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 plot(filename, outputend):
# 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'][:]
ae = 6.371229e6
# Open a workstation.
wks_type = "pdf"
rlist = Ngl.Resources()
rlist.wkColorMap = "WhBlReWh"
# Create contour and map resource lists.
for it in range(13):
wks = Ngl.open_wks(wks_type,"Moving_"+str(it)+outputend, rlist)
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 = 0, 90.#qmoving(it*24.*3600., 12.*24.*3600., 0.25*np.pi, ae)
cnres.mpCenterLonF = x_c*r2d # Rotate the projection.
cnres.mpCenterLatF = y_c*r2d # Rotate the projection.
cnres.mpOutlineOn = False
cnres.sfXArray = lon[it, 0:ncols[it]]
cnres.sfYArray = lat[it, 0:ncols[it]]
cnres.cnLinesOn = False
cnres.cnFillOn = True
cnres.cnLineLabelsOn = False
cnres.cnInfoLabelOn = False
cnres.lbLabelBarOn = True
cnres.cnLevelSelectionMode = "ManualLevels"
cnres.cnMinLevelValF = 0.4
cnres.cnMaxLevelValF = 1.6
cnres.cnLevelSpacingF = 0.05
cnres.pmTickMarkDisplayMode = "Never"
contour1 = Ngl.contour_map(wks,q[it, 0:ncols[it]],cnres)
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,contour1,vertx[it, :, nc],verty[it, :, nc],gsres)
# Ngl.draw(contour1)
Ngl.frame(wks)
Ngl.destroy(wks)
Ngl.end()
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 draw_clubb_tau(ptype, cseason, ncases, cases, casenames, nsite, lats, lons,
filepath, filepathobs, casedir, varis, vname, cscale,
chscale, pname):
# ncases, the number of models
# cases, the name of models
# casename, the name of cases
# filepath, model output filepath
# filepathobs, filepath for observational data
# inptrs = [ncases]
if not os.path.exists(casedir):
os.mkdir(casedir)
_Font = 25
interp = 2
extrap = False
mkres = Ngl.Resources()
mkres.gsMarkerIndex = 2
mkres.gsMarkerColor = "Red"
mkres.gsMarkerSizeF = 15.
infiles = ["" for x in range(ncases)]
ncdfs = ["" for x in range(ncases)]
nregions = nsite
nvaris = len(varis)
plottau = ["" for x in range(nsite * ncases)]
alphas = [
'a) ', 'b) ', 'c) ', 'd) ', 'e) ', 'f) ', 'g) ', 'h) ', 'i) ', 'j) ',
'k) ', 'l) ', 'm) ', 'n) ', 'o) ', 'p) ', 'q) ', 'r) ', 's) ', 't) ',
'u) ', 'v) ', 'w) ', 'x) ', 'y) ', 'z) '
]
for ire in range(0, nsite):
for im in range(0, ncases):
if not os.path.exists(casedir + '/' + str(lons[ire]) + 'E_' +
str(lats[ire]) + 'N'):
os.mkdir(casedir + '/' + str(lons[ire]) + 'E_' +
str(lats[ire]) + 'N')
plotname = casedir + '/' + str(lons[ire]) + 'E_' + str(
lats[ire]) + 'N/' + pname + '_' + casenames[im] + "_" + str(
lons[ire]) + "E_" + str(lats[ire]) + "N_" + cseason
plottau[im +
ncases * ire] = pname + '_' + casenames[im] + "_" + str(
lons[ire]) + "E_" + str(lats[ire]) + "N_" + cseason
wks = Ngl.open_wks(ptype, plotname)
Ngl.define_colormap(wks, "radar")
plot = []
res = Ngl.Resources()
res.nglMaximize = False
res.nglDraw = False
res.nglFrame = False
res.vpWidthF = 0.30 # set width and height
res.vpHeightF = 0.30
# if (lats[ire] > 0 and lons[ire]!=240 ):
# res.trYMinF = 400.
# res.trYMaxF = 1000.
# else:
res.trYMinF = 100.
res.trYMaxF = 1000.
res.tiMainFont = _Font
res.tmYLLabelFont = _Font
res.tmXBLabelFont = _Font
res.tiYAxisFont = _Font
res.tiXAxisFont = _Font
res.tmXBLabelFontHeightF = 0.01
res.tmXBLabelFontThicknessF = 1.0
res.xyMarkLineMode = "MarkLines"
res.xyLineThicknesses = [
3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3., 2., 2., 2., 2, 2, 2, 2, 2, 2,
2
]
res.xyDashPatterns = np.arange(0, 24, 1)
res.xyMarkers = np.arange(16, 40, 1)
res.xyMarkerSizeF = 0.005
res.pmLegendDisplayMode = "Never"
res.pmLegendSide = "top" # Change location of
res.pmLegendParallelPosF = 0.75 # move units right
res.pmLegendOrthogonalPosF = -0.65 # more neg = down
res.pmLegendWidthF = 0.1 # Change width and
res.pmLegendHeightF = 0.15 # height of legend.
res.lgLabelFontHeightF = .02 # change font height
res.lgLabelFontThicknessF = 1.
# res.lgBoxMinorExtentF = 0.2
res.lgPerimOn = True
res.tiYAxisString = "Pressure [hPa]"
res.trYReverse = True
pres = Ngl.Resources()
pres.nglFrame = False
pres.txFont = _Font
pres.nglPanelYWhiteSpacePercent = 5
pres.nglPanelXWhiteSpacePercent = 5
pres.nglPanelTop = 0.93
pres.txFont = _Font
pres.nglMaximize = False
pres.txFont = _Font
pres.nglPanelTop = 0.935
pres.nglPanelFigureStrings = alphas
pres.nglPanelFigureStringsJust = 'Topright'
pres.nglPanelFigureStringsFontHeightF = 0.015
for iv in range(0, nvaris):
if (varis[iv] == 'tau_no_N2_zm'):
budget_ends = ['tau_no_N2_zm', 'bkgnd', 'sfc', 'shear']
budget_name = [
'1/~F8~t~N~~F10~~B~noN2~N~',
'1/~F8~t~N~~F10~~B~bkgnd~N~',
'1/~F8~t~N~~F10~~B~surf~N~',
'1/~F8~t~N~~F10~~B~shear~N~'
]
nterms = len(budget_ends)
if (varis[iv] == 'tau_xp2_zm'):
budget_ends = ['tau_xp2_zm', 'tau_no_N2_zm']
budget_name = [
"1/~F8~t~N~~F10~~B~x'~S~2~N~~N~",
'1/~F8~t~N~~F10~~B~noN2~N~'
]
nterms = len(budget_ends)
if (varis[iv] == 'tau_wp2_zm'):
budget_ends = ['tau_wp2_zm', 'tau_no_N2_zm', 'bvpos']
budget_name = [
"1/~F8~t~N~~F10~~B~w'~S~2~N~~N~",
'1/~F8~t~N~~F10~~B~noN2~N~', '1/~F8~t~N~~F10~~B~bv~N~'
]
nterms = len(budget_ends)
if (varis[iv] == 'tau_wpxp_zm'):
budget_ends = [
'tau_wpxp_zm', 'tau_no_N2_zm', 'bvpos', 'clear'
]
budget_name = [
"1/~F8~t~N~~F10~~B~w'x'~N~",
"1/~F8~t~N~~F10~~B~noN2~N~", '1/~F8~t~N~~F10~~B~bv~N~',
'1/~F8~t~N~~F10~~B~clr~N~'
]
nterms = len(budget_ends)
if (varis[iv] == 'tau_wp3_zm'):
budget_ends = [
'tau_wpxp_zm', 'tau_no_N2_zm', 'bvpos', 'clear'
]
budget_name = [
"1/~F8~t~N~~F10~~B~w'x'~N~",
'1/~F8~t~N~~F10~~B~noN2~N~', '1/~F8~t~N~~F10~~B~bv~N~',
'1/~F8~t~N~~F10~~B~clr~N~'
]
nterms = len(budget_ends)
ncdfs[im] = './data/' + cases[im] + '_site_location.nc'
infiles[im] = filepath[im] + cases[im] + '/' + cases[
im] + '_' + cseason + '_climo.nc'
inptrs = Dataset(infiles[im], 'r') # pointer to file1
lat = inptrs.variables['lat'][:]
nlat = len(lat)
lon = inptrs.variables['lon'][:]
nlon = len(lon)
ilev = inptrs.variables['ilev'][:]
nilev = len(ilev)
ncdf = Dataset(ncdfs[im], 'r')
n = ncdf.variables['n'][:]
idx_cols = ncdf.variables['idx_cols'][:, :]
ncdf.close()
A_field = np.zeros((nterms, nilev), np.float32)
if (varis[iv] == 'tau_no_N2_zm' or varis[iv] == 'tau_wp2_zm'
or varis[iv] == 'tau_xp2_zm'
or varis[iv] == 'tau_wp3_zm'
or varis[iv] == 'tau_wpxp_zm'):
theunits = str(chscale[iv]) + '~F10~' + inptrs.variables[
varis[iv]].units + '~S~-1~N~'
else:
theunits = str(
chscale[iv]) + '~F10~' + inptrs.variables[varis[iv] +
'_bt'].units
res.tiXAxisString = '1/~F8~t~N~~F25~~B~' + vname[
iv] + "~N~ " + ' Unit= ' + theunits
res1 = Ngl.Resources()
res1 = res
for it in range(0, nterms):
for subc in range(0, n[ire]):
npoint = idx_cols[ire, n[subc] - 1] - 1
if (varis[iv] == 'tau_wp2_zm'
or varis[iv] == 'tau_xp2_zm'
or varis[iv] == 'tau_wp3_zm'
or varis[iv] == 'tau_wpxp_zm'
or varis[iv] == 'tau_no_N2_zm'):
varis_tau = budget_ends[it]
if ( varis_tau == 'bkgnd' or varis_tau == 'shear' \
or varis_tau == 'sfc' ):
tmp = inptrs.variables['invrs_tau_' +
varis_tau][0, :, npoint]
if (varis_tau == 'tau_no_N2_zm'):
tmp = inptrs.variables[varis_tau][0, :, npoint]
tmp = 1 / tmp
if (varis[iv] == 'tau_wp2_zm'):
if (varis_tau == 'tau_wp2_zm'):
tmp = inptrs.variables[varis_tau][0, :,
npoint]
tmp = 1 / tmp
if (varis_tau == 'bvpos'):
tmp0 = inptrs.variables['tau_wp2_zm'][
0, :, npoint]
tmp1 = inptrs.variables['tau_no_N2_zm'][
0, :, npoint]
tmp = tmp0
tmp = 1 / tmp0 - 1 / tmp1
if (varis[iv] == 'tau_zm'):
if (varis_tau == 'tau_zm'):
tmp = inptrs.variables[varis_tau][0, :,
npoint]
tmp = 1 / tmp
if (varis_tau == 'bvpos'):
tmp0 = inptrs.variables['tau_zm'][0, :,
npoint]
tmp1 = inptrs.variables['tau_no_N2_zm'][
0, :, npoint]
tmp = tmp0
tmp = 1 / tmp0 - 1 / tmp1
if (varis[iv] == 'tau_xp2_zm'):
if (varis_tau == 'tau_xp2_zm'):
tmp = inptrs.variables[varis_tau][0, :,
npoint]
tmp = 1 / tmp
if (varis_tau == 'Rich'):
tmp0 = inptrs.variables['tau_xp2_zm'][
0, :, npoint]
tmp1 = inptrs.variables['tau_no_N2_zm'][
0, :, npoint]
tmp = tmp0
tmp = 1 / tmp0 / (1 / tmp1) / 1000
if (varis[iv] == 'tau_wp3_zm'):
if (varis_tau == 'tau_wp3_zm'
or varis_tau == 'tau_wpxp_zm'):
tmp = inptrs.variables[varis_tau][0, :,
npoint]
tmp = 1 / tmp
tmp[0:10] = 0
if (varis_tau == 'bvpos'):
tmp0 = inptrs.variables['tau_wp2_zm'][
0, :, npoint]
tmp1 = inptrs.variables['tau_no_N2_zm'][
0, :, npoint]
tmp = tmp0
tmp = 1 / tmp0 - 1 / tmp1
if (varis_tau == 'clear'):
tmp0 = inptrs.variables['tau_wp3_zm'][
0, :, npoint]
tmp1 = inptrs.variables['tau_wp2_zm'][
0, :, npoint]
tmp = tmp0
tmp = 1 / tmp0 - 1 / tmp1
tmp[0:10] = 0
if (varis[iv] == 'tau_wpxp_zm'):
if (varis_tau == 'tau_wpxp_zm'
or varis_tau == 'tau_zm'):
tmp = inptrs.variables[varis_tau][0, :,
npoint]
tmp = 1 / tmp
if (varis_tau == 'bvpos'):
tmp0 = inptrs.variables['tau_zm'][0, :,
npoint]
tmp1 = inptrs.variables['tau_no_N2_zm'][
0, :, npoint]
tmp = tmp0
tmp = 2 / tmp0 - 1 / tmp1
if (varis_tau == 'clear'):
tmp0 = inptrs.variables['tau_wpxp_zm'][
0, :, npoint]
tmp1 = inptrs.variables['tau_zm'][0, :,
npoint]
tmp = tmp0
tmp = 1 / tmp0 / 5 - 2 / tmp1
else:
varis_tau = varis[iv] + budget_ends[it]
tmp0 = inptrs.variables[varis[iv]][0, :, npoint]
tmp = inptrs.variables[varis_tau][0, :, npoint]
tmp = tmp * cscale[iv]
A_field[it, :] = (A_field[it, :] +
tmp[:] / n[ire]).astype(np.float32)
inptrs.close()
res.pmLegendDisplayMode = "Never"
if (varis[iv] == 'tau_wp3_zm'):
res.trXMinF = 0
res.trXMaxF = 450
res.xyMarkerColors = [
'black', 'orange', 'purple', 'firebrick'
]
res.xyLineColors = [
'black', 'orange', 'purple', 'firebrick'
]
if (varis[iv] == 'tau_no_N2_zm'):
res.trXMinF = 0
res.trXMaxF = 20
res.xyMarkerColors = ['black', 'red', 'green', 'blue']
res.xyLineColors = ['black', 'red', 'green', 'blue']
if (varis[iv] == 'tau_wp2_zm'):
res.trXMinF = 0
res.trXMaxF = 12
res.xyMarkerColors = ['black', 'orange', 'purple']
res.xyLineColors = ['black', 'orange', 'purple']
if (varis[iv] == 'tau_xp2_zm'):
res.trXMinF = 0
res.trXMaxF = 35
res.xyMarkerColors = ['black', 'orange']
res.xyLineColors = ['black', 'orange']
p = Ngl.xy(wks, A_field, ilev, res)
plot.append(p)
xp = np.mod(iv, 2)
yp = int(iv / 2)
if (varis[iv] == 'tau_wp3_zm'):
res.trXMinF = 0
res.trXMaxF = 450
res.xyMarkerColors = [
'black', 'orange', 'purple', 'firebrick'
]
res.xyLineColors = [
'black', 'orange', 'purple', 'firebrick'
]
Common_functions.create_legend(
wks, budget_name[:], 0.02,
['black', 'orange', 'purple', 'firebrick'],
0.3 + xp * 0.5, 0.8 - yp * 0.5)
if (varis[iv] == 'tau_no_N2_zm'):
res.trXMinF = 0
res.trXMaxF = 20
res.xyMarkerColors = ['black', 'red', 'green', 'blue']
res.xyLineColors = ['black', 'red', 'green', 'blue']
Common_functions.create_legend(
wks, budget_name[:], 0.02,
['black', 'red', 'green', 'blue'], 0.3 + xp * 0.5,
0.8 - yp * 0.5)
if (varis[iv] == 'tau_wp2_zm'):
res.trXMinF = 0
res.trXMaxF = 12
res.xyMarkerColors = ['black', 'orange', 'purple']
res.xyLineColors = ['black', 'orange', 'purple']
Common_functions.create_legend(
wks, budget_name[:], 0.02,
['black', 'orange', 'purple'], 0.3 + xp * 0.5,
0.8 - yp * 0.5)
if (varis[iv] == 'tau_xp2_zm'):
res.trXMinF = 0
res.trXMaxF = 35
res.xyMarkerColors = ['black', 'orange']
res.xyLineColors = ['black', 'orange']
Common_functions.create_legend(wks, budget_name[:], 0.02,
['black', 'orange'],
0.3 + xp * 0.5,
0.8 - yp * 0.5)
Ngl.panel(wks, plot[:], [nvaris / 2, 2], pres)
txres = Ngl.Resources()
txres.txFont = _Font
txres.txFontHeightF = 0.020
Ngl.text_ndc(
wks, casenames[im] + " BUDGET at " + str(lons[ire]) + "E," +
str(lats[ire]) + "N", 0.5, 0.95, txres)
Ngl.frame(wks)
Ngl.destroy(wks)
return (plottau)
def clubb_skw_prf(ptype, cseason, ncases, cases, casenames, nsite, lats, lons,
filepath, filepathobs, casedir, dofv):
# ncases, the number of models
# cases, the name of models
# casename, the name of cases
# filepath, model output filepath
# filepathobs, filepath for observational data
# inptrs = [ncases]
if not os.path.exists(casedir):
os.mkdir(casedir)
_Font = 25
interp = 2
extrap = False
mkres = Ngl.Resources()
mkres.gsMarkerIndex = 2
mkres.gsMarkerColor = "Red"
mkres.gsMarkerSizeF = 15.
infiles = ["" for x in range(ncases)]
ncdfs = ["" for x in range(ncases)]
nregions = nsite
varis = [
"C6rt_Skw_fnc", "C11_Skw_fnc", "C1_Skw_fnc", "C7_Skw_fnc", "Lscale",
"Richardson_num", "Kh_zm", "tau_zm", "Skw_velocity"
]
varisobs = [
"CC_ISBL", "OMEGA", "SHUM", "CLWC_ISBL", "THETA", "RELHUM", "U",
"CIWC_ISBL", "T"
]
nvaris = len(varis)
cunits = [
"%", "mba/day", "g/kg", "g/kg", "K", "%", "m/s", "g/kg", "m/s", "m/s",
"K", "m"
]
cscale = [
1, 1, 1, 1, 1., 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
]
cscaleobs = [100, 1, 1, 1000, 1., 1, 1, 1000, 1, 1, 1, 1, 1, 1, 1]
obsdataset = [
"ERAI", "ERAI", "ERAI", "ERAI", "ERAI", "ERAI", "ERAI", "ERAI", "ERAI",
"ERAI"
]
plotskw = ["" for x in range(nsite)]
for ire in range(0, nsite):
if not os.path.exists(casedir + '/' + str(lons[ire]) + 'E_' +
str(lats[ire]) + 'N'):
os.mkdir(casedir + '/' + str(lons[ire]) + 'E_' + str(lats[ire]) +
'N')
plotname = casedir + '/' + str(lons[ire]) + 'E_' + str(
lats[ire]) + 'N/CLUBB_skewfunc_' + str(lons[ire]) + "E_" + str(
lats[ire]) + "N_" + cseason
plotskw[ire] = 'CLUBB_skewfunc_' + str(lons[ire]) + "E_" + str(
lats[ire]) + "N_" + cseason
wks = Ngl.open_wks(ptype, plotname)
Ngl.define_colormap(wks, "GMT_paired")
plot = []
res = Ngl.Resources()
res.nglDraw = False
res.nglFrame = False
res.lgLabelFontHeightF = .012 # change font height
res.lgPerimOn = False # no box around
res.vpWidthF = 0.30 # set width and height
res.vpHeightF = 0.30
#res.vpXF = 0.04
# res.vpYF = 0.30
res.tmYLLabelFont = _Font
res.tmXBLabelFont = _Font
res.tmXBLabelFontHeightF = 0.01
res.tmXBLabelFontThicknessF = 1.0
# res.tmXBLabelAngleF = 45
res.xyMarkLineMode = "Lines"
res.xyLineThicknesses = [
3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3., 3., 3., 3., 3, 3, 3, 3, 3, 3, 3
]
res.xyDashPatterns = np.arange(0, 24, 1)
# res.xyMarkers = np.arange(16,40,1)
# res.xyMarkerSizeF = 0.005
pres = Ngl.Resources()
# pres.nglMaximize = True
pres.nglFrame = False
pres.txFont = _Font
pres.nglPanelYWhiteSpacePercent = 5
pres.nglPanelXWhiteSpacePercent = 5
pres.nglPanelTop = 0.88
pres.wkWidth = 2500
pres.wkHeight = 2500
for iv in range(0, nvaris):
if (iv == nvaris - 1):
res.pmLegendDisplayMode = "NEVER"
res.xyExplicitLegendLabels = casenames[:]
res.pmLegendSide = "top"
res.pmLegendParallelPosF = 0.6
res.pmLegendOrthogonalPosF = -0.5
res.pmLegendWidthF = 0.10
res.pmLegendHeightF = 0.10
res.lgLabelFontHeightF = .02
res.lgLabelFontThicknessF = 1.5
res.lgPerimOn = False
else:
res.pmLegendDisplayMode = "NEVER"
# if(obsdataset[iv] =="CCCM"):
# if(cseason == "ANN"):
# fileobs = "/Users/guoz/databank/CLD/CCCm/cccm_cloudfraction_2007-"+cseason+".nc"
# else:
# fileobs = "/Users/guoz/databank/CLD/CCCm/cccm_cloudfraction_2007-2010-"+cseason+".nc"
# inptrobs = Dataset(fileobs,'r')
# B=inptrobs.variables[varisobs[iv]][:,(lats[ire]),(lons[ire])]
# else:
# if (varisobs[iv] =="PRECT"):
# fileobs = filepathobs+'/GPCP_'+cseason+'_climo.nc'
# else:
# fileobs = filepathobs + obsdataset[iv]+'_'+cseason+'_climo.nc'
# inptrobs = Dataset(fileobs,'r')
# if (varisobs[iv] =="THETA"):
# B = inptrobs.variables['T'][0,:,(lats[ire]),(lons[ire])]
# pre1 = inptrobs.variables['lev'][:]
# for il1 in range (0, len(pre1)):
# B[il1] = B[il1]*(1000/pre1[il1])**0.286
# else:
# pre1 = inptrobs.variables['lev'][:]
# B = inptrobs.variables[varisobs[iv]][0,:,(lats[ire]),(lons[ire])]
#
# B[:]=B[:] * cscaleobs[iv]
for im in range(0, ncases):
ncdfs[im] = './data/' + cases[im] + '_site_location.nc'
infiles[im] = filepath[im] + '/' + cases[
im] + '_' + cseason + '_climo.nc'
inptrs = Dataset(infiles[im], 'r') # pointer to file1
lat = inptrs.variables['lat'][:]
nlat = len(lat)
lon = inptrs.variables['lon'][:]
nlon = len(lon)
lev = inptrs.variables['ilev'][:]
nlev = len(lev)
ncdf = Dataset(ncdfs[im], 'r')
n = ncdf.variables['n'][:]
idx_cols = ncdf.variables['idx_cols'][:, :]
if (dofv):
idx_lats = ncdf.variables['idx_coord_lat'][:, :]
idx_lons = ncdf.variables['idx_coord_lon'][:, :]
ncdf.close()
if (im == 0):
A_field = np.zeros((ncases, nlev), np.float32)
for subc in range(0, n[ire]):
npoint = idx_cols[ire, n[subc] - 1] - 1
if (dofv):
npointlat = idx_lats[ire, 0]
npointlon = idx_lons[ire, 0]
if (dofv):
tmp = inptrs.variables[varis[iv]][0, :, npointlat,
npointlon]
else:
tmp = inptrs.variables[varis[iv]][0, :, npoint]
theunits = str(
cscale[iv]) + inptrs.variables[varis[iv]].units
A_field[im, :] = (A_field[im, :] + tmp[:] / n[ire]).astype(
np.float32)
A_field[im, :] = A_field[im, :] * cscale[iv]
inptrs.close()
res.tiMainString = varis[iv] + " " + theunits
# res.trXMinF = min(np.min(A_field[0, :]))
# res.trXMaxF = max(np.max(A_field[0, :]))
res.trYReverse = True
res.xyLineColors = np.arange(3, 20, 2)
res.xyMarkerColors = np.arange(2, 20, 2)
p = Ngl.xy(wks, A_field, lev, res)
# res.trYReverse = False
# res.xyLineColors = ["black"]
# pt = Ngl.xy(wks,B,pre1,res)
# Ngl.overlay(p,pt)
plot.append(p)
pres.txString = "Skewness Functions at" + str(lons[ire]) + "E," + str(
lats[ire]) + "N"
txres = Ngl.Resources()
txres.txFontHeightF = 0.020
txres.txFont = _Font
Ngl.text_ndc(
wks, "Skewness Functions at" + str(lons[ire]) + "E," +
str(lats[ire]) + "N", 0.5, 0.92 + ncases * 0.01, txres)
Common_functions.create_legend(wks, casenames, 0.02,
np.arange(3, 20,
2), 0.1, 0.89 + ncases * 0.01)
Ngl.panel(wks, plot[:], [nvaris / 3, 3], pres)
Ngl.frame(wks)
Ngl.destroy(wks)
return plotskw
# # 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()
def plot_rain_around_point(path, data_rain_tot_sum, hour_start, hour_end, point, stat_processing, member):
if stat_processing == 'max':
data_processed = (data_rain_tot_sum[hour_end, :, :] - data_rain_tot_sum[hour_start, :, :]).max(axis=0)
elif stat_processing == 'member_extract':
data_processed = data_rain_tot_sum[hour_end, member-1, :] - data_rain_tot_sum[hour_start, member-1, :]
########################################################################
if stat_processing == 'max':
member_text_filename = 'max'
elif stat_processing == 'member_extract':
member_text_filename = 'm{:02d}'.format(member)
plot_name = 'iconeueps_rain_{}_{:02d}Z-{:02d}Z_{}'.format(\
point['name'], hour_start+12, hour_end+12, member_text_filename)
mpi_file = nc.Dataset(path['base'] + path['grid'] + 'icon_grid_0028_R02B07_N02.nc', 'r')
vlat = mpi_file.variables['clat_vertices'][:].data * 180./np.pi
vlon = mpi_file.variables['clon_vertices'][:].data * 180./np.pi
clat = mpi_file.variables['clat'][:].data * 180./np.pi
clon = mpi_file.variables['clon'][:].data * 180./np.pi
mpi_file.close()
########################################################################
x_resolution = 800
y_resolution = 800
wks_res = Ngl.Resources()
wks_res.wkWidth = x_resolution
wks_res.wkHeight = y_resolution
wks_type = 'png'
wks = Ngl.open_wks(wks_type, path['base'] + path['plots'] + plot_name, wks_res)
resources = Ngl.Resources()
resources.mpProjection = 'Hammer'
resources.mpCenterLonF = point['lon']
resources.mpCenterLatF = point['lat']
radius = 500 # image radius in km around centered point
cutout_plot = dict(
lat_min = point['lat'] - radius / 111.2,
lat_max = point['lat'] + radius / 111.2,
lon_min = point['lon'] - radius / (111.2 * np.cos(point['lat']*np.pi/180)),
lon_max = point['lon'] + radius / (111.2 * np.cos(point['lat']*np.pi/180)),
)
resources.mpLimitMode = 'latlon'
resources.mpMinLonF = cutout_plot['lon_min']
resources.mpMaxLonF = cutout_plot['lon_max']
resources.mpMinLatF = cutout_plot['lat_min']
resources.mpMaxLatF = cutout_plot['lat_max']
resources.nglMaximize = False
resources.vpXF = 0.05
resources.vpYF = 0.95
resources.vpWidthF = 0.7
resources.vpHeightF = 0.7
########################################################################
# Turn on filled map areas:
resources.mpFillOn = True
# Set colors for [FillValue, Ocean, Land , InlandWater]:
resources.mpFillColors = ['pink','blue','white','blue']
resources.mpDataBaseVersion = 'MediumRes'
resources.mpDataSetName = 'Earth..4'
resources.mpOutlineBoundarySets = 'national'
resources.mpGeophysicalLineThicknessF = 7.0 * x_resolution / 1000
resources.mpNationalLineThicknessF = 7.0 * x_resolution / 1000
#resources.mpGridAndLimbDrawOrder = 'postdraw'
resources.mpGridAndLimbOn = False
#resources.mpLimbLineColor = 'black'
#resources.mpLimbLineThicknessF = 10
#resources.mpGridLineColor = 'black'
#resources.mpGridLineThicknessF = 1.0
#resources.mpGridSpacingF = 1
resources.mpPerimOn = True
resources.mpPerimLineColor = 'black'
resources.mpPerimLineThicknessF = 8.0 * x_resolution / 1000
resources.tmXBOn = False
resources.tmXTOn = False
resources.tmYLOn = False
resources.tmYROn = False
resources.sfDataArray = data_processed
resources.sfXArray = clon
resources.sfYArray = clat
resources.sfXCellBounds = vlon
resources.sfYCellBounds = vlat
resources.sfMissingValueV = 9999
resources.cnFillOn = True
resources.cnFillMode = 'CellFill'
resources.cnCellFillEdgeColor = 'black'
resources.cnMissingValFillColor = 'black'
resources.cnFillPalette = 'WhiteBlueGreenYellowRed'
resources.cnLevelSelectionMode = 'ManualLevels'
minlevel = 0.0
maxlevel = 50.0
numberoflevels = 250
resources.cnMinLevelValF = minlevel
resources.cnMaxLevelValF = maxlevel
resources.cnLevelSpacingF = (maxlevel - minlevel) / numberoflevels
# Turn off contour lines and labels:
resources.cnLinesOn = False
resources.cnLineLabelsOn = False
# Set resources for a nice label bar
resources.lbLabelBarOn = True
resources.lbAutoManage = False
resources.lbOrientation = 'vertical'
resources.lbLabelOffsetF = 0.05
#resources.lbBoxMinorExtentF = 0.2
resources.lbLabelStride = 25
resources.lbLabelFontHeightF = 0.02
resources.lbBoxSeparatorLinesOn = False
resources.lbBoxLineThicknessF = 4.0
#resources.lbBoxEndCapStyle = 'TriangleBothEnds'
resources.lbLabelAlignment = 'BoxCenters'
resources.lbTitleString = 'mm'
resources.lbTitleFontHeightF = 0.02
resources.lbTitlePosition = 'Right'
resources.lbTitleDirection = 'Across'
resources.lbTitleAngleF = 90.0
resources.lbTitleExtentF = 0.1
resources.lbTitleOffsetF = 0.0
resources.nglFrame = False
plot = Ngl.contour_map(wks, data_processed, resources)
########################################################################
polymarker_res_1 = Ngl.Resources()
polymarker_res_1.gsMarkerColor = 'black'
polymarker_res_1.gsMarkerIndex = 16
polymarker_res_1.gsMarkerSizeF = 0.012
polymarker_res_1.gsMarkerThicknessF = 1
Ngl.polymarker(wks, plot, point['lon'], point['lat'], polymarker_res_1)
########################################################################
polymarker_res_2 = Ngl.Resources()
polymarker_res_2.gsMarkerColor = 'white'
polymarker_res_2.gsMarkerIndex = 16
polymarker_res_2.gsMarkerSizeF = 0.008
polymarker_res_2.gsMarkerThicknessF = 1
Ngl.polymarker(wks, plot, point['lon'], point['lat'], polymarker_res_2)
########################################################################
if stat_processing == 'max':
member_text = 'max member of each cell'
elif stat_processing == 'member_extract':
member_text = 'member {:d}'.format(member)
text = 'rain_gsp + rain_con, 07.08.19 {}Z-{}Z, {}'.format(hour_start+12, hour_end+12, member_text)
x = 0.1
y = 0.95
text_res_1 = Ngl.Resources()
text_res_1.txFontHeightF = 0.02
text_res_1.txJust = 'BottomLeft'
Ngl.text_ndc(wks, text, x, y, text_res_1)
Ngl.frame(wks)
Ngl.destroy(wks)
return
def rain_prf (ptype,cseason, ncases, cases, casenames, nsite, lats, lons, filepath, filepathobs,casedir,varis,cscale,chscale,pname):
# ncases, the number of models
# cases, the name of models
# casename, the name of cases
# filepath, model output filepath
# filepathobs, filepath for observational data
# inptrs = [ncases]
if not os.path.exists(casedir):
os.mkdir(casedir)
_Font = 25
interp = 2
extrap = False
mkres = Ngl.Resources()
mkres.gsMarkerIndex = 2
mkres.gsMarkerColor = 'Red'
mkres.gsMarkerSizeF = 15.
infiles = ['' for x in range(ncases)]
ncdfs = ['' for x in range(ncases)]
nregions = nsite
varisobs = ['CC_ISBL', 'OMEGA','SHUM','CLWC_ISBL', 'THETA','RELHUM','U','CIWC_ISBL','T' ]
nvaris = len(varis)
cunits = ['%','mba/day','g/kg','g/kg','K', '%', 'm/s', 'g/kg', 'm/s', 'm/s','K','m' ]
cscaleobs = [100, 1, 1, 1000 , 1., 1, 1, 1000, 1,1,1,1,1,1,1]
obsdataset =['ERAI', 'ERAI', 'ERAI', 'ERAI', 'ERAI', 'ERAI', 'ERAI', 'ERAI','ERAI','ERAI']
plotrain=['' for x in range(nsite)]
for ire in range (0, nsite):
if not os.path.exists(casedir+'/'+str(lons[ire])+'E_'+str(lats[ire])+'N'):
os.mkdir(casedir+'/'+str(lons[ire])+'E_'+str(lats[ire])+'N')
plotname = casedir+'/'+str(lons[ire])+'E_'+str(lats[ire])+'N/'+pname+'_'+str(lons[ire])+'E_'+str(lats[ire])+'N_'+cseason
plotrain[ire] = pname+'_'+str(lons[ire])+'E_'+str(lats[ire])+'N_'+cseason
wks= Ngl.open_wks(ptype,plotname)
Ngl.define_colormap(wks,'GMT_paired')
plot = []
res = Ngl.Resources()
res.nglDraw = False
res.nglFrame = False
res.lgLabelFontHeightF = .02 # change font height
res.lgPerimOn = False # no box around
res.vpWidthF = 0.30 # set width and height
res.vpHeightF = 0.30
#res.vpXF = 0.04
# res.vpYF = 0.30
res.tmYLLabelFont = _Font
res.tmXBLabelFont = _Font
res.tiMainFont = _Font
res.tmXBLabelFontHeightF = 0.015
res.tmXBLabelFontThicknessF = 1.0
# res.tmXBLabelAngleF = 45
res.xyMarkLineMode = 'Lines'
res.xyLineThicknesses = [3.0, 3.0, 3.0, 3.0, 3.0, 3.0,3.,3.,3.,3.,3,3,3,3,3,3,3]
res.xyDashPatterns = np.arange(0,24,1)
# res.xyMarkers = np.arange(16,40,1)
# res.xyMarkerSizeF = 0.005
pres = Ngl.Resources()
# pres.nglMaximize = True
pres.nglFrame = False
pres.txFont = _Font
pres.nglPanelYWhiteSpacePercent = 5
pres.nglPanelXWhiteSpacePercent = 5
pres.nglPanelTop = 0.88
pres.wkWidth = 2500
pres.wkHeight = 2500
for iv in range (0, nvaris):
if(iv == nvaris-1):
res.pmLegendDisplayMode = 'Never'
res.xyExplicitLegendLabels = casenames[:]
res.pmLegendSide = 'top'
res.pmLegendParallelPosF = 0.6
res.pmLegendOrthogonalPosF = -0.5
res.pmLegendWidthF = 0.10
res.pmLegendHeightF = 0.10
res.lgLabelFontHeightF = .02
res.lgLabelFontThicknessF = 1.5
res.lgPerimOn = False
else:
res.pmLegendDisplayMode = 'NEVER'
for im in range (0,ncases):
ncdfs[im] = './data/'+cases[im]+'_site_location.nc'
infiles[im]= filepath[im]+cases[im]+'/'+cases[im]+'_'+cseason+'_climo.nc'
inptrs = Dataset(infiles[im],'r') # pointer to file1
lat=inptrs.variables['lat'][:]
nlat=len(lat)
lon=inptrs.variables['lon'][:]
nlon=len(lon)
lev=inptrs.variables['lev'][:]
nlev=len(lev)
ncdf= Dataset(ncdfs[im],'r')
n =ncdf.variables['n'][:]
idx_cols=ncdf.variables['idx_cols'][:,:]
ncdf.close()
if (im ==0):
A_field = np.zeros((ncases,nlev),np.float32)
for subc in range( 0, n[ire]):
npoint=idx_cols[ire,n[subc]-1]-1
tmp=inptrs.variables[varis[iv]][0,:,npoint]
theunits=str(chscale[iv])+'x'+inptrs.variables[varis[iv]].units
A_field[im,:] = (A_field[im,:]+tmp[:]/n[ire]).astype(np.float32 )
A_field[im,:] = A_field[im,:] *cscale[iv]
inptrs.close()
res.tiMainString = varis[iv]+' '+theunits
# res.trXMinF = min(np.min(A_field[0, :]))
# res.trXMaxF = max(np.max(A_field[0, :]))
res.trYReverse = True
res.xyLineColors = np.arange(3,20,2)
res.xyMarkerColors = np.arange(2,20,2)
p = Ngl.xy(wks,A_field,lev,res)
# res.trYReverse = False
# res.xyLineColors = ['black']
# pt = Ngl.xy(wks,B,pre1,res)
# Ngl.overlay(p,pt)
plot.append(p)
pres.txString = pname+' at'+ str(lons[ire])+'E,'+str(lats[ire])+'N'
txres = Ngl.Resources()
txres.txFontHeightF = 0.020
txres.txFont = _Font
Ngl.text_ndc(wks,pname+' at'+ str(lons[ire])+'E,'+str(lats[ire])+'N',0.5,0.92+ncases*0.01,txres)
Common_functions.create_legend(wks,casenames,np.arange(3,20,2),0.1,0.89+ncases*0.01)
Ngl.panel(wks,plot[:],[nvaris/2,2],pres)
Ngl.frame(wks)
Ngl.destroy(wks)
return plotrain
# Set up info for box. width = 0.07 # Box width height = 0.05 # Box height # Set coordinates for top left corner of the eight boxes. xpos = [0.20,0.40,0.60,0.80,0.20, 0.40, 0.60, 0.80] ypos = [0.13,0.13,0.13,0.13,0.055,0.055,0.055,0.055] # Text resources. txres = Ngl.Resources() txres.txFontHeightF = 0.015 # Decrease size of font txres.txJust = "CenterLeft" # Left justify nboxes = len(xpos) for i in xrange(nboxes): xp = xpos[i] yp = ypos[i] xbox = [xp,xp+width,xp+width,xp,xp] ybox = [yp,yp,yp-height,yp-height,yp] gsres.gsFillColor = colors[i+2] Ngl.polygon_ndc(wks,xbox,ybox,gsres) # Fill the box Ngl.polyline_ndc(wks,xbox,ybox) # Outline the box Ngl.text_ndc(wks,labels[i],xp+width+0.005,yp-height/2.,txres) Ngl.frame(wks) # Now advance the frame. Ngl.end()
def histogram(wks, data, res):
"""Plot a histogram.
The NumPy histogram function is used to define the histogram.
Arguments:
wks -- Ngl wrokstation.
data -- 1D array of data to construct a histogram from.
res -- Ngl resources variable. Valid resources are:
"""
# Define a function to compute bar locations.
def bar_position(x, y, dx, ymin, bar_width_perc):
"""Compute the coordinates required to draw a specified bar."""
dxp = dx * bar_width_perc
xbar = np.array([x, x+dxp, x+dxp, x, x])
ybar = np.array([ymin, ymin, y, y, ymin])
return xbar, ybar
# Make a local copy of resources so they can be modified.
res = copy(res)
# Intercept and turn off draw and frame resources. These will be applied
# if necessary once the histogram has been constructed.
frame_on = getattr(res, 'nglFrame', True)
draw_on = getattr(res, 'nglDraw', True)
res.nglFrame = False
res.nglDraw = False
# Set default values of special resources that will not be recognised by
# Ngl.
resdefaults = {
'nglHistogramBarWidthPercent': 1.,
'nglHistogramBinIntervals': None,
'nglHistogramNumberOfBins': 10,
'nglxHistogramRange': None,
'nglxHistogramBarColor': 0,
'nglxHistogramBarOutlineColor': 1,
'nglxHistogramDensity': True,
}
# Record the values of the special resources, and remove them from the
# resource list.
specialres = dict()
for resname in resdefaults.keys():
specialres[resname] = getattr(res, resname, resdefaults[resname])
try:
delattr(res, resname)
except AttributeError:
pass
# Work out the values of histogram parameters.
nbins = specialres['nglHistogramBinIntervals'] or \
specialres['nglHistogramNumberOfBins']
hrange = specialres['nglxHistogramRange']
density = specialres['nglxHistogramDensity']
# Compute the histogram with the NumPy routine.
hist, binedges = np.histogram(data, bins=nbins, range=hrange, density=density)
dx = binedges[1] - binedges[0]
ymin = 0.
# Draw up to three bars, the first and last and the tallest, if they are
# different. This sets up the plot correctly. The user specified plotting
# resources are respected during this process. The lines drawn here will
# be covered by the histogram.
xdum, ydum = list(), list()
xbar, ybar = bar_position(binedges[0], hist[0], dx, ymin, # first bar
specialres['nglHistogramBarWidthPercent'])
xdum.append(xbar)
ydum.append(ybar)
if nbins > 1:
xbar, ybar = bar_position(binedges[-2], hist[-1], dx, ymin, # last bar
specialres['nglHistogramBarWidthPercent'])
xdum.append(xbar)
ydum.append(ybar)
i = np.argmax(hist)
if i not in (0, nbins-1):
xbar, ybar = bar_position(binedges[i], hist[i], dx, ymin, # tallest bar
specialres['nglHistogramBarWidthPercent'])
xdum.append(xbar)
ydum.append(ybar)
plot = xy(wks, np.array(xdum), np.array(ydum), res)
# Create resources for shading the bars and drawing outlines around them.
fillres = Ngl.Resources()
fillres.gsFillColor = specialres['nglxHistogramBarColor']
lineres = Ngl.Resources()
lineres.gsLineColor = specialres['nglxHistogramBarOutlineColor']
# Draw the bars and their outlines.
plot._histbars = list()
plot._histlines = list()
for bar in xrange(nbins):
xbar, ybar = bar_position(binedges[bar], hist[bar], dx, ymin,
specialres['nglHistogramBarWidthPercent'])
plot._histbars.append(Ngl.add_polygon(wks, plot, xbar, ybar, fillres))
plot._histlines.append(Ngl.add_polyline(wks, plot, xbar, ybar, lineres))
# Apply drawing and frame advancing if they were specified in the input
# resources.
if draw_on:
Ngl.draw(plot)
if frame_on:
Ngl.frame(plot)
# Return a plot identifier.
return plot
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)
def __call__(self, wks, plots, dims, res=None):
"""Panel a collection of plots.
Arguments:
wks -- An Ngl workstation to plot onto.
plots -- A collection of plots to put together into a panel
plot.
dims -- Dimensions of the panel plot. It should be specified as
(rows, cols) or, if the panel resource 'nglPanelRowSpec' is
set to True, as (cols0, cols1, cols2) where each entry
is a row and its value specifies the number of columns in
the row.
res -- An Ngl resources object. The following resources are
understood:
'nglPanelRowSpec'
'nglPanelCenter'
'nglPanelScalePlotIndex'
'nglPanelTop'
'nglPanelLeft'
'nglPanelRight'
'nglPanelXF'
'nglPanelYF'
'nglPanelXWhiteSpacePercent'
'nglPanelYWhiteSpacePercent'
'nglPanelTitleString'
'nglPanelTitleFont'
'nglPanelTitleFontheightF'
'nglPanelTitleFontColor'
'nglPanelTitleOffsetXF'
'nglPanelTitleOffsetYF'
'nglPanelFigureStrings' X not implemented
'nglPanelLabelBar' X not implemented
'nglPanelDebug'
"""
# Get the unified panel specification. These details can be used to
# produce the panel plot independently of the user's choice of panel
# specification format.
self.number_rows, self.number_columns, self.row_spec, \
self.number_panels = self._get_panel_spec(dims, res)
# Get the number of plots that can actually be plotted. We cannot plot
# more plots than panels that are defined.
self.number_plots = self._get_number_plots(plots)
# Retrieve the width and height of the plots from the plot objects.
# Only one plot is considered and the others assumed to be the same
# size. Unless otherwise specified, the plot this information comes
# from will be the first plot.
self.plot_width, self.plot_height = \
self._get_plot_dimensions(plots, res)
# Compute the space to be left between plots. This consists of a base
# size plus an offset. The offset can be user specified via the
# resources variable.
self.delta_x, self.delta_y = self._get_plot_spacing(res)
# Compute the total width of the panel plot.
self.total_width = self.number_columns * self.plot_width + \
(self.number_columns - 1) * self.delta_x
# Compute the top and left coordinates of the panel plot.
self.panel_x0, self.panel_center_x = self._get_panel_xcoord(res)
self.panel_y0 = self._get_panel_ycoord(res)
# Work out where to draw each of the plots.
self.plot_coordinates = self._get_plot_coords(res)
# Draw each plot on the workstation.
self._draw_plots(plots, res)
# Draw panel labels if required.
self._draw_panel_labels(wks, res)
# Draw a main title if required.
self._draw_main_title(wks, res)
# Finish the panelling by advancing the frame unless requested not to.
if getattr(res, 'nglPanelFrame', True):
Ngl.frame(wks)
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_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)
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_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)
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)
line_res.gsLineDashPattern = 2 # dashed lines
Ngl.polyline(wks, plot, lon2d[::4, 0], lat2d[::4, 0], line_res)
Ngl.polyline(wks, plot, lon2d[::4, -1], lat2d[::4, -1], line_res)
#
# Add a text string explaining the lines.
#
text_res = Ngl.Resources() # text resources
text_res.txFontHeightF = 0.03 # font height
text_res.txFontColor = "Red"
Ngl.text_ndc(wks, "dashed red line shows area with no data", 0.5, 0.17,
text_res)
Ngl.frame(wks) # Now advance frame.
#
# Add cyclic points. Since lat2d/lon2d are 2D arrays, make them
# cyclic the same way you do the 2D data array.
#
u = Ngl.add_cyclic(urot[::4, ::4])
v = Ngl.add_cyclic(vrot[::4, ::4])
lon = Ngl.add_cyclic(lon2d[::4, ::4])
lat = Ngl.add_cyclic(lat2d[::4, ::4])
#
# Specify new coordinate arrays for data.
#
resources.vfXArray = lon
resources.vfYArray = lat
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_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 main():
import numpy
import Ngl
options = parse_input()
u_array2, v_array2, s_array2, x_min, x_max, y_min, y_max = read_data(options)
wks_type = 'ps'
wks = Ngl.open_wks(wks_type, options.data)
color_res = Ngl.Resources()
gs_res = Ngl.Resources()
text_res = Ngl.Resources()
rgb_min = []
for s in options.rgb_min.split():
rgb_min.append(float(s))
rgb_max = []
for s in options.rgb_max.split():
rgb_max.append(float(s))
l = []
l.append([1.00, 1.00, 1.00])
l.append([0.00, 0.00, 0.00])
f_l = []
n = 100
for f in range(n):
f_l.append(float((f + 1) * (1.0 / n)))
for f in f_l:
r = rgb_min[0] + f * (rgb_max[0] - rgb_min[0])
g = rgb_min[1] + f * (rgb_max[1] - rgb_min[1])
b = rgb_min[2] + f * (rgb_max[2] - rgb_min[2])
l.append([r, g, b])
rgb_map = numpy.array(l, 'f')
color_res.wkColorMap = rgb_map
Ngl.set_values(wks, color_res)
vector_res = get_vector_res(options, x_min, x_max, y_min, y_max)
plot = Ngl.vector_scalar(wks, u_array2, v_array2, s_array2, vector_res)
color_res.wkColorMap = "default"
Ngl.set_values(wks, color_res)
line_thickness = 2.0
gs_res.gsMarkerSizeF = 10.0
gs_res.gsMarkerThicknessF = 2.0
gs_res.gsLineDashPattern = 2
gs_res.gsLineThicknessF = line_thickness
text_res.txFont = 21
text_res.txFontHeightF = 0.015
text_res.txFontColor = options.text_color
for atom in read_molecule(options):
gs_res.gsMarkerIndex = 16
gs_res.gsMarkerColor = "White"
Ngl.polymarker(wks, plot, [atom[2]], [atom[1]], gs_res)
gs_res.gsMarkerIndex = 4
gs_res.gsMarkerColor = options.text_color
Ngl.polymarker(wks, plot, [atom[2]], [atom[1]], gs_res)
Ngl.text(wks, plot, atom[0], [atom[2] + 1.0], [atom[1]], text_res)
Ngl.frame(wks)
del plot
Ngl.end()
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()
def draw_gh_500hPa(sel_year, sel_month):
print("darwing 500hPa gh plot for " + str(sel_month).zfill(2) + " " +
str(sel_year))
files_cli = sorted(
glob.glob(
os.path.join(
'/home/alley/work/Dong/mongo/seasonal_analysis/data/data/download_from_mongo/cli/',
'gh_500_*.grb')))
f_cli = xr.open_mfdataset(files_cli,
concat_dim="time",
combine="nested",
engine="cfgrib",
parallel=True)
h = f_cli["z"]
h = h.mean(dim="time")
h_cli = h / 98
lat = f_cli["latitude"].values
lon = f_cli["longitude"].values
file_cur = "/home/alley/work/Dong/mongo/seasonal_analysis/data/data/download_from_mongo/cur/gh_500_" + str(
sel_year) + str(sel_month).zfill(2) + ".grb"
# print(file_cur)
f_cur = xr.open_mfdataset(file_cur, engine="cfgrib", parallel=True)
h_cur = f_cur["z"] / 98
h_ano = h_cur - h_cli
leftString = "500hPa Height & Anomaly for " + str(sel_month) + str(
sel_year)
rightString = "dagpm"
wks_type = 'png'
wks = Ngl.open_wks(
wks_type,
'/home/alley/work/Dong/mongo/seasonal_analysis/images/gh_500hPa_' +
str(sel_year) + str(sel_month).zfill(2) + '.png')
res = Ngl.Resources()
res.nglFrame = False
res.nglDraw = False
res.mpLimitMode = "LatLon"
res.mpMinLonF = 60
res.mpMaxLonF = 180
res.mpMinLatF = 0
res.mpMaxLatF = 60
plot_map = Ngl.map(wks, res)
fres = Ngl.Resources()
fres.nglFrame = False
fres.nglDraw = False
fres.cnFillOn = True
fres.sfXArray = lon
fres.sfYArray = lat
fres.lbOrientation = "Horizontal" # horizontal labelbar
fres.cnLinesOn = False
fres.cnLineLabelsOn = False
fres.cnLevelSelectionMode = "ExplicitLevels"
fres.cnFillPalette = "BlueDarkRed18"
fres.cnLevels = np.arange(-5, 6, 1)
plot_cn = Ngl.contour(wks, h_ano, fres)
cnres = Ngl.Resources()
cnres.nglDraw = False
cnres.nglFrame = False
cnres.cnFillOn = False
cnres.cnLinesOn = True
cnres.cnLineLabelsOn = True
cnres.cnInfoLabelOn = False
cnres.sfXArray = lon
cnres.sfYArray = lat
cnres.cnLevelSelectionMode = "ExplicitLevels"
cnres.cnLevels = np.arange(500, 600, 4)
cnres.cnLineColor = "black"
cnres.cnLineThicknessF = 5
plot2 = Ngl.contour(wks, h_cur, cnres)
Ngl.overlay(plot_map, plot_cn)
Ngl.overlay(plot_map, plot2)
#
txres = Ngl.Resources()
txres.txFontHeightF = 0.024
#
Ngl.text_ndc(wks, leftString, 0.3, 0.83, txres)
Ngl.text_ndc(wks, rightString, 0.85, 0.83, txres)
#
Ngl.maximize_plot(wks, plot_map)
Ngl.draw(plot_map)
Ngl.frame(wks)
Ngl.end()
print("Finish darwing 500hPa gh plot plot for " + str(sel_month).zfill(2) +
" " + str(sel_year))
def plot_oope_map(data, figname, size_class=None, percentage=1):
''' Draws 2D OOPE maps.
:param xarray.Dataset data: 2D OOPE array. Dims must be (y, x, comm, size)
:param str figname: Name of the figure file (must end by .png or .pdf)
:param list size_class: Size classes to output (in m)
:param float percentage: percentage used to saturate colorbar from percentile.
Colorbar is saturated from values of the (X) and (100 - X) percentile.
:return: None
'''
if size_class is None:
size_class = [1e-3, 1e-2, 1e-1, 1]
# sort size class in ascending order, and add 0 and infinity as size bounds
size_class = np.sort(size_class)
if size_class[0] != 0:
size_class = np.concatenate(([0], size_class), axis=0)
if size_class[-1] != np.Inf:
size_class = np.concatenate((size_class, [np.Inf]), axis=0)
# Check that the OOPE dataset has 4 dimensions (i.e. no time dimension)
ndims = len(data['OOPE'].dims)
if ndims != 4:
message = 'Data must have dimensions of size (lat, lon, comm, wei)'
print(message)
sys.exit(0)
# Recover data variables
length = data['length'].values
oope = data['OOPE'].values
lon = data['longitude'][:].values
lat = data['latitude'][:].values
comm = data['community'][:].values.astype(np.int)
# mask oope where land
oope = np.ma.masked_where(np.isnan(oope), oope)
comm_string = misc.extract_community_names(data)
if figname.endswith('png'):
form = 'png'
elif figname.endswith('pdf'):
form = 'pdf'
else:
message = 'Figure name should end with png or pdf'
print(message)
sys.exit(0)
# opens the document
wks = Ngl.open_wks(form, figname)
# set the document colormap
# resngl = Ngl.Resources()
# resngl.wkColorMap = 'precip2_15lev'
# Ngl.set_values(wks, resngl)
# Add gray to the workspace
Ngl.new_color(wks, 0.7, 0.7, 0.7)
# init the plot resources
# For a detailed description, see https://www.ncl.ucar.edu/Document/Graphics/Resources/
res = Ngl.Resources()
# not necessary, just a good habit
res.nglDraw = False
res.nglFrame = False
# Set map resources.
res.mpLimitMode = "LatLon" # limit map via lat/lon
res.mpMinLatF = lat.min() # map area
res.mpMaxLatF = lat.max() # latitudes
res.mpMinLonF = lon.min() # and
res.mpMaxLonF = lon.max() # longitudes
res.mpFillOn = True
res.mpLandFillColor = "LightGray"
res.mpOceanFillColor = -1
res.mpInlandWaterFillColor = "LightBlue"
res.mpGeophysicalLineThicknessF = 1 # thickness of coastlines
# coordinates for contour plots
res.sfXArray = lon
res.sfYArray = lat
res.cnFillOn = True # filled contour
res.cnLinesOn = False # no lines
res.cnLineLabelsOn = False # no labels
res.cnInfoLabelOn = False # no info about contours
res.cnFillMode = 'CellFill' # contourf=AreaFill, pcolor="CellFill" or "RasterFill"
res.lbOrientation = "Horizontal" # colorbar orientation
res.lbLabelFontHeightF = 0.012 # colorbar label fontsize
res.lbTitlePosition = "Bottom" # position of colorbar title
res.lbTitleFontHeightF = 0.012 # title font height
# res.cnFillPalette = "wgne15"
res.cnFillPalette = "WhiteBlueGreenYellowRed"
txres = Ngl.Resources()
txres.txJust = "BottomCenter"
txres.txFontHeightF = 0.02
res.cnLevelSelectionMode = 'ExplicitLevels'
res.cnMaxLevelCount = 41
res.mpGridAndLimbOn = True
# Equations are complicated with NCARG
# see https://www.ncl.ucar.edu/Applications/fcodes.shtml
res.lbTitleString = "OOPE (J.kg~S~-1~N~.m~S~-2~N~)" # title of colorbar
# Loop over communities
for icom in comm:
# Loop over size classes
for isize in xrange(0, len(size_class) - 1):
# Extract sizes comprised between the size class bound
iw = np.nonzero((length >= size_class[isize]) & (length < size_class[isize+1]))[0]
if iw.size == 0:
continue
# Integrate OOPE for the given community and given size class
temp = oope[:, :, icom, iw]
temp = np.sum(temp, axis=-1)
# Finds the colorbar limits
cmin, cmax = misc.find_percentile(temp, percentage=1)
# draw the contour maps
# defines the contour
res.cnLevels = np.linspace(cmin, cmax, res.cnMaxLevelCount)
mapplot = Ngl.contour_map(wks, temp, res)
# add title
title = 'Community=%s, %.2E m <= L < %.2E m' % (comm_string[icom], size_class[isize], size_class[isize + 1])
Ngl.text_ndc(wks, title, 0.5, 0.85, txres)
# draws the map
Ngl.draw(mapplot)
# add a page to the pdf output
Ngl.frame(wks)
def draw_2D_plot(ptype, cseason, ncases, cases, casenames, nsite, lats, lons,
filepath, filepathobs, casedir):
# ncases, the number of models
# cases, the name of models
# casename, the name of cases
# filepath, model output filepath
# filepathobs, filepath for observational data
# inptrs = [ncases]
if not os.path.exists(casedir):
os.mkdir(casedir)
if not os.path.exists(casedir + '/2D'):
os.mkdir(casedir + '/2D')
_Font = 25
interp = 2
extrap = False
infiles = ['' for x in range(ncases)]
ncdfs = ['' for x in range(ncases)]
alpha = ['A', 'B', 'C', 'D', 'E', 'F']
cunits = ['']
varis = [
'SWCF', 'LWCF', 'PRECT', 'LHFLX', 'SHFLX', 'TMQ', 'PS', 'TS', 'U10',
'CLDTOT', 'CLDLOW', 'CLDHGH', 'TGCLDLWP'
]
varisobs = [
'SWCF', 'LWCF', 'PRECT', 'LHFLX', 'SHFLX', 'PREH2O', 'PS', 'TS', 'U10',
'CLDTOT_CAL', 'CLDTOT_CAL', 'CLDTOT_CAL', 'TGCLDLWP_OCEAN'
]
nvaris = len(varis)
cscale = [1, 1, 86400000, 1, 1, 1, 1, 1, 1, 100, 100, 100, 1000, 1, 1, 1]
cscaleobs = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
# cntrs = [[0 for col in range(11)] for row in range(nvaris)]
cntrs = np.zeros((nvaris, 11), np.float32)
obsdataset = [
'CERES-EBAF', 'CERES-EBAF', 'GPCP', 'ERAI', 'NCEP', 'ERAI', 'NCEP',
'ERAI', 'ERAI', 'CALIPSOCOSP', 'CALIPSOCOSP', 'CALIPSOCOSP', 'NVAP'
]
plot2d = ['' for x in range(nvaris)]
for iv in range(0, nvaris):
# make plot for each field
if (varis[iv] == 'CLDTOT' or varis[iv] == 'CLDLOW'
or varis[iv] == 'CLDHGH'):
cntrs[iv, :] = [2, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90]
if (varis[iv] == 'LWCF'):
cntrs[iv, :] = [1, 2, 5, 10, 15, 20, 25, 30, 35, 40, 45]
if (varis[iv] == 'SWCF' or varis[iv] == 'FLUT'):
cntrs[iv, :] = [
-40, -50, -60, -70, -80, -90, -100, -110, -120, -130, -140
]
if (varis[iv] == 'PRECT' or varis[iv] == 'QFLX'):
cntrs[iv, :] = [0.5, 1.5, 3, 4.5, 6, 7.5, 9, 10.5, 12, 13.5, 15]
if (varis[iv] == 'LHFLX'):
cntrs[iv, :] = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110]
if (varis[iv] == 'SHFLX'):
cntrs[iv, :] = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110]
if (varis[iv] == 'U10'):
cntrs[iv, :] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
if (varis[iv] == 'TMQ'):
cntrs[iv, :] = [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55]
if (varis[iv] == 'TGCLDLWP'):
cntrs[iv, :] = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110]
# Observational data
if (obsdataset[iv] == 'CCCM'):
if (cseason == 'ANN'):
fileobs = '/Users/guoz/databank/CLD/CCCm/cccm_cloudfraction_2007-' + cseason + '.nc'
else:
fileobs = '/Users/guoz/databank/CLD/CCCm/cccm_cloudfraction_2007-2010-' + cseason + '.nc'
else:
if (varisobs[iv] == 'PRECT'):
fileobs = filepathobs + '/GPCP_' + cseason + '_climo.nc'
else:
fileobs = filepathobs + obsdataset[
iv] + '_' + cseason + '_climo.nc'
# infiles[im]=filepath[im]+cases[im]+'/run/'+cases[im]+'_'+cseason+'_climo.nc'
inptrobs = Dataset(fileobs, 'r')
latobs = inptrobs.variables['lat'][:]
lonobs = inptrobs.variables['lon'][:]
if (varisobs[iv] == 'U10'):
B0 = inptrobs.variables[varisobs[iv]][0, :, :]
B1 = inptrobs.variables['V10'][0, :, :]
B = (B0 * B0 + B1 * B1)
B = B * cscaleobs[iv]
B = np.sqrt(B)
else:
B = inptrobs.variables[varisobs[iv]][0, :, :]
B = B * cscaleobs[iv]
#************************************************
# create plot
#************************************************
plotname = casedir + '/2D/Horizontal_' + varis[iv] + '_' + cseason
plot2d[iv] = 'Horizontal_' + varis[iv] + '_' + cseason
wks = Ngl.open_wks(ptype, plotname)
Ngl.define_colormap(wks, 'cmocean_thermal')
# Ngl.define_colormap(wks,'MPL_coolwarm')
# ngl_define_colormap(wks,'prcp_1')
plot = []
textres = Ngl.Resources()
textres.txFontHeightF = 0.02 # Size of title.
textres.txFont = _Font
Ngl.text_ndc(wks, varis[iv], 0.1, .97, textres)
pres = Ngl.Resources()
pres.nglMaximize = True
pres.nglPanelYWhiteSpacePercent = 5
pres.nglPanelXWhiteSpacePercent = 5
pres.nglPanelBottom = 0.20
pres.nglPanelTop = 0.9
pres.pmLabelBarWidthF = 0.8
pres.nglFrame = False
pres.nglPanelLabelBar = True # Turn on panel labelbar
pres.nglPanelLabelBarLabelFontHeightF = 0.015 # Labelbar font height
pres.nglPanelLabelBarHeightF = 0.0750 # Height of labelbar
pres.nglPanelLabelBarWidthF = 0.700 # Width of labelbar
pres.lbLabelFont = 'helvetica-bold' # Labelbar font
pres.nglPanelTop = 0.935
pres.nglPanelFigureStrings = alpha
pres.nglPanelFigureStringsJust = 'BottomRight'
res = Ngl.Resources()
res.nglDraw = False #-- don't draw plots
res.nglFrame = False
res.cnFillOn = True
res.cnFillMode = 'RasterFill'
res.cnLinesOn = False
res.nglMaximize = True
res.mpFillOn = True
res.mpCenterLonF = 180
res.tiMainFont = _Font
res.tiMainFontHeightF = 0.025
res.tiXAxisString = ''
res.tiXAxisFont = _Font
res.tiXAxisFontHeightF = 0.025
res.tiYAxisString = ''
res.tiYAxisFont = _Font
res.tiYAxisOffsetXF = 0.0
res.tiYAxisFontHeightF = 0.025
res.tmXBLabelFont = _Font
res.tmYLLabelFont = _Font
res.tiYAxisFont = _Font
res.vpWidthF = 0.80 # set width and height
res.vpHeightF = 0.40
res.vpXF = 0.04
res.vpYF = 0.30
res.cnInfoLabelOn = False
res.cnFillOn = True
res.cnLinesOn = False
res.cnLineLabelsOn = False
res.lbLabelBarOn = False
# res.vcRefMagnitudeF = 5.
# res.vcMinMagnitudeF = 1.
# res.vcRefLengthF = 0.04
# res.vcRefAnnoOn = True#False
# res.vcRefAnnoZone = 3
# res.vcRefAnnoFontHeightF = 0.02
# res.vcRefAnnoString2 =''
# res.vcRefAnnoOrthogonalPosF = -1.0
# res.vcRefAnnoArrowLineColor = 'blue' # change ref vector color
# res.vcRefAnnoArrowUseVecColor = False
# res.vcMinDistanceF = .05
# res.vcMinFracLengthF = .
# res.vcRefAnnoParallelPosF = 0.997
# res.vcFillArrowsOn = True
# res.vcLineArrowThicknessF = 3.0
# res.vcLineArrowHeadMinSizeF = 0.01
# res.vcLineArrowHeadMaxSizeF = 0.03
# res.vcGlyphStyle = 'CurlyVector' # turn on curley vectors
# res@vcGlyphStyle ='Fillarrow'
# res.vcMonoFillArrowFillColor = True
# res.vcMonoLineArrowColor = True
# res.vcLineArrowColor = 'green' # change vector color
# res.vcFillArrowEdgeColor ='white'
# res.vcPositionMode ='ArrowTail'
# res.vcFillArrowHeadInteriorXF =0.1
# res.vcFillArrowWidthF =0.05 #default
# res.vcFillArrowMinFracWidthF =.5
# res.vcFillArrowHeadMinFracXF =.5
# res.vcFillArrowHeadMinFracYF =.5
# res.vcFillArrowEdgeThicknessF = 2.0
res.mpFillOn = False
res.cnLevelSelectionMode = 'ExplicitLevels'
res.cnLevels = cntrs[iv][:]
for im in range(0, ncases):
ncdfs[im] = './data/' + cases[im] + '_site_location.nc'
infiles[im] = filepath[im] + cases[im] + '/' + cases[
im] + '_' + cseason + '_climo.nc'
inptrs = Dataset(infiles[im], 'r') # pointer to file1
lat = inptrs.variables['lat'][:]
nlat = len(lat)
lon = inptrs.variables['lon'][:]
nlon = len(lon)
area = inptrs.variables['area'][:]
area_wgt = np.zeros(nlat)
sits = np.linspace(0, nsite - 1, nsite)
ncdf = Dataset(ncdfs[im], 'r')
n = ncdf.variables['n'][:]
idx_cols = ncdf.variables['idx_cols'][:]
if (varis[iv] == 'PRECT'):
A = inptrs.variables['PRECC'][
0, :] + inptrs.variables['PRECL'][0, :]
else:
A = inptrs.variables[varis[iv]][0, :]
if (varis[iv] == 'FLUT'):
A = inptrs.variables['FLUT'][0, :] - inptrs.variables['FLNS'][
0, :]
else:
A = inptrs.variables[varis[iv]][0, :]
if (varis[iv] == 'U10'):
A = inptrs.variables['U10'][0, :] * inptrs.variables['U10'][
0, :]
A = np.sqrt(A)
else:
A = inptrs.variables[varis[iv]][0, :]
A_xy = A
A_xy = A_xy * cscale[iv]
ncdf.close()
inptrs.close()
if im == 0:
dsizes = len(A_xy)
field_xy = [[0 for col in range(dsizes)]
for row in range(ncases)]
field_xy[im][:] = A_xy
res.lbLabelBarOn = False
if (np.mod(im, 2) == 0):
res.tiYAxisOn = True
else:
res.tiYAxisOn = False
res.tiXAxisOn = False
res.sfXArray = lon
res.sfYArray = lat
res.mpLimitMode = 'LatLon'
res.mpMaxLonF = max(lon)
res.mpMinLonF = min(lon)
res.mpMinLatF = min(lat)
res.mpMaxLatF = max(lat)
res.tiMainString = 'GLB=' + str(
np.sum(A_xy[:] * area[:] / np.sum(area)))
textres.txFontHeightF = 0.015
Ngl.text_ndc(wks, alpha[im] + ' ' + casenames[im], 0.3,
.135 - im * 0.03, textres)
p = Ngl.contour_map(wks, A_xy, res)
plot.append(p)
# observation
# res.nglLeftString = obsdataset[iv]
# res@lbLabelBarOn = True
# res@lbOrientation = 'vertical' # vertical label bars
res.lbLabelFont = _Font
res.tiYAxisOn = True
res.tiXAxisOn = True
res.tiXAxisFont = _Font
rad = 4.0 * np.arctan(1.0) / 180.0
re = 6371220.0
rr = re * rad
dlon = abs(lonobs[2] - lonobs[1]) * rr
dx = dlon * np.cos(latobs * rad)
jlat = len(latobs)
dy = np.zeros(jlat, dtype=float)
# close enough
dy[0] = abs(lat[2] - lat[1]) * rr
dy[1:jlat - 2] = abs(lat[2:jlat - 1] - lat[0:jlat - 3]) * rr * 0.5
dy[jlat - 1] = abs(lat[jlat - 1] - lat[jlat - 2]) * rr
area_wgt = dx * dy #
is_SE = False
sum1 = 0
sum2 = 0
for j in range(0, jlat - 1):
for i in range(0, len(lonobs) - 1):
if (np.isnan(B[j][i]) != '--'):
sum1 = sum1 + area_wgt[j] * B[j][i]
sum2 = sum2 + area_wgt[j]
glb = sum1 / sum2
res.sfXArray = lonobs
res.sfYArray = latobs
res.mpLimitMode = 'LatLon'
res.mpMaxLonF = max(lonobs)
res.mpMinLonF = min(lonobs)
res.mpMinLatF = min(latobs)
res.mpMaxLatF = max(latobs)
res.tiMainString = 'GLB=' + str(glb)
p = Ngl.contour_map(wks, B, res)
if (iv == 0):
poly_res = Ngl.Resources()
poly_res.gsMarkerIndex = 16
poly_res.gsMarkerSizeF = 0.005
poly_res.gsMarkerColor = 'green'
dum = Ngl.add_polymarker(wks, p, lons, lats, poly_res)
plot.append(p)
if (np.mod(ncases + 1, 2) == 1):
Ngl.panel(wks, plot[:], [(ncases + 1) / 2 + 1, 2], pres)
else:
Ngl.panel(wks, plot[:], [(ncases + 1) / 2, 2], pres)
Ngl.frame(wks)
Ngl.destroy(wks)
# Ngl.end()
return plot2d
ycoord = [16.16,17.1,16.75,16.91,17.26]
polyres.gsMarkerColor = "red"
for gg in range(5):
Ngl.polymarker(wks,xyplot,xcoord[gg],ycoord[gg],polyres)
# 3rd set
ycoord = [24.9,26.6,29.1,27.7]
xcoord = [3.12,3.69,4.54,5.12]
polyres.gsMarkerColor = "green"
for gg in range(4):
Ngl.polymarker(wks,xyplot,xcoord[gg],ycoord[gg],polyres)
Ngl.draw(xyplot)
# Ngl.draw_ndc_grid(wks) # For debug purposes.
#
# Draw a text string on side of plot.
#
txres.txFontHeightF = 0.01
txres.txAngleF = 90.
Ngl.text_ndc(wks,"Central Antarctica",0.665,0.61,txres)
Ngl.text_ndc(wks,"Tropical Atlantic", 0.665,0.75,txres)
Ngl.text_ndc(wks,"North Atlantic", 0.665,0.86,txres)
Ngl.frame(wks) # Now advance frame, we're done!
Ngl.end()
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
# 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.
Ngl.destroy(wks)
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
import Ngl
#
# Draw four wind barbs of the same magnitude, but at different
# locations and in different directions.
#
# Open a workstation.
#
wks_type = "ps"
wks = Ngl.open_wks(wks_type, "wmbarb")
#
# Draw wind barbs.
#
x = [0.25, 0.75, 0.75, 0.25] # x,y,u,v can also be Numeric arrays.
y = [0.25, 0.25, 0.75, 0.75]
u = [50., -50., -50., 50.0]
v = [50., 50., -50., -50.0]
Ngl.wmsetp("wbs", 0.2) # Scale the size.
Ngl.wmbarb(wks, x, y, u, v) # Draw barbs.
Ngl.frame(wks) # Draw plot.
#
# Retrieve the value of the wbs parameter.
#
size = Ngl.wmgetp("wbs")
print size
Ngl.end()
def clubb_std_prf(ptype, cseason, ncases, cases, casenames, nsite, lats, lons,
filepath, filepathobs, casedir, varis, vname, cscale,
chscale, pname):
# ncases, the number of models
# cases, the name of models
# casename, the name of cases
# filepath, model output filepath
# filepathobs, filepath for observational data
# inptrs = [ncases]
if not os.path.exists(casedir):
os.mkdir(casedir)
_Font = 25
interp = 2
extrap = False
mkres = Ngl.Resources()
mkres.gsMarkerIndex = 2
mkres.gsMarkerColor = "Red"
mkres.gsMarkerSizeF = 15.
alphas = [
'a) ', 'b) ', 'c) ', 'd) ', 'e) ', 'f) ', 'g) ', 'h) ', 'i) ', 'j) ',
'k) ', 'l) ', 'm) ', 'n) ', 'o) ', 'p) ', 'q) ', 'r) ', 's) ', 't) ',
'u) ', 'v) ', 'w) ', 'x) ', 'y) ', 'z) '
]
infiles = ["" for x in range(ncases)]
ncdfs = ["" for x in range(ncases)]
nregions = nsite
nvaris = len(varis)
plotstd = ["" for x in range(nsite)]
for ire in range(0, nsite):
if not os.path.exists(casedir + '/' + str(lons[ire]) + 'E_' +
str(lats[ire]) + 'N'):
os.mkdir(casedir + '/' + str(lons[ire]) + 'E_' + str(lats[ire]) +
'N')
plotname = casedir + '/' + str(lons[ire]) + 'E_' + str(
lats[ire]) + 'N/' + pname + '_' + str(lons[ire]) + "E_" + str(
lats[ire]) + "N_" + cseason
plotstd[ire] = pname + '_' + str(lons[ire]) + "E_" + str(
lats[ire]) + "N_" + cseason
wks = Ngl.open_wks(ptype, plotname)
Ngl.define_colormap(wks, "GMT_paired")
plot = []
res = Ngl.Resources()
res.nglMaximize = False
if (lats[ire] > 0 and lons[ire] < 230):
res.trYMinF = 400.
res.trYMaxF = 1000.
else:
res.trYMinF = 700.
res.trYMaxF = 1000.
res.nglDraw = False
res.nglFrame = False
res.lgLabelFontHeightF = .02 # change font height
res.lgPerimOn = False # no box around
res.vpWidthF = 0.30 # set width and height
res.vpHeightF = 0.30
res.vpXF = 0.04
res.vpYF = 0.30
res.tmYLLabelFont = _Font
res.tmXBLabelFont = _Font
res.tiYAxisFont = _Font
res.tiXAxisFont = _Font
res.tmXBLabelFontHeightF = 0.01
res.tmXBLabelFontThicknessF = 2.0
res.xyMarkLineMode = "Lines"
res.xyLineThicknesses = [
3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3., 3., 3., 3., 3, 3, 3, 3, 3, 3, 3
]
res.xyDashPatterns = np.arange(0, 24, 1)
# res.xyMarkers = np.arange(16,40,1)
# res.xyMarkerSizeF = 0.005
res.tiYAxisString = "Pressure [hPa]"
for iv in range(0, nvaris):
if (iv == nvaris - 1):
res.pmLegendDisplayMode = "NEVER"
res.xyExplicitLegendLabels = casenames[:]
res.pmLegendSide = "top"
res.pmLegendParallelPosF = 0.6
res.pmLegendOrthogonalPosF = -0.5
res.pmLegendWidthF = 0.10
res.pmLegendHeightF = 0.10
res.lgLabelFontHeightF = .02
res.lgLabelFontThicknessF = 1.5
res.lgPerimOn = True
else:
res.pmLegendDisplayMode = "NEVER"
# if(obsdataset[iv] =="CCCM"):
# if(cseason == "ANN"):
# fileobs = "/Users/guoz/databank/CLD/CCCm/cccm_cloudfraction_2007-"+cseason+".nc"
# else:
# fileobs = "/Users/guoz/databank/CLD/CCCm/cccm_cloudfraction_2007-2010-"+cseason+".nc"
# inptrobs = Dataset(fileobs,'r')
# B=inptrobs.variables[varisobs[iv]][:,(lats[ire]),(lons[ire])]
# else:
# if (varisobs[iv] =="PRECT"):
# fileobs = filepathobs+'/GPCP_'+cseason+'_climo.nc'
# else:
# fileobs = filepathobs + obsdataset[iv]+'_'+cseason+'_climo.nc'
# inptrobs = Dataset(fileobs,'r')
# if (varisobs[iv] =="THETA"):
# B = inptrobs.variables['T'][0,:,(lats[ire]),(lons[ire])]
# pre1 = inptrobs.variables['lev'][:]
# for il1 in range (0, len(pre1)):
# B[il1] = B[il1]*(1000/pre1[il1])**0.286
# else:
# pre1 = inptrobs.variables['lev'][:]
# B = inptrobs.variables[varisobs[iv]][0,:,(lats[ire]),(lons[ire])]
#
# B[:]=B[:] * cscaleobs[iv]
for im in range(0, ncases):
ncdfs[im] = './data/' + cases[im] + '_site_location.nc'
infiles[im] = filepath[im] + cases[im] + '/' + cases[
im] + '_' + cseason + '_climo.nc'
inptrs = Dataset(infiles[im], 'r') # pointer to file1
lat = inptrs.variables['lat'][:]
nlat = len(lat)
lon = inptrs.variables['lon'][:]
nlon = len(lon)
lev = inptrs.variables['ilev'][:]
nlev = len(lev)
ncdf = Dataset(ncdfs[im], 'r')
n = ncdf.variables['n'][:]
idx_cols = ncdf.variables['idx_cols'][:, :]
ncdf.close()
if (im == 0):
A_field = np.zeros((ncases, nlev), np.float32)
for subc in range(0, n[ire]):
npoint = idx_cols[ire, n[subc] - 1] - 1
if (varis[iv] == 'THETA'):
tmp = inptrs.variables['T'][0, :, npoint]
hyam = inptrs.variables['hyam'][:]
hybm = inptrs.variables['hybm'][:]
ps = inptrs.variables['PS'][0, npoint]
ps = ps
p0 = inptrs.variables['P0']
pre = np.zeros((nlev), np.float32)
for il in range(0, nlev):
pre[il] = hyam[il] * p0 + hybm[il] * ps
tmp[il] = tmp[il] * (100000 / pre[il])**0.286
theunits = str(
chscale[iv]) + inptrs.variables['T'].units
else:
tmp = inptrs.variables[varis[iv]][0, :, npoint]
# tmp2=inptrs.variables['C6rt_Skw_fnc'][0,:,npoint]
# tmp3=inptrs.variables['tau_zm'][0,:,npoint]
# tmp4=inptrs.variables['tau_wpxp_zm'][0,:,npoint]
theunits = str(
chscale[iv]) + inptrs.variables[varis[iv]].units
if (varis[iv] == 'tau_zm' or varis[iv] == 'tau_wp2_zm' \
or varis[iv] == 'tau_wp3_zm' or varis[iv] == 'tau_xp2_zm' \
or varis[iv] == 'tau_no_N2_zm' or varis[iv] == 'tau_wpxp_zm'):
tmp = 1 / tmp
tmp[0:10] = 0.0
theunits = str(chscale[iv]) + inptrs.variables[
varis[iv]].units + '^-1'
A_field[im, :] = (A_field[im, :] + tmp[:] / n[ire]).astype(
np.float32)
A_field[im, :] = A_field[im, :] * cscale[iv]
inptrs.close()
res.tiXAxisString = vname[iv] + " Unit= " + theunits
res.trYReverse = True
res.xyLineColors = np.arange(3, 20, 2)
res.xyMarkerColors = np.arange(2, 20, 2)
p = Ngl.xy(wks, A_field, lev, res)
plot.append(p)
pres = Ngl.Resources()
pres.nglFrame = False
pres.txFont = _Font
pres.nglMaximize = False
pres.txFont = _Font
# pres.nglPanelYWhiteSpacePercent = 5
# pres.nglPanelXWhiteSpacePercent = 5
# pres.wkPaperWidthF = 17 # in inches
# pres.wkPaperHeightF = 28 # in inches
pres.nglPanelTop = 0.935
pres.nglPanelFigureStrings = alphas
pres.nglPanelFigureStringsJust = 'Topleft'
pres.nglPanelFigureStringsFontHeightF = 0.015
pres.gsnPaperOrientation = "Landscape"
txres = Ngl.Resources()
txres.txFontHeightF = 0.02
txres.txFont = _Font
Ngl.text_ndc(
wks,
"CLUBB VAR at " + str(lons[ire]) + "E," + str(lats[ire]) + "N",
0.5, 0.75, txres)
Common_functions.create_legend(wks, casenames, 0.015,
np.arange(3, 20, 2), 0.3,
0.59 + ncases * 0.01)
Ngl.panel(wks, plot[:], [nvaris / 2, 2], pres)
Ngl.frame(wks)
Ngl.destroy(wks)
return plotstd
line_res.gsLineThicknessF = 1.5 # line thickness scale line_res.gsLineDashPattern = 2 # dashed lines Ngl.polyline(wks,plot,lon2d[:,0],lat2d[:,0],line_res) Ngl.polyline(wks,plot,lon2d[:,-1],lat2d[:,-1],line_res) # # Add a text string explaining the lines. # text_res = Ngl.Resources() # text resources text_res.txFontHeightF = 0.03 # font height text_res.txFontColor = "Red" Ngl.text_ndc(wks,"dashed red line shows area with no data",0.5,0.17,text_res) Ngl.frame(wks) # Now advance frame. # # Add cyclic points. Since lat2d/lon2d are 2D arrays, make them # cyclic the same way you do the 2D data array. # u = Ngl.add_cyclic(urot) v = Ngl.add_cyclic(vrot) lon = Ngl.add_cyclic(lon2d) lat = Ngl.add_cyclic(lat2d) # # Specify new coordinate arrays for data. # resources.vfXArray = lon resources.vfYArray = lat
polyg = Ngl.add_polygon(wks1,map,x1d,y1d,pgres) #-- add a labelbar lbres = Ngl.Resources() lbres.vpWidthF = 0.85 lbres.vpHeightF = 0.15 lbres.lbOrientation = 'Horizontal' lbres.lbFillPattern = 'SolidFill' lbres.lbMonoFillPattern = 21 #-- must be 21 for color solid fill lbres.lbMonoFillColor = False #-- use multiple colors lbres.lbFillColors = colors #-- indices from loaded colormap lbres.lbBoxCount = len(colormap[colors,:]) lbres.lbLabelFontHeightF= 0.014 lbres.lbLabelAlignment = 'InteriorEdges' lbres.lbLabelStrings = labels lb = Ngl.labelbar_ndc(wks1,nlevs+1,labels,0.1,0.24,lbres) #-- maximize and draw the plot and advance the frame Ngl.maximize_plot(wks1, map) Ngl.draw(map) Ngl.frame(wks1) #-- get wallclock time t2 = time.time() print '' print 'Wallclock time: %0.3f seconds' % (t2-t1) print '' Ngl.end()
def large_scale_prf (ptype,cseason, ncases, cases, casenames, nsite, lats, lons, filepath, filepathobs, casedir):
# ncases, the number of models
# cases, the name of models
# casename, the name of cases
# filepath, model output filepath
# filepathobs, filepath for observational data
# inptrs = [ncases]
if not os.path.exists(casedir):
os.mkdir(casedir)
_Font = 25
interp = 2
extrap = False
mkres = Ngl.Resources()
mkres.gsMarkerIndex = 2
mkres.gsMarkerColor = 'Red'
mkres.gsMarkerSizeF = 15.
infiles = ['' for x in range(ncases)]
ncdfs = ['' for x in range(ncases)]
nregions = nsite
localnames=np.append('ERAI',casenames)
varis = [ 'CLOUD' , 'CLDLIQ', 'THETA','RELHUM']
varisobs = ['CC_ISBL', 'CLWC_ISBL', 'THETA','RELHUM' ]
alphas = ['a) ','b) ','c) ','d) ','e) ','f) ','g) ','h) ','i) ', 'j) ', 'k) ', 'l) ', 'm) ', 'n) ', 'o) ', 'p) ', 'q) ', 'r) ', 's) ', 't) ', 'u) ', 'v) ', 'w) ', 'x) ', 'y) ', 'z) ']
nvaris = len(varis)
cunits = ['%','g/kg','K', '%' ]
cscale = [100, 1000 , 1., 1 ]
cscaleobs = [100, 1000 , 1., 1, 1, 1000, 1,1,1,1,1,1,1]
obsdataset =['ERAI', 'ERAI', 'ERAI', 'ERAI', 'ERAI', 'ERAI', 'ERAI', 'ERAI','ERAI','ERAI']
plotlgs=['' for x in range(nsite)]
for ire in range (0, nsite):
if not os.path.exists(casedir+'/'+str(lons[ire])+'E_'+str(lats[ire])+'N'):
os.mkdir(casedir+'/'+str(lons[ire])+'E_'+str(lats[ire])+'N')
plotname = casedir+'/'+str(lons[ire])+'E_'+str(lats[ire])+'N/Largescale_'+str(lons[ire])+'E_'+str(lats[ire])+'N_'+cseason
plotlgs[ire] = 'Largescale_'+str(lons[ire])+'E_'+str(lats[ire])+'N_'+cseason
wks= Ngl.open_wks(ptype,plotname)
Ngl.define_colormap(wks,'GMT_paired')
plot = []
res = Ngl.Resources()
res.nglMaximize = False
res.nglDraw = False
res.nglFrame = False
res.lgPerimOn = False # no box around
res.vpWidthF = 0.30 # set width and height
res.vpHeightF = 0.30
res.tiYAxisString = "Pressure [hPa]"
if (lats[ire] > 0 and lons[ire]<230 ):
res.trYMinF = 400.
res.trYMaxF = 1000.
else:
res.trYMinF = 700.
res.trYMaxF = 1000.
res.tiMainFont = _Font
res.tmYLLabelFont = _Font
res.tmXBLabelFont = _Font
res.tiYAxisFont = _Font
res.tiXAxisFont = _Font
res.tmXBLabelFontHeightF = 0.01
res.tmXBLabelFontThicknessF = 1.0
res.xyMarkLineMode = 'Lines'
res.xyLineThicknesses = [3.0, 3.0, 3.0, 3.0, 3.0, 3.0,3.,3.,3.,3.,3,3,3,3,3,3,3]
res.xyDashPatterns = np.arange(0,24,1)
pres = Ngl.Resources()
pres.nglFrame = False
pres.txFont = _Font
pres.nglPanelYWhiteSpacePercent = 5
pres.nglPanelXWhiteSpacePercent = 5
# pres.nglPanelTop = 0.88
pres.wkPaperWidthF = 17 # in inches
pres.wkPaperHeightF = 28 # in inches
pres.nglMaximize = True
pres.nglPanelTop = 0.935
pres.nglPanelFigureStrings = alphas
pres.nglPanelFigureStringsJust = 'Topleft'
pres.nglPanelFigureStringsFontHeightF = 0.015
for iv in range (0, nvaris):
if(iv == nvaris-1):
res.pmLegendDisplayMode = 'NEVER'
res.xyExplicitLegendLabels = casenames[:]
res.pmLegendSide = 'top'
res.pmLegendParallelPosF = 0.6
res.pmLegendOrthogonalPosF = -0.5
res.pmLegendWidthF = 0.10
res.pmLegendHeightF = 0.10
res.lgLabelFontHeightF = .02
res.lgLabelFontThicknessF = 1.5
res.lgPerimOn = True
else:
res.pmLegendDisplayMode = 'NEVER'
if(obsdataset[iv] =='CCCM'):
if(cseason == 'ANN'):
fileobs = '/Users/guoz/databank/CLD/CCCm/cccm_cloudfraction_2007-'+cseason+'.nc'
else:
fileobs = '/Users/guoz/databank/CLD/CCCm/cccm_cloudfraction_2007-2010-'+cseason+'.nc'
inptrobs = Dataset(fileobs,'r')
latobs=inptrobs.variables['lat'][:]
latobs_idx = np.abs(latobs - lats[ire]).argmin()
lonobs=inptrobs.variables['lon'][:]
lonobs_idx = np.abs(lonobs - lons[ire]).argmin()
B=inptrobs.variables[varisobs[iv]][:,latobs_idx,lonobs_idx]
else:
if (varisobs[iv] =='PRECT'):
fileobs = filepathobs+'/GPCP_'+cseason+'_climo.nc'
else:
fileobs = filepathobs + obsdataset[iv]+'_'+cseason+'_climo.nc'
inptrobs = Dataset(fileobs,'r')
if (varisobs[iv] =='THETA'):
latobs=inptrobs.variables['lat'][:]
latobs_idx = np.abs(latobs - lats[ire]).argmin()
lonobs=inptrobs.variables['lon'][:]
lonobs_idx = np.abs(lonobs - lons[ire]).argmin()
B = inptrobs.variables['T'][0,:,latobs_idx,lonobs_idx]
pre1 = inptrobs.variables['lev'][:]
for il1 in range (0, len(pre1)):
B[il1] = B[il1]*(1000/pre1[il1])**0.286
else:
pre1 = inptrobs.variables['lev'][:]
latobs=inptrobs.variables['lat'][:]
latobs_idx = np.abs(latobs - lats[ire]).argmin()
lonobs=inptrobs.variables['lon'][:]
lonobs_idx = np.abs(lonobs - lons[ire]).argmin()
B = inptrobs.variables[varisobs[iv]][0,:,latobs_idx,lonobs_idx]
B[:]=B[:] * cscaleobs[iv]
for im in range (0,ncases):
ncdfs[im] = './data/'+cases[im]+'_site_location.nc'
infiles[im]= filepath[im]+cases[im]+'/'+cases[im]+'_'+cseason+'_climo.nc'
inptrs = Dataset(infiles[im],'r') # pointer to file1
lat=inptrs.variables['lat'][:]
nlat=len(lat)
lon=inptrs.variables['lon'][:]
nlon=len(lon)
lev=inptrs.variables['lev'][:]
nlev=len(lev)
ncdf= Dataset(ncdfs[im],'r')
n =ncdf.variables['n'][:]
idx_cols=ncdf.variables['idx_cols'][:,:]
ncdf.close()
if (im ==0 ):
A_field = np.zeros((ncases,nlev),np.float32)
for subc in range( 0, n[ire]):
npoint=idx_cols[ire,n[subc]-1]-1
if (varis[iv] == 'THETA'):
tmp = inptrs.variables['T'][0,:,npoint]
hyam =inptrs.variables['hyam'][:]
hybm =inptrs.variables['hybm'][:]
ps=inptrs.variables['PS'][0,npoint]
ps=ps
p0=inptrs.variables['P0']
pre = np.zeros((nlev),np.float32)
for il in range (0, nlev):
pre[il] = hyam[il]*p0 + hybm[il] * ps
tmp[il] = tmp[il] * (100000/pre[il])**0.286
theunits=str(cscale[iv])+inptrs.variables['T'].units
else:
tmp=inptrs.variables[varis[iv]][0,:,npoint]
theunits=str(cscale[iv])+inptrs.variables[varis[iv]].units
A_field[im,:] = (A_field[im,:]+tmp[:]/n[ire]).astype(np.float32 )
A_field[im,:] = A_field[im,:] *cscale[iv]
inptrs.close()
textres = Ngl.Resources()
textres.txFontHeightF = 0.02
textres.txFont = _Font
textres.txFontHeightF = 0.015
res.tiXAxisString = varis[iv]+ " Unit= " + cunits[iv]
res.trXMinF = min(np.min(A_field[0, :]),np.min(B))
res.trXMaxF = max(np.max(A_field[0, :]),np.max(B))
if(varis[iv] == 'CLOUD') :
if (lats[ire] >= 0 and lons[ire]<230):
res.trXMinF = 0.
res.trXMaxF = 50.
else:
res.trXMinF = 0.
res.trXMaxF = 100.
if(varis[iv] =='CLDLIQ') :
if (lats[ire] >= 0):
res.trXMinF = 0.
res.trXMaxF = 0.1
else:
res.trXMinF = 0.
res.trXMaxF = 0.1
if(varis[iv] =='RELHUM') :
res.trXMinF = 0.
res.trXMaxF = 100.
if(varis[iv] == 'THETA') :
if (lats[ire] >= 0 and lons[ire]<230 ):
res.trXMinF = 290.
res.trXMaxF = 340.
else:
res.trXMinF = 280
res.trXMaxF = 320
if(varis[iv] == 'T') :
res.trXMinF = 180
res.trXMaxF = 300
if(varis[iv] == 'U') :
res.trXMinF = -40
res.trXMaxF = 40
res.trYReverse = True
res.xyLineColors = np.arange(3,20,2)
res.xyMarkerColors = np.arange(2,20,2)
p = Ngl.xy(wks,A_field,lev,res)
res.trYReverse = False
res.xyLineColors = ['black']
pt = Ngl.xy(wks,B,pre1,res)
Ngl.overlay(p,pt)
plot.append(p)
Ngl.panel(wks,plot[:],[nvaris/2,2],pres)
txres = Ngl.Resources()
txres.txFontHeightF = 0.02
txres.txFont = _Font
Ngl.text_ndc(wks,'Large-scale VAR at '+ str(lons[ire])+'E,'+str(lats[ire])+'N',0.5,0.93,txres)
Common_functions.create_legend(wks,localnames,0.02,np.append('black',np.arange(3,20,2)),0.25,0.85)
Ngl.frame(wks)
Ngl.destroy(wks)
return plotlgs
def large_scale_prf(ptype, cseason, ncases, cases, casenames, nsite, lats,
lons, filepath, filepathobs, casedir, dofv):
# ncases, the number of models
# cases, the name of models
# casename, the name of cases
# filepath, model output filepath
# filepathobs, filepath for observational data
# inptrs = [ncases]
if not os.path.exists(casedir):
os.mkdir(casedir)
_Font = 25
interp = 2
extrap = False
mkres = Ngl.Resources()
mkres.gsMarkerIndex = 2
mkres.gsMarkerColor = "Red"
mkres.gsMarkerSizeF = 15.
infiles = ["" for x in range(ncases)]
ncdfs = ["" for x in range(ncases)]
nregions = nsite
varis = [
"CLOUD", "OMEGA", "Q", "CLDLIQ", "THETA", "RELHUM", "U", "CLDICE", "T"
]
varisobs = [
"CC_ISBL", "OMEGA", "SHUM", "CLWC_ISBL", "THETA", "RELHUM", "U",
"CIWC_ISBL", "T"
]
nvaris = len(varis)
cunits = [
"%", "mba/day", "g/kg", "g/kg", "K", "%", "m/s", "g/kg", "m/s", "m/s",
"K", "m"
]
cscale = [100, 864, 1000, 1000, 1., 1, 1, 1000, 1, 1, 1, 1, 1, 1, 1]
cscaleobs = [100, 1, 1, 1000, 1., 1, 1, 1000, 1, 1, 1, 1, 1, 1, 1]
obsdataset = [
"ERAI", "ERAI", "ERAI", "ERAI", "ERAI", "ERAI", "ERAI", "ERAI", "ERAI",
"ERAI"
]
plotlgs = ["" for x in range(nsite)]
for ire in range(0, nsite):
if not os.path.exists(casedir + '/' + str(lons[ire]) + 'E_' +
str(lats[ire]) + 'N'):
os.mkdir(casedir + '/' + str(lons[ire]) + 'E_' + str(lats[ire]) +
'N')
plotname = casedir + '/' + str(lons[ire]) + 'E_' + str(
lats[ire]) + 'N/Largescale_' + str(lons[ire]) + "E_" + str(
lats[ire]) + "N_" + cseason
plotlgs[ire] = 'Largescale_' + str(lons[ire]) + "E_" + str(
lats[ire]) + "N_" + cseason
wks = Ngl.open_wks(ptype, plotname)
Ngl.define_colormap(wks, "GMT_paired")
plot = []
res = Ngl.Resources()
res.nglMaximize = False
res.nglDraw = False
res.nglFrame = False
res.lgPerimOn = False # no box around
res.vpWidthF = 0.30 # set width and height
res.vpHeightF = 0.30
res.tiYAxisString = "Pressure [hPa]"
res.tiMainFont = _Font
res.tmYLLabelFont = _Font
res.tmXBLabelFont = _Font
res.tiYAxisFont = _Font
res.tiXAxisFont = _Font
res.tmXBLabelFontHeightF = 0.01
res.tmXBLabelFontThicknessF = 1.0
res.xyMarkLineMode = 'Lines'
# res.tmXBLabelAngleF = 45
res.xyLineThicknesses = [
3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3., 3., 3., 3., 3, 3, 3, 3, 3, 3, 3
]
res.xyDashPatterns = np.arange(0, 24, 1)
pres = Ngl.Resources()
pres.nglFrame = False
pres.txString = "Large-scale VAR at" + str(lons[ire]) + "E," + str(
lats[ire]) + "N"
pres.txFont = _Font
pres.nglPanelYWhiteSpacePercent = 5
pres.nglPanelXWhiteSpacePercent = 5
pres.nglPanelTop = 0.88
pres.wkPaperWidthF = 17 # in inches
pres.wkPaperHeightF = 28 # in inches
pres.nglMaximize = True
pres.wkWidth = 10000
pres.wkHeight = 10000
for iv in range(0, nvaris):
if (iv == nvaris - 1):
res.pmLegendDisplayMode = "NEVER"
res.xyExplicitLegendLabels = casenames[:]
res.pmLegendSide = "top"
res.pmLegendParallelPosF = 0.6
res.pmLegendOrthogonalPosF = -0.5
res.pmLegendWidthF = 0.10
res.pmLegendHeightF = 0.10
res.lgLabelFontHeightF = .02
res.lgLabelFontThicknessF = 1.5
res.lgPerimOn = True
else:
res.pmLegendDisplayMode = "NEVER"
if (obsdataset[iv] == "CCCM"):
if (cseason == "ANN"):
fileobs = "/Users/guoz/databank/CLD/CCCm/cccm_cloudfraction_2007-" + cseason + ".nc"
else:
fileobs = "/Users/guoz/databank/CLD/CCCm/cccm_cloudfraction_2007-2010-" + cseason + ".nc"
inptrobs = Dataset(fileobs, 'r')
latobs = inptrobs.variables['lat'][:]
latobs_idx = np.abs(latobs - lats[ire]).argmin()
lonobs = inptrobs.variables['lon'][:]
lonobs_idx = np.abs(lonobs - lons[ire]).argmin()
B = inptrobs.variables[varisobs[iv]][:, latobs_idx, lonobs_idx]
else:
if (varisobs[iv] == "PRECT"):
fileobs = filepathobs + '/GPCP_' + cseason + '_climo.nc'
else:
fileobs = filepathobs + obsdataset[
iv] + '_' + cseason + '_climo.nc'
inptrobs = Dataset(fileobs, 'r')
if (varisobs[iv] == "THETA"):
latobs = inptrobs.variables['lat'][:]
latobs_idx = np.abs(latobs - lats[ire]).argmin()
lonobs = inptrobs.variables['lon'][:]
lonobs_idx = np.abs(lonobs - lons[ire]).argmin()
B = inptrobs.variables['T'][0, :, latobs_idx, lonobs_idx]
pre1 = inptrobs.variables['lev'][:]
for il1 in range(0, len(pre1)):
B[il1] = B[il1] * (1000 / pre1[il1])**0.286
else:
pre1 = inptrobs.variables['lev'][:]
latobs = inptrobs.variables['lat'][:]
latobs_idx = np.abs(latobs - lats[ire]).argmin()
lonobs = inptrobs.variables['lon'][:]
lonobs_idx = np.abs(lonobs - lons[ire]).argmin()
B = inptrobs.variables[varisobs[iv]][0, :, latobs_idx,
lonobs_idx]
B[:] = B[:] * cscaleobs[iv]
for im in range(0, ncases):
ncdfs[im] = './data/' + cases[im] + '_site_location.nc'
infiles[im] = filepath[im] + '/' + cases[
im] + '_' + cseason + '_climo.nc'
inptrs = Dataset(infiles[im], 'r') # pointer to file1
lat = inptrs.variables['lat'][:]
nlat = len(lat)
lon = inptrs.variables['lon'][:]
nlon = len(lon)
lev = inptrs.variables['lev'][:]
nlev = len(lev)
ncdf = Dataset(ncdfs[im], 'r')
n = ncdf.variables['n'][:]
idx_cols = ncdf.variables['idx_cols'][:, :]
if (dofv):
idx_lats = ncdf.variables['idx_coord_lat'][:, :]
idx_lons = ncdf.variables['idx_coord_lon'][:, :]
ncdf.close()
theunits = "[units]"
if (im == 0):
A_field = np.zeros((ncases, nlev), np.float32)
for subc in range(0, n[ire]):
npoint = idx_cols[ire, n[subc] - 1] - 1
if (dofv):
npointlat = idx_lats[ire, 0]
npointlon = idx_lons[ire, 0]
if (varis[iv] == 'THETA'):
if (dofv):
tmp = inptrs.variables['T'][0, :, npointlat,
npointlon]
else:
tmp = inptrs.variables['T'][0, :, npoint]
hyam = inptrs.variables['hyam'][:]
hybm = inptrs.variables['hybm'][:]
if (dofv):
ps = inptrs.variables['PS'][0, npointlat,
npointlon]
else:
ps = inptrs.variables['PS'][0, npoint]
ps = ps
p0 = 100000.0 #CAM uses a hard-coded p0
pre = np.zeros((nlev), np.float32)
for il in range(0, nlev):
pre[il] = hyam[il] * p0 + hybm[il] * ps
tmp[il] = tmp[il] * (100000 / pre[il])**0.286
theunits = str(
cscale[iv]) + "x" + inptrs.variables['T'].units
else:
if (dofv):
tmp = inptrs.variables[varis[iv]][0, :, npointlat,
npointlon]
else:
tmp = inptrs.variables[varis[iv]][0, :, npoint]
theunits = str(cscale[iv]) + "x" + inptrs.variables[
varis[iv]].units
##import pdb; pdb.set_trace()
A_field[im, :] = (A_field[im, :] + tmp[:] / n[ire]).astype(
np.float32)
A_field[im, :] = A_field[im, :] * cscale[iv]
inptrs.close()
res.tiMainString = varis[iv] + " " + theunits
res.trXMinF = min(np.min(A_field[0, :]), np.min(B))
res.trXMaxF = max(np.max(A_field[0, :]), np.max(B))
if (varis[iv] == "THETA"):
res.trXMinF = 270.
res.trXMaxF = 400.
if (varis[iv] == "CLOUD" or varis[iv] == "RELHUM"):
res.trXMinF = 0.
res.trXMaxF = 100.
if (varis[iv] == "T"):
res.trXMinF = 180
res.trXMaxF = 300
if (varis[iv] == "U"):
res.trXMinF = -40
res.trXMaxF = 40
res.trYReverse = True
res.xyLineColors = np.arange(3, 20, 2)
res.xyMarkerColors = np.arange(2, 20, 2)
p = Ngl.xy(wks, A_field, lev, res)
res.trYReverse = False
res.xyLineColors = ["black"]
pt = Ngl.xy(wks, B, pre1, res)
Ngl.overlay(p, pt)
plot.append(p)
Ngl.panel(wks, plot[:], [nvaris / 3, 3], pres)
txres = Ngl.Resources()
txres.txFontHeightF = 0.02
txres.txFont = _Font
Ngl.text_ndc(
wks, "Large-scale VAR at" + str(lons[ire]) + "E," +
str(lats[ire]) + "N", 0.5, 0.92 + ncases * 0.01, txres)
Common_functions.create_legend(wks, casenames, 0.02,
np.arange(3, 20,
2), 0.1, 0.89 + ncases * 0.01)
Ngl.frame(wks)
Ngl.destroy(wks)
return plotlgs
polyg = Ngl.add_polygon(wks1, map, x1d, y1d, pgres)
#-- add a labelbar
lbres = Ngl.Resources()
lbres.vpWidthF = 0.85
lbres.vpHeightF = 0.15
lbres.lbOrientation = 'Horizontal'
lbres.lbFillPattern = 'SolidFill'
lbres.lbMonoFillPattern = 21 #-- must be 21 for color solid fill
lbres.lbMonoFillColor = False #-- use multiple colors
lbres.lbFillColors = colors #-- indices from loaded colormap
lbres.lbBoxCount = len(colormap[colors, :])
lbres.lbLabelFontHeightF = 0.014
lbres.lbLabelAlignment = 'InteriorEdges'
lbres.lbLabelStrings = labels
lb = Ngl.labelbar_ndc(wks1, nlevs + 1, labels, 0.1, 0.24, lbres)
#-- maximize and draw the plot and advance the frame
Ngl.maximize_plot(wks1, map)
Ngl.draw(map)
Ngl.frame(wks1)
#-- get wallclock time
t2 = time.time()
print('')
print('Wallclock time: %0.3f seconds' % (t2 - t1))
print('')
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) Ngl.draw(xy)
def draw_3D_plot(ptype, clevel, cseason, ncases, cases, casenames, nsite, lats,
lons, filepath, filepathobs, casedir):
# ncases, the number of models
# cases, the name of models
# casename, the name of cases
# filepath, model output filepath
# filepathobs, filepath for observational data
# inptrs = [ncases]
if not os.path.exists(casedir):
os.mkdir(casedir)
if not os.path.exists(casedir + "/2D"):
os.mkdir(casedir + "/2D")
_Font = 25
interp = 2
extrap = False
mkres = Ngl.Resources()
mkres.gsMarkerIndex = 2
mkres.gsMarkerColor = "Red"
mkres.gsMarkerSizeF = 15.
infiles = ["" for x in range(ncases)]
ncdfs = ["" for x in range(ncases)]
varis = ["T", "OMEGA", "Z3"]
varisobs = ["T", "OMEGA", "Z3"]
alpha = ["A", "B", "C", "D", "E", "F"]
nvaris = len(varis)
cunits = [""]
cscale = [1, 864, 1, 1, 1, 1]
cscaleobs = [1, 1, 1, 1, 0.04]
cntrs = np.zeros((nvaris, 11), np.float32)
obsdataset = ["ERAI", "ERAI", "ERAI", "ERAI", "ERAI"]
level = [
1000., 925., 850., 700., 600., 500., 400., 300., 250., 200., 150., 100.
]
plot3d = ["" for x in range(nvaris)]
for iv in range(0, nvaris):
# make plot for each field
# Observational data
if (obsdataset[iv] == "CCCM"):
if (cseason == "ANN"):
fileobs = "/Users/guoz/databank/CLD/CCCm/cccm_cloudfraction_2007-" + cseason + ".nc"
else:
fileobs = "/Users/guoz/databank/CLD/CCCm/cccm_cloudfraction_2007-2010-" + cseason + ".nc"
else:
if (varisobs[iv] == "PRECT"):
fileobs = filepathobs + '/GPCP_' + cseason + '_climo.nc'
else:
fileobs = filepathobs + obsdataset[
iv] + '_' + cseason + '_climo.nc'
inptrobs = Dataset(fileobs, 'r')
latobs = inptrobs.variables['lat'][:]
lonobs = inptrobs.variables['lon'][:]
levobs = inptrobs.variables['lev'][:]
levobs_idx = np.abs(levobs - clevel).argmin()
B = inptrobs.variables[varisobs[iv]][0, levobs_idx, :, :]
B = B * cscaleobs[iv]
# cntrs= np.arange(np.min(B),np.max(B),12)
if (varis[iv] == "OMEGA"):
cntrs[iv, :] = [-50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50]
if (varis[iv] == "Z3"):
if (clevel == 500):
cntrs[iv, :] = [
5300, 5400, 5500, 5600, 5700, 5800, 5900, 6000, 6100, 6200,
6300
]
else:
cntrs[iv, :] = [
300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300
]
if (varis[iv] == "T"):
if (clevel == 500):
cntrs[iv, :] = [
210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310
]
else:
cntrs[iv, :] = [
292, 294, 296, 298, 300, 302, 304, 306, 308, 310, 312
]
if (varis[iv] == "Q"):
if (clevel == 500):
cntrs[iv, :] = [
210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310
]
else:
cntrs[iv, :] = [
292, 294, 296, 298, 300, 302, 304, 306, 308, 310, 312
]
#************************************************
# create plot
#************************************************
plotname = casedir + '/2D/Horizontal_' + varis[
iv] + '_' + cseason + str(clevel)
plot3d[iv] = 'Horizontal_' + varis[iv] + '_' + cseason + str(clevel)
wks = Ngl.open_wks(ptype, plotname)
Ngl.define_colormap(wks, "amwg256")
plot = []
textres = Ngl.Resources()
textres.txFontHeightF = 0.02 # Size of title.
textres.txFont = _Font
Ngl.text_ndc(wks, varis[iv], 0.1, .97, textres)
pres = Ngl.Resources()
# pres.nglMaximize = True
pres.nglFrame = False
pres.nglPanelYWhiteSpacePercent = 5
pres.nglPanelXWhiteSpacePercent = 5
pres.nglPanelBottom = 0.2
pres.nglPanelTop = 0.9
pres.nglPanelLabelBar = True
pres.pmLabelBarWidthF = 0.8
pres.nglFrame = False
pres.nglPanelLabelBar = True # Turn on panel labelbar
pres.nglPanelLabelBarLabelFontHeightF = 0.015 # Labelbar font height
pres.nglPanelLabelBarHeightF = 0.0750 # Height of labelbar
pres.nglPanelLabelBarWidthF = 0.700 # Width of labelbar
pres.lbLabelFont = "helvetica-bold" # Labelbar font
pres.nglPanelTop = 0.93
pres.nglPanelFigureStrings = alpha
pres.nglPanelFigureStringsJust = "BottomRight"
res = Ngl.Resources()
res.nglDraw = False #-- don't draw plots
res.nglFrame = False
res.cnFillOn = True
res.cnFillMode = "RasterFill"
res.cnLinesOn = False
res.nglMaximize = True
res.mpFillOn = True
res.mpCenterLonF = 180
res.tiMainFont = _Font
res.tiMainFontHeightF = 0.025
res.tiXAxisString = ""
res.tiXAxisFont = _Font
res.tiXAxisFontHeightF = 0.025
res.tiYAxisString = ""
res.tiYAxisFont = _Font
res.tiYAxisOffsetXF = 0.0
res.tiYAxisFontHeightF = 0.025
res.tmXBLabelFont = _Font
res.tmYLLabelFont = _Font
res.tiYAxisFont = _Font
# res.nglStringFont = _Font
# res.nglStringFontHeightF = 0.04
# res.nglRightString = ""#"Cloud Fraction"
# res.nglScalarContour = True
res.cnInfoLabelOn = False
res.cnFillOn = True
res.cnLinesOn = False
res.cnLineLabelsOn = False
res.lbLabelBarOn = False
# res.vcRefMagnitudeF = 5.
# res.vcMinMagnitudeF = 1.
# res.vcRefLengthF = 0.04
# res.vcRefAnnoOn = True#False
# res.vcRefAnnoZone = 3
# res.vcRefAnnoFontHeightF = 0.02
# res.vcRefAnnoString2 =""
# res.vcRefAnnoOrthogonalPosF = -1.0
# res.vcRefAnnoArrowLineColor = "blue" # change ref vector color
# res.vcRefAnnoArrowUseVecColor = False
# res.vcMinDistanceF = .05
# res.vcMinFracLengthF = .
# res.vcRefAnnoParallelPosF = 0.997
# res.vcFillArrowsOn = True
# res.vcLineArrowThicknessF = 3.0
# res.vcLineArrowHeadMinSizeF = 0.01
# res.vcLineArrowHeadMaxSizeF = 0.03
# res.vcGlyphStyle = "CurlyVector" # turn on curley vectors
# res@vcGlyphStyle ="Fillarrow"
# res.vcMonoFillArrowFillColor = True
# res.vcMonoLineArrowColor = True
# res.vcLineArrowColor = "green" # change vector color
# res.vcFillArrowEdgeColor ="white"
# res.vcPositionMode ="ArrowTail"
# res.vcFillArrowHeadInteriorXF =0.1
# res.vcFillArrowWidthF =0.05 #default
# res.vcFillArrowMinFracWidthF =.5
# res.vcFillArrowHeadMinFracXF =.5
# res.vcFillArrowHeadMinFracYF =.5
# res.vcFillArrowEdgeThicknessF = 2.0
res.mpFillOn = False
res.cnLevelSelectionMode = "ExplicitLevels"
res.cnLevels = cntrs[iv, :]
for im in range(0, ncases):
ncdfs[im] = './data/' + cases[im] + '_site_location.nc'
infiles[im] = filepath[im] + '/' + cases[
im] + '_' + cseason + '_climo.nc'
inptrs = Dataset(infiles[im], 'r') # pointer to file1
lat = inptrs.variables['lat'][:]
nlat = len(lat)
lon = inptrs.variables['lon'][:]
nlon = len(lon)
#area=inptrs.variables['area'][:]
lev = inptrs.variables['lev'][:]
lev_idx = np.abs(lev - clevel).argmin()
area_wgt = np.zeros(nlat)
# area_wgt[:] = gw[:]
sits = np.linspace(0, nsite - 1, nsite)
ncdf = Dataset(ncdfs[im], 'r')
n = ncdf.variables['n'][:]
idx_cols = ncdf.variables['idx_cols'][:]
A = inptrs.variables[varis[iv]][0, lev_idx, :]
A_xy = A
A_xy = A_xy * cscale[iv]
ncdf.close()
inptrs.close()
if im == 0:
dsizes = len(A_xy)
field_xy = [[0 for col in range(dsizes)]
for row in range(ncases)]
field_xy[im][:] = A_xy
res.lbLabelBarOn = False
if (np.mod(im, 2) == 0):
res.tiYAxisOn = True
else:
res.tiYAxisOn = False
res.tiXAxisOn = False
res.sfXArray = lon
res.sfYArray = lat
res.mpLimitMode = "LatLon"
res.mpMaxLonF = max(lon)
res.mpMinLonF = min(lon)
res.mpMinLatF = min(lat)
res.mpMaxLatF = max(lat)
#res.tiMainString = "GLB="+str(np.sum(A_xy[:]*area[:]/np.sum(area)))
res.tiMainString = "GLB="
textres.txFontHeightF = 0.015
Ngl.text_ndc(wks, alpha[im] + " " + casenames[im], 0.3,
.135 - im * 0.03, textres)
p = Ngl.contour_map(wks, A_xy, res)
plot.append(p)
# observation
# res.nglLeftString = obsdataset[iv]
# res@lbLabelBarOn = True
# res@lbOrientation = "vertical" # vertical label bars
res.lbLabelFont = _Font
res.tiYAxisOn = True
res.tiXAxisOn = True
res.tiXAxisFont = _Font
rad = 4.0 * np.arctan(1.0) / 180.0
re = 6371220.0
rr = re * rad
dlon = abs(lonobs[2] - lonobs[1]) * rr
dx = dlon * np.cos(latobs * rad)
jlat = len(latobs)
dy = np.zeros(jlat, dtype=float)
# close enough
dy[0] = abs(lat[2] - lat[1]) * rr
dy[1:jlat - 2] = abs(lat[2:jlat - 1] - lat[0:jlat - 3]) * rr * 0.5
dy[jlat - 1] = abs(lat[jlat - 1] - lat[jlat - 2]) * rr
area_wgt = dx * dy #
is_SE = False
sum1 = 0
sum2 = 0
for j in range(0, jlat - 1):
for i in range(0, len(lonobs) - 1):
if (np.isnan(B[j][i]) != "--"):
sum1 = sum1 + area_wgt[j] * B[j][i]
sum2 = sum2 + area_wgt[j]
res.sfXArray = lonobs
res.sfYArray = latobs
res.mpLimitMode = "LatLon"
res.mpMaxLonF = max(lonobs)
res.mpMinLonF = min(lonobs)
res.mpMinLatF = min(latobs)
res.mpMaxLatF = max(latobs)
res.tiMainString = "GLB=" + str(
sum1 / sum2) #Common_functions.area_avg(B, area_wgt,is_SE))
p = Ngl.contour_map(wks, B, res)
plot.append(p)
if (np.mod(ncases + 1, 2) == 1):
Ngl.panel(wks, plot[:], [(ncases + 1) / 2 + 1, 2], pres)
else:
Ngl.panel(wks, plot[:], [(ncases + 1) / 2, 2], pres)
Ngl.frame(wks)
Ngl.destroy(wks)
return plot3d
cnres.cnFillOn = True cnres.cnLinesOn = False cnres.cnLineLabelsOn = False cnres.cnFillMode = "CellFill" cnres.cnCellFillEdgeColor = "Black" cnres.cnMonoFillColor = True cnres.cnFillColor = "Transparent" cnres.cnCellFillMissingValEdgeColor = "Red" cnres.lbLabelBarOn = False cnres.tiMainString = "Pop Grid cells -- grid stride [::3,::3]" plot = Ngl.contour_map(wks,temp[:-2:3,:-2:3],cnres) Ngl.frame(wks) # # Now set up a vector resource variable. # vcres = Ngl.Resources() # # Set coordinate arrays for data. This is necessary in order to overlay # the vectors on a map. # vcres.vfXArray = lon vcres.vfYArray = lat vcres.mpProjection = "Orthographic" vcres.mpFillOn = True
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) Ngl.draw(xy)
import numpy as np
import Ngl, Nio
#-- open file and read variables
f = Nio.open_file("/mnt/e/SYSU/data/NCEP/air.mon.mean.nc", "r")
var = f.variables["air"][0, 0, :, :]
lat = f.variables["lat"][:]
lon = f.variables["lon"][:]
wks = Ngl.open_wks("png", "/mnt/e/SYSU/p_py")
#-- resource settings
res = Ngl.Resources()
res.nglFrame = False
res.cnFillOn = True
res.cnFillPalette = "NCL_default"
res.cnLineLabelsOn = False
res.lbOrientation = "horizontal" #-- horizontal labelbar
res.sfXArray = lon
res.sfYArray = lat
#-- create the contour plot
plot = Ngl.contour_map(wks, var, res)
#-- write variable long_name and units to the plot
txres = Ngl.Resources()
txres.txFontHeightF = 0.012
Ngl.text_ndc(wks,f.variables["air"].attributes['long_name'],\
0.14,0.82,txres)
Ngl.text_ndc(wks,f.variables["air"].attributes['units'], \
0.95,0.82,txres)
#-- advance the frame
Ngl.frame(wks)
Ngl.end()
def draw_clubb_bgt(ptype, cseason, ncases, cases, casenames, nsite, lats, lons,
filepath, filepathobs, casedir, varis, vname, cscale,
chscale, pname):
# ncases, the number of models
# cases, the name of models
# casename, the name of cases
# filepath, model output filepath
# filepathobs, filepath for observational data
# inptrs = [ncases]
if not os.path.exists(casedir):
os.mkdir(casedir)
_Font = 25
interp = 2
extrap = False
mkres = Ngl.Resources()
mkres.gsMarkerIndex = 2
mkres.gsMarkerColor = "Red"
mkres.gsMarkerSizeF = 15.
infiles = ["" for x in range(ncases)]
ncdfs = ["" for x in range(ncases)]
nregions = nsite
nvaris = len(varis)
plotbgt = ["" for x in range(nsite * ncases)]
alphas = [
'a) ', 'b) ', 'c) ', 'd) ', 'e) ', 'f) ', 'g) ', 'h) ', 'i) ', 'j) ',
'k) ', 'l) ', 'm) ', 'n) ', 'o) ', 'p) ', 'q) ', 'r) ', 's) ', 't) ',
'u) ', 'v) ', 'w) ', 'x) ', 'y) ', 'z) '
]
for ire in range(0, nsite):
for im in range(0, ncases):
if not os.path.exists(casedir + '/' + str(lons[ire]) + 'E_' +
str(lats[ire]) + 'N'):
os.mkdir(casedir + '/' + str(lons[ire]) + 'E_' +
str(lats[ire]) + 'N')
plotname = casedir + '/' + str(lons[ire]) + 'E_' + str(
lats[ire]) + 'N/' + pname + '_' + casenames[im] + "_" + str(
lons[ire]) + "E_" + str(lats[ire]) + "N_" + cseason
plotbgt[im +
ncases * ire] = pname + '_' + casenames[im] + "_" + str(
lons[ire]) + "E_" + str(lats[ire]) + "N_" + cseason
wks = Ngl.open_wks(ptype, plotname)
Ngl.define_colormap(wks, "radar")
plot = []
res = Ngl.Resources()
res.nglDraw = False
res.nglFrame = False
res.nglScale = False
# res.vpWidthF = 0.40 # set width and height
# res.vpHeightF = 0.40
# res.vpXF = 0.04
# res.vpYF = 0.30
res.nglMaximize = False
if (lats[ire] > 0 and lons[ire] < 230):
res.trYMinF = 400.
res.trYMaxF = 1000.
else:
res.trYMinF = 700.
res.trYMaxF = 1000.
res.tiMainFont = _Font
res.tmYLLabelFont = _Font
res.tmXBLabelFont = _Font
res.tiYAxisFont = _Font
res.tiXAxisFont = _Font
res.tmXBLabelFontHeightF = 0.02
res.tmXBLabelFontThicknessF = 1.0
res.xyMarkLineMode = "MarkLines"
res.xyLineThicknesses = [
3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3., 2., 2., 2., 2, 2, 2, 2, 2, 2,
2
]
res.xyLineColors = np.arange(1, 16, 4)
res.xyDashPatterns = np.arange(0, 24, 1)
res.xyMarkers = np.arange(16, 40, 1)
res.xyMarkerSizeF = 0.005
res.xyMarkerColors = np.arange(1, 16, 4)
res.pmLegendDisplayMode = "NEVER"
res.pmLegendSide = "top" # Change location of
res.pmLegendParallelPosF = 0.75 # move units right
res.pmLegendOrthogonalPosF = -0.65 # more neg = down
res.pmLegendWidthF = 0.05 # Change width and
res.pmLegendHeightF = 0.06 # height of legend.
res.lgLabelFontHeightF = 0.03 # change font height
res.lgLabelFontThicknessF = 1.
# res.lgBoxMinorExtentF = 0.5
res.lgPerimOn = False
res.tiYAxisString = "Pressure [hPa]"
res.tmYLLabelFontHeightF = 0.02
res.trYReverse = True
res1 = Ngl.Resources()
res1 = res
pres = Ngl.Resources()
# pres.gsnPaperOrientation= "Landscape"
# pres.wkPaperWidthF = 20 # in inches
# pres.wkPaperHeightF = 28 # in inches
pres.nglMaximize = False
# pres.vpWidthF = 0.40 # set width and height
# pres.vpHeightF = 0.40
pres.nglFrame = False
pres.txFont = _Font
pres.nglPanelYWhiteSpacePercent = 5
pres.nglPanelXWhiteSpacePercent = 3
pres.nglPanelTop = .9
# pres.nglPanelBottom=0.3
# pres.nglPanelLeft= 0.2
# pres.nglPanelRight= 0.95
pres.nglPanelFigureStrings = alphas
pres.nglPanelFigureStringsJust = 'Topleft'
pres.nglPanelFigureStringsFontHeightF = 0.015
for iv in range(0, nvaris):
if (varis[iv] == "rtp2" or varis[iv] == "thlp2"):
budget_ends = ["_ta", "_tp", "_dp1"]
budget_name = [
'~F10~turb adv', '~F10~turb prod', '~F10~dissipation'
]
nterms = len(budget_ends)
if (varis[iv] == "wprtp"):
budget_ends = [
"_bt", "_ma", "_ta", "_tp", "_ac", "_bp", "_pr1",
"_pr2", "_pr3", "_dp1", "_mfl", "_cl", "_sicl", "_pd",
"_forcing"
]
budget_name = budget_ends
nterms = len(budget_ends)
if (varis[iv] == "wpthlp"):
budget_ends = ["_tp", "_bp", "_pr1"]
budget_name = [
'~F10~turb prod', '~F10~buoy+pres', '~F10~ret to iso'
]
nterms = len(budget_ends)
if (varis[iv] == "rtpthlp"):
budget_ends = [
"_bt", "_ma", "_ta", "_tp1", "_tp2", "_dp1", "_dp2",
"_cl", "_sf", "_forcing"
]
budget_name = budget_ends
nterms = len(budget_ends)
if (varis[iv] == "wp2"):
budget_ends = ["_ta", "_bp", "_pr1", "_dp1", "_dp2"]
budget_name = budget_ends
nterms = len(budget_ends)
if (varis[iv] == "wp3"):
budget_ends = [
"_ta", "_tp", "_bp1", "_bp2", "_pr1", "_pr2", "_dp1"
]
budget_name = budget_ends
nterms = len(budget_ends)
if (varis[iv] == "up2" or varis[iv] == "vp2"):
budget_ends = [
"_bt", "_ma", "_ta", "_tp", "_dp1", "_dp2", "_pr1",
"_pr2", "_cl", "_pd", "_sf"
]
budget_name = budget_ends
nterms = len(budget_ends)
if (varis[iv] == "um" or varis[iv] == "vm"):
budget_ends = ["_bt", "_ma", "_ta", "_gf", "_f"]
budget_name = budget_ends
nterms = len(budget_ends)
if (varis[iv] == "thlm" or varis[iv] == "rtm"):
budget_ends = ["_bt", "_ma", "_ta", "_cl", "_mc"]
budget_name = budget_ends
nterms = len(budget_ends)
if (varis[iv] == 'tau_no_N2_zm'):
budget_ends = ['tau_no_N2_zm', 'bkgnd', 'sfc', 'shear']
budget_name = [
'~F8~t~N~~F25~~B~noN2~N~', '~F8~t~N~~F25~~B~bkgnd~N~',
'~F8~t~N~~F25~~B~shear~N~', '~F8~t~N~~F25~~B~shear~N~'
]
nterms = len(budget_ends)
if (varis[iv] == 'tau_xp2_zm'):
budget_ends = ['tau_xp2_zm', 'tau_no_N2_zm']
budget_name = [
'~F8~t~N~~F25~~B~x`~S~2~N~~N~',
'~F8~t~N~~F25~~B~noN2~N~'
]
nterms = len(budget_ends)
if (varis[iv] == 'tau_wp2_zm'):
budget_ends = ['tau_wp2_zm', 'tau_no_N2_zm', 'bvpos']
budget_name = [
'~F8~t~N~~F25~~B~w`~S~2~N~~N~',
'~F8~t~N~~F25~~B~noN2~N~', '~F8~t~N~~F25~~B~bv~N~'
]
nterms = len(budget_ends)
if (varis[iv] == 'tau_wpxp_zm'):
budget_ends = [
'tau_wpxp_zm', 'tau_no_N2_zm', 'bvpos', 'clear'
]
budget_name = [
'~F8~t~N~~F25~~B~w`x`~N~', '~F8~t~N~~F25~~B~noN2~N~',
'~F8~t~N~~F25~~B~bv~N~', '~F8~t~N~~F25~~B~clr~N~'
]
nterms = len(budget_ends)
if (varis[iv] == 'tau_wp3_zm'):
budget_ends = [
'tau_wp3_zm', 'tau_no_N2_zm', 'bvpos', 'clear'
]
budget_name = [
'~F8~t~N~~F25~~B~w`~S~3~N~~N~',
'~F8~t~N~~F25~~B~noN2~N~', '~F8~t~N~~F25~~B~bv~N~',
'~F8~t~N~~F25~~B~clr~N~'
]
nterms = len(budget_ends)
ncdfs[im] = './data/' + cases[im] + '_site_location.nc'
infiles[im] = filepath[im] + cases[im] + '/' + cases[
im] + '_' + cseason + '_climo.nc'
inptrs = Dataset(infiles[im], 'r') # pointer to file1
lat = inptrs.variables['lat'][:]
nlat = len(lat)
lon = inptrs.variables['lon'][:]
nlon = len(lon)
ilev = inptrs.variables['ilev'][:]
nilev = len(ilev)
ncdf = Dataset(ncdfs[im], 'r')
n = ncdf.variables['n'][:]
idx_cols = ncdf.variables['idx_cols'][:, :]
ncdf.close()
A_field = np.zeros((nterms, nilev), np.float32)
for it in range(0, nterms):
for subc in range(0, n[ire]):
npoint = idx_cols[ire, n[subc] - 1] - 1
varis_bgt = varis[iv] + budget_ends[it]
tmp0 = inptrs.variables[varis[iv]][0, :, npoint]
theunits0 = "~F25~" + inptrs.variables[varis[iv]].units
if (varis[iv] == 'wpthlp'
and budget_ends[it] == '_bp'):
tmp = inptrs.variables[varis_bgt][
0, :, npoint] + inptrs.variables['wpthlp_pr3'][
0, :, npoint]
else:
tmp = inptrs.variables[varis_bgt][0, :, npoint]
theunits = str(
chscale[iv]) + "~F25~" + inptrs.variables[
varis[iv] + '_bt'].units
if (varis[iv] == "wprtp" or varis[iv] == "thlp2"):
tmp[0:10] = 0.0
tmp0[0:10] = 0.0
txres = Ngl.Resources()
txres.txFontHeightF = 0.02
txres.txFont = _Font
tmp = tmp * cscale[iv]
A_field[it, :] = (A_field[it, :] +
tmp[:] / n[ire]).astype(np.float32)
inptrs.close()
res.tiXAxisString = vname[iv] + ' Unit= ' + theunits
if (varis[iv] == "wpthlp" or varis[iv] == "thlp2"
or varis[iv] == "wp3" or varis[iv] == "wp2"):
res1.tiXAxisString = vname[iv] + ' Unit= ' + theunits0
p0 = Ngl.xy(wks, tmp0, ilev, res1)
plot.append(p0)
if (varis[iv] == 'wpthlp'):
if (lons[ire] < 230):
res.trXMinF = -1.
res.trXMaxF = 1.
Common_functions.create_legend(wks, budget_name, 0.013,
np.arange(1, 16, 4),
0.77, 0.85 - iv * 0.45)
else:
res.trXMinF = -0.4
res.trXMaxF = 0.4
Common_functions.create_legend(wks, budget_name, 0.013,
np.arange(1, 16, 4),
0.77, 0.85 - iv * 0.45)
if (varis[iv] == 'thlp2'):
if (lons[ire] < 230):
res.trXMinF = -2.8
res.trXMaxF = 2.8
Common_functions.create_legend(wks, budget_name, 0.013,
np.arange(1, 16, 4),
0.77, 0.85 - iv * 0.45)
else:
res.trXMinF = -1.
res.trXMaxF = 1.
Common_functions.create_legend(wks, budget_name, 0.013,
np.arange(1, 16, 4),
0.77, 0.85 - iv * 0.45)
res.tiXAxisString = vname[iv] + ' Unit= ' + theunits
p = Ngl.xy(wks, A_field, ilev, res)
plot.append(p)
del (res.trXMaxF)
del (res.trXMinF)
xp = np.mod(iv, 2)
yp = int(iv / 2)
Ngl.panel(wks, plot[:], [nvaris, 2], pres)
txres = Ngl.Resources()
txres.txFont = _Font
txres.txFontHeightF = 0.02
Ngl.text_ndc(
wks, casenames[im] + " BUDGET at " + str(lons[ire]) + "E," +
str(lats[ire]) + "N", 0.5, 0.91, txres)
Ngl.frame(wks)
Ngl.destroy(wks)
return (plotbgt)
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
def plot_contourmap(path, date, fcst_hours, variable, stat_processing_methods):
##### generate subpath and filename #####
subpath = 'run_{}{:02}{:02}{:02}/{}/'.format(\
date['year'], date['month'], date['day'], date['hour'], variable)
filename1 = 'icon-eu-eps_europe_icosahedral_single-level_{}{:02}{:02}{:02}_{:03}_{}.grib2'.format(\
date['year'], date['month'], date['day'], date['hour'], fcst_hours[0], variable)
filename2 = 'icon-eu-eps_europe_icosahedral_single-level_{}{:02}{:02}{:02}_{:03}_{}.grib2'.format(\
date['year'], date['month'], date['day'], date['hour'], fcst_hours[1], variable)
########################################################################
### read data ###
########################################################################
##### create empty numpy arrays #####
##### 2 fcst_hours, 40 members, 75948 eu gridpoints #####
data_raw = np.empty((2, 40, 75948))
data_members = np.empty((40, 75948))
##### every time in loop open next grib msg from grib file #####
##### grib messages in dwd file are sorted by increasing member number #####
with open(path['base'] + path['data'] + subpath + filename1, 'rb') as file:
for member in range(1, 41):
print('read data from member {}'.format(member))
grib_msg_id = eccodes.codes_grib_new_from_file(file)
data_raw[0, member - 1, :] = eccodes.codes_get_array(
grib_msg_id, 'values')
eccodes.codes_release(grib_msg_id)
with open(path['base'] + path['data'] + subpath + filename2, 'rb') as file:
for member in range(1, 41):
print('read data from member {}'.format(member))
grib_msg_id = eccodes.codes_grib_new_from_file(file)
data_raw[1, member - 1, :] = eccodes.codes_get_array(
grib_msg_id, 'values')
eccodes.codes_release(grib_msg_id)
##### take the difference of the two accumulated total precipitation arrays #####
data_members = data_raw[1, :, :] - data_raw[0, :, :]
del data_raw
##### open icon-eps grid file #####
icongrid_file = nc.Dataset(
path['base'] + path['grid'] + 'icon_grid_0028_R02B07_N02.nc', 'r')
vlat = icongrid_file.variables['clat_vertices'][:].data * 180. / np.pi
vlon = icongrid_file.variables['clon_vertices'][:].data * 180. / np.pi
clat = icongrid_file.variables['clat'][:].data * 180. / np.pi
clon = icongrid_file.variables['clon'][:].data * 180. / np.pi
icongrid_file.close()
for stat_processing in stat_processing_methods:
########################################################################
### statistically process data ###
########################################################################
if stat_processing == 'mean':
data_processed = data_members.mean(axis=0)
elif stat_processing == 'max':
data_processed = data_members.max(axis=0)
elif stat_processing == 'min':
data_processed = data_members.min(axis=0)
elif stat_processing == '90p':
data_processed = np.percentile(data_members, 90, axis=0)
elif stat_processing == '75p':
data_processed = np.percentile(data_members, 75, axis=0)
elif stat_processing == '50p':
data_processed = np.percentile(data_members, 50, axis=0)
elif stat_processing == '25p':
data_processed = np.percentile(data_members, 25, axis=0)
elif stat_processing == '10p':
data_processed = np.percentile(data_members, 10, axis=0)
#print('shape of data_members: {}'.format(np.shape(data_members)))
#print('shape of data_processed: {}'.format(np.shape(data_processed)))
########################################################################
### plot data on world map ###
########################################################################
##### set domain due to center point and radius #####
center_point = dict(lat=48.5, lon=9.0)
radius = 1800 # domain radius in km around center_point
domain = dict(
lat_min=center_point['lat'] - radius / 111.2,
lat_max=center_point['lat'] + radius / 111.2,
lon_min=center_point['lon'] - radius /
(111.2 * np.cos(center_point['lat'] * np.pi / 180)),
lon_max=center_point['lon'] + radius /
(111.2 * np.cos(center_point['lat'] * np.pi / 180)),
)
##### or set domain manually in deg N/E #####
'''domain = dict(
lat_min = 0.0,
lat_max = 20.0,
lon_min = 0.0,
lon_max = 20.0,
)'''
##### set image size (should be squared) #####
##### plotting area in pyngl can not exceed squared area even if plotting on rectangular images #####
##### for obtaining rectangular plots on has to cut manually afterwards e.g. with pillow package #####
x_resolution = 800
y_resolution = 800
wks_res = Ngl.Resources()
wks_res.wkWidth = x_resolution
wks_res.wkHeight = y_resolution
plot_name = 'contourplot_icon-eu-eps_tot_prec_{:02d}-{:02d}h_{}'.format(\
fcst_hours[0], fcst_hours[1], stat_processing)
wks_type = 'png'
wks = Ngl.open_wks(wks_type, path['base'] + path['plots'] + plot_name,
wks_res)
resources = Ngl.Resources(
) # create resources object containing all the plot settings
resources.mpProjection = 'Hammer' # projection type
resources.mpCenterLonF = (domain['lon_max'] + domain['lon_min']
) / 2 # projection center point
resources.mpCenterLatF = (domain['lat_max'] + domain['lat_min']) / 2
resources.mpLimitMode = 'latlon'
resources.mpMinLonF = domain['lon_min']
resources.mpMaxLonF = domain['lon_max']
resources.mpMinLatF = domain['lat_min']
resources.mpMaxLatF = domain['lat_max']
##### set plot area #####
resources.nglMaximize = False
resources.vpXF = 0.05
resources.vpYF = 0.9
resources.vpWidthF = 0.7
resources.vpHeightF = 0.7
##### set all map plot settings #####
resources.mpFillOn = True # turn on filled map areas
resources.mpFillColors = [
'pink', 'blue', 'white', 'white'
] # set colors for [FillValue, Ocean, Land , InlandWater]
resources.mpDataBaseVersion = 'MediumRes' # quality of national borders
resources.mpDataSetName = 'Earth..4'
resources.mpOutlineBoundarySets = 'national'
#resources.mpDataBaseVersion = 'HighRes'
#resources.mpDataResolution = 'Fine'
resources.mpGeophysicalLineThicknessF = 7.0 * x_resolution / 1000 # keep borders thickness resolution-independent
resources.mpNationalLineThicknessF = 7.0 * x_resolution / 1000
#resources.mpGridAndLimbDrawOrder = 'postdraw'
resources.mpGridAndLimbOn = False # turn off geographic coordinates grid
#resources.mpLimbLineColor = 'black'
#resources.mpLimbLineThicknessF = 10
#resources.mpGridLineColor = 'black'
#resources.mpGridLineThicknessF = 1.0
#resources.mpGridSpacingF = 1
resources.mpPerimOn = True # turn on perimeter around plot
resources.mpPerimLineColor = 'black'
resources.mpPerimLineThicknessF = 8.0 * x_resolution / 1000 # keep perimeter thickness resolution-independent
resources.tmXBOn = False # turn off location ticks around plot
resources.tmXTOn = False
resources.tmYLOn = False
resources.tmYROn = False
resources.sfDataArray = data_processed # data input file to plot
resources.sfXArray = clon # array with cell center locations
resources.sfYArray = clat
resources.sfXCellBounds = vlon # array with cell vertices locations
resources.sfYCellBounds = vlat
resources.sfMissingValueV = 9999 # in case you want to mask values
resources.cnFillOn = True
resources.cnFillMode = 'CellFill'
#resources.cnCellFillEdgeColor = 'black' # uncomment this for plotting the cell edges
resources.cnMissingValFillColor = 'black'
resources.cnFillPalette = 'WhiteBlueGreenYellowRed' # color palette
resources.cnLevelSelectionMode = 'ManualLevels'
minlevel = 0.0 # min level of colorbar
maxlevel = 50.0 # max level of colorbar
numberoflevels = 250 # number of levels of colorbar, max. 250 with this color palette
resources.cnMinLevelValF = minlevel
resources.cnMaxLevelValF = maxlevel
resources.cnLevelSpacingF = (maxlevel - minlevel) / numberoflevels
resources.cnLinesOn = False # turn off contour lines
resources.cnLineLabelsOn = False # turn off contour labels
##### set resources for a nice colorbar #####
resources.lbLabelBarOn = True
resources.lbAutoManage = False
resources.lbOrientation = 'vertical'
resources.lbLabelOffsetF = 0.05
#resources.lbBoxMinorExtentF = 0.2
resources.lbLabelStride = 25 # print a tick every 25 levels
resources.lbLabelFontHeightF = 0.016
resources.lbBoxSeparatorLinesOn = False
resources.lbBoxLineThicknessF = 4.0
#resources.lbBoxEndCapStyle = 'TriangleBothEnds'
resources.lbLabelAlignment = 'BoxCenters'
resources.lbTitleString = 'mm'
resources.lbTitleFontHeightF = 0.016
resources.lbTitlePosition = 'Right'
resources.lbTitleDirection = 'Across'
resources.lbTitleAngleF = 90.0
resources.lbTitleExtentF = 0.1
resources.lbTitleOffsetF = -0.15
resources.nglFrame = False # hold on frame because will plot text afterwards on same plot
Ngl.contour_map(wks, data_processed, resources) # plot the actual plot
##### plot title text #####
text = '{:02d}-{:02d}h Total Precipitation {}, ICON-EPS run {:02}.{:02}.{} {:02}Z'.format(\
fcst_hours[0], fcst_hours[1], stat_processing,\
date['day'], date['month'], date['year'], date['hour'])
x = 0.5
y = 0.95
text_res_1 = Ngl.Resources()
text_res_1.txFontHeightF = 0.018
text_res_1.txJust = 'TopCenter'
Ngl.text_ndc(wks, text, x, y, text_res_1)
Ngl.frame(wks) # advance frame
Ngl.destroy(
wks
) # delete workspace to free memory, relevant if plotting various plots in one script
print('plotted "{}.png"'.format(plot_name))
return
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
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
lres.lbTitleFontHeightF = 0.01 # title font height
Ngl.labelbar_ndc (wks,4,labels,0.23,0.15,lres)
Ngl.frame(wks) # Advance the frame.
Ngl.end()
def draw_e3sm_bgt (ptype,cseason, ncases, cases, casenames, nsite, lats, lons, filepath, filepathobs,casedir,dpsc):
# ncases, the number of models
# cases, the name of models
# casename, the name of cases
# filepath, model output filepath
# filepathobs, filepath for observational data
# inptrs = [ncases]
if not os.path.exists(casedir):
os.mkdir(casedir)
_Font = 25
interp = 2
extrap = False
mkres = Ngl.Resources()
mkres.gsMarkerIndex = 2
mkres.gsMarkerColor = "Red"
mkres.gsMarkerSizeF = 15.
infiles = ["" for x in range(ncases)]
ncdfs = ["" for x in range(ncases)]
nregions = nsite
varis = [ "DCQ","DCCLDLIQ","DCCLDICE","PTEQ","PTTEND","DTCOND"]
nvaris = len(varis)
cscale = [1E8, 1E8, 1E8, 1E8, 1E4, 1E4]
chscale = ['1E-8', '1E-8', '1E-8', '1E-8', '1E-4', '1E-4']
plote3smbgt=["" for x in range(nsite*ncases)]
for ire in range (0, nsite):
for im in range (0,ncases):
if not os.path.exists(casedir+'/'+str(lons[ire])+'E_'+str(lats[ire])+'N'):
os.mkdir(casedir+'/'+str(lons[ire])+'E_'+str(lats[ire])+'N')
plotname = casedir+'/'+str(lons[ire])+'E_'+str(lats[ire])+'N/E3SM_Budgets_'+casenames[im]+"_"+str(lons[ire])+"E_"+str(lats[ire])+"N_"+cseason
plote3smbgt[im+ncases*ire] = 'E3SM_Budgets_'+casenames[im]+"_"+str(lons[ire])+"E_"+str(lats[ire])+"N_"+cseason
wks= Ngl.open_wks(ptype,plotname)
Ngl.define_colormap(wks,"radar")
plot = []
res = Ngl.Resources()
res.nglDraw = False
res.nglFrame = False
res.lgLabelFontHeightF = .012 # change font height
res.lgPerimOn = False # no box around
res.vpWidthF = 0.30 # set width and height
res.vpHeightF = 0.30
# res.txFontHeightF = .01
# res.vpXF = 0.04
# res.vpYF = 0.30
res.tmYLLabelFont = 12
res.tmXBLabelFont = 12
res.tmXBLabelFontHeightF = 0.01
res.tmXBLabelFontThicknessF = 1.0
res.xyMarkLineMode = "MarkLines"
res.xyLineThicknesses = [2.0, 2.0, 2.0, 2.0, 2.0, 2.0,2.,2.,2.,2.,2,2,2,2,2,2,2]
res.xyLineColors = np.arange(2,16,2)
res.xyDashPatterns = np.arange(0,24,1)
res.xyMarkers = np.arange(16,40,1)
res.xyMarkerSizeF = 0.005
res.xyMarkerColors = np.arange(2,16,2)
res.pmLegendDisplayMode = "ALWAYS"
res.pmLegendSide = "top" # Change location of
res.pmLegendParallelPosF = 0.6 # move units right
res.pmLegendOrthogonalPosF = -0.55 # more neg = down
res.pmLegendWidthF = 0.2 # Decrease width
res.pmLegendHeightF = 0.1 # Decrease height
res.lgBoxMinorExtentF = 0.1 # Shorten the legend lines
res.lgLabelFontHeightF = 0.015 # Change the font size
res.lgPerimOn = True
res.tiYAxisString = "PRESSURE"
res.trYReverse = True
pres = Ngl.Resources()
pres.nglMaximize = True
pres.wkWidth = 2000
pres.wkHeight = 2000
pres.nglFrame = False
pres.txFont = 12
pres.nglPanelYWhiteSpacePercent = 5
pres.nglPanelXWhiteSpacePercent = 5
pres.nglPanelTop = 0.93
for iv in range (0, nvaris):
if (varis[iv] == "DCQ" ):
if (dpsc[im] == "zm" ):
budget_ends = ["MPDQ", "RVMTEND_CLUBB","ZMDQ", "EVAPQZM"]
else:
budget_ends = ["MPDQ", "RVMTEND_CLUBB"]
nterms = len (budget_ends)
if (varis[iv] == "DTCOND" ):
if (dpsc[im] == "zm" ):
budget_ends = ["STEND_CLUBB", "MPDT", "DPDLFT","ZMDT", "EVAPTZM", "ZMMTT"]
else:
budget_ends = ["STEND_CLUBB", "MPDT", "DPDLFT"]
nterms = len (budget_ends)
if (varis[iv] == "PTTEND") :
budget_ends = ["DTCOND", "QRS", "QRL", "TTGW"]
nterms = len (budget_ends)
if (varis[iv] == "PTEQ") :
if (dpsc[im] == "zm" ):
budget_ends = ["MPDQ", "RVMTEND_CLUBB","ZMDQ", "EVAPQZM"]
else:
budget_ends = ["MPDQ", "RVMTEND_CLUBB"]
nterms = len (budget_ends)
if (varis[iv] == "DCCLDLIQ") :
if (dpsc[im] == "zm" ):
budget_ends = ["MPDLIQ", "RCMTEND_CLUBB", "DPDLFLIQ","ZMDLIQ"]
else:
budget_ends = ["MPDLIQ", "RCMTEND_CLUBB", "DPDLFLIQ"]
nterms = len (budget_ends)
if (varis[iv] == "DCCLDICE") :
if (dpsc[im] == "zm" ):
budget_ends = ["MPDICE", "RIMTEND_CLUBB", "DPDLFICE","ZMDICE"]
else:
budget_ends = ["MPDICE", "RIMTEND_CLUBB", "DPDLFICE"]
nterms = len (budget_ends)
ncdfs[im] = './data/'+cases[im]+'_site_location.nc'
infiles[im]= filepath[im]+cases[im]+'/'+cases[im]+'_'+cseason+'_climo.nc'
inptrs = Dataset(infiles[im],'r') # pointer to file1
lat=inptrs.variables['lat'][:]
nlat=len(lat)
lon=inptrs.variables['lon'][:]
nlon=len(lon)
ilev=inptrs.variables['lev'][:]
nilev=len(ilev)
ncdf= Dataset(ncdfs[im],'r')
n =ncdf.variables['n'][:]
idx_cols=ncdf.variables['idx_cols'][:,:]
ncdf.close()
A_field = np.zeros((nterms,nilev),np.float32)
theunits=str(chscale[iv])+"x"+inptrs.variables[varis[iv]].units
res.tiMainString = varis[iv]+" "+theunits
for it in range(0, nterms):
for subc in range( 0, n[ire]):
varis_bgt= budget_ends[it]
npoint=idx_cols[ire,n[subc]-1]-1
tmp=inptrs.variables[varis_bgt][0,:,npoint] #/n[ire]
tmp=tmp*cscale[iv]
if (varis_bgt == "MPDT" or varis_bgt == "STEND_CLUBB" ):
tmp=tmp/1004
A_field[it,:] = (A_field[it,:]+tmp[:]/n[ire]).astype(np.float32 )
inptrs.close()
res.xyExplicitLegendLabels = budget_ends[:]
p = Ngl.xy(wks,A_field,ilev,res)
plot.append(p)
xp=np.mod(iv,2)
yp=int(iv/2)
Ngl.panel(wks,plot[:],[nvaris/2,2],pres)
txres = Ngl.Resources()
txres.txFont = _Font
txres.txFontHeightF = 0.020
Ngl.text_ndc(wks,casenames[im]+" BUDGET at" +str(lons[ire])+"E,"+str(lats[ire])+"N",0.5,0.95,txres)
Ngl.frame(wks)
Ngl.destroy(wks)
return (plote3smbgt)
