def wks_setting(res):
image_name = "Shimen_topography"
wks_type = "png"
print("\033[44m---Plot: {}.{} ---\033[0m".format(image_name,wks_type))
rlist = Ngl.Resources()
rlist.wkWidth = 3000 #page resolution
rlist.wkHeight = 3000
wks = Ngl.open_wks(wks_type,image_name,rlist)
'''
cmap = Ngl.read_colormap_file("MPL_terrain")
cmap1 = []
for cp in cmap:
print(cp)
print('---')
#print(str(cp).replace('[','').replace(']','').replace(' ',' ').strip().split(' '))
dum = str(cp).replace('[','').replace(']','').strip()
dum = dum.replace(' ',' ').replace(' ',' ').replace(' ',' ')
dum = dum.replace(' ',',').split(',')[0:3]
print(dum)
cmap1.append(dum)
cmap1 = [[1,1,1],[0,0,0]]+[cmap1[0]]+ cmap1[30:len(cmap1)]
print(cmap1)
cmap1 = np.float32(cmap1)
print(cmap1)
#cmap1 = np.concatenate(([cmap[0]], cmap[30:]), axis=0)
#print(len(cmap1))
#print(cmap1)
#Ngl.define_colormap(wks, cmap1 )
'''
Ngl.define_colormap(wks, colorbar_tables.TW_terrain())
#Ngl.define_colormap(wks, "MPL_terrain")
res.nglMaximize = True
res.nglDraw = False; res.nglFrame = False
#res.vpWidthF = 0.74; res.vpHeightF = 0.54
return wks, res
def create_workstation(colorBarName, image_path):
# 3.1打开工作台
wkres = Ngl.Resources()
wkres.wkWidth = 930
wkres.wkHeight = 930
workstation = Ngl.open_wks("png", image_path, wkres)
# 3.2获取底板颜色
cmap = colorTool.getColorMap(colorBarName)
Ngl.define_colormap(workstation, cmap)
return workstation
def wks_setting(res):
image_name = "Falcon_track_20191031"
wks_type = "png"
print("\033[44m---Plot: {}.{} ---\033[0m".format(image_name,wks_type))
rlist = Ngl.Resources()
rlist.wkWidth = 3000 #page resolution
rlist.wkHeight = 3000
wks = Ngl.open_wks(wks_type,image_name,rlist)
Ngl.define_colormap(wks, colorbar_tables.TW_terrain())
#Ngl.define_colormap(wks, "MPL_terrain")
res.nglMaximize = True
res.nglDraw = False; res.nglFrame = False
#res.vpWidthF = 0.74; res.vpHeightF = 0.54
return wks, res
def wks_setting(res, vn):
image_name = "{}_timeseries_Falcon_20190603".format(vn)
wks_type = "png"
print("\033[44m---Plot: {}.{} ---\033[0m".format(image_name, wks_type))
rlist = Ngl.Resources()
rlist.wkWidth = 3000 #page resolution
rlist.wkHeight = 3000
wks = Ngl.open_wks(wks_type, image_name, rlist)
Ngl.define_colormap(wks, colorbar_tables.TW_terrain())
#Ngl.define_colormap(wks, "MPL_terrain")
res.nglMaximize = True
res.nglDraw = False
res.nglFrame = False
res.vpWidthF = 0.9
res.vpHeightF = 0.3
return wks, res
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
wkres = Ngl.Resources()
#wkres.wkColorMap=colors
rlist = Ngl.Resources()
wks_type = "png"
if (wks_type == "ps" or wks_type == "pdf"):
rlist.wkOrientation = "Portrait" # For PS or PDF output only.
wks = Ngl.open_wks(wks_type, "J8_teb_current_validation_urban_mean_teb_class",
wkres)
#wks2 = Ngl.open_wks(wks_type,"J8_teb_current_all_3h_validation3",wkres)
colors = [
"White", "Black", "SkyBlue", "Turquoise", "Blue", "Green", "Yellow",
"Orange", "tomato", "Magenta", "PaleVioletRed", "Maroon", "red4", "Red"
]
#colors=["White","Black","SkyBlue","Turquoise","PaleVioletRed","Red"]
Ngl.define_colormap(wks, colors)
#Ngl.define_colormap(wks,"BlGrYeOrReVi200")
#Ngl.define_colormap(wks,"hotres")
#Ngl.define_colormap(wks2,colors)
#Ngl.define_colormap(wks,"GHRSST_anomaly")
resources = Ngl.Resources()
Ngl.set_values(wks, resources)
resources.pmLabelBarDisplayMode = "Never" # Turn on label bar.
resources.pmLabelBarDisplayMode = "Never" # Turn on label bar.
#resources.pmLabelBarWidthF = 0.50
resources.nglDraw = False # dont draw
resources.nglFrame = False # dont advance frame
#resources.nglAddCyclic = False
#
# Generate the data.
#
# Note: we have not verified the accuracy of this data!
#
x = numpy.array(range(1,9))
y = numpy.array([154900,56600,40000,30200,29700,24400,21700,13900])
colors = ["white","black","FireBrick","Red","Orange","Green",
"Navy", "Blue", "SkyBlue","SlateBlue"]
labels = ["Lung","Colon","Breast","Prostate","Pancreas",\
"Lymphoma","Leukemias","Ovary"]
wks_type = "ps"
wks = Ngl.open_wks(wks_type,"bar2")
Ngl.define_colormap(wks,colors)
res = Ngl.Resources()
res.nglMaximize = False # Need to set to False if using
# vp resources.
res.vpYF = 0.92 # Move plot up a little.
res.vpHeightF = 0.77 # Make plot higher than
res.vpWidthF = 0.70 # it is high.
res.tmXBOn = False # Turn off bottom tickmarks & labes
res.tmXTOn = False # Turn off top tickmarks & labes
res.tmYROn = False # Turn off right tickmarks & labes
res.tmYLMajorLengthF = 0.0 # Turn off Y major tickmarks
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
# # Import Ngl support functions. # import Ngl # # Qualifiers for China provinces and India states. # boundaries = ["China:states", "India:States"] # # Change the color map. # wks_type = "png" wks = Ngl.open_wks(wks_type, "ctrydiv1") Ngl.define_colormap(wks, "StepSeq25") # # Create the plot. # # # Map resources. # res = Ngl.Resources() res.mpDataSetName = "Earth..4" # Change database res.mpDataBaseVersion = "MediumRes" # Medium resolution database res.mpOutlineOn = True # Turn on map outlines res.mpOutlineBoundarySets = "National"
def draw_clubb_bgt (ptype,cseason, ncases, cases, casenames, nsite, lats, lons, filepath, filepathobs,casedir,varis,cscale,chscale,pname,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
varisobs = [ "CLOUD", "OMEGA","SHUM","CLWC_ISBL", "THATA","RELHUM"]
nvaris = len(varis)
cunits = ["%", "mba/day","g/kg","g/kg","K", "%", "mba/day", "K", "g/kg", "m/s", "m/s","K","m"]
cscaleobs = [100., 100/86400., 1., 1000, 1., 1., 1, 1,1,1]
obsdataset =["CCCM", "ERAI", "ERAI", "ERAI", "ERAI", "ERAI", "ERAI", "ERAI","ERAI","ERAI"]
plotbgt=["" 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/'+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.vpWidthF = 0.30 # set width and height
res.vpHeightF = 0.30
# res.txFontHeightF = .01
# res.vpXF = 0.04
# res.vpYF = 0.30
res.tmYLLabelFont = _Font
res.tmXBLabelFont = _Font
res.tmXBLabelFontHeightF = 0.005
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(2,16,1)
res.xyDashPatterns = np.arange(0,24,1)
res.xyMarkers = np.arange(16,40,1)
res.xyMarkerSizeF = 0.005
res.xyMarkerColors = np.arange(2,16,1)
res.pmLegendDisplayMode = "ALWAYS"
res.pmLegendSide = "top" # Change location of
res.pmLegendParallelPosF = 0.75 # move units right
res.pmLegendOrthogonalPosF = -0.65 # more neg = down
res.pmLegendWidthF = 0.10 # Change width and
res.pmLegendHeightF = 0.15 # height of legend.
res.lgLabelFontHeightF = .01 # change font height
res.lgLabelFontThicknessF = 1.
res.lgBoxMinorExtentF = 0.1
res.lgPerimOn = True
res.tiYAxisString = "PRESSURE"
res.tiYAxisString = "Pressure [hPa]"
res.tmYLLabelFontHeightF = 0.02
res.trYReverse = True
pres = Ngl.Resources()
# pres.nglMaximize = True
pres.nglFrame = False
pres.txFont = _Font
pres.nglPanelYWhiteSpacePercent = 5
pres.nglPanelXWhiteSpacePercent = 5
pres.nglPanelTop = 0.93
for iv in range (0, nvaris):
if (varis[iv] == "rtp2" or varis[iv] == "thlp2"):
budget_ends = ["_bt", "_ma", "_ta", "_tp", "_dp1", "_dp2", "_cl", "_pd", "_sf", "_forcing"]
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"]
nterms = len (budget_ends)
if (varis[iv] == "wpthlp") :
budget_ends = ["_bt", "_ma", "_ta", "_tp", "_ac","_bp","_pr1","_pr2", "_pr3","_dp1","_mfl", "_cl", "_sicl", "_forcing"]
nterms = len (budget_ends)
if (varis[iv] == "rtpthlp") :
budget_ends = ["_bt", "_ma", "_ta", "_tp1","_tp2","_dp1","_dp2", "_cl", "_sf", "_forcing"]
nterms = len (budget_ends)
if (varis[iv] == "wp2") :
budget_ends = ["_bt", "_ma", "_ta", "_ac","_bp","_pr1","_pr2", "_pr3","_dp1","_dp2", "_cl", "_pd", "_sf"]
nterms = len (budget_ends)
if (varis[iv] == "wp3") :
budget_ends = ["_bt", "_ma", "_ta", "_tp", "_ac","_bp1","_bp2","_pr1","_pr2","_dp1", "_cl"]
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"]
nterms = len (budget_ends)
if (varis[iv] == "um" or varis[iv] == "vm") :
budget_ends = ["_bt", "_ma","_ta","_gf", "_f"]
nterms = len (budget_ends)
if (varis[iv] == "thlm" or varis[iv] == "rtm") :
budget_ends = ["_bt", "_ma","_ta","_cl", "_mc"]
nterms = len (budget_ends)
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)
ilev=inptrs.variables['ilev'][:]
nilev=len(ilev)
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()
A_field = np.zeros((nterms,nilev),np.float32)
theunits=str(chscale[iv])+"x"+inptrs.variables[varis[iv]+'_bt'].units
res.tiMainString = varis[iv]+" "+theunits
for it in range(0, nterms):
for subc in range( 0, n[ire]):
varis_bgt= varis[iv]+budget_ends[it]
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_bgt][0,:,npointlat,npointlon]
else:
tmp=inptrs.variables[varis_bgt][0,:,npoint] #/n[ire]
if (varis[iv] == "wprtp" ) :
tmp [0:10] = 0.0
tmp=tmp*cscale[iv]
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)
# pres.txFontHeightF = 0.02
# pres.txString = casenames[im]+" BUDGET at" +str(lons[ire])+"E,"+str(lats[ire])+"N"
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 (plotbgt)
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
#
# Draw two vertical strings on left side of plot.
#
txres.txFontHeightF = 0.009
txres.txAngleF = 90.
Ngl.text_ndc(wks,"cooling",0.11,0.835,txres)
Ngl.text_ndc(wks,"warming",0.11,0.95,txres)
Ngl.draw(plot) # Draw the plot; all of the attached lines and
# text strings will also get drawn.
#==========================================================================
print "Beginning robinson map projection section"
Ngl.define_colormap(wks,"wh-bl-gr-ye-re")
mpres = Ngl.Resources() # New resource list for map plots.
mpres.nglMaximize = False
mpres.nglFrame = False
mpres.vpWidthF = 0.5
mpres.vpHeightF = 0.32
mpres.vpXF = .11
mpres.vpYF = .75
mpres.sfXArray = lon
mpres.sfYArray = lat
mpres.mpProjection = "Robinson" # choose projection
mpres.mpFillOn = False # turn on map fill
# Import necessary modules
import numpy, os, Nio, Ngl
# Open data file
data_dir = Ngl.pynglpath("data")
cdffile = Nio.open_file(os.path.join(data_dir,"cdf","941110_P.cdf"),"r")
# Read data
psl = cdffile.variables["Psl"]
psl_lon = cdffile.variables["lon"][:]
psl_lat = cdffile.variables["lat"][:]
# Open workstation
wks_type = "ps"
wks = Ngl.open_wks(wks_type,"tickmark2")
Ngl.define_colormap(wks,"BlueDarkRed18")
# Set resources
res = Ngl.Resources()
#---Define X/Y values of data.
res.sfXArray = psl_lon
res.sfYArray = psl_lat
#---Contour resources
res.cnFillOn = True
res.cnLineLabelsOn = False
res.cnLevelSelectionMode = "ManualLevels"
res.cnMinLevelValF = 950
res.cnMaxLevelValF = 1040
# A single visualization is produced showing the filled U.S. states. # # # Import numpy and os # import numpy,os # # Import Ngl,Nio support functions. # import Ngl, Nio wks_type = "ps" wks = Ngl.open_wks (wks_type,"shapefile1") Ngl.define_colormap(wks,"rainbow+gray") # # Map resources. # res = Ngl.Resources() res.mpProjection = "LambertConformal" res.mpLambertParallel1F = 33 # two parallels res.mpLambertParallel2F = 45 res.mpLambertMeridianF = -98 # central meridian res.mpLimitMode = "Corners" # limit map via two opposite corners res.mpLeftCornerLatF = 22 # left corner res.mpLeftCornerLonF = -125 # left corner
#
# Draw two vertical strings on left side of plot.
#
txres.txFontHeightF = 0.009
txres.txAngleF = 90.
Ngl.text_ndc(wks, "cooling", 0.11, 0.835, txres)
Ngl.text_ndc(wks, "warming", 0.11, 0.95, txres)
Ngl.draw(plot) # Draw the plot; all of the attached lines and
# text strings will also get drawn.
#==========================================================================
print("Beginning robinson map projection section")
Ngl.define_colormap(wks, "wh-bl-gr-ye-re")
mpres = Ngl.Resources() # New resource list for map plots.
mpres.nglMaximize = False
mpres.nglFrame = False
mpres.vpWidthF = 0.5
mpres.vpHeightF = 0.32
mpres.vpXF = .11
mpres.vpYF = .75
mpres.sfXArray = lon
mpres.sfYArray = lat
mpres.mpProjection = "Robinson" # choose projection
mpres.mpFillOn = False # turn on map fill
def silhs_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
varis = [ "SILHS_CLUBB_PRECIP_FRAC","SILHS_CLUBB_ICE_SS_FRAC","HR","AWNC","AWNI","ADSNOW","ANSNOW","AQSNOW","RHW","QRS","QRL","AQRAIN" ]
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 ]
plotsilhs=["" 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/silhs_'+str(lons[ire])+"E_"+str(lats[ire])+"N_"+cseason
plotsilhs[ire] = 'CLUBB_standard_'+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.txFontHeightF = .01
#res.vpXF = 0.04
# res.vpYF = 0.30
res.tmYLLabelFont = _Font
res.tmXBLabelFont = _Font
res.tmXBLabelFontHeightF = 0.005
res.tmXBLabelFontThicknessF = 1.0
res.tmXBLabelAngleF = 45
res.xyMarkLineMode = "Lines"
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.xyDashPatterns = np.arange(0,24,1)
# res.xyMarkers = np.arange(16,40,1)
# res.xyMarkerSizeF = 0.005
pres = Ngl.Resources()
pres.nglMaximize = True
pres.txString = str(lons[ire])+"E,"+str(lats[ire])+"N"
pres.txFont = _Font
pres.nglPanelYWhiteSpacePercent = 5
pres.nglPanelXWhiteSpacePercent = 5
pres.nglPanelTop = 0.93
txres = Ngl.Resources()
txres.txFontHeightF = 0.01
for iv in range (0, nvaris):
if(iv == nvaris-1):
res.pmLegendDisplayMode = "ALWAYS"
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['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(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()
res.tiMainString = varis[iv]+" x"+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 = 280.
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(2,20,1)
res.xyMarkerColors = np.arange(2,20,2)
p = Ngl.xy(wks,A_field,lev,res)
plot.append(p)
Ngl.panel(wks,plot[:],[nvaris/3,3],pres)
txres = Ngl.Resources()
txres.txFontHeightF = 0.020
Ngl.text_ndc(wks,"SILHS VAR at"+ str(lons[ire])+"E,"+str(lats[ire])+"N",0.5,0.05,txres)
Ngl.frame(wks)
Ngl.destroy(wks)
return plotsilhs
losing_teams_nm.append(team) # Name of losing team
losing_colors[nl, :] = numpy.array(teams[team]["colors"]) / 100.
nl += 1
# Store the winning and losing counts in numpy arrays.
y_win = numpy.array(winning_teams_ct)
x_win = numpy.array(range(1, y_win.shape[0] + 1))
y_lose = numpy.array(losing_teams_ct)
x_lose = numpy.array(range(1, y_lose.shape[0] + 1))
#
# Start the graphics portion of the script.
#
wks_type = "png"
wks = Ngl.open_wks(wks_type, "bar1")
Ngl.define_colormap(wks, winning_colors)
res = Ngl.Resources()
res.nglMaximize = False # Need to set to False if using
# vp resources.
res.vpXF = 0.12 # Move plot to left a little.
res.vpYF = 0.98 # Move plot up a little.
res.vpHeightF = 0.90 # Make plot higher than
res.vpWidthF = 0.80 # it is wide.
res.tmXBOn = False # Turn off bottom tickmarks & labes
res.tmXTOn = False # Turn off top tickmarks & labes
res.tmYROn = False # Turn off right tickmarks & labes
res.tmYLMinorOn = False # Turn off left minor tickmarks
def plot_Robinson_pyngl(var,lon,lat,wks_name='plot', clim=None, cspace=None, cmap=None):
#
# Select a colormap and open a workstation.
#
rlist = Ngl.Resources()
wks_type = "png"
wks = Ngl.open_wks(wks_type,wks_name,rlist)
if cmap is None:
mycmap = 'BlAqGrYeOrReVi200'
Ngl.define_colormap(wks,mycmap)
else:
try:
mycmap = '/Users/frederic/python/cmap/' + cmap
mycmap = np.loadtxt(mycmap)
except:
mycmap = cmap
try:
Ngl.define_colormap(wks,mycmap)
except:
raise Warning, 'Unknown colormap'
#
# The next set of resources will apply to the contour plot and the labelbar.
#
resources = Ngl.Resources()
resources.sfXArray = lon
resources.sfYArray = lat
resources.gsnMaximize = True # use full page
resources.cnFillOn = True
resources.cnFillMode = "RasterFill"
resources.cnMaxLevelCount = 255
resources.cnLinesOn = False
resources.cnLineLabelsOn = False
if clim is not None:
resources.cnLevelSelectionMode = "ManualLevels" # set manual contour levels
resources.cnMinLevelValF = clim[0] # set min contour level
resources.cnMaxLevelValF = clim[1] # set max contour level
if cspace is None:
LevelSpacing = (clim[1] - clim[0]) / 100.
resources.cnLevelSpacingF = LevelSpacing # set contour spacing
else:
resources.cnLevelSpacingF = cspace # set contour spacing
resources.lbOrientation = "Horizontal" # Default is vertical.
resources.lbBoxLinesOn = False
resources.lbLabelFontHeightF = 0.01 # label font height
resources.lbBoxMinorExtentF = 0.15
#
# The contour plot is not very interesting, so don't draw it.
#
resources.nglDraw = False
resources.nglFrame = False
contour = Ngl.contour(wks,var,resources)
#
# Retrieve the actual lat/lon end points of the scalar array so
# we know where to overlay on map.
#
xs = Ngl.get_float(contour.sffield,"sfXCActualStartF")
xe = Ngl.get_float(contour.sffield,"sfXCActualEndF")
ys = Ngl.get_float(contour.sffield,"sfYCActualStartF")
ye = Ngl.get_float(contour.sffield,"sfYCActualEndF")
resources.nglDraw = True # Turn these resources back on.
resources.nglFrame = True
resources.mpProjection = "Robinson"
resources.mpCenterLonF = 270
resources.mpCenterLatF = 0
resources.mpGeophysicalLineThicknessF = 2
map = Ngl.contour_map(wks,var,resources)
Ngl.end()
def draw_micro_bgt(ptype, cseason, ncases, cases, casenames, nsite, lats, lons,
filepath, filepathobs, casedir, varis, vname, cscale,
chscale, pname, 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
nvaris = len(varis)
plotmicrobgt = ['' 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/' + pname + '_' + casenames[im] + '_' + str(
lons[ire]) + 'E_' + str(lats[ire]) + 'N_' + cseason
plotmicrobgt[
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.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 = [
3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3., 3., 3., 3., 3, 3, 3, 3, 3, 3,
3
]
res.xyLineColors = np.arange(2, 16, 1)
res.xyDashPatterns = np.arange(0, 24, 1)
res.xyMarkers = np.arange(16, 40, 1)
res.xyMarkerSizeF = 0.005
res.xyMarkerColors = np.arange(2, 16, 1)
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.nglLeftString = varis[iv]
# res.nglRightString = cunits[iv]
res.trYReverse = True
pres = Ngl.Resources()
# pres.nglMaximize = True
pres.nglFrame = False
pres.txFont = 12
pres.nglPanelYWhiteSpacePercent = 5
pres.nglPanelXWhiteSpacePercent = 5
pres.nglPanelTop = 0.93
txres = Ngl.Resources()
# txres.txFontHeightF = 0.01
for iv in range(0, nvaris):
if (varis[iv] == 'MPDLIQ'): # LIQ
budget_ends = [
'PRCO', 'PRAO', 'MNUCCCO', 'MNUCCTO', 'MSACWIO',
'PSACWSO', 'BERGSO', 'BERGO'
]
# in fortran prc*cld, pra*cld,mnuccc*cld,mnucct*cld,msacwi*cld,psacws*cld,bergs*cld, berg
# (-pra-prc-mnuccc-mnucct-msacwi- psacws-bergs)*lcldm-berg
nterms = len(budget_ends)
if (varis[iv] == 'MPDICE'): # ICE
budget_ends = [
'PRCIO', 'PRAIO', 'MSACWIO', 'MNUCCCO', 'MNUCCTO',
'mnudepo', 'BERGO', 'CMEIOUT', 'mnuccrio'
]
# in fortran prci*cld,prai*cld,msacwi*cld,mnuccc*cld, mnucct*cld, berg, vap_dep + ice_sublim + mnuccd
# (mnuccc+mnucct+mnudep+msacwi)*lcldm+(-prci-prai)*icldm+(vap_dep+ice_sublim+mnuccd)+berg+mnuccri*precip_frac
nterms = len(budget_ends)
if (varis[iv] == 'QRSEDTEN'): # RAIN
budget_ends = [
'PRAO', 'PRCO', 'PRACSO', 'EVAPPREC', 'MNUCCRO',
'mnuccrio'
]
# pra*cld,prc*cld, psacs*prf, -pre*prf(nevapr),mnuccr*prf
# (pra+prc)*lcldm+(pre-pracs- mnuccr-mnuccri)*precip_frac
nterms = len(budget_ends)
if (varis[iv] == 'QSSEDTEN'): # SNOW
budget_ends = [
'PRAIO', 'PRCIO', 'PSACWSO', 'PRACSO', 'EVAPSNOW',
'MNUCCRO', 'BERGSO'
]
# prai*cld,prci*cld,psacws*cld,psacs*prf, -prds*prc, mnuccr*prf,bergs*cld
# (prai+prci)*icldm+(psacws+bergs)*lcldm+(prds+ pracs+mnuccr)*precip_frac
nterms = len(budget_ends)
if (varis[iv] == 'QISEVAP'): # Vapor
budget_ends = [
'EVAPPREC', 'EVAPSNOW', 'CMEIOUT', 'mnudepo'
]
# -pre*prf(nevapr),-prds*prc ,vap_dep + ice_sublim + mnuccd
# -(pre+prds)*precip_frac-vap_dep-ice_sublim-mnuccd-mnudep*lcldm
nterms = len(budget_ends)
if (varis[iv] == 'nnuccco'): # NUM of LIQ
budget_ends = [
'nnuccco', 'nnuccto', 'npsacwso', 'nsubco', 'nprao',
'nprc1o'
]
# nnuccc*cld,nnucct*cld,npsacws*cld,nsubc*cld,npra*cld,nprc1*cld
# (-nnuccc-nnucct-npsacws+nsubc-npra-nprc1)*lcldm
nterms = len(budget_ends)
if (varis[iv] == 'nnuccdo'): # NUM of ICE
budget_ends = [
'nnuccdo', 'nnuccto', 'tmpfrzo', 'nnudepo', 'nsacwio',
'nsubio', 'nprcio', 'npraio', 'nnuccrio', 'DETNICETND'
]
# nnuccd ,nnucct*lcld,tmpfrz*lcld,nnudep*lcld,nsacwi*lcld,nsubi*icld,nprci*icld,nprai*icld,nnuccri*prf
# nnuccd+ (nnucct+tmpfrz+nnudep+nsacwi)*lcldm+(nsubi-nprci- nprai)*icldm+nnuccri*precip_frac
nterms = len(budget_ends)
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)
ilev = inptrs.variables['lev'][:]
nilev = len(ilev)
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()
A_field = np.zeros((nterms, nilev), np.float32)
theunits = str(
chscale[iv]) + 'x' + inptrs.variables[varis[iv]].units
res.tiMainString = vname[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
if (dofv):
npointlat = idx_lats[ire, 0]
npointlon = idx_lons[ire, 0]
if (dofv):
tmp = inptrs.variables[varis_bgt][
0, :, npointlat, npointlon] #/n[ire]
else:
tmp = inptrs.variables[varis_bgt][0, :,
npoint] #/n[ire]
tmp = tmp * cscale[iv]
if (dofv):
lcldm = inptrs.variables['CLOUD'][0, :, npointlat,
npointlon]
else:
lcldm = inptrs.variables['CLOUD'][0, :, npoint]
icldm = lcldm
if (dofv):
precip_frac = inptrs.variables['FREQR'][0, :,
npointlat,
npointlon]
else:
precip_frac = inptrs.variables['FREQR'][0, :,
npoint]
if (varis_bgt == 'MPDT' or varis_bgt == 'STEND_CLUBB'):
tmp = tmp / 1004
if (varis[iv] == 'MPDLIQ'): # LIQ
if (varis_bgt == 'PRCO' or varis_bgt == 'PRCIO' or varis_bgt == 'PRAO' or varis_bgt == 'PRAICSO' or varis_bgt == 'PRAIO' \
or varis_bgt == 'MNUCCCO' or varis_bgt == 'MNUCCTO' or varis_bgt == 'MSACWIO' or varis_bgt == 'PSACWSO' or varis_bgt == 'BERGSO'
or varis_bgt == 'BERGO'):
tmp = tmp * (-1)
if (varis[iv] == 'MPDICE'): # ICE
if (varis_bgt == 'PRCIO' or varis_bgt == 'PRAIO'):
tmp = tmp * (-1)
if (varis[iv] == 'QRSEDTEN'): # RAIN
if (varis_bgt == 'MNUCCRO' or varis_bgt == 'PRACSO'
or varis_bgt == 'EVAPPREC'
or varis_bgt == 'mnuccrio'):
tmp = tmp * (-1)
if (varis[iv] == 'QSSEDTEN'): # SNOW
if (varis_bgt == 'EVAPSNOW'):
tmp = tmp * (-1)
if (varis[iv] == 'QISEVAP'): # Vapor
if (varis_bgt == 'CMEIOUT'
or varis_bgt == 'mnudepo'):
tmp = -1 * tmp
if (varis[iv] == 'nnuccco'): # NUM of LIQ
if (varis_bgt == 'nnuccco'
or varis_bgt == 'nnuccto'
or varis_bgt == 'npsacwso'
or varis_bgt == 'nprao'
or varis_bgt == 'nprc1o'):
tmp = -1 * tmp
if (varis[iv] == 'nnuccdo'): # NUM of ICE
if (varis_bgt == 'nprcio'
or varis_bgt == 'npraio'):
tmp = -1 * tmp
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)
pres.txFontHeightF = 0.02
pres.txFont = _Font
pres.txString = casenames[im] + ' Microphy BUDGET at' + str(
lons[ire]) + 'E,' + str(lats[ire]) + 'N'
if (np.mod(nvaris, 2) == 1):
Ngl.panel(wks, plot[:], [(nvaris) / 2 + 1, 2], pres)
else:
Ngl.panel(wks, plot[:], [(nvaris) / 2, 2], pres)
txres = Ngl.Resources()
txres.txFontHeightF = 0.020
txres.txFont = _Font
Ngl.text_ndc(
wks, casenames[im] + ' Microphy BUDGET at' + str(lons[ire]) +
'E,' + str(lats[ire]) + 'N', 0.5, 0.95, txres)
Ngl.frame(wks)
Ngl.destroy(wks)
return (plotmicrobgt)
def plot_Robinson_pyngl(var,
lon,
lat,
wks_name='plot',
clim=None,
cspace=None,
cmap=None):
#
# Select a colormap and open a workstation.
#
rlist = Ngl.Resources()
wks_type = "png"
wks = Ngl.open_wks(wks_type, wks_name, rlist)
if cmap is None:
mycmap = 'BlAqGrYeOrReVi200'
Ngl.define_colormap(wks, mycmap)
else:
try:
mycmap = '/Users/frederic/python/cmap/' + cmap
mycmap = np.loadtxt(mycmap)
except:
mycmap = cmap
try:
Ngl.define_colormap(wks, mycmap)
except:
raise Warning('Unknown colormap')
#
# The next set of resources will apply to the contour plot and the labelbar.
#
resources = Ngl.Resources()
resources.sfXArray = lon
resources.sfYArray = lat
resources.gsnMaximize = True # use full page
resources.cnFillOn = True
resources.cnFillMode = "RasterFill"
resources.cnMaxLevelCount = 255
resources.cnLinesOn = False
resources.cnLineLabelsOn = False
if clim is not None:
resources.cnLevelSelectionMode = "ManualLevels" # set manual contour levels
resources.cnMinLevelValF = clim[0] # set min contour level
resources.cnMaxLevelValF = clim[1] # set max contour level
if cspace is None:
LevelSpacing = (clim[1] - clim[0]) / 100.
resources.cnLevelSpacingF = LevelSpacing # set contour spacing
else:
resources.cnLevelSpacingF = cspace # set contour spacing
resources.lbOrientation = "Horizontal" # Default is vertical.
resources.lbBoxLinesOn = False
resources.lbLabelFontHeightF = 0.01 # label font height
resources.lbBoxMinorExtentF = 0.15
#
# The contour plot is not very interesting, so don't draw it.
#
resources.nglDraw = False
resources.nglFrame = False
contour = Ngl.contour(wks, var, resources)
#
# Retrieve the actual lat/lon end points of the scalar array so
# we know where to overlay on map.
#
xs = Ngl.get_float(contour.sffield, "sfXCActualStartF")
xe = Ngl.get_float(contour.sffield, "sfXCActualEndF")
ys = Ngl.get_float(contour.sffield, "sfYCActualStartF")
ye = Ngl.get_float(contour.sffield, "sfYCActualEndF")
resources.nglDraw = True # Turn these resources back on.
resources.nglFrame = True
resources.mpProjection = "Robinson"
resources.mpCenterLonF = 270
resources.mpCenterLatF = 0
resources.mpGeophysicalLineThicknessF = 2
map = Ngl.contour_map(wks, var, resources)
Ngl.end()
#
f = Nio.open_file(os.path.join(Ngl.pynglpath("data"), "cdf", "hgt.nc"), "r")
hgt = f.variables["HGT"][:, :, :]
lat = Ngl.fspan(-90, 90, 73)
lon = Ngl.fspan(0, 357.5, 144)
# Add a cyclic point in the longitude dimension.
hgt0, lon = Ngl.add_cyclic(hgt[0, :, :], lon)
#
# Start graphics.
#
wks_type = "png"
wks = Ngl.open_wks(wks_type, "spaghetti")
Ngl.define_colormap(wks, "default") # Change color map.
mpres = Ngl.Resources()
mpres.nglDraw = False # Do not draw until the end.
mpres.nglFrame = False # Do not automatically advance frame.
#
# Set contour resources first, because we'll make a copy of them
# to use later for when we generate contours only.
#
mpres.sfXArray = lon # Area of map to overlay
mpres.sfYArray = lat # contours on.
mpres.cnLevelSelectionMode = "ExplicitLevels" # explicit contour levels
mpres.cnLevels = 5500 # which level(s) to plot
from __future__ import print_function
import numpy, os, Nio, Ngl
# Open data file
data_dir = Ngl.pynglpath("data")
cdffile = Nio.open_file(os.path.join(data_dir, "cdf", "941110_P.cdf"), "r")
# Read data
psl = cdffile.variables["Psl"][:]
psl_lon = cdffile.variables["lon"][:]
psl_lat = cdffile.variables["lat"][:]
# Open workstation
wks_type = "png"
wks = Ngl.open_wks(wks_type, "tickmark2")
Ngl.define_colormap(wks, "BlueDarkRed18") # Could also set
# res.cnFillPalette = "BlueDarkRed18"
# Set resources
res = Ngl.Resources()
#---Define X/Y values of data.
res.sfXArray = psl_lon
res.sfYArray = psl_lat
#---Contour resources
res.cnFillOn = True
res.cnLineLabelsOn = False
res.cnLevelSelectionMode = "ManualLevels"
res.cnMinLevelValF = 950
res.cnMaxLevelValF = 1040
#
# Perimeter.
#
pres.gsLineColor = 1
Ngl.polyline_ndc(wks, xx, yy, pres)
#
# Show how to put four plots, illustrating wind barb procedures,
# on a single frame.
#
wks_type = "png"
wks = Ngl.open_wks(wks_type, "wmstnm02")
cmap = numpy.array([[1., 1., 1.], [0., 0., 0.], [1., 0., 0.]]) # color map
Ngl.define_colormap(wks, cmap)
#############################
# #
# Lower left quatrant. #
# #
#############################
#
# Use the procedure wmbarb to produce a table of the association
# of wind barb symbols with wind speeds. This plot simply uses
# NDC coordinates - the three other plots show how to plot
# wind barbs over maps and position those plots on the frame.
#
width = 0.42
height = 0.42
xl = 0.06
def draw_clm_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
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 = ["SOILWATER_10CM", "QBOT"]
varisobs = ["PRECT" ,"PRECT"]
alpha = ["A","B","C","D","E","F"]
nvaris = len(varis)
cunits = [""]
cscale = [1,1,1,86400000, 1, 1,1,100,100,1,1,1000,1,1,1,1]
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= ["GPCP","GPCP", "NCEP", "ERAI", "CALIPSOCOSP","CALIPSOCOSP","NCEP","NCEP","NVAP"]
plotclm=["" for x in range(nvaris)]
for iv in range(0, nvaris):
# make plot for each field
if(varis[iv] == "SOILWATER_10CM" or varis[iv] == "QBOT" ):
cntrs[iv,:] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
# 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/Land_'+varis[iv]+'_'+cseason
plotclm[iv] = 'Land_'+varis[iv]+'_'+cseason
wks= Ngl.open_wks(ptype,plotname)
Ngl.define_colormap(wks,"cmocean_thermal")
# 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.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+'_clm.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)
# varM = A_xy
# for ix in range(0, nlon-1):
# if ( np.isnan(A_xy[ix]) or not np.isfinite(A_xy[ix])):
# varM[ix]=0.0
p = Ngl.contour_map(wks,A_xy,res)
plot.append(p)
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)
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 plotclm
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
#----------------------------------------------------------------------
# Main code
#----------------------------------------------------------------------
# Read variables
f = Nio.open_file("pres.mon.ltm.nc")
pres = f.variables["pres"][0, :, :]
lat = f.variables["lat"][:]
lon = f.variables["lon"][:]
#print(pres)
#----------------------------------------------------------------------
wks = Ngl.open_wks("x11", "PyNGL_China_boundary")
Ngl.define_colormap(wks, "gui_default")
res = Ngl.Resources()
res.nglMaximize = True
res.nglDraw = False
res.nglFrame = False
#---------------------------------------------------------------
# set for the map
res.sfXArray = lon
res.sfYArray = lat
res.mpLimitMode = "LatLon"
res.mpMinLatF = 17.
res.mpMaxLatF = 55.
res.mpMinLonF = 72.
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 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
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
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
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 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)
# A single visualization is produced showing the filled U.S. states. # # # Import numpy and os # import numpy,os # # Import Ngl,Nio support functions. # import Ngl, Nio wks_type = "png" wks = Ngl.open_wks (wks_type,"shapefile1") Ngl.define_colormap(wks,"rainbow+gray") # # Map resources. # res = Ngl.Resources() res.mpProjection = "LambertConformal" res.mpLambertParallel1F = 33 # two parallels res.mpLambertParallel2F = 45 res.mpLambertMeridianF = -98 # central meridian res.mpLimitMode = "Corners" # limit map via two opposite corners res.mpLeftCornerLatF = 22 # left corner res.mpLeftCornerLonF = -125 # left corner
# # Import Ngl support functions. # import Ngl # # Qualifiers for China provinces and India states. # boundaries = ["China:states", "India:States"] # # Change the color map. # wks_type = "ps" wks = Ngl.open_wks(wks_type, "ctrydiv1") Ngl.define_colormap(wks, "StepSeq25") # # Create the plot. # # # Map resources. # res = Ngl.Resources() res.mpDataSetName = "Earth..4" # Change database res.mpDataBaseVersion = "MediumRes" # Medium resolution database res.mpOutlineOn = True # Turn on map outlines res.mpOutlineBoundarySets = "National"
