def save_ps_map(self, title, data, labels_on=True):
"""
Directly create a PS file of data with filename=title.
Assumes normalized data between 0 and 1.
INPUT: title a string describing the dataset data a numpy array
containing the map to be drawn
"""
# Change the levels of contouring
resources = self.resources
resources.cnLevelSelectionMode = "ExplicitLevels" # Define own levels.
resources.cnLevels = np.arange(0., 1., 0.05)
wks_type = "ps"
wks = Ngl.open_wks(wks_type, title, resources)
if labels_on:
resources.lbTitleString = title
# Reshape for visualization on the sphere
data.shape = (self.grid.grid_size()["lat"],
self.grid.grid_size()["lon"])
# Generate map plot
cmap = Ngl.contour_map(wks, data, resources)
# Clear map
del cmap
Ngl.destroy(wks)
# Clean up
del resources
Ngl.end()
def save_ps_map(self, title, data, labels_on=True):
"""
Directly create a PS file of data with filename=title.
Assumes normalized data between 0 and 1.
INPUT: title a string describing the dataset data a numpy array
containing the map to be drawn
"""
# Change the levels of contouring
resources = self.resources
resources.cnLevelSelectionMode = "ExplicitLevels" # Define own levels.
resources.cnLevels = np.arange(0., 1., 0.05)
wks_type = "ps"
wks = Ngl.open_wks(wks_type, title, resources)
if labels_on:
resources.lbTitleString = title
# Reshape for visualization on the sphere
data.shape = (self.grid.grid_size()["lat"],
self.grid.grid_size()["lon"])
# Generate map plot
cmap = Ngl.contour_map(wks, data, resources)
# Clear map
del cmap
Ngl.destroy(wks)
# Clean up
del resources
Ngl.end()
def save(self,filename = 'plot'):
#'eps' workstation doesn't work reliably, but 'ps' is OK
weps = Ngl.open_wks('ps',filename,self.WorkstationResources)
Ngl.change_workstation(self.plot,weps)
Ngl.draw(self.plot)
Ngl.change_workstation(self.plot,self.workstation)
Ngl.destroy(weps)
def save(self, filename='plot'):
#'eps' workstation doesn't work reliably, but 'ps' is OK
weps = Ngl.open_wks('ps', filename, self.WorkstationResources)
Ngl.change_workstation(self.plot, weps)
Ngl.draw(self.plot)
Ngl.change_workstation(self.plot, self.workstation)
Ngl.destroy(weps)
def generate_multiple_map_plots_npy(self,
map_names,
map_scales,
title_on=True,
labels_on=True):
"""
Method for very large datasets (RAM issues) and useful for PARALLEL
code. Generates map plots from the datasets stored in the npy files
and the list of titles. The data is sorted as parallel computation
mixes it up. Stores the plots in the file indicated by filename in the
current directory.
"""
# Set resources
resources = self.resources
# Set plot title
if title_on:
resources.tiMainString = self.title
for k in range(len(self.map_mult_data)):
# Open a workstation for every map, only wks_type = "ps" allows
# multiple workstation
# Sort dataset, as parallel code will mix it
self.map_mult_data[k].sort()
# Define own levels
resources.cnLevelSelectionMode = "ExplicitLevels"
resources.cnLevels = map_scales[k]
wks_type = "pdf"
wks = Ngl.open_wks(wks_type, map_names[k], resources)
#
# Generate map plots
#
for ititle in self.map_mult_data[k]:
# Set title
if labels_on:
resources.lbTitleString = ititle
data = np.load(str(k) + "_" + ititle + ".npy")
# Reshape for visualization on the sphere
data.shape = (self.grid.grid_size()["lat"],
self.grid.grid_size()["lon"])
# Generate map plot
cmap = Ngl.contour_map(wks, data, resources)
# Clear map
del cmap
Ngl.destroy(wks)
# Clean up
for file_name in glob.glob('*.npy'):
os.remove(file_name)
del resources
Ngl.end()
def generate_multiple_map_plots_npy(self, map_names, map_scales,
title_on=True, labels_on=True):
"""
Method for very large datasets (RAM issues) and useful for PARALLEL
code. Generates map plots from the datasets stored in the npy files
and the list of titles. The data is sorted as parallel computation
mixes it up. Stores the plots in the file indicated by filename in the
current directory.
"""
# Set resources
resources = self.resources
# Set plot title
if title_on:
resources.tiMainString = self.title
for k in range(len(self.map_mult_data)):
# Open a workstation for every map, only wks_type = "ps" allows
# multiple workstation
# Sort dataset, as parallel code will mix it
self.map_mult_data[k].sort()
# Define own levels
resources.cnLevelSelectionMode = "ExplicitLevels"
resources.cnLevels = map_scales[k]
wks_type = "pdf"
wks = Ngl.open_wks(wks_type, map_names[k], resources)
#
# Generate map plots
#
for ititle in self.map_mult_data[k]:
# Set title
if labels_on:
resources.lbTitleString = ititle
data = np.load(str(k) + "_" + ititle + ".npy")
# Reshape for visualization on the sphere
data.shape = (self.grid.grid_size()["lat"],
self.grid.grid_size()["lon"])
# Generate map plot
cmap = Ngl.contour_map(wks, data, resources)
# Clear map
del cmap
Ngl.destroy(wks)
# Clean up
for file_name in glob.glob('*.npy'):
os.remove(file_name)
del resources
Ngl.end()
def plot(filename, outputend):
# set up parameters
f = Nio.open_file(filename)
q = f.variables['q'][:, 0, :]
r2d = 57.2957795
lat = f.variables['lat'][:, :]*r2d
lon = f.variables['lon'][:, :]*r2d
vertx = f.variables['vertx'][:, :, :]*r2d
verty = f.variables['verty'][:, :, :]*r2d
ncols = f.variables['nCols'][:]
ae = 6.371229e6
# Open a workstation.
wks_type = "pdf"
rlist = Ngl.Resources()
rlist.wkColorMap = "WhBlReWh"
# Create contour and map resource lists.
for it in range(13):
wks = Ngl.open_wks(wks_type,"Moving_"+str(it)+outputend, rlist)
cnres = Ngl.Resources()
cnres.nglFrame = False
# cnres.nglDraw = False
cnres.mpGridAndLimbOn = False
cnres.mpOutlineOn = False
cnres.mpPerimOn = False# ; turn off box around plot
cnres.nglMaximize = True
cnres.mpProjection = "Orthographic" # Change the map projection.
# x_c, y_c = 0, 90.#qmoving(it*24.*3600., 12.*24.*3600., 0.25*np.pi, ae)
cnres.mpCenterLonF = x_c*r2d # Rotate the projection.
cnres.mpCenterLatF = y_c*r2d # Rotate the projection.
cnres.mpOutlineOn = False
cnres.sfXArray = lon[it, 0:ncols[it]]
cnres.sfYArray = lat[it, 0:ncols[it]]
cnres.cnLinesOn = False
cnres.cnFillOn = True
cnres.cnLineLabelsOn = False
cnres.cnInfoLabelOn = False
cnres.lbLabelBarOn = True
cnres.cnLevelSelectionMode = "ManualLevels"
cnres.cnMinLevelValF = 0.4
cnres.cnMaxLevelValF = 1.6
cnres.cnLevelSpacingF = 0.05
cnres.pmTickMarkDisplayMode = "Never"
contour1 = Ngl.contour_map(wks,q[it, 0:ncols[it]],cnres)
gsres = Ngl.Resources()
gsres.gsLineColor = "Gray25"
gsres.gsLineThicknessF = 3.0
# print(vertx[0, :, 0],verty[0, :, 0])
for nc in range(ncols[it]):
Ngl.polyline(wks,contour1,vertx[it, :, nc],verty[it, :, nc],gsres)
# Ngl.draw(contour1)
Ngl.frame(wks)
Ngl.destroy(wks)
Ngl.end()
def generate_multiple_map_plots(self,
map_names,
map_scales,
title_on=True,
labels_on=True):
"""
Generate map plots from the datasets stored in the :attr:`map_data`
list of dictionaries. Stores the plots in the file indicated by
filename in the current directory.
"""
for k in range(len(self.map_mult_data)):
# Set resources
resources = self.resources
# Set plot title
if title_on:
resources.tiMainString = self.title
# Open a workstation for every map, only wks_type = "ps" allows
# multiple workstations
# Define own levels
resources.cnLevelSelectionMode = "ExplicitLevels"
resources.cnLevels = map_scales[k]
wks_type = "pdf"
wks = Ngl.open_wks(wks_type, map_names[k], resources)
#
# Generate map plots
#
for dataset in self.map_mult_data[k]:
# Set title
if labels_on:
resources.lbTitleString = dataset["title"]
# Reshape for visualization on the sphere
dataset["data"].shape = (self.grid.grid_size()["lat"],
self.grid.grid_size()["lon"])
# Generate map plot
cmap = Ngl.contour_map(wks, dataset["data"], resources)
# Clear map
del cmap
Ngl.destroy(wks)
# Clean up
del resources
Ngl.end()
def generate_multiple_map_plots(self, map_names, map_scales, title_on=True,
labels_on=True):
"""
Generate map plots from the datasets stored in the :attr:`map_data`
list of dictionaries. Stores the plots in the file indicated by
filename in the current directory.
"""
for k in xrange(len(self.map_mult_data)):
# Set resources
resources = self.resources
# Set plot title
if title_on:
resources.tiMainString = self.title
# Open a workstation for every map, only wks_type = "ps" allows
# multiple workstations
# Define own levels
resources.cnLevelSelectionMode = "ExplicitLevels"
resources.cnLevels = map_scales[k]
wks_type = "pdf"
wks = Ngl.open_wks(wks_type, map_names[k], resources)
#
# Generate map plots
#
for dataset in self.map_mult_data[k]:
# Set title
if labels_on:
resources.lbTitleString = dataset["title"]
# Reshape for visualization on the sphere
dataset["data"].shape = (self.grid.grid_size()["lat"],
self.grid.grid_size()["lon"])
# Generate map plot
cmap = Ngl.contour_map(wks, dataset["data"], resources)
# Clear map
del cmap
Ngl.destroy(wks)
# Clean up
del resources
Ngl.end()
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
res2.tiMainString = str(sinefreq2)+"-Hz Sine Wave"+ \
"~C~~B~"+leftname
res3.trYMinF = -10.0 # Limits for Y axis
res3.trYMaxF = 10.0
res3.xyLineColors = ["navy"]
res3.tiMainString = "Simulated Signal of Mixed Sine Waves With White Noise, "+ \
"~C~SNR = {} dB".format(SNR)
plot = []
plot.append(Ngl.xy(wks,t,sig1,res))
plot.append(Ngl.xy(wks,t,sig2,res2))
plot.append(Ngl.xy(wks,t,data,res3))
panelres = Ngl.Resources()
Ngl.panel(wks,plot[0:3+1],[3,1],panelres)
Ngl.destroy(wks); del res, panelres, plot
""" Plot 2-1 """
image_name = "FFT_ex3_2"
resf = Ngl.Resources()
(wks,resf,dummy) = wks_setting(resf,image_name)
resf.xyLineColors = ["navy"]
resf.xyLineThicknesses = [25]
resf.vpWidthF = 0.74; resf.vpHeightF = 0.54
resf.tiMainFontHeightF = 0.025
resf.tiXAxisString = "~F25~Frequency (Hz)"
resf.tiYAxisString = "~F25~Power Spectrum (dB)"
leftname = "~F25~Period from ~F34~%~F25~ to {} seconds".format(1./faxis[-1])
resf.tiMainString = "Spectral Analysis (FFT)"+ \
"~C~~B~"+leftname
resf.trYMinF = -100 # Limits for Y axis
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)
# use colormap and type information to setup output background
wks_type = "pdf"
wks = Ngl.open_wks(wks_type,"HighRes"+str(it), rlist)
# resources for the contour
res = Ngl.Resources()
res.lbLabelBarOn = False
# Filled contour/labels/labelinfos
res.cnLinesOn = False
res.cnFillOn = True
res.cnLineLabelsOn = False
res.cnInfoLabelOn = False
res.mpGridAndLimbOn = False
#Level selection
res.cnLevelSelectionMode = "ExplicitLevels" # Set explicit contour levels
res.cnLevels = np.arange(1e-5,8.e-4, 4e-5) # 0,5,10,...,70
# maximize the plot
contour = []
res.nglMaximize = True
res.nglFrame = False
# coordinate settings
NewTracerPlot, LonPlot = Ngl.add_cyclic(NewTracer_AI_High[it, lev, :, :], lon_high)
res.sfXArray = LonPlot
res.sfYArray = lat_high[:]
res.vpWidthF = 1
res.vpHeightF = 0.5
Ngl.contour_map(wks,NewTracerPlot,res)
Ngl.frame(wks)
Ngl.destroy(wks)
Ngl.end()
def plot_statistical_value_around_point(path, run, data_tmax_24h, point,
stat_processing):
if stat_processing['method'] == 'max':
data_processed = data_tmax_24h.max(axis=0)
elif stat_processing['method'] == 'min':
data_processed = data_tmax_24h.min(axis=0)
elif stat_processing['method'] == 'median':
data_processed = np.percentile(data_tmax_24h, 50, axis=0)
elif stat_processing['method'] == 'spread':
data_processed = data_tmax_24h.std(axis=0)
elif stat_processing['method'] == 'member_extract':
data_processed = data_tmax_24h[stat_processing['member'] - 1, :]
########################################################################
mpi_file = nc.Dataset(
path['base'] + path['grid'] + 'icon_grid_0028_R02B07_N02.nc', 'r')
vlat = mpi_file.variables['clat_vertices'][:].data * 180. / np.pi
vlon = mpi_file.variables['clon_vertices'][:].data * 180. / np.pi
clat = mpi_file.variables['clat'][:].data * 180. / np.pi
clon = mpi_file.variables['clon'][:].data * 180. / np.pi
mpi_file.close()
########################################################################
if stat_processing['method'] == 'max'\
or stat_processing['method'] == 'min'\
or stat_processing['method'] == 'spread'\
or stat_processing['method'] == 'median':
member_text_filename = stat_processing['method']
elif stat_processing['method'] == 'member_extract':
member_text_filename = 'm{:02d}'.format(stat_processing['member'])
plot_name = 'iconeueps_tmax_24h_{}_run_{:02d}.{:02d}._{}'.format(\
member_text_filename, run['day'], run['month'], point['name'])
########################################################################
x_resolution = 800
y_resolution = 800
wks_res = Ngl.Resources()
wks_res.wkWidth = x_resolution
wks_res.wkHeight = y_resolution
wks_type = 'png'
wks = Ngl.open_wks(wks_type, path['base'] + path['plots'] + plot_name,
wks_res)
resources = Ngl.Resources()
resources.mpProjection = 'Hammer'
resources.mpCenterLonF = point['lon']
resources.mpCenterLatF = point['lat']
radius = 700 # image radius in km around centered point
cutout_plot = dict(
lat_min=point['lat'] - radius / 111.2,
lat_max=point['lat'] + radius / 111.2,
lon_min=point['lon'] - radius /
(111.2 * np.cos(point['lat'] * np.pi / 180)),
lon_max=point['lon'] + radius /
(111.2 * np.cos(point['lat'] * np.pi / 180)),
)
resources.mpLimitMode = 'latlon'
resources.mpMinLonF = cutout_plot['lon_min']
resources.mpMaxLonF = cutout_plot['lon_max']
resources.mpMinLatF = cutout_plot['lat_min']
resources.mpMaxLatF = cutout_plot['lat_max']
resources.nglMaximize = False
resources.vpXF = 0.05
resources.vpYF = 0.95
resources.vpWidthF = 0.7
resources.vpHeightF = 0.7
########################################################################
# Turn on filled map areas:
resources.mpFillOn = True
# Set colors for [FillValue, Ocean, Land , InlandWater]:
resources.mpFillColors = ['pink', 'blue', 'white', 'blue']
resources.mpDataBaseVersion = 'MediumRes'
resources.mpDataSetName = 'Earth..4'
resources.mpOutlineBoundarySets = 'national'
resources.mpGeophysicalLineThicknessF = 7.0 * x_resolution / 1000
resources.mpNationalLineThicknessF = 7.0 * x_resolution / 1000
#resources.mpGridAndLimbDrawOrder = 'postdraw'
resources.mpGridAndLimbOn = False
#resources.mpLimbLineColor = 'black'
#resources.mpLimbLineThicknessF = 10
#resources.mpGridLineColor = 'black'
#resources.mpGridLineThicknessF = 1.0
#resources.mpGridSpacingF = 1
resources.mpPerimOn = True
resources.mpPerimLineColor = 'black'
resources.mpPerimLineThicknessF = 8.0 * x_resolution / 1000
resources.tmXBOn = False
resources.tmXTOn = False
resources.tmYLOn = False
resources.tmYROn = False
resources.sfDataArray = data_processed
resources.sfXArray = clon
resources.sfYArray = clat
resources.sfXCellBounds = vlon
resources.sfYCellBounds = vlat
resources.sfMissingValueV = 9999
resources.cnFillOn = True
resources.cnFillMode = 'CellFill'
#resources.cnCellFillEdgeColor = 'black'
resources.cnMissingValFillColor = 'black'
resources.cnFillPalette = 'GMT_wysiwygcont'
resources.cnLevelSelectionMode = 'ManualLevels'
minlevel = 0.0
maxlevel = 45.0
numberoflevels = 45
resources.cnMinLevelValF = minlevel
resources.cnMaxLevelValF = maxlevel
resources.cnLevelSpacingF = (maxlevel - minlevel) / numberoflevels
if stat_processing['method'] == 'spread':
resources.cnFillPalette = 'WhiteYellowOrangeRed'
minlevel = 0.0
maxlevel = 4.0
# Turn off contour lines and labels:
resources.cnLinesOn = False
resources.cnLineLabelsOn = False
# Set resources for a nice label bar
resources.lbLabelBarOn = True
resources.lbAutoManage = False
resources.lbOrientation = 'vertical'
resources.lbLabelOffsetF = 0.05
#resources.lbBoxMinorExtentF = 0.2
resources.lbLabelStride = 5 #25
resources.lbLabelFontHeightF = 0.02
resources.lbBoxSeparatorLinesOn = False
resources.lbBoxLineThicknessF = 4.0
#resources.lbBoxEndCapStyle = 'TriangleBothEnds'
resources.lbLabelAlignment = 'BoxCenters'
resources.lbTitleString = 'K'
resources.lbTitleFontHeightF = 0.02
resources.lbTitlePosition = 'Right'
resources.lbTitleDirection = 'Across'
#resources.lbTitleAngleF = 90.0
resources.lbTitleExtentF = 0.1
resources.lbTitleOffsetF = 0.0
resources.nglFrame = False
plot = Ngl.contour_map(wks, data_processed, resources)
########################################################################
'''polymarker_res_1 = Ngl.Resources()
polymarker_res_1.gsMarkerColor = 'black'
polymarker_res_1.gsMarkerIndex = 16
polymarker_res_1.gsMarkerSizeF = 0.012
polymarker_res_1.gsMarkerThicknessF = 1
Ngl.polymarker(wks, plot, point['lon'], point['lat'], polymarker_res_1)
########################################################################
polymarker_res_2 = Ngl.Resources()
polymarker_res_2.gsMarkerColor = 'white'
polymarker_res_2.gsMarkerIndex = 16
polymarker_res_2.gsMarkerSizeF = 0.008
polymarker_res_2.gsMarkerThicknessF = 1
Ngl.polymarker(wks, plot, point['lon'], point['lat'], polymarker_res_2)'''
########################################################################
if stat_processing['method'] == 'max'\
or stat_processing['method'] == 'min'\
or stat_processing['method'] == 'spread'\
or stat_processing['method'] == 'median':
member_text = '{} of members'.format(stat_processing['method'])
elif stat_processing['method'] == 'member_extract':
member_text = 'member {:d}'.format(stat_processing['member'])
text = 'tmax 00Z-00Z for 25.07.19, icon-eu-eps run {:02d}.{:02d}.19, {}'.format(\
run['day'], run['month'], member_text)
x = 0.1
y = 0.95
text_res_1 = Ngl.Resources()
text_res_1.txFontHeightF = 0.018
text_res_1.txJust = 'BottomLeft'
Ngl.text_ndc(wks, text, x, y, text_res_1)
Ngl.frame(wks)
Ngl.destroy(wks)
return
def large_scale_prf (ptype,cseason, ncases, cases, casenames, nsite, lats, lons, filepath, filepathobs, casedir):
# ncases, the number of models
# cases, the name of models
# casename, the name of cases
# filepath, model output filepath
# filepathobs, filepath for observational data
# inptrs = [ncases]
if not os.path.exists(casedir):
os.mkdir(casedir)
_Font = 25
interp = 2
extrap = False
mkres = Ngl.Resources()
mkres.gsMarkerIndex = 2
mkres.gsMarkerColor = 'Red'
mkres.gsMarkerSizeF = 15.
infiles = ['' for x in range(ncases)]
ncdfs = ['' for x in range(ncases)]
nregions = nsite
localnames=np.append('ERAI',casenames)
varis = [ 'CLOUD' , 'CLDLIQ', 'THETA','RELHUM']
varisobs = ['CC_ISBL', 'CLWC_ISBL', 'THETA','RELHUM' ]
alphas = ['a) ','b) ','c) ','d) ','e) ','f) ','g) ','h) ','i) ', 'j) ', 'k) ', 'l) ', 'm) ', 'n) ', 'o) ', 'p) ', 'q) ', 'r) ', 's) ', 't) ', 'u) ', 'v) ', 'w) ', 'x) ', 'y) ', 'z) ']
nvaris = len(varis)
cunits = ['%','g/kg','K', '%' ]
cscale = [100, 1000 , 1., 1 ]
cscaleobs = [100, 1000 , 1., 1, 1, 1000, 1,1,1,1,1,1,1]
obsdataset =['ERAI', 'ERAI', 'ERAI', 'ERAI', 'ERAI', 'ERAI', 'ERAI', 'ERAI','ERAI','ERAI']
plotlgs=['' for x in range(nsite)]
for ire in range (0, nsite):
if not os.path.exists(casedir+'/'+str(lons[ire])+'E_'+str(lats[ire])+'N'):
os.mkdir(casedir+'/'+str(lons[ire])+'E_'+str(lats[ire])+'N')
plotname = casedir+'/'+str(lons[ire])+'E_'+str(lats[ire])+'N/Largescale_'+str(lons[ire])+'E_'+str(lats[ire])+'N_'+cseason
plotlgs[ire] = 'Largescale_'+str(lons[ire])+'E_'+str(lats[ire])+'N_'+cseason
wks= Ngl.open_wks(ptype,plotname)
Ngl.define_colormap(wks,'GMT_paired')
plot = []
res = Ngl.Resources()
res.nglMaximize = False
res.nglDraw = False
res.nglFrame = False
res.lgPerimOn = False # no box around
res.vpWidthF = 0.30 # set width and height
res.vpHeightF = 0.30
res.tiYAxisString = "Pressure [hPa]"
if (lats[ire] > 0 and lons[ire]<230 ):
res.trYMinF = 400.
res.trYMaxF = 1000.
else:
res.trYMinF = 700.
res.trYMaxF = 1000.
res.tiMainFont = _Font
res.tmYLLabelFont = _Font
res.tmXBLabelFont = _Font
res.tiYAxisFont = _Font
res.tiXAxisFont = _Font
res.tmXBLabelFontHeightF = 0.01
res.tmXBLabelFontThicknessF = 1.0
res.xyMarkLineMode = 'Lines'
res.xyLineThicknesses = [3.0, 3.0, 3.0, 3.0, 3.0, 3.0,3.,3.,3.,3.,3,3,3,3,3,3,3]
res.xyDashPatterns = np.arange(0,24,1)
pres = Ngl.Resources()
pres.nglFrame = False
pres.txFont = _Font
pres.nglPanelYWhiteSpacePercent = 5
pres.nglPanelXWhiteSpacePercent = 5
# pres.nglPanelTop = 0.88
pres.wkPaperWidthF = 17 # in inches
pres.wkPaperHeightF = 28 # in inches
pres.nglMaximize = True
pres.nglPanelTop = 0.935
pres.nglPanelFigureStrings = alphas
pres.nglPanelFigureStringsJust = 'Topleft'
pres.nglPanelFigureStringsFontHeightF = 0.015
for iv in range (0, nvaris):
if(iv == nvaris-1):
res.pmLegendDisplayMode = 'NEVER'
res.xyExplicitLegendLabels = casenames[:]
res.pmLegendSide = 'top'
res.pmLegendParallelPosF = 0.6
res.pmLegendOrthogonalPosF = -0.5
res.pmLegendWidthF = 0.10
res.pmLegendHeightF = 0.10
res.lgLabelFontHeightF = .02
res.lgLabelFontThicknessF = 1.5
res.lgPerimOn = True
else:
res.pmLegendDisplayMode = 'NEVER'
if(obsdataset[iv] =='CCCM'):
if(cseason == 'ANN'):
fileobs = '/Users/guoz/databank/CLD/CCCm/cccm_cloudfraction_2007-'+cseason+'.nc'
else:
fileobs = '/Users/guoz/databank/CLD/CCCm/cccm_cloudfraction_2007-2010-'+cseason+'.nc'
inptrobs = Dataset(fileobs,'r')
latobs=inptrobs.variables['lat'][:]
latobs_idx = np.abs(latobs - lats[ire]).argmin()
lonobs=inptrobs.variables['lon'][:]
lonobs_idx = np.abs(lonobs - lons[ire]).argmin()
B=inptrobs.variables[varisobs[iv]][:,latobs_idx,lonobs_idx]
else:
if (varisobs[iv] =='PRECT'):
fileobs = filepathobs+'/GPCP_'+cseason+'_climo.nc'
else:
fileobs = filepathobs + obsdataset[iv]+'_'+cseason+'_climo.nc'
inptrobs = Dataset(fileobs,'r')
if (varisobs[iv] =='THETA'):
latobs=inptrobs.variables['lat'][:]
latobs_idx = np.abs(latobs - lats[ire]).argmin()
lonobs=inptrobs.variables['lon'][:]
lonobs_idx = np.abs(lonobs - lons[ire]).argmin()
B = inptrobs.variables['T'][0,:,latobs_idx,lonobs_idx]
pre1 = inptrobs.variables['lev'][:]
for il1 in range (0, len(pre1)):
B[il1] = B[il1]*(1000/pre1[il1])**0.286
else:
pre1 = inptrobs.variables['lev'][:]
latobs=inptrobs.variables['lat'][:]
latobs_idx = np.abs(latobs - lats[ire]).argmin()
lonobs=inptrobs.variables['lon'][:]
lonobs_idx = np.abs(lonobs - lons[ire]).argmin()
B = inptrobs.variables[varisobs[iv]][0,:,latobs_idx,lonobs_idx]
B[:]=B[:] * cscaleobs[iv]
for im in range (0,ncases):
ncdfs[im] = './data/'+cases[im]+'_site_location.nc'
infiles[im]= filepath[im]+cases[im]+'/'+cases[im]+'_'+cseason+'_climo.nc'
inptrs = Dataset(infiles[im],'r') # pointer to file1
lat=inptrs.variables['lat'][:]
nlat=len(lat)
lon=inptrs.variables['lon'][:]
nlon=len(lon)
lev=inptrs.variables['lev'][:]
nlev=len(lev)
ncdf= Dataset(ncdfs[im],'r')
n =ncdf.variables['n'][:]
idx_cols=ncdf.variables['idx_cols'][:,:]
ncdf.close()
if (im ==0 ):
A_field = np.zeros((ncases,nlev),np.float32)
for subc in range( 0, n[ire]):
npoint=idx_cols[ire,n[subc]-1]-1
if (varis[iv] == 'THETA'):
tmp = inptrs.variables['T'][0,:,npoint]
hyam =inptrs.variables['hyam'][:]
hybm =inptrs.variables['hybm'][:]
ps=inptrs.variables['PS'][0,npoint]
ps=ps
p0=inptrs.variables['P0']
pre = np.zeros((nlev),np.float32)
for il in range (0, nlev):
pre[il] = hyam[il]*p0 + hybm[il] * ps
tmp[il] = tmp[il] * (100000/pre[il])**0.286
theunits=str(cscale[iv])+inptrs.variables['T'].units
else:
tmp=inptrs.variables[varis[iv]][0,:,npoint]
theunits=str(cscale[iv])+inptrs.variables[varis[iv]].units
A_field[im,:] = (A_field[im,:]+tmp[:]/n[ire]).astype(np.float32 )
A_field[im,:] = A_field[im,:] *cscale[iv]
inptrs.close()
textres = Ngl.Resources()
textres.txFontHeightF = 0.02
textres.txFont = _Font
textres.txFontHeightF = 0.015
res.tiXAxisString = varis[iv]+ " Unit= " + cunits[iv]
res.trXMinF = min(np.min(A_field[0, :]),np.min(B))
res.trXMaxF = max(np.max(A_field[0, :]),np.max(B))
if(varis[iv] == 'CLOUD') :
if (lats[ire] >= 0 and lons[ire]<230):
res.trXMinF = 0.
res.trXMaxF = 50.
else:
res.trXMinF = 0.
res.trXMaxF = 100.
if(varis[iv] =='CLDLIQ') :
if (lats[ire] >= 0):
res.trXMinF = 0.
res.trXMaxF = 0.1
else:
res.trXMinF = 0.
res.trXMaxF = 0.1
if(varis[iv] =='RELHUM') :
res.trXMinF = 0.
res.trXMaxF = 100.
if(varis[iv] == 'THETA') :
if (lats[ire] >= 0 and lons[ire]<230 ):
res.trXMinF = 290.
res.trXMaxF = 340.
else:
res.trXMinF = 280
res.trXMaxF = 320
if(varis[iv] == 'T') :
res.trXMinF = 180
res.trXMaxF = 300
if(varis[iv] == 'U') :
res.trXMinF = -40
res.trXMaxF = 40
res.trYReverse = True
res.xyLineColors = np.arange(3,20,2)
res.xyMarkerColors = np.arange(2,20,2)
p = Ngl.xy(wks,A_field,lev,res)
res.trYReverse = False
res.xyLineColors = ['black']
pt = Ngl.xy(wks,B,pre1,res)
Ngl.overlay(p,pt)
plot.append(p)
Ngl.panel(wks,plot[:],[nvaris/2,2],pres)
txres = Ngl.Resources()
txres.txFontHeightF = 0.02
txres.txFont = _Font
Ngl.text_ndc(wks,'Large-scale VAR at '+ str(lons[ire])+'E,'+str(lats[ire])+'N',0.5,0.93,txres)
Common_functions.create_legend(wks,localnames,0.02,np.append('black',np.arange(3,20,2)),0.25,0.85)
Ngl.frame(wks)
Ngl.destroy(wks)
return plotlgs
def 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
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)
def grid_order_contourplot(model, grid, first_varying_var, cut_domain):
# define paths #
path = dict(base='/', plots='data/plots/experimental/gridpoint_order/')
print('plotting model: {}'.format(model))
# load icosahedral grid information #
if grid == 'icosahedral':
clat, clon, vlat, vlon = read_grid_coordinates(model, grid)
data_array1 = np.arange(clat.shape[0], dtype='float32')
else:
clat, clon = read_grid_coordinates(model, grid)
clon = clon - 180
if first_varying_var == 'lon':
data_array1 = np.arange(clat.shape[0] * clon.shape[0],
dtype='float32').reshape(
(clat.shape[0], clon.shape[0]))
elif first_varying_var == 'lat':
data_array1 = np.arange(clat.shape[0] * clon.shape[0],
dtype='float32').reshape(
(clon.shape[0], clat.shape[0])).T
print('data_array1:', data_array1.shape, 'clat:', clat.shape, 'clon:',
clon.shape)
if grid == 'icosahedral':
print('vlat:', vlat.shape, 'vlon:', vlon.shape)
print('data_array1 min,max:', data_array1.min(), data_array1.max())
print('clat min,max:', clat.min(), clat.max())
print('clon min,max:', clon.min(), clon.max())
if grid == 'icosahedral':
print('vlat min,max:', vlat.min(), vlat.max())
print('vlon min,max:', vlon.min(), vlon.max())
print('------------------------------------------')
if cut_domain['name'] == 'uncut':
data_array1_cut = data_array1
else:
margin_deg = 0
if grid == 'icosahedral':
data_array1_cut, clat, clon, vlat, vlon \
= cut_by_domain(cut_domain, grid, data_array1, clat, clon, vlat, vlon, margin_deg)
else:
data_array1_cut, clat, clon \
= cut_by_domain(cut_domain, grid, data_array1, clat, clon, None, None, margin_deg)
print('data_array1:', data_array1_cut.shape, 'clat:', clat.shape, 'clon:',
clon.shape)
if grid == 'icosahedral':
print('vlat:', vlat.shape, 'vlon:', vlon.shape)
print('data_array1_cut min,max:', data_array1_cut.min(),
data_array1_cut.max())
print('clat min,max:', clat.min(), clat.max())
print('clon min,max:', clon.min(), clon.max())
if grid == 'icosahedral':
print('vlat min,max:', vlat.min(), vlat.max())
print('vlon min,max:', vlon.min(), vlon.max())
if grid == 'icosahedral':
plot_name = 'gridpoint_order_{}_{}_global_{}'.format(
model, grid, cut_domain['name'])
else:
plot_name = 'gridpoint_order_{}_{}_{}-varying-first_global_{}'.format(
model, grid, first_varying_var, cut_domain['name'])
# plot basic map with borders #
wks_res = Ngl.Resources()
x_resolution = 800
y_resolution = 800
wks_res.wkWidth = x_resolution
wks_res.wkHeight = y_resolution
wks_res.wkColorMap = 'BkBlAqGrYeOrReViWh200'
levels1 = np.linspace(data_array1.min(), data_array1.max(), 200)
wks_type = 'png'
wks = Ngl.open_wks(wks_type, path['base'] + path['plots'] + plot_name,
wks_res)
mpres = Ngl.Resources()
mpres.mpProjection = 'CylindricalEquidistant'
mpres.mpLimitMode = 'LatLon'
mpres.mpCenterLonF = 0.0
mpres.mpCenterLatF = 0.0
mpres.mpMinLonF = -180.
mpres.mpMaxLonF = 180.
mpres.mpMinLatF = -90.
mpres.mpMaxLatF = 90.
mpres.nglMaximize = False
mpres.vpXF = 0.003
mpres.vpYF = 1.00
mpres.vpWidthF = 0.86
mpres.vpHeightF = 1.00
mpres.mpMonoFillColor = 'True'
mpres.mpFillColor = -1
#Ngl.set_values(wks,mpres)
mpres.mpFillOn = True
#resources.cnFillDrawOrder = 'Predraw' # draw contours first
mpres.mpGridAndLimbOn = False
mpres.mpGreatCircleLinesOn = False
#mpres.mpOutlineDrawOrder = 'PreDraw'
mpres.mpDataBaseVersion = 'LowRes'
mpres.mpDataSetName = 'Earth..4'
mpres.mpOutlineBoundarySets = 'national'
mpres.mpGeophysicalLineColor = 'black'
mpres.mpNationalLineColor = 'black'
mpres.mpGeophysicalLineThicknessF = 2. * x_resolution / 1000
mpres.mpNationalLineThicknessF = 2. * x_resolution / 1000
mpres.mpPerimOn = True
mpres.mpPerimLineColor = 'black'
mpres.mpPerimLineThicknessF = 8.0 * x_resolution / 1000
mpres.tmXBOn = False
mpres.tmXTOn = False
mpres.tmYLOn = False
mpres.tmYROn = False
mpres.nglDraw = False #-- don't draw plot
mpres.nglFrame = False #-- don't advance frame
basic_map = Ngl.map(wks, mpres)
# plot variable1 in shading/contours #
v1res = Ngl.Resources()
v1res.sfDataArray = data_array1_cut
v1res.sfXArray = clon
v1res.sfYArray = clat
if grid == 'icosahedral':
v1res.cnFillMode = 'CellFill'
v1res.sfXCellBounds = vlon
v1res.sfYCellBounds = vlat
else:
v1res.cnFillMode = 'RasterFill'
v1res.sfMissingValueV = 9999
v1res.cnLinesOn = False # Turn off contour lines.
v1res.cnFillOn = True
#v1res.cnFillOpacityF = 0.5
#v1res.cnFillDrawOrder = 'Predraw'
v1res.cnLineLabelsOn = False
v1res.cnLevelSelectionMode = 'ExplicitLevels'
v1res.cnLevels = levels1
# set resources for a nice label bar #
v1res.lbLabelBarOn = True
v1res.lbAutoManage = False
v1res.lbOrientation = 'vertical'
v1res.lbLabelOffsetF = 0.04 # minor axis fraction: the distance between colorbar and numbers
v1res.lbBoxMinorExtentF = 0.20 # minor axis fraction: width of the color boxes when labelbar down
v1res.lbTopMarginF = 0.05 # make a little more space at top for the unit label
v1res.lbRightMarginF = 0.0
v1res.lbBottomMarginF = 0.0
v1res.lbLeftMarginF = -0.35
v1res.cnLabelBarEndStyle = 'ExcludeOuterBoxes'
#v1res.cnLabelBarEndStyle = 'IncludeOuterBoxes'
#v1res.cnExplicitLabelBarLabelsOn = True
#v1res.pmLabelBarDisplayMode = 'Always'
v1res.pmLabelBarWidthF = 0.10
#v1res.lbLabelStrings = label_str_list
v1res.lbLabelFontHeightF = 0.010
#v1res.lbBoxCount = 40
v1res.lbBoxSeparatorLinesOn = False
v1res.lbBoxLineThicknessF = 4
#v1res.lbBoxEndCapStyle = 'TriangleBothEnds'
v1res.lbLabelAlignment = 'InteriorEdges'
v1res.lbLabelStride = 1
v1res.nglFrame = False
v1res.nglDraw = False
v1plot = Ngl.contour(wks, data_array1_cut, v1res)
# plot label bar unit #
text_str = 'Index'
text_res_1 = Ngl.Resources()
text_res_1.txFontColor = 'black'
text_x = 0.975
text_y = 0.72
text_res_1.txFontHeightF = 0.012
Ngl.overlay(basic_map, v1plot)
Ngl.draw(basic_map)
Ngl.text_ndc(wks, text_str, text_x, text_y, text_res_1)
Ngl.frame(wks)
Ngl.destroy(wks)
#Ngl.end()
# cut top and bottom whitespace of plot #
im = Image.open(path['base'] + path['plots'] + plot_name + '.png')
image_array = np.asarray(im.convert('L'))
image_array = np.where(image_array < 255, 1, 0)
image_filter = np.amax(image_array, axis=1)
vmargins = [
np.nonzero(image_filter)[0][0],
np.nonzero(image_filter[::-1])[0][0]
]
#print(vmargins)
#print(im.size)
im_cropped = Image.new(
'RGB', (im.size[0], im.size[1] - vmargins[0] - vmargins[1]),
(255, 255, 255))
im_cropped.paste(
im.crop((0, vmargins[0], im.size[0], im.size[1] - vmargins[1])),
(0, 0))
#print(im_cropped.size)
im.close()
im_cropped.save(path['base'] + path['plots'] + plot_name + '.png', 'png')
im_cropped.close()
del data_array1, data_array1_cut, clat, clon
if grid == 'icosahedral':
del vlat, vlon
return
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_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 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
res2.trXMinF = faxis[0] # Limits for X axis
res2.trXMaxF = faxis[-1]
res2.tiXAxisString = "~F25~Frequency (Hz)"
res2.tiYAxisString = "~F25~Power Spectrum"
leftname = "Period from ~F34~%~F25~ to {} seconds".format(1./faxis[-1])
res2.tiMainString = "Spectral Analysis (FFT)"+ \
"~C~~B~"+leftname
plot = []
plot.append(Ngl.xy(wks,t,sig,res))
plot.append(Ngl.xy(wks,faxis,PS,res2))
#wrf_tools.ngl_Strings(wks, plot[0], left=leftname, center="", right="")
panelres = Ngl.Resources()
Ngl.panel(wks,plot[0:1+1],[2,1],panelres)
#Ngl.draw(plot[0])
#Ngl.frame(wks)
Ngl.destroy(wks); del res, plot
Ngl.end()
end_time = cpu_time(); end_time = (end_time - start_time)/60.0
print(os.path.basename(__file__)+" has done!\nTime elapsed: {:.2f}".format(end_time), "mins.")
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 plot_rain_around_point(path, data_rain_tot_sum, hour_start, hour_end, point, stat_processing, member):
if stat_processing == 'max':
data_processed = (data_rain_tot_sum[hour_end, :, :] - data_rain_tot_sum[hour_start, :, :]).max(axis=0)
elif stat_processing == 'member_extract':
data_processed = data_rain_tot_sum[hour_end, member-1, :] - data_rain_tot_sum[hour_start, member-1, :]
########################################################################
if stat_processing == 'max':
member_text_filename = 'max'
elif stat_processing == 'member_extract':
member_text_filename = 'm{:02d}'.format(member)
plot_name = 'iconeueps_rain_{}_{:02d}Z-{:02d}Z_{}'.format(\
point['name'], hour_start+12, hour_end+12, member_text_filename)
mpi_file = nc.Dataset(path['base'] + path['grid'] + 'icon_grid_0028_R02B07_N02.nc', 'r')
vlat = mpi_file.variables['clat_vertices'][:].data * 180./np.pi
vlon = mpi_file.variables['clon_vertices'][:].data * 180./np.pi
clat = mpi_file.variables['clat'][:].data * 180./np.pi
clon = mpi_file.variables['clon'][:].data * 180./np.pi
mpi_file.close()
########################################################################
x_resolution = 800
y_resolution = 800
wks_res = Ngl.Resources()
wks_res.wkWidth = x_resolution
wks_res.wkHeight = y_resolution
wks_type = 'png'
wks = Ngl.open_wks(wks_type, path['base'] + path['plots'] + plot_name, wks_res)
resources = Ngl.Resources()
resources.mpProjection = 'Hammer'
resources.mpCenterLonF = point['lon']
resources.mpCenterLatF = point['lat']
radius = 500 # image radius in km around centered point
cutout_plot = dict(
lat_min = point['lat'] - radius / 111.2,
lat_max = point['lat'] + radius / 111.2,
lon_min = point['lon'] - radius / (111.2 * np.cos(point['lat']*np.pi/180)),
lon_max = point['lon'] + radius / (111.2 * np.cos(point['lat']*np.pi/180)),
)
resources.mpLimitMode = 'latlon'
resources.mpMinLonF = cutout_plot['lon_min']
resources.mpMaxLonF = cutout_plot['lon_max']
resources.mpMinLatF = cutout_plot['lat_min']
resources.mpMaxLatF = cutout_plot['lat_max']
resources.nglMaximize = False
resources.vpXF = 0.05
resources.vpYF = 0.95
resources.vpWidthF = 0.7
resources.vpHeightF = 0.7
########################################################################
# Turn on filled map areas:
resources.mpFillOn = True
# Set colors for [FillValue, Ocean, Land , InlandWater]:
resources.mpFillColors = ['pink','blue','white','blue']
resources.mpDataBaseVersion = 'MediumRes'
resources.mpDataSetName = 'Earth..4'
resources.mpOutlineBoundarySets = 'national'
resources.mpGeophysicalLineThicknessF = 7.0 * x_resolution / 1000
resources.mpNationalLineThicknessF = 7.0 * x_resolution / 1000
#resources.mpGridAndLimbDrawOrder = 'postdraw'
resources.mpGridAndLimbOn = False
#resources.mpLimbLineColor = 'black'
#resources.mpLimbLineThicknessF = 10
#resources.mpGridLineColor = 'black'
#resources.mpGridLineThicknessF = 1.0
#resources.mpGridSpacingF = 1
resources.mpPerimOn = True
resources.mpPerimLineColor = 'black'
resources.mpPerimLineThicknessF = 8.0 * x_resolution / 1000
resources.tmXBOn = False
resources.tmXTOn = False
resources.tmYLOn = False
resources.tmYROn = False
resources.sfDataArray = data_processed
resources.sfXArray = clon
resources.sfYArray = clat
resources.sfXCellBounds = vlon
resources.sfYCellBounds = vlat
resources.sfMissingValueV = 9999
resources.cnFillOn = True
resources.cnFillMode = 'CellFill'
resources.cnCellFillEdgeColor = 'black'
resources.cnMissingValFillColor = 'black'
resources.cnFillPalette = 'WhiteBlueGreenYellowRed'
resources.cnLevelSelectionMode = 'ManualLevels'
minlevel = 0.0
maxlevel = 50.0
numberoflevels = 250
resources.cnMinLevelValF = minlevel
resources.cnMaxLevelValF = maxlevel
resources.cnLevelSpacingF = (maxlevel - minlevel) / numberoflevels
# Turn off contour lines and labels:
resources.cnLinesOn = False
resources.cnLineLabelsOn = False
# Set resources for a nice label bar
resources.lbLabelBarOn = True
resources.lbAutoManage = False
resources.lbOrientation = 'vertical'
resources.lbLabelOffsetF = 0.05
#resources.lbBoxMinorExtentF = 0.2
resources.lbLabelStride = 25
resources.lbLabelFontHeightF = 0.02
resources.lbBoxSeparatorLinesOn = False
resources.lbBoxLineThicknessF = 4.0
#resources.lbBoxEndCapStyle = 'TriangleBothEnds'
resources.lbLabelAlignment = 'BoxCenters'
resources.lbTitleString = 'mm'
resources.lbTitleFontHeightF = 0.02
resources.lbTitlePosition = 'Right'
resources.lbTitleDirection = 'Across'
resources.lbTitleAngleF = 90.0
resources.lbTitleExtentF = 0.1
resources.lbTitleOffsetF = 0.0
resources.nglFrame = False
plot = Ngl.contour_map(wks, data_processed, resources)
########################################################################
polymarker_res_1 = Ngl.Resources()
polymarker_res_1.gsMarkerColor = 'black'
polymarker_res_1.gsMarkerIndex = 16
polymarker_res_1.gsMarkerSizeF = 0.012
polymarker_res_1.gsMarkerThicknessF = 1
Ngl.polymarker(wks, plot, point['lon'], point['lat'], polymarker_res_1)
########################################################################
polymarker_res_2 = Ngl.Resources()
polymarker_res_2.gsMarkerColor = 'white'
polymarker_res_2.gsMarkerIndex = 16
polymarker_res_2.gsMarkerSizeF = 0.008
polymarker_res_2.gsMarkerThicknessF = 1
Ngl.polymarker(wks, plot, point['lon'], point['lat'], polymarker_res_2)
########################################################################
if stat_processing == 'max':
member_text = 'max member of each cell'
elif stat_processing == 'member_extract':
member_text = 'member {:d}'.format(member)
text = 'rain_gsp + rain_con, 07.08.19 {}Z-{}Z, {}'.format(hour_start+12, hour_end+12, member_text)
x = 0.1
y = 0.95
text_res_1 = Ngl.Resources()
text_res_1.txFontHeightF = 0.02
text_res_1.txJust = 'BottomLeft'
Ngl.text_ndc(wks, text, x, y, text_res_1)
Ngl.frame(wks)
Ngl.destroy(wks)
return
res.cnLevelSelectionMode = "ManualLevels" res.cnMinLevelValF = 1000.0 res.cnMaxLevelValF = 6000.0 res.cnLevelSpacingF = 250.0 res.cnLineThicknessF = 2.5 # create PWAT plot for analysis data MD_plot = ngl.contour_map(wks,MD,res) ngl.maximize_plot(wks, MD_plot) ngl.draw(MD_plot) ngl.frame(wks) ngl.destroy(wks) del res del MD #del CIN ################################################################################################### # open forecast file f_fili = "GFS_forecast_%s_%s.nc" % (init_dt[:8], init_dt[8:10]) forecast = nio.open_file(diri+f_fili) # loop through forecast times for i in range(0, len(fore)):
def delete(self):
Ngl.destroy(self.workstation)
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_prob_of_exceedance_around_point(path, run, data_tmax_24h, domain,
threshold):
data_processed = np.where(data_tmax_24h >= threshold, 1,
0).sum(axis=0) / 40 * 100
data_processed += 0.1
########################################################################
mpi_file = nc.Dataset(
path['base'] + path['grid'] + 'icon_grid_0028_R02B07_N02.nc', 'r')
vlat = mpi_file.variables['clat_vertices'][:].data * 180. / np.pi
vlon = mpi_file.variables['clon_vertices'][:].data * 180. / np.pi
clat = mpi_file.variables['clat'][:].data * 180. / np.pi
clon = mpi_file.variables['clon'][:].data * 180. / np.pi
mpi_file.close()
########################################################################
color = 'heat'
plot_name = 'iconeueps_tmax_24h_prob_over_{:.0f}C_run_{:02d}.{:02d}._{}_{}'.format(\
threshold, run['day'], run['month'], domain['name'], color)
subfolder = 'prob_of_exceedance/{}'.format(domain['name'])
if not os.path.isdir(path['base'] + path['plots'] + subfolder):
os.mkdir(path['base'] + path['plots'] + subfolder)
path['plots_subfolder'] = subfolder + '/'
########################################################################
colorpalette_source = 'hclwizard'
if colorpalette_source == 'tristenca':
filename = 'colorscale_tristenca_tot_prec_monohue_blues.txt'
with open(path['base'] + path['colorpalette'] + filename, 'r') as f:
line = f.read()
hex_colors = []
for i in range(40):
start = i * 8 + 1
end = start + 6
hex_colors.append(line[start:end])
custom_palette_list = [[255, 255, 255]]
for hex_color in hex_colors[:]:
rgb_color = [
int(hex_str, 16)
for hex_str in [hex_color[:2], hex_color[2:4], hex_color[4:]]
]
custom_palette_list.append(rgb_color)
custom_palette = np.array(custom_palette_list) / 255
elif colorpalette_source == 'hclwizard':
filename = 'colorscale_hclwizard_t2m_prob_{}.txt'.format(color)
with open(path['base'] + path['colorpalette'] + filename, 'r') as f:
lines = f.readlines()
hex_colors = []
for line in lines:
hex_colors.append(line[2:8])
custom_palette_list = [[0, 255,
0]] # extra color for correct LabelBar view
custom_palette_list.append([255, 255, 255]) # color for 1% category
for hex_color in hex_colors[:]:
rgb_color = [
int(hex_str, 16)
for hex_str in [hex_color[:2], hex_color[2:4], hex_color[4:]]
]
custom_palette_list.append(rgb_color)
custom_palette_list.append([255, 0, 0
]) # extra color for correct LabelBar view
custom_palette = np.array(custom_palette_list)
'''custom_palette = np.array([[255, 255, 255],\
[255, 255, 0],\
[250, 0, 0],\
[ 0, 250, 0],\
[ 0, 0, 250],\
[100, 0, 0],\
[ 0, 100, 0],\
[ 0, 0, 100],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[100, 100, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[ 0, 0, 0],\
[250, 100, 100],\
[100, 250, 100],\
[100, 100, 250]])'''
custom_palette = custom_palette / 255
#print(custom_palette.shape)
########################################################################
x_resolution = 1200
y_resolution = 1200
wks_res = Ngl.Resources()
wks_res.wkWidth = x_resolution
wks_res.wkHeight = y_resolution
wks_res.wkColorMap = custom_palette
wks_type = 'png'
wks = Ngl.open_wks(
wks_type,
path['base'] + path['plots'] + path['plots_subfolder'] + plot_name,
wks_res)
resources = Ngl.Resources()
if domain['method'] == 'centerpoint':
resources.mpProjection = 'Hammer'
resources.mpCenterLonF = domain['lon']
resources.mpCenterLatF = domain['lat']
cutout_plot = dict(
lat_min=domain['lat'] - domain['radius'] / 111.2,
lat_max=domain['lat'] + domain['radius'] / 111.2,
lon_min=domain['lon'] - domain['radius'] /
(111.2 * np.cos(domain['lat'] * np.pi / 180)),
lon_max=domain['lon'] + domain['radius'] /
(111.2 * np.cos(domain['lat'] * np.pi / 180)),
)
resources.mpLimitMode = 'latlon'
resources.mpMinLonF = cutout_plot['lon_min']
resources.mpMaxLonF = cutout_plot['lon_max']
resources.mpMinLatF = cutout_plot['lat_min']
resources.mpMaxLatF = cutout_plot['lat_max']
elif domain['method'] == 'deltalatlon':
resources.mpProjection = 'Hammer'
resources.mpCenterLonF = domain['lon']
resources.mpCenterLatF = domain['lat']
cutout_plot = dict(
lat_min=domain['lat'] - domain['deltalat'] / 111.2,
lat_max=domain['lat'] + domain['deltalat'] / 111.2,
lon_min=domain['lon'] - domain['deltalon'] /
(111.2 * np.cos(domain['lat'] * np.pi / 180)),
lon_max=domain['lon'] + domain['deltalon'] /
(111.2 * np.cos(domain['lat'] * np.pi / 180)),
)
resources.mpLimitMode = 'latlon'
resources.mpMinLonF = cutout_plot['lon_min']
resources.mpMaxLonF = cutout_plot['lon_max']
resources.mpMinLatF = cutout_plot['lat_min']
resources.mpMaxLatF = cutout_plot['lat_max']
else:
print('domain method "{}" not implemented!'.format(domain['method']))
exit()
resources.nglMaximize = False
resources.vpXF = 0.02
resources.vpYF = 0.92
resources.vpWidthF = 0.82
resources.vpHeightF = 0.70
########################################################################
# Turn on filled map areas:
resources.mpFillOn = True
# Set colors for [FillValue, Ocean, Land , InlandWater]:
resources.mpFillColors = ['pink', 'blue', 'white', 'blue']
resources.mpDataBaseVersion = 'MediumRes'
resources.mpDataSetName = 'Earth..4'
resources.mpOutlineBoundarySets = 'national'
resources.mpGeophysicalLineThicknessF = 3.0 * x_resolution / 1000
resources.mpNationalLineThicknessF = 3.0 * x_resolution / 1000
#resources.mpGridAndLimbDrawOrder = 'postdraw'
resources.mpGridAndLimbOn = False
#resources.mpLimbLineColor = 'black'
#resources.mpLimbLineThicknessF = 10
#resources.mpGridLineColor = 'black'
#resources.mpGridLineThicknessF = 1.0
#resources.mpGridSpacingF = 1
resources.mpPerimOn = True
resources.mpPerimLineColor = 'black'
resources.mpPerimLineThicknessF = 8.0 * x_resolution / 1000
resources.tmXBOn = False
resources.tmXTOn = False
resources.tmYLOn = False
resources.tmYROn = False
resources.sfDataArray = data_processed
resources.sfXArray = clon
resources.sfYArray = clat
resources.sfXCellBounds = vlon
resources.sfYCellBounds = vlat
resources.sfMissingValueV = 9999
resources.cnFillOn = True
resources.cnFillMode = 'CellFill'
#resources.cnCellFillEdgeColor = 'black'
resources.cnMissingValFillColor = 'black'
resources.cnFillPalette = custom_palette
resources.cnLevelSelectionMode = 'ExplicitLevels'
resources.cnLevels = np.arange(0, 103, 2.5)
minlevel = 0.0
maxlevel = 100.0
numberoflevels = 40
resources.cnMinLevelValF = minlevel
resources.cnMaxLevelValF = maxlevel
resources.cnLevelSpacingF = (maxlevel - minlevel) / numberoflevels
# Turn off contour lines and labels:
resources.cnLinesOn = False
resources.cnLineLabelsOn = False
# Set resources for a nice label bar
resources.lbLabelBarOn = True
resources.lbAutoManage = False
resources.lbOrientation = 'vertical'
resources.lbLabelOffsetF = 0.05
#resources.lbBoxMinorExtentF = 0.2
#label_str_list = ['{:d}'.format(int(x)) for x in list(range(0, 101, 10))]
resources.cnLabelBarEndStyle = 'ExcludeOuterBoxes'
#resources.cnExplicitLabelBarLabelsOn = True
#resources.pmLabelBarDisplayMode = 'Always'
resources.pmLabelBarWidthF = 0.10
#resources.lbLabelStrings = label_str_list
resources.lbLabelStride = 4
resources.lbLabelFontHeightF = 0.018
#resources.lbBoxCount = 40
resources.lbBoxSeparatorLinesOn = False
resources.lbBoxLineThicknessF = 4.0
#resources.lbBoxEndCapStyle = 'TriangleBothEnds'
resources.lbLabelAlignment = 'BoxCenters'
resources.lbTitleString = '%'
resources.lbTitleFontHeightF = 0.018
resources.lbTitlePosition = 'Right'
resources.lbTitleDirection = 'Across'
#resources.lbTitleAngleF = 90.0
resources.lbTitleExtentF = 0.1
resources.lbTitleOffsetF = 0.0
resources.nglFrame = False
plot = Ngl.contour_map(wks, data_processed, resources)
########################################################################
'''polymarker_res_1 = Ngl.Resources()
polymarker_res_1.gsMarkerColor = 'black'
polymarker_res_1.gsMarkerIndex = 16
polymarker_res_1.gsMarkerSizeF = 0.012
polymarker_res_1.gsMarkerThicknessF = 1
Ngl.polymarker(wks, plot, point['lon'], point['lat'], polymarker_res_1)
########################################################################
polymarker_res_2 = Ngl.Resources()
polymarker_res_2.gsMarkerColor = 'white'
polymarker_res_2.gsMarkerIndex = 16
polymarker_res_2.gsMarkerSizeF = 0.008
polymarker_res_2.gsMarkerThicknessF = 1
Ngl.polymarker(wks, plot, point['lon'], point['lat'], polymarker_res_2)'''
########################################################################
run_time = datetime.datetime(run['year'], run['month'], run['day'],
run['hour'])
timeshift = 2
if timeshift == 1:
time_code = 'CET' # UTC+1
elif timeshift == 2:
time_code = 'CEST' # UTC+2
run_time = run_time + datetime.timedelta(0, 3600 * int(timeshift))
valid_time = run_time + datetime.timedelta(0, 3600 * int(4 * 24 + 12))
text1 = '4-Tages-Vorhersage der Maximaltemperatur vom 21.07.19: \
Wahrscheinlichkeit, dass am 25.07.19 Temperaturen von \
36 ~S~o~N~C, 38 ~S~o~N~C, 40 ~S~o~N~C u~H-13V2F35~H~FV-2H3~berschritten werden'
text1 = 'Vorhersage der Hitzewelle '
text1 = 'Modell: ICON-EU-EPS, Bildautor: Marco Wurth, IMK-TRO (KIT)'
#text2 = 'Wahrscheinlichkeit fu~H-13V2F35~H~FV-2H3~r u~H-13V2F35~H~FV-2H3~ber {:d} ~S~o~N~C'.format(int(threshold))
text_res_1 = Ngl.Resources()
text_res_1.txJust = 'BottomLeft'
text_res_1.txFontHeightF = 0.009
x = 0.01
y = 0.01
Ngl.text_ndc(wks, text1, x, y, text_res_1)
text_res_1.txFontHeightF = 0.018
x = 0.25
y = 0.94
#Ngl.text_ndc(wks, text2, x, y, text_res_1)
text_res_1.txFontHeightF = 0.014
x = 0.575
y = 0.93
#Ngl.text_ndc(wks, text3, x, y, text_res_1)
text_res_1.txFontHeightF = 0.014
x = 0.025
y = 0.192
#Ngl.text_ndc(wks, text4, x, y, text_res_1)
Ngl.frame(wks)
Ngl.destroy(wks)
return
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 delete(self):
Ngl.destroy(self.workstation)
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
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 plot_contourmap(path, date, fcst_hours, variable, stat_processing_methods):
##### generate subpath and filename #####
subpath = 'run_{}{:02}{:02}{:02}/{}/'.format(\
date['year'], date['month'], date['day'], date['hour'], variable)
filename1 = 'icon-eu-eps_europe_icosahedral_single-level_{}{:02}{:02}{:02}_{:03}_{}.grib2'.format(\
date['year'], date['month'], date['day'], date['hour'], fcst_hours[0], variable)
filename2 = 'icon-eu-eps_europe_icosahedral_single-level_{}{:02}{:02}{:02}_{:03}_{}.grib2'.format(\
date['year'], date['month'], date['day'], date['hour'], fcst_hours[1], variable)
########################################################################
### read data ###
########################################################################
##### create empty numpy arrays #####
##### 2 fcst_hours, 40 members, 75948 eu gridpoints #####
data_raw = np.empty((2, 40, 75948))
data_members = np.empty((40, 75948))
##### every time in loop open next grib msg from grib file #####
##### grib messages in dwd file are sorted by increasing member number #####
with open(path['base'] + path['data'] + subpath + filename1, 'rb') as file:
for member in range(1, 41):
print('read data from member {}'.format(member))
grib_msg_id = eccodes.codes_grib_new_from_file(file)
data_raw[0, member - 1, :] = eccodes.codes_get_array(
grib_msg_id, 'values')
eccodes.codes_release(grib_msg_id)
with open(path['base'] + path['data'] + subpath + filename2, 'rb') as file:
for member in range(1, 41):
print('read data from member {}'.format(member))
grib_msg_id = eccodes.codes_grib_new_from_file(file)
data_raw[1, member - 1, :] = eccodes.codes_get_array(
grib_msg_id, 'values')
eccodes.codes_release(grib_msg_id)
##### take the difference of the two accumulated total precipitation arrays #####
data_members = data_raw[1, :, :] - data_raw[0, :, :]
del data_raw
##### open icon-eps grid file #####
icongrid_file = nc.Dataset(
path['base'] + path['grid'] + 'icon_grid_0028_R02B07_N02.nc', 'r')
vlat = icongrid_file.variables['clat_vertices'][:].data * 180. / np.pi
vlon = icongrid_file.variables['clon_vertices'][:].data * 180. / np.pi
clat = icongrid_file.variables['clat'][:].data * 180. / np.pi
clon = icongrid_file.variables['clon'][:].data * 180. / np.pi
icongrid_file.close()
for stat_processing in stat_processing_methods:
########################################################################
### statistically process data ###
########################################################################
if stat_processing == 'mean':
data_processed = data_members.mean(axis=0)
elif stat_processing == 'max':
data_processed = data_members.max(axis=0)
elif stat_processing == 'min':
data_processed = data_members.min(axis=0)
elif stat_processing == '90p':
data_processed = np.percentile(data_members, 90, axis=0)
elif stat_processing == '75p':
data_processed = np.percentile(data_members, 75, axis=0)
elif stat_processing == '50p':
data_processed = np.percentile(data_members, 50, axis=0)
elif stat_processing == '25p':
data_processed = np.percentile(data_members, 25, axis=0)
elif stat_processing == '10p':
data_processed = np.percentile(data_members, 10, axis=0)
#print('shape of data_members: {}'.format(np.shape(data_members)))
#print('shape of data_processed: {}'.format(np.shape(data_processed)))
########################################################################
### plot data on world map ###
########################################################################
##### set domain due to center point and radius #####
center_point = dict(lat=48.5, lon=9.0)
radius = 1800 # domain radius in km around center_point
domain = dict(
lat_min=center_point['lat'] - radius / 111.2,
lat_max=center_point['lat'] + radius / 111.2,
lon_min=center_point['lon'] - radius /
(111.2 * np.cos(center_point['lat'] * np.pi / 180)),
lon_max=center_point['lon'] + radius /
(111.2 * np.cos(center_point['lat'] * np.pi / 180)),
)
##### or set domain manually in deg N/E #####
'''domain = dict(
lat_min = 0.0,
lat_max = 20.0,
lon_min = 0.0,
lon_max = 20.0,
)'''
##### set image size (should be squared) #####
##### plotting area in pyngl can not exceed squared area even if plotting on rectangular images #####
##### for obtaining rectangular plots on has to cut manually afterwards e.g. with pillow package #####
x_resolution = 800
y_resolution = 800
wks_res = Ngl.Resources()
wks_res.wkWidth = x_resolution
wks_res.wkHeight = y_resolution
plot_name = 'contourplot_icon-eu-eps_tot_prec_{:02d}-{:02d}h_{}'.format(\
fcst_hours[0], fcst_hours[1], stat_processing)
wks_type = 'png'
wks = Ngl.open_wks(wks_type, path['base'] + path['plots'] + plot_name,
wks_res)
resources = Ngl.Resources(
) # create resources object containing all the plot settings
resources.mpProjection = 'Hammer' # projection type
resources.mpCenterLonF = (domain['lon_max'] + domain['lon_min']
) / 2 # projection center point
resources.mpCenterLatF = (domain['lat_max'] + domain['lat_min']) / 2
resources.mpLimitMode = 'latlon'
resources.mpMinLonF = domain['lon_min']
resources.mpMaxLonF = domain['lon_max']
resources.mpMinLatF = domain['lat_min']
resources.mpMaxLatF = domain['lat_max']
##### set plot area #####
resources.nglMaximize = False
resources.vpXF = 0.05
resources.vpYF = 0.9
resources.vpWidthF = 0.7
resources.vpHeightF = 0.7
##### set all map plot settings #####
resources.mpFillOn = True # turn on filled map areas
resources.mpFillColors = [
'pink', 'blue', 'white', 'white'
] # set colors for [FillValue, Ocean, Land , InlandWater]
resources.mpDataBaseVersion = 'MediumRes' # quality of national borders
resources.mpDataSetName = 'Earth..4'
resources.mpOutlineBoundarySets = 'national'
#resources.mpDataBaseVersion = 'HighRes'
#resources.mpDataResolution = 'Fine'
resources.mpGeophysicalLineThicknessF = 7.0 * x_resolution / 1000 # keep borders thickness resolution-independent
resources.mpNationalLineThicknessF = 7.0 * x_resolution / 1000
#resources.mpGridAndLimbDrawOrder = 'postdraw'
resources.mpGridAndLimbOn = False # turn off geographic coordinates grid
#resources.mpLimbLineColor = 'black'
#resources.mpLimbLineThicknessF = 10
#resources.mpGridLineColor = 'black'
#resources.mpGridLineThicknessF = 1.0
#resources.mpGridSpacingF = 1
resources.mpPerimOn = True # turn on perimeter around plot
resources.mpPerimLineColor = 'black'
resources.mpPerimLineThicknessF = 8.0 * x_resolution / 1000 # keep perimeter thickness resolution-independent
resources.tmXBOn = False # turn off location ticks around plot
resources.tmXTOn = False
resources.tmYLOn = False
resources.tmYROn = False
resources.sfDataArray = data_processed # data input file to plot
resources.sfXArray = clon # array with cell center locations
resources.sfYArray = clat
resources.sfXCellBounds = vlon # array with cell vertices locations
resources.sfYCellBounds = vlat
resources.sfMissingValueV = 9999 # in case you want to mask values
resources.cnFillOn = True
resources.cnFillMode = 'CellFill'
#resources.cnCellFillEdgeColor = 'black' # uncomment this for plotting the cell edges
resources.cnMissingValFillColor = 'black'
resources.cnFillPalette = 'WhiteBlueGreenYellowRed' # color palette
resources.cnLevelSelectionMode = 'ManualLevels'
minlevel = 0.0 # min level of colorbar
maxlevel = 50.0 # max level of colorbar
numberoflevels = 250 # number of levels of colorbar, max. 250 with this color palette
resources.cnMinLevelValF = minlevel
resources.cnMaxLevelValF = maxlevel
resources.cnLevelSpacingF = (maxlevel - minlevel) / numberoflevels
resources.cnLinesOn = False # turn off contour lines
resources.cnLineLabelsOn = False # turn off contour labels
##### set resources for a nice colorbar #####
resources.lbLabelBarOn = True
resources.lbAutoManage = False
resources.lbOrientation = 'vertical'
resources.lbLabelOffsetF = 0.05
#resources.lbBoxMinorExtentF = 0.2
resources.lbLabelStride = 25 # print a tick every 25 levels
resources.lbLabelFontHeightF = 0.016
resources.lbBoxSeparatorLinesOn = False
resources.lbBoxLineThicknessF = 4.0
#resources.lbBoxEndCapStyle = 'TriangleBothEnds'
resources.lbLabelAlignment = 'BoxCenters'
resources.lbTitleString = 'mm'
resources.lbTitleFontHeightF = 0.016
resources.lbTitlePosition = 'Right'
resources.lbTitleDirection = 'Across'
resources.lbTitleAngleF = 90.0
resources.lbTitleExtentF = 0.1
resources.lbTitleOffsetF = -0.15
resources.nglFrame = False # hold on frame because will plot text afterwards on same plot
Ngl.contour_map(wks, data_processed, resources) # plot the actual plot
##### plot title text #####
text = '{:02d}-{:02d}h Total Precipitation {}, ICON-EPS run {:02}.{:02}.{} {:02}Z'.format(\
fcst_hours[0], fcst_hours[1], stat_processing,\
date['day'], date['month'], date['year'], date['hour'])
x = 0.5
y = 0.95
text_res_1 = Ngl.Resources()
text_res_1.txFontHeightF = 0.018
text_res_1.txJust = 'TopCenter'
Ngl.text_ndc(wks, text, x, y, text_res_1)
Ngl.frame(wks) # advance frame
Ngl.destroy(
wks
) # delete workspace to free memory, relevant if plotting various plots in one script
print('plotted "{}.png"'.format(plot_name))
return
def 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
