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 skewt(fname, p, tc, tdc, z, wspd, wdir, saveas='pdf', barbstride=20):
wks = Ngl.open_wks(saveas,fname)
dataOpts = Ngl.Resources() # Options describing
""" # data and plotting.
dataOpts.sktHeightWindBarbsOn = True # Plot wind barbs at
# height levels.
dataOpts.sktPressureWindBarbComponents = "SpeedDirection" # Wind speed and
# dir [else: u,v].
dataOpts.sktHeightWindBarbPositions = hght # height of wind reports
dataOpts.sktHeightWindBarbSpeeds = hspd # speed
# [or u components]
dataOpts.sktHeightWindBarbDirections = hdir # direction
# [or v components]
"""
skewtOpts = Ngl.Resources()
skewtOpts.sktHeightScaleOn = True # default is False
skewtOpts.sktHeightScaleUnits = "km" # default is "feet"
skewtOpts.sktColoredBandsOn = False # default is False
skewtOpts.sktGeopotentialWindBarbColor = "Red"
dataOpts.sktPressureWindBarbStride = barbstride
skewtOpts.tiMainString = "Graw Launch WBB - Env. Instr Lab 5"
# create a background
skewt_bkgd = Ngl.skewt_bkg(wks, skewtOpts)
# plot the darn profile...
skewt_data = Ngl.skewt_plt(wks, skewt_bkgd, p, tc, tdc, z, \
wspd, wdir, dataOpts)
Ngl.draw(skewt_bkgd)
Ngl.draw(skewt_data)
Ngl.frame(wks)
Ngl.end()
def 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(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 generate_map_plots(self, file_name, title_on=True, labels_on=True):
"""
Generate and save map plots.
Store the plots in the file indicated by ``file_name`` in the current
directory.
Map plots are stored in a PDF file, with each map occupying its own
page.
:arg str file_name: The name for the PDF file containing map plots.
:arg bool title_on: Determines, whether main title is plotted.
:arg bool labels_on: Determines whether individual map titles are
plotted.
"""
# Set resources
resources = self.resources
# Set plot title
if title_on:
resources.tiMainString = self.title
# Open a workstation, display in X11 window
# Alternatively wks_type = "ps", wks_type = "pdf" or wks_type = "x11"
wks_type = "pdf"
wks = Ngl.open_wks(wks_type, file_name, resources)
#
# Generate map plots
#
for dataset in self.map_data:
# Set title
if labels_on:
resources.lbTitleString = dataset["title"]
# Generate map plot
cmap = Ngl.contour_map(wks, dataset["data"], resources)
# Clean up
del cmap
del resources
Ngl.end()
def main(data_path = DEFAULT_PATH):
#get data to memory
[data, times, x_indices, y_indices] = data_select.get_data_from_file(data_path)
the_mean = np.mean(data, axis = 0)
lons2d, lats2d = polar_stereographic.lons, polar_stereographic.lats
lons = lons2d[x_indices, y_indices]
lats = lats2d[x_indices, y_indices]
#colorbar
wres = Ngl.Resources()
wres.wkColorMap = "BlGrYeOrReVi200"
wks_type = "ps"
wks = Ngl.open_wks(wks_type,"test_pyngl", wres)
#plot resources
res = Ngl.Resources()
res.cnFillMode = "RasterFill"
#res.cnFillOn = True # Turn on contour fill
#res.cnMonoFillPattern = True # Turn solid fill back on.
#res.cnMonoFillColor = False # Use multiple colors.
res.cnLineLabelsOn = False # Turn off line labels.
res.cnInfoLabelOn = False # Turn off informational
res.pmLabelBarDisplayMode = "Always" # Turn on label bar.
res.cnLinesOn = False # Turn off contour lines.
res.mpProjection = "LambertConformal"
res.mpDataBaseVersion = "MediumRes"
# res.mpLimitMode = "LatLon" # limit map via lat/lon
# res.mpMinLatF = np.min(lats) # map area
# res.mpMaxLatF = np.max(lats) # latitudes
# res.mpMinLonF = np.min( lons ) # and
# res.mpMaxLonF = np.max( lons ) # longitudes
print np.min(lons), np.max(lons)
res.tiMainFont = 26
res.tiXAxisFont = 26
res.tiYAxisFont = 26
res.sfXArray = lons2d
res.sfYArray = lats2d
#
# Set title resources.
#
res.tiMainString = "Logarithm of mean annual streamflow m**3/s"
to_plot = np.ma.masked_all(lons2d.shape)
to_plot[x_indices, y_indices] = np.log(the_mean[:])
# for i, j, v in zip(x_indices, y_indices, the_mean):
# to_plot[i, j] = v
Ngl.contour_map(wks, to_plot[:,:], res)
Ngl.end()
pass
wkres.wkColorMap = "rainbow+gray" Ngl.set_values(wks,wkres) vcres.nglSpreadColorStart = 24 vcres.nglSpreadColorEnd = -2 # Don't include last color, which is gray vcres.mpOceanFillColor = "Transparent" vcres.mpLandFillColor = "Gray" vcres.vcRefAnnoArrowLineColor = "Black" vcres.vcMaxLevelCount = 23 vcres.vcMonoLineArrowColor = False vcres.tiMainString = "Curly vectors colored by magnitude" plot = Ngl.vector_map(wks,u,v,vcres) # # Change to filled arrows. # vcres.vcGlyphStyle = "FillArrow" vcres.vcMonoFillArrowFillColor = False vcres.vcRefMagnitudeF = 0.0 vcres.vcRefAnnoOrthogonalPosF = -0.20 vcres.tiMainString = "Filled arrows" plot = Ngl.vector_map(wks,u,v,vcres) Ngl.end()
#---Open file for graphics wks_type = "png" wks = Ngl.open_wks(wks_type, "wrf3") # Set some map options based on information in WRF output file res = get_pyngl(tc) res.tfDoNDCOverlay = True # required for native projection #---Contour options res.cnFillOn = True # turn on contour fill res.cnLinesOn = False # turn off contour lines res.cnLineLabelsOn = False # turn off line labels res.cnFillMode = "RasterFill" # These two resources res.trGridType = "TriangularMesh" # can speed up plotting. res.cnFillPalette = "ncl_default" res.lbOrientation = "horizontal" # default is vertical res.pmLabelBarHeightF = 0.08 res.pmLabelBarWidthF = 0.65 res.lbTitleString = "%s (%s)" % (tc.description, tc.units) res.lbTitleFontHeightF = 0.015 res.lbLabelFontHeightF = 0.015 res.tiMainString = filename res.tiMainFont = "helvetica-bold" res.tiMainFontHeightF = 0.02 plot = Ngl.contour_map(wks, tc[0, :, :], res) Ngl.end()
def plot_Robinson_pyngl(var,
lon,
lat,
wks_name='plot',
clim=None,
cspace=None,
cmap=None):
#
# Select a colormap and open a workstation.
#
rlist = Ngl.Resources()
wks_type = "png"
wks = Ngl.open_wks(wks_type, wks_name, rlist)
if cmap is None:
mycmap = 'BlAqGrYeOrReVi200'
Ngl.define_colormap(wks, mycmap)
else:
try:
mycmap = '/Users/frederic/python/cmap/' + cmap
mycmap = np.loadtxt(mycmap)
except:
mycmap = cmap
try:
Ngl.define_colormap(wks, mycmap)
except:
raise Warning('Unknown colormap')
#
# The next set of resources will apply to the contour plot and the labelbar.
#
resources = Ngl.Resources()
resources.sfXArray = lon
resources.sfYArray = lat
resources.gsnMaximize = True # use full page
resources.cnFillOn = True
resources.cnFillMode = "RasterFill"
resources.cnMaxLevelCount = 255
resources.cnLinesOn = False
resources.cnLineLabelsOn = False
if clim is not None:
resources.cnLevelSelectionMode = "ManualLevels" # set manual contour levels
resources.cnMinLevelValF = clim[0] # set min contour level
resources.cnMaxLevelValF = clim[1] # set max contour level
if cspace is None:
LevelSpacing = (clim[1] - clim[0]) / 100.
resources.cnLevelSpacingF = LevelSpacing # set contour spacing
else:
resources.cnLevelSpacingF = cspace # set contour spacing
resources.lbOrientation = "Horizontal" # Default is vertical.
resources.lbBoxLinesOn = False
resources.lbLabelFontHeightF = 0.01 # label font height
resources.lbBoxMinorExtentF = 0.15
#
# The contour plot is not very interesting, so don't draw it.
#
resources.nglDraw = False
resources.nglFrame = False
contour = Ngl.contour(wks, var, resources)
#
# Retrieve the actual lat/lon end points of the scalar array so
# we know where to overlay on map.
#
xs = Ngl.get_float(contour.sffield, "sfXCActualStartF")
xe = Ngl.get_float(contour.sffield, "sfXCActualEndF")
ys = Ngl.get_float(contour.sffield, "sfYCActualStartF")
ye = Ngl.get_float(contour.sffield, "sfYCActualEndF")
resources.nglDraw = True # Turn these resources back on.
resources.nglFrame = True
resources.mpProjection = "Robinson"
resources.mpCenterLonF = 270
resources.mpCenterLatF = 0
resources.mpGeophysicalLineThicknessF = 2
map = Ngl.contour_map(wks, var, resources)
Ngl.end()
def draw_gh_500hPa(sel_year, sel_month):
print("darwing 500hPa gh plot for " + str(sel_month).zfill(2) + " " +
str(sel_year))
files_cli = sorted(
glob.glob(
os.path.join(
'/home/alley/work/Dong/mongo/seasonal_analysis/data/data/download_from_mongo/cli/',
'gh_500_*.grb')))
f_cli = xr.open_mfdataset(files_cli,
concat_dim="time",
combine="nested",
engine="cfgrib",
parallel=True)
h = f_cli["z"]
h = h.mean(dim="time")
h_cli = h / 98
lat = f_cli["latitude"].values
lon = f_cli["longitude"].values
file_cur = "/home/alley/work/Dong/mongo/seasonal_analysis/data/data/download_from_mongo/cur/gh_500_" + str(
sel_year) + str(sel_month).zfill(2) + ".grb"
# print(file_cur)
f_cur = xr.open_mfdataset(file_cur, engine="cfgrib", parallel=True)
h_cur = f_cur["z"] / 98
h_ano = h_cur - h_cli
leftString = "500hPa Height & Anomaly for " + str(sel_month) + str(
sel_year)
rightString = "dagpm"
wks_type = 'png'
wks = Ngl.open_wks(
wks_type,
'/home/alley/work/Dong/mongo/seasonal_analysis/images/gh_500hPa_' +
str(sel_year) + str(sel_month).zfill(2) + '.png')
res = Ngl.Resources()
res.nglFrame = False
res.nglDraw = False
res.mpLimitMode = "LatLon"
res.mpMinLonF = 60
res.mpMaxLonF = 180
res.mpMinLatF = 0
res.mpMaxLatF = 60
plot_map = Ngl.map(wks, res)
fres = Ngl.Resources()
fres.nglFrame = False
fres.nglDraw = False
fres.cnFillOn = True
fres.sfXArray = lon
fres.sfYArray = lat
fres.lbOrientation = "Horizontal" # horizontal labelbar
fres.cnLinesOn = False
fres.cnLineLabelsOn = False
fres.cnLevelSelectionMode = "ExplicitLevels"
fres.cnFillPalette = "BlueDarkRed18"
fres.cnLevels = np.arange(-5, 6, 1)
plot_cn = Ngl.contour(wks, h_ano, fres)
cnres = Ngl.Resources()
cnres.nglDraw = False
cnres.nglFrame = False
cnres.cnFillOn = False
cnres.cnLinesOn = True
cnres.cnLineLabelsOn = True
cnres.cnInfoLabelOn = False
cnres.sfXArray = lon
cnres.sfYArray = lat
cnres.cnLevelSelectionMode = "ExplicitLevels"
cnres.cnLevels = np.arange(500, 600, 4)
cnres.cnLineColor = "black"
cnres.cnLineThicknessF = 5
plot2 = Ngl.contour(wks, h_cur, cnres)
Ngl.overlay(plot_map, plot_cn)
Ngl.overlay(plot_map, plot2)
#
txres = Ngl.Resources()
txres.txFontHeightF = 0.024
#
Ngl.text_ndc(wks, leftString, 0.3, 0.83, txres)
Ngl.text_ndc(wks, rightString, 0.85, 0.83, txres)
#
Ngl.maximize_plot(wks, plot_map)
Ngl.draw(plot_map)
Ngl.frame(wks)
Ngl.end()
print("Finish darwing 500hPa gh plot plot for " + str(sel_month).zfill(2) +
" " + str(sel_year))
def main():
import numpy
import Ngl
options = parse_input()
u_array2, v_array2, s_array2, x_min, x_max, y_min, y_max = read_data(options)
wks_type = 'ps'
wks = Ngl.open_wks(wks_type, options.data)
color_res = Ngl.Resources()
gs_res = Ngl.Resources()
text_res = Ngl.Resources()
rgb_min = []
for s in options.rgb_min.split():
rgb_min.append(float(s))
rgb_max = []
for s in options.rgb_max.split():
rgb_max.append(float(s))
l = []
l.append([1.00, 1.00, 1.00])
l.append([0.00, 0.00, 0.00])
f_l = []
n = 100
for f in range(n):
f_l.append(float((f + 1) * (1.0 / n)))
for f in f_l:
r = rgb_min[0] + f * (rgb_max[0] - rgb_min[0])
g = rgb_min[1] + f * (rgb_max[1] - rgb_min[1])
b = rgb_min[2] + f * (rgb_max[2] - rgb_min[2])
l.append([r, g, b])
rgb_map = numpy.array(l, 'f')
color_res.wkColorMap = rgb_map
Ngl.set_values(wks, color_res)
vector_res = get_vector_res(options, x_min, x_max, y_min, y_max)
plot = Ngl.vector_scalar(wks, u_array2, v_array2, s_array2, vector_res)
color_res.wkColorMap = "default"
Ngl.set_values(wks, color_res)
line_thickness = 2.0
gs_res.gsMarkerSizeF = 10.0
gs_res.gsMarkerThicknessF = 2.0
gs_res.gsLineDashPattern = 2
gs_res.gsLineThicknessF = line_thickness
text_res.txFont = 21
text_res.txFontHeightF = 0.015
text_res.txFontColor = options.text_color
for atom in read_molecule(options):
gs_res.gsMarkerIndex = 16
gs_res.gsMarkerColor = "White"
Ngl.polymarker(wks, plot, [atom[2]], [atom[1]], gs_res)
gs_res.gsMarkerIndex = 4
gs_res.gsMarkerColor = options.text_color
Ngl.polymarker(wks, plot, [atom[2]], [atom[1]], gs_res)
Ngl.text(wks, plot, atom[0], [atom[2] + 1.0], [atom[1]], text_res)
Ngl.frame(wks)
del plot
Ngl.end()
def main():
import numpy
import Ngl
options = parse_input()
u_array2, v_array2, s_array2, x_min, x_max, y_min, y_max = read_data(
options)
wks_type = 'ps'
wks = Ngl.open_wks(wks_type, options.data)
color_res = Ngl.Resources()
gs_res = Ngl.Resources()
text_res = Ngl.Resources()
rgb_min = []
for s in options.rgb_min.split():
rgb_min.append(float(s))
rgb_max = []
for s in options.rgb_max.split():
rgb_max.append(float(s))
l = []
l.append([1.00, 1.00, 1.00])
l.append([0.00, 0.00, 0.00])
f_l = []
n = 100
for f in range(n):
f_l.append(float((f + 1) * (1.0 / n)))
for f in f_l:
r = rgb_min[0] + f * (rgb_max[0] - rgb_min[0])
g = rgb_min[1] + f * (rgb_max[1] - rgb_min[1])
b = rgb_min[2] + f * (rgb_max[2] - rgb_min[2])
l.append([r, g, b])
rgb_map = numpy.array(l, 'f')
color_res.wkColorMap = rgb_map
Ngl.set_values(wks, color_res)
vector_res = get_vector_res(options, x_min, x_max, y_min, y_max)
plot = Ngl.vector_scalar(wks, u_array2, v_array2, s_array2, vector_res)
color_res.wkColorMap = "default"
Ngl.set_values(wks, color_res)
line_thickness = 2.0
gs_res.gsMarkerSizeF = 10.0
gs_res.gsMarkerThicknessF = 2.0
gs_res.gsLineDashPattern = 2
gs_res.gsLineThicknessF = line_thickness
text_res.txFont = 21
text_res.txFontHeightF = 0.015
text_res.txFontColor = options.text_color
for atom in read_molecule(options):
gs_res.gsMarkerIndex = 16
gs_res.gsMarkerColor = "White"
Ngl.polymarker(wks, plot, [atom[2]], [atom[1]], gs_res)
gs_res.gsMarkerIndex = 4
gs_res.gsMarkerColor = options.text_color
Ngl.polymarker(wks, plot, [atom[2]], [atom[1]], gs_res)
Ngl.text(wks, plot, atom[0], [atom[2] + 1.0], [atom[1]], text_res)
Ngl.frame(wks)
del plot
Ngl.end()
def cwrf_contour(fname, vname, days=None):
if not pth.exists(fname):
print_("You do not have the necessary '%s' file to run this script." %
fname)
sys.exit(1)
dset = Nio.open_file(fname + ".nc")
lat = dset.variables["XLAT"][0, :, :]
lon = dset.variables["XLONG"][0, :, :]
region, subm = re.split(os.sep, pth.dirname(fname))[-2:]
cur_date = get_date(dset.START_DATE)
dates = get_list_times(dset)
if days is None:
days = len(dates) / 10 * 10
elif days in ('30', '60', '90'):
days = int(days)
else:
print_("%s invalid, between 30, 60 and 90" % days)
sys.exit(1)
if vname == "RAINC":
vdat, base = get_rain_data(dset, cur_date.month - 1, days)
blon, blat = get_coordinates("rainc")
title = "Precipitacion Total"
unit = "mm"
levels = (30, 1200, 150)
blevels = [-300., -180., -90., -30., 30., 90., 180., 300.]
palete = 'WhBlGrYeRe'
bpalete = 'precip_diff_12lev'
elif vname == "T2":
vdat, base = get_temp_data(dset, cur_date.month - 1, days)
blon, blat = get_coordinates("t2ave")
title = "Temperatura Media a 2 m"
unit = "~S~o~N~C"
levels = (-5, 35, 5)
blevels = [-3, -2, -1, -0.3, 0.3, 1, 2, 3]
palete = 'MPL_coolwarm'
bpalete = 'temp_diff_18lev'
else:
print_("%s not supported" % vname)
sys.exit(1)
rbase = regrid((blon, blat), (lon, lat), base)
wks_type = "png"
wks_res = Ngl.Resources()
wks_res.wkHeight = 600
wks_res.wkWidth = 650
wks = Ngl.open_wks(
wks_type, DEFAULT_FILE_FORMAT %
(cur_date.year, cur_date.month, days, VARIABLES[vname]), wks_res)
panelres = Ngl.Resources()
panelres.nglMaximize = True
panelres.nglPanelBottom = 0.03
res = Ngl.Resources()
res.nglDraw = False
res.nglFrame = False
# Mapa
set_map(res, (np.min(lon), np.min(lat)), (np.max(lon), np.max(lat)))
#set_map(res, (REGIONS["SA"][0][0], REGIONS["SA"][0][1]),
# (REGIONS["SA"][1][0], REGIONS["SA"][1][1]))
set_labelbar(res, unit)
res.tiMainFontHeightF = 0.025
res.tiMainString = "Totales"
res.sfXArray = lon
res.sfYArray = lat
set_contour(res, levels, cnFillPalette=palete)
plot1 = Ngl.contour_map(wks, vdat, res)
#res.tmYLOn = False
#res.tmYROn = True
#res.tmYRLabelsOn = True
res.tiMainString = "Anomalias"
#res.sfXArray = blon
#res.sfYArray = blat
set_contour(res, blevels, cnFillPalette=bpalete)
plot2 = Ngl.contour_map(wks, vdat - rbase, res)
txres = Ngl.Resources()
txres.txFontHeightF = 0.03
Ngl.text_ndc(wks, title, 0.5, 0.97, txres)
txres.txFontHeightF = 0.02
last = days - 1
if days > 89:
last = days + 1
Ngl.text_ndc(
wks, "Promedio %d dias desde %s hasta %s" %
(days, dates[0].strftime("%d%b%Y"), dates[last].strftime("%d%b%Y")),
0.5, 0.91, txres)
txres.txFontHeightF = 0.015
y1 = 0.120
y2 = 0.090
if region == "PARAGUAY":
y1 = 0.190
y2 = 0.160
Ngl.text_ndc(wks, "CWRF-CAM-%s OLE~S~2~N~ (clima.pol.una.py) " % subm, 0.8,
y1, txres)
txres.txFontHeightF = 0.013
Ngl.text_ndc(
wks, "Proyecto 14-INV-054 / GUYRA Paraguay / Facultad "
"Politecnica", 0.75, y2, txres)
Ngl.panel(wks, [plot1, plot2], [1, 2], panelres)
Ngl.end()
def main():
print('')
#-- open file and read variable and time
home = os.environ.get('HOME')
fname = os.path.join(home,'NCL/PyNGL/User_Guide_examples/rectilinear_grid_2D.nc')
ds = xr.open_dataset(fname)
var = ds.tsurf
time = ds.time
#-- xarray deletes the units and long_name attributes, so we have to get
#-- them on another way
print('--> time attributes:', ds.time.attrs)
print('')
units = var.attrs['units']
lname = var.attrs['long_name']
#-- print some information about the variable and the time coordinate
print('--> var: ',var)
print('')
#-- convert the time values to date strings using a user defined function
date_labels = conv_time_netcdf(ds)
print('--> date_labels ',type(date_labels))
print('')
#-- for explicit x-axis generate simple time array
time = np.arange(0,len(ds.time),1)
#-- compute the area mean without weighting
areamean = np.average(var,axis=(1,2))
print('--> areamean: ',areamean)
print('')
#-- open a workstation
wks = Ngl.open_wks('png','plot_xy_plot_timeseries') #-- graphics output
#-- set resources/attributes
res = Ngl.Resources() #-- generate an res object for plot
res.tiMainString = 'DKRZ Example: xy-plot timeseries' #-- draw a title
res.tiMainOffsetYF = 0.02
res.nglMaximize = False
res.nglPointTickmarksOutward = True #-- point tickmarks outward
res.nglDraw = False
res.nglFrame = False
res.vpWidthF = 0.7
res.vpHeightF = 0.7
res.vpXF = 0.2
res.vpYF = 0.85
res.tmXBMode = 'Explicit' #-- use explicit values
res.tmXBValues = time
res.tmXBLabels = list(date_labels)
res.tmXBLabelFontHeightF = 0.006
res.tmXBLabelJust = 'CenterRight'
res.tmXBLabelDeltaF = 0.2
res.tmXBLabelAngleF = 40.0
res.tmXBLabelStride = 4
#-- draw the plot
plot = Ngl.xy(wks,time,areamean,res)
#-- add additional strings to plot (like NCL's gsnLeftString and gsnRightString)
ngl_Strings(wks, plot, left=lname, right=units, ytitle=lname)
#-- done
Ngl.draw(plot)
Ngl.frame(wks)
Ngl.end()
def plot_Robinson_pyngl(var,lon,lat,wks_name='plot', clim=None, cspace=None, cmap=None):
#
# Select a colormap and open a workstation.
#
rlist = Ngl.Resources()
wks_type = "png"
wks = Ngl.open_wks(wks_type,wks_name,rlist)
if cmap is None:
mycmap = 'BlAqGrYeOrReVi200'
Ngl.define_colormap(wks,mycmap)
else:
try:
mycmap = '/Users/frederic/python/cmap/' + cmap
mycmap = np.loadtxt(mycmap)
except:
mycmap = cmap
try:
Ngl.define_colormap(wks,mycmap)
except:
raise Warning, 'Unknown colormap'
#
# The next set of resources will apply to the contour plot and the labelbar.
#
resources = Ngl.Resources()
resources.sfXArray = lon
resources.sfYArray = lat
resources.gsnMaximize = True # use full page
resources.cnFillOn = True
resources.cnFillMode = "RasterFill"
resources.cnMaxLevelCount = 255
resources.cnLinesOn = False
resources.cnLineLabelsOn = False
if clim is not None:
resources.cnLevelSelectionMode = "ManualLevels" # set manual contour levels
resources.cnMinLevelValF = clim[0] # set min contour level
resources.cnMaxLevelValF = clim[1] # set max contour level
if cspace is None:
LevelSpacing = (clim[1] - clim[0]) / 100.
resources.cnLevelSpacingF = LevelSpacing # set contour spacing
else:
resources.cnLevelSpacingF = cspace # set contour spacing
resources.lbOrientation = "Horizontal" # Default is vertical.
resources.lbBoxLinesOn = False
resources.lbLabelFontHeightF = 0.01 # label font height
resources.lbBoxMinorExtentF = 0.15
#
# The contour plot is not very interesting, so don't draw it.
#
resources.nglDraw = False
resources.nglFrame = False
contour = Ngl.contour(wks,var,resources)
#
# Retrieve the actual lat/lon end points of the scalar array so
# we know where to overlay on map.
#
xs = Ngl.get_float(contour.sffield,"sfXCActualStartF")
xe = Ngl.get_float(contour.sffield,"sfXCActualEndF")
ys = Ngl.get_float(contour.sffield,"sfYCActualStartF")
ye = Ngl.get_float(contour.sffield,"sfYCActualEndF")
resources.nglDraw = True # Turn these resources back on.
resources.nglFrame = True
resources.mpProjection = "Robinson"
resources.mpCenterLonF = 270
resources.mpCenterLatF = 0
resources.mpGeophysicalLineThicknessF = 2
map = Ngl.contour_map(wks,var,resources)
Ngl.end()
def draw_sst(sel_year, sel_month):
st = time.time()
np.seterr(divide='ignore', invalid='ignore')
print("darwing sst plot for " + str(sel_month).zfill(2) + " " +
str(sel_year))
files_cli = sorted(
glob.glob(
os.path.join(
'/home/alley/work/Dong/mongo/seasonal_analysis/data/data/download_from_mongo/cli',
'sst_*.grb')))
f_cli = xr.open_mfdataset(files_cli,
concat_dim="time",
combine="nested",
engine="cfgrib",
parallel=True)
h_cli = f_cli["sst"]
h_cli_ori = h_cli.mean(dim="time").values
file_cur = "/home/alley/new_disk/data/sst_" + str(sel_year) + str(
sel_month).zfill(2) + ".grb"
f_cur = xr.open_mfdataset(file_cur, engine="cfgrib", parallel=True)
h_cur_ori = f_cur["sst"]
h_cur = (h_cur_ori.values - 273.15)
h_cur = np.nan_to_num(h_cur, nan=-999)
lat = f_cur["latitude"].values
lon = f_cur["longitude"].values
h_ano = h_cur_ori - h_cli_ori
h_ano = np.nan_to_num(h_ano.values, nan=-999)
# print(h_ano)
et1 = time.time()
# print(et1 - st)
# leftString = "SST in " + str(sel_month) + str(sel_year)
# rightString = "~S~o~N~C"
wks_type = 'png'
wks = Ngl.open_wks(
wks_type, '/home/alley/work/Dong/mongo/seasonal_analysis/images/sst_' +
str(sel_year) + str(sel_month).zfill(2) + '.png')
res = Ngl.Resources()
res.nglFrame = False
res.nglDraw = False
res.mpLimitMode = "LatLon"
# res.mpFillOn = True #-- turn on map fill
# res.mpLandFillColor = "gray" #-- change land color to gray
# res.mpMinLonF= 50
# res.mpMaxLonF = 280
res.mpMinLatF = -45
res.mpMaxLatF = 45
res.cnFillOn = True
res.mpCenterLonF = 120
res.sfMissingValueV = -999
res.sfXArray = lon
res.sfYArray = lat
res.lbOrientation = "Horizontal" # horizontal labelbar
res.cnLinesOn = False
res.tiMainFontHeightF = 0.015
res.cnLineLabelsOn = False
res.cnFillDrawOrder = "Predraw"
res.cnFillPalette = "BlAqGrYeOrRe"
res.pmLabelBarDisplayMode = "Always" #-- turn on a labelbar
res.tiMainString = "SST in " + str(sel_month).zfill(2) + " " + str(
sel_year) + " (degC)"
res.cnLevelSelectionMode = "ExplicitLevels"
res.cnLevels = np.arange(20, 35, 1)
plot_cur = Ngl.contour_map(wks, h_cur, res)
res.cnLevelSelectionMode = "ExplicitLevels"
res.tiMainString = "SST anomaly in " + str(sel_month).zfill(2) + " " + str(
sel_year) + " (degC)"
res.cnFillPalette = "GMT_polar"
res.cnLevels = np.arange(-3, 4, 1)
res.pmLabelBarHeightF = 0.3
plot_ano = Ngl.contour_map(wks, h_ano, res)
Ngl.panel(wks, [plot_cur, plot_ano], [2, 1], False)
Ngl.end()
et2 = time.time()
# print(et2 - et1)
print("Finish darwing sst plot for " + str(sel_month).zfill(2) + " " +
str(sel_year))
