["algeria","angola","angola-exclave-called-cabinda","benin","botswana", \
"burundi","cameroon","central-african-republic","chad","congo","djibouti", \
"egypt","equatorial-guinea","ethiopia","gabon","gambia","ghana","guinea", \
"guinea-bissau","ivory-coast","kenya","lesotho","liberia","libya", \
"madagascar","malawi","mali","mauritania","mauritius","morocco", \
"mozambique","namibia","niger","nigeria","rwanda","senegal","sierra-leone",\
"somalia","south-africa","sudan","swaziland","tanzania","togo","tunisia", \
"uganda","upper-volta","western-sahara","zaire","zambia","zimbabwe"]
#
# Open a workstation.
#
wks_type = "ps"
wks = Ngl.open_wks(wks_type,"cn12p")
dirc = Ngl.ncargpath("data")
z = Ngl.asciiread(dirc+"/asc/cn12n.asc",len_dims,"float")
resources = Ngl.Resources()
resources.sfXCStartV = -18.0
resources.sfXCEndV = 52.0
resources.sfYCStartV = -35.0
resources.sfYCEndV = 38.0
resources.vpXF = 0.1
resources.mpMaskAreaSpecifiers = mask_specs
resources.mpFillAreaSpecifiers = fill_specs
resources.pmLabelBarDisplayMode = "always"
Ngl.contour_map(wks,z[:,:],resources)
del resources
Ngl.end()
#
# Import all names from the NetCDF module.
#
from Scientific.IO.NetCDF import NetCDFFile
#
# Import Ngl support functions.
#
import Ngl
#
# Open the netCDF file.
#
file = NetCDFFile(Ngl.ncargpath("data") + "/cdf/pop.nc","r")
#
# Open a workstation.
#
wks_type = "ps"
rlist = Ngl.Resources()
rlist.wkColorMap = ["White","Black","Tan1","SkyBlue","Red"]
wks = Ngl.open_wks(wks_type,"streamline",rlist)
#
# Get the u/v and lat/lon variables.
#
urot = file.variables["urot"]
vrot = file.variables["vrot"]
lat2d = file.variables["lat2d"][:,:]
lon2d = file.variables["lon2d"][:,:]
#
# Import the NetCDF reader.
#
from Scientific.IO.NetCDF import NetCDFFile
#
# Import Ngl support functions.
#
import Ngl
#
# Open the netCDF file. This file is a netCDF version of the file:
#
# ncargpath("data") + "/grb/ced1.lf00.t00z.eta.grb"
#
file = NetCDFFile(Ngl.ncargpath("data") + "/cdf/ced1.lf00.t00z.eta.nc","r")
names = file.variables.keys() # Get the variable names
print "\nVariable names:" # and print them out.
print names
#
# For variable in names[1], retrieve and print all attributes
# and their values (exclude printing the built-in functions).
#
print "\nThe attributes and their values for variable " + names[1] + ":"
for attrib in dir(file.variables[names[1]]):
t = getattr(file.variables[names[1]],attrib)
if (type(t) != types.BuiltinFunctionType):
print "Attribute " + "'" + attrib + "' has value:", t
#
import Numeric
#
# Import the NetCDF reader.
#
from Scientific.IO.NetCDF import NetCDFFile
#
# Import Ngl support functions.
#
import Ngl
#
# Open three netCDF files and get variables.
#
data_dir = Ngl.ncargpath("data")
cdf_file1 = NetCDFFile(data_dir + "/cdf/941110_P.cdf","r")
cdf_file2 = NetCDFFile(data_dir + "/cdf/sstdata_netcdf.nc","r")
cdf_file3 = NetCDFFile(data_dir + "/cdf/Pstorm.cdf","r")
psl = cdf_file1.variables["Psl"]
sst = cdf_file2.variables["sst"]
pf = cdf_file3.variables["p"]
psl_lon = cdf_file1.variables["lon"][:]
psl_lat = cdf_file1.variables["lat"][:]
psl_nlon = len(psl_lon)
psl_nlat = len(psl_lat)
sst_lon = cdf_file2.variables["lon"][:]
sst_lat = cdf_file2.variables["lat"][:]
output[:,0] = x[:,0]
output[:,nj-1] = x[:,nj-1]
#
# Return smoothed array.
#
return output
#
# Main program.
#
#
# Read in the data variables from the NetCDF file.
#
cdf_file = NetCDFFile(Ngl.ncargpath("data") + "/cdf/meteo_data.nc","r")
tempisobar = cdf_file.variables["tempisobar"][:,:] # temperature
levels = cdf_file.variables["levels"][:] # levels
taus = cdf_file.variables["taus"][:] # taus
rh = cdf_file.variables["rh"][:,:] # realtive humidity
ugrid = cdf_file.variables["ugrid"][:,:] # horizontal winds
vgrid = cdf_file.variables["vgrid"][:,:] # vertical winds
rain03 = cdf_file.variables["rain03"][:] # rainfall
tempht = cdf_file.variables["tempht"][:] # surface temperatures
#
# Smooth temperature and humidity.
#
smothtemp = smth9(tempisobar, 0.50, -0.25)
smothrh = smth9( rh, 0.50, -0.25)
# PO 3007, Boulder, Colorado
#
# Date: Tue Mar 1 14:58:28 MST 2005
#
# Description:
# Produces two plots using real data:
# 1. Shows the full radiosonde.
# 2. This "thins" the number of wind barbs plotted and
# uses a Centigrade scale. Setting Wthin to 3 causes
# the plotting of every third wind barb.
#
import Ngl
nlvl = 65
ncol = 7
TestData = Ngl.asciiread(Ngl.ncargpath("data") + "/asc/sounding_ATS.asc", \
[nlvl,ncol], "float")
#
# Order: Surface is 'bottom' eg: 1000,950,935,897,...
#
p = TestData[:,0] # pressure [mb / hPa]
tc = TestData[:,1] # temperature [C]
tdc = TestData[:,2] # dew pt temp [C]
z = TestData[:,4] # geopotential [gpm]
wspd = TestData[:,5] # wind speed [knots or m/s]
wdir = TestData[:,6] # meteorological wind dir
wks_type = "ps"
wks = Ngl.open_wks(wks_type, "skewt3")
import Numeric,sys,os
#
# Import the NetCDF reader.
#
from Scientific.IO.NetCDF import NetCDFFile
#
# Import Ngl support functions.
#
import Ngl
#
# Open the netCDF file.
#
cdf_file = NetCDFFile(Ngl.ncargpath("data") + "/cdf/contour.cdf","r")
#
# Associate Python variables with NetCDF variables.
# These variables have associated attributes.
#
temp = cdf_file.variables["T"] # temperature
Z = cdf_file.variables["Z"] # geopotential height
pres = cdf_file.variables["Psl"] # pressure at mean sea level
lat = cdf_file.variables["lat"] # latitude
lon = cdf_file.variables["lon"] # longitude
#
# Open a workstation.
#
wks_type = "ps"
resources.tiYAxisString = "Y Axis" # Label for the Y axis
resources.tiMainFont = "Helvetica" # Font for title
resources.tiXAxisFont = "Helvetica" # Font for X axis label
resources.tiYAxisFont = "Helvetica" # Font for Y axis label
resources.xyMarkLineModes = ["Lines","Markers","MarkLines"]
resources.xyMarkers = [0,1,3] # (none, dot, asterisk)
resources.xyMarkerColor = 3 # Marker color
resources.xyMarkerSizeF = 0.03 # Marker size (default
# is 0.01)
plot = Ngl.xy(wks,x,y2,resources) # Draw an XY plot.
#---------- Begin fifth plot ------------------------------------------
filename = Ngl.ncargpath("data") + "/asc/xy.asc"
data = Ngl.asciiread(filename,(129,4),"float")
#
# Define a two-dimensional array of data values based on
# columns two and three of the input data.
#
uv = Numeric.zeros((2,129),Numeric.Float0)
uv[0,:] = data[:,1]
uv[1,:] = data[:,2]
#
# Use the first column of the input data file (which is simply the
# row number) for longitude values. The fourth column of data is
# not used in this example.
#