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 = 107 # 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 = os.path.join(Ngl.pynglpath("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 = numpy.zeros((2,129),'f')
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.
#
lon = data[:,0]
# This example produce two visualizations:
# 1.) A Raob sounding with no winds.
# 2,) A Raob sounding with wind barbs at height levels and a
# height scale in feet.
#
# Notes:
# This example was updated in January 2006 to include the new
# Skew-T resource names decided on.
#
import Ngl
import numpy
nlvl = 30
ncol = 16
TestData = Ngl.asciiread(Ngl.pynglpath("data") + "/asc/sounding_testdata.asc", \
[nlvl,ncol], "float")
p = TestData[:,1]
z = TestData[:,2]
tc = TestData[:,5] + 2. # for demo purposes
tdc = TestData[:,9]
#
# Set winds to missing values so that they will not be plotted.
#
wspd = -999.*numpy.ones(nlvl,'f')
wdir = -999.*numpy.ones(nlvl,'f')
#
# Plot 1 - Create background skew-T and plot sounding.
#
# This example produces two visualizations:
# 1.) A colored contour plot with a label bar where negative
# interpolated values are allowed.
# 2.) A colored contour plot with a label bar where negative
# interpolated values are not allowed.
#
# Notes:
#
import numpy
import Ngl
#
# Open the ASCII file of seismic data.
#
dirc = Ngl.pynglpath("data")
seismic = Ngl.asciiread(dirc + "/asc/seismic.asc" ,[52,3],"float")
#
# Extract coordinate triples from the data file.
#
x = numpy.array(seismic[:,0],'f')
y = numpy.array(seismic[:,1],'f')
z = numpy.array(seismic[:,2],'f')-785.
#
# Define output grid for the calls to "natgrid".
#
numxout, numyout = 20, 20
xmin, xmax, ymin, ymax = min(x), max(x), min(y), max(y)
xc = (xmax-xmin)/(numxout-1)
yc = (ymax-ymin)/(numyout-1)
#
# Import numpy.
#
import numpy
#
# Import Ngl support functions.
#
import Ngl
import Nio
#
# Open the ASCII file.
#
dirc = Ngl.pynglpath("data")
seismic = Ngl.asciiread(dirc + "/asc/seismic.asc" ,[52,3],"float")
#
# Read in X,Y,Z data.
#
x = numpy.array(seismic[:,0],'f')
y = numpy.array(seismic[:,1],'f')
z = numpy.array(seismic[:,2],'f')
#
# Interpolate to a 2D output grid using natgrid.
#
numxout = 20 # Define output grid for call to "natgrid".
numyout = 20
xmin = min(x)
ymin = min(y)
"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 numpy.
#
import numpy, os
#
# Import Ngl support functions.
#
import Ngl
#
# Open the ASCII file.
#
dirc = Ngl.pynglpath("data")
seismic = Ngl.asciiread(os.path.join(dirc, "asc", "seismic.asc"), [52, 3],
"float")
x = numpy.array(seismic[:, 0], 'f')
y = numpy.array(seismic[:, 1], 'f')
z = numpy.array(seismic[:, 2], 'f')
numxout = 20 # Define output grid for call to "natgrid".
numyout = 20
xmin = min(x)
ymin = min(y)
xmax = max(x)
ymax = max(y)
xc = (xmax - xmin) / (numxout - 1)
yc = (ymax - ymin) / (numyout - 1)
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 = 107 # 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 = os.path.join(Ngl.pynglpath("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 = numpy.zeros((2, 129), 'f')
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.
#
lon = data[:, 0]
#
from __future__ import print_function
import Ngl
import os
#
# xy.asc has 4 vars of length 129 longitudes, u, v, and t.
#
# The data is taken at 43N latitude. Longitude is an index
# 1-129 standing for 0 deg - 360 deg in steps of 360/128.
# u and v are in m/s, and t is in deg K.
#
dirc = Ngl.pynglpath("data")
data = Ngl.asciiread(os.path.join(dirc,
"asc",
"xy.asc"),
[516],
"float")
lon = data[0:512:4] # First column of data is longitude.
u = data[1:513:4] # Second column is u
v = data[2:514:4] # Third column is v (not used in this example)
t = data[3:515:4] # Fourth column is t
lon = (lon-1.) * 360./128.
t = (t-273.15) * 9./5. + 32.
#
# Open a workstation.
#
wks_type = "png"
# 1.) Shows the full radiosonde.
# 2.) "thins" the number of wind barbs plotted and
# uses a Centigrade scale. Setting "Wthin" to 3
# causes the plotting of every third wind barb.
#
# Notes:
# This example was updated in January 2006 to include the new
# Skew-T resource names decided on.
#
from __future__ import print_function
import Ngl, os
nlvl = 65
ncol = 7
TestData = Ngl.asciiread(os.path.join(Ngl.pynglpath("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 = "png"
wks = Ngl.open_wks(wks_type, "skewt3")
#
# Import numpy.
#
import numpy, os
#
# Import Ngl support functions.
#
import Ngl
#
# Open the ASCII file containing the climate division numbers
# and additional data.
#
dirc = Ngl.pynglpath("data")
filename = os.path.join(dirc,"asc","climdivcorr.txt")
cldata = Ngl.asciiread(filename,[345,3],"float")
clmin = min(cldata[:,2])
clmax = max(cldata[:,2])
# Group each datum into 1 of 20 equally-spaced bins
bins = Ngl.fspan(clmin,clmax,20)
lencl = len(cldata[:,0])
databin = numpy.zeros([lencl],'i')
for i in range(lencl):
ii = numpy.greater_equal(bins,cldata[i,2])
for j in range(len(ii)):
if ii[j]:
databin[i] = j + 1
break
#
# 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")
#
2018-08-28 kmf
"""
from __future__ import print_function
import numpy as np
import Ngl
#-- file has 21361 lines but 1 header line
#-- 3 columns
nrows = 21360 #-- file has 21361 lines but 1 header line
ncols = 3 #-- file has 3 columns
num_lon = 240 #-- number of longitudes
num_lat = 89 #-- number of latitudes
#-- read all data
data = Ngl.asciiread("asc6.txt", (nrows, ncols), "float")
#-- select lat, lon and temp data
lat = data[::num_lon, 0]
lon = data[:num_lon, 1]
temp1D = data[:, 2]
#-- size of lat, lon and temp1d
nlats = len(lat)
nlons = len(lon)
ntemp = len(temp1D)
#-- reshape temp1d to 2D-array temp2d with size (89,240)
temp2D = np.reshape(temp1D, (nlats, nlons))
#-- print information
#
# Import NGL support functions.
#
import Ngl
#
# Open a workstation.
#
wks_type = "png"
wks = Ngl.open_wks(wks_type,"ngl11p")
dirc = Ngl.pynglpath("data")
data = Ngl.asciiread(os.path.join(dirc,
"asc",
"u.cocos"),
(39,14),
"float")
pressure = data[:,0] # First column of data is pressure (mb).
height = data[:,1] # Second column is height (km).
u = data[:,2:14] # Rest of columns are climatological zonal winds
# (u: m/s)
unew = Ngl.add_cyclic(u) # Add cyclic points to u.
#----------- Begin first plot -----------------------------------------
resources = Ngl.Resources()
resources.tiMainString = "~F26~Cocos Island" # Main title.
resources.tiYAxisString = "~F25~Pressure (mb)" # Y axes label.
#
# Import Nio for a NetCDF reader.
#
import Nio
#
# Import PyNGL support functions.
#
import Ngl
import os, string, types
# Read data off file.
filename = os.path.join(Ngl.pynglpath("data"), "asc", "fcover.dat")
data = Ngl.asciiread(filename, [3, 73, 73], "float")
# Send graphics to PNG file
wks_type = "png"
wks = Ngl.open_wks(wks_type, "streamline2")
stres = Ngl.Resources()
cnres = Ngl.Resources()
mpres = Ngl.Resources()
cnres.nglDraw = False
cnres.nglFrame = False
mpres.nglDraw = False
mpres.nglFrame = False
stres.nglDraw = False
stres.nglFrame = False
#
#
# Import Ngl support functions.
#
import Ngl
#
# xy.asc has 4 vars of length 129 longitudes, u, v, and t.
#
# The data is taken at 43N latitude. Longitude is an index
# 1-129 standing for 0 deg - 360 deg in steps of 360/128.
# u and v are in m/s, and t is in deg K.
#
dirc = Ngl.pynglpath("data")
data = Ngl.asciiread(dirc+"/asc/xy.asc",[516],"float")
lon = data[0:512:4] # First column of data is longitude.
u = data[1:513:4] # Second column is u
v = data[2:514:4] # Third column is v (not used in this example)
t = data[3:515:4] # Fourth column is t
lon = (lon-1.) * 360./128.
t = (t-273.15) * 9./5. + 32.
#
# Open a workstation.
#
wks_type = "ps"
wks = Ngl.open_wks(wks_type,"multi_y")
#
# Import numpy.
#
import numpy, os
#
# Import Ngl support functions.
#
import Ngl
#
# Open the ASCII file.
#
dirc = Ngl.pynglpath("data")
seismic = Ngl.asciiread(os.path.join(dirc,"asc","seismic.asc") ,[52,3],"float")
x = numpy.array(seismic[:,0],'f')
y = numpy.array(seismic[:,1],'f')
z = numpy.array(seismic[:,2],'f')
numxout = 20 # Define output grid for call to "natgrid".
numyout = 20
xmin = min(x)
ymin = min(y)
xmax = max(x)
ymax = max(y)
xc = (xmax-xmin)/(numxout-1)
yc = (ymax-ymin)/(numyout-1)
#
import numpy
#
# Import NGL support functions.
#
import Ngl
#
# Open a workstation.
#
wks_type = "png"
wks = Ngl.open_wks(wks_type,"ngl11p")
dirc = Ngl.pynglpath("data")
data = Ngl.asciiread(dirc+"/asc/u.cocos",(39,14),"float")
pressure = data[:,0] # First column of data is pressure (mb).
height = data[:,1] # Second column is height (km).
u = data[:,2:14] # Rest of columns are climatological zonal winds
# (u: m/s)
unew = Ngl.add_cyclic(u) # Add cyclic points to u.
#----------- Begin first plot -----------------------------------------
resources = Ngl.Resources()
resources.tiMainString = "~F26~Cocos Island" # Main title.
resources.tiYAxisString = "~F25~Pressure (mb)" # Y axes label.
resources.sfYCStartV = float(max(pressure)) # Indicate start and end of left
#
#
# Import Ngl support functions.
#
import Ngl
#
# xy.asc has 4 vars of length 129 longitudes, u, v, and t.
#
# The data is taken at 43N latitude. Longitude is an index
# 1-129 standing for 0 deg - 360 deg in steps of 360/128.
# u and v are in m/s, and t is in deg K.
#
dirc = Ngl.pynglpath("data")
data = Ngl.asciiread(dirc + "/asc/xy.asc", [516], "float")
lon = data[0:512:4] # First column of data is longitude.
u = data[1:513:4] # Second column is u
v = data[2:514:4] # Third column is v (not used in this example)
t = data[3:515:4] # Fourth column is t
lon = (lon - 1.) * 360. / 128.
t = (t - 273.15) * 9. / 5. + 32.
#
# Open a workstation.
#
wks_type = "png"
wks = Ngl.open_wks(wks_type, "multi_y")
#
import Ngl
#
# Define a color table and open a workstation.
#
cmap = numpy.zeros((2,3),'f')
cmap[0] = [1.,1.,1.]
cmap[1] = [0.,0.,0.]
rlist = Ngl.Resources()
rlist.wkColorMap = cmap
wks_type = "ps"
wks = Ngl.open_wks(wks_type,"ngl11p",rlist)
dirc = Ngl.pynglpath("data")
data = Ngl.asciiread(dirc+"/asc/u.cocos",(39,14),"float")
pressure = data[:,0] # First column of data is pressure (mb).
height = data[:,1] # Second column is height (km).
u = data[:,2:14] # Rest of columns are climatological zonal winds
# (u: m/s)
unew = Ngl.add_cyclic(u) # Add cyclic points to u.
#----------- Begin first plot -----------------------------------------
resources = Ngl.Resources()
resources.tiMainString = "~F26~Cocos Island" # Main title.
resources.tiYAxisString = "~F25~Pressure (mb)" # Y axes label.
resources.sfYCStartV = float(max(pressure)) # Indicate start and end of left
# This example produce two visualizations:
# 1.) A Raob sounding with no winds.
# 2,) A Raob sounding with wind barbs at height levels and a
# height scale in feet.
#
# Notes:
# This example was updated in January 2006 to include the new
# Skew-T resource names decided on.
#
import Ngl
import numpy
nlvl = 30
ncol = 16
TestData = Ngl.asciiread(Ngl.pynglpath("data") + "/asc/sounding_testdata.asc", \
[nlvl,ncol], "float")
p = TestData[:, 1]
z = TestData[:, 2]
tc = TestData[:, 5] + 2. # for demo purposes
tdc = TestData[:, 9]
#
# Set winds to missing values so that they will not be plotted.
#
wspd = -999. * numpy.ones(nlvl, 'f')
wdir = -999. * numpy.ones(nlvl, 'f')
#
# Plot 1 - Create background skew-T and plot sounding.
#
2.00;3.50;5.10;8.20
2.40;3.10;4.80;8.90
2.60;3.70;5.30;10.10
2.75;3.90;5.55;10.25
3.00;4.10;6.05;10.50
2018-08-28 kmf
"""
import numpy as np
import Ngl
#-- data file name
diri = "./"
fili = "Test_6h.csv"
#-- number of lines and columns in input file
nrows = 5
ncols = 4
#-- read all data
vals = Ngl.asciiread(diri + fili, (nrows, ncols), "float", sep=';')
#-- print information
print("vals: {}".format(vals))
print("")
print("--> rank of vals: {}".format(len(vals.shape)))
print("--> shape vals: {}".format(vals.shape))
exit()
#
# Import NumPy.
#
import Numeric
#
# Import Ngl support functions.
#
import Ngl
#
# Open the ASCII file.
#
seismic = Ngl.asciiread("seismic.asc" ,[52,3],"float")
x = Numeric.array(seismic[:,0],Numeric.Float0)
y = Numeric.array(seismic[:,1],Numeric.Float0)
z = Numeric.array(seismic[:,2],Numeric.Float0)
numxout = 20 # Define output grid for call to "natgrid".
numyout = 20
xmin = min(x)
ymin = min(y)
xmax = max(x)
ymax = max(y)
xc = (xmax-xmin)/(numxout-1)
yc = (ymax-ymin)/(numyout-1)
xo = xmin + xc*Numeric.arange(0,numxout)
yo = ymin + yc*Numeric.arange(0,numxout)
#
from __future__ import print_function
import numpy, os
#
# Import Ngl support functions.
#
import Ngl
#
# Open the ASCII file containing the climate division numbers
# and additional data.
#
dirc = Ngl.pynglpath("data")
filename = os.path.join(dirc, "asc", "climdivcorr.txt")
cldata = Ngl.asciiread(filename, [345, 3], "float")
clmin = min(cldata[:, 2])
clmax = max(cldata[:, 2])
# Group each datum into 1 of 20 equally-spaced bins
bins = Ngl.fspan(clmin, clmax, 20)
lencl = len(cldata[:, 0])
databin = numpy.zeros([lencl], 'i')
for i in range(lencl):
ii = numpy.greater_equal(bins, cldata[i, 2])
for j in range(len(ii)):
if ii[j]:
databin[i] = j + 1
break
#
# Start the graphics.