def plot_map(wks, x, y, data, **kwargs):
# Set up annoying plot resources
res = ngl.Resources()
res.cnFillOn = True
res.cnLinesOn = False
res.cnFillPalette = 'MPL_viridis'
# Add cyclic point if we need to
if len(data.shape) == 2 and x.max() < 360:
data, x = ngl.add_cyclic(data, x)
# If passed 2d coordinate arrays assume they represent cell vertices,
# otherwise assume cell centers
if len(x.shape) == 2:
res.cnFillMode = 'CellFill'
res.sfXCellBounds = x
res.sfYCellBounds = y
else:
res.cnFillMode = 'RasterFill'
res.sfXArray = x
res.sfYArray = y
# Tweak plot appearance
res.mpGridAndLimbOn = False
res.mpPerimOn = False
# Additional options passed via kwargs
for key, val in kwargs.items():
setattr(res, key, val)
# Make the plot
plot = ngl.contour_map(wks, data, res)
return plot
def plot_time_series(time, data, **kwargs):
# Setup a plot resources object
res = ngl.Resources()
# Set resource values by keyword
for key, val in kwargs.items():
setattr(res, key, val)
# Make plot
pl = ngl.xy(wks, time, data, res)
return pl
def plot_image_gdal(
wks, band1, band2, band3,
lat1=-90, lat2=90, lon1=0, lon2=360,
**kwargs):
# Clip if over maximum
band1[band1>255] = 255
band2[band2>255] = 255
band3[band3>255] = 255
# Construct RGBA colormaps
ramp = np.linspace(0.,1.,255)
reds = np.zeros((255,4))
greens = np.zeros((255,4))
blues = np.zeros((255,4))
reds[:,0] = ramp
greens[:,1] = ramp
blues[:,2] = ramp
# Set alpha channel (a = 1: 100% opaque; a = 0: 100% transparent)
reds[:,3] = 1.0 #0.8
greens[:,3] = 0.0 #0.44444
blues[:,3] = 0.0 #0.3077
# Generate coordinate variables since they do not exist in the data
y = ngl.fspan(-90, 90, band1.shape[0])
x = ngl.fspan(0, 360, band1.shape[1])
# Set up plot resources
mapres = ngl.Resources()
mapres.nglDraw = True
mapres.nglFrame = False
mapres.cnFillOn = True
mapres.cnLinesOn = False
mapres.cnLineLabelsOn = False
mapres.cnInfoLabelOn = False
mapres.cnFillMode = 'RasterFill'
mapres.cnLevelSelectionMode = "ExplicitLevels"
mapres.cnLevels = np.arange(0,254,1)
mapres.cnFillBackgroundColor = [1., 1., 1., 1.] #set it to black
mapres.cnLinesOn = False
mapres.cnLineLabelsOn = False
mapres.lbLabelBarOn = False
mapres.sfXArray = x
mapres.sfYArray = y
# NOTE: pyngl will complain about these for the non-map plots,
# but I want everything set ahead of time so that I can override
# with kwargs passed into this function
mapres.mpFillOn = False
mapres.mpGridAndLimbOn = False
mapres.mpProjection = "Orthographic"
mapres.mpLimitMode = "LatLon"
mapres.mpMinLatF = lat1
mapres.mpMaxLatF = lat2
mapres.mpMinLonF = lon1
mapres.mpMaxLonF = lon2
# Set additional plot resource passed via kwargs
for key, value in kwargs.items(): setattr(mapres, key, value)
# The green and blue maps will be overlaid on top of the red map,
# The red map serves as the base upon which the others are overlaid,
# so we can make this one an actual map. NOTE: I am manually overlaying
# these plots, because I want them all to use the same plot resources.
mapres.cnFillColors = reds
redMap = ngl.contour_map(wks,band1,mapres)
mapres.cnFillColors = greens
greenMap = ngl.contour_map(wks,band2,mapres)
mapres.cnFillColors = blues
blueMap = ngl.contour_map(wks,band3,mapres)
# Return plot objects
return redMap, greenMap, blueMap
def increase_workspace_memory(value=10000000000):
ws_id = ngl.get_workspace_id()
rlist = ngl.Resources()
rlist.wsMaximumSize = value
ngl.set_values(ws_id,rlist)