def __init__(self, lons, lats, face_nodes, img):
"""
lons, lats : 1d array in node index order
face_nodes: iterable of iterable of node index
img: 1d array of source data. Same length as face nodes
"""
# convert to 3d space
self._geocent = ccrs.Geocentric(globe=ccrs.Globe())
self.img = img
xyz = self._geocent.transform_points(ccrs.Geodetic(), lons, lats)
self._nodes_xyz = xyz
start = time.time()
self._kd = KDTree(xyz)
end = time.time()
logging.info('KD Construction time ({} points): {}'.format(
lons.size, end - start))
self._face_nodes = np.array(face_nodes)
self._node_faces = fmgc.create_node_faces_array(self._face_nodes,
num_nodes=len(lons))
def test_transform_points_xyz(self):
# Test geodetic transforms when using z value
rx = np.array([2574.32516e3])
ry = np.array([837.562e3])
rz = np.array([5761.325e3])
src_proj = ccrs.Geocentric()
target_proj = ccrs.Geodetic()
res = target_proj.transform_points(x=rx, y=ry, z=rz, src_crs=src_proj)
glat = res[..., 0]
glon = res[..., 1]
galt = res[..., 2]
# Solution generated by pyproj
solx = np.array([18.0224043189])
soly = np.array([64.9796515089])
solz = np.array([5048.03893734])
assert_arr_almost_eq(glat, solx)
assert_arr_almost_eq(glon, soly)
assert_arr_almost_eq(galt, solz)
def mpl_plot(data2d,lon,lat,title,longname,units,proj,clev,cmap,gllfile):
# Setup the plot
figure = pyplot.figure(figsize=(15, 10))
dataproj=crs.PlateCarree()
# pcolor/tripcolor doesn't use nelvels or contour intervals
if len(clev)==1:
vmin=None
vmax=None
nlevels=int(round(clev[0]))
else:
vmin=clev[0]
vmax=clev[1]
nlevels=int(round( (clev[1]-clev[0])/clev[2] ))
if proj=="latlon":
plotproj=crs.PlateCarree(central_longitude=0.0)
ax = pyplot.axes(projection=plotproj)
ax.set_global()
elif proj=="US1":
plotproj=plotproj=crs.PlateCarree(central_longitude=0.0)
ax = pyplot.axes(projection=plotproj)
ax.set_extent([-180, 0, -30, 75],crs=dataproj)
elif proj=="andes":
plotproj=plotproj=crs.PlateCarree(central_longitude=0.0)
ax = pyplot.axes(projection=plotproj)
ax.set_extent([-100, -40, -40, 15],crs=dataproj)
elif proj=="himalaya":
plotproj=plotproj=crs.PlateCarree(central_longitude=90.0)
ax = pyplot.axes(projection=plotproj)
ax.set_extent([50, 110, 0, 60],crs=dataproj)
elif proj=="oro":
plotproj=crs.Orthographic(central_longitude=-45.0, central_latitude=45.0)
ax = pyplot.axes(projection=plotproj)
ax.set_global()
else:
print("Bad projection argument: ",projection)
sys.exit(3)
ax.coastlines(linewidth=0.2)
# strucgtured lat/lon or unstructured data?
struct=False
if len(lon)*len(lat) == numpy.prod(data2d.shape): struct=True
compute_tri=True
if ~struct and os.path.isfile(gllfile):
cfile = Nio.open_file(gllfile,"r")
ec=cfile.variables["element_corners"]
nd=ec.shape
# by Euler, number of subcells is number of gll nodes -2
if (nd[1] == len(lat)-2):
ntris=nd[1]*2
tri=numpy.empty((ntris,3), dtype=int)
tri[::2,0]=ec[0,:]
tri[::2,1]=ec[1,:]
tri[::2,2]=ec[2,:]
tri[1::2,0]=ec[0,:]
tri[1::2,1]=ec[2,:]
tri[1::2,2]=ec[3,:]
tri=tri-1 # zero indexing
compute_tri=False
print("data min/max=",numpy.amin(data2d),numpy.amax(data2d))
print("colormap min/max=",vmin,vmax)
if struct:
data2d_ext, lon2 = add_cyclic_point(data2d, coord=lon,axis=1)
print("MPL plotting structured data (with added cyclic point)")
pl=ax.pcolormesh(lon2, lat, data2d_ext,vmin=vmin,vmax=vmax,
transform=dataproj, cmap=cmap)
#pl=ax.contourf(lon2, lat, data2d_ext, nlevels,vmin=vmin,vmax=vmax,
# transform=dataproj, cmap=cmap)
elif compute_tri:
print("MPL plot using internal Delaunay triangulation")
# do the triangulation in the plot coordinates for better results
tcoords = plotproj.transform_points(dataproj,lon[:],lat[:])
# need to remove non-visible points
xi=tcoords[:,0]!=numpy.inf
tc=tcoords[xi,:]
datai=data2d[:][xi] # convert to numpy array, then subset
pl = ax.tripcolor(tc[:,0],tc[:,1], datai,vmin=vmin, vmax=vmax,
shading='gouraud',cmap=cmap)
else:
print("MPL plot using gll subcell triangulation")
# latlon->cartesian->local coords. this will put any seams at plot boundaries
proj3d=crs.Geocentric() # for cartesian (x,y,z) representation
x3d = proj3d.transform_points(dataproj,lon[:],lat[:])
tcoords = plotproj.transform_points(proj3d,x3d[:,0],x3d[:,1],x3d[:,2])
#Remove bad triangles:
x0=tcoords[tri[:,0],0]
y0=tcoords[tri[:,0],1]
x1=tcoords[tri[:,1],0]
y1=tcoords[tri[:,1],1]
x2=tcoords[tri[:,2],0]
y2=tcoords[tri[:,2],1]
d=numpy.empty(tri.shape)
d[:,0]=((x0-x1)**2 + (y0-y1)**2)**0.5
d[:,1]=((x0-x2)**2 + (y0-y2)**2)**0.5
d[:,2]=((x1-x2)**2 + (y1-y2)**2)**0.5
dmax=numpy.amax(d,axis=1)
gmin=numpy.nanmin(dmax[dmax != numpy.inf])
gmax=numpy.nanmax(dmax[dmax != numpy.inf])
print("triangle max lengths: ",gmin,gmax)
mask = numpy.logical_or( dmax > 25*gmin, numpy.isnan(dmax))
# gouraud shading requires we remove non-visable triangles
pl = ax.tripcolor(tcoords[:,0],tcoords[:,1],tri,data2d,vmin=vmin,vmax=vmax,
mask=mask,shading='gouraud',cmap=cmap)
# plot some of the triangles to make sure they are ok:
#ax.triplot(tcoords[:,0],tcoords[:,1],tri[1:100,:],'go-')
cb = pyplot.colorbar(pl, orientation='horizontal',
label='%s (%s)'%(longname, units),shrink=0.75, pad=0.1)