def test_lin_interp():
X = np.array([[0., 0.], [10., 0.], [10., 10.], [0., 10.]])
F = np.array([1., 2., 3., 4.])
f = field.XYZField(X=X, F=F)
elev = f.interpolate([2, 3])
out = f.interpolate(X)
assert np.allclose(out, F)
def test_peanut():
# like a figure 8, or a peanut
r = 100
thetas = np.linspace(0, 2 * np.pi, 1000)
peanut = np.zeros((len(thetas), 2), np.float64)
peanut[:, 0] = r * (0.5 + 0.3 * np.cos(2 * thetas)) * np.cos(thetas)
peanut[:, 1] = r * (0.5 + 0.3 * np.cos(2 * thetas)) * np.sin(thetas)
min_pnt = peanut.min(axis=0)
max_pnt = peanut.max(axis=0)
d_data = np.array([[min_pnt[0], min_pnt[1], 1.5],
[min_pnt[0], max_pnt[1], 1.5],
[max_pnt[0], min_pnt[1], 8],
[max_pnt[0], max_pnt[1], 8]])
density = field.XYZField(X=d_data[:, :2], F=d_data[:, 2])
trifront_wrapper([peanut], density, label='peanut')
def test_peanut():
# like a figure 8, or a peanut
r = 100
thetas = np.linspace(0,2*np.pi,1000)
peanut = np.zeros( (len(thetas),2), np.float64)
peanut[:,0] = r*(0.5+0.3*np.cos(2*thetas))*np.cos(thetas)
peanut[:,1] = r*(0.5+0.3*np.cos(2*thetas))*np.sin(thetas)
min_pnt = peanut.min(axis=0)
max_pnt = peanut.max(axis=0)
d_data = np.array([ [min_pnt[0],min_pnt[1], 1.5],
[min_pnt[0],max_pnt[1], 1.5],
[max_pnt[0],min_pnt[1], 8],
[max_pnt[0],max_pnt[1], 8]])
density = field.XYZField(X=d_data[:,:2],F=d_data[:,2])
p=paver.Paving(peanut,density)
p.pave_all()
params('west_marsh_fill',
src=field.ConstantField(10),
priority=105,
data_mode='diffuser(),min()',
alpha_mode='feather_in(2)')
##
# And do something a bit better for the mouth -- pull from
# the UCB grid.
pdo_grid = unstructured_grid.RgfGrid(
"../data/ucb-model/MouthVariation/EpMp/PDO_EpMp.grd")
pdo_grid.add_node_field('z_bed_node', -pdo_grid.nodes['depth_node'])
# Convert that to a Field
pdo_field = field.XYZField(X=pdo_grid.nodes['x'],
F=pdo_grid.nodes['z_bed_node'])
pdo_field._tri = pdo_grid.mpl_triangulation()
##
params('mouth',
src=pdo_field,
priority=100,
data_mode='blur(5)',
alpha_mode='feather_in(10.0),blur_alpha(4)')
params('lag_thalweg',
src=field.ConstantField(0.0),
priority=110,
data_mode='min()',
alpha_mode='feather_in(5.0),blur_alpha(10)')
# leave this test in.
if os.path.exists(map_fn):
if not os.path.exists(ic_fns[0]):
# Get a baseline, global 2D salt field from Polaris/Peterson data
if extrap_bay:
from sfb_dfm_utils import initial_salinity
usgs_init_salt = initial_salinity.samples_from_usgs(
run_start, field='salinity')
usgs_init_temp = initial_salinity.samples_from_usgs(
run_start, field='temperature')
cc_salt = initial_salinity.samples_to_cells(usgs_init_salt, g)
cc_temp = initial_salinity.samples_to_cells(usgs_init_temp, g)
salt_extrap_field = field.XYZField(X=cc_salt[:, :2],
F=cc_salt[:, 2])
temp_extrap_field = field.XYZField(X=cc_temp[:, :2],
F=cc_temp[:, 2])
salt_extrap_field.build_index()
temp_extrap_field.build_index()
missing_val = -999
else:
missing_val = 32.5
# Scan the coastal model files, find one close to our start date
for fn in coastal_files:
snap = xr.open_dataset(fn)
if snap.time.ndim > 0:
snap = snap.isel(time=0)
if snap.time > run_start:
print("Will use %s for initial condition" % fn)
def set_lsb_bathy(grid):
check_bathy()
# First, load in the original sfb_dfm grid to get bathymetry
log.info("Loading SFB DFM v2 grid")
sfb_dfm_grid=xr.open_dataset('sfb_dfm/sfei_v19_net.nc')
sfb_X=np.c_[ sfb_dfm_grid.NetNode_x.values,
sfb_dfm_grid.NetNode_y.values]
sfb_dfm_field=field.XYZField(X=sfb_X,
F=sfb_dfm_grid.NetNode_z.values)
lsb_X=grid.nodes['x']
# This will set all points within the convex hull of the original
# grid. These elevations are used in the output (a) outside the
# LSB merged_2m DEM, and (b) to prioritize which nodes to use when
# moving depths from edges to nodes.
# Could argue that it would be better to pull point elevations here
# from the DEM where they overlap. Probably makes very little difference
lsb_z=sfb_dfm_field(lsb_X) # still some nans at this point
#
log.info("Loading LSB dem from %s"%merged_2m_path)
dem=field.GdalGrid(merged_2m_path)
#
# Use the 2m DEM to find an effective minimum depth for each edge covered
# by the DEM.
edge_min_depths=depth_connectivity.edge_connection_depth(grid,dem,centers='lowest')
# move those edge depths to node depths
node_depths=depth_connectivity.greedy_edgemin_to_node(grid,lsb_z,edge_min_depths)
# Still have some nodes with nan depth, first try filling in with the DEM.
missing=np.isnan(node_depths)
node_depths[missing]=dem( lsb_X[missing,:] )
# And wrap it up with a more forgiving interpolation from the original sfb_dfm_v2
# grid (about 12 points on the convex hull)
still_missing=np.isnan(node_depths)
node_depths[still_missing]=sfb_dfm_field.interpolate( lsb_X[still_missing,:],
'nearest' )
assert np.isnan(node_depths).sum()==0
# Update the grid
grid.nodes['depth']=node_depths
if 0: # caller is going to deal with I/O
out_file='lsb_v99_bathy_net.nc'
os.path.exists(out_file) and os.unlink(out_file)
dfm_grid.write_dfm(grid,out_file)
if 0: # plot for development.
plt.figure(10).clf()
fig,ax=plt.subplots(num=10)
edge_mins=grid.nodes['depth'][grid.edges['nodes']].min(axis=1)
ecoll=grid.plot_edges(lw=1.5,values=edge_mins)
ncoll=grid.plot_nodes(values=grid.nodes['depth'])
plt.setp([ecoll,ncoll],clim=[-3,3])
plot_utils.cbar(ncoll,extras=[ecoll])
# Modified in place, but return just in case
return grid # QED.
scat = ax.scatter(source_ds.x.values[close_samples, 0],
source_ds.x.values[close_samples,
1], 30, close_distances[:, 1])
scat.set_clim([-40, 40])
scat.set_cmap('seismic')
ax.axis((647301.6747810617, 647489.8763778533, 4185618.735872294,
4185781.4072091985))
##
# Plot those in the plane:
plt.figure(2).clf()
fig, (ax, ax_dem) = plt.subplots(2, 1, num=2)
valid = np.isfinite(close_distances[:, 0])
f = field.XYZField(X=close_distances[valid],
F=source_ds.z.values[close_samples][valid])
clim = [f.F.min(), f.F.max()]
scat = ax.scatter(f.X[:, 0], f.X[:, 1], 30, f.F, cmap='jet', clim=clim)
fg = f.to_grid(nx=500, ny=500, aspect=5)
fg.plot(zorder=-2, ax=ax, cmap='jet', clim=clim)
scat2 = ax.scatter([0.0], [0.0], 50, [dem(x_target)], cmap='jet')
scat2.set_clim(clim)
plt.colorbar(scat2)
# and plot things in geographical coordinates
poly_pnts = np.array(poly.exterior)
if np.all(poly_pnts[-1] == poly_pnts[0]):
poly_pnts = poly_pnts[:-1]
new_values = xyz_sub.interpolate(poly_pnts, 'nearest')
poly_nodes = []
for pnt, value in zip(poly_pnts, new_values):
poly_nodes.append(cdt.add_node(x=pnt, value=value))
from stompy import utils
for a, b in utils.circular_pairs(poly_nodes):
cdt.add_constraint(a, b)
##
xyz_aug = field.XYZField(X=cdt.nodes['x'], F=cdt.nodes['value'])
aug_tri = delaunay.Triangulation(xyz_aug.X[:, 0], xyz_aug.X[:, 1],
cdt.cells['nodes'][~cdt.cells['deleted']])
xyz_aug._tri = aug_tri
dem2 = xyz_aug.to_grid(dx=5, dy=10, interp='linear')
##
plt.figure(20).clf()
#fig,ax=plt.subplots(1,1,num=20,sharex=True,sharey=True)
fig, (ax, ax2) = plt.subplots(1, 2, num=20, sharex=True, sharey=True)
plot_wkb.plot_polygon(poly, ax=ax, fc='none')
alpha = 10 # controls the degree of anistropy
coords = np.c_[cc[nbrs], alpha * psi[nbrs]]
coord0 = np.array([cc[c, 0], cc[c, 1], alpha * psi[c]])
dists = utils.mag(coords - coord0)
# Will take the median of a subset of those:
N = int(len(dists) * 0.15)
subsel = np.argsort(dists)[:N]
close_nbrs = nbrs[subsel]
d_med[c] = np.median(d_orig[close_nbrs])
plt.figure(13).clf()
fig, axs = plt.subplots(1, 3, num=13, sharex=True, sharey=True)
axs[0].set_adjustable('box', share=True)
clim = [-4, 0]
gr.plot_cells(values=d_orig, cmap=turbo, clim=clim, ax=axs[0])
gr.plot_cells(values=d_med, cmap=turbo, clim=clim, ax=axs[1])
gr.plot_cells(values=d_med - d_orig, cmap='coolwarm', clim=[-1, 1], ax=axs[2])
axs[0].axis(zoom)
fig.tight_layout()
##
from stompy.spatial import field
dem_smooth = field.XYZField(gr.cells_center(), d_med).rectify()
dem_smooth.write_gdal('dwr_smooth_v01.tif')
## plt.figure(10).clf() fig,ax=plt.subplots(1,1,num=10) img1=dem_crop.plot(clim=[-5,2.2],ax=ax,cmap='gray') gd.contour_node_values(psi_node,40,linewidths=0.5,colors='orange') gd.contour_node_values(phi_node,300,linewidths=0.5,colors='red') ax.axis(zoom) ## # Convert that to rasters phi_fld=field.XYZField(gr.cells_center(),phi).rectify() psi_fld=field.XYZField(gr.cells_center(),psi).rectify() ## # add psi and phi coordinates to condensed. xy=condensed.loc[:,['x','y']].values phi_fld.default_interpolation='linear' psi_fld.default_interpolation='linear' condensed['phi']=phi_fld(xy) condensed['psi']=psi_fld(xy) ## plt.figure(12).clf()
# Checking the coverage of a 2m xyz file from Ed -
# seems to be just a 2m merging of the 2m DWR datasets.
from stompy.spatial import field
##
fn = "/home/rusty/mirrors/ucd-X/UnTRIM/JANET Files/Bay-Delta Bathymetry Data/2m.xyz"
xyz = np.loadtxt(fn, delimiter=",")
fx = field.XYZField(X=xyz[:, :2], F=xyz[:, 2])
f = fx.rectify(2.0, 2.0)
f.write_gdal('2m.tif')
lat_min=sub_ds.lat.values.min()
lat_max=sub_ds.lat.values.max()
lon_min=sub_ds.lon.values.min()
lon_max=sub_ds.lon.values.max()
sel=((hy_bathy.Latitude.values>=lat_min) &
(hy_bathy.Latitude.values<=lat_max) &
(hy_bathy.Longitude.values>=lon_min) &
(hy_bathy.Longitude.values<=lon_max))
bathy_xyz=np.c_[ hy_bathy.Longitude.values[sel],
hy_bathy.Latitude.values[sel],
hy_bathy.bathymetry.isel(MT=0).values[sel] ]
bathy_xyz[:,:2]=ll2utm(bathy_xyz[:,:2])
bathy=field.XYZField(X=bathy_xyz[:,:2],F=bathy_xyz[:,2])
##
# counterclockwise along border, north to south.
nodes_bc=g.select_nodes_boundary_segment([[457184, 4289741],
[592899, 4037040]])
# g.plot_nodes(mask=nodes_bc,ax=ax) # good
##
# rotation matrix to take true east/north vector, rotate to local UTM easting/northing
# centered on given utm x,y
def vec_ll2utm(utm_xy):
ll0=utm2ll(utm_xy)