def pnts_to_xA(pnts, datum='MSL'):
samples = linestring_utils.upsample_linearring(pnts, 5, closed_ring=False)
z = dem(samples)
dists = utils.dist_along(samples)
z0 = ds.z.sel(datum=datum).item()
depths = (z0 - z).clip(0, np.inf)
xA = np.trapz(depths, dists)
return xA
def test_peninsula():
r = 100
thetas = np.linspace(0, 2 * np.pi, 1000)
pen = np.zeros((len(thetas), 2), np.float64)
pen[:, 0] = r * (0.2 + np.abs(np.sin(2 * thetas))**0.2) * np.cos(thetas)
pen[:, 1] = r * (0.2 + np.abs(np.sin(2 * thetas))**0.2) * np.sin(thetas)
density = field.ConstantField(10.0)
pen2 = upsample_linearring(pen, density)
trifront_wrapper([pen2], density, label='peninsula')
print("Loading DEM for %s" % name)
dem = field.GdalGrid(dem_fn)
if gen_weirs:
print("Generating fixed weirs")
fixed_weir_fn = "fixed_weirs-%s.pliz" % name
fixed_weirs = [] # suitable for write_pli
dx = 5.0 # m. discretize lines at this resolution
for i in range(len(lines)):
feat = lines[i]
if feat['type'] != 'fixed_weir': continue
print(f"Processing levee feature {feat['name']}")
xy = np.array(feat['geom'])
xy = linestring_utils.upsample_linearring(xy,
dx,
closed_ring=False)
z = dem(xy)
fixed_weirs.append((feat['name'], np.c_[xy, z, 0 * z, 0 * z]))
dio.write_pli(fixed_weir_fn, fixed_weirs)
if gen_grids:
print("Setting bathymetry for %s" % name)
if 0: # Simplest option:
# Put bathy on nodes, just direct sampling.
z_node = dem(g.nodes['x'])
if 1: # Bias deep
name += "_deep"
# Maybe a good match with bedlevtype=5.
# BLT=5: edges get shallower node, cells get deepest edge.
# count total features so that files can be concatenated without issue.
total_count = 0
# no longer split by class, all filtering already complete in the input
# shapefile
g = unstructured_grid.UnstructuredGrid(extra_node_fields=[('elev_m', 'f8')],
extra_edge_fields=[('mean_elev_m', 'f8')
])
for ix, sel_i in enumerate(np.nonzero(sel)[0]):
geom = inv['geom'][sel_i]
coords = np.array(geom)
# not 100% sure about whether it's necessary to test for closed_ring
new_coords = linestring_utils.upsample_linearring(coords,
res,
closed_ring=0)
nodes = [
g.add_or_find_node(x=xy, tolerance=0.0, elev_m=np.nan)
for xy in new_coords
]
for a, b in zip(nodes[:-1], nodes[1:]):
j = g.add_edge(nodes=[a, b], mean_elev_m=inv['Z_Mean'][sel_i])
# pull out point elevations at the nodes:
g.nodes['elev_m'] = dem(g.nodes['x'])
# drastic, but go ahead and delete any nodes which failed to get an elevation
