def create_dataset(args):
"""
Parse the shapefile and source definitions, but do not render
any of the DEM.
"""
paths = args.path
# field.CompositeField has a generic interface for sources, via
# a 'factory' method.
def factory(attrs):
geo_bounds = attrs['geom'].bounds
if attrs['src_name'].startswith('py:'):
expr = attrs['src_name'][3:]
# something like 'ConstantField(-1.0)'
# a little sneaky... make it look like it's running
# after a "from stompy.spatial.field import *"
# and also it gets fields of the shapefile
field_hash = dict(field.__dict__)
# convert the attrs into a dict suitable for passing to eval
attrs_dict = {}
for name in attrs.dtype.names:
attrs_dict[name] = attrs[name]
return eval(expr, field_hash, attrs_dict)
# Otherwise assume src_name is a file name or file pattern.
for p in paths:
full_path = os.path.join(p, attrs['src_name'])
files = glob.glob(full_path)
if len(files) > 1:
mrf = field.MultiRasterField(files)
return mrf
elif len(files) == 1:
gg = field.GdalGrid(files[0], geo_bounds=geo_bounds)
gg.default_interpolation = 'linear'
return gg
log.warning("Source %s was not found -- ignoring" % attrs['src_name'])
return None
comp_field = field.CompositeField(shp_fn=args.shapefile,
shp_query=args.query,
target_date=args.date,
factory=factory,
priority_field='priority',
data_mode='data_mode',
alpha_mode='alpha_mode')
return comp_field
priority=95,
src=field.ConstantField(0.7),
alpha_mode='blur_alpha(4.0)',
data_mode='min()')
south_channel_dem = field.GdalGrid("north_channel_and_ditch-1m.tif")
# And now include that channel
params('marsh_south_channel',
priority=90,
src=south_channel_dem,
data_mode='min()',
alpha_mode='valid(),feather_in(2.0)',
geom=field_bounds(lagoon_dem))
comp_field = field.CompositeField(shp_data=shp_data, factory=factory)
res = 2.0
# This can then be used below with the ContourInterpolator
dem_dry = comp_field.to_grid(dx=res, dy=res, bounds=clip)
# Assume the western pan is sort of radially similar.
from stompy.grid import unstructured_grid, exact_delaunay
from stompy.spatial import interp_concentric
west = adj_regions['geom'][adj_regions['name'] == 'west_marsh_inundated'][0]
center = adj_regions['geom'][adj_regions['name'] ==
'center_marsh_inundated_west'][0]
# Go out about 10.0m to get some dry land to pull from the DEM
region = west.union(center).buffer(10.0)
# a little sneaky... make it look like it's running
# after a "from stompy.spatial.field import *"
# and also it gets fields of the shapefile
field_hash = dict(field.__dict__)
# convert the attrs into a dict suitable for passing to eval
attrs_dict = {}
for name in attrs.dtype.names:
attrs_dict[name] = attrs[name]
return eval(expr, field_hash, attrs_dict)
assert False
src_shp = 'roughness_sources.shp'
mbf = field.CompositeField(shp_fn=src_shp,
factory=factory,
priority_field='priority',
data_mode='data_mode',
alpha_mode='alpha_mode')
xxyy = [642800, 649400, 4183200, 4188400]
bleed = 50
xxyy_pad = [xxyy[0] - bleed, xxyy[1] + bleed, xxyy[2] - bleed, xxyy[3] + bleed]
dem = mbf.to_grid(dx=2, dy=2, bounds=xxyy_pad)
dem = dem.crop(xxyy)
out_fn = 'composite-roughness.tif'
os.path.exists(out_fn) and os.unlink(out_fn)
dem.write_gdal(out_fn)