def get_matrix_A(fn, gauge_name):
# points to read information from
point_reader = reader(file(gauge_name))
points = []
point_name = []
for i, row in enumerate(point_reader):
if i == 0:
for j, value in enumerate(row):
if value.strip() == 'easting': easting = j
if value.strip() == 'northing': northing = j
if value.strip() == 'name': name = j
if value.strip() == 'elevation': elevation = j
else:
#points.append([float(row[easting]),float(row[northing])])
points.append([float(row[easting]), float(row[northing])])
point_name.append(row[name])
points_array = np.array(points, np.float)
dim_gauge = int(np.sqrt(points_array.shape[0]))
interpolation_points = ensure_absolute(points_array)
# read the sww file to extract something
fid = NetCDFFile(fn, netcdf_mode_r)
xllcorner = fid.xllcorner
yllcorner = fid.yllcorner
zone = fid.zone
x = fid.variables['x'][:]
y = fid.variables['y'][:]
triangles = fid.variables['volumes'][:]
x = np.reshape(x, (len(x), 1))
y = np.reshape(y, (len(y), 1))
vertex_coordinates = np.concatenate((x, y), axis=1)
ele = fid.variables['elevation'][:]
fid.close()
vertex_coordinates = ensure_absolute(vertex_coordinates)
triangles = ensure_numeric(triangles)
interpolation_points = ensure_numeric(interpolation_points)
interpolation_points[:, 0] -= xllcorner
interpolation_points[:, 1] -= yllcorner
mesh = Mesh(vertex_coordinates, triangles)
mesh_boundary_polygon = mesh.get_boundary_polygon()
indices = outside_polygon(interpolation_points, mesh_boundary_polygon)
interp = Interpolate(vertex_coordinates, triangles)
matrix_A, inside_poly_indices, outside_poly_indices, centroids = interp._build_interpolation_matrix_A(
interpolation_points, output_centroids=False, verbose=False)
ele = matrix_A * ele
ele = np.asarray(ele)
ele = ele.reshape((100, 100))
return ele, matrix_A
def get_interpolation_object(self):
"""Get object I that will allow linear interpolation using this mesh
This is a time consuming process but it needs only to be
once for the mesh.
Interpolation can then be done using
result = I.interpolate_block(vertex_values, interpolation_points)
where vertex values have been obtained from a quantity using
vertex_values, triangles = self.get_vertex_values()
"""
if hasattr(self, 'interpolation_object'):
I = self.interpolation_object
else:
from anuga.fit_interpolate.interpolate import Interpolate
# Get discontinuous mesh - this will match internal
# representation of vertex values
triangles = self.get_disconnected_triangles()
vertex_coordinates = self.get_vertex_coordinates()
I = Interpolate(vertex_coordinates, triangles)
self.interpolation_object = I
return I
def calc_grid_values(nrows, ncols, cellsize, NODATA_value, x, y, norms,
volumes, result, grid_values):
grid_points = num.zeros((ncols * nrows, 2), num.float)
vertex_points = num.concatenate((x[:, num.newaxis], y[:, num.newaxis]),
axis=1)
assert len(vertex_points.shape) == 2
for i in xrange(nrows):
yg = i * cellsize
# if out_ext == '.asc':
# yg = i * cellsize
# else:
# # this will flip the order of the y values for ers
# yg = (nrows-i) * cellsize
for j in xrange(ncols):
xg = j * cellsize
k = i * ncols + j
grid_points[k, 0] = xg
grid_points[k, 1] = yg
# Interpolate
from anuga.fit_interpolate.interpolate import Interpolate
# Remove loners from vertex_points, volumes here
vertex_points, volumes = remove_lone_verts(vertex_points, volumes)
# export_mesh_file('monkey.tsh',{'vertices':vertex_points, 'triangles':volumes})
interp = Interpolate(vertex_points, volumes, verbose=verbose)
# Interpolate using quantity values
if verbose: log.critical('Interpolating')
grid_values[:] = interp.interpolate(result,
grid_points,
NODATA_value=NODATA_value,
verbose=verbose).flatten()
#print grid_values.shape
return
def calc_grid_values_old(vertex_points, volumes, result):
grid_points = num.zeros ((ncols*nrows, 2), num.float)
for i in xrange(nrows):
if out_ext == '.asc':
yg = i * cellsize
else:
# this will flip the order of the y values for ers
yg = (nrows-i) * cellsize
for j in xrange(ncols):
xg = j * cellsize
k = i*ncols + j
grid_points[k, 0] = xg
grid_points[k, 1] = yg
# Interpolate
from anuga.fit_interpolate.interpolate import Interpolate
# Remove loners from vertex_points, volumes here
vertex_points, volumes = remove_lone_verts(vertex_points, volumes)
# export_mesh_file('monkey.tsh',{'vertices':vertex_points, 'triangles':volumes})
interp = Interpolate(vertex_points, volumes, verbose = verbose)
bprint = 0
# Interpolate using quantity values
if verbose: log.critical('Interpolating')
grid_values = interp.interpolate(bprint, result, grid_points).flatten()
outside_indices = interp.get_outside_poly_indices()
for i in outside_indices:
#print 'change grid_value',NODATA_value
grid_values[i] = NODATA_value
return grid_values
def calc_grid_values(nrows, ncols, cellsize, NODATA_value,
x,y, norms, volumes, result, grid_values):
grid_points = num.zeros ((ncols*nrows, 2), num.float)
vertex_points = num.concatenate ((x[:,num.newaxis], y[:,num.newaxis]), axis=1)
assert len(vertex_points.shape) == 2
for i in xrange(nrows):
yg = i * cellsize
# if out_ext == '.asc':
# yg = i * cellsize
# else:
# # this will flip the order of the y values for ers
# yg = (nrows-i) * cellsize
for j in xrange(ncols):
xg = j * cellsize
k = i*ncols + j
grid_points[k, 0] = xg
grid_points[k, 1] = yg
# Interpolate
from anuga.fit_interpolate.interpolate import Interpolate
# Remove loners from vertex_points, volumes here
vertex_points, volumes = remove_lone_verts(vertex_points, volumes)
# export_mesh_file('monkey.tsh',{'vertices':vertex_points, 'triangles':volumes})
interp = Interpolate(vertex_points, volumes, verbose = verbose)
# Interpolate using quantity values
if verbose: log.critical('Interpolating')
grid_values[:] = interp.interpolate(result, grid_points,
NODATA_value= NODATA_value,
verbose=verbose).flatten()
#print grid_values.shape
return
def calc_grid_values_old(vertex_points, volumes, result):
grid_points = num.zeros((ncols * nrows, 2), num.float)
for i in range(nrows):
if out_ext == '.asc':
yg = i * cellsize
else:
# this will flip the order of the y values for ers
yg = (nrows - i) * cellsize
for j in range(ncols):
xg = j * cellsize
k = i * ncols + j
grid_points[k, 0] = xg
grid_points[k, 1] = yg
# Interpolate
from anuga.fit_interpolate.interpolate import Interpolate
# Remove loners from vertex_points, volumes here
vertex_points, volumes = remove_lone_verts(vertex_points, volumes)
# export_mesh_file('monkey.tsh',{'vertices':vertex_points, 'triangles':volumes})
interp = Interpolate(vertex_points, volumes, verbose=verbose)
bprint = 0
# Interpolate using quantity values
if verbose: log.critical('Interpolating')
grid_values = interp.interpolate(bprint, result, grid_points).flatten()
outside_indices = interp.get_outside_poly_indices()
for i in outside_indices:
#print 'change grid_value',NODATA_value
grid_values[i] = NODATA_value
return grid_values
yg = i * cellsize
else:
# this will flip the order of the y values for ers
yg = (nrows-i) * cellsize
for j in xrange(ncols):
xg = j * cellsize
k = i*ncols + j
grid_points[k, 0] = xg
grid_points[k, 1] = yg
# Remove loners from vertex_points, volumes here
vertex_points, volumes = remove_lone_verts(vertex_points, volumes)
# export_mesh_file('monkey.tsh',{'vertices':vertex_points, 'triangles':volumes})
interp = Interpolate(vertex_points, volumes, verbose=verbose)
log.debug('Interpolating')
# Interpolate using quantity values
grid_values = interp.interpolate(result, grid_points).flatten()
log.debug('Interpolated values are in [%f, %f]'
% (num.min(grid_values), num.max(grid_values)))
# Assign NODATA_value to all points outside bounding polygon (from interpolation mesh)
P = interp.mesh.get_boundary_polygon()
outside_indices = outside_polygon(grid_points, P, closed=True)
for i in outside_indices:
grid_values[i] = NODATA_value
# Post condition: Now q has dimension: number_of_points
assert len(q.shape) == 1
assert q.shape[0] == number_of_points
if verbose:
log.critical('Processed values for %s are in [%f, %f]' %
(quantity, min(q), max(q)))
# Create grid and update xll/yll corner and x,y
vertex_points = num.concatenate((x[:, num.newaxis], y[:, num.newaxis]),
axis=1)
assert len(vertex_points.shape) == 2
# Interpolate
from anuga.fit_interpolate.interpolate import Interpolate
interp = Interpolate(vertex_points, volumes, verbose=verbose)
# Interpolate using quantity values
if verbose: log.critical('Interpolating')
interpolated_values = interp.interpolate(q, data_points).flatten()
if verbose:
log.critical(
'Interpolated values are in [%f, %f]' %
(num.min(interpolated_values), num.max(interpolated_values)))
# Assign NODATA_value to all points outside bounding polygon
# (from interpolation mesh)
P = interp.mesh.get_boundary_polygon()
outside_indices = outside_polygon(data_points, P, closed=True)
# Post condition: Now q has dimension: number_of_points
assert len(q.shape) == 1
assert q.shape[0] == number_of_points
if verbose:
log.critical('Processed values for %s are in [%f, %f]'
% (quantity, min(q), max(q)))
# Create grid and update xll/yll corner and x,y
vertex_points = num.concatenate((x[:, num.newaxis], y[:, num.newaxis]), axis=1)
assert len(vertex_points.shape) == 2
# Interpolate
from anuga.fit_interpolate.interpolate import Interpolate
interp = Interpolate(vertex_points, volumes, verbose=verbose)
# Interpolate using quantity values
if verbose: log.critical('Interpolating')
interpolated_values = interp.interpolate(q, data_points).flatten()
if verbose:
log.critical('Interpolated values are in [%f, %f]'
% (num.min(interpolated_values),
num.max(interpolated_values)))
# Assign NODATA_value to all points outside bounding polygon
# (from interpolation mesh)
P = interp.mesh.get_boundary_polygon()
outside_indices = outside_polygon(data_points, P, closed=True)
def sww2pts(name_in,
name_out=None,
data_points=None,
quantity=None,
timestep=None,
reduction=None,
NODATA_value=-9999,
verbose=False,
origin=None):
"""Read SWW file and convert to interpolated values at selected points
The parameter 'quantity' must be the name of an existing quantity or
an expression involving existing quantities. The default is 'elevation'.
if timestep (an index) is given, output quantity at that timestep.
if reduction is given use that to reduce quantity over all timesteps.
data_points (Nx2 array) give locations of points where quantity is to
be computed.
"""
import sys
from anuga.geometry.polygon import inside_polygon, outside_polygon
from anuga.abstract_2d_finite_volumes.util import \
apply_expression_to_dictionary
from anuga.geospatial_data.geospatial_data import Geospatial_data
if quantity is None:
quantity = 'elevation'
if reduction is None:
reduction = max
basename_in, in_ext = os.path.splitext(name_in)
if name_out != None:
basename_out, out_ext = os.path.splitext(name_out)
else:
basename_out = basename_in + '_%s' % quantity
out_ext = '.pts'
name_out = basename_out + out_ext
if in_ext != '.sww':
raise IOError('Input format for %s must be .sww' % name_in)
if out_ext != '.pts':
raise IOError('Output format for %s must be .pts' % name_out)
# Read sww file
if verbose: log.critical('Reading from %s' % name_in)
from anuga.file.netcdf import NetCDFFile
fid = NetCDFFile(name_in)
# Get extent and reference
x = fid.variables['x'][:]
y = fid.variables['y'][:]
volumes = fid.variables['volumes'][:]
try: # works with netcdf4
number_of_timesteps = len(fid.dimensions['number_of_timesteps'])
number_of_points = len(fid.dimensions['number_of_points'])
except: #works with scientific.io.netcdf
number_of_timesteps = fid.dimensions['number_of_timesteps']
number_of_points = fid.dimensions['number_of_points']
if origin is None:
# Get geo_reference
# sww files don't have to have a geo_ref
try:
geo_reference = Geo_reference(NetCDFObject=fid)
except AttributeError as e:
geo_reference = Geo_reference() # Default georef object
xllcorner = geo_reference.get_xllcorner()
yllcorner = geo_reference.get_yllcorner()
zone = geo_reference.get_zone()
else:
zone = origin[0]
xllcorner = origin[1]
yllcorner = origin[2]
# FIXME: Refactor using code from file_function.statistics
# Something like print swwstats(swwname)
if verbose:
x = fid.variables['x'][:]
y = fid.variables['y'][:]
times = fid.variables['time'][:]
log.critical('------------------------------------------------')
log.critical('Statistics of SWW file:')
log.critical(' Name: %s' % swwfile)
log.critical(' Reference:')
log.critical(' Lower left corner: [%f, %f]' %
(xllcorner, yllcorner))
log.critical(' Start time: %f' % fid.starttime[0])
log.critical(' Extent:')
log.critical(' x [m] in [%f, %f], len(x) == %d' %
(num.min(x), num.max(x), len(x.flat)))
log.critical(' y [m] in [%f, %f], len(y) == %d' %
(num.min(y), num.max(y), len(y.flat)))
log.critical(' t [s] in [%f, %f], len(t) == %d' %
(min(times), max(times), len(times)))
log.critical(' Quantities [SI units]:')
for name in ['stage', 'xmomentum', 'ymomentum', 'elevation']:
q = fid.variables[name][:].flat
log.critical(' %s in [%f, %f]' % (name, min(q), max(q)))
# Get quantity and reduce if applicable
if verbose: log.critical('Processing quantity %s' % quantity)
# Turn NetCDF objects into numeric arrays
quantity_dict = {}
for name in list(fid.variables.keys()):
quantity_dict[name] = fid.variables[name][:]
# Convert quantity expression to quantities found in sww file
q = apply_expression_to_dictionary(quantity, quantity_dict)
if len(q.shape) == 2:
# q has a time component and needs to be reduced along
# the temporal dimension
if verbose: log.critical('Reducing quantity %s' % quantity)
q_reduced = num.zeros(number_of_points, num.float)
for k in range(number_of_points):
q_reduced[k] = reduction(q[:, k])
q = q_reduced
# Post condition: Now q has dimension: number_of_points
assert len(q.shape) == 1
assert q.shape[0] == number_of_points
if verbose:
log.critical('Processed values for %s are in [%f, %f]' %
(quantity, min(q), max(q)))
# Create grid and update xll/yll corner and x,y
vertex_points = num.concatenate((x[:, num.newaxis], y[:, num.newaxis]),
axis=1)
assert len(vertex_points.shape) == 2
# Interpolate
from anuga.fit_interpolate.interpolate import Interpolate
interp = Interpolate(vertex_points, volumes, verbose=verbose)
# Interpolate using quantity values
if verbose: log.critical('Interpolating')
interpolated_values = interp.interpolate(q, data_points).flatten()
if verbose:
log.critical(
'Interpolated values are in [%f, %f]' %
(num.min(interpolated_values), num.max(interpolated_values)))
# Assign NODATA_value to all points outside bounding polygon
# (from interpolation mesh)
P = interp.mesh.get_boundary_polygon()
outside_indices = outside_polygon(data_points, P, closed=True)
for i in outside_indices:
interpolated_values[i] = NODATA_value
# Store results
G = Geospatial_data(data_points=data_points,
attributes=interpolated_values)
G.export_points_file(name_out, absolute=True)
fid.close()
