def test_ferret2sww_nz_origin(self):
from anuga.coordinate_transforms.redfearn import redfearn
#Call conversion (with nonzero origin)
ferret2sww(self.test_MOST_file, verbose=self.verbose,
origin = (56, 100000, 200000))
#Work out the UTM coordinates for first point
zone, e, n = redfearn(-34.5, 150.66667)
#Read output file 'small.sww'
#fid = NetCDFFile('small.sww', netcdf_mode_r)
fid = NetCDFFile(self.test_MOST_file + '.sww')
x = fid.variables['x'][:]
y = fid.variables['y'][:]
#Check that first coordinate is correctly represented
assert num.allclose(x[0], e-100000)
assert num.allclose(y[0], n-200000)
fid.close()
#Cleanup
os.remove(self.test_MOST_file + '.sww')
def test_ferret2sww_2(self):
"""Test that georeferencing etc works when converting from
ferret format (lat/lon) to sww format (UTM)
"""
#The test file has
# LON = 150.66667, 150.83334, 151, 151.16667
# LAT = -34.5, -34.33333, -34.16667, -34 ;
# TIME = 0, 0.1, 0.6, 1.1, 1.6, 2.1 ;
#
# First value (index=0) in small_ha.nc is 0.3400644 cm,
# Fourth value (index==3) is -6.50198 cm
from anuga.coordinate_transforms.redfearn import redfearn
#fid = NetCDFFile('small_ha.nc')
fid = NetCDFFile(self.test_MOST_file + '_ha.nc')
#Pick a coordinate and a value
time_index = 1
lat_index = 0
lon_index = 2
test_value = fid.variables['HA'][:][time_index, lat_index, lon_index]
test_time = fid.variables['TIME'][:][time_index]
test_lat = fid.variables['LAT'][:][lat_index]
test_lon = fid.variables['LON'][:][lon_index]
linear_point_index = lat_index * 4 + lon_index
fid.close()
#Call conversion (with zero origin)
ferret2sww(self.test_MOST_file,
verbose=self.verbose,
origin=(56, 0, 0))
#Work out the UTM coordinates for test point
zone, e, n = redfearn(test_lat, test_lon)
#Read output file 'small.sww'
fid = NetCDFFile(self.test_MOST_file + '.sww')
x = fid.variables['x'][:]
y = fid.variables['y'][:]
#Check that test coordinate is correctly represented
assert num.allclose(x[linear_point_index], e)
assert num.allclose(y[linear_point_index], n)
#Check test value
stage = fid.variables['stage'][:]
assert num.allclose(stage[time_index, linear_point_index],
old_div(test_value, 100))
fid.close()
#Cleanup
import os
os.remove(self.test_MOST_file + '.sww')
def test_ferret2sww_2(self):
"""Test that georeferencing etc works when converting from
ferret format (lat/lon) to sww format (UTM)
"""
#The test file has
# LON = 150.66667, 150.83334, 151, 151.16667
# LAT = -34.5, -34.33333, -34.16667, -34 ;
# TIME = 0, 0.1, 0.6, 1.1, 1.6, 2.1 ;
#
# First value (index=0) in small_ha.nc is 0.3400644 cm,
# Fourth value (index==3) is -6.50198 cm
from anuga.coordinate_transforms.redfearn import redfearn
#fid = NetCDFFile('small_ha.nc')
fid = NetCDFFile(self.test_MOST_file + '_ha.nc')
#Pick a coordinate and a value
time_index = 1
lat_index = 0
lon_index = 2
test_value = fid.variables['HA'][:][time_index, lat_index, lon_index]
test_time = fid.variables['TIME'][:][time_index]
test_lat = fid.variables['LAT'][:][lat_index]
test_lon = fid.variables['LON'][:][lon_index]
linear_point_index = lat_index*4 + lon_index
fid.close()
#Call conversion (with zero origin)
ferret2sww(self.test_MOST_file, verbose=self.verbose,
origin = (56, 0, 0))
#Work out the UTM coordinates for test point
zone, e, n = redfearn(test_lat, test_lon)
#Read output file 'small.sww'
fid = NetCDFFile(self.test_MOST_file + '.sww')
x = fid.variables['x'][:]
y = fid.variables['y'][:]
#Check that test coordinate is correctly represented
assert num.allclose(x[linear_point_index], e)
assert num.allclose(y[linear_point_index], n)
#Check test value
stage = fid.variables['stage'][:]
assert num.allclose(stage[time_index, linear_point_index], test_value/100)
fid.close()
#Cleanup
import os
os.remove(self.test_MOST_file + '.sww')
def test_ferret2sww_nz_origin(self):
from anuga.coordinate_transforms.redfearn import redfearn
#Call conversion (with nonzero origin)
ferret2sww(self.test_MOST_file,
verbose=self.verbose,
origin=(56, 100000, 200000))
#Work out the UTM coordinates for first point
zone, e, n = redfearn(-34.5, 150.66667)
#Read output file 'small.sww'
#fid = NetCDFFile('small.sww', netcdf_mode_r)
fid = NetCDFFile(self.test_MOST_file + '.sww')
x = fid.variables['x'][:]
y = fid.variables['y'][:]
#Check that first coordinate is correctly represented
assert num.allclose(x[0], e - 100000)
assert num.allclose(y[0], n - 200000)
fid.close()
#Cleanup
os.remove(self.test_MOST_file + '.sww')
def test_ferret2sww_lat_longII(self):
# Test that min lat long works
#The test file has
# LON = 150.66667, 150.83334, 151, 151.16667
# LAT = -34.5, -34.33333, -34.16667, -34 ;
#Read
from anuga.coordinate_transforms.redfearn import redfearn
fid = NetCDFFile(self.test_MOST_file + '_ha.nc')
first_value = fid.variables['HA'][:][0, 0, 0]
fourth_value = fid.variables['HA'][:][0, 0, 3]
fid.close()
#Call conversion (with zero origin)
#ferret2sww('small', verbose=False,
# origin = (56, 0, 0))
ferret2sww(self.test_MOST_file,
verbose=False,
origin=(56, 0, 0),
minlat=-34.4,
maxlat=-34.2)
#Work out the UTM coordinates for first point
zone, e, n = redfearn(-34.5, 150.66667)
#print zone, e, n
#Read output file 'small.sww'
#fid = NetCDFFile('small.sww')
fid = NetCDFFile(self.test_MOST_file + '.sww')
x = fid.variables['x'][:]
y = fid.variables['y'][:]
#Check that first coordinate is correctly represented
assert 12 == len(x)
fid.close()
#Cleanup
import os
os.remove(self.test_MOST_file + '.sww')
def keep_point(lat, long, seg, max_distance):
"""
seg is two points, UTM
"""
from math import sqrt
_, x0, y0 = redfearn(lat, long)
x1 = seg[0][0]
y1 = seg[0][1]
x2 = seg[1][0]
y2 = seg[1][1]
x2_1 = x2 - x1
y2_1 = y2 - y1
num = (x2_1) * (x2_1) + (y2_1) * (y2_1)
if sqrt(num) == 0 and abs(num) == 0:
return True
else:
d = old_div(abs((x2_1) * (y1 - y0) - (x1 - x0) * (y2_1)), num)
return d <= max_distance
def keep_point(lat, long, seg, max_distance):
"""
seg is two points, UTM
"""
from math import sqrt
_ , x0, y0 = redfearn(lat, long)
x1 = seg[0][0]
y1 = seg[0][1]
x2 = seg[1][0]
y2 = seg[1][1]
x2_1 = x2-x1
y2_1 = y2-y1
num = (x2_1)*(x2_1)+(y2_1)*(y2_1)
if sqrt(num) == 0 and abs(num) == 0:
return True
else:
d = abs((x2_1)*(y1-y0)-(x1-x0)*(y2_1))/num
return d <= max_distance
def test_ferret2sww_lat_longII(self):
# Test that min lat long works
#The test file has
# LON = 150.66667, 150.83334, 151, 151.16667
# LAT = -34.5, -34.33333, -34.16667, -34 ;
#Read
from anuga.coordinate_transforms.redfearn import redfearn
fid = NetCDFFile(self.test_MOST_file + '_ha.nc')
first_value = fid.variables['HA'][:][0,0,0]
fourth_value = fid.variables['HA'][:][0,0,3]
fid.close()
#Call conversion (with zero origin)
#ferret2sww('small', verbose=False,
# origin = (56, 0, 0))
ferret2sww(self.test_MOST_file, verbose=False,
origin = (56, 0, 0), minlat=-34.4, maxlat=-34.2)
#Work out the UTM coordinates for first point
zone, e, n = redfearn(-34.5, 150.66667)
#print zone, e, n
#Read output file 'small.sww'
#fid = NetCDFFile('small.sww')
fid = NetCDFFile(self.test_MOST_file + '.sww')
x = fid.variables['x'][:]
y = fid.variables['y'][:]
#Check that first coordinate is correctly represented
assert 12 == len(x)
fid.close()
#Cleanup
import os
os.remove(self.test_MOST_file + '.sww')
def ferret2sww(
basename_in,
name_out=None,
verbose=False,
minlat=None,
maxlat=None,
minlon=None,
maxlon=None,
mint=None,
maxt=None,
mean_stage=0,
origin=None,
zscale=1,
fail_on_NaN=True,
NaN_filler=0,
elevation=None,
inverted_bathymetry=True): #FIXME: Bathymetry should be obtained
#from MOST somehow.
#Alternatively from elsewhere
#or, as a last resort,
#specified here.
#The value of -100 will work
#for the Wollongong tsunami
#scenario but is very hacky
"""Convert MOST and 'Ferret' NetCDF format for wave propagation to
sww format native to abstract_2d_finite_volumes.
Specify only basename_in and read files of the form
basefilename_ha.nc, basefilename_ua.nc, basefilename_va.nc containing
relative height, x-velocity and y-velocity, respectively.
Also convert latitude and longitude to UTM. All coordinates are
assumed to be given in the GDA94 datum.
min's and max's: If omitted - full extend is used.
To include a value min may equal it, while max must exceed it.
Lat and lon are assuemd to be in decimal degrees
origin is a 3-tuple with geo referenced
UTM coordinates (zone, easting, northing)
nc format has values organised as HA[TIME, LATITUDE, LONGITUDE]
which means that longitude is the fastest
varying dimension (row major order, so to speak)
ferret2sww uses grid points as vertices in a triangular grid
counting vertices from lower left corner upwards, then right
"""
from anuga.file.netcdf import NetCDFFile
_assert_lat_long(minlat, maxlat, minlon, maxlon)
if name_out != None and name_out[-4:] != '.sww':
raise IOError('Output file %s should be of type .sww.' % name_out)
# Get NetCDF data
if verbose: log.critical('Reading files %s_*.nc' % basename_in)
# Wave amplitude (cm)
file_h = NetCDFFile(basename_in + '_ha.nc', netcdf_mode_r)
# Velocity (x) (cm/s)
file_u = NetCDFFile(basename_in + '_ua.nc', netcdf_mode_r)
# Velocity (y) (cm/s)
file_v = NetCDFFile(basename_in + '_va.nc', netcdf_mode_r)
# Elevation (z) (m)
file_e = NetCDFFile(basename_in + '_e.nc', netcdf_mode_r)
if name_out is None:
swwname = basename_in + '.sww'
else:
swwname = name_out
# Get dimensions of file_h
for dimension in list(file_h.dimensions.keys()):
if dimension[:3] == 'LON':
dim_h_longitude = dimension
if dimension[:3] == 'LAT':
dim_h_latitude = dimension
if dimension[:4] == 'TIME':
dim_h_time = dimension
times = file_h.variables[dim_h_time]
latitudes = file_h.variables[dim_h_latitude]
longitudes = file_h.variables[dim_h_longitude]
kmin, kmax, lmin, lmax = get_min_max_indices(latitudes[:], longitudes[:],
minlat, maxlat, minlon,
maxlon)
# get dimensions for file_e
for dimension in list(file_e.dimensions.keys()):
if dimension[:3] == 'LON':
dim_e_longitude = dimension
if dimension[:3] == 'LAT':
dim_e_latitude = dimension
# get dimensions for file_u
for dimension in list(file_u.dimensions.keys()):
if dimension[:3] == 'LON':
dim_u_longitude = dimension
if dimension[:3] == 'LAT':
dim_u_latitude = dimension
# get dimensions for file_v
for dimension in list(file_v.dimensions.keys()):
if dimension[:3] == 'LON':
dim_v_longitude = dimension
if dimension[:3] == 'LAT':
dim_v_latitude = dimension
# Precision used by most for lat/lon is 4 or 5 decimals
e_lat = num.around(file_e.variables[dim_e_latitude][:], 5)
e_lon = num.around(file_e.variables[dim_e_longitude][:], 5)
# Check that files are compatible
assert num.allclose(latitudes, file_u.variables[dim_u_latitude])
assert num.allclose(latitudes, file_v.variables[dim_v_latitude])
assert num.allclose(latitudes, e_lat)
assert num.allclose(longitudes, file_u.variables[dim_u_longitude])
assert num.allclose(longitudes, file_v.variables[dim_v_longitude])
assert num.allclose(longitudes, e_lon)
if mint is None:
jmin = 0
mint = times[0]
else:
jmin = num.searchsorted(times, mint)
# numpy.int32 didn't work in slicing of amplitude below
jmin = int(jmin)
if maxt is None:
jmax = len(times)
maxt = times[-1]
else:
jmax = num.searchsorted(times, maxt)
# numpy.int32 didn't work in slicing of amplitude below
jmax = int(jmax)
kmin, kmax, lmin, lmax = get_min_max_indices(latitudes[:], longitudes[:],
minlat, maxlat, minlon,
maxlon)
times = times[jmin:jmax]
latitudes = latitudes[kmin:kmax]
longitudes = longitudes[lmin:lmax]
if verbose: log.critical('cropping')
zname = 'ELEVATION'
amplitudes = file_h.variables['HA'][jmin:jmax, kmin:kmax, lmin:lmax]
uspeed = file_u.variables['UA'][jmin:jmax, kmin:kmax, lmin:lmax] #Lon
vspeed = file_v.variables['VA'][jmin:jmax, kmin:kmax, lmin:lmax] #Lat
elevations = file_e.variables[zname][kmin:kmax, lmin:lmax]
# Get missing values
nan_ha = file_h.variables['HA'].missing_value
nan_ua = file_u.variables['UA'].missing_value
nan_va = file_v.variables['VA'].missing_value
if hasattr(file_e.variables[zname], 'missing_value'):
nan_e = file_e.variables[zname].missing_value
else:
nan_e = None
# Cleanup
missing = (amplitudes == nan_ha)
if num.sometrue(missing):
if fail_on_NaN:
msg = 'NetCDFFile %s contains missing values' \
% basename_in + '_ha.nc'
raise_(DataMissingValuesError, msg)
else:
amplitudes = amplitudes * (missing == 0) + missing * NaN_filler
missing = (uspeed == nan_ua)
if num.sometrue(missing):
if fail_on_NaN:
msg = 'NetCDFFile %s contains missing values' \
% basename_in + '_ua.nc'
raise_(DataMissingValuesError, msg)
else:
uspeed = uspeed * (missing == 0) + missing * NaN_filler
missing = (vspeed == nan_va)
if num.sometrue(missing):
if fail_on_NaN:
msg = 'NetCDFFile %s contains missing values' \
% basename_in + '_va.nc'
raise_(DataMissingValuesError, msg)
else:
vspeed = vspeed * (missing == 0) + missing * NaN_filler
missing = (elevations == nan_e)
if num.sometrue(missing):
if fail_on_NaN:
msg = 'NetCDFFile %s contains missing values' \
% basename_in + '_e.nc'
raise_(DataMissingValuesError, msg)
else:
elevations = elevations * (missing == 0) + missing * NaN_filler
number_of_times = times.shape[0]
number_of_latitudes = latitudes.shape[0]
number_of_longitudes = longitudes.shape[0]
assert amplitudes.shape[0] == number_of_times
assert amplitudes.shape[1] == number_of_latitudes
assert amplitudes.shape[2] == number_of_longitudes
if verbose:
_show_stats((latitudes, longitudes), times, amplitudes, \
(uspeed, vspeed), elevations)
# print number_of_latitudes, number_of_longitudes
number_of_points = number_of_latitudes * number_of_longitudes
number_of_volumes = (number_of_latitudes - 1) * (number_of_longitudes -
1) * 2
file_h.close()
file_u.close()
file_v.close()
file_e.close()
# NetCDF file definition
outfile = NetCDFFile(swwname, netcdf_mode_w)
description = 'Converted from Ferret files: %s, %s, %s, %s' \
% (basename_in + '_ha.nc',
basename_in + '_ua.nc',
basename_in + '_va.nc',
basename_in + '_e.nc')
# Create new file
starttime = times[0]
sww = Write_sww(['elevation'], ['stage', 'xmomentum', 'ymomentum'])
sww.store_header(outfile,
times,
number_of_volumes,
number_of_points,
description=description,
verbose=verbose,
sww_precision=netcdf_float)
# Store
from anuga.coordinate_transforms.redfearn import redfearn
x = num.zeros(number_of_points, num.float) #Easting
y = num.zeros(number_of_points, num.float) #Northing
if verbose:
log.critical('Making triangular grid')
# Check zone boundaries
refzone, _, _ = redfearn(latitudes[0], longitudes[0])
vertices = {}
i = 0
for k, lat in enumerate(latitudes): # Y direction
for l, lon in enumerate(longitudes): # X direction
vertices[l, k] = i
_, easting, northing = redfearn(lat, lon)
#msg = 'Zone boundary crossed at longitude =', lon
#assert zone == refzone, msg
#print '%7.2f %7.2f %8.2f %8.2f' %(lon, lat, easting, northing)
x[i] = easting
y[i] = northing
i += 1
#Construct 2 triangles per 'rectangular' element
volumes = []
for l in range(number_of_longitudes - 1): # X direction
for k in range(number_of_latitudes - 1): # Y direction
v1 = vertices[l, k + 1]
v2 = vertices[l, k]
v3 = vertices[l + 1, k + 1]
v4 = vertices[l + 1, k]
volumes.append([v1, v2, v3]) #Upper element
volumes.append([v4, v3, v2]) #Lower element
volumes = num.array(volumes, num.int) #array default#
if origin is None:
origin = Geo_reference(refzone, min(x), min(y))
geo_ref = write_NetCDF_georeference(origin, outfile)
if elevation is not None:
z = elevation
else:
if inverted_bathymetry:
z = -1 * elevations
else:
z = elevations
#FIXME: z should be obtained from MOST and passed in here
#FIXME use the Write_sww instance(sww) to write this info
z = num.resize(z, outfile.variables['elevation'][:].shape)
outfile.variables['x'][:] = x - geo_ref.get_xllcorner()
outfile.variables['y'][:] = y - geo_ref.get_yllcorner()
#outfile.variables['z'][:] = z #FIXME HACK for bacwards compat.
outfile.variables['elevation'][:] = z
outfile.variables['volumes'][:] = volumes.astype(
num.int32) #For Opteron 64
#Time stepping
stage = outfile.variables['stage']
xmomentum = outfile.variables['xmomentum']
ymomentum = outfile.variables['ymomentum']
if verbose:
log.critical('Converting quantities')
n = len(times)
for j in range(n):
if verbose and j % (old_div((n + 10), 10)) == 0:
log.critical(' Doing %d of %d' % (j, n))
i = 0
for k in range(number_of_latitudes): # Y direction
for l in range(number_of_longitudes): # X direction
w = old_div(zscale * amplitudes[j, k, l], 100) + mean_stage
stage[j, i] = w
h = w - z[i]
xmomentum[j, i] = old_div(uspeed[j, k, l], 100) * h
ymomentum[j, i] = old_div(vspeed[j, k, l], 100) * h
i += 1
#outfile.close()
#FIXME: Refactor using code from file_function.statistics
#Something like print swwstats(swwname)
if verbose:
time_info = times, starttime, mint, maxt
_show_sww_stats(outfile, swwname, geo_ref, time_info)
outfile.close()
def test_file_boundary_stsIV_sinewave_ordering(self):
"""test_file_boundary_stsIV_sinewave_ordering(self):
Read correct points from ordering file and apply sts to boundary
This one uses a sine wave and compares to time boundary
"""
lat_long_points=[[6.01, 97.0], [6.02, 97.0], [6.05, 96.9], [6.0, 97.0]]
bounding_polygon=[[6.0, 97.0], [6.01, 97.0], [6.02,97.0], \
[6.02,97.02], [6.00,97.02]]
tide = 0.35
time_step_count = 50
time_step = 0.1
times_ref = num.arange(0, time_step_count*time_step, time_step)
n=len(lat_long_points)
first_tstep=num.ones(n,num.int)
last_tstep=(time_step_count)*num.ones(n,num.int)
gauge_depth=20*num.ones(n,num.float)
ha1=num.ones((n,time_step_count),num.float)
ua1=3.*num.ones((n,time_step_count),num.float)
va1=2.*num.ones((n,time_step_count),num.float)
for i in range(n):
ha1[i]=num.sin(times_ref)
base_name, files = self.write_mux2(lat_long_points,
time_step_count, time_step,
first_tstep, last_tstep,
depth=gauge_depth,
ha=ha1,
ua=ua1,
va=va1)
# Write order file
file_handle, order_base_name = tempfile.mkstemp("")
os.close(file_handle)
os.remove(order_base_name)
d=","
order_file=order_base_name+'order.txt'
fid=open(order_file,'w')
# Write Header
header='index, longitude, latitude\n'
fid.write(header)
indices=[3,0,1]
for i in indices:
line=str(i)+d+str(lat_long_points[i][1])+d+\
str(lat_long_points[i][0])+"\n"
fid.write(line)
fid.close()
sts_file=base_name
urs2sts(base_name, basename_out=sts_file,
ordering_filename=order_file,
mean_stage=tide,
verbose=False)
self.delete_mux(files)
# Now read the sts file and check that values have been stored correctly.
fid = NetCDFFile(sts_file + '.sts')
# Check the time vector
times = fid.variables['time'][:]
#print times
# Check sts quantities
stage = fid.variables['stage'][:]
xmomentum = fid.variables['xmomentum'][:]
ymomentum = fid.variables['ymomentum'][:]
elevation = fid.variables['elevation'][:]
# Create beginnings of boundary polygon based on sts_boundary
boundary_polygon = create_sts_boundary(base_name)
os.remove(order_file)
# Append the remaining part of the boundary polygon to be defined by
# the user
bounding_polygon_utm=[]
for point in bounding_polygon:
zone,easting,northing=redfearn(point[0],point[1])
bounding_polygon_utm.append([easting,northing])
boundary_polygon.append(bounding_polygon_utm[3])
boundary_polygon.append(bounding_polygon_utm[4])
#print 'boundary_polygon', boundary_polygon
plot=False
if plot:
from pylab import plot,show,axis
boundary_polygon=ensure_numeric(boundary_polygon)
bounding_polygon_utm=ensure_numeric(bounding_polygon_utm)
#plot(lat_long_points[:,0],lat_long_points[:,1],'o')
plot(boundary_polygon[:,0], boundary_polygon[:,1])
plot(bounding_polygon_utm[:,0],bounding_polygon_utm[:,1])
show()
assert num.allclose(bounding_polygon_utm,boundary_polygon)
extent_res=1000000
meshname = 'urs_test_mesh' + '.tsh'
interior_regions=None
boundary_tags={'ocean': [0,1], 'otherocean': [2,3,4]}
# have to change boundary tags from last example because now bounding
# polygon starts in different place.
create_mesh_from_regions(boundary_polygon,
boundary_tags=boundary_tags,
maximum_triangle_area=extent_res,
filename=meshname,
interior_regions=interior_regions,
verbose=False)
domain_fbound = Domain(meshname)
domain_fbound.set_quantity('stage', tide)
Bf = File_boundary(sts_file+'.sts',
domain_fbound,
boundary_polygon=boundary_polygon)
Br = Reflective_boundary(domain_fbound)
domain_fbound.set_boundary({'ocean': Bf,'otherocean': Br})
finaltime=time_step*(time_step_count-1)
yieldstep=time_step
temp_fbound=num.zeros(int(finaltime/yieldstep)+1,num.float)
for i, t in enumerate(domain_fbound.evolve(yieldstep=yieldstep,
finaltime=finaltime,
skip_initial_step=False)):
temp_fbound[i]=domain_fbound.quantities['stage'].centroid_values[2]
domain_time = Domain(meshname)
domain_time.set_quantity('stage', tide)
Br = Reflective_boundary(domain_time)
Bw = Time_boundary(domain=domain_time,
function=lambda t: [num.sin(t)+tide,3.*(20.+num.sin(t)+tide),2.*(20.+num.sin(t)+tide)])
domain_time.set_boundary({'ocean': Bw,'otherocean': Br})
temp_time=num.zeros(int(finaltime/yieldstep)+1,num.float)
domain_time.set_starttime(domain_fbound.get_starttime())
for i, t in enumerate(domain_time.evolve(yieldstep=yieldstep,
finaltime=finaltime,
skip_initial_step=False)):
temp_time[i]=domain_time.quantities['stage'].centroid_values[2]
assert num.allclose(temp_fbound, temp_time)
assert num.allclose(domain_fbound.quantities['stage'].vertex_values,
domain_time.quantities['stage'].vertex_values)
assert num.allclose(domain_fbound.quantities['xmomentum'].vertex_values,
domain_time.quantities['xmomentum'].vertex_values)
assert num.allclose(domain_fbound.quantities['ymomentum'].vertex_values,
domain_time.quantities['ymomentum'].vertex_values)
try:
os.remove(sts_file+'.sts')
except:
# Windoze can't remove this file for some reason
pass
os.remove(meshname)
def sequential_time_varying_file_boundary_sts(self):
"""sequential_ltest_time_varying_file_boundary_sts_sequential(self):
Read correct points from ordering file and apply sts to boundary. The boundary is time varying. FIXME add to test_urs2sts.
"""
lat_long_points=[[6.01,97.0],[6.02,97.0],[6.05,96.9],[6.0,97.0]]
bounding_polygon=[[6.0,97.0],[6.01,97.0],[6.02,97.0],
[6.02,97.02],[6.00,97.02]]
tide = 3.0
time_step_count = 65
time_step = 2.
n=len(lat_long_points)
first_tstep=num.ones(n,num.int)
last_tstep=(time_step_count)*num.ones(n,num.int)
finaltime=num.float(time_step*(time_step_count-1))
yieldstep=num.float(time_step)
gauge_depth=20*num.ones(n,num.float)
ha=2*num.ones((n,time_step_count),num.float)
ua=10*num.ones((n,time_step_count),num.float)
va=-10*num.ones((n,time_step_count),num.float)
times=num.arange(0., num.float(time_step_count*time_step), time_step)
for i in range(n):
#ha[i]+=num.sin(times)
ha[i]+=times/finaltime
sts_file="test"
if myid==0:
base_name, files = self.write_mux2(lat_long_points,
time_step_count,
time_step,
first_tstep,
last_tstep,
depth=gauge_depth,
ha=ha,
ua=ua,
va=va)
# base name will not exist, but 3 other files are created
# Write order file
file_handle, order_base_name = tempfile.mkstemp("")
os.close(file_handle)
os.remove(order_base_name)
d=","
order_file=order_base_name+'order.txt'
fid=open(order_file,'w')
# Write Header
header='index, longitude, latitude\n'
fid.write(header)
indices=[3,0,1]
for i in indices:
line=str(i)+d+str(lat_long_points[i][1])+d+\
str(lat_long_points[i][0])+"\n"
fid.write(line)
fid.close()
urs2sts(base_name,
basename_out=sts_file,
ordering_filename=order_file,
mean_stage=tide,
verbose=verbose)
self.delete_mux(files)
assert(os.access(sts_file+'.sts', os.F_OK))
os.remove(order_file)
barrier()
boundary_polygon = create_sts_boundary(sts_file)
# Append the remaining part of the boundary polygon to be defined by
# the user
bounding_polygon_utm=[]
for point in bounding_polygon:
zone,easting,northing=redfearn(point[0],point[1])
bounding_polygon_utm.append([easting,northing])
boundary_polygon.append(bounding_polygon_utm[3])
boundary_polygon.append(bounding_polygon_utm[4])
assert num.allclose(bounding_polygon_utm,boundary_polygon)
extent_res=1000000
meshname = 'urs_test_mesh' + '.tsh'
interior_regions=None
boundary_tags={'ocean': [0,1], 'otherocean': [2,3,4]}
# have to change boundary tags from last example because now bounding
# polygon starts in different place.
if myid==0:
create_mesh_from_regions(boundary_polygon,
boundary_tags=boundary_tags,
maximum_triangle_area=extent_res,
filename=meshname,
interior_regions=interior_regions,
verbose=verbose)
barrier()
domain_fbound = Domain(meshname)
domain_fbound.set_quantities_to_be_stored(None)
domain_fbound.set_quantity('stage', tide)
if verbose: print "Creating file boundary condition"
Bf = File_boundary(sts_file+'.sts',
domain_fbound,
boundary_polygon=boundary_polygon)
Br = Reflective_boundary(domain_fbound)
domain_fbound.set_boundary({'ocean': Bf,'otherocean': Br})
temp_fbound=num.zeros(int(finaltime/yieldstep)+1,num.float)
if verbose: print "Evolving domain with file boundary condition"
for i, t in enumerate(domain_fbound.evolve(yieldstep=yieldstep,
finaltime=finaltime,
skip_initial_step = False)):
temp_fbound[i]=domain_fbound.quantities['stage'].centroid_values[2]
if verbose: domain_fbound.write_time()
domain_drchlt = Domain(meshname)
domain_drchlt.set_quantities_to_be_stored(None)
domain_drchlt.set_starttime(time_step)
domain_drchlt.set_quantity('stage', tide)
Br = Reflective_boundary(domain_drchlt)
#Bd = Dirichlet_boundary([2.0+tide,220+10*tide,-220-10*tide])
Bd = Time_boundary(domain=domain_drchlt, f=lambda t: [2.0+t/finaltime+tide,220.+10.*tide+10.*t/finaltime,-220.-10.*tide-10.*t/finaltime])
#Bd = Time_boundary(domain=domain_drchlt,f=lambda t: [2.0+num.sin(t)+tide,10.*(2+20.+num.sin(t)+tide),-10.*(2+20.+num.sin(t)+tide)])
domain_drchlt.set_boundary({'ocean': Bd,'otherocean': Br})
temp_drchlt=num.zeros(int(finaltime/yieldstep)+1,num.float)
for i, t in enumerate(domain_drchlt.evolve(yieldstep=yieldstep,
finaltime=finaltime,
skip_initial_step = False)):
temp_drchlt[i]=domain_drchlt.quantities['stage'].centroid_values[2]
#domain_drchlt.write_time()
#print domain_fbound.quantities['stage'].vertex_values
#print domain_drchlt.quantities['stage'].vertex_values
assert num.allclose(temp_fbound,temp_drchlt),temp_fbound-temp_drchlt
assert num.allclose(domain_fbound.quantities['stage'].vertex_values,
domain_drchlt.quantities['stage'].vertex_values)
assert num.allclose(domain_fbound.quantities['xmomentum'].vertex_values,
domain_drchlt.quantities['xmomentum'].vertex_values)
assert num.allclose(domain_fbound.quantities['ymomentum'].vertex_values,
domain_drchlt.quantities['ymomentum'].vertex_values)
if not sys.platform == 'win32':
if myid==0: os.remove(sts_file+'.sts')
if myid==0: os.remove(meshname)
def esri2sww(bath_dir,
elevation_dir,
ucur_dir,
vcur_dir,
sww_file,
minlat=None,
maxlat=None,
minlon=None,
maxlon=None,
zscale=1,
mean_stage=0,
fail_on_NaN=True,
elevation_NaN_filler=0,
bath_prefix='ba',
elevation_prefix='el',
verbose=False):
"""
Produce an sww boundary file, from esri ascii data from CSIRO.
Also convert latitude and longitude to UTM. All coordinates are
assumed to be given in the GDA94 datum.
assume:
All files are in esri ascii format
4 types of information
bathymetry
elevation
u velocity
v velocity
Assumptions
The metadata of all the files is the same
Each type is in a seperate directory
One bath file with extention .000
The time period is less than 24hrs and uniform.
"""
from anuga.file.netcdf import NetCDFFile
from anuga.coordinate_transforms.redfearn import redfearn
if sww_file[-4:] != '.sww':
raise IOError('Output file %s should be of type .sww.' % sww_file)
# So if we want to change the precision it's done here
precision = netcdf_float
# go in to the bath dir and load the only file,
bath_files = os.listdir(bath_dir)
bath_file = bath_files[0]
bath_dir_file = bath_dir + os.sep + bath_file
bath_metadata, bath_grid = _read_asc(bath_dir_file)
#Use the date.time of the bath file as a basis for
#the start time for other files
base_start = bath_file[-12:]
#go into the elevation dir and load the 000 file
elevation_dir_file = elevation_dir + os.sep + elevation_prefix \
+ base_start
elevation_files = os.listdir(elevation_dir)
ucur_files = os.listdir(ucur_dir)
vcur_files = os.listdir(vcur_dir)
elevation_files.sort()
# the first elevation file should be the
# file with the same base name as the bath data
assert elevation_files[0] == 'el' + base_start
number_of_latitudes = bath_grid.shape[0]
number_of_longitudes = bath_grid.shape[1]
number_of_volumes = (number_of_latitudes - 1) * (number_of_longitudes -
1) * 2
longitudes = [bath_metadata['xllcorner'] + x*bath_metadata['cellsize'] \
for x in range(number_of_longitudes)]
latitudes = [bath_metadata['yllcorner'] + y*bath_metadata['cellsize'] \
for y in range(number_of_latitudes)]
# reverse order of lat, so the first lat represents the first grid row
latitudes.reverse()
kmin, kmax, lmin, lmax = get_min_max_indices(latitudes[:],
longitudes[:],
minlat=minlat,
maxlat=maxlat,
minlon=minlon,
maxlon=maxlon)
bath_grid = bath_grid[kmin:kmax, lmin:lmax]
latitudes = latitudes[kmin:kmax]
longitudes = longitudes[lmin:lmax]
number_of_latitudes = len(latitudes)
number_of_longitudes = len(longitudes)
number_of_times = len(os.listdir(elevation_dir))
number_of_points = number_of_latitudes * number_of_longitudes
number_of_volumes = (number_of_latitudes - 1) * (number_of_longitudes -
1) * 2
#Work out the times
if len(elevation_files) > 1:
# Assume: The time period is less than 24hrs.
time_period = (int(elevation_files[1][-3:]) \
- int(elevation_files[0][-3:])) * 60*60
times = [x * time_period for x in range(len(elevation_files))]
else:
times = [0.0]
if verbose:
log.critical('------------------------------------------------')
log.critical('Statistics:')
log.critical(' Extent (lat/lon):')
log.critical(' lat in [%f, %f], len(lat) == %d' %
(min(latitudes), max(latitudes), len(latitudes)))
log.critical(' lon in [%f, %f], len(lon) == %d' %
(min(longitudes), max(longitudes), len(longitudes)))
log.critical(' t in [%f, %f], len(t) == %d' %
(min(times), max(times), len(times)))
######### WRITE THE SWW FILE #############
# NetCDF file definition
outfile = NetCDFFile(sww_file, netcdf_mode_w)
#Create new file
outfile.institution = 'Geoscience Australia'
outfile.description = 'Converted from XXX'
#For sww compatibility
outfile.smoothing = 'Yes'
outfile.order = 1
#Start time in seconds since the epoch (midnight 1/1/1970)
outfile.starttime = starttime = times[0]
# dimension definitions
outfile.createDimension('number_of_volumes', number_of_volumes)
outfile.createDimension('number_of_vertices', 3)
outfile.createDimension('number_of_points', number_of_points)
outfile.createDimension('number_of_timesteps', number_of_times)
# variable definitions
outfile.createVariable('x', precision, ('number_of_points', ))
outfile.createVariable('y', precision, ('number_of_points', ))
outfile.createVariable('elevation', precision, ('number_of_points', ))
#FIXME: Backwards compatibility
#outfile.createVariable('z', precision, ('number_of_points',))
#################################
outfile.createVariable('volumes', netcdf_int,
('number_of_volumes', 'number_of_vertices'))
outfile.createVariable('time', precision, ('number_of_timesteps', ))
outfile.createVariable('stage', precision,
('number_of_timesteps', 'number_of_points'))
outfile.createVariable('xmomentum', precision,
('number_of_timesteps', 'number_of_points'))
outfile.createVariable('ymomentum', precision,
('number_of_timesteps', 'number_of_points'))
#Store
from anuga.coordinate_transforms.redfearn import redfearn
x = num.zeros(number_of_points, num.float) #Easting
y = num.zeros(number_of_points, num.float) #Northing
if verbose: log.critical('Making triangular grid')
#Get zone of 1st point.
refzone, _, _ = redfearn(latitudes[0], longitudes[0])
vertices = {}
i = 0
for k, lat in enumerate(latitudes):
for l, lon in enumerate(longitudes):
vertices[l, k] = i
zone, easting, northing = redfearn(lat, lon)
#msg = 'Zone boundary crossed at longitude =', lon
#assert zone == refzone, msg
#print '%7.2f %7.2f %8.2f %8.2f' %(lon, lat, easting, northing)
x[i] = easting
y[i] = northing
i += 1
#Construct 2 triangles per 'rectangular' element
volumes = []
for l in range(number_of_longitudes - 1): #X direction
for k in range(number_of_latitudes - 1): #Y direction
v1 = vertices[l, k + 1]
v2 = vertices[l, k]
v3 = vertices[l + 1, k + 1]
v4 = vertices[l + 1, k]
#Note, this is different to the ferrit2sww code
#since the order of the lats is reversed.
volumes.append([v1, v3, v2]) #Upper element
volumes.append([v4, v2, v3]) #Lower element
volumes = num.array(volumes, num.int) #array default#
geo_ref = Geo_reference(refzone, min(x), min(y))
geo_ref.write_NetCDF(outfile)
# This will put the geo ref in the middle
#geo_ref = Geo_reference(refzone, (max(x)+min(x))/2., (max(x)+min(y))/2.)
if verbose:
log.critical('------------------------------------------------')
log.critical('More Statistics:')
log.critical(' Extent (/lon):')
log.critical(' x in [%f, %f], len(lat) == %d' %
(min(x), max(x), len(x)))
log.critical(' y in [%f, %f], len(lon) == %d' %
(min(y), max(y), len(y)))
log.critical('geo_ref: ', geo_ref)
z = num.resize(bath_grid, outfile.variables['elevation'][:].shape)
outfile.variables['x'][:] = x - geo_ref.get_xllcorner()
outfile.variables['y'][:] = y - geo_ref.get_yllcorner()
# FIXME (Ole): Remove once viewer has been recompiled and changed
# to use elevation instead of z
#outfile.variables['z'][:] = z
outfile.variables['elevation'][:] = z
outfile.variables['volumes'][:] = volumes.astype(
num.int32) # On Opteron 64
stage = outfile.variables['stage']
xmomentum = outfile.variables['xmomentum']
ymomentum = outfile.variables['ymomentum']
outfile.variables['time'][:] = times #Store time relative
if verbose: log.critical('Converting quantities')
n = number_of_times
for j in range(number_of_times):
# load in files
elevation_meta, elevation_grid = \
_read_asc(elevation_dir + os.sep + elevation_files[j])
_, u_momentum_grid = _read_asc(ucur_dir + os.sep + ucur_files[j])
_, v_momentum_grid = _read_asc(vcur_dir + os.sep + vcur_files[j])
#cut matrix to desired size
elevation_grid = elevation_grid[kmin:kmax, lmin:lmax]
u_momentum_grid = u_momentum_grid[kmin:kmax, lmin:lmax]
v_momentum_grid = v_momentum_grid[kmin:kmax, lmin:lmax]
# handle missing values
missing = (elevation_grid == elevation_meta['NODATA_value'])
if num.sometrue(missing):
if fail_on_NaN:
msg = 'File %s contains missing values' \
% (elevation_files[j])
raise_(DataMissingValuesError, msg)
else:
elevation_grid = elevation_grid*(missing==0) \
+ missing*elevation_NaN_filler
if verbose and j % (old_div((n + 10), 10)) == 0:
log.critical(' Doing %d of %d' % (j, n))
i = 0
for k in range(number_of_latitudes): #Y direction
for l in range(number_of_longitudes): #X direction
w = zscale * elevation_grid[k, l] + mean_stage
stage[j, i] = w
h = w - z[i]
xmomentum[j, i] = u_momentum_grid[k, l] * h
ymomentum[j, i] = v_momentum_grid[k, l] * h
i += 1
outfile.close()
def test_ferret2sww1(self):
"""Test that georeferencing etc works when converting from
ferret format (lat/lon) to sww format (UTM)
"""
import os, sys
#The test file has
# LON = 150.66667, 150.83334, 151, 151.16667
# LAT = -34.5, -34.33333, -34.16667, -34 ;
# TIME = 0, 0.1, 0.6, 1.1, 1.6, 2.1 ;
#
# First value (index=0) in small_ha.nc is 0.3400644 cm,
# Fourth value (index==3) is -6.50198 cm
#Read
from anuga.coordinate_transforms.redfearn import redfearn
#fid = NetCDFFile(self.test_MOST_file)
fid = NetCDFFile(self.test_MOST_file + '_ha.nc')
first_value = fid.variables['HA'][:][0, 0, 0]
fourth_value = fid.variables['HA'][:][0, 0, 3]
fid.close()
#Call conversion (with zero origin)
#ferret2sww('small', verbose=False,
# origin = (56, 0, 0))
ferret2sww(self.test_MOST_file,
verbose=self.verbose,
origin=(56, 0, 0))
#Work out the UTM coordinates for first point
zone, e, n = redfearn(-34.5, 150.66667)
#print zone, e, n
#Read output file 'small.sww'
#fid = NetCDFFile('small.sww')
fid = NetCDFFile(self.test_MOST_file + '.sww')
x = fid.variables['x'][:]
y = fid.variables['y'][:]
#Check that first coordinate is correctly represented
assert num.allclose(x[0], e)
assert num.allclose(y[0], n)
#Check first value
stage = fid.variables['stage'][:]
xmomentum = fid.variables['xmomentum'][:]
ymomentum = fid.variables['ymomentum'][:]
#print ymomentum
assert num.allclose(stage[0, 0], old_div(first_value, 100)) #Meters
#Check fourth value
assert num.allclose(stage[0, 3], old_div(fourth_value, 100)) #Meters
fid.close()
#Cleanup
import os
os.remove(self.test_MOST_file + '.sww')
def urs2sts(basename_in, basename_out=None,
weights=None,
verbose=False,
origin=None,
zone=None,
central_meridian=None,
mean_stage=0.0,
zscale=1.0,
ordering_filename=None):
"""Convert URS mux2 format for wave propagation to sts format
Also convert latitude and longitude to UTM. All coordinates are
assumed to be given in the GDA94 datum
origin is a 3-tuple with geo referenced
UTM coordinates (zone, easting, northing)
inputs:
basename_in: list of source file prefixes
These are combined with the extensions:
WAVEHEIGHT_MUX2_LABEL = '-z-mux2' for stage
EAST_VELOCITY_MUX2_LABEL = '-e-mux2' xmomentum
NORTH_VELOCITY_MUX2_LABEL = '-n-mux2' and ymomentum
to create a 2D list of mux2 file. The rows are associated with each
quantity and must have the above extensions
the columns are the list of file prefixes.
ordering: a .txt file name specifying which mux2 gauge points are
to be stored. This is indicated by the index of the gauge
in the ordering file.
ordering file format:
1st line: 'index,longitude,latitude\n'
other lines: index,longitude,latitude
If ordering is None or ordering file is empty then
all points are taken in the order they
appear in the mux2 file.
output:
basename_out: name of sts file in which mux2 data is stored.
NOTE: South is positive in mux files so sign of y-component of velocity is reverted
"""
import os
from anuga.file.netcdf import NetCDFFile
from operator import __and__
if not isinstance(basename_in, list):
if verbose: log.critical('Reading single source')
basename_in = [basename_in]
# This is the value used in the mux file format to indicate NAN data
# FIXME (Ole): This should be changed everywhere to IEEE NAN when
# we upgrade to Numpy
NODATA = 99
# Check that basename is a list of strings
if not reduce(__and__, map(lambda z:isinstance(z,basestring), basename_in)):
msg= 'basename_in must be a string or list of strings'
raise Exception, msg
# Find the number of sources to be used
numSrc = len(basename_in)
# A weight must be specified for each source
if weights is None:
# Default is equal weighting
weights = num.ones(numSrc, num.float) / numSrc
else:
weights = ensure_numeric(weights)
msg = 'When combining multiple sources must specify a weight for ' \
'mux2 source file'
assert len(weights) == numSrc, msg
if verbose: log.critical('Weights used in urs2sts: %s' % str(weights))
# Check output filename
if basename_out is None:
msg = 'STS filename must be specified as basename_out ' \
'in function urs2sts'
raise Exception, msg
if basename_out.endswith('.sts'):
stsname = basename_out
else:
stsname = basename_out + '.sts'
# Create input filenames from basenames and check their existence
files_in = [[], [], []]
for files in basename_in:
files_in[0].append(files + WAVEHEIGHT_MUX2_LABEL),
files_in[1].append(files + EAST_VELOCITY_MUX2_LABEL)
files_in[2].append(files + NORTH_VELOCITY_MUX2_LABEL)
quantities = ['HA','UA','VA'] # Quantity names used in the MUX2 format
for i in range(len(quantities)):
for file_in in files_in[i]:
if (os.access(file_in, os.R_OK) == 0):
msg = 'File %s does not exist or is not accessible' % file_in
raise IOError, msg
# Establish permutation array
if ordering_filename is not None:
if verbose is True: log.critical('Reading ordering file %s'
% ordering_filename)
# Read ordering file
try:
fid = open(ordering_filename, 'r')
file_header = fid.readline().split(',')
ordering_lines = fid.readlines()
fid.close()
except:
msg = 'Cannot open %s' % ordering_filename
raise Exception, msg
reference_header = 'index, longitude, latitude\n'
reference_header_split = reference_header.split(',')
for i in range(3):
if not file_header[i].strip() == reference_header_split[i].strip():
msg = 'File must contain header: ' + reference_header
raise Exception, msg
if len(ordering_lines) < 2:
msg = 'File must contain at least two points'
raise Exception, msg
permutation = [int(line.split(',')[0]) for line in ordering_lines]
permutation = ensure_numeric(permutation)
else:
permutation = None
# Read MUX2 files
if (verbose): log.critical('reading mux2 file')
mux={}
times_old = 0.0
latitudes_old = 0.0
longitudes_old = 0.0
elevation_old = 0.0
starttime_old = 0.0
for i, quantity in enumerate(quantities):
# For each quantity read the associated list of source mux2 file with
# extention associated with that quantity
times, latitudes, longitudes, elevation, mux[quantity], starttime \
= read_mux2_py(files_in[i], weights, permutation, verbose=verbose)
# Check that all quantities have consistent time and space information
if quantity != quantities[0]:
msg = '%s, %s and %s have inconsistent gauge data' \
% (files_in[0], files_in[1], files_in[2])
assert num.allclose(times, times_old), msg
assert num.allclose(latitudes, latitudes_old), msg
assert num.allclose(longitudes, longitudes_old), msg
assert num.allclose(elevation, elevation_old), msg
assert num.allclose(starttime, starttime_old), msg
times_old = times
latitudes_old = latitudes
longitudes_old = longitudes
elevation_old = elevation
starttime_old = starttime
# Self check - can be removed to improve speed
#ref_longitudes = [float(line.split(',')[1]) for line in ordering_lines]
#ref_latitudes = [float(line.split(',')[2]) for line in ordering_lines]
#
#msg = 'Longitudes specified in ordering file do not match those ' \
# 'found in mux files. ' \
# 'I got %s instead of %s (only beginning shown)' \
# % (str(longitudes[:10]) + '...',
# str(ref_longitudes[:10]) + '...')
#assert allclose(longitudes, ref_longitudes), msg
#
#msg = 'Latitudes specified in ordering file do not match those ' \
# 'found in mux files. '
# 'I got %s instead of %s (only beginning shown)' \
# % (str(latitudes[:10]) + '...',
# str(ref_latitudes[:10]) + '...')
#assert allclose(latitudes, ref_latitudes), msg
# Store timeseries in STS file
msg = 'File is empty and or clipped region not in file region'
assert len(latitudes > 0), msg
number_of_points = latitudes.shape[0] # Number of stations retrieved
number_of_times = times.shape[0] # Number of timesteps
number_of_latitudes = latitudes.shape[0] # Number latitudes
number_of_longitudes = longitudes.shape[0] # Number longitudes
# The permutation vector of contains original indices
# as given in ordering file or None in which case points
# are assigned the trivial indices enumerating them from
# 0 to number_of_points-1
if permutation is None:
permutation = num.arange(number_of_points, dtype=num.int)
# NetCDF file definition
outfile = NetCDFFile(stsname, netcdf_mode_w)
description = 'Converted from URS mux2 files: %s' % basename_in
# Create new file
sts = Write_sts()
sts.store_header(outfile,
times+starttime,
number_of_points,
description=description,
verbose=verbose,
sts_precision=netcdf_float)
# Store
from anuga.coordinate_transforms.redfearn import redfearn
x = num.zeros(number_of_points, num.float) # Easting
y = num.zeros(number_of_points, num.float) # Northing
# Check zone boundaries
if zone is None:
refzone, _, _ = redfearn(latitudes[0], longitudes[0],
central_meridian=central_meridian)
else:
refzone = zone
old_zone = refzone
old_easting = 0.0
old_northing = 0.0
for i in range(number_of_points):
computed_zone, easting, northing = redfearn(latitudes[i], longitudes[i],
zone=zone,
central_meridian=central_meridian)
x[i] = easting
y[i] = northing
if computed_zone != refzone:
msg = 'All sts gauges need to be in the same zone. \n'
msg += 'offending gauge:Zone %d,%.4f, %4f\n' \
% (computed_zone, easting, northing)
msg += 'previous gauge:Zone %d,%.4f, %4f' \
% (old_zone, old_easting, old_northing)
raise Exception, msg
old_zone = computed_zone
old_easting = easting
old_northing = northing
if origin is None:
origin = Geo_reference(refzone, min(x), min(y))
geo_ref = write_NetCDF_georeference(origin, outfile)
elevation = num.resize(elevation, outfile.variables['elevation'][:].shape)
outfile.variables['permutation'][:] = permutation.astype(num.int32) # Opteron 64
outfile.variables['x'][:] = x - geo_ref.get_xllcorner()
outfile.variables['y'][:] = y - geo_ref.get_yllcorner()
outfile.variables['elevation'][:] = elevation
stage = outfile.variables['stage']
xmomentum = outfile.variables['xmomentum']
ymomentum = outfile.variables['ymomentum']
if verbose: log.critical('Converting quantities')
for j in range(len(times)):
for i in range(number_of_points):
ha = mux['HA'][i,j]
ua = mux['UA'][i,j]
va = mux['VA'][i,j]
if ha == NODATA:
if verbose:
msg = 'Setting nodata value %d to 0 at time = %f, ' \
'point = %d' % (ha, times[j], i)
log.critical(msg)
ha = 0.0
ua = 0.0
va = 0.0
w = zscale*ha + mean_stage
h = w - elevation[i]
stage[j,i] = w
xmomentum[j,i] = ua * h
ymomentum[j,i] = -va * h # South is positive in mux files
outfile.close()
if verbose:
log.critical('Wrote sts file ' + stsname)
def ferret2sww(basename_in, name_out=None,
verbose=False,
minlat=None, maxlat=None,
minlon=None, maxlon=None,
mint=None, maxt=None, mean_stage=0,
origin=None, zscale=1,
fail_on_NaN=True,
NaN_filler=0,
elevation=None,
inverted_bathymetry=True
): #FIXME: Bathymetry should be obtained
#from MOST somehow.
#Alternatively from elsewhere
#or, as a last resort,
#specified here.
#The value of -100 will work
#for the Wollongong tsunami
#scenario but is very hacky
"""Convert MOST and 'Ferret' NetCDF format for wave propagation to
sww format native to abstract_2d_finite_volumes.
Specify only basename_in and read files of the form
basefilename_ha.nc, basefilename_ua.nc, basefilename_va.nc containing
relative height, x-velocity and y-velocity, respectively.
Also convert latitude and longitude to UTM. All coordinates are
assumed to be given in the GDA94 datum.
min's and max's: If omitted - full extend is used.
To include a value min may equal it, while max must exceed it.
Lat and lon are assuemd to be in decimal degrees
origin is a 3-tuple with geo referenced
UTM coordinates (zone, easting, northing)
nc format has values organised as HA[TIME, LATITUDE, LONGITUDE]
which means that longitude is the fastest
varying dimension (row major order, so to speak)
ferret2sww uses grid points as vertices in a triangular grid
counting vertices from lower left corner upwards, then right
"""
from anuga.file.netcdf import NetCDFFile
_assert_lat_long(minlat, maxlat, minlon, maxlon)
if name_out != None and name_out[-4:] != '.sww':
raise IOError('Output file %s should be of type .sww.' % name_out)
# Get NetCDF data
if verbose: log.critical('Reading files %s_*.nc' % basename_in)
# Wave amplitude (cm)
file_h = NetCDFFile(basename_in + '_ha.nc', netcdf_mode_r)
# Velocity (x) (cm/s)
file_u = NetCDFFile(basename_in + '_ua.nc', netcdf_mode_r)
# Velocity (y) (cm/s)
file_v = NetCDFFile(basename_in + '_va.nc', netcdf_mode_r)
# Elevation (z) (m)
file_e = NetCDFFile(basename_in + '_e.nc', netcdf_mode_r)
if name_out is None:
swwname = basename_in + '.sww'
else:
swwname = name_out
# Get dimensions of file_h
for dimension in file_h.dimensions.keys():
if dimension[:3] == 'LON':
dim_h_longitude = dimension
if dimension[:3] == 'LAT':
dim_h_latitude = dimension
if dimension[:4] == 'TIME':
dim_h_time = dimension
times = file_h.variables[dim_h_time]
latitudes = file_h.variables[dim_h_latitude]
longitudes = file_h.variables[dim_h_longitude]
kmin, kmax, lmin, lmax = get_min_max_indices(latitudes[:],
longitudes[:],
minlat, maxlat,
minlon, maxlon)
# get dimensions for file_e
for dimension in file_e.dimensions.keys():
if dimension[:3] == 'LON':
dim_e_longitude = dimension
if dimension[:3] == 'LAT':
dim_e_latitude = dimension
# get dimensions for file_u
for dimension in file_u.dimensions.keys():
if dimension[:3] == 'LON':
dim_u_longitude = dimension
if dimension[:3] == 'LAT':
dim_u_latitude = dimension
# get dimensions for file_v
for dimension in file_v.dimensions.keys():
if dimension[:3] == 'LON':
dim_v_longitude = dimension
if dimension[:3] == 'LAT':
dim_v_latitude = dimension
# Precision used by most for lat/lon is 4 or 5 decimals
e_lat = num.around(file_e.variables[dim_e_latitude][:], 5)
e_lon = num.around(file_e.variables[dim_e_longitude][:], 5)
# Check that files are compatible
assert num.allclose(latitudes, file_u.variables[dim_u_latitude])
assert num.allclose(latitudes, file_v.variables[dim_v_latitude])
assert num.allclose(latitudes, e_lat)
assert num.allclose(longitudes, file_u.variables[dim_u_longitude])
assert num.allclose(longitudes, file_v.variables[dim_v_longitude])
assert num.allclose(longitudes, e_lon)
if mint is None:
jmin = 0
mint = times[0]
else:
jmin = num.searchsorted(times, mint)
# numpy.int32 didn't work in slicing of amplitude below
jmin = int(jmin)
if maxt is None:
jmax = len(times)
maxt = times[-1]
else:
jmax = num.searchsorted(times, maxt)
# numpy.int32 didn't work in slicing of amplitude below
jmax = int(jmax)
kmin, kmax, lmin, lmax = get_min_max_indices(latitudes[:],
longitudes[:],
minlat, maxlat,
minlon, maxlon)
times = times[jmin:jmax]
latitudes = latitudes[kmin:kmax]
longitudes = longitudes[lmin:lmax]
if verbose: log.critical('cropping')
zname = 'ELEVATION'
amplitudes = file_h.variables['HA'][jmin:jmax, kmin:kmax, lmin:lmax]
uspeed = file_u.variables['UA'][jmin:jmax, kmin:kmax, lmin:lmax] #Lon
vspeed = file_v.variables['VA'][jmin:jmax, kmin:kmax, lmin:lmax] #Lat
elevations = file_e.variables[zname][kmin:kmax, lmin:lmax]
# Get missing values
nan_ha = file_h.variables['HA'].missing_value
nan_ua = file_u.variables['UA'].missing_value
nan_va = file_v.variables['VA'].missing_value
if hasattr(file_e.variables[zname],'missing_value'):
nan_e = file_e.variables[zname].missing_value
else:
nan_e = None
# Cleanup
missing = (amplitudes == nan_ha)
if num.sometrue (missing):
if fail_on_NaN:
msg = 'NetCDFFile %s contains missing values' \
% basename_in + '_ha.nc'
raise DataMissingValuesError, msg
else:
amplitudes = amplitudes*(missing==0) + missing*NaN_filler
missing = (uspeed == nan_ua)
if num.sometrue (missing):
if fail_on_NaN:
msg = 'NetCDFFile %s contains missing values' \
% basename_in + '_ua.nc'
raise DataMissingValuesError, msg
else:
uspeed = uspeed*(missing==0) + missing*NaN_filler
missing = (vspeed == nan_va)
if num.sometrue (missing):
if fail_on_NaN:
msg = 'NetCDFFile %s contains missing values' \
% basename_in + '_va.nc'
raise DataMissingValuesError, msg
else:
vspeed = vspeed*(missing==0) + missing*NaN_filler
missing = (elevations == nan_e)
if num.sometrue (missing):
if fail_on_NaN:
msg = 'NetCDFFile %s contains missing values' \
% basename_in + '_e.nc'
raise DataMissingValuesError, msg
else:
elevations = elevations*(missing==0) + missing*NaN_filler
number_of_times = times.shape[0]
number_of_latitudes = latitudes.shape[0]
number_of_longitudes = longitudes.shape[0]
assert amplitudes.shape[0] == number_of_times
assert amplitudes.shape[1] == number_of_latitudes
assert amplitudes.shape[2] == number_of_longitudes
if verbose:
_show_stats((latitudes, longitudes), times, amplitudes, \
(uspeed, vspeed), elevations)
# print number_of_latitudes, number_of_longitudes
number_of_points = number_of_latitudes * number_of_longitudes
number_of_volumes = (number_of_latitudes-1) * (number_of_longitudes-1) * 2
file_h.close()
file_u.close()
file_v.close()
file_e.close()
# NetCDF file definition
outfile = NetCDFFile(swwname, netcdf_mode_w)
description = 'Converted from Ferret files: %s, %s, %s, %s' \
% (basename_in + '_ha.nc',
basename_in + '_ua.nc',
basename_in + '_va.nc',
basename_in + '_e.nc')
# Create new file
starttime = times[0]
sww = Write_sww(['elevation'], ['stage', 'xmomentum', 'ymomentum'])
sww.store_header(outfile, times, number_of_volumes,
number_of_points, description=description,
verbose=verbose, sww_precision=netcdf_float)
# Store
from anuga.coordinate_transforms.redfearn import redfearn
x = num.zeros(number_of_points, num.float) #Easting
y = num.zeros(number_of_points, num.float) #Northing
if verbose:
log.critical('Making triangular grid')
# Check zone boundaries
refzone, _, _ = redfearn(latitudes[0], longitudes[0])
vertices = {}
i = 0
for k, lat in enumerate(latitudes): # Y direction
for l, lon in enumerate(longitudes): # X direction
vertices[l, k] = i
_, easting, northing = redfearn(lat, lon)
#msg = 'Zone boundary crossed at longitude =', lon
#assert zone == refzone, msg
#print '%7.2f %7.2f %8.2f %8.2f' %(lon, lat, easting, northing)
x[i] = easting
y[i] = northing
i += 1
#Construct 2 triangles per 'rectangular' element
volumes = []
for l in range(number_of_longitudes-1): # X direction
for k in range(number_of_latitudes-1): # Y direction
v1 = vertices[l, k+1]
v2 = vertices[l, k]
v3 = vertices[l+1, k+1]
v4 = vertices[l+1, k]
volumes.append([v1, v2, v3]) #Upper element
volumes.append([v4, v3, v2]) #Lower element
volumes = num.array(volumes, num.int) #array default#
if origin is None:
origin = Geo_reference(refzone, min(x), min(y))
geo_ref = write_NetCDF_georeference(origin, outfile)
if elevation is not None:
z = elevation
else:
if inverted_bathymetry:
z = -1 * elevations
else:
z = elevations
#FIXME: z should be obtained from MOST and passed in here
#FIXME use the Write_sww instance(sww) to write this info
z = num.resize(z, outfile.variables['elevation'][:].shape)
outfile.variables['x'][:] = x - geo_ref.get_xllcorner()
outfile.variables['y'][:] = y - geo_ref.get_yllcorner()
#outfile.variables['z'][:] = z #FIXME HACK for bacwards compat.
outfile.variables['elevation'][:] = z
outfile.variables['volumes'][:] = volumes.astype(num.int32) #For Opteron 64
#Time stepping
stage = outfile.variables['stage']
xmomentum = outfile.variables['xmomentum']
ymomentum = outfile.variables['ymomentum']
if verbose:
log.critical('Converting quantities')
n = len(times)
for j in range(n):
if verbose and j % ((n+10)/10) == 0:
log.critical(' Doing %d of %d' % (j, n))
i = 0
for k in range(number_of_latitudes): # Y direction
for l in range(number_of_longitudes): # X direction
w = zscale * amplitudes[j, k, l] / 100 + mean_stage
stage[j, i] = w
h = w - z[i]
xmomentum[j, i] = uspeed[j, k, l]/100*h
ymomentum[j, i] = vspeed[j, k, l]/100*h
i += 1
#outfile.close()
#FIXME: Refactor using code from file_function.statistics
#Something like print swwstats(swwname)
if verbose:
time_info = times, starttime, mint, maxt
_show_sww_stats(outfile, swwname, geo_ref, time_info)
outfile.close()
def parallel_time_varying_file_boundary_sts(self):
""" parallel_test_time_varying_file_boundary_sts_sequential(self):
Read correct points from ordering file and apply sts to boundary.
The boundary is time varying. Compares sequential result with
distributed result found using anuga_parallel
"""
#------------------------------------------------------------
# Define test variables
#------------------------------------------------------------
lat_long_points = [[6.01, 97.0], [6.02, 97.0], [6.05, 96.9],
[6.0, 97.0]]
bounding_polygon = [[6.0, 97.0], [6.01, 97.0], [6.02, 97.0],
[6.02, 97.02], [6.00, 97.02]]
tide = 3.0
time_step_count = 65
time_step = 2
n = len(lat_long_points)
first_tstep = num.ones(n, num.int)
last_tstep = (time_step_count) * num.ones(n, num.int)
finaltime = num.float(time_step * (time_step_count - 1))
yieldstep = num.float(time_step)
gauge_depth = 20 * num.ones(n, num.float)
ha = 2 * num.ones((n, time_step_count), num.float)
ua = 10 * num.ones((n, time_step_count), num.float)
va = -10 * num.ones((n, time_step_count), num.float)
times = num.arange(0, time_step_count * time_step, time_step)
for i in range(n):
#ha[i]+=num.sin(times)
ha[i] += times / finaltime
#------------------------------------------------------------
# Write mux data to file then convert to sts format
#------------------------------------------------------------
sts_file = "test"
if myid == 0:
base_name, files = self.write_mux2(lat_long_points,
time_step_count,
time_step,
first_tstep,
last_tstep,
depth=gauge_depth,
ha=ha,
ua=ua,
va=va)
# base name will not exist, but 3 other files are created
# Write order file
file_handle, order_base_name = tempfile.mkstemp("")
os.close(file_handle)
os.remove(order_base_name)
d = ","
order_file = order_base_name + 'order.txt'
fid = open(order_file, 'w')
# Write Header
header = 'index, longitude, latitude\n'
fid.write(header)
indices = [3, 0, 1]
for i in indices:
line=str(i)+d+str(lat_long_points[i][1])+d+\
str(lat_long_points[i][0])+"\n"
fid.write(line)
fid.close()
urs2sts(base_name,
basename_out=sts_file,
ordering_filename=order_file,
mean_stage=tide,
verbose=verbose)
self.delete_mux(files)
assert (os.access(sts_file + '.sts', os.F_OK))
os.remove(order_file)
barrier()
#------------------------------------------------------------
# Define boundary_polygon on each processor. This polygon defines the
# urs boundary and lies on a portion of the bounding_polygon
#------------------------------------------------------------
boundary_polygon = create_sts_boundary(sts_file)
# Append the remaining part of the boundary polygon to be defined by
# the user
bounding_polygon_utm = []
for point in bounding_polygon:
zone, easting, northing = redfearn(point[0], point[1])
bounding_polygon_utm.append([easting, northing])
boundary_polygon.append(bounding_polygon_utm[3])
boundary_polygon.append(bounding_polygon_utm[4])
assert num.allclose(bounding_polygon_utm, boundary_polygon)
extent_res = 10000
meshname = 'urs_test_mesh' + '.tsh'
interior_regions = None
boundary_tags = {'ocean': [0, 1], 'otherocean': [2, 3, 4]}
#------------------------------------------------------------
# Create mesh on the master processor and store in file. This file
# is read in by each slave processor when needed
#------------------------------------------------------------
if myid == 0:
create_mesh_from_regions(boundary_polygon,
boundary_tags=boundary_tags,
maximum_triangle_area=extent_res,
filename=meshname,
interior_regions=interior_regions,
verbose=verbose)
# barrier()
domain_fbound = Domain(meshname)
domain_fbound.set_quantities_to_be_stored(None)
domain_fbound.set_quantity('stage', tide)
# print domain_fbound.mesh.get_boundary_polygon()
else:
domain_fbound = None
barrier()
if (verbose and myid == 0):
print 'DISTRIBUTING PARALLEL DOMAIN'
domain_fbound = distribute(domain_fbound)
#--------------------------------------------------------------------
# Find which sub_domain in which the interpolation points are located
#
# Sometimes the interpolation points sit exactly
# between two centroids, so in the parallel run we
# reset the interpolation points to the centroids
# found in the sequential run
#--------------------------------------------------------------------
interpolation_points = [[279000, 664000], [280250, 664130],
[279280, 665400], [280500, 665000]]
interpolation_points = num.array(interpolation_points)
#if myid==0:
# import pylab as P
# boundary_polygon=num.array(boundary_polygon)
# P.plot(boundary_polygon[:,0],boundary_polygon[:,1])
# P.plot(interpolation_points[:,0],interpolation_points[:,1],'ko')
# P.show()
fbound_gauge_values = []
fbound_proc_tri_ids = []
for i, point in enumerate(interpolation_points):
fbound_gauge_values.append([]) # Empty list for timeseries
try:
k = domain_fbound.get_triangle_containing_point(point)
if domain_fbound.tri_full_flag[k] == 1:
fbound_proc_tri_ids.append(k)
else:
fbound_proc_tri_ids.append(-1)
except:
fbound_proc_tri_ids.append(-2)
if verbose: print 'P%d has points = %s' % (myid, fbound_proc_tri_ids)
#------------------------------------------------------------
# Set boundary conditions
#------------------------------------------------------------
Bf = File_boundary(sts_file + '.sts',
domain_fbound,
boundary_polygon=boundary_polygon)
Br = Reflective_boundary(domain_fbound)
domain_fbound.set_boundary({'ocean': Bf, 'otherocean': Br})
#------------------------------------------------------------
# Evolve the domain on each processor
#------------------------------------------------------------
for i, t in enumerate(
domain_fbound.evolve(yieldstep=yieldstep,
finaltime=finaltime,
skip_initial_step=False)):
stage = domain_fbound.get_quantity('stage')
for i in range(4):
if fbound_proc_tri_ids[i] > -1:
fbound_gauge_values[i].append(
stage.centroid_values[fbound_proc_tri_ids[i]])
#------------------------------------------------------------
# Create domain to be run sequntially on each processor
#------------------------------------------------------------
domain_drchlt = Domain(meshname)
domain_drchlt.set_quantities_to_be_stored(None)
domain_drchlt.set_starttime(time_step)
domain_drchlt.set_quantity('stage', tide)
Br = Reflective_boundary(domain_drchlt)
#Bd = Dirichlet_boundary([2.0+tide,220+10*tide,-220-10*tide])
Bd = Time_boundary(
domain=domain_drchlt,
function=lambda t: [
2.0 + t / finaltime + tide, 220. + 10. * tide + 10. * t /
finaltime, -220. - 10. * tide - 10. * t / finaltime
])
#Bd = Time_boundary(domain=domain_drchlt,function=lambda t: [2.0+num.sin(t)+tide,10.*(2+20.+num.sin(t)+tide),-10.*(2+20.+num.sin(t)+tide)])
domain_drchlt.set_boundary({'ocean': Bd, 'otherocean': Br})
drchlt_gauge_values = []
drchlt_proc_tri_ids = []
for i, point in enumerate(interpolation_points):
drchlt_gauge_values.append([]) # Empty list for timeseries
try:
k = domain_drchlt.get_triangle_containing_point(point)
if domain_drchlt.tri_full_flag[k] == 1:
drchlt_proc_tri_ids.append(k)
else:
drchlt_proc_tri_ids.append(-1)
except:
drchlt_proc_tri_ids.append(-2)
if verbose: print 'P%d has points = %s' % (myid, drchlt_proc_tri_ids)
#------------------------------------------------------------
# Evolve entire domain on each processor
#------------------------------------------------------------
for i, t in enumerate(
domain_drchlt.evolve(yieldstep=yieldstep,
finaltime=finaltime,
skip_initial_step=False)):
stage = domain_drchlt.get_quantity('stage')
for i in range(4):
drchlt_gauge_values[i].append(
stage.centroid_values[drchlt_proc_tri_ids[i]])
#------------------------------------------------------------
# Compare sequential values with parallel values
#------------------------------------------------------------
barrier()
success = True
for i in range(4):
if fbound_proc_tri_ids[i] > -1:
fbound_gauge_values[i] = num.array(fbound_gauge_values[i])
drchlt_gauge_values[i] = num.array(drchlt_gauge_values[i])
#print i,fbound_gauge_values[i][4]
#print i,drchlt_gauge_values[i][4]
success = success and num.allclose(fbound_gauge_values[i],
drchlt_gauge_values[i])
assert success #, (fbound_gauge_values[i]-drchlt_gauge_values[i])
#assert_(success)
if not sys.platform == 'win32':
if myid == 0: os.remove(sts_file + '.sts')
if myid == 0: os.remove(meshname)
def esri2sww(
bath_dir,
elevation_dir,
ucur_dir,
vcur_dir,
sww_file,
minlat=None,
maxlat=None,
minlon=None,
maxlon=None,
zscale=1,
mean_stage=0,
fail_on_NaN=True,
elevation_NaN_filler=0,
bath_prefix="ba",
elevation_prefix="el",
verbose=False,
):
"""
Produce an sww boundary file, from esri ascii data from CSIRO.
Also convert latitude and longitude to UTM. All coordinates are
assumed to be given in the GDA94 datum.
assume:
All files are in esri ascii format
4 types of information
bathymetry
elevation
u velocity
v velocity
Assumptions
The metadata of all the files is the same
Each type is in a seperate directory
One bath file with extention .000
The time period is less than 24hrs and uniform.
"""
from anuga.file.netcdf import NetCDFFile
from anuga.coordinate_transforms.redfearn import redfearn
if sww_file[-4:] != ".sww":
raise IOError("Output file %s should be of type .sww." % sww_file)
# So if we want to change the precision it's done here
precision = netcdf_float
# go in to the bath dir and load the only file,
bath_files = os.listdir(bath_dir)
bath_file = bath_files[0]
bath_dir_file = bath_dir + os.sep + bath_file
bath_metadata, bath_grid = _read_asc(bath_dir_file)
# Use the date.time of the bath file as a basis for
# the start time for other files
base_start = bath_file[-12:]
# go into the elevation dir and load the 000 file
elevation_dir_file = elevation_dir + os.sep + elevation_prefix + base_start
elevation_files = os.listdir(elevation_dir)
ucur_files = os.listdir(ucur_dir)
vcur_files = os.listdir(vcur_dir)
elevation_files.sort()
# the first elevation file should be the
# file with the same base name as the bath data
assert elevation_files[0] == "el" + base_start
number_of_latitudes = bath_grid.shape[0]
number_of_longitudes = bath_grid.shape[1]
number_of_volumes = (number_of_latitudes - 1) * (number_of_longitudes - 1) * 2
longitudes = [bath_metadata["xllcorner"] + x * bath_metadata["cellsize"] for x in range(number_of_longitudes)]
latitudes = [bath_metadata["yllcorner"] + y * bath_metadata["cellsize"] for y in range(number_of_latitudes)]
# reverse order of lat, so the first lat represents the first grid row
latitudes.reverse()
kmin, kmax, lmin, lmax = get_min_max_indices(
latitudes[:], longitudes[:], minlat=minlat, maxlat=maxlat, minlon=minlon, maxlon=maxlon
)
bath_grid = bath_grid[kmin:kmax, lmin:lmax]
latitudes = latitudes[kmin:kmax]
longitudes = longitudes[lmin:lmax]
number_of_latitudes = len(latitudes)
number_of_longitudes = len(longitudes)
number_of_times = len(os.listdir(elevation_dir))
number_of_points = number_of_latitudes * number_of_longitudes
number_of_volumes = (number_of_latitudes - 1) * (number_of_longitudes - 1) * 2
# Work out the times
if len(elevation_files) > 1:
# Assume: The time period is less than 24hrs.
time_period = (int(elevation_files[1][-3:]) - int(elevation_files[0][-3:])) * 60 * 60
times = [x * time_period for x in range(len(elevation_files))]
else:
times = [0.0]
if verbose:
log.critical("------------------------------------------------")
log.critical("Statistics:")
log.critical(" Extent (lat/lon):")
log.critical(" lat in [%f, %f], len(lat) == %d" % (min(latitudes), max(latitudes), len(latitudes)))
log.critical(" lon in [%f, %f], len(lon) == %d" % (min(longitudes), max(longitudes), len(longitudes)))
log.critical(" t in [%f, %f], len(t) == %d" % (min(times), max(times), len(times)))
######### WRITE THE SWW FILE #############
# NetCDF file definition
outfile = NetCDFFile(sww_file, netcdf_mode_w)
# Create new file
outfile.institution = "Geoscience Australia"
outfile.description = "Converted from XXX"
# For sww compatibility
outfile.smoothing = "Yes"
outfile.order = 1
# Start time in seconds since the epoch (midnight 1/1/1970)
outfile.starttime = starttime = times[0]
# dimension definitions
outfile.createDimension("number_of_volumes", number_of_volumes)
outfile.createDimension("number_of_vertices", 3)
outfile.createDimension("number_of_points", number_of_points)
outfile.createDimension("number_of_timesteps", number_of_times)
# variable definitions
outfile.createVariable("x", precision, ("number_of_points",))
outfile.createVariable("y", precision, ("number_of_points",))
outfile.createVariable("elevation", precision, ("number_of_points",))
# FIXME: Backwards compatibility
# outfile.createVariable('z', precision, ('number_of_points',))
#################################
outfile.createVariable("volumes", netcdf_int, ("number_of_volumes", "number_of_vertices"))
outfile.createVariable("time", precision, ("number_of_timesteps",))
outfile.createVariable("stage", precision, ("number_of_timesteps", "number_of_points"))
outfile.createVariable("xmomentum", precision, ("number_of_timesteps", "number_of_points"))
outfile.createVariable("ymomentum", precision, ("number_of_timesteps", "number_of_points"))
# Store
from anuga.coordinate_transforms.redfearn import redfearn
x = num.zeros(number_of_points, num.float) # Easting
y = num.zeros(number_of_points, num.float) # Northing
if verbose:
log.critical("Making triangular grid")
# Get zone of 1st point.
refzone, _, _ = redfearn(latitudes[0], longitudes[0])
vertices = {}
i = 0
for k, lat in enumerate(latitudes):
for l, lon in enumerate(longitudes):
vertices[l, k] = i
zone, easting, northing = redfearn(lat, lon)
# msg = 'Zone boundary crossed at longitude =', lon
# assert zone == refzone, msg
# print '%7.2f %7.2f %8.2f %8.2f' %(lon, lat, easting, northing)
x[i] = easting
y[i] = northing
i += 1
# Construct 2 triangles per 'rectangular' element
volumes = []
for l in range(number_of_longitudes - 1): # X direction
for k in range(number_of_latitudes - 1): # Y direction
v1 = vertices[l, k + 1]
v2 = vertices[l, k]
v3 = vertices[l + 1, k + 1]
v4 = vertices[l + 1, k]
# Note, this is different to the ferrit2sww code
# since the order of the lats is reversed.
volumes.append([v1, v3, v2]) # Upper element
volumes.append([v4, v2, v3]) # Lower element
volumes = num.array(volumes, num.int) # array default#
geo_ref = Geo_reference(refzone, min(x), min(y))
geo_ref.write_NetCDF(outfile)
# This will put the geo ref in the middle
# geo_ref = Geo_reference(refzone, (max(x)+min(x))/2., (max(x)+min(y))/2.)
if verbose:
log.critical("------------------------------------------------")
log.critical("More Statistics:")
log.critical(" Extent (/lon):")
log.critical(" x in [%f, %f], len(lat) == %d" % (min(x), max(x), len(x)))
log.critical(" y in [%f, %f], len(lon) == %d" % (min(y), max(y), len(y)))
log.critical("geo_ref: ", geo_ref)
z = num.resize(bath_grid, outfile.variables["elevation"][:].shape)
outfile.variables["x"][:] = x - geo_ref.get_xllcorner()
outfile.variables["y"][:] = y - geo_ref.get_yllcorner()
# FIXME (Ole): Remove once viewer has been recompiled and changed
# to use elevation instead of z
# outfile.variables['z'][:] = z
outfile.variables["elevation"][:] = z
outfile.variables["volumes"][:] = volumes.astype(num.int32) # On Opteron 64
stage = outfile.variables["stage"]
xmomentum = outfile.variables["xmomentum"]
ymomentum = outfile.variables["ymomentum"]
outfile.variables["time"][:] = times # Store time relative
if verbose:
log.critical("Converting quantities")
n = number_of_times
for j in range(number_of_times):
# load in files
elevation_meta, elevation_grid = _read_asc(elevation_dir + os.sep + elevation_files[j])
_, u_momentum_grid = _read_asc(ucur_dir + os.sep + ucur_files[j])
_, v_momentum_grid = _read_asc(vcur_dir + os.sep + vcur_files[j])
# cut matrix to desired size
elevation_grid = elevation_grid[kmin:kmax, lmin:lmax]
u_momentum_grid = u_momentum_grid[kmin:kmax, lmin:lmax]
v_momentum_grid = v_momentum_grid[kmin:kmax, lmin:lmax]
# handle missing values
missing = elevation_grid == elevation_meta["NODATA_value"]
if num.sometrue(missing):
if fail_on_NaN:
msg = "File %s contains missing values" % (elevation_files[j])
raise DataMissingValuesError, msg
else:
elevation_grid = elevation_grid * (missing == 0) + missing * elevation_NaN_filler
if verbose and j % ((n + 10) / 10) == 0:
log.critical(" Doing %d of %d" % (j, n))
i = 0
for k in range(number_of_latitudes): # Y direction
for l in range(number_of_longitudes): # X direction
w = zscale * elevation_grid[k, l] + mean_stage
stage[j, i] = w
h = w - z[i]
xmomentum[j, i] = u_momentum_grid[k, l] * h
ymomentum[j, i] = v_momentum_grid[k, l] * h
i += 1
outfile.close()
def sequential_time_varying_file_boundary_sts(self):
"""sequential_ltest_time_varying_file_boundary_sts_sequential(self):
Read correct points from ordering file and apply sts to boundary. The boundary is time varying. FIXME add to test_urs2sts.
"""
lat_long_points = [[6.01, 97.0], [6.02, 97.0], [6.05, 96.9],
[6.0, 97.0]]
bounding_polygon = [[6.0, 97.0], [6.01, 97.0], [6.02, 97.0],
[6.02, 97.02], [6.00, 97.02]]
tide = 3.0
time_step_count = 65
time_step = 2.
n = len(lat_long_points)
first_tstep = num.ones(n, num.int)
last_tstep = (time_step_count) * num.ones(n, num.int)
finaltime = num.float(time_step * (time_step_count - 1))
yieldstep = num.float(time_step)
gauge_depth = 20 * num.ones(n, num.float)
ha = 2 * num.ones((n, time_step_count), num.float)
ua = 10 * num.ones((n, time_step_count), num.float)
va = -10 * num.ones((n, time_step_count), num.float)
times = num.arange(0., num.float(time_step_count * time_step),
time_step)
for i in range(n):
#ha[i]+=num.sin(times)
ha[i] += times / finaltime
sts_file = "test"
if myid == 0:
base_name, files = self.write_mux2(lat_long_points,
time_step_count,
time_step,
first_tstep,
last_tstep,
depth=gauge_depth,
ha=ha,
ua=ua,
va=va)
# base name will not exist, but 3 other files are created
# Write order file
file_handle, order_base_name = tempfile.mkstemp("")
os.close(file_handle)
os.remove(order_base_name)
d = ","
order_file = order_base_name + 'order.txt'
fid = open(order_file, 'w')
# Write Header
header = 'index, longitude, latitude\n'
fid.write(header)
indices = [3, 0, 1]
for i in indices:
line=str(i)+d+str(lat_long_points[i][1])+d+\
str(lat_long_points[i][0])+"\n"
fid.write(line)
fid.close()
urs2sts(base_name,
basename_out=sts_file,
ordering_filename=order_file,
mean_stage=tide,
verbose=verbose)
self.delete_mux(files)
assert (os.access(sts_file + '.sts', os.F_OK))
os.remove(order_file)
barrier()
boundary_polygon = create_sts_boundary(sts_file)
# Append the remaining part of the boundary polygon to be defined by
# the user
bounding_polygon_utm = []
for point in bounding_polygon:
zone, easting, northing = redfearn(point[0], point[1])
bounding_polygon_utm.append([easting, northing])
boundary_polygon.append(bounding_polygon_utm[3])
boundary_polygon.append(bounding_polygon_utm[4])
assert num.allclose(bounding_polygon_utm, boundary_polygon)
extent_res = 1000000
meshname = 'urs_test_mesh' + '.tsh'
interior_regions = None
boundary_tags = {'ocean': [0, 1], 'otherocean': [2, 3, 4]}
# have to change boundary tags from last example because now bounding
# polygon starts in different place.
if myid == 0:
create_mesh_from_regions(boundary_polygon,
boundary_tags=boundary_tags,
maximum_triangle_area=extent_res,
filename=meshname,
interior_regions=interior_regions,
verbose=verbose)
barrier()
domain_fbound = Domain(meshname)
domain_fbound.set_quantities_to_be_stored(None)
domain_fbound.set_quantity('stage', tide)
if verbose: print "Creating file boundary condition"
Bf = File_boundary(sts_file + '.sts',
domain_fbound,
boundary_polygon=boundary_polygon)
Br = Reflective_boundary(domain_fbound)
domain_fbound.set_boundary({'ocean': Bf, 'otherocean': Br})
temp_fbound = num.zeros(int(finaltime / yieldstep) + 1, num.float)
if verbose: print "Evolving domain with file boundary condition"
for i, t in enumerate(
domain_fbound.evolve(yieldstep=yieldstep,
finaltime=finaltime,
skip_initial_step=False)):
temp_fbound[i] = domain_fbound.quantities['stage'].centroid_values[
2]
if verbose: domain_fbound.write_time()
domain_drchlt = Domain(meshname)
domain_drchlt.set_quantities_to_be_stored(None)
domain_drchlt.set_starttime(time_step)
domain_drchlt.set_quantity('stage', tide)
Br = Reflective_boundary(domain_drchlt)
#Bd = Dirichlet_boundary([2.0+tide,220+10*tide,-220-10*tide])
Bd = Time_boundary(
domain=domain_drchlt,
f=lambda t: [
2.0 + t / finaltime + tide, 220. + 10. * tide + 10. * t /
finaltime, -220. - 10. * tide - 10. * t / finaltime
])
#Bd = Time_boundary(domain=domain_drchlt,f=lambda t: [2.0+num.sin(t)+tide,10.*(2+20.+num.sin(t)+tide),-10.*(2+20.+num.sin(t)+tide)])
domain_drchlt.set_boundary({'ocean': Bd, 'otherocean': Br})
temp_drchlt = num.zeros(int(finaltime / yieldstep) + 1, num.float)
for i, t in enumerate(
domain_drchlt.evolve(yieldstep=yieldstep,
finaltime=finaltime,
skip_initial_step=False)):
temp_drchlt[i] = domain_drchlt.quantities['stage'].centroid_values[
2]
#domain_drchlt.write_time()
#print domain_fbound.quantities['stage'].vertex_values
#print domain_drchlt.quantities['stage'].vertex_values
assert num.allclose(temp_fbound,
temp_drchlt), temp_fbound - temp_drchlt
assert num.allclose(domain_fbound.quantities['stage'].vertex_values,
domain_drchlt.quantities['stage'].vertex_values)
assert num.allclose(
domain_fbound.quantities['xmomentum'].vertex_values,
domain_drchlt.quantities['xmomentum'].vertex_values)
assert num.allclose(
domain_fbound.quantities['ymomentum'].vertex_values,
domain_drchlt.quantities['ymomentum'].vertex_values)
if not sys.platform == 'win32':
if myid == 0: os.remove(sts_file + '.sts')
if myid == 0: os.remove(meshname)
def parallel_time_varying_file_boundary_sts(self):
""" parallel_test_time_varying_file_boundary_sts_sequential(self):
Read correct points from ordering file and apply sts to boundary.
The boundary is time varying. Compares sequential result with
distributed result found using anuga_parallel
"""
#------------------------------------------------------------
# Define test variables
#------------------------------------------------------------
lat_long_points=[[6.01,97.0],[6.02,97.0],[6.05,96.9],[6.0,97.0]]
bounding_polygon=[[6.0,97.0],[6.01,97.0],[6.02,97.0],
[6.02,97.02],[6.00,97.02]]
tide = 3.0
time_step_count = 65
time_step = 2
n=len(lat_long_points)
first_tstep=num.ones(n,num.int)
last_tstep=(time_step_count)*num.ones(n,num.int)
finaltime=num.float(time_step*(time_step_count-1))
yieldstep=num.float(time_step)
gauge_depth=20*num.ones(n,num.float)
ha=2*num.ones((n,time_step_count),num.float)
ua=10*num.ones((n,time_step_count),num.float)
va=-10*num.ones((n,time_step_count),num.float)
times=num.arange(0, time_step_count*time_step, time_step)
for i in range(n):
#ha[i]+=num.sin(times)
ha[i]+=times/finaltime
#------------------------------------------------------------
# Write mux data to file then convert to sts format
#------------------------------------------------------------
sts_file="test"
if myid==0:
base_name, files = self.write_mux2(lat_long_points,
time_step_count,
time_step,
first_tstep,
last_tstep,
depth=gauge_depth,
ha=ha,
ua=ua,
va=va)
# base name will not exist, but 3 other files are created
# Write order file
file_handle, order_base_name = tempfile.mkstemp("")
os.close(file_handle)
os.remove(order_base_name)
d=","
order_file=order_base_name+'order.txt'
fid=open(order_file,'w')
# Write Header
header='index, longitude, latitude\n'
fid.write(header)
indices=[3,0,1]
for i in indices:
line=str(i)+d+str(lat_long_points[i][1])+d+\
str(lat_long_points[i][0])+"\n"
fid.write(line)
fid.close()
urs2sts(base_name,
basename_out=sts_file,
ordering_filename=order_file,
mean_stage=tide,
verbose=verbose)
self.delete_mux(files)
assert(os.access(sts_file+'.sts', os.F_OK))
os.remove(order_file)
barrier()
#------------------------------------------------------------
# Define boundary_polygon on each processor. This polygon defines the
# urs boundary and lies on a portion of the bounding_polygon
#------------------------------------------------------------
boundary_polygon = create_sts_boundary(sts_file)
# Append the remaining part of the boundary polygon to be defined by
# the user
bounding_polygon_utm=[]
for point in bounding_polygon:
zone,easting,northing=redfearn(point[0],point[1])
bounding_polygon_utm.append([easting,northing])
boundary_polygon.append(bounding_polygon_utm[3])
boundary_polygon.append(bounding_polygon_utm[4])
assert num.allclose(bounding_polygon_utm,boundary_polygon)
extent_res=10000
meshname = 'urs_test_mesh' + '.tsh'
interior_regions=None
boundary_tags={'ocean': [0,1], 'otherocean': [2,3,4]}
#------------------------------------------------------------
# Create mesh on the master processor and store in file. This file
# is read in by each slave processor when needed
#------------------------------------------------------------
if myid==0:
create_mesh_from_regions(boundary_polygon,
boundary_tags=boundary_tags,
maximum_triangle_area=extent_res,
filename=meshname,
interior_regions=interior_regions,
verbose=verbose)
# barrier()
domain_fbound = Domain(meshname)
domain_fbound.set_quantities_to_be_stored(None)
domain_fbound.set_quantity('stage', tide)
# print domain_fbound.mesh.get_boundary_polygon()
else:
domain_fbound=None
barrier()
if ( verbose and myid == 0 ):
print 'DISTRIBUTING PARALLEL DOMAIN'
domain_fbound = distribute(domain_fbound)
#--------------------------------------------------------------------
# Find which sub_domain in which the interpolation points are located
#
# Sometimes the interpolation points sit exactly
# between two centroids, so in the parallel run we
# reset the interpolation points to the centroids
# found in the sequential run
#--------------------------------------------------------------------
interpolation_points = [[279000,664000], [280250,664130],
[279280,665400], [280500,665000]]
interpolation_points=num.array(interpolation_points)
#if myid==0:
# import pylab as P
# boundary_polygon=num.array(boundary_polygon)
# P.plot(boundary_polygon[:,0],boundary_polygon[:,1])
# P.plot(interpolation_points[:,0],interpolation_points[:,1],'ko')
# P.show()
fbound_gauge_values = []
fbound_proc_tri_ids = []
for i, point in enumerate(interpolation_points):
fbound_gauge_values.append([]) # Empty list for timeseries
try:
k = domain_fbound.get_triangle_containing_point(point)
if domain_fbound.tri_full_flag[k] == 1:
fbound_proc_tri_ids.append(k)
else:
fbound_proc_tri_ids.append(-1)
except:
fbound_proc_tri_ids.append(-2)
if verbose: print 'P%d has points = %s' %(myid, fbound_proc_tri_ids)
#------------------------------------------------------------
# Set boundary conditions
#------------------------------------------------------------
Bf = File_boundary(sts_file+'.sts',
domain_fbound,
boundary_polygon=boundary_polygon)
Br = Reflective_boundary(domain_fbound)
domain_fbound.set_boundary({'ocean': Bf,'otherocean': Br})
#------------------------------------------------------------
# Evolve the domain on each processor
#------------------------------------------------------------
for i, t in enumerate(domain_fbound.evolve(yieldstep=yieldstep,
finaltime=finaltime,
skip_initial_step = False)):
stage = domain_fbound.get_quantity('stage')
for i in range(4):
if fbound_proc_tri_ids[i] > -1:
fbound_gauge_values[i].append(stage.centroid_values[fbound_proc_tri_ids[i]])
#------------------------------------------------------------
# Create domain to be run sequntially on each processor
#------------------------------------------------------------
domain_drchlt = Domain(meshname)
domain_drchlt.set_quantities_to_be_stored(None)
domain_drchlt.set_starttime(time_step)
domain_drchlt.set_quantity('stage', tide)
Br = Reflective_boundary(domain_drchlt)
#Bd = Dirichlet_boundary([2.0+tide,220+10*tide,-220-10*tide])
Bd = Time_boundary(domain=domain_drchlt, function=lambda t: [2.0+t/finaltime+tide,220.+10.*tide+10.*t/finaltime,-220.-10.*tide-10.*t/finaltime])
#Bd = Time_boundary(domain=domain_drchlt,function=lambda t: [2.0+num.sin(t)+tide,10.*(2+20.+num.sin(t)+tide),-10.*(2+20.+num.sin(t)+tide)])
domain_drchlt.set_boundary({'ocean': Bd,'otherocean': Br})
drchlt_gauge_values = []
drchlt_proc_tri_ids = []
for i, point in enumerate(interpolation_points):
drchlt_gauge_values.append([]) # Empty list for timeseries
try:
k = domain_drchlt.get_triangle_containing_point(point)
if domain_drchlt.tri_full_flag[k] == 1:
drchlt_proc_tri_ids.append(k)
else:
drchlt_proc_tri_ids.append(-1)
except:
drchlt_proc_tri_ids.append(-2)
if verbose: print 'P%d has points = %s' %(myid, drchlt_proc_tri_ids)
#------------------------------------------------------------
# Evolve entire domain on each processor
#------------------------------------------------------------
for i, t in enumerate(domain_drchlt.evolve(yieldstep=yieldstep,
finaltime=finaltime,
skip_initial_step = False)):
stage = domain_drchlt.get_quantity('stage')
for i in range(4):
drchlt_gauge_values[i].append(stage.centroid_values[drchlt_proc_tri_ids[i]])
#------------------------------------------------------------
# Compare sequential values with parallel values
#------------------------------------------------------------
barrier()
success = True
for i in range(4):
if fbound_proc_tri_ids[i] > -1:
fbound_gauge_values[i]=num.array(fbound_gauge_values[i])
drchlt_gauge_values[i]=num.array(drchlt_gauge_values[i])
#print i,fbound_gauge_values[i][4]
#print i,drchlt_gauge_values[i][4]
success = success and num.allclose(fbound_gauge_values[i], drchlt_gauge_values[i])
assert success#, (fbound_gauge_values[i]-drchlt_gauge_values[i])
#assert_(success)
if not sys.platform == 'win32':
if myid==0: os.remove(sts_file+'.sts')
if myid==0: os.remove(meshname)
def urs2sts(basename_in,
basename_out=None,
weights=None,
verbose=False,
origin=None,
zone=None,
central_meridian=None,
mean_stage=0.0,
zscale=1.0,
ordering_filename=None):
"""Convert URS mux2 format for wave propagation to sts format
Also convert latitude and longitude to UTM. All coordinates are
assumed to be given in the GDA94 datum
origin is a 3-tuple with geo referenced
UTM coordinates (zone, easting, northing)
inputs:
basename_in: list of source file prefixes
These are combined with the extensions:
WAVEHEIGHT_MUX2_LABEL = '-z-mux2' for stage
EAST_VELOCITY_MUX2_LABEL = '-e-mux2' xmomentum
NORTH_VELOCITY_MUX2_LABEL = '-n-mux2' and ymomentum
to create a 2D list of mux2 file. The rows are associated with each
quantity and must have the above extensions
the columns are the list of file prefixes.
ordering: a .txt file name specifying which mux2 gauge points are
to be stored. This is indicated by the index of the gauge
in the ordering file.
ordering file format:
1st line: 'index,longitude,latitude\n'
other lines: index,longitude,latitude
If ordering is None or ordering file is empty then
all points are taken in the order they
appear in the mux2 file.
output:
basename_out: name of sts file in which mux2 data is stored.
NOTE: South is positive in mux files so sign of y-component of velocity is reverted
"""
import os
from anuga.file.netcdf import NetCDFFile
from operator import __and__
if not isinstance(basename_in, list):
if verbose: log.critical('Reading single source')
basename_in = [basename_in]
# This is the value used in the mux file format to indicate NAN data
# FIXME (Ole): This should be changed everywhere to IEEE NAN when
# we upgrade to Numpy
NODATA = 99
# Check that basename is a list of strings
if not reduce(__and__, map(lambda z: isinstance(z, basestring),
basename_in)):
msg = 'basename_in must be a string or list of strings'
raise Exception, msg
# Find the number of sources to be used
numSrc = len(basename_in)
# A weight must be specified for each source
if weights is None:
# Default is equal weighting
weights = num.ones(numSrc, num.float) / numSrc
else:
weights = ensure_numeric(weights)
msg = 'When combining multiple sources must specify a weight for ' \
'mux2 source file'
assert len(weights) == numSrc, msg
if verbose: log.critical('Weights used in urs2sts: %s' % str(weights))
# Check output filename
if basename_out is None:
msg = 'STS filename must be specified as basename_out ' \
'in function urs2sts'
raise Exception, msg
if basename_out.endswith('.sts'):
stsname = basename_out
else:
stsname = basename_out + '.sts'
# Create input filenames from basenames and check their existence
files_in = [[], [], []]
for files in basename_in:
files_in[0].append(files + WAVEHEIGHT_MUX2_LABEL),
files_in[1].append(files + EAST_VELOCITY_MUX2_LABEL)
files_in[2].append(files + NORTH_VELOCITY_MUX2_LABEL)
quantities = ['HA', 'UA', 'VA'] # Quantity names used in the MUX2 format
for i in range(len(quantities)):
for file_in in files_in[i]:
if (os.access(file_in, os.R_OK) == 0):
msg = 'File %s does not exist or is not accessible' % file_in
raise IOError, msg
# Establish permutation array
if ordering_filename is not None:
if verbose is True:
log.critical('Reading ordering file %s' % ordering_filename)
# Read ordering file
try:
fid = open(ordering_filename, 'r')
file_header = fid.readline().split(',')
ordering_lines = fid.readlines()
fid.close()
except:
msg = 'Cannot open %s' % ordering_filename
raise Exception, msg
reference_header = 'index, longitude, latitude\n'
reference_header_split = reference_header.split(',')
for i in range(3):
if not file_header[i].strip() == reference_header_split[i].strip():
msg = 'File must contain header: ' + reference_header
raise Exception, msg
if len(ordering_lines) < 2:
msg = 'File must contain at least two points'
raise Exception, msg
permutation = [int(line.split(',')[0]) for line in ordering_lines]
permutation = ensure_numeric(permutation)
else:
permutation = None
# Read MUX2 files
if (verbose): log.critical('reading mux2 file')
mux = {}
times_old = 0.0
latitudes_old = 0.0
longitudes_old = 0.0
elevation_old = 0.0
starttime_old = 0.0
for i, quantity in enumerate(quantities):
# For each quantity read the associated list of source mux2 file with
# extention associated with that quantity
times, latitudes, longitudes, elevation, mux[quantity], starttime \
= read_mux2_py(files_in[i], weights, permutation, verbose=verbose)
# Check that all quantities have consistent time and space information
if quantity != quantities[0]:
msg = '%s, %s and %s have inconsistent gauge data' \
% (files_in[0], files_in[1], files_in[2])
assert num.allclose(times, times_old), msg
assert num.allclose(latitudes, latitudes_old), msg
assert num.allclose(longitudes, longitudes_old), msg
assert num.allclose(elevation, elevation_old), msg
assert num.allclose(starttime, starttime_old), msg
times_old = times
latitudes_old = latitudes
longitudes_old = longitudes
elevation_old = elevation
starttime_old = starttime
# Self check - can be removed to improve speed
#ref_longitudes = [float(line.split(',')[1]) for line in ordering_lines]
#ref_latitudes = [float(line.split(',')[2]) for line in ordering_lines]
#
#msg = 'Longitudes specified in ordering file do not match those ' \
# 'found in mux files. ' \
# 'I got %s instead of %s (only beginning shown)' \
# % (str(longitudes[:10]) + '...',
# str(ref_longitudes[:10]) + '...')
#assert allclose(longitudes, ref_longitudes), msg
#
#msg = 'Latitudes specified in ordering file do not match those ' \
# 'found in mux files. '
# 'I got %s instead of %s (only beginning shown)' \
# % (str(latitudes[:10]) + '...',
# str(ref_latitudes[:10]) + '...')
#assert allclose(latitudes, ref_latitudes), msg
# Store timeseries in STS file
msg = 'File is empty and or clipped region not in file region'
assert len(latitudes > 0), msg
number_of_points = latitudes.shape[0] # Number of stations retrieved
number_of_times = times.shape[0] # Number of timesteps
number_of_latitudes = latitudes.shape[0] # Number latitudes
number_of_longitudes = longitudes.shape[0] # Number longitudes
# The permutation vector of contains original indices
# as given in ordering file or None in which case points
# are assigned the trivial indices enumerating them from
# 0 to number_of_points-1
if permutation is None:
permutation = num.arange(number_of_points, dtype=num.int)
# NetCDF file definition
outfile = NetCDFFile(stsname, netcdf_mode_w)
description = 'Converted from URS mux2 files: %s' % basename_in
# Create new file
sts = Write_sts()
sts.store_header(outfile,
times + starttime,
number_of_points,
description=description,
verbose=verbose,
sts_precision=netcdf_float)
# Store
from anuga.coordinate_transforms.redfearn import redfearn
x = num.zeros(number_of_points, num.float) # Easting
y = num.zeros(number_of_points, num.float) # Northing
# Check zone boundaries
if zone is None:
refzone, _, _ = redfearn(latitudes[0],
longitudes[0],
central_meridian=central_meridian)
else:
refzone = zone
old_zone = refzone
old_easting = 0.0
old_northing = 0.0
for i in range(number_of_points):
computed_zone, easting, northing = redfearn(
latitudes[i],
longitudes[i],
zone=zone,
central_meridian=central_meridian)
x[i] = easting
y[i] = northing
if computed_zone != refzone:
msg = 'All sts gauges need to be in the same zone. \n'
msg += 'offending gauge:Zone %d,%.4f, %4f\n' \
% (computed_zone, easting, northing)
msg += 'previous gauge:Zone %d,%.4f, %4f' \
% (old_zone, old_easting, old_northing)
raise Exception, msg
old_zone = computed_zone
old_easting = easting
old_northing = northing
if origin is None:
origin = Geo_reference(refzone, min(x), min(y))
geo_ref = write_NetCDF_georeference(origin, outfile)
elevation = num.resize(elevation, outfile.variables['elevation'][:].shape)
outfile.variables['permutation'][:] = permutation.astype(
num.int32) # Opteron 64
outfile.variables['x'][:] = x - geo_ref.get_xllcorner()
outfile.variables['y'][:] = y - geo_ref.get_yllcorner()
outfile.variables['elevation'][:] = elevation
stage = outfile.variables['stage']
xmomentum = outfile.variables['xmomentum']
ymomentum = outfile.variables['ymomentum']
if verbose: log.critical('Converting quantities')
for j in range(len(times)):
for i in range(number_of_points):
ha = mux['HA'][i, j]
ua = mux['UA'][i, j]
va = mux['VA'][i, j]
if ha == NODATA:
if verbose:
msg = 'Setting nodata value %d to 0 at time = %f, ' \
'point = %d' % (ha, times[j], i)
log.critical(msg)
ha = 0.0
ua = 0.0
va = 0.0
w = zscale * ha + mean_stage
h = w - elevation[i]
stage[j, i] = w
xmomentum[j, i] = ua * h
ymomentum[j, i] = -va * h # South is positive in mux files
outfile.close()
if verbose:
log.critical('Wrote sts file ' + stsname)
def test_ferret2sww1(self):
"""Test that georeferencing etc works when converting from
ferret format (lat/lon) to sww format (UTM)
"""
import os, sys
#The test file has
# LON = 150.66667, 150.83334, 151, 151.16667
# LAT = -34.5, -34.33333, -34.16667, -34 ;
# TIME = 0, 0.1, 0.6, 1.1, 1.6, 2.1 ;
#
# First value (index=0) in small_ha.nc is 0.3400644 cm,
# Fourth value (index==3) is -6.50198 cm
#Read
from anuga.coordinate_transforms.redfearn import redfearn
#fid = NetCDFFile(self.test_MOST_file)
fid = NetCDFFile(self.test_MOST_file + '_ha.nc')
first_value = fid.variables['HA'][:][0,0,0]
fourth_value = fid.variables['HA'][:][0,0,3]
fid.close()
#Call conversion (with zero origin)
#ferret2sww('small', verbose=False,
# origin = (56, 0, 0))
ferret2sww(self.test_MOST_file, verbose=self.verbose,
origin = (56, 0, 0))
#Work out the UTM coordinates for first point
zone, e, n = redfearn(-34.5, 150.66667)
#print zone, e, n
#Read output file 'small.sww'
#fid = NetCDFFile('small.sww')
fid = NetCDFFile(self.test_MOST_file + '.sww')
x = fid.variables['x'][:]
y = fid.variables['y'][:]
#Check that first coordinate is correctly represented
assert num.allclose(x[0], e)
assert num.allclose(y[0], n)
#Check first value
stage = fid.variables['stage'][:]
xmomentum = fid.variables['xmomentum'][:]
ymomentum = fid.variables['ymomentum'][:]
#print ymomentum
assert num.allclose(stage[0,0], first_value/100) #Meters
#Check fourth value
assert num.allclose(stage[0,3], fourth_value/100) #Meters
fid.close()
#Cleanup
import os
os.remove(self.test_MOST_file + '.sww')
def test_urs_ungridded2sww (self):
#Zone: 50
#Easting: 240992.578 Northing: 7620442.472
#Latitude: -21 30 ' 0.00000 '' Longitude: 114 30 ' 0.00000 ''
lat_long = [[-21.5,114.5],[-21,114.5],[-21,115]]
time_step_count = 2
time_step = 400
tide = 9000000
base_name, files = self.write_mux(lat_long,
time_step_count, time_step)
urs_ungridded2sww(base_name, mean_stage=tide,
verbose=self.verbose)
# now I want to check the sww file ...
sww_file = base_name + '.sww'
#Let's interigate the sww file
# Note, the sww info is not gridded. It is point data.
fid = NetCDFFile(sww_file)
# Make x and y absolute
x = fid.variables['x'][:]
y = fid.variables['y'][:]
geo_reference = Geo_reference(NetCDFObject=fid)
points = geo_reference.get_absolute(map(None, x, y))
points = ensure_numeric(points)
x = points[:,0]
y = points[:,1]
#Check that first coordinate is correctly represented
#Work out the UTM coordinates for first point
zone, e, n = redfearn(lat_long[0][0], lat_long[0][1])
assert num.allclose([x[0],y[0]], [e,n])
#Check the time vector
times = fid.variables['time'][:]
times_actual = []
for i in range(time_step_count):
times_actual.append(time_step * i)
assert num.allclose(ensure_numeric(times),
ensure_numeric(times_actual))
#Check first value
stage = fid.variables['stage'][:]
xmomentum = fid.variables['xmomentum'][:]
ymomentum = fid.variables['ymomentum'][:]
elevation = fid.variables['elevation'][:]
assert num.allclose(stage[0,0], e +tide) #Meters
#Check the momentums - ua
#momentum = velocity*(stage-elevation)
# elevation = - depth
#momentum = velocity_ua *(stage+depth)
# = n*(e+tide+n) based on how I'm writing these files
#
answer_x = n*(e+tide+n)
actual_x = xmomentum[0,0]
#print "answer_x",answer_x
#print "actual_x",actual_x
assert num.allclose(answer_x, actual_x) #Meters
#Check the momentums - va
#momentum = velocity*(stage-elevation)
# elevation = - depth
#momentum = velocity_va *(stage+depth)
# = e*(e+tide+n) based on how I'm writing these files
#
answer_y = -1*e*(e+tide+n)
actual_y = ymomentum[0,0]
#print "answer_y",answer_y
#print "actual_y",actual_y
assert num.allclose(answer_y, actual_y) #Meters
# check the stage values, first time step.
# These arrays are equal since the Easting values were used as
# the stage
assert num.allclose(stage[0], x +tide) #Meters
# check the elevation values.
# -ve since urs measures depth, sww meshers height,
# these arrays are equal since the northing values were used as
# the elevation
assert num.allclose(-elevation, y) #Meters
fid.close()
self.delete_mux(files)
os.remove(sww_file)
