def build_xbeach(diroutmain,
nf,
sigma,
variable_name,
interp_fn_orig,
l_max,
l_min=1):
total_length_x = 1250
# set up parameters for XBeach input
p = dict(
#processes
order=1,
bedfriccoef=55,
flow=1,
sedtrans=0,
morphology=0,
avalanching=0,
cyclic=0,
wavemodel='nonh',
#grid
dx=total_length_x / nf,
dy=0,
vardx=1,
posdwn=0,
xfile='x.txt',
xori=0,
yori=0,
Hrms=2.5,
Trep=10,
dir0=270,
instat='stat',
front='nonh_1d',
#output
outputformat='netcdf',
tintg=200,
tintm=200,
tstop=200,
nglobalvar=['zb', 'zs', 'hh'],
nmeanvar=['zs', 'u'])
#nx = 2**6)
b = dict(height=2, length_x=total_length_x)
logger = logging.getLogger(__name__)
logger.info('setup.py is called for')
# create directory where XBeach will run
path = os.path.join(diroutmain, 'test')
if not os.path.exists(path):
os.makedirs(path)
# if less than minimum level considered in algorithm, return 0
if nf <= l_min:
maximum_surge = 0
return maximum_surge
if variable_name == 'bed_slope':
xb = XBeachModel(**p)
bp = b.copy()
bp.update(p)
bathy = Bathymetry(**bp)
bed_slope = sigma
bathy.mc_slope(b['length_x'] / (l_max), 0.6, bed_slope)
interp_fn = interpolate.interp1d(bathy.x, bathy.z)
dx = b['length_x'] / nf
x = np.arange(0, b['length_x'] + dx, dx)
z = interp_fn(x)
else:
bed_slope = 0.15
exec("p['" + variable_name + "'] = " + str(sigma))
dx = b['length_x'] / nf
x = np.arange(0, b['length_x'] + dx, dx)
z = interp_fn_orig(x)
xb = XBeachModel(**p)
bp = b.copy()
bp.update(p)
bathy = Bathymetry(**bp)
XBbathy = XBeachBathymetry(x, z)
logger.debug(XBbathy)
xb['bathymetry'] = XBbathy
os.chdir(path)
xb.write(path)
# run XBeach
subprocess.check_call(
"/rds/general/user/mc4117/home/trunk/src/xbeach/xbeach > /dev/null",
shell=True)
# collect output
dataset = Dataset('xboutput.nc')
x_array = dataset.variables['globalx'][0]
a = (dataset.variables['zs_max'][0] -
dataset.variables['zb'][0]).data[0][0:len(x_array) - 1]
# find point at which the water level stops being above the bed level
count = len(a[a > 0.00000001])
maximum_surge = x_array[count - 1] / total_length_x
return maximum_surge
def sample():
np.random.seed()
return (np.random.uniform(0.035, 0.5, 1))[0]
M = 2 # integer factor that meshsize is refined by at each level
Lmin = 2 # minimum refinement level
Lmax = 12 # maximum refinement level
L = 7
# list of all levels considered
l_element = []
for i in range(Lmin, Lmax+1):
l_element.append(i)
bathy = Bathymetry()
bathy.length_x = 1250
bathy.dx = bathy.length_x/M**(Lmax+1)
bathy.mc_slope(bathy.dx, 0.6, 0.15)
# interpolation function used to generate bed on new x and y grid
interp_orig_fn = interpolate.interp1d(bathy.x, bathy.z)
# epsilon values - for CDF calculation this should be set to epsilon = [0.0002]
epsilon = [0.0002, 0.0005, 0.0008, 0.0012, 0.002, 0.003]
N0 = 750 # number of samples used in preliminary run
# set the function which builds XBeach inputs, runs XBeach and records output
build_output = bd_fns.build_xbeach
def sample():
np.random.seed()
return (np.random.uniform(0.035, 0.5, 1))[0]
M = 2 # integer factor that meshsize is refined by at each level
Lmin = 2 # minimum refinement level
Lmax = 8 # maximum refinement level
L = 7
# list of all levels considered
l_element = []
for i in range(Lmin, Lmax + 1):
l_element.append(i)
bathy = Bathymetry()
bathy.length_x = 1250
bathy.length_y = 1000
bathy.dx = bathy.length_x / M**Lmax
bathy.dy = bathy.length_y / M**Lmax
bathy.mc_slope_test(bathy.dx, bathy.dy, 0.6, 0.15, 0.15)
# interpolation function used to generate bed on new x and y grid
interp_orig_fn = interpolate.interp2d(bathy.x, bathy.y, bathy.z)
# epsilon values - for CDF calculation this should be set to epsilon = [0.001]
epsilon = [0.001, 0.0017, 0.0025, 0.0035, 0.005, 0.006]
N0 = 500 # number of samples used in preliminary run
# set the function which builds XBeach inputs, runs XBeach and records output
# set the function which builds XBeach inputs, runs XBeach and records output
build_output = bd_fns.build_xbeach
no_parallel_processes = 48 # should be equal to number of cores
# directory where XBeach files will be dumped and code will run - this needs to be changed to reflect name of folder
path_stem = '/rds/general/user/mc4117/home/MLMC_Code/slope_1d_mc/1/'
variable_name = 'bed_slope' # name of uncertain variable
M = 2 # integer factor that meshsize is refined by at each level
t1 = time.time()
L_eps = 13 # fineness of grid
bathy = Bathymetry()
bathy.length_x = 1250
bathy.dx = bathy.length_x / M**L_eps
bathy.mc_slope(bathy.dx, 0.6, 0.15)
# interpolation function used to generate bed on new x and y grid
interp_orig_fn = interpolate.interp1d(bathy.x, bathy.z)
# run monte carlo algorithm
outputf, real_soln, file_name = mc_fn.monte_carlo_main(2000, M**L_eps,
build_output, path_stem,
sample, variable_name,
no_parallel_processes,
interp_orig_fn)
elif runs[k] in ['m1n3']:
p['mmpi'] = 1
p['nmpi'] = 3
nprocesses = 3
elif runs[k] in ['m3n3']:
p['mmpi'] = 3
p['nmpi'] = 3
nprocesses = 9
###MAKING THE BATHYMETRIES AND CREATING THE XBEACH INPUT FILES###
xb = XBeachModel(**p)
bp = b.copy()
bp.update(p)
bathy = Bathymetry(**bp)
if runs[k] in ['m1', 'm3']:
if b['shape'][i] in ['flat']:
bathy.flat_1d()
elif b['shape'][i] in ['dune']:
bathy.dune_1d()
XBbathy = XBeachBathymetry(bathy.x, bathy.z)
else:
if b['shape'][i] in ['flat']:
bathy.flat_2d()
elif b['shape'][i] in ['dune']:
bathy.dune_2d()
XBbathy = XBeachBathymetry(bathy.x, bathy.y, bathy.z)