Simple Parallel DemoΒΆ

# Std import block
import time

import numpy as np
import matplotlib.pyplot as plt
import math

import sys

from pysit import *
from pysit.gallery import horizontal_reflector

from pysit.util.parallel import *

from mpi4py import MPI

if __name__ == '__main__':
	# Setup

	comm = MPI.COMM_WORLD
#	comm = MPI.COMM_SELF
	size = comm.Get_size()
	rank = comm.Get_rank()
	
	pwrap = ParallelWrapShot(comm=MPI.COMM_WORLD)

	if rank == 0:
		ttt = time.time()

	#	Define Domain
	pmlx = PML(0.1, 100)
	pmlz = PML(0.1, 100)
	
	x_config = (0.1, 1.0, pmlx, pmlx)
	z_config = (0.1, 0.8, pmlz, pmlz)

	d = RectangularDomain(x_config, z_config)
	
	m = CartesianMesh(d, 90, 70)

	#	Generate true wave speed
	C, C0, m, d = horizontal_reflector(m)

	# Set up shots
	zmin = d.z.lbound
	zmax = d.z.rbound
	zpos = zmin + (1./9.)*zmax
	
	Nshots = size
	sys.stdout.write("{0}: {1}\n".format(rank, Nshots / size))
	
	shots = equispaced_acquisition(m,
	                               RickerWavelet(10.0),
	                               sources=Nshots,
	                               source_depth=zpos,
	                               source_kwargs={},
	                               receivers='max',
	                               receiver_depth=zpos,
	                               receiver_kwargs={},
	                               parallel_shot_wrap=pwrap,
	                               )
	              
	
	# Define and configure the wave solver
	trange = (0.0,3.0)

	solver = ConstantDensityAcousticWave(m, 
	                                     formulation='scalar',
	                                     model_parameters={'C': C}, 
		                                 spatial_accuracy_order=6,
		                                 trange=trange,
		                                 use_cpp_acceleration=True)

	# Generate synthetic Seismic data
	sys.stdout.write('Generating data...')
	base_model = solver.ModelParameters(m,{'C': C})
	tt = time.time()
	generate_seismic_data(shots, solver, base_model)
	sys.stdout.write('{1}:Data generation: {0}s\n'.format(time.time()-tt,rank))
	
	sys.stdout.flush()
	
	comm.Barrier()
	
	if rank == 0:
		tttt = time.time()-ttt
		sys.stdout.write('Total wall time: {0}\n'.format(tttt))
		sys.stdout.write('Total wall time/shot: {0}\n'.format(tttt/Nshots))
	
	objective = TemporalLeastSquares(solver, parallel_wrap_shot=pwrap)
	
	# Define the inversion algorithm
#	invalg = GradientDescent(objective)
	invalg = LBFGS(objective, memory_length=10)
	initial_value = solver.ModelParameters(m, {'C': C0})

	# Execute inversion algorithm
	print('Running LBFGS...')
	tt = time.time()

	nsteps = 2
	
	status_configuration = {'value_frequency'           : 1, 
	                 'residual_frequency'        : 1, 
	                 'residual_length_frequency' : 1, 
	                 'objective_frequency'       : 1, 
	                 'step_frequency'            : 1,
	                 'step_length_frequency'     : 1,
	                 'gradient_frequency'        : 1, 
	                 'gradient_length_frequency' : 1, 
	                 'run_time_frequency'        : 1, 
	                 'alpha_frequency'           : 1,
	                }
	
	
	line_search = 'backtrack'
	
	result = invalg(shots, initial_value, nsteps, 
	                line_search=line_search, 
	                status_configuration=status_configuration, verbose=True)

	print 'Run time:  {0}s'.format(time.time()-tt)


	if rank == 0:
		
		model = result.C.reshape(m.shape(as_grid=True))
		
		from scipy.io import savemat
		
		out = {'result':model, 'true':C.reshape(m.shape(as_grid=True))}
		savemat('test.mat',out)
	
		
	# Do something to visualize the results
#	display_on_grid(C, d, shade_pml=True)
#	display_on_grid(result.C, d, shade_pml=True)
	#display_seismogram(shots[0], clim=[-1,1])
	#display_seismogram(shots[0], wiggle=True, wiggle_skip=1)
	# animate_wave_evolution(ps, domain=d, display_rate=10, shade_pml=True)

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