def test_divides(self):
divisible = [(1, 1), (2, 1), (100, 1), (50, 2), (12, 3)]
not_divisible = [(1, 0), (5, 0), (5, 4), (13, 2), (3, 6)]
for (n, m) in divisible:
self.assertTrue(tools.divides(n, m))
for (n, m) in not_divisible:
self.assertFalse(tools.divides(n, m))
def test_divides(self):
divisible = [(1, 1), (2, 1), (100, 1), (50, 2), (12, 3)]
not_divisible = [(1, 0), (5, 0), (5, 4), (13, 2), (3, 6)]
for (n, m) in divisible:
self.assertTrue(tools.divides(n, m))
for (n, m) in not_divisible:
self.assertFalse(tools.divides(n, m))
def evaluate_gradient(self):
""" Performs adjoint simulation to evaluate gradient
"""
system.run('solver', 'eval_grad',
path=PATH.GRAD,
export_traces=divides(optimize.iter, PAR.SAVETRACES))
self.write_gradient(path=PATH.GRAD, suffix='new')
def evaluate_gradient(self):
""" Performs adjoint simulation to evaluate gradient
"""
system.run('solver', 'eval_grad',
path=PATH.GRAD,
export_traces=divides(optimize.iter, PAR.SAVETRACES))
self.write_gradient(path=PATH.GRAD, suffix='new')
def finalize(self):
""" Saves results from current model update iteration
"""
system.checkpoint()
if divides(optimize.iter, PAR.SAVEMODEL):
self.save_model()
if divides(optimize.iter, PAR.SAVEGRADIENT):
self.save_gradient()
if divides(optimize.iter, PAR.SAVEKERNELS):
self.save_kernels()
if divides(optimize.iter, PAR.SAVETRACES):
self.save_traces()
if divides(optimize.iter, PAR.SAVERESIDUALS):
self.save_residuals()
def finalize(self):
""" Saves results from current model update iteration
"""
self.checkpoint()
if divides(optimize.iter, PAR.SAVEMODEL):
self.save_model()
if divides(optimize.iter, PAR.SAVEGRADIENT):
self.save_gradient()
if divides(optimize.iter, PAR.SAVEKERNELS):
self.save_kernels()
if divides(optimize.iter, PAR.SAVETRACES):
self.save_traces()
if divides(optimize.iter, PAR.SAVERESIDUALS):
self.save_residuals()
def evaluate_gradient(self):
""" Performs adjoint simulation to evaluate gradient
"""
system.run('solver',
'eval_grad',
hosts='all',
path=PATH.GRAD,
export_traces=divides(optimize.iter, PAR.SAVETRACES))
postprocess.write_gradient(path=PATH.GRAD)
src = join(PATH.GRAD, 'gradient')
dst = join(PATH.OPTIMIZE, 'g_new')
savenpy(dst, solver.merge(solver.load(src, suffix='_kernel')))
