def test_randomfunc(brng, meth_args):
meth, args = meth_args
mesh = UnitSquareMesh(10, 10)
V0 = VectorFunctionSpace(mesh, "CG", 1)
V1 = FunctionSpace(mesh, "CG", 1)
V = V0 * V1
seed = 123456789
# Original
bgen = getattr(randomgen, brng)(seed=seed)
if brng == 'PCG64':
state = bgen.state
state['state'] = {'state': seed, 'inc': V.comm.Get_rank()}
bgen.state = state
rg_base = randomgen.Generator(bgen)
# Firedrake wrapper
fgen = getattr(randomfunctiongen, brng)(seed=seed)
if brng == 'PCG64':
state = fgen.state
state['state'] = {'state': seed, 'inc': V.comm.Get_rank()}
fgen.state = state
rg_wrap = randomfunctiongen.Generator(fgen)
for i in range(1, 10):
f = getattr(rg_wrap, meth)(V, *args)
with f.dat.vec_ro as v:
if meth in ('rand', 'randn'):
assert np.allclose(getattr(rg_base, meth)(v.local_size), v.array[:])
else:
kwargs = {'size': (v.local_size, )}
assert np.allclose(getattr(rg_base, meth)(*args, **kwargs), v.array[:])
def test_randomfunc_parallel_philox():
mesh = UnitSquareMesh(10, 10)
V0 = VectorFunctionSpace(mesh, "CG", 1)
V1 = FunctionSpace(mesh, "CG", 1)
V = V0 * V1
key_size = 2
# Original
key = np.zeros(key_size, dtype=np.uint64)
key[0] = V.comm.Get_rank()
rg_base = randomgen.Generator(randomgen.Philox(counter=12345678910, key=key))
# Firedrake wrapper
rg_wrap = randomfunctiongen.Generator(randomfunctiongen.Philox(counter=12345678910))
for i in range(1, 10):
f = rg_wrap.beta(V, 0.3, 0.5)
with f.dat.vec_ro as v:
assert np.allclose(rg_base.beta(0.3, 0.5, size=(v.local_size,)), v.array[:])
def test_randomfunc_parallel_pcg64():
mesh = UnitSquareMesh(10, 10)
V0 = VectorFunctionSpace(mesh, "CG", 1)
V1 = FunctionSpace(mesh, "CG", 1)
V = V0 * V1
seed = 123456789
# Original
bgen = randomgen.PCG64(seed=seed)
state = bgen.state
state['state'] = {'state': seed, 'inc': V.comm.Get_rank()}
bgen.state = state
rg_base = randomgen.Generator(bgen)
# Firedrake wrapper
fgen = randomfunctiongen.PCG64(seed=seed)
state = fgen.state
state['state'] = {'state': seed, 'inc': V.comm.Get_rank()}
fgen.state = state
rg_wrap = randomfunctiongen.Generator(fgen)
for i in range(1, 10):
f = rg_wrap.beta(V, 0.3, 0.5)
with f.dat.vec_ro as v:
assert np.allclose(rg_base.beta(0.3, 0.5, size=(v.local_size,)), v.array[:])
def test_randomgen_brng(brng, meth_args_kwargs):
meth, args, kwargs = meth_args_kwargs
seed = 123456789
# Original
bgen = getattr(randomgen, brng)(seed=seed)
if brng == 'PCG64':
state = bgen.state
state['state'] = {'state': seed, 'inc': 0}
bgen.state = state
rg_base = randomgen.Generator(bgen)
# Firedrake wrapper
fgen = getattr(randomfunctiongen, brng)(seed=seed)
if brng == 'PCG64':
state = fgen.state
state['state'] = {'state': seed, 'inc': 0}
fgen.state = state
rg_wrap = randomfunctiongen.Generator(fgen)
if meth == 'bytes':
a0 = getattr(rg_base, meth)(17)
a1 = getattr(rg_wrap, meth)(17)
assert a0 == a1
return
elif meth == 'shuffle':
arr0 = np.arange(17)
arr1 = np.arange(17)
getattr(rg_base, meth)(arr0)
getattr(rg_wrap, meth)(arr1)
assert np.allclose(arr0, arr1)
return
for i in range(1, 10):
assert np.allclose(
getattr(rg_base, meth)(*args, **kwargs),
getattr(rg_wrap, meth)(*args, **kwargs))
def test_randomfunc(brng, meth):
mesh = UnitSquareMesh(10, 10)
V0 = VectorFunctionSpace(mesh, "CG", 1)
V1 = FunctionSpace(mesh, "CG", 1)
V = V0 * V1
seed = 123456789
# Original
bgen = getattr(randomgen, brng)(seed=seed)
if brng == 'PCG64':
state = bgen.state
state['state'] = {'state': seed, 'inc': V.comm.Get_rank()}
bgen.state = state
rg_base = randomgen.Generator(bgen)
# Firedrake wrapper
fgen = getattr(randomfunctiongen, brng)(seed=seed)
if brng == 'PCG64':
state = fgen.state
state['state'] = {'state': seed, 'inc': V.comm.Get_rank()}
fgen.state = state
rg_wrap = randomfunctiongen.Generator(fgen)
if meth == 'beta':
args = (0.3, 0.5)
elif meth == 'binomial':
args = (7, 0.3)
elif meth == 'chisquare':
args = (7,)
elif meth == 'choice':
args = (1234 * 5678,)
elif meth == 'exponential':
args = ()
elif meth == 'f':
args = (7, 17)
elif meth == 'gamma':
args = (3.14,)
elif meth == 'geometric':
args = (0.5,)
elif meth == 'gumbel':
args = ()
elif meth == 'hypergeometric':
args = (17, 2 * 17, 3 * 17 - 1)
elif meth == 'laplace':
args = ()
elif meth == 'logistic':
args = ()
elif meth == 'lognormal':
args = ()
elif meth == 'logseries':
args = (0.3,)
elif meth == 'negative_binomial':
args = (7, 0.3)
elif meth == 'noncentral_chisquare':
args = (7, 3.14)
elif meth == 'noncentral_f':
args = (7, 17, 3.14)
elif meth == 'normal':
args = ()
elif meth == 'pareto':
args = (3.14,)
elif meth == 'poisson':
args = ()
elif meth == 'power':
args = (3.14,)
elif meth == 'random':
args = ()
elif meth == 'rayleigh':
args = ()
elif meth == 'standard_cauchy':
args = ()
elif meth == 'standard_exponential':
args = ()
elif meth == 'standard_gamma':
args = (3.14,)
elif meth == 'standard_normal':
args = ()
elif meth == 'standard_t':
args = (7,)
elif meth == 'triangular':
args = (2.71, 3.14, 10.)
elif meth == 'uniform':
args = ()
elif meth == 'vonmises':
args = (2.71, 3.14)
elif meth == 'wald':
args = (2.71, 3.14)
elif meth == 'weibull':
args = (3.14,)
elif meth == 'zipf':
args = (3.14,)
else:
raise RuntimeError("Unknown method: add test for %s." % meth)
for i in range(1, 10):
f = getattr(rg_wrap, meth)(V, *args)
with f.dat.vec_ro as v:
if meth in ('rand', 'randn'):
assert np.allclose(getattr(rg_base, meth)(v.local_size), v.array[:])
else:
kwargs = {'size': (v.local_size, )}
assert np.allclose(getattr(rg_base, meth)(*args, **kwargs), v.array[:])
def test_randomgen_brng(brng, meth):
seed = 123456789
# Original
bgen = getattr(randomgen, brng)(seed=seed)
if brng == 'PCG64':
state = bgen.state
state['state'] = {'state': seed, 'inc': 0}
bgen.state = state
rg_base = randomgen.Generator(bgen)
# Firedrake wrapper
fgen = getattr(randomfunctiongen, brng)(seed=seed)
if brng == 'PCG64':
state = fgen.state
state['state'] = {'state': seed, 'inc': 0}
fgen.state = state
rg_wrap = randomfunctiongen.Generator(fgen)
if meth == 'beta':
args = (0.3, 0.5)
kwargs = {'size': 17}
elif meth == 'binomial':
args = (7, 0.3)
kwargs = {'size': 17}
elif meth == 'bytes':
a0 = getattr(rg_base, meth)(17 + 1)
a1 = getattr(rg_wrap, meth)(17 + 1)
assert a0 == a1
return
elif meth == 'chisquare':
args = (7, )
kwargs = {'size': 17}
elif meth == 'choice':
args = (17, )
kwargs = {'size': 17}
elif meth == 'complex_normal':
args = ()
kwargs = {'size': 17}
elif meth == 'dirichlet':
args = (np.arange(1, 17 + 1), )
kwargs = {'size': 17}
elif meth == 'exponential':
args = ()
kwargs = {'size': 17}
elif meth == 'f':
args = (7, 17)
kwargs = {'size': 17}
elif meth == 'gamma':
args = (3.14, )
kwargs = {'size': 17}
elif meth == 'geometric':
args = (0.5, )
kwargs = {'size': 17}
elif meth == 'gumbel':
args = ()
kwargs = {'size': 17}
elif meth == 'hypergeometric':
args = (17, 2 * 17, 3 * 17 - 1)
kwargs = {'size': 17}
elif meth == 'laplace':
args = ()
kwargs = {'size': 17}
elif meth == 'logistic':
args = ()
kwargs = {'size': 17}
elif meth == 'lognormal':
args = ()
kwargs = {'size': 17}
elif meth == 'logseries':
args = (0.3, )
kwargs = {'size': 17}
elif meth == 'multinomial':
args = (17, [0.1, 0.2, 0.3, 0.4])
kwargs = {'size': 17}
elif meth == 'multivariate_hypergeometric':
args = ([16, 8, 4], 6)
kwargs = {'size': 3}
elif meth == 'multivariate_normal':
args = ([3.14, 2.71], [[1.00, 0.01], [0.01, 2.00]])
kwargs = {'size': 17}
elif meth == 'negative_binomial':
args = (7, 0.3)
kwargs = {'size': 17}
elif meth == 'noncentral_chisquare':
args = (7, 3.14)
kwargs = {'size': 17}
elif meth == 'noncentral_f':
args = (7, 17, 3.14)
kwargs = {'size': 17}
elif meth == 'normal':
args = ()
kwargs = {'size': 17}
elif meth == 'pareto':
args = (3.14, )
kwargs = {'size': 17}
elif meth == 'permutation':
args = (10 * 17, )
kwargs = {}
elif meth == 'poisson':
args = ()
kwargs = {'size': 17}
elif meth == 'power':
args = (3.14, )
kwargs = {'size': 17}
elif meth == 'rand':
args = (17, )
kwargs = {}
elif meth == 'random':
args = ()
kwargs = {'size': 17}
elif meth == 'rayleigh':
args = ()
kwargs = {'size': 17}
elif meth == 'shuffle':
arr0 = np.arange(17 + 10)
arr1 = np.arange(17 + 10)
getattr(rg_base, meth)(arr0)
getattr(rg_wrap, meth)(arr1)
assert np.allclose(arr0, arr1)
return
elif meth == 'standard_cauchy':
args = ()
kwargs = {'size': 17}
elif meth == 'standard_exponential':
args = ()
kwargs = {'size': 17}
elif meth == 'standard_gamma':
args = (3.14, )
kwargs = {'size': 17}
elif meth == 'standard_normal':
args = ()
kwargs = {'size': 17}
elif meth == 'standard_t':
args = (7, )
kwargs = {'size': 17}
elif meth == 'tomaxint':
args = (7, )
kwargs = {}
elif meth == 'triangular':
args = (2.71, 3.14, 10.)
kwargs = {'size': 17}
elif meth == 'uniform':
args = ()
kwargs = {'size': 17}
elif meth == 'vonmises':
args = (2.71, 3.14)
kwargs = {'size': 17}
elif meth == 'wald':
args = (2.71, 3.14)
kwargs = {'size': 17}
elif meth == 'weibull':
args = (3.14, )
kwargs = {'size': 17}
elif meth == 'zipf':
args = (3.14, )
kwargs = {'size': 17}
else:
raise RuntimeError("Unknown method: add test for %s." % meth)
for i in range(1, 10):
assert np.allclose(
getattr(rg_base, meth)(*args, **kwargs),
getattr(rg_wrap, meth)(*args, **kwargs))