def test_arithmetic(self):
n_tests = 100
max_dim = 5
max_len = 10
ops = [
operator.abs,
operator.neg,
lambda x: x.real,
lambda x: x.imag,
]
binops = [
operator.add,
operator.sub,
operator.mul,
operator.div,
operator.pow,
]
rbinops = [
operator.add,
operator.sub,
operator.mul,
operator.div,
]
for _ in range(n_tests):
n_dim = numpy.random.randint(1, max_dim)
data_shape = numpy.random.randint(1, max_len, (n_dim))
a = BootPack.random(data_shape)
b = BootPack.random(data_shape)
c = numpy.random.random()
for binop in binops:
assert numpy.all(
BootPack(binop(a.true, b.true), binop(a.boot, b.boot))
== binop(a, b))
assert numpy.all(
BootPack(binop(a.true, c), binop(a.boot, c))
== binop(a, c))
for rbinop in rbinops:
assert numpy.all(
BootPack(rbinop(b.true, a.true), rbinop(b.boot, a.boot))
== rbinop(b, a))
assert numpy.all(
BootPack(rbinop(c, a.true), rbinop(c, a.boot))
== rbinop(c, a))
for op in ops:
assert numpy.all(BootPack(op(a.true), op(a.boot)) == op(a))
def test_concatenate(self):
n_dim = numpy.random.randint(2, 5)
data_shape = numpy.random.randint(1, 10, (n_dim))
n_bps = numpy.random.randint(2, 5)
axis = numpy.random.randint(n_dim-1)
bps = [BootPack.random(data_shape) for i in range(n_bps)]
assert numpy.all(
BootPack(
numpy.concatenate(tuple(bp.true for bp in bps), axis=axis),
numpy.concatenate(tuple(bp.boot for bp in bps), axis=axis)) ==
BootPack.concatenate(bps, axis=axis))