def test_save_load():
p = PointMass(n=0, m=10.0, tag="pointmass")
file = Path(tempdir) / "point_mass.toml"
p.save(file)
p_loaded = p.load(file)
assert p == p_loaded
def test_local_index():
n = 0
mx = 1.0
my = 2.0
p = PointMass(n=n, mx=mx, my=my)
assert p.dof_local_index().x_0 == 0
assert p.dof_local_index().y_0 == 1
def random_var(self, is_random, *args):
"""Generate a list of objects as random attributes.
This function creates a list of objects with random values for selected
attributes from ross.PointMass.
Parameters
----------
is_random : list
List of the object attributes to become stochastic.
*args : dict
Dictionary instanciating the ross.PointMass class.
The attributes that are supposed to be stochastic should be
set as lists of random variables.
Returns
-------
f_list : generator
Generator of random objects.
"""
args_dict = args[0]
new_args = []
for i in range(len(args_dict[is_random[0]])):
arg = []
for key, value in args_dict.items():
if key in is_random:
arg.append(value[i])
else:
arg.append(value)
new_args.append(arg)
f_list = (PointMass(*arg) for arg in new_args)
return f_list
def test_point_mass():
# fmt: off
m0 = np.array([[10.0, 0], [0, 10.0]])
# fmt: on
p = PointMass(n=0, m=10.0)
assert_allclose(m0, p.M())
assert_allclose(np.zeros((2, 2)), p.K())
assert_allclose(np.zeros((2, 2)), p.C())
assert_allclose(np.zeros((2, 2)), p.G())
def test_pickle():
p = PointMass(n=0, m=10.0, tag="pointmass")
p_pickled = pickle.loads(pickle.dumps(p))
assert p == p_pickled