def test_to_ndim_1(self):
for v in [
429., [429.], [[429.]],
np.array(429.),
np.array([429.]),
np.array([[429.]])
]:
r = npu.to_ndim_1(v)
self.assertTrue(checks.is_numpy_array(r))
self.assertEqual(np.shape(r), (1, ))
npt.assert_almost_equal(r, np.array([429.]))
for v in [[429., 5.], [[429., 5.]], [[[429.], [5.]]],
np.array([429., 5.]),
np.array([[429., 5.]]),
np.array([[[429.], [5.]]])]:
r = npu.to_ndim_1(v)
self.assertTrue(checks.is_numpy_array(r))
self.assertEqual(np.shape(r), (2, ))
npt.assert_almost_equal(r, np.array([429., 5.]))
for v in [[429., 5., 2., 14.], [[429., 5., 2., 14.]],
[[429., 5.], [2., 14.]], [[429.], [5.], [2.], [14.]],
np.array([429., 5., 2., 14.]),
np.array([[429., 5., 2., 14.]]),
np.array([[429., 5.], [2., 14.]]),
np.array([[[429.], [5.], [2.], [14.]]])]:
r = npu.to_ndim_1(v)
self.assertTrue(checks.is_numpy_array(r))
self.assertEqual(np.shape(r), (4, ))
npt.assert_almost_equal(r, np.array([429., 5., 2., 14.]))
npt.assert_equal(npu.to_ndim_1(None), np.array([None]))
a = np.array([2., 14.])
b = npu.to_ndim_1(a, copy=False)
b[1] = 42.
npt.assert_almost_equal(b, np.array([2., 42.]))
npt.assert_almost_equal(a, np.array([2., 42.]))
a = [2., 14.]
b = npu.to_ndim_1(a, copy=False)
b[1] = 42.
npt.assert_almost_equal(b, np.array([2., 42.]))
npt.assert_almost_equal(a, np.array([2., 14.]))
a = [2., 14.]
b = npu.to_ndim_1(a, copy=True)
b[1] = 42.
npt.assert_almost_equal(b, np.array([2., 42.]))
npt.assert_almost_equal(a, np.array([2., 14.]))
def immutable_copy_of(arg):
if checks.is_numpy_array(arg):
result = np.copy(arg) if arg.flags.writeable else arg
else:
result = np.array(arg)
result.flags.writeable = False
return result
def sign(arg):
if isinstance(arg, dt.timedelta):
arg = arg.total_seconds()
elif checks.is_numpy_array(
arg) and arg.dtype == object and np.size(arg) > 0 and isinstance(
arg.item(0), dt.timedelta):
arg = np.vectorize(lambda x: x.total_seconds())(arg)
return np.sign(arg)
def to_scalar(arg, raise_value_error=True):
try:
if checks.is_float(arg): return arg
elif checks.is_numpy_array(arg): return np.asscalar(arg)
else: return np.asscalar(np.array(arg))
except:
if raise_value_error: raise
return arg
def __init__(self, obs_matrix):
super().__init__()
if not checks.is_numpy_array(obs_matrix) and not checks.is_iterable(obs_matrix):
obs_matrix = (obs_matrix,)
self._obs_matrix = npu.make_immutable(
block_diag(
*[npu.to_ndim_2(om, ndim_1_to_col=False, copy=False) for om in obs_matrix]))
self._to_string_helper_KalmanFilterObsModel = None
self._str_KalmanFilterObsModel = None
def are_views_of_same(arg1, arg2):
if not checks.is_numpy_array(arg1) or not checks.is_numpy_array(arg2):
return False
return (arg1.base is arg2) or (arg2.base is arg1) or (
(arg1.base is arg2.base) and arg1.base is not None)
def is_view_of(arg1, arg2):
if not checks.is_numpy_array(arg1) or not checks.is_numpy_array(arg2):
return False
return arg1.base is arg2
