def test_point_in_tri():
test_datasets = [
{
'triangle': np.array([[0., 0.], [1., 0.], [0., 1.]]),
'points_inside': [np.array([0.1, 0.1]),
np.array([0.3, 0.3])],
'points_outside': [np.array([5., 5.])],
},
]
for dataset in test_datasets:
for point in dataset['points_inside']:
assert point_in_tri(dataset['triangle'], point)
for point in dataset['points_outside']:
assert ~point_in_tri(dataset['triangle'], point)
def locate_faces(self,
points,
method='celltree',
_copy=False,
_memo=True,
_hash=None):
"""
Returns the face indices, one per point.
Points that are not in the mesh will have an index of -1
If a single point is passed in, a single index will be returned
If a sequence of points is passed in an array of indexes will be returned.
:param points: The points that you want to locate -- (lon, lat). If the shape of point
is 1D, function will return a scalar index. If it is 2D, it will return
a 1D array of indices
:type point: array-like containing one or more points: shape (2,) for one point, shape (N, 2)
for more than one point.
:param method='celltree': method to use. Options are 'celltree', 'simple'.
for 'celltree' the celltree2d pacakge must be installed:
https://github.com/NOAA-ORR-ERD/cell_tree2d/
'simple' is very, very slow for large grids.
:type simple: str
This version utilizes the CellTree data structure.
"""
points = np.asarray(points, dtype=np.float64)
just_one = (points.ndim == 1)
points.shape = (-1, 2)
if not hasattr(self, '_ind_memo_dict'):
self._ind_memo_dict = OrderedDict()
if _memo:
if _hash is None:
_hash = self._hash_of_pts(points)
result = self._get_memoed(points, self._ind_memo_dict, _copy,
_hash)
if result is not None:
return result
if method == 'celltree':
try:
import cell_tree2d
except ImportError:
raise ImportError(
"the cell_tree2d package must be installed to use the celltree search:\n"
"https://github.com/NOAA-ORR-ERD/cell_tree2d/")
if self._tree is None:
self.build_celltree()
indices = self._tree.locate(points)
elif method == 'simple':
indices = np.zeros((points.shape[0]), dtype=IND_DT)
for n, point in enumerate(points):
for i, face in enumerate(self._faces):
f = self._nodes[face]
if point_in_tri(f, point):
indices[n] = i
break
else:
indices[n] = -1
else:
raise ValueError('"method" must be one of: "celltree", "simple"')
if _memo:
self._add_memo(points, indices, self._ind_memo_dict, _copy, _hash)
if just_one:
return indices[0]
else:
return indices