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Deprecated np.object -> object #15

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merged 1 commit into from
Sep 5, 2024
Merged

Deprecated np.object -> object #15

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2 changes: 1 addition & 1 deletion spine/io/read/hdf5.py
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Original file line number Diff line number Diff line change
Expand Up @@ -188,7 +188,7 @@ def load_key(self, in_file, event, data, key):
# If the reference points at a group, unpack
el_refs = in_file[key]['index'][region_ref].flatten()
if len(in_file[key]['index'].shape) == 1:
ret = np.empty(len(el_refs), dtype=np.object)
ret = np.empty(len(el_refs), dtype=object)
ret[:] = [in_file[key]['elements'][r] for r in el_refs]
if len(in_file[key]['elements'].shape) > 1:
for i in range(len(el_refs)):
Expand Down
79 changes: 18 additions & 61 deletions spine/utils/vertex.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,15 +5,9 @@
from .globals import TRACK_SHP, INTER_COL, PRINT_COL, VTX_COLS


def get_vertex(start_points,
end_points,
directions,
semantics,
anchor_vertex=True,
touching_threshold=2.0,
return_mode=False):
'''
Reconstruct the vertex of an individual interaction.
def get_vertex(start_points, end_points, directions, semantics,
anchor_vertex=True, touching_threshold=2.0, return_mode=False):
"""Reconstruct the vertex of an individual interaction.

Parameters
----------
Expand All @@ -30,7 +24,9 @@ def get_vertex(start_points,
with the expection of interactions only composed of showers.
touching_threshold : float, default 2.0
Maximum distance for two particle points to be considered touching
'''
return_mode : bool, default False
If `True`, return the method used to find the vertex
"""
# If there is no particle: return default values
if not len(start_points):
if return_mode:
Expand Down Expand Up @@ -98,11 +94,10 @@ def angular_loss(candidates: nb.float32[:,:],
points: nb.float32[:,:],
directions: nb.float32[:,:],
use_cos: bool = True) -> nb.float32:
'''
Computes the angular/cosine distance between vectors that
join candidate points to the start points of particles and their
respective direction estimates. Values are normalized between
0 (perfect fit) and 1 (complete disagreement).
"""Computes the angular/cosine distance between vectors that join candidate
points to the start points of particles and their respective direction
estimates. Values are normalized between 0 (perfect fit) and 1
(complete disagreement).

Parameters
----------
Expand All @@ -119,7 +114,7 @@ def angular_loss(candidates: nb.float32[:,:],
-------
np.ndarray
(C) Loss for each of the candidates
'''
"""
n_c = len(candidates)
losses = np.empty(n_c, dtype=np.float32)
for i, c in enumerate(candidates):
Expand All @@ -141,8 +136,7 @@ def angular_loss(candidates: nb.float32[:,:],
def get_confluence_points(start_points: nb.float32[:,:],
end_points: nb.float32[:,:] = None,
touching_threshold: nb.float32 = 2.0) -> nb.types.List(nb.float32[:]):
'''
Find the points where multiple particles touch.
"""Find the points where multiple particles touch.

Parameters
----------
Expand All @@ -157,7 +151,7 @@ def get_confluence_points(start_points: nb.float32[:,:],
-------
List[np.ndarray]
List of vertices that correspond to the confluence points
'''
"""
# Create a particle-to-particle distance matrix
n_part = len(start_points)
dist_mat = np.zeros((n_part, n_part), dtype=start_points.dtype)
Expand Down Expand Up @@ -223,10 +217,8 @@ def get_confluence_points(start_points: nb.float32[:,:],
def get_pseudovertex(start_points: nb.float32[:,:],
directions: nb.float32[:,:],
dim: int = 3) -> nb.float32[:]:
'''
Finds the vertex which minimizes the total distance
from itself to all the lines defined by the start points
of particles and their directions.
"""Finds the vertex which minimizes the total distance from itself to all
the lines defined by the start points of particles and their directions.

Parameters
----------
Expand All @@ -236,9 +228,9 @@ def get_pseudovertex(start_points: nb.float32[:,:],
(P, 3) Particle directions
dim : int
Number of dimensions
'''
assert len(start_points),\
'Cannot reconstruct pseudovertex without points'
"""
assert len(start_points), (
"Cannot reconstruct pseudovertex without points.")

if len(start_points) == 1:
return start_points[0]
Expand All @@ -254,38 +246,3 @@ def get_pseudovertex(start_points: nb.float32[:,:],
pseudovtx = np.linalg.pinv(S) @ C

return pseudovtx


def get_truth_vertex(cluster_label,
data_idx,
inter_idx,
primary_label=1):
"""
Getting true vertex for interaction identified by inter_idx

Look at cluster labels, selecting only primary particles
within this interaction, and get vertex which occurs the most.

Parameters
----------
cluster_label: list of np.ndarray
Cluster labels.
data_idx: int
Which entry we are looking at (labels).
inter_idx: int
The true interaction id for which we want the vertex.
primary_label: int, default 1
What integer tags primary particles in kinematics labels
("primary particles" ~ particles coming out of the vertex).

Output
------
np.ndarray
True vertex coordinates. Shape (3,)
"""
inter_mask = cluster_label[data_idx][:, INTER_COL] == inter_idx
primary_mask = cluster_label[data_idx][:, PRINT_COL] == primary_label
mask = inter_mask if (inter_mask & primary_mask).sum() == 0 else inter_mask & primary_mask
vtx, counts = np.unique(cluster_label[data_idx][mask][:, [VTX_COLS[0], VTX_COLS[1], VTX_COLS[2]]], axis=0, return_counts=True)
vtx = vtx[np.argmax(counts)]
return vtx
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