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import numpy as np
import torch
from torch_scatter import scatter_add, scatter_sum
from sklearn.preprocessing import StandardScaler
from torch_scatter import scatter_sum
from src.dataset.functions_data import (
get_ratios,
find_mask_no_energy,
find_cluster_id,
get_particle_features,
get_hit_features,
calculate_distance_to_boundary,
concatenate_Particles_GT,
)
def create_inputs_from_table(
output, hits_only, prediction=False, hit_chis=False, pos_pxpy=False, is_Ks=False
):
"""Used by graph creation to get nodes and edge features
Args:
output (_type_): input from the root reading
hits_only (_type_): reading only hits or also tracks
prediction (bool, optional): if running in eval mode. Defaults to False.
Returns:
_type_: all information to construct a graph
"""
number_hits = np.int32(np.sum(output["pf_mask"][0]))
number_part = np.int32(np.sum(output["pf_mask"][1]))
(
pos_xyz_hits,
pos_pxpypz,
p_hits,
e_hits,
hit_particle_link,
pandora_cluster,
pandora_cluster_energy,
pfo_energy,
pandora_mom,
pandora_ref_point,
unique_list_particles,
cluster_id,
hit_type_feature,
pandora_pfo_link,
daughters,
hit_link_modified,
connection_list,
chi_squared_tracks,
) = get_hit_features(
output,
number_hits,
prediction,
number_part,
hit_chis=hit_chis,
pos_pxpy=pos_pxpy,
is_Ks=is_Ks,
)
# features particles
y_data_graph = get_particle_features(
unique_list_particles, output, prediction, connection_list
)
assert len(y_data_graph) == len(unique_list_particles)
# remove particles that have no energy, no hits or only track hits
mask_hits, mask_particles = find_mask_no_energy(
cluster_id,
hit_type_feature,
e_hits,
y_data_graph,
daughters,
prediction,
is_Ks=is_Ks,
)
# create mapping from links to number of particles in the event
cluster_id, unique_list_particles = find_cluster_id(hit_particle_link[~mask_hits])
y_data_graph.mask(~mask_particles)
if prediction:
if is_Ks:
result = [
y_data_graph, # y_data_graph[~mask_particles],
p_hits[~mask_hits],
e_hits[~mask_hits],
cluster_id,
hit_particle_link[~mask_hits],
pos_xyz_hits[~mask_hits],
pos_pxpypz[~mask_hits],
pandora_cluster[~mask_hits],
pandora_cluster_energy[~mask_hits],
pandora_mom[~mask_hits],
pandora_ref_point[~mask_hits],
pfo_energy[~mask_hits],
pandora_pfo_link[~mask_hits],
hit_type_feature[~mask_hits],
hit_link_modified[~mask_hits],
daughters[~mask_hits]
]
else:
result = [
y_data_graph, # y_data_graph[~mask_particles],
p_hits[~mask_hits],
e_hits[~mask_hits],
cluster_id,
hit_particle_link[~mask_hits],
pos_xyz_hits[~mask_hits],
pos_pxpypz[~mask_hits],
pandora_cluster[~mask_hits],
pandora_cluster_energy[~mask_hits],
pandora_mom,
pandora_ref_point,
pfo_energy[~mask_hits],
pandora_pfo_link[~mask_hits],
hit_type_feature[~mask_hits],
hit_link_modified[~mask_hits],
]
else:
result = [
y_data_graph, # y_data_graph[~mask_particles],
p_hits[~mask_hits],
e_hits[~mask_hits],
cluster_id,
hit_particle_link[~mask_hits],
pos_xyz_hits[~mask_hits],
pos_pxpypz[~mask_hits],
pandora_cluster,
pandora_cluster_energy,
pandora_mom,
pandora_ref_point,
pfo_energy,
pandora_pfo_link,
hit_type_feature[~mask_hits],
hit_link_modified[~mask_hits],
daughters[~mask_hits]
]
if hit_chis:
result.append(
chi_squared_tracks[~mask_hits],
)
else:
result.append(None)
hit_type = hit_type_feature[~mask_hits]
# if hits only remove tracks, otherwise leave tracks
if hits_only:
hit_mask = (hit_type == 0) | (hit_type == 1)
hit_mask = ~hit_mask
for i in range(1, len(result)):
if result[i] is not None:
result[i] = result[i][hit_mask]
hit_type_one_hot = torch.nn.functional.one_hot(
hit_type_feature[~mask_hits][hit_mask] - 2, num_classes=2
)
else:
# if we want the tracks keep only 1 track hit per charged particle.
hit_mask = hit_type == 10
hit_mask = ~hit_mask
for i in range(1, len(result)):
if result[i] is not None:
# if len(result[i].shape) == 2 and result[i].shape[0] == 3:
# result[i] = result[i][:, hit_mask]
# else:
# result[i] = result[i][hit_mask]
result[i] = result[i][hit_mask]
hit_type_one_hot = torch.nn.functional.one_hot(
hit_type_feature[~mask_hits][hit_mask], num_classes=5
)
result.append(hit_type_one_hot)
result.append(connection_list)
return result
def remove_hittype0(graph):
filt = graph.ndata["hit_type"] == 0
# graph.ndata["hit_type"] -= 1
return dgl.remove_nodes(graph, torch.where(filt)[0])
def store_track_at_vertex_at_track_at_calo(graph):
# To make it compatible with clustering, remove the 0 hit type nodes and store them as pos_pxpypz_at_vertex
tracks_at_calo = graph.ndata["hit_type"] == 1
tracks_at_vertex = graph.ndata["hit_type"] == 0
part = graph.ndata["particle_number"].long()
assert (part[tracks_at_calo] == part[tracks_at_vertex]).all()
graph.ndata["pos_pxpypz_at_vertex"] = torch.zeros_like(graph.ndata["pos_pxpypz"])
graph.ndata["pos_pxpypz_at_vertex"][tracks_at_calo] = graph.ndata["pos_pxpypz"][tracks_at_vertex]
return remove_hittype0(graph)
def create_graph(
output,
config=None,
n_noise=0,
):
ks_dataset = np.float32(np.sum(output["pf_mask"][2]))
hits_only = config.graph_config.get(
"only_hits", False
) # Whether to only include hits in the graph
# standardize_coords = config.graph_config.get("standardize_coords", False)
extended_coords = config.graph_config.get("extended_coords", False)
prediction = config.graph_config.get("prediction", False)
hit_chis = config.graph_config.get("hit_chis_track", False)
pos_pxpy = config.graph_config.get("pos_pxpy", False)
is_Ks = (torch.sum(torch.Tensor([ks_dataset])))!=0 #config.graph_config.get("ks", False)
(
y_data_graph,
p_hits,
e_hits,
cluster_id,
hit_particle_link,
pos_xyz_hits,
pos_pxpypz,
pandora_cluster,
pandora_cluster_energy,
pandora_mom,
pandora_ref_point,
pandora_pfo_energy,
pandora_pfo_link,
hit_type,
hit_link_modified,
daugthers,
chi_squared_tracks,
hit_type_one_hot,
connections_list,
) = create_inputs_from_table(
output,
hits_only=hits_only,
prediction=prediction,
hit_chis=hit_chis,
pos_pxpy=pos_pxpy,
is_Ks=is_Ks,
)
graph_coordinates = pos_xyz_hits # / 3330 # divide by detector size
if pos_xyz_hits.shape[0] > 0:
graph_empty = False
g = dgl.graph(([], []))
g.add_nodes(graph_coordinates.shape[0])
if hits_only == False:
hit_features_graph = torch.cat(
(graph_coordinates, hit_type_one_hot, e_hits, p_hits), dim=1
) # dims = 8
else:
hit_features_graph = torch.cat(
(graph_coordinates, hit_type_one_hot, e_hits, p_hits), dim=1
) # dims = 9
g.ndata["h"] = hit_features_graph
g.ndata["pos_hits_xyz"] = pos_xyz_hits
g.ndata["pos_pxpypz"] = pos_pxpypz
g = calculate_distance_to_boundary(g)
g.ndata["hit_type"] = hit_type
g.ndata[
"e_hits"
] = e_hits # if no tracks this is e and if there are tracks this fills the tracks e values with p
if hit_chis:
g.ndata["chi_squared_tracks"] = chi_squared_tracks
g.ndata["particle_number"] = cluster_id
g.ndata["hit_link_modified"] = hit_link_modified
g.ndata["daugthers"] = daugthers
g.ndata["particle_number_nomap"] = hit_particle_link
if prediction:
g.ndata["pandora_cluster"] = pandora_cluster
g.ndata["pandora_pfo"] = pandora_pfo_link
g.ndata["pandora_cluster_energy"] = pandora_cluster_energy
g.ndata["pandora_pfo_energy"] = pandora_pfo_energy
if is_Ks:
g.ndata["pandora_momentum"] = pandora_mom
g.ndata["pandora_reference_point"] = pandora_ref_point
y_data_graph.calculate_corrected_E(g, connections_list)
if ks_dataset>0: #is_Ks == True:
if y_data_graph.pid.flatten().shape[0] == 4 and np.count_nonzero(y_data_graph.pid.flatten() == 22) == 4:
graph_empty = False
else:
graph_empty = True
if g.ndata["h"].shape[0] < 10 or (set(g.ndata["hit_type"].unique().tolist()) == set([0, 1]) and g.ndata["hit_type"][g.ndata["hit_type"] == 1].shape[0] < 10):
graph_empty = True # less than 10 hits
if is_Ks == False:
if len(y_data_graph) < 4:
graph_empty = True
else:
graph_empty = True
g = 0
y_data_graph = 0
if pos_xyz_hits.shape[0] < 10:
graph_empty = True
print("graph_empty", graph_empty, pos_xyz_hits.shape[0])
if graph_empty:
return [g, y_data_graph], graph_empty
return [store_track_at_vertex_at_track_at_calo(g), y_data_graph], graph_empty
def graph_batch_func(list_graphs):
"""collator function for graph dataloader
Args:
list_graphs (list): list of graphs from the iterable dataset
Returns:
batch dgl: dgl batch of graphs
"""
list_graphs_g = [el[0] for el in list_graphs]
# list_y = add_batch_number(list_graphs)
# ys = torch.cat(list_y, dim=0)
# ys = torch.reshape(ys, [-1, list_y[0].shape[1]])
ys = concatenate_Particles_GT(list_graphs)
bg = dgl.batch(list_graphs_g)
# reindex particle number
return bg, ys
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