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track_efficiency.py
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track_efficiency.py
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from torch_geometric.data import Data, Dataset
from datasets.particle import TrackMLParticleTrackingDataset
from models.EdgeNetWithCategories import EdgeNetWithCategories
from models.InteractionNetwork import InteractionNetwork
# from princeton_gnn_tracking.models.EC1.ec1 import GNNSegmentClassifier
import pickle
import copy
import argparse
import yaml
import torch
import numpy as np
from tqdm import tqdm
from unionfind import unionfind
from matplotlib import pyplot as plt
def main(args):
"""Main function"""
# Parse the command line
# args = parse_args()
group = args.group
model_group = args.model
type = args.type
config_file = '/data/gnn_code/training_data/' + group + '/config.yaml'
# Nevents = args.n_events
# pt_range = [args.pt_min, args.pt_max]
with open(config_file) as f:
config = yaml.load(f)
selection = config['selection']
# n_events = config['n_files']
n_events = 200
# n_events = 500
# n_events = 10
# n_events = 24
trackml_data = TrackMLParticleTrackingDataset('/data/gnn_code/training_data/' + group,
# trackml_data = TrackMLParticleTrackingDataset('/data/gnn_code/training_data/test_geometric',
volume_layer_ids=selection['volume_layer_ids'],
layer_pairs=selection['layer_pairs'],
layer_pairs_plus=selection['layer_pairs_plus'],
pt_range=selection['pt_range'],
# pt_range=pt_range,
eta_range=selection['eta_range'],
phi_slope_max=selection['phi_slope_max'],
# phi_slope_max=.001,
z0_max=selection['z0_max'],
diff_phi=selection['diff_phi'],
diff_z=selection['diff_z'],
# z0_max=150,
n_phi_sections=selection['n_phi_sections'],
n_eta_sections=selection['n_eta_sections'],
# n_phi_sections=4,
# n_eta_sections=2,
# augments=selection['construct_augmented_graphs'],
augments=False,
intersect=selection['remove_intersecting_edges'],
hough=selection['hough_transform'],
noise=selection['noise'],
duplicates=selection['duplicates'],
secondaries=selection['secondaries'],
tracking=True,
# n_workers=24,
n_tasks=1,
n_events=n_events,
data_type=config['data_type'],
mmap=selection['module_map'],
N_modules=selection['N_modules']
# n_events=Nevents,
# directed=True,
# layer_pairs_plus=True,
)
if (args.reprocess):
trackml_data.process(True)
# Print properties of the data set
print()
print("Graph Construction Features")
print("number_of_graphs = " + str(n_events))
print("number_of_phi_sections = " + str(selection['n_phi_sections']))
print("number_of_eta_sections = " + str(selection['n_eta_sections']))
print("edge_features_used = " + str(selection['hough_transform']))
print("layer_pairs_plus_used = " + str(selection['layer_pairs_plus']))
print()
print("Truth Level Cuts Applied")
print("pt_range = " + str(selection['pt_range']))
print("noise_hits_present = " + str(selection['noise']))
print("duplicate_hits_from_same_paticle_within_layer_present = " + str(selection['duplicates']))
print()
print("Geometric Cuts Applied")
print("eta_range = " + str(selection['eta_range']))
print("phi_slope_max = " + str(selection['phi_slope_max']))
print("z0_max = " + str(selection['z0_max']))
print("diff_phi = " + str(selection['diff_phi']))
print("diff_z = " + str(selection['diff_z']))
print("intersecting_line_cut_used = " + str(selection['remove_intersecting_edges']))
print()
print("Inference Graph Node Properties")
print("Average_Graph_Node_Count = " + str(trackml_data.average_node_count))
print("Average_Total_Pixel_Node_Count = " + str(trackml_data.average_total_pixel_node_count))
print("Average_Total_Global_Node_Count = " + str(trackml_data.average_total_node_count))
print()
print("Inference Graph Edge Properties")
print("Average_Graph_Edge_Count = " + str(trackml_data.average_edge_count))
print("Average_Graph_True_Edge_Count = " + str(trackml_data.average_true_edge_count))
print("Average_Graph_Purity = " + str(trackml_data.average_true_edge_count / trackml_data.average_edge_count))
print("Average_Pruned_Pixel_True_Edge_Count = " + str(trackml_data.average_pruned_pixel_true_edge_count))
print("Average_Total_Pixel_True_Edge_Count = " + str(trackml_data.average_total_pixel_true_edge_count))
print("Average_Total_Global_True_Edge_Count = " + str(trackml_data.average_total_true_edge_count))
print()
print("Inference Graph Track Properties")
print("Average_Pixel_Track_Threshold_Count = " + str(trackml_data.average_pixel_track_threshold_count))
print("Average_Pixel_Track_Count = " + str(trackml_data.average_pixel_track_count))
print("Average_Global_Track_Count = " + str(trackml_data.average_total_track_count))
print()
# trackml_data.draw(0)
# print(config_file)
N_attr = trackml_data[0].edge_attr.shape[1]
Hits_minimum = 3
pt_cut_for_eta = 1.0
if (type == "edge_classifier"):
if(N_attr == 4):
model_fname = '/data/gnn_code/hgcal_ldrd/output/'+model_group+'/checkpoints/model_checkpoint_EdgeNetWithCategories_259587_d1ebe35288_atkinsn2.best.pth.tar'
else:
model_fname = '/data/gnn_code/hgcal_ldrd/output/'+model_group+'/checkpoints/model_checkpoint_EdgeNetWithCategories_259075_140efb4178_atkinsn2.best.pth.tar'
mdl = EdgeNetWithCategories(input_dim=3, hidden_dim=64, edge_dim=N_attr, output_dim=2, n_iters=6).to('cuda:0')
# mdl = EdgeNetWithCategories(input_dim=3, hidden_dim=64, edge_dim=N_attr, output_dim=2, n_iters=6).to('cpu:0')
elif (type == "interaction_network"):
# model_fname = '/data/gnn_code/hgcal_ldrd/output/'+model_group+'/checkpoints/model_checkpoint_InteractionNetwork_6448_77ce67e079_atkinsn2.best.pth.tar'
# model_fname = '/data/gnn_code/hgcal_ldrd/output/'+model_group+'/checkpoints/model_checkpoint_InteractionNetwork_9558_f9fcff2bfe_atkinsn2.best.pth.tar'
# model_fname = '/data/gnn_code/hgcal_ldrd/output/'+model_group+'/checkpoints/model_checkpoint_InteractionNetwork_22488_7d94caeb8a_atkinsn2.best.pth.tar'
# model_fname = '/data/gnn_code/hgcal_ldrd/output/'+model_group+'/checkpoints/model_checkpoint_InteractionNetwork_73658_99bfbda303_atkinsn2.best.pth.tar'
# model_fname = '/data/gnn_code/hgcal_ldrd/output/'+model_group+'/checkpoints/model_checkpoint_InteractionNetwork_282308_620b37ae37_atkinsn2.best.pth.tar'
model_fname = '/data/gnn_code/hgcal_ldrd/output/'+model_group+'/checkpoints/model_checkpoint_InteractionNetwork_16721_5e48b5fd7c_atkinsn2.best.pth.tar'
mdl = InteractionNetwork(input_dim=3, hidden_dim=40, edge_dim=N_attr, output_dim=2, n_iters=6).to('cuda:0')
mdl.load_state_dict(torch.load(model_fname)['model'])
mdl.eval()
# Initializing vectors
truth_pt = []
truth_eta = []
tru_pt = []
tru_eta = []
gnn_pt = []
gnn_eta = []
d_tru_pt = []
d_tru_eta = []
d_gnn_pt = []
d_gnn_eta = []
u_gnn_pt = []
u_gnn_eta = []
n_gnn_pt = []
n_gnn_eta = []
recon_pt = []
recon_eta = []
m_exa_pt = []
m_exa_eta = []
r_exa_pt = []
r_exa_eta = []
pred_edges_all = []
y_all = []
# Loop over number of events to average
for idx in tqdm(range(n_events)):
# Perform Inference
with torch.no_grad():
pred_edges = mdl(trackml_data[idx].to('cuda:0')).detach()
# pred_edges = mdl(trackml_data[idx].to('cpu:0')).detach()
pred_edges_np = pred_edges.cpu().numpy()
if (type == "edge_classifier"):
pred_edges_np = -pred_edges_np / (2* np.amin(pred_edges_np))
pred_edges_np = pred_edges_np[:,1] - pred_edges_np[:,0] + .5
# out = np.greater(pred_edges_np, 0.5).astype(np.int_)
out = np.greater(pred_edges_np, 0.511).astype(np.int_) #Triplets TrackML
# out = np.greater(pred_edges_np, 0.522).astype(np.int_) #Triplets
# out = np.greater(pred_edges_np, 0.555).astype(np.int_) #Triplets
# out = np.greater(pred_edges_np, 0.5195).astype(np.int_) #Doublets
elif (type == "interaction_network"):
# out = np.transpose(np.greater(pred_edges_np, 0.5).astype(np.int_))[0]
# out = np.transpose(np.greater(pred_edges_np, 0.1).astype(np.int_))[0]
out = np.transpose(np.greater(pred_edges_np, 0.96).astype(np.int_))[0]
pred_edges_np = np.transpose(pred_edges_np)[0]
# Extract Graph features
X = trackml_data[idx].x.cpu().numpy()
index = trackml_data[idx].edge_index.cpu().numpy().T
y = trackml_data[idx].y.cpu().numpy()
particles = trackml_data[idx].particles.cpu().numpy()
track_attr = trackml_data[idx].track_attr.cpu().numpy()
pred_edges_all.append(pred_edges_np)
y_all.append(y)
if trackml_data.directed:
f_pred_true_index = index[out == 1]
f_pred_true_weights = pred_edges_np[out == 1]
else:
f_out, b_out = np.split(out, 2, axis=0)
f_index, b_index = np.split(index, 2, axis=0)
f_weight, b_weight = np.split(pred_edges_np, 2, axis=0)
f_pred_true_index = f_index[f_out == 1]
f_pred_true_weights = f_weight[f_out == 1]
b_pred_true_index = b_index[b_out == 1]
b_pred_true_weights = b_weight[b_out == 1]
if (trackml_data.data_type == 'ATLAS'):
track_attr = track_attr[track_attr[:,3] < 200000] #Prunes Secondaries from Denominator
track_attr = np.delete(track_attr, 3, 1)
track_prune = track_attr[track_attr[:,3] >= Hits_minimum] # Prunes Denominator
# track_prune = track_attr # Full Denominator
# track_prune =
# Truth particle parameters
truth_pt.append(track_prune[:,1])
track_prune_eta = track_prune[track_prune[:,1] >= pt_cut_for_eta]
truth_eta.append(track_prune_eta[:,2])
# ExaTrk defines Reconstructable as particles with atleast 5 hits
track_recon = track_attr[track_attr[:,3] >= 4]
recon_pt.append(track_recon[:,1])
track_recon_eta = track_recon[track_recon[:,1] >= pt_cut_for_eta]
recon_eta.append(track_recon_eta[:,2])
# Union Find the True edges
finder_tru = unionfind(X.shape[0])
for i in range(index.shape[0]):
if y[i] == 1:
finder_tru.unite(index[i,0], index[i,1])
tru_roots = np.array([finder_tru.find(i) for i in range(X.shape[0])], dtype=np.uint32)
tru_clusters = np.unique(tru_roots, return_inverse=True, return_counts=True)
trus = tru_clusters[0][np.where(tru_clusters[2] >= Hits_minimum)]
tru_clusters_sel = {i: np.where(tru_roots == tru)[0] for i, tru in enumerate(trus)}
# True Graph particle parameters
found_particles = np.empty(0, dtype=np.float64)
for i in range(len(tru_clusters_sel)):
track_particles = [particles[j] for j in tru_clusters_sel[i]]
match = track_particles.count(track_particles[0]) == len(track_particles)
found_previously = np.isin(track_particles[0], found_particles)
if(match and not found_previously):
track_pt, track_eta = get_particle_pt_eta(track_particles[0], track_attr)
tru_pt.append(track_pt)
if(track_pt > pt_cut_for_eta):
tru_eta.append(track_eta)
found_particles = np.append(found_particles, track_particles[0])
elif(match and found_previously):
track_pt, track_eta = get_particle_pt_eta(track_particles[0], track_attr)
d_tru_pt.append(track_pt)
if(track_pt > pt_cut_for_eta):
d_tru_eta.append(track_eta)
# Union Find the Inferenced edges
finder_gnn = unionfind(X.shape[0])
for i in range(index.shape[0]):
if out[i] == 1:
finder_gnn.unite(index[i,0], index[i,1])
gnn_roots = np.array([finder_gnn.find(i) for i in range(X.shape[0])], dtype=np.uint32)
gnn_clusters = np.unique(gnn_roots, return_inverse=True, return_counts=True)
gnns = gnn_clusters[0][np.where(gnn_clusters[2] >= Hits_minimum)]
gnn_clusters_sel = {i: np.where(gnn_roots == gnn)[0] for i, gnn in enumerate(gnns)}
# Inferenced particle parameters
found_particles = np.empty(0, dtype=np.float64)
found_particles_exa_p = np.empty(0, dtype=np.float64)
found_particles_exa_t = np.empty(0, dtype=np.float64)
for i in range(len(gnn_clusters_sel)):
all_paths = [gnn_clusters_sel[i]]
######################## WRANGLER ###########################
# if trackml_data.directed:
# new_paths = find_all_paths(gnn_clusters_sel[i], f_pred_true_index, f_pred_true_weights)
# else:
# # new_paths = find_all_paths(list(gnn_clusters_sel[i]), f_pred_true_index, f_pred_true_weights)
#
# # new_paths = find_all_paths(list(gnn_clusters_sel[i])[::-1], b_pred_true_index, b_pred_true_weights)
#
# f_paths = find_all_paths(list(gnn_clusters_sel[i]), f_pred_true_index, f_pred_true_weights)
# b_paths = find_all_paths(list(gnn_clusters_sel[i])[::-1], b_pred_true_index, b_pred_true_weights)
# b_paths = [path[::-1] for path in b_paths]
# new_paths = []
# for i in f_paths + b_paths:
# if i not in new_paths:
# new_paths.append(i)
# if len(new_paths) > 0:
# new_paths = [new_paths[np.argmax(np.asarray([len(i) for i in new_paths]))]] # Longest Path
# if len(new_paths[0]) >= Hits_minimum:
# all_paths = new_paths
######################## WRANGLER ###########################
for track_candidate in all_paths:
track_particles = [particles[j] for j in track_candidate]
#Markus strict matching criteria
particle, count = np.unique(np.array(track_particles), return_counts=True)
purity = np.max(count) / np.sum(count)
particle = particle[np.argmax(count)]
threshold = 1.0
# threshold = 0.5
found_previously = np.isin(particle, found_particles)
if(purity >= threshold and particle == 0): # Noise Track
track_pos = [X[j] for j in gnn_clusters_sel[i]]
track_pt = 0
track_eta = np.asarray([get_node_eta(track_pos[i]) for i in range(len(track_pos))])
n_gnn_pt.append(track_pt)
if(track_pt > pt_cut_for_eta):
n_gnn_eta.append(track_eta.mean())
elif(purity >= threshold and not found_previously): # Good Track
track_pt, track_eta = get_particle_pt_eta(particle, track_attr)
gnn_pt.append(track_pt)
if(track_pt > pt_cut_for_eta):
gnn_eta.append(track_eta)
found_particles = np.append(found_particles, particle)
elif(purity >= threshold and found_previously): # Duplicate Track
track_pt, track_eta = get_particle_pt_eta(particle, track_attr)
d_gnn_pt.append(track_pt)
if(track_pt > pt_cut_for_eta):
d_gnn_eta.append(track_eta)
else: # Bad Mixed Track
track_particles = [x for x in track_particles if x > 0] #prunes out noise hits
track_pt = np.asarray([get_particle_pt_eta(track_particles[i], track_attr)[0] for i in range(len(track_particles))])
track_eta = np.asarray([get_particle_pt_eta(track_particles[i], track_attr)[1] for i in range(len(track_particles))])
u_gnn_pt.append(track_pt.mean())
if(track_pt.mean() > pt_cut_for_eta):
u_gnn_eta.append(track_eta.mean())
#ExaTrk looser matching criteria
particle, count = np.unique(np.array(track_particles), return_counts=True)
ratio = np.max(count) / np.sum(count)
index = np.argmax(count)
count = np.max(count)
particle = particle[index]
found_previously_exa_p = np.isin(particle, found_particles_exa_p)
found_previously_exa_t = np.isin(particle, found_particles_exa_t)
#Require majority of hits from one particle
if (ratio > .5):
index = np.where(track_attr[:,0] == particle)
ratio = count / track_attr[index,3]
if (ratio > .2 and not found_previously_exa_p): #Reduced for inner detector only
found_particles_exa_p = np.append(found_particles_exa_p, particle)
track_pt, track_eta = get_particle_pt_eta(particle, track_attr)
m_exa_pt.append(track_pt)
if(track_pt > pt_cut_for_eta):
m_exa_eta.append(track_eta)
index = np.where(track_recon[:,0] == particle)
ratio = count / track_recon[index,3]
if (ratio > .2 and not found_previously_exa_t): #Reduced for inner detector only
found_particles_exa_t = np.append(found_particles_exa_t, particle)
track_pt, track_eta = get_particle_pt_eta(particle, track_recon)
r_exa_pt.append(track_pt)
if(track_pt > pt_cut_for_eta):
r_exa_eta.append(track_eta)
# # This chunk of code finds the optimal edge weight values to cut on
# pred_edges_all = np.concatenate(pred_edges_all, axis=0)
# y_all = np.concatenate(y_all, axis=0)
#
# fp = []
# tn = []
# xc = []
# for i in tqdm(range(1001)):
# out_new = np.greater(pred_edges_all, i*.001).astype(np.int_)
# errors = out_new - y_all
# xc.append(i*.001)
# fp.append(np.count_nonzero(errors == 1))
# tn.append(np.count_nonzero(errors == -1))
# N = y_all.shape[0]
# true_N = np.sum(y_all)
# xc = np.asarray(xc)
# fp = np.asarray(fp)/(N-true_N)
# tn = np.asarray(tn)/true_N
# tot = fp+tn
# print(xc[np.argmin(tot)])
#
# figg, (axg) = plt.subplots(1, 1, dpi=600, figsize=(6, 6))
# axg.scatter(xc, fp, s=1, label="False Positives")
# axg.scatter(xc, tn, s=1, label="True Negatives")
# axg.set_xlabel('cut value')
# axg.set_ylabel('percentage')
# axg.set_ylim(.00000001, 1)
# axg.set_yscale('log')
# axg.legend()#loc='upper right')
# figg.savefig('pos_neg.png', dpi=600)
#
# false_weights = pred_edges_all[y_all == 0]
# true_weights = pred_edges_all[y_all == 1]
#
# figh, (axh) = plt.subplots(1, 1, dpi=600, figsize=(6, 6))
# axh.hist(false_weights, bins=100, range=(0,1), histtype='step', edgecolor='blue', fill=False, label='False Edges')
# axh.hist(true_weights, bins=100, range=(0,1), histtype='step', edgecolor='orange', fill=False, label='True Edges')
# axh.set_xlabel('edge score')
# axh.set_ylabel('count')
# # axh.set_ylim(.00000001, 1)
# axh.set_yscale('log')
# axh.legend()#loc='upper right')
# figh.savefig('weights_distro.png', dpi=600)
# Convert lists to numpy arrays for processing
truth_pt = np.concatenate(truth_pt, axis=0)
truth_eta = np.concatenate(truth_eta, axis=0)
recon_pt = np.concatenate(recon_pt, axis=0)
recon_eta = np.concatenate(recon_eta, axis=0)
tru_pt = np.asarray(tru_pt)
tru_eta = np.asarray(tru_eta)
gnn_pt = np.asarray(gnn_pt)
gnn_eta = np.asarray(gnn_eta)
d_tru_pt = np.asarray(d_tru_pt)
d_tru_eta = np.asarray(d_tru_eta)
d_gnn_pt = np.asarray(d_gnn_pt)
d_gnn_eta = np.asarray(d_gnn_eta)
u_gnn_pt = np.asarray(u_gnn_pt)
u_gnn_eta = np.asarray(u_gnn_eta)
n_gnn_pt = np.asarray(n_gnn_pt)
n_gnn_eta = np.asarray(n_gnn_eta)
m_exa_pt = np.asarray(m_exa_pt)
m_exa_eta = np.asarray(m_exa_eta)
r_exa_pt = np.asarray(r_exa_pt)
r_exa_eta = np.asarray(r_exa_eta)
# Construct histograms
bins = [0, .1, .2, .3, .4, .5, .6, .7, .8, .9, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0]
hist_truth_pt = np.histogram(truth_pt, bins=bins, range=(.5,5))
hist_tru_pt = np.histogram(tru_pt, bins=bins, range=(.5,5))
hist_gnn_pt = np.histogram(gnn_pt, bins=bins, range=(.5,5))
hist_truth_eta = np.histogram(truth_eta, bins=20, range=(-4,4))
hist_tru_eta = np.histogram(tru_eta, bins=20, range=(-4,4))
hist_gnn_eta = np.histogram(gnn_eta, bins=20, range=(-4,4))
hist_d_tru_pt = np.histogram(d_tru_pt, bins=bins, range=(.5,5))
hist_d_gnn_pt = np.histogram(d_gnn_pt, bins=bins, range=(.5,5))
hist_u_gnn_pt = np.histogram(u_gnn_pt, bins=bins, range=(.5,5))
hist_n_gnn_pt = np.histogram(n_gnn_pt, bins=bins, range=(.5,5))
hist_d_tru_eta = np.histogram(d_tru_eta, bins=20, range=(-4,4))
hist_d_gnn_eta = np.histogram(d_gnn_eta, bins=20, range=(-4,4))
hist_u_gnn_eta = np.histogram(u_gnn_eta, bins=20, range=(-4,4))
hist_n_gnn_eta = np.histogram(n_gnn_eta, bins=20, range=(-4,4))
hist_recon_pt = np.histogram(recon_pt, bins=bins, range=(.5,5))
hist_recon_eta = np.histogram(recon_eta, bins=20, range=(-4,4))
hist_m_exa_pt = np.histogram(m_exa_pt, bins=bins, range=(.5,5))
hist_m_exa_eta = np.histogram(m_exa_eta, bins=20, range=(-4,4))
hist_r_exa_pt = np.histogram(r_exa_pt, bins=bins, range=(.5,5))
hist_r_exa_eta = np.histogram(r_exa_eta, bins=20, range=(-4,4))
e_hist_truth_pt = div(hist_truth_pt[0] , np.sqrt(hist_truth_pt[0]))
e_hist_tru_pt = div(hist_tru_pt[0] , np.sqrt(hist_tru_pt[0]))
e_hist_gnn_pt = div(hist_gnn_pt[0] , np.sqrt(hist_gnn_pt[0]))
e_hist_truth_eta = div(hist_truth_eta[0] , np.sqrt(hist_truth_eta[0]))
e_hist_tru_eta = div(hist_tru_eta[0] , np.sqrt(hist_tru_eta[0]))
e_hist_gnn_eta = div(hist_gnn_eta[0] , np.sqrt(hist_gnn_eta[0]))
e_hist_d_tru_pt = div(hist_d_tru_pt[0] , np.sqrt(hist_d_tru_pt[0]))
e_hist_d_gnn_pt = div(hist_d_gnn_pt[0] , np.sqrt(hist_d_gnn_pt[0]))
e_hist_u_gnn_pt = div(hist_u_gnn_pt[0] , np.sqrt(hist_u_gnn_pt[0]))
e_hist_n_gnn_pt = div(hist_n_gnn_pt[0] , np.sqrt(hist_n_gnn_pt[0]))
e_hist_d_tru_eta = div(hist_d_tru_eta[0] , np.sqrt(hist_d_tru_eta[0]))
e_hist_d_gnn_eta = div(hist_d_gnn_eta[0] , np.sqrt(hist_d_gnn_eta[0]))
e_hist_u_gnn_eta = div(hist_u_gnn_eta[0] , np.sqrt(hist_u_gnn_eta[0]))
e_hist_n_gnn_eta = div(hist_n_gnn_eta[0] , np.sqrt(hist_n_gnn_eta[0]))
e_hist_recon_pt = div(hist_recon_pt[0] , np.sqrt(hist_recon_pt[0]))
e_hist_recon_eta = div(hist_recon_eta[0] , np.sqrt(hist_recon_eta[0]))
e_hist_m_exa_pt = div(hist_m_exa_pt[0] , np.sqrt(hist_m_exa_pt[0]))
e_hist_m_exa_eta = div(hist_m_exa_eta[0] , np.sqrt(hist_m_exa_eta[0]))
e_hist_r_exa_pt = div(hist_r_exa_pt[0] , np.sqrt(hist_r_exa_pt[0]))
e_hist_r_exa_eta = div(hist_r_exa_eta[0] , np.sqrt(hist_r_exa_eta[0]))
# Calculate efficiencies
gra_pt_efficiency = div(hist_tru_pt[0] , hist_truth_pt[0])
gnn_pt_efficiency = div(hist_gnn_pt[0] , hist_tru_pt[0])
tot_pt_efficiency = div(hist_gnn_pt[0] , hist_truth_pt[0])
gra_eta_efficiency = div(hist_tru_eta[0] , hist_truth_eta[0])
gnn_eta_efficiency = div(hist_gnn_eta[0] , hist_tru_eta[0])
tot_eta_efficiency = div(hist_gnn_eta[0] , hist_truth_eta[0])
phys_pt_efficiency = div(hist_m_exa_pt[0] , hist_truth_pt[0])
tech_pt_efficiency = div(hist_r_exa_pt[0] , hist_recon_pt[0])
phys_eta_efficiency = div(hist_m_exa_eta[0] , hist_truth_eta[0])
tech_eta_efficiency = div(hist_r_exa_eta[0] , hist_recon_eta[0])
e_gra_pt_efficiency = div_err(gra_pt_efficiency ,e_hist_tru_pt ,hist_tru_pt[0] ,e_hist_truth_pt ,hist_truth_pt[0])
e_gnn_pt_efficiency = div_err(gnn_pt_efficiency ,e_hist_gnn_pt ,hist_gnn_pt[0] ,e_hist_tru_pt ,hist_tru_pt[0])
e_tot_pt_efficiency = div_err(tot_pt_efficiency ,e_hist_gnn_pt ,hist_gnn_pt[0] ,e_hist_truth_pt ,hist_truth_pt[0])
e_gra_eta_efficiency = div_err(gra_eta_efficiency ,e_hist_tru_eta ,hist_tru_eta[0] ,e_hist_truth_eta ,hist_truth_eta[0])
e_gnn_eta_efficiency = div_err(gnn_eta_efficiency ,e_hist_gnn_eta ,hist_gnn_eta[0] ,e_hist_tru_eta ,hist_tru_eta[0])
e_tot_eta_efficiency = div_err(tot_eta_efficiency ,e_hist_gnn_eta ,hist_gnn_eta[0] ,e_hist_truth_eta ,hist_truth_eta[0])
e_phys_pt_efficiency = div_err(phys_pt_efficiency , e_hist_m_exa_pt ,hist_m_exa_pt[0] , e_hist_truth_pt , hist_truth_pt[0])
e_tech_pt_efficiency = div_err(tech_pt_efficiency , e_hist_r_exa_pt ,hist_r_exa_pt[0] , e_hist_recon_pt , hist_recon_pt[0])
e_phys_eta_efficiency = div_err(phys_eta_efficiency, e_hist_m_exa_eta ,hist_m_exa_eta[0] , e_hist_truth_eta, hist_truth_eta[0])
e_tech_eta_efficiency = div_err(tech_eta_efficiency, e_hist_r_exa_eta ,hist_r_exa_eta[0] , e_hist_recon_eta, hist_recon_eta[0])
# Calculate fake fractions
sum_tru_pt = hist_tru_pt[0] + hist_d_tru_pt[0]
sum_gnn_pt = hist_gnn_pt[0] + hist_d_gnn_pt[0] + hist_u_gnn_pt[0] + hist_n_gnn_pt[0]
sum_tru_eta = hist_tru_eta[0] + hist_d_tru_eta[0]
sum_gnn_eta = hist_gnn_eta[0] + hist_d_gnn_eta[0] + hist_u_gnn_eta[0] + hist_n_gnn_eta[0]
e_sum_tru_pt = sum_err(e_hist_tru_pt, e_hist_d_tru_pt)
e_sum_gnn_pt = sum_err(e_hist_gnn_pt, e_hist_d_gnn_pt, e_hist_u_gnn_pt, e_hist_n_gnn_pt)
e_sum_tru_eta = sum_err(e_hist_tru_eta, e_hist_d_tru_eta)
e_sum_gnn_eta = sum_err(e_hist_gnn_eta, e_hist_d_gnn_eta, e_hist_u_gnn_eta, e_hist_n_gnn_eta)
gra_pt_dfrac = div(hist_d_tru_pt[0] , sum_tru_pt)
gnn_pt_dfrac = div(hist_d_gnn_pt[0] , sum_gnn_pt)
gnn_pt_ufrac = div(hist_u_gnn_pt[0] , sum_gnn_pt)
gnn_pt_nfrac = div(hist_n_gnn_pt[0] , sum_gnn_pt)
gra_eta_dfrac = div(hist_d_tru_eta[0] , sum_tru_eta)
gnn_eta_dfrac = div(hist_d_gnn_eta[0] , sum_gnn_eta)
gnn_eta_ufrac = div(hist_u_gnn_eta[0] , sum_gnn_eta)
gnn_eta_nfrac = div(hist_n_gnn_eta[0] , sum_gnn_eta)
e_gra_pt_dfrac = div_err(gra_pt_dfrac ,e_hist_d_tru_pt ,hist_d_tru_pt[0] ,e_sum_tru_pt ,sum_tru_pt)
e_gnn_pt_dfrac = div_err(gnn_pt_dfrac ,e_hist_d_gnn_pt ,hist_d_gnn_pt[0] ,e_sum_gnn_pt ,sum_gnn_pt)
e_gnn_pt_ufrac = div_err(gnn_pt_ufrac ,e_hist_u_gnn_pt ,hist_u_gnn_pt[0] ,e_sum_gnn_pt ,sum_gnn_pt)
e_gnn_pt_nfrac = div_err(gnn_pt_nfrac ,e_hist_n_gnn_pt ,hist_n_gnn_pt[0] ,e_sum_gnn_pt ,sum_gnn_pt)
e_gra_eta_dfrac = div_err(gra_eta_dfrac ,e_hist_d_tru_eta ,hist_d_tru_eta[0] ,e_sum_tru_eta ,sum_tru_eta)
e_gnn_eta_dfrac = div_err(gnn_eta_dfrac ,e_hist_d_gnn_eta ,hist_d_gnn_eta[0] ,e_sum_gnn_eta ,sum_gnn_eta)
e_gnn_eta_ufrac = div_err(gnn_eta_ufrac ,e_hist_u_gnn_eta ,hist_u_gnn_eta[0] ,e_sum_gnn_eta ,sum_gnn_eta)
e_gnn_eta_nfrac = div_err(gnn_eta_nfrac ,e_hist_n_gnn_eta ,hist_n_gnn_eta[0] ,e_sum_gnn_eta ,sum_gnn_eta)
# Make the plots
pt_bins = [.05, .15, .25, .35, .45, .55, .65, .75, .85, .95, 1.1, 1.3, 1.5, 1.7, 1.9, 2.25, 2.75, 3.25, 3.75, 4.5]
pt_err = [.05, .05, .05, .05, .05, .05, .05, .05, .05, .05, 0.1, 0.1, 0.1, 0.1, 0.1, 0.25, 0.25, 0.25, 0.25, 0.5]
eta_bins = hist_truth_eta[1] + .2
eta_bins = eta_bins[eta_bins < 4]
eta_err = .2
dpi=600
file_root = group + '_' + model_group
fig0, (ax0) = plt.subplots(1, 1, dpi=dpi, figsize=(6, 6))
ax0.hist(truth_pt, bins=bins, range=(0,5), histtype='step', edgecolor='blue', fill=False, label='Truth Tracks')
ax0.hist(tru_pt, bins=bins, range=(0,5), histtype='step', edgecolor='orange', fill=False, label='Graph Tracks')
ax0.hist(gnn_pt, bins=bins, range=(0,5), histtype='step', edgecolor='green', fill=False, label='Inferenced Tracks')
ax0.legend(loc='upper right')
ax0.set_xlabel('pT [GeV]')
ax0.set_xlim(0, 5)
ax0.set_ylabel('Particle Tracks')
fig0.savefig(file_root + '_pt_counts.pdf', dpi=dpi)
fig1, (ax1) = plt.subplots(1, 1, dpi=dpi, figsize=(6, 6))
ax1.hist(truth_eta, bins=20, range=(-4,4), histtype='step', edgecolor='blue', fill=False, label='Truth Tracks')
ax1.hist(tru_eta, bins=20, range=(-4,4), histtype='step', edgecolor='orange', fill=False, label='Graph Tracks')
ax1.hist(gnn_eta, bins=20, range=(-4,4), histtype='step', edgecolor='green', fill=False, label='Inferenced Tracks')
ax1.legend(loc='upper right')
ax1.set_xlabel('eta')
ax1.set_xlim(-4, 4)
ax1.set_ylabel('Particle Tracks')
ax1.set_ylim(.9*hist_gnn_eta[0].min(), 1.2*hist_truth_eta[0].max())
fig1.savefig(file_root + '_eta_counts.pdf', dpi=dpi)
fig2, (ax2) = plt.subplots(1, 1, dpi=dpi, figsize=(6, 6))
ax2.errorbar(pt_bins, gra_pt_efficiency, xerr=pt_err, yerr=e_gra_pt_efficiency, fmt='o', label='Graph Construction Efficiency')
ax2.errorbar(pt_bins, gnn_pt_efficiency, xerr=pt_err, yerr=e_gnn_pt_efficiency, fmt='o', label='GNN Inference Efficiency')
ax2.errorbar(pt_bins, tot_pt_efficiency, xerr=pt_err, yerr=e_tot_pt_efficiency, fmt='o', label='Total Efficiency')
ax2.legend(loc='lower right')
ax2.set_xlabel('pT [GeV]')
ax2.set_xlim(0, 5)
ax2.set_ylabel('Track Efficiency')
ax2.set_ylim(0, 1)
# ax2.set_ylim(.9, 1)
fig2.savefig(file_root + '_pt_efficiencies.pdf', dpi=dpi)
fig2.savefig(file_root + '_pt_efficiencies.png', dpi=dpi)
fig3, (ax3) = plt.subplots(1, 1, dpi=dpi, figsize=(6, 6))
ax3.errorbar(eta_bins, gra_eta_efficiency, xerr=eta_err, yerr=e_gra_eta_efficiency, fmt='o', label='Graph Construction Efficiency')
ax3.errorbar(eta_bins, gnn_eta_efficiency, xerr=eta_err, yerr=e_gnn_eta_efficiency, fmt='o', label='GNN Inference Efficiency')
ax3.errorbar(eta_bins, tot_eta_efficiency, xerr=eta_err, yerr=e_tot_eta_efficiency, fmt='o', label='Total Efficiency')
ax3.legend(loc='lower right')
ax3.set_xlabel('eta')
ax3.set_xlim(-4, 4)
ax3.set_ylabel('Track Efficiency')
# ax3.set_ylim(0, 1)
ax3.set_ylim(.5, 1)
# ax3.set_ylim(.8, 1)
# ax3.set_ylim(.9, 1)
fig3.savefig(file_root + '_eta_efficiencies.pdf', dpi=dpi)
fig3.savefig(file_root + '_eta_efficiencies.png', dpi=dpi)
with open(f'./{file_root}.pkl', 'wb') as fid:
pickle.dump(fig3, fid)
np.savez(file_root + '.npz', eta_bins, tot_eta_efficiency, eta_err, e_tot_eta_efficiency)
fig4, (ax4) = plt.subplots(1, 1, dpi=dpi, figsize=(6, 6))
ax4.hist(d_tru_pt, bins=bins, range=(0,5), histtype='step', edgecolor='blue', fill=False, label='Graph Construction: Duplicated Tracks')
ax4.hist(d_gnn_pt, bins=bins, range=(0,5), histtype='step', edgecolor='orange', fill=False, label='GNN Inference: Duplicated Tracks')
ax4.hist(u_gnn_pt, bins=bins, range=(0,5), histtype='step', edgecolor='green', fill=False, label='GNN Inference: Mixed Tracks')
# ax4.hist(n_gnn_pt, bins=bins, range=(0,5), histtype='step', edgecolor='red', fill=False, label='GNN Inference: Noise Tracks')
ax4.legend(loc='upper right')
ax4.set_xlabel('pT [GeV]')
ax4.set_xlim(0, 5)
ax4.set_ylabel('Fake Tracks')
fig4.savefig(file_root + '_pt_fake_counts.pdf', dpi=dpi)
fig5, (ax5) = plt.subplots(1, 1, dpi=dpi, figsize=(6, 6))
ax5.hist(d_tru_eta, bins=20, range=(-4,4), histtype='step', edgecolor='blue', fill=False, label='Graph Construction: Duplicated Tracks')
ax5.hist(d_gnn_eta, bins=20, range=(-4,4), histtype='step', edgecolor='orange', fill=False, label='GNN Inference: Duplicated Tracks')
ax5.hist(u_gnn_eta, bins=20, range=(-4,4), histtype='step', edgecolor='green', fill=False, label='GNN Inference: Mixed Tracks')
# ax5.hist(n_gnn_eta, bins=20, range=(-4,4), histtype='step', edgecolor='red', fill=False, label='GNN Inference: Noise Tracks')
ax5.legend(loc='upper center')
ax5.set_xlabel('eta')
ax5.set_xlim(-4, 4)
ax5.set_ylabel('Fake Tracks')
# ax5.set_ylim(.9*hist_gnn_eta[0].min(), 1.2*hist_truth_eta[0].max())
fig5.savefig(file_root + '_eta_fake_counts.pdf', dpi=dpi)
fig6, (ax6) = plt.subplots(1, 1, dpi=dpi, figsize=(6, 6))
ax6.errorbar(pt_bins, gra_pt_dfrac, xerr=pt_err, yerr=e_gra_pt_dfrac, fmt='o', label='Graph Construction: Duplication Fraction')
ax6.errorbar(pt_bins, gnn_pt_dfrac, xerr=pt_err, yerr=e_gnn_pt_dfrac, fmt='o', label='GNN Inference: Duplication Fraction')
ax6.errorbar(pt_bins, gnn_pt_ufrac, xerr=pt_err, yerr=e_gnn_pt_ufrac, fmt='o', label='GNN Inference: Mixed Track Fraction')
# ax6.errorbar(pt_bins, gnn_pt_nfrac, xerr=pt_err, yerr=e_gnn_pt_nfrac, fmt='o', label='GNN Inference: Noise Track Fraction')
ax6.legend(loc='best')
ax6.set_xlabel('pT [GeV]')
ax6.set_xlim(0, 5)
ax6.set_ylabel('Fake Fraction')
ax6.set_ylim(.001, .3)
ax6.set_yscale('log')
fig6.savefig(file_root + '_pt_fake_fractions.pdf', dpi=dpi)
fig7, (ax7) = plt.subplots(1, 1, dpi=dpi, figsize=(6, 6))
ax7.errorbar(eta_bins, gra_eta_dfrac, xerr=eta_err, yerr=e_gra_eta_dfrac, fmt='o', label='Graph Construction: Duplication Fraction')
ax7.errorbar(eta_bins, gnn_eta_dfrac, xerr=eta_err, yerr=e_gnn_eta_dfrac, fmt='o', label='GNN Inference: Duplication Fraction')
ax7.errorbar(eta_bins, gnn_eta_ufrac, xerr=eta_err, yerr=e_gnn_eta_ufrac, fmt='o', label='GNN Inference: Mixed Track Fraction')
# ax7.errorbar(eta_bins, gnn_eta_nfrac, xerr=eta_err, yerr=e_gnn_eta_nfrac, fmt='o', label='GNN Inference: Noise Track Fraction')
ax7.legend(loc='best')
ax7.set_xlabel('eta')
ax7.set_xlim(-4, 4)
ax7.set_ylabel('Fake Fraction')
ax7.set_ylim(.001, .3)
ax7.set_yscale('log')
fig7.savefig(file_root + '_eta_fake_fractions.pdf', dpi=dpi)
#ExaTrk plots
fig8, (ax8) = plt.subplots(1, 1, dpi=dpi, figsize=(6, 6))
ax8.hist(truth_pt, bins=bins, range=(0,5), histtype='step', edgecolor='blue', fill=False, label='Selected')
ax8.hist(recon_pt, bins=bins, range=(0,5), histtype='step', edgecolor='orange', fill=False, label='Reconstructable')
ax8.hist(m_exa_pt, bins=bins, range=(0,5), histtype='step', edgecolor='green', fill=False, label='Matched')
ax8.legend(loc='upper right')
ax8.set_xlabel('pT [GeV]')
ax8.set_xlim(0, 5)
ax8.set_ylabel('Particles')
fig8.savefig(file_root + '_pt_counts_exa.pdf', dpi=dpi)
fig9, (ax9) = plt.subplots(1, 1, dpi=dpi, figsize=(6, 6))
ax9.hist(truth_eta, bins=20, range=(-4,4), histtype='step', edgecolor='blue', fill=False, label='Selected')
ax9.hist(recon_eta, bins=20, range=(-4,4), histtype='step', edgecolor='orange', fill=False, label='Reconstructable')
ax9.hist(m_exa_eta, bins=20, range=(-4,4), histtype='step', edgecolor='green', fill=False, label='Matched')
ax9.legend(loc='upper right')
ax9.set_xlabel('eta')
ax9.set_xlim(-4, 4)
ax9.set_ylabel('Particles')
ax9.set_ylim(.9*hist_m_exa_eta[0].min(), 1.2*hist_truth_eta[0].max())
fig9.savefig(file_root + '_eta_counts_exa.pdf', dpi=dpi)
fig10, (ax10) = plt.subplots(1, 1, dpi=dpi, figsize=(6, 6))
ax10.errorbar(pt_bins, phys_pt_efficiency, xerr=pt_err, yerr=e_phys_pt_efficiency, fmt='o', label='Physics Efficiency')
ax10.errorbar(pt_bins, tech_pt_efficiency, xerr=pt_err, yerr=e_tech_pt_efficiency, fmt='o', label='Technical Efficiency')
ax10.legend(loc='lower right')
ax10.set_xlabel('pT [GeV]')
ax10.set_xlim(0, 5)
ax10.set_ylabel('Track Efficiency')
ax10.set_ylim(0, 1)
# ax10.set_ylim(.75, 1)
fig10.savefig(file_root + '_pt_efficiencies_exa.pdf', dpi=dpi)
fig11, (ax11) = plt.subplots(1, 1, dpi=dpi, figsize=(6, 6))
ax11.errorbar(eta_bins, phys_eta_efficiency, xerr=eta_err, yerr=e_phys_eta_efficiency, fmt='o', label='Physics Efficiency')
ax11.errorbar(eta_bins, tech_eta_efficiency, xerr=eta_err, yerr=e_tech_eta_efficiency, fmt='o', label='Technical Efficiency')
ax11.legend(loc='lower right')
ax11.set_xlabel('eta')
ax11.set_xlim(-4, 4)
ax11.set_ylabel('Track Efficiency')
ax11.set_ylim(0, 1)
# ax11.set_ylim(.75, 1)
fig11.savefig(file_root + '_eta_efficiencies_exa.pdf', dpi=dpi)
# fig12, (ax12) = plt.subplots(1, 1, dpi=dpi, figsize=(6, 6))
# np.savez_compressed('/data/gnn_code/eta_phi.npz', gra_eta_efficiency)
#
# data = np.load('/data/gnn_code/eta_no_mm.npz')
# gra_eta_efficiency_no_mm = data[data.files[0]]
# data_1 = np.load('/data/gnn_code/eta_geo.npz')
# gra_eta_efficiency_geo = data_1[data_1.files[0]]
# data_3 = np.load('/data/gnn_code/eta_phi.npz')
# gra_eta_efficiency_phi = data_3[data_3.files[0]]
# # data_10 = np.load('/data/gnn_code/eta_mm_10.npz')
# # gra_eta_efficiency_mm_10 = data_10[data_10.files[0]]
# # data_30 = np.load('/data/gnn_code/eta_mm_30.npz')
# # gra_eta_efficiency_mm_30 = data_30[data_30.files[0]]
# # data_100 = np.load('/data/gnn_code/eta_mm_100.npz')
# # gra_eta_efficiency_mm_100 = data_100[data_100.files[0]]
# # data_300 = np.load('/data/gnn_code/eta_mm_300.npz')
# # gra_eta_efficiency_mm_300 = data_300[data_300.files[0]]
# # data_1000 = np.load('/data/gnn_code/eta_mm_1000.npz')
# # gra_eta_efficiency_mm_1000 = data_1000[data_1000.files[0]]
#
# ax12.errorbar(eta_bins, gra_eta_efficiency_phi/gra_eta_efficiency_no_mm, xerr=eta_err, yerr=e_gra_eta_efficiency, fmt='o', label='phi on')
# ax12.errorbar(eta_bins, gra_eta_efficiency_geo/gra_eta_efficiency_no_mm, xerr=eta_err, yerr=e_gra_eta_efficiency, fmt='o', label='phi and z0 on')
#
#
# # ax12.errorbar(eta_bins, gra_eta_efficiency_mm_1/gra_eta_efficiency_no_mm, xerr=eta_err, yerr=e_gra_eta_efficiency, fmt='o', label='cut > 1')
# # ax12.errorbar(eta_bins, gra_eta_efficiency_mm_3/gra_eta_efficiency_no_mm, xerr=eta_err, yerr=e_gra_eta_efficiency, fmt='o', label='cut > 3')
# # ax12.errorbar(eta_bins, gra_eta_efficiency_mm_10/gra_eta_efficiency_no_mm, xerr=eta_err, yerr=e_gra_eta_efficiency, fmt='o', label='cut > 10')
# # ax12.errorbar(eta_bins, gra_eta_efficiency_mm_30/gra_eta_efficiency_no_mm, xerr=eta_err, yerr=e_gra_eta_efficiency, fmt='o', label='cut > 30')
# # ax12.errorbar(eta_bins, gra_eta_efficiency_mm_100/gra_eta_efficiency_no_mm, xerr=eta_err, yerr=e_gra_eta_efficiency, fmt='o', label='cut > 100')
# # ax12.errorbar(eta_bins, gra_eta_efficiency_mm_300/gra_eta_efficiency_no_mm, xerr=eta_err, yerr=e_gra_eta_efficiency, fmt='o', label='cut > 300')
# # ax12.errorbar(eta_bins, gra_eta_efficiency_mm_1000/gra_eta_efficiency_no_mm, xerr=eta_err, yerr=e_gra_eta_efficiency, fmt='o', label='cut > 1000')
#
# ax12.legend(loc='lower right')
# ax12.set_xlabel('eta')
# ax12.set_xlim(-4, 4)
# ax12.set_ylabel('Track Efficiency')
# ax12.set_ylim(.96, 1)
# fig12.savefig(file_root + '_eta_mm_efficiencies.pdf', dpi=dpi)
# fig12.savefig(file_root + '_eta_mm_efficiencies.png', dpi=dpi)
#Calculate and Print Totals
N_truth = np.sum(hist_truth_pt[0])
N_graph = np.sum(hist_tru_pt[0])
N_gnn = np.sum(hist_gnn_pt[0])
N_d_graph = np.sum(hist_d_tru_pt[0])
N_d_gnn = np.sum(hist_d_gnn_pt[0])
N_unmatched = np.sum(hist_u_gnn_pt[0])
N_noise = np.sum(hist_n_gnn_pt[0])
e_N_truth = div(N_truth , np.sqrt(N_truth))
e_N_graph = div(N_graph , np.sqrt(N_graph))
e_N_gnn = div(N_gnn , np.sqrt(N_gnn))
e_N_d_graph = div(N_d_graph , np.sqrt(N_d_graph))
e_N_d_gnn = div(N_d_gnn , np.sqrt(N_d_gnn))
e_N_unmatched = div(N_unmatched , np.sqrt(N_unmatched))
e_N_noise = div(N_noise , np.sqrt(N_noise))
N_graph_clu = N_graph + N_d_graph
N_gnn_clu = N_gnn + N_d_gnn + N_unmatched + N_noise
e_N_graph_clu = sum_err(e_N_graph, e_N_d_graph)
e_N_gnn_clu = sum_err(e_N_gnn, e_N_d_gnn, e_N_unmatched, e_N_noise)
gra_eff = div(N_graph , N_truth)
gnn_eff = div(N_gnn , N_graph)
tot_eff = div(N_gnn , N_truth)
e_gra_eff = div_err(gra_eff ,e_N_graph, N_graph, e_N_truth, N_truth)
e_gnn_eff = div_err(gnn_eff ,e_N_gnn , N_gnn, e_N_graph, N_graph)
e_tot_eff = div_err(tot_eff ,e_N_gnn , N_gnn, e_N_truth, N_truth)
gra_df = div(N_d_graph , N_graph_clu)
gnn_df = div(N_d_gnn , N_gnn_clu)
gnn_uf = div(N_unmatched , N_gnn_clu)
gnn_nf = div(N_noise , N_gnn_clu)
e_gra_df = div_err(gra_df ,e_N_d_graph ,N_d_graph, e_N_graph_clu, N_graph_clu)
e_gnn_df = div_err(gnn_df ,e_N_d_gnn ,N_d_gnn, e_N_gnn_clu, N_gnn_clu)
e_gnn_uf = div_err(gnn_uf ,e_N_unmatched ,N_unmatched, e_N_gnn_clu, N_gnn_clu)
e_gnn_nf = div_err(gnn_nf ,e_N_noise ,N_noise, e_N_gnn_clu, N_gnn_clu)
print('Track Efficiencies [-4,4]')
print('Graph Construction Efficiency = ' +str(gra_eff) +' +/- ' +str(e_gra_eff))
print('GNN Inference Efficiency = ' +str(gnn_eff) +' +/- ' +str(e_gnn_eff))
print('Total Efficiency = ' +str(tot_eff) +' +/- ' +str(e_tot_eff))
print()
print('Fake Fractions [-4,4]')
print('Graph Construction: Duplicate Track Fraction = ' +str(gra_df) +' +/- ' +str(e_gra_df))
print('GNN Inference: Duplicate Track Fraction = ' +str(gnn_df) +' +/- ' +str(e_gnn_df))
print('GNN Inference: Unmatched Track Fraction = ' +str(gnn_uf) +' +/- ' +str(e_gnn_uf))
print('GNN Inference: Noise Track Fraction = ' +str(gnn_nf) +' +/- ' +str(e_gnn_nf))
print('True Tracks: = ' + str(N_truth / n_events))
print('Graph Clusters: = ' + str(N_graph_clu / n_events))
print('GNN Track Candidates: = ' + str(N_gnn_clu / n_events))
print()
#Calculate and Print Totals (Barrel Region Only)
N_truth = np.sum(hist_truth_eta[0][5:15])
N_graph = np.sum(hist_tru_eta[0][5:15])
N_gnn = np.sum(hist_gnn_eta[0][5:15])
N_d_graph = np.sum(hist_d_tru_eta[0][5:15])
N_d_gnn = np.sum(hist_d_gnn_eta[0][5:15])
N_unmatched = np.sum(hist_u_gnn_eta[0][5:15])
N_noise = np.sum(hist_n_gnn_eta[0][5:15])
e_N_truth = div(N_truth , np.sqrt(N_truth))
e_N_graph = div(N_graph , np.sqrt(N_graph))
e_N_gnn = div(N_gnn , np.sqrt(N_gnn))
e_N_d_graph = div(N_d_graph , np.sqrt(N_d_graph))
e_N_d_gnn = div(N_d_gnn , np.sqrt(N_d_gnn))
e_N_unmatched = div(N_unmatched , np.sqrt(N_unmatched))
e_N_noise = div(N_noise , np.sqrt(N_noise))
N_graph_clu = N_graph + N_d_graph
N_gnn_clu = N_gnn + N_d_gnn + N_unmatched + N_noise
e_N_graph_clu = sum_err(e_N_graph, e_N_d_graph)
e_N_gnn_clu = sum_err(e_N_gnn, e_N_d_gnn, e_N_unmatched, e_N_noise)
gra_eff = div(N_graph , N_truth)
gnn_eff = div(N_gnn , N_graph)
tot_eff = div(N_gnn , N_truth)
e_gra_eff = div_err(gra_eff ,e_N_graph, N_graph, e_N_truth, N_truth)
e_gnn_eff = div_err(gnn_eff ,e_N_gnn , N_gnn, e_N_graph, N_graph)
e_tot_eff = div_err(tot_eff ,e_N_gnn , N_gnn, e_N_truth, N_truth)
gra_df = div(N_d_graph , N_graph_clu)
gnn_df = div(N_d_gnn , N_gnn_clu)
gnn_uf = div(N_unmatched , N_gnn_clu)
gnn_nf = div(N_noise , N_gnn_clu)
e_gra_df = div_err(gra_df ,e_N_d_graph ,N_d_graph, e_N_graph_clu, N_graph_clu)
e_gnn_df = div_err(gnn_df ,e_N_d_gnn ,N_d_gnn, e_N_gnn_clu, N_gnn_clu)
e_gnn_uf = div_err(gnn_uf ,e_N_unmatched ,N_unmatched, e_N_gnn_clu, N_gnn_clu)
e_gnn_nf = div_err(gnn_nf ,e_N_noise ,N_noise, e_N_gnn_clu, N_gnn_clu)
print('Track Efficiencies (Barrel Region Only [-2,2])')
print('Graph Construction Efficiency = ' +str(gra_eff) +' +/- ' +str(e_gra_eff))
print('GNN Inference Efficiency = ' +str(gnn_eff) +' +/- ' +str(e_gnn_eff))
print('Total Efficiency = ' +str(tot_eff) +' +/- ' +str(e_tot_eff))
print()
print('Fake Fractions (Barrel Region Only [-2,2])')
print('Graph Construction: Duplicate Track Fraction = ' +str(gra_df) +' +/- ' +str(e_gra_df))
print('GNN Inference: Duplicate Track Fraction = ' +str(gnn_df) +' +/- ' +str(e_gnn_df))
print('GNN Inference: Unmatched Track Fraction = ' +str(gnn_uf) +' +/- ' +str(e_gnn_uf))
print('GNN Inference: Noise Track Fraction = ' +str(gnn_nf) +' +/- ' +str(e_gnn_nf))
print()
#Calculate and Print Totals (Endcaps Only)
N_truth = np.sum(hist_truth_eta[0][:5] + hist_truth_eta[0][15:])
N_graph = np.sum(hist_tru_eta [0][:5] + hist_tru_eta [0][15:])
N_gnn = np.sum(hist_gnn_eta [0][:5] + hist_gnn_eta [0][15:])
N_d_graph = np.sum(hist_d_tru_eta[0][:5] + hist_d_tru_eta[0][15:])
N_d_gnn = np.sum(hist_d_gnn_eta[0][:5] + hist_d_gnn_eta[0][15:])
N_unmatched = np.sum(hist_u_gnn_eta[0][:5] + hist_u_gnn_eta[0][15:])
N_noise = np.sum(hist_n_gnn_eta[0][:5] + hist_n_gnn_eta[0][15:])
e_N_truth = div(N_truth , np.sqrt(N_truth))
e_N_graph = div(N_graph , np.sqrt(N_graph))
e_N_gnn = div(N_gnn , np.sqrt(N_gnn))
e_N_d_graph = div(N_d_graph , np.sqrt(N_d_graph))
e_N_d_gnn = div(N_d_gnn , np.sqrt(N_d_gnn))
e_N_unmatched = div(N_unmatched , np.sqrt(N_unmatched))
e_N_noise = div(N_noise , np.sqrt(N_noise))
N_graph_clu = N_graph + N_d_graph
N_gnn_clu = N_gnn + N_d_gnn + N_unmatched + N_noise
e_N_graph_clu = sum_err(e_N_graph, e_N_d_graph)
e_N_gnn_clu = sum_err(e_N_gnn, e_N_d_gnn, e_N_unmatched, e_N_noise)
gra_eff = div(N_graph , N_truth)
gnn_eff = div(N_gnn , N_graph)
tot_eff = div(N_gnn , N_truth)
e_gra_eff = div_err(gra_eff ,e_N_graph, N_graph, e_N_truth, N_truth)
e_gnn_eff = div_err(gnn_eff ,e_N_gnn , N_gnn, e_N_graph, N_graph)
e_tot_eff = div_err(tot_eff ,e_N_gnn , N_gnn, e_N_truth, N_truth)
gra_df = div(N_d_graph , N_graph_clu)
gnn_df = div(N_d_gnn , N_gnn_clu)
gnn_uf = div(N_unmatched , N_gnn_clu)
gnn_nf = div(N_noise , N_gnn_clu)
e_gra_df = div_err(gra_df ,e_N_d_graph ,N_d_graph, e_N_graph_clu, N_graph_clu)
e_gnn_df = div_err(gnn_df ,e_N_d_gnn ,N_d_gnn, e_N_gnn_clu, N_gnn_clu)
e_gnn_uf = div_err(gnn_uf ,e_N_unmatched ,N_unmatched, e_N_gnn_clu, N_gnn_clu)
e_gnn_nf = div_err(gnn_nf ,e_N_noise ,N_noise, e_N_gnn_clu, N_gnn_clu)
print('Track Efficiencies (Endcaps Only)')
print('Graph Construction Efficiency = ' +str(gra_eff) +' +/- ' +str(e_gra_eff))
print('GNN Inference Efficiency = ' +str(gnn_eff) +' +/- ' +str(e_gnn_eff))
print('Total Efficiency = ' +str(tot_eff) +' +/- ' +str(e_tot_eff))
print()
print('Fake Fractions (Endcaps Only)')
print('Graph Construction: Duplicate Track Fraction = ' +str(gra_df) +' +/- ' +str(e_gra_df))
print('GNN Inference: Duplicate Track Fraction = ' +str(gnn_df) +' +/- ' +str(e_gnn_df))
print('GNN Inference: Unmatched Track Fraction = ' +str(gnn_uf) +' +/- ' +str(e_gnn_uf))
print('GNN Inference: Noise Track Fraction = ' +str(gnn_nf) +' +/- ' +str(e_gnn_nf))
print()
def get_particle_pt_eta(particle_id, track_attr):
particle = track_attr[track_attr[:,0] == particle_id]
# print(particle.shape)
# print(particle[0,1], particle[0,2])
return particle[0,1], particle[0,2]
def get_node_eta(pos):
r = pos[0]
z = pos[2]
return np.arcsinh(z/r)
def div(a,b):
return np.divide(a,b, out=np.zeros_like(a, dtype=float), where=b!=0)
def div_err(c, a_e, a, b_e, b):
return c * np.sqrt(div(a_e,a)**2 + div(b_e,b)**2)
def sum_err(a, b, c=0, d=0):
return np.sqrt(a**2 + b**2 + c**2 + d**2)
def find_all_paths(node_cluster, edge_index, edge_weights):
# Create adjacency matrix for track candidate
edges = []
for j in range(len(node_cluster)):
edges.append(edge_index[np.where(edge_index == node_cluster[j])[0]])
edges = np.concatenate(edges, axis=0)
if edges.shape[0] == 0: return []
edges = np.unique(edges, axis=0)
# Gather edge weights for track candidate
weights = []
for j in range(edges.shape[0]):
weights.append( edge_weights[np.where(np.all(edge_index == edges[j], axis=1))] )
weights = np.concatenate(weights, axis=0)
# Check if any nodes have multiple neighbors, if not, algorithm finished
ot_counts = np.asarray([np.count_nonzero(edges[:,0] == j) for j in node_cluster])
in_counts = np.asarray([np.count_nonzero(edges[:,1] == j) for j in node_cluster])
if (ot_counts[ot_counts > 1].shape[0] == 0 and in_counts[in_counts == 1].shape[0] > 0):
# return [node_cluster], [list(-np.asarray(node_cluster))]
return [node_cluster]
# Find all starting and ending path nodes
start = np.asarray(node_cluster)[in_counts == 0]
end = np.asarray(node_cluster)[ot_counts == 0]
good_paths = []
edge_scores = []
for start_point in start:
for end_point in end:
all_paths = []
path_weights = []
path_queue = [[start_point]]
weight_queue = [[]]
while len(path_queue) > 0:
path = path_queue.pop(0)
weight = weight_queue.pop(0)
next_step = edges[edges[:,0] == path[-1]][:,1]
next_weight = weights[edges[:,0] == path[-1]]
# remove any steps to nodes already in the path
prune = []
for i in range(len(next_step)):
if (next_step[i] in path):
prune.append(i)
if len(prune) > 0:
prune = prune[::-1]
for i in prune:
next_step = np.delete(next_step, i)
next_weight = np.delete(next_weight, i)
good_step = []
if (len(next_weight) > 0):
# good_weight = next_weight[next_weight > 0.555]
good_weight = next_weight[next_weight > 0.8]
if (len(good_weight) > 0):
# good_step = next_step[next_weight > 0.555]
good_step = next_step[next_weight > 0.8]
else:
good_weight = [max(next_weight)]
# if (good_weight[0] > 0.555):