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DataLoader.py
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DataLoader.py
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import ROOT
import numpy as np
from larcv import larcv
from larlite import larlite
from array import *
from larlite import larutil
from ublarcvapp import ublarcvapp
from MiscFunctions import cropped_np, unravel_array, reravel_array, paste_target
from LArMatchModel import LArMatchConvNet
class DataLoader_MC:
def __init__(self, PARAMS, verbose=False, all_train = False,all_valid = False,deploy=False):
self.PARAMS = PARAMS
self.verbose = verbose
self.truthtrack_SCE = ublarcvapp.mctools.TruthTrackSCE()
self.SCEUBooNE = larutil.SpaceChargeMicroBooNE()
self.NeutrinoVertexer = ublarcvapp.mctools.NeutrinoVertex()
self.LArbysMC = ublarcvapp.mctools.LArbysMC()
self.LArbysMC.initialize()
self.iocv = None
self.LArMatchNet = None
if PARAMS['USE_CONV_IM'] == False:
self.iocv = larcv.IOManager(larcv.IOManager.kREAD,"io",larcv.IOManager.kTickBackward)
self.iocv.set_verbosity(5)
self.iocv.reverse_all_products() # Do I need this?
self.iocv.add_in_file(self.PARAMS['INFILE'])
self.iocv.initialize()
else:
self.LArMatchNet = LArMatchConvNet(self.PARAMS)
if deploy == True:
self.iocv = larcv.IOManager(larcv.IOManager.kREAD,"io",larcv.IOManager.kTickBackward)
self.iocv.set_verbosity(5)
self.iocv.reverse_all_products() # Do I need this?
self.iocv.add_in_file(self.PARAMS['INFILE'])
self.iocv.initialize()
self.ioll = larlite.storage_manager(larlite.storage_manager.kREAD)
self.ioll.add_in_filename(self.PARAMS['INFILE'])
self.ioll.open()
self.nentries_ll = self.ioll.get_entries()
self.nentries_train = int(self.nentries_ll*0.8)
self.nentries_val = self.nentries_ll-int(self.nentries_ll*0.8)
self.nentry_val_buffer = self.nentries_train
self.currentEntry = 0
if all_train:
self.nentries_train = self.nentries_ll
self.nentries_val = 0
self.nentry_val_buffer = self.nentries_train
elif all_valid:
self.nentries_val = self.nentries_ll
self.nentries_train = 0
self.nentry_val_buffer = self.nentries_train
print()
print("Total Events in File: ", self.nentries_ll)
print("Total Events in Training: ", self.nentries_train)
print("Total Events in Validation: ", self.nentries_val)
print()
self.PDG_to_Part = {
2212:"PROTON",
2112:"NEUTRON",
211:"PIPLUS",
-211:"PIMINUS",
111:"PI0",
11:"ELECTRON",
-11:"POSITRON",
13:"MUON",
-13:"ANTIMUON",
}
def load_dlreco_inputs_onestop(self, START_ENTRY, END_ENTRY, MAX_TRACKS_PULL = -1, is_val=False):
training_data = []
entries_v = []
mctrack_idx_v = []
mctrack_length_v = []
mctrack_pdg_v = []
mctrack_energy_v = []
runs_v = []
subruns_v = []
event_ids_v = []
buffer = 0
max_entry = self.nentries_train
if is_val:
buffer = self.nentry_val_buffer
max_entry = self.nentries_val
if END_ENTRY > max_entry:
END_ENTRY = max_entry
assert END_ENTRY > START_ENTRY
assert START_ENTRY >= 0
is_val_string = "validation" if is_val else "training"
print("Loading Entries from",START_ENTRY+buffer, "to",END_ENTRY+buffer,"for",is_val_string)
for i in range(START_ENTRY+buffer, END_ENTRY+buffer):
if self.verbose:
print()
print("Loading Entry:", i, "of range", START_ENTRY+buffer, END_ENTRY+buffer)
if self.PARAMS['USE_CONV_IM'] == False:
self.iocv.read_entry(i)
else:
self.iocv.read_entry(i)
self.ioll.go_to(i)
ev_mctrack = self.ioll.get_data(larlite.data.kMCTrack, "mcreco")
# Get Wire ADC Image to a Numpy Array
meta = None
run = -1
subrun = -1
event = -1
if self.PARAMS['USE_CONV_IM']:
y_wire_np, run, subrun, event, meta = self.LArMatchNet.get_larmatch_features(i)
else:
ev_wire = self.iocv.get_data(larcv.kProductImage2D,"wire")
img_v = ev_wire.Image2DArray()
y_wire_image2d = img_v[2]
y_wire_np = larcv.as_ndarray(y_wire_image2d) # I am Speed.
run = ev_wire.run()
subrun = ev_wire.subrun()
event = ev_wire.event()
meta = y_wire_image2d.meta()
ev_wire = self.iocv.get_data(larcv.kProductImage2D,"wire")
img_v = ev_wire.Image2DArray()
y_wire_image2d = img_v[2]
y_defwire_np = larcv.as_ndarray(y_wire_image2d) # I am Speed.
ev_ancestor = self.iocv.get_data(larcv.kProductImage2D,"ancestor")
anc_v = ev_ancestor.Image2DArray()
y_anc_image2d = anc_v[2]
y_anc_np = larcv.as_ndarray(y_anc_image2d) # I am Speed.
# Get MC Track X Y Points
trk_xpt_list = []
trk_ypt_list = []
this_event_track_pdgs = []
trk_idx = -1
if self.verbose:
print("N Tracks", len(ev_mctrack))
for mctrack in ev_mctrack:
trk_idx += 1
if mctrack.PdgCode() not in self.PDG_to_Part or self.PDG_to_Part[mctrack.PdgCode()] not in ["PROTON","MUON","PIPLUS","PIMINUS","PI0"]:
continue
print(mctrack.PdgCode())
track_length = mctrack_length(mctrack)
if self.verbose:
print("Track Index:",trk_idx)
print(" Track Length", track_length)
if mctrack.PdgCode() in self.PDG_to_Part:
print(" Track PDG:", self.PDG_to_Part[mctrack.PdgCode()])
else:
print(" Track PDG:", mctrack.PdgCode())
if track_length < self.PARAMS['MIN_TRACK_LENGTH']:
if self.verbose:
print("Skipping Short Track")
continue
xpt_list = []
ypt_list = []
last_x = 0
last_y = 0
last_z = 0
step_idx = -1
sce_track = self.truthtrack_SCE.applySCE(mctrack)
for pos_idx in range(sce_track.NumberTrajectoryPoints()):
sce_step = sce_track.LocationAtPoint(pos_idx)
step_idx += 1
x = sce_step.X()
y = sce_step.Y()
z = sce_step.Z()
if is_inside_boundaries(x,y,z) == False:
continue
if step_idx != 0:
step_dist = ((x-last_x)**2 + (y-last_y)**2 + (z-last_z)**2)**0.5
# step_dist_3d.append(step_dist)
last_x = x
last_y = y
last_z = z
# if trk_idx == 6:
# print(str(round(x)).zfill(4),str(round(y)).zfill(4),str(round(z)).zfill(4),str(round(t)).zfill(4))
col,row = getprojectedpixel(meta,x,y,z)
if len(xpt_list) !=0 and col == xpt_list[len(xpt_list)-1] and row == ypt_list[len(ypt_list)-1]:
continue
xpt_list.append(col)
ypt_list.append(row)
full_image = y_wire_np
steps_x = xpt_list
steps_y = ypt_list
# FLAG
if self.verbose:
print(" Original Track Points", len(steps_x))
new_steps_x, new_steps_y = insert_cropedge_steps(steps_x,steps_y,self.PARAMS['PADDING'],always_edge=self.PARAMS['ALWAYS_EDGE'])
steps_x = new_steps_x
steps_y = new_steps_y
if self.verbose:
print(" After Inserted Track Points", len(steps_x))
if len(steps_x) < 2: #Don't include tracks without a step and then endpoint
continue
# Many of the following categories are just a reformatting of each other
# They are duplicated to allow for easy network mode switching
stepped_images = [] # List of cropped images as 2D numpy array
stepped_wire_images = []
flat_stepped_images = [] # list of cropped images as flattened 1D np array
next_positions = [] # list of next step positions as np(x,y)
flat_next_positions = [] # list of next step positions in flattened single coord idx
flat_area_positions = [] # list of np_zeros with 1s pasted in a square around target
xy_shifts = [] # list of X,Y shifts to take the next step
charge_in_wire_v = []
charge_in_truth_v = []
for idx in range(len(steps_x)):
# if idx > 1:
# continue
step_x = steps_x[idx]
step_y = steps_y[idx]
next_step_x = -1.0
next_step_y = -1.0
if idx != len(steps_x)-1:
next_step_x = steps_x[idx+1]
next_step_y = steps_y[idx+1]
cropped_step_image = cropped_np(full_image, step_x, step_y, self.PARAMS['PADDING'])
cropped_wire_image = cropped_np(y_defwire_np, step_x, step_y, self.PARAMS['PADDING'])
cropped_anc_image = cropped_np(y_anc_np, step_x, step_y, self.PARAMS['PADDING'])
cropped_anc_image[cropped_anc_image < 0] = 0
cropped_anc_image[cropped_anc_image > 0] = 1
chg_in_wire = np.sum(cropped_wire_image)
chg_in_truth = np.sum(cropped_anc_image*cropped_wire_image)
charge_in_wire_v.append(chg_in_wire)
charge_in_truth_v.append(chg_in_truth)
required_padding_x = self.PARAMS['PADDING'] - step_x
required_padding_y = self.PARAMS['PADDING'] - step_y
stepped_images.append(cropped_step_image)
flat_stepped_images.append(unravel_array(cropped_step_image))
stepped_wire_images.append(cropped_wire_image)
if idx != len(steps_x)-1:
target_x = required_padding_x + next_step_x
target_y = required_padding_y + next_step_y
np_step_target = np.array([target_x*1.0,target_y*1.0])
flat_np_step_target = target_x*cropped_step_image.shape[1]+target_y
if self.PARAMS['AREA_TARGET']:
zeros_np = np.zeros((cropped_step_image.shape[0],cropped_step_image.shape[1]))
flat_area_positions.append(unravel_array(paste_target(zeros_np,target_x,target_y,self.PARAMS['TARGET_BUFFER'])))
next_positions.append(np_step_target)
flat_next_positions.append(flat_np_step_target)
np_xy_shift = np.array([target_x*1.0-self.PARAMS['PADDING'],target_y*1.0-self.PARAMS['PADDING'] ])
xy_shifts.append(np_xy_shift)
else:
if self.PARAMS['AREA_TARGET']:
next_positions.append(np.array([self.PARAMS['PADDING'],self.PARAMS['PADDING']])) #should correspond to centerpoint
flat_next_positions.append((self.PARAMS['NUM_CLASSES']-1)/2) #should correspond to centerpoint
targ_np = np.zeros((cropped_step_image.shape[0],cropped_step_image.shape[1]))
flat_area_positions.append(unravel_array(paste_target(targ_np,self.PARAMS['PADDING'],self.PARAMS['PADDING'],self.PARAMS['TARGET_BUFFER']))) #should correspond to centerpoint
np_xy_shift = np.array([0.0,0.0])
xy_shifts.append(np_xy_shift)
elif self.PARAMS['CENTERPOINT_ISEND']:
next_positions.append(np.array([self.PARAMS['PADDING'],self.PARAMS['PADDING']])) #should correspond to centerpoint
flat_next_positions.append((self.PARAMS['NUM_CLASSES']-1)/2) #should correspond to centerpoint
np_xy_shift = np.array([0.0,0.0])
xy_shifts.append(np_xy_shift)
else:
next_positions.append(np.array([-1.0,-1.0]))
flat_next_positions.append(self.PARAMS['NUM_CLASSES']-1)
np_xy_shift = np.array([0.0,0.0])
xy_shifts.append(np_xy_shift)
if self.PARAMS['CLASSIFIER_NOT_DISTANCESHIFTER']:
training_data.append((stepped_images,flat_next_positions,flat_area_positions,stepped_wire_images,charge_in_wire_v,charge_in_truth_v))
entries_v.append(i)
mctrack_idx_v.append(trk_idx)
mctrack_length_v.append(track_length)
mctrack_pdg_v.append(mctrack.PdgCode())
mctrack_energy_v.append(mctrack.Start().E())
runs_v.append(run)
subruns_v.append(subrun)
event_ids_v.append(event)
if len(training_data) == MAX_TRACKS_PULL:
print("Clipping Training Load Size at ",len(training_data))
is_val_string = "validation" if is_val else "training"
print("Loading ",len(training_data), "tracks for",is_val_string)
return training_data, entries_v, mctrack_idx_v, mctrack_length_v, mctrack_pdg_v, mctrack_energy_v, runs_v, subruns_v, event_ids_v
else:
training_data.append((stepped_images,xy_shifts))
entries_v.append(i)
mctrack_idx_v.append(trk_idx)
mctrack_length_v.append(track_length)
mctrack_pdg_v.append(mctrack.PdgCode())
mctrack_energy_v.append(mctrack.Start().E())
runs_v.append(run)
subruns_v.append(subrun)
event_ids_v.append(event)
if len(training_data) == MAX_TRACKS_PULL:
print("Clipping Training Load Size at ",len(training_data))
is_val_string = "validation" if is_val else "training"
print("Loading ",len(training_data), "tracks for",is_val_string)
return training_data, entries_v, mctrack_idx_v, mctrack_length_v, mctrack_pdg_v, mctrack_energy_v, runs_v, subruns_v, event_ids_v
# FLAG
# End of MCTrack Loop
is_val_string = "validation" if is_val else "training"
print("Loading ",len(training_data), "tracks for",is_val_string)
return training_data, entries_v, mctrack_idx_v, mctrack_length_v, mctrack_pdg_v, mctrack_energy_v, runs_v, subruns_v, event_ids_v
def load_deploy_versatile(self, mode='MCNU', prongDict=None):
deployDict = {}
deployDict['seedX'] = None
deployDict['seedY'] = None
deployDict['entry'] = None
deployDict['run'] = None
deployDict['subrun'] = None
deployDict['event'] = None
deployDict['featureImages'] = None
deployDict['mcProngs'] = None
deployDict['mcProngs_thresh']= None
print("Loading Entry:", self.currentEntry)
deployDict['entry'] = self.currentEntry
self.iocv.read_entry(self.currentEntry)
self.ioll.go_to(self.currentEntry)
if prongDict != None:
self.LArbysMC.process(self.iocv, self.ioll)
nPart = self.LArbysMC._nproton + self.LArbysMC._nlepton + self.LArbysMC._nmeson
nPart_thresh = self.LArbysMC._nproton_60mev + self.LArbysMC._nlepton_35mev + self.LArbysMC._nmeson_35mev
deployDict['mcProngs'] = nPart
deployDict['mcProngs_thresh'] = nPart_thresh
if mode in ["MCNU","MCNU_NUE","MCNU_BNB"]:
passFlag = self.getDeployDictMCNU(deployDict)
self.currentEntry += 1
return deployDict, passFlag
def getDeployDictMCNU(self, deployDict):
neutrino_vertex = self.NeutrinoVertexer.getPos3DwSCE(self.ioll, self.SCEUBooNE)
if is_inside_boundaries(neutrino_vertex[0],neutrino_vertex[1],neutrino_vertex[2]) == False:
if self.verbose:
print(neutrino_vertex[0],neutrino_vertex[1],neutrino_vertex[2], " Out of Bounds")
return 0
larmatchFeat, deployDict['run'], deployDict['subrun'], deployDict['event'], meta = \
self.LArMatchNet.get_larmatch_features(self.currentEntry)
ev_wire = self.iocv.get_data(larcv.kProductImage2D,"wire")
y_defwire_np = larcv.as_ndarray(ev_wire.Image2DArray()[2]) # I am Speed.
y_defwire_np = np.expand_dims(y_defwire_np,axis=2)
deployDict['featureImages'] = np.concatenate((larmatchFeat,y_defwire_np),axis=2)
deployDict['seedX'],deployDict['seedY'] = getprojectedpixel(meta,neutrino_vertex[0],neutrino_vertex[1],neutrino_vertex[2])
return 1
def load_dlreco_inputs_onestop_deploy_neutrinovtx(self, START_ENTRY, END_ENTRY, MAX_TRACKS_PULL = -1, run_backwards = False, is_val=True, showermode=False):
training_data = []
entries_v = []
mctrack_idx_v = []
mctrack_length_v = []
mctrack_pdg_v = []
mctrack_energy_v = []
runs_v = []
subruns_v = []
event_ids_v = []
larmatch_images_v = []
wire_images_v = []
x_starts_v = []
y_starts_v = []
ssnettrack_ims_v = []
ssnetshower_ims_v = []
n_mcProngs = []
n_mcProngs_thresh = []
buffer = 0
max_entry = self.nentries_train
if is_val:
buffer = self.nentry_val_buffer
max_entry = self.nentries_val
if END_ENTRY > max_entry:
END_ENTRY = max_entry
assert END_ENTRY > START_ENTRY
assert START_ENTRY >= 0
is_val_string = "validation" if is_val else "training"
print("Loading Entries from",START_ENTRY+buffer, "to",END_ENTRY+buffer,"for",is_val_string)
for i in range(START_ENTRY+buffer, END_ENTRY+buffer):
if self.verbose:
print()
print("Loading Entry:", i, "of range", START_ENTRY+buffer, END_ENTRY+buffer)
self.iocv.read_entry(i)
self.ioll.go_to(i)
self.LArbysMC.process(self.iocv, self.ioll)
nPart = self.LArbysMC._nproton + self.LArbysMC._nlepton + self.LArbysMC._nmeson
nPart_thresh = self.LArbysMC._nproton_60mev + self.LArbysMC._nlepton_35mev + self.LArbysMC._nmeson_35mev
meta = None
run = -1
subrun = -1
event = -1
if self.PARAMS['USE_CONV_IM']:
y_wire_np, run, subrun, event, meta = self.LArMatchNet.get_larmatch_features(i)
else:
ev_wire = self.iocv.get_data(larcv.kProductImage2D,"wire")
img_v = ev_wire.Image2DArray()
y_wire_image2d = img_v[2]
y_wire_np = larcv.as_ndarray(y_wire_image2d) # I am Speed.
run = ev_wire.run()
subrun = ev_wire.subrun()
event = ev_wire.event()
meta = y_wire_image2d.meta()\
# Deply needs wire image as well:
ev_wire = self.iocv.get_data(larcv.kProductImage2D,"wire")
img_v = ev_wire.Image2DArray()
y_wire_image2d = img_v[2]
y_defwire_np = larcv.as_ndarray(y_wire_image2d) # I am Speed.
ev_ancestor = self.iocv.get_data(larcv.kProductImage2D,"ancestor")
anc_v = ev_ancestor.Image2DArray()
y_anc_image2d = anc_v[2]
y_anc_np = larcv.as_ndarray(y_anc_image2d) # I am Speed.
# ev_ssnet = self.iocv.get_data(larcv.kProductImage2D,"ubspurn_plane2")
ev_ssnet = self.iocv.get_data(larcv.kProductSparseImage,"sparseuresnetout")
# // 0 -> HIP (Pions+ProtonsTruth)
# // 1 -> MIP (Muons)
# // 2 -> Shower
# // 3 -> Delta Ray
# // 4 -> Michel
ssnet_v = ev_ssnet.SparseImageArray().at(2).as_Image2D();
# y_ssnet_image2d = ssnet_v[2]
y_ssnet_track_np = larcv.as_ndarray(ssnet_v[0]) + larcv.as_ndarray(ssnet_v[1])# I am Speed.
y_ssnet_shower_np = larcv.as_ndarray(ssnet_v[2]) + larcv.as_ndarray(ssnet_v[3]) + larcv.as_ndarray(ssnet_v[4])# I am Speed.
########
neutrino_vertex = self.NeutrinoVertexer.getPos3DwSCE(self.ioll, self.SCEUBooNE)
# ev_mctruth = self.ioll.get_data(larlite.data.kMCTruth,"generator");
# mctruth = ev_mctruth.at(0)
# start = mctruth.GetNeutrino().Nu().Trajectory().front()
# tick = CrossingPointsAnaMethods.getTick(start, 4050.0, None)
# x = start.X()
# y = start.Y()
# z = start.Z()
# neutrino_vertex = [x,y,z]
########
if is_inside_boundaries(neutrino_vertex[0],neutrino_vertex[1],neutrino_vertex[2]) == False:
if self.verbose:
print(neutrino_vertex[0],neutrino_vertex[1],neutrino_vertex[2], " Out of Bounds")
continue
col,row = getprojectedpixel(meta,neutrino_vertex[0],neutrino_vertex[1],neutrino_vertex[2])
stepped_images = [] # List of cropped images as 2D numpy array
stepped_wire_images = []
flat_stepped_images = [] # list of cropped images as flattened 1D np array
next_positions = [] # list of next step positions as np(x,y)
flat_next_positions = [] # list of next step positions in flattened single coord idx
flat_area_positions = [] # list of np_zeros with 1s pasted in a square around target
xy_shifts = [] # list of X,Y shifts to take the next step
charge_in_wire_v = []
charge_in_truth_v = []
cropped_step_image = cropped_np(y_wire_np, col, row, self.PARAMS['PADDING'])
cropped_wire_image = cropped_np(y_defwire_np, col, row, self.PARAMS['PADDING'])
cropped_anc_image = cropped_np(y_anc_np, col, row, self.PARAMS['PADDING'])
cropped_anc_image[cropped_anc_image < 0] = 0
cropped_anc_image[cropped_anc_image > 0] = 1
chg_in_wire = np.sum(cropped_wire_image)
chg_in_truth = np.sum(cropped_anc_image*cropped_wire_image)
charge_in_wire_v.append(chg_in_wire)
charge_in_truth_v.append(chg_in_truth)
required_padding_x = self.PARAMS['PADDING'] - col
required_padding_y = self.PARAMS['PADDING'] - row
stepped_images.append(cropped_step_image)
flat_stepped_images.append(unravel_array(cropped_step_image))
stepped_wire_images.append(cropped_wire_image)
if self.PARAMS['AREA_TARGET']:
next_positions.append(np.array([self.PARAMS['PADDING'],self.PARAMS['PADDING']])) #should correspond to centerpoint
flat_next_positions.append((self.PARAMS['NUM_CLASSES']-1)/2) #should correspond to centerpoint
targ_np = np.zeros((cropped_step_image.shape[0],cropped_step_image.shape[1]))
flat_area_positions.append(unravel_array(paste_target(targ_np,self.PARAMS['PADDING'],self.PARAMS['PADDING'],self.PARAMS['TARGET_BUFFER']))) #should correspond to centerpoint
np_xy_shift = np.array([0.0,0.0])
xy_shifts.append(np_xy_shift)
elif self.PARAMS['CENTERPOINT_ISEND']:
next_positions.append(np.array([self.PARAMS['PADDING'],self.PARAMS['PADDING']])) #should correspond to centerpoint
flat_next_positions.append((self.PARAMS['NUM_CLASSES']-1)/2) #should correspond to centerpoint
np_xy_shift = np.array([0.0,0.0])
xy_shifts.append(np_xy_shift)
else:
next_positions.append(np.array([-1.0,-1.0]))
flat_next_positions.append(self.PARAMS['NUM_CLASSES']-1)
np_xy_shift = np.array([0.0,0.0])
xy_shifts.append(np_xy_shift)
training_data.append((stepped_images,flat_next_positions,flat_area_positions,stepped_wire_images,charge_in_wire_v,charge_in_truth_v))
entries_v.append(i)
mctrack_idx_v.append(0) # Always 1 neutrino idx
mctrack_length_v.append(-1) # No track length for a vertex
mctrack_pdg_v.append(-1)#mctrack.PdgCode()) # No PDG
mctrack_energy_v.append(-1)#mctrack.Start().E())
runs_v.append(run)
subruns_v.append(subrun)
event_ids_v.append(event)
larmatch_images_v.append(np.copy(y_wire_np))
wire_images_v.append(np.copy(y_defwire_np))
x_starts_v.append(col)
y_starts_v.append(row)
ssnettrack_ims_v.append(y_ssnet_track_np)
ssnetshower_ims_v.append(y_ssnet_shower_np)
n_mcProngs.append(nPart)
n_mcProngs_thresh.append(nPart_thresh)
if len(training_data) == MAX_TRACKS_PULL:
print("Clipping Training Load Size at ",len(training_data))
is_val_string = "validation" if is_val else "training"
print("Loading ",len(training_data), "tracks for",is_val_string)
return training_data, entries_v, mctrack_idx_v, mctrack_length_v, mctrack_pdg_v, mctrack_energy_v, runs_v, subruns_v, event_ids_v, larmatch_images_v, wire_images_v, x_starts_v, y_starts_v, ssnettrack_ims_v, ssnetshower_ims_v, n_mcProngs, n_mcProngs_thresh
return training_data, entries_v, mctrack_idx_v, mctrack_length_v, mctrack_pdg_v, mctrack_energy_v, runs_v, subruns_v, event_ids_v, larmatch_images_v, wire_images_v, x_starts_v, y_starts_v, ssnettrack_ims_v, ssnetshower_ims_v, n_mcProngs, n_mcProngs_thresh
def load_dlreco_inputs_onestop_deploy(self, START_ENTRY, END_ENTRY, MAX_TRACKS_PULL = -1, run_backwards = False, is_val=True, showermode=False):
training_data = []
entries_v = []
mctrack_idx_v = []
mctrack_length_v = []
mctrack_pdg_v = []
mctrack_energy_v = []
runs_v = []
subruns_v = []
event_ids_v = []
larmatch_images_v = []
wire_images_v = []
x_starts_v = []
y_starts_v = []
buffer = 0
max_entry = self.nentries_train
if is_val:
buffer = self.nentry_val_buffer
max_entry = self.nentries_val
if END_ENTRY > max_entry:
END_ENTRY = max_entry
assert END_ENTRY > START_ENTRY
assert START_ENTRY >= 0
is_val_string = "validation" if is_val else "training"
print("Loading Entries from",START_ENTRY+buffer, "to",END_ENTRY+buffer,"for",is_val_string)
for i in range(START_ENTRY+buffer, END_ENTRY+buffer):
if self.verbose:
print()
print("Loading Entry:", i, "of range", START_ENTRY+buffer, END_ENTRY+buffer)
if self.PARAMS['USE_CONV_IM'] == False:
self.iocv.read_entry(i)
else:
self.iocv.read_entry(i)
self.ioll.go_to(i)
ev_mctrack = self.ioll.get_data(larlite.data.kMCTrack, "mcreco")
if showermode:
# ev_mctrack = self.ioll.get_data(larlite.data.kMCTruth, "generator" );
ev_mctrack = self.ioll.get_data(larlite.data.kMCShower, "mcreco")
# Get Wire ADC Image to a Numpy Array
meta = None
run = -1
subrun = -1
event = -1
if self.PARAMS['USE_CONV_IM']:
y_wire_np, run, subrun, event, meta = self.LArMatchNet.get_larmatch_features(i)
else:
ev_wire = self.iocv.get_data(larcv.kProductImage2D,"wire")
img_v = ev_wire.Image2DArray()
y_wire_image2d = img_v[2]
y_wire_np = larcv.as_ndarray(y_wire_image2d) # I am Speed.
run = ev_wire.run()
subrun = ev_wire.subrun()
event = ev_wire.event()
meta = y_wire_image2d.meta()\
# Deply needs wire image as well:
ev_wire = self.iocv.get_data(larcv.kProductImage2D,"wire")
img_v = ev_wire.Image2DArray()
y_wire_image2d = img_v[2]
y_defwire_np = larcv.as_ndarray(y_wire_image2d) # I am Speed.
ev_ancestor = self.iocv.get_data(larcv.kProductImage2D,"ancestor")
anc_v = ev_ancestor.Image2DArray()
y_anc_image2d = anc_v[2]
y_anc_np = larcv.as_ndarray(y_anc_image2d) # I am Speed.
# Get MC Track X Y Points
trk_xpt_list = []
trk_ypt_list = []
this_event_track_pdgs = []
trk_idx = -1
if self.verbose:
print("N Tracks", len(ev_mctrack))
mctrack = None
nTracks = len(ev_mctrack) #if not showermode else 1
# for mctrack in ev_mctrack:
for mc_idx in range(nTracks):
mctrack = ev_mctrack.at(mc_idx)
# mctrack = ev_mctrack.at(mc_idx) if not showermode else ev_mctrack.at(0).GetNeutrino().Nu()
trk_idx += 1
if showermode:
if trk_idx != 0:
continue
if not showermode:
if mctrack.PdgCode() not in self.PDG_to_Part or self.PDG_to_Part[mctrack.PdgCode()] not in ["PROTON","MUON","PIPLUS","PIMINUS","PI0"]:
continue
print(mctrack.PdgCode())
track_length = -1
if not showermode:
track_length = mctrack_length(mctrack)
if self.verbose:
print("Track Index:",trk_idx)
print(" Track Length", track_length)
if mctrack.PdgCode() in self.PDG_to_Part:
print(" Track PDG:", self.PDG_to_Part[mctrack.PdgCode()])
else:
print(" Track PDG:", mctrack.PdgCode())
if track_length < self.PARAMS['MIN_TRACK_LENGTH']:
if self.verbose:
print("Skipping Short Track")
continue
xpt_list = []
ypt_list = []
last_x = 0
last_y = 0
last_z = 0
step_idx = -1
sce_track = None
if showermode:
sce_track = mctrack
sce_step = sce_track.Start()
x = sce_step.X()
y = sce_step.Y()
z = sce_step.Z()
if is_inside_boundaries(x,y,z) == False:
if self.verbose:
print(x,y,z, " Out of Bounds")
continue
if step_idx != 0:
step_dist = ((x-last_x)**2 + (y-last_y)**2 + (z-last_z)**2)**0.5
# step_dist_3d.append(step_dist)
last_x = x
last_y = y
last_z = z
# if trk_idx == 6:
# print(str(round(x)).zfill(4),str(round(y)).zfill(4),str(round(z)).zfill(4),str(round(t)).zfill(4))
col,row = getprojectedpixel(meta,x,y,z)
if len(xpt_list) !=0 and col == xpt_list[len(xpt_list)-1] and row == ypt_list[len(ypt_list)-1]:
continue
xpt_list.append(col)
ypt_list.append(row)
track_length = ((sce_track.End().Z() - z)**2 + (sce_track.End().Y() - y)**2 + (sce_track.End().X() - x)**2 )**0.2
if self.verbose:
print("Breaking Shower")
else:
sce_track = self.truthtrack_SCE.applySCE(mctrack)
for pos_idx in range(sce_track.NumberTrajectoryPoints()):
sce_step = None
if run_backwards:
sce_step = sce_track.LocationAtPoint(sce_track.NumberTrajectoryPoints()-1-pos_idx)
else:
sce_step = sce_track.LocationAtPoint(pos_idx)
step_idx += 1
x = sce_step.X()
y = sce_step.Y()
z = sce_step.Z()
if is_inside_boundaries(x,y,z) == False:
continue
if step_idx != 0:
step_dist = ((x-last_x)**2 + (y-last_y)**2 + (z-last_z)**2)**0.5
# step_dist_3d.append(step_dist)
last_x = x
last_y = y
last_z = z
# if trk_idx == 6:
# print(str(round(x)).zfill(4),str(round(y)).zfill(4),str(round(z)).zfill(4),str(round(t)).zfill(4))
col,row = getprojectedpixel(meta,x,y,z)
if len(xpt_list) !=0 and col == xpt_list[len(xpt_list)-1] and row == ypt_list[len(ypt_list)-1]:
continue
xpt_list.append(col)
ypt_list.append(row)
break
if len(xpt_list) == 1:
print("Printing Col and Row:\n",col, row)
full_image = y_wire_np
steps_x = xpt_list
steps_y = ypt_list
# FLAG
if self.verbose:
print(" Original Track Points", len(steps_x))
new_steps_x, new_steps_y = insert_cropedge_steps(steps_x,steps_y,self.PARAMS['PADDING'],always_edge=self.PARAMS['ALWAYS_EDGE'])
steps_x = new_steps_x
steps_y = new_steps_y
if self.verbose:
print(" After Inserted Track Points", len(steps_x))
# if len(steps_x) < 2: #Don't include tracks without a step and then endpoint
# continue
# Many of the following categories are just a reformatting of each other
# They are duplicated to allow for easy network mode switching
stepped_images = [] # List of cropped images as 2D numpy array
stepped_wire_images = []
flat_stepped_images = [] # list of cropped images as flattened 1D np array
next_positions = [] # list of next step positions as np(x,y)
flat_next_positions = [] # list of next step positions in flattened single coord idx
flat_area_positions = [] # list of np_zeros with 1s pasted in a square around target
xy_shifts = [] # list of X,Y shifts to take the next step
charge_in_wire_v = []
charge_in_truth_v = []
for idx in range(len(steps_x)):
# if idx > 1:
# continue
step_x = steps_x[idx]
step_y = steps_y[idx]
next_step_x = -1.0
next_step_y = -1.0
if idx != len(steps_x)-1:
next_step_x = steps_x[idx+1]
next_step_y = steps_y[idx+1]
cropped_step_image = cropped_np(full_image, step_x, step_y, self.PARAMS['PADDING'])
cropped_wire_image = cropped_np(y_defwire_np, step_x, step_y, self.PARAMS['PADDING'])
cropped_anc_image = cropped_np(y_anc_np, step_x, step_y, self.PARAMS['PADDING'])
cropped_anc_image[cropped_anc_image < 0] = 0
cropped_anc_image[cropped_anc_image > 0] = 1
chg_in_wire = np.sum(cropped_wire_image)
chg_in_truth = np.sum(cropped_anc_image*cropped_wire_image)
charge_in_wire_v.append(chg_in_wire)
charge_in_truth_v.append(chg_in_truth)
required_padding_x = self.PARAMS['PADDING'] - step_x
required_padding_y = self.PARAMS['PADDING'] - step_y
stepped_images.append(cropped_step_image)
flat_stepped_images.append(unravel_array(cropped_step_image))
stepped_wire_images.append(cropped_wire_image)
if idx != len(steps_x)-1:
target_x = required_padding_x + next_step_x
target_y = required_padding_y + next_step_y
np_step_target = np.array([target_x*1.0,target_y*1.0])
flat_np_step_target = target_x*cropped_step_image.shape[1]+target_y
if self.PARAMS['AREA_TARGET']:
zeros_np = np.zeros((cropped_step_image.shape[0],cropped_step_image.shape[1]))
flat_area_positions.append(unravel_array(paste_target(zeros_np,target_x,target_y,self.PARAMS['TARGET_BUFFER'])))
next_positions.append(np_step_target)
flat_next_positions.append(flat_np_step_target)
np_xy_shift = np.array([target_x*1.0-self.PARAMS['PADDING'],target_y*1.0-self.PARAMS['PADDING'] ])
xy_shifts.append(np_xy_shift)
else:
if self.PARAMS['AREA_TARGET']:
next_positions.append(np.array([self.PARAMS['PADDING'],self.PARAMS['PADDING']])) #should correspond to centerpoint
flat_next_positions.append((self.PARAMS['NUM_CLASSES']-1)/2) #should correspond to centerpoint
targ_np = np.zeros((cropped_step_image.shape[0],cropped_step_image.shape[1]))
flat_area_positions.append(unravel_array(paste_target(targ_np,self.PARAMS['PADDING'],self.PARAMS['PADDING'],self.PARAMS['TARGET_BUFFER']))) #should correspond to centerpoint
np_xy_shift = np.array([0.0,0.0])
xy_shifts.append(np_xy_shift)
elif self.PARAMS['CENTERPOINT_ISEND']:
next_positions.append(np.array([self.PARAMS['PADDING'],self.PARAMS['PADDING']])) #should correspond to centerpoint
flat_next_positions.append((self.PARAMS['NUM_CLASSES']-1)/2) #should correspond to centerpoint
np_xy_shift = np.array([0.0,0.0])
xy_shifts.append(np_xy_shift)
else:
next_positions.append(np.array([-1.0,-1.0]))
flat_next_positions.append(self.PARAMS['NUM_CLASSES']-1)
np_xy_shift = np.array([0.0,0.0])
xy_shifts.append(np_xy_shift)
if self.PARAMS['CLASSIFIER_NOT_DISTANCESHIFTER']:
training_data.append((stepped_images,flat_next_positions,flat_area_positions,stepped_wire_images,charge_in_wire_v,charge_in_truth_v))
entries_v.append(i)
mctrack_idx_v.append(trk_idx)
mctrack_length_v.append(track_length)
mctrack_pdg_v.append(mctrack.PdgCode())
mctrack_energy_v.append(mctrack.Start().E())
runs_v.append(run)
subruns_v.append(subrun)
event_ids_v.append(event)
larmatch_images_v.append(np.copy(full_image))
wire_images_v.append(np.copy(y_defwire_np))
x_starts_v.append(steps_x[0])
y_starts_v.append(steps_y[0])
if len(training_data) == MAX_TRACKS_PULL:
print("Clipping Training Load Size at ",len(training_data))
is_val_string = "validation" if is_val else "training"
print("Loading ",len(training_data), "tracks for",is_val_string)
return training_data, entries_v, mctrack_idx_v, mctrack_length_v, mctrack_pdg_v, mctrack_energy_v, runs_v, subruns_v, event_ids_v, larmatch_images_v, wire_images_v, x_starts_v, y_starts_v
else:
training_data.append((stepped_images,xy_shifts))
entries_v.append(i)
mctrack_idx_v.append(trk_idx)
mctrack_length_v.append(track_length)
mctrack_pdg_v.append(mctrack.PdgCode())
mctrack_energy_v.append(mctrack.Start().E())
runs_v.append(run)
subruns_v.append(subrun)
event_ids_v.append(event)
larmatch_images_v.append(np.copy(full_image))
wire_images_v.append(np.copy(y_defwire_np))
x_starts_v.append(steps_x[0])
y_starts_v.append(steps_y[0])
if len(training_data) == MAX_TRACKS_PULL:
print("Clipping Training Load Size at ",len(training_data))
is_val_string = "validation" if is_val else "training"
print("Loading ",len(training_data), "tracks for",is_val_string)
return training_data, entries_v, mctrack_idx_v, mctrack_length_v, mctrack_pdg_v, mctrack_energy_v, runs_v, subruns_v, event_ids_v, larmatch_images_v, wire_images_v, x_starts_v, y_starts_v
# FLAG
# End of MCTrack Loop
is_val_string = "validation" if is_val else "training"
print("Loading ",len(training_data), "tracks for",is_val_string)
return training_data, entries_v, mctrack_idx_v, mctrack_length_v, mctrack_pdg_v, mctrack_energy_v, runs_v, subruns_v, event_ids_v, larmatch_images_v, wire_images_v, x_starts_v, y_starts_v
def get_net_inputs_mc(self, START_ENTRY, END_ENTRY, MAX_TRACKS_PULL = -1, is_val=False):
image_list, xs, ys, runs, subruns, events, filepaths, entries, track_pdgs = self.load_rootfile_MC_Positions(START_ENTRY, END_ENTRY, is_val=is_val)
training_data = []
full_images = []
event_ids = []
steps_x = []
steps_y = []
for EVENT_IDX in range(len(image_list)):
if self.verbose:
print("Doing Event:", EVENT_IDX)
print("N MC Tracks:", len(xs[EVENT_IDX]))
for TRACK_IDX in range(len(xs[EVENT_IDX])):
# if TRACK_IDX != 0:
# continue
if self.verbose:
print(" Doing Track:", TRACK_IDX)
full_image = image_list[EVENT_IDX]
steps_x = xs[EVENT_IDX][TRACK_IDX]
steps_y = ys[EVENT_IDX][TRACK_IDX]
if self.verbose:
print(" Original Track Points", len(steps_x))
new_steps_x, new_steps_y = insert_cropedge_steps(steps_x,steps_y,self.PARAMS['PADDING'],always_edge=self.PARAMS['ALWAYS_EDGE'])
steps_x = new_steps_x
steps_y = new_steps_y
if self.verbose:
print(" After Inserted Track Points", len(steps_x))
if len(steps_x) < 2: #Don't include tracks without a step and then endpoint
continue
# Many of the following categories are just a reformatting of each other
# They are duplicated to allow for easy network mode switching
stepped_images = [] # List of cropped images as 2D numpy array
flat_stepped_images = [] # list of cropped images as flattened 1D np array
next_positions = [] # list of next step positions as np(x,y)
flat_next_positions = [] # list of next step positions in flattened single coord idx
flat_area_positions = [] # list of np_zeros with 1s pasted in a square around target
xy_shifts = [] # list of X,Y shifts to take the next step
for idx in range(len(steps_x)):
# if idx > 1:
# continue
step_x = steps_x[idx]
step_y = steps_y[idx]
next_step_x = -1.0
next_step_y = -1.0
if idx != len(steps_x)-1:
next_step_x = steps_x[idx+1]
next_step_y = steps_y[idx+1]
cropped_step_image = cropped_np(full_image, step_x, step_y, self.PARAMS['PADDING'])
required_padding_x = self.PARAMS['PADDING'] - step_x
required_padding_y = self.PARAMS['PADDING'] - step_y
stepped_images.append(cropped_step_image)
flat_stepped_images.append(unravel_array(cropped_step_image))
if idx != len(steps_x)-1:
target_x = required_padding_x + next_step_x
target_y = required_padding_y + next_step_y
np_step_target = np.array([target_x*1.0,target_y*1.0])
flat_np_step_target = target_x*cropped_step_image.shape[1]+target_y
if self.PARAMS['AREA_TARGET']:
zeros_np = np.zeros((cropped_step_image.shape[0],cropped_step_image.shape[1]))
flat_area_positions.append(unravel_array(paste_target(zeros_np,target_x,target_y,self.PARAMS['TARGET_BUFFER'])))
next_positions.append(np_step_target)
flat_next_positions.append(flat_np_step_target)
np_xy_shift = np.array([target_x*1.0-self.PARAMS['PADDING'],target_y*1.0-self.PARAMS['PADDING'] ])
xy_shifts.append(np_xy_shift)
else:
if self.PARAMS['AREA_TARGET']:
next_positions.append(np.array([self.PARAMS['PADDING'],self.PARAMS['PADDING']])) #should correspond to centerpoint
flat_next_positions.append((self.PARAMS['NUM_CLASSES']-1)/2) #should correspond to centerpoint
targ_np = np.zeros((cropped_step_image.shape[0],cropped_step_image.shape[1]))
flat_area_positions.append(unravel_array(paste_target(targ_np,self.PARAMS['PADDING'],self.PARAMS['PADDING'],self.PARAMS['TARGET_BUFFER']))) #should correspond to centerpoint
np_xy_shift = np.array([0.0,0.0])
xy_shifts.append(np_xy_shift)
elif self.PARAMS['CENTERPOINT_ISEND']:
next_positions.append(np.array([self.PARAMS['PADDING'],self.PARAMS['PADDING']])) #should correspond to centerpoint
flat_next_positions.append((self.PARAMS['NUM_CLASSES']-1)/2) #should correspond to centerpoint
np_xy_shift = np.array([0.0,0.0])
xy_shifts.append(np_xy_shift)
else:
next_positions.append(np.array([-1.0,-1.0]))
flat_next_positions.append(self.PARAMS['NUM_CLASSES']-1)
np_xy_shift = np.array([0.0,0.0])
xy_shifts.append(np_xy_shift)
if self.PARAMS['CLASSIFIER_NOT_DISTANCESHIFTER']:
training_data.append((stepped_images,flat_next_positions,flat_area_positions))
event_ids.append(EVENT_IDX)
if len(training_data) == MAX_TRACKS_PULL:
print("Clipping Training Load Size at ",len(training_data))
is_val_string = "validation" if is_val else "training"
print("Loading ",len(training_data), "tracks for",is_val_string)
return training_data
else:
training_data.append((stepped_images,xy_shifts))
event_ids.append(EVENT_IDX)
if len(training_data) == MAX_TRACKS_PULL:
print("Clipping Training Load Size at ",len(training_data))
is_val_string = "validation" if is_val else "training"
print("Loading ",len(training_data), "tracks for",is_val_string)
return training_data
# rse_pdg_dict = {}
# rse_pdg_dict['runs'] = runs
# rse_pdg_dict['subruns'] = subruns
# rse_pdg_dict['events'] = events
# rse_pdg_dict['filepaths'] = filepaths
# rse_pdg_dict['pdgs'] = track_pdgs
# rse_pdg_dict['file_idx'] = entries
is_val_string = "validation" if is_val else "training"
print("Loading ",len(training_data), "tracks for",is_val_string)
return training_data #Could return more: full_images, steps_x, steps_y, event_ids, rse_pdg_dict
def get_net_inputs_mc_fullout(self, START_ENTRY, END_ENTRY, MAX_TRACKS_PULL = -1, is_val=False):
image_list, xs, ys, runs, subruns, events, filepaths, entries, track_pdgs = self.load_rootfile_MC_Positions(START_ENTRY, END_ENTRY, is_val=is_val)
training_data = []
full_images = []
event_ids = []
steps_x = []
steps_y = []
entry_num = []
track_idx = []
for EVENT_IDX in range(len(image_list)):
if self.verbose:
print("Doing Event:", EVENT_IDX)
print("N MC Tracks:", len(xs[EVENT_IDX]))
for TRACK_IDX in range(len(xs[EVENT_IDX])):
# if TRACK_IDX != 0:
# continue
if self.verbose:
print(" Doing Track:", TRACK_IDX)
full_image = image_list[EVENT_IDX]
steps_x = xs[EVENT_IDX][TRACK_IDX]
steps_y = ys[EVENT_IDX][TRACK_IDX]
if self.verbose:
print(" Original Track Points", len(steps_x))
new_steps_x, new_steps_y = insert_cropedge_steps(steps_x,steps_y,self.PARAMS['PADDING'],always_edge=self.PARAMS['ALWAYS_EDGE'])
steps_x = new_steps_x
steps_y = new_steps_y
if self.verbose:
print(" After Inserted Track Points", len(steps_x))
if len(steps_x) < 2: #Don't include tracks without a step and then endpoint
continue
# Many of the following categories are just a reformatting of each other
# They are duplicated to allow for easy network mode switching
stepped_images = [] # List of cropped images as 2D numpy array
flat_stepped_images = [] # list of cropped images as flattened 1D np array
next_positions = [] # list of next step positions as np(x,y)
flat_next_positions = [] # list of next step positions in flattened single coord idx
flat_area_positions = [] # list of np_zeros with 1s pasted in a square around target
xy_shifts = [] # list of X,Y shifts to take the next step
for idx in range(len(steps_x)):
# if idx > 1:
# continue
step_x = steps_x[idx]
step_y = steps_y[idx]
next_step_x = -1.0
next_step_y = -1.0
if idx != len(steps_x)-1:
next_step_x = steps_x[idx+1]
next_step_y = steps_y[idx+1]
cropped_step_image = cropped_np(full_image, step_x, step_y, self.PARAMS['PADDING'])
required_padding_x = self.PARAMS['PADDING'] - step_x
required_padding_y = self.PARAMS['PADDING'] - step_y
stepped_images.append(cropped_step_image)
flat_stepped_images.append(unravel_array(cropped_step_image))
if idx != len(steps_x)-1:
target_x = required_padding_x + next_step_x
target_y = required_padding_y + next_step_y
np_step_target = np.array([target_x*1.0,target_y*1.0])
flat_np_step_target = target_x*cropped_step_image.shape[1]+target_y
if self.PARAMS['AREA_TARGET']:
zeros_np = np.zeros((cropped_step_image.shape[0],cropped_step_image.shape[1]))
flat_area_positions.append(unravel_array(paste_target(zeros_np,target_x,target_y,self.PARAMS['TARGET_BUFFER'])))
next_positions.append(np_step_target)
flat_next_positions.append(flat_np_step_target)
np_xy_shift = np.array([target_x*1.0-self.PARAMS['PADDING'],target_y*1.0-self.PARAMS['PADDING'] ])
xy_shifts.append(np_xy_shift)
else:
if self.PARAMS['AREA_TARGET']: