import numpy as np
from numpy.typing import ArrayLike
from uproot import TTree
from ..common import FancyDict
from concurrent.futures import ThreadPoolExecutor
from .pxdFilter import FindUnselectedClusters
import warnings
class PXD(FancyDict):
def __init__(self, data: dict = None) -> None:
self.name = 'pxd'
# list of pxd panels
self.panels = [[[-0.89 , 0.36 , 0.36 , -0.89 , -0.89 ], [ 1.4 , 1.4 , 1.4 , 1.4 , 1.4 ], [-3.12, -3.12, 5.92, 5.92, -3.12]], # 00
[[ 1.25 , 0.365, 0.365, 1.25 , 1.25 ], [ 0.72 , 1.615, 1.615, 0.72 , 0.72 ], [-3.12, -3.12, 5.92, 5.92, -3.12]], # 01
[[ 1.4 , 1.4 , 1.4 , 1.4 , 1.4 ], [-0.36 , 0.89 , 0.89 , -0.36 , -0.36 ], [-3.12, -3.12, 5.92, 5.92, -3.12]], # 02
[[ 0.72 , 1.615, 1.615, 0.72 , 0.72 ], [-1.25 , -0.365, -0.365, -1.25 , -1.25 ], [-3.12, -3.12, 5.92, 5.92, -3.12]], # 03
[[ 0.89 , -0.36 , -0.36 , 0.89 , 0.89 ], [-1.4 , -1.4 , -1.4 , -1.4 , -1.4 ], [-3.12, -3.12, 5.92, 5.92, -3.12]], # 04
[[-1.25 , -0.365, -0.365, -1.25 , -1.25 ], [-0.72 , -1.615, -1.615, -0.72 , -0.72 ], [-3.12, -3.12, 5.92, 5.92, -3.12]], # 05
[[-1.4 , -1.4 , -1.4 , -1.4 , -1.4 ], [ 0.36 , -0.89 , -0.89 , 0.36 , 0.36 ], [-3.12, -3.12, 5.92, 5.92, -3.12]], # 06
[[-0.72 , -1.615, -1.615, -0.72 , -0.72 ], [ 1.25 , 0.365, 0.365, 1.25 , 1.25 ], [-3.12, -3.12, 5.92, 5.92, -3.12]], # 07
[[-0.89 , 0.36 , 0.36 , -0.89 , -0.89 ], [ 2.2 , 2.2 , 2.2 , 2.2 , 2.2 ], [-4.28, -4.28, 8.08, 8.08, -4.28]], # 08
[[ 0.345, 1.4 , 1.4 , 0.345, 0.345], [ 2.35 , 1.725, 1.725, 2.35 , 2.35 ], [-4.28, -4.28, 8.08, 8.08, -4.28]], # 09
[[ 1.48 , 2.1 , 2.1 , 1.48 , 1.48 ], [ 1.85 , 0.78 , 0.78 , 1.85 , 1.85 ], [-4.28, -4.28, 8.08, 8.08, -4.28]], # 10
[[ 2.2 , 2.2 , 2.2 , 2.2 , 2.2 ], [ 0.89 , -0.36 , -0.36 , 0.89 , 0.89 ], [-4.28, -4.28, 8.08, 8.08, -4.28]], # 11
[[ 2.35 , 1.725, 1.725, 2.35 , 2.35 ], [-0.345, -1.4 , -1.4 , -0.345, -0.345], [-4.28, -4.28, 8.08, 8.08, -4.28]], # 12
[[ 1.85 , 0.78 , 0.78 , 1.85 , 1.85 ], [-1.48 , -2.1 , -2.1 , -1.48 , -1.48 ], [-4.28, -4.28, 8.08, 8.08, -4.28]], # 13
[[ 0.89 , -0.36 , -0.36 , 0.89 , 0.89 ], [-2.2 , -2.2 , -2.2 , -2.2 , -2.2 ], [-4.28, -4.28, 8.08, 8.08, -4.28]], # 14
[[-0.345, -1.4 , -1.4 , -0.345, -0.345], [-2.35 , -1.725, -1.725, -2.35 , -2.35 ], [-4.28, -4.28, 8.08, 8.08, -4.28]], # 15
[[-1.48 , -2.1 , -2.1 , -1.48 , -1.48 ], [-1.85 , -0.78 , -0.78 , -1.85 , -1.85 ], [-4.28, -4.28, 8.08, 8.08, -4.28]], # 16
[[-2.2 , -2.2 , -2.2 , -2.2 , -2.2 ], [-0.89 , 0.36 , 0.36 , -0.89 , -0.89 ], [-4.28, -4.28, 8.08, 8.08, -4.28]], # 17
[[-2.35 , -1.725, -1.725, -2.35 , -2.35 ], [ 0.345, 1.4 , 1.4 , 0.345, 0.345], [-4.28, -4.28, 8.08, 8.08, -4.28]], # 18
[[-1.85 , -0.78 , -0.78 , -1.85 , -1.85 ], [ 1.48 , 2.1 , 2.1 , 1.48 , 1.48 ], [-4.28, -4.28, 8.08, 8.08, -4.28]]] # 19
# these are the branch names for cluster info in the root file
self.clusters = ['PXDClusters/PXDClusters.m_clsCharge',
'PXDClusters/PXDClusters.m_seedCharge',
'PXDClusters/PXDClusters.m_clsSize',
'PXDClusters/PXDClusters.m_uSize',
'PXDClusters/PXDClusters.m_vSize',
'PXDClusters/PXDClusters.m_uPosition',
'PXDClusters/PXDClusters.m_vPosition',
'PXDClusters/PXDClusters.m_sensorID']
# these are the branch names for cluster digits in the root file
self.digits = ['PXDDigits/PXDDigits.m_uCellID',
'PXDDigits/PXDDigits.m_vCellID',
'PXDDigits/PXDDigits.m_charge']
# this establishes the relationship between clusters and digits
# because for some reaseon the branch for digits has a different
# size than the cluster branch
self.clusterToDigis = 'PXDClustersToPXDDigits/m_elements/m_elements.m_to'
# these are the branch names for monte carlo data in the root file
self.mcData = ['MCParticles/MCParticles.m_pdg',
'MCParticles/MCParticles.m_momentum_x',
'MCParticles/MCParticles.m_momentum_y',
'MCParticles/MCParticles.m_momentum_z']
# these two establish the relation ship to an from clusters and monte carlo
# there more entries than in the cluster data, but there still mc data missing
# for some cluster files
self.clusterToMC = 'PXDClustersToMCParticles/m_elements/m_elements.m_to'
self.mcToCluster = 'PXDClustersToMCParticles/m_elements/m_elements.m_from'
# these are the sensor IDs of the pxd modules/panels from the root file, they are
# use to identify on which panels a cluster event happened
self.panelIDs = np.array([ 8480, 8512, 8736, 8768, 8992, 9024, 9248, 9280,
9504, 9536, 9760, 9792, 10016, 10048, 10272, 10304,
16672, 16704, 16928, 16960, 17184, 17216, 17440, 17472,
17696, 17728, 17952, 17984, 18208, 18240, 18464, 18496,
18720, 18752, 18976, 19008, 19232, 19264, 19488, 19520])
# every line in this corresponds to one entry in the array above, this is used
# to put the projected uv plane in the right position
self.panelShifts = np.array([[1.3985 , 0.2652658 , 3.68255],
[ 1.3985 , 0.23238491, -0.88255],
[ 0.80146531, 1.17631236, 3.68255],
[ 0.82407264, 1.15370502, -0.88255],
[-0.2582769 , 1.3985 , 3.68255],
[-0.2322286 , 1.3985 , -0.88255],
[-1.17531186, 0.80246583, 3.68255 ],
[-1.15510614, 0.82267151, -0.88255],
[-1.3985 , -0.2645974 , 3.68255],
[-1.3985 , -0.23012119, -0.88255],
[-0.80591227, -1.17186534, 3.68255],
[-0.82344228, -1.15433536, -0.88255],
[ 0.26975836, -1.3985 , 3.68255],
[ 0.23326624, -1.3985 , -0.88255],
[ 1.1746111 , -0.80316652, 3.68255],
[ 1.15205703, -0.82572062, -0.88255],
[ 2.2015 , 0.26959865, 5.01305],
[ 2.2015 , 0.2524582 , -1.21305],
[ 1.77559093, 1.32758398, 5.01305],
[ 1.78212569, 1.31626522, -1.21305],
[ 0.87798948, 2.03516717, 5.01305],
[ 0.88478563, 2.03124357, -1.21305],
[-0.26129975, 2.2015 , 5.01305],
[-0.25184137, 2.2015 , -1.21305],
[-1.32416655, 1.77756402, 5.01305],
[-1.31417539, 1.78333226, -1.21305],
[-2.03421133, 0.87964512, 5.01305],
[-2.02960691, 0.88762038, -1.21305],
[-2.2015 , -0.25954151, 5.01305],
[-2.2015 , -0.24969109, -1.21305],
[-1.77636043, -1.32625112, 5.01305],
[-1.78138268, -1.31755219, -1.21305],
[-0.87493138, -2.03693277, 5.01305 ],
[-0.8912978 , -2.02748378, -1.21305],
[ 0.26489725, -2.2015 , 5.01305],
[ 0.25364439, -2.2015 , -1.21305],
[ 1.3269198 , -1.7759744 , 5.01305],
[ 1.32258793, -1.77847528, -1.21305],
[ 2.03616649, -0.87625871, 5.01305],
[ 2.02936825, -0.8880338 , -1.21305]])
# every entry here corresponds to the entries in the array above, these are
# used for rotating the projected uv plane
self.panelRotations = np.array([ 90, 90, 135, 135, 180, 180, 225, 225, 270, 270, 315, 315, 360,
360, 405, 405, 90, 90, 120, 120, 150, 150, 180, 180, 210, 210,
240, 240, 270, 270, 300, 300, 330, 330, 360, 360, 390, 390, 420,
420])
# the layer and ladder arrays, for finding them from sensor id
self.panelLayer = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
self.panelLadder = np.array([1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 12, 12, 13, 13, 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 20, 20, 21, 21])
# all transpormaations are stored in a dict, with the sensor id as a keyword
self.transformation = {}
self.layersLadders = {}
for i in range(len(self.panelIDs)):
self.transformation[str(self.panelIDs[i])] = [self.panelShifts[i], self.panelRotations[i]]
self.layersLadders[str(self.panelIDs[i])] = [self.panelLayer[i], self.panelLadder[i]]
# parameter for checking if coordinates have been loaded
self.gotClusters = False
self.gotCoordinates = False
self.gotSphericals = False
self.gotLayers = False
self.gotDigits = False
self.gotMatrices = False
self.gotMCData = False
self.gotFiltered = False
# this dict stores the data
self.data = data if data is not None else {}
# inorder to find roi unselected clusters
self.findUnselectedClusters = FindUnselectedClusters()
self.includeUnSelected = False
def getClusters(self, eventTree: TTree, includeUnSelected: bool = False) -> None:
"""
this uses the array from __init__ to load different branches into the data dict
"""
self.gotClusters = True
self.includeUnSelected = includeUnSelected
for branch in self.clusters:
data = self._getData(eventTree, branch)
keyword = branch.split('_')[-1]
self.data[keyword] = data
self.data['roiSelected'] = np.array([True] * len(self.data['clsCharge']))
clusters = eventTree.arrays('PXDClusters/PXDClusters.m_clsCharge', library='np')['PXDClusters/PXDClusters.m_clsCharge']
self._getEventNumbers(clusters)
if includeUnSelected:
unselectedClusters = self.findUnselectedClusters.getClusters(eventTree)
for key in unselectedClusters:
self.data[key] = np.concatenate((self.data[key], unselectedClusters[key]))
self.data['detector'] = np.array(['pxd'] * len(self.data['clsCharge']))
def _getEventNumbers(self, clusters: np.ndarray, offset: int = 0) -> None:
"""
this generates event numbers from the structure of pxd clusters
"""
eventNumbers = []
for i in range(len(clusters)):
eventNumbers.append(np.array([i]*len(clusters[i])) + offset)
self.data['eventNumber'] = np.hstack(eventNumbers)
def _getData(self, eventTree: TTree, keyword: str, library: str = 'np') -> np.ndarray:
"""
a private method for converting branches into something useful, namely
into numpy arrays, if the keyward library is set to np.
keyword: str = the full branch name
library: str = can be 'np' (numpy), 'pd' (pandas) or 'ak' (akward)
see uproot documentation for more info
"""
try:
data = eventTree.arrays(keyword, library=library)[keyword]
return np.hstack(data)
except:
return KeyError
def getDigits(self, eventTree: TTree) -> None:
"""
reorganizes digits, so that they fit to the clusters
"""
uCellIDs = eventTree.arrays(self.digits[0], library='np')[self.digits[0]]
vCellIDs = eventTree.arrays(self.digits[1], library='np')[self.digits[1]]
cellCharges = eventTree.arrays(self.digits[2], library='np')[self.digits[2]]
# this establishes the relation between digits and clusters, it's still
# shocking to me, that this is necessary, why aren't digits stored in the
# same way as clusters, than one wouldn't need to jump through hoops just
# to have the data in a usable und sensible manner
# root is such a retarded file format
clusterDigits = eventTree.arrays(self.clusterToDigis, library='np')[self.clusterToDigis]
self.data['uCellIDs'] = []
self.data['vCellIDs'] = []
self.data['cellCharges'] = []
for event in range(len(clusterDigits)):
for cls in clusterDigits[event]:
self.data['uCellIDs'].append(uCellIDs[event][cls])
self.data['vCellIDs'].append(vCellIDs[event][cls])
self.data['cellCharges'].append(cellCharges[event][cls])
self.data['uCellIDs'] = np.array(self.data['uCellIDs'], dtype=object)
self.data['vCellIDs'] = np.array(self.data['vCellIDs'], dtype=object)
self.data['cellCharges'] = np.array(self.data['cellCharges'], dtype=object)
if self.includeUnSelected:
unselectedClusters = self.findUnselectedClusters.getDigits(eventTree)
for key in unselectedClusters:
self.data[key] = np.concatenate((self.data[key], unselectedClusters[key]))
self.gotDigits = True
def getMatrices(self, eventTree: TTree, matrixSize: tuple = (9, 9)) -> None:
"""
Loads the digit branches into arrays and converts them into adc matrices
"""
popDigits = False
if self.gotDigits is False:
self.getDigits(eventTree)
popDigits = True
uCellIDs = self.data['uCellIDs']
vCellIDs = self.data['vCellIDs']
cellCharges = self.data['cellCharges']
indexChunks = np.array_split(range(len(cellCharges)), 4)
with ThreadPoolExecutor(max_workers=None) as executor:
futures = [executor.submit(self._getMatrices, chunk, uCellIDs, vCellIDs, cellCharges, matrixSize) for chunk in indexChunks]
results = [future.result() for future in futures]
# Combine the results from all chunks
self.data['matrix'] = np.concatenate(results).astype('int')
if popDigits is True:
self.data.pop('uCellIDs')
self.data.pop('vCellIDs')
self.data.pop('cellCharges')
self.gotDigits = False
self.gotMatrices = True
@staticmethod
def _getMatrices(indexChunks: ArrayLike, uCellIDs: ArrayLike, vCellIDs: ArrayLike, cellCharges: ArrayLike, matrixSize: tuple = (9, 9)) -> np.ndarray:
"""
this takes the ragged/jagged digit arrays and converts them into 9x9 matrices
it's a rather slow process because of all the looping
"""
plotRange = np.array(matrixSize) // 2
events = []
for event in indexChunks:
# Since uCellIDs, vCellIDs, and cellCharges are now directly associated with clusters,
# we don't need digitIndices or maxChargeIndex
digitsU, digitsV, digitsCharge = np.array(uCellIDs[event]), np.array(vCellIDs[event]), np.array(cellCharges[event])
adcValues = []
# Find the center of the cluster (digit with the max charge)
uMax, vMax = digitsU[digitsCharge.argmax()], digitsV[digitsCharge.argmax()]
uPos, vPos = digitsU - uMax + plotRange[0], digitsV - vMax + plotRange[1]
valid_indices = (uPos >= 0) & (uPos < matrixSize[0]) & (vPos >= 0) & (vPos < matrixSize[1])
cacheImg = np.zeros(matrixSize)
cacheImg[uPos[valid_indices].astype(int), vPos[valid_indices].astype(int)] = digitsCharge[valid_indices]
adcValues.append(cacheImg)
events.extend(adcValues)
return np.array(events, dtype=object)
def getCoordinates(self, eventTree: TTree) -> None:
"""
converting the uv coordinates, together with sensor ids, into xyz coordinates
"""
# checking if cluster parameters have been loaded
if self.gotClusters is False:
self.getClusters(eventTree)
# setting a bool for checking if coordinates were calculated
self.gotCoordinates = True
indexChunnks = np.array_split(range(len(self.data['sensorID'])), 4)
with ThreadPoolExecutor(max_workers=None) as executor:
futures = [executor.submit(self._getCoordinates, self.data['uPosition'][chunk], self.data['vPosition'][chunk], self.data['sensorID'][chunk]) for chunk in indexChunnks]
xResults, yResults, zResults = [], [], []
for future in futures:
x, y, z = future.result()
xResults.append(x)
yResults.append(y)
zResults.append(z)
self.data['xPosition'] = np.concatenate(xResults)
self.data['yPosition'] = np.concatenate(yResults)
self.data['zPosition'] = np.concatenate(zResults)
self.gotCoordinates = True
def _getCoordinates(self, uPositions: ArrayLike, vPositions: ArrayLike, sensorIDs: ArrayLike) -> tuple[np.ndarray]:
"""
a private method for transposing/converting 2d uv coords into 3d xyz coordinates
"""
length = len(sensorIDs)
xArr, yArr, zArr = np.zeros(length), np.zeros(length), np.zeros(length)
# iterting over the cluster arrays
for index, (u, v, sensor_id) in enumerate(zip(uPositions, vPositions, sensorIDs)):
# grabbing the shift vector and rotation angle
shift, angle = self.transformation[str(sensor_id)]
# setting up rotation matrix
theta = np.deg2rad(angle)
rotMatrix = np.array([[np.cos(theta), -np.sin(theta), 0], [np.sin(theta), np.cos(theta), 0], [0, 0, 1]])
# projecting uv coordinates into 3d space
point = np.array([u, 0, v])
# shifting and rotating the projected vector
shifted = rotMatrix.dot(point) + shift
xArr[index], yArr[index], zArr[index] = shifted
return xArr, yArr, zArr
def getSphericals(self) -> None:
"""
Calculate spherical coordinates for each cluster.
"""
# Checking if coordinates have been loaded
popCoords = False
if self.gotCoordinates is False:
self.getCoordinates()
popCoords = True
xPosition = self.data['xPosition']
yPosition = self.data['yPosition']
zPosition = self.data['zPosition']
r, theta, phi = self._calcSphericals(xPosition, yPosition, zPosition)
self.data['rPosition'] = r
self.data['thetaPosition'] = theta
self.data['phiPosition'] = phi
self.gotSphericals = True
if popCoords:
self.data.pop('xPosition')
self.data.pop('yPosition')
self.data.pop('zPosition')
self.gotCoordinates = False
@staticmethod
def _calcSphericals(xPosition: np.ndarray, yPosition: np.ndarray, zPosition: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
xSquare = np.square(xPosition)
ySquare = np.square(yPosition)
zSquare = np.square(zPosition)
# Avoid division by zero by replacing zeros with a small number
r = np.sqrt(xSquare + ySquare + zSquare)
rSafe = np.where(r == 0, 1e-10, r)
theta = np.arccos(zPosition / rSafe)
phi = np.arctan2(yPosition, xPosition)
return r, theta, phi
def getLayers(self, eventTree: TTree) -> None:
"""
looks up the corresponding layers and ladders for every cluster
"""
if self.gotClusters is False:
self.getClusters(eventTree)
layers, ladders = [], []
for id in self.data['sensorID']:
layer, ladder = self.layersLadders[str(id)]
layers.append(layer)
ladders.append(ladder)
self.data['layer'] = np.array(layers, dtype=int)
self.data['ladder'] = np.array(ladders, dtype=int)
self.gotLayers = True
def getMCData(self, eventTree: TTree) -> None:
"""
this loads the monte carlo from the root file
"""
if self.includeUnSelected:
warnings.warn('mc data are not supported on roi unselected data')
# the monte carlo data, they are longer than the cluster data
pdg = eventTree.arrays(self.mcData[0], library='np')[self.mcData[0]]
momentumX = eventTree.arrays(self.mcData[1], library='np')[self.mcData[1]]
momentumY = eventTree.arrays(self.mcData[2], library='np')[self.mcData[2]]
momentumZ = eventTree.arrays(self.mcData[3], library='np')[self.mcData[3]]
# this loads the relation ships to and from clusters and mc data
# this is the same level of retardedness as with the cluster digits
clusterToMC = eventTree.arrays(self.clusterToMC, library='np')[self.clusterToMC]
mcToCluster = eventTree.arrays(self.mcToCluster, library='np')[self.mcToCluster]
# it need the cluster charge as a jagged/ragged array, maybe I could simply
# use the event numbers, but I am too tired to fix this shitty file format
clsCharge = eventTree.arrays('PXDClusters/PXDClusters.m_clsCharge', library='np')['PXDClusters/PXDClusters.m_clsCharge']
# reorganizing MC data
momentumXList = []
momentumYList = []
momentumZList = []
pdgList = []
clusterNumbersList = []
for i in range(len(clusterToMC)):
# _fillMCList fills in the missing spots, because there are not mc data for
# every cluster, even though there are more entries in this branch than
# in the cluster branch... as I said, the root format is retarded
fullClusterReferences = self._fillMCList(mcToCluster[i], clusterToMC[i], len(clsCharge[i]))
clusterNumbersList.append(fullClusterReferences)
pdgs, xmom, ymom, zmom = self._getMCData(fullClusterReferences, pdg[i], momentumX[i], momentumY[i], momentumZ[i])
momentumXList.append(xmom)
momentumYList.append(ymom)
momentumZList.append(zmom)
pdgList.append(pdgs)
self.data['momentumX'] = np.hstack(momentumXList).astype(float)
self.data['momentumY'] = np.hstack(momentumYList).astype(float)
self.data['momentumZ'] = np.hstack(momentumZList).astype(float)
self.data['pdg'] = np.hstack(pdgList).astype(int)
self.data['clsNumber'] = np.hstack(clusterNumbersList).astype(int)
self.gotMCData = True
if self.includeUnSelected:
sampleSize = np.sum(self.data['roiSelected'] == False)
missingMCData = self.findUnselectedClusters.fillMCData({
'momentumX': self.data['momentumX'],
'momentumY': self.data['momentumY'],
'momentumZ': self.data['momentumZ'],
'pdg': self.data['pdg'],
'clsNumber': self.data['clsNumber']
})
for key in missingMCData:
self.data[key] = np.hstack((self.data[key], missingMCData[key][0:sampleSize]))
@staticmethod
def _findMissing(lst: list, length: int) -> list:
"""
a private method for finding missing elements in mc data arrays
"""
return sorted(set(range(0, length)) - set(lst))
def _fillMCList(self, fromClusters: ArrayLike, toClusters: ArrayLike, length: ArrayLike) -> list:
"""
a private method for filling MC data arrays where clusters don't have
any information
"""
missingIndex = self._findMissing(fromClusters, length)
testList = [-1] * length
fillIndex = 0
for i in range(len(testList)):
if i in missingIndex:
testList[i] = -1
else:
try:
testList[i] = int(toClusters[fillIndex])
except TypeError:
testList[i] = int(toClusters[fillIndex][0])
fillIndex += 1
return testList
@staticmethod
def _getMCData(toClusters: ArrayLike, pdgs: ArrayLike, xMom: ArrayLike, yMom: ArrayLike, zMom: ArrayLike) -> tuple[np.ndarray]:
"""
after filling and reorganizing MC data arrays one can finally collect the
actual MC data, where there's data missing I will with zeros
"""
pxList, pyList, pzList = [], [], []
pdgList = []
for references in toClusters:
if references == -1:
pxList.append(0)
pyList.append(0)
pzList.append(0)
pdgList.append(0)
else:
pxList.append(xMom[references])
pyList.append(yMom[references])
pzList.append(zMom[references])
pdgList.append(pdgs[references])
return np.array(pdgList,dtype=list), np.array(pxList,dtype=list), np.array(pyList,dtype=list), np.array(pzList,dtype=list)