import numpy as np
import uproot as ur
class fromRoot:
def __init__(self):
# panel ids werden benutzt um layer und lader zu bestimmen und um u/v zu kartesisch um zu rechnen
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])
# die koordinaten verschiebung der u/v koordinaten in korrekte panel 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]])
# drehwinkel um panels korrekt auszurichten
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])
# ladder und layer ids
self.panelLayer = np.array([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, 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])
# generierung der look-up tabels für u/v -> x/y/z transformation
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]]
self.gotClusters = False
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']
self.digits = ['PXDDigits/PXDDigits.m_uCellID',
'PXDDigits/PXDDigits.m_vCellID',
'PXDDigits/PXDDigits.m_charge']
self.clusterToDigis = 'PXDClustersToPXDDigits/m_elements/m_elements.m_to'
self.mcData = ['MCParticles/MCParticles.m_pdg',
'MCParticles/MCParticles.m_momentum_x',
'MCParticles/MCParticles.m_momentum_y',
'MCParticles/MCParticles.m_momentum_z']
self.clusterToMC = 'PXDClustersToMCParticles/m_elements/m_elements.m_to'
self.mcToCluster = 'PXDClustersToMCParticles/m_elements/m_elements.m_from'
self.data = {}
def __getitem__(self, index):
if isinstance(index, str):
return self.data[index]
else:
return {key: value[index] for key, value in self.data.items()}
def loadData(self, file):
self.eventTree = ur.open(f'{file}:tree')
self._genEventNumbers()
# liest den event tree aus, man muss das voll schlüsselwort angeben
def _getData(self, keyword: str):
try:
data = self.eventTree.arrays(keyword, library='np')[keyword]
return self._flatten(data)
except:
return KeyError
def _flatten(self, structure, max_depth=None, current_depth=0):
flat_list = []
for element in structure:
if isinstance(element, (list, np.ndarray)) and (max_depth is None or current_depth < max_depth):
flat_list.extend(self._flatten(element, max_depth, current_depth + 1))
else:
flat_list.append(element)
return np.array(flat_list, dtype=object)
# generiert für jeden cluster eine event nummer
def _genEventNumbers(self):
eventNumbers = []
clusters = self.eventTree.arrays('PXDClusters/PXDClusters.m_clsCharge', library='np')['PXDClusters/PXDClusters.m_clsCharge']
for i in range(len(clusters)):
eventNumbers.append(np.array([i]*len(clusters[i])))
self.data['eventNumbers'] = self._flatten(eventNumbers)
def getClusters(self):
self.gotClusters = True
for branch in self.clusters:
data = self._getData(branch)
keyword = branch.split('_')[-1]
self.data[keyword] = data
def getMatrices(self):
uCellIDs = self.eventTree.arrays(self.digits[0], library='np')[self.digits[0]]
vCellIDs = self.eventTree.arrays(self.digits[1], library='np')[self.digits[1]]
cellCharges = self.eventTree.arrays(self.digits[2], library='np')[self.digits[2]]
clusterDigits = self.eventTree.arrays(self.clusterToDigis, library='np')[self.clusterToDigis]
self.data['ADCs'] = self._genMatrices(uCellIDs, vCellIDs, cellCharges, clusterDigits)
# generiert alle pixel matrizen aller events
def _genMatrices(self, uCellIDs, vCellIDs, cellCharges, clusterDigits, matrixSize=(9, 9)):
plotRange = np.array(matrixSize) // 2
events = []
for event in range(len(cellCharges)):
adcValues = []
digitsU = np.array(uCellIDs[event])
digitsV = np.array(vCellIDs[event])
digitsCharge = np.array(cellCharges[event])
digitIndices = clusterDigits[event]
for indices in digitIndices:
cacheImg = np.zeros(matrixSize)
maxChargeIndex = digitsCharge[indices].argmax()
uMax, vMax = digitsU[indices[maxChargeIndex]], digitsV[indices[maxChargeIndex]]
uPos, vPos = digitsU[indices] - uMax + plotRange[0], digitsV[indices] - vMax + plotRange[1]
valid_indices = (uPos >= 0) & (uPos < matrixSize[0]) & (vPos >= 0) & (vPos < matrixSize[1])
cacheImg[uPos[valid_indices].astype(int), vPos[valid_indices].astype(int)] = digitsCharge[indices][valid_indices]
adcValues.append(cacheImg)
events.extend(adcValues)
return np.array(events, dtype=object)
def genCoordisnate(self):
if self.gotClusters is False:
self.getClusters()
xcoords, ycoords, zcoords = self._getCartesian(self.data['uPosition'], self.data['vPosition'], self.data['sensorID'])
self.data['xcoords'] = xcoords
self.data['ycoords'] = ycoords
self.data['zcoords'] = zcoords
def _getCartesian(self, uPositions, vPositions, sensorIDs):
length = len(sensorIDs)
xArr, yArr, zArr = np.zeros(length), np.zeros(length), np.zeros(length)
for index, (u, v, sensor_id) in enumerate(zip(uPositions, vPositions, sensorIDs)):
shift, angle = self.transformation[str(sensor_id)]
theta = np.deg2rad(angle)
rotMatrix = np.array([[np.cos(theta), -np.sin(theta), 0], [np.sin(theta), np.cos(theta), 0], [0, 0, 1]])
point = np.array([u, 0, v])
shifted = rotMatrix.dot(point) + shift
xArr[index], yArr[index], zArr[index] = shifted
return xArr, yArr, zArr
# bestimmt die layer und ladder nummer eines events
def getLayers(self):
if self.gotClusters is False:
self.getClusters()
layers, ladders = [], []
for id in self.data['sensorID']:
layer, ladder = self.layersLadders[str(id)]
layers.append(layer)
ladders.append(ladder)
self.data['layers'] = np.array(layers)
self.data['ladder'] = np.array(ladders)
def getMCData(self):
pdg = self.eventTree.arrays(self.mcData[0], library='np')[self.mcData[0]]
momentumX = self.eventTree.arrays(self.mcData[1], library='np')[self.mcData[1]]
momentumY = self.eventTree.arrays(self.mcData[2], library='np')[self.mcData[2]]
momentumZ = self.eventTree.arrays(self.mcData[3], library='np')[self.mcData[3]]
clusterToMC = self.eventTree.arrays(self.clusterToMC, library='np')[self.clusterToMC]
mcToCluster = self.eventTree.arrays(self.mcToCluster, library='np')[self.mcToCluster]
clsCharge = self.eventTree.arrays('PXDClusters/PXDClusters.m_clsCharge', library='np')['PXDClusters/PXDClusters.m_clsCharge']
# mc umorganisieren
momentumXList = []
momentumYList = []
momentumZList = []
pdgList = []
clusterNumbersList = []
for i in range(len(clusterToMC)):
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'] = self._flatten(momentumXList)
self.data['momentumY'] = self._flatten(momentumYList)
self.data['momentumZ'] = self._flatten(momentumZList)
self.data['pdg'] = self._flatten(pdgList)
self.data['clsNumber'] = self._flatten(clusterNumbersList)
# findet die fehlenden event referenzen in mc daten, setzt sie gleich -1
def _findMissing(self, lst: list, length: int) -> list:
return sorted(set(range(0, length)) - set(lst))
# füllt die mc-cluster beziehungs arrays mit fehlenden werten auf
def _fillMCList(self, fromClusters, toClusters, length):
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
# organisiert mc daten eines events um
def _getMCData(self, toClusters, pdgs, xMom, yMom, zMom):
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)