245 lines
13 KiB
Python
245 lines
13 KiB
Python
|
|
import os
|
|
import numpy as np
|
|
from ROOT import TH1D, TH2D, TCanvas, TF1
|
|
from multiprocessing import Pool
|
|
from array import array
|
|
import h5py
|
|
from multiprocessing import Pool
|
|
import aare
|
|
|
|
_cfg = {} ### global config
|
|
_aduToKev3DMap = None ### global caliFile
|
|
_pedestalAduFrame = None ### global pedestalAduFrame
|
|
_noiseEneFrame = None ### global noiseEneFrame
|
|
h1_ClusterEnergy, h1_PixelEnergy, h1_CenterPixelEnergy, h2_Qc = None, None, None, None
|
|
sumFrame = np.zeros((400, 400), dtype=np.float32) ### global sumFrame for all frames
|
|
EtaxLUT, EtayLUT = None, None ### global LUT for charge weighted center
|
|
nChunks = 16
|
|
|
|
def init(cfg):
|
|
global _cfg
|
|
global _aduToKev3DMap
|
|
_cfg = cfg
|
|
_aduToKev3DMap = cfg['caliFile']
|
|
Roi = cfg['Roi']
|
|
global sumFrame
|
|
sumFrame = sumFrame[Roi[2]:Roi[3], Roi[0]:Roi[1]] ### crop the sumFrame to the ROI
|
|
if 'etaxLUT' in cfg and 'etayLUT' in cfg:
|
|
global EtaxLUT, EtayLUT
|
|
EtaxLUT = np.load(cfg['etaxLUT'])
|
|
EtayLUT = np.load(cfg['etayLUT'])
|
|
global nChunks
|
|
nChunks = _cfg.get('NChunks', 16)
|
|
|
|
def convertAduFrameToEnergyFrame(aduFrame):
|
|
if _aduToKev3DMap is None:
|
|
return aduFrame
|
|
NX, NY = _cfg['NX'], _cfg['NY']
|
|
if _aduToKev3DMap.shape[0] == 1640:
|
|
idxFrame = (np.abs(aduFrame//10)).astype(np.int32)
|
|
idxFrame[idxFrame > _aduToKev3DMap.shape[0]-2] = _aduToKev3DMap.shape[0]-2
|
|
NY, NX = _aduToKev3DMap.shape[1:3]
|
|
_energyFrame0 = _aduToKev3DMap[idxFrame, np.arange(NY).reshape(-1, 1), np.arange(NX)]
|
|
_energyFrame1 = _aduToKev3DMap[idxFrame+1, np.arange(NY).reshape(-1, 1), np.arange(NX)]
|
|
energyFrame = _energyFrame0 + (_energyFrame1 - _energyFrame0)/10 * (np.abs(aduFrame) - idxFrame*10)
|
|
energyFrame *= np.sign(aduFrame)
|
|
|
|
else:
|
|
aduPerBin = 10
|
|
nExtraBins = 1000 // aduPerBin
|
|
idxFrame = (aduFrame//aduPerBin).astype(np.int32) + nExtraBins
|
|
idxFrame[idxFrame > _aduToKev3DMap.shape[0]-2] = _aduToKev3DMap.shape[0]-2
|
|
NY, NX = _aduToKev3DMap.shape[1:3]
|
|
_energyFrame0 = _aduToKev3DMap[idxFrame, np.arange(NY).reshape(-1, 1), np.arange(NX)]
|
|
_energyFrame1 = _aduToKev3DMap[idxFrame+1, np.arange(NY).reshape(-1, 1), np.arange(NX)]
|
|
energyFrame = _energyFrame0 + (_energyFrame1 - _energyFrame0)/aduPerBin * (aduFrame - (idxFrame-nExtraBins)*aduPerBin)
|
|
if 'energyScalingFactor' in _cfg:
|
|
energyFrame *= _cfg['energyScalingFactor']
|
|
return energyFrame
|
|
|
|
def bookHistograms(energy, suffix = '', energyBinWidth = 0.1, isMC = False):
|
|
histMaxEnergy = energy * 5.5
|
|
h1_ClusterEnergy = TH1D(f'h1_ClusterEnergy{suffix}', f'h1_ClusterEnergy{suffix}', int(histMaxEnergy//energyBinWidth), -1, histMaxEnergy)
|
|
h1_ClusterEnergy.SetDirectory(0)
|
|
h1_ClusterEnergy.SetTitle(f'Cluster Energy;Energy [keV];Counts')
|
|
h1_PixelEnergy = TH1D(f'h1_PixelEnergy{suffix}', f'h1_PixelEnergy{suffix}', int(histMaxEnergy//energyBinWidth), -1, histMaxEnergy)
|
|
h1_PixelEnergy.SetDirectory(0)
|
|
h1_PixelEnergy.SetTitle(f'Pixel Energy;Energy [keV];Counts')
|
|
h1_CenterPixelEnergy = TH1D(f'h1_CenterPixelEnergy{suffix}', f'h1_CenterPixelEnergy{suffix}', int(histMaxEnergy//energyBinWidth), -1, histMaxEnergy)
|
|
h1_CenterPixelEnergy.SetDirectory(0)
|
|
h1_CenterPixelEnergy.SetTitle(f'Center Pixel Energy;Energy [keV];Counts')
|
|
h2_Qc = TH2D(f'h2_Qc{suffix}', f'h2_Qc{suffix}', 201, 0, 1, 201, 0, 1)
|
|
h2_Qc.SetDirectory(0)
|
|
h2_Qc.SetTitle(f'Charge weighted center;#eta_x;#eta_y;Counts')
|
|
h2_counts = TH2D(f'h2_counts{suffix}', f'h2_counts{suffix}', _cfg['Roi'][1] - _cfg['Roi'][0], _cfg['Roi'][0], _cfg['Roi'][1], _cfg['Roi'][3] - _cfg['Roi'][2], _cfg['Roi'][2], _cfg['Roi'][3])
|
|
h2_counts.SetDirectory(0)
|
|
h2_HitsSumFrame = TH2D(f'h2_HitsSumFrame{suffix}', f'h2_HitsSumFrame{suffix}', _cfg['Roi'][1] - _cfg['Roi'][0], _cfg['Roi'][0], _cfg['Roi'][1], _cfg['Roi'][3] - _cfg['Roi'][2], _cfg['Roi'][2], _cfg['Roi'][3])
|
|
h2_HitsSumFrame.SetDirectory(0)
|
|
interpolationBins = 10
|
|
xBins = (_cfg['Roi'][1] - _cfg['Roi'][0]) * interpolationBins
|
|
yBins = (_cfg['Roi'][3] - _cfg['Roi'][2]) * interpolationBins
|
|
h2_interpolated = TH2D(f'h2_interpolated{suffix}', f'h2_interpolated{suffix}', xBins, _cfg['Roi'][0], _cfg['Roi'][1], yBins, _cfg['Roi'][2], _cfg['Roi'][3])
|
|
h2_interpolated.SetDirectory(0)
|
|
h2_interpolatedSubpixel = TH2D(f'h2_interpolatedSubpixel{suffix}', f'h2_interpolatedSubpixel{suffix}', interpolationBins, 0, 1, interpolationBins, 0, 1)
|
|
h2_interpolatedSubpixel.SetDirectory(0)
|
|
|
|
if isMC:
|
|
h1_ChargeCollectionEfficiency = TH1D(f'h1_ChargeCollectionEfficiency{suffix}', f'h1_ChargeCollectionEfficiency{suffix}', 400, 0.8, 1.01)
|
|
return h1_ClusterEnergy.Clone(), h1_PixelEnergy.Clone(), h1_CenterPixelEnergy.Clone(), h2_Qc.Clone(), h1_ChargeCollectionEfficiency.Clone()
|
|
|
|
return h1_ClusterEnergy, h1_PixelEnergy, h1_CenterPixelEnergy, h2_Qc, h2_counts, h2_HitsSumFrame, h2_interpolated, h2_interpolatedSubpixel
|
|
|
|
def _processFrames(idxChunk): ### for both single and double photon events, using
|
|
startFrame, endFrame = _cfg['NFrame'] * idxChunk // nChunks, min(_cfg['NFrame'] * (idxChunk + 1) // nChunks, _cfg['NFrame'])
|
|
print(f'Processing frames from {startFrame} to {endFrame}...')
|
|
Energy = _cfg['energy']
|
|
selectionRange = _cfg['selectionRange']
|
|
signalFileNames = _cfg['signalFileNames']
|
|
NX = _cfg['NX']
|
|
NY = _cfg['NY']
|
|
headerSize = _cfg['headerSize']
|
|
Roi = _cfg['Roi']
|
|
nFramePerFile = os.path.getsize(f'{signalFileNames[0]}') // (NX * NY + 56) // 2
|
|
_h1_PixelEnergy, _h1_ClusterEnergy, _h1_CenterPixelEnergy, _h2_Qc, _h2_counts, _h2_HitsSumFrame, _h2_interpolated, _h2_interpolatedSubpixel = bookHistograms(Energy, f'_{idxChunk}')
|
|
|
|
### create a cluster finder
|
|
nxROI = Roi[1] - Roi[0]
|
|
nyROI = Roi[3] - Roi[2]
|
|
CF = aare.VarClusterFinder((nxROI, nyROI), 5) ### arg1: frame size, arg2: threshold
|
|
CF.set_peripheralThresholdFactor(2)
|
|
CF.set_noiseMap(_noiseEneFrame)
|
|
|
|
if 'writeClusters' in _cfg and _cfg['writeClusters'] == True:
|
|
h5_file = h5py.File(f'clusters_chunk{idxChunk}.h5', 'w')
|
|
# 创建可扩展数据集
|
|
dset_clusters = h5_file.create_dataset(
|
|
'clusters', (0, 3, 3), maxshape=(None, 3, 3), dtype='f4',
|
|
chunks=True, compression='gzip'
|
|
)
|
|
dset_refs = h5_file.create_dataset(
|
|
'referencePoint', (0, 2), maxshape=(None, 2), dtype='i4',
|
|
chunks=True
|
|
)
|
|
|
|
buffer_size = 50_000
|
|
cluster_list = []
|
|
refpoint_list = []
|
|
def flush_h5_buffer():
|
|
if not cluster_list:
|
|
return
|
|
old_len = dset_clusters.shape[0]
|
|
new_len = old_len + len(cluster_list)
|
|
dset_clusters.resize((new_len, 3, 3))
|
|
dset_refs.resize((new_len, 2))
|
|
dset_clusters[old_len:new_len] = cluster_list
|
|
dset_refs[old_len:new_len] = refpoint_list
|
|
cluster_list.clear()
|
|
refpoint_list.clear()
|
|
|
|
for idxFrame in range(startFrame, endFrame):
|
|
if idxFrame % 10000 == 0:
|
|
print(f'Processing frame {idxFrame}...')
|
|
idxFile, idxFrame = divmod(idxFrame, nFramePerFile)
|
|
try:
|
|
if 'nFiber' in _cfg and _cfg['nFiber'] == 2:
|
|
offset = (headerSize + idxFrame * (NX * NY//2 + headerSize)) * 2
|
|
fileName1 = signalFileNames[idxFile]
|
|
fileName2 = fileName1.replace('d0', 'd1')
|
|
_signalAduFrame1 = np.fromfile(f'{fileName1}', dtype=np.uint16, offset=offset, count=NX * NY //2).astype(np.int32).reshape(NY//2, NX)
|
|
_signalAduFrame2 = np.fromfile(f'{fileName2}', dtype=np.uint16, offset=offset, count=NX * NY //2).astype(np.int32).reshape(NY//2, NX)
|
|
signalAduFrame = np.concatenate([_signalAduFrame1, _signalAduFrame2], axis=0)
|
|
del _signalAduFrame1, _signalAduFrame2
|
|
else:
|
|
offset = (headerSize + idxFrame * (NX * NY + headerSize)) * 2
|
|
signalAduFrame = np.fromfile(f'{signalFileNames[idxFile]}', dtype=np.uint16, offset=offset, count=NX * NY).astype(np.int32).reshape(NX, NY)
|
|
except Exception as e:
|
|
print(f'Error reading frame {idxFrame} from file {signalFileNames[idxFile]}: {e}, stop processing this chunk ({startFrame}-{endFrame})')
|
|
return _h1_ClusterEnergy.Clone(), _h1_PixelEnergy.Clone(), _h1_CenterPixelEnergy.Clone(), _h2_Qc.Clone(), _h2_counts.Clone(), _h2_HitsSumFrame.Clone(), _h2_interpolated.Clone(), _h2_interpolatedSubpixel.Clone()
|
|
|
|
signalAduFrame = signalAduFrame - _pedestalAduFrame
|
|
signalEneFrame = convertAduFrameToEnergyFrame(signalAduFrame)
|
|
|
|
CF.find_clusters_X(signalEneFrame[Roi[2]:Roi[3], Roi[0]:Roi[1]])
|
|
clusters = CF.hits()
|
|
for idxCluster in range(len(clusters)):
|
|
energy = clusters['energy'][idxCluster]
|
|
_h1_ClusterEnergy.Fill(energy)
|
|
xs = clusters['cols'][idxCluster]
|
|
ys = clusters['rows'][idxCluster]
|
|
enes = clusters['enes'][idxCluster]
|
|
if energy < 5:
|
|
continue
|
|
for i in range(len(xs)):
|
|
_h1_PixelEnergy.Fill(enes[i])
|
|
_h2_HitsSumFrame.Fill(xs[i]+Roi[0], ys[i]+Roi[2], enes[i])
|
|
|
|
if 'writeClusters' in _cfg and _cfg['writeClusters'] == True:
|
|
flush_h5_buffer()
|
|
h5_file.close()
|
|
return _h1_ClusterEnergy, _h1_PixelEnergy, _h1_CenterPixelEnergy, _h2_Qc, _h2_counts, _h2_HitsSumFrame, _h2_interpolated, _h2_interpolatedSubpixel
|
|
|
|
def process():
|
|
with Pool(_cfg['NThread'], maxtasksperchild=1) as p:
|
|
results = p.map(_processFrames, range(nChunks))
|
|
|
|
global h1_ClusterEnergy, h1_PixelEnergy, h1_CenterPixelEnergy, h2_Qc, h2_counts, h2_HitsSumFrame, h2_interpolated, h2_interpolatedSubpixel
|
|
h1_ClusterEnergy, h1_PixelEnergy, h1_CenterPixelEnergy, h2_Qc, h2_counts, h2_HitsSumFrame, h2_interpolated, h2_interpolatedSubpixel = bookHistograms(_cfg['energy'])
|
|
for h in (h1_ClusterEnergy, h1_PixelEnergy, h1_CenterPixelEnergy, h2_Qc, h2_counts, h2_HitsSumFrame, h2_interpolated, h2_interpolatedSubpixel):
|
|
h.SetDirectory(0)
|
|
|
|
global sumFrame
|
|
for _h1_ClusterEnergy, _h1_PixelEnergy, _h1_CenterPixelEnergy, _h2_Qc, _h2_counts, _h2_HitsSumFrame, _h2_interpolated, _h2_interpolatedSubpixel in results:
|
|
h1_ClusterEnergy.Add(_h1_ClusterEnergy)
|
|
h1_PixelEnergy.Add(_h1_PixelEnergy)
|
|
h1_CenterPixelEnergy.Add(_h1_CenterPixelEnergy)
|
|
h2_Qc.Add(_h2_Qc)
|
|
h2_interpolated.Add(_h2_interpolated)
|
|
h2_interpolatedSubpixel.Add(_h2_interpolatedSubpixel)
|
|
h2_counts.Add(_h2_counts)
|
|
h2_HitsSumFrame.Add(_h2_HitsSumFrame)
|
|
del _h1_ClusterEnergy, _h1_PixelEnergy, _h1_CenterPixelEnergy, _h2_Qc, _h2_counts, _h2_HitsSumFrame, _h2_interpolated, _h2_interpolatedSubpixel
|
|
|
|
def getPedestalAndNoise():
|
|
NX, NY = _cfg['NX'], _cfg['NY']
|
|
pedestalFileName = _cfg['pedestalFileName']
|
|
if 'nFiber' in _cfg and _cfg['nFiber'] == 2:
|
|
pedestalFileName2 = pedestalFileName.replace('d0', 'd1')
|
|
_pedestalAduFrames1 = np.fromfile(f'{pedestalFileName}', dtype=np.uint16).astype(np.int32)
|
|
_pedestalAduFrames1 = _pedestalAduFrames1.reshape(-1, NX * NY //2 + 56)
|
|
_pedestalAduFrames1 = _pedestalAduFrames1[:, 56:].reshape(-1, NY//2, NX)
|
|
_pedestalAduFrames2 = np.fromfile(f'{pedestalFileName2}', dtype=np.uint16).astype(np.int32)
|
|
_pedestalAduFrames2 = _pedestalAduFrames2.reshape(-1, NX * NY //2 + 56)
|
|
_pedestalAduFrames2 = _pedestalAduFrames2[:, 56:].reshape(-1, NY//2, NX)
|
|
pedestalAduFrames = np.concatenate([_pedestalAduFrames1, _pedestalAduFrames2], axis=1)
|
|
del _pedestalAduFrames1, _pedestalAduFrames2
|
|
else:
|
|
pedestalAduFrames = np.fromfile(f'{pedestalFileName}', dtype=np.uint16).astype(np.int32)
|
|
pedestalAduFrames = pedestalAduFrames.reshape(-1, NX * NY + 56)
|
|
pedestalAduFrames = pedestalAduFrames[:, 56:].reshape(-1, NX, NY)
|
|
|
|
pedestalAduFrames = pedestalAduFrames[pedestalAduFrames.shape[0]//10:] # skip the first 10% frames
|
|
global _pedestalAduFrame, _noiseEneFrame
|
|
_pedestalAduFrame = np.mean(pedestalAduFrames, axis=0)
|
|
noiseAduFrame = np.std(pedestalAduFrames, axis=0, ddof=1)
|
|
with Pool(16) as p:
|
|
pedestalEneFrames = p.map(convertAduFrameToEnergyFrame, pedestalAduFrames)
|
|
_noiseEneFrame = np.std(pedestalEneFrames, axis=0, ddof=1)
|
|
print(f'Average noise = {np.mean(_noiseEneFrame):.3f} keV; {np.mean(noiseAduFrame):.3f} ADU')
|
|
del pedestalAduFrames, pedestalEneFrames
|
|
|
|
def getPedestalAndNoise_simplified():
|
|
NX, NY = _cfg['NX'], _cfg['NY']
|
|
pedestalFileName = _cfg['pedestalFileName']
|
|
pedestalAduFrames = np.fromfile(f'{pedestalFileName}', dtype=np.uint16).astype(np.int32)
|
|
pedestalAduFrames = pedestalAduFrames.reshape(-1, NX * NY + 56)
|
|
pedestalAduFrames = pedestalAduFrames[:, 56:].reshape(-1, NX, NY)
|
|
pedestalAduFrames = pedestalAduFrames[pedestalAduFrames.shape[0]//10:] # skip the first 10% frames
|
|
global _pedestalAduFrame, _noiseEneFrame
|
|
_pedestalAduFrame = np.mean(pedestalAduFrames, axis=0)
|
|
noiseAduFrame = np.std(pedestalAduFrames, axis=0, ddof=1)
|
|
_noiseEneFrame = convertAduFrameToEnergyFrame(noiseAduFrame)
|
|
|
|
print(f'Average noise = {np.mean(_noiseEneFrame):.3f} keV; {np.mean(noiseAduFrame):.3f} ADU')
|
|
del pedestalAduFrames
|