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Use truncated 1ph sample; fix 2ph center problem; exclude samples crossing the Roi border

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xie_x1
2026-03-17 17:50:18 +01:00
parent 4da9380073
commit c25c9c31d5
3 changed files with 58 additions and 30 deletions
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Configs.py
+2 -2
View File
@@ -17,9 +17,9 @@ Configs['SiemenStarLowerLeft'] = {
'writeClusters': True,
# 'outputFolder': '/mnt/sls_det_storage/moench_data/MLXID/Samples/Measurement/2504_SOLEIL_SiemenStarClusters_MOENCH040_150V/',
'outputFolder': '/home/xie_x1/MLXID/DataProcess/Samples/SiemenStarLowerLeft/',
'NFrame': 2_000_0, ### 20_000_000 in total
'NFrame': 20_000_000, ### 20_000_000 in total
'NThread': 16,
'NChunks': 16,
'NChunks': 160,
'runName': 'SiemenStarLowerLeft',
}
DataAnalysis.py
+5 -6
View File
@@ -1,12 +1,11 @@
import UsefulFuncs
import numpy as np
from Configs import Configs
measurementConfig = Configs['SiemenStarLowerLeft'] ### SiemenStarLowerLeft, SiemenStarLowerRight
# measurementConfig['writeClusters'] = True ### default is False
# measurementConfig['NFrame'] = 20_000_000 ### 20_000_000 in total
from argparse import ArgumentParser
argParser = ArgumentParser()
argParser.add_argument('--runName', type=str, default='SiemenStarLowerLeft')
args = argParser.parse_args()
measurementConfig = Configs[args.runName]
UsefulFuncs.init(measurementConfig)
UsefulFuncs.getPedestalAndNoise_simplified()
UsefulFuncs.py
+51 -22
View File
@@ -14,12 +14,13 @@ _pedestalAduFrame = None ### global pedestalAduFrame
_noiseEneFrame = None ### global noiseEneFrame
nChunks = 16
clusterSize2Photon = 7
clusterSize1Photon = 3
def init(cfg):
global _cfg
global _aduToKev3DMap
_cfg = cfg
_aduToKev3DMap = cfg['caliFile']
_aduToKev3DMap = np.load(cfg['caliFileName'])
Roi = cfg['Roi']
global nChunks
nChunks = _cfg.get('NChunks', 16)
@@ -104,6 +105,11 @@ def bookHistograms(energy, suffix='', energyBinWidth=0.1, isMC=False):
)
histograms['h1_1PhotonEta_Y'].SetTitle('1-Photon Eta Y;Eta Y;Counts')
histograms['h1_2PhotonClusterEnergy'] = TH1D(
f'h1_2PhotonClusterEnergy{suffix}', f'h1_2PhotonClusterEnergy{suffix}', nEnergyBins, -1, energy*4
)
histograms['h1_2PhotonClusterEnergy'].SetTitle('2-Photon Cluster Energy;Energy [keV];Counts')
histograms['h1_2PhotonClusterSize'] = TH1D(
f'h1_2PhotonClusterSize{suffix}', f'h1_2PhotonClusterSize{suffix}', 30, 0, 30
)
@@ -139,6 +145,11 @@ def bookHistograms(energy, suffix='', energyBinWidth=0.1, isMC=False):
)
histograms['h2_1PhotonSumFrame'].SetTitle('1-Photon Hits Sum Frame;X [pixel];Y [pixel];Energy Sum [keV]')
histograms['h2_1PhotonSumSample'] = TH2D(
f'h2_1PhotonSumSample{suffix}', f'h2_1PhotonSumSample{suffix}', clusterSize1Photon, 0, clusterSize1Photon, clusterSize1Photon, 0, clusterSize1Photon
)
histograms['h2_1PhotonSumSample'].SetTitle('1-Photon Sum Sample;Sum Sample;Counts')
histograms['h2_2PhotonSumSample'] = TH2D(
f'h2_2PhotonSumSample{suffix}', f'h2_2PhotonSumSample{suffix}', clusterSize2Photon, 0, clusterSize2Photon, clusterSize2Photon, 0, clusterSize2Photon
)
@@ -186,9 +197,9 @@ def _processFrames(idxChunk): ### for both single and double photon events, usin
CF.set_noiseMap(_noiseEneFrame)
if 'writeClusters' in _cfg and _cfg['writeClusters'] == True:
h5_1Photon_file = h5py.File(f'{_cfg["outputFolder"]}/1Photon_CS3_chunk{idxChunk}.h5', 'w')
h5_1Photon_file = h5py.File(f'{_cfg["outputFolder"]}/1Photon_CS{clusterSize1Photon}_chunk{idxChunk}.h5', 'w')
dset_1Photon_clusters = h5_1Photon_file.create_dataset(
'clusters', (0, 3, 3), maxshape=(None, 3, 3), dtype='f4',
'clusters', (0, clusterSize1Photon, clusterSize1Photon), maxshape=(None, clusterSize1Photon, clusterSize1Photon), dtype='f4',
chunks=True, compression='gzip'
)
dset_1photon_refs = h5_1Photon_file.create_dataset(
@@ -210,11 +221,12 @@ def _processFrames(idxChunk): ### for both single and double photon events, usin
)
cluster_2Photon_list = []
refpoint_2Photon_list = []
BUFFER_THRESHOLD = 10000
def flush_h5_buffer():
if not cluster_1Photon_list: return # no new clusters to write
old_len = dset_1Photon_clusters.shape[0]
new_len = old_len + len(cluster_1Photon_list)
dset_1Photon_clusters.resize((new_len, 3, 3))
dset_1Photon_clusters.resize((new_len, clusterSize1Photon, clusterSize1Photon))
dset_1photon_refs.resize((new_len, 2))
dset_1Photon_clusters[old_len:new_len] = cluster_1Photon_list
dset_1photon_refs[old_len:new_len] = refpoint_1Photon_list
@@ -252,7 +264,8 @@ def _processFrames(idxChunk): ### for both single and double photon events, usin
signalEneFrame = convertAduFrameToEnergyFrame(signalAduFrame)
sumFrame += signalEneFrame
CF.find_clusters_X(signalEneFrame[Roi[2]:Roi[3], Roi[0]:Roi[1]])
signalEneFrame = signalEneFrame[Roi[2]:Roi[3], Roi[0]:Roi[1]]
CF.find_clusters_X(signalEneFrame)
clusters = CF.hits()
for idxCluster in range(len(clusters)):
clusterSize = clusters['size'][idxCluster]
@@ -264,6 +277,10 @@ def _processFrames(idxChunk): ### for both single and double photon events, usin
### single photon events
if Energy - selectionRange < energy < Energy + selectionRange:
ref_x = clusters['col'][idxCluster] - 1 ### refered to the lower-left corner of the cluster
ref_y = clusters['row'][idxCluster] - 1
if int(ref_x) < 0 or int(ref_x)+clusterSize1Photon > signalEneFrame.shape[1] or int(ref_y) < 0 or int(ref_y)+clusterSize1Photon > signalEneFrame.shape[0]:
continue ### skip clusters too close to the border
for i in range(len(xs)):
_hists['h2_1PhotonSumFrame'].Fill(xs[i]+Roi[0], ys[i]+Roi[2], enes[i])
_hists['h1_1PhotonClusterSize'].Fill(clusterSize)
@@ -271,18 +288,19 @@ def _processFrames(idxChunk): ### for both single and double photon events, usin
diameterY = max(ys) - min(ys) + 1
_hists['h1_1PhotonClusterDiameterX'].Fill(diameterX)
_hists['h1_1PhotonClusterDiameterY'].Fill(diameterY)
_hists['h1_1PhotonClusterEnergy'].Fill(energy)
maxX, maxY = clusters['col'][idxCluster], clusters['row'][idxCluster]
ref_x = maxX - 1 ### refered to the lower-left corner of the cluster
ref_y = maxY - 1
cluster_1Photon = np.zeros((3, 3), dtype=np.float32)
for i in range(len(xs)):
x_rel = xs[i] - int(ref_x)
y_rel = ys[i] - int(ref_y)
_hists['h1_1PhotonClusterPixelEnergy'].Fill(enes[i])
if 0 <= x_rel < 3 and 0 <= y_rel < 3:
cluster_1Photon[y_rel, x_rel] = enes[i]
cluster_1Photon = np.zeros((clusterSize1Photon, clusterSize1Photon), dtype=np.float32)
# for i in range(len(xs)):
# x_rel = xs[i] - int(ref_x)
# y_rel = ys[i] - int(ref_y)
# _hists['h1_1PhotonClusterPixelEnergy'].Fill(enes[i])
# if 0 <= x_rel < clusterSize1Photon and 0 <= y_rel < clusterSize1Photon:
# cluster_1Photon[y_rel, x_rel] = enes[i]
cluster_1Photon = signalEneFrame[int(ref_y):int(ref_y)+clusterSize1Photon, int(ref_x):int(ref_x)+clusterSize1Photon]
_hists['h1_1PhotonClusterEnergy'].Fill(np.sum(cluster_1Photon)) # use the sum of 3x3 cluster as the cluster energy for single photon events, which is more accurate than the original cluster energy given by the cluster finder, since the cluster finder may miss some pixels with low energy far from the center pixel
for i in range(3):
for j in range(3):
_hists['h2_1PhotonSumSample'].Fill(i, j, cluster_1Photon[i, j])
if 'writeClusters' in _cfg and _cfg['writeClusters'] == True:
cluster_1Photon_list.append(cluster_1Photon)
refpoint_1Photon_list.append([int(ref_x)+Roi[0], int(ref_y)+Roi[2]])
@@ -297,7 +315,14 @@ def _processFrames(idxChunk): ### for both single and double photon events, usin
_hists['h1_1PhotonEta_Y'].Fill(etaY)
### double photon events
if 2 * Energy - selectionRange < energy < 2 * Energy + selectionRange:
if 2 * Energy - 2 * selectionRange < energy < 2 * Energy + 2 * selectionRange:
x_center = np.sum(xs * enes) / np.sum(enes)
y_center = np.sum(ys * enes) / np.sum(enes)
ref_x = int(x_center - clusterSize2Photon / 2) + 1 ### refered to the lower-left corner of the cluster
ref_y = int(y_center - clusterSize2Photon / 2) + 1
if int(ref_x) < 0 or int(ref_x)+clusterSize2Photon > signalEneFrame.shape[1] or int(ref_y) < 0 or int(ref_y)+clusterSize2Photon > signalEneFrame.shape[0]:
continue ### skip clusters too close to the border
for i in range(len(xs)):
_hists['h2_2PhotonSumFrame'].Fill(xs[i]+Roi[0], ys[i]+Roi[2], enes[i])
_hists['h1_2PhotonClusterPixelEnergy'].Fill(enes[i])
@@ -307,8 +332,6 @@ def _processFrames(idxChunk): ### for both single and double photon events, usin
_hists['h1_2PhotonClusterDiameterX'].Fill(diameterX)
_hists['h1_2PhotonClusterDiameterY'].Fill(diameterY)
ref_x = (min(xs) + max(xs)) // 2 - (clusterSize2Photon // 2) ### refered to the lower-left corner of the cluster
ref_y = (min(ys) + max(ys)) // 2 - (clusterSize2Photon // 2)
cluster_2Photon = np.zeros((clusterSize2Photon, clusterSize2Photon), dtype=np.float32)
for i in range(len(xs)):
x_rel = xs[i] - int(ref_x)
@@ -316,7 +339,11 @@ def _processFrames(idxChunk): ### for both single and double photon events, usin
if 0 <= x_rel < clusterSize2Photon and 0 <= y_rel < clusterSize2Photon:
cluster_2Photon[y_rel, x_rel] = enes[i]
_hists['h2_2PhotonSumSample'].Fill(x_rel, y_rel, enes[i])
# cluster_2Photon = signalEneFrame[int(ref_y):int(ref_y)+clusterSize2Photon, int(ref_x):int(ref_x)+clusterSize2Photon]
_hists['h1_2PhotonClusterEnergy'].Fill(np.sum(cluster_2Photon))
for i in range(clusterSize2Photon):
for j in range(clusterSize2Photon):
_hists['h2_2PhotonSumSample'].Fill(i, j, cluster_2Photon[i, j])
if 'writeClusters' in _cfg and _cfg['writeClusters'] == True:
cluster_2Photon_list.append(cluster_2Photon)
refpoint_2Photon_list.append([int(ref_x)+Roi[0], int(ref_y)+Roi[2]])
@@ -329,10 +356,12 @@ def _processFrames(idxChunk): ### for both single and double photon events, usin
for i in range(len(xs)):
_hists['h1_PixelEnergy'].Fill(enes[i])
_hists['h2_HitsSumFrame'].Fill(xs[i]+Roi[0], ys[i]+Roi[2], enes[i])
if 'writeClusters' in _cfg and _cfg['writeClusters'] == True:
if len(cluster_1Photon_list) > BUFFER_THRESHOLD or len(cluster_2Photon_list) > BUFFER_THRESHOLD:
flush_h5_buffer()
for _x in range(Roi[0], Roi[1]):
for _y in range(Roi[2], Roi[3]):
signalEneFrame[_x, _y]
_hists['h2_SumFrame'].Fill(_x, _y, sumFrame[_y, _x])
if 'writeClusters' in _cfg and _cfg['writeClusters'] == True:
flush_h5_buffer()