Change to npz output; add h5 output; change to python script
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@@ -1,2 +1,3 @@
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*.npy
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Samples/
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*.pyc
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@@ -0,0 +1,78 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(625000, 4)\n",
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"2.5738147384822754 2.3159877105736997 14979.560000000001\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<matplotlib.collections.PathCollection at 0x7fe5d47a16a0>"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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truncated
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import h5py\n",
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"\n",
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"data = np.load('/home/xie_x1/MLXID/McGeneration/Samples/15keV_Moench040_150V_15.npz')\n",
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"samples = data['samples']\n",
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"labels = data['labels']\n",
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"# with h5py.File('/home/xie_x1/MLXID/McGeneration/Samples/15keV_Moench040_150V_15.h5', 'r') as hf:\n",
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"# samples = hf['samples'][:]\n",
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"# labels = hf['labels'][:]\n",
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"print(labels.shape)\n",
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"idx = 6\n",
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"plt.imshow(samples[idx], origin='lower', extent = (0, samples.shape[1], 0, samples.shape[2]))\n",
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"x,y,z,e = labels[idx]\n",
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"print(x,y,e)\n",
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"plt.scatter(x, y, s=200, facecolors='none', edgecolors='r')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "pytorch_nightly",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.18"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@@ -1,179 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Welcome to JupyROOT 6.28/10\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import numpy as np\n",
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"from ROOT import *\n",
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"from multiprocessing import Pool\n",
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"from array import array\n",
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"from scipy.stats import gennorm\n",
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"import imp"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Processing Moench040 150V 15keV simu2 with 128 bins\n",
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"Alpha fitting chi2/NDF = 1.9768728049976632\n",
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"Beta fitting chi2/NDF = 1.3047556961613742\n",
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" FCN=245.132 FROM MIGRAD STATUS=CONVERGED 96 CALLS 97 TOTAL\n",
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" EDM=3.53827e-13 STRATEGY= 1 ERROR MATRIX ACCURATE \n",
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" EXT PARAMETER STEP FIRST \n",
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" NO. NAME VALUE ERROR SIZE DERIVATIVE \n",
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" 1 p0 5.20982e-01 1.41176e-02 3.53269e-05 -1.23898e-06\n",
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" 2 p1 2.73449e+00 1.50597e-02 1.01242e-05 -3.50997e-04\n",
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" 3 p2 -9.48420e-02 3.32013e-03 1.69238e-06 -3.00452e-03\n",
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" 4 p3 4.99907e-04 2.88035e-05 3.33069e-08 -1.76453e-01\n",
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" FCN=160.485 FROM MIGRAD STATUS=CONVERGED 412 CALLS 413 TOTAL\n",
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" EDM=3.93066e-07 STRATEGY= 1 ERROR MATRIX ACCURATE \n",
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" EXT PARAMETER STEP FIRST \n",
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" NO. NAME VALUE ERROR SIZE DERIVATIVE \n",
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" 1 p0 8.57590e-01 1.20963e-01 5.07321e-05 -4.75170e-02\n",
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" 2 p1 -2.47722e+00 1.29467e+00 1.23369e-03 -3.44551e-03\n",
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" 3 p2 -3.91474e-01 3.56163e-02 1.94337e-05 -5.53030e-02\n",
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" 4 p3 -3.36737e-01 1.31473e-01 3.89495e-04 -1.89786e-03\n",
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" 5 p4 -2.81985e+00 1.71232e+00 3.71391e-03 7.06359e-04\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Info in <TCanvas::MakeDefCanvas>: created default TCanvas with name c1\n"
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]
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},
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{
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"data": {
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"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<IPython.core.display.Image object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import McGenerator\n",
|
||||
"imp.reload(McGenerator)\n",
|
||||
"\n",
|
||||
"attenuationLengthDict = { ### https://henke.lbl.gov/optical_constants/atten2.html\n",
|
||||
" '5keV': 18.0101, '10keV':133.706, '15keV':442.607, '20keV':1037.80, '25keV':1991.45,\n",
|
||||
"}\n",
|
||||
"element = '15keV'\n",
|
||||
"mcConfig = {\n",
|
||||
" 'element': element,\n",
|
||||
" 'energy': 15000, ### eV\n",
|
||||
" 'attenuationLength': attenuationLengthDict[element],\n",
|
||||
" 'sensorCode': 'Moench040',\n",
|
||||
" 'nTotalIncident': 10000000,\n",
|
||||
" 'nThread': 16,\n",
|
||||
" 'Roi': [50, 350, 50, 350], \n",
|
||||
" 'clusterWidth': 5, ### pixel\n",
|
||||
" 'pixelSize': 25, ### um\n",
|
||||
" 'noiseEneFrame': np.ones((400, 400)) * 0, \n",
|
||||
" 'shotNoiseFactor': 0.0,\n",
|
||||
" 'calibrationNoise': 0.0,\n",
|
||||
" 'sensorThickness': 650, ### moench040: 650 um, 34.7 V\n",
|
||||
" 'T': 273 + 20.0 + 14.8,\n",
|
||||
" 'zBins': 128,\n",
|
||||
" 'hv': 150,\n",
|
||||
" 'depletionVoltage': 34.7,\n",
|
||||
" 'simulationMethod': 'simu2', ### 'simu1' (cpu) or 'simu2' (gpu)\n",
|
||||
" 'resultsPath': '/home/xie_x1/MLED/McSimulation/BatchResults',\n",
|
||||
" 'sampleOutputPath': '/home/xie_x1/MLXID/McGeneration/Samples'\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"McGenerator.init(mcConfig)\n",
|
||||
"McGenerator.parameterization().Draw()\n",
|
||||
"McGenerator.generateFromParameters()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"(625000, 4)\n",
|
||||
"2.96790016863627 2.287150706089626 15088.160000000002\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.collections.PathCollection at 0x7f2f39e45d00>"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZwAAAGiCAYAAADTMXDkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAVyklEQVR4nO3df4yVhbng8ecwwEH5MTIowiw/SrVq5Ye7grVjrFWx5HINkTS9qb3WsDV3E7poMSxpi95EydKOae6mMaElYhurtzXYpqJmtxLprUBNQxewrCy1Fi1bpnsRrnqZgUEOMLz7R6+zUkDnDDPPmRk+n+SNnuP7vufJkeGb9z3vvKdUFEURANDLBtV6AADODYIDQArBASCF4ACQQnAASCE4AKQQHABSCA4AKQQHgBSCA0CKqoLz4IMPRqlUOmkZN25cb80GwAAyuNoNpk6dGj//+c87H9fV1fXoQAAMTFUHZ/DgwY5qAKha1cHZtWtXNDY2RrlcjmuvvTa++c1vxkc/+tEzrl+pVKJSqXQ+PnHiRLzzzjsxZsyYKJVK3ZsagJooiiIOHjwYjY2NMWhQdZcBlKr5eoLnn38+Dh8+HJdddlns27cvVqxYEb/73e9i586dMWbMmNNu8+CDD8by5curGgqAvq2lpSUmTJhQ1TZVBecvtbe3xyWXXBJf/epXY8mSJadd5y+PcFpbW2PSpElxffx1DI4h3X1pAGrgeByLl+JnceDAgaivr69q26pPqb3f8OHDY/r06bFr164zrlMul6NcLp/mhYfE4JLgAPQr/3aI0p2PRM7q93AqlUq8+uqrMX78+LPZDQDngKqCs3Tp0ti4cWPs3r07fv3rX8fnPve5aGtriwULFvTWfAAMEFWdUvvTn/4UX/jCF+Ktt96Kiy66KD75yU/G5s2bY/Lkyb01HwADRFXBWbNmTW/NAcAA515qAKQQHABSCA4AKQQHgBSCA0AKwQEgheAAkEJwAEghOACkEBwAUggOACkEB4AUggNACsEBIIXgAJBCcABIITgApBAcAFIIDgApBAeAFIIDQArBASCF4ACQQnAASCE4AKQQHABSCA4AKQQHgBSCA0AKwQEgheAAkEJwAEghOACkEBwAUggOACkEB4AUggNACsEBIIXgAJBCcABIITgApBAcAFIIDgApBAeAFIIDQArBASCF4ACQQnAASCE4AKQQHABSCA4AKQQHgBSCA0AKwQEgheAAkEJwAEghOACkEBwAUggOACkEB4AUggNACsEBIIXgAJDirILT3NwcpVIp7r333h4aB4CBqtvB2bJlS6xevTpmzJjRk/MAMEB1KziHDh2KO+64Ix599NEYPXp0T88EwAA0uDsbLVq0KG699da45ZZbYsWKFR+4bqVSiUql0vm4ra2tOy8JZ1Q39fJaj9A/HD1W6wn6hY5df6j1CANW1cFZs2ZNvPzyy7Fly5Yurd/c3BzLly+vejAABpaqTqm1tLTE4sWL44c//GEMGzasS9ssW7YsWltbO5eWlpZuDQpA/1bVEc62bdti//79MXPmzM7nOjo6YtOmTbFy5cqoVCpRV1d30jblcjnK5XLPTAtAv1VVcGbPnh07duw46bkvfelLccUVV8TXvva1U2IDAO+pKjgjR46MadOmnfTc8OHDY8yYMac8DwDv504DAKTo1mXR77dhw4YeGAOAgc4RDgApBAeAFIIDQArBASCF4ACQQnAASCE4AKQQHABSCA4AKQQHgBSCA0AKwQEgheAAkEJwAEghOACkEBwAUggOACkEB4AUggNACsEBIIXgAJBCcABIITgApBAcAFIIDgApBAeAFIIDQArBASCF4ACQQnAASCE4AKQQHABSCA4AKQQHgBSCA0AKwQEgheAAkEJwAEghOACkEBwAUggOACkEB4AUggNACsEBIIXgAJBCcABIITgApBAcAFIIDgApBAeAFIIDQArBASCF4ACQQnAASCE4AKQQHABSCA4AKQQHgBSCA0AKwQEgheAAkEJwAEghOACkqCo4q1atihkzZsSoUaNi1KhR0dTUFM8//3xvzQbAAFJVcCZMmBAPPfRQbN26NbZu3Ro333xz3HbbbbFz587emg+AAWJwNSvPmzfvpMff+MY3YtWqVbF58+aYOnVqjw4GwMBSVXDer6OjI37yk59Ee3t7NDU1nXG9SqUSlUql83FbW1t3X/KcU3fhmFqP0C/8bP1TtR6hX3j16OFaj9AvLJn6mVqP0KcNKo5GtHdz22o32LFjR4wYMSLK5XIsXLgw1q5dG1deeeUZ129ubo76+vrOZeLEid2bFIB+rergXH755bF9+/bYvHlzfPnLX44FCxbEb3/72zOuv2zZsmhtbe1cWlpazmpgAPqnqk+pDR06NC699NKIiJg1a1Zs2bIlHn744XjkkUdOu365XI5yuXx2UwLQ75317+EURXHSZzQAcDpVHeHcd999MXfu3Jg4cWIcPHgw1qxZExs2bIh169b11nwADBBVBWffvn1x5513xt69e6O+vj5mzJgR69ati898xlUdAHywqoLz/e9/v7fmAGCAcy81AFIIDgApBAeAFIIDQArBASCF4ACQQnAASCE4AKQQHABSCA4AKQQHgBSCA0AKwQEgheAAkEJwAEghOACkEBwAUggOACkEB4AUggNACsEBIIXgAJBCcABIITgApBAcAFIIDgApBAeAFIIDQArBASCF4ACQQnAASCE4AKQQHABSCA4AKQbXegCghx0rIn5/NGLv8YhSRIwfHHHZ0FpPBYIDA0JRRLx4OEqPt0ZsfDdKleLk/zysFBM/XY537jw/2q8vR5RKNRqUc5ngQH/XcixKS/dHadO7UUwbGsXXG6K4eljEpCERRUT88VjEb47E0B+3xke++E4cvKkc//zQBXF8XF2tJ+ccIzjQn219N0pf3BsxfFCc+NH4iJvOP/XoZfzgiE+eF2/cNTRG/FMlGu87EJf81b/EH59oiCMznGojj4sGoL967WiU/nZvxBVDo/jFxIibh3/wqbJSKQ7dMizeeGFsHP1IXUz+4tsxdPfxvHk55wkO9EfHiyh9ZV/EuLooftgYUd/102MdFwyKPz4+JjouGBSNSw9EdBQfug30BMGB/uhHbRH/uxLFwxdHjKj+x/hE/aD453+4IIZvPRoXPP1uLwwIpxIc6G+KIkqPtUb81fCI/zCs27s5/IlyHLyxHA1PtPfgcHBmggP9ze+PRem1o1H87aiz3tW/3n5+nPfKsRiyx2c59D7Bgf7mfx358z+v6f7RzXsOX/Pnq9TO23HsrPcFH0ZwoJ8p/Z9jUYyvixh19r9H03FhXRwfPcjVaqQQHOhvjkfE4J67U0AxOKLU0WO7gzMSHOhnioZBEW919MjlzKVKEXUHTsTx0f4qoPf5Uwb9zfRylN4tInYdPetdlV87FoOORRyZNqQHBoMPJjjQ3/z7YVGcX4rSfz901ruq/x9HomNUKY5cKTj0PsGB/mb4oIjPjYz4x7aI9hPd3s2gthNxwVOH41//5vwohrl7NL1PcKAfKv7z6IiDJ6L0jbe7vY9x/7UtSseKePvvRvTgZHBmggP90eQhUdw/5s93HPhRa9WbN/ygPUb/+HC8+fej4nijrykgh68ngP7qrvooXj8Wg5b+SxR/PB7Ff2mIKH/wqbHSkSLG/kNbXPhoe7z1d8PjwO3nJw0LggP9V6kUxTcvjGJcXZT+2ztRWncoioWjI+aPiDj/L05etJ+I0T9ujzGPHooh/7cj3rx/VLz9nz7k6wyghwkO9GelUsTihig+MzxKzW9Haen+iK/tj7h86Mnf+Pn7ozG+iDg4e1i0rG6IymWuSiOf4MBAcGU5in9sjNhzLOLFw1F6pRKx93hEKSJmDoviP9bH658qxbF/50ee2vGnDwaSSUMiFtTH6e5BcOzo4fRx4P1cpQZACsEBIIXgAJBCcABIITgApBAcAFJUFZzm5ua45pprYuTIkTF27NiYP39+vPbaa701GwADSFXB2bhxYyxatCg2b94c69evj+PHj8ecOXOivb29t+YDYICo6hc/161bd9Ljxx57LMaOHRvbtm2LG2644bTbVCqVqFQqnY/b2tq6MSYA/d1Z3WmgtfXPt0VvaGg44zrNzc2xfPnys3mZc1ZxyJFjV1z+2JdrPUK/UHfYjTq7YlLlf9Z6hD6tKI51e9tuXzRQFEUsWbIkrr/++pg2bdoZ11u2bFm0trZ2Li0tLd19SQD6sW4f4dx9993xyiuvxEsvvfSB65XL5SiXy919GQAGiG4F55577onnnnsuNm3aFBMmTOjpmQAYgKoKTlEUcc8998TatWtjw4YNMWXKlN6aC4ABpqrgLFq0KJ588sl49tlnY+TIkfHmm29GRER9fX2cd955vTIgAANDVRcNrFq1KlpbW+PGG2+M8ePHdy5PPfVUb80HwABR9Sk1AOgO91IDIIXgAJBCcABIITgApBAcAFIIDgApBAeAFIIDQArBASCF4ACQQnAASCE4AKQQHABSCA4AKQQHgBSCA0AKwQEgheAAkEJwAEghOACkEBwAUggOACkEB4AUggNACsEBIIXgAJBCcABIITgApBAcAFIIDgApBAeAFIIDQArBASCF4ACQQnAASCE4AKQQHABSCA4AKQQHgBSCA0AKwQEgheAAkEJwAEghOACkEBwAUggOACkEB4AUggNACsEBIIXgAJBCcABIITgApBAcAFIIDgApBAeAFIIDQArBASCF4ACQQnAASCE4AKQQHABSCA4AKQQHgBRVB2fTpk0xb968aGxsjFKpFM8880wvjAXAQFN1cNrb2+Oqq66KlStX9sY8AAxQg6vdYO7cuTF37twur1+pVKJSqXQ+bmtrq/YlARgAqg5OtZqbm2P58uW9/TID0okjR2o9Qr/wkb/fXOsRGECKoqj1CH1aURzv9ra9ftHAsmXLorW1tXNpaWnp7ZcEoA/q9SOccrkc5XK5t18GgD7OZdELine truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"samples = np.load('/home/xie_x1/MLXID/McGeneration/Samples/15keV_Moench040_150V_0_samples.npy')\n",
|
||||
"labels = np.load('/home/xie_x1/MLXID/McGeneration/Samples/15keV_Moench040_150V_0_labels.npy')\n",
|
||||
"print(labels.shape)\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"idx = 6\n",
|
||||
"plt.imshow(samples[idx], origin='lower', extent = (0, samples.shape[1], 0, samples.shape[2]))\n",
|
||||
"x,y,z,e = labels[idx]\n",
|
||||
"print(x,y,e)\n",
|
||||
"plt.scatter(x, y, s=200, facecolors='none', edgecolors='r')"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "pytorch_nightly",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.18"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+7
-4
@@ -4,6 +4,7 @@ from ROOT import *
|
||||
from multiprocessing import Pool
|
||||
from array import array
|
||||
from scipy.stats import gennorm
|
||||
import h5py
|
||||
|
||||
EnergyPerPair = 3.62 # eV
|
||||
FanoFactor = 0.13
|
||||
@@ -184,10 +185,12 @@ def singleProcess(thredIdx):
|
||||
labels[i] = np.array([x_center/pixelSize, y_center/pixelSize, z0, np.sum(_energyArray)])
|
||||
sampleOutputPath = _cfg['sampleOutputPath']
|
||||
element = _cfg['element']
|
||||
np.save(f'{sampleOutputPath}/{element}_{_cfg["sensorCode"]}_{_cfg["hv"]}V_{thredIdx}_samples.npy', samples)
|
||||
np.save(f'{sampleOutputPath}/{element}_{_cfg["sensorCode"]}_{_cfg["hv"]}V_{thredIdx}_labels.npy', labels)
|
||||
### save samples and labels into one npy file
|
||||
np.save(f'{sampleOutputPath}/{element}_{_cfg["sensorCode"]}_{_cfg["hv"]}V_{thredIdx}_samples_labels.npy', (samples, labels))
|
||||
### save samples and labels into npz file
|
||||
np.savez(f'{sampleOutputPath}/{element}_{_cfg["sensorCode"]}_{_cfg["hv"]}V_{thredIdx}.npz', samples=samples, labels=labels)
|
||||
### h5 file
|
||||
with h5py.File(f'{sampleOutputPath}/{element}_{_cfg["sensorCode"]}_{_cfg["hv"]}V_{thredIdx}.h5', 'w') as hf:
|
||||
hf['samples'] = samples
|
||||
hf['labels'] = labels
|
||||
|
||||
def getDriftTime_allpix2_8p23(z0):
|
||||
### unit: V, cm, ns, K
|
||||
|
||||
@@ -0,0 +1,40 @@
|
||||
import os
|
||||
import numpy as np
|
||||
from ROOT import *
|
||||
from multiprocessing import Pool
|
||||
from array import array
|
||||
from scipy.stats import gennorm
|
||||
import McGenerator
|
||||
|
||||
gROOT.SetBatch(True)
|
||||
|
||||
attenuationLengthDict = { ### https://henke.lbl.gov/optical_constants/atten2.html
|
||||
'5keV': 18.0101, '10keV':133.706, '15keV':442.607, '20keV':1037.80, '25keV':1991.45,
|
||||
}
|
||||
element = '15keV'
|
||||
mcConfig = {
|
||||
'element': element,
|
||||
'energy': 15000, ### eV
|
||||
'attenuationLength': attenuationLengthDict[element],
|
||||
'sensorCode': 'Moench040',
|
||||
'nTotalIncident': 10_000_000,
|
||||
'nThread': 16,
|
||||
'Roi': [50, 350, 50, 350],
|
||||
'clusterWidth': 5, ### pixel
|
||||
'pixelSize': 25, ### um
|
||||
'noiseEneFrame': np.ones((400, 400)) * 0,
|
||||
'shotNoiseFactor': 0.0,
|
||||
'calibrationNoise': 0.0,
|
||||
'sensorThickness': 650, ### moench040: 650 um, 34.7 V
|
||||
'T': 273 + 20.0 + 14.8,
|
||||
'zBins': 128,
|
||||
'hv': 150,
|
||||
'depletionVoltage': 34.7,
|
||||
'simulationMethod': 'simu2', ### 'simu1' (cpu) or 'simu2' (gpu)
|
||||
'resultsPath': '/home/xie_x1/MLED/McSimulation/BatchResults',
|
||||
'sampleOutputPath': '/home/xie_x1/MLXID/McGeneration/Samples'
|
||||
}
|
||||
|
||||
McGenerator.init(mcConfig)
|
||||
McGenerator.parameterization()
|
||||
McGenerator.generateFromParameters()
|
||||
Reference in new issue
Block a user