{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Welcome to JupyROOT 6.16/00\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/disk/lhcb_data2/davide/miniconda3/envs/root_env/lib/python3.7/site-packages/root_numpy/__init__.py:46: RuntimeWarning: numpy 1.17.2 is currently installed but you installed root_numpy against numpy 1.9.3. Please consider reinstalling root_numpy for this numpy version.\n",
" RuntimeWarning)\n"
]
}
],
"source": [
"import root_numpy as rn\n",
"import ROOT as r\n",
"import numpy as np\n",
"#import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"\n",
"file_name='B2Dmunu'\n",
"file_path='/disk/lhcb_data2/davide/HCAL_project_full_event/'+file_name+'.root'\n",
"tree_name='Bd2Dmu/DecayTree'\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#r.TBrowser()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"f = r.TFile(file_path)\n",
"t = f.Get(tree_name)\n",
"j=t.GetEntries()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"j"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"batch_size=20000\n",
"n_batches=1\n",
"#N = j\n",
"#n_batches= N//batch_size"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"particle = 'K'\n",
"variable = 'ET'\n",
"\n",
"cal_zone = 'inner'\n",
"\n",
"cellsET_inner_dict={}\n",
"\n",
"for j in range(n_batches):\n",
" cellsET_inner_dict[j]=rn.root2array(\n",
" filenames=file_path, \n",
" treename=tree_name,\n",
" branches=particle+'_L0Calo_HCAL_Cells'+variable+'_'+cal_zone,#\"B0_L0HadronDecision_TOS\",\"B0_L0HadronDecision_TIS\"],\n",
" start=j*batch_size,\n",
" stop=(j+1)*batch_size,\n",
" )\n",
" \n",
" cellsET_inner_dict[j]=np.array([cellsET_inner_dict[j][i] for i in range(batch_size)])\n",
" #np.save('/disk/lhcb_data/davide/HCAL_project_full_event/npy/'+file_name+'/'+variable+'_'+cal_zone+'/batch_'+str(j), cellsET_inner_dict[j])\n",
"\n",
"#if N % batch_size != 0:\n",
"# \n",
"# cellsET_inner_dict[j+1]=rn.root2array(\n",
"# filenames=file_path,\n",
"# treename=tree_name,\n",
"# branches=particle+'_L0Calo_HCAL_Cells'+variable+'_'+cal_zone,# \"B0_L0HadronDecision_TOS\",\"B0_L0HadronDecision_TIS\"],\n",
"# start=n_batches*batch_size,\n",
"# stop=N,\n",
"# ) \n",
"# cellsET_inner_dict[j+1]=np.array([cellsET_inner_dict[j+1][i] for i in range((N % batch_size))])\n",
"# np.save('/disk/lhcb_data/davide/HCAL_project_full_event/npy/'+file_name+'/'+variable+'_'+cal_zone+'/batch_'+str(j+1), cellsET_inner_dict[j+1])\n",
" \n",
"particle = 'K'\n",
"variable = 'ET'\n",
"\n",
"cal_zone='outer'\n",
"\n",
"cellsET_outer_dict ={}\n",
"\n",
"for j in range(n_batches):\n",
" cellsET_outer_dict[j]=rn.root2array(\n",
" filenames=file_path, \n",
" treename=tree_name,\n",
" branches=particle+'_L0Calo_HCAL_Cells'+variable+'_'+cal_zone,# \"B0_L0HadronDecision_TOS\",\"B0_L0HadronDecision_TIS\"],\n",
" start=j*batch_size,\n",
" stop=(j+1)*batch_size,\n",
" )\n",
" cellsET_outer_dict[j]=np.array([cellsET_outer_dict[j][i] for i in range(batch_size)])\n",
" #np.save('/disk/lhcb_data/davide/HCAL_project_full_event/npy/'+file_name+'/'+variable+'_'+cal_zone+'/batch_'+str(j), cellsET_outer_dict[j])\n",
"\n",
"#if N % batch_size != 0:\n",
"# \n",
"# cellsET_outer_dict[j+1]=rn.root2array(\n",
"# filenames=file_path,\n",
"# treename=tree_name,\n",
"# branches=particle+'_L0Calo_HCAL_Cells'+variable+'_'+cal_zone,# \"B0_L0HadronDecision_TOS\",\"B0_L0HadronDecision_TIS\"],\n",
"# start=n_batches*batch_size,\n",
"# stop=N,\n",
"# ) \n",
"# cellsET_outer_dict[j+1]=np.array([cellsET_outer_dict[j+1][i] for i in range((N % batch_size))])\n",
"# np.save('/disk/lhcb_data/davide/HCAL_project_full_event/npy/'+file_name+'/'+variable+'_'+cal_zone+'/batch_'+str(j+1), cellsET_outer_dict[j+1]) "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"particle='K'\n",
"L0Calo_HCAL_TriggerET ={}\n",
"\n",
"for j in range(n_batches):\n",
" L0Calo_HCAL_TriggerET[j]=rn.root2array(\n",
" filenames=file_path, \n",
" treename=tree_name,\n",
" branches=particle+'_L0Calo_HCAL_TriggerET',\n",
" start=j*batch_size,\n",
" stop=(j+1)*batch_size,\n",
" )\n",
" L0Calo_HCAL_TriggerET[j]=np.array([L0Calo_HCAL_TriggerET[j][i] for i in range(batch_size)])\n",
" #np.save('/disk/lhcb_data/davide/HCAL_project_full_event/npy/'+file_name+'/'+variable+'_'+cal_zone+'/batch_'+str(j), cellsET_outer_dict[j])\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6577"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(L0Calo_HCAL_TriggerET[0]<0).sum()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(L0Calo_HCAL_TriggerET[0][L0Calo_HCAL_TriggerET[0]<0],bins=30);"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fdf22912470>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"batch=0\n",
"n=10\n",
"plt.imshow(cellsET_inner_dict[batch][n],cmap='gray')"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [24., 0., 0., ..., 0., 24., 0.],\n",
" ...,\n",
" [ 0., 0., 24., ..., 24., 24., 24.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cellsET_inner_dict[batch][n]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fdf22b0e940>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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F2IfdWD+rLoHPZOZlwJ8CX4+Iz86ojnlyL7AO2AAsAXf2teOIOBV4FLgtM9/pa79j1NH7MckJBnmtM4uwvwGct+L3c4E3Z1AHmflm9fMQ8DiDjxizMtYAntOWmQerF9qHwHfp6ZhUE5A8CvwgMx+rFvd+TIbVMatjUu37mAd5rTOLsL8ArI+IT0TEycBXGAxe2auIWBsRpx15DHwB2N281lTNxQCeR15Mlevp4ZhUsw7dB+zJzLtWNPV6TOrq6PuYTG2Q176uMB51tfEaBlc69wN/NaMaPsmgJ+BnwM/7rAN4iMHbwQ8YvNP5c+BjwLPAL6ufZ86ojn8CXgF2VS+uxR7q2MTgo9wu4OXqzzV9H5OGOno9JsAlwH9U+9sN/HW1fKLj4TfopEL4DTqpEIZdKoRhlwph2KVCGHapEIZdKoRhlwph2KVC/B+rBIuj+AaCVAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"batch=0\n",
"n=3\n",
"plt.imshow(cellsET_outer_dict[batch][n],cmap='gray')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"for j in range(n_batches):\n",
"\n",
" np.save('/disk/lhcb_data/davide/HCAL_project_full_event/npy/'+file_name+'/'+variable+'_inner/batch_'+str(j), cellsET_inner_dict[j])\n",
" #np.save('/disk/lhcb_data/davide/HCAL_project_full_event/npy/'+file_name+'/'+variable+'_'+cal_zone+'/batch_'+str(j+1), cellsET_inner_dict[j+1])\n",
"\n",
" np.save('/disk/lhcb_data/davide/HCAL_project_full_event/npy/'+file_name+'/'+variable+'_outer/batch_'+str(j), cellsET_outer_dict[j])\n",
" #np.save('/disk/lhcb_data/davide/HCAL_project_full_event/npy/'+file_name+'/'+variable+'_'+cal_zone+'/batch_'+str(j+1), cellsET_outer_dict[j+1]) \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"#particle = 'piplus'\n",
"#cal_zone = 'inner'\n",
"#variable = 'X'\n",
"#\n",
"#cellsX_inner_dict={}\n",
"#\n",
"#for j in range(n_batches):\n",
"# cellsX_inner_dict[j]=rn.root2array(\n",
"# filenames=file_path, \n",
"# treename=tree_name,\n",
"# branches=particle+'_L0Calo_HCAL_Cells'+variable+'_'+cal_zone,\n",
"# start=j*batch_size,\n",
"# stop=(j+1)*batch_size,\n",
"# )\n",
"# \n",
"# cellsX_inner_dict[j]=np.array([cellsX_inner_dict[j][i] for i in range(batch_size)])\n",
"# np.save('/disk/lhcb_data/davide/HCAL_project_full_event/npy/'+file_name+'/'+variable+'_'+cal_zone+'/batch_'+str(j), cellsX_inner_dict[j])\n",
"#\n",
"#if N % batch_size != 0:\n",
"# \n",
"# cellsX_inner_dict[j+1]=rn.root2array(\n",
"# filenames=file_path,\n",
"# treename=tree_name,\n",
"# branches=particle+'_L0Calo_HCAL_Cells'+variable+'_'+cal_zone,\n",
"# start=n_batches*batch_size,\n",
"# stop=N,\n",
"# ) \n",
"# cellsX_inner_dict[j+1]=np.array([cellsX_inner_dict[j+1][i] for i in range((N % batch_size))])\n",
"# np.save('/disk/lhcb_data/davide/HCAL_project_full_event/npy/'+file_name+'/'+variable+'_'+cal_zone+'/batch_'+str(j+1), cellsX_inner_dict[j+1])\n",
"# \n",
"#variable = 'Y'\n",
"#\n",
"#cellsY_inner_dict ={}\n",
"#\n",
"#for j in range(n_batches):\n",
"# cellsY_inner_dict[j]=rn.root2array(\n",
"# filenames=file_path, \n",
"# treename=tree_name,\n",
"# branches=particle+'_L0Calo_HCAL_Cells'+variable+'_'+cal_zone,\n",
"# start=j*batch_size,\n",
"# stop=(j+1)*batch_size,\n",
"# )\n",
"# cellsY_inner_dict[j]=np.array([cellsY_inner_dict[j][i] for i in range(batch_size)])\n",
"# np.save('/disk/lhcb_data/davide/HCAL_project_full_event/npy/'+file_name+'/'+variable+'_'+cal_zone+'/batch_'+str(j), cellsY_inner_dict[j])\n",
"#\n",
"#if N % batch_size != 0:\n",
"# \n",
"# cellsY_inner_dict[j+1]=rn.root2array(\n",
"# filenames=file_path,\n",
"# treename=tree_name,\n",
"# branches=particle+'_L0Calo_HCAL_Cells'+variable+'_'+cal_zone,\n",
"# start=n_batches*batch_size,\n",
"# stop=N,\n",
"# ) \n",
"# cellsY_inner_dict[j+1]=np.array([cellsY_inner_dict[j+1][i] for i in range((N % batch_size))])\n",
"# np.save('/disk/lhcb_data/davide/HCAL_project_full_event/npy/'+file_name+'/'+variable+'_'+cal_zone+'/batch_'+str(j+1), cellsY_inner_dict[j+1])\n",
"\n",
"#particle = 'piplus'\n",
"#cal_zone = 'outer'\n",
"#variable = 'X'\n",
"#\n",
"#cellsX_outer_dict={}\n",
"#\n",
"#for j in range(n_batches):\n",
"# cellsX_outer_dict[j]=rn.root2array(\n",
"# filenames=file_path, \n",
"# treename=tree_name,\n",
"# branches=particle+'_L0Calo_HCAL_Cells'+variable+'_'+cal_zone,\n",
"# start=j*batch_size,\n",
"# stop=(j+1)*batch_size,\n",
"# )\n",
"# \n",
"# cellsX_outer_dict[j]=np.array([cellsX_outer_dict[j][i] for i in range(batch_size)])\n",
"# np.save('/disk/lhcb_data/davide/HCAL_project_full_event/npy/'+file_name+'/'+variable+'_'+cal_zone+'/batch_'+str(j), cellsX_outer_dict[j])\n",
"#\n",
"#if N % batch_size != 0:\n",
"# \n",
"# cellsX_outer_dict[j+1]=rn.root2array(\n",
"# filenames=file_path,\n",
"# treename=tree_name,\n",
"# branches=particle+'_L0Calo_HCAL_Cells'+variable+'_'+cal_zone,\n",
"# start=n_batches*batch_size,\n",
"# stop=N,\n",
"# ) \n",
"# cellsX_outer_dict[j+1]=np.array([cellsX_outer_dict[j+1][i] for i in range((N % batch_size))])\n",
"# np.save('/disk/lhcb_data/davide/HCAL_project_full_event/npy/'+file_name+'/'+variable+'_'+cal_zone+'/batch_'+str(j+1), cellsX_outer_dict[j+1])\n",
"# \n",
"#variable = 'Y'\n",
"#\n",
"#cellsY_outer_dict ={}\n",
"#\n",
"#for j in range(n_batches):\n",
"# cellsY_outer_dict[j]=rn.root2array(\n",
"# filenames=file_path, \n",
"# treename=tree_name,\n",
"# branches=particle+'_L0Calo_HCAL_Cells'+variable+'_'+cal_zone,\n",
"# start=j*batch_size,\n",
"# stop=(j+1)*batch_size,\n",
"# )\n",
"# cellsY_outer_dict[j]=np.array([cellsY_outer_dict[j][i] for i in range(batch_size)])\n",
"# np.save('/disk/lhcb_data/davide/HCAL_project_full_event/npy/'+file_name+'/'+variable+'_'+cal_zone+'/batch_'+str(j), cellsY_outer_dict[j])\n",
"#\n",
"#if N % batch_size != 0:\n",
"# \n",
"# cellsY_outer_dict[j+1]=rn.root2array(\n",
"# filenames=file_path,\n",
"# treename=tree_name,\n",
"# branches=particle+'_L0Calo_HCAL_Cells'+variable+'_'+cal_zone,\n",
"# start=n_batches*batch_size,\n",
"# stop=N,\n",
"# ) \n",
"# cellsY_outer_dict[j+1]=np.array([cellsY_outer_dict[j+1][i] for i in range((N % batch_size))])\n",
"# np.save('/disk/lhcb_data/davide/HCAL_project_full_event/npy/'+file_name+'/'+variable+'_'+cal_zone+'/batch_'+str(j+1), cellsY_outer_dict[j+1]) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Davide Lancierini