{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/hep/davide/miniconda3/envs/root_env/lib/python2.7/site-packages/root_numpy/_tree.py:5: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility\n",
" from . import _librootnumpy\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": 4,
"metadata": {},
"outputs": [],
"source": [
"\n",
"file_name='B2Dmunu_full'\n",
"file_path='/disk/lhcb_data/davide/HCAL_project_full_event/'+file_name+'.root'\n",
"tree_name='Bd2Dmu/DecayTree'\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"f = r.TFile(file_path)\n",
"t = f.Get(tree_name)\n",
"j=t.GetEntries()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#b=r.TBrowser()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"batch_size=20000\n",
"#n_batches=2\n",
"N = j\n",
"n_batches= N//batch_size"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"particle = 'pi1'\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 = 'pi1'\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": 7,
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-7-7befa847dcbe>, line 6)",
"output_type": "error",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-7-7befa847dcbe>\"\u001b[0;36m, line \u001b[0;32m6\u001b[0m\n\u001b[0;31m if cellsET_inner_dict[j][i][1]: and not cellsET_inner_dict[j][i][2]:\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"cellsET_inner_dict_TOS={}\n",
"\n",
"for j in range(n_batches):\n",
" cellsET_inner_dict_TOS[j]=[]\n",
" for i in range(100):\n",
" if cellsET_inner_dict[j][i][1]: and not cellsET_inner_dict[j][i][2]:\n",
" cellsET_inner_dict_TOS[j].append(cellsET_inner_dict[j][i][0])\n",
" cellsET_inner_dict_TOS[j]=np.array(cellsET_inner_dict_TOS[j])\n",
" \n",
"cellsET_inner_dict_TIS={}\n",
"\n",
"for j in range(n_batches):\n",
" cellsET_inner_dict_TIS[j]=[]\n",
" for i in range(100):\n",
" if cellsET_inner_dict[j][i][2]:\n",
" cellsET_inner_dict_TIS[j].append(cellsET_inner_dict[j][i][0])\n",
" cellsET_inner_dict_TIS[j]=np.array(cellsET_inner_dict_TIS[j])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(17, 32, 32)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cellsET_inner_dict_TOS[0].shape"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"cellsET_outer_dict_TOS={}\n",
"\n",
"for j in range(n_batches):\n",
" cellsET_outer_dict_TOS[j]=[]\n",
" for i in range(100):\n",
" if cellsET_outer_dict[j][i][1] and not cellsET_inner_dict[j][i][2]:\n",
" cellsET_outer_dict_TOS[j].append(cellsET_outer_dict[j][i][0])\n",
" cellsET_outer_dict_TOS[j]=np.array(cellsET_outer_dict_TOS[j])\n",
" \n",
"cellsET_outer_dict_TIS={}\n",
"\n",
"for j in range(n_batches):\n",
" cellsET_outer_dict_TIS[j]=[]\n",
" for i in range(100):\n",
" if cellsET_outer_dict[j][i][2]:\n",
" cellsET_outer_dict_TIS[j].append(cellsET_outer_dict[j][i][0])\n",
" cellsET_outer_dict_TIS[j]=np.array(cellsET_outer_dict_TIS[j])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fcc1894d710>"
]
},
"execution_count": 18,
"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=10\n",
"n=4\n",
"plt.imshow(cellsET_inner_dict[batch][n],cmap='gray')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fcc18901d90>"
]
},
"execution_count": 19,
"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=10\n",
"n=4\n",
"plt.imshow(cellsET_outer_dict[batch][n],cmap='gray')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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+'_'+cal_zone+'/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+'_'+cal_zone+'/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": 125,
"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 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.15"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Davide Lancierini