{
"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": 3,
"metadata": {},
"outputs": [],
"source": [
"file_name='DplusMuNu_Full'\n",
"file_path='/disk/lhcb_data/davide/HCAL_project_full_event/'+file_name+'.root'\n",
"tree_name='ntuple/DecayTree'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"f = r.TFile(file_path)\n",
"t = f.Get(tree_name)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"j=t.GetEntries()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"batch_size=25000\n",
"#n_batches=3\n",
"N = j\n",
"n_batches= N//batch_size"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"particle = 'piplus'\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,\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,\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 = 'piplus'\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,\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,\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": 26,
"metadata": {},
"outputs": [],
"source": [
"batch=10\n",
"n=4"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f310df9f350>"
]
},
"execution_count": 27,
"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": [
"plt.imshow(cellsET_inner_dict[batch][n],cmap='gray')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f310df929d0>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADe1JREFUeJzt3W+IHPd9x/H3t/Yl8V/asySbWqJqa/rAKEJNJdyAU+KWgLHypBhsWgSmuD4ojopTCm1daBTogwYiu7gYgkAmrSWEwQkp1FZt94FxDG6CCIr+WEpjt26kYlkKSm0TXDjb3z7YEZzE7Z+bm5mdvd/7BeJ2Z3Znvve7/Wh257e/30RmIqk8vzDtAiRNh+GXCmX4pUIZfqlQhl8qlOGXCmX4pUIZfqlQhl8q1NWreXJE3A18HbgK+MfM/Lsxj/frhFLLMjMmeVzU/XpvRHwS+BHwOeAc8BqwkJk/GPEcwy+1bNLwr+Zt/x3Aycw8k5mLwDPAzlVsT1KHVhP+jcCZJffPVsskzYBVfeafREQsAAtt70fSyqwm/GeBTUvub6yWXSYz9wH7wM/8Up+s5m3/94EtEbExIuaA+4HDzZQlqW21j/yZ+X8R8SfACwz+EzmQmUcaq0xSq2p39dXamW/7pdZ10dUnaYYZfqlQhl8qlOGXCmX4pUIZfqlQhl8qlOGXCmX4pUIZfqlQhl8qVOvj+afl6quH/2offvjhTNfR9Da7rLHLttdoHvmlQhl+qVCGXyqU4ZcKZfilQhl+qVC96epbq11zo55Td199785bzTa7qqPr9hilyb/nSrblkV8qlOGXCmX4pUIZfqlQhl8qlOGXCrWqK/ZExFvA+8BHwIeZuX3M49PRXpOp2xXVl/atU0cfusqmoem/zaRX7GmiBe7KzJ82sB1JHfJtv1So1YY/gZci4nhE7G6iIEndWO3b/s9m5rmI2AD8a0SczsyXlj4gIhaAhVXuR1LDVnXkz8xz1c/zwLPAjmUesy8zt487GSipW7XDHxHXRcS1l24DdwOvN1WYpHat5m3/zcB3IiKBa4FngH8e9YSIYG5ubtl1fRmp1rQ2Ru413U3VRjt2OQKyjjZG7i0uLg5dt23btqHrTp48uezytl/DtVs0M/8T2NpgLZI6ZFefVCjDLxXK8EuFMvxSoQy/VKhVjepb8c4G3YKawCx0Y3apy+sTjtL3iUlh8lF9HvmlQhl+qVCGXyqU4ZcKZfilQvVnIrMOXXPNNUPXffDBByveXhtnjmfhrHKd37uNgUJdXmJtLfHILxXK8EuFMvxSoQy/VCjDLxXK8EuF6nxgT18uJ1XHLNeucjiwR9JIhl8qlOGXCmX4pUIZfqlQhl8q1Niuvoh4CvgicD4zt1TL5hlcnusW4G3g/sz82did1ZzDr+kutlGj+h577LGh6/bu3bvs8jfeeKNWHZo9szC3YpNdfd9kcBHOpb4KHM7MTwOHq/uSZsjY8GfmK8DFKxbvBJ6ubh+o7kuaIXU/86/PzAsA1c8NzZUkqQutz+QTEQvAQtv7kbQydY/8FyJiPUD18/ywB2bmvszcnpnba+5LUgvqhv95YFd1exeDk36SZsgkXX2HgM8D64B3gK8A32HQ1Xdztey+zLzypOBy2xq6sy4ngxy1r7m5uaHrFhcXG61D5eiyi3DSrr7eXKvP8Gst62P4/YafVCjDLxXK8EuFMvxSoQy/VKjenO0fpelRfbMwMkv91OVrp+6+PNsvaSTDLxXK8EuFMvxSoQy/VCjDLxVqJrr6hqkzGAjszlO/Xjt1urJHPceuPkkjGX6pUIZfKpThlwpl+KVCtT51dxOaHtgjtaHuQJxhU8eNek4Tr32P/FKhDL9UKMMvFcrwS4Uy/FKhDL9UqEku1/UU8EXgfGZuqZbtAR4CLlQPezQznx+7sw4H9rQxv1/T+9LaUvf1OOx5u3fvHvqcxx9/fOi6Jgf2fBO4e7n9Z+a26t/Y4Evql7Hhz8xXgLEX4ZQ0W1bzmf/hiDgdEQcj4qbGKpLUibrhfxK4DbgdeBN4YtgDI2IhIo5ExJGa+5LUglrf7c/MSyf6iIhvAC+PeOw+YF/12O6mDZI0Uq0jf0RsWHL3XuD1ZsqR1JWxR/6IOAR8HlgXEWeBrwB3RcRW4BPAT4AH2yyyaZs3bx667r333qu1TmvHjTfeOHTdqNdAne68Uc977bXXhj6nCWPDn5l/sMzi/S3UIqlDfsNPKpThlwpl+KVCGX6pUIZfKtRMX66rDaO6ZB544IFll+/fb+fHrGljRGhf9ufluiSNZPilQhl+qVCGXyqU4ZcKZfilQs10V1/d7pOdO3cOXffcc8+tqiZpOU139Y26fqVdfZJGMvxSoQy/VCjDLxXK8EuFmomz/XUuodUGL8u1dsz6wB7P9kuqzfBLhTL8UqEMv1Qowy8VyvBLhRrb1RcRm4CDwDyDy3Ptz8yvRcQ88AxwC/A2cH9m/mzMtno/h9/8/PzQdRcvXuywEqmeJrv6FoEvZeYW4LeAP46IbcBXgcOZ+WngcHVf0owYG/7MPJeZx6rb7wPHgFuBncDT1cMOVPclzYgVfeaPiM3ADuBVYH1mXgCofm4Y/kxJfTPx92Yj4nrgWeCRzHw3YqKPFUTEArBQrzxJbZnoyB8Rc8C3gEOZ+e1q8YWIWF+tXw+cX+65mbkvM7dn5vYmCpbUjLHhj8Ehfj9wKjP3Lln1PLCrur2LwUk/STNikq6+O4HvAseBj6vFjwLfY9DVdzPwDnBfZo7sC+tLV1/dUYKO6ls7uh7VV0ed1+lKRvXNxJDephl+GX6/4ScVy/BLhTL8UqEMv1Qowy8VqvOZMUdNPNgHfalD09OXnoC29+WRXyqU4ZcKZfilQhl+qVCGXyqU4ZcKtWYH9vRl8E4bdfSlK2qUpq+v2Jffa5S+/F0c2CNpJMMvFcrwS4Uy/FKhDL9UqN6c7e/LmdK+1FFX3wdONd0LMM4s9N40uS+n8ZI0luGXCmX4pUIZfqlQhl8qlOGXCjXJ5bo2AQeBeeATwP7M/FpE7AEeAi5UD300M58fs61a/Yp1uzyaVqcbba12HUK39c/Pzw9dd/HiyKvELasvv9cobXf1TbL1ReBLmXksIm4AfhARL1TrHs/Mr6+4QklTNzb8mXkOOFfdfj8ijgG3tl2YpHat6DN/RGwGdgCvVosejojTEXEwIm5quDZJLZo4/BFxPfAs8Ehmvgs8CdwG3A68CTwx5HkLEXEkIo40UK+khkz03f6ImAP+BXgxM/cus/6XgZcz8zfGbMcTfhM+ry/6Ur8n/CbT6Hf7IyKA/cCppcGPiA1LHnYv8PpKC5U0PZN09d0JfBc4DnxcLX4U+ENgK4Puv58AD2bmmTHbqjWqb5iu/4fucsTcLByZRpnl0YWz/rpqrKsvM18FltvYyD59Sf3mN/ykQhl+qVCGXyqU4ZcKZfilQnU7m2JNJU7g2ZcusbpK/Js1re3ub4/8UqEMv1Qowy8VyvBLhTL8UqEMv1Sozrv6uhrtVbf7p+lrqtX9vZre5ix0h81CjW0Y9ru13R4e+aVCGX6pUIZfKpThlwpl+KVCGX6pUBNN3d3YzmpO3S1pco1N3S1pbTL8UqEMv1Qowy8VyvBLhZrkWn2fqq6yezQifhwRfx8D8xHxUkQcj4gXI+KXuihYUjMmuVZfANdm5s+rq/W+CvwV8PvAf2XmYxHxZeBXM/NPx2zLrj6pZY119eXAz6u7c8BVwHlgJ/B0tfxAdV/SjJjoM39EXBURRxmE/uXMPAGsz8wLANXPDaO2IalfJprMIzM/ArZFxC8CL0TEXZPuICIWgIWa9UlqyYq/3hsRfwMsAg8Bd2TmhYhYD/x7Zv76mOf6mV9qWWOf+SNiXUTcUN2+BvgCcAJ4HthVPWwXcLheqZKmYZKz/VuBfwIC+BRwKDP3RMRNwDPAzcA7wH2ZeXHMtjzySy2b9MjvqD5pjXFUn6SRDL9UKMMvFcrwS4Uy/FKhur5c10+B/65ur6vuT5t1XM46LjdrdfzKpBvstKvvsh1HHMnM7VPZuXVYh3X4tl8qleGXCjXN8O+b4r6Xso7LWcfl1mwdU/vML2m6fNsvFWoq4Y+IuyPiREScioi/nEYNVR1vVROQHo2IIx3u96mIOB8RJ5Ys63xC1CF17ImI/6na5GhE3NNBHZsi4pXqNfEfEfEX1fJO22REHZ22SWeT5mZmp/+ATwJvAZsYzAl4BPhM13VUtbwFrJvCfn8H+AxwYsmyfwD+rLr9ZeCJKdWxB/jzjtvjFmBrdfsG4MfAtq7bZEQdnbYJg+Hz11W354DvAb/bdHtM48h/B3AyM89k5iKDOQGKmvwzM18Brpz7oPMJUYfU0bnMPJeZx6rb7wPHgFvpuE1G1NGpHGh90txphH8jcGbJ/bPVsmlI4NLbqN1TquGSPk2I+nBEnI6Ig9WkLZ2JiM3ADgZTxE+tTa6oAzpuky4mzS39hN9nM/M3gd8D/igivjDtgnrgSeA24HbgTeCJrnYcEdcDzwKPZOa7Xe13gjo6b5PM/CgztzE4MH5uJZPmTmoa4T/L4PP+JRurZZ3LzHPVz/MM/tg7plFH5dJEqFQ/z0+jiMy8UL3wPga+QUdtUl0Q5lsMpon7drW48zZZro5ptUm17/8FngN+m4bbYxrh/z6wJSI2Vg19P1OY/DMirouIay/dBu4GXu+6jiV6MSFqRCx9K3kvHbRJdVWo/cCpzNy7ZFWnbTKsjq7bpLNJc7s6g3nF2cx7gJPAKeCvp1TDrzE4ofNDBmd1/5bqS08d7PsQ8DaDKdDPAg8CNwH/Bhyvfs5PqY4DVbucBl4ENnVQx50Mzr8cA45W/+7puk1G1NFpmwBbq33/EPgRsKda3mh7+A0/qVCln/CTimX4pUIZfqlQhl8qlOGXCmX4pUIZfqlQhl8q1P8DunFJofehXT0AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(cellsET_outer_dict[batch][n],cmap='gray')"
]
},
{
"cell_type": "code",
"execution_count": null,
"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]) "
]
}
],
"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