{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"import pickle\n",
"import sys"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"width_X=8404.0\n",
"width_Y=6828.0\n",
"#number of events\n",
"batch_size=20000\n",
"X_pixels=64\n",
"Y_pixels=52"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with open('/disk/lhcb_data/davide/HCAL_project_full_event/csv/MCtracker_info.pickle', 'rb') as f:\n",
" tracks=pickle.load(f)\n",
" \n",
"n_batches=len(tracks['xProjections'])\n",
"\n",
"tracks['pi1_xProjection'][0][0] in tracks['xProjections'][0][0][0]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"for j in range(n_batches):\n",
" for i in range(batch_size):\n",
" tracks['xProjections'][j][i][0]/=(width_X/2)\n",
" tracks['xProjections'][j][i][0]*=32\n",
" tracks['xProjections'][j][i][0]+=32\n",
"\n",
" \n",
"for j in range(n_batches):\n",
" for i in range(batch_size):\n",
" tracks['yProjections'][j][i][0]/=(width_Y/2)\n",
" tracks['yProjections'][j][i][0]*=26\n",
" tracks['yProjections'][j][i][0]+=26\n",
"\n",
"for j in range(n_batches):\n",
" for i in range(batch_size):\n",
" tracks['pi1_xProjection'][j][i]/=(width_X/2)\n",
" tracks['pi1_xProjection'][j][i]*=32\n",
" tracks['pi1_xProjection'][j][i]+=32\n",
"\n",
" \n",
"for j in range(n_batches):\n",
" for i in range(batch_size):\n",
" tracks['pi1_yProjection'][j][i]/=(width_Y/2)\n",
" tracks['pi1_yProjection'][j][i]*=26\n",
" tracks['pi1_yProjection'][j][i]+=26 \n",
" \n",
"for j in range(n_batches):\n",
" for i in range(batch_size):\n",
" tracks['pi2_xProjection'][j][i]/=(width_X/2)\n",
" tracks['pi2_xProjection'][j][i]*=32\n",
" tracks['pi2_xProjection'][j][i]+=32\n",
"\n",
" \n",
"for j in range(n_batches):\n",
" for i in range(batch_size):\n",
" tracks['pi2_yProjection'][j][i]/=(width_Y/2)\n",
" tracks['pi2_yProjection'][j][i]*=26\n",
" tracks['pi2_yProjection'][j][i]+=26\n",
"\n",
"for j in range(n_batches):\n",
" for i in range(batch_size):\n",
" tracks['K_xProjection'][j][i]/=(width_X/2)\n",
" tracks['K_xProjection'][j][i]*=32\n",
" tracks['K_xProjection'][j][i]+=32\n",
"\n",
" \n",
"for j in range(n_batches):\n",
" for i in range(batch_size):\n",
" tracks['K_yProjection'][j][i]/=(width_Y/2)\n",
" tracks['K_yProjection'][j][i]*=26\n",
" tracks['K_yProjection'][j][i]+=26 "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 26.366026 , 32.283485 , 43.76675 , 40.5049 ,\n",
" 26.017092 , 25.059456 , 11.166256 , 32.01572 ,\n",
" 46.174244 , 37.074677 , 29.652033 , 42.82162 ,\n",
" -4.8200912 , 5.0072193 , 17.069355 , 23.731718 ,\n",
" 44.455063 , 55.73885 , 30.468227 , 56.224937 ,\n",
" 26.463978 , 12.707497 , 41.476982 , 32.009785 ,\n",
" 11.1635895 , 29.462578 , 43.270424 , 40.199158 ,\n",
" 37.376354 , 31.979446 , -5.9480743 , 33.48986 ,\n",
" -0.35655975, 11.592337 , 46.973446 , 40.964615 ,\n",
" 24.896637 , 40.067432 , -4.4417343 , 10.579229 ,\n",
" -16.302044 , 30.109873 , 36.19085 , 10.674828 ,\n",
" 34.354465 , 51.943687 , 37.02441 , 68.699234 ,\n",
" 4.2089787 , 29.306755 , 31.927109 , 17.94367 ,\n",
" 46.051395 , 40.864464 , 9.20113 , 30.551847 ,\n",
" 59.26765 , 21.558033 , 36.4137 , 43.763763 ,\n",
" 31.440235 , 29.761326 , 39.14627 , 21.2994 ,\n",
" 13.478554 , 31.528143 , 62.70546 , 18.237343 ,\n",
" 35.2656 , 12.071615 , 20.394001 , 28.785568 ,\n",
" 35.621407 , 54.64473 , 8.858938 , 72.147156 ,\n",
" 47.763863 , 25.496136 , 26.082474 , 26.931595 ,\n",
" 27.804598 , 38.213768 , 63.684364 , -12.207516 ,\n",
" -1.8446388 , 44.428505 , -7.9377556 , 50.85122 ,\n",
" 45.11862 , 35.516087 , 62.596184 , 9.868935 ],\n",
" dtype=float32),)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tracks['xProjections'][0][0]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#for j in range(n_batches):\n",
"# for i in range(batch_size):\n",
"# tracks[\"xProjections\"][j][i][0]/=(width_X/2)\n",
"# tracks[\"yProjections\"][j][i][0]/=(width_Y/2)\n",
"# \n",
"# tracks[\"xProjections\"][j][i][0]*=32\n",
"# tracks[\"yProjections\"][j][i][0]*=26\n",
"# \n",
"# tracks[\"xProjections\"][j][i][0]+=32\n",
"# tracks[\"yProjections\"][j][i][0]+=26\n",
"# "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"pics_dict_flip = {}\n",
"pics_dict = {}\n",
"for n in range(n_batches):\n",
" \n",
" pic = np.zeros(shape=(Y_pixels,X_pixels,1),dtype=np.float32)\n",
" batch_size = tracks['pi1_region'][0].shape[0]\n",
" pics_dict[n]={}\n",
" pics_dict_flip[n]={}\n",
" pics_dict[n]['pic']=np.array([pic for i in range(0,batch_size)])\n",
" pics_dict_flip[n]['pic']=np.array([pic for i in range(0,batch_size)])\n",
" \n",
" pics_dict[n]['L0HadronDec_TIS']=np.empty(shape=(batch_size,),dtype='bool')\n",
" pics_dict[n]['L0HadronDec_TOS']=np.empty(shape=(batch_size,),dtype='bool')\n",
" \n",
" for event in range(batch_size):\n",
" #if event not in pos_rejected[n]:\n",
" pics_dict[n]['L0HadronDec_TIS'][event]=tracks['L0HadronDec_TIS'][n][event]\n",
" pics_dict[n]['L0HadronDec_TOS'][event]=tracks['L0HadronDec_TOS'][n][event]\n",
" \n",
" for ntrack in range(tracks['yProjections'][n][event][0].shape[0]):\n",
" \n",
" y=int(np.floor(tracks['yProjections'][n][event][0][ntrack]))\n",
" x=int(np.floor(tracks['xProjections'][n][event][0][ntrack]))\n",
" \n",
" if 0<x<X_pixels:\n",
" if 0<y<Y_pixels:\n",
" \n",
" pics_dict_flip[n]['pic'][event][y,x]+=tracks['realETs'][n][event][0][ntrack]\n",
" \n",
" pics_dict[n]['pic'][event]=np.flip(pics_dict_flip[n]['pic'][event], axis=0)\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"#pics_dict_filtered={}\n",
"#for n in range(n_batches):\n",
"# \n",
"# pics_dict_filtered[n]={}\n",
"# \n",
"#for n in range(n_batches):\n",
"# \n",
"# pics_dict_filtered[n]['pic']=np.delete(pics_dict[n]['pic'],pos_rejected[n],axis=0)\n",
"# pics_dict_filtered[n]['L0HadronDec_TIS']=np.delete(pics_dict[n]['L0HadronDec_TIS'],pos_rejected[n],axis=0)\n",
"# pics_dict_filtered[n]['L0HadronDec_TOS']=np.delete(pics_dict[n]['L0HadronDec_TOS'],pos_rejected[n],axis=0)\n",
"#"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f294be28208>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"i=2\n",
"plt.imshow(pics_dict[0]['pic'][i].reshape(52,64));\n",
"plt.colorbar()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"for i in range(n_batches):\n",
" with open('/disk/lhcb_data/davide/HCAL_project_full_event/true/sample'+str(i)+'.pickle', 'wb') as handle:\n",
" pickle.dump(pics_dict[i], handle, protocol=pickle.HIGHEST_PROTOCOL)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.hist(pics_dict[0]['pic'].sum(axis=(1,2))/1000, range=(0,300),bins=70);"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"#def make_pics_dict(X_grid, Y_grid, tracks, n_batches=n_batches):\n",
"# X_pixels=64\n",
"# Y_pixels=np.int(np.ceil((X_pixels*width_Y)/width_X))\n",
"# pic = np.zeros(shape=(Y_pixels,X_pixels,1),dtype=np.float32)\n",
"# pics_dict = {}\n",
"# \n",
"# for n in range(1):\n",
"# \n",
"# batch_size = tracks['regions'][0].shape[0]\n",
"# pics_dict[n]=np.array([pic for i in range(0,batch_size)])\n",
"# \n",
"# for k in range(batch_size):\n",
"# event=k\n",
"# if tracks['regions'][n][event][0]>=0: #and particle_1[n][\"region\"][event]>=0 and particle_2[n][\"region\"][event]>=0:\n",
"# #event = n*batch_size+k\n",
"# \n",
"# #print(event)\n",
"# for i in range(X_grid.shape[0]-1):\n",
"# for j in range(Y_grid.shape[1]-1):\n",
"# for ntrack in range(tracks['xProjections'][n][event][0].shape[0]):\n",
"# \n",
"# xtrack = tracks['xProjections'][n][event][0][ntrack]*X_pixels/2\n",
"# ytrack = tracks['yProjections'][n][event][0][ntrack]*Y_pixels/2 \n",
"# \n",
"# if X_grid[i,j] < xtrack < X_grid[i,j+1]:\n",
"# if Y_grid[i,j] < ytrack < Y_grid[i+1,j]:\n",
"#\n",
"# \n",
"# x_primed=int(Y_pixels/2-Y_grid[i,j])\n",
"# y_primed=int(X_pixels/2+X_grid[i,j])\n",
"#\n",
"# #pics_dict[n][k][x_primed,y_primed,0]=1\n",
"# \n",
"# pics_dict[n][k][x_primed-1,y_primed-1,0]=tracks[\"realETs\"][n][event][0][ntrack]\n",
"# #total_pic[x_primed-1,y_primed-1,0]=1\n",
"# #total_pic[x_primed-1,y_primed-1,0]=true_events['true_ET'][event]\n",
"# \n",
"# \n",
"# print('Converted '+str(n+1)+'/'+str(n_batches)+' batches of ' +str(batch_size)+' images')\n",
"# \n",
"# # \n",
"# # if N % batch_size != 0:\n",
"# # \n",
"# # print('Converting last batch of '+str(N%batch_size)+' images') \n",
"# # \n",
"# # for k in range(batch_size*n_batches,N):\n",
"# # #if true_events['region'][k]>=0:\n",
"# # \n",
"# # for i in range(X_grid.shape[0]):\n",
"# # for j in range(Y_grid.shape[1]):\n",
"# # if X_grid[i,j-1] < X_norm[n_batches][k]*X_pixels/2 < X_grid[i,j]:\n",
"# # if Y_grid[i-1,j] < Y_norm[n_batches][k]*Y_pixels/2 < Y_grid[i,j]:\n",
"# # \n",
"# # x_primed=int(Y_pixels/2-Y_grid[i,j])\n",
"# # y_primed=int(X_pixels/2+X_grid[i,j])\n",
"# # \n",
"# # pics_dict[n_batches][k-(N-1)][x_primed,y_primed,0]=true_events['true_ET'][event]\n",
"# # #total_pic[x_primed,y_primed,0]=true_events['true_ET'][event]\n",
"# return pics_dict\n",
"#\n",
"#\n",
"#\n",
"#"
]
},
{
"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