05_delayed_extraction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Setup and Load Data ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lax Version : 1.5.2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/project/lgrandi/anaconda3/envs/pax_head/lib/python3.4/site-packages/seaborn-0.8.1-py3.4.egg/seaborn/apionly.py:6: UserWarning: As seaborn no longer sets a default style on import, the seaborn.apionly module is deprecated. It will be removed in a future version.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "run ../initialize.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mpl.rcParams['axes.linewidth'] = 2.5\n",
    "R_tpc=pax_config['DEFAULT']['tpc_radius']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_file_list(path, pattern, remove_string='', remove_path=True):\n",
    "    '''\n",
    "    Get a list of files matching pattern in path. Optional to remove a part of the path (i.e. the extention)\n",
    "    Optional to remove the path\n",
    "    '''\n",
    "    file_list = glob.glob(path + pattern)\n",
    "    # Remove path\n",
    "    for i, f in enumerate(file_list):\n",
    "        if remove_path:\n",
    "            f = f.replace(path, '')\n",
    "        if remove_string != '':\n",
    "            f = f.replace(remove_string, '')\n",
    "        file_list[i] = f\n",
    "    file_list = np.sort(file_list)\n",
    "    return file_list\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract Hist from pre-processed pickles: SR0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed data available selection: 0 rows removed (100.00% passed)\n",
      "Source in place selection: 1390 rows removed (4.86% passed)\n",
      "Source in place selection: 559 rows removed (61.74% passed)\n",
      "Source in place selection: 1372 rows removed (6.09% passed)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/project/lgrandi/anaconda3/envs/pax_head/lib/python3.4/site-packages/hax-2.5.0-py3.4.egg/hax/cuts.py:130: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  d.cut_history = prev_cuts + [passthrough_dict]\n"
     ]
    }
   ],
   "source": [
    "datasets = hax.runs.datasets \n",
    "datasets = hax.runs.tags_selection(include=['*sciencerun0'],\n",
    "                                  exclude=['bad','messy', 'test',\n",
    "                                           'nofield','lowfield',\n",
    "                                           'commissioning', 'flash',\n",
    "                                           'pmttrip','trip','_pmttrip',\n",
    "                                           'source_opening',\n",
    "                                           ],\n",
    "                                  )\n",
    "datasets= hax.cuts.selection(datasets, datasets['location'] != '', 'Processed data available')\n",
    "\n",
    "#Radon\n",
    "datasets_rn = hax.cuts.selection(datasets, datasets['source__type']=='Rn220', 'Source in place')\n",
    "dataset_names_rn = datasets_rn['name']\n",
    "\n",
    "#Bkg\n",
    "datasets_bkg = hax.cuts.selection(datasets, datasets['source__type']=='none', 'Source in place')\n",
    "dataset_names_bkg = datasets_bkg['name']\n",
    "\n",
    "#Krypton\n",
    "datasets_kr = hax.cuts.selection(datasets, datasets['source__type']=='Kr83m', 'Source in place')\n",
    "dataset_names_kr = datasets_kr['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching for Processed Pickle Files\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/5707 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 5707 datasets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5707/5707 [14:12<00:00,  6.70it/s]\n",
      "  0%|          | 1/5702 [00:00<17:42,  5.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 5702 processed files \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5702/5702 [02:44<00:00, 34.57it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 17s, sys: 1min 34s, total: 2min 52s\n",
      "Wall time: 16min 57s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print('Searching for Processed Pickle Files')\n",
    "#simpath= '/project/lgrandi/jpienaar/cache_files/'\n",
    "simpath= '/scratch/midway2/jpienaar/cache_files/'\n",
    "\n",
    "#Find datasets\n",
    "datasets = get_file_list(simpath, '*dt.pkl', ' ')\n",
    "print('Found %d datasets' % len(datasets))\n",
    "\n",
    "processed_list=[]\n",
    "for dataset in tqdm(datasets):\n",
    "    with open(simpath+dataset, 'rb') as handle:\n",
    "        data_temp = pickle.load(handle)\n",
    "        try:\n",
    "            version = data_temp['version']\n",
    "            if version =='2.0':\n",
    "                processed_list.append(dataset)\n",
    "        except KeyError:\n",
    "            continue\n",
    "print(\"Found %i processed files \" %len(processed_list))\n",
    "datasets=processed_list\n",
    "\n",
    "\n",
    "dicts_datasets_rn={}\n",
    "dicts_datasets_kr={}\n",
    "dicts_datasets_bkg={}\n",
    "for dataset in tqdm(datasets[:]):\n",
    "    key=dataset[:-7]\n",
    "    if dataset_names_rn.isin([dataset[:-7]]).any():\n",
    "        with open(simpath+dataset, 'rb') as handle:\n",
    "            data_dict = pickle.load(handle)\n",
    "            dataset_temp = datasets_rn.loc[datasets_rn.name==dataset[:-7]]\n",
    "            time_diff=dataset_temp.end-dataset_temp.start\n",
    "            data_dict['duration'] =  time_diff.iloc[0].seconds\n",
    "            data_dict['trigger_rate']=dataset_temp.iloc[0].trigger__events_built\n",
    "        dicts_datasets_rn[key]=data_dict\n",
    "              \n",
    "    elif dataset_names_kr.isin([dataset[:-7]]).any():\n",
    "        with open(simpath+dataset, 'rb') as handle:\n",
    "            data_dict = pickle.load(handle)\n",
    "            dataset_temp = datasets_kr.loc[datasets_kr.name==dataset[:-7]]\n",
    "            time_diff=dataset_temp.end-dataset_temp.start\n",
    "            data_dict['duration'] =  time_diff.iloc[0].seconds\n",
    "            data_dict['trigger_rate']=dataset_temp.iloc[0].trigger__events_built\n",
    "        dicts_datasets_kr[key]=data_dict\n",
    "        \n",
    "    elif dataset_names_bkg.isin([dataset[:-7]]).any():\n",
    "        with open(simpath+dataset, 'rb') as handle:\n",
    "            data_dict = pickle.load(handle)\n",
    "            dataset_temp = datasets_bkg.loc[datasets_bkg.name==dataset[:-7]]\n",
    "            time_diff=dataset_temp.end-dataset_temp.start\n",
    "            data_dict['duration'] =  time_diff.iloc[0].seconds\n",
    "            data_dict['trigger_rate']=dataset_temp.iloc[0].trigger__events_built\n",
    "        dicts_datasets_bkg[key]=data_dict\n",
    "    \n",
    "#df_PI=pd.concat(df_PI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Krypton 89\n",
      "Background 901\n",
      "Radon 71\n"
     ]
    }
   ],
   "source": [
    "dict_source_sr0={'Radon' : dicts_datasets_rn, 'Background' : dicts_datasets_bkg, 'Krypton': dicts_datasets_kr,}\n",
    "for source, dict_data in dict_source_sr0.items():\n",
    "    print(source, len(dict_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract Hist from pre-processed pickles: SR1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed data available selection: 0 rows removed (100.00% passed)\n",
      "Source in place selection: 7229 rows removed (4.56% passed)\n",
      "Source in place selection: 1185 rows removed (84.35% passed)\n",
      "Source in place selection: 7119 rows removed (6.01% passed)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/project/lgrandi/anaconda3/envs/pax_head/lib/python3.4/site-packages/hax-2.5.0-py3.4.egg/hax/cuts.py:130: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  d.cut_history = prev_cuts + [passthrough_dict]\n"
     ]
    }
   ],
   "source": [
    "datasets = hax.runs.datasets \n",
    "datasets = hax.runs.tags_selection(include=['*sciencerun1'],\n",
    "                                  exclude=['bad','messy', 'test',\n",
    "                                           'nofield','lowfield',\n",
    "                                           'commissioning', 'flash',\n",
    "                                           'pmttrip','trip','_pmttrip',\n",
    "                                           'source_opening',\n",
    "                                           ],\n",
    "                                  )\n",
    "datasets= hax.cuts.selection(datasets, datasets['location'] != '', 'Processed data available')\n",
    "\n",
    "#Radon\n",
    "datasets_rn = hax.cuts.selection(datasets, datasets['source__type']=='Rn220', 'Source in place')\n",
    "dataset_names_rn = datasets_rn['name']\n",
    "\n",
    "#Bkg\n",
    "datasets_bkg = hax.cuts.selection(datasets, datasets['source__type']=='none', 'Source in place')\n",
    "dataset_names_bkg = datasets_bkg['name']\n",
    "\n",
    "#Krypton\n",
    "datasets_kr = hax.cuts.selection(datasets, datasets['source__type']=='Kr83m', 'Source in place')\n",
    "dataset_names_kr = datasets_kr['name']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/5707 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching for Processed Pickle Files\n",
      "Found 5707 datasets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5707/5707 [11:10<00:00,  8.51it/s]\n",
      "  1%|          | 49/5707 [00:00<00:11, 486.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 5707 processed files \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5707/5707 [05:26<00:00, 17.49it/s] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 43s, sys: 1min 25s, total: 3min 8s\n",
      "Wall time: 16min 37s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print('Searching for Processed Pickle Files')\n",
    "#simpath= '/project/lgrandi/jpienaar/cache_files/'\n",
    "simpath= '/scratch/midway2/jpienaar/cache_files/'\n",
    "\n",
    "#Find datasets\n",
    "datasets = get_file_list(simpath, '*dt.pkl', ' ')\n",
    "print('Found %d datasets' % len(datasets))\n",
    "\n",
    "processed_list=[]\n",
    "for dataset in tqdm(datasets):\n",
    "    with open(simpath+dataset, 'rb') as handle:\n",
    "        data_temp = pickle.load(handle)\n",
    "        try:\n",
    "            data_temp['version']\n",
    "            processed_list.append(dataset)\n",
    "        except KeyError:\n",
    "            continue\n",
    "print(\"Found %i processed files \" %len(processed_list))\n",
    "datasets=processed_list\n",
    "\n",
    "dicts_datasets_rn={}\n",
    "dicts_datasets_kr={}\n",
    "dicts_datasets_bkg={}\n",
    "for dataset in tqdm(datasets[:]):\n",
    "    key=dataset[:-7]\n",
    "    if dataset_names_rn.isin([dataset[:-7]]).any():\n",
    "        with open(simpath+dataset, 'rb') as handle:\n",
    "            data_dict = pickle.load(handle)\n",
    "            dataset_temp = datasets_rn.loc[datasets_rn.name==dataset[:-7]]\n",
    "            time_diff=dataset_temp.end-dataset_temp.start\n",
    "            data_dict['duration'] =  time_diff.iloc[0].seconds\n",
    "            data_dict['trigger_rate']=dataset_temp.iloc[0].trigger__events_built\n",
    "        dicts_datasets_rn[key]=data_dict\n",
    "              \n",
    "    elif dataset_names_kr.isin([dataset[:-7]]).any():\n",
    "        with open(simpath+dataset, 'rb') as handle:\n",
    "            data_dict = pickle.load(handle)\n",
    "            dataset_temp = datasets_kr.loc[datasets_kr.name==dataset[:-7]]\n",
    "            time_diff=dataset_temp.end-dataset_temp.start\n",
    "            data_dict['duration'] =  time_diff.iloc[0].seconds\n",
    "            data_dict['trigger_rate']=dataset_temp.iloc[0].trigger__events_built\n",
    "        dicts_datasets_kr[key]=data_dict\n",
    "        \n",
    "    elif dataset_names_bkg.isin([dataset[:-7]]).any():\n",
    "        with open(simpath+dataset, 'rb') as handle:\n",
    "            data_dict = pickle.load(handle)\n",
    "            dataset_temp = datasets_bkg.loc[datasets_bkg.name==dataset[:-7]]\n",
    "            time_diff=dataset_temp.end-dataset_temp.start\n",
    "            data_dict['duration'] =  time_diff.iloc[0].seconds\n",
    "            data_dict['trigger_rate']=dataset_temp.iloc[0].trigger__events_built\n",
    "        dicts_datasets_bkg[key]=data_dict\n",
    "    \n",
    "#df_PI=pd.concat(df_PI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Krypton 297\n",
      "Background 2059\n",
      "Radon 200\n"
     ]
    }
   ],
   "source": [
    "dict_source_sr1={'Krypton': dicts_datasets_kr,\n",
    "                 'Radon' : dicts_datasets_rn,\n",
    "                 'Background' : dicts_datasets_bkg\n",
    "                }\n",
    "for source, dict_data in dict_source_sr1.items():\n",
    "    print(source, len(dict_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/project/lgrandi/anaconda3/envs/pax_head/lib/python3.4/site-packages/hax-2.5.0-py3.4.egg/hax/cuts.py:130: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  d.cut_history = prev_cuts + [passthrough_dict]\n",
      "100%|██████████| 89/89 [00:00<00:00, 1909.72it/s]\n",
      "  0%|          | 0/901 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Krypton\n",
      "Source in place selection: 19791 rows removed (7.37% passed)\n",
      "Background\n",
      "Source in place selection: 7082 rows removed (66.85% passed)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 901/901 [00:00<00:00, 2060.49it/s]\n",
      "100%|██████████| 71/71 [00:00<00:00, 2916.53it/s]\n",
      "100%|██████████| 297/297 [00:00<00:00, 2781.97it/s]\n",
      "  0%|          | 0/2059 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Radon\n",
      "Source in place selection: 19803 rows removed (7.31% passed)\n",
      "Krypton\n",
      "Source in place selection: 19791 rows removed (7.37% passed)\n",
      "Background\n",
      "Source in place selection: 7082 rows removed (66.85% passed)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2059/2059 [00:00<00:00, 2598.61it/s]\n",
      "100%|██████████| 200/200 [00:00<00:00, 2438.44it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Radon\n",
      "Source in place selection: 19803 rows removed (7.31% passed)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Plots average lifetime over a run, versus run number\n",
    "\n",
    "#plt.figure(figsize=(10, 8))\n",
    "source_key={'Radon': 'Rn220', 'Krypton': 'Kr83m', 'Background': 'none'}\n",
    "colours=['blue', 'orange', 'green']\n",
    "index=0\n",
    "_xmax=10**8\n",
    "_tpoints=np.linspace(0, _xmax, 201)\n",
    "_xmin=_tpoints[1]\n",
    "_ymax=1.7e-7\n",
    "livet_hist=0\n",
    "events=0\n",
    "source_dfs_sr0={}\n",
    "source_dfs_sr1={}\n",
    "\n",
    "for source, dict_data in dict_source_sr0.items():\n",
    "    print(source)\n",
    "    #plt.figure(figsize=(10, 8))\n",
    "    \n",
    "    average_array=[]\n",
    "    datasets_array=[]\n",
    "    event_rate_array=[]\n",
    "    livetime_array=[]\n",
    "    trigger_rate_array=[]\n",
    "    \n",
    "    datasets = hax.runs.datasets\n",
    "    datasets_source = hax.cuts.selection(datasets, datasets['source__type']==source_key[source], 'Source in place')\n",
    "\n",
    "    for key, data in tqdm(dict_data.items()):\n",
    "        livet_hist\n",
    "        livet_hist=data['livet_hist']\n",
    "        events_hist=data['deltat']\n",
    "        events=data['events']\n",
    "        duration=data['duration']\n",
    "        trigger_rate=data['trigger_rate']\n",
    "\n",
    "        livet_hist=livet_hist.sum('s2_area')\n",
    "        livet_hist=livet_hist/events\n",
    "        \n",
    "        events_hist=events_hist.sum('s2_area')\n",
    "        events_hist=events_hist/events\n",
    "        \n",
    "        event_rate_array.append(events/duration)\n",
    "        trigger_rate_array.append(trigger_rate/duration)\n",
    "        average_array.append(np.average(events_hist[100:]))\n",
    "        livetime_array.append(np.average(livet_hist[100:]))\n",
    "        datasets_array.append(key)\n",
    "        \n",
    "    source_datasets=datasets.loc[datasets.name.isin(datasets_array)]\n",
    "    assert len(average_array)==len(source_datasets)\n",
    "    #source_datasets['average_livetime']=average_array\n",
    "    test_df=pd.DataFrame({'name': datasets_array, 'average': average_array, 'event_rate': event_rate_array, \n",
    "                         'trigger_rate': trigger_rate_array, 'livetime': livetime_array})\n",
    "    test_df=pd.merge(source_datasets, test_df, on='name', how='outer')\n",
    "    \n",
    "    source_dfs_sr0[source]=test_df\n",
    "\n",
    "livet_hist=0\n",
    "events=0   \n",
    "for source, dict_data in dict_source_sr1.items():\n",
    "    print(source)\n",
    "    #plt.figure(figsize=(10, 8))\n",
    "    \n",
    "    average_array=[]\n",
    "    datasets_array=[]\n",
    "    event_rate_array=[]\n",
    "    livetime_array=[]\n",
    "    trigger_rate_array=[]\n",
    "    \n",
    "    datasets = hax.runs.datasets\n",
    "    datasets_source = hax.cuts.selection(datasets, datasets['source__type']==source_key[source], 'Source in place')\n",
    "\n",
    "    for key, data in tqdm(dict_data.items()):\n",
    "        livet_hist\n",
    "        livet_hist=data['livet_hist']\n",
    "        events_hist=data['deltat']\n",
    "        events=data['events']\n",
    "        duration=data['duration']\n",
    "        trigger_rate=data['trigger_rate']\n",
    "\n",
    "        livet_hist=livet_hist.sum('s2_area')\n",
    "        livet_hist=livet_hist/events\n",
    "        \n",
    "        events_hist=events_hist.sum('s2_area')\n",
    "        events_hist=events_hist/events\n",
    "        \n",
    "        event_rate_array.append(events/duration)\n",
    "        trigger_rate_array.append(trigger_rate/duration)\n",
    "        average_array.append(np.average(events_hist[100:]))\n",
    "        livetime_array.append(np.average(livet_hist[100:]))\n",
    "        datasets_array.append(key)\n",
    "        \n",
    "    source_datasets=datasets.loc[datasets.name.isin(datasets_array)]\n",
    "    assert len(average_array)==len(source_datasets)\n",
    "    #source_datasets['average_livetime']=average_array\n",
    "    test_df=pd.DataFrame({'name': datasets_array, 'average': average_array, 'event_rate': event_rate_array, \n",
    "                         'trigger_rate': trigger_rate_array, 'livetime': livetime_array})\n",
    "    test_df=pd.merge(source_datasets, test_df, on='name', how='outer')\n",
    "                   \n",
    "    source_dfs_sr1[source]=test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ysvjmm2/YvXs3Ho+H3bt3U1RURHFxMT169DhhT9bIkSMpKiri22+/Dep5HTp0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\nMzMzM7MCcXNmZmZmViD/D40eelGdq/F5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f2b1cea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bal23zalwwubr60tycjI///wzs2fP5hk31N8oKy9V1RYvXszvv/+O0+lky5Yt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\n/zUQSR6o9zruAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f2b4f4470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PPvkkP/7xj33t33zzDb/85S85cuQIABdeeGGdxQY1+vbty80338zzzz/PQw89xHPPPcdV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QMYxHvB4LimOiZMgKde2cI/P7yErky407hbN3s6wzETqyaS6m2HfAwa+m6a2ZXp4r4t1/OtWy/i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\nImXkvwHILDfZQy0ojgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f2b25c1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NB1Dtu9rpjTZRf8lpNl9xJy0rvHNmuudxX7SPhvqGlGPFKHQJrQspGBQpc353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\nDgEAGF8rvavOpUK36gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f4b85e5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "index=0\n",
    "for key, df in source_dfs_sr0.items():\n",
    "    plt.plot(df['start'], df['average'], marker='o', linewidth=0, color=colours[index], label=key+'SR0')\n",
    "    index+=1\n",
    "index=0\n",
    "for key, df in source_dfs_sr1.items():\n",
    "    plt.plot(df['start'], df['average'], marker='o', linewidth=0, color=colours[index], label=key+'SR1', alpha =0.5)\n",
    "    index+=1\n",
    "plt.xlabel('Date', fontsize=12)\n",
    "#plt.xlim([datetime.date(2017, 2, 3), datetime.date(2017, 2, 6)])\n",
    "#plt.ylim([0, 0.01])\n",
    "plt.legend(loc='best')\n",
    "plt.ylabel('Average Peak Rate', fontsize=12)\n",
    "plt.xticks(rotation='vertical')\n",
    "#plt.savefig(\"fig_date_livet.png\", bboxes='tight')\n",
    "#plt.savefig(\"fig_date_livet.pdf\", bboxes='tight')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "index=0\n",
    "for key, df in source_dfs_sr0.items():\n",
    "    plt.plot(df['trigger_rate'], df['event_rate'], marker='o', \n",
    "             linewidth=0, color=colours[index], label=key+'SR0')\n",
    "    index+=1\n",
    "index=0\n",
    "for key, df in source_dfs_sr1.items():\n",
    "    plt.plot(df['trigger_rate'], df['event_rate'], marker='o', \n",
    "             linewidth=0, color=colours[index], label=key+'SR1', alpha =0.5)\n",
    "    index+=1\n",
    "plt.xlabel('Trigger Rate', fontsize=12)\n",
    "#plt.xlim([datetime.date(2017, 2, 3), datetime.date(2017, 2, 6)])\n",
    "#plt.ylim([0, 5000])\n",
    "plt.legend(loc='best')\n",
    "plt.ylabel('Event Rate', fontsize=12)\n",
    "plt.xticks(rotation='vertical')\n",
    "#plt.savefig(\"fig_date_livet.png\", bboxes='tight')\n",
    "#plt.savefig(\"fig_date_livet.pdf\", bboxes='tight')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "index=0\n",
    "for key, df in source_dfs_sr0.items():\n",
    "    plt.plot(df['trigger_rate'], df['average'], marker='o', \n",
    "             linewidth=0, color=colours[index], label=key+'SR0')\n",
    "    index+=1\n",
    "index=0\n",
    "for key, df in source_dfs_sr1.items():\n",
    "    plt.plot(df['trigger_rate'], df['average'], marker='o', \n",
    "             linewidth=0, color=colours[index], label=key+'SR1', alpha =0.5)\n",
    "    index+=1\n",
    "plt.xlabel('Trigger Rate', fontsize=12)\n",
    "#plt.xlim([datetime.date(2017, 2, 3), datetime.date(2017, 2, 6)])\n",
    "#plt.ylim([0, 50000])\n",
    "plt.legend(loc='best')\n",
    "plt.ylabel('Average Peak Rate', fontsize=12)\n",
    "plt.xticks(rotation='vertical')\n",
    "#plt.savefig(\"fig_date_livet.png\", bboxes='tight')\n",
    "#plt.savefig(\"fig_date_livet.pdf\", bboxes='tight')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "index=0\n",
    "for key, df in source_dfs_sr0.items():\n",
    "    plt.plot(df['trigger_rate'], df['average']/df['livetime'], marker='o', \n",
    "             linewidth=0, color=colours[index], label=key+'SR0')\n",
    "    index+=1\n",
    "index=0\n",
    "for key, df in source_dfs_sr1.items():\n",
    "    plt.plot(df['trigger_rate'], df['average']/df['livetime'], marker='o', \n",
    "             linewidth=0, color=colours[index], label=key+'SR1', alpha =0.5)\n",
    "    index+=1\n",
    "plt.xlabel('Trigger Rate', fontsize=12)\n",
    "#plt.xlim([0, 10])\n",
    "plt.ylim([0, 6.5*10**-6])\n",
    "plt.legend(loc='best')\n",
    "plt.ylabel('Corrected Average Peak Rate', fontsize=12)\n",
    "plt.xticks(rotation='vertical')\n",
    "#plt.savefig(\"fig_date_livet.png\", bboxes='tight')\n",
    "#plt.savefig(\"fig_date_livet.pdf\", bboxes='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Livetime - all events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Plots Live Time Fraction#\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "colours=['blue', 'orange', 'green', 'magenta', 'cyan', 'red']\n",
    "index=0\n",
    "_xmax=10**8\n",
    "_tpoints=np.linspace(0, _xmax, 201)\n",
    "_xmin=_tpoints[1]\n",
    "_ymax=1.7e-7\n",
    "events_hist=0\n",
    "events=0\n",
    "plt.set_cmap(\"Blues\")\n",
    "\"\"\"\n",
    "for source, dict_data in dict_source_sr0.items():\n",
    "    print(source)\n",
    "    del events_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        try:\n",
    "            events_hist\n",
    "            events_hist+=data['livet_hist']\n",
    "            weight_hist+=data['livet_weights']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            events_hist=data['livet_hist']\n",
    "            weight_hist=data['livet_weights']\n",
    "            events=data['events']\n",
    "        except KeyError:\n",
    "            print(source, key)\n",
    "    \n",
    "    events_hist=events_hist.sum('s2_area')\n",
    "    events_hist=events_hist\n",
    "    weight_hist=weight_hist.sum('s2_area')\n",
    "    weight_hist=weight_hist\n",
    "\n",
    "    #Confidence Bands\n",
    "    x = _tpoints\n",
    "    x = np.sort(np.array(list(x[1:]) + list(x[:-1])))\n",
    "    y1=events_hist-np.sqrt(weight_hist)\n",
    "    y2=events_hist+np.sqrt(weight_hist)\n",
    "    y_single = np.repeat(y1[:], 2)\n",
    "    y_total = np.repeat(y2[:], 2)\n",
    "        \n",
    "    #Plot\n",
    "    plt.step(_tpoints[1:], events_hist/events, linewidth=3,  color=colours[index], label=source+' SR0')\n",
    "    plt.fill_between(x, y_single/events, y_total/events, facecolor=colours[index], interpolate=True, alpha = 0.25)\n",
    "    index+=1\n",
    "    \n",
    "    print(events)\n",
    "\"\"\"   \n",
    "for source, dict_data in dict_source_sr1.items():\n",
    "    print(source)\n",
    "    del events_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        try:\n",
    "            events_hist\n",
    "            events_hist+=data['livet_hist']\n",
    "            weight_hist+=data['livet_weights']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            events_hist=data['livet_hist']\n",
    "            weight_hist=data['livet_weights']\n",
    "            events=data['events']\n",
    "        except KeyError:\n",
    "            print(source, key)\n",
    "    \n",
    "    events_hist=events_hist.sum('s2_area')\n",
    "    events_hist=events_hist\n",
    "    weight_hist=weight_hist.sum('s2_area')\n",
    "    weight_hist=weight_hist\n",
    "\n",
    "    #Confidence Bands\n",
    "    x = _tpoints\n",
    "    x = np.sort(np.array(list(x[1:]) + list(x[:-1])))\n",
    "    y1=events_hist-np.sqrt(weight_hist)\n",
    "    y2=events_hist+np.sqrt(weight_hist)\n",
    "    y_single = np.repeat(y1[:], 2)\n",
    "    y_total = np.repeat(y2[:], 2)\n",
    "        \n",
    "    #Plot\n",
    "    plt.step(_tpoints[1:], events_hist/events, linewidth=3,  color=colours[index], label=source+' SR1')\n",
    "    plt.fill_between(x, y_single/events, y_total/events, facecolor=colours[index], interpolate=True, alpha = 0.25)\n",
    "    index+=1\n",
    "    \n",
    "    print(events)\n",
    "\n",
    "\n",
    "plt.xlim([0, _xmax])\n",
    "plt.xlabel('Time Since S2 [ns]', fontsize=12)\n",
    "plt.ylabel('Live Time Fraction [/bin]', fontsize=12)\n",
    "axes = plt.gca()\n",
    "axes.tick_params(width=2, length=10)\n",
    "plt.legend(loc='best', fontsize=12)\n",
    "#plt.savefig(\"fig_livet_example.png\", bboxes='tight')\n",
    "#plt.savefig(\"fig_livet_example.pdf\", bboxes='tight')\n",
    "#plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uncorrected Peak Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Plots uncorrected event with error added#\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "index=0\n",
    "window_length=10**8\n",
    "_tpoints=np.linspace(0, window_length, 201)\n",
    "_xmin=_tpoints[1]\n",
    "_ymax=1.7e-7\n",
    "events_hist=0\n",
    "events=0\n",
    "index=0\n",
    "plt.set_cmap(\"Blues\")\n",
    "\"\"\"\n",
    "for source, dict_data in dict_source_sr0.items():\n",
    "    print(source)\n",
    "    del events_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        try:\n",
    "            events_hist\n",
    "            events_hist+=data['deltat']\n",
    "            weight_hist+=data['deltat_weights']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            events_hist=data['deltat']\n",
    "            weight_hist=data['deltat_weights']\n",
    "            events=data['events']\n",
    "        except KeyError:\n",
    "            print(source, key)\n",
    "        \n",
    "    events_hist=events_hist.sum('s2_area')\n",
    "    events_hist=events_hist\n",
    "    weight_hist=weight_hist.sum('s2_area')\n",
    "    weight_hist=weight_hist\n",
    "\n",
    "    #Confidence Bands\n",
    "    x = _tpoints\n",
    "    x = np.sort(np.array(list(x[1:]) + list(x[:-1])))\n",
    "    y1=events_hist-np.sqrt(weight_hist)\n",
    "    y2=events_hist+np.sqrt(weight_hist)\n",
    "    y_single = np.repeat(y1[:], 2)\n",
    "    y_total = np.repeat(y2[:], 2)\n",
    "        \n",
    "    #Plot\n",
    "    plt.step(_tpoints[1:], events_hist/events, linewidth=3,  color=colours[index], label=source+' SR0')\n",
    "    plt.fill_between(x, y_single/events, y_total/events, facecolor=colours[index], interpolate=True, alpha = 0.25)\n",
    "    index+=1\n",
    "    \n",
    "    print(events)\n",
    "\"\"\"    \n",
    "for source, dict_data in dict_source_sr1.items():\n",
    "    print(source)\n",
    "    del events_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        try:\n",
    "            events_hist\n",
    "            events_hist+=data['deltat']\n",
    "            weight_hist+=data['deltat_weights']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            events_hist=data['deltat']\n",
    "            weight_hist=data['deltat_weights']\n",
    "            events=data['events']\n",
    "        except KeyError:\n",
    "            print(source, key)\n",
    "        \n",
    "    events_hist=events_hist.sum('s2_area')\n",
    "    events_hist=events_hist\n",
    "    weight_hist=weight_hist.sum('s2_area')\n",
    "    weight_hist=weight_hist\n",
    "\n",
    "    #Confidence Bands\n",
    "    x = _tpoints\n",
    "    x = np.sort(np.array(list(x[1:]) + list(x[:-1])))\n",
    "    y1=events_hist-np.sqrt(weight_hist)\n",
    "    y2=events_hist+np.sqrt(weight_hist)\n",
    "    y_single = np.repeat(y1[:], 2)\n",
    "    y_total = np.repeat(y2[:], 2)\n",
    "        \n",
    "    #Plot\n",
    "    plt.step(_tpoints[1:], events_hist/events, linewidth=3,  color=colours[index], label=source+' SR1')\n",
    "    plt.fill_between(x, y_single/events, y_total/events, facecolor=colours[index], interpolate=True, alpha = 0.25)\n",
    "    index+=1\n",
    "    \n",
    "    print(events)\n",
    "\n",
    "plt.xlim([0, window_length])\n",
    "plt.xlabel('Time Since S2 [ns]', fontsize=12)\n",
    "plt.ylabel('Uncorrected Peak Rate [ns$^{-1}$]', fontsize=12)\n",
    "axes = plt.gca()\n",
    "axes.tick_params(width=2, length=10)\n",
    "plt.legend(loc='best', fontsize=12)\n",
    "plt.savefig(\"fig_deltat_example.png\", bboxes='tight')\n",
    "plt.savefig(\"fig_deltat_example.pdf\", bboxes='tight')\n",
    "#plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dict_source_sr0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Live Time Corrected Peak Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Plots corrected event with error added#\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "index=0\n",
    "window_length=10**8\n",
    "_tpoints=np.linspace(0, window_length, 201)\n",
    "_xmin=_tpoints[1]\n",
    "_ymax=1.7e-7\n",
    "events_hist=0\n",
    "events=0\n",
    "index=0\n",
    "\"\"\"    \n",
    "for source, dict_data in dict_source_sr0.items():\n",
    "    print(source)\n",
    "    del events_hist\n",
    "    del livet_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        try:\n",
    "            events_hist\n",
    "            events_hist+=data['deltat']\n",
    "            weight_hist+=data['deltat_weights']\n",
    "            livet_hist+=data['livet_hist']\n",
    "            livet_weight+=data['livet_weights']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            events_hist=data['deltat']\n",
    "            weight_hist=data['deltat_weights']\n",
    "            livet_hist=data['livet_hist']\n",
    "            livet_weight=data['livet_weights']\n",
    "            events=data['events']\n",
    "        except KeyError:\n",
    "            print(source, key)\n",
    "        \n",
    "    events_hist=events_hist.sum('s2_area')\n",
    "    weight_hist=weight_hist.sum('s2_area')\n",
    "    livet_hist=livet_hist.sum('s2_area')\n",
    "    livet_weight=livet_weight.sum('s2_area')\n",
    "    \n",
    "    #Error\n",
    "    num_value=np.array(events_hist)\n",
    "    denum_value=np.array(livet_hist)\n",
    "    num_error = np.sqrt(weight_hist)\n",
    "    denum_error = np.sqrt(livet_weight)\n",
    "    e1 = num_error\n",
    "    e2 = denum_error\n",
    "    b1 = num_value\n",
    "    b2 = denum_value\n",
    "    w=b1/b2\n",
    "    \n",
    "    ratio_error = np.abs(((1-2*w)*e1*e1 + w*w*e2*e2)/(b2*b2))       \n",
    "    ratio = num_value/denum_value \n",
    "    \n",
    "    #Confidence Bands\n",
    "    x = _tpoints\n",
    "    x = np.sort(np.array(list(x[1:]) + list(x[:-1])))\n",
    "    y1=ratio-np.sqrt(ratio_error)\n",
    "    y2=ratio+np.sqrt(ratio_error)\n",
    "    y_lower = np.repeat(y1[:], 2)\n",
    "    y_upper = np.repeat(y2[:], 2)\n",
    "        \n",
    "    #Plot\n",
    "    plt.step(_tpoints[1:], ratio*1000000, linewidth=3,  color=colours[index], label=source+' SR0')\n",
    "    plt.fill_between(x, y_lower*1000000, y_upper*1000000, facecolor=colours[index], interpolate=True, alpha = 0.25)\n",
    "    index+=1\n",
    "    \n",
    "    print(events)\n",
    "\"\"\"  \n",
    "for source, dict_data in dict_source_sr1.items():\n",
    "    print(source)\n",
    "    del events_hist\n",
    "    del livet_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        try:\n",
    "            events_hist\n",
    "            events_hist+=data['deltat']\n",
    "            weight_hist+=data['deltat_weights']\n",
    "            livet_hist+=data['livet_hist']\n",
    "            livet_weight+=data['livet_weights']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            events_hist=data['deltat']\n",
    "            weight_hist=data['deltat_weights']\n",
    "            livet_hist=data['livet_hist']\n",
    "            livet_weight=data['livet_weights']\n",
    "            events=data['events']\n",
    "        except KeyError:\n",
    "            print(source, key)\n",
    "        \n",
    "    events_hist=events_hist.sum('s2_area')\n",
    "    weight_hist=weight_hist.sum('s2_area')\n",
    "    livet_hist=livet_hist.sum('s2_area')\n",
    "    livet_weight=livet_weight.sum('s2_area')\n",
    "    \n",
    "    #Error\n",
    "    num_value=np.array(events_hist)\n",
    "    denum_value=np.array(livet_hist)\n",
    "    num_error = np.sqrt(weight_hist)\n",
    "    denum_error = np.sqrt(livet_weight)\n",
    "    e1 = num_error\n",
    "    e2 = denum_error\n",
    "    b1 = num_value\n",
    "    b2 = denum_value\n",
    "    w=b1/b2\n",
    "    \n",
    "    ratio_error = np.abs(((1-2*w)*e1*e1 + w*w*e2*e2)/(b2*b2))       \n",
    "    ratio = num_value/denum_value \n",
    "    \n",
    "    #Confidence Bands\n",
    "    x = _tpoints\n",
    "    x = np.sort(np.array(list(x[1:]) + list(x[:-1])))\n",
    "    y1=ratio-np.sqrt(ratio_error)\n",
    "    y2=ratio+np.sqrt(ratio_error)\n",
    "    y_lower = np.repeat(y1[:], 2)\n",
    "    y_upper = np.repeat(y2[:], 2)\n",
    "        \n",
    "    #Plot\n",
    "    plt.step(_tpoints[1:], ratio*1000000, linewidth=3,  color=colours[index], label=source+' SR1')\n",
    "    plt.fill_between(x, y_lower*1000000, y_upper*1000000, facecolor=colours[index], interpolate=True, alpha = 0.25)\n",
    "    index+=1\n",
    "    \n",
    "    print(events)\n",
    "\n",
    "plt.xlim([_tpoints[5], _tpoints[-2]])\n",
    "plt.ylim([1.5, 6.5])\n",
    "plt.xlabel('Time Since S2 [ns]', fontsize=12)\n",
    "plt.ylabel('Corrected Peak Rate [ns$^{-1}$]', fontsize=12)\n",
    "axes = plt.gca()\n",
    "axes.tick_params(width=2, length=10)\n",
    "plt.legend(loc='best', fontsize=12)\n",
    "plt.savefig(\"fig_peak_rate_100ms.png\", bboxes='tight')\n",
    "plt.savefig(\"fig_peak_rate_100ms.pdf\", bboxes='tight')\n",
    "#plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Questions:\n",
    "    \n",
    " 1.) Why Kr_SR0 higher than Kr_SR1? Probably event rate related. Look at individual calibrations. Should decrease with Kr source strength. If so, need some kind of cut to remove peaks from other events.\n",
    " \n",
    " 2.)Why is Rn_SR0 higher than Rn_SR1. Source has not changed in activity signficantly. Injection procedure might have changed however. Could be lower overall event rate in SR1 if Kr explanation from (1) holds. Simple to check event rate.\n",
    " \n",
    " 3.) Might be different \"decay\" tmes in SR0 and SR1 background. Could be bacground related? More alpha backgrounds in SR0 possibly. Check individual energy lines to be sure\n",
    " \n",
    "Update:\n",
    "\n",
    "After applying AFT cut:\n",
    "\n",
    " 1.) . Kr still processing\n",
    "\n",
    " 2.) Radon SR0 is now lower than Radon SR1, and more pronounced decay. Lower overall rate due to lower e-lifetime. Checks out.\n",
    " \n",
    " 3.) Background more or less equivalent still, though higher rate at small times, posisbly due to higher extraction field and thsu charge yield."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split By Date of Krypton Dataset\n",
    "\n",
    "  * If peak rate is related to event rate, should see decreasing peak rate trend in Krypton data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colours=['blue', 'orange', 'green', 'magenta', 'cyan', 'red', 'grey', 'black', 'brown']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "kr_cal_dates=[datetime.date(2017, 1, 17), datetime.date(2017, 2, 10), datetime.date(2017, 2, 17), \n",
    "              datetime.date(2017, 2, 24), datetime.date(2017, 3, 3), datetime.date(2017, 3, 10), \n",
    "              datetime.date(2017, 3, 20), datetime.date(2017, 4, 7), datetime.date(2017, 4, 20)]\n",
    "\n",
    "date_label=[\"5 Feb\", \"12 Feb\", \"21 Feb\", \"26 Feb\", \"5 Mar\", \"9 Mar\",  \"2 April\", \"16 April\"]\n",
    "print('Load Data')\n",
    "simpath= '/project/lgrandi/jpienaar/cache_files/'\n",
    "#simpath= '/scratch/midway2/jpienaar/cache_files/'\n",
    "\n",
    "#Find datasets\n",
    "datasets = get_file_list(simpath, '*dt.pkl', ' ')\n",
    "print('Found %d datasets' % len(datasets))\n",
    "n_files=len(datasets)\n",
    "\n",
    "dict_kr_calibrations={}\n",
    "for t_index, date in enumerate(kr_cal_dates[:-1]):\n",
    "    df=datasets_kr\n",
    "    df_temp=hax.cuts.selection(df, df.start>kr_cal_dates[t_index], 'TimeCut Low')\n",
    "    df_temp=hax.cuts.selection(df_temp, df_temp.start<kr_cal_dates[t_index+1], 'TimeCut High')\n",
    "    dataset_names=df_temp['name']\n",
    "    dict_calibration={}\n",
    "    for dataset in tqdm(datasets[:]):\n",
    "        key=dataset[:-7]\n",
    "        if dataset_names.isin([dataset[:-7]]).any():\n",
    "            with open(simpath+dataset, 'rb') as handle:\n",
    "                data_dict = pickle.load(handle)\n",
    "                dict_calibration[key]=data_dict\n",
    "    dict_kr_calibrations[date_label[t_index]]=dict_calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "index=0\n",
    "for key, df in source_dfs_sr1.items():\n",
    "    if key == 'Krypton':\n",
    "        for t_index, date in enumerate(kr_cal_dates[:-1]):\n",
    "            print(kr_cal_dates[t_index], kr_cal_dates[t_index+1])\n",
    "            df_temp=hax.cuts.selection(df, df.start>kr_cal_dates[t_index], 'TimeCut Low')\n",
    "            df_temp=hax.cuts.selection(df_temp, df_temp.start<kr_cal_dates[t_index+1], 'TimeCut High')\n",
    "            plt.plot(df_temp['start'], df_temp['average_livetime'], \n",
    "                     marker='o', linewidth=0, color=colours[index], label=key)\n",
    "            index+=1\n",
    "        #plt.plot(df['start'], df['average_livetime'], \n",
    "        #        marker='s', linewidth=0, color='black', label=key, markersize=10)\n",
    "\n",
    "plt.xlabel('Date', fontsize=12)\n",
    "#plt.xlim([datetime.date(2017, 3, 1), datetime.date(2017, 4, 20)])\n",
    "plt.legend(loc='best')\n",
    "plt.ylabel('Average Live Time Fraction', fontsize=12)\n",
    "plt.xticks(rotation='vertical')\n",
    "#plt.savefig(\"fig_date_livet.png\", bboxes='tight')\n",
    "#plt.savefig(\"fig_date_livet.pdf\", bboxes='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Plots corrected event with error added#\n",
    "colours=['blue', 'orange', 'green', 'magenta', 'cyan', 'red', 'grey', 'black', 'brown']\n",
    "plt.figure(figsize=(10, 8))\n",
    "index=0\n",
    "window_length=10**8\n",
    "_tpoints=np.linspace(0, window_length, 201)\n",
    "_xmin=_tpoints[1]\n",
    "_ymax=1.7e-7\n",
    "events_hist=0\n",
    "events=0\n",
    "index=0\n",
    "\n",
    "for source, dict_data in dict_kr_calibrations.items():\n",
    "    print(source, len(dict_data))\n",
    "    del events_hist\n",
    "    #del livet_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        try:\n",
    "            events_hist\n",
    "            events_hist+=data['deltat']\n",
    "            weight_hist+=data['deltat_weights']\n",
    "            livet_hist+=data['livet_hist']\n",
    "            livet_weight+=data['livet_weights']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            events_hist=data['deltat']\n",
    "            weight_hist=data['deltat_weights']\n",
    "            livet_hist=data['livet_hist']\n",
    "            livet_weight=data['livet_weights']\n",
    "            events=data['events']\n",
    "        except KeyError:\n",
    "            print(source, key)\n",
    "    \n",
    "    events_hist=events_hist.sum('s2_area')\n",
    "    weight_hist=weight_hist.sum('s2_area')\n",
    "    livet_hist=livet_hist.sum('s2_area')\n",
    "    livet_weight=livet_weight.sum('s2_area')\n",
    "    #Error\n",
    "    num_value=np.array(events_hist)\n",
    "    denum_value=np.array(livet_hist)\n",
    "    num_error = np.sqrt(weight_hist)\n",
    "    denum_error = np.sqrt(livet_weight)\n",
    "    e1 = num_error\n",
    "    e2 = denum_error\n",
    "    b1 = num_value\n",
    "    b2 = denum_value\n",
    "    w=b1/b2\n",
    "    \n",
    "    ratio_error = np.abs(((1-2*w)*e1*e1 + w*w*e2*e2)/(b2*b2))       \n",
    "    ratio = num_value/denum_value \n",
    "    \n",
    "    #Confidence Bands\n",
    "    x = _tpoints\n",
    "    x = np.sort(np.array(list(x[1:]) + list(x[:-1])))\n",
    "    y1=ratio-np.sqrt(ratio_error)\n",
    "    y2=ratio+np.sqrt(ratio_error)\n",
    "    y_lower = np.repeat(y1[:], 2)\n",
    "    y_upper = np.repeat(y2[:], 2)\n",
    "        \n",
    "    #Plot\n",
    "    plt.step(_tpoints[1:], ratio*1000000, linewidth=3,  color=colours[index], label=source)\n",
    "    plt.fill_between(x, y_lower*1000000, y_upper*1000000, facecolor=colours[index], interpolate=True, alpha = 0.25)\n",
    "    index+=1\n",
    "    \n",
    "    print(events)\n",
    "\n",
    "plt.xlim([_tpoints[5], _tpoints[-2]])\n",
    "plt.ylim([2, 3.75])\n",
    "plt.xlabel('Time Since S2 [ns]', fontsize=12)\n",
    "plt.ylabel('Corrected Peak Rate [ms$^{-1}$]', fontsize=12)\n",
    "axes = plt.gca()\n",
    "axes.tick_params(width=2, length=10)\n",
    "plt.legend(loc='best', fontsize=12)\n",
    "#plt.savefig(\"fig_deltat_example.png\", bboxes='tight')\n",
    "#plt.savefig(\"fig_deltat_example.pdf\", bboxes='tight')\n",
    "#plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.) Peak rate related to event rate seems to be true in Kr data, though 6th injection (Cal 5), is an outlier. Problematic. Decrease trend aslo not consistent with Rb83 half life"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split by Date of Rn Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "rn_cal_dates=[datetime.date(2017, 1, 17), datetime.date(2017, 4, 1), datetime.date(2017, 6, 1), \n",
    "              datetime.date(2017, 7, 1), datetime.date(2017, 8, 1), datetime.date(2017, 9, 1)]\n",
    "\n",
    "print('Load Data')\n",
    "#simpath= '/project/lgrandi/jpienaar/cache_files/'\n",
    "simpath= '/scratch/midway2/jpienaar/cache_files/'\n",
    "\n",
    "date_label=[\"13 Mar\", \"9 May\", \"12 Jun\", \"10 Jul\", \"7 Aug\"]\n",
    "\n",
    "#Find datasets\n",
    "datasets = get_file_list(simpath, '*dt.pkl', ' ')\n",
    "print('Found %d datasets' % len(datasets))\n",
    "n_files=len(datasets)\n",
    "\n",
    "dict_rn_calibrations={}\n",
    "for t_index, date in enumerate(rn_cal_dates[:-1]):\n",
    "    df=datasets_rn\n",
    "    df_temp=hax.cuts.selection(df, df.start>rn_cal_dates[t_index], 'TimeCut Low')\n",
    "    df_temp=hax.cuts.selection(df_temp, df_temp.start<rn_cal_dates[t_index+1], 'TimeCut High')\n",
    "    dataset_names=df_temp['name']\n",
    "    dict_calibration={}\n",
    "    for dataset in tqdm(datasets[:]):\n",
    "        key=dataset[:-7]\n",
    "        if dataset_names.isin([dataset[:-7]]).any():\n",
    "            with open(simpath+dataset, 'rb') as handle:\n",
    "                data_dict = pickle.load(handle)\n",
    "                dict_calibration[key]=data_dict\n",
    "    dict_rn_calibrations[date_label[t_index]]=dict_calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "index=0\n",
    "for key, df in source_dfs_sr1.items():\n",
    "    if key == 'Radon':\n",
    "        for t_index, date in enumerate(rn_cal_dates[:-1]):\n",
    "            print(rn_cal_dates[t_index], rn_cal_dates[t_index+1])\n",
    "            df_temp=hax.cuts.selection(df, df.start>rn_cal_dates[t_index], 'TimeCut Low')\n",
    "            df_temp=hax.cuts.selection(df_temp, df_temp.start<rn_cal_dates[t_index+1], 'TimeCut High')\n",
    "            plt.plot(df_temp['start'], df_temp['average_livetime'], \n",
    "                     marker='o', linewidth=0, color=colours[index], label=key)\n",
    "            index+=1\n",
    "            print(df_temp.iloc[0].start)\n",
    "        #plt.plot(df['start'], df['average_livetime'], \n",
    "        #        marker='s', linewidth=0, color='black', label=key, markersize=10)\n",
    "\n",
    "plt.xlabel('Date', fontsize=12)\n",
    "#plt.xlim([datetime.date(2017, 3, 1), datetime.date(2017, 4, 20)])\n",
    "plt.legend(loc='best')\n",
    "plt.ylabel('Average Live Time Fraction', fontsize=12)\n",
    "plt.xticks(rotation='vertical')\n",
    "#plt.savefig(\"fig_date_livet.png\", bboxes='tight')\n",
    "#plt.savefig(\"fig_date_livet.pdf\", bboxes='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Plots corrected event with error added#\n",
    "colours=['blue', 'orange', 'green', 'magenta', 'cyan', 'red', 'grey', 'black', 'brown']\n",
    "plt.figure(figsize=(10, 8))\n",
    "index=0\n",
    "window_length=10**8\n",
    "_tpoints=np.linspace(0, window_length, 201)\n",
    "_xmin=_tpoints[1]\n",
    "_ymax=1.7e-7\n",
    "events_hist=0\n",
    "events=0\n",
    "index=0\n",
    "\n",
    "for source, dict_data in dict_rn_calibrations.items():\n",
    "    print(source, len(dict_data))\n",
    "    del events_hist\n",
    "    #del livet_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        try:\n",
    "            events_hist\n",
    "            events_hist+=data['deltat']\n",
    "            weight_hist+=data['deltat_weights']\n",
    "            livet_hist+=data['livet_hist']\n",
    "            livet_weight+=data['livet_weights']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            events_hist=data['deltat']\n",
    "            weight_hist=data['deltat_weights']\n",
    "            livet_hist=data['livet_hist']\n",
    "            livet_weight=data['livet_weights']\n",
    "            events=data['events']\n",
    "        except KeyError:\n",
    "            print(source, key)\n",
    "        \n",
    "    events_hist=events_hist.sum('s2_area')\n",
    "    weight_hist=weight_hist.sum('s2_area')\n",
    "    livet_hist=livet_hist.sum('s2_area')\n",
    "    livet_weight=livet_weight.sum('s2_area')\n",
    "    \n",
    "    #Error\n",
    "    num_value=np.array(events_hist)\n",
    "    denum_value=np.array(livet_hist)\n",
    "    num_error = np.sqrt(weight_hist)\n",
    "    denum_error = np.sqrt(livet_weight)\n",
    "    e1 = num_error\n",
    "    e2 = denum_error\n",
    "    b1 = num_value\n",
    "    b2 = denum_value\n",
    "    w=b1/b2\n",
    "    \n",
    "    ratio_error = np.abs(((1-2*w)*e1*e1 + w*w*e2*e2)/(b2*b2))       \n",
    "    ratio = num_value/denum_value \n",
    "    \n",
    "    #Confidence Bands\n",
    "    x = _tpoints\n",
    "    x = np.sort(np.array(list(x[1:]) + list(x[:-1])))\n",
    "    y1=ratio-np.sqrt(ratio_error)\n",
    "    y2=ratio+np.sqrt(ratio_error)\n",
    "    y_lower = np.repeat(y1[:], 2)\n",
    "    y_upper = np.repeat(y2[:], 2)\n",
    "        \n",
    "    #Plot\n",
    "    plt.step(_tpoints[1:], ratio*1000000, linewidth=3,  color=colours[index], label=source)\n",
    "    plt.fill_between(x, y_lower*1000000, y_upper*1000000, facecolor=colours[index], interpolate=True, alpha = 0.25)\n",
    "    index+=1\n",
    "    \n",
    "    print(events)\n",
    "\n",
    "plt.xlim([_tpoints[5], _tpoints[-2]])\n",
    "plt.ylim([3.2, 7.5])\n",
    "plt.xlabel('Time Since S2 [ns]', fontsize=12)\n",
    "plt.ylabel('Corrected Peak Rate [ms$^{-1}$]', fontsize=12)\n",
    "axes = plt.gca()\n",
    "axes.tick_params(width=2, length=10)\n",
    "plt.legend(loc='best', fontsize=12)\n",
    "#plt.savefig(\"fig_deltat_example.png\", bboxes='tight')\n",
    "#plt.savefig(\"fig_deltat_example.pdf\", bboxes='tight')\n",
    "#plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rn data also shows decreasing trend in peak rate. But why though? This would not be expected, as Th228 does not decay over months timescale, and increasing e-lifetime should lead to higher not lower single electron rate.\n",
    "\n",
    "Look at Bkg data. De-couples \"opening the valve\" effect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split Bkg data by Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "bkg_cal_dates=[datetime.date(2017, 1, 17), datetime.date(2017, 3, 1), datetime.date(2017, 4, 1), \n",
    "              datetime.date(2017, 5, 1), datetime.date(2017, 6, 1), datetime.date(2017, 7, 1)]\n",
    "\n",
    "print('Load Data')\n",
    "simpath= '/project/lgrandi/jpienaar/cache_files/'\n",
    "#simpath= '/scratch/midway2/jpienaar/cache_files/'\n",
    "\n",
    "date_label=['Feb', 'Mar', 'Apr', 'May', 'Jun']\n",
    "\n",
    "#Find datasets\n",
    "datasets = get_file_list(simpath, '*dt.pkl', ' ')\n",
    "print('Found %d datasets' % len(datasets))\n",
    "n_files=len(datasets)\n",
    "\n",
    "dict_bkg_calibrations={}\n",
    "for t_index, date in enumerate(bkg_cal_dates[:-1]):\n",
    "    df=datasets_bkg\n",
    "    df_temp=hax.cuts.selection(df, df.start>bkg_cal_dates[t_index], 'TimeCut Low')\n",
    "    df_temp=hax.cuts.selection(df_temp, df_temp.start<bkg_cal_dates[t_index+1], 'TimeCut High')\n",
    "    dataset_names=df_temp['name']\n",
    "    dict_calibration={}\n",
    "    for dataset in tqdm(datasets[:]):\n",
    "        key=dataset[:-7]\n",
    "        if dataset_names.isin([dataset[:-7]]).any():\n",
    "            with open(simpath+dataset, 'rb') as handle:\n",
    "                data_dict = pickle.load(handle)\n",
    "                dict_calibration[key]=data_dict\n",
    "    dict_bkg_calibrations[date_label[t_index]]=dict_calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "index=0\n",
    "for key, df in source_dfs_sr1.items():\n",
    "    if key == 'Background':\n",
    "        for t_index, date in enumerate(bkg_cal_dates[:-1]):\n",
    "            print(bkg_cal_dates[t_index], bkg_cal_dates[t_index+1])\n",
    "            df_temp=hax.cuts.selection(df, df.start>bkg_cal_dates[t_index], 'TimeCut Low')\n",
    "            df_temp=hax.cuts.selection(df_temp, df_temp.start<bkg_cal_dates[t_index+1], 'TimeCut High')\n",
    "            plt.plot(df_temp['start'], df_temp['average_livetime'], \n",
    "                     marker='o', linewidth=0, color=colours[index], label=key)\n",
    "            index+=1\n",
    "        #plt.plot(df['start'], df['average_livetime'], \n",
    "        #        marker='s', linewidth=0, color='black', label=key, markersize=10)\n",
    "\n",
    "plt.xlabel('Date', fontsize=12)\n",
    "#plt.xlim([datetime.date(2017, 2, 1), datetime.date(2017, 3, 1)])\n",
    "plt.ylim([0, 0.005])\n",
    "plt.legend(loc='best')\n",
    "plt.ylabel('Average Live Time Fraction', fontsize=12)\n",
    "plt.xticks(rotation='vertical')\n",
    "#plt.savefig(\"fig_date_livet.png\", bboxes='tight')\n",
    "#plt.savefig(\"fig_date_livet.pdf\", bboxes='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Plots corrected event with error added#\n",
    "colours=['blue', 'orange', 'green', 'magenta', 'cyan', 'red', 'grey', 'black', 'brown']\n",
    "plt.figure(figsize=(10, 8))\n",
    "index=0\n",
    "window_length=10**8\n",
    "_tpoints=np.linspace(0, window_length, 201)\n",
    "_xmin=_tpoints[1]\n",
    "_ymax=1.7e-7\n",
    "events_hist=0\n",
    "events=0\n",
    "index=0\n",
    "\n",
    "for source, dict_data in dict_bkg_calibrations.items():\n",
    "    print(source, len(dict_data))\n",
    "    del events_hist\n",
    "    #del livet_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        try:\n",
    "            events_hist\n",
    "            events_hist+=data['deltat']\n",
    "            weight_hist+=data['deltat_weights']\n",
    "            livet_hist+=data['livet_hist']\n",
    "            livet_weight+=data['livet_weights']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            events_hist=data['deltat']\n",
    "            weight_hist=data['deltat_weights']\n",
    "            livet_hist=data['livet_hist']\n",
    "            livet_weight=data['livet_weights']\n",
    "            events=data['events']\n",
    "        except KeyError:\n",
    "            print(source, key)\n",
    "        \n",
    "    events_hist=events_hist.sum('s2_area')\n",
    "    weight_hist=weight_hist.sum('s2_area')\n",
    "    livet_hist=livet_hist.sum('s2_area')\n",
    "    livet_weight=livet_weight.sum('s2_area')\n",
    "    \n",
    "    #Error\n",
    "    num_value=np.array(events_hist)\n",
    "    denum_value=np.array(livet_hist)\n",
    "    num_error = np.sqrt(weight_hist)\n",
    "    denum_error = np.sqrt(livet_weight)\n",
    "    e1 = num_error\n",
    "    e2 = denum_error\n",
    "    b1 = num_value\n",
    "    b2 = denum_value\n",
    "    w=b1/b2\n",
    "    \n",
    "    ratio_error = np.abs(((1-2*w)*e1*e1 + w*w*e2*e2)/(b2*b2))       \n",
    "    ratio = num_value/denum_value \n",
    "    \n",
    "    #Confidence Bands\n",
    "    x = _tpoints\n",
    "    x = np.sort(np.array(list(x[1:]) + list(x[:-1])))\n",
    "    y1=ratio-np.sqrt(ratio_error)\n",
    "    y2=ratio+np.sqrt(ratio_error)\n",
    "    y_lower = np.repeat(y1[:], 2)\n",
    "    y_upper = np.repeat(y2[:], 2)\n",
    "        \n",
    "    #Plot\n",
    "    plt.step(_tpoints[1:], ratio*1000000, linewidth=3,  color=colours[index], label=source)\n",
    "    plt.fill_between(x, y_lower*1000000, y_upper*1000000, facecolor=colours[index], interpolate=True, alpha = 0.25)\n",
    "    index+=1\n",
    "    \n",
    "    print(events)\n",
    "\n",
    "plt.xlim([_tpoints[5], _tpoints[-2]])\n",
    "plt.ylim([1.0, 6])\n",
    "plt.xlabel('Time Since S2 [ns]', fontsize=12)\n",
    "plt.ylabel('Corrected Peak Rate [ms$^{-1}$]', fontsize=12)\n",
    "axes = plt.gca()\n",
    "axes.tick_params(width=2, length=10)\n",
    "plt.legend(loc='best', fontsize=12)\n",
    "#plt.savefig(\"fig_deltat_example.png\", bboxes='tight')\n",
    "#plt.savefig(\"fig_deltat_example.pdf\", bboxes='tight')\n",
    "#plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From background seems different \"rates\" between different radon and krypton calibrations much more likely to be correlated to different \"valve\" conditions than enything else"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two-D plots. No Cuts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Todo: Normalize next 3 cells by number of S2s of given size \n",
    " * Not needed, livetime should take care of that already "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "##Live Time Corrected 2d Plots##\n",
    "#plt.figure(figsize=(10, 8))\n",
    "index=0\n",
    "_xmax=10**8\n",
    "_tpoints=np.linspace(0, _xmax, 201)\n",
    "_xmin=_tpoints[1]\n",
    "_ymax=1.7e-7\n",
    "livet_hist=0\n",
    "events=0\n",
    "plt.set_cmap(\"Oranges\")\n",
    "\n",
    "_s2bins=50\n",
    "_tbins=200\n",
    "for source, dict_data in dict_source_sr1.items():\n",
    "    print(source, len(dict_data))\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    del events_hist\n",
    "    del livet_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        try:\n",
    "            events_hist\n",
    "            events_hist+=data['deltat']\n",
    "            livet_hist+=data['livet_hist']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            events_hist=data['deltat']\n",
    "            livet_hist=data['livet_hist']\n",
    "            events=data['events']\n",
    "    \n",
    "   \n",
    "    s2_norm_hist=events_hist.sum('delta_T')\n",
    "    \n",
    "    blank_hist=events_hist.similar_blank_histogram()\n",
    "        \n",
    "    for idt in range(_tbins):\n",
    "        for ids2 in range (_s2bins):\n",
    "            #blank_hist[idt][ids2]=\n",
    "            bin_content=events_hist[idt][ids2]/livet_hist[idt][ids2]\n",
    "            if np.isnan(bin_content):\n",
    "                blank_hist[idt][ids2]=0\n",
    "            else:\n",
    "                blank_hist[idt][ids2]=bin_content\n",
    "    \n",
    "    blank_hist=blank_hist*10**6 #ms adjustment\n",
    "    blank_hist.plot(cblabel = 'Corrected Event Rate [ms$^{-1}$.S2$^{-1}$]', vmax=1*10**1, vmin =0)\n",
    "    plt.xlabel('Time Since S2 [ns]', fontsize=12)\n",
    "    plt.ylabel('S2 Size [Log10(PE)]', fontsize=12)\n",
    "    #plt.savefig(\"fig_%s_2d_rate_corrected.png\" %source, bboxes='tight')\n",
    "    #plt.savefig(\"fig_%s_2d_rate_corrected.pdf\" %source, bboxes='tight')\n",
    "    plt.show()    \n",
    "\n",
    "    \n",
    "   \n",
    "    #new_hist.plot(label=source, color=colours[index])\n",
    "    #index+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#plt.figure(figsize=(10, 8))\n",
    "index=0\n",
    "_xmax=10**8\n",
    "_tpoints=np.linspace(0, _xmax, 201)\n",
    "_xmin=_tpoints[1]\n",
    "_ymax=1.7e-7\n",
    "events_hist=0\n",
    "events=0\n",
    "plt.set_cmap(\"Oranges\")\n",
    "for source, dict_data in dict_source_sr1.items():\n",
    "    print(source)\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    del events_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        try:\n",
    "            events_hist\n",
    "            events_hist+=data['deltat']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            events_hist=data['deltat']\n",
    "            events=data['events']\n",
    "    \n",
    "    new_hist=events_hist/events\n",
    "    new_hist.plot(cblabel = 'Uncorrected Events [ns$^{-1}$.S2$^{-1}$]', norm=LogNorm(), vmin=10**-11, vmax=10**-7)\n",
    "    plt.xlabel('Time Since S2 [ns]', fontsize=12)\n",
    "    plt.ylabel('S2 Size [Log10(PE)]', fontsize=12)\n",
    "    #plt.savefig(\"fig_%s_2d_rate.png\" %source, bboxes='tight')\n",
    "    #plt.savefig(\"fig_%s_2d_rate.pdf\" %source, bboxes='tight')\n",
    "    plt.show()    \n",
    "\n",
    "   \n",
    "    #new_hist.plot(label=source, color=colours[index])\n",
    "    #index+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#plt.figure(figsize=(10, 8))\n",
    "index=0\n",
    "_xmax=10**8\n",
    "_tpoints=np.linspace(0, _xmax, 201)\n",
    "_xmin=_tpoints[1]\n",
    "_ymax=1.7e-7\n",
    "events_hist=0\n",
    "events=0\n",
    "plt.set_cmap(\"Oranges\")\n",
    "for source, dict_data in dict_source_sr1.items():\n",
    "    print(source)\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    del events_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        try:\n",
    "            events_hist\n",
    "            events_hist+=data['livet_hist']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            events_hist=data['livet_hist']\n",
    "            events=data['events']\n",
    "    \n",
    "    new_hist=events_hist/events\n",
    "    new_hist.plot(cblabel = 'Live Time Fraction [bin$^{-1}$.S2$^{-1}$]', norm=LogNorm(), vmin=10**-6, vmax=10**-2)\n",
    "    plt.xlabel('Time Since S2 [ns]', fontsize=12)\n",
    "    plt.ylabel('S2 Size [Log10(PE)]', fontsize=12)\n",
    "    #plt.savefig(\"fig_%s_2d_livetime.png\" %source, bboxes='tight')\n",
    "    #plt.savefig(\"fig_%s_2d_livetime.pdf\" %source, bboxes='tight')\n",
    "    plt.show()    \n",
    "\n",
    "   \n",
    "    #new_hist.plot(label=source, color=colours[index])\n",
    "    #index+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#plt.figure(figsize=(10, 8))\n",
    "index=0\n",
    "_xmax=10**8\n",
    "_tpoints=np.linspace(0, _xmax, 201)\n",
    "_xmin=_tpoints[1]\n",
    "_ymax=1.7e-7\n",
    "peaks_hist=0\n",
    "events=0\n",
    "for source, dict_data in dict_source_sr1.items():\n",
    "    print(source)\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    del peaks_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        try:\n",
    "            peaks_hist\n",
    "            peaks_hist+=data['peaks']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            peaks_hist=data['peaks']\n",
    "            events=data['events']\n",
    "    \n",
    "    new_hist=peaks_hist/events\n",
    "    new_hist.plot(cblabel = 'Uncorrected Events [ns$^{-1}$]' ,norm=LogNorm(), vmin=10**-11, vmax=10**-7)\n",
    "    plt.xlabel('Time Since S2 [ns]', fontsize=12)\n",
    "    plt.ylabel('Peak Size [Log10(PE)]', fontsize=12)\n",
    "    #plt.savefig(\"fig_%s_2d_peaks.png\" %source, bboxes='tight')\n",
    "    #plt.savefig(\"fig_%s_2d_peaks.pdf\" %source, bboxes='tight')\n",
    "    plt.show()    \n",
    "\n",
    "   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at energy lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Plots corrected event with error added#\n",
    "\n",
    "colours=['blue', 'orange', 'green', 'magenta', 'cyan', 'red', 'grey', 'black', 'brown']\n",
    "#[5.3, 6]\n",
    "#[3, 3.7]\n",
    "#[4.5, 5.2]\n",
    "#[2, 2.7]\n",
    "\n",
    "lower_cs2=2\n",
    "upper_cs2=3\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "index=0\n",
    "window_length=10**8\n",
    "_tpoints=np.linspace(0, window_length, 201)\n",
    "_xmin=_tpoints[1]\n",
    "_ymax=1.7e-7\n",
    "events_hist=0\n",
    "events=0\n",
    "index=0\n",
    "\n",
    "for source, dict_data in dict_source_sr0.items():\n",
    "    print(source)\n",
    "    del events_hist\n",
    "    del livet_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        try:\n",
    "            events_hist\n",
    "            events_hist+=data['deltat']\n",
    "            weight_hist+=data['deltat_weights']\n",
    "            livet_hist+=data['livet_hist']\n",
    "            livet_weight+=data['livet_weights']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            events_hist=data['deltat']\n",
    "            weight_hist=data['deltat_weights']\n",
    "            livet_hist=data['livet_hist']\n",
    "            livet_weight=data['livet_weights']\n",
    "            events=data['events']\n",
    "        except KeyError:\n",
    "            print(source, key)\n",
    "        \n",
    "    events_hist=events_hist.slicesum(lower_cs2, upper_cs2, 's2_area')\n",
    "    weight_hist=weight_hist.slicesum(lower_cs2, upper_cs2, 's2_area')\n",
    "    livet_hist=livet_hist.slicesum(lower_cs2, upper_cs2, 's2_area')\n",
    "    livet_weight=livet_weight.slicesum(lower_cs2, upper_cs2, 's2_area')\n",
    "    \n",
    "    #Error\n",
    "    num_value=np.array(events_hist)\n",
    "    denum_value=np.array(livet_hist)\n",
    "    num_error = np.sqrt(weight_hist)\n",
    "    denum_error = np.sqrt(livet_weight)\n",
    "    e1 = num_error\n",
    "    e2 = denum_error\n",
    "    b1 = num_value\n",
    "    b2 = denum_value\n",
    "    w=b1/b2\n",
    "    \n",
    "    ratio_error = np.abs(((1-2*w)*e1*e1 + w*w*e2*e2)/(b2*b2))       \n",
    "    ratio = num_value/denum_value \n",
    "    \n",
    "    #Confidence Bands\n",
    "    x = _tpoints\n",
    "    x = np.sort(np.array(list(x[1:]) + list(x[:-1])))\n",
    "    y1=ratio-np.sqrt(ratio_error)\n",
    "    y2=ratio+np.sqrt(ratio_error)\n",
    "    y_lower = np.repeat(y1[:], 2)\n",
    "    y_upper = np.repeat(y2[:], 2)\n",
    "        \n",
    "    #Plot\n",
    "    plt.step(_tpoints[1:], ratio*1000000, linewidth=3,  color=colours[index], label=source+' SR0')\n",
    "    plt.fill_between(x, y_lower*1000000, y_upper*1000000, facecolor=colours[index], interpolate=True, alpha = 0.25)\n",
    "    index+=1\n",
    "    \n",
    "    print(events)\n",
    "    \n",
    "for source, dict_data in dict_source_sr1.items():\n",
    "    print(source)\n",
    "    del events_hist\n",
    "    del livet_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        try:\n",
    "            events_hist\n",
    "            events_hist+=data['deltat']\n",
    "            weight_hist+=data['deltat_weights']\n",
    "            livet_hist+=data['livet_hist']\n",
    "            livet_weight+=data['livet_weights']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            events_hist=data['deltat']\n",
    "            weight_hist=data['deltat_weights']\n",
    "            livet_hist=data['livet_hist']\n",
    "            livet_weight=data['livet_weights']\n",
    "            events=data['events']\n",
    "        except KeyError:\n",
    "            print(source, key)\n",
    "        \n",
    "    events_hist=events_hist.slicesum(lower_cs2, upper_cs2, 's2_area')\n",
    "    weight_hist=weight_hist.slicesum(lower_cs2, upper_cs2, 's2_area')\n",
    "    livet_hist=livet_hist.slicesum(lower_cs2, upper_cs2, 's2_area')\n",
    "    livet_weight=livet_weight.slicesum(lower_cs2, upper_cs2, 's2_area')\n",
    "    \n",
    "    #Error\n",
    "    num_value=np.array(events_hist)\n",
    "    denum_value=np.array(livet_hist)\n",
    "    num_error = np.sqrt(weight_hist)\n",
    "    denum_error = np.sqrt(livet_weight)\n",
    "    e1 = num_error\n",
    "    e2 = denum_error\n",
    "    b1 = num_value\n",
    "    b2 = denum_value\n",
    "    w=b1/b2\n",
    "    \n",
    "    ratio_error = np.abs(((1-2*w)*e1*e1 + w*w*e2*e2)/(b2*b2))       \n",
    "    ratio = num_value/denum_value \n",
    "    \n",
    "    #Confidence Bands\n",
    "    x = _tpoints\n",
    "    x = np.sort(np.array(list(x[1:]) + list(x[:-1])))\n",
    "    y1=ratio-np.sqrt(ratio_error)\n",
    "    y2=ratio+np.sqrt(ratio_error)\n",
    "    y_lower = np.repeat(y1[:], 2)\n",
    "    y_upper = np.repeat(y2[:], 2)\n",
    "        \n",
    "    #Plot\n",
    "    plt.step(_tpoints[1:], ratio*1000000, linewidth=3,  color=colours[index], label=source+' SR1')\n",
    "    plt.fill_between(x, y_lower*1000000, y_upper*1000000, facecolor=colours[index], interpolate=True, alpha = 0.25)\n",
    "    index+=1\n",
    "    \n",
    "    print(events)\n",
    "\n",
    "plt.xlim([_tpoints[5], _tpoints[-2]])\n",
    "plt.ylim([0, 10])\n",
    "plt.xlabel('Time Since S2 [ms]', fontsize=12)\n",
    "plt.ylabel('Corrected Peak Rate [ns$^{-1}$]', fontsize=12)\n",
    "axes = plt.gca()\n",
    "axes.tick_params(width=2, length=10)\n",
    "plt.legend(loc='best', fontsize=12)\n",
    "#plt.savefig(\"fig_deltat_example.png\", bboxes='tight')\n",
    "#plt.savefig(\"fig_deltat_example.pdf\", bboxes='tight')\n",
    "#plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Radon SR1 seems consistely off. Only first calibration injection is very high. Does exluding this move Radon SR1 closer to other datasets?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Excluding First SR1 Radon Injection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "rn_cal_dates=[datetime.date(2017, 1, 17), datetime.date(2017, 4, 1), datetime.date(2017, 6, 1), \n",
    "              datetime.date(2017, 7, 1), datetime.date(2017, 8, 1), datetime.date(2017, 9, 1)]\n",
    "\n",
    "print('Load Data')\n",
    "#simpath= '/home/jpienaar/Test_files/'\n",
    "simpath= '/scratch/midway2/jpienaar/cache_files/'\n",
    "\n",
    "#Find datasets\n",
    "datasets = get_file_list(simpath, '*dt.pkl', ' ')\n",
    "print('Found %d datasets' % len(datasets))\n",
    "n_files=len(datasets)\n",
    "\n",
    "df=datasets_rn\n",
    "df_temp=hax.cuts.selection(df, df.start>rn_cal_dates[1], 'TimeCut Low')\n",
    "dataset_names=df_temp['name']\n",
    "dict_calibration={}\n",
    "for dataset in tqdm(datasets[:]):\n",
    "    key=dataset[:-7]\n",
    "    if dataset_names.isin([dataset[:-7]]).any():\n",
    "         with open(simpath+dataset, 'rb') as handle:\n",
    "            data_dict = pickle.load(handle)\n",
    "            dict_calibration[key]=data_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dict_source_sr1={'Radon' : dicts_datasets_rn, 'Krypton': dicts_datasets_kr, \n",
    "                 'Background' : dicts_datasets_bkg, 'Reduced Radon': dict_calibration}\n",
    "for source, dict_data in dict_source_sr1.items():\n",
    "    print(source, len(dict_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Plots corrected event with error added#\n",
    "\n",
    "colours=['blue', 'orange', 'green', 'magenta', 'cyan', 'red', 'grey', 'black', 'brown']\n",
    "#[5.3, 6]\n",
    "#[3, 3.7]\n",
    "#[4.5, 5.2]\n",
    "#[2, 2.7]\n",
    "\n",
    "lower_cs2=3\n",
    "upper_cs2=3.7\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "index=0\n",
    "window_length=10**8\n",
    "_tpoints=np.linspace(0, window_length, 201)\n",
    "_xmin=_tpoints[1]\n",
    "_ymax=1.7e-7\n",
    "events_hist=0\n",
    "events=0\n",
    "index=0\n",
    "\n",
    "for source, dict_data in dict_source_sr0.items():\n",
    "    print(source)\n",
    "    del events_hist\n",
    "    del livet_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        try:\n",
    "            events_hist\n",
    "            events_hist+=data['deltat']\n",
    "            weight_hist+=data['deltat_weights']\n",
    "            livet_hist+=data['livet_hist']\n",
    "            livet_weight+=data['livet_weights']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            events_hist=data['deltat']\n",
    "            weight_hist=data['deltat_weights']\n",
    "            livet_hist=data['livet_hist']\n",
    "            livet_weight=data['livet_weights']\n",
    "            events=data['events']\n",
    "        except KeyError:\n",
    "            print(source, key)\n",
    "        \n",
    "    events_hist=events_hist.slicesum(lower_cs2, upper_cs2, 's2_area')\n",
    "    weight_hist=weight_hist.slicesum(lower_cs2, upper_cs2, 's2_area')\n",
    "    livet_hist=livet_hist.slicesum(lower_cs2, upper_cs2, 's2_area')\n",
    "    livet_weight=livet_weight.slicesum(lower_cs2, upper_cs2, 's2_area')\n",
    "    \n",
    "    #Error\n",
    "    num_value=np.array(events_hist)\n",
    "    denum_value=np.array(livet_hist)\n",
    "    num_error = np.sqrt(weight_hist)\n",
    "    denum_error = np.sqrt(livet_weight)\n",
    "    e1 = num_error\n",
    "    e2 = denum_error\n",
    "    b1 = num_value\n",
    "    b2 = denum_value\n",
    "    w=b1/b2\n",
    "    \n",
    "    ratio_error = np.abs(((1-2*w)*e1*e1 + w*w*e2*e2)/(b2*b2))       \n",
    "    ratio = num_value/denum_value \n",
    "    \n",
    "    #Confidence Bands\n",
    "    x = _tpoints\n",
    "    x = np.sort(np.array(list(x[1:]) + list(x[:-1])))\n",
    "    y1=ratio-np.sqrt(ratio_error)\n",
    "    y2=ratio+np.sqrt(ratio_error)\n",
    "    y_lower = np.repeat(y1[:], 2)\n",
    "    y_upper = np.repeat(y2[:], 2)\n",
    "        \n",
    "    #Plot\n",
    "    plt.step(_tpoints[1:], ratio*1000000, linewidth=3,  color=colours[index], label=source+' SR0')\n",
    "    plt.fill_between(x, y_lower*1000000, y_upper*1000000, facecolor=colours[index], interpolate=True, alpha = 0.25)\n",
    "    index+=1\n",
    "    \n",
    "    print(events)\n",
    "    \n",
    "for source, dict_data in dict_source_sr1.items():\n",
    "    print(source)\n",
    "    del events_hist\n",
    "    del livet_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        try:\n",
    "            events_hist\n",
    "            events_hist+=data['deltat']\n",
    "            weight_hist+=data['deltat_weights']\n",
    "            livet_hist+=data['livet_hist']\n",
    "            livet_weight+=data['livet_weights']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            events_hist=data['deltat']\n",
    "            weight_hist=data['deltat_weights']\n",
    "            livet_hist=data['livet_hist']\n",
    "            livet_weight=data['livet_weights']\n",
    "            events=data['events']\n",
    "        except KeyError:\n",
    "            print(source, key)\n",
    "        \n",
    "    events_hist=events_hist.slicesum(lower_cs2, upper_cs2, 's2_area')\n",
    "    weight_hist=weight_hist.slicesum(lower_cs2, upper_cs2, 's2_area')\n",
    "    livet_hist=livet_hist.slicesum(lower_cs2, upper_cs2, 's2_area')\n",
    "    livet_weight=livet_weight.slicesum(lower_cs2, upper_cs2, 's2_area')\n",
    "    \n",
    "    #Error\n",
    "    num_value=np.array(events_hist)\n",
    "    denum_value=np.array(livet_hist)\n",
    "    num_error = np.sqrt(weight_hist)\n",
    "    denum_error = np.sqrt(livet_weight)\n",
    "    e1 = num_error\n",
    "    e2 = denum_error\n",
    "    b1 = num_value\n",
    "    b2 = denum_value\n",
    "    w=b1/b2\n",
    "    \n",
    "    ratio_error = np.abs(((1-2*w)*e1*e1 + w*w*e2*e2)/(b2*b2))       \n",
    "    ratio = num_value/denum_value \n",
    "    \n",
    "    #Confidence Bands\n",
    "    x = _tpoints\n",
    "    x = np.sort(np.array(list(x[1:]) + list(x[:-1])))\n",
    "    y1=ratio-np.sqrt(ratio_error)\n",
    "    y2=ratio+np.sqrt(ratio_error)\n",
    "    y_lower = np.repeat(y1[:], 2)\n",
    "    y_upper = np.repeat(y2[:], 2)\n",
    "        \n",
    "    #Plot\n",
    "    plt.step(_tpoints[1:], ratio*1000000, linewidth=3,  color=colours[index], label=source+' SR1')\n",
    "    plt.fill_between(x, y_lower*1000000, y_upper*1000000, facecolor=colours[index], interpolate=True, alpha = 0.25)\n",
    "    index+=1\n",
    "    \n",
    "    print(events)\n",
    "\n",
    "plt.xlim([_tpoints[5], _tpoints[-2]])\n",
    "plt.ylim([0, 10])\n",
    "plt.xlabel('Time Since S2 [ms]', fontsize=12)\n",
    "plt.ylabel('Corrected Peak Rate [ns$^{-1}$]', fontsize=12)\n",
    "axes = plt.gca()\n",
    "axes.tick_params(width=2, length=10)\n",
    "plt.legend(loc='best', fontsize=12)\n",
    "#plt.savefig(\"fig_deltat_example.png\", bboxes='tight')\n",
    "#plt.savefig(\"fig_deltat_example.pdf\", bboxes='tight')\n",
    "#plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Position Correlation Stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Load random Sampled (Generated at end of notebook if needed)\n",
    "with open('random_source_sampling.pkl', 'rb') as handle:\n",
    "    dfs_source_distrib_hist = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#plt.figure(figsize=(10, 8))\n",
    "colours=['blue', 'orange', 'green', 'red', 'magenta', 'cyan']\n",
    "index=0\n",
    "_ymin=8*10**-4\n",
    "_xmax=(47.9)**2\n",
    "_rpoints=np.linspace(0, (R_tpc)**2, 101)\n",
    "_ymax=0.8*10**0\n",
    "r2_hist=0\n",
    "for source, dict_data in dict_source_sr1.items():\n",
    "    plt.figure(figsize=(10, 4))\n",
    "    print(source, len(dict_data))\n",
    "    del r2_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        #print(key)\n",
    "        try:\n",
    "            r2_hist\n",
    "            r2_hist+=data['dt_r2']\n",
    "            weight_hist+=data['dt_r2_weights'] \n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            r2_hist = data['dt_r2']\n",
    "            weight_hist=data['dt_r2_weights'] \n",
    "            events=data['events']\n",
    "    \n",
    "    events_hist=r2_hist.sum('delta_T')\n",
    "    weight_hist=weight_hist.sum('delta_T')\n",
    "\n",
    "    #Confidence Bands\n",
    "    x = _rpoints\n",
    "    x = np.sort(np.array(list(x[1:]) + list(x[:-1])))\n",
    "    y1=events_hist-np.sqrt(weight_hist)\n",
    "    y2=events_hist+np.sqrt(weight_hist)\n",
    "    y_single = np.repeat(y1[:], 2)\n",
    "    y_total = np.repeat(y2[:], 2)\n",
    "        \n",
    "    #Plot\n",
    "    plt.step(_rpoints[1:], events_hist/events, linewidth=3,  color=colours[index], label=source+' SR1')\n",
    "    plt.fill_between(x, y_single/events, y_total/events, facecolor=colours[index], interpolate=True, alpha = 0.25)\n",
    "    \n",
    "    del r2_hist\n",
    "    del weight_hist\n",
    "    del events\n",
    "    dict_data=dict_source_sr0[source]\n",
    "    for key, data in dict_data.items():\n",
    "        #print(key)\n",
    "        try:\n",
    "            r2_hist\n",
    "            r2_hist+=data['dt_r2']\n",
    "            weight_hist+=data['dt_r2_weights'] \n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            r2_hist = data['dt_r2']\n",
    "            weight_hist=data['dt_r2_weights'] \n",
    "            events=data['events']\n",
    "    \n",
    "    events_hist=r2_hist.sum('delta_T')\n",
    "    weight_hist=weight_hist.sum('delta_T')\n",
    "\n",
    "    #Confidence Bands\n",
    "    x = _rpoints\n",
    "    x = np.sort(np.array(list(x[1:]) + list(x[:-1])))\n",
    "    y1=events_hist-np.sqrt(weight_hist)\n",
    "    y2=events_hist+np.sqrt(weight_hist)\n",
    "    y_single = np.repeat(y1[:], 2)\n",
    "    y_total = np.repeat(y2[:], 2)\n",
    "        \n",
    "    #Plot\n",
    "    plt.step(_rpoints[1:], events_hist/events, linewidth=3,  color=colours[index+1], label=source+' SR0')\n",
    "    plt.fill_between(x, y_single/events, y_total/events, facecolor=colours[index+1], interpolate=True, alpha = 0.25)\n",
    "\n",
    "\n",
    "    random_comp = dfs_source_distrib_hist[source]['alpha']\n",
    "    r_norms = dfs_source_distrib_hist[source]['r_norms']\n",
    "\n",
    "    content, bins = np.histogram(random_comp[:, 0]**2, weights=1/r_norms, bins=100, range=[0, (R_tpc)**2])\n",
    "    scaling_factor=np.sum(events_hist[:]/events)/np.sum(content)\n",
    "    plt.hist(random_comp[:, 0]**2, weights=scaling_factor/r_norms, range=[0, (R_tpc)**2], bins=100, \n",
    "             histtype='stepfilled', linewidth=2, label='%s Sim' %source, color='green', alpha=0.5)\n",
    "    #print(np.nansum(random_comp[:,0]**2/r_norms))\n",
    "    \n",
    "    \n",
    "    #event_max=np.max(new_hist)\n",
    "    #plt.hlines(0.1*event_max, 0, _xmax, color=colours[index], linestyle=\":\")\n",
    "   \n",
    "    idx=1\n",
    "    for bin_value in new_hist[1:]:\n",
    "        if bin_value<(content[idx]*scaling_factor):\n",
    "            plt.vlines(_rpoints[idx], 0, _ymax, color='red', \n",
    "                       linestyle=\"dashed\", label='%.2f cm' %float(np.sqrt(_rpoints[idx])))\n",
    "            break\n",
    "        idx+=1\n",
    "    \n",
    "    plt.xlim([0, _xmax])\n",
    "    plt.ylim([_ymin, _ymax])\n",
    "    plt.xlabel('Distance [cm$^{2}$]', fontsize=12)\n",
    "    plt.ylabel('Normalized Peaks [cm$^{-2}$.100ms$^{-1}$]', fontsize=12)\n",
    "    plt.legend(loc='best', fontsize=12)\n",
    "    plt.yscale('log')\n",
    "    #plt.savefig(\"fig_%s_r_dependence_100ms.png\" %source, bboxes='tight')\n",
    "    #plt.savefig(\"fig_%s_r_dependence_100ms.pdf\" %source, bboxes='tight')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#t_bins=[0, 5*10**8, 2*10**9]\n",
    "livet_tbin_width=0.5*10**6\n",
    "t_bins=np.linspace(0, 1*10**8, 7)\n",
    "_xmax=(R_tpc)**2\n",
    "r_bins=np.linspace(0, (R_tpc)**2, 101)\n",
    "for source, dict_data in dict_source_sr0.items():\n",
    "    print(source)\n",
    "    plt.figure(figsize=(10, 4))\n",
    "    dt_r2_hist=0\n",
    "    livet_hist=0\n",
    "    events=0\n",
    "    del dt_r2_hist\n",
    "    del livet_hist\n",
    "    for key, data in dict_data.items():\n",
    "        try:\n",
    "            dt_r2_hist\n",
    "            dt_r2_hist+=data['dt_r2'] \n",
    "            weights_hist+=data['dt_r2_weights'] \n",
    "            livet_hist+=data['livet_hist']  \n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            dt_r2_hist = data['dt_r2']\n",
    "            weights_hist=data['dt_r2_weights'] \n",
    "            livet_hist = data['livet_hist']  \n",
    "            events = data['events']           \n",
    "            \n",
    "            \n",
    "    livet_hist=livet_hist.sum('s2_area')\n",
    "    for index, delta_t in enumerate(t_bins[::2]):      \n",
    "        start_time = t_bins[index]\n",
    "        stop_time  = t_bins[index+1]\n",
    "        #print(start_time, stop_time)\n",
    "        new_hist=dt_r2_hist.slicesum(start=start_time, stop=stop_time,axis='delta_T')\n",
    "        new_weight_hist=weights_hist.slicesum(start=start_time, stop=stop_time,axis='delta_T')\n",
    "\n",
    "        #Livetime in slice\n",
    "        start_bin = int(np.floor(start_time/(livet_tbin_width)))\n",
    "        stop_bin  = int(np.floor(stop_time/(livet_tbin_width)))\n",
    "        livet_array=np.array\n",
    "        livet_correction = np.average(livet_hist[start_bin:stop_bin])\n",
    "        #print(start_bin, stop_bin, livet_correction)        \n",
    "\n",
    "        #Confidence Bands\n",
    "        x = _rpoints\n",
    "        x = np.sort(np.array(list(x[1:]) + list(x[:-1])))\n",
    "        y1=new_hist-np.sqrt(new_weight_hist)\n",
    "        y2=new_hist+np.sqrt(new_weight_hist)\n",
    "        y_single = np.repeat(y1[:], 2)\n",
    "        y_total = np.repeat(y2[:], 2)\n",
    "\n",
    "        #Plot\n",
    "        plt.step(_rpoints[1:], new_hist/livet_correction, linewidth=2,  color=colours[index],\n",
    "                 label='%d - %d ms'%(start_time/10**6, stop_time/10**6))\n",
    "        plt.fill_between(x, y_single/livet_correction, \n",
    "                         y_total/livet_correction, facecolor=colours[index], interpolate=True, alpha = 0.25)\n",
    "        \n",
    "\n",
    "    plt.xlim([0, _xmax])\n",
    "    plt.xlabel('Distance [cm$^{2}$]', fontsize=12)\n",
    "    plt.ylabel('Normalized Peaks [cm$^{-2}$.ms$^{-1}$]', fontsize=12)\n",
    "    plt.legend(loc='best', fontsize=12)\n",
    "    plt.ylim([1*10**-1, 3*10])\n",
    "    plt.yscale('log')\n",
    "    #plt.savefig(\"fig_%s_r_dep_timeslices.png\" %source, bboxes='tight')\n",
    "    #plt.savefig(\"fig_%s_r_dep_timeslices.pdf\" %source, bboxes='tight')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "t_bins=np.linspace(0, 1*10**8, 5)\n",
    "_xmax=(R_tpc)**2\n",
    "r_bins=np.linspace(0, (R_tpc)**2, 101)\n",
    "for source, dict_data in dict_source_sr0.items():\n",
    "    print(source)\n",
    "    plt.figure(figsize=(10, 4))\n",
    "    dt_r2_hist=0\n",
    "    livet_hist=0\n",
    "    events=0\n",
    "    del dt_r2_hist\n",
    "    del livet_hist\n",
    "    for key, data in dict_data.items():\n",
    "        try:\n",
    "            dt_r2_hist\n",
    "            dt_r2_hist+=data['dt_r2'] \n",
    "            livet_hist+=data['livet_hist']\n",
    "            weights_hist+=data['dt_r2_weights'] \n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            dt_r2_hist = data['dt_r2']\n",
    "            livet_hist = data['livet_hist']\n",
    "            weights_hist=data['dt_r2_weights'] \n",
    "            events = data['events']\n",
    " \n",
    "    livet_hist=livet_hist.sum('s2_area')\n",
    "    for index, delta_t in enumerate(t_bins[::2]):\n",
    "        #Time Slice Hist\n",
    "        start_time = t_bins[index]\n",
    "        stop_time  = t_bins[index+1]\n",
    "        #print(start_time, stop_time)\n",
    "        new_hist=dt_r2_hist.slicesum(start=start_time, stop=stop_time,axis='delta_T')\n",
    "        new_weight_hist=weights_hist.slicesum(start=start_time, stop=stop_time,axis='delta_T')\n",
    "\n",
    "        #Livetime in slice\n",
    "        start_bin = int(np.floor(start_time/(livet_tbin_width)))\n",
    "        stop_bin  = int(np.floor(stop_time/(livet_tbin_width)))\n",
    "        livet_correction = np.average(livet_hist[start_bin:stop_bin])\n",
    "        #print(start_bin, stop_bin, livet_correction)        \n",
    "        \n",
    "        new_hist=new_hist/livet_correction\n",
    "        new_weight_hist=new_weight_hist/livet_correction\n",
    "        \n",
    "        #Reference time window hist\n",
    "        ref_start_time=t_bins[-2]\n",
    "        ref_stop_time=t_bins[-1]\n",
    "        #print(ref_start_time, ref_stop_time)\n",
    "        ref_hist=dt_r2_hist.slicesum(start=ref_start_time, stop=ref_stop_time,axis='delta_T')\n",
    "        ref_weight=weights_hist.slicesum(start=ref_start_time, stop=ref_stop_time,axis='delta_T')\n",
    "        \n",
    "        #Livetime in reference slice\n",
    "        ref_start_bin = int(np.floor(ref_start_time/(livet_tbin_width)))\n",
    "        ref_stop_bin  = int(np.floor(ref_stop_time/(livet_tbin_width)))\n",
    "        ref_livet_correction = np.average(livet_hist[ref_start_bin:ref_stop_bin])\n",
    "        #print(ref_start_bin, ref_stop_bin, ref_livet_correction)  \n",
    "        \n",
    "        ref_hist=ref_hist/ref_livet_correction\n",
    "        ref_weight=ref_weight/ref_livet_correction\n",
    "\n",
    "        #Error\n",
    "        num_value=np.array(new_hist)\n",
    "        denum_value=np.array(ref_hist)\n",
    "        num_error = np.sqrt(new_weight_hist)\n",
    "        denum_error = np.sqrt(ref_weight)\n",
    "        e1 = num_error\n",
    "        e2 = denum_error\n",
    "        b1 = num_value\n",
    "        b2 = denum_value\n",
    "        w=b1/b2\n",
    "\n",
    "        ratio_error = np.abs(((1-2*w)*e1*e1 + w*w*e2*e2)/(b2*b2))       \n",
    "        ratio = num_value/denum_value \n",
    "\n",
    "        #Confidence Bands\n",
    "        x = _rpoints\n",
    "        x = np.sort(np.array(list(x[1:]) + list(x[:-1])))\n",
    "        y1=ratio-np.sqrt(ratio_error)\n",
    "        y2=ratio+np.sqrt(ratio_error)\n",
    "        y_lower = np.repeat(y1[:], 2)\n",
    "        y_upper = np.repeat(y2[:], 2)\n",
    "\n",
    "        #Plot\n",
    "        plt.step(_rpoints[1:], ratio-1, linewidth=3,  color=colours[index], \n",
    "                 label='%d - %d ms'%(start_time/10**6, stop_time/10**6))\n",
    "        plt.fill_between(x, y_lower-1, y_upper-1, facecolor=colours[index], interpolate=True, alpha = 0.25)\n",
    "        index+=1\n",
    "\n",
    "        #Difference Hist\n",
    "        #diff_hist=(new_hist-ref_hist)/ref_hist\n",
    "        #diff_hist.plot(label='%d - %d ms'%(start_time/10**6, stop_time/10**6))\n",
    "             \n",
    "        #const_std=np.std(diff_hist[25:75])\n",
    "        #const_ave=np.average(diff_hist[25:75])\n",
    "        plt.hlines(0, 0, _xmax, color='r', linestyle=\":\")\n",
    "        #plt.fill_between(r_bins, [const_ave-const_std]*len(r_bins), [const_ave+const_std]*len(r_bins),alpha=0.25,facecolor=colours[index])\n",
    "        #print(const_std, )\n",
    "        \n",
    "        \n",
    "        #new_hist.plot(label=index)\n",
    "    plt.xlim([0, _xmax])\n",
    "    plt.xlabel('Distance [cm$^{2}$]', fontsize=12)\n",
    "    plt.ylabel('Relative Difference', fontsize=12)\n",
    "    plt.legend(loc='best', fontsize=12)\n",
    "    plt.ylim([-0.35, 1])\n",
    "    #plt.yscale('log')\n",
    "    #plt.savefig(\"fig_%s_r_diff_timeslices.png\" %source, bboxes='tight')\n",
    "    #plt.savefig(\"fig_%s_r_diff_timeslices.pdf\" %source, bboxes='tight')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#plt.figure(figsize=(10, 8))\n",
    "colours=['blue', 'orange', 'green', 'red', 'magenta', 'cyan']\n",
    "index=0\n",
    "_ymin=8*10**-4\n",
    "_xmax=(47.9)**2\n",
    "_rpoints=np.linspace(0, (R_tpc)**2, 101)\n",
    "_ymax=0.8*10**0\n",
    "r2_hist=0\n",
    "for source, dict_data in dict_source_sr1.items():\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    print(source, len(dict_data))\n",
    "    del r2_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        #print(key)\n",
    "        try:\n",
    "            r2_hist\n",
    "            r2_hist+=data['dt_xy']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            r2_hist = data['dt_xy']\n",
    "            events=data['events']\n",
    "    \n",
    "    events_hist=r2_hist.sum('delta_T')/events\n",
    "    events_hist.plot(norm=LogNorm(), vmin=1*10**-4)\n",
    "    \n",
    " \n",
    "    \n",
    "    #plt.xlim([0, _xmax])\n",
    "    #plt.ylim([_ymin, _ymax])\n",
    "    plt.xlabel('Delta X [cm]', fontsize=12)\n",
    "    plt.ylabel('Delta Y [cm]', fontsize=12)\n",
    "    plt.legend(loc='best', fontsize=12)\n",
    "    #plt.yscale('log')\n",
    "    #plt.savefig(\"fig_%s_r_dependence_100ms.png\" %source, bboxes='tight')\n",
    "    #plt.savefig(\"fig_%s_r_dependence_100ms.pdf\" %source, bboxes='tight')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#plt.figure(figsize=(10, 8))\n",
    "colours=['blue', 'orange', 'green', 'red', 'magenta', 'cyan']\n",
    "index=0\n",
    "_ymin=8*10**-4\n",
    "_xmax=(47.9)**2\n",
    "_rpoints=np.linspace(0, (R_tpc)**2, 101)\n",
    "_ymax=0.8*10**0\n",
    "r2_hist=0\n",
    "plt.set_cmap(\"RdBu\")\n",
    "for source, dict_data in dict_source_sr1.items():\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    print(source, len(dict_data))\n",
    "    del r2_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        #print(key)\n",
    "        try:\n",
    "            r2_hist\n",
    "            r2_hist+=data['dt_xy']\n",
    "            livet_hist+=data['livet_hist']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            r2_hist = data['dt_xy']\n",
    "            livet_hist=data['livet_hist']\n",
    "            events=data['events']\n",
    "            \n",
    "    livet_hist=livet_hist.sum('s2_area')\n",
    "    \n",
    "    ref_hist=r2_hist.slicesum(75*10**6, 100*10**6, 'delta_T')\n",
    "    ref_livet=np.average(livet_hist[150:200])\n",
    "    ref_hist=ref_hist/ref_livet\n",
    "    \n",
    "    events_hist=r2_hist.slicesum(5*10**6, 25*10**6, 'delta_T')\n",
    "    events_livet=np.average(livet_hist[10:50])\n",
    "    events_hist=events_hist/events_livet\n",
    "    \n",
    "    plot_hist=(events_hist-ref_hist)/ref_hist\n",
    "    plot_hist.plot(vmax=1, vmin=-1)\n",
    "    \n",
    " \n",
    "    \n",
    "    #plt.xlim([0, _xmax])\n",
    "    #plt.ylim([_ymin, _ymax])\n",
    "    plt.xlabel('Delta X [cm]', fontsize=12)\n",
    "    plt.ylabel('Delta Y [cm]', fontsize=12)\n",
    "    plt.legend(loc='best', fontsize=12)\n",
    "    #plt.yscale('log')\n",
    "    #plt.savefig(\"fig_%s_r_dependence_100ms.png\" %source, bboxes='tight')\n",
    "    #plt.savefig(\"fig_%s_r_dependence_100ms.pdf\" %source, bboxes='tight')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### X/Y Slixes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "_spatial_bins=50\n",
    "_xbins=np.linspace(-50, 50, _spatial_bins+1)\n",
    "_ybins=np.linspace(-50, 50, _spatial_bins+1)\n",
    "xv, yv = np.meshgrid(_xbins, _ybins)\n",
    "array_hist = np.zeros((_spatial_bins, _spatial_bins))\n",
    "xc = np.zeros_like(array_hist)\n",
    "yc = np.zeros_like(array_hist)\n",
    "\n",
    "r_lower=15\n",
    "r_upper=20\n",
    "key_search=[]\n",
    "\n",
    "for idx, x_edge in tqdm(enumerate(xv[:-1])):\n",
    "    for idy, y_edge in enumerate(yv[:-1]):\n",
    "        xc[idx, idy]=xv[idx][idy]+(xv[idx][idy+1]-xv[idx][idy])/2\n",
    "        yc[idx, idy]=yv[idx][idy]+(yv[idx+1][idy]-yv[idx][idy])/2\n",
    "        \n",
    "        rc = np.sqrt(xc[idx, idy]**2+yc[idx, idy]**2)\n",
    "        \n",
    "        if rc>r_lower and rc<r_upper:\n",
    "            key_search.append('%d_%d' %(xc[idx, idy], yc[idx, idy]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "print('Data')\n",
    "#simpath= '/scratch/midway2/jpienaar/cache_files/'\n",
    "simpath= '/scratch/midway2/jpienaar/cache_files/'\n",
    "\n",
    "#Find datasets\n",
    "datasets = get_file_list(simpath, '*special.pkl', ' ')\n",
    "print('Found %d datasets' % len(datasets))\n",
    "n_files=len(datasets)\n",
    "\n",
    "dicts_datasets_rn_xy={}\n",
    "dicts_datasets_kr_xy={}\n",
    "dicts_datasets_bkg_xy={}\n",
    "for dataset in tqdm(datasets[:]):\n",
    "    #print(key)\n",
    "    key=dataset[:-7]\n",
    "    if dataset_names_rn.isin([dataset[:-15]]).any():\n",
    "        #print('Radon')\n",
    "        with open(simpath+dataset, 'rb') as handle:\n",
    "            data_dict = pickle.load(handle)\n",
    "    dicts_datasets_rn_xy[dataset]=data_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp_dict={'Radon': dicts_datasets_rn_xy}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#plt.figure(figsize=(10, 8))\n",
    "colours=['blue', 'orange', 'green', 'red', 'magenta', 'cyan']\n",
    "r2_hist=0\n",
    "for source, dict_data in temp_dict.items():\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    print(source, len(dict_data))\n",
    "    del r2_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        #print(key)\n",
    "        try:\n",
    "            r2_hist\n",
    "            r2_hist+=data['dt_xy']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            r2_hist = data['dt_xy']\n",
    "            events=data['events']\n",
    "    \n",
    "    events_hist=r2_hist.sum('delta_T')\n",
    "    events_hist.plot(vmin=1)\n",
    "    \n",
    " \n",
    "    \n",
    "    #plt.xlim([-10, 60])\n",
    "    #plt.ylim([_ymin, _ymax])\n",
    "    plt.xlabel('Delta X [cm]', fontsize=12)\n",
    "    plt.ylabel('Delta Y [cm]', fontsize=12)\n",
    "    plt.legend(loc='best', fontsize=12)\n",
    "    #plt.yscale('log')\n",
    "    #plt.savefig(\"fig_%s_r_dependence_100ms.png\" %source, bboxes='tight')\n",
    "    #plt.savefig(\"fig_%s_r_dependence_100ms.pdf\" %source, bboxes='tight')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "colours=['blue', 'orange', 'green', 'red', 'magenta', 'cyan']\n",
    "index=0\n",
    "_ymin=8*10**-4\n",
    "_xmax=(47.9)**2\n",
    "_rpoints=np.linspace(0, (R_tpc)**2, 101)\n",
    "_ymax=0.8*10**0\n",
    "r2_hist=0\n",
    "plt.set_cmap(\"RdBu\")\n",
    "for source, dict_data in temp_dict.items():\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    print(source, len(dict_data))\n",
    "    del r2_hist\n",
    "    events=0\n",
    "    for key, data in dict_data.items():\n",
    "        #print(key)\n",
    "        try:\n",
    "            r2_hist\n",
    "            r2_hist+=data['dt_xy']\n",
    "            livet_hist+=data['livet_hist']\n",
    "            events+=data['events']\n",
    "        except NameError:\n",
    "            r2_hist = data['dt_xy']\n",
    "            livet_hist=data['livet_hist']\n",
    "            events=data['events']\n",
    "            \n",
    "    livet_hist=livet_hist.sum('s2_area')\n",
    "    \n",
    "    ref_hist=r2_hist.slicesum(75*10**6, 100*10**6, 'delta_T')\n",
    "    ref_livet=np.average(livet_hist[150:200])\n",
    "    ref_hist=ref_hist/ref_livet\n",
    "    \n",
    "    events_hist=r2_hist.slicesum(5*10**6, 25*10**6, 'delta_T')\n",
    "    events_livet=np.average(livet_hist[10:50])\n",
    "    events_hist=events_hist/events_livet\n",
    "    \n",
    "    plot_hist=(events_hist-ref_hist)/ref_hist\n",
    "    plot_hist.plot(vmax=1, vmin=-1)\n",
    "    \n",
    " \n",
    "    \n",
    "    #plt.xlim([0, _xmax])\n",
    "    #plt.ylim([_ymin, _ymax])\n",
    "    plt.xlabel('Delta X [cm]', fontsize=12)\n",
    "    plt.ylabel('Delta Y [cm]', fontsize=12)\n",
    "    plt.legend(loc='best', fontsize=12)\n",
    "    #plt.yscale('log')\n",
    "    #plt.savefig(\"fig_%s_r_dependence_100ms.png\" %source, bboxes='tight')\n",
    "    #plt.savefig(\"fig_%s_r_dependence_100ms.pdf\" %source, bboxes='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dict_source_xy={'Radon' : dicts_datasets_rn_xy, 'Krypton': dicts_datasets_kr_xy, 'Background' : dicts_datasets_bkg_xy}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#plt.figure(figsize=(10, 8))\n",
    "index=0\n",
    "_ymin=10**-3\n",
    "_xmax=(2*47.9)**2\n",
    "_rpoints=np.linspace(0, (2*R_tpc)**2, 101)\n",
    "_ymax=10\n",
    "xy_hist=0\n",
    "t_bins=np.linspace(0, 2*10**8, 5)\n",
    "for source, dict_data in dict_source_xy.items():\n",
    "    numrows=1\n",
    "    numcols=len(t_bins)-2\n",
    "    plt.figure(figsize=(10*numcols, 8))\n",
    "    print(source)\n",
    "    if source=='Background':\n",
    "        continue\n",
    "    del xy_hist\n",
    "    for key, data in dict_data.items():\n",
    "        #print(key)\n",
    "        try:\n",
    "            xy_hist\n",
    "            xy_hist+=data\n",
    "        except NameError:\n",
    "            xy_hist = data\n",
    "            \n",
    "    new_hist=xy_hist\n",
    "    new_hist=new_hist/len(dict_data)\n",
    "    \n",
    "    #plt.hlines(0.1*event_max, 0, _xmax, color=colours[index], linestyle=\":\")\n",
    "\n",
    "    for index, delta_t in enumerate(t_bins[::2]):\n",
    "        plt.subplot2grid((numrows,numcols), (0, index))\n",
    "        ax = plt.gca()\n",
    "        circle1 = plt.Circle((0, 0),  r_lower, color='red', fill=False, linewidth=3, linestyle=':')\n",
    "        ax.add_artist(circle1)\n",
    "        circle1 = plt.Circle((0, 0),  r_upper, color='red', fill=False, linewidth=3, linestyle=':')\n",
    "        ax.add_artist(circle1)\n",
    "        \n",
    "        slice_hist=new_hist.slicesum(start=t_bins[index], stop=t_bins[index+1],axis='delta_T')\n",
    "        slice_hist.plot(norm=LogNorm(), cblabel='Normalized Entries', vmin=5*10**-11, vmax=5*10**-9)\n",
    "        plt.xlabel('X  [cm]', fontsize=12)\n",
    "        plt.ylabel('Y  [cm]', fontsize=12)\n",
    "\n",
    "\n",
    "    plt.savefig(\"fig_%s_%i_peaks_xy.png\" %(source, r_lower), bboxes='tight')\n",
    "    plt.savefig(\"fig_%s_%i_peaks_xy.pdf\" %(source, r_lower), bboxes='tight')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "numcols\n",
    "np.floor(len(t_bins)/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#plt.figure(figsize=(10, 8))\n",
    "colours=['blue', 'orange', 'green']\n",
    "index=0\n",
    "_ymin=10**-3\n",
    "_xmax=(2*47.9)**2\n",
    "_rpoints=np.linspace(0, (2*R_tpc)**2, 101)\n",
    "_ymax=10\n",
    "xy_hist=0\n",
    "for source, dict_data in dict_source_xy.items():\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    print(source)\n",
    "    if source=='Background':\n",
    "        continue\n",
    "    del xy_hist\n",
    "    for key, data in dict_data.items():\n",
    "        #print(key)\n",
    "        try:\n",
    "            xy_hist\n",
    "            xy_hist+=data\n",
    "        except NameError:\n",
    "            xy_hist = data\n",
    "            \n",
    "    new_hist=xy_hist\n",
    "    new_hist=new_hist/len(dict_data)\n",
    "    \n",
    "    #plt.hlines(0.1*event_max, 0, _xmax, color=colours[index], linestyle=\":\")\n",
    "\n",
    "    ax = plt.gca()\n",
    "    circle1 = plt.Circle((0, 0),  r_lower, color='red', fill=False, linewidth=3, linestyle=':')\n",
    "    ax.add_artist(circle1)\n",
    "    circle1 = plt.Circle((0, 0),  r_upper, color='red', fill=False, linewidth=3, linestyle=':')\n",
    "    ax.add_artist(circle1)\n",
    "    \n",
    "    new_hist=new_hist.slicesum(start=1*10**8, stop=2*10**8,axis='delta_T')\n",
    "    print(np.average(new_hist))\n",
    "    new_hist.plot(label=source, norm=LogNorm())\n",
    "    index+=1\n",
    "\n",
    "#plt.xlim([0, _xmax])\n",
    "#plt.ylim([_ymin, _ymax])\n",
    "    plt.xlabel('X  [cm]', fontsize=12)\n",
    "    plt.ylabel('Y  [cm]', fontsize=12)\n",
    "#plt.legend(loc='best', fontsize=12)\n",
    "#plt.yscale('log')\n",
    "    #plt.savefig(\"fig_%s_%i_peaks_xy.png\" %(source, r_lower), bboxes='tight')\n",
    "    #plt.savefig(\"fig_%s_%i_peaks_xy.pdf\" %(source, r_lower), bboxes='tight')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing R normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Uniform Distrib\n",
    "coords=np.random.random([100000, 2])\n",
    "new_array=[]\n",
    "closed_array=[]\n",
    "open_array=[]\n",
    "alpha_array=[]\n",
    "r_norms=[]\n",
    "s2_ypos=20\n",
    "s2_xpos=-23\n",
    "rpos=np.sqrt(s2_xpos**2+s2_ypos**2)\n",
    "for pos in coords:\n",
    "    xpos=R_tpc*(2*pos[0]-1)\n",
    "    ypos=R_tpc*(2*pos[1]-1)\n",
    "    if (ypos**2+xpos**2)>R_tpc**2:\n",
    "        continue\n",
    "    else:\n",
    "        xpos=R_tpc*(2*pos[0]-1)\n",
    "        ypos=R_tpc*(2*pos[1]-1)\n",
    "        new_array.append([xpos, ypos])\n",
    "        \n",
    "        r_dist=np.sqrt((xpos-(s2_xpos))**2+(ypos-(s2_ypos))**2)\n",
    "        alpha = np.arccos((r_dist**2+rpos**2-R_tpc**2)/(2*rpos*r_dist))\n",
    "        r_norm = (alpha/np.pi)\n",
    "        if np.isnan(alpha):\n",
    "            alpha=-1\n",
    "            r_norm=1\n",
    "            closed_array.append([xpos, ypos])\n",
    "        elif alpha>0.5*np.pi:\n",
    "            open_array.append([xpos, ypos])\n",
    "        new_array.append([xpos, ypos])\n",
    "        alpha_array.append([r_dist, alpha])\n",
    "        r_norms.append(r_norm)\n",
    "        \n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "new_array=np.array(new_array)\n",
    "closed_array=np.array(closed_array)\n",
    "open_array=np.array(open_array)\n",
    "plt.scatter(new_array[:, 0], new_array[:, 1])\n",
    "plt.scatter(closed_array[:, 0], closed_array[:, 1])\n",
    "plt.scatter(open_array[:, 0], open_array[:, 1])\n",
    "plt.scatter(s2_xpos, s2_ypos, color='r', s=20)\n",
    "plt.xlabel('X  [cm]', fontsize=12)\n",
    "plt.ylabel('Y  [cm]', fontsize=12)\n",
    "#plt.savefig('fig_r_norm_illustration.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data and Produce Random Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "#Load Data To Sample From\n",
    "\n",
    "print('Radon Data')\n",
    "#simpath= '/home/jpienaar/Test_files/'\n",
    "simpath= '/scratch/midway2/jpienaar/cache_files/'\n",
    "\n",
    "#Find datasets\n",
    "datasets = get_file_list(simpath, '*Pre_trigger.hdf5', ' ')\n",
    "print('Found %d datasets' % len(datasets))\n",
    "n_files=len(datasets)\n",
    "\n",
    "datasets_rn=[]\n",
    "datasets_kr=[]\n",
    "datasets_bkg=[]\n",
    "for dataset in tqdm(datasets[:]):\n",
    "    run_number=dataset[:-17]\n",
    "    if dataset_names_rn.isin([run_number]).any():\n",
    "        while len(datasets_rn)<10:\n",
    "            cache_file_name = '/scratch/midway2/jpienaar/cache_files/'+run_number+ '_Pre_trigger.hdf5'\n",
    "            #print (run_number, cache_file_name)\n",
    "            df = hax.minitrees.load(cache_file = cache_file_name)\n",
    "            datasets_rn.append(df)\n",
    "            \n",
    "    elif dataset_names_bkg.isin([run_number]).any():\n",
    "        while len(datasets_bkg)<10:\n",
    "            cache_file_name = '/scratch/midway2/jpienaar/cache_files/'+run_number+ '_Pre_trigger.hdf5'\n",
    "            #print (run_number, cache_file_name)\n",
    "            df = hax.minitrees.load(cache_file = cache_file_name)\n",
    "            datasets_bkg.append(df)\n",
    "            \n",
    "    elif dataset_names_kr.isin([run_number]).any():\n",
    "        while len(datasets_kr)<10:\n",
    "            cache_file_name = '/scratch/midway2/jpienaar/cache_files/'+run_number+ '_Pre_trigger.hdf5'\n",
    "            #print (run_number, cache_file_name)\n",
    "            df = hax.minitrees.load(cache_file = cache_file_name)\n",
    "            datasets_kr.append(df)\n",
    "              \n",
    "    \n",
    "datasets_rn=pd.concat(datasets_rn)\n",
    "datasets_kr=pd.concat(datasets_kr)\n",
    "datasets_bkg=pd.concat(datasets_bkg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfs_source_distrib={'Background': {'data':datasets_bkg},\n",
    "                    'Radon': {'data': datasets_rn},\n",
    "                    'Krypton': {'data': datasets_kr}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Distrib from Data\n",
    "dfs_source_distrib_hist={}\n",
    "sample_num=1000\n",
    "for key, source in dfs_source_distrib.items():\n",
    "    print(key)\n",
    "    df=source['data']\n",
    "    alpha_array=[]\n",
    "    new_array=[]\n",
    "    r_norms=[]\n",
    "    unique_y=pd.unique(df['y_s2_tpf'].values)\n",
    "    unique_x=pd.unique(df['x_s2_tpf'].values)\n",
    "    unique_events=[[x, y] for x, y in zip(unique_x, unique_y)]\n",
    "    sampled_df=df.filter(['x_p_tpf','y_p_tpf'])\n",
    "    sampled_df=sampled_df.sample(sample_num)\n",
    "    for unique_s2 in tqdm(unique_events[:sample_num]):\n",
    "        s2_ypos=unique_s2[1]\n",
    "        s2_xpos=unique_s2[0]\n",
    "        rpos=np.sqrt(s2_xpos**2+s2_ypos**2)\n",
    "        for peak_key, peak in sampled_df.iterrows():\n",
    "            xpos=peak['x_p_tpf']\n",
    "            ypos=peak['y_p_tpf']\n",
    "            new_array.append([xpos, ypos])\n",
    "\n",
    "            r_dist=np.sqrt((xpos-(s2_xpos))**2+(ypos-(s2_ypos))**2)\n",
    "            alpha = np.arccos((r_dist**2+rpos**2-R_tpc**2)/(2*rpos*r_dist))\n",
    "            r_norm = (alpha/np.pi)\n",
    "            if np.isnan(alpha):\n",
    "                alpha=-1\n",
    "                r_norm=1\n",
    "            alpha_array.append([r_dist, alpha])\n",
    "            r_norms.append(r_norm)\n",
    "\n",
    "    alpha_array=np.array(alpha_array)\n",
    "    r_norms=np.array(r_norms)\n",
    "    dfs_source_distrib_hist[key]={'alpha': alpha_array, 'r_norms': r_norms}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "for key, source in dfs_source_distrib_hist.items():\n",
    "    alpha_array=source['alpha']\n",
    "    r_norms=source['r_norms']\n",
    "    print(key,len(alpha_array), len(r_norms))\n",
    "    plt.hist(alpha_array[:, 0]**2, weights=1/r_norms, range=[0, (2*R_tpc)**2], bins=100, histtype='step', linewidth=2, label=key)\n",
    "    #plt.vlines((R_tpc-rpos)**2, 0, 5000)\n",
    "    #plt.vlines((R_tpc+rpos)**2, 0, 5000)\n",
    "#plt.yscale('log')\n",
    "plt.xlim([0, (2*R_tpc)**2])\n",
    "plt.ylabel('Normalized Events [cm$^{-2}$]', fontsize=12)\n",
    "plt.xlabel('R  [cm$^{2}$]', fontsize=12)\n",
    "plt.legend(loc='best')\n",
    "#plt.savefig('fig_r2_norm_illustration_weighted.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "plt.hist(alpha_array[:, 0]**2, range=[0, (2*R_tpc)**2], bins=100, histtype='step', linewidth=2)\n",
    "#plt.vlines((R_tpc-rpos)**2, 0, 5000)\n",
    "#plt.vlines((R_tpc+rpos)**2, 0, 5000)\n",
    "#plt.ylim([0, 5000])\n",
    "plt.yscale('log')\n",
    "plt.ylabel('Normalized Events [cm$^{-2}$]', fontsize=12)\n",
    "plt.xlabel('R  [cm$^{2}$]', fontsize=12)\n",
    "#plt.savefig('fig_r2_norm_illustration.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.scatter(alpha_array[:, 0], r_norms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('random_source_sampling.pkl', 'wb') as handle:\n",
    "    pickle.dump(dfs_source_distrib_hist, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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