{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IC170922A_wGFU\n",
    "\n",
    ".. contents:: :local:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Introduction\n",
    "\n",
    "The IC170922A_wGFU analysis is a multi-dataset time-integrated single source\n",
    "analysis with a two-component likelihood function using a spacial and an energy\n",
    "event PDF."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "Import analysis creation function from \"i3skyllh\" project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from i3skyllh.analyses.IC170922A_wGFU.analysis import create_analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the required imports for the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import numpy as np\n",
    "\n",
    "# Classes to define the source hypothesis.\n",
    "from skyllh.physics.source import PointLikeSource\n",
    "\n",
    "# Classes for utility functionality.\n",
    "from skyllh.core.config import CFG\n",
    "from skyllh.core.random import RandomStateService\n",
    "from skyllh.core.timing import TimeLord\n",
    "\n",
    "# Logging setup utilities.\n",
    "from skyllh.core.debugging import (\n",
    "    setup_logger,\n",
    "    setup_console_handler,\n",
    "    setup_file_handler\n",
    ")\n",
    "\n",
    "# The pre-defined data samples.\n",
    "from i3skyllh.datasets import data_samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define TXS 0506+056 location to use as a point source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def TXS_location():\n",
    "    src_ra  = np.radians(77.358)\n",
    "    src_dec = np.radians(5.693)\n",
    "    return (src_ra, src_dec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup logging first to get log messages from \"skyllh\" project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "setup_logger('skyllh', logging.DEBUG)\n",
    "log_format = '%(asctime)s %(processName)s %(name)s %(levelname)s: '\\\n",
    "             '%(message)s'\n",
    "setup_console_handler('skyllh', logging.INFO, log_format)\n",
    "#setup_file_handler('skyllh', logging.DEBUG, log_format, 'debug.log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set a number of CPUs available for parallel calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "CFG['multiproc']['ncpu'] = 16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define which datasets seasons to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_seasons = [\n",
    "    (\"PointSourceTracks\", \"IC40\"),\n",
    "    (\"PointSourceTracks\", \"IC59\"),\n",
    "    (\"PointSourceTracks\", \"IC79\"),\n",
    "    (\"PointSourceTracks\", \"IC86, 2011\"),\n",
    "    (\"PointSourceTracks\", \"IC86, 2012\"),\n",
    "    (\"PointSourceTracks\", \"IC86, 2013\"),\n",
    "    (\"PointSourceTracks\", \"IC86, 2014\"),\n",
    "    (\"PointSourceTracks\", \"IC86, 2015\"),\n",
    "    (\"PointSourceTracks\", \"IC86, 2016\"),\n",
    "    (\"PointSourceTracks\", \"IC86, 2017\"),\n",
    "    (\"GFU\", \"IC86, 2015-2018\")\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = []\n",
    "for (sample, season) in sample_seasons:\n",
    "    # Get the dataset from the correct dataset collection.\n",
    "    dsc = data_samples[sample].create_dataset_collection()\n",
    "    datasets.append(dsc.get_dataset(season))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set a rss seed and define a random state service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "rss_seed = 1\n",
    "rss = RandomStateService(rss_seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "source = PointLikeSource(*TXS_location())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a TimeLord instance. It is used to keep track of execution times of specific code segments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "tl = TimeLord()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[==========================================================] 100% ELT 0h:03m:57s\n",
      "[==========================================================] 100% ELT 0h:01m:34s\n"
     ]
    }
   ],
   "source": [
    "with tl.task_timer('Creating analysis.'):\n",
    "    ana = create_analysis(\n",
    "        datasets, source,\n",
    "        data_base_path=None,\n",
    "        compress_data=False,\n",
    "        tl=tl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unblind the data and print best fit parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TS = 25.1273\n",
      "ns_fit = 15.8888\n",
      "gamma_fit = 1.98958\n"
     ]
    }
   ],
   "source": [
    "with tl.task_timer('Unblinding data.'):\n",
    "    (TS, fitparam_dict, status) = ana.unblind(rss)\n",
    "print('TS = %g'%(TS))\n",
    "print('ns_fit = %g'%(fitparam_dict['ns']))\n",
    "print('gamma_fit = %g'%(fitparam_dict['gamma']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate some signal events."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('n_sig: %d', 98)\n",
      "signal datasets: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n"
     ]
    }
   ],
   "source": [
    "with tl.task_timer('Generating signal events.'):\n",
    "    (n_sig, signal_events_dict) = ana.sig_generator.generate_signal_events(rss, 100)\n",
    "print('n_sig: %d', n_sig)\n",
    "print('signal datasets: '+str(signal_events_dict.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the TimeLord instance to get a summary of execution times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executed tasks:\n",
      "[                                     Loading grl data from disk.] 0.622766 sec\n",
      "[                                     Loading exp data from disk.] 2.55504 sec\n",
      "[                                      Loading mc data from disk.] 64.3951 sec\n",
      "[Selected only the experimental data that matches the GRL for dat] 0.0118048 sec\n",
      "[                Preparing data of dataset \"IC40\" by \"data_prep\".] 0.0924909 sec\n",
      "[             Appending IceCube-specific data fields to exp data.] 0.0445914 sec\n",
      "[              Appending IceCube-specific data fields to MC data.] 4.54178 sec\n",
      "[                                              Cleaning exp data.] 0.0034306 sec\n",
      "[                                               Cleaning MC data.] 0.179492 sec\n",
      "[                                          Asserting data format.] 0.000287294 sec\n",
      "[Selected only the experimental data that matches the GRL for dat] 0.013073 sec\n",
      "[                Preparing data of dataset \"IC59\" by \"data_prep\".] 0.22692 sec\n",
      "[Selected only the experimental data that matches the GRL for dat] 0.00786304 sec\n",
      "[                Preparing data of dataset \"IC79\" by \"data_prep\".] 0.216612 sec\n",
      "[Selected only the experimental data that matches the GRL for dat] 0.0117018 sec\n",
      "[          Preparing data of dataset \"IC86, 2011\" by \"data_prep\".] 0.0715699 sec\n",
      "[Selected only the experimental data that matches the GRL for dat] 0.01633 sec\n",
      "[          Preparing data of dataset \"IC86, 2012\" by \"data_prep\".] 1.39812 sec\n",
      "[Selected only the experimental data that matches the GRL for dat] 0.0136781 sec\n",
      "[          Preparing data of dataset \"IC86, 2013\" by \"data_prep\".] 2.32966 sec\n",
      "[Selected only the experimental data that matches the GRL for dat] 0.0154629 sec\n",
      "[          Preparing data of dataset \"IC86, 2014\" by \"data_prep\".] 1.78734 sec\n",
      "[Selected only the experimental data that matches the GRL for dat] 0.0154121 sec\n",
      "[          Preparing data of dataset \"IC86, 2015\" by \"data_prep\".] 1.34278 sec\n",
      "[Selected only the experimental data that matches the GRL for dat] 0.0102689 sec\n",
      "[          Preparing data of dataset \"IC86, 2016\" by \"data_prep\".] 1.51473 sec\n",
      "[Selected only the experimental data that matches the GRL for dat] 0.015995 sec\n",
      "[          Preparing data of dataset \"IC86, 2017\" by \"data_prep\".] 1.33161 sec\n",
      "[Selected only the experimental data that matches the GRL for dat] 0.114225 sec\n",
      "[     Preparing data of dataset \"IC86, 2015-2018\" by \"data_prep\".] 1.10847 sec\n",
      "[                                              Creating analysis.] 335.913 sec\n",
      "[                                                Unblinding data.] 0.361189 sec\n",
      "[                                       Generating signal events.] 0.0934389 sec\n"
     ]
    }
   ],
   "source": [
    "print(tl)"
   ]
  }
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