{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dc687af9",
   "metadata": {},
   "source": [
    "# Blazar-neutrino chance coincidence simulation\n",
    "\n",
    "We demonstrate how to run a simulation of a blazar population and diffuse neutrino flux that have no underlying connection. The simulation makes use of the configuration files provided with the package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9122b472",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-22T12:25:54.400974Z",
     "iopub.status.busy": "2022-07-22T12:25:54.400697Z",
     "iopub.status.idle": "2022-07-22T12:25:58.179069Z",
     "shell.execute_reply": "2022-07-22T12:25:58.177564Z"
    }
   },
   "outputs": [],
   "source": [
    "from popsynth.utils.logging import quiet_mode\n",
    "from icecube_tools.detector.effective_area import EffectiveArea\n",
    "from icecube_tools.detector.energy_resolution import EnergyResolution\n",
    "from icecube_tools.detector.angular_resolution import AngularResolution\n",
    "\n",
    "from nu_coincidence.blazar_nu.coincidence import BlazarNuCoincidenceSim\n",
    "from nu_coincidence.blazar_nu.coincidence import BlazarNuCoincidenceResults\n",
    "from nu_coincidence.utils.package_data import get_available_config\n",
    "\n",
    "quiet_mode()  # silence popsynth logs/output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a17a1197",
   "metadata": {},
   "source": [
    "Firstly, we make sure that we have the necessary files for running an IceCube simulation by using the `icecube_tools` package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e419726e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-22T12:25:58.184318Z",
     "iopub.status.busy": "2022-07-22T12:25:58.183237Z",
     "iopub.status.idle": "2022-07-22T12:26:39.299558Z",
     "shell.execute_reply": "2022-07-22T12:26:39.298769Z"
    }
   },
   "outputs": [
    {
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      "\n"
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    }
   ],
   "source": [
    "my_aeff = EffectiveArea.from_dataset(\"20181018\")\n",
    "my_aeff = EffectiveArea.from_dataset(\"20131121\")\n",
    "my_eres = EnergyResolution.from_dataset(\"20150820\")\n",
    "my_angres = AngularResolution.from_dataset(\"20181018\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03908347",
   "metadata": {},
   "source": [
    "The inputs to `BlazarNuCoincidenceSim` are specified by the `.yml` config files provided with the `nu_coincidence` package. We can check what is available using the helper function `get_available_config()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "47c582d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-22T12:26:39.303906Z",
     "iopub.status.busy": "2022-07-22T12:26:39.303498Z",
     "iopub.status.idle": "2022-07-22T12:26:39.320813Z",
     "shell.execute_reply": "2022-07-22T12:26:39.319677Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['nu_connected_hese.yml',\n",
       " 'fsrq_high.yml',\n",
       " 'bllac_rbcu.yml',\n",
       " 'nu_diffuse_tracks.yml',\n",
       " 'fsrq_ref.yml',\n",
       " 'nu_diffuse_ehe_hard.yml',\n",
       " 'nu_diffuse_hese_soft.yml',\n",
       " 'bllac_high.yml',\n",
       " 'nu_connected_tracks.yml',\n",
       " 'bllac_low.yml',\n",
       " 'bllac_ref.yml',\n",
       " 'fsrq_gpsel.yml',\n",
       " 'fsrq_nobcu.yml',\n",
       " 'nu_diffuse_ehe_soft.yml',\n",
       " 'bllac_gpsel.yml',\n",
       " 'fsrq_low.yml',\n",
       " 'bllac_nobcu.yml',\n",
       " 'nu_diffuse_hese.yml',\n",
       " 'nu_diffuse_hese_hard.yml',\n",
       " 'nu_connected_ehe.yml',\n",
       " 'fsrq_rbcu.yml',\n",
       " 'nu_diffuse_ehe.yml']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_available_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "462a6550",
   "metadata": {},
   "source": [
    "For this example, we can use the reference blazar model (`bllac_ref.yml` and `fsrq_ref.yml`) together with the diffuse IceCube HESE and EHE alerts models (`nu_diffuse_hese.yml` and `nu_diffuse_ehe.yml`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "46b7be0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-22T12:26:39.324559Z",
     "iopub.status.busy": "2022-07-22T12:26:39.323973Z",
     "iopub.status.idle": "2022-07-22T12:29:05.555361Z",
     "shell.execute_reply": "2022-07-22T12:29:05.554484Z"
    }
   },
   "outputs": [],
   "source": [
    "sim = BlazarNuCoincidenceSim(\n",
    "    file_name=\"output/test_coincidence_sim.h5\",  # output file name\n",
    "    N=2,  # number of independent simulations to run\n",
    "    bllac_config=\"bllac_ref.yml\",\n",
    "    fsrq_config=\"fsrq_ref.yml\",\n",
    "    nu_hese_config=\"nu_diffuse_hese.yml\",\n",
    "    nu_ehe_config=\"nu_diffuse_ehe.yml\",\n",
    "    seed=42,\n",
    ")\n",
    "\n",
    "sim.run(parallel=False)  # define number of parallel jobs to run"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dafe6b54",
   "metadata": {},
   "source": [
    "The results are organised to store relevant information on the number of different coincidences, divided into the `results.bllac` and `results.fsrq` populations. We study *spatial*, *variable* and *flaring* coincidences. In just 2 simulations, we expect to see spatial and variable coincidences, but flaring coincidences are unlikely. In the event that flaring coincidences occur, several other population/neutrino properties are also stored for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f809efad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-22T12:29:05.560234Z",
     "iopub.status.busy": "2022-07-22T12:29:05.559770Z",
     "iopub.status.idle": "2022-07-22T12:29:05.570799Z",
     "shell.execute_reply": "2022-07-22T12:29:05.569518Z"
    }
   },
   "outputs": [],
   "source": [
    "results = BlazarNuCoincidenceResults.load([\"output/test_coincidence_sim.h5\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a5b7bbc5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-22T12:29:05.574032Z",
     "iopub.status.busy": "2022-07-22T12:29:05.573787Z",
     "iopub.status.idle": "2022-07-22T12:29:05.580037Z",
     "shell.execute_reply": "2022-07-22T12:29:05.579344Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('n_spatial', array([ 5., 15.])),\n",
       "             ('n_spatial_astro', array([1., 5.])),\n",
       "             ('n_variable', array([0., 1.])),\n",
       "             ('n_variable_astro', array([0., 0.])),\n",
       "             ('n_flaring', array([0., 0.])),\n",
       "             ('n_flaring_astro', array([0., 0.])),\n",
       "             ('matched_flare_amplitudes', array([], dtype=float64)),\n",
       "             ('matched_nu_ras', []),\n",
       "             ('matched_nu_decs', []),\n",
       "             ('matched_nu_ang_errs', []),\n",
       "             ('matched_nu_times', []),\n",
       "             ('pop_ras', []),\n",
       "             ('pop_decs', []),\n",
       "             ('pop_fluxes', [])])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.bllac"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfc95a8e",
   "metadata": {},
   "source": [
    "As we ran 2 simulations, we have a coincidence count for each case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "25fc8856",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-22T12:29:05.583954Z",
     "iopub.status.busy": "2022-07-22T12:29:05.583489Z",
     "iopub.status.idle": "2022-07-22T12:29:05.588352Z",
     "shell.execute_reply": "2022-07-22T12:29:05.587628Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5., 15.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.bllac[\"n_spatial\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "acc02bd9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-22T12:29:05.592190Z",
     "iopub.status.busy": "2022-07-22T12:29:05.591741Z",
     "iopub.status.idle": "2022-07-22T12:29:05.597480Z",
     "shell.execute_reply": "2022-07-22T12:29:05.596785Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('n_spatial', array([2., 8.])),\n",
       "             ('n_spatial_astro', array([0., 4.])),\n",
       "             ('n_variable', array([0., 2.])),\n",
       "             ('n_variable_astro', array([0., 1.])),\n",
       "             ('n_flaring', array([0., 0.])),\n",
       "             ('n_flaring_astro', array([0., 0.])),\n",
       "             ('matched_flare_amplitudes', array([], dtype=float64)),\n",
       "             ('matched_nu_ras', []),\n",
       "             ('matched_nu_decs', []),\n",
       "             ('matched_nu_ang_errs', []),\n",
       "             ('matched_nu_times', []),\n",
       "             ('pop_ras', []),\n",
       "             ('pop_decs', []),\n",
       "             ('pop_fluxes', [])])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.fsrq"
   ]
  }
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