{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using high-level science data\n",
    "\n",
    "High-level science data includes information provided as functions, histograms, tables etc. containing data derived from e.g. simulations like detector acceptance, background expectations etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Loaded catalog from cache.\n"
     ]
    }
   ],
   "source": [
    "from openkm3.store import KM3Store\n",
    "store = KM3Store()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lookup table: Detector acceptance\n",
    "The detector acceptance (here for the ANTARES 2007-2017 neutrino sample) is provided as lookup table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Loaded entry ana20_01_acc as <class 'openkm3.dataclasses.LookUpTable'>.\n"
     ]
    }
   ],
   "source": [
    "acceptance = store.get(\"ana20_01_acc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "| Parameter | Name | Description | Unit | Symbol | Range | \n",
       "| --------- | ---- | ----------- | ---- | ------ | ----- | \n",
       "| xaxis | Declination | Source declination | deg | %delta | [-90, 90] | \n",
       "| yaxis | Spectral index | Exponential of the energy power spectrum E^{-x} |  | %lambda | [1.5, 3.0] | \n",
       "| returnvalue | Detector acceptance | Acceptance of the detector to a given neutrino point source flux | GeV^{-1} cm^{2} s | Acceptance |  | \n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acceptance.show_paraminfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:x value 100 out of range. Range is [-90, 90]\n"
     ]
    }
   ],
   "source": [
    "acceptance.lookup(xvalue = 100, yvalue = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = acceptance.get_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind = \"line\", logy=True, legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function: Number of background events estimate\n",
    "\n",
    "This information (again for the ANTARES 2007-2017 sample) is provided as polynomial function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Loading data from cache.\n",
      "INFO:root:Loaded entry ana20_01_bkg as <class 'openkm3.dataclasses.Function'>.\n"
     ]
    }
   ],
   "source": [
    "bkg = store.get(\"ana20_01_bkg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'polynomial'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bkg.functiontype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "| Parameter | Name | Description | Unit | Symbol | Range | \n",
       "| --------- | ---- | ----------- | ---- | ------ | ----- | \n",
       "| xvalue | Declination | Source declination | deg | %delta | [-90, 90] | \n",
       "| returnvalue | Number of background events | Estimated number of muon background events |  | N_{\\mu, bkg} |  | \n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bkg.show_paraminfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7893.0656496"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bkg.evaluate(20) # returns function result for given value"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}