{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Studying Ptolemy's sun declination using visualization tools\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kanon.units import Sexagesimal\n",
    "from kanon.units.precision import set_precision, TruncatureMode\n",
    "from kanon.tables import HTable\n",
    "import math\n",
    "from astropy.units import arcsecond\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function redefines what is in the declination notebook\n",
    "\n",
    "def build_declination(sin_table, obliquity):\n",
    "    arcsin_table = sin_table.copy(set_index=\"Val\")\n",
    "    obl = sin_table.get(obliquity)\n",
    "    obl_table = sin_table.apply(\"Val\", lambda x: x * obl)\n",
    "    return obl_table.apply(\"Val\", lambda x: round(arcsin_table.get(x), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "OBLIQUITY = \"23;51,20\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sin = lambda x: math.sin(x * math.pi / 180)\n",
    "asin = lambda x: math.asin(x) * 180 / math.pi\n",
    "\n",
    "# This table holds computed sine values\n",
    "sin_table_true = HTable([\n",
    "    list(Sexagesimal.range(0, 91)),\n",
    "    [round(\n",
    "        Sexagesimal.from_float(sin(x), 3)\n",
    "    ) for x in range(0, 91)]\n",
    "], names=(\"Arg\", \"Val\"), index=\"Arg\")\n",
    "\n",
    "# This table holds computed declination values\n",
    "decl_table_true = HTable([\n",
    "    list(Sexagesimal.range(1, 91)),\n",
    "    [round(Sexagesimal.from_float(\n",
    "        asin(sin(x) * sin(Sexagesimal(OBLIQUITY))\n",
    "    ), 2)) for x in range(1, 91)]\n",
    "], names=(\"Arg\", \"Val\"), index=\"Arg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# We import Ptolemy's sun declination table from DISHAS\n",
    "ptolemy_decl = HTable.read(214, format=\"dishas\")\n",
    "\n",
    "ptolemy_decl.plot2d()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We rebuild a declination table from a sine table without odd arguments\n",
    "\n",
    "sin_table_grid2 = sin_table_true[[i for i in range(0, 91, 2)]]\n",
    "decl_table_grid2 = build_declination(sin_table_grid2, Sexagesimal(OBLIQUITY))[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Let's compare Ptolemy's declination with our reconstructed tables\n",
    "\n",
    "residue_nogrid = ptolemy_decl.apply(\"Entries\", lambda x: x - decl_table_true[\"Val\"], \"Declination residue\")\n",
    "residue_nogrid[\"Declination residue\"] = residue_nogrid[\"Declination residue\"].to(arcsecond).astype(float)\n",
    "# For the computed declination table residue, we plot in blue\n",
    "residue_nogrid.plot2d('bx', linestyle='dashed', lw=0.4)\n",
    "\n",
    "residue_grid2 = ptolemy_decl[\n",
    "    [i for i in range(1,91,2)]\n",
    "].apply(\"Entries\", lambda x: x - decl_table_grid2[\"Val\"], \"Declination residue\")\n",
    "residue_grid2[\"Declination residue\"] = residue_grid2[\"Declination residue\"].to(arcsecond).astype(float)\n",
    "# For the built declination with grid 2 sine residue, we plot in red\n",
    "residue_grid2.plot2d('r+', linestyle='dashed', lw=0.4)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Residue with no interpolation\nmean : 0.3111, std : 2.2639\nquartiles : [-1.  0.  1.] \n\nResidue with grid 2 interpolation\nmean : -3.0222, std : 3.1516\nquartiles : [-5. -3. -1.]\n"
     ]
    }
   ],
   "source": [
    "arr_nogrid = residue_nogrid['Declination residue']\n",
    "arr_grid2 = residue_grid2['Declination residue']\n",
    "\n",
    "print(\"Residue with no interpolation\")\n",
    "print(f\"mean : {np.mean(arr_nogrid):.4f}, std : {np.std(arr_nogrid):.4f}\")\n",
    "print(f\"quartiles : {np.quantile(arr_nogrid, [0.25, 0.5, 0.75])} \\n\")\n",
    "print(\"Residue with grid 2 interpolation\")\n",
    "print(f\"mean : {np.mean(arr_grid2):.4f}, std : {np.std(arr_grid2):.4f}\")\n",
    "print(f\"quartiles : {np.quantile(arr_grid2, [0.25, 0.5, 0.75])}\")"
   ]
  }
 ],
 "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": "3.9.0"
  },
  "metadata": {
   "interpreter": {
    "hash": "4cd7ab41f5fca4b9b44701077e38c5ffd31fe66a6cab21e0214b68d958d0e462"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}