{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# General testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mpcorbfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Class to drive\n",
    "f = mpcorbfile.mpcorb_file()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "J9611 1996-01-01 00:00:00\n",
      "J961A 1996-01-10 00:00:00\n",
      "J969U 1996-09-30 00:00:00\n",
      "J96A1 1996-10-01 00:00:00\n",
      "K01AM 2001-10-22 00:00:00\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "CALCULATE DATES FROM COMPRESSED EPOCH\n",
    "Examples:\n",
    "\n",
    "        1996 Jan. 1    = J9611\n",
    "        1996 Jan. 10   = J961A\n",
    "        1996 Sept.30   = J969U\n",
    "        1996 Oct. 1    = J96A1\n",
    "        2001 Oct. 22   = K01AM\n",
    "\n",
    "        This system can be extended to dates with non-integral days. The decimal fraction of the day is simply appended to the five characters defined above.\n",
    "\n",
    "        Examples:\n",
    "\n",
    "        1998 Jan. 18.73     = J981I73\n",
    "        2001 Oct. 22.138303 = K01AM138303\n",
    "\"\"\"\n",
    "for p in [\"J9611\", \"J961A\", \"J969U\", \"J96A1\", \"K01AM\"]:\n",
    "    print(p, f.compressed_epoch_to_datetime(p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "reading: 100%|\u001b[32m██████████\u001b[0m| 13.2k/13.2k [00:00<00:00, 134k bodies/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['packed_designation',\n",
       " 'H',\n",
       " 'G',\n",
       " 'Epoch',\n",
       " 'M',\n",
       " 'Peri',\n",
       " 'Node',\n",
       " 'i',\n",
       " 'e',\n",
       " 'n',\n",
       " 'a',\n",
       " 'U',\n",
       " 'Ref',\n",
       " 'Num_obs',\n",
       " 'Num_opps',\n",
       " 'Arc_length',\n",
       " 'rms',\n",
       " 'Perturbers',\n",
       " 'Perturbers_2',\n",
       " 'Computer',\n",
       " 'Hex_flags',\n",
       " 'Number',\n",
       " 'Name',\n",
       " 'Last_obs',\n",
       " 'epochJD',\n",
       " 'designation',\n",
       " 'discover_date',\n",
       " 'orbit_type']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load MPCORB.DAT\n",
    "bodies = f.read(\"MPCORB_TEST.DAT\")\n",
    "# bodies = f.read(\"MPCORB.DAT\")\n",
    "f.colnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'packed_designation': 'a7943',\n",
       " 'H': 24.19,\n",
       " 'G': 0.15,\n",
       " 'Epoch': datetime.datetime(2025, 5, 5, 0, 0),\n",
       " 'M': 184.7538,\n",
       " 'Peri': 195.63684,\n",
       " 'Node': 146.91033,\n",
       " 'i': 11.6102,\n",
       " 'e': 0.0894684,\n",
       " 'n': 1.13494811,\n",
       " 'a': 0.9102319,\n",
       " 'U': '2',\n",
       " 'Ref': 'E2024-JU2',\n",
       " 'Num_obs': 1055,\n",
       " 'Num_opps': 2,\n",
       " 'Arc_length': '2012-2013',\n",
       " 'rms': 0.36,\n",
       " 'Perturbers': 'M-v',\n",
       " 'Perturbers_2': '3Ek',\n",
       " 'Computer': 'Veres',\n",
       " 'Hex_flags': 2049,\n",
       " 'Number': '(367943)',\n",
       " 'Name': 'Duende',\n",
       " 'Last_obs': '20130221',\n",
       " 'epochJD': 2460800.5,\n",
       " 'designation': '367943',\n",
       " 'discover_date': nan,\n",
       " 'orbit_type': 'NEO;Athen'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bodies[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "writting: 100%|\u001b[34m██████████\u001b[0m| 13.2k/13.2k [00:00<00:00, 65.2k bodies/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write new file\n",
    "f.write(\"new_mcorb.dat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>packed_designation</th>\n",
       "      <th>H</th>\n",
       "      <th>G</th>\n",
       "      <th>Epoch</th>\n",
       "      <th>M</th>\n",
       "      <th>Peri</th>\n",
       "      <th>Node</th>\n",
       "      <th>i</th>\n",
       "      <th>e</th>\n",
       "      <th>n</th>\n",
       "      <th>...</th>\n",
       "      <th>Perturbers_2</th>\n",
       "      <th>Computer</th>\n",
       "      <th>Hex_flags</th>\n",
       "      <th>Number</th>\n",
       "      <th>Name</th>\n",
       "      <th>Last_obs</th>\n",
       "      <th>epochJD</th>\n",
       "      <th>designation</th>\n",
       "      <th>discover_date</th>\n",
       "      <th>orbit_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a7943</td>\n",
       "      <td>24.19</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2025-05-05</td>\n",
       "      <td>184.75380</td>\n",
       "      <td>195.63684</td>\n",
       "      <td>146.91033</td>\n",
       "      <td>11.61020</td>\n",
       "      <td>0.089468</td>\n",
       "      <td>1.134948</td>\n",
       "      <td>...</td>\n",
       "      <td>3Ek</td>\n",
       "      <td>Veres</td>\n",
       "      <td>2049</td>\n",
       "      <td>(367943)</td>\n",
       "      <td>Duende</td>\n",
       "      <td>20130221</td>\n",
       "      <td>2460800.5</td>\n",
       "      <td>367943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NEO;Athen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00001</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2025-05-05</td>\n",
       "      <td>188.70269</td>\n",
       "      <td>73.27343</td>\n",
       "      <td>80.25221</td>\n",
       "      <td>10.58780</td>\n",
       "      <td>0.079401</td>\n",
       "      <td>0.214247</td>\n",
       "      <td>...</td>\n",
       "      <td>30k</td>\n",
       "      <td>MPCLINUX</td>\n",
       "      <td>16384</td>\n",
       "      <td>(1)</td>\n",
       "      <td>Ceres</td>\n",
       "      <td>20241101</td>\n",
       "      <td>2460800.5</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MB II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00002</td>\n",
       "      <td>4.11</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2025-05-05</td>\n",
       "      <td>168.79870</td>\n",
       "      <td>310.91037</td>\n",
       "      <td>172.89530</td>\n",
       "      <td>34.92402</td>\n",
       "      <td>0.230540</td>\n",
       "      <td>0.213766</td>\n",
       "      <td>...</td>\n",
       "      <td>28k</td>\n",
       "      <td>Pan</td>\n",
       "      <td>16384</td>\n",
       "      <td>(2)</td>\n",
       "      <td>Pallas</td>\n",
       "      <td>20240927</td>\n",
       "      <td>2460800.5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MB II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00003</td>\n",
       "      <td>5.18</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2025-05-05</td>\n",
       "      <td>172.45499</td>\n",
       "      <td>247.85972</td>\n",
       "      <td>169.82994</td>\n",
       "      <td>12.98651</td>\n",
       "      <td>0.255947</td>\n",
       "      <td>0.225826</td>\n",
       "      <td>...</td>\n",
       "      <td>3Ek</td>\n",
       "      <td>Pan</td>\n",
       "      <td>16384</td>\n",
       "      <td>(3)</td>\n",
       "      <td>Juno</td>\n",
       "      <td>20240607</td>\n",
       "      <td>2460800.5</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MB II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00004</td>\n",
       "      <td>3.25</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2025-05-05</td>\n",
       "      <td>332.45069</td>\n",
       "      <td>151.58335</td>\n",
       "      <td>103.70297</td>\n",
       "      <td>7.14394</td>\n",
       "      <td>0.090107</td>\n",
       "      <td>0.271613</td>\n",
       "      <td>...</td>\n",
       "      <td>18k</td>\n",
       "      <td>Pan</td>\n",
       "      <td>16384</td>\n",
       "      <td>(4)</td>\n",
       "      <td>Vesta</td>\n",
       "      <td>20240306</td>\n",
       "      <td>2460800.5</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MB I</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  packed_designation      H     G      Epoch          M       Peri       Node  \\\n",
       "0              a7943  24.19  0.15 2025-05-05  184.75380  195.63684  146.91033   \n",
       "1              00001   3.34  0.15 2025-05-05  188.70269   73.27343   80.25221   \n",
       "2              00002   4.11  0.15 2025-05-05  168.79870  310.91037  172.89530   \n",
       "3              00003   5.18  0.15 2025-05-05  172.45499  247.85972  169.82994   \n",
       "4              00004   3.25  0.15 2025-05-05  332.45069  151.58335  103.70297   \n",
       "\n",
       "          i         e         n  ...  Perturbers_2  Computer Hex_flags  \\\n",
       "0  11.61020  0.089468  1.134948  ...           3Ek     Veres      2049   \n",
       "1  10.58780  0.079401  0.214247  ...           30k  MPCLINUX     16384   \n",
       "2  34.92402  0.230540  0.213766  ...           28k       Pan     16384   \n",
       "3  12.98651  0.255947  0.225826  ...           3Ek       Pan     16384   \n",
       "4   7.14394  0.090107  0.271613  ...           18k       Pan     16384   \n",
       "\n",
       "     Number    Name  Last_obs    epochJD designation discover_date orbit_type  \n",
       "0  (367943)  Duende  20130221  2460800.5      367943           NaN  NEO;Athen  \n",
       "1       (1)   Ceres  20241101  2460800.5           1           NaN      MB II  \n",
       "2       (2)  Pallas  20240927  2460800.5           2           NaN      MB II  \n",
       "3       (3)    Juno  20240607  2460800.5           3           NaN      MB II  \n",
       "4       (4)   Vesta  20240306  2460800.5           4           NaN       MB I  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df=pd.DataFrame(bodies)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Some plot to be used as a cover in documentation\n",
    "df.loc[0:50,['packed_designation','Peri','Node']].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test json support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.write_json(\"mpcorb.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in f.get_chunks(13):\n",
    "    c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:00<00:00, 157006.03it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Critical_list_numbered_object_flag': 1,\n",
       " 'H': 3.34,\n",
       " 'G': 0.15,\n",
       " 'Num_obs': 7330,\n",
       " 'rms': 0.8,\n",
       " 'U': '0',\n",
       " 'Arc_years': '1801-2024',\n",
       " 'Perturbers': 'M-v',\n",
       " 'Perturbers_2': '30k',\n",
       " 'Number': '(1)',\n",
       " 'Name': 'Ceres',\n",
       " 'Principal_desig': 'A801 AA',\n",
       " 'Other_desigs': ['A899 OF', '1943 XB'],\n",
       " 'Epoch': datetime.date(2025, 5, 5),\n",
       " 'M': 188.70269,\n",
       " 'Peri': 73.27343,\n",
       " 'Node': 80.25221,\n",
       " 'i': 10.5878,\n",
       " 'e': 0.0794013,\n",
       " 'n': 0.21424651,\n",
       " 'a': 2.7660512,\n",
       " 'Ref': 'E2024-V47',\n",
       " 'Num_opps': 125,\n",
       " 'Computer': 'MPCLINUX',\n",
       " 'Hex_flags': 16384,\n",
       " 'Last_obs': '20241101',\n",
       " 'Tp': 2461600.03376,\n",
       " 'Orbital_period': 4.6003441,\n",
       " 'Perihelion_dist': 2.5464231,\n",
       " 'Aphelion_dist': 2.9856793,\n",
       " 'Semilatus_rectum': 1.3743062,\n",
       " 'Synodic_period': 1.2777512,\n",
       " 'Orbit_type': 'MBA',\n",
       " 'packed_designation': '00001',\n",
       " 'Arc_length': '1801-2024'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bodies1=f.read_json('mpcorb_extended.json')\n",
    "bodies1 = f.read_json(\"mpcorb_extended_test.json\")\n",
    "bodies1[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "writting: 100%|\u001b[34m██████████\u001b[0m| 7.00/7.00 [00:00<00:00, 40.3k bodies/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.write(\"new_mpcorb1.dat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.write_json(\"mpcorb1.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K2555\n",
      "K2555\n",
      "K2555\n",
      "K2555\n",
      "K2555\n",
      "K2555\n",
      "K2555\n"
     ]
    }
   ],
   "source": [
    "for b in f.bodies:\n",
    "    print(f.datetime_compressed_epoch(b[\"Epoch\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1995 XA \t J95X00A\n",
      "1995 XL1 \t J95X01L\n",
      "1995 FB13 \t J95F13B\n",
      "1998 SQ108 \t J98SA8Q\n",
      "1998 SV127 \t J98SC7V\n",
      "2099 AZ193 \t K99AJ3Z\n",
      "2007 TA418 \t K07Tf8A\n",
      "(98) \t 00098\n",
      "(100345) \t A0345\n",
      "(360017) \t a0017\n",
      "(203289) \t K3289\n"
     ]
    }
   ],
   "source": [
    "for d in [\n",
    "    \"1995 XA\",\n",
    "    \"1995 XL1\",\n",
    "    \"1995 FB13\",\n",
    "    \"1998 SQ108\",\n",
    "    \"1998 SV127\",\n",
    "    \"2099 AZ193\",\n",
    "    \"2007 TA418\",\n",
    "    \"(98)\",\n",
    "    \"(100345)\",\n",
    "    \"(360017)\",\n",
    "    \"(203289)\",\n",
    "]:\n",
    "    print(d, \"\\t\", f.pack_designation(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3140113) ~AZaz\n",
      "(15396335) ~zzzz\n",
      "(620061) ~000z\n",
      "(620000) ~0000\n"
     ]
    }
   ],
   "source": [
    "for d in [\"(3140113)\", \"(15396335)\", \"(620061)\", \"(620000)\"]:\n",
    "    print(d, f.pack_designation(d))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create new files from scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "writting: 100%|\u001b[34m██████████\u001b[0m| 1.00/1.00 [00:00<00:00, 16.4k bodies/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "mpc = mpcorbfile.mpcorb_file()\n",
    "Name = \"2026 AA1\"\n",
    "epoch = datetime.datetime(2026, 1, 1)\n",
    "packed_designation = mpc.pack_designation(Name)\n",
    "body = {\n",
    "    \"packed_designation\": packed_designation,\n",
    "    \"H\": 3.2,\n",
    "    \"G\": 0.15,\n",
    "    \"Epoch\": epoch,\n",
    "    \"M\": 0.0,\n",
    "    \"Peri\": 0.0,\n",
    "    \"Node\": 0.0,\n",
    "    \"i\": 0.0,\n",
    "    \"e\": 0.0,\n",
    "    \"n\": 0.0,\n",
    "    \"a\": 0.0,\n",
    "    \"U\": 0.0,\n",
    "    \"Ref\": \"MPC 12345\",\n",
    "    \"Num\": 12345,\n",
    "    \"Name\": \"Test\",\n",
    "}\n",
    "mpc.add(body)\n",
    "mpc.write(\"test.dat\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mpcorbfile-4k_20HRF-py3.12",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
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