Final_comments.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "41174962",
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.io import fits\n",
    "from astropy.wcs import WCS\n",
    "from astropy.utils.data import get_pkg_data_filename\n",
    "from sqlite3 import connect\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord, search_around_sky\n",
    "from astroquery.vizier import Vizier\n",
    "import pandas as pd\n",
    "import sqlite3\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "from os import path\n",
    "from astropy.coordinates import SkyCoord\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as plp\n",
    "import joblib\n",
    "import lamy\n",
    "from matplotlib.patches import Ellipse, Circle\n",
    "from pathlib import Path\n",
    "\n",
    "import Image_module as im"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "882ed39a",
   "metadata": {},
   "source": [
    "## Чтение fits файла, определение центра и углового размера снятого поля, загрузка каталога снимка"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4564319",
   "metadata": {},
   "source": [
    "Проверка отработки для пяти объектов:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "adf5737a",
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = SkyCoord('12:32:48.7171 -1:29:22.243', unit=(u.hourangle, u.deg)) #AT2021lfa\n",
    "c2 = SkyCoord('15:21:25.48 68:20:34.0', unit=(u.hourangle, u.deg)) #150818A\n",
    "c3 = SkyCoord('16:15:46.6130 +14:23:07.714', unit=(u.hourangle, u.deg)) #210610B_R\n",
    "c4 = SkyCoord('1:48:07.2695 -6:20:49.995', unit=(u.hourangle, u.deg)) #210722A_r\n",
    "c5 = SkyCoord('129.8925066 79.2215748', unit=(u.deg, u.deg)) #210912a\n",
    "\n",
    "circle1 = 1\n",
    "circle2 = 5\n",
    "circle3 = 4.514\n",
    "circle4 = 11.879\n",
    "circle5 = 0.851\n",
    "\n",
    "objects = {1: (c1.ra.value, c1.dec.value, circle1),\n",
    "           2: (c2.ra.value, c2.dec.value, circle2),\n",
    "           3: (c3.ra.value, c3.dec.value, circle3),\n",
    "           4: (c4.ra.value, c4.dec.value, circle4),\n",
    "           5: (c5.ra.value, c5.dec.value, circle5)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13e3e5bc",
   "metadata": {},
   "source": [
    "Выбор события"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 783,
   "id": "8ef69abb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(188.20298791666664, -1.4895119444444445, 1)"
      ]
     },
     "execution_count": 783,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 1\n",
    "obj = objects[n]\n",
    "           \n",
    "fits_name = 'test' + str(n) + '.fits'\n",
    "im_db_path = 'test' + str(n) + '.db'\n",
    "obj"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3551dfd",
   "metadata": {},
   "source": [
    "Первый объект - фильтр r; центр (188.19982497, -1.49268559); квадрат (22.04, 22.04); (PS, SDSS), координаты объекта\n",
    "(188.20298792, -1.48951194)\n",
    "\n",
    "Второй объект - фильтр R; центр (230.28175944, 68.38398499); квадрат (18.29, 18.29); (PS); координаты объекта\n",
    "(230.35616667, 68.34277778)\n",
    "\n",
    "Третий объект - фильтр R; центр (243.88638324, 14.41489765); квадрат (18.29, 18.29); (PS, SDSS); координаты объекта\n",
    "(243.94422083, 14.38547611)\n",
    "\n",
    "Четвёртый объект - фильтр r; центр(27.03028958333333, -6.347220833333333); квадрат (22.06, 22.06); координаты объекта\n",
    "(27.03382825, -6.346988)\n",
    "\n",
    "Пятый объект - фильтр r; центр(129.89651801, 79.22134282); квадрат (13.56, 13.56); координаты объекта\n",
    "(129.8925066, 79.2215748)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 784,
   "id": "2506910a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: FITSFixedWarning: RADECSYS= 'FK5 ' / Equatorial coordinate system \n",
      "the RADECSYS keyword is deprecated, use RADESYSa. [astropy.wcs.wcs]\n"
     ]
    }
   ],
   "source": [
    "img = im.imread(fits_name)\n",
    "w = WCS(img.hdr)\n",
    "\n",
    "field_center = SkyCoord(*w.wcs.crval, unit = [u.deg, u.deg])\n",
    "region_width = img.width * (abs(w.wcs.cdelt) * u.deg).to(u.arcsec)[0]\n",
    "region_height = img.height * (abs(w.wcs.cdelt) * u.deg).to(u.arcsec)[1]\n",
    "\n",
    "cat_db_path = 'catalogs.db' \n",
    "eps = 1 #input(\"Введите значение epsilon\")\n",
    "model = 'Maidanak' #input(\"Выберите название обсерватории\")\n",
    "\n",
    "# print(field_center)\n",
    "# print(region_width/60, region_height/60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 785,
   "id": "a36d3ae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>RA1</th>\n",
       "      <th>e_RA1</th>\n",
       "      <th>DE1</th>\n",
       "      <th>e_DE1</th>\n",
       "      <th>r1</th>\n",
       "      <th>e_r1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>187.990416102568</td>\n",
       "      <td>0.005057</td>\n",
       "      <td>-1.34337177398464</td>\n",
       "      <td>0.007076</td>\n",
       "      <td>13.523146</td>\n",
       "      <td>0.085988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>187.992885114294</td>\n",
       "      <td>1.502449</td>\n",
       "      <td>-1.35578648381857</td>\n",
       "      <td>1.688250</td>\n",
       "      <td>21.147539</td>\n",
       "      <td>0.133722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>187.995464776883</td>\n",
       "      <td>0.433014</td>\n",
       "      <td>-1.37032560910434</td>\n",
       "      <td>0.606173</td>\n",
       "      <td>20.333571</td>\n",
       "      <td>0.101317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>187.998126782129</td>\n",
       "      <td>7.854701</td>\n",
       "      <td>-1.35080854770134</td>\n",
       "      <td>10.990025</td>\n",
       "      <td>20.838474</td>\n",
       "      <td>0.114701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>187.999016169859</td>\n",
       "      <td>0.605724</td>\n",
       "      <td>-1.39665097046421</td>\n",
       "      <td>0.848311</td>\n",
       "      <td>20.918145</td>\n",
       "      <td>0.110413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>188.402057237663</td>\n",
       "      <td>0.063886</td>\n",
       "      <td>-1.62852446223497</td>\n",
       "      <td>0.076902</td>\n",
       "      <td>19.878124</td>\n",
       "      <td>2.204601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>188.404570605031</td>\n",
       "      <td>0.100146</td>\n",
       "      <td>-1.61537881754912</td>\n",
       "      <td>0.115736</td>\n",
       "      <td>20.240113</td>\n",
       "      <td>2.204767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>188.405363729061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.63930914913251</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.385395</td>\n",
       "      <td>2.204864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>188.405444729276</td>\n",
       "      <td>0.076628</td>\n",
       "      <td>-1.63928804028764</td>\n",
       "      <td>0.107085</td>\n",
       "      <td>19.594960</td>\n",
       "      <td>2.204521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>188.405475737529</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.63932011501443</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.456437</td>\n",
       "      <td>2.204928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1973 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   RA1     e_RA1                DE1      e_DE1         r1  \\\n",
       "0     187.990416102568  0.005057  -1.34337177398464   0.007076  13.523146   \n",
       "1     187.992885114294  1.502449  -1.35578648381857   1.688250  21.147539   \n",
       "2     187.995464776883  0.433014  -1.37032560910434   0.606173  20.333571   \n",
       "3     187.998126782129  7.854701  -1.35080854770134  10.990025  20.838474   \n",
       "4     187.999016169859  0.605724  -1.39665097046421   0.848311  20.918145   \n",
       "...                ...       ...                ...        ...        ...   \n",
       "1968  188.402057237663  0.063886  -1.62852446223497   0.076902  19.878124   \n",
       "1969  188.404570605031  0.100146  -1.61537881754912   0.115736  20.240113   \n",
       "1970  188.405363729061  0.000000  -1.63930914913251   0.000000  20.385395   \n",
       "1971  188.405444729276  0.076628  -1.63928804028764   0.107085  19.594960   \n",
       "1972  188.405475737529  0.000000  -1.63932011501443   0.000000  20.456437   \n",
       "\n",
       "          e_r1  \n",
       "0     0.085988  \n",
       "1     0.133722  \n",
       "2     0.101317  \n",
       "3     0.114701  \n",
       "4     0.110413  \n",
       "...        ...  \n",
       "1968  2.204601  \n",
       "1969  2.204767  \n",
       "1970  2.204864  \n",
       "1971  2.204521  \n",
       "1972  2.204928  \n",
       "\n",
       "[1973 rows x 6 columns]"
      ]
     },
     "execution_count": 785,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not path.isfile(im_db_path):\n",
    "        raise IOError(f\"No such file \\\"{im_db_path}\\\"\")\n",
    "        \n",
    "con1 = sqlite3.connect(im_db_path)\n",
    "df1 = pd.read_sql_query(\"SELECT * from objects\", con1)[['ra_deg', 'ra_err', 'dec_deg', 'dec_err', 'mag', 'mag_err']]\n",
    "\n",
    "\n",
    "df1.rename(columns={df1.columns[0]: 'RA1',\n",
    "                    df1.columns[1]: 'e_RA1',\n",
    "                    df1.columns[2]: 'DE1',\n",
    "                    df1.columns[3]: 'e_DE1',\n",
    "                    df1.columns[4]: 'r1',\n",
    "                    df1.columns[5]: 'e_r1'}, inplace=True)\n",
    "\n",
    "if fits_name == 'test2' or 'test3':\n",
    "    df1.r1 += 0.21\n",
    "\n",
    "\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "596199a6",
   "metadata": {},
   "source": [
    "### 1. Удалённый каталог"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 786,
   "id": "e96c79b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "catalogs = {'sdss': 'V/147/sdss12', 'ps': 'II/349/ps1'}\n",
    "\n",
    "sdss_columns_to_import = ['RA_ICRS', 'e_RA_ICRS', 'DE_ICRS', 'e_DE_ICRS', 'class', 'gmag','e_gmag','rmag','e_rmag','imag','e_imag','zmag','e_zmag']\n",
    "ps_columns_to_import = ['RAJ2000', 'e_RAJ2000', 'DEJ2000', 'e_DEJ2000', 'gmag','e_gmag','rmag','e_rmag','imag','e_imag','zmag','e_zmag']\n",
    "\n",
    "columns_to_import = {'sdss': sdss_columns_to_import, 'ps':ps_columns_to_import}\n",
    "cat_name = 'ps'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 787,
   "id": "2a996593",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 174 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "try: \n",
    "    outer_cat = Vizier(catalog=catalogs[cat_name], columns=columns_to_import[cat_name], row_limit=-1).query_region(field_center,\n",
    "                                                        width=region_width + 4/60*u.deg,\n",
    "                                                        height=region_height + 4/60*u.deg)[0]\n",
    "except IndexError:\n",
    "    print('Указанного поля нет в каталоге')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 788,
   "id": "e9ffc922",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame()\n",
    "\n",
    "for  i in columns_to_import[cat_name]:\n",
    "    df2[i] = outer_cat[i].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 789,
   "id": "d0829e92",
   "metadata": {},
   "outputs": [],
   "source": [
    "if cat_name == 'ps':  \n",
    "    \n",
    "    df2.rename(columns={df2.columns[0]: 'RA2',\n",
    "                        df2.columns[1]: 'e_RA2',\n",
    "                        df2.columns[2]: 'DE2',\n",
    "                        df2.columns[3]: 'e_DE2',\n",
    "                        df2.columns[4]: 'g2',\n",
    "                        df2.columns[5]: 'e_g2',\n",
    "                        df2.columns[6]: 'r2',\n",
    "                        df2.columns[7]: 'e_r2',\n",
    "                        df2.columns[8]: 'i2',\n",
    "                        df2.columns[9]: 'e_i2',\n",
    "                        df2.columns[10]: 'z2',\n",
    "                        df2.columns[11]: 'e_z2'}, inplace=True)\n",
    "    \n",
    "else:\n",
    "    pass\n",
    "\n",
    "if cat_name == 'sdss':\n",
    "    \n",
    "    df2.rename(columns={df2.columns[0]: 'RA2',\n",
    "                        df2.columns[1]: 'e_RA2',\n",
    "                        df2.columns[2]: 'DE2',\n",
    "                        df2.columns[3]: 'e_DE2',\n",
    "                        df2.columns[4]: 'Class',\n",
    "                        df2.columns[5]: 'g2',\n",
    "                        df2.columns[6]: 'e_g2',\n",
    "                        df2.columns[7]: 'r2',\n",
    "                        df2.columns[8]: 'e_r2',\n",
    "                        df2.columns[9]: 'i2',\n",
    "                        df2.columns[10]: 'e_i2',\n",
    "                        df2.columns[11]: 'z2',\n",
    "                        df2.columns[12]: 'e_z2'}, inplace=True)\n",
    "else:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 790,
   "id": "6c61042c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RA2</th>\n",
       "      <th>e_RA2</th>\n",
       "      <th>DE2</th>\n",
       "      <th>e_DE2</th>\n",
       "      <th>g2</th>\n",
       "      <th>e_g2</th>\n",
       "      <th>r2</th>\n",
       "      <th>e_r2</th>\n",
       "      <th>i2</th>\n",
       "      <th>e_i2</th>\n",
       "      <th>z2</th>\n",
       "      <th>e_z2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>188.325973</td>\n",
       "      <td>0.0818</td>\n",
       "      <td>-1.704784</td>\n",
       "      <td>0.0818</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.6410</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>21.0920</td>\n",
       "      <td>0.2210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>188.327129</td>\n",
       "      <td>0.0339</td>\n",
       "      <td>-1.701004</td>\n",
       "      <td>0.0342</td>\n",
       "      <td>21.6165</td>\n",
       "      <td>0.1515</td>\n",
       "      <td>21.6359</td>\n",
       "      <td>0.0767</td>\n",
       "      <td>20.9182</td>\n",
       "      <td>0.0459</td>\n",
       "      <td>20.5741</td>\n",
       "      <td>0.0735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>188.283283</td>\n",
       "      <td>0.0573</td>\n",
       "      <td>-1.707915</td>\n",
       "      <td>0.0573</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0656</td>\n",
       "      <td>0.0999</td>\n",
       "      <td>20.8918</td>\n",
       "      <td>0.1273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>188.280048</td>\n",
       "      <td>0.0638</td>\n",
       "      <td>-1.706350</td>\n",
       "      <td>0.0302</td>\n",
       "      <td>12.7100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.3050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.1370</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0620</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>188.276814</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>-1.703709</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>17.5081</td>\n",
       "      <td>0.1005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.4041</td>\n",
       "      <td>0.0959</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3993</th>\n",
       "      <td>188.237235</td>\n",
       "      <td>0.0860</td>\n",
       "      <td>-1.279913</td>\n",
       "      <td>0.0860</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.7781</td>\n",
       "      <td>0.1611</td>\n",
       "      <td>21.5698</td>\n",
       "      <td>0.1815</td>\n",
       "      <td>21.3314</td>\n",
       "      <td>0.1679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3994</th>\n",
       "      <td>188.237228</td>\n",
       "      <td>0.0616</td>\n",
       "      <td>-1.280105</td>\n",
       "      <td>0.0616</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.5185</td>\n",
       "      <td>0.1373</td>\n",
       "      <td>21.4500</td>\n",
       "      <td>0.0792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3995</th>\n",
       "      <td>188.238407</td>\n",
       "      <td>0.0621</td>\n",
       "      <td>-1.284536</td>\n",
       "      <td>0.0613</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.6272</td>\n",
       "      <td>0.0538</td>\n",
       "      <td>21.4702</td>\n",
       "      <td>0.1296</td>\n",
       "      <td>21.4311</td>\n",
       "      <td>0.1746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3996</th>\n",
       "      <td>188.190843</td>\n",
       "      <td>0.0948</td>\n",
       "      <td>-1.281908</td>\n",
       "      <td>0.0948</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.2060</td>\n",
       "      <td>0.0990</td>\n",
       "      <td>21.2997</td>\n",
       "      <td>0.1552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3997</th>\n",
       "      <td>188.192441</td>\n",
       "      <td>0.0878</td>\n",
       "      <td>-1.279844</td>\n",
       "      <td>0.0878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.5108</td>\n",
       "      <td>0.1271</td>\n",
       "      <td>21.4944</td>\n",
       "      <td>0.1918</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3998 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             RA2   e_RA2       DE2   e_DE2       g2    e_g2       r2    e_r2  \\\n",
       "0     188.325973  0.0818 -1.704784  0.0818      NaN     NaN      NaN     NaN   \n",
       "1     188.327129  0.0339 -1.701004  0.0342  21.6165  0.1515  21.6359  0.0767   \n",
       "2     188.283283  0.0573 -1.707915  0.0573      NaN     NaN      NaN     NaN   \n",
       "3     188.280048  0.0638 -1.706350  0.0302  12.7100     NaN  12.3050     NaN   \n",
       "4     188.276814  0.0089 -1.703709  0.0089  17.5081  0.1005      NaN     NaN   \n",
       "...          ...     ...       ...     ...      ...     ...      ...     ...   \n",
       "3993  188.237235  0.0860 -1.279913  0.0860      NaN     NaN  21.7781  0.1611   \n",
       "3994  188.237228  0.0616 -1.280105  0.0616      NaN     NaN  21.5185  0.1373   \n",
       "3995  188.238407  0.0621 -1.284536  0.0613      NaN     NaN  21.6272  0.0538   \n",
       "3996  188.190843  0.0948 -1.281908  0.0948      NaN     NaN      NaN     NaN   \n",
       "3997  188.192441  0.0878 -1.279844  0.0878      NaN     NaN  21.5108  0.1271   \n",
       "\n",
       "           i2    e_i2       z2    e_z2  \n",
       "0     21.6410  0.0153  21.0920  0.2210  \n",
       "1     20.9182  0.0459  20.5741  0.0735  \n",
       "2     21.0656  0.0999  20.8918  0.1273  \n",
       "3     12.1370     NaN  12.0620     NaN  \n",
       "4     18.4041  0.0959      NaN     NaN  \n",
       "...       ...     ...      ...     ...  \n",
       "3993  21.5698  0.1815  21.3314  0.1679  \n",
       "3994  21.4500  0.0792      NaN     NaN  \n",
       "3995  21.4702  0.1296  21.4311  0.1746  \n",
       "3996  21.2060  0.0990  21.2997  0.1552  \n",
       "3997  21.4944  0.1918      NaN     NaN  \n",
       "\n",
       "[3998 rows x 12 columns]"
      ]
     },
     "execution_count": 790,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "088becad",
   "metadata": {},
   "source": [
    "### 2. Локальный каталог"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 791,
   "id": "f16c0292",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "\n",
    "# if not path.isfile(cat_db_path):\n",
    "#         raise IOError(f\"No such file \\\"{cat_db_path}\\\"\")\n",
    "        \n",
    "# con2 = sqlite3.connect(cat_db_path)\n",
    "\n",
    "# df2 = pd.read_sql_query(\"SELECT * FROM {} WHERE ({} BETWEEN ? AND ?) AND ({} BETWEEN ? AND ?)\".format(cat_name, cat_name + '_RA',cat_name + '_DE'),\n",
    "#                         con2,\n",
    "#                         params = [field_center.ra.value - region_width.value/7200 - 1/60,\n",
    "#                                   field_center.ra.value + region_width.value/7200 + 1/60,\n",
    "#                                   field_center.dec.value - region_height.value/7200 - 1/60,\n",
    "#                                   field_center.dec.value + region_height.value/7200 + 1/60])\n",
    "\n",
    "# if cat_name == 'ps':  \n",
    "    \n",
    "#     df2.rename(columns={df2.columns[1]: 'RA2',\n",
    "#                         df2.columns[2]: 'e_RA2',\n",
    "#                         df2.columns[3]: 'DE2',\n",
    "#                         df2.columns[4]: 'e_DE2',\n",
    "#                         df2.columns[5]: 'g2',\n",
    "#                         df2.columns[6]: 'e_g2',\n",
    "#                         df2.columns[7]: 'r2',\n",
    "#                         df2.columns[8]: 'e_r2',\n",
    "#                         df2.columns[9]: 'i2',\n",
    "#                         df2.columns[10]: 'e_i2',\n",
    "#                         df2.columns[11]: 'z2',\n",
    "#                         df2.columns[12]: 'e_z2',\n",
    "#                         df2.columns[13]: 'y2',\n",
    "#                         df2.columns[14]: 'e_y2'}, inplace=True)\n",
    "# else:\n",
    "#     pass\n",
    "\n",
    "# if cat_name == 'sdss':\n",
    "    \n",
    "#     df2.rename(columns={df2.columns[1]: 'RA2',\n",
    "#                         df2.columns[2]: 'e_RA2',\n",
    "#                         df2.columns[3]: 'DE2',\n",
    "#                         df2.columns[4]: 'e_DE2',\n",
    "#                         df2.columns[5]: 'u2',\n",
    "#                         df2.columns[6]: 'e_u2',\n",
    "#                         df2.columns[7]: 'g2',\n",
    "#                         df2.columns[8]: 'e_g2',\n",
    "#                         df2.columns[9]: 'r2',\n",
    "#                         df2.columns[10]: 'e_r2',\n",
    "#                         df2.columns[11]: 'i2',\n",
    "#                         df2.columns[12]: 'e_i2',\n",
    "#                         df2.columns[13]: 'z2',\n",
    "#                         df2.columns[14]: 'e_z2'}, inplace=True)\n",
    "# else:\n",
    "#     pass\n",
    "\n",
    "# df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 792,
   "id": "c1034f0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj_coord = SkyCoord(obj[0]*u.deg, obj[1]*u.deg)\n",
    "df1_coord = SkyCoord(df1.iloc[:, 0].values.astype(float).tolist(), df1.iloc[:, 2].values.astype(float).tolist(), frame=\"icrs\", unit=\"deg\")\n",
    "df2_coord = SkyCoord(df2.iloc[:, 0].values.astype(float).tolist(), df2.iloc[:, 2].values.astype(float).tolist(), frame=\"icrs\", unit=\"deg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "553883c8",
   "metadata": {},
   "source": [
    "## Визуализация событий "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f06b88e",
   "metadata": {},
   "source": [
    "Соседи в каталоге изображения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 793,
   "id": "9665a907",
   "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>RA1</th>\n",
       "      <th>e_RA1</th>\n",
       "      <th>DE1</th>\n",
       "      <th>e_DE1</th>\n",
       "      <th>r1</th>\n",
       "      <th>e_r1</th>\n",
       "      <th>sep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1026</th>\n",
       "      <td>188.203039046218</td>\n",
       "      <td>0.116334</td>\n",
       "      <td>-1.48947962158306</td>\n",
       "      <td>0.162575</td>\n",
       "      <td>20.205963</td>\n",
       "      <td>0.139593</td>\n",
       "      <td>0.21771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   RA1     e_RA1                DE1     e_DE1         r1  \\\n",
       "1026  188.203039046218  0.116334  -1.48947962158306  0.162575  20.205963   \n",
       "\n",
       "          e_r1      sep  \n",
       "1026  0.139593  0.21771  "
      ]
     },
     "execution_count": 793,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seplimit = 15*u.arcsec\n",
    "df1_cp_id = obj_coord.separation(df1_coord) < seplimit\n",
    "df1_cp = df1.iloc[df1_cp_id].copy()\n",
    "\n",
    "# Расстояния\n",
    "df1_cp['sep'] = obj_coord.separation(df1_coord).to(u.arcsec)[df1_cp_id].value\n",
    "df1_cp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 794,
   "id": "baee8035",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Событие в каталоге изображения:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RA1       188.203039046218\n",
       "e_RA1             0.116334\n",
       "DE1      -1.48947962158306\n",
       "e_DE1             0.162575\n",
       "r1               20.205963\n",
       "e_r1              0.139593\n",
       "Name: 1026, dtype: object"
      ]
     },
     "execution_count": 794,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_loc_df1 = SkyCoord.match_to_catalog_sky(obj_coord, df1_coord)\n",
    "obj_df1 = df1.loc[obj_loc_df1[0]]\n",
    "\n",
    "print('Событие в каталоге изображения:')\n",
    "obj_df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21ecaf72",
   "metadata": {},
   "source": [
    "Соседи во втором каталоге"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 795,
   "id": "f49caa55",
   "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>RA2</th>\n",
       "      <th>e_RA2</th>\n",
       "      <th>DE2</th>\n",
       "      <th>e_DE2</th>\n",
       "      <th>g2</th>\n",
       "      <th>e_g2</th>\n",
       "      <th>r2</th>\n",
       "      <th>e_r2</th>\n",
       "      <th>i2</th>\n",
       "      <th>e_i2</th>\n",
       "      <th>z2</th>\n",
       "      <th>e_z2</th>\n",
       "      <th>sep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2142</th>\n",
       "      <td>188.201316</td>\n",
       "      <td>0.074</td>\n",
       "      <td>-1.493134</td>\n",
       "      <td>0.074</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.1086</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>21.5832</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>21.1052</td>\n",
       "      <td>0.3539</td>\n",
       "      <td>14.360882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             RA2  e_RA2       DE2  e_DE2  g2  e_g2       r2    e_r2       i2  \\\n",
       "2142  188.201316  0.074 -1.493134  0.074 NaN   NaN  22.1086  0.0034  21.5832   \n",
       "\n",
       "        e_i2       z2    e_z2        sep  \n",
       "2142  0.0073  21.1052  0.3539  14.360882  "
      ]
     },
     "execution_count": 795,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seplimit = 15*u.arcsec\n",
    "df2_cp_id = obj_coord.separation(df2_coord) < seplimit\n",
    "df2_cp = df2.iloc[df2_cp_id].copy()\n",
    "\n",
    "# Расстояния\n",
    "df2_cp['sep'] = obj_coord.separation(df2_coord).to(u.arcsec)[df2_cp_id].value\n",
    "df2_cp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0368ca70",
   "metadata": {},
   "source": [
    "Ближайший"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 796,
   "id": "e2a636b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "До ближайшего соседа во внешнем каталоге 14.54 угл. секунд, этот объект:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RA2      188.201316\n",
       "e_RA2      0.074000\n",
       "DE2       -1.493134\n",
       "e_DE2      0.074000\n",
       "g2              NaN\n",
       "e_g2            NaN\n",
       "r2        22.108600\n",
       "e_r2       0.003400\n",
       "i2        21.583200\n",
       "e_i2       0.007300\n",
       "z2        21.105200\n",
       "e_z2       0.353900\n",
       "Name: 2142, dtype: float64"
      ]
     },
     "execution_count": 796,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df1_coord = SkyCoord(float(obj_df1.RA1)*u.deg, float(obj_df1.DE1)*u.deg)\n",
    "\n",
    "obj_df1_loc_df2 = SkyCoord.match_to_catalog_sky(obj_df1_coord, df2_coord)\n",
    "\n",
    "print('До ближайшего соседа во внешнем каталоге {} угл. секунд, этот объект:'.format(np.round(obj_df1_loc_df2[1].value[0]*3600, 2)))\n",
    "df2.iloc[obj_df1_loc_df2[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1e74170",
   "metadata": {},
   "source": [
    "Отображение id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 797,
   "id": "7ec4cad5",
   "metadata": {},
   "outputs": [],
   "source": [
    "sep = 25*u.arcsec\n",
    "\n",
    "im_cent = df1.loc[obj_coord.separation(df1_coord) < sep]\n",
    "cat_cent = df2.loc[obj_coord.separation(df2_coord) < sep]\n",
    "\n",
    "im_cent_sk  = SkyCoord(im_cent.iloc[:, 0].values.astype(float).tolist(), im_cent.iloc[:, 2].values.astype(float).tolist(), frame=\"icrs\", unit=\"deg\")\n",
    "cat_cent_sk = SkyCoord(cat_cent.iloc[:, 0].values.astype(float).tolist(), cat_cent.iloc[:, 2].values.astype(float).tolist(), frame=\"icrs\", unit=\"deg\")\n",
    "\n",
    "im_text = np.array((im_cent.index), dtype = str)\n",
    "cat_text = np.array((cat_cent.index), dtype = str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 798,
   "id": "fad41a86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Минимум: 719, максимум: 64160, медиана: 758.0\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e786fa4fb24f470495ac1618db088ef3",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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width=1000.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib ipympl\n",
    "import scipy.stats as st\n",
    "\n",
    "q = st.chi2(df=2).ppf(.99997)\n",
    "delta = 50\n",
    "\n",
    "fig, [ax, ax1] = plt.subplots(1, 2, figsize=(10,10))\n",
    "\n",
    "#Центрируем на событие\n",
    "cent = np.array((w.world_to_pixel(obj_coord)[0], w.world_to_pixel(obj_coord)[1]))\n",
    "\n",
    "# объекты из каталога\n",
    "ax.scatter(w.world_to_pixel(cat_cent_sk)[0], w.world_to_pixel(cat_cent_sk)[1], s=60, marker='o', label='Outer catalog', \n",
    "                   linewidths = 2,\n",
    "                   edgecolors = 'orange',\n",
    "                   facecolors='none',\n",
    "                   alpha=0.75)\n",
    "\n",
    "for i in range(len(cat_text)):\n",
    "    ax.annotate(cat_text[i],  (w.world_to_pixel(cat_cent_sk[i])[0] - np.random.rand()*6 - 3, w.world_to_pixel(cat_cent_sk[i])[1] + np.random.rand()*6 - 3), c = 'orange', size=7, alpha=1)\n",
    "    \n",
    "    \n",
    "#объекты с изображения\n",
    "ax.scatter(w.world_to_pixel(im_cent_sk)[0], w.world_to_pixel(im_cent_sk)[1], s=10, color='r', label='Image catalog')\n",
    "\n",
    "for i in range(len(im_text)):\n",
    "    ax.annotate(im_text[i],  (w.world_to_pixel(im_cent_sk[i])[0] + np.random.rand()*6 - 3, w.world_to_pixel(im_cent_sk[i])[1] - np.random.rand()*6 - 3), c = 'r', size=7, alpha=1)\n",
    "    \n",
    "    \n",
    "    \n",
    "ax.scatter(w.world_to_pixel(obj_coord)[0], w.world_to_pixel(obj_coord)[1], color='k', marker=\"x\", alpha=.5, label='Event', s=3000)\n",
    "\n",
    "thr = img.data.mean() - img.data.min() \n",
    "m = np.median(img.data)\n",
    "\n",
    "ax.imshow(img.data, vmin=img.data.min(), vmax=m + thr)\n",
    "ax1.imshow(img.data, vmin=img.data.min(), vmax=m + thr)\n",
    "\n",
    "ax.set_xlim(cent[0] - delta, cent[0] + delta)\n",
    "ax1.set_xlim(cent[0] - delta, cent[0] + delta)\n",
    "\n",
    "ax.set_ylim(cent[1] - delta, cent[1] + delta)\n",
    "ax1.set_ylim(cent[1] - delta, cent[1] + delta)\n",
    "\n",
    "ax.set_xlabel('pixel', fontsize=15)\n",
    "ax.set_ylabel('pixel', fontsize=15)\n",
    "ax1.set_xlabel('pixel', fontsize=15)\n",
    "ax1.set_ylabel('pixel', fontsize=15)\n",
    "\n",
    "ax.grid()\n",
    "ax1.grid()\n",
    "ax.legend(loc=4,prop={'size': 8})\n",
    "\n",
    "# plt.savefig('first slide.png')\n",
    "print('Минимум: {}, максимум: {}, медиана: {}'.format(img.data.min(), img.data.max(), np.median(img.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f301ffcf",
   "metadata": {},
   "source": [
    "## Сопоставление"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 799,
   "id": "a594b617",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 18 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "nnbs = SkyCoord.match_to_catalog_sky(df1_coord, df2_coord)\n",
    "eps=2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "118e382a",
   "metadata": {},
   "source": [
    "Группа a0: каждый объект с изображения нашёл соседа в eps, и этот сосед не сопоставлен ни с кем другим"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 800,
   "id": "3c4822e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Всего объектов: 1973, объектов в a0: 1392\n"
     ]
    }
   ],
   "source": [
    "temp_ar = nnbs[1].to(u.arcsec).value < eps\n",
    "\n",
    "temp_df = df1.loc[temp_ar].copy()\n",
    "temp_df['id2'] = nnbs[0][temp_ar]\n",
    "\n",
    "a0 = temp_df.loc[~temp_df.id2.duplicated(keep=False)].copy()\n",
    "a0.reset_index(drop=False, inplace=True)\n",
    "a0.rename(columns={'index': 'id1'}, inplace=True)\n",
    "\n",
    "print('Всего объектов: {}, объектов в a0: {}'.format(len(df1), len(a0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a7edd24",
   "metadata": {},
   "source": [
    "Группа a1: объект никого не нашёл в eps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 801,
   "id": "f8e370e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Всего объектов: 1973, объектов в a1: 473\n"
     ]
    }
   ],
   "source": [
    "temp_ar = nnbs[1].to(u.arcsec).value > eps\n",
    "\n",
    "a1 = df1.loc[temp_ar].copy()\n",
    "\n",
    "print('Всего объектов: {}, объектов в a1: {}'.format(len(df1), len(a1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ef33a37",
   "metadata": {},
   "source": [
    "Группа a2: более одного объекта с изображения сопоставляются с одним и тем же объектом в каталоге "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 802,
   "id": "8f22114d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Всего объектов: 1973, объектов в a2: 108\n"
     ]
    }
   ],
   "source": [
    "temp_ar = nnbs[1].to(u.arcsec).value < eps\n",
    "\n",
    "temp_df = df1.loc[temp_ar].copy()\n",
    "temp_df['id2'] = nnbs[0][temp_ar]\n",
    "\n",
    "a2= temp_df.loc[temp_df.id2.duplicated(keep=False)].copy()\n",
    "a2.reset_index(drop=False, inplace=True)\n",
    "a2.rename(columns={'index': 'id1'}, inplace=True)\n",
    "\n",
    "print('Всего объектов: {}, объектов в a2: {}'.format(len(df1), len(a2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 803,
   "id": "06a88fbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(len(a0) + len(a1) + len(a2) == len(df1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6738e34d",
   "metadata": {},
   "source": [
    "Работа с группой a1: для каждого объекта в расширенном радиусе (~20arcsec) запускаем сопоставление по блеску. Если находим компаньон, проверяем, чтобы его не было в группе a0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 804,
   "id": "447b2e14",
   "metadata": {},
   "outputs": [
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       "      id1               RA1     e_RA1                DE1      e_DE1  \\\n",
       "0       1  187.992885114294  1.502449  -1.35578648381857   1.688250   \n",
       "1       1  187.992885114294  1.502449  -1.35578648381857   1.688250   \n",
       "2       1  187.992885114294  1.502449  -1.35578648381857   1.688250   \n",
       "3       3  187.998126782129  7.854701  -1.35080854770134  10.990025   \n",
       "4       3  187.998126782129  7.854701  -1.35080854770134  10.990025   \n",
       "..    ...               ...       ...                ...        ...   \n",
       "952  1958  188.393361497813  0.553573  -1.64282074274228   0.773243   \n",
       "953  1958  188.393361497813  0.553573  -1.64282074274228   0.773243   \n",
       "954  1958  188.393361497813  0.553573  -1.64282074274228   0.773243   \n",
       "955  1966  188.401424896036  0.000000  -1.61214696921092   0.000000   \n",
       "956  1966  188.401424896036  0.000000  -1.61214696921092   0.000000   \n",
       "\n",
       "            r1      e_r1   id2         RA2   e_RA2  ...       g2    e_g2  \\\n",
       "0    21.147539  0.133722  3655  187.994119  0.0590  ...      NaN     NaN   \n",
       "1    21.147539  0.133722  3658  187.991663  0.0088  ...  18.3089  0.0043   \n",
       "2    21.147539  0.133722  3660  187.994049  0.0285  ...  21.9428  0.2365   \n",
       "3    20.838474  0.114701  3652  188.000685  0.0714  ...      NaN     NaN   \n",
       "4    20.838474  0.114701  3655  187.994119  0.0590  ...      NaN     NaN   \n",
       "..         ...       ...   ...         ...     ...  ...      ...     ...   \n",
       "952  20.523236  2.376424  1203  188.396901  0.0322  ...  21.9059  0.0414   \n",
       "953  20.523236  2.376424  1210  188.389843  0.0407  ...  21.9696  0.0946   \n",
       "954  20.523236  2.376424  1217  188.390244  0.0280  ...  20.7829  0.0258   \n",
       "955  20.394262  2.204876  1237  188.404662  0.0260  ...  22.2123  0.1857   \n",
       "956  20.394262  2.204876  1363  188.401888  0.0135  ...  20.4383  0.0786   \n",
       "\n",
       "          r2    e_r2       i2    e_i2       z2    e_z2        sep  phm_test  \n",
       "0    21.4164  0.1632  20.8704  0.1786  20.4922  0.0945  17.174798     False  \n",
       "1    18.0153  0.0033  17.9091  0.0068  17.8912  0.0054  15.304649     False  \n",
       "2    21.7344  0.0972  20.9368  0.0378  20.4032  0.0718  17.103772      True  \n",
       "3    21.2616  0.1944  20.6839  0.0814      NaN     NaN  14.402689     False  \n",
       "4    21.4164  0.1632  20.8704  0.1786  20.4922  0.0945  14.486329     False  \n",
       "..       ...     ...      ...     ...      ...     ...        ...       ...  \n",
       "952  20.9146  0.0569  20.4745  0.0184  20.0534  0.0363  13.520308     False  \n",
       "953  21.4770  0.0736  21.0986  0.0542  20.8732  0.0285  17.105912     False  \n",
       "954  19.6038  0.0227  18.7235  0.0131  18.2557  0.0141  11.687680     False  \n",
       "955  21.4599  0.0691  20.8095  0.0486  20.5054  0.0355  16.764140     False  \n",
       "956  21.3633  0.0402  19.8259  0.0144  19.1227  0.0112   9.156586     False  \n",
       "\n",
       "[957 rows x 22 columns]"
      ]
     },
     "execution_count": 804,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noid_coord = SkyCoord(a1.iloc[:, 0].values.astype(float).tolist(), a1.iloc[:, 2].values.astype(float).tolist(), frame=\"icrs\", unit=\"deg\")\n",
    "res = search_around_sky(noid_coord, df2_coord, seplimit=20*u.arcsec)\n",
    "\n",
    "temp_df_1 = a1.iloc[res[0]].copy()\n",
    "temp_df_1.reset_index(drop=False, inplace=True)\n",
    "temp_df_1.rename(columns={'index': 'id1'}, inplace=True)\n",
    "\n",
    "\n",
    "temp_df_2 = df2.iloc[res[1]].copy()\n",
    "temp_df_2.reset_index(drop=False, inplace=True)\n",
    "temp_df_2.rename(columns={'index': 'id2'}, inplace=True)\n",
    "\n",
    "preprod1 = pd.concat([temp_df_1, temp_df_2], axis=1)\n",
    "preprod1['sep'] = res[2].value*3600\n",
    "\n",
    "#Сопоставление по магнитудам\n",
    "phm_test = preprod1.apply(lamy.photometry_test, axis = 1, args = (model, ))\n",
    "preprod1['phm_test'] = phm_test\n",
    "preprod1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 805,
   "id": "9722a5b3",
   "metadata": {},
   "outputs": [
    {
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       "      <td>22.0626</td>\n",
       "      <td>0.1416</td>\n",
       "      <td>21.6850</td>\n",
       "      <td>0.0634</td>\n",
       "      <td>21.3760</td>\n",
       "      <td>0.1942</td>\n",
       "      <td>21.3373</td>\n",
       "      <td>0.1660</td>\n",
       "      <td>2.227531</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>33</td>\n",
       "      <td>188.014285158929</td>\n",
       "      <td>0.627492</td>\n",
       "      <td>-1.46655597377263</td>\n",
       "      <td>0.879649</td>\n",
       "      <td>20.314208</td>\n",
       "      <td>0.068481</td>\n",
       "      <td>3053</td>\n",
       "      <td>188.014340</td>\n",
       "      <td>0.0794</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.4008</td>\n",
       "      <td>0.0780</td>\n",
       "      <td>20.4168</td>\n",
       "      <td>0.1434</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.370500</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>90</td>\n",
       "      <td>188.029822862657</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.5881201290822</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.012911</td>\n",
       "      <td>0.160115</td>\n",
       "      <td>912</td>\n",
       "      <td>188.027815</td>\n",
       "      <td>0.0292</td>\n",
       "      <td>...</td>\n",
       "      <td>22.1392</td>\n",
       "      <td>0.0946</td>\n",
       "      <td>21.3673</td>\n",
       "      <td>0.0449</td>\n",
       "      <td>20.8294</td>\n",
       "      <td>0.0964</td>\n",
       "      <td>20.6539</td>\n",
       "      <td>0.0392</td>\n",
       "      <td>16.757670</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>115</td>\n",
       "      <td>188.035964981285</td>\n",
       "      <td>0.255192</td>\n",
       "      <td>-1.35288514881942</td>\n",
       "      <td>0.280867</td>\n",
       "      <td>20.377728</td>\n",
       "      <td>0.087504</td>\n",
       "      <td>3676</td>\n",
       "      <td>188.038144</td>\n",
       "      <td>0.0168</td>\n",
       "      <td>...</td>\n",
       "      <td>21.8200</td>\n",
       "      <td>0.0564</td>\n",
       "      <td>21.0626</td>\n",
       "      <td>0.0673</td>\n",
       "      <td>20.7618</td>\n",
       "      <td>0.0411</td>\n",
       "      <td>20.4660</td>\n",
       "      <td>0.0325</td>\n",
       "      <td>8.931935</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>313</td>\n",
       "      <td>188.069974639552</td>\n",
       "      <td>3.083713</td>\n",
       "      <td>-1.35575538587231</td>\n",
       "      <td>4.318313</td>\n",
       "      <td>20.471479</td>\n",
       "      <td>0.107065</td>\n",
       "      <td>3374</td>\n",
       "      <td>188.074709</td>\n",
       "      <td>0.0197</td>\n",
       "      <td>...</td>\n",
       "      <td>21.9252</td>\n",
       "      <td>0.0693</td>\n",
       "      <td>20.8842</td>\n",
       "      <td>0.0267</td>\n",
       "      <td>19.8156</td>\n",
       "      <td>0.0102</td>\n",
       "      <td>19.3247</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>19.326367</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>572</td>\n",
       "      <td>188.11817332184</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.32117094954602</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.444226</td>\n",
       "      <td>0.122188</td>\n",
       "      <td>3820</td>\n",
       "      <td>188.114224</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>...</td>\n",
       "      <td>22.8637</td>\n",
       "      <td>0.2730</td>\n",
       "      <td>21.4518</td>\n",
       "      <td>0.0596</td>\n",
       "      <td>20.2402</td>\n",
       "      <td>0.0284</td>\n",
       "      <td>19.6542</td>\n",
       "      <td>0.0128</td>\n",
       "      <td>18.589107</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>618</td>\n",
       "      <td>188.124362513126</td>\n",
       "      <td>0.225713</td>\n",
       "      <td>-1.31928292545998</td>\n",
       "      <td>0.633347</td>\n",
       "      <td>20.926210</td>\n",
       "      <td>0.089052</td>\n",
       "      <td>3546</td>\n",
       "      <td>188.129646</td>\n",
       "      <td>0.0591</td>\n",
       "      <td>...</td>\n",
       "      <td>21.8789</td>\n",
       "      <td>0.0816</td>\n",
       "      <td>21.8911</td>\n",
       "      <td>0.0874</td>\n",
       "      <td>21.4821</td>\n",
       "      <td>0.0599</td>\n",
       "      <td>20.6657</td>\n",
       "      <td>0.0413</td>\n",
       "      <td>19.958638</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>665</td>\n",
       "      <td>188.132673319771</td>\n",
       "      <td>3.007423</td>\n",
       "      <td>-1.68786247662682</td>\n",
       "      <td>1.205008</td>\n",
       "      <td>20.383470</td>\n",
       "      <td>0.054185</td>\n",
       "      <td>130</td>\n",
       "      <td>188.135726</td>\n",
       "      <td>0.0362</td>\n",
       "      <td>...</td>\n",
       "      <td>20.8335</td>\n",
       "      <td>0.0798</td>\n",
       "      <td>20.8181</td>\n",
       "      <td>0.0878</td>\n",
       "      <td>21.3389</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.040879</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>710</td>\n",
       "      <td>188.142940019803</td>\n",
       "      <td>0.214342</td>\n",
       "      <td>-1.53703486356258</td>\n",
       "      <td>0.299602</td>\n",
       "      <td>20.607325</td>\n",
       "      <td>0.148962</td>\n",
       "      <td>2088</td>\n",
       "      <td>188.143773</td>\n",
       "      <td>0.0496</td>\n",
       "      <td>...</td>\n",
       "      <td>21.5676</td>\n",
       "      <td>0.0980</td>\n",
       "      <td>21.3068</td>\n",
       "      <td>0.0798</td>\n",
       "      <td>20.9329</td>\n",
       "      <td>0.0584</td>\n",
       "      <td>20.5597</td>\n",
       "      <td>0.1226</td>\n",
       "      <td>12.971266</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>751</td>\n",
       "      <td>188.151241370357</td>\n",
       "      <td>0.349401</td>\n",
       "      <td>-1.43088061651935</td>\n",
       "      <td>0.488390</td>\n",
       "      <td>20.529188</td>\n",
       "      <td>0.075798</td>\n",
       "      <td>3212</td>\n",
       "      <td>188.149180</td>\n",
       "      <td>0.0492</td>\n",
       "      <td>...</td>\n",
       "      <td>21.8156</td>\n",
       "      <td>0.0623</td>\n",
       "      <td>21.4560</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>21.3953</td>\n",
       "      <td>0.0550</td>\n",
       "      <td>20.7080</td>\n",
       "      <td>0.3950</td>\n",
       "      <td>15.380139</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>787</td>\n",
       "      <td>188.157853852953</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.44050713722009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.691657</td>\n",
       "      <td>0.163561</td>\n",
       "      <td>2300</td>\n",
       "      <td>188.157786</td>\n",
       "      <td>0.0602</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.5783</td>\n",
       "      <td>0.1123</td>\n",
       "      <td>21.5231</td>\n",
       "      <td>0.1088</td>\n",
       "      <td>20.7807</td>\n",
       "      <td>0.2412</td>\n",
       "      <td>15.113443</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>909</td>\n",
       "      <td>188.181769073709</td>\n",
       "      <td>0.505263</td>\n",
       "      <td>-1.3098882697892</td>\n",
       "      <td>0.358802</td>\n",
       "      <td>20.651694</td>\n",
       "      <td>0.063006</td>\n",
       "      <td>3572</td>\n",
       "      <td>188.183884</td>\n",
       "      <td>0.0322</td>\n",
       "      <td>...</td>\n",
       "      <td>21.7416</td>\n",
       "      <td>0.0531</td>\n",
       "      <td>21.4841</td>\n",
       "      <td>0.0368</td>\n",
       "      <td>21.4935</td>\n",
       "      <td>0.0774</td>\n",
       "      <td>21.3640</td>\n",
       "      <td>0.0782</td>\n",
       "      <td>8.699024</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>1218</td>\n",
       "      <td>188.238705776829</td>\n",
       "      <td>2.934664</td>\n",
       "      <td>-1.4566950494716</td>\n",
       "      <td>4.102194</td>\n",
       "      <td>20.473800</td>\n",
       "      <td>0.056299</td>\n",
       "      <td>2377</td>\n",
       "      <td>188.236311</td>\n",
       "      <td>0.0307</td>\n",
       "      <td>...</td>\n",
       "      <td>21.5241</td>\n",
       "      <td>0.0416</td>\n",
       "      <td>20.9608</td>\n",
       "      <td>0.0746</td>\n",
       "      <td>20.8867</td>\n",
       "      <td>0.0296</td>\n",
       "      <td>20.6030</td>\n",
       "      <td>0.0537</td>\n",
       "      <td>9.835393</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>1378</td>\n",
       "      <td>188.269741419575</td>\n",
       "      <td>0.445732</td>\n",
       "      <td>-1.47953999745738</td>\n",
       "      <td>0.623239</td>\n",
       "      <td>20.604094</td>\n",
       "      <td>0.125900</td>\n",
       "      <td>2338</td>\n",
       "      <td>188.269071</td>\n",
       "      <td>0.0699</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.8103</td>\n",
       "      <td>0.2083</td>\n",
       "      <td>21.4829</td>\n",
       "      <td>0.1296</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.942740</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>1504</td>\n",
       "      <td>188.292177757442</td>\n",
       "      <td>10.866007</td>\n",
       "      <td>-1.46924697614917</td>\n",
       "      <td>14.012205</td>\n",
       "      <td>20.607887</td>\n",
       "      <td>0.075013</td>\n",
       "      <td>2366</td>\n",
       "      <td>188.292284</td>\n",
       "      <td>0.0103</td>\n",
       "      <td>...</td>\n",
       "      <td>21.8310</td>\n",
       "      <td>0.0412</td>\n",
       "      <td>20.6898</td>\n",
       "      <td>0.0305</td>\n",
       "      <td>19.4906</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>18.9382</td>\n",
       "      <td>0.0119</td>\n",
       "      <td>17.214506</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>1588</td>\n",
       "      <td>188.308484285034</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.46461392711991</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.768125</td>\n",
       "      <td>0.086907</td>\n",
       "      <td>1975</td>\n",
       "      <td>188.305546</td>\n",
       "      <td>0.0126</td>\n",
       "      <td>...</td>\n",
       "      <td>21.7431</td>\n",
       "      <td>0.1583</td>\n",
       "      <td>21.2944</td>\n",
       "      <td>0.0389</td>\n",
       "      <td>20.2041</td>\n",
       "      <td>0.0332</td>\n",
       "      <td>19.6637</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>17.026453</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>1663</td>\n",
       "      <td>188.322581</td>\n",
       "      <td>0.999000</td>\n",
       "      <td>-1.370634</td>\n",
       "      <td>0.999000</td>\n",
       "      <td>20.476839</td>\n",
       "      <td>0.104716</td>\n",
       "      <td>2680</td>\n",
       "      <td>188.323579</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0988</td>\n",
       "      <td>0.0944</td>\n",
       "      <td>21.0774</td>\n",
       "      <td>0.0577</td>\n",
       "      <td>20.5552</td>\n",
       "      <td>0.0299</td>\n",
       "      <td>20.3271</td>\n",
       "      <td>0.0314</td>\n",
       "      <td>14.691726</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>1674</td>\n",
       "      <td>188.324781256123</td>\n",
       "      <td>0.326719</td>\n",
       "      <td>-1.53723588835276</td>\n",
       "      <td>0.824347</td>\n",
       "      <td>20.066616</td>\n",
       "      <td>0.144904</td>\n",
       "      <td>1788</td>\n",
       "      <td>188.320508</td>\n",
       "      <td>0.0320</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0927</td>\n",
       "      <td>0.0393</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.8844</td>\n",
       "      <td>0.0918</td>\n",
       "      <td>15.537589</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>1773</td>\n",
       "      <td>188.343589368982</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.4518442418183</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.696898</td>\n",
       "      <td>0.153858</td>\n",
       "      <td>2006</td>\n",
       "      <td>188.340675</td>\n",
       "      <td>0.0214</td>\n",
       "      <td>...</td>\n",
       "      <td>21.5052</td>\n",
       "      <td>0.0397</td>\n",
       "      <td>21.1670</td>\n",
       "      <td>0.0403</td>\n",
       "      <td>21.0174</td>\n",
       "      <td>0.0669</td>\n",
       "      <td>20.9971</td>\n",
       "      <td>0.0324</td>\n",
       "      <td>12.340696</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>1801</td>\n",
       "      <td>188.349141571464</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.543452461729</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.859662</td>\n",
       "      <td>0.130819</td>\n",
       "      <td>1768</td>\n",
       "      <td>188.350588</td>\n",
       "      <td>0.0187</td>\n",
       "      <td>...</td>\n",
       "      <td>20.5504</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>20.2655</td>\n",
       "      <td>0.0169</td>\n",
       "      <td>20.1119</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>20.1498</td>\n",
       "      <td>0.0342</td>\n",
       "      <td>8.377879</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>1880</td>\n",
       "      <td>188.36688812149</td>\n",
       "      <td>1.109583</td>\n",
       "      <td>-1.39125913319074</td>\n",
       "      <td>2.101199</td>\n",
       "      <td>21.093358</td>\n",
       "      <td>0.089951</td>\n",
       "      <td>2655</td>\n",
       "      <td>188.371475</td>\n",
       "      <td>0.0931</td>\n",
       "      <td>...</td>\n",
       "      <td>22.1929</td>\n",
       "      <td>0.0104</td>\n",
       "      <td>21.7251</td>\n",
       "      <td>0.0955</td>\n",
       "      <td>21.5614</td>\n",
       "      <td>0.0682</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.731308</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id1               RA1      e_RA1                DE1      e_DE1  \\\n",
       "2       1  187.992885114294   1.502449  -1.35578648381857   1.688250   \n",
       "13      5  187.999256458139   0.532886  -1.40376904859222   0.490235   \n",
       "22     33  188.014285158929   0.627492  -1.46655597377263   0.879649   \n",
       "63     90  188.029822862657   0.000000   -1.5881201290822   0.000000   \n",
       "89    115  188.035964981285   0.255192  -1.35288514881942   0.280867   \n",
       "209   313  188.069974639552   3.083713  -1.35575538587231   4.318313   \n",
       "327   572   188.11817332184   0.000000  -1.32117094954602   0.000000   \n",
       "344   618  188.124362513126   0.225713  -1.31928292545998   0.633347   \n",
       "368   665  188.132673319771   3.007423  -1.68786247662682   1.205008   \n",
       "396   710  188.142940019803   0.214342  -1.53703486356258   0.299602   \n",
       "405   751  188.151241370357   0.349401  -1.43088061651935   0.488390   \n",
       "426   787  188.157853852953   0.000000  -1.44050713722009   0.000000   \n",
       "477   909  188.181769073709   0.505263   -1.3098882697892   0.358802   \n",
       "602  1218  188.238705776829   2.934664   -1.4566950494716   4.102194   \n",
       "697  1378  188.269741419575   0.445732  -1.47953999745738   0.623239   \n",
       "756  1504  188.292177757442  10.866007  -1.46924697614917  14.012205   \n",
       "789  1588  188.308484285034   0.000000  -1.46461392711991   0.000000   \n",
       "814  1663        188.322581   0.999000          -1.370634   0.999000   \n",
       "819  1674  188.324781256123   0.326719  -1.53723588835276   0.824347   \n",
       "864  1773  188.343589368982   0.000000   -1.4518442418183   0.000000   \n",
       "880  1801  188.349141571464   0.000000    -1.543452461729   0.000000   \n",
       "916  1880   188.36688812149   1.109583  -1.39125913319074   2.101199   \n",
       "\n",
       "            r1      e_r1   id2         RA2   e_RA2  ...       g2    e_g2  \\\n",
       "2    21.147539  0.133722  3660  187.994049  0.0285  ...  21.9428  0.2365   \n",
       "13   20.967038  0.122435  3626  187.998888  0.0694  ...  22.0626  0.1416   \n",
       "22   20.314208  0.068481  3053  188.014340  0.0794  ...      NaN     NaN   \n",
       "63   21.012911  0.160115   912  188.027815  0.0292  ...  22.1392  0.0946   \n",
       "89   20.377728  0.087504  3676  188.038144  0.0168  ...  21.8200  0.0564   \n",
       "209  20.471479  0.107065  3374  188.074709  0.0197  ...  21.9252  0.0693   \n",
       "327  21.444226  0.122188  3820  188.114224  0.0131  ...  22.8637  0.2730   \n",
       "344  20.926210  0.089052  3546  188.129646  0.0591  ...  21.8789  0.0816   \n",
       "368  20.383470  0.054185   130  188.135726  0.0362  ...  20.8335  0.0798   \n",
       "396  20.607325  0.148962  2088  188.143773  0.0496  ...  21.5676  0.0980   \n",
       "405  20.529188  0.075798  3212  188.149180  0.0492  ...  21.8156  0.0623   \n",
       "426  20.691657  0.163561  2300  188.157786  0.0602  ...      NaN     NaN   \n",
       "477  20.651694  0.063006  3572  188.183884  0.0322  ...  21.7416  0.0531   \n",
       "602  20.473800  0.056299  2377  188.236311  0.0307  ...  21.5241  0.0416   \n",
       "697  20.604094  0.125900  2338  188.269071  0.0699  ...      NaN     NaN   \n",
       "756  20.607887  0.075013  2366  188.292284  0.0103  ...  21.8310  0.0412   \n",
       "789  20.768125  0.086907  1975  188.305546  0.0126  ...  21.7431  0.1583   \n",
       "814  20.476839  0.104716  2680  188.323579  0.0153  ...  22.0988  0.0944   \n",
       "819  20.066616  0.144904  1788  188.320508  0.0320  ...      NaN     NaN   \n",
       "864  20.696898  0.153858  2006  188.340675  0.0214  ...  21.5052  0.0397   \n",
       "880  19.859662  0.130819  1768  188.350588  0.0187  ...  20.5504  0.0202   \n",
       "916  21.093358  0.089951  2655  188.371475  0.0931  ...  22.1929  0.0104   \n",
       "\n",
       "          r2    e_r2       i2    e_i2       z2    e_z2        sep  phm_test  \n",
       "2    21.7344  0.0972  20.9368  0.0378  20.4032  0.0718  17.103772      True  \n",
       "13   21.6850  0.0634  21.3760  0.1942  21.3373  0.1660   2.227531      True  \n",
       "22   20.4008  0.0780  20.4168  0.1434      NaN     NaN  18.370500      True  \n",
       "63   21.3673  0.0449  20.8294  0.0964  20.6539  0.0392  16.757670      True  \n",
       "89   21.0626  0.0673  20.7618  0.0411  20.4660  0.0325   8.931935      True  \n",
       "209  20.8842  0.0267  19.8156  0.0102  19.3247  0.0149  19.326367      True  \n",
       "327  21.4518  0.0596  20.2402  0.0284  19.6542  0.0128  18.589107      True  \n",
       "344  21.8911  0.0874  21.4821  0.0599  20.6657  0.0413  19.958638      True  \n",
       "368  20.8181  0.0878  21.3389  0.0553      NaN     NaN  11.040879      True  \n",
       "396  21.3068  0.0798  20.9329  0.0584  20.5597  0.1226  12.971266      True  \n",
       "405  21.4560  0.1238  21.3953  0.0550  20.7080  0.3950  15.380139      True  \n",
       "426  21.5783  0.1123  21.5231  0.1088  20.7807  0.2412  15.113443      True  \n",
       "477  21.4841  0.0368  21.4935  0.0774  21.3640  0.0782   8.699024      True  \n",
       "602  20.9608  0.0746  20.8867  0.0296  20.6030  0.0537   9.835393      True  \n",
       "697  21.8103  0.2083  21.4829  0.1296      NaN     NaN  19.942740      True  \n",
       "756  20.6898  0.0305  19.4906  0.0110  18.9382  0.0119  17.214506      True  \n",
       "789  21.2944  0.0389  20.2041  0.0332  19.6637  0.0153  17.026453      True  \n",
       "814  21.0774  0.0577  20.5552  0.0299  20.3271  0.0314  14.691726      True  \n",
       "819  20.0927  0.0393      NaN     NaN  19.8844  0.0918  15.537589      True  \n",
       "864  21.1670  0.0403  21.0174  0.0669  20.9971  0.0324  12.340696      True  \n",
       "880  20.2655  0.0169  20.1119  0.0180  20.1498  0.0342   8.377879      True  \n",
       "916  21.7251  0.0955  21.5614  0.0682      NaN     NaN  16.731308      True  \n",
       "\n",
       "[22 rows x 22 columns]"
      ]
     },
     "execution_count": 805,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "found = preprod1.loc[preprod1.phm_test == True]\n",
    "rel = found.loc[found.id2.isin(a0.id2.values) == False]\n",
    "\n",
    "#Выбираем взаимно-однозначные сопоставления по блеску\n",
    "temp_rel = rel.loc[~rel.id1.duplicated(keep=False)]\n",
    "relevant = temp_rel.loc[~temp_rel.id2.duplicated(keep=False)]\n",
    "relevant"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06b2e6bf",
   "metadata": {},
   "source": [
    "Работа с группой a2: для каждого объекта 1 из пар дубликатов (одному объекту из внеш. каталога оказались сопоставлены два и более объектов с изображения) и сопоставленного ему объекта проводим два теста: первый тест использует указанные в каталогах ошибки локализации объектов и проверяет гипотезу о том, что пара - один и тот же объект, второй тест - обученная модель для классификации объектов по блеску. В каждом из тестов: True объекты следует сопоставить, False - сопоставление следует отклонить. Все пары, имеющие меньше двух True отправляются в группу кандидатов. Пары с идентичными координатами группируются."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33162df1",
   "metadata": {},
   "source": [
    "Дубликаты"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 806,
   "id": "34e75e43",
   "metadata": {},
   "outputs": [],
   "source": [
    "preprod_dup = a2.join(df2, on='id2').copy()\n",
    "\n",
    "\n",
    "preprod_dup['sep'] = preprod_dup.apply(lamy.separation, axis = 1)\n",
    "preprod_dup['position_test'] = preprod_dup.apply(lamy.position_test, axis = 1, args = (0.99997, ))\n",
    "preprod_dup['photometry_test'] = preprod_dup.apply(lamy.photometry_test, axis = 1, args = (model, ))\n",
    "\n",
    "\n",
    "# prod = preprod.copy()\n",
    "# prod['tests_sum'] = 1*prod['position_test'] + 1*prod['photometry_test']\n",
    "# prod\n",
    "preprod_dup['sum'] = 1*preprod_dup['position_test'] + 1*preprod_dup['photometry_test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 807,
   "id": "d4a02ed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "prod = preprod_dup.loc[preprod_dup['sum'] < 2]\n",
    "a22 = prod.loc[~prod.RA1.duplicated(keep='first')].iloc[:, :8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 808,
   "id": "a1fd7518",
   "metadata": {},
   "outputs": [],
   "source": [
    "preprod_dup_a0 = preprod_dup.loc[preprod_dup['sum'] == 2]\n",
    "preprod_dup_a0.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "070fcb1d",
   "metadata": {},
   "source": [
    "Сшиваем идентифицировавшихся"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 809,
   "id": "b60fcf74",
   "metadata": {},
   "outputs": [],
   "source": [
    "a00 = pd.concat([a0, preprod_dup_a0.iloc[:, :8]], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 810,
   "id": "3320e8ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "a000 = pd.concat([a00, relevant.iloc[:, :8]], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb342bcd",
   "metadata": {},
   "source": [
    "Не потерялось ли событие?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 811,
   "id": "22893e40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 811,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(obj_df1.name).isin(a000.id1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 812,
   "id": "93b601f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RA1</th>\n",
       "      <th>e_RA1</th>\n",
       "      <th>DE1</th>\n",
       "      <th>e_DE1</th>\n",
       "      <th>r1</th>\n",
       "      <th>e_r1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>187.995464776883</td>\n",
       "      <td>0.433014</td>\n",
       "      <td>-1.37032560910434</td>\n",
       "      <td>0.606173</td>\n",
       "      <td>20.333571</td>\n",
       "      <td>0.101317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>187.998126782129</td>\n",
       "      <td>7.854701</td>\n",
       "      <td>-1.35080854770134</td>\n",
       "      <td>10.990025</td>\n",
       "      <td>20.838474</td>\n",
       "      <td>0.114701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>187.999016169859</td>\n",
       "      <td>0.605724</td>\n",
       "      <td>-1.39665097046421</td>\n",
       "      <td>0.848311</td>\n",
       "      <td>20.918145</td>\n",
       "      <td>0.110413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>188.000430693676</td>\n",
       "      <td>0.445598</td>\n",
       "      <td>-1.36590739653148</td>\n",
       "      <td>0.623707</td>\n",
       "      <td>20.346704</td>\n",
       "      <td>0.099567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>188.003925164606</td>\n",
       "      <td>0.287365</td>\n",
       "      <td>-1.43172843741522</td>\n",
       "      <td>1.281178</td>\n",
       "      <td>21.419434</td>\n",
       "      <td>0.162724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>188.394703397214</td>\n",
       "      <td>0.167995</td>\n",
       "      <td>-1.60674065537863</td>\n",
       "      <td>0.234981</td>\n",
       "      <td>20.417064</td>\n",
       "      <td>2.204951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>188.401424896036</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.61214696921092</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.394262</td>\n",
       "      <td>2.204876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>188.405363729061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.63930914913251</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.385395</td>\n",
       "      <td>2.204864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>188.405444729276</td>\n",
       "      <td>0.076628</td>\n",
       "      <td>-1.63928804028764</td>\n",
       "      <td>0.107085</td>\n",
       "      <td>19.594960</td>\n",
       "      <td>2.204521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>188.405475737529</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.63932011501443</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.456437</td>\n",
       "      <td>2.204928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>553 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   RA1     e_RA1                DE1      e_DE1         r1  \\\n",
       "2     187.995464776883  0.433014  -1.37032560910434   0.606173  20.333571   \n",
       "3     187.998126782129  7.854701  -1.35080854770134  10.990025  20.838474   \n",
       "4     187.999016169859  0.605724  -1.39665097046421   0.848311  20.918145   \n",
       "6     188.000430693676  0.445598  -1.36590739653148   0.623707  20.346704   \n",
       "16    188.003925164606  0.287365  -1.43172843741522   1.281178  21.419434   \n",
       "...                ...       ...                ...        ...        ...   \n",
       "1961  188.394703397214  0.167995  -1.60674065537863   0.234981  20.417064   \n",
       "1966  188.401424896036  0.000000  -1.61214696921092   0.000000  20.394262   \n",
       "1970  188.405363729061  0.000000  -1.63930914913251   0.000000  20.385395   \n",
       "1971  188.405444729276  0.076628  -1.63928804028764   0.107085  19.594960   \n",
       "1972  188.405475737529  0.000000  -1.63932011501443   0.000000  20.456437   \n",
       "\n",
       "          e_r1  \n",
       "2     0.101317  \n",
       "3     0.114701  \n",
       "4     0.110413  \n",
       "6     0.099567  \n",
       "16    0.162724  \n",
       "...        ...  \n",
       "1961  2.204951  \n",
       "1966  2.204876  \n",
       "1970  2.204864  \n",
       "1971  2.204521  \n",
       "1972  2.204928  \n",
       "\n",
       "[553 rows x 6 columns]"
      ]
     },
     "execution_count": 812,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates = df1.loc[~df1.index.isin(a000.id1)]\n",
    "candidates"
   ]
  }
 ],
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