{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "作业8\n",
    "• 对于作业5中的星系，依据fits图像画出其一维的表\n",
    "面亮度轮廓，并计算其星等\n",
    "• 注意流量单位\n",
    "• 一维轮廓：\n",
    "✓椭圆测光：椭圆是等面亮度轮廓，半径沿着长轴方向\n",
    "✓圆测光: 圆孔径内的平均表面亮度\n",
    "• 星等：\n",
    "✓积分（求和）到某个等亮度半径处\n",
    "✓根据面亮度轮廓模型，积分到无穷远处"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'DESI NGC 5557')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from astropy.io import fits\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.wcs import WCS\n",
    "\n",
    "DESI = fits.open('cutout_214.6066_36.4927.fits')\n",
    "\n",
    "data = DESI[0].data\n",
    "\n",
    "plt.figure(figsize=(3,3), dpi=200)\n",
    "plt.imshow(data, vmin=0.54, vmax=4.8, origin='lower')\n",
    "plt.colorbar(fraction = 0.05)\n",
    "plt.title('DESI NGC 5557')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testimage: head background at: 0.0 sec\n",
      "testimage: head background at: 0.0 sec\n",
      "testimage: head psf at: 1.0 sec\n",
      "testimage: head psf at: 1.0 sec\n",
      "testimage: PSF assumed to be: 4.0000e+00 pix\n",
      "testimage: head center at: 1.0 sec\n",
      "testimage: head center at: 1.0 sec\n",
      "testimage: head isophoteinit at: 2.2 sec\n",
      "testimage: head isophoteinit at: 2.2 sec\n",
      "testimage: init scale: 237.376314 pix\n",
      "testimage: head isophotefit at: 4.2 sec\n",
      "testimage: head isophotefit at: 4.2 sec\n",
      "testimage: Completed isohpote fit in 235 itterations\n",
      "testimage: head isophoteextract at: 15.7 sec\n",
      "testimage: head isophoteextract at: 15.7 sec\n",
      "testimage: R complete in range [1.0,368.4]\n",
      "testimage: head checkfit at: 17.3 sec\n",
      "testimage: head checkfit at: 17.3 sec\n",
      "testimage: head writeprof at: 17.5 sec\n",
      "testimage: head writeprof at: 17.5 sec\n",
      "testimage: Processing Complete! (at 17.5 sec)\n",
      "testimage: Processing Complete! (at 17.5 sec)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'background': 0.999046802520752,\n",
       " 'psf': 0.0002942085266113281,\n",
       " 'center': 1.1950459480285645,\n",
       " 'isophoteinit': 2.016838788986206,\n",
       " 'isophotefit': 11.444736957550049,\n",
       " 'isophoteextract': 1.6451389789581299,\n",
       " 'checkfit': 0.18798017501831055,\n",
       " 'writeprof': 0.00841212272644043}"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from autoprof import Pipeline\n",
    "\n",
    "config_file = './galaxy_profile/test_config.txt'\n",
    "PIPELINE = Pipeline.Isophote_Pipeline(loggername='xxx')\n",
    "PIPELINE.Process_ConfigFile(config_file=config_file)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Example Image](./galaxy_profile/fit_ellipse_testimage.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Example Image](./galaxy_profile/photometry_testimage.jpg)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>R</th>\n",
       "      <th>SB</th>\n",
       "      <th>SB_e</th>\n",
       "      <th>totmag</th>\n",
       "      <th>totmag_e</th>\n",
       "      <th>ellip</th>\n",
       "      <th>ellip_e</th>\n",
       "      <th>pa</th>\n",
       "      <th>pa_e</th>\n",
       "      <th>pixels</th>\n",
       "      <th>maskedpixels</th>\n",
       "      <th>totmag_direct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.262000</td>\n",
       "      <td>17.092673</td>\n",
       "      <td>6.140689e-07</td>\n",
       "      <td>19.466914</td>\n",
       "      <td>0.001913</td>\n",
       "      <td>0.479344</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>89.869803</td>\n",
       "      <td>0.057296</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>19.248592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.288200</td>\n",
       "      <td>17.092673</td>\n",
       "      <td>6.355799e-07</td>\n",
       "      <td>19.259951</td>\n",
       "      <td>0.001676</td>\n",
       "      <td>0.479344</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>89.869803</td>\n",
       "      <td>0.057296</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>19.248592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.317020</td>\n",
       "      <td>17.092674</td>\n",
       "      <td>6.588771e-07</td>\n",
       "      <td>19.052987</td>\n",
       "      <td>0.001415</td>\n",
       "      <td>0.479344</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>89.869803</td>\n",
       "      <td>0.057296</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>19.248592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.348722</td>\n",
       "      <td>17.092674</td>\n",
       "      <td>6.812414e-07</td>\n",
       "      <td>18.846024</td>\n",
       "      <td>0.001238</td>\n",
       "      <td>0.479344</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>89.869803</td>\n",
       "      <td>0.057296</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>19.248592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.383594</td>\n",
       "      <td>17.092674</td>\n",
       "      <td>7.020016e-07</td>\n",
       "      <td>18.639061</td>\n",
       "      <td>0.001123</td>\n",
       "      <td>0.479344</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>89.869803</td>\n",
       "      <td>0.057296</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>18.808363</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>58</th>\n",
       "      <td>65.929082</td>\n",
       "      <td>24.174502</td>\n",
       "      <td>2.467159e-02</td>\n",
       "      <td>10.592996</td>\n",
       "      <td>0.002780</td>\n",
       "      <td>0.158602</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>77.945325</td>\n",
       "      <td>1.161707</td>\n",
       "      <td>1383</td>\n",
       "      <td>8</td>\n",
       "      <td>10.553279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>72.521991</td>\n",
       "      <td>25.519933</td>\n",
       "      <td>2.604948e-02</td>\n",
       "      <td>10.586938</td>\n",
       "      <td>0.002789</td>\n",
       "      <td>0.158493</td>\n",
       "      <td>0.001123</td>\n",
       "      <td>82.579165</td>\n",
       "      <td>0.358423</td>\n",
       "      <td>15347</td>\n",
       "      <td>40</td>\n",
       "      <td>10.546838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>79.774190</td>\n",
       "      <td>99.999000</td>\n",
       "      <td>9.999900e+01</td>\n",
       "      <td>10.585334</td>\n",
       "      <td>0.002793</td>\n",
       "      <td>0.158493</td>\n",
       "      <td>0.001123</td>\n",
       "      <td>82.579165</td>\n",
       "      <td>0.358423</td>\n",
       "      <td>13229</td>\n",
       "      <td>1</td>\n",
       "      <td>10.546582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>87.751609</td>\n",
       "      <td>99.999000</td>\n",
       "      <td>9.999900e+01</td>\n",
       "      <td>10.585334</td>\n",
       "      <td>0.002793</td>\n",
       "      <td>0.158493</td>\n",
       "      <td>0.001123</td>\n",
       "      <td>82.579165</td>\n",
       "      <td>0.358423</td>\n",
       "      <td>8387</td>\n",
       "      <td>61</td>\n",
       "      <td>10.548515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>96.526769</td>\n",
       "      <td>99.999000</td>\n",
       "      <td>9.999900e+01</td>\n",
       "      <td>10.585334</td>\n",
       "      <td>0.002793</td>\n",
       "      <td>0.158493</td>\n",
       "      <td>0.001123</td>\n",
       "      <td>82.579165</td>\n",
       "      <td>0.358423</td>\n",
       "      <td>3885</td>\n",
       "      <td>7</td>\n",
       "      <td>10.550314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            R         SB          SB_e     totmag  totmag_e     ellip  \\\n",
       "0    0.262000  17.092673  6.140689e-07  19.466914  0.001913  0.479344   \n",
       "1    0.288200  17.092673  6.355799e-07  19.259951  0.001676  0.479344   \n",
       "2    0.317020  17.092674  6.588771e-07  19.052987  0.001415  0.479344   \n",
       "3    0.348722  17.092674  6.812414e-07  18.846024  0.001238  0.479344   \n",
       "4    0.383594  17.092674  7.020016e-07  18.639061  0.001123  0.479344   \n",
       "..        ...        ...           ...        ...       ...       ...   \n",
       "58  65.929082  24.174502  2.467159e-02  10.592996  0.002780  0.158602   \n",
       "59  72.521991  25.519933  2.604948e-02  10.586938  0.002789  0.158493   \n",
       "60  79.774190  99.999000  9.999900e+01  10.585334  0.002793  0.158493   \n",
       "61  87.751609  99.999000  9.999900e+01  10.585334  0.002793  0.158493   \n",
       "62  96.526769  99.999000  9.999900e+01  10.585334  0.002793  0.158493   \n",
       "\n",
       "     ellip_e         pa      pa_e  pixels  maskedpixels  totmag_direct  \n",
       "0   0.001000  89.869803  0.057296      15             0      19.248592  \n",
       "1   0.001000  89.869803  0.057296      15             0      19.248592  \n",
       "2   0.001000  89.869803  0.057296      15             0      19.248592  \n",
       "3   0.001000  89.869803  0.057296      15             0      19.248592  \n",
       "4   0.001000  89.869803  0.057296      15             0      18.808363  \n",
       "..       ...        ...       ...     ...           ...            ...  \n",
       "58  0.001000  77.945325  1.161707    1383             8      10.553279  \n",
       "59  0.001123  82.579165  0.358423   15347            40      10.546838  \n",
       "60  0.001123  82.579165  0.358423   13229             1      10.546582  \n",
       "61  0.001123  82.579165  0.358423    8387            61      10.548515  \n",
       "62  0.001123  82.579165  0.358423    3885             7      10.550314  \n",
       "\n",
       "[63 rows x 12 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dat = pd.read_csv('./galaxy_profile/testimage.prof', header=1)\n",
    "dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.001913\n",
       "1     0.001676\n",
       "2     0.001415\n",
       "3     0.001238\n",
       "4     0.001123\n",
       "        ...   \n",
       "58    0.002780\n",
       "59    0.002789\n",
       "60    0.002793\n",
       "61    0.002793\n",
       "62    0.002793\n",
       "Name: totmag_e, Length: 63, dtype: float64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use(['science','nature','no-latex'])\n",
    "R = dat['R']\n",
    "mag = dat['totmag']\n",
    "mag_e = dat['totmag_e']\n",
    "plt.figure(figsize=(3,2), dpi=200)\n",
    "# plt.scatter(R, mag, c='r', s=3)\n",
    "plt.errorbar(R, mag, xerr=mag_e, fmt='o', color='blue', elinewidth=1, capsize=5, capthick=1, ms=2)\n",
    "plt.gca().invert_yaxis() \n",
    "plt.xlabel('Semi-major axis [arcsec]')\n",
    "plt.ylabel('Magnitude [mag]')\n",
    "plt.xlim(0, 70)\n",
    "plt.ylim(20, 10)\n",
    "mag_e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "• 对于所有波段K改正为0mag的天体来说，其SED可以由什么函数描述$f(\\lambda)$?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Example Image](./K-1.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Example Image](./K-2.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "作业9、某标准烛光B波段的星等测量值为25等（内禀绝对\n",
    "星等为-3.0），测量误差为0.1个星等，该天体方向\n",
    "上银河系尘埃的红化E(B-V)=0.2星等（不确定性为\n",
    "0.05个星等），请问该天体的距离为多少，距离测\n",
    "量的不确定性是多少？\n",
    "• 假设银河系的消光曲线Rv=3.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "解：$m_B = 25 mag$, $M_B = -3 mag$,  ${m_B}_e = 0.1 mag$, $E(B-V) = 0.2 mag$, $E(B-V)_e = 0.05 mag$, $R_v = 3.1$\n",
    "\n",
    "$m_B - M_B = 5lgd - 5 + A_B$\n",
    "\n",
    "$A_B = R_v \\times E(B-V)$\n",
    "\n",
    "$d = 10^{\\frac{m_B-M_B+5-R_v \\cdot E(B-V)}{5}}$\n",
    "\n",
    "$d = 3 \\times 10^6 pc$\n",
    "\n",
    "$d_e = 62169 pc$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "作业10\n",
    "• 某星系（Ra=180，Dec=-10），观测到其Ha\n",
    "谱线的（真空中）波长被红移到7000A处，在r\n",
    "波段观测到的其AB星等为20等，其光谱的能量\n",
    "分布fλ 在静止波长（5000-7500A）范围内近似\n",
    "为常数，请计算其r波段的绝对星等。\n",
    "• 宇宙学（$H_0 \\sim 100 h kms^{-1} Mpc^{-1} Ω_0=0.28，\n",
    "Ω_Λ=0.72$）\n",
    "• r波段的波长范围近似为5500-6700A，滤光片响应\n",
    "曲线近似为常数0.6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "解：红移$z = \\frac{\\lambda_{observed}-\\lambda_{emitted}}{\\lambda_{emitted}} = 0.067$\n",
    "\n",
    "$v = H_0 \\times d$, $v = c \\times z$\n",
    "\n",
    "$d = \\frac{c \\times z}{H_0}$\n",
    "\n",
    "$d = \\frac{201}{h} Mpc$, $h = 0.7$\n",
    "\n",
    "$d = 287 Mpc$\n",
    "\n",
    "$d_l = d*(1+z) = 306.229 Mpc$\n",
    "\n",
    "这里$f_{\\lambda} = 常数$，所以K改正等于0\n",
    "\n",
    "$M_r = m_r - 5(logd_l - 1) = -17.43 mag$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 距离不能这么简单的算了"
   ]
  }
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