css_PA_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eaa368ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\n",
    "import pandas as pd\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "32dbab22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# write Position Angles table with single space character separator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "026fc0f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting css_ds9_pa.txt\n"
     ]
    }
   ],
   "source": [
    "%%file css_ds9_pa.txt\n",
    "t z op uv r\n",
    "1 0.017 150 0 41\n",
    "2 0.536 109 0 70\n",
    "3 0.361 70 63 82\n",
    "4 0.091 36 24 39\n",
    "5 0.404 54 64 44\n",
    "6 0.445 23 45 44\n",
    "7 0.371 132 73 71\n",
    "8 0.171 156 86 93\n",
    "9 0.195 157 0 22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "99648440",
   "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>source</th>\n",
       "      <th>redshift</th>\n",
       "      <th>optical</th>\n",
       "      <th>uv</th>\n",
       "      <th>radio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.017</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.536</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.361</td>\n",
       "      <td>70</td>\n",
       "      <td>63</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.091</td>\n",
       "      <td>36</td>\n",
       "      <td>24</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.404</td>\n",
       "      <td>54</td>\n",
       "      <td>64</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.445</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.371</td>\n",
       "      <td>132</td>\n",
       "      <td>73</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.171</td>\n",
       "      <td>156</td>\n",
       "      <td>86</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.195</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   source  redshift  optical  uv  radio\n",
       "0       1     0.017      150   0     41\n",
       "1       2     0.536      109   0     70\n",
       "2       3     0.361       70  63     82\n",
       "3       4     0.091       36  24     39\n",
       "4       5     0.404       54  64     44\n",
       "5       6     0.445       23  45     44\n",
       "6       7     0.371      132  73     71\n",
       "7       8     0.171      156  86     93\n",
       "8       9     0.195      157   0     22"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('css_ds9_pa.txt', sep=' ', header=0)\n",
    "df.columns=['source','redshift','optical','uv', 'radio']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5001c88f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "386b2fcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_plot(fig):\n",
    "    \n",
    "    for ax in fig.get_axes():\n",
    "\n",
    "        ax.yaxis.labelpad = 10\n",
    "        ax.yaxis.set_ticks_position('both')    # ticks on all sides of plot\n",
    "        ax.xaxis.set_ticks_position('both')\n",
    "        \n",
    "        ax.yaxis.set_minor_locator(AutoMinorLocator())\n",
    "        ax.xaxis.set_minor_locator(AutoMinorLocator())\n",
    "        \n",
    "        ax.tick_params(axis='both',direction='in',which='both',length=8, pad=8.0, labelsize=10) #width=1.0\n",
    "        ax.tick_params(which='minor',length=4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e0e34335",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, axes = plt.subplots(1, 2, figsize=(14,5)) \n",
    "plt.subplots_adjust(wspace=0.3)\n",
    "plt.rcParams['font.family'] = 'sans-serif'\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "\n",
    "set_plot(fig1)\n",
    "\n",
    "axes[0].scatter(df['optical'], df['radio'], s=40, marker='D', facecolors='#00b3b3', edgecolors='k', alpha=0.8)\n",
    "axes[0].set_xlim([-25,220])\n",
    "axes[0].set_ylim([-15,135])\n",
    "axes[0].set_xlabel('HST/V PA (deg)', fontsize=14, labelpad=12)\n",
    "axes[0].set_ylabel('Radio PA (deg)', fontsize=14)\n",
    "\n",
    "#axes[1].plot(df['uv'], df['uv'],linestyle=':', color='k', zorder=1)\n",
    "axes[1].axline((0, 0), slope=1, linestyle=':', color='k', zorder=1)\n",
    "axes[1].scatter(df['uv'], df['radio'], s=50, marker='o', facecolors='#ff4d4d', edgecolors='k', alpha=0.9)\n",
    "axes[1].set_xlim([5,110])\n",
    "axes[1].set_ylim([-15,135])\n",
    "axes[1].set_xlabel('HST/UV PA (deg)', fontsize=14, labelpad=12)\n",
    "axes[1].set_ylabel('Radio PA (deg)', fontsize=14)\n",
    "\n",
    "#plt.savefig('PA_plot1.pdf',facecolor='w', dpi=1000, bbox_inches='tight', pad_inches=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3634bb8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2842b729",
   "metadata": {},
   "outputs": [],
   "source": [
    "def uvL(dict_input):\n",
    "\n",
    "    c = 3.0e18\n",
    "    hst_L = []\n",
    "    data = pd.read_csv('~/phd_research/CSS2/SFR_calc/gal_corr_sfr_mags.csv', header=[0])\n",
    "    hst_mAB = data['hst_uv'].values\n",
    "    hst_Fnu = [ 10**(- (k + 48.6) / 2.5) for k in hst_mAB ]\n",
    "    \n",
    "    target_name = list(dict_input.keys())\n",
    "    \n",
    "    for i in range (len(target_name)):\n",
    "    \n",
    "        if target_name[i] == '1037+30':\n",
    "            eff_lambda = 2341.0\n",
    "        elif target_name[i] == '1221-1221':\n",
    "            eff_lambda = 2715.3\n",
    "        else:\n",
    "            eff_lambda = 3361.1 \n",
    "        \n",
    "        # L in ergs/sec\n",
    "        hst_L.append((4 * math.pi * d[i]**2) * hst_Fnu[i] * (c/eff_lambda)) \n",
    "                 \n",
    "    return hst_L\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0c5521cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.2553891712903737e+43,\n",
       " 1.1084592472900565e+43,\n",
       " 1.301614924893452e+43,\n",
       " 3.554034769110349e+42,\n",
       " 1.2439127786113606e+43,\n",
       " 4.3828043715156916e+43]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic = {'1025+390':1940.5, '1037+30':417.2, '1128+455':2217.0, '1201+394':2488.2, \n",
    "              '1203+645':2004.1, '1221-423':826.8}  # (in Mpc)\n",
    "\n",
    "#(1 Mpc =  3.08e24 cm)\n",
    "\n",
    "d = np.zeros(6, dtype=np.float32)\n",
    "i = 0\n",
    "for source in dic:\n",
    "    d[i] = dic[source] * 3.08e24\n",
    "    i = i+1\n",
    "\n",
    "    \n",
    "uv_lum = uvL(dic)\n",
    "uv_lum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3d60ba44",
   "metadata": {},
   "outputs": [],
   "source": [
    "lum_list=[]\n",
    "for m in range(len(uv_lum)):\n",
    "    #lum_list.append(np.nan)   #NaN element for 3 non-uv sources\n",
    "    lum_list.append(uv_lum[m]/1e43)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cb3ad000",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, axes2 = plt.subplots(1, 2, figsize=(14,5)) \n",
    "plt.subplots_adjust(wspace=0.3)\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "\n",
    "set_plot(fig2)\n",
    "\n",
    "y = (df['uv'] - df['radio']).to_numpy()\n",
    "z = df['redshift'].to_numpy()\n",
    "\n",
    "align_list = [y[x] for x in range(2,8)]\n",
    "redshift = [z[x] for x in range(2,8)]\n",
    "\n",
    "axes2[0].scatter(redshift, align_list, s=100, marker='+', facecolors='#005ce6')\n",
    "axes2[0].set_xlim([-0.1,0.7])\n",
    "axes2[0].set_ylim([-55,55])\n",
    "axes2[0].set_xlabel('Redshift', fontsize=14, labelpad=12)\n",
    "axes2[0].set_ylabel('Alignment (deg)', fontsize=14)\n",
    "axes2[0].xaxis.set_major_locator(plt.MaxNLocator(7))\n",
    "axes2[0].yaxis.set_major_locator(plt.MaxNLocator(6))\n",
    "\n",
    "axes2[1].scatter(lum_list, align_list, s=50, marker='x', facecolors='#005ce6')\n",
    "axes2[1].set_xlim([-0.5,5.5])\n",
    "axes2[1].set_ylim([-35,35])\n",
    "axes2[1].set_xlabel('UV luminosity (x 10$^{43}$ ergs sec$^{-1}$)', fontsize=14, labelpad=12)\n",
    "axes2[1].set_ylabel('Alignment (deg)', fontsize=14)\n",
    "axes2[1].xaxis.set_major_locator(plt.MaxNLocator(7))\n",
    "axes2[1].yaxis.set_major_locator(plt.MaxNLocator(6))\n",
    "\n",
    "\n",
    "#plt.savefig('PA_plot2.pdf',facecolor='w', dpi=1000, bbox_inches='tight', pad_inches=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9188b242",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-19, -15, 20, 1, 2, -7]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "align_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d9d18f51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.4 GHz radio luminosity\n",
    "# x 10^27 W/Hz\n",
    "\n",
    "# 1025: 0.296, 1037: 0.008, 1128: 1.201, 1201: 0.356, 1203: 1.781, 1221: 0.205\n",
    "\n",
    "radio_lum = [0.296, 0.008, 1.201, 0.356, 1.781, 0.205]\n",
    "#radio_lum = [radio_lum[x]*10 for x in range(len(radio_lum))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bdb21d13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.296, 0.008, 1.201, 0.356, 1.781, 0.205]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "radio_lum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "86442d0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3, axes3 = plt.subplots(1, 2, figsize=(14,5)) \n",
    "plt.subplots_adjust(wspace=0.3)\n",
    "plt.rcParams['axes.linewidth'] = 1.0\n",
    "\n",
    "set_plot(fig3)\n",
    "\n",
    "y = (df['uv'] - df['radio']).to_numpy()\n",
    "z = df['redshift'].to_numpy()\n",
    "\n",
    "align_list = [y[x] for x in range(2,8)]\n",
    "redshift = [z[x] for x in range(2,8)]\n",
    "\n",
    "axes3[0].scatter(redshift, align_list, s=100, marker='+', facecolors='#005ce6')\n",
    "axes3[0].set_xlim([-0.1,0.7])\n",
    "axes3[0].set_ylim([-55,55])\n",
    "axes3[0].set_xlabel('Redshift', fontsize=14, labelpad=12)\n",
    "axes3[0].set_ylabel('Alignment (deg)', fontsize=14)\n",
    "axes3[0].xaxis.set_major_locator(plt.MaxNLocator(7))\n",
    "axes3[0].yaxis.set_major_locator(plt.MaxNLocator(6))\n",
    "\n",
    "axes3[1].scatter(radio_lum, align_list, s=50, marker='x', facecolors='#005ce6')\n",
    "#axes3[1].set_xlim([-1.5,21.5])\n",
    "axes3[1].set_xlim([-0.4,2.6])\n",
    "axes3[1].set_ylim([-40,40])\n",
    "axes3[1].set_xlabel('P$_{1.4\\mathrm{\\ GHz}}$ (x 10$^{27}$ W Hz$^{-1}$)', fontsize=14, labelpad=12)\n",
    "axes3[1].set_ylabel('Alignment (deg)', fontsize=14)\n",
    "axes3[1].xaxis.set_major_locator(plt.MaxNLocator(7))\n",
    "axes3[1].yaxis.set_major_locator(plt.MaxNLocator(6))\n",
    "\n",
    "#plt.savefig('PA_plot3.pdf',facecolor='w', dpi=1000, bbox_inches='tight', pad_inches=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8c5b0d0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8a2406a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace(0, \"\\nodata\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "73928762",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{ccccc}\n",
      "\\hline\n",
      "  source  &  redshift  &  optical  &  uv   &  radio  \\\\\n",
      "\\hline\n",
      "    1     &   0.017    &    150    & odata &   41    \\\\\n",
      "    2     &   0.536    &    109    & odata &   70    \\\\\n",
      "    3     &   0.361    &    70     &  63   &   82    \\\\\n",
      "    4     &   0.091    &    36     &  24   &   39    \\\\\n",
      "    5     &   0.404    &    54     &  64   &   44    \\\\\n",
      "    6     &   0.445    &    23     &  45   &   44    \\\\\n",
      "    7     &   0.371    &    132    &  73   &   71    \\\\\n",
      "    8     &   0.171    &    156    &  86   &   93    \\\\\n",
      "    9     &   0.195    &    157    & odata &   22    \\\\\n",
      "\\hline\n",
      "\\end{tabular}\n"
     ]
    }
   ],
   "source": [
    "from tabulate import tabulate\n",
    "print(tabulate(df.replace(0, '\\nodata'), tablefmt=\"latex\", headers=\"keys\", showindex=False, numalign=\"center\", stralign=\"center\"))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}