make_paper_plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib,numpy,astropy\n",
    "from astropy.wcs import WCS\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "from astroquery.vizier import Vizier\n",
    "from astropy.table import QTable, join\n",
    "from astropy.table import Table\n",
    "from astropy import units\n",
    "from astropy.coordinates import SkyCoord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "font = {'size'   : 16, 'family' : 'serif', 'serif' : 'cm'}\n",
    "plt.rc('font', **font)\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['lines.linewidth'] = 1\n",
    "plt.rcParams['axes.linewidth'] = 1\n",
    "plt.rcParams['hatch.linewidth'] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "unresolved_list = [38,49,57,62,89,95]\n",
    "falsegroup_list = [48,54]\n",
    "baddata_list = [89]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "HCGs_VLA_HI = Table.read('tables/HCGs_VLA_HI.vo', format='votable')\n",
    "HCGs_VLA_HI.add_index('HCG')\n",
    "\n",
    "HCGs_global_HI = Table.read('tables/HCGs_global_HI.vo', format='votable')\n",
    "HCGs_global_HI.add_index('HCG')\n",
    "\n",
    "HCGs_HI = astropy.table.join(HCGs_VLA_HI,HCGs_global_HI,keys=['HCG'])\n",
    "HCGs_HI.add_index('HCG')\n",
    "\n",
    "HCG_mems = Table.read('tables/HCG_members.vo',format='votable')\n",
    "HCG_mems.add_index('HCG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for HCG in falsegroup_list:\n",
    "    inx = list(HCGs_VLA_HI['HCG']).index(HCG)\n",
    "    HCGs_VLA_HI.remove_row(inx)\n",
    "    inx = list(HCGs_global_HI['HCG']).index(HCG)\n",
    "    HCGs_global_HI.remove_row(inx)\n",
    "    inx = list(HCGs_HI['HCG']).index(HCG)\n",
    "    HCGs_HI.remove_row(inx)\n",
    "    \n",
    "for HCG in baddata_list:\n",
    "    inx = list(HCGs_VLA_HI['HCG']).index(HCG)\n",
    "    HCGs_VLA_HI.remove_row(inx)\n",
    "    inx = list(HCGs_global_HI['HCG']).index(HCG)\n",
    "    HCGs_global_HI.remove_row(inx)\n",
    "    inx = list(HCGs_HI['HCG']).index(HCG)\n",
    "    HCGs_HI.remove_row(inx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "HCGs_HI['Nmem'] = numpy.array(numpy.zeros(len(HCGs_HI)),dtype='int')\n",
    "\n",
    "for HCG in HCGs_HI['HCG']:\n",
    "    HCGs_HI.loc[HCG]['Nmem'] = len(HCG_mems.loc[HCG])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "HCGs_HI['logMHI_p'] = numpy.array(numpy.zeros(len(HCGs_HI)),dtype='str')\n",
    "HCGs_HI['HIphase'] = numpy.array(numpy.zeros(len(HCGs_HI)),dtype='str')\n",
    "\n",
    "HCG_mems_HI = Table.read('tables/HCG_mems_VLA_HI.vo',format='votable')\n",
    "\n",
    "for HCG in list(HCGs_HI['HCG']):\n",
    "    \n",
    "    HCGs_HI.loc[HCG]['logMHI_p'] = r'$'+str(HCGs_HI.loc[HCG]['logMHI_pred_1'])+r' \\pm '+str(HCGs_HI.loc[HCG]['e_logMHI_pred_1'])+r'$'\n",
    "    \n",
    "    count = numpy.sum(numpy.array(numpy.isfinite(HCG_mems_HI[HCG_mems_HI['HCG'] == HCG]['logMHI']),dtype='int'))\n",
    "    \n",
    "    if HCGs_HI.loc[HCG]['ex_frac'] > 0.75 or (hasattr(HCGs_HI.loc[HCG]['logMHI_tot'],'mask') and hasattr(HCGs_HI.loc[HCG]['ex_frac'],'mask')):\n",
    "        HCGs_HI.loc[HCG]['HIphase'] = '3a'\n",
    "    elif HCGs_HI.loc[HCG]['ex_frac'] > 0.25:\n",
    "        HCGs_HI.loc[HCG]['HIphase'] = '2'\n",
    "    elif count > 1:\n",
    "        HCGs_HI.loc[HCG]['HIphase'] = '1'\n",
    "    else:\n",
    "        HCGs_HI.loc[HCG]['HIphase'] = '3c'\n",
    "    \n",
    "    \n",
    "    if HCG in falsegroup_list:\n",
    "        HCGs_HI.loc[HCG]['HIphase'] = ''\n",
    "    elif HCG in unresolved_list and not hasattr(HCGs_HI.loc[HCG]['logMHI_tot'],'mask'):\n",
    "        HCGs_HI.loc[HCG]['HIphase'] = ''\n",
    "    #elif hasattr(HCGs_HI.loc[HCG]['logMHI_tot'],'mask') and hasattr(HCGs_HI.loc[HCG]['logMHI_GBT'],'mask'):\n",
    "    elif hasattr(HCGs_HI.loc[HCG]['logMHI_tot'],'mask'):\n",
    "        HCGs_HI.loc[HCG]['HIphase'] = '3a_nondet'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "HCGs_WISE = Vizier(catalog='J/ApJ/821/113/table1', columns=['Group', 'Name', 'Class', '_RA', '_DE'], row_limit=-1).query_constraints()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "HCGs_HI['IR_class'] = numpy.array(numpy.zeros(len(HCGs_HI)),dtype='str')\n",
    "\n",
    "for HCG in list(HCGs_HI['HCG']):\n",
    "    namestr = 'HCG'+str(HCG).zfill(2)\n",
    "    \n",
    "    tmp = HCGs_WISE[HCGs_WISE['Group'] == namestr]\n",
    "    \n",
    "    classes = list(tmp['Class'])\n",
    "    \n",
    "    N_active = classes.count('Active')\n",
    "    N_quiet = classes.count('Quiescent')\n",
    "    N_canyon = classes.count('Canyon')\n",
    "    N_unknown = int(HCGs_HI.loc[HCG]['Nmem'])-N_active-N_quiet-N_canyon\n",
    "    \n",
    "    if float(N_active)/float(HCGs_HI.loc[HCG]['Nmem']) > 0.5:\n",
    "        HCGs_HI.loc[HCG]['IR_class'] = 'Active'\n",
    "    elif float(N_quiet+N_canyon)/float(HCGs_HI.loc[HCG]['Nmem']) > 0.5:\n",
    "        HCGs_HI.loc[HCG]['IR_class'] = 'Quiescent'\n",
    "    elif float(N_quiet+N_canyon+N_unknown)/float(HCGs_HI.loc[HCG]['Nmem']) > 0.5 or float(N_active+N_unknown)/float(HCGs_HI.loc[HCG]['Nmem']) > 0.5: \n",
    "        HCGs_HI.loc[HCG]['IR_class'] = 'Unknown'\n",
    "    else:\n",
    "        HCGs_HI.loc[HCG]['IR_class'] = 'Intermediate'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "counter_nondet = 0\n",
    "\n",
    "for HCG in list(HCGs_HI['HCG']):\n",
    "        \n",
    "    if HCGs_HI.loc[HCG]['HIphase'] == '1':\n",
    "        col = '#56B4E9'\n",
    "    elif HCGs_HI.loc[HCG]['HIphase'] == '2':\n",
    "        col = '#009E73'\n",
    "    elif HCGs_HI.loc[HCG]['HIphase'] == '3a':\n",
    "        col = '#D55E00'\n",
    "    elif HCGs_HI.loc[HCG]['HIphase'] == '3a_nondet':\n",
    "        col = '#D55E00'\n",
    "    elif HCGs_HI.loc[HCG]['HIphase'] == '3c':\n",
    "        col = '#CC79A7'\n",
    "    else:\n",
    "        continue\n",
    "        \n",
    "    if HCGs_HI.loc[HCG]['IR_class'] == 'Active':\n",
    "        symbol = '*'\n",
    "        size = 300\n",
    "        ecol = 'none'\n",
    "        fcol = col\n",
    "        order = 2\n",
    "    elif HCGs_HI.loc[HCG]['IR_class'] == 'Quiescent':\n",
    "        symbol = 's'\n",
    "        size = 125\n",
    "        ecol = 'none'\n",
    "        fcol = col\n",
    "        order = 1\n",
    "    elif HCGs_HI.loc[HCG]['IR_class'] == 'Unknown':\n",
    "        symbol = 'o'\n",
    "        size = 100\n",
    "        ecol = col\n",
    "        fcol = 'none'\n",
    "        order = 0\n",
    "    elif HCGs_HI.loc[HCG]['IR_class'] == 'Intermediate':\n",
    "        symbol = 'X'\n",
    "        size = 150\n",
    "        ecol = 'none'\n",
    "        fcol = col\n",
    "        order = 4\n",
    "    else:\n",
    "        print(\"Error: Unmatched IR classification.\")\n",
    "        break    \n",
    "    \n",
    "    if HCGs_HI.loc[HCG]['HIphase'] == '3a_nondet':\n",
    "        if not hasattr(HCGs_HI.loc[HCG]['logMHI_GBT'],'mask'):\n",
    "            HIdef_val = HCGs_HI.loc[HCG]['HIdef_GBT']\n",
    "            plt.scatter(HIdef_val,1.,marker=symbol,s=size,edgecolor=ecol,facecolor=fcol,lw=4,zorder=order)\n",
    "            if len(HCG_mems.loc[HCG]) == 3:\n",
    "                plt.scatter(HIdef_val,1.,edgecolor='k',marker='o',s=300, ls='--',facecolor='none')\n",
    "        else:\n",
    "            HIdef_val = 1.5\n",
    "            plt.scatter(HIdef_val,1.+counter_nondet*0.05,marker=symbol,s=size,edgecolor=ecol,facecolor=fcol,lw=4,zorder=order)\n",
    "            plt.errorbar(HIdef_val,1.+counter_nondet*0.05,c='#D55E00',xlolims=True,xerr=0.05,lw=0,elinewidth=1)\n",
    "            if len(HCG_mems.loc[HCG]) == 3:\n",
    "                plt.scatter(HIdef_val,1.+counter_nondet*0.05,edgecolor='k',marker='o',s=300, ls='--',facecolor='none')\n",
    "            counter_nondet += 1\n",
    "        continue\n",
    "    else:\n",
    "        plt.scatter(HCGs_HI.loc[HCG]['HIdef'],HCGs_HI.loc[HCG]['ex_frac'],marker=symbol,s=size,edgecolor=ecol,facecolor=fcol,lw=4,zorder=order)\n",
    "    if len(HCG_mems.loc[HCG]) == 3:\n",
    "        plt.scatter(HCGs_HI.loc[HCG]['HIdef'],HCGs_HI.loc[HCG]['ex_frac'],edgecolor='k',marker='o',s=300, ls='--',facecolor='none')\n",
    "    \n",
    "style = \"Simple, tail_width=10, head_width=25, head_length=20\"\n",
    "\n",
    "a1 = patches.FancyArrowPatch(posA=(0., 0.), posB=(0.,0.25),color='#56B4E9', lw=0, arrowstyle=style,alpha=0.25)\n",
    "a2 = patches.FancyArrowPatch(posA=(0.,0.25), posB=(0.,0.75),color='#009E73', lw=0, arrowstyle=style,alpha=0.25)\n",
    "a3 = patches.FancyArrowPatch(posA=(0.,0.775), posB=(1.0,1.0),color='#D55E00',connectionstyle=\"arc3,rad=-.2\", lw=0, arrowstyle=style,alpha=0.25)\n",
    "a4 = patches.FancyArrowPatch(posA=(1.5,0.9), posB=(0.8,0.0125),color='#CC79A7',connectionstyle=\"arc3,rad=-0.3\", lw=0, arrowstyle=style,alpha=0.25)\n",
    "\n",
    "plt.gca().add_patch(a1)\n",
    "plt.gca().add_patch(a2)\n",
    "plt.gca().add_patch(a3)\n",
    "plt.gca().add_patch(a4)\n",
    "    \n",
    "plt.axhline(0.25,lw=1,c='k',ls='dashed')\n",
    "plt.axhline(0.75,lw=1,c='k',ls='dashed')\n",
    "plt.ylim(-0.05,1.05)\n",
    "plt.xlim(-0.5,1.6)\n",
    "\n",
    "plt.scatter([],[],c='#56B4E9',marker='D',s=100,label='Phase 1')\n",
    "plt.scatter([],[],c='#009E73',marker='D',s=100,label='Phase 2')\n",
    "plt.scatter([],[],c='#D55E00',marker='D',s=100,label='Phase 3a')\n",
    "plt.scatter([],[],c='#CC79A7',marker='D',s=100,label='Phase 3c')\n",
    "plt.scatter([],[],c='grey',marker='*',s=300,label='Active')\n",
    "plt.scatter([],[],c='grey',marker='X',s=150,label='Intermediate')\n",
    "plt.scatter([],[],c='grey',marker='s',s=125,label='Quiescent')\n",
    "plt.scatter([],[],edgecolor='grey',marker='o',s=100,facecolor='none',lw=4,label='Unknown')\n",
    "plt.scatter([],[],edgecolor='k',marker='o',s=300, ls='--',facecolor='none',label='Triplet')\n",
    "\n",
    "plt.legend(loc=7)\n",
    "\n",
    "plt.xlabel('HI-deficiency [dex]')\n",
    "plt.ylabel(r'Extended $M_\\mathrm{HI}$ / Total $M_\\mathrm{HI}$')\n",
    "\n",
    "plt.savefig('HCG_tidal_frac.png',bbox_inches='tight')\n",
    "plt.savefig('HCG_tidal_frac.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "ph1_HIdef = []\n",
    "ph2_HIdef = []\n",
    "ph3_HIdef = []\n",
    "\n",
    "for HCG in list(HCGs_HI['HCG']):\n",
    "        \n",
    "    if HCGs_HI.loc[HCG]['HIphase'] == '1':\n",
    "        ph1_HIdef.append(HCGs_HI.loc[HCG]['HIdef'])\n",
    "    elif HCGs_HI.loc[HCG]['HIphase'] == '2':\n",
    "        ph2_HIdef.append(HCGs_HI.loc[HCG]['HIdef'])\n",
    "    elif HCGs_HI.loc[HCG]['HIphase'] == '3a':\n",
    "        ph3_HIdef.append(HCGs_HI.loc[HCG]['HIdef'])\n",
    "    elif HCGs_HI.loc[HCG]['HIphase'] == '3a_nondet':\n",
    "        if not hasattr(HCGs_HI.loc[HCG]['logMHI_GBT'],'mask'):\n",
    "            ph3_HIdef.append(HCGs_HI.loc[HCG]['HIdef_GBT'])\n",
    "        else:\n",
    "            continue\n",
    "    elif HCGs_HI.loc[HCG]['HIphase'] == '3c': \n",
    "        ph3_HIdef.append(HCGs_HI.loc[HCG]['HIdef'])\n",
    "    else:\n",
    "        continue\n",
    "        \n",
    "defbins = numpy.arange(-0.75,2.01,0.25)\n",
    "\n",
    "all_HIdef = []\n",
    "all_HIdef.extend(ph1_HIdef)\n",
    "all_HIdef.extend(ph2_HIdef)\n",
    "all_HIdef.extend(ph3_HIdef)\n",
    "\n",
    "all_binvals, all_edges = numpy.histogram(all_HIdef,defbins)\n",
    "ph1_binvals, ph1_edges = numpy.histogram(ph1_HIdef,defbins)\n",
    "ph2_binvals, ph2_edges = numpy.histogram(ph2_HIdef,defbins)\n",
    "ph3_binvals, ph3_edges = numpy.histogram(ph3_HIdef,defbins)\n",
    "\n",
    "plt.bar(defbins[:-1],all_binvals,width=0.25,align='edge',color='grey',alpha=0.25,label='All HCGs')\n",
    "plt.bar(defbins[:-1],ph3_binvals,width=0.25,align='edge',facecolor='none',edgecolor='#E69F00',hatch='/',lw=2,label='Phase 3')\n",
    "plt.bar(defbins[:-1],ph2_binvals,width=0.25,align='edge',facecolor='none',edgecolor='#009E73',hatch='\\\\\\\\',lw=2,label='Phase 2')\n",
    "plt.bar(defbins[:-1],ph1_binvals,width=0.25,align='edge',facecolor='none',edgecolor='#56B4E9',hatch='+',lw=2,label='Phase 1')\n",
    "\n",
    "plt.axvline(-0.25,lw=1,ls='dashed',c='k')\n",
    "plt.axvline(0.25,lw=1,ls='dashed',c='k')\n",
    "\n",
    "plt.annotate('HI-normal',[0,9.25],ha='center')\n",
    "plt.annotate('HI-rich',[-0.5,9.25],ha='center')\n",
    "plt.annotate('HI-poor',[0.5,9.25],ha='center')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.xlim(-0.75,1.75)\n",
    "plt.ylim(0.,9.75)\n",
    "\n",
    "plt.xlabel('HI-deficiency [dex]')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "plt.savefig('HCG_HIdef_hist.png',bbox_inches='tight')\n",
    "plt.savefig('HCG_HIdef_hist.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "ph1_HIdef = []\n",
    "ph2_HIdef = []\n",
    "ph3_HIdef = []\n",
    "\n",
    "for HCG in list(HCGs_HI['HCG']):\n",
    "        \n",
    "    if HCGs_HI.loc[HCG]['HIphase'] == '1':\n",
    "        ph1_HIdef.append(HCGs_HI.loc[HCG]['HIdef'])\n",
    "    elif HCGs_HI.loc[HCG]['HIphase'] == '2':\n",
    "        ph2_HIdef.append(HCGs_HI.loc[HCG]['HIdef'])\n",
    "    elif HCGs_HI.loc[HCG]['HIphase'] == '3a':\n",
    "        ph3_HIdef.append(HCGs_HI.loc[HCG]['HIdef'])\n",
    "    elif HCGs_HI.loc[HCG]['HIphase'] == '3a_nondet':\n",
    "        if not hasattr(HCGs_HI.loc[HCG]['logMHI_GBT'],'mask'):\n",
    "            ph3_HIdef.append(HCGs_HI.loc[HCG]['HIdef_GBT'])\n",
    "        else:\n",
    "            continue\n",
    "    elif HCGs_HI.loc[HCG]['HIphase'] == '3c': \n",
    "        ph3_HIdef.append(HCGs_HI.loc[HCG]['HIdef'])\n",
    "    else:\n",
    "        continue\n",
    "        \n",
    "defbins = numpy.arange(-0.75,2.01,0.25)\n",
    "\n",
    "all_HIdef = []\n",
    "all_HIdef.extend(ph1_HIdef)\n",
    "all_HIdef.extend(ph2_HIdef)\n",
    "all_HIdef.extend(ph3_HIdef)\n",
    "\n",
    "all_binvals, all_edges = numpy.histogram(all_HIdef,defbins)\n",
    "ph1_binvals, ph1_edges = numpy.histogram(ph1_HIdef,defbins)\n",
    "ph2_binvals, ph2_edges = numpy.histogram(ph2_HIdef,defbins)\n",
    "ph3_binvals, ph3_edges = numpy.histogram(ph3_HIdef,defbins)\n",
    "\n",
    "plt.bar(defbins[:-1],ph3_binvals,bottom=ph2_binvals+ph1_binvals,width=0.25,align='edge',facecolor='none',edgecolor='#E69F00',hatch='/',lw=2,label='Phase 3')\n",
    "plt.bar(defbins[:-1],ph2_binvals,bottom=ph1_binvals,width=0.25,align='edge',facecolor='none',edgecolor='#009E73',hatch='\\\\\\\\',lw=2,label='Phase 2')\n",
    "plt.bar(defbins[:-1],ph1_binvals,width=0.25,align='edge',facecolor='none',edgecolor='#56B4E9',hatch='+',lw=2,label='Phase 1')\n",
    "\n",
    "plt.axvline(-0.25,lw=1,ls='dashed',c='k')\n",
    "plt.axvline(0.25,lw=1,ls='dashed',c='k')\n",
    "\n",
    "plt.annotate('HI-normal',[0,9.25],ha='center')\n",
    "plt.annotate('HI-rich',[-0.5,9.25],ha='center')\n",
    "plt.annotate('HI-poor',[0.5,9.25],ha='center')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.xlim(-0.75,1.75)\n",
    "plt.ylim(0.,9.75)\n",
    "\n",
    "plt.xlabel('HI-deficiency [dex]')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "plt.savefig('HCG_HIdef_hist_stacked.png',bbox_inches='tight')\n",
    "plt.savefig('HCG_HIdef_hist_stacked.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "for HCG in list(HCGs_HI['HCG']):\n",
    "        \n",
    "    if HCGs_HI.loc[HCG]['HIphase'] == '1':\n",
    "        col = '#56B4E9'\n",
    "    elif HCGs_HI.loc[HCG]['HIphase'] == '2':\n",
    "        col = '#009E73'\n",
    "    elif HCGs_HI.loc[HCG]['HIphase'] == '3a':\n",
    "        col = '#D55E00'\n",
    "    elif HCGs_HI.loc[HCG]['HIphase'] == '3a_nondet':\n",
    "        col = '#D55E00'\n",
    "    elif HCGs_HI.loc[HCG]['HIphase'] == '3c':\n",
    "        col = '#CC79A7'\n",
    "    else:\n",
    "        continue\n",
    "        \n",
    "    if HCGs_HI.loc[HCG]['IR_class'] == 'Active':\n",
    "        symbol = '*'\n",
    "        size = 300\n",
    "        ecol = 'none'\n",
    "        fcol = col\n",
    "        order = 2\n",
    "    elif HCGs_HI.loc[HCG]['IR_class'] == 'Quiescent':\n",
    "        symbol = 's'\n",
    "        size = 125\n",
    "        ecol = 'none'\n",
    "        fcol = col\n",
    "        order = 1\n",
    "    elif HCGs_HI.loc[HCG]['IR_class'] == 'Unknown':\n",
    "        symbol = 'o'\n",
    "        size = 100\n",
    "        ecol = col\n",
    "        fcol = 'none'\n",
    "        order = 0\n",
    "    elif HCGs_HI.loc[HCG]['IR_class'] == 'Intermediate':\n",
    "        symbol = 'X'\n",
    "        size = 150\n",
    "        ecol = 'none'\n",
    "        fcol = col\n",
    "        order = 4\n",
    "    else:\n",
    "        print(\"Error: Unmatched IR classification.\")\n",
    "        break    \n",
    "        \n",
    "    tmp = HCG_mems[HCG_mems['HCG'] == HCG]\n",
    "    early_frac = len(tmp[tmp['T'] <= 0])/float(len(tmp))\n",
    "    \n",
    "    if HCGs_HI.loc[HCG]['HIphase'] == '3a_nondet':\n",
    "        if not hasattr(HCGs_HI.loc[HCG]['logMHI_GBT'],'mask'):\n",
    "            HIdef_val = HCGs_HI.loc[HCG]['HIdef_GBT']\n",
    "            plt.scatter(HIdef_val,early_frac,marker=symbol,s=size,edgecolor=ecol,facecolor=fcol,lw=4,zorder=order)\n",
    "            if len(HCG_mems.loc[HCG]) == 3:\n",
    "                plt.scatter(HIdef_val,early_frac,edgecolor='k',marker='o',s=300, ls='--',facecolor='none')\n",
    "        else:\n",
    "            HIdef_val = 1.5\n",
    "            plt.scatter(HIdef_val,early_frac,marker=symbol,s=size,edgecolor=ecol,facecolor=fcol,lw=4,zorder=order)\n",
    "            plt.errorbar(HIdef_val,early_frac,c='#D55E00',xlolims=True,xerr=0.05,lw=0,elinewidth=1)\n",
    "            if len(HCG_mems.loc[HCG]) == 3:\n",
    "                plt.scatter(HIdef_val,early_frac,edgecolor='k',marker='o',s=300, ls='--',facecolor='none')\n",
    "        continue\n",
    "    else:\n",
    "        plt.scatter(HCGs_HI.loc[HCG]['HIdef'],early_frac,marker=symbol,s=size,edgecolor=ecol,facecolor=fcol,lw=4,zorder=order)\n",
    "    if len(HCG_mems.loc[HCG]) == 3:\n",
    "        plt.scatter(HCGs_HI.loc[HCG]['HIdef'],early_frac,edgecolor='k',marker='o',s=300, ls='--',facecolor='none')\n",
    "\n",
    "plt.ylim(-0.05,1.05)\n",
    "plt.xlim(-0.5,1.6)\n",
    "\n",
    "plt.scatter([],[],c='#56B4E9',marker='D',s=100,label='Phase 1')\n",
    "plt.scatter([],[],c='#009E73',marker='D',s=100,label='Phase 2')\n",
    "plt.scatter([],[],c='#D55E00',marker='D',s=100,label='Phase 3a')\n",
    "plt.scatter([],[],c='#CC79A7',marker='D',s=100,label='Phase 3c')\n",
    "plt.scatter([],[],c='grey',marker='*',s=300,label='Active')\n",
    "plt.scatter([],[],c='grey',marker='X',s=150,label='Intermediate')\n",
    "plt.scatter([],[],c='grey',marker='s',s=125,label='Quiescent')\n",
    "plt.scatter([],[],edgecolor='grey',marker='o',s=100,facecolor='none',lw=4,label='Unknown')\n",
    "plt.scatter([],[],edgecolor='k',marker='o',s=300, ls='--',facecolor='none',label='Triplet')\n",
    "\n",
    "#plt.legend(loc=3)\n",
    "\n",
    "plt.xlabel('HI-deficiency [dex]')\n",
    "plt.ylabel('Fraction of early-type members')\n",
    "\n",
    "plt.savefig('HCG_ETG_frac.png',bbox_inches='tight')\n",
    "plt.savefig('HCG_ETG_frac.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "plt.scatter(HCGs_VLA_HI['logMHI_pred'],HCGs_VLA_HI['logMHI_tot'],c='k',marker='+',s=80,lw=1.5)\n",
    "m_tmp = numpy.arange(7.35,11.01,0.01)\n",
    "plt.plot(m_tmp,m_tmp,lw=1,c='k')\n",
    "plt.plot(m_tmp,m_tmp+0.13,lw=1,c='k',ls='--')\n",
    "plt.plot(m_tmp,m_tmp-0.13,lw=1,c='k',ls='--')\n",
    "plt.plot(m_tmp,m_tmp+2.*0.13,lw=1,c='k',ls='dotted')\n",
    "plt.plot(m_tmp,m_tmp-2.*0.13,lw=1,c='k',ls='dotted')\n",
    "\n",
    "for HCG in HCGs_VLA_HI['HCG']:\n",
    "    if hasattr(HCGs_VLA_HI.loc[HCG]['logMHI_tot'],'mask') and hasattr(HCGs_global_HI.loc[HCG]['logMHI_GBT'],'mask'):\n",
    "        v_wid_fid = 100. #km/s\n",
    "        detlim = numpy.log10(3.*(0.001*HCGs_global_HI.loc[HCG]['VLA_rms']/numpy.sqrt(v_wid_fid/abs(HCGs_global_HI.loc[HCG]['chan_wid'])))*v_wid_fid*2.356E5*HCGs_VLA_HI.loc[HCG]['dist']**2.)\n",
    "        plt.scatter(HCGs_global_HI.loc[HCG]['logMHI_pred'],detlim,c='k',marker='+',s=80.,lw=1.5)\n",
    "        plt.errorbar(HCGs_global_HI.loc[HCG]['logMHI_pred'],detlim,c='k',uplims=True,yerr=0.05,lw=0,elinewidth=1.5)\n",
    "        \n",
    "    if hasattr(HCGs_VLA_HI.loc[HCG]['logMHI_tot'],'mask') and not hasattr(HCGs_global_HI.loc[HCG]['logMHI_GBT'],'mask'):\n",
    "        plt.scatter(HCGs_global_HI.loc[HCG]['logMHI_pred'],HCGs_global_HI.loc[HCG]['logMHI_GBT'],c='k',marker='+',s=80,lw=1.5)\n",
    "        \n",
    "for HCG in HCGs_VLA_HI['HCG']:\n",
    "    if len(HCG_mems.loc[HCG]) == 3:\n",
    "        plt.scatter(HCGs_VLA_HI.loc[HCG]['logMHI_pred'],HCGs_VLA_HI.loc[HCG]['logMHI_pred']-HCGs_VLA_HI.loc[HCG]['HIdef'],\n",
    "                    edgecolor='k',marker='o',s=150, ls='--',facecolor='none')\n",
    "\n",
    "plt.xlabel(r'$\\log\\,M_\\mathrm{HI}/\\mathrm{M_\\odot}$ (predicted)')\n",
    "plt.ylabel(r'$\\log\\,M_\\mathrm{HI}/\\mathrm{M_\\odot}$ (measured)')\n",
    "plt.xlim(8.75,11.)\n",
    "plt.ylim(7.35,11.)\n",
    "\n",
    "plt.savefig('HCG_def.png',bbox_inches='tight')\n",
    "plt.savefig('HCG_def.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "plt.scatter(HCGs_VLA_HI['logMHI_pred_HG84'],HCGs_VLA_HI['logMHI_tot'],c='g',marker='o',s=50,lw=1.5,label='HG84 relation')\n",
    "plt.scatter(HCGs_VLA_HI['logMHI_pred'],HCGs_VLA_HI['logMHI_tot'],c='k',marker='+',s=80,lw=1.5,label='Jones+18 relation')\n",
    "m_tmp = numpy.arange(7.35,11.01,0.01)\n",
    "plt.plot(m_tmp,m_tmp,lw=1,c='k')\n",
    "plt.plot(m_tmp,m_tmp+0.13,lw=1,c='k',ls='--')\n",
    "plt.plot(m_tmp,m_tmp-0.13,lw=1,c='k',ls='--')\n",
    "plt.plot(m_tmp,m_tmp+2.*0.13,lw=1,c='k',ls='dotted')\n",
    "plt.plot(m_tmp,m_tmp-2.*0.13,lw=1,c='k',ls='dotted')\n",
    "\n",
    "for HCG in HCGs_VLA_HI['HCG']:\n",
    "    plt.plot([HCGs_VLA_HI.loc[HCG]['logMHI_pred_HG84'],HCGs_VLA_HI.loc[HCG]['logMHI_pred']],\n",
    "             [HCGs_VLA_HI.loc[HCG]['logMHI_tot'],HCGs_VLA_HI.loc[HCG]['logMHI_tot']],lw=1,c='g',ls='--')\n",
    "    \n",
    "    if hasattr(HCGs_VLA_HI.loc[HCG]['logMHI_tot'],'mask') and hasattr(HCGs_global_HI.loc[HCG]['logMHI_GBT'],'mask'):\n",
    "        v_wid_fid = 100. #km/s\n",
    "        detlim = numpy.log10(3.*(0.001*HCGs_global_HI.loc[HCG]['VLA_rms']/numpy.sqrt(v_wid_fid/abs(HCGs_global_HI.loc[HCG]['chan_wid'])))*v_wid_fid*2.356E5*HCGs_VLA_HI.loc[HCG]['dist']**2.)\n",
    "        plt.scatter(HCGs_global_HI.loc[HCG]['logMHI_pred'],detlim,c='k',marker='+',s=80.,lw=1.5)\n",
    "        plt.errorbar(HCGs_global_HI.loc[HCG]['logMHI_pred'],detlim,c='k',uplims=True,yerr=0.05,lw=0,elinewidth=1.5)\n",
    "        plt.scatter(HCGs_global_HI.loc[HCG]['logMHI_pred_HG84'],detlim,c='g',marker='o',s=50.,lw=1.5)\n",
    "        plt.errorbar(HCGs_global_HI.loc[HCG]['logMHI_pred_HG84'],detlim,c='g',uplims=True,yerr=0.05,lw=0,elinewidth=1.5)\n",
    "        plt.plot([HCGs_VLA_HI.loc[HCG]['logMHI_pred_HG84'],HCGs_VLA_HI.loc[HCG]['logMHI_pred']],[detlim,detlim],lw=1,c='g',ls='--')\n",
    "        \n",
    "    if hasattr(HCGs_VLA_HI.loc[HCG]['logMHI_tot'],'mask') and not hasattr(HCGs_global_HI.loc[HCG]['logMHI_GBT'],'mask'):\n",
    "        plt.scatter(HCGs_global_HI.loc[HCG]['logMHI_pred'],HCGs_global_HI.loc[HCG]['logMHI_GBT'],c='k',marker='+',s=80,lw=1.5)\n",
    "        plt.scatter(HCGs_global_HI.loc[HCG]['logMHI_pred_HG84'],HCGs_global_HI.loc[HCG]['logMHI_GBT'],c='g',marker='o',s=50,lw=1.5)\n",
    "        plt.plot([HCGs_VLA_HI.loc[HCG]['logMHI_pred_HG84'],HCGs_VLA_HI.loc[HCG]['logMHI_pred']],\n",
    "                 [HCGs_global_HI.loc[HCG]['logMHI_GBT'],HCGs_global_HI.loc[HCG]['logMHI_GBT']],lw=1,c='g',ls='--')\n",
    "        \n",
    "for HCG in HCGs_VLA_HI['HCG']:\n",
    "    if len(HCG_mems.loc[HCG]) == 3:\n",
    "        plt.scatter(HCGs_VLA_HI.loc[HCG]['logMHI_pred'],HCGs_VLA_HI.loc[HCG]['logMHI_pred']-HCGs_VLA_HI.loc[HCG]['HIdef'],\n",
    "                    edgecolor='k',marker='o',s=150, ls='--',facecolor='none')\n",
    "\n",
    "plt.xlabel(r'$\\log\\,M_\\mathrm{HI}/\\mathrm{M_\\odot}$ (predicted)')\n",
    "plt.ylabel(r'$\\log\\,M_\\mathrm{HI}/\\mathrm{M_\\odot}$ (measured)')\n",
    "plt.xlim(8.75,11.)\n",
    "plt.ylim(7.35,11.)\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig('HCG_def_HG84comp.png',bbox_inches='tight')\n",
    "plt.savefig('HCG_def_HG84comp.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "plt.scatter(HCGs_global_HI['logMHI_GBT'],HCGs_global_HI['logMHI_VLA_inGBTbeam'],c='k')\n",
    "m_tmp = numpy.arange(5.5,10.76,0.01)\n",
    "plt.plot(m_tmp,m_tmp,lw=1,c='k')\n",
    "#plt.plot(m_tmp,m_tmp-0.25,lw=1,c='k',ls='--')\n",
    "plt.xlabel('log M_HI (GBT)')\n",
    "plt.ylabel('log M_HI (VLA weighted to GBT beam)')\n",
    "\n",
    "plt.xlabel(r'$\\log\\,M_\\mathrm{HI}/\\mathrm{M_\\odot}$ (GBT)')\n",
    "plt.ylabel(r'$\\log\\,M_\\mathrm{HI}/\\mathrm{M_\\odot}$ (VLA within GBT beam)')\n",
    "plt.xlim(8,10.75)\n",
    "plt.ylim(5.75,10.75)\n",
    "\n",
    "plt.savefig('HCG_GBT_VLA_HImass.png',bbox_inches='tight')\n",
    "plt.savefig('HCG_GBT_VLA_HImass.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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