NEOWISE_lightcurve_construction_original.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from astropy import stats\n",
    "from astropy import constants as const\n",
    "from astropy import units as u\n",
    "import astropy.time\n",
    "from astropy.time import Time\n",
    "import dateutil.parser\n",
    "from astropy.coordinates import SkyCoord\n",
    "\n",
    "import scipy\n",
    "from scipy import stats\n",
    "\n",
    "from uncertainties import ufloat\n",
    "from uncertainties.umath import *\n",
    "from uncertainties import unumpy as unp\n",
    "from uncertainties import ufloat_fromstr\n",
    "\n",
    "import math\n",
    "import pandas as pd\n",
    "import pyphot\n",
    "import pyphot.ezunits.pint as pint\n",
    "import scipy.constants as const\n",
    "\n",
    "import extinction #https://extinction.readthedocs.io/en/latest/\n",
    "from extinction import ccm89, remove, apply\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Location of WISE catalog tables\n",
    "root = '/home/treynolds/data/LIRGS/WISE/WISE_analysis/Data/'\n",
    "\n",
    "### Make sure you have the following columns selected when downloading the data:\n",
    "# cc_flags\n",
    "# ph_qual\n",
    "# qual_frame\n",
    "# qi_fact\n",
    "# saa_sep\n",
    "# moon_masked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define source\n",
    "source = 'ASASSN-15lh'\n",
    "neowise_path = f'{root}{source}_NeoWISE.tbl'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WISE zero points\n",
    "lib = pyphot.get_library()\n",
    "\n",
    "f0_wise_3_4 = lib['WISE_RSR_W1'].Vega_zero_Jy.magnitude*1e8\n",
    "f0_wise_4_6 = lib['WISE_RSR_W2'].Vega_zero_Jy.magnitude*1e8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert observation date such as 20171131 into Julian day\n",
    "\n",
    "def mjday(day):\n",
    "    return astropy.time.Time(dateutil.parser.parse(day)).jd - 2400000.5\n",
    "\n",
    "# Convert flux density to magnitude and vice versa\n",
    "\n",
    "def fluxdens_to_mag(flux, f0):\n",
    "    return -2.5 * unp.log10(flux / f0)\n",
    "\n",
    "def mag_to_fluxdens(mag, f0):\n",
    "    return f0 * 10**(-mag/2.5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in WISE data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct header and identify start of data\n",
    "neowise_header = []\n",
    "\n",
    "for idx,line in enumerate(open(neowise_path).readlines()):\n",
    "    if line.startswith('|'):\n",
    "        skiprows = idx + 4 # skip over comments and header info\n",
    "        for i in line.split('|')[1:-1]:\n",
    "            neowise_header.append(i.lstrip().rstrip())\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in data in Pandas dataframe\n",
    "neowise_read_df = pd.read_fwf(neowise_path, skiprows = skiprows, header = None, names = neowise_header)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for any astrometric outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check for any measurements offset from median position\n",
    "ra_median = np.median(neowise_read_df['ra'])\n",
    "dec_median = np.median(neowise_read_df['dec'])\n",
    "pos_median = SkyCoord(ra_median*u.deg,dec_median*u.deg, frame='icrs')\n",
    "\n",
    "# Add column with separation in arcseconds\n",
    "neowise_read_df['sep'] = pos_median.separation(SkyCoord(neowise_read_df['ra']*u.deg,neowise_read_df['dec']*u.deg, frame='icrs')).arcsec\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set allowed max offset in arcsec from median position (WISE FWHM ~ 6\") based on positional distribution\n",
    "allowed_sep = 1.5\n",
    "\n",
    "fig, ax = plt.subplots(1,1)\n",
    "\n",
    "for ra, dec, sep in zip(neowise_read_df['ra'], neowise_read_df['dec'], neowise_read_df['sep']):\n",
    "    if sep > allowed_sep: \n",
    "        ax.plot(ra, dec, color = 'red', marker = 'o', markersize=5)\n",
    "    else:\n",
    "        ax.plot(ra, dec, color = 'black', marker = 'o', markersize=5)\n",
    "\n",
    "ax.set_xlabel('ra')\n",
    "ax.set_ylabel('dec')\n",
    "\n",
    "fig.set_size_inches(7,7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diagnostic plots\n",
    "Measurements with qual_frame = 0 and offset sources should always be masked. Upper limits too. The other filters sometimes represent a significant fraction of data, so should be analysed more carefully before filtering out. For example, if there are a lot of sources with SAA separation < 5, but they don't show significant magnitude offsets, consider not filtering for them. See plot below for visualization of filters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1)\n",
    "\n",
    "ax.errorbar(neowise_read_df['w1mpro'], neowise_read_df['w2mpro'],\\\n",
    "            xerr = neowise_read_df['w1sigmpro'], yerr = neowise_read_df['w2sigmpro'],\\\n",
    "           linestyle = '')\n",
    "\n",
    "# Poor quality\n",
    "qual_mask = neowise_read_df['qual_frame'] == 0\n",
    "ax.errorbar(neowise_read_df['w1mpro'][qual_mask], neowise_read_df['w2mpro'][qual_mask],\\\n",
    "            xerr = neowise_read_df['w1sigmpro'][qual_mask], yerr = neowise_read_df['w2sigmpro'][qual_mask],\\\n",
    "           linestyle = '', color = 'red', marker = 'o', label = 'poor qual')\n",
    "\n",
    "# Flagged as upper limit or no profile-fit\n",
    "qual_mask = [('X' in i or 'U' in i) for i in neowise_read_df['ph_qual']]\n",
    "ax.errorbar(neowise_read_df['w1mpro'][qual_mask], neowise_read_df['w2mpro'][qual_mask],\\\n",
    "            xerr = neowise_read_df['w1sigmpro'][qual_mask], yerr = neowise_read_df['w2sigmpro'][qual_mask],\\\n",
    "           linestyle = '', color = 'orange', marker = 'o', label = 'photometry flag')\n",
    "\n",
    "# Too far offset\n",
    "qual_mask = neowise_read_df['sep'] > allowed_sep\n",
    "ax.errorbar(neowise_read_df['w1mpro'][qual_mask], neowise_read_df['w2mpro'][qual_mask],\\\n",
    "            xerr = neowise_read_df['w1sigmpro'][qual_mask], yerr = neowise_read_df['w2sigmpro'][qual_mask],\\\n",
    "           linestyle = '', color = 'black', marker = 'o', label = 'offset')\n",
    "\n",
    "# Close to SAA\n",
    "qual_mask = [abs(i) < 5 for i in neowise_read_df['saa_sep']]\n",
    "ax.errorbar(neowise_read_df['w1mpro'][qual_mask], neowise_read_df['w2mpro'][qual_mask],\\\n",
    "            xerr = neowise_read_df['w1sigmpro'][qual_mask], yerr = neowise_read_df['w2sigmpro'][qual_mask],\\\n",
    "           linestyle = '', color = 'purple', marker = 'o', label = 'poor qual')\n",
    "\n",
    "# Within the moon-mask area\n",
    "qual_mask = neowise_read_df['moon_masked'] != 0\n",
    "ax.errorbar(neowise_read_df['w1mpro'][qual_mask], neowise_read_df['w2mpro'][qual_mask],\\\n",
    "            xerr = neowise_read_df['w1sigmpro'][qual_mask], yerr = neowise_read_df['w2sigmpro'][qual_mask],\\\n",
    "           linestyle = '', color = 'green', marker = 'o', label = 'moon mask')\n",
    "\n",
    "# Flagged as known artifact\n",
    "qual_mask = [(i != 0.0 and i != '0000') for i in neowise_read_df['cc_flags']]\n",
    "ax.errorbar(neowise_read_df['w1mpro'][qual_mask], neowise_read_df['w2mpro'][qual_mask],\\\n",
    "            xerr = neowise_read_df['w1sigmpro'][qual_mask], yerr = neowise_read_df['w2sigmpro'][qual_mask],\\\n",
    "           linestyle = '', color = 'pink', marker = 'o', label = 'artifact flag')\n",
    "\n",
    "\n",
    "ax.legend()\n",
    "ax.set_xlabel('W1')\n",
    "ax.set_ylabel('W2')\n",
    "fig.set_size_inches(7,7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create mask to filter out poor quality measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Comment out constraints you don't want to include (qi_fact is same as qual_frame as far as I know)\n",
    "\n",
    "neowise_mask = [all(constraint) for constraint in zip(\n",
    "    neowise_read_df['sep'] < allowed_sep,\\\n",
    "    neowise_read_df['qual_frame'] > 0,\\\n",
    "    neowise_read_df['qi_fact'] > 0,\\\n",
    "    [('X' not in i and 'U' not in i) for i in neowise_read_df['ph_qual']],\\\n",
    "    [abs(i) > 5 for i in neowise_read_df['saa_sep']],\\\n",
    "    neowise_read_df['moon_masked'] == 0,\\\n",
    "    [(i == 0.0 or i == '0000') for i in neowise_read_df['cc_flags']],\\\n",
    "    ~np.isnan(neowise_read_df['w1mpro']),\\\n",
    "    ~np.isnan(neowise_read_df['w2mpro']))]\n",
    "\n",
    "neowise_df = pd.DataFrame({})\n",
    "neowise_df['mjd'] = neowise_read_df['mjd'][neowise_mask]\n",
    "neowise_df['w1mag'] = neowise_read_df['w1mpro'][neowise_mask]\n",
    "neowise_df['w1sig'] = neowise_read_df['w1sigmpro'][neowise_mask]\n",
    "neowise_df['w2mag'] = neowise_read_df['w2mpro'][neowise_mask]\n",
    "neowise_df['w2sig'] = neowise_read_df['w2sigmpro'][neowise_mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bin measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.893296434606418\n"
     ]
    }
   ],
   "source": [
    "# Define bin edges\n",
    "start_epoch = np.min(neowise_df['mjd'])\n",
    "end_epoch = np.max(neowise_df['mjd'])\n",
    "yr = u.year.to(u.d)\n",
    "\n",
    "cycles = (end_epoch - start_epoch)/yr * 2\n",
    "print(cycles) # should be close to .5 or .0, as WISE is on a 6 month cycle. Inspect below\n",
    "cycles = round(cycles,0)\n",
    "\n",
    "bins = [start_epoch - yr/4 + a*(yr/2) for a in np.arange(cycles + 2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inspect data and bin edges\n",
    "fig, (ax1, ax2) = plt.subplots(1,2)\n",
    "\n",
    "ax1.errorbar(neowise_df['mjd'], neowise_df['w1mag'], yerr=neowise_df['w1sig'],\\\n",
    "            label=r\"W1 measurements\", color='orange', linestyle = '', marker = 'o', markersize=5)\n",
    "\n",
    "ax2.errorbar(neowise_df['mjd'], neowise_df['w2mag'], yerr=neowise_df['w2sig'],\\\n",
    "            label=r\"W2 measurements\", color='red', linestyle = '', marker = 'o', markersize=5)\n",
    "\n",
    "for epoch in bins:\n",
    "    ax1.axvline(epoch)\n",
    "    ax2.axvline(epoch)\n",
    "\n",
    "fig.set_size_inches(12,6)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Binning measurements\n",
    "\n",
    "neowise_bin_df = pd.DataFrame({})\n",
    "\n",
    "# Bin mean mjd according to bin edges\n",
    "neowise_bin_df['mjd'] = scipy.stats.binned_statistic(neowise_df['mjd'].values, neowise_df['mjd'].values,\\\n",
    "                                               statistic=np.mean, bins=bins, range=None)[0]\n",
    "\n",
    "# Bin mean magnitude, with error standard error of mean\n",
    "neowise_bin_df['w1mag'] = unp.uarray(scipy.stats.binned_statistic(neowise_df['mjd'].values, neowise_df['w1mag'].values,\\\n",
    "                                               statistic=np.mean, bins=bins, range=None)[0],\\\n",
    "                                  scipy.stats.binned_statistic(neowise_df['mjd'].values, neowise_df['w1mag'].values,\\\n",
    "                                               statistic=scipy.stats.sem, bins=bins, range=None)[0])\n",
    "\n",
    "neowise_bin_df['w2mag'] = unp.uarray(scipy.stats.binned_statistic(neowise_df['mjd'].values, neowise_df['w2mag'].values,\\\n",
    "                                               statistic=np.mean, bins=bins, range=None)[0],\\\n",
    "                                  scipy.stats.binned_statistic(neowise_df['mjd'].values, neowise_df['w2mag'].values,\\\n",
    "                                               statistic=scipy.stats.sem, bins=bins, range=None)[0])\n",
    "\n",
    "neowise_bin_df['w1flux'] = mag_to_fluxdens(neowise_bin_df['w1mag'], f0_wise_3_4)\n",
    "neowise_bin_df['w2flux'] = mag_to_fluxdens(neowise_bin_df['w2mag'], f0_wise_4_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optional: Add systematic error term reflecting systematic uncertainty in photometry between epochs,\\\n",
    "# 2.4% in flux for W1 and 2.8% for W2\n",
    "\n",
    "# wise_bin_df['w1flux'] = unp.uarray(unp.nominal_values(wise_bin_df['w1flux']),\\\n",
    "#                                    np.sqrt(unp.std_devs(wise_bin_df['w1flux'])**2 +\\\n",
    "#                                            (unp.nominal_values(wise_bin_df['w1flux']) * 0.024)**2))\n",
    "# wise_bin_df['w2flux'] = unp.uarray(unp.nominal_values(wise_bin_df['w2flux']),\\\n",
    "#                                    np.sqrt(unp.std_devs(wise_bin_df['w2flux'])**2 +\\\n",
    "#                                            (unp.nominal_values(wise_bin_df['w2flux']) * 0.028)**2))\n",
    "\n",
    "# wise_bin_df['w1mag'] = fluxdens_to_mag(wise_bin_df['w1flux'], f0_wise_3_4)\n",
    "# wise_bin_df['w2mag'] = fluxdens_to_mag(wise_bin_df['w2flux'], f0_wise_4_6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot measurements and binned values\n",
    "fig, (ax1, ax2) = plt.subplots(1,2)\n",
    "\n",
    "# W1\n",
    "ax1.errorbar(neowise_df['mjd'], neowise_df['w1mag'], yerr=neowise_df['w1sig'],\\\n",
    "            label=r\"W1 measurements\", color='black', linestyle = '', marker = 'o', markersize=5, alpha = .1, zorder = 0)\n",
    "\n",
    "ax1.errorbar(neowise_bin_df['mjd'], unp.nominal_values(neowise_bin_df['w1mag']),\\\n",
    "             yerr = unp.std_devs(neowise_bin_df['w1mag']),\\\n",
    "            label=r\"W1 mean\", color='blue', linestyle = '', marker = 'o', markersize=5, capsize = 3, elinewidth = 1, zorder=1)\n",
    "\n",
    "# W2\n",
    "ax2.errorbar(neowise_df['mjd'], neowise_df['w2mag'], yerr=neowise_df['w2sig'],\\\n",
    "            label=r\"W2 measurements\", color='black', linestyle = '', marker = 'o', markersize=5, alpha = .1, zorder = 0)\n",
    "\n",
    "ax2.errorbar(neowise_bin_df['mjd'], unp.nominal_values(neowise_bin_df['w2mag']),\\\n",
    "             yerr = unp.std_devs(neowise_bin_df['w2mag']),\\\n",
    "            label=r\"W2 mean\", color='red', linestyle = '', marker = 'o', markersize=5, capsize = 3, elinewidth = 1, zorder=1)\n",
    "\n",
    "# Optional: explosion/discovery epoch\n",
    "eplosion_epoch = mjday('20150614')\n",
    "ax1.axvline(x=eplosion_epoch, color='black', linestyle = ':', linewidth = 2.0)\n",
    "ax2.axvline(x=eplosion_epoch, color='black', linestyle = ':', linewidth = 2.0)\n",
    "\n",
    "\n",
    "ax1.set_ylim(ymin = max(neowise_df['w1mag']) + 0.1, ymax = min(neowise_df['w1mag']) - 0.1)\n",
    "ax2.set_ylim(ymin = max(neowise_df['w2mag']) + 0.1, ymax = min(neowise_df['w2mag']) - 0.1)\n",
    "\n",
    "ax1.set_title(source+' W1')\n",
    "ax2.set_title(source+' W2')\n",
    "\n",
    "ax1.set_xlabel(r'MJD')\n",
    "ax2.set_xlabel(r'MJD')\n",
    "ax1.set_ylabel(r'mag')\n",
    "\n",
    "ax1.legend()\n",
    "ax2.legend()\n",
    "\n",
    "\n",
    "fig.set_size_inches(14,7)\n",
    "fig.savefig(f'Output_figures/{source}_NeoWISE_lightcurve.png', bbox_inches='tight', dpi=600)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write out result to pandas table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write masked and binned magnitudes to file\n",
    "neowise_df.to_csv(f'Output_photometry/{source}_NeoWISE_masked.tbl', header = True, index = None, sep = '\\t')\n",
    "neowise_bin_df.to_csv(f'Output_photometry/{source}_NeoWISE_binned.tbl', header = True, index = None, sep = '\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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