homeworks/Homework_part3_Group1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ASTR598 - Astrostatistics     \n",
    "## Homework 2, part 3\n",
    "### Group 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import astropy as apy\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from astropy import units as units\n",
    "from astropy.table import Table\n",
    "from astropy.io import fits\n",
    "from astropy.coordinates import SkyCoord\n",
    "\n",
    "from astroML.stats import binned_statistic_2d\n",
    "from astroML.plotting import setup_text_plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "% matplotlib inline\n",
    "setup_text_plots(fontsize=16, usetex=True)\n",
    "\n",
    "mpl.rcParams['figure.figsize'] = (10,10)\n",
    "mpl.rcParams[\"axes.labelsize\"] = 20\n",
    "mpl.rcParams[\"font.size\"] = 24\n",
    "mpl.rcParams['xtick.labelsize'] = 14\n",
    "mpl.rcParams['ytick.labelsize'] = 14"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read all GalFast simulated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.loadtxt('data/Galfast-Stripe82.txt')\n",
    "table = Table(rows=data, names=(\"ra\", \"dec\", \"glon\", \"glat\", \"pmra\", \"pmdec\", \"px\", \"rv\", \"Teff\", \"FeH\",\n",
    "                                \"log(g)\", \"u\", \"g\", \"r\", \"i\", \"z\", \"y\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter the data for objects of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def filter_rmag(table, rlim=(20.5, 21), **kwargs):\n",
    "    \"\"\"\n",
    "    Applies a r-band magnitude filter to the table, such that the returned dataset satisfies:\n",
    "        rlim[0] < table < rlim[1]\n",
    "    \"\"\"\n",
    "    rmask = (table['r']>rlim[0]) & (table['r']<rlim[1])\n",
    "    return table[rmask]\n",
    "\n",
    "def filter_color(table, gmilim=(0.3, 0.4), **kwargs):\n",
    "    \"\"\"\n",
    "    Applies a g-i color filter to the table, such that the returned dataset satisfies:\n",
    "        gmilim[0] < table < gmilim[1]\n",
    "    \"\"\"\n",
    "    gmi = table['g']-table['i']\n",
    "    gmimask = (gmi>gmilim[0]) & (gmi<gmilim[1])\n",
    "    return table[gmimask]\n",
    "\n",
    "def filter_ra(table, ralim=(25, 40), **kwargs):\n",
    "    \"\"\"\n",
    "    Applies a right ascension coordiante filter to the table, such that the returned dataset satisfies:\n",
    "        ralim[0] < table < ralim[1]\n",
    "    \"\"\"\n",
    "    ramask = (table['ra']>ralim[0]) & (table['ra']<ralim[1])\n",
    "    return table[ramask]\n",
    "\n",
    "def filter_abs_pm(table, pmlim = (-0.001, 100), **kwargs):\n",
    "    \"\"\"\n",
    "    Applies an absolute proper motion filter to the table, such that the returned dataset satisfies:\n",
    "        pmlim[0] < table < pmlim[1]\n",
    "    where abspm is taken to be np.sqrt(pmra**2 + pmdec**2)\n",
    "    \"\"\"\n",
    "    abspm = np.sqrt(table['pmra']**2 + table['pmdec']**2)\n",
    "    abspmmask = (abspm>pmlim[0]) & (abspm<pmlim[1])\n",
    "    return table[abspmmask]  \n",
    "\n",
    "filters = [filter_rmag, filter_color, filter_ra, filter_abs_pm]\n",
    "def apply_filters(table, filters=filters, **kwargs):\n",
    "    \"\"\"\n",
    "    Applies filters provided in the filters kwarg, in order in which they are given, to the table.\n",
    "    \n",
    "    table:   dataset (numpy.void, astropy.Table...)\n",
    "    filters: an iterable containing the filter functions in order in which they are to be applied \n",
    "             to the dataset\n",
    "    \"\"\"\n",
    "    for f in filters:\n",
    "        table = f(table, **kwargs)\n",
    "    return table\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def hess_gri_pm(table, title=\"\", colorby=\"abspm\", colorlabel=None, statistic=\"median\"):    \n",
    "    \"\"\"\n",
    "    For a given dataset constructs a Hess plot of Ra vs r-band magnitudes where the coloring represents\n",
    "    the median absolute proper motion of objects in that bin. \n",
    "    \n",
    "    table: dataset to be plotted, has to contain keywords \"pmra\", \"pmdec\", \"ra\" and \"r\"\n",
    "    title: title of the plot\n",
    "    \"\"\"\n",
    "    if colorby == \"abspm\":\n",
    "        stat = np.sqrt(table['pmra']**2 + table['pmdec']**2)\n",
    "        colorlabel = r'Proper Motion (mas/yr)'\n",
    "    else:\n",
    "        stat = table[colorby]\n",
    "        colorlabel = colorlabel\n",
    "    nbins = int(3*len(table)**(1.0 / 4.0))\n",
    "    \n",
    "    medpm, xedges, yedges = binned_statistic_2d(table['ra'], table['r'], stat, \n",
    "                                                statistic=statistic, bins=nbins)\n",
    "    \n",
    "    plt.imshow(medpm.T, origin='lower',\n",
    "                   extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]],\n",
    "                   aspect='auto', interpolation='nearest')\n",
    "\n",
    "    cb = plt.colorbar(orientation='horizontal')\n",
    "    cb.set_label(colorlabel, fontsize=22)\n",
    "    #plt.clim(0, 30) # This was set by hand to draw out as much detail as possible\n",
    "\n",
    "    plt.xlabel(r'Ra', fontsize=22)\n",
    "    plt.ylabel(r'r', fontsize=22)\n",
    "    plt.gca().invert_yaxis()\n",
    "    plt.gca().invert_xaxis()\n",
    "    plt.title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot data and analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1945 objects with 20.5<r<21, 0.3<g-i<0.4 and 25<Ra<40\n"
     ]
    },
    {
     "data": {
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Po9eUpHe/EX9bP3h0PnrN7bPRS0qSzv3Jk/UU7kHdlztpKjsWToUQ19LuZUpu5pcxUXaN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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f567cb8dbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table1 = apply_filters(table, ralim=(25, 40), pmlim=(-0.00001, 30))\n",
    "print(r\"There are {0} objects with 20.5<r<21, 0.3<g-i<0.4 and 25<Ra<40\".format(len(table1)))\n",
    "\n",
    "hess_gri_pm(table1, r\"Hess plot of PM in 25$^\\circ<\\alpha<$40$^\\circ$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2102 objects with 20.5<r<21, 0.3<g-i<0.4 and 0<Ra<15\n"
     ]
    },
    {
     "data": {
      "image/png": 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3PJ5VCHzj95/UjRP78jRquWtj52X20/GXb0Wtd/Tt21HrPfSFuPX+7Ifj/am8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5677c76510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table2 = apply_filters(table, ralim=(0, 15))\n",
    "print(r\"There are {0} objects with 20.5<r<21, 0.3<g-i<0.4 and 0<Ra<15\".format(len(table2)))\n",
    "\n",
    "hess_gri_pm(table2, title=r\"Hess plot of PM in 0$^\\circ<\\alpha<$15$^\\circ$\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}