12_Angular-2-point-correlation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import csv\n",
    "from astroML.correlation import bootstrap_two_point_angular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This cell will convert the txt to csv\n",
    "datContent = [i.strip().split() for i in open(\"BCS05hr_reduced.txt\").readlines()]\n",
    "with open(\"BCS05hr_reduced.csv\", \"wb\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    writer.writerows(datContent)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Applying the given constraints\n",
    "data  = pd.read_csv('BCS05hr_reduced.csv')\n",
    "data = data[data['r-mag']<20]\n",
    "data = data[data['r-mag']>17]\n",
    "data = data[data['spread_model']>0.002]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to compute Two point angular coorealtaion\n",
    "def two_point_correlation(N):\n",
    "    np.random.seed(0)\n",
    "    bins = 10 ** np.linspace(np.log10(1. / 60.), np.log10(6), 16)\n",
    "    results = [bins]\n",
    "    for D in [data]:\n",
    "        results += bootstrap_two_point_angular(D['#RA'],D['DEC'],bins=bins,method='landy-szalay',Nbootstraps=N)\n",
    "    return results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating Two point angular coorealtaion\n",
    "(bins, r_corr, r_corr_err, r_bootstraps) = two_point_correlation(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLot the results\n",
    "bin_centers = 0.5 * (bins[1:] + bins[:-1])\n",
    "corr = [r_corr]\n",
    "corr_err = [r_corr_err]\n",
    "bootstraps = [r_bootstraps]\n",
    "plt.errorbar(bin_centers, corr[0], corr_err[0],fmt='.k', ecolor='gray', lw=1)\n",
    "plt.xscale('log')\n",
    "plt.yscale('linear')\n",
    "plt.xlabel(r'$\\theta\\ (deg)$')\n",
    "plt.ylabel(r'$w(\\theta)$')\n",
    "plt.title('Two-point Angular Correlation')\n",
    "plt.show()"
   ]
  }
 ],
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