tomr-stargazer/gist:50deda1f8fefff484773

## gistfile1.txt
{
 "metadata": {
  "name": "",
  "signature": "sha256:bdad813af58e34dc0582a258505db7bd92308dfcb54d7e5edcf2a1dc2e24cfd1"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%run aatau_analysis.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Auto-detected table type: fits\n",
        "Auto-detected table type: fits"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Auto-detected table type: fits"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Auto-detected table type: fits"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Auto-detected table type: fits"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Auto-detected table type: fits"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 1\n",
        "Case 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 1\n",
        "Case 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 1\n",
        "Case 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 1\n",
        "Case 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 3.5"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 3.5"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 1\n",
        "Case 3"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 3.5"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 1\n",
        "Case 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 1\n",
        "Case 1\n",
        "Case 1\n",
        "Case 3.5\n",
        "Case 3.5\n",
        "Case 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Case 1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Auto-detected table type: fits"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Auto-detected table type: fits\n",
        "Auto-detected table type: fits\n",
        "Auto-detected table type: fits"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Auto-detected table type: fits"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Auto-detected table type: fits"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Auto-detected table type: fits"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Auto-detected table type: fits"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Auto-detected table type: fits"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Auto-detected table type: fits"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Auto-detected table type: fits"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Auto-detected table type: fits"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Auto-detected table type: fits"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Auto-detected table type: fits"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Auto-detected table type: fits"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "There are 1203 total variables, and 539 total periodic variables."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Of these, 73 are AA Taus (periodic 'dippers').\n",
        "This is 6.1% of all variables and 13.5% of periodic variables.\n",
        "\n",
        "Class distribution of AA Taus:\n",
        "2 class \"P\"\n",
        "63 class \"D\"\n",
        "3 class \"ND\"\n",
        "5 unknown class\n",
        "\n",
        "There are 641 disked stars, and 229 periodic disked stars.\n",
        "Thus 9.8% of all disked stars are of AA Tau type,\n",
        "and 27.5% of periodic disked stars are of AA Tau type\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(aatau_periods, aatau_spread.k_meanr, 'r.')\n",
      "ylabel(\"$K$ mag\")\n",
      "xlabel(\"AA Tau period\")\n",
      "gca().invert_yaxis()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEPCAYAAABCyrPIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJ0v8BOToQ6f/bV4cAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109a0ecd0>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "valid = ~np.isnan(aatau_periods) & (aatau_periods < 12)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "-c:1: RuntimeWarning: invalid value encountered in less\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "median_coeff = np.corrcoef(aatau_periods[valid], aatau_spread.k_medianr[valid])[1,0]\n",
      "mean_coeff = np.corrcoef(aatau_periods[valid], aatau_spread.k_meanr[valid])[1,0]\n",
      "min_coeff = np.corrcoef(aatau_periods[valid], aatau_spread.k_minr[valid])[1,0]\n",
      "max_coeff = np.corrcoef(aatau_periods[valid], aatau_spread.k_maxr[valid])[1,0]\n",
      "\n",
      "print median_coeff\n",
      "print mean_coeff\n",
      "print min_coeff, \"***\"\n",
      "print max_coeff"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-0.613429439228\n",
        "-0.612248552579\n",
        "-0.625513955499 ***\n",
        "-0.58103966683\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subplot(221)\n",
      "plt.plot(aatau_periods, aatau_spread.k_medianr, 'r.')\n",
      "gca().invert_yaxis()\n",
      "text(10,15, \"median\\n{:.3f}\".format(median_coeff))\n",
      "ylim(16,9)\n",
      "\n",
      "subplot(222)\n",
      "plt.plot(aatau_periods, aatau_spread.k_meanr, 'r.')\n",
      "gca().invert_yaxis()\n",
      "text(10,15, \"mean\\n{:.3f}\".format(mean_coeff))\n",
      "ylim(16,9)\n",
      "\n",
      "subplot(223)\n",
      "plt.plot(aatau_periods, aatau_spread.k_minr, 'r.')\n",
      "gca().invert_yaxis()\n",
      "text(10,15, \"min\\n{:.3f}\".format(min_coeff), fontsize=14, color='b')\n",
      "ylim(16,9)\n",
      "\n",
      "subplot(224)\n",
      "plt.plot(aatau_periods, aatau_spread.k_maxr, 'r.')\n",
      "gca().invert_yaxis()\n",
      "text(10,15, \"max\\n{:.3f}\".format(max_coeff))\n",
      "ylim(16,9)\n",
      "\n",
      "suptitle(\"Does the correlation work best for mean, median, min, or max $K$ mag?\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "<matplotlib.text.Text at 0x116db0e10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAESCAYAAAAPEjVtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4wf8f1eDmij3JmLWAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1155f9210>"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "valid_irac1 = ~np.isnan(aatau_periods) & (aatau_periods < 12) & ~np.isnan(aatau_megeath['3.6mag'])\n",
      "valid_irac2 = ~np.isnan(aatau_periods) & (aatau_periods < 12) & ~np.isnan(aatau_megeath['4.5mag'])\n",
      "valid_irac3 = ~np.isnan(aatau_periods) & (aatau_periods < 12) & ~np.isnan(aatau_megeath['5.8mag']) & (aatau_megeath['5.8mag'] > 1)\n",
      "valid_irac4 = ~np.isnan(aatau_periods) & (aatau_periods < 12) & ~np.isnan(aatau_megeath['8.0mag']) & (aatau_megeath['8.0mag'] > 1)\n",
      "\n",
      "\n",
      "irac1_coeff = np.corrcoef(aatau_periods[valid_irac1], aatau_megeath['3.6mag'][valid_irac1])[1,0]\n",
      "irac2_coeff = np.corrcoef(aatau_periods[valid_irac2], aatau_megeath['4.5mag'][valid_irac2])[1,0]\n",
      "irac3_coeff = np.corrcoef(aatau_periods[valid_irac3], aatau_megeath['5.8mag'][valid_irac3])[1,0]\n",
      "irac4_coeff = np.corrcoef(aatau_periods[valid_irac4], aatau_megeath['8.0mag'][valid_irac4])[1,0]\n",
      "\n",
      "\n",
      "print irac1_coeff\n",
      "print irac2_coeff\n",
      "print irac3_coeff, \"***\"\n",
      "print irac4_coeff"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-0.618813180115\n",
        "-0.629388057895\n",
        "-0.64219767644 ***\n",
        "-0.590084254537\n"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\tplt.subplot(221)\n",
      "\tplt.plot(aatau_periods, aatau_megeath['3.6mag'], 'b.')\n",
      "\tplt.gca().invert_yaxis()\n",
      "\tplt.text(10, 12.5, \"[3.6]\\n{:.3f}\".format(irac1_coeff), fontsize=14)\n",
      "\n",
      "\tplt.subplot(222)\n",
      "\tplt.plot(aatau_periods, aatau_megeath['4.5mag'], 'g.')\n",
      "\tplt.gca().invert_yaxis()\n",
      "\tplt.text(10, 12, \"[4.5]\\n{:.3f}\".format(irac2_coeff), fontsize=14)\n",
      "\n",
      "\tplt.subplot(223)\n",
      "\tplt.plot(aatau_periods, aatau_megeath['5.8mag'], 'r.')\n",
      "\tplt.xlabel(\"AA Tau period (days)\")\n",
      "\tplt.ylabel(\"Magnitude\")\n",
      "\tplt.gca().invert_yaxis()\t\n",
      "\tplt.ylim(12, 7.5)\n",
      "\tplt.text(10, 11.5, \"[5.8]\\n{:.3f}\".format(irac3_coeff), fontsize=14)\n",
      "\n",
      "\tplt.subplot(224)\n",
      "\tplt.plot(aatau_periods, aatau_megeath['8.0mag'], 'm.')\n",
      "\tplt.gca().invert_yaxis()\t\n",
      "\tplt.ylim(12, 7)\t\n",
      "\tplt.text(10, 11.5, \"[8.0]\\n{:.3f}\".format(irac4_coeff), fontsize=14)\n",
      "\n",
      "\tplt.suptitle(\"AA Tau magnitude-period relation for Spitzer photometry\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "<matplotlib.text.Text at 0x118474f50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEhCAYAAABhpec9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLv8HhieQ+1LVtGIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1180b4790>"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}