line_fitting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 203.  495.]\n",
      " [ 210.  479.]\n",
      " [ 202.  504.]\n",
      " [ 198.  510.]\n",
      " [ 158.  416.]\n",
      " [ 165.  393.]\n",
      " [ 201.  442.]\n",
      " [ 157.  317.]\n",
      " [ 131.  311.]\n",
      " [ 166.  400.]\n",
      " [ 160.  337.]\n",
      " [ 186.  423.]\n",
      " [ 125.  334.]\n",
      " [ 218.  533.]\n",
      " [ 146.  344.]\n",
      " [  50.  300.]\n",
      " [  60.  310.]\n",
      " [  70.  305.]\n",
      " [  80.  304.]\n",
      " [  90.  290.]\n",
      " [ 100.  309.]\n",
      " [ 120.  302.]]\n",
      "[ 203.  210.  202.  198.  158.  165.  201.  157.  131.  166.  160.  186.\n",
      "  125.  218.  146.   50.   60.   70.   80.   90.  100.  120.] [ 495.  479.  504.  510.  416.  393.  442.  317.  311.  400.  337.  423.\n",
      "  334.  533.  344.  300.  310.  305.  304.  290.  309.  302.]\n"
     ]
    }
   ],
   "source": [
    "# https://docs.scipy.org/doc/numpy/reference/routines.random.html\n",
    "# googled \"load text from ascii table python\", second hit\n",
    "data = np.loadtxt(\"data.dat\", skiprows = 2) # we are going to hit the bad rows\n",
    "print data # notice how it is loaded in as a 2D array?\n",
    "x = data[:,0] # numpy ndarray slicing\n",
    "y = data[:,1]\n",
    "print x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x,y = np.loadtxt(\"data.dat\", skiprows = 2, unpack = True) # could have unpacked (maybe show them table comments after)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lN/vYOy4jar5yp6UJAR/1BVCmCRfBGC1Jp4XnN0a0RcXddwv/K7sl6SA8bsfSQxthuL7o\nz7V0EUj/UWiK6TyX3exj77iU1XzlTksTAr6RF4AU/UWQpNALO3T3Q0ly92fhQrhR6AFFI/b6xrjr\n7oNwjOrnGHGzj7rjUlbzPDot0Qd8TL84M4j2IhijL2k1PG5J+jCiLVZdd38sXYxf3g/tH/S5BxSF\nEfU15VwXJ16jPM/DN/smKKv5Kp2WqAO+4ReA1ICLoMTP+lxbW9L+iLbomFnP3Z+Fx12d93gGtW7o\ncw8oFmX1RX+uzWz4dzfW83xxs1dzOi7Fmr9omzZHog54NfQCkJpzEYRfls7gl2bQSwgB2Xf3w7K2\n2goOhusOtT0Nq5b+W7r4Wb4Lx7ytu+4R5/pSfbGd6+GaC4oTr1GdZ6n0Zh99x6Wk5it3WqLfiyYs\nBTqV1C78oD2FSYdY17WGgH/s7g8LbV/8LADmq7C081TnPfRv3X2/LDdiyZKymsNLo36OSjkSfcAD\nAGYT+xANAGBGBDwAJIqAB4BEEfAAkCgCHgASRcADQKIIeABIFAEPAIn6f4w4aRLPuqOlAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ba21385d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y) # look at data\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "yCut = y[np.where(x>115)] # we can use where to get rid of data we don't want\n",
    "xCut = x[np.where(x>115)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3sNqtf5Av7Rh7eutd770FT0H+yszepfHH3nhk77FSTm6at8F+u/tB7+Qqdb/IufT7XN2J\nox1JG+nzkEO/X0tqpFURjTS3FLHfwy7SJj5yc9UM6XfRz8Vkr7Nqe8UAWG19Szwv1b0y/dbdj9O7\nk3N1VwPtp+d+UVtVw/qdHir0uZjotQh2AIhlaYdiAADTIdgBIBiCHQCCIdgBIBiCHQCCIdgBIBiC\nHQCCIdgBIJj/B9gmr/Y+h+w1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b9f9e5550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(xCut,yCut) # yay, good data\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.linregress.html\n",
    "# google \"fitting a straight line python\", 4th hit\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(xCut,yCut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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O8tgKIAVAnserIqJ+s9sl3tpfjsffLkJ1fTN+9I2xeGjeRESG8cxAL1QNBSGESUppEUKs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\n4eHmqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ba214ff90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make nice plots\n",
    "plt.scatter(xCut,yCut, label = \"data\")\n",
    "fitX = np.linspace(np.min(xCut), np.max(xCut),100)\n",
    "fitY = slope*fitX + intercept\n",
    "plt.plot(fitX, fitY, label = 'fit: $y=$' + str(round(slope,3)) +'$x$' + str(round(intercept,3)))\n",
    "plt.xlabel('$x$')\n",
    "plt.ylabel('$y$')\n",
    "plt.legend(loc = 'upper left')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}