Atik383L+LinearityTest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Linearity test of an Atik 383L+ CCD camera\n",
    "\n",
    "All images were captured using a Baader HA filter to enable longer exposures to elimnate shutter artifacts.\n",
    "All images were binned 2x2.\n",
    "An EL panel was used as the light source.\n",
    "\n",
    "The camera seems to be linear almost to about 50,000 adu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f80b14b6e10>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QLBRFFAAAAPApb7zxegYM2C0vv/xSundfOVdd9Zesvvo3yh2LJs4zogAAAIBP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\nY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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f80b14d3cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from astropy import units as u\n",
    "from ccdproc import CCDData\n",
    "import matplotlib.pyplot as plt\n",
    "from photutils import aperture_photometry, CircularAperture\n",
    "%matplotlib inline\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "\n",
    "fits_dir = '/home/dokeeffe/Pictures/linear-test'\n",
    "\n",
    "#Define the x,y for the central 1/3 of the image\n",
    "ccd_central_region_width_pixels = int(1663/3)\n",
    "ccd_central_region_height_pixels = int(1252/3)\n",
    "\n",
    "x_points = []\n",
    "y_points = []\n",
    "for filename in os.listdir(fits_dir):\n",
    "    ccd_image =  CCDData.read(os.path.join(fits_dir, filename), unit=u.adu)\n",
    "    x_points.append(ccd_image.header['EXPTIME'])\n",
    "    y_points.append(ccd_image.data[ccd_central_region_height_pixels:-1*ccd_central_region_height_pixels, \n",
    "                                        ccd_central_region_width_pixels:-1*ccd_central_region_width_pixels].mean())\n",
    "\n",
    "#Sort, then plot the x and y points\n",
    "y_points = np.sort(y_points)\n",
    "x_points = np.sort(x_points)\n",
    "\n",
    "#Create a fit to the data by removing the first 1 point and the last 10 points\n",
    "fit = np.polyfit(x_points[1:-10],y_points[1:-10],1)\n",
    "fit_fn = np.poly1d(fit) \n",
    "\n",
    "fig=plt.figure(figsize=(18, 16), dpi= 80, facecolor='w', edgecolor='k')\n",
    "plt.plot(x_points,y_points, 'yo', x_points, fit_fn(x_points), '--k', label='rrr')\n",
    "fig.suptitle('Atik 383L+ Linearity test')\n",
    "plt.xlabel('Seconds')\n",
    "plt.ylabel('ADU')\n",
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  7709.53382904,  11302.43275141,  14893.35449804,  18445.48654252,\n",
       "        21985.24598905,  25477.40930213,  28952.34905815,  32403.33893702,\n",
       "        35820.96253287,  39208.62854864,  42541.33300573,  45845.96254149,\n",
       "        49152.97739989])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_points[1:-10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.2+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}