Temporal Error.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "%matplotlib nbagg\n",
    "from matplotlib import pyplot\n",
    "from tqdm import tnrange\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import soapy\n",
    "import aotools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "CONFIGFILE = \"conf/simulation/temporalerror.yaml\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "FRAME_TIMES = (1./numpy.array([100, 200, 400, 500, 600, 800, 1000]))\n",
    "INTEGRATION_TIME = 10 #seconds\n",
    "WIND_SPEEDS = [5, 10, 20, 40]\n",
    "RUNS = 1\n",
    "TEL_DIAM = 8.\n",
    "R0 = 0.20\n",
    "WAVELENGTH = 500e-9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "***\n",
      "Set Wind speed to 5m/s, loop time to 10.0ms\"\n",
      "***\n",
      "Run for 1000 frames\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "1 of 1: Loaded configuration file successfully!Starting Sim: 2017-05-19-16-13-49\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "Creating mask...\n",
      "Turbulence summary @ 500 nm:\n",
      "| r0 = 0.20 m (0.52\" seeing)\n",
      "| Vbar_5/3 = 5.00 m/s\n",
      "| tau0 = 12.56 ms\n",
      "Initialise Infinite Phase Screen 1\n",
      "Initialising WFSs....\n",
      "WFS 0: 632 measurements\n",
      "Initialising 1 DMs...\n",
      "Making DM Influence Functions...\n",
      "DM 0: 441 active actuators\n",
      "441 total DM Actuators\n",
      "Initialising Reconstructor...\n",
      "Initialising 1 Science Cams...\n",
      "Generating fftw3 plan....\n",
      "If this takes too long, change fftw_FLAGS.\n",
      "Done!\n",
      "Initialise Data Storage...\n",
      "Initialisation Complete!\n",
      "Making interaction Matrices...\n",
      "Creating Interaction Matrix for DM 0 \n",
      "141 of 441: Generating 441 Actuator DM iMat\n",
      "440 of 441: Generating 441 Actuator DM iMat\n",
      "441 of 441: Generating 441 Actuator DM iMatChecking for redundant actuators...\n",
      "DM 0 has 429 valid actuators (12 dropped)\n",
      "Interaction Matrices Done\n",
      "Creating Command Matrix\n",
      "Invert iMat with conditioning: 0.0300\n",
      "Command Matrix Generated!\n",
      "999 of 1000: temporalerror  Strehl -- sci_0: inst 0.86, long 0.86 \n",
      "1000 of 1000: temporalerror  Strehl -- sci_0: inst 0.84, long 0.86 \n",
      "\n",
      "Science Camera 0: Long Exposure Strehl Ratio: 0.86\n",
      "\n",
      "\n",
      "Time moving atmosphere: 0.00\n",
      "Time making IMats and CMats: 16.32\n",
      "Time in WFS: 30.58\n",
      "\t of which time spent in : 0.00\n",
      "Time in Reconstruction: 0.27\n",
      "Time in DM: 0.74\n",
      "Time making science image: 2.55\n",
      "\n",
      "\n",
      "\n",
      "***\n",
      "Set Wind speed to 5m/s, loop time to 5.0ms\"\n",
      "***\n",
      "Run for 2000 frames\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "1 of 1: Loaded configuration file successfully!Starting Sim: 2017-05-19-16-14-41\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "Creating mask...\n",
      "Turbulence summary @ 500 nm:\n",
      "| r0 = 0.20 m (0.52\" seeing)\n",
      "| Vbar_5/3 = 5.00 m/s\n",
      "| tau0 = 12.56 ms\n",
      "Initialise Infinite Phase Screen 1\n",
      "Initialising WFSs....\n",
      "WFS 0: 632 measurements\n",
      "Initialising 1 DMs...\n",
      "Making DM Influence Functions...\n",
      "DM 0: 441 active actuators\n",
      "441 total DM Actuators\n",
      "Initialising Reconstructor...\n",
      "Initialising 1 Science Cams...\n",
      "Generating fftw3 plan....\n",
      "If this takes too long, change fftw_FLAGS.\n",
      "Done!\n",
      "Initialise Data Storage...\n",
      "Initialisation Complete!\n",
      "Making interaction Matrices...\n",
      "Creating Interaction Matrix for DM 0 \n",
      "440 of 441: Generating 441 Actuator DM iMat\n",
      "441 of 441: Generating 441 Actuator DM iMatChecking for redundant actuators...\n",
      "DM 0 has 429 valid actuators (12 dropped)\n",
      "Interaction Matrices Done\n",
      "Creating Command Matrix\n",
      "Invert iMat with conditioning: 0.0300\n",
      "Command Matrix Generated!\n",
      "1999 of 2000: temporalerror  Strehl -- sci_0: inst 0.86, long 0.90 \n",
      "2000 of 2000: temporalerror  Strehl -- sci_0: inst 0.86, long 0.90 \n",
      "\n",
      "Science Camera 0: Long Exposure Strehl Ratio: 0.90\n",
      "\n",
      "\n",
      "Time moving atmosphere: 0.00\n",
      "Time making IMats and CMats: 14.53\n",
      "Time in WFS: 58.77\n",
      "\t of which time spent in : 0.00\n",
      "Time in Reconstruction: 0.54\n",
      "Time in DM: 1.42\n",
      "Time making science image: 5.00\n",
      "\n",
      "\n",
      "\n",
      "***\n",
      "Set Wind speed to 5m/s, loop time to 2.5ms\"\n",
      "***\n",
      "Run for 4000 frames\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "1 of 1: Loaded configuration file successfully!Starting Sim: 2017-05-19-16-16-05\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "Creating mask...\n",
      "Turbulence summary @ 500 nm:\n",
      "| r0 = 0.20 m (0.52\" seeing)\n",
      "| Vbar_5/3 = 5.00 m/s\n",
      "| tau0 = 12.56 ms\n",
      "Initialise Infinite Phase Screen 1\n",
      "Initialising WFSs....\n",
      "WFS 0: 632 measurements\n",
      "Initialising 1 DMs...\n",
      "Making DM Influence Functions...\n",
      "DM 0: 441 active actuators\n",
      "441 total DM Actuators\n",
      "Initialising Reconstructor...\n",
      "Initialising 1 Science Cams...\n",
      "Generating fftw3 plan....\n",
      "If this takes too long, change fftw_FLAGS.\n",
      "Done!\n",
      "Initialise Data Storage...\n",
      "Initialisation Complete!\n",
      "Making interaction Matrices...\n",
      "Creating Interaction Matrix for DM 0 \n",
      "440 of 441: Generating 441 Actuator DM iMat\n",
      "441 of 441: Generating 441 Actuator DM iMatChecking for redundant actuators...\n",
      "DM 0 has 429 valid actuators (12 dropped)\n",
      "Interaction Matrices Done\n",
      "Creating Command Matrix\n",
      "Invert iMat with conditioning: 0.0300\n",
      "Command Matrix Generated!\n",
      "3999 of 4000: temporalerror  Strehl -- sci_0: inst 0.93, long 0.91 \n",
      "4000 of 4000: temporalerror  Strehl -- sci_0: inst 0.93, long 0.91 \n",
      "\n",
      "Science Camera 0: Long Exposure Strehl Ratio: 0.91\n",
      "\n",
      "\n",
      "Time moving atmosphere: 0.00\n",
      "Time making IMats and CMats: 12.01\n",
      "Time in WFS: 104.57\n",
      "\t of which time spent in : 0.00\n",
      "Time in Reconstruction: 1.02\n",
      "Time in DM: 2.44\n",
      "Time making science image: 9.47\n",
      "\n",
      "\n",
      "\n",
      "***\n",
      "Set Wind speed to 5m/s, loop time to 2.0ms\"\n",
      "***\n",
      "Run for 5000 frames\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "1 of 1: Loaded configuration file successfully!Starting Sim: 2017-05-19-16-18-20\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "Creating mask...\n",
      "Turbulence summary @ 500 nm:\n",
      "| r0 = 0.20 m (0.52\" seeing)\n",
      "| Vbar_5/3 = 5.00 m/s\n",
      "| tau0 = 12.56 ms\n",
      "Initialise Infinite Phase Screen 1\n",
      "Initialising WFSs....\n",
      "WFS 0: 632 measurements\n",
      "Initialising 1 DMs...\n",
      "Making DM Influence Functions...\n",
      "DM 0: 441 active actuators\n",
      "441 total DM Actuators\n",
      "Initialising Reconstructor...\n",
      "Initialising 1 Science Cams...\n",
      "Generating fftw3 plan....\n",
      "If this takes too long, change fftw_FLAGS.\n",
      "Done!\n",
      "Initialise Data Storage...\n",
      "Initialisation Complete!\n",
      "Making interaction Matrices...\n",
      "Creating Interaction Matrix for DM 0 \n",
      "440 of 441: Generating 441 Actuator DM iMat\n",
      "441 of 441: Generating 441 Actuator DM iMatChecking for redundant actuators...\n",
      "DM 0 has 429 valid actuators (12 dropped)\n",
      "Interaction Matrices Done\n",
      "Creating Command Matrix\n",
      "Invert iMat with conditioning: 0.0300\n",
      "Command Matrix Generated!\n",
      "4999 of 5000: temporalerror  Strehl -- sci_0: inst 0.93, long 0.91 \n",
      "5000 of 5000: temporalerror  Strehl -- sci_0: inst 0.93, long 0.91 \n",
      "\n",
      "Science Camera 0: Long Exposure Strehl Ratio: 0.91\n",
      "\n",
      "\n",
      "Time moving atmosphere: 0.00\n",
      "Time making IMats and CMats: 12.00\n",
      "Time in WFS: 123.93\n",
      "\t of which time spent in : 0.00\n",
      "Time in Reconstruction: 1.07\n",
      "Time in DM: 3.39\n",
      "Time making science image: 12.20\n",
      "\n",
      "\n",
      "\n",
      "***\n",
      "Set Wind speed to 5m/s, loop time to 1.6666666666666667ms\"\n",
      "***\n",
      "Run for 6000 frames\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "1 of 1: Loaded configuration file successfully!Starting Sim: 2017-05-19-16-21-00\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "Creating mask...\n",
      "Turbulence summary @ 500 nm:\n",
      "| r0 = 0.20 m (0.52\" seeing)\n",
      "| Vbar_5/3 = 5.00 m/s\n",
      "| tau0 = 12.56 ms\n",
      "Initialise Infinite Phase Screen 1\n",
      "Initialising WFSs....\n",
      "WFS 0: 632 measurements\n",
      "Initialising 1 DMs...\n",
      "Making DM Influence Functions...\n",
      "DM 0: 441 active actuators\n",
      "441 total DM Actuators\n",
      "Initialising Reconstructor...\n",
      "Initialising 1 Science Cams...\n",
      "Generating fftw3 plan....\n",
      "If this takes too long, change fftw_FLAGS.\n",
      "Done!\n",
      "Initialise Data Storage...\n",
      "Initialisation Complete!\n",
      "Making interaction Matrices...\n",
      "Creating Interaction Matrix for DM 0 \n",
      "440 of 441: Generating 441 Actuator DM iMat\n",
      "441 of 441: Generating 441 Actuator DM iMatChecking for redundant actuators...\n",
      "DM 0 has 429 valid actuators (12 dropped)\n",
      "Interaction Matrices Done\n",
      "Creating Command Matrix\n",
      "Invert iMat with conditioning: 0.0300\n",
      "Command Matrix Generated!\n",
      "5999 of 6000: temporalerror  Strehl -- sci_0: inst 0.95, long 0.90 \n",
      "6000 of 6000: temporalerror  Strehl -- sci_0: inst 0.94, long 0.90 \n",
      "\n",
      "Science Camera 0: Long Exposure Strehl Ratio: 0.90\n",
      "\n",
      "\n",
      "Time moving atmosphere: 0.00\n",
      "Time making IMats and CMats: 10.37\n",
      "Time in WFS: 130.94\n",
      "\t of which time spent in : 0.00\n",
      "Time in Reconstruction: 1.16\n",
      "Time in DM: 3.64\n",
      "Time making science image: 13.38\n",
      "\n",
      "\n",
      "\n",
      "***\n",
      "Set Wind speed to 5m/s, loop time to 1.25ms\"\n",
      "***\n",
      "Run for 8000 frames\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "1 of 1: Loaded configuration file successfully!Starting Sim: 2017-05-19-16-23-47\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "Creating mask...\n",
      "Turbulence summary @ 500 nm:\n",
      "| r0 = 0.20 m (0.52\" seeing)\n",
      "| Vbar_5/3 = 5.00 m/s\n",
      "| tau0 = 12.56 ms\n",
      "Initialise Infinite Phase Screen 1\n",
      "Initialising WFSs....\n",
      "WFS 0: 632 measurements\n",
      "Initialising 1 DMs...\n",
      "Making DM Influence Functions...\n",
      "DM 0: 441 active actuators\n",
      "441 total DM Actuators\n",
      "Initialising Reconstructor...\n",
      "Initialising 1 Science Cams...\n",
      "Generating fftw3 plan....\n",
      "If this takes too long, change fftw_FLAGS.\n",
      "Done!\n",
      "Initialise Data Storage...\n",
      "Initialisation Complete!\n",
      "Making interaction Matrices...\n",
      "Creating Interaction Matrix for DM 0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "440 of 441: Generating 441 Actuator DM iMat\n",
      "441 of 441: Generating 441 Actuator DM iMatChecking for redundant actuators...\n",
      "DM 0 has 429 valid actuators (12 dropped)\n",
      "Interaction Matrices Done\n",
      "Creating Command Matrix\n",
      "Invert iMat with conditioning: 0.0300\n",
      "Command Matrix Generated!\n",
      "7999 of 8000: temporalerror  Strehl -- sci_0: inst 0.93, long 0.90 \n",
      "8000 of 8000: temporalerror  Strehl -- sci_0: inst 0.93, long 0.90 \n",
      "\n",
      "Science Camera 0: Long Exposure Strehl Ratio: 0.90\n",
      "\n",
      "\n",
      "Time moving atmosphere: 0.00\n",
      "Time making IMats and CMats: 11.55\n",
      "Time in WFS: 177.05\n",
      "\t of which time spent in : 0.00\n",
      "Time in Reconstruction: 1.65\n",
      "Time in DM: 5.07\n",
      "Time making science image: 18.59\n",
      "\n",
      "\n",
      "\n",
      "***\n",
      "Set Wind speed to 5m/s, loop time to 1.0ms\"\n",
      "***\n",
      "Run for 10000 frames\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "1 of 1: Loaded configuration file successfully!Starting Sim: 2017-05-19-16-27-32\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "Creating mask...\n",
      "Turbulence summary @ 500 nm:\n",
      "| r0 = 0.20 m (0.52\" seeing)\n",
      "| Vbar_5/3 = 5.00 m/s\n",
      "| tau0 = 12.56 ms\n",
      "Initialise Infinite Phase Screen 1\n",
      "Initialising WFSs....\n",
      "WFS 0: 632 measurements\n",
      "Initialising 1 DMs...\n",
      "Making DM Influence Functions...\n",
      "DM 0: 441 active actuators\n",
      "441 total DM Actuators\n",
      "Initialising Reconstructor...\n",
      "Initialising 1 Science Cams...\n",
      "Generating fftw3 plan....\n",
      "If this takes too long, change fftw_FLAGS.\n",
      "Done!\n",
      "Initialise Data Storage...\n",
      "Initialisation Complete!\n",
      "Making interaction Matrices...\n",
      "Creating Interaction Matrix for DM 0 \n",
      "440 of 441: Generating 441 Actuator DM iMat\n",
      "441 of 441: Generating 441 Actuator DM iMatChecking for redundant actuators...\n",
      "DM 0 has 429 valid actuators (12 dropped)\n",
      "Interaction Matrices Done\n",
      "Creating Command Matrix\n",
      "Invert iMat with conditioning: 0.0300\n",
      "Command Matrix Generated!\n",
      "9999 of 10000: temporalerror  Strehl -- sci_0: inst 0.91, long 0.89 \n",
      "10000 of 10000: temporalerror  Strehl -- sci_0: inst 0.91, long 0.89 \n",
      "\n",
      "Science Camera 0: Long Exposure Strehl Ratio: 0.89\n",
      "\n",
      "\n",
      "Time moving atmosphere: 0.00\n",
      "Time making IMats and CMats: 10.45\n",
      "Time in WFS: 219.90\n",
      "\t of which time spent in : 0.00\n",
      "Time in Reconstruction: 2.26\n",
      "Time in DM: 6.68\n",
      "Time making science image: 25.11\n",
      "\n",
      "\n",
      "\n",
      "***\n",
      "Set Wind speed to 10m/s, loop time to 10.0ms\"\n",
      "***\n",
      "Run for 1000 frames\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "1 of 1: Loaded configuration file successfully!Starting Sim: 2017-05-19-16-32-12\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "Creating mask...\n",
      "Turbulence summary @ 500 nm:\n",
      "| r0 = 0.20 m (0.52\" seeing)\n",
      "| Vbar_5/3 = 10.00 m/s\n",
      "| tau0 = 6.28 ms\n",
      "Initialise Infinite Phase Screen 1\n",
      "Initialising WFSs....\n",
      "WFS 0: 632 measurements\n",
      "Initialising 1 DMs...\n",
      "Making DM Influence Functions...\n",
      "DM 0: 441 active actuators\n",
      "441 total DM Actuators\n",
      "Initialising Reconstructor...\n",
      "Initialising 1 Science Cams...\n",
      "Generating fftw3 plan....\n",
      "If this takes too long, change fftw_FLAGS.\n",
      "Done!\n",
      "Initialise Data Storage...\n",
      "Initialisation Complete!\n",
      "Making interaction Matrices...\n",
      "Creating Interaction Matrix for DM 0 \n",
      "440 of 441: Generating 441 Actuator DM iMat\n",
      "441 of 441: Generating 441 Actuator DM iMatChecking for redundant actuators...\n",
      "DM 0 has 429 valid actuators (12 dropped)\n",
      "Interaction Matrices Done\n",
      "Creating Command Matrix\n",
      "Invert iMat with conditioning: 0.0300\n",
      "Command Matrix Generated!\n",
      "999 of 1000: temporalerror  Strehl -- sci_0: inst 0.72, long 0.71 \n",
      "1000 of 1000: temporalerror  Strehl -- sci_0: inst 0.74, long 0.71 \n",
      "\n",
      "Science Camera 0: Long Exposure Strehl Ratio: 0.71\n",
      "\n",
      "\n",
      "Time moving atmosphere: 0.00\n",
      "Time making IMats and CMats: 10.37\n",
      "Time in WFS: 23.58\n",
      "\t of which time spent in : 0.00\n",
      "Time in Reconstruction: 0.18\n",
      "Time in DM: 0.58\n",
      "Time making science image: 2.28\n",
      "\n",
      "\n",
      "\n",
      "***\n",
      "Set Wind speed to 10m/s, loop time to 5.0ms\"\n",
      "***\n",
      "Run for 2000 frames\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "1 of 1: Loaded configuration file successfully!Starting Sim: 2017-05-19-16-32-50\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "Creating mask...\n",
      "Turbulence summary @ 500 nm:\n",
      "| r0 = 0.20 m (0.52\" seeing)\n",
      "| Vbar_5/3 = 10.00 m/s\n",
      "| tau0 = 6.28 ms\n",
      "Initialise Infinite Phase Screen 1\n",
      "Initialising WFSs....\n",
      "WFS 0: 632 measurements\n",
      "Initialising 1 DMs...\n",
      "Making DM Influence Functions...\n",
      "DM 0: 441 active actuators\n",
      "441 total DM Actuators\n",
      "Initialising Reconstructor...\n",
      "Initialising 1 Science Cams...\n",
      "Generating fftw3 plan....\n",
      "If this takes too long, change fftw_FLAGS.\n",
      "Done!\n",
      "Initialise Data Storage...\n",
      "Initialisation Complete!\n",
      "Making interaction Matrices...\n",
      "Creating Interaction Matrix for DM 0 \n",
      "440 of 441: Generating 441 Actuator DM iMat\n",
      "441 of 441: Generating 441 Actuator DM iMatChecking for redundant actuators...\n",
      "DM 0 has 429 valid actuators (12 dropped)\n",
      "Interaction Matrices Done\n",
      "Creating Command Matrix\n",
      "Invert iMat with conditioning: 0.0300\n",
      "Command Matrix Generated!\n",
      "1999 of 2000: temporalerror  Strehl -- sci_0: inst 0.82, long 0.87 \n",
      "2000 of 2000: temporalerror  Strehl -- sci_0: inst 0.81, long 0.87 \n",
      "\n",
      "Science Camera 0: Long Exposure Strehl Ratio: 0.87\n",
      "\n",
      "\n",
      "Time moving atmosphere: 0.00\n",
      "Time making IMats and CMats: 9.64\n",
      "Time in WFS: 49.57\n",
      "\t of which time spent in : 0.00\n",
      "Time in Reconstruction: 0.40\n",
      "Time in DM: 1.26\n",
      "Time making science image: 4.78\n",
      "\n",
      "\n",
      "\n",
      "***\n",
      "Set Wind speed to 10m/s, loop time to 2.5ms\"\n",
      "***\n",
      "Run for 4000 frames\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "1 of 1: Loaded configuration file successfully!Starting Sim: 2017-05-19-16-33-59\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "Creating mask...\n",
      "Turbulence summary @ 500 nm:\n",
      "| r0 = 0.20 m (0.52\" seeing)\n",
      "| Vbar_5/3 = 10.00 m/s\n",
      "| tau0 = 6.28 ms\n",
      "Initialise Infinite Phase Screen 1\n",
      "Initialising WFSs....\n",
      "WFS 0: 632 measurements\n",
      "Initialising 1 DMs...\n",
      "Making DM Influence Functions...\n",
      "DM 0: 441 active actuators\n",
      "441 total DM Actuators\n",
      "Initialising Reconstructor...\n",
      "Initialising 1 Science Cams...\n",
      "Generating fftw3 plan....\n",
      "If this takes too long, change fftw_FLAGS.\n",
      "Done!\n",
      "Initialise Data Storage...\n",
      "Initialisation Complete!\n",
      "Making interaction Matrices...\n",
      "Creating Interaction Matrix for DM 0 \n",
      "440 of 441: Generating 441 Actuator DM iMat\n",
      "441 of 441: Generating 441 Actuator DM iMatChecking for redundant actuators...\n",
      "DM 0 has 429 valid actuators (12 dropped)\n",
      "Interaction Matrices Done\n",
      "Creating Command Matrix\n",
      "Invert iMat with conditioning: 0.0300\n",
      "Command Matrix Generated!\n",
      "3999 of 4000: temporalerror  Strehl -- sci_0: inst 0.93, long 0.89 \n",
      "4000 of 4000: temporalerror  Strehl -- sci_0: inst 0.92, long 0.89 \n",
      "\n",
      "Science Camera 0: Long Exposure Strehl Ratio: 0.89\n",
      "\n",
      "\n",
      "Time moving atmosphere: 0.00\n",
      "Time making IMats and CMats: 11.41\n",
      "Time in WFS: 104.85\n",
      "\t of which time spent in : 0.00\n",
      "Time in Reconstruction: 0.88\n",
      "Time in DM: 2.65\n",
      "Time making science image: 9.82\n",
      "\n",
      "\n",
      "\n",
      "***\n",
      "Set Wind speed to 10m/s, loop time to 2.0ms\"\n",
      "***\n",
      "Run for 5000 frames\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "1 of 1: Loaded configuration file successfully!Starting Sim: 2017-05-19-16-36-14\n",
      "Pixel Scale: 12.50 pxls/m\n",
      "subScreenSize: 102 simulation pixels\n",
      "Creating mask...\n",
      "Turbulence summary @ 500 nm:\n",
      "| r0 = 0.20 m (0.52\" seeing)\n",
      "| Vbar_5/3 = 10.00 m/s\n",
      "| tau0 = 6.28 ms\n",
      "Initialise Infinite Phase Screen 1\n",
      "Initialising WFSs....\n",
      "WFS 0: 632 measurements\n",
      "Initialising 1 DMs...\n",
      "Making DM Influence Functions...\n",
      "DM 0: 441 active actuators\n",
      "441 total DM Actuators\n",
      "Initialising Reconstructor...\n",
      "Initialising 1 Science Cams...\n",
      "Generating fftw3 plan....\n",
      "If this takes too long, change fftw_FLAGS.\n",
      "Done!\n",
      "Initialise Data Storage...\n",
      "Initialisation Complete!\n",
      "Making interaction Matrices...\n",
      "Creating Interaction Matrix for DM 0 \n",
      "440 of 441: Generating 441 Actuator DM iMat\n",
      "441 of 441: Generating 441 Actuator DM iMatChecking for redundant actuators...\n",
      "DM 0 has 429 valid actuators (12 dropped)\n",
      "Interaction Matrices Done\n",
      "Creating Command Matrix\n",
      "Invert iMat with conditioning: 0.0300\n",
      "Command Matrix Generated!\n",
      "4939 of 5000: temporalerror  Strehl -- sci_0: inst 0.90, long 0.89 "
     ]
    }
   ],
   "source": [
    "data_frame = pandas.DataFrame()\n",
    "\n",
    "for r in tnrange(RUNS):\n",
    "    for nw in tnrange(len(WIND_SPEEDS)):\n",
    "        for nf in range(len(FRAME_TIMES)):\n",
    "\n",
    "            total_frames = int(INTEGRATION_TIME / FRAME_TIMES[nf])\n",
    "            print('\\n***\\nSet Wind speed to {}m/s, loop time to {}ms\"\\n***'.format(WIND_SPEEDS[nw], 1000*FRAME_TIMES[nf]))\n",
    "            print(\"Run for {} frames\".format(total_frames))\n",
    "            \n",
    "            sim = soapy.Sim(CONFIGFILE)\n",
    "            sim.config.sim.nIters=total_frames\n",
    "\n",
    "            sim.config.sim.loopTime = FRAME_TIMES[nf]\n",
    "            sim.config.atmos.windSpeeds[0] = WIND_SPEEDS[nw]\n",
    "            \n",
    "            sim.aoinit()\n",
    "            sim.makeIMat()\n",
    "            sim.aoloop()\n",
    "\n",
    "            data_series = pandas.Series(dict(sim.config.sim.saveHeader))\n",
    "            data_series[\"wind_speed\"] = WIND_SPEEDS[nw]\n",
    "            data_series[\"loop_time\"] = FRAME_TIMES[nf]\n",
    "            \n",
    "            data_series[\"long_strehl\"] = sim.longStrehl[0][-1]\n",
    "            data_series[\"wavefront_error\"] = sim.WFE[0][-1]\n",
    "            \n",
    "            \n",
    "            data_frame = data_frame.append(data_series, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = pyplot.figure()\n",
    "for ws in WIND_SPEEDS:\n",
    "    df = data_frame[data_frame.wind_speed==ws]\n",
    "    pyplot.plot(df.loop_time, df.long_strehl, label=\"Wind Speed: {}\".format(ws))\n",
    "    \n",
    "    pyplot.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x119970978>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = pyplot.figure()\n",
    "pyplot.plot(N_SUBAPS[:n-1], strehls.T[:n-1], label=\"Strehl Ratio @ 1.65um\")\n",
    "pyplot.plot(N_SUBAPS[:n-1], theoretical_strehl[:n-1], label=\"Theoretical Strehl\")\n",
    "# pyplot.plot(N_SUBAPS, wfe.T, label=\"Wavefront Error\")\n",
    "pyplot.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 581.70265574  542.27144355  502.01775669  348.97719574]]\n"
     ]
    }
   ],
   "source": [
    "measured_wfe = numpy.sqrt(-1 * numpy.log(strehls[:,:n-1])) * (1650 / (2*numpy.pi))\n",
    "print(measured_wfe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11c51b9e8>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = pyplot.figure()\n",
    "pyplot.plot(N_SUBAPS[:n-1], measured_wfe.T[:n-1], label=\"Estimated RMS WFE\")\n",
    "pyplot.plot(N_SUBAPS[:n-1], theoretical_wfe[:n-1], label=\"Theoretical RMS WFE\")\n",
    "# pyplot.plot(N_SUBAPS, wfe.T, label=\"Wavefront Error\")\n",
    "pyplot.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = pyplot.subplots()\n",
    "pyplot.grid()\n",
    "\n",
    "ax1.plot(N_SUBAPS[:n-1], strehls.T[:n-1], label=\"Measured Strehl\")\n",
    "ax1.plot(N_SUBAPS[:n-1], theoretical_strehl.T[:n-1], label=\"Theoretical Strehl\", color=\"b\", linestyle=\":\")\n",
    "\n",
    "ax1.set_xlabel('Number of Sub-apertures (s)')\n",
    "# Make the y-axis label, ticks and tick labels match the line color.\n",
    "ax1.set_ylabel('Strehl ratio @ 1.65um', color='b')\n",
    "ax1.tick_params('y', colors='b')\n",
    "pyplot.legend(loc=0)\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(N_SUBAPS[:n-1], measured_wfe.T[:n-1], color=\"r\", label=\"Measured WFE\")\n",
    "ax2.plot(N_SUBAPS[:n-1], theoretical_wfe[:n-1], color=\"r\", linestyle=\":\", label=\"Theoretical WFE\")\n",
    "ax2.set_ylabel('Wavefront Error (nm)', color='r')\n",
    "ax2.tick_params('y', colors='r')\n",
    "pyplot.legend()\n",
    "\n",
    "\n",
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