NotebookGUI.ipynb 493 KB
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{
 "cells": [
  {
   "cell_type": "code",
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   "execution_count": 7,
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   "metadata": {
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    "hideCode": false,
    "hidePrompt": false
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   },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
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   "source": [
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    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%load_ext autoreload\n",
    "\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
    "\n",
    "%autoreload 2\n",
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    "%matplotlib inline\n",
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    "\n",
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    "import sys\n",
    "sys.stdout = open('output.txt', 'w')"
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   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 2,
   "metadata": {},
   "outputs": [],
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   "source": [
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    "from Scripts.DevicePool import DevicePool\n",
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    "from Scripts.MeasurementManager import MeasurementManager\n",
    "from Scripts.GUI import DevicePoolInterface, IoInterface, MeasurementManagerInterface\n",
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    "from Scripts.DataManager import IO"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 3,
   "metadata": {
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    "scrolled": false
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   },
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   "outputs": [],
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   "source": [
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    "device_pool = DevicePool()\n",
    "io = IO('Data/test/')\n",
    "measurement_manager = MeasurementManager(device_pool, io)"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 4,
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   "metadata": {
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    "scrolled": true
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   },
   "outputs": [
    {
     "data": {
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      "application/vnd.jupyter.widget-view+json": {
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       "model_id": "f25b76441d3845daa4f5f7a7284b62d6"
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      }
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     },
     "metadata": {},
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     "output_type": "display_data"
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    }
   ],
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   "source": [
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    "device_pool_interface = DevicePoolInterface(device_pool)\n",
    "device_pool_interface.show()"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 5,
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   "metadata": {
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    "scrolled": false
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   },
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "model_id": "282f85cb7b4848eeb6e0e611b57b9ba3"
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      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
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   "source": [
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    "IoInterface(io, device_pool, device_pool_interface).show()"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 6,
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   "metadata": {
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    "scrolled": true
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   },
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   "outputs": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PlasmaCamHorizontal  config time:  1.1920928955078125e-06\n",
      "FaradayCup  config time:  2.384185791015625e-06\n",
      "test_device1  config time:  1.1920928955078125e-06\n",
      "DelayGenerator  config time:  1.1920928955078125e-06\n",
      "DRS4  config time:  7.152557373046875e-07\n",
      "FocusCamera  config time:  9.5367431640625e-07\n",
      "ScreenCamera  config time:  1.1920928955078125e-06\n",
      "Periscope  config time:  1.9073486328125e-06\n",
      "BladePositioning  config time:  9.5367431640625e-07\n",
      "Interferometer  config time:  7.152557373046875e-07\n",
      "Laser  config time:  9.5367431640625e-07\n",
      "ParabolicMirror  config time:  7.152557373046875e-07\n",
      "test_device2  config time:  7.152557373046875e-07\n",
      "Choose parameter!!!\n",
      "Choose parameter!!!\n"
     ]
    },
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    {
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     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "model_id": "ea246539d2704e42b3dedd065e00b2ff"
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      }
     },
     "metadata": {},
     "output_type": "display_data"
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    }
   ],
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   "source": [
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    "MeasurementManagerInterface(measurement_manager).show()"
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   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check excentricity"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 230,
   "metadata": {},
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "desired position:  -2.8\n",
      "cor:  0.0\n",
      "desired position:  -2.8\n",
      "cor:  0.0\n",
      "desired position:  -2.8\n",
      "cor:  -0.0\n"
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     ]
    }
   ],
   "source": [
    "blade_config = {'pos_R':342.65, 'pos_L':-2.8}\n",
    "blade = BladePositioning(blade_config, init = 0)\n",
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    "im1 = plasmacam.measure()['im']\n",
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    "\n",
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    "blade_config = {'pos_R':342.65 - 40, 'pos_L':-2.8}\n",
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    "blade = BladePositioning(blade_config, init = 0)\n",
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    "im2 = plasmacam.measure()['im']\n",
    "\n",
    "blade_config = {'pos_R':342.65 - 80, 'pos_L':-2.8}\n",
    "blade = BladePositioning(blade_config, init = 0)\n",
    "im3 = plasmacam.measure()['im']"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 375,
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   "metadata": {
    "scrolled": true
   },
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.Circle at 0x7fda5f19a400>"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZwUjEQiinK73jIiRiqNUl7wAvFeGz8zPCIl4CwMFAW+hvjGXpa6hmWBEh5JRT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\n0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3xiK4ZP6MATX17iL8bfZazDxnbi1m+uxa5VY7Hu8anaTtOWsZQQExtD03oYZTCgzwuK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\ncsJlirZYirGVpoVIWmbSVCwVa8SoaONywG33H8K+B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KuSMQHA5iFgzn8ZsGBb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q2u054SK0m3nFcweCjBnfyREG+kz8Mbr52HXnsa16x4Ab991d9gariAHY9diH3BCo3WkM5H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\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DFWtkUqB6NI7F9d2zmX4bhrXH44hwU+YtKQn2MUK0zsl2YzU4MsXlogZVDIJ4vngojDg7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\nhoaGe8H/A805BSaVUeu5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda5f1ddda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
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   "source": [
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    "plt.figure(figsize = (9,9))\n",
    "\n",
    "plt.imshow(im1.astype(float) + im2.astype(float) + im3.astype(float))\n",
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    "\n",
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    "px = 528\n",
    "py = 575\n",
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    "\n",
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    "r0 = 49\n",
    "r1 = 66\n",
    "r2 = 72\n",
    "r3 = 80\n",
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    "\n",
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    "psize = plasmacam.psize\n",
    "\n",
    "plt.xlim(px - 150,px + 150)\n",
    "plt.ylim(py - 150,py + 150)\n",
    "\n",
    "\n",
    "\n",
    "circle1 = plt.Circle((px, py), r0, color='y', fill=False)\n",
    "circle2 = plt.Circle((px, py), r1, color='y', fill=False)\n",
    "circle3 = plt.Circle((px, py), r2, color='y', fill=False)\n",
    "circle4 = plt.Circle((px, py), r3, color='y', fill=False)\n",
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    "plt.hlines((py), 0, 1000, color = 'y')\n",
    "plt.vlines((px), 0, 1000, color = 'y')\n",
    "plt.gca().add_artist(circle1)\n",
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    "plt.gca().add_artist(circle2)\n",
    "plt.gca().add_artist(circle3)\n",
    "plt.gca().add_artist(circle4)\n"
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   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {
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    "collapsed": true
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   },
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   "outputs": [],
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   "source": [
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    "## Check excentricity\n",
    "\n",
    "blade_config = {'pos_R':342.65, 'pos_L':-2.8}\n",
    "blade = BladePositioning(blade_config, init = 0)\n",
    "im1 = plasmacam.measure()['im']\n",
    "\n",
    "blade_config = {'pos_R':342.65 - 40, 'pos_L':-2.8}\n",
    "blade = BladePositioning(blade_config, init = 0)\n",
    "im2 = plasmacam.measure()['im']\n",
    "\n",
    "blade_config = {'pos_R':342.65 - 80, 'pos_L':-2.8}\n",
    "blade = BladePositioning(blade_config, init = 0)\n",
    "im3 = plasmacam.measure()['im']\n",
    "\n",
    "plt.figure(figsize = (9,9))\n",
    "\n",
    "plt.imshow(im1.astype(float) + im2.astype(float) + im3.astype(float))\n",
    "\n",
    "px = 528\n",
    "py = 575\n",
    "\n",
    "r0 = 49\n",
    "r1 = 66\n",
    "r2 = 72\n",
    "r3 = 80\n",
    "\n",
    "psize = plasmacam.psize\n",
    "\n",
    "plt.xlim(px - 150,px + 150)\n",
    "plt.ylim(py - 150,py + 150)\n",
    "\n",
    "\n",
    "\n",
    "circle1 = plt.Circle((px, py), r0, color='y', fill=False)\n",
    "circle2 = plt.Circle((px, py), r1, color='y', fill=False)\n",
    "circle3 = plt.Circle((px, py), r2, color='y', fill=False)\n",
    "circle4 = plt.Circle((px, py), r3, color='y', fill=False)\n",
    "plt.hlines((py), 0, 1000, color = 'y')\n",
    "plt.vlines((px), 0, 1000, color = 'y')\n",
    "plt.gca().add_artist(circle1)\n",
    "plt.gca().add_artist(circle2)\n",
    "plt.gca().add_artist(circle3)\n",
    "plt.gca().add_artist(circle4)\n",
    "## Check excentricity\n",
    "\n",
    "blade_config = {'pos_R':342.65, 'pos_L':-2.8}\n",
    "blade = BladePositioning(blade_config, init = 0)\n",
    "im1 = plasmacam.measure()['im']\n",
    "\n",
    "blade_config = {'pos_R':342.65 - 40, 'pos_L':-2.8}\n",
    "blade = BladePositioning(blade_config, init = 0)\n",
    "im2 = plasmacam.measure()['im']\n",
    "\n",
    "blade_config = {'pos_R':342.65 - 80, 'pos_L':-2.8}\n",
    "blade = BladePositioning(blade_config, init = 0)\n",
    "im3 = plasmacam.measure()['im']\n",
    "\n",
    "plt.figure(figsize = (9,9))\n",
    "\n",
    "plt.imshow(im1.astype(float) + im2.astype(float) + im3.astype(float))\n",
    "\n",
    "px = 528\n",
    "py = 575\n",
    "\n",
    "r0 = 49\n",
    "r1 = 66\n",
    "r2 = 72\n",
    "r3 = 80\n",
    "\n",
    "psize = plasmacam.psize\n",
    "\n",
    "plt.xlim(px - 150,px + 150)\n",
    "plt.ylim(py - 150,py + 150)\n",
    "\n",
    "\n",
    "\n",
    "circle1 = plt.Circle((px, py), r0, color='y', fill=False)\n",
    "circle2 = plt.Circle((px, py), r1, color='y', fill=False)\n",
    "circle3 = plt.Circle((px, py), r2, color='y', fill=False)\n",
    "circle4 = plt.Circle((px, py), r3, color='y', fill=False)\n",
    "plt.hlines((py), 0, 1000, color = 'y')\n",
    "plt.vlines((px), 0, 1000, color = 'y')\n",
    "plt.gca().add_artist(circle1)\n",
    "plt.gca().add_artist(circle2)\n",
    "plt.gca().add_artist(circle3)\n",
    "plt.gca().add_artist(circle4)\n",
    "## Check excentricity\n",
    "\n",
    "blade_config = {'pos_R':342.65, 'pos_L':-2.8}\n",
    "blade = BladePositioning(blade_config, init = 0)\n",
    "im1 = plasmacam.measure()['im']\n",
    "\n",
    "blade_config = {'pos_R':342.65 - 40, 'pos_L':-2.8}\n",
    "blade = BladePositioning(blade_config, init = 0)\n",
    "im2 = plasmacam.measure()['im']\n",
    "\n",
    "blade_config = {'pos_R':342.65 - 80, 'pos_L':-2.8}\n",
    "blade = BladePositioning(blade_config, init = 0)\n",
    "im3 = plasmacam.measure()['im']\n",
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    "\n",
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    "plt.figure(figsize = (9,9))\n",
    "\n",
    "plt.imshow(im1.astype(float) + im2.astype(float) + im3.astype(float))\n",
    "\n",
    "px = 528\n",
    "py = 575\n",
    "\n",
    "r0 = 49\n",
    "r1 = 66\n",
    "r2 = 72\n",
    "r3 = 80\n",
    "\n",
    "psize = plasmacam.psize\n",
    "\n",
    "plt.xlim(px - 150,px + 150)\n",
    "plt.ylim(py - 150,py + 150)\n",
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    "\n",
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    "\n",
    "\n",
    "circle1 = plt.Circle((px, py), r0, color='y', fill=False)\n",
    "circle2 = plt.Circle((px, py), r1, color='y', fill=False)\n",
    "circle3 = plt.Circle((px, py), r2, color='y', fill=False)\n",
    "circle4 = plt.Circle((px, py), r3, color='y', fill=False)\n",
    "plt.hlines((py), 0, 1000, color = 'y')\n",
    "plt.vlines((px), 0, 1000, color = 'y')\n",
    "plt.gca().add_artist(circle1)\n",
    "plt.gca().add_artist(circle2)\n",
    "plt.gca().add_artist(circle3)\n",
    "plt.gca().add_artist(circle4)\n"
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   ]
  },
  {
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   "cell_type": "markdown",
   "metadata": {},
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   "source": [
388
    "## Optimisation of Focal Spot Size"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
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    "#import sys\n",
    "#sys.path.append('../pyAPT/')\n",
    "#from pyAPT import Z825B\n",
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    "\n",
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    "#Cz = Z825B(83829619)#27251098)#83829619)"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 41,
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   "metadata": {
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    "scrolled": true
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   },
   "outputs": [
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    {
     "data": {
      "text/plain": [
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       "0"
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      ]
     },
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     "execution_count": 41,
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     "metadata": {},
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     "output_type": "execute_result"
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    }
   ],
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   "source": [
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    "#import Pyro4\n",
    "#ns = Pyro4.locateNS(host='pc9730.psi.ch', port = 9090) \n",
    "#Cz = Pyro4.Proxy(\"PYRONAME:APTserver\")\n",
    "#Cz.add_motor(27501952)"
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   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 13,
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   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
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    "#Cz.home()"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 37,
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   "metadata": {
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    "hideCode": false,
    "hidePrompt": false,
    "scrolled": true
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   },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581
      "-------------------------------------------\n",
      "Parameters:  (5.5, 4.3999825093283578, -6.6138116604477615)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.015916397563465967, 0.014907590694770434, 0.015416766782984759, 0.02309690963969091, 0.013703524455816574, 0.013860690330998349, 0.015340794044664818, 0.015112240702656266, 0.00057206851119895319, 0.023058115856174233, 0.00068202345657475405, 0.015363164750672009, 0.0018218181818179247, 0.014039999300777595, 0.022402872087279313, 0.001661336192810392, 0.00017238777369410485, 0.03072422094759153, 0.014325714626447716, 0.014361613912771798]\n",
      "FWHMys:  [0.022041713307659583, 0.020322467815409029, 0.022763415208240989, 0.029497687842837239, 0.018975794012558334, 0.0214390784145293, 0.019729822811122011, 0.02083914540604348, 0.0013355921855922048, 0.027606435489044845, 0.00044244190980213816, 0.019524940079089781, 0.00084692509472450439, 0.018753324502226887, 0.026643945544287506, 0.0022455174427089553, 0.00041659403254956651, 0.041081671728340652, 0.020611618457300196, 0.019833087514689041]\n",
      "maxints:  [4095, 4095, 3605, 2846, 4095, 3957, 4037, 4095, 3132, 3235, 2523, 2710, 3132, 3985, 3095, 2607, 4095, 2382, 4095, 4095]\n",
      "mean(maxits):  3495.55\n",
      "mean:  0.000339213031684 error:  6.61304828886e-05\n",
      "-------------------------------------------\n",
      "Parameters:  (5.7750000000000004, 4.3999825093283578, -6.6138116604477615)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n",
      "FWHMys:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n",
      "maxints:  [887, 845, 755, 859, 868, 718, 863, 788, 802, 816, 882, 788, 770, 760, 929, 793, 830, 784, 779, 793]\n",
      "mean(maxits):  815.45\n",
      "mean:  0.0004 error:  0.0\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5, 4.619981634794776, -6.6138116604477615)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n",
      "FWHMys:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n",
      "maxints:  [709, 830, 830, 723, 765, 765, 816, 751, 718, 751, 732, 704, 737, 802, 727, 727, 713, 788, 741, 713]\n",
      "mean(maxits):  752.1\n",
      "mean:  0.0004 error:  0.0\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5, 4.3999825093283578, -6.9445022434701489)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.031843303224054154, 0.035790685550506574, 0.0094479770114945794, 0.032603603166037054, 0.022063653751528634, 0.03698464155064185, 0.016378397350458318, 0.02, 0.039435473399855425, 0.00030025798868438258, 0.02130228149679958, 0.023070472769543215, 0.0024886107665040846, 0.017853471584677205, 0.013913792256517343, 0.016228371788370222, 0.016022489231846127, 0.015405960858586365, 0.018295829954590559, 0.016614746920511347]\n",
      "FWHMys:  [0.0307976705641273, 0.032432876783148323, 0.010806874602332872, 0.033468566318046733, 0.026599224194608695, 0.041147368696106357, 0.019836578621091894, 0.02, 0.039854365937473935, 0.0016021086626138992, 0.020647326551761713, 0.015162548094358907, 0.0033981594042222785, 0.020476016573295786, 0.019861397765414512, 0.019847500069170687, 0.019095682389881796, 0.017893651128958243, 0.021119382504940276, 0.020610848337732524]\n",
      "maxints:  [2705, 2255, 2893, 2560, 2185, 2302, 2485, 1791, 2054, 2827, 2762, 4095, 3240, 2616, 2677, 2663, 2921, 2879, 2691, 2621]\n",
      "mean(maxits):  2661.1\n",
      "mean:  0.000546290956064 error:  9.89802178098e-05\n",
      "-------------------------------------------\n",
      "Parameters:  (5.6833333333333318, 4.5466485929726366, -6.2831210774253732)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n",
      "FWHMys:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n",
      "maxints:  [793, 755, 1027, 770, 849, 882, 788, 774, 802, 812, 765, 765, 835, 859, 835, 821, 835, 835, 905, 934]\n",
      "mean(maxits):  832.05\n",
      "mean:  0.0004 error:  0.0\n",
      "-------------------------------------------\n",
      "Parameters:  (5.6374999999999993, 4.5099820720615664, -6.4484663689365673)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n",
      "FWHMys:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n",
      "maxints:  [1145, 1220, 1041, 901, 1548, 943, 835, 980, 957, 896, 905, 821, 1051, 999, 971, 901, 1182, 1191, 938, 1074]\n",
      "mean(maxits):  1024.95\n",
      "mean:  0.0004 error:  0.0\n",
      "###################\n",
      "step done\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5458333333333325, 4.4366490302394279, -6.7791569519589547)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.02, 0.0024460989635244346, 0.02, 0.012951361459984767, 0.02, 0.0055404303278687728, 0.02, 0.02, 0.00069994033220455165, 0.012097432624113669, 0.031366342181860851, 0.0012933717194614225, 0.02, 0.02, 0.02, 0.00018260090455424205, 0.029175118913857379, 0.00052153551892386574, 0.012241493476015997, 0.029010315792200814]\n",
      "FWHMys:  [0.02, 0.0015776682692307809, 0.02, 0.013882511002031306, 0.02, 0.0068320146678171323, 0.02, 0.02, 0.00071292290199531649, 0.014566205563504298, 0.037119398952302429, 0.0031706200671421936, 0.02, 0.02, 0.02, 0.00048933721889832249, 0.040984671615602597, 0.00077125129399591152, 0.013374177767354389, 0.041116144297576929]\n",
      "maxints:  [1454, 2251, 1698, 2279, 1805, 2251, 1824, 1651, 2213, 2232, 2007, 2279, 1632, 1899, 1970, 2091, 2021, 2255, 2077, 2049]\n",
      "mean(maxits):  1996.9\n",
      "mean:  0.000374304484366 error:  8.68748267287e-05\n",
      "###################\n",
      "step done\n",
      "-------------------------------------------\n",
      "Parameters:  (5.7138888888888886, 4.2044277311359863, -6.7240418547885561)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n",
      "FWHMys:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n",
      "maxints:  [1130, 1196, 1163, 1140, 1041, 1243, 1074, 1238, 1159, 1079, 1032, 1163, 1154, 1046, 1074, 1154, 1234, 1121, 1140, 1112]\n",
      "mean(maxits):  1134.65\n",
      "mean:  0.0004 error:  0.0\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5534722222222221, 4.5160931588800786, -6.6413692090329599)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n",
      "FWHMys:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n",
      "maxints:  [1065, 1107, 1276, 1121, 1018, 1224, 1004, 1234, 1032, 1220, 976, 1023, 1126, 1159, 910, 1201, 905, 1266, 1107, 1130]\n",
      "mean(maxits):  1105.2\n",
      "mean:  0.0004 error:  0.0\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5229166666666663, 4.4183157697838933, -6.6964843062033577)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.014711992203118918, 0.00052202662072220463, 0.017020328918036842, 0.0015321638380396685, 0.015369596920290274, 0.01304932025540273, 0.012745158649503541, 0.0010219532467528047, 0.01550981637125215, 0.013607531739314283, 0.014809606902689954, 0.014296940456277163, 0.0013198780233527607, 0.017209631881826226, 0.014922980625931004, 0.038434060363865985, 0.014092663596230182, 0.015104001095815445, 0.014671127707770903, 0.034309730134964855]\n",
      "FWHMys:  [0.018145683565719661, 0.00052106614897451387, 0.020229128775025362, 0.0014283079910601959, 0.0186615655737703, 0.016668308289241596, 0.015206324460314447, 0.00032658359456649855, 0.019129767868852454, 0.018470628088023577, 0.018673229128971736, 0.016313400207900175, 0.0012786158357178667, 0.021962967536669487, 0.017886629046967695, 0.04598606871470956, 0.017191722222222428, 0.019202872181211639, 0.018273469153387234, 0.039608018489776908]\n",
      "maxints:  [2668, 2288, 2466, 2612, 2855, 2724, 2804, 3320, 3090, 2762, 2827, 3193, 2823, 2410, 2860, 2237, 2598, 2870, 2776, 2387]\n",
      "mean(maxits):  2728.5\n",
      "mean:  0.000353773480409 error:  9.41905947492e-05\n",
      "-------------------------------------------\n",
      "Parameters:  (5.6375000000000002, 4.3999825093283578, -6.6138116604477615)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.019412939320388567, 0.020547769209066313, 0.0013359002687374222, 0.025740385106383101, 0.024686392973856552, 0.024088373405535268, 0.022104166235155986, 0.0014032768756999658, 0.026303633894622536, 0.022211002043113215, 0.020605107053799365, 0.022560942500827519, 0.02, 0.020284292565946771, 0.02213080483525065, 0.02, 0.0010165217391304715, 0.024457579945799779, 0.02, 0.025576198224852131]\n",
      "FWHMys:  [0.013496811538461495, 0.016244813227640931, 0.001405814247877224, 0.015852471011370772, 0.017246869376457585, 0.014466477239988729, 0.013560741719690572, 0.00073109343125254966, 0.015677555827182665, 0.014333926766201044, 0.014983303821656246, 0.017824628019323718, 0.02, 0.012509555228758185, 0.016468882730923617, 0.02, 0.00014910714285720772, 0.015160317444717242, 0.02, 0.016776884962049321]\n",
      "maxints:  [2471, 2387, 2101, 2035, 2124, 2209, 2495, 2251, 2176, 2209, 2302, 2016, 1866, 2654, 2316, 1923, 2059, 2030, 1852, 2237]\n",
      "mean(maxits):  2185.65\n",
      "mean:  0.000328500026885 error:  3.21673613615e-05\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5, 4.5099820720615664, -6.6138116604477615)\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n",
      "FWHMys:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n",
      "maxints:  [1215, 1079, 1726, 1055, 1721, 1374, 1482, 1238, 1070, 1721, 990, 1262, 1102, 971, 1168, 1187, 1660, 1126, 1205, 1173]\n",
      "mean(maxits):  1276.25\n",
      "mean:  0.0004 error:  0.0\n",
      "###################\n",
      "step done\n",
      "-------------------------------------------\n",
      "Parameters:  (5.6069444444444443, 4.3022051202321734, -6.6689267576181592)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.018724313060817455, 0.0015255550416277686, 0.018045949472096812, 0.021265318299191094, 0.015543120219295936, 0.0018519923510464942, 0.02003631708864706, 0.020194811072480778, 0.018957956162390932, 0.021768102345416374, 0.020289676635569354, 0.019877131871426368, 0.0041947867298577357, 0.018070809389755027, 0.019143694255784105, 0.019782301418175052, 0.026809133433283616, 0.017096564524663904, 0.020003047583514899, 0.022497118144884087]\n",
      "FWHMys:  [0.012361972625411721, 0.0024463451659451074, 0.017640195726495667, 0.011446305989851635, 0.021295156320743414, 0.0016689552052052692, 0.011469158064983387, 0.012218037844904051, 0.01936218257921718, 0.011579005568155165, 0.011925576996518528, 0.011833052952602841, 0.0045613675643402241, 0.014696229282744944, 0.018765233333333353, 0.012527254519765552, 0.028726311283133965, 0.015902863363363329, 0.01225514397291172, 0.012001120478660865]\n",
      "maxints:  [4095, 3910, 3530, 3807, 3798, 4095, 4095, 4095, 3723, 3826, 4095, 4095, 3470, 4095, 3816, 4027, 2626, 4095, 4095, 4095]\n",
      "mean(maxits):  3874.15\n",
      "mean:  0.000277295661193 error:  3.40398587458e-05\n",
      "-------------------------------------------\n",
      "Parameters:  (5.6604166666666664, 4.198316644317476, -6.6964843062033577)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n",
      "FWHMys:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n",
      "maxints:  [1313, 1271, 1623, 1529, 1426, 1501, 1112, 1679, 1252, 1384, 1655, 1173, 1510, 1796, 1534, 1341, 1520, 1440, 1351, 1843]\n",
      "mean(maxits):  1462.65\n",
      "mean:  0.0004 error:  0.0\n",
      "###################\n",
      "step done\n",
      "-------------------------------------------\n",
      "Parameters:  (5.6400462962962967, 4.3164643228086987, -6.5678824128057647)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.032450353016176603, 0.04061993916497153, 0.040621342696629092, 0.029886028982865298, 0.025438726940480549, 0.039027526863380402, 0.042821749763400252, 0.026847169796222925, 0.027251422121548519, 0.038729744737561411, 0.025082379938357935, 0.028303471339678143, 0.040194159637116034, 0.019660157740951867, 0.017447882912403845, 0.031491057148282042, 0.0022271898496244447, 0.041757654909150954, 0.022095780844155755, 0.02]\n",
      "FWHMys:  [0.01194904773282357, 0.018961390524569688, 0.010500214100384375, 0.027825157376166976, 0.021144292867031567, 0.011798879778830984, 0.021083572748886392, 0.024231064570245597, 0.011750799937365275, 0.029647308188599397, 0.029286378550724579, 0.018239800377065518, 0.012583679650453705, 0.016550271577380982, 0.027059146641197129, 0.018131663858639846, 0.00037401041666684787, 0.017321673953202055, 0.01865652280115182, 0.02]\n",
      "maxints:  [2977, 2377, 4095, 2499, 2565, 2710, 2251, 2513, 3249, 2063, 2462, 2734, 2738, 3305, 3071, 2734, 2687, 2509, 3198, 1871]\n",
      "mean(maxits):  2730.4\n",
      "mean:  0.000683315326286 error:  6.31695745997e-05\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5521990740740739, 4.3928529080400942, -6.6643338328539592)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.015077269326510212, 0.0085211242965046452, 0.020862520183982713, 0.002434804007088065, 0.014841392118112307, 0.023333632627293266, 0.015532546296296079, 0.0018036349372385629, 0.024038872988924265, 0.015178525386522868, 0.019143595118649603, 0.019190932937330096, 0.021721673182413248, 0.016604678296305853, 0.016313709120214792, 0.0020944307677797092, 0.018777126672184341, 0.0004170384190160803, 0.019464853499285617, 0.015662285925144737]\n",
      "FWHMys:  [0.018863846300380072, 0.0017528949359919554, 0.018935968428252847, 0.0018996925566341361, 0.019285309484964097, 0.021464397360704046, 0.017980295990588813, 0.00095845340552869196, 0.021115722656182889, 0.017560888983835454, 0.027818096982758389, 0.025441054280800346, 0.021016808058599512, 0.019843551707789731, 0.018856883312253103, 0.00035020028158383454, 0.028209499140666283, 0.002005746095262495, 0.033605361201298534, 0.016000046775110022]\n",
      "maxints:  [4095, 4095, 3788, 3554, 4095, 4095, 4095, 3184, 4095, 4095, 3071, 3474, 4095, 4095, 3877, 3835, 2963, 3690, 2752, 4009]\n",
      "mean(maxits):  3752.6\n",
      "mean:  0.000317949556152 error:  4.72613939359e-05\n",
      "###################\n",
      "step done\n",
      "-------------------------------------------\n",
      "Parameters:  (5.6977623456790116, 4.3300445157387246, -6.6842365068321579)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n",
      "FWHMys:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]\n",
      "maxints:  [1370, 1585, 1398, 1880, 1337, 1309, 1412, 1655, 1285, 1501, 1829, 1435, 1721, 1866, 1585, 1220, 1632, 1295, 1843, 1491]\n",
      "mean(maxits):  1532.45\n",
      "mean:  0.0004 error:  0.0\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5494405864197525, 4.3824980109309495, -6.6314178720438601)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.021078604428044656, 0.015919585918030776, 0.019571171626119543, 0.020583916897506604, 0.019322753680532934, 0.015512662481555584, 0.018204131332487439, 0.016722904747515699, 0.019365786652054684, 0.018690967123637492, 0.017069011403509027, 0.019088535945769092, 0.018855445608496169, 0.020408964009288155, 0.018715989418968793, 0.018399170728876424, 0.019866272572037325, 0.020202976758629365, 0.015864059160885624, 0.019078363801510623]\n",
      "FWHMys:  [0.019613364801266853, 0.017850214066973624, 0.019423496477053215, 0.018488666513888585, 0.019056524127704977, 0.0183003104551962, 0.020268979448329505, 0.017405932974150495, 0.018981810742707328, 0.018411635896224965, 0.02556232112736212, 0.019691541137375701, 0.018957268357696844, 0.018088697440968637, 0.018680866375314631, 0.018430092534943654, 0.018634847523219866, 0.016545685296188051, 0.017344441410694023, 0.018632704546253542]\n",
      "maxints:  [3882, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 3432, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095]\n",
      "mean(maxits):  4051.2\n",
      "will be attenuated\n",
      "mean:  0.00035519974023 error:  9.81237536209e-06\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5795717592592595, 4.3475290141361338, -6.6666302952360592)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.018661002506863422, 0.019273796080375938, 0.020967600212653092, 0.016780932161807538, 0.021205044588407329, 0.017238473401244736, 0.0168522348802842, 0.020188583144998162, 0.019592493328118188, 0.017344026897846643, 0.016641685828508113, 0.019309301573342985, 0.019766621441437149, 0.019606974466349403, 0.018725620911478114, 0.01991698125429231, 0.017739572383275171, 0.019981183251321699, 0.019759406131167889, 0.018573102287057441]\n",
      "FWHMys:  [0.014865783063004456, 0.016073845267133624, 0.014801572268571928, 0.015864767692744808, 0.014614714406475882, 0.015927917618025478, 0.014855575089230744, 0.01479033921478079, 0.015647249559643028, 0.015559376496078969, 0.015004699207846861, 0.015406539813400899, 0.014920847409886306, 0.01363768207046645, 0.01509041678472034, 0.016355780578471535, 0.01496346752249289, 0.015001684445252406, 0.014291836655001444, 0.014813577186446047]\n",
      "maxints:  [4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095]\n",
      "mean(maxits):  4095.0\n",
      "will be attenuated\n",
      "mean:  0.000294211296285 error:  5.36309860506e-06\n",
      "-------------------------------------------\n",
      "Parameters:  (5.6222222222222218, 4.3510938147802651, -6.6413692090329608)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.00013516404104629842, 0.001382383532442244, 0.02148006554074211, 0.024535286285900071, 0.014895660157387969, 0.024573188677250002, 0.024302662319732882, 5.1667313916148316e-05, 0.020865703598379248, 0.021585842296511615, 0.0025543803904146678, 0.022078664319482577, 0.034154284113196987, 0.00046770775392124264, 0.034580838852716678, 0.00059346251865433075, 0.02760205158730189, 0.021644158001084879, 0.021798242464702966, 0.0005303890973733516]\n",
      "FWHMys:  [8.9213767229257002e-05, 0.0015457151620819065, 0.012716907003688549, 0.029128993777140066, 0.0087242369866020741, 0.02597218745016483, 0.017778208301167053, 3.9554039979128319e-05, 0.012522861666505403, 0.012316756457828038, 0.0017870241144845034, 0.028277030963664962, 0.013692533476058921, 0.00028488588737507481, 0.012520656776557004, 0.00081475358893556837, 0.028031368768997034, 0.011950457368045675, 0.012586635778269351, 0.0017849273964132273]\n",
      "maxints:  [3165, 3043, 4095, 2429, 2420, 2888, 3348, 3587, 3634, 3751, 2663, 2607, 2846, 3127, 2945, 3099, 2823, 4095, 3291, 3413]\n",
      "mean(maxits):  3163.45\n",
      "mean:  0.000314287701553 error:  6.15142994714e-05\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5534722222222221, 4.3510938147802651, -6.6413692090329608)\n"
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    },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.017401304703437148, 0.019589865416667074, 0.019655963755571992, 0.018364218650107844, 0.018280796818395117, 0.017577875323681269, 0.018602708718992833, 0.019451856442458393, 0.017755469771749688, 0.019193951796190323, 0.018996829535425253, 0.018596862719527429, 0.018133741234915401, 0.020480916159168583, 0.019554509052116753, 0.018026861457742793, 0.020210773421783124, 0.018444327817197959, 0.01814259712509747, 0.017890688172557567]\n",
      "FWHMys:  [0.017625519026973624, 0.017191845158176067, 0.017188163578088722, 0.01716388564491067, 0.017315546406832971, 0.016981633515306793, 0.016932727227329414, 0.017435452433383736, 0.01708678093645466, 0.015855877115118155, 0.016415214406356737, 0.017554507695112798, 0.016856492652213673, 0.015763758293358299, 0.017246090425281135, 0.016024912597819796, 0.015609856069271455, 0.016838772116124279, 0.014813541142817077, 0.015915158134634289]\n",
      "maxints:  [4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4095]\n",
      "mean(maxits):  4095.0\n",
      "will be attenuated\n",
      "mean:  0.000315116737901 error:  4.0328679538e-06\n",
      "###################\n",
      "step done\n",
      "38.07 38.07\n",
      "laser attenuated\n",
      "-------------------------------------------\n",
      "Parameters:  (5.6523533950617271, 4.3161248179854503, -6.6765816322251599)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02910866354827979, 0.027400165158205425, 0.02, 0.02, 0.02]\n",
      "FWHMys:  [0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.024709362959246972, 0.010082020178257323, 0.02, 0.02, 0.02]\n",
      "maxints:  [1501, 1454, 1548, 1820, 1468, 1430, 1534, 1566, 1557, 1810, 1787, 1487, 1632, 1665, 1365, 2082, 2345, 1918, 1679, 1613]\n",
      "mean(maxits):  1663.05\n",
      "mean:  0.000417757077325 error:  1.56279516378e-05\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5781925154320984, 4.342351565581561, -6.6501723148310106)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.016860861604154564, 0.018034285430537533, 0.022988458293664582, 0.015829301271295559, 0.01928919029135967, 0.017194984447207684, 0.017326993071446584, 0.017294044856632773, 0.017591151700036001, 0.01608849228009257, 0.018046622178157445, 0.016357573755932275, 0.017004982951555725, 0.016605217772584613, 0.021113728493679318, 0.020889892214640771, 0.021722770588235463, 0.015581022574634673, 0.022584282195887706, 0.018964922695035469]\n",
      "FWHMys:  [0.012439239130110469, 0.014078723241044644, 0.013930583333333191, 0.012626846641948575, 0.013288559731783667, 0.012632955346991603, 0.013295331879793704, 0.012140327030921605, 0.012957916434672256, 0.013485716391713254, 0.012267613995006088, 0.012769177536461895, 0.013229669260522381, 0.013950579612450942, 0.014427275987334065, 0.016236568737477142, 0.014228965087220602, 0.013959798448752059, 0.013555819812332337, 0.013522054961714769]\n",
      "maxints:  [4095, 3709, 2963, 4095, 3638, 4065, 4095, 4095, 4095, 4095, 4095, 4027, 4095, 4095, 3451, 3113, 3165, 4095, 4095, 3652]\n",
      "mean(maxits):  3841.4\n",
      "mean:  0.000262101167015 error:  1.10964858758e-05\n",
      "###################\n",
      "step done\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5542502572016481, 4.3102966518529797, -6.6824503694238588)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.015837678846294612, 0.014616810365038369, 0.01652661209589068, 0.023438037468336059, 0.018373895885084757, 0.015125762829777845, 0.016376736265995007, 0.014954544840768413, 0.014813695929305926, 0.01416089708051782, 0.015109375481603848, 0.015373640009022527, 0.019504667380021434, 0.019196622792811979, 0.01325527460999254, 0.014378604934059958, 0.015279367670761435, 0.018439719648435382, 0.015197674394769045, 0.016191847361621381]\n",
      "FWHMys:  [0.013868432474634873, 0.013738106869815581, 0.01310379147776719, 0.014008338719043056, 0.015707558069481209, 0.014361135033807204, 0.013826290166904176, 0.015208117342561556, 0.014386832898673374, 0.014342049134343782, 0.014119262872008931, 0.014436682674674239, 0.013463137579266427, 0.013243060060466494, 0.014146335465313076, 0.014171793935145893, 0.014444941679875312, 0.013356614991427485, 0.014890007310978781, 0.013403988017658408]\n",
      "maxints:  [4095, 4095, 3657, 2940, 2827, 3952, 4074, 4095, 4095, 4095, 4095, 4095, 3432, 3479, 4023, 4095, 4095, 3310, 3896, 3760]\n",
      "mean(maxits):  3810.25\n",
      "mean:  0.000235468071352 error:  8.98828539087e-06\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5202642746913604, 4.2898980703893379, -6.7029909496193083)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.012177334603019396, 0.012953906394822745, 0.013610882734912177, 0.015007870218579189, 0.012802275549367526, 0.012936202131378138, 0.014107741566754761, 0.01325598869856659, 0.01539460678527238, 0.013913600688468364, 0.01324139647525735, 0.012521825303179135, 0.01301522192482274, 0.013430453396634956, 0.01400880342420896, 0.013510059361386162, 0.014200142577582398, 0.012766259395828872, 0.013240643725020096, 0.013332879826777333]\n",
      "FWHMys:  [0.017503509520258476, 0.017350520863862351, 0.016645299235780509, 0.015113325209330064, 0.015530017390531392, 0.017039574476376718, 0.016653452637871768, 0.0164965119434618, 0.016019061292680492, 0.017463751582045606, 0.016873007179364352, 0.017031578342749598, 0.016283054129317298, 0.016806331882821435, 0.01611126550162012, 0.016289309465540525, 0.01545792838404092, 0.015809566155675303, 0.018136357673764536, 0.018169732900616076]\n",
      "maxints:  [4095, 4095, 3952, 3812, 4070, 4095, 4095, 4095, 3315, 3652, 4095, 4095, 4095, 4095, 4095, 4095, 4095, 4093, 3451, 3690]\n",
      "mean(maxits):  3958.75\n",
      "mean:  0.000229806332402 error:  2.89112615484e-06\n",
      "###################\n",
      "step done\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5573623971193413, 4.2754408233325805, -6.6814297194762577)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.016710596241403142, 0.014723605311838917, 0.016101086309523893, 0.015683190665404911, 0.017558466940475803, 0.016493899639123732, 0.015811280934452832, 0.017894153161694515, 0.017196000765987929, 0.017990278422273409, 0.017315168420810156, 0.015773615458732682, 0.016673549237473217, 0.016551592838742302, 0.01624622987006985, 0.016172009554782374, 0.018794315921664584, 0.01549963752665251, 0.02009511037714562, 0.017907890632567813]\n",
      "FWHMys:  [0.015334656010815739, 0.014117760019917547, 0.013440794872291384, 0.013824864339042509, 0.013592590689655182, 0.013955804086507984, 0.014933303953972354, 0.014726846288348361, 0.013765018855263711, 0.014592888102815949, 0.015468565444051396, 0.014276937812911783, 0.014783184744424804, 0.014008905073667421, 0.014584465317076023, 0.015238648137157917, 0.01564672287694413, 0.014002873557036, 0.01367857523510968, 0.013465617738507241]\n",
      "maxints:  [3324, 3948, 4055, 3835, 3446, 3680, 3666, 3305, 3765, 3470, 3338, 3746, 3357, 3587, 3357, 3498, 3141, 4065, 3301, 3596]\n",
      "mean(maxits):  3574.0\n",
      "mean:  0.000246384808361 error:  5.22003875093e-06\n",
      "###################\n",
      "step done\n",
      "-------------------------------------------\n",
      "Parameters:  (5.4969350137174207, 4.3029218526368123, -6.6874685649995573)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.012075646758746394, 0.012767251275464186, 0.012270334824911799, 0.011178486739028326, 0.01209512467486018, 0.011029426649679319, 0.011982860405912454, 0.012382484372904567, 0.011988360689620947, 0.01213363617886154, 0.012571929364115775, 0.011827764268029117, 0.012778489175465868, 0.012725219416102718, 0.012559794473012253, 0.011457523143126647, 0.012712415242866371, 0.010951877684697298, 0.012045390905905062, 0.013540788658724878]\n",
      "FWHMys:  [0.018940384199238158, 0.017708750818698604, 0.017737669789513855, 0.018645554360255612, 0.019123345082008636, 0.020333625052781135, 0.022761475910246243, 0.017886185994458681, 0.018673322111104396, 0.018524961870021506, 0.018357127789046745, 0.018785422374556826, 0.018532764467759866, 0.018336966918153785, 0.016981245369617426, 0.019441581368843219, 0.017440868341998872, 0.020625672428273734, 0.020237880653927176, 0.020071230585878563]\n",
      "maxints:  [4095, 4095, 4095, 4095, 4095, 4095, 3446, 4095, 4095, 4095, 4095, 4093, 3718, 4095, 4095, 3765, 4095, 4095, 3685, 3601]\n",
      "mean(maxits):  3981.9\n",
      "mean:  0.00025459819714 error:  5.2316571577e-06\n",
      "###################\n",
      "step done\n",
      "-------------------------------------------\n",
      "Parameters:  (5.4715152749199838, 4.2364889319909267, -6.7310871745657384)\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.012126602223636596, 0.012248340131977731, 0.013232101113752393, 0.011668506692013025, 0.01255556457138729, 0.012332718550106758, 0.011581502925402276, 0.014404816178760438, 0.011851680484346261, 8.5189689538189839e-05, 0.011586653478650621, 0.012244533010012493, 0.013088498484848277, 0.012255811979954512, 0.012769540581220262, 0.012690591278052388, 0.011561492544934993, 0.012187254951162796, 0.012209144917189718, 0.02]\n",
      "FWHMys:  [0.023708410041841044, 0.023248855882352981, 0.03019415270213649, 0.025913345684915101, 0.02724661445221449, 0.030207891655550601, 0.026136136990748238, 0.030582604895104948, 0.02981919764488572, 0.0020092798594847361, 0.02206254563279858, 0.026323945240489366, 0.020499755465551717, 0.025432969924812032, 0.024856228518929768, 0.029681600654448859, 0.020662082622740274, 0.02714781761225199, 0.023135020607086387, 0.02]\n",
      "maxints:  [3020, 3305, 2532, 2870, 2902, 2584, 3071, 2879, 2560, 2687, 2865, 2476, 2635, 2870, 2827, 2602, 3085, 2846, 2846, 1984]\n",
      "mean(maxits):  2772.3\n",
      "mean:  0.000396457116777 error:  2.67256403756e-05\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5515232053040702, 4.3158859071839029, -6.670401029764693)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.017122182215895343, 0.017446766413183212, 0.014740907912468071, 0.015856817803884482, 0.01531664652514575, 0.018107455281892726, 0.014046708818226339, 0.015167589319132091, 0.017730689660733212, 0.018174199207634967, 0.015942745993590357, 0.017031860086579975, 0.016730808533251817, 0.015021874031715843, 0.013850916483042486, 0.017303775879916827, 0.016502516284119473, 0.01880522984777766, 0.015895194709314886, 0.018026879085458525]\n",
      "FWHMys:  [0.014397774181758249, 0.015257768719391551, 0.015538655788655764, 0.016212338291488049, 0.014827840826422189, 0.014162813273951524, 0.01465828479468001, 0.015171049414835713, 0.014684036404627898, 0.015038202154191715, 0.014843218508573619, 0.015248098290598255, 0.018130222691356446, 0.014906892601314947, 0.015737473458964146, 0.014953585931624991, 0.015144116666666596, 0.014610951772606739, 0.014509025010190801, 0.015255418371212137]\n",
      "maxints:  [3451, 3226, 3488, 3263, 3582, 3427, 4095, 4095, 3273, 3099, 3812, 3104, 3057, 3934, 4095, 3123, 3268, 3043, 3451, 3207]\n",
      "mean(maxits):  3454.65\n",
      "mean:  0.000251455735504 error:  5.36993347523e-06\n",
      "###################\n",
      "step done\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5891649043590927, 4.2845613479670686, -6.6824125675739472)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.018916866383440034, 0.019283703641962635, 0.026354450330387191, 0.016886527513227723, 0.024650770045172354, 0.020021393711965985, 0.021084104242424129, 0.020375126412018663, 0.019067007561302862, 0.017621982507522116, 0.020503217399594487, 0.02546898965051092, 0.021270659787134605, 0.021793827020239576, 0.019722333046101159, 0.024629613929829208, 0.018219276808610907, 0.019722826635489632, 0.017480072991407525, 0.019774159396320989]\n",
      "FWHMys:  [0.012925668821100467, 0.011813685966787557, 0.013994129093846341, 0.013136481091707064, 0.013498806496718196, 0.012688030249350501, 0.014682090327687636, 0.013131441712811553, 0.011727719489981858, 0.01192982989568514, 0.012237821833742224, 0.014097130758807697, 0.013954004065040437, 0.012895662700994515, 0.012802853080265852, 0.013445598405692594, 0.011902440025252425, 0.013715292723381567, 0.013155431432973819, 0.012901252461706814]\n",
      "maxints:  [3441, 3418, 2326, 3760, 2841, 3277, 2715, 3109, 3245, 3863, 3263, 2518, 2902, 3048, 3296, 2776, 3568, 3015, 3554, 3259]\n",
      "mean(maxits):  3159.7\n",
      "mean:  0.000301771278011 error:  1.39497228267e-05\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5199924863778387, 4.2983317264693763, -6.6862045656431546)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.012860833020324591, 0.013380407509704373, 0.013300259098555145, 0.012592150380354994, 0.012728309879231681, 0.012932465929512915, 0.012821577613236101, 0.014005920973783059, 0.014066474109121874, 0.015098361022927609, 0.012718300344234379, 0.013390605311856429, 0.013895101387255249, 0.013293526560476554, 0.014463564423336628, 0.013323703523927755, 0.014098834630692725, 0.015141760839305185, 0.013214263144011973, 0.01315998580808575]\n",
      "FWHMys:  [0.016236339648519893, 0.015966070494548013, 0.016180647390453995, 0.015915421104976013, 0.01615944750912468, 0.016632344974506608, 0.017035887397288674, 0.017230339350699064, 0.016614598655252499, 0.017226235862535577, 0.016470151998698168, 0.016720024537037026, 0.017038015513084592, 0.015468167610298122, 0.017236407650273233, 0.0173169122323511, 0.01615708282112982, 0.019157213876754842, 0.017119561261707661, 0.015186855531092003]\n",
      "maxints:  [4060, 3952, 4095, 4095, 4095, 4095, 4095, 3085, 3568, 3441, 4095, 3545, 3690, 3779, 3446, 3620, 3662, 3085, 4095, 4095]\n",
      "mean(maxits):  3784.65\n",
      "mean:  0.000230734735047 error:  4.81369615165e-06\n",
      "###################\n",
      "step done\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5135562334882895, 4.2598945062769609, -6.7100157933944544)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.012766325492413078, 0.016164878883623057, 0.012835071681986943, 0.012385509459449384, 0.015881255339250444, 0.016335876582279418, 0.015345801282051319, 0.013508774859067429, 0.015953723566469957, 0.013692702125076117, 0.012480970319732165, 0.014384266666667145, 0.013418322397193538, 0.014986722185331924, 0.013308056108997324, 0.016637983021415614, 0.015339843337313308, 0.015798981657178857, 0.013324969595537084, 0.0013944949880961843]\n",
      "FWHMys:  [0.017564661627453071, 0.021776026468155518, 0.018618253347262526, 0.018269812484883186, 0.019369742847112514, 0.022435394721544044, 0.018459297707711875, 0.019553811217397077, 0.021152032531500553, 0.018676824484419208, 0.018648857371225569, 0.023403108101489045, 0.020049177134781271, 0.019054873321123322, 0.017369782095145003, 0.019850263443797811, 0.01908447553666015, 0.017051827612221737, 0.01851543543543549, 0.0020894452080323922]\n",
      "maxints:  [3662, 3005, 3587, 3807, 3024, 2607, 3043, 3315, 2780, 3812, 3605, 3085, 2973, 3221, 3615, 2795, 3090, 3329, 3638, 2509]\n",
      "mean(maxits):  3225.1\n",
      "mean:  0.000280665629443 error:  1.70305635956e-05\n",
      "-------------------------------------------\n",
      "Parameters:  (5.542031462350125, 4.3018880569571678, -6.6803047206721331)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.015184302846416475, 0.015152861556063879, 0.014937264360138425, 0.013159188344067374, 0.015922880681818352, 0.01519657169027866, 0.014710981281622626, 0.013322907593581235, 0.01840591397849467, 0.022100472248812952, 0.014240688378854838, 0.014991012867646702, 0.014645414779226762, 0.014215677952091887, 0.01308573515081024, 0.015441652661064342, 0.015084633992665353, 0.015471398509277812, 0.016165241180122969, 0.013789435548017259]\n",
      "FWHMys:  [0.015267370687041426, 0.014799232262507012, 0.014795833885087983, 0.016424658795347602, 0.014812145313576108, 0.01563197748887879, 0.01693604807692306, 0.015005460357824019, 0.016851040042047516, 0.017969465281398977, 0.015600468115095523, 0.014068430743644611, 0.01601123103347224, 0.015822176243971531, 0.015102012738885229, 0.015779264542468474, 0.016744305589090236, 0.01581033137866561, 0.015245771943392272, 0.015933784469710832]\n",
      "maxints:  [3507, 3868, 3596, 3882, 3488, 3441, 3835, 3774, 2668, 2452, 3765, 3695, 3648, 3549, 3905, 3310, 3343, 3287, 3432, 4023]\n",
      "mean(maxits):  3523.4\n",
      "mean:  0.000242483167511 error:  9.59022713548e-06\n",
      "###################\n",
      "step done\n",
      "-------------------------------------------\n",
      "Parameters:  (5.4974964184935402, 4.3179710792113415, -6.6982371044801399)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.012158139663567269, 0.013183261949979208, 0.012512942511235803, 0.012259565530916117, 0.013809019133532718, 0.01247880900064402, 0.012592007180133091, 0.011881762489827086, 0.011754655100250755, 0.012287894038810698, 0.011070688510018911, 0.013126801801801147, 0.011521670115375304, 0.011319756927370861, 0.011525716401971309, 0.012173735217527959, 0.016899787859069981, 0.012402696959094239, 0.010965780825991533, 0.0035598404847694276]\n",
      "FWHMys:  [0.018161940374493368, 0.020177898689367479, 0.019704483741554202, 0.01918265212114878, 0.019140378411268255, 0.019078689598050369, 0.02022153852080133, 0.019296908665105472, 0.020139423606409523, 0.020051550688852227, 0.018944572370121482, 0.022466687438638555, 0.019911146619124631, 0.019601674394641999, 0.019066530571174578, 0.022815680414425166, 0.017305935188581256, 0.019033674391684796, 0.019680473018739986, 0.00065093251107717354]\n",
      "maxints:  [4095, 3605, 3695, 4095, 3474, 4095, 4095, 4095, 3877, 3774, 4065, 3343, 4095, 4095, 4095, 3385, 4095, 3877, 4095, 3962]\n",
      "mean(maxits):  3900.35\n",
      "mean:  0.000259085600272 error:  1.38385018434e-05\n",
      "-------------------------------------------\n",
      "Parameters:  (5.542395902462891, 4.2860733873022703, -6.6856315657272285)\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.014446736839301089, 0.015954258145185918, 0.015161780128204683, 0.015053509301156343, 0.017373032836861757, 0.017458307759563141, 0.015646831671899619, 0.015059672948281477, 0.013829674093103428, 0.016333333825336194, 0.013311170275284834, 0.013575552481334796, 0.025299808488612463, 0.014008564902360465, 0.016424862079262414, 0.016763454632324315, 0.019062408897388305, 0.01513882850765258, 0.014725688888888477, 0.015342793138265698]\n",
      "FWHMys:  [0.015859300169143276, 0.017714182675971579, 0.015545425813008107, 0.017644544188054279, 0.016903634387351762, 0.016042257985258046, 0.015321674731541557, 0.015876381517728633, 0.015011893515947228, 0.017059311083693851, 0.015872196771095193, 0.015039154683403444, 0.018419018705036039, 0.016239584373232252, 0.015476670172332563, 0.016700369525898484, 0.017219602245670984, 0.015340692847822024, 0.016805305108877788, 0.015885941840722895]\n",
      "maxints:  [3549, 3020, 3455, 3118, 2874, 2996, 3577, 3441, 3896, 3071, 3915, 3962, 2284, 3601, 3212, 3066, 2687, 3404, 3465, 3282]\n",
      "mean(maxits):  3293.75\n",
      "mean:  0.000264503250231 error:  1.31252947545e-05\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5201283805345991, 4.2941148984293571, -6.6945977576312314)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.00029517493384023652, 0.013789914415058835, 0.014063729485329635, 0.017403454273549279, 0.014162158604211772, 0.012457677796136934, 0.013782999315803401, 0.015108823466951637, 0.012336582588230893, 0.012880692492125867, 0.013670633706913904, 0.014748312326622326, 0.01399631275627522, 0.013836331854290229, 0.013322447134139992, 0.01326860520183093, 0.01615139211965877, 0.015375621845631571, 0.013779058478386563, 0.012933044159189055]\n",
      "FWHMys:  [0.0013475389958567119, 0.016339247572815463, 0.016215640569395173, 0.015970605008417404, 0.016305651444315505, 0.017354252924191238, 0.016853579388020457, 0.016748152149604789, 0.015566363068596822, 0.016795029288702912, 0.016353506616002322, 0.017702823477139695, 0.016531133075640025, 0.017007796252927365, 0.016578433297774309, 0.015321040349195791, 0.017868229767685651, 0.017591433272495904, 0.018962321650124014, 0.016388760818998094]\n",
      "maxints:  [4095, 3559, 3699, 3080, 4095, 4095, 3760, 3530, 4095, 3704, 3648, 2996, 3615, 3460, 3746, 3891, 3123, 3034, 3259, 3901]\n",
      "mean(maxits):  3619.25\n",
      "mean:  0.000228383131379 error:  1.2553470226e-05\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5311478685207423, 4.2958930636732529, -6.6916478351457211)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.012772396972855482, 0.013236738157564432, 0.015610317580666777, 0.013672219343251246, 0.014410286618808499, 0.013242476045065565, 0.013912563124893751, 0.013991300443117805, 0.013665050700681025, 0.014204858124033048, 0.016023335482672874, 0.014541922124774231, 0.014251591065379543, 0.013257534049958331, 0.017079913945277969, 0.015690855614973298, 0.014238042469226109, 0.016414595148001254, 0.012692523011982093, 0.015774599290180991]\n",
      "FWHMys:  [0.014768770158960054, 0.016596039699185439, 0.017009962695411107, 0.015837039413999721, 0.015339691463520988, 0.015794024999999934, 0.016006943257725892, 0.016563850067579322, 0.016230113300631621, 0.015798129763553326, 0.016528631790744486, 0.014338081253227775, 0.015668912644567268, 0.015230939220931217, 0.017410275289014177, 0.014000155674039005, 0.015932482652975533, 0.017067167659284732, 0.015883240044806568, 0.016892256892875657]\n",
      "maxints:  [4095, 4095, 3155, 3920, 3634, 4023, 3474, 3643, 3596, 3629, 3202, 3868, 3723, 4037, 3020, 3620, 3718, 3113, 3929, 3324]\n",
      "mean(maxits):  3640.9\n",
      "mean:  0.000232427515082 error:  5.95032443342e-06\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5388133359053509, 4.2826694468609592, -6.6922103345477826)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.015205499008592138, 0.018337191606023318, 0.020499901291016531, 0.016546020203470846, 0.016515376732971099, 0.015674117666609533, 0.014385092628246277, 0.015255594828129659, 0.014515231208185764, 0.016245926731078608, 0.016814720658643978, 0.018476786430076331, 0.013503430573663877, 0.013088154346364345, 0.017888452331161098, 0.013997009159910423, 0.016259193490852031, 0.014017504651504531, 0.019666705131285767, 0.014963200000000398]\n",
      "FWHMys:  [0.017870342294900254, 0.017321529985868223, 0.01885107386363627, 0.016718216132797004, 0.015747646783303426, 0.016126577563389088, 0.01608132017829822, 0.01581726068712308, 0.015913324088709047, 0.016217759462759385, 0.015844539578262817, 0.018873385714285762, 0.015842100919342017, 0.014625462539540779, 0.015476765632603362, 0.014041477605478359, 0.016513428961748566, 0.015385068884922304, 0.015376842526355405, 0.014066452889245462]\n",
      "maxints:  [3338, 2954, 2302, 2973, 3240, 3287, 3610, 3390, 3699, 3188, 3198, 3104, 3830, 4095, 3127, 4095, 3141, 4095, 2795, 3816]\n",
      "mean(maxits):  3363.85\n",
      "mean:  0.000262482096577 error:  1.06726959786e-05\n",
      "###################\n",
      "step done\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5088803465924503, 4.3039345748003388, -6.7006140270497241)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.012575227564493474, 0.011959951601096197, 0.014826877192643195, 0.012423614687810502, 0.012760875522157988, 0.013822717927681971, 0.013442769342817407, 0.012514362094833142, 0.012304633466404979, 0.011599401910226348, 0.012597456549229946, 0.012555046092355582, 0.01408240630920865, 0.012351687779551757, 0.012371894090045021, 0.012602644943079166, 0.013079765991118375, 0.01298645783132546, 0.012092910637315768, 0.013397908740386377]\n",
      "FWHMys:  [0.017574636728930115, 0.018875732841909354, 0.017002461272653369, 0.018277573754213683, 0.017425982437789478, 0.017421271874480282, 0.017492451445062396, 0.018764930209652531, 0.018270654806491815, 0.019176441622938412, 0.018227224919093898, 0.017566020290507134, 0.017120238718236425, 0.017369009171368077, 0.017156650926207107, 0.018504905043162267, 0.017379865871047229, 0.017730803117890104, 0.017787878798480428, 0.01679295828413474]\n",
      "maxints:  [4095, 4084, 3460, 3938, 4095, 3760, 3816, 3596, 3957, 4095, 4095, 4095, 3676, 4060, 3929, 3554, 3849, 4032, 4095, 3887]\n",
      "mean(maxits):  3908.4\n",
      "mean:  0.000240986435651 error:  1.96352346793e-06\n",
      "-------------------------------------------\n",
      "Parameters:  (5.5163635939206754, 4.2986182928154939, -6.6985131039242383)\n",
      "c\n",
      "x\n",
      "y\n",
      "z\n",
      "FWHMxs:  [0.013146347926439095, 0.014426835036688068, 0.012097067029081643, 0.013539831102332034, 0.014465276423965445, 0.013679335204578091, 0.013802690227018299, 0.013170254269088133, 0.012847144680886213, 0.013335054703420557, 0.014028324580994234, 0.013744158585562882, 0.012990646988795707, 0.012752239716801395, 0.012195527953630769, 0.012346295019997289, 0.014503236987557955, 0.012561086782014286, 0.013239072272941854, 0.013579682239180713]\n",
      "FWHMys:  [0.018247816849816689, 0.019423102264449654, 0.015