NotebookGUI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "%load_ext autoreload\n",
    "\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: Qt5Agg\n"
     ]
    }
   ],
   "source": [
    "%autoreload\n",
    "%matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Load old configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "for i in range(-1,0):\n",
    "    config = io.load(i)\n",
    "    print (i ,config['comment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best configuration of parabolic mirror (good automatic optimization)\n"
     ]
    }
   ],
   "source": [
    "import DataManager as dm\n",
    "io = dm.IO('Data/')\n",
    "config = io.load(-2)\n",
    "print (config['comment'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## FocusCamera"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from Devices.FocusCamera import FocusCamera\n",
    "\n",
    "try:\n",
    "    para_config = config['FocusCamera']\n",
    "except:\n",
    "    para_config = {'ExposureTimeAbs': 10000,\n",
    "                 'min_intensity': 2000,\n",
    "                  'measure_full' : True}\n",
    "    \n",
    "focus = FocusCamera(para_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "meas = focus.measure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "focus.plot_measure(meas)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## ParabolicMirror"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from Devices.ParabolicMirror import ParabolicMirror\n",
    "\n",
    "\n",
    "try:\n",
    "    para_config = config['ParabolicMirror']\n",
    "except:\n",
    "    para_config = {'pos_Pay': 3,\n",
    "                 'pos_Paz': 3}\n",
    "    \n",
    "para = ParabolicMirror(para_config, home = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Periscope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connnection with motors sucessful!\n"
     ]
    }
   ],
   "source": [
    "from Devices.Periscope import Periscope\n",
    "\n",
    "\n",
    "try:\n",
    "    per_config = config['Periscope']\n",
    "except:\n",
    "    per_config = {'pos_l_x': 0.1,\n",
    "              'pos_l_y': 0.1,\n",
    "              'pos_l_z': 0.1, \n",
    "              'pos_g_origin_x': -25./2,\n",
    "              'pos_g_origin_y': 25./2,\n",
    "              'pos_g_origin_z': 25./2,\n",
    "              'deltaz_Mz': 0,\n",
    "              'deltaz_Cz': 0,\n",
    "              'sync_camera': True}\n",
    "    \n",
    "per = Periscope(per_config, home = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## BladePositioning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from Devices.BladePositioning import BladePositioning\n",
    "\n",
    "try:\n",
    "    blade_config = config['BladePositioning']\n",
    "except:\n",
    "    blade_config = {'pos_R':344.25, 'pos_L':-2.8}\n",
    "blade = BladePositioning(blade_config, init = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "blade.get_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Interferometer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "available cameras:\n",
      "<DeviceInfo Basler scA1400-17gm (21145133)>\n",
      "<DeviceInfo Basler avA2300-25gmDSY (21547524)>\n",
      "camera found =) Be happy\n"
     ]
    }
   ],
   "source": [
    "from Devices.Interferometer import Interferometer\n",
    "\n",
    "try:\n",
    "    inter_config = config['Interferometer']\n",
    "except:\n",
    "    inter_config = {'xmax':6.0,\n",
    "                    'xmin':2.6,\n",
    "                    'ymin':3, \n",
    "                    'ymax':12,\n",
    "                    'ExposureTimeAbs':35}\n",
    "inter = Interferometer(inter_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#measurement = inter.measure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#inter.plot_measure(measurement)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## DelayGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from Devices.DelayGenerator import DelayGenerator\n",
    "\n",
    "try:\n",
    "    delay_config = config['DelayGenerator']\n",
    "except:\n",
    "    delay_config = {'jet_triggered': True,\n",
    "                    'mode': 'single shot',\n",
    "                    'rate': 1.,\n",
    "                    't_sleep': 0.3}\n",
    "\n",
    "delay = DelayGenerator(delay_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time': '2017-03-29_10-39-30.348962'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delay.measure()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## DRS4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from Devices.DRS4 import DRS4\n",
    "\n",
    "try:\n",
    "    drs_config = config['DRS4']\n",
    "except:\n",
    "    drs_config = {'trigger_level_CH0': 100,\n",
    "                  'trigger_mode':2,\n",
    "                  'delay': 0.0}\n",
    "drs = DRS4(drs_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Laser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.3315204836\n"
     ]
    }
   ],
   "source": [
    "from Devices.Laser import Laser\n",
    "\n",
    "try:\n",
    "    laser_config = config['Laser']\n",
    "except:\n",
    "    laser_config = {'max_angle': -22.9,\n",
    "                    'attenuation': 100,\n",
    "                    'comp_lin': 37.796,\n",
    "                    'channel': 1}\n",
    "\n",
    "laser = Laser(laser_config, drs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## FaradayCup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from Devices.FaradayCup import FaradayCup\n",
    "\n",
    "try:\n",
    "    fara_config = config['FaradayCup']\n",
    "except:\n",
    "    fara_config = {'channel':2 }\n",
    "\n",
    "fara = FaradayCup(fara_config, drs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## test_devices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from Devices.test_devices import test_device1, test_device2\n",
    "\n",
    "td1 = test_device1(2)\n",
    "td2 = test_device2(4.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# IO und GUI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: Qt5Agg\n"
     ]
    }
   ],
   "source": [
    "import DataManager as dm\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "io = dm.IO('Data/')\n",
    "\n",
    "%matplotlib\n",
    "\n",
    "import GUI\n",
    "%autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "devices = [delay,laser, inter, drs, fara, per, blade]  #use this to run the LWFA\n",
    "#devices = [inter, per]\n",
    "#devices = [per, para, laser, focus]              #use this to do focus optimization\n",
    "#devices = [td1, td2]                             #Use this do debug the gui and data menager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9687c609909a4a75862ad3471c1722d2"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "550ad84aa4a6422caeedb061d82c5ece"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a74d3f227f414c22a418441cc8eff8e2"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b99db782ef5d4d038250a93f49c1c001"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9ddf7ac6d426498c9817a24edc3cf1a4"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3032d69feffc464184f033ce44e0760f"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2bfb23e80c594eae8c297a15f2f7a2f5"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6e5c019ba24743e6978fae40eff0e151"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "66c4f1b4d0a243eaac3b87dede72a7c8"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5415b8fd31c546a6ad19f5dd596024c8"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c75838b0f39d4c3183c8d37dbed4c19a"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb4894a731c5486cb5138f9fdf771bc8"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ddf9af4d8dfa4144a7214b2a04470b88"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b67122affb1d4434a5ccc82c8d328c9c"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7086fe7ca4c04da5b5f5f5dac9ee9091"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ion()\n",
    "GUI.main(devices, io, parallel = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "dm.set_config(devices,io.load(29, elog = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "list_of_measurements = io.search(kind = 'measure')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "io.search(output = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%autoreload\n",
    "para.optimize(focus, laser, per, 0.006, output = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "class test:\n",
    "    def __init__(self, du):\n",
    "        self.du = du\n",
    "        \n",
    "    def give(self):\n",
    "        print (self.du)\n",
    "        return self.du\n",
    "    def kase(si):\n",
    "        print (si.du)\n",
    "        \n",
    "    def __str__(self):\n",
    "        return 'hege: '+str(self.du)\n",
    "    \n",
    "    def __add__(self, right):\n",
    "        return test(self.du + right.du)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "t1 = test(2)\n",
    "t2 = test(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "t1.give()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "t1.du"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "t1.kase()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "t1.du = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "t1.give()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "print (t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "print (t1 + t2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  },
  "widgets": {
   "state": {
    "0065df34925c4ad18b5e33837ed8762e": {
     "views": [
      {
       "cell_index": 34
      }
     ]
    },
    "0c32490fdf0e49209a83fab858376cf1": {
     "views": [
      {
       "cell_index": 34
      }
     ]
    },
    "0f446a29d17d40c9af519c71acb6f891": {
     "views": [
      {
       "cell_index": 34
      }
     ]
    },
    "151870f89a704faf89bc8b2cbbe5611c": {
     "views": [
      {
       "cell_index": 34
      }
     ]
    },
    "16f9122ce08d48c39fcf8ee0df28a79c": {
     "views": [
      {
       "cell_index": 34
      }
     ]
    },
    "1ab5a59ccf9f4fef839a79fd4b7a65b0": {
     "views": [
      {
       "cell_index": 34
      }
     ]
    },
    "2fddeec661ed482995d338a7c5312139": {
     "views": [
      {
       "cell_index": 34
      }
     ]
    },
    "3a819ca20ac949e3be372d4c4a9a9487": {
     "views": [
      {
       "cell_index": 34
      }
     ]
    },
    "448db5ef3bc0457190f5f33f19b5f06c": {
     "views": [
      {
       "cell_index": 34
      }
     ]
    },
    "4f4f1ff5355c42a886b75d3a6c044d2c": {
     "views": [
      {
       "cell_index": 34
      }
     ]
    },
    "54a5678c069d48069ab3dfadf649b6dd": {
     "views": [
      {
       "cell_index": 34