{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*This notebook was created by [Sergey Tomin](http://www.xfel.eu/organization/staff/tomin_sergey/) for Workshop: [Designing future X-ray FELs](http://www.xrayfels.co.uk/). Source and license info is on [GitHub](https://github.com/iagapov/ocelot/tree/dev/docs). August 2016.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An Introduction to Ocelot\n",
    "\n",
    "Ocelot is a multiphysics simulation toolkit designed for studying FEL and storage ring based light sources. Ocelot is written in Python. Its central concept is the writing of python's scripts for simulations with the usage of Ocelot's modules and functions and the standard Python libraries. \n",
    "\n",
    "Ocelot includes following main modules:\n",
    "* **Charged particle beam dynamics module (CPBD)**\n",
    "    - optics\n",
    "    - tracking\n",
    "    - matching\n",
    "    - collective effects (description can be found [here](http://vrws.de/ipac2017/papers/wepab031.pdf) )\n",
    "        - Space Charge (true 3D Laplace solver) \n",
    "        - CSR (Coherent Synchrotron Radiation) (1D model with arbitrary number of dipoles) (under development).\n",
    "        - Wakefields (Taylor expansion up to second order for arbitrary geometry).\n",
    "    - MOGA (Multi Objective Genetics Algorithm). (under development but we have already applied it for a storage ring [application](http://accelconf.web.cern.ch/AccelConf/ipac2016/papers/thpmb034.pdf))\n",
    "* **Native module for spontaneous radiation calculation**\n",
    "* **FEL calculations: interface to GENESIS and pre/post-processing**\n",
    "* **Modules for online beam control and online optimization of accelerator performances.** [Work1](http://accelconf.web.cern.ch/accelconf/IPAC2014/papers/mopro086.pdf), [work2](https://jacowfs.jlab.org/conf/y15/ipac15/prepress/TUPWA037.PDF), [work3](http://accelconf.web.cern.ch/AccelConf/ipac2016/papers/wepoy036.pdf), [work4](https://arxiv.org/pdf/1704.02335.pdf).\n",
    "\n",
    "Ocelot extensively  uses Python's [NumPy (Numerical Python)](http://numpy.org) and [SciPy (Scientific Python)](http://scipy.org) libraries, which enable efficient in-core numerical and scientific computation within Python and give you access to various mathematical and optimization techniques and algorithms. To produce high quality figures Python's [matplotlib](http://matplotlib.org/index.html) library is used.\n",
    "\n",
    "It is an open source project and it is being developed by physicists from  [The European XFEL](http://www.xfel.eu/), [DESY](http://www.desy.de/) (Germany), [NRC Kurchatov Institute](http://www.nrcki.ru/) (Russia).\n",
    "\n",
    "We still have no documentation but you can find a lot of examples in ocelot/demos/ \n",
    "\n",
    "\n",
    "## Ocelot user profile\n",
    "\n",
    "Ocelot is designed for researchers who want to have the flexibility that is given by high-level languages such as Matlab, Python (with Numpy and SciPy) or Mathematica.\n",
    "However if someone needs a GUI  it can be developed using Python's libraries like a [PyQtGraph](http://www.pyqtgraph.org/) or [PyQt](http://pyqt.sourceforge.net/Docs/PyQt4/). \n",
    "\n",
    "For example, you can see GUI for SASE optimization (uncomment and run next block)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "#Image(filename='gui_example.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorials\n",
    "* Preliminaries: Setup & introduction\n",
    "* Beam dynamics\n",
    "* [Tutorial N1. Linear optics.](#tutorial1). [Web version](http://nbviewer.jupyter.org/github/iagapov/ocelot/blob/dev/demos/ipython_tutorials/1_introduction.ipynb).\n",
    "    - Linear optics. Double Bend Achromat (DBA). Simple example of usage OCELOT functions to get periodic solution for a storage ring cell. \n",
    "* [Tutorial N2. Tracking.](2_tracking.ipynb). [Web version](http://nbviewer.jupyter.org/github/iagapov/ocelot/blob/dev/demos/ipython_tutorials/2_tracking.ipynb).\n",
    "    - Linear optics of the European XFEL Injector. \n",
    "    - Tracking. First and second order. \n",
    "* [Tutorial N3. Space Charge.](3_space_charge.ipynb). [Web version](http://nbviewer.jupyter.org/github/iagapov/ocelot/blob/dev/demos/ipython_tutorials/3_space_charge.ipynb).\n",
    "    - Tracking through RF cavities with SC effects and RF focusing.\n",
    "* [Tutorial N4. Wakefields.](4_wake.ipynb). [Web version](http://nbviewer.jupyter.org/github/iagapov/ocelot/blob/dev/demos/ipython_tutorials/4_wake.ipynb).\n",
    "    - Tracking through corrugated structure (energy chirper) with Wakefields\n",
    "* [Tutorial N5. CSR.](5_CSR.ipynb). [Web version](http://nbviewer.jupyter.org/github/iagapov/ocelot/blob/dev/demos/ipython_tutorials/5_CSR.ipynb).\n",
    "    - Tracking trough bunch compressor with CSR effect.\n",
    "* [Tutorial N6. RF Coupler Kick.](6_coupler_kick.ipynb). [Web version](http://nbviewer.jupyter.org/github/iagapov/ocelot/blob/dev/demos/ipython_tutorials/6_coupler_kick.ipynb).\n",
    "    - Coupler Kick. Example of RF coupler kick influence on trajjectory and optics.\n",
    "* [Tutorial N7. Lattice design.](7_lattice_design.ipynb). [Web version](http://nbviewer.jupyter.org/github/iagapov/ocelot/blob/dev/demos/ipython_tutorials/7_lattice_design.ipynb).\n",
    "    - Lattice design, twiss matching, twiss backtracking "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminaries\n",
    "\n",
    "The tutorial includes 7 simple examples dediacted to beam dynamics and optics. However, you should have a basic understanding of Computer Programming terminologies. A basic understanding of Python language is a plus.\n",
    "\n",
    "##### This tutorial requires the following packages:\n",
    "\n",
    "- Python 3.4-3.6 (python 2.7 can work as well)\n",
    "- `numpy` version 1.8 or later: http://www.numpy.org/\n",
    "- `scipy` version 0.15 or later: http://www.scipy.org/\n",
    "- `matplotlib` version 1.5 or later: http://matplotlib.org/\n",
    "- `ipython` version 2.4 or later, with notebook support: http://ipython.org\n",
    "\n",
    "**Optional** to speed up python \n",
    "- numexpr (version 2.6.1)\n",
    "- pyfftw (version 0.10)\n",
    "\n",
    "The easiest way to get these is to download and install the (very large) [Anaconda software distribution](https://www.continuum.io/).\n",
    "\n",
    "Alternatively, you can download and install [miniconda](http://conda.pydata.org/miniconda.html).\n",
    "The following command will install all required packages:\n",
    "```\n",
    "$ conda install numpy scipy matplotlib ipython-notebook\n",
    "```\n",
    "\n",
    "##### Ocelot installation\n",
    "1. you have to download from GitHub [zip file](https://github.com/iagapov/ocelot/archive/master.zip). \n",
    "2. Unzip ocelot-master.zip to your working folder **/your_working_dir/**. \n",
    "3. Rename folder **../your_working_dir/ocelot-master** to **/your_working_dir/ocelot**. \n",
    "4. Add **../your_working_dir/** to PYTHONPATH\n",
    "    - **Windows 7:** go to Control Panel -> System and Security -> System -> Advance System Settings -> Environment Variables.\n",
    "    and in User variables add **/your_working_dir/** to PYTHONPATH. If variable PYTHONPATH does not exist, create it\n",
    "    \n",
    "    Variable name: PYTHONPATH\n",
    "    \n",
    "    Variable value: ../your_working_dir/\n",
    "    - Linux: \n",
    "    ```\n",
    "    $ export PYTHONPATH=/your_working_dir/:$PYTHONPATH\n",
    "    ```\n",
    "    \n",
    "#### To launch \"ipython notebook\" or \"jupyter notebook\"\n",
    "in command line run following commands:\n",
    "\n",
    "```\n",
    "$ ipython notebook\n",
    "```\n",
    "\n",
    "or\n",
    "```\n",
    "$ ipython notebook --notebook-dir=\"path_to_your_directory\"\n",
    "```\n",
    "\n",
    "or\n",
    "```\n",
    "$ jupyter notebook --notebook-dir=\"path_to_your_directory\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking your installation\n",
    "\n",
    "You can run the following code to check the versions of the packages on your system:\n",
    "\n",
    "(in IPython notebook, press `shift` and `return` together to execute the contents of a cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IPython: 5.1.0\n",
      "numpy: 1.11.2\n",
      "scipy: 0.18.1\n",
      "matplotlib: 1.5.1\n",
      "initializing ocelot...\n",
      "ocelot: 17.05.rc\n"
     ]
    }
   ],
   "source": [
    "import IPython\n",
    "print('IPython:', IPython.__version__)\n",
    "\n",
    "import numpy\n",
    "print('numpy:', numpy.__version__)\n",
    "\n",
    "import scipy\n",
    "print('scipy:', scipy.__version__)\n",
    "\n",
    "import matplotlib\n",
    "print('matplotlib:', matplotlib.__version__)\n",
    "\n",
    "import ocelot\n",
    "print('ocelot:', ocelot.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"tutorial1\"></a>\n",
    "## Tutorial N1. Double Bend Achromat.\n",
    "\n",
    "We designed a simple lattice to demonstrate the basic concepts and syntax of the optics functions calculation. \n",
    "Also, we chose DBA to demonstrate the periodic solution for the optical functions calculation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initializing ocelot...\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "# the output of plotting commands is displayed inline within frontends, \n",
    "# directly below the code cell that produced it\n",
    "%matplotlib inline\n",
    "\n",
    "# import from Ocelot main modules and functions\n",
    "from ocelot import *\n",
    "\n",
    "# import from Ocelot graphical modules\n",
    "from ocelot.gui.accelerator import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating lattice\n",
    "Ocelot has following elements: Drift, Quadrupole, Sextupole, Octupole, Bend, SBend, RBend, Edge, Multipole, Hcor, Vcor, Solenoid, Cavity, Monitor, Marker, Undulator. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# defining of the drifts\n",
    "D1 = Drift(l=2.)\n",
    "D2 = Drift(l=0.6)\n",
    "D3 = Drift(l=0.3)\n",
    "D4 = Drift(l=0.7)\n",
    "D5 = Drift(l=0.9)\n",
    "D6 = Drift(l=0.2)\n",
    "\n",
    "# defining of the quads\n",
    "Q1 = Quadrupole(l=0.4, k1=-1.3)\n",
    "Q2 = Quadrupole(l=0.8, k1=1.4)\n",
    "Q3 = Quadrupole(l=0.4, k1=-1.7)\n",
    "Q4 = Quadrupole(l=0.5, k1=1.3)\n",
    "\n",
    "# defining of the bending magnet\n",
    "B = Bend(l=2.7, k1=-.06, angle=2*pi/16., e1=pi/16., e2=pi/16.)\n",
    "\n",
    "# defining of the sextupoles\n",
    "SF = Sextupole(l=0.01, k2=1.5) #random value\n",
    "SD = Sextupole(l=0.01, k2=-1.5) #random value\n",
    "\n",
    "# cell creating\n",
    "cell = (D1, Q1, D2, Q2, D3, Q3, D4, B, D5, SD, D5, SF, D6, Q4, D6, SF, D5, SD, D5, B, D4, Q3, D3, Q2, D2, Q1, D1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*hint: to see a simple description of the function put cursor inside () and press **Shift-Tab** or you can type sign **?** before function. To extend dialog window press **+** *\n",
    "\n",
    "The cell is a list of the simple objects which contain a physical information of lattice elements such as length, strength, voltage and so on. In order to create a transport map for every element and bind it with lattice object we have to create new Ocelot object - MagneticLattice() which makes these things automatically. \n",
    "\n",
    "MagneticLattice(sequence, start=None, stop=None, method=MethodTM()):     \n",
    "* sequence - list of the elements,\n",
    "\n",
    "other paramenters we will consider in tutorial N2. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of the cell:  20.34 m\n"
     ]
    }
   ],
   "source": [
    "lat = MagneticLattice(cell)\n",
    "\n",
    "# to see total lenth of the lattice \n",
    "print(\"length of the cell: \", lat.totalLen, \"m\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optical function calculation\n",
    "Uses: \n",
    "* twiss() function and,\n",
    "* Twiss() object contains twiss parameters and other information at one certain position (s) of lattice\n",
    "\n",
    "To calculate twiss parameters you have to run **twiss(lattice, tws0=None, nPoints=None)** function. If you want to get a periodic solution leave tws0 by default. \n",
    "\n",
    "You can change the number of points over the cell, If nPoints=None, then twiss parameters are calculated at the end of each element.\n",
    "twiss() function returns list of Twiss() objects.\n",
    "\n",
    "##### You will see the Twiss object contains more information than just twiss parameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "emit_x  = 0.0\n",
      "emit_y  = 0.0\n",
      "beta_x  = 0.527161369596\n",
      "beta_y  = 0.51659778953\n",
      "alpha_x = 1.21756687446e-15\n",
      "alpha_y = -1.83246512915e-15\n",
      "gamma_x = 1.89695235211\n",
      "gamma_y = 1.93574192586\n",
      "Dx      = 0.166739277081\n",
      "Dy      = 0.0\n",
      "Dxp     = -1.91513471748e-15\n",
      "Dyp     = 0.0\n",
      "mux     = 0.0\n",
      "muy     = 0.0\n",
      "nu_x    = 0.0\n",
      "nu_y    = 0.0\n",
      "E       = 0.0\n",
      "s        = 0.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tws=twiss(lat)\n",
    "\n",
    "# to see twiss paraments at the begining of the cell, uncomment next line\n",
    "# print(tws[0])\n",
    "\n",
    "# to see twiss paraments at the end of the cell, uncomment next line\n",
    "print(tws[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkYAAAGHCAYAAABVgZ3gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nihdVjBRFURRFUbz8P4+UBaTtQkIRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa69f550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot optical functions.\n",
    "plot_opt_func(lat, tws, top_plot = [\"Dx\", \"Dy\"], legend=False, font_size=10)\n",
    "plt.show()\n",
    "\n",
    "# you also can use standard matplotlib functions for plotting\n",
    "#s = [tw.s for tw in tws]\n",
    "#bx = [tw.beta_x for tw in tws]\n",
    "#plt.plot(s, bx)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkMAAAGHCAYAAACzqFakAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nvco4enhTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4e1a9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# you can play with quadrupole strength and try to make achromat\n",
    "Q4.k1 = 1.18\n",
    "\n",
    "# to make achromat uncomment next line\n",
    "# Q4.k1 =  1.18543769836\n",
    "# To use matching function, please see ocelot/demos/ebeam/dba.py \n",
    "\n",
    "# updating transfer maps after changing element parameters. \n",
    "lat.update_transfer_maps()\n",
    "\n",
    "# recalculate twiss parameters \n",
    "tws=twiss(lat, nPoints=1000)\n",
    "\n",
    "plot_opt_func(lat, tws, legend=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": 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": "3.5.2"
  },
  "name": ""
 },
 "nbformat": 4,
 "nbformat_minor": 0
}