1_introduction.ipynb

{
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "name": "",
  "signature": "sha256:c8d2d8f915aad33d24944b2847a7b4fffcef70247e6363027ee7d89dec9cc5cc"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "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",
      "* **Charge particle beam dynamics module (CPBD)**\n",
      "    - optics\n",
      "    - tracking\n",
      "    - matching\n",
      "    - collective effects\n",
      "        - Space Charge (true 3D Laplace solver) \n",
      "        - CSR (Coherent Synchrotron Radiation) (1D model with arbitrary number of dipole) (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 applyed it for a storage ring [aplication](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).\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",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "#Image(filename='gui_example.png') "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Outline\n",
      "* Preliminaries: Setup & introduction\n",
      "* #### Beam dynamics\n",
      "* [Tutorial N1. Linear optics.](#tutorial1). [Web version](http://nbviewer.jupyter.org/github/iagapov/ocelot/blob/master/test/workshop/1_introduction.ipynb).\n",
      "    - Linear optics. DBA.\n",
      "* [Tutorial N2. Tracking.](2_tracking.ipynb). [Web version](http://nbviewer.jupyter.org/github/iagapov/ocelot/blob/master/test/workshop/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/master/test/workshop/3_space_charge.ipynb).\n",
      "    - Tracking with SC effects.\n",
      "* [Tutorial N4. Wakefields.](4_wake.ipynb). [Web version](http://nbviewer.jupyter.org/github/iagapov/ocelot/blob/master/test/workshop/4_wake.ipynb).\n",
      "    - Tracking with Wakefields\n",
      "* #### FEL calculation\n",
      "* [Tutorial N5: Genesis preprocessor](5_Genesis_preprocessor.ipynb). [Web version](http://nbviewer.jupyter.org/github/iagapov/ocelot/blob/master/test/workshop/5_Genesis_preprocessor.ipynb).\n",
      "* [Tutorial N6. Genesis postprocessor](6_Genesis_postprocessor.ipynb). [Web version](http://nbviewer.jupyter.org/github/iagapov/ocelot/blob/master/test/workshop/6_Genesis_postprocessor.ipynb).\n",
      "\n",
      "\n",
      "All IPython (jupyter) notebooks (*.ipynb) have analogues in the form of python scripts (*.py). \n",
      "\n",
      "All these notebooks as well as additional files (beam distribution, wakes, ...) you can download [here](https://github.com/sergey-tomin/workshop/archive/master.zip).\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Preliminaries\n",
      "\n",
      "The tutorial includes 4 simple examples dediacted to beam dynamics. 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 version 2.7 or 3.4-3.5\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",
      "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",
     "collapsed": false,
     "input": [
      "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__)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "('IPython:', '2.4.1')\n",
        "('numpy:', '1.9.2')"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "('scipy:', '0.15.1')\n",
        "('matplotlib:', '1.4.3')"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "initializing ocelot..."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "('ocelot:', '16.8.rc')\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "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",
     "collapsed": false,
     "input": [
      "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 *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "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",
     "collapsed": true,
     "input": [
      "# 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)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "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",
     "collapsed": false,
     "input": [
      "lat = MagneticLattice(cell)\n",
      "\n",
      "# to see total lenth of the lattice \n",
      "print(\"length of the cell: \", lat.totalLen, \"m\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "length of the cell:  20.34 m\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "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",
     "collapsed": false,
     "input": [
      "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[-1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "emit_x  = 0.0\n",
        "emit_y  = 0.0\n",
        "beta_x  = 0.527161369596\n",
        "beta_y  = 0.51659778953\n",
        "alpha_x = -6.66133814775e-15\n",
        "alpha_y = 8.43769498715e-15\n",
        "gamma_x = 1.89695235211\n",
        "gamma_y = 1.93574192586\n",
        "Dx      = 0.166739277081\n",
        "Dy      = 0.0\n",
        "Dxp     = -1.11022302463e-16\n",
        "Dyp     = 0.0\n",
        "mux     = 7.10073199212\n",
        "muy     = 5.66988435162\n",
        "nu_x    = 1.13011659612\n",
        "nu_y    = 0.902390121319\n",
        "E       = 0.0\n",
        "s        = 20.34\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(tws)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "32"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 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()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAaUAAAEZCAYAAAA32jqgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xdf110d0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 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()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAaIAAAEZCAYAAADVBiHZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xe48a190>"
       ]
      }
     ],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}