PS_Extraction_Fringe_Fields.ipynb

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    "# imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import Markdown as md\n",
    "from IPython.core.display import display, HTML, Markdown\n",
    "from cpymad.madx import Madx\n",
    "from tqdm.notebook import tqdm\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
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   "id": "backed-gregory",
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   "source": [
    "%%capture\n",
    "tqdm().pandas()"
   ]
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  {
   "cell_type": "markdown",
   "id": "pursuant-january",
   "metadata": {},
   "source": [
    "### Function to extracting a Matrix from MADX\n",
    "https://gitlab.cern.ch/abt-optics-and-code-repository/simulation-codes/cpymad_examples/-/blob/master/MATRIX_extraction.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "mobile-bacteria",
   "metadata": {
    "ExecuteTime": {
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   "source": [
    "def get_matrix_from_twiss(twiss):\n",
    "    elem = twiss.iloc[-1] # iloc[-1] takes last row of the data frame\n",
    "    matExtracted = np.matrix([\n",
    "        elem[['re11', 're12', 're13', 're14', 're15', 're16']].values.astype(float),\n",
    "        elem[['re21', 're22', 're23', 're24', 're25', 're26']].values.astype(float),\n",
    "        elem[['re31', 're32', 're33', 're34', 're35', 're36']].values.astype(float),\n",
    "        elem[['re41', 're42', 're43', 're44', 're45', 're46']].values.astype(float),\n",
    "        elem[['re51', 're52', 're53', 're54', 're55', 're56']].values.astype(float),\n",
    "        elem[['re61', 're62', 're63', 're64', 're65', 're66']].values.astype(float),\n",
    "    ])\n",
    "    return matExtracted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "retained-albania",
   "metadata": {},
   "source": [
    "## The model D16STRAY and F16SHIM deflects the beam strongly back towards the inside of the ring  with quad K1 and sextupole K2 components.\n",
    "\n",
    "#### They are placed as seen in: https://gitlab.cern.ch/acc-models/acc-models-tls/-/blob/2021/ps_extraction/tt2tt10_lhc_q20/stitched/get_initial_conditions_lhc_q20.madx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "happy-novelty",
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     "text": [
      "\n",
      "  ++++++++++++++++++++++++++++++++++++++++++++\n",
      "  +     MAD-X 5.06.01  (64 bit, Linux)       +\n",
      "  + Support: mad@cern.ch, http://cern.ch/mad +\n",
      "  + Release   date: 2020.09.01               +\n",
      "  + Execution date: 2021.07.26 14:37:15      +\n",
      "  ++++++++++++++++++++++++++++++++++++++++++++\n"
     ]
    }
   ],
   "source": [
    "madx = Madx(command_log=\"commandlog\") #Writes the errors in this file once madx has quit\n",
    "madx.option(verbose=False, debug=False, echo=True, warn=False, twiss_print=False)\n",
    "\n",
    "madx.beam(particle='proton', pc=26, exn=3.5e-6,eyn=3.5E-6)\n",
    "madx.input('BRHO:= BEAM->PC * 3.3356;')\n",
    "\n",
    "len_ft16 = 7.701173928\n",
    "\n",
    "madx.command.sequence.clone('FT16', refer=\"entry\", l=len_ft16)\n",
    "madx.command.endsequence()\n",
    "\n",
    "madx.command.rbend.clone('D16STRAY', L=2.20480, ANGLE=0.0, K1=-.047, K2=0.33)\n",
    "madx.command.rbend.clone('F16SHIM', L=2.19624,ANGLE=0.021,K1=0.0)\n",
    "\n",
    "madx.command.seqedit(sequence='FT16')\n",
    "madx.command.install(element='D16STRAY_1', class_='D16STRAY', at='2.700072902') # Random positions, remeber starts from middle\n",
    "madx.command.install(element='F16SHIM_1', class_='F16SHIM', at='4.904893572')\n",
    "madx.command.endedit()\n",
    "\n",
    "madx.use(sequence=\"FT16\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "honey-tuner",
   "metadata": {},
   "source": [
    "#### Initial conditions from MADX stitched model of PS extraction to TT10 LHC Q20 beam\n",
    "https://gitlab.cern.ch/acc-models/acc-models-tls/-/blob/2021/ps_extraction/tt2tt10_lhc_q20/stitched/get_initial_conditions_lhc_q20.madx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "extended-baltimore",
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     "start_time": "2021-07-26T12:37:15.549639Z"
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   },
   "outputs": [],
   "source": [
    "betx0 = 2.5103613982e+01 ;\n",
    "bety0 = 8.3789531557e+00 ;\n",
    "alfx0 = -2.3322645191e+00 ;\n",
    "alfy0 = 9.7954156718e-01 ;\n",
    "dx0 = 3.0982680415e+00 ;\n",
    "dy0 = 0.0000000000e+00 ;\n",
    "dpx0 = 2.6262543044e-01 ;\n",
    "dpy0 = 0.0000000000e+00 ;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "spanish-tower",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-22T07:20:01.322779Z",
     "start_time": "2021-07-22T07:20:01.317291Z"
    }
   },
   "source": [
    "#### Initial conditions from https://codimd.web.cern.ch/05LpRgDIRIKUpRnTWTfbEA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "extended-motel",
   "metadata": {
    "ExecuteTime": {
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   "source": [
    "LHCx = 1.078422e-01\n",
    "LHCxp = 3.131043e-02\n",
    "TOFx = 1.173072e-01\n",
    "TOFxp = 2.975372e-02\n",
    "ADx = 1.101851e-01\n",
    "ADxp = 2.973021e-02\n",
    "\n",
    "# Choose beam type here\n",
    "initialValues = [LHCx,LHCxp,0,0,0,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unauthorized-apache",
   "metadata": {},
   "source": [
    "## Reference RXX Matrix coefficient calculation\n",
    "\n",
    "- I'm varying one parameter at a time (x, xp, y, yp, T, PT) with the other coordinates set to zero such as:\n",
    "\n",
    "\\begin{equation}\n",
    "\\left(\\begin{array}{c}\n",
    "{\\pmb{x^{New}}}  \\\\\n",
    "{x'^{New}}  \\\\\n",
    "{y^{New}}  \\\\\n",
    "{y'^{New}}  \\\\\n",
    "{t^{New}}  \\\\\n",
    "{(\\frac{\\delta p}{p})^{New}}  \\\\\n",
    "\\end{array}\\right) =\n",
    "\\left(\\begin{array}{cccccc}\n",
    "R_{11} & R_{12} & R_{13} & R_{14} & R_{15} & R_{16} \\\\\n",
    "R_{21} & R_{22} & R_{23} & R_{24} & R_{25} & R_{26} \\\\\n",
    "R_{31} & R_{32} & R_{33} & R_{34} & R_{35} & R_{36} \\\\\n",
    "R_{41}  & R_{42} & R_{43} & R_{44} & R_{45} & R_{46} \\\\\n",
    "R_{51} & R_{52} & R_{53} & R_{54} & R_{55} & R_{56} \\\\\n",
    "R_{61} & R_{62} & R_{63} & R_{64} & R_{65} & R_{66} \\\\\n",
    "\\end{array}\\right)\n",
    "\\left(\\begin{array}{c}\n",
    "{\\pmb{x^{Old}}}  \\\\\n",
    "{0}  \\\\\n",
    "{0}  \\\\\n",
    "{0}  \\\\\n",
    "{0}  \\\\\n",
    "{0}  \\\\\n",
    "\\end{array}\\right)\n",
    "\\end{equation}\n",
    "\n",
    "\n",
    " with $x^{Old}$ = [-0.01  -0.005  0.     0.005  0.01 ] for example\n",
    "- Plotting $x^{New}$ as a function of $x^{Old}$\n",
    "- then $x^{New}$ as a function of $x'^{Old}$\n",
    "- ...\n",
    "- finally $(\\frac{\\delta p}{p})^{New}$ as a function of $(\\frac{\\delta p}{p})^{Old}$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accompanied-designation",
   "metadata": {},
   "source": [
    "#### Settings for the computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "extensive-wesley",
   "metadata": {
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   "outputs": [],
   "source": [
    "var_names = (\"x\", \"xp\", \"y\", \"yp\", \"T\", \"PT\")\n",
    "var_units = (\"[m]\", \"[rad]\", \"[m]\", \"[rad]\", \"[m]\", \"[ ]\")\n",
    "values_lim = 0.01 # calculate the coefficient in the range +/-values_lim\n",
    "values_no = 50      # choose how many points you want to calculate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tough-toronto",
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   "source": [
    "### Varying the x and px component as input \n",
    "\n",
    "#### Remark: turning off the sextupole component K2 basically removes all fringe fields effect \n",
    "\n",
    "#### Linear regression:\n",
    "The value $R^2=1$ corresponds to sum of squared residuals (SSR) $\\sum_{i}(y{i}-f(x_{i}))^2=0$ and corresponds to a perfect fit."
   ]
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       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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   "source": [
    "fig, ax = plt.subplots(4, 4, figsize=(10,10), constrained_layout=True, sharex=True, squeeze=False)\n",
    "#squeeze argument if I only have one sublot instead of 6x6 filled\n",
    "\n",
    "xvalues = np.linspace(-values_lim, values_lim, values_no)# This centers the linspace around the position of the beam\n",
    "yvalues = np.zeros(values_no)\n",
    "\n",
    "# Reference Matrix\n",
    "twiss = madx.twiss(betx=betx0, bety=bety0,\n",
    "                   alfx=alfx0, alfy=alfy0,\n",
    "                   x=initialValues[0],px=initialValues[1],\n",
    "                   dx=dx0, dy=dy0,\n",
    "                   dpx=dpx0, dpy=dpy0,\n",
    "                   rmatrix=True).dframe()\n",
    "matExtractedRef = get_matrix_from_twiss(twiss)\n",
    "\n",
    "for k in tqdm(range (4),desc=\"k loop\"):\n",
    "    vectorOld = np.array([[0.], [0.], [0.], [0.], [0.], [0.]])\n",
    "    vectorNew = np.array([0.,0.,0.,0.,0.,0.])\n",
    "    for j in tqdm(range (4),desc=\"j loop\", leave=False):\n",
    "        for i in range (0,values_no):\n",
    "            vectorOld[k]=xvalues[i]+initialValues[k]\n",
    "            vectorNew = np.dot(matExtractedRef,vectorOld) # Matrix multiplication \n",
    "            yvalues[i]=vectorNew[j]\n",
    "        ax[j,k].plot(xvalues, yvalues, marker = 'o', markersize=1, color = 'b', alpha=0.5, label=\"reference particle\")\n",
    "\n",
    "        yvalues = np.zeros(values_no)\n",
    "        for i in tqdm(range (0,values_no),desc=\"i loop\", leave=False): # Change the twiss inputs around the reference particle\n",
    "            vectorOld[k]=xvalues[i]+initialValues[k]\n",
    "\n",
    "            if k==0:\n",
    "                twiss = madx.twiss(betx=betx0, bety=bety0, # We reevaluate the twiss and transport matrix\n",
    "                                   alfx=alfx0, alfy=alfy0,\n",
    "                                   x=xvalues[i]+initialValues[0], px=initialValues[1],\n",
    "                                   y=0, py=0,\n",
    "                                   dx=dx0, dy=dy0,\n",
    "                                   dpx=dpx0, dpy=dpy0,\n",
    "                                   rmatrix=True).dframe()\n",
    "            if k==1:\n",
    "                twiss = madx.twiss(betx=betx0, bety=bety0,\n",
    "                                   alfx=alfx0, alfy=alfy0,\n",
    "                                   x=initialValues[0], px=xvalues[i]+initialValues[1],\n",
    "                                   y=0, py=0,\n",
    "                                   dx=dx0, dy=dy0,\n",
    "                                   dpx=dpx0, dpy=dpy0,\n",
    "                                   rmatrix=True).dframe()     \n",
    "            if k==2:\n",
    "                twiss = madx.twiss(betx=betx0, bety=bety0,\n",
    "                                   alfx=alfx0, alfy=alfy0,\n",
    "                                   x=initialValues[0], px=initialValues[1],\n",
    "                                   y=xvalues[i], py=0,\n",
    "                                   dx=dx0, dy=dy0,\n",
    "                                   dpx=dpx0, dpy=dpy0,\n",
    "                                   rmatrix=True).dframe()\n",
    "            if k==3:\n",
    "                twiss = madx.twiss(betx=betx0, bety=bety0,\n",
    "                                   alfx=alfx0, alfy=alfy0,\n",
    "                                   x=initialValues[0], px=initialValues[1],\n",
    "                                   y=0, py=xvalues[i],\n",
    "                                   dx=dx0, dy=dy0,\n",
    "                                   dpx=dpx0, dpy=dpy0,\n",
    "                                   rmatrix=True).dframe()\n",
    "            \n",
    "            matExtracted = get_matrix_from_twiss(twiss)\n",
    "\n",
    "            vectorNew = np.dot(matExtracted,vectorOld) # Matrix multiplication    \n",
    "            yvalues[i]=vectorNew[j]\n",
    "        ax[j,k].scatter(xvalues, yvalues, marker = 'o', s=5, color = 'r', alpha=0.5, label=\"fringe effect\")\n",
    "        ax[j,k].set_xlabel(\"input \"+var_names[k]+\" \"+var_units[k])\n",
    "        ax[j,k].set_ylabel(\"output \"+var_names[j]+\" \"+var_units[j])\n",
    "        \n",
    "        #Linear regression\n",
    "        xlin = xvalues.reshape((-1,1))\n",
    "        model = LinearRegression().fit(xlin,yvalues)\n",
    "        r_sq = model.score(xlin, yvalues)\n",
    "        y_pred = model.predict(xlin)\n",
    "        ax[j,k].plot(xvalues, y_pred, color = 'r', alpha=0.5, label=\"linear regression\")\n",
    "        ax[j,k].title.set_text('R^2 = '+str(round(r_sq, 3)))\n",
    "\n",
    "fig.suptitle(\"Extracted matrix coefficient\", fontsize=10)\n",
    "ax[0,0].legend(loc=(-0.5, 1.5),fontsize=10)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "complex-possible",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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