Gonio_ID22.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "unlikely-arkansas",
   "metadata": {},
   "source": [
    "# Calibration of the Goniometer of ESRF ID22\n",
    "\n",
    "Features the refinement of the incident wavelength.\n",
    "13 images taken at 10° step from 0 to 120° in angle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "extreme-account",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib nbagg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "referenced-steel",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "from matplotlib.pyplot import subplots\n",
    "from matplotlib.lines import Line2D\n",
    "import pyFAI.goniometer\n",
    "import hdf5plugin\n",
    "import h5py\n",
    "import time\n",
    "import fabio\n",
    "from pyFAI.gui import jupyter\n",
    "from pyFAI.goniometer import GeometryTransformation, GoniometerRefinement, Goniometer, ExtendedTransformation\n",
    "from pyFAI.calibrant import get_calibrant\n",
    "from pyFAI.ext.bilinear import Bilinear\n",
    "from scipy import ndimage\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy.optimize import bisect\n",
    "import scipy.signal\n",
    "from numpy import pi\n",
    "start_time = time.perf_counter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bridal-subscriber",
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File(\"id222103_LaB6_DS_15500eV.h5\", \"r\") as h:\n",
    "    images = h['/LaB6_DS_15500eV_0001_3.1/measurement/eiger'][()]\n",
    "    tth = h['/LaB6_DS_15500eV_0001_3.1/measurement/tth_enc'][()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "inside-amber",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\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"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = subplots(3)\n",
    "jupyter.display(images[0], ax=ax[0])\n",
    "jupyter.display(images[1], ax=ax[1])\n",
    "jupyter.display(images[2], ax=ax[2])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "corresponding-export",
   "metadata": {},
   "outputs": [],
   "source": [
    "#in the neighboring of the gaps, pixels look too intense: masking out.\n",
    "\n",
    "mask0= (images.min(axis=0) == 0)\n",
    "mask = ndimage.binary_dilation(mask0, iterations=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "welsh-wonder",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Detector Eiger2 CdTe 2M-W\t PixelSize= 7.500e-05, 7.500e-05 m\n",
       "Wavelength= 7.998981e-11m\n",
       "SampleDetDist= 7.939243e-01m\tPONI= 2.075750e-02, 1.421967e-01m\trot1=0.171007  rot2= 0.000000  rot3= 0.000000 rad\n",
       "DirectBeamDist= 805.676mm\tCenter: x=67.880, y=276.767 pix\tTilt=9.798 deg  tiltPlanRotation= 180.000 deg"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo1 = pyFAI.load(\"slice1.poni\")\n",
    "geo2 = pyFAI.load(\"slice2.poni\")\n",
    "geo3 = pyFAI.load(\"slice3.poni\")\n",
    "wavelength = geo1.wavelength\n",
    "geo1.mask = geo2.mask = geo3.mask = mask\n",
    "eiger = geo1.detector\n",
    "geo1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "economic-particle",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LaB6 Calibrant with 92 reflections at wavelength 7.998980544077437e-11 15.5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "7.998980544077437e-11"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LaB6 = get_calibrant(\"LaB6\")\n",
    "LaB6.wavelength = wavelength\n",
    "energy = pyFAI.units.hc/wavelength*1e-10\n",
    "print(LaB6, energy)\n",
    "pyFAI.units.hc/energy*1e-10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "least-wrestling",
   "metadata": {},
   "outputs": [],
   "source": [
    "goniotrans = ExtendedTransformation(param_names = [\"dist\", \n",
    "                                                   \"poni1\", \"poni2\",\n",
    "                                                   \"rot1\", \"rot2\", \n",
    "                                                   \"ex01\", \"ex02\",\n",
    "                                                   \"ex11\", \"ex12\",\n",
    "                                                   \"ex21\", \"ex22\",\n",
    "                                                   \"scale1\", \"scale2\",\n",
    "                                                   \"energy\"],\n",
    "                                    dist_expr=\"dist   + ex01*sin(pos) + ex02*cos(pos)\",\n",
    "                                    poni1_expr=\"poni1 + ex11*sin(pos) + ex12*cos(pos)\",\n",
    "                                    poni2_expr=\"poni2 + ex21*sin(pos) + ex22*cos(pos)\",\n",
    "                                    rot1_expr=\"pos*scale1 + rot1\",\n",
    "                                    rot2_expr=\"pos*scale2 + rot2\",\n",
    "                                    rot3_expr=\"pi/2\",\n",
    "                                    wavelength_expr=\"hc/energy*1e-10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "equipped-implementation",
   "metadata": {},
   "outputs": [],
   "source": [
    "param = {\"dist\":   0.8,\n",
    "         \"poni1\":  0.02,\n",
    "         \"poni2\":  0.14,\n",
    "         \"rot1\":   0.0,\n",
    "         \"rot2\":   0.0,\n",
    "         \"scale1\": 1.0,\n",
    "         \"scale2\": 0.0,\n",
    "         \"ex01\":   0.0,\n",
    "         \"ex11\":   0.0,\n",
    "         \"ex21\":   0.0,\n",
    "         \"ex02\":   0.0,\n",
    "         \"ex12\":   0.0,\n",
    "         \"ex22\":   0.0,\n",
    "         \"energy\": 15.5,\n",
    "        }\n",
    "#Defines the bounds for some variables\n",
    "bounds = {\"dist\":   ( 0.7, 0.9),\n",
    "          \"poni1\":  ( 0.0, 0.2),\n",
    "          \"poni2\":  ( 0.0, 0.2),\n",
    "          \"rot1\":   (-1.0, 1.0),\n",
    "          \"rot2\":   (-1.0, 1.0),\n",
    "          \"scale1\": (-1.1, 1.1),\n",
    "          \"scale2\": (-1.1, 1.1),\n",
    "          \"ex01\":   (-1.0, 1.0),\n",
    "          \"ex11\":   (-1.0, 1.0),\n",
    "          \"ex21\":   (-1.0, 1.0),\n",
    "          \"ex02\":   (-1.0, 1.0),\n",
    "          \"ex12\":   (-1.0, 1.0),\n",
    "          \"ex22\":   (-1.0, 1.0), \n",
    "          \"energy\": (15.0,16.0)\n",
    "         }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "convinced-temple",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty refinement object: GoniometerRefinement with 0 geometries labeled: .\n",
      "Empty refinement object: GoniometerRefinement with 3 geometries labeled: 1, 2, 3.\n",
      "0.17458276570653847 0.34911453884399146 0.5236183509211757\n"
     ]
    }
   ],
   "source": [
    "gonioref = GoniometerRefinement(param, #initial guess\n",
    "                                bounds=bounds,\n",
    "                                pos_function=lambda index: tth[index]*pi/180,\n",
    "                                trans_function=goniotrans,\n",
    "                                detector=eiger, wavelength=wavelength)\n",
    "print(\"Empty refinement object:\", gonioref)\n",
    "sg1 = gonioref.new_geometry(\"1\", image=images[1], metadata=1, control_points=\"slice1.npt\", calibrant=LaB6)\n",
    "sg2 = gonioref.new_geometry(\"2\", image=images[2], metadata=2, control_points=\"slice2.npt\", calibrant=LaB6)\n",
    "sg3 = gonioref.new_geometry(\"3\", image=images[3], metadata=3, control_points=\"slice3.npt\", calibrant=LaB6)\n",
    "print(\"Empty refinement object:\", gonioref)\n",
    "print(sg1.get_position(), sg2.get_position(), sg3.get_position())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "polar-novelty",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost function before refinement: 3.775148547981588e-05\n",
      "[ 0.8   0.02  0.14  0.    0.    0.    0.    0.    0.    0.    0.    1.\n",
      "  0.   15.5 ]\n",
      "     fun: 5.278658875907129e-09\n",
      "     jac: array([ 5.10327330e-09,  3.84692412e-08, -1.13651742e-06,  4.42618161e-07,\n",
      "        2.21404161e-08,  1.02196949e-07, -4.97671554e-08, -5.54592982e-08,\n",
      "        5.81860657e-08, -3.03139766e-07, -6.93780080e-07,  1.86614338e-07,\n",
      "       -5.36995165e-08,  9.60210506e-09])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 181\n",
      "     nit: 12\n",
      "    njev: 12\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.97396830e-01,  2.03028892e-02,  1.41871213e-01, -1.46023035e-03,\n",
      "        2.59393766e-04, -1.01967552e-03, -2.37445581e-03,  9.71730677e-05,\n",
      "        2.83948595e-04,  5.39270072e-04,  1.79101090e-03,  9.99564789e-01,\n",
      "        8.71064889e-05,  1.55001157e+01])\n",
      "Cost function after refinement: 5.278658875907129e-09\n",
      "GonioParam(dist=0.7973968296977896, poni1=0.02030288918938589, poni2=0.14187121264463703, rot1=-0.0014602303468810112, rot2=0.00025939376630337006, ex01=-0.0010196755239564531, ex02=-0.002374455810221054, ex11=9.717306768669535e-05, ex12=0.0002839485948219522, ex21=0.0005392700722196733, ex22=0.0017910109029303067, scale1=0.9995647889786868, scale2=8.710648885853944e-05, energy=15.500115711976022)\n",
      "maxdelta on: dist (0) 0.8 --> 0.7973968296977896\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 7.97396830e-01,  2.03028892e-02,  1.41871213e-01, -1.46023035e-03,\n",
       "        2.59393766e-04, -1.01967552e-03, -2.37445581e-03,  9.71730677e-05,\n",
       "        2.83948595e-04,  5.39270072e-04,  1.79101090e-03,  9.99564789e-01,\n",
       "        8.71064889e-05,  1.55001157e+01])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gonioref.refine2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "inclusive-benchmark",
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_all():\n",
    "    \"Display all images with associated calibration\"\n",
    "    nimg = len(gonioref.single_geometries)\n",
    "    fig,ax = subplots(nimg, 1, figsize=(8,nimg))\n",
    "    keys = list(gonioref.single_geometries.keys())\n",
    "    keys.sort(key=lambda x:int(x))\n",
    "    for i, k in enumerate(keys):\n",
    "        sg = gonioref.single_geometries[k]\n",
    "        param = gonioref.trans_function(gonioref.param, sg.get_position())\n",
    "        sg.geometry_refinement.set_param(param)\n",
    "        jupyter.display(sg=sg, ax=ax[i])\n",
    "        l = ax[i].get_legend()\n",
    "        if l: l.remove()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "medium-madness",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\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"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n",
      "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n"
     ]
    }
   ],
   "source": [
    "display_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "vital-methodology",
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize_with_new_images(list_index, pts_per_deg=5):\n",
    "    sg = None\n",
    "    for idx in list_index:\n",
    "        base = str(idx)\n",
    "        image = images[idx]\n",
    "        sg = gonioref.new_geometry(base, image=image, metadata=idx,\n",
    "                                   calibrant=LaB6)\n",
    "        print(sg.extract_cp(pts_per_deg=pts_per_deg))\n",
    "    gonioref.refine2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "alert-evidence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ControlPoints instance containing 1 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998920829823548e-11\n",
      "Containing 1 groups of points:\n",
      "# j ring 0: 75 points\n",
      "ControlPoints instance containing 12 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998920829823548e-11\n",
      "Containing 12 groups of points:\n",
      "# k ring 6: 30 points\n",
      "# l ring 7: 30 points\n",
      "# m ring 8: 30 points\n",
      "# n ring 9: 25 points\n",
      "# o ring 10: 25 points\n",
      "# p ring 11: 25 points\n",
      "# q ring 12: 19 points\n",
      "# r ring 13: 25 points\n",
      "# s ring 14: 25 points\n",
      "# t ring 15: 25 points\n",
      "# u ring 16: 25 points\n",
      "# v ring 17: 25 points\n",
      "ControlPoints instance containing 13 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998920829823548e-11\n",
      "Containing 13 groups of points:\n",
      "# w ring 11: 25 points\n",
      "# x ring 12: 25 points\n",
      "# y ring 13: 25 points\n",
      "# z ring 14: 25 points\n",
      "#aa ring 15: 25 points\n",
      "#ab ring 16: 25 points\n",
      "#ac ring 17: 25 points\n",
      "#ad ring 18: 25 points\n",
      "#ae ring 19: 25 points\n",
      "#af ring 20: 25 points\n",
      "#ag ring 21: 20 points\n",
      "#ah ring 22: 20 points\n",
      "#ai ring 23: 20 points\n",
      "Cost function before refinement: 7.872219858610941e-09\n",
      "[ 7.97396830e-01  2.03028892e-02  1.41871213e-01 -1.46023035e-03\n",
      "  2.59393766e-04 -1.01967552e-03 -2.37445581e-03  9.71730677e-05\n",
      "  2.83948595e-04  5.39270072e-04  1.79101090e-03  9.99564789e-01\n",
      "  8.71064889e-05  1.55001157e+01]\n",
      "     fun: 4.026767099033416e-09\n",
      "     jac: array([ 2.53644918e-08,  2.79028496e-07, -4.42463118e-07, -1.19660896e-07,\n",
      "        2.25677134e-07, -3.18128278e-07,  1.62706667e-07,  5.04503961e-08,\n",
      "        2.74361337e-07,  2.26298326e-07,  3.36492478e-07, -1.50619778e-08,\n",
      "        3.79560829e-08,  1.00071771e-08])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 407\n",
      "     nit: 27\n",
      "    njev: 27\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.97108907e-01,  2.02601685e-02,  1.42887433e-01, -1.45248065e-04,\n",
      "        3.37675475e-04, -2.35523625e-03, -1.48861879e-03,  3.87013265e-04,\n",
      "        1.27823342e-04,  9.41043284e-04,  1.75849192e-03,  9.99854283e-01,\n",
      "        4.37677569e-04,  1.55002024e+01])\n",
      "Cost function after refinement: 4.026767099033416e-09\n",
      "GonioParam(dist=0.7971089071452742, poni1=0.020260168537198524, poni2=0.14288743310323307, rot1=-0.00014524806528371466, rot2=0.000337675475473811, ex01=-0.0023552362543095644, ex02=-0.001488618788431497, ex11=0.0003870132647155313, ex12=0.00012782334226120045, ex21=0.000941043283918638, ex22=0.001758491920827077, scale1=0.999854283364752, scale2=0.00043767756919271654, energy=15.500202354411662)\n",
      "maxdelta on: ex01 (5) -0.0010196755239564531 --> -0.0023552362543095644\n"
     ]
    }
   ],
   "source": [
    "optimize_with_new_images([0,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "subjective-alexander",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ControlPoints instance containing 15 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998876117763193e-11\n",
      "Containing 15 groups of points:\n",
      "#aj ring 17: 25 points\n",
      "#ak ring 18: 25 points\n",
      "#al ring 19: 25 points\n",
      "#am ring 20: 25 points\n",
      "#an ring 21: 20 points\n",
      "#ao ring 22: 20 points\n",
      "#ap ring 23: 20 points\n",
      "#aq ring 24: 20 points\n",
      "#ar ring 25: 15 points\n",
      "#as ring 26: 20 points\n",
      "#at ring 27: 20 points\n",
      "#au ring 28: 20 points\n",
      "#av ring 29: 20 points\n",
      "#aw ring 30: 20 points\n",
      "#ax ring 31: 20 points\n",
      "ControlPoints instance containing 17 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998876117763193e-11\n",
      "Containing 17 groups of points:\n",
      "#ay ring 23: 20 points\n",
      "#az ring 24: 15 points\n",
      "#ba ring 25: 20 points\n",
      "#bb ring 26: 20 points\n",
      "#bc ring 27: 20 points\n",
      "#bd ring 28: 20 points\n",
      "#be ring 29: 15 points\n",
      "#bf ring 30: 20 points\n",
      "#bg ring 31: 20 points\n",
      "#bh ring 32: 20 points\n",
      "#bi ring 33: 0 points\n",
      "#bj ring 34: 20 points\n",
      "#bk ring 35: 20 points\n",
      "#bl ring 36: 20 points\n",
      "#bm ring 37: 20 points\n",
      "#bn ring 38: 15 points\n",
      "#bo ring 39: 15 points\n",
      "ControlPoints instance containing 16 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998876117763193e-11\n",
      "Containing 16 groups of points:\n",
      "#bp ring 30: 20 points\n",
      "#bq ring 31: 20 points\n",
      "#br ring 32: 20 points\n",
      "#bs ring 33: 20 points\n",
      "#bt ring 34: 20 points\n",
      "#bu ring 35: 20 points\n",
      "#bv ring 36: 20 points\n",
      "#bw ring 37: 20 points\n",
      "#bx ring 38: 20 points\n",
      "#by ring 39: 20 points\n",
      "#bz ring 41: 15 points\n",
      "#ca ring 42: 15 points\n",
      "#cb ring 43: 15 points\n",
      "#cc ring 44: 15 points\n",
      "#cd ring 45: 15 points\n",
      "#ce ring 46: 15 points\n",
      "ControlPoints instance containing 17 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998876117763193e-11\n",
      "Containing 17 groups of points:\n",
      "#cf ring 38: 15 points\n",
      "#cg ring 39: 15 points\n",
      "#ch ring 40: 15 points\n",
      "#ci ring 41: 15 points\n",
      "#cj ring 42: 15 points\n",
      "#ck ring 43: 15 points\n",
      "#cl ring 44: 15 points\n",
      "#cm ring 45: 15 points\n",
      "#cn ring 46: 15 points\n",
      "#co ring 47: 15 points\n",
      "#cp ring 48: 15 points\n",
      "#cq ring 49: 15 points\n",
      "#cr ring 50: 15 points\n",
      "#cs ring 51: 15 points\n",
      "#ct ring 52: 15 points\n",
      "#cu ring 53: 15 points\n",
      "#cv ring 54: 15 points\n",
      "Cost function before refinement: 5.550563441625292e-08\n",
      "[ 7.97108907e-01  2.02601685e-02  1.42887433e-01 -1.45248065e-04\n",
      "  3.37675475e-04 -2.35523625e-03 -1.48861879e-03  3.87013265e-04\n",
      "  1.27823342e-04  9.41043284e-04  1.75849192e-03  9.99854283e-01\n",
      "  4.37677569e-04  1.55002024e+01]\n",
      "     fun: 2.4992787363588178e-09\n",
      "     jac: array([-1.77948950e-08,  1.63057287e-08, -3.73746327e-08,  2.24654659e-08,\n",
      "        2.18867931e-08, -7.45606818e-09,  5.08930957e-08,  1.99402074e-08,\n",
      "        6.04644229e-09, -4.58574265e-08,  7.47954279e-08, -8.12105583e-08,\n",
      "        2.57657934e-08, -1.95150365e-08])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 526\n",
      "     nit: 35\n",
      "    njev: 35\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94843107e-01,  2.02076309e-02,  1.45967536e-01,  2.37308073e-03,\n",
      "        3.83618993e-04, -6.71044978e-04,  2.35916839e-04,  3.94179577e-04,\n",
      "        4.61822221e-05,  4.45785955e-04,  8.40161379e-04,  9.99912126e-01,\n",
      "        5.53425262e-04,  1.55006241e+01])\n",
      "Cost function after refinement: 2.4992787363588178e-09\n",
      "GonioParam(dist=0.7948431065609595, poni1=0.020207630934463858, poni2=0.14596753570116397, rot1=0.0023730807280716833, rot2=0.0003836189927402655, ex01=-0.000671044978137258, ex02=0.00023591683920310094, ex11=0.0003941795766804939, ex12=4.61822220744487e-05, ex21=0.0004457859550112717, ex22=0.0008401613787348661, scale1=0.9999121258056136, scale2=0.0005534252616439375, energy=15.500624110520976)\n",
      "maxdelta on: poni2 (2) 0.14288743310323307 --> 0.14596753570116397\n"
     ]
    }
   ],
   "source": [
    "optimize_with_new_images([6,7,8,9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "entire-repeat",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ControlPoints instance containing 17 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998658476534927e-11\n",
      "Containing 17 groups of points:\n",
      "#cw ring 46: 15 points\n",
      "#cx ring 47: 15 points\n",
      "#cy ring 48: 15 points\n",
      "#cz ring 49: 15 points\n",
      "#da ring 50: 15 points\n",
      "#db ring 51: 15 points\n",
      "#dc ring 52: 15 points\n",
      "#dd ring 53: 15 points\n",
      "#de ring 54: 15 points\n",
      "#df ring 55: 20 points\n",
      "#dg ring 56: 20 points\n",
      "#dh ring 57: 0 points\n",
      "#di ring 58: 20 points\n",
      "#dj ring 59: 20 points\n",
      "#dk ring 60: 20 points\n",
      "#dl ring 61: 20 points\n",
      "#dm ring 62: 20 points\n",
      "ControlPoints instance containing 17 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998658476534927e-11\n",
      "Containing 17 groups of points:\n",
      "#dn ring 53: 15 points\n",
      "#do ring 54: 15 points\n",
      "#dp ring 55: 15 points\n",
      "#dq ring 56: 15 points\n",
      "#dr ring 57: 20 points\n",
      "#ds ring 58: 20 points\n",
      "#dt ring 59: 20 points\n",
      "#du ring 60: 20 points\n",
      "#dv ring 61: 20 points\n",
      "#dw ring 62: 20 points\n",
      "#dx ring 63: 20 points\n",
      "#dy ring 64: 20 points\n",
      "#dz ring 65: 20 points\n",
      "#ea ring 66: 20 points\n",
      "#eb ring 67: 20 points\n",
      "#ec ring 68: 25 points\n",
      "#ed ring 69: 25 points\n",
      "ControlPoints instance containing 15 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998658476534927e-11\n",
      "Containing 15 groups of points:\n",
      "#ee ring 61: 20 points\n",
      "#ef ring 62: 20 points\n",
      "#eg ring 63: 20 points\n",
      "#eh ring 64: 20 points\n",
      "#ei ring 66: 20 points\n",
      "#ej ring 67: 25 points\n",
      "#ek ring 68: 25 points\n",
      "#el ring 69: 25 points\n",
      "#em ring 70: 25 points\n",
      "#en ring 71: 25 points\n",
      "#eo ring 72: 1 points\n",
      "#ep ring 73: 25 points\n",
      "#eq ring 74: 25 points\n",
      "#er ring 75: 25 points\n",
      "#es ring 76: 25 points\n",
      "Cost function before refinement: 8.708810200866916e-09\n",
      "[ 7.94843107e-01  2.02076309e-02  1.45967536e-01  2.37308073e-03\n",
      "  3.83618993e-04 -6.71044978e-04  2.35916839e-04  3.94179577e-04\n",
      "  4.61822221e-05  4.45785955e-04  8.40161379e-04  9.99912126e-01\n",
      "  5.53425262e-04  1.55006241e+01]\n",
      "     fun: 2.706319441773673e-09\n",
      "     jac: array([ 1.85957136e-07, -1.81162731e-07,  1.09684618e-08, -1.68560735e-09,\n",
      "       -1.60602708e-07, -1.97690621e-07, -1.08327577e-07, -4.25902994e-08,\n",
      "       -1.65764814e-07,  3.75167964e-08, -1.74441970e-08,  2.64745966e-08,\n",
      "       -4.77493199e-08, -1.18900163e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 451\n",
      "     nit: 30\n",
      "    njev: 30\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94820482e-01,  2.01824656e-02,  1.46406901e-01,  2.09897043e-03,\n",
      "        3.39077671e-04, -6.22829766e-04,  2.33107203e-04,  3.91144290e-04,\n",
      "        1.67729672e-05,  9.49470834e-04,  2.19660070e-04,  1.00079982e+00,\n",
      "        5.09740838e-04,  1.55010768e+01])\n",
      "Cost function after refinement: 2.706319441773673e-09\n",
      "GonioParam(dist=0.794820481878865, poni1=0.02018246563570308, poni2=0.14640690141129406, rot1=0.0020989704328371775, rot2=0.00033907767135782916, ex01=-0.0006228297664527629, ex02=0.00023310720306741052, ex11=0.0003911442896513936, ex12=1.677296720221402e-05, ex21=0.0009494708338890352, ex22=0.00021966007036946158, scale1=1.000799824632789, scale2=0.0005097408377695113, energy=15.501076780837918)\n",
      "maxdelta on: scale1 (11) 0.9999121258056136 --> 1.000799824632789\n"
     ]
    }
   ],
   "source": [
    "optimize_with_new_images([10, 11, 12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "approximate-norwegian",
   "metadata": {},
   "outputs": [],
   "source": [
    "def renew(lst):\n",
    "    lst = list(lst)\n",
    "    numpy.random.shuffle(lst)\n",
    "    for i in lst:\n",
    "        \n",
    "        print(f\"Re-extract frame #{i}\")\n",
    "        gonioref.single_geometries.pop(str(i))\n",
    "        optimize_with_new_images([i])\n",
    "        print(\"*\"*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "coordinate-invention",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-extract frame #12\n",
      "ControlPoints instance containing 15 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424895647684e-11\n",
      "Containing 15 groups of points:\n",
      "#et ring 61: 20 points\n",
      "#eu ring 62: 20 points\n",
      "#ev ring 63: 20 points\n",
      "#ew ring 64: 20 points\n",
      "#ex ring 66: 20 points\n",
      "#ey ring 67: 25 points\n",
      "#ez ring 68: 25 points\n",
      "#fa ring 69: 25 points\n",
      "#fb ring 70: 25 points\n",
      "#fc ring 71: 25 points\n",
      "#fd ring 72: 25 points\n",
      "#fe ring 73: 25 points\n",
      "#ff ring 74: 25 points\n",
      "#fg ring 75: 25 points\n",
      "#fh ring 76: 25 points\n",
      "Cost function before refinement: 2.671749982801582e-09\n",
      "[ 7.94820482e-01  2.01824656e-02  1.46406901e-01  2.09897043e-03\n",
      "  3.39077671e-04 -6.22829766e-04  2.33107203e-04  3.91144290e-04\n",
      "  1.67729672e-05  9.49470834e-04  2.19660070e-04  1.00079982e+00\n",
      "  5.09740838e-04  1.55010768e+01]\n",
      "     fun: 2.6657878229458544e-09\n",
      "     jac: array([ 2.62406334e-07, -1.80333363e-07,  2.44774477e-07, -1.74844059e-07,\n",
      "       -1.66382143e-07, -1.57710997e-07, -1.17284190e-07, -4.00809981e-08,\n",
      "       -1.68603693e-07, -4.80343297e-07,  8.10635168e-08,  2.08671894e-08,\n",
      "       -5.79593937e-08, -1.86595140e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 61\n",
      "     nit: 4\n",
      "    njev: 4\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819968e-01,  2.01828123e-02,  1.46406672e-01,  2.09911355e-03,\n",
      "        3.39398007e-04, -6.22552742e-04,  2.33383285e-04,  3.91221181e-04,\n",
      "        1.70956266e-05,  9.49876652e-04,  2.21498091e-04,  1.00080139e+00,\n",
      "        5.09853731e-04,  1.55010773e+01])\n",
      "Cost function after refinement: 2.6657878229458544e-09\n",
      "GonioParam(dist=0.7948199679661985, poni1=0.020182812326614112, poni2=0.1464066719991098, rot1=0.0020991135523488126, rot2=0.00033939800677105483, ex01=-0.0006225527421768684, ex02=0.00023338328475114813, ex11=0.00039122118083147054, ex12=1.7095626575125736e-05, ex21=0.0009498766516526422, ex22=0.0002214980914251059, scale1=1.0008013884306994, scale2=0.0005098537308719589, energy=15.50107733462685)\n",
      "maxdelta on: ex22 (10) 0.00021966007036946158 --> 0.0002214980914251059\n",
      "**************************************************\n",
      "Re-extract frame #6\n",
      "ControlPoints instance containing 15 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424609897275e-11\n",
      "Containing 15 groups of points:\n",
      "#fi ring 17: 25 points\n",
      "#fj ring 18: 25 points\n",
      "#fk ring 19: 25 points\n",
      "#fl ring 20: 25 points\n",
      "#fm ring 21: 20 points\n",
      "#fn ring 22: 20 points\n",
      "#fo ring 23: 20 points\n",
      "#fp ring 24: 20 points\n",
      "#fq ring 25: 20 points\n",
      "#fr ring 26: 20 points\n",
      "#fs ring 27: 20 points\n",
      "#ft ring 28: 20 points\n",
      "#fu ring 29: 20 points\n",
      "#fv ring 30: 20 points\n",
      "#fw ring 31: 20 points\n",
      "Cost function before refinement: 2.6704038295393077e-09\n",
      "[ 7.94819968e-01  2.01828123e-02  1.46406672e-01  2.09911355e-03\n",
      "  3.39398007e-04 -6.22552742e-04  2.33383285e-04  3.91221181e-04\n",
      "  1.70956266e-05  9.49876652e-04  2.21498091e-04  1.00080139e+00\n",
      "  5.09853731e-04  1.55010773e+01]\n",
      "     fun: 2.670277907463485e-09\n",
      "     jac: array([ 2.98295062e-07, -1.78330436e-07,  8.31436341e-07, -6.46815309e-07,\n",
      "       -1.61246279e-07, -1.26106073e-07, -9.90018626e-08, -3.85044769e-08,\n",
      "       -1.67495442e-07,  2.86631614e-08,  3.74436887e-07, -4.73662114e-07,\n",
      "       -5.27393980e-08, -2.24283283e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 17\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819924e-01,  2.01828383e-02,  1.46406551e-01,  2.09920783e-03,\n",
      "        3.39421510e-04, -6.22534361e-04,  2.33397715e-04,  3.91226793e-04,\n",
      "        1.71200410e-05,  9.49872474e-04,  2.21443513e-04,  1.00080146e+00,\n",
      "        5.09861418e-04,  1.55010774e+01])\n",
      "Cost function after refinement: 2.670277907463485e-09\n",
      "GonioParam(dist=0.7948199244861711, poni1=0.020182838320380873, poni2=0.14640655080744572, rot1=0.0020992078333174624, rot2=0.00033942151032017155, ex01=-0.0006225343607276999, ex02=0.00023339771544160425, ex11=0.0003912267933136292, ex12=1.712004101356913e-05, ex21=0.0009498724736584186, ex22=0.00022144351282757783, scale1=1.0008014574725455, scale2=0.0005098614182619457, energy=15.501077367318786)\n",
      "maxdelta on: poni2 (2) 0.1464066719991098 --> 0.14640655080744572\n",
      "**************************************************\n",
      "Re-extract frame #9\n",
      "ControlPoints instance containing 17 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424593028514e-11\n",
      "Containing 17 groups of points:\n",
      "#fx ring 38: 15 points\n",
      "#fy ring 39: 15 points\n",
      "#fz ring 40: 15 points\n",
      "#ga ring 41: 15 points\n",
      "#gb ring 42: 15 points\n",
      "#gc ring 43: 15 points\n",
      "#gd ring 44: 15 points\n",
      "#ge ring 45: 15 points\n",
      "#gf ring 46: 15 points\n",
      "#gg ring 47: 15 points\n",
      "#gh ring 48: 15 points\n",
      "#gi ring 49: 15 points\n",
      "#gj ring 50: 15 points\n",
      "#gk ring 51: 15 points\n",
      "#gl ring 52: 12 points\n",
      "#gm ring 53: 15 points\n",
      "#gn ring 54: 15 points\n",
      "Cost function before refinement: 2.5286300879481057e-09\n",
      "[ 7.94819924e-01  2.01828383e-02  1.46406551e-01  2.09920783e-03\n",
      "  3.39421510e-04 -6.22534361e-04  2.33397715e-04  3.91226793e-04\n",
      "  1.71200410e-05  9.49872474e-04  2.21443513e-04  1.00080146e+00\n",
      "  5.09861418e-04  1.55010774e+01]\n",
      "     fun: 2.5283098011965923e-09\n",
      "     jac: array([ 1.82319907e-07, -1.75640579e-07, -8.63255482e-07,  7.30213084e-07,\n",
      "       -1.52248691e-07, -2.35658751e-07, -1.05578922e-07, -3.68377609e-08,\n",
      "       -1.65994413e-07, -1.45079862e-06,  8.14911821e-08,  1.39542819e-06,\n",
      "       -4.02538598e-08, -5.39322788e-08])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 17\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819903e-01,  2.01828587e-02,  1.46406651e-01,  2.09912302e-03,\n",
      "        3.39439194e-04, -6.22506989e-04,  2.33409978e-04,  3.91231072e-04,\n",
      "        1.71393211e-05,  9.50040983e-04,  2.21434048e-04,  1.00080130e+00,\n",
      "        5.09866094e-04,  1.55010774e+01])\n",
      "Cost function after refinement: 2.5283098011965923e-09\n",
      "GonioParam(dist=0.7948199033098661, poni1=0.020182858720887556, poni2=0.14640665107384937, rot1=0.0020991230196772985, rot2=0.00033943919387853576, ex01=-0.0006225069891605293, ex02=0.0002334099783452268, ex11=0.00039123107198880513, ex12=1.7139321126038226e-05, ex21=0.0009500409827072972, ex22=0.0002214340476948044, scale1=1.0008012953947258, scale2=0.0005098660937141643, energy=15.501077373582975)\n",
      "maxdelta on: ex21 (9) 0.0009498724736584186 --> 0.0009500409827072972\n",
      "**************************************************\n",
      "Re-extract frame #10\n",
      "ControlPoints instance containing 16 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424589796245e-11\n",
      "Containing 16 groups of points:\n",
      "#go ring 46: 15 points\n",
      "#gp ring 47: 15 points\n",
      "#gq ring 48: 15 points\n",
      "#gr ring 49: 15 points\n",
      "#gs ring 50: 15 points\n",
      "#gt ring 51: 15 points\n",
      "#gu ring 52: 15 points\n",
      "#gv ring 53: 15 points\n",
      "#gw ring 54: 15 points\n",
      "#gx ring 55: 20 points\n",
      "#gy ring 56: 20 points\n",
      "#gz ring 58: 18 points\n",
      "#ha ring 59: 20 points\n",
      "#hb ring 60: 20 points\n",
      "#hc ring 61: 20 points\n",
      "#hd ring 62: 20 points\n",
      "Cost function before refinement: 2.522657006087966e-09\n",
      "[ 7.94819903e-01  2.01828587e-02  1.46406651e-01  2.09912302e-03\n",
      "  3.39439194e-04 -6.22506989e-04  2.33409978e-04  3.91231072e-04\n",
      "  1.71393211e-05  9.50040983e-04  2.21434048e-04  1.00080130e+00\n",
      "  5.09866094e-04  1.55010774e+01]\n",
      "     fun: 2.522657006087966e-09\n",
      "     jac: array([ 2.27636677e-07, -1.79526909e-07,  5.60934499e-07, -4.17895643e-07,\n",
      "       -1.53990854e-07, -1.96478031e-07, -1.05982889e-07, -3.96738366e-08,\n",
      "       -1.67357479e-07, -2.76575764e-07,  3.57714121e-07, -1.30246675e-07,\n",
      "       -4.11098534e-08, -1.93575625e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 15\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819903e-01,  2.01828587e-02,  1.46406651e-01,  2.09912302e-03,\n",
      "        3.39439194e-04, -6.22506989e-04,  2.33409978e-04,  3.91231072e-04,\n",
      "        1.71393211e-05,  9.50040983e-04,  2.21434048e-04,  1.00080130e+00,\n",
      "        5.09866094e-04,  1.55010774e+01])\n",
      "Cost function after refinement: 2.522657006087966e-09\n",
      "GonioParam(dist=0.7948199033098661, poni1=0.020182858720887556, poni2=0.14640665107384937, rot1=0.0020991230196772985, rot2=0.00033943919387853576, ex01=-0.0006225069891605293, ex02=0.0002334099783452268, ex11=0.00039123107198880513, ex12=1.7139321126038226e-05, ex21=0.0009500409827072972, ex22=0.0002214340476948044, scale1=1.0008012953947258, scale2=0.0005098660937141643, energy=15.501077373582975)\n",
      "Restore wavelength and former parameters\n",
      "GonioParam(dist=0.7948199033098661, poni1=0.020182858720887556, poni2=0.14640665107384937, rot1=0.0020991230196772985, rot2=0.00033943919387853576, ex01=-0.0006225069891605293, ex02=0.0002334099783452268, ex11=0.00039123107198880513, ex12=1.7139321126038226e-05, ex21=0.0009500409827072972, ex22=0.0002214340476948044, scale1=1.0008012953947258, scale2=0.0005098660937141643, energy=15.501077373582975)\n",
      "**************************************************\n",
      "Re-extract frame #0\n",
      "ControlPoints instance containing 1 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424589796245e-11\n",
      "Containing 1 groups of points:\n",
      "#he ring 0: 80 points\n",
      "Cost function before refinement: 2.5348483012130513e-09\n",
      "[ 7.94819903e-01  2.01828587e-02  1.46406651e-01  2.09912302e-03\n",
      "  3.39439194e-04 -6.22506989e-04  2.33409978e-04  3.91231072e-04\n",
      "  1.71393211e-05  9.50040983e-04  2.21434048e-04  1.00080130e+00\n",
      "  5.09866094e-04  1.55010774e+01]\n",
      "     fun: 2.5348483012130513e-09\n",
      "     jac: array([ 2.19616175e-07, -1.80366871e-07,  5.20756118e-07, -3.84765647e-07,\n",
      "       -1.54677990e-07, -1.96210246e-07, -1.13549116e-07, -3.96197793e-08,\n",
      "       -1.68214012e-07, -2.76197888e-07,  3.17812456e-07, -1.30070375e-07,\n",
      "       -4.10538474e-08, -1.92903086e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 15\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819903e-01,  2.01828587e-02,  1.46406651e-01,  2.09912302e-03,\n",
      "        3.39439194e-04, -6.22506989e-04,  2.33409978e-04,  3.91231072e-04,\n",
      "        1.71393211e-05,  9.50040983e-04,  2.21434048e-04,  1.00080130e+00,\n",
      "        5.09866094e-04,  1.55010774e+01])\n",
      "Cost function after refinement: 2.5348483012130513e-09\n",
      "GonioParam(dist=0.7948199033098661, poni1=0.020182858720887556, poni2=0.14640665107384937, rot1=0.0020991230196772985, rot2=0.00033943919387853576, ex01=-0.0006225069891605293, ex02=0.0002334099783452268, ex11=0.00039123107198880513, ex12=1.7139321126038226e-05, ex21=0.0009500409827072972, ex22=0.0002214340476948044, scale1=1.0008012953947258, scale2=0.0005098660937141643, energy=15.501077373582975)\n",
      "Restore wavelength and former parameters\n",
      "GonioParam(dist=0.7948199033098661, poni1=0.020182858720887556, poni2=0.14640665107384937, rot1=0.0020991230196772985, rot2=0.00033943919387853576, ex01=-0.0006225069891605293, ex02=0.0002334099783452268, ex11=0.00039123107198880513, ex12=1.7139321126038226e-05, ex21=0.0009500409827072972, ex22=0.0002214340476948044, scale1=1.0008012953947258, scale2=0.0005098660937141643, energy=15.501077373582975)\n",
      "**************************************************\n",
      "Re-extract frame #7\n",
      "ControlPoints instance containing 16 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424589796245e-11\n",
      "Containing 16 groups of points:\n",
      "#hf ring 23: 20 points\n",
      "#hg ring 24: 20 points\n",
      "#hh ring 25: 20 points\n",
      "#hi ring 26: 20 points\n",
      "#hj ring 27: 20 points\n",
      "#hk ring 28: 20 points\n",
      "#hl ring 29: 20 points\n",
      "#hm ring 30: 20 points\n",
      "#hn ring 31: 20 points\n",
      "#ho ring 32: 20 points\n",
      "#hp ring 34: 20 points\n",
      "#hq ring 35: 20 points\n",
      "#hr ring 36: 20 points\n",
      "#hs ring 37: 15 points\n",
      "#ht ring 38: 15 points\n",
      "#hu ring 39: 15 points\n",
      "Cost function before refinement: 2.525867489822442e-09\n",
      "[ 7.94819903e-01  2.01828587e-02  1.46406651e-01  2.09912302e-03\n",
      "  3.39439194e-04 -6.22506989e-04  2.33409978e-04  3.91231072e-04\n",
      "  1.71393211e-05  9.50040983e-04  2.21434048e-04  1.00080130e+00\n",
      "  5.09866094e-04  1.55010774e+01]\n",
      "     fun: 2.525867489822442e-09\n",
      "     jac: array([ 1.42323790e-07, -1.79260626e-07,  1.88627648e-07, -1.10164712e-07,\n",
      "       -1.54322602e-07, -2.68294318e-07, -1.39726886e-07, -3.87568388e-08,\n",
      "       -1.67690812e-07, -5.87259268e-07,  2.04025667e-07,  2.04965600e-07,\n",
      "       -4.08207946e-08, -1.61398846e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 15\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819903e-01,  2.01828587e-02,  1.46406651e-01,  2.09912302e-03,\n",
      "        3.39439194e-04, -6.22506989e-04,  2.33409978e-04,  3.91231072e-04,\n",
      "        1.71393211e-05,  9.50040983e-04,  2.21434048e-04,  1.00080130e+00,\n",
      "        5.09866094e-04,  1.55010774e+01])\n",
      "Cost function after refinement: 2.525867489822442e-09\n",
      "GonioParam(dist=0.7948199033098661, poni1=0.020182858720887556, poni2=0.14640665107384937, rot1=0.0020991230196772985, rot2=0.00033943919387853576, ex01=-0.0006225069891605293, ex02=0.0002334099783452268, ex11=0.00039123107198880513, ex12=1.7139321126038226e-05, ex21=0.0009500409827072972, ex22=0.0002214340476948044, scale1=1.0008012953947258, scale2=0.0005098660937141643, energy=15.501077373582975)\n",
      "Restore wavelength and former parameters\n",
      "GonioParam(dist=0.7948199033098661, poni1=0.020182858720887556, poni2=0.14640665107384937, rot1=0.0020991230196772985, rot2=0.00033943919387853576, ex01=-0.0006225069891605293, ex02=0.0002334099783452268, ex11=0.00039123107198880513, ex12=1.7139321126038226e-05, ex21=0.0009500409827072972, ex22=0.0002214340476948044, scale1=1.0008012953947258, scale2=0.0005098660937141643, energy=15.501077373582975)\n",
      "**************************************************\n",
      "Re-extract frame #2\n",
      "ControlPoints instance containing 7 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424589796245e-11\n",
      "Containing 7 groups of points:\n",
      "#hv ring 0: 70 points\n",
      "#hw ring 1: 60 points\n",
      "#hx ring 2: 50 points\n",
      "#hy ring 3: 7 points\n",
      "#hz ring 4: 40 points\n",
      "#ia ring 5: 35 points\n",
      "#ib ring 6: 30 points\n",
      "Cost function before refinement: 2.0289716081658178e-09\n",
      "[ 7.94819903e-01  2.01828587e-02  1.46406651e-01  2.09912302e-03\n",
      "  3.39439194e-04 -6.22506989e-04  2.33409978e-04  3.91231072e-04\n",
      "  1.71393211e-05  9.50040983e-04  2.21434048e-04  1.00080130e+00\n",
      "  5.09866094e-04  1.55010774e+01]\n",
      "     fun: 2.0285338145698263e-09\n",
      "     jac: array([ 9.52151763e-08, -1.80376325e-07, -1.39827660e-06,  1.16836431e-06,\n",
      "       -1.55609419e-07, -2.88337305e-07, -1.87382272e-07, -3.88588862e-08,\n",
      "       -1.68729827e-07, -1.13809199e-06, -1.28680108e-06,  6.54376594e-07,\n",
      "       -4.11082444e-08, -1.31254202e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 17\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819891e-01,  2.01828814e-02,  1.46406827e-01,  2.09897641e-03,\n",
      "        3.39458720e-04, -6.22470808e-04,  2.33433491e-04,  3.91235948e-04,\n",
      "        1.71604935e-05,  9.50183792e-04,  2.21595517e-04,  1.00080121e+00,\n",
      "        5.09871252e-04,  1.55010774e+01])\n",
      "Cost function after refinement: 2.0285338145698263e-09\n",
      "GonioParam(dist=0.7948198913621536, poni1=0.02018288135472124, poni2=0.14640682653124487, rot1=0.0020989764119467144, rot2=0.000339458719931755, ex01=-0.0006224708082557962, ex02=0.00023343349129348876, ex11=0.0003912359480476279, ex12=1.7160493543541974e-05, ex21=0.0009501837918304427, ex22=0.00022159551701039803, scale1=1.00080121328278, scale2=0.0005098712520250934, energy=15.501077390052906)\n",
      "maxdelta on: poni2 (2) 0.14640665107384937 --> 0.14640682653124487\n",
      "**************************************************\n",
      "Re-extract frame #3\n",
      "ControlPoints instance containing 9 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424581297901e-11\n",
      "Containing 9 groups of points:\n",
      "#ic ring 3: 40 points\n",
      "#id ring 4: 40 points\n",
      "#ie ring 5: 35 points\n",
      "#if ring 6: 30 points\n",
      "#ig ring 7: 30 points\n",
      "#ih ring 8: 30 points\n",
      "#ii ring 9: 25 points\n",
      "#ij ring 10: 25 points\n",
      "#ik ring 11: 25 points\n",
      "Cost function before refinement: 1.994201345790515e-09\n",
      "[ 7.94819891e-01  2.01828814e-02  1.46406827e-01  2.09897641e-03\n",
      "  3.39458720e-04 -6.22470808e-04  2.33433491e-04  3.91235948e-04\n",
      "  1.71604935e-05  9.50183792e-04  2.21595517e-04  1.00080121e+00\n",
      "  5.09871252e-04  1.55010774e+01]\n",
      "     fun: 1.9935107318016827e-09\n",
      "     jac: array([ 1.07245337e-07, -1.83771703e-07,  2.21438422e-06, -1.71622834e-06,\n",
      "       -1.58572456e-07, -2.87020019e-07, -1.94399477e-07, -3.97455358e-08,\n",
      "       -1.70991485e-07,  1.08582775e-06,  9.85127205e-07, -1.67476275e-06,\n",
      "       -4.29763050e-08, -3.18360401e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 17\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819880e-01,  2.01829007e-02,  1.46406593e-01,  2.09915715e-03,\n",
      "        3.39475419e-04, -6.22440582e-04,  2.33453964e-04,  3.91240134e-04,\n",
      "        1.71785007e-05,  9.50069443e-04,  2.21491773e-04,  1.00080139e+00,\n",
      "        5.09875778e-04,  1.55010774e+01])\n",
      "Cost function after refinement: 1.9935107318016827e-09\n",
      "GonioParam(dist=0.7948198800681063, poni1=0.020182900707789365, poni2=0.14640659333359882, rot1=0.002099157148607708, rot2=0.0003394754192576077, ex01=-0.0006224405820681487, ex02=0.0002334539635764792, ex11=0.0003912401336651575, ex12=1.717850072202974e-05, ex21=0.0009500694428961104, ex22=0.00022149177288883167, scale1=1.0008013896526833, scale2=0.00050987577787616, energy=15.501077423579561)\n",
      "maxdelta on: poni2 (2) 0.14640682653124487 --> 0.14640659333359882\n",
      "**************************************************\n",
      "Re-extract frame #1\n",
      "ControlPoints instance containing 3 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.99842456399843e-11\n",
      "Containing 3 groups of points:\n",
      "#il ring 0: 80 points\n",
      "#im ring 1: 55 points\n",
      "#in ring 2: 45 points\n",
      "Cost function before refinement: 1.9916305875263023e-09\n",
      "[ 7.94819880e-01  2.01829007e-02  1.46406593e-01  2.09915715e-03\n",
      "  3.39475419e-04 -6.22440582e-04  2.33453964e-04  3.91240134e-04\n",
      "  1.71785007e-05  9.50069443e-04  2.21491773e-04  1.00080139e+00\n",
      "  5.09875778e-04  1.55010774e+01]\n",
      "     fun: 1.9916305875263023e-09\n",
      "     jac: array([ 5.56824503e-08, -1.88743452e-07, -2.05453283e-07,  2.28408156e-07,\n",
      "       -1.61812935e-07, -3.11046507e-07, -2.22815162e-07, -3.84833353e-08,\n",
      "       -1.77842868e-07, -5.82243219e-07,  3.58378192e-08,  3.81958456e-07,\n",
      "       -4.01560290e-08, -1.37393564e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 15\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819880e-01,  2.01829007e-02,  1.46406593e-01,  2.09915715e-03,\n",
      "        3.39475419e-04, -6.22440582e-04,  2.33453964e-04,  3.91240134e-04,\n",
      "        1.71785007e-05,  9.50069443e-04,  2.21491773e-04,  1.00080139e+00,\n",
      "        5.09875778e-04,  1.55010774e+01])\n",
      "Cost function after refinement: 1.9916305875263023e-09\n",
      "GonioParam(dist=0.7948198800681063, poni1=0.020182900707789365, poni2=0.14640659333359882, rot1=0.002099157148607708, rot2=0.0003394754192576077, ex01=-0.0006224405820681487, ex02=0.0002334539635764792, ex11=0.0003912401336651575, ex12=1.717850072202974e-05, ex21=0.0009500694428961104, ex22=0.00022149177288883167, scale1=1.0008013896526833, scale2=0.00050987577787616, energy=15.501077423579561)\n",
      "Restore wavelength and former parameters\n",
      "GonioParam(dist=0.7948198800681063, poni1=0.020182900707789365, poni2=0.14640659333359882, rot1=0.002099157148607708, rot2=0.0003394754192576077, ex01=-0.0006224405820681487, ex02=0.0002334539635764792, ex11=0.0003912401336651575, ex12=1.717850072202974e-05, ex21=0.0009500694428961104, ex22=0.00022149177288883167, scale1=1.0008013896526833, scale2=0.00050987577787616, energy=15.501077423579561)\n",
      "**************************************************\n",
      "Re-extract frame #8\n",
      "ControlPoints instance containing 17 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.99842456399843e-11\n",
      "Containing 17 groups of points:\n",
      "#io ring 30: 20 points\n",
      "#ip ring 31: 20 points\n",
      "#iq ring 32: 20 points\n",
      "#ir ring 33: 20 points\n",
      "#is ring 34: 20 points\n",
      "#it ring 35: 20 points\n",
      "#iu ring 36: 20 points\n",
      "#iv ring 37: 20 points\n",
      "#iw ring 38: 20 points\n",
      "#ix ring 39: 20 points\n",
      "#iy ring 40: 15 points\n",
      "#iz ring 41: 15 points\n",
      "#ja ring 42: 15 points\n",
      "#jb ring 43: 15 points\n",
      "#jc ring 44: 15 points\n",
      "#jd ring 45: 15 points\n",
      "#je ring 46: 15 points\n",
      "Cost function before refinement: 1.984762272072607e-09\n",
      "[ 7.94819880e-01  2.01829007e-02  1.46406593e-01  2.09915715e-03\n",
      "  3.39475419e-04 -6.22440582e-04  2.33453964e-04  3.91240134e-04\n",
      "  1.71785007e-05  9.50069443e-04  2.21491773e-04  1.00080139e+00\n",
      "  5.09875778e-04  1.55010774e+01]\n",
      "     fun: 1.984762272072607e-09\n",
      "     jac: array([ 1.09516021e-08, -1.89095614e-07, -1.00117469e-07,  1.37806413e-07,\n",
      "       -1.64704755e-07, -3.53567378e-07, -2.29609919e-07, -3.94466778e-08,\n",
      "       -1.77297198e-07, -4.76919911e-07,  5.38153760e-08,  2.55224862e-07,\n",
      "       -4.49709261e-08, -1.42918198e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 15\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819880e-01,  2.01829007e-02,  1.46406593e-01,  2.09915715e-03,\n",
      "        3.39475419e-04, -6.22440582e-04,  2.33453964e-04,  3.91240134e-04,\n",
      "        1.71785007e-05,  9.50069443e-04,  2.21491773e-04,  1.00080139e+00,\n",
      "        5.09875778e-04,  1.55010774e+01])\n",
      "Cost function after refinement: 1.984762272072607e-09\n",
      "GonioParam(dist=0.7948198800681063, poni1=0.020182900707789365, poni2=0.14640659333359882, rot1=0.002099157148607708, rot2=0.0003394754192576077, ex01=-0.0006224405820681487, ex02=0.0002334539635764792, ex11=0.0003912401336651575, ex12=1.717850072202974e-05, ex21=0.0009500694428961104, ex22=0.00022149177288883167, scale1=1.0008013896526833, scale2=0.00050987577787616, energy=15.501077423579561)\n",
      "Restore wavelength and former parameters\n",
      "GonioParam(dist=0.7948198800681063, poni1=0.020182900707789365, poni2=0.14640659333359882, rot1=0.002099157148607708, rot2=0.0003394754192576077, ex01=-0.0006224405820681487, ex02=0.0002334539635764792, ex11=0.0003912401336651575, ex12=1.717850072202974e-05, ex21=0.0009500694428961104, ex22=0.00022149177288883167, scale1=1.0008013896526833, scale2=0.00050987577787616, energy=15.501077423579561)\n",
      "**************************************************\n",
      "Re-extract frame #5\n",
      "ControlPoints instance containing 13 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.99842456399843e-11\n",
      "Containing 13 groups of points:\n",
      "#jf ring 11: 25 points\n",
      "#jg ring 12: 25 points\n",
      "#jh ring 13: 25 points\n",
      "#ji ring 14: 25 points\n",
      "#jj ring 15: 25 points\n",
      "#jk ring 16: 25 points\n",
      "#jl ring 17: 25 points\n",
      "#jm ring 18: 25 points\n",
      "#jn ring 19: 25 points\n",
      "#jo ring 20: 25 points\n",
      "#jp ring 21: 20 points\n",
      "#jq ring 22: 20 points\n",
      "#jr ring 23: 20 points\n",
      "Cost function before refinement: 1.984863239256766e-09\n",
      "[ 7.94819880e-01  2.01829007e-02  1.46406593e-01  2.09915715e-03\n",
      "  3.39475419e-04 -6.22440582e-04  2.33453964e-04  3.91240134e-04\n",
      "  1.71785007e-05  9.50069443e-04  2.21491773e-04  1.00080139e+00\n",
      "  5.09875778e-04  1.55010774e+01]\n",
      "     fun: 1.984647554383448e-09\n",
      "     jac: array([ 4.46312265e-08, -1.89010625e-07, -9.12151353e-07,  7.93468661e-07,\n",
      "       -1.65070805e-07, -3.27766515e-07, -2.07962001e-07, -3.93815703e-08,\n",
      "       -1.77242564e-07, -1.09899272e-06, -4.68127602e-07,  8.27421665e-07,\n",
      "       -4.52903701e-08, -1.05302099e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 17\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819875e-01,  2.01829219e-02,  1.46406696e-01,  2.09906824e-03,\n",
      "        3.39493916e-04, -6.22403855e-04,  2.33477266e-04,  3.91244547e-04,\n",
      "        1.71983614e-05,  9.50192589e-04,  2.21544228e-04,  1.00080130e+00,\n",
      "        5.09880853e-04,  1.55010774e+01])\n",
      "Cost function after refinement: 1.984647554383448e-09\n",
      "GonioParam(dist=0.794819875067016, poni1=0.020182921887113178, poni2=0.14640669554345512, rot1=0.0020990682375760256, rot2=0.0003394939160379866, ex01=-0.0006224038546453908, ex02=0.00023347726647047164, ex11=0.0003912445465124657, ex12=1.7198361392032484e-05, ex21=0.000950192589002446, ex22=0.00022154422827878477, scale1=1.0008012969370947, scale2=0.0005098808528258458, energy=15.501077435379042)\n",
      "maxdelta on: ex21 (9) 0.0009500694428961104 --> 0.000950192589002446\n",
      "**************************************************\n",
      "Re-extract frame #11\n",
      "ControlPoints instance containing 17 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424557909998e-11\n",
      "Containing 17 groups of points:\n",
      "#js ring 53: 15 points\n",
      "#jt ring 54: 13 points\n",
      "#ju ring 55: 15 points\n",
      "#jv ring 56: 15 points\n",
      "#jw ring 57: 20 points\n",
      "#jx ring 58: 20 points\n",
      "#jy ring 59: 20 points\n",
      "#jz ring 60: 20 points\n",
      "#ka ring 61: 20 points\n",
      "#kb ring 62: 20 points\n",
      "#kc ring 63: 20 points\n",
      "#kd ring 64: 20 points\n",
      "#ke ring 65: 20 points\n",
      "#kf ring 66: 20 points\n",
      "#kg ring 67: 20 points\n",
      "#kh ring 68: 25 points\n",
      "#ki ring 69: 25 points\n",
      "Cost function before refinement: 1.978969654683388e-09\n",
      "[ 7.94819875e-01  2.01829219e-02  1.46406696e-01  2.09906824e-03\n",
      "  3.39493916e-04 -6.22403855e-04  2.33477266e-04  3.91244547e-04\n",
      "  1.71983614e-05  9.50192589e-04  2.21544228e-04  1.00080130e+00\n",
      "  5.09880853e-04  1.55010774e+01]\n",
      "     fun: 1.9785686410547333e-09\n",
      "     jac: array([ 8.26543650e-08, -1.93008765e-07,  1.19421641e-06, -9.03533695e-07,\n",
      "       -1.66417769e-07, -2.98664509e-07, -2.05836346e-07, -4.22219419e-08,\n",
      "       -1.78348302e-07,  7.11051890e-07, -4.86300381e-07, -1.79879025e-06,\n",
      "       -4.53342754e-08, -3.53841215e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 17\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819865e-01,  2.01829452e-02,  1.46406551e-01,  2.09917755e-03,\n",
      "        3.39514051e-04, -6.22367720e-04,  2.33502170e-04,  3.91249655e-04,\n",
      "        1.72199395e-05,  9.50106560e-04,  2.21603065e-04,  1.00080151e+00,\n",
      "        5.09886338e-04,  1.55010775e+01])\n",
      "Cost function after refinement: 1.9785686410547333e-09\n",
      "GonioParam(dist=0.7948198650667798, poni1=0.0201829452389741, poni2=0.14640655105687944, rot1=0.002099177554855674, rot2=0.0003395140506914149, ex01=-0.0006223677196436267, ex02=0.00023350217032250945, ex11=0.00039124965488624526, ex12=1.721993950399456e-05, ex21=0.0009501065598274794, ex22=0.0002216030650825976, scale1=1.000801514570214, scale2=0.0005098863377565018, energy=15.501077478189796)\n",
      "maxdelta on: scale1 (11) 1.0008012969370947 --> 1.000801514570214\n",
      "**************************************************\n",
      "Re-extract frame #4\n",
      "ControlPoints instance containing 12 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424535820013e-11\n",
      "Containing 12 groups of points:\n",
      "#kj ring 6: 30 points\n",
      "#kk ring 7: 30 points\n",
      "#kl ring 8: 30 points\n",
      "#km ring 9: 25 points\n",
      "#kn ring 10: 25 points\n",
      "#ko ring 11: 25 points\n",
      "#kp ring 12: 25 points\n",
      "#kq ring 13: 25 points\n",
      "#kr ring 14: 25 points\n",
      "#ks ring 15: 25 points\n",
      "#kt ring 16: 25 points\n",
      "#ku ring 17: 25 points\n",
      "Cost function before refinement: 1.984122302522027e-09\n",
      "[ 7.94819865e-01  2.01829452e-02  1.46406551e-01  2.09917755e-03\n",
      "  3.39514051e-04 -6.22367720e-04  2.33502170e-04  3.91249655e-04\n",
      "  1.72199395e-05  9.50106560e-04  2.21603065e-04  1.00080151e+00\n",
      "  5.09886338e-04  1.55010775e+01]\n",
      "     fun: 1.983744385149349e-09\n",
      "     jac: array([ 1.09147864e-07, -1.81261788e-07, -1.01333506e-06,  8.73180189e-07,\n",
      "       -1.54178507e-07, -2.83074401e-07, -1.71016622e-07, -3.48081219e-08,\n",
      "       -1.70066412e-07, -9.87300464e-07, -1.29101148e-06,  2.28059006e-07,\n",
      "       -3.61436556e-08, -1.82680190e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 17\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819848e-01,  2.01829739e-02,  1.46406711e-01,  2.09903936e-03,\n",
      "        3.39538452e-04, -6.22322918e-04,  2.33529237e-04,  3.91255164e-04,\n",
      "        1.72468554e-05,  9.50262817e-04,  2.21807390e-04,  1.00080148e+00,\n",
      "        5.09892058e-04,  1.55010775e+01])\n",
      "Cost function after refinement: 1.983744385149349e-09\n",
      "GonioParam(dist=0.794819847792252, poni1=0.020182973926771403, poni2=0.1464067114345903, rot1=0.002099039359064439, rot2=0.00033953845209330486, ex01=-0.0006223229182482523, ex02=0.00023352923664584575, ex11=0.0003912551638705468, ex12=1.724685544037037e-05, ex21=0.00095026281711605, ex22=0.00022180738986642284, scale1=1.0008014784759518, scale2=0.0005098920581119873, energy=15.50107750710208)\n",
      "maxdelta on: ex22 (10) 0.0002216030650825976 --> 0.00022180738986642284\n",
      "**************************************************\n",
      "CPU times: user 1min 4s, sys: 54.7 ms, total: 1min 4s\n",
      "Wall time: 11.4 s\n"
     ]
    }
   ],
   "source": [
    "%time renew(range(13))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "unauthorized-slovenia",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-extract frame #5\n",
      "ControlPoints instance containing 13 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424520901518e-11\n",
      "Containing 13 groups of points:\n",
      "#kv ring 11: 25 points\n",
      "#kw ring 12: 25 points\n",
      "#kx ring 13: 25 points\n",
      "#ky ring 14: 25 points\n",
      "#kz ring 15: 25 points\n",
      "#la ring 16: 25 points\n",
      "#lb ring 17: 25 points\n",
      "#lc ring 18: 25 points\n",
      "#ld ring 19: 25 points\n",
      "#le ring 20: 25 points\n",
      "#lf ring 21: 20 points\n",
      "#lg ring 22: 20 points\n",
      "#lh ring 23: 20 points\n",
      "Cost function before refinement: 1.992331051763856e-09\n",
      "[ 7.94819848e-01  2.01829739e-02  1.46406711e-01  2.09903936e-03\n",
      "  3.39538452e-04 -6.22322918e-04  2.33529237e-04  3.91255164e-04\n",
      "  1.72468554e-05  9.50262817e-04  2.21807390e-04  1.00080148e+00\n",
      "  5.09892058e-04  1.55010775e+01]\n",
      "     fun: 1.992076311888785e-09\n",
      "     jac: array([ 1.16277690e-07, -1.89127407e-07,  5.05439359e-07, -3.54844468e-07,\n",
      "       -1.61940122e-07, -2.84445183e-07, -1.69937840e-07, -3.99442344e-08,\n",
      "       -1.75274827e-07,  1.41175323e-07, -6.98938837e-07, -1.10997217e-06,\n",
      "       -4.24704024e-08, -2.95231984e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 17\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819824e-01,  2.01830130e-02,  1.46406607e-01,  2.09911258e-03,\n",
      "        3.39571867e-04, -6.22264225e-04,  2.33564302e-04,  3.91263406e-04,\n",
      "        1.72830223e-05,  9.50233686e-04,  2.21951612e-04,  1.00080171e+00,\n",
      "        5.09900822e-04,  1.55010776e+01])\n",
      "Cost function after refinement: 1.992076311888785e-09\n",
      "GonioParam(dist=0.7948198237990555, poni1=0.020183012952065392, poni2=0.14640660714024212, rot1=0.00209911257906966, rot2=0.0003395718674559602, ex01=-0.0006222642247087417, ex02=0.0002335643022891949, ex11=0.0003912634061212142, ex12=1.72830223386136e-05, ex21=0.000950233686443795, ex22=0.00022195161165898, scale1=1.0008017075119824, scale2=0.0005099008216220978, energy=15.50107756802141)\n",
      "maxdelta on: scale1 (11) 1.0008014784759518 --> 1.0008017075119824\n",
      "**************************************************\n",
      "Re-extract frame #10\n",
      "ControlPoints instance containing 16 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424489467662e-11\n",
      "Containing 16 groups of points:\n",
      "#li ring 46: 15 points\n",
      "#lj ring 47: 15 points\n",
      "#lk ring 48: 15 points\n",
      "#ll ring 49: 15 points\n",
      "#lm ring 50: 15 points\n",
      "#ln ring 51: 15 points\n",
      "#lo ring 52: 15 points\n",
      "#lp ring 53: 15 points\n",
      "#lq ring 54: 15 points\n",
      "#lr ring 55: 20 points\n",
      "#ls ring 56: 20 points\n",
      "#lt ring 58: 20 points\n",
      "#lu ring 59: 20 points\n",
      "#lv ring 60: 20 points\n",
      "#lw ring 61: 20 points\n",
      "#lx ring 62: 20 points\n",
      "Cost function before refinement: 1.995729855184429e-09\n",
      "[ 7.94819824e-01  2.01830130e-02  1.46406607e-01  2.09911258e-03\n",
      "  3.39571867e-04 -6.22264225e-04  2.33564302e-04  3.91263406e-04\n",
      "  1.72830223e-05  9.50233686e-04  2.21951612e-04  1.00080171e+00\n",
      "  5.09900822e-04  1.55010776e+01]\n",
      "     fun: 1.995450220430426e-09\n",
      "     jac: array([ 8.61402316e-08, -1.86188824e-07, -3.16058854e-07,  3.04800829e-07,\n",
      "       -1.70377993e-07, -3.11708171e-07, -1.66900584e-07, -3.76711485e-08,\n",
      "       -1.74524503e-07, -5.46854434e-07, -8.14341988e-07, -1.86980120e-07,\n",
      "       -5.85516099e-08, -2.07383282e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 17\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819792e-01,  2.01830827e-02,  1.46406726e-01,  2.09899835e-03,\n",
      "        3.39635718e-04, -6.22147410e-04,  2.33626849e-04,  3.91277524e-04,\n",
      "        1.73484263e-05,  9.50438623e-04,  2.22256791e-04,  1.00080178e+00,\n",
      "        5.09922764e-04,  1.55010776e+01])\n",
      "Cost function after refinement: 1.995450220430426e-09\n",
      "GonioParam(dist=0.7948197915175383, poni1=0.02018308272733293, poni2=0.146406725585005, rot1=0.002098998353312612, rot2=0.0003396357175290331, ex01=-0.000622147410387853, ex02=0.00023362684918461788, ex11=0.00039127752358759905, ex12=1.7348426338819653e-05, ex21=0.0009504386230969113, ex22=0.00022225679071752986, scale1=1.0008017775837925, scale2=0.0005099227641541743, energy=15.501077645739414)\n",
      "maxdelta on: ex22 (10) 0.00022195161165898 --> 0.00022225679071752986\n",
      "**************************************************\n",
      "Re-extract frame #9\n",
      "ControlPoints instance containing 17 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424449365831e-11\n",
      "Containing 17 groups of points:\n",
      "#ly ring 38: 15 points\n",
      "#lz ring 39: 15 points\n",
      "#ma ring 40: 15 points\n",
      "#mb ring 41: 15 points\n",
      "#mc ring 42: 15 points\n",
      "#md ring 43: 15 points\n",
      "#me ring 44: 15 points\n",
      "#mf ring 45: 15 points\n",
      "#mg ring 46: 15 points\n",
      "#mh ring 47: 15 points\n",
      "#mi ring 48: 15 points\n",
      "#mj ring 49: 15 points\n",
      "#mk ring 50: 15 points\n",
      "#ml ring 51: 15 points\n",
      "#mm ring 52: 15 points\n",
      "#mn ring 53: 15 points\n",
      "#mo ring 54: 15 points\n",
      "Cost function before refinement: 2.001590296727567e-09\n",
      "[ 7.94819792e-01  2.01830827e-02  1.46406726e-01  2.09899835e-03\n",
      "  3.39635718e-04 -6.22147410e-04  2.33626849e-04  3.91277524e-04\n",
      "  1.73484263e-05  9.50438623e-04  2.22256791e-04  1.00080178e+00\n",
      "  5.09922764e-04  1.55010776e+01]\n",
      "     fun: 2.001331714824601e-09\n",
      "     jac: array([ 1.14776324e-07, -1.89347178e-07,  1.11100860e-06, -8.47046181e-07,\n",
      "       -1.77979487e-07, -2.97402427e-07, -1.48208910e-07, -3.92112030e-08,\n",
      "       -1.77057536e-07,  5.04938157e-07, -1.79163693e-07, -1.37042139e-06,\n",
      "       -6.77793995e-08, -3.03773295e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 17\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819778e-01,  2.01831047e-02,  1.46406597e-01,  2.09909644e-03,\n",
      "        3.39656327e-04, -6.22112972e-04,  2.33644012e-04,  3.91282064e-04,\n",
      "        1.73689293e-05,  9.50380152e-04,  2.22277538e-04,  1.00080194e+00,\n",
      "        5.09930613e-04,  1.55010777e+01])\n",
      "Cost function after refinement: 2.001331714824601e-09\n",
      "GonioParam(dist=0.7948197782266168, poni1=0.020183104653444556, poni2=0.14640659693191935, rot1=0.0020990964399560754, rot2=0.0003396563272795509, ex01=-0.0006221129716447511, ex02=0.0002336440115481796, ex11=0.0003912820641845317, ex12=1.736892932880696e-05, ex21=0.0009503801520358917, ex22=0.00022227753759730592, scale1=1.000801936276479, scale2=0.0005099306129042717, energy=15.501077680915895)\n",
      "maxdelta on: scale1 (11) 1.0008017775837925 --> 1.000801936276479\n",
      "**************************************************\n",
      "Re-extract frame #11\n",
      "ControlPoints instance containing 17 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424431215065e-11\n",
      "Containing 17 groups of points:\n",
      "#mp ring 53: 15 points\n",
      "#mq ring 54: 15 points\n",
      "#mr ring 55: 15 points\n",
      "#ms ring 56: 15 points\n",
      "#mt ring 57: 20 points\n",
      "#mu ring 58: 20 points\n",
      "#mv ring 59: 20 points\n",
      "#mw ring 60: 20 points\n",
      "#mx ring 61: 20 points\n",
      "#my ring 62: 20 points\n",
      "#mz ring 63: 20 points\n",
      "#na ring 64: 20 points\n",
      "#nb ring 65: 20 points\n",
      "#nc ring 66: 20 points\n",
      "#nd ring 67: 20 points\n",
      "#ne ring 68: 25 points\n",
      "#nf ring 69: 25 points\n",
      "Cost function before refinement: 2.021531328595229e-09\n",
      "[ 7.94819778e-01  2.01831047e-02  1.46406597e-01  2.09909644e-03\n",
      "  3.39656327e-04 -6.22112972e-04  2.33644012e-04  3.91282064e-04\n",
      "  1.73689293e-05  9.50380152e-04  2.22277538e-04  1.00080194e+00\n",
      "  5.09930613e-04  1.55010777e+01]\n",
      "     fun: 2.0213552093631653e-09\n",
      "     jac: array([ 1.52068561e-07, -1.78319947e-07, -7.19583460e-07,  6.25053812e-07,\n",
      "       -1.89204061e-07, -2.56878534e-07, -1.73401609e-07, -2.98089192e-08,\n",
      "       -1.78225167e-07, -1.06421899e-06, -2.19362362e-07,  8.60898983e-07,\n",
      "       -9.20176504e-08, -1.05564671e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 17\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819761e-01,  2.01831251e-02,  1.46406679e-01,  2.09902483e-03,\n",
      "        3.39678004e-04, -6.22083542e-04,  2.33663877e-04,  3.91285479e-04,\n",
      "        1.73893478e-05,  9.50502075e-04,  2.22302669e-04,  1.00080184e+00,\n",
      "        5.09941155e-04,  1.55010777e+01])\n",
      "Cost function after refinement: 2.0213552093631653e-09\n",
      "GonioParam(dist=0.7948197608048234, poni1=0.020183125082738097, poni2=0.14640667937127594, rot1=0.0020990248304250557, rot2=0.0003396780035158121, ex01=-0.0006220835422564752, ex02=0.0002336638773703706, ex11=0.00039128547925485624, ex12=1.738934776384714e-05, ex21=0.0009505020746943708, ex22=0.00022230266893086666, scale1=1.0008018376472552, scale2=0.0005099411549418821, energy=15.501077693009952)\n",
      "maxdelta on: ex21 (9) 0.0009503801520358917 --> 0.0009505020746943708\n",
      "**************************************************\n",
      "Re-extract frame #7\n",
      "ControlPoints instance containing 16 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424424974634e-11\n",
      "Containing 16 groups of points:\n",
      "#ng ring 23: 20 points\n",
      "#nh ring 24: 20 points\n",
      "#ni ring 25: 20 points\n",
      "#nj ring 26: 20 points\n",
      "#nk ring 27: 20 points\n",
      "#nl ring 28: 20 points\n",
      "#nm ring 29: 20 points\n",
      "#nn ring 30: 20 points\n",
      "#no ring 31: 20 points\n",
      "#np ring 32: 20 points\n",
      "#nq ring 34: 20 points\n",
      "#nr ring 35: 20 points\n",
      "#ns ring 36: 20 points\n",
      "#nt ring 37: 15 points\n",
      "#nu ring 38: 15 points\n",
      "#nv ring 39: 15 points\n",
      "Cost function before refinement: 2.037492714682155e-09\n",
      "[ 7.94819761e-01  2.01831251e-02  1.46406679e-01  2.09902483e-03\n",
      "  3.39678004e-04 -6.22083542e-04  2.33663877e-04  3.91285479e-04\n",
      "  1.73893478e-05  9.50502075e-04  2.22302669e-04  1.00080184e+00\n",
      "  5.09941155e-04  1.55010777e+01]\n",
      "     fun: 2.037407442157624e-09\n",
      "     jac: array([ 1.97438362e-07, -1.80825675e-07,  6.19159617e-07, -4.54851387e-07,\n",
      "       -1.93360842e-07, -2.19084984e-07, -1.58409781e-07, -3.14960610e-08,\n",
      "       -1.79548750e-07,  5.71702925e-08,  1.36723724e-07, -4.98305837e-07,\n",
      "       -9.64473092e-08, -2.23844992e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 17\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819731e-01,  2.01831521e-02,  1.46406587e-01,  2.09909282e-03,\n",
      "        3.39706908e-04, -6.22050792e-04,  2.33687557e-04,  3.91290187e-04,\n",
      "        1.74161877e-05,  9.50493529e-04,  2.22282231e-04,  1.00080191e+00,\n",
      "        5.09955572e-04,  1.55010777e+01])\n",
      "Cost function after refinement: 2.037407442157624e-09\n",
      "GonioParam(dist=0.7948197312906387, poni1=0.020183152113566093, poni2=0.14640658681585159, rot1=0.0020990928241409476, rot2=0.0003397069081702759, ex01=-0.0006220507922142037, ex02=0.0002336875573457436, ex11=0.0003912901874612864, ex12=1.7416187709954596e-05, ex21=0.0009504935285609813, ex22=0.00022228223070793013, scale1=1.000801912136784, scale2=0.0005099555724222475, energy=15.501077726471546)\n",
      "maxdelta on: poni2 (2) 0.14640667937127594 --> 0.14640658681585159\n",
      "**************************************************\n",
      "Re-extract frame #1\n",
      "ControlPoints instance containing 3 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424407708736e-11\n",
      "Containing 3 groups of points:\n",
      "#nw ring 0: 80 points\n",
      "#nx ring 1: 55 points\n",
      "#ny ring 2: 45 points\n",
      "Cost function before refinement: 2.0288533035716235e-09\n",
      "[ 7.94819731e-01  2.01831521e-02  1.46406587e-01  2.09909282e-03\n",
      "  3.39706908e-04 -6.22050792e-04  2.33687557e-04  3.91290187e-04\n",
      "  1.74161877e-05  9.50493529e-04  2.22282231e-04  1.00080191e+00\n",
      "  5.09955572e-04  1.55010777e+01]\n",
      "     fun: 2.0288533035716235e-09\n",
      "     jac: array([ 1.83257715e-07, -1.49760937e-07, -5.27204478e-08,  8.66858976e-08,\n",
      "       -1.67833900e-07, -2.27550609e-07, -1.63983481e-07, -2.53726898e-08,\n",
      "       -1.49610588e-07, -4.98811342e-07, -1.09187832e-08,  2.07964252e-07,\n",
      "       -9.09932509e-08, -1.61033470e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 15\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819731e-01,  2.01831521e-02,  1.46406587e-01,  2.09909282e-03,\n",
      "        3.39706908e-04, -6.22050792e-04,  2.33687557e-04,  3.91290187e-04,\n",
      "        1.74161877e-05,  9.50493529e-04,  2.22282231e-04,  1.00080191e+00,\n",
      "        5.09955572e-04,  1.55010777e+01])\n",
      "Cost function after refinement: 2.0288533035716235e-09\n",
      "GonioParam(dist=0.7948197312906387, poni1=0.020183152113566093, poni2=0.14640658681585159, rot1=0.0020990928241409476, rot2=0.0003397069081702759, ex01=-0.0006220507922142037, ex02=0.0002336875573457436, ex11=0.0003912901874612864, ex12=1.7416187709954596e-05, ex21=0.0009504935285609813, ex22=0.00022228223070793013, scale1=1.000801912136784, scale2=0.0005099555724222475, energy=15.501077726471546)\n",
      "Restore wavelength and former parameters\n",
      "GonioParam(dist=0.7948197312906387, poni1=0.020183152113566093, poni2=0.14640658681585159, rot1=0.0020990928241409476, rot2=0.0003397069081702759, ex01=-0.0006220507922142037, ex02=0.0002336875573457436, ex11=0.0003912901874612864, ex12=1.7416187709954596e-05, ex21=0.0009504935285609813, ex22=0.00022228223070793013, scale1=1.000801912136784, scale2=0.0005099555724222475, energy=15.501077726471546)\n",
      "**************************************************\n",
      "Re-extract frame #8\n",
      "ControlPoints instance containing 17 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424407708736e-11\n",
      "Containing 17 groups of points:\n",
      "#nz ring 30: 20 points\n",
      "#oa ring 31: 20 points\n",
      "#ob ring 32: 20 points\n",
      "#oc ring 33: 20 points\n",
      "#od ring 34: 20 points\n",
      "#oe ring 35: 20 points\n",
      "#of ring 36: 20 points\n",
      "#og ring 37: 20 points\n",
      "#oh ring 38: 20 points\n",
      "#oi ring 39: 20 points\n",
      "#oj ring 40: 15 points\n",
      "#ok ring 41: 15 points\n",
      "#ol ring 42: 15 points\n",
      "#om ring 43: 15 points\n",
      "#on ring 44: 15 points\n",
      "#oo ring 45: 15 points\n",
      "#op ring 46: 15 points\n",
      "Cost function before refinement: 2.0544713808554787e-09\n",
      "[ 7.94819731e-01  2.01831521e-02  1.46406587e-01  2.09909282e-03\n",
      "  3.39706908e-04 -6.22050792e-04  2.33687557e-04  3.91290187e-04\n",
      "  1.74161877e-05  9.50493529e-04  2.22282231e-04  1.00080191e+00\n",
      "  5.09955572e-04  1.55010777e+01]\n",
      "     fun: 2.054372713776567e-09\n",
      "     jac: array([ 1.63235500e-07, -1.49212963e-07, -3.60931373e-07,  3.38239619e-07,\n",
      "       -1.67404696e-07, -2.47268656e-07, -1.67460191e-07, -2.48330415e-08,\n",
      "       -1.49515441e-07, -8.02340157e-07, -6.44373086e-08,  5.59200931e-07,\n",
      "       -9.03939558e-08, -1.31720441e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 17\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819709e-01,  2.01831728e-02,  1.46406637e-01,  2.09904590e-03,\n",
      "        3.39730132e-04, -6.22016489e-04,  2.33710789e-04,  3.91293633e-04,\n",
      "        1.74369298e-05,  9.50604836e-04,  2.22291170e-04,  1.00080183e+00,\n",
      "        5.09968113e-04,  1.55010777e+01])\n",
      "Cost function after refinement: 2.054372713776567e-09\n",
      "GonioParam(dist=0.7948197086451868, poni1=0.020183172813689372, poni2=0.1464066368873986, rot1=0.002099045900591878, rot2=0.0003397301320092527, ex01=-0.0006220164889502249, ex02=0.0002337107888835396, ex11=0.00039129363251732603, ex12=1.7436929795597158e-05, ex21=0.000950604836183163, ex22=0.00022229117001321664, scale1=1.000801834559555, scale2=0.0005099681126599467, energy=15.501077744744954)\n",
      "maxdelta on: ex21 (9) 0.0009504935285609813 --> 0.000950604836183163\n",
      "**************************************************\n",
      "Re-extract frame #3\n",
      "ControlPoints instance containing 9 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424398279813e-11\n",
      "Containing 9 groups of points:\n",
      "#oq ring 3: 40 points\n",
      "#or ring 4: 40 points\n",
      "#os ring 5: 35 points\n",
      "#ot ring 6: 30 points\n",
      "#ou ring 7: 30 points\n",
      "#ov ring 8: 30 points\n",
      "#ow ring 9: 25 points\n",
      "#ox ring 10: 25 points\n",
      "#oy ring 11: 25 points\n",
      "Cost function before refinement: 2.0506342667690335e-09\n",
      "[ 7.94819709e-01  2.01831728e-02  1.46406637e-01  2.09904590e-03\n",
      "  3.39730132e-04 -6.22016489e-04  2.33710789e-04  3.91293633e-04\n",
      "  1.74369298e-05  9.50604836e-04  2.22291170e-04  1.00080183e+00\n",
      "  5.09968113e-04  1.55010777e+01]\n",
      "     fun: 2.050530880156948e-09\n",
      "     jac: array([ 1.83006062e-07, -1.60717053e-07,  6.79348250e-07, -4.99564733e-07,\n",
      "       -1.77458790e-07, -2.34664537e-07, -1.58160263e-07, -3.08235713e-08,\n",
      "       -1.58943205e-07, -4.15686695e-08,  3.47541597e-07, -3.51421886e-07,\n",
      "       -9.62298967e-08, -2.10396482e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 17\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819677e-01,  2.01832004e-02,  1.46406520e-01,  2.09913160e-03,\n",
      "        3.39760575e-04, -6.21976232e-04,  2.33737922e-04,  3.91298920e-04,\n",
      "        1.74641968e-05,  9.50611967e-04,  2.22231549e-04,  1.00080189e+00,\n",
      "        5.09984621e-04,  1.55010778e+01])\n",
      "Cost function after refinement: 2.050530880156948e-09\n",
      "GonioParam(dist=0.7948196772502014, poni1=0.020183200384959278, poni2=0.14640652034410984, rot1=0.0020991316017281696, rot2=0.00033976057535106994, ex01=-0.0006219762318702135, ex02=0.0002337379215319168, ex11=0.00039129892035073745, ex12=1.746419675891243e-05, ex21=0.0009506119673555036, ex22=0.00022223154869139766, scale1=1.0008018948465467, scale2=0.0005099846210540477, energy=15.501077780838811)\n",
      "maxdelta on: poni2 (2) 0.1464066368873986 --> 0.14640652034410984\n",
      "**************************************************\n",
      "Re-extract frame #6\n",
      "ControlPoints instance containing 15 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.99842437965569e-11\n",
      "Containing 15 groups of points:\n",
      "#oz ring 17: 25 points\n",
      "#pa ring 18: 25 points\n",
      "#pb ring 19: 25 points\n",
      "#pc ring 20: 25 points\n",
      "#pd ring 21: 20 points\n",
      "#pe ring 22: 20 points\n",
      "#pf ring 23: 20 points\n",
      "#pg ring 24: 20 points\n",
      "#ph ring 25: 20 points\n",
      "#pi ring 26: 20 points\n",
      "#pj ring 27: 20 points\n",
      "#pk ring 28: 20 points\n",
      "#pl ring 29: 20 points\n",
      "#pm ring 30: 20 points\n",
      "#pn ring 31: 20 points\n",
      "Cost function before refinement: 2.061056132548845e-09\n",
      "[ 7.94819677e-01  2.01832004e-02  1.46406520e-01  2.09913160e-03\n",
      "  3.39760575e-04 -6.21976232e-04  2.33737922e-04  3.91298920e-04\n",
      "  1.74641968e-05  9.50611967e-04  2.22231549e-04  1.00080189e+00\n",
      "  5.09984621e-04  1.55010778e+01]\n",
      "     fun: 2.061056132548845e-09\n",
      "     jac: array([ 2.02294548e-07, -1.64476352e-07, -1.42986373e-07,  1.62421053e-07,\n",
      "       -1.86898189e-07, -2.13112399e-07, -1.47877855e-07, -3.49504221e-08,\n",
      "       -1.60333605e-07, -6.66622030e-07,  7.06346140e-08,  4.11553010e-07,\n",
      "       -1.06874218e-07, -1.46553271e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 15\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819677e-01,  2.01832004e-02,  1.46406520e-01,  2.09913160e-03,\n",
      "        3.39760575e-04, -6.21976232e-04,  2.33737922e-04,  3.91298920e-04,\n",
      "        1.74641968e-05,  9.50611967e-04,  2.22231549e-04,  1.00080189e+00,\n",
      "        5.09984621e-04,  1.55010778e+01])\n",
      "Cost function after refinement: 2.061056132548845e-09\n",
      "GonioParam(dist=0.7948196772502014, poni1=0.020183200384959278, poni2=0.14640652034410984, rot1=0.0020991316017281696, rot2=0.00033976057535106994, ex01=-0.0006219762318702135, ex02=0.0002337379215319168, ex11=0.00039129892035073745, ex12=1.746419675891243e-05, ex21=0.0009506119673555036, ex22=0.00022223154869139766, scale1=1.0008018948465467, scale2=0.0005099846210540477, energy=15.501077780838811)\n",
      "Restore wavelength and former parameters\n",
      "GonioParam(dist=0.7948196772502014, poni1=0.020183200384959278, poni2=0.14640652034410984, rot1=0.0020991316017281696, rot2=0.00033976057535106994, ex01=-0.0006219762318702135, ex02=0.0002337379215319168, ex11=0.00039129892035073745, ex12=1.746419675891243e-05, ex21=0.0009506119673555036, ex22=0.00022223154869139766, scale1=1.0008018948465467, scale2=0.0005099846210540477, energy=15.501077780838811)\n",
      "**************************************************\n",
      "Re-extract frame #4\n",
      "ControlPoints instance containing 12 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.99842437965569e-11\n",
      "Containing 12 groups of points:\n",
      "#po ring 6: 30 points\n",
      "#pp ring 7: 30 points\n",
      "#pq ring 8: 30 points\n",
      "#pr ring 9: 25 points\n",
      "#ps ring 10: 25 points\n",
      "#pt ring 11: 25 points\n",
      "#pu ring 12: 25 points\n",
      "#pv ring 13: 25 points\n",
      "#pw ring 14: 25 points\n",
      "#px ring 15: 25 points\n",
      "#py ring 16: 25 points\n",
      "#pz ring 17: 25 points\n",
      "Cost function before refinement: 2.0731561865826213e-09\n",
      "[ 7.94819677e-01  2.01832004e-02  1.46406520e-01  2.09913160e-03\n",
      "  3.39760575e-04 -6.21976232e-04  2.33737922e-04  3.91298920e-04\n",
      "  1.74641968e-05  9.50611967e-04  2.22231549e-04  1.00080189e+00\n",
      "  5.09984621e-04  1.55010778e+01]\n",
      "     fun: 2.072957057644144e-09\n",
      "     jac: array([ 2.30030043e-07, -1.75745618e-07,  6.88487942e-07, -5.15722163e-07,\n",
      "       -1.97905997e-07, -1.95283953e-07, -1.26631579e-07, -4.21943241e-08,\n",
      "       -1.68966214e-07, -1.32148293e-07,  7.07570506e-07, -6.18934410e-08,\n",
      "       -1.14559321e-07, -1.80462595e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 17\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819616e-01,  2.01832471e-02,  1.46406338e-01,  2.09926855e-03,\n",
      "        3.39813129e-04, -6.21924375e-04,  2.33771548e-04,  3.91310125e-04,\n",
      "        1.75090653e-05,  9.50647059e-04,  2.22043655e-04,  1.00080191e+00,\n",
      "        5.10015042e-04,  1.55010778e+01])\n",
      "Cost function after refinement: 2.072957057644144e-09\n",
      "GonioParam(dist=0.7948196161662981, poni1=0.020183247053768946, poni2=0.14640633751785695, rot1=0.0020992685504600808, rot2=0.0003398131287941569, ex01=-0.000621924374704389, ex02=0.0002337715482314376, ex11=0.0003913101249480904, ex12=1.7509065314936607e-05, ex21=0.0009506470590045492, ex22=0.0002220436551112529, scale1=1.0008019112821953, scale2=0.0005100150419955145, energy=15.501077828760202)\n",
      "maxdelta on: ex22 (10) 0.00022223154869139766 --> 0.0002220436551112529\n",
      "**************************************************\n",
      "Re-extract frame #2\n",
      "ControlPoints instance containing 7 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.99842435492866e-11\n",
      "Containing 7 groups of points:\n",
      "#qa ring 0: 70 points\n",
      "#qb ring 1: 60 points\n",
      "#qc ring 2: 50 points\n",
      "#qd ring 3: 10 points\n",
      "#qe ring 4: 40 points\n",
      "#qf ring 5: 35 points\n",
      "#qg ring 6: 30 points\n",
      "Cost function before refinement: 2.0699351195566363e-09\n",
      "[ 7.94819616e-01  2.01832471e-02  1.46406338e-01  2.09926855e-03\n",
      "  3.39813129e-04 -6.21924375e-04  2.33771548e-04  3.91310125e-04\n",
      "  1.75090653e-05  9.50647059e-04  2.22043655e-04  1.00080191e+00\n",
      "  5.10015042e-04  1.55010778e+01]\n",
      "     fun: 2.0697986717713375e-09\n",
      "     jac: array([ 2.21898558e-07, -1.47856949e-07, -3.48785870e-07,  3.17256333e-07,\n",
      "       -1.73894973e-07, -2.00758791e-07, -1.26224013e-07, -3.22023797e-08,\n",
      "       -1.43425898e-07, -8.71616644e-07,  2.47059433e-07,  7.89583298e-07,\n",
      "       -1.05255514e-07, -1.09421309e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 17\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819584e-01,  2.01832688e-02,  1.46406389e-01,  2.09922197e-03,\n",
      "        3.39838661e-04, -6.21894898e-04,  2.33790081e-04,  3.91314853e-04,\n",
      "        1.75301238e-05,  9.50775034e-04,  2.22007381e-04,  1.00080180e+00,\n",
      "        5.10030496e-04,  1.55010778e+01])\n",
      "Cost function after refinement: 2.0697986717713375e-09\n",
      "GonioParam(dist=0.794819583586059, poni1=0.02018326876285449, poni2=0.1464063887283163, rot1=0.0020992219693226526, rot2=0.00033983866091061, ex01=-0.0006218948983098678, ex02=0.00023379008106277728, ex11=0.00039131485306008766, ex12=1.7530123811798643e-05, ex21=0.0009507750340535289, ex22=0.00022200738062838716, scale1=1.0008017953516863, scale2=0.00051003049612848, energy=15.501077844825979)\n",
      "maxdelta on: ex21 (9) 0.0009506470590045492 --> 0.0009507750340535289\n",
      "**************************************************\n",
      "Re-extract frame #0\n",
      "ControlPoints instance containing 1 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424346638855e-11\n",
      "Containing 1 groups of points:\n",
      "#qh ring 0: 80 points\n",
      "Cost function before refinement: 2.072342705793995e-09\n",
      "[ 7.94819584e-01  2.01832688e-02  1.46406389e-01  2.09922197e-03\n",
      "  3.39838661e-04 -6.21894898e-04  2.33790081e-04  3.91314853e-04\n",
      "  1.75301238e-05  9.50775034e-04  2.22007381e-04  1.00080180e+00\n",
      "  5.10030496e-04  1.55010778e+01]\n",
      "     fun: 2.072202601072138e-09\n",
      "     jac: array([ 2.60160583e-07, -1.58610592e-07,  6.39619402e-07, -4.80415054e-07,\n",
      "       -1.82894262e-07, -1.90111704e-07, -9.96313950e-08, -3.33620672e-08,\n",
      "       -1.53283231e-07, -1.42609901e-07,  5.06698036e-07, -1.66299557e-07,\n",
      "       -1.06781275e-07, -1.96844974e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 17\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819522e-01,  2.01833063e-02,  1.46406237e-01,  2.09933571e-03,\n",
      "        3.39881963e-04, -6.21849888e-04,  2.33813670e-04,  3.91322752e-04,\n",
      "        1.75664149e-05,  9.50808798e-04,  2.21887416e-04,  1.00080183e+00,\n",
      "        5.10055777e-04,  1.55010779e+01])\n",
      "Cost function after refinement: 2.072202601072138e-09\n",
      "GonioParam(dist=0.7948195219908408, poni1=0.020183306315253384, poni2=0.14640623729301372, rot1=0.0020993357116495157, rot2=0.0003398819626737394, ex01=-0.0006218498877561624, ex02=0.00023381366963811024, ex11=0.00039132275181164, ex12=1.7566414913056872e-05, ex21=0.0009508087981536737, ex22=0.00022188741558340586, scale1=1.000801834724513, scale2=0.0005100557774982815, energy=15.501077891430691)\n",
      "maxdelta on: poni2 (2) 0.1464063887283163 --> 0.14640623729301372\n",
      "**************************************************\n",
      "Re-extract frame #12\n",
      "ControlPoints instance containing 15 group of point:\n",
      "LaB6 Calibrant with 92 reflections at wavelength 7.998424322591221e-11\n",
      "Containing 15 groups of points:\n",
      "#qi ring 61: 20 points\n",
      "#qj ring 62: 20 points\n",
      "#qk ring 63: 20 points\n",
      "#ql ring 64: 20 points\n",
      "#qm ring 66: 20 points\n",
      "#qn ring 67: 25 points\n",
      "#qo ring 68: 25 points\n",
      "#qp ring 69: 25 points\n",
      "#qq ring 70: 25 points\n",
      "#qr ring 71: 25 points\n",
      "#qs ring 72: 25 points\n",
      "#qt ring 73: 25 points\n",
      "#qu ring 74: 25 points\n",
      "#qv ring 75: 25 points\n",
      "#qw ring 76: 25 points\n",
      "Cost function before refinement: 2.0756394825016054e-09\n",
      "[ 7.94819522e-01  2.01833063e-02  1.46406237e-01  2.09933571e-03\n",
      "  3.39881963e-04 -6.21849888e-04  2.33813670e-04  3.91322752e-04\n",
      "  1.75664149e-05  9.50808798e-04  2.21887416e-04  1.00080183e+00\n",
      "  5.10055777e-04  1.55010779e+01]\n",
      "     fun: 2.075483678340505e-09\n",
      "     jac: array([ 3.67885274e-07, -1.56813718e-07,  5.23696311e-07, -3.90956169e-07,\n",
      "       -1.79398635e-07, -8.94531283e-08, -1.74928107e-07, -3.31377555e-08,\n",
      "       -1.49998277e-07, -1.10250149e-07, -2.72116018e-07, -7.44973072e-07,\n",
      "       -1.03774733e-07, -3.06642918e-07])\n",
      " message: 'Optimization terminated successfully'\n",
      "    nfev: 17\n",
      "     nit: 1\n",
      "    njev: 1\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 7.94819443e-01,  2.01833399e-02,  1.46406125e-01,  2.09941953e-03,\n",
      "        3.39920426e-04, -6.21830709e-04,  2.33851174e-04,  3.91329857e-04,\n",
      "        1.75985745e-05,  9.50832436e-04,  2.21945757e-04,  1.00080199e+00,\n",
      "        5.10078027e-04,  1.55010780e+01])\n",
      "Cost function after refinement: 2.075483678340505e-09\n",
      "GonioParam(dist=0.7948194431163379, poni1=0.020183339936072006, poni2=0.14640612501266675, rot1=0.002099419532546058, rot2=0.0003399204256923382, ex01=-0.0006218307090297266, ex02=0.00023385117417677227, ex11=0.00039132985653742643, ex12=1.759857450288529e-05, ex21=0.0009508324357556939, ex22=0.0002219457571837978, scale1=1.0008019944465374, scale2=0.0005100780267729206, energy=15.50107795717485)\n",
      "maxdelta on: scale1 (11) 1.000801834724513 --> 1.0008019944465374\n",
      "**************************************************\n",
      "CPU times: user 1min, sys: 84.1 ms, total: 1min\n",
      "Wall time: 11 s\n"
     ]
    }
   ],
   "source": [
    "%time renew(range(13))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "hispanic-papua",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\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"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n",
      "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n",
      "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n",
      "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n",
      "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n",
      "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n",
      "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n",
      "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n",
      "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n",
      "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n",
      "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n",
      "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n"
     ]
    }
   ],
   "source": [
    "display_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "threatened-fashion",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total fit time: 63.686s\n"
     ]
    }
   ],
   "source": [
    "fit_time = time.perf_counter()\n",
    "print(f\"total fit time: {fit_time-start_time:.3f}s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "sudden-cosmetic",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultiGeometry integrator with 13 geometries on (0, 131.88) radial range (2th_deg) and (-180, 180) azimuthal range (deg)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\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"
    },
    {
     "data": {
      "text/html": [