IK_circle_intersections

IK_circle_intersections.ipynb

{
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          "start_time": "2021-06-06T07:13:24.479730Z",
          "end_time": "2021-06-06T07:13:24.487394Z"
        },
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      "source": "%matplotlib notebook\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom numpy.linalg import norm\ntau = 2 * np.pi",
      "execution_count": 3,
      "outputs": []
    },
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      "metadata": {
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          "start_time": "2021-06-06T07:13:25.109513Z",
          "end_time": "2021-06-06T07:13:25.205263Z"
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      "source": "class Circle:\n    def __init__(self, x, y, r):\n        self.x = x\n        self.y = y\n        self.r = r\n\ndef intersects(x0, y0, r0, x1, y1, r1):\n    d = ((x1 - x0)**2 + (y1 - y0)**2)**0.5\n    if d > r0 + r1 :# non intersecting\n        print('non intersects')\n        return None\n    if d < abs(r0 - r1):# One circle within other\n        print('within')\n        return None\n    if d == 0 and r0 == r1:# coincident circles\n        print('coincident')\n        return None\n    else:\n        a = (r0**2 - r1**2 + d**2) / (2*d)\n        h = (r0**2 - a**2)**0.5\n        x2 = x0 + a*(x1 - x0) / d   \n        y2 = y0 + a*(y1 - y0) / d   \n        x3 = x2 + h*(y1 - y0) / d     \n        y3 = y2 - h*(x1 - x0) / d\n        x4 = x2 - h*(y1 - y0) / d\n        y4 = y2 + h*(x1 - x0) / d       \n        return [x3, y3], [x4, y4]\n    \ndef Plot(LV):\n    fig, ax = plt.subplots(figsize = (5,5))\n    ax.axis([-2, N+1, -2, N+1])\n    ax.grid()\n    ax.plot(TV[0], TV[1], 'rx', ms=10)\n    for i in range(N):\n        ax.add_patch(plt.Circle((LC[i].x, LC[i].y), LR[i], fill=False, color='b', ls='--', alpha=0.2)) # link circle\n        ax.plot([LV[i][0],LV[i+1][0]],[LV[i][1],LV[i+1][1]], 'b-')\n        ax.plot([LV[i][0],LV[i+1][0]],[LV[i][1],LV[i+1][1]], 'bo')\n        s = 'LV[' + str(i+1) + ']'\n        ax.text(LV[i+1][0]+0.1, LV[i+1][1], s, fontsize=8, color='b', ha='left', va='center')\n    \n    ax.add_patch(plt.Circle((CP[0], CP[1]), CR, fill=False, color='red', ls='--', alpha=0.4)) # base circle\n    ax.plot([0,TV[0]], [0, TV[1]], 'r-', alpha=0.4) # T:horizontal line\n    ax.plot([TV[0]/2, CP[0]], [TV[1]/2, CP[1]], 'r-', alpha=0.4) # T:perpendicular line\n    ax.plot(CP[0], CP[1], 'r.')\n    ax.text(TV[0], TV[1]-0.15, 'TV', fontsize=8, color='r', ha='center', va='top')\n    ax.text(CP[0]+0.1, CP[1], 'CP', fontsize=8, color='r', ha='left', va='center')\n    ax.text(LV[0][0], LV[0][1]-0.1, s='LV[0]=(0,0)', fontsize=8, color='b', ha='center', va='top')",
      "execution_count": 4,
      "outputs": []
    },
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      "metadata": {
        "ExecuteTime": {
          "start_time": "2021-06-06T07:13:26.302719Z",
          "end_time": "2021-06-06T07:13:26.427019Z"
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      "cell_type": "code",
      "source": "N = 4                        # the number of Links\nTV = np.array([2., 1])       # Target vector\nLR = [1.0] * N               # length(radius) of each Link\n# LR = [1.0 - i*0.1 for i in range(N)]  # case of different lengths\nscaler = LR[0] / norm(TV)    # initial value for scaler, |TV| > 0\nCP = np.array([-TV[1], TV[0]]) * scaler + TV/2 # center point of Guide-circle\nCR = norm(CP)                # radius of Guide-circle\nC = Circle(CP[0], CP[1], CR) # Guide-circle\n\nLC = []\nLV = [[0, 0]]\nfor i in range(N):\n    LC.append(Circle(LV[i][0], LV[i][1], LR[i])) # Link-circle\n    v1, v2 = intersects(LV[i][0], LV[i][1], LR[i], C.x, C.y, C.r) # intersentions of Link-circle and Guide-circle\n    LV.append(v2) # list of the upper intersections of Link-circle and Guide-circle\n\nbaseTheta = np.arctan2(TV[1]-CP[1], TV[0]-CP[0]) - np.arctan2(0-CP[1], 0-CP[0]) # angle at ((0,0), CP, TV)\nlinkTheta = np.arctan2(0-CP[1], 0-CP[0]) - np.arctan2(LV[-1][1]-CP[1], LV[-1][0]-CP[0]) # angle at ((0,0), CP, LV[N])\nThetas = baseTheta % tau + linkTheta % tau # sum of angles\n\nPlot(LV)",
      "execution_count": 5,
      "outputs": [
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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\nmpl.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 */\nfunction 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\nmpl.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 * mpl.ratio;\n    var y = canvas_pos.y * mpl.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\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar 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\nmpl.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};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n    var width = fig.canvas.width / mpl.ratio;\n    fig.root.removeEventListener('remove', this._remove_fig_handler);\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\nmpl.figure.prototype.close_ws = function (fig, msg) {\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n};\n\nmpl.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 / mpl.ratio;\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n};\n\nmpl.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\nmpl.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\nmpl.figure.prototype._remove_fig_handler = function () {\n    this.close_ws(this, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n    el.addEventListener('remove', this._remove_fig_handler);\n};\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    fig.ondownload(fig, null);\n};\n\nmpl.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.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n"
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\" width=\"500\">"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Inverse Kinematics"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2021-06-06T07:13:43.247374Z",
          "end_time": "2021-06-06T07:13:43.327938Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "loop = 0\nwhile True:\n    Error = Thetas - tau\n    if abs(Error) < 1e-4:\n        print('Error:', Error, ' loop:', loop)\n        break\n    scaler += Error * 0.1\n    CP = np.array([-TV[1], TV[0]]) * scaler + TV/2 # center point of the base circle\n    CR = norm(CP)  # radius of the base circle\n    C.x = CP[0]    # x-coordinate of the base circle\n    C.y = CP[1]    # y-coordinate of the base circle\n    C.r = CR\n    for i in range(N):\n        LC[i].x = LV[i][0] # x-coordinate of i-th link\n        LC[i].y = LV[i][1] # y-coordinate of i-th link\n        v1, v2 = intersects(LC[i].x, LC[i].y, LR[i], C.x, C.y, C.r)\n        LV[i+1] = v2\n        \n    baseTheta = np.arctan2(TV[1]-CP[1], TV[0]-CP[0]) - np.arctan2(0-CP[1], 0-CP[0]) # angle at ((0,0), CP, TV)\n    linkTheta = np.arctan2(0-CP[1], 0-CP[0]) - np.arctan2(LV[-1][1]-CP[1], LV[-1][0]-CP[0]) # angle at ((0,0), CP, LV[N])\n    Thetas = baseTheta % tau + linkTheta % tau # sum of angles\n    \n    loop += 1\n    if loop > 1000:\n        print('1000 loops')\n        break\n\nPlot(LV)",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Error: -9.707965942507002e-05  loop: 12\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.Javascript object>",
            "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.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\nmpl.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 (mpl.ratio !== 1) {\n            fig.send_message('set_dpi_ratio', { dpi_ratio: mpl.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\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.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    mpl.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    var resizeObserver = new 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 * mpl.ratio);\n                canvas.setAttribute('height', height * mpl.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 (width != 0 && height != 0) {\n                fig.request_resize(width, height);\n            }\n        }\n    });\n    resizeObserver.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.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 / mpl.ratio,\n        fig.canvas.height / mpl.ratio\n    );\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.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\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.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\nmpl.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\nmpl.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.\nmpl.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\nmpl.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 */\nfunction 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\nmpl.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 * mpl.ratio;\n    var y = canvas_pos.y * mpl.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\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar 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\nmpl.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};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n    var width = fig.canvas.width / mpl.ratio;\n    fig.root.removeEventListener('remove', this._remove_fig_handler);\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\nmpl.figure.prototype.close_ws = function (fig, msg) {\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n};\n\nmpl.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 / mpl.ratio;\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n};\n\nmpl.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\nmpl.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\nmpl.figure.prototype._remove_fig_handler = function () {\n    this.close_ws(this, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n    el.addEventListener('remove', this._remove_fig_handler);\n};\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    fig.ondownload(fig, null);\n};\n\nmpl.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.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n"
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\" width=\"500\">"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Interactive mode: Arm follows Mouse"
    },
    {
      "metadata": {
        "trusted": true,
        "ExecuteTime": {
          "start_time": "2021-06-07T13:02:14.073151Z",
          "end_time": "2021-06-07T13:02:14.180302Z"
        },
        "code_folding": [
          16
        ]
      },
      "cell_type": "code",
      "source": "N = 4                        # the number of links\nTV = np.array([2., 1])       # target vector\nLR = [1.0] * N               # length(radius) of each link\n# LR = [1.0 - i*0.1 for i in range(N)]  # case of different length\nscaler = LR[0] / norm(TV)    # initial value for scaler, |TV| > 0\nCP = np.array([-TV[1], TV[0]]) * scaler + TV/2 # center point of the base circle\nCR = norm(CP)                # radius of the base circle\nC = Circle(CP[0], CP[1], CR) # base circle\n\nLC = []\nLV = [[0, 0]]\nfor i in range(N):\n    LC.append(Circle(LV[i][0], LV[i][1], LR[i])) # link-circle\n    v1, v2 = intersects(LV[i][0], LV[i][1], LR[i], C.x, C.y, C.r) # intersentions of link-circle and base-circle\n    LV.append(v2) # list of the upper intersections of link-circle and base-circle\n\ndef motion(event):\n    global ax, scaler, TV, CP, LV\n    mx = event.xdata\n    my = event.ydata\n    TV = np.array([mx, my])\n    \n    baseTheta = np.arctan2(TV[1]-CP[1], TV[0]-CP[0]) - np.arctan2(0-CP[1], 0-CP[0]) # angle at ((0,0), CP, TV)\n    linkTheta = np.arctan2(0-CP[1], 0-CP[0]) - np.arctan2(LV[-1][1]-CP[1], LV[-1][0]-CP[0]) # angle at ((0,0), CP, LV[N])\n    Thetas = baseTheta % tau + linkTheta % tau # sum of angles\n    \n    Error = Thetas - tau\n    scaler += Error * 0.1\n    CP = np.array([-TV[1], TV[0]]) * scaler + TV/2 # center point of the base circle\n    CR = norm(CP)  # radius of the base circle\n    C.x = CP[0]    # x-coordinate of the base circle\n    C.y = CP[1]    # y-coordinate of the base circle\n    C.r = CR\n    for i in range(N):\n        LC[i].x = LV[i][0] # x-coordinate of i-th link\n        LC[i].y = LV[i][1] # y-coordinate of i-th link\n        v1, v2 = intersects(LC[i].x, LC[i].y, LR[i], C.x, C.y, C.r) # returns two intersections \n        LV[i+1] = v2 # use the upper intersection \n    \n    # update drawing\n    Mouse.set_data(mx, my)\n    for i in range(N):\n        LinkCircle[i].set_center((LC[i].x, LC[i].y))\n        LinkCircle[i].set_radius(LR[i])\n\n    for i in range(N):\n        Link[i][0].set_data([LV[i][0], LV[i+1][0]], [LV[i][1], LV[i+1][1]])\n        Joint[i][0].set_data(LV[i+1][0], LV[i+1][1])\n        TX[i].set_position((LV[i+1][0]+0.1, LV[i+1][1]))\n    \n    GC.set_center((CP[0], CP[1]))\n    GC.set_radius(CR)\n    HL.set_data([0,TV[0]], [0, TV[1]])\n    PL.set_data([TV[0]/2, CP[0]], [TV[1]/2, CP[1]])\n    CPP.set_data(CP[0], CP[1])\n    TXTV.set_position((TV[0], TV[1]-0.15))\n    TXCP.set_position((CP[0]+0.1, CP[1]))\n    plt.draw()\n\nfig, ax = plt.subplots(figsize = (5,5))\nax.axis([-2, N+1, -2, N+1])\nax.grid()\nMouse, = ax.plot([], [], 'rx', ms=10)\nLinkCircle = [plt.Circle((LC[i].x, LC[i].y), LR[i], fill=False, color='b', ls='--', alpha=0.2) for i in range(N)]\nfor i in range(N):\n    ax.add_patch(LinkCircle[i])\nLink =  [ax.plot([],[], 'b-') for i in range(N)]\nJoint = [ax.plot([],[], 'bo') for i in range(N)]\nax.plot(0, 0, 'bo')\n\nTX = [ax.text([], [], 'LV[' + str(i+1) + ']', fontsize=8, color='b', ha='left', va='center') for i in range(N)]\nGC = plt.Circle((CP[0], CP[1]), CR, fill=False, color='red', ls='--', alpha=0.4)\nax.add_patch(GC) # Guide-circle\nHL, = ax.plot([0,TV[0]], [0, TV[1]], 'r-', alpha=0.4) # T:horizontal line\nPL, = ax.plot([TV[0]/2, CP[0]], [TV[1]/2, CP[1]], 'r-', alpha=0.4) # T:perpendicular line\nCPP, = ax.plot(CP[0], CP[1], 'r.')\nTXTV = ax.text(TV[0], TV[1]-0.15, 'TV', fontsize=8, color='r', ha='center', va='top')\nTXCP = ax.text(CP[0]+0.1, CP[1], 'CP', fontsize=8, color='r', ha='left', va='center')\nax.text(LV[0][0], LV[0][1]-0.1, s='LV[0]=(0,0)', fontsize=8, color='b', ha='center', va='top')    \n\nplt.connect('motion_notify_event', motion)\nplt.show()",
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.Javascript object>",
            "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.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\nmpl.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 (mpl.ratio !== 1) {\n            fig.send_message('set_dpi_ratio', { dpi_ratio: mpl.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\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.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    mpl.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    var resizeObserver = new 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 * mpl.ratio);\n                canvas.setAttribute('height', height * mpl.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 (width != 0 && height != 0) {\n                fig.request_resize(width, height);\n            }\n        }\n    });\n    resizeObserver.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.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 / mpl.ratio,\n        fig.canvas.height / mpl.ratio\n    );\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.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\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.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\nmpl.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\nmpl.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.\nmpl.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\nmpl.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 */\nfunction 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\nmpl.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 * mpl.ratio;\n    var y = canvas_pos.y * mpl.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\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar 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\nmpl.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};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n    var width = fig.canvas.width / mpl.ratio;\n    fig.root.removeEventListener('remove', this._remove_fig_handler);\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\nmpl.figure.prototype.close_ws = function (fig, msg) {\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n};\n\nmpl.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 / mpl.ratio;\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n};\n\nmpl.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\nmpl.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\nmpl.figure.prototype._remove_fig_handler = function () {\n    this.close_ws(this, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n    el.addEventListener('remove', this._remove_fig_handler);\n};\n\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    fig.ondownload(fig, null);\n};\n\nmpl.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.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "_draft": {
      "nbviewer_url": "https://gist.github.com/97340a14de085961a140f4a9f6e9ca51"
    },
    "gist": {
      "id": "97340a14de085961a140f4a9f6e9ca51",
      "data": {
        "description": "IK_circle_intersections",
        "public": true
      }
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.5",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}