LoadPlotAfterTuesday.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "flush-rebecca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "table deleted\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",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\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 (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": {
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       "<img 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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\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 (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"
    },
    {
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\" 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       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((673, datetime.datetime(2021, 2, 11, 8, 7, 28), 18.4681, 22.8598, 20.5281, 18.8395, 21.1362, 22.6232, 19.2109, 18.8732, 19.2447, 22.488, 22.1162, 21.4065, 20.8997, 20.3592, 23.1979, 23.3331, 23.2317, 23.0626, 22.995),)\n",
      "Load in kWh is: 7.167154474816182e-06\n",
      "heat Capacity in kWh is:  -1.7354398165865836e-06\n",
      "values in teh list are: 673\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6300192266609699 0.003946252594180832 -5.187740468238238\n",
      "result is ((674, datetime.datetime(2021, 2, 11, 8, 7, 30), 18.4681, 22.8598, 20.5619, 18.8395, 21.1362, 22.6232, 19.2109, 18.8395, 19.2447, 22.5218, 22.0824, 21.3727, 20.8659, 20.3254, 23.1979, 23.3331, 23.2317, 23.0626, 23.0288),)\n",
      "Load in kWh is: 1.4334308949632364e-05\n",
      "heat Capacity in kWh is:  -2.0868197868631108e-05\n",
      "values in teh list are: 674\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6380194708090458 0.004002129411673854 2.9012208599907954\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((675, datetime.datetime(2021, 2, 11, 8, 7, 32), 18.5018, 22.8936, 20.5619, 18.8395, 21.1362, 22.6232, 19.2109, 18.8395, 19.2447, 22.5218, 22.1162, 21.3727, 20.8997, 20.3592, 23.1979, 23.3331, 23.2317, 23.0626, 22.995),)\n",
      "Load in kWh is: 3.004975109444678e-05\n",
      "heat Capacity in kWh is:  -2.2603560559201186e-05\n",
      "values in teh list are: 675\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6380194708090458 0.003996363738233922 4.260273039056376\n",
      "result is ((677, datetime.datetime(2021, 2, 11, 8, 7, 35), 18.4681, 22.8598, 20.5281, 18.8395, 21.1362, 22.6232, 19.2447, 18.8732, 19.2447, 22.5218, 22.1162, 21.4065, 20.8997, 20.3592, 23.1979, 23.3331, 23.2317, 23.0626, 22.995),)\n",
      "Load in kWh is: 3.721690556926296e-05\n",
      "heat Capacity in kWh is:  -6.660256829323878e-06\n",
      "values in teh list are: 677\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6400195318460646 0.004008891524247195 6.622276415880069\n",
      "result is ((678, datetime.datetime(2021, 2, 11, 8, 7, 37), 18.5018, 22.8598, 20.5619, 18.8732, 21.1362, 22.6232, 19.2109, 18.8395, 19.2447, 22.5218, 22.1162, 21.3727, 20.8997, 20.3592, 23.1979, 23.3331, 23.2317, 23.0626, 22.995),)\n",
      "Load in kWh is: 4.438406004407914e-05\n",
      "heat Capacity in kWh is:  1.7762087024434558e-05\n",
      "values in teh list are: 678\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((679, datetime.datetime(2021, 2, 11, 8, 7, 39), 18.5018, 22.8598, 20.5619, 18.8732, 21.1024, 22.657, 19.2109, 18.8395, 19.2447, 22.5218, 22.1162, 21.4065, 20.8997, 20.3592, 23.1979, 23.3331, 23.2317, 23.0626, 23.0288),)\n",
      "Load in kWh is: 5.162291788466379e-05\n",
      "heat Capacity in kWh is:  1.605187991088608e-05\n",
      "values in teh list are: 679\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6320192876979889 0.003964491830999805 0.5340774213713702\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((681, datetime.datetime(2021, 2, 11, 8, 7, 42), 18.5018, 22.8598, 20.5619, 18.8395, 21.1362, 22.6232, 19.2109, 18.8057, 19.2447, 22.5218, 22.1162, 21.4065, 20.8997, 20.3592, 23.1979, 23.2993, 23.2317, 23.0626, 23.0288),)\n",
      "Load in kWh is: 5.451592852448115e-05\n",
      "heat Capacity in kWh is:  1.4341672797337603e-05\n",
      "values in teh list are: 681\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6320192876979889 0.003964491830999805 0.5340774213713702\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((682, datetime.datetime(2021, 2, 11, 8, 7, 44), 18.4681, 22.8598, 20.5281, 18.8395, 21.1024, 22.657, 19.2109, 18.8395, 19.2447, 22.5218, 22.1162, 21.3727, 20.8997, 20.3592, 23.1979, 23.3669, 23.1979, 23.0626, 22.995),)\n",
      "Load in kWh is: 5.743788211520152e-05\n",
      "heat Capacity in kWh is:  1.2606232980751019e-05\n",
      "values in teh list are: 682\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((683, datetime.datetime(2021, 2, 11, 8, 7, 46), 18.4681, 22.8598, 20.5619, 18.8395, 21.1024, 22.6232, 19.2109, 18.8395, 19.2447, 22.5218, 22.1162, 21.3727, 20.8997, 20.3592, 23.1979, 23.3669, 23.2317, 23.0626, 22.995),)\n",
      "Load in kWh is: 6.46050365900177e-05\n",
      "heat Capacity in kWh is:  1.0895949859194083e-05\n",
      "values in teh list are: 683\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6300192266609699 0.003951945970775122 -0.25497039150178974\n",
      "flow rates HP is =  0.6320192876979889 0.003958780380194105 -2.825737091414544\n",
      "result is ((684, datetime.datetime(2021, 2, 11, 8, 7, 47), 18.4681, 22.8598, 20.5619, 18.8732, 21.1024, 22.6232, 19.1771, 18.8732, 19.2784, 22.5218, 22.1162, 21.4065, 20.8997, 20.3592, 23.1979, 23.3331, 23.2317, 23.0626, 22.995),)\n",
      "Load in kWh is: 6.323774118414939e-05\n",
      "heat Capacity in kWh is:  4.7442927239365554e-07\n",
      "values in teh list are: 684\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6300192266609699 0.003951945970775122 -0.25497039150178974\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((686, datetime.datetime(2021, 2, 11, 8, 7, 51), 18.4681, 22.8598, 20.5619, 18.8732, 21.1362, 22.6232, 19.2109, 18.8395, 19.2447, 22.5218, 22.1162, 21.4065, 20.8659, 20.3592, 23.1979, 23.3331, 23.2317, 23.0626, 22.995),)\n",
      "Load in kWh is: 6.185660798896763e-05\n",
      "heat Capacity in kWh is:  -1.2358538491632797e-06\n",
      "values in teh list are: 686\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((687, datetime.datetime(2021, 2, 11, 8, 7, 52), 18.5018, 22.8598, 20.5619, 18.8732, 21.1362, 22.6232, 19.2109, 18.8732, 19.2447, 22.5218, 22.1162, 21.3727, 20.8659, 20.3592, 23.1979, 23.2993, 23.2317, 23.0964, 22.995),)\n",
      "Load in kWh is: 6.902376246378381e-05\n",
      "heat Capacity in kWh is:  -2.946060962711757e-06\n",
      "values in teh list are: 687\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6320192876979889 0.003964491830999805 0.5340774213713702\n",
      "flow rates HP is =  0.6300192266609699 0.003946252594180832 -5.187740468238238\n",
      "result is ((688, datetime.datetime(2021, 2, 11, 8, 7, 54), 18.5018, 22.8598, 20.5281, 18.8732, 21.1024, 22.6232, 19.1771, 18.8395, 19.2447, 22.5218, 22.1162, 21.3727, 20.8659, 20.3592, 23.164, 23.2993, 23.2317, 23.0964, 22.995),)\n",
      "Load in kWh is: 7.191677310360116e-05\n",
      "heat Capacity in kWh is:  -2.235938152509741e-05\n",
      "values in teh list are: 688\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((689, datetime.datetime(2021, 2, 11, 8, 7, 56), 18.4681, 22.8598, 20.5619, 18.8732, 21.1024, 22.6232, 19.1771, 18.8395, 19.2447, 22.5218, 22.1162, 21.4065, 20.8997, 20.3592, 23.1979, 23.2993, 23.2317, 23.0626, 23.0288),)\n",
      "Load in kWh is: 7.908392757841734e-05\n",
      "heat Capacity in kWh is:  -2.4069664646654346e-05\n",
      "values in teh list are: 689\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((691, datetime.datetime(2021, 2, 11, 8, 7, 59), 18.5018, 22.8598, 20.5619, 18.8732, 21.1024, 22.657, 19.2109, 18.8732, 19.2447, 22.488, 22.1162, 21.3727, 20.8997, 20.3592, 23.1979, 23.3331, 23.2317, 23.0626, 23.0288),)\n",
      "Load in kWh is: 8.6322785419002e-05\n",
      "heat Capacity in kWh is:  -2.5779871760202825e-05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "values in teh list are: 691\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6300192266609699 0.003951945970775122 -0.25497039150178974\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((692, datetime.datetime(2021, 2, 11, 8, 8, 1), 18.4681, 22.8598, 20.5281, 18.8732, 21.1362, 22.657, 19.2109, 18.8395, 19.2447, 22.5218, 22.1162, 21.3727, 20.8997, 20.3592, 23.1979, 23.3331, 23.2317, 23.0626, 22.995),)\n",
      "Load in kWh is: 8.492783475985779e-05\n",
      "heat Capacity in kWh is:  -2.7515311576789407e-05\n",
      "values in teh list are: 692\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((693, datetime.datetime(2021, 2, 11, 8, 8, 3), 18.4681, 22.8598, 20.5619, 18.8395, 21.1362, 22.657, 19.2109, 18.8395, 19.2447, 22.5218, 22.0824, 21.4065, 20.8659, 20.3254, 23.164, 23.3331, 23.2317, 23.0964, 22.995),)\n",
      "Load in kWh is: 9.216669260044245e-05\n",
      "heat Capacity in kWh is:  -2.9225594698346343e-05\n",
      "values in teh list are: 693\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6380194708090458 0.003996363738233922 4.260273039056376\n",
      "result is ((694, datetime.datetime(2021, 2, 11, 8, 8, 5), 18.4681, 22.8598, 20.5281, 18.8395, 21.1362, 22.6232, 19.2109, 18.8395, 19.2447, 22.5218, 22.0824, 21.3727, 20.8659, 20.393, 23.1979, 23.3331, 23.2317, 23.0626, 23.0288),)\n",
      "Load in kWh is: 9.933384707525863e-05\n",
      "heat Capacity in kWh is:  -1.3282290968469034e-05\n",
      "values in teh list are: 694\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6400195318460646 0.004008891524247195 6.622276415880069\n",
      "result is ((696, datetime.datetime(2021, 2, 11, 8, 8, 8), 18.4343, 22.8598, 20.5619, 18.8395, 21.1024, 22.6232, 19.2109, 18.8395, 19.2447, 22.5218, 22.1162, 21.3727, 20.8997, 20.3254, 23.1979, 23.3331, 23.1979, 23.0964, 22.9612),)\n",
      "Load in kWh is: 0.00010650100155007481\n",
      "heat Capacity in kWh is:  1.1142226652070524e-05\n",
      "values in teh list are: 696\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6360194097720268 0.003989583551449171 2.1121730471176354\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((697, datetime.datetime(2021, 2, 11, 8, 8, 10), 18.5018, 22.8598, 20.5281, 18.8732, 21.1024, 22.6232, 19.2109, 18.8395, 19.2447, 22.5218, 22.1162, 21.3727, 20.8997, 20.3592, 23.1979, 23.3331, 23.1979, 23.0626, 23.0288),)\n",
      "Load in kWh is: 0.00011794229985989011\n",
      "heat Capacity in kWh is:  9.406863961500443e-06\n",
      "values in teh list are: 697\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6320192876979889 0.003964491830999805 0.5340774213713702\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((698, datetime.datetime(2021, 2, 11, 8, 8, 11), 18.4681, 22.8598, 20.5619, 18.8395, 21.1362, 22.6232, 19.1771, 18.8395, 19.2447, 22.5218, 22.1162, 21.4065, 20.8997, 20.3592, 23.164, 23.2993, 23.2655, 23.0964, 22.995),)\n",
      "Load in kWh is: 0.00012083531049970747\n",
      "heat Capacity in kWh is:  7.696580839943507e-06\n",
      "values in teh list are: 698\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6380194708090458 0.004002129411673854 2.9012208599907954\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((699, datetime.datetime(2021, 2, 11, 8, 8, 13), 18.5018, 22.826, 20.5619, 18.8395, 21.1362, 22.657, 19.1771, 18.8395, 19.2784, 22.5218, 22.1162, 21.3727, 20.8997, 20.3254, 23.1979, 23.3331, 23.2317, 23.0964, 22.995),)\n",
      "Load in kWh is: 0.0001365505143316591\n",
      "heat Capacity in kWh is:  1.459425101237983e-05\n",
      "values in teh list are: 699\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6380194708090458 0.003996363738233922 4.260273039056376\n",
      "result is ((701, datetime.datetime(2021, 2, 11, 8, 8, 17), 18.4681, 22.8598, 20.5619, 18.8395, 21.1024, 22.6232, 19.2109, 18.8732, 19.2447, 22.5556, 22.1162, 21.3727, 20.8659, 20.3592, 23.1979, 23.2993, 23.2317, 23.0626, 22.995),)\n",
      "Load in kWh is: 0.00014371766880647528\n",
      "heat Capacity in kWh is:  3.030644282131109e-05\n",
      "values in teh list are: 701\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6320192876979889 0.003958780380194105 -2.825737091414544\n",
      "result is ((702, datetime.datetime(2021, 2, 11, 8, 8, 18), 18.5018, 22.8598, 20.5281, 18.8395, 21.1024, 22.6232, 19.2109, 18.8732, 19.2447, 22.5556, 22.1162, 21.4065, 20.8659, 20.3254, 23.1979, 23.3331, 23.2655, 23.0964, 23.0288),)\n",
      "Load in kWh is: 0.00015088482328129148\n",
      "heat Capacity in kWh is:  1.9732101194833532e-05\n",
      "values in teh list are: 702\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6360194097720268 0.003989583551449171 2.1121730471176354\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((703, datetime.datetime(2021, 2, 11, 8, 8, 20), 18.4681, 22.8598, 20.5619, 18.8732, 21.1024, 22.6232, 19.2109, 18.8395, 19.2447, 22.5218, 22.1162, 21.3727, 20.8997, 20.3592, 23.1979, 23.3331, 23.2317, 23.0964, 22.995),)\n",
      "Load in kWh is: 0.00016232612159110678\n",
      "heat Capacity in kWh is:  1.8021818073276597e-05\n",
      "values in teh list are: 703\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6300192266609699 0.003951945970775122 -0.25497039150178974\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((705, datetime.datetime(2021, 2, 11, 8, 8, 24), 18.4681, 22.8598, 20.5619, 18.8732, 21.1362, 22.6232, 19.2109, 18.8395, 19.2784, 22.5218, 22.1162, 21.4065, 20.8997, 20.3592, 23.164, 23.3331, 23.2317, 23.0964, 22.995),)\n",
      "Load in kWh is: 0.00016095882618523847\n",
      "heat Capacity in kWh is:  1.631153495171966e-05\n",
      "values in teh list are: 705\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6300192266609699 0.003946252594180832 -5.187740468238238\n",
      "result is ((706, datetime.datetime(2021, 2, 11, 8, 8, 25), 18.4681, 22.8598, 20.5281, 18.8395, 21.1024, 22.6232, 19.2109, 18.8395, 19.2447, 22.5218, 22.0824, 21.4065, 20.8997, 20.3592, 23.1979, 23.3331, 23.2317, 23.0626, 23.0626),)\n",
      "Load in kWh is: 0.00016812598066005466\n",
      "heat Capacity in kWh is:  -3.102648411330198e-06\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-2d069a3e6b17>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    438\u001b[0m     \u001b[0mGrossDf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtoCsv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdfs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist2df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLoad_kWh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Load_kW'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist2df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Heat_Capacity_kW'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m     \u001b[0;31m# Plot tem values as a seperate first fig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplotObject\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    441\u001b[0m     \u001b[0;31m#plot load_kW in different fig2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m     \u001b[0mfig2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplotObject_Load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGrossDf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-4-2d069a3e6b17>\u001b[0m in \u001b[0;36mplotObject\u001b[0;34m(df, ax, fig, alpha, scm)\u001b[0m\n\u001b[1;32m    250\u001b[0m \u001b[0mfig_size\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplotObject\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    253\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         ax.plot(df.index, df.iloc[:, i], color= scm.to_rgba(i, alpha=alpha),\\\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mclear\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1175\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1176\u001b[0m         \u001b[0;34m\"\"\"Clear the axes.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1177\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcla\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1178\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1179\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mcla\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1071\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_scale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'linear'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1072\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1073\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1074\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1075\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mset_ylim\u001b[0;34m(self, bottom, top, emit, auto, ymin, ymax)\u001b[0m\n\u001b[1;32m   3563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3564\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process_unit_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mydata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbottom\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3565\u001b[0;31m         \u001b[0mbottom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_converted_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbottom\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_yunits\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3566\u001b[0m         \u001b[0mtop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_converted_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_yunits\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_validate_converted_limits\u001b[0;34m(self, limit, convert)\u001b[0m\n\u001b[1;32m   3207\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlimit\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3208\u001b[0m             \u001b[0mconverted_limit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3209\u001b[0;31m             if (isinstance(converted_limit, Real)\n\u001b[0m\u001b[1;32m   3210\u001b[0m                     and not np.isfinite(converted_limit)):\n\u001b[1;32m   3211\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Axis limits cannot be NaN or Inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/abc.py\u001b[0m in \u001b[0;36m__instancecheck__\u001b[0;34m(cls, instance)\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m__instancecheck__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m             \u001b[0;34m\"\"\"Override for isinstance(instance, cls).\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0m_abc_instancecheck\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m__subclasscheck__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubclass\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import time\n",
    "import board\n",
    "import busio\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "from matplotlib import pylab\n",
    "import matplotlib.pyplot as plt\n",
    "from pylab import *\n",
    "import pylab\n",
    "import numpy as np\n",
    "import matplotlib.colors as clrs\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib\n",
    "from collections import deque\n",
    "#matplotlib.use('TkAgg')\n",
    "%matplotlib notebook\n",
    "#%matplotlib inline\n",
    "\n",
    "\n",
    "#import time\n",
    "import sys\n",
    "import sqlite3\n",
    "#from time import sleep\n",
    "\n",
    "#import adafruit_ads1x15.ads1015 as ADS\n",
    "import adafruit_ads1x15.ads1115 as ADS\n",
    "from adafruit_ads1x15.analog_in import AnalogIn\n",
    "\n",
    "import MySQLdb as db\n",
    "\n",
    "HOST = \"10.208.8.122\"\n",
    "PORT = 3306\n",
    "USER = \"yogi\"\n",
    "PASSWORD = \"bittoo\"\n",
    "DB = \"TemaccessToRemoteRp2\"\n",
    "'''\n",
    "try:\n",
    "    connection = db.Connection(host=HOST, port=PORT,\n",
    "                               user=USER, passwd=PASSWORD, db=DB)\n",
    "\n",
    "    c = connection.cursor()\n",
    "    c.execute(\"SELECT * from temSensor\")\n",
    "    result = c.fetchall()\n",
    "    for item in result:\n",
    "        print (item)\n",
    "\n",
    "except Exception as e:\n",
    "    print (e)\n",
    "'''\n",
    "connectionL = db.connect(host=\"10.208.8.121\",\n",
    "                     user=\"yogi\",\n",
    "                     passwd=\"bittoo\",\n",
    "                     db=\"allSensors\")\n",
    "\n",
    "#c = conn.cursor()\n",
    "connectionR = db.Connection(host=HOST, port=PORT,user=USER, passwd=PASSWORD, db=DB)\n",
    "\n",
    "cR = connectionR.cursor()\n",
    "cL =connectionL.cursor()\n",
    "\n",
    "i2c = busio.I2C(board.SCL, board.SDA)\n",
    "\n",
    "# Create the ADC object using the I2C bus\n",
    "ads = ADS.ADS1115(i2c)\n",
    "\n",
    "ads.gain = 1\n",
    "\n",
    "\n",
    "cL.execute('DROP TABLE IF EXISTS flowReadings;')\n",
    "print ('table deleted')\n",
    "\n",
    "\n",
    "cL.execute('CREATE TABLE flowReadings(id INT AUTO_INCREMENT PRIMARY KEY, ts TIMESTAMP DEFAULT CURRENT_TIMESTAMP, flowHP FLOAT, flowLoad FLOAT);')\n",
    "\n",
    "\n",
    "#cvsFileName = import os.path\n",
    "#os.path.exists(file_path)\n",
    "\n",
    "lol=[[], [], []]\n",
    "flowRateLoad  = []\n",
    "\n",
    "def flatten(l_of_l):\n",
    "    T = l_of_l[0]\n",
    "    flattend_l = [val for sublist in T for val in sublist]\n",
    "    #print ('flattened list is =', flattend_l)\n",
    "    return flattend_l\n",
    "\n",
    "def mct(Lol):\n",
    "    mHP = Lol[2]\n",
    "    T = flatten(Lol)\n",
    "    mL = Lol[1]\n",
    "    #this is density as polynomial function of T\n",
    "    p_LperH = [999.8473664794213 + 6.29265190e-02*x[2] - 8.42930922e-03*x[2]**2 + 6.77190849e-05*x[2]**3 \\\n",
    " - 4.40840180e-07*x[2]**4 + 1.29302849e-09*x[2]**5 for x in T]\n",
    "\n",
    "    #pV = [(999.8473664794213 + 6.29265190e-02*x[2] - 8.42930922e-03*x[2]**2 + 6.77190849e-05*x[2]**3 - 4.40840180e-07*x[2]**4 + 1.29302849e-09*x[2]**5)*2.7777e-07*y for x, y in zip(T, mHP)  ]\n",
    "    # this is mass flow  in kg/S =  density*V(flow rate in m^3/sec)= d * (1000^-3/3600)\n",
    "    mF_kgPerS =[ x*2.7777e-07*y for x, y in zip(p_LperH, mHP)]\n",
    "    #this is Cp as functiono of T linear function\n",
    "    cP_kjPerkgK = [4.253264761904763 - 0.00470305*b[2] for b in T]\n",
    "    #mCT1 = sum([(4.253264761904763 - 0.00470305*b[2])*a*(b[2]-b[3]) for a, b in zip(mHP, T)])\n",
    "    #mCT_kW = [(4.253264761904763 - 0.00470305*b[2])*a*(b[3]-b[4]) for a, b in zip(mHP, T)]\n",
    "    #Cp*M*deltaT\n",
    "    mCT_kW = [c*a*(b[3]-b[4]) for c, a, b in zip(cP_kjPerkgK,mF_kgPerS, T)]\n",
    "    # sum(mct* 5/3600 (time stamp))  = kWh\n",
    "    mCT2_kWh = sum([m*c*(dt[3]-dt[4])*0.0013888889 for m, c, dt in zip(mF_kgPerS,  cP_kjPerkgK, T)])\n",
    "    print ('heat Capacity in kWh is: ', mCT2_kWh)\n",
    "    \n",
    "    #print (T[-1], mHP,'mCpDeltaT =', mCT)\n",
    "    #print ('mCT is:',mCT)\n",
    "    #print ('mF', mF_kgPerS, mHP[-1])\n",
    "    #print (' mCT_kW is:', mCT_kW)\n",
    "    #print ('mCTkWh is ',mCT2_kWh)\n",
    "    return mCT_kW\n",
    "\n",
    "l_id = []\n",
    "\n",
    "def idGenerator(Id, l_id):\n",
    "    if Id not in l_id:\n",
    "       l_id.append(Id) \n",
    "    #print (l_id)\n",
    "    return l_id\n",
    "\n",
    "#df_conc= None\n",
    "def dfs(Lol):\n",
    "    mHP = Lol[2]\n",
    "    T = flatten(Lol)\n",
    "    mL = Lol[1]\n",
    "    StartTemTestingtank = [ 'id', 'dateTime', 'Tamb', 'TtopTestTankHPCir', 'TbottomTestTankHpCir', 'TtopSourceTank', \\\n",
    "'TloadTankMix','TTopTestTankLoadCir', 'TloadMix', 'TbottomSourceTank', 'TbottomTestTankLoadCir']\n",
    "    l_T = ['T{}'.format(x) for x in range(0,10)]\n",
    "    L = StartTemTestingtank +l_T\n",
    "    df_mHP = DataFrame(mHP, columns=['flowHP'])\n",
    "    df_mL = DataFrame(mL, columns=['flowLoad'])\n",
    "    df_tem = DataFrame(T, columns = L)\n",
    "    df_conc = pd.concat([df_tem, df_mHP, df_mL], axis=1)\n",
    "    \n",
    "    #print (df_mL)\n",
    "    #print (df_mHP)\n",
    "    #print (df_tem)\n",
    "    #print ('TOut and Tin in Df is:', df_conc[['TTopTestTankLoadCir', 'TbottomTestTankLoadCir']])\n",
    "    return df_conc\n",
    "\n",
    "def list2df(lis, column_name = None):\n",
    "    df  = pd.DataFrame(lis, columns = [column_name]) \n",
    "    return df\n",
    "    \n",
    "def toCsv(df_full, load_kW, heatCapacity_kW):\n",
    "    df_Tem_Flow_Load_capacity_conc = pd.concat([df_full, load_kW, heatCapacity_kW], axis=1)\n",
    "    import os.path\n",
    "    import datetime\n",
    "    file_name = 'csvFile_{}'.format(datetime.datetime.now().strftime(\"%Y_%m_%d\"))\n",
    "    suffix = '.csv'\n",
    "    filePath = os.path.join(os.getcwd(), file_name + suffix)\n",
    "    df_Tem_Flow_Load_capacity_conc.to_csv(filePath, header=True)\n",
    "    return df_Tem_Flow_Load_capacity_conc\n",
    "\n",
    "def Load_kWh(Lol):\n",
    "    \n",
    "    mHP = Lol[2]\n",
    "    T = flatten(Lol)\n",
    "    mL = Lol[1]\n",
    "    #this is density as polynomial function of T\n",
    "    p_LperH = [999.8473664794213 + 6.29265190e-02*x[10] - 8.42930922e-03*x[10]**2 + 6.77190849e-05*x[10]**3 \\\n",
    " - 4.40840180e-07*x[10]**4 + 1.29302849e-09*x[10]**5 for x in T]\n",
    "    #print ('denisty is:', p_LperH )\n",
    "    #print ('Tem is:', [x[10] for x in T])\n",
    "    # this is mass flow  in kg/S =  density*V(flow rate in m^3/sec)= d * (1000^-3/3600)\n",
    "    mF_kgPerS =[ x*2.7777e-07*y for x, y in zip(p_LperH, mL)]\n",
    "    #this is Cp as functiono of T linear function\n",
    "    cP_kjPerkgK = [4.253264761904763 - 0.00470305*b[10] for b in T]\n",
    "    #print ('Cp is:', cP_kjPerkgK)\n",
    "    #Cp*M*deltaT\n",
    "    mCT_kW = [c*a*(b[7]-b[10]) for c, a, b in zip(cP_kjPerkgK,mF_kgPerS, T)]\n",
    "    mCT2_kWh = sum([m*c*(dt[7]-dt[10])*0.0013888889 for m, c, dt in zip(mF_kgPerS,  cP_kjPerkgK, T)])\n",
    "    print ('Load in kWh is:', mCT2_kWh)\n",
    "    return mCT_kW\n",
    "\n",
    "'''\n",
    "def Load_kWh(Lol):\n",
    "    \n",
    "    mHP = Lol[2]\n",
    "    T = flatten(Lol)\n",
    "    mL = Lol[1]\n",
    "    #this is density as polynomial function of T\n",
    "    p_LperH = [999.8473664794213 + 6.29265190e-02*x[7] - 8.42930922e-03*x[7]**2 + 6.77190849e-05*x[7]**3 \\\n",
    " - 4.40840180e-07*x[7]**4 + 1.29302849e-09*x[7]**5 for x in T]\n",
    "    p_LperH2 = [999.8473664794213 + 6.29265190e-02*x[10] - 8.42930922e-03*x[10]**2 + 6.77190849e-05*x[10]**3 \\\n",
    " - 4.40840180e-07*x[10]**4 + 1.29302849e-09*x[10]**5 for x in T]\n",
    "    #print ('denisty is:', p_LperH )\n",
    "    #print ('Tem is:', [x[10] for x in T])\n",
    "    # this is mass flow  in kg/S =  density*V(flow rate in m^3/sec)= d * (1000^-3/3600)\n",
    "    mF_kgPerS =[ x*2.7777e-07*y for x, y in zip(p_LperH, mL)]\n",
    "    #this is Cp as functiono of T linear function\n",
    "    cP_kjPerkgK = [4.253264761904763 - 0.00470305*b[7] for b in T]\n",
    "    #print ('Cp is:', cP_kjPerkgK)\n",
    "    #Cp*M*deltaT\n",
    "    mCT_kW = [c*a*(b[7]-b[10]) for c, a, b in zip(cP_kjPerkgK,mF_kgPerS, T)]\n",
    "    mCT2_kWh = sum([m*c*(dt[7]-dt[10])*0.0013888889 for m, c, dt in zip(mF_kgPerS,  cP_kjPerkgK, T)])\n",
    "    print ('Load in kWh is:', mCT2_kWh)\n",
    "    TOut = [x[7] for x in T]\n",
    "    Tin = [x[10] for x in T]\n",
    "    #print ('tem Out in is values:',TOut[-1],  Tin[-1] )\n",
    "    print ('denisty 1 is:',p_LperH )\n",
    "    print ('denisty 2 is:',p_LperH2 )\n",
    "    return mCT_kW\n",
    "'''\n",
    "def tankStrat(df):\n",
    "    dfT = df.iloc[:, 11:-2]\n",
    "    #print (dfT.columns)\n",
    "    #print (dfT)\n",
    "    dfT = dfT[['T4', 'T3', 'T2', 'T1', 'T0', 'T9', 'T8', 'T7', 'T6', 'T5']]\n",
    "    dfT = dfT.rename({'T4':'T0', 'T3':'T1', 'T2':'T2', 'T1':'T3', 'T0':'T4', 'T9':'T5', 'T8':'T6',\\\n",
    "              'T7':'T7','T6':'T8','T5':'T9'}, axis='columns')\n",
    "    #dfT = dfT[['T4', 'T3', 'T2', 'T1', 'T0', 'T5', 'T6', 'T7', 'T8', 'T9']]\n",
    "    #dfT = dfT[['T4', 'T3', 'T2', 'T1', 'T0', 'T8', 'T9', 'T5', 'T6', 'T7']]\n",
    "    #dfT = dfT.rename({'T4':'T0', 'T3':'T1', 'T2':'T2', 'T1':'T3', 'T0':'T4', 'T8':'T5', 'T9':'T6',\\\n",
    "    #           'T5':'T7','T6':'T8','T7':'T9'}, axis='columns')\n",
    "    #dfT = dfT.rename({'T4':'T0', 'T3':'T1', 'T2':'T2', 'T1':'T3', 'T0':'T4', 'T5':'T5', 'T6':'T6',\\\n",
    "    #          'T7':'T7','T8':'T8','T9':'T9'}, axis='columns')\n",
    "    #dfT.columns = ['T4', 'T3', 'T2', 'T1', 'T0', 'T5', 'T6', 'T7', 'T8', 'T9']\n",
    "    #print (dfT)\n",
    "    return dfT\n",
    "#tankStratTem = tankStrat(df_conc)\n",
    "\n",
    "\n",
    "    \n",
    "def create_color_step_obj(cmap_name, n):\n",
    "    \"\"\"\n",
    "    Return scalarMap object with n colors in gradient from color map\n",
    "    given in cmap_name.\n",
    "    \"\"\"\n",
    "    cmap = plt.get_cmap(cmap_name)\n",
    "    values = range(n)\n",
    "    cNorm  = clrs.Normalize(vmin=values[0], vmax=values[-1])\n",
    "    scalarMap = cm.ScalarMappable(norm=cNorm, cmap=cmap)\n",
    "    return scalarMap\n",
    "\n",
    "#import logging\n",
    "#logging.getLogger().setLevel(logging.CRITICAL)\n",
    "\n",
    "# colors\n",
    "cmap_name = \"jet\"\n",
    "scm = create_color_step_obj(cmap_name, 10)\n",
    "\n",
    "plt.ion()\n",
    "#plt.rcParams[\"figure.figsize\"]=5,5\n",
    "fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "fig_size[0] = 8\n",
    "fig_size[1] = 3\n",
    "def plotObject(df, ax, fig, alpha =0.7, scm=None):\n",
    "    ax.clear()\n",
    "    for i in range (0, df.shape[1]):\n",
    "        ax.plot(df.index, df.iloc[:, i], color= scm.to_rgba(i, alpha=alpha),\\\n",
    "                label = 'Layer ' + str(i))\n",
    "        ax.legend(bbox_to_anchor=(0.4,0.8))\n",
    "    fig.canvas.draw()\n",
    "    ax.legend()\n",
    "    #plt.legend(loc='upper left')\n",
    "    return fig, ax\n",
    "    \n",
    "# initialize figure and interactivity\n",
    "fig = plt.figure(1)\n",
    "fig2 = plt.figure(2)\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(121)\n",
    "ax_2 = fig.add_subplot(122)\n",
    "ax2 = fig2.add_subplot(122)\n",
    "ax2_1 = fig2.add_subplot(121)\n",
    "\n",
    "fig.canvas.draw()\n",
    "#plt.ion()\n",
    "\n",
    "fig.show()\n",
    "\n",
    "ax.legend(loc='best')\n",
    "ax.set_xlabel('Index')\n",
    "ax.set_ylabel('Tem, C', color='r')\n",
    "#################################\n",
    "#Plot for HPin, Hpout, TmixL tem.\n",
    "\n",
    "fig.show()\n",
    "fig.canvas.draw()\n",
    "ax_2.legend(loc='best')\n",
    "ax_2.set_xlabel('Index')\n",
    "ax_2.set_ylabel('Load in kW', color='r')\n",
    "def plotObject_Tem_HpAndLoad(df, ax_2, fig):\n",
    "    ax_2.clear()\n",
    "    ax_2.plot(df.index, df.iloc[:, 3], label = 'Hp to Testing tank top')\n",
    "    ax_2.plot(df.index, df.iloc[:, 4], label = 'Testing tank to HP Bottom')\n",
    "    ax_2.plot(df.index, df.iloc[:, 8], label = 'load tem Mixed')\n",
    "    \n",
    "    ax_2.legend(bbox_to_anchor=(0.4,0.8))\n",
    "    fig.canvas.draw()\n",
    "    ax_2.legend()\n",
    "    #plt.legend(loc='upper left')\n",
    "    return fig, ax_2\n",
    "##################################\n",
    "\n",
    "#fig2 = plt.figure(2)\n",
    "\n",
    "#plt.ion()\n",
    "\n",
    "fig2.show()\n",
    "fig2.canvas.draw()\n",
    "ax2.legend(loc='best')\n",
    "ax2.set_xlabel('Index')\n",
    "ax2.set_ylabel('Load in kW', color='r')\n",
    "def plotObject_Load(df, ax2, fig2):\n",
    "    ax2.clear()\n",
    "    ax2.plot(df.index, df.iloc[:, -2], label = 'Load in kW')\n",
    "    ax2.legend(bbox_to_anchor=(0.4,0.8))\n",
    "    fig2.canvas.draw()\n",
    "    ax2.legend()\n",
    "    #plt.legend(loc='upper left')\n",
    "    return fig2, ax2\n",
    "    \n",
    "# initialize figure and interactivity\n",
    "####Plot for heat_Capacity:\n",
    "\n",
    "fig2.show()\n",
    "fig2.canvas.draw()\n",
    "ax2_1.legend(loc='best')\n",
    "ax2_1.set_xlabel('Index')\n",
    "ax2_1.set_ylabel('Heat_Capacity in kW', color='r')\n",
    "def plotObject_Heat_Capacity(df, ax2_1, fig2):\n",
    "    ax2_1.clear()\n",
    "    ax2_1.plot(df.index, df.iloc[:, -1], label = 'heat_Capacity in kW')\n",
    "    ax2_1.legend(bbox_to_anchor=(0.4,0.8))\n",
    "    fig2.canvas.draw()\n",
    "    ax2_1.legend()\n",
    "    #plt.legend(loc='upper left')\n",
    "    return fig2, ax2_1\n",
    "############\n",
    "\n",
    "\n",
    "'''\n",
    "def plotObject(df, ax1,  fig, alpha =0.7, scm=None):\n",
    "    #ax2.clear()\n",
    "    ax1.clear()\n",
    "    for i in range (0, df.shape[1]):\n",
    "        ax1.plot(df.index, df.iloc[:, i], color= scm.to_rgba(i, alpha=alpha),\\\n",
    "                label = 'Layer ' + str(i+1))\n",
    "        ax1.legend(bbox_to_anchor=(0.4,0.8))\n",
    "    \n",
    "    #ax2.plot(dfFlowtem.index, dfFlowtem.iloc[:, 1])\n",
    "    #ax3.plot(dfFlowtem.index, dfFlowtem.iloc[:, 2])\n",
    "    \n",
    "    fig.canvas.draw()\n",
    "    #ax2.legend()\n",
    "    #ax3.legend()\n",
    "    #legend_without_duplicate_labels(ax2)\n",
    "    #plt.legend(loc='upper left')\n",
    "    return fig, ax1, ax2, ax3\n",
    "\n",
    "\n",
    "# initialize figure and interactivity\n",
    "#fig = plt.figure()\n",
    "fig, ax1 = plt.subplots(1)\n",
    "plt.ion()\n",
    "\n",
    "fig.show()\n",
    "fig.canvas.draw()\n",
    "ax1.legend(loc='best')\n",
    "ax1.set_xlabel('Index')\n",
    "ax1.set_ylabel('Tem, C', color='r')\n",
    "\n",
    "\n",
    "ax2.legend(loc='best')\n",
    "ax2.set_xlabel('Index')\n",
    "ax2.set_ylabel('Tank outlet, C', color='r')\n",
    "\n",
    "\n",
    "ax3.legend(loc='best')\n",
    "ax3.set_xlabel('Index')\n",
    "ax3.set_ylabel('TankInletTem, C', color='r')\n",
    "'''\n",
    "\n",
    "#plt.legend(loc='best')\n",
    "\n",
    "\n",
    "while True:\n",
    "    #c.execute(\"INSERT INTO flowReadings(flowHp, flowLoad) VALUES(?,?,?,?)\", (chan2.voltage, chan1.voltage))\n",
    "    #connection.commit()\n",
    "    chan1 = AnalogIn(ads, ADS.P0)\n",
    "    chan2 = AnalogIn(ads, ADS.P1)\n",
    "\n",
    "    chan1Vol = chan1.voltage\n",
    "    chan1curr = chan1Vol/159.42\n",
    "\n",
    "    chan2Vol = chan2.voltage\n",
    "    chan2curr=chan2Vol/159.65\n",
    "    #flow1 = ((chan1Vol/159.42)*1000 -4)/16*1000\n",
    "    flow1 = ((chan1Vol/159.42)-0.003956)/0.0000159\n",
    "    #flow2 = ((chan2Vol/159.65)*1000 -4)/16*4000 +20\n",
    "    #flow2 = ((chan2Vol/159.65)- 0.0005468893873066417)/1.09561608e-05\n",
    "    flow2 = ((chan2Vol/159.65)- 0.003973767754877122)/5.3038815e-06\n",
    "    cL.execute(\"INSERT INTO flowReadings(flowHp, flowLoad) VALUES(%s, %s)\", (flow2, flow1))\n",
    "    connectionL.commit()\n",
    "    #print('flow HP:',\"{:>5}\\t{:>5.3f}\".format(chan2.value, chan2.voltage), '\\n\\n')\n",
    "    #print('flow load:',\"{:>5}\\t{:>5.3f}\".format(chan1.value, chan1.voltage, '\\n\\n'))\n",
    "    print('flow rates load is = ',chan1Vol, chan1curr,flow1 )\n",
    "    print('flow rates HP is = ',chan2Vol, chan2curr,flow2 )\n",
    "\n",
    "    connectionR = db.Connection(host=HOST, port=PORT,user=USER, passwd=PASSWORD, db=DB)\n",
    "    cR = connectionR.cursor()\n",
    "    #c.execute(\"SELECT * from temSensor\")\n",
    "    cR.execute(\"SELECT * FROM temSensor ORDER BY id DESC LIMIT 1\")\n",
    "    result = cR.fetchall()\n",
    "    print ('result is',result)\n",
    "    id = result[0][0]\n",
    "    idGenerator(id, l_id)\n",
    "    #print ('id is :', id)\n",
    "    flowRateLoad.append(flow1)\n",
    "\n",
    "    if lol[0] ==[] or id < lol[0][-1][0][0] :\n",
    "        lol[0].append(result)\n",
    "        #print (lol)\n",
    "        lol[1].append(flow1)\n",
    "        lol[2].append(flow2)\n",
    "    elif lol[0] != [] and id > lol[0][-1][0][0]:\n",
    "\n",
    "        lol[0].append(result)\n",
    "        lol[1].append(flow1)\n",
    "        lol[2].append(flow2)\n",
    "\n",
    "\n",
    "    #mct(lol)\n",
    "    #Load = Load_kWh(lol)\n",
    "    #print ('load in kWh is:', Load)\n",
    "    #print ('lol is:',lol)\n",
    "    #print ('df of tem is:',df_tem)\n",
    "    #print ('df is:', tankStrat(dfs(lol)))\n",
    "    df = tankStrat(dfs(lol))\n",
    "    #restTem = InletTem(dfs(lol))\n",
    "    #print (df)\n",
    "    GrossDf = toCsv(dfs(lol), list2df(Load_kWh(lol), 'Load_kW'), list2df(mct(lol),'Heat_Capacity_kW'))\n",
    "    # Plot tem values as a seperate first fig\n",
    "    fig, ax = plotObject(df, ax,  fig, alpha=0.7, scm=scm)\n",
    "    #plot load_kW in different fig2 \n",
    "    fig2, ax2 = plotObject_Load(GrossDf, ax2, fig2)\n",
    "    #Plot heat_Capacity in the same fig as fig2 but as a subplot\n",
    "    fig2, ax2_1 =plotObject_Heat_Capacity(GrossDf, ax2_1, fig2)\n",
    "    #This plot the HP in and out and tmixL laod tem.\n",
    "    fig, ax_1 = plotObject_Tem_HpAndLoad(GrossDf, ax_2, fig)\n",
    "    #print ('df whole is:', dfs(lol))\n",
    "    \n",
    "    #print ('df is:', df)\n",
    "    #plot(tankStrat(dfs(lol)), plotObject(tankStrat(dfs(lol))))\n",
    "    #plotObject(tankStrat(dfs(lol)))for i in range (0, 10):\n",
    "    #x = tankStrat(dfs(lol)).index[-1]\n",
    "    #y =tankStrat(dfs(lol)).iloc[-1]\n",
    "    #print ('x is:',x,'y is :',y)\n",
    "    #restTem = InletTem(dfs(lol))\n",
    "    #print ('rets of inlet tem values are:', restTem.iloc[:, 1])\n",
    "    print ('values in teh list are:', lol[0][-1][0][0])\n",
    "    print('________________________________________________________________')\n",
    "    time.sleep(0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "legislative-carolina",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "palestinian-trinidad",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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