plotDischargeEff.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "vocational-processing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "table flowReadings 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"
    },
    {
     "data": {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "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 ((394, datetime.datetime(2021, 3, 17, 7, 48, 4), 19.8188, 25.3961, 20.697, 19.346, 25.7344, 25.5991, 22.5218, 16.848, 21.2038, 23.7726, 23.8064, 23.6036, 23.4007, 22.9612, 26.0051, 25.3961, 24.5504, 23.9755, 23.9079),)\n",
      "electricity in khW is: 2.789377777777778e-06\n",
      "cop is: -0.5006407246581669\n",
      "Load in kWh is: 3.7014012320999813e-06\n",
      "heat addition in kwh is: -1.396476112012054e-06\n",
      "Index([0], dtype='object')\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 ((397, datetime.datetime(2021, 3, 17, 7, 48, 9), 19.8525, 25.3623, 20.697, 19.3122, 25.7344, 25.5652, 22.5218, 16.848, 21.2038, 23.7388, 23.7726, 23.5698, 23.4345, 22.9612, 26.0051, 25.3961, 24.5166, 23.9755, 23.9079),)\n",
      "electricity in khW is: 7.684544444444446e-06\n",
      "cop is: 6.259067694479601\n",
      "Load in kWh is: 1.2883888259151576e-05\n",
      "heat addition in kwh is: 4.8098083879014926e-05\n",
      "RangeIndex(start=0, stop=2, step=1)\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 ((398, datetime.datetime(2021, 3, 17, 7, 48, 11), 19.8525, 25.3961, 20.697, 19.3122, 25.7344, 25.5652, 22.5218, 16.848, 21.2038, 23.7726, 23.8064, 23.5698, 23.4345, 22.9274, 26.0389, 25.3284, 24.5166, 23.9755, 23.9417),)\n",
      "electricity in khW is: 1.3203569444444447e-05\n",
      "cop is: 3.5370488865714793\n",
      "Load in kWh is: 1.2176094250312734e-05\n",
      "heat addition in kwh is: 4.670167060224144e-05\n",
      "RangeIndex(start=0, stop=3, step=1)\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 ((400, datetime.datetime(2021, 3, 17, 7, 48, 15), 19.8863, 25.3961, 20.7308, 19.3122, 25.7344, 25.5652, 22.5556, 16.848, 21.2038, 23.7388, 23.8064, 23.6036, 23.4683, 22.9274, 26.0051, 25.3623, 24.5166, 23.8741, 23.9417),)\n",
      "electricity in khW is: 1.8397319444444448e-05\n",
      "cop is: 2.387796645030357\n",
      "Load in kWh is: 1.5141278396358329e-05\n",
      "heat addition in kwh is: 4.3929057646996205e-05\n",
      "RangeIndex(start=0, stop=4, step=1)\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 ((401, datetime.datetime(2021, 3, 17, 7, 48, 16), 19.8863, 25.3623, 20.7308, 19.346, 25.7344, 25.5991, 22.488, 16.848, 21.2038, 23.7388, 23.8064, 23.6036, 23.4007, 22.9274, 26.0389, 25.3623, 24.5504, 23.9079, 23.9079),)\n",
      "electricity in khW is: 2.1425347222222226e-05\n",
      "cop is: 2.018213559867824\n",
      "Load in kWh is: 1.699197901240832e-05\n",
      "heat addition in kwh is: 4.3240926288765315e-05\n",
      "RangeIndex(start=0, stop=5, step=1)\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 ((403, datetime.datetime(2021, 3, 17, 7, 48, 20), 19.8863, 25.3961, 20.697, 19.3122, 25.7344, 25.5991, 22.488, 16.848, 21.2038, 23.7388, 23.7726, 23.6036, 23.4345, 22.9274, 26.0051, 25.3284, 24.5504, 24.0432, 23.9079),)\n",
      "electricity in khW is: 2.7268091666666672e-05\n",
      "cop is: 1.4833537420091456\n",
      "Load in kWh is: 1.5565443037638596e-05\n",
      "heat addition in kwh is: 4.044822581119841e-05\n",
      "RangeIndex(start=0, stop=6, step=1)\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 ((405, datetime.datetime(2021, 3, 17, 7, 48, 23), 19.9201, 25.3961, 20.697, 19.3122, 25.7006, 25.5314, 22.5218, 16.8817, 21.2038, 23.7388, 23.8064, 23.6036, 23.4345, 22.9274, 26.0051, 25.3623, 24.5166, 23.9755, 23.9079),)\n",
      "electricity in khW is: 3.342034722222223e-05\n",
      "cop is: 0.509212684695696\n",
      "Load in kWh is: 2.1032448024791147e-05\n",
      "heat addition in kwh is: 1.701806473249013e-05\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-1-a356ac48f442>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    647\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[1;32m    648\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 649\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    650\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    651\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-1-a356ac48f442>\u001b[0m in \u001b[0;36mplotObject\u001b[0;34m(df, ax, fig, alpha, scm)\u001b[0m\n\u001b[1;32m    289\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    290\u001b[0m         ax.plot(df.index, df.iloc[:, i], color= scm.to_rgba(i, alpha=alpha),\\\n\u001b[0;32m--> 291\u001b[0;31m                 label = 'Layer ' + str(i))\n\u001b[0m\u001b[1;32m    292\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbbox_to_anchor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.8\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    293\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\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/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1743\u001b[0m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1744\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1745\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\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   1746\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_request_autoscale_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscalex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscaley\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1747\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlines\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;36madd_line\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   1962\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1964\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_line_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\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   1965\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_label\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   1966\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_line%d'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlines\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;36m_update_line_limits\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   2021\u001b[0m                                           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mignore_existing_data_limits\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2022\u001b[0m                                           \u001b[0mupdatex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mupdatex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2023\u001b[0;31m                                           updatey=updatey)\n\u001b[0m\u001b[1;32m   2024\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mignore_existing_data_limits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36mupdate_from_path\u001b[0;34m(self, path, ignore, updatex, updatey)\u001b[0m\n\u001b[1;32m    872\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m         points, minpos, changed = update_path_extents(\n\u001b[0;32m--> 874\u001b[0;31m             path, None, self._points, self._minpos, ignore)\n\u001b[0m\u001b[1;32m    875\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    876\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mchanged\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",
    "\n",
    "import time\n",
    "import struct\n",
    "from struct import *\n",
    "from pyModbusTCP.client import ModbusClient\n",
    "\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 flowReadings deleted')\n",
    "\n",
    "\n",
    "cL.execute('CREATE TABLE flowReadings(id INT AUTO_INCREMENT PRIMARY KEY,  \\\n",
    "ts TIMESTAMP DEFAULT CURRENT_TIMESTAMP, \\\n",
    "flowHP FLOAT, flowLoad FLOAT, watt FLOAT) ENGINE=MyISAM;')\n",
    "\n",
    "#cvsFileName = import os.path\n",
    "#os.path.exists(file_path)\n",
    "\n",
    "lol=[[], [], [], [], []]\n",
    "flowRateLoad  = []\n",
    "wattMeterKw = []\n",
    "################################\n",
    "##Cop functions, takes Tiem stamp of both Wattmegter and Tem values\n",
    "###\n",
    "###############################\n",
    "def flatten_watt(l_of_l):\n",
    "    '''this to flatten the watt tuple/list  whish is \n",
    "    present in lol[1]\n",
    "    '''\n",
    "    T = l_of_l[1]\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",
    "\n",
    "timePeriod_watt = []\n",
    "def timeStampCreator_watt(Lol):\n",
    "    '''calculating timestamp of wattmeter'''\n",
    "    mHP = Lol[2]\n",
    "    T = flatten_watt(Lol)\n",
    "    if len(mHP) == 1:\n",
    "        timeStamp = 2/3600\n",
    "        timePeriod_watt.append(timeStamp)\n",
    "    elif len(mHP) > 1:\n",
    "        timeLast = T[-1][1]\n",
    "        timeSecondLast = T[-2][1]\n",
    "        timeStamp = timeLast -timeSecondLast\n",
    "        timeStamp = timeStamp.total_seconds()/3600\n",
    "        timePeriod_watt.append(timeStamp)\n",
    "    return \n",
    "\n",
    "def flatten_tem(l_of_l):\n",
    "    '''flatting the time lol to calculate time stamp b/w each'''\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",
    "timePeriod_tem = []\n",
    "def timeStampCreator_cop(Lol):\n",
    "    '''calculatng tem time stamp to calculate accuractly the mCT '''\n",
    "    mHP = Lol[2]\n",
    "    T = flatten_tem(Lol)\n",
    "    if len(mHP) == 1:\n",
    "        timeStamp = 2/3600\n",
    "        timePeriod_tem.append(timeStamp)\n",
    "    elif len(mHP) > 1:\n",
    "        timeLast = T[-1][1]\n",
    "        timeSecondLast = T[-2][1]\n",
    "        timeStamp = timeLast -timeSecondLast\n",
    "        timeStamp = timeStamp.total_seconds()/3600\n",
    "        timePeriod_tem.append(timeStamp)\n",
    "    return \n",
    "\n",
    "copPlotterList = []\n",
    "def Cop(Lol, time_stamp_tem, time_stamp_watt):\n",
    "    '''this function takes both time stamp Tem and Watt\n",
    "     because both Tem and watt are running at different timetamps\n",
    "    '''\n",
    "    mHP = Lol[2]\n",
    "    T = flatten_tem(Lol)\n",
    "    mL = Lol[3]\n",
    "    watt_kW = Lol[4]\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",
    "    mF_kgPerS =[ x*2.7777e-07*y for x, y in zip(p_LperH, mHP)]\n",
    "    cP_kjPerkgK = [4.253264761904763 - 0.00470305*b[2] for b in T]\n",
    "    mCT_kW = [c*a*(b[3]-b[4]) for c, a, b in zip(cP_kjPerkgK,mF_kgPerS, T)]\n",
    "    mCT_kWh_l = [x*y for x,y in zip (time_stamp_tem, mCT_kW)]\n",
    "    mCT_kWh = sum(mCT_kWh_l)\n",
    "    #electricty consumed:\n",
    "    watt_kW_l = [x*y for x,y in zip (time_stamp_watt,  watt_kW)]\n",
    "    watt_kWh = sum(watt_kW_l)\n",
    "    #cop \n",
    "    cop =mCT_kWh/watt_kWh\n",
    "    copPlotterList.append(cop)\n",
    "    #print ('cop of system is:', cop)\n",
    "    #print ('kwh heat addition is :', mCT_kWh)\n",
    "    #print ('whole kW list is:', mCT_kW)\n",
    "    print ('electricity in khW is:', watt_kWh)\n",
    "    \n",
    "    #print ('electricity consumption kW :', watt_kW)\n",
    "    return cop\n",
    "############################################################\n",
    "\n",
    "def MCT_timeStamp(Lol, time_stamp_tem):\n",
    "    mHP = Lol[2]\n",
    "    T = flatten_tem(Lol)\n",
    "    mL = Lol[3]\n",
    "    watt_kW = Lol[4]\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",
    "    mF_kgPerS =[ x*2.7777e-07*y for x, y in zip(p_LperH, mHP)]\n",
    "    cP_kjPerkgK = [4.253264761904763 - 0.00470305*b[2] for b in T]\n",
    "    mCT_kW = [c*a*(b[3]-b[4]) for c, a, b in zip(cP_kjPerkgK,mF_kgPerS, T)]\n",
    "    mCT_kWh_l = [x*y for x,y in zip (time_stamp_tem, mCT_kW)]\n",
    "    mCT_kWh = sum(mCT_kWh_l)\n",
    "    print ('heat addition in kwh is:', mCT_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_tem(Lol)\n",
    "    mL = Lol[3]\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",
    "    return df_conc\n",
    "\n",
    "def list2df(lis, column_name = None):\n",
    "    df  = pd.DataFrame(lis, columns = [column_name]) \n",
    "    return df\n",
    "\n",
    "\n",
    "def toCsv(df_full, load_kW, heatCapacity_kW, electrictyUsed_kW, COP, nDis, timePeriod):\n",
    "    df_Tem_Flow_Load_capacity_conc = pd.concat([df_full, load_kW, heatCapacity_kW, electrictyUsed_kW, \\\n",
    "                                                COP, nDis, timePeriod], axis=1)\n",
    "    #df_Tem_Flow_Load_capacity_conc = df_Tem_Flow_Load_capacity_conc.replace(',',np.nan)\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, na_rep=np.nan)\n",
    "    #print ('gross df with NAN is: ', df_Tem_Flow_Load_capacity_conc )\n",
    "    return df_Tem_Flow_Load_capacity_conc\n",
    "\n",
    "def Load_kWh(Lol, time_stamp_tem):\n",
    "    \n",
    "    mHP = Lol[2]\n",
    "    T = flatten_tem(Lol)\n",
    "    mL = Lol[3]\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[7] for b in T]\n",
    "    #print ('Cp if load function is:')\n",
    "    #print ('Cp is:', cP_kjPerkgK)\n",
    "    #Cp*M*deltaT\n",
    "    load_mCT_kW = [c*a*(b[7]-b[10]) for c, a, b in zip(cP_kjPerkgK,mF_kgPerS, T)]\n",
    "    load_mCT_kW_l = [x*y for x,y in zip (time_stamp_tem, load_mCT_kW )]\n",
    "    load_mCT_kWh = sum(load_mCT_kW_l)\n",
    "    print ('Load in kWh is:', load_mCT_kWh)\n",
    "    #print ('Load in kW:', load_mCT_kW)\n",
    "    return load_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",
    "\n",
    "    return dfT\n",
    "#tankStratTem = tankStrat(df_conc)\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",
    "axTwin = ax2.twinx()\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",
    "    axTwin.clear()\n",
    "    ax2.plot(df.index, df.iloc[:, -6],label = 'Load in kW')\n",
    "    axTwin.plot(df.index, Nan2Zero(df).iloc[:, -2]*100, label = 'Discharge Efficiency', color = 'b')\n",
    "    axTwin.set_ylabel('nDischarge',color ='b')\n",
    "    #ax2.plot(df.index, COP, label = 'cop')\n",
    "    print (df.index)\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",
    "def Nan2Zero(df):\n",
    "    #df.replace('NaN', 0)\n",
    "     return df.fillna(0) \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",
    "    #axTwin = ax2_1.twinx()\n",
    "    #axTwin.clear()\n",
    "    \n",
    "    ax2_1.plot(df.index, df.iloc[:, -5], label = 'heat_Capacity in kW')\n",
    "    ax2_1.plot(df.index, df.iloc[:, -4], label = 'electrcity_kW')\n",
    "    ax2_1.plot(df.index, df.iloc[:, -3], label = 'cop of system')\n",
    "    #axTwin.plot(df.index, Nan2Zero(df).iloc[:, -2]*100, label = 'Discharge Efficiency', color = 'b')\n",
    "    #axTwin.set_ylabel('nDischarge',color ='b')\n",
    "    \n",
    "    ax2_1.legend(bbox_to_anchor=(0.4,0.8))\n",
    "    plt.subplots_adjust(wspace = 0.5)\n",
    "    fig2.canvas.draw()\n",
    "    ax2_1.legend()\n",
    "    fig2.tight_layout()\n",
    "    #plt.legend(loc='upper left')\n",
    "    return fig2, ax2_1\n",
    "\n",
    "#######################\n",
    "###Discharge efficiency\n",
    "#######################\n",
    "def densityFumction(temVlaue):\n",
    "    #tcalculating density at a specific temp.\n",
    "    return temVlaue.map(lambda x: 999.8473664794213 + 6.29265190e-02*x - \\\n",
    "    8.42930922e-03*x**2 + 6.77190849e-05*x*3 \\\n",
    "    - 4.40840180e-07*x**4 + 1.29302849e-09*x**5)\n",
    "\n",
    "def flowCubeMeterPerSec(flow):\n",
    "    return flow.map(lambda x: x*0.001*0.000277777778)\n",
    "\n",
    "def specificHeatkJPerKgK(temValue):\n",
    "    return temValue.map(lambda x: 4.253264761904763 - 0.00470305*x)\n",
    "\n",
    "def dfMultiplication(df1_p_hot, cp_df_hot, df1_p_cold, cp_df_cold, df_V,  Tin, Tout, timeStamp_tem):\n",
    "    \n",
    "    #df_V_litersPerH =  df_V.map(lambda x: x*1000*3600)\n",
    "    #if df_V_litersPerH.iloc[-1] > 5:\n",
    "    \n",
    "    #Hot side p*V*Cp*T_hot in kj/sec, I will convert it to kwh by multiplying by timeStamp.Then to KJ\n",
    "    #Finally because I need to divide teh net energy extraccted by total energy mstore*C*dT which is \n",
    "    #in kJ.\n",
    "    #deltaT_df = Tout.subtract(Tin)\n",
    "    l2df_timeStamp_df= pd.DataFrame(timeStamp_tem)\n",
    "    #print ('type of timestamp object is:', type (l2df_timeStamp_df))\n",
    "    pcp_df_hot = df1_p_hot.multiply( cp_df_hot)\n",
    "    pVCp_df_hot = pcp_df_hot.multiply(df_V)\n",
    "    pCpVT_df_hot = pVCp_df_hot.multiply(Tout)\n",
    "    \n",
    "    #Cold side pCpVT_df_cold is in kJ/s:\n",
    "    # I am seperating hot from cold becasue density, Cp are funstion of temp.otherwise I would have\n",
    "    # only done pVCp [Thot -Tcold] without any problem, however, p&Cp are function of Thot and Tcold\n",
    "    #So I calculate them during hot and during cold side and just subtract them\n",
    "    pcp_df_cold = df1_p_cold.multiply(cp_df_cold)\n",
    "    pVCp_df_cold = pcp_df_cold.multiply(df_V)\n",
    "    pCpVT_df_cold = pVCp_df_cold.multiply(Tin)\n",
    "    \n",
    "    pVCpDeltaT_df_kJPersec = pCpVT_df_hot.subtract( pCpVT_df_cold )\n",
    "    #convert pandas series to pandas dataframe.otherwise it will not multiply with other PD series in\n",
    "    #next step:\n",
    "    pVCpDeltaT_df_kJPersec = pVCpDeltaT_df_kJPersec.to_frame()\n",
    "    #print ('type of timestamp object is:', type (pVCpDeltaT_df_kJPersec))\n",
    "    # convert the kJ/sec to kwH, as energy = power in time, \n",
    "    pVCpDeltaT_df_kWh = pVCpDeltaT_df_kJPersec.multiply(l2df_timeStamp_df, fill_value=0)\n",
    "    #pVCpDeltaT_sum  = pVCpDeltaT_df.sum()\n",
    "    pVCpDeltaT_df_kWh_sum  = pVCpDeltaT_df_kWh.sum()\n",
    "    #convert kwh to kJ as I need to divide mstore * C *DeltaT = kJ to calculate teh discharge\n",
    "    #efficiency, so energy taken out must be in kJ instead of kwh. To make sense \n",
    "    pVCpDeltaT_df_kJ_sum = pVCpDeltaT_df_kWh_sum*3600\n",
    "    pVCpDeltaT_df_kJ_sum  = pd.to_numeric(pVCpDeltaT_df_kJ_sum)\n",
    "    #print ('flow lools like this:',df_V)\n",
    "    print ('Total sum discharge in kJ is :',pVCpDeltaT_df_kJ_sum )\n",
    "    #print ('discharge in kJ/sec:',pVCpDeltaT_df_kJPersec ,'tiem df is',  l2df_timeStamp_df\\\n",
    "    #       , 'multiplied df is:', pVCpDeltaT_df_kWh)\n",
    "    #print ('multiplied df is :', )\n",
    "    #pVCpDeltaT = pVCp.multiply(deltaT_df)\n",
    "    return pVCpDeltaT_df_kJ_sum\n",
    "\n",
    "def TankVolumeDischarged(Lol,timeStamp):\n",
    "    mLoad = Lol[3]\n",
    "    #print ('len of Mload is:', len (mLoad))\n",
    "    #print ('len of tiem stamp is:', len (timeStamp))\n",
    "    litersDischarged = sum([x*y for x, y in zip (mLoad , timeStamp)])\n",
    "    print ('liters discharged is :',litersDischarged )\n",
    "    return litersDischarged\n",
    "\n",
    "nDischargePlotterList = []\n",
    "def nDicharge(df_conc_all, gross_DF):\n",
    "    '''\n",
    "    T_initial should be average tem of tank i.e. mean of 10 sensors just before the discharge\n",
    "    initiates \n",
    "\n",
    "    '''\n",
    "    df_flowGreaterThan5 = df_conc_all.loc[(df_conc_all['flowLoad']>0.5)&(df_conc_all['flowLoad']<5)]\n",
    "    #print ('df for just before discharge starts is:', df_flowGreaterThan5['flowLoad'], df_flowGreaterThan5['id'] )\n",
    "    df_flowGreaterThan5_temValues = df_flowGreaterThan5.iloc[-1, -12:-2]\n",
    "    print ('df for just before discharge starts is:', df_flowGreaterThan5_temValues, df_flowGreaterThan5.iloc[-1]['id'] )\n",
    "    df_flowGreaterThan5_mean  = df_flowGreaterThan5_temValues.mean()\n",
    "    T_inlet = df_conc_all['TbottomTestTankLoadCir']\n",
    "    T_inlet_lastValue  =  T_inlet.iloc[-1]\n",
    "    cP_kjPerkgK = 4.253264761904763 - 0.00470305*df_flowGreaterThan5_mean \n",
    "    p_LperH = 999.8473664794213 + 6.29265190e-02*df_flowGreaterThan5_mean - \\\n",
    "    8.42930922e-03*df_flowGreaterThan5_mean**2 + 6.77190849e-05*df_flowGreaterThan5_mean**3 \\\n",
    " - 4.40840180e-07*df_flowGreaterThan5_mean**4 + 1.29302849e-09*df_flowGreaterThan5_mean**5 \n",
    "    \n",
    "    mcT_kJ = 0.397*p_LperH*cP_kjPerkgK*(df_flowGreaterThan5_mean-T_inlet_lastValue)\n",
    "    #mcT_kJ shoudl be constant throught the discharge process, as its the energy content of tank \n",
    "    #from very begineening of tank.\n",
    "    flowLoad  = df_conc_all['flowLoad']\n",
    "    T_out  = df_conc_all['TTopTestTankLoadCir']\n",
    "    #T_inlet = df_conc_all['TbottomTestTankLoadCir']\n",
    "    if not abs(T_out.iloc[-1]-T_inlet.iloc[-1]) <  0.8*abs(df_flowGreaterThan5_mean-T_inlet.iloc[-1]):\n",
    "        print ('OK ok oK oK ok if not small run ')\n",
    "        '''\n",
    "        flowLoad  = df_conc_all['flowLoad']\n",
    "        waterDensityAtInletTem_df_series = densityFumction(T_inlet)\n",
    "        flow_kgPerS = flowCubeMeterPerSec(flowLoad)*waterDensityAtInletTem_df_series\n",
    "        T_out  = df_conc_all['TTopTestTankLoadCir']\n",
    "        T_inlet = df_conc_all['TbottomTestTankLoadCir']\n",
    "        mcT_sum = flowLoad\n",
    "        '''\n",
    "        \n",
    "        numerator = dfMultiplication(densityFumction(gross_DF['TTopTestTankLoadCir']), \\\n",
    "                         specificHeatkJPerKgK(gross_DF['TTopTestTankLoadCir']), \\\n",
    "                         densityFumction(gross_DF['TbottomTestTankLoadCir']),\\\n",
    "                         specificHeatkJPerKgK(gross_DF['TbottomTestTankLoadCir']),\\\n",
    "                         flowCubeMeterPerSec(gross_DF ['flowLoad']), \\\n",
    "                         gross_DF['TbottomTestTankLoadCir'], \\\n",
    "                         gross_DF['TTopTestTankLoadCir'], timePeriod_tem)\n",
    "        denominator = mcT_kJ\n",
    "        dischargeEfficiency =  numerator/denominator\n",
    "        dischargeEfficiency= dischargeEfficiency.to_numpy()[0]\n",
    "        print('type of nDis now is ', dischargeEfficiency)\n",
    "        #dischargeEfficiency = pd.to_numeric(dischargeEfficiency)\n",
    "        nDischargePlotterList.append(dischargeEfficiency)\n",
    "        print ('energy content of tank from the begineening of discharge in kJ, Inlet, mean, cp, p:' \\\n",
    "               , denominator, T_inlet_lastValue, df_flowGreaterThan5_mean, cP_kjPerkgK,p_LperH )\n",
    "        #print ('energy content of tank from the begineening of discharge in kJ:', denominator)\n",
    "        print ('energy/sec discharged from tank in kJ/sec:', numerator)\n",
    "        print ('discharge efficiency is:', dischargeEfficiency)\n",
    "\n",
    "        return dischargeEfficiency\n",
    "        \n",
    "    '''\n",
    "    #print ('mean of the T_initial  and index is :', df_flowGreaterThan5.iloc[-1][['id']], df_flowGreaterThan5_mean)\n",
    "    #print ('mean of the T_initial  and index is :', df_flowGreaterThan5.iloc[-1][['id']], df_flowGreaterThan5_mean)\n",
    "    print('heat capacity of water mixed is:',cP_kjPerkgK )\n",
    "    print ('condition check Tout-T_in:', T_out.iloc[-1]-T_inlet.iloc[-1])\n",
    "    print ('80% condition check :', 0.8*abs(df_flowGreaterThan5_mean-T_inlet.iloc[-1]))\n",
    "    #print ('Inlet and mean to teh tank and mct and density is :', T_inlet_lastValue, df_flowGreaterThan5_mean,  mcT_kJ, p_LperH)\n",
    "    #if df_flowGreaterThan5\n",
    "    #.iloc[-12:-3]\n",
    "    #T_initial = df__conc_all.iloc[-1, 0:11].mean()\n",
    "    return df_flowGreaterThan5_temValues\n",
    "    '''\n",
    "while True:\n",
    "    #c.execute(\"INSERT INTO flowReadings(flowHp, flowLoad) VALUES(?,?,?,?)\", (chan2.voltage, chan1.voltage))\n",
    "    #connection.commit()\n",
    "    \n",
    "    ###################################################################################\n",
    "    ################################################\n",
    "    ##Modbus connection for wattmeter readings in kW\n",
    "    ##https://stackoverflow.com/questions/28186746/python-convert-2-ints-to-float\n",
    "    ################################################\n",
    "    \n",
    "    c = ModbusClient(host=\"10.208.8.125\", port=502, auto_open=True)\n",
    "    \n",
    "    #This reads the register address 19026 where all teh meteric(values of wattmeter) \n",
    "    #values are stored in a 16 bit values.county = 2, means read two values in the\n",
    "    #same register address.Register is a column (like in spreadsheet). each register\n",
    "    #store a 16 bit value, we read two 16 bit value and would convert those 2 16 bit \n",
    "    #value to a 32 bit floating point value by the formula\n",
    "    regs = c.read_input_registers(19026, 2)\n",
    "    #this is forst value of two counts\n",
    "    a = regs[0]\n",
    "    #This is second value of two counts\n",
    "    b = regs[1]\n",
    "    #This is how we convert two 16 bit value to a single 32 floating point number.\n",
    "    #from stackoverflow\n",
    "    mypack = pack('>HH',a,b)\n",
    "    #print (mypack)\n",
    "    fl = unpack('>f', mypack)\n",
    "    #fl = struct.unpack('!f', bytes.fromhex('{0:02x}'.format(a) + '{0:02x}'.format(b)))\n",
    "    #fl is a tuple (5.0186,) I need to unpack it index it with [0] \n",
    "    fl = fl[0]*10**-3\n",
    "    #wattMeterKw.append(fl)\n",
    "    #lol[3].append(fl)\n",
    "    #print ('watter meter reaidngs are:', wattMeterKw)\n",
    "    #print ('gross lol is:', lol)\n",
    "    ####################################################################################\n",
    "    \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, watt) VALUES(%s, %s, %s)\", (flow2, flow1, fl))\n",
    "    connectionL.commit()\n",
    "    \n",
    "    #cL =connectionL.cursor()\n",
    "    cL.execute(\"SELECT * FROM flowReadings ORDER BY id DESC LIMIT 1\")\n",
    "    FlowFormDb = cL.fetchall()\n",
    "    #print ('flow and watt from FlowWattDB is :',FlowFormDb[-1][2],FlowFormDb )\n",
    "    #print ('Time period of flow load and watt is :',timePeriod_watt, [x* 3600 for x in timePeriod_watt])\n",
    "\n",
    "    #c.execute(\"SELECT * from temSensor\")\n",
    "    #cR.execute(\"SELECT * FROM temSensor ORDER BY id DESC LIMIT 1\")\n",
    "    #result = cR.fetchall()\n",
    "    \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",
    "    id_flowWatt = FlowFormDb[0][0]\n",
    "    flowHP  = FlowFormDb[-1][2]\n",
    "    flowLoad  = FlowFormDb[-1][3]\n",
    "    wattUsed = FlowFormDb[-1][4]\n",
    "    #print ('flow HP, flow Load, watt is:' ,flowHP, flowLoad, wattUsed)\n",
    "    #idGenerator(id, l_id)\n",
    "    #print ('id is :', id)\n",
    "    \n",
    "    flowRateLoad.append(flow1)\n",
    "    if lol[0] ==[] or id < lol[0][-1][0][0] :\n",
    "        lol[0].append(result)\n",
    "        #print (lol)\n",
    "        if lol[1] ==[] or id_flowWatt < lol[1][-1][0][0]:\n",
    "            lol[1].append(FlowFormDb)\n",
    "            lol[2].append(flowHP)\n",
    "            lol[3].append(flowLoad)\n",
    "            lol[4].append(wattUsed)\n",
    "            timeStampCreator_cop(lol)\n",
    "            timeStampCreator_watt(lol)\n",
    "            wattMeterKw.append(fl)\n",
    "    elif lol[0] != [] and id > lol[0][-1][0][0]:\n",
    "        lol[0].append(result)\n",
    "        if lol[1] != [] and id > lol[1][-1][0][0]:\n",
    "            lol[1].append(FlowFormDb)\n",
    "            lol[2].append(flowHP)\n",
    "            lol[3].append(flowLoad)\n",
    "            lol[4].append(wattUsed)\n",
    "            timeStampCreator_cop(lol)\n",
    "            timeStampCreator_watt(lol)\n",
    "            wattMeterKw.append(fl)\n",
    "        \n",
    "\n",
    "    print ('cop is:', Cop(lol, timePeriod_tem, timePeriod_watt ))\n",
    "    \n",
    "\n",
    "    df = tankStrat(dfs(lol))\n",
    "\n",
    "\n",
    "    GrossDf = toCsv(dfs(lol), list2df(Load_kWh(lol, timePeriod_tem), 'Load_kW'), \\\n",
    "                    list2df(MCT_timeStamp(lol, timePeriod_tem),'Heat_Capacity_kW'),\\\n",
    "                    list2df(wattMeterKw, 'electrcity_kW'), list2df(copPlotterList, 'COP'), \\\n",
    "                   list2df(nDischargePlotterList, 'nDis'), list2df(timePeriod_tem, 'timeStamp_Temp'))\n",
    "    \n",
    "    #pint ('gorss DF is :',GrossDf )\n",
    "    #GrossDf = toCsv(dfs(lol), list2df(Load_kWh(lol), 'Load_kW'), list2df(MCT_timeStamp(lol, timePeriod),'Heat_Capacity_kW'))\n",
    "    #heatAddition_kwh, cop = MCT_timeStamp(lol, timePeriod)\n",
    "    # Plot tem values as a seperate first fig\n",
    "\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",
    "\n",
    "    #print ('fetch the flow load:', nDicharge(dfs(lol)))\n",
    "    #nDicharge(dfs(lol))\n",
    "    #print ('lol len and lol itself is :', len (lol[0]), lol[0])\n",
    "    #print ('Density of df is :',densityFumction(GrossDf ['TbottomTestTankLoadCir']))\n",
    "    #print ('fow in cubeic meter per sec  :',flowCubeMeterPerSec(GrossDf ['flowLoad']))\n",
    "    #flowCubeMeterPerSec(flow)\n",
    "    #print ('discharge mcdelta T is :',dfMultiplication(densityFumction(), df2_V, Tin, Tout), flowCubeMeterPerSec(GrossDf ['flowLoad']))\n",
    "    #dfMultiplication(df1_p, df2_V, Tin, Tout)\n",
    "    #print ('flow in m3/sec:', flowCubeMeterPerSec(GrossDf ['flowLoad']))\n",
    "    #print ('hot water cp, cold water cp:', specificHeatkJPerKgK(GrossDf['TTopTestTankLoadCir']),specificHeatkJPerKgK(GrossDf['TbottomTestTankLoadCir']))\n",
    "    #print ('how water density, cold water density:', densityFumction(GrossDf['TTopTestTankLoadCir']),densityFumction(GrossDf['TbottomTestTankLoadCir']))\n",
    "    #nDicharge(dfs(lol), GrossDf)\n",
    "    #print ('tiem perod is :', pd.DataFrame({'time_tem':timePeriod_tem}))\n",
    "    #print ('nDis plottre list is:',nDischargePlotterList, len(nDischargePlotterList))\n",
    "    #print ('nDis plotter df looks like:',list2df(nDischargePlotterList, 'nDis'))\n",
    "    #if nDischargePlotterList\n",
    "    #print ('gross df wiuth Nan is :', Nan2Zero(GrossDf))\n",
    "    #print ('nDis df looks like thuis :',Nan2Zero(list2df(nDischargePlotterList, 'nDis')))\n",
    "    if GrossDf ['flowLoad'].iloc[-1]>5:\n",
    "        #print ('Highre discharge calculating Discharge Efficiency',GrossDf ['flowLoad'].iloc[-1] )\n",
    "        nDicharge(dfs(lol), GrossDf)\n",
    "        TankVolumeDischarged(lol,timePeriod_watt)\n",
    "    else : \n",
    "        #This else conditions fills the nDischargePlotterList list even when discharge is not going on\n",
    "        #otehrwise its problem. \n",
    "        nDischargePlotterList.append(np.nan)\n",
    "        \n",
    "        \n",
    "\n",
    "\n",
    "    '''\n",
    "    dfMultiplication(densityFumction(GrossDf['TTopTestTankLoadCir']), \\\n",
    "                     specificHeatkJPerKgK(GrossDf['TTopTestTankLoadCir']), \\\n",
    "                     densityFumction(GrossDf['TbottomTestTankLoadCir']),\\\n",
    "                     specificHeatkJPerKgK(GrossDf['TbottomTestTankLoadCir']),\\\n",
    "                     flowCubeMeterPerSec(GrossDf ['flowLoad']), \\\n",
    "                     GrossDf['TbottomTestTankLoadCir'], \\\n",
    "                     GrossDf['TTopTestTankLoadCir'], timePeriod_tem)\n",
    "    '''\n",
    "    print('________________________________________________________________')\n",
    "    time.sleep(0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "recent-house",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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