DischargeEfficiency.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "regional-blame",
   "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": {
      "text/html": [
       "<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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       "<img 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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 =  0.6320192876979889 0.003964491830999805 0.5340774213713702\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((196, datetime.datetime(2021, 4, 9, 12, 37, 11), 22.0148, 33.3556, 30.3728, 14.9926, 22.2852, 33.4573, 23.4683, 14.2507, 25.5991, 33.6608, 33.3895, 32.5418, 32.1689, 27.2234, 33.8304, 33.9321, 33.8643, 33.7964, 33.7964),)\n",
      "electricity in khW is: 3.7610499999999998e-06\n",
      "cop is: -0.2349893694375595\n",
      "Load in kWh is: 2.6683400726786894e-06\n",
      "heat addition in kwh is: -8.83806767923133e-07\n",
      "RangeIndex(start=0, stop=1, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6320192876979889 0.003964491830999805 0.5340774213713702\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((198, datetime.datetime(2021, 4, 9, 12, 37, 15), 22.0148, 33.3895, 30.4067, 15.06, 22.2514, 33.4573, 23.4683, 14.2507, 25.5991, 33.6608, 33.3895, 32.5418, 32.1689, 27.1896, 33.7286, 33.9321, 33.8643, 33.6947, 33.7964),)\n",
      "electricity in khW is: 1.0210427777777777e-05\n",
      "cop is: 0.6220918060604445\n",
      "Load in kWh is: 8.005020218036069e-06\n",
      "heat addition in kwh is: 6.351823456927508e-06\n",
      "RangeIndex(start=0, stop=2, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6320192876979889 0.003958780380194105 -2.825737091414544\n",
      "result is ((200, datetime.datetime(2021, 4, 9, 12, 37, 18), 22.0148, 33.3556, 30.3389, 15.0263, 22.2176, 33.4573, 23.4345, 14.2507, 25.6329, 33.6947, 33.3895, 32.5079, 32.1689, 27.2234, 33.7964, 33.9321, 33.8643, 33.966, 33.7964),)\n",
      "electricity in khW is: 1.5059877777777777e-05\n",
      "cop is: -0.12072705464516084\n",
      "Load in kWh is: 1.7877777848002872e-05\n",
      "heat addition in kwh is: -1.8181346874272211e-06\n",
      "RangeIndex(start=0, stop=3, 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 ((202, datetime.datetime(2021, 4, 9, 12, 37, 22), 22.0148, 33.3556, 30.3389, 15.0263, 22.2514, 33.4573, 23.4345, 14.2507, 25.6667, 33.6947, 33.4234, 32.5079, 32.1689, 27.2573, 33.7286, 33.966, 33.8304, 33.6608, 33.7964),)\n",
      "electricity in khW is: 1.9777019444444444e-05\n",
      "cop is: -0.18232461390359297\n",
      "Load in kWh is: 1.5352186583624522e-05\n",
      "heat addition in kwh is: -3.605837434372184e-06\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 =  0.6380194708090458 0.003996363738233922 4.260273039056376\n",
      "result is ((204, datetime.datetime(2021, 4, 9, 12, 37, 25), 22.0148, 33.3556, 30.3728, 15.06, 22.2514, 33.4573, 23.4683, 14.3181, 25.6667, 33.6608, 33.4234, 32.5418, 32.2028, 27.2234, 33.7625, 33.966, 33.8643, 33.593, 33.7964),)\n",
      "electricity in khW is: 2.820010277777778e-05\n",
      "cop is: 0.30401685721703\n",
      "Load in kWh is: 2.5181829981779974e-05\n",
      "heat addition in kwh is: 8.573306619697237e-06\n",
      "RangeIndex(start=0, stop=5, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6320192876979889 0.003964491830999805 0.5340774213713702\n",
      "flow rates HP is =  0.6380194708090458 0.003996363738233922 4.260273039056376\n",
      "result is ((206, datetime.datetime(2021, 4, 9, 12, 37, 29), 22.0148, 33.3895, 30.3728, 15.0263, 22.2176, 33.4573, 23.4345, 14.2844, 25.6667, 33.593, 33.3895, 32.5418, 32.2028, 27.2573, 33.8304, 33.9321, 33.8982, 33.8643, 33.7964),)\n",
      "electricity in khW is: 3.278206111111111e-05\n",
      "cop is: 0.7625122320896869\n",
      "Load in kWh is: 3.0472100232026654e-05\n",
      "heat addition in kwh is: 2.4996722590333854e-05\n",
      "RangeIndex(start=0, stop=6, 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 ((208, datetime.datetime(2021, 4, 9, 12, 37, 32), 22.0148, 33.3895, 30.3389, 15.06, 22.2176, 33.4573, 23.4683, 14.2844, 25.7006, 33.6269, 33.3895, 32.5757, 32.1689, 27.2234, 33.7625, 33.9321, 33.8643, 33.9321, 33.7625),)\n",
      "electricity in khW is: 3.816324444444444e-05\n",
      "cop is: 0.6194672116050289\n",
      "Load in kWh is: 3.44223501142615e-05\n",
      "heat addition in kwh is: 2.3640878621801112e-05\n",
      "RangeIndex(start=0, stop=7, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((211, datetime.datetime(2021, 4, 9, 12, 37, 37), 22.0148, 33.3556, 30.2711, 15.0937, 22.2176, 33.4234, 23.4345, 14.2844, 25.7344, 33.593, 33.3895, 32.5418, 32.1689, 27.2234, 33.7286, 33.9321, 33.8982, 33.6608, 33.7964),)\n",
      "electricity in khW is: 4.6212494444444444e-05\n",
      "cop is: 0.7139583063708664\n",
      "Load in kWh is: 5.058990111966335e-05\n",
      "heat addition in kwh is: 3.299379426672863e-05\n",
      "RangeIndex(start=0, stop=8, 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 ((213, datetime.datetime(2021, 4, 9, 12, 37, 41), 22.0148, 33.3556, 30.305, 15.06, 22.2514, 33.4234, 23.4683, 14.3181, 25.7344, 33.6269, 33.3895, 32.5418, 32.2028, 27.2234, 33.7625, 33.966, 33.8643, 33.966, 33.7964),)\n",
      "electricity in khW is: 5.167982777777778e-05\n",
      "cop is: 0.6034463280873178\n",
      "Load in kWh is: 6.352394192398484e-05\n",
      "heat addition in kwh is: 3.118600230868497e-05\n",
      "RangeIndex(start=0, stop=9, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6360194097720268 0.003989583551449171 2.1121730471176354\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((215, datetime.datetime(2021, 4, 9, 12, 37, 44), 22.0148, 33.3556, 30.305, 15.0937, 22.2514, 33.4234, 23.4683, 14.3518, 25.7682, 33.6608, 33.3895, 32.5079, 32.1689, 27.1896, 33.7964, 33.966, 33.8643, 33.6608, 33.7964),)\n",
      "electricity in khW is: 5.6343469444444444e-05\n",
      "cop is: 0.5294341763878285\n",
      "Load in kWh is: 7.894051809391296e-05\n",
      "heat addition in kwh is: 2.9830158340152226e-05\n",
      "RangeIndex(start=0, stop=10, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6320192876979889 0.003958780380194105 -2.825737091414544\n",
      "result is ((216, datetime.datetime(2021, 4, 9, 12, 37, 46), 22.0148, 33.3556, 30.2711, 15.1275, 22.2514, 33.4234, 23.4683, 14.3181, 25.7682, 33.6269, 33.3895, 32.5757, 32.2028, 27.1896, 33.7625, 33.8982, 33.8304, 33.8643, 33.7964),)\n",
      "electricity in khW is: 6.396300277777777e-05\n",
      "cop is: 0.37929906223553667\n",
      "Load in kWh is: 8.537880491985247e-05\n",
      "heat addition in kwh is: 2.4261106971380135e-05\n",
      "RangeIndex(start=0, stop=11, 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 ((218, datetime.datetime(2021, 4, 9, 12, 37, 49), 22.0148, 33.3556, 30.2373, 15.1275, 22.2852, 33.4234, 23.4345, 14.3518, 25.7682, 33.6947, 33.3895, 32.5757, 32.1689, 27.2234, 33.7286, 33.9321, 33.8643, 33.7286, 33.7964),)\n",
      "electricity in khW is: 7.059579166666667e-05\n",
      "cop is: 0.3240302727868927\n",
      "Load in kWh is: 8.927699434168248e-05\n",
      "heat addition in kwh is: 2.2875173631356648e-05\n",
      "RangeIndex(start=0, stop=12, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((220, datetime.datetime(2021, 4, 9, 12, 37, 53), 22.0148, 33.3895, 30.2711, 15.1275, 22.2176, 33.4573, 23.4007, 14.3518, 25.8359, 33.6608, 33.3895, 32.5418, 32.2028, 27.1557, 33.7964, 33.9321, 33.8643, 33.8304, 33.7964),)\n",
      "electricity in khW is: 7.552525e-05\n",
      "cop is: 0.4030405757588238\n",
      "Load in kWh is: 0.00010209549620971778\n",
      "heat addition in kwh is: 3.0439740244329105e-05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=13, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6300192266609699 0.003951945970775122 -0.25497039150178974\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((222, datetime.datetime(2021, 4, 9, 12, 37, 56), 21.981, 33.3895, 30.2034, 15.1612, 22.2852, 33.4234, 23.4345, 14.3855, 25.8359, 33.6608, 33.4234, 32.5418, 32.2028, 27.1896, 33.7286, 33.9321, 33.8982, 33.8304, 33.7964),)\n",
      "electricity in khW is: 7.999923333333333e-05\n",
      "cop is: 0.4529618535915367\n",
      "Load in kWh is: 0.00010025112195959916\n",
      "heat addition in kwh is: 3.6236601016568514e-05\n",
      "RangeIndex(start=0, stop=14, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6320192876979889 0.003964491830999805 0.5340774213713702\n",
      "flow rates HP is =  0.6320192876979889 0.003958780380194105 -2.825737091414544\n",
      "result is ((224, datetime.datetime(2021, 4, 9, 12, 37, 59), 22.0486, 33.3556, 30.2034, 15.1612, 22.2176, 33.4234, 23.4007, 14.3855, 25.8697, 33.6608, 33.4234, 32.5418, 32.2028, 27.1896, 33.7964, 33.9321, 33.8643, 33.7286, 33.8304),)\n",
      "electricity in khW is: 8.669875555555556e-05\n",
      "cop is: 0.31949788012610986\n",
      "Load in kWh is: 0.00010409707753148141\n",
      "heat addition in kwh is: 2.770006860957179e-05\n",
      "RangeIndex(start=0, stop=15, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6320192876979889 0.003958780380194105 -2.825737091414544\n",
      "result is ((226, datetime.datetime(2021, 4, 9, 12, 38, 3), 22.0148, 33.3556, 30.2034, 15.1949, 22.2514, 33.4234, 23.4007, 14.3518, 25.8697, 33.6269, 33.3895, 32.5418, 32.2028, 27.1896, 33.7964, 33.9321, 33.8643, 33.8643, 33.7964),)\n",
      "electricity in khW is: 9.168211388888889e-05\n",
      "cop is: 0.17797911792476268\n",
      "Load in kWh is: 0.00011680111133390959\n",
      "heat addition in kwh is: 1.6317501759422077e-05\n",
      "RangeIndex(start=0, stop=16, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6380194708090458 0.003996363738233922 4.260273039056376\n",
      "result is ((228, datetime.datetime(2021, 4, 9, 12, 38, 6), 22.0148, 33.3895, 30.305, 15.1949, 22.2514, 33.3895, 23.4683, 14.3855, 25.8697, 33.6947, 33.3895, 32.5757, 32.1689, 27.2234, 33.7625, 33.8982, 33.8643, 33.6269, 33.7964),)\n",
      "electricity in khW is: 9.737827222222222e-05\n",
      "cop is: 0.2969029838528786\n",
      "Load in kWh is: 0.00012628637617638738\n",
      "heat addition in kwh is: 2.891189958521566e-05\n",
      "RangeIndex(start=0, stop=17, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((230, datetime.datetime(2021, 4, 9, 12, 38, 10), 22.0148, 33.3556, 30.2711, 15.2286, 22.2514, 33.4234, 23.4683, 14.4193, 25.9036, 33.6608, 33.4234, 32.5418, 32.1689, 27.2573, 33.7625, 33.9321, 33.8982, 33.593, 33.7964),)\n",
      "electricity in khW is: 0.00010199937222222223\n",
      "cop is: 0.35680839311311563\n",
      "Load in kWh is: 0.00013893279394675112\n",
      "heat addition in kwh is: 3.6394232101157674e-05\n",
      "RangeIndex(start=0, stop=18, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6320192876979889 0.003964491830999805 0.5340774213713702\n",
      "flow rates HP is =  0.6300192266609699 0.003946252594180832 -5.187740468238238\n",
      "result is ((232, datetime.datetime(2021, 4, 9, 12, 38, 13), 22.0148, 33.3556, 30.2373, 15.1949, 22.2514, 33.4234, 23.4345, 14.4193, 25.9036, 33.6269, 33.4234, 32.5418, 32.2028, 27.2573, 33.7286, 33.9321, 33.8304, 33.7625, 33.7625),)\n",
      "electricity in khW is: 0.00010700005555555556\n",
      "cop is: 0.19523309495615573\n",
      "Load in kWh is: 0.00014276130712962575\n",
      "heat addition in kwh is: 2.0889952006591716e-05\n",
      "RangeIndex(start=0, stop=19, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6360194097720268 0.003989583551449171 2.1121730471176354\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((233, datetime.datetime(2021, 4, 9, 12, 38, 15), 21.981, 33.3895, 30.2373, 15.2286, 22.2852, 33.4234, 23.5021, 14.4193, 25.9374, 33.6608, 33.3895, 32.5757, 32.1689, 27.2573, 33.7286, 33.9321, 33.8643, 33.8982, 33.7964),)\n",
      "electricity in khW is: 0.00011438693333333333\n",
      "cop is: 0.21605096137280394\n",
      "Load in kWh is: 0.00015280947302941664\n",
      "heat addition in kwh is: 2.47134069151535e-05\n",
      "RangeIndex(start=0, stop=20, 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 ((235, datetime.datetime(2021, 4, 9, 12, 38, 18), 22.0148, 33.3895, 30.1695, 15.2624, 22.2514, 33.3895, 23.4345, 14.453, 25.9036, 33.6269, 33.4234, 32.5418, 32.1689, 27.2234, 33.7286, 33.966, 33.8643, 33.9321, 33.7625),)\n",
      "electricity in khW is: 0.000120455075\n",
      "cop is: 0.19328594374423783\n",
      "Load in kWh is: 0.0001509899687281768\n",
      "heat addition in kwh is: 2.328227285015795e-05\n",
      "RangeIndex(start=0, stop=21, 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 ((237, datetime.datetime(2021, 4, 9, 12, 38, 22), 22.0148, 33.3895, 30.1695, 15.2624, 22.2514, 33.3895, 23.4683, 14.4867, 25.9374, 33.6608, 33.4234, 32.5418, 32.1689, 27.2234, 33.7964, 33.966, 33.8643, 33.8982, 33.7964),)\n",
      "electricity in khW is: 0.00012552359166666667\n",
      "cop is: 0.01542094313019113\n",
      "Load in kWh is: 0.0001635219371096538\n",
      "heat addition in kwh is: 1.935692168589e-06\n",
      "RangeIndex(start=0, stop=22, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6360194097720268 0.003989583551449171 2.1121730471176354\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((239, datetime.datetime(2021, 4, 9, 12, 38, 25), 22.0486, 33.3556, 30.2034, 15.2624, 22.2176, 33.4234, 23.5021, 14.4867, 25.9712, 33.6608, 33.4234, 32.5418, 32.1689, 27.2234, 33.7964, 33.966, 33.8304, 33.7964, 33.7964),)\n",
      "electricity in khW is: 0.000130156325\n",
      "cop is: 0.004108569994690414\n",
      "Load in kWh is: 0.00017852542083642296\n",
      "heat addition in kwh is: 5.347563715141737e-07\n",
      "RangeIndex(start=0, stop=23, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6400195318460646 0.004014675271898536 3.6902686728639007\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((241, datetime.datetime(2021, 4, 9, 12, 38, 29), 22.0148, 33.3556, 30.2034, 15.2961, 22.2514, 33.3895, 23.4683, 14.453, 25.9712, 33.6947, 33.3895, 32.5757, 32.2028, 27.2234, 33.7625, 33.8982, 33.8643, 33.593, 33.8304),)\n",
      "electricity in khW is: 0.0001370899138888889\n",
      "cop is: 0.05967845679508357\n",
      "Load in kWh is: 0.0002133174702560001\n",
      "heat addition in kwh is: 8.181314503059783e-06\n",
      "RangeIndex(start=0, stop=24, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6360194097720268 0.003989583551449171 2.1121730471176354\n",
      "flow rates HP is =  0.6300192266609699 0.003946252594180832 -5.187740468238238\n",
      "result is ((243, datetime.datetime(2021, 4, 9, 12, 38, 32), 22.0148, 33.3556, 30.1356, 15.3298, 22.2176, 33.3556, 23.4345, 14.5204, 25.9712, 33.6608, 33.3895, 32.5079, 32.1689, 27.2573, 33.8304, 33.9321, 33.8643, 33.7964, 33.7964),)\n",
      "electricity in khW is: 0.00014283394722222224\n",
      "cop is: -0.05480924640370761\n",
      "Load in kWh is: 0.00022818445252056015\n",
      "heat addition in kwh is: -7.828621008116946e-06\n",
      "RangeIndex(start=0, stop=25, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6300192266609699 0.003951945970775122 -0.25497039150178974\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((245, datetime.datetime(2021, 4, 9, 12, 38, 36), 21.981, 33.3556, 30.2034, 15.3298, 22.2514, 33.3895, 23.5021, 14.5204, 26.0051, 33.6608, 33.3895, 32.5418, 32.1689, 27.2573, 33.7964, 33.9321, 33.8643, 33.559, 33.7964),)\n",
      "electricity in khW is: 0.00014857242222222225\n",
      "cop is: -0.0012230479134382913\n",
      "Load in kWh is: 0.00022579168180849208\n",
      "heat addition in kwh is: -1.8171119099336173e-07\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=26, 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 ((247, datetime.datetime(2021, 4, 9, 12, 38, 39), 22.0148, 33.3556, 30.1018, 15.2961, 22.2176, 33.3895, 23.4007, 14.4867, 26.0389, 33.6608, 33.3895, 32.5757, 32.1689, 27.2234, 33.6947, 33.9321, 33.8643, 33.559, 33.7964),)\n",
      "electricity in khW is: 0.0001542202\n",
      "cop is: -0.10608014582227766\n",
      "Load in kWh is: 0.00023506131870558192\n",
      "heat addition in kwh is: -1.6359701304740826e-05\n",
      "RangeIndex(start=0, stop=27, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6300192266609699 0.003951945970775122 -0.25497039150178974\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((249, datetime.datetime(2021, 4, 9, 12, 38, 43), 21.981, 33.3556, 30.1356, 15.3636, 22.2514, 33.3895, 23.4345, 14.5204, 26.0051, 33.593, 33.4234, 32.5757, 32.1689, 27.2234, 33.7964, 33.966, 33.8982, 33.9321, 33.7964),)\n",
      "electricity in khW is: 0.00015785444166666667\n",
      "cop is: -0.05415315298683214\n",
      "Load in kWh is: 0.00023266854799351385\n",
      "heat addition in kwh is: -8.54831572922597e-06\n",
      "RangeIndex(start=0, stop=28, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6320192876979889 0.003958780380194105 -2.825737091414544\n",
      "result is ((252, datetime.datetime(2021, 4, 9, 12, 38, 48), 22.0148, 33.3895, 30.1695, 15.3636, 22.2514, 33.3556, 23.4345, 14.5541, 26.0389, 33.6269, 33.4234, 32.5418, 32.1689, 27.2234, 33.7964, 33.9321, 33.8643, 33.6947, 33.7964),)\n",
      "electricity in khW is: 0.00016382715277777778\n",
      "cop is: -0.14089578812308123\n",
      "Load in kWh is: 0.00024804669224589627\n",
      "heat addition in kwh is: -2.3082555806585435e-05\n",
      "RangeIndex(start=0, stop=29, 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 ((254, datetime.datetime(2021, 4, 9, 12, 38, 51), 22.0148, 33.3895, 30.1018, 15.3636, 22.2514, 33.3895, 23.4683, 14.5541, 26.0728, 33.593, 33.3895, 32.5418, 32.1689, 27.2234, 33.7625, 33.9321, 33.8643, 33.7286, 33.7964),)\n",
      "electricity in khW is: 0.0001684408527777778\n",
      "cop is: -0.1457115589141078\n",
      "Load in kWh is: 0.00025727313900931814\n",
      "heat addition in kwh is: -2.454377924307173e-05\n",
      "RangeIndex(start=0, stop=30, 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 ((256, datetime.datetime(2021, 4, 9, 12, 38, 55), 22.0148, 33.3895, 30.1356, 15.3298, 22.2514, 33.3556, 23.4007, 14.5541, 26.1066, 33.6269, 33.3895, 32.5418, 32.1689, 27.2234, 33.7625, 33.9321, 33.8982, 33.7625, 33.7625),)\n",
      "electricity in khW is: 0.00017534978611111112\n",
      "cop is: -0.15096709151799992\n",
      "Load in kWh is: 0.00025492457722014463\n",
      "heat addition in kwh is: -2.6472047207497826e-05\n",
      "RangeIndex(start=0, stop=31, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6300192266609699 0.003951945970775122 -0.25497039150178974\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((257, datetime.datetime(2021, 4, 9, 12, 38, 56), 21.981, 33.3895, 30.1018, 15.3973, 22.2514, 33.3556, 23.4345, 14.5541, 26.0728, 33.6947, 33.3895, 32.5079, 32.1689, 27.1896, 33.7625, 33.9321, 33.8304, 33.593, 33.8304),)\n",
      "electricity in khW is: 0.0001803567861111111\n",
      "cop is: -0.13572066263336915\n",
      "Load in kWh is: 0.000254334671064495\n",
      "heat addition in kwh is: -2.4478142521424833e-05\n",
      "RangeIndex(start=0, stop=32, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6320192876979889 0.003958780380194105 -2.825737091414544\n",
      "result is ((259, datetime.datetime(2021, 4, 9, 12, 39), 22.0148, 33.3895, 30.1356, 15.431, 22.2176, 33.3895, 23.4683, 14.5879, 26.1404, 33.593, 33.4234, 32.5418, 32.1689, 27.1896, 33.7286, 33.9321, 33.8643, 33.6269, 33.7625),)\n",
      "electricity in khW is: 0.00018536475277777778\n",
      "cop is: -0.1954414021343786\n",
      "Load in kWh is: 0.00026652178199023947\n",
      "heat addition in kwh is: -3.622794718918134e-05\n",
      "RangeIndex(start=0, stop=33, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6360194097720268 0.003989583551449171 2.1121730471176354\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((261, datetime.datetime(2021, 4, 9, 12, 39, 3), 22.0148, 33.3895, 30.1356, 15.431, 22.2176, 33.3556, 23.4683, 14.5879, 26.1404, 33.6269, 33.4234, 32.5757, 32.1689, 27.2234, 33.7286, 33.9321, 33.8643, 33.966, 33.8304),)\n",
      "electricity in khW is: 0.00019014008611111113\n",
      "cop is: -0.19813890344240967\n",
      "Load in kWh is: 0.0002810446591484229\n",
      "heat addition in kwh is: -3.767414816250091e-05\n",
      "RangeIndex(start=0, stop=34, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6380194708090458 0.004002129411673854 2.9012208599907954\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((263, datetime.datetime(2021, 4, 9, 12, 39, 7), 22.0148, 33.3895, 30.0679, 15.3973, 22.2514, 33.3556, 23.4683, 14.5879, 26.1404, 33.6947, 33.4234, 32.5079, 32.2028, 27.1896, 33.7964, 33.9321, 33.8304, 33.6608, 33.7964),)\n",
      "electricity in khW is: 0.00019508754444444445\n",
      "cop is: -0.20320382524576686\n",
      "Load in kWh is: 0.00030764230464656926\n",
      "heat addition in kwh is: -3.9642535288914666e-05\n",
      "RangeIndex(start=0, stop=35, 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 ((265, datetime.datetime(2021, 4, 9, 12, 39, 10), 22.0148, 33.3895, 30.0679, 15.431, 22.2514, 33.3217, 23.4683, 14.6216, 26.1743, 33.593, 33.4234, 32.5757, 32.1689, 27.2234, 33.7625, 33.9321, 33.8304, 33.9321, 33.7964),)\n",
      "electricity in khW is: 0.0002021283777777778\n",
      "cop is: -0.2034292566228948\n",
      "Load in kWh is: 0.00031665397522525387\n",
      "heat addition in kwh is: -4.1118825633724984e-05\n",
      "RangeIndex(start=0, stop=36, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6320192876979889 0.003958780380194105 -2.825737091414544\n",
      "result is ((267, datetime.datetime(2021, 4, 9, 12, 39, 13), 22.0148, 33.3895, 30.1018, 15.431, 22.2514, 33.3217, 23.4683, 14.5879, 26.1743, 33.6269, 33.4234, 32.5757, 32.1689, 27.2234, 33.7625, 33.9321, 33.8643, 33.7964, 33.8304),)\n",
      "electricity in khW is: 0.00020668588611111113\n",
      "cop is: -0.24202290105916263\n",
      "Load in kWh is: 0.0003256656458039385\n",
      "heat addition in kwh is: -5.0022717764594806e-05\n",
      "RangeIndex(start=0, stop=37, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6380194708090458 0.003996363738233922 4.260273039056376\n",
      "result is ((269, datetime.datetime(2021, 4, 9, 12, 39, 17), 22.0148, 33.3895, 30.1018, 15.4647, 22.2852, 33.3556, 23.4345, 14.6216, 26.1404, 33.593, 33.3895, 32.5418, 32.1689, 27.2234, 33.7964, 33.8982, 33.8643, 33.7625, 33.7964),)\n",
      "electricity in khW is: 0.00021157791111111113\n",
      "cop is: -0.15183027662701185\n",
      "Load in kWh is: 0.0003377957644050657\n",
      "heat addition in kwh is: -3.2123932772165325e-05\n",
      "RangeIndex(start=0, stop=38, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6360194097720268 0.003989583551449171 2.1121730471176354\n",
      "flow rates HP is =  0.6380194708090458 0.003996363738233922 4.260273039056376\n",
      "result is ((271, datetime.datetime(2021, 4, 9, 12, 39, 20), 22.0148, 33.3895, 30.0001, 15.4985, 22.2852, 33.3556, 23.5021, 14.6553, 26.2081, 33.6947, 33.4234, 32.5418, 32.1689, 27.2573, 33.7964, 33.9321, 33.8982, 33.8982, 33.7964),)\n",
      "electricity in khW is: 0.0002167580527777778\n",
      "cop is: -0.08435483721042905\n",
      "Load in kWh is: 0.0003521810032241504\n",
      "heat addition in kwh is: -1.8284590256119037e-05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=39, 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 ((272, datetime.datetime(2021, 4, 9, 12, 39, 22), 22.0148, 33.3895, 30.0001, 15.431, 22.2176, 33.3217, 23.4345, 14.6216, 26.1743, 33.593, 33.3895, 32.5757, 32.2028, 27.2234, 33.7625, 33.9321, 33.8304, 33.8643, 33.7964),)\n",
      "electricity in khW is: 0.00022363247500000003\n",
      "cop is: -0.08625255804383782\n",
      "Load in kWh is: 0.00035102329100769033\n",
      "heat addition in kwh is: -1.928887303042461e-05\n",
      "RangeIndex(start=0, stop=40, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6320192876979889 0.003964491830999805 0.5340774213713702\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((274, datetime.datetime(2021, 4, 9, 12, 39, 25), 21.981, 33.3556, 30.034, 15.4985, 22.2852, 33.3556, 23.4683, 14.6553, 26.2081, 33.6947, 33.3895, 32.5418, 32.1689, 27.2573, 33.8304, 33.9321, 33.8643, 33.9321, 33.8304),)\n",
      "electricity in khW is: 0.00022850585833333336\n",
      "cop is: -0.05796560604361846\n",
      "Load in kWh is: 0.0003546606995461983\n",
      "heat addition in kwh is: -1.3245480562808891e-05\n",
      "RangeIndex(start=0, stop=41, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6380194708090458 0.003996363738233922 4.260273039056376\n",
      "result is ((277, datetime.datetime(2021, 4, 9, 12, 39, 31), 21.981, 33.3895, 30.0679, 15.4985, 22.2514, 33.3556, 23.4345, 14.689, 26.2081, 33.6947, 33.3895, 32.5079, 32.1689, 27.2573, 33.7964, 33.966, 33.8304, 33.8982, 33.8304),)\n",
      "electricity in khW is: 0.0002344532138888889\n",
      "cop is: 0.05920490529872139\n",
      "Load in kWh is: 0.0003726834354421683\n",
      "heat addition in kwh is: 1.3880780325272539e-05\n",
      "RangeIndex(start=0, stop=42, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6320192876979889 0.003964491830999805 0.5340774213713702\n",
      "flow rates HP is =  0.6380194708090458 0.003996363738233922 4.260273039056376\n",
      "result is ((279, datetime.datetime(2021, 4, 9, 12, 39, 34), 22.0148, 33.3895, 30.0001, 15.4985, 22.2514, 33.3556, 23.4345, 14.689, 26.2419, 33.7286, 33.4234, 32.5418, 32.2028, 27.2234, 33.7964, 33.9321, 33.8304, 33.593, 33.7964),)\n",
      "electricity in khW is: 0.0002413790027777778\n",
      "cop is: 0.1148406552447272\n",
      "Load in kWh is: 0.00037630347067716996\n",
      "heat addition in kwh is: 2.772012284131883e-05\n",
      "RangeIndex(start=0, stop=43, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6300192266609699 0.003951945970775122 -0.25497039150178974\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((281, datetime.datetime(2021, 4, 9, 12, 39, 37), 22.0148, 33.3895, 30.0679, 15.5322, 22.2514, 33.3217, 23.4345, 14.689, 26.2419, 33.6947, 33.3895, 32.5418, 32.2028, 27.1896, 33.7625, 33.9321, 33.8304, 33.593, 33.7964),)\n",
      "electricity in khW is: 0.0002477882472222222\n",
      "cop is: 0.13625842931815724\n",
      "Load in kWh is: 0.00037458349046961544\n",
      "heat addition in kwh is: 3.3763237369999235e-05\n",
      "RangeIndex(start=0, stop=44, 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 ((284, datetime.datetime(2021, 4, 9, 12, 39, 43), 21.981, 33.3556, 30.0001, 15.5322, 22.2176, 33.3556, 23.4007, 14.7228, 26.2758, 33.6608, 33.4234, 32.5079, 32.1689, 27.2234, 33.7625, 33.9321, 33.8643, 33.8982, 33.7964),)\n",
      "electricity in khW is: 0.0002546431916666667\n",
      "cop is: 0.12087653111510241\n",
      "Load in kWh is: 0.00037114369427125165\n",
      "heat addition in kwh is: 3.078038568074482e-05\n",
      "RangeIndex(start=0, stop=45, 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 ((286, datetime.datetime(2021, 4, 9, 12, 39, 46), 22.0148, 33.3895, 29.9662, 15.5659, 22.2514, 33.3217, 23.4007, 14.689, 26.2758, 33.593, 33.3556, 32.5418, 32.1689, 27.2234, 33.7625, 33.9321, 33.8643, 33.8982, 33.7964),)\n",
      "electricity in khW is: 0.00026001705\n",
      "cop is: 0.05291820878405\n",
      "Load in kWh is: 0.00038002612102451387\n",
      "heat addition in kwh is: 1.3759636539312767e-05\n",
      "RangeIndex(start=0, stop=46, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6360194097720268 0.003989583551449171 2.1121730471176354\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((288, datetime.datetime(2021, 4, 9, 12, 39, 50), 22.0148, 33.3895, 29.9662, 15.5659, 22.2852, 33.3217, 23.4683, 14.7228, 26.2758, 33.593, 33.3895, 32.5757, 32.1689, 27.2234, 33.7964, 33.9321, 33.8982, 33.966, 33.7625),)\n",
      "electricity in khW is: 0.0002664555833333333\n",
      "cop is: 0.08280488850197236\n",
      "Load in kWh is: 0.0003989319935292945\n",
      "heat addition in kwh is: 2.206382486864467e-05\n",
      "RangeIndex(start=0, stop=47, 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 ((289, datetime.datetime(2021, 4, 9, 12, 39, 51), 22.0148, 33.3556, 29.9324, 15.5322, 22.2852, 33.2878, 23.4345, 14.7228, 26.2758, 33.7286, 33.3895, 32.5757, 32.1689, 27.2234, 33.7625, 33.9321, 33.8643, 33.6947, 33.7964),)\n",
      "electricity in khW is: 0.00027094031666666663\n",
      "cop is: 0.07956245219972362\n",
      "Load in kWh is: 0.00040187855708133065\n",
      "heat addition in kwh is: 2.1556675993769643e-05\n",
      "RangeIndex(start=0, stop=48, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((292, datetime.datetime(2021, 4, 9, 12, 39, 56), 22.0148, 33.4234, 29.9324, 15.5659, 22.2514, 33.3217, 23.4345, 14.7565, 26.3096, 33.593, 33.3895, 32.5757, 32.1689, 27.2234, 33.7964, 33.8982, 33.8643, 33.9321, 33.7964),)\n",
      "electricity in khW is: 0.00027758003888888884\n",
      "cop is: 0.11579432567845796\n",
      "Load in kWh is: 0.0004166108835184154\n",
      "heat addition in kwh is: 3.214219342493902e-05\n",
      "RangeIndex(start=0, stop=49, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6380194708090458 0.003996363738233922 4.260273039056376\n",
      "result is ((294, datetime.datetime(2021, 4, 9, 12, 40), 22.0148, 33.3895, 29.9662, 15.5659, 22.2514, 33.3217, 23.4345, 14.7902, 26.3096, 33.6947, 33.3895, 32.5418, 32.2028, 27.2234, 33.7625, 33.9321, 33.8643, 33.6947, 33.7964),)\n",
      "electricity in khW is: 0.0002840839944444444\n",
      "cop is: 0.178747160480414\n",
      "Load in kWh is: 0.00042839674466808327\n",
      "heat addition in kwh is: 5.077920734487815e-05\n",
      "RangeIndex(start=0, stop=50, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6320192876979889 0.003964491830999805 0.5340774213713702\n",
      "flow rates HP is =  0.6300192266609699 0.003946252594180832 -5.187740468238238\n",
      "result is ((296, datetime.datetime(2021, 4, 9, 12, 40, 3), 21.981, 33.3895, 29.9662, 15.5997, 22.2514, 33.3217, 23.4683, 14.7565, 26.3435, 33.593, 33.3895, 32.6096, 32.1689, 27.2234, 33.7964, 33.966, 33.8643, 33.8643, 33.7964),)\n",
      "electricity in khW is: 0.00028885530277777776\n",
      "cop is: 0.1168670786003272\n",
      "Load in kWh is: 0.000431947318194829\n",
      "heat addition in kwh is: 3.3757675373851864e-05\n",
      "RangeIndex(start=0, stop=51, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6380194708090458 0.003996363738233922 4.260273039056376\n",
      "result is ((298, datetime.datetime(2021, 4, 9, 12, 40, 7), 22.0148, 33.3895, 29.9324, 15.6334, 22.2852, 33.2878, 23.4683, 14.7902, 26.3096, 33.6947, 33.3895, 32.5418, 32.135, 27.2234, 33.8304, 33.9321, 33.8304, 33.6269, 33.7964),)\n",
      "electricity in khW is: 0.0002939443111111111\n",
      "cop is: 0.17887300458480523\n",
      "Load in kWh is: 0.00044367620059448275\n",
      "heat addition in kwh is: 5.257870210905519e-05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=52, 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 ((300, datetime.datetime(2021, 4, 9, 12, 40, 10), 21.981, 33.4234, 29.9324, 15.6334, 22.2176, 33.3217, 23.4683, 14.7902, 26.3435, 33.6947, 33.3895, 32.5418, 32.1689, 27.2573, 33.7964, 33.8982, 33.8643, 33.8304, 33.8304),)\n",
      "electricity in khW is: 0.0003006651333333333\n",
      "cop is: 0.16971389313004598\n",
      "Load in kWh is: 0.0004472267741212285\n",
      "heat addition in kwh is: 5.102705030646435e-05\n",
      "RangeIndex(start=0, stop=53, 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 ((302, datetime.datetime(2021, 4, 9, 12, 40, 14), 21.981, 33.3895, 29.8646, 15.6334, 22.2514, 33.3217, 23.4345, 14.8239, 26.3773, 33.6947, 33.3895, 32.5418, 32.1689, 27.2911, 33.7625, 33.8982, 33.8304, 33.9321, 33.8304),)\n",
      "electricity in khW is: 0.0003058236583333333\n",
      "cop is: 0.16002061896711728\n",
      "Load in kWh is: 0.00045889773239843313\n",
      "heat addition in kwh is: 4.893809110128819e-05\n",
      "RangeIndex(start=0, stop=54, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6300192266609699 0.003951945970775122 -0.25497039150178974\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((304, datetime.datetime(2021, 4, 9, 12, 40, 17), 22.0148, 33.3556, 29.8985, 15.6671, 22.2514, 33.3217, 23.4683, 14.7902, 26.3773, 33.593, 33.3895, 32.5418, 32.1689, 27.2573, 33.7625, 33.9321, 33.8643, 33.8982, 33.7964),)\n",
      "electricity in khW is: 0.0003115205\n",
      "cop is: 0.17728440339308968\n",
      "Load in kWh is: 0.00045721096830574824\n",
      "heat addition in kwh is: 5.522772598721699e-05\n",
      "RangeIndex(start=0, stop=55, 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 ((306, datetime.datetime(2021, 4, 9, 12, 40, 21), 22.0148, 33.3895, 29.8646, 15.6334, 22.2514, 33.2878, 23.4345, 14.8239, 26.4111, 33.7625, 33.3895, 32.5757, 32.135, 27.2234, 33.7625, 33.9321, 33.8643, 33.6947, 33.7964),)\n",
      "electricity in khW is: 0.00031774236666666663\n",
      "cop is: 0.1672388338133299\n",
      "Load in kWh is: 0.00046187576297815894\n",
      "heat addition in kwh is: 5.313886285442079e-05\n",
      "RangeIndex(start=0, stop=56, step=1)\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 ((307, datetime.datetime(2021, 4, 9, 12, 40, 22), 21.981, 33.3895, 29.9324, 15.6334, 22.2514, 33.2878, 23.5021, 14.7902, 26.4111, 33.593, 33.3895, 32.5418, 32.1689, 27.2573, 33.8304, 33.9321, 33.8304, 33.593, 33.7964),)\n",
      "electricity in khW is: 0.00032263756666666663\n",
      "cop is: 0.18737179094958092\n",
      "Load in kWh is: 0.00046476492024148745\n",
      "heat addition in kwh is: 6.045317869394814e-05\n",
      "RangeIndex(start=0, stop=57, 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 ((309, datetime.datetime(2021, 4, 9, 12, 40, 26), 22.0148, 33.3895, 29.8646, 15.6671, 22.2514, 33.2878, 23.5021, 14.8577, 26.4111, 33.6608, 33.3895, 32.5757, 32.1689, 27.2234, 33.7625, 33.9321, 33.8643, 33.8982, 33.7964),)\n",
      "electricity in khW is: 0.0003269669166666666\n",
      "cop is: 0.11342218533479342\n",
      "Load in kWh is: 0.0004763215492948014\n",
      "heat addition in kwh is: 3.7085302220512615e-05\n",
      "RangeIndex(start=0, stop=58, 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 ((312, datetime.datetime(2021, 4, 9, 12, 40, 31), 21.981, 33.3895, 29.8985, 15.6334, 22.2852, 33.2878, 23.4683, 14.8239, 26.445, 33.6947, 33.3895, 32.5418, 32.1689, 27.1896, 33.8304, 33.966, 33.8643, 33.5251, 33.7964),)\n",
      "electricity in khW is: 0.0003311947388888888\n",
      "cop is: 0.10416595383912547\n",
      "Load in kWh is: 0.0004821235202996039\n",
      "heat addition in kwh is: 3.4499215882861205e-05\n",
      "RangeIndex(start=0, stop=59, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((314, datetime.datetime(2021, 4, 9, 12, 40, 34), 21.981, 33.4234, 29.8307, 15.6671, 22.2514, 33.2878, 23.4683, 14.8914, 26.445, 33.6269, 33.3895, 32.5418, 32.1689, 27.2234, 33.7625, 33.9321, 33.8982, 33.8304, 33.7964),)\n",
      "electricity in khW is: 0.0003362681638888888\n",
      "cop is: 0.12203312144946134\n",
      "Load in kWh is: 0.0004907478518797892\n",
      "heat addition in kwh is: 4.103585368344014e-05\n",
      "RangeIndex(start=0, stop=60, 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 ((316, datetime.datetime(2021, 4, 9, 12, 40, 38), 21.981, 33.3895, 29.8307, 15.6671, 22.2514, 33.2878, 23.4683, 14.8577, 26.4788, 33.6947, 33.3556, 32.5418, 32.1689, 27.1896, 33.7286, 33.9321, 33.8643, 33.7625, 33.7964),)\n",
      "electricity in khW is: 0.00034242197499999993\n",
      "cop is: 0.05093760814330079\n",
      "Load in kWh is: 0.0005021896153577237\n",
      "heat addition in kwh is: 1.7442156382205135e-05\n",
      "RangeIndex(start=0, stop=61, 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 ((318, datetime.datetime(2021, 4, 9, 12, 40, 41), 22.0148, 33.3895, 29.8307, 15.6671, 22.2852, 33.2878, 23.4345, 14.8914, 26.445, 33.6608, 33.3895, 32.5418, 32.1689, 27.2234, 33.7625, 33.9321, 33.8643, 33.8982, 33.7964),)\n",
      "electricity in khW is: 0.0003466815749999999\n",
      "cop is: 0.045749307918322366\n",
      "Load in kWh is: 0.0005108139469379089\n",
      "heat addition in kwh is: 1.5860442124283965e-05\n",
      "RangeIndex(start=0, stop=62, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6380194708090458 0.003996363738233922 4.260273039056376\n",
      "result is ((319, datetime.datetime(2021, 4, 9, 12, 40, 43), 22.0148, 33.4234, 29.8646, 15.6671, 22.2514, 33.3217, 23.4683, 14.8914, 26.4788, 33.6608, 33.3895, 32.5757, 32.135, 27.2234, 33.7286, 33.9321, 33.8982, 33.593, 33.7964),)\n",
      "electricity in khW is: 0.00034964946666666657\n",
      "cop is: 0.07306686774974372\n",
      "Load in kWh is: 0.0005165633112574241\n",
      "heat addition in kwh is: 2.5547791339701752e-05\n",
      "RangeIndex(start=0, stop=63, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6320192876979889 0.003964491830999805 0.5340774213713702\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((321, datetime.datetime(2021, 4, 9, 12, 40, 46), 22.0148, 33.4234, 29.8646, 15.7346, 22.2852, 33.2878, 23.5021, 14.9251, 26.5127, 33.6269, 33.3895, 32.5418, 32.1689, 27.2234, 33.7964, 33.9321, 33.8643, 33.8643, 33.8304),)\n",
      "electricity in khW is: 0.00035536426666666655\n",
      "cop is: 0.09011162896123792\n",
      "Load in kWh is: 0.0005200097232549955\n",
      "heat addition in kwh is: 3.2022452943949064e-05\n",
      "RangeIndex(start=0, stop=64, step=1)\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 ((323, datetime.datetime(2021, 4, 9, 12, 40, 50), 21.981, 33.4234, 29.7969, 15.7346, 22.2176, 33.2878, 23.4683, 14.9251, 26.5465, 33.6608, 33.3895, 32.5418, 32.1689, 27.1896, 33.7286, 33.966, 33.8643, 33.559, 33.8304),)\n",
      "electricity in khW is: 0.00036038173333333324\n",
      "cop is: 0.17401919736257496\n",
      "Load in kWh is: 0.0005313366424271565\n",
      "heat addition in kwh is: 6.271333997880018e-05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=65, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6320192876979889 0.003964491830999805 0.5340774213713702\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((325, datetime.datetime(2021, 4, 9, 12, 40, 53), 21.981, 33.3895, 29.8646, 15.7683, 22.2514, 33.2878, 23.4683, 14.8914, 26.5127, 33.593, 33.3895, 32.5757, 32.1689, 27.1896, 33.7964, 33.9321, 33.8643, 33.7286, 33.7964),)\n",
      "electricity in khW is: 0.00036631711666666657\n",
      "cop is: 0.18870704593224896\n",
      "Load in kWh is: 0.0005347830544247279\n",
      "heat addition in kwh is: 6.912662096058565e-05\n",
      "RangeIndex(start=0, stop=66, 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 ((327, datetime.datetime(2021, 4, 9, 12, 40, 57), 22.0148, 33.3895, 29.7969, 15.7683, 22.2852, 33.2878, 23.4683, 14.9251, 26.5465, 33.6947, 33.3895, 32.5079, 32.1689, 27.2234, 33.7964, 33.9321, 33.8304, 33.9321, 33.8304),)\n",
      "electricity in khW is: 0.0003714972166666666\n",
      "cop is: 0.18034492766043192\n",
      "Load in kWh is: 0.0005326003327108178\n",
      "heat addition in kwh is: 6.699763866580179e-05\n",
      "RangeIndex(start=0, stop=67, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6400195318460646 0.004014675271898536 3.6902686728639007\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((329, datetime.datetime(2021, 4, 9, 12, 41), 21.981, 33.4234, 29.763, 15.8021, 22.2176, 33.2878, 23.4345, 14.9251, 26.5465, 33.6608, 33.3556, 32.5757, 32.1689, 27.2234, 33.8304, 33.9321, 33.8643, 33.8982, 33.7964),)\n",
      "electricity in khW is: 0.0003797370722222221\n",
      "cop is: 0.19396960879252587\n",
      "Load in kWh is: 0.0005562938038815175\n",
      "heat addition in kwh is: 7.365745134296356e-05\n",
      "RangeIndex(start=0, stop=68, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6380194708090458 0.003996363738233922 4.260273039056376\n",
      "result is ((331, datetime.datetime(2021, 4, 9, 12, 41, 4), 21.981, 33.3895, 29.763, 15.8021, 22.2852, 33.2878, 23.4345, 14.9251, 26.5465, 33.6947, 33.3895, 32.5418, 32.2028, 27.2573, 33.8304, 33.9321, 33.8643, 33.7625, 33.7964),)\n",
      "electricity in khW is: 0.00038478495555555547\n",
      "cop is: 0.24273721339130244\n",
      "Load in kWh is: 0.0005676207230536785\n",
      "heat addition in kwh is: 9.34016278664517e-05\n",
      "RangeIndex(start=0, stop=69, 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 ((333, datetime.datetime(2021, 4, 9, 12, 41, 7), 21.981, 33.3895, 29.763, 15.8021, 22.2176, 33.2538, 23.4007, 14.9588, 26.5803, 33.6947, 33.3895, 32.5757, 32.1689, 27.1896, 33.7625, 33.966, 33.8643, 33.8304, 33.7964),)\n",
      "electricity in khW is: 0.0003881755638888888\n",
      "cop is: 0.2364645244185723\n",
      "Load in kWh is: 0.0005760300716427645\n",
      "heat addition in kwh is: 9.178975010589722e-05\n",
      "RangeIndex(start=0, stop=70, 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 ((334, datetime.datetime(2021, 4, 9, 12, 41, 9), 22.0148, 33.4234, 29.7291, 15.7683, 22.2514, 33.2538, 23.4683, 14.9588, 26.6142, 33.6269, 33.3895, 32.5418, 32.1689, 27.2234, 33.7964, 33.9321, 33.8982, 33.7286, 33.7964),)\n",
      "electricity in khW is: 0.0003934034388888888\n",
      "cop is: 0.23053973650564044\n",
      "Load in kWh is: 0.0005816075585763722\n",
      "heat addition in kwh is: 9.069512514185724e-05\n",
      "RangeIndex(start=0, stop=71, 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 ((336, datetime.datetime(2021, 4, 9, 12, 41, 12), 22.0148, 33.4234, 29.763, 15.8021, 22.2514, 33.2878, 23.4683, 14.9588, 26.5803, 33.6608, 33.3556, 32.5418, 32.1689, 27.1896, 33.7625, 33.9321, 33.8643, 33.7964, 33.7964),)\n",
      "electricity in khW is: 0.0003995850388888888\n",
      "cop is: 0.22290187553515536\n",
      "Load in kWh is: 0.0005799788029106417\n",
      "heat addition in kwh is: 8.906825460412131e-05\n",
      "RangeIndex(start=0, stop=72, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6380194708090458 0.003996363738233922 4.260273039056376\n",
      "result is ((338, datetime.datetime(2021, 4, 9, 12, 41, 16), 22.0148, 33.4234, 29.6953, 15.8358, 22.2514, 33.2538, 23.4345, 14.9926, 26.6142, 33.559, 33.3895, 32.5418, 32.135, 27.1896, 33.7964, 33.8982, 33.8643, 33.559, 33.7625),)\n",
      "electricity in khW is: 0.0004043603638888888\n",
      "cop is: 0.27046332956404456\n",
      "Load in kWh is: 0.0005911337767778572\n",
      "heat addition in kwh is: 0.00010936465036111752\n",
      "RangeIndex(start=0, stop=73, 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 ((341, datetime.datetime(2021, 4, 9, 12, 41, 21), 22.0148, 33.4234, 29.7291, 15.8358, 22.2514, 33.2538, 23.4345, 14.9926, 26.6142, 33.6608, 33.3556, 32.5757, 32.1689, 27.1896, 33.7625, 33.8982, 33.8643, 33.7286, 33.7964),)\n",
      "electricity in khW is: 0.00041090051944444434\n",
      "cop is: 0.2594985474712553\n",
      "Load in kWh is: 0.0005967621116524357\n",
      "heat addition in kwh is: 0.00010662808795101759\n",
      "RangeIndex(start=0, stop=74, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6280191656239509 0.003939400110550439 -1.0440182043749497\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((343, datetime.datetime(2021, 4, 9, 12, 41, 24), 22.0148, 33.4234, 29.7291, 15.8021, 22.2514, 33.2538, 23.5359, 15.0263, 26.6142, 33.6608, 33.3895, 32.474, 32.1689, 27.2234, 33.7625, 33.9321, 33.8643, 33.7964, 33.8304),)\n",
      "electricity in khW is: 0.00042034340833333323\n",
      "cop is: 0.24976280922598265\n",
      "Load in kWh is: 0.0005901607105333939\n",
      "heat addition in kwh is: 0.00010498615050495763\n",
      "RangeIndex(start=0, stop=75, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6300192266609699 0.003951945970775122 -0.25497039150178974\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((345, datetime.datetime(2021, 4, 9, 12, 41, 28), 22.0148, 33.4234, 29.7291, 15.8695, 22.2514, 33.2538, 23.5021, 15.0263, 26.648, 33.559, 33.4234, 32.5418, 32.1689, 27.2234, 33.7625, 33.966, 33.8643, 33.8304, 33.8304),)\n",
      "electricity in khW is: 0.0004264643833333332\n",
      "cop is: 0.2671916598590947\n",
      "Load in kWh is: 0.0005880221679687566\n",
      "heat addition in kwh is: 0.00011394772645361855\n",
      "RangeIndex(start=0, stop=76, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((347, datetime.datetime(2021, 4, 9, 12, 41, 31), 21.981, 33.4234, 29.6953, 15.8695, 22.2852, 33.2878, 23.4683, 15.0263, 26.648, 33.6269, 33.3556, 32.5757, 32.1689, 27.2234, 33.7964, 33.9321, 33.8982, 33.8643, 33.8304),)\n",
      "electricity in khW is: 0.00043214177499999987\n",
      "cop is: 0.2793775584583629\n",
      "Load in kWh is: 0.000596388251028232\n",
      "heat addition in kwh is: 0.00012073071400736317\n",
      "RangeIndex(start=0, stop=77, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6380194708090458 0.004002129411673854 2.9012208599907954\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((349, datetime.datetime(2021, 4, 9, 12, 41, 34), 21.981, 33.3895, 29.6953, 15.8358, 22.2852, 33.2878, 23.4345, 15.0263, 26.6142, 33.6608, 33.3556, 32.5757, 32.1689, 27.2234, 33.7964, 33.8982, 33.8643, 33.8304, 33.7625),)\n",
      "electricity in khW is: 0.00043754784999999985\n",
      "cop is: 0.2721730788790932\n",
      "Load in kWh is: 0.0006148267780022369\n",
      "heat addition in kwh is: 0.00011908874549142759\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=78, 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 ((351, datetime.datetime(2021, 4, 9, 12, 41, 38), 22.0148, 33.4234, 29.6953, 15.8695, 22.2514, 33.2538, 23.4683, 15.0263, 26.648, 33.593, 33.3895, 32.5757, 32.1689, 27.1896, 33.7286, 33.9321, 33.8643, 33.7286, 33.7964),)\n",
      "electricity in khW is: 0.00044554924999999987\n",
      "cop is: 0.26232670263154684\n",
      "Load in kWh is: 0.0006259244358582533\n",
      "heat addition in kwh is: 0.00011687946561245868\n",
      "RangeIndex(start=0, stop=79, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6380194708090458 0.004002129411673854 2.9012208599907954\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((354, datetime.datetime(2021, 4, 9, 12, 41, 43), 22.0148, 33.4234, 29.6275, 15.9033, 22.2852, 33.2538, 23.5021, 15.06, 26.6819, 33.6608, 33.3895, 32.5757, 32.135, 27.1896, 33.7964, 33.9321, 33.8643, 33.8982, 33.7964),)\n",
      "electricity in khW is: 0.0004524438944444443\n",
      "cop is: 0.2837689087322679\n",
      "Load in kWh is: 0.0006561841080683473\n",
      "heat addition in kwh is: 0.00012838951018907737\n",
      "RangeIndex(start=0, stop=80, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6320192876979889 0.003958780380194105 -2.825737091414544\n",
      "result is ((355, datetime.datetime(2021, 4, 9, 12, 41, 45), 22.0148, 33.4573, 29.6614, 15.8695, 22.2514, 33.2538, 23.4683, 15.0263, 26.648, 33.559, 33.3895, 32.5418, 32.135, 27.1557, 33.6947, 33.9321, 33.8304, 33.8304, 33.7625),)\n",
      "electricity in khW is: 0.000456646111111111\n",
      "cop is: 0.2661492701882228\n",
      "Load in kWh is: 0.0006617329369963555\n",
      "heat addition in kwh is: 0.0001215360292065123\n",
      "RangeIndex(start=0, stop=81, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6320192876979889 0.003964491830999805 0.5340774213713702\n",
      "flow rates HP is =  0.6300192266609699 0.003946252594180832 -5.187740468238238\n",
      "result is ((358, datetime.datetime(2021, 4, 9, 12, 41, 50), 22.0148, 33.4234, 29.6275, 15.937, 22.2176, 33.2878, 23.4345, 15.06, 26.6819, 33.6608, 33.3556, 32.6096, 32.135, 27.1896, 33.7964, 33.9321, 33.8643, 33.593, 33.7964),)\n",
      "electricity in khW is: 0.0004655240416666665\n",
      "cop is: 0.19350339722149237\n",
      "Load in kWh is: 0.0006673321692295072\n",
      "heat addition in kwh is: 9.008048355077954e-05\n",
      "RangeIndex(start=0, stop=82, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6320192876979889 0.003958780380194105 -2.825737091414544\n",
      "result is ((360, datetime.datetime(2021, 4, 9, 12, 41, 53), 22.0148, 33.4234, 29.6614, 15.937, 22.2852, 33.2878, 23.4683, 15.0937, 26.6819, 33.6947, 33.3895, 32.5079, 32.135, 27.2234, 33.7286, 33.9321, 33.8643, 33.6269, 33.8304),)\n",
      "electricity in khW is: 0.0004724023416666665\n",
      "cop is: 0.16911870345407087\n",
      "Load in kWh is: 0.000675655140049801\n",
      "heat addition in kwh is: 7.989207153133365e-05\n",
      "RangeIndex(start=0, stop=83, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6320192876979889 0.003964491830999805 0.5340774213713702\n",
      "flow rates HP is =  0.6300192266609699 0.003946252594180832 -5.187740468238238\n",
      "result is ((362, datetime.datetime(2021, 4, 9, 12, 41, 57), 21.981, 33.4234, 29.6275, 15.937, 22.2514, 33.2878, 23.4683, 15.0937, 26.6819, 33.6947, 33.3895, 32.5418, 32.135, 27.2234, 33.7286, 33.9321, 33.8643, 33.7964, 33.7964),)\n",
      "electricity in khW is: 0.00047773494999999984\n",
      "cop is: 0.11455405895523592\n",
      "Load in kWh is: 0.0006801345258363224\n",
      "heat addition in kwh is: 5.472647762727667e-05\n",
      "RangeIndex(start=0, stop=84, step=1)\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 ((364, datetime.datetime(2021, 4, 9, 12, 42), 22.0148, 33.4234, 29.5598, 15.937, 22.2514, 33.2538, 23.4683, 15.0937, 26.6819, 33.593, 33.3895, 32.5418, 32.1689, 27.2234, 33.7964, 33.9321, 33.8304, 33.5251, 33.7964),)\n",
      "electricity in khW is: 0.0004851405499999998\n",
      "cop is: 0.16335150525126435\n",
      "Load in kWh is: 0.0006884146590405623\n",
      "heat addition in kwh is: 7.924843910092624e-05\n",
      "RangeIndex(start=0, stop=85, step=1)\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 ((366, datetime.datetime(2021, 4, 9, 12, 42, 4), 22.0148, 33.4234, 29.5936, 15.937, 22.2176, 33.2538, 23.4683, 15.1275, 26.6819, 33.6608, 33.4234, 32.5079, 32.1689, 27.1896, 33.7964, 33.9321, 33.8643, 33.8304, 33.7964),)\n",
      "electricity in khW is: 0.0004896762166666665\n",
      "cop is: 0.22802486497845265\n",
      "Load in kWh is: 0.0006994548366462156\n",
      "heat addition in kwh is: 0.00011165835318857614\n",
      "RangeIndex(start=0, stop=86, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((368, datetime.datetime(2021, 4, 9, 12, 42, 7), 21.981, 33.4234, 29.5936, 15.937, 22.2514, 33.2538, 23.4683, 15.0937, 26.7157, 33.6269, 33.3895, 32.5757, 32.1689, 27.2234, 33.7625, 33.9321, 33.8304, 33.7964, 33.7964),)\n",
      "electricity in khW is: 0.0004943943249999998\n",
      "cop is: 0.2399428350447004\n",
      "Load in kWh is: 0.000707691990822043\n",
      "heat addition in kwh is: 0.00011862637597051095\n",
      "RangeIndex(start=0, stop=87, 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 ((371, datetime.datetime(2021, 4, 9, 12, 42, 12), 21.981, 33.4234, 29.5598, 15.9707, 22.2852, 33.2199, 23.5698, 15.1275, 26.7157, 33.6608, 33.4234, 32.5757, 32.1689, 27.2234, 33.7625, 33.8982, 33.8643, 33.7964, 33.7964),)\n",
      "electricity in khW is: 0.0005002747805555553\n",
      "cop is: 0.23140137582650855\n",
      "Load in kWh is: 0.0007213493984762586\n",
      "heat addition in kwh is: 0.00011576427251186014\n",
      "RangeIndex(start=0, stop=88, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6360194097720268 0.003989583551449171 2.1121730471176354\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((372, datetime.datetime(2021, 4, 9, 12, 42, 14), 22.0148, 33.4234, 29.5598, 15.937, 22.2176, 33.2199, 23.4683, 15.1275, 26.7157, 33.6269, 33.3556, 32.5418, 32.1689, 27.1896, 33.8304, 33.9321, 33.8304, 33.8982, 33.7625),)\n",
      "electricity in khW is: 0.0005066493805555552\n",
      "cop is: 0.22623038373124954\n",
      "Load in kWh is: 0.0007300701641531021\n",
      "heat addition in kwh is: 0.00011461948378028315\n",
      "RangeIndex(start=0, stop=89, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((374, datetime.datetime(2021, 4, 9, 12, 42, 17), 22.0148, 33.4234, 29.5259, 15.937, 22.2176, 33.2199, 23.4683, 15.1275, 26.7496, 33.6269, 33.3556, 32.5418, 32.1689, 27.1896, 33.7964, 33.9321, 33.7964, 33.8304, 33.7964),)\n",
      "electricity in khW is: 0.0005112535722222219\n",
      "cop is: 0.23806260126790307\n",
      "Load in kWh is: 0.0007382215086103749\n",
      "heat addition in kwh is: 0.00012171035531072989\n",
      "RangeIndex(start=0, stop=90, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6320192876979889 0.003964491830999805 0.5340774213713702\n",
      "flow rates HP is =  0.6320192876979889 0.003958780380194105 -2.825737091414544\n",
      "result is ((376, datetime.datetime(2021, 4, 9, 12, 42, 21), 21.981, 33.4234, 29.5936, 16.0045, 22.2514, 33.2538, 23.4683, 15.1275, 26.7157, 33.593, 33.3895, 32.5418, 32.1689, 27.2573, 33.7964, 33.8982, 33.8643, 33.966, 33.7964),)\n",
      "electricity in khW is: 0.0005161202222222219\n",
      "cop is: 0.20902173572169258\n",
      "Load in kWh is: 0.0007426547082614683\n",
      "heat addition in kwh is: 0.00010788034468995451\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=91, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6380194708090458 0.004002129411673854 2.9012208599907954\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((378, datetime.datetime(2021, 4, 9, 12, 42, 24), 21.981, 33.4234, 29.5598, 15.9707, 22.2852, 33.2538, 23.5021, 15.1612, 26.7157, 33.593, 33.3895, 32.5418, 32.1689, 27.2234, 33.7964, 33.9321, 33.8643, 33.7286, 33.7625),)\n",
      "electricity in khW is: 0.0005210050388888885\n",
      "cop is: 0.20376594215129035\n",
      "Load in kWh is: 0.0007607162717042092\n",
      "heat addition in kwh is: 0.00010616308261476402\n",
      "RangeIndex(start=0, stop=92, step=1)\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 ((380, datetime.datetime(2021, 4, 9, 12, 42, 28), 22.0148, 33.4234, 29.5259, 16.0045, 22.2852, 33.2199, 23.4683, 15.1949, 26.7496, 33.6947, 33.4234, 32.5079, 32.135, 27.2573, 33.7625, 33.9321, 33.8304, 33.6269, 33.7964),)\n",
      "electricity in khW is: 0.0005269700388888885\n",
      "cop is: 0.2640490004699594\n",
      "Load in kWh is: 0.0007715847309805729\n",
      "heat addition in kwh is: 0.00013914591204622662\n",
      "RangeIndex(start=0, stop=93, 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 ((382, datetime.datetime(2021, 4, 9, 12, 42, 31), 22.0148, 33.4234, 29.5598, 16.0045, 22.2514, 33.2199, 23.4345, 15.1612, 26.7157, 33.6608, 33.3895, 32.5757, 32.135, 27.2234, 33.7964, 33.9321, 33.8643, 33.6269, 33.7964),)\n",
      "electricity in khW is: 0.0005320609388888885\n",
      "cop is: 0.2254177472511768\n",
      "Load in kWh is: 0.0007797791755731023\n",
      "heat addition in kwh is: 0.0001199359782446793\n",
      "RangeIndex(start=0, stop=94, 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 ((384, datetime.datetime(2021, 4, 9, 12, 42, 35), 22.0148, 33.4234, 29.5259, 16.0045, 22.2514, 33.2199, 23.4683, 15.1612, 26.7157, 33.6608, 33.3895, 32.5079, 32.1689, 27.2234, 33.7286, 33.9321, 33.8643, 33.8643, 33.7964),)\n",
      "electricity in khW is: 0.0005369116388888885\n",
      "cop is: 0.21907945935731318\n",
      "Load in kWh is: 0.0007907051016964748\n",
      "heat addition in kwh is: 0.00011762631157042665\n",
      "RangeIndex(start=0, stop=95, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6360194097720268 0.003983835952220649 1.898269662232681\n",
      "result is ((386, datetime.datetime(2021, 4, 9, 12, 42, 38), 21.981, 33.4234, 29.5259, 16.0045, 22.2514, 33.2199, 23.4683, 15.1949, 26.7496, 33.593, 33.3895, 32.5418, 32.135, 27.2234, 33.7964, 33.9321, 33.8643, 33.966, 33.8304),)\n",
      "electricity in khW is: 0.0005413589472222218\n",
      "cop is: 0.2303785868302074\n",
      "Load in kWh is: 0.0007988564461537475\n",
      "heat addition in kwh is: 0.0001247175092289443\n",
      "RangeIndex(start=0, stop=96, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6360194097720268 0.003989583551449171 2.1121730471176354\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((388, datetime.datetime(2021, 4, 9, 12, 42, 42), 22.0148, 33.4234, 29.5259, 16.0382, 22.2514, 33.2199, 23.5021, 15.1612, 26.7496, 33.559, 33.3895, 32.5757, 32.1689, 27.1896, 33.7286, 33.9321, 33.8643, 33.7286, 33.7964),)\n",
      "electricity in khW is: 0.0005495028083333329\n",
      "cop is: 0.22276108638272518\n",
      "Load in kWh is: 0.0008162062406786673\n",
      "heat addition in kwh is: 0.00012240784255469165\n",
      "RangeIndex(start=0, stop=97, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6340193487350078 0.0039770376912244875 1.3231252342444757\n",
      "flow rates HP is =  0.6320192876979889 0.003958780380194105 -2.825737091414544\n",
      "result is ((390, datetime.datetime(2021, 4, 9, 12, 42, 45), 21.981, 33.4573, 29.492, 16.0382, 22.2514, 33.2199, 23.4345, 15.1949, 26.7157, 33.6947, 33.3895, 32.5757, 32.1689, 27.1896, 33.8304, 33.8982, 33.8643, 33.9321, 33.7964),)\n",
      "electricity in khW is: 0.0005540767749999995\n",
      "cop is: 0.2015394901149991\n",
      "Load in kWh is: 0.0008244006852711966\n",
      "heat addition in kwh is: 0.00011166835071806299\n",
      "RangeIndex(start=0, stop=98, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6380194708090458 0.004002129411673854 2.9012208599907954\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((392, datetime.datetime(2021, 4, 9, 12, 42, 48), 22.0148, 33.4234, 29.492, 16.0382, 22.2514, 33.2199, 23.4683, 15.2286, 26.7496, 33.6947, 33.3895, 32.5079, 32.1689, 27.1896, 33.7964, 33.966, 33.8982, 33.6269, 33.7964),)\n",
      "electricity in khW is: 0.0005590168666666662\n",
      "cop is: 0.19663276791537995\n",
      "Load in kWh is: 0.0008422740942078308\n",
      "heat addition in kwh is: 0.00010992103380404948\n",
      "RangeIndex(start=0, stop=99, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6360194097720268 0.003989583551449171 2.1121730471176354\n",
      "flow rates HP is =  0.6340193487350078 0.003971308166207377 -0.4637337145910134\n",
      "result is ((395, datetime.datetime(2021, 4, 9, 12, 42, 54), 22.0148, 33.4234, 29.4581, 16.0719, 22.2176, 33.2199, 23.4345, 15.1949, 26.7496, 33.6269, 33.3895, 32.5079, 32.1689, 27.1896, 33.7625, 33.9321, 33.8643, 33.593, 33.7964),)\n",
      "electricity in khW is: 0.0005668702138888885\n",
      "cop is: 0.18769069806908042\n",
      "Load in kWh is: 0.0008682987859952104\n",
      "heat addition in kwh is: 0.00010639626615937441\n",
      "RangeIndex(start=0, stop=100, step=1)\n",
      "________________________________________________________________\n",
      "flow rates load is =  0.6360194097720268 0.003989583551449171 2.1121730471176354\n",
      "flow rates HP is =  0.6320192876979889 0.003958780380194105 -2.825737091414544\n",
      "result is ((397, datetime.datetime(2021, 4, 9, 12, 42, 57), 22.0148, 33.4234, 29.4581, 16.0382, 22.2852, 33.2199, 23.5359, 15.2286, 26.7496, 33.6608, 33.4234, 32.5079, 32.1689, 27.1896, 33.7964, 33.9321, 33.8304, 33.6608, 33.7964),)\n",
      "electricity in khW is: 0.0005715479388888886\n",
      "cop is: 0.16736525797613583\n",
      "Load in kWh is: 0.0008813111318889003\n",
      "heat addition in kwh is: 9.565726823786754e-05\n",
      "RangeIndex(start=0, stop=101, 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 ((398, datetime.datetime(2021, 4, 9, 12, 42, 59), 22.0148, 33.4573, 29.4243, 16.0719, 22.2514, 33.2199, 23.5021, 15.2286, 26.7496, 33.593, 33.3895, 32.6096, 32.1689, 27.1896, 33.7964, 33.966, 33.8643, 33.559, 33.8304),)\n",
      "electricity in khW is: 0.0005764143638888886\n",
      "cop is: 0.1638791328365605\n",
      "Load in kWh is: 0.0008867453615270821\n",
      "heat addition in kwh is: 9.446228610864869e-05\n",
      "RangeIndex(start=0, stop=102, 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 ((400, datetime.datetime(2021, 4, 9, 12, 43, 2), 22.0148, 33.4234, 29.4581, 16.0719, 22.2176, 33.2199, 23.4345, 15.2624, 26.7834, 33.6947, 33.4234, 32.5079, 32.1689, 27.2234, 33.7286, 33.9321, 33.8643, 33.966, 33.7964),)\n",
      "electricity in khW is: 0.0005829405416666664\n",
      "cop is: 0.1590211962634746\n",
      "Load in kWh is: 0.0008851828601177593\n",
      "heat addition in kwh is: 9.269990228631115e-05\n",
      "RangeIndex(start=0, stop=103, step=1)\n"
     ]
    }
   ],
   "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):\n",
    "    df_Tem_Flow_Load_capacity_conc = pd.concat([df_full, load_kW, heatCapacity_kW, electrictyUsed_kW, \\\n",
    "                                                COP], axis=1)\n",
    "    import os.path\n",
    "    import datetime\n",
    "    file_name = 'csvFile_{}'.format(datetime.datetime.now().strftime(\"%Y_%m_%d\"))\n",
    "    suffix = '.csv'\n",
    "    filePath = os.path.join(os.getcwd(), file_name + suffix)\n",
    "    df_Tem_Flow_Load_capacity_conc.to_csv(filePath, header=True)\n",
    "    return df_Tem_Flow_Load_capacity_conc\n",
    "\n",
    "def Load_kWh(Lol, 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[10] for b in T]\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",
    "\n",
    "fig.canvas.draw()\n",
    "#plt.ion()\n",
    "\n",
    "fig.show()\n",
    "\n",
    "ax.legend(loc='best')\n",
    "ax.set_xlabel('Index')\n",
    "ax.set_ylabel('Tem, C', color='r')\n",
    "#################################\n",
    "#Plot for HPin, Hpout, TmixL tem.\n",
    "\n",
    "fig.show()\n",
    "fig.canvas.draw()\n",
    "ax_2.legend(loc='best')\n",
    "ax_2.set_xlabel('Index')\n",
    "ax_2.set_ylabel('Load in kW', color='r')\n",
    "def plotObject_Tem_HpAndLoad(df, ax_2, fig):\n",
    "    ax_2.clear()\n",
    "    ax_2.plot(df.index, df.iloc[:, 3], label = 'Hp to Testing tank top')\n",
    "    ax_2.plot(df.index, df.iloc[:, 4], label = 'Testing tank to HP Bottom')\n",
    "    ax_2.plot(df.index, df.iloc[:, 8], label = 'load tem Mixed')\n",
    "    \n",
    "    ax_2.legend(bbox_to_anchor=(0.4,0.8))\n",
    "    fig.canvas.draw()\n",
    "    ax_2.legend()\n",
    "    #plt.legend(loc='upper left')\n",
    "    return fig, ax_2\n",
    "##################################\n",
    "\n",
    "#fig2 = plt.figure(2)\n",
    "\n",
    "#plt.ion()\n",
    "\n",
    "fig2.show()\n",
    "fig2.canvas.draw()\n",
    "ax2.legend(loc='best')\n",
    "ax2.set_xlabel('Index')\n",
    "ax2.set_ylabel('Load in kW', color='r')\n",
    "def plotObject_Load(df, ax2, fig2):\n",
    "    ax2.clear()\n",
    "    ax2.plot(df.index, df.iloc[:, -4],label = 'Load in kW')\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",
    "fig2.show()\n",
    "fig2.canvas.draw()\n",
    "ax2_1.legend(loc='best')\n",
    "ax2_1.set_xlabel('Index')\n",
    "ax2_1.set_ylabel('Heat_Capacity in kW', color='r')\n",
    "def plotObject_Heat_Capacity(df, ax2_1, fig2):\n",
    "    ax2_1.clear()\n",
    "    ax2_1.plot(df.index, df.iloc[:, -3], label = 'heat_Capacity in kW')\n",
    "    ax2_1.plot(df.index, df.iloc[:, -2], label = 'electrcity_kW')\n",
    "    ax2_1.plot(df.index, df.iloc[:, -1], label = 'cop of system')\n",
    "    ax2_1.legend(bbox_to_anchor=(0.4,0.8))\n",
    "    fig2.canvas.draw()\n",
    "    ax2_1.legend()\n",
    "    #plt.legend(loc='upper left')\n",
    "    return fig2, ax2_1\n",
    "\n",
    "#######################\n",
    "###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",
    "    #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",
    "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",
    "    df_flowGreaterThan5_temValues = df_flowGreaterThan5.iloc[-1, -12:-2]\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",
    "        nDischargePlotterList.append(dischargeEfficiency)\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",
    "        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",
    "    \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",
    "    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",
    "        \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(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "graduate-liberal",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "conventional-innocent",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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