code-examples.ipynb

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     ]
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   ],
   "source": [
    "!pip install pyvaporation"
   ]
  },
  {
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   "metadata": {
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     "text": [
      "Collecting matplotlib==3.2.2\n",
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    }
   ],
   "source": [
    "!pip install matplotlib==3.2.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wu0ulYJbdsXd"
   },
   "source": [
    "# PyVaporation Code Examples\n",
    "\n",
    "## Main classes and methods overview\n",
    "\n",
    "* Component\n",
    "  - Create\n",
    "  - Methods overview\n",
    "* Mixture creation\n",
    "  - Create\n",
    "  - Methods overview\n",
    "* Permeance\n",
    "  - Create\n",
    "  - Convert\n",
    "* Membrane\n",
    "  - Create/Load\n",
    "  - Methods overview\n",
    "* Pervaporation Function\n",
    "  - Create / (best) Fit\n",
    "  - Plot\n",
    "* Pervaporation (ideal)\n",
    "  - Create\n",
    "  - Model ideal diffusion curve\n",
    "  - Plot diffusion curve\n",
    "* Pervaporation (non-ideal)\n",
    "  - Create\n",
    "  - Model non-ideal diffusion curve\n",
    "  - Plot diffusion curve\n",
    "* Pervaporation (non-ideal)\n",
    "  - Modelling non-idael diffusion curves with different initial permeances\n",
    "* Process Model\n",
    "  - Create\n",
    "  - Conditions\n",
    "  - Ideal isothermal process\n",
    "\n",
    "## Examples of applied usage\n",
    "\n",
    "* Illustration of the influence of the precision value on the obtained flux values\n",
    "  - Calculate partial fluxes\n",
    "* Fitting of a strongly non-ideal diffusion curve with `find_best_fit()`\n",
    "* Evaluation of a non-ideal isothermal process model against experimental data provided in the literature\n",
    "* Evaluation of a non-ideal isothermal process model against obtained experimental data\n",
    "*Evaluation of a non-ideal non-isothermal process model against experimental data provided in the literature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "-zIJpyiTf2qJ"
   },
   "outputs": [],
   "source": [
    "import pyvaporation\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-6fr9fLDg2K5",
    "outputId": "f4d1956a-b3ef-4b27-efd7-702661724f9e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Methanol vapor pressure: 55.57396777330733 kPa\n",
      "MTBE vapor pressure: 86.30665730589656 kPa\n",
      "Methanol specific heat capacity: 86.65695527172431 J/(mol*K)\n",
      "MTBE specific heat capacity: 195.91742978431822 J/(mol*K)\n"
     ]
    }
   ],
   "source": [
    "# Example 1. Creation of the Component object, \n",
    "# calculation of some of the components’ properties\n",
    "\n",
    "from pyvaporation import Components, Component\n",
    "from pyvaporation import VaporPressureConstants, HeatCapacityConstants\n",
    "\n",
    "methanol = Components.MeOH\n",
    "\n",
    "mtbe = Component(\n",
    "    name=\"MTBE\",\n",
    "    molecular_weight=88.15,\n",
    "    vapour_pressure_constants=VaporPressureConstants(\n",
    "        a=6.050931522,\n",
    "        b=-1139.816725,\n",
    "        c=-46.15171,\n",
    "    ),\n",
    "    heat_capacity_constants=HeatCapacityConstants(\n",
    "        a=147.329712,\n",
    "        b=-9.7850807e-2,\n",
    "        c=9.216480e-4,\n",
    "        d=-4.75200e-7,\n",
    "    ),\n",
    ")\n",
    "\n",
    "print(\n",
    "    'Methanol vapor pressure:', \n",
    "    methanol.get_vapor_pressure(323.15), \n",
    "    'kPa'\n",
    ")\n",
    "\n",
    "print(\n",
    "    'MTBE vapor pressure:', \n",
    "    mtbe.get_vapor_pressure(323.15), \n",
    "    'kPa'\n",
    ")\n",
    "\n",
    "print(\n",
    "    'Methanol specific heat capacity:', \n",
    "    methanol.get_specific_heat(323.15), \n",
    "    'J/(mol*K)'\n",
    ")\n",
    "\n",
    "print(\n",
    "    'MTBE specific heat capacity:', \n",
    "    mtbe.get_specific_heat(323.15), \n",
    "    'J/(mol*K)'\n",
    ")"
   ]
  },
  {
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   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fiUC3c4zhOK8",
    "outputId": "4fa7b3ff-b41b-46ba-fb57-5c15b4e5863f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saturated vapour pressures of (water, ethanol) in kPa at 333.15 K\n",
      "(0.0, 46.89836939159766)\n",
      "(8.891134632430656, 37.466800075630296)\n",
      "(13.245231502587709, 31.66032683488933)\n",
      "(15.397007508848512, 27.95931933290596)\n",
      "(16.511008564375675, 25.442571233550673)\n",
      "(17.1642007104559, 23.47758805609483)\n",
      "(17.64548411500568, 21.53220662174932)\n",
      "(18.101979793349827, 19.046502948023534)\n",
      "(18.610193493757325, 15.324493735775249)\n",
      "(19.211072426116353, 9.420253102872836)\n",
      "(19.92758517292072, 0.0)\n"
     ]
    }
   ],
   "source": [
    "# Example 2. Creation of a Mixture object, \n",
    "# calculation of saturated vapour pressure of the components \n",
    "# at 333.15 oK in a stated composition range\n",
    "\n",
    "\n",
    "from pyvaporation import (\n",
    "    Mixtures, Composition, \n",
    "    CompositionType, \n",
    "    get_nrtl_partial_pressures\n",
    ")\n",
    "\n",
    "h2o_etoh = Mixtures.H2O_EtOH\n",
    "\n",
    "composition_range = [\n",
    "   Composition(p=i / 10, type=CompositionType.weight) for i in range(11)\n",
    "]\n",
    "\n",
    "print('Saturated vapour pressures of (water, ethanol) in kPa at 333.15 K')\n",
    "for composition in composition_range:\n",
    "    print(\n",
    "       get_nrtl_partial_pressures(\n",
    "           temperature=333.15,\n",
    "           mixture=h2o_etoh,\n",
    "           composition=composition\n",
    "       )\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ur-QIFYbiS5B",
    "outputId": "2ee6c34e-516f-42c4-ceef-2bcbdf71deae"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Permeance(value=0.07754020415999999, units='kg/(m2*h*kPa)')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example 3. Creation of a Permeance object, conversion between permeance units\n",
    "\n",
    "from pyvaporation import Permeance, Units\n",
    "\n",
    "permeance = Permeance(value=3568, units=Units.GPU)\n",
    "permeance.convert(to_units=Units.kg_m2_h_kPa, component=Components.H2O)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3eCYLXlXi4aE",
    "outputId": "3f61be56-4539-497a-eb10-7d9d0ec86617"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Romakon PM 102 H2O permeance at 323.15 K is Permeance(value=0.067127, units='kg/(m2*h*kPa)')\n",
      "Romakon PM 102 ideal selectivity H2O/EtOH at 333.15 K is 889.7388722741106\n",
      "Romakon PM 102 H2O pure component flux at 313.15 K is 0.35079725864427985\n"
     ]
    }
   ],
   "source": [
    "# Example 4. Loading of a Membrane object from a directory,\n",
    "# calculation of some parameters based on an IdealExperiments object loaded\n",
    "\n",
    "from pyvaporation import Membrane, Components\n",
    "\n",
    "romakon_pm_102 = Membrane.load(\"tests/default_membranes/RomakonPM_102\")\n",
    "\n",
    "h2o = Components.H2O\n",
    "etoh = Components.EtOH\n",
    "\n",
    "romakon_pm_102.calculate_activation_energy(component=h2o)\n",
    "\n",
    "romakon_pm_102.calculate_activation_energy(component=etoh)\n",
    "\n",
    "print(\n",
    "    'Romakon PM 102 H2O permeance at 323.15 K is',\n",
    "    romakon_pm_102.get_permeance(\n",
    "        temperature=323.15,\n",
    "        component=Components.H2O\n",
    "    )\n",
    ")\n",
    "\n",
    "\n",
    "print(\n",
    "    'Romakon PM 102 ideal selectivity H2O/EtOH at 333.15 K is',\n",
    "    romakon_pm_102.get_ideal_selectivity(\n",
    "          temperature=333.15,\n",
    "          first_component=h2o,\n",
    "          second_component=etoh\n",
    "    )\n",
    ")\n",
    "\n",
    "\n",
    "print(\n",
    "    'Romakon PM 102 H2O pure component flux at 313.15 K is',\n",
    "    romakon_pm_102.get_estimated_pure_component_flux(\n",
    "          temperature=313.15,\n",
    "          component=h2o,\n",
    "          permeate_pressure=1\n",
    "    )\n",
    ")"
   ]
  },
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   "cell_type": "code",
   "execution_count": 27,
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     "height": 846
    },
    "id": "McqANBv0jF__",
    "outputId": "08c09d6a-ee82-4714-bc8e-540899fe83ba",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H2O permeance at 333.15 K 0.05343349152297439 kg/(m2*h*kPa)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example 5. Creation, calling and plotting of a PervaporationFunction object\n",
    "\n",
    "from pyvaporation import Membrane, Measurements, find_best_fit\n",
    "\n",
    "\n",
    "pervap_2510 = Membrane.load('tests/default_membranes/Pervap_2510')\n",
    "\n",
    "curve_set = pervap_2510.diffusion_curve_sets[0]\n",
    "\n",
    "data_h2o = Measurements.from_diffusion_curves_first(curve_set)\n",
    "data_ipoh = Measurements.from_diffusion_curves_second(curve_set)\n",
    "\n",
    "fit_h2o = find_best_fit(data_h2o)\n",
    "fit_ipoh = find_best_fit(data_ipoh)\n",
    "\n",
    "print('H2O permeance at 333.15 K', fit_h2o(0.15, 333.15), 'kg/(m2*h*kPa)')\n",
    "\n",
    "fit_h2o.plot(experimental_data=data_h2o, temperature=373.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\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",
       "    fig.rubberband_canvas.style.cursor = msg['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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "                img\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 https://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",
       " * https://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.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\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\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"432\">"
      ],
      "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_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\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",
       "    fig.rubberband_canvas.style.cursor = msg['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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "                img\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 https://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",
       " * https://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.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\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\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "fit_h2o.plot(experimental_data=data_h2o)\n",
    "fit_ipoh.plot(experimental_data=data_ipoh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 335
    },
    "id": "gVNpsG7Gk6or",
    "outputId": "e680c32d-3b4d-453e-a046-dd6149421013"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cSfBjgEtAKFDY4ZWZJkBHEYkDPgdaisisbMaplO8KK2o9Cdt7CSQnwrR2sGwoXDnr6ciUn8twyj77BiK7jTG1cnQSkebAMGNMe2fb6ZR9yu9duwhrXodNH0GhUtDuPaimUx6r7MvWlH0OVohIq1yOSan8KaSgNV3gY6utlv3nPayHpC6c9HRkyg+50oI/j066rVTuS7xm9cuvewdCCkHHsVC9g6ejUj4mRy14nXRbKTcJCoFmL8D/rYMiZWFeL1gyGK5k5TETpTLmrFxwpLMdxVI21yNSKr+5uToM+A6aDoMdc20jbTZ4OirlB5y14N8RkYUi8qiI1BSRm0WkvIi0FJH/ABuA6nkUp1L+LSgE7h0F/b62KlZOawff/hsSr3o6MuXDnPbBi0gNoCfWkMfSWMMl9wErgAXGmCu5GYz2wSsFXL0Aq16C2GlQqhY8OBFK1fR0VMpLOeuDzzDBi8itxpg8rX2qCV4pB/u/hqVDrPHyLUdBoyet1r1SDrJ7k3WyiGwSkTdFpLmIBLkpPqVUeqq2gcGboHIr+HYUTO8IZw57OirlQ5yVC74fq/b7WqAzsElEFtlqx5TPm/CUyucKloDus+CBj6yCZR83ge1zdfYo5RKnwySNMVeMMV8bY56x/QnwHBAEjBeRzXkSoVL5nQjU6wlPbLD65JcMgi96w8XTno5MeTlXnmS1M8b8Zoz5yBjTEbjbTTEppdJTtAL0XQ73vQr/+wY+ugv+t8rTUSkv5sqEH+dtE304vv4A5onIbXkQo1IqRUAgNHkGHl8DBUvCnK7WDFLXLno6MuWFXJ3w43mgDFAWGAbMwaoQOcVtkSmlMnZLLRi4Bho/DTFT4dN74MRuT0elvIwrCb6jMeZTY8x5Y8w5Y8xEoLUxZh5Q1M3xKaUyElQAWv3Hmibw2kWY1NJK9noDVtm4kuAviUg3EQmwvboBKQ846U+SUp5WsSkMWg+Rd8PyobDwMa1nowDXEnxPoDdwEvjT9r6XiIQBQ9wYm1LKVQVLQM8FcO+/Yc8SmNhM54FVLiX4M8aYDsaYEsaYksaYDkCSMeayMUbnHlPKWwQEQNPnoO9XkHAFJt8HWyZrl00+5kqCXyYi9vLAIlIdWOa+kJRSOVKhkdVlU/Ee+Oo5a0IRnR4wX3Ilwb+OleQLiUgDYAHQy71hKaVypGBxeOQL+McrsG8ZfNoMjm3zdFQqj7ky4cdXwPvAKmAa0NkYs929YSmlciwgAO4eCv1WQlICfNYKfp6oXTb5SIYFxERkHKlHyRQBDgFDRARjzNPuDk4plQvK3wmDfoQlT8DK5yFuHXQcD2E3eToy5WbOKkSmrdsb685AlFJuFF4MenwOG8fD6tHWg1Fdp0KZBp6OTLlRhgneGDM9LwNRSrmZCDR+CsrdBQv6wWetrQel7hxkrVN+J0vFxrJCREJFZLOI7BCRPSLyirvOpZTKgnJ3WBN9V74Pvh5hTfZ9+W9PR6XcwG0JHrgKtDTG1AHqAm1E5C43nk8p5arwYvDwHGj9hlWZ8tNmWsvGD7ktwRvLBdvHYNtLb98r5S1EoNFg6P81JF2Dz+6D3Ys8HZXKRVlO8CLyuogMF5HiLmwbKCLbscocfGuM+TmdbQaKSIyIxMTHx2c1HKVUTpWNhoE/wC21rb751aMhOcnTUalckJ0W/GYgEWtsvFPGmCRjTF2sMsMNRaRWOttMNMZEG2OiS5YsmY1wlFI5VrgU9FkO0f1h/fswp5v2y/uBLE+kbYxZko19zojIGqANoB19SnmjoBBo/z7cEgUrnoeJLaDHXLi5uqcjU9nkyoxOb4tIhIgEi8h3IhIvIpmWKhCRkiJyk+19GHAf8EuOI1ZKuVd0P1vBsksw6V7Yu9TTEalscqWLppUx5hzQHogDKmHN8JSZ0sAaEdkJbMHqg1+e3UCVUnmo/J1Wv/zN1a0Jvr9/DZKTPR2VyiJXumhStmkHzDfGnBUXHoowxuwE6uUgNqWUJ0WUhn4r4KtnYd07cGIXPDgRQot4OjLlIlda8MtF5BegAfCdiJTk+oxOSil/FlTAqltz/7twcLXVZRP/P09HpVzkSjXJEUBjINoYkwBcAh5wd2BKKS8hAg0fh0eXwpUz1tyvv6zwdFTKBa7cZA0HBgMf2xbdCkS7MyillBeKbAID10Lx2+HzHrD2Le2X93KudNFMBa5hteIBjgKvuS0ipZT3KlLWevK1Tg9Y+7p1A1Yn+PZariT4240xbwMJAMaYS4CWnlMqvwoOg04fQ5s3Yf9KmPwPOHXQ01GpdLiS4K/ZxrEbABG5HauQmFIqvxKBu56AR5fAxXiYfC/Erfd0VCoNVxL8aOBroJyIzAa+A4a7MyillI+oeA8MXAOFSsGMTrBjnqcjUg4yHQdvjFklIrHAXVhdM88YY065PTKllG8oGgmPfQPzesPigfB3HDR7QScR8QKujKKZCSQaY76yPYlaUES+c39oSimfEVYUei26fvN1yWBIvObpqPI9V55kXQ/8LCLPAmWwyhQ859aolFK+JyjEuvlatKKV5M/+Ad1n6eTeHuRKF82nIrIHWAOcAuoZY064PTKllO8RgebDoWgF+HIIfNYKes63Pqs850oXTW9gCvAoMA1YISJ13ByXUsqX1XkYei+GCyesETZHYj0dUb7kyiiah4C7jTFzjTEvAoOA6e4NSynl8yo2hce+heBwmNYO9i3zdET5jiu1aDoZY046fN4MNHRrVEop/1CyKgz4DkrVtEbZbJwARqdmziuZ9sGLyJQMVvXP5ViUUv6oUEnos8waQvnNv+Cv36ynYAOzPKGcyiJX/oW/cngfCnQGjrknHKWUXwoJh64zYPW/4adx1gibhz6DAoU8HZlfc2UUzULHzyIyF2vopFJKuS4gAFq9Zj0YteJ5mNoWHvnCmlhEuYUrN1nTqgzcnNuBKKXyiTsGQI95cPqQNcLmzz2ejshvuTJM8ryInEv5CixDa9EopXKiSivovxJMMnzWGg7qw/Hu4MoomsLGmAiHr1XSdtsopVSWla5jjbApWgHmdINdCzwdkd/JsA9eROo729EYszX3w1FK5StFylgTe895GBYOgCtn4Y7HPB2V33B2k/W/TtYZoGUux6KUyo9Ci0CvhTC/L3z1rDXv693PajXKXOAswX9sjPlCRG4zxvya1QOLSDlgBlAK6z+EicaYD7MZp1LKn4WEw8OzYckT8N2rcPlvuO8/muRzyFkf/Ajb1+x2jCUCzxljamDVkn9SRGpk81hKKX8XGAydJ8Idj1tj5Zc+BclJno7KpzlrwZ8WkVVARRFZmnalMaajswMbY44Dx23vz4vIPqxyw3tzEK9Syp8FBMD971j15de9bfXJPzQZggp4OjKf5CzBtwPqAzNx3h+fKRGJBOoBP6ezbiAwEKB8+fI5OY1Syh+IQMuXrCT/zYvWCJvus/Wp12wQk0nhHxEpaYyJz/YJRAoBPwBjjDGLnG0bHR1tYmJisnsqpZS/2TYblg6BW+tbdeXDi3kkjORkQ0JyMglJhsSkZK4lXX+fkJTMtURDQlIyicnX319/Xf98zWGfhCTDtUTrfVhwIE/dWzlbsYlIrDEmOr11rpQqyElyDwYWArMzS+5KKf+WkiQTkwyJSYZrtoSYaE+AKUnyekJMLNiKIo3HUXvjUC58ch8b7prE+ZCS1jqH/exJM9mQkGgd41pSsm25Sb29fZsbE256STkhyZCU7L4KmEEBQqmI0GwneKfHzvUj2oiIAJ8B+4wx77nrPEr5s6Tk60kv0f41g2X25GklsZTk5phEE9Mut+2bdh97Qkw5pkPrNSUBW+tty5JSWrgp57p+3pREm/0cWZRGAS8wKem/1Py6G70S/sVhUyrdLYMDheDAAIIChJCgAIICAggOEoIDAqzltvUp24WFBBASKLbtbMtT9gkMSLWt6+8DCAmyHdP23jq3tV2Iw3ZBAUJAgPtGCrmzXmcToDewS0S225b9yxizwo3nVPmIMYZkAwlJySSlJKqUpJVsSLIlnZQkaX21WmMpySfVumRDUvL1Fptj8kyVTB0SY9pjX1+XfH2/NMdML7mmxJyQlJzqOvKydHqwLdEF2ZJQUEriCxSCbMnIniQDAggJCiA8MIDgAHFInNZ2QYG2xOmw/fUE65BQbds6Jmb7cRy3DWzKX6cbUear3qwJeIMzD30OpWrZjpESqyA6rDIVZ0+yjsMav54uY8zTzg5sjFkP6L92HkhJdIm2ZJaS3BKTrycz+3KHxHTDcluCu77++teUJJRquxvOkyZBOmyX0Tntydn+Of19EpPS38cTAgOsZJKSyIIChECH5Gets71PSXgBQlhwIIEFguxJKzBQ7AkuyJYk0ybUlOM4Jt+0y4IDhcCA64k07fbBaZLz9X2urwv0heR4a1Mo9TXM7Ezx+Z3hkflQ/k5PR+XVnLXgfeZu57krCfZEk2yuJ7gkYyWLpOTryS/JIRklOyQWxySTsl9iksPx0tvuhuMkZ7jt9e2S0z1GegnWisOkSbjpn8PTAm1JwjHZOX7OeLn1Z3RocMqy68knvQSaat9U69LZ1yEhXt/GcXna5Oq4bep9HM/t9YnQn91cDfp/DTM7Wa/uM6HSPzwdldfKMMEbY3xm3tWGY1ZzJSHZY+cPEFIlm7QJLDDd5VbfW1CAECC21p3D+tTb244deH17x8+BYiWqGxKqQ0xp48l0/8A0584gsfpM60/5j6IVoP83MPNBq4bNQ5OgZmdPR+WVXJmyryRWeeAaWDM6AWCM8ZpaNC+2rY4xxpYUAwgMwJ6YAhySUKCkTmopicxKmOkn4rTJOPU2AQQImtyUymuFboa+y2FOd5jfD66cgwZ9PB2V13HlJutsYB7Wg0+DgD5AtodOukOfxpGeDkEpldfCboLei+GLR2HZ05CcqJUo03BlRqfixpjPgARjzA/GmP5oJUmllDdIKVJWpY1ViXLLZ56OyKu4kuATbF+Pi0g7EakHeOZxMqWUSiuoAHSboUk+Ha500bwmIkWA54BxQATwT7dGpZRSWZGS5L941EryGGvu13zOlVIFy21vzwIt3BuOUkplkz3J94GvnrOW5fMk7+xBpxeMMW9n9MBTZg86KaVUngsqAN2ma5K3cdaC32f76jMPPCmllCb565w96LTM9vaSMWa+4zoR6erWqJRSKifSJnljoOHjno4qz7kyiuZFF5cppZT3SEnyVdrCimGweZKnI8pzzvrg2wL3A2VEZKzDqgis+VaVUsq7OY6uWTHMWpaPWvLOWvDHsPrfrwCxDq+lQGv3h6aUUrkgKMQ2Tj7/teSd9cHvEJHdQGtfKjymlFI3SEny+awl77QP3hiTBJQTkZA8ikcppdwjJclXvT/ftORdeZL1N2CDiCwFLqYs1Gn4lFI+JygEuk6H+X3yRUvelVE0h4Dltm0LO7yUUsr3pCT5fNCSd6VUwSt5EYhSSuWZfNKSd3XCjxeAmnjphB9KKZVl9iTf10ryIQWh7iOejipXudJFMxv4BagIvALEAVvcGJNSSuWNoBDoOhUqNoMvh8C+5Znv40PcNuGHiEwRkZO2oZZKKeWdggrAw3Pg1nqwoB/8+oOnI8o17pzwYxrQJruBKaVUnilQCHrOh+KVYG4POBLr6YhyhSsJ3nHCj2HAZFyY8MMYsw74K2fhKaVUHgkvZs3xWqgkzH4ITu7LfB8vl2GCF5FQERmK1Qp/GPjFGNPCGNPAGLM0twIQkYEiEiMiMfHxXjWXt1Iqvyl8C/ReAoEFYGZn+DvO0xHliLMW/HQgGtgFtAX+644AjDETjTHRxpjokiVLuuMUSinlumIVrZZ8wmWY0QnOn/B0RNnmLMHXMMb0MsZ8CnQBmuZRTEop5VmlakCvhXDhJMx8EC75Zm+zs3HwKTdXMcYkikgehJNOEAkJHDlyhCtXrnjk/Eop/xMaGkrZsmUJDg7OeKOy0fDwbJjTzXo9+qU1Vt6HOEvwdUTknO29AGG2zwIYY0yEswOLyFygOVBCRI4AL9uGW2bJkSNHKFy4MJGRkXjqPxmllP8wxnD69GmOHDlCxYoVnW98ewt46DPridfPe8Ij86xhlT4iwy4aY0ygMSbC9ipsjAlyeO80udv272GMKW2MCTbGlM1Ocge4cuUKxYsX1+SulMoVIkLx4sVd7xWo0RE6jodf18DCAZCc5N4Ac5ErwyQ9TpO7Uio3ZTmn1OsJrd+AfUth2TPWHK8+wJVywUoppRoNhst/w7q3IbQItHoNvLzx6RMteE8LDAykbt269ldcXByNGzfO0jE++OADLl26lO66hIQERowYQeXKlalfvz6NGjVi5cqVuRG6x8TFxTFnzpws7zdt2jSGDBmSalnz5s2JiYnh0qVLtGvXjmrVqlGzZk1GjBiRaruJEydSrVo1qlWrRsOGDVm/fn2OrkGpG7T4FzQcCBvHw49uGTmeqzTBuyAsLIzt27fbX5GRkfz00083bJeYmPFc5M4S/KhRozh+/Di7d+9m69atLFmyhPPnz+da/J6Q3QSfmWHDhvHLL7+wbds2NmzYYP+PcPny5Xz66aesX7+eX375hU8++YRHHnmEEyd8dwyz8kIi0OYtiOoO3/8Htkz2dERO+VQXzSvL9rD32LnMN8yCGrdG8HKHmlner1ChQly4cIG1a9cyatQoihYtak883bp148iRIyQlJTFq1Cj+/PNPjh07RosWLShRogRr1qyxH+fSpUtMmjSJ3377jQIFrLvzpUqVolu3bgDMnTuX119/HWMM7dq146233rKf/4knnmDFihWULl2a119/nRdeeIHDhw/zwQcf0LFjR6ZNm8bixYs5e/YsR48epVevXrz88ssAvPfee0yZMgWAAQMGMHToUOLi4mjbti133303P/30E2XKlOHLL78kLCyMQ4cO8eSTTxIfH094eDiTJk2iWrVq9O3bl4iICGJiYjhx4gRvv/02Xbp0YcSIEezbt4+6devSp08f/vnPTKtbZCo8PJwWLVoAEBISQv369Tly5AgAb731Fu+88w4lSpQAoH79+vTp04cJEybwn//8J8fnVsouIAAemABXzsFXw6BAEYjq6umo0uVTCd5TLl++TN26dQGoWLEiixcvTrV+69at7N69m4oVK7Jw4UJuvfVWvvrqKwDOnj1LkSJFeO+991izZo09AaU4ePAg5cuXJyLixoFJx44dY/jw4cTGxlK0aFFatWrFkiVL6NSpExcvXqRly5a88847dO7cmZEjR/Ltt9+yd+9e+vTpQ8eOHQHYvHkzu3fvJjw8nDvuuIN27dohIkydOpWff/4ZYwx33nknzZo1o2jRohw4cIC5c+cyadIkunXrxsKFC+nVqxcDBw7kk08+oXLlyvz8888MHjyY77//HoDjx4/bW84dO3akS5cuvPnmm7z77rssX5718qvz5s1L1b1y8ODBG7Y5c+YMy5Yt45lnngFgz549NGjQINU20dHRTJ+u88UrNwgMhq7TYHYXWDIIQiOgSmtPR3UDn0rw2Wlp54aULpqMNGzY0D6etnbt2jz33HMMHz6c9u3b07Rp9h8A3rJlC82bNyelhEPPnj1Zt24dnTp1IiQkhDZt2tjPWaBAAYKDg6lduzZxcXH2Y9x3330UL14cgAcffJD169cjInTu3JmCBQval//444907NiRihUr2v8za9CgAXFxcVy4cIGffvqJrl2vt1KuXr1qf9+pUycCAgKoUaMGf/75Z7avN0X37t0ZP368/XPz5s1TrU9MTKRHjx48/fTT3HbbbTk+n1LZEhxqlRme0RG+6AN9v4KyDTLfLw9pH3wuSEmUAFWqVGHr1q3Url2bkSNH8uqrrzrdt1KlShw+fJhz57LW9RQcHGwf6hUQEGDv3gkICEh1LyDtcLDMhoelHAesm8uJiYkkJydz0003pboPsW/fvnT3MS4MH3vppZfsN6yzY+DAgVSuXJmhQ4fal9WoUYPY2NQlXmNjY6lZ0zONApVPhEbAI/Oh0M0wt7vXFSfTBJ/Ljh07Rnh4OL169eL5559n69atABQuXDjdG6fh4eE89thjPPPMM1y7dg2A+Ph45s+fT8OGDfnhhx84deoUSUlJzJ07l2bNmmUpnm+//Za//vqLy5cvs2TJEpo0aULTpk1ZsmQJly5d4uLFiyxevNjpXxoRERFUrFiR+fPnA1YS37Fjh9PzZnS9AGPGjLH/R5FVI0eO5OzZs3zwwQeplr/wwgsMHz6c06dPA7B9+3amTZvG4MGDs3wOpbKkUEnouQCSEmB2V2sopZfQBJ/Ldu3aRcOGDalbty6vvPIKI0eOBKxWZ5s2bew3CR299tprlCxZkho1alCrVi3at29PREQEpUuX5s0336RFixbUqVOHBg0a8MADD2QpnoYNG/LQQw8RFRXFQw89RHR0NPXr16dv3740bNiQO++8kwEDBlCvXj2nx5k9ezafffYZderUoWbNmnz55ZdOt4+KiiIwMJA6derw/vvvZynmjBw5coQxY8awd+9e6tevT926dZk82RrF0LFjR/r370/jxo2pVq0ajz/+OLNmzaJ06dK5cm6lnCpZxequ+TsO5vWGxKuZ7pIXxJU/qfNKdHS0iYmJSbVs3759VK9e3UMR+bZp06YRExOTqj9bKWVxS27ZOR8WDbCGUXb+NE8ehBKRWGNMdHrrfOomq1JKebWornAmDr5/DW6qAC1f8mg4muD9WN++fenbt6+nw1Aqf2k6DP7+3SppULQC1OvlsVA0wSulVG4Sgfbvw9kjVmGyiDJW2WEP0JusSimV2wKDodt0KFEVvngU/tzrkTA0wSullDuEFoGeX0BwuDV88tzxPA9BE7xSSrlLkbJWkr/8t/Ug1NULeXp6TfAuGDNmDDVr1iQqKoq6devy888/53kMa9eupX379umu27x5M/fccw9Vq1alXr16DBgwIMPKlb5i2rRpHDt2LMv7RUZGUrt2bfuTsk8//XS6x7t27RpDhw6lUqVKVK5cmQceeMBeuAysYm5p40lbxji3OX6P165dm6pi6SeffMKMGTOc7u8Yoyvbp5X2msGqClqrVq0sHcfR+PHjqVSpEiLCqVOnsn0cR7GxsdSuXZtKlSrx9NNP25+eHjVqlP13tFWrVtn6+XGL0nWsujUndsGC/pCUcdXZ3KYJPhMbN25k+fLlbN26lZ07d7J69WrKlSvn6bDs/vzzT7p27cpbb73F/v372bZtG23atPH5csPZTfAAa9assT8pO3bs2HSP969//Yvz58+zf/9+Dhw4QKdOnXjwwQddKrWQF9Im+EGDBvHoo4+6vH9Wt3eXJk2asHr1aipUqJDlfUePHs20adNuWP7EE08wadIkDhw4wIEDB/j6668BeP7559m5cyfbt2+nffv2mZYJyVNVWsH978KBb+Dr4Xk2I5RvjaJZOcL6XzA33VIb2r6Z4erjx49TokQJe70Vx2qQsbGxPPvss1y4cIESJUowbdo0SpcuzcGDBxk0aBDx8fEEBgYyf/58brvtNl544QVWrlyJiDBy5Ei6d+/O2rVrGT16NCVKlGD37t00aNCAWbNmISJ8/fXXDB06lPDwcO6+++5045swYQJ9+vShUaNG9mVdunQB4K+//qJ///78+uuvhIeHM3HiRKKiohg9ejS//fYbv/76K4cPH+b9999n06ZNrFy5kjJlyrBs2TKCg4OJjIykW7durFy5krCwMObMmUOlSpWIi4ujf//+nDp1ipIlSzJ16lTKly+fYelggHfeeYcvvviCq1ev0rlzZ1555ZUMyxN/9dVXxMTE0LNnT8LCwti4cSNhYWHZ/hYvWLAg1fE2bNjA1KlT+e233wgMDASgX79+TJkyhe+//5577703W+eZNm0aS5Ys4eLFixw4cIBhw4Zx7do1Zs6cSYECBVixYgXFihWjefPmvPvuu0RHR3Pq1Cmio6NTFYiLi4vjk08+ITAwkFmzZjFu3Di+++47ChUqxLBhw2jevDl16tThhx9+IDExkSlTptCwYcNUsYwePdq+fUalnn/77TceeeQRLly44PQJ6cTERHr27MnWrVupWbMmM2bMYNOmTYwdO5YlS5YAVkmMjz766IZKqxk9IX3x4kWeeuopdu/eTUJCAqNHj3bpKe3jx49z7tw57rrrLgAeffRRlixZQtu2bVNVZL148aL3TfV5x2Nw5nfY8CEUjYTGT7n9lNqCz0SrVq34448/qFKlCoMHD+aHH34ArFmYnnrqKRYsWEBsbCz9+/fnpZeshxp69uzJk08+yY4dO/jpp58oXbo0ixYtYvv27ezYsYPVq1fz/PPPc/y4ddNl27ZtfPDBB+zdu5dff/2VDRs2cOXKFR5//HGWLVtGbGxshhNXpPynkJ6XX36ZevXqsXPnTl5//fVULbpDhw7x/fffs3TpUnr16kWLFi3YtWsXYWFh9lLHAEWKFGHXrl0MGTLEXtzrqaeeok+fPuzcuZOePXvau0Hgeung5cuX22dcWrVqFQcOHGDz5s1s376d2NhY1q1bB8CBAwd48skn2bNnDzfddBMLFy6kS5cuREdHM3v2bLZv357l5N6iRQt7F837779/w/EOHTqUbonm6Oho9uzZA1wvEZ3y+ve//+3SuXfv3s2iRYvYsmULL730EuHh4Wzbto1GjRq53GUSGRnJoEGD+Oc//8n27dvTrRN06dIltm/fzkcffUT//v2dHm/gwIGMGzeO2NhY3n33XXt9nmeeeYYnnniCXbt2OS3psH//fgYPHsy+ffuIiIjgo48+okWLFvzyyy/Ex8cDMHXq1EzjcDRmzBhatmzJ5s2bWbNmDc8//zwXL17MdL+jR49StmxZ++eyZcty9OhR++eXXnqJcuXKMXv2bO9qwae4dzTU6ASrRsKeJW4/nW+14J20tN2lUKFCxMbG8uOPP7JmzRq6d+/Om2++SXR0NLt37+a+++4DICkpidKlS3P+/HmOHj1K586dAQgNDQVg/fr19OjRg8DAQEqVKkWzZs3YsmULERERNGzY0P5DmzIlYKFChahYsSKVK1cGoFevXkycODFLsa9fv56FCxcC0LJlS06fPm2vWtm2bVt7eeGkpKRUpYcdW5M9evSwf02ZtGPjxo0sWrQIgN69e/PCCy/Yt0+vdPCqVatYtWqVvTV34cIFDhw4QPny5dMtT5xT6dXdz6q0JaJTyj5kpkWLFhQuXJjChQtTpEgROnToAFj/rjt37sxRTI5Svi/33HMP586d48yZM+lu56zU84YNG+w/H71792b48OHpHqNcuXI0adIEsH4Ox44dy7Bhw+jduzezZs2iX79+bNy4MUt9/qtWrWLp0qW8++67AFy5coXDhw+TmJhI7969AThx4gQhISH2wnLfffddpscdM2YMY8aM4Y033mD8+PG88sorLseUJwICoPMncP44LP4/iLgVyjXMfL9scmuCF5E2wIdAIDDZGJP3GToXBAYG0rx5c5o3b07t2rWZPn06DRo0oGbNmmzcuDHVttnp+06vRK+ratasSWxsbJaLkDmWF05bejijcsOu/MmbXulgYwwvvvgi//d//5dq27i4uBuu/fLly06P/8cff9iT5qBBgxg0aFCmMaV1++23c/jwYc6fP0/hwoXty2NjYzO8ke0qx+vJqIxzUFAQycnJgJXYssPVMtCOpZ5dOU5WztWvXz86dOhAaGgoXbt2JSjI9XRijGHhwoVUrVr1hnUpsY4ePZrIyMhUT2Nfu3Yt1c3wI0eOUKZMmRuO0bNnT+6//37vS/AAwWHw8Fz47B8w92EYsBqKuWdeA7d10YhIIDABaAvUAHqISA13nc9dUm7Cpdi+fTsVKlSgatWqxMfH2xN8QkICe/bsoXDhwpQtW9beN3n16lUuXbpE06ZNmTdvHklJScTHx7Nu3bob+k0dVatWjbi4OA4dOgRYU/elZ8iQIUyfPj3VyJ5Fixbx559/0rRpU2bPng1YN+1KlCiR7sxRzsybN8/+NaWfv3Hjxnz++eeAVWUys0lNWrduzZQpU7hwwRoidvToUU6ePOl0n4zKDZcrV85+AzUryd3xeAULFqRPnz48++yzJCUlATBjxgwuXbpEy5YtMz3W4sWLefHFF10+d1qRkZH22vULFizINN70pHxf1q9fT5EiRShSpEi62zkr9dykSZNU38eMHD582P5zPmfOHPv9oFtvvZVbb72V1157jX79+mW4f3pat27NuHHj7I2Abdu2ubRf6dKliYiIYNOmTRhjmDFjhr1x4/h7+uWXX1KtWrUsxZSnCha3SgwbY42Rv/SXW07jzj74hsBBY8yvxphrwOdA1pqZXuDChQv06dOHGjVqEBUVxd69exk9ejQhISEsWLCA4cOHU6dOHerWrWsf9TBz5kzGjh1LVFQUjRs35sSJE3Tu3JmoqCjq1KlDy5Ytefvtt7nlllsyPG9oaCgTJ06kXbt21K9fn5tvvjnd7UqVKsXnn3/OsGHDqFq1KtWrV+ebb76hcOHCjB49mtjYWKKiohgxYkS2pq/7+++/iYqK4sMPP7SX/R03bhxTp04lKiqKmTNn8uGHHzo9RqtWrXjkkUdo1KgRtWvXpkuXLpn+pdO3b18GDRpE3bp1M23Vp+XYB59y3yHt8d544w1CQ0OpUqUKlStXZv78+SxevNilFu2hQ4ey/B+lo2HDhvHxxx9Tr169DIcOdujQgcWLF1O3bl1+/PHHG9aHhoZSr149Bg0axGeffeb0fBmVev7www+ZMGECtWvXTtWPnVbVqlWZMGEC1atX5++//+aJJ56wr+vZsyflypXLsCrj2LFjKVu2LEeOHCEqKooBAwYA1pDGhIQEoqKiqFmzJqNGjXJ6DY4++ugjBgwYQKVKlbj99ttp27YtACNGjKBWrVpERUWxatWqTH8uPa747dBjLpz5Az5/BBKy99ecU8YYt7yALljdMimfewPj09luIBADxJQvX96ktXfv3huWqbxRoUIFEx8f7+kwvE7Pnj3NyZMnPXb+Zs2amS1btnjs/I6efPJJM3nyZE+HkS1ek1t2LTTmyyHGJF7L1u5AjMkgD3v8JqsxZiIwEax68B4OR6lMzZo1y9MheIUGDRpQsGBB/vvf/3o6FN9W60Hr5QbuTPBHAccngsralikfkRsjWlTuW7t2radDALhhDlzlfdzZB78FqCwiFUUkBHgYWJqdAxkvebpQKeUf8ktOcVuCN8YkAkOAb4B9wBfGmD1ZPU5oaCinT5/ON98QpZR7GWM4ffq0/RkVf+bWPnhjzApgRU6OkXIHPuWJOaWUyqnQ0NBUT8T6K4/fZM1McHAwFStW9HQYSinlc7QWjVJK+SlN8Eop5ac0wSullJ8SbxqdIiLxwO/Z3L0EkDtTxniev1yLv1wH6LV4I3+5DsjZtVQwxpRMb4VXJficEJEYY0y0p+PIDf5yLf5yHaDX4o385TrAfdeiXTRKKeWnNMErpZSf8qcEn7Xpjrybv1yLv1wH6LV4I3+5DnDTtfhNH7xSSqnU/KkFr5RSyoEmeKWU8lNen+BFpI2I7BeRgyIyIp3174vIdtvrfyJyxmFdHxE5YHv1ydPA05HDa0lyWJetssu5yYVrKS8ia0Rkm4jsFJH7Hda9aNtvv4i0ztvIb5TdaxGRSBG57PB9+STvo08VZ2bXUUFEvrNdw1oRKeuwztd+V5xdi9f8rojIFBE5KSK7M1gvIjLWdp07RaS+w7qcf08ymurJG15AIHAIuA0IAXYANZxs/xQwxfa+GPCr7WtR2/uivngtts8XPP39yMq1YN00esL2vgYQ5/B+B1AAqGg7TqCPXksksNvT348sXMd8oI/tfUtgpu29z/2uZHQtts/e9LtyD1A/o58T4H5gJSDAXcDPufk98fYWfFYn7u4BzLW9bw18a4z5yxjzN/At0Mat0TqXk2vxNq5ciwFSZqYuAhyzvX8A+NwYc9UY8xtw0HY8T8nJtXgTV66jBvC97f0ah/W++LuS0bV4FWPMOuAvJ5s8AMwwlk3ATSJSmlz6nnh7gi8D/OHw+Yht2Q1EpAJWizDlm+7yvnkkJ9cCECoiMSKySUQ6uS1K17hyLaOBXiJyBGtOgKeysG9eysm1AFS0dd38ICJN3Rqpc65cxw4gZfLPzkBhESnu4r55KSfXAt71u5KZjK41V74n3p7gs+JhYIExJsnTgeSC9K6lgrEeZX4E+EBEbvdMaC7rAUwzxpTF+jN0poj46s9bRtdyHChvjKkHPAvMEZEIJ8fxtGFAMxHZBjTDmiPZV39fnF2Lr/2uuI23/8JlZeLuh0ndpeFtk37n5Fowxhy1ff0VWAvUy/0QXebKtTwGfAFgjNkIhGIVVPLF70u612LrZjptWx6L1W9cxe0Rpy/T6zDGHDPGPGj7D+kl27Izruybx3JyLd72u5KZjK41d74nnr4JkckNiiCsmwsVuX6zpWY621UD4rA9uOVwk+I3rBsURW3vi/notRQFCtjelwAO4OQGrTdcC9aNo76299Wx+q0FqEnqm6y/4tmbrDm5lpIpsWPdEDzqqZ8xF6+jBBBgez8GeNX23ud+V5xci1f9rtjiiCTjm6ztSH2TdXNufk88dtFZ+Me5H/gfVuvoJduyV4GODtuMBt5MZ9/+WDfxDgL9fPVagMbALtsP+i7gMW+/FqybYBtsMW8HWjns+5Jtv/1AW1+9FuAhYI9t2Vagg5dfRxdbwvsfMDklEdrW+dTvSkbX4m2/K1h/iR8HErD60R8DBgGDbOsFmGC7zl1AdG5+T7RUgVJK+Slv74NXSimVTZrglVLKT2mCV0opP6UJXiml/JQmeKWU8lOa4JVXEpELaT73FZHxtvfPisheW/W972ylHVK2qyki39sqER4QkVEiIm6Mc4WI3JTJNmtF5IYJlUWkrmOVzTTrmtiuL0ZEKtuW3SQiq3z4iWCVx/QHRfmibVjjhaOABcDbACISBizFeo6gKlAHa1z0YHcFYoy539ieoMyGuljjvdPznG3dUKxx0wAjgdeNMcnZPJ/KZzTBK59jjFljjLlk+7gJ6zFusGqPbDDGrLJtdwkYAtxQT9yRiEwQkY6294tFZIrtfX8RGWN730tENttqjH8qIoG25XEiUsL2fpTtL4f1IjJXRIY5nKarbf//iUhTEQnBenCnu+2Y3dOElQCE214Jtnoq5Ywxa7P8D6byrSBPB6BUBsJEZLvD52JYrfO0HsN61BusMgixjiuNMYdEpJCIRBhjzmVwrh+BprbjlwFK25Y3BT4XkepAd6CJMSZBRD4CegIzUg4gIndgPdlaBwjGerLVMZYgY0xDW5fMy8aYf4jIv7H+EhmSTkxv2I5/GegNvIvVglfKZZrglbe6bIypm/JBRPoCqfqxRaSXbVmzHJ7rR2CoiNQA9gJFbTW5GwFPA32ABsAWW3d+GHAyzTGaAF8aY64AV0RkWZr1i2xfY7FqkzhljNmOVZsEEbkH63F3EZF5WK3754wxf2btMlV+owle+SQR+QdWTZtmxpirtsV7sWbQcdzuNqwZfjJqvWOMOWq7UdoGWIf110I3237nbTdppxtjXsxByCkxJpGF3zvbuUdiVRgdB7yA9R/E09iqKCqVEe2DVz5HROoBn2IVnnJsSc8G7rYl/5SbrmO5fhO2oYjMSHs8m01YNzTXYbXoh9m+AnwHdBGRm23HKeY4csdmA9BBREJFpBDQ3oVLOQ8UzmSbR4EVxpi/sPrjk22vcBeOr/I5TfDKF70DFALmi8PEysaYy1hToI0Ukf1Y1fm2AONt+5XH6tNOz49Y/eQHsfrPi9mWYYzZi9WKXiUiO7GmTyvtuLMxZgtWH/5OrHsCu4CzmVzHGqBGBjdZEZFwoC9WtUGA97BmlPoA8OgE38o3aDVJlW+IyDtYkzPvdNPxCxljLtgS8zpgoDFmqzvOpZQrNMErlUtEZA5W7fhQrD77NzwcksrnNMErpZSf0j54pZTyU5rglVLKT2mCV0opP6UJXiml/JQmeKWU8lP/D/9Xuraqd4FUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example 6. Creation of a Pervaporation object and \n",
    "# modelling an ideal diffusion curve \n",
    "# (permeance is assumed independent of feed composition) of \n",
    "# Romakon PM 102 membranes at 335.15 K; \n",
    "# plotting of partial fluxes and separation factor \n",
    "# as functions of water weight fraction in the feed\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from pyvaporation import (\n",
    "    Membrane, \n",
    "    Pervaporation,\n",
    "    Mixtures, \n",
    "    Composition,\n",
    "    CompositionType,\n",
    ")\n",
    "\n",
    "romakon_pm_102 = Membrane.load(\"tests/default_membranes/RomakonPM_102\")\n",
    "\n",
    "h2o_etoh = Mixtures.H2O_EtOH\n",
    "composition_range = [\n",
    "    Composition(p=c,type=CompositionType.weight)\n",
    "    for c in np.linspace(0.7, 1, 20)\n",
    "]\n",
    "\n",
    "pervaporation = Pervaporation(membrane=romakon_pm_102, mixture=h2o_etoh)\n",
    "\n",
    "curve = pervaporation.ideal_diffusion_curve(\n",
    "   feed_temperature=335.15,\n",
    "   compositions=composition_range,\n",
    "   permeate_temperature=263.15\n",
    ")\n",
    "\n",
    "curve.plot(curve.partial_fluxes,'Partial fluxes, kg/(m2*h*kPa)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "id": "B_evLsOymhjE",
    "outputId": "b0796175-3a62-4601-883d-6fac1045a1c9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Iq1atYtCgQZ7LL+D8oS5cuJBZs2Z5ehCbPXs2GzduZMmSJaxYsYLk5GTmz58PwMaNG7nttttYs2YN1apV46OPPqJfv34kJSXx3nvvsWLFCipUqFCoz6xr166eS0Pjxo07aX+bN2+mfv36VKlSJdt2SUlJrFmzBoCjR49m65Xu3//+d25vdZKffvqJadOmsXTpUu6//34qVqzI8uXL6dixo8+XamJiYhgxYgR33nknK1asyLWCbUpKCitWrODll1/mxhtvzHd/w4cP58UXXyQ5OZlnnnmGW2+9FYA77riDW265hdWrV+dbimPDhg3ceuutrFu3jipVqvDyyy/TtWtX1q9f7+nz96233iowDm9jx46lW7duLFmyhLlz53L33Xdz5MiRArfbuXMndevW9UzXrVuXnTt3nrTem2++Sa9exV8fLfiKzhWkWQ/nieP5T0FsX6jV1P/v+flo2L26ePd5Rhz0eiLPxZUrVyY5OZkFCxYwd+5crrnmGp544gmSkpL46aef6N69OwAZGRnUqVOHQ4cOsXPnTs+3o6ioKAAWLlzIwIEDCQ0N5fTTT+eCCy5g6dKlVKlShfbt23t+ubO6wqxcuTINGzakaVPn5zp48GAmTJhQqENbuHAhH330EQDdunVj3759niqnvXr18pStzsjIyFbS2ruM9cCBAz2vd955J+BcLss6Q7nuuuu45557POvnVpJ69uzZzJ4921PP6PDhw2zcuJH69evnWvb6VM2dO/ek4n6FlbP0eFa5joJ07dqV6OhooqOjqVq1Kpdffjng/FyzzsiKQ9bncv755/PXX3/leT08vxLiixYt8vx+XHfdddk6PvJWr149zj33XMD5PXzhhRe46667uO6663j33Xe54YYb+P777wvVJjF79mxmzpzpOWs5duwY27ZtIz09neuuuw6A3bt3ExER4ek46ZtvvvFp32PHjiUsLIxBgwb5HI+v/JoIRKQn8DwQCryuqif9ZxKRq4ExgAIrVfVaf8bkk55PwuY58MkdMGQWhJTPE6fQ0FC6dOlCly5diIuL4+2336Zdu3bExsby/fffZ1u3KNfmcyv97KvY2FiSk5MLXYzOu2x1zpLWeZWx9qWz79xKUqsq9913H3/729+yrbt169aTjr2gy0Dbt2/3/HMdMWIEI0aMKDCmnBo3bsy2bds4dOgQ0dHRnvnJycl5Nsj7yvt48ioPHhYWRmZmJuD8AywKX8uLe5cQ92U/hXmvG264gcsvv5yoqCj69+/vuSzjC1Xlo48+onnz5icty4p1zJgxxMTEMHToUM+y1NTUbJd8duzYwVlnneWZnjhxIrNmzeKbb74pUuf0BfHbfzgRCQXGA72AVsBAEWmVY52mwH3AuaoaC/zdX/EUSvTpTvmJXxfBj2/7//16PQE3fFq8Qz5nA4CnMTHLihUraNCgAc2bN2fPnj2eRJCWlsaaNWuIjo6mbt26nuuWx48fJyUlhc6dOzNlyhQyMjLYs2cP8+fPp3379nm+b4sWLdi6dSubN28GnC4rczNy5EjefvvtbHcyTZs2jd9//53OnTvz3nvvAU5bRK1atU66HFKQrLaMKVOmeNohOnXqxOTJkwGnKmlBne9cfPHFvPnmmxw+fBhwTu//+OOPfLfJq4x1vXr1PA3BhUkC3vurVKkSQ4YM4R//+AcZGRkAvPPOO6SkpNCtW7cC9zV9+nTuu+8+n987p5iYGE/fCd7tKHnFm5usz2XhwoVUrVqVqlWr5rqedwlxcP4Br1y5EnAacr0/x7xs27bN83v+/vvvc9555wFw5plncuaZZ/LYY49xww035Ll9bi6++GJefPFFz5eF5cuX+7RdnTp1qFKlCosXL0ZVeeeddzxfgr744gueeuopZs6cScWKFQsVj6/8+VW3PbBJVbeoaiowGcj59e5mYLyqHgBQ1fz/ikpSm8EQ0xm+eggOlb+Owg8fPsyQIUNo1aoV8fHxrF27ljFjxhAREcHUqVO59957SUhIIDEx0XOXx6RJk3jhhReIj4+nU6dO7N69myuuuIL4+HgSEhLo1q0bTz31FGeccUae7xsVFcWECRO49NJLadu2Laeddlqu651++ulMnjyZu+66i+bNm9OyZUu+/PJLoqOjGTNmDMnJycTHxzN69GjefrvwyfrAgQPEx8fz/PPPe8pJv/jii7z11lvEx8czadIknn8+/3aiHj16cO2113oaJvv161fgmdPQoUMZMWJEkRqLvdsIstpFcu7vP//5D1FRUTRr1oymTZvy4YcfMn36dJ++RW7evLnQCdXbXXfdxX//+1/atGmT5y2Vl19+OdOnT/c0FucUFRVFmzZtGDFiBG+88Ua+75dXCfHnn3+e8ePHExcXl+t19izNmzdn/PjxtGzZkgMHDnDLLbd4lg0aNIh69erlWSH4hRdeoG7duuzYsYP4+HiGDRsGwIMPPkhaWhrx8fHExsby4IMP5nsM3l5++WWGDRtGkyZNaNy4sactYOTIkRw6dIju3buTmJhYpLPFAuXVh+WpDkA/nMtBWdPXAS/lWGcG8BSwCFgM9Cxov6fUZ3Fh7d2k+kht1cmDi33XpaHP4mCVVz/LwW7QoEH6xx9/BOz9L7jggpP6cA6U2267TV9//fVAh1Fkhe2zONAXv8OApkAXYCDwmohUy7mSiAwXkWUisiyrNb9E1GwMXe6FdTNh3aySe19jAuDdd9/19CMdzNq1a8eqVasYPHhwoEMpMf5sLN4JeD+hVNed520H8IOqpgG/iMjPOIlhqfdKqjoBmABOxzR+izg3nUbBT9Pgs7ugYWeIyv2apSk7iuMOHlP85s2bF+gQAE7qIzoY+POMYCnQVEQaikgEMACYmWOdGThnA4hILaAZsMWPMRVeaDhc/gIc/h2+fjjQ0RhjTLHzWyJQ1XRgJPAlsA74n6quEZFHRKS3u9qXwD4RWQvMBe5W1X3+iqnI6rZz+jle9gZsW1xsu9VS8ti/Mab8KMr/leDts7iwjh+Glzs6Tx+PWAhhkQVvk49ffvmF6Ohoatas6Zf7go0xwUdV2bdvH4cOHaJhw4bZluXXZ7E9WeyryMpw2bPwXj9Y8Cx0Lfr91oDn1rMSbfw2xpR7UVFR2cpV+MISQWE07Q6t+8GC/3PKUJzWosi7Cg8PPyljG2NMIAT69tGyp+cTztnBJ6PAfZzeGGPKMksEhVW5ttOJzfYfnMZjY4wp4ywRFEXCQGjUxbmd9M+8H2E3xpiywBJBUYjAZeNAM2Dm7VDG7rwyxhhvPicCEankVhQ1ADUaQfdHYPM3sOzNQEdjjDFFlmciEJEQEblWRD4VkT+A9cAuEVkrIk+LSJOSC7OUSrrJuUQ0+0HYX7oeiDbGGF/ld0YwF2iM01/AGapaT1VPA87DqRT6pIgET1Wm3ISEQJ/xEBIG02+BzIxAR2SMMYWWXyK4SFUfVdVVquq5T1JV96vqR6p6FTDF/yGWclXrQq8nYfti+P6lQEdjjDGFlmcicCuCeojIaSJSP2vIbZ2glTAAWlwGcx6D39cGOhpjjCmUAhuLRaS3iGwEfgG+BbYCn/s5rrJFBC57DiKrwPS/QXpqoCMyxhif+XLX0KNAB+BnVW0IXIjTRmC8Va4Nlz8Hu1fB/KcDHY0xxvjMl0SQ5paGDhGREFWdC+RawS7otbwc4gc4tYh2Bl/nFsaYssmXRHBQRCoD84H3ROR54Ih/wyrDej0J0WfA9BGQVrjOyY0xJhB8SQS9gRTgTuALYDNwuT+DKtMqVIM+L8Hen+GbRwMdjTHGFCi/B8rOEZGVwB/AIqCZqr6tqi+Uyl7ESpPG3eDsYbD4Zdi6MNDRGGNMvvI7IxgP3AXUBJ4FxpVIROVF90egRkOYcQscPxToaIwxJk/5JYIQVf1KVY+r6odA7ZIKqlyIqAR9X4E/d8CX/wp0NMYYk6f8eiirJiJX5jWtqtP8F1Y5Uf8c6DQKFj0HzS+F5j0DHZExxpwkv0TwLdkbhb2nFbBE4Iuu/4JN38DHt8KIRVClTqAjMsaYbPJMBKp6A4CIRKrqce9lIlLD34GVG2GR0O9NmHABTLsZrv8YQqyatzGm9PDl9tFpIuJJGCJyBvCVLzsXkZ4iskFENonI6FyWDxWRPSKywh2G+R56GVK7GVzyDGxdAAueDXQ0xhiTjS+JYAbwoYiEikgMMBunNHW+3E5sxgO9gFbAQBFplcuqU1Q10R1e9znysibxWoi7GuY9Dr9+F+hojDHGo8BEoKqvAV/jJIRPgBGqOtuHfbcHNqnqFlVNBSYDfU4h1rJNBC57FqrHwEfDIGV/oCMyxhgg/wfK/pE1AFFAfWAF0MGdV5CzgO1e0zvceTldJSKrRGSqiNTzPfQyKDLaaS84/Ad8PNL6OjbGlAr5nRFEew2Vce4S2uQ1rzh8AsSoajxOu8Pbua0kIsNFZJmILNuzZ08xvXWAnNnGedhsw6ew5LVAR2OMMfneNfSwiNRR1V0icoaq7i7kvncC3t/w67rzvN/Du1TF68BTecQyAZgAkJSUVPa/Rne4BX75FmbfD/U7QJ34QEdkjAliBbURPCIitYCHi7DvpUBTEWkoIhHAAGCm9woi4n1TfW9gXRHep+wRgT4vQ8WaMPUGOH440BEZY4JYfm0EQ4BtwBJgu4hcX5gdq2o6MBL4Eucf/P9UdY2IPCIivd3VRonIGre43ShgaBGOoWyqVBOufA32bYbP7g50NMaYIJbfk8XzgPOAtcAWnAqkhaKqnwGf5Zj3b6/x+/DhVtRyq2FnuOAe+PZJaHSB0/exMcaUsPw6r/8V6IxzyeY8d9oUt/PvgfqdYNY/YO+mQEdjjAlC+bYRqOoIVc1U1VtLKqCgExoGV70GYRFOe0H68YK3McaYYlTgA2Ui0kxEJojIbBGZkzWURHBBo2pdp/F49yr48v5AR2OMCTL5tRFk+RB4Bef2zgz/hhPEWlwCHW6DxeOhbpK1FxhjSowviSBdVf/r90gMdH8Ydq2ET+6A01pCnYRAR2SMCQL53T5awy03/YmI3CoidbLmWRlqPwkNh/5vQYUaMGWw1SMyxpSI/M4IknE6oBF32vtmdwUa+SuooFb5NLhmErzVCz66CQZNtf4LjDF+ld/tow1VtZH7mnOwJOBPdZPgkqdh8xyY81igozHGlHP53jUkIo+5r4+UTDjGo91QaDsEFj4La2cWuLoxxhRVQbePLhWR8cCykgjG5HDJ03BWO5hxC+z5OdDRGGPKqfwaix8CugEDgW4i8u+81jV+EhYJV78DYVEwZRAc+yvQERljyqH82giyKo52dKft8lAgVK0L/Sc6xelm3AKZmYGOyBhTzhR0aegtVd0AvFUSwZg8NOwMPR6F9bNg0bhAR2OMKWcKSgRbRKSxqq70niki1pNKSetwK7S+Cr55FDZ9E+hojDHlSH5tBFcD64GP3D4DzvZaPNHfgZkcRKD3i3BaK5h6IxzYGuiIjDHlRH5nBP8C2qlqInADMElErnCXSZ5bGf+JqAQD3gUUPrgWjh8KdETGmHIgv0QQqqq7AFR1CdAVeEBERuE8WWwCoUYj6PcW7FnvnBlkpAc6ImNMGZdfIjgkIo2zJtyk0AXoA8T6OS6TnyYXwiVPwcbZMNvKVhtjTk1+tYZuIcclIFU9JCI9gav9GpUp2NnDYN8Wp2x1jcZwzvBAR2SMKaPyTAS53ClUxWv9z/0ZlPFRj0fhwC/wxb1QPQaa9Qh0RMaYMsiXHsr+JiK7gVU4FUmTsZITpUNIKFz5Gpze2unmcvdPgY7IGFMGFZgIgLuA1qoaU9jqoyLSU0Q2iMgmERmdz3pXiYiKSJKvgRtXZGW4dgpERsP718Ch3YGOyBhTxviSCDYDKYXdsYiEAuOBXkArYKCItMplvWjgDuCHwr6HcVU500kGRw/ABwMgtdAflzEmiPmSCO4DvhORV0XkhazBh+3aA5tUdYuqpgKTce44yulR4EngmM9Rm5PVSYB+b8BvK2D6cKtJZIzxmS+J4FVgDrCYE20EyT5sdxaw3Wt6hzvPQ0TaAvVU9VOfojX5a94LLn4c1n0C34wJdDTGmDLCl87rw1X1H8X9xiISAjwLDPVh3eHAcID69esXdyjlS4dbYN8mWPS8c1tpuyGBjsgYU8r5ckbwuYgML0Ln9TuBel7Tdd15WaKB1sA8EdkKdABm5tZgrKoTVDVJVZNq167tw1sHMRHo9RQ0vhA+/QdsmRfoiIwxpZwviWAgbjsBhbt9dCnQVEQaikgEMADw9Lmoqn+qai33bqQYnEtPvVXVbk09VaFhTh8GtZrBlOtg16pAR2SMKcUKTARF7bxeVdOBkcCXwDrgf6q6RkQeEZHepx66yVdUFRj0IURWgXevcjq2McaYXIhq7vXjROQ8VV2Y54bOk8b1VbVEn2JKSkrSZcvspMFne36GNy92nje4cTZUqRPoiIwxASAiyaqa67Na+Z0RXCUi34nIv0XkUhFpLyLni8iNIjIJmAVU8EvEpvjUbgaDp8KRffDulc6zBsYY4yW/PovvBC4DdgH9ce73/wfQFHhVVc9X1aUlEqU5NWe1gwHvOXcTvX+NPXBmjMkmv0tDHYHFmtcKAWKXhk7B2o/hw6HOHUUDP4DQ8EBHZIwpIUW9NHQ9kCwik0VkqIic4Z/wTIlp1QcuGwebvoIZt9jTx8YYIP8y1LcAiEgLnHpBE0WkKjAX+AJYpKoZJRKlKT7thkLKfvjmYahQ3XnmQKznUWOCWYFPFqvqepxO7MeJSAWcLiv74zwVbNVCy6Lz7oSUffD9S1CxFnS5N9ARGWMCqMBEkMtTxIuBr1Q1zT8hGb8TgR6POXcQzXscKtaA9jcHOipjTID4UmvoR5xSEQdwuq6sBuwWkd+Bm1XVlwJ0prQRgctfcJLBZ3c7l4ni+gU6KmNMAPhSYuIr4BK3HERNnPaCT4FbgZf9GZzxs9Aw6PcmNOgE0//mVC01xgQdXxJBB1X9MmtCVWe78xYDkX6LzJSM8AowcDKc2ca5tXTdrEBHZIwpYb4kgl0icq+INHCHe4A/3B7I7P7D8iCqCgz+yE0GQywZGBNkfEkE1+KUkJ4BTMdpLxgIhAJX+y0yU7KiqloyMCZI+ZIIolX1dlVto6ptVfV2IEZVU1V1k78DNCUoKxnUSXSSwXrrOM6YYOBLIvhIRDxdTIrI+cCb/gvJBFRUVbhumpMM/ne9JQNjgoAvieBvwAwROUNELgFeBC7xb1gmoCwZGBNUfOmYZikwCpgNjAEuUtXt+W5kyr5sycAuExlTnuWZCETkExGZKSIzcbqqrAgcB95w55nyzpMM4i0ZGFOO5fdk8TMlFoUpvaKqwnXTYdIVTjK4+m1ocWmgozLGFKP8qo9+W5KBmFIsZzK4cgK0vjLQURljiokvjcXGnEgGdZNg6o2wzG4cM6a8sERgfBdVFQZPg6Y9YNadMP8ZKF0d2BljisCviUBEeorIBhHZJCKjc1k+QkRWi8gKEVkoIq38GY8pBhEVnf6P46+BOY/Cl/dbT2fGlHG+lKHORkTeBlKA8ar6Uz7rhQLjge7ADmCpiMxU1bVeq72vqq+46/fG6eymZ2FjMiUsNBz6vuKUrl48Ho7uh94vWh/IxpRRRTkjeAn4GriugPXaA5tUdYuqpgKTgT7eK6jqX16TlQC7zlBWhIRAzyeg6wOw8gOYch2kHQ10VMaYIvD5jEBEKqpqivuA2VLgowI2OQvwfvBsB3BOLvu9DfgHEAF08zUeUwqIwAV3Q8Xq8Old8O5VMPADpy3BGFNmFHhGICKdRGQtTr/FiEiCiBRbhzSqOl5VGwP3Ag/kEcNwEVkmIsv27NlTXG9tisvZw6DfG7B9CUy8FA7/EeiIjDGF4MuloXHAxcA+AFVdCZzvw3Y7cUpWZ6nrzsvLZKBvbgtUdYKqJqlqUu3atX14a1PiWl8F106GfZvhzYvhwK+BjsgY4yOf2ghyqS2U4cNmS4GmItJQRCKAAUC20hQi0tRr8lJgoy/xmFKqyUVw/UxI2e8kg9153ktgjClFfEkE20WkE6AiEi4idwHrCtpIVdOBkcCX7vr/U9U1IvKIe4cQwEgRWSMiK3DaCYYU6ShM6VHvbLjxC0CcZPDzlwVuYowJLNECHggSkVrA88BFgOBUIb1DVff5P7yTJSUl6bJlywLx1qYw/toFHwyA3avg4sfhnBFO47IxJiBEJFlVk3JbVuBdQ6q6FxhU7FGZ8q1KHbjhM5g2HL4YDXt/hl5P2bMGxpRCvtw19LaIVPOari4iVmjGFCyiElw9Cc79u1Ob6L3+cPRgoKMyxuTgSxtBvKoezJpQ1QNAG79FZMqXkBDo/jD0fgm2LoA3esD+XwIdlTHGiy+JIEREqmdNiEgNilCawgS5ttfBdTPg8O/w+oWwbXGgIzLGuHxJBP8HfC8ij4rIY8B3wFP+DcuUSw07w7BvnCeP374cVv0v0BEZY/Ctz+J3gKuA34HdwJWqOsnfgZlyqlYTJxnUbQ/Tboa5j1spa2MCzNeic+uBaTgPhB0Wkfr+C8mUexVrOJ3cJA6Cb5+E/10Hx/4qeDtjjF8UeK1fRG4HHsI5I8jAeZZAgXj/hmbKtbAI6DMeTmsJXz0Ef3SFa951po0xJcqXM4I7gOaqGquq8aoap6qWBMypE4FOt8OQmc4ZwWvdYPXUQEdlTNDxqcQE8Ke/AzFBLOY8+Nt8OCMeProJPrsH0lMDHZUxQcOX20C3APNE5FPgeNZMVX3Wb1GZ4FOlDgydBV/9Gxa/DLtWQP+JUOXMQEdmTLnnyxnBNuArnI5jor0GY4pXaDj0/A/0e9OpXPrq+fDL/EBHZUy550utoYdLIhBjPFpfBafFOncTvdMHLnwIzr3DitYZ4ye+3DVUG7gHiAWisuarqnUrafzntBZw8xz4eCR8/RDsWAp9X7ZuMI3xA18uDb2H8xxBQ+BhYCtOpzPG+FdktNNOcPHjsOFz51LRdvvVM6a4+ZIIaqrqG0Caqn6rqjdincybkiICHW9zSlprptPZzbwnISM90JEZU274kgjS3NddInKpiLQBavgxJmNOVr8DjFgIcf1g3uMw8RI4sDXQURlTLviSCB4TkarAP4G7gNeBO/0alTG5iaoKV06AK1+HP9bBf8+DlVOsVpExp8iXu4ZmuaN/Al39G44xPojvD/Xaw/S/wfThsHE2XPp/UKFaoCMzpkzypYeyZiLyjYj85E7Hi8gD/g/NmHxUbwBDP4VuD8Ca6fDKefDrd4GOypgyyZdLQ68B9+G2FajqKmCAP4MyxichoXD+3XDTbAgJg4mXwjePQkZawdsaYzx8SQQVVXVJjnk+3bIhIj1FZIOIbBKR0bks/4eIrBWRVe5ZRwNf9mtMNnWTYMQCSLwWFjwDr1/kPJlsjPGJL4lgr4g0xik9jYj0A3YVtJGIhALjgV5AK2CgiLTKsdpyIMmtZjoV6/nMFFVktFPW+up34K+dMOECmDMW0o8XvK0xQc6XRHAb8CrQQkR2An8HbvFhu/bAJlXdoqqpwGSgj/cKqjpXVVPcycVAXV8DNyZXrfrAbUugdT+Y/xS80hm25zyhNcZ486Wryi2qehFQG2ihquep6lYf9n0WTgnrLDvceXm5Cfjch/0ak7+KNeDKV2HQVEg9Am/0gM9HO+PGmJP4UmuoGnA9EAOEiVv4S1VHFVcQIjIYSAIuyGP5cGA4QP361kum8VHT7nDbYvj6Yfjhv7DhU7j8BWhsd0Eb482XS0Of4SSB1UCy11CQnUA9r+m67rxsROQi4H6gt6rmekFXVSeoapKqJtWuXduHtzbGFRkNlz4DN3wOoREwqS98fBscPRjoyIwpNXzpmCZKVf9RhH0vBZqKSEOcBDAAuNZ7BbdcxatAT1X9owjvYYxvGnSCEYvg2ydg0Quw8Wu45GloebmVtzZBz5czgkkicrOI1BGRGllDQRupajowEvgSWAf8T1XXiMgjItLbXe1poDLwoYisEJGZRT0QYwoUHgUXjXHKW1eu7fR38O5VsHdjoCMzJqBEC6jTIiK3AWOBg7i3kAKqqo38G1rukpKSdNmyZYF4a1OeZKTD0tdh7lhIOwodb3UeTou0zvdM+SQiyaqalNsyX84I/gk0UdUYVW3oDgFJAsYUm9Aw6DACbk+G+Gtg0fPw0tmw6kMrYmeCji+JYBOQUuBaxpRFlU+DvuNh2DcQfQZMGwZvXWJPJpug4ktj8RFghYjMBTx39RTn7aPGBFzdJBg2B5ZPgm8ehlc7w9nDoOu/oEL1QEdnjF/5kghmuIMx5VtICLQbAq16w9zHnTaEnz6CC/8NiYOdy0nGlEP5Nha79YK+VtVS8wSONRabErN7NXx2D2z7Dmq3gAsfgua97HZTUyYVubFYVTOATLeHMmOCyxlxTl/JV0+CzHSYPBDe7AnbFgc6MmOKlS/nuoeB1SLyFU57AWBtBCZIiDiXippf4rQfzHsC3rzYmb7wITitRaAjNOaU+ZIIprmDMcErNAySbnBuNf3hv7DwOfhvR6cPhC73QVUrnGvKrgIfKAMQkQpAfVXd4P+Q8mdtBKZUSNkPC/4PlkwACYH2w+G8O53Kp8aUQqf0QJmIXA6sAL5wpxOtFIQJehVrwMVjnQfSYq+E716EFxKdS0dHDwQ6OmMKxZcHysbgdDJzEEBVVwD2ZLExANXqwxX/hVsWQUxnmPcfGBcH3zwCR/YFOjpjfOJLIkhT1T9zzMv0RzDGlFmnx8KA95wKp00vggXPwnNxMPsBOPR7oKMzJl++JII1InItECoiTUXkReA7P8dlTNl0RmvoPxFu+wFaXgbfj4fn4+Hze+Gv3wIdnTG58iUR3A7E4pSXeB/4E6ffYmNMXmo3hysnwMhlENfPeUr5+QSYdScc3Bbo6IzJJs+7hkQkChgBNMHpnewNt4+BgLK7hkyZdOBXWPQcLH8XNNNpYO54K5zZJtCRmSBR1LuG3sbpR3g10At4xg+xGRMcqjeAy8bBqBXOraYbPocJXeDNXrBuFmRmBDpCE8TyOyNYrapx7ngYsERV25ZkcLmxMwJTLhz7E36cBD+8Cn9ug+oxcM4t0GaQdY5j/KKoZwRpWSOl4ZKQMeVKVFXoNBJGLYf+b0Pl0+GLe+HZVvDl/daOYEpUfmcEGZyoLSRABZwOagSnq8oqJRJhDnZGYMqtHcmweDysmeFMt7zcuYzUoJNVPDWnLL8zAp9KTJQmlghMuXdwu1O6IvltOP4n1GoG7YZCwkArYWGKzBKBMWVR6hFYMx2SJ8KOpRAaCa36OEnBzhJMIeWXCKzLJWNKq4hK0GawM+z+CX58G1ZOgdX/s7MEU6x8eaCsyESkp4hsEJFNIjI6l+Xni8iPIpIuIv38GYsxZdoZreGSp+Gf66HPyxBVDb78F/xfc/hoGGz5FjKt8ospGr+dEbjdXI4HugM7gKUiMlNV13qttg0YCtzlrziMKVciKjq3mLYZBL+vcS4brZwCqz+EKmc5TzHHX+PUPjLGR/68NNQe2KSqWwBEZDLQB/AkAlXd6i7z+1eZZVv3s3DTXipFhFExMtR5jQilUmQYFSKyT1eMCCUyLASxa7CmNDs91jlL6P4IbPgMVv3PqW206Hk4vbWTEOL6QZUzAx2pKeX8mQjOArZ7Te8AzvHj++Vr2a8HeO7rjT6vHyJ4kkZFN0k4QxiVIkOpEO6+eiWRrPEKXutmbVchazo8lJAQSzCmGIVXgNZXOcORvU4D88rJ8NWD8NW/oeH5kDDAuR3VHlYrs9IyMlGFiLDiv6Lvt7uG3Gv+PVV1mDt9HXCOqo7MZd2JwCxVnZrHvoYDwwHq16/f7tdffy1STBmZSkpqOimpGRw57vWalkHK8QyOpKaTcjydI6kZHE3Nms5wl6dzJDXdne8sP3zcmU7NKNwJTWRYiCdRZCWNCuHu2Ul4aLZ52cfDss3P7TUqPJRQSzQGYN9m5yxh1RQ48AuEVYDmPaFlb2jaAyIrBzrCoJWansmfR9PcIZX9R9I4cCSV/SmpHDiSyr4jqdmm9x9J5a9j6TxxZRwD2tcv0nsG6q6hnUA9r+m67rxCU9UJwARwbh8takChIUJ0VDjRUeFF3UWu0jMyPckkK9E4g3fiODH/aFrWskyOpp2Yv+fQcVJS0zmWlunZz/H0wl81iwgLcZKDmyCiwkOpEB7ijIeFEpWVPMJDiQp31o3KWubOy/6atcxrOjyEyDBLOqVazcbQ9T7oMtq5/XTVFFj7sXPGEBYFjS+EVr2hWU+oUC3Q0ZYpqemZHDqWxqFj6e6QxqHjXuPu659H0ziYkub1T98ZUlLzri0VERZCzUoRVK8YQY1KEdSrXpEa7nTrs6r65Xj8mQiWAk1FpCFOAhgAXOvH9wuYsNAQqoSGUKWYEwxAZqa6iSODY2leCSbNmT6amukmDyfBHE3NJCUtnWOehJPBsbRMjrnrH0xJc8czT+wjLYOinhiGhwqRYScSQ2R4CFE5XiPD3GVhIe506Il57vKIsBAiQkOIDA91X0OIDHXmR4aFOsvdITxUiAwNJTxMiAgNISzUrze/lX0iUK+9M/R6CrYthnUzYd0nsOFTCAmHRhc4ZwotLoVKtQIdcbHIzFRSMzI56v4tOH8f+b8ePu5cFTjsfqk77HXlIOsKwaHj6aT68AUtKjyEahUiqFohnKoVw6lXoyKtK4RTrUK4Z15VdzzrH33NyhFUCA8t8fZJvz5QJiKXAM8BocCbqjpWRB4BlqnqTBE5G5gOVAeOAbtVNd/bHeyBsuKnqhxPz+R4WibH0p0/imPpJxLI0bQMjnslj+NpGRzzWv/kV2fd1PRMjqc7ZzXO/k+MH0vLID2zeH73QgTCPUkjhPDQrEE887NNZy0PCyE8RAgLFcJCs8ZDCAsVwkOcdcJChfBQITQkxH0VwkKc6bCsbb2mQ90hRJxlIXJim6xxZzme9UKypkUQObE8JEQQIEQEERDcV3c8REDkxGuhP/fMDHTHj7BuJrJ+JnJgKyohaP1zyWhxGZlNepBepT6ZqmSq83uSqTjTmSfGMzKdIT1TyVQlPcOdp0pGZiYZmZCemUl6hpKemUlahuYYzyQt03lNz3D+eaemZ554Tc8kzR0/npFJmrvseJrz+3Us7cTvmWc8LbPQl2wBIkJDPDeTVHLbBytHnriRpJK7LDoqzL26cOK1cmQYVbLGo8IIL2VfUOzJYlMqpWc4ScH7jz7rDzo1PfOk19SMDPdVs/1zyBo/7jUv3f02mObOS3P/waRlDeknpnP7p5TVMFce5MwRuR+X0kp+pWfoEnqFLKVpiHMVd1PmmczJbMPczESWZTYnrQSfQQ0Pdc74shJ5hNeZY1bSjwrPeXYZmn1+ePbLpN7tblHu/IoRYe7lUWf78soSgTFFkOEmhPRMJcNNFlnffNPzmPZ8I1Yl0/0m7HxrJtu3Y/X6Nq2Ks77XN+0M99u1KihZ38idcXW/nTv7cPbjLdtULssEQJyzjWxnGpxIGtWObifmwCIa7FvImQeTCdU0UkMr8VvNjvxW+zx21e5MasXTEJwzm6yzn7CQEM9ZT1iIc7aTdTYU7p55hYXIibOtkBD3jOzEeNY/e7t9u3hZiQljisD5h1Z+vyHmrynQzRk9fhh++ZaIjbOJ+Xk2MWu+dubXSXDuPmrUBeqeDWGRgQrWnCI7IzDG+E7VeaJ545ew8SvY/oPT9WZYBWjQERpe4Dy3UCcBgjaJlk52acgY4x9HD8Kvi+CX+U69oz3rnPlR1SDmPOdsoeEFUKupVUsNMLs0ZIzxjwrVnFtOW1zqTB/63UkKv8yDLfNh/SxnfnQdqN/RHc5xSmDYGUOpYYnAGFN8ok+H+P7OoOo80bzlW9i6wHl+Yc00Z72IaKh3tpMY6p0DdZOcstsmICwRGGP8QwRqNHKGpBuceQe3Owlh2/dO+8LcxwEFCXXaFep3cJLCmW2heoxdTioh1kZgjAmcowed8hfbvodtP8DOZZB+zFlWoQac1dZJCme1c8YrnxbQcMsyayMwxpROFapB0+7OAJCR5tyV9NuPsNMdNj/j3JkEUKWukxDOauucQZwRX25KYgSSJQJjTOkRGg5nJjpD0o3OvNQjsGulmxiSnSSxbuaJbaLrOI3PZ8Q5PbmdEe9cjrLGaJ9ZIjDGlG4RlaBBJ2fIkrIfdq9y+nLevRp+/wm2zIXMdGd5eEU4rZWTHE6PhdrNoXZL5+zB2h1OYonAGFP2VKzhPKPQqMuJeenHYc+GE4lh92qn5HbyWyfWqVADardwEsNpLd0E0QIqnx7UCcISgTGmfAiLhDrxzpBFFQ7thj3rvYYNJyeIqKpQq7nTh0PNxlCjMdRs4lxiCoIOfCwRGGPKLxGoUscZGnc9MV8VDv9xIjHsWQd7NzrPPKz8IPs+ouu4icErSVRvANUaQFSVkj0eP7FEYIwJPiLOw2/Rpzud8nhLPQL7tzhdfe7b5I5vgvWfQsre7OtWqO4871CtwYnkUL0BVIuBavXKTCE+SwTGGOMtopJ7B1LcycuOHnQSw8Ff4cCv7utWpz1iw2eQkeq1sjjPPVQ5C6rWPTF4T1c6DUIC34GNJQJjjPFVhWonnmPIKTMDDu3yShC/wl874M+d8Mc62PQ1pKVk3yYk3LlsFX0mRJ/hXIbK9uoOkVX82phticAYY4pDSOiJb/qce/JyVTh6AP7cAX/tdF6zxg/tdh6k2/QNpB46edvwik5C6Ho/xPUr9tAtERhjTEkQcW57rVgj+51NOR0/5FRxPbTLSRCHdzuvh3ZBxZp+Cc0SgTHGlCaR0c5Qq0mJvWXgWymMMcYElF8TgYj0FJENIrJJREbnsjxSRKa4y38QkRh/xmOMMeZkfksEIhIKjAd6Aa2AgSLSKsdqNwEHVLUJMA540l/xGGOMyZ0/zwjaA5tUdYuqpgKTgT451ukDvO2OTwUuFAnigh/GGBMA/kwEZwHbvaZ3uPNyXUdV04E/Af80ixtjjMlVmWgsFpHhIrJMRJbt2bMn0OEYY0y54s9EsBOo5zVd152X6zoiEgZUBfbl3JGqTlDVJFVNql27tp/CNcaY4OTPRLAUaCoiDUUkAhgAzMyxzkxgiDveD5ijZa0TZWOMKeP82nm9iFwCPAeEAm+q6lgReQRYpqozRSQKmAS0AfYDA1R1SwH73AP8WsSQagF7C1yrbLBjKX3Ky3GAHUtpdSrH0kBVc72k4tdEUNqIyDJVTQp0HMXBjqX0KS/HAXYspZW/jqVMNBYbY4zxH0sExhgT5IItEUwIdADFyI6l9CkvxwF2LKWVX44lqNoIjDHGnCzYzgiMMcbkUG4SQUGVTt11rhaRtSKyRkTe95o/REQ2usOQ3LYtSad4LBkissIdcj63UaJ8qD47zivWn0XkoNeyMvWZFHAspeYzceMp6Fjqi8hcEVkuIqvc28Czlt3nbrdBRC4u2chPirNIxyEiMSJy1OszeaXkoz8p1oKOpYGIfOMexzwRqeu17NT/VlS1zA84zylsBhoBEcBKoFWOdZoCy4Hq7vRp7msNYIv7Wt0dr14Wj8UdPxzoz8PX48ix/u04z5qUyc8kr2MpTZ9JIX6/JgC3uOOtgK1e4yuBSKChu5/QMngcMcBPgf4sCnksHwJD3PFuwCR3vFj+VsrLGYEvlU5vBsar6gEAVf3DnX8x8JWq7neXfQX0LKG4c3Mqx1Ka+HIc3gYCH7jjZfEz8eZ9LKWNL8eiQBV3vCrwmzveB5isqsdV9Rdgk7u/QDiV4yhtfDmWVsAcd3yu1/Ji+VspL4nAl0qnzYBmIrJIRBaLSM9CbFuSTuVYAKLcAn2LRaSvn2PNj88/VxFpgPMNM+sXvSx+JkCuxwKl5zMB345lDDBYRHYAn+Gc4fi6bUk5leMAaOheMvpWRDr7NdKC+XIsK4Er3fErgGgRqenjtgUKpj6Lw3AuqXTBKYA3X0TiAhpR0eV6LKp6EOcx8p0i0giYIyKrVXVz4EL1yQBgqqpmBDqQYpDbsZS1z2QgMFFV/09EOgKTRKR1oIMqgryOYxdQX1X3iUg7YIaIxKrqXwGNNn93AS+JyFBgPk7BzmL7eykvZwS+VDrdAcxU1TT3tPZnnH+mvmxbkk7lWFDVne7rFmAeTh2nQCjMz3UA2S+llMXPJEvOYylNnwn4diw3Af8DUNXvgSicGjel6XMp8nG4l7b2ufOTca7PN/N7xHkr8FhU9TdVvVJV2wD3u/MO+rKtTwLdUFJMjS1hOI0kDTnR2BKbY52ewNvueC2c06maOI0sv+A0tFR3x2uU0WOpDkR6zd9IPo2agT4Od70WwFbcZ1rceWXuM8nnWErNZ1KI36/PgaHueEuca+sCxJK9sXgLgWssPpXjqJ0VN04D7c7S/vvl/u6EuONjgUfc8WL5WwnIgfvph3kJzjfjzcD97rxHgN7uuADPAmuB1TiVTrO2vRGn4WsTcENZPRagkzu90n29qTQfhzs9Bngil23L1GeS17GUts/Ex9+vVsAiN+YVQA+vbe93t9sA9CqLxwFcBaxx5/0IXF4GPpN+OF8ifgZex/1y4S475b8Ve7LYGGOCXHlpIzDGGFNElgiMMSbIWSIwxpggZ4nAGGOCnCUCY4wJcpYITJklIodzTA8VkZfc8X+41VlXuVUbG3itFysic9xqjxtF5EERET/G+ZmIVCtgnXkiclJftCKS6F39M8eyc93jWyYiTd151URktojY37bxmf2ymPJqOZCkqvHAVOApABGpAMzEud+/OZCAc6//rf4KRFUvUecp0KJIxLnHPDf/dJf9HRjhznsAeFxVM4v4fiYIWSIw5ZKqzlXVFHdyMc6j9wDXAotUdba7XgowEsi134csIjJeRHq749NF5E13/EYRGeuODxaRJW6N+1dFJNSdv1VEarnjD7pnIgtF5AMRucvrbfq72/8sIp1FJALnoaJr3H1ekyOsNKCiO6SJSGOgnqrOK/QPzAS1YCo6Z8qfCiKywmu6Bs63/Zxuwik3AE6ZhGTvhaq6WUQqi0gVzbvw2AKgs7v/s4A67vzOwGQRaQlcA5yrqmki8jIwCHgnawcicjbOU60JQDjOU63esYSpanv3UtBDqnqRiPwb58xmZC4x/cfd/1HgOuAZnDMCYwrFEoEpy46qamLWhFuZMdt1dhEZ7M674BTfawHwdxFphVPao7qI1AE6AqOAIUA7YKnb3FAByNlPxLnAx6p6DDgmIp/kWD7NfU3G6TwlX6q6AugAICLn41TVFBGZgnO28E9V/b1wh2mCkSUCU26JyEU4tXEuUNXj7uy1wPk51muE04tYnmWI1SkjXQ2n4N98nLOPq93tDrmNzW+r6n2nEHJWjBkU4m/Tfe8HcCqfvgjcg5NIRuFWqjQmP9ZGYMolEWkDvIpTtMv7m/l7wHlukshqPH6BE43J7UXknZz7cy3GaZidj3OGcJf7CvAN0E9ETnP3U8P7TiXXIuByEYkSkcrAZT4cyiEguoB1rgc+U9X9OO0Fme5Q0Yf9G2OJwJRbTwOVgQ/Fq9N4VT2K083fAyKyAaci6FLgJXe7+jjX3HOzAOc6/iac6/s13Hmo6lqcb+WzRWQVTpeBdbw3VtWlOG0Mq3DaLFYDfxZwHHOBVnk0FiMiFYGhwHh31rM4vXE9BwS8U3ZTNlj1UWO8iMjTOB2Dr/LT/iur6mH3H/h8YLiq/uiP9zLGV5YIjClBIvI+Tp38KJw2hf8EOCRjLBEYY0ywszYCY4wJcpYIjDEmyFkiMMaYIGeJwBhjgpwlAmOMCXKWCIwxJsj9P/Q4gJBUeW+DAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example 7. Creation of a Pervaporation object and modelling \n",
    "# an non-ideal diffusion curve \n",
    "# (permeance are assumed dependent on feed composition) of \n",
    "# Romakon PM 102 membranes at 335.15 K\n",
    "\n",
    "from pyvaporation import (\n",
    "    Membrane, \n",
    "    Pervaporation,\n",
    "    Mixtures, \n",
    "    Composition,\n",
    "    CompositionType,\n",
    ")\n",
    "\n",
    "romakon_pm_102 = Membrane.load(\"tests/default_membranes/RomakonPM_102\")\n",
    "\n",
    "h2o_etoh = Mixtures.H2O_EtOH\n",
    "curve_set = romakon_pm_102.diffusion_curve_sets[0]\n",
    "\n",
    "pervaporation = Pervaporation(membrane=romakon_pm_102, mixture=h2o_etoh)\n",
    "\n",
    "initial_feed = Composition(p=0.9, type=CompositionType.weight)\n",
    "\n",
    "curve = pervaporation.non_ideal_diffusion_curve(\n",
    "   feed_temperature=335.15,\n",
    "   diffusion_curve_set=curve_set,\n",
    "   initial_feed_composition=initial_feed,\n",
    "   delta_composition= -0.01,\n",
    "   number_of_steps= 30,\n",
    "   n_first=5,\n",
    ")\n",
    "\n",
    "curve.plot(curve.permeances, 'Permeance, kg/(m2*h*kPa) ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "vedOC899m2nk",
    "outputId": "27fb16a2-2397-4cdf-94b0-57923d565d82"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example 8. Loading of a Membrane object; \n",
    "# modelling non-ideal diffusion curves using \n",
    "# various “dry” initial permeance values of \n",
    "# Pervap 4100 membrane\n",
    "\n",
    "from pyvaporation import (\n",
    "    Membrane, \n",
    "    Pervaporation,\n",
    "    Mixtures, \n",
    "    Composition,\n",
    "    CompositionType,\n",
    "    Permeance,\n",
    "    Units,\n",
    "    DiffusionCurve\n",
    ")\n",
    "\n",
    "pervap_4100 = Membrane.load(path=\"tests/default_membranes/Pervap_4100\")\n",
    "\n",
    "pervaporation = Pervaporation(membrane=pervap_4100, mixture=Mixtures.H2O_EtOH)\n",
    "\n",
    "modelled_curve_50 = pervaporation.non_ideal_diffusion_curve(\n",
    "   diffusion_curve_set=pervaporation.membrane.diffusion_curve_sets[1],\n",
    "   feed_temperature=368.15,\n",
    "   initial_feed_composition=Composition(p=0.4745, type=CompositionType.weight),\n",
    "   delta_composition=-0.005,\n",
    "   number_of_steps=90,\n",
    ")\n",
    "\n",
    "modelled_curve_25 = pervaporation.non_ideal_diffusion_curve(\n",
    "    diffusion_curve_set=pervaporation.membrane.diffusion_curve_sets[1],\n",
    "    feed_temperature=368.15,\n",
    "    initial_feed_composition=Composition(p=0.2570, type=CompositionType.weight),\n",
    "    initial_permeances=(\n",
    "        Permeance(value=3598.018023, units=Units.GPU),\n",
    "        Permeance(value=13.01528204, units=Units.GPU),\n",
    "    ),\n",
    "    delta_composition=-0.005,\n",
    "    number_of_steps=50,\n",
    ")\n",
    "\n",
    "modelled_curve_15 = pervaporation.non_ideal_diffusion_curve(\n",
    "    diffusion_curve_set=pervaporation.membrane.diffusion_curve_sets[1],\n",
    "    feed_temperature=368.15,\n",
    "    initial_feed_composition=Composition(p=0.12686, type=CompositionType.weight),\n",
    "    delta_composition=-0.0025,\n",
    "    number_of_steps=48,\n",
    "    initial_permeances=(\n",
    "        Permeance(value=2586.349948, units=Units.GPU),\n",
    "        Permeance(value=1.677562465, units=Units.GPU),\n",
    "    ),\n",
    ")\n",
    "\n",
    "modelled_curve_6 = pervaporation.non_ideal_diffusion_curve(\n",
    "    diffusion_curve_set=pervaporation.membrane.diffusion_curve_sets[1],\n",
    "    feed_temperature=368.15,\n",
    "    initial_feed_composition=Composition(p=0.05961, type=CompositionType.weight),\n",
    "    delta_composition=-0.001,\n",
    "    number_of_steps=50,\n",
    "    initial_permeances=(\n",
    "        Permeance(value=1133.045025, units=Units.GPU),\n",
    "        Permeance(value=0.2100162934, units=Units.GPU),\n",
    "    ),\n",
    ")\n",
    "\n",
    "compositions_25 = [\n",
    "    0.0053734337,\n",
    "    0.0308656506,\n",
    "    0.0376454955,\n",
    "    0.1789374627,\n",
    "    0.2239556327,\n",
    "    0.2570412757,\n",
    "]\n",
    "permeances_25_h2o = [\n",
    "    1212.734594,\n",
    "    1561.012113,\n",
    "    1703.565186,\n",
    "    3202.285314,\n",
    "    3329.107307,\n",
    "    3598.018023,\n",
    "]\n",
    "permeances_25_etoh = [\n",
    "    1.778198976,\n",
    "    2.426177206,\n",
    "    2.522262532,\n",
    "    10.21010056,\n",
    "    11.92617288,\n",
    "    13.01528204,\n",
    "]\n",
    "experimental_25 = DiffusionCurve(\n",
    "    mixture=Mixtures.H2O_EtOH,\n",
    "    membrane_name=\"Pervap 4100\",\n",
    "    feed_temperature=368.15,\n",
    "    feed_compositions=[\n",
    "        Composition(p=composition, type=CompositionType.weight)\n",
    "        for composition in compositions_25\n",
    "    ],\n",
    "    permeances=[\n",
    "        (\n",
    "            Permeance(value=permeances_25_h2o[i], units=Units.GPU),\n",
    "            Permeance(value=permeances_25_etoh[i], units=Units.GPU),\n",
    "        )\n",
    "        for i in range(len(compositions_25))\n",
    "    ],\n",
    ")\n",
    "\n",
    "\n",
    "compositions_15 = [\n",
    "    1.188208494, \n",
    "    1.513641048,\n",
    "    9.052828551,\n",
    "    11.22237891,\n",
    "    12.68682541\n",
    "]\n",
    "\n",
    "permeances_15_h2o = [\n",
    "    970.0097218,\n",
    "    1018.264961,\n",
    "    2171.612439,\n",
    "    2171.612439,\n",
    "    2586.349948,\n",
    "]\n",
    "permeances_15_etoh = [\n",
    "    0.513111450,\n",
    "    0.479394567,\n",
    "    1.115750557,\n",
    "    1.464338579,\n",
    "    1.677562465,\n",
    "]\n",
    "\n",
    "experimental_15 = DiffusionCurve(\n",
    "    mixture=Mixtures.H2O_EtOH,\n",
    "    membrane_name=\"Pervap 4100\",\n",
    "    feed_temperature=368.15,\n",
    "    feed_compositions=[\n",
    "        Composition(p=composition / 100, type=CompositionType.weight)\n",
    "        for composition in compositions_15\n",
    "    ],\n",
    "    permeances=[\n",
    "        (\n",
    "            Permeance(value=permeances_15_h2o[i], units=Units.GPU),\n",
    "            Permeance(value=permeances_15_etoh[i], units=Units.GPU),\n",
    "        )\n",
    "        for i in range(len(compositions_15))\n",
    "    ],\n",
    ")\n",
    "\n",
    "compositions_6 = [\n",
    "    1.703476704,\n",
    "    1.94755112, \n",
    "    4.876444107,\n",
    "    5.473070456,\n",
    "    5.961219287\n",
    "]\n",
    "\n",
    "permeances_6_h2o = [\n",
    "    746.3089286,\n",
    "    838.5364554,\n",
    "    1038.232656,\n",
    "    1122.096546,\n",
    "    1133.045025\n",
    "]\n",
    "\n",
    "permeances_6_etoh = [\n",
    "    0.2100162934,\n",
    "    0.2100162934,\n",
    "    0.2141346138,\n",
    "    0.2550303347,\n",
    "    0.2100162934,\n",
    "]\n",
    "\n",
    "experimental_6 = DiffusionCurve(\n",
    "    mixture=Mixtures.H2O_EtOH,\n",
    "    membrane_name=\"Pervap 4100\",\n",
    "    feed_temperature=368.15,\n",
    "    feed_compositions=[\n",
    "        Composition(p=composition / 100, type=CompositionType.weight)\n",
    "        for composition in compositions_6\n",
    "    ],\n",
    "    permeances=[\n",
    "        (\n",
    "            Permeance(value=permeances_6_h2o[i], units=Units.GPU),\n",
    "            Permeance(value=permeances_6_etoh[i], units=Units.GPU),\n",
    "        )\n",
    "        for i in range(len(compositions_6))\n",
    "    ],\n",
    ")\n",
    "\n",
    "compositions_50 = [\n",
    "    0.0240858057,\n",
    "    0.2326338341,\n",
    "    0.2532445626,\n",
    "    0.2754824538,\n",
    "    0.4110793513,\n",
    "    0.4284357542,\n",
    "    0.4517584206,\n",
    "    0.4745386994,\n",
    "]\n",
    "\n",
    "permeances_50_h2o = [\n",
    "    2688.778689,\n",
    "    5623.966085,\n",
    "    5790.19941,\n",
    "    6078.245449,\n",
    "    7525.770499,\n",
    "    7823.818024,\n",
    "    7525.770499,\n",
    "    7525.770499,\n",
    "]\n",
    "permeances_50_etoh = [\n",
    "    23.76315076,\n",
    "    107.036062,\n",
    "    120.2633877,\n",
    "    132.5265399,\n",
    "    207.1487026,\n",
    "    215.3525347,\n",
    "    205.1470494,\n",
    "    205.1470494,\n",
    "]\n",
    "\n",
    "experimental_50 = DiffusionCurve(\n",
    "    mixture=Mixtures.H2O_EtOH,\n",
    "    membrane_name=\"Pervap 4100\",\n",
    "    feed_temperature=368.15,\n",
    "    feed_compositions=[\n",
    "        Composition(p=composition, type=CompositionType.weight)\n",
    "        for composition in compositions_50\n",
    "    ],\n",
    "    permeances=[\n",
    "        (\n",
    "            Permeance(value=permeances_50_h2o[i], units=Units.GPU),\n",
    "            Permeance(value=permeances_50_etoh[i], units=Units.GPU),\n",
    "        )\n",
    "        for i in range(len(compositions_50))\n",
    "    ],\n",
    ")\n",
    "\n",
    "\n",
    "fig1, ax = plt.subplots()\n",
    "\n",
    "ax.plot(\n",
    "    [c.first for c in modelled_curve_50.feed_compositions],\n",
    "    [\n",
    "        p[0].convert(to_units=\"GPU\", component=Components.H2O).value\n",
    "        for p in modelled_curve_50.permeances\n",
    "    ], \n",
    "    label=\"H2O,\\nFR = 1.00  RMSD 1.95 %\",\n",
    ")\n",
    "ax.scatter(\n",
    "    [c.first for c in experimental_50.feed_compositions],\n",
    "    [\n",
    "        p[0].convert(to_units=\"GPU\", component=Components.H2O).value\n",
    "        for p in experimental_50.permeances\n",
    "    ],\n",
    ")\n",
    "ax.plot(\n",
    "    [c.first for c in modelled_curve_50.feed_compositions],\n",
    "    [\n",
    "        p[1].convert(to_units=\"GPU\", component=Components.EtOH).value\n",
    "        for p in modelled_curve_50.permeances\n",
    "    ],\n",
    "    color=\"y\", label=\"EtOH,\\nFR = 1.00  RMSD 2.88 %\",\n",
    ")\n",
    "ax.scatter(\n",
    "    [c.first for c in experimental_50.feed_compositions],\n",
    "    [\n",
    "        p[1].convert(to_units=\"GPU\", component=Components.EtOH).value\n",
    "        for p in experimental_50.permeances\n",
    "    ],\n",
    "    color=\"y\",\n",
    ")\n",
    "\n",
    "ax.set_xlabel(\"Water weight fraction, %\")\n",
    "ax.set_ylabel(\"Permeance, GPU\")\n",
    "fig1.suptitle(\"Base curve\")\n",
    "ax.set_yscale(\"log\")\n",
    "ax.legend()\n",
    "plt.show()\n",
    "\n",
    "fig2, ax = plt.subplots()\n",
    "ax.plot(\n",
    "    [c.first for c in modelled_curve_25.feed_compositions],\n",
    "    [\n",
    "        p[0].convert(to_units=\"GPU\", component=Components.H2O).value\n",
    "        for p in modelled_curve_25.permeances\n",
    "    ], \n",
    "    label=\"H2O,\\nFR = 0.607  RMSD 6.96 %\",\n",
    ")\n",
    "ax.scatter(\n",
    "    [c.first for c in experimental_25.feed_compositions],\n",
    "    [\n",
    "        p[0].convert(to_units=\"GPU\", component=Components.H2O).value\n",
    "        for p in experimental_25.permeances\n",
    "    ],\n",
    ")\n",
    "ax.plot(\n",
    "    [c.first for c in modelled_curve_25.feed_compositions],\n",
    "    [\n",
    "        p[1].convert(to_units=\"GPU\", component=Components.EtOH).value\n",
    "        for p in modelled_curve_25.permeances\n",
    "    ],\n",
    "    color=\"y\", label=\"EtOH,\\nFR = 0.105  RMSD 16.90 %\",\n",
    ")\n",
    "ax.scatter(\n",
    "    [c.first for c in experimental_25.feed_compositions],\n",
    "    [\n",
    "        p[1].convert(to_units=\"GPU\", component=Components.EtOH).value\n",
    "        for p in experimental_25.permeances\n",
    "    ],\n",
    "    color=\"y\",\n",
    ")\n",
    "ax.set_xlabel(\"Water weight fraction, %\")\n",
    "ax.set_ylabel(\"Permeance, GPU\")\n",
    "ax.set_yscale(\"log\")\n",
    "ax.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "fig3, ax = plt.subplots()\n",
    "ax.plot(\n",
    "    [c.first for c in modelled_curve_15.feed_compositions],\n",
    "    [\n",
    "        p[0].convert(to_units=\"GPU\", component=Components.H2O).value\n",
    "        for p in modelled_curve_15.permeances\n",
    "    ], \n",
    "    label=\"H2O,\\nFR = 0.660  RMSD 22.83 %\",\n",
    ")\n",
    "ax.scatter(\n",
    "    [c.first for c in experimental_15.feed_compositions],\n",
    "    [\n",
    "        p[0].convert(to_units=\"GPU\", component=Components.H2O).value\n",
    "        for p in experimental_15.permeances\n",
    "    ],\n",
    ")\n",
    "ax.plot(\n",
    "    [c.first for c in modelled_curve_15.feed_compositions],\n",
    "    [\n",
    "        p[1].convert(to_units=\"GPU\", component=Components.EtOH).value\n",
    "        for p in modelled_curve_15.permeances\n",
    "    ],\n",
    "    color=\"y\", label=\"EtOH,\\nFR = 0.034  RMSD 3.06 %\",\n",
    ")\n",
    "ax.scatter(\n",
    "    [c.first for c in experimental_15.feed_compositions],\n",
    "    [\n",
    "        p[1].convert(to_units=\"GPU\", component=Components.EtOH).value\n",
    "        for p in experimental_15.permeances\n",
    "    ],\n",
    "    color=\"y\",\n",
    ")\n",
    "ax.set_xlabel(\"Water weight fraction, %\")\n",
    "ax.set_ylabel(\"Permeance, GPU\")\n",
    "ax.set_yscale(\"log\")\n",
    "ax.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "fig4, ax = plt.subplots()\n",
    "ax.plot(\n",
    "    [c.first for c in modelled_curve_6.feed_compositions],\n",
    "    [\n",
    "        p[0].convert(to_units=\"GPU\", component=Components.H2O).value\n",
    "        for p in modelled_curve_6.permeances\n",
    "    ], \n",
    "    label=\"H2O,\\nFR = 0.366  RMSD 10.01 %\",\n",
    ")\n",
    "ax.scatter(\n",
    "    [c.first for c in experimental_6.feed_compositions],\n",
    "    [\n",
    "        p[0].convert(to_units=\"GPU\", component=Components.H2O).value\n",
    "        for p in experimental_6.permeances\n",
    "    ],\n",
    ")\n",
    "ax.plot(\n",
    "    [c.first for c in modelled_curve_6.feed_compositions],\n",
    "    [\n",
    "        p[1].convert(to_units=\"GPU\", component=Components.iPOH).value\n",
    "        for p in modelled_curve_6.permeances\n",
    "    ],\n",
    "    color=\"y\", label=\"EtOH,\\nFR = 0.008  RMSD 26.05 %\",\n",
    ")\n",
    "ax.scatter(\n",
    "    [c.first for c in experimental_6.feed_compositions],\n",
    "    [\n",
    "        p[1].convert(to_units=\"GPU\", component=Components.iPOH).value\n",
    "        for p in experimental_6.permeances\n",
    "    ],\n",
    "    color=\"y\",\n",
    ")\n",
    "ax.set_xlabel(\"Water weight fraction, %\")\n",
    "ax.set_ylabel(\"Permeance, GPU\")\n",
    "ax.set_yscale(\"log\")\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 541
    },
    "id": "felEZWFymiBr",
    "outputId": "fee84477-2d25-4352-f2b6-3d254b1c40f3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example 9. Creation of a ProcessModel object; \n",
    "# modelling isothermal ideal process of H2O/EtOH mixture separation \n",
    "# with a Romakon PM 102 membrane \n",
    "\n",
    "from pyvaporation import Membrane, Mixtures, Pervaporation, Conditions\n",
    "\n",
    "romakon_pm_102 = Membrane.load(\"tests/default_membranes/RomakonPM_102\")\n",
    "\n",
    "h2o_etoh = Mixtures.H2O_EtOH\n",
    "\n",
    "pervaporation = Pervaporation(membrane=romakon_pm_102, mixture=h2o_etoh)\n",
    "\n",
    "conditions = Conditions(\n",
    "    membrane_area=0.0048,\n",
    "    initial_feed_temperature=335.15,\n",
    "    initial_feed_amount=0.200,\n",
    "    initial_feed_composition=Composition(p=0.94, type=CompositionType.weight)\n",
    ")\n",
    "\n",
    "model = pervaporation.ideal_isothermal_process(\n",
    "    number_of_steps=50,\n",
    "    delta_hours=0.2,\n",
    "    conditions=conditions,\n",
    ")\n",
    "\n",
    "model.plot(model.feed_compositions, 'Water fraction in feed, wt%')\n",
    "model.plot(model.feed_mass, 'Feed amount, kg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qXTde3ZQnbwD",
    "outputId": "4af3a0b5-ac3d-4fcd-a67f-01528f23cb44"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Partial fluxes\n",
      "(0.25079337140976277, 0.1637411760363952)\n",
      "(0.255980578053858, 0.16142891854790636)\n",
      "(0.255786119219273, 0.16151387957176908)\n",
      "(0.2557618303846792, 0.16152450094117343)\n",
      "(0.2557587951348898, 0.16152582838440846)\n"
     ]
    }
   ],
   "source": [
    "# Example 10. Illustration of the influence of the precision value \n",
    "# on the obtained flux values \n",
    "\n",
    "from pyvaporation import (\n",
    "    Membrane,\n",
    "    IdealExperiment,\n",
    "    IdealExperiments,\n",
    "    Pervaporation\n",
    ")\n",
    "\n",
    "experiment_meoh_1 = IdealExperiment(\n",
    "    name=\"PAI_SPI_1(wt%)_asym\",\n",
    "    temperature=293.15,\n",
    "    component=Components.MeOH,\n",
    "    permeance=Permeance(0.0267),\n",
    "    activation_energy=20904.65064,\n",
    ")\n",
    "\n",
    "experiment_meoh_2 = IdealExperiment(\n",
    "    name=\"PAI_SPI_1(wt%)_asym\",\n",
    "    temperature=313.15,\n",
    "    component=Components.MeOH,\n",
    "    permeance=Permeance(0.04498),\n",
    "    activation_energy=20904.65064,\n",
    ")\n",
    "\n",
    "experiment_mtbe_1 = IdealExperiment(\n",
    "    name=\"PAI_SPI_1(wt%)_asym\",\n",
    "    temperature=293.15,\n",
    "    component=Components.MTBE,\n",
    "    permeance=Permeance(0.01077),\n",
    "    activation_energy=-39656.76576,\n",
    ")\n",
    "\n",
    "experiment_mtbe_2 = IdealExperiment(\n",
    "    name=\"PAI_SPI_1(wt%)_asym\",\n",
    "    temperature=313.15,\n",
    "    component=Components.MTBE,\n",
    "    permeance=Permeance(0.00425),\n",
    "    activation_energy=-39656.76576,\n",
    ")\n",
    "\n",
    "ideal_experiments = IdealExperiments(\n",
    "    experiments=[\n",
    "        experiment_meoh_1,\n",
    "        experiment_meoh_2,\n",
    "        experiment_mtbe_1,\n",
    "        experiment_mtbe_2,\n",
    "    ]\n",
    ")\n",
    "\n",
    "pai_spi = Membrane(ideal_experiments=ideal_experiments, name=\"PAI_SPI_1(wt%)_asym\")\n",
    "meoh_mtbe = Mixtures.MeOH_MTBE\n",
    "\n",
    "pervaporation = Pervaporation(membrane=pai_spi, mixture=meoh_mtbe)\n",
    "\n",
    "precision = [1e-1, 1e-2, 1e-3, 1e-4, 1e-5]\n",
    "composition = Composition(p=0.1, type=CompositionType.weight)\n",
    "\n",
    "print('Partial fluxes')\n",
    "for p in precision:\n",
    "    print(\n",
    "       pervaporation.calculate_partial_fluxes(\n",
    "           feed_temperature=313.15,\n",
    "           composition=composition,\n",
    "           permeate_temperature=293.15,\n",
    "           precision=p\n",
    "       )\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 599
    },
    "id": "n5Nh0rhOpwq3",
    "outputId": "7dcc50a0-278f-407f-d21c-ceaa2aca7473"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example 11. Example of fitting experimental data with find_best_fit() function\n",
    "\n",
    "from pyvaporation import (\n",
    "    DiffusionCurve, Mixtures, Composition, \n",
    "    CompositionType, Measurements, find_best_fit\n",
    ")\n",
    "\n",
    "compositions = [\n",
    "    0.9999,\n",
    "    0.883,\n",
    "    0.756,\n",
    "    0.658,\n",
    "    0.490,\n",
    "    0.430,\n",
    "    0.401,\n",
    "    0.280,\n",
    "    0.240,\n",
    "    0.044,\n",
    "]\n",
    "\n",
    "flux_h2o_40 = [\n",
    "    0.7848,\n",
    "    0.3480,\n",
    "    0.2566,\n",
    "    0.4406,\n",
    "    0.3340,\n",
    "    0.2773,\n",
    "    0.2816,\n",
    "    0.2292,\n",
    "    0.1066,\n",
    "    0.0247,\n",
    "]\n",
    "\n",
    "flux_etoh_40 = [\n",
    "    0,\n",
    "    0.0017,\n",
    "    0.0013,\n",
    "    0.0136,\n",
    "    0.0684,\n",
    "    0.0568,\n",
    "    0.0577,\n",
    "    0.0573,\n",
    "    0.0457,\n",
    "    0.0165,\n",
    "]\n",
    "\n",
    "spi_diffusion_curve = DiffusionCurve(\n",
    "   mixture=Mixtures.H2O_EtOH,\n",
    "   membrane_name=\"SPI dense\",\n",
    "   feed_temperature=313.15,\n",
    "   feed_compositions=[\n",
    "       Composition(p=c, type=CompositionType.weight) for c in compositions\n",
    "   ],\n",
    "   partial_fluxes=[\n",
    "       (flux_h2o_40[i], flux_etoh_40[i]) for i in range(len(compositions))\n",
    "   ],\n",
    ")\n",
    "\n",
    "measurements_h2o = Measurements.from_diffusion_curve_first(spi_diffusion_curve)\n",
    "measurements_etoh = Measurements.from_diffusion_curve_second(spi_diffusion_curve)\n",
    "\n",
    "fit_h2o = find_best_fit(measurements_h2o, n=9)\n",
    "fit_etoh = find_best_fit(measurements_etoh)\n",
    "\n",
    "fit_h2o = find_best_fit(measurements_h2o, n=9)\n",
    "fit_etoh = find_best_fit(measurements_etoh)\n",
    "\n",
    "fit_h2o.plot(measurements_h2o, temperature=313.15)\n",
    "fit_etoh.plot(measurements_etoh, temperature=313.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 544
    },
    "id": "0utLKYBoFxqJ",
    "outputId": "06e98773-0d6e-4b2a-93f0-1465412d9d7d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example 12. Modelling of a non-ideal isothermal process\n",
    "# described in the literature using the membrane performance data \n",
    "# provided in an independent source\n",
    "\n",
    "from pyvaporation import Membrane, Mixtures, Pervaporation, Conditions\n",
    "\n",
    "membrane = Membrane.load(\"tests/default_membranes/Pervap_4101\")\n",
    "\n",
    "pv = Pervaporation(\n",
    "    membrane=membrane,\n",
    "    mixture=Mixtures.H2O_EtOH,\n",
    ")\n",
    "\n",
    "con = Conditions(\n",
    "    membrane_area=0.017,\n",
    "    initial_feed_temperature=368.15,\n",
    "    initial_feed_amount=1.5,\n",
    "    initial_feed_composition=Composition(p=0.1, type=CompositionType.weight),\n",
    "    permeate_pressure=0,\n",
    ")\n",
    "\n",
    "model = pv.non_ideal_isothermal_process(\n",
    "    conditions=con,\n",
    "    diffusion_curve_set=membrane.diffusion_curve_sets[0],\n",
    "    initial_permeances=(\n",
    "       Permeance(0.0153),\n",
    "       Permeance(0.00000632),\n",
    "    ),\n",
    "    number_of_steps=50,\n",
    "    delta_hours=0.2,\n",
    ")\n",
    "\n",
    "experiment_time_hours = [\n",
    "    0,\n",
    "    0.5163,\n",
    "    1.0148,\n",
    "    1.5133,\n",
    "    2.0118,\n",
    "    3.0087,\n",
    "    4.0057,\n",
    "    5.0173,\n",
    "    6.0143,\n",
    "    7.0112,\n",
    "    8.0082,\n",
    "    9.0052,\n",
    "    10,\n",
    "]\n",
    "\n",
    "experiment_water_fraction = [\n",
    "    0.1,\n",
    "    0.0946,\n",
    "    0.0938,\n",
    "    0.0907,\n",
    "    0.0887,\n",
    "    0.0842,\n",
    "    0.0791,\n",
    "    0.0730,\n",
    "    0.0702,\n",
    "    0.0669,\n",
    "    0.0625,\n",
    "    0.0588,\n",
    "    0.0573,\n",
    "]\n",
    "\n",
    "experiment_water_flux = [\n",
    "    0.4863,\n",
    "    0.5209,\n",
    "    0.5301,\n",
    "    0.5865,\n",
    "    0.4517,\n",
    "    0.4736,\n",
    "    0.4353,\n",
    "    0.3789,\n",
    "    0.3770,\n",
    "    0.3370,\n",
    "    0.2987,\n",
    "    0.2823,\n",
    "    0.2823,\n",
    "]\n",
    "\n",
    "fig1, ax = plt.subplots()\n",
    "ax.plot(\n",
    "    model.time,\n",
    "    [c.first for c in model.feed_compositions], \n",
    "    label=\"Model\",\n",
    ")\n",
    "\n",
    "ax.scatter(\n",
    "    experiment_time_hours,\n",
    "    experiment_water_fraction,\n",
    "    label=\"Experiment\",\n",
    ")\n",
    "\n",
    "ax.set_xlabel(\"Process time, hours\")\n",
    "ax.set_ylabel(\"Water fraction in feed, wt%\")\n",
    "ax.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "fig2, ax = plt.subplots()\n",
    "ax.plot(\n",
    "    model.time,\n",
    "    [f[0] for f in model.partial_fluxes], \n",
    "    label=\"Model\", \n",
    "    color=\"y\"\n",
    ")\n",
    "\n",
    "ax.scatter(\n",
    "    experiment_time_hours,\n",
    "    experiment_water_flux,\n",
    "    label=\"Experiment\",\n",
    "    color=\"y\"\n",
    ")\n",
    "\n",
    "ax.set_xlabel(\"Process time, hours\")\n",
    "ax.set_ylabel(\"Flux, kg / (m2 * h)\")\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "M5MsCtkCIE0u",
    "outputId": "4f6f9cf6-c6aa-480c-afcd-77807d37d2b4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example 13. Modelling a non-ideal isothermal process of \n",
    "# water/ethanol mixture concentration with Romakon-PM 102 membranes\n",
    "\n",
    "from pyvaporation import Membrane, Mixtures, Pervaporation, Conditions\n",
    "\n",
    "membrane = Membrane.load(\"tests/default_membranes/RomakonPM_102\")\n",
    "\n",
    "pv = Pervaporation(\n",
    "    membrane=membrane,\n",
    "    mixture=Mixtures.H2O_EtOH,\n",
    ")\n",
    "\n",
    "initial_composition = Composition(p=0.9134, type=CompositionType.weight)\n",
    "\n",
    "conditions = Conditions(\n",
    "    membrane_area=2.55e-3,\n",
    "    initial_feed_temperature=341.95,\n",
    "    initial_feed_amount=0.06,\n",
    "    initial_feed_composition=initial_composition,\n",
    "    permeate_pressure=1,\n",
    ")\n",
    "\n",
    "model = pv.non_ideal_isothermal_process(\n",
    "    conditions=conditions,\n",
    "    diffusion_curve_set=membrane.diffusion_curve_sets[0],\n",
    "    number_of_steps=50,\n",
    "    delta_hours=0.1,\n",
    ")\n",
    "\n",
    "exp_time = [\n",
    "    0,\n",
    "    2.60,\n",
    "    3.62,\n",
    "    4.28,\n",
    "    4.78,\n",
    "]\n",
    "exp_fraction = [0.0866, 0.1176, 0.1472, 0.1630, 0.2098]\n",
    "\n",
    "fig4, ax = plt.subplots()\n",
    "ax.plot(\n",
    "    model.time,\n",
    "    [c.second for c in model.feed_compositions], \n",
    "    label=\"Model\",\n",
    ")\n",
    "\n",
    "ax.scatter(\n",
    "    exp_time,\n",
    "    exp_fraction,\n",
    "    label=\"Experiment\",\n",
    ")\n",
    "\n",
    "ax.set_xlabel(\"Process time, hours\")\n",
    "ax.set_ylabel(\"EtOH in feed, wt%\")\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 541
    },
    "id": "hEaOhrQzJ22-",
    "outputId": "8b7045b2-ee7f-4ae2-f822-4a42397b67cb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example 14. Modelling of a non-ideal non-isothermal process\n",
    "# described in the literature\n",
    "\n",
    "from pyvaporation import Membrane, Mixtures, Pervaporation, Conditions\n",
    "\n",
    "membrane = Membrane.load(\"tests/default_membranes/Chang_et_al_1998\")\n",
    "\n",
    "pv = Pervaporation(\n",
    "    membrane=membrane,\n",
    "    mixture=Mixtures.H2O_EtOH,\n",
    ")\n",
    "\n",
    "model_temp = []\n",
    "model_area = []\n",
    "model_fraction = [0.062]\n",
    "for i in range(4):\n",
    "    conditions = Conditions(\n",
    "        membrane_area=1,\n",
    "        initial_feed_temperature=368.475,\n",
    "        initial_feed_amount=12.106,\n",
    "        initial_feed_composition=Composition(p=model_fraction[i], type=CompositionType.weight),\n",
    "        permeate_pressure=1.3,\n",
    "    )\n",
    "\n",
    "    model = pv.non_ideal_non_isothermal_process(\n",
    "        conditions=conditions,\n",
    "        diffusion_curve_set=membrane.diffusion_curve_sets[0],\n",
    "        number_of_steps=11,\n",
    "        delta_hours=0.1,\n",
    "    )\n",
    "    area=[t+i for t in model.time]\n",
    "    model_temp.extend(model.feed_temperature)\n",
    "    model_area.extend(area)\n",
    "    model_fraction.append(model.feed_compositions[-1].first)\n",
    "\n",
    "\n",
    "exp_area_initial_temp=[0,1,2,3]\n",
    "exp_initial_temp=[94.09241735, 94.76834214,96.18163942,96.18163942]\n",
    "\n",
    "exp_area_ret_temp=[1,2,3,4]\n",
    "exp_ret_temp=[75.59665724, 78.66904264, 84.62947032, 92.67912007]\n",
    "\n",
    "exp_area_fraction=[0,1,2,3,4]\n",
    "exp_fraction=[\n",
    "    0.06162740899,\n",
    "    0.04331905782,\n",
    "    0.02886509636,\n",
    "    0.01794432548,\n",
    "    0.01141327623,\n",
    "]\n",
    "\n",
    "fig41, ax = plt.subplots()\n",
    "ax.plot(\n",
    "    model_area,\n",
    "    [(t-273.15) for t in model_temp], \n",
    "    label=\"Model\"\n",
    ")\n",
    "\n",
    "ax.scatter(\n",
    "    exp_area_initial_temp,\n",
    "    exp_initial_temp,\n",
    "    label=\"Experiment\",\n",
    "    color=\"y\",\n",
    ")\n",
    "\n",
    "ax.scatter(\n",
    "    exp_area_ret_temp,\n",
    "    exp_ret_temp,\n",
    "    color=\"y\",\n",
    ")\n",
    "\n",
    "ax.set_xlabel(\"Membrane area, m2\")\n",
    "ax.set_ylabel(\"Feed temperature, oC\")\n",
    "ax.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "fig42, ax = plt.subplots()\n",
    "ax.plot(\n",
    "    exp_area_fraction,\n",
    "    model_fraction, label=\"Model\"\n",
    ")\n",
    "\n",
    "ax.scatter(\n",
    "    exp_area_fraction,\n",
    "    exp_fraction,\n",
    "    label=\"Experiment\"\n",
    ")\n",
    "\n",
    "ax.set_xlabel(\"Membrane area, m2\")\n",
    "ax.set_ylabel(\"Water in feed, wt%\")\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "PyVaporation-Demo.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}