examples/linearization.ipynb

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    " <table style=\"width:100%\">\n",
    " <tr>\n",
    "    <td> <img src=\"https://upload.wikimedia.org/wikipedia/en/thumb/e/e0/KTH_Royal_Institute_of_Technology_logo.svg/1200px-KTH_Royal_Institute_of_Technology_logo.svg.png\"\n",
    "  width=\"100\"\n",
    "  height=\"100\"\n",
    "  style=\"float:left;\"/> </td>\n",
    "    <td style=\"text-align:left\"> <h1> Numerical Linearization </h1> \n",
    "         <h2><span style=\"color:gray\"> with PowerFactory and PFPy</span> </h2> \n",
    "            </td>\n",
    "    <td style=\"text-align:right\"> \n",
    "            </td>\n",
    "    </tr>\n",
    " </table>"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
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    "## Introduction of the problem\n",
    "\n",
    "Power systems are typically described with the following set of DAEs:\n",
    "\\begin{align}\n",
    "\\dot{x}(t) &= f\\left(x(t), z(t), u(t)\\right) \\\\\n",
    "0 &= g\\left(x(t), z(t), u(t)\\right) \\\\\n",
    "y(t) &= h\\left(x(t),z(t),u(t)\\right)\n",
    "\\end{align}\n",
    "\n",
    "For various purposes, such as small-signal analysis, these equations can be linearized and Kron-reduction can be performed to obtain the following LTI system:\n",
    "\\begin{align}\n",
    "\\Delta \\dot{x}(t) = A\\Delta x(t) + B\\Delta u(t) \\\\\n",
    "\\Delta y(t) = C \\Delta y(t) + D\\Delta u(t) \n",
    "\\end{align}\n",
    "\n",
    "Typically, simulation tools perform some sort of linearization of the model. In the case of PowerFactory, **the only available part of the linearized model is the $A$ matrix!** However, all $x(t), z(t), u(t)$ and $y(t)$ are available.\n",
    "\n",
    "***\n",
    "\n",
    "<font color=red>**Problem**</font>: Given the posibility to arbitrarily set the inputs to the model $u(t)$, and the simulated values of $x(t), z(t)$ and $y(t)$, find the linearized state space model defined as its defined above. "
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    "## Solutions\n",
    "\n",
    "Two different solutions have been implemented.\n",
    "\n",
    "### Approach #1:\n",
    "First, let's tackle the $C$ matrix. Assume we sufficiently excite the system’s dynamics by perturbing the system, and then observe the free response:\n",
    "\n",
    "\\begin{align}\n",
    "\\Delta y(t) = C \\Delta x(t) + D \\underbrace{\\Delta u(t)}_{=0}, \\ \\ \\ \\Delta x(0) \\neq 0, 0<t<t_{end}\n",
    "\\end{align}\n",
    "\n",
    "The simulated vectors $\\Delta x$ and $\\Delta y$ at each time step can be arranged in the following two matrices:\n",
    "\\begin{align}\n",
    "Y &= \\begin{bmatrix}\\vdots & \\vdots & \\vdots & & \\vdots \\\\\n",
    "                    \\Delta y(0) & \\Delta y(T_s) & \\Delta y(2T_s) &\\cdots& \\Delta y(t_{end}/T_s) \\\\\n",
    "                    \\vdots & \\vdots & \\vdots & & \\vdots\n",
    "                    \\end{bmatrix} \\in \\mathbb{R}^{n_y \\times t_{end}/T_s} \\\\\n",
    "X &= \\begin{bmatrix}\\vdots & \\vdots & \\vdots & & \\vdots \\\\\n",
    "                    \\Delta x(0) & \\Delta x(T_s) & \\Delta x(2T_s) &\\cdots& \\Delta x(t_{end}/T_s) \\\\\n",
    "                    \\vdots & \\vdots & \\vdots & & \\vdots\n",
    "                    \\end{bmatrix} \\in \\mathbb{R}^{n_x \\times t_{end}/T_s}\n",
    "\\end{align}\n",
    "where $T_s$ is the simulation time step.\n",
    "\n",
    "These two matrices are related to each other through the matrix $C$:\n",
    "\n",
    "\\begin{align}\n",
    "Y = CX\n",
    "\\end{align}\n",
    "Since $X$ and $Y$ are non-square, $C$ cannot be obtained by multiplying from the left with $X^{-1}$. But it can be obtained as the least squares solution:\n",
    "\n",
    "\\begin{align} \\boxed{\n",
    "C = YX^T\\left[ XX^T\\right]^{-1}}\n",
    "\\end{align}\n",
    "\n",
    "\n",
    "------\n",
    "\n",
    "How do we obtain $D$? Assume there is only a single input to the system. Then we can write:\n",
    "\n",
    "\\begin{align}\n",
    "\\Delta y(t) = C\\Delta x(t) + D_i \\Delta u_i(t) , \\ \\ \\ D_i \\in \\mathbb{R}^{n_y \\times 1}\n",
    "\\end{align}\n",
    "\n",
    "We can force the input $\\Delta u_i(t)$ to be a step signal, and all others to be zero. After the transient ($\\Delta \\dot{x}(t_{end}$) = 0) we will have that:\n",
    "\n",
    "\\begin{align}\\boxed{\n",
    "\\Delta y(t_{end}) = C \\Delta x(t_{end}) + D_i u_{mag} \\Rightarrow D_i = \\frac{\\Delta y(t_{end})-C\\Delta x(t_{end})}{u_{mag}}}\n",
    "\\end{align}\n",
    "\n",
    "This process can be repeated $\\forall i \\in \\{1,..., n_u\\}$ and the $D$ matrix can be formed as:\n",
    "\n",
    "\\begin{align}\n",
    "D &= \\begin{bmatrix}\\vdots & \\vdots & & \\vdots \\\\\n",
    "                    D_1 & D_2  &\\cdots& D_{n_u} \\\\\n",
    "                    \\vdots  & \\vdots & & \\vdots\n",
    "                    \\end{bmatrix} \\in \\mathbb{R}^{n_y \\times n_u}\n",
    "\\end{align}\n",
    "\n",
    "With that, the matrices $C$ and $D$ of the output equation are obtained.\n",
    "\n",
    "---------\n",
    "\n",
    "A similar process can be carried out for the state equation. Again, given only a single input we have that: \n",
    "\n",
    "\\begin{align}\n",
    "\\Delta \\dot{x}(t) = A\\Delta x(t) + B_i \\Delta u_i(t), \\ \\ \\ B_i \\in \\mathbb{R}^{n_x \\times 1}\n",
    "\\end{align}\n",
    "\n",
    "**Note**: Here it is assumed that the $A$ matrix is available from PowerFactory.\n",
    "\n",
    "After the transient, it can be written that:\n",
    "\n",
    "\\begin{align}\\boxed{\n",
    "\\Delta \\dot{x} (t_{end}) = 0 = A\\Delta x(t_{end}) + B_i u_{mag} \\Rightarrow B_i = - \\frac{ A\\Delta x(t_{end})}{u_{mag}}}\n",
    "\\end{align}\n",
    "\n",
    "This process can be repeated $\\forall i \\in \\{1,..., n_u\\}$ in parallel with the one for matrix $D$ and $B$ can be formed as:\n",
    "\n",
    "\\begin{align}\n",
    "B &= \\begin{bmatrix}\\vdots & \\vdots & & \\vdots \\\\\n",
    "                    B_1 & B_2  &\\cdots& B_{n_u} \\\\\n",
    "                    \\vdots  & \\vdots & & \\vdots\n",
    "                    \\end{bmatrix} \\in \\mathbb{R}^{n_x \\times n_u}\n",
    "\\end{align}\n",
    "\n",
    "-----\n",
    "\n",
    "### Approach #2:\n",
    "\n",
    "The idea taken from system identification, specifically [1]. There, the problem is more complex since the states are typically not observed like here.\n",
    "\n",
    "[1] Lennart Ljung, *System Identification Theory for the User 2nd Edition’*, Prentice Hall, 1999., pp. 208-209\n",
    "\n",
    "The linearized model can also be written in the discrete form as follows:\n",
    "\n",
    "\\begin{align}\n",
    "\\Delta x(k+1) = A_d \\Delta x(k) + B_d \\Delta u(k) \\\\\n",
    "\\Delta y(k) = C_d x(k) + D_d \\Delta u(k)\n",
    "\\end{align}\n",
    "\n",
    "where $k \\in \\mathbb{Z}$.\n",
    "After the simulation, the following matrices can be formed:\n",
    "\n",
    "\\begin{align}\n",
    "Y &= \\begin{bmatrix}\n",
    "\\Delta x(2) & \\Delta x(3) & \\cdots & \\Delta x(k_{end}) \\\\\n",
    "\\Delta y(1) & \\Delta x(2) & \\cdots & \\Delta x(k_{end}-1) \\\\\n",
    "\\end{bmatrix}\\\\\n",
    "\\Phi &= \\begin{bmatrix}\n",
    "\\Delta x(1) & \\Delta x(2) & \\cdots & \\Delta x(k_{end}-1) \\\\\n",
    "\\Delta u(1) & \\Delta u(2) & \\cdots & \\Delta u(k_{end}-1) \\\\\n",
    "\\end{bmatrix}\n",
    "\\end{align}\n",
    "\n",
    "and we can denote by $\\Theta$ the following matrix:\n",
    "\n",
    "\\begin{align}\n",
    "\\Theta = \\begin{bmatrix}\n",
    "A_d & B_d \\\\ C_d & D_d\n",
    "\\end{bmatrix}\n",
    "\\end{align}\n",
    "\n",
    "Similarly as in the first approach, these matrices can be related as:\n",
    "\n",
    "\\begin{align}\n",
    "Y = \\Theta \\Phi\n",
    "\\end{align}\n",
    "\n",
    "and $\\Theta$ can be found as the following least-squares solution:\n",
    "\n",
    "\\begin{align}\\boxed{\n",
    "    \\Theta = Y\\Phi^T\\left[  \\Phi \\Phi^T \\right]^{-1}}\n",
    "\\end{align}\n",
    "\n",
    "The continuous-time LTI system can then be obtained from the discrete-time LTI system using, e.g. the equivalent of Matlab's `d2c` function. \n",
    "\n",
    "**Note**: The $A$ matrix is not taken from PowerFactory. Also, the coordinate system is fixed by the state and output variables used in $Y$ and $\\Phi$ so the $A$ matrix should correspond to the $A$ matrix from PowerFactory."
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code Demo\n",
    "First, some modules need to be imported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "sys.path.insert(1, os.path.abspath('..')) # Project root\n",
    "sys.path.insert(1, os.path.abspath('../examples')) # Examples folder\n",
    "sys.path.insert(1, os.path.abspath('../examples/linearization')) # Examples folder with linearization\n",
    "from pfpy import models, wavegen\n",
    "from scipy import signal\n",
    "import copy\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook\n",
    "#plt.style.use('science')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find the linearized model numerically, input signals, $\\Delta u$, and output signals, $\\Delta y$, need to be specified. They are provided in:\n",
    "- `examples/modaldata_v1.py` for Approach #1, and\n",
    "- `examples/modaldata_v2.py` for Approach #2\n",
    "\n",
    "and they are imported below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import modaldata_v1, modaldata_v2 # Approach #1 and #2 input specifications"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, a PowerFactory model is defined that will be linearized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = models.PowerFactoryModel(project_name = 'PFPY_DEMO', \n",
    "                                 study_case='01 - Base Case', \n",
    "                                 networks='MV_EUR',\n",
    "                                 outputs = modaldata_v1.outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Approach # 1\n",
    "Then the linearization method of approach #1 is executed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "A1, B1, C1, D1 = model.linearize(modaldata_v1.inputs_c, modaldata_v1.inputs_bd, modaldata_v1.outputs, modaldata_v1.ref_angle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Approach #2\n",
    "\n",
    "Also, the linearization method of approach #2 is executed, and the discrete-time LTI system is converted to the continous-time LTI system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "A2d, B2d, C2d, D2d = model.linearize2(modaldata_v2.inputs, modaldata_v2.outputs, modaldata_v2.ref_angle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "A2, B2, C2, D2 = models.d2c(A2d, B2d, C2d, D2d, 0.001, method='zoh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparisons\n",
    "\n",
    "To check if the original nonlinear and the linearized models match around the operating point, the systems are simulated by using small disturbances as inputs. They are a dip in voltage at the PCC of 0.05 pu lasting 500ms followed by a dip in frequency at the PCC of 0.005pu lasting 100ms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = copy.deepcopy(modaldata_v2.inputs)\n",
    "inputs[0]['wave_specs'].update({'type':'dip', 't0':1, 'deltat': 0.5, 'deltay' : 0.05})\n",
    "inputs[1]['wave_specs'].update({'type':'dip', 't0':5, 'deltat' : 0.1, 'deltay' : 0.005})\n",
    "model.inputs = inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simulation of the nonlinear model is run, and the simulation results are centered w.r.t. the initial stead-state conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = model.simulate(0, 10, 0.001)\n",
    "res -= res.iloc[0, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, a linear state-space model is created by using the `A, B, C` and `D` matrices obtained from the linearization procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "sys1 = signal.StateSpace(A1,B1,C1,D1) # LTI system of Approach #1\n",
    "sys2 = signal.StateSpace(A2,B2,C2,D2) # LTI system of Approach #2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Also, the inputs are centered with respect to the steady-state below for LTI systems' simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sig_v = wavegen.dip(**inputs[0]['wave_specs'])\n",
    "sig_f = wavegen.dip(**inputs[1]['wave_specs'])\n",
    "t = sig_v['time']\n",
    "u = np.stack((sig_v['y1']-inputs[0]['wave_specs']['y0'],sig_f['y1']-inputs[1]['wave_specs']['y0']),axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The linear models are simulated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tout1, y1, x1 = signal.lsim(sys1, u, t)\n",
    "tout2, y2, x2 = signal.lsim(sys2, u, t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------\n",
    "\n",
    "Finally, the differences between the model outputs is plotted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * mpl.ratio);\n",
       "                canvas.setAttribute('height', height * mpl.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\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'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / mpl.ratio,\n",
       "        fig.canvas.height / mpl.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / mpl.ratio;\n",
       "    fig.root.removeEventListener('remove', this._remove_fig_handler);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\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 / mpl.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 () {\n",
       "    this.close_ws(this, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "    el.addEventListener('remove', this._remove_fig_handler);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"862.9630239248646\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2)\n",
    "axs[0].plot(tout1, y1[:,0],label='LTI System - Approach 1')\n",
    "axs[0].plot(tout2, y2[:,0],'--',label='LTI System - Approach 2')\n",
    "axs[0].plot(res['SourcePCC.ElmVac\\\\m:Qsum:bus1'],'--',label='PowerFactory Simulation')\n",
    "axs[0].set_ylabel('Reactive Power [MVar]')\n",
    "axs[0].set_xlabel('Time (s)')\n",
    "axs[0].set_xlim(0,10)\n",
    "axs[0].legend()\n",
    "axs[0].grid()\n",
    "axs[1].plot(tout1, y1[:,1],label='LTI System - Approach 1')\n",
    "axs[1].plot(tout2, y2[:,1],'--',label='LTI System - Approach 2')\n",
    "axs[1].plot(res['SourcePCC.ElmVac\\\\m:Psum:bus1'],'--',label='PowerFactory Simulation')\n",
    "axs[1].set_ylabel('Active Power [MW]')\n",
    "axs[1].set_xlabel('Time (s)')\n",
    "axs[1].set_xlim(0,10)\n",
    "axs[1].legend()\n",
    "axs[1].grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "One can also look at the RMSE between the nonlinear and linear simulations to compare the two approaches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------ RMSE for Approach 1 ------\n",
      "Reactive Power: 0.019070650935459062\n",
      "Active Power: 0.02665167806990526 \n",
      "\n",
      "------ RMSE for Approach 2 ------\n",
      "Reactive Power: 0.014507085003583439\n",
      "Active Power: 0.01640056387704473\n"
     ]
    }
   ],
   "source": [
    "def rmse(x,y):\n",
    "    return np.sqrt(1/x.shape[0]*np.sum((x-y)**2))\n",
    "\n",
    "Q = res['SourcePCC.ElmVac\\\\m:Qsum:bus1'].values[:-1]\n",
    "P = res['SourcePCC.ElmVac\\\\m:Psum:bus1'].values[:-1]\n",
    "print('------ RMSE for Approach 1 ------')\n",
    "print('Reactive Power:', rmse(y1[:,0],Q))\n",
    "print('Active Power:', rmse(y1[:,1],P),'\\n')\n",
    "print('------ RMSE for Approach 2 ------')\n",
    "print('Reactive Power:', rmse(y2[:,0],Q))\n",
    "print('Active Power:', rmse(y2[:,1],P))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----\n",
    "\n",
    "The following plot show the comparison of eigenvalues of the $A$ matrix obtained from PowerFactory (same as approach 1) and the $A$ matrix obtained from the approach 2. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
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       "        return MozWebSocket;\n",
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       "        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",
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       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (mpl.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: mpl.ratio });\n",
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       "        fig.send_message('refresh', {});\n",
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       "        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",
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       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
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       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
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       "        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",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * mpl.ratio);\n",
       "                canvas.setAttribute('height', height * mpl.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\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'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / mpl.ratio,\n",
       "        fig.canvas.height / mpl.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / mpl.ratio;\n",
       "    fig.root.removeEventListener('remove', this._remove_fig_handler);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\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 / mpl.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 () {\n",
       "    this.close_ws(this, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "    el.addEventListener('remove', this._remove_fig_handler);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
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     "output_type": "display_data"
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\" width=\"862.9630239248646\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.linalg import eigvals\n",
    "eigs2 = eigvals(A2)\n",
    "eigspf = eigvals(A1)\n",
    "\n",
    "fig, axs = plt.subplots(1,2)\n",
    "axs[0].plot(np.real(eigs2), np.imag(eigs2),'x', label='Eigvals - Approach 2')\n",
    "axs[0].plot(np.real(eigspf), np.imag(eigspf),'o', fillstyle='none', label='Eigvals - PowerFactory')\n",
    "axs[0].set_xlabel('Real Part')\n",
    "axs[0].set_ylabel('Imaginary Part')\n",
    "axs[0].legend()\n",
    "axs[0].grid()\n",
    "axs[1].plot(np.real(eigs2), np.imag(eigs2),'x', label='Eigvals - Approach 2')\n",
    "axs[1].plot(np.real(eigspf), np.imag(eigspf),'o', fillstyle='none', label='Eigvals - PowerFactory')\n",
    "axs[1].set_xlim(-2,0)\n",
    "axs[1].set_xlabel('Real Part')\n",
    "axs[1].set_ylabel('Imaginary Part')\n",
    "axs[1].legend()\n",
    "axs[1].grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Conclusions\n",
    "\n",
    "- Approach #2 seems to be matching the time-domain response better than approach #1, but this might be due to a higher degree of freedom (in A matrix)\n",
    "\n",
    "\n",
    "- This also indicates that the full model is not needed when creating dynamic equivalents of distribution networks to match the time-domain response.\n",
    "\n",
    "\n",
    "- Approach #1 might be better suited for control design purposes since the \"correct\" A matrix is taken from PowerFactory \n",
    "\n",
    "\n",
    "- A more careful design of input signals is needed for Approach #2. I think there was not enough excitation in the input signals. Likely, some form of a Gaussian process would yield better results.\n",
    "\n",
    "\n",
    "- Implementation of the approach #2 and it's input specifications are simpler than in the case of approach #1.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.6 64-bit ('venv': venv)",
   "language": "python",
   "name": "python37664bitvenvvenv16d9569c0fae4eb2bf04eaf0a2a8f008"
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