From fdb892709bc260f5706044f58495fd0e0fb5f284 Mon Sep 17 00:00:00 2001
From: Johan Mabille <johan.mabille@gmail.com>
Date: Tue, 21 Nov 2023 14:28:55 +0100
Subject: [PATCH] Linter

---
 notebooks/microwave-waveguide.ipynb |   688 +-
 notebooks/xeus-octave.ipynb         | 18624 +++++++++++++-------------
 2 files changed, 9656 insertions(+), 9656 deletions(-)

diff --git a/notebooks/microwave-waveguide.ipynb b/notebooks/microwave-waveguide.ipynb
index 7611f20..c4fbb67 100644
--- a/notebooks/microwave-waveguide.ipynb
+++ b/notebooks/microwave-waveguide.ipynb
@@ -1,353 +1,353 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "427a089b-9140-41fb-b5e6-3cf39f0d6ca8",
-   "metadata": {},
-   "source": [
-    "# Analysis and simulation of a rectangular waveguide"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "16b1aa80-81cd-477c-b603-35187b994e18",
-   "metadata": {},
-   "source": [
-    "In this excercise a rectangular waveguide filled by a dielectric is designed and analysed, according to the following parameters:\n",
-    "\n",
-    "$$ a = 101 \\textit{mm} $$\n",
-    "$$ b = 40.4 \\textit{mm} $$\n",
-    "$$ \\epsilon_r = 3.8 $$\n",
-    "\n",
-    "The dielectric behaviour in the electromagnetic field is then described by the following constants:\n",
-    "\n",
-    "$$ \\mu = \\mu_0 $$\n",
-    "$$ \\epsilon = \\epsilon_0 \\epsilon_r $$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "427ccee4-3be3-4dd8-bed5-88f62368b5e4",
-   "metadata": {
-    "tags": [
-     "remove_cell"
-    ],
-    "vscode": {
-     "languageId": "octave"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "a = 0.1010;\n",
-    "b = 0.0404;\n",
-    "e_r = 3.8;\n",
-    "m = 1;\n",
-    "n = 4;\n",
-    "l = 1;\n",
-    "\n",
-    "mu_0 = 4e-7*pi;\n",
-    "e_0 = 8.85e-12;\n",
-    "c_0 = 1/sqrt(mu_0*e_0);\n",
-    "mu = mu_0;\n",
-    "e = e_0*e_r;\n",
-    "c = 1/sqrt(mu*e);\n",
-    "\n",
-    "E0 = 1;\n",
-    "\n",
-    "f = 7.68e9;\n",
-    "w = 2*pi*f;\n",
-    "\n",
-    "k = w*sqrt(mu*e);\n",
-    "kc = sqrt((m*pi/a)^2 + (n*pi/b)^2);\n",
-    "be = sqrt(k^2-kc^2);\n",
-    "\n",
-    "P = 50;  % Grid precision\n",
-    "\n",
-    "[X,Y] = meshgrid(linspace(0,a,P),linspace(0,b,P));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7e0d3d16-da82-409b-8c5e-e9ab452daf90",
-   "metadata": {},
-   "source": [
-    "## Properties of the transmitted mode"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "f3695b5c-bf2b-4592-8cb9-7385e37e059e",
-   "metadata": {
-    "tags": [
-     "remove_cell"
-    ],
-    "vscode": {
-     "languageId": "octave"
-    }
-   },
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "Fc14 = 7.6532e+09\n"
+      "cell_type": "markdown",
+      "id": "427a089b-9140-41fb-b5e6-3cf39f0d6ca8",
+      "metadata": {},
+      "source": [
+        "# Analysis and simulation of a rectangular waveguide"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "% Cutoff frequency of the analysed mode\n",
-    "Fc14 = c_0/2/sqrt(e_r)*sqrt((m/a)^2+(n/b)^2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "40249f48-2b9e-4abd-bc92-840d135167ea",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "The transmitted mode being analysed is the $\\textit{TM}_{(1,4)}$, with cutoff frequency\n",
-    "\n",
-    "$$ f_{c(m,n)} = \\frac{1}{2\\sqrt{\\mu\\epsilon}} \\sqrt{\\left(\\frac{m}{a}\\right)^2 + \\left(\\frac{n}{b}\\right)^2} \\approx 7.65 \\textit{GHz} $$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a72024a1-abab-4534-b1dd-01e6d872fb68",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Field components\n",
-    "\n",
-    "The mode is _transverse magnetic_, which means that the longitudinal component of the magnetic field is null ($H_z = 0$).\n",
-    "\n",
-    "The longitudinal component of the electric field is:\n",
-    "\n",
-    "$$ E_z(x,y,z) = E_0 \\sin \\frac{m \\pi x}{a}\\sin\\frac{n \\pi y}{b} e^{-j\\beta z} $$\n",
-    "\n",
-    "where $\\beta$ is a function of the frequency of the transmitted wave and of the geometry of the waveguide ($a$ and $b$). \n",
-    "\n",
-    "For TM modes the Maxwell equations (written in phasor form for linear media and without sources) can be reduced as follows:\n",
-    "\n",
-    "$$ \\vec{E_t} = \\frac{-j \\beta}{k_c^2} \\nabla E_z $$\n",
-    "$$ \\vec{H_t} = \\frac{\\hat{z} \\times \\vec{E_t}}{Z_{TM}} $$\n",
-    "\n",
-    "where $Z_{TM} = \\frac{\\beta}{\\omega\\epsilon}$ is the modal impedance.\n",
-    "\n",
-    "All the other components can then be calculated as a function of the longitudinal component of the electric field:\n",
-    "\n",
-    "$$ E_x = \\frac{-j \\beta}{k_c^2}\\frac{m\\pi}{a} E_0 \\cos \\frac{m \\pi x}{a}\\sin\\frac{n \\pi y}{b} e^{-j\\beta z} $$\n",
-    "$$ E_y = \\frac{-j \\beta}{k_c^2}\\frac{n\\pi}{b} E_0 \\sin \\frac{m \\pi x}{a}\\cos\\frac{n \\pi y}{b} e^{-j\\beta z} $$\n",
-    "$$ H_x = \\frac{ j \\omega \\epsilon}{k_c^2}\\frac{n\\pi}{b} E_0 \\sin \\frac{m \\pi x}{a}\\cos\\frac{n \\pi y}{b} e^{-j\\beta z} $$\n",
-    "$$ H_y = \\frac{-j \\omega \\epsilon}{k_c^2}\\frac{m\\pi}{a} E_0 \\cos \\frac{m \\pi x}{a}\\sin\\frac{n \\pi y}{b} e^{-j\\beta z} $$\n",
-    "\n",
-    "#### Electric field representation "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "654bca41-cf11-4f0d-a15d-d13922ed3b27",
-   "metadata": {
-    "tags": [
-     "remove_cell"
-    ],
-    "vscode": {
-     "languageId": "octave"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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"
-     },
-     "metadata": {
-      "image/png": {
-       "height": 900,
-       "width": 1200
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "% Phase in the time dimension \n",
-    "p = pi/4;    # Change it from 0 to 2*pi to see the variation of the E field\n",
-    "% Phase in the z dimension\n",
-    "z = 0;       # Change it from 0 to 2*pi to see the variation of the E field\n",
-    "\n",
-    "EX0 = -j*E0*be*m*pi/kc^2/a;\n",
-    "EY0 = -j*E0*be*n*pi/kc^2/b;\n",
-    "EZ0 = E0;\n",
-    "\n",
-    "% Phasorial fields\n",
-    "Ex = EX0.*cos((m*pi/a).*X).*sin((n*pi/b).*Y);\n",
-    "Ey = EY0.*sin((m*pi/a).*X).*cos((n*pi/b).*Y);\n",
-    "Ez = EZ0.*sin((m*pi/a).*X).*sin((n*pi/b).*Y);\n",
-    "\n",
-    "% Vector fields\n",
-    "Fx = real(Ex.*exp(i*p).*exp(j*be*z));\n",
-    "Fy = real(Ey.*exp(i*p).*exp(j*be*z));\n",
-    "Fz = real(Ez.*exp(i*p).*exp(j*be*z));\n",
-    "\n",
-    "F = sqrt(Fx.^2+Fy.^2+Fz.^2);\n",
-    "\n",
-    "EX0_ = abs(EX0);\n",
-    "EY0_ = abs(EY0);\n",
-    "EZ0_ = abs(EZ0);\n",
-    "\n",
-    "figure(\"position\", [0 0 1200 900])\n",
-    "colormap(\"turbo\");\n",
-    "subplot(2,2,1);\n",
-    "imagesc([0 a],[0 b],Fx,[-EX0_,EX0_]);\n",
-    "colorbar;\n",
-    "title(\"X component\");\n",
-    "subplot(2,2,2);\n",
-    "imagesc([0 a],[0 b],Fy,[-EY0_,EY0_]);\n",
-    "colorbar;\n",
-    "title(\"Y component\");\n",
-    "subplot(2,2,3);\n",
-    "imagesc([0 a],[0 b],Fz,[-EZ0_,EZ0_]);\n",
-    "colorbar;\n",
-    "title(\"Z component\");\n",
-    "subplot(2,2,4);\n",
-    "imagesc([0 a],[0 b],F,[0,E0]);\n",
-    "colorbar;\n",
-    "title(\"ABS value\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "768afe71-880c-48c7-86d6-f31c94306ad8",
-   "metadata": {},
-   "source": [
-    "### Frequency dependent properties\n",
-    "\n",
-    "Below are reported in a range of $[1.1 \\div 2]f_{c(1,4)}$ the main mode properties that are a function of frequency:\n",
-    "\n",
-    "* the propagation constant $\\beta = 2\\pi f \\sqrt{\\mu \\epsilon} \\sqrt{1-\\left(\\frac{f_{c(1,4)}}{f}\\right)^2}$\n",
-    "\n",
-    "* the group velocity $v_g = c \\sqrt{1-\\left(\\frac{f_{c(1,4)}}{f}\\right)^2}$\n",
-    "\n",
-    "* the phase velocity $v_f = \\frac{c}{\\sqrt{1-\\left(\\frac{f_{c(1,4)}}{f}\\right)^2}}$\n",
-    "\n",
-    "* the wavelength $\\lambda = \\frac{2 \\pi }{\\beta}$\n",
-    "\n",
-    "* the modal impedance $Z_{TM} = \\frac{\\beta}{2 \\pi f \\epsilon}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "31968a5e-6e20-4f8b-846f-29091da3d399",
-   "metadata": {
-    "tags": [
-     "remove_cell"
-    ],
-    "vscode": {
-     "languageId": "octave"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "F = linspace(1.1*Fc14, 2*Fc14);\n",
-    "sqrt_f = sqrt(1-(Fc14./F).^2);\n",
-    "\n",
-    "be_f = (2*pi*sqrt(mu*e)).*F.*sqrt_f;\n",
-    "vg_f = c.*sqrt_f;\n",
-    "vf_f = c./sqrt_f;\n",
-    "lm_f = (2*pi)./be_f;\n",
-    "zm_f = be_f./(2*pi*e)./F;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "c8974bb8-02cb-4fd4-b870-afb2100fd01d",
-   "metadata": {
-    "tags": [
-     "remove_input"
-    ],
-    "vscode": {
-     "languageId": "octave"
-    }
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "id": "16b1aa80-81cd-477c-b603-35187b994e18",
+      "metadata": {},
+      "source": [
+        "In this excercise a rectangular waveguide filled by a dielectric is designed and analysed, according to the following parameters:\n",
+        "\n",
+        "$$ a = 101 \\textit{mm} $$\n",
+        "$$ b = 40.4 \\textit{mm} $$\n",
+        "$$ \\epsilon_r = 3.8 $$\n",
+        "\n",
+        "The dielectric behaviour in the electromagnetic field is then described by the following constants:\n",
+        "\n",
+        "$$ \\mu = \\mu_0 $$\n",
+        "$$ \\epsilon = \\epsilon_0 \\epsilon_r $$"
+      ]
+    },
     {
-     "data": {
-      "image/png": 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HxsYGBAQIIVpbW4UQx48fF0Lce++9PXr0EELo/vM3tr4JpqsaOXKkNA5o5MiRQogff/xRf9uTJ08KIXSf+tJgFl9f387uMyQkpG/fvgcPHvTx8Rk3blzv3r1TUlLal2q6d6ZLNah3797z5s175JFHXF1d58yZU1FRcdNNdO+m6bdm6tSpUt+lgeg6hYWF0hQ2khEjRri7uzs5OXl6egohxo4dK4To37+/6MAbBwD2JCAg4P77779y5cpvf/tbjUYjhGhsbExNTTW2vhTiFBUVSbnAp59+KoQICgqS/s8/ffq0FNAcPHhQf6uLFy9KV+Ls2rWrtbVVGn2g75NPPpGmnpVuXaxLeYy58847hRC7d++Wyti5c6eJlW969I5U8vnnn7/55puTJ08+cODA6dOnBw4c+PXXX9fV1alUKiFEU1NTVFRUVFRUTU3N559/LlWlb+bMmU5OTm+99VZNTY30CdWRDY292tK2X3zxhbTavffe6+XlJY2jaa/955qxQxvro/62UsAhTdpirGtarfb3v/993759Fy9erFAo9uzZ05FX2JiOv9ftK/n3v/+9e/duaayNtKgb1XIrvvzyy/Hjx0tJTUdeNMDu9ZC7AAAyGz9+/O233y7lMgsXLhwyZIh0D4IOntYzeAJNOo3T2tr6u9/9bsGCBTNnzpTWkRIH3T0g/+d//kd6YGx9EzpYlW4FfcZOkXV2n9Lpnc2bN2/fvv3gwYNHjx794IMPKisr2+zWdO9Ml2qQUqmURravX79+3rx5W7ZsCQsLM72J7t00VoyJM2n6Z7Ta1GxwEQAcyurVqydPnpyfn9+/f/+AgIB//OMfPXv2HD58uMGVw8PD77//fmmQ5ogRI95//31nZ+dly5Z5eXl5e3ufO3fu7rvvdnV1PXr0qP5WPXr0mD59+uTJk6VYQXdXHR3p8zQnJ+fixYsFBQXC+KAbycSJE4cOHfrtt99GRER4enpK83oYc9Ojd6QSjUazYsWKTZs2JSQk1NTU1NXV+fn59e/ff+DAgREREYWFhXPnzv3pp5/eeeedkSNHvvzyy212279///Hjx0sTskg35YmIiDC4oTTNiulXOygoKCwsrLS0NCoqSqFQlJSUTJw40cfHR4rV9A8qhMjIyHjhhRd0o0VMHLqystJgH/X3GRwcLIT417/+ZaJrO3fufOuttz766KM2cYzpV9iYjr/X7SuRviQ8/vjj58+fb2hoaGxsvOlAno4oKCjQnekx9oNx60cBbAjfpAFHl52dXVBQsGvXrjfeeMPf37/9CsZONEnPGjyBZuw0jrT/PXv2SOeddKdrbnrap/15KtNVmWbsFFln93nT0ztS2Z09qWWi1xJpsj0hhDSPj3ShewcZK8bEmTT9M1oAgDZUKtVXX301d+5cb29vtVodGRn5xRdfSLN0GbR9+/Ynn3zyu+++27FjR0RExJdffin9o/7uu+8GBAScOXPG09Pz+eef199k1KhRixYt+uKLLwYNGvT222/rX08qmTFjhr+//6VLl/7+97//5S9/8fb2vnjxYvuTBDo9evR49913lUrlsWPH6uvrjd1mu4NH70gl06dPf+utt9zd3d98883PP/88NjZ2165d0ifLzp07f/WrX+Xn53/66aePPfbY559/Lg2nbUO6uGbAgAERERFSS0c2NPhqKxSKTz75JCEh4dixYxUVFbNnz5bmBmpj0aJFwcHB27dvl6ZN0Wfw0Cb6qBMcHDxo0KA2Q1zbdG3v3r0jRoyYPn364sWLO/4KG3tHOvVet6lEelmkHOfw4cNCCNPvfgcVFRXpfkE68qIB9k+GeWwAWIc2E/3qtJluUPvz9Lfh4eFJSUk9e/Z0dnb+6quvdPP833HHHbNmzZIGnhQUFGh/vkFD3759f/3rX0tXuPTr10+r1d64cUO6SCcyMlK6W4EQ4tlnnzW2vvbnGzrMmTPn7NmzBif6NVhV+7kS9TvY2toqDS2ZOnVqVFSU0LuTRaf2uWPHDiHE4MGD09LSZs+eLYTw8/OTZkPUL9tY70yUqr+5fuXSPRSEEJMnT541a5abm5sQYs+ePfrrGLv7krRorJiWlpahQ4cKIUaPHi29JuLniQAXL16sm5Kwzd6kDK6+vl773xP4MTkfAHSL9p/Ixmg0Gmnm1I67cuXKN9980y1Hv8VKHMqf//xnJyenNp/v+hYuXCjNoHzmzBkhxOeff95mBXO81wZJI3qkm2E9+uij/fv3v3btWpt12nzr6EZ8l4DjIIYEcHPGTusJIyfQjJ3GcXJy2rVr13333Xf8+PGrV6/qxiebOO1j4jyViapMM3GKrFP7NHF6R7/szp7UMt1rIcSQIUPOnj2bn58/ePDg3Nxc6SZKHWSsGBNn0vTPaAEArFPv3r1veilrG25ubtLkYrJX4lAWLlzYr1+/N954w9gKYWFhX3/9dVFR0f/+7/8aXMHy7/XZs2e3b9++fPly3RXobVRXV2/cuFH/2rFb9Mknn+Tn53fX3gBrJ3cqBMBWmT6BZvA0zvnz5wsLC8vLy6XF5557Tgjx5ptvGlvfbnRX76SRMo888oiJdW56zspEMV07k9YeZ7cAoFt0bayKfRzdjuXm5t53333tR51Irl+/npCQ4ObmJg3CbT9SxmJ0I2XWrl07ffr0n376qf06+mePSktLu+vQuht08l0CjoCJfgGYhcHTOD/++OODDz7Y2tr6xhtveHh4rF+/vnfv3tLFMvZ9Ys3yvZPOWcXGxrafLc9EMd1y1vSTTz7Zv3//Le4EACCE8Pb21pqcxtWOj27HZs+eLQUuBh08eNDPz6++vv7AgQN5eXm/+MUvLFmbPj8/P+kHYPTo0fPmzTO4Tv/+/c3xQ7Jv375u3ydgtQhlAFiOt7f3zp07V65cuWzZMmdn56CgoD/96U9+fn5y12UzBg8evHnz5iFDhtx0zWPHjs2dO7e0tNTytzDIyMgoLS218EEBALAPw4YN+/rrr0eNGtXc3PzCCy9IdycAYMcU5N8AAAAAAACWx0S/AAAAAAAAMiCUAQAAAAAAkAGhDAAAAAAAgAwIZQAAAAAAAGRAKAMAAAAAACADQhkAAAAAAAAZEMoAAAAAAADIgFDGQX333XcLFiwIDQ2NiYkpLS2VGquqquLj48PCwubPn3/p0iUTjQAAwKwuXbp06dKlyMjIf/7zn53a0OAH9/79+x988MGQkJBHH3301KlTZqgXAAB0BaGMg/rjH/8YFhZWWlqakpLy1FNPXblypbW1NTU1NTY2tqSkxNvbOy0tTQhhsBEAAJjb888/HxERsXnz5uHDh3d8K4Mf3FevXl20aFF8fHxJSUlISEhKSorZqgYAAJ1DKOOITpw4UVtb+8QTT/Tu3TsqKurIkSPu7u4VFRWNjY1JSUlubm4pKSnl5eUXLlww2Ch3+QAA2Ln8/Pwff/xRqVTqWsrKyqZPnx4WFhYWFvb444/X1tYa3NDgB/fZs2dbWlpmz57t4eGRmJh4+vTp5uZmS3UFAACY0kPuAiCDb775ZuDAgU8//fTevXv79++/bNmysWPHqtVqf39/hUIhhHB3d/fy8qqurjbY2K9fv/b7VKlUlu4GAMAMuLZFdnV1dX/961/fe++92NhYqaW2tnbhwoXr168PDg7euHFjSUnJoEGD9Ddpbm7u1auXEMLgB3dwcLBSqczNzY2JicnJyQkJCZFWbo9PcwCwD3ya2xBCGUek0Wj+/ve/x8XFPfvss0VFRQsWLPjss8+am5tdXV1167i5uTU2NhpsbL9DlUol46+9vEc3H/plc+y1a/TLttx6v+z1lbEVWq326aefXrJkiaenp64xPz9/woQJY8aMEUIsWLAgJyfn3Llz3t7e0rMPP/zwxo0bpZzF4Ae3i4vLsmXLUlNTV61aJYTYunWriQL4QO929Mu20C/bQr/MtwdYDKGMI3J3dw8MDPzVr34lhHjooYdWr15dUVGhVCo1Go1unaamJqVSabBRhooBAHAMeXl5Xl5e0dHR+o21tbXl5eVTp06VFt3c3C5fvqwLZfQZ/OA+ceJEZmZmbm5uUFBQaWnpvHnz8vPz9UMfAAAgF0IZRzRkyJAbN27oFpVKpYuLy5AhQ9RqtdSi0WgaGhp8fHy0Wm37RhkqBgDAMRw+fPjTTz/duXOntBgbG7t48WJPT8/IyMisrCyp8ejRoyqVateuXZmZmUKIhoaGGTNmODk5TZw4MS4urv0H97Zt28LDw8PCwoQQU6ZM2bBhw6FDh6ZNmyZH/wAAwH9hol9HNHr06B9++OGDDz5oaWnJz8+vr68PCwsLDQ11cXHJz89vbW3Nzs4eO3asl5eXwUa5ywcAwG69/vrrp37m4eHxySefLFiwICYmZu/evcePH7927dq6devS09OdnZ1jYmKKi4uLi4tVKtWOHTuKi4tXrFhh8IN79OjRpaWlBw4c0Gg0BQUFarV61KhRcncUAAAIQSjjmJycnLKzs3fs2BEREZGbm7tu3Tp3d3eFQrF27dq8vLzx48dXVlZmZGQIIQw2AgAASxo+fPhLL72Unp4eERGxb9++NWvWSFP5tmfwgzs8PHzp0qUrV66MjIzcsGFDVlYW414BALASCq1WK3cNsHnyziNVXV3t6+sr19HNh37ZHHvtGv2yLbfeL6YGdGR8oJsD/bIt9Mu20C9j+DS3IYyUAQAAAAAAkAGhDAAAAAAAgAwIZQAAAAAAAGTALbEBAAAAADBMpVLJXYIBTBljNwhlAAAAAAAwygoTEKbytRtcvgQAAAAAACADQhkAAAAAAAAZEMoAAAAAAADIgFAGAAAAAABABoQyAAAAAAAAMiCUAQAAAABAZtHR0Tt27JAejx8/vri4WN56YBmEMgAAAAAAyCwhIWH79u1CiLNnz9bU1EyaNEnuimAJhDIAAAAAAMhs5syZu3fvvn79+tatW5OSkpyc+G/dIfSQuwAAAAAAABxdnz59xo0bt2fPnvfff/+9994TQlRVVaWlpXl4eNTU1Hz22WdyFwizIJQBAAAAAKBDFEu6baoX7Wv3tmlJSEh49dVXe/XqdeeddwohMjMzX3vtNS8vr3HjxnXXQWFtCGUAAAAAAOiQ9klKN4qLi3viiSfeeOMNabG+vt7Pz08I8Ytf/MJ8B4W8CGUAAAAAAJCfq6urRqPRLfbr1+/06dOenp41NTUyVgWzIpQBAAAAAMDq/PGPf0xJSVEqlSqVSu5aYC6EMgAAAAAAWJ2TJ08uW7bMxcUlLy9P7lpgLoQyAAAAAABYnfvvv/+dd9754Ycfnn/+eblrgbkQygAAAAAAYHXc3Nx+//vfy10FzMtJ7gIAAAAAAAAcEaEMAAAAAACADAhlAAAAAAAAZEAoAwAAAAAAIANCGQAAAAAAABkQygAAAAAAAMiAUAYAAABCCPFx0A9ylwAAjis6OnrHjh3S4/HjxxcXF8tbDyyDUAYAAAAAAJklJCRs375dCHH27NmamppJkybJXREsgVAGAAAAQggRd8Kt6pFBclcBAA5q5syZu3fvvn79+tatW5OSkpycnL755psZM2bIXRfMq4fcBQAAAAAAYBu6Mbz231arv9inT59x48bt2bPn/ffff++994QQb7zxRktLS3cdDtaJUAYAAAAAgA5pk6R0r4SEhFdffbVXr1533nmnECIuLu7y5cvmOxysAZcvAQAA4D/8t9VyBRMAyCUuLu7gwYNz5syRuxBYDiNlAAAAAACQn6urq0ajkbsKWBQjZQAAAAAAAGTASBkAAAD8H+kKJrNOmgAA6IgHHnhA7hJgdoyUAQAAAAAAkAGhDAAAAAAAgAwIZQAAgBBCKJYUK5YUy10FrAL3YAIAwDKYUwYAAEcnZTHa1+4VQqg+kbsaAAAAh0EoAwCA49KPYwAAgEEqlUruEmC3CGUAAHBExDEwjXswAYDk1KlTFj5idXW1r6+vhQ8KuRDKAADgWIhjAAAArAShDAAAjoI4Bp3CYBkAAMyNuy8BAFwSA4EAACAASURBVGD/pDsraV+7l0TGGnz88cdTp04NCQlJSEg4efJk+xWSk5NVPxszZkyndl5VVRUfHx8WFjZ//vxLly5Jjfv373/wwQdDQkIeffRRy4/DBwAAxhDKAABgz4hjrE1NTc1zzz23atWqffv2jRw5Mi0trc0KUVFRZ86c2bx5c1lZWVlZWWFhYcd33trampqaGhsbW1JS4u3tLe386tWrixYtio+PLykpCQkJSUlJ6c7+AACAW8DlSwAA2CcuVrJOlZWVQUFBoaGhQoikpKT333+/tbXVyek/58laW1vPnTt38OBBV1fXNhuWlZW98sor586dCwoK+tvf/mZw5xUVFY2NjUlJSQqFIiUlJTIy8sKFC+fPn29paZk9e7azs3NiYuKmTZuam5t79erVkWq5ggkAALMilAEAwN4Qx1iz6Ojo6OhoIURTU1NBQcGYMWN0iYwQor6+XqFQzJ8//8SJE8OGDUtPTw8PD6+trX3ggQfWr1+/detWIcTGjRv1d5idnf3YY49JIYtarfb391coFEIId3d3Ly+v6urq4OBgpVKZm5sbExOTk5MTEhJiIpHRv+1rUVGR9KC6urobXwFjampqLHAUy6NftoV+2Rb6pS8qKqrbK4EFEMoAAGA/hq7+VohviWOs35EjR+bMmaPVavPy8vTbGxsbQ0JCUlNTAwIC8vPzn3zyyaKiovz8/AkTJkiTy2i12pycnOnTp3t7e0ubFBQUPPLII1LO0tzcrD/Exs3NrbGx0cXFZdmyZampqatWrRJCSMmOMQZmnLHgYBl7vQUs/bIt9Mu20C8d/T/g+gk7rByhDAAA9kAaHXN60TB7/XpqZ0JDQ48cOVJcXPzYY4+VlZW5u7tL7QEBATk5OdLjWbNm5eXlVVRU1NbWlpeXT506VWp3c3O7fPmyLpTRp1QqNRqNbrGpqUmpVJ44cSIzMzM3NzcoKKi0tHTevHn5+fmenp5m7iIAALg5QhkAAGyb/sVKlrnMBLdix44dTU1NiYmJLi4uUVFRK1asOHnyZHh4uPTs8ePHNRrNuHHjpMUePXrcdtttnp6ekZGRWVlZUuPRo0dVKtWuXbsyMzOFEA0NDTNmzHBycpo4cWJcXJxarZZW02g0DQ0NPj4+27ZtCw8PDwsLE0JMmTJlw4YNhw4dmjZtmqV7DgAA2uHuSwAA2CrurGSLPDw8Vq9efeTIkaamppycnJaWFpVKpdVqv/jiiwsXLly9enXBggWHDh1qaWnZuXPn999/HxYWFhMTs3fv3uPHj1+7dm3dunXp6enOzs4xMTHFxcXFxcUqlWrHjh3FxcUrVqwIDQ11cXHJz89vbW3Nzs4eO3asl5fX6NGjS0tLDxw4oNFoCgoK1Gr1qFGjOlWzNN2vmV4QAAAcGSNlAACwPUzla7vuueeeuXPnpqamXr58WaVSrVu3rk+fPjdu3Jg3b966desmTZqUnp6+cuXKurq6kSNHbtiwwdXVdfjw4S+99FJ6enpdXV1gYOCaNWukqXzbUygUa9euXb58eUZGRmBgYEZGhhAiPDx86dKlK1eurK+vHzZsWFZWlo+Pj2U7DQAADCOUAQDAlhDH2IHk5OTk5GT9FmdnZ90EjQkJCQkJCW02iYqKMnZbje3bt+sv+vn5bdmypc06M2fOnDlz5i0VDQAAzIBQBgAA20AcAxn5W/AeTAAAOA5CGQAArB1xDAAAgF0ilAEAwHoRx8B6MFgGAIBuRygDAIA1Io4BAACwe4QyAABYF+IYWC0GywAA0L0IZQAAsBbEMQAAAA6FUAYAAPkRx8BWMFgGAIBuRCgDAICciGMAAAAcFqEMAADyII4BAABwcIQyAABYGnEMbBpXMAEA0F0IZQAAsBziGAAAAOgQygAAYAnEMbAnDJYBAKBbOMldAOSRnJys+tmYMWOkxqqqqvj4+LCwsPnz51+6dMlEIwCg4xRLihVLirWv3UsiAwAAAH2EMg7qzJkzmzdvLisrKysrKywsFEK0trampqbGxsaWlJR4e3unpaUZawQAdBBxDOyYNFhG7ioAALBtXL7kiFpbW8+dOxccHOzq6qprrKioaGxsTEpKUigUKSkpkZGRFy5cUKvV7Rv79esnY/EAYBO4WAkAAAA3RSjjiOrr6xUKxfz580+cODFs2LD09PTw8HC1Wu3v769QKIQQ7u7uXl5e1dXVBhsNhjIqlUp/saioyDJ9EULU1NRY7FiWRL9sjr12jX511tDV3wohTi8aJoSorq4201GM6UK/oqKizFEJHAEzywAAcIsIZRxRY2NjSEhIampqQEBAfn7+k08+WVRU1NzcrD9wxs3NrbGx0WCjwX2eOnXK7HUb5+vrK+PRzYd+2Rx77Rr96iArGR3T2X61+QPeJmQHAACA+RDKOKKAgICcnBzp8axZs/Ly8ioqKpRKpUaj0a3T1NSkVCoNNlq6XACwelYSxwCWx2AZAABuBaGMIzp+/LhGoxk3bpy02KNHj9tuu83T01OtVkstGo2moaHBx8dHq9W2b5SnaACwSsQxAAAA6DLuvuSIrl69umDBgkOHDrW0tOzcufP7778PCwsLDQ11cXHJz89vbW3Nzs4eO3asl5eXwUa5ywcAq8CdlQAJt2ECAKDLGCnjiCIiItLT01euXFlXVzdy5MgNGzZIE8esXbt2+fLlGRkZgYGBGRkZQgiFQtG+EQAcHKNjAAAA0C0IZRxUQkJCQkJCm0Y/P78tW7Z0pBEAHBNxDGAQM8sAANA1hDIAANwccQwAAAC6HaEMAACmEMcAHcFgGQAAuoBQBgAAw4hjAAAAYFaEMgAAtEUcA3QBg2UAAOgsQhkAAP4PcQxwK8hlAADoFEIZAACEII4BAACAxRHKAAAcHXEM0I0YLAMAQMcRygAAHBdxDAAAAGREKAMAcERDV38rxLfEMYA5MFgGAIAOIpQBADgWaXTM6UXDfH195a4FAAAADo1QBgDgKPQvVqqurpa5GsCuMVgGAICOIJQBANg/5o4BLI9cBgCAmyKUAQDYM+IYAAAAWC1CGQCAfSKOAWTHYBkAAEwjlAEA2BviGAAAANgEQhkAgP0gjgGsDYNlAAAwgVAGAGAPiGMAq0UuAwCAMYQyAADbRhwDAAAAG0UoAwCwVcQxgK1gsAwAAAYRygAAbA9xDGBzyGUAAGiPUAYAYEuIYwAAAGA3CGUAADZDsaSYOAawXQyWAQCgDSe5CwAA4OYUS4pJZOAgPv7446lTp4aEhCQkJJw8ebKzm1dVVcXHx4eFhc2fP//SpUtS4/79+x988MGQkJBHH3301KlT3V1yJ0i5jIwFAABgVQhlAABWTRfHkMjAEdTU1Dz33HOrVq3at2/fyJEj09LSOrV5a2trampqbGxsSUmJt7e3tPnVq1cXLVoUHx9fUlISEhKSkpJintoBAECnEcoAAKwUcQwcUGVlZVBQUGhoqJubW1JS0r/+9a/W1lYhRFlZ2fTp08PCwh5//PHaWqOX/1RUVDQ2NiYlJbm5uaWkpJSXl1+4cOHs2bMtLS2zZ8/28PBITEw8ffp0c3OzBfvUFoNlAADQYU4ZAIDVYTZfOKzo6Ojo6GghRFNTU0FBwZgxY5ycnGpraxcuXLh+/frg4OCNGzempKR88MEH+ls1Nzf36tVLCKFWq/39/RUKhRDC3d3dy8ururo6ODhYqVTm5ubGxMTk5OSEhIRIKxukUql0j4uKiszUzZ6Z+6seGdQzc79+Y01NjZkOJy/6ZVvol22hX/qioqK6vRJYAKEMAMCKEMcAQogjR47MmTNHq9Xm5eUJIfLz8ydMmDBmzBghxIIFC3Jycs6dO+ft7S2t/PDDD2/cuFHKWZqbm11dXXX7cXNza2xsdHFxWbZsWWpq6qpVq4QQW7duNXFoi804UyWEr69vm8b2LfaBftkW+mVb6JeO/h9w/YQdVo5QBgBgFYhjAJ3Q0NAjR44UFxc/9thjZWVltbW15eXlU6dOlZ51c3O7fPmyLpTRp1QqNRqNbrGpqUmpVJ44cSIzMzM3NzcoKKi0tHTevHn5+fmenp4W6owR3IkJAABBKAMAkB1xDKCzY8eOpqamxMREFxeXqKioFStWnDx50tPTMzIyMisrS1rn6NGjKpVq165dmZmZQoiGhoYZM2Y4OTlNnDgxLi5OrVZLq2k0moaGBh8fn23btoWHh4eFhQkhpkyZsmHDhkOHDk2bNk2uPuqQywAAQCgDAJANcQzQhoeHx8svvzxy5Eh/f/+PPvqopaVFpVJ5eHgkJCQcP35cpVJt3rz5ww8/LCwsjImJiYmJET9fvqRUKoUQWq3WxcUlPz8/Li4uOzt77NixXl5eo0ePfvfddw8cOBAcHLx37161Wj1q1Ci5O/of5DIAAAdHKAMAkAFxDGDQPffcM3fu3NTU1MuXL6tUqnXr1vXp06dPnz4vvfRSenp6XV1dYGDgmjVrpKl821MoFGvXrl2+fHlGRkZgYGBGRoYQIjw8fOnSpStXrqyvrx82bFhWVpaPj49luwUAAAwjlAEAWBRxDGBacnJycnJym8aoqChjt9XYvn27/qKfn9+WLVvarDNz5syZM2d2Y5HdiMEyAABHRigDALAQ4hgABkm5TJs7ZAMA4AgIZQAAZkccA8A0KZcRjJcBADgYQhkAgBkRxwDooJ6Z+7mOCQDgaAhlAADmolhSTBwDAAAAGEMoAwDofgyQAdAFTPoLAHA0hDIAgO5EHAPgVpDLAAAcCqEMAKB7EMcA6BbkMgAAx0EoAwC4VcQxALoXuQwAwEEQygAAuo44BoCZkMsAABwBoQwAoCuIYwAAAIBbRCgDAOgc4hgAlsFgGQCA3SOUAQB0FHEMAAsjlwEA2DdCGQDAzRHHAJALuQwAwI4RygAATBm6+lshviWOASAjchkAgL1ykrsAAID1UiwpPr1oGIkMANlJuYzcVQAA0M0IZQAABiiWFCuWFBPHALAe5DIAAPvD5UsAgP/C9DEArBbXMQEA7AyhDADgP4hjAFg/chkAgD0hlAEAEMcAsCXkMgAAu0EoAwAOjTgGgC0ilwEA2AdCGQBwUMQxAGwauQwAwA4QygCAwyGOAWAfyGUAALaOUAYAHAhxDAA7Qy4DALBphDIA4CgUS4qJYwDYH3IZAIDtIpQBAPvHABkA9o1cBgBgowhlAMCeEccAcBDkMgAAW0QoAwD2iTgGgKMhlwEA2BxCGQCwN8QxAByWlMtID+SuBQCAmyOUAQD7QRwDAFIcw5AZAIBNIJQBAHtAHAMA+riUCQBgEwhlAMC2EccAgEHkMgAA60coAwA2TLGkmDgGAIwhlwEAWDlCGQCwSQyQAYCOYOpfAIA1c5K7AMjp0qVLkZGR//znP6XFqqqq+Pj4sLCw+fPnX7p0yUQjABkplhRLA2RIZACgI/y31eqiGQAArAqhjEN7/vnnz58/Lz1ubW1NTU2NjY0tKSnx9vZOS0sz1ghALsQxANBl5DIAACvE5UuOKz8//8cff1QqldJiRUVFY2NjUlKSQqFISUmJjIy8cOGCWq1u39ivXz95KwccEBcrAcCtY4oZAIC1IZRxUHV1dX/961/fe++92NhYqUWtVvv7+ysUCiGEu7u7l5dXdXW1wUaDoYxKpdJfLCoqMn8n/qOmpsZix7Ik+mVzzNS1oau/FUKcXjRMCFFdXW2OQ5hmr28Z/dKJiooyRyWAdWKKGQCAVSGUcURarfbpp59esmSJp6enrrG5udnV1VW36Obm1tjYaLDR4D5PnTplvoJvytfXV8ajmw/9sjnd3jUrubmSvb5l9EvS5g94m5AdsD9SHMOQGQCANSCUcUR5eXleXl7R0dH6jUqlUqPR6BabmpqUSqXBRssVCjgwrlcCALNiyAwAwBoQyjiiw4cPf/rppzt37pQWY2NjFy9eHB4erlarpRaNRtPQ0ODj46PVats3ylM04DCIYwDAMhgyAwCQHaGMI3r99ddff/116XFEREROTs7w4cO1Wq2Li0t+fn5cXFx2dvbYsWO9vLw8PT3bN8pbPGDHiGMAwPKY/RcAICNuiY3/UCgUa9euzcvLGz9+fGVlZUZGhrFGAN2Oe10DgIykXIYbZgMALI+RMo6uvLxc99jPz2/Lli1tVjDYCKC7MDoGAKwBlzIBAGTBSBkAkAejYwCHtX///ri4uLvuumv69On79u3bt29fmxWSk5NVPxszZkyndl5VVRUfHx8WFjZ//vxLly7pjvjggw+GhIQ8+uij8t4w0coxZAYAYGGEMgAgA+IYwGFduXLlqaeemjdv3v79++Pj4xcuXDh8+PA265w5c2bz5s1lZWVlZWWFhYUd33lra2tqampsbGxJSYm3t3daWpoQ4urVq4sWLYqPjy8pKQkJCUlJSenO/tgd/221uhszAQBgbly+BAAWxfVKgIOrrKzs06dPbGysECIxMfHtt9+urKwcMGCA9Gxra2twcPDBgwddXV3bbFhWVvbKK6+cO3cuKCjob3/7m8GdV1RUNDY2JiUlKRSKlJSUyMjICxcunD9/vqWlZfbs2c7OzomJiZs2bWpubu7Vq5dZu2nruGE2AMAyCGUAwEKIYwAIIUJCQnSTtZ0/f76hoWHUqFG6Z+vr6xUKxfz580+cODFs2LD09PTw8PDa2toHHnhACLF+/frg4OCNGzfq7zA7O/uxxx6TQha1Wu3v769QKIQQ7u7uXl5e1dXVwcHBSqUyNzc3JiYmJycnJCTERCKjUql0j4uKirq16zdRU1NjycPdVM/M/UKIqkcGSQ+6zNr61V3ol22hX7ala/2Kiorq9kpgAYQyAGB2xDEAdHr37t27d28hxOHDh9PT05944gndMBkhRGNjY0hISGpqakBAQH5+/pNPPllUVJSfnz9hwoQ33nhDCKHVanNycqZPn+7t7S1tUlBQ8Mgjj0g5S3Nzs/4QGzc3t8bGRhcXl2XLlqWmpq5atUoIsXXrVhPlyTvjjK+vr4xHN6w7hsxYY7+6A/2yLfTLtnShX/p/wPUTdlg5QhkAMCPiGADtaTSaF198cf/+/cuWLZs2bZr+UwEBATk5OdLjWbNm5eXlVVRU1NbWlpeXT506VWp3c3O7fPmyLpTRp1QqNRqNbrGpqUmpVJ44cSIzMzM3NzcoKKi0tHTevHn5+fmenp5m65+90d2YSXA1EwCguxHKAIC5SLP5yl0FAOty48aN5ORkHx+fgoKC9pcRHT9+XKPRjBs3Tlrs0aPHbbfd5unpGRkZmZWVJTUePXpUpVLt2rUrMzNTCNHQ0DBjxgwnJ6eJEyfGxcWp1WppNY1G09DQ4OPjs23btvDw8LCwMCHElClTNmzYcOjQoTZhEG6Ke2YDAMyBUAYAuh8DZAAY88UXXzQ0NLz55pvXrl27du2aEMLV1bVHjx5ffvllUFDQ1atXFyxYsGHDhpCQkIKCgu+//z4sLGzQoEEJCQnHjx9XqVSbN2/+8MMPCwsLY2JiYmJihBAPP/zwxo0blUqlEEKr1bq4uOTn58fFxWVnZ48dO9bLy2v06NHvvvvugQMHgoOD9+7dq1ar9WexQacwATAAoHsRygBAdyKOAWBaZWXl2bNnIyIidC1r1qyZNGnSvHnz1q1bN2nSpPT09JUrV9bV1Y0cOXLDhg2urq7Dhw9/6aWX0tPT6+rqAgMD16xZI03l255CoVi7du3y5cszMjICAwMzMjKEEOHh4UuXLl25cmV9ff2wYcOysrJ8fHws1Ft7xNVMAIBuRCgDAN1j6OpvhfiWOAaAaSkpKSkpKe3bdRM0JiQkJCQktHk2KirK2G01tm/frr/o5+enu7uTzsyZM2fOnNnFimEI0QwAoFsQygDArZJGx5xeNMxe5/8HABjERDMAgFtEKAMAXad/sVJ1dbXM1QAA5MBEMwCALiOUAYAu4uZKAAAJVzMBALqGUAYAOo3ZfAEA7RHNAAA6i1AGADqBOAYAYBrRDACg4whlAKBDiGMAAB2nH80IohkAgBGEMgBwc0wfAwDoAv9ttdXV1YyaAQAYQygDAKYwQAYAcIu4oAkAYAyhDAAYRhwDAOhGRDMAgPYIZQCgLeIYAICZEM0AAPQRygDA/yGOAQBYANEMAEBCKAMA/8FsvgAAS/qvOzSRzgCAQyKUAQAGyAAAZKPLYhg4AwAOiFAGgEMjjgEAWAmuaQIAB0QoA8BBEccAAKwQ1zQBgEMhlAHgiJg+BgBgzbimCQAcBKEMAMfCABkAgA1h4AwA2DdCGQCOgjgGAGCjGDgDAPaKUAaA/SOOAQDYBwbOAICdIZQBYM+IYwAA9oeBMwBgNwhlANgtZvMFANg3Bs4AgK0jlAFghxggAwBwHG0GzgjSGQCwHYQyAOwKcQwAwGGRzgCAzSGUAWAniGMAAJCQzgCArSCUAWAPmD4GAID2SGcAwMoRygCwbQyQAQDgpkhnAMA6EcoAsFXEMQAAdBbpDABYFUIZADaJ65UAALgVpDMAYA0IZQDYGAbIAADQjUhnAEBGhDIAbAZxDAAA5tM+nREENABgZoQyAGwAcQwAABajH8QwfAYAzIpQBoC1Y/oYAADkwvAZADArQhkA1osBMgAAWIn2w2eq2rUDADqLUAaANSKOAQDAavlvq62urvb19RVc3wQAt4ZQBoDV4XolAABsBdc3AcCtIJQBYEUYIAMAgI0yOD2wIKABAJMIZQBYBeIYAADsBgENAHQQoQwAmRHHAABgx4wFNIKMBgAIZQDIi+ljAABwHG1SGAbRAAChDAB5MEAGAAAHxyAaACCUAWBpxDEAAKANBtEAcEyEMgAsaujqb4ljAACAaQyiAeAgCGUAWIg0QOb0omFyFwIAAGyJiUE07Z8FANtCKAPA7PSvV6qurpa5GgAAYMvIaADYE0IZAObF/ZUAoFP279//8ssvnzlzxtfXd+nSpUKICRMmdHzzqqqqZ5555vTp02FhYS+//LKHh4e0z1WrVp05cyYgIGDFihUqlcpc1QMWR0YDwKYRygAwFyb0BYDOunLlylNPPbVixYrJkyd/9NFHCxcuLCoq6vjmra2tqamp8fHxDz300F/+8pe0tLQNGzZcvXp10aJFixcvjo2NXb9+fUpKSmFhofm6AMjLdEbTfgUAkBehDIDuRxwDAF1TWVnZp0+f2NhYIURiYuLbb79dWVk5YMCAsrKyV1555dy5c0FBQS+++KIQYtCgtv9qCiEqKioaGxuTkpIUCkVKSkpkZOSFCxfOnz/f0tIye/ZsZ2fnxMTETZs2NTc39+rVy9J9A+TQPoJhKA0Aq0IoA6Cbcb0SAHRZSEjIli1bpMfnz59vaGgYNWpUbW3twoUL169fHxwcvHHjxpSUlA8++EB/K13Iolar/f39FQqFEMLd3d3Ly6u6ujo4OFipVObm5sbExOTk5ISEhJhIZPSvbOrUIJ1bV1NTY8nDWQz9sjY9M/frL7YfSlP93yvYB9t9v0yjX/qioqK6vRJYAKEMgG7DABkAuEW9e/fu3bu3EOLw4cPp6elPPPHEgAED3nrrrQkTJowZM0YIsWDBgpycnHPnznl7e0ubPPzwwxs3bpRylubmZldXV93e3NzcGhsbXVxcli1blpqaumrVKiHE1q1bTRRw6tQp8/Xupnx9fWU8uvnQL6v23yNlqqurry/9ZZtV7GM0jZ28X+3QLx39P+DMHWZDCGUAdAPiGADoLhqN5sUXX9y/f/+yZcumTZsmhKitrS0vL586daq0gpub2+XLl3WhjD6lUqnRaHSLTU1NSqXyxIkTmZmZubm5QUFBpaWl8+bNy8/P9/T0tEx3AJtz0yueDK4DAF1DKAPgVnG9EgB0lxs3biQnJ/v4+BQUFOguMvL09IyMjMzKypIWjx49qlKpdu3alZmZKYRoaGiYMWOGk5PTxIkT4+Li1Gq1tJpGo2loaPDx8dm2bVt4eHhYWJgQYsqUKRs2bDh06JAU9wDoiI7ENAZXA4CbIpQB0HUMkAGA7vXFF180NDS8+eab165du3btmhDC1dU1JiYmISHh+PHjKpVq8+bNH374YWFhYUxMTExMjPj58iWlUimE0Gq1Li4u+fn5cXFx2dnZY8eO9fLyGj169LvvvnvgwIHg4OC9e/eq1epRo0bJ3E/AxhnMX0hqAHQBoQyAriCOAQBzqKysPHv2bEREhK5lzZo1U6ZMeemll9LT0+vq6gIDA9esWSNN5dueQqFYu3bt8uXLMzIyAgMDMzIyhBDh4eFLly5duXJlfX39sGHDsrKyfHx8LNQfwJF0MKkhpgGgj1AGQKdxvRIAmElKSkpKSkr79qioKGO31di+fbv+op+fn+7+TTozZ86cOXNmdxUJoOM6eOmTwTUBOAJCGQCdwAAZAACAW2EsfOHqJ8AxEco4qI8//nj16tUNDQ0jRoxYsWLFiBEjhBBVVVXPPPPM6dOnw8LCXn75ZQ8PD2ONcEDEMQAAAObT8XlqjK0MwBYRyjiimpqa5557btOmTcOHD8/KykpLS9u5c2dra2tqamp8fPxDDz30l7/8JS0tbcOGDQYb5S4fMuB6JQAAAMvr1LAaE+sDsFqEMo6osrIyKCgoNDRUCJGUlPT++++3trZWVFQ0NjYmJSUpFIqUlJTIyMgLFy6o1er2jf369ZO7B7AcBsgAAABYm86GNUIIQV4DWCVCGUcUHR0dHR0thGhqaiooKBgzZoyTk5Narfb395fu5uDu7u7l5VVdXW2w0WAoo1Kp9BeLioos0hUhhKipqbHYsSzJGvo1dPW3pxcNE0JUV1d31z6toV9mYq9do1+2pQv9MjZ9LADA5hgLa6qrqxlcA1gnQhnHdeTIkTlz5mi12ry8PCFEc3Ozq6ur7lk3N7fGxkaDjQb3durUKXMXbIKvr6+MRzcfGftl1gEy9vp+CfvtGv2yLZ3tV5s/4G1CdgCAfejK4BoiG8D8CGUcV2ho6JEjR4qLix977LGysjKlUqnRaHTPNjU1KZVKumRsWQAAIABJREFUg41yFAvL4XolAAAAx2E6djER2ZDXAN2CUMYR7dixo6mpKTEx0cXFJSoqasWKFSdPnhwyZIharZZW0Gg0DQ0NPj4+Wq22faN8hcPsmNAXAAAAOiaSF4bYAN2CUMYReXh4vPzyyyNHjvT39//oo49aWlpUKpW7u7uLi0t+fn5cXFx2dvbYsWO9vLw8PT3bN8pdPsyCATIAAADouC4PsenI5oDjIJRxRPfcc8/cuXNTU1MvX76sUqnWrVvXp08fIcTatWuXL1+ekZERGBiYkZEhhFAoFO0bYWeIYwAAANC9bpq5kNoAEkIZB5WcnJycnNym0c/Pb8uWLR1phN3geiUAAABY3q2nNkKInpn7u6kcQDaEMoCDYoAMAAAArFZHRspUPTKoqjv2A8iIUAZwRAyQAQAAgK3rmbnf19fX9DodGXEjyG4gH0IZwLEwQAYAAACOo4NpSwezm47vEOggQhnAgTBABgAAAGiv41EL8Q26F6EM4BAYIAMAAADcOnPEN53dM+wJoQxg54hjAAAAAMvrbMiiH+LcdALjLuwf1olQBrBnXK8EAAAA2ARdyFJdXX3TCYzFTUbiuHVPTTA/QhnAPjFABgAAALBjpkbKqFQWLAS3hFAGsEMMkAEAAAAA60coA9gVBsgAAAAAgK0glAHsBwNkAAAAAMCGEMoA9oABMgAAAABgcwhlANtGHAMAAAAANopQBrBhXK8EAAAAALaLUAawSQyQAQAAAABbRygD2B4GyAAAAACAHSCUAWwJA2QAAAAAwG4QygA2gwEyAAAAAGBPCGUAG8AAGQAAAACwP4QygLVjgAwAAAAA2CVCGcB6MUAGAAAAAOwYoQxgpYau/vb/s3f/cVFV+R/Hz/CjVITGgFIJRUxQEELkV4GWPxI0KK1Ic8t9lGtCD4lCQ9JadStt1KTvamqJpqImmpuopVSoa4AmpC24rdCiqICEGo4yoKIz3z+uzRK/RJzhzgyv52P/mHu4c+/nYOs5vuecO8QxAAAAAGDBCGUAkyMtkDkZ5y53IQAAAAAAIyKUAUyL/gkyJSUlctcCAAAAADAiQhnAVPAEGQAAAADoUAhlAJPAVywBAAAAQEdDKAPIjAUyAAAAANAxEcoAcmKBDAAAAAB0WIQygDxYIAMAAAAAHRyhDCADFsgAAAAAAAhlgHbFAhkAAAAAgIRQBmg/LJABAAAAAOgRygDtgQUyAAAAAIAGrOQuALB80gIZEhkAQH0qlSojI6PJH02ZMsXzd4GBgbd12aKioujo6ICAgJiYmKqqKqkxJyfnySef9PPze/755wsLC++0dAAAYCCEMoARKabvZcsSAKCBmpqazMzMtLS0Jn8aHh7+8ssvr127Njs7Ozs7e8+ePa2/slarTUhIiIyM3Ldvn4uLS2JionS7uLi46Ojoffv2+fn5xcfHG6YbAADgjrF9CTAW4hgAQJMWLVqUn5+v0+ka/0ir1ZaVlfn6+trZ2TX4UXZ29sKFC8vKynx8fD777LMmr5yXl6dWqydNmqRQKOLj40NDQy9cuHD+/Pm6urqJEydaW1u/8MILa9asqa2t7dy5s+E7BgAAbhOhDGAUJDIAgObMmTNHCDFx4sTGP6qoqFAoFDExMQUFBe7u7klJSUFBQeXl5U888cSnn366ZcuW1atXN3hLSkrKn/70JylkKS4u9vDwUCgUQggHBwdnZ+eSkhJfX1+lUrlx48YxY8akpqb6+fm1kMh4enrqXze3u8pISktL2/N27YZ+mRf6ZV7oV33h4eEGrwTtgFAGMDCe6QsAaDO1Wu3n55eQkDBgwID09PRXX301IyMjPT19yJAh0sNlYmNjH3nkkaeeesrFxUV6y+7du5999lkpZ6mtra2/xMbe3l6tVtva2s6cOTMhIeGDDz4QQmzZsqWFAuR94oybm5uMdzce+mVe6Jd5oV969f8Cr5+ww8QRygCGxAIZAMCdGDBgQGpqqvR6/PjxmzZtysvLKy8vP3To0KhRo6R2e3v7ixcv6kOZ+pRKpUaj0R9WV1crlcqCgoLFixdv3LjRx8cnKytr6tSp6enpTk5O7dAdAADQMkIZwDBYIAMAuHP5+fkajebhhx+WDm1sbDp16uTk5BQaGpqcnCw1Hj161NPT8+uvv168eLEQorKycuzYsVZWVkOHDo2KiiouLpZO02g0lZWVrq6uX3zxRVBQUEBAgBBixIgRq1atys3NHT16tBz9AwAAf0AoAxgAC2QAAHdCp9P985//9PHxqampiY2NXbVqlZ+f3+7du8+dOxcQENCzZ88JEybk5+d7enquXbv2H//4x549e8aMGTNmzBghxDPPPLN69WqlUildx9bWNj09PSoqKiUlJTg42NnZefDgwRs2bDh48KCvr++BAweKi4sHDhwod48BAIAQhDLAHWKBDADgzmm12qlTp37yySePPfZYUlLSvHnzzp496+XltWrVKjs7u379+s2fPz8pKens2bPe3t7Lly+XHuXbmEKhWLly5axZs1Qqlbe3t0qlEkIEBQXNmDFj3rx5FRUV7u7uycnJrq6u7ds/AADQNEIZoO1YIAMAaLNNmzbpX1tbW+sf0DhhwoQJEyY0ODk8PLy5r9XYtm1b/cO+ffumpaU1OGfcuHHjxo2704oBAIChWcldAGCuSGQAAAAAAHeClTLAbWPLEgAAAADgzhHKALeHBTIAAAAAAIMglAFaiwUyAAAAAAADIpQBWoUFMgAAAAAAw+JBv8CtkcgAAAAAAAyOlTJAS9iyBAAAAAAwEkIZoFkskAEAAAAAGA/bl4CmkcgAAAAAAIyKlTJAQ2xZAgAAAAC0A0IZ4A9YIAMAAAAAaB+EMsD/kMgAAAAAMCnSQv7b4mGMOmAchDKAEGxZAgAAAGAcbUhVhDihf9WGf6R47rr9G0ImhDIAC2QAAAAAtKRNwcpNt/tvjZKSEjc3tzbfDuaFUAYdHYkMAAAA0EG0OVvhnwwwEkIZdFxsWQIAAADMV5+lJ+pv82kNJv8wNYQy6KBYIAMAAACYjjasYTkZ5842H5g7Qhl0RCQyAAAAgLHdVs7Shvl5SUnJ7b4FMDWEMuhY2LIEAAAAtJmxcxagoyGUQQfCAhkAAACgsdZHLUynAcMilEFHQSIDAACADuWPUUtLD8RlngzIhVAGlo8tSwAAALAkrVzYop8Al5SU8EBcwDQRynRQOTk5CxYsOH36tJub24wZM4YMGSKEKCoqmj179smTJwMCAhYsWNCtW7fmGs0IC2QAAABgRloTuDC/BSwGoUxHdOnSpddee23u3LnDhg378ssvp02blpGRcd999yUkJERHRz/99NMfffRRYmLiqlWrtFpt40a5y78NJDIAAAAwKbfMXJi+Ah0KoUxHdOzYsXvuuScyMlII8cILL6xbt+7YsWMODg5qtXrSpEkKhSI+Pj40NPTChQvFxcWNGx0dHeXuQauQyAAAAKCdkbkAuC2EMh2Rn59fWlqa9Pr8+fOVlZUDBw7ct2+fh4eHQqEQQjg4ODg7O5eUlBQXFzdubDKU8fT0rH+YkZFh/H7cVFpa2qClz9ITQoiTce4lJSXtVobBNe6XZbDUfgnL7Rr9Mi9t6Fd4eLgxKgEAi0TmAsCwCGU6oi5dunTp0kUI8eOPPyYlJU2ePLl79+61tbV2dnb6c+zt7dVqdZONTV6zsLDQ2GW3oP5zyyxpgYylPo/NUvslLLdr9Mu83G6/GvwF3iBkB4COpuXYxWLmmQBMBKFMB6XRaN5///2cnJyZM2eOHj1aCKFUKjUajf6E6upqpVLZZKMM5baaJSUyAAAAMAZiFwCmg1CmI7px48aUKVNcXV13797duXNnqbF3797FxcXSa41GU1lZ6erqqtPpGjfKU3QrkMgAAABA0kLy0sKM0aw3vwMwR4QyHdH+/fsrKys//vjjq1evXr16VQhhZ2fn7+9va2ubnp4eFRWVkpISHBzs7Ozs5OTUuFHu8psgDbokMgAAAB1K25IXADAdhDId0bFjx86cORMSEqJvWb58+YgRI1auXDlr1iyVSuXt7a1SqYQQCoWicaOp6bP0BIMuAACApSJ5AWDBCGU6ovj4+Pj4+Mbtffv21X8rU8uNpkMxfe/JOHe5qwAAAMCdai58IXkBYMEIZWDGpIfIsPUXAADAjBC+AIAeoQzMEg+RAQAAMHGELwBwS4QyMD98yxIAAIBJaTJ/YcIGALdEKAMzQyIDAAAgo3r5ywl9I9MzAGgbQhmYExIZAACAdtPC+peSkhI3N7f2LggALA6hDMwGiQwAAICRsP8IAGRBKAMzwGN9AQAADKhxBMNECwBkQSgDU8cCGQAAgDZjCQwAmDJCGZg0EhkAAIDWYwkMAJgXQhmYLhIZAACAFhDBAIC5I5SBiSKRAQAAaKBBCsNkCQDMHaEMTBGJDACgg1OpVEIIPz+/8PDw23pjUVHR7NmzT548GRAQsGDBgm7dugkhcnJyPvjgg9OnTw8YMGDu3Lmenp5GKRqGxkIYALB4hDIwLXzREgCgg6upqTl48GBaWtqCBQtuN5HRarUJCQnR0dFPP/30Rx99lJiYuGrVqpqamri4uNdffz0yMvLTTz+Nj4/fs2ePkYrHHWIhDAB0NIQyMCEskAEAYNGiRfn5+Tqdrn5jdnb2woULy8rKhBA+Pj7vv/9+z549G783Ly9PrVZPmjRJoVDEx8eHhoZeuHDh/PnzdXV1EydOtLa2fuGFF9asWVNbW9u5c+d26g9aRAoDAB0coQxMBYkMAABCiDlz5gghJk6cqG8pLy+fNm3ap59+6uvru3r16n379jVIZPQhS3FxsYeHh0KhEEI4ODg4OzuXlJT4+voqlcqNGzeOGTMmNTXVz8+vhUSm/s6mjIwMg/euBaWlpe15u3bToF99lp6of3gyzr3+YUlJSTuUZBAd5M/LYtAv89K2ft3u4kqYCEIZmAQSGQAAmpOenj5kyJDAwEAhRGxsbGpqallZmYuLi/TTZ555ZvXq1VLOUltba2dnp3+jvb29Wq22tbWdOXNmQkLCBx98IITYsmVLC/cqLCw0Yk9uxc3NTca7G0mfpSeE+F8QY0kTHov88xL0y9zQL736f4Hz7DAzQigD+ZHIAADQgvLy8kOHDo0aNUo6tLe3v3jxoj6UqU+pVGo0Gv1hdXW1UqksKChYvHjxxo0bfXx8srKypk6dmp6e7uTk1E7VdzwNdiSdjHO31H80AgDuHKEMZEYiAwBAy5ycnEJDQ5OTk6XDo0ePenp6fv3114sXLxZCVFZWjh071srKaujQoVFRUcXFxdJpGo2msrLS1dX1iy++CAoKCggIEEKMGDFi1apVubm5o0ePlqs7Fql+ENNgYmNGO5IAAO2PUAZyIpEBAOCWxowZM2HChPz8fE9Pz7Vr1/7jH//Ys2fPmDFjxowZI37fvqRUKoUQOp3O1tY2PT09KioqJSUlODjY2dl58ODBGzZsOHjwoK+v74EDB4qLiwcOHCh3n8weD+gFABgEoQxkQyIDAEBr9OvXb/78+UlJSWfPnvX29l6+fLn0KN/GFArFypUrZ82apVKpvL29VSqVECIoKGjGjBnz5s2rqKhwd3dPTk52dXVt3x5YiBaWwwAA0DaEMpAHiQwAAC3YtGlT/cPw8PDmvlZj27Zt9Q/79u2blpbW4Jxx48aNGzfOsBV2EAQxAACjIpRBe5MmN0xrAACAaSKIAQC0G0IZtCsWyAAAABNEEAMAkAWhDNoPiQwAADAdBDEAANkRyqCdkMgAAADZEcQAAEwKoQzaA4kMAACQC0EMAMBkEcrA6EhkAABA+9NnMcxDAAAmi1AGxkUiAwAA2g2LYgAA5oVQBkZEIgMAANoBi2IAAGaKUAbGQiIDAACMiiwGAGDuCGVgFCQyAADAGNigBACwJIQyMDwSGQAAYFgsigEAWCRCGRgYiQwAADAUshgAgGUjlIEhkcgAAIA7RxYDAOggCGVgMCQyAADgTpDFAAA6GkIZGAaJDAAAaBuyGABAh0UoA8NgFgUAAG6XFMdIs4iSkhKZqwEAoN0RygAAAKBdsTQGAAAJoQwAAADaA1kMAAANEMoAAADAuOpvUwIAAHqEMgAAADAKlsYAANAyQhkAAAAYGEtjAABoDUIZAAAAGAZLYwAAuC2EMgAAALhTLI0BAKANCGUAAADQdsQxAAC0GaEMAAAAbhs7lQAAuHOEMgAAALgNLI0BAMBQCGUAAADQKsQxAAAYFqEMAAAAboE4BgAAYyCUAQAAQLOIYwAAMB5CGQAAADSBOAYAAGMjlAEAAMAfEMcAANA+CGUAAABwE3EMAADtiVAGAAAAQghRFLmCOAYAgPZkJXcBAAAAMAkeu2LlLgEAgI6FUAYAAAAAAEAGhDIAAAAAAAAyIJQBAAAAAACQAaEMAAAAAACADAhlAAAAAAAAZEAoAwAAAAAAIANCGQAAAAAAABkQygAAAAAAAMiAUAYAAAAAAEAGhDIwe+Hh4XKXYBT0y+xYatfol3mx1H6hI7DU/3rpl3mhX+aFfsECEMp0aCqVKiMjQ39YVFQUHR0dEBAQExNTVVXVQiMAADCSKVOmeP4uMDAwMDDwtt7eeOBev3695x+tWLHCOLUDAIDbQyjTQdXU1GRmZqalpelbtFptQkJCZGTkvn37XFxcEhMTm2sEAADGc/r06bVr12ZnZ2dnZ+/Zsyc3N7f1721y4I6Ojs7+3XfffderV68xY8YYrXwAAHAbbOQuAPJYtGhRfn6+TqfTt+Tl5anV6kmTJikUivj4+NDQ0AsXLhQXFzdudHR0lLFyAAAsmFarLSsr8/X1tbOzq9+enZ29cOHCsrIyHx+f999/XwjRs2fPxm9vcjR3dHTs3LmzdMKCBQtefPHF3r17t0NfAADALRHKdFBz5swRQkycOFHfUlxc7OHhoVAohBAODg7Ozs4lJSVNNjYZynh6erZX7U2Q9+7GQ7/MjqV2jX6ZF0vtVwdRUVGhUChiYmIKCgrc3d2TkpKCgoLKy8unTZv26aef+vr6rl69Oj4+fuvWrfXfVVtbK8UuLQ/chYWF2dnZ6enpLRTAgG4M9Mu80C/zQr9g7ghlcFNtbW39D+Xs7e3VanWTjY3fW1hY2B4lAgBg6dRqtZ+fX0JCwoABA9LT01999dWMjIz09PQhQ4ZID5eJjY1NTU0tKytzcXGR3vLMM8+sXr1aCmVaHriTk5Pj4uKsra2buzsDOgAA7YxQBjcplUqNRqM/rK6uViqVTTbKUR0AAB3CgAEDUlNTpdfjx4/ftGlTXl5eeXn5oUOHRo0aJbXb29tfvHhRH8rU18LA/csvv/znP/9Zvny5kXsAAABuA6EMburdu3dxcbH0WqPRVFZWurq66nS6xo3y1QgAgIXLz8/XaDQPP/ywdGhjY9OpUycnJ6fQ0NDk5GSp8ejRo56enl9//fXixYuFEJWVlWPHjrWysho6dGhUVFRzA/fWrVufeuopKyu+5AEAABPCwIyb/P39bW1t09PTtVptSkpKcHCws7Nzk41yVwoAgMWqqamJjY3Nzc2tq6vbsWPHuXPnAgICxowZc+DAgfz8/KtXr37yySdJSUnW1tZjxozZu3fv3r17PT09t2/fvnfv3rlz57YwcH/77bePPvqovL0DAAANEMrgJoVCsXLlyk2bNoWFhR07dkylUjXXCAAAjCQkJCQpKWnevHkhISFbt25dtWqVnZ1dv3795s+fn5SUFBIS8v333y9fvlx6lG9jzQ3cp06dOnfu3MCBA9uxKwAA4NYU9b8UGQAAAAAAAO2DlTIAAAAAAAAyIJQBAAAAAACQAaEMAAAAAACADAhlAAAAAAAAZEAoAzP2/fffR0VFBQYGTp069cKFC3KXYwAqlSojI0N/WFRUFB0dHRAQEBMTU1VVJWNhd6hBv1poNDsNepGTkxMVFfXQQw899dRT33//vYyF3aEG/dq5c+eoUaP8/PwmTJhw/PhxGQu7Q03+V1dVVRUaGvrLL7/IUpJBNOjXlClTPH8XGBgoY2FAa1jeaC4Y0M0No7l5YTSHhSGUgbk6c+bMtGnTEhMTs7KyBgwY8Prrr8td0R2pqanJzMx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"
-     },
-     "metadata": {
-      "image/png": {
-       "height": 900,
-       "width": 1200
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "427ccee4-3be3-4dd8-bed5-88f62368b5e4",
+      "metadata": {
+        "tags": [
+          "remove_cell"
+        ],
+        "vscode": {
+          "languageId": "octave"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "a = 0.1010;\n",
+        "b = 0.0404;\n",
+        "e_r = 3.8;\n",
+        "m = 1;\n",
+        "n = 4;\n",
+        "l = 1;\n",
+        "\n",
+        "mu_0 = 4e-7*pi;\n",
+        "e_0 = 8.85e-12;\n",
+        "c_0 = 1/sqrt(mu_0*e_0);\n",
+        "mu = mu_0;\n",
+        "e = e_0*e_r;\n",
+        "c = 1/sqrt(mu*e);\n",
+        "\n",
+        "E0 = 1;\n",
+        "\n",
+        "f = 7.68e9;\n",
+        "w = 2*pi*f;\n",
+        "\n",
+        "k = w*sqrt(mu*e);\n",
+        "kc = sqrt((m*pi/a)^2 + (n*pi/b)^2);\n",
+        "be = sqrt(k^2-kc^2);\n",
+        "\n",
+        "P = 50;  % Grid precision\n",
+        "\n",
+        "[X,Y] = meshgrid(linspace(0,a,P),linspace(0,b,P));"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7e0d3d16-da82-409b-8c5e-e9ab452daf90",
+      "metadata": {},
+      "source": [
+        "## Properties of the transmitted mode"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "f3695b5c-bf2b-4592-8cb9-7385e37e059e",
+      "metadata": {
+        "tags": [
+          "remove_cell"
+        ],
+        "vscode": {
+          "languageId": "octave"
+        }
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Fc14 = 7.6532e+09\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "% Cutoff frequency of the analysed mode\n",
+        "Fc14 = c_0/2/sqrt(e_r)*sqrt((m/a)^2+(n/b)^2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "40249f48-2b9e-4abd-bc92-840d135167ea",
+      "metadata": {
+        "tags": []
+      },
+      "source": [
+        "The transmitted mode being analysed is the $\\textit{TM}_{(1,4)}$, with cutoff frequency\n",
+        "\n",
+        "$$ f_{c(m,n)} = \\frac{1}{2\\sqrt{\\mu\\epsilon}} \\sqrt{\\left(\\frac{m}{a}\\right)^2 + \\left(\\frac{n}{b}\\right)^2} \\approx 7.65 \\textit{GHz} $$"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a72024a1-abab-4534-b1dd-01e6d872fb68",
+      "metadata": {
+        "tags": []
+      },
+      "source": [
+        "### Field components\n",
+        "\n",
+        "The mode is _transverse magnetic_, which means that the longitudinal component of the magnetic field is null ($H_z = 0$).\n",
+        "\n",
+        "The longitudinal component of the electric field is:\n",
+        "\n",
+        "$$ E_z(x,y,z) = E_0 \\sin \\frac{m \\pi x}{a}\\sin\\frac{n \\pi y}{b} e^{-j\\beta z} $$\n",
+        "\n",
+        "where $\\beta$ is a function of the frequency of the transmitted wave and of the geometry of the waveguide ($a$ and $b$). \n",
+        "\n",
+        "For TM modes the Maxwell equations (written in phasor form for linear media and without sources) can be reduced as follows:\n",
+        "\n",
+        "$$ \\vec{E_t} = \\frac{-j \\beta}{k_c^2} \\nabla E_z $$\n",
+        "$$ \\vec{H_t} = \\frac{\\hat{z} \\times \\vec{E_t}}{Z_{TM}} $$\n",
+        "\n",
+        "where $Z_{TM} = \\frac{\\beta}{\\omega\\epsilon}$ is the modal impedance.\n",
+        "\n",
+        "All the other components can then be calculated as a function of the longitudinal component of the electric field:\n",
+        "\n",
+        "$$ E_x = \\frac{-j \\beta}{k_c^2}\\frac{m\\pi}{a} E_0 \\cos \\frac{m \\pi x}{a}\\sin\\frac{n \\pi y}{b} e^{-j\\beta z} $$\n",
+        "$$ E_y = \\frac{-j \\beta}{k_c^2}\\frac{n\\pi}{b} E_0 \\sin \\frac{m \\pi x}{a}\\cos\\frac{n \\pi y}{b} e^{-j\\beta z} $$\n",
+        "$$ H_x = \\frac{ j \\omega \\epsilon}{k_c^2}\\frac{n\\pi}{b} E_0 \\sin \\frac{m \\pi x}{a}\\cos\\frac{n \\pi y}{b} e^{-j\\beta z} $$\n",
+        "$$ H_y = \\frac{-j \\omega \\epsilon}{k_c^2}\\frac{m\\pi}{a} E_0 \\cos \\frac{m \\pi x}{a}\\sin\\frac{n \\pi y}{b} e^{-j\\beta z} $$\n",
+        "\n",
+        "#### Electric field representation "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "654bca41-cf11-4f0d-a15d-d13922ed3b27",
+      "metadata": {
+        "tags": [
+          "remove_cell"
+        ],
+        "vscode": {
+          "languageId": "octave"
+        }
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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"
+          },
+          "metadata": {
+            "image/png": {
+              "height": 900,
+              "width": 1200
+            }
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "% Phase in the time dimension \n",
+        "p = pi/4;    # Change it from 0 to 2*pi to see the variation of the E field\n",
+        "% Phase in the z dimension\n",
+        "z = 0;       # Change it from 0 to 2*pi to see the variation of the E field\n",
+        "\n",
+        "EX0 = -j*E0*be*m*pi/kc^2/a;\n",
+        "EY0 = -j*E0*be*n*pi/kc^2/b;\n",
+        "EZ0 = E0;\n",
+        "\n",
+        "% Phasorial fields\n",
+        "Ex = EX0.*cos((m*pi/a).*X).*sin((n*pi/b).*Y);\n",
+        "Ey = EY0.*sin((m*pi/a).*X).*cos((n*pi/b).*Y);\n",
+        "Ez = EZ0.*sin((m*pi/a).*X).*sin((n*pi/b).*Y);\n",
+        "\n",
+        "% Vector fields\n",
+        "Fx = real(Ex.*exp(i*p).*exp(j*be*z));\n",
+        "Fy = real(Ey.*exp(i*p).*exp(j*be*z));\n",
+        "Fz = real(Ez.*exp(i*p).*exp(j*be*z));\n",
+        "\n",
+        "F = sqrt(Fx.^2+Fy.^2+Fz.^2);\n",
+        "\n",
+        "EX0_ = abs(EX0);\n",
+        "EY0_ = abs(EY0);\n",
+        "EZ0_ = abs(EZ0);\n",
+        "\n",
+        "figure(\"position\", [0 0 1200 900])\n",
+        "colormap(\"turbo\");\n",
+        "subplot(2,2,1);\n",
+        "imagesc([0 a],[0 b],Fx,[-EX0_,EX0_]);\n",
+        "colorbar;\n",
+        "title(\"X component\");\n",
+        "subplot(2,2,2);\n",
+        "imagesc([0 a],[0 b],Fy,[-EY0_,EY0_]);\n",
+        "colorbar;\n",
+        "title(\"Y component\");\n",
+        "subplot(2,2,3);\n",
+        "imagesc([0 a],[0 b],Fz,[-EZ0_,EZ0_]);\n",
+        "colorbar;\n",
+        "title(\"Z component\");\n",
+        "subplot(2,2,4);\n",
+        "imagesc([0 a],[0 b],F,[0,E0]);\n",
+        "colorbar;\n",
+        "title(\"ABS value\");"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "768afe71-880c-48c7-86d6-f31c94306ad8",
+      "metadata": {},
+      "source": [
+        "### Frequency dependent properties\n",
+        "\n",
+        "Below are reported in a range of $[1.1 \\div 2]f_{c(1,4)}$ the main mode properties that are a function of frequency:\n",
+        "\n",
+        "* the propagation constant $\\beta = 2\\pi f \\sqrt{\\mu \\epsilon} \\sqrt{1-\\left(\\frac{f_{c(1,4)}}{f}\\right)^2}$\n",
+        "\n",
+        "* the group velocity $v_g = c \\sqrt{1-\\left(\\frac{f_{c(1,4)}}{f}\\right)^2}$\n",
+        "\n",
+        "* the phase velocity $v_f = \\frac{c}{\\sqrt{1-\\left(\\frac{f_{c(1,4)}}{f}\\right)^2}}$\n",
+        "\n",
+        "* the wavelength $\\lambda = \\frac{2 \\pi }{\\beta}$\n",
+        "\n",
+        "* the modal impedance $Z_{TM} = \\frac{\\beta}{2 \\pi f \\epsilon}$"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "31968a5e-6e20-4f8b-846f-29091da3d399",
+      "metadata": {
+        "tags": [
+          "remove_cell"
+        ],
+        "vscode": {
+          "languageId": "octave"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "F = linspace(1.1*Fc14, 2*Fc14);\n",
+        "sqrt_f = sqrt(1-(Fc14./F).^2);\n",
+        "\n",
+        "be_f = (2*pi*sqrt(mu*e)).*F.*sqrt_f;\n",
+        "vg_f = c.*sqrt_f;\n",
+        "vf_f = c./sqrt_f;\n",
+        "lm_f = (2*pi)./be_f;\n",
+        "zm_f = be_f./(2*pi*e)./F;"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "c8974bb8-02cb-4fd4-b870-afb2100fd01d",
+      "metadata": {
+        "tags": [
+          "remove_input"
+        ],
+        "vscode": {
+          "languageId": "octave"
+        }
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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"
+          },
+          "metadata": {
+            "image/png": {
+              "height": 900,
+              "width": 1200
+            }
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "figure(\"position\", [0 0 1200 900])\n",
+        "\n",
+        "subplot(2,2,1);\n",
+        "plot(F/1e9, be_f);\n",
+        "title('Propagation constant \\beta [rad/m]');\n",
+        "xlabel(\"Frequency [GHz]\");\n",
+        "xlim([1.1*Fc14 2*Fc14]/1e9);\n",
+        "grid on;\n",
+        "\n",
+        "subplot(2,2,2);\n",
+        "plot(F/1e9, vg_f, F/1e9, vf_f);\n",
+        "title('Group and phase velocities (v_g and v_f) [m/s]');\n",
+        "xlabel(\"Frequency [GHz]\");\n",
+        "legend('v_g', 'v_f');\n",
+        "xlim([1.1*Fc14 2*Fc14]/1e9);\n",
+        "grid on;\n",
+        "\n",
+        "subplot(2,2,3);\n",
+        "plot(F/1e9, lm_f*1e3);\n",
+        "title('Wavelength \\lambda [mm]');\n",
+        "xlabel(\"Frequency [GHz]\");\n",
+        "xlim([1.1*Fc14 2*Fc14]/1e9);\n",
+        "grid on;\n",
+        "\n",
+        "subplot(2,2,4);\n",
+        "plot(F/1e9, zm_f);\n",
+        "title('Modal impedance Z_{TM} [\\Omega]');\n",
+        "xlabel(\"Frequency [GHz]\");\n",
+        "xlim([1.1*Fc14 2*Fc14]/1e9);\n",
+        "grid on;"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Octave (xoctave)",
+      "language": "Octave",
+      "name": "xoctave"
+    },
+    "language_info": {
+      "version": "7.3.0",
+      "codemirror_mode": "octave",
+      "file_extension": ".m",
+      "mimetype": "text/x-octave",
+      "name": "Octave",
+      "nbconvert_exporter": "",
+      "pygments_lexer": "octave"
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "state": {},
+        "version_major": 2,
+        "version_minor": 0
       }
-     },
-     "output_type": "display_data"
     }
-   ],
-   "source": [
-    "figure(\"position\", [0 0 1200 900])\n",
-    "\n",
-    "subplot(2,2,1);\n",
-    "plot(F/1e9, be_f);\n",
-    "title('Propagation constant \\beta [rad/m]');\n",
-    "xlabel(\"Frequency [GHz]\");\n",
-    "xlim([1.1*Fc14 2*Fc14]/1e9);\n",
-    "grid on;\n",
-    "\n",
-    "subplot(2,2,2);\n",
-    "plot(F/1e9, vg_f, F/1e9, vf_f);\n",
-    "title('Group and phase velocities (v_g and v_f) [m/s]');\n",
-    "xlabel(\"Frequency [GHz]\");\n",
-    "legend('v_g', 'v_f');\n",
-    "xlim([1.1*Fc14 2*Fc14]/1e9);\n",
-    "grid on;\n",
-    "\n",
-    "subplot(2,2,3);\n",
-    "plot(F/1e9, lm_f*1e3);\n",
-    "title('Wavelength \\lambda [mm]');\n",
-    "xlabel(\"Frequency [GHz]\");\n",
-    "xlim([1.1*Fc14 2*Fc14]/1e9);\n",
-    "grid on;\n",
-    "\n",
-    "subplot(2,2,4);\n",
-    "plot(F/1e9, zm_f);\n",
-    "title('Modal impedance Z_{TM} [\\Omega]');\n",
-    "xlabel(\"Frequency [GHz]\");\n",
-    "xlim([1.1*Fc14 2*Fc14]/1e9);\n",
-    "grid on;"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Octave (xoctave)",
-   "language": "Octave",
-   "name": "xoctave"
-  },
-  "language_info": {
-   "codemirror_mode": "octave",
-   "file_extension": ".m",
-   "mimetype": "text/x-octave",
-   "name": "Octave",
-   "nbconvert_exporter": "",
-   "pygments_lexer": "octave",
-   "version": "7.3.0"
   },
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {},
-    "version_major": 2,
-    "version_minor": 0
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "nbformat": 4,
+  "nbformat_minor": 5
 }
diff --git a/notebooks/xeus-octave.ipynb b/notebooks/xeus-octave.ipynb
index 066c9c4..6510f7a 100644
--- a/notebooks/xeus-octave.ipynb
+++ b/notebooks/xeus-octave.ipynb
@@ -1,9370 +1,9370 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# *Xeus-Octave*: A native Octave kernel for jupyter with advanced visualization\n",
-    "\n",
-    "*Xeus-Octave* is an Octave kernel for Jupyter built with Xeus, a C++ framework\n",
-    "for building Jupyter kernels, which means that it is completely native, and does\n",
-    "not run a virtual Octave session in a forked process.\n",
-    "\n",
-    "Thanks to its being native, xeus-octave has access to the internal representation\n",
-    "of all Octave objects, and thus it is able to provide advanced visualisation of\n",
-    "many types with ease."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Native Octave plots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "image/png": 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"
-     },
-     "metadata": {
-      "image/png": {
-       "height": 420,
-       "width": 560
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "graphics_toolkit(\"notebook\")  % The default\n",
-    "sombrero"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Advanced plots using [Plotly](https://github.com/plotly/plotly.js), with zoom, cursors, and hover tooltips (experimental)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# *Xeus-Octave*: A native Octave kernel for jupyter with advanced visualization\n",
+        "\n",
+        "*Xeus-Octave* is an Octave kernel for Jupyter built with Xeus, a C++ framework\n",
+        "for building Jupyter kernels, which means that it is completely native, and does\n",
+        "not run a virtual Octave session in a forked process.\n",
+        "\n",
+        "Thanks to its being native, xeus-octave has access to the internal representation\n",
+        "of all Octave objects, and thus it is able to provide advanced visualisation of\n",
+        "many types with ease."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Native Octave plots"
+      ]
+    },
     {
-     "data": {
-      "application/vnd.plotly.v1+json": {
-       "data": [
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
         {
-         "autocolorscale": "false",
-         "cauto": "false",
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-           "rgb(42,119,142)"
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-           0.4126984126984127,
-           "rgb(40,123,142)"
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-           0.42857142857142855,
-           "rgb(39,127,142)"
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-           0.6507936507936508,
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-           0.6666666666666666,
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-           0.7142857142857143,
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-           "rgb(90,199,99)"
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-           "rgb(117,208,83)"
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-        },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "graphics_toolkit(\"notebook\")  % The default\n",
+        "sombrero"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Advanced plots using [Plotly](https://github.com/plotly/plotly.js), with zoom, cursors, and hover tooltips (experimental)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [
         {
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+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "graphics_toolkit(\"plotly\")\n",
+        "\n",
+        "a = [1:0.1:13];\n",
+        "theta = linspace (0,2*pi,1000);\n",
+        "rho = sin (7*theta);\n",
+        "\n",
+        "subplot(2,2,1);\n",
+        "sombrero()\n",
+        "grid on;\n",
+        "box on;\n",
+        "\n",
+        "subplot(2,2,2);\n",
+        "plot(theta, rho);\n",
+        "grid on;\n",
+        "box on;\n",
+        "\n",
+        "subplot(2,2,3);\n",
+        "polar(a,a)\n",
+        "grid on;\n",
+        "box on;\n",
+        "\n",
+        "subplot(2,2,4);\n",
+        "polar(theta, rho);\n",
+        "grid on;\n",
+        "box on;"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2020-08-26T09:02:37.693846Z",
+          "iopub.status.busy": "2020-08-26T09:02:37.693787Z",
+          "iopub.status.idle": "2020-08-26T09:02:37.698708Z",
+          "shell.execute_reply": "2020-08-26T09:02:37.698628Z",
+          "shell.execute_reply.started": "2020-08-26T09:02:37.693821Z"
+        }
+      },
+      "source": [
+        "## Matrices displayed as tables or $\\LaTeX$"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "tags": []
+      },
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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "graphics_toolkit(\"plotly\")\n",
-    "\n",
-    "a = [1:0.1:13];\n",
-    "theta = linspace (0,2*pi,1000);\n",
-    "rho = sin (7*theta);\n",
-    "\n",
-    "subplot(2,2,1);\n",
-    "sombrero()\n",
-    "grid on;\n",
-    "box on;\n",
-    "\n",
-    "subplot(2,2,2);\n",
-    "plot(theta, rho);\n",
-    "grid on;\n",
-    "box on;\n",
-    "\n",
-    "subplot(2,2,3);\n",
-    "polar(a,a)\n",
-    "grid on;\n",
-    "box on;\n",
-    "\n",
-    "subplot(2,2,4);\n",
-    "polar(theta, rho);\n",
-    "grid on;\n",
-    "box on;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2020-08-26T09:02:37.693846Z",
-     "iopub.status.busy": "2020-08-26T09:02:37.693787Z",
-     "iopub.status.idle": "2020-08-26T09:02:37.698708Z",
-     "shell.execute_reply": "2020-08-26T09:02:37.698628Z",
-     "shell.execute_reply.started": "2020-08-26T09:02:37.693821Z"
-    }
-   },
-   "source": [
-    "## Matrices displayed as tables or $\\LaTeX$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+      ],
+      "source": [
+        "disp(\"A table matrix: \\n\")\n",
+        "displayformat matrix html;\n",
+        "m = ones(3,3)\n",
+        "disp(\"\\nAnd a latex one:\\n\")\n",
+        "displayformat matrix latex;\n",
+        "m"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "A table matrix: \n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Structs displayed as interactive tree views"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "<table><tr><th><i>m<i></th><th>1</th><th>2</th><th>3</th></tr><tr><th>1</th><td>1</td><td>1</td><td>1</td></tr><tr><th>2</th><td>1</td><td>1</td><td>1</td></tr><tr><th>3</th><td>1</td><td>1</td><td>1</td></tr></table>"
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/json": {
+              "age": 33,
+              "knows_octave": true,
+              "name": "Alice",
+              "pets": [
+                {
+                  "name": "Rex",
+                  "type": "Dog"
+                },
+                {
+                  "name": "Felix",
+                  "type": "Cat"
+                }
+              ]
+            },
+            "text/plain": [
+              "ans =     name = Alice\n",
+              "    age = 33\n",
+              "    knows_octave = 1\n",
+              "    pets =\n",
+              "\n",
+              "      2x1 struct array containing the fields:\n",
+              "\n",
+              "        name\n",
+              "        type\n",
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "m =    1   1   1\n",
-       "   1   1   1\n",
-       "   1   1   1\n"
+      "source": [
+        "struct(\n",
+        "    \"name\",\"Alice\",\n",
+        "    \"age\", 33,\n",
+        "    \"knows_octave\", true,\n",
+        "    \"pets\", [\n",
+        "        struct(\"name\", \"Rex\", \"type\", \"Dog\"),\n",
+        "        struct(\"name\", \"Felix\", \"type\", \"Cat\")\n",
+        "    ]\n",
+        ")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "And a latex one:\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2020-08-26T09:07:10.387277Z",
+          "iopub.status.busy": "2020-08-26T09:07:10.387090Z",
+          "iopub.status.idle": "2020-08-26T09:07:10.395463Z",
+          "shell.execute_reply": "2020-08-26T09:07:10.395377Z",
+          "shell.execute_reply.started": "2020-08-26T09:07:10.387194Z"
+        }
+      },
+      "source": [
+        "## Symbolic classes displayed in $\\LaTeX$"
+      ]
     },
     {
-     "data": {
-      "text/latex": [
-       "$$ m = \\begin{bmatrix} 1 & 1 & 1 \\\\ 1 & 1 & 1 \\\\ 1 & 1 & 1 \\end{bmatrix}$$"
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/latex": [
+              "$$f=\\sin^{3}{\\left(\\frac{x}{2} \\right)}$$"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/latex": [
+              "$$ans=\\frac{3 \\sin^{2}{\\left(\\frac{x}{2} \\right)} \\cos{\\left(\\frac{x}{2} \\right)}}{2}$$"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "m =    1   1   1\n",
-       "   1   1   1\n",
-       "   1   1   1\n"
+      "source": [
+        "pkg load symbolic\n",
+        "\n",
+        "syms x\n",
+        "\n",
+        "f = (sin(x/2))^3 \n",
+        "diff(f, x)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "disp(\"A table matrix: \\n\")\n",
-    "displayformat matrix html;\n",
-    "m = ones(3,3)\n",
-    "disp(\"\\nAnd a latex one:\\n\")\n",
-    "displayformat matrix latex;\n",
-    "m"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Structs displayed as interactive tree views"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "application/json": {
-       "age": 33,
-       "knows_octave": true,
-       "name": "Alice",
-       "pets": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Input support"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
         {
-         "name": "Rex",
-         "type": "Dog"
+          "name": "stdin",
+          "output_type": "stream",
+          "text": [
+            "Enter value:  \"Marina\"\n"
+          ]
         },
         {
-         "name": "Felix",
-         "type": "Cat"
+          "data": {
+            "text/plain": [
+              "name = Marina\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
         }
-       ]
-      },
-      "text/plain": [
-       "ans =     name = Alice\n",
-       "    age = 33\n",
-       "    knows_octave = 1\n",
-       "    pets =\n",
-       "\n",
-       "      2x1 struct array containing the fields:\n",
-       "\n",
-       "        name\n",
-       "        type\n",
-       "\n"
+      ],
+      "source": [
+        "name = input(\"Enter value: \")"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "struct(\n",
-    "    \"name\",\"Alice\",\n",
-    "    \"age\", 33,\n",
-    "    \"knows_octave\", true,\n",
-    "    \"pets\", [\n",
-    "        struct(\"name\", \"Rex\", \"type\", \"Dog\"),\n",
-    "        struct(\"name\", \"Felix\", \"type\", \"Cat\")\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2020-08-26T09:07:10.387277Z",
-     "iopub.status.busy": "2020-08-26T09:07:10.387090Z",
-     "iopub.status.idle": "2020-08-26T09:07:10.395463Z",
-     "shell.execute_reply": "2020-08-26T09:07:10.395377Z",
-     "shell.execute_reply.started": "2020-08-26T09:07:10.387194Z"
-    }
-   },
-   "source": [
-    "## Symbolic classes displayed in $\\LaTeX$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/latex": [
-       "$$f=\\sin^{3}{\\left(\\frac{x}{2} \\right)}$$"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Formatted help "
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/latex": [
-       "$$ans=\\frac{3 \\sin^{2}{\\left(\\frac{x}{2} \\right)} \\cos{\\left(\\frac{x}{2} \\right)}}{2}$$"
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/x-texinfo": "\n @deftypefn  {} {} plot (@var{y})\n @deftypefnx {} {} plot (@var{x}, @var{y})\n @deftypefnx {} {} plot (@var{x}, @var{y}, @var{fmt})\n @deftypefnx {} {} plot (@dots{}, @var{property}, @var{value}, @dots{})\n @deftypefnx {} {} plot (@var{x1}, @var{y1}, @dots{}, @var{xn}, @var{yn})\n @deftypefnx {} {} plot (@var{hax}, @dots{})\n @deftypefnx {} {@var{h} =} plot (@dots{})\n Produce 2-D plots.\n\n Many different combinations of arguments are possible.  The simplest\n form is\n\n @example\n plot (@var{y})\n @end example\n\n @noindent\n where the argument is taken as the set of @var{y} coordinates and the\n @var{x} coordinates are taken to be the range @code{1:numel (@var{y})}.\n\n If more than one argument is given, they are interpreted as\n\n @example\n plot (@var{y}, @var{property}, @var{value}, @dots{})\n @end example\n\n @noindent\n or\n\n @example\n plot (@var{x}, @var{y}, @var{property}, @var{value}, @dots{})\n @end example\n\n @noindent\n or\n\n @example\n plot (@var{x}, @var{y}, @var{fmt}, @dots{})\n @end example\n\n @noindent\n and so on.  Any number of argument sets may appear.  The @var{x} and\n @var{y} values are interpreted as follows:\n\n @itemize @bullet\n @item\n If a single data argument is supplied, it is taken as the set of @var{y}\n coordinates and the @var{x} coordinates are taken to be the indices of\n the elements, starting with 1.\n\n @item\n If @var{x} and @var{y} are scalars, a single point is plotted.\n\n @item\n @code{squeeze()} is applied to arguments with more than two dimensions,\n but no more than two singleton dimensions.\n\n @item\n If both arguments are vectors, the elements of @var{y} are plotted versus\n the elements of @var{x}.\n\n @item\n If @var{x} is a vector and @var{y} is a matrix, then\n the columns (or rows) of @var{y} are plotted versus @var{x}.\n (using whichever combination matches, with columns tried first.)\n\n @item\n If the @var{x} is a matrix and @var{y} is a vector,\n @var{y} is plotted versus the columns (or rows) of @var{x}.\n (using whichever combination matches, with columns tried first.)\n\n @item\n If both arguments are matrices, the columns of @var{y} are plotted\n versus the columns of @var{x}.  In this case, both matrices must have\n the same number of rows and columns and no attempt is made to transpose\n the arguments to make the number of rows match.\n @end itemize\n\n Multiple property-value pairs may be specified, but they must appear\n in pairs.  These arguments are applied to the line objects drawn by\n @code{plot}.  Useful properties to modify are @qcode{\"linestyle\"},\n @qcode{\"linewidth\"}, @qcode{\"color\"}, @qcode{\"marker\"},\n @qcode{\"markersize\"}, @qcode{\"markeredgecolor\"}, @qcode{\"markerfacecolor\"}.\n The full list of properties is documented at\n @ref{Line Properties}.\n\n The @var{fmt} format argument can also be used to control the plot style.\n It is a string composed of four optional parts:\n \"<linestyle><marker><color><;displayname;>\".\n When a marker is specified, but no linestyle, only the markers are\n plotted.  Similarly, if a linestyle is specified, but no marker, then\n only lines are drawn.  If both are specified then lines and markers will\n be plotted.  If no @var{fmt} and no @var{property}/@var{value} pairs are\n given, then the default plot style is solid lines with no markers and the\n color determined by the @qcode{\"colororder\"} property of the current axes.\n\n Format arguments:\n\n @table @asis\n @item linestyle\n\n @multitable @columnfractions 0.06 0.94\n @item @samp{-}  @tab Use solid lines (default).\n @item @samp{--} @tab Use dashed lines.\n @item @samp{:}  @tab Use dotted lines.\n @item @samp{-.} @tab Use dash-dotted lines.\n @end multitable\n\n @item marker\n\n @multitable @columnfractions 0.06 0.94\n @item @samp{+} @tab crosshair\n @item @samp{o} @tab circle\n @item @samp{*} @tab star\n @item @samp{.} @tab point\n @item @samp{x} @tab cross\n @item @samp{|} @tab vertical line\n @item @samp{_} @tab horizontal line\n @item @samp{s} @tab square\n @item @samp{d} @tab diamond\n @item @samp{^} @tab upward-facing triangle\n @item @samp{v} @tab downward-facing triangle\n @item @samp{>} @tab right-facing triangle\n @item @samp{<} @tab left-facing triangle\n @item @samp{p} @tab pentagram\n @item @samp{h} @tab hexagram\n @end multitable\n\n @item color\n\n @multitable @columnfractions 0.21 0.79\n @item @samp{k}, @qcode{\"black\"}   @tab blacK\n @item @samp{r}, @qcode{\"red\"}     @tab Red\n @item @samp{g}, @qcode{\"green\"}   @tab Green\n @item @samp{b}, @qcode{\"blue\"}    @tab Blue\n @item @samp{y}, @qcode{\"yellow\"}  @tab Yellow\n @item @samp{m}, @qcode{\"magenta\"} @tab Magenta\n @item @samp{c}, @qcode{\"cyan\"}    @tab Cyan\n @item @samp{w}, @qcode{\"white\"}   @tab White\n @end multitable\n\n @item @qcode{\";displayname;\"}\n The text between semicolons is used to set the @qcode{\"displayname\"}\n property which determines the label used for the plot legend.\n\n @end table\n\n The @var{fmt} argument may also be used to assign legend labels.\n To do so, include the desired label between semicolons after the\n formatting sequence described above, e.g., @qcode{\"+b;Data Series 3;\"}.\n Note that the last semicolon is required and Octave will generate\n an error if it is left out.\n\n Here are some plot examples:\n\n @example\n plot (x, y, \"or\", x, y2, x, y3, \"m\", x, y4, \"+\")\n @end example\n\n This command will plot @code{y} with red circles, @code{y2} with solid\n lines, @code{y3} with solid magenta lines, and @code{y4} with points\n displayed as @samp{+}.\n\n @example\n plot (b, \"*\", \"markersize\", 10)\n @end example\n\n This command will plot the data in the variable @code{b},\n with points displayed as @samp{*} and a marker size of 10.\n\n @example\n @group\n t = 0:0.1:6.3;\n plot (t, cos(t), \"-;cos(t);\", t, sin(t), \"-b;sin(t);\");\n @end group\n @end example\n\n This will plot the cosine and sine functions and label them accordingly\n in the legend.\n\n If the first argument @var{hax} is an axes handle, then plot into this axes,\n rather than the current axes returned by @code{gca}.\n\n The optional return value @var{h} is a vector of graphics handles to\n the created line objects.\n\n To save a plot, in one of several image formats such as PostScript\n or PNG, use the @code{print} command.\n\n @seealso{axis, box, grid, hold, legend, title, xlabel, ylabel, xlim, ylim,\n ezplot, errorbar, fplot, line, plot3, polar, loglog, semilogx, semilogy,\n subplot}\n @end deftypefn\n",
+            "text/html": [
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "<dl class=\"def\">\n",
+              "<dt  id=\"index-plot\" style='float:unset;width:unset;margin-left:0px;'><span class=\"category\">: </span><span><em></em> <strong>plot</strong> <em>(<var>y</var>)</em><a href='#index-plot' class='copiable-anchor'> &para;</a></span></dt>\n",
+              "<dt  id=\"index-plot-1\" style='float:unset;width:unset;margin-left:0px;'><span class=\"category\">: </span><span><em></em> <strong>plot</strong> <em>(<var>x</var>, <var>y</var>)</em><a href='#index-plot-1' class='copiable-anchor'> &para;</a></span></dt>\n",
+              "<dt  id=\"index-plot-2\" style='float:unset;width:unset;margin-left:0px;'><span class=\"category\">: </span><span><em></em> <strong>plot</strong> <em>(<var>x</var>, <var>y</var>, <var>fmt</var>)</em><a href='#index-plot-2' class='copiable-anchor'> &para;</a></span></dt>\n",
+              "<dt  id=\"index-plot-3\" style='float:unset;width:unset;margin-left:0px;'><span class=\"category\">: </span><span><em></em> <strong>plot</strong> <em>(&hellip;, <var>property</var>, <var>value</var>, &hellip;)</em><a href='#index-plot-3' class='copiable-anchor'> &para;</a></span></dt>\n",
+              "<dt  id=\"index-plot-4\" style='float:unset;width:unset;margin-left:0px;'><span class=\"category\">: </span><span><em></em> <strong>plot</strong> <em>(<var>x1</var>, <var>y1</var>, &hellip;, <var>xn</var>, <var>yn</var>)</em><a href='#index-plot-4' class='copiable-anchor'> &para;</a></span></dt>\n",
+              "<dt  id=\"index-plot-5\" style='float:unset;width:unset;margin-left:0px;'><span class=\"category\">: </span><span><em></em> <strong>plot</strong> <em>(<var>hax</var>, &hellip;)</em><a href='#index-plot-5' class='copiable-anchor'> &para;</a></span></dt>\n",
+              "<dt  id=\"index-plot-6\" style='float:unset;width:unset;margin-left:0px;'><span class=\"category\">: </span><span><em><var>h</var> =</em> <strong>plot</strong> <em>(&hellip;)</em><a href='#index-plot-6' class='copiable-anchor'> &para;</a></span></dt>\n",
+              "<dd  style='float:unset;width:unset;font-weight:unset;margin-left:40px'><p>Produce 2-D plots.\n",
+              "</p>\n",
+              "<p>Many different combinations of arguments are possible.  The simplest\n",
+              "form is\n",
+              "</p>\n",
+              "<div class=\"example\">\n",
+              "<pre class=\"example\">plot (<var>y</var>)\n",
+              "</pre></div>\n",
+              "\n",
+              "<p>where the argument is taken as the set of <var>y</var> coordinates and the\n",
+              "<var>x</var> coordinates are taken to be the range <code>1:numel (<var>y</var>)</code>.\n",
+              "</p>\n",
+              "<p>If more than one argument is given, they are interpreted as\n",
+              "</p>\n",
+              "<div class=\"example\">\n",
+              "<pre class=\"example\">plot (<var>y</var>, <var>property</var>, <var>value</var>, &hellip;)\n",
+              "</pre></div>\n",
+              "\n",
+              "<p>or\n",
+              "</p>\n",
+              "<div class=\"example\">\n",
+              "<pre class=\"example\">plot (<var>x</var>, <var>y</var>, <var>property</var>, <var>value</var>, &hellip;)\n",
+              "</pre></div>\n",
+              "\n",
+              "<p>or\n",
+              "</p>\n",
+              "<div class=\"example\">\n",
+              "<pre class=\"example\">plot (<var>x</var>, <var>y</var>, <var>fmt</var>, &hellip;)\n",
+              "</pre></div>\n",
+              "\n",
+              "<p>and so on.  Any number of argument sets may appear.  The <var>x</var> and\n",
+              "<var>y</var> values are interpreted as follows:\n",
+              "</p>\n",
+              "<ul>\n",
+              "<li> If a single data argument is supplied, it is taken as the set of <var>y</var>\n",
+              "coordinates and the <var>x</var> coordinates are taken to be the indices of\n",
+              "the elements, starting with 1.\n",
+              "\n",
+              "</li><li> If <var>x</var> and <var>y</var> are scalars, a single point is plotted.\n",
+              "\n",
+              "</li><li> <code>squeeze()</code> is applied to arguments with more than two dimensions,\n",
+              "but no more than two singleton dimensions.\n",
+              "\n",
+              "</li><li> If both arguments are vectors, the elements of <var>y</var> are plotted versus\n",
+              "the elements of <var>x</var>.\n",
+              "\n",
+              "</li><li> If <var>x</var> is a vector and <var>y</var> is a matrix, then\n",
+              "the columns (or rows) of <var>y</var> are plotted versus <var>x</var>.\n",
+              "(using whichever combination matches, with columns tried first.)\n",
+              "\n",
+              "</li><li> If the <var>x</var> is a matrix and <var>y</var> is a vector,\n",
+              "<var>y</var> is plotted versus the columns (or rows) of <var>x</var>.\n",
+              "(using whichever combination matches, with columns tried first.)\n",
+              "\n",
+              "</li><li> If both arguments are matrices, the columns of <var>y</var> are plotted\n",
+              "versus the columns of <var>x</var>.  In this case, both matrices must have\n",
+              "the same number of rows and columns and no attempt is made to transpose\n",
+              "the arguments to make the number of rows match.\n",
+              "</li></ul>\n",
+              "\n",
+              "<p>Multiple property-value pairs may be specified, but they must appear\n",
+              "in pairs.  These arguments are applied to the line objects drawn by\n",
+              "<code>plot</code>.  Useful properties to modify are <code>&quot;linestyle&quot;</code>,\n",
+              "<code>&quot;linewidth&quot;</code>, <code>&quot;color&quot;</code>, <code>&quot;marker&quot;</code>,\n",
+              "<code>&quot;markersize&quot;</code>, <code>&quot;markeredgecolor&quot;</code>, <code>&quot;markerfacecolor&quot;</code>.\n",
+              "The full list of properties is documented at\n",
+              "&lsquo;Line Properties&rsquo;.\n",
+              "</p>\n",
+              "<p>The <var>fmt</var> format argument can also be used to control the plot style.\n",
+              "It is a string composed of four optional parts:\n",
+              "&quot;&lt;linestyle&gt;&lt;marker&gt;&lt;color&gt;&lt;;displayname;&gt;&quot;.\n",
+              "When a marker is specified, but no linestyle, only the markers are\n",
+              "plotted.  Similarly, if a linestyle is specified, but no marker, then\n",
+              "only lines are drawn.  If both are specified then lines and markers will\n",
+              "be plotted.  If no <var>fmt</var> and no <var>property</var>/<var>value</var> pairs are\n",
+              "given, then the default plot style is solid lines with no markers and the\n",
+              "color determined by the <code>&quot;colororder&quot;</code> property of the current axes.\n",
+              "</p>\n",
+              "<p>Format arguments:\n",
+              "</p>\n",
+              "<dl compact=\"compact\">\n",
+              "<dt  style='float:unset;width:unset;margin-left:0px;'><span>linestyle</span></dt>\n",
+              "<dd  style='float:unset;width:unset;font-weight:unset;margin-left:40px'>\n",
+              "<table>\n",
+              "<tr><td width=\"6%\">&lsquo;<samp>-</samp>&rsquo;</td><td width=\"94%\">Use solid lines (default).</td></tr>\n",
+              "<tr><td width=\"6%\">&lsquo;<samp>--</samp>&rsquo;</td><td width=\"94%\">Use dashed lines.</td></tr>\n",
+              "<tr><td width=\"6%\">&lsquo;<samp>:</samp>&rsquo;</td><td width=\"94%\">Use dotted lines.</td></tr>\n",
+              "<tr><td width=\"6%\">&lsquo;<samp>-.</samp>&rsquo;</td><td width=\"94%\">Use dash-dotted lines.</td></tr>\n",
+              "</table>\n",
+              "\n",
+              "</dd>\n",
+              "<dt  style='float:unset;width:unset;margin-left:0px;'><span>marker</span></dt>\n",
+              "<dd  style='float:unset;width:unset;font-weight:unset;margin-left:40px'>\n",
+              "<table>\n",
+              "<tr><td width=\"6%\">&lsquo;<samp>+</samp>&rsquo;</td><td width=\"94%\">crosshair</td></tr>\n",
+              "<tr><td width=\"6%\">&lsquo;<samp>o</samp>&rsquo;</td><td width=\"94%\">circle</td></tr>\n",
+              "<tr><td width=\"6%\">&lsquo;<samp>*</samp>&rsquo;</td><td width=\"94%\">star</td></tr>\n",
+              "<tr><td width=\"6%\">&lsquo;<samp>.</samp>&rsquo;</td><td width=\"94%\">point</td></tr>\n",
+              "<tr><td width=\"6%\">&lsquo;<samp>x</samp>&rsquo;</td><td width=\"94%\">cross</td></tr>\n",
+              "<tr><td width=\"6%\">&lsquo;<samp>|</samp>&rsquo;</td><td width=\"94%\">vertical line</td></tr>\n",
+              "<tr><td width=\"6%\">&lsquo;<samp>_</samp>&rsquo;</td><td width=\"94%\">horizontal line</td></tr>\n",
+              "<tr><td width=\"6%\">&lsquo;<samp>s</samp>&rsquo;</td><td width=\"94%\">square</td></tr>\n",
+              "<tr><td width=\"6%\">&lsquo;<samp>d</samp>&rsquo;</td><td width=\"94%\">diamond</td></tr>\n",
+              "<tr><td width=\"6%\">&lsquo;<samp>^</samp>&rsquo;</td><td width=\"94%\">upward-facing triangle</td></tr>\n",
+              "<tr><td width=\"6%\">&lsquo;<samp>v</samp>&rsquo;</td><td width=\"94%\">downward-facing triangle</td></tr>\n",
+              "<tr><td width=\"6%\">&lsquo;<samp>&gt;</samp>&rsquo;</td><td width=\"94%\">right-facing triangle</td></tr>\n",
+              "<tr><td width=\"6%\">&lsquo;<samp>&lt;</samp>&rsquo;</td><td width=\"94%\">left-facing triangle</td></tr>\n",
+              "<tr><td width=\"6%\">&lsquo;<samp>p</samp>&rsquo;</td><td width=\"94%\">pentagram</td></tr>\n",
+              "<tr><td width=\"6%\">&lsquo;<samp>h</samp>&rsquo;</td><td width=\"94%\">hexagram</td></tr>\n",
+              "</table>\n",
+              "\n",
+              "</dd>\n",
+              "<dt  style='float:unset;width:unset;margin-left:0px;'><span>color</span></dt>\n",
+              "<dd  style='float:unset;width:unset;font-weight:unset;margin-left:40px'>\n",
+              "<table>\n",
+              "<tr><td width=\"21%\">&lsquo;<samp>k</samp>&rsquo;, <code>&quot;black&quot;</code></td><td width=\"79%\">blacK</td></tr>\n",
+              "<tr><td width=\"21%\">&lsquo;<samp>r</samp>&rsquo;, <code>&quot;red&quot;</code></td><td width=\"79%\">Red</td></tr>\n",
+              "<tr><td width=\"21%\">&lsquo;<samp>g</samp>&rsquo;, <code>&quot;green&quot;</code></td><td width=\"79%\">Green</td></tr>\n",
+              "<tr><td width=\"21%\">&lsquo;<samp>b</samp>&rsquo;, <code>&quot;blue&quot;</code></td><td width=\"79%\">Blue</td></tr>\n",
+              "<tr><td width=\"21%\">&lsquo;<samp>y</samp>&rsquo;, <code>&quot;yellow&quot;</code></td><td width=\"79%\">Yellow</td></tr>\n",
+              "<tr><td width=\"21%\">&lsquo;<samp>m</samp>&rsquo;, <code>&quot;magenta&quot;</code></td><td width=\"79%\">Magenta</td></tr>\n",
+              "<tr><td width=\"21%\">&lsquo;<samp>c</samp>&rsquo;, <code>&quot;cyan&quot;</code></td><td width=\"79%\">Cyan</td></tr>\n",
+              "<tr><td width=\"21%\">&lsquo;<samp>w</samp>&rsquo;, <code>&quot;white&quot;</code></td><td width=\"79%\">White</td></tr>\n",
+              "</table>\n",
+              "\n",
+              "</dd>\n",
+              "<dt  style='float:unset;width:unset;margin-left:0px;'><span><code>&quot;;displayname;&quot;</code></span></dt>\n",
+              "<dd  style='float:unset;width:unset;font-weight:unset;margin-left:40px'><p>The text between semicolons is used to set the <code>&quot;displayname&quot;</code>\n",
+              "property which determines the label used for the plot legend.\n",
+              "</p>\n",
+              "</dd>\n",
+              "</dl>\n",
+              "\n",
+              "<p>The <var>fmt</var> argument may also be used to assign legend labels.\n",
+              "To do so, include the desired label between semicolons after the\n",
+              "formatting sequence described above, e.g., <code>&quot;+b;Data Series 3;&quot;</code>.\n",
+              "Note that the last semicolon is required and Octave will generate\n",
+              "an error if it is left out.\n",
+              "</p>\n",
+              "<p>Here are some plot examples:\n",
+              "</p>\n",
+              "<div class=\"example\">\n",
+              "<pre class=\"example\">plot (x, y, &quot;or&quot;, x, y2, x, y3, &quot;m&quot;, x, y4, &quot;+&quot;)\n",
+              "</pre></div>\n",
+              "\n",
+              "<p>This command will plot <code>y</code> with red circles, <code>y2</code> with solid\n",
+              "lines, <code>y3</code> with solid magenta lines, and <code>y4</code> with points\n",
+              "displayed as &lsquo;<samp>+</samp>&rsquo;.\n",
+              "</p>\n",
+              "<div class=\"example\">\n",
+              "<pre class=\"example\">plot (b, &quot;*&quot;, &quot;markersize&quot;, 10)\n",
+              "</pre></div>\n",
+              "\n",
+              "<p>This command will plot the data in the variable <code>b</code>,\n",
+              "with points displayed as &lsquo;<samp>*</samp>&rsquo; and a marker size of 10.\n",
+              "</p>\n",
+              "<div class=\"example\">\n",
+              "<pre class=\"example\">t = 0:0.1:6.3;\n",
+              "plot (t, cos(t), &quot;-;cos(t);&quot;, t, sin(t), &quot;-b;sin(t);&quot;);\n",
+              "</pre></div>\n",
+              "\n",
+              "<p>This will plot the cosine and sine functions and label them accordingly\n",
+              "in the legend.\n",
+              "</p>\n",
+              "<p>If the first argument <var>hax</var> is an axes handle, then plot into this axes,\n",
+              "rather than the current axes returned by <code>gca</code>.\n",
+              "</p>\n",
+              "<p>The optional return value <var>h</var> is a vector of graphics handles to\n",
+              "the created line objects.\n",
+              "</p>\n",
+              "<p>To save a plot, in one of several image formats such as PostScript\n",
+              "or PNG, use the <code>print</code> command.\n",
+              "</p>\n",
+              "\n",
+              "<p><strong>See also:</strong> axis, box, grid, hold, legend, title, xlabel, ylabel, xlim, ylim,\n",
+              "ezplot, errorbar, fplot, line, plot3, polar, loglog, semilogx, semilogy,\n",
+              "subplot.\n",
+              "</p></dd></dl>\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plot?"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pkg load symbolic\n",
-    "\n",
-    "syms x\n",
-    "\n",
-    "f = (sin(x/2))^3 \n",
-    "diff(f, x)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Input support"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "Enter value:  \"Marina\"\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Example: notch filter\n",
+        "\n",
+        "### Parameters"
+      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "name = Marina\n"
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "w0 = 2*pi*1000;     % Target frequency\n",
+        "Q = 100;            % Analog selectivity\n",
+        "Tc = 1/50e3;        % Sampling time    "
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "name = input(\"Enter value: \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Formatted help "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "application/x-texinfo": "\n @deftypefn  {} {} plot (@var{y})\n @deftypefnx {} {} plot (@var{x}, @var{y})\n @deftypefnx {} {} plot (@var{x}, @var{y}, @var{fmt})\n @deftypefnx {} {} plot (@dots{}, @var{property}, @var{value}, @dots{})\n @deftypefnx {} {} plot (@var{x1}, @var{y1}, @dots{}, @var{xn}, @var{yn})\n @deftypefnx {} {} plot (@var{hax}, @dots{})\n @deftypefnx {} {@var{h} =} plot (@dots{})\n Produce 2-D plots.\n\n Many different combinations of arguments are possible.  The simplest\n form is\n\n @example\n plot (@var{y})\n @end example\n\n @noindent\n where the argument is taken as the set of @var{y} coordinates and the\n @var{x} coordinates are taken to be the range @code{1:numel (@var{y})}.\n\n If more than one argument is given, they are interpreted as\n\n @example\n plot (@var{y}, @var{property}, @var{value}, @dots{})\n @end example\n\n @noindent\n or\n\n @example\n plot (@var{x}, @var{y}, @var{property}, @var{value}, @dots{})\n @end example\n\n @noindent\n or\n\n @example\n plot (@var{x}, @var{y}, @var{fmt}, @dots{})\n @end example\n\n @noindent\n and so on.  Any number of argument sets may appear.  The @var{x} and\n @var{y} values are interpreted as follows:\n\n @itemize @bullet\n @item\n If a single data argument is supplied, it is taken as the set of @var{y}\n coordinates and the @var{x} coordinates are taken to be the indices of\n the elements, starting with 1.\n\n @item\n If @var{x} and @var{y} are scalars, a single point is plotted.\n\n @item\n @code{squeeze()} is applied to arguments with more than two dimensions,\n but no more than two singleton dimensions.\n\n @item\n If both arguments are vectors, the elements of @var{y} are plotted versus\n the elements of @var{x}.\n\n @item\n If @var{x} is a vector and @var{y} is a matrix, then\n the columns (or rows) of @var{y} are plotted versus @var{x}.\n (using whichever combination matches, with columns tried first.)\n\n @item\n If the @var{x} is a matrix and @var{y} is a vector,\n @var{y} is plotted versus the columns (or rows) of @var{x}.\n (using whichever combination matches, with columns tried first.)\n\n @item\n If both arguments are matrices, the columns of @var{y} are plotted\n versus the columns of @var{x}.  In this case, both matrices must have\n the same number of rows and columns and no attempt is made to transpose\n the arguments to make the number of rows match.\n @end itemize\n\n Multiple property-value pairs may be specified, but they must appear\n in pairs.  These arguments are applied to the line objects drawn by\n @code{plot}.  Useful properties to modify are @qcode{\"linestyle\"},\n @qcode{\"linewidth\"}, @qcode{\"color\"}, @qcode{\"marker\"},\n @qcode{\"markersize\"}, @qcode{\"markeredgecolor\"}, @qcode{\"markerfacecolor\"}.\n The full list of properties is documented at\n @ref{Line Properties}.\n\n The @var{fmt} format argument can also be used to control the plot style.\n It is a string composed of four optional parts:\n \"<linestyle><marker><color><;displayname;>\".\n When a marker is specified, but no linestyle, only the markers are\n plotted.  Similarly, if a linestyle is specified, but no marker, then\n only lines are drawn.  If both are specified then lines and markers will\n be plotted.  If no @var{fmt} and no @var{property}/@var{value} pairs are\n given, then the default plot style is solid lines with no markers and the\n color determined by the @qcode{\"colororder\"} property of the current axes.\n\n Format arguments:\n\n @table @asis\n @item linestyle\n\n @multitable @columnfractions 0.06 0.94\n @item @samp{-}  @tab Use solid lines (default).\n @item @samp{--} @tab Use dashed lines.\n @item @samp{:}  @tab Use dotted lines.\n @item @samp{-.} @tab Use dash-dotted lines.\n @end multitable\n\n @item marker\n\n @multitable @columnfractions 0.06 0.94\n @item @samp{+} @tab crosshair\n @item @samp{o} @tab circle\n @item @samp{*} @tab star\n @item @samp{.} @tab point\n @item @samp{x} @tab cross\n @item @samp{|} @tab vertical line\n @item @samp{_} @tab horizontal line\n @item @samp{s} @tab square\n @item @samp{d} @tab diamond\n @item @samp{^} @tab upward-facing triangle\n @item @samp{v} @tab downward-facing triangle\n @item @samp{>} @tab right-facing triangle\n @item @samp{<} @tab left-facing triangle\n @item @samp{p} @tab pentagram\n @item @samp{h} @tab hexagram\n @end multitable\n\n @item color\n\n @multitable @columnfractions 0.21 0.79\n @item @samp{k}, @qcode{\"black\"}   @tab blacK\n @item @samp{r}, @qcode{\"red\"}     @tab Red\n @item @samp{g}, @qcode{\"green\"}   @tab Green\n @item @samp{b}, @qcode{\"blue\"}    @tab Blue\n @item @samp{y}, @qcode{\"yellow\"}  @tab Yellow\n @item @samp{m}, @qcode{\"magenta\"} @tab Magenta\n @item @samp{c}, @qcode{\"cyan\"}    @tab Cyan\n @item @samp{w}, @qcode{\"white\"}   @tab White\n @end multitable\n\n @item @qcode{\";displayname;\"}\n The text between semicolons is used to set the @qcode{\"displayname\"}\n property which determines the label used for the plot legend.\n\n @end table\n\n The @var{fmt} argument may also be used to assign legend labels.\n To do so, include the desired label between semicolons after the\n formatting sequence described above, e.g., @qcode{\"+b;Data Series 3;\"}.\n Note that the last semicolon is required and Octave will generate\n an error if it is left out.\n\n Here are some plot examples:\n\n @example\n plot (x, y, \"or\", x, y2, x, y3, \"m\", x, y4, \"+\")\n @end example\n\n This command will plot @code{y} with red circles, @code{y2} with solid\n lines, @code{y3} with solid magenta lines, and @code{y4} with points\n displayed as @samp{+}.\n\n @example\n plot (b, \"*\", \"markersize\", 10)\n @end example\n\n This command will plot the data in the variable @code{b},\n with points displayed as @samp{*} and a marker size of 10.\n\n @example\n @group\n t = 0:0.1:6.3;\n plot (t, cos(t), \"-;cos(t);\", t, sin(t), \"-b;sin(t);\");\n @end group\n @end example\n\n This will plot the cosine and sine functions and label them accordingly\n in the legend.\n\n If the first argument @var{hax} is an axes handle, then plot into this axes,\n rather than the current axes returned by @code{gca}.\n\n The optional return value @var{h} is a vector of graphics handles to\n the created line objects.\n\n To save a plot, in one of several image formats such as PostScript\n or PNG, use the @code{print} command.\n\n @seealso{axis, box, grid, hold, legend, title, xlabel, ylabel, xlim, ylim,\n ezplot, errorbar, fplot, line, plot3, polar, loglog, semilogx, semilogy,\n subplot}\n @end deftypefn\n",
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "<dl class=\"def\">\n",
-       "<dt  id=\"index-plot\" style='float:unset;width:unset;margin-left:0px;'><span class=\"category\">: </span><span><em></em> <strong>plot</strong> <em>(<var>y</var>)</em><a href='#index-plot' class='copiable-anchor'> &para;</a></span></dt>\n",
-       "<dt  id=\"index-plot-1\" style='float:unset;width:unset;margin-left:0px;'><span class=\"category\">: </span><span><em></em> <strong>plot</strong> <em>(<var>x</var>, <var>y</var>)</em><a href='#index-plot-1' class='copiable-anchor'> &para;</a></span></dt>\n",
-       "<dt  id=\"index-plot-2\" style='float:unset;width:unset;margin-left:0px;'><span class=\"category\">: </span><span><em></em> <strong>plot</strong> <em>(<var>x</var>, <var>y</var>, <var>fmt</var>)</em><a href='#index-plot-2' class='copiable-anchor'> &para;</a></span></dt>\n",
-       "<dt  id=\"index-plot-3\" style='float:unset;width:unset;margin-left:0px;'><span class=\"category\">: </span><span><em></em> <strong>plot</strong> <em>(&hellip;, <var>property</var>, <var>value</var>, &hellip;)</em><a href='#index-plot-3' class='copiable-anchor'> &para;</a></span></dt>\n",
-       "<dt  id=\"index-plot-4\" style='float:unset;width:unset;margin-left:0px;'><span class=\"category\">: </span><span><em></em> <strong>plot</strong> <em>(<var>x1</var>, <var>y1</var>, &hellip;, <var>xn</var>, <var>yn</var>)</em><a href='#index-plot-4' class='copiable-anchor'> &para;</a></span></dt>\n",
-       "<dt  id=\"index-plot-5\" style='float:unset;width:unset;margin-left:0px;'><span class=\"category\">: </span><span><em></em> <strong>plot</strong> <em>(<var>hax</var>, &hellip;)</em><a href='#index-plot-5' class='copiable-anchor'> &para;</a></span></dt>\n",
-       "<dt  id=\"index-plot-6\" style='float:unset;width:unset;margin-left:0px;'><span class=\"category\">: </span><span><em><var>h</var> =</em> <strong>plot</strong> <em>(&hellip;)</em><a href='#index-plot-6' class='copiable-anchor'> &para;</a></span></dt>\n",
-       "<dd  style='float:unset;width:unset;font-weight:unset;margin-left:40px'><p>Produce 2-D plots.\n",
-       "</p>\n",
-       "<p>Many different combinations of arguments are possible.  The simplest\n",
-       "form is\n",
-       "</p>\n",
-       "<div class=\"example\">\n",
-       "<pre class=\"example\">plot (<var>y</var>)\n",
-       "</pre></div>\n",
-       "\n",
-       "<p>where the argument is taken as the set of <var>y</var> coordinates and the\n",
-       "<var>x</var> coordinates are taken to be the range <code>1:numel (<var>y</var>)</code>.\n",
-       "</p>\n",
-       "<p>If more than one argument is given, they are interpreted as\n",
-       "</p>\n",
-       "<div class=\"example\">\n",
-       "<pre class=\"example\">plot (<var>y</var>, <var>property</var>, <var>value</var>, &hellip;)\n",
-       "</pre></div>\n",
-       "\n",
-       "<p>or\n",
-       "</p>\n",
-       "<div class=\"example\">\n",
-       "<pre class=\"example\">plot (<var>x</var>, <var>y</var>, <var>property</var>, <var>value</var>, &hellip;)\n",
-       "</pre></div>\n",
-       "\n",
-       "<p>or\n",
-       "</p>\n",
-       "<div class=\"example\">\n",
-       "<pre class=\"example\">plot (<var>x</var>, <var>y</var>, <var>fmt</var>, &hellip;)\n",
-       "</pre></div>\n",
-       "\n",
-       "<p>and so on.  Any number of argument sets may appear.  The <var>x</var> and\n",
-       "<var>y</var> values are interpreted as follows:\n",
-       "</p>\n",
-       "<ul>\n",
-       "<li> If a single data argument is supplied, it is taken as the set of <var>y</var>\n",
-       "coordinates and the <var>x</var> coordinates are taken to be the indices of\n",
-       "the elements, starting with 1.\n",
-       "\n",
-       "</li><li> If <var>x</var> and <var>y</var> are scalars, a single point is plotted.\n",
-       "\n",
-       "</li><li> <code>squeeze()</code> is applied to arguments with more than two dimensions,\n",
-       "but no more than two singleton dimensions.\n",
-       "\n",
-       "</li><li> If both arguments are vectors, the elements of <var>y</var> are plotted versus\n",
-       "the elements of <var>x</var>.\n",
-       "\n",
-       "</li><li> If <var>x</var> is a vector and <var>y</var> is a matrix, then\n",
-       "the columns (or rows) of <var>y</var> are plotted versus <var>x</var>.\n",
-       "(using whichever combination matches, with columns tried first.)\n",
-       "\n",
-       "</li><li> If the <var>x</var> is a matrix and <var>y</var> is a vector,\n",
-       "<var>y</var> is plotted versus the columns (or rows) of <var>x</var>.\n",
-       "(using whichever combination matches, with columns tried first.)\n",
-       "\n",
-       "</li><li> If both arguments are matrices, the columns of <var>y</var> are plotted\n",
-       "versus the columns of <var>x</var>.  In this case, both matrices must have\n",
-       "the same number of rows and columns and no attempt is made to transpose\n",
-       "the arguments to make the number of rows match.\n",
-       "</li></ul>\n",
-       "\n",
-       "<p>Multiple property-value pairs may be specified, but they must appear\n",
-       "in pairs.  These arguments are applied to the line objects drawn by\n",
-       "<code>plot</code>.  Useful properties to modify are <code>&quot;linestyle&quot;</code>,\n",
-       "<code>&quot;linewidth&quot;</code>, <code>&quot;color&quot;</code>, <code>&quot;marker&quot;</code>,\n",
-       "<code>&quot;markersize&quot;</code>, <code>&quot;markeredgecolor&quot;</code>, <code>&quot;markerfacecolor&quot;</code>.\n",
-       "The full list of properties is documented at\n",
-       "&lsquo;Line Properties&rsquo;.\n",
-       "</p>\n",
-       "<p>The <var>fmt</var> format argument can also be used to control the plot style.\n",
-       "It is a string composed of four optional parts:\n",
-       "&quot;&lt;linestyle&gt;&lt;marker&gt;&lt;color&gt;&lt;;displayname;&gt;&quot;.\n",
-       "When a marker is specified, but no linestyle, only the markers are\n",
-       "plotted.  Similarly, if a linestyle is specified, but no marker, then\n",
-       "only lines are drawn.  If both are specified then lines and markers will\n",
-       "be plotted.  If no <var>fmt</var> and no <var>property</var>/<var>value</var> pairs are\n",
-       "given, then the default plot style is solid lines with no markers and the\n",
-       "color determined by the <code>&quot;colororder&quot;</code> property of the current axes.\n",
-       "</p>\n",
-       "<p>Format arguments:\n",
-       "</p>\n",
-       "<dl compact=\"compact\">\n",
-       "<dt  style='float:unset;width:unset;margin-left:0px;'><span>linestyle</span></dt>\n",
-       "<dd  style='float:unset;width:unset;font-weight:unset;margin-left:40px'>\n",
-       "<table>\n",
-       "<tr><td width=\"6%\">&lsquo;<samp>-</samp>&rsquo;</td><td width=\"94%\">Use solid lines (default).</td></tr>\n",
-       "<tr><td width=\"6%\">&lsquo;<samp>--</samp>&rsquo;</td><td width=\"94%\">Use dashed lines.</td></tr>\n",
-       "<tr><td width=\"6%\">&lsquo;<samp>:</samp>&rsquo;</td><td width=\"94%\">Use dotted lines.</td></tr>\n",
-       "<tr><td width=\"6%\">&lsquo;<samp>-.</samp>&rsquo;</td><td width=\"94%\">Use dash-dotted lines.</td></tr>\n",
-       "</table>\n",
-       "\n",
-       "</dd>\n",
-       "<dt  style='float:unset;width:unset;margin-left:0px;'><span>marker</span></dt>\n",
-       "<dd  style='float:unset;width:unset;font-weight:unset;margin-left:40px'>\n",
-       "<table>\n",
-       "<tr><td width=\"6%\">&lsquo;<samp>+</samp>&rsquo;</td><td width=\"94%\">crosshair</td></tr>\n",
-       "<tr><td width=\"6%\">&lsquo;<samp>o</samp>&rsquo;</td><td width=\"94%\">circle</td></tr>\n",
-       "<tr><td width=\"6%\">&lsquo;<samp>*</samp>&rsquo;</td><td width=\"94%\">star</td></tr>\n",
-       "<tr><td width=\"6%\">&lsquo;<samp>.</samp>&rsquo;</td><td width=\"94%\">point</td></tr>\n",
-       "<tr><td width=\"6%\">&lsquo;<samp>x</samp>&rsquo;</td><td width=\"94%\">cross</td></tr>\n",
-       "<tr><td width=\"6%\">&lsquo;<samp>|</samp>&rsquo;</td><td width=\"94%\">vertical line</td></tr>\n",
-       "<tr><td width=\"6%\">&lsquo;<samp>_</samp>&rsquo;</td><td width=\"94%\">horizontal line</td></tr>\n",
-       "<tr><td width=\"6%\">&lsquo;<samp>s</samp>&rsquo;</td><td width=\"94%\">square</td></tr>\n",
-       "<tr><td width=\"6%\">&lsquo;<samp>d</samp>&rsquo;</td><td width=\"94%\">diamond</td></tr>\n",
-       "<tr><td width=\"6%\">&lsquo;<samp>^</samp>&rsquo;</td><td width=\"94%\">upward-facing triangle</td></tr>\n",
-       "<tr><td width=\"6%\">&lsquo;<samp>v</samp>&rsquo;</td><td width=\"94%\">downward-facing triangle</td></tr>\n",
-       "<tr><td width=\"6%\">&lsquo;<samp>&gt;</samp>&rsquo;</td><td width=\"94%\">right-facing triangle</td></tr>\n",
-       "<tr><td width=\"6%\">&lsquo;<samp>&lt;</samp>&rsquo;</td><td width=\"94%\">left-facing triangle</td></tr>\n",
-       "<tr><td width=\"6%\">&lsquo;<samp>p</samp>&rsquo;</td><td width=\"94%\">pentagram</td></tr>\n",
-       "<tr><td width=\"6%\">&lsquo;<samp>h</samp>&rsquo;</td><td width=\"94%\">hexagram</td></tr>\n",
-       "</table>\n",
-       "\n",
-       "</dd>\n",
-       "<dt  style='float:unset;width:unset;margin-left:0px;'><span>color</span></dt>\n",
-       "<dd  style='float:unset;width:unset;font-weight:unset;margin-left:40px'>\n",
-       "<table>\n",
-       "<tr><td width=\"21%\">&lsquo;<samp>k</samp>&rsquo;, <code>&quot;black&quot;</code></td><td width=\"79%\">blacK</td></tr>\n",
-       "<tr><td width=\"21%\">&lsquo;<samp>r</samp>&rsquo;, <code>&quot;red&quot;</code></td><td width=\"79%\">Red</td></tr>\n",
-       "<tr><td width=\"21%\">&lsquo;<samp>g</samp>&rsquo;, <code>&quot;green&quot;</code></td><td width=\"79%\">Green</td></tr>\n",
-       "<tr><td width=\"21%\">&lsquo;<samp>b</samp>&rsquo;, <code>&quot;blue&quot;</code></td><td width=\"79%\">Blue</td></tr>\n",
-       "<tr><td width=\"21%\">&lsquo;<samp>y</samp>&rsquo;, <code>&quot;yellow&quot;</code></td><td width=\"79%\">Yellow</td></tr>\n",
-       "<tr><td width=\"21%\">&lsquo;<samp>m</samp>&rsquo;, <code>&quot;magenta&quot;</code></td><td width=\"79%\">Magenta</td></tr>\n",
-       "<tr><td width=\"21%\">&lsquo;<samp>c</samp>&rsquo;, <code>&quot;cyan&quot;</code></td><td width=\"79%\">Cyan</td></tr>\n",
-       "<tr><td width=\"21%\">&lsquo;<samp>w</samp>&rsquo;, <code>&quot;white&quot;</code></td><td width=\"79%\">White</td></tr>\n",
-       "</table>\n",
-       "\n",
-       "</dd>\n",
-       "<dt  style='float:unset;width:unset;margin-left:0px;'><span><code>&quot;;displayname;&quot;</code></span></dt>\n",
-       "<dd  style='float:unset;width:unset;font-weight:unset;margin-left:40px'><p>The text between semicolons is used to set the <code>&quot;displayname&quot;</code>\n",
-       "property which determines the label used for the plot legend.\n",
-       "</p>\n",
-       "</dd>\n",
-       "</dl>\n",
-       "\n",
-       "<p>The <var>fmt</var> argument may also be used to assign legend labels.\n",
-       "To do so, include the desired label between semicolons after the\n",
-       "formatting sequence described above, e.g., <code>&quot;+b;Data Series 3;&quot;</code>.\n",
-       "Note that the last semicolon is required and Octave will generate\n",
-       "an error if it is left out.\n",
-       "</p>\n",
-       "<p>Here are some plot examples:\n",
-       "</p>\n",
-       "<div class=\"example\">\n",
-       "<pre class=\"example\">plot (x, y, &quot;or&quot;, x, y2, x, y3, &quot;m&quot;, x, y4, &quot;+&quot;)\n",
-       "</pre></div>\n",
-       "\n",
-       "<p>This command will plot <code>y</code> with red circles, <code>y2</code> with solid\n",
-       "lines, <code>y3</code> with solid magenta lines, and <code>y4</code> with points\n",
-       "displayed as &lsquo;<samp>+</samp>&rsquo;.\n",
-       "</p>\n",
-       "<div class=\"example\">\n",
-       "<pre class=\"example\">plot (b, &quot;*&quot;, &quot;markersize&quot;, 10)\n",
-       "</pre></div>\n",
-       "\n",
-       "<p>This command will plot the data in the variable <code>b</code>,\n",
-       "with points displayed as &lsquo;<samp>*</samp>&rsquo; and a marker size of 10.\n",
-       "</p>\n",
-       "<div class=\"example\">\n",
-       "<pre class=\"example\">t = 0:0.1:6.3;\n",
-       "plot (t, cos(t), &quot;-;cos(t);&quot;, t, sin(t), &quot;-b;sin(t);&quot;);\n",
-       "</pre></div>\n",
-       "\n",
-       "<p>This will plot the cosine and sine functions and label them accordingly\n",
-       "in the legend.\n",
-       "</p>\n",
-       "<p>If the first argument <var>hax</var> is an axes handle, then plot into this axes,\n",
-       "rather than the current axes returned by <code>gca</code>.\n",
-       "</p>\n",
-       "<p>The optional return value <var>h</var> is a vector of graphics handles to\n",
-       "the created line objects.\n",
-       "</p>\n",
-       "<p>To save a plot, in one of several image formats such as PostScript\n",
-       "or PNG, use the <code>print</code> command.\n",
-       "</p>\n",
-       "\n",
-       "<p><strong>See also:</strong> axis, box, grid, hold, legend, title, xlabel, ylabel, xlim, ylim,\n",
-       "ezplot, errorbar, fplot, line, plot3, polar, loglog, semilogx, semilogy,\n",
-       "subplot.\n",
-       "</p></dd></dl>\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Filter design\n",
+        "\n",
+        "The analog solution has the following transfer function:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example: notch filter\n",
-    "\n",
-    "### Parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "w0 = 2*pi*1000;     % Target frequency\n",
-    "Q = 100;            % Analog selectivity\n",
-    "Tc = 1/50e3;        % Sampling time    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Filter design\n",
-    "\n",
-    "The analog solution has the following transfer function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/latex": [
-       "$$ \\mathit{F(s)} = \\frac{2.533\\mathrm{ᴇ\\text{-}}{8} s^2 + 1}{2.533\\mathrm{ᴇ\\text{-}}{8} s^2 + 1.592\\mathrm{ᴇ\\text{-}}{6} s + 1} $$"
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/latex": [
+              "$$ \\mathit{F(s)} = \\frac{2.533\\mathrm{\u1d07\\text{-}}{8} s^2 + 1}{2.533\\mathrm{\u1d07\\text{-}}{8} s^2 + 1.592\\mathrm{\u1d07\\text{-}}{6} s + 1} $$"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Continuous-time model.\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "pkg load signal\n",
+        "pkg load control\n",
+        "\n",
+        "%% Analog solution\n",
+        "s = tf('s');\n",
+        "N = 1+(s/w0)^2;                  \n",
+        "D = 1 + s/(Q*w0) + (s/w0)^2;\n",
+        "F = N/D                          % Analog notch filter                "
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Continuous-time model.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "pkg load signal\n",
-    "pkg load control\n",
-    "\n",
-    "%% Analog solution\n",
-    "s = tf('s');\n",
-    "N = 1+(s/w0)^2;                  \n",
-    "D = 1 + s/(Q*w0) + (s/w0)^2;\n",
-    "F = N/D                          % Analog notch filter                "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The discrete solution has the following transfer function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The discrete solution has the following transfer function:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/latex": [
+              "$$ \\mathit{F_d(z)} = \\frac{z^2 - 1.984 z + 1}{z^2 - 1.983 z + 0.9987} $$"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sampling time: 2e-05 s\n",
+            "Discrete-time model.\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "F_d = c2d(F,Tc,'zoh')            % Discretisation   "
+      ]
+    },
     {
-     "data": {
-      "text/latex": [
-       "$$ \\mathit{F_d(z)} = \\frac{z^2 - 1.984 z + 1}{z^2 - 1.983 z + 0.9987} $$"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Bode diagram"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sampling time: 2e-05 s\n",
-      "Discrete-time model.\n",
-      "\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "tags": []
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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"
+          },
+          "metadata": {
+            "image/png": {
+              "height": 420,
+              "width": 560
+            }
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "graphics_toolkit(\"notebook\")\n",
+        "bode(F,F_d);\n",
+        "legend(\"F\", \"F_d\")"
+      ]
     }
-   ],
-   "source": [
-    "F_d = c2d(F,Tc,'zoh')            % Discretisation   "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Bode diagram"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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"
-     },
-     "metadata": {
-      "image/png": {
-       "height": 420,
-       "width": 560
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Octave (xoctave)",
+      "language": "Octave",
+      "name": "xoctave"
+    },
+    "language_info": {
+      "version": "7.2.0",
+      "codemirror_mode": "octave",
+      "file_extension": ".m",
+      "mimetype": "text/x-octave",
+      "name": "Octave",
+      "pygments_lexer": "octave"
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "state": {},
+        "version_major": 2,
+        "version_minor": 0
       }
-     },
-     "output_type": "display_data"
     }
-   ],
-   "source": [
-    "graphics_toolkit(\"notebook\")\n",
-    "bode(F,F_d);\n",
-    "legend(\"F\", \"F_d\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Octave (xoctave)",
-   "language": "Octave",
-   "name": "xoctave"
-  },
-  "language_info": {
-   "codemirror_mode": "octave",
-   "file_extension": ".m",
-   "mimetype": "text/x-octave",
-   "name": "Octave",
-   "pygments_lexer": "octave",
-   "version": "7.2.0"
   },
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {},
-    "version_major": 2,
-    "version_minor": 0
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }