laplacewt.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resolución de la Ecuación de Laplace en un cable sometido a una diferencia de potencial armónica\n",
    "=========================================\n",
    "\n",
    "\n",
    "La idea es resolver la ecuación de Laplace en forma aproximada, para la transformada de Fourier del potencial respecto al tiempo, en presencia de un conductor cilíndrico:\n",
    "$$\n",
    "\\nabla^2 \\Phi = 0\n",
    "$$\n",
    "con $\\Phi=\\Phi(r,z)$ un potencial con simetría cilíndrica. Para esto, introducimos una celda elemental correspondiente a un elemento de volumen  $\\Delta r \\times \\Delta z \\times r \\Delta \\phi$, donde asumiremos además que $\\Delta r=\\Delta z=\\Delta$. La ecuación de Laplace discretizada en esta geometría se expresa según\n",
    "\n",
    "$$\\Phi(r,z)= \\frac{(1+\\frac{\\Delta}{2 r} )\\Phi(r+\\Delta,z)+(1-\\frac{\\Delta}{2 r} )\\Phi(r-\\Delta,z)+\\Phi(r,z+\\Delta)+\\Phi(r,z-\\Delta)}{4} + {\\cal O}(\\Delta^2)\n",
    "$$\n",
    "\n",
    "con la condición \n",
    "$\\Phi(-\\Delta,z)=\\Phi(\\Delta,z)$\n",
    "\n",
    "Proponemos entonces que\n",
    "$$\n",
    "\\Phi(\\Delta i,\\Delta j)=\\Phi_{i,j}\n",
    "$$\n",
    "\n",
    "de manera que los $\\Phi_{i,j}$ satisfacen las condiciones anteriores.\n",
    "\n",
    "Si ahora consideramos regiones conductoras, la solución quedará determinada en términos de las condiciones de borde, impuestas sobre el conductor, y sobre los límites de la región a estudiar. En el caso de las condiciones de Dirichlet (potencial fijo) la condición se establece directamente fijando el valor del potencial en los correspondientes elementos $\\Phi_{i,j}$\n",
    "\n",
    "Condiciones tipo Neumann, o de discontinuidad en las derivadas se pueden imponer modificando la condición de actualización sobre los correspondientes elementos. Por ejemplo, si queremos imponer que la derivada radiar se anule en un elemento $i,j$, basta imponer\n",
    "$$\n",
    "\\Phi_{i,j}=\\Phi_{i-1,j}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En la superficie de un conductor cilíndrico, de conductividad finita $\\sigma$, la condición de conservación de la carga en su superficie lateral implica que\n",
    "$$\n",
    "\\Sigma = -\\frac{\\sigma}{{\\bf i}\\omega}\\check{r}\\cdot\\nabla \\Phi\n",
    "$$\n",
    "\n",
    "con $\\Sigma$ la densidad superficial de carga que, de acuerdo con la Ley de Gauss,\n",
    "$$\n",
    "\\Sigma = \\left.\\varepsilon \\check{r}\\cdot\\nabla \\Phi\\right|_{r=a-\\epsilon}\n",
    "-\\left.\\varepsilon \\check{r}\\cdot\\nabla \\Phi\\right|_{r=a+\\epsilon}\n",
    "$$\n",
    "por lo que\n",
    "$$\n",
    "\\left.\\check{r}\\cdot\\nabla \\Phi\\right|_{r=a+\\epsilon}  =\n",
    "\\left.(\\frac{\\varepsilon_{int}}{\\varepsilon_{ext} } +\\frac{\\sigma}{{\\bf i}\\omega\\varepsilon_{ext}} )\\check{r}\\cdot\\nabla \\Phi\\right|_{r=a-\\epsilon} \n",
    "$$\n",
    "\n",
    " \n",
    "Sobre las celdas correspondientes a la interface, podemos establecer esta condición de acuerdo con\n",
    "\n",
    "$$\n",
    "\\Phi_{i,j}=\\frac{\\Phi_{(i+1),j}+ (\\frac{\\varepsilon_{int}}{\\varepsilon_{ext}}+\\frac{\\sigma}{{\\bf i}\\omega \\varepsilon_{ext}})\\Phi_{(i-1),j}}{1+\\frac{\\varepsilon_{int}}{\\varepsilon_{ext}}+\\frac{\\sigma}{{\\bf i}\\omega \\varepsilon_{ext}}}\n",
    "$$\n",
    "\n",
    "\n",
    "Finalmente, para resolver el conjunto de relaciones entre los valores de una celda y sus vecinas, proponemos una configuración inicial en la que el potencial toma el valor especificado en las celdas sujetas a condiciones de Dirichlet, y en el resto toma valores cercanos a los reales. Luego, recorriendo las celdas actualizamos cada valor de acuerdo a las condiciones, e iteramos hasta la convergencia."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "Calculo del campo en la superficie y densidad de carga\n",
    "=============================================\n",
    "\n",
    "\n",
    "\n",
    "Una vez resuelto el sistema, además de disponer de una descripción cualitativa del potencial (que mejorará al refinar el mallado) podemos estimar algunas cantidades de interés. Una de estas cantidades tiene que ver con las componentes del campo eléctrico. Estas pueden ser calculadas en diferencias finitas de acuerdo con\n",
    "\n",
    "$$\n",
    "\\vec{E}_{r}\\approx  \\frac{\\Phi_{i-1,j}-\\Phi_{i+1,j}}{2\\Delta}\n",
    "$$\n",
    "$$\n",
    "\\vec{E}_{z}\\approx  \\frac{\\Phi_{i,j-1}-\\Phi_{i,j+1}}{2\\Delta}\n",
    "$$\n",
    "\n",
    "De manera análoga, podemos estimar la densidad superficial de carga en un punto de la interface  $r=a$\n",
    "según\n",
    "$$\n",
    "\\Sigma = \\varepsilon_0 \\frac{2\\Phi_{i,j}-\\Phi_{i+1,j}+\\Phi_{i-1,j}}{\\Delta}\n",
    "$$\n",
    "y sobre una interface con $z=\\pm L/2$ según\n",
    "$$\n",
    "\\Sigma = \\varepsilon_0 \\frac{2\\Phi_{i,j}-\\Phi_{i,j+1}+\\Phi_{i,j-1}}{\\Delta}\n",
    "$$\n",
    "\n",
    "\n",
    "\n",
    "Capacidad y momento dipolar. Parámetros adimensionales\n",
    "================================================\n",
    "\n",
    "\n",
    "A partir de la densidad superficial de carga podemos estimar la capacidad del conductor como el cociente entre la carga total separada y la diferencia de potencial aplicada.  Como el conductor es globalmente neutro, la carga separada puede expresarse como\n",
    "$$\n",
    "Q=\\frac{1}{2}\\iint |\\Sigma| dS \\approx \\frac{\\varepsilon_0 \\Delta^2}{2} \\sum_{i,j \\in {\\rm borde}}|\\Sigma_{ij}|\n",
    "$$\n",
    "Luego,\n",
    "$$\n",
    "C=\\frac{1}{2}\\iint |\\Sigma| dS \\approx \\frac{\\varepsilon_0 \\Delta^2}{2 (\\Delta V)} \\sum_{i,j \\in {\\rm borde}}|\\Sigma_{ij}|\n",
    "$$\n",
    "\n",
    "De manera semejante, el momento dipolar inducido \n",
    "$$\n",
    "\\vec{p}=\\frac{1}{2}\\iint \\vec{r}\\Sigma dS \\approx \\frac{\\varepsilon_0 \\Delta^3}{2} \\sum_{i,j \\in {\\rm borde}}j\\Sigma_{ij}\\check{e}_z\n",
    "$$\n",
    "\n",
    "Todas estas cantidades dependen explícitamente de las escalas de longitud y otros parámetros con dimensiones ($\\sigma$, $\\varepsilon_{int/ext}$, $\\omega$). Al hacer simulaciones numéricas es conveniente expresar tanto los parámetros de entrada, como las cantidades calculadas como cantidades adimensionales.\n",
    "\n",
    "Como entradas, conviene entonces trabajar con los cocientes\n",
    "$$\n",
    "\\zeta=\\frac{\\varepsilon_{int}}{\\varepsilon_{ext}}+\\frac{\\sigma}{{\\bf i}\\omega \\varepsilon_{ext}}\n",
    "$$\n",
    "que define una permitividad relativa compleja entre los medios, y\n",
    "$$\n",
    "t=L/a\n",
    "$$\n",
    "la relación entre la longitud y el radio del alambre. \n",
    "\n",
    "El resto de los parámetros a introducir tienen que ver con la discretización del modelo en un recinto finito:\n",
    "$n_z$ y $n_r$ son el número de celdas en que se divide la región a estudiar, y $L/\\Delta=l*n_z$ el número de celdas que ocupa el alambre en la dirección del eje z. El límite del continuo se obtiene cuando $l n_z \\rightarrow \\infty$, mientras que el límite de \"recinto infinito\" (sin superficies limitadoras) se obtiene cuando $l\\rightarrow 0$. Naturalmente, elegiremos $nz$ tan pequeño y $l$ tan grande como sea posible, de manera que las cantidades de interés se vuelvan independientes de estas cantidades.\n",
    "\n",
    "\n",
    "Habiendo definido los parámetros que definen el problema, conviene introducir las cantidades\n",
    "$$\n",
    "c = \\frac{C}{L}\n",
    "$$\n",
    "la capacidad por unidad  de longitud (medida en unidades de $\\varepsilon_0$) y\n",
    "$$\n",
    "\\vec{\\cal P}=\\frac{\\vec{p}}{\\Delta V \\pi a^2}\n",
    "$$\n",
    "una \"polarizabilidad efectiva\", que indica cuanto vale el momento dipolar inducido en el volumen del alambre. Esta cantidad es importante para estimar la radiación dipolar eléctrica emitida por el alambre a grandes distancias.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-10T11:43:16.655957Z",
     "start_time": "2020-07-10T11:43:16.425007Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#Tamaño de la malla:\n",
    "nz=400\n",
    "nr=int(nz/2)\n",
    "# dimensiones del alambre\n",
    "l = .25       # lo bastante chica para tener suficiente \"vacío\" alrededor del cable.\n",
    "a =  l     # relativo a l\n",
    "##\n",
    "DeltaV = 1.\n",
    "epsint = None   # Asumimos que en el exterior, eps = eps0. Dentro, epsrel + \\sigma/(I \\omega)\n",
    "omega = None     # la variable queda implícita en sigma. Sólo para fijar el límite omega=0\n",
    "##\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-10T11:44:59.837904Z",
     "start_time": "2020-07-10T11:44:59.791778Z"
    },
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "jmax = None\n",
    "jmin = None\n",
    "boundary1 = None\n",
    "boundary2 = None\n",
    "boundary3 = None\n",
    "pot = None\n",
    "\n",
    "\n",
    "\n",
    "def inicializar():\n",
    "    global pot, cable, exterior, boundary1, boundary2, boundary3, jmax, jmin\n",
    "    pot = np.zeros((nr,nz),dtype=complex)\n",
    "    # limites\n",
    "    jmax = min(nz,int(.5*nz*(1+l)))-1\n",
    "    jmin = max(0,int(.5*nz*(1.-l)))\n",
    "\n",
    "\n",
    "    # Superficie lateral del cilindro\n",
    "    boundary1 = [(int(nz*a),j) for j in  range(jmin,jmax+1)] \n",
    "    #Tapas\n",
    "    boundary2 = [(i,jmin) for i in  range(0, int(nz*a))] \n",
    "    boundary3 = [(i,jmax) for i in  range(0, int(nz*a))] \n",
    "\n",
    "\n",
    "    cable = []\n",
    "    exterior = []\n",
    "\n",
    "    # Construimos listas de puntos adentro y afuera del cable:\n",
    "    for i in range(1,nr-1):\n",
    "        for j in range(1,nz-1):\n",
    "            if (i,j) not in boundary1 and \\\n",
    "               (i,j) not in boundary2 and \\\n",
    "               (i,j) not in boundary3:\n",
    "                if i <  int(a*nz) and   \\\n",
    "                        abs(2.*j/nz-1) < l:\n",
    "                    cable.append((i,j))\n",
    "                else:\n",
    "                    exterior.append((i,j))\n",
    "\n",
    "    \n",
    "    # Relleno el exterior con la solución tipo alambre infinito/disco\n",
    "    for p in exterior:\n",
    "        if p[1]<jmin:\n",
    "            cc = a*nz\n",
    "            zz = (jmin-p[1]+.01)/cc\n",
    "            cc = cc**2\n",
    "            rrsq = p[0]**2/cc + zz**2\n",
    "            zeta = np.sqrt(2)*zz/np.sqrt(np.sqrt((rrsq-1)**2+4*zz**2)-rrsq+1)\n",
    "            pot[p] = .5*DeltaV*(1-2/np.pi*np.arctan(zeta))    \n",
    "        elif p[1]>jmax:\n",
    "            cc = a*nz\n",
    "            zz = (p[1]-jmax+.01)/cc\n",
    "            cc = cc**2\n",
    "            rrsq = p[0]**2/cc + zz**2\n",
    "            zeta = np.sqrt(2)*zz/np.sqrt(np.sqrt((rrsq-1)**2+4*zz**2)-rrsq+1)\n",
    "            pot[p] = -.5*DeltaV*(1-2/np.pi*np.arctan(zeta))    \n",
    "        else:\n",
    "            val = -.5*DeltaV/l * (p[1]-.5*nz)/(jmax-jmin) \n",
    "            val = val * np.log( max(.5*l*nz/p[0],1 ) )/np.log( .5*l/(a+1/nz))\n",
    "            pot[p] = val\n",
    "    # Relleno el interior con la solución lineal\n",
    "    for j in range(jmin,jmax):\n",
    "        for i in range(0, int(nz*a)):\n",
    "            val = -.5*DeltaV/l * (j-.5*nz)/(jmax-jmin) \n",
    "            pot[i,j] = val\n",
    "    \n",
    "    for p in boundary2:\n",
    "        pot[p] = .5* DeltaV\n",
    "\n",
    "    for p in boundary3:\n",
    "        pot[p] = -.5* DeltaV\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def update_pot(eps=None, sigma=None, omega=None):\n",
    "    if pot is None or nr != len(pot) or nz != len(pot[0]):\n",
    "        inicializar()\n",
    "        \n",
    "    if omega is not None:\n",
    "        omega = omega\n",
    "    if eps is not None:\n",
    "        epsint = eps\n",
    "        sigma = - epsint.imag\n",
    "    elif sigma is not None:\n",
    "        if omega != 0:\n",
    "            epsint = 1 - sigma*1j/omega\n",
    "        \n",
    "    if epsint is None:\n",
    "        return\n",
    "    # Sobre el eje, condición de derivada nula\n",
    "    for j in range(1,nz-1):\n",
    "        if (0,j) in boundary2 or \\\n",
    "            (0,j) in boundary3:\n",
    "            continue\n",
    "        pot[0,j] = .25*(2.*pot[1,j]+pot[0,j+1]+pot[0,j-1])\n",
    "\n",
    "    for p in cable:\n",
    "        pot[p] =  .25*((1+.5/p[0])*pot[p[0]+1,p[1]]+(1-.5/p[0])*pot[p[0]-1,p[1]]+pot[p[0],p[1]+1]+pot[p[0],p[1]-1])\n",
    "\n",
    "    for p in exterior:\n",
    "        pot[p] =  .25*((1+.5/p[0])*pot[p[0]+1,p[1]]+(1-.5/p[0])*pot[p[0]-1,p[1]]+pot[p[0],p[1]+1]+pot[p[0],p[1]-1])\n",
    "\n",
    "    if sigma == 0:\n",
    "        for p in boundary1:\n",
    "            pot[p] =  .25*((1+.5/p[0])*pot[p[0]+1,p[1]]+(1-.5/p[0])*pot[p[0]-1,p[1]]+pot[p[0],p[1]+1]+pot[p[0],p[1]-1])\n",
    "    else:\n",
    "        if omega == 0:\n",
    "            for p in boundary1:\n",
    "                # Derivada radial nula\n",
    "                pot[p] = pot[p[0]-1,p[1]]\n",
    "        else:\n",
    "            \n",
    "            for p in boundary1:\n",
    "                pot[p] = (pot[p[0]+1,p[1]]+ epsint *pot[p[0]-1,p[1]])/(1+epsint)\n",
    "                \n",
    "#inicializar()\n",
    "\n",
    "\n",
    "def carga_superficial():\n",
    "    sigmas = []\n",
    "    for p in boundary1[2:-2]:\n",
    "        sigmas.append(2 * pot[p[0],p[1]]-pot[p[0]-1,p[1]]-pot[p[0]+1,p[1]] )\n",
    "    return np.array(sigmas)\n",
    "\n",
    "\n",
    "def capacidad_unidad_long():    \n",
    "    sigmas = []\n",
    "    for p in boundary2[:-2]:\n",
    "        sigmas.append( (2*pot[p[0],p[1]]-pot[p[0],p[1]-1]-pot[p[0],p[1]+1]).real * p[0])\n",
    "             \n",
    "    for p in boundary3[:-2]:\n",
    "        sigmas.append( (2*pot[p[0],p[1]]-pot[p[0],p[1]-1]-pot[p[0],p[1]+1]).real * p[0])\n",
    "    \n",
    "    qlateral = sum(abs(carga_superficial().real)) * a*nz\n",
    "    q = sum([abs(s) for s in sigmas]) + qlateral\n",
    "    return np.pi*q.real/DeltaV/(l *nz) \n",
    "\n",
    "\n",
    "\n",
    "def polarizabilidad():    \n",
    "    sigmas = []\n",
    "    for p in boundary2[:-1]:\n",
    "        sigmas.append( (2*pot[p[0],p[1]]-pot[p[0],p[1]-1]-pot[p[0],p[1]+1]) * p[0])\n",
    "\n",
    "    res = sum([s for s in sigmas])*(jmin)\n",
    "    sigmas = []\n",
    "    \n",
    "    for p in boundary3[:-1]:\n",
    "        sigmas.append( (2*pot[p[0],p[1]]-pot[p[0],p[1]-1]-pot[p[0],p[1]+1]) * p[0])\n",
    "\n",
    "    res = res + sum([s for s in sigmas])*(jmax)\n",
    "    res = res/(a*nz) + np.dot(carga_superficial(), np.array([p[1] for p in boundary1[2:-2]]))\n",
    "    return res/(.5*a*nz*DeltaV)\n",
    "\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2020-07-10T12:04:57.131Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   C/L:  3.056824327264187 pF/m  P:  (-1.189503213452195-0.0015575490125195074j) pC/m^2\n",
      "   C/L:  3.0568243272665923 pF/m  P:  (-1.1895032134573675-0.0015575490125948903j) pC/m^2\n",
      "   C/L:  3.0568243272665923 pF/m  P:  (-1.1895032134573675-0.0015575490125949915j) pC/m^2\n",
      "   C/L:  3.0568243272665923 pF/m  P:  (-1.1895032134573675-0.0015575490125949915j) pC/m^2\n",
      "   C/L:  3.0568243272665923 pF/m  P:  (-1.1895032134573675-0.0015575490125949915j) pC/m^2\n",
      "   C/L:  3.0568243272665923 pF/m  P:  (-1.1895032134573675-0.0015575490125949915j) pC/m^2\n",
      "   C/L:  3.0568243272665923 pF/m  P:  (-1.1895032134573675-0.0015575490125949915j) pC/m^2\n",
      "   C/L:  3.0568243272665923 pF/m  P:  (-1.1895032134573675-0.0015575490125949915j) pC/m^2\n",
      "   C/L:  3.0568243272665923 pF/m  P:  (-1.1895032134573675-0.0015575490125949915j) pC/m^2\n"
     ]
    }
   ],
   "source": [
    "sigma = 0.\n",
    "omega = 1.\n",
    "l = .2\n",
    "a = .2\n",
    "nz = 50\n",
    "nr = int(nz/2)\n",
    "\n",
    "for j in range(10):\n",
    "    for i in range(500):\n",
    "        update_pot(eps=1.-.1*1j, sigma=sigma, omega=omega)\n",
    "    print(\"   C/L: \" ,capacidad_unidad_long(), \"pF/m  P: \" ,polarizabilidad(),\"pC/m^2\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-10T12:04:45.052179Z",
     "start_time": "2020-07-10T12:04:44.235838Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "########################### Graficos   ################################\n",
    "fig,axs = plt.subplots(2,2,figsize=(12,8))\n",
    "\n",
    "####          \n",
    "ax = axs[0][0]\n",
    "\n",
    "ax.contourf(pot.real)\n",
    "bdry=np.array(boundary2+boundary1+boundary3)\n",
    "ax.plot(bdry[:,1],bdry[:,0])\n",
    "ax.set_title(\"Potencial en el plano r-z\")\n",
    "ax = axs[0][1]\n",
    "\n",
    "ax.plot(pot[0,:].real,label=\"real eje\")\n",
    "ax.plot(pot[0,:].imag,label=\"imag eje\")\n",
    "ax.plot(pot[int(2*a*nz/3),:].real,label=\"real 2a/3\")\n",
    "ax.plot(pot[int(2*a*nz/3),:].imag,label=\"imag 2a/3\")\n",
    "ax.plot(pot[int(a*nz),:].real,label=\"real superficie\")\n",
    "ax.plot(pot[int(a*nz),:].imag,label=\"imag superficie\")\n",
    "\n",
    "ax.set_title(\"Potencial en el eje y en la superficie\")\n",
    "ax.legend()\n",
    "\n",
    "\n",
    "ax = axs[1][0]\n",
    "\n",
    "\n",
    "ax.plot(carga_superficial().real)\n",
    "ax.set_title(\"Densidad superficial de carga en la cara lateral\")\n",
    "\n",
    "\n",
    "\n",
    "ax = axs[1][1]\n",
    "\n",
    "sigmas = []\n",
    "for p in boundary2[:-2]:\n",
    "    sigmas.append( (2*pot[p[0],p[1]]-pot[p[0],p[1]-1]-pot[p[0],p[1]+1]).real)\n",
    "\n",
    "ax.plot(np.array( boundary2[:-2])[:,0]/(a*nz), np.array(sigmas).real)\n",
    "ax.set_title(\"Densidad superficial de carga en la tapa\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": false,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}