{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Implementación de un pasabanda mediante el conversor generalizado de impedancias\n", "\n", " \n", "\n", "#### Por Mariano Llamedo Soria" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Resumen \n", "\n", "En este notebook se analizará la respuesta en frecuencia de un **filtro pasabanda activo** implementado mediante el conversor generalizado de impedancias de [Andreas Antoniou](https://ieeexplore.ieee.org/author/37276841000) en 1969 [1]. Se aprovecha el ejemplo para demostrar el uso de las siguientes funciones de PyTC2:\n", "\n", "* Análisis de la respuesta en frecuencia: [analyze_sys](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/sistemas_lineales/index.html#pytc2.sistemas_lineales.analyze_sys), [bodePlot](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/sistemas_lineales/index.html#pytc2.sistemas_lineales.bodePlot), [pzmap](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/sistemas_lineales/index.html#pytc2.sistemas_lineales.pzmap), [pretty_print_bicuad_omegayq](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/sistemas_lineales/index.html#pytc2.sistemas_lineales.pretty_print_bicuad_omegayq), [GroupDelay](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/sistemas_lineales/index.html#pytc2.sistemas_lineales.GroupDelay), [symbfunc2tf](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.symbfunc2tf), [factorSOS](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.factorSOS)\n", "\n", "[1] A. Anotniou. [*Realisation of gyrators using operational amplifiers, and their use in RC-active-network synthesis*](https://drive.google.com/file/d/1joumLPy2mgb4ejy2ld_ywwJKN_i40eeL/view?usp=drive_link). PROC. IEE, Vol. 116, No. II, NOVEMBER 1969.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Introducción\n", "\n", "En este documento se analizará el conversor generalizado de inmitancias (impedancias o admitancias) propuesto por Antoniou (GIC de aquí en adelante). Este circuito es especialmente útil para implementar funciones de excitación en su puerto de entrada que dependen de las 5 admitancias que componen la red. Algunos ejemplos que se estudiarán son giradores, resistores negativos dependientes de la frecuencia (FDNR), entre otras posibilidades que se pueden implementar con esta red. El circuito que representa al GIC de Antoniou es muy llamativo:\n", "\n", "\"GIC\n", "\n", "Se procede ahora al análisis de la red para hallar concretamente la admitancia en el puerto 1, $Y_1 = \\frac{I_1}{V_1}$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Planteamos las ecuaciones de nodos, quedando en forma matricial de la siguiente forma\n", "\n", "$$ v.Y-i = 0$$\n", "\n", "que se procederá a resolver con el solver de ecuaciones lineales de SymPy" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "code_folding": [], "scrolled": true }, "outputs": [], "source": [ "import sympy as sp\n", "from pytc2.remociones import remover_polo_dc\n", "from pytc2.general import a_equal_b_latex_s, print_latex, s, symbfunc2tf, factorSOS\n", "from pytc2.sistemas_lineales import bodePlot\n", "\n", "# tensiones y corrientes que usaremos para nuestros análisis\n", "I1, V1, V2, V3, V4, V5 = sp.symbols(\"I1, V1, V2, V3, V4, V5\")\n", "Y1, Y2, Y3, Y4, Y5, As = sp.symbols(\"Y1, Y2, Y3, Y4, Y5, As\")\n", "G, C, wt, w0 = sp.symbols(\"G, C, wt, w0\", real = True, posistive = True) \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se aprovecharán las capacidades simbólicas para mostrar lo sencillo que puede ser analizar un circuito como el GIC, con diferentes complejidades algebraicas. Para ello planteamos tres análisis en orden de complejidad creciente:\n", "\n", "1. Amplificadores operacionales (OpAmp) ideales, realimentados negativamente\n", "2. OpAmp's ideales, sin considerar realimentación negativa.\n", "3. OpAmp's basados en el modelo de un solo polo en el origen o integrador.\n", "\n", "### OpAmp's ideales, realimentados negativamente\n", "\n", "El caso más sencillo se modela con la siguiente ecuación matricial, que arroja el resultado más conocido del GIC.\n", " " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle Z_1 = \\frac{V_1}{I_1}=\\frac{Y_{2} Y_{4}}{Y_{1} Y_{3} Y_{5}}$" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# modelo ideal negativamente realimentado\n", "aa = sp.solve([ \n", " V1*Y1 - V2*Y1 - I1, \n", " -V2*Y2 + V1*(Y2+Y3) -V3*Y3,\n", " -V3*Y4 + V1*(Y4+Y5)\n", " ], \n", " [V1, V2, V3])\n", "Z1 = aa[V1]/I1\n", "\n", "print_latex(a_equal_b_latex_s('Z_1 = \\\\frac{V_1}{I_1}', Z1))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sin asumir realimentación negativa\n", "\n", "Un paso de complejidad siguiente podría ser considerar que los OpAmp's no están realimentados negativamente, y en consecuencia la tensión en su pata inversora y no inversora no son iguales. Luego se debería imponer que la ganancia de cada OpAmp tiende a infinito, y analizar la impedancia $Z_1$. Para ello se plantea:\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle Z_{12}=\\frac{As^{2} I_{1} Y_{2} Y_{4} + As I_{1} Y_{2} Y_{4} + As I_{1} Y_{2} Y_{5} + As I_{1} Y_{3} Y_{4} + As I_{1} Y_{3} Y_{5} + I_{1} Y_{2} Y_{4} + I_{1} Y_{2} Y_{5} + I_{1} Y_{3} Y_{4} + I_{1} Y_{3} Y_{5}}{I_{1} \\left(As^{2} Y_{1} Y_{3} Y_{5} + As Y_{1} Y_{2} Y_{4} + As Y_{1} Y_{2} Y_{5} + As Y_{1} Y_{3} Y_{4} + As Y_{1} Y_{3} Y_{5} + Y_{1} Y_{2} Y_{4} + Y_{1} Y_{2} Y_{5} + Y_{1} Y_{3} Y_{4} + Y_{1} Y_{3} Y_{5}\\right)}$" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "aa = sp.solve([ \n", " V1*Y1 - V2*Y1 - I1, \n", " -V2*Y2 + V3*(Y2+Y3) -V4*Y3,\n", " -V4*Y4 + V5*(Y4+Y5),\n", " As*(V5-V3) - V2, \n", " As*(V1-V3) - V4, \n", " ], \n", " [V1, V2, V3, V4, V5])\n", "\n", "Z12 = aa[V1]/I1\n", "\n", "print_latex(a_equal_b_latex_s('Z_{12}', Z12))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como se observa la expresión se complica enormemente, a menos que consideremos $A_s \\to \\infty$" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "code_folding": [] }, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle Z_{12} = Z_1=\\frac{Y_{2} Y_{4}}{Y_{1} Y_{3} Y_{5}}$" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# modelo ideal sin asumir realimentación negativa\n", "Z122 = sp.limit(Z12, As, sp.oo)\n", "\n", "print_latex(a_equal_b_latex_s('Z_{12} = Z_1', Z122))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Esto quiere decir que la asunción de realimentación negativa es correcta.\n", "\n", "A partir de ahora se considerará un modelo de OpAmp más complicado, que permitirá acercarse un poco más a una simulación más realista.\n", "\n", "### Análisis con OpAmp como integrador\n", "\n", "Desde el punto conceptual y algebraico, basta con asumir \n", "\n", "$$ A_s = \\frac{\\omega_t}{s}$$\n", "\n", "esto trae aparejado expresiones un tanto más complejas, que deben analizarse con más cuidado. Recordar que $\\omega_t$ puede interpretarse como la pulsación a la cual un opamp tiene una ganancia unitaria o de 0 dB. También se lo puede asemejar al **producto ganancia-ancho de banda** (GBP).\n", "\n", "Por este motivo, primero se revisará para el caso más sencillo cómo es el comportamiento del GIC en su configuración como *girador* de impedancias. \n", "\n", "$$ Z_1 = \\frac{r^2}{Z_2}$$\n", "\n", "Donde el comportamiento del girador anticipa que una $Z_2$ conectada en un puerto, será *girada* en su naturaleza reactiva cuando se la observe desde el otro puerto, como $Z_1$. La constante $r$ sirve como factor de proporcionalidad entre ambas impedancias y se mide en ohms. Esto quiere decir que una reactancia capacitiva en $Z_2$ se verá como **inductiva** en $Z_1$. En nuestro GIC, haciendo $Y_2 = s.C$ y todas las demás $Y_x = 1/R$ (x:1,3-5) resulta en\n", "\n", "$$ Z_1 = s. L_{eq} = s.R^2.C$$\n", "\n", "con $L_{eq} = R^2.C$. \n", "\n", "Ahora si se procede a aumentar la complejidad algebraica al considerar $ A_s = \\frac{\\omega_t}{s}$" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle Z_{13}=\\frac{Y_{2} Y_{4} s^{2} + Y_{2} Y_{4} s wt + Y_{2} Y_{4} wt^{2} + Y_{2} Y_{5} s^{2} + Y_{2} Y_{5} s wt + Y_{3} Y_{4} s^{2} + Y_{3} Y_{4} s wt + Y_{3} Y_{5} s^{2} + Y_{3} Y_{5} s wt}{Y_{1} \\left(Y_{2} Y_{4} s^{2} + Y_{2} Y_{4} s wt + Y_{2} Y_{5} s^{2} + Y_{2} Y_{5} s wt + Y_{3} Y_{4} s^{2} + Y_{3} Y_{4} s wt + Y_{3} Y_{5} s^{2} + Y_{3} Y_{5} s wt + Y_{3} Y_{5} wt^{2}\\right)}$" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Z13 = sp.simplify(sp.expand(Z12.subs(As, wt/s)))\n", "\n", "print_latex(a_equal_b_latex_s('Z_{13}', Z13))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora se procede a configurar al GIC como girador, para obtener" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle Z_{1}=\\frac{s \\left(2 C s^{2} + 2 C s wt + C wt^{2} + 2 G s + 2 G wt\\right)}{G \\left(2 C s^{3} + 2 C s^{2} wt + 2 G s^{2} + 2 G s wt + G wt^{2}\\right)}$" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Z13 = sp.simplify(sp.expand(Z13.subs({Y1:G, Y2:s*C, Y3:G, Y4:G, Y5:G})))\n", "\n", "print_latex(a_equal_b_latex_s('Z_{1}', Z13))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si bien la expresión se simplifica notablemente, aún conserva bastante complejidad, sobretodo si se recuerda que el comportamiento debería ser similar a un inductor de valor $L_{eq}$, como el que fue hallado más arriba.\n", "\n", "A partir de ahora se sugiere pasar a un análisis numérico para ganar algo de perspectiva respecto a la respuesta en frecuencia de $Z_1$. Para ello asignaremos valores *razonables* a las variables $\\omega_t$, $C$ y $G$.\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle Z_{1}=\\frac{s \\left(s^{2} + 101 s + 5100\\right)}{s^{3} + 101 s^{2} + 100 s + 5000}$" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Z1num = sp.simplify(sp.expand(Z13.subs({wt:100, G:1, C:1})))\n", "\n", "print_latex(a_equal_b_latex_s('Z_{1}', Z1num))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Asumimos una $\\omega_t=100$, lo que implicaría que el GBP del OpAmp está 100 veces por encima de la frecuencia de operación del GIC. Este valor persigue una finalidad didáctica antes que realista, dado que se pretende mantener coeficientes no demasiado grandes. En la práctica este valor podría ser bastante más elevado. Factorizando la expresión se obtiene" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle Z_{1}=\\frac{s \\left(s^{2} + 101.0 s + 5100.0\\right)}{\\left(s + 100.5\\right) \\left(s^{2} + 0.502 s + 49.76\\right)}$" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Z1num, _, _ = factorSOS(Z1num)\n", "\n", "print_latex(a_equal_b_latex_s('Z_{1}', Z1num))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y de esta manera se comienza a tomar algo de perspectiva del comportamiento realista del GIC. El comportamiento inductivo se afectará por la/s singularidades de menor frecuencia. En este caso, siguiendo el orden: 1) los polos complejos conjugados de radio $\\sqrt{49.8} \\approx 7$, luego 2) los ceros de $\\sqrt{5100} \\approx 71$ y finalmente 3) el polo en 101.\n", "\n", "En el siguiente análisis se observa cómo a dichas frecuencias se obtienen puntos de inflexión en las curvas de respuesta.\n", "\n", "Primero inicializamos el entorno gráfico" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Inicialización e importación de módulos\n", "\n", "# Módulos externos\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "\n", "# Esta parte de código la agregamos SOLO en los notebooks para fijar el estilo de los gráficos.\n", "fig_sz_x = 13\n", "fig_sz_y = 7\n", "fig_dpi = 80 # dpi\n", "fig_font_size = 11\n", "\n", "mpl.rcParams['figure.figsize'] = (fig_sz_x, fig_sz_y)\n", "mpl.rcParams['figure.dpi'] = fig_dpi\n", "plt.rcParams.update({'font.size':fig_font_size})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "ahora si continuamos con la respuesta en frecuencia" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig_id, axes_hdl = bodePlot(symbfunc2tf(Z1num), filter_description='$\\omega_t = {:d}$'.format(100), fig_id=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En el siguiente análsis, se observa la dependencia del GIC respecto al GBP del OpAmp y en comparativa se incorpora el comportamiento del girador idealizado." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Z1_ideal = sp.simplify(sp.expand(Z1.subs({Y1:G, Y2:s*C, Y3:G, Y4:G, Y5:G})))\n", "Z1_ideal = Z1_ideal.subs({G:1, C:1})\n", "\n", "wt_all = [10, 100, 1000]\n", "fig_id, axes_hdl = bodePlot(symbfunc2tf(Z1_ideal), filter_description='Inductor ideal')\n", "\n", "for wti in wt_all:\n", "\n", " fig_id, axes_hdl = bodePlot(symbfunc2tf(sp.simplify(sp.expand(Z13.subs({wt:wti, G:1, C:1})))), filter_description='$\\omega_t = {:d}$'.format(wti), fig_id=1)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notar cómo el alejamiento de las tres curvas respecto del comportamiento idealizado indica el ancho de banda útil del GIC de Antoniou. Para el caso analizado de $\\omega_t=100$, esto ocurre algo antes de los 7 r/s (curva verde). Ciertamente a mayor GBP, mayor ancho de banda en el que el girador se comportará como tal." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Un modelo circuital de $Z_1$\n", "\n", "Ahora se intentará obtener una red que responda al comportamiento de $Z_1$, esto es realizar la síntesis de la impedancia de entrada. Para la mayoría de estudiantes que revisa este documento al mismo tiempo que está cursando la asignatura, es posible que no siga al detalle el siguiente procedimiento todavía. Es que se trata de hallar una red a partir de una respuesta en frecuencia, o una función matemática que define una función de excitación. Este tema se verá durante el segundo cuatrimestre, y se puede saltear el siguiente procedimiento." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle Z_{1}=\\frac{s \\left(s^{2} + 101.0 s + 5100.0\\right)}{\\left(s + 100.5\\right) \\left(s^{2} + 0.502 s + 49.76\\right)}$" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print_latex(a_equal_b_latex_s('Z_{1}', Z1num))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "Z1 = sp.simplify(sp.expand(Z13.subs({wt:100, G:1, C:1})))\n", "\n", "Y2, YLeq = remover_polo_dc(1/Z1)\n", "\n", "Leq = 1/YLeq/s\n", "\n", "# parte imaginaria de la variable compleja s\n", "ww = sp.symbols(\"\\omega\", real = True)\n", "\n", "re_Y2 = sp.simplify(sp.expand(sp.re(Y2.subs({s:(sp.I*ww)}))))\n", "\n", "G1 = sp.minimum(re_Y2, ww, domain=sp.Reals)\n", "\n", "R1 = 1/G1\n", "\n", "Y4 = sp.simplify(sp.expand(Y2 - G1))\n", "\n", "Z4 = 1/Y4\n", "\n", "Z6, ZC1 = remover_polo_dc(Z4)\n", "\n", "C1 = 1/ZC1/s\n", "\n", "re_Z6 = sp.simplify(sp.expand(sp.re(Z6.subs({s:(sp.I*ww)}))))\n", "\n", "R2 = sp.minimum(re_Z6, ww, domain=sp.Reals)\n", "\n", "Z8 = sp.simplify(sp.expand(Z6 - R2))\n", "\n", "Y8 = 1/Z8\n", "\n", "Y8 = sp.expand(Y8).as_ordered_terms()\n", "\n", "R3 = Y8[0]\n", "L2 = 1/Y8[1]/s\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Una vez obtenido los elementos circuitales, podemos agruparlos en un circuito gracias a las posibilidades del módulo [schemdraw](https://schemdraw.readthedocs.io/en/stable/) y [PyTC2](https://pytc2.readthedocs.io/en/stable/)." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", " \n", " \n", " \n", " \n", " 2024-12-20T16:56:48.306572\n", " image/svg+xml\n", " \n", " \n", " Matplotlib v3.7.5, https://matplotlib.org/\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from schemdraw import Drawing\n", "from pytc2.dibujar import dibujar_puerto_entrada, dibujar_funcion_exc_abajo, dibujar_elemento_serie, dibujar_elemento_derivacion, dibujar_espaciador\n", "\n", "d = Drawing(unit=4)\n", "d = dibujar_puerto_entrada(d)\n", "d = dibujar_funcion_exc_abajo(d, \n", " 'Z_1', \n", " Z1, \n", " hacia_salida = True)\n", "\n", "d = dibujar_elemento_serie(d, \"L\", sym_label=Leq)\n", "d = dibujar_elemento_derivacion(d, \"R\", sym_label=R1)\n", "d = dibujar_elemento_serie(d, \"C\", sym_label=C1.evalf(3))\n", "d = dibujar_elemento_serie(d, \"R\", sym_label=R2.evalf(3))\n", "d = dibujar_elemento_derivacion(d, \"R\", sym_label=R3.evalf(3))\n", "d = dibujar_espaciador(d)\n", "d = dibujar_elemento_derivacion(d, \"L\", sym_label=L2.evalf(3), with_nodes=False)\n", "display(d)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notar cómo el comportamiento inductivo se destaca en bajas frecuencias aproximándose al comportamiento del girador ideal con valor $L_{eq} = 1$.\n", "\n", "### Finalmente, la simulación circuital\n", "\n", "Una vez que se tiene una buena perspectiva respecto al comportamiento idealizado del GIC, incluso al haber analizado el efecto del GBP en este circuito, se puede continuar con la simulación circuital. Especialmente con el objetivo de utilizar modelos reales de OpAmp's provistos por los fabricantes. Para tal fin se utilizará un resonador pasivo RLC sintonizado a $\\omega_0 = 1$ r/s y $Q = 1$, con la finalidad de *activar* el inductor mediante un GIC.\n", "\n", "\"GIC\n", "\n", "\n", "Notar que en la parametrización de la red, el GBW (GBP en este documento) está 4 órdenes de magnitud por encima de la frecuencia de operación del resonador RLC para asegurar el comportamiento adecuado del GIC, a esa frecuencia y otras que fueron analizadas en otros escenarios de simulación. \n", "\n", "\n", "\"GIC\n", "\n", "\n", "El comportamiento es el esperado para el caso ideal, al verificarse que la red responde de manera similar al análisis numérico y simbólico. A continuación se observa la respuesta inductiva del GIC (en azul), superpuesta a la respuesta pasabanda del resonador (en rojo). Se observa la respuesta en frecuencia para $\\mathrm{GBW} = f_0 * 10^k$ para $k = 2, 3, 4$, y la respuesta pasabanda que no cambia significativamente.\n", "\n", "\n", "\"GIC\n" ] } ], "metadata": { "_draft": { "nbviewer_url": "https://gist.github.com/ac51adac5315287242cfd3dfb27a0196" }, "gist": { "data": { "description": "mariano/Docencia/TC2/scripts/digital filters/python/Filtrado digital. Aplicaciones electrocardiografía.ipynb", "public": true }, "id": "ac51adac5315287242cfd3dfb27a0196" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 4 }