image_processing/1_transduccion/1_Transduccion_Analogico_Digital.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Determinación de los parámetros de adquisición de una señal fisiológica\n",
    "\n",
    "La medida de una magnitud física suele llevarse a cabo en la siguiente secuencia: transducción, muestreo y cuantización.\n",
    "\n",
    "La transducción consiste en la conversión de la magnitud física (posición, ángulo, velocidad, presión, tensión mecánica, temperatura, etc.) en una señal de voltaje o intensidad, esto es, una señal eléctrica. El muestreo consiste en pasar de esa señal eléctrica que varía contínuamente en el tiempo, a una señal en paquetes de tiempo (valores promedios, por ejemplo), esto es, discreta en el tiempo. La cuantización consiste en pasar de estos valores de señal que pueden tomar valores arbitrariamente próximos entre ellos, a valores agrupados por tramos, esto es, que pueden tomar valores discretos o digitales.\n",
    "\n",
    "Así, el resultado de una medida de una magnitud física continua o analógica se ha convertido en un listado digital de números enteros o, cuando menos, (a través de coeficientes multiplicativos de calibración) discretos, cada uno correspondiente a un tiempo de muestreo.\n",
    "\n",
    "El objetivo de este ejercicio es que usted determine, a partir del análisis del archivo de datos proporcionado, procedente de una medida de presión intracardiaca con una tarjeta digitalizadora Keithley, cuáles fueron los settings de dicha tarjeta usados para la digitalización.\n",
    "\n",
    "**TAREA**\n",
    "\n",
    "En particular, deberá usted determinar:\n",
    "\n",
    "- frecuencia de muestreo.\n",
    "- frecuencia de Nyquist de la señal.\n",
    "- bits de cuantificación.\n",
    "- ganancia de la digitalización (factor multiplicador de calibración).\n",
    "- polaridad de la medida (es decir, si la medida original era monopolar, esto es, sólo comprendía valores positivos de voltaje o si, por el contrario, era bipolar, esto es, comprendía valores tanto positivos como negativos de la señal eléctrica).\n",
    "    \n",
    "**DATOS**\n",
    "\n",
    "Archivo de texto con dos columnas `1800031_PSentronMP.dat`; tiempo en segundos y presión calibrada en mmHg."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Referencias\n",
    "\n",
    "[Frecuencia de muestreo](https://es.wikipedia.org/wiki/Frecuencia_de_muestreo)\n",
    "\n",
    "[Teorema de muestreo Numpy](https://ernestocrespo13.wordpress.com/2013/02/19/teorema-de-muestreo-explicado-con-numpy/)\n",
    "\n",
    "[Muestreo, Cuantificacion, Codificacion](https://www.youtube.com/watch?v=9GxcNyGQsuk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seconds</th>\n",
       "      <th>mmHg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001</td>\n",
       "      <td>95.007489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002</td>\n",
       "      <td>95.007489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.003</td>\n",
       "      <td>95.128993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.004</td>\n",
       "      <td>95.250498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005</td>\n",
       "      <td>95.250498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   seconds       mmHg\n",
       "0    0.001  95.007489\n",
       "1    0.002  95.007489\n",
       "2    0.003  95.128993\n",
       "3    0.004  95.250498\n",
       "4    0.005  95.250498"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "\n",
    "# Data\n",
    "DATA_PATH = \"data/\"\n",
    "\n",
    "# Figures\n",
    "FIGURES_PATH = \"figures/\"\n",
    "\n",
    "df = pd.read_csv(\n",
    "    DATA_PATH + \"1_d1800031_PSentronMP.dat\", \n",
    "    names=[\"seconds\", \"mmHg\"]\n",
    ")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 5))\n",
    "plt.plot(df[\"seconds\"], df[\"mmHg\"], label=\"mmHg\")\n",
    "plt.xlabel(\"segundos\")\n",
    "plt.ylabel(\"mmHg\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig(FIGURES_PATH + \"plot_mmhg_vs_tiempo.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Frecuencias de muestreo\n",
    "\n",
    "Será la inversa de nuestro paso de tiempo. En nuestro caso tenemos un muestreo uniforme (entre muestras consecutivas siempre hay la misma cantidad de tiempo)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intervalo de muestreo (s):  0.001\n",
      "Frecuencia de muestreo (Hz):  1000.0\n"
     ]
    }
   ],
   "source": [
    "intervalo_muestreo = df.loc[1, \"seconds\"] - df.loc[0, \"seconds\"]\n",
    "f_muestreo = 1 / intervalo_muestreo\n",
    "\n",
    "print(\"Intervalo de muestreo (s): \", intervalo_muestreo)\n",
    "print(\"Frecuencia de muestreo (Hz): \", f_muestreo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Frecuencia de Nyquist\n",
    "\n",
    "El período (T) de nuestra muestra corresponde al tiempo que tarda la señal en dar una vuelta, esto es, dado un punto de la señal, el período será la diferencia de tiempo para el cual la señal vuelve a tomar dicho valor.\n",
    "\n",
    "Con el objetivo de estimar el período de nuestra gráfica veamos los puntos para los cuales la presión coincide con nuestra primera muesta de datos (no es necesaria la primera muestra ya que se trata de una onda periódica y supondremos que habrá repetición debido a la gran cantidad de datos)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frecuencia de Nyquist (Hz):  500.0\n"
     ]
    }
   ],
   "source": [
    "f_nyquist = f_muestreo / 2\n",
    "\n",
    "print(\"Frecuencia de Nyquist (Hz): \", f_nyquist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.loc[df[\"mmHg\"] == df.loc[0, \"mmHg\"], [\"seconds\", \"mmHg\"]]\n",
    "# df.loc[df[\"mmHg\"] == df.loc[0, \"mmHg\"], \"seconds\"].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#El período será la diferencia de tiempos consecutivos en nuestro anterior dataframe. Por ejemplo `(0.855 - 0.002)`. Es importante puntualizar que el período se encuentra alrededor del valor 0.852 ya que será necesario eliminar de nuestro cálculo aquellos valores de T que se alejen de este valor. Con estos datos podemos estimar la **frecuencia de Nyquist**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dn = df.loc[df[\"mmHg\"] == df.loc[1000, \"mmHg\"], \"seconds\"]\n",
    "#fn = []\n",
    "#T = []\n",
    "\n",
    "#for i in range(0, len(dn)-1):\n",
    "#    T.append(dn[dn.index[i+1]] - dn[dn.index[i]])\n",
    "#    if T[i] > 0.7 and T[i] < 1.0: # eliminar valores extraños\n",
    "#        nyquist = 2 * 1/(T[i])\n",
    "#        fn.append(nyquist)\n",
    "#        print(\"Frecuencia de Nyquist estimada: \", nyquist)\n",
    "#print(\" \")        \n",
    "#print(\"Frecuencia de Nyquist media: \", sum(fn)/len(fn))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cuantificación\n",
    "\n",
    "La cuantificación es el proceso por el cual establecemos una precisión en el eje vertival de la señal. Vendrá determinada por el número de valores únicos en el eje vertical.\n",
    "\n",
    "En primer lugar ordenamos valores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seconds</th>\n",
       "      <th>mmHg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2987</th>\n",
       "      <td>2.988</td>\n",
       "      <td>5.823069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2194</th>\n",
       "      <td>2.195</td>\n",
       "      <td>5.823069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2195</th>\n",
       "      <td>2.196</td>\n",
       "      <td>5.823069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2196</th>\n",
       "      <td>2.197</td>\n",
       "      <td>5.823069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2197</th>\n",
       "      <td>2.198</td>\n",
       "      <td>5.823069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      seconds      mmHg\n",
       "2987    2.988  5.823069\n",
       "2194    2.195  5.823069\n",
       "2195    2.196  5.823069\n",
       "2196    2.197  5.823069\n",
       "2197    2.198  5.823069"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rl = df.sort_values(by=\"mmHg\", ascending=True)\n",
    "df_rl.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eliminamos repetidos y creamos set de datos para próxima regresión lineal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Niveles de cuantificación:  766\n"
     ]
    }
   ],
   "source": [
    "yu = df_rl[\"mmHg\"].unique()\n",
    "x = np.array(range(0, len(yu)))\n",
    "xm = np.arange(0, 10, 10/len(yu)) # mapeo x a tensión rango 0-10\n",
    "\n",
    "print(\"Niveles de cuantificación: \", len(yu))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bits de cuantificación\n",
    "\n",
    "A partir de la cuantificación aplicamos el logaritmo para estimar los bits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bits de cuatificación:  10.0\n",
      "Bits de cuatificación:  10.0\n"
     ]
    }
   ],
   "source": [
    "bits = 2 ** np.array(range(0, 24))\n",
    "\n",
    "# Dos métodos, debería dar el mismo resultado\n",
    "print(\"Bits de cuatificación: \", np.log2([c for c in bits if c > len(yu)][0]))\n",
    "print(\"Bits de cuatificación: \", np.ceil(np.log2(len(yu))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ganancia\n",
    "\n",
    "Corresponde con la pendiente de la regresión lineal según la ecuación.\n",
    "\n",
    "$$\n",
    "mmHg = ganancia \\cdot tension + B\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.999\n",
      "Model:                            OLS   Adj. R-squared:                  0.999\n",
      "Method:                 Least Squares   F-statistic:                 1.488e+06\n",
      "Date:                Mon, 30 Aug 2021   Prob (F-statistic):               0.00\n",
      "Time:                        19:11:41   Log-Likelihood:                -780.32\n",
      "No. Observations:                 766   AIC:                             1565.\n",
      "Df Residuals:                     764   BIC:                             1574.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          4.5062      0.048     93.019      0.000       4.411       4.601\n",
      "x1            10.2469      0.008   1220.025      0.000      10.230      10.263\n",
      "==============================================================================\n",
      "Omnibus:                     1122.483   Durbin-Watson:                   0.002\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               62.720\n",
      "Skew:                           0.288   Prob(JB):                     2.40e-14\n",
      "Kurtosis:                       1.722   Cond. No.                         11.8\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      " \n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = sm.add_constant(xm, prepend=True)\n",
    "\n",
    "ols1 = sm.OLS(yu, X)\n",
    "ols_result1 = ols1.fit()\n",
    "print(ols_result1.summary())\n",
    "print(\" \")\n",
    "\n",
    "y_pred = ols_result1.predict(X)\n",
    "\n",
    "plt.figure(figsize = (8, 4))\n",
    "plt.grid(True)\n",
    "plt.plot(xm, yu, label=\"datos\")\n",
    "plt.plot(xm, y_pred, color=\"red\", label=\"Regresión lineal\")\n",
    "plt.figtext(.56, .4, f\"ganancia: (%.1E $\\pm$ %.1E)\" % (ols_result1.params[1], ols_result1.bse[1]))\n",
    "plt.figtext(.62, .3, f\"cte: (%.1E $\\pm$ %.1E)\" % (ols_result1.params[0], ols_result1.bse[0]))\n",
    "plt.figtext(.75, .2, f\"$R^2 \\sim$ %.2f\" % ols_result1.rsquared)\n",
    "plt.title(\"Regresión lineal\")\n",
    "plt.xlabel(\"Tensión (V)\")\n",
    "plt.ylabel(\"mmHg\")\n",
    "plt.legend()\n",
    "plt.savefig(FIGURES_PATH + \"plot_regresion.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
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    "#### Polaridad\n",
    "\n",
    "- Señal monopolar si todos los valores son positivos.\n",
    "\n",
    "- Señal bipolar si hay valores negativos y positivos.\n",
    "\n",
    "Hay que evaluar según coeficiente e intercepto de la regresión lineal."
   ]
  }
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