Laponite_Er_TG_final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d99a0dbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\marco\\AppData\\Local\\Temp\\ipykernel_23972\\1865039913.py:6: DeprecationWarning: Please use `gaussian_filter1d` from the `scipy.ndimage` namespace, the `scipy.ndimage.filters` namespace is deprecated.\n",
      "  from scipy.ndimage.filters import gaussian_filter1d\n"
     ]
    }
   ],
   "source": [
    "# Importación de paquetes.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.signal as ss\n",
    "from scipy.ndimage.filters import gaussian_filter1d\n",
    "from scipy.interpolate import interp1d\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c0513399",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Función para aplicar suavizado utilizando el filtro Savitzky-Golay en una región específica\n",
    "def apply_savgol_smoothing(y, transition_index, window_length1, polyorder1, window_length2, polyorder2):\n",
    "    y_smooth = np.copy(y)\n",
    "    y_smooth[:transition_index+1] = ss.savgol_filter(y[:transition_index+1], window_length=window_length1, polyorder=polyorder1)\n",
    "    y_smooth[transition_index:] = ss.savgol_filter(y[transition_index:], window_length=window_length2, polyorder=polyorder2)\n",
    "    return y_smooth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1d8254ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Función para aplicar suavizado con kernel gaussiano en una región específica\n",
    "def apply_gaussian_smoothing(y, transition_index, sigma1, sigma2):\n",
    "    y_smooth = np.copy(y)\n",
    "    y_smooth[:transition_index+1] = gaussian_filter1d(y[:transition_index+1], sigma=sigma1, order=0)\n",
    "    y_smooth[transition_index:] = gaussian_filter1d(y[transition_index:], sigma=sigma2, order=0)\n",
    "    return y_smooth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a93ff052",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Función para aplicar suavizado con kernel gaussiano en una región específica\n",
    "def apply_mix_smoothing(y, transition_index, window_length1, polyorder1, sigma2):\n",
    "    y_smooth = np.copy(y)\n",
    "    y_smooth[:transition_index+1] = ss.savgol_filter(y[:transition_index+1], window_length=window_length1, polyorder=polyorder1)\n",
    "    y_smooth[transition_index:] = gaussian_filter1d(y[transition_index:], sigma=sigma2, order=0)\n",
    "    return y_smooth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "33a2dcf0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Importación de datos TG de Laponita funcionalizada.\n",
    "df1 = pd.read_csv('./nuevaCarpeta/Laponite_Er_TG.txt',sep='\\t',header=None,names=['Temperatura','TG','Heat Flow','H2O'], \n",
    "        usecols=['Temperatura','TG','Heat Flow','H2O'],decimal=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9da09bf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df1[\"Temperatura\"].values\n",
    "y = df1[\"TG\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "caf03ce7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtrado de datos.\n",
    "minim = min(df1[\"Temperatura\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6218947b",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = df1.loc[df1[\"Temperatura\"]==minim].index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "80a9f854",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x204d9d92ad0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 9))\n",
    "plt.plot(x[a:], y[a:], linewidth=3, label=\"y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5deda465",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x[a:]\n",
    "y = y[a:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "59aa88c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Interpolación de los puntos del eje x.\n",
    "x_interpolated = np.linspace(x.min(), x.max(), len(x))\n",
    "y_interpolated = interp1d(x, y)(x_interpolated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0b976c82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.30657386 -0.1954011  -0.07592589 ...  0.          0.\n",
      "  0.        ]\n"
     ]
    }
   ],
   "source": [
    "def calculate_derivative(x, y):\n",
    "    dx = np.gradient(x)  # Calcular el espaciado entre puntos en el eje x\n",
    "    dy = np.gradient(y)  # Calcular la diferencia entre puntos en el eje y\n",
    "    derivative = dy / dx  # Calcular la derivada punto por punto\n",
    "    return derivative\n",
    "derivative_raw = calculate_derivative(x_interpolated, y_interpolated)\n",
    "print(derivative_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "49a9294a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x204dbe93d60>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 9))\n",
    "plt.plot(x_interpolated[:], derivative_raw[:], linewidth=3, label=\"y_dev_smooth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "83ba1534",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "385\n"
     ]
    }
   ],
   "source": [
    "end_start = np.where(x_interpolated > 110)[0][0]\n",
    "print(end_start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9e97a446",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definir las regiones donde se aplicará el suavizado\n",
    "transition_index = end_start\n",
    "#window_length1=300, polyorder1=5\n",
    "# Aplicar suavizado en cada región con diferentes parámetros\n",
    "y_smooth =  gaussian_filter1d(y_interpolated, sigma=30, order=0)\n",
    "#y_smooth =  ss.savgol_filter(y_interpolated, window_length=250, polyorder=3)\n",
    "#apply_gaussian_smoothing(y_interpolated, transition_index,sigma1=20,sigma2=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "54ffaacb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x204dbf0fdc0>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 9))\n",
    "plt.plot(x_interpolated, y_smooth, linewidth=2, label=\"y_smoothed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2735c52f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-5.47798904e-04 -8.21454943e-04 -1.36837466e-03 ...  1.65954175e-04\n",
      "  9.99093536e-05  6.67186941e-05]\n"
     ]
    }
   ],
   "source": [
    "derivative = calculate_derivative(x_interpolated, y_smooth)\n",
    "print(derivative)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "af7654bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x204dbf7ae60>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 9))\n",
    "plt.plot(x_interpolated, derivative, linewidth=3, label=\"y_dev_smooth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b632533b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "y_dev_smooth = derivative\n",
    "a = np.where(y_dev_smooth < 0)[0][0]  # Filtrado de datos\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2555268b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.4\n",
      "-0.0005477989040458541\n"
     ]
    }
   ],
   "source": [
    "print(x_interpolated[a])\n",
    "print(y_dev_smooth[a])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b531ccf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exportación de datos DTG de Laponita funcionalizada.\n",
    "df2 = pd.DataFrame({\"Temperatura\":x_interpolated,\"TG_smooth\":y_smooth,\"TG_dev\":y_dev_smooth})\n",
    "df2.to_csv('./nuevaCarpeta/Laponita_Er_TG_smooth.txt',sep='\\t',index=False,decimal=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0e76a9ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#x_interpolated[:], derivative[:]\n",
    "df3 = pd.DataFrame({\"Temperatura\":x,\"DTG_raw\":derivative_raw[:],\"DTG_processed\":y_dev_smooth})\n",
    "df3.to_csv('./nuevaCarpeta/ejemploLaponitaFuncionalizada.txt',sep='\\t',index=False,decimal=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b6222549",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "718"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.where(x_interpolated>184.2)[0][0] #filtrado de datos\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "662ee4d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "184.2847048096649\n",
      "-0.028162833981579514\n"
     ]
    }
   ],
   "source": [
    "print(x_interpolated[b])\n",
    "print(y_dev_smooth[b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0babe58a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100.33410075]\n",
      "[-0.17903243]\n"
     ]
    }
   ],
   "source": [
    "min_temp_1 = np.where(y_dev_smooth == min(y_dev_smooth[a:b]))\n",
    "print(x_interpolated[min_temp_1])\n",
    "print(y_dev_smooth[min_temp_1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6fcff02e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-14.206799776301644"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Integración numérica con la regla trapezoidal.\n",
    "resultado_original = np.trapz(y_dev_smooth[a:b],x_interpolated[a:b])\n",
    "resultado_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8cb4733e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resultado desplazado: -14.109296339120188\n",
      "error_integral: 0.09750343718145515\n"
     ]
    }
   ],
   "source": [
    "# Cálculo del error\n",
    "a_e = np.where(x>(24.4-0.1))[0][0]\n",
    "b_e = np.where(x>(184.2+0.5))[0][0]\n",
    "resultado_desplazado = np.trapz(y_dev_smooth[a_e:b_e], x_interpolated[a_e:b_e])\n",
    "print(\"resultado desplazado:\",resultado_desplazado)\n",
    "error_integral = np.abs(resultado_original - resultado_desplazado)\n",
    "print(\"error_integral:\",error_integral)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d593a500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3397"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.where(x_interpolated>780.7)[0][0] #filtrado de datos\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "89362e87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3594"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = np.where(x_interpolated>824.6)[0][0] #filtrado de datos\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e05fc544",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[799.99669478]\n",
      "[-0.26779281]\n"
     ]
    }
   ],
   "source": [
    "min_temp_2 = np.where(y_dev_smooth == min(y_dev_smooth[c:d]))\n",
    "print(x_interpolated[min_temp_2])\n",
    "print(y_dev_smooth[min_temp_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a50b9fb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-6.080628362427558"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Integración numérica con la regla trapezoidal.\n",
    "resultado_original_hidrox = np.trapz(y_dev_smooth[c:d],x_interpolated[c:d])\n",
    "resultado_original_hidrox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "115fa6ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resultado desplazado: -6.060573531636637\n",
      "error_integral: 0.020054830790921407\n"
     ]
    }
   ],
   "source": [
    "# Cálculo del error\n",
    "c_e = np.where(x>(780.7-0.5))[0][0]\n",
    "d_e = np.where(x>(824.6+0.5))[0][0]\n",
    "resultado_desplazado_hidrox = np.trapz(y_dev_smooth[c_e:d_e], x_interpolated[c_e:d_e])\n",
    "print(\"resultado desplazado:\",resultado_desplazado_hidrox)\n",
    "error_integral_hidrox = np.abs(resultado_original_hidrox - resultado_desplazado_hidrox)\n",
    "print(\"error_integral:\",error_integral_hidrox)"
   ]
  }
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