Laponite_TG_final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "267cb1c4",
   "metadata": {},
   "outputs": [],
   "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.interpolate import interp1d\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "23c65dd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Función para aplicar suavizado utilizando el filtro Savitzky-Golay en una región específica\n",
    "def apply_savgol_smoothing(x, 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": "26f68ab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importación de datos TG de Laponita pura.\n",
    "df1 = pd.read_csv('./nuevaCarpeta/Laponita_TG.txt',sep='\\t',header=None,names=['Temperatura','H2O','TG'], \n",
    "        usecols=['Temperatura','H2O','TG'],decimal=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "21d906c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         19.71000\n",
       "1         19.71000\n",
       "2         19.54000\n",
       "3         19.45000\n",
       "4         19.47000\n",
       "           ...    \n",
       "4703   1,001.20000\n",
       "4704   1,001.40000\n",
       "4705   1,001.60000\n",
       "4706   1,001.80000\n",
       "4707   1,002.10000\n",
       "Name: Temperatura, Length: 4708, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.float_format = \"{:,.5f}\".format\n",
    "df1 = df1.astype(float)\n",
    "df1[\"Temperatura\"][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2e3d7050",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df1[\"Temperatura\"].values\n",
    "y = df1[\"TG\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a690f49c",
   "metadata": {
    "scrolled": true
   },
   "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>Temperatura</th>\n",
       "      <th>H2O</th>\n",
       "      <th>TG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.71000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.00217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>29.21000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.00034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>29.38000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.00338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>29.53000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.00661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>29.70000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.01042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4703</th>\n",
       "      <td>1,001.20000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-16.52500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4704</th>\n",
       "      <td>1,001.40000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-16.52200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4705</th>\n",
       "      <td>1,001.60000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-16.51900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4706</th>\n",
       "      <td>1,001.80000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-16.51800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4707</th>\n",
       "      <td>1,002.10000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-16.51700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4302 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Temperatura     H2O        TG\n",
       "1        19.71000 0.00000  -0.00217\n",
       "407      29.21000 0.00000  -0.00034\n",
       "408      29.38000 0.00000  -0.00338\n",
       "409      29.53000 0.00000  -0.00661\n",
       "410      29.70000 0.00000  -0.01042\n",
       "...           ...     ...       ...\n",
       "4703  1,001.20000 0.00000 -16.52500\n",
       "4704  1,001.40000 0.00000 -16.52200\n",
       "4705  1,001.60000 0.00000 -16.51900\n",
       "4706  1,001.80000 0.00000 -16.51800\n",
       "4707  1,002.10000 0.00000 -16.51700\n",
       "\n",
       "[4302 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtrado de datos.\n",
    "df1.loc[df1[\"TG\"]<=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a0503639",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x[407:]\n",
    "y = y[407:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "483cff8c",
   "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": 9,
   "id": "d266b064",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.01785559 -0.01958047 -0.02198344 ...  0.01        0.004\n",
      "  0.00333333]\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",
    "#(x_interpolated, y_smooth)\n",
    "derivative = calculate_derivative(x, y)\n",
    "print(derivative)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "626a714f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x224870348e0>]"
      ]
     },
     "execution_count": 10,
     "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": 11,
   "id": "22bb8351",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "534\n"
     ]
    }
   ],
   "source": [
    "end_start = np.where(x_interpolated > 150)[0][0]  # Filtrado de datos\n",
    "print(end_start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6efc5cf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definir las regiones donde se aplicará el suavizado\n",
    "transition_index = end_start\n",
    "\n",
    "# Aplicar suavizado en cada región con diferentes parámetros\n",
    "y_smooth = apply_savgol_smoothing(x_interpolated, y_interpolated, transition_index, window_length1=20, polyorder1=2, window_length2=100, polyorder2=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a154de02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x224896522c0>]"
      ]
     },
     "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, y_smooth, linewidth=3, label=\"y_smoothed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a5a704fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.02324097 -0.02316649 -0.02301753 ...  0.00102347  0.00106845\n",
      "  0.00109094]\n"
     ]
    }
   ],
   "source": [
    "derivative = calculate_derivative(x_interpolated, y_smooth)\n",
    "print(derivative)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "06bd8701",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x224896c6530>]"
      ]
     },
     "execution_count": 15,
     "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": 16,
   "id": "a7234bd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x224890fa5c0>]"
      ]
     },
     "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": [
    "# Aplicación del filtro de suavizado con kernel gaussiano en la primera derivada.\n",
    "y_dev_smooth = ss.savgol_filter(derivative,window_length=350,polyorder=2,deriv=0)\n",
    "a = np.where(y_dev_smooth < 0)[0][0]  # Filtrado de datos\n",
    "print(a)\n",
    "plt.figure(figsize=(12, 9))\n",
    "plt.plot(x_interpolated[a:], y_dev_smooth[a:], linewidth=3, label=\"y_dev_smooth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9369e7ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.662506976744186\n",
      "-1.3516210813204225e-05\n"
     ]
    }
   ],
   "source": [
    "print(x_interpolated[a])\n",
    "print(y_dev_smooth[a])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "92266830",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exportación de datos DTG de Laponita pura.\n",
    "df2 = pd.DataFrame({\"Temperatura\":x_interpolated,\"TG_smooth\":y_smooth,\"TG_dev\":y_dev_smooth})\n",
    "df2.to_csv('./nuevaCarpeta/Laponita_TG_smooth.txt',sep='\\t',index=False,decimal=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f6bbdd07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "569"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.where(x_interpolated>157.9)[0][0] #filtrado de datos\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4dd71b1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157.94823488372094\n",
      "-0.025774456524857763\n"
     ]
    }
   ],
   "source": [
    "print(x_interpolated[b])\n",
    "print(y_dev_smooth[b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "69cbf124",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[90.97720233]\n",
      "[-0.13307534]\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": 29,
   "id": "71c0c36a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-10.013552391276402"
      ]
     },
     "execution_count": 29,
     "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": 44,
   "id": "2a59d756",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resultado desplazado: -9.654497366549872\n",
      "error_integral: 0.35905502472652984\n"
     ]
    }
   ],
   "source": [
    "# Cálculo del error\n",
    "a_e = np.where(x>(x_interpolated[a]-0.1))[0][0]\n",
    "b_e = np.where(x>(x_interpolated[b]+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": 36,
   "id": "cbf8b05d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2702"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.where(x_interpolated>640.4)[0][0] #filtrado de datos\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a026cf28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3361"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = np.where(x_interpolated>789.5)[0][0] #filtrado de datos\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f047b891",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[735.34713721]\n",
      "[-0.02568378]\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": 41,
   "id": "e8cdfd72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.413753696820382"
      ]
     },
     "execution_count": 41,
     "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": 42,
   "id": "667f2d5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "resultado desplazado: -2.54333382218509\n",
      "error_integral: 0.12958012536470775\n"
     ]
    }
   ],
   "source": [
    "# Cálculo del error\n",
    "c_e = np.where(x>(x_interpolated[c]-0.5))[0][0]\n",
    "d_e = np.where(x>(x_interpolated[d]+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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