Dimensión Fractal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importar librerías necesarias\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.optimize as spopt\n",
    "import scipy.fftpack as spfft\n",
    "import scipy.ndimage as spimg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cargar base de datos\n",
    "\n",
    "df_catalogo = pd.read_csv('catalogo_sismos.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FIltrar las columnas a utlizar\n",
    "\n",
    "df_punto2 = df_catalogo[['coordenadaX','coordenadaY','DEPTH']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4172: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  method=method,\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "#Eliminar valores nulos\n",
    "\n",
    "nan_value = float(\"NaN\")\n",
    "df_punto2.replace(\"\", nan_value, inplace=True)\n",
    "df_punto2.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1109496.14333474],\n",
       "       [1245631.26053738],\n",
       "       [-147735.21796565]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cálculo del centroide\n",
    "\n",
    "z=df_punto2['DEPTH']*-1000\n",
    "y=df_punto2['coordenadaY']\n",
    "x=df_punto2['coordenadaX']\n",
    "\n",
    "z_prom=np.mean(z)\n",
    "x_prom=np.mean(x)\n",
    "y_prom=np.mean(y)\n",
    "\n",
    "puntocentral=np.vstack((x_prom, y_prom, z_prom))\n",
    "puntocentral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cálculo de N, Cr y la distancia al centroide\n",
    "\n",
    "dist=np.sqrt(((x-x_prom)**2)+((y-y_prom)**2)+((z-z_prom)**2))\n",
    "N=len(df_punto2)\n",
    "\n",
    "def Nvalues(dist, r):\n",
    "    Nv=np.count_nonzero(dist < r)\n",
    "    Nv=np.mean(Nv)\n",
    "    return Nv\n",
    "\n",
    "def cr(Nr,N):\n",
    "    cr=(2*Nr)/(N*(N-1))\n",
    "    return cr\n",
    "\n",
    "amin = np.ceil(np.min(dist))\n",
    "amax = np.ceil(np.max(dist))\n",
    "\n",
    "r=np.linspace(amin, amax, N)\n",
    "c=np.zeros(len(r))\n",
    "Nr=np.zeros(len(r))\n",
    "\n",
    "for i in range (len(r)):\n",
    "    Nr[i] = Nvalues(dist, r[i])\n",
    "    \n",
    "for i in range (len(r)):\n",
    "    c[i]=cr(Nr[i],N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "#Definición de las variables r y c\n",
    "\n",
    "df_punto2['r']=r \n",
    "df_punto2['c']=c "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Definir límite de la regresión\n",
    "\n",
    "Limite=7000\n",
    "valo_1= df_punto2['r'] < Limite\n",
    "x=np.log10(df_punto2['r'][valo_1]) \n",
    "y=np.log10(df_punto2['c'][valo_1])\n",
    "\n",
    "def predlog(x0, x):\n",
    "    return 10**(x0[0] + (x0[1]*np.log10(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-14.87199489],\n",
       "       [  2.94336859],\n",
       "       [  0.99165399]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Realizar regresión por la norma L1\n",
    "\n",
    "from scipy import stats\n",
    "slope, intercept, r_value, p_value, std_err=stats.linregress(x,y)\n",
    "r2_value=r_value*r_value\n",
    "lreg=np.vstack((intercept, slope, r2_value))\n",
    "lreg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gráfica de la dimensión fractal\n",
    "\n",
    "fig2 = plt.figure()\n",
    "ax = plt.gca()\n",
    "\n",
    "fig2.suptitle(\"Dimension Fractal\", fontsize=16)\n",
    "ax.scatter(r, c, marker=\".\", color=\"blue\")\n",
    "ax.plot(r, predlog(lreg, r), color=\"red\")\n",
    "ax.set_yscale('log')\n",
    "ax.set_xscale('log')\n",
    "ax.set_ylim(1e-7, 1e-3)\n",
    "ax.set_xlim(7e2, 4e4)\n",
    "\n",
    "fig2.savefig('Dimension_fractal.png', dpi=400)"
   ]
  },
  {
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}