Intro_problema_v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Proyecto Volcano\n",
    " \n",
    " Datos obtenidos del grupo @Natura??"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Análisis exploratorio de los datos\n",
    "\n",
    "En este cuaderno vamos a abrir y explorar en que consiste la base de datos.\n",
    "\n",
    "Para ello, comenzamos importamos las bibliotecas que necesitaremos más adelante: pandas, numpy, pyploy (en matplotlib) y signal (en scipy)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.signal as signal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La base de datos, que se encuentra en el archivo \"MicSigV1_v1_1.json\", que utilizamos a continuación, fue conformada por el Instituto Geofísico de la Escuela Politécnica Nacional de Ecuador. Estos datos corresponden a un catálogo de diferentes eventos sísmicos registrados en el volcán Cotopaxi.\n",
    "\n",
    "Como solo contamos con permiso para uso académico, no podemos compartir el archivo con terceros.\n",
    "\n",
    "Entonces, leemos el archivo y hacemos una primera inspección de la serie de datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Network</th>\n",
       "      <th>Station</th>\n",
       "      <th>SampleRate</th>\n",
       "      <th>Component</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Type</th>\n",
       "      <th>Duration</th>\n",
       "      <th>StartPoint</th>\n",
       "      <th>EndPoint</th>\n",
       "      <th>Data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EC</td>\n",
       "      <td>VC1</td>\n",
       "      <td>100</td>\n",
       "      <td>SHZ</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>VT</td>\n",
       "      <td>23</td>\n",
       "      <td>1001</td>\n",
       "      <td>3301</td>\n",
       "      <td>[23.355529956093186, -26.21249638722304, 7.478...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EC</td>\n",
       "      <td>VC1</td>\n",
       "      <td>100</td>\n",
       "      <td>SHZ</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>LP</td>\n",
       "      <td>47</td>\n",
       "      <td>1001</td>\n",
       "      <td>5701</td>\n",
       "      <td>[-9.39456702711197, 5.797439374378022, -23.901...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EC</td>\n",
       "      <td>VC1</td>\n",
       "      <td>100</td>\n",
       "      <td>SHZ</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>LP</td>\n",
       "      <td>56</td>\n",
       "      <td>1001</td>\n",
       "      <td>6601</td>\n",
       "      <td>[-3.915706523024276, 0.420532668769778, -21.37...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EC</td>\n",
       "      <td>VC1</td>\n",
       "      <td>100</td>\n",
       "      <td>SHZ</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>VT</td>\n",
       "      <td>49</td>\n",
       "      <td>1001</td>\n",
       "      <td>5901</td>\n",
       "      <td>[-2.5686254658527723, 23.91770870655175, -10.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EC</td>\n",
       "      <td>VC1</td>\n",
       "      <td>100</td>\n",
       "      <td>SHZ</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>VT</td>\n",
       "      <td>31</td>\n",
       "      <td>1001</td>\n",
       "      <td>4101</td>\n",
       "      <td>[55.05791051352128, 41.33929940327658, -19.424...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Network Station  SampleRate Component  Year  Month Type  Duration  \\\n",
       "0      EC     VC1         100       SHZ  2012      1   VT        23   \n",
       "1      EC     VC1         100       SHZ  2012      1   LP        47   \n",
       "2      EC     VC1         100       SHZ  2012      1   LP        56   \n",
       "3      EC     VC1         100       SHZ  2012      1   VT        49   \n",
       "4      EC     VC1         100       SHZ  2012      1   VT        31   \n",
       "\n",
       "   StartPoint  EndPoint                                               Data  \n",
       "0        1001      3301  [23.355529956093186, -26.21249638722304, 7.478...  \n",
       "1        1001      5701  [-9.39456702711197, 5.797439374378022, -23.901...  \n",
       "2        1001      6601  [-3.915706523024276, 0.420532668769778, -21.37...  \n",
       "3        1001      5901  [-2.5686254658527723, 23.91770870655175, -10.7...  \n",
       "4        1001      4101  [55.05791051352128, 41.33929940327658, -19.424...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_file = './dataset/MicSigV1_v1_1.json'\n",
    "sism_events = pd.read_json(json_file)\n",
    "# Mostramos el DataFrame con las primeros 5 filas, para tener una primera idea\n",
    "sism_events.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "Una breve descripción de las columnas que componen el DataFrame.\n",
    "\n",
    "* **Network:** red sísmica;\n",
    "* **Station:** nombre de la estación sísmica,\n",
    "* **SampleRate:** cantidad de datos del digitalizador por unidad de tiempo (segundos);\n",
    "* **Component:** conjunto de tres letras cuya última establece la dirección de medición. En este caso, Z indica la dirección vertical del movimiento.\n",
    "* **Type:** tipo de evento sísmico;\n",
    "* **Duration:** duración del evento, desde StartPoint hasta EndPoint, en segundos;\n",
    "* **StartPoint:** inicio del evento en de la serie temporal del conjunto de datos (Data); > ¿por defecto siempre descarta los primeros 1000 puntos?\n",
    "* **EndPoint:** fin del evento en de la serie temporal del conjunto de datos (Data);\n",
    "* **Data:** conjunto de datos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los datos de mayor interés, son los grupos de datos que se encuentran en **Data**.\n",
    "Todas nuestras columnas, o variables, parecen ser categóricas, salvo por Data.\n",
    "Intentamos sacar información adicional, del resto de las columnas. Por ejemplo, cuantas redes y estaciones sísmicas hay."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EC    1187\n",
       "Name: Network, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sism_events.Network.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BREF    706\n",
       "VC1     481\n",
       "Name: Station, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sism_events.Station.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bien, contamos con un tipo de red sísmica (lo cual nos dice nada, se podría descartar) y dos estaciones sísmicas.\n",
    "Ahora, veamos los tipos de componentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BHZ    706\n",
       "SHZ    481\n",
       "Name: Component, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sism_events.Component.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por último, tenemos los eventos sísmicos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nro. de eventos totales:  1187\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LP          1044\n",
       "VT           101\n",
       "REGIONAL      27\n",
       "HB             8\n",
       "ICEQUAKE       7\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Nro. de eventos totales: ', len(sism_events.Type))\n",
    "sism_events.Type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cada evento sísmico refleja un proceso con origen en distintos tipos de fuente. Los sismos más destacados, en actividad volcánica, son:\n",
    "\n",
    "**VT (volcano-tectonic):** son consecuencia de la ruptura frágil de la corteza;\n",
    "**LP (long period):** se deben a la agitación de un sistema que involucra un fluido.\n",
    "\n",
    "Quedemonos con los sismos tipos LP, que son los mayoritarios, y grafiquemos un conjunto de datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nuestro nuevo DataFrame, con solo eventos tipo LP\n",
    "LP_events = sism_events[sism_events.Type == 'LP']\n",
    "\n",
    "# N° de filas de LP_events\n",
    "n_LP = len(LP_events)\n",
    "\n",
    "# Otra forma, dimension\n",
    "#dim_LP = LP_events.shape\n",
    "\n",
    "# Nos quedamos con el primer evento\n",
    "LP1 = LP_events.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 10.0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1368x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# forma de onda\n",
    "LP_waveform = LP1.Data[LP1.StartPoint:LP1.EndPoint]\n",
    "\n",
    "N_points = len(LP_waveform)\n",
    "\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "time = np.linspace(0, LP1.Duration, N_points)\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(19, 3), gridspec_kw=dict(width_ratios=[10,3]))\n",
    "\n",
    "ax1.plot(time, LP_waveform, 'k', lw=0.9)\n",
    "ax1.set_ylabel('cuentas')\n",
    "ax1.set_xlabel('segundos')\n",
    "ax1.set_xlim(0, LP1.Duration)\n",
    "\n",
    "# PSD\n",
    "f, PSD = signal.welch(LP_waveform, LP1.SampleRate, nperseg=1024, scaling='density')\n",
    "ax2.plot(f, PSD, color='k')\n",
    "ax2.set_xlabel('frequency [Hz]')\n",
    "ax2.set_ylabel(r'PSD [cuentas$^2$/Hz]')\n",
    "ax2.grid()\n",
    "ax2.set_xlim(0, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Planteando el problema"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No todas las señales LP son iguales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'LPs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-2bfb0fd2f80f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m19\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgridspec_kw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwidth_ratios\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhspace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mLP_k\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLPs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1044\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mLP_waveform\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLP_k\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mData\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mLP_k\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mStartPoint\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mLP_k\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mEndPoint\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mN_points\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mLP_waveform\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'LPs' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x216 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 2, figsize=(19, 3), gridspec_kw=dict(width_ratios=[10,3], hspace=0.5))\n",
    "for k in range(3):\n",
    "    LP_k = LPs.iloc[np.random.randint(0, 1044)]\n",
    "    LP_waveform = LP_k.Data[LP_k.StartPoint:LP_k.EndPoint]\n",
    "    N_points = len(LP_waveform)\n",
    "    time = np.linspace(0, LP_k.Duration, N_points)\n",
    "    axes[k][0].plot(time, LP_waveform, 'k', lw=0.9)\n",
    "    axes[k][0].set_ylabel('cuentas')\n",
    "    axes[k][0].set_xlabel('segundos')\n",
    "    axes[k][0].set_xlim(0, LP_k.Duration)\n",
    "\n",
    "    nps = 1024\n",
    "    if N_points < nps:\n",
    "        nps = 512\n",
    "\n",
    "    f, PSD = signal.welch(LP_waveform, LP_k.SampleRate, nperseg=nps, scaling='density')\n",
    "    axes[k][1].plot(f, PSD, color='k')\n",
    "    axes[k][1].set_xlabel('freq. [Hz]')\n",
    "    axes[k][1].set_ylabel(r'PSD')\n",
    "    axes[k][1].grid()\n",
    "    axes[k][1].set_xlim(0, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clasificando manualmente, en subgrupos a partir de un análisis visual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## El objetivo del proyecto es: \n",
    "\n",
    "Diseñar un algortimo no-supervisado o semisupervisado a partir de features extraidos de las señales capaz de clasificar estos LP en subcategorias o subtipos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
  },
  "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": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}