Agregar archivos iniciales

calibracion.ipynb

-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "a4d202eb-9d92-4857-b2af-aba670f9090b",
-   "metadata": {},
-   "source": [
-    "# Análisis de Datos Racimo Tormenta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52bc4c5f-1baf-4e8f-8a1d-0b0e51fa8695",
-   "metadata": {},
-   "source": [
-    "Notebook para el análisis de datos del proyecto racimo tormenta. \n",
-    "A continuación se realiza el análisis de un archivo de datos de una estación *Racimo tormenta*. Resumiendo se realiza lo siguiente:\n",
-    "\n",
-    "1. Se cargan la librerias necesarias.\n",
-    "2. Se descarga el archivo de datos desde el repositorio de datos **Dataverse**.\n",
-    "3. Se analiza el archivo de datos y se genera una nueva versión del mismo.\n",
-    "4. Por último, se publica la nueva versión de los datos en el repositorio **Dataverse**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d277a53c-cccc-4996-8944-620130575372",
-   "metadata": {},
-   "source": [
-    "## Cargar Librerias "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e96a3270-365f-4f90-9d5a-d2673f176f11",
-   "metadata": {},
-   "source": [
-    "Importar las librerias necesarias para el análisis e interacciones de los datos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5d4d6478-6b92-46a4-a849-7fc4d5a3b119",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pylab as plt\n",
-    "import scipy\n",
-    "from scipy import stats\n",
-    "from scipy.fftpack import fftfreq, irfft, rfft\n",
-    "import sys\n",
-    "import os\n",
-    "from matplotlib import cm\n",
-    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
-    "import math\n",
-    "import datetime as datetime\n",
-    "import time\n",
-    "import matplotlib.dates as md\n",
-    "\n",
-    "#!pip install --user -e git+https://github.com/IQSS/dataverse-client-python.git#egg=dataverse\n",
-    "\n",
-    "%matplotlib inline\n",
-    "sys.getdefaultencoding()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e994b24-64db-40ca-8d89-698456ba2f5b",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "97659221-fc8b-4e1a-b6d9-2ef11fd5410c",
-   "metadata": {},
-   "source": [
-    "## Descargar datos "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "14c40ec4-7904-4690-8619-f9cf1cbdafec",
-   "metadata": {},
-   "source": [
-    "En esta sección descargaremos el archivo de datos desde el repositorio **Dataverse**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "14901417-d762-42f9-83e5-2bed2e3c3138",
-   "metadata": {},
-   "source": [
-    "<img src=\"imagenes/dataverse-jupyter_datos.jpg\" alt=\"drawing\" width=\"600\"/>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b0683242-aecf-4fee-91f6-1fbd91aff17e",
-   "metadata": {},
-   "source": [
-    "Ir a https://dataverse.redclara.net/dataverseuser.xhtml?selectTab=apiTokenTab y copiar el **Token** de usuario"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "89723f93-88a1-4bd4-ac98-76a5d4e75c48",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%env API_TOKEN=TOKENdeUSUARIO"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eac7066f-c26e-4297-85ad-4f1c632ade0b",
-   "metadata": {},
-   "source": [
-    "Descargar dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bf02c4eb-bfed-478e-8315-2ca2bfbe6470",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%env SERVER_URL=https://dataverse.redclara.net\n",
-    "%env PERSISTENT_ID=doi:10.21348/FK2/RBNNPC\n",
-    "%env VERSION=DRAFT \n",
-    "!curl -L -O -J -H \"X-Dataverse-key:$API_TOKEN\" $SERVER_URL/api/access/dataset/:persistentId/?persistentId=$PERSISTENT_ID"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52c1b78f-5325-4a21-9933-d25cf8e927ab",
-   "metadata": {},
-   "source": [
-    "Descomprimir archivo de datos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0c7d3e5e-31fe-42b7-be08-e9c9f793a7da",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!unzip dataverse_files.zip\n",
-    "!rm dataverse_files.zip\n",
-    "!rm MANIFEST.TXT"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "927a9fb3-a065-45f2-b075-cf650d73d8b5",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "960fdf13-2b85-4fca-a1fb-ed0aa016e468",
-   "metadata": {},
-   "source": [
-    "## Calibración del detector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3b8b509b-c69e-4c8d-98e2-6f9d7cc825c3",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "En esta sección realizaremos un análisis del archivo de datos descargado y generaremos una nueva versión del mismo. Graficamos la amplitud y la frecuencia para de manera visual, selecccionar el subconjunto de datos donde se presenta el evento."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "440109c1-3272-4702-9a0c-fd5e2b8e6b1d",
-   "metadata": {},
-   "source": [
-    "### Vista preliminar de los datos de calibración"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4ed58223-c091-4de4-b9c7-2cf4b3dd674f",
-   "metadata": {},
-   "source": [
-    "Cargar datos en formato *array numpy*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "78a31578-66f3-4363-80b4-856aabede298",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = np.loadtxt('Descargas/Lighting_2021_11_10_01_56.dat', comments='#')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7c03fdf2-6a4a-4d30-87e3-3b025a43f6c1",
-   "metadata": {},
-   "source": [
-    "Describir datos "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6733522b-ab2c-4b49-841a-a45bc4dfd377",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy import stats\n",
-    "stats.describe(data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c754f55c-22ff-46b9-a414-7fa07a4497d6",
-   "metadata": {},
-   "source": [
-    "### Amplitud y frecuencia de la señal"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "badb8819-ef05-4e8c-867c-8b4a03f554d2",
-   "metadata": {},
-   "source": [
-    "Función para graficar amplitud y frecuencia."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "05f9f0b6-dd57-4e7e-92ca-4718cf85808e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def Lightning_Analysis(data, dt, Np):\n",
-    "    \n",
-    "    mean = np.mean(data[:,1])\n",
-    "    sigma = np.std(data[:,1])\n",
-    "    peaks = []\n",
-    "    MTFt = []  # Multiple-termination flash (MTF) relative times\n",
-    "    MTSt = [] # Multiple.termination stroke (MTS) relative times\n",
-    "    MTSv = []\n",
-    "    T_count = 0 # Terminations counter\n",
-    "    MTFc = 1 # MTF counter\n",
-    "    MTSc = 0 # MTS counter\n",
-    "    \n",
-    "    \n",
-    "    N = len(data)\n",
-    "    \n",
-    "    MTSw = 1e-3 # time window for differentiating MTF and MTS events\n",
-    "    \n",
-    "    threshold = mean + 5*sigma # Peak threshold\n",
-    "    \n",
-    "    # Termination identification\n",
-    "    for i in range(N):\n",
-    "        if (data[i,1] > threshold):\n",
-    "            T_count += 1\n",
-    "            peaks.append(i)\n",
-    "            t1 = data[i,0]\n",
-    "            \n",
-    "            if T_count > 1:\n",
-    "                Td = t1 - t0\n",
-    "                if Td > MTSw:\n",
-    "                    MTFt.append(Td)\n",
-    "                    MTFc += 1\n",
-    "                    MTSv.append(MTSc)\n",
-    "                    MTSc = 0\n",
-    "                else:\n",
-    "                    MTSt.append(Td)\n",
-    "                    MTSc += 1\n",
-    "            t0 = t1\n",
-    "            \n",
-    "    print (u'Terminations above 5\\u03C3 = %d\\n' %T_count)\n",
-    "    print (u'Number of strokes = %d\\n' %MTFc)\n",
-    "    \n",
-    "    s = data[:,1]\n",
-    "    \n",
-    "    Y = np.fft.fft(s)\n",
-    "    N = len(Y)/2+1\n",
-    "    fa = 1.0/dt\n",
-    "\n",
-    "    X = np.linspace(0, fa/2, int(N), endpoint=True)\n",
-    "    sfft = np.abs(Y[:int(N)])\n",
-    "\n",
-    "    print('Sample Time = %.5f s' % dt)\n",
-    "    print('Frequency = %.2f Hz' % fa)\n",
-    "    \n",
-    "    sfft = np.array(sfft)\n",
-    "    pos = int(np.where(sfft[1:-1] == np.amax(sfft[1:-1]))[0])\n",
-    "    frec_pico = 868.35 # X[pos+1]\n",
-    "\n",
-    "    print (\"Maximum frequency = %.2f Hz\" %frec_pico)\n",
-    "\n",
-    "    if T_count >= Np:\n",
-    "        \n",
-    "        # Signal plot\n",
-    "\n",
-    "        plt.figure(figsize = (16,4))\n",
-    "        plt.subplot(1,2,1)\n",
-    "        plt.plot(data[:,0], data[:,1])\n",
-    "        plt.axhline(threshold, color='red')\n",
-    "        plt.xlabel('Time [s]', fontsize = 20)\n",
-    "        plt.ylabel('Amplitude [ADC]', fontsize = 20)\n",
-    "        plt.savefig(\"amplitude.png\", dpi=150)\n",
-    "\n",
-    "        # Spectrum plotting\n",
-    "\n",
-    "        plt.subplot(1,2,2)\n",
-    "        plt.axvline(frec_pico, color='red')\n",
-    "        plt.loglog(X, sfft)\n",
-    "        plt.xlabel('Frequency [Hz]', fontsize = 20)\n",
-    "        plt.axis([1e-1,1e5,1e1,1e7])\n",
-    "        plt.grid()\n",
-    "        plt.show()\n",
-    "\n",
-    "    return frec_pico, peaks, MTFt, MTSt, T_count, MTFc, MTSv"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4da5b0b2-97c4-455b-b9f9-028e817f3fa9",
-   "metadata": {},
-   "source": [
-    "Graficar "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "66982705-0db4-43d7-8bd0-22ec2ae7b907",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dt = 10e-6 # sampling period\n",
-    "Np = 0. # filter signals per number of peaks above 5 sigma\n",
-    "\n",
-    "fp1, peaks1, MTFt, MTSt, pN1, MTFc, MTSc = Lightning_Analysis(data, dt, Np) # Returns maximum peak frequency and peak positions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eccbba23-922e-430b-816b-41ca7212cae5",
-   "metadata": {},
-   "source": [
-    "### Escalar señal y enfocar"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c15cc352-a82b-404d-9a47-09860ae25950",
-   "metadata": {},
-   "source": [
-    "Definir parámetros de escalado y foco"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aaea4b8a-0639-479e-8f66-ad59fb179ac8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "R1 = 0\n",
-    "R2 = 200\n",
-    "P = 5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5a59c5cb-19db-4872-9046-6f0471498da0",
-   "metadata": {},
-   "source": [
-    "Crear nuevo *array* de datos."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0d8a87c1-5ab1-4e9f-b2d8-8d10572ebad8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newdata = data[R1:R2]*[1,P]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6abec110-ecf4-44e0-b88c-3a3a94b8b4aa",
-   "metadata": {},
-   "source": [
-    "Graficar señal escalada"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "53d02f88-4245-4b93-a2c4-7da1cb9c4448",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dt = 10e-6 # sampling period\n",
-    "Np = 0. # filter signals per number of peaks above 5 sigma\n",
-    "\n",
-    "fp1, peaks1, MTFt, MTSt, pN1, MTFc, MTSc = Lightning_Analysis(newdata, dt, Np) # Returns maximum peak frequency and peak positions"
-   ]
-  }
- ],
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