ejercicio datos taller

.gitignore

+data/
+.ipynb_checkpoints/
+src/

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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d277a53c-cccc-4996-8944-620130575372",
+   "metadata": {},
+   "source": [
+    "## 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",
+    "%matplotlib inline\n",
+    "sys.getdefaultencoding()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97659221-fc8b-4e1a-b6d9-2ef11fd5410c",
+   "metadata": {},
+   "source": [
+    "## Descargar datos "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50ba171a-6d09-4d7a-90ff-b76ce313b09d",
+   "metadata": {},
+   "source": [
+    "Ir a https://dataverse.redclara.net/dataverseuser.xhtml?selectTab=apiTokenTab y copiar el **API Token** para definirlo en el siguiente campo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89723f93-88a1-4bd4-ac98-76a5d4e75c48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%env API_TOKEN=42401228-20ba-4de1-a889-6aa8ccd89087"
+   ]
+  },
+  {
+   "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/EIQEXC\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": "960fdf13-2b85-4fca-a1fb-ed0aa016e468",
+   "metadata": {},
+   "source": [
+    "## Calibración del detector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b8b509b-c69e-4c8d-98e2-6f9d7cc825c3",
+   "metadata": {},
+   "source": [
+    "Calibración de las mediciones del detector"
+   ]
+  },
+  {
+   "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('data/Lighting_2021_04_13_00_4.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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6cd50a1f-6003-4002-a1aa-f98af31ffcab",
+   "metadata": {},
+   "source": [
+    "### Publicar nuevos datos en dataverse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78c305e5-7806-432c-8ba6-f179d1f6d863",
+   "metadata": {},
+   "source": [
+    "Conectar a dataverse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c365000c-82c3-4972-aa80-ad7d76801b43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dataverse import Connection\n",
+    "\n",
+    "API_TOKEN = os.environ['API_TOKEN']\n",
+    "host = 'dataverse.redclara.net'                  # All clients >4.0 are supported\n",
+    "# Conexión a repositorio\n",
+    "connection = Connection(host, API_TOKEN)\n",
+    "# Selección de dataverse a user (storm para Racimo Tormenta)\n",
+    "dataverse = connection.get_dataverse('storm')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f189374-3ad4-4bfc-a11a-67f2c8a0e6eb",
+   "metadata": {},
+   "source": [
+    "Guardar *array* en nuevo archivo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae9b7fd8-9850-4d7e-ab2e-0397c266416b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.savetxt('data/Lighting_2021_04_13_00_4-'+connection.token+'.dat', newdata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a1f7583-ad98-4aba-a67e-518af2319572",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = dataverse.get_dataset_by_title('Lighting_2021_04_13')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71796740-f56e-4dfa-8d8e-4fac88d4a762",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset.upload_filepath('data/Lighting_2021_04_13_00_4-'+connection.token+'.dat')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e1d125e8-8bf1-464b-b012-cc648cc8691d",
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}