diff --git a/calibracion.ipynb b/calibracion.ipynb
index 53083b2bacb19c6d39b4222788da6caac2fe67dc..b960609a8ab32dfdcf99ccfd18abe6946698f6b7 100644
--- a/calibracion.ipynb
+++ b/calibracion.ipynb
@@ -80,7 +80,7 @@
"metadata": {},
"outputs": [],
"source": [
- "%env API_TOKEN=42401228-20ba-4de1-a889-6aa8ccd89087"
+ "%env API_TOKEN=xxxx-xxxxx--xxxxx--xxxx--xxx"
]
},
{
diff --git "a/calibraci\303\263n.ipynb" "b/calibraci\303\263n.ipynb"
deleted file mode 100644
index 164ea00d549e3ebabaea4bce4aa5407367ef5ff8..0000000000000000000000000000000000000000
--- "a/calibraci\303\263n.ipynb"
+++ /dev/null
@@ -1,476 +0,0 @@
-{
- "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=XXXXXXXXX"
- ]
- },
- {
- "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
-}