From 6d73bb888f31477e31f5bd0babc4b1e1c9acc12b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Alexander=20Mart=C3=ADnez=20M=C3=A9ndez?=
<alexander2198160@correo.uis.edu.co>
Date: Wed, 10 Aug 2022 13:44:43 +0000
Subject: [PATCH] Agregar archivos iniciales
---
.ipynb_checkpoints/amplitude-checkpoint.png | Bin 31315 -> 0 bytes
.../calibraci\303\263n-checkpoint.ipynb" | 332 -
Lightning/Lighting_2021_08_29_13_23.dat | 120009 ---------------
amplitude.png | Bin 56242 -> 0 bytes
calibracion.ipynb | 447 -
5 files changed, 120788 deletions(-)
delete mode 100644 .ipynb_checkpoints/amplitude-checkpoint.png
delete mode 100644 ".ipynb_checkpoints/calibraci\303\263n-checkpoint.ipynb"
delete mode 100644 Lightning/Lighting_2021_08_29_13_23.dat
delete mode 100644 amplitude.png
delete mode 100644 calibracion.ipynb
diff --git a/.ipynb_checkpoints/amplitude-checkpoint.png b/.ipynb_checkpoints/amplitude-checkpoint.png
deleted file mode 100644
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diff --git "a/.ipynb_checkpoints/calibraci\303\263n-checkpoint.ipynb" "b/.ipynb_checkpoints/calibraci\303\263n-checkpoint.ipynb"
deleted file mode 100644
index 02e5c56..0000000
--- "a/.ipynb_checkpoints/calibraci\303\263n-checkpoint.ipynb"
+++ /dev/null
@@ -1,332 +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": 1,
- "id": "5d4d6478-6b92-46a4-a849-7fc4d5a3b119",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'utf-8'"
- ]
- },
- "execution_count": 1,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "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": "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"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "78a31578-66f3-4363-80b4-856aabede298",
- "metadata": {},
- "outputs": [],
- "source": [
- "data = np.loadtxt('../Data/Lighting_2021_04_13_00_6.dat', comments='#')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7c03fdf2-6a4a-4d30-87e3-3b025a43f6c1",
- "metadata": {},
- "source": [
- "Ver encabezado"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "c5141991-6dd0-42d6-a4bd-4cfe0a164bba",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[0.00000e+00, 0.00000e+00],\n",
- " [1.00000e-05, 9.00000e+01],\n",
- " [2.00000e-05, 3.90000e+01],\n",
- " ...,\n",
- " [1.19995e+00, 3.30000e+01],\n",
- " [1.19996e+00, 5.30000e+01],\n",
- " [1.19997e+00, 1.80000e+01]])"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "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": 4,
- "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": 5,
- "id": "66982705-0db4-43d7-8bd0-22ec2ae7b907",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Terminations above 5σ = 7\n",
- "\n",
- "Number of strokes = 1\n",
- "\n",
- "Sample Time = 0.00001 s\n",
- "Frequency = 100000.00 Hz\n",
- "Maximum frequency = 868.35 Hz\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1152x288 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "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": "code",
- "execution_count": null,
- "id": "2c168963-a095-45b0-bcf1-e7943b3ba423",
- "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
-}
diff --git a/Lightning/Lighting_2021_08_29_13_23.dat b/Lightning/Lighting_2021_08_29_13_23.dat
deleted file mode 100644
index b92c70c..0000000
--- a/Lightning/Lighting_2021_08_29_13_23.dat
+++ /dev/null
@@ -1,120009 +0,0 @@
-# RACiMo Storm
-# # This is a LiMoNet (Lightning Monitoring Network) raw data file
-# Version 1.0
-# Latitud 0708.1993 N
-# Longitud 07307.1182 W
-# GPS Activo
-# R 488 uV/UADC : Resolucion del ADC
-# C 58072180 : Contador de ciclos de reloj (10 ns de paso)
-# S 1630261402 : Tiempo Unix del evento
-# F 10 us : Resolucion temporal
-# Tiempo (s) UADC : Columnas de datos
-0 0
-1e-05 3
-2e-05 3
-3e-05 3
-4e-05 3
-5e-05 3
-6e-05 3
-7e-05 3
-8e-05 3
-9e-05 3
-0.0001 3
-0.00011 3
-0.00012 3
-0.00013 3
-0.00014 3
-0.00015 3
-0.00016 3
-0.00017 517
-0.00018 0
-0.00019 63
-0.0002 0
-0.00021 35
-0.00022 0
-0.00023 0
-0.00024 0
-0.00025 0
-0.00026 0
-0.00027 0
-0.00028 0
-0.00029 0
-0.0003 0
-0.00031 0
-0.00032 2
-0.00033 0
-0.00034 0
-0.00035 0
-0.00036 0
-0.00037 0
-0.00038 0
-0.00039 0
-0.0004 0
-0.00041 0
-0.00042 0
-0.00043 0
-0.00044 0
-0.00045 0
-0.00046 0
-0.00047 0
-0.00048 0
-0.00049 5
-0.0005 15
-0.00051 0
-0.00052 0
-0.00053 0
-0.00054 0
-0.00055 0
-0.00056 0
-0.00057 0
-0.00058 0
-0.00059 1
-0.0006 0
-0.00061 0
-0.00062 0
-0.00063 0
-0.00064 0
-0.00065 0
-0.00066 0
-0.00067 13
-0.00068 0
-0.00069 0
-0.0007 0
-0.00071 0
-0.00072 0
-0.00073 0
-0.00074 0
-0.00075 0
-0.00076 3
-0.00077 0
-0.00078 0
-0.00079 0
-0.0008 0
-0.00081 0
-0.00082 0
-0.00083 6
-0.00084 0
-0.00085 0
-0.00086 0
-0.00087 0
-0.00088 0
-0.00089 0
-0.0009 0
-0.00091 0
-0.00092 0
-0.00093 0
-0.00094 0
-0.00095 0
-0.00096 5
-0.00097 0
-0.00098 0
-0.00099 0
-0.001 0
-0.00101 0
-0.00102 0
-0.00103 0
-0.00104 0
-0.00105 0
-0.00106 0
-0.00107 0
-0.00108 0
-0.00109 0
-0.0011 0
-0.00111 0
-0.00112 0
-0.00113 0
-0.00114 0
-0.00115 0
-0.00116 0
-0.00117 0
-0.00118 0
-0.00119 0
-0.0012 0
-0.00121 0
-0.00122 0
-0.00123 0
-0.00124 0
-0.00125 0
-0.00126 0
-0.00127 0
-0.00128 0
-0.00129 0
-0.0013 0
-0.00131 0
-0.00132 0
-0.00133 0
-0.00134 0
-0.00135 0
-0.00136 0
-0.00137 0
-0.00138 0
-0.00139 0
-0.0014 0
-0.00141 0
-0.00142 0
-0.00143 0
-0.00144 0
-0.00145 0
-0.00146 0
-0.00147 0
-0.00148 0
-0.00149 0
-0.0015 0
-0.00151 0
-0.00152 0
-0.00153 0
-0.00154 0
-0.00155 0
-0.00156 0
-0.00157 0
-0.00158 0
-0.00159 0
-0.0016 0
-0.00161 0
-0.00162 0
-0.00163 0
-0.00164 0
-0.00165 0
-0.00166 0
-0.00167 0
-0.00168 0
-0.00169 6
-0.0017 0
-0.00171 0
-0.00172 0
-0.00173 0
-0.00174 0
-0.00175 0
-0.00176 0
-0.00177 0
-0.00178 0
-0.00179 0
-0.0018 0
-0.00181 0
-0.00182 0
-0.00183 0
-0.00184 0
-0.00185 0
-0.00186 0
-0.00187 0
-0.00188 0
-0.00189 0
-0.0019 0
-0.00191 0
-0.00192 0
-0.00193 0
-0.00194 0
-0.00195 0
-0.00196 0
-0.00197 0
-0.00198 0
-0.00199 0
-0.002 0
-0.00201 0
-0.00202 0
-0.00203 0
-0.00204 0
-0.00205 0
-0.00206 0
-0.00207 0
-0.00208 0
-0.00209 0
-0.0021 0
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diff --git a/calibracion.ipynb b/calibracion.ipynb
deleted file mode 100644
index 2f95eec..0000000
--- a/calibracion.ipynb
+++ /dev/null
@@ -1,447 +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. \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"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "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": "3.8.10"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
--
GitLab