{ "cells": [ { "cell_type": "markdown", "id": "caef4bd3-b240-4cac-bf94-18dc7a42c28c", "metadata": { "tags": [] }, "source": [ "# F: Fotometría de la secuencia de una ocultación\n", "\n", "En este `notebook` vamos a analizar (de manera rápida y no muy precisa) los datos obtenidos en la ocultación de una estrella por parte de [Tritón](https://es.wikipedia.org/wiki/Trit%C3%B3n_(sat%C3%A9lite)) (satélite de Neptuno). Partiremos de una serie de `FITS` que tenemos en la carpeta `imagenes/ocultacionTriton/` (son un subconjunto de los datos obtenidos). Si observamos uno de dichos fotogramas veremos algo similar a esto:\n", "\n", "[![](./ficherosAuxiliares/ocultacionTritonAnotada.png)]" ] }, { "cell_type": "code", "execution_count": 1, "id": "5904ef7d-5553-418f-b841-171ebc91c8ff", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", "import numpy as np\n", "import glob\n", "\n", "from astropy.io import fits\n", "from astropy.stats import sigma_clipped_stats\n", "\n", "from photutils.detection import DAOStarFinder\n", "from photutils.aperture import aperture_photometry, CircularAperture, CircularAnnulus" ] }, { "cell_type": "markdown", "id": "1919086b-b093-4a4f-954b-38297f722ca7", "metadata": {}, "source": [ "Para analizar la secuencia de imágenes vamos a definir una serie de funciones que nos facilitarán estructurar nuestro código y conseguir la curva de luz de la ocultación:" ] }, { "cell_type": "code", "execution_count": 2, "id": "90ac5335-42dc-42d2-b748-99d1760aa857", "metadata": {}, "outputs": [], "source": [ "def getImageData(file):\n", " \"\"\"\n", " Abre un fichero fits y devuelve el array de pixeles.\n", " ----------\n", " file\n", " El fichero FITS a abrir\n", " \"\"\"\n", " \n", " hdul = fits.open(light) # Abrimos la imagen\n", " data = hdul[0].data\n", " \n", " return data" ] }, { "cell_type": "code", "execution_count": 3, "id": "db925320-b0cf-4400-b9f4-dc7afee8d23e", "metadata": {}, "outputs": [], "source": [ "def getMaximumSource(data):\n", " \"\"\"\n", " Obtiene la posición de la fuente más brillante de la imagen. Hemos ajustado los parámetros del algoritmo de detección de fuentes\n", " para que encuentre a Neptuno, que es la que vamos a usar para alinear las imágenes.\n", " ----------\n", " data\n", " Los datos de la imagen sobre la cual buscar la fuente más brillante.\n", " \"\"\"\n", " mean, median, std = sigma_clipped_stats(data, sigma=3.0) # Obtenemos datos generales de la imagen\n", "\n", " daofind = DAOStarFinder(fwhm=12.0, threshold=5.*std) # Encontramos fuentes \"gordas\" (en nuestro caso queremos localizar Neptuno, en el centro de la imagen\n", " sources = daofind(data - median) \n", "\n", "\n", " # print(sources)\n", "\n", " maxIndex = 0; # Encontramos la fuente más brillante (máximo \"flux\")\n", " maxValue = 0;\n", " for (i, value) in enumerate(sources['flux']):\n", " if value > maxValue:\n", " maxValue = value\n", " maxIndex = i\n", "\n", " sourceMax = np.array([sources['xcentroid'][maxIndex], sources['ycentroid'][maxIndex]]) # Coordenadas de la fuente más brillante\n", "\n", " return sourceMax" ] }, { "cell_type": "code", "execution_count": 4, "id": "164a29bd-3e93-4334-8a9f-41b4e91fdd41", "metadata": {}, "outputs": [], "source": [ "def doPhotometry(data, positions):\n", " \"\"\"\n", " Hacemos la fotometría propiamente dicha en las posiciones sobre una imagen y en las coordenadas que nos interesan.\n", " ----------\n", " data\n", " Los datos de la imagen sobre los que hacer la fotometría\n", " positions\n", " Coordenadas de las fuentes que queremos analizar\n", " \"\"\"\n", " aperture = CircularAperture(positions, r=3.) # Mediremos en un círculo de radio 3\n", "\n", " photTable = aperture_photometry(data, aperture) # Hacemos la fotometría\n", " \n", " res = photTable['aperture_sum'].tolist() # Devolvemos la suma de cuentas\n", " \n", " return res" ] }, { "cell_type": "code", "execution_count": 5, "id": "51d3a862-37d1-4eaf-b860-f3e83dfc33c3", "metadata": {}, "outputs": [], "source": [ "def graficasFotometria(cuentasAnalizar, cuentasReferencia):\n", " \"\"\"\n", " Muestra un par de gráficas. \n", " La primera mostrará los datos de la fuente ocultada y la fuente de referencia.\n", " La segunda será la curva ajustando los valores con los de la fuente de referencia.\n", " ----------\n", " cuentasAnalizar\n", " Los datos de la fuente a analizar\n", " cuentasReferencia\n", " Los datos de la fuente de referencia\n", " \"\"\"\n", " plt.rcParams[\"figure.figsize\"] = (20,10)\n", " plt.plot(cuentasAnalizar.tolist(), color =\"red\")\n", " plt.plot(cuentasReferencia.tolist(), color =\"blue\")\n", " plt.show()\n", "\n", " plt.plot((cuentasAnalizar/cuentasReferencia).tolist(), color =\"green\")\n", " plt.show()" ] }, { "cell_type": "markdown", "id": "50bce1e6-8a69-4f28-8280-e2cf4e1f6d2d", "metadata": {}, "source": [ "A continuación programamos nuestra rutina que irá analizando cada imagen, obtenendo la posición de Neptuno para alinear los fotogramas, y ejecutando la fotometría propiamente dicha. Acabaremos con 2 arrays que contendrán las cuentas de la estrella que se oculta y la estrella de referencia." ] }, { "cell_type": "code", "execution_count": 6, "id": "533a2e1c-f521-4358-bf32-281164a87425", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "....................................................................................................\n", "....................................................................................................\n", "...............................\n" ] } ], "source": [ "lights_list = sorted(glob.glob('imagenes/ocultacionTriton/*.fit'))\n", "#lights_list\n", "\n", "fuenteAnalizar = np.array([453.44, 326.02])\n", "fuenteReferencia = np.array([153.11, 570.52])\n", "\n", "coordInit = (-10, -10)\n", "\n", "cuentasAnalizar = np.array([])\n", "cuentasReferencia = np.array([])\n", "\n", "numIm = 0\n", "\n", "for light in lights_list:\n", " print(\".\", end='') # Imprimimos puntitos\n", " #\n", " if (numIm + 1)%100 == 0: #\n", " print(\"\") #\n", " \n", " if (numIm % 1) == 0: # Solo vamos a analizar de 10 en 10 (o el número que pongamos)\n", " \n", " data = getImageData(light) # Obtenemos datos de la imagen\n", "\n", " fuenteMax = getMaximumSource(data) # Obtenemos la posición de la fuente más brillante (Neptuno en este caso)\n", "\n", " #print(fuenteMax)\n", "\n", " if (coordInit[0] == -10): # Si es la primera imagen guardamos sus coordenadas para luego alinear el resto.\n", " coordInit = np.array(fuenteMax) #\n", "\n", " desv = coordInit - fuenteMax # Calculamos la desviación de la fuente más brillante con respecto a la primera imagen \n", "\n", " nuevaFuenteAnalizar = fuenteAnalizar - desv # Coordenada de las fuentes que buscamos actualizadas\n", " nuevaFuenteReferencia = fuenteReferencia - desv #\n", "\n", "\n", " posicionesAnalizar = [nuevaFuenteAnalizar, nuevaFuenteReferencia]\n", " \n", " #print(light)\n", " #print(posicionesAnalizar)\n", " \n", " phot = doPhotometry(data, posicionesAnalizar) # Hacemos la fotometría de las dos fuentes\n", "\n", " cuentasAnalizar = np.append(cuentasAnalizar, phot[0]) # Guardamos las fotometrias para graficarlas más tarde\n", " cuentasReferencia = np.append(cuentasReferencia, phot[1]) #\n", "\n", " numIm += 1 \n", " \n", "print(\"\")" ] }, { "cell_type": "markdown", "id": "f2bbcc21-d5d5-478d-a15f-7961a9149b25", "metadata": {}, "source": [ "Una vez que tenemos los datos de la secuencia de imágenes podemos graficarlas. En la primera gráfica en rojo tenemos los datos de la estrella ocultada. En azul la estrella de referencia. La ocultación se aprecia perfectamente pero comprobamos que tanto la estrella de referencia como la ocultada tienen un incremento más o menos lineal en su brillo (probablemente por cambios en las condiciones del cielo durante la ocultación). En la última gráfica en verde ajustamos los datos de la ocultación dividiendo por los datos de referencia para corregir dicho problema y los pequeños efectos en cada fotograma." ] }, { "cell_type": "code", "execution_count": 7, "id": "fb71b0f8-e911-4f6a-b263-da762b77fcca", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#print(cuentasAnalizar)\n", "#print(cuentasReferencia)\n", "\n", "graficasFotometria(cuentasAnalizar, cuentasReferencia)\n", "\n", "curvaLuz = cuentasAnalizar / cuentasReferencia\n", "\n", "\n", "# Grabamos los datos de la curva de luz\n", "import json\n", "\n", "with open('salidas/curvaLuz.json', 'w') as fich:\n", " fich.write(json.dumps(curvaLuz.tolist()))" ] }, { "cell_type": "markdown", "id": "07283b62-1896-44e4-bf3b-dd28fc77c422", "metadata": {}, "source": [ "***\n", "**Ejercicio F.1:**\n", "\n", "Mejorar la función que calcula la fotometría para restar el fondo utilizando un anillo alrededor de la estrella `CircularAnnules`. Para ello habrá que calcular el brillo medio de dicho anillo y restarle a las cuentas de la estrella el brillo medio multiplicado por el área de la apertura (`aperture.area`)." ] }, { "cell_type": "code", "execution_count": 8, "id": "3837d25a-e0f5-4d74-a347-5ef9115d0433", "metadata": {}, "outputs": [], "source": [ "def doPhotometry2(data, positions):\n", " # Tu código aquí\n", " return res" ] }, { "cell_type": "markdown", "id": "94eef602-f64f-4113-86a9-8cd1e96936f0", "metadata": {}, "source": [ "***\n", "**Ejercicio F.2:**\n", "\n", "Suavizar la curva de luz obtenida usando una convolución." ] }, { "cell_type": "code", "execution_count": 9, "id": "80f603b1-3099-440f-bdb6-b1414f922df9", "metadata": {}, "outputs": [], "source": [ "# Tu código aquí" ] } ], "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": "3.8.16" } }, "nbformat": 4, "nbformat_minor": 5 }