{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# How to open a light curve in Excel?" ] }, { "cell_type": "markdown", "metadata": { "id": "3S_YbDpLL6Zf" }, "source": [ "## Learning Goals\n", "\n", "By the end of this tutorial, you will:\n", "\n", "* Learn how to search and download TESS light curves.\n", "* Make a quick plot of a light curve you found.\n", "* Understand how to load the data into Excel.\n", "\n", "This tutorial is aimed at first time users who may not have used *Lightkurve* before." ] }, { "cell_type": "markdown", "metadata": { "id": "yo6N8AQeM4E9" }, "source": [ "## Imports\n", "\n", "First we must install and import the [*Lightkurve*](https://docs.lightkurve.org/index.html) package. We can do this as follows:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "eNbRDXQlKtMp", "outputId": "8698e825-4263-4baf-c0e3-c064615e74be" }, "outputs": [], "source": [ "# This step is not necessary if you have already installed Lightkurve\n", "!pip install lightkurve --quiet" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "6MerVfAeKyeh" }, "outputs": [], "source": [ "%matplotlib inline \n", "import lightkurve as lk " ] }, { "cell_type": "markdown", "metadata": { "id": "0p5kFB7ZNkGC" }, "source": [ "## 1. Downloading a TESS light curve\n", "\n", "The light curves of stars created by the TESS mission are stored at the [Mikulksi Archive for Space Telescopes](https://archive.stsci.edu/tess/) (MAST) archive, along with metadata about the observations, such as which CCD channel was used at each time.\n", "\n", "*Lightkurve’s* built-in tools allow us to search for light curve files in the archive, and download them and their metadata. In this example, we will start by downloading one sector of TESS data for a star named V1357 Cyg, also known as [Cygnus X-1](https://en.wikipedia.org/wiki/Cygnus_X-1). V1357 Cyg is a galactic X-ray source in the constellation Cygnus. \n", "\n", "Using Lightkurve’s [search_lightcurve](https://docs.lightkurve.org/reference/api/lightkurve.search_lightcurve.html?highlight=search_lightcurve) function, we can find an itemized list of light curves produced by different data analysis pipelines:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 141 }, "id": "myq63GjDKzFp", "outputId": "702fabe7-8441-4c19-ac3a-abdeb0ad6712" }, "outputs": [ { "data": { "text/html": [ "SearchResult containing 3 data products.\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
#missionyearauthorexptimetarget_namedistance
sarcsec
0TESS Sector 142019SPOC1201026046450.0
1TESS Sector 142019TESS-SPOC18001026046450.0
2TESS Sector 142019QLP18001026046450.0
" ], "text/plain": [ "SearchResult containing 3 data products.\n", "\n", " # mission year author exptime target_name distance\n", " s arcsec \n", "--- -------------- ---- --------- ------- ----------- --------\n", " 0 TESS Sector 14 2019 SPOC 120 102604645 0.0\n", " 1 TESS Sector 14 2019 TESS-SPOC 1800 102604645 0.0\n", " 2 TESS Sector 14 2019 QLP 1800 102604645 0.0" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "search_result = lk.search_lightcurve(\"V1357 Cyg\")\n", "search_result " ] }, { "cell_type": "markdown", "metadata": { "id": "08VkWMhTOiT0" }, "source": [ "By clicking on each of the \"author\" links above, you will obtain a description of the product and how each one is different from the other. The acronym *SPOC* refers to the TESS data calibration pipeline provided by NASA Ames, and *QLP* refers to the Quiclook Pipeline in use by the mission team at MIT.\n", "\n", "We can [download](https://docs.lightkurve.org/reference/search.html?highlight=download) the first data product by specifying its index number in rectangular brackets:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "E3BvFsOCLSYm" }, "outputs": [], "source": [ "lc = search_result[0].download()" ] }, { "cell_type": "markdown", "metadata": { "id": "GE5q3jTAPEMB" }, "source": [ "## 2. Plotting a light curve\n", "\n", "Before loading the light curve data into Excel, it can be useful to have a quick look at the data using *Lightkurve's* [plot](https://docs.lightkurve.org/reference/api/lightkurve.LightCurve.plot.html?highlight=plot#lightkurve.LightCurve.plot) function:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 404 }, "id": "wCG3HtCELVpI", "outputId": "00d46754-7fd5-42f7-b418-b0408eac54c0" }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lc.plot();" ] }, { "cell_type": "markdown", "metadata": { "id": "wgivwJj4PSkz" }, "source": [ "## 3. Download the light curve as an Excel spreadsheet\n", "\n", "If you are new to Python, it is likely that you feel more comfortable inspecting and analyzing a light curve in Excel than in Python. That is fine! We can save the light curve data as an Excel spreadsheet named \"V1357Cyg.xlsx\" as follows:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "otJ2P1dmLYpx" }, "outputs": [], "source": [ "lc.to_excel(\"V1357Cyg.xlsx\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This file will be available on your local drive. If you run this notebook in a Google Colab environment, you can download the file as follows:" ] }, { "cell_type": "markdown", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "id": "ujEkjqPALCAQ", "outputId": "7cdc2db2-8a92-4a02-bd7c-8139ac11bac6" }, "source": [ "```python\n", "from google.colab import files\n", "files.download(\"V1357Cyg.xlsx\")\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "How to create a light curve for Excel.ipynb", "provenance": [] }, "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": "3.9.13" } }, "nbformat": 4, "nbformat_minor": 4 }