From 24da5b61ac52da863753703157e0786912024c20 Mon Sep 17 00:00:00 2001 From: Thomas Dutkiewicz <106269091+ttdu@users.noreply.github.com> Date: Thu, 4 Jan 2024 16:05:29 -0500 Subject: [PATCH 1/6] adding changes from hack session --- .../notebook_template/notebook_template.ipynb | 231 ++++++++++++++---- 1 file changed, 184 insertions(+), 47 deletions(-) diff --git a/contributing/notebook_template/notebook_template.ipynb b/contributing/notebook_template/notebook_template.ipynb index db45ef429..d85fdc4ca 100644 --- a/contributing/notebook_template/notebook_template.ipynb +++ b/contributing/notebook_template/notebook_template.ipynb @@ -8,15 +8,16 @@ "# Tutorial Title\n", "***\n", "## Learning Goals\n", - "Write three to five learning goals using [Bloom's taxonomy](https://tips.uark.edu/using-blooms-taxonomy/) as a guide:\n", + "\n", + "Write three to five learning goals. We highly recommend using [Bloom's taxonomy](https://tips.uark.edu/using-blooms-taxonomy/) as a guide. Bloom’s Taxonomy is a classification of the different objectives and skills that educators set for their audience. The taxonomy is divided into six hierarchical categories, making it easy to communicate the goals of your Notebook and classify the expected difficulty level.\n", + "\n", + "(Delete the above text after you've added your goals)\n", "\n", "```\n", "By the end of this tutorial, you will:\n", "\n", - "- Understand how to use aperture photometry to turn a series of two-dimensional\n", - " images into a one-dimensional time series.\n", - "- Be able to determine the most useful aperture for photometry on a *Kepler/K2*\n", - " target.\n", + "- Understand how to use aperture photometry to turn a series of two-dimensional images into a one-dimensional time series.\n", + "- Determine the most useful aperture for photometry on a *Kepler/K2* target.\n", "- Create your own light curve for a single quarter/campaign of *Kepler/K2* data.\n", "```" ] @@ -29,21 +30,19 @@ } }, "source": [ + "## Table of Contents\n", + "* [Introduction](#Introduction)\n", + "* [Main Content](#Main-Content-(Rename))\n", + " * [Loading Data](#Loading-Data-(Rename))\n", + " * [File and Data Information](#File-and-Data-Information)\n", + " * [Visualization](#Visualization)\n", + "* [Exercises](#Exercises)\n", + "* [Additional Resources](#Additional-Resources)\n", + "\n", "## Introduction\n", "Write a short introduction explaining the purpose of the tutorial. Define any terms or common acronyms that your audience may not know. If you're using some kind of domain-specific astronomical symbol or unusual mathematical concept, make sure you define it (for example, in its mathematical form) and link to any definitions (from literature, Wikipedia, etc.).\n", "\n", - "If there are background materials or resources that may be useful to the reader to provide additional context, you may link to it here. If your tutorial is a continuation from another tutorial, or there are other tutorials that would be useful for the reader to read before or after your tutorial, mention that here as well.\n", - "\n", - "Finally, under this section you should add a description of the workflow in your notebook. This will essentially be a table of contents outlining the functional cells of the notebook, i.e. the main sections. Each section should link users to the actual section for easier navigation through the notebook. Refer to the example below for how to hyperlink sections on a Jupyter Notebook: \n", - "\n", - "\n", - "The workflow for this notebook consists of:\n", - "* [Main Content](#Main-Content)\n", - " * [Loading Data](#Loading-Data)\n", - " * [File and Data Information](#File-and-Data-Information)\n", - "* [Visualization](#Visualization)\n", - "* [Exercises](#Exercises)\n", - "* [Additional Resources](#Additional-Resources)" + "If there are background materials or resources that may be useful to the reader, link to it here. If your tutorial is a continuation from another tutorial, or there are other tutorials that would be useful for the reader to read before or after your tutorial, mention that here as well." ] }, { @@ -56,6 +55,7 @@ "source": [ "## Imports\n", "Describe the main packages we're using here and their use-case for this notebook. If there's something unusual, explain what the library is, and why we need it.\n", + "\n", "- *numpy* to handle array functions\n", "- *astropy.io fits* for accessing FITS files\n", "- *astropy.table Table* for creating tidy tables of the data\n", @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "slideshow": { "slide_type": "fragment" @@ -94,9 +94,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Main Content\n", + "## Main Content (Rename)\n", "\n", - "The main content of your tutorial should be subdivided into sections with useful, descriptive headings that make sense based on the content. Break sections up with standard Markdown syntax headings:\n", + "Where reasonable, \"Main Content\" should be renamed with the overall objective of the Notebook.\n", + "\n", + "The main content of your tutorial should be subdivided into sections with useful, **descriptive headings** that make sense based on the content. Break sections up with standard Markdown syntax headings:\n", "\n", "```\n", "## Section 1\n", @@ -111,7 +113,9 @@ "\n", "A complete thought that's as important as Section 1 but doesn't need subsections.\n", "\n", - "```" + "```\n", + "\n", + "Code should have comments. Text in cells is necessary, but not sufficient, to explain code." ] }, { @@ -122,10 +126,12 @@ } }, "source": [ - "### Loading Data\n", + "### Loading Data (Rename)\n", "\n", "Loading data and file information should appear within your main content, at the same time the data is going to be used, if possible. These elements of your tutorial can be their own sections within the main content, but avoid generic or vague headings like “Loading Data” and instead use descriptive headings pertinent to the content of the tutorial and the actual data being downloaded or files being used.\n", "\n", + "## TD FIX THIS ^^\n", + "\n", "If the user needs to download data to run the tutorial properly, where possible, use [astroquery](https://astroquery.readthedocs.io/en/latest/) (or similar) to retrieve files. If this is not possible, see the [data guide](https://github.com/spacetelescope/style-guides/blob/master/guides/where-to-put-your-data.md) for other options." ] }, @@ -142,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "slideshow": { "slide_type": "fragment" @@ -167,23 +173,85 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Where possible (if the code supports it), use code examples that visually display the data in the tutorial. For example, if you are showing an object such as a Table, display a preview of the table:" + "Where possible (if the code supports it), use code examples that visually display the data in the tutorial. For example, if you are showing an object such as an Astropy Table, display a preview:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
Table masked=True length=1\n", + "\n", + "\n", + "\n", + "\n", + "
obsIDobs_collectiondataproduct_typeobs_iddescriptiontypedataURIproductTypeproductGroupDescriptionproductSubGroupDescriptionproductDocumentationURLprojectprvversionproposal_idproductFilenamesizeparent_obsiddataRightscalib_level
str6str6str10str36str59str1str110str7str28str8str1str6str1str7str44int64str6str6int64
549936Keplertimeserieskplr008957091_lc_Q000000000011111111Target Pixel Long Cadence (TPL) - Q14Cmast:KEPLER/url/missions/kepler/target_pixel_files/0089/008957091/kplr008957091-2012277125453_lpd-targ.fits.gzSCIENCEMinimum Recommended ProductsLPD-TARG--Kepler--GO30032kplr008957091-2012277125453_lpd-targ.fits.gz4365449549936PUBLIC2
" + ], + "text/plain": [ + "\n", + "obsID obs_collection dataproduct_type ... parent_obsid dataRights calib_level\n", + " str6 str6 str10 ... str6 str6 int64 \n", + "------ -------------- ---------------- ... ------------ ---------- -----------\n", + "549936 Kepler timeseries ... 549936 PUBLIC 2" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yourProd[0:5]" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More text here to guide\n", + "\n", + "e.g.: " + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:KEPLER/url/missions/kepler/target_pixel_files/0089/008957091/kplr008957091-2012277125453_lpd-targ.fits.gz to ./mastDownload/Kepler/kplr008957091_lc_Q000000000011111111/kplr008957091-2012277125453_lpd-targ.fits.gz ... [Done]\n" + ] + }, + { + "data": { + "text/html": [ + "
Table length=1\n", + "
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Local PathStatusMessageURL
str103str8objectobject
./mastDownload/Kepler/kplr008957091_lc_Q000000000011111111/kplr008957091-2012277125453_lpd-targ.fits.gzCOMPLETENoneNone
" + ], + "text/plain": [ + "\n", + " Local Path ...\n", + " str103 ...\n", + "------------------------------------------------------------------------------------------------------- ...\n", + "./mastDownload/Kepler/kplr008957091_lc_Q000000000011111111/kplr008957091-2012277125453_lpd-targ.fits.gz ..." + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Download the products\n", "output = Observations.download_products(yourProd, mrp_only=False, cache=False)\n", @@ -211,9 +279,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The HDU list of our output file:\n", + "\n", + "Filename: ./mastDownload/Kepler/kplr008957091_lc_Q000000000011111111/kplr008957091-2012277125453_lpd-targ.fits.gz\n", + "No. Name Ver Type Cards Dimensions Format\n", + " 0 PRIMARY 1 PrimaryHDU 58 () \n", + " 1 TARGETTABLES 1 BinTableHDU 287 4757R x 13C [D, E, J, 64J, 64E, 64E, 64E, 64E, 64E, J, E, E, 40E] \n", + " 2 APERTURE 1 ImageHDU 48 (8, 8) int32 \n", + "None\n" + ] + } + ], "source": [ "file = output['Local Path'][0]\n", "print('The HDU list of our output file:\\n')\n", @@ -229,28 +312,48 @@ "source": [ "## Visualization\n", "\n", - "When presenting any visuals and/or plots from the data, make sure you are using color palettes that are color-blind friendly and using language that keeps accessibility in mind. The most common form of color vision deficiency involves differentiating between red and green, so avoiding colormaps with both red and green will avoid many problems in general. Use descriptive keywords not pertaining to the color of the object you are referring to. It is good practice to make your plots and images large enough to ensure that important details are not hard to see. On the same note, make sure that tick labels, legends, and other plot notations are not too small, and make sure they are descriptive enough that the user can understand what is being represented by the data. " + "When presenting any visuals and/or plots from the data, make sure you are using color palettes that are color-blind friendly and using language that keeps accessibility in mind. The most common form of color vision deficiency involves differentiating between red and green, so avoiding colormaps with both red and green will avoid many problems in general. Use descriptive keywords not pertaining to the color of the object you are referring to. It is good practice to make your plots and images large enough to ensure that important details are not hard to see. On the same note, make sure that tick labels, legends, and other plot notations are not too small, and make sure they are descriptive enough that the user can understand what is being represented by the data. \n", + "\n", + "### TD\n", + "- maybe visualization shouldn't be its own heading (rename or reorganize)\n", + "- give more specific advice: what are color-blind friendly options in matplotlib? (link to style guide resources)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's plot the first four images of the Kepler TPF we just downloaded to see where the center of the PSF is located..." + "Let's plot the first four images of the Kepler TPF we just downloaded to see where the center of the PSF is located...\n", + "\n", + "## TD\n", + "add more descriptive code comments in the below" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "# set the number of desired images\n", "imgs = 4\n", "\n", "fig, axs = plt.subplots(1, imgs, figsize=(20, 20))\n", "\n", + "# create 4 images using a loop\n", "for idx in range(0, imgs):\n", " # Plotting\n", " axs[idx].imshow(data[idx], cmap='bone', origin='lower')\n", @@ -269,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -283,9 +386,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plotting\n", "plt.figure(1, figsize=(10, 6))\n", @@ -293,9 +407,13 @@ "\n", "# Formatting\n", "obj = fits.getheader(file)['OBJECT']\n", + "\n", + "# Add title and labels \n", "plt.title(f'Object: {obj}', fontsize=25)\n", "plt.xlabel('Time - 2454833 (BKJD days)', fontsize=20)\n", "plt.ylabel('Flux (e-/s)', fontsize=20)\n", + "\n", + "# Set up tick sizes on both axes\n", "plt.tick_params(axis='both', which='major', labelsize=15)" ] }, @@ -303,8 +421,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercises\n", - "Exercises are optional, but encouraged. Exercises can be woven into the main content of your tutorial, or appear in their own section toward the end of the tutorial. Final exercises can be more challenging, similar to homework problems. They can be minimal or take as long as 30 minutes to an hour to complete. If you do have one or more exercises in your tutorial, be sure to leave a blank code cell underneath each to show the reader that they're meant to try out their new skill right there. You may also want to include a \"solutions\" notebook next to your main notebook for the reader to check their work after they have finished their attempt." + "## Exercises (optional)\n", + "\n", + "Exercises are optional, but encouraged. Exercises are often most effective when woven into the main content of your tutorial, but they can appear in their own section towards the end. Final exercises might be more challenging, similar to homework problems. They can be minimal or take as long as 30 minutes to an hour to complete. \n", + "\n", + "[TALK ABOUT BLOOM'S TAXONOMY HERE]\n", + "\n", + "If you do have one or more exercises in your tutorial, be sure to leave a blank code cell underneath each to show the reader that they're meant to try out their new skill right there. You may also want to include a \"solutions\" notebook next to your main notebook for the reader to check their work after they have finished their attempt." ] }, { @@ -348,12 +471,12 @@ }, "source": [ "## About this Notebook\n", - "Let the world know who the author of this great tutorial is! If possible and appropriate, include a contact email address for users who might need support (for example, `archive@stsci.edu`). You can also optionally include keywords, your funding source, or a last update date in this section.\n", + "Let the world know who the author of this great tutorial is! If possible and appropriate, include a contact email address for users who might need support (for example, `archive@stsci.edu`). You should also include keywords and a last updated date in this section.\n", "\n", "**Author(s):** Jessie Blogs, Jenny V. Medina, Thomas Dutkiewicz
\n", "**Keyword(s):** Tutorial, lightkurve, kepler, introduction, template
\n", "**Last Updated:** Aug 2022
\n", - "**Next Review:** Mar 2023\n", + "\n", "***\n", "[Top of Page](#top)\n", "\"Space " @@ -361,10 +484,24 @@ } ], "metadata": { - "language_info": { - "name": "python" - } + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 4 - } + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From e7a6fd9ab34051fa78d4dd2a5240f6d17be06f30 Mon Sep 17 00:00:00 2001 From: Thomas Dutkiewicz <106269091+ttdu@users.noreply.github.com> Date: Thu, 18 Jan 2024 14:42:12 -0500 Subject: [PATCH 2/6] addtional updates --- .../notebook_template/notebook_template.ipynb | 247 +++++------------- 1 file changed, 62 insertions(+), 185 deletions(-) diff --git a/contributing/notebook_template/notebook_template.ipynb b/contributing/notebook_template/notebook_template.ipynb index d85fdc4ca..ddd16195d 100644 --- a/contributing/notebook_template/notebook_template.ipynb +++ b/contributing/notebook_template/notebook_template.ipynb @@ -17,8 +17,8 @@ "By the end of this tutorial, you will:\n", "\n", "- Understand how to use aperture photometry to turn a series of two-dimensional images into a one-dimensional time series.\n", - "- Determine the most useful aperture for photometry on a *Kepler/K2* target.\n", - "- Create your own light curve for a single quarter/campaign of *Kepler/K2* data.\n", + "- Determine the most useful aperture for photometry on a Kepler/K2 target.\n", + "- Create your own light curve for a single quarter/campaign of Kepler/K2 data.\n", "```" ] }, @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -126,13 +126,13 @@ } }, "source": [ - "### Loading Data (Rename)\n", + "## Querying for TESS Data (Rename)\n", "\n", - "Loading data and file information should appear within your main content, at the same time the data is going to be used, if possible. These elements of your tutorial can be their own sections within the main content, but avoid generic or vague headings like “Loading Data” and instead use descriptive headings pertinent to the content of the tutorial and the actual data being downloaded or files being used.\n", + "Many tutorials include a section on loading or downloading data. Try avoid generic or vague headings like “Loading Data” and instead use descriptive headings pertinent to the content of the tutorial, data downloaded, or particular files (e.g. \"Accessing light curves\" or \"Querying MAST for Kepler Observations\"). \n", "\n", - "## TD FIX THIS ^^\n", + "If the user needs to download data to run the tutorial properly, where possible, use [astroquery](https://astroquery.readthedocs.io/en/latest/) (or similar) to retrieve files. If this is not possible, see the [data guide](https://github.com/spacetelescope/style-guides/blob/master/guides/where-to-put-your-data.md) for other options.\n", "\n", - "If the user needs to download data to run the tutorial properly, where possible, use [astroquery](https://astroquery.readthedocs.io/en/latest/) (or similar) to retrieve files. If this is not possible, see the [data guide](https://github.com/spacetelescope/style-guides/blob/master/guides/where-to-put-your-data.md) for other options." + "Let's do an example query for MAST data:" ] }, { @@ -148,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -156,10 +156,14 @@ }, "outputs": [], "source": [ - "keplerObs = Observations.query_criteria(target_name='kplr008957091', obs_collection='Kepler')\n", - "keplerProds = Observations.get_product_list(keplerObs[0])\n", - "yourProd = Observations.filter_products(keplerProds,extension='kplr008957091-2012277125453_lpd-targ.fits.gz',\n", - " mrp_only=False)" + "# Query for a reproducible Kepler Observation\n", + "TESSObs = Observations.query_criteria(target_name='8262242', t_exptime=[120,120])\n", + "\n", + "# Get the list of corresponding products\n", + "TESSProds = Observations.get_product_list(TESSObs[0])\n", + "\n", + "# Filter for the products we want\n", + "yourProd = Observations.filter_products(TESSProds,description='Light curves')" ] }, { @@ -178,33 +182,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Table masked=True length=1\n", - "
\n", - "\n", - "\n", - "\n", - "
obsIDobs_collectiondataproduct_typeobs_iddescriptiontypedataURIproductTypeproductGroupDescriptionproductSubGroupDescriptionproductDocumentationURLprojectprvversionproposal_idproductFilenamesizeparent_obsiddataRightscalib_level
str6str6str10str36str59str1str110str7str28str8str1str6str1str7str44int64str6str6int64
549936Keplertimeserieskplr008957091_lc_Q000000000011111111Target Pixel Long Cadence (TPL) - Q14Cmast:KEPLER/url/missions/kepler/target_pixel_files/0089/008957091/kplr008957091-2012277125453_lpd-targ.fits.gzSCIENCEMinimum Recommended ProductsLPD-TARG--Kepler--GO30032kplr008957091-2012277125453_lpd-targ.fits.gz4365449549936PUBLIC2
" - ], - "text/plain": [ - "\n", - "obsID obs_collection dataproduct_type ... parent_obsid dataRights calib_level\n", - " str6 str6 str10 ... str6 str6 int64 \n", - "------ -------------- ---------------- ... ------------ ---------- -----------\n", - "549936 Kepler timeseries ... 549936 PUBLIC 2" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ + "# Display the first five products in the results table\n", + "# (in this case, there is only one product to display)\n", "yourProd[0:5]" ] }, @@ -212,49 +195,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "More text here to guide\n", + "### Downloads to a Local Machine\n", "\n", - "e.g.: " + "Be cognizant of file size when writing a tutorial; users should not need to wait for more than ~60 seconds for something to download. A 10MB file is likely appropriate. A 10 GB file is definitely not." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:KEPLER/url/missions/kepler/target_pixel_files/0089/008957091/kplr008957091-2012277125453_lpd-targ.fits.gz to ./mastDownload/Kepler/kplr008957091_lc_Q000000000011111111/kplr008957091-2012277125453_lpd-targ.fits.gz ... [Done]\n" - ] - }, - { - "data": { - "text/html": [ - "
Table length=1\n", - "
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Local PathStatusMessageURL
str103str8objectobject
./mastDownload/Kepler/kplr008957091_lc_Q000000000011111111/kplr008957091-2012277125453_lpd-targ.fits.gzCOMPLETENoneNone
" - ], - "text/plain": [ - "\n", - " Local Path ...\n", - " str103 ...\n", - "------------------------------------------------------------------------------------------------------- ...\n", - "./mastDownload/Kepler/kplr008957091_lc_Q000000000011111111/kplr008957091-2012277125453_lpd-targ.fits.gz ..." - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# Download the products\n", - "output = Observations.download_products(yourProd, mrp_only=False, cache=False)\n", + "# Download the products to our local directory\n", + "output = Observations.download_products(yourProd)\n", "output" ] }, @@ -279,142 +232,65 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The HDU list of our output file:\n", - "\n", - "Filename: ./mastDownload/Kepler/kplr008957091_lc_Q000000000011111111/kplr008957091-2012277125453_lpd-targ.fits.gz\n", - "No. Name Ver Type Cards Dimensions Format\n", - " 0 PRIMARY 1 PrimaryHDU 58 () \n", - " 1 TARGETTABLES 1 BinTableHDU 287 4757R x 13C [D, E, J, 64J, 64E, 64E, 64E, 64E, 64E, J, E, E, 40E] \n", - " 2 APERTURE 1 ImageHDU 48 (8, 8) int32 \n", - "None\n" - ] - } - ], + "outputs": [], "source": [ + "# Get the path to the file\n", "file = output['Local Path'][0]\n", + "\n", + "# Print out some information about our file\n", "print('The HDU list of our output file:\\n')\n", "print(fits.info(file))\n", "\n", - "data = fits.getdata(file, 1)['FLUX']\n", - "time = fits.getdata(file, 1)['TIME']" + "# Load in the flux and time data\n", + "time = fits.getdata(file, 1)['time']\n", + "flux = fits.getdata(file, 1)['pdcsap_flux']" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "## Visualization\n", + "## Visualization, where relevant\n", "\n", - "When presenting any visuals and/or plots from the data, make sure you are using color palettes that are color-blind friendly and using language that keeps accessibility in mind. The most common form of color vision deficiency involves differentiating between red and green, so avoiding colormaps with both red and green will avoid many problems in general. Use descriptive keywords not pertaining to the color of the object you are referring to. It is good practice to make your plots and images large enough to ensure that important details are not hard to see. On the same note, make sure that tick labels, legends, and other plot notations are not too small, and make sure they are descriptive enough that the user can understand what is being represented by the data. \n", + "When presenting any visuals and/or plots from the data, make sure you are using color palettes that are color-blind friendly and using language that keeps accessibility in mind. The most common form of color vision deficiency involves differentiating between red and green, so avoiding colormaps with both red and green (e.g. `viridis`) will avoid many problems in general. \n", "\n", - "### TD\n", - "- maybe visualization shouldn't be its own heading (rename or reorganize)\n", - "- give more specific advice: what are color-blind friendly options in matplotlib? (link to style guide resources)" + "Use descriptive keywords not pertaining to the color of the object you are referring to. It is also good practice to make your plots and images large enough to ensure that important details are not hard to see. On the same note, make sure that tick labels, legends, and other plot notations are not too small, and make sure they are descriptive enough that the user can understand what is being represented by the data. " ] }, { "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's plot the first four images of the Kepler TPF we just downloaded to see where the center of the PSF is located...\n", - "\n", - "## TD\n", - "add more descriptive code comments in the below" - ] - }, - { - "cell_type": "code", - "execution_count": 6, "metadata": { - "scrolled": true + "jp-MarkdownHeadingCollapsed": true, + "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# set the number of desired images\n", - "imgs = 4\n", - "\n", - "fig, axs = plt.subplots(1, imgs, figsize=(20, 20))\n", - "\n", - "# create 4 images using a loop\n", - "for idx in range(0, imgs):\n", - " # Plotting\n", - " axs[idx].imshow(data[idx], cmap='bone', origin='lower')\n", - " \n", - " # Formatting\n", - " axs[idx].set_title(f'Image #{idx}', fontsize=25)\n", - " axs[idx].tick_params(axis='both', which='major', labelsize=20)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, "source": [ - "Looks like it's typically located around (x,y)=(4,4). Let's gather all the images and extract the flux at (4,4) from each of them to patch our lightcurve together..." + "Let's plot the light curve from the file we just downloaded." ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": null, + "metadata": { + "scrolled": true, + "tags": [] + }, "outputs": [], "source": [ - "lightcurve = []\n", - "\n", - "for idx in range(0, len(data)):\n", - " \n", - " flux = data[idx][4, 4]\n", - " lightcurve.append(flux)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plotting\n", - "plt.figure(1, figsize=(10, 6))\n", - "plt.plot(time, lightcurve)\n", - "\n", - "# Formatting\n", - "obj = fits.getheader(file)['OBJECT']\n", + "# Create the figure\n", + "fig, ax = plt.subplots(1, figsize=(5,5))\n", + "# Show the image data\n", + "ax.plot(time, flux)\n", "\n", - "# Add title and labels \n", - "plt.title(f'Object: {obj}', fontsize=25)\n", - "plt.xlabel('Time - 2454833 (BKJD days)', fontsize=20)\n", - "plt.ylabel('Flux (e-/s)', fontsize=20)\n", + "# Set x/y labels\n", + "ax.set_xlabel(\"Date (BTJD)\")\n", + "ax.set_ylabel(\"Flux (e-/s)\")\n", "\n", - "# Set up tick sizes on both axes\n", - "plt.tick_params(axis='both', which='major', labelsize=15)" + "# Set the title for the image, make the font large\n", + "ax.set_title(\"TIC 8262242 light curve\", fontsize=25)" ] }, { @@ -423,9 +299,9 @@ "source": [ "## Exercises (optional)\n", "\n", - "Exercises are optional, but encouraged. Exercises are often most effective when woven into the main content of your tutorial, but they can appear in their own section towards the end. Final exercises might be more challenging, similar to homework problems. They can be minimal or take as long as 30 minutes to an hour to complete. \n", + "Exercises are optional, but encouraged. Exercises are often **most effective when woven into the main content** of your tutorial, but they can appear in their own section towards the end. Final exercises might be more challenging, similar to homework problems. They can be minimal or take as long as 30 minutes to an hour to complete. \n", "\n", - "[TALK ABOUT BLOOM'S TAXONOMY HERE]\n", + "It may be helpful to again reference [Bloom's taxonomy](https://tips.uark.edu/using-blooms-taxonomy/) as you're writing the exercises. This may help you to classify the level of difficulty and design exercises appropriate to the learning goals.\n", "\n", "If you do have one or more exercises in your tutorial, be sure to leave a blank code cell underneath each to show the reader that they're meant to try out their new skill right there. You may also want to include a \"solutions\" notebook next to your main notebook for the reader to check their work after they have finished their attempt." ] @@ -439,8 +315,8 @@ "This section is optional. Try to weave resource links into the main content of your tutorial so that they are falling in line with the context of your writing. For resources that do not fit cleanly into your narrative, you may include an additional resources section at the end of your tutorial. Usually a list of links using Markdown bullet list plus link format is appropriate:\n", "\n", "- [MAST API](https://mast.stsci.edu/api/v0/index.html)\n", - "- [Kepler Archive Page (MAST)](https://archive.stsci.edu/kepler/)\n", - "- [Kepler Archive Manual](https://archive.stsci.edu/kepler/manuals/archive_manual.pdf)\n", + "- [TESS Archive Page (MAST)](https://archive.stsci.edu/tess/)\n", + "- [TESS Archive Manual](https://outerspace.stsci.edu/display/TESS/TESS+Archive+Manual)\n", "- [Exo.MAST website](https://exo.mast.stsci.edu/)" ] }, @@ -475,6 +351,7 @@ "\n", "**Author(s):** Jessie Blogs, Jenny V. Medina, Thomas Dutkiewicz
\n", "**Keyword(s):** Tutorial, lightkurve, kepler, introduction, template
\n", + "**Published:** \n", "**Last Updated:** Aug 2022
\n", "\n", "***\n", From ee6e897a6dbefbf3648063b6c88f9a33a811858e Mon Sep 17 00:00:00 2001 From: Thomas Dutkiewicz <106269091+ttdu@users.noreply.github.com> Date: Thu, 18 Jan 2024 14:55:25 -0500 Subject: [PATCH 3/6] remove references to Kepler --- .../notebook_template/notebook_template.ipynb | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/contributing/notebook_template/notebook_template.ipynb b/contributing/notebook_template/notebook_template.ipynb index ddd16195d..a359e8b8e 100644 --- a/contributing/notebook_template/notebook_template.ipynb +++ b/contributing/notebook_template/notebook_template.ipynb @@ -16,9 +16,8 @@ "```\n", "By the end of this tutorial, you will:\n", "\n", - "- Understand how to use aperture photometry to turn a series of two-dimensional images into a one-dimensional time series.\n", - "- Determine the most useful aperture for photometry on a Kepler/K2 target.\n", - "- Create your own light curve for a single quarter/campaign of Kepler/K2 data.\n", + "- Understand how to query the MAST Archive for TESS Observations.\n", + "- Create your own light curve using TESS data.\n", "```" ] }, @@ -143,7 +142,7 @@ } }, "source": [ - "For example, if we wanted to query for data from MAST for Kepler we might do something like:" + "For example, if we wanted to query for data from MAST for TESS we might do something like:" ] }, { @@ -156,7 +155,7 @@ }, "outputs": [], "source": [ - "# Query for a reproducible Kepler Observation\n", + "# Query for a reproducible TESS Observation\n", "TESSObs = Observations.query_criteria(target_name='8262242', t_exptime=[120,120])\n", "\n", "# Get the list of corresponding products\n", @@ -215,7 +214,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Explain pertinent details about the file you've just downloaded. For example, if working with Kepler light curves, explain what's in the different file extensions:\n", + "Explain pertinent details about the file you've just downloaded. For example, if working with TESS light curves, explain what's in the different file extensions:\n", "\n", "```\n", "- No. 0 (Primary): This HDU contains metadata related to the entire file.\n", @@ -350,7 +349,7 @@ "Let the world know who the author of this great tutorial is! If possible and appropriate, include a contact email address for users who might need support (for example, `archive@stsci.edu`). You should also include keywords and a last updated date in this section.\n", "\n", "**Author(s):** Jessie Blogs, Jenny V. Medina, Thomas Dutkiewicz
\n", - "**Keyword(s):** Tutorial, lightkurve, kepler, introduction, template
\n", + "**Keyword(s):** Tutorial, lightkurve, TESS, introduction, template
\n", "**Published:** \n", "**Last Updated:** Aug 2022
\n", "\n", From f325be15a18c8439a7ce540b64e192795b7e0f16 Mon Sep 17 00:00:00 2001 From: Thomas Dutkiewicz <106269091+ttdu@users.noreply.github.com> Date: Tue, 23 Jan 2024 12:26:22 -0500 Subject: [PATCH 4/6] add C. Pacifici comments --- contributing/notebook_template/notebook_template.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/contributing/notebook_template/notebook_template.ipynb b/contributing/notebook_template/notebook_template.ipynb index a359e8b8e..5780d59b3 100644 --- a/contributing/notebook_template/notebook_template.ipynb +++ b/contributing/notebook_template/notebook_template.ipynb @@ -32,7 +32,7 @@ "## Table of Contents\n", "* [Introduction](#Introduction)\n", "* [Main Content](#Main-Content-(Rename))\n", - " * [Loading Data](#Loading-Data-(Rename))\n", + " * [Loading Data](#Loading-Data-(Rename,-e.g.-Querying-for-TESS-Data))\n", " * [File and Data Information](#File-and-Data-Information)\n", " * [Visualization](#Visualization)\n", "* [Exercises](#Exercises)\n", @@ -125,7 +125,7 @@ } }, "source": [ - "## Querying for TESS Data (Rename)\n", + "## Loading Data (Rename, e.g. Querying for TESS Data)\n", "\n", "Many tutorials include a section on loading or downloading data. Try avoid generic or vague headings like “Loading Data” and instead use descriptive headings pertinent to the content of the tutorial, data downloaded, or particular files (e.g. \"Accessing light curves\" or \"Querying MAST for Kepler Observations\"). \n", "\n", @@ -350,8 +350,8 @@ "\n", "**Author(s):** Jessie Blogs, Jenny V. Medina, Thomas Dutkiewicz
\n", "**Keyword(s):** Tutorial, lightkurve, TESS, introduction, template
\n", - "**Published:** \n", - "**Last Updated:** Aug 2022
\n", + "**First published:** Sep 2020
\n", + "**Last updated:** Aug 2022
\n", "\n", "***\n", "[Top of Page](#top)\n", From 5dd11720df85fd018087d4f69921a51cf3e95ecc Mon Sep 17 00:00:00 2001 From: Thomas Dutkiewicz <106269091+ttdu@users.noreply.github.com> Date: Tue, 23 Jan 2024 12:38:25 -0500 Subject: [PATCH 5/6] update image link, assuming master->main --- contributing/notebook_template/notebook_template.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/contributing/notebook_template/notebook_template.ipynb b/contributing/notebook_template/notebook_template.ipynb index 5780d59b3..2e881dcb6 100644 --- a/contributing/notebook_template/notebook_template.ipynb +++ b/contributing/notebook_template/notebook_template.ipynb @@ -355,7 +355,7 @@ "\n", "***\n", "[Top of Page](#top)\n", - "\"Space " + "\"Space " ] } ], From 01e174d97bb5415a62cb706a402c766b768c01bd Mon Sep 17 00:00:00 2001 From: Thomas Dutkiewicz <106269091+ttdu@users.noreply.github.com> Date: Wed, 24 Jan 2024 12:21:11 -0500 Subject: [PATCH 6/6] final toc updates --- contributing/notebook_template/notebook_template.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/contributing/notebook_template/notebook_template.ipynb b/contributing/notebook_template/notebook_template.ipynb index 2e881dcb6..a5f6dc43d 100644 --- a/contributing/notebook_template/notebook_template.ipynb +++ b/contributing/notebook_template/notebook_template.ipynb @@ -31,10 +31,10 @@ "source": [ "## Table of Contents\n", "* [Introduction](#Introduction)\n", - "* [Main Content](#Main-Content-(Rename))\n", - " * [Loading Data](#Loading-Data-(Rename,-e.g.-Querying-for-TESS-Data))\n", + "* [Main Content (Rename)](#Main-Content-(Rename))\n", + " * [Loading Data (Rename)](#Loading-Data-(Rename,-e.g.-Querying-for-TESS-Data))\n", " * [File and Data Information](#File-and-Data-Information)\n", - " * [Visualization](#Visualization)\n", + " * [Visualization, where relevant](#Visualization,-where-relevant)\n", "* [Exercises](#Exercises)\n", "* [Additional Resources](#Additional-Resources)\n", "\n",