From 2778c29a863a50029bcab27f039badb7843a0ba6 Mon Sep 17 00:00:00 2001 From: bsipocz Date: Thu, 12 Sep 2024 21:19:34 +0000 Subject: [PATCH] Merge pull request #321 from bsipocz/MAINT_warning_cleanups MAINT: cleanup rendering warnings 6d66e51cc2e1f6da9f78cafba472ca6c3aa6f0e9 --- .buildinfo | 2 +- .doctrees/README.doctree | Bin 9365 -> 0 bytes .doctrees/documentation/README.doctree | Bin 110116 -> 117283 bytes .doctrees/documentation/index.doctree | Bin 3279 -> 0 bytes .../notebook_review_process.doctree | Bin 17186 -> 0 bytes .doctrees/environment.pickle | Bin 156508 -> 126895 bytes .../forced_photometry/code_src/README.doctree | Bin 3208 -> 0 bytes .../forced_photometry/data/README.doctree | Bin 4106 -> 0 bytes .../multiband_photometry.doctree | Bin 102248 -> 101907 bytes .../forced_photometry/output/README.doctree | Bin 3902 -> 0 bytes .doctrees/light_curves/ML_AGNzoo.doctree | Bin 99863 -> 99286 bytes .../light_curves/code_src/README.doctree | Bin 4185 -> 0 bytes .doctrees/light_curves/data/README.doctree | Bin 4053 -> 0 bytes .../light_curve_classifier.doctree | Bin 98684 -> 98353 bytes .../light_curve_generator.doctree | Bin 73541 -> 76202 bytes .doctrees/light_curves/output/README.doctree | Bin 3855 -> 0 bytes .doctrees/spectroscopy/README.doctree | Bin 3828 -> 0 bytes .../spectroscopy/code_src/README.doctree | Bin 3611 -> 0 bytes .../spectroscopy/explore_Euclid_data.doctree | Bin 16459 -> 0 bytes .../spectroscopy/spectra_generator.doctree | Bin 53929 -> 0 bytes README.html | 535 ----------- _sources/README.md | 24 - 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z`@``QaD~Zvq?`ak+hba^%r>LuEnI%aSNl!uJi#+(3O=K?*?Ze)GkL<8oO8Op?m5yn zo${9V372$ZxQPZ`J?uMq;lerKADJAG1+(fi{iKM05e?e6^C^^E8!^M}iZaZ^Y!|y} zFoKI@T~Y;d-h`h+(~>a743$?3FfE|b^leDo%pNwV!XqcIqq)4#kKm;rQczjrIGnkYjn#1HbmaE4WAybl zjMtgF*boj=v#yT`9y6)<^gXJ04;$9u;2CiyYK|?(i~HVWJReU=GRM4lYV>Xl-*2@I9vU!=`{!e7^;N vBNl;~0&1ZlW(p-PP>EYNFcU!~8lnwQ;`gY;og0|x2ybA6<(YaA%xC^Dy^5ow diff --git a/README.html b/README.html deleted file mode 100644 index 15b00bcd..00000000 --- a/README.html +++ /dev/null @@ -1,535 +0,0 @@ - - - - - - - - - - - Executive Summary — Fornax Demo Notebooks - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Executive Summary

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Executive Summary#

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The Fornax Initiative is a NASA Astrophysics Archives project to collaboratively among the three archives HEASARC, IRSA, and MAST, create cloud systems, cloud software, and cloud standards for the astronomical community.

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The Fornax Science Console is a cloud compute system near to NASA data in the AWS cloud which provides a place where astronomers can do data-intensive research with reduced barriers. The Fornax Initiative provides increased compute, increased memory, increased ease of use by pre-installing astronomical software, increased reproducibility of big data results, and increased inclusion by removing some of these barriers to entry, tutorial notebooks, and documentation.

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This repo houses tutorial notebooks of fully worked science use cases for all users. Common goals of the notebooks are the usage of archival data from all NASA archives, cross-archive work, big data, and computationally intensive science. Currently, there are two major topics for which we have notebooks. The “Photometry” notebook starts with a catalog and a set of archival images. The notebook grabs all necessary images and measures photometry at all positions listed in the catalog on all images. The “Time Domain” notebooks are twofold. The first generates light curves from all available archival data for any user-supplied sample of targets. The second notebook runs ML algorithms to classify those generated light curves.

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Documentation#

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The user cases and documentation of the Fornax Initiative are currently available at the https://nasa-fornax.github.io/fornax-demo-notebooks/ URL, while the source code for the documentation can be found in the fornax-documentation repository.

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Content contributing#

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In this repository, we follow the standard practice of the Scientific Python ecosystem and use Jupytext and MyST Markdown Notebooks. -You will need jupytext installed for your browser to recognise the markdown files as notebooks (see more about the motivation and technicalities e.g. here: https://numpy.org/numpy-tutorials/content/pairing.html).

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If you already have an ipynb file, convert it to Markdown using the following command, and commit -only the markdown file to the repo:

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-By Fornax developers -

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- - © Copyright 2022-2023, Fornax developers. -
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- - \ No newline at end of file diff --git a/_sources/README.md b/_sources/README.md deleted file mode 100644 index 11248225..00000000 --- a/_sources/README.md +++ /dev/null @@ -1,24 +0,0 @@ -## Executive Summary - -The Fornax Initiative is a NASA Astrophysics Archives project to collaboratively among the three archives HEASARC, IRSA, and MAST, create cloud systems, cloud software, and cloud standards for the astronomical community. - -The Fornax Science Console is a cloud compute system near to NASA data in the AWS cloud which provides a place where astronomers can do data-intensive research with reduced barriers. The Fornax Initiative provides increased compute, increased memory, increased ease of use by pre-installing astronomical software, increased reproducibility of big data results, and increased inclusion by removing some of these barriers to entry, tutorial notebooks, and documentation. - -This repo houses tutorial notebooks of fully worked science use cases for all users. Common goals of the notebooks are the usage of archival data from all NASA archives, cross-archive work, big data, and computationally intensive science. Currently, there are two major topics for which we have notebooks. The "Photometry" notebook starts with a catalog and a set of archival images. The notebook grabs all necessary images and measures photometry at all positions listed in the catalog on all images. The "Time Domain" notebooks are twofold. The first generates light curves from all available archival data for any user-supplied sample of targets. The second notebook runs ML algorithms to classify those generated light curves. - - -## Documentation - -The user cases and documentation of the Fornax Initiative are currently available at the https://nasa-fornax.github.io/fornax-demo-notebooks/ URL, while the source code for the documentation can be found in the [fornax-documentation](https://github.com/nasa-fornax/fornax-documentation) repository. - -## Content contributing - -In this repository, we follow the standard practice of the Scientific Python ecosystem and use Jupytext and MyST Markdown Notebooks. -You will need ``jupytext`` installed for your browser to recognise the markdown files as notebooks (see more about the motivation and technicalities e.g. here: https://numpy.org/numpy-tutorials/content/pairing.html). - -If you already have an ``ipynb`` file, convert it to Markdown using the following command, and commit -only the markdown file to the repo: - -``` -jupytext --from notebook --to myst yournotebook.ipynb -``` diff --git a/_sources/documentation/README.md b/_sources/documentation/README.md index 570d8d44..3e532d69 100644 --- a/_sources/documentation/README.md +++ b/_sources/documentation/README.md @@ -9,12 +9,33 @@ The Fornax Science Console supports many astronomical use cases, but users will * that require complicated software pre-installed on the platform; or * that are parallelizable and require more compute than they currently have access to. -### Fornax Science Console basic capabilities - * CPUs: Upon logging in, users will have access to 4 CPUs provided by AWS. These are useful for smaller analyses and to test out larger analyses. Once a user has completed testing and is ready to scale up an analysis, they can request up to 128 CPUs. +### Fornax Science Console basic capabilities and selection guidelines + +After logging in, users will be presented with choices for their working environment. + +* Compute size: the amount of compute (CPUs) and memory (RAM) available is configurable. Options are briefly described below. +* Compute environment: the software environment including the operating system, system library versions, and pre-installed software selections. + +The compute size you select largely determines the costs you will accrue while it is running. We therefore need our users to be more conscious of the compute you are using than you would be on your own hardware. We ask users to follow these guidelines: + +* You will have a choice of server types. Please test your use case on the smallest server that can serve as a test, and run tests with a limited scope (e.g., number of sources, iterations, etc.). Only expand the scope and/or select a larger server when you have successfully tested the smaller case. +* Please also note that if you start an instance, closing your browser window does not necessarily stop the instance you started. Especially with the larger instances, **please explicitly use the “Stop My Server” button** (under File->Hub Control Panel) when done. +* Any time you need to use the biggest 128-core compute instance, please reach out to the Help Desk for explicit permission _before_ starting it. This will help us to be aware of resource usage for this development system, which does not yet have fully automated resource usage guardrails. Give us a brief idea of what the science use case is and an estimate for the _total_ run time. Note that if your job (or jobs) runs significantly longer than we expect, we may contact you about terminating it. If you need to stop and restart your instance, you don’t need to get permission every time as long as you are still within the original estimated total run time. If you need more time than expected, please write to the help desk with a brief justification. Our review process is more about visibility than control, so you’ll probably be approved. + +The compute options: + + * CPUs: By default, users will have access to 4 CPUs provided by AWS. These are useful for smaller analyses and to test out larger analyses. Once a user has completed testing and is ready to scale up an analysis, they can request up to 128 CPUs. * RAM: Upon logging in, users will have access to up to 16 GB of RAM. Up to 512 GB of RAM are available upon request. * User Storage: Upon logging in, users will have access to 10 GB of storage; additional storage is available upon request. * GPUs: There are currently no GPUs available. +Software environment options: + +* A base environment contains common astronomy libraries such as Astropy. +* The forced photometry tool called Tractor is available in a customized environment. +* The HEASoft high-energy astrophysics software is available in another customized environment. +* More environments will be created as need. + ### Data access within the Fornax Science Console Users of the Fornax Science Console will have access to data curated and published by data repositories around the world. @@ -22,10 +43,6 @@ Users of the Fornax Science Console will have access to data curated and publish * Many data repositories provide data access through application program interfaces (APIs), which can be invoked by users of the Fornax Science Console through standard Python (e.g. astroquery, pyvo) or command line methods (e.g. wget, curl). * Users can upload a limited amount of datasets to AWS and interact with them as though they were on the local filesystem. -### Software pre-installed on the Fornax Science Console - -Under construction: How can users get a list of pre-installed software without logging into the Fornax Science Console? - ## Quick Start Guide ### 1. Get an account on the Fornax Science Console diff --git a/_sources/documentation/index.md b/_sources/documentation/index.md deleted file mode 100644 index aa6be22a..00000000 --- a/_sources/documentation/index.md +++ /dev/null @@ -1,8 +0,0 @@ -# User Documentation - -```{toctree} ---- -maxdepth: 2 ---- - -README diff --git a/_sources/documentation/notebook_review_process.md b/_sources/documentation/notebook_review_process.md deleted file mode 100644 index c8826445..00000000 --- a/_sources/documentation/notebook_review_process.md +++ /dev/null @@ -1,49 +0,0 @@ -# How Does Fornax Review Notebooks? -*** - -This is a sort of requirements document for notebooks contributed to Fornax by Fornax team members. When a notebook is deemed "finished" by its authors, it should go through a two part review; a science review and a tech review. Suggested checklists for those reviews are below. These should be applied within reason to notebooks, and especially lightly to contributed notebooks. Checklists have been written and maintained by the distributed Fornax team. - -For authors: consider these checklists requirements for your code. - -## Who should participate in these reviews? -- JK's suggestion is that if everyone on Fornax helps to write the below checklists, then the whole team does not need to be involved in code reviews. One developer per tech review, one scientist per science review, from any team. - - - -## Science Review Checklist -- Is there a use case in the introduction which motivates the code? will our community understand this motivation/code? -- Does the code do what the intro says it is going to do? -- Is it scientifically accurate? -- Does it include all three archives HEASARC, MAST, IRSA?\ - - if not, is that justified -- Does it include work linked to a buzzword: - - big data, spectroscopy, time domain, forced photometry, cloud -- Has each NASA archive been given option to comment on modules for their relevant data access?\ - - TODO: a preferred contact method for each archive should be listed here, ie., archive helpdesk, NN slack channel #fornaxdev-daskhub? ??? - - Is archival data accessed in the most efficient way according to that archive? -## Tech Review Checklist -- Documentation: - - Is every function documented? - - Does it follow the style guide? https://github.com/spacetelescope/style-guides/blob/master/guides/jupyter-notebooks.md - - Do all code cells have corresponding narratives/comments? - - Include information about runtime on fiducial Fornax server - - Include information about which "image" the notebook uses when loggin into Fornax, ie., "Astrophysics default image" -- Notebook execution, error handling, etc.: - - Does the notebook run end-to-end, out of the box? - - Are errors handled appropriately, with `try`/`except` statements that are narrow in scope? - - Have warnings been dealt with appropriately, preferably by updating the code to avoid them (i.e., not by simply silencing them)? -- Efficiency: - - Is data accessed from the cloud where possible? - - Is the code parallelized where possible? - - If the notebook is intended to be scaled up, does it do that efficiently? - - Is memory usage optimized where possible? -- Cleanup: - - Have blocks of code that need to be re-used been turned into functions (rather than being duplicated)? - - Have un-used libraries been removed from the requirements.txt file and the `import` statements? - - Has un-used code been removed (e.g., unused functions and commented-out lines)? - - Are comment lines wrapped so all fit within a max of 90 - 100 characters per line? - - Are code lines reasonably short where possible? some code lines can't easily be wrapped and that is ok - -```python - -``` diff --git a/_sources/forced_photometry/code_src/README.md b/_sources/forced_photometry/code_src/README.md deleted file mode 100644 index d8a4fa54..00000000 --- a/_sources/forced_photometry/code_src/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# Code - Forced Photometry - -This directory stores code for the forced photometry use case. diff --git a/_sources/forced_photometry/data/README.md b/_sources/forced_photometry/data/README.md deleted file mode 100644 index dce2cae6..00000000 --- a/_sources/forced_photometry/data/README.md +++ /dev/null @@ -1,8 +0,0 @@ -# Data - Forced Photometry - -This directory is used for data that is downloaded or otherwise retrieved for the forced photometry use case. -Users may also store additional data here. Note that there is a separate directory for output generated by the -notebook or its users. - -New data should not be committed to the code repository. This directory is included in the .gitignore file to -prevent accidental commits. diff --git a/_sources/forced_photometry/multiband_photometry.md b/_sources/forced_photometry/multiband_photometry.md index ffa75ea0..fbbd30cf 100644 --- a/_sources/forced_photometry/multiband_photometry.md +++ b/_sources/forced_photometry/multiband_photometry.md @@ -12,7 +12,7 @@ kernelspec: --- # Automated Multiband Forced Photometry on Large Datasets -*** + ## Learning Goals: By the end of this tutorial, you will be able to: @@ -177,7 +177,7 @@ print("Number of objects: ", len(cosmos_table)) +++ -#### Use the fornax cloud access API to obtain the IRAC data from the IRSA S3 bucket. +### Use the fornax cloud access API to obtain the IRAC data from the IRSA S3 bucket. Details here may change as the prototype code is being added to the appropriate libraries, as well as the data holding to the appropriate NGAP storage as opposed to IRSA resources. @@ -250,7 +250,7 @@ fornax_download(spitzer, access_url_column='sia_url', fname_filter='go2_sci', data_subdirectory='IRAC', verbose=False) ``` -#### Use IVOA image search and Fornax download to obtain Galex from the MAST archive +### Use IVOA image search and Fornax download to obtain Galex from the MAST archive ```{code-cell} ipython3 #the Galex mosaic of COSMOS is broken into 4 seperate images diff --git a/_sources/forced_photometry/output/README.md b/_sources/forced_photometry/output/README.md deleted file mode 100644 index 9356ab4b..00000000 --- a/_sources/forced_photometry/output/README.md +++ /dev/null @@ -1,7 +0,0 @@ -# Output - Forced Photometry - -This directory is used for output generated by the forced photometry use case. This includes results, figures, -logs, etc. Users may also store their own output here. - -Output should not be committed to the code repository. This directory is included in the .gitignore file to -prevent accidental commits. diff --git a/_sources/light_curves/ML_AGNzoo.md b/_sources/light_curves/ML_AGNzoo.md index f3bfae4f..80d2584f 100644 --- a/_sources/light_curves/ML_AGNzoo.md +++ b/_sources/light_curves/ML_AGNzoo.md @@ -15,7 +15,7 @@ kernelspec: By the IPAC Science Platform Team, last edit: Feb 16th, 2024 -*** + ## Learning Goals @@ -97,7 +97,7 @@ colors = [ custom_cmap = LinearSegmentedColormap.from_list("custom_theme", colors[1:]) ``` -*** + ## 1) Loading data @@ -879,7 +879,3 @@ Datasets: Packages: * [`SOMPY`](https://github.com/sevamoo/SOMPY) * [`umap`](https://github.com/lmcinnes/umap) - - - -[Top of Page](#top) diff --git a/_sources/light_curves/code_src/README.md b/_sources/light_curves/code_src/README.md deleted file mode 100644 index 5861022c..00000000 --- a/_sources/light_curves/code_src/README.md +++ /dev/null @@ -1,11 +0,0 @@ -# Code - Light Curves - -This directory stores code for the light curve use cases including the ML use cases. -Notebooks using this directory are: -- light_curve_generator -- lc_classifier -- ML_AGNzoo - -```python - -``` diff --git a/_sources/light_curves/data/README.md b/_sources/light_curves/data/README.md deleted file mode 100644 index 96e6e440..00000000 --- a/_sources/light_curves/data/README.md +++ /dev/null @@ -1,8 +0,0 @@ -# Data - Light Curves - -This directory is used for data that is downloaded or otherwise retrieved for the light curve use case. Users may -also store additional data here. Note that there is a separate directory for output generated by the notebooks or -its users. - -Data should not be committed to the code repository. This directory is included in the .gitignore file to prevent -accidental commits. diff --git a/_sources/light_curves/light_curve_classifier.md b/_sources/light_curves/light_curve_classifier.md index 4dbce4b5..883ae5d1 100644 --- a/_sources/light_curves/light_curve_classifier.md +++ b/_sources/light_curves/light_curve_classifier.md @@ -12,7 +12,7 @@ kernelspec: --- # Light Curve Classifier -*** + ## Learning Goals By the end of this tutorial, you will be able to: @@ -55,7 +55,7 @@ Trained classifiers as well as estimates of their accuracy and plots of confusio As of 2024 August, this notebook takes ~170s to run to completion on Fornax using the 'Astrophysics Default Image' and the 'Large' server with 16GB RAM/ 4CPU. ## Authors -Jessica Krick, Shooby Hemmati, Troy Raen, Brigitta Sipocz, Andreas Faisst, Vandana Desai, Dave Shoop +Jessica Krick, Shoubaneh Hemmati, Troy Raen, Brigitta Sipőcz, Andreas Faisst, Vandana Desai, David Shupe ## Acknowledgements Stephanie La Massa diff --git a/_sources/light_curves/light_curve_generator.md b/_sources/light_curves/light_curve_generator.md index 704036eb..79e954f3 100644 --- a/_sources/light_curves/light_curve_generator.md +++ b/_sources/light_curves/light_curve_generator.md @@ -12,60 +12,61 @@ kernelspec: --- # Make Multi-Wavelength Light Curves Using Archival Data -*** + ## Learning Goals By the end of this tutorial, you will be able to: - • Automatically load a catalog of target sources - • Automatically & efficiently search NASA and non-NASA resources for the light curves of up to ~500 targets - • Store & manipulate light curves in a Pandas MultiIndex dataframe - • Plot all light curves on the same plot + * Automatically load a catalog of target sources + * Automatically & efficiently search NASA and non-NASA resources for the light curves of up to ~500 targets + * Store & manipulate light curves in a Pandas MultiIndex dataframe + * Plot all light curves on the same plot ## Introduction: - • A user has a sample of interesting targets for which they would like to see a plot of available archival light curves. We start with a small set of changing look AGN from Yang et al., 2018, which are automatically downloaded. Changing look AGN are cases where the broad emission lines appear or disappear (and not just that the flux is variable). + * A user has a sample of interesting targets for which they would like to see a plot of available archival light curves. We start with a small set of changing look AGN from Yang et al., 2018, which are automatically downloaded. Changing look AGN are cases where the broad emission lines appear or disappear (and not just that the flux is variable). - • We model light curve plots after van Velzen et al. 2021. We search through a curated list of time-domain NASA holdings as well as non-NASA sources. HEASARC catalogs used are Fermi and Beppo-Sax, IRSA catalogs used are ZTF and WISE, and MAST catalogs used are Pan-STARRS, TESS, Kepler, and K2. Non-NASA sources are Gaia and IceCube. This list is generalized enough to include many types of targets to make this notebook interesting for many types of science. All of these time-domain archives are searched in an automated and efficient fashion using astroquery, pyvo, pyarrow or APIs. + * We model light curve plots after van Velzen et al. 2021. We search through a curated list of time-domain NASA holdings as well as non-NASA sources. HEASARC catalogs used are Fermi and Beppo-Sax, IRSA catalogs used are ZTF and WISE, and MAST catalogs used are Pan-STARRS, TESS, Kepler, and K2. Non-NASA sources are Gaia and IceCube. This list is generalized enough to include many types of targets to make this notebook interesting for many types of science. All of these time-domain archives are searched in an automated and efficient fashion using astroquery, pyvo, pyarrow or APIs. - • Light curve data storage is a tricky problem. Currently we are using a MultiIndex Pandas dataframe, as the best existing choice for right now. One downside is that we need to manually track the units of flux and time instead of relying on an astropy storage scheme which would be able to do some of the units worrying for us (even astropy can't do all magnitude to flux conversions). Astropy does not currently have a good option for multi-band light curve storage. + * Light curve data storage is a tricky problem. Currently we are using a MultiIndex Pandas dataframe, as the best existing choice for right now. One downside is that we need to manually track the units of flux and time instead of relying on an astropy storage scheme which would be able to do some of the units worrying for us (even astropy can't do all magnitude to flux conversions). Astropy does not currently have a good option for multi-band light curve storage. - • This notebook walks through the individual steps required to collect the targets and their light curves and create figures. It also shows how to speed up the collection of light curves using python's `multiprocessing`. This is expected to be sufficient for up to ~500 targets. For a larger number of targets, consider using the bash script demonstrated in the neighboring notebook [scale_up](scale_up.md). + * This notebook walks through the individual steps required to collect the targets and their light curves and create figures. It also shows how to speed up the collection of light curves using python's `multiprocessing`. This is expected to be sufficient for up to ~500 targets. For a larger number of targets, consider using the bash script demonstrated in the neighboring notebook [scale_up](scale_up.md). - • ML work using these time-series light curves is in two neighboring notebooks: [ML_AGNzoo](ML_AGNzoo.md) and [light_curve_classifier](light_curve_classifier.md). + * ML work using these time-series light curves is in two neighboring notebooks: [ML_AGNzoo](ML_AGNzoo.md) and [light_curve_classifier](light_curve_classifier.md). As written, this notebook is expected to require at least 2 CPU and 8G RAM. ## Input: - • choose from a list of known changing look AGN from the literature + * choose from a list of known changing look AGN from the literature OR - - • input your own sample + * input your own sample ## Output: - • an archival optical + IR + neutrino light curve + * an archival optical + IR + neutrino light curve ## Authors: -Jessica Krick, Shoubaneh Hemmati, Andreas Faisst, Troy Raen, Brigitta Sipőcz, Dave Shupe +Jessica Krick, Shoubaneh Hemmati, Andreas Faisst, Troy Raen, Brigitta Sipőcz, David Shupe ## Acknowledgements: Suvi Gezari, Antara Basu-zych, Stephanie LaMassa MAST, HEASARC, & IRSA Fornax teams ## Imports: - • `acstools` to work with HST magnitude to flux conversion - • `astropy` to work with coordinates/units and data structures - • `astroquery` to interface with archives APIs - • `hpgeom` to locate coordinates in HEALPix space - • `lightkurve` to search TESS, Kepler, and K2 archives - • `matplotlib` for plotting - • `multiprocessing` to use the power of multiple CPUs to get work done faster - • `numpy` for numerical processing - • `pandas` for their data structure DataFrame and all the accompanying functions - • `pyarrow` to work with Parquet files for WISE and ZTF - • `pyvo` for accessing Virtual Observatory(VO) standard data - • `requests` to get information from URLs - • `scipy` to do statistics - • `tqdm` to track progress on long running jobs - • `urllib` to handle archive searches with website interface + * `acstools` to work with HST magnitude to flux conversion + * `astropy` to work with coordinates/units and data structures + * `astroquery` to interface with archives APIs + * `hpgeom` to locate coordinates in HEALPix space + * `lightkurve` to search TESS, Kepler, and K2 archives + * `matplotlib` for plotting + * `multiprocessing` to use the power of multiple CPUs to get work done faster + * `numpy` for numerical processing + * `pandas` with their `[aws]` extras for their data structure DataFrame and all the accompanying functions + * `pyarrow` to work with Parquet files for WISE and ZTF + * `pyvo` for accessing Virtual Observatory(VO) standard data + * `requests` to get information from URLs + * `scipy` to do statistics + * `tqdm` to track progress on long running jobs + * `urllib` to handle archive searches with website interface + This cell will install them if needed: @@ -433,11 +434,11 @@ _ = create_figures(df_lc = parallel_df_lc, # either df_lc (serial call) or paral This work made use of: -• Astroquery; Ginsburg et al., 2019, 2019AJ....157...98G -• Astropy; Astropy Collaboration 2022, Astropy Collaboration 2018, Astropy Collaboration 2013, 2022ApJ...935..167A, 2018AJ....156..123A, 2013A&A...558A..33A -• Lightkurve; Lightkurve Collaboration 2018, 2018ascl.soft12013L -• acstools; https://zenodo.org/record/7406933#.ZBH1HS-B0eY -• unWISE light curves; Meisner et al., 2023, 2023AJ....165...36M +* Astroquery; Ginsburg et al., 2019, 2019AJ....157...98G +* Astropy; Astropy Collaboration 2022, Astropy Collaboration 2018, Astropy Collaboration 2013, 2022ApJ...935..167A, 2018AJ....156..123A, 2013A&A...558A..33A +* Lightkurve; Lightkurve Collaboration 2018, 2018ascl.soft12013L +* acstools; https://zenodo.org/record/7406933#.ZBH1HS-B0eY +* unWISE light curves; Meisner et al., 2023, 2023AJ....165...36M ```{code-cell} ipython3 diff --git a/_sources/light_curves/output/README.md b/_sources/light_curves/output/README.md deleted file mode 100644 index bc229bfc..00000000 --- a/_sources/light_curves/output/README.md +++ /dev/null @@ -1,7 +0,0 @@ -# Output - Light Curves - -This directory is used for output generated by the light curve use case. This includes results, figures, logs, -etc. Users may also store their own output here. - -Output should not be committed to the code repository. This directory is included in the .gitignore file to -prevent accidental commits. diff --git a/_sources/spectroscopy/README.md b/_sources/spectroscopy/README.md deleted file mode 100644 index bf22cac9..00000000 --- a/_sources/spectroscopy/README.md +++ /dev/null @@ -1,15 +0,0 @@ -# Spectra Generator - -In this User Case Scenario we work towards creating a spectroscopic library -from multiple archival and publication resources and classifying and analysing -them. - - - -```{toctree} ---- -maxdepth: 1 ---- -spectra_generator - -``` diff --git a/_sources/spectroscopy/code_src/README.md b/_sources/spectroscopy/code_src/README.md deleted file mode 100644 index a6498360..00000000 --- a/_sources/spectroscopy/code_src/README.md +++ /dev/null @@ -1,10 +0,0 @@ -# Code - Spectra generator - -This directory stores code for the spectroscopic use cases. -Notebooks using this directory are: - - • spectra_generator - -```python - -``` diff --git a/_sources/spectroscopy/explore_Euclid_data.md b/_sources/spectroscopy/explore_Euclid_data.md deleted file mode 100644 index a8b13fe4..00000000 --- a/_sources/spectroscopy/explore_Euclid_data.md +++ /dev/null @@ -1,83 +0,0 @@ - -# Explore Euclid Data -*** - -## Learning Goals -By the end of this tutorial, you will be able to: - - • work with data in the cloud - - • get and visualize Euclid images - - • get and visualize Euclid spectra - - -## Introduction -This notebook is written to be used in Fornax which is a cloud based computing platform using AWS. It will access Euclid data stored in the cloud. The user does not need to know where the actual data are stored. We will want to make sure the data access methods work from various locations. - - -## 1. Define the Sample - -Start with the MOSDEF sample Kriek et al 2015 -- We think these galaxies will be included the May 2024 early data release -- Redshift range of this sample is good match to Euclid - -*TODO: need coords of this sample -- https://mosdef.astro.berkeley.edu/for-scientists/data-releases/ links arent working -- Kriek et al., 2015 doesn't have it -- Du et al., 2021 coords are on Vizier. Is this an ok sample to start with? - - 157 Star-forming Galaxies at z~2, some fraction of which are in COSMOS - - - - -## 2. Query the Euclid catalog for the sample -MER is the merged catalog of Euclid imaging & spectroscopy as well as external photometry sources - -Return a data structure of targets with Euclid data - -*TODO: how do we access MER?\ -*TODO: define this data structure (astropy table?) and what columns are interesting to keep\ -*TODO: May data release will be [early release observations](https://www.cosmos.esa.int/web/euclid/ero-data-release), what fraction of this notebook can we do with that dataset, and what fraction will we need to test on other datasets - - - - - -## 3. Grab the images, 1D coadded spectrum, & individual spectra -*TODO: Define the data structure to hold this data - - Is the data structure here similar to the spectroscopy notebook where some rows have single band photometry and some have spectroscopy and hold arrays of flux, unc? - -*TODO: How do we access the imaging? - - images might be available via SIAv2 - -*TODO: How do we access the spectra? - - From Anastasia, there will be 2 ways to access Euclid spectra - - 1. Do a TAP search on the MER photometry catalog. The results will include a service descriptor to get the spectrum for rows of interest. - 2. Query the Euclid datalink service directly for spectra. - -4/19 update: -- IRSA datalink service is not yet ready, maybe in a couple weeks -- Alternatively, we could explore CADC -- CADC runs almost entirely off datalink - their SIA and ObsTAP services return datalink tables that you have to follow to get to any of their products. - - - -## 4. Visualize Euclid data - -```python -# plot object-scale cutouts of the MER images - -``` - -```python -# plot coadded 1D spectrum -``` - -```python -# plot individual dithers to get a sense of the quality of the data -``` - -```python - -``` diff --git a/_sources/spectroscopy/spectra_generator.md b/_sources/spectroscopy/spectra_generator.md deleted file mode 100644 index ad9a3eb9..00000000 --- a/_sources/spectroscopy/spectra_generator.md +++ /dev/null @@ -1,303 +0,0 @@ ---- -jupyter: - jupytext: - text_representation: - extension: .md - format_name: markdown - format_version: '1.3' - jupytext_version: 1.15.2 - kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 ---- - - -# Extract Multi-Wavelength Spectroscopy from Archival Data -*** - -## Learning Goals -By the end of this tutorial, you will be able to: - - • automatically load a catalog of sources - - • search NASA and non-NASA resources for fully reduced spectra and load them using specutils - - • store the spectra in a Pandas multiindex dataframe - - • plot all the spectra of a given source - - -## Introduction: - -### Motivation -A user has a source (or a sample of sources) for which they want to obtain spectra covering ranges of wavelengths from the UV to the far-IR. The large amount of spectra available enables multi-wavelength spectroscopic studies, which is crucial to understand the physics of stars, galaxies, and AGN. However, gathering and analysing spectra is a difficult endeavor as the spectra are distributed over different archives and in addition they have different formats which complicates their handling. This notebook showcases a tool for the user to conveniently query the spectral archives and collect the spectra for a set of objects in a format that can be read in using common software such as the Python `specutils` package. For simplicity, we limit the tool to query already reduced and calibrated spectra. -The notebook may focus on the COSMOS field for now, which has a large overlap of spectroscopic surveys such as with SDSS, DESI, Keck, HST, JWST, Spitzer, and Herschel. In addition, the tool enables the capability to search and ingest spectra from Euclid and SPHEREx in the feature. For this to work, the `specutils` functions may have to be update or a wrapper has to be implemented. - - -### List of Spectroscopic Archives and Status - - -| Archive | Spectra | Description | Access point | Status | -| ------- | ------- | ----------- | ------------ | ------ | -| IRSA | Keck | About 10,000 spectra on the COSMOS field from [Hasinger et al. (2018)](https://ui.adsabs.harvard.edu/abs/2018ApJ...858...77H/abstract) | [IRSA Archive](https://irsa.ipac.caltech.edu/cgi-bin/Gator/nph-scan?projshort=COSMOS) | Implemented with `astroquery.ipac.irsa`. (Table gives URLs to spectrum FITS files.) Note: only implemented for absolute calibrated spectra. | -| IRSA | Spitzer IRS | ~17,000 merged low-resolution IRS spectra | [IRS Enhanced Product](https://irsa.ipac.caltech.edu/cgi-bin/Gator/nph-dd?catalog=irs_enhv211) | Implemented with `astroquery.ipac.irsa`. (Table gives URLs to spectrum IPAC tables.) | -| IRSA | IRTF* | Large library of stellar spectra | | does `astroquery.ipac.irsa` work?? | -| ESA | Herschel* | Some spectra | astroquery.esa.hsa | implemented with [astroquery](https://astroquery.readthedocs.io/en/latest/esa/hsa/hsa.html) | -| IRSA | Euclid | Spectra hosted at IRSA in FY25 -> preparation for ingestion | | Will use mock spectra with correct format for testing | -| IRSA | SPHEREx | Spectra/cubes will be hosted at IRSA, first release in FY25 -> preparation for ingestion | | Will use mock spectra with correct format for testing | -| MAST | HST* | Slitless spectra would need reduction and extraction. There are some reduced slit spectra from COS in the Hubble Archive | `astroquery.mast` | Implemented using `astroquery.mast` | -| MAST | JWST* | Reduced slit MSA and Slit spectra that can be queried | `astroquery.mast` | Implemented using `astroquery.mast` | -| SDSS | SDSS optical| Optical spectra that are reduced | [Sky Server](https://skyserver.sdss.org/dr18/SearchTools) or `astroquery.sdss` (preferred) | Implemented using `astroquery.sdss`. | -| DESI | DESI* | Optical spectra | [DESI public data release](https://data.desi.lbl.gov/public/) | Implemented with `SPARCL` library | -| BOSS | BOSS* | Optical spectra | [BOSS webpage (part of SDSS)](https://www.sdss4.org/surveys/boss/) | Implemented with `SPARCL` library together with DESI | -| HEASARC | None | Could link to Chandra observations to check AGN occurrence. | `astroquery.heasarc` | More thoughts on how to include scientifically. | - -The ones with an asterisk (*) are the challenging ones. - -## Input: - - • Coordinates for a single source or a sample on the COSMOS field - - - -## Output: - - • A Pandas data frame including the spectra from different facilities - - • A plot comparing the different spectra extracted for each source - -## Non-standard Imports: - -• ... -## Runtime - -As of 2024 August, this notebook takes ~300s to run to completion on Fornax using the 'Astrophysics Default Image' and the 'Large' server with 16GB RAM/ 4CPU. - -## Authors: -Andreas Faisst, Jessica Krick, Shoubaneh Hemmati, Troy Raen, Brigitta Sipőcz, Dave Shupe - -## Acknowledgements: -... - -## Open Issues: - -• Implement queries for: Herschel, Euclid (use mock data), SPHEREx (use mock data) -• Match to HEASARC -• Make more efficient (especially MAST searches) - - - - -### Datasets that were considered but didn't end up being used: -#### IRTF: - - https://irsa.ipac.caltech.edu/Missions/irtf.html \ - - The IRTF is a 3.2 meter telescope, optimized for infrared observations, and located at the summit of Mauna Kea, Hawaiʻi. \ - - large library of stellar spectra \ - - Not included here because the data are not currently available in an easily accessible, searchable format - - -```python -# Ensure all dependencies are installed - -!pip install -r requirements.txt -``` - -```python -## IMPORTS -import sys, os -import numpy as np -import os - -import matplotlib.pyplot as plt -import matplotlib as mpl - - -from astropy.coordinates import SkyCoord -import astropy.units as u -from astropy.table import Table - -sys.path.append('code_src/') -from data_structures_spec import MultiIndexDFObject -from sample_selection import clean_sample -from desi_functions import DESIBOSS_get_spec -from spitzer_functions import SpitzerIRS_get_spec -from sdss_functions import SDSS_get_spec -from mast_functions import HST_get_spec, JWST_get_spec -from keck_functions import KeckDEIMOS_get_spec -from plot_functions import create_figures -from herschel_functions import Herschel_get_spec -``` - -## 1. Define the sample - -Here we will define the sample of galaxies. For now, we just enter some "random" coordinates to test the code. - -```python -coords = [] -labels = [] - -coords.append(SkyCoord("{} {}".format("09 54 49.40" , "+09 16 15.9"), unit=(u.hourangle, u.deg) )) -labels.append("NGC3049") - -coords.append(SkyCoord("{} {}".format("12 45 17.44 " , "27 07 31.8"), unit=(u.hourangle, u.deg) )) -labels.append("NGC4670") - -coords.append(SkyCoord("{} {}".format("14 01 19.92" , "−33 04 10.7"), unit=(u.hourangle, u.deg) )) -labels.append("Tol_89") - -coords.append(SkyCoord(233.73856 , 23.50321, unit=u.deg )) -labels.append("Arp220") - -coords.append(SkyCoord( 150.091 , 2.2745833, unit=u.deg )) -labels.append("COSMOS1") - -coords.append(SkyCoord( 150.1024475 , 2.2815559, unit=u.deg )) -labels.append("COSMOS2") - -coords.append(SkyCoord("{} {}".format("150.000" , "+2.00"), unit=(u.deg, u.deg) )) -labels.append("COSMOS3") - -coords.append(SkyCoord("{} {}".format("+53.15508" , "-27.80178"), unit=(u.deg, u.deg) )) -labels.append("JADESGS-z7-01-QU") - -coords.append(SkyCoord("{} {}".format("+53.15398", "-27.80095"), unit=(u.deg, u.deg) )) -labels.append("TestJWST") - -sample_table = clean_sample(coords, labels, precision=2.0* u.arcsecond , verbose=1) - -``` - -### 1.2 Write out your sample to disk - -At this point you may wish to write out your sample to disk and reuse that in future work sessions, instead of creating it from scratch again. Note that we first check if the `data` directory exists and if not, we will create one. - -For the format of the save file, we would suggest to choose from various formats that fully support astropy objects(eg., SkyCoord). One example that works is Enhanced Character-Separated Values or ['ecsv'](https://docs.astropy.org/en/stable/io/ascii/ecsv.html) - -```python -if not os.path.exists("./data"): - os.mkdir("./data") -sample_table.write('data/input_sample.ecsv', format='ascii.ecsv', overwrite = True) -``` - -### 1.3 Load the sample table from disk - -Do only this step from this section when you have a previously generated sample table - -```python -sample_table = Table.read('data/input_sample.ecsv', format='ascii.ecsv') -``` - -### 1.4 Initialize data structure to hold the spectra -Here, we initialize the MultiIndex data structure that will hold the spectra. - -```python -df_spec = MultiIndexDFObject() -``` - -## 2. Find spectra for these targets in NASA and other ancillary catalogs - -We search a curated list of NASA astrophysics archives. Because each archive is different, and in many cases each catalog is different, each function to access a catalog is necesarily specialized to the location and format of that particular catalog. - - -### 2.1 IRSA Archive - -This archive includes spectra taken by - - • Keck - - • Spitzer/IRS - - - - -```python -%%time -## Get Keck Spectra (COSMOS only) -df_spec_DEIMOS = KeckDEIMOS_get_spec(sample_table = sample_table, search_radius_arcsec=1) -df_spec.append(df_spec_DEIMOS) -``` - -```python -%%time -## Get Spitzer IRS Spectra -df_spec_IRS = SpitzerIRS_get_spec(sample_table, search_radius_arcsec=1 , COMBINESPEC=False) -df_spec.append(df_spec_IRS) -``` - -### 2.2 MAST Archive - -This archive includes spectra taken by - - • HST (including slit spectroscopy) - - • JWST (including MSA and slit spectroscopy) - - -```python -%%time -## Get Spectra for HST -df_spec_HST = HST_get_spec(sample_table , search_radius_arcsec = 0.5, datadir = "./data/", verbose = False) -df_spec.append(df_spec_HST) -``` - -### 2.3 ESA Archive -```python -# Herschel PACS & SPIRE from ESA TAP using astroquery -#This search is fully functional, but is commented out because it takes ~4 hours to run to completion -herschel_radius = 1.1 -herschel_download_directory = 'data/herschel' - -#if not os.path.exists(herschel_download_directory): -# os.makedirs(herschel_download_directory, exist_ok=True) -#df_spec_herschel = Herschel_get_spec(sample_table, herschel_radius, herschel_download_directory, delete_tarfiles = True) -#df_spec.append(df_spec_herschel) -``` - -### 2.4 SDSS Archive -```python -%%time -## Get Spectra for JWST -df_jwst = JWST_get_spec(sample_table , search_radius_arcsec = 0.5, datadir = "./data/", verbose = False) -df_spec.append(df_jwst) -``` - - -This includes SDSS spectra. - -```python -%%time -## Get SDSS Spectra -df_spec_SDSS = SDSS_get_spec(sample_table , search_radius_arcsec=5, data_release=17) -df_spec.append(df_spec_SDSS) -``` - -### 2.5 DESI Archive - -This includes DESI spectra. Here, we use the `SPARCL` query. Note that this can also be used -for SDSS searches, however, according to the SPARCL webpage, only up to DR16 is included. Therefore, we will not include SDSS DR16 here (this is treated in the SDSS search above). - -```python -%%time -## Get DESI and BOSS spectra with SPARCL -df_spec_DESIBOSS = DESIBOSS_get_spec(sample_table, search_radius_arcsec=5) -df_spec.append(df_spec_DESIBOSS) -``` - -## 3. Make plots of luminosity as a function of time -We show flux in mJy as a function of time for all available bands for each object. `show_nbr_figures` controls how many plots are actually generated and returned to the screen. If you choose to save the plots with `save_output`, they will be put in the output directory and labelled by sample number. - - - -```python -### Plotting #### -create_figures(df_spec = df_spec, - bin_factor=5, - show_nbr_figures = 10, - save_output = False, - ) -``` - - - - diff --git a/documentation/README.html b/documentation/README.html index bd5e006c..5baa58e1 100644 --- a/documentation/README.html +++ b/documentation/README.html @@ -411,9 +411,8 @@

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