Margin Cache#

For more discussion of the whys and hows of margin caches, please see the demo notebook in LSDB documentation and Max’s AAS iPoster for more information.

This page discusses topics around setting up a pipeline to generate a margin cache from an existing HATS catalog on disk.

At a minimum, you need arguments that include where to find the input files, and where to put the output files. A minimal arguments block will look something like:

from hats_import.margin_cache.margin_cache_arguments import MarginCacheArguments

args = MarginCacheArguments(
    input_catalog_path="./my_data/my_catalog",
    output_path="./output",
    margin_threshold=10.0,
    output_artifact_name="my_catalog_10arcs",
)

More details on each of these parameters is provided in sections below.

For the curious, see the API documentation for hats_import.margin_cache.margin_cache_arguments.MarginCacheArguments.

Dask setup#

We will either use a user-provided dask Client, or create a new one with arguments:

dask_tmp - str - directory for dask worker space. this should be local to the execution of the pipeline, for speed of reads and writes. For much more information, see Temporary files and disk usage

dask_n_workers - int - number of workers for the dask client. Defaults to 1.

dask_threads_per_worker - int - number of threads per dask worker. Defaults to 1.

If you find that you need additional parameters for your dask client (e.g are creating a SLURM worker pool), you can instead create your own dask client and pass along to the pipeline, ignoring the above arguments. This would look like:

from dask.distributed import Client
from hats_import.pipeline import pipeline_with_client

args = MarginCacheArguments(...)
with Client('scheduler:port') as client:
    pipeline_with_client(args, client)

If you’re running within a .py file, we recommend you use a main guard to potentially avoid some python threading issues with dask:

from hats_import.pipeline import pipeline

def margin_pipeline():
    args = MarginCacheArguments(...)
    pipeline(args)

if __name__ == '__main__':
    margin_pipeline()

Input Catalog#

For this pipeline, you will need to have already transformed your catalog into hats parquet format. Provide the path to the catalog data with the argument input_catalog_path.

The input hats catalog will provide its own right ascension and declination that will be used when computing margin populations.

Margin calculation parameters#

When creating a margin catalog, we need to know how large of a margin to include around each pixel in the input catalog.

margin_threshold is the size of the margin cache boundary, given in arcseconds. This defaults to 5 arcseconds, but you should set this value to whatever is appropriate for the astrometry error/PSF width for your instruments. If you’re not sure how to determine this, please reach out! We’d love to help! Contact us.

This is equivalent to setting the margin_order. We use a lookup, with roughly the following table of values. This is the minimum separation angle possible within healpix pixels of a given order.

`margin_order`	minimum separation angle
10	2.15 arcmin
11	1.07 arcmin
12	32.21 arcsec
13	16.10 arcsec
14	8.05 arcsec
15	4.03 arcsec
16	2.01 arcsec
17	1.01 arcsec
18	0.50 arcsec
19	0.25 arcsec
20	0.13 arcsec
21	62.91 msec
22	31.45 msec

For each input catalog partition, we can quickly determine all possible neighboring healpix pixels at the given margin_order. All of these partitions may contain points that are inside the margin_threshold. For each point in the input catalog, we can quickly determine the healpix pixel at margin_order and filter points based on this.

In the figure below, the central yellow pixel is the primary catalog pixel at order 10, and the surrounding pink order 13 pixels represent the margin for 10 arcsec.

Visual of primary catalog pixel and the margin pixels.#

For reasons of runtime performance and numerical precision, we do not perform precise boundary checking on individual points.

Progress Reporting#

By default, we will display some progress bars during pipeline execution. To disable these (e.g. when you expect no output to standard out), you can set progress_bar=False.

There are several stages to the pipeline execution, and you can expect progress reporting to look like the following:

Mapping  : 100%|██████████| 2352/2352 [9:25:00<00:00, 14.41s/it]
Reducing : 100%|██████████| 2385/2385 [00:43<00:00, 54.47it/s]
Finishing: 100%|██████████| 4/4 [00:03<00:00,  1.15it/s]

For very long-running pipelines (e.g. multi-TB inputs), you can get an email notification when the pipeline completes using the completion_email_address argument. This will send a brief email, for either pipeline success or failure.

Output#

You must specify a name for the margin catalog, using output_artifact_name. A good convention is the name of the primary input catalog, followed by the margin threshold, e.g. gaia_10arcs would be a margin catalog based on gaia that uses 10 arcseconds for margins.

You must specify where you want your margin data to be written, using output_path. This path should be the base directory for your catalogs, as the full path for the margin will take the form of output_path/output_artifact_name.

If you’re writing to cloud storage, or otherwise have some filesystem credential dict, initialize output_path using universal_pathlib’s utilities.

In addition, you can specify directories to use for various intermediate files:

dask worker space (dask_tmp)
sharded parquet files (tmp_dir)

Most users are going to be ok with simply setting the tmp_dir for all intermediate file use. For more information on these parameters, when you would use each, and demonstrations of temporary file use see Temporary files and disk usage

Margin Cache

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