Skip to main content

No project description provided

Project description

heinlein

heinlein is a high-level tool for interacting with local versions of astronomical survey datasets. heinlein empowers astronomers who work with large survey datasets to stop thinking about files and start thinking about astronomy. Let's say you had downloaded some catalogs from the Dark Energy Survey. You could add these catalogs to heinlein with a single command:

> heinlein add hsc catalog /path/to/catalogs

Once this is done retrieving data in a python script becomes a simple as:

from heinlein import load dataset
import astropy.units as u

des = load_dataset("des")
catalog = data.cone_search(center=(141.23246, 2.32358), radius=120*u.arcsec)
#Returns a standard astropy table

heinlein understands that it's pointless to load an entire dataset when you only need one small piece of it, so it contains tools to intelligently portions of the data based on what's needed at the moment. You can easily setup your previously-downloaded catalogs to work with these features by calling:

> heinlein split des /path/to/catalogs

heinlein also knows that if you're getting data from one part of the sky, there's a decent chance you'll come back and try to get data from a nearby part of the sky. heinlein caches data so queries nearby a perviously-queried area will return substantially faster.

But heinlein doesn't only work with catalogs. For analyses that rely on photometry, it can be necesary to remove objects from a catalog that fall within a mask provided by the survey team (often because of a nearby bright star). It's easy to use heinlein to manage these masks and apply them to catalogs:

des = load_dataset("des")
data = data.cone_search(center=(141.23246, 2.32358), radius=120*u.arcsec, dtypes=["catalg", "mask"])
catalog = data["catalog"]
mask = data["mask"]
masked_catalog = catalog[mask]

heinlein will happily keep track of any data you give it, but it only contains built-in tools for certain datatypes (currently catalogs and masks).

Currently supported surveys: DES, HSC SSP, CFHTLS

Data types with built-in utilities: Catalogs (plaintext (csv, tsv etc), sqlite) Masks (.fits, .reg, mangle)

Interested in adding something to these lists? Don't hesitate to add it in "Issues."

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

heinlein-0.9.1.tar.gz (23.9 kB view details)

Uploaded Source

Built Distribution

heinlein-0.9.1-py3-none-any.whl (32.4 kB view details)

Uploaded Python 3

File details

Details for the file heinlein-0.9.1.tar.gz.

File metadata

  • Download URL: heinlein-0.9.1.tar.gz
  • Upload date:
  • Size: 23.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.12.1 Linux/6.5.0-1021-azure

File hashes

Hashes for heinlein-0.9.1.tar.gz
Algorithm Hash digest
SHA256 8ae7ea4a2b9deb5834208b169fb99bc6e724fab4175045fc13f3cf442f2ede17
MD5 4adbb438966c87f69fe94cdc74c0edcf
BLAKE2b-256 272ab8df0649954f2ef8a4cb1319a2ecd44a5f03ca9587bf7086723c99b5ad87

See more details on using hashes here.

File details

Details for the file heinlein-0.9.1-py3-none-any.whl.

File metadata

  • Download URL: heinlein-0.9.1-py3-none-any.whl
  • Upload date:
  • Size: 32.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.12.1 Linux/6.5.0-1021-azure

File hashes

Hashes for heinlein-0.9.1-py3-none-any.whl
Algorithm Hash digest
SHA256 2ddc3fb7197bdb4ae8cab04cef099ef59c9ab07cf85949714d5514bf7b28d94d
MD5 e9b1e92f2ab0c8ae4c7a1b10bae867a7
BLAKE2b-256 22da3bb9ccfd791d08f80aaeff77db4ff40d5fd79d4c8f245c6346fd24d2f05d

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page