Downloadable Data Management (


A number of Astropy’s tools work with data sets that are either awkwardly large (e.g., solar_system_ephemeris) or regularly updated (e.g., IERS_B) or both (e.g., IERS_A). This kind of data - authoritative data made available on the Web, and possibly updated from time to time - is reasonably common in astronomy. The Astropy Project therefore provides some tools for working with such data.

The primary tool for this is the astropy cache. This is a repository of downloaded data, indexed by the URL where it was obtained. The tool download_file and various other things built upon it can use this cache to request the contents of a URL, and (if they choose to use the cache) the data will only be downloaded if it is not already present in the cache. The tools can be instructed to obtain a new copy of data that is in the cache but has been updated online.

The astropy cache is stored in a centralized place (on Linux machines by default it is $HOME/.astropy/cache; see Configuration System (astropy.config) for more details). You can check its location on your machine:

>>> import astropy.config.paths
>>> astropy.config.paths.get_cache_dir()  

This centralization means that the cache is persistent and shared between all astropy runs in any virtualenv by one user on one machine (possibly more if your home directory is shared between multiple machines). This can dramatically accelerate astropy operations and reduce the load on servers, like those of the IERS, that were not designed for heavy Web traffic. If you find the cache has corrupted or outdated data in it, you can remove an entry or clear the whole thing with clear_download_cache.

The files in the cache directory are named according to a cryptographic hash of their URL (currently MD5, so in principle malevolent entities can cause collisions, though the security risks this poses are marginal at most). The modification times on these files normally indicate when they were last downloaded from the Internet.

Usage Within Astropy

For the most part, you can ignore the caching mechanism and rely on astropy to have the correct data when you need it. For example, precise time conversions and sky locations need measured tables of the Earth’s rotation from the IERS. The table IERS_Auto provides the infrastructure for many of these calculations. It makes available Earth rotation parameters, and if you request them for a time more recent than its tables cover, it will download updated tables from the IERS. So for example asking what time it is in UT1 (a timescale that reflects the irregularity of the Earth’s rotation) probably triggers a download of the IERS data:

>>> from astropy.time import Time
|============================================| 3.2M/3.2M (100.00%)         1s
<Time object: scale='ut1' format='datetime' value=2019-09-22 08:39:03.812731>

But running it a second time does not require any new download:

<Time object: scale='ut1' format='datetime' value=2019-09-22 08:41:21.588836>

Some data is also made available from the Astropy data server either for use within astropy or for your convenience. These are available more conveniently with the get_pkg_data_* functions:

>>> from import get_pkg_data_contents
>>> print(get_pkg_data_contents("coordinates/sites-un-ascii"))  
# these are all mappings from the name in sites.json (which is ASCII-only) to the "true" unicode names

Usage From Outside Astropy

Users of astropy can also make use of astropy’s caching and downloading mechanism. In its simplest form, this amounts to using download_file with the cache=True argument to obtain their data, from the cache if the data is there:

>>> from astropy.utils.iers import IERS_B_URL, IERS_B
>>> from import download_file
>>>, cache=True))["year","month","day"][-3:]  
 <IERS_B length=3>
 year month  day
int64 int64 int64
----- ----- -----
 2019     8     4
 2019     8     5
 2019     8     6

If users want to update the cache to a newer version of the data (note that here the data was already up to date; users will have to decide for themselves when to obtain new versions), they can use the cache='update' argument:

...                           cache='update')
... )["year","month","day"][-3:]  
|=========================================| 3.2M/3.2M (100.00%)         0s
<IERS_B length=3>
 year month  day
int64 int64 int64
----- ----- -----
 2019     8    18
 2019     8    19
 2019     8    20

If they are concerned that the primary source of the data may be overloaded or unavailable, they can use the sources argument to provide a list of sources to attempt downloading from, in order. This need not include the original source. Regardless, the data will be stored in the cache under the original URL requested:

>>> f = download_file("",
...     cache=True,
...     sources=['',
...              ''])  
Downloading from
|========================================|  65M/ 65M (100.00%)        19s

Cache Management

Because the cache is persistent, it is possible for it to become inconveniently large, or become filled with irrelevant data. While it is simply a directory on disk, each file is supposed to represent the contents of a URL, and many URLs do not make acceptable on-disk filenames (for example, containing troublesome characters like “:” and “~”). There is reason to worry that multiple astropy processes accessing the cache simultaneously might lead to cache corruption. The data is therefore stored in a subdirectory named after the hash of the URL, and write access is handled in a way that is resistant to concurrency problems. So access to the cache is more convenient with a few helpers provided by data.

If your cache starts behaving oddly you can use check_download_cache to examine your cache contents and raise an exception if it finds any anomalies. If a single file is undesired or damaged, it can be removed by calling clear_download_cache with an argument that is the URL it was obtained from, the filename of the downloaded file, or the hash of its contents. Should the cache ever become badly corrupted, clear_download_cache with no arguments will simply delete the whole directory, freeing the space and removing any inconsistent data. Of course, if you remove data using either of these tools, any processes currently using that data may be disrupted (or, under Windows, deleting the cache may not be possible until those processes terminate). So use clear_download_cache with care.

To check the total space occupied by the cache, use cache_total_size. The contents of the cache can be listed with get_cached_urls, and the presence of a particular URL in the cache can be tested with is_url_in_cache. More general manipulations can be carried out using cache_contents, which returns a dict mapping URLs to on-disk filenames of their contents.

If you want to transfer the cache to another computer, or preserve its contents for later use, you can use the functions export_download_cache to produce a ZIP file listing some or all of the cache contents, and import_download_cache to load the astropy cache from such a ZIP file.

The Astropy cache has changed format - once in the Python 2 to Python 3 transition, and again before Astropy version 4.0.2 to resolve some concurrency problems that arose on some compute clusters. Each version of the cache is in its own subdirectory, so the old versions do not interfere with the new versions and vice versa, but their contents are not used by this version and are not cleared by clear_download_cache. To remove these old cache directories, you can run:

>>> from shutil import rmtree
>>> from os.path import join
>>> from astropy.config.paths import get_cache_dir
>>> rmtree(join(get_cache_dir(), 'download', 'py2'), ignore_errors=True)  
>>> rmtree(join(get_cache_dir(), 'download', 'py3'), ignore_errors=True)  

Using Astropy With Limited or No Internet Access

You might want to use astropy on a telescope control machine behind a strict firewall. Or you might be running continuous integration (CI) on your astropy server and want to avoid hammering astronomy servers on every pull request for every architecture. Or you might not have access to US government or military web servers. Whichever is the case, you may need to avoid astropy needing data from the Internet. There is no simple and complete solution to this problem at the moment, but there are tools that can help.

Exactly which external data your project depends on will depend on what parts of astropy you use and how. The most general solution is to use a computer that can access the Internet to run a version of your calculation that pulls in all of the data files you will require, including sufficiently up-to-date versions of files like the IERS data that update regularly. Then once the cache on this connected machine is loaded with everything necessary, transport the cache contents to your target machine by whatever means you have available, whether by copying via an intermediate machine, portable disk drive, or some other tool. The cache directory itself is somewhat portable between machines of the same UNIX flavour; this may be sufficient if you can persuade your CI system to cache the directory between runs. For greater portability, though, you can simply use export_download_cache and import_download_cache, which are portable and will allow adding files to an existing cache directory.

If your application needs IERS data specifically, you can download the appropriate IERS table, covering the appropriate time span, by any means you find convenient. You can then load this file into your application and use the resulting table rather than IERS_Auto. In fact, the IERS B table is small enough that a version (not necessarily recent) is bundled with astropy as astropy.utils.iers.IERS_B_FILE. Using a specific non-automatic table also has the advantage of giving you control over exactly which version of the IERS data your application is using. See also Working offline.

If your issue is with certain specific servers, even if they are the ones astropy normally uses, if you can anticipate exactly which files will be needed (or just pick up after astropy fails to obtain them) and make those files available somewhere else, you can request they be downloaded to the cache using download_file with the sources argument set to locations you know do work. You can also set sources to an empty list to ensure that download_file does not attempt to use the Internet at all.

If you have a particular URL that is giving you trouble, you can download it using some other tool (e.g., wget), possibly on another machine, and then use import_file_to_cache.

Astropy Data and Clusters

Astronomical calculations often require the use of a large number of different processes on different machines with a shared home filesystem. This can pose certain complexities. In particular, if the many different processes attempt to download a file simultaneously this can overload a server or trigger security systems. The parallel access to the home directory can also trigger concurrency problems in the Astropy data cache, though we have tried to minimize these. We therefore recommend the following guidelines:

  • Write a simple script that sets astropy.utils.iers.conf.auto_download = True and then accesses all cached resources your code will need, including source name lookups and IERS tables. Run it on the head node from time to time (frequently enough to beat the timeout astropy.utils.iers.conf.auto_max_age, which defaults to 30 days) to ensure all data is up to date.

  • Make an Astropy config file (see Configuration System (astropy.config)) that sets astropy.utils.iers.conf.auto_download = False so that the worker jobs will not suddenly notice an out-of-date table all at once and frantically attempt to download it.

  • Optionally, in this file, set = 0 to prevent any attempt to download any file from the worker nodes; if you do this, you will need to override this setting in your script that does the actual downloading.

Now your worker nodes should not need to obtain anything from the Internet and all should run smoothly.