IERS data access (astropy.utils.iers)


The iers package provides access to the tables provided by the International Earth Rotation and Reference Systems (IERS) service, in particular allowing interpolation of published UT1-UTC values for given times. These are used in astropy.time to provide UT1 values. The polar motions are also used for determining Earth orientation for celestial-to-terrestrial coordinate transformations (in astropy.coordinates).

Getting started

Starting with astropy 1.2, the latest IERS values (which include approximately one year of predictive values) are automatically downloaded from the IERS service when required. This happens when a time or coordinate transformation needs a value which is not already available via the download cache. In most cases there is no need for invoking the iers classes oneself, but it is useful to understand the situations when a download will occur and how this can be controlled.

Basic usage

The IERS data are managed via a instances of the IERS_Auto class. These instances are created internally within the relevant time and coordinate objects during transformations. If the astropy data cache does not have the required IERS data file then astropy will request the file from the IERS service. This will occur the first time such a transform is done for a new setup or on a new machine. Here is an example that shows the typical download progress bar:

>>> from astropy.time import Time
>>> t = Time('2016:001')
>>> t.ut1  
|==================================================================| 3.0M/3.0M (100.00%)         6s
<Time object: scale='ut1' format='yday' value=2016:001:00:00:00.082>

Note that you can forcibly clear the download cache as follows:

>>> from import clear_download_cache
>>> clear_download_cache()

The default IERS data used automatically is updated by the service every 7 days and includes transforms dating back to 1973-01-01.


The IERS_Auto class contains machinery to ensure that the IERS table is kept up to date by auto-downloading the latest version as needed. This means that the IERS table is assured of having the state-of-the-art definitive and predictive values for Earth rotation. As a user it is your responsibility to understand the accuracy of IERS predictions if your science depends on that. If you request UT1-UTC or polar motions for times beyond the range of IERS table data then the nearest available values will be provided.

Configuration parameters

There are three configuration parameters that control the behavior of the automatic IERS downloading:

Enable auto-downloading of the latest IERS data. If set to False then the local IERS-B file will be used by default (even if the full IERS file with predictions was already downloaded and cached). This replicates the behavior prior to astropy 1.2. (default=True)
Maximum age of predictive data before auto-downloading (days). See next section for details. (default=30)
URL for auto-downloading IERS file data
Remote timeout downloading IERS file data (seconds)

Auto refresh behavior

The first time that one attempts a time or coordinate transformation that requires IERS data, the latest version of the IERS table (from 1973 through one year into the future) will be downloaded and stored in the astropy cache.

Transformations will then use the cached data file if possible. However, the IERS_Auto table is automatically updated in place from the network if the following two conditions a met when the table is queried for UT1-UTC or polar motion values:

  • Any of the requested IERS values are predictive, meaning that they have been extrapolated into the future with a model that is fit to measured data. The IERS table contains approximately one year of predictive data from the time it is created.
  • The first predictive values in the table are at least conf.auto_max_age days old relative to the current actual time (i.e. This means that the IERS table is out of date and a newer version can be found on the IERS service.

The IERS Service provides the default online table ( and updates the content once each 7 days. The default value of auto_max_age is 30 days to avoid unnecessary network access, but one can reduce this to as low as 10 days.

Working offline

If you are working without an internet connection and doing transformations that require IERS data, there are a couple of options.

Disable auto downloading

Here you can do:

>>> from astropy.utils import iers
>>> iers.conf.auto_download = False  

In this case any transforms will use the bundled IERS-B data which covers the time range from 1962 to just before the astropy release date. Any transforms outside of this range will not be allowed.

Set the auto-download max age parameter

Only do this if you understand what you are doing, THIS CAN GIVE INACCURATE ANSWERS! Assuming you have previously been connected to the internet and have downloaded and cached the IERS auto values previously, then do the following:

>>> iers.conf.auto_max_age = None  

This disables the check of whether the IERS values are sufficiently recent, and all the transformations (even those outside the time range of available IERS data) will succeed with at most warnings.

Direct table access

In most cases the automatic interface will suffice, but you may need to directly load and manipulate IERS tables. IERS-B values are provided as part of astropy and can be used for transformations. For example:

>>> from astropy.utils import iers
>>> t = Time('2010:001')
>>> iers_b =
>>> iers_b.ut1_utc(t)
<Quantity 0.1140749 s>
>>> t.delta_ut1_utc = iers_b.ut1_utc(t)
>>> t.ut1.iso
'2010-01-01 00:00:00.114'

Instead of local copies of IERS files, one can also download them, using iers.IERS_A_URL and iers.IERS_B_URL:

>>> iers_a =  

For coordinate transformations that require IERS polar motion values, setting the values manually can be done as follows (where one could also select IERS_B):

>>> iers.conf.auto_download = False
>>> iers.IERS.iers_table =  

To see the internal IERS data that gets used in astropy you can do the following:

>>> dat =  
>>> dat  
<IERS_Auto length=16196>
 year month  day    MJD   PolPMFlag_A ... UT1Flag    PM_x     PM_y   PolPMFlag
                     d                ...           arcsec   arcsec
int64 int64 int64 float64     str1    ... unicode1 float64  float64   unicode1
----- ----- ----- ------- ----------- ... -------- -------- -------- ---------
   73     1     2 41684.0           I ...        B    0.143    0.137         B
   73     1     3 41685.0           I ...        B    0.141    0.134         B
   73     1     4 41686.0           I ...        B    0.139    0.131         B
   73     1     5 41687.0           I ...        B    0.137    0.128         B
  ...   ...   ...     ...         ... ...      ...      ...      ...       ...
   17     5     2 57875.0           P ...        P 0.007211  0.44884         P
   17     5     3 57876.0           P ...        P 0.008757 0.450321         P
   17     5     4 57877.0           P ...        P 0.010328 0.451777         P
   17     5     5 57878.0           P ...        P 0.011924 0.453209         P
   17     5     6 57879.0           P ...        P 0.013544 0.454617         P

The explanation for most of the columns can be found in the file named iers.IERS_A_README. The important columns of this table are MJD, UT1_UTC, UT1Flag, PM_x, PM_y, PolPMFlag:

>>> dat['MJD', 'UT1_UTC', 'UT1Flag', 'PM_x', 'PM_y', 'PolPMFlag']  
<IERS_Auto length=16196>
  MJD    UT1_UTC   UT1Flag    PM_x     PM_y   PolPMFlag
   d        s                arcsec   arcsec
float64  float64   unicode1 float64  float64   unicode1
------- ---------- -------- -------- -------- ---------
41684.0     0.8075        B    0.143    0.137         B
41685.0     0.8044        B    0.141    0.134         B
41686.0     0.8012        B    0.139    0.131         B
41687.0     0.7981        B    0.137    0.128         B
    ...        ...      ...      ...      ...       ...
57875.0 -0.6545408        P 0.007211  0.44884         P
57876.0 -0.6559528        P 0.008757 0.450321         P
57877.0 -0.6573705        P 0.010328 0.451777         P
57878.0 -0.6587712        P 0.011924 0.453209         P
57879.0  -0.660187        P 0.013544 0.454617         P