FITS#

Reading and writing tables in FITS format is supported with format='fits'. In most cases, existing FITS files should be automatically identified as such based on the header of the file, but if not, or if writing to disk, then the format should be explicitly specified.

Reading#

If a FITS table file contains only a single table, then it can be read in as shown below. In this example we use a file that is installed with astropy:

>>> from astropy.table import Table
>>> from astropy.utils.data import get_pkg_data_filename
>>> chandra_events = get_pkg_data_filename('data/chandra_time.fits',
...                                        package='astropy.io.fits.tests')
>>> t = Table.read(chandra_events)

A benefit of using the unified interface to read the table is that it will reconstruct any Mixin Columns that were written to that HDU.

If more than one table is present in the file, you can select the HDU by index or by name:

>>> t = Table.read(chandra_events, hdu="EVENTS")

In this case if the hdu argument is omitted, then the first table found will be read in and a warning will be emitted.

You can also read a table from the HDUs of an in-memory FITS file.

>>> from astropy.io import fits
>>> with fits.open(chandra_events) as hdul:
...     t = Table.read(hdul["EVENTS"])

If a column contains unit information, it will have an associated astropy.units object:

>>> t["energy"].unit
Unit("eV")

It is also possible to get directly a table with columns as Quantity objects by using the QTable class:

>>> from astropy.table import QTable
>>> t2 = QTable.read(chandra_events, hdu=1)
>>> t2['energy']
<Quantity [7782.7305, 5926.725 ] eV>

Writing#

To write a table t to a new file:

>>> t.write('new_table.fits')

If the file already exists and you want to overwrite it, then set the overwrite keyword:

>>> t.write('existing_table.fits', overwrite=True)

If you want to append a table to an existing file, set the append keyword:

>>> t.write('existing_table.fits', append=True)

Alternatively, you can use the convenience function table_to_hdu() to create a single binary table HDU and insert or append that to an existing HDUList.

There is support for writing a table which contains Mixin Columns such as Time or SkyCoord. This uses FITS COMMENT cards to capture additional information needed order to fully reconstruct the mixin columns when reading back from FITS. The information is a Python dict structure which is serialized using YAML.

Keywords#

The FITS keywords associated with an HDU table are represented in the meta ordered dictionary attribute of a Table. After reading a table you can view the available keywords in a readable format using:

>>> for key, value in t.meta.items():
...     print(f'{key} = {value}')
EXTNAME = EVENTS
HDUNAME = EVENTS
TLMIN2 = 0
...

This does not include the “internal” FITS keywords that are required to specify the FITS table properties (e.g., NAXIS, TTYPE1). HISTORY and COMMENT keywords are treated specially and are returned as a list of

>>> t.meta['MY_KEYWD'] = 'my value'
>>> t.meta['COMMENT'] = ['First comment', 'Second comment', 'etc']
>>> t.write('my_table.fits', overwrite=True)

The keyword names (e.g., MY_KEYWD) will be automatically capitalized prior to writing.

TDISPn Keyword#

TDISPn FITS keywords will map to and from the Column format attribute if the display format is convertible to and from a Python display format. Below are the rules used for both conversion directions.

TDISPn to Python format string#

TDISPn format characters are defined in the table below.

Format

Description

Aw

Character

Lw

Logical

Iw.m

Integer

Bw.m

Binary, integers only

Ow.m

Octal, integers only

Zw.m

Hexadecimal, integers only

Fw.d

Floating-point, fixed decimal notation

Ew.dEe

Floating-point, exponential notation

ENw.d

Engineering; E format with exponent multiple of three

ESw.d

Scientific; same as EN but non-zero leading digit if not zero

Gw.dEe

General; appears as F if significance not lost, also E

Dw.dEe

Floating-point, exponential notation, double precision

Where w is the width in characters of displayed values, m is the minimum number of digits displayed, d is the number of digits to the right of decimal, and e is the number of digits in the exponent. The .m and Ee fields are optional.

The A (character), L (logical), F (floating point), and G (general) display formats can be directly translated to Python format strings. The other formats need to be modified to match Python display formats.

For the integer formats (I, B, O, and Z), the width (w) value is used to add space padding to the left of the column value. The minimum number (m) value is not used. For the E, G, D, EN, and ES formats (floating point exponential) the width (w) and precision (d) are both used, but the exponential (e) is not used.

Python format string to TDISPn#

The conversion from Python format strings back to TDISPn is slightly more complicated.

Python strings map to the TDISP format A if the Python formatting string does not contain right space padding. It will accept left space padding. The same applies to the logical format L.

The integer formats (decimal integer, binary, octal, hexadecimal) map to the I, B, O, and Z TDISP formats respectively. Integer formats do not accept a zero padded format string or a format string with no left padding defined (a width is required in the TDISP format standard for the Integer formats).

For all float and exponential values, zero padding is not accepted. There must be at least a width or precision defined. If only a width is defined, there is no precision set for the TDISPn format. If only a precision is defined, the width is set to the precision plus an extra padding value depending on format type, and both are set in the TDISPn format. Otherwise, if both a width and precision are present they are both set in the TDISPn format. A Python f or F map to TDISP F format. The Python g or G map to TDISP G format. The Python e and E map to TDISP E format.

Masked Columns#

Tables that contain MaskedColumn columns can be written to FITS. By default this will replace the masked data elements with certain sentinel values according to the FITS standard:

  • NaN for float columns.

  • Value of TNULLn for integer columns, as defined by the column fill_value attribute.

  • Null string for string columns (not currently implemented).

When the file is read back those elements are marked as masked in the returned table, but see issue #4708 for problems in all three cases. It is possible to deactivate the masking with mask_invalid=False.

The FITS standard has a few limitations:

  • Not all data types are supported (e.g., logical / boolean).

  • Integer columns require picking one value as the NULL indicator. If all possible values are represented in valid data (e.g., an unsigned int columns with all 256 possible values in valid data), then there is no way to represent missing data.

  • The masked data values are permanently lost, precluding the possibility of later unmasking the values.

astropy provides a work-around for this limitation that users can choose to use. The key part is to use the serialize_method='data_mask' keyword argument when writing the table. This tells the FITS writer to split each masked column into two separate columns, one for the data and one for the mask. When it gets read back that process is reversed and the two columns are merged back into one masked column.

>>> from astropy.table.table_helpers import simple_table
>>> t = simple_table(masked=True)
>>> t['d'] = [False, False, True]
>>> t['d'].mask = [True, False, False]
>>> t
<Table masked=True length=3>
  a      b     c     d
int64 float64 str1  bool
----- ------- ---- -----
   --     1.0    c    --
    2     2.0   -- False
    3      --    e  True
>>> t.write('data.fits', serialize_method='data_mask', overwrite=True)
>>> Table.read('data.fits')
<Table length=3>
  a      b      c      d
int64 float64 bytes1  bool
----- ------- ------ -----
   --     1.0      c    --
    2     2.0     -- False
    3      --      e  True

Warning

This option goes outside of the established FITS standard for representing missing data, so users should be careful about choosing this option, especially if other (non-astropy) users will be reading the file(s). Behind the scenes, astropy is converting the masked columns into two distinct data and mask columns, then writing metadata into COMMENT cards to allow reconstruction of the original data.

astropy Native Objects (Mixin Columns)#

It is possible to store not only standard Column objects to a FITS table HDU, but also any astropy native objects (Mixin Columns) within a Table or QTable. This includes Time, Quantity, SkyCoord, and many others.

In general, a mixin column may contain multiple data components as well as object attributes beyond the standard Column attributes like format or description. Abiding by the rules set by the FITS standard requires the mapping of these data components and object attributes to the appropriate FITS table columns and keywords. Thus, a well defined protocol has been developed to allow the storage of these mixin columns in FITS while allowing the object to “round-trip” through the file with no loss of data or attributes.

Quantity#

A Quantity mixin column in a QTable is represented in a FITS table using the TUNITn FITS column keyword to incorporate the unit attribute of Quantity. For example:

>>> from astropy.table import QTable
>>> import astropy.units as u
>>> t = QTable([[1, 2] * u.angstrom])
>>> t.write('my_table.fits', overwrite=True)
>>> qt = QTable.read('my_table.fits')
>>> qt
<QTable length=2>
  col0
Angstrom
float64
--------
     1.0
     2.0

Time#

astropy provides the following features for reading and writing Time:

  • Writing and reading Time Table columns to and from FITS tables.

  • Reading time coordinate columns in FITS tables (compliant with the time standard) as Time Table columns.

Writing and reading astropy Time columns#

By default, a Time mixin column within a Table or QTable will be written to FITS in full precision. This will be done using the FITS time standard by setting the necessary FITS header keywords.

The default behavior for reading a FITS table into a Table has historically been to convert all FITS columns to Column objects, which have closely matching properties. For some columns, however, closer native astropy representations are possible, and you can indicate these should be used by passing astropy_native=True (for backwards compatibility, this is not done by default). This will convert columns conforming to the FITS time standard to Time instances, avoiding any loss of precision and preserving information about the time system if set in the fits header.

Example#

To read a FITS table into Table:

>>> from astropy.time import Time
>>> from astropy.table import Table
>>> from astropy.coordinates import EarthLocation
>>> t = Table()
>>> t['a'] = Time([100.0, 200.0], scale='tt', format='mjd',
...               location=EarthLocation(-2446354, 4237210, 4077985, unit='m'))
>>> t.write('my_table.fits', overwrite=True)
>>> tm = Table.read('my_table.fits', astropy_native=True)
>>> tm['a']
<Time object: scale='tt' format='jd' value=[2400100.5 2400200.5]>
>>> tm['a'].location
<EarthLocation (-2446354., 4237210., 4077985.) m>
>>> all(tm['a'] == t['a'])
True

The same will work with QTable.

In addition to binary table columns, various global time informational FITS keywords are treated specially with astropy_native=True. In particular, the keywords DATE, DATE-* (ISO 8601 datetime strings), and the MJD-* (MJD date values) will be returned as Time objects in the Table meta. For more details regarding the FITS time paper and the implementation, refer to FITS Tables with Time Columns.

Since not all FITS readers are able to use the FITS time standard, it is also possible to store Time instances using the _time_format. For this case, none of the special header keywords associated with the FITS time standard will be set. When reading this back into astropy, the column will be an ordinary Column instead of a Time object. See the Details section below for an example.

Reading FITS standard compliant time coordinate columns in binary tables#

Reading FITS files which are compliant with the FITS time standard is supported by astropy by following the multifarious rules and conventions set by the standard. The standard was devised in order to describe time coordinates in an unambiguous and comprehensive manner and also to provide flexibility for its multiple use cases. Thus, while reading time coordinate columns in FITS- compliant files, multiple aspects of the standard are taken into consideration.

Time coordinate columns strictly compliant with the two-vector JD subset of the standard (described in the Details section below) can be read as native Time objects. The other subsets of the standard are also supported by astropy; a thorough examination of the FITS standard time- related keywords is done and the time data is interpreted accordingly.

The standard describes the various components in the specification of time:

  • Time coordinate frame

  • Time unit

  • Corrections, errors, etc.

  • Durations

The keywords used to specify times define these components. Using these keywords, time coordinate columns are identified and read as Time objects. Refer to FITS Tables with Time Columns for the specification of these keywords and their description.

There are two aspects of the standard that require special attention due to the subtleties involved while handling them. These are:

  • Column named TIME with time unit

A common convention found in existing FITS files is that a FITS binary table column with TTYPEn = ‘TIME’ represents a time coordinate column. Many astronomical data files, including official data products from major observatories, follow this convention that predates the FITS standard. The FITS time standard states that such a column will be controlled by the global time reference frame keywords, and this will still be compliant with the present standard.

Using this convention which has been incorporated into the standard, astropy can read time coordinate columns from all such FITS tables as native Time objects. Common examples of FITS files following this convention are Chandra, XMM, and HST files.

Examples#

The following is an example of a Header extract of a Chandra event list:

COMMENT      ---------- Globally valid key words ----------------
DATE    = '2016-01-27T12:34:24' / Date and time of file creation
TIMESYS = 'TT      '           / Time system
MJDREF  =  5.0814000000000E+04 / [d] MJD zero point for times
TIMEUNIT= 's       '           / Time unit
TIMEREF = 'LOCAL   '           / Time reference (barycenter/local)

COMMENT      ---------- Time Column -----------------------
TTYPE1  = 'time    '           / S/C TT corresponding to mid-exposure
TFORM1  = '1D      '           / format of field
TUNIT1  = 's       '

When reading such a FITS table with astropy_native=True, astropy checks whether the name of a column is “TIME”/ “time” (TTYPEn = ‘TIME’) and whether its unit is a FITS recognized time unit (TUNITn is a time unit).

For example, reading a Chandra event list which has the above mentioned header and the time coordinate column time as [1, 2] will give:

>>> from astropy.table import Table
>>> from astropy.time import Time, TimeDelta
>>> from astropy.utils.data import get_pkg_data_filename
>>> chandra_events = get_pkg_data_filename('data/chandra_time.fits',
...                                        package='astropy.io.fits.tests')
>>> native = Table.read(chandra_events, astropy_native=True)  
>>> native['time']  
<Time object: scale='tt' format='mjd' value=[57413.76033393 57413.76033393]>
>>> non_native = Table.read(chandra_events)
>>> # MJDREF  =  5.0814000000000E+04, TIMESYS = 'TT'
>>> ref_time = Time(non_native.meta['MJDREF'], format='mjd',
...                 scale=non_native.meta['TIMESYS'].lower())
>>> # TTYPE1  = 'time', TUNIT1 = 's'
>>> delta_time = TimeDelta(non_native['time'])
>>> all(ref_time + delta_time == native['time'])
True

By default, FITS table columns will be read as standard Column objects without taking the FITS time standard into consideration.

  • String time column in ISO 8601 Datetime format

FITS uses a subset of ISO 8601 (which in itself does not imply a particular timescale) for several time-related keywords, such as DATE-xxx. Following the FITS standard, its values must be written as a character string in the following datetime format:

[+/-C]CCYY-MM-DD[Thh:mm:ss[.s...]]

A time coordinate column can be constructed using this representation of time. The following is an example of an ISO 8601 datetime format time column:

TIME
----
1999-01-01T00:00:00
1999-01-01T00:00:40
1999-01-01T00:01:06
.
.
.
1999-01-20T01:10:00

The criteria for identifying a time coordinate column in ISO 8601 format is as follows:

A time column is identified using the time coordinate frame keywords as described in FITS Tables with Time Columns. Once it has been identified, its datatype is checked in order to determine its representation format. Since ISO 8601 datetime format is the only string representation of time, a time coordinate column having string datatype will be automatically read as a Time object with format='fits' (‘fits’ represents the FITS ISO 8601 format).

As this format does not imply a particular timescale, it is determined using the timescale keywords in the header (TCTYP or TIMESYS) or their defaults. The other time coordinate information is also determined in the same way, using the time coordinate frame keywords. All ISO 8601 times are relative to a globally accepted zero point (year 0 corresponds to 1 BCE) and are thus not relative to the reference time keywords (MJDREF, JDREF, or DATEREF). Hence, these keywords will be ignored while dealing with ISO 8601 time columns.

Note

Reading FITS files with time coordinate columns may fail. astropy supports a large subset of these files, but there are still some FITS files which are not compliant with any aspect of the standard. If you have such a file, please do not hesitate to let us know (by opening an issue in the issue tracker).

Also, reading a column having TTYPEn = ‘TIME’ as Time will fail if TUNITn for the column is not a FITS-recognized time unit.

Details#

Time as a dimension in astronomical data presents challenges in its representation in FITS files. The standard has therefore been extended to describe rigorously the time coordinate in the World Coordinate System framework. Refer to FITS WCS paper IV for details.

Allowing Time columns to be written as time coordinate columns in FITS tables thus involves storing time values in a way that ensures retention of precision and mapping the associated metadata to the relevant FITS keywords.

In accordance with the standard, which states that in binary tables one may use pairs of doubles, the astropy Time column is written in such a table as a vector of two doubles (TFORMn = ‘2D’) (jd1, jd2) where JD = jd1 + jd2. This reproduces the time values to double-double precision and is the “lossless” version, exploiting the higher precision provided in binary tables. Note that jd1 is always a half-integer or integer, while abs(jd2) < 1. “Round-tripping” of astropy-written FITS binary tables containing time coordinate columns has been partially achieved by mapping selected metadata, scale and singular location of Time, to corresponding keywords.

Examples#

Consider the following Time column:

>>> t = Table()
>>> t['a'] = Time([100.0, 200.0], scale='tt', format='mjd')

The FITS standard requires an additional translation layer back into the desired format. The Time column t['a'] will undergo the translation Astropy Time --> FITS --> Astropy Time which corresponds to the format conversion mjd --> (jd1, jd2) --> jd. Thus, the final conversion from (jd1, jd2) will require a software implementation which is fully compliant with the FITS time standard.

Taking this into consideration, the functionality to read/write Time from/to FITS can be explicitly turned off, by opting to store the time representation values in the format specified by the format attribute of the Time column, instead of the (jd1, jd2) format, with no extra metadata in the header. This is the “lossy” version, but can help with portability. For the above example, the FITS column corresponding to t['a'] will then store [100.0 200.0] instead of [[ 2400100.5, 0. ], [ 2400200.5, 0. ]]. This is done by setting the Table Serialization Methods for Time columns when writing, as in the following example:

>>> from astropy.time import Time
>>> from astropy.table import Table
>>> from astropy.coordinates import EarthLocation
>>> t = Table()
>>> t['a'] = Time([100.0, 200.0], scale='tt', format='mjd')
>>> t.write('my_table.fits', overwrite=True,
...         serialize_method={Time: 'formatted_value'})
>>> tm = Table.read('my_table.fits')
>>> tm['a']
<Column name='a' dtype='float64' length=2>
100.0
200.0
>>> all(tm['a'] == t['a'].value)
True

By default, serialize_method for Time columns is equal to 'jd1_jd2', that is, Time columns will be written in full precision.

Note

The astropy Time object does not precisely map to the FITS time standard.

  • FORMAT

    The FITS format considers only three formats: ISO 8601, JD, and MJD. astropy Time allows for many other formats like unix or cxcsec for representing the values.

    Hence, the format attribute of Time is not stored. After reading from FITS the user must set the format as desired.

  • LOCATION

    In the FITS standard, the reference position for a time coordinate is a scalar expressed via keywords. However, vectorized reference position or location can be supported by the Green Bank Keyword Convention which is a Registered FITS Convention. In astropy Time, location can be an array which is broadcastable to the Time values.

    Hence, vectorized location attribute of Time is stored and read following this convention.