Unified file read/write interface

Astropy provides a unified interface for reading and writing data in different formats. For many common cases this will simplify the process of file I/O and reduce the need to master the separate details of all the I/O packages within Astropy. This functionality is still in active development and the number of supported formats will be increasing. For details on the implementation see I/O Registry (astropy.io.registry).

Getting started with Table I/O

The Table class includes two methods, read() and write(), that make it possible to read from and write to files. A number of formats are automatically supported (see Built-in table readers/writers) and new file formats and extensions can be registered with the Table class (see I/O Registry (astropy.io.registry)).

To use this interface, first import the Table class, then simply call the Table read() method with the name of the file and the file format, for instance 'ascii.daophot':

>>> from astropy.table import Table
>>> t = Table.read('photometry.dat', format='ascii.daophot')

It is possible to load tables directly from the Internet using URLs. For example, download tables from Vizier catalogues in CDS format ('ascii.cds'):

>>> t = Table.read("ftp://cdsarc.u-strasbg.fr/pub/cats/VII/253/snrs.dat",
...         readme="ftp://cdsarc.u-strasbg.fr/pub/cats/VII/253/ReadMe",
...         format="ascii.cds")

For certain file formats, the format can be automatically detected, for example from the filename extension:

>>> t = Table.read('table.tex')

Similarly, for writing, the format can be explicitly specified:

>>> t.write(filename, format='latex')

As for the read() method, the format may be automatically identified in some cases.

The underlying file handler will also automatically detect various compressed data formats and transparently uncompress them as far as supported by the Python installation (see get_readable_fileobj()).

Any additional arguments specified will depend on the format. For examples of this see the section Built-in table readers/writers. This section also provides the full list of choices for the format argument.

Built-in table readers/writers

The Table class has built-in support for various input and output formats including ASCII formats, -FITS, HDF5, and VO Tables.

A full list of the supported formats and corresponding classes is shown in the table below. The Write column indicates those formats that support write functionality, and the Suffix column indicates the filename suffix indicating a particular format. If the value of Suffix is auto, the format is auto-detected from the file itself. Not all formats support auto-detection.

Format	Write	Suffix	Description
ascii	Yes		ASCII table in any supported format (uses guessing)
ascii.aastex	Yes		AASTex: AASTeX deluxetable used for AAS journals
ascii.basic	Yes		Basic: Basic table with custom delimiters
ascii.cds	No		Cds: CDS format table
ascii.commented_header	Yes		CommentedHeader: Column names in a commented line
ascii.csv	Yes	.csv	Csv: Basic table with comma-separated values
ascii.daophot	No		Daophot: IRAF DAOphot format table
ascii.ecsv	Yes	.ecsv	Ecsv: Basic table with Enhanced CSV (supporting metadata)
ascii.fixed_width	Yes		FixedWidth: Fixed width
ascii.fixed_width_no_header	Yes		FixedWidthNoHeader: Fixed width with no header
ascii.fixed_width_two_line	Yes		FixedWidthTwoLine: Fixed width with second header line
ascii.html	Yes	.html	HTML: HTML table
ascii.ipac	Yes		Ipac: IPAC format table
ascii.latex	Yes	.tex	Latex: LaTeX table
ascii.no_header	Yes		NoHeader: Basic table with no headers
ascii.rdb	Yes	.rdb	Rdb: Tab-separated with a type definition header line
ascii.rst	Yes	.rst	RST: reStructuredText simple format table
ascii.sextractor	No		SExtractor: SExtractor format table
ascii.tab	Yes		Tab: Basic table with tab-separated values
fits	Yes	auto	fits: Flexible Image Transport System file
hdf5	Yes	auto	HDF5: Hierarchical Data Format binary file
votable	Yes	auto	votable: Table format used by Virtual Observatory (VO) initiative

ASCII formats

The read() and write() methods can be used to read and write formats supported by astropy.io.ascii.

Use format='ascii' in order to interface to the generic read() and write() functions from astropy.io.ascii. When reading a table this means that all supported ASCII table formats will be tried in order to successfully parse the input. For example:

>>> t = Table.read('astropy/io/ascii/tests/t/latex1.tex', format='ascii')
>>> print(t)
cola colb colc
---- ---- ----
   a    1    2
   b    3    4

When writing a table with format='ascii' the output is a basic character-delimited file with a single header line containing the column names.

All additional arguments are passed to the astropy.io.ascii read() and write() functions. Further details are available in the sections on Parameters for read() and Parameters for write(). For example, to change column delimiter and the output format for the colc column use:

>>> t.write(sys.stdout, format='ascii', delimiter='|', formats={'colc': '%0.2f'})
cola|colb|colc
a|1|2.00
b|3|4.00

Note

When specifying a specific ASCII table format using the unified interface, the format name is prefixed with ascii in order to identify the format as ASCII-based. Compare the table above to the astropy.io.ascii list of supported formats where the prefix is not needed. Therefore the following are equivalent:

>>> dat = ascii.read('file.dat', format='daophot')
>>> dat = Table.read('file.dat', format='ascii.daophot')

For compatibility with astropy version 0.2 and earlier, the following format values are also allowed in Table.read(): daophot, ipac, html, latex, and rdb.

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 with:

>>> from astropy.table import Table
>>> t = Table.read('data.fits')

If more than one table is present in the file, you can select the HDU as follows:

>>> t = Table.read('data.fits', hdu=3)

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

>>> t = Table.read('data.fits')
WARNING: hdu= was not specified but multiple tables are present, reading in first available table (hdu=1) [astropy.io.fits.connect]

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)

At this time there is no support for appending an HDU to an existing file or writing multi-HDU files using the Table interface. Instead one can use the convenience function table_to_hdu() to create a single binary table HDU and insert or append that to an existing HDUList.

Keywords

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

>>> for key, value in t.meta.items():
...     print('{0} = {1}'.format(key, value))

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 values.

Conversely, the following shows examples of setting user keyword values for a table t:

>>> 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.

At this time, the meta attribute of the Table class is simply an ordered dictionary and does not fully represent the structure of a FITS header (for example, keyword comments are dropped).

Astropy native objects (mixin columns)

It is possible to store not only standard Column objects to a FITS table HDU, but also the following Astropy native objects (Mixin columns) within a Table or QTable:

• astropy.time.Time
• astropy.units.Quantity

Other mixin columns such as SkyCoord or EarthLocation are not currently supported due to reasons including extensive metadata and no precise mapping to the FITS standard.

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 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.

>>> 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

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 behaviour for reading a FITS table into an 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 one 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. For 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',
...               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>
>>> tm['a'] == t['a']
array([ True,  True], dtype=bool)

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 Table 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.

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 (TFORM n = ‘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. Note that the arbitrary metadata allowed in Table objects within the meta dict is not written and will be lost.

The FITS standard requires an additional translation layer back into the desired format. In the example stated above, the Time column t['a'] undergoes the translation Astropy Time --> FITS --> Astropy Time which corresponds to the format conversion mjd --> (jd1, jd2) --> jd. Thus, the final conversion from (jd1, jd2) requires 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 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 using a special info.serialize_method attribute, 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['a'].info.serialize_method['fits'] = 'formatted_value'
>>> t.write('my_table.fits', overwrite=True)
>>> tm = Table.read('my_table.fits')
>>> tm['a']
<Column name='a' dtype='float64' length=2>
100.0
200.0
>>> tm['a'] == t['a'].value
array([ True,  True], dtype=bool)

By default, serialize_method['fits'] in a Time column info is equal to 'jd1_jd2', that is, Time column 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.

• LOCATION

  In Astropy Time, location can be an array which is broadcastable to the Time values. In the FITS standard, location is a scalar expressed via keywords.

Hence the format attribute and a vector location attribute are not stored. After reading from FITS the user must set the format as desired.

Reading FITS files with time coordinate columns which are not written by Astropy may fail. Astropy supports only a small subset of the rather complicated standard.

HDF5

Reading/writing from/to HDF5 files is supported with format='hdf5' (this requires h5py to be installed). However, the .hdf5 file extension is automatically recognized when writing files, and HDF5 files are automatically identified (even with a different extension) when reading in (using the first few bytes of the file to identify the format), so in most cases you will not need to explicitly specify format='hdf5'.

Since HDF5 files can contain multiple tables, the full path to the table should be specified via the path= argument when reading and writing. For example, to read a table called data from an HDF5 file named observations.hdf5, you can do:

>>> t = Table.read('observations.hdf5', path='data')

To read a table nested in a group in the HDF5 file, you can do:

>>> t = Table.read('observations.hdf5', path='group/data')

To write a table to a new file, the path should also be specified:

>>> t.write('new_file.hdf5', path='updated_data')

It is also possible to write a table to an existing file using append=True:

>>> t.write('observations.hdf5', path='updated_data', append=True)

As with other formats, the overwrite=True argument is supported for overwriting existing files. To overwrite only a single table within an HDF5 file that has multiple datasets, use both the overwrite=True and append=True arguments.

If the metadata of the table cannot be written directly to the HDF5 file (e.g. dictionaries), or if you want to preserve the units and description of tables and columns, use serialize_meta=True:

>>> t.write('observations.hdf5', path='updated_data', serialize_meta=True)

The way serialized meta are saved in the HDF5 dataset have changed in Astropy 3.0. Files in the old format are still read correctly. If for some reason the user wants to write in the old format, they will specify the (deprecated) compatibility_mode keyword

>>> t.write('observations.hdf5', path='updated_data', serialize_meta=True, compatibility_mode=True)

Finally, when writing to HDF5 files, the compression= argument can be used to ensure that the data is compressed on disk:

>>> t.write('new_file.hdf5', path='updated_data', compression=True)

VO Tables

Reading/writing from/to VO table files is supported with format='votable'. In most cases, existing VO tables 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.

If a VO table file contains only a single table, then it can be read in with:

>>> t = Table.read('aj285677t3_votable.xml')

If more than one table is present in the file, an error will be raised, unless the table ID is specified via the table_id= argument:

>>> t = Table.read('catalog.xml')
Traceback (most recent call last):
...
ValueError: Multiple tables found: table id should be set via the table_id= argument. The available tables are twomass, spitzer

>>> t = Table.read('catalog.xml', table_id='twomass')

To write to a new file, the ID of the table should also be specified (unless t.meta['ID'] is defined):

>>> t.write('new_catalog.xml', table_id='updated_table', format='votable')

When writing, the compression=True argument can be used to force compression of the data on disk, and the overwrite=True argument can be used to overwrite an existing file.