astro py:docs

- Index
- Modules

### Navigation

- next »
- « previous |
- Astropy v1.1.1 »
- Unified file read/write interface

# 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 full list of built-in readers and writers is shown in the table below:

Format | Read | Write | Auto-identify | Deprecated
--- | --- | --- | --- | ---
aastex | Yes | Yes | No | Yes
ascii | Yes | Yes | No |
ascii.aastex | Yes | Yes | No |
ascii.basic | Yes | Yes | No |
ascii.cds | Yes | No | No |
ascii.commented_header | Yes | Yes | No |
ascii.daophot | Yes | No | No |
ascii.ecsv | Yes | Yes | No |
ascii.fixed_width | Yes | Yes | No |
ascii.fixed_width_no_header | Yes | Yes | No |
ascii.fixed_width_two_line | Yes | Yes | No |
ascii.html | Yes | Yes | Yes |
ascii.ipac | Yes | Yes | No |
ascii.latex | Yes | Yes | Yes |
ascii.no_header | Yes | Yes | No |
ascii.rdb | Yes | Yes | Yes |
ascii.sextractor | Yes | No | No |
ascii.tab | Yes | Yes | No |
ascii.csv | Yes | Yes | Yes |
cds | Yes | No | No | Yes
daophot | Yes | No | No | Yes
fits | Yes | Yes | Yes |
hdf5 | Yes | Yes | Yes |
html | Yes | Yes | No | Yes
ipac | Yes | Yes | No | Yes
latex | Yes | Yes | No | Yes
rdb | Yes | Yes | No | Yes
votable | Yes | Yes | Yes |

Deprecated format names like `aastex` will be removed in a future version. Use the full name (e.g. `ascii.aastex`) instead.

### 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

A full list of the supported `format` values and corresponding format types for ASCII tables is given below. The `Suffix` column indicates the filename suffix where the format will be auto-detected, while the `Write` column indicates which support write functionality.

Format | Suffix | Write | 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` |  |  | `Cds`: CDS format table
`ascii.commented_header` |  | Yes | `CommentedHeader`: Column names in a commented line
`ascii.daophot` |  |  | `Daophot`: IRAF DAOphot format table
`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.ipac` |  | Yes | `Ipac`: IPAC format table
`ascii.html` | .html | Yes | `HTML`: HTML table
`ascii.latex` | .tex | Yes | `Latex`: LaTeX table
`ascii.no_header` |  | Yes | `NoHeader`: Basic table with no headers
`ascii.rdb` | .rdb | Yes | `Rdb`: Tab-separated with a type definition header line
`ascii.sextractor` |  |  | `SExtractor`: SExtractor format table
`ascii.tab` |  | Yes | `Tab`: Basic table with tab-separated values
`ascii.csv` | .csv | Yes | `Csv`: Basic table with comma-separated values
`ascii.ecsv` | .ecsv | Yes | `Ecsv`: Basic table with Enhanced CSV (supporting metadata)

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. 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 lower-level astropy.io.fits interface.

#### 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).

### 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 using `serialize_meta=True`:

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