.. include:: references.txt

.. _astropy.io.ascii_read:

Reading tables
**************

The majority of commonly encountered ASCII tables can be easily read with the |read|
function::

  >>> from astropy.io import ascii
  >>> data = ascii.read(table)  # doctest: +SKIP

where ``table`` is the name of a file, a string representation of a table, or a
list of table lines.  The return value (``data`` in this case) is a :ref:`Table
<astropy-table>` object.

By default |read| will try to `guess the table format <#guess-table-format>`_
by trying all the supported formats.  If this does not work (for unusually
formatted tables) then one needs give `astropy.io.ascii` additional hints about the
format, for example::

   >>> data = astropy.io.ascii.read('t/nls1_stackinfo.dbout', data_start=2, delimiter='|')  # doctest: +SKIP
   >>> data = astropy.io.ascii.read('t/simple.txt', quotechar="'")  # doctest: +SKIP
   >>> data = astropy.io.ascii.read('t/simple4.txt', format='no_header', delimiter='|')  # doctest: +SKIP
   >>> data = astropy.io.ascii.read('t/tab_and_space.txt', delimiter=r'\s')  # doctest: +SKIP

The |read| function accepts a number of parameters that specify the detailed
table format.  Different formats can define different defaults, so the
descriptions below sometimes mention "typical" default values.  This refers to
the :class:`~astropy.io.ascii.Basic` format reader and other similar character-separated formats.

.. _io_ascii_read_parameters:

Parameters for ``read()``
=========================

**table** : input table
  There are four ways to specify the table to be read:

  - Name of a file (string)
  - Single string containing all table lines separated by newlines
  - File-like object with a callable read() method
  - List of strings where each list element is a table line

  The first two options are distinguished by the presence of a newline in the string.
  This assumes that valid file names will not normally contain a newline.

**format** : file format (default='basic')
  This specifies the top-level format of the ASCII table, for example
  if it is a basic character delimited table, fixed format table, or
  a CDS-compatible table, etc.  The value of this parameter must
  be one of the :ref:`supported_formats`.

**guess** : try to guess table format (default=True)
  If set to True then |read| will try to guess the table format by cycling
  through a number of possible table format permutations and attempting to read
  the table in each case.  See the `Guess table format`_ section for further details.

**delimiter** : column delimiter string
  A one-character string used to separate fields which typically defaults to
  the space character.  Other common values might be "\\s" (whitespace), "," or
  "|" or "\\t" (tab).  A value of "\\s" allows any combination of the tab and
  space characters to delimit columns.

**comment** : regular expression defining a comment line in table
  If the ``comment`` regular expression matches the beginning of a table line then that line
  will be discarded from header or data processing.  For the ``basic`` format this
  defaults to "\\s*#" (any whitespace followed by #).

**quotechar** : one-character string to quote fields containing special characters
  This specifies the quote character and will typically be either the single or double
  quote character.  This is can be useful for reading text fields with spaces in a space-delimited
  table.  The default is typically the double quote.

**header_start** : line index for the header line
  This includes only significant non-comment lines and counting starts at 0. If
  set to None this indicates that there is no header line and the column names
  will be auto-generated.  See `Specifying header and data location`_ for more
  details.

**data_start** : line index for the start of data counting
  This includes only significant non-comment lines and counting starts at 0.  See
  `Specifying header and data location`_ for more details.

**data_end** : line index for the end of data
  This includes only significant non-comment line and can be negative to count
  from end.  See `Specifying header and data location`_ for more details.

**encoding**: encoding to read the file (default=`None`)
  When `None` use `locale.getpreferredencoding` as an encoding.  This matches
  the default behavior of the built-in `open` when no ``mode`` argument is
  provided.

**converters** : dict of data type converters
  See the `Converters`_ section for more information.

**names** : list of names corresponding to each data column
  Define the complete list of names for each data column.  This will override
  names found in the header (if it exists).  If not supplied then
  use names from the header or auto-generated names if there is no header.

**include_names** : list of names to include in output
  From the list of column names found from the header or the ``names``
  parameter, select for output only columns within this list.  If not supplied
  then include all names.

**exclude_names** : list of names to exclude from output
  Exclude these names from the list of output columns.  This is applied *after*
  the ``include_names`` filtering.  If not specified then no columns are excluded.

**fill_values** : list of fill value specifiers
  Specify input table entries which should be masked in the output table
  because they are bad or missing.  See the `Bad or missing values`_ section
  for more information and examples.  The default is that any blank table
  values are treated as missing.
**fill_include_names** : list of column names, which are affected by ``fill_values``.
  If not supplied, then ``fill_values`` can affect all columns.

**fill_exclude_names** : list of column names, which are not affected by ``fill_values``.
  If not supplied, then ``fill_values`` can affect all columns.

**Outputter** : Outputter class
  This converts the raw data tables value into the
  output object that gets returned by |read|.  The default is
  :class:`~astropy.io.ascii.TableOutputter`, which returns a
  :class:`~astropy.table.Table` object (see :ref:`Data Tables <astropy-table>`).

**Inputter** : Inputter class
  This is generally not specified.

**data_Splitter** : Splitter class to split data columns

**header_Splitter** : Splitter class to split header columns

**fast_reader** : whether to use the C engine
  This can be ``True`` or ``False``, and also be a dict with options.
  (see :ref:`fast_ascii_io`)

**Reader** : Reader class (*deprecated* in favor of ``format``)
  This specifies the top-level format of the ASCII table, for example
  if it is a basic character delimited table, fixed format table, or
  a CDS-compatible table, etc.  The value of this parameter must
  be a Reader class.  For basic usage this means one of the
  built-in :ref:`extension_reader_classes`.

Specifying header and data location
===================================

The three parameters ``header_start``, ``data_start`` and ``data_end`` make it
possible to read a table file that has extraneous non-table data included.
This is a case where you need to help out ``io.ascii`` and tell it where to
find the header and data.

When processing of a file into a header and data components any blank lines
(which might have whitespace characters) and commented lines (starting with the
comment character, typically ``#``) are stripped out *before* the header and data
parsing code sees the table content. For example imagine you have the file
below. The column on the left is not part of the file but instead shows how
``io.ascii`` is viewing each line and the line count index.  ::

  Index    Table content
  ------ ----------------------------------------------------------------
     -  | # This is the start of my data file
     -  |
     0  | Automatically generated by my_script.py at 2012-01-01T12:13:14
     1  | Run parameters: None
     2  | Column header line:
     -  |
     3  | x y z
     -  |
     4  | Data values section:
     -  |
     5  | 1 2 3
     6  | 4 5 6
     -  |
     7  | Run completed at 2012:01-01T12:14:01

In this case you would have ``header_start=3``, ``data_start=5``, and
``data_end=7``.  The convention for ``data_end`` follows the normal Python
slicing convention where to select data rows 5 and 6 you would do
``rows[5:7]``.  For ``data_end`` you can also supply a negative index to
count backward from the end, so ``data_end=-1`` (like ``rows[5:-1]``) would
work in this case.

.. note::

   Prior to astropy v1.1 there was a bug in which a blank line that had one or
   more whitespace characters was mistakenly counted for ``header_start`` but
   was (correctly) not counted for ``data_start`` and ``data_end``.  If you
   have code that was depending on the incorrect pre-1.1 behavior then it needs
   to be modified.

.. _replace_bad_or_missing_values:

Bad or missing values
=====================

ASCII data tables can contain bad or missing values.  A common case is when a table
contains blank entries with no available data, for example::

  >>> weather_data = """
  ...   day,precip,type
  ...   Mon,1.5,rain
  ...   Tues,,
  ...   Wed,1.1,snow
  ...   """

By default |read| will interpret blank entries as being bad/missing and output a masked
Table with those entries masked out by setting the corresponding mask value set to
``True``::

  >>> dat = ascii.read(weather_data)
  >>> print(dat)
  day  precip type
  ---- ------ ----
   Mon    1.5 rain
  Tues     --   --
   Wed    1.1 snow

If you want to replace the masked (missing) values with particular values, set the masked
column ``fill_value`` attribute and then get the "filled" version of the table.  This
looks like the following::

  >>> dat['precip'].fill_value = -999
  >>> dat['type'].fill_value = 'N/A'
  >>> print(dat.filled())
  day  precip type
  ---- ------ ----
   Mon    1.5 rain
  Tues -999.0  N/A
   Wed    1.1 snow

ASCII tables may also have other indicators of bad or missing data.  For
example a table may contain string values that are not a valid representation
of a number, e.g. ``"..."``, or a table may have special values like ``-999``
that are chosen to indicate missing data.  The |read| function has a flexible
system to accommodate these cases by marking specified character sequences in
the input data as "missing data" during the conversion process.  Whenever
missing data is found then the output will be a masked table.

This is done with the ``fill_values`` keyword argument, which can be set to a
single missing-value specification ``<missing_spec>`` or a list of ``<missing_spec>`` tuples::

  fill_values = <missing_spec> | [<missing_spec1>, <missing_spec2>, ...]
  <missing_spec> = (<match_string>, '0', <optional col name 1>, <optional col name 2>, ...)

When reading a table the second element of a ``<missing_spec>`` should always
be the string ``'0'``,
otherwise you may get unexpected behavior [#f1]_.  By default the
``<missing_spec>`` is applied to all columns unless column name strings are
supplied.  An alternate way to limit the columns is via the
``fill_include_names`` and ``fill_exclude_names`` keyword arguments in |read|.

In the example below we read back the weather table after filling the missing
values in with typical placeholders::

  >>> table = ['day   precip  type',
  ...          ' Mon     1.5  rain',
  ...          'Tues  -999.0   N/A',
  ...          ' Wed     1.1  snow']
  >>> t = ascii.read(table, fill_values=[('-999.0', '0', 'precip'), ('N/A', '0', 'type')])
  >>> print(t)
  day  precip type
  ---- ------ ----
   Mon    1.5 rain
  Tues     --   --
   Wed    1.1 snow

.. note::

   The default in |read| is ``fill_values=('','0')``.  This marks blank entries as being
   missing for any data type (int, float, or string).  If ``fill_values`` is explicitly
   set in the call to |read| then the default behavior of marking blank entries as missing
   no longer applies.  For instance setting ``fill_values=None`` will disable this
   auto-masking without setting any other fill values.  This can be useful for a string
   column where one of values happens to be ``""``.


.. [#f1] The requirement to put the ``'0'`` there is the legacy of an old
         interface which is maintained for backward compatibility and also to
         match the format of ``fill_value`` for reading with the format of
         ``fill_value`` used for writing tables. On reading, the second
         element of the ``<missing_spec>`` tuple can actually be an arbitrary
         string value which replaces occurrences of the ``<match_string>``
         string in the input stream prior to type conversion.  This ends up
         being the value "behind the mask", which should never be directly
         accessed.  Only the value ``'0'`` is neutral when attempting to detect
         the column data type and perform type conversion.  For instance if you
         used ``'nan'`` for the ``<match_string>`` value then integer columns
         would wind up as float.

.. _guess_formats:

Guess table format
==================

If the ``guess`` parameter in |read| is set to True (which is the default) then
|read| will try to guess the table format by cycling through a number of
possible table format permutations and attempting to read the table in each case.
The first format which succeeds and will be used to read the table. To succeed
the table must be successfully parsed by the Reader and satisfy the following
column requirements:

 * At least two table columns
 * No column names are a float or int number
 * No column names begin or end with space, comma, tab, single quote, double quote, or
   a vertical bar (|).

These requirements reduce the chance for a false positive where a table is
successfully parsed with the wrong format.  A common situation is a table
with numeric columns but no header row, and in this case ``astropy.io.ascii`` will
auto-assign column names because of the restriction on column names that
look like a number.

Guess order
-----------
The order of guessing is shown by this Python code, where ``Reader`` is the
class which actually implements reading the different file formats::

  for Reader in (Ecsv, FixedWidthTwoLine, Rst, FastBasic, Basic,
                 FastRdb, Rdb, FastTab, Tab, Cds, Daophot, SExtractor,
                 Ipac, Latex, AASTex):
      read(Reader=Reader)

  for Reader in (CommentedHeader, FastBasic, Basic, FastNoHeader, NoHeader):
      for delimiter in ("|", ",", " ", "\\s"):
          for quotechar in ('"', "'"):
              read(Reader=Reader, delimiter=delimiter, quotechar=quotechar)

Note that the :class:`~astropy.io.ascii.FixedWidth` derived-readers are not included
in the default guess sequence (this causes problems), so to read such tables
one must explicitly specify the format with the ``format`` keyword. Also notice
that formats compatible with the fast reading engine attempt to use the fast
engine before the ordinary reading engine.

If none of the guesses succeed in reading the table (subject to the column
requirements) a final try is made using just the user-supplied parameters but
without checking the column requirements.  In this way a table with only one
column or column names that look like a number can still be successfully read.

The guessing process respects any values of the Reader, delimiter, and
quotechar parameters as well as options for the fast reader that were
supplied to the read() function.  Any guesses that would conflict are
skipped.  For example the call::

 >>> data = ascii.read(table, Reader=ascii.NoHeader, quotechar="'")

would only try the four delimiter possibilities, skipping all the conflicting
Reader and quotechar combinations.  Similarly with any setting of
``fast_reader`` that requires use of the fast engine, only the fast
variants in the Reader list above will be tried.

Disabling
---------

Guessing can be disabled in two ways::

  import astropy.io.ascii
  data = astropy.io.ascii.read(table)               # guessing enabled by default
  data = astropy.io.ascii.read(table, guess=False)  # disable for this call
  astropy.io.ascii.set_guess(False)                 # set default to False globally
  data = astropy.io.ascii.read(table)               # guessing disabled

Debugging
---------

In order to get more insight into the guessing process and possibly debug if
something isn't working as expected, use the
`~astropy.io.ascii.get_read_trace()` function.  This returns a traceback of the
attempted read formats for the last call to `~astropy.io.ascii.read()`.

Comments and metadata
=====================

Any comment lines detected during reading are inserted into the output table
via the ``comments`` key in the table's ``.meta`` dictionary. For example::

 >>> table='''# TELESCOPE = 30 inch
 ...          # TARGET = PV Ceph
 ...          # BAND = V
 ...          MJD mag
 ...          55555 12.3
 ...          55556 12.4'''
 >>> dat = ascii.read(table)
 >>> print(dat.meta['comments'])
 ['TELESCOPE = 30 inch', 'TARGET = PV Ceph', 'BAND = V']

While :mod:`astropy.io.ascii` will not do any post-processing on comment lines,
custom post-processing can be accomplished by re-reading with the metadata line
comments. Here is one example, where comments are of the form "# KEY = VALUE"::

 >>> header = ascii.read(dat.meta['comments'], delimiter='=',
 ...                     format='no_header', names=['key', 'val'])
 >>> print(header)
    key      val
 --------- -------
 TELESCOPE 30 inch
    TARGET PV Ceph
      BAND       V


Converters
==========

:mod:`astropy.io.ascii` converts the raw string values from the table into
numeric data types by using converter functions such as the Python ``int`` and
``float`` functions.  For example ``int("5.0")`` will fail while float("5.0")
will succeed and return 5.0 as a Python float.

The default converters are::

    default_converters = [astropy.io.ascii.convert_numpy(numpy.int),
                          astropy.io.ascii.convert_numpy(numpy.float),
                          astropy.io.ascii.convert_numpy(numpy.str)]

These take advantage of the :func:`~astropy.io.ascii.convert_numpy`
function which returns a 2-element tuple ``(converter_func, converter_type)``
as described in the previous section.  The type provided to
:func:`~astropy.io.ascii.convert_numpy` must be a valid `numpy type
<https://docs.scipy.org/doc/numpy/user/basics.types.html>`_, for example
``numpy.int``, ``numpy.uint``, ``numpy.int8``, ``numpy.int64``,
``numpy.float``, ``numpy.float64``, ``numpy.str``.

The default converters for each column can be overridden with the
``converters`` keyword::

  >>> import numpy as np
  >>> converters = {'col1': [ascii.convert_numpy(np.uint)],
  ...               'col2': [ascii.convert_numpy(np.float32)]}
  >>> ascii.read('file.dat', converters=converters)  # doctest: +SKIP


.. _fortran_style_exponents:

Fortran-style exponents
=======================

The :ref:`fast converter <fast_conversion_opts>` available with the C
input parser provides an ``exponent_style`` option to define a custom
character instead of the standard ``'e'`` for exponential formats in
the input file, to read for example Fortran-style double precision
numbers like ``'1.495978707D+13'``:

  >>> ascii.read('double.dat', format='basic', guess=False,
  ...            fast_reader={'exponent_style': 'D'})  # doctest: +SKIP

The special setting ``'fortran'`` is provided to allow for the
auto-detection of any valid Fortran exponent character (``'E'``,
``'D'``, ``'Q'``), as well as of triple-digit exponents prefixed with no
character at all (e.g. ``'2.1127123261674622-107'``).
All values and exponent characters in the input data are
case-insensitive; any value other than the default ``'E'`` implies the
automatic setting of ``'use_fast_converter': True``.

Advanced customization
======================

Here we provide a few examples that demonstrate how to extend the base
functionality to handle special cases.  To go beyond these simple examples the
best reference is to read the code for the existing
:ref:`extension_reader_classes`.

**Define custom readers by class inheritance**

The most useful way to define a new reader class is by inheritance.
This is the way all the build-in readers are defined, so there are plenty
of examples in the code.

In most cases, you will define one class to handle the header,
one class that handles the data and a reader class that ties it all together.
Here is a simple example from the code that defines a reader that is just like
the basic reader, but header and data start in different lines of the file::

  # Note: NoHeader is already included in astropy.io.ascii for convenience.
  class NoHeaderHeader(BasicHeader):
      '''Reader for table header without a header

      Set the start of header line number to `None`, which tells the basic
      reader there is no header line.
      '''
      start_line = None

  class NoHeaderData(BasicData):
      '''Reader for table data without a header

      Data starts at first uncommented line since there is no header line.
      '''
      start_line = 0

  class NoHeader(Basic):
      """Read a table with no header line.  Columns are autonamed using
      header.auto_format which defaults to "col%d".  Otherwise this reader
      the same as the :class:`Basic` class from which it is derived.  Example::

        # Table data
        1 2 "hello there"
        3 4 world
      """
      _format_name = 'no_header'
      _description = 'Basic table with no headers'
      header_class = NoHeaderHeader
      data_class = NoHeaderData

In a slightly more involved case, the implementation can also override some of
the methods in the base class::

  # Note: CommentedHeader is already included in astropy.io.ascii for convenience.
  class CommentedHeaderHeader(BasicHeader):
      """Header class for which the column definition line starts with the
      comment character.  See the :class:`CommentedHeader` class  for an example.
      """
      def process_lines(self, lines):
          """Return only lines that start with the comment regexp.  For these
          lines strip out the matching characters."""
          re_comment = re.compile(self.comment)
          for line in lines:
              match = re_comment.match(line)
              if match:
                  yield line[match.end():]

      def write(self, lines):
          lines.append(self.write_comment + self.splitter.join(self.colnames))


  class CommentedHeader(Basic):
      """Read a file where the column names are given in a line that begins with
      the header comment character. ``header_start`` can be used to specify the
      line index of column names, and it can be a negative index (for example -1
      for the last commented line).  The default delimiter is the <space>
      character.::

        # col1 col2 col3
        # Comment line
        1 2 3
        4 5 6
      """
      _format_name = 'commented_header'
      _description = 'Column names in a commented line'

      header_class = CommentedHeaderHeader
      data_class = NoHeaderData


**Define a custom reader functionally**
Instead of defining a new class, it is also possible to obtain an instance
of a reader and then to modify the properties of this one reader instance
in a function::

   def read_rdb_table(table):
       reader = astropy.io.ascii.Basic()
       reader.header.splitter.delimiter = '\t'
       reader.data.splitter.delimiter = '\t'
       reader.header.splitter.process_line = None
       reader.data.splitter.process_line = None
       reader.data.start_line = 2

       return reader.read(table)


**Create a custom splitter.process_val function**
::

   # The default process_val() normally just strips whitespace.
   # In addition have it replace empty fields with -999.
   def process_val(x):
       """Custom splitter process_val function: Remove whitespace at the beginning
       or end of value and substitute -999 for any blank entries."""
       x = x.strip()
       if x == '':
           x = '-999'
       return x

   # Create an RDB reader and override the splitter.process_val function
   rdb_reader = astropy.io.ascii.get_reader(Reader=astropy.io.ascii.Rdb)
   rdb_reader.data.splitter.process_val = process_val

.. _chunk_reading:

Reading large tables in chunks
==============================

The default process for reading ASCII tables is not memory efficient and may
temporarily require much more memory than the size of the file (up to a factor
of 5 to 10).  In cases where the temporary memory requirement exceeds available
memory this can cause significant slowdown when disk cache gets used.

In this situation there is a way to read the table in smaller chunks which are
limited in size.  There are two possible ways to do this:

- Read the table in chunks and aggregate the final table along the way.  This
  uses only somewhat more memory than the final table requires.
- Use a Python generator function to return a `~astropy.table.Table` object for
  each chunk of the input table.  This allows for scanning through arbitrarily
  large tables since it never returns the final aggregate table.

The chunk reading functionality is most useful for very large tables, so this is
available only for the :ref:`fast_ascii_io` readers.  The following formats are
supported: ``tab``, ``csv``, ``no_header``, ``rdb``, and ``basic``.  The
``commented_header`` format is not directly supported, but as a workaround one
can read using the ``no_header`` format and explicitly supply the column names
using the ``names`` argument.

In order to read a table in chunks one must provide the ``fast_reader`` keyword
argument with a ``dict`` that includes the ``chunk_size`` key with the value
being the approximate size (in bytes) of each chunk of the input table to read.
In addition, if one provides a ``chunk_generator`` key which is set to
``True``, then instead of returning a single table for the whole input it
returns an iterator that provides a table for each chunk of the input.

**Example**: Reading an entire table while limiting peak memory usage.
::

  # Read a large CSV table in 100 Mb chunks.

  tbl = ascii.read('large_table.csv', format='csv', guess=False,
                   fast_reader={'chunk_size': 100 * 1000000})

**Example**: Reading the table in chunks with an iterator.

Here we iterate over a CSV table and select all rows where the ``Vmag`` column is
less than 8.0 (e.g. all stars in table brighter than 8.0 mag).  We collect all
these sub-tables and then stack them at the end.
::

  from astropy.table import vstack

  # tbls is an iterator over the chunks (no actual reading done yet)
  tbls = ascii.read('large_table.csv', format='csv', guess=False,
                    fast_reader={'chunk_size': 100 * 1000000,
                                 'chunk_generator': True})

  out_tbls = []

  # At this point the file is actually read in chunks.
  for tbl in tbls:
      bright = tbl['Vmag'] < 8.0
      if np.count_nonzero(bright):
          out_tbls.append(tbl[bright])

  out_tbl = vstack(out_tbls)

.. Note:: **Performance**

  Specifying the ``format`` explicitly and using ``guess=False`` is a good idea
  for large tables.  This prevent unnecessary guessing in the typical case
  where the format is already known.

  The ``chunk_size`` should generally be set to the largest value that is 
  reasonable given available system memory.  There is overhead associated
  with processing each chunk, so the fewer chunks the better.
