ObservationTable¶

class gammapy.data.ObservationTable(data=None, masked=None, names=None, dtype=None, meta=None, copy=True, rows=None, copy_indices=True, **kwargs)[source]¶

Bases: astropy.table.Table

Observation table.

Data format specification: Observation index table

Attributes Summary

`ColumnClass`
`colnames`
`dtype`
`groups`
`has_mixin_columns`	True if table has any mixin columns (defined as columns that are not Column subclasses).
`iloc`	Return a TableILoc object that can be used for retrieving indexed rows in the order they appear in the index.
`indices`	Return the indices associated with columns of the table as a TableIndices object.
`info`
`loc`	Return a TableLoc object that can be used for retrieving rows by index in a given data range.
`loc_indices`	Return a TableLocIndices object that can be used for retrieving the row indices corresponding to given table index key value or values.
`mask`
`masked`
`meta`
`pointing_galactic`	Pointing positions as Galactic (`SkyCoord`)
`pointing_radec`	Pointing positions as ICRS (`SkyCoord`)

Methods Summary

`add_column`(col[, index, name, …])	Add a new Column object `col` to the table.
`add_columns`(cols[, indexes, names, copy, …])	Add a list of new Column objects `cols` to the table.
`add_index`(colnames[, engine, unique])	Insert a new index among one or more columns.
`add_row`([vals, mask])	Add a new row to the end of the table.
`argsort`([keys, kind])	Return the indices which would sort the table according to one or more key columns.
`as_array`([keep_byteorder])	Return a new copy of the table in the form of a structured np.ndarray or np.ma.MaskedArray object (as appropriate).
`convert_bytestring_to_unicode`()	Convert bytestring columns (dtype.kind=’S’) to unicode (dtype.kind=’U’) using UTF-8 encoding.
`convert_unicode_to_bytestring`()	Convert unicode columns (dtype.kind=’U’) to bytestring (dtype.kind=’S’) using UTF-8 encoding.
`copy`([copy_data])	Return a copy of the table.
`create_gti`(obs_id)	Returns a GTI table containing TSTART and TSTOP from the table.
`field`(item)	Return column[item] for recarray compatibility.
`filled`([fill_value])	Return a copy of self, with masked values filled.
`from_pandas`(dataframe)	Create a `Table` from a `pandas.DataFrame` instance
`get_obs_idx`(obs_id)	Get row index for given `obs_id`.
`group_by`(keys)	Group this table by the specified `keys`
`index_column`(name)	Return the positional index of column `name`.
`index_mode`(mode)	Return a context manager for an indexing mode.
`insert_row`(index[, vals, mask])	Add a new row before the given `index` position in the table.
`itercols`()	Iterate over the columns of this table.
`keep_columns`(names)	Keep only the columns specified (remove the others).
`keys`()
`more`([max_lines, max_width, show_name, …])	Interactively browse table with a paging interface.
`pformat`([max_lines, max_width, show_name, …])	Return a list of lines for the formatted string representation of the table.
`pprint`([max_lines, max_width, show_name, …])	Print a formatted string representation of the table.
`read`(filename, **kwargs)	Read an observation table from file.
`remove_column`(name)	Remove a column from the table.
`remove_columns`(names)	Remove several columns from the table.
`remove_indices`(colname)	Remove all indices involving the given column.
`remove_row`(index)	Remove a row from the table.
`remove_rows`(row_specifier)	Remove rows from the table.
`rename_column`(name, new_name)	Rename a column.
`replace_column`(name, col)	Replace column `name` with the new `col` object.
`reverse`()	Reverse the row order of table rows.
`select_linspace_subset`(num)	Select subset of observations.
`select_obs_id`(obs_id)	Get `ObservationTable` containing only `obs_id`.
`select_observations`([selection])	Select subset of observations.
`select_range`(selection_variable, value_range)	Make an observation table, applying some selection.
`select_time_range`(selection_variable, time_range)	Make an observation table, applying a time selection.
`show_in_browser`([max_lines, jsviewer, …])	Render the table in HTML and show it in a web browser.
`show_in_notebook`([tableid, css, …])	Render the table in HTML and show it in the IPython notebook.
`sort`([keys])	Sort the table according to one or more keys.
`summary`()	Info string (str)
`to_pandas`()	Return a `pandas.DataFrame` instance
`write`(args, *kwargs)	Write this Table object out in the specified format.

Attributes Documentation

ColumnClass¶

colnames¶

dtype¶

groups¶

has_mixin_columns¶: True if table has any mixin columns (defined as columns that are not Column subclasses).

iloc¶: Return a TableILoc object that can be used for retrieving indexed rows in the order they appear in the index.

indices¶: Return the indices associated with columns of the table as a TableIndices object.

info¶

loc¶: Return a TableLoc object that can be used for retrieving rows by index in a given data range. Note that both loc and iloc work only with single-column indices.

loc_indices¶: Return a TableLocIndices object that can be used for retrieving the row indices corresponding to given table index key value or values.

mask¶

masked¶

meta¶

pointing_galactic¶: Pointing positions as Galactic (SkyCoord)

pointing_radec¶: Pointing positions as ICRS (SkyCoord)

Methods Documentation

add_column(col, index=None, name=None, rename_duplicate=False, copy=True)¶

Add a new Column object col to the table. If index is supplied then insert column before index position in the list of columns, otherwise append column to the end of the list.

Parameters:	col : Column Column object to add. index : int or `None` Insert column before this position or at end (default). name : str Column name rename_duplicate : bool Uniquify column name if it already exist. Default is False. copy : bool Make a copy of the new column. Default is True.

Examples

Create a table with two columns ‘a’ and ‘b’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> print(t)
 a   b
--- ---
  1 0.1
  2 0.2
  3 0.3

Create a third column ‘c’ and append it to the end of the table:

>>> col_c = Column(name='c', data=['x', 'y', 'z'])
>>> t.add_column(col_c)
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Add column ‘d’ at position 1. Note that the column is inserted before the given index:

>>> col_d = Column(name='d', data=['a', 'b', 'c'])
>>> t.add_column(col_d, 1)
>>> print(t)
 a   d   b   c
--- --- --- ---
  1   a 0.1   x
  2   b 0.2   y
  3   c 0.3   z

Add second column named ‘b’ with rename_duplicate:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> col_b = Column(name='b', data=[1.1, 1.2, 1.3])
>>> t.add_column(col_b, rename_duplicate=True)
>>> print(t)
 a   b  b_1
--- --- ---
  1 0.1 1.1
  2 0.2 1.2
  3 0.3 1.3

Add an unnamed column or mixin object in the table using a default name or by specifying an explicit name with name. Name can also be overridden:

>>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b'))
>>> col_c = Column(data=['x', 'y'])
>>> t.add_column(col_c)
>>> t.add_column(col_c, name='c')
>>> col_b = Column(name='b', data=[1.1, 1.2])
>>> t.add_column(col_b, name='d')
>>> print(t)
 a   b  col2  c   d
--- --- ---- --- ---
  1 0.1    x   x 1.1
  2 0.2    y   y 1.2

To add several columns use add_columns.

add_columns(cols, indexes=None, names=None, copy=True, rename_duplicate=False)¶

Add a list of new Column objects cols to the table. If a corresponding list of indexes is supplied then insert column before each index position in the original list of columns, otherwise append columns to the end of the list.

Parameters:	cols : list of Columns Column objects to add. indexes : list of ints or `None` Insert column before this position or at end (default). names : list of str Column names copy : bool Make a copy of the new columns. Default is True. rename_duplicate : bool Uniquify new column names if they duplicate the existing ones. Default is False.

Examples

Create a table with two columns ‘a’ and ‘b’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> print(t)
 a   b
--- ---
  1 0.1
  2 0.2
  3 0.3

Create column ‘c’ and ‘d’ and append them to the end of the table:

>>> col_c = Column(name='c', data=['x', 'y', 'z'])
>>> col_d = Column(name='d', data=['u', 'v', 'w'])
>>> t.add_columns([col_c, col_d])
>>> print(t)
 a   b   c   d
--- --- --- ---
  1 0.1   x   u
  2 0.2   y   v
  3 0.3   z   w

Add column ‘c’ at position 0 and column ‘d’ at position 1. Note that the columns are inserted before the given position:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> col_c = Column(name='c', data=['x', 'y', 'z'])
>>> col_d = Column(name='d', data=['u', 'v', 'w'])
>>> t.add_columns([col_c, col_d], [0, 1])
>>> print(t)
 c   a   d   b
--- --- --- ---
  x   1   u 0.1
  y   2   v 0.2
  z   3   w 0.3

Add second column ‘b’ and column ‘c’ with rename_duplicate:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> col_b = Column(name='b', data=[1.1, 1.2, 1.3])
>>> col_c = Column(name='c', data=['x', 'y', 'z'])
>>> t.add_columns([col_b, col_c], rename_duplicate=True)
>>> print(t)
 a   b  b_1  c
--- --- --- ---
  1 0.1 1.1  x
  2 0.2 1.2  y
  3 0.3 1.3  z

Add unnamed columns or mixin objects in the table using default names or by specifying explicit names with names. Names can also be overridden:

>>> t = Table()
>>> col_a = Column(data=['x', 'y'])
>>> col_b = Column(name='b', data=['u', 'v'])
>>> t.add_columns([col_a, col_b])
>>> t.add_columns([col_a, col_b], names=['c', 'd'])
>>> print(t)
col0  b   c   d
---- --- --- ---
   x   u   x   u
   y   v   y   v

add_index(colnames, engine=None, unique=False)¶

Insert a new index among one or more columns. If there are no indices, make this index the primary table index.

Parameters:	colnames : str or list List of column names (or a single column name) to index engine : type or None Indexing engine class to use, from among SortedArray, BST, FastBST, FastRBT, and SCEngine. If the supplied argument is None (by default), use SortedArray. unique : bool Whether the values of the index must be unique. Default is False.

add_row(vals=None, mask=None)¶

Add a new row to the end of the table.

The vals argument can be:

sequence (e.g. tuple or list): Column values in the same order as table columns.
mapping (e.g. dict): Keys corresponding to column names. Missing values will be filled with np.zeros for the column dtype.
None: All values filled with np.zeros for the column dtype.

This method requires that the Table object “owns” the underlying array data. In particular one cannot add a row to a Table that was initialized with copy=False from an existing array.

The mask attribute should give (if desired) the mask for the values. The type of the mask should match that of the values, i.e. if vals is an iterable, then mask should also be an iterable with the same length, and if vals is a mapping, then mask should be a dictionary.

Parameters:	vals : tuple, list, dict or `None` Use the specified values in the new row mask : tuple, list, dict or `None` Use the specified mask values in the new row

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1,2],[4,5],[7,8]], names=('a','b','c'))
>>> print(t)
 a   b   c
--- --- ---
  1   4   7
  2   5   8

Adding a new row with entries ‘3’ in ‘a’, ‘6’ in ‘b’ and ‘9’ in ‘c’:

>>> t.add_row([3,6,9])
>>> print(t)
  a   b   c
  --- --- ---
  1   4   7
  2   5   8
  3   6   9

argsort(keys=None, kind=None)¶

Return the indices which would sort the table according to one or more key columns. This simply calls the numpy.argsort function on the table with the order parameter set to keys.

Parameters:	keys : str or list of str The column name(s) to order the table by kind : {‘quicksort’, ‘mergesort’, ‘heapsort’}, optional Sorting algorithm.
Returns:	index_array : ndarray, int Array of indices that sorts the table by the specified key column(s).

as_array(keep_byteorder=False)¶

Return a new copy of the table in the form of a structured np.ndarray or np.ma.MaskedArray object (as appropriate).

Parameters:	keep_byteorder : bool, optional By default the returned array has all columns in native byte order. However, if this option is `True` this preserves the byte order of all columns (if any are non-native).
Returns:	table_array : np.ndarray (unmasked) or np.ma.MaskedArray (masked) Copy of table as a numpy structured array

convert_bytestring_to_unicode()¶

Convert bytestring columns (dtype.kind=’S’) to unicode (dtype.kind=’U’) using UTF-8 encoding.

Internally this changes string columns to represent each character in the string with a 4-byte UCS-4 equivalent, so it is inefficient for memory but allows scripts to manipulate string arrays with natural syntax.

convert_unicode_to_bytestring()¶

Convert unicode columns (dtype.kind=’U’) to bytestring (dtype.kind=’S’) using UTF-8 encoding.

When exporting a unicode string array to a file, it may be desirable to encode unicode columns as bytestrings.

copy(copy_data=True)¶

Return a copy of the table.

Parameters:	copy_data : bool If `True` (the default), copy the underlying data array. Otherwise, use the same data array. The `meta` is always deepcopied regardless of the value for `copy_data`.

create_gti(obs_id)[source]¶

Returns a GTI table containing TSTART and TSTOP from the table.

TODO: This method can be removed once GTI tables are explicitly required in Gammapy.

Parameters:	obs_id : int ID of the observation for which the GTI table will be created
Returns:	gti : `GTI` GTI table containing one row (TSTART and TSTOP of the observation with `obs_id`)

field(item)¶: Return column[item] for recarray compatibility.

filled(fill_value=None)¶

Return a copy of self, with masked values filled.

If input fill_value supplied then that value is used for all masked entries in the table. Otherwise the individual fill_value defined for each table column is used.

Parameters:	fill_value : str If supplied, this `fill_value` is used for all masked entries in the entire table.
Returns:	filled_table : Table New table with masked values filled

classmethod from_pandas(dataframe)¶

Create a Table from a pandas.DataFrame instance

Parameters:	dataframe : `pandas.DataFrame` The pandas `pandas.DataFrame` instance
Returns:	table : `Table` A `Table` (or subclass) instance

get_obs_idx(obs_id)[source]¶

Get row index for given obs_id.

Raises KeyError if observation is not available.

Parameters:	obs_id : int, list observation ids
Returns:	idx : list indices corresponding to obs_id

group_by(keys)¶

Group this table by the specified keys

This effectively splits the table into groups which correspond to unique values of the keys grouping object. The output is a new TableGroups which contains a copy of this table but sorted by row according to keys.

The keys input to group_by can be specified in different ways:

String or list of strings corresponding to table column name(s)

Numpy array (homogeneous or structured) with same length as this table

Table with same length as this table

Parameters:	keys : str, list of str, numpy array, or `Table` Key grouping object
Returns:	out : `Table` New table with groups set

index_column(name)¶

Return the positional index of column name.

Parameters:	name : str column name
Returns:	index : int Positional index of column `name`.

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Get index of column ‘b’ of the table:

>>> t.index_column('b')
1

index_mode(mode)¶

Return a context manager for an indexing mode.

Parameters:

mode : str: Either ‘freeze’, ‘copy_on_getitem’, or ‘discard_on_copy’. In ‘discard_on_copy’ mode, indices are not copied whenever columns or tables are copied. In ‘freeze’ mode, indices are not modified whenever columns are modified; at the exit of the context, indices refresh themselves based on column values. This mode is intended for scenarios in which one intends to make many additions or modifications in an indexed column. In ‘copy_on_getitem’ mode, indices are copied when taking column slices as well as table slices, so col[i0:i1] will preserve indices.

insert_row(index, vals=None, mask=None)¶

Add a new row before the given index position in the table.

The vals argument can be:

sequence (e.g. tuple or list): Column values in the same order as table columns.
mapping (e.g. dict): Keys corresponding to column names. Missing values will be filled with np.zeros for the column dtype.
None: All values filled with np.zeros for the column dtype.

The mask attribute should give (if desired) the mask for the values. The type of the mask should match that of the values, i.e. if vals is an iterable, then mask should also be an iterable with the same length, and if vals is a mapping, then mask should be a dictionary.

Parameters:	vals : tuple, list, dict or `None` Use the specified values in the new row mask : tuple, list, dict or `None` Use the specified mask values in the new row

itercols()¶

Iterate over the columns of this table.

Examples

To iterate over the columns of a table:

>>> t = Table([[1], [2]])
>>> for col in t.itercols():
...     print(col)
col0
----
   1
col1
----
   2

Using itercols() is similar to for col in t.columns.values() but is syntactically preferred.

keep_columns(names)¶

Keep only the columns specified (remove the others).

Parameters:	names : list A list containing the names of the columns to keep. All other columns will be removed.

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3],[0.1, 0.2, 0.3],['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Specifying only a single column name keeps only this column. Keep only column ‘a’ of the table:

>>> t.keep_columns('a')
>>> print(t)
 a
---
  1
  2
  3

Specifying a list of column names is keeps is also possible. Keep columns ‘a’ and ‘c’ of the table:

>>> t = Table([[1, 2, 3],[0.1, 0.2, 0.3],['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> t.keep_columns(['a', 'c'])
>>> print(t)
 a   c
--- ---
  1   x
  2   y
  3   z

keys()¶

more(max_lines=None, max_width=None, show_name=True, show_unit=None, show_dtype=False)¶

Interactively browse table with a paging interface.

Supported keys:

f, <space> : forward one page
b : back one page
r : refresh same page
n : next row
p : previous row
< : go to beginning
> : go to end
q : quit browsing
h : print this help

Parameters:

max_lines : int: Maximum number of lines in table output
max_width : int or None: Maximum character width of output
show_name : bool: Include a header row for column names. Default is True.
show_unit : bool: Include a header row for unit. Default is to show a row for units only if one or more columns has a defined value for the unit.
show_dtype : bool: Include a header row for column dtypes. Default is True.

pformat(max_lines=None, max_width=None, show_name=True, show_unit=None, show_dtype=False, html=False, tableid=None, align=None, tableclass=None)¶

Return a list of lines for the formatted string representation of the table.

If no value of max_lines is supplied then the height of the screen terminal is used to set max_lines. If the terminal height cannot be determined then the default is taken from the configuration item astropy.conf.max_lines. If a negative value of max_lines is supplied then there is no line limit applied.

The same applies for max_width except the configuration item is astropy.conf.max_width.

Parameters:

max_lines : int or None: Maximum number of rows to output
max_width : int or None: Maximum character width of output
show_name : bool: Include a header row for column names. Default is True.
show_unit : bool: Include a header row for unit. Default is to show a row for units only if one or more columns has a defined value for the unit.
show_dtype : bool: Include a header row for column dtypes. Default is True.
html : bool: Format the output as an HTML table. Default is False.
tableid : str or None: An ID tag for the table; only used if html is set. Default is “table{id}”, where id is the unique integer id of the table object, id(self)
align : str or list or tuple or None: Left/right alignment of columns. Default is right (None) for all columns. Other allowed values are ‘>’, ‘<’, ‘^’, and ‘0=’ for right, left, centered, and 0-padded, respectively. A list of strings can be provided for alignment of tables with multiple columns.
tableclass : str or list of str or None: CSS classes for the table; only used if html is set. Default is None.

Returns:

lines : list: Formatted table as a list of strings.

pprint(max_lines=None, max_width=None, show_name=True, show_unit=None, show_dtype=False, align=None)¶

Print a formatted string representation of the table.

If no value of max_lines is supplied then the height of the screen terminal is used to set max_lines. If the terminal height cannot be determined then the default is taken from the configuration item astropy.conf.max_lines. If a negative value of max_lines is supplied then there is no line limit applied.

The same applies for max_width except the configuration item is astropy.conf.max_width.

Parameters:

max_lines : int: Maximum number of lines in table output.
max_width : int or None: Maximum character width of output.
show_name : bool: Include a header row for column names. Default is True.
show_unit : bool: Include a header row for unit. Default is to show a row for units only if one or more columns has a defined value for the unit.
show_dtype : bool: Include a header row for column dtypes. Default is True.
align : str or list or tuple or None: Left/right alignment of columns. Default is right (None) for all columns. Other allowed values are ‘>’, ‘<’, ‘^’, and ‘0=’ for right, left, centered, and 0-padded, respectively. A list of strings can be provided for alignment of tables with multiple columns.

classmethod read(filename, **kwargs)[source]¶

Read an observation table from file.

Parameters:	filename : `Path`, str Filename

remove_column(name)¶

Remove a column from the table.

This can also be done with:

del table[name]

Parameters:	name : str Name of column to remove

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Remove column ‘b’ from the table:

>>> t.remove_column('b')
>>> print(t)
 a   c
--- ---
  1   x
  2   y
  3   z

To remove several columns at the same time use remove_columns.

remove_columns(names)¶

Remove several columns from the table.

Parameters:	names : list A list containing the names of the columns to remove

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...     names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Remove columns ‘b’ and ‘c’ from the table:

>>> t.remove_columns(['b', 'c'])
>>> print(t)
 a
---
  1
  2
  3

Specifying only a single column also works. Remove column ‘b’ from the table:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...     names=('a', 'b', 'c'))
>>> t.remove_columns('b')
>>> print(t)
 a   c
--- ---
  1   x
  2   y
  3   z

This gives the same as using remove_column.

remove_indices(colname)¶

Remove all indices involving the given column. If the primary index is removed, the new primary index will be the most recently added remaining index.

Parameters:	colname : str Name of column

remove_row(index)¶

Remove a row from the table.

Parameters:	index : int Index of row to remove

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Remove row 1 from the table:

>>> t.remove_row(1)
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  3 0.3   z

To remove several rows at the same time use remove_rows.

remove_rows(row_specifier)¶

Remove rows from the table.

Parameters:	row_specifier : slice, int, or array of ints Specification for rows to remove

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Remove rows 0 and 2 from the table:

>>> t.remove_rows([0, 2])
>>> print(t)
 a   b   c
--- --- ---
  2 0.2   y

Note that there are no warnings if the slice operator extends outside the data:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> t.remove_rows(slice(10, 20, 1))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

rename_column(name, new_name)¶

Rename a column.

This can also be done directly with by setting the name attribute for a column:

table[name].name = new_name

TODO: this won’t work for mixins

Parameters:	name : str The current name of the column. new_name : str The new name for the column

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1,2],[3,4],[5,6]], names=('a','b','c'))
>>> print(t)
 a   b   c
--- --- ---
  1   3   5
  2   4   6

Renaming column ‘a’ to ‘aa’:

>>> t.rename_column('a' , 'aa')
>>> print(t)
 aa  b   c
--- --- ---
  1   3   5
  2   4   6

replace_column(name, col)¶

Replace column name with the new col object.

Parameters:	name : str Name of column to replace col : column object (list, ndarray, Column, etc) New column object to replace the existing column

Examples

Replace column ‘a’ with a float version of itself:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> float_a = t['a'].astype(float)
>>> t.replace_column('a', float_a)

reverse()¶

Reverse the row order of table rows. The table is reversed in place and there are no function arguments.

Examples

Create a table with three columns:

>>> t = Table([['Max', 'Jo', 'John'], ['Miller','Miller','Jackson'],
...         [12,15,18]], names=('firstname','name','tel'))
>>> print(t)
firstname   name  tel
--------- ------- ---
      Max  Miller  12
       Jo  Miller  15
     John Jackson  18

Reversing order:

>>> t.reverse()
>>> print(t)
firstname   name  tel
--------- ------- ---
     John Jackson  18
       Jo  Miller  15
      Max  Miller  12

select_linspace_subset(num)[source]¶

Select subset of observations.

This is mostly useful for testing, if you want to make the analysis run faster.

Parameters:	num : int Number of samples to select.
Returns:	table : `ObservationTable` Subset observation table (a copy).

select_obs_id(obs_id)[source]¶

Get ObservationTable containing only obs_id.

Raises KeyError if observation is not available.

Parameters:	obs_id: int, list observation ids

select_observations(selection=None)[source]¶

Select subset of observations.

Returns a new observation table representing the subset.

There are 3 main kinds of selection criteria, according to the value of the type keyword in the selection dictionary:

sky regions (boxes or circles)
time intervals (min, max)
intervals (min, max) on any other parameter present in the observation table, that can be casted into an Quantity object

Allowed selection criteria are interpreted using the following keywords in the selection dictionary under the type key.

sky_box and sky_circle are 2D selection criteria acting on sky coordinates
- sky_box is a squared region delimited by the lon and lat keywords: both tuples of format (min, max); uses select_sky_box
- sky_circle is a circular region centered in the coordinate marked by the lon and lat keywords, and radius radius; uses select_sky_circle
in each case, the coordinate system can be specified by the frame keyword (built-in Astropy coordinate frames are supported, e.g. icrs or galactic); an aditional border can be defined using the border keyword
time_box is a 1D selection criterion acting on the observation start time (TSTART); the interval is set via the time_range keyword; uses select_time_range
par_box is a 1D selection criterion acting on any parameter defined in the observation table that can be casted into an Quantity object; the parameter name and interval can be specified using the keywords variable and value_range respectively; min = max selects exact values of the parameter; uses select_range

In all cases, the selection can be inverted by activating the inverted flag, in which case, the selection is applied to keep all elements outside the selected range.

A few examples of selection criteria are given below.

Parameters:	selection : dict Dictionary with a few keywords for applying selection cuts.
Returns:	obs_table : `ObservationTable` Observation table after selection.

Examples

>>> selection = dict(type='sky_box', frame='icrs',
...                  lon=Angle([150, 300], 'deg'),
...                  lat=Angle([-50, 0], 'deg'),
...                  border=Angle(2, 'deg'))
>>> selected_obs_table = obs_table.select_observations(selection)

>>> selection = dict(type='sky_circle', frame='galactic',
...                  lon=Angle(0, 'deg'),
...                  lat=Angle(0, 'deg'),
...                  radius=Angle(5, 'deg'),
...                  border=Angle(2, 'deg'))
>>> selected_obs_table = obs_table.select_observations(selection)

>>> selection = dict(type='time_box',
...                  time_range=Time(['2012-01-01T01:00:00', '2012-01-01T02:00:00']))
>>> selected_obs_table = obs_table.select_observations(selection)

>>> selection = dict(type='par_box', variable='ALT',
...                  value_range=Angle([60., 70.], 'deg'))
>>> selected_obs_table = obs_table.select_observations(selection)

>>> selection = dict(type='par_box', variable='OBS_ID',
...                  value_range=[2, 5])
>>> selected_obs_table = obs_table.select_observations(selection)

>>> selection = dict(type='par_box', variable='N_TELS',
...                  value_range=[4, 4])
>>> selected_obs_table = obs_table.select_observations(selection)

select_range(selection_variable, value_range, inverted=False)[source]¶

Make an observation table, applying some selection.

Generic function to apply a 1D box selection (min, max) to a table on any variable that is in the observation table and can be casted into a Quantity object.

If the range length is 0 (min = max), the selection is applied to the exact value indicated by the min value. This is useful for selection of exact values, for instance in discrete variables like the number of telescopes.

If the inverted flag is activated, the selection is applied to keep all elements outside the selected range.

Parameters:	selection_variable : str Name of variable to apply a cut (it should exist on the table). value_range : `Quantity`-like Allowed range of values (min, max). The type should be consistent with the selection_variable. inverted : bool, optional Invert selection: keep all entries outside the (min, max) range.
Returns:	obs_table : `ObservationTable` Observation table after selection.

select_time_range(selection_variable, time_range, inverted=False)[source]¶

Make an observation table, applying a time selection.

Apply a 1D box selection (min, max) to a table on any time variable that is in the observation table. It supports both fomats: absolute times in Time variables and [MET].

If the inverted flag is activated, the selection is applied to keep all elements outside the selected range.

Parameters:	selection_variable : str Name of variable to apply a cut (it should exist on the table). time_range : `Time` Allowed time range (min, max). inverted : bool, optional Invert selection: keep all entries outside the (min, max) range.
Returns:	obs_table : `ObservationTable` Observation table after selection.

show_in_browser(max_lines=5000, jsviewer=False, browser='default', jskwargs={'use_local_files': True}, tableid=None, table_class='display compact', css=None, show_row_index='idx')¶

Render the table in HTML and show it in a web browser.

Parameters:

max_lines : int: Maximum number of rows to export to the table (set low by default to avoid memory issues, since the browser view requires duplicating the table in memory). A negative value of max_lines indicates no row limit.
jsviewer : bool: If True, prepends some javascript headers so that the table is rendered as a DataTables data table. This allows in-browser searching & sorting.
browser : str: Any legal browser name, e.g. 'firefox', 'chrome', 'safari' (for mac, you may need to use 'open -a "/Applications/Google Chrome.app" {}' for Chrome). If 'default', will use the system default browser.
jskwargs : dict: Passed to the astropy.table.JSViewer init. Defaults to {'use_local_files': True} which means that the JavaScript libraries will be served from local copies.
tableid : str or None: An html ID tag for the table. Default is table{id}, where id is the unique integer id of the table object, id(self).
table_class : str or None: A string with a list of HTML classes used to style the table. Default is “display compact”, and other possible values can be found in https://www.datatables.net/manual/styling/classes
css : string: A valid CSS string declaring the formatting for the table. Defaults to astropy.table.jsviewer.DEFAULT_CSS.
show_row_index : str or False: If this does not evaluate to False, a column with the given name will be added to the version of the table that gets displayed. This new column shows the index of the row in the table itself, even when the displayed table is re-sorted by another column. Note that if a column with this name already exists, this option will be ignored. Defaults to “idx”.

show_in_notebook(tableid=None, css=None, display_length=50, table_class='astropy-default', show_row_index='idx')¶

Render the table in HTML and show it in the IPython notebook.

Parameters:

tableid : str or None: An html ID tag for the table. Default is table{id}-XXX, where id is the unique integer id of the table object, id(self), and XXX is a random number to avoid conflicts when printing the same table multiple times.
table_class : str or None: A string with a list of HTML classes used to style the table. The special default string (‘astropy-default’) means that the string will be retrieved from the configuration item astropy.table.default_notebook_table_class. Note that these table classes may make use of bootstrap, as this is loaded with the notebook. See this page for the list of classes.
css : string: A valid CSS string declaring the formatting for the table. Defaults to astropy.table.jsviewer.DEFAULT_CSS_NB.
display_length : int, optional: Number or rows to show. Defaults to 50.
show_row_index : str or False: If this does not evaluate to False, a column with the given name will be added to the version of the table that gets displayed. This new column shows the index of the row in the table itself, even when the displayed table is re-sorted by another column. Note that if a column with this name already exists, this option will be ignored. Defaults to “idx”.

Notes

Currently, unlike show_in_browser (with jsviewer=True), this method needs to access online javascript code repositories. This is due to modern browsers’ limitations on accessing local files. Hence, if you call this method while offline (and don’t have a cached version of jquery and jquery.dataTables), you will not get the jsviewer features.

sort(keys=None)¶

Sort the table according to one or more keys. This operates on the existing table and does not return a new table.

Parameters:	keys : str or list of str The key(s) to order the table by. If None, use the primary index of the Table.

Examples

Create a table with 3 columns:

>>> t = Table([['Max', 'Jo', 'John'], ['Miller','Miller','Jackson'],
...         [12,15,18]], names=('firstname','name','tel'))
>>> print(t)
firstname   name  tel
--------- ------- ---
      Max  Miller  12
       Jo  Miller  15
     John Jackson  18

Sorting according to standard sorting rules, first ‘name’ then ‘firstname’:

>>> t.sort(['name','firstname'])
>>> print(t)
firstname   name  tel
--------- ------- ---
     John Jackson  18
       Jo  Miller  15
      Max  Miller  12

summary()[source]¶: Info string (str)

to_pandas()¶

Return a pandas.DataFrame instance

Returns:	dataframe : `pandas.DataFrame` A pandas `pandas.DataFrame` instance
Raises:	ImportError If pandas is not installed ValueError If the Table contains mixin or multi-dimensional columns

write(*args, **kwargs)¶

Write this Table object out in the specified format.

This function provides the Table interface to the astropy unified I/O layer. This allows easily writing a file in many supported data formats using syntax such as:

>>> from astropy.table import Table
>>> dat = Table([[1, 2], [3, 4]], names=('a', 'b'))
>>> dat.write('table.dat', format='ascii')

Parameters:	format : str File format specifier. serialize_method : str, dict, optional Serialization method specifier for columns. args : tuple, optional Positional arguments passed through to data writer. If supplied the first argument is the output filename. *kwargs : dict, optional Keyword arguments passed through to data writer.

Notes

The available built-in formats are:

Format	Read	Write	Auto-identify	Deprecated
ascii	Yes	Yes	No
ascii.aastex	Yes	Yes	No
ascii.basic	Yes	Yes	No
ascii.commented_header	Yes	Yes	No
ascii.csv	Yes	Yes	No
ascii.ecsv	Yes	Yes	Yes
ascii.fast_basic	Yes	Yes	No
ascii.fast_commented_header	Yes	Yes	No
ascii.fast_csv	Yes	Yes	No
ascii.fast_no_header	Yes	Yes	No
ascii.fast_rdb	Yes	Yes	No
ascii.fast_tab	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.rst	Yes	Yes	No
ascii.tab	Yes	Yes	No
fits	Yes	Yes	Yes
hdf5	Yes	Yes	Yes
jsviewer	No	Yes	No
votable	Yes	Yes	Yes
aastex	Yes	Yes	No	Yes
csv	Yes	Yes	Yes	Yes
html	Yes	Yes	No	Yes
ipac	Yes	Yes	No	Yes
latex	Yes	Yes	No	Yes
rdb	Yes	Yes	No	Yes

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