nbodykit.io.fits module

class nbodykit.io.fits.FITSFile(path, ext=None)[source]

Bases: nbodykit.io.base.FileType

A file object to handle the reading of FITS data using the fitsio package.

See also: https://github.com/esheldon/fitsio

Parameters:
  • path (str) – the file path to load
  • ext (number or string, optional) – The extension. Either the numerical extension from zero or a string extension name. If not sent, data is read from the first HDU that has data.

Attributes

columns A list of the names of the columns in the file.
dtype A numpy.dtype object holding the data types of each column in the file.
ncol The number of data columns in the file.
shape The shape of the file, which defaults to (size, )
size The size of the file, i.e., number of rows

Methods

asarray() Return a view of the file, where the fields of the
get_dask(column[, blocksize]) Return the specified column as a dask array, which
keys() Aliased function to return columns
read(columns, start, stop[, step]) Read the specified column(s) over the given range
__getitem__(s)

This function provides numpy-like array indexing of the file object.

It supports:

  1. integer, slice-indexing similar to arrays
  2. string indexing using column names in keys()
  3. array-like indexing using integer lists or boolean arrays

Note

If a single column is being returned, a numpy array holding the data is returned, rather than a structured array with only a single field.

asarray()

Return a view of the file, where the fields of the structured array are stacked in columns of a single numpy array

Examples

Start with a file object with three named columns, ra, dec, and z

>>> ff.dtype
dtype([('ra', '<f4'), ('dec', '<f4'), ('z', '<f4')])
>>> ff.shape
(1000,)
>>> ff.columns
['ra', 'dec', 'z']
>>> ff[:3]
array([(235.63442993164062, 59.39099884033203, 0.6225500106811523),
       (140.36181640625, -1.162310004234314, 0.5026500225067139),
       (129.96627807617188, 45.970130920410156, 0.4990200102329254)],
      dtype=(numpy.record, [('ra', '<f4'), ('dec', '<f4'), ('z', '<f4')]))

Select a subset of columns and switch the ordering and convert output to a single numpy array

>>> x = ff[['dec', 'ra']].asarray()
>>> x.dtype
dtype('float32')
>>> x.shape
(1000, 2)
>>> x.columns
['dec', 'ra']
>>> x[:3]
array([[  59.39099884,  235.63442993],
       [  -1.16231   ,  140.36181641],
       [  45.97013092,  129.96627808]], dtype=float32)

Now, select only the first column (dec)

>>> dec = x[:,0]
>>> dec[:3]
array([ 59.39099884,  -1.16231   ,  45.97013092], dtype=float32)
Returns:a file object that will return a numpy array with the columns representing the fields
Return type:FileType
columns

A list of the names of the columns in the file.

This defaults to the named fields in the file’s dtype attribute, but differ from this if a view of the file has been returned with asarray()

dtype

A numpy.dtype object holding the data types of each column in the file.

get_dask(column, blocksize=100000)

Return the specified column as a dask array, which delays the explicit reading of the data until dask.compute() is called

The dask array is chunked into blocks of size blocksize

Parameters:
  • column (str) – the name of the column to return
  • blocksize (int, optional) – the size of the chunks in the dask array
Returns:

the dask array holding the column, which computes the necessary functions to read the data, but delays evaluating until the user specifies
Return type:

dask.array.Array
keys()

Aliased function to return columns

logger = <logging.Logger object>
ncol

The number of data columns in the file.

read(columns, start, stop, step=1)[source]

Read the specified column(s) over the given range

‘start’ and ‘stop’ should be between 0 and size, which is the total size of the file

Parameters:
  • columns (str, list of str) – the name of the column(s) to return
  • start (int) – the row integer to start reading at
  • stop (int) – the row integer to stop reading at
  • step (int, optional) – the step size to use when reading; default is 1
Returns:

structured array holding the requested columns over the specified range of rows
Return type:

numpy.array
shape

The shape of the file, which defaults to (size, )

Multiple dimensions can be introduced into the shape if a view of the file has been returned with asarray()

size

The size of the file, i.e., number of rows