fitsio 0.9.3

A python library to read and write data to FITS files using cfitsio.

Description
-----------

This is a python extension written in c and python. Data are read into
numerical python arrays.

A patched version of cfitsio is bundled with this package, there is no need to
install your own, nor will this conflict with a version you have installed; see
below for details.


Some Features
-------------

- Read from and write to image, binary, and ascii table extensions.
- Read arbitrary subsets of table columns and rows without loading all
the data to memory.
- Read image subsets without reading the whole image.
- Write and read variable length table columns. Can read into fixed length
arrays with the maximum size, or object arrays to save memory.
- Read data using slice notation similar to numpy arrays. This is
like a more powerful memmap, since it is column-aware for tables.
- Append rows to an existing table.
- Query the columns and rows in a table.
- Read and write header keywords.
- Read and write images in tile-compressed format (RICE,GZIP,PLIO,HCOMPRESS).
- Read/write gzip files directly. Read unix compress files (.Z,.zip).
- TDIM information is used to return array columns in the correct shape.
- Write and read string table columns, including array columns of arbitrary
shape.
- Read and write unsigned integer types and signed bytes.
- Write checksums into the header and verify them.
- Insert new columns into tables in-place.
- Iterate over rows in a table. Data are buffered for efficiency.
- data are guaranteed to conform to the FITS standard.


Examples
--------

>>> import fitsio

# Often you just want to quickly read or write data without bothering to
# create a FITS object. In that case, you can use the read and write
# convienience functions.

# read all data from the first hdu with data
>>> data = fitsio.read(filename)
# read a subset of rows and columns from the specified extension
>>> data = fitsio.read(filename, rows=rows, columns=columns, ext=ext)
# read the header, or both at once
>>> h = fitsio.read_header(filename, extension)
>>> data,h = fitsio.read(filename, ext=ext, header=True)

# open the file, write a new binary table extension, and then write the
# data from "recarray" into the table. By default a new extension is
# added to the file. use clobber=True to overwrite an existing file
# instead. To append rows to an existing table, see below.
>>> fitsio.write(filename, recarray)
# write an image
>>> fitsio.write(filename, image)

# NOTE when reading row subsets, the data must still be read from disk.
# This is most efficient if the data are read in the order they appear in
# the file. For this reason, the rows are always returned in row-sorted
# order.

#
# the FITS class gives the you the ability to explore the data, and gives
# more control
#

# open a FITS file for reading and explore
>>> fits=fitsio.FITS('data.fits')

# see what is in here; the FITS object prints itself
>>> fits

file: data.fits
mode: READONLY
extnum hdutype hduname
0 IMAGE_HDU
1 BINARY_TBL mytable

# explore the extensions, either by extension number or
# extension name if available
>>> fits[0]

file: data.fits
extension: 0
type: IMAGE_HDU
image info:
data type: f8
dims: [4096,2048]

>>> fits['mytable'] # can also use fits[1]

file: data.fits
extension: 1
type: BINARY_TBL
extname: mytable
rows: 4328342
column info:
i1scalar u1
f f4
fvec f4 array[2]
darr f8 array[3,2]
dvarr f8 varray[10]
s S5
svec S6 array[3]
svar S0 vstring[8]
sarr S2 array[4,3]

# See bottom for how to get more information for an extension

# [-1] to refers the last HDU
>>> fits[-1]
...

# if there are multiple HDUs with the same name, and an EXTVER
# is set, you can use it. Here extver=2
# fits['mytable',2]


# read the image from extension zero
>>> img = fits[0].read()
>>> img = fits[0][:,:]

# read a subset of the image without reading the whole image
>>> img = fits[0][25:35, 45:55]


# read all rows and columns from a binary table extension
>>> data = fits[1].read()
>>> data = fits['mytable'].read()
>>> data = fits[1][:]

# read a subset of rows and columns. By default uses a case-insensitive
# match. The result retains the names with original case. If columns is a
# sequence, a recarray is returned
>>> data = fits[1].read(rows=[1,5], columns=['index','x','y'])

# Similar but using slice notation
# row subsets
>>> data = fits[1][10:20]
>>> data = fits[1][10:20:2]
>>> data = fits[1][[1,5,18]]

# all rows of column 'x'
>>> data = fits[1]['x'][:]

# Read a few columns at once. This is more efficient than separate read for
# each column
>>> data = fits[1]['x','y'][:]

# General column and row subsets. As noted above, the data are returned
# in row sorted order for efficiency reasons.
>>> data = fits[1][columns][rows]

# iterate over rows in a table hdu
# faster if we buffer some rows, let's buffer 1000 at a time
fits=fitsio.FITS(filename,iter_row_buffer=1000)
for row in fits[1]:
print row

# iterate over HDUs in a FITS object
for hdu in fits:
data=hdu.read()

# Note dvarr shows type varray[10] and svar shows type vstring[8]. These
# are variable length columns and the number specified is the maximum size.
# By default they are read into fixed-length fields in the output array.
# You can over-ride this by constructing the FITS object with the vstorage
# keyword or specifying vstorage when reading. Sending vstorage='object'
# will store the data in variable size object fields to save memory; the
# default is vstorage='fixed'. Object fields can also be written out to a
# new FITS file as variable length to save disk space.

>>> fits = fitsio.FITS(filename,vstorage='object')
# OR
>>> data = fits[1].read(vstorage='object')
>>> print data['dvarr'].dtype
dtype('object')


# you can grab a FITSHDU object to simplify notation
>>> hdu1 = fits[1]
>>> data = hdu1['x','y'][35:50]

# get rows that satisfy the input expression. See "Row Filtering
# Specification" in the cfitsio manual
>>> w=fits[1].where("x > 0.25 && y < 35.0")
>>> data = fits[1][w]

# read the header
>>> h = fits[0].read_header()
>>> h['BITPIX']
-64

>>> fits.close()


# now write some data
>>> fits = FITS('test.fits','rw')


# create a rec array. Note vstr
# is a variable length string
>>> nrows=35
>>> data = numpy.zeros(nrows, dtype=[('index','i4'),('vstr','O'),('x','f8'),('arr','f4',(3,4))])
>>> data['index'] = numpy.arange(nrows,dtype='i4')
>>> data['x'] = numpy.random.random(nrows)
>>> data['vstr'] = [str(i) for i in xrange(nrows)]
>>> data['arr'] = numpy.arange(nrows*3*4,dtype='f4').reshape(nrows,3,4)

# create a new table extension and write the data
>>> fits.write(data)

# can also be a list of ordinary arrays if you send the names
array_list=[xarray,yarray,namearray]
names=['x','y','name']
>>> fits.write(array_list, names=names)

# similarly a dict of arrays
>>> fits.write(dict_of_arrays)
>>> fits.write(dict_of_arrays, names=names) # control name order

# note under the hood writing data to a HDU does the following
>>> fits.create_table_hdu(data=data)
>>> fits[-1].write(data)
>>> fits.update_hdu_list()

# append more rows to the table. The fields in data2 should match columns
# in the table. missing columns will be filled with zeros
>>> fits[-1].append(data2)

# insert a new column into a table
>>> fits[-1].insert_column('newcol', data)
# insert with a specific colnum
>>> fits[-1].insert_column('newcol', data, colnum=2)


# create an image
>>> img=numpy.arange(20,30,dtype='i4')

# write an image in a new HDU (if this is a new file, the primary HDU)
>>> fits.write(img)

# write an image with rice compression
>>> fits.write(img, compress='rice')


# add checksums for the data
>>> fits[-1].write_checksum()

# you can also write a header at the same time. The header can be
# - a simple dict (no comments)
# - a list of dicts with 'name','value','comment' fields
# - a FITSHDR object

>>> header = {'somekey': 35, 'location': 'kitt peak'}
>>> fits.write(data, header=header)

# you can add individual keys to an existing HDU
>>> fits[1].write_key(name, value, comment="my comment")

# Write multiple header keys to an existing HDU. Here records is a dict of
# keyword-value pairs, a FITSHDR, or list of dicts with 'name','value', and
# optionally 'comment' fields
>>> fits[1].write_keys(records)

>>> fits.close()

# using a context, the file is closed automatically after leaving the block
with FITS('path/to/file') as fits:
data = fits[ext].read()

# you can check if a header exists using "in":
f=fitsio.FITS(filename)
if 'blah' in f:
data=f['blah'].read()
if 2 in f:
data=f[2].read()

# how to get more information about an extension
f[1].get_info() # lots of info about the extension
f[1].get_extname()
f[1].get_extver()
f[1].get_extnum() # return zero-offset extension number
f[1].get_exttype() # 'BINARY_TBL' or 'ASCII_TBL' or 'IMAGE_HDU'
f[1].get_colnames()
f[1].get_colname(colnum) # find the column name from column number
f[1].get_nrows() # for tables
f[1].get_rec_dtype() # for tables
f[1].get_rec_column_descr() # for tables
f[1].get_vstorage() # storage mechanism for variable length columns
f[1].has_data() # returns True if data is present in extension
f[1].is_compressed()

# public attributes you can feel free to change as needed
f[1].lower # If True, lower case colnames on output
f[1].upper # If True, upper case colnames on output
f[1].case_sensitive # if True, names are matched case sensitive

Installation
------------
The easiest way is using pip

pip install fitsio

Also, you can download the source from github or use git clone

git clone https://github.com/esheldon/fitsio.git

Enter the fitsio directory and type

python setup.py install

optionally with a prefix

python setup.py install --prefix=/some/path

Requirements
------------

- you need a c compiler and build tools like Make
- You need a recent python, probably >= 2.5, but this has not been
extensively tested.
- You need numerical python (numpy).

test
----
The unit tests should all pass for full support.

import fitsio
fitsio.test.test()

TODO
----

- separate classes for image, ascii and binary table HDUs. Inherit from base
class.
- More testing of variable length columns in ascii tables.
- More error checking in c code for python lists and dicts.
- complex table columns. bit? logical?
- HDU groups?

Notes on cfitsio bundling
-------------------------

We bundle partly because many deployed versions of cfitsio in the wild do not
have support for interesting features like tiled image compression. Bundling
a version that meets our needs is a safe alternative. The patches to 3.28 fix
the ability to read float and double images from tile-compressed HDUs, support
to read very large compressed images, and add back support for tile-compressed
byte and unsigned byte images.

Note on array ordering
----------------------

Since numpy uses C order, FITS uses fortran order, we have to write the TDIM
and image dimensions in reverse order, but write the data as is. Then we need
to also reverse the dims as read from the header when creating the numpy dtype,
but read as is.