fitsio 0.9.11

A full featured python library to read from and write to FITS files.

A python library to read from and write to FITS files.


Do not use numpy 1.10.0 or 1.10.1
----------------------------------
There is a serious performance regression in numpy 1.10 that results
in fitsio running tens to hundreds of times slower. A fix may be
forthcoming in a later release. Please comment here if this
has already impacted your work https://github.com/numpy/numpy/issues/6467

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

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

A 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.


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 subsets to existing images.
- Write and read variable length table columns.
- Read images and tables 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 (.Z,.zip) and bzip2 (.bz2) files.
- 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 complex, bool (logical), unsigned integer, signed bytes types.
- 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.
- python 3 support


Examples
--------

```python
import fitsio
from fitsio import FITS,FITSHDR

# 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
filename='data.fits'
data = fitsio.read(filename)

# read a subset of rows and columns from a table
data = fitsio.read(filename, rows=[35,1001], columns=['x','y'], ext=2)

# 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
print(fits)

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

# at the python prompt, you could just type "fits" and it will automatically
# print itself. Same for ipython.
>>> fits
file: data.fits
... etc

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

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

print(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
print(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.
columns=['index','x','y']
rows=[1,5]
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 FITS HDU 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 (note no temporary table is
# created in this case, contrary to the cfitsio docs)
w=fits[1].where("x > 0.25 && y < 35.0")
data = fits[1][w]

# read the header
h = fits[0].read_header()
print(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

# 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)

# overwrite rows
fits[-1].write(data)

# overwrite starting at a particular row. The table will grow if needed
fits[-1].write(data, firstrow=350)


# create an image
img=numpy.arange(2*3,dtype='i4').reshape(2,3)

# 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')

# overwrite the image
fits[ext].write(img2)

# write into an existing image, starting at the location [300,400]
# the image will be expanded if needed
fits[ext].write(img3, start=[300,400])

# change the shape of the image on disk
fits[ext].reshape([250,100])

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

# can later verify data integridy
fits[-1].verify_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

hdict = {'somekey': 35, 'location': 'kitt peak'}
fits.write(data, header=hdict)
hlist = [{'name':'observer', 'value':'ES', 'comment':'who'},
{'name':'location','value':'CTIO'},
{'name':'photometric','value':True}]
fits.write(data, header=hlist)
hdr=FITSHDR(hlist)
fits.write(data, header=hdr)

# 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 the same as sent with header= above
fits[1].write_keys(records)

# write special COMMENT fields
fits[1].write_comment("observer JS")
fits[1].write_comment("we had good weather")

# write special history fields
fits[1].write_history("processed with software X")
fits[1].write_history("re-processed with software Y")

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":
if 'blah' in fits:
data=fits['blah'].read()
if 2 in f:
data=fits[2].read()

# methods to get more information about extension. For extension 1:
f[1].get_info() # lots of info about the extension
f[1].has_data() # returns True if data is present in 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_offsets() # byte offsets (header_start, data_start, data_end)
f[1].is_compressed() # for images. True if tile-compressed
f[1].get_colnames() # for tables
f[1].get_colname(colnum) # for tables find the 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() # for tables, storage mechanism for variable
# length columns

# 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. To get the latest release

pip install fitsio

# update fitsio (and everything else)
pip install fitsio --upgrade

# if pip refuses to update to a newer version
pip install fitsio --upgrade --ignore-installed

# if you only want to upgrade fitsio
pip install fitsio --no-deps --upgrade --ignore-installed

You can also get the latest source tarball release from

https://pypi.python.org/pypi/fitsio

or the bleeding edge source from github or use git. To check out
the code for the first time

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

Or at a later time to update to the latest

cd fitsio
git update

Use tar xvfz to untar the file, enter the fitsio directory and type

python setup.py install

optionally with a prefix

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

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

- python 2 or python 3
- you need a c compiler and build tools like Make
- You need numerical python (numpy).

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

import fitsio
fitsio.test.test()

Some tests may fail if certain libraries are not available, such
as bzip2. This failure only implies that bzipped files cannot
be read, without affecting other functionality.

TODO
----

- bit columns
- HDU groups: does anyone use these? If so open an issue!

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.

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.