# Getting Started¶

A CCDData object can be created from a numpy array (masked or not) or from a FITS file:

>>> import numpy as np
>>> from astropy import units as u
>>> from astropy.nddata import CCDData
>>> import ccdproc
>>> image_1 = CCDData(np.ones((10, 10)), unit="adu")


An example of reading from a FITS file is image_2 = astropy.nddata.CCDData.read('my_image.fits', unit="electron") (the electron unit is defined as part of ccdproc).

The metadata of a CCDData object may be any dictionary-like object, including a FITS header. When a CCDData object is initialized from FITS file its metadata is a FITS header.

The data is accessible either by indexing directly or through the data attribute:

>>> sub_image = image_1[:, 1:-3]  # a CCDData object
>>> sub_data =  image_1.data[:, 1:-3]  # a numpy array


See the documentation for CCDData for a complete list of attributes.

Most operations are performed by functions in ccdproc:

>>> dark = CCDData(np.random.normal(size=(10, 10)), unit="adu")
>>> dark_sub = ccdproc.subtract_dark(image_1, dark,
...                                  dark_exposure=30*u.second,
...                                  data_exposure=15*u.second,
...                                  scale=True)


See the documentation for subtract_dark for more compact ways of providing exposure times.

Every function returns a copy of the data with the operation performed.

Every function in ccdproc supports logging through the addition of information to the image metadata.

>>> dark_sub_gained = ccdproc.gain_correct(dark_sub, 1.5 * u.photon/u.adu, add_keyword='gain_corrected')


Logging can be more complicated – add several keyword/value pairs by passing a dictionary to add_keyword:

>>> my_log = {'gain_correct': 'Gain value was 1.5',
...           'calstat': 'G'}
>>> dark_sub_gained = ccdproc.gain_correct(dark_sub,


You might wonder why there is a gain_correct at all, since the implemented gain correction simple multiplies by a constant. There are two things you get with gain_correct that you do not get with multiplication:

• Appropriate scaling of uncertainties.
• Units

The same advantages apply to operations that are more complex, like flat correction, in which one image is divided by another:

>>> flat = CCDData(np.random.normal(1.0, scale=0.1, size=(10, 10)),
>>> image_1_flat = ccdproc.flat_correct(image_1, flat)


In addition to doing the necessary division, flat_correct propagates uncertainties (if they are set).

The function wcs_project allows you to reproject an image onto a different WCS.

To make applying the same operations to a set of files in a directory easier, use an ImageFileCollection. It constructs, given a directory, a Table containing the values of user-selected keywords in the directory. It also provides methods for iterating over the files. The example below was used to find an image in which the sky background was high for use in a talk:

>>> from ccdproc import ImageFileCollection
>>> import numpy as np
>>> from glob import glob

>>> for d in dirs: