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- N-Dimensional Datasets (`astropy.nddata`) »
- CCDData Class

# CCDData Class

## Getting Started

### Getting Data In

Creating a `CCDData` object from any array-like data using `astropy.nddata` is convenient:

    >>> import numpy as np
    >>> from astropy.nddata import CCDData
    >>> from astropy.utils.data import get_pkg_data_filename
    >>> ccd = CCDData(np.arange(10), unit="adu")

Note that behind the scenes, this creates references to (not copies of) your data when possible, so modifying the data in `ccd` will modify the underlying data.

You are required to provide a unit for your data. The most frequently used units for these objects are likely to be `adu`, `photon`, and `electron`, which can be set either by providing the string name of the unit (as in the example above) or from unit objects:

    >>> from astropy import units as u
    >>> ccd_photon = CCDData([1, 2, 3], unit=u.photon)
    >>> ccd_electron = CCDData([1, 2, 3], unit="electron")

If you prefer not to use the unit functionality, then use the special unit `u.dimensionless_unscaled` when you create your `CCDData` images:

    >>> ccd_unitless = CCDData(np.zeros((10, 10)),
    ...                        unit=u.dimensionless_unscaled)

A `CCDData` object can also be initialized from a FITS filename or URL:

    >>> ccd = CCDData.read('my_file.fits', unit="adu")
    >>> ccd = CCDData.read(get_pkg_data_filename('tutorials/FITS-images/HorseHead.fits'), unit="adu", cache=True)

If there is a unit in the FITS file (in the `BUNIT` keyword), that will be used, but explicitly providing a unit in `read` will override any unit in the FITS file.

There is no restriction at all on what the unit can be — any unit in `astropy.units` or another that you create yourself will work.

In addition, the user can specify the extension in a FITS file to use:

    >>> ccd = CCDData.read('my_file.fits', hdu=1, unit="adu")

If `hdu` is not specified, it will assume the data is in the primary extension. If there is no data in the primary extension, the first extension with image data will be used.

### Metadata

When initializing from a FITS file, the `header` property is initialized using the header of the FITS file. Metadata is optional, and can be provided by any dictionary or dict-like object:

    >>> ccd_simple = CCDData(np.arange(10), unit="adu")
    >>> my_meta = {'observer': 'Edwin Hubble', 'exposure': 30.0}
    >>> ccd_simple.header = my_meta  # or use ccd_simple.meta = my_meta

Whether the metadata is case-sensitive or not depends on how it is initialized. A FITS header, for example, is not case-sensitive, but a Python dictionary is.

### Getting Data Out

A `CCDData` object behaves like a `numpy` array (masked if the `CCDData` mask is set) in expressions, and the underlying data (ignoring any mask) is accessed through the `data` attribute:

    >>> ccd_masked = CCDData([1, 2, 3], unit="adu", mask=[0, 0, 1])
    >>> 2 * np.ones(3) * ccd_masked   # one return value will be masked
    masked_array(data=[2.0, 4.0, --],
                 mask=[False, False,  True],
           fill_value=1e+20)
    >>> 2 * np.ones(3) * ccd_masked.data   # ignores the mask
    array([2., 4., 6.])

You can force conversion to a `numpy` array with:

    >>> np.asarray(ccd_masked)
    array([1, 2, 3])
    >>> np.ma.array(ccd_masked.data, mask=ccd_masked.mask)
    masked_array(data=[1, 2, --],
                 mask=[False, False,  True],
           fill_value=999999)

A method for converting a `CCDData` object to a FITS HDU list is also available. It converts the metadata to a FITS header:

    >>> hdulist = ccd_masked.to_hdu()

You can also write directly to a FITS file:

    >>> ccd_masked.write('my_image.fits')

### Masks and Flags

Although it is not required when a `CCDData` image is created, you can also specify a mask and/or flags.

A mask is a boolean array the same size as the data in which a value of `True` indicates that a particular pixel should be masked (i.e., not be included in arithmetic operations or aggregation).

Flags are one or more additional arrays (of any type) whose shape matches the shape of the data. One particularly useful type of flag is a bit planes; for more details about bit planes and the functions `astropy` provides for converting them to binary masks, see Utility Functions for Handling Bit Masks and Mask Arrays. For more details on setting flags, see `NDData`.

### WCS

The `wcs` attribute of a `CCDData` object can be set two ways.

- If the `CCDData` object is created from a FITS file that has WCS keywords in the header, the `wcs` attribute is set to a `WCS` object using the information in the FITS header.

- The WCS can also be provided when the `CCDData` object is constructed with the `wcs` argument.

Either way, the `wcs` attribute is kept up to date if the `CCDData` image is trimmed.

### PSF

The `psf` attributes of a `CCDData` object can be set two ways.

- If the FITS file has an image HDU extension matching the appropriate name (defaulted to `"PSFIMAGE"`), the `psf` attribute is loaded from that image HDU.

- The PSF can also be provided when the `CCDData` object is constructed with the `psf` argument.

The `psf` attribute should be a normalized image representing the PSF at the center of the `CCDData`, sized appropriately for the data; users are responsible for managing and interpreting it in context. For more on normalizing a PSF image, see Normalization.

The `psf` attribute is set to `None` in the output of an arithmetic operation, no matter the inputs. A warning message is emitted if either of the input images contain a non-`None` PSF; users are responsible for determining the appropriate thing to do in that context.

## Uncertainty

You can set the uncertainty directly, either by creating a `StdDevUncertainty` object first:

    >>> rng = np.random.default_rng()
    >>> data = rng.normal(size=(10, 10), loc=1.0, scale=0.1)
    >>> ccd = CCDData(data, unit="electron")
    >>> from astropy.nddata.nduncertainty import StdDevUncertainty
    >>> uncertainty = 0.1 * ccd.data  # can be any array whose shape matches the data
    >>> my_uncertainty = StdDevUncertainty(uncertainty)
    >>> ccd.uncertainty = my_uncertainty

Or by providing a `ndarray` with the same shape as the data:

    >>> ccd.uncertainty = 0.1 * ccd.data
    INFO: array provided for uncertainty; assuming it is a StdDevUncertainty. [...]

In this case, the uncertainty is assumed to be `StdDevUncertainty`.

Two other uncertainty classes are available for which error propagation is also supported: `VarianceUncertainty` and `InverseVariance`. Using one of these three uncertainties is required to enable error propagation in `CCDData`.

If you want access to the underlying uncertainty, use its `.array` attribute:

    >>> ccd.uncertainty.array
    array(...)

## Arithmetic with Images

Methods are provided to perform arithmetic operations with a `CCDData` image and a number, an `astropy` `Quantity` (a number with units), or another `CCDData` image.

Using these methods propagates errors correctly (if the errors are uncorrelated), takes care of any necessary unit conversions, and applies masks appropriately. Note that the metadata of the result is not set if the operation is between two `CCDData` objects.

    >>> result = ccd.multiply(0.2 * u.adu)
    >>> uncertainty_ratio = result.uncertainty.array[0, 0]/ccd.uncertainty.array[0, 0]
    >>> round(uncertainty_ratio, 5)
    0.2
    >>> result.unit
    Unit("adu electron")

Note

The affiliated package ccdproc provides functions for many common data reduction operations. Those functions try to construct a sensible header for the result and provide a mechanism for logging the action of the function in the header.

The arithmetic operators `*`, `/`, `+`, and `-` are not overridden.

Note

If two images have different WCS values, the `wcs` on the first `CCDData` object will be used for the resultant object.