reproject_and_coadd

reproject.mosaicking.reproject_and_coadd(input_data, output_projection, shape_out=None, input_weights=None, hdu_in=None, hdu_weights=None, reproject_function=None, combine_function='mean', match_background=False, background_reference=None, output_array=None, output_footprint=None, block_sizes=None, progress_bar=None, blank_pixel_value=0, intermediate_memmap=False, **kwargs)[source]

Given a set of input data, reproject and co-add these to a single final image.

Parameters:
input_dataiterable

One or more input datasets to reproject and co-add. This should be an iterable containing one entry for each dataset, where a single dataset is one of:

  • The name of a FITS file

  • An HDUList object

  • An image HDU object such as a PrimaryHDU, ImageHDU, or CompImageHDU instance

  • A tuple where the first element is an ndarray and the second element is either a WCS or a Header object

  • An NDData object from which the .data and .wcs attributes will be used as the input data.

output_projectionBaseLowLevelWCS or BaseHighLevelWCS or Header

The output projection, which can be either a BaseLowLevelWCS, BaseHighLevelWCS, or a Header instance.

shape_outtuple, optional

If output_projection is a WCS instance, the shape of the output data should be specified separately.

input_weightsiterable

If specified, this should be an iterable with the same length as input_data, where each item is one of:

hdu_inint or str, optional

If one or more items in input_data is a FITS file or an HDUList instance, specifies the HDU to use.

hdu_weightsint or str, optional

If one or more items in input_weights is a FITS file or an HDUList instance, specifies the HDU to use.

reproject_functioncallable

The function to use for the reprojection.

combine_function{ ‘mean’, ‘sum’, ‘first’, ‘last’, ‘min’, ‘max’ }

The type of function to use for combining the values into the final image. For ‘first’ and ‘last’, respectively, the reprojected images are simply overlaid on top of each other. With respect to the order of the input images in input_data, either the first or the last image to cover a region of overlap determines the output data for that region.

match_backgroundbool

Whether to match the backgrounds of the images.

background_referenceNone or int

If None, the background matching will make it so that the average of the corrections for all images is zero. If an integer, this specifies the index of the image to use as a reference.

output_arrayarray or None

The final output array. Specify this if you already have an appropriately-shaped array to store the data in. Must match shape specified with shape_out or derived from the output projection.

output_footprintarray or None

The final output footprint array. Specify this if you already have an appropriately-shaped array to store the data in. Must match shape specified with shape_out or derived from the output projection.

block_sizeslist of tuples or None

The block size to use for each dataset. Could also be a single tuple if you want the sample block size for all data sets.

progress_barcallable, optional

If specified, use this as a progress_bar to track loop iterations over data sets.

blank_pixel_valuefloat, optional

Value to use for areas of the resulting mosaic that do not have input data.

intermediate_memmapbool, optional

If True, use numpy.memmap to store intermediate output arrays for reprojected data.

**kwargs

Keyword arguments to be passed to the reprojection function.

Returns:
arrayndarray

The co-added array.

footprintndarray

Footprint of the co-added array. Values of 0 indicate no coverage or valid values in the input image, while values of 1 indicate valid values.