ResampleData

class jwst.resample.resample.ResampleData(input_models, output=None, single=False, blendheaders=True, pixfrac=1.0, kernel='square', fillval='INDEF', weight_type='ivm', good_bits=0, pscale_ratio=1.0, **kwargs)[source]

Bases: object

This is the controlling routine for the resampling process.

Notes

This routine performs the following operations:

1. Extracts parameter settings from input model, such as pixfrac,
   weight type, exposure time (if relevant), and kernel, and merges
   them with any user-provided values.
2. Creates output WCS based on input images and define mapping function
   between all input arrays and the output array.
3. Updates output data model with output arrays from drizzle, including
   a record of metadata from all input models.
Parameters
  • input_models (list of objects) – list of data models, one for each input image

  • output (str) – filename for output

  • kwargs (dict) – Other parameters

Methods Summary

blend_output_metadata(output_model)

Create new output metadata based on blending all input metadata.

do_drizzle()

Pick the correct drizzling mode based on self.single

drizzle_arrays(insci, inwht, input_wcs, …)

Low level routine for performing ‘drizzle’ operation on one image.

resample_many_to_many()

Resample many inputs to many outputs where outputs have a common frame.

resample_many_to_one()

Resample and coadd many inputs to a single output.

resample_variance_array(name, output_model)

Resample variance arrays from self.input_models to the output_model

update_exposure_times(output_model)

Modify exposure time metadata in-place

update_fits_wcs(model)

Update FITS WCS keywords of the resampled image.

update_slit_metadata(model)

Update slit attributes in the resampled slit image.

Methods Documentation

blend_output_metadata(output_model)[source]

Create new output metadata based on blending all input metadata.

do_drizzle()[source]

Pick the correct drizzling mode based on self.single

static drizzle_arrays(insci, inwht, input_wcs, output_wcs, outsci, outwht, outcon, uniqid=1, xmin=None, xmax=None, ymin=None, ymax=None, pixfrac=1.0, kernel='square', fillval='INDEF')[source]

Low level routine for performing ‘drizzle’ operation on one image.

The interface is compatible with STScI code. All images are Python ndarrays, instead of filenames. File handling (input and output) is performed by the calling routine.

Parameters
  • insci (2d array) – A 2d numpy array containing the input image to be drizzled.

  • inwht (2d array) – A 2d numpy array containing the pixel by pixel weighting. Must have the same dimensions as insci. If none is supplied, the weghting is set to one.

  • input_wcs (gwcs.WCS object) – The world coordinate system of the input image.

  • output_wcs (gwcs.WCS object) – The world coordinate system of the output image.

  • outsci (2d array) – A 2d numpy array containing the output image produced by drizzling. On the first call it should be set to zero. Subsequent calls it will hold the intermediate results. This is modified in-place.

  • outwht (2d array) – A 2d numpy array containing the output counts. On the first call it should be set to zero. On subsequent calls it will hold the intermediate results. This is modified in-place.

  • outcon (2d or 3d array, optional) – A 2d or 3d numpy array holding a bitmap of which image was an input for each output pixel. Should be integer zero on first call. Subsequent calls hold intermediate results. This is modified in-place.

  • uniqid (int, optional) – The id number of the input image. Should be one the first time this function is called and incremented by one on each subsequent call.

  • xmin (float, optional) – This and the following three parameters set a bounding rectangle on the input image. Only pixels on the input image inside this rectangle will have their flux added to the output image. Xmin sets the minimum value of the x dimension. The x dimension is the dimension that varies quickest on the image. If the value is zero, no minimum will be set in the x dimension. All four parameters are zero based, counting starts at zero.

  • xmax (float, optional) – Sets the maximum value of the x dimension on the bounding box of the input image. If the value is zero, no maximum will be set in the x dimension, the full x dimension of the output image is the bounding box.

  • ymin (float, optional) – Sets the minimum value in the y dimension on the bounding box. The y dimension varies less rapidly than the x and represents the line index on the input image. If the value is zero, no minimum will be set in the y dimension.

  • ymax (float, optional) – Sets the maximum value in the y dimension. If the value is zero, no maximum will be set in the y dimension, the full x dimension of the output image is the bounding box.

  • pixfrac (float, optional) – The fraction of a pixel that the pixel flux is confined to. The default value of 1 has the pixel flux evenly spread across the image. A value of 0.5 confines it to half a pixel in the linear dimension, so the flux is confined to a quarter of the pixel area when the square kernel is used.

  • kernel (str, optional) – The name of the kernel used to combine the input. The choice of kernel controls the distribution of flux over the kernel. The kernel names are: “square”, “gaussian”, “point”, “tophat”, “turbo”, “lanczos2”, and “lanczos3”. The square kernel is the default.

  • fillval (str, optional) – The value a pixel is set to in the output if the input image does not overlap it. The default value of INDEF does not set a value.

Returns

  • A tuple with three values (a version string, the number of pixels)

  • on the input image that do not overlap the output image, and the

  • number of complete lines on the input image that do not overlap the

  • output input image.

resample_many_to_many()[source]

Resample many inputs to many outputs where outputs have a common frame.

Coadd only different detectors of the same exposure, i.e. map NRCA5 and NRCB5 onto the same output image, as they image different areas of the sky.

Used for outlier detection

resample_many_to_one()[source]

Resample and coadd many inputs to a single output.

Used for stage 3 resampling

resample_variance_array(name, output_model)[source]

Resample variance arrays from self.input_models to the output_model

Loop through self.input_models and resample the name variance array to the same name in output_model. This modifies output_model in-place.

update_exposure_times(output_model)[source]

Modify exposure time metadata in-place

update_fits_wcs(model)[source]

Update FITS WCS keywords of the resampled image.

update_slit_metadata(model)[source]

Update slit attributes in the resampled slit image.

This is needed because model.slit attributes are not in model.meta, so the normal update() method doesn’t work with them. Updates output_model in-place.