Resample Utilities¶

romancal.resample.resample_utils.build_driz_weight(model, weight_type=None, good_bits: str = None)[source]¶

Builds the drizzle weight map for resampling.

Parameters:

model (object) – The input model.
weight_type (str, optional) – The type of weight to use. Allowed values are ‘ivm’ or ‘exptime’. Defaults to None.
good_bits (str, optional) – The good bits to use for building the mask. Defaults to None.

Returns:

The drizzle weight map.

Return type:

numpy.ndarray

Raises:

ValueError – If an invalid weight type is provided.

Examples

model = get_input_model()
weight_map = build_driz_weight(model, weight_type='ivm', good_bits=[1, 2, 3])
print(weight_map)

romancal.resample.resample_utils.build_mask(dqarr, bitvalue)[source]¶

Build a bit mask from an input DQ array and a bitvalue flag.

Parameters:

dqarr (numpy.ndarray) – Input DQ array.
bitvalue (str) – Bitvalue flag.

Returns:

Bit mask where 1 represents good and 0 represents bad.

Return type:

ndarray

Notes

The function interprets the bitvalue flag using the astropy.nddata.bitmask.interpret_bit_flags function.
If the bitvalue is None, the function returns a bit mask with all elements set to 1.
Otherwise, the function performs a bitwise AND operation between the dqarr and the complement of the bitvalue, and then applies a logical NOT operation to obtain the bit mask.
The resulting bit mask is returned as a numpy.ndarray of dtype numpy.uint8.

romancal.resample.resample_utils.calc_gwcs_pixmap(in_wcs, out_wcs, shape=None)[source]¶

Generate a pixel map grid using the input and output WCS.

Parameters:

in_wcs (WCS) – Input WCS.
out_wcs (WCS) – Output WCS.
shape (tuple, optional) – Shape of the data. If provided, the bounding box will be calculated from the shape. If not provided, the bounding box will be calculated from the input WCS.

Returns:

pixmap – The calculated pixel map grid.

Return type:

ndarray

romancal.resample.resample_utils.decode_context(context, x, y)[source]¶

Get 0-based indices of input images that contributed to (resampled) output pixel with coordinates x and y.

Parameters:

context (numpy.ndarray) – A 3D ndarray of integral data type.
x (int, list of integers, numpy.ndarray of integers) – X-coordinate of pixels to decode (3rd index into the context array)
y (int, list of integers, numpy.ndarray of integers) – Y-coordinate of pixels to decode (2nd index into the context array)

Returns:

A list of numpy.ndarray objects each containing indices of input images
that have contributed to an output pixel with coordinates x and y.
The length of returned list is equal to the number of input coordinate
arrays x and y.

Examples

An example context array for an output image of array shape (5, 6) obtained by resampling 80 input images.

con = np.array(
    [[[0, 0, 0, 0, 0, 0],
      [0, 0, 0, 36196864, 0, 0],
      [0, 0, 0, 0, 0, 0],
      [0, 0, 0, 0, 0, 0],
      [0, 0, 537920000, 0, 0, 0]],
     [[0, 0, 0, 0, 0, 0,],
      [0, 0, 0, 67125536, 0, 0],
      [0, 0, 0, 0, 0, 0],
      [0, 0, 0, 0, 0, 0],
      [0, 0, 163856, 0, 0, 0]],
     [[0, 0, 0, 0, 0, 0],
      [0, 0, 0, 8203, 0, 0],
      [0, 0, 0, 0, 0, 0],
      [0, 0, 0, 0, 0, 0],
      [0, 0, 32865, 0, 0, 0]]],
    dtype=np.int32
)
decode_context(con, [3, 2], [1, 4])
[array([ 9, 12, 14, 19, 21, 25, 37, 40, 46, 58, 64, 65, 67, 77]),
array([ 9, 20, 29, 36, 47, 49, 64, 69, 70, 79])]

romancal.resample.resample_utils.make_output_wcs(input_models, pscale_ratio=None, pscale=None, rotation=None, shape=None, crpix: Tuple[float, float] = None, crval: Tuple[float, float] = None)[source]¶

Generate output WCS here based on footprints of all input WCS objects

Parameters:

input_models (list of roman_datamodels.datamodels.DataModel) – Each datamodel must have a gwcs.wcs.WCS object.
pscale_ratio (float, optional) – Ratio of input to output pixel scale. Ignored when pscale is provided.
pscale (float, None, optional) – Absolute pixel scale in degrees. When provided, overrides pscale_ratio.
rotation (float, None, optional) – Position angle (in degrees) of output image’s Y-axis relative to North. A value of 0.0 would orient the final output image to be North up. The default of None specifies that the images will not be rotated, but will instead be resampled in the default orientation for the camera with the x and y axes of the resampled image corresponding approximately to the detector axes.
shape (tuple of int, None, optional) – Shape of the image (data array) using numpy.ndarray convention (ny first and nx second). This value will be assigned to pixel_shape and array_shape properties of the returned WCS object.
crpix (tuple of float, None, optional) – Position of the reference pixel in the image array. If ref_pixel is not specified, it will be set to the center of the bounding box of the returned WCS object.
crval (tuple of float, None, optional) – Right ascension and declination of the reference pixel. Automatically computed if not provided.

Returns:

output_wcs – WCS object, with defined domain, covering entire set of input frames

Return type:

object

romancal.resample.resample_utils.reproject(wcs1, wcs2)[source]¶

Given two WCSs or transforms return a function which takes pixel coordinates in the first WCS or transform and computes them in the second one. It performs the forward transformation of wcs1 followed by the inverse of wcs2.

Parameters:

wcs1 (astropy.wcs.WCS or gwcs.wcs.WCS or astropy.modeling.Model) – WCS objects.
wcs2 (astropy.wcs.WCS or gwcs.wcs.WCS or astropy.modeling.Model) – WCS objects.

Returns:

Function to compute the transformations. It takes (x, y) positions in wcs1 and returns (x, y) positions in wcs2.

Return type:

func