DummyObservation

class lsst.meas.extensions.scarlet.DummyObservation(psfs, model_psf, bbox, dtype)

Bases: scarlet.lite.models.LiteObservation

An observation that does not have any image data

In order to reproduce a model in an observed seeing we make use of the scarlet LiteObservation class, but since we are not fitting the model to data we can use empty arrays for the image, variance, and weight data, and zero for the noise_rms.

Parameters:

psfs : numpy.ndarray

The array of PSF images in each band

psf_model : numpy.ndarray

The image of the model PSF.

bbox : scarlet.bbox.Box

dtype : numpy.dtype

The data type of the model that is generated.

Attributes Summary

convolution_bounds	Build the slices needed for convolution in real space
data	Mirror of Observation.data to make APIs match
dtype	The dtype of the observation is the dtype of the images
n_bands	The number of bands in the observation
shape	The shape of the iamges, variance, etc.

Methods Summary

convolve(image[, mode, grad])	Convolve the model into the observed seeing in each band.
render(model)	Mirror of `Observation.render to make APIs match

Attributes Documentation

convolution_bounds

Build the slices needed for convolution in real space

data

Mirror of Observation.data to make APIs match

dtype

The dtype of the observation is the dtype of the images

n_bands

The number of bands in the observation

shape

The shape of the iamges, variance, etc.

Methods Documentation

convolve(image, mode=None, grad=False)

Convolve the model into the observed seeing in each band.

Parameters:

image: `~numpy.array`

The image to convolve

mode: `str`

The convolution mode to use. This should be “real” or “fft” or None, where None will use the default convolution_mode specified during init.

grad: `bool`

Whether this is a backward gradient convolution (grad==True) or a pure convolution with the PSF.

render(model)

Mirror of `Observation.render to make APIs match