lsst.scarlet.lite.observation.Observation Class Reference

Inheritance diagram for lsst.scarlet.lite.observation.Observation:

Public Member Functions
	__init__ (self, np.ndarray|Image images, np.ndarray|Image variance, np.ndarray|Image weights, np.ndarray psfs, np.ndarray|None model_psf=None, np.ndarray|None noise_rms=None, Box|None bbox=None, tuple|None bands=None, int padding=3, str convolution_mode="fft")
tuple	bands (self)
Box	bbox (self)
Image	convolve (self, Image image, str|None mode=None, bool grad=False)
float	log_likelihood (self, Image model)
Observation	__getitem__ (self, Any indices)
Observation	__copy__ (self)
Observation	__deepcopy__ (self, dict[int, Any] memo)
Observation	copy (self, bool deep=False)
tuple[int, int, int]	shape (self)
int	n_bands (self)
npt.DTypeLike	dtype (self)
tuple[int, int, int, int]	convolution_bounds (self)

Static Public Member Functions
Observation	empty (tuple[Any] bands, np.ndarray psfs, np.ndarray model_psf, Box bbox, npt.DTypeLike dtype)

Public Attributes
	images = images
	variance = _set_image_like(variance, bands, bbox)
	weights = _set_image_like(weights, bands, bbox)
	psfs = psfs
	mode = convolution_mode
	noise_rms = noise_rms
	model_psf = model_psf
	padding = padding
Fourier|None	diff_kernel = cast(Fourier, match_kernel(psfs, model_psf, padding=padding))
Fourier|None	grad_kernel = Fourier(diff_img[:, ::-1, ::-1])
	bands

Protected Attributes
tuple[int, int, int, int]|None	_convolution_bounds = None

Detailed Description

A single observation

This class contains all of the observed images and derived
properties, like PSFs, variance map, and weight maps,
required for most optimizers.
This includes methods to match a scarlet model PSF to the oberved PSF
in each band.

Notes
-----
This is effectively a combination of the `Observation` and
`Renderer` class from scarlet main, greatly simplified due
to the assumptions that the observations are all resampled
onto the same pixel grid and that the `images` contain all
of the information for all of the model bands.

Parameters
----------
images:
    (bands, y, x) array of observed images.
variance:
    (bands, y, x) array of variance for each image pixel.
weights:
    (bands, y, x) array of weights to use when calculate the
    likelihood of each pixel.
psfs:
    (bands, y, x) array of the PSF image in each band.
model_psf:
    (bands, y, x) array of the model PSF image in each band.
    If `model_psf` is `None` then convolution is performed,
    which should only be done when the observation is a
    PSF matched coadd, and the scarlet model has the same PSF.
noise_rms:
    Per-band average noise RMS. If `noise_rms` is `None` then the mean
    of the sqrt of the variance is used.
bbox:
    The bounding box containing the model. If `bbox` is `None` then
    a `Box` is created that is the shape of `images` with an origin
    at `(0, 0)`.
padding:
    Padding to use when performing an FFT convolution.
convolution_mode:
    The method of convolution. This should be either "fft" or "real".

Member Function Documentation

__copy__()

Observation lsst.scarlet.lite.observation.Observation.__copy__

(

self

)

Create a copy of the observation

Returns
-------
result:
    The copy of the observation.

__deepcopy__()

Observation lsst.scarlet.lite.observation.Observation.__deepcopy__	(		self,
		dict[int, Any]	memo )

Create a deep copy of the observation

Parameters
----------
memo: dict[int, Any]
    The memoization dictionary used by `copy.deepcopy`.

Returns
-------
result:
    The deep copy of the observation.

__getitem__()

Observation lsst.scarlet.lite.observation.Observation.__getitem__	(		self,
		Any	indices )

Get a view for the subset of an image

Parameters
----------
indices:
    The indices to select a subsection of the image.

Returns
-------
result:
    The resulting image obtained by selecting subsets of the iamge
    based on the `indices`.

bands()

tuple lsst.scarlet.lite.observation.Observation.bands

(

self

)

The bands in the observations.

bbox()

Box lsst.scarlet.lite.observation.Observation.bbox

(

self

)

The bounding box for the full observation.

convolution_bounds()

tuple[int, int, int, int] lsst.scarlet.lite.observation.Observation.convolution_bounds

(

self

)

Build the slices needed for convolution in real space

convolve()

Image lsst.scarlet.lite.observation.Observation.convolve	(		self,
		Image	image,
		str | None	mode = None,
		bool	grad = False )

Convolve the model into the observed seeing in each band.

Parameters
----------
image:
    The 3D image to convolve.
mode:
    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:
    Whether this is a backward gradient convolution
    (`grad==True`) or a pure convolution with the PSF.

Returns
-------
result:
    The convolved image.

Reimplemented in lsst.scarlet.lite.models.fit_psf.FittedPsfObservation.

copy()

Observation lsst.scarlet.lite.observation.Observation.copy	(		self,
		bool	deep = False )

Create a copy of the observation

Parameters
----------
deep:
    Whether to perform a deep copy or not.

Returns
-------
result:
    The copy of the observation.

dtype()

npt.DTypeLike lsst.scarlet.lite.observation.Observation.dtype

(

self

)

The dtype of the observation is the dtype of the images

log_likelihood()

float lsst.scarlet.lite.observation.Observation.log_likelihood	(		self,
		Image	model )

Calculate the log likelihood of the given model

Parameters
----------
model:
    Model to compare with the observed images.

Returns
-------
result:
    The log-likelihood of the given model.

n_bands()

int lsst.scarlet.lite.observation.Observation.n_bands

(

self

)

The number of bands in the observation

shape()

tuple[int, int, int] lsst.scarlet.lite.observation.Observation.shape

(

self

)

The shape of the images, variance, etc.

---

The documentation for this class was generated from the following file:

• python/lsst/scarlet/lite/observation.py