Inheritance diagram for lsst.scarlet.lite.models.fit_psf.FittedPsfObservation:
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") | |
| np.ndarray | grad_fit_kernel (self, np.ndarray input_grad, np.ndarray kernel, np.ndarray model_fft) |
| np.ndarray | prox_kernel (self, np.ndarray kernel) |
| np.ndarray | fitted_kernel (self) |
| cached_kernel (self) | |
| Image | convolve (self, Image image, str|None mode=None, bool grad=False) |
| update (self, int it, np.ndarray input_grad, Image model) | |
| None | parameterize (self, Callable parameterization) |
Public Attributes | |
| axes | |
| fft_shape | |
Protected Attributes | |
| _fitted_kernel | |
Detailed Description
An observation that fits the PSF used to convolve the model.
Definition at line 36 of file fit_psf.py.
Constructor & Destructor Documentation
◆ __init__()
| lsst.scarlet.lite.models.fit_psf.FittedPsfObservation.__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" ) |
Initialize a `FitPsfObservation` See `Observation` for a description of the parameters.
Reimplemented from lsst.scarlet.lite.observation.Observation.
Definition at line 39 of file fit_psf.py.
51 ):
52 """Initialize a `FitPsfObservation`
53
54 See `Observation` for a description of the parameters.
55 """
56 super().__init__(
57 images,
58 variance,
59 weights,
60 psfs,
61 model_psf,
62 noise_rms,
63 bbox,
64 bands,
65 padding,
66 convolution_mode,
67 )
68
69 self.axes = (-2, -1)
70
71 self.fft_shape = get_fft_shape(self.images.data[0], psfs[0], padding, self.axes)
72
73 # Make the DFT of the psf a fittable parameter
74 self._fitted_kernel = parameter(cast(Fourier, self.diff_kernel).fft(self.fft_shape, self.axes))
75
Member Function Documentation
◆ cached_kernel()
| lsst.scarlet.lite.models.fit_psf.FittedPsfObservation.cached_kernel | ( | self | ) |
Definition at line 93 of file fit_psf.py.
93 def cached_kernel(self):
94 return self.fitted_kernel.real - self.fitted_kernel.imag * 1j
95
◆ convolve()
| Image lsst.scarlet.lite.models.fit_psf.FittedPsfObservation.convolve | ( | self, | |
| Image | image, | ||
| str | None | mode = None, | ||
| bool | grad = False ) |
Convolve the model into the observed seeing in each band.
Parameters
----------
image:
The 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.
Reimplemented from lsst.scarlet.lite.observation.Observation.
Definition at line 96 of file fit_psf.py.
96 def convolve(self, image: Image, mode: str | None = None, grad: bool = False) -> Image:
97 """Convolve the model into the observed seeing in each band.
98
99 Parameters
100 ----------
101 image:
102 The image to convolve
103 mode:
104 The convolution mode to use.
105 This should be "real" or "fft" or `None`,
106 where `None` will use the default `convolution_mode`
107 specified during init.
108 grad:
109 Whether this is a backward gradient convolution
110 (`grad==True`) or a pure convolution with the PSF.
111 """
112 if grad:
113 kernel = self.cached_kernel
114 else:
115 kernel = self.fitted_kernel
116
117 if mode != "fft" and mode is not None:
118 return super().convolve(image, mode, grad)
119
120 fft_image = Fourier(image.data)
121 fft = fft_image.fft(self.fft_shape, self.axes)
122
123 result = Fourier.from_fft(fft * kernel, self.fft_shape, image.shape, self.axes)
124 return Image(result.image, bands=image.bands, yx0=image.yx0)
125
◆ fitted_kernel()
| np.ndarray lsst.scarlet.lite.models.fit_psf.FittedPsfObservation.fitted_kernel | ( | self | ) |
Definition at line 89 of file fit_psf.py.
89 def fitted_kernel(self) -> np.ndarray:
90 return self._fitted_kernel.x
91
◆ grad_fit_kernel()
| np.ndarray lsst.scarlet.lite.models.fit_psf.FittedPsfObservation.grad_fit_kernel | ( | self, | |
| np.ndarray | input_grad, | ||
| np.ndarray | kernel, | ||
| np.ndarray | model_fft ) |
Definition at line 76 of file fit_psf.py.
78 ) -> np.ndarray:
79 # Transform the upstream gradient into k-space
80 grad_fft = Fourier(input_grad)
81 _grad_fft = grad_fft.fft(self.fft_shape, self.axes)
82 return _grad_fft * model_fft
83
◆ parameterize()
| None lsst.scarlet.lite.models.fit_psf.FittedPsfObservation.parameterize | ( | self, | |
| Callable | parameterization ) |
Convert the component parameter arrays into Parameter instances
Parameters
----------
parameterization: Callable
A function to use to convert parameters of a given type into
a `Parameter` in place. It should take a single argument that
is the `Component` or `Source` that is to be parameterized.
Definition at line 131 of file fit_psf.py.
131 def parameterize(self, parameterization: Callable) -> None:
132 """Convert the component parameter arrays into Parameter instances
133
134 Parameters
135 ----------
136 parameterization: Callable
137 A function to use to convert parameters of a given type into
138 a `Parameter` in place. It should take a single argument that
139 is the `Component` or `Source` that is to be parameterized.
140 """
141 # Update the spectrum and morph in place
142 parameterization(self)
143 # update the parameters
144 self._fitted_kernel.grad = self.grad_fit_kernel
145 self._fitted_kernel.prox = self.prox_kernel
146
147
◆ prox_kernel()
| np.ndarray lsst.scarlet.lite.models.fit_psf.FittedPsfObservation.prox_kernel | ( | self, | |
| np.ndarray | kernel ) |
Definition at line 84 of file fit_psf.py.
84 def prox_kernel(self, kernel: np.ndarray) -> np.ndarray:
85 # No prox for now
86 return kernel
87
◆ update()
| lsst.scarlet.lite.models.fit_psf.FittedPsfObservation.update | ( | self, | |
| int | it, | ||
| np.ndarray | input_grad, | ||
| Image | model ) |
Definition at line 126 of file fit_psf.py.
126 def update(self, it: int, input_grad: np.ndarray, model: Image):
127 _model = Fourier(model.data[:, ::-1, ::-1])
128 model_fft = _model.fft(self.fft_shape, self.axes)
129 self._fitted_kernel.update(it, input_grad, model_fft)
130
Member Data Documentation
◆ _fitted_kernel
|
protected |
Definition at line 74 of file fit_psf.py.
◆ axes
| lsst.scarlet.lite.models.fit_psf.FittedPsfObservation.axes |
Definition at line 69 of file fit_psf.py.
◆ fft_shape
| lsst.scarlet.lite.models.fit_psf.FittedPsfObservation.fft_shape |
Definition at line 71 of file fit_psf.py.
The documentation for this class was generated from the following file:
- /j/snowflake/release/lsstsw/stack/lsst-scipipe-8.0.0/Linux64/scarlet_lite/gae0086650b+585e252eca/python/lsst/scarlet/lite/models/fit_psf.py
