LSST Applications 27.0.0,g0265f82a02+469cd937ee,g02d81e74bb+21ad69e7e1,g1470d8bcf6+cbe83ee85a,g2079a07aa2+e67c6346a6,g212a7c68fe+04a9158687,g2305ad1205+94392ce272,g295015adf3+81dd352a9d,g2bbee38e9b+469cd937ee,g337abbeb29+469cd937ee,g3939d97d7f+72a9f7b576,g487adcacf7+71499e7cba,g50ff169b8f+5929b3527e,g52b1c1532d+a6fc98d2e7,g591dd9f2cf+df404f777f,g5a732f18d5+be83d3ecdb,g64a986408d+21ad69e7e1,g858d7b2824+21ad69e7e1,g8a8a8dda67+a6fc98d2e7,g99cad8db69+f62e5b0af5,g9ddcbc5298+d4bad12328,ga1e77700b3+9c366c4306,ga8c6da7877+71e4819109,gb0e22166c9+25ba2f69a1,gb6a65358fc+469cd937ee,gbb8dafda3b+69d3c0e320,gc07e1c2157+a98bf949bb,gc120e1dc64+615ec43309,gc28159a63d+469cd937ee,gcf0d15dbbd+72a9f7b576,gdaeeff99f8+a38ce5ea23,ge6526c86ff+3a7c1ac5f1,ge79ae78c31+469cd937ee,gee10cc3b42+a6fc98d2e7,gf1cff7945b+21ad69e7e1,gfbcc870c63+9a11dc8c8f
LSST Data Management Base Package
|
Classes | |
class | CartesianFrame |
class | EllipseFrame |
class | EllipticalParametricComponent |
class | ParametricComponent |
Functions | |
np.ndarray | gaussian2d (np.ndarray params, EllipseFrame ellipse) |
np.ndarray | grad_gaussian2 (np.ndarray input_grad, np.ndarray params, np.ndarray morph, np.ndarray spectrum, EllipseFrame ellipse) |
np.ndarray | circular_gaussian (Sequence[int] center, CartesianFrame frame, float sigma) |
np.ndarray | grad_circular_gaussian (np.ndarray input_grad, np.ndarray params, np.ndarray morph, np.ndarray spectrum, CartesianFrame frame, float sigma) |
integrated_gaussian (np.ndarray params, CartesianFrame frame) | |
np.ndarray | grad_integrated_gaussian (np.ndarray input_grad, np.ndarray params, np.ndarray morph, np.ndarray spectrum, CartesianFrame frame) |
np.ndarray | bounded_prox (np.ndarray params, np.ndarray proxmin, np.ndarray proxmax) |
np.ndarray | sersic (np.ndarray params, EllipseFrame ellipse) |
np.ndarray | grad_sersic (np.ndarray input_grad, np.ndarray params, np.ndarray morph, np.ndarray spectrum, EllipseFrame ellipse) |
Variables | |
int | MIN_RADIUS = 1e-20 |
SQRT_PI_2 = np.sqrt(np.pi / 2) | |
SERSIC_B1 = gamma.ppf(0.5, 2) | |
np.ndarray lsst.scarlet.lite.models.parametric.bounded_prox | ( | np.ndarray | params, |
np.ndarray | proxmin, | ||
np.ndarray | proxmax ) |
A bounded proximal operator This function updates `params` in place. Parameters ---------- params: The array of parameters to constrain. proxmin: The array of minimum values for each parameter. proxmax: The array of maximum values for each parameter. Returns ------- result: The updated parameters.
Definition at line 535 of file parametric.py.
np.ndarray lsst.scarlet.lite.models.parametric.circular_gaussian | ( | Sequence[int] | center, |
CartesianFrame | frame, | ||
float | sigma ) |
Model of a circularly symmetric Gaussian Parameters ---------- center: The center of the Gaussian. frame: The frame in which to generate the image of the circular Gaussian sigma: The standard deviation. Returns ------- result: The image of the circular Gaussian.
Definition at line 388 of file parametric.py.
np.ndarray lsst.scarlet.lite.models.parametric.gaussian2d | ( | np.ndarray | params, |
EllipseFrame | ellipse ) |
Model of a 2D elliptical gaussian Parameters ---------- params: The parameters of the function. In this case there are none outside of the ellipticity ellipse: The ellipse parameters to scale the radius in all directions. Returns ------- result: The 2D guassian for the given ellipse parameters
Definition at line 335 of file parametric.py.
np.ndarray lsst.scarlet.lite.models.parametric.grad_circular_gaussian | ( | np.ndarray | input_grad, |
np.ndarray | params, | ||
np.ndarray | morph, | ||
np.ndarray | spectrum, | ||
CartesianFrame | frame, | ||
float | sigma ) |
Gradient of the the component model wrt the Gaussian morphology parameters Parameters ---------- input_grad: Gradient of the likelihood wrt the component model params: The parameters of the morphology. morph: The model of the morphology. spectrum: The model of the spectrum. frame: The frame in which to generate the image of the circular Gaussian. sigma: The standard deviation.
Definition at line 411 of file parametric.py.
np.ndarray lsst.scarlet.lite.models.parametric.grad_gaussian2 | ( | np.ndarray | input_grad, |
np.ndarray | params, | ||
np.ndarray | morph, | ||
np.ndarray | spectrum, | ||
EllipseFrame | ellipse ) |
Gradient of the the component model wrt the Gaussian morphology parameters Parameters ---------- input_grad: Gradient of the likelihood wrt the component model params: The parameters of the morphology. morph: The model of the morphology. spectrum: The model of the spectrum. ellipse: The ellipse parameters to scale the radius in all directions.
Definition at line 354 of file parametric.py.
np.ndarray lsst.scarlet.lite.models.parametric.grad_integrated_gaussian | ( | np.ndarray | input_grad, |
np.ndarray | params, | ||
np.ndarray | morph, | ||
np.ndarray | spectrum, | ||
CartesianFrame | frame ) |
Gradient of the component model wrt the Gaussian morphology parameters Parameters ---------- input_grad: Gradient of the likelihood wrt the component model parameters. params: The parameters of the morphology. morph: The model of the morphology. spectrum: The model of the spectrum. frame: The frame in which to generate the image of the circular Gaussian. Returns ------- result: The gradient of the component morphology.
Definition at line 478 of file parametric.py.
np.ndarray lsst.scarlet.lite.models.parametric.grad_sersic | ( | np.ndarray | input_grad, |
np.ndarray | params, | ||
np.ndarray | morph, | ||
np.ndarray | spectrum, | ||
EllipseFrame | ellipse ) |
Gradient of the component model wrt the morphology parameters Parameters ---------- input_grad: Gradient of the likelihood wrt the component model params: The parameters of the morphology. morph: The model of the morphology. spectrum: The model of the spectrum. ellipse: The ellipse parameters to scale the radius in all directions.
Definition at line 589 of file parametric.py.
lsst.scarlet.lite.models.parametric.integrated_gaussian | ( | np.ndarray | params, |
CartesianFrame | frame ) |
Model of a circularly symmetric Gaussian integrated over pixels This differs from `circularGaussian` because the gaussian function is integrated over each pixel to replicate the pixelated image version of a Gaussian function. Parameters ---------- params: The center of the Gaussian. frame: The frame in which to generate the image of the circular Gaussian Returns ------- result: The image of the circular Gaussian.
Definition at line 448 of file parametric.py.
np.ndarray lsst.scarlet.lite.models.parametric.sersic | ( | np.ndarray | params, |
EllipseFrame | ellipse ) |
Generate a Sersic Model. Parameters ---------- params: The parameters of the function. In this case the only parameter is the sersic index ``n``. ellipse: The ellipse parameters to scale the radius in all directions. Returns ------- result: The model for the given ellipse parameters
Definition at line 561 of file parametric.py.
int lsst.scarlet.lite.models.parametric.MIN_RADIUS = 1e-20 |
Definition at line 51 of file parametric.py.
lsst.scarlet.lite.models.parametric.SERSIC_B1 = gamma.ppf(0.5, 2) |
Definition at line 57 of file parametric.py.
lsst.scarlet.lite.models.parametric.SQRT_PI_2 = np.sqrt(np.pi / 2) |
Definition at line 54 of file parametric.py.