LSST Applications g013ef56533+d2224463a4,g199a45376c+0ba108daf9,g19c4beb06c+9f335b2115,g1fd858c14a+2459ca3e43,g210f2d0738+2d3d333a78,g262e1987ae+abbb004f04,g2825c19fe3+eedc38578d,g29ae962dfc+0cb55f06ef,g2cef7863aa+aef1011c0b,g35bb328faa+8c5ae1fdc5,g3fd5ace14f+19c3a54948,g47891489e3+501a489530,g4cdb532a89+a047e97985,g511e8cfd20+ce1f47b6d6,g53246c7159+8c5ae1fdc5,g54cd7ddccb+890c8e1e5d,g5fd55ab2c7+951cc3f256,g64539dfbff+2d3d333a78,g67b6fd64d1+501a489530,g67fd3c3899+2d3d333a78,g74acd417e5+0ea5dee12c,g786e29fd12+668abc6043,g87389fa792+8856018cbb,g89139ef638+501a489530,g8d7436a09f+5ea4c44d25,g8ea07a8fe4+81eaaadc04,g90f42f885a+34c0557caf,g9486f8a5af+165c016931,g97be763408+d5e351dcc8,gbf99507273+8c5ae1fdc5,gc2a301910b+2d3d333a78,gca7fc764a6+501a489530,gce8aa8abaa+8c5ae1fdc5,gd7ef33dd92+501a489530,gdab6d2f7ff+0ea5dee12c,ge410e46f29+501a489530,geaed405ab2+e3b4b2a692,gf9a733ac38+8c5ae1fdc5,w.2025.41
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.