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LSST Applications g013ef56533+b8d55c8942,g083dd6704c+a047e97985,g199a45376c+0ba108daf9,g1fd858c14a+7a3b874d60,g210f2d0738+7416ca6900,g262e1987ae+1d557ba9a3,g29ae962dfc+519d34895e,g2cef7863aa+aef1011c0b,g30d7c61c20+36d16ea71a,g35bb328faa+8c5ae1fdc5,g3fd5ace14f+cb326ad149,g47891489e3+f459a6810c,g53246c7159+8c5ae1fdc5,g54cd7ddccb+890c8e1e5d,g5a60e81ecd+6240c63dbc,g64539dfbff+7416ca6900,g67b6fd64d1+f459a6810c,g6ebf1fc0d4+8c5ae1fdc5,g74acd417e5+0bae3c876a,g786e29fd12+668abc6043,g87389fa792+8856018cbb,g89139ef638+f459a6810c,g8d7436a09f+dee7680868,g8ea07a8fe4+81eaaadc04,g90f42f885a+34c0557caf,g97be763408+14b8164b5b,g98a1a72a9c+8389601a76,g98df359435+fff771c62d,gb8cb2b794d+6728931916,gbf99507273+8c5ae1fdc5,gc2a301910b+7416ca6900,gca7fc764a6+f459a6810c,gd7ef33dd92+f459a6810c,gdab6d2f7ff+0bae3c876a,ge410e46f29+f459a6810c,ge41e95a9f2+7416ca6900,geaed405ab2+e3b4b2a692,gf9a733ac38+8c5ae1fdc5,w.2025.43
LSST Data Management Base Package
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A class that evaluates 2D Gaussians and renders them in images. More...
#include <evaluate.h>
Public Types | |
| typedef Image< T, Data > | DataT |
| typedef ImageArray< T, Data > | ImageArrayT |
| typedef Image< idx_type, Indices > | IndicesT |
Public Member Functions | |
| GaussianEvaluator (int x=0, const std::shared_ptr< const ConvolvedGaussians > gaussians=nullptr) | |
| GaussianEvaluator (const std::shared_ptr< const ConvolvedGaussians > gaussians, const std::shared_ptr< const Image< T, Data > > data=nullptr, const std::shared_ptr< const Image< T, Data > > sigma_inv=nullptr, const std::shared_ptr< Image< T, Data > > output=nullptr, const std::shared_ptr< Image< T, Data > > residual=nullptr, const std::shared_ptr< ImageArrayT > grads=nullptr, const std::shared_ptr< const Image< idx_type, Indices > > grad_param_map=nullptr, const std::shared_ptr< const Image< T, Data > > grad_param_factor=nullptr, const std::shared_ptr< const Image< idx_type, Indices > > extra_param_map=nullptr, const std::shared_ptr< const Image< T, Data > > extra_param_factor=nullptr, const std::shared_ptr< const Image< T, Data > > background=nullptr) | |
| Construct a GaussianEvaluator, inferring outputs from inputs. | |
| ~GaussianEvaluator () override=default | |
| Data const & | IMAGE_NULL_CONST () const |
| Indices const & | INDICES_NULL_CONST () const |
| ImageArray< T, Data > const & | IMAGEARRAY_NULL_CONST () const |
| const CoordinateSystem & | get_coordsys () const |
| size_t | get_n_cols () const |
| size_t | get_n_rows () const |
| size_t | get_size () const |
| double | loglike_pixel (bool to_add=false) |
| Compute the model and/or log-likelihood and/or gradient (d(log-likelihood)/dx) and/or Jacobian (dmodel/dx) for a Gaussian mixture model. | |
| std::string | repr (bool name_keywords=false, std::string_view namespace_separator=Object::CC_NAMESPACE_SEPARATOR) const override |
| Return a full, callable string representation of this. | |
| std::string | str () const override |
| Return a brief, human-readable string representation of this. | |
Static Public Member Functions | |
| static std::string_view | null_str (const std::string_view &namespace_separator) |
Static Public Attributes | |
| static constexpr std::string_view | CC_NAMESPACE_SEPARATOR = "::" |
| The C++ namespace separator. | |
| static constexpr std::string_view | NULL_STR_GENERAL = "None" |
| static constexpr std::string_view | PY_NAMESPACE_SEPARATOR = "." |
A class that evaluates 2D Gaussians and renders them in images.
This class is designed for repeated re-evaluation of Gaussian mixture models by caching references to the inputs and outputs.
| T | The data type (e.g. float, int) |
| Data | The data array class |
| Indices | The index array class (usually a size_t array) |
Definition at line 668 of file evaluate.h.
| typedef Image<T, Data> lsst::gauss2d::GaussianEvaluator< T, Data, Indices >::DataT |
Definition at line 670 of file evaluate.h.
| typedef ImageArray<T, Data> lsst::gauss2d::GaussianEvaluator< T, Data, Indices >::ImageArrayT |
Definition at line 671 of file evaluate.h.
| typedef Image<idx_type, Indices> lsst::gauss2d::GaussianEvaluator< T, Data, Indices >::IndicesT |
Definition at line 672 of file evaluate.h.
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inline |
Definition at line 674 of file evaluate.h.
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inline |
Construct a GaussianEvaluator, inferring outputs from inputs.
| gaussians | N x 6 matrix of Gaussian parameters [cen_x, cen_y, flux, sigma_x, sigma_y, rho] |
| coordsys | Coordinate system for all images. |
| data | 2D input Data matrix. |
| sigma_inv | 2D inverse sigma (sqrt variance) map of the same size as data. |
| output | 2D output matrix of the same size as ImageD. |
| residual | 2D output matrix for residual ((data-model)/sigma) of the same size as data. |
| grads | Output for gradients. Can either an M x 1 vector or M x Data 3D Jacobian matrix, where M <= N x 6 to allow for condensing gradients based on grad_param_map. |
| grad_param_map | Nx6 matrix of indices of grad to add each gradient to. For example, if four gaussians share the same cen_x, one could set grad_param_map[0:4,0] = 0. All values must have index < grad.size(). |
| grad_param_factor | Nx6 matrix of multiplicative factors for each gradient term. For example, if a Gaussian is a sub-component of a multi-Gaussian component with a total flux parameter but fixed ratios, as in multi-Gaussian Sersic models. |
| extra_param_map | Nx2 matrix of indices to add to an extra (meta)parameter. The first item is the index of the Gaussian to add and the second is the index of the metaparameter. |
| extra_param_factor | Nx3 matrix of multiplicative factors for each extra gradient term. The factors are ordered L, sigma_x, sigma_y. |
| background | A background model image. Only 1x1 (constant level) backgrounds are supported. |
Definition at line 699 of file evaluate.h.
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overridedefault |
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inline |
Definition at line 878 of file evaluate.h.
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inline |
Definition at line 879 of file evaluate.h.
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inline |
Definition at line 880 of file evaluate.h.
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inline |
Definition at line 881 of file evaluate.h.
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Definition at line 874 of file evaluate.h.
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Definition at line 876 of file evaluate.h.
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Definition at line 875 of file evaluate.h.
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Compute the model and/or log-likelihood and/or gradient (d(log-likelihood)/dx) and/or Jacobian (dmodel/dx) for a Gaussian mixture model.
This function calls a series of templated functions with explicit instantiations. This is solely for the purpose of avoiding having to manually write a series of nested conditionals. My hope is that the templating will insert no-op functions wherever there's nothing to do instead of a needless branch inside each pixel's loop, and that the compiler will actually inline everything for maximum performance. Whether that actually happens or not is anyone's guess.
TODO: Consider override to compute LL and Jacobian, even if it's only useful for debug purposes.
Definition at line 897 of file evaluate.h.
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inlinestaticinherited |
Definition at line 49 of file object.h.
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inlineoverridevirtual |
Return a full, callable string representation of this.
| name_keywords | Whether to prefix arguments with "{name}=", where name is the arg name in the header (as with keyword arguments in Python). |
| namespace_separator | The string to use to delimit namespaces, i.e. :: in C++ and . in Python. |
Implements lsst::gauss2d::Object.
Definition at line 907 of file evaluate.h.
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inlineoverridevirtual |
Return a brief, human-readable string representation of this.
Implements lsst::gauss2d::Object.
Definition at line 929 of file evaluate.h.
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staticconstexprinherited |
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staticconstexprinherited |
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staticconstexprinherited |