LSST Applications g1653933729+34a971ddd9,g1a997c3884+34a971ddd9,g28da252d5a+e9c12036e6,g2bbee38e9b+387d105147,g2bc492864f+387d105147,g2ca4be77d2+2af33ed832,g2cdde0e794+704103fe75,g3156d2b45e+6e87dc994a,g347aa1857d+387d105147,g35bb328faa+34a971ddd9,g3a166c0a6a+387d105147,g3bc1096a96+da0d0eec6b,g3e281a1b8c+8ec26ec694,g4005a62e65+ba0306790b,g414038480c+9f5be647b3,g41af890bb2+260fbe2614,g5065538af8+ba676e4b71,g5a0bb5165c+019e928339,g717e5f8c0f+90540262f6,g80478fca09+bbe9b7c29a,g8204df1d8d+90540262f6,g82479be7b0+c8d705dbd9,g858d7b2824+90540262f6,g9125e01d80+34a971ddd9,g91f4dbe722+fd1343598d,ga5288a1d22+cbf2f5b209,gae0086650b+34a971ddd9,gb58c049af0+ace264a4f2,gc28159a63d+387d105147,gcf0d15dbbd+c403bb023e,gd6b7c0dfd1+f7139e6704,gda6a2b7d83+c403bb023e,gdaeeff99f8+7774323b41,ge2409df99d+d3bbf40f76,ge33fd446bb+90540262f6,ge79ae78c31+387d105147,gf0baf85859+890af219f9,gf5289d68f6+d7e5a322af,w.2024.37
LSST Data Management Base Package
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Functions
observation.cc File Reference
#include <pybind11/attr.h>
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include <memory>
#include <string>
#include "lsst/gauss2d/fit/observation.h"
#include "lsst/gauss2d/fit/parametric.h"
#include "lsst/gauss2d/python/image.h"
#include "pybind11.h"

Go to the source code of this file.

Functions

template<typename T >
void declare_observation (py::module &m, std::string str_type)
 
void bind_observation (py::module &m)
 

Function Documentation

◆ bind_observation()

void bind_observation ( py::module & m)

Definition at line 64 of file observation.cc.

64 {
65 declare_observation<double>(m, "D");
66 declare_observation<float>(m, "F");
67}
int m
Definition SpanSet.cc:48

◆ declare_observation()

template<typename T >
void declare_observation ( py::module & m,
std::string str_type )

Definition at line 43 of file observation.cc.

43 {
44 typedef g2p::Image<T> Image;
45 typedef g2p::Image<bool> Mask;
46 typedef g2f::Observation<T, Image, Mask> Observation;
47 std::string pyclass_name = std::string("Observation") + str_type;
48 py::class_<Observation, std::shared_ptr<Observation>, g2f::Parametric>(m, pyclass_name.c_str())
50 const g2f::Channel &>(),
51 "image"_a, "sigma_inv"_a, "mask_inv"_a, "channel"_a)
52 .def_property_readonly("channel", &Observation::get_channel)
53 .def_property_readonly("image", &Observation::get_image)
54 .def_property_readonly("mask_inv", &Observation::get_mask_inverse)
55 .def_property_readonly("sigma_inv", &Observation::get_sigma_inverse)
56 .def_property_readonly("n_cols", &Observation::get_n_cols)
57 .def_property_readonly("n_rows", &Observation::get_n_rows)
58 .def("parameters", &Observation::get_parameters, "parameters"_a = g2f::ParamRefs(),
59 "paramfilter"_a = nullptr)
60 .def("__repr__", [](const Observation &self) { return self.repr(true); })
61 .def("__str__", &Observation::str);
62}
T c_str(T... args)
An observational channel, usually representing some range of wavelengths of light.
Definition channel.h:29
An observed single-channel image with an associated variance and mask.
Definition observation.h:35
A parametric object that can return and filter its Parameter instances.
Definition parametric.h:13
A Python image using numpy arrrays for storage.
Definition image.h:72
g2d::python::Image< double > Image
Definition test_image.cc:14
g2d::python::Image< bool > Mask
Definition test_image.cc:16