LSST Applications 30.0.7,g0e76e35be5+e8e946ae08,g19811a7679+138f7293ba,g199a45376c+5e234f8357,g1fd858c14a+2f48dbc4c4,g262e1987ae+fb36cac54d,g29ae962dfc+d9108a0941,g2c21b0017a+4f59a27f16,g31e44d4a5c+b0138be388,g33ac35c1f1+28b9f72785,g35bb328faa+b0138be388,g40c9b15c53+823ad735c1,g47891489e3+bcc48a0b46,g53246c7159+b0138be388,g64539dfbff+e8e946ae08,g67b6fd64d1+bcc48a0b46,g74acd417e5+422380537a,g76965917b2+a5ca99c4d9,g786e29fd12+796b79145d,g7aefaa3e3d+dc0c200193,g86b635cae8+734fe384f0,g87389fa792+d8b5378923,g89139ef638+bcc48a0b46,g8bbb235e95+3f4f7f9447,g8ea07a8fe4+78a4c88802,g9290983e33+ffdc83c6f7,g92c671f44c+e8e946ae08,gaa753fd333+03f406da14,gbf99507273+b0138be388,gc49b57b85e+8df26ee1f0,gca7fc764a6+bcc48a0b46,gd7ef33dd92+bcc48a0b46,gdab6d2f7ff+422380537a,ge1c02a5578+b0138be388,ge410e46f29+bcc48a0b46,ge80df9fc40+e6db5413d1,geaed405ab2+1de65a85c6,gf5dcc679e7+35a0ce2edd,gf5f1c85443+e8e946ae08
LSST Data Management Base Package
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observation.cc File Reference
#include <memory>
#include <string>
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#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 {
67}
void declare_observation(py::module &m, std::string str_type)

◆ 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::classh<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 Python image using numpy arrrays for storage.
Definition image.h:72
std::vector< ParamBaseRef > ParamRefs
Definition param_defs.h:13
g2d::python::Image< double > Image
Definition test_image.cc:14
g2d::python::Image< bool > Mask
Definition test_image.cc:16