MPI_IS_gaussian_process Directory Reference

Directory dependency graph for MPI_IS_gaussian_process:

Directories
	src
	tools

Detailed Description

Due to mechanical imprecisions, many telescope mounts show periodic tracking errors. This work uses the Gaussian process regression framework to predict and reduce the periodoc error.

Compile Instructions

So far, this feature is optional and needs to be activated with CMake to be compiled. The GP guiding feature is activated by calling cmake with the additional flag -DGUIDING_GAUSSIAN_PROCESS=true. The rest of the build process remains the same.

Files Overview

The GP guiding project introduced new files to PHD2 guiding. These files are listed, together with a short description, in the following.

Files in the Project Root

File name	Description
guide_algorithm_gaussian_process.cpp	Provides the UI for the GP-based guider.
guide_algorithm_gaussian_process.h	Header for GP guider UI.

Files in contributions/MPI_IS_gaussian_process

File name	Description
CMakeLists.txt	CMake file for this subtree.
README.md	This file.
src/covariance_functions.cpp	Provides the necessary covariance functions / kernels for the Gaussian process.
src/covariance_functions.h	Header for covariance functions.
src/gaussian_process.cpp	Provides the Gaussian process inference and prediction functionality.
src/gaussian_process.h	Header for the Gaussian process.
src/gaussian_process_guider.cpp	Provides the GP-based control algorithm for the right ascension axis.
src/gaussian_process_guider.h	Header for the Gaussian process guider.
tools/math_tools.cpp	Mathematical tools for the GP implementation.
tools/math_tools.h	Header for the math tools.
tools/plot_dec_data.py	Python plotting for debugging.
tools/plot_gp_data.py	Python plotting for debugging.
tools/plot_spectrum.py	Python plotting for debugging.
tools/analyze_data.py	Python script that analyzes and plots data from testers.
tools/optimize_params.py	Python script for rudimentary parameter optimization.
tests/gaussian_process/gaussian_process_test.cpp	Unittests for the GP.
tests/gaussian_process/math_tools_test.cpp	Unittests for the math tools.
tests/gaussian_process/dataset01.csv	Real-world dataset for certain tests.
tests/gaussian_process/dataset02.csv	Real-world dataset for certain tests.
tests/gaussian_process/dataset03.csv	Real-world dataset for certain tests.
tests/gaussian_process/performance_dataset01.csv	Real-world dataset for performance tests.
tests/gaussian_process/performance_dataset02.csv	Real-world dataset for performance tests.
tests/gaussian_process/performance_dataset03.csv	Real-world dataset for performance tests.
tests/gaussian_process/performance_dataset04.csv	Real-world dataset for performance tests.
tests/gaussian_process/performance_dataset05.csv	Real-world dataset for performance tests.
tests/gaussian_process/performance_dataset06.csv	Real-world dataset for performance tests.
tests/gaussian_process/performance_dataset07.csv	Real-world dataset for performance tests.
tests/gaussian_process/performance_dataset08.csv	Real-world dataset for performance tests.

Copyright and Licensing

Copyright 2014-2017, Max Planck Society. All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.