This is a C++ version of the machine-learning photometric redshift code GPZ (Almosallam et al. 2016a,2016b,2017). Below is a list of the main selling points and important facts:
- GPz++ is free software and does not require a MatLab license.
- GPz++ runs up to 20 times faster than the MatLab version.
- GPz++ implements GPz v2.0, with support for noisy and missing data.
- GPz++ can only predict a scalar output, while the MatLab version can predict vectors.
If you use this code for your own work, please cite this repository, as well as Almosallam et al. (2016a,2016b) where GPz was first introduced, and Almosallam (2017) where all the features of GPz v2.0 are described.
To install GPz++, you first need to build it. For this, you will need a recent C++ compiler, git and CMake. All of these tools are available by default on most modern linux distributions, and for MacOS you may need to install them with MacPorts. From there installing GPz++ is very easy. Navigate to a directory of your choosing where the code will be downloaded (it will be placed in a subdirectory called gpzpp), then execute the following commands:
# Download the code and dependencies
git clone https://github.com/cschreib/gpzpp.git
cd gpzpp
# Compile
mkdir build
cd build
cmake ../
make install
This will create an executable called gpz++ in the gpzpp/bin directory, which you can use immediately.
To run GPz++, all you need is to specify the path to a parameter file containing all the required information (paths to catalogs, options of the GPz model, ...):
gpz++ gpz.param
See the gpz.param file in the example directory for a full description of the content of this file.
The C++ version of GPz was developed by Corentin Schreiber for the Euclid space mission, with funding from the UK Space Agency, under the supervision of Matt Jarvis.
Particular thanks go to the author of the original GPz implementation, Ibrahim Almosallam.