Getting started tutorial

Welcome to the WaveDiff tutorial!

This tutorial serves as a walk-through guide of how to set up runs of WaveDiff with different configuration settings.

Basic Execution

The WaveDiff pipeline is launched and managed by wf_psf/run.py script.

A list of command-line arguments can be displayed using the --help option:

> python wf_psf/run.py --help
usage: run.py [-h] --conffile CONFFILE --repodir REPODIR --outputdir OUTPUTDIR

optional arguments:
  -h, --help            show this help message and exit
  --conffile CONFFILE, -c CONFFILE
                        a configuration file containing program settings.
  --repodir REPODIR, -r REPODIR
                        the path of the code repository directory.
  --outputdir OUTPUTDIR, -o OUTPUTDIR
                        the path of the output directory.

There are three arguments, which the user should specify when launching the pipeline.

The first argument: --confile CONFFILE specifies the path to the configuration file storing the parameter options for running the pipeline.

The second argument: --repodir REPODIR is the path to the wf-psf repository.

The third argument: --outputdir OUTPUTDIR is used to set the path to the output directory, which stores the WaveDiff results.

To run WaveDiff, use the following command:

> python wf_psf/run.py -c /path/to/config/file -r /path/to/wf-psf -o /path/to/output/dir

You can test this now using the configuration file configs.yaml provided in the subdirectory configs inside the wf-psf repository. Launching this script will initiate training of the semi parametric DataDriven PSF model. The outputs will be stored in a directory called wf-outputs located by path provided with the argument -o.

Next, we describe to some detail the configuration file structures and content.

Configuration

The input configuration files into WaveDiff are constructed using YAML (Yet Another Markup Language). There is the option to provide a single configuration file with all the parameter settings, but this could lead to a very lengthy file. The other option is to provide a set of configuration files for the each component of the WaveDiff pipeline, i.e. training, metrics evaluation, and plotting. In our implementation, we have a directory named config which stores the various sets of configuration files

config
├── configs.yaml
├── logging.conf
├── metrics_config.yaml
├── plotting_config.yaml
└── training_config.yaml

For example, the training part of the pipeline can contain all of the associated training parameters in a configuration file called training_config.yaml.
The exception is that for logging we use ini file syntax. There is the option to provide as an input a master file configs.yaml which lists the subcomponent configuration files.

---
  training_conf: config/training_config.yaml