Miscellaneous

Logging

fastDFE uses the standard Python logging module for logging. By default, fastDFE logs to the console at the INFO level. You can change the logging level, to for example DEBUG as follows

fastdfe <- load_fastdfe()

fastdfe$logger$setLevel("DEBUG")

You can also disable the progress bar like this

fastdfe$Settings$disable_pbar <- TRUE

Debugging

If you encounter an unexpected error, you might want to disable parallelization to obtain a more descriptive stack trace (see parallelize in BaseInference, JointInference, etc.). Unfortunately, this being a Python package, the stack trace will be in Python.

Seeding

fastDFE is seeded by default to ensure reproducibility (see seed in BaseInference, Parser, etc.). Randomness is required for various computational tasks, such as bootstrapping, choosing initial values for different optimization runs, and taking subsamples during VCF parsing.

Object-Oriented Design

The fastDFE Python library is implemented using an object-oriented design which carries over to the R interface. This paradigm may be less familiar to R users. To aid comprehension, our documentation often demonstrates how methods are called on the class itself rather than on an instance. For example:

inference <- fastdfe$BaseInference(...)

fastdfe$BaseInference$run(inference)

In this style, the run() method is called on the BaseInference class, and its instance is explicitly passed as an argument.

Alternatively, run() can also be called directly on an instance of a class:

inference$run()

This is more in line with traditional object-oriented programming. Both approaches are functionally equivalent, but the first method is more verbose.