pypesto.objective.julia

Julia objective

class pypesto.objective.julia.JuliaObjective(module: str, source_file: str | None = None, fun: str | None = None, grad: str | None = None, hess: str | None = None, res: str | None = None, sres: str | None = None)[source]

Bases: Objective

Wrapper around an objective defined in Julia.

This class provides objective function wrappers around Julia objects. It expects the corresponding Julia objects to be defined in a source_file within a module.

We use the PyJulia package to access Julia from inside Python. It can be installed via pip install pypesto[julia], however requires additional Julia dependencies to be installed via:

>>> python -c "import julia; julia.install()"

For further information, see https://pyjulia.readthedocs.io/en/latest/installation.html.

There are some known problems, e.g. with statically linked Python interpreters, see https://pyjulia.readthedocs.io/en/latest/troubleshooting.html for details. Possible solutions are to pass compiled_modules=False to the Julia constructor early in your code:

>>> from julia.api import Julia
>>> jl = Julia(compiled_modules=False)

This however slows down loading and using Julia packages, especially for large ones. An alternative is to use the python-jl command shipped with PyJulia:

>>> python-jl MY_SCRIPT.py

This basically launches a Python interpreter inside Julia. When using Jupyter notebooks, this wrapper can be installed as an additional kernel via:

>>> python -m ipykernel install --name python-jl [--prefix=/path/to/python/env]

And changing the first argument in /path/to/python/env/share/jupyter/kernels/python-jl/kernel.json to python-jl.

Model simulations are eagerly converted to Python objects (specifically, numpy.ndarray and pandas.DataFrame). This can introduce overhead and could be avoided by an alternative lazy implementation.

Parameters:

  • module – Julia module name.

  • source_file – Julia source file name. Defaults to {module}.jl.

  • fun – Names of callables within the Julia code of the corresponding objective functions and derivatives.

  • grad – Names of callables within the Julia code of the corresponding objective functions and derivatives.

  • hess – Names of callables within the Julia code of the corresponding objective functions and derivatives.

  • res – Names of callables within the Julia code of the corresponding objective functions and derivatives.

  • sres – Names of callables within the Julia code of the corresponding objective functions and derivatives.

__init__(module: str, source_file: str | None = None, fun: str | None = None, grad: str | None = None, hess: str | None = None, res: str | None = None, sres: str | None = None)[source]
get(name: str, as_array: bool = False) → Callable | None[source]

Get variable from Julia module.

Use this function to access any variable from the Julia module.

pypesto.objective.julia.display_source_ipython(source_file: str)[source]

Display source code as syntax highlighted HTML within IPython.