import os import sys import warnings from types import ModuleType from typing import cast import juliapkg # Check if JuliaCall is already loaded, and if so, warn the user # about the relevant environment variables. If not loaded, # set up sensible defaults. if "juliacall" in sys.modules: warnings.warn( "juliacall module already imported. " "Make sure that you have set the environment variable `PYTHON_JULIACALL_HANDLE_SIGNALS=yes` to avoid segfaults. " "Also note that PySR will not be able to configure `PYTHON_JULIACALL_THREADS` or `PYTHON_JULIACALL_OPTLEVEL` for you." ) else: # Required to avoid segfaults (https://juliapy.github.io/PythonCall.jl/dev/faq/) if os.environ.get("PYTHON_JULIACALL_HANDLE_SIGNALS", "yes") != "yes": warnings.warn( "PYTHON_JULIACALL_HANDLE_SIGNALS environment variable is set to something other than 'yes' or ''. " + "You will experience segfaults if running with multithreading." ) if os.environ.get("PYTHON_JULIACALL_THREADS", "auto") != "auto": warnings.warn( "PYTHON_JULIACALL_THREADS environment variable is set to something other than 'auto', " "so PySR was not able to set it. You may wish to set it to `'auto'` for full use " "of your CPU." ) # TODO: Remove these when juliapkg lets you specify this for k, default in ( ("PYTHON_JULIACALL_HANDLE_SIGNALS", "yes"), ("PYTHON_JULIACALL_THREADS", "auto"), ("PYTHON_JULIACALL_OPTLEVEL", "3"), ): os.environ[k] = os.environ.get(k, default) juliapkg.require_julia("~1.6.7, ~1.7, ~1.8, ~1.9, =1.10.0, ^1.10.3") juliapkg.add( "SymbolicRegression", "8254be44-1295-4e6a-a16d-46603ac705cb", version="=0.24.5", ) juliapkg.add("Serialization", "9e88b42a-f829-5b0c-bbe9-9e923198166b", version="1") autoload_extensions = os.environ.get("PYSR_AUTOLOAD_EXTENSIONS") if autoload_extensions is not None: # Deprecated; so just pass to juliacall os.environ["PYTHON_JULIACALL_AUTOLOAD_IPYTHON_EXTENSION"] = autoload_extensions from juliacall import Main as jl # type: ignore jl = cast(ModuleType, jl) jl_version = (jl.VERSION.major, jl.VERSION.minor, jl.VERSION.patch) jl.seval("using SymbolicRegression") SymbolicRegression = jl.SymbolicRegression jl.seval("using Pkg: Pkg") Pkg = jl.Pkg