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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Function caching"""
import os
from joblib import Memory
from decorator import FunctionMaker
def _decorator_apply(dec, func):
return FunctionMaker.create(
func,
"return decfunc(%(shortsignature)s)",
dict(decfunc=dec(func)),
__wrapped__=func,
)
class CacheManager(object):
"""The librosa cache manager class wraps joblib.Memory
with a __call__ attribute, so that it may act as a function.
Additionally, it provides a caching level filter, so that
different functions can be cached or not depending on the user's
preference for speed vs. storage usage.
"""
def __init__(self, *args, **kwargs):
level = kwargs.pop("level", 10)
# Initialize the memory object
self.memory = Memory(*args, **kwargs)
# The level parameter controls which data we cache
# smaller numbers mean less caching
self.level = level
def __call__(self, level):
"""Example usage:
@cache(level=2)
def semi_important_function(some_arguments):
...
"""
def wrapper(function):
"""Decorator function. Adds an input/output cache to
the specified function."""
if self.memory.location is not None and self.level >= level:
return _decorator_apply(self.memory.cache, function)
else:
return function
return wrapper
def clear(self, *args, **kwargs):
return self.memory.clear(*args, **kwargs)
def eval(self, *args, **kwargs):
return self.memory.eval(*args, **kwargs)
def format(self, *args, **kwargs):
return self.memory.format(*args, **kwargs)
def reduce_size(self, *args, **kwargs):
return self.memory.reduce_size(*args, **kwargs)
def warn(self, *args, **kwargs):
return self.memory.warn(*args, **kwargs)
# Instantiate the cache from the environment
cache = CacheManager(
os.environ.get("LIBROSA_CACHE_DIR", None),
mmap_mode=os.environ.get("LIBROSA_CACHE_MMAP", None),
compress=os.environ.get("LIBROSA_CACHE_COMPRESS", False),
verbose=int(os.environ.get("LIBROSA_CACHE_VERBOSE", 0)),
level=int(os.environ.get("LIBROSA_CACHE_LEVEL", 10)),
)
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