functools

Using functools.lru_cache on functions with constant but non-hashable objects

由 核能气质少年 提交于 2021-02-16 04:53:39
问题 Is it possible to use functools.lru_cache for caching a partial function created by functools.partial ? My problem is a function that takes hashable parameters and contant, non-hashable objects such as NumPy arrays. Consider this toy example: import numpy as np from functools import lru_cache, partial def foo(key, array): print('%s:' % key, array) a = np.array([1,2,3]) Since NumPy arrays are not hashable, this will not work: @lru_cache(maxsize=None) def foo(key, array): print('%s:' % key,

Using functools.lru_cache on functions with constant but non-hashable objects

由 这一生的挚爱 提交于 2021-02-16 04:47:09
问题 Is it possible to use functools.lru_cache for caching a partial function created by functools.partial ? My problem is a function that takes hashable parameters and contant, non-hashable objects such as NumPy arrays. Consider this toy example: import numpy as np from functools import lru_cache, partial def foo(key, array): print('%s:' % key, array) a = np.array([1,2,3]) Since NumPy arrays are not hashable, this will not work: @lru_cache(maxsize=None) def foo(key, array): print('%s:' % key,

Use functools' @lru_cache without specifying maxsize parameter

由 末鹿安然 提交于 2020-06-22 15:04:11
问题 The documentation for lru_cache gives the function definition: @functools.lru_cache(maxsize=128, typed=False) This says to me that maxsize is optional. However, it doesn't like being called without an argument: Python 3.6.3 (default, Oct 24 2017, 14:48:20) [GCC 7.2.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import functools >>> @functools.lru_cache ... def f(): ... ... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr

Why does functools.lru_cache break this function?

由 ╄→гoц情女王★ 提交于 2020-05-28 18:08:11
问题 Consider the following function, which returns all the unique permutations of a set of elements: def get_permutations(elements): if len(elements) == 0: yield () else: unique_elements = set(elements) for first_element in unique_elements: remaining_elements = list(elements) remaining_elements.remove(first_element) for subpermutation in get_permutations(tuple(remaining_elements)): yield (first_element,) + subpermutation for permutation in get_permutations((1, 1, 2)): print(permutation) This

Why does functools.lru_cache break this function?

由 老子叫甜甜 提交于 2020-05-28 18:07:11
问题 Consider the following function, which returns all the unique permutations of a set of elements: def get_permutations(elements): if len(elements) == 0: yield () else: unique_elements = set(elements) for first_element in unique_elements: remaining_elements = list(elements) remaining_elements.remove(first_element) for subpermutation in get_permutations(tuple(remaining_elements)): yield (first_element,) + subpermutation for permutation in get_permutations((1, 1, 2)): print(permutation) This

Non Negative ODE Solutions with functools in R?

由 南楼画角 提交于 2020-05-28 10:19:20
问题 I am trying to implement an R algortihm dealing with non-negative ODE Systems. I need something like ode45 in MATLAB to define states which have to be none-negative. I discussed about that already 3 years ago but with no real solution. deSolve is still not the way to go. I found some python code which looks very promising. Maybe this is possible in R as well. In the end I have to define a function wraper, as functools in python. What it does is pretty simple. Here is the code the of the

Non Negative ODE Solutions with functools in R?

由 给你一囗甜甜゛ 提交于 2020-05-28 10:15:46
问题 I am trying to implement an R algortihm dealing with non-negative ODE Systems. I need something like ode45 in MATLAB to define states which have to be none-negative. I discussed about that already 3 years ago but with no real solution. deSolve is still not the way to go. I found some python code which looks very promising. Maybe this is possible in R as well. In the end I have to define a function wraper, as functools in python. What it does is pretty simple. Here is the code the of the

Python-functools模块

由 非 Y 不嫁゛ 提交于 2020-01-07 19:16:22
【推荐】2019 Java 开发者跳槽指南.pdf(吐血整理) >>> Python-进阶-functools模块小结 functools.partial functool.update_wrapper functool.wraps functools.reduce functools.cmp_to_key functools.total_ordering 文档 地址 functools.partial 作用: functools.partial 通过包装手法，允许我们 "重新定义" 函数签名 用一些默认参数包装一个可调用对象,返回结果是可调用对象，并且可以像原始对象一样对待 冻结部分函数位置函数或关键字参数，简化函数,更少更灵活的函数参数调用 #args/keywords 调用partial时参数 def partial(func, *args, **keywords): def newfunc(*fargs, **fkeywords): newkeywords = keywords.copy() newkeywords.update(fkeywords) return func(*(args + fargs), **newkeywords) #合并，调用原始函数，此时用了partial的参数 newfunc.func = func newfunc.args = args

python 装饰器及标准库functools中的wraps

由 混江龙づ霸主 提交于 2020-01-07 18:23:35
【推荐】2019 Java 开发者跳槽指南.pdf(吐血整理) >>> 最近在看 flask的视图装饰器 时,忽然想起预(复)习一下python的装饰器. 这里有一篇比较好的讲解装饰器的书写的 Python装饰器学习（九步入门） . 这里不单独记录装饰器的书写格式了,重点是工作流程. 首先常见的 装饰器 格式就是通过@语法糖,简便的写法,让流程有些不太清楚. 装饰器不带参数的情况下: def deco(func): def _deco(): print("before myfunc() called.") func() print(" after myfunc() called.") return _deco @deco def myfunc(): print(" myfunc() called.") myfunc() 运行结果: before myfunc() called. myfunc() called. after myfunc() called. myfunc() called. 这个@语法糖的作用是: def myfunc(): print(" myfunc() called.") myfunc = deco(myfunc) 也就是现在的myfunc不再是一开始定义的那个了,而变成了 def _deco(): print("before myfunc() called.

implementing functools.partial that prepends additional arguments

由 痞子三分冷 提交于 2020-01-01 09:06:37
问题 The documentation for functools.partial says that it is "roughly equivalent to": def partial(func, *args, **keywords): def newfunc(*fargs, **fkeywords): newkeywords = keywords.copy() newkeywords.update(fkeywords) return func(*(args + fargs), **newkeywords) # line to change newfunc.func = func newfunc.args = args newfunc.keywords = keywords return newfunc If I wanted to implement a version that prepends the additional arguments, it seems like I'd just have to change the indicated line. Are
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