Invalidating package

from cache_utils.decorators import cached @cached(60) def foo(x, y=0): print 'foo is called' return x y foo(1,2) # foo is called foo(1,2) foo(5,6) # foo is called foo(5,6) foo.invalidate(1,2) foo(1,2) # foo is called foo(5,6) foo(x=2) # foo is called foo(x=2) class Foo(object): @cached(60) def foo(self, x,y): print "foo is called" return x y obj = Foo() obj.foo(1,2) # foo is called obj.foo(1,2) from import models from cache_utils.decorators import cached class City Manager(models. 'self' parameter is ignored @cached(60*60*24, 'cities') def default(self): return self.active()[0] # cache a method result.

However since we can rarely need more than a few dozens of tags, practically it is still O(1).That's massive, because functional magnetic resonance imaging (f MRI) is one of the best tools we have to measure brain activity, and if it’s flawed, it means all those conclusions about what our brains look like during things like exercise, gaming, love, and drug addiction are wrong."Despite the popularity of f MRI as a tool for studying brain function, the statistical methods used have rarely been validated using real data," researchers led by Anders Eklund from Linköping University in Sweden assert.The main problem here is in how scientists use f MRI scans to find sparks of activity in certain regions of the brain.The bug was corrected in May 2015, at the time the researchers started writing up their paper, but the fact that it remained undetected for over a decade shows just how easy it was for something like this to happen, because researchers just haven't had reliable methods for validating f MRI results.The other issue is that because software is the thing that's actually interpreting the data from the f MRI scans, your results are only as good as your computer, and programs used to validate the results have been prohibitively slow.Memoization stores the value of function call and returns the cached result when the function is called again with the same arguments.

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