How to multiply functions in python?

You can use your hack class as a decorator pretty much as it’s written, though you’d likely want to choose a more appropriate name for the class. Like this: class Composable(object): def __init__(self, function): self.function = function def __call__(self, *args, **kwargs): return self.function(*args, **kwargs) def __mul__(self, other): @Composable def composed(*args, **kwargs): return self.function(other(*args, **kwargs)) return … Read more

How can I modify the XMLHttpRequest responsetext received by another function?

Edit: See the second code option below (it is tested and works). The first one has some limitations. Since you can’t modify any of those functions, it appears you have to go after the XMLHttpRequest prototype. Here’s one idea (untested, but you can see the direction): (function() { var open = XMLHttpRequest.prototype.open; XMLHttpRequest.prototype.open = function(method, … Read more

How to Mock an HTTP request in a unit testing scenario in Python

Starting a web server for unit testing is definitely not a good practice. Unit tests should be simple and isolated, which means that they should avoid performing IO operations for example. If what you want to write are really unit tests then you should craft your own test inputs and also look into mock objects. … Read more

Extending builtin classes in python

Just subclass the type >>> class X(str): … def my_method(self): … return int(self) … >>> s = X(“Hi Mom”) >>> s.lower() ‘hi mom’ >>> s.my_method() Traceback (most recent call last): File “<stdin>”, line 1, in <module> File “<stdin>”, line 3, in my_method ValueError: invalid literal for int() with base 10: ‘Hi Mom’ >>> z = … Read more

Conjugate transpose operator “.H” in numpy

You can subclass the ndarray object like: from numpy import ndarray class myarray(ndarray): @property def H(self): return self.conj().T such that: a = np.random.rand(3, 3).view(myarray) a.H will give you the desired behavior. Edit: As suggested by @slek120, you can force to transpose only the last 2 axes with: self.swapaxes(-2, -1).conj() instead of self.conj().T.

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