Is there a difference in the results returned by Python’s built-in pow(x, y) (no third argument) and the values returned by math.pow(), in the case of two float arguments.
numpy has three different functions which seem like they can be used for the same things — except that numpy.maximum can only be used element-wise, while numpy.max and numpy.amax can be used on particular axes, or all elements. Why is there more than just numpy.max? Is there some subtlety to this in performance?
I figure that if I understand a problem well enough to write a program that can figure it out, I don’t need to do the work manually.
sorted([2, float('nan'), 1]) returns [2, nan, 1]
I need to do auto-correlation of a set of numbers, which as I understand it is just the correlation of the set with itself.
How would you find the signed angle theta from vector a to b?
Which one is more efficient using math.pow or the ** operator? When should I use one over the other?
Python’s math module contain handy functions like floor & ceil. These functions take a floating point number and return the nearest integer below or above it. However these functions return the answer as a floating point number. For example:
According to the SciPy documentation it is possible to minimize functions with multiple variables, yet it doesn’t tell how to optimize on such functions.
I want to be able to get a estimate of the distance between two (latitude, longitude) points. I want to undershoot, as this will be for A* graph search and I want it to be fast. The points will be at most 800 km apart.