2020年7月2日 · numpy.amax() will find the max value in an array, and numpy.amin() does the same for the min value. If I want to find both max and min, I have to call both functions, which requires passing over t...

2015年1月22日 · I'm working on a problem that has to do with calculating angles of refraction and what not. However, it seems that I'm unable to use the numpy.sin() function in degrees. I have tried to use numpy.degrees() and numpy.rad2deg(). numpy.sin(90) numpy.degrees(numpy.sin(90)) Both return ~ 0.894 and ~ 51.2 respectively. Thanks for your …

Most NumPy functions are just a bit slower than the equivalent function written in C. This is true when the NumPy function is merely a thin wrapper around a C (or Fortran) function. In contrast, a np.vectorized function still has to call a Python function once for each element in the array, so it performs more like Python code than C code ...

2010年9月6日 · You should use numpy function to deal with numpy's types and use regular python function to deal with regular python types. Worst performance usually occurs when mixing python builtins with numpy, because of types conversion. Those type conversion have been optimized lately, but it's still often better to not use them.

2016年2月5日 · The vectorized numpy-version uses a lot of additional memory and memory-accesses. Numexp-library tries to tile the numpy-arrays and thus get a better cache utilization: # less cache misses than numpy-functionality import numexpr as ne def ne_f(x): return ne.evaluate("x+2*x*x+4*x*x*x") Leads to the following comparison:

2018年6月11日 · On 1D numpy arrays of integers like yours, any will give you True if and only if some element is non-zero, whereas all will give you True if and only if all elements are non-zero. So your first snippet of code translates into:

2015年12月18日 · For a vectorized but less accurate approximation, you can use numpy.trapz or scipy.integrate.simps. Your function definition (at least the one shown in the question) is implemented using numpy functions that all support broadcasting, so given a grid of x values on [0, 1], you can do this:

2014年9月13日 · No, not in a significant way. The reason np.sum is faster than sum is that sum is implemented to "naively" iterate over the iterable (in this case, the numpy array), calling elements' __add__ operator (which imposes a significant overhead), while numpy's implementation of sum is optimized, e.g. taking advantage of the fact it knows the type (dtype) of the elements, and …

2014年4月1日 · What is a good way to produce a numpy array containing the values of a function evaluated on an n-dimensional grid of points? For example, suppose I want to evaluate the function defined by def f...

2013年8月5日 · However, when comparing Numpy ufuncs with standard Python functions I find that the latter are much faster. For example, aa = np.arange(1000000, dtype = float) %timeit np.mean(aa) # 1000 loops, best of 3: 1.15 ms per loop %timeit aa.mean # 10000000 loops, best of 3: 69.5 ns per loop I got similar results with other Numpy functions like max, power.