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Numerically Integrating Signals with Absolute Value

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Improve performance of a for loop in Python (possibly with numpy or numba)

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Where do `@numba.jitclass`ed special methods go?

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Install numba 0.30.1 on ubuntu 16.04 lts

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Elegant way to complete a parallel operation on two arrays of unequal lengths

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How to specify field of a numba jitclass when it contains a jitted function?

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TypeError raised when using Numba to accelerate Python code

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Trouble with speeding up functions with numba JIT

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How do I speed up profiled NumPy code - vectorizing, Numba?

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Optionally passing parameters onto another function with jit

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Using numba.autojit on a lambdify'd sympy expression

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Can this loop in a numba function be optimized to run faster?

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Problem by dictionaries to use numba njit parallelization to accelerate the code

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Numba CUDA `vectorize` and `reduce` decorators slower than expected

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