Round in numpy to Nearest Step

Question

I would like to know how I can round a number in numpy to an upper or lower threshold which is function of predefined step size. Hopefully stated in a clearer way, if I have the number 123 and a step size equal to 50, I need to round 123 to the closest of either 150 or 100, in this case 100. I came out with function below which does the work but I wonder if there is a better, more succint, way to do this.

Thanks in advance,

Paolo

def getRoundedThresholdv1(a, MinClip):
    import numpy as np
    import math
    digits = int(math.log10(MinClip))+1
    b = np.round(a, -digits)
    if b > a:  # rounded-up
        c = b - MinClip
        UpLow = np.array((b,c))
    else:  # rounded-down
        c = b + MinClip
        UpLow = np.array((c,b))
    AbsDelta = np.abs(a - UpLow)
    return UpLow[AbsDelta.argmin()]




getRoundedThresholdv1(143, 50)

Answer 1

The solution by pb360 is much better, using the second argument of builtin round in python3.

I think you don't need numpy:

def getRoundedThresholdv1(a, MinClip):
    return round(float(a) / MinClip) * MinClip

here a is a single number, if you want to vectorize this function you only need to replace round with np.round and float(a) with np.array(a, dtype=float)

Answer 2

Summary: This is a correct way to do it, the top answer has cases that do not work:

def round_step_size(quantity: Union[float, Decimal], step_size: Union[float, Decimal]) -> float:
    """Rounds a given quantity to a specific step size
    :param quantity: required
    :param step_size: required
    :return: decimal
    """
    precision: int = int(round(-math.log(step_size, 10), 0))
    return float(round(quantity, precision))

My reputation is too low to post a comment on the top answer from Ruggero Turra and point out the issue. However it has cases which did not work for example:

def getRoundedThresholdv1(a, MinClip):
    return round(float(a) / MinClip) * MinClip

getRoundedThresholdv1(quantity=13.200000000000001, step_size=0.0001)

Returns 13.200000000000001 right back whether using numpy or the standard library round. I didn't even find this by stress testing the function. It just came up when using it in production code and spat an error.

Note full credit for this answer comes out of an open source github repo which is not mine found here