Question

我需要计算一些纽菲方向阵列。我将360度分成16个组, 每个组覆盖22.5度。我要一组中间的 0 度, 也就是在 - 11. 25 度和 11. 25 度之间获得方向。但是问题在于我如何得到168. 75 度和 - 168. 75 度之间的一组?

a[numpy.where(a<0)] = a[numpy.where(a<0)]+360
for m in range (0,3600,225):
        b = (a*10 > m)-(a*10 >= m+225).astype(float)    
        c = numpy.apply_over_axes(numpy.sum,b,0)

Answer 1

如果您想要将数据分为16组, 中间为0度, 您为什么要在范围( 0, 3600, 225) 上写 m 的 < code>?

>>> [x/10. for x in range(0,3600,225)]
[0.0, 22.5, 45.0, 67.5, 90.0, 112.5, 135.0, 157.5, 180.0, 202.5, 225.0, 247.5,
 270.0, 292.5, 315.0, 337.5]
## this sectors are not the ones you want!

我认为你应该从 for m in range (1125,36000,2250) (注意我现在使用的是100因数而不是10) 开始,这将使你拥有你想要的群体...

wind_sectors = [x/100.0 for x in range(-1125,36000,2250)]
for m in wind_sectors:
    #DO THINGS

I have to say I don t really understand your script and the goal of it... To deal with circle degrees, I would suggest something like:

a condition, where you put your problematic data, i.e., the one where you have to deal with the transition around zero;
a condition where you put all the other data.

例如,在这种情况下,我正在打印我阵列中属于每个部门的所有元素:

import numpy

def wind_sectors(a_array, nsect = 16):
    step = 360./nsect
    init = step/2
    sectores = [x/100.0 for x in range(int(init*100),36000,int(step*100))]

    a_array[a_array<0] = a_arraya_array[a_array<0]+360

    for i, m in enumerate(sectores):
        print  Sector +str(i)+ (max_threshold =  +str(m)+ ) 
        if i == 0:
            for b in a_array:
                if b <= m or b > sectores[-1]:
                    print b

        else:
            for b in a_array:
                if b <= m and b > sectores[i-1]:
                    print b
    return "it works!"

# TESTING IF THE FUNCTION IS WORKING:
a = numpy.array([2,67,89,3,245,359,46,342])

print wind_sectors(a, 16)

# WITH NDARRAYS:

b = numpy.array([[250,31,27,306], [142,54,260,179], [86,93,109,311]])

print wind_sectors(b.flat[:], 16)

about ballpall 和 reshape 功能:

>>> a = numpy.array([[0,1,2,3], [4,5,6,7], [8,9,10,11]]) >>> original = a.shape >>> b = a.flat[:] >>> c = b.reshape(original) >>> a array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> b array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) >>> c array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]])

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