过滤直方图边缘和计数

Question

考虑一个返回百分比的numpy数组的直方图计算:

# 500 random numbers between 0 and 10,000
values = np.random.uniform(0,10000,500)

# Histogram using e.g. 200 buckets
perc, edges = np.histogram(values, bins=200,
                           weights=np.zeros_like(values) + 100/values.size)

以上返回两个数组:

• perc包含%每对连续edges[ix]和edges[ix+1]总数中的值(即百分比).
• edges 长度 len(hist)+1

现在,假设我想过滤perc,edges以便我最终 得到新范围内包含的值的百分比和边缘[m, M]."

即,我想与工作子阵列的perc和edges对应于中值的间隔[m, M].不用说,新的百分比数组仍然指的是输入数组的总分数.我们只想过滤perc并edges最终得到正确的子阵列.

我怎样才能后处理perc和edges这样做呢？

值的m和M可以是任何数量的课程.在上面的例子中,我们可以假设例如m = 0和M = 200.

Answer 1

m = 0; M = 200
mask = [(m < edges) & (edges < M)]
>>> edges[mask]
array([  37.4789683 ,   87.07491593,  136.67086357,  186.2668112 ])

让我们处理一个较小的数据集，以便更容易理解：

np.random.seed(0)
values = np.random.uniform(0, 100, 10)
values.sort()
>>> values
array([ 38.34415188,  42.36547993,  43.75872113,  54.4883183 ,
        54.88135039,  60.27633761,  64.58941131,  71.51893664,
        89.17730008,  96.36627605])

# Histogram using e.g. 10 buckets
perc, edges = np.histogram(values, bins=10,
                           weights=np.zeros_like(values) + 100./values.size)

>>> perc
array([ 30.,   0.,  20.,  10.,  10.,  10.,   0.,   0.,  10.,  10.])

>>> edges
array([ 38.34415188,  44.1463643 ,  49.94857672,  55.75078913,
        61.55300155,  67.35521397,  73.15742638,  78.9596388 ,
        84.76185122,  90.56406363,  96.36627605])

m = 0; M = 50
mask = (m <= edges) & (edges < M)
>>> mask
array([ True,  True,  True, False, False, False, False, False, False,
       False, False], dtype=bool)

>>> edges[mask]
array([ 38.34415188,  44.1463643 ,  49.94857672])

>>> perc[mask[:-1]][:-1]
array([ 30.,   0.])

m = 40; M = 60
mask = (m < edges) & (edges < M)
>>> edges[mask]
array([ 44.1463643 ,  49.94857672,  55.75078913])
>>> perc[mask[:-1]][:-1]
array([  0.,  20.])