python 一维数组所有元素是否大于_如何最好在python中将一维数组连续元素分组...
我有以下一维数组：
[0, 0, 0, 1, 0, 0, 16, 249, 142, 149, 189, 135, 141, 146, 294, 3, 2, 0, 3, 3, 6, 2, 3, 4, 21, 22, 138, 95, 86, 110, 72, 89, 79, 138, 14, 18, 18, 18, 12, 15, 21, 22, 11, 20, 26, 90, 62, 128, 94, 117, 81, 81, 137, 7, 13, 14, 6, 10, 8, 11, 10, 13, 21, 18, 140, 69, 147, 110, 112, 88, 100, 197, 9, 20, 5, 6, 5, 4, 7, 10, 21, 32, 42, 56, 41, 156, 95, 112, 81, 93, 152, 14, 19, 9, 12, 20, 18, 14, 21, 18, 18, 14, 91, 47, 43, 63, 41, 45, 43, 85, 15, 16, 14, 10, 11]
我可以看到尖峰所在的模式。所以我想上面的数组分组如下：
[[0, 0, 0, 1, 0, 0, 16], [249, 142, 149, 189, 135, 141, 146, 294], [3, 2, 0, 3, 3, 6, 2, 3, 4, 21, 22], [138, 95, 86, 110, 72, 89, 79, 138]....so on]
我尝试使用K均值，均值和标准偏差的某种组合。但是，没有一个导致这种分组。请帮忙！
编辑：这些数据是沿x轴的灰度图像的暗像素值的总和，在y轴上相加。较高的范围组代表写行，而较低的范围组代表空白行。这意味着，我想将图像上的书面行和空白行分开。因此有一种模式。写入的线将具有相同的宽度，即它们的组长度将相同。由于背景噪声，空白行可能会突然出现尖峰。但总体而言，我可以手动看到空白行的模式。我需要以编程方式。
解决方案
在这种情况下，将使用基于阈值的简单方法。
x = np.array([0, 0, 0, 1, 0, 0, 16, 249, 142, 149, 189, 135, 141, 146, 294, 3, 2,
0, 3, 3, 6, 2, 3, 4, 21, 22, 138, 95, 86, 110, 72, 89, 79, 138, 14,
18, 18, 18, 12, 15, 21, 22, 11, 20, 26, 90, 62, 128, 94, 117, 81,
81, 137, 7, 13, 14, 6, 10, 8, 11, 10, 13, 21, 18, 140, 69, 147,
110, 112, 88, 100, 197, 9, 20, 5, 6, 5, 4, 7, 10, 21, 32, 42, 56,
41, 156, 95, 112, 81, 93, 152, 14, 19, 9, 12, 20, 18, 14, 21, 18,
18, 14, 91, 47, 43, 63, 41, 45, 43, 85, 15, 16, 14, 10, 11])
mask = x > 30 # Mark values above/below threshold
cuts = np.flatnonzero(np.diff(mask)) # find indices where mask changes
cuts = np.hstack([0, cuts + 1, -1]) # let indices point after the change and add beginning and end of the array.
groups = []
for a, b in zip(cuts[:-1], cuts[1:]): # iterate over index pairs
groups.append(x[a:b].tolist())
print(groups)
# [[0, 0, 0, 1, 0, 0, 16], [249, 142, 149, 189, 135, 141, 146, 294], [3, 2, 0, 3, 3, 6, 2, 3, 4, 21, 22], [138, 95, 86, 110, 72, 89, 79, 138], [14, 18, 18, 18, 12, 15, 21, 22, 11, 20, 26], [90, 62, 128, 94, 117, 81, 81, 137], [7, 13, 14, 6, 10, 8, 11, 10, 13, 21, 18], [140, 69, 147, 110, 112, 88, 100, 197], [9, 20, 5, 6, 5, 4, 7, 10, 21], [32, 42, 56, 41, 156, 95, 112, 81, 93, 152], [14, 19, 9, 12, 20, 18, 14, 21, 18, 18, 14], [91, 47, 43, 63, 41, 45, 43, 85], [15, 16, 14, 10]]
更复杂的方法可能涉及拟合分段常数模型或检测统计不平稳性，但是通常最好坚持使用最简单可行的方法。
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