def get_minima(array):
sdiff = np.diff(np.sign(np.diff(array)))
rising_1 = (sdiff == 2)
rising_2 = (sdiff[:-1] == 1) & (sdiff[1:] == 1)
rising_all = rising_1
rising_all[1:] = rising_all[1:] | rising_2
min_ind = np.where(rising_all)[0] + 1
minima = list(zip(min_ind, array[min_ind]))
return sorted(minima, key=lambda x: x[1])通过使用我拥有的数据数组运行这段代码,它将产生:
[(59, 7.958373616052042e-10),
(69, 6.5364637051479655e-09),
(105, 1.0748381102806489e-08),
(88, 2.953895857338913e-07),
(27, 9.083111768048306e-07)]这很好--它是我数据集中的所有最小值。但我只需要存储最小值--即这个特定示例中的(59,7.958373616052042e-10)点。我想不出该怎么做。我尝试了一些使用np.amin和布尔比较的方法,但是我对表示法和语法非常困惑,因为现在它是一个列表数组,我以前从未真正使用过它。
感谢你的帮助!
发布于 2020-03-04 14:06:51
与其对所有的最小值进行排序,您还可以得到最低的一对:
def get_minima(array):
sdiff = np.diff(np.sign(np.diff(array)))
rising_1 = (sdiff == 2)
rising_2 = (sdiff[:-1] == 1) & (sdiff[1:] == 1)
rising_all = rising_1
rising_all[1:] = rising_all[1:] | rising_2
min_ind = np.where(rising_all)[0] + 1
minima = list(zip(min_ind, array[min_ind]))
return min(minima, key=lambda pair: pair[1])例如:
minima = [(59, 7.958373616052042e-10),
(69, 6.5364637051479655e-09),
(105, 1.0748381102806489e-08),
(88, 2.953895857338913e-07),
(27, 9.083111768048306e-07)]
minimum = min(minima, key=lambda pair: pair[1])
print(minimum)
>>> (59, 7.958373616052042e-10)发布于 2020-03-04 13:47:39
看起来太简单了但你试过了吗?
minima = list(zip(min_ind, array[min_ind]))
minima.sort(key=lambda x: x[1])
return minima[0]https://stackoverflow.com/questions/60527392
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