我不明白interp1d报告的结果.我收到NAN,我应该收到号码.
In [131]: bb
Out[131]:
array([ 0. , 1.80286595, 1.87443683, 2.70410611, 3.02764722,
3.11305985, 3.11534355, 3.18695351, 3.20693444])
In [132]: alphas1
Out[134]:
array([ 3.80918778e+00, 2.06547222e+00, 1.99234191e+00,
7.55942418e-01, 2.56971574e-01, 1.05144676e-01,
9.30852046e-02, 1.52574183e-02, 1.23664407e-07])
In [135]: bb.shape
Out[135]: (9,)
In [136]: alphas1.shape
Out[140]: (9,)
In [141]: pol = interp1d(alphas1, bb, bounds_error=False)
In [149]: pol(pol.x)
Out[149]: array([ nan, nan, nan, nan, nan, nan, nan, nan, nan]) # I was expecting to receive nan only at the borders.
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小智 8
我想,如果你检查一下这个类的源代码,那就可以看出问题interp1d了_check_bounds:
def _check_bounds(self, x_new):
...
below_bounds = x_new < self.x[0]
above_bounds = x_new > self.x[-1]
# !! Could provide more information about which values are out of bounds
if self.bounds_error and below_bounds.any():
raise ValueError("A value in x_new is below the interpolation "
"range.")
if self.bounds_error and above_bounds.any():
raise ValueError("A value in x_new is above the interpolation "
"range.")
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该方法检查您尝试放入的x的值是否小于(在您的情况下)self.x[0]的第一个元素.由于是列表中最大的元素,因此此后的每个元素都将"超出范围",即小于第一个元素.xalphas1alphas1[0]x
解决这个问题的方法是扭转你的x并y列出:
bb = bb[::-1]
alphas1 = alphas[::-1]
pol = interp1d(alphas1, bb, bounds_error=False)
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现在alphas1将随着scipy的预期而增加,并将按预期pol(pol.x)返回bb(现在逆转).
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