Python 插值在numpy中未按预期工作

Python 插值在numpy中未按预期工作,python,matlab,numpy,interpolation,Python,Matlab,Numpy,Interpolation,我在Matlab中有一行用于插值 result = interp1(1:size(STD_TOT,2),STD_TOT,0.1:0.1:size(STD_TOT,2)); 标准TOT为1x1929矩阵 STD_TOT = np.array([0.022329,0.038252,0.026183,0.039864,0.071397,0.0093877,0.035645,0.038868,0.03461,0.034803,0.025023,0.019119,0.017909,0.027522,

我在Matlab中有一行用于插值

result = interp1(1:size(STD_TOT,2),STD_TOT,0.1:0.1:size(STD_TOT,2));  
标准TOT为1x1929矩阵

STD_TOT = 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结果是:

[NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.0223287, 0.023921, 0.0255133, ... , 0.0783331, 0.0729199, 0.0675067, 0.0620934]
请注意,结果由19.290个元素组成,其中9个元素(第一个)为NA。根据我的研究,它们是某种缺失的价值观或类似的东西

我想在Python中实现同样的功能

以下是我已经做过的:

Numpy interp:

result = np.interp(np.arange(0.1, STD_TOT.shape[0]+0.1, 0.1), np.arange(1, STD_TOT.size+1, dtype=int), STD_TOT)
结果是

[0.022329  0.022329  0.022329  0.022329  0.022329  0.022329  0.022329  0.022329  0.022329 0.022329  0.023921  0.0255136  0.0271059  ... 0.07292 0.06750 0.06209 ]
如您所见,numpy的结果与我在Matlab中得到的结果非常相似,但9 NA值被0.022329替换(我不希望出现这种情况)


我能做些什么来获得完全相同的结果?我已经对这件事失去了理智一天了,我还尝试了scipy
interpolate.interp1d
,但我甚至无法运行它。我真的迷路了

来自文档:

numpy.interp(x,xp,fp,left=None,right=None,period=None)

与要返回的fp值相对应的leftoptional float或complex x Right与要返回的fp值相对应的可选浮点或复数 x>xp[-1],默认值为fp[-1]


左侧
右侧
替换为所需的越界值。MATLAB的默认值是NaN。

这段代码相当混乱;请在文章中确保清楚什么是MATLAB代码,什么是Python,以及哪些东西可以用这两种语言工作,哪些不可以。例如,在您的第一行代码中,您说这是MATLAB,但随后使用NumPy定义了一个变量,这显然在MATLAB中不起作用。另外,请阅读并将代码减少到最小,而不是丢弃1929个元素,使用例如5或6来重现相同的问题。嗨。很明显,只有第一行代码在Matlab中。STD_TOT数组已经是Python语法了,因为我让其他人更容易重现我的问题。不能处理减少的数据,因为它当然会改变结果,使请求变得毫无意义。这不是真的。您可以使用数据中的10个值创建一个数组,以再现您的行为(实际上只使用10个STD_TOT值)。这是一个简单的线性插值,可以减少。是的,但是如果我想在Python中使用这些NA值呢?我不希望他们被其他价值观所取代。这是因为这之后的所有数学运算给出的结果都与我预期的不同,因为存在实际值而不是NA值。尝试左=np.nan,但我不知道NA是否等于np.nanhonest@claw91我喜欢它。事实上,AFAIK python没有NA(显然numpy没有),而是NaN。本质上是一样的