Python 单变量样条插值都是NaN，但没有错误参数的迹象

我创建了一个函数来创建一个样条插值函数，该函数对输入数据进行错误检查，如果通过，则创建一个点数量是原始数量的数倍的样条。但它似乎不起作用，即使我的任何错误检查都没有引起注意

我检查了以下错误：

• x和y数据大小相同
• “倍数”（要创建的点的系数）必须大于零，1表示样条曲线中的多个点，1表示相同数量的点
• 样条曲线的顺序必须介于1和5之间（包括1和5）
• x数据必须严格单调递增
• x或y数据中至少有一个NaN

我觉得这似乎是一个完整的列表，它会导致样条曲线创建一个包含所有NaN的样条曲线，但当我向它提供数据时，这些错误都不会出现

这是密码

##
# Univariate Spline Interpolation
##

## This function interpolates the data by creating multiple times the amount of points in the data set and fitting a spline to it
## Input:
# dataX - X axis that you corresponds to dataset
# dataY - Y axis of data to fit spline on (must be same size as dataX)
# multiple - the multiplication factor, default is 2 ( <1 - Less points, 1 - same amount of points, >1 - more points)
# order - order of spline, default is 4 (3 - Cubic, 4 - Quartic)
## Output
# spline - interpolation spline object to be used for peak detection
# splinedDataX - splined X Axis
# splinedDataY - splined Y Axis

#import scipy modules for spling creation and class methods
from scipy.interpolate import UnivariateSpline, LSQUnivariateSpline

#import numpy module for linear spacing creation
from numpy import linspace, NaN

def univariate_spline_interpolation(dataX, dataY, multiple=2, order=4):

    #Libraries
    from scipy.interpolate import UnivariateSpline, LSQUnivariateSpline
    from myUnivariateSpline import MyUnivariateSpline

    #Find sizes of x and y axis for comparison and multiple
    sizeX = len(dataX)
    sizeY = len(dataY)

    #Error catching
    if(sizeX != sizeY):
        print "Data X axis and Y axis must have same size"
        return

    if(multiple <= 0):
        print "Multiple must be greater than 0"
        return

    if(order < 1 or order >5):
        print "Order must be 1 <= order <= 5"
        return

    #check for monotonic increasting function
    for indx, val in enumerate(dataX): #set first value as largest value, need to have all following increase
        if indx == 0:
            high = val
            highIndx = indx
            continue
        #if the curent value is lower than 
        if val <= high:
            print "timestamp out of order"
            print "value at ", highIndx, "is ", high
            print "value at ", indx, "is ", val
            break

    #check for NaN in x and y
    for indx, val in enumerate(dataY):
        if(val == NaN):
            print "Value in Data Y at indx", indx, "is NaN"
            return

    for indx, val in enumerate(dataX):
        if(val == NaN):
            print "Value in Data X at indx", indx, "is NaN"
            return

    #Create Spline
    spline = UnivariateSpline(dataX, dataY, k=order, s=0)   

    #Create new axis based on numPoints
    numPoints = sizeX * multiple   #Find number of points for spline
    startPt = dataX[0]   #find value of first point on x axis
    endPt = dataX[-1]   #find value of last point on x axis
    splinedDataX = linspace(startPt, endPt, numPoints)   #create evenly spaced points on axis based on start, end, and number of desired data points

    #Create Y axis of splined Data
    splinedDataY = spline(splinedDataX)   #Create new Y axis with numPoints etnries of data splined to fit the original data

    return spline, splinedDataX, splinedDataY

##
#单变量样条插值
##
##此函数通过在数据集中创建数倍的点并拟合样条曲线来插值数据
##输入：
#dataX—与数据集对应的X轴
#dataY-拟合样条曲线的数据Y轴（必须与dataX大小相同）
#倍数-乘法因子，默认值为2（1-更多点数）
#阶数-样条曲线的阶数，默认值为4（3-三次，4-四次）
##输出
#样条曲线-用于峰值检测的插值样条曲线对象
#花键数据-花键X轴
#花键数据-花键Y轴
#导入scipy模块以创建spling和类方法
从scipy.interpolate导入单变量样条线，lsqunivariate样条线
#导入numpy模块以创建线性间距
来自南澳大利亚州numpy import linspace
def单变量样条插值（数据x，数据y，倍数=2，顺序=4）：
#图书馆
从scipy.interpolate导入单变量样条线，lsqunivariate样条线
从myUnivariateSpline导入myUnivariateSpline
#查找x轴和y轴的尺寸，以进行比较和多重比较
sizeX=len（dataX）
sizeY=len（数据Y）
#错误捕获
如果（sizeX！=sizeY）：
打印“数据X轴和Y轴必须具有相同的大小”
回来
如果（多个5）：
打印“顺序必须是1第152行和第153行上的X数据是相同的：5.86868286133。这可能是原因。我不知道为什么您的检查代码没有捕捉到这一点，因为我不熟悉Python。但我想您应该能够找到它