scipy fftconvolve声称输入参数的维数不相同。我要解析什么？

我试图创建一个使用一个类fftconvolve从scipy.signal与类实例的方法中高斯进行卷积一些数据。但是，每次创建实例并调用方法expand_smooth（在按右箭头键时发生）时，我从fftconvolve收到一条错误消息，指出：ValueError：in1和in2应该具有相同的维数。def fftconvolve(in1, in2, mode="full"):当该行的elif not in1.ndim == in2.ndim:值为True时，这就会在函数中发生。不过我行print vals.ndim == gs.ndim打印真正调用之前fftconvolve，都丘壑和GS的尺寸是（101）。所以，如果我不解析瓦尔斯和GS到fftconvolve我是什么分析？为什么不起作用？

class Smoother(object):
    import sys
    sys.path.append("/DATA/Pythonfunktioner")
    from scipy.signal import fftconvolve
    import pyximport; pyximport.install()
    from fitting6 import gs_smooth1
    """
    This class allows the user to smooth any function of one variable with a gaussian using fftconvolve while looking at the smoothed function. The smoothing parameter is changed with the arrow keys and finally chosen with enter.
    """

    def __init__(self, data):
        self.data = data
        self.sigma = 1 #smallest possible sigma for this smoothing
        self.arr = np.arange(len(self.data.get_ydata()), dtype='float64') - len(self.data.get_ydata())/2
        self.stack = [data]
        self.line = data
        self.active = True

    def connect(self):
        self.cidkpress = self.data.figure.canvas.mpl_connect('key_press_event', self.key)

    def key(self, event):
        if event.key == 'right':
            self.enlarge_smooth()
        elif event.key == 'left':
            self.lower_smooth()
        elif event.key == 'enter':
            self.term(event)

    def enlarge_smooth(self):
        if 0: #Check if larger smooth is already in stack
            pass#set larger smooth as current
        else:
            gs = self.gs_smooth1(self.arr.copy(), self.sigma) #Gaussian core centered at 0
            vals = self.data.get_ydata().copy()
            print vals.ndim == gs.ndim
            print vals.ndim, type(vals), vals.dtype
            print gs.ndim, type(gs), gs.dtype
#            print vals, type(vals), vals.dtype
#            print gs, type(gs), gs.dtype
            newsmooth = self.fftconvolve(vals, gs)
            self.line = Line2D(self.data.get_xdata(), newsmooth)
            self.stack.append(self.line)

    def lower_smooth(self):
        if 1: #Check if current smooth is lowest possible
            print "Cannot smooth less. Least smooth already active."
        else:
            pass#Set lesser smooth as current

    def term(self, event):
        self.active = False
        self.disconnect()

    def disconnect(self):
        self.data.figure.canvas.mpl_disconnect(self.cidkpress)

我还尝试了解析vals[0]，gs[0]然后检查是否要解析两个长度为101的列表。事实证明，我实际上只是解析了两个标量，而ftconvolve会退出并显示错误：TypeError：不支持的操作数类型*：“平滑”和“浮动”。

好像我正在解析该类本身的实例。我只是不知道如何。

如果它有助于通过即时通讯测试我的班级对以下函数的调用

def smoothBF(datalist):
    from matplotlib import pyplot as plt
    for i in xrange(len(datalist)):
        fig, axs = plt.subplots(nrows=1, ncols=1)
        data, = axs.plot(datalist[i][0], datalist[i][1])
        smoother = Smoother(data)
        smoother.connect()
        while smoother.active:
            plt.pause(0.1)
        #Return current result
        plt.close(fig)

其中datalist是仅包含元组的列表(np.arange(101), np.random.random(101))

更新：这似乎与在类定义中导入fftconvolve有关。添加一些打印语句以获取scipy fftconvolve函数内部的尺寸类型和数量，这表明in1某种程度上是一种更平滑的类型。但是，当我from scipy.signal import fftconvolve在模块顶部newsmooth = fftconvolve(vals, gs)而不是在类定义内部调用而不是时，它也会产生不同的结果newsmooth = self.fftconvolve(vals, gs)。然后我收到错误消息AttributeError：'numpy.ndarray'对象没有来自fftconvolve的属性'ndims'。