convolve1d:ndarray 与输入形状相同的卷积数组 例子: >>>fromscipy.ndimageimportconvolve1d>>>convolve1d([2,8,0,4,1,9,9,0], weights=[1,3]) array([14,24,4,13,12,36,27,0])
scipy.signal.convolve 1d 原理`scipy.signal.convolve`的1D卷积操作遵循线性卷积的定义。对于两个长度为N的1D数组,卷积操作将这两个数组的对应元素相乘,然后将乘积相加。具体来说,卷积操作在两个数组的长度上滑动,每次都取当前位置的元素相乘并相加,最后返回所有结果的累积和。 例如,如果我们有两个1D数组a和b,a的...
defconvolve(sequence, rule, **kwds):"""Wrapper around scipy.ndimage.convolve1dthat allows complex input."""dtype = np.result_type(float, np.ravel(sequence)[0]) seq = np.asarray(sequence, dtype=dtype)ifnp.iscomplexobj(seq):return(convolve1d(seq.real, rule, **kwds) +1j*convolve1d(se...
implemented with a filter."arr = np.array(arr, copy=False)globalconvolve1difconvolve1disNone:try:fromscipy.ndimageimportconvolve1dexceptImportError:raiseValueError("'filter' method requires SciPy.")ifaxisisNone:raiseValueError("An `axis` value of None is not supported.")ifwindow <1:raiseValueError...
# 需要导入模块: from scipy import ndimage [as 别名]# 或者: from scipy.ndimage importconvolve1d[as 别名]deftest_correlate01(self):array = numpy.array([1,2]) weights = numpy.array([2]) expected = [2,4] output = ndimage.correlate(array, weights) ...
示例1: smooth1d ▲点赞 5▼ # 需要导入模块: from scipy.ndimage import filters [as 别名]# 或者: from scipy.ndimage.filters importconvolve1d[as 别名]defsmooth1d(array, window_size=None, kernel='gaussian'):"""Apply a centered window smoothing to a 1D array. ...