gaussian_filter1d#
- scipy.ndimage.gaussian_filter1d(input, sigma, axis=-1, order=0, output=None, mode='reflect', cval=0.0, truncate=4.0, *, radius=None)[source]#
- 1-D Gaussian filter. - Parameters:
- inputarray_like
- The input array. 
- sigmascalar
- standard deviation for Gaussian kernel 
- axisint, optional
- The axis of input along which to calculate. Default is -1. 
- orderint, optional
- An order of 0 corresponds to convolution with a Gaussian kernel. A positive order corresponds to convolution with that derivative of a Gaussian. 
- outputarray or dtype, optional
- The array in which to place the output, or the dtype of the returned array. By default an array of the same dtype as input will be created. 
- mode{‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional
- The mode parameter determines how the input array is extended beyond its boundaries. Default is ‘reflect’. Behavior for each valid value is as follows: - ‘reflect’ (d c b a | a b c d | d c b a)
- The input is extended by reflecting about the edge of the last pixel. This mode is also sometimes referred to as half-sample symmetric. 
- ‘constant’ (k k k k | a b c d | k k k k)
- The input is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter. 
- ‘nearest’ (a a a a | a b c d | d d d d)
- The input is extended by replicating the last pixel. 
- ‘mirror’ (d c b | a b c d | c b a)
- The input is extended by reflecting about the center of the last pixel. This mode is also sometimes referred to as whole-sample symmetric. 
- ‘wrap’ (a b c d | a b c d | a b c d)
- The input is extended by wrapping around to the opposite edge. 
 - For consistency with the interpolation functions, the following mode names can also be used: - ‘grid-mirror’
- This is a synonym for ‘reflect’. 
- ‘grid-constant’
- This is a synonym for ‘constant’. 
- ‘grid-wrap’
- This is a synonym for ‘wrap’. 
 
- cvalscalar, optional
- Value to fill past edges of input if mode is ‘constant’. Default is 0.0. 
- truncatefloat, optional
- Truncate the filter at this many standard deviations. Default is 4.0. 
- radiusNone or int, optional
- Radius of the Gaussian kernel. If specified, the size of the kernel will be - 2*radius + 1, and truncate is ignored. Default is None.
 
- Returns:
- gaussian_filter1dndarray
 
 - Notes - The Gaussian kernel will have size - 2*radius + 1along each axis. If radius is None, a default- radius = round(truncate * sigma)will be used.- Examples - >>> from scipy.ndimage import gaussian_filter1d >>> import numpy as np >>> gaussian_filter1d([1.0, 2.0, 3.0, 4.0, 5.0], 1) array([ 1.42704095, 2.06782203, 3. , 3.93217797, 4.57295905]) >>> gaussian_filter1d([1.0, 2.0, 3.0, 4.0, 5.0], 4) array([ 2.91948343, 2.95023502, 3. , 3.04976498, 3.08051657]) >>> import matplotlib.pyplot as plt >>> rng = np.random.default_rng() >>> x = rng.standard_normal(101).cumsum() >>> y3 = gaussian_filter1d(x, 3) >>> y6 = gaussian_filter1d(x, 6) >>> plt.plot(x, 'k', label='original data') >>> plt.plot(y3, '--', label='filtered, sigma=3') >>> plt.plot(y6, ':', label='filtered, sigma=6') >>> plt.legend() >>> plt.grid() >>> plt.show() 