358 lines
13 KiB
Python
358 lines
13 KiB
Python
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import numpy as np
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from scipy.linalg import lstsq
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from scipy._lib._util import float_factorial
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from scipy.ndimage import convolve1d
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from ._arraytools import axis_slice
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def savgol_coeffs(window_length, polyorder, deriv=0, delta=1.0, pos=None,
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use="conv"):
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"""Compute the coefficients for a 1-D Savitzky-Golay FIR filter.
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Parameters
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----------
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window_length : int
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The length of the filter window (i.e., the number of coefficients).
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polyorder : int
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The order of the polynomial used to fit the samples.
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`polyorder` must be less than `window_length`.
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deriv : int, optional
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The order of the derivative to compute. This must be a
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nonnegative integer. The default is 0, which means to filter
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the data without differentiating.
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delta : float, optional
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The spacing of the samples to which the filter will be applied.
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This is only used if deriv > 0.
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pos : int or None, optional
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If pos is not None, it specifies evaluation position within the
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window. The default is the middle of the window.
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use : str, optional
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Either 'conv' or 'dot'. This argument chooses the order of the
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coefficients. The default is 'conv', which means that the
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coefficients are ordered to be used in a convolution. With
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use='dot', the order is reversed, so the filter is applied by
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dotting the coefficients with the data set.
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Returns
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-------
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coeffs : 1-D ndarray
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The filter coefficients.
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See Also
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--------
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savgol_filter
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Notes
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-----
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.. versionadded:: 0.14.0
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References
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----------
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A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of Data by
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Simplified Least Squares Procedures. Analytical Chemistry, 1964, 36 (8),
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pp 1627-1639.
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Jianwen Luo, Kui Ying, and Jing Bai. 2005. Savitzky-Golay smoothing and
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differentiation filter for even number data. Signal Process.
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85, 7 (July 2005), 1429-1434.
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Examples
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--------
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>>> import numpy as np
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>>> from scipy.signal import savgol_coeffs
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>>> savgol_coeffs(5, 2)
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array([-0.08571429, 0.34285714, 0.48571429, 0.34285714, -0.08571429])
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>>> savgol_coeffs(5, 2, deriv=1)
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array([ 2.00000000e-01, 1.00000000e-01, 2.07548111e-16, -1.00000000e-01,
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-2.00000000e-01])
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Note that use='dot' simply reverses the coefficients.
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>>> savgol_coeffs(5, 2, pos=3)
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array([ 0.25714286, 0.37142857, 0.34285714, 0.17142857, -0.14285714])
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>>> savgol_coeffs(5, 2, pos=3, use='dot')
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array([-0.14285714, 0.17142857, 0.34285714, 0.37142857, 0.25714286])
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>>> savgol_coeffs(4, 2, pos=3, deriv=1, use='dot')
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array([0.45, -0.85, -0.65, 1.05])
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`x` contains data from the parabola x = t**2, sampled at
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t = -1, 0, 1, 2, 3. `c` holds the coefficients that will compute the
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derivative at the last position. When dotted with `x` the result should
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be 6.
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>>> x = np.array([1, 0, 1, 4, 9])
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>>> c = savgol_coeffs(5, 2, pos=4, deriv=1, use='dot')
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>>> c.dot(x)
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6.0
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"""
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# An alternative method for finding the coefficients when deriv=0 is
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# t = np.arange(window_length)
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# unit = (t == pos).astype(int)
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# coeffs = np.polyval(np.polyfit(t, unit, polyorder), t)
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# The method implemented here is faster.
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# To recreate the table of sample coefficients shown in the chapter on
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# the Savitzy-Golay filter in the Numerical Recipes book, use
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# window_length = nL + nR + 1
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# pos = nL + 1
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# c = savgol_coeffs(window_length, M, pos=pos, use='dot')
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if polyorder >= window_length:
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raise ValueError("polyorder must be less than window_length.")
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halflen, rem = divmod(window_length, 2)
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if pos is None:
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if rem == 0:
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pos = halflen - 0.5
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else:
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pos = halflen
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if not (0 <= pos < window_length):
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raise ValueError("pos must be nonnegative and less than "
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"window_length.")
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if use not in ['conv', 'dot']:
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raise ValueError("`use` must be 'conv' or 'dot'")
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if deriv > polyorder:
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coeffs = np.zeros(window_length)
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return coeffs
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# Form the design matrix A. The columns of A are powers of the integers
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# from -pos to window_length - pos - 1. The powers (i.e., rows) range
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# from 0 to polyorder. (That is, A is a vandermonde matrix, but not
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# necessarily square.)
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x = np.arange(-pos, window_length - pos, dtype=float)
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if use == "conv":
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# Reverse so that result can be used in a convolution.
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x = x[::-1]
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order = np.arange(polyorder + 1).reshape(-1, 1)
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A = x ** order
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# y determines which order derivative is returned.
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y = np.zeros(polyorder + 1)
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# The coefficient assigned to y[deriv] scales the result to take into
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# account the order of the derivative and the sample spacing.
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y[deriv] = float_factorial(deriv) / (delta ** deriv)
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# Find the least-squares solution of A*c = y
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coeffs, _, _, _ = lstsq(A, y)
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return coeffs
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def _polyder(p, m):
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"""Differentiate polynomials represented with coefficients.
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p must be a 1-D or 2-D array. In the 2-D case, each column gives
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the coefficients of a polynomial; the first row holds the coefficients
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associated with the highest power. m must be a nonnegative integer.
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(numpy.polyder doesn't handle the 2-D case.)
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"""
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if m == 0:
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result = p
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else:
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n = len(p)
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if n <= m:
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result = np.zeros_like(p[:1, ...])
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else:
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dp = p[:-m].copy()
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for k in range(m):
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rng = np.arange(n - k - 1, m - k - 1, -1)
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dp *= rng.reshape((n - m,) + (1,) * (p.ndim - 1))
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result = dp
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return result
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def _fit_edge(x, window_start, window_stop, interp_start, interp_stop,
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axis, polyorder, deriv, delta, y):
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"""
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Given an N-d array `x` and the specification of a slice of `x` from
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`window_start` to `window_stop` along `axis`, create an interpolating
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polynomial of each 1-D slice, and evaluate that polynomial in the slice
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from `interp_start` to `interp_stop`. Put the result into the
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corresponding slice of `y`.
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"""
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# Get the edge into a (window_length, -1) array.
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x_edge = axis_slice(x, start=window_start, stop=window_stop, axis=axis)
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if axis == 0 or axis == -x.ndim:
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xx_edge = x_edge
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swapped = False
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else:
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xx_edge = x_edge.swapaxes(axis, 0)
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swapped = True
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xx_edge = xx_edge.reshape(xx_edge.shape[0], -1)
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# Fit the edges. poly_coeffs has shape (polyorder + 1, -1),
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# where '-1' is the same as in xx_edge.
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poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),
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xx_edge, polyorder)
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if deriv > 0:
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poly_coeffs = _polyder(poly_coeffs, deriv)
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# Compute the interpolated values for the edge.
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i = np.arange(interp_start - window_start, interp_stop - window_start)
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values = np.polyval(poly_coeffs, i.reshape(-1, 1)) / (delta ** deriv)
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# Now put the values into the appropriate slice of y.
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# First reshape values to match y.
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shp = list(y.shape)
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shp[0], shp[axis] = shp[axis], shp[0]
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values = values.reshape(interp_stop - interp_start, *shp[1:])
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if swapped:
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values = values.swapaxes(0, axis)
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# Get a view of the data to be replaced by values.
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y_edge = axis_slice(y, start=interp_start, stop=interp_stop, axis=axis)
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y_edge[...] = values
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def _fit_edges_polyfit(x, window_length, polyorder, deriv, delta, axis, y):
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"""
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Use polynomial interpolation of x at the low and high ends of the axis
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to fill in the halflen values in y.
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This function just calls _fit_edge twice, once for each end of the axis.
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"""
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halflen = window_length // 2
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_fit_edge(x, 0, window_length, 0, halflen, axis,
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polyorder, deriv, delta, y)
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n = x.shape[axis]
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_fit_edge(x, n - window_length, n, n - halflen, n, axis,
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polyorder, deriv, delta, y)
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def savgol_filter(x, window_length, polyorder, deriv=0, delta=1.0,
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axis=-1, mode='interp', cval=0.0):
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""" Apply a Savitzky-Golay filter to an array.
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This is a 1-D filter. If `x` has dimension greater than 1, `axis`
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determines the axis along which the filter is applied.
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Parameters
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----------
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x : array_like
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The data to be filtered. If `x` is not a single or double precision
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floating point array, it will be converted to type ``numpy.float64``
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before filtering.
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window_length : int
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The length of the filter window (i.e., the number of coefficients).
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If `mode` is 'interp', `window_length` must be less than or equal
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to the size of `x`.
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polyorder : int
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The order of the polynomial used to fit the samples.
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`polyorder` must be less than `window_length`.
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deriv : int, optional
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The order of the derivative to compute. This must be a
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nonnegative integer. The default is 0, which means to filter
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the data without differentiating.
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delta : float, optional
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The spacing of the samples to which the filter will be applied.
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This is only used if deriv > 0. Default is 1.0.
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axis : int, optional
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The axis of the array `x` along which the filter is to be applied.
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Default is -1.
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mode : str, optional
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Must be 'mirror', 'constant', 'nearest', 'wrap' or 'interp'. This
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determines the type of extension to use for the padded signal to
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which the filter is applied. When `mode` is 'constant', the padding
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value is given by `cval`. See the Notes for more details on 'mirror',
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'constant', 'wrap', and 'nearest'.
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When the 'interp' mode is selected (the default), no extension
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is used. Instead, a degree `polyorder` polynomial is fit to the
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last `window_length` values of the edges, and this polynomial is
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used to evaluate the last `window_length // 2` output values.
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cval : scalar, optional
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Value to fill past the edges of the input if `mode` is 'constant'.
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Default is 0.0.
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Returns
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-------
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y : ndarray, same shape as `x`
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The filtered data.
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See Also
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--------
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savgol_coeffs
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Notes
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-----
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Details on the `mode` options:
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'mirror':
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Repeats the values at the edges in reverse order. The value
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closest to the edge is not included.
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'nearest':
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The extension contains the nearest input value.
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'constant':
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The extension contains the value given by the `cval` argument.
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'wrap':
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The extension contains the values from the other end of the array.
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For example, if the input is [1, 2, 3, 4, 5, 6, 7, 8], and
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`window_length` is 7, the following shows the extended data for
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the various `mode` options (assuming `cval` is 0)::
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mode | Ext | Input | Ext
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-----------+---------+------------------------+---------
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'mirror' | 4 3 2 | 1 2 3 4 5 6 7 8 | 7 6 5
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'nearest' | 1 1 1 | 1 2 3 4 5 6 7 8 | 8 8 8
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'constant' | 0 0 0 | 1 2 3 4 5 6 7 8 | 0 0 0
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'wrap' | 6 7 8 | 1 2 3 4 5 6 7 8 | 1 2 3
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.. versionadded:: 0.14.0
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Examples
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--------
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>>> import numpy as np
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>>> from scipy.signal import savgol_filter
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>>> np.set_printoptions(precision=2) # For compact display.
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>>> x = np.array([2, 2, 5, 2, 1, 0, 1, 4, 9])
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Filter with a window length of 5 and a degree 2 polynomial. Use
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the defaults for all other parameters.
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>>> savgol_filter(x, 5, 2)
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array([1.66, 3.17, 3.54, 2.86, 0.66, 0.17, 1. , 4. , 9. ])
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Note that the last five values in x are samples of a parabola, so
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when mode='interp' (the default) is used with polyorder=2, the last
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three values are unchanged. Compare that to, for example,
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`mode='nearest'`:
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>>> savgol_filter(x, 5, 2, mode='nearest')
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array([1.74, 3.03, 3.54, 2.86, 0.66, 0.17, 1. , 4.6 , 7.97])
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"""
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if mode not in ["mirror", "constant", "nearest", "interp", "wrap"]:
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raise ValueError("mode must be 'mirror', 'constant', 'nearest' "
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"'wrap' or 'interp'.")
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x = np.asarray(x)
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# Ensure that x is either single or double precision floating point.
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if x.dtype != np.float64 and x.dtype != np.float32:
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x = x.astype(np.float64)
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coeffs = savgol_coeffs(window_length, polyorder, deriv=deriv, delta=delta)
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if mode == "interp":
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if window_length > x.shape[axis]:
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raise ValueError("If mode is 'interp', window_length must be less "
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"than or equal to the size of x.")
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# Do not pad. Instead, for the elements within `window_length // 2`
|
||
|
|
# of the ends of the sequence, use the polynomial that is fitted to
|
||
|
|
# the last `window_length` elements.
|
||
|
|
y = convolve1d(x, coeffs, axis=axis, mode="constant")
|
||
|
|
_fit_edges_polyfit(x, window_length, polyorder, deriv, delta, axis, y)
|
||
|
|
else:
|
||
|
|
# Any mode other than 'interp' is passed on to ndimage.convolve1d.
|
||
|
|
y = convolve1d(x, coeffs, axis=axis, mode=mode, cval=cval)
|
||
|
|
|
||
|
|
return y
|