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914 lines
30 KiB
Python
914 lines
30 KiB
Python
"""
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Numerical Python functions written for compatibility with MATLAB
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commands with the same names. Most numerical Python functions can be found in
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the `NumPy`_ and `SciPy`_ libraries. What remains here is code for performing
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spectral computations and kernel density estimations.
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.. _NumPy: https://numpy.org
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.. _SciPy: https://www.scipy.org
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Spectral functions
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------------------
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`cohere`
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Coherence (normalized cross spectral density)
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`csd`
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Cross spectral density using Welch's average periodogram
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`detrend`
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Remove the mean or best fit line from an array
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`psd`
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Power spectral density using Welch's average periodogram
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`specgram`
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Spectrogram (spectrum over segments of time)
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`complex_spectrum`
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Return the complex-valued frequency spectrum of a signal
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`magnitude_spectrum`
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Return the magnitude of the frequency spectrum of a signal
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`angle_spectrum`
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Return the angle (wrapped phase) of the frequency spectrum of a signal
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`phase_spectrum`
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Return the phase (unwrapped angle) of the frequency spectrum of a signal
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`detrend_mean`
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Remove the mean from a line.
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`detrend_linear`
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Remove the best fit line from a line.
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`detrend_none`
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Return the original line.
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"""
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import functools
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from numbers import Number
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import numpy as np
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from matplotlib import _api, _docstring, cbook
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def window_hanning(x):
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"""
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Return *x* times the Hanning (or Hann) window of len(*x*).
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See Also
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--------
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window_none : Another window algorithm.
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"""
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return np.hanning(len(x))*x
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def window_none(x):
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"""
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No window function; simply return *x*.
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See Also
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--------
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window_hanning : Another window algorithm.
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"""
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return x
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def detrend(x, key=None, axis=None):
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"""
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Return *x* with its trend removed.
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Parameters
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----------
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x : array or sequence
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Array or sequence containing the data.
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key : {'default', 'constant', 'mean', 'linear', 'none'} or function
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The detrending algorithm to use. 'default', 'mean', and 'constant' are
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the same as `detrend_mean`. 'linear' is the same as `detrend_linear`.
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'none' is the same as `detrend_none`. The default is 'mean'. See the
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corresponding functions for more details regarding the algorithms. Can
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also be a function that carries out the detrend operation.
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axis : int
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The axis along which to do the detrending.
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See Also
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--------
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detrend_mean : Implementation of the 'mean' algorithm.
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detrend_linear : Implementation of the 'linear' algorithm.
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detrend_none : Implementation of the 'none' algorithm.
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"""
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if key is None or key in ['constant', 'mean', 'default']:
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return detrend(x, key=detrend_mean, axis=axis)
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elif key == 'linear':
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return detrend(x, key=detrend_linear, axis=axis)
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elif key == 'none':
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return detrend(x, key=detrend_none, axis=axis)
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elif callable(key):
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x = np.asarray(x)
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if axis is not None and axis + 1 > x.ndim:
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raise ValueError(f'axis(={axis}) out of bounds')
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if (axis is None and x.ndim == 0) or (not axis and x.ndim == 1):
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return key(x)
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# try to use the 'axis' argument if the function supports it,
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# otherwise use apply_along_axis to do it
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try:
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return key(x, axis=axis)
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except TypeError:
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return np.apply_along_axis(key, axis=axis, arr=x)
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else:
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raise ValueError(
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f"Unknown value for key: {key!r}, must be one of: 'default', "
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f"'constant', 'mean', 'linear', or a function")
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def detrend_mean(x, axis=None):
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"""
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Return *x* minus the mean(*x*).
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Parameters
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----------
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x : array or sequence
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Array or sequence containing the data
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Can have any dimensionality
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axis : int
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The axis along which to take the mean. See `numpy.mean` for a
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description of this argument.
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See Also
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--------
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detrend_linear : Another detrend algorithm.
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detrend_none : Another detrend algorithm.
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detrend : A wrapper around all the detrend algorithms.
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"""
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x = np.asarray(x)
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if axis is not None and axis+1 > x.ndim:
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raise ValueError('axis(=%s) out of bounds' % axis)
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return x - x.mean(axis, keepdims=True)
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def detrend_none(x, axis=None):
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"""
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Return *x*: no detrending.
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Parameters
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----------
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x : any object
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An object containing the data
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axis : int
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This parameter is ignored.
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It is included for compatibility with detrend_mean
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See Also
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--------
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detrend_mean : Another detrend algorithm.
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detrend_linear : Another detrend algorithm.
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detrend : A wrapper around all the detrend algorithms.
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"""
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return x
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def detrend_linear(y):
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"""
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Return *x* minus best fit line; 'linear' detrending.
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Parameters
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----------
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y : 0-D or 1-D array or sequence
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Array or sequence containing the data
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See Also
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--------
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detrend_mean : Another detrend algorithm.
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detrend_none : Another detrend algorithm.
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detrend : A wrapper around all the detrend algorithms.
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"""
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# This is faster than an algorithm based on linalg.lstsq.
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y = np.asarray(y)
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if y.ndim > 1:
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raise ValueError('y cannot have ndim > 1')
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# short-circuit 0-D array.
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if not y.ndim:
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return np.array(0., dtype=y.dtype)
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x = np.arange(y.size, dtype=float)
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C = np.cov(x, y, bias=1)
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b = C[0, 1]/C[0, 0]
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a = y.mean() - b*x.mean()
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return y - (b*x + a)
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def _spectral_helper(x, y=None, NFFT=None, Fs=None, detrend_func=None,
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window=None, noverlap=None, pad_to=None,
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sides=None, scale_by_freq=None, mode=None):
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"""
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Private helper implementing the common parts between the psd, csd,
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spectrogram and complex, magnitude, angle, and phase spectrums.
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"""
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if y is None:
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# if y is None use x for y
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same_data = True
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else:
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# The checks for if y is x are so that we can use the same function to
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# implement the core of psd(), csd(), and spectrogram() without doing
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# extra calculations. We return the unaveraged Pxy, freqs, and t.
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same_data = y is x
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if Fs is None:
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Fs = 2
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if noverlap is None:
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noverlap = 0
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if detrend_func is None:
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detrend_func = detrend_none
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if window is None:
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window = window_hanning
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# if NFFT is set to None use the whole signal
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if NFFT is None:
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NFFT = 256
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if noverlap >= NFFT:
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raise ValueError('noverlap must be less than NFFT')
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if mode is None or mode == 'default':
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mode = 'psd'
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_api.check_in_list(
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['default', 'psd', 'complex', 'magnitude', 'angle', 'phase'],
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mode=mode)
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if not same_data and mode != 'psd':
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raise ValueError("x and y must be equal if mode is not 'psd'")
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# Make sure we're dealing with a numpy array. If y and x were the same
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# object to start with, keep them that way
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x = np.asarray(x)
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if not same_data:
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y = np.asarray(y)
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if sides is None or sides == 'default':
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if np.iscomplexobj(x):
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sides = 'twosided'
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else:
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sides = 'onesided'
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_api.check_in_list(['default', 'onesided', 'twosided'], sides=sides)
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# zero pad x and y up to NFFT if they are shorter than NFFT
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if len(x) < NFFT:
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n = len(x)
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x = np.resize(x, NFFT)
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x[n:] = 0
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if not same_data and len(y) < NFFT:
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n = len(y)
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y = np.resize(y, NFFT)
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y[n:] = 0
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if pad_to is None:
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pad_to = NFFT
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if mode != 'psd':
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scale_by_freq = False
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elif scale_by_freq is None:
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scale_by_freq = True
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# For real x, ignore the negative frequencies unless told otherwise
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if sides == 'twosided':
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numFreqs = pad_to
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if pad_to % 2:
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freqcenter = (pad_to - 1)//2 + 1
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else:
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freqcenter = pad_to//2
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scaling_factor = 1.
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elif sides == 'onesided':
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if pad_to % 2:
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numFreqs = (pad_to + 1)//2
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else:
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numFreqs = pad_to//2 + 1
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scaling_factor = 2.
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if not np.iterable(window):
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window = window(np.ones(NFFT, x.dtype))
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if len(window) != NFFT:
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raise ValueError(
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"The window length must match the data's first dimension")
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result = np.lib.stride_tricks.sliding_window_view(
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x, NFFT, axis=0)[::NFFT - noverlap].T
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result = detrend(result, detrend_func, axis=0)
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result = result * window.reshape((-1, 1))
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result = np.fft.fft(result, n=pad_to, axis=0)[:numFreqs, :]
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freqs = np.fft.fftfreq(pad_to, 1/Fs)[:numFreqs]
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if not same_data:
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# if same_data is False, mode must be 'psd'
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resultY = np.lib.stride_tricks.sliding_window_view(
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y, NFFT, axis=0)[::NFFT - noverlap].T
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resultY = detrend(resultY, detrend_func, axis=0)
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resultY = resultY * window.reshape((-1, 1))
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resultY = np.fft.fft(resultY, n=pad_to, axis=0)[:numFreqs, :]
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result = np.conj(result) * resultY
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elif mode == 'psd':
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result = np.conj(result) * result
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elif mode == 'magnitude':
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result = np.abs(result) / window.sum()
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elif mode == 'angle' or mode == 'phase':
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# we unwrap the phase later to handle the onesided vs. twosided case
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result = np.angle(result)
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elif mode == 'complex':
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result /= window.sum()
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if mode == 'psd':
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# Also include scaling factors for one-sided densities and dividing by
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# the sampling frequency, if desired. Scale everything, except the DC
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# component and the NFFT/2 component:
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# if we have a even number of frequencies, don't scale NFFT/2
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if not NFFT % 2:
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slc = slice(1, -1, None)
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# if we have an odd number, just don't scale DC
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else:
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slc = slice(1, None, None)
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result[slc] *= scaling_factor
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# MATLAB divides by the sampling frequency so that density function
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# has units of dB/Hz and can be integrated by the plotted frequency
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# values. Perform the same scaling here.
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if scale_by_freq:
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result /= Fs
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# Scale the spectrum by the norm of the window to compensate for
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# windowing loss; see Bendat & Piersol Sec 11.5.2.
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result /= (window**2).sum()
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else:
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# In this case, preserve power in the segment, not amplitude
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result /= window.sum()**2
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t = np.arange(NFFT/2, len(x) - NFFT/2 + 1, NFFT - noverlap)/Fs
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if sides == 'twosided':
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# center the frequency range at zero
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freqs = np.roll(freqs, -freqcenter, axis=0)
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result = np.roll(result, -freqcenter, axis=0)
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elif not pad_to % 2:
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# get the last value correctly, it is negative otherwise
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freqs[-1] *= -1
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# we unwrap the phase here to handle the onesided vs. twosided case
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if mode == 'phase':
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result = np.unwrap(result, axis=0)
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return result, freqs, t
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def _single_spectrum_helper(
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mode, x, Fs=None, window=None, pad_to=None, sides=None):
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"""
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Private helper implementing the commonality between the complex, magnitude,
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angle, and phase spectrums.
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"""
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_api.check_in_list(['complex', 'magnitude', 'angle', 'phase'], mode=mode)
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if pad_to is None:
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pad_to = len(x)
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spec, freqs, _ = _spectral_helper(x=x, y=None, NFFT=len(x), Fs=Fs,
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detrend_func=detrend_none, window=window,
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noverlap=0, pad_to=pad_to,
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sides=sides,
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scale_by_freq=False,
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mode=mode)
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if mode != 'complex':
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spec = spec.real
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if spec.ndim == 2 and spec.shape[1] == 1:
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spec = spec[:, 0]
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return spec, freqs
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# Split out these keyword docs so that they can be used elsewhere
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_docstring.interpd.register(
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Spectral="""\
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Fs : float, default: 2
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The sampling frequency (samples per time unit). It is used to calculate
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the Fourier frequencies, *freqs*, in cycles per time unit.
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window : callable or ndarray, default: `.window_hanning`
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A function or a vector of length *NFFT*. To create window vectors see
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`.window_hanning`, `.window_none`, `numpy.blackman`, `numpy.hamming`,
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`numpy.bartlett`, `scipy.signal`, `scipy.signal.get_window`, etc. If a
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function is passed as the argument, it must take a data segment as an
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argument and return the windowed version of the segment.
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sides : {'default', 'onesided', 'twosided'}, optional
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Which sides of the spectrum to return. 'default' is one-sided for real
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data and two-sided for complex data. 'onesided' forces the return of a
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one-sided spectrum, while 'twosided' forces two-sided.""",
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Single_Spectrum="""\
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pad_to : int, optional
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The number of points to which the data segment is padded when performing
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the FFT. While not increasing the actual resolution of the spectrum (the
|
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minimum distance between resolvable peaks), this can give more points in
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the plot, allowing for more detail. This corresponds to the *n* parameter
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in the call to `~numpy.fft.fft`. The default is None, which sets *pad_to*
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equal to the length of the input signal (i.e. no padding).""",
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PSD="""\
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pad_to : int, optional
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The number of points to which the data segment is padded when performing
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the FFT. This can be different from *NFFT*, which specifies the number
|
|
of data points used. While not increasing the actual resolution of the
|
|
spectrum (the minimum distance between resolvable peaks), this can give
|
|
more points in the plot, allowing for more detail. This corresponds to
|
|
the *n* parameter in the call to `~numpy.fft.fft`. The default is None,
|
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which sets *pad_to* equal to *NFFT*
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NFFT : int, default: 256
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The number of data points used in each block for the FFT. A power 2 is
|
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most efficient. This should *NOT* be used to get zero padding, or the
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scaling of the result will be incorrect; use *pad_to* for this instead.
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detrend : {'none', 'mean', 'linear'} or callable, default: 'none'
|
|
The function applied to each segment before fft-ing, designed to remove
|
|
the mean or linear trend. Unlike in MATLAB, where the *detrend* parameter
|
|
is a vector, in Matplotlib it is a function. The :mod:`~matplotlib.mlab`
|
|
module defines `.detrend_none`, `.detrend_mean`, and `.detrend_linear`,
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but you can use a custom function as well. You can also use a string to
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choose one of the functions: 'none' calls `.detrend_none`. 'mean' calls
|
|
`.detrend_mean`. 'linear' calls `.detrend_linear`.
|
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|
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scale_by_freq : bool, default: True
|
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Whether the resulting density values should be scaled by the scaling
|
|
frequency, which gives density in units of 1/Hz. This allows for
|
|
integration over the returned frequency values. The default is True for
|
|
MATLAB compatibility.""")
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|
|
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|
@_docstring.interpd
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|
def psd(x, NFFT=None, Fs=None, detrend=None, window=None,
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noverlap=None, pad_to=None, sides=None, scale_by_freq=None):
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r"""
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|
Compute the power spectral density.
|
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|
The power spectral density :math:`P_{xx}` by Welch's average
|
|
periodogram method. The vector *x* is divided into *NFFT* length
|
|
segments. Each segment is detrended by function *detrend* and
|
|
windowed by function *window*. *noverlap* gives the length of
|
|
the overlap between segments. The :math:`|\mathrm{fft}(i)|^2`
|
|
of each segment :math:`i` are averaged to compute :math:`P_{xx}`.
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|
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If len(*x*) < *NFFT*, it will be zero padded to *NFFT*.
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|
|
Parameters
|
|
----------
|
|
x : 1-D array or sequence
|
|
Array or sequence containing the data
|
|
|
|
%(Spectral)s
|
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|
|
%(PSD)s
|
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|
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noverlap : int, default: 0 (no overlap)
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The number of points of overlap between segments.
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Returns
|
|
-------
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Pxx : 1-D array
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The values for the power spectrum :math:`P_{xx}` (real valued)
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|
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freqs : 1-D array
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The frequencies corresponding to the elements in *Pxx*
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|
|
References
|
|
----------
|
|
Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John
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|
Wiley & Sons (1986)
|
|
|
|
See Also
|
|
--------
|
|
specgram
|
|
`specgram` differs in the default overlap; in not returning the mean of
|
|
the segment periodograms; and in returning the times of the segments.
|
|
|
|
magnitude_spectrum : returns the magnitude spectrum.
|
|
|
|
csd : returns the spectral density between two signals.
|
|
"""
|
|
Pxx, freqs = csd(x=x, y=None, NFFT=NFFT, Fs=Fs, detrend=detrend,
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window=window, noverlap=noverlap, pad_to=pad_to,
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sides=sides, scale_by_freq=scale_by_freq)
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|
return Pxx.real, freqs
|
|
|
|
|
|
@_docstring.interpd
|
|
def csd(x, y, NFFT=None, Fs=None, detrend=None, window=None,
|
|
noverlap=None, pad_to=None, sides=None, scale_by_freq=None):
|
|
"""
|
|
Compute the cross-spectral density.
|
|
|
|
The cross spectral density :math:`P_{xy}` by Welch's average
|
|
periodogram method. The vectors *x* and *y* are divided into
|
|
*NFFT* length segments. Each segment is detrended by function
|
|
*detrend* and windowed by function *window*. *noverlap* gives
|
|
the length of the overlap between segments. The product of
|
|
the direct FFTs of *x* and *y* are averaged over each segment
|
|
to compute :math:`P_{xy}`, with a scaling to correct for power
|
|
loss due to windowing.
|
|
|
|
If len(*x*) < *NFFT* or len(*y*) < *NFFT*, they will be zero
|
|
padded to *NFFT*.
|
|
|
|
Parameters
|
|
----------
|
|
x, y : 1-D arrays or sequences
|
|
Arrays or sequences containing the data
|
|
|
|
%(Spectral)s
|
|
|
|
%(PSD)s
|
|
|
|
noverlap : int, default: 0 (no overlap)
|
|
The number of points of overlap between segments.
|
|
|
|
Returns
|
|
-------
|
|
Pxy : 1-D array
|
|
The values for the cross spectrum :math:`P_{xy}` before scaling (real
|
|
valued)
|
|
|
|
freqs : 1-D array
|
|
The frequencies corresponding to the elements in *Pxy*
|
|
|
|
References
|
|
----------
|
|
Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John
|
|
Wiley & Sons (1986)
|
|
|
|
See Also
|
|
--------
|
|
psd : equivalent to setting ``y = x``.
|
|
"""
|
|
if NFFT is None:
|
|
NFFT = 256
|
|
Pxy, freqs, _ = _spectral_helper(x=x, y=y, NFFT=NFFT, Fs=Fs,
|
|
detrend_func=detrend, window=window,
|
|
noverlap=noverlap, pad_to=pad_to,
|
|
sides=sides, scale_by_freq=scale_by_freq,
|
|
mode='psd')
|
|
|
|
if Pxy.ndim == 2:
|
|
if Pxy.shape[1] > 1:
|
|
Pxy = Pxy.mean(axis=1)
|
|
else:
|
|
Pxy = Pxy[:, 0]
|
|
return Pxy, freqs
|
|
|
|
|
|
_single_spectrum_docs = """\
|
|
Compute the {quantity} of *x*.
|
|
Data is padded to a length of *pad_to* and the windowing function *window* is
|
|
applied to the signal.
|
|
|
|
Parameters
|
|
----------
|
|
x : 1-D array or sequence
|
|
Array or sequence containing the data
|
|
|
|
{Spectral}
|
|
|
|
{Single_Spectrum}
|
|
|
|
Returns
|
|
-------
|
|
spectrum : 1-D array
|
|
The {quantity}.
|
|
freqs : 1-D array
|
|
The frequencies corresponding to the elements in *spectrum*.
|
|
|
|
See Also
|
|
--------
|
|
psd
|
|
Returns the power spectral density.
|
|
complex_spectrum
|
|
Returns the complex-valued frequency spectrum.
|
|
magnitude_spectrum
|
|
Returns the absolute value of the `complex_spectrum`.
|
|
angle_spectrum
|
|
Returns the angle of the `complex_spectrum`.
|
|
phase_spectrum
|
|
Returns the phase (unwrapped angle) of the `complex_spectrum`.
|
|
specgram
|
|
Can return the complex spectrum of segments within the signal.
|
|
"""
|
|
|
|
|
|
complex_spectrum = functools.partial(_single_spectrum_helper, "complex")
|
|
complex_spectrum.__doc__ = _single_spectrum_docs.format(
|
|
quantity="complex-valued frequency spectrum",
|
|
**_docstring.interpd.params)
|
|
magnitude_spectrum = functools.partial(_single_spectrum_helper, "magnitude")
|
|
magnitude_spectrum.__doc__ = _single_spectrum_docs.format(
|
|
quantity="magnitude (absolute value) of the frequency spectrum",
|
|
**_docstring.interpd.params)
|
|
angle_spectrum = functools.partial(_single_spectrum_helper, "angle")
|
|
angle_spectrum.__doc__ = _single_spectrum_docs.format(
|
|
quantity="angle of the frequency spectrum (wrapped phase spectrum)",
|
|
**_docstring.interpd.params)
|
|
phase_spectrum = functools.partial(_single_spectrum_helper, "phase")
|
|
phase_spectrum.__doc__ = _single_spectrum_docs.format(
|
|
quantity="phase of the frequency spectrum (unwrapped phase spectrum)",
|
|
**_docstring.interpd.params)
|
|
|
|
|
|
@_docstring.interpd
|
|
def specgram(x, NFFT=None, Fs=None, detrend=None, window=None,
|
|
noverlap=None, pad_to=None, sides=None, scale_by_freq=None,
|
|
mode=None):
|
|
"""
|
|
Compute a spectrogram.
|
|
|
|
Compute and plot a spectrogram of data in *x*. Data are split into
|
|
*NFFT* length segments and the spectrum of each section is
|
|
computed. The windowing function *window* is applied to each
|
|
segment, and the amount of overlap of each segment is
|
|
specified with *noverlap*.
|
|
|
|
Parameters
|
|
----------
|
|
x : array-like
|
|
1-D array or sequence.
|
|
|
|
%(Spectral)s
|
|
|
|
%(PSD)s
|
|
|
|
noverlap : int, default: 128
|
|
The number of points of overlap between blocks.
|
|
mode : str, default: 'psd'
|
|
What sort of spectrum to use:
|
|
'psd'
|
|
Returns the power spectral density.
|
|
'complex'
|
|
Returns the complex-valued frequency spectrum.
|
|
'magnitude'
|
|
Returns the magnitude spectrum.
|
|
'angle'
|
|
Returns the phase spectrum without unwrapping.
|
|
'phase'
|
|
Returns the phase spectrum with unwrapping.
|
|
|
|
Returns
|
|
-------
|
|
spectrum : array-like
|
|
2D array, columns are the periodograms of successive segments.
|
|
|
|
freqs : array-like
|
|
1-D array, frequencies corresponding to the rows in *spectrum*.
|
|
|
|
t : array-like
|
|
1-D array, the times corresponding to midpoints of segments
|
|
(i.e the columns in *spectrum*).
|
|
|
|
See Also
|
|
--------
|
|
psd : differs in the overlap and in the return values.
|
|
complex_spectrum : similar, but with complex valued frequencies.
|
|
magnitude_spectrum : similar single segment when *mode* is 'magnitude'.
|
|
angle_spectrum : similar to single segment when *mode* is 'angle'.
|
|
phase_spectrum : similar to single segment when *mode* is 'phase'.
|
|
|
|
Notes
|
|
-----
|
|
*detrend* and *scale_by_freq* only apply when *mode* is set to 'psd'.
|
|
|
|
"""
|
|
if noverlap is None:
|
|
noverlap = 128 # default in _spectral_helper() is noverlap = 0
|
|
if NFFT is None:
|
|
NFFT = 256 # same default as in _spectral_helper()
|
|
if len(x) <= NFFT:
|
|
_api.warn_external("Only one segment is calculated since parameter "
|
|
f"NFFT (={NFFT}) >= signal length (={len(x)}).")
|
|
|
|
spec, freqs, t = _spectral_helper(x=x, y=None, NFFT=NFFT, Fs=Fs,
|
|
detrend_func=detrend, window=window,
|
|
noverlap=noverlap, pad_to=pad_to,
|
|
sides=sides,
|
|
scale_by_freq=scale_by_freq,
|
|
mode=mode)
|
|
|
|
if mode != 'complex':
|
|
spec = spec.real # Needed since helper implements generically
|
|
|
|
return spec, freqs, t
|
|
|
|
|
|
@_docstring.interpd
|
|
def cohere(x, y, NFFT=256, Fs=2, detrend=detrend_none, window=window_hanning,
|
|
noverlap=0, pad_to=None, sides='default', scale_by_freq=None):
|
|
r"""
|
|
The coherence between *x* and *y*. Coherence is the normalized
|
|
cross spectral density:
|
|
|
|
.. math::
|
|
|
|
C_{xy} = \frac{|P_{xy}|^2}{P_{xx}P_{yy}}
|
|
|
|
Parameters
|
|
----------
|
|
x, y
|
|
Array or sequence containing the data
|
|
|
|
%(Spectral)s
|
|
|
|
%(PSD)s
|
|
|
|
noverlap : int, default: 0 (no overlap)
|
|
The number of points of overlap between segments.
|
|
|
|
Returns
|
|
-------
|
|
Cxy : 1-D array
|
|
The coherence vector.
|
|
freqs : 1-D array
|
|
The frequencies for the elements in *Cxy*.
|
|
|
|
See Also
|
|
--------
|
|
:func:`psd`, :func:`csd` :
|
|
For information about the methods used to compute :math:`P_{xy}`,
|
|
:math:`P_{xx}` and :math:`P_{yy}`.
|
|
"""
|
|
if len(x) < 2 * NFFT:
|
|
raise ValueError(
|
|
"Coherence is calculated by averaging over *NFFT* length "
|
|
"segments. Your signal is too short for your choice of *NFFT*.")
|
|
Pxx, f = psd(x, NFFT, Fs, detrend, window, noverlap, pad_to, sides,
|
|
scale_by_freq)
|
|
Pyy, f = psd(y, NFFT, Fs, detrend, window, noverlap, pad_to, sides,
|
|
scale_by_freq)
|
|
Pxy, f = csd(x, y, NFFT, Fs, detrend, window, noverlap, pad_to, sides,
|
|
scale_by_freq)
|
|
Cxy = np.abs(Pxy) ** 2 / (Pxx * Pyy)
|
|
return Cxy, f
|
|
|
|
|
|
class GaussianKDE:
|
|
"""
|
|
Representation of a kernel-density estimate using Gaussian kernels.
|
|
|
|
Parameters
|
|
----------
|
|
dataset : array-like
|
|
Datapoints to estimate from. In case of univariate data this is a 1-D
|
|
array, otherwise a 2D array with shape (# of dims, # of data).
|
|
bw_method : {'scott', 'silverman'} or float or callable, optional
|
|
The method used to calculate the estimator bandwidth. If a
|
|
float, this will be used directly as `kde.factor`. If a
|
|
callable, it should take a `GaussianKDE` instance as only
|
|
parameter and return a float. If None (default), 'scott' is used.
|
|
|
|
Attributes
|
|
----------
|
|
dataset : ndarray
|
|
The dataset passed to the constructor.
|
|
dim : int
|
|
Number of dimensions.
|
|
num_dp : int
|
|
Number of datapoints.
|
|
factor : float
|
|
The bandwidth factor, obtained from `kde.covariance_factor`, with which
|
|
the covariance matrix is multiplied.
|
|
covariance : ndarray
|
|
The covariance matrix of *dataset*, scaled by the calculated bandwidth
|
|
(`kde.factor`).
|
|
inv_cov : ndarray
|
|
The inverse of *covariance*.
|
|
|
|
Methods
|
|
-------
|
|
kde.evaluate(points) : ndarray
|
|
Evaluate the estimated pdf on a provided set of points.
|
|
kde(points) : ndarray
|
|
Same as kde.evaluate(points)
|
|
"""
|
|
|
|
# This implementation with minor modification was too good to pass up.
|
|
# from scipy: https://github.com/scipy/scipy/blob/master/scipy/stats/kde.py
|
|
|
|
def __init__(self, dataset, bw_method=None):
|
|
self.dataset = np.atleast_2d(dataset)
|
|
if not np.array(self.dataset).size > 1:
|
|
raise ValueError("`dataset` input should have multiple elements.")
|
|
|
|
self.dim, self.num_dp = np.array(self.dataset).shape
|
|
|
|
if bw_method is None:
|
|
pass
|
|
elif cbook._str_equal(bw_method, 'scott'):
|
|
self.covariance_factor = self.scotts_factor
|
|
elif cbook._str_equal(bw_method, 'silverman'):
|
|
self.covariance_factor = self.silverman_factor
|
|
elif isinstance(bw_method, Number):
|
|
self._bw_method = 'use constant'
|
|
self.covariance_factor = lambda: bw_method
|
|
elif callable(bw_method):
|
|
self._bw_method = bw_method
|
|
self.covariance_factor = lambda: self._bw_method(self)
|
|
else:
|
|
raise ValueError("`bw_method` should be 'scott', 'silverman', a "
|
|
"scalar or a callable")
|
|
|
|
# Computes the covariance matrix for each Gaussian kernel using
|
|
# covariance_factor().
|
|
|
|
self.factor = self.covariance_factor()
|
|
# Cache covariance and inverse covariance of the data
|
|
if not hasattr(self, '_data_inv_cov'):
|
|
self.data_covariance = np.atleast_2d(
|
|
np.cov(
|
|
self.dataset,
|
|
rowvar=1,
|
|
bias=False))
|
|
self.data_inv_cov = np.linalg.inv(self.data_covariance)
|
|
|
|
self.covariance = self.data_covariance * self.factor ** 2
|
|
self.inv_cov = self.data_inv_cov / self.factor ** 2
|
|
self.norm_factor = (np.sqrt(np.linalg.det(2 * np.pi * self.covariance))
|
|
* self.num_dp)
|
|
|
|
def scotts_factor(self):
|
|
return np.power(self.num_dp, -1. / (self.dim + 4))
|
|
|
|
def silverman_factor(self):
|
|
return np.power(
|
|
self.num_dp * (self.dim + 2.0) / 4.0, -1. / (self.dim + 4))
|
|
|
|
# Default method to calculate bandwidth, can be overwritten by subclass
|
|
covariance_factor = scotts_factor
|
|
|
|
def evaluate(self, points):
|
|
"""
|
|
Evaluate the estimated pdf on a set of points.
|
|
|
|
Parameters
|
|
----------
|
|
points : (# of dimensions, # of points)-array
|
|
Alternatively, a (# of dimensions,) vector can be passed in and
|
|
treated as a single point.
|
|
|
|
Returns
|
|
-------
|
|
(# of points,)-array
|
|
The values at each point.
|
|
|
|
Raises
|
|
------
|
|
ValueError : if the dimensionality of the input points is different
|
|
than the dimensionality of the KDE.
|
|
|
|
"""
|
|
points = np.atleast_2d(points)
|
|
|
|
dim, num_m = np.array(points).shape
|
|
if dim != self.dim:
|
|
raise ValueError(f"points have dimension {dim}, dataset has "
|
|
f"dimension {self.dim}")
|
|
|
|
result = np.zeros(num_m)
|
|
|
|
if num_m >= self.num_dp:
|
|
# there are more points than data, so loop over data
|
|
for i in range(self.num_dp):
|
|
diff = self.dataset[:, i, np.newaxis] - points
|
|
tdiff = np.dot(self.inv_cov, diff)
|
|
energy = np.sum(diff * tdiff, axis=0) / 2.0
|
|
result = result + np.exp(-energy)
|
|
else:
|
|
# loop over points
|
|
for i in range(num_m):
|
|
diff = self.dataset - points[:, i, np.newaxis]
|
|
tdiff = np.dot(self.inv_cov, diff)
|
|
energy = np.sum(diff * tdiff, axis=0) / 2.0
|
|
result[i] = np.sum(np.exp(-energy), axis=0)
|
|
|
|
result = result / self.norm_factor
|
|
|
|
return result
|
|
|
|
__call__ = evaluate
|