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Classe « Axes »

Méthode matplotlib.figure.Axes.cohere

Signature de la méthode cohere

def cohere(self, x, y, *, NFFT=256, Fs=2, Fc=0, detrend=<function detrend_none at 0x0000020D9ABF6660>, window=<function window_hanning at 0x0000020D9ABF63E0>, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, data=None, **kwargs) 

Description

help(Axes.cohere)

Plot 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
----------
Fs : float, default: 2
    The sampling frequency (samples per time unit).  It is used to calculate
    the Fourier frequencies, *freqs*, in cycles per time unit.

window : callable or ndarray, default: `.window_hanning`
    A function or a vector of length *NFFT*.  To create window vectors see
    `.window_hanning`, `.window_none`, `numpy.blackman`, `numpy.hamming`,
    `numpy.bartlett`, `scipy.signal`, `scipy.signal.get_window`, etc.  If a
    function is passed as the argument, it must take a data segment as an
    argument and return the windowed version of the segment.

sides : {'default', 'onesided', 'twosided'}, optional
    Which sides of the spectrum to return. 'default' is one-sided for real
    data and two-sided for complex data. 'onesided' forces the return of a
    one-sided spectrum, while 'twosided' forces two-sided.

pad_to : int, optional
    The number of points to which the data segment is padded when performing
    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,
    which sets *pad_to* equal to *NFFT*

NFFT : int, default: 256
    The number of data points used in each block for the FFT.  A power 2 is
    most efficient.  This should *NOT* be used to get zero padding, or the
    scaling of the result will be incorrect; use *pad_to* for this instead.

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`,
    but you can use a custom function as well.  You can also use a string to
    choose one of the functions: 'none' calls `.detrend_none`. 'mean' calls
    `.detrend_mean`. 'linear' calls `.detrend_linear`.

scale_by_freq : bool, default: True
    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.

noverlap : int, default: 0 (no overlap)
    The number of points of overlap between blocks.

Fc : int, default: 0
    The center frequency of *x*, which offsets the x extents of the
    plot to reflect the frequency range used when a signal is acquired
    and then filtered and downsampled to baseband.

Returns
-------
Cxy : 1-D array
    The coherence vector.

freqs : 1-D array
    The frequencies for the elements in *Cxy*.

Other Parameters
----------------
data : indexable object, optional
    If given, the following parameters also accept a string ``s``, which is
    interpreted as ``data[s]`` if ``s`` is a key in ``data``:

    *x*, *y*

**kwargs
    Keyword arguments control the `.Line2D` properties:

    Properties:
    agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image
    alpha: scalar or None
    animated: bool
    antialiased or aa: bool
    clip_box: `~matplotlib.transforms.BboxBase` or None
    clip_on: bool
    clip_path: Patch or (Path, Transform) or None
    color or c: :mpltype:`color`
    dash_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}
    dash_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}
    dashes: sequence of floats (on/off ink in points) or (None, None)
    data: (2, N) array or two 1D arrays
    drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'
    figure: `~matplotlib.figure.Figure` or `~matplotlib.figure.SubFigure`
    fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}
    gapcolor: :mpltype:`color` or None
    gid: str
    in_layout: bool
    label: object
    linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
    linewidth or lw: float
    marker: marker style string, `~.path.Path` or `~.markers.MarkerStyle`
    markeredgecolor or mec: :mpltype:`color`
    markeredgewidth or mew: float
    markerfacecolor or mfc: :mpltype:`color`
    markerfacecoloralt or mfcalt: :mpltype:`color`
    markersize or ms: float
    markevery: None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool]
    mouseover: bool
    path_effects: list of `.AbstractPathEffect`
    picker: float or callable[[Artist, Event], tuple[bool, dict]]
    pickradius: float
    rasterized: bool
    sketch_params: (scale: float, length: float, randomness: float)
    snap: bool or None
    solid_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}
    solid_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}
    transform: unknown
    url: str
    visible: bool
    xdata: 1D array
    ydata: 1D array
    zorder: float

References
----------
Bendat & Piersol -- Random Data: Analysis and Measurement Procedures,
John Wiley & Sons (1986)

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