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File Content: _peak_finding.cpython-35.pyc

DB�W�E������������������@���s��d��Z��d�d�l�m�Z�m�Z�m�Z�d�d�l�Z�d�d�l�m�Z�d�d�l	�m
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Functions for identifying peaks in signals.
�����)�division�print_function�absolute_importN)�xrange)�cwt�ricker)�scoreatpercentile�	argrelmin�	argrelmax�
argrelextrema�find_peaks_cwt����Zclipc�������������C���s��t��|���|�k�s�|�d�k��r*�t�d�����|��j�|�}�t�j�d�|���}�t�j�|��j�d�t��}�|��j�|�d�|�d�|��}�x��t�d�|�d���D]{�}	�|��j�|�|	�d�|�d�|��}
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    Calculate the relative extrema of `data`.

Relative extrema are calculated by finding locations where
    ``comparator(data[n], data[n+1:n+order+1])`` is True.

Parameters
    ----------
    data : ndarray
        Array in which to find the relative extrema.
    comparator : callable
        Function to use to compare two data points.
        Should take 2 numbers as arguments.
    axis : int, optional
        Axis over which to select from `data`.  Default is 0.
    order : int, optional
        How many points on each side to use for the comparison
        to consider ``comparator(n,n+x)`` to be True.
    mode : str, optional
        How the edges of the vector are treated.  'wrap' (wrap around) or
        'clip' (treat overflow as the same as the last (or first) element).
        Default 'clip'.  See numpy.take

Returns
    -------
    extrema : ndarray
        Boolean array of the same shape as `data` that is True at an extrema,
        False otherwise.

See also
    --------
    argrelmax, argrelmin

Examples
    --------
    >>> testdata = np.array([1,2,3,2,1])
    >>> _boolrelextrema(testdata, np.greater, axis=0)
    array([False, False,  True, False, False], dtype=bool)

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    Calculate the relative minima of `data`.

Parameters
    ----------
    data : ndarray
        Array in which to find the relative minima.
    axis : int, optional
        Axis over which to select from `data`.  Default is 0.
    order : int, optional
        How many points on each side to use for the comparison
        to consider ``comparator(n, n+x)`` to be True.
    mode : str, optional
        How the edges of the vector are treated.
        Available options are 'wrap' (wrap around) or 'clip' (treat overflow
        as the same as the last (or first) element).
        Default 'clip'. See numpy.take

Returns
    -------
    extrema : tuple of ndarrays
        Indices of the minima in arrays of integers.  ``extrema[k]`` is
        the array of indices of axis `k` of `data`.  Note that the
        return value is a tuple even when `data` is one-dimensional.

See Also
    --------
    argrelextrema, argrelmax

Notes
    -----
    This function uses `argrelextrema` with np.less as comparator.

.. versionadded:: 0.11.0

Examples
    --------
    >>> from scipy.signal import argrelmin
    >>> x = np.array([2, 1, 2, 3, 2, 0, 1, 0])
    >>> argrelmin(x)
    (array([1, 5]),)
    >>> y = np.array([[1, 2, 1, 2],
    ...               [2, 2, 0, 0],
    ...               [5, 3, 4, 4]])
    ...
    >>> argrelmin(y, axis=1)
    (array([0, 2]), array([2, 1]))

)r���r���Zless)r���r���r���r���r���r���r���r	���K���s����2c�������������C���s���t��|��t�j�|�|�|���S)a~��
    Calculate the relative maxima of `data`.

Parameters
    ----------
    data : ndarray
        Array in which to find the relative maxima.
    axis : int, optional
        Axis over which to select from `data`.  Default is 0.
    order : int, optional
        How many points on each side to use for the comparison
        to consider ``comparator(n, n+x)`` to be True.
    mode : str, optional
        How the edges of the vector are treated.
        Available options are 'wrap' (wrap around) or 'clip' (treat overflow
        as the same as the last (or first) element).
        Default 'clip'.  See `numpy.take`.

Returns
    -------
    extrema : tuple of ndarrays
        Indices of the maxima in arrays of integers.  ``extrema[k]`` is
        the array of indices of axis `k` of `data`.  Note that the
        return value is a tuple even when `data` is one-dimensional.

See Also
    --------
    argrelextrema, argrelmin

Notes
    -----
    This function uses `argrelextrema` with np.greater as comparator.

.. versionadded:: 0.11.0

Examples
    --------
    >>> from scipy.signal import argrelmax
    >>> x = np.array([2, 1, 2, 3, 2, 0, 1, 0])
    >>> argrelmax(x)
    (array([3, 6]),)
    >>> y = np.array([[1, 2, 1, 2],
    ...               [2, 2, 0, 0],
    ...               [5, 3, 4, 4]])
    ...
    >>> argrelmax(y, axis=1)
    (array([0]), array([1]))
    )r���r����greater)r���r���r���r���r���r���r���r
�������s����1c�������������C���s%���t��|��|�|�|�|���}�t�j�|���S)a���
    Calculate the relative extrema of `data`.

Parameters
    ----------
    data : ndarray
        Array in which to find the relative extrema.
    comparator : callable
        Function to use to compare two data points.
        Should take 2 numbers as arguments.
    axis : int, optional
        Axis over which to select from `data`.  Default is 0.
    order : int, optional
        How many points on each side to use for the comparison
        to consider ``comparator(n, n+x)`` to be True.
    mode : str, optional
        How the edges of the vector are treated.  'wrap' (wrap around) or
        'clip' (treat overflow as the same as the last (or first) element).
        Default is 'clip'.  See `numpy.take`.

See Also
    --------
    argrelmin, argrelmax

Notes
    -----

.. versionadded:: 0.11.0

Examples
    --------
    >>> from scipy.signal import argrelextrema
    >>> x = np.array([2, 1, 2, 3, 2, 0, 1, 0])
    >>> argrelextrema(x, np.greater)
    (array([3, 6]),)
    >>> y = np.array([[1, 2, 1, 2],
    ...               [2, 2, 0, 0],
    ...               [5, 3, 4, 4]])
    ...
    >>> argrelextrema(y, np.less, axis=1)
    (array([0, 2]), array([2, 1]))

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    Identify ridges in the 2-D matrix.

Expect that the width of the wavelet feature increases with increasing row
    number.

Parameters
    ----------
    matr : 2-D ndarray
        Matrix in which to identify ridge lines.
    max_distances : 1-D sequence
        At each row, a ridge line is only connected
        if the relative max at row[n] is within
        `max_distances`[n] from the relative max at row[n+1].
    gap_thresh : int
        If a relative maximum is not found within `max_distances`,
        there will be a gap. A ridge line is discontinued if
        there are more than `gap_thresh` points without connecting
        a new relative maximum.

Returns
    -------
    ridge_lines : tuple
        Tuple of 2 1-D sequences. `ridge_lines`[ii][0] are the rows of the
        ii-th ridge-line, `ridge_lines`[ii][1] are the columns. Empty if none
        found.  Each ridge-line will be sorted by row (increasing), but the
        order of the ridge lines is not specified.

References
    ----------
    Bioinformatics (2006) 22 (17): 2059-2065.
    doi: 10.1093/bioinformatics/btl355
    http://bioinformatics.oxfordjournals.org/content/22/17/2059.long

Examples
    --------
    >>> data = np.random.rand(5,5)
    >>> ridge_lines = _identify_ridge_lines(data, 1, 1)

Notes
    -----
    This function is intended to be used in conjunction with `cwt`
    as part of `find_peaks_cwt`.

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    Filter ridge lines according to prescribed criteria. Intended
    to be used for finding relative maxima.

Parameters
    ----------
    cwt : 2-D ndarray
        Continuous wavelet transform from which the `ridge_lines` were defined.
    ridge_lines : 1-D sequence
        Each element should contain 2 sequences, the rows and columns
        of the ridge line (respectively).
    window_size : int, optional
        Size of window to use to calculate noise floor.
        Default is ``cwt.shape[1] / 20``.
    min_length : int, optional
        Minimum length a ridge line needs to be acceptable.
        Default is ``cwt.shape[0] / 4``, ie 1/4-th the number of widths.
    min_snr : float, optional
        Minimum SNR ratio. Default 1. The signal is the value of
        the cwt matrix at the shortest length scale (``cwt[0, loc]``), the
        noise is the `noise_perc`th percentile of datapoints contained within a
        window of `window_size` around ``cwt[0, loc]``.
    noise_perc : float, optional
        When calculating the noise floor, percentile of data points
        examined below which to consider noise. Calculated using
        scipy.stats.scoreatpercentile.

References
    ----------
    Bioinformatics (2006) 22 (17): 2059-2065. doi: 10.1093/bioinformatics/btl355
    http://bioinformatics.oxfordjournals.org/content/22/17/2059.long

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min_length�min_snr�noisesr���r����	filt_func���s����6z&_filter_ridge_lines.<locals>.filt_func)r���r����ceilr����divmodr-���r*����max�minr����list�filter)r���r0���Zwindow_sizer7���r8����
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window_endr:���r���)r���r7���r8���r9���r����_filter_ridge_linesd��s ����#
rC���c����������	���C���s����|�d�k�r�t��j�|�d���}�|�d�k�r5�|�d�}�|�d�k�rG�t�}�t�|��|�|���}�t�|�|�|���}	�t�|�|	�d�|�d�|�d�|��}
�d�d����|
�D��}�t�|���S)	ar
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    Attempt to find the peaks in a 1-D array.

The general approach is to smooth `vector` by convolving it with
    `wavelet(width)` for each width in `widths`. Relative maxima which
    appear at enough length scales, and with sufficiently high SNR, are
    accepted.

Parameters
    ----------
    vector : ndarray
        1-D array in which to find the peaks.
    widths : sequence
        1-D array of widths to use for calculating the CWT matrix. In general,
        this range should cover the expected width of peaks of interest.
    wavelet : callable, optional
        Should take two parameters and return a 1-D array to convolve
        with `vector`. The first parameter determines the number of points 
        of the returned wavelet array, the second parameter is the scale 
        (`width`) of the wavelet. Should be normalized and symmetric.
        Default is the ricker wavelet.
    max_distances : ndarray, optional
        At each row, a ridge line is only connected if the relative max at
        row[n] is within ``max_distances[n]`` from the relative max at
        ``row[n+1]``.  Default value is ``widths/4``.
    gap_thresh : float, optional
        If a relative maximum is not found within `max_distances`,
        there will be a gap. A ridge line is discontinued if there are more
        than `gap_thresh` points without connecting a new relative maximum.
        Default is 2.
    min_length : int, optional
        Minimum length a ridge line needs to be acceptable.
        Default is ``cwt.shape[0] / 4``, ie 1/4-th the number of widths.
    min_snr : float, optional
        Minimum SNR ratio. Default 1. The signal is the value of
        the cwt matrix at the shortest length scale (``cwt[0, loc]``), the
        noise is the `noise_perc`th percentile of datapoints contained within a
        window of `window_size` around ``cwt[0, loc]``.
    noise_perc : float, optional
        When calculating the noise floor, percentile of data points
        examined below which to consider noise. Calculated using
        `stats.scoreatpercentile`.  Default is 10.

Returns
    -------
    peaks_indices : list
        Indices of the locations in the `vector` where peaks were found.
        The list is sorted.

Name	Size	Permission
__init__.cpython-35.opt-1.pyc	12413 bytes	0644
__init__.cpython-35.pyc	12413 bytes	0644
_arraytools.cpython-35.opt-1.pyc	5169 bytes	0644
_arraytools.cpython-35.pyc	5169 bytes	0644
_max_len_seq.cpython-35.opt-1.pyc	4571 bytes	0644
_max_len_seq.cpython-35.pyc	4571 bytes	0644
_peak_finding.cpython-35.opt-1.pyc	16682 bytes	0644
_peak_finding.cpython-35.pyc	16682 bytes	0644
_savitzky_golay.cpython-35.opt-1.pyc	10925 bytes	0644
_savitzky_golay.cpython-35.pyc	10925 bytes	0644
_upfirdn.cpython-35.opt-1.pyc	5062 bytes	0644
_upfirdn.cpython-35.pyc	5062 bytes	0644
bsplines.cpython-35.opt-1.pyc	12779 bytes	0644
bsplines.cpython-35.pyc	12779 bytes	0644
filter_design.cpython-35.opt-1.pyc	107048 bytes	0644
filter_design.cpython-35.pyc	107328 bytes	0644
fir_filter_design.cpython-35.opt-1.pyc	24042 bytes	0644
fir_filter_design.cpython-35.pyc	24042 bytes	0644
lti_conversion.cpython-35.opt-1.pyc	13089 bytes	0644
lti_conversion.cpython-35.pyc	13089 bytes	0644
ltisys.cpython-35.opt-1.pyc	106889 bytes	0644
ltisys.cpython-35.pyc	107162 bytes	0644
setup.cpython-35.opt-1.pyc	1246 bytes	0644
setup.cpython-35.pyc	1246 bytes	0644
signaltools.cpython-35.opt-1.pyc	93816 bytes	0644
signaltools.cpython-35.pyc	93816 bytes	0644
spectral.cpython-35.opt-1.pyc	31743 bytes	0644
spectral.cpython-35.pyc	31743 bytes	0644
waveforms.cpython-35.opt-1.pyc	13560 bytes	0644
waveforms.cpython-35.pyc	13560 bytes	0644
wavelets.cpython-35.opt-1.pyc	10619 bytes	0644
wavelets.cpython-35.pyc	10619 bytes	0644
windows.cpython-35.opt-1.pyc	54254 bytes	0644
windows.cpython-35.pyc	54254 bytes	0644