dff in thunder

gistfile1.txt

# from thunder/rdds/timeseries.py
# found at https://github.com/thunder-project/thunder/blob/b15ba0a38642312d597a98643cf3514e2d46b69d/thunder/rdds/timeseries.py

def normalize(self, baseline='percentile', window=None, perc=20):
        """
        Normalize each time series by subtracting and dividing by a baseline.
        Baseline can be derived from a global mean or percentile,
        or a smoothed percentile estimated within a rolling window.
        Parameters
        ----------
        baseline : str, optional, default = 'percentile'
            Quantity to use as the baseline, options are 'mean', 'percentile', 'window', or 'window-fast'
        window : int, optional, default = 6
            Size of window for baseline estimation, for 'window' and 'window-fast' baseline only
        perc : int, optional, default = 20
            Percentile value to use, for 'percentile', 'window', or 'window-fast' baseline only
        """
        checkParams(baseline, ['mean', 'percentile', 'window', 'window-fast'])
        method = baseline.lower()
    
        from warnings import warn
        if not (method == 'window' or method == 'window-fast') and window is not None:
            warn('Setting window without using method "window" has no effect')

        if method == 'mean':
            baseFunc = mean

        if method == 'percentile':
            baseFunc = lambda x: percentile(x, perc)

        if method == 'window':
            if window & 0x1:
                left, right = (ceil(window/2), ceil(window/2) + 1)
            else:
                left, right = (window/2, window/2)

            n = len(self.index)
            baseFunc = lambda x: asarray([percentile(x[max(ix-left, 0):min(ix+right+1, n)], perc)
                                          for ix in arange(0, n)])

        if method == 'window-fast':
            from scipy.ndimage.filters import percentile_filter
            baseFunc = lambda x: percentile_filter(x.astype(float64), perc, window, mode='nearest')

        def get(y):
            b = baseFunc(y)
            return (y - b) / (b + 0.1)

        return self.applyValues(get, keepIndex=True)