baselines/common/plot_util.py
def one_sided_ema(xolds, yolds, low=None, high=None, n=512, decay_steps=1., low_counts_threshold=1e-8):
    '''
    perform one-sided (causal) EMA (exponential moving average)
    smoothing and resampling to an even grid with n points.
    Does not do extrapolation, so we assume
    xolds[0] <= low && high <= xolds[-1]

    Arguments:

    xolds: array or list  - x values of data. Needs to be sorted in ascending order
    yolds: array of list  - y values of data. Has to have the same length as xolds

    low: float            - min value of the new x grid. By default equals to xolds[0]
    high: float           - max value of the new x grid. By default equals to xolds[-1]

    n: int                - number of points in new x grid

    decay_steps: float    - EMA decay factor, expressed in new x grid steps.

    low_counts_threshold: float or int
                          - y values with counts less than this value will be set to NaN

    Returns:
        tuple sum_ys, count_ys where
            xs        - array with new x grid
            ys        - array of EMA of y at each point of the new x grid
            count_ys  - array of EMA of y counts at each point of the new x grid

    '''

    low = xolds[0] if low is None else low
    high = xolds[-1] if high is None else high

    assert xolds[0] <= low, 'low = {} < xolds[0] = {} - extrapolation not permitted!'.format(low, xolds[0])
    assert xolds[-1] >= high, 'high = {} > xolds[-1] = {}  - extrapolation not permitted!'.format(high, xolds[-1])
    assert len(xolds) == len(yolds), 'length of xolds ({}) and yolds ({}) do not match!'.format(len(xolds), len(yolds))


    xolds = xolds.astype('float64')
    yolds = yolds.astype('float64')

    luoi = 0 # last unused old index
    sum_y = 0.
    count_y = 0.
    xnews = np.linspace(low, high, n)
    decay_period = (high - low) / (n - 1) * decay_steps
    interstep_decay = np.exp(- 1. / decay_steps)
    sum_ys = np.zeros_like(xnews)
    count_ys = np.zeros_like(xnews)
    for i in range(n):
        xnew = xnews[i]
        sum_y *= interstep_decay
        count_y *= interstep_decay
        while True:
            xold = xolds[luoi]
            if xold <= xnew:
                decay = np.exp(- (xnew - xold) / decay_period)
                sum_y += decay * yolds[luoi]
                count_y += decay
                luoi += 1
            else:
                break
            if luoi >= len(xolds):
                break
        sum_ys[i] = sum_y
        count_ys[i] = count_y

    ys = sum_ys / count_ys
    ys[count_ys < low_counts_threshold] = np.nan

    return xnews, ys, count_ys
def one_sided_ema(xolds, yolds, low=None, high=None, n=512, decay_steps=1., low_counts_threshold=1e-8):
    '''
    perform one-sided (causal) EMA (exponential moving average)
    smoothing and resampling to an even grid with n points.
    Does not do extrapolation, so we assume
    xolds[0] <= low && high <= xolds[-1]

    Arguments:

    xolds: array or list  - x values of data. Needs to be sorted in ascending order
    yolds: array of list  - y values of data. Has to have the same length as xolds

    low: float            - min value of the new x grid. By default equals to xolds[0]
    high: float           - max value of the new x grid. By default equals to xolds[-1]

    n: int                - number of points in new x grid

    decay_steps: float    - EMA decay factor, expressed in new x grid steps.

    low_counts_threshold: float or int
                          - y values with counts less than this value will be set to NaN

    Returns:
        tuple sum_ys, count_ys where
            xs        - array with new x grid
            ys        - array of EMA of y at each point of the new x grid
            count_ys  - array of EMA of y counts at each point of the new x grid

    '''

    low = xolds[0] if low is None else low
    high = xolds[-1] if high is None else high

    assert xolds[0] <= low, 'low = {} < xolds[0] = {} - extrapolation not permitted!'.format(low, xolds[0])
    assert xolds[-1] >= high, 'high = {} > xolds[-1] = {}  - extrapolation not permitted!'.format(high, xolds[-1])
    assert len(xolds) == len(yolds), 'length of xolds ({}) and yolds ({}) do not match!'.format(len(xolds), len(yolds))


    xolds = xolds.astype('float64')
    yolds = yolds.astype('float64')

    luoi = 0 # last unused old index
    sum_y = 0.
    count_y = 0.
    xnews = np.linspace(low, high, n)
    decay_period = (high - low) / (n - 1) * decay_steps
    interstep_decay = np.exp(- 1. / decay_steps)
    sum_ys = np.zeros_like(xnews)
    count_ys = np.zeros_like(xnews)
    for i in range(n):
        xnew = xnews[i]
        sum_y *= interstep_decay
        count_y *= interstep_decay
        while True:
            xold = xolds[luoi]
            if xold <= xnew:
                decay = np.exp(- (xnew - xold) / decay_period)
                sum_y += decay * yolds[luoi]
                count_y += decay
                luoi += 1
            else:
                break
            if luoi >= len(xolds):
                break
        sum_ys[i] = sum_y
        count_ys[i] = count_y

    ys = sum_ys / count_ys
    ys[count_ys < low_counts_threshold] = np.nan

    return xnews, ys, count_ys
def one_sided_ema ( xolds , yolds , low = None , high = None , n = 512 , decay_steps = 1. , low_counts_threshold = 1e-8 ) : low = xolds [ 0 ] if low is None else low high = xolds [ - 1 ] if high is None else high assert xolds [ 0 ] <= low , 'low = {} < xolds[0] = {} - extrapolation not permitted!' . format ( low , xolds [ 0 ] ) assert xolds [ - 1 ] >= high , 'high = {} > xolds[-1] = {}  - extrapolation not permitted!' . format ( high , xolds [ - 1 ] ) assert len ( xolds ) == len ( yolds ) , 'length of xolds ({}) and yolds ({}) do not match!' . format ( len ( xolds ) , len ( yolds ) ) xolds = xolds . astype ( 'float64' ) yolds = yolds . astype ( 'float64' ) luoi = 0 # last unused old index sum_y = 0. count_y = 0. xnews = np . linspace ( low , high , n ) decay_period = ( high - low ) / ( n - 1 ) * decay_steps interstep_decay = np . exp ( - 1. / decay_steps ) sum_ys = np . zeros_like ( xnews ) count_ys = np . zeros_like ( xnews ) for i in range ( n ) : xnew = xnews [ i ] sum_y *= interstep_decay count_y *= interstep_decay while True : xold = xolds [ luoi ] if xold <= xnew : decay = np . exp ( - ( xnew - xold ) / decay_period ) sum_y += decay * yolds [ luoi ] count_y += decay luoi += 1 else : break if luoi >= len ( xolds ) : break sum_ys [ i ] = sum_y count_ys [ i ] = count_y ys = sum_ys / count_ys ys [ count_ys < low_counts_threshold ] = np . nan return xnews , ys , count_ys
perform one-sided (causal) EMA (exponential moving average)
    smoothing and resampling to an even grid with n points.
    Does not do extrapolation, so we assume
    xolds[0] <= low && high <= xolds[-1]

    Arguments:

    xolds: array or list  - x values of data. Needs to be sorted in ascending order
    yolds: array of list  - y values of data. Has to have the same length as xolds

    low: float            - min value of the new x grid. By default equals to xolds[0]
    high: float           - max value of the new x grid. By default equals to xolds[-1]

    n: int                - number of points in new x grid

    decay_steps: float    - EMA decay factor, expressed in new x grid steps.

    low_counts_threshold: float or int
                          - y values with counts less than this value will be set to NaN

    Returns:
        tuple sum_ys, count_ys where
            xs        - array with new x grid
            ys        - array of EMA of y at each point of the new x grid
            count_ys  - array of EMA of y counts at each point of the new x grid
perform one - sided ( causal ) EMA ( exponential moving average ) smoothing and resampling to an even grid with n points . Does not do extrapolation so we assume xolds [ 0 ] < = low && high < = xolds [ - 1 ]