pybaselines.Baseline.std_distribution
- Baseline.std_distribution(data, half_window=None, interp_half_window=5, fill_half_window=3, num_std=1.1, smooth_half_window=None, weights=None, pad_kwargs=None, **kwargs)[source]
Identifies baseline segments by analyzing the rolling standard deviation distribution.
The rolling standard deviations are split into two distributions, with the smaller distribution assigned to noise. Baseline points are then identified as any point where the rolling standard deviation is less than a multiple of the median of the noise's standard deviation distribution.
- Parameters:
- dataarray_like, shape (N,)
The y-values of the measured data, with N data points.
- half_window
int, optional The half-window to use for the rolling standard deviation calculation. Should be approximately equal to the full-width-at-half-maximum of the peaks or features in the data. Default is None, which will use half of the value from
optimize_window(), which is not always a good value, but at least scales with the number of data points and gives a starting point for tuning the parameter.- interp_half_window
int, optional When interpolating between baseline segments, will use the average of
data[i-interp_half_window:i+interp_half_window+1], where i is the index of the peak start or end, to fit the linear segment. Default is 5.- fill_half_window
int, optional When a point is identified as a peak point, all points +- fill_half_window are likewise set as peak points. Default is 3.
- num_std
float, optional The number of standard deviations to include when thresholding. Higher values will assign more points as baseline. Default is 1.1.
- smooth_half_window
int, optional The half window to use for smoothing the interpolated baseline with a moving average. Default is None, which will use half_window. Set to 0 to not smooth the baseline.
- weightsarray_like, shape (N,), optional
The weighting array, used to override the function's baseline identification to designate peak points. Only elements with 0 or False values will have an effect; all non-zero values are considered baseline points. If None (default), then will be an array with size equal to N and all values set to 1.
- pad_kwargs
dict, optional A dictionary of keyword arguments to pass to
pad_edges()for padding the edges of the data to prevent edge effects from smoothing. Default is None.- **kwargs
Deprecated since version 1.2.0: Passing additional keyword arguments is deprecated and will be removed in version 1.4.0. Pass keyword arguments using pad_kwargs.
- Returns:
- baseline
numpy.ndarray, shape (N,) The calculated baseline.
- params
dict A dictionary with the following items:
- 'mask': numpy.ndarray, shape (N,)
The boolean array designating baseline points as True and peak points as False.
- baseline
References
Wang, K.C., et al. Distribution-Based Classification Method for Baseline Correction of Metabolomic 1D Proton Nuclear Magnetic Resonance Spectra. Analytical Chemistry. 2013, 85, 1231-1239.