Module `iaf.stats`

Statistics functionalities.

Functions

def hist_bins(values: numpy.ndarray, bin_size: float) ‑> tuple

Return the bins to be used for the passed values and bin size.

values : np.ndarray: One-dimensional array of values for which to determine the ideal histogram bins.
bin_size : float: Bin size to use.

bin_edges : np.ndarray: Array of bin edges (to use with np.histogram()).
bin_centers : np.ndarray: Array of bin centers.
bin_width:: Bin width.

def ideal_hist_bins(values: numpy.ndarray, scott: bool = False)

Calculate the ideal histogram bins using the Freedman-Diaconis rule.

values : np.ndarray: One-dimensional array of values for which to determine the ideal histogram bins.
scott : bool: Whether to use Scott's normal reference rule (if the data is normally distributed).

bin_edges : np.ndarray: Array of bin edges (to use with np.histogram()).
bin_centers : np.ndarray: Array of bin centers.
bin_size:: Bin width.

def prepare_histogram(values: numpy.ndarray, normalize: bool = True, auto_bins: bool = True, scott: bool = False, bin_size: float = 0.0)

Return histogram counts and bins for given values with provided or automatically calculated bin number.

values : np.ndarray: Array of values. It may contain NaNs.
normalize : bool: Whether to normalize the histogram to a probability mass function (PMF). The integral of the PMF is 1.0.
auto_bins : bool: Whether to automatically calculate the bin size from the data.
scott : bool: Whether to use Scott's normal reference rule (the data should be normally distributed). This is used only if auto_bins is True.
bin_size : float: Bin size to use if auto_bins is False. It will be ignored if auto_bins is True.

n : np.ndarray: Histogram counts (optionally normalized to sum to 1.0).
bin_edges : np.ndarray: Array of bin edges (to use with np.histogram()).
bin_centers : np.ndarray: Array of bin centers.
bin_width:: Bin width.