Utilities

Various auxiliary functions for calculating stats.

calc_log_fraction_of_spans(req_xs, req_ys, xs, ys, lower_better=True)

Calculate fraction of range where the curve complies with requirements.

By default “complies” means the curve is below the requirement points. This function interpollates the curve points linerarly, but does not interpolate the requirement points.

find_line_segments_by_var_of_prop(cent, prop, lin_threshold)

Primitive linearity estimator.

Determines linearity by requiring the absolute difference in successive points in prop varies sufficiently slowly, ie the differences are all small.

find_positive_spans(xs, ys, where='post')

Find regions where the provided curve is positive.

Parameters:

xs

x-coordinates of the input points

ys

y-coordinates of the input points

where:

Where the points provided are supposed to be in the spands of the stepwise function provided. Accepted values are “pre”, “mid”, and “post”, behaving like in matplotlib.step. Default is “post”

Returns:

spanslist[tuple]

A list of tuples, each tuple being the start and stop positions in x.

Treat input points as a stepwise function and finds the sections

where the ys are positive. By default considers each constant

section as starting at point xs[i] and going to xs[i+1],

having the value ys[i] in the section.

Results are sensitive to the density of points provided.

normalize_along_axis(arr: ndarray, axis: int = 1, threshold_frac=0.0001, fill_value=nan) → ndarray

Normalize an ndarray along the given axis so that the sum along that axis is 1.0.

Parameters:

arrnp.ndarray

The input array.

axisint

The axis along which to normalize.

threshold_frac: float

if the integral along the axis is below this fraction of the total integral, mask off this row as having too low stats to plot. This prevents low-stats values form saturating the color scale

fill_value:

value to replace low-stats entries with

Returns:

np.ndarray

A new array normalized along the specified axis.

rms_profile_from_2d_hist(hist, density: bool = False, threshold=15) → tuple[ndarray, ndarray, ndarray]

Calculate RMS and mean per x-bin in a 2d histogram.

Parameters:

hist: Metric|hist.Histogram

Metric to profile, must be 2D

density: bool

If False, assumes the histogram is proportional to counts per bin; otherwise, assumes it is proportional to a density. For constant bin widths, these are equivalent, but the distinction is important when bin widths vary (see Notes). If None (default), sets density=True for backwards compatibility, but warns if the bin widths are variable. Set density explicitly to silence the warning.

Returns:

tuple[np.ndarray, np.ndarray, np.ndarray]:

y_rms, y_mean, x_bin_center