# staircase.Stairs.resample¶

Stairs.resample(x, how='right', aggfunc=None, window=(0, 0), lower_how='right', upper_how='left')

Evaluates the value of the step function at one, or more, points and creates a new Stairs instance whose step changes occur at a subset of these points. The new instance and self have the same values when evaluated at x.

Parameters: x (int, float or vector data) – Values at which to evaluate the function how ({'left', 'right'}, default 'right') – If points where step changes occur do not coincide with x then this parameter has no effect. Where a step changes occurs at a point given by x, this parameter determines if the step function is evaluated at the interval to the left, or the right. aggfunc ({'mean', 'median', 'mode', 'max', 'min', 'std', None}. Default None.) – A string corresponding to the aggregating function window (array-like of int, float or pandas.Timedelta, optional) – Only relevant if aggfunc is not None. Should be length of 2. Defines distances from focal point to window boundaries. lower_how ({'left', 'right'}, default 'right') – Only relevant if aggfunc is not None. Determines how the left window boundary should be evaluated. If ‘left’ then $$\lim_{x \to lower_how^{-}} f(x)$$ is included in the window. upper_how ({'left', 'right'}, default 'left') – Only relevant if aggfunc is not None. Determines how the right window boundary should be evaluated. If ‘right’ then $$\lim_{x \to upper_how^{+}} f(x)$$ is included in the window. Stairs

staircase.resample()
>>> stair_list = [s1, s1.resample([1.5,2.5,4,4.5])]