staircase.sample(collection, points=None, how='right', expand_key=True)

Takes a dict-like collection of Stairs instances and evaluates their values across a common set of points.

Technically the results of this function should be considered as \(\lim_{x \to z^{-}} f(x)\) or \(\lim_{x \to z^{+}} f(x)\), when how = ‘left’ or how = ‘right’ respectively. See A note on interval endpoints for an explanation.

  • collection (dictionary or pandas.Series) – The Stairs instances at which to evaluate
  • points (int, float or vector data) – The points at which to sample the Stairs instances
  • 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.
  • expand_key (boolean, default True) – used when collection is a multi-index pandas.Series. Indicates if index should be expanded from tuple to columns in a dataframe.

A dataframe, in tidy format, with three columns: points, key, value. The column key contains the identifiers used in the dict-like object specified by ‘collection’.

Return type:


See also



>>> stair_list = [s1, s2]
>>> fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(17,5), sharey=True, sharex=True)
>>> for ax, title, stair_instance in zip(axes, (['s1', 's2']), stair_list):
...     stair_instance.plot(ax)
...     ax.set_title(title)
>>> sc.sample({"s1":s1, "s2":s2}, [1, 1.5, 2.5, 4])
   points   key   value
0     1.0    s1     1.0
1     1.5    s1     1.0
2     2.5    s1     0.0
3     4.0    s1    -1.0
4     1.0    s2     0.5
5     1.5    s2     0.5
6     2.5    s2     0.0
7     4.0    s2    -1.0