# algorithms.statistics.models.utils

## Module: `algorithms.statistics.models.utils`

Inheritance diagram for `nipy.algorithms.statistics.models.utils`:

General matrix and other utilities for statistics

## Class

## `StepFunction`

class nipy.algorithms.statistics.models.utils.StepFunction(x, y, ival=0.0, sorted=False)

  Bases: `object`

  A basic step function

  Values at the ends are handled in the simplest way possible: everything to the left of `x[0]` is set to ival; everything to the right of `x[-1]` is set to `y[-1]`.

  Examples

      >>> x = np.arange(20)
      >>> y = np.arange(20)
      >>> f = StepFunction(x, y)
      >>>
      >>> f(3.2)
      3.0
      >>> res = f([[3.2, 4.5],[24, -3.1]])
      >>> np.all(res == [[ 3, 4],
      ...                [19, 0]])
      True

  __init__(x, y, ival=0.0, sorted=False)

## Functions

nipy.algorithms.statistics.models.utils.ECDF(values)

  Return the ECDF of an array as a step function.

nipy.algorithms.statistics.models.utils.mad(a, c=0.6745, axis=0)

  Median Absolute Deviation:

  median(abs(a - median(a))) / c

nipy.algorithms.statistics.models.utils.monotone_fn_inverter(fn, x, vectorized=True, **keywords)

  Given a monotone function x (no checking is done to verify monotonicity) and a set of x values, return an linearly interpolated approximation to its inverse from its values on x.