smartgrid.util.interpolate¶

smartgrid.util.interpolate(value, old_bounds, new_bounds)[source]¶

Interpolates a value (or array of values) from a domain to a new one.

For example, if the value is 0, the interpolation from [-1, 1] to [0, 1] gives 0.5.

This function is particularly useful for manipulating reward ranges, and to convert actions and observations between the [0, 1] domain that is typically used in learning algorithm (easier to manipulate) and the actual domain expected from the simulator.

It supports interpolating differently for each dimension, when an array of values is passed.

Parameters:

value – Either a single value (float) or an array of multiple values. It must match old_bounds and new_bounds. The value(s) will be interpolated from their old_bounds to their new_bounds.
old_bounds – The previous domain (in which value is currently). If value is a scalar, old_bounds must be a 1D array of size 2, e.g., [-1, 1]. Otherwise, if value is an array, old_bounds must be a 2D array, of the same size as value, each element being an array of size 2, e.g., [ [-1, 1], [0, 1], [-100, 100] ] assuming that value contains 3 elements.
new_bounds – The new domain (in which the returned value will be). Similarly to old_bounds, if value is a scalar, new_bounds must be a 1D array of size 2, e.g., [0, 1]. Otherwise, if value is an array, new_bounds must be a 2D array, of the same size as value, each element being an array of size 2, e.g., [ [0, 1], [-10, 10], [-1, 1] ], assuming that value contains 3 elements.

Returns:

If value is a scalar (i.e., has no len()), a scalar interpolated from the old domain to the new one. Otherwise, if value is an array, a numpy ndarray is returned, in which each element was interpolated from its corresponding old domain to its new one.