Technical reference

class elem_effects.permutations.PermutationsArray(num_dims, num_levels=4, lower=0.0, upper=1.0, index_array=True)

Create an array of possible input parameters, of dimensions (number of levels per parameter, number of parameters).

Can return either the percentages, from 0 to 1, which can be translated back into parameter space using the percentage point function; or the integer indices, from 0 to num_levels - 1, which are more easily used with a table of pre-calculated values which correspond to the percentages.

Args:

• num_dims (integer): Number of input parmeters (number of parameters in model)
• num_levels (integer, optional, default 4): Number of possible levels per parameter
• lower (float, optional, default 0): Minimum parameter value
• upper (float, optional, default 1): Maximum parameter value
• index_array (boolean, optional, default True): Provide integer indices instead of percentages

create()

Create the array of possible permutations.

Returns:

• permutations array (NumPy array)

class elem_effects.elem_effects.ElementaryEffects(model, trajectories)

Given an array of possible permutations and a model, compute the parameters that can be rejected using the method of elementary effects.

class elem_effects.trajectories.Trajectory(start, end, order)

A trajectory of points where each point has a unique set of values for each parameter in the model.

The Method of Elementary Effects looks at the difference in model outputs from a change in one parameter at a time, and a trajectory is all parameter values needed so that each paramter changes once.

Args:

• start (1-d numpy array): A numpy array of point coordinates at start of trajectory.
• end (1-d numpy array): A numpy array of point coordinates at end of trajectory.
• order (1-d integer numpy array): An array that lists the order that each parameter should be changed, e.g. [2, 0, 1] would mean the middle parameter changes first, then the last, and finally the first parameter is changed.

get_at_index(i)

Get trajectory at index i, where is is step number, starting at zero (so when i=0, this is the first step in the trajectory, not the starting position).

Use self.reset() to revert to starting coordinates.

Args:

• i (integer): Index to retrieve trajectory from.

Returns:

• 1-d numpy array of coordinates.

points()

Get a 2-d numpy array of all point coordinates for the entire trajectory.

Returns:

• 2-d numpy array of coordinates of entire trajectory, where the first row is the starting coordinates, and the last row is the ending coordinates.

reset()

Reset to starting coordinates.

set_index(i)

Replace first i starting elements with final values. Useful in more expensive computations where the entire trajectory is not evaluated at once.

Does not return anything, but sets self.current to the correct values.

Args:

• i (integer): Index to jump to (i.e. the number of trajectory steps already evaluated)

class elem_effects.trajectories.TrajectoryPicker(permutations, num_trajs=500, pick=10, seed=None, verbose=True)

Pick a set of trajectories, maximizing distance from each other.

calculate_distance(a, b)

Calculate the summed Manhattan distance from every point in the trajectory a to every point in the trajectory b.

create_trajectory()

Create a randomized Trajectory object

kennard_stone(distances, pick)

Pick n objects by maximizing the minimum distance from next candiadate.