Utilities

These provide functionality that is not easily grouped into another set. More...

Modules
	Chebyshev
	These functions provide methods for discrete derivative and integral calculations.

Classes
class	SUAVE.Methods.Utilities.Cubic_Spline_Blender.Cubic_Spline_Blender

Functions
def	SUAVE.Methods.Utilities.latin_hypercube_sampling.latin_hypercube_sampling (num_dimensions, num_samples, bounds=None, criterion='random')
	If needed for mapping to normal distribution: from scipy.stats.distributions import norm. More...
def	SUAVE.Methods.Utilities.soft_max.soft_max (x1, x2)

Detailed Description

These provide functionality that is not easily grouped into another set.

Most of these provide some type of mathematical functionality.

Function Documentation

latin_hypercube_sampling()

def SUAVE.Methods.Utilities.latin_hypercube_sampling.latin_hypercube_sampling	(		num_dimensions,
			num_samples,
			bounds = None,
			criterion = 'random'
	)

If needed for mapping to normal distribution: from scipy.stats.distributions import norm.

Provides an array of chosen dimensionality and number of samples taken according
to latin hypercube sampling. Bounds can be optionally specified.

Assumptions:
None

Source:
None

Inputs:
num_dimensions       [-]
num_samples          [-]
bounds (optional)    [-]      Default is 0 to 1. Input value should be in the form (with numpy arrays)
                              (array([low_bnd_1,low_bnd_2,..]), array([up_bnd_1,up_bnd_2,..]))
criterion            <string> Possible values: random and center. Determines if samples are 
                              taken at the center of a bucket or randomly from within it.
                     
Outputs:             
lhd                  [-]      Array of samples

Properties Used:
N/A

soft_max()

def SUAVE.Methods.Utilities.soft_max.soft_max	(		x1,
			x2
	)

Computes the soft maximum of two inputs.

Assumptions:
None

Source:
http://www.johndcook.com/blog/2010/01/20/how-to-compute-the-soft-maximum/

Inputs:
x1   [-]
x2   [-]

Outputs:             
f    [-] The soft max

Properties Used:
N/A