Base Functions¶

EcoPy contains several basic functions:

wt_mean()

wt_var()

wt_scale()

wt_mean(x, wt=None)¶

Calculates as weighted mean. Returns a float.

$\mu = \frac{\sum x_iw_i}{\sum w_i}$

Parameters

x: numpy.ndarray or list: A vector of input observations
wt: numpy.ndarray or list: A vector of weights. If this vector does not sum to 1, this will be transformed internally by dividing each weight by the sum of weights

Example

Weighted mean:

import ecopy as ep
print ep.wt_mean([1,3,5], [1,2,1])

wt_var(x, wt=None)¶

Calculates as weighted variance. Returns a float.

$\sigma^2 = \frac{\sum w_i(x_i - \mu_w)^2}{\sum w_i}$

where $\mu_w$ is the weighted mean.

Parameters

x: numpy.ndarray or list: A vector of input observations
wt: numpy.ndarray or list: A vector of weights. If this vector does not sum to 1, this will be transformed internally by dividing each weight by the sum of weights

Example

Weighted variance:

import ecopy as ep
print ep.wt_var([1,3,5], [1,2,1])

wt_var(x, wt=None)

Returns a vector of scaled, weighted observations.

$z = \frac{x-\mu_w}{\sigma_w}$

where $\mu_w$ is the weighted mean and $\sigma_w$ is weighted standard deviation (the square root of weighted variance).

Parameters

x: numpy.ndarray or list: A vector of input observations
wt: numpy.ndarray or list: A vector of weights. If this vector does not sum to 1, this will be transformed internally by dividing each weight by the sum of weights

Example

Weighted variance:

import ecopy as ep
print ep.wt_scale([1,3,5], [1,2,1])