gumbi

gumbi.aggregation

Classes

DataSet(data, outputs[, names_column, ...])

Container for tabular data, allowing simple access to standardized values and wide or tidy dataframe formats.

MetaFrame(df, outputs[, log_vars, ...])

Abstract Base Class for WideData and TidyData.

Standardizer([log_vars, logit_vars])

Container for dict of mean (μ) and variance (σ2) for every parameter.

TidyData(df, outputs[, log_vars, ...])

Container for tidy-form tabular data, allowing simple access to standardized and/or transformed values.

WideData(df, outputs[, log_vars, ...])

Container for wide-form tabular data, allowing simple access to standardized and/or transformed values.

gumbi.arrays

Classes

LayeredArray([stdzr])

An array with one or more named values at every index.

LogitNormal([loc, scale])

A logit-normal continuous random variable.

MVUncertainParameterArray(*uparrays, cor[, ...])

Structured array of multiple parameter means and variances along with correlations.

MultivariateNormalish(mean, cov, **kwargs)

A multivariate Normal distribution built from and callable on ParameterArrays.

ParameterArray(stdzr[, stdzd])

Array of parameter values, allowing simple transformation.

UncertainArray(name, μ, σ2[, stdzr])

Structured array containing mean and variance of a normal distribution at each point.

UncertainParameterArray(name, μ, σ2, stdzr)

Structured array of parameter means and variances, allowing transformation with uncertainty handling.

gumbi.plotting

Classes

ParrayPlotter(x, y[, z, stdzr, x_scale, ...])

Wrapper for a matplotlib.pyplot function; adjusts ticks and labels according to plotter settings.

gumbi.regression

Classes

GP(dataset[, outputs, seed])

Gaussian Process surface learning and prediction.

GPC(dataset[, outputs, seed])

Regressor(dataset[, outputs, seed])

Surface learning and prediction.

Submodules

gumbi.utils

Submodules