hypertools.DataGeometry([fig, ax, line_ani, ...]) |
Hypertools data object class |
hypertools.load(dataset[, reduce, ndims, ...]) |
Load a .geo file or example data |
hypertools.analyze(data[, normalize, ...]) |
Wrapper function for normalize -> reduce -> align transformations. |
hypertools.plot(x[, fmt, marker, markers, ...]) |
Plots dimensionality reduced data and parses plot arguments |
hypertools.normalize(*args, **kwargs) |
Z-transform the columns or rows of an array, or list of arrays |
hypertools.reduce(*args, **kwargs) |
Reduces dimensionality of an array, or list of arrays |
hypertools.align(*args, **kwargs) |
Aligns a list of arrays |
hypertools.cluster(*args, **kwargs) |
Performs clustering analysis and returns a list of cluster labels |
hypertools.describe(x[, reduce, max_dims, show]) |
Create plot describing covariance with as a function of number of dimensions |
hypertools.tools.format_data(x[, ppca]) |
Formats data into a list of numpy arrays |
hypertools.tools.describe_pca(x[, show]) |
Create plot describing covariance with as a function of number of dimensions |
hypertools.tools.procrustes(source, target) |
Function to project from one space to another using Procrustean transformation (shift + scaling + rotation + reflection). |
hypertools.tools.missing_inds(x) |
Returns indices of missing data |
hypertools.tools.df2mat(data[, return_labels]) |
Transforms a Pandas DataFrame into a Numpy array with binarized text columns |