Gradient analyses (skbio.math.gradient)

This module provides functionality for performing gradient analyses. The algorithms included in this module mainly allows performing analysis of volatility on time series data, but they can be applied to any data that contains a gradient.

Classes

GradientANOVA(coords, prop_expl, metadata_map) Base class for the Trajectory algorithms
AverageGradientANOVA(coords, prop_expl, ...) Perform trajectory analysis using the RMS average algorithm
TrajectoryGradientANOVA(coords, prop_expl, ...) Perform trajectory analysis using the RMS trajectory algorithm
FirstDifferenceGradientANOVA(coords, ...[, ...]) Perform trajectory analysis using the first difference algorithm
WindowDifferenceGradientANOVA(coords, ...) Perform trajectory analysis using the modified first difference
GroupResults Store the trajectory results of a group of a metadata category
CategoryResults Store the trajectory results of a metadata category
GradientANOVAResults Store the trajectory results

Examples

Assume we have the following coordinates:

>>> import numpy as np
>>> import pandas as pd
>>> from skbio.math.gradient import AverageGradientANOVA
>>> coord_data = {'PC.354': np.array([0.2761, -0.0341, 0.0633, 0.1004]),
...               'PC.355': np.array([0.2364, 0.2186, -0.0301, -0.0225]),
...               'PC.356': np.array([0.2208, 0.0874, -0.3519, -0.0031]),
...               'PC.607': np.array([-0.1055, -0.4140, -0.15, -0.116]),
...               'PC.634': np.array([-0.3716, 0.1154, 0.0721, 0.0898])}
>>> coords = pd.DataFrame.from_dict(coord_data, orient='index')

the following metadata map:

>>> metadata_map = {'PC.354': {'Treatment': 'Control', 'Weight': '60'},
...            'PC.355': {'Treatment': 'Control', 'Weight': '55'},
...            'PC.356': {'Treatment': 'Control', 'Weight': '50'},
...            'PC.607': {'Treatment': 'Fast', 'Weight': '65'},
...            'PC.634': {'Treatment': 'Fast', 'Weight': '68'}}
>>> metadata_map = pd.DataFrame.from_dict(metadata_map, orient='index')

and the following array with the proportion explained of each coord:

>>> prop_expl = np.array([25.6216, 15.7715, 14.1215, 11.6913, 9.8304])

Then to compute the average trajectory of this data:

>>> av = AverageGradientANOVA(coords, prop_expl, metadata_map,
...                     trajectory_categories=['Treatment'],
...                     sort_category='Weight')
>>> trajectory_results = av.get_trajectories()

Check the algorithm used to compute the trajectory_results:

>>> print trajectory_results.algorithm
avg

Check if we weighted the data or not:

>>> print trajectory_results.weighted
False

Check the trajectory_results results of one of the categories:

>>> print trajectory_results.categories[0].category
Treatment
>>> print trajectory_results.categories[0].probability
0.0118478282382

Check the trajectory_results results of one group of one of the categories:

>>> print trajectory_results.categories[0].groups[0].name
Control
>>> print trajectory_results.categories[0].groups[0].trajectory
[ 3.52199973  2.29597001  3.20309816]
>>> print trajectory_results.categories[0].groups[0].info
{'avg': 3.007022633956606}