def run_trajectory_analysis(ord_res,
metadata_map,
trajectory_categories=None,
sort_category=None,
algorithm='avg',
axes=3,
weighted=False,
window_size=None):
"""Executes the volatility analysis using 'algorithm' on the input data
Parameters
----------
ord_res : skbio.maths.stats.ordination.OrdinationResults
The ordination results
metadata_map : pandas.DataFrame
The metadata map where index are samples ids and columns are the
metadata categories
trajectory_categories : list of str, optional
A list of metadata categories to use to create the trajectories. If
None is passed, the trajectories for all metadata categories are
computed. Default: None, compute all of them.
sort_category : str, optional
The metadata category to use to sort the trajectories. Default: None,
sort by sample id.
algorithm : str, optional
The algorithm used to perform the volatility analysis. Default: avg.
axes : int, optional
The number of axes to account while doing the trajectory specific
calculations. Pass 0 to compute all of them. Default: 3
weighted : bool, optional
If true, the output is weighted by the space between samples in the
`sort_category` column
window_size : int or long, optional
If `algorithm` is 'wdiff', the window size to use while computing the
difference
Returns
-------
skbio.maths.gradient.GradientANOVAResults
The results of running the volatility analysis over the input data
Raises
------
ValueError
If `algorithm` is not a recognized.
"""
coords_dict = {
sid: coord
for sid, coord in zip(ord_res.site_ids, ord_res.site)
}
coords = pd.DataFrame.from_dict(coords_dict, orient='index')
if algorithm == 'avg':
alg = AverageGradientANOVA(coords, ord_res.proportion_explained,
metadata_map, trajectory_categories,
sort_category, axes, weighted)
elif algorithm == 'trajectory':
alg = TrajectoryGradientANOVA(coords, ord_res.proportion_explained,
metadata_map, trajectory_categories,
sort_category, axes, weighted)
elif algorithm == 'diff':
alg = FirstDifferenceGradientANOVA(coords,
ord_res.proportion_explained,
metadata_map, trajectory_categories,
sort_category, axes, weighted)
elif algorithm == 'wdiff':
alg = WindowDifferenceGradientANOVA(
coords,
ord_res.proportion_explained,
metadata_map,
window_size,
trajectory_categories=trajectory_categories,
sort_category=sort_category,
axes=axes,
weighted=weighted)
else:
raise ValueError("Algorithm %s not recognized" % algorithm)
return alg.get_trajectories()
def run_trajectory_analysis(
ord_res,
metadata_map,
trajectory_categories=None,
sort_category=None,
algorithm="avg",
axes=3,
weighted=False,
window_size=None,
):
"""Executes the volatility analysis using 'algorithm' on the input data
Parameters
----------
ord_res : skbio.maths.stats.ordination.OrdinationResults
The ordination results
metadata_map : pandas.DataFrame
The metadata map where index are samples ids and columns are the
metadata categories
trajectory_categories : list of str, optional
A list of metadata categories to use to create the trajectories. If
None is passed, the trajectories for all metadata categories are
computed. Default: None, compute all of them.
sort_category : str, optional
The metadata category to use to sort the trajectories. Default: None,
sort by sample id.
algorithm : str, optional
The algorithm used to perform the volatility analysis. Default: avg.
axes : int, optional
The number of axes to account while doing the trajectory specific
calculations. Pass 0 to compute all of them. Default: 3
weighted : bool, optional
If true, the output is weighted by the space between samples in the
`sort_category` column
window_size : int or long, optional
If `algorithm` is 'wdiff', the window size to use while computing the
difference
Returns
-------
skbio.maths.gradient.GradientANOVAResults
The results of running the volatility analysis over the input data
Raises
------
ValueError
If `algorithm` is not a recognized.
"""
coords_dict = {sid: coord for sid, coord in zip(ord_res.site_ids, ord_res.site)}
coords = pd.DataFrame.from_dict(coords_dict, orient="index")
if algorithm == "avg":
alg = AverageGradientANOVA(
coords, ord_res.proportion_explained, metadata_map, trajectory_categories, sort_category, axes, weighted
)
elif algorithm == "trajectory":
alg = TrajectoryGradientANOVA(
coords, ord_res.proportion_explained, metadata_map, trajectory_categories, sort_category, axes, weighted
)
elif algorithm == "diff":
alg = FirstDifferenceGradientANOVA(
coords, ord_res.proportion_explained, metadata_map, trajectory_categories, sort_category, axes, weighted
)
elif algorithm == "wdiff":
alg = WindowDifferenceGradientANOVA(
coords,
ord_res.proportion_explained,
metadata_map,
window_size,
trajectory_categories=trajectory_categories,
sort_category=sort_category,
axes=axes,
weighted=weighted,
)
else:
raise ValueError("Algorithm %s not recognized" % algorithm)
return alg.get_trajectories()