parameter_descriptions={
'metric': 'The beta diversity metric to be computed.',
'n_jobs': sklearn_n_jobs_description
},
output_descriptions={'distance_matrix': 'The resulting distance matrix.'},
name='Beta diversity',
description=("Computes a user-specified beta diversity metric for all "
"pairs of samples in a feature table."))
plugin.methods.register_function(
function=q2_diversity.alpha_phylogenetic,
inputs={
'table': FeatureTable[Frequency],
'phylogeny': Phylogeny[Rooted]
},
parameters={'metric': Str % Choices(alpha.phylogenetic_metrics())},
outputs=[('alpha_diversity',
SampleData[AlphaDiversity] % Properties('phylogenetic'))],
input_descriptions={
'table': ('The feature table containing the samples for which alpha '
'diversity should be computed.'),
'phylogeny': ('Phylogenetic tree containing tip identifiers that '
'correspond to the feature identifiers in the table. '
'This tree can contain tip ids that are not present in '
'the table, but all feature ids in the table must be '
'present in this tree.')
},
parameter_descriptions={
'metric': 'The alpha diversity metric to be computed.'
},
output_descriptions={
'diversity should be computed.')
},
parameter_descriptions={
'metric': 'The beta diversity metric to be computed.'
},
output_descriptions={'distance_matrix': 'The resulting distance matrix.'},
name='Beta diversity',
description=("Computes a user-specified beta diversity metric for all "
"pairs of samples in a feature table.")
)
plugin.methods.register_function(
function=q2_diversity.alpha_phylogenetic,
inputs={'table': FeatureTable[Frequency] % Properties('uniform-sampling'),
'phylogeny': Phylogeny[Rooted]},
parameters={'metric': Str % Choices(alpha.phylogenetic_metrics())},
outputs=[('alpha_diversity',
SampleData[AlphaDiversity] % Properties('phylogenetic'))],
input_descriptions={
'table': ('The feature table containing the samples for which alpha '
'diversity should be computed.'),
'phylogeny': ('Phylogenetic tree containing tip identifiers that '
'correspond to the feature identifiers in the table. '
'This tree can contain tip ids that are not present in '
'the table, but all feature ids in the table must be '
'present in this tree.')
},
parameter_descriptions={
'metric': 'The alpha diversity metric to be computed.'
},
output_descriptions={