P, R = TypeMap({Choices(True): C1[Foo], Choices(False): Foo})
dummy_plugin.methods.register_function(
function=bool_flag_swaps_output_method,
inputs={'a': Bar},
parameters={'b': Bool % P},
outputs=[('x', R)],
name='Bool Flag Swaps Output Method',
description='Test if a parameter can change output')
del P, R
def predicates_preserved_method(a: EchoFormat) -> EchoFormat:
return a
P = TypeMatch(
[Properties('A'),
Properties('B'),
Properties('C'),
Properties('X', 'Y')])
dummy_plugin.methods.register_function(
function=predicates_preserved_method,
inputs={'a': Foo % P},
parameters={},
outputs=[('x', Foo % P)],
name='Predicates Preserved Method',
description='Test that predicates are preserved')
del P
'vmin': 'The minimum value to use for anchoring the colormap. If '
'"auto", vmin is set to the minimum value in the data.',
'vmax': 'The maximum value to use for anchoring the colormap. If '
'"auto", vmax is set to the maximum value in the data.',
'palette': 'The color palette to use for plotting.'
},
name='Make a confusion matrix from sample classifier predictions.',
description='Make a confusion matrix and calculate accuracy of predicted '
'vs. true values for a set of samples classified using a '
'sample classifier. If per-sample class probabilities are '
'provided, will also generate Receiver Operating '
'Characteristic curves and calculate area under the curve for '
'each class.')
T = TypeMatch([
Frequency, RelativeFrequency, PresenceAbsence, Balance,
PercentileNormalized
])
plugin.methods.register_function(
function=split_table,
inputs={'table': FeatureTable[T]},
parameters={
'random_state': parameters['base']['random_state'],
'missing_samples': parameters['base']['missing_samples'],
**parameters['splitter'], 'metadata':
MetadataColumn[Numeric | Categorical],
**parameters['regressor']
},
outputs=[('training_table', FeatureTable[T]),
('test_table', FeatureTable[T])],
input_descriptions={
'table':
'int1': Int,
'int2': Int
},
outputs=[('concatenated_ints', IntSequence1)],
name='Concatenate integers',
description='This method concatenates integers into a single sequence in '
'the order they are provided.',
citations=[citations['baerheim1994effect']],
examples={
'concatenate_ints_simple': concatenate_ints_simple,
'concatenate_ints_complex': concatenate_ints_complex,
'comments_only': comments_only
},
)
T = TypeMatch([IntSequence1, IntSequence2])
dummy_plugin.methods.register_function(
function=split_ints,
inputs={'ints': T},
parameters={},
outputs=[('left', T), ('right', T)],
name='Split sequence of integers in half',
description='This method splits a sequence of integers in half, returning '
'the two halves (left and right). If the input sequence\'s '
'length is not evenly divisible by 2, the right half will '
'have one more element than the left.',
citations=[
citations['witcombe2006sword'], citations['reimers2012response']
])
dummy_plugin.methods.register_function(
parameter_descriptions={
'fraction': ('The fraction of sequences to retain in subsample.')
},
output_descriptions={
'subsampled_sequences': 'The subsampled sequences.'
},
name='Subsample paired-end sequences without replacement.',
description=('Generate a random subsample of paired-end sequences '
'containing approximately the fraction of input sequences '
'specified by the fraction parameter. The number of output '
'samples will always be equal to the number of input '
'samples, even if some of those samples contain no '
'sequences after subsampling.')
)
T = TypeMatch([SequencesWithQuality, PairedEndSequencesWithQuality,
JoinedSequencesWithQuality])
plugin.methods.register_function(
function=q2_demux.filter_samples,
inputs={'demux': SampleData[T]},
parameters={'metadata': Metadata,
'where': Str,
'exclude_ids': Bool},
outputs=[
('filtered_demux', SampleData[T])
],
input_descriptions={
'demux': 'The demultiplexed data from which samples should be '
'filtered.'
},
parameter_descriptions={
'metadata': 'Sample metadata indicating which sample ids to filter. '
outputs=[('merged_data', FeatureData[Taxonomy])],
input_descriptions={
'data': 'The collection of feature taxonomies to be merged.',
},
parameter_descriptions={},
output_descriptions={
'merged_data': ('The resulting collection of feature taxonomies '
'containing all feature taxonomies provided.')
},
name="Combine collections of feature taxonomies",
description="Combines a pair of feature data objects which may or may not "
"contain data for the same features. If different feature "
"data is present for the same feature id in the inputs, "
"the data from the first will be propagated to the result.")
T1 = TypeMatch([Frequency, RelativeFrequency, PresenceAbsence, Composition])
plugin.methods.register_function(
function=q2_feature_table.rename_ids,
inputs={
'table': FeatureTable[T1],
},
parameters={
'metadata': MetadataColumn[Categorical],
'strict': Bool,
'axis': Str % Choices({'sample', 'feature'})
},
outputs=[('renamed_table', FeatureTable[T1])],
input_descriptions={
'table': 'The table to be renamed',
},
'supports interactive visualization of phylogenetic '
'trees.'),
short_description='Plugin for visualizing phylogenies with Empress.')
TREE_DESC = 'The phylogenetic tree to visualize.'
FM_DESC = (
'Feature metadata. Can be used to color nodes (tips and/or '
'internal nodes) in the tree, and to display tip-level barplots. '
'Features described in the metadata that are not present in the tree '
'will be automatically filtered out of the visualization.')
# Accept various types of feature tables -- we (currently) convert things to
# presence/absence, anyway. This line adapted from
# https://github.com/qiime2/q2-feature-table/blob/9ed6160adad45445ec054e5ce034a3b3ba25a9b4/q2_feature_table/plugin_setup.py#L243.
AcceptedTableTypes = TypeMatch([Frequency, RelativeFrequency, PresenceAbsence])
plugin.visualizers.register_function(
function=community_plot,
inputs={
'tree': Phylogeny[Rooted],
'feature_table': FeatureTable[AcceptedTableTypes],
'pcoa': PCoAResults,
},
parameters={
'sample_metadata': Metadata,
'feature_metadata': Metadata,
'ignore_missing_samples': Bool,
'filter_extra_samples': Bool,
'filter_missing_features': Bool,
'number_of_features': Int % Range(1, None),
parameter_descriptions={
'level': ('The level that masking should be performed at. "mask" will '
'mask only positions annotated as "mask", while "caution" '
'will mask positions annotated as "mask" or "caution".'),
'mask_terminal_gaps':
('Mask alignment positions that are terminal (i.e., end) gaps in '
'all reference sequences.'),
},
output_descriptions={
'masked_alignment': ('The alignment with masked positions removed.')
},
name='Mask alignment',
description='Apply pre-computed alignment mask to filter positions.'
)
T = TypeMatch([Sequence, AlignedSequence])
plugin.methods.register_function(
function=label_seqs,
inputs={'seqs': FeatureData[T]},
parameters={
'delimiter': Str % Choices('|', ',', '+', ':', ';'),
'metadata': Metadata,
'columns': List[Str],
'missing_value': Str,
},
outputs=[('labeled_seqs', FeatureData[T])],
input_descriptions={'seqs': 'The sequences to be re-labeled.'},
parameter_descriptions={
'delimiter': 'The delimiter between the sequence id and each metadata'
' entry.',
input_descriptions={
'data': 'The collection of feature taxonomies to be merged.',
},
parameter_descriptions={},
output_descriptions={
'merged_data': ('The resulting collection of feature taxonomies '
'containing all feature taxonomies provided.')
},
name="Combine collections of feature taxonomies",
description="Combines a pair of feature data objects which may or may not "
"contain data for the same features. If different feature "
"data is present for the same feature id in the inputs, "
"the data from the first will be propagated to the result."
)
T = TypeMatch([Frequency, RelativeFrequency, PresenceAbsence, Composition])
plugin.methods.register_function(
function=q2_feature_table.rename_ids,
inputs={
'table': FeatureTable[T],
},
parameters={
'metadata': MetadataColumn[Categorical],
'strict': Bool,
'axis': Str % Choices({'sample', 'feature'})
},
outputs=[
('renamed_table', FeatureTable[T])
],
input_descriptions={
version='1',
website='https://github.com/richrr/coremicro',
package='q2_coremicrobiome',
description=('This QIIME 2 plugin calculates coremicrobiome '
'using presence/absence data. '
'Refer http://coremic2.appspot.com/help for online tool.'),
short_description='Plugin for exploring coremicrobiome.',
citation_text='Rodrigues RR, Rodgers NC, Wu X, Williams MA. (2018) '
'COREMIC: a web-tool to search for a niche associated '
'CORE MICrobiome. PeerJ 6:e4395'
#citations=[citations['Rodrigues2018']]
)
# https://dev.qiime2.org/latest/actions/visualizers/
# you do not provide outputs or output_descriptions when making this call, as Visualizers, by definition, only return a single visualization. Since the visualization output path is a required parameter, you do not include this in an outputs list
T = TypeMatch([Frequency, RelativeFrequency])
plugin.visualizers.register_function(
function=q2_coremicrobiome.full_pipeline,
inputs={'table': FeatureTable[T]},
parameters={
'groupfile': Metadata,
'factor': Str,
'group': Str,
'outputfile': Str,
'max_out_presence': Float,
'max_p': Float,
'min_frac': Float,
'min_abundance': Float,
'p_val_adj': Str,
'make_relative': Bool,
'quantile_normalize': Bool
},
output_descriptions={'tree': 'The resulting phylogenetic tree.'},
name=('Construct a phylogenetic tree with IQ-TREE with bootstrap '
'supports.'),
description=('Construct a phylogenetic tree using IQ-TREE '
'(http://www.iqtree.org/) with automatic '
'model selection and bootstrap supports.'),
citations=[
citations['Minh2020iqtree'],
citations['Kalyaanamoorthy2017modelfinder'],
citations['Minh2013ultrafastbootstrap'],
citations['Hoang2017ultrafastbootstrap2']
],
)
T1 = TypeMatch([Frequency, RelativeFrequency, PresenceAbsence])
plugin.methods.register_function(
function=q2_phylogeny.filter_table,
inputs={
'table': FeatureTable[T1],
'tree': Phylogeny[Rooted | Unrooted]
},
parameters={},
outputs=[('filtered_table', FeatureTable[T1])],
input_descriptions={
'table':
'Feature table that features should be filtered from.',
'tree': ('Tree where tip identifiers are the feature identifiers that '
'should be retained in the table.')
},
