'representative_fragments': ('The consensus sequence fragments '
'to be used for fragment insertion.'),
'tree': 'The tree with all the reconstructed features',
'placements': ('The location of the representative fragments '
'in the phylogenetic tree')
},
parameter_descriptions={
'n_threads': 'the number of threads to use during fragment insertion',
},
)
plugin.register_formats(KmerMapFormat,
KmerMapDirFmt,
KmerAlignFormat,
KmerAlignDirFmt,
SidleReconFormat,
SidleReconDirFormat,
ReconSummaryFormat,
ReconSummaryDirFormat,
)
plugin.register_semantic_types(KmerMap,
KmerAlignment,
SidleReconstruction,
ReconstructionSummary,
)
plugin.register_semantic_type_to_format(FeatureData[KmerMap],
KmerMapDirFmt)
version=q2_micom.__version__,
website="https://github.com/micom-dev/q2-micom",
package="q2_micom",
description=(""),
short_description="Plugin for metabolic modeling of microbial communities.",
citations=[citations["micom"]],
)
plugin.register_formats(
SBMLFormat,
SBMLDirectory,
JSONFormat,
JSONDirectory,
CommunityModelFormat,
CommunityModelManifest,
CommunityModelDirectory,
GrowthRates,
Fluxes,
MicomResultsDirectory,
MicomMediumFile,
MicomMediumDirectory,
TradeoffResultsDirectory,
)
plugin.register_semantic_types(MetabolicModels, CommunityModels, MicomResults,
MicomMedium)
plugin.register_semantic_type_to_format(MetabolicModels[SBML], SBMLDirectory)
plugin.register_semantic_type_to_format(MetabolicModels[JSON], JSONDirectory)
plugin.register_semantic_type_to_format(CommunityModels[Pickle],
CommunityModelDirectory)
plugin.register_semantic_type_to_format(MicomResults, MicomResultsDirectory)
plugin.register_semantic_type_to_format(TradeoffResults,
import q2_covid
from q2_covid.common import (IDSelectionDirFmt, IDSelection, Selection,
IDMetadataFormat, UNIXListFormat)
from q2_covid.subsample_random import subsample_random
plugin = Plugin(
name='covid',
website='https://github.com/caporaso-lab/q2-covid',
package='q2_covid',
version=q2_covid.__version__,
description='Tools for genomic epidemiology focused on the SARS-CoV-2'
' virus.',
short_description='Tools for genomic epidemiology.'
)
plugin.register_formats(IDSelectionDirFmt)
plugin.register_semantic_types(Selection)
plugin.register_semantic_type_to_format(FeatureData[Selection],
artifact_format=IDSelectionDirFmt)
@plugin.register_transformer
def _1(obj: IDSelection) -> IDSelectionDirFmt:
result = IDSelectionDirFmt()
inclusion = obj.inclusion
assert not inclusion.index.has_duplicates
include = inclusion.index[inclusion]
exclude = inclusion.index[~inclusion]
with open(result.included.path_maker(), 'w') as fh:
fh.write('\n'.join(include))
'ids_to_keep':
'List of IDs to keep (as Metadata). Selecting these '
'IDs occurs after inclusion and exclusion filtering.',
'include':
'List of search terms. Taxa containing one or more of '
'these terms will be retained. Inclusion filtering occurs '
'prior to exclusion filtering and selecting `ids_to_keep`.',
'exclude':
'List of search terms. Taxa containing one or more of '
'these terms will be excluded. Exclusion filtering occurs '
'after inclusion filtering and prior to selecting '
'`ids_to_keep`.'
},
output_descriptions={'filtered_taxonomy': 'The filtered taxonomy.'},
name='Filter taxonomy by list of IDs or search criteria.',
description=('Filter taxonomy by list of IDs or search criteria.'),
)
# Registrations
plugin.register_semantic_types(SILVATaxonomy, SILVATaxidMap, RNASequence)
plugin.register_semantic_type_to_format(
FeatureData[SILVATaxonomy], artifact_format=SILVATaxonomyDirectoryFormat)
plugin.register_semantic_type_to_format(
FeatureData[SILVATaxidMap], artifact_format=SILVATaxidMapDirectoryFormat)
plugin.register_semantic_type_to_format(
FeatureData[RNASequence], artifact_format=RNASequencesDirectoryFormat)
plugin.register_formats(SILVATaxonomyFormat, SILVATaxonomyDirectoryFormat,
SILVATaxidMapFormat, SILVATaxidMapDirectoryFormat,
RNAFASTAFormat, RNASequencesDirectoryFormat)
importlib.import_module('rescript.types._transformer')
@plugin.register_transformer
def _5(ff: BooleanSeriesFormat) -> (pd.Series):
with ff.open() as fh:
df = _read_dataframe(fh)
return df.iloc[:, 0]
@plugin.register_transformer
def _6(ff: BooleanSeriesFormat) -> (qiime2.Metadata):
with ff.open() as fh:
return qiime2.Metadata(_read_dataframe(fh))
plugin.register_formats(BooleanSeriesFormat, BooleanSeriesDirectoryFormat)
plugin.register_semantic_types(BooleanSeries)
plugin.register_semantic_type_to_format(
SampleData[BooleanSeries], artifact_format=BooleanSeriesDirectoryFormat)
description = ('Predicts a {0} sample metadata column using a {1}. Splits '
'input data into training and test sets. The training set is '
'used to train and test the estimator using a stratified '
'k-fold cross-validation scheme. This includes optional steps '
'for automated feature extraction and hyperparameter '
'optimization. The test set validates classification accuracy '
'of the optimized estimator. Outputs classification results '
'for test set. For more details on the learning algorithm, '
'see http://scikit-learn.org/stable/supervised_learning.html')
version='0.0.0-dev',
website='https://github.com/qiime2/qiime2',
package='qiime2.core.testing',
user_support_text='For help, see https://qiime2.org',
citations=[citations['unger1998does'], citations['berry1997flying']]
)
import_module('qiime2.core.testing.transformer')
# Register semantic types
dummy_plugin.register_semantic_types(IntSequence1, IntSequence2, Mapping,
FourInts, Kennel, Dog, Cat, SingleInt)
# Register formats
dummy_plugin.register_formats(
IntSequenceFormatV2, MappingFormat, IntSequenceV2DirectoryFormat,
MappingDirectoryFormat)
dummy_plugin.register_formats(
FourIntsDirectoryFormat, UnimportableDirectoryFormat, UnimportableFormat,
citations=[citations['baerheim1994effect']])
dummy_plugin.register_views(
int, IntSequenceFormat, IntSequenceDirectoryFormat,
SingleIntFormat, RedundantSingleIntDirectoryFormat,
citations=[citations['mayer2012walking']])
dummy_plugin.register_semantic_type_to_format(
IntSequence1,
artifact_format=IntSequenceDirectoryFormat
)
#citations = Citations.load('citations.bib', package='q2_classo')
plugin = Plugin(
name='classo',
version='0.0.0.dev0',
website='https://github.com/Leo-Simpson/q2-classo',
package='q2-classo',
short_description=('Package for constrained sparse regression and classification'),
description=('This is QIIME 2 plugin that enables sparse and robust linear regression and classification with linear equality constraints on the model parameters.')
)
CLASSOProblem = SemanticType("CLASSOProblem")
ConstraintMatrix = SemanticType("ConstraintMatrix")
plugin.register_formats(CLASSOProblemDirectoryFormat,ConstraintDirectoryFormat)
plugin.register_semantic_type_to_format(ConstraintMatrix,
artifact_format=ConstraintDirectoryFormat)
plugin.register_semantic_type_to_format(CLASSOProblem,
artifact_format=CLASSOProblemDirectoryFormat)
plugin.methods.register_function(
function=regress,
inputs={'features': FeatureTable[Composition], 'c':ConstraintMatrix},
parameters=regress_parameters,
outputs= [('result',CLASSOProblem)],
input_descriptions={'features': 'Matrix representing the data of the problem','c': 'Constraint matrix, default is the zero-sum',},
parameter_descriptions=regress_parameter_descriptions,
'subsample_size':
Float % Range(0, 1, inclusive_start=False, inclusive_end=True),
'random_seed': Int % Range(1, None)
},
outputs=[('sample_sequences', FeatureData[T])],
input_descriptions={'sequences': 'Sequences to subsample from.'},
parameter_descriptions={
'subsample_size': 'Size of the random sample as a '
'fraction of the total count',
'random_seed': 'Seed to be used for random sampling.'
},
output_descriptions={
'sample_sequences': 'Sample of original sequences.', },
name='Subsample an indicated number of sequences from a FASTA file.',
description=(
"Subsample a set of sequences (either plain or aligned DNA)"
"based on a fraction of original sequences."),
)
# Registrations
plugin.register_semantic_types(SILVATaxonomy, SILVATaxidMap)
plugin.register_semantic_type_to_format(
FeatureData[SILVATaxonomy],
artifact_format=SILVATaxonomyDirectoryFormat)
plugin.register_semantic_type_to_format(
FeatureData[SILVATaxidMap],
artifact_format=SILVATaxidMapDirectoryFormat)
plugin.register_formats(SILVATaxonomyFormat, SILVATaxonomyDirectoryFormat,
SILVATaxidMapFormat, SILVATaxidMapDirectoryFormat)
importlib.import_module('rescript.types._transformer')
def create_plugin(**filters):
plugin = Plugin(
name='mystery-stew',
project_name='q2-mystery-stew',
version=q2_mystery_stew.__version__,
website='https://github.com/qiime2/q2-mystery-stew',
package='q2_mystery_stew',
description=('This QIIME 2 plugin templates out arbitrary '
'QIIME 2 actions to test interfaces. '),
short_description='Plugin for generating arbitrary QIIME 2 '
'actions.')
plugin.register_semantic_types(SingleInt1, SingleInt2, IntWrapper,
WrappedInt1, WrappedInt2, EchoOutput)
plugin.register_formats(SingleIntFormat, SingleIntDirectoryFormat,
EchoOutputFmt, EchoOutputDirFmt)
plugin.register_semantic_type_to_format(SingleInt1,
SingleIntDirectoryFormat)
plugin.register_semantic_type_to_format(SingleInt2,
SingleIntDirectoryFormat)
plugin.register_semantic_type_to_format(
IntWrapper[WrappedInt1 | WrappedInt2], SingleIntDirectoryFormat)
plugin.register_semantic_type_to_format(EchoOutput, EchoOutputDirFmt)
# This transformer is being registered in this file right now because it is
# needed by the registration functions so it needs to be registered before
# it is called
@plugin.register_transformer
def _0(data: int) -> SingleIntFormat:
ff = SingleIntFormat()
with ff.open() as fh:
fh.write('%d\n' % data)
return ff
selected_types = []
basics = {
'ints': int_params,
'floats': float_params,
'strings': string_params,
'bools': bool_params
}
selected_types.append(artifact_params())
for key, generator in basics.items():
if not filters or filters.get(key, False):
selected_types.append(generator())
if not filters or filters.get('collections', False):
selected_types.append(list_params(generator()))
selected_types.append(set_params(generator()))
if not selected_types:
raise ValueError("Must select at least one parameter type to use")
selected_types.append(primitive_unions())
register_single_type_tests(plugin, selected_types)
return plugin
'table': FeatureTable[Frequency],
'phylogeny': Phylogeny[Unrooted],
},
parameters={
'metadata': Metadata,
'formula': Str,
'family': Str,
'choice': Str % qiime2.plugin.Choices(_CHOICE_OPT),
'nfactors': Int,
'ncores': Int
},
outputs=[('featuretable', FeatureTable[Frequency]),
('tree', Phylogeny[Unrooted]), ('factors', FeatureData[Factors])],
input_descriptions={
'table':
'The sample by observation table',
'phylogeny': ('The phylogenetic tree describing the '
'relationship of the observations in '
'table')
},
parameter_descriptions={},
output_descriptions={},
name='Run Phylofactor',
description='Phylofactor defines clades that are associated with '
'metadata columns of interest')
plugin.register_formats(FactorsFormat, FactorsDirFmt)
plugin.register_semantic_types(Factors)
plugin.register_semantic_type_to_format(FeatureData[Factors], FactorsDirFmt)
importlib.import_module('q2_phylofactor._transform')
'display conserved positions on.',
'nterm_offset':
'Number of the amino acids that'
'are missing from the N-terminus'
'of the PDB structure. Defaults'
'to 1.'
},
name='PCA loadings plot',
description=(
'Visualise principal component loadings to find which positions '
'within the alignment/protein sequence contribute most/least to'
'the observed variance. Ultimately, find which positions are most/'
'least conserved within a protein sequence.'))
# Registrations
plugin.register_formats(PositionMappingFormat, PositionMappingDirectoryFormat)
plugin.register_formats(RankedProteinAlignmentFormat,
RankedProteinAlignmentDirectoryFormat)
plugin.register_semantic_types(PositionMapping)
plugin.register_semantic_types(RankedProteinAlignment)
plugin.register_semantic_type_to_format(
FeatureData[PositionMapping],
artifact_format=PositionMappingDirectoryFormat)
plugin.register_semantic_type_to_format(
FeatureData[RankedProteinAlignment],
artifact_format=RankedProteinAlignmentDirectoryFormat)
importlib.import_module('q2_protein_pca._transformer')
website='https://github.com/qiime2/qiime2',
package='qiime2.core.testing',
citation_text='No relevant citation.',
user_support_text='For help, see https://qiime2.org'
)
import_module('qiime2.core.testing.transformer')
# Register semantic types
dummy_plugin.register_semantic_types(IntSequence1, IntSequence2, Mapping,
FourInts, Kennel, Dog, Cat, SingleInt)
# Register formats
dummy_plugin.register_formats(
IntSequenceFormat, IntSequenceFormatV2, MappingFormat, SingleIntFormat,
IntSequenceDirectoryFormat, IntSequenceV2DirectoryFormat,
MappingDirectoryFormat, FourIntsDirectoryFormat, UnimportableFormat,
UnimportableDirectoryFormat, RedundantSingleIntDirectoryFormat
)
dummy_plugin.register_semantic_type_to_format(
IntSequence1,
artifact_format=IntSequenceDirectoryFormat
)
dummy_plugin.register_semantic_type_to_format(
IntSequence2,
artifact_format=IntSequenceV2DirectoryFormat
)
dummy_plugin.register_semantic_type_to_format(
Mapping,
artifact_format=MappingDirectoryFormat
)
name='ninja-ops',
version=q2_ninja_ops.__version__,
website='https://github.com/knights-lab/q2-ninja-ops',
package='q2_ninja_ops',
citation_text='Al-Ghalith GA, Montassier E, Ward HN, Knights D. '
'NINJA-OPS: Fast Accurate Marker Gene Alignment Using '
'Concatenated Ribosomes. PLoS Computational Biology. '
'2016 Jan;12(1).',
short_description='Plugin for OTU picking with NINJA-OPS.',
description='This plugin wraps the NINJA-OPS application and provides '
'methods for clustering sequence data into OTUs.'
)
plugin.register_semantic_types(NinjaOpsDB)
plugin.register_formats(NinjaOpsDBDirFmt, Bowtie2IndexFormat,
TerrificCompressedFormat, NinjaReplicateMapFormat)
plugin.register_semantic_type_to_format(NinjaOpsDB,
artifact_format=NinjaOpsDBDirFmt)
plugin.methods.register_function(
function=cluster_closed_reference,
inputs={
'sequences': SampleData[Sequences],
'reference_database': NinjaOpsDB
},
parameters={
# TODO expose relevant NINJA-OPS parameters here
},
outputs=[
('clustered_table', FeatureTable[Frequency]),
plugin = Plugin(
name='demux',
version=q2_demux.__version__,
website='https://github.com/qiime2/q2-demux',
package='q2_demux',
description=('This QIIME 2 plugin supports demultiplexing of '
'single-end and paired-end sequence reads and '
'visualization of sequence quality information.'),
short_description='Plugin for demultiplexing & viewing sequence quality.')
plugin.register_semantic_types(RawSequences, EMPSingleEndSequences,
EMPPairedEndSequences)
plugin.register_formats(EMPMultiplexedDirFmt, EMPSingleEndDirFmt,
EMPSingleEndCasavaDirFmt, EMPPairedEndDirFmt,
EMPPairedEndCasavaDirFmt)
# TODO: remove when aliasing exists
plugin.register_semantic_type_to_format(RawSequences,
artifact_format=EMPSingleEndDirFmt)
plugin.register_semantic_type_to_format(EMPSingleEndSequences,
artifact_format=EMPSingleEndDirFmt)
plugin.register_semantic_type_to_format(EMPPairedEndSequences,
artifact_format=EMPPairedEndDirFmt)
plugin.methods.register_function(
function=q2_demux.emp_single,
# TODO: remove RawSequences by creating an alias to EMPSequences
"code is used at all, which is useful for debugging. For n_jobs below -1, "
"(n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one "
"are used. (Description from sklearn.metrics.pairwise_distances)")
plugin = Plugin(
name="mlab",
version=q2_mlab.__version__,
website="https://dev.qiime2.org/",
package="q2_mlab",
citations=Citations.load("citations.bib", package="q2_mlab"),
description=("This QIIME 2 plugin is in development"
" and does sweet stuff."),
short_description="Plugin for machine learning automated benchmarking.",
)
plugin.register_formats(ResultsFormat, ResultsDirectoryFormat)
plugin.register_semantic_types(Target)
plugin.register_semantic_type_to_format(
SampleData[Target], artifact_format=PredictionsDirectoryFormat)
plugin.register_semantic_types(Results)
plugin.register_semantic_type_to_format(SampleData[Results],
artifact_format=ResultsDirectoryFormat)
plugin.pipelines.register_function(
function=q2_mlab.preprocess,
inputs={
"table": FeatureTable[Frequency],
"phylogeny": Phylogeny[Rooted]
},
plugin = Plugin(
name='longitudinal',
version=q2_longitudinal.__version__,
website="https://github.com/qiime2/q2-longitudinal",
package='q2_longitudinal',
description=(
'This QIIME 2 plugin supports methods for analysis of time series '
'data, involving either paired sample comparisons or longitudinal '
'study designs.'),
short_description='Plugin for paired sample and time series analyses.',
citations=[citations['bokulich2017q2']])
plugin.register_semantic_types(FirstDifferences)
plugin.register_formats(FirstDifferencesFormat,
FirstDifferencesDirectoryFormat)
plugin.register_semantic_type_to_format(
SampleData[FirstDifferences],
artifact_format=FirstDifferencesDirectoryFormat)
miscellaneous_parameters = {
'state_column': Str,
'replicate_handling': Str % Choices(['error', 'random', 'drop'])
}
shared_parameters = {
'metadata': Metadata,
'individual_id_column': Str,
}
plugin = Plugin(
name='woltka',
version=__version__,
website="https://github.com/qiyunzhu/woltka",
citations=[],
short_description='Plugin for metagenomics analysis',
description=('This is a QIIME 2 plugin supporting various analyses of '
'shotgun metagenome datasets'),
package='woltka')
plugin.register_semantic_types(SeqAlnMap, BLAST6Out, SimpleMap, NCBINodes,
GeneCoordinates)
plugin.register_formats(SeqAlnMapFormat, SeqAlnMapDirFmt, BLAST6OutFormat,
BLAST6OutDirFmt, SimpleMapFormat, SimpleMapDirFmt,
NCBINodesFormat, NCBINodesDirFmt, GeneCoordFormat,
GeneCoordDirFmt)
plugin.register_semantic_type_to_format(FeatureData[SeqAlnMap],
artifact_format=SeqAlnMapDirFmt)
plugin.register_semantic_type_to_format(FeatureData[BLAST6Out],
artifact_format=BLAST6OutDirFmt)
plugin.register_semantic_type_to_format(FeatureData[SimpleMap],
artifact_format=SimpleMapDirFmt)
plugin.register_semantic_type_to_format(FeatureData[NCBINodes],
artifact_format=NCBINodesDirFmt)
plugin.register_semantic_type_to_format(GeneCoordinates,
artifact_format=GeneCoordDirFmt)
alnfmts = SeqAlnMap | BLAST6Out | SimpleMap
plugin = Plugin(
name='SCNIC',
version=q2_SCNIC.__version__,
website="https://github.com/shafferm/q2-SCNIC",
package='q2_SCNIC',
description=('This QIIME 2 plugin allows for use of the SCNIC methods '
'to build correlation networks as well as detect and '
'summarize modules of highy intercorrelated features'),
short_description='Plugin for SCNIC usage.',
citations=[citations['SciPyProceedings_11']])
plugin.register_semantic_types(Network)
plugin.register_semantic_types(PairwiseFeatureData)
plugin.register_semantic_types(ModuleMembership)
plugin.register_formats(GraphModelingLanguageFormat)
plugin.register_formats(GraphModelingLanguageDirectoryFormat)
plugin.register_formats(PairwiseFeatureDataFormat)
plugin.register_formats(PairwiseFeatureDataDirectoryFormat)
plugin.register_formats(ModuleMembershipTSVFormat)
plugin.register_formats(ModuleMembershipTSVDirectoryFormat)
plugin.register_semantic_type_to_format(
Network, artifact_format=GraphModelingLanguageDirectoryFormat)
plugin.register_semantic_type_to_format(
PairwiseFeatureData, artifact_format=PairwiseFeatureDataDirectoryFormat)
plugin.register_semantic_type_to_format(
ModuleMembership, artifact_format=ModuleMembershipTSVDirectoryFormat)
plugin.methods.register_function(
function=sparcc_filter,
'max_centroids_per_class': Int,
'feature_extractor_specification': Str,
'knn_classifier_specification': Str,
'n_jobs': Int,
'random_state': Int
},
outputs=[('nearest_neighbors', q2_clawback.PrecalculatedNearestNeighbors)],
name='Calculate nearest neighbors for estimating class weight importance',
description=('Fit a kNN classifier to the optionally undersampled '
'reference data and cache the neighbors'))
plugin.visualizers.register_function(
function=q2_clawback.kNN_LOOCV_F_measures,
inputs={
'nearest_neighbors': q2_clawback.PrecalculatedNearestNeighbors,
'class_weight': FeatureTable[RelativeFrequency]
},
parameters={},
name='Estimate importance of class weights',
description=('Calculated k Nearest Neighbors Leave-One-Out Cross '
'Validated F-measures for weighted and uniform assumptions'))
plugin.register_semantic_types(q2_clawback.PrecalculatedNearestNeighbors)
plugin.register_formats(
q2_clawback.PrecalculatedNearestNeighborsFormat,
q2_clawback.PrecalculatedNearestNeighborsDirectoryFormat)
plugin.register_semantic_type_to_format(
q2_clawback.PrecalculatedNearestNeighbors,
artifact_format=q2_clawback.PrecalculatedNearestNeighborsDirectoryFormat)
importlib.import_module('q2_clawback._transformer')
'table')
},
parameter_descriptions={},
output_descriptions={},
name='Run Phylofactor',
description='Phylofactor defines clades that are associated with '
'metadata columns of interest')
plugin.methods.register_function(
function=cross_validate_map,
inputs={
'table': FeatureTable[Frequency],
'groups': FeatureData[FactorGroups],
'phylogeny': Phylogeny[Unrooted],
'full_phylogeny': Phylogeny[Unrooted],
},
parameters={},
outputs=[('featuretable', FeatureTable[Frequency]),
('groups', FeatureData[FactorGroups])],
input_descriptions={},
parameter_descriptions={},
output_descriptions={},
name='Cross validate factor groups',
description='Applies factor groupings to new data sets')
plugin.register_formats(FactorGroupsFormat, FactorGroupsDirFmt)
plugin.register_semantic_types(FactorGroups)
plugin.register_semantic_type_to_format(FeatureData[FactorGroups],
FactorGroupsDirFmt)
importlib.import_module('q2_phylofactor._transform')
plugin = Plugin(
name='ninja-ops',
version=q2_ninja_ops.__version__,
website='https://github.com/knights-lab/q2-ninja-ops',
package='q2_ninja_ops',
citation_text='Al-Ghalith GA, Montassier E, Ward HN, Knights D. '
'NINJA-OPS: Fast Accurate Marker Gene Alignment Using '
'Concatenated Ribosomes. PLoS Computational Biology. '
'2016 Jan;12(1).',
short_description='Plugin for OTU picking with NINJA-OPS.',
description='This plugin wraps the NINJA-OPS application and provides '
'methods for clustering sequence data into OTUs.')
plugin.register_semantic_types(NinjaOpsDB)
plugin.register_formats(NinjaOpsDBDirFmt, Bowtie2IndexFormat,
TerrificCompressedFormat, NinjaReplicateMapFormat)
plugin.register_semantic_type_to_format(NinjaOpsDB,
artifact_format=NinjaOpsDBDirFmt)
plugin.methods.register_function(
function=cluster_closed_reference,
inputs={
'sequences': SampleData[Sequences],
'reference_database': NinjaOpsDB
},
parameters={
# TODO expose relevant NINJA-OPS parameters here
},
outputs=[('clustered_table', FeatureTable[Frequency]),
('unmatched_sequences', SampleData[Sequences])],
'If there are more samples than this number the tree will'
'divide itself again. If there is fewer than this number '
'of samples in a bin the tree will not subdivide again.',
'x_coord': 'Metadata column containing x coordinates, i.e. longitude.',
'y_coord': 'Metadata column containing y coordinates, i.e. latitude'},
name='Divide samples into bins by quadtrees based on'
'x and y coordinates',
description='Objective binning of samples based on spatial data '
'by quadtrees. While there are samples '
'(greater than the threshold) in a partition '
'that partition will split into four allowing for quick '
'binning based both on location and sample density.',
)
# Registrations
plugin.register_formats(CoordinatesFormat, CoordinatesDirectoryFormat)
plugin.register_semantic_types(Coordinates)
plugin.register_semantic_type_to_format(
SampleData[Coordinates],
artifact_format=CoordinatesDirectoryFormat)
plugin.register_formats(QuadTreeFormat, QuadTreeDirectoryFormat)
plugin.register_semantic_types(QuadTree)
plugin.register_semantic_type_to_format(
SampleData[QuadTree],
artifact_format=QuadTreeDirectoryFormat)
importlib.import_module('q2_coordinates._transformer')
from genome_sampler.combine import combine_selections
from genome_sampler.summarize import summarize_selections
from genome_sampler.label_seqs import label_seqs, label_unaligned_seqs
from genome_sampler.mask import mask
citations = Citations.load('citations.bib', package='genome_sampler')
plugin = Plugin(name='genome-sampler',
website='https://caporasolab.us/genome-sampler',
package='genome_sampler',
version=genome_sampler.__version__,
description='Tools for sampling from collections of genomes.',
short_description='Genome sampler.',
citations=[citations['genomesampler']])
plugin.register_formats(IDSelectionDirFmt)
plugin.register_formats(GISAIDDNAFASTAFormat)
plugin.register_formats(VCFLikeMaskFormat)
plugin.register_formats(VCFLikeMaskDirFmt)
plugin.register_semantic_types(Selection)
plugin.register_semantic_types(AlignmentMask)
plugin.register_semantic_type_to_format(FeatureData[Selection],
artifact_format=IDSelectionDirFmt)
plugin.register_semantic_type_to_format(AlignmentMask,
artifact_format=VCFLikeMaskDirFmt)
@plugin.register_transformer
def _1(obj: IDSelection) -> IDSelectionDirFmt:
result = IDSelectionDirFmt()
name='demux',
version=q2_demux.__version__,
website='https://github.com/qiime2/q2-demux',
package='q2_demux',
description=('This QIIME 2 plugin supports demultiplexing of '
'single-end and paired-end sequence reads and '
'visualization of sequence quality information.'),
short_description='Plugin for demultiplexing & viewing sequence quality.'
)
plugin.register_semantic_types(
RawSequences, EMPSingleEndSequences, EMPPairedEndSequences,
ErrorCorrectionDetails)
plugin.register_formats(EMPMultiplexedDirFmt, ErrorCorrectionDetailsDirFmt,
EMPSingleEndDirFmt, EMPSingleEndCasavaDirFmt,
EMPPairedEndDirFmt, EMPPairedEndCasavaDirFmt)
# TODO: remove when aliasing exists
plugin.register_semantic_type_to_format(
RawSequences,
artifact_format=EMPSingleEndDirFmt
)
plugin.register_semantic_type_to_format(
EMPSingleEndSequences,
artifact_format=EMPSingleEndDirFmt
)
plugin.register_semantic_type_to_format(
EMPPairedEndSequences,
'filtered based on importance scores; samples are optionally '
'grouped by sample metadata; and a heatmap is generated that '
'displays (normalized) feature abundances per sample.')
# Registrations
plugin.register_semantic_types(SampleEstimator, BooleanSeries, Importance,
ClassifierPredictions, RegressorPredictions,
Classifier, Regressor, Probabilities)
plugin.register_semantic_type_to_format(SampleEstimator[Classifier],
artifact_format=SampleEstimatorDirFmt)
plugin.register_semantic_type_to_format(SampleEstimator[Regressor],
artifact_format=SampleEstimatorDirFmt)
plugin.register_semantic_type_to_format(
SampleData[BooleanSeries], artifact_format=BooleanSeriesDirectoryFormat)
plugin.register_semantic_type_to_format(
SampleData[RegressorPredictions],
artifact_format=PredictionsDirectoryFormat)
plugin.register_semantic_type_to_format(
SampleData[ClassifierPredictions],
artifact_format=PredictionsDirectoryFormat)
plugin.register_semantic_type_to_format(
FeatureData[Importance], artifact_format=ImportanceDirectoryFormat)
plugin.register_semantic_type_to_format(
SampleData[Probabilities], artifact_format=ProbabilitiesDirectoryFormat)
plugin.register_formats(SampleEstimatorDirFmt, BooleanSeriesFormat,
BooleanSeriesDirectoryFormat, ImportanceFormat,
ImportanceDirectoryFormat, PredictionsFormat,
PredictionsDirectoryFormat, ProbabilitiesFormat,
ProbabilitiesDirectoryFormat)
importlib.import_module('q2_sample_classifier._transformer')
version='0.0.0-dev',
website='https://github.com/qiime2/qiime2',
package='qiime2.core.testing',
user_support_text='For help, see https://qiime2.org',
citations=[citations['unger1998does'], citations['berry1997flying']])
import_module('qiime2.core.testing.transformer')
# Register semantic types
dummy_plugin.register_semantic_types(IntSequence1, IntSequence2, Mapping,
FourInts, Kennel, Dog, Cat, SingleInt, C1,
C2, C3, Foo, Bar, Baz)
# Register formats
dummy_plugin.register_formats(IntSequenceFormatV2, MappingFormat,
IntSequenceV2DirectoryFormat,
MappingDirectoryFormat, EchoDirectoryFormat,
EchoFormat)
dummy_plugin.register_formats(FourIntsDirectoryFormat,
UnimportableDirectoryFormat,
UnimportableFormat,
citations=[citations['baerheim1994effect']])
dummy_plugin.register_views(int,
IntSequenceFormat,
IntSequenceDirectoryFormat,
SingleIntFormat,
RedundantSingleIntDirectoryFormat,
citations=[citations['mayer2012walking']])
dummy_plugin.register_semantic_type_to_format(
return ff
@plugin.register_transformer
def _5(ff: BooleanSeriesFormat) -> (pd.Series):
with ff.open() as fh:
return _read_dataframe(fh)
@plugin.register_transformer
def _6(ff: BooleanSeriesFormat) -> (qiime2.Metadata):
with ff.open() as fh:
return qiime2.Metadata(_read_dataframe(fh))
plugin.register_formats(CoordinatesFormat, CoordinatesDirectoryFormat,
BooleanSeriesFormat, BooleanSeriesDirectoryFormat)
plugin.register_semantic_types(Coordinates, BooleanSeries)
plugin.register_semantic_type_to_format(
SampleData[Coordinates],
artifact_format=CoordinatesDirectoryFormat)
plugin.register_semantic_type_to_format(
SampleData[BooleanSeries],
artifact_format=BooleanSeriesDirectoryFormat)
description = ('Predicts a {0} sample metadata category using a {1}. Splits '
'input data into training and test sets. The training set is '
'used to train and test the estimator using a stratified '
'k-fold cross-validation scheme. This includes optional steps '
plugin = Plugin(
name='network',
version=q2_network.__version__,
website="https://github.com/shafferm/q2-network",
package='q2_network',
description=(
'This QIIME 2 plugin supports methods for analysis of netwoks '
'generated from correlations or other sources and provides '
'rudimentary network statistics.'),
short_description='Plugin for network analysis.',
)
plugin.register_semantic_types(Network)
plugin.register_semantic_types(PairwiseFeatureData)
plugin.register_formats(GraphModelingLanguageFormat)
plugin.register_formats(GraphModelingLanguageDirectoryFormat)
plugin.register_formats(PairwiseFeatureDataFormat)
plugin.register_formats(PairwiseFeatureDataDirectoryFormat)
plugin.register_semantic_type_to_format(
Network, artifact_format=GraphModelingLanguageDirectoryFormat)
plugin.register_semantic_type_to_format(
PairwiseFeatureData, artifact_format=PairwiseFeatureDataDirectoryFormat)
plugin.methods.register_function(
function=calculate_correlations,
inputs={'table': FeatureTable[Frequency]
}, # TODO: Generalize, don't require frequency
parameters={
'corr_method': Str % Choices(["kendall", "pearson", "spearman"]),
'formula': Str,
'learning_rate': Float,
'beta1': Float,
'beta2': Float
},
outputs=[('differentials', FeatureData[Differential]),
('posterior', StrayPosterior)],
input_descriptions={
'table': 'The feature table of abundances.'
},
parameter_descriptions={
'metadata': 'Sample metadata',
'formula': 'The experimental condition of interest.',
'learning_rate': 'The learning rate of the gradient descent.',
'beta1': 'First momentum operator in ADAM gradient descent',
'beta2': 'Second momentum operator in ADAM gradient descent'
},
output_descriptions={
'differentials': 'The estimated per-feature differentials.',
'posterior': 'The posterior distribution over all of the differentials.'
}
)
# TODO: Need to add a visualizer to summarize the stray results
plugin.register_formats(StrayPosteriorFormat, StrayPosteriorDirFmt)
plugin.register_semantic_types(StrayPosterior)
plugin.register_semantic_type_to_format(
StrayPosterior, StrayPosteriorDirFmt)
importlib.import_module('q2_stray._transformer')
