from qiime2.plugin import Plugin, Str, Range, Choices, Float, Int, Bool, List
from q2_types.feature_table import FeatureTable, Frequency
from q2_types.tree import Phylogeny, Rooted
plugin = Plugin(
name='qemistree',
version=q2_qemistree.__version__,
website='https://github.com/biocore/q2-qemistree',
package='q2_qemistree',
description='Hierarchical orderings for mass spectrometry data',
short_description='Plugin for exploring chemical diversity.',
)
# type registration
plugin.register_views(MGFDirFmt)
plugin.register_semantic_types(MassSpectrometryFeatures)
plugin.register_semantic_type_to_format(MassSpectrometryFeatures,
artifact_format=MGFDirFmt)
plugin.register_views(SiriusDirFmt)
plugin.register_semantic_types(SiriusFolder)
plugin.register_semantic_type_to_format(SiriusFolder,
artifact_format=SiriusDirFmt)
plugin.register_views(ZodiacDirFmt)
plugin.register_semantic_types(ZodiacFolder)
plugin.register_semantic_type_to_format(ZodiacFolder,
artifact_format=ZodiacDirFmt)
plugin.register_views(CSIDirFmt)
plugin.register_semantic_types(CSIFolder)
output_descriptions={
'heatmap':
'Heatmap of important features.',
'filtered_table':
'Filtered feature table containing data displayed '
'in heatmap.'
},
name='Generate heatmap of important features.',
description='Generate a heatmap of important features. Features are '
'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)
# ----------------------------------------------------------------------------
import q2_ili
from ._plot import plot
from ._semantics import STLDirFmt, Model
from qiime2.plugin import Plugin, Metadata, Citations
plugin = Plugin(
name='ili',
version=q2_ili.__version__,
website='https://ili.embl.de/',
citations=Citations.load('citations.bib', package='q2_ili'),
package='q2_ili',
description=('This QIIME 2 plugin wraps `ili and '
'supports interactive visualization of 3D models'),
short_description='Plugin for spatial mapping with `ili')
# type registration
plugin.register_views(STLDirFmt)
plugin.register_semantic_types(Model)
plugin.register_semantic_type_to_format(Model, artifact_format=STLDirFmt)
plugin.visualizers.register_function(
function=plot,
inputs={'model': Model},
parameters={'metadata': Metadata},
input_descriptions={'model': 'The model where the data will be plotted.'},
parameter_descriptions={'metadata': 'Metadata used to color the model'},
name='Visualize and interact with 3D models',
description='Visualize and interact with 3D models colored using metadata')
import q2_network
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]
'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')
citations = Citations.load('citations.bib', package='q2_demux')
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,
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
from ._type import SeqAlnMap, BLAST6Out, SimpleMap, NCBINodes, GeneCoordinates
from woltka import __version__
from woltka.q2.plugin import classify, psfilter, collapse, coverage
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,
'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')
citations = Citations.load('citations.bib', package='q2_shogun')
plugin = Plugin(
name='shogun',
version=q2_shogun.__version__,
website='https://github.com/qiime2/q2-shogun',
package='q2_shogun',
description=('A QIIME 2 plugin wrapper for the SHOGUN shallow shotgun '
'sequencing taxonomy profiler.'),
short_description='Shallow shotgun sequencing taxonomy profiler.',
citations=[citations['Hillmann320986']])
plugin.register_views(Bowtie2IndexDirFmt,
citations=[citations['langmead2012fast']])
plugin.register_semantic_types(Bowtie2Index)
plugin.register_semantic_type_to_format(Bowtie2Index, Bowtie2IndexDirFmt)
plugin.methods.register_function(
function=nobunaga,
inputs={
'query': FeatureData[Sequence],
'reference_reads': FeatureData[Sequence],
'reference_taxonomy': FeatureData[Taxonomy],
'database': Bowtie2Index
},
parameters={
'taxacut': Float % Range(0.0, 1.0, inclusive_end=True),
'threads': Int % Range(1, None),
'percent_id': Float % Range(0.0, 1.0, inclusive_end=True)
},
"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]
},
parameters={
"metadata": Metadata,
from qiime2.plugin import Plugin, Str, Range, Choices, Float, Int
from q2_types.feature_table import FeatureTable, Frequency
from q2_types.tree import Phylogeny, Rooted
plugin = Plugin(
name='chemistree',
version='0.0.0',
website='https://github.com/biocore/q2-chemistree',
package='q2_chemistree',
description='Hierarchical orderings for mass spectrometry data',
short_description='Plugin for exploring chemical diversity.',
)
# type registration
plugin.register_views(MGFDirFmt)
plugin.register_semantic_types(MassSpectrometryFeatures)
plugin.register_semantic_type_to_format(MassSpectrometryFeatures,
artifact_format=MGFDirFmt)
PARAMS = {
'database': Str % Choices(['all', 'pubchem']),
'sirius_path': Str,
'profile': Str % Choices(['qtof', 'orbitrap', 'fticr']),
'fingerid_db': Str % Choices(['all', 'pubchem', 'bio', 'kegg', 'hmdb']),
'ppm_max': Int % Range(0, 30, inclusive_end=True),
'n_jobs': Int % Range(1, None),
'num_candidates': Int % Range(5, 100, inclusive_end=True),
'tree_timeout': Int % Range(600, 3000, inclusive_end=True),
'maxmz': Int % Range(100, 850, inclusive_end=True),
'zodiac_threshold': Float % Range(0, 1, inclusive_end=True),
}
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
name='dummy-plugin',
description='Description of dummy plugin.',
short_description='Dummy plugin for testing.',
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')
import_module('qiime2.core.testing.validator')
# Register semantic types
dummy_plugin.register_semantic_types(IntSequence1, IntSequence2, IntSequence3,
Mapping, FourInts, Kennel, Dog, Cat,
SingleInt, C1, C2, C3, Foo, Bar, Baz,
AscIntSequence, Squid, Octopus,
Cuttlefish)
# Register formats
dummy_plugin.register_formats(IntSequenceFormatV2, MappingFormat,
IntSequenceV2DirectoryFormat,
IntSequenceMultiFileDirectoryFormat,
MappingDirectoryFormat, EchoDirectoryFormat,
EchoFormat, Cephalapod,
CephalapodDirectoryFormat)
dummy_plugin.register_formats(FourIntsDirectoryFormat,
UnimportableDirectoryFormat,
UnimportableFormat,
citations=[citations['baerheim1994effect']])
'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')
'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')
@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')
inputs = {'table': FeatureTable[Frequency]}
import q2_ninja_ops
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
citations = Citations.load('citations.bib', package='qiime2.core.testing')
dummy_plugin = Plugin(
name='dummy-plugin',
description='Description of dummy plugin.',
short_description='Dummy plugin for testing.',
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']])
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()
inclusion = obj.inclusion
assert not inclusion.index.has_duplicates
include = inclusion.index[inclusion]
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))
with open(result.excluded.path_maker(), 'w') as fh:
@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 '
'for automated feature extraction and hyperparameter '
'optimization. The test set validates classification accuracy '
'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')
from ._type import RawSequences, EMPSingleEndSequences, EMPPairedEndSequences
from ._format import (EMPMultiplexedDirFmt, EMPSingleEndDirFmt,
EMPSingleEndCasavaDirFmt, EMPPairedEndDirFmt,
EMPPairedEndCasavaDirFmt)
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.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)
plugin.register_semantic_type_to_format(FeatureData[KmerAlignment],
KmerAlignDirFmt)
plugin.register_semantic_type_to_format(FeatureData[SidleReconstruction],
SidleReconDirFormat)
citations = Citations.load('citations.bib', package='q2_longitudinal')
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,
from q2_beast.formats import (PosteriorLogFormat, NexusFormat,
BEASTControlFileFormat, BEASTOpsFileFormat,
BEASTPosteriorDirFmt, NexusDirFmt)
plugin = Plugin(name='beast',
website='',
package='q2_beast',
version=q2_beast.__version__,
description='',
short_description='')
plugin.register_formats(PosteriorLogFormat, NexusFormat,
BEASTControlFileFormat, BEASTOpsFileFormat,
BEASTPosteriorDirFmt, NexusDirFmt)
plugin.register_semantic_types(Chain, BEAST, MCC)
plugin.register_semantic_type_to_format(Chain[BEAST],
artifact_format=BEASTPosteriorDirFmt)
plugin.register_semantic_type_to_format(Phylogeny[MCC],
artifact_format=NexusDirFmt)
importlib.import_module('q2_beast.transformers')
NONZERO_INT = Int % Range(1, None)
NONNEGATIVE_INT = Int % Range(0, None)
plugin.methods.register_function(
function=gtr_single_partition,
inputs={'alignment': FeatureData[AlignedSequence]},
parameters={
'time': MetadataColumn[Numeric],
'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')
package='q2_phylogenomics',
description='A QIIME 2 plugin for phylogenomics analyses.',
short_description='A QIIME 2 plugin for phylogenomics analyses.',
)
plugin.register_formats(GenBankFormat, GenBankDirFmt, citations=[])
plugin.register_formats(BAMFormat,
SAMFormat,
BAMFilesDirFmt,
SAMFilesDirFmt,
PileUpTSVFormat,
PileUpFilesDirFmt,
citations=[])
plugin.register_formats(FASTAFilesDirFmt)
plugin.register_semantic_types(AlignmentMap, PileUp, ConsensusSequences,
ReferenceSequence)
# before release we want to use GenBank format for this,
# but I think it's broken with skbio < 0.5.6 - I get this
# error when trying to load a genbank file:
# ValueError: cannot set WRITEABLE flag to True of this array
# plugin.register_semantic_type_to_format(ReferenceSequence, GenBankDirFmt)
plugin.register_semantic_type_to_format(ReferenceSequence,
DNASequencesDirectoryFormat)
plugin.register_semantic_type_to_format(SampleData[PileUp], PileUpFilesDirFmt)
plugin.register_semantic_type_to_format(SampleData[AlignmentMap],
BAMFilesDirFmt)
plugin.register_semantic_type_to_format(SampleData[ConsensusSequences],
FASTAFilesDirFmt)
importlib.import_module('q2_phylogenomics._transformers')
import q2_SCNIC
citations = Citations.load('citations.bib', package='q2_SCNIC')
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(
citations = Citations.load('citations.bib', package='qiime2.core.testing')
dummy_plugin = Plugin(
name='dummy-plugin',
description='Description of dummy plugin.',
short_description='Dummy plugin for testing.',
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, IntSequence3,
Mapping, FourInts, Kennel, Dog, Cat,
SingleInt, C1, C2, C3, Foo, Bar, Baz)
# Register formats
dummy_plugin.register_formats(IntSequenceFormatV2, MappingFormat,
IntSequenceV2DirectoryFormat,
IntSequenceMultiFileDirectoryFormat,
MappingDirectoryFormat, EchoDirectoryFormat,
EchoFormat)
dummy_plugin.register_formats(FourIntsDirectoryFormat,
UnimportableDirectoryFormat,
UnimportableFormat,
citations=[citations['baerheim1994effect']])
dummy_plugin.register_views(int,
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
