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galaxy.datatypes package¶
Subpackages¶
- galaxy.datatypes.converters package
- Submodules
- galaxy.datatypes.converters.bed_to_gff_converter module
- galaxy.datatypes.converters.bgzip module
- galaxy.datatypes.converters.cram_to_bam module
- galaxy.datatypes.converters.fasta_to_len module
- galaxy.datatypes.converters.fasta_to_tabular_converter module
- galaxy.datatypes.converters.fastq_to_fqtoc module
- galaxy.datatypes.converters.fastqsolexa_to_fasta_converter module
- galaxy.datatypes.converters.fastqsolexa_to_qual_converter module
- galaxy.datatypes.converters.gff_to_bed_converter module
- galaxy.datatypes.converters.gff_to_interval_index_converter module
- galaxy.datatypes.converters.interval_to_bed_converter module
- galaxy.datatypes.converters.interval_to_bedstrict_converter module
- galaxy.datatypes.converters.interval_to_fli module
- galaxy.datatypes.converters.interval_to_interval_index_converter module
- galaxy.datatypes.converters.interval_to_tabix_converter module
- galaxy.datatypes.converters.lped_to_fped_converter module
- galaxy.datatypes.converters.lped_to_pbed_converter module
- galaxy.datatypes.converters.maf_to_fasta_converter module
- galaxy.datatypes.converters.maf_to_interval_converter module
- galaxy.datatypes.converters.parquet_to_csv_converter module
- galaxy.datatypes.converters.pbed_ldreduced_converter module
- galaxy.datatypes.converters.pbed_to_lped_converter module
- galaxy.datatypes.converters.picard_interval_list_to_bed6_converter module
- galaxy.datatypes.converters.pileup_to_interval_index_converter module
- galaxy.datatypes.converters.ref_to_seq_taxonomy_converter module
- galaxy.datatypes.converters.tabular_csv module
- galaxy.datatypes.converters.tabular_to_dbnsfp module
- galaxy.datatypes.converters.vcf_to_interval_index_converter module
- galaxy.datatypes.converters.vcf_to_vcf_bgzip module
- galaxy.datatypes.converters.wiggle_to_simple_converter module
- galaxy.datatypes.dataproviders package
- Submodules
- galaxy.datatypes.dataproviders.base module
- galaxy.datatypes.dataproviders.chunk module
- galaxy.datatypes.dataproviders.column module
ColumnarDataProvider
ColumnarDataProvider.settings
ColumnarDataProvider.__init__()
ColumnarDataProvider.parse_filter()
ColumnarDataProvider.create_numeric_filter()
ColumnarDataProvider.create_string_filter()
ColumnarDataProvider.create_list_filter()
ColumnarDataProvider.get_default_parsers()
ColumnarDataProvider.filter()
ColumnarDataProvider.parse_columns_from_line()
ColumnarDataProvider.parse_column_at_index()
ColumnarDataProvider.parse_value()
ColumnarDataProvider.get_column_type()
ColumnarDataProvider.filter_by_columns()
DictDataProvider
- galaxy.datatypes.dataproviders.dataset module
DatasetDataProvider
DatasetDataProvider.__init__()
DatasetDataProvider.get_column_metadata_from_dataset()
DatasetDataProvider.get_metadata_column_types()
DatasetDataProvider.get_metadata_column_names()
DatasetDataProvider.get_indeces_by_column_names()
DatasetDataProvider.get_metadata_column_index_by_name()
DatasetDataProvider.get_genomic_region_indeces()
DatasetDataProvider.settings
ConvertedDatasetDataProvider
DatasetColumnarDataProvider
DatasetDictDataProvider
GenomicRegionDataProvider
IntervalDataProvider
FastaDataProvider
TwoBitFastaDataProvider
WiggleDataProvider
BigWigDataProvider
DatasetSubprocessDataProvider
SamtoolsDataProvider
SQliteDataProvider
SQliteDataTableProvider
SQliteDataDictProvider
- galaxy.datatypes.dataproviders.decorators module
- galaxy.datatypes.dataproviders.exceptions module
- galaxy.datatypes.dataproviders.external module
- galaxy.datatypes.dataproviders.hierarchy module
- galaxy.datatypes.dataproviders.line module
FilteredLineDataProvider
RegexLineDataProvider
BlockDataProvider
BlockDataProvider.__init__()
BlockDataProvider.init_new_block()
BlockDataProvider.filter()
BlockDataProvider.is_new_block()
BlockDataProvider.add_line_to_block()
BlockDataProvider.assemble_current_block()
BlockDataProvider.filter_block()
BlockDataProvider.handle_last_block()
BlockDataProvider.settings
- galaxy.datatypes.display_applications package
- Submodules
- galaxy.datatypes.display_applications.application module
quote_plus_string()
DisplayApplicationLink
DynamicDisplayApplicationBuilder
PopulatedDisplayApplicationLink
PopulatedDisplayApplicationLink.__init__()
PopulatedDisplayApplicationLink.display_ready()
PopulatedDisplayApplicationLink.get_param_value()
PopulatedDisplayApplicationLink.preparing_display()
PopulatedDisplayApplicationLink.prepare_display()
PopulatedDisplayApplicationLink.get_prepare_steps()
PopulatedDisplayApplicationLink.display_url()
PopulatedDisplayApplicationLink.get_param_name_by_url()
PopulatedDisplayApplicationLink.allow_cors
DisplayApplication
- galaxy.datatypes.display_applications.parameters module
DisplayApplicationParameter
DisplayApplicationParameter.type
DisplayApplicationParameter.from_elem()
DisplayApplicationParameter.__init__()
DisplayApplicationParameter.get_value()
DisplayApplicationParameter.prepare()
DisplayApplicationParameter.ready()
DisplayApplicationParameter.is_preparing()
DisplayApplicationParameter.build_url()
DisplayApplicationDataParameter
DisplayApplicationTemplateParameter
DisplayParameterValueWrapper
DisplayDataValueWrapper
- galaxy.datatypes.display_applications.util module
- galaxy.datatypes.util package
- Submodules
- galaxy.datatypes.util.generic_util module
- galaxy.datatypes.util.gff_util module
- galaxy.datatypes.util.maf_utilities module
maketrans()
src_split()
src_merge()
get_species_in_block()
tool_fail()
TempFileHandler
RegionAlignment
RegionAlignment.DNA_COMPLEMENT
RegionAlignment.MAX_SEQUENCE_SIZE
RegionAlignment.__init__()
RegionAlignment.add_species()
RegionAlignment.get_species_names()
RegionAlignment.get_sequence()
RegionAlignment.get_sequence_reverse_complement()
RegionAlignment.set_position()
RegionAlignment.set_range()
RegionAlignment.flush()
GenomicRegionAlignment
SplicedAlignment
maf_index_by_uid()
open_or_build_maf_index()
build_maf_index_species_chromosomes()
build_maf_index()
component_overlaps_region()
chop_block_by_region()
orient_block_by_region()
get_oriented_chopped_blocks_for_region()
get_oriented_chopped_blocks_with_index_offset_for_region()
iter_blocks_split_by_src()
iter_blocks_split_by_species()
get_chopped_blocks_for_region()
get_chopped_blocks_with_index_offset_for_region()
get_region_alignment()
reduce_block_by_primary_genome()
fill_region_alignment()
get_spliced_region_alignment()
line_enumerator()
get_starts_ends_fields_from_gene_bed()
iter_components_by_src()
get_components_by_src()
iter_components_by_src_start()
get_components_by_src_start()
sort_block_components_by_block()
get_species_in_maf()
parse_species_option()
remove_temp_index_file()
get_fasta_header()
get_attributes_from_fasta_header()
iter_fasta_alignment()
Submodules¶
galaxy.datatypes.annotation module¶
- class galaxy.datatypes.annotation.SnapHmm(**kwd)[source]¶
Bases:
Text
- file_ext = 'snaphmm'¶
- edam_data = 'data_1364'¶
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
SNAP model files start with zoeHMM
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.annotation.Augustus(**kwd)[source]¶
Bases:
CompressedArchive
Class describing an Augustus prediction model
- file_ext = 'augustus'¶
- edam_data = 'data_0950'¶
- compressed = True¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.anvio module¶
Datatypes for Anvi’o https://github.com/merenlab/anvio
- class galaxy.datatypes.anvio.AnvioComposite(**kwd)[source]¶
Bases:
Html
Base class to use for Anvi’o composite datatypes. Generally consist of a sqlite database, plus optional additional files
- file_ext = 'anvio_composite'¶
- generate_primary_file(dataset: HasExtraFilesAndMetadata) str [source]¶
This is called only at upload to write the html file cannot rename the datasets here - they come with the default unfortunately
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.anvio.AnvioDB(*args, **kwd)[source]¶
Bases:
AnvioComposite
Class for AnvioDB database files.
- file_ext = 'anvio_db'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the anvio_basename based upon actual extra_files_path contents.
- metadata_spec: metadata.MetadataSpecCollection = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.anvio.AnvioStructureDB(*args, **kwd)[source]¶
Bases:
AnvioDB
Class for Anvio Structure DB database files.
- file_ext = 'anvio_structure_db'¶
- metadata_spec: metadata.MetadataSpecCollection = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.anvio.AnvioGenomesDB(*args, **kwd)[source]¶
Bases:
AnvioDB
Class for Anvio Genomes DB database files.
- file_ext = 'anvio_genomes_db'¶
- metadata_spec: metadata.MetadataSpecCollection = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.anvio.AnvioContigsDB(*args, **kwd)[source]¶
Bases:
AnvioDB
Class for Anvio Contigs DB database files.
- file_ext = 'anvio_contigs_db'¶
- metadata_spec: metadata.MetadataSpecCollection = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.anvio.AnvioProfileDB(*args, **kwd)[source]¶
Bases:
AnvioDB
Class for Anvio Profile DB database files.
- file_ext = 'anvio_profile_db'¶
- metadata_spec: metadata.MetadataSpecCollection = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.anvio.AnvioPanDB(*args, **kwd)[source]¶
Bases:
AnvioDB
Class for Anvio Pan DB database files.
- file_ext = 'anvio_pan_db'¶
- metadata_spec: metadata.MetadataSpecCollection = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.anvio.AnvioSamplesDB(*args, **kwd)[source]¶
Bases:
AnvioDB
Class for Anvio Samples DB database files.
- file_ext = 'anvio_samples_db'¶
- metadata_spec: metadata.MetadataSpecCollection = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.assembly module¶
velvet datatypes James E Johnson - University of Minnesota for velvet assembler tool in galaxy
- class galaxy.datatypes.assembly.Amos(**kwd)[source]¶
Bases:
Text
Class describing the AMOS assembly file
- edam_data = 'data_0925'¶
- edam_format = 'format_3582'¶
- file_ext = 'afg'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is an amos assembly file format Example:
{CTG iid:1 eid:1 seq: CCTCTCCTGTAGAGTTCAACCGA-GCCGGTAGAGTTTTATCA . qlt: DDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD . {TLE src:1027 off:0 clr:618,0 gap: 250 612 . } }
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.assembly.Sequences(**kwd)[source]¶
Bases:
Fasta
Class describing the Sequences file generated by velveth
- edam_data = 'data_0925'¶
- file_ext = 'sequences'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is a velveth produced fasta format The id line has 3 fields separated by tabs: sequence_name sequence_index category:
>SEQUENCE_0_length_35 1 1 GGATATAGGGCCAACCCAACTCAACGGCCTGTCTT >SEQUENCE_1_length_35 2 1 CGACGAATGACAGGTCACGAATTTGGCGGGGATTA
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.assembly.Roadmaps(**kwd)[source]¶
Bases:
Text
Class describing the Sequences file generated by velveth
- edam_format = 'format_2561'¶
- file_ext = 'roadmaps'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- Determines whether the file is a velveth produced RoadMap::
142858 21 1 ROADMAP 1 ROADMAP 2 …
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.assembly.Velvet(**kwd)[source]¶
Bases:
Html
- file_ext = 'velvet'¶
- regenerate_primary_file(dataset: DatasetProtocol) None [source]¶
cannot do this until we are setting metadata
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'long_reads': <galaxy.model.metadata.MetadataElementSpec object>, 'paired_end_reads': <galaxy.model.metadata.MetadataElementSpec object>, 'short2_reads': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.binary module¶
Binary classes
- class galaxy.datatypes.binary.Binary(**kwd)[source]¶
Bases:
Data
Binary data
- edam_format = 'format_2333'¶
- file_ext = 'binary'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Ab1(**kwd)[source]¶
Bases:
Binary
Class describing an ab1 binary sequence file
- file_ext = 'ab1'¶
- edam_format = 'format_3000'¶
- edam_data = 'data_0924'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Idat(**kwd)[source]¶
Bases:
Binary
Binary data in idat format
- file_ext = 'idat'¶
- edam_format = 'format_2058'¶
- edam_data = 'data_2603'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Cel(**kwd)[source]¶
Bases:
Binary
Cel File format described at: http://media.affymetrix.com/support/developer/powertools/changelog/gcos-agcc/cel.html
- file_ext = 'cel'¶
- edam_format = 'format_1638'¶
- edam_data = 'data_3110'¶
- sniff(filename: str) bool [source]¶
Try to guess if the file is a Cel file.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('affy_v_agcc.cel') >>> Cel().sniff(fname) True >>> fname = get_test_fname('affy_v_3.cel') >>> Cel().sniff(fname) True >>> fname = get_test_fname('affy_v_4.cel') >>> Cel().sniff(fname) True >>> fname = get_test_fname('test.gal') >>> Cel().sniff(fname) False
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set metadata for Cel file.
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'version': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.MashSketch(**kwd)[source]¶
Bases:
Binary
Mash Sketch file. Sketches are used by the MinHash algorithm to allow fast distance estimations with low storage and memory requirements. To make a sketch, each k-mer in a sequence is hashed, which creates a pseudo-random identifier. By sorting these identifiers (hashes), a small subset from the top of the sorted list can represent the entire sequence (these are min-hashes). The more similar another sequence is, the more min-hashes it is likely to share.
- file_ext = 'msh'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.CompressedArchive(**kwd)[source]¶
Bases:
Binary
Class describing an compressed binary file This class can be sublass’ed to implement archive filetypes that will not be unpacked by upload.py.
- file_ext = 'compressed_archive'¶
- compressed = True¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Meryldb(**kwd)[source]¶
Bases:
CompressedArchive
MerylDB is a tar.gz archive, with 128 files. 64 data files and 64 index files.
- file_ext = 'meryldb'¶
- sniff(filename: str) bool [source]¶
Try to guess if the file is a Cel file.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('affy_v_agcc.cel') >>> Meryldb().sniff(fname) False >>> fname = get_test_fname('read-db.meryldb') >>> Meryldb().sniff(fname) True
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Visium(**kwd)[source]¶
Bases:
CompressedArchive
Visium is a tar.gz archive with at least a ‘Spatial’ subfolder, a filtered h5 file and a raw h5 file.
- file_ext = 'visium.tar.gz'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Bref3(**kwd)[source]¶
Bases:
Binary
Bref3 format is a binary format for storing phased, non-missing genotypes for a list of samples.
- file_ext = 'bref3'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.DynamicCompressedArchive(**kwd)[source]¶
Bases:
CompressedArchive
- matches_any(target_datatypes: List[Any]) bool [source]¶
Treat two aspects of compressed datatypes separately.
- metadata_spec: metadata.MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.GzDynamicCompressedArchive(**kwd)[source]¶
Bases:
DynamicCompressedArchive
- metadata_spec: metadata.MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Bz2DynamicCompressedArchive(**kwd)[source]¶
Bases:
DynamicCompressedArchive
- metadata_spec: metadata.MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.CompressedZipArchive(**kwd)[source]¶
Bases:
CompressedArchive
Class describing an compressed binary file This class can be sublass’ed to implement archive filetypes that will not be unpacked by upload.py.
- file_ext = 'zip'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.GenericAsn1Binary(**kwd)[source]¶
Bases:
Binary
Class for generic ASN.1 binary format
- file_ext = 'asn1-binary'¶
- edam_format = 'format_1966'¶
- edam_data = 'data_0849'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.BamNative(**kwd)[source]¶
Bases:
CompressedArchive
,_BamOrSam
Class describing a BAM binary file that is not necessarily sorted
- edam_format = 'format_2572'¶
- edam_data = 'data_0863'¶
- file_ext = 'unsorted.bam'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- static merge(split_files: List[str], output_file: str) None [source]¶
Merges BAM files
- Parameters:
split_files – List of bam file paths to merge
output_file – Write merged bam file to this location
- to_archive(dataset: DatasetProtocol, name: str = '') Iterable [source]¶
Collect archive paths and file handles that need to be exported when archiving dataset.
- Parameters:
dataset – HistoryDatasetAssociation
name – archive name, in collection context corresponds to collection name(s) and element_identifier, joined by ‘/’, e.g ‘fastq_collection/sample1/forward’
- groom_dataset_content(file_name: str) None [source]¶
Ensures that the BAM file contents are coordinate-sorted. This function is called on an output dataset after the content is initially generated.
- get_chunk(trans, dataset: HasFileName, offset: int = 0, ck_size: Optional[int] = None) str [source]¶
- display_data(trans, dataset: DatasetHasHidProtocol, preview: bool = False, filename: Optional[str] = None, to_ext: Optional[str] = None, offset: Optional[int] = None, ck_size: Optional[int] = None, **kwd)[source]¶
Displays data in central pane if preview is True, else handles download.
Datatypes should be very careful if overridding this method and this interface between datatypes and Galaxy will likely change.
TOOD: Document alternatives to overridding this method (data providers?).
- validate(dataset: DatasetProtocol, **kwd) DatatypeValidation [source]¶
- metadata_spec: metadata.MetadataSpecCollection = {'bam_header': <galaxy.model.metadata.MetadataElementSpec object>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Bam(**kwd)[source]¶
Bases:
BamNative
Class describing a BAM binary file
- edam_format = 'format_2572'¶
- edam_data = 'data_0863'¶
- file_ext = 'bam'¶
- get_index_flag(file_name: str) str [source]¶
Return pysam flag for bai index (default) or csi index (contig size > (2**29 - 1) )
- dataset_content_needs_grooming(file_name: str) bool [source]¶
Check if file_name is a coordinate-sorted BAM file
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, metadata_tmp_files_dir: Optional[str] = None, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- line_dataprovider(dataset: DatasetProtocol, **settings) FilteredLineDataProvider [source]¶
- regex_line_dataprovider(dataset: DatasetProtocol, **settings) RegexLineDataProvider [source]¶
- column_dataprovider(dataset: DatasetProtocol, **settings) ColumnarDataProvider [source]¶
- dict_dataprovider(dataset: DatasetProtocol, **settings) DictDataProvider [source]¶
- header_dataprovider(dataset: DatasetProtocol, **settings) RegexLineDataProvider [source]¶
- id_seq_qual_dataprovider(dataset: DatasetProtocol, **settings) DictDataProvider [source]¶
- genomic_region_dataprovider(dataset: DatasetProtocol, **settings) ColumnarDataProvider [source]¶
- genomic_region_dict_dataprovider(dataset: DatasetProtocol, **settings) DictDataProvider [source]¶
- samtools_dataprovider(dataset: DatasetProtocol, **settings) SamtoolsDataProvider [source]¶
Generic samtools interface - all options available through settings.
- dataproviders: Dict[str, Any] = {'base': <function Data.base_dataprovider>, 'chunk': <function Data.chunk_dataprovider>, 'chunk64': <function Data.chunk64_dataprovider>, 'column': <function Bam.column_dataprovider>, 'dict': <function Bam.dict_dataprovider>, 'genomic-region': <function Bam.genomic_region_dataprovider>, 'genomic-region-dict': <function Bam.genomic_region_dict_dataprovider>, 'header': <function Bam.header_dataprovider>, 'id-seq-qual': <function Bam.id_seq_qual_dataprovider>, 'line': <function Bam.line_dataprovider>, 'regex-line': <function Bam.regex_line_dataprovider>, 'samtools': <function Bam.samtools_dataprovider>}¶
- metadata_spec: metadata.MetadataSpecCollection = {'bam_csi_index': <galaxy.model.metadata.MetadataElementSpec object>, 'bam_header': <galaxy.model.metadata.MetadataElementSpec object>, 'bam_index': <galaxy.model.metadata.MetadataElementSpec object>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.ProBam(**kwd)[source]¶
Bases:
Bam
Class describing a BAM binary file - extended for proteomics data
- edam_format = 'format_3826'¶
- edam_data = 'data_0863'¶
- file_ext = 'probam'¶
- metadata_spec: metadata.MetadataSpecCollection = {'bam_csi_index': <galaxy.model.metadata.MetadataElementSpec object>, 'bam_header': <galaxy.model.metadata.MetadataElementSpec object>, 'bam_index': <galaxy.model.metadata.MetadataElementSpec object>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.BamInputSorted(**kwd)[source]¶
Bases:
BamNative
A class for BAM files that can formally be unsorted or queryname sorted. Alignments are either ordered based on the order with which the queries appear when producing the alignment, or ordered by their queryname. This notaby keeps alignments produced by paired end sequencing adjacent.
- file_ext = 'qname_input_sorted.bam'¶
- dataset_content_needs_grooming(file_name: str) bool [source]¶
Groom if the file is coordinate sorted
- metadata_spec: metadata.MetadataSpecCollection = {'bam_header': <galaxy.model.metadata.MetadataElementSpec object>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.BamQuerynameSorted(**kwd)[source]¶
Bases:
BamInputSorted
A class for queryname sorted BAM files.
- file_ext = 'qname_sorted.bam'¶
- dataset_content_needs_grooming(file_name: str) bool [source]¶
Check if file_name is a queryname-sorted BAM file
- metadata_spec: metadata.MetadataSpecCollection = {'bam_header': <galaxy.model.metadata.MetadataElementSpec object>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.CRAM(**kwd)[source]¶
Bases:
Binary
- file_ext = 'cram'¶
- edam_format = 'format_3462'¶
- edam_data = 'data_0863'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, metadata_tmp_files_dir: Optional[str] = None, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'cram_index': <galaxy.model.metadata.MetadataElementSpec object>, 'cram_version': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.BaseBcf(**kwd)[source]¶
Bases:
CompressedArchive
- edam_format = 'format_3020'¶
- edam_data = 'data_3498'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Bcf(**kwd)[source]¶
Bases:
BaseBcf
Class describing a (BGZF-compressed) BCF file
- file_ext = 'bcf'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, metadata_tmp_files_dir: Optional[str] = None, **kwd) None [source]¶
Creates the index for the BCF file.
- metadata_spec: MetadataSpecCollection = {'bcf_index': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.BcfUncompressed(**kwd)[source]¶
Bases:
BaseBcf
Class describing an uncompressed BCF file
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('1.bcf_uncompressed') >>> BcfUncompressed().sniff(fname) True >>> fname = get_test_fname('1.bcf') >>> BcfUncompressed().sniff(fname) False
- file_ext = 'bcf_uncompressed'¶
- compressed = False¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.H5(**kwd)[source]¶
Bases:
Binary
Class describing an HDF5 file
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.mz5') >>> H5().sniff(fname) True >>> fname = get_test_fname('interval.interval') >>> H5().sniff(fname) False
- file_ext = 'h5'¶
- edam_format = 'format_3590'¶
- get_structured_content(dataset, content_type=None, path='/', dtype='origin', format='json', flatten=False, selection=None, **kwargs)[source]¶
Implements h5grove protocol (https://silx-kit.github.io/h5grove/). This allows the h5web visualization tool (https://github.com/silx-kit/h5web) to be used directly with Galaxy datasets.
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Loom(**kwd)[source]¶
Bases:
H5
Class describing a Loom file: http://loompy.org/
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.loom') >>> Loom().sniff(fname) True >>> fname = get_test_fname('test.mz5') >>> Loom().sniff(fname) False
- file_ext = 'loom'¶
- edam_format = 'format_3590'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'col_attrs_count': <galaxy.model.metadata.MetadataElementSpec object>, 'col_attrs_names': <galaxy.model.metadata.MetadataElementSpec object>, 'col_graphs_count': <galaxy.model.metadata.MetadataElementSpec object>, 'col_graphs_names': <galaxy.model.metadata.MetadataElementSpec object>, 'creation_date': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'description': <galaxy.model.metadata.MetadataElementSpec object>, 'doi': <galaxy.model.metadata.MetadataElementSpec object>, 'layers_count': <galaxy.model.metadata.MetadataElementSpec object>, 'layers_names': <galaxy.model.metadata.MetadataElementSpec object>, 'loom_spec_version': <galaxy.model.metadata.MetadataElementSpec object>, 'row_attrs_count': <galaxy.model.metadata.MetadataElementSpec object>, 'row_attrs_names': <galaxy.model.metadata.MetadataElementSpec object>, 'row_graphs_count': <galaxy.model.metadata.MetadataElementSpec object>, 'row_graphs_names': <galaxy.model.metadata.MetadataElementSpec object>, 'shape': <galaxy.model.metadata.MetadataElementSpec object>, 'title': <galaxy.model.metadata.MetadataElementSpec object>, 'url': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Anndata(**kwd)[source]¶
Bases:
H5
Class describing an HDF5 anndata files: http://anndata.rtfd.io
>>> from galaxy.datatypes.sniff import get_test_fname >>> Anndata().sniff(get_test_fname('pbmc3k_tiny.h5ad')) True >>> Anndata().sniff(get_test_fname('test.mz5')) False >>> Anndata().sniff(get_test_fname('import.loom.krumsiek11.h5ad')) True >>> Anndata().sniff(get_test_fname('adata_0_6_small2.h5ad')) True >>> Anndata().sniff(get_test_fname('adata_0_6_small.h5ad')) True >>> Anndata().sniff(get_test_fname('adata_0_7_4_small2.h5ad')) True >>> Anndata().sniff(get_test_fname('adata_0_7_4_small.h5ad')) True >>> Anndata().sniff(get_test_fname('adata_unk2.h5ad')) True >>> Anndata().sniff(get_test_fname('adata_unk.h5ad')) True
- file_ext = 'h5ad'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'anndata_spec_version': <galaxy.model.metadata.MetadataElementSpec object>, 'creation_date': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'description': <galaxy.model.metadata.MetadataElementSpec object>, 'doi': <galaxy.model.metadata.MetadataElementSpec object>, 'layers_count': <galaxy.model.metadata.MetadataElementSpec object>, 'layers_names': <galaxy.model.metadata.MetadataElementSpec object>, 'obs_count': <galaxy.model.metadata.MetadataElementSpec object>, 'obs_layers': <galaxy.model.metadata.MetadataElementSpec object>, 'obs_names': <galaxy.model.metadata.MetadataElementSpec object>, 'obs_size': <galaxy.model.metadata.MetadataElementSpec object>, 'obsm_count': <galaxy.model.metadata.MetadataElementSpec object>, 'obsm_layers': <galaxy.model.metadata.MetadataElementSpec object>, 'raw_var_count': <galaxy.model.metadata.MetadataElementSpec object>, 'raw_var_layers': <galaxy.model.metadata.MetadataElementSpec object>, 'raw_var_size': <galaxy.model.metadata.MetadataElementSpec object>, 'row_attrs_count': <galaxy.model.metadata.MetadataElementSpec object>, 'shape': <galaxy.model.metadata.MetadataElementSpec object>, 'title': <galaxy.model.metadata.MetadataElementSpec object>, 'uns_count': <galaxy.model.metadata.MetadataElementSpec object>, 'uns_layers': <galaxy.model.metadata.MetadataElementSpec object>, 'url': <galaxy.model.metadata.MetadataElementSpec object>, 'var_count': <galaxy.model.metadata.MetadataElementSpec object>, 'var_layers': <galaxy.model.metadata.MetadataElementSpec object>, 'var_size': <galaxy.model.metadata.MetadataElementSpec object>, 'varm_count': <galaxy.model.metadata.MetadataElementSpec object>, 'varm_layers': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Grib(**kwd)[source]¶
Bases:
Binary
Class describing an GRIB file
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.grib') >>> Grib().sniff_prefix(FilePrefix(fname)) True >>> fname = FilePrefix(get_test_fname('interval.interval')) >>> Grib().sniff_prefix(fname) False
- file_ext = 'grib'¶
- edam_format = 'format_2333'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the GRIB edition.
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'grib_edition': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.binary.GmxBinary(**kwd)[source]¶
Bases:
Binary
Base class for GROMACS binary files - xtc, trr, cpt
- file_ext = ''¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: metadata.MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.binary.Trr(**kwd)[source]¶
Bases:
GmxBinary
Class describing an trr file from the GROMACS suite
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('md.trr') >>> Trr().sniff(fname) True >>> fname = get_test_fname('interval.interval') >>> Trr().sniff(fname) False
- file_ext = 'trr'¶
- metadata_spec: metadata.MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Cpt(**kwd)[source]¶
Bases:
GmxBinary
Class describing a checkpoint (.cpt) file from the GROMACS suite
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('md.cpt') >>> Cpt().sniff(fname) True >>> fname = get_test_fname('md.trr') >>> Cpt().sniff(fname) False
- file_ext = 'cpt'¶
- metadata_spec: metadata.MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Xtc(**kwd)[source]¶
Bases:
GmxBinary
Class describing an xtc file from the GROMACS suite
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('md.xtc') >>> Xtc().sniff(fname) True >>> fname = get_test_fname('md.trr') >>> Xtc().sniff(fname) False
- file_ext = 'xtc'¶
- metadata_spec: metadata.MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Edr(**kwd)[source]¶
Bases:
GmxBinary
Class describing an edr file from the GROMACS suite
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('md.edr') >>> Edr().sniff(fname) True >>> fname = get_test_fname('md.trr') >>> Edr().sniff(fname) False
- file_ext = 'edr'¶
- metadata_spec: metadata.MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Biom2(**kwd)[source]¶
Bases:
H5
Class describing a biom2 file (http://biom-format.org/documentation/biom_format.html)
- file_ext = 'biom2'¶
- edam_format = 'format_3746'¶
- sniff(filename: str) bool [source]¶
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('biom2_sparse_otu_table_hdf5.biom2') >>> Biom2().sniff(fname) True >>> fname = get_test_fname('test.mz5') >>> Biom2().sniff(fname) False >>> fname = get_test_fname('wiggle.wig') >>> Biom2().sniff(fname) False
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'creation_date': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'format': <galaxy.model.metadata.MetadataElementSpec object>, 'format_url': <galaxy.model.metadata.MetadataElementSpec object>, 'format_version': <galaxy.model.metadata.MetadataElementSpec object>, 'generated_by': <galaxy.model.metadata.MetadataElementSpec object>, 'id': <galaxy.model.metadata.MetadataElementSpec object>, 'nnz': <galaxy.model.metadata.MetadataElementSpec object>, 'shape': <galaxy.model.metadata.MetadataElementSpec object>, 'type': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Cool(**kwd)[source]¶
Bases:
H5
Class describing the cool format (https://github.com/mirnylab/cooler)
- file_ext = 'cool'¶
- sniff(filename: str) bool [source]¶
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('matrix.cool') >>> Cool().sniff(fname) True >>> fname = get_test_fname('test.mz5') >>> Cool().sniff(fname) False >>> fname = get_test_fname('wiggle.wig') >>> Cool().sniff(fname) False >>> fname = get_test_fname('biom2_sparse_otu_table_hdf5.biom2') >>> Cool().sniff(fname) False
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.MCool(**kwd)[source]¶
Bases:
H5
Class describing the multi-resolution cool format (https://github.com/mirnylab/cooler)
- file_ext = 'mcool'¶
- sniff(filename: str) bool [source]¶
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('matrix.mcool') >>> MCool().sniff(fname) True >>> fname = get_test_fname('matrix.cool') >>> MCool().sniff(fname) False >>> fname = get_test_fname('test.mz5') >>> MCool().sniff(fname) False >>> fname = get_test_fname('wiggle.wig') >>> MCool().sniff(fname) False >>> fname = get_test_fname('biom2_sparse_otu_table_hdf5.biom2') >>> MCool().sniff(fname) False
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.H5MLM(**kwd)[source]¶
Bases:
H5
Machine learning model generated by Galaxy-ML.
- file_ext = 'h5mlm'¶
- TARGET_URL = 'https://github.com/goeckslab/Galaxy-ML'¶
- max_peek_size = 1000¶
- max_preview_size = 1000000¶
- CONFIG = '-model_config-'¶
- HTTP_REPR = '-http_repr-'¶
- HYPERPARAMETER = '-model_hyperparameters-'¶
- REPR = '-repr-'¶
- URL = '-URL-'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, metadata_tmp_files_dir: Optional[str] = None, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- display_data(trans, dataset: DatasetHasHidProtocol, preview: bool = False, filename: Optional[str] = None, to_ext: Optional[str] = None, **kwd)[source]¶
Displays data in central pane if preview is True, else handles download.
Datatypes should be very careful if overridding this method and this interface between datatypes and Galaxy will likely change.
TOOD: Document alternatives to overridding this method (data providers?).
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'hyper_params': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.LudwigModel(**kwd)[source]¶
Bases:
Html
Composite datatype that encloses multiple files for a Ludwig trained model.
- file_ext = 'ludwig_model'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.HexrdMaterials(**kwd)[source]¶
Bases:
H5
Class describing a Hexrd Materials file: https://github.com/HEXRD/hexrd
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('hexrd.materials.h5') >>> HexrdMaterials().sniff(fname) True >>> fname = get_test_fname('test.loom') >>> HexrdMaterials().sniff(fname) False
- file_ext = 'hexrd.materials.h5'¶
- edam_format = 'format_3590'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'LatticeParameters': <galaxy.model.metadata.MetadataElementSpec object>, 'SpaceGroupNumber': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'materials': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Scf(**kwd)[source]¶
Bases:
Binary
Class describing an scf binary sequence file
- edam_format = 'format_1632'¶
- edam_data = 'data_0924'¶
- file_ext = 'scf'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Sff(**kwd)[source]¶
Bases:
Binary
Standard Flowgram Format (SFF)
- edam_format = 'format_3284'¶
- edam_data = 'data_0924'¶
- file_ext = 'sff'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.binary.BigWig(**kwd)[source]¶
Bases:
Binary
Accessing binary BigWig files from UCSC. The supplemental info in the paper has the binary details: http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btq351v1
- edam_format = 'format_3006'¶
- edam_data = 'data_3002'¶
- file_ext = 'bigwig'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.binary.BigBed(**kwd)[source]¶
Bases:
BigWig
BigBed support from UCSC.
- edam_format = 'format_3004'¶
- edam_data = 'data_3002'¶
- file_ext = 'bigbed'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.TwoBit(**kwd)[source]¶
Bases:
Binary
Class describing a TwoBit format nucleotide file
- edam_format = 'format_3009'¶
- edam_data = 'data_0848'¶
- file_ext = 'twobit'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.binary.SQlite(**kwd)[source]¶
Bases:
Binary
Class describing a Sqlite database
- file_ext = 'sqlite'¶
- edam_format = 'format_3621'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- sqlite_dataprovider(dataset: DatasetProtocol, **settings) SQliteDataProvider [source]¶
- sqlite_datatableprovider(dataset: DatasetProtocol, **settings) SQliteDataTableProvider [source]¶
- sqlite_datadictprovider(dataset: DatasetProtocol, **settings) SQliteDataDictProvider [source]¶
- dataproviders: Dict[str, Any] = {'base': <function Data.base_dataprovider>, 'chunk': <function Data.chunk_dataprovider>, 'chunk64': <function Data.chunk64_dataprovider>, 'sqlite': <function SQlite.sqlite_dataprovider>, 'sqlite-dict': <function SQlite.sqlite_datadictprovider>, 'sqlite-table': <function SQlite.sqlite_datatableprovider>}¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object>, 'tables': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.GeminiSQLite(**kwd)[source]¶
Bases:
SQlite
Class describing a Gemini Sqlite database
- file_ext = 'gemini.sqlite'¶
- edam_format = 'format_3622'¶
- edam_data = 'data_3498'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'gemini_version': <galaxy.model.metadata.MetadataElementSpec object>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object>, 'tables': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.ChiraSQLite(**kwd)[source]¶
Bases:
SQlite
Class describing a ChiRAViz Sqlite database
- file_ext = 'chira.sqlite'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object>, 'tables': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.CuffDiffSQlite(**kwd)[source]¶
Bases:
SQlite
Class describing a CuffDiff SQLite database
- file_ext = 'cuffdiff.sqlite'¶
- edam_format = 'format_3621'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'cuffdiff_version': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'genes': <galaxy.model.metadata.MetadataElementSpec object>, 'samples': <galaxy.model.metadata.MetadataElementSpec object>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object>, 'tables': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.MzSQlite(**kwd)[source]¶
Bases:
SQlite
Class describing a Proteomics Sqlite database
- file_ext = 'mz.sqlite'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object>, 'tables': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.PQP(**kwd)[source]¶
Bases:
SQlite
Class describing a Peptide query parameters file
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.pqp') >>> PQP().sniff(fname) True >>> fname = get_test_fname('test.osw') >>> PQP().sniff(fname) False
- file_ext = 'pqp'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- sniff(filename: str) bool [source]¶
table definition according to https://github.com/grosenberger/OpenMS/blob/develop/src/openms/source/ANALYSIS/OPENSWATH/TransitionPQPFile.cpp#L264 for now VERSION GENE PEPTIDE_GENE_MAPPING are excluded, since there is test data wo these tables, see also here https://github.com/OpenMS/OpenMS/issues/4365
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object>, 'tables': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.OSW(**kwd)[source]¶
Bases:
SQlite
Class describing OpenSwath output
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.osw') >>> OSW().sniff(fname) True >>> fname = get_test_fname('test.sqmass') >>> OSW().sniff(fname) False
- file_ext = 'osw'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object>, 'tables': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.SQmass(**kwd)[source]¶
Bases:
SQlite
Class describing a Sqmass database
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.sqmass') >>> SQmass().sniff(fname) True >>> fname = get_test_fname('test.pqp') >>> SQmass().sniff(fname) False
- file_ext = 'sqmass'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object>, 'tables': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.BlibSQlite(**kwd)[source]¶
Bases:
SQlite
Class describing a Proteomics Spectral Library Sqlite database
- file_ext = 'blib'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'blib_version': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object>, 'tables': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.DlibSQlite(**kwd)[source]¶
Bases:
SQlite
Class describing a Proteomics Spectral Library Sqlite database DLIBs only have the “entries”, “metadata”, and “peptidetoprotein” tables populated. ELIBs have the rest of the tables populated too, such as “peptidequants” or “peptidescores”.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.dlib') >>> DlibSQlite().sniff(fname) True >>> fname = get_test_fname('interval.interval') >>> DlibSQlite().sniff(fname) False
- file_ext = 'dlib'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'dlib_version': <galaxy.model.metadata.MetadataElementSpec object>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object>, 'tables': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.ElibSQlite(**kwd)[source]¶
Bases:
SQlite
Class describing a Proteomics Chromatagram Library Sqlite database DLIBs only have the “entries”, “metadata”, and “peptidetoprotein” tables populated. ELIBs have the rest of the tables populated too, such as “peptidequants” or “peptidescores”.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.elib') >>> ElibSQlite().sniff(fname) True >>> fname = get_test_fname('test.dlib') >>> ElibSQlite().sniff(fname) False
- file_ext = 'elib'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object>, 'tables': <galaxy.model.metadata.MetadataElementSpec object>, 'version': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.IdpDB(**kwd)[source]¶
Bases:
SQlite
Class describing an IDPicker 3 idpDB (sqlite) database
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.idpdb') >>> IdpDB().sniff(fname) True >>> fname = get_test_fname('interval.interval') >>> IdpDB().sniff(fname) False
- file_ext = 'idpdb'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object>, 'tables': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.GAFASQLite(**kwd)[source]¶
Bases:
SQlite
Class describing a GAFA SQLite database
- file_ext = 'gafa.sqlite'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'gafa_schema_version': <galaxy.model.metadata.MetadataElementSpec object>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object>, 'tables': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.NcbiTaxonomySQlite(**kwd)[source]¶
Bases:
SQlite
Class describing the NCBI Taxonomy database stored in SQLite as done by rust-ncbitaxonomy
- file_ext = 'ncbitaxonomy.sqlite'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'ncbitaxonomy_schema_version': <galaxy.model.metadata.MetadataElementSpec object>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object>, 'tables': <galaxy.model.metadata.MetadataElementSpec object>, 'taxon_count': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Xlsx(**kwd)[source]¶
Bases:
Binary
Class for Excel 2007 (xlsx) files
- file_ext = 'xlsx'¶
- compressed = True¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.binary.ExcelXls(**kwd)[source]¶
Bases:
Binary
Class describing an Excel (xls) file
- file_ext = 'excel.xls'¶
- edam_format = 'format_3468'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.binary.Sra(**kwd)[source]¶
Bases:
Binary
Sequence Read Archive (SRA) datatype originally from mdshw5/sra-tools-galaxy
- file_ext = 'sra'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
The first 8 bytes of any NCBI sra file is ‘NCBI.sra’, and the file is binary. For details about the format, see http://www.ncbi.nlm.nih.gov/books/n/helpsra/SRA_Overview_BK/#SRA_Overview_BK.4_SRA_Data_Structure
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.binary.RData(**kwd)[source]¶
Bases:
CompressedArchive
Generic R Data file datatype implementation, i.e. files generated with R’s save or save.img function see https://www.loc.gov/preservation/digital/formats/fdd/fdd000470.shtml and https://cran.r-project.org/doc/manuals/r-patched/R-ints.html#Serialization-Formats
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.rdata') >>> RData().sniff(fname) True >>> from galaxy.util.bunch import Bunch >>> dataset = Bunch() >>> dataset.metadata = Bunch >>> dataset.file_name = fname >>> dataset.has_data = lambda: True >>> RData().set_meta(dataset) >>> dataset.metadata.version '3'
- VERSION_2_PREFIX = b'RDX2\nX\n'¶
- VERSION_3_PREFIX = b'RDX3\nX\n'¶
- file_ext = 'rdata'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'version': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.binary.RDS(**kwd)[source]¶
Bases:
CompressedArchive
File using a serialized R object generated with R’s saveRDS function see https://cran.r-project.org/doc/manuals/r-patched/R-ints.html#Serialization-Formats
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('int-r3.rds') >>> RDS().sniff(fname) True >>> fname = get_test_fname('int-r4.rds') >>> RDS().sniff(fname) True >>> fname = get_test_fname('int-r3-version2.rds') >>> RDS().sniff(fname) True >>> from galaxy.util.bunch import Bunch >>> dataset = Bunch() >>> dataset.metadata = Bunch >>> dataset.file_name = get_test_fname('int-r4.rds') >>> dataset.has_data = lambda: True >>> RDS().set_meta(dataset) >>> dataset.metadata.version '3' >>> dataset.metadata.rversion '4.1.1' >>> dataset.metadata.minrversion '3.5.0'
- file_ext = 'rds'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'minrversion': <galaxy.model.metadata.MetadataElementSpec object>, 'rversion': <galaxy.model.metadata.MetadataElementSpec object>, 'version': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.binary.OxliBinary(**kwd)[source]¶
Bases:
Binary
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.OxliCountGraph(**kwd)[source]¶
Bases:
OxliBinary
OxliCountGraph starts with “OXLI” + one byte version number + 8-bit binary ‘1’ Test file generated via:
load-into-counting.py --n_tables 1 --max-tablesize 1 \ oxli_countgraph.oxlicg khmer/tests/test-data/100-reads.fq.bz2
using khmer 2.0
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('sequence.csfasta') >>> OxliCountGraph().sniff(fname) False >>> fname = get_test_fname("oxli_countgraph.oxlicg") >>> OxliCountGraph().sniff(fname) True
- file_ext = 'oxlicg'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.OxliNodeGraph(**kwd)[source]¶
Bases:
OxliBinary
OxliNodeGraph starts with “OXLI” + one byte version number + 8-bit binary ‘2’ Test file generated via:
load-graph.py --n_tables 1 --max-tablesize 1 oxli_nodegraph.oxling \ khmer/tests/test-data/100-reads.fq.bz2
using khmer 2.0
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('sequence.csfasta') >>> OxliNodeGraph().sniff(fname) False >>> fname = get_test_fname("oxli_nodegraph.oxling") >>> OxliNodeGraph().sniff(fname) True
- file_ext = 'oxling'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.OxliTagSet(**kwd)[source]¶
Bases:
OxliBinary
OxliTagSet starts with “OXLI” + one byte version number + 8-bit binary ‘3’ Test file generated via:
load-graph.py --n_tables 1 --max-tablesize 1 oxli_nodegraph.oxling \ khmer/tests/test-data/100-reads.fq.bz2; mv oxli_nodegraph.oxling.tagset oxli_tagset.oxlits
using khmer 2.0
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('sequence.csfasta') >>> OxliTagSet().sniff(fname) False >>> fname = get_test_fname("oxli_tagset.oxlits") >>> OxliTagSet().sniff(fname) True
- file_ext = 'oxlits'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.OxliStopTags(**kwd)[source]¶
Bases:
OxliBinary
OxliStopTags starts with “OXLI” + one byte version number + 8-bit binary ‘4’ Test file adapted from khmer 2.0’s “khmer/tests/test-data/goodversion-k32.stoptags”
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('sequence.csfasta') >>> OxliStopTags().sniff(fname) False >>> fname = get_test_fname("oxli_stoptags.oxlist") >>> OxliStopTags().sniff(fname) True
- file_ext = 'oxlist'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.OxliSubset(**kwd)[source]¶
Bases:
OxliBinary
OxliSubset starts with “OXLI” + one byte version number + 8-bit binary ‘5’ Test file generated via:
load-graph.py -k 20 example tests/test-data/random-20-a.fa; partition-graph.py example; mv example.subset.0.pmap oxli_subset.oxliss
using khmer 2.0
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('sequence.csfasta') >>> OxliSubset().sniff(fname) False >>> fname = get_test_fname("oxli_subset.oxliss") >>> OxliSubset().sniff(fname) True
- file_ext = 'oxliss'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.OxliGraphLabels(**kwd)[source]¶
Bases:
OxliBinary
OxliGraphLabels starts with “OXLI” + one byte version number + 8-bit binary ‘6’ Test file generated via:
python -c "from khmer import GraphLabels; \ gl = GraphLabels(20, 1e7, 4); \ gl.consume_fasta_and_tag_with_labels('tests/test-data/test-labels.fa'); \ gl.save_labels_and_tags('oxli_graphlabels.oxligl')"
using khmer 2.0
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('sequence.csfasta') >>> OxliGraphLabels().sniff(fname) False >>> fname = get_test_fname("oxli_graphlabels.oxligl") >>> OxliGraphLabels().sniff(fname) True
- file_ext = 'oxligl'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.PostgresqlArchive(**kwd)[source]¶
Bases:
CompressedArchive
Class describing a Postgresql database packed into a tar archive
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('postgresql_fake.tar.bz2') >>> PostgresqlArchive().sniff(fname) True >>> fname = get_test_fname('test.fast5.tar') >>> PostgresqlArchive().sniff(fname) False
- file_ext = 'postgresql'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'version': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.MongoDBArchive(**kwd)[source]¶
Bases:
CompressedArchive
Class describing a Mongo database packed into a tar archive
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('mongodb_fake.tar.bz2') >>> MongoDBArchive().sniff(fname) True >>> fname = get_test_fname('test.fast5.tar') >>> MongoDBArchive().sniff(fname) False
- file_ext = 'mongodb'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'version': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.GeneNoteBook(**kwd)[source]¶
Bases:
MongoDBArchive
Class describing a bzip2-compressed GeneNoteBook archive
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('mongodb_fake.tar.bz2') >>> GeneNoteBook().sniff(fname) True >>> fname = get_test_fname('test.fast5.tar.gz') >>> GeneNoteBook().sniff(fname) False
- file_ext = 'genenotebook'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'version': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Fast5Archive(**kwd)[source]¶
Bases:
CompressedArchive
Class describing a FAST5 archive
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.fast5.tar') >>> Fast5Archive().sniff(fname) True
- file_ext = 'fast5.tar'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Fast5ArchiveGz(**kwd)[source]¶
Bases:
Fast5Archive
Class describing a gzip-compressed FAST5 archive
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.fast5.tar.gz') >>> Fast5ArchiveGz().sniff(fname) True >>> fname = get_test_fname('test.fast5.tar.bz2') >>> Fast5ArchiveGz().sniff(fname) False >>> fname = get_test_fname('test.fast5.tar') >>> Fast5ArchiveGz().sniff(fname) False
- file_ext = 'fast5.tar.gz'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Fast5ArchiveBz2(**kwd)[source]¶
Bases:
Fast5Archive
Class describing a bzip2-compressed FAST5 archive
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.fast5.tar.bz2') >>> Fast5ArchiveBz2().sniff(fname) True >>> fname = get_test_fname('test.fast5.tar.gz') >>> Fast5ArchiveBz2().sniff(fname) False >>> fname = get_test_fname('test.fast5.tar') >>> Fast5ArchiveBz2().sniff(fname) False
- file_ext = 'fast5.tar.bz2'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.SearchGuiArchive(**kwd)[source]¶
Bases:
CompressedArchive
Class describing a SearchGUI archive
- file_ext = 'searchgui_archive'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'searchgui_major_version': <galaxy.model.metadata.MetadataElementSpec object>, 'searchgui_version': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.NetCDF(**kwd)[source]¶
Bases:
Binary
Binary data in netCDF format
- file_ext = 'netcdf'¶
- edam_format = 'format_3650'¶
- edam_data = 'data_0943'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.binary.Dcd(**kwd)[source]¶
Bases:
Binary
Class describing a dcd file from the CHARMM molecular simulation program
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test_glucose_vacuum.dcd') >>> Dcd().sniff(fname) True >>> fname = get_test_fname('interval.interval') >>> Dcd().sniff(fname) False
- file_ext = 'dcd'¶
- edam_data = 'data_3842'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Vel(**kwd)[source]¶
Bases:
Binary
Class describing a velocity file from the CHARMM molecular simulation program
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test_charmm.vel') >>> Vel().sniff(fname) True >>> fname = get_test_fname('interval.interval') >>> Vel().sniff(fname) False
- file_ext = 'vel'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.DAA(**kwd)[source]¶
Bases:
Binary
Class describing an DAA (diamond alignment archive) file
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('diamond.daa') >>> DAA().sniff(fname) True >>> fname = get_test_fname('interval.interval') >>> DAA().sniff(fname) False
- file_ext = 'daa'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.binary.RMA6(**kwd)[source]¶
Bases:
Binary
Class describing an RMA6 (MEGAN6 read-match archive) file
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('diamond.rma6') >>> RMA6().sniff(fname) True >>> fname = get_test_fname('interval.interval') >>> RMA6().sniff(fname) False
- file_ext = 'rma6'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.binary.DMND(**kwd)[source]¶
Bases:
Binary
Class describing an DMND file
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('diamond_db.dmnd') >>> DMND().sniff(fname) True >>> fname = get_test_fname('interval.interval') >>> DMND().sniff(fname) False
- file_ext = 'dmnd'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.binary.ICM(**kwd)[source]¶
Bases:
Binary
Class describing an ICM (interpolated context model) file, used by Glimmer
- file_ext = 'icm'¶
- edam_data = 'data_0950'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Parquet(**kwd)[source]¶
Bases:
Binary
Class describing Apache Parquet file (https://parquet.apache.org/)
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('example.parquet') >>> Parquet().sniff(fname) True >>> fname = get_test_fname('test.mz5') >>> Parquet().sniff(fname) False
- file_ext = 'parquet'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.binary.BafTar(**kwd)[source]¶
Bases:
CompressedArchive
Base class for common behavior of tar files of directory-based raw file formats
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('brukerbaf.d.tar') >>> BafTar().sniff(fname) True >>> fname = get_test_fname('test.fast5.tar') >>> BafTar().sniff(fname) False
- edam_data = 'data_2536'¶
- edam_format = 'format_3712'¶
- file_ext = 'brukerbaf.d.tar'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.YepTar(**kwd)[source]¶
Bases:
BafTar
A tar’d up .d directory containing Agilent/Bruker YEP format data
- file_ext = 'agilentbrukeryep.d.tar'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.TdfTar(**kwd)[source]¶
Bases:
BafTar
A tar’d up .d directory containing Bruker TDF format data
- file_ext = 'brukertdf.d.tar'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.MassHunterTar(**kwd)[source]¶
Bases:
BafTar
A tar’d up .d directory containing Agilent MassHunter format data
- file_ext = 'agilentmasshunter.d.tar'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.MassLynxTar(**kwd)[source]¶
Bases:
BafTar
A tar’d up .d directory containing Waters MassLynx format data
- file_ext = 'watersmasslynx.raw.tar'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.WiffTar(**kwd)[source]¶
Bases:
BafTar
A tar’d up .wiff/.scan pair containing Sciex WIFF format data
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('some.wiff.tar') >>> WiffTar().sniff(fname) True >>> fname = get_test_fname('brukerbaf.d.tar') >>> WiffTar().sniff(fname) False >>> fname = get_test_fname('test.fast5.tar') >>> WiffTar().sniff(fname) False
- file_ext = 'wiff.tar'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Wiff2Tar(**kwd)[source]¶
Bases:
BafTar
A tar’d up .wiff2/.scan pair containing Sciex WIFF format data
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('some.wiff2.tar') >>> Wiff2Tar().sniff(fname) True >>> fname = get_test_fname('brukerbaf.d.tar') >>> Wiff2Tar().sniff(fname) False >>> fname = get_test_fname('test.fast5.tar') >>> Wiff2Tar().sniff(fname) False
- file_ext = 'wiff2.tar'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Pretext(**kwd)[source]¶
Bases:
Binary
PretextMap contact map file Try to guess if the file is a Pretext file.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('sample.pretext') >>> Pretext().sniff(fname) True
- file_ext = 'pretext'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.binary.JP2(**kwd)[source]¶
Bases:
Binary
JPEG 2000 binary image format
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.jp2') >>> JP2().sniff(fname) True >>> fname = get_test_fname('interval.interval') >>> JP2().sniff(fname) False
- file_ext = 'jp2'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.Npz(**kwd)[source]¶
Bases:
CompressedArchive
Class describing an Numpy NPZ file
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('hexrd.images.npz') >>> Npz().sniff(fname) True >>> fname = get_test_fname('interval.interval') >>> Npz().sniff(fname) False
- file_ext = 'npz'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'files': <galaxy.model.metadata.MetadataElementSpec object>, 'nfiles': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.HexrdImagesNpz(**kwd)[source]¶
Bases:
Npz
Class describing an HEXRD Images Numpy NPZ file
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('hexrd.images.npz') >>> HexrdImagesNpz().sniff(fname) True >>> fname = get_test_fname('eta_ome.npz') >>> HexrdImagesNpz().sniff(fname) False
- file_ext = 'hexrd.images.npz'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'files': <galaxy.model.metadata.MetadataElementSpec object>, 'nfiles': <galaxy.model.metadata.MetadataElementSpec object>, 'nframes': <galaxy.model.metadata.MetadataElementSpec object>, 'omegas': <galaxy.model.metadata.MetadataElementSpec object>, 'panel_id': <galaxy.model.metadata.MetadataElementSpec object>, 'shape': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.HexrdEtaOmeNpz(**kwd)[source]¶
Bases:
Npz
Class describing an HEXRD Eta-Ome Numpy NPZ file
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('hexrd.eta_ome.npz') >>> HexrdEtaOmeNpz().sniff(fname) True >>> fname = get_test_fname('hexrd.images.npz') >>> HexrdEtaOmeNpz().sniff(fname) False
- file_ext = 'hexrd.eta_ome.npz'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'HKLs': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'files': <galaxy.model.metadata.MetadataElementSpec object>, 'nfiles': <galaxy.model.metadata.MetadataElementSpec object>, 'nframes': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.binary.FITS(**kwd)[source]¶
Bases:
Binary
FITS (Flexible Image Transport System) file data format, widely used in astronomy Represents sky images (in celestial coordinates) and tables https://fits.gsfc.nasa.gov/fits_primer.html
- file_ext = 'fits'¶
- sniff(filename: str) bool [source]¶
Determines whether the file is a FITS file >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname(‘test.fits’) >>> FITS().sniff(fname) True >>> fname = FilePrefix(get_test_fname(‘interval.interval’)) >>> FITS().sniff(fname) False
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'HDUs': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.blast module¶
NCBI BLAST datatypes.
Covers the blastxml
format and the BLAST databases.
- class galaxy.datatypes.blast.BlastXml(**kwd)[source]¶
Bases:
GenericXml
NCBI Blast XML Output data
- file_ext = 'blastxml'¶
- edam_format = 'format_3331'¶
- edam_data = 'data_0857'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is blastxml
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('megablast_xml_parser_test1.blastxml') >>> BlastXml().sniff(fname) True >>> fname = get_test_fname('tblastn_four_human_vs_rhodopsin.blastxml') >>> BlastXml().sniff(fname) True >>> fname = get_test_fname('interval.interval') >>> BlastXml().sniff(fname) False
- static merge(split_files: List[str], output_file: str) None [source]¶
Merging multiple XML files is non-trivial and must be done in subclasses.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.blast.BlastNucDb(**kwd)[source]¶
Bases:
_BlastDb
Class for nucleotide BLAST database files.
- file_ext = 'blastdbn'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.blast.BlastProtDb(**kwd)[source]¶
Bases:
_BlastDb
Class for protein BLAST database files.
- file_ext = 'blastdbp'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.blast.BlastDomainDb(**kwd)[source]¶
Bases:
_BlastDb
Class for domain BLAST database files.
- file_ext = 'blastdbd'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.blast.LastDb(**kwd)[source]¶
Bases:
Data
Class for LAST database files.
- file_ext = 'lastdb'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.blast.BlastNucDb5(**kwd)[source]¶
Bases:
_BlastDb
Class for nucleotide BLAST database files.
- file_ext = 'blastdbn5'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.blast.BlastProtDb5(**kwd)[source]¶
Bases:
_BlastDb
Class for protein BLAST database files.
- file_ext = 'blastdbp5'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.blast.BlastDomainDb5(**kwd)[source]¶
Bases:
_BlastDb
Class for domain BLAST database files.
- file_ext = 'blastdbd5'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.checkers module¶
Module proxies galaxy.util.checkers
for backward compatibility.
External datatypes may make use of these functions.
- galaxy.datatypes.checkers.check_bz2(file_path: str, check_content: bool = True) Tuple[bool, bool] [source]¶
- galaxy.datatypes.checkers.check_gzip(file_path: str, check_content: bool = True) Tuple[bool, bool] [source]¶
- galaxy.datatypes.checkers.check_html(name, file_path: bool = True) bool [source]¶
Returns True if the file/string contains HTML code.
- galaxy.datatypes.checkers.check_image(file_path: str)[source]¶
Simple wrapper around image_type to yield a True/False verdict
galaxy.datatypes.chrominfo module¶
- class galaxy.datatypes.chrominfo.ChromInfo(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'len'¶
- metadata_spec: MetadataSpecCollection = {'chrom': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'length': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.constructive_solid_geometry module¶
Constructive Solid Geometry file formats.
- class galaxy.datatypes.constructive_solid_geometry.Ply(**kwd)[source]¶
Bases:
object
The PLY format describes an object as a collection of vertices, faces and other elements, along with properties such as color and normal direction that can be attached to these elements. A PLY file contains the description of exactly one object.
- subtype = ''¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
The structure of a typical PLY file: Header, Vertex List, Face List, (lists of other elements)
- sniff(filename)¶
- class galaxy.datatypes.constructive_solid_geometry.PlyAscii(**kwd)[source]¶
-
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.plyascii') >>> PlyAscii().sniff(fname) True >>> fname = get_test_fname('test.vtkascii') >>> PlyAscii().sniff(fname) False
- file_ext = 'plyascii'¶
- subtype = 'ascii'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'face': <galaxy.model.metadata.MetadataElementSpec object>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object>, 'other_elements': <galaxy.model.metadata.MetadataElementSpec object>, 'vertex': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.constructive_solid_geometry.PlyBinary(**kwd)[source]¶
-
- file_ext = 'plybinary'¶
- subtype = 'binary'¶
- metadata_spec: metadata.MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'face': <galaxy.model.metadata.MetadataElementSpec object>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object>, 'other_elements': <galaxy.model.metadata.MetadataElementSpec object>, 'vertex': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.constructive_solid_geometry.Vtk(**kwd)[source]¶
Bases:
object
The Visualization Toolkit provides a number of source and writer objects to read and write popular data file formats. The Visualization Toolkit also provides some of its own file formats.
There are two different styles of file formats available in VTK. The simplest are the legacy, serial formats that are easy to read and write either by hand or programmatically. However, these formats are less flexible than the XML based file formats which support random access, parallel I/O, and portable data compression and are preferred to the serial VTK file formats whenever possible.
All keyword phrases are written in ASCII form whether the file is binary or ASCII. The binary section of the file (if in binary form) is the data proper; i.e., the numbers that define points coordinates, scalars, cell indices, and so forth.
Binary data must be placed into the file immediately after the newline (’\n’) character from the previous ASCII keyword and parameter sequence.
TODO: only legacy formats are currently supported and support for XML formats should be added.
- subtype = ''¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
VTK files can be either ASCII or binary, with two different styles of file formats: legacy or XML. We’ll assume if the file contains a valid VTK header, then it is a valid VTK file.
- set_structure_metadata(line: str, dataset: DatasetProtocol, dataset_type: Optional[str]) Tuple[DatasetProtocol, Optional[str]] [source]¶
The fourth part of legacy VTK files is the dataset structure. The geometry part describes the geometry and topology of the dataset. This part begins with a line containing the keyword DATASET followed by a keyword describing the type of dataset. Then, depending upon the type of dataset, other keyword/ data combinations define the actual data.
- sniff(filename)¶
- class galaxy.datatypes.constructive_solid_geometry.VtkAscii(**kwd)[source]¶
-
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.vtkascii') >>> VtkAscii().sniff(fname) True >>> fname = get_test_fname('test.vtkbinary') >>> VtkAscii().sniff(fname) False
- file_ext = 'vtkascii'¶
- subtype = 'ASCII'¶
- metadata_spec: metadata.MetadataSpecCollection = {'cells': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dataset_type': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'dimensions': <galaxy.model.metadata.MetadataElementSpec object>, 'field_components': <galaxy.model.metadata.MetadataElementSpec object>, 'field_names': <galaxy.model.metadata.MetadataElementSpec object>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object>, 'lines': <galaxy.model.metadata.MetadataElementSpec object>, 'origin': <galaxy.model.metadata.MetadataElementSpec object>, 'points': <galaxy.model.metadata.MetadataElementSpec object>, 'polygons': <galaxy.model.metadata.MetadataElementSpec object>, 'spacing': <galaxy.model.metadata.MetadataElementSpec object>, 'triangle_strips': <galaxy.model.metadata.MetadataElementSpec object>, 'vertices': <galaxy.model.metadata.MetadataElementSpec object>, 'vtk_version': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.constructive_solid_geometry.VtkBinary(**kwd)[source]¶
-
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.vtkbinary') >>> VtkBinary().sniff(fname) True >>> fname = get_test_fname('test.vtkascii') >>> VtkBinary().sniff(fname) False
- file_ext = 'vtkbinary'¶
- subtype = 'BINARY'¶
- metadata_spec: metadata.MetadataSpecCollection = {'cells': <galaxy.model.metadata.MetadataElementSpec object>, 'dataset_type': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'dimensions': <galaxy.model.metadata.MetadataElementSpec object>, 'field_components': <galaxy.model.metadata.MetadataElementSpec object>, 'field_names': <galaxy.model.metadata.MetadataElementSpec object>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object>, 'lines': <galaxy.model.metadata.MetadataElementSpec object>, 'origin': <galaxy.model.metadata.MetadataElementSpec object>, 'points': <galaxy.model.metadata.MetadataElementSpec object>, 'polygons': <galaxy.model.metadata.MetadataElementSpec object>, 'spacing': <galaxy.model.metadata.MetadataElementSpec object>, 'triangle_strips': <galaxy.model.metadata.MetadataElementSpec object>, 'vertices': <galaxy.model.metadata.MetadataElementSpec object>, 'vtk_version': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.constructive_solid_geometry.STL(**kwd)[source]¶
Bases:
Data
- file_ext = 'stl'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.constructive_solid_geometry.NeperTess(**kwd)[source]¶
Bases:
Text
Neper Tessellation File
Example:
***tess **format format **general dim type **cell number_of_cells
- file_ext = 'neper.tess'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Neper tess format, starts with
***tess
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.neper.tess') >>> NeperTess().sniff(fname) True >>> fname = get_test_fname('test.neper.tesr') >>> NeperTess().sniff(fname) False
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'cells': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'dimension': <galaxy.model.metadata.MetadataElementSpec object>, 'format': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.constructive_solid_geometry.NeperTesr(**kwd)[source]¶
Bases:
Binary
Neper Raster Tessellation File
Example:
***tesr **format format **general dimension size_x size_y [size_z] voxsize_x voxsize_y [voxsize_z] [*origin origin_x origin_y [origin_z]] [*hasvoid has_void] [**cell number_of_cells
- file_ext = 'neper.tesr'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Neper tesr format, starts with
***tesr
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.neper.tesr') >>> NeperTesr().sniff(fname) True >>> fname = get_test_fname('test.neper.tess') >>> NeperTesr().sniff(fname) False
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'cells': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'dimension': <galaxy.model.metadata.MetadataElementSpec object>, 'format': <galaxy.model.metadata.MetadataElementSpec object>, 'origin': <galaxy.model.metadata.MetadataElementSpec object>, 'size': <galaxy.model.metadata.MetadataElementSpec object>, 'voxsize': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.constructive_solid_geometry.NeperPoints(**kwd)[source]¶
Bases:
Text
Neper Position File Neper position format has 1 - 3 floats per line separated by white space.
- file_ext = 'neper.points'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'dimension': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.constructive_solid_geometry.NeperPointsTabular(**kwd)[source]¶
Bases:
NeperPoints
,Tabular
Neper Position File Neper position format has 1 - 3 floats per line separated by TABs.
- file_ext = 'neper.points.tsv'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Tries to determine the number of columns as well as those columns that contain numerical values in the dataset. A skip parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many invalid comment lines should be skipped. Using None for skip will cause skip to be zero, but the first line will be processed as a header. A max_data_lines parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many data lines should be processed to ensure that the non-optional metadata parameters are properly set; if used, optional metadata parameters will be set to None, unless the entire file has already been read. Using None for max_data_lines will process all data lines.
Items of interest:
We treat ‘overwrite’ as always True (we always want to set tabular metadata when called).
If a tabular file has no data, it will have one column of type ‘str’.
We used to check only the first 100 lines when setting metadata and this class’s set_peek() method read the entire file to determine the number of lines in the file. Since metadata can now be processed on cluster nodes, we’ve merged the line count portion of the set_peek() processing here, and we now check the entire contents of the file.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'dimension': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.constructive_solid_geometry.NeperMultiScaleCell(**kwd)[source]¶
Bases:
Text
Neper Multiscale Cell File
- file_ext = 'neper.mscell'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.constructive_solid_geometry.GmshMsh(**kwd)[source]¶
Bases:
Binary
Gmsh Mesh File
- file_ext = 'gmsh.msh'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Gmsh msh format, starts with
$MeshFormat
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.gmsh.msh') >>> GmshMsh().sniff(fname) True >>> fname = get_test_fname('test.neper.tesr') >>> GmshMsh().sniff(fname) False
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'format': <galaxy.model.metadata.MetadataElementSpec object>, 'version': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.constructive_solid_geometry.GmshGeo(**kwd)[source]¶
Bases:
Text
Gmsh geometry File
- file_ext = 'gmsh.geo'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.constructive_solid_geometry.ZsetGeof(**kwd)[source]¶
Bases:
Text
Z-set geof File
- file_ext = 'zset.geof'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.coverage module¶
Coverage datatypes
- class galaxy.datatypes.coverage.LastzCoverage(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'coverage'¶
- metadata_spec: MetadataSpecCollection = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'forwardCol': <galaxy.model.metadata.MetadataElementSpec object>, 'positionCol': <galaxy.model.metadata.MetadataElementSpec object>, 'reverseCol': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.data module¶
- class galaxy.datatypes.data.DatatypeValidation(state: str, message: str)[source]¶
Bases:
object
- static validated() DatatypeValidation [source]¶
- static invalid(message: str) DatatypeValidation [source]¶
- static unvalidated() DatatypeValidation [source]¶
- galaxy.datatypes.data.validate(dataset_instance: DatasetProtocol) DatatypeValidation [source]¶
- galaxy.datatypes.data.get_params_and_input_name(converter, deps: Optional[Dict], target_context: Optional[Dict] = None) Tuple[Dict, str] [source]¶
- class galaxy.datatypes.data.DataMeta(name, bases, dict_)[source]¶
Bases:
type
Metaclass for Data class. Sets up metadata spec.
- class galaxy.datatypes.data.Data(**kwd)[source]¶
Bases:
object
Base class for all datatypes. Implements basic interfaces as well as class methods for metadata.
>>> class DataTest( Data ): ... MetadataElement( name="test" ) ... >>> DataTest.metadata_spec.test.name 'test' >>> DataTest.metadata_spec.test.desc 'test' >>> type( DataTest.metadata_spec.test.param ) <class 'galaxy.model.metadata.MetadataParameter'>
- edam_data = 'data_0006'¶
- edam_format = 'format_1915'¶
- file_ext = 'data'¶
- CHUNKABLE = False¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- primary_file_name = 'index'¶
- dataproviders: Dict[str, Any] = {'base': <function Data.base_dataprovider>, 'chunk': <function Data.chunk_dataprovider>, 'chunk64': <function Data.chunk64_dataprovider>}¶
- classmethod is_datatype_change_allowed() bool [source]¶
Returns the value of the allow_datatype_change class attribute if set in a subclass, or True iff the datatype is not composite.
- dataset_content_needs_grooming(file_name: str) bool [source]¶
This function is called on an output dataset file after the content is initially generated.
- groom_dataset_content(file_name: str) None [source]¶
This function is called on an output dataset file if dataset_content_needs_grooming returns True.
- set_meta(dataset: DatasetProtocol, *, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- missing_meta(dataset: HasMetadata, check: Optional[List] = None, skip: Optional[List] = None) bool [source]¶
Checks for empty metadata values. Returns False if no non-optional metadata is missing and the missing metadata key otherwise. Specifying a list of ‘check’ values will only check those names provided; when used, optionality is ignored Specifying a list of ‘skip’ items will return True even when a named metadata value is missing; when used, optionality is ignored
- to_archive(dataset: DatasetProtocol, name: str = '') Iterable [source]¶
Collect archive paths and file handles that need to be exported when archiving dataset.
- Parameters:
dataset – HistoryDatasetAssociation
name – archive name, in collection context corresponds to collection name(s) and element_identifier, joined by ‘/’, e.g ‘fastq_collection/sample1/forward’
- display_data(trans, dataset: DatasetHasHidProtocol, preview: bool = False, filename: Optional[str] = None, to_ext: Optional[str] = None, **kwd)[source]¶
Displays data in central pane if preview is True, else handles download.
Datatypes should be very careful if overridding this method and this interface between datatypes and Galaxy will likely change.
TOOD: Document alternatives to overridding this method (data providers?).
- display_as_markdown(dataset_instance: DatasetProtocol) str [source]¶
Prepare for embedding dataset into a basic Markdown document.
This is a somewhat experimental interface and should not be implemented on datatypes not tightly tied to a Galaxy version (e.g. datatypes in the Tool Shed).
Speaking very losely - the datatype should should load a bounded amount of data from the supplied dataset instance and prepare for embedding it into Markdown. This should be relatively vanilla Markdown - the result of this is bleached and it should not contain nested Galaxy Markdown directives.
If the data cannot reasonably be displayed, just indicate this and do not throw an exception.
- add_display_app(app_id: str, label: str, file_function: str, links_function: str) None [source]¶
Adds a display app to the datatype. app_id is a unique id label is the primary display label, e.g., display at ‘UCSC’ file_function is a string containing the name of the function that returns a properly formatted display links_function is a string containing the name of the function that returns a list of (link_name,link)
- add_display_application(display_application: DisplayApplication) None [source]¶
New style display applications
- get_display_application(key: str, default: Optional[DisplayApplication] = None) DisplayApplication [source]¶
- get_display_applications_by_dataset(dataset: DatasetProtocol, trans) Dict[str, DisplayApplication] [source]¶
- as_display_type(dataset: DatasetProtocol, type: str, **kwd) Union[IO[str], TextIOWrapper, GzipFile, BZ2File, IO[bytes], str] [source]¶
Returns modified file contents for a particular display type
- get_display_links(dataset: DatasetProtocol, type: str, app, base_url: str, request, target_frame: str = '_blank', **kwd)[source]¶
Returns a list of tuples of (name, link) for a particular display type. No check on ‘access’ permissions is done here - if you can view the dataset, you can also save it or send it to a destination outside of Galaxy, so Galaxy security restrictions do not apply anyway.
- get_converter_types(original_dataset: HasExt, datatypes_registry: Registry) Dict[str, Dict] [source]¶
Returns available converters by type for this dataset
- find_conversion_destination(dataset: DatasetProtocol, accepted_formats: List[str], datatypes_registry, **kwd) Tuple[bool, Optional[str], Any] [source]¶
Returns ( direct_match, converted_ext, existing converted dataset )
- convert_dataset(trans, original_dataset: DatasetHasHidProtocol, target_type: str, return_output: bool = False, visible: bool = True, deps: Optional[Dict] = None, target_context: Optional[Dict] = None, history=None)[source]¶
This function adds a job to the queue to convert a dataset to another type. Returns a message about success/failure.
- after_setting_metadata(dataset: HasClearAssociatedFiles) None [source]¶
This function is called on the dataset after metadata is set.
- before_setting_metadata(dataset: HasClearAssociatedFiles) None [source]¶
This function is called on the dataset before metadata is set.
- property writable_files¶
- property has_resolution¶
- matches_any(target_datatypes: List[Any]) bool [source]¶
Check if this datatype is of any of the target_datatypes or is a subtype thereof.
- static merge(split_files: List[str], output_file: str) None [source]¶
Merge files with copy.copyfileobj() will not hit the max argument limitation of cat. gz and bz2 files are also working.
- has_dataprovider(data_format: str) bool [source]¶
Returns True if data_format is available in dataproviders.
- dataprovider(dataset: DatasetProtocol, data_format: str, **settings)[source]¶
Base dataprovider factory for all datatypes that returns the proper provider for the given data_format or raises a NoProviderAvailable.
- validate(dataset: DatasetProtocol, **kwd) DatatypeValidation [source]¶
- base_dataprovider(dataset: DatasetProtocol, **settings) DataProvider [source]¶
- chunk_dataprovider(dataset: DatasetProtocol, **settings) ChunkDataProvider [source]¶
- chunk64_dataprovider(dataset: DatasetProtocol, **settings) Base64ChunkDataProvider [source]¶
- class galaxy.datatypes.data.Text(**kwd)[source]¶
Bases:
Data
- edam_format = 'format_2330'¶
- file_ext = 'txt'¶
- line_class = 'line'¶
- set_meta(dataset: DatasetProtocol, *, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- estimate_file_lines(dataset: DatasetProtocol) Optional[int] [source]¶
Perform a rough estimate by extrapolating number of lines from a small read.
- count_data_lines(dataset: HasFileName) Optional[int] [source]¶
Count the number of lines of data in dataset, skipping all blank lines and comments.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- classmethod split(input_datasets: List, subdir_generator_function: Callable, split_params: Dict) None [source]¶
Split the input files by line.
- line_dataprovider(dataset: DatasetProtocol, **settings) FilteredLineDataProvider [source]¶
Returns an iterator over the dataset’s lines (that have been stripped) optionally excluding blank lines and lines that start with a comment character.
- regex_line_dataprovider(dataset: DatasetProtocol, **settings) RegexLineDataProvider [source]¶
Returns an iterator over the dataset’s lines optionally including/excluding lines that match one or more regex filters.
- dataproviders: Dict[str, Any] = {'base': <function Data.base_dataprovider>, 'chunk': <function Data.chunk_dataprovider>, 'chunk64': <function Data.chunk64_dataprovider>, 'line': <function Text.line_dataprovider>, 'regex-line': <function Text.regex_line_dataprovider>}¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.data.Directory(**kwd)[source]¶
Bases:
Data
Class representing a directory of files.
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.data.GenericAsn1(**kwd)[source]¶
Bases:
Text
Class for generic ASN.1 text format
- edam_data = 'data_0849'¶
- edam_format = 'format_1966'¶
- file_ext = 'asn1'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.data.LineCount(**kwd)[source]¶
Bases:
Text
Dataset contains a single line with a single integer that denotes the line count for a related dataset. Used for custom builds.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.data.Newick(**kwd)[source]¶
Bases:
Text
New Hampshire/Newick Format
- edam_data = 'data_0872'¶
- edam_format = 'format_1910'¶
- file_ext = 'newick'¶
- sniff(filename: str) bool [source]¶
Returning false as the newick format is too general and cannot be sniffed.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.data.Nexus(**kwd)[source]¶
Bases:
Text
Nexus format as used By Paup, Mr Bayes, etc
- edam_data = 'data_0872'¶
- edam_format = 'format_1912'¶
- file_ext = 'nex'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
All Nexus Files Simply puts a ‘#NEXUS’ in its first line
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- galaxy.datatypes.data.get_file_peek(file_name, width=256, line_count=5, skipchars=None, line_wrap=True)[source]¶
Returns the first line_count lines wrapped to width.
>>> def assert_peek_is(file_name, expected, *args, **kwd): ... path = get_test_fname(file_name) ... peek = get_file_peek(path, *args, **kwd) ... assert peek == expected, "%s != %s" % (peek, expected) >>> assert_peek_is('0_nonewline', u'0') >>> assert_peek_is('0.txt', u'0\n') >>> assert_peek_is('4.bed', u'chr22\t30128507\t31828507\tuc003bnx.1_cds_2_0_chr22_29227_f\t0\t+\n', line_count=1) >>> assert_peek_is('1.bed', u'chr1\t147962192\t147962580\tCCDS989.1_cds_0_0_chr1_147962193_r\t0\t-\nchr1\t147984545\t147984630\tCCDS990.1_cds_0_0_chr1_147984546_f\t0\t+\n', line_count=2)
galaxy.datatypes.flow module¶
Flow analysis datatypes.
- class galaxy.datatypes.flow.FCS(**kwd)[source]¶
Bases:
Binary
Class describing an FCS binary file
- file_ext = 'fcs'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Checking if the file is in FCS format. Should read FCS2.0, FCS3.0 and FCS3.1
Based on flowcore: https://github.com/RGLab/flowCore/blob/27141b792ad65ae8bd0aeeef26e757c39cdaefe7/R/IO.R#L667
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
galaxy.datatypes.genetics module¶
rgenetics datatypes Use at your peril Ross Lazarus for the rgenetics and galaxy projects
genome graphs datatypes derived from Interval datatypes genome graphs datasets have a header row with appropriate columnames The first column is always the marker - eg columname = rs, first row= rs12345 if the rows are snps subsequent row values are all numeric ! Will fail if any non numeric (eg ‘+’ or ‘NA’) values ross lazarus for rgenetics august 20 2007
- class galaxy.datatypes.genetics.GenomeGraphs(**kwd)[source]¶
Bases:
Tabular
Tab delimited data containing a marker id and any number of numeric values
- file_ext = 'gg'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Tries to determine the number of columns as well as those columns that contain numerical values in the dataset. A skip parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many invalid comment lines should be skipped. Using None for skip will cause skip to be zero, but the first line will be processed as a header. A max_data_lines parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many data lines should be processed to ensure that the non-optional metadata parameters are properly set; if used, optional metadata parameters will be set to None, unless the entire file has already been read. Using None for max_data_lines will process all data lines.
Items of interest:
We treat ‘overwrite’ as always True (we always want to set tabular metadata when called).
If a tabular file has no data, it will have one column of type ‘str’.
We used to check only the first 100 lines when setting metadata and this class’s set_peek() method read the entire file to determine the number of lines in the file. Since metadata can now be processed on cluster nodes, we’ve merged the line count portion of the set_peek() processing here, and we now check the entire contents of the file.
- as_ucsc_display_file(dataset: DatasetProtocol, **kwd) Union[IO[str], TextIOWrapper, GzipFile, BZ2File, IO[bytes], str] [source]¶
Returns file
- ucsc_links(dataset: DatasetProtocol, type: str, app, base_url: str, request) List [source]¶
from the ever-helpful angie hinrichs angie@soe.ucsc.edu a genome graphs call looks like this
http://genome.ucsc.edu/cgi-bin/hgGenome?clade=mammal&org=Human&db=hg18&hgGenome_dataSetName=dname &hgGenome_dataSetDescription=test&hgGenome_formatType=best%20guess&hgGenome_markerType=best%20guess &hgGenome_columnLabels=best%20guess&hgGenome_maxVal=&hgGenome_labelVals= &hgGenome_maxGapToFill=25000000&hgGenome_uploadFile=http://galaxy.esphealth.org/datasets/333/display/index &hgGenome_doSubmitUpload=submit
Galaxy gives this for an interval file
http://genome.ucsc.edu/cgi-bin/hgTracks?db=hg18&position=chr1:1-1000&hgt.customText= http%3A%2F%2Fgalaxy.esphealth.org%2Fdisplay_as%3Fid%3D339%26display_app%3Ducsc
- make_html_table(dataset: DatasetProtocol, **kwargs) str [source]¶
Create HTML table, used for displaying peek
- validate(dataset: DatasetProtocol, **kwd) DatatypeValidation [source]¶
Validate a gg file - all numeric after header row
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in gg format
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'test_space.txt' ) >>> GenomeGraphs().sniff( fname ) False >>> fname = get_test_fname( '1.gg' ) >>> GenomeGraphs().sniff( fname ) True
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'markerCol': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.genetics.rgTabList(**kwd)[source]¶
Bases:
Tabular
for sampleid and for featureid lists of exclusions or inclusions in the clean tool featureid subsets on statistical criteria -> specialized display such as gg
- file_ext = 'rgTList'¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.rgSampleList(**kwd)[source]¶
Bases:
rgTabList
for sampleid exclusions or inclusions in the clean tool output from QC eg excess het, gender error, ibd pair member,eigen outlier,excess mendel errors,… since they can be uploaded, should be flexible but they are persistent at least same infrastructure for expression?
- file_ext = 'rgSList'¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.rgFeatureList(**kwd)[source]¶
Bases:
rgTabList
for featureid lists of exclusions or inclusions in the clean tool output from QC eg low maf, high missingness, bad hwe in controls, excess mendel errors,… featureid subsets on statistical criteria -> specialized display such as gg same infrastructure for expression?
- file_ext = 'rgFList'¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.Rgenetics(**kwd)[source]¶
Bases:
Html
base class to use for rgenetics datatypes derived from html - composite datatype elements stored in extra files path
- file_ext = 'rgenetics'¶
- regenerate_primary_file(dataset: DatasetProtocol) None [source]¶
cannot do this until we are setting metadata
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.SNPMatrix(**kwd)[source]¶
Bases:
Rgenetics
BioC SNPMatrix Rgenetics data collections
- file_ext = 'snpmatrix'¶
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.Lped(**kwd)[source]¶
Bases:
Rgenetics
linkage pedigree (ped,map) Rgenetics data collections
- file_ext = 'lped'¶
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.Pphe(**kwd)[source]¶
Bases:
Rgenetics
Plink phenotype file - header must have FID IID… Rgenetics data collections
- file_ext = 'pphe'¶
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.Fphe(**kwd)[source]¶
Bases:
Rgenetics
fbat pedigree file - mad format with ! as first char on header row Rgenetics data collections
- file_ext = 'fphe'¶
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.Phe(**kwd)[source]¶
Bases:
Rgenetics
Phenotype file
- file_ext = 'phe'¶
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.Fped(**kwd)[source]¶
Bases:
Rgenetics
FBAT pedigree format - single file, map is header row of rs numbers. Strange. Rgenetics data collections
- file_ext = 'fped'¶
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.Pbed(**kwd)[source]¶
Bases:
Rgenetics
Plink Binary compressed 2bit/geno Rgenetics data collections
- file_ext = 'pbed'¶
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.ldIndep(**kwd)[source]¶
Bases:
Rgenetics
LD (a good measure of redundancy of information) depleted Plink Binary compressed 2bit/geno This is really a plink binary, but some tools work better with less redundancy so are constrained to these files
- file_ext = 'ldreduced'¶
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.Eigenstratgeno(**kwd)[source]¶
Bases:
Rgenetics
Eigenstrat format - may be able to get rid of this if we move to shellfish Rgenetics data collections
- file_ext = 'eigenstratgeno'¶
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.Eigenstratpca(**kwd)[source]¶
Bases:
Rgenetics
Eigenstrat PCA file for case control adjustment Rgenetics data collections
- file_ext = 'eigenstratpca'¶
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.Snptest(**kwd)[source]¶
Bases:
Rgenetics
BioC snptest Rgenetics data collections
- file_ext = 'snptest'¶
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.IdeasPre(**kwd)[source]¶
Bases:
Html
This datatype defines the input format required by IDEAS: https://academic.oup.com/nar/article/44/14/6721/2468150 The IDEAS preprocessor tool produces an output using this format. The extra_files_path of the primary input dataset contains the following files and directories. - chromosome_windows.txt (optional) - chromosomes.bed (optional) - IDEAS_input_config.txt - compressed archived tmp directory containing a number of compressed bed files.
- file_ext = 'ideaspre'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'chrom_bed': <galaxy.model.metadata.MetadataElementSpec object>, 'chrom_windows': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'input_config': <galaxy.model.metadata.MetadataElementSpec object>, 'tmp_archive': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.Pheno(**kwd)[source]¶
Bases:
Tabular
base class for pheno files
- file_ext = 'pheno'¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.RexpBase(**kwd)[source]¶
Bases:
Html
base class for BioC data structures in Galaxy must be constructed with the pheno data in place since that goes into the metadata for each instance
- file_ext = 'rexpbase'¶
- html_table = None¶
- generate_primary_file(dataset: HasExtraFilesAndMetadata) str [source]¶
This is called only at upload to write the html file cannot rename the datasets here - they come with the default unfortunately
- get_phecols(phenolist: List, maxConc: int = 20) List [source]¶
sept 2009: cannot use whitespace to split - make a more complex structure here and adjust the methods that rely on this structure return interesting phenotype column names for an rexpression eset or affybatch to use in array subsetting and so on. Returns a data structure for a dynamic Galaxy select parameter. A column with only 1 value doesn’t change, so is not interesting for analysis. A column with a different value in every row is equivalent to a unique identifier so is also not interesting for anova or limma analysis - both these are removed after the concordance (count of unique terms) is constructed for each column. Then a complication - each remaining pair of columns is tested for redundancy - if two columns are always paired, then only one is needed :)
- get_pheno(dataset)[source]¶
expects a .pheno file in the extra_files_dir - ugh note that R is wierd and adds the row.name in the header so the columns are all wrong - unless you tell it not to. A file can be written as write.table(file=’foo.pheno’,pData(foo),sep=’ ‘,quote=F,row.names=F)
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
expects a .pheno file in the extra_files_dir - ugh note that R is weird and does not include the row.name in the header. why?
- get_file_peek(filename: str) str [source]¶
Read and return the first max_lines. (Can’t really peek at a filename - need the extra_files_path and such?)
- regenerate_primary_file(dataset: DatasetProtocol) None [source]¶
cannot do this until we are setting metadata
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
NOTE we apply the tabular machinary to the phenodata extracted from a BioC eSet or affybatch.
- make_html_table(pp: str = 'nothing supplied from peek\n') str [source]¶
Create HTML table, used for displaying peek
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.Affybatch(**kwd)[source]¶
Bases:
RexpBase
derived class for BioC data structures in Galaxy
- file_ext = 'affybatch'¶
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.Eset(**kwd)[source]¶
Bases:
RexpBase
derived class for BioC data structures in Galaxy
- file_ext = 'eset'¶
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.MAlist(**kwd)[source]¶
Bases:
RexpBase
derived class for BioC data structures in Galaxy
- file_ext = 'malist'¶
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.LinkageStudies(**kwd)[source]¶
Bases:
Text
superclass for classical linkage analysis suites
- test_files = ['linkstudies.allegro_fparam', 'linkstudies.alohomora_gts', 'linkstudies.linkage_datain', 'linkstudies.linkage_map']¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.genetics.GenotypeMatrix(**kwd)[source]¶
Bases:
LinkageStudies
Sample matrix of genotypes - GTs as columns
- file_ext = 'alohomora_gts'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
>>> classname = GenotypeMatrix >>> from galaxy.datatypes.sniff import get_test_fname >>> extn_true = classname().file_ext >>> file_true = get_test_fname("linkstudies." + extn_true) >>> classname().sniff(file_true) True >>> false_files = list(LinkageStudies.test_files) >>> false_files.remove("linkstudies." + extn_true) >>> result_true = [] >>> for fname in false_files: ... file_false = get_test_fname(fname) ... res = classname().sniff(file_false) ... if res: ... result_true.append(fname) >>> >>> result_true []
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.genetics.MarkerMap(**kwd)[source]¶
Bases:
LinkageStudies
Map of genetic markers including physical and genetic distance Common input format for linkage programs
chrom, genetic pos, markername, physical pos, Nr
- file_ext = 'linkage_map'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
>>> classname = MarkerMap >>> from galaxy.datatypes.sniff import get_test_fname >>> extn_true = classname().file_ext >>> file_true = get_test_fname("linkstudies." + extn_true) >>> classname().sniff(file_true) True >>> false_files = list(LinkageStudies.test_files) >>> false_files.remove("linkstudies." + extn_true) >>> result_true = [] >>> for fname in false_files: ... file_false = get_test_fname(fname) ... res = classname().sniff(file_false) ... if res: ... result_true.append(fname) >>> >>> result_true []
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.genetics.DataIn(**kwd)[source]¶
Bases:
LinkageStudies
Common linkage input file for intermarker distances and recombination rates
- file_ext = 'linkage_datain'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
>>> classname = DataIn >>> from galaxy.datatypes.sniff import get_test_fname >>> extn_true = classname().file_ext >>> file_true = get_test_fname("linkstudies." + extn_true) >>> classname().sniff(file_true) True >>> false_files = list(LinkageStudies.test_files) >>> false_files.remove("linkstudies." + extn_true) >>> result_true = [] >>> for fname in false_files: ... file_false = get_test_fname(fname) ... res = classname().sniff(file_false) ... if res: ... result_true.append(fname) >>> >>> result_true []
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.genetics.AllegroLOD(**kwd)[source]¶
Bases:
LinkageStudies
Allegro output format for LOD scores
- file_ext = 'allegro_fparam'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
>>> classname = AllegroLOD >>> from galaxy.datatypes.sniff import get_test_fname >>> extn_true = classname().file_ext >>> file_true = get_test_fname("linkstudies." + extn_true) >>> classname().sniff(file_true) True >>> false_files = list(LinkageStudies.test_files) >>> false_files.remove("linkstudies." + extn_true) >>> result_true = [] >>> for fname in false_files: ... file_false = get_test_fname(fname) ... res = classname().sniff(file_false) ... if res: ... result_true.append(fname) >>> >>> result_true []
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
galaxy.datatypes.gis module¶
GIS classes
- class galaxy.datatypes.gis.Shapefile(**kwd)[source]¶
Bases:
Binary
The Shapefile data format: For more information please see http://en.wikipedia.org/wiki/Shapefile
- file_ext = 'shp'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.goldenpath module¶
- class galaxy.datatypes.goldenpath.GoldenPath(**kwd)[source]¶
Bases:
Tabular
Class describing NCBI’s Golden Path assembly format
- edam_format = 'format_3693'¶
- file_ext = 'agp'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Tries to determine the number of columns as well as those columns that contain numerical values in the dataset. A skip parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many invalid comment lines should be skipped. Using None for skip will cause skip to be zero, but the first line will be processed as a header. A max_data_lines parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many data lines should be processed to ensure that the non-optional metadata parameters are properly set; if used, optional metadata parameters will be set to None, unless the entire file has already been read. Using None for max_data_lines will process all data lines.
Items of interest:
We treat ‘overwrite’ as always True (we always want to set tabular metadata when called).
If a tabular file has no data, it will have one column of type ‘str’.
We used to check only the first 100 lines when setting metadata and this class’s set_peek() method read the entire file to determine the number of lines in the file. Since metadata can now be processed on cluster nodes, we’ve merged the line count portion of the set_peek() processing here, and we now check the entire contents of the file.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Checks for and does cursory validation on data that looks like AGP
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('eg1.agp') >>> GoldenPath().sniff(fname) True >>> fname = get_test_fname('eg2.agp') >>> GoldenPath().sniff(fname) True >>> fname = get_test_fname('1.bed') >>> GoldenPath().sniff(fname) False >>> fname = get_test_fname('2.tabular') >>> GoldenPath().sniff(fname) False
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- exception galaxy.datatypes.goldenpath.AGPError(fname, line_number, message='Error in AGP file.')[source]¶
Bases:
Exception
Exception raised for AGP related errors.
- class galaxy.datatypes.goldenpath.AGPFile(in_file)[source]¶
Bases:
object
A class storing the contents of an AGP v2.1 file. https://www.ncbi.nlm.nih.gov/assembly/agp/AGP_Specification/
The class is able to read new AGP lines in order to sequentially build the complete file.
The class should be capable of checking the validity of the file, as well as writing the AGP contents to a file stream.
- Common abbreviations:
“comp”: AGP component “obj”: AGP object “pid”: AGP part number
- property agp_version¶
- property fname¶
- property num_lines¶
Calculate the number of lines in the current state of the AGP file.
- class galaxy.datatypes.goldenpath.AGPObject(agp_fname, in_agp_line)[source]¶
Bases:
object
Represents an AGP object. Objects will consist of AGP lines, and have to adhere to certain rules. By organizing AGP lines into the objects that they comprise, we can easily calculate stats about the assembly (the collection of objects).
- property obj¶
- property obj_len¶
- property num_lines¶
- class galaxy.datatypes.goldenpath.AGPLine(fname, line_number, obj, obj_beg, obj_end, pid, comp_type)[source]¶
Bases:
object
An abstract base class representing a single AGP file line. Inheriting subclasses should override or implement new methods to check the validity of a single AFP line. Validity checks that involve multiple lines should not be considered.
- class galaxy.datatypes.goldenpath.AGPSeqLine(fname, line_number, obj, obj_beg, obj_end, pid, comp_type, comp, comp_beg, comp_end, orientation)[source]¶
Bases:
AGPLine
A subclass of AGPLine specifically for AGP lines that represent sequences.
- allowed_orientations = {'+', '-', '0', '?', 'na'}¶
- class galaxy.datatypes.goldenpath.AGPGapLine(fname, line_number, obj, obj_beg, obj_end, pid, comp_type, gap_len, gap_type, linkage, linkage_evidence)[source]¶
Bases:
AGPLine
A subclass of AGPLine specifically for AGP lines that represent sequence gaps.
- allowed_linkage_types = {'no', 'yes'}¶
- allowed_gap_types = {'centromere', 'contamination', 'contig', 'heterochromatin', 'repeat', 'scaffold', 'short_arm', 'telomere'}¶
- allowed_evidence_types = {'align_genus', 'align_trnscpt', 'align_xgenus', 'clone_contig', 'map', 'na', 'paired-ends', 'pcr', 'proximity_ligation', 'strobe', 'unspecified', 'within_clone'}¶
galaxy.datatypes.graph module¶
Graph content classes.
- class galaxy.datatypes.graph.Xgmml(**kwd)[source]¶
Bases:
GenericXml
XGMML graph format (http://wiki.cytoscape.org/Cytoscape_User_Manual/Network_Formats).
- file_ext = 'xgmml'¶
- static merge(split_files: List[str], output_file: str) None [source]¶
Merging multiple XML files is non-trivial and must be done in subclasses.
- node_edge_dataprovider(dataset: DatasetProtocol, **settings) XGMMLGraphDataProvider [source]¶
- dataproviders: Dict[str, Any] = {'base': <function Data.base_dataprovider>, 'chunk': <function Data.chunk_dataprovider>, 'chunk64': <function Data.chunk64_dataprovider>, 'line': <function Text.line_dataprovider>, 'node-edge': <function Xgmml.node_edge_dataprovider>, 'regex-line': <function Text.regex_line_dataprovider>, 'xml': <function GenericXml.xml_dataprovider>}¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.graph.Sif(**kwd)[source]¶
Bases:
Tabular
SIF graph format (http://wiki.cytoscape.org/Cytoscape_User_Manual/Network_Formats).
First column: node id Second column: relationship type Third to Nth column: target ids for link
- file_ext = 'sif'¶
- static merge(split_files: List[str], output_file: str) None [source]¶
Merge files with copy.copyfileobj() will not hit the max argument limitation of cat. gz and bz2 files are also working.
- node_edge_dataprovider(dataset: DatasetProtocol, **settings) SIFGraphDataProvider [source]¶
- dataproviders: Dict[str, Any] = {'base': <function Data.base_dataprovider>, 'chunk': <function Data.chunk_dataprovider>, 'chunk64': <function Data.chunk64_dataprovider>, 'column': <function TabularData.column_dataprovider>, 'dataset-column': <function TabularData.dataset_column_dataprovider>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider>, 'dict': <function TabularData.dict_dataprovider>, 'line': <function Text.line_dataprovider>, 'node-edge': <function Sif.node_edge_dataprovider>, 'regex-line': <function Text.regex_line_dataprovider>}¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.graph.XGMMLGraphDataProvider(source, selector=None, max_depth=None, **kwargs)[source]¶
Bases:
XMLDataProvider
Provide two lists: nodes, edges:
'nodes': contains objects of the form: { 'id' : <some string id>, 'data': <any extra data> } 'edges': contains objects of the form: { 'source' : <an index into nodes>, 'target': <an index into nodes>, 'data': <any extra data> }
- class galaxy.datatypes.graph.SIFGraphDataProvider(source, indeces=None, column_count=None, column_types=None, parsers=None, parse_columns=True, deliminator='\t', filters=None, **kwargs)[source]¶
Bases:
ColumnarDataProvider
Provide two lists: nodes, edges:
'nodes': contains objects of the form: { 'id' : <some string id>, 'data': <any extra data> } 'edges': contains objects of the form: { 'source' : <an index into nodes>, 'target': <an index into nodes>, 'data': <any extra data> }
- settings: Dict[str, str] = {'column_count': 'int', 'column_types': 'list:str', 'comment_char': 'str', 'deliminator': 'str', 'filters': 'list:str', 'indeces': 'list:int', 'invert': 'bool', 'limit': 'int', 'offset': 'int', 'parse_columns': 'bool', 'provide_blank': 'bool', 'regex_list': 'list:escaped', 'strip_lines': 'bool', 'strip_newlines': 'bool'}¶
galaxy.datatypes.hdf5 module¶
Composite datatype for the HDF5SummarizedExperiment R data object.
This datatype was created for use with the iSEE interactive tool.
- class galaxy.datatypes.hdf5.HDF5SummarizedExperiment(**kwd)[source]¶
Bases:
Data
Composite datatype to represent HDF5SummarizedExperiment objects.
A lightweight shell file se.rds is read into memory by R, and provides an interface to the much larger assays.h5 files which contains the experiment data.
Within R, the HDF5SummarizedExperiment object is conventionally referenced by the parent directory name of these two files. In Galaxy tool commands, the parent directory can be accessed through param_name.extra_files_path.
- file_ext = 'rdata.se'¶
- init_meta(dataset: HasMetadata, copy_from: Optional[HasMetadata] = None) None [source]¶
Override parent init metadata.
- generate_primary_file(dataset: HasExtraFilesAndMetadata) str [source]¶
Generate primary file to represent dataset.
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.images module¶
Image classes
- class galaxy.datatypes.images.Image(**kwd)[source]¶
Bases:
Data
Class describing an image
- edam_data = 'data_2968'¶
- edam_format = 'format_3547'¶
- file_ext = ''¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Jpg(**kwd)[source]¶
Bases:
Image
- edam_format = 'format_3579'¶
- file_ext = 'jpg'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Png(**kwd)[source]¶
Bases:
Image
- edam_format = 'format_3603'¶
- file_ext = 'png'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Tiff(**kwd)[source]¶
Bases:
Image
- edam_format = 'format_3591'¶
- file_ext = 'tiff'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.OMETiff(**kwd)[source]¶
Bases:
Tiff
- file_ext = 'ome.tiff'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, metadata_tmp_files_dir: Optional[str] = None, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'offsets': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Hamamatsu(**kwd)[source]¶
Bases:
Image
- file_ext = 'vms'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Mirax(**kwd)[source]¶
Bases:
Image
- file_ext = 'mrxs'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Sakura(**kwd)[source]¶
Bases:
Image
- file_ext = 'svslide'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Nrrd(**kwd)[source]¶
Bases:
Image
- file_ext = 'nrrd'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Bmp(**kwd)[source]¶
Bases:
Image
- edam_format = 'format_3592'¶
- file_ext = 'bmp'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Gif(**kwd)[source]¶
Bases:
Image
- edam_format = 'format_3467'¶
- file_ext = 'gif'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Im(**kwd)[source]¶
Bases:
Image
- edam_format = 'format_3593'¶
- file_ext = 'im'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Pcd(**kwd)[source]¶
Bases:
Image
- edam_format = 'format_3594'¶
- file_ext = 'pcd'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Pcx(**kwd)[source]¶
Bases:
Image
- edam_format = 'format_3595'¶
- file_ext = 'pcx'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Ppm(**kwd)[source]¶
Bases:
Image
- edam_format = 'format_3596'¶
- file_ext = 'ppm'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Psd(**kwd)[source]¶
Bases:
Image
- edam_format = 'format_3597'¶
- file_ext = 'psd'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Xbm(**kwd)[source]¶
Bases:
Image
- edam_format = 'format_3598'¶
- file_ext = 'xbm'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Xpm(**kwd)[source]¶
Bases:
Image
- edam_format = 'format_3599'¶
- file_ext = 'xpm'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Rgb(**kwd)[source]¶
Bases:
Image
- edam_format = 'format_3600'¶
- file_ext = 'rgb'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Pbm(**kwd)[source]¶
Bases:
Image
- edam_format = 'format_3601'¶
- file_ext = 'pbm'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Pgm(**kwd)[source]¶
Bases:
Image
- edam_format = 'format_3602'¶
- file_ext = 'pgm'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Eps(**kwd)[source]¶
Bases:
Image
- edam_format = 'format_3466'¶
- file_ext = 'eps'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Rast(**kwd)[source]¶
Bases:
Image
- edam_format = 'format_3605'¶
- file_ext = 'rast'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Pdf(**kwd)[source]¶
Bases:
Image
- edam_format = 'format_3508'¶
- file_ext = 'pdf'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Tck(**kwd)[source]¶
Bases:
Binary
Tracks file format (.tck) format https://mrtrix.readthedocs.io/en/latest/getting_started/image_data.html#tracks-file-format-tck
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('fibers_sparse_top_6_lines.tck') >>> Tck().sniff( fname ) True >>> fname = get_test_fname('2.txt') >>> Tck().sniff( fname ) False
- file_ext = 'tck'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.images.Trk(**kwd)[source]¶
Bases:
Binary
Track File format (.trk) is the tractography file format. http://trackvis.org/docs/?subsect=fileformat
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('IIT2mean_top_2000bytes.trk') >>> Trk().sniff( fname ) True >>> fname = get_test_fname('2.txt') >>> Trk().sniff( fname ) False
- file_ext = 'trk'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.images.Mrc2014(**kwd)[source]¶
Bases:
Binary
MRC/CCP4 2014 file format (.mrc). https://www.ccpem.ac.uk/mrc_format/mrc2014.php
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('1.mrc') >>> Mrc2014().sniff(fname) True >>> fname = get_test_fname('2.txt') >>> Mrc2014().sniff(fname) False
- file_ext = 'mrc'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Gmaj(**kwd)[source]¶
Bases:
Data
Deprecated class. Exists for limited backwards compatibility.
- edam_format = 'format_3547'¶
- file_ext = 'gmaj.zip'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Analyze75(**kwd)[source]¶
Bases:
Binary
Mayo Analyze 7.5 files http://www.imzml.org
- file_ext = 'analyze75'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Nifti1(**kwd)[source]¶
Bases:
Binary
Nifti1 format https://nifti.nimh.nih.gov/pub/dist/src/niftilib/nifti1.h
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('T1_top_350bytes.nii1') >>> Nifti1().sniff( fname ) True >>> fname = get_test_fname('2.txt') >>> Nifti1().sniff( fname ) False
- file_ext = 'nii1'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.images.Nifti2(**kwd)[source]¶
Bases:
Binary
Nifti2 format https://brainder.org/2015/04/03/the-nifti-2-file-format/
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('avg152T1_LR_nifti2_top_100bytes.nii2') >>> Nifti2().sniff( fname ) True >>> fname = get_test_fname('T1_top_350bytes.nii1') >>> Nifti2().sniff( fname ) False
- file_ext = 'nii2'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.images.Gifti(**kwd)[source]¶
Bases:
GenericXml
Class describing a Gifti format
- file_ext = 'gii'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is a Gifti file
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('Human.colin.R.activations.label.gii') >>> Gifti().sniff(fname) True >>> fname = get_test_fname('interval.interval') >>> Gifti().sniff(fname) False >>> fname = get_test_fname('megablast_xml_parser_test1.blastxml') >>> Gifti().sniff(fname) False >>> fname = get_test_fname('tblastn_four_human_vs_rhodopsin.blastxml') >>> Gifti().sniff(fname) False
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.images.Star(**kwd)[source]¶
Bases:
Text
Base format class for Relion STAR (Self-defining Text Archiving and Retrieval) image files. https://relion.readthedocs.io/en/latest/Reference/Conventions.html
- file_ext = 'star'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Each file must have one or more data blocks. The start of a data block is defined by the keyword
data_
followed by an optional string for identification (e.g.,data_images
). All text before the firstdata_
keyword are comments>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('1.star') >>> Star().sniff(fname) True >>> fname = get_test_fname('interval.interval') >>> Star().sniff(fname) False
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.images.Html(**kwd)[source]¶
Bases:
Html
Deprecated class. This class should not be used anymore, but the galaxy.datatypes.text:Html one. This is for backwards compatibilities only.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.images.Laj(**kwd)[source]¶
Bases:
Text
Deprecated class. Exists for limited backwards compatibility.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.interval module¶
Interval datatypes
- class galaxy.datatypes.interval.Interval(**kwd)[source]¶
Bases:
Tabular
Tab delimited data containing interval information
- edam_data = 'data_3002'¶
- edam_format = 'format_3475'¶
- file_ext = 'interval'¶
- line_class = 'region'¶
- set_meta(dataset: DatasetProtocol, *, overwrite: bool = True, first_line_is_header: bool = False, **kwd) None [source]¶
Tries to guess from the line the location number of the column for the chromosome, region start-end and strand
- get_estimated_display_viewport(dataset: DatasetProtocol, chrom_col: Optional[int] = None, start_col: Optional[int] = None, end_col: Optional[int] = None) Tuple[Optional[str], Optional[str], Optional[str]] [source]¶
Return a chrom, start, stop tuple for viewing a file.
- as_ucsc_display_file(dataset: DatasetProtocol, **kwd) Union[IO[str], TextIOWrapper, GzipFile, BZ2File, IO[bytes], str] [source]¶
Returns file contents with only the bed data
- ucsc_links(dataset: DatasetProtocol, type: str, app, base_url: str, request) List [source]¶
Generate links to UCSC genome browser sites based on the dbkey and content of dataset.
- validate(dataset: DatasetProtocol, **kwd) DatatypeValidation [source]¶
Validate an interval file using the bx GenomicIntervalReader
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Checks for ‘intervalness’
This format is mostly used by galaxy itself. Valid interval files should include a valid header comment, but this seems to be loosely regulated.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'test_space.txt' ) >>> Interval().sniff( fname ) False >>> fname = get_test_fname( 'interval.interval' ) >>> Interval().sniff( fname ) True
- genomic_region_dataprovider(dataset: DatasetProtocol, **settings) GenomicRegionDataProvider [source]¶
- genomic_region_dict_dataprovider(dataset: DatasetProtocol, **settings) GenomicRegionDataProvider [source]¶
- interval_dataprovider(dataset: DatasetProtocol, **settings) IntervalDataProvider [source]¶
- interval_dict_dataprovider(dataset: DatasetProtocol, **settings) IntervalDataProvider [source]¶
- dataproviders: Dict[str, Any] = {'base': <function Data.base_dataprovider>, 'chunk': <function Data.chunk_dataprovider>, 'chunk64': <function Data.chunk64_dataprovider>, 'column': <function TabularData.column_dataprovider>, 'dataset-column': <function TabularData.dataset_column_dataprovider>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider>, 'dict': <function TabularData.dict_dataprovider>, 'genomic-region': <function Interval.genomic_region_dataprovider>, 'genomic-region-dict': <function Interval.genomic_region_dict_dataprovider>, 'interval': <function Interval.interval_dataprovider>, 'interval-dict': <function Interval.interval_dict_dataprovider>, 'line': <function Text.line_dataprovider>, 'regex-line': <function Text.regex_line_dataprovider>}¶
- metadata_spec: MetadataSpecCollection = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.interval.BedGraph(**kwd)[source]¶
Bases:
Interval
Tab delimited chrom/start/end/datavalue dataset
- edam_format = 'format_3583'¶
- file_ext = 'bedgraph'¶
- as_ucsc_display_file(dataset: DatasetProtocol, **kwd) Union[IO[str], TextIOWrapper, GzipFile, BZ2File, IO[bytes], str] [source]¶
Returns file contents as is with no modifications. TODO: this is a functional stub and will need to be enhanced moving forward to provide additional support for bedgraph.
- get_estimated_display_viewport(dataset: DatasetProtocol, chrom_col: Optional[int] = 0, start_col: Optional[int] = 1, end_col: Optional[int] = 2) Tuple[Optional[str], Optional[str], Optional[str]] [source]¶
Set viewport based on dataset’s first 100 lines.
- metadata_spec: MetadataSpecCollection = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.interval.Bed(**kwd)[source]¶
Bases:
Interval
Tab delimited data in BED format
- edam_format = 'format_3003'¶
- file_ext = 'bed'¶
- check_required_metadata = True¶
- column_names = ['Chrom', 'Start', 'End', 'Name', 'Score', 'Strand', 'ThickStart', 'ThickEnd', 'ItemRGB', 'BlockCount', 'BlockSizes', 'BlockStarts']¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Sets the metadata information for datasets previously determined to be in bed format.
- as_ucsc_display_file(dataset: DatasetProtocol, **kwd) Union[IO[str], TextIOWrapper, GzipFile, BZ2File, IO[bytes], str] [source]¶
Returns file contents with only the bed data. If bed 6+, treat as interval.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Checks for ‘bedness’
BED lines have three required fields and nine additional optional fields. The number of fields per line must be consistent throughout any single set of data in an annotation track. The order of the optional fields is binding: lower-numbered fields must always be populated if higher-numbered fields are used. The data type of all 12 columns is: 1-str, 2-int, 3-int, 4-str, 5-int, 6-str, 7-int, 8-int, 9-int or list, 10-int, 11-list, 12-list
For complete details see http://genome.ucsc.edu/FAQ/FAQformat#format1
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'test_tab.bed' ) >>> Bed().sniff( fname ) True >>> fname = get_test_fname( 'interv1.bed' ) >>> Bed().sniff( fname ) True >>> fname = get_test_fname( 'complete.bed' ) >>> Bed().sniff( fname ) True
- metadata_spec: MetadataSpecCollection = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.interval.ProBed(**kwd)[source]¶
Bases:
Bed
Tab delimited data in proBED format - adaptation of BED for proteomics data.
- edam_format = 'format_3827'¶
- file_ext = 'probed'¶
- column_names = ['Chrom', 'Start', 'End', 'Name', 'Score', 'Strand', 'ThickStart', 'ThickEnd', 'ItemRGB', 'BlockCount', 'BlockSizes', 'BlockStarts', 'ProteinAccession', 'PeptideSequence', 'Uniqueness', 'GenomeReferenceVersion', 'PsmScore', 'Fdr', 'Modifications', 'Charge', 'ExpMassToCharge', 'CalcMassToCharge', 'PsmRank', 'DatasetID', 'Uri']¶
- metadata_spec: MetadataSpecCollection = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.interval.BedStrict(**kwd)[source]¶
Bases:
Bed
Tab delimited data in strict BED format - no non-standard columns allowed
- edam_format = 'format_3584'¶
- file_ext = 'bedstrict'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Sets the metadata information for datasets previously determined to be in bed format.
- metadata_spec: MetadataSpecCollection = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.interval.Bed6(**kwd)[source]¶
Bases:
BedStrict
Tab delimited data in strict BED format - no non-standard columns allowed; column count forced to 6
- edam_format = 'format_3585'¶
- file_ext = 'bed6'¶
- metadata_spec: MetadataSpecCollection = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.interval.Bed12(**kwd)[source]¶
Bases:
BedStrict
Tab delimited data in strict BED format - no non-standard columns allowed; column count forced to 12
- edam_format = 'format_3586'¶
- file_ext = 'bed12'¶
- metadata_spec: MetadataSpecCollection = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.interval.Gff(**kwd)[source]¶
Bases:
Tabular
,_RemoteCallMixin
Tab delimited data in Gff format
- edam_data = 'data_1255'¶
- edam_format = 'format_2305'¶
- file_ext = 'gff'¶
- valid_gff_frame = ['.', '0', '1', '2']¶
- column_names = ['Seqname', 'Source', 'Feature', 'Start', 'End', 'Score', 'Strand', 'Frame', 'Group']¶
- data_sources: Dict[str, str] = {'data': 'interval_index', 'feature_search': 'fli', 'index': 'bigwig'}¶
- set_attribute_metadata(dataset: DatasetProtocol) None [source]¶
Sets metadata elements for dataset’s attributes.
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Tries to determine the number of columns as well as those columns that contain numerical values in the dataset. A skip parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many invalid comment lines should be skipped. Using None for skip will cause skip to be zero, but the first line will be processed as a header. A max_data_lines parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many data lines should be processed to ensure that the non-optional metadata parameters are properly set; if used, optional metadata parameters will be set to None, unless the entire file has already been read. Using None for max_data_lines will process all data lines.
Items of interest:
We treat ‘overwrite’ as always True (we always want to set tabular metadata when called).
If a tabular file has no data, it will have one column of type ‘str’.
We used to check only the first 100 lines when setting metadata and this class’s set_peek() method read the entire file to determine the number of lines in the file. Since metadata can now be processed on cluster nodes, we’ve merged the line count portion of the set_peek() processing here, and we now check the entire contents of the file.
- get_estimated_display_viewport(dataset: DatasetProtocol) Tuple[Optional[str], Optional[str], Optional[str]] [source]¶
Return a chrom, start, stop tuple for viewing a file. There are slight differences between gff 2 and gff 3 formats. This function should correctly handle both…
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in gff format
GFF lines have nine required fields that must be tab-separated.
For complete details see http://genome.ucsc.edu/FAQ/FAQformat#format3
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('gff.gff3') >>> Gff().sniff( fname ) False >>> fname = get_test_fname('test.gff') >>> Gff().sniff( fname ) True
- genomic_region_dataprovider(dataset: DatasetProtocol, **settings) GenomicRegionDataProvider [source]¶
- genomic_region_dict_dataprovider(dataset: DatasetProtocol, **settings) GenomicRegionDataProvider [source]¶
- dataproviders: Dict[str, Any] = {'base': <function Data.base_dataprovider>, 'chunk': <function Data.chunk_dataprovider>, 'chunk64': <function Data.chunk64_dataprovider>, 'column': <function TabularData.column_dataprovider>, 'dataset-column': <function TabularData.dataset_column_dataprovider>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider>, 'dict': <function TabularData.dict_dataprovider>, 'genomic-region': <function Gff.genomic_region_dataprovider>, 'genomic-region-dict': <function Gff.genomic_region_dict_dataprovider>, 'interval': <function Gff.interval_dataprovider>, 'interval-dict': <function Gff.interval_dict_dataprovider>, 'line': <function Text.line_dataprovider>, 'regex-line': <function Text.regex_line_dataprovider>}¶
- metadata_spec: MetadataSpecCollection = {'attribute_types': <galaxy.model.metadata.MetadataElementSpec object>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.interval.Gff3(**kwd)[source]¶
Bases:
Gff
Tab delimited data in Gff3 format
- edam_format = 'format_1975'¶
- file_ext = 'gff3'¶
- valid_gff3_strand = ['+', '-', '.', '?']¶
- valid_gff3_phase = ['.', '0', '1', '2']¶
- column_names = ['Seqid', 'Source', 'Type', 'Start', 'End', 'Score', 'Strand', 'Phase', 'Attributes']¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Tries to determine the number of columns as well as those columns that contain numerical values in the dataset. A skip parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many invalid comment lines should be skipped. Using None for skip will cause skip to be zero, but the first line will be processed as a header. A max_data_lines parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many data lines should be processed to ensure that the non-optional metadata parameters are properly set; if used, optional metadata parameters will be set to None, unless the entire file has already been read. Using None for max_data_lines will process all data lines.
Items of interest:
We treat ‘overwrite’ as always True (we always want to set tabular metadata when called).
If a tabular file has no data, it will have one column of type ‘str’.
We used to check only the first 100 lines when setting metadata and this class’s set_peek() method read the entire file to determine the number of lines in the file. Since metadata can now be processed on cluster nodes, we’ve merged the line count portion of the set_peek() processing here, and we now check the entire contents of the file.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in GFF version 3 format
GFF 3 format:
adds a mechanism for representing more than one level of hierarchical grouping of features and subfeatures.
separates the ideas of group membership and feature name/id
constrains the feature type field to be taken from a controlled vocabulary.
allows a single feature, such as an exon, to belong to more than one group at a time.
provides an explicit convention for pairwise alignments
provides an explicit convention for features that occupy disjunct regions
The format consists of 9 columns, separated by tabs (NOT spaces).
Undefined fields are replaced with the “.” character, as described in the original GFF spec.
For complete details see http://song.sourceforge.net/gff3.shtml
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'test.gff' ) >>> Gff3().sniff( fname ) False >>> fname = get_test_fname( 'test.gtf' ) >>> Gff3().sniff( fname ) False >>> fname = get_test_fname('gff.gff3') >>> Gff3().sniff( fname ) True >>> fname = get_test_fname( 'grch37.75.gtf' ) >>> Gff3().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'attribute_types': <galaxy.model.metadata.MetadataElementSpec object>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.interval.Gtf(**kwd)[source]¶
Bases:
Gff
Tab delimited data in Gtf format
- edam_format = 'format_2306'¶
- file_ext = 'gtf'¶
- column_names = ['Seqname', 'Source', 'Feature', 'Start', 'End', 'Score', 'Strand', 'Frame', 'Attributes']¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in gtf format
GTF lines have nine required fields that must be tab-separated. The first eight GTF fields are the same as GFF. The group field has been expanded into a list of attributes. Each attribute consists of a type/value pair. Attributes must end in a semi-colon, and be separated from any following attribute by exactly one space. The attribute list must begin with the two mandatory attributes:
gene_id value - A globally unique identifier for the genomic source of the sequence. transcript_id value - A globally unique identifier for the predicted transcript.
For complete details see http://genome.ucsc.edu/FAQ/FAQformat#format4
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( '1.bed' ) >>> Gtf().sniff( fname ) False >>> fname = get_test_fname( 'test.gff' ) >>> Gtf().sniff( fname ) False >>> fname = get_test_fname( 'test.gtf' ) >>> Gtf().sniff( fname ) True >>> fname = get_test_fname( 'grch37.75.gtf' ) >>> Gtf().sniff( fname ) True
- metadata_spec: MetadataSpecCollection = {'attribute_types': <galaxy.model.metadata.MetadataElementSpec object>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.interval.Wiggle(**kwd)[source]¶
Bases:
Tabular
,_RemoteCallMixin
Tab delimited data in wiggle format
- edam_format = 'format_3005'¶
- file_ext = 'wig'¶
- get_estimated_display_viewport(dataset: DatasetProtocol) Tuple[Optional[str], Optional[str], Optional[str]] [source]¶
Return a chrom, start, stop tuple for viewing a file.
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Tries to determine the number of columns as well as those columns that contain numerical values in the dataset. A skip parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many invalid comment lines should be skipped. Using None for skip will cause skip to be zero, but the first line will be processed as a header. A max_data_lines parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many data lines should be processed to ensure that the non-optional metadata parameters are properly set; if used, optional metadata parameters will be set to None, unless the entire file has already been read. Using None for max_data_lines will process all data lines.
Items of interest:
We treat ‘overwrite’ as always True (we always want to set tabular metadata when called).
If a tabular file has no data, it will have one column of type ‘str’.
We used to check only the first 100 lines when setting metadata and this class’s set_peek() method read the entire file to determine the number of lines in the file. Since metadata can now be processed on cluster nodes, we’ve merged the line count portion of the set_peek() processing here, and we now check the entire contents of the file.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines wether the file is in wiggle format
The .wig format is line-oriented. Wiggle data is preceeded by a track definition line, which adds a number of options for controlling the default display of this track. Following the track definition line is the track data, which can be entered in several different formats.
The track definition line begins with the word ‘track’ followed by the track type. The track type with version is REQUIRED, and it currently must be wiggle_0. For example, track type=wiggle_0…
For complete details see http://genome.ucsc.edu/goldenPath/help/wiggle.html
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'interv1.bed' ) >>> Wiggle().sniff( fname ) False >>> fname = get_test_fname( 'wiggle.wig' ) >>> Wiggle().sniff( fname ) True
- wiggle_dataprovider(dataset: DatasetProtocol, **settings) WiggleDataProvider [source]¶
- wiggle_dict_dataprovider(dataset: DatasetProtocol, **settings) WiggleDataProvider [source]¶
- dataproviders: Dict[str, Any] = {'base': <function Data.base_dataprovider>, 'chunk': <function Data.chunk_dataprovider>, 'chunk64': <function Data.chunk64_dataprovider>, 'column': <function TabularData.column_dataprovider>, 'dataset-column': <function TabularData.dataset_column_dataprovider>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider>, 'dict': <function TabularData.dict_dataprovider>, 'line': <function Text.line_dataprovider>, 'regex-line': <function Text.regex_line_dataprovider>, 'wiggle': <function Wiggle.wiggle_dataprovider>, 'wiggle-dict': <function Wiggle.wiggle_dict_dataprovider>}¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.interval.CustomTrack(**kwd)[source]¶
Bases:
Tabular
UCSC CustomTrack
- edam_format = 'format_3588'¶
- file_ext = 'customtrack'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Tries to determine the number of columns as well as those columns that contain numerical values in the dataset. A skip parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many invalid comment lines should be skipped. Using None for skip will cause skip to be zero, but the first line will be processed as a header. A max_data_lines parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many data lines should be processed to ensure that the non-optional metadata parameters are properly set; if used, optional metadata parameters will be set to None, unless the entire file has already been read. Using None for max_data_lines will process all data lines.
Items of interest:
We treat ‘overwrite’ as always True (we always want to set tabular metadata when called).
If a tabular file has no data, it will have one column of type ‘str’.
We used to check only the first 100 lines when setting metadata and this class’s set_peek() method read the entire file to determine the number of lines in the file. Since metadata can now be processed on cluster nodes, we’ve merged the line count portion of the set_peek() processing here, and we now check the entire contents of the file.
- get_estimated_display_viewport(dataset: DatasetProtocol, chrom_col: Optional[int] = None, start_col: Optional[int] = None, end_col: Optional[int] = None) Tuple[Optional[str], Optional[str], Optional[str]] [source]¶
Return a chrom, start, stop tuple for viewing a file.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in customtrack format.
CustomTrack files are built within Galaxy and are basically bed or interval files with the first line looking something like this.
track name=”User Track” description=”User Supplied Track (from Galaxy)” color=0,0,0 visibility=1
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'complete.bed' ) >>> CustomTrack().sniff( fname ) False >>> fname = get_test_fname( 'ucsc.customtrack' ) >>> CustomTrack().sniff( fname ) True
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.interval.ENCODEPeak(**kwd)[source]¶
Bases:
Interval
Human ENCODE peak format. There are both broad and narrow peak formats. Formats are very similar; narrow peak has an additional column, though.
Broad peak ( http://genome.ucsc.edu/FAQ/FAQformat#format13 ): This format is used to provide called regions of signal enrichment based on pooled, normalized (interpreted) data. It is a BED 6+3 format.
Narrow peak http://genome.ucsc.edu/FAQ/FAQformat#format12 and : This format is used to provide called peaks of signal enrichment based on pooled, normalized (interpreted) data. It is a BED6+4 format.
- edam_format = 'format_3612'¶
- file_ext = 'encodepeak'¶
- column_names = ['Chrom', 'Start', 'End', 'Name', 'Score', 'Strand', 'SignalValue', 'pValue', 'qValue', 'Peak']¶
- metadata_spec: MetadataSpecCollection = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.interval.ChromatinInteractions(**kwd)[source]¶
Bases:
Interval
Chromatin interactions obtained from 3C/5C/Hi-C experiments.
- file_ext = 'chrint'¶
- column_names = ['Chrom1', 'Start1', 'End1', 'Chrom2', 'Start2', 'End2', 'Value']¶
- metadata_spec: MetadataSpecCollection = {'chrom1Col': <galaxy.model.metadata.MetadataElementSpec object>, 'chrom2Col': <galaxy.model.metadata.MetadataElementSpec object>, 'chromCol': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'end1Col': <galaxy.model.metadata.MetadataElementSpec object>, 'end2Col': <galaxy.model.metadata.MetadataElementSpec object>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object>, 'start1Col': <galaxy.model.metadata.MetadataElementSpec object>, 'start2Col': <galaxy.model.metadata.MetadataElementSpec object>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object>, 'valueCol': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.interval.ScIdx(**kwd)[source]¶
Bases:
Tabular
ScIdx files are 1-based and consist of strand-specific coordinate counts. They always have 5 columns, and the first row is the column labels: ‘chrom’, ‘index’, ‘forward’, ‘reverse’, ‘value’. Each line following the first consists of data: chromosome name (type str), peak index (type int), Forward strand peak count (type int), Reverse strand peak count (type int) and value (type int). The value of the 5th ‘value’ column is the sum of the forward and reverse peak count values.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('cntrl_hg19.scidx') >>> ScIdx().sniff(fname) True >>> Bed().sniff(fname) False >>> fname = get_test_fname('empty.txt') >>> ScIdx().sniff(fname) False
- file_ext = 'scidx'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Checks for ‘scidx-ness.’
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.interval.IntervalTabix(**kwd)[source]¶
Bases:
Interval
Class describing the bgzip format (http://samtools.github.io/hts-specs/SAMv1.pdf) As tabix is just a bgzip sorted for chr,start,end with an index
- file_ext = 'interval_tabix.gz'¶
- edam_format = 'format_3616'¶
- compressed = True¶
- compressed_format = 'gzip'¶
- sniff_prefix(file_prefix: FilePrefix)[source]¶
Checks for ‘intervalness’
This format is mostly used by galaxy itself. Valid interval files should include a valid header comment, but this seems to be loosely regulated.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'test_space.txt' ) >>> Interval().sniff( fname ) False >>> fname = get_test_fname( 'interval.interval' ) >>> Interval().sniff( fname ) True
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, first_line_is_header: bool = False, metadata_tmp_files_dir: Optional[str] = None, **kwd) None [source]¶
Tries to guess from the line the location number of the column for the chromosome, region start-end and strand
- metadata_spec: MetadataSpecCollection = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object>, 'tabix_index': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.interval.JuicerMediumTabix(**kwd)[source]¶
Bases:
IntervalTabix
Class describing a tabix file built from a juicer medium format: https://github.com/aidenlab/juicer/wiki/Pre#medium-format <readname> <str1> <chr1> <pos1> <frag1> <str2> <chr2> <pos2> <frag2> <mapq1> <mapq2>
str = strand (0 for forward, anything else for reverse) chr = chromosome (must be a chromosome in the genome) pos = position frag = restriction site fragment mapq = mapping quality score
- file_ext = 'juicer_medium_tabix.gz'¶
- metadata_spec: MetadataSpecCollection = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object>, 'tabix_index': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.interval.BedTabix(**kwd)[source]¶
Bases:
IntervalTabix
Class describing a tabix file built from a bed file
- file_ext = 'bed_tabix.gz'¶
- metadata_spec: MetadataSpecCollection = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object>, 'tabix_index': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.interval.GffTabix(**kwd)[source]¶
Bases:
IntervalTabix
Class describing a tabix file built from a bed file
- file_ext = 'gff_tabix.gz'¶
- metadata_spec: MetadataSpecCollection = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object>, 'tabix_index': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.isa module¶
ISA datatype
See https://github.com/ISA-tools
- class galaxy.datatypes.isa.IsaTab(**kwd)[source]¶
Bases:
_Isa
- file_ext = 'isa-tab'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.media module¶
Video classes
- class galaxy.datatypes.media.Audio(**kwd)[source]¶
Bases:
Binary
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'audio_codecs': <galaxy.model.metadata.MetadataElementSpec object>, 'audio_streams': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'duration': <galaxy.model.metadata.MetadataElementSpec object>, 'sample_rates': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.media.Video(**kwd)[source]¶
Bases:
Binary
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Unimplemented method, allows guessing of metadata from contents of file
- metadata_spec: MetadataSpecCollection = {'audio_codecs': <galaxy.model.metadata.MetadataElementSpec object>, 'audio_streams': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'fps': <galaxy.model.metadata.MetadataElementSpec object>, 'resolution_h': <galaxy.model.metadata.MetadataElementSpec object>, 'resolution_w': <galaxy.model.metadata.MetadataElementSpec object>, 'video_codecs': <galaxy.model.metadata.MetadataElementSpec object>, 'video_streams': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.media.Mkv(**kwd)[source]¶
Bases:
Video
- file_ext = 'mkv'¶
- metadata_spec: MetadataSpecCollection = {'audio_codecs': <galaxy.model.metadata.MetadataElementSpec object>, 'audio_streams': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'fps': <galaxy.model.metadata.MetadataElementSpec object>, 'resolution_h': <galaxy.model.metadata.MetadataElementSpec object>, 'resolution_w': <galaxy.model.metadata.MetadataElementSpec object>, 'video_codecs': <galaxy.model.metadata.MetadataElementSpec object>, 'video_streams': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.media.Mp4(**kwd)[source]¶
Bases:
Video
Class that reads MP4 video file. >>> from galaxy.datatypes.sniff import sniff_with_cls >>> sniff_with_cls(Mp4, ‘video_1.mp4’) True >>> sniff_with_cls(Mp4, ‘audio_1.mp4’) False
- file_ext = 'mp4'¶
- metadata_spec: MetadataSpecCollection = {'audio_codecs': <galaxy.model.metadata.MetadataElementSpec object>, 'audio_streams': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'fps': <galaxy.model.metadata.MetadataElementSpec object>, 'resolution_h': <galaxy.model.metadata.MetadataElementSpec object>, 'resolution_w': <galaxy.model.metadata.MetadataElementSpec object>, 'video_codecs': <galaxy.model.metadata.MetadataElementSpec object>, 'video_streams': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.media.Flv(**kwd)[source]¶
Bases:
Video
- file_ext = 'flv'¶
- metadata_spec: MetadataSpecCollection = {'audio_codecs': <galaxy.model.metadata.MetadataElementSpec object>, 'audio_streams': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'fps': <galaxy.model.metadata.MetadataElementSpec object>, 'resolution_h': <galaxy.model.metadata.MetadataElementSpec object>, 'resolution_w': <galaxy.model.metadata.MetadataElementSpec object>, 'video_codecs': <galaxy.model.metadata.MetadataElementSpec object>, 'video_streams': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.media.Mpg(**kwd)[source]¶
Bases:
Video
- file_ext = 'mpg'¶
- metadata_spec: MetadataSpecCollection = {'audio_codecs': <galaxy.model.metadata.MetadataElementSpec object>, 'audio_streams': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'fps': <galaxy.model.metadata.MetadataElementSpec object>, 'resolution_h': <galaxy.model.metadata.MetadataElementSpec object>, 'resolution_w': <galaxy.model.metadata.MetadataElementSpec object>, 'video_codecs': <galaxy.model.metadata.MetadataElementSpec object>, 'video_streams': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.media.Mp3(**kwd)[source]¶
Bases:
Audio
Class that reads MP3 audio file. >>> from galaxy.datatypes.sniff import sniff_with_cls >>> sniff_with_cls(Mp3, ‘audio_2.mp3’) True >>> sniff_with_cls(Mp3, ‘audio_1.wav’) False
- file_ext = 'mp3'¶
- metadata_spec: MetadataSpecCollection = {'audio_codecs': <galaxy.model.metadata.MetadataElementSpec object>, 'audio_streams': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'duration': <galaxy.model.metadata.MetadataElementSpec object>, 'sample_rates': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.media.Wav(**kwd)[source]¶
Bases:
Audio
Class that reads WAV audio file >>> from galaxy.datatypes.sniff import sniff_with_cls >>> sniff_with_cls(Wav, ‘hello.wav’) True >>> sniff_with_cls(Wav, ‘audio_2.mp3’) False >>> sniff_with_cls(Wav, ‘drugbank_drugs.cml’) False
- file_ext = 'wav'¶
- blurb = 'RIFF WAV Audio file'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the metadata for this dataset from the file contents.
- metadata_spec: MetadataSpecCollection = {'audio_codecs': <galaxy.model.metadata.MetadataElementSpec object>, 'audio_streams': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'duration': <galaxy.model.metadata.MetadataElementSpec object>, 'nchannels': <galaxy.model.metadata.MetadataElementSpec object>, 'nframes': <galaxy.model.metadata.MetadataElementSpec object>, 'rate': <galaxy.model.metadata.MetadataElementSpec object>, 'sample_rates': <galaxy.model.metadata.MetadataElementSpec object>, 'sampwidth': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.metacyto module¶
MetaCyto analysis datatypes.
- class galaxy.datatypes.metacyto.mStats(**kwd)[source]¶
Bases:
Tabular
Class describing the table of cluster statistics output from MetaCyto
- file_ext = 'metacyto_stats.txt'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Quick test on file headings
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.metacyto.mSummary(**kwd)[source]¶
Bases:
Tabular
Class describing the summary table output by MetaCyto after FCS preprocessing
- file_ext = 'metacyto_summary.txt'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.metadata module¶
Expose the model metadata module as a datatype module also, allowing it to live in galaxy.model means the model module doesn’t have any dependencies on th datatypes module. This module will need to remain here for datatypes living in the tool shed so we might as well keep and use this interface from the datatypes module.
- class galaxy.datatypes.metadata.Statement(target)[source]¶
Bases:
object
This class inserts its target into a list in the surrounding class. the data.Data class has a metaclass which executes these statements. This is how we shove the metadata element spec into the class.
- class galaxy.datatypes.metadata.MetadataCollection(parent: Union[DatasetInstance, NoneDataset], session: Optional[Union[galaxy_scoped_session, SessionlessContext]] = None)[source]¶
Bases:
Mapping
MetadataCollection is not a collection at all, but rather a proxy to the real metadata which is stored as a Dictionary. This class handles processing the metadata elements when they are set and retrieved, returning default values in cases when metadata is not set.
- __init__(parent: Union[DatasetInstance, NoneDataset], session: Optional[Union[galaxy_scoped_session, SessionlessContext]] = None) None [source]¶
- property parent¶
- property spec¶
- element_is_set(name) bool [source]¶
check if the meta data with the given name is set, i.e.
if the such a metadata actually exists and
if its value differs from no_value
- Parameters:
name – the name of the metadata element
- Returns:
True if the value differes from the no_value False if its equal of if no metadata with the name is specified
- property requires_dataset_id¶
- class galaxy.datatypes.metadata.MetadataSpecCollection(*args, **kwds)[source]¶
Bases:
OrderedDict
A simple extension of OrderedDict which allows cleaner access to items and allows the values to be iterated over directly as if it were a list. append() is also implemented for simplicity and does not “append”.
- class galaxy.datatypes.metadata.MetadataParameter(spec)[source]¶
Bases:
object
- make_copy(value, target_context: MetadataCollection, source_context)[source]¶
- classmethod marshal(value)[source]¶
This method should/can be overridden to convert the incoming value to whatever type it is supposed to be.
- class galaxy.datatypes.metadata.MetadataElementSpec(datatype, name=None, desc=None, param=<class 'galaxy.model.metadata.MetadataParameter'>, default=None, no_value=None, visible=True, set_in_upload=False, optional=False, **kwargs)[source]¶
Bases:
object
Defines a metadata element and adds it to the metadata_spec (which is a MetadataSpecCollection) of datatype.
- class galaxy.datatypes.metadata.SelectParameter(spec)[source]¶
Bases:
MetadataParameter
- class galaxy.datatypes.metadata.DBKeyParameter(spec)[source]¶
Bases:
SelectParameter
- make_copy(value, target_context: MetadataCollection, source_context)[source]¶
- class galaxy.datatypes.metadata.RangeParameter(spec)[source]¶
Bases:
SelectParameter
- class galaxy.datatypes.metadata.ColumnParameter(spec)[source]¶
Bases:
RangeParameter
- class galaxy.datatypes.metadata.ColumnTypesParameter(spec)[source]¶
Bases:
MetadataParameter
- class galaxy.datatypes.metadata.ListParameter(spec)[source]¶
Bases:
MetadataParameter
- class galaxy.datatypes.metadata.DictParameter(spec)[source]¶
Bases:
MetadataParameter
- class galaxy.datatypes.metadata.PythonObjectParameter(spec)[source]¶
Bases:
MetadataParameter
- class galaxy.datatypes.metadata.FileParameter(spec)[source]¶
Bases:
MetadataParameter
- make_copy(value, target_context: MetadataCollection, source_context)[source]¶
- classmethod marshal(value)[source]¶
This method should/can be overridden to convert the incoming value to whatever type it is supposed to be.
- from_external_value(value, parent, path_rewriter=None)[source]¶
Turns a value read from a external dict into its value to be pushed directly into the metadata dict.
galaxy.datatypes.microarrays module¶
- class galaxy.datatypes.microarrays.GenericMicroarrayFile(**kwd)[source]¶
Bases:
Text
Abstract class for most of the microarray files.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'block_count': <galaxy.model.metadata.MetadataElementSpec object>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.microarrays.Gal(**kwd)[source]¶
Bases:
GenericMicroarrayFile
Gal File format described at: http://mdc.custhelp.com/app/answers/detail/a_id/18883/#gal
- edam_format = 'format_3829'¶
- edam_data = 'data_3110'¶
- file_ext = 'gal'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to guess if the file is a Gal file.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.gal') >>> Gal().sniff(fname) True >>> fname = get_test_fname('test.gpr') >>> Gal().sniff(fname) False
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set metadata for Gal file.
- metadata_spec: MetadataSpecCollection = {'block_count': <galaxy.model.metadata.MetadataElementSpec object>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.microarrays.Gpr(**kwd)[source]¶
Bases:
GenericMicroarrayFile
Gpr File format described at: http://mdc.custhelp.com/app/answers/detail/a_id/18883/#gpr
- edam_format = 'format_3829'¶
- edam_data = 'data_3110'¶
- file_ext = 'gpr'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to guess if the file is a Gpr file.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.gpr') >>> Gpr().sniff(fname) True >>> fname = get_test_fname('test.gal') >>> Gpr().sniff(fname) False
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set metadata for Gpr file.
- metadata_spec: MetadataSpecCollection = {'block_count': <galaxy.model.metadata.MetadataElementSpec object>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
galaxy.datatypes.molecules module¶
- galaxy.datatypes.molecules.count_lines(filename, non_empty=False)[source]¶
counting the number of lines from the ‘filename’ file
- class galaxy.datatypes.molecules.GenericMolFile(**kwd)[source]¶
Bases:
Text
Abstract class for most of the molecule files.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- element_symbols = ['Ac', 'Ag', 'Al', 'Am', 'Ar', 'As', 'At', 'Au', 'B ', 'Ba', 'Be', 'Bh', 'Bi', 'Bk', 'Br', 'C ', 'Ca', 'Cd', 'Ce', 'Cf', 'Cl', 'Cm', 'Co', 'Cr', 'Cs', 'Cu', 'Ds', 'Db', 'Dy', 'Er', 'Es', 'Eu', 'F ', 'Fe', 'Fm', 'Fr', 'Ga', 'Gd', 'Ge', 'H ', 'He', 'Hf', 'Hg', 'Ho', 'Hs', 'I ', 'In', 'Ir', 'K ', 'Kr', 'La', 'Li', 'Lr', 'Lu', 'Md', 'Mg', 'Mn', 'Mo', 'Mt', 'N ', 'Na', 'Nb', 'Nd', 'Ne', 'Ni', 'No', 'Np', 'O ', 'Os', 'P ', 'Pa', 'Pb', 'Pd', 'Pm', 'Po', 'Pr', 'Pt', 'Pu', 'Ra', 'Rb', 'Re', 'Rf', 'Rg', 'Rh', 'Rn', 'Ru', 'S ', 'Sb', 'Sc', 'Se', 'Sg', 'Si', 'Sm', 'Sn', 'Sr', 'Ta', 'Tb', 'Tc', 'Te', 'Th', 'Ti', 'Tl', 'Tm', 'U ', 'V ', 'W ', 'Xe', 'Y ', 'Yb', 'Zn', 'Zr']¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.molecules.MOL(**kwd)[source]¶
Bases:
GenericMolFile
- file_ext = 'mol'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number molecules, in the case of MOL its always one.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.molecules.SDF(**kwd)[source]¶
Bases:
GenericMolFile
- file_ext = 'sdf'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to guess if the file is a SDF2 file.
An SDfile (structure-data file) can contain multiple compounds.
Each compound starts with a block in V2000 or V3000 molfile format, which ends with a line equal to ‘M END’. This is followed by a non-structural data block, which ends with a line equal to ‘$$$$’.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('drugbank_drugs.sdf') >>> SDF().sniff(fname) True >>> fname = get_test_fname('github88.v3k.sdf') >>> SDF().sniff(fname) True >>> fname = get_test_fname('chebi_57262.v3k.mol') >>> SDF().sniff(fname) False
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of molecules in dataset.
- classmethod split(input_datasets: List, subdir_generator_function: Callable, split_params: Dict) None [source]¶
Split the input files by molecule records.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.molecules.MOL2(**kwd)[source]¶
Bases:
GenericMolFile
- file_ext = 'mol2'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to guess if the file is a MOL2 file.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('drugbank_drugs.mol2') >>> MOL2().sniff(fname) True >>> fname = get_test_fname('drugbank_drugs.cml') >>> MOL2().sniff(fname) False
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- classmethod split(input_datasets: List, subdir_generator_function: Callable, split_params: Dict) None [source]¶
Split the input files by molecule records.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.molecules.FPS(**kwd)[source]¶
Bases:
GenericMolFile
chemfp fingerprint file: http://code.google.com/p/chem-fingerprints/wiki/FPS
- file_ext = 'fps'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to guess if the file is a FPS file.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('q.fps') >>> FPS().sniff(fname) True >>> fname = get_test_fname('drugbank_drugs.cml') >>> FPS().sniff(fname) False
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- classmethod split(input_datasets: List, subdir_generator_function: Callable, split_params: Dict) None [source]¶
Split the input files by fingerprint records.
- static merge(split_files: List[str], output_file: str) None [source]¶
Merging fps files requires merging the header manually. We take the header from the first file.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.molecules.OBFS(**kwd)[source]¶
Bases:
Binary
OpenBabel Fastsearch format (fs).
- file_ext = 'obfs'¶
- __init__(**kwd)[source]¶
A Fastsearch Index consists of a binary file with the fingerprints and a pointer the actual molecule file.
- static merge(split_files: List[str], output_file: str) None [source]¶
Merging Fastsearch indices is not supported.
- classmethod split(input_datasets: List, subdir_generator_function: Callable, split_params: Dict) None [source]¶
Splitting Fastsearch indices is not supported.
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.molecules.DRF(**kwd)[source]¶
Bases:
GenericMolFile
- file_ext = 'drf'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.molecules.PHAR(**kwd)[source]¶
Bases:
GenericMolFile
Pharmacophore database format from silicos-it.
- file_ext = 'phar'¶
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.molecules.PDB(**kwd)[source]¶
Bases:
GenericMolFile
Protein Databank format. http://www.wwpdb.org/documentation/format33/v3.3.html
- file_ext = 'pdb'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to guess if the file is a PDB file.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('5e5z.pdb') >>> PDB().sniff(fname) True >>> fname = get_test_fname('drugbank_drugs.cml') >>> PDB().sniff(fname) False
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Find Chain_IDs for metadata.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'chain_ids': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.molecules.PDBQT(**kwd)[source]¶
Bases:
GenericMolFile
PDBQT Autodock and Autodock Vina format http://autodock.scripps.edu/faqs-help/faq/what-is-the-format-of-a-pdbqt-file
- file_ext = 'pdbqt'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to guess if the file is a PDBQT file.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('NuBBE_1_obabel_3D.pdbqt') >>> PDBQT().sniff(fname) True >>> fname = get_test_fname('drugbank_drugs.cml') >>> PDBQT().sniff(fname) False
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.molecules.PQR(**kwd)[source]¶
Bases:
GenericMolFile
Protein Databank format. https://apbs-pdb2pqr.readthedocs.io/en/latest/formats/pqr.html
- file_ext = 'pqr'¶
- get_matcher() Pattern [source]¶
- Atom and HETATM line fields are space separated, match group:
- 0: Field_name
A string which specifies the type of PQR entry: ATOM or HETATM.
- 1: Atom_number
An integer which provides the atom index.
- 2: Atom_name
A string which provides the atom name.
- 3: Residue_name
A string which provides the residue name.
- 5: Chain_ID (Optional, group 4 is whole field)
An optional string which provides the chain ID of the atom. Note that chain ID support is a new feature of APBS 0.5.0 and later versions.
- 6: Residue_number
An integer which provides the residue index.
- 7: X 8: Y 9: Z
3 floats which provide the atomic coordinates (in angstroms)
- 10: Charge
A float which provides the atomic charge (in electrons).
- 11: Radius
A float which provides the atomic radius (in angstroms).
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to guess if the file is a PQR file. >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname(‘5e5z.pqr’) >>> PQR().sniff(fname) True >>> fname = get_test_fname(‘drugbank_drugs.cml’) >>> PQR().sniff(fname) False
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Find Optional Chain_IDs for metadata.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'chain_ids': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.molecules.Cell(**kwd)[source]¶
Bases:
GenericMolFile
CASTEP CELL format.
- file_ext = 'cell'¶
- meta_error = False¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to guess if the file is a CASTEP CELL file.
A fingerprint for CELL files is the use of %BLOCK and %ENDBLOCK to denote data blocks (not case sensitive).
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('Si_uppercase.cell') >>> Cell().sniff(fname) True >>> fname = get_test_fname('Si_lowercase.cell') >>> Cell().sniff(fname) True >>> fname = get_test_fname('Si.cif') >>> Cell().sniff(fname) False
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Find Atom IDs for metadata.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'atom_data': <galaxy.model.metadata.MetadataElementSpec object>, 'chemical_formula': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'is_periodic': <galaxy.model.metadata.MetadataElementSpec object>, 'lattice_parameters': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_atoms': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.molecules.CIF(**kwd)[source]¶
Bases:
GenericMolFile
CIF format.
- file_ext = 'cif'¶
- meta_error = False¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to guess if the file is a CIF file.
The CIF format and the Relion STAR format have a shared origin. Note therefore that STAR files and the STAR sniffer also use
data_
blocks. STAR files will not pass the CIF sniffer, but CIF files can pass the STAR sniffer.>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('Si.cif') >>> CIF().sniff(fname) True >>> fname = get_test_fname('Si_lowercase.cell') >>> CIF().sniff(fname) False >>> fname = get_test_fname('1.star') >>> CIF().sniff(fname) False
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Find Atom IDs for metadata.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'atom_data': <galaxy.model.metadata.MetadataElementSpec object>, 'chemical_formula': <galaxy.model.metadata.MetadataElementSpec object>, 'data_block_names': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'is_periodic': <galaxy.model.metadata.MetadataElementSpec object>, 'lattice_parameters': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_atoms': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.molecules.XYZ(**kwd)[source]¶
Bases:
GenericMolFile
XYZ format.
- file_ext = 'xyz'¶
- meta_error = False¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to guess if the file is a XYZ file.
XYZ has no fingerprint phrases, so the whole prefix must be checked for the correct structure. If the prefix passes, assume the whole file passes.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('Si.xyz') >>> XYZ().sniff(fname) True >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('Si_multi.xyz') >>> XYZ().sniff(fname) True >>> fname = get_test_fname('Si.cif') >>> XYZ().sniff(fname) False >>> fname = get_test_fname('not_a_xyz_file.txt') >>> XYZ().sniff(fname) False
- read_blocks(lines: List) List [source]¶
Parses and returns a list of dictionaries representing XYZ structure blocks (aka frames).
Raises IndexError, TypeError, ValueError
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Find Atom IDs for metadata.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'atom_data': <galaxy.model.metadata.MetadataElementSpec object>, 'chemical_formula': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'is_periodic': <galaxy.model.metadata.MetadataElementSpec object>, 'lattice_parameters': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_atoms': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.molecules.ExtendedXYZ(**kwd)[source]¶
Bases:
XYZ
Extended XYZ format.
Uses specification from https://github.com/libAtoms/extxyz.
- file_ext = 'extxyz'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to guess if the file is an Extended XYZ file.
XYZ files will not pass the ExtendedXYZ sniffer, but ExtendedXYZ files can pass the XYZ sniffer.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('Si.extxyz') >>> ExtendedXYZ().sniff(fname) True >>> fname = get_test_fname('Si.xyz') >>> ExtendedXYZ().sniff(fname) False
- read_blocks(lines: List) List [source]¶
Parses and returns a list of XYZ structure blocks (aka frames).
Raises IndexError, TypeError, ValueError
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'atom_data': <galaxy.model.metadata.MetadataElementSpec object>, 'chemical_formula': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'is_periodic': <galaxy.model.metadata.MetadataElementSpec object>, 'lattice_parameters': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_atoms': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.molecules.grd(**kwd)[source]¶
Bases:
Text
- file_ext = 'grd'¶
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.molecules.grdtgz(**kwd)[source]¶
Bases:
Binary
- file_ext = 'grd.tgz'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.molecules.InChI(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'inchi'¶
- column_names = ['InChI']¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to guess if the file is a InChI file.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('drugbank_drugs.inchi') >>> InChI().sniff(fname) True >>> fname = get_test_fname('drugbank_drugs.cml') >>> InChI().sniff(fname) False
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.molecules.SMILES(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'smi'¶
- column_names = ['SMILES', 'TITLE']¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.molecules.CML(**kwd)[source]¶
Bases:
GenericXml
Chemical Markup Language http://cml.sourceforge.net/
- file_ext = 'cml'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to guess if the file is a CML file.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('interval.interval') >>> CML().sniff(fname) False >>> fname = get_test_fname('drugbank_drugs.cml') >>> CML().sniff(fname) True
- classmethod split(input_datasets: List, subdir_generator_function: Callable, split_params: Dict) None [source]¶
Split the input files by molecule records.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.molecules.GRO(**kwd)[source]¶
Bases:
GenericMolFile
GROMACS structure format. https://manual.gromacs.org/current/reference-manual/file-formats.html#gro
- file_ext = 'gro'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to guess if the file is a GRO file.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('5e5z.gro') >>> GRO().sniff_prefix(fname) True >>> fname = get_test_fname('5e5z.pdb') >>> GRO().sniff_prefix(fname) False
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.mothur module¶
Mothur Metagenomics Datatypes
- class galaxy.datatypes.mothur.Otu(**kwd)[source]¶
Bases:
Text
- file_ext = 'mothur.otu'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set metadata for Otu files.
>>> from galaxy.datatypes.sniff import get_test_fname >>> from galaxy.util.bunch import Bunch >>> dataset = Bunch() >>> dataset.metadata = Bunch >>> otu = Otu() >>> dataset.file_name = get_test_fname( 'mothur_datatypetest_true.mothur.otu' ) >>> dataset.has_data = lambda: True >>> otu.set_meta(dataset) >>> dataset.metadata.columns 100 >>> len(dataset.metadata.labels) == 37 True >>> len(dataset.metadata.otulabels) == 98 True
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is otu (operational taxonomic unit) format
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.otu' ) >>> Otu().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.otu' ) >>> Otu().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'labels': <galaxy.model.metadata.MetadataElementSpec object>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.mothur.Sabund(**kwd)[source]¶
Bases:
Otu
- file_ext = 'mothur.sabund'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is otu (operational taxonomic unit) format label<TAB>count[<TAB>value(1..n)]
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.sabund' ) >>> Sabund().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.sabund' ) >>> Sabund().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'labels': <galaxy.model.metadata.MetadataElementSpec object>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.mothur.GroupAbund(**kwd)[source]¶
Bases:
Otu
- file_ext = 'mothur.shared'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, skip: Optional[int] = 1, **kwd) None [source]¶
Set metadata for Otu files.
>>> from galaxy.datatypes.sniff import get_test_fname >>> from galaxy.util.bunch import Bunch >>> dataset = Bunch() >>> dataset.metadata = Bunch >>> otu = Otu() >>> dataset.file_name = get_test_fname( 'mothur_datatypetest_true.mothur.otu' ) >>> dataset.has_data = lambda: True >>> otu.set_meta(dataset) >>> dataset.metadata.columns 100 >>> len(dataset.metadata.labels) == 37 True >>> len(dataset.metadata.otulabels) == 98 True
- sniff_prefix(file_prefix: FilePrefix, vals_are_int=False) bool [source]¶
Determines whether the file is a otu (operational taxonomic unit) Shared format label<TAB>group<TAB>count[<TAB>value(1..n)] The first line is column headings as of Mothur v 1.2
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.shared' ) >>> GroupAbund().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.shared' ) >>> GroupAbund().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'groups': <galaxy.model.metadata.MetadataElementSpec object>, 'labels': <galaxy.model.metadata.MetadataElementSpec object>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.mothur.SecondaryStructureMap(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'mothur.map'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is a secondary structure map format A single column with an integer value which indicates the row that this row maps to. Check to make sure if structMap[10] = 380 then structMap[380] = 10 and vice versa.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.map' ) >>> SecondaryStructureMap().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.map' ) >>> SecondaryStructureMap().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.mothur.AlignCheck(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'mothur.align.check'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Tries to determine the number of columns as well as those columns that contain numerical values in the dataset. A skip parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many invalid comment lines should be skipped. Using None for skip will cause skip to be zero, but the first line will be processed as a header. A max_data_lines parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many data lines should be processed to ensure that the non-optional metadata parameters are properly set; if used, optional metadata parameters will be set to None, unless the entire file has already been read. Using None for max_data_lines will process all data lines.
Items of interest:
We treat ‘overwrite’ as always True (we always want to set tabular metadata when called).
If a tabular file has no data, it will have one column of type ‘str’.
We used to check only the first 100 lines when setting metadata and this class’s set_peek() method read the entire file to determine the number of lines in the file. Since metadata can now be processed on cluster nodes, we’ve merged the line count portion of the set_peek() processing here, and we now check the entire contents of the file.
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.mothur.AlignReport(**kwd)[source]¶
Bases:
Tabular
QueryName QueryLength TemplateName TemplateLength SearchMethod SearchScore AlignmentMethod QueryStart QueryEnd TemplateStart TemplateEnd PairwiseAlignmentLength GapsInQuery GapsInTemplate LongestInsert SimBtwnQuery&Template AY457915 501 82283 1525 kmer 89.07 needleman 5 501 1 499 499 2 0 0 97.6
- file_ext = 'mothur.align.report'¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.mothur.DistanceMatrix(**kwd)[source]¶
Bases:
Text
- file_ext = 'mothur.dist'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, skip: Optional[int] = 0, **kwd) None [source]¶
Set the number of lines of data in dataset.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.mothur.LowerTriangleDistanceMatrix(**kwd)[source]¶
Bases:
DistanceMatrix
- file_ext = 'mothur.lower.dist'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is a lower-triangle distance matrix (phylip) format The first line has the number of sequences in the matrix. The remaining lines have the sequence name followed by a list of distances from all preceeding sequences
5 # possibly but not always preceded by a tab :/ U68589 U68590 0.3371 U68591 0.3609 0.3782 U68592 0.4155 0.3197 0.4148 U68593 0.2872 0.1690 0.3361 0.2842
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.lower.dist' ) >>> LowerTriangleDistanceMatrix().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.lower.dist' ) >>> LowerTriangleDistanceMatrix().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.mothur.SquareDistanceMatrix(**kwd)[source]¶
Bases:
DistanceMatrix
- file_ext = 'mothur.square.dist'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is a square distance matrix (Column-formatted distance matrix) format The first line has the number of sequences in the matrix. The following lines have the sequence name in the first column plus a column for the distance to each sequence in the row order in which they appear in the matrix.
3 U68589 0.0000 0.3371 0.3610 U68590 0.3371 0.0000 0.3783 U68590 0.3371 0.0000 0.3783
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.square.dist' ) >>> SquareDistanceMatrix().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.square.dist' ) >>> SquareDistanceMatrix().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.mothur.PairwiseDistanceMatrix(**kwd)[source]¶
Bases:
DistanceMatrix
,Tabular
- file_ext = 'mothur.pair.dist'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, skip: Optional[int] = None, **kwd) None [source]¶
Tries to determine the number of columns as well as those columns that contain numerical values in the dataset. A skip parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many invalid comment lines should be skipped. Using None for skip will cause skip to be zero, but the first line will be processed as a header. A max_data_lines parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many data lines should be processed to ensure that the non-optional metadata parameters are properly set; if used, optional metadata parameters will be set to None, unless the entire file has already been read. Using None for max_data_lines will process all data lines.
Items of interest:
We treat ‘overwrite’ as always True (we always want to set tabular metadata when called).
If a tabular file has no data, it will have one column of type ‘str’.
We used to check only the first 100 lines when setting metadata and this class’s set_peek() method read the entire file to determine the number of lines in the file. Since metadata can now be processed on cluster nodes, we’ve merged the line count portion of the set_peek() processing here, and we now check the entire contents of the file.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is a pairwise distance matrix (Column-formatted distance matrix) format The first and second columns have the sequence names and the third column is the distance between those sequences.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.pair.dist' ) >>> PairwiseDistanceMatrix().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.pair.dist' ) >>> PairwiseDistanceMatrix().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.mothur.Names(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'mothur.names'¶
- __init__(**kwd)[source]¶
http://www.mothur.org/wiki/Name_file Name file shows the relationship between a representative sequence(col 1) and the sequences(comma-separated) it represents(col 2)
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.mothur.Summary(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'mothur.summary'¶
- __init__(**kwd)[source]¶
summarizes the quality of sequences in an unaligned or aligned fasta-formatted sequence file
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.mothur.Group(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'mothur.groups'¶
- __init__(**kwd)[source]¶
http://www.mothur.org/wiki/Groups_file Group file assigns sequence (col 1) to a group (col 2)
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, skip: Optional[int] = None, max_data_lines: Optional[int] = None, **kwd) None [source]¶
Tries to determine the number of columns as well as those columns that contain numerical values in the dataset. A skip parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many invalid comment lines should be skipped. Using None for skip will cause skip to be zero, but the first line will be processed as a header. A max_data_lines parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many data lines should be processed to ensure that the non-optional metadata parameters are properly set; if used, optional metadata parameters will be set to None, unless the entire file has already been read. Using None for max_data_lines will process all data lines.
Items of interest:
We treat ‘overwrite’ as always True (we always want to set tabular metadata when called).
If a tabular file has no data, it will have one column of type ‘str’.
We used to check only the first 100 lines when setting metadata and this class’s set_peek() method read the entire file to determine the number of lines in the file. Since metadata can now be processed on cluster nodes, we’ve merged the line count portion of the set_peek() processing here, and we now check the entire contents of the file.
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'groups': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.mothur.AccNos(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'mothur.accnos'¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.mothur.Oligos(**kwd)[source]¶
Bases:
Text
- file_ext = 'mothur.oligos'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
http://www.mothur.org/wiki/Oligos_File Determines whether the file is a otu (operational taxonomic unit) format
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.oligos' ) >>> Oligos().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.oligos' ) >>> Oligos().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.mothur.Frequency(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'mothur.freq'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is a frequency tabular format for chimera analysis
#1.14.0 0 0.000 1 0.000 ... 155 0.975
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.freq' ) >>> Frequency().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.freq' ) >>> Frequency().sniff( fname ) False >>> # Expression count matrix (EdgeR wrapper) >>> fname = get_test_fname( 'mothur_datatypetest_false_2.mothur.freq' ) >>> Frequency().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.mothur.Quantile(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'mothur.quan'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is a quantiles tabular format for chimera analysis
1 0 0 0 0 0 0 2 0.309198 0.309198 0.37161 0.37161 0.37161 0.37161 3 0.510982 0.563213 0.693529 0.858939 1.07442 1.20608 ...
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.quan' ) >>> Quantile().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.quan' ) >>> Quantile().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'filtered': <galaxy.model.metadata.MetadataElementSpec object>, 'masked': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.mothur.LaneMask(**kwd)[source]¶
Bases:
Text
- file_ext = 'mothur.filter'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is a lane mask filter: 1 line consisting of zeros and ones.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.filter' ) >>> LaneMask().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.filter' ) >>> LaneMask().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.mothur.CountTable(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'mothur.count_table'¶
- __init__(**kwd)[source]¶
http://www.mothur.org/wiki/Count_File A table with first column names and following columns integer counts # Example 1: Representative_Sequence total U68630 1 U68595 1 U68600 1 # Example 2 (with group columns): Representative_Sequence total forest pasture U68630 1 1 0 U68595 1 1 0 U68600 1 1 0 U68591 1 1 0 U68647 1 0 1
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, skip: Optional[int] = 1, max_data_lines: Optional[int] = None, **kwd) None [source]¶
Tries to determine the number of columns as well as those columns that contain numerical values in the dataset. A skip parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many invalid comment lines should be skipped. Using None for skip will cause skip to be zero, but the first line will be processed as a header. A max_data_lines parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many data lines should be processed to ensure that the non-optional metadata parameters are properly set; if used, optional metadata parameters will be set to None, unless the entire file has already been read. Using None for max_data_lines will process all data lines.
Items of interest:
We treat ‘overwrite’ as always True (we always want to set tabular metadata when called).
If a tabular file has no data, it will have one column of type ‘str’.
We used to check only the first 100 lines when setting metadata and this class’s set_peek() method read the entire file to determine the number of lines in the file. Since metadata can now be processed on cluster nodes, we’ve merged the line count portion of the set_peek() processing here, and we now check the entire contents of the file.
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'groups': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.mothur.RefTaxonomy(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'mothur.ref.taxonomy'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is a Reference Taxonomy
http://www.mothur.org/wiki/Taxonomy_outline A table with 2 or 3 columns:
SequenceName
Taxonomy (semicolon-separated taxonomy in descending order)
integer ?
Example: 2-column (http://www.mothur.org/wiki/Taxonomy_outline)
X56533.1 Eukaryota;Alveolata;Ciliophora;Intramacronucleata;Oligohymenophorea;Hymenostomatida;Tetrahymenina;Glaucomidae;Glaucoma; X97975.1 Eukaryota;Parabasalidea;Trichomonada;Trichomonadida;unclassified_Trichomonadida; AF052717.1 Eukaryota;Parabasalidea;
Example: 3-column (http://vamps.mbl.edu/resources/databases.php)
v3_AA008 Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus 5 v3_AA016 Bacteria 120 v3_AA019 Archaea;Crenarchaeota;Marine_Group_I 1
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.ref.taxonomy' ) >>> RefTaxonomy().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.ref.taxonomy' ) >>> RefTaxonomy().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.mothur.ConsensusTaxonomy(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'mothur.cons.taxonomy'¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.mothur.TaxonomySummary(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'mothur.tax.summary'¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.mothur.Axes(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'mothur.axes'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is an axes format The first line may have column headings. The following lines have the name in the first column plus float columns for each axis.
group axis1 axis2 forest 0.000000 0.145743 pasture 0.145743 0.000000
axis1 axis2 U68589 0.262608 -0.077498 U68590 0.027118 0.195197 U68591 0.329854 0.014395
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.axes' ) >>> Axes().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.axes' ) >>> Axes().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.mothur.SffFlow(**kwd)[source]¶
Bases:
Tabular
https://mothur.org/wiki/flow_file/ The first line is the total number of flow values - 800 for Titanium data. For GS FLX it would be 400. Following lines contain:
SequenceName
the number of useable flows as defined by 454’s software
the flow intensity for each base going in the order of TACG.
Example:
800 GQY1XT001CQL4K 85 1.04 0.00 1.00 0.02 0.03 1.02 0.05 ... GQY1XT001CQIRF 84 1.02 0.06 0.98 0.06 0.09 1.05 0.07 ... GQY1XT001CF5YW 88 1.02 0.02 1.01 0.04 0.06 1.02 0.03 ...
- file_ext = 'mothur.sff.flow'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, skip: Optional[int] = 1, max_data_lines: Optional[int] = None, **kwd) None [source]¶
Tries to determine the number of columns as well as those columns that contain numerical values in the dataset. A skip parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many invalid comment lines should be skipped. Using None for skip will cause skip to be zero, but the first line will be processed as a header. A max_data_lines parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many data lines should be processed to ensure that the non-optional metadata parameters are properly set; if used, optional metadata parameters will be set to None, unless the entire file has already been read. Using None for max_data_lines will process all data lines.
Items of interest:
We treat ‘overwrite’ as always True (we always want to set tabular metadata when called).
If a tabular file has no data, it will have one column of type ‘str’.
We used to check only the first 100 lines when setting metadata and this class’s set_peek() method read the entire file to determine the number of lines in the file. Since metadata can now be processed on cluster nodes, we’ve merged the line count portion of the set_peek() processing here, and we now check the entire contents of the file.
- make_html_table(dataset: DatasetProtocol, skipchars: Optional[List] = None, **kwargs) str [source]¶
Create HTML table, used for displaying peek
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'flow_order': <galaxy.model.metadata.MetadataElementSpec object>, 'flow_values': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.msa module¶
- class galaxy.datatypes.msa.InfernalCM(**kwd)[source]¶
Bases:
Text
- file_ext = 'cm'¶
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'infernal_model.cm' ) >>> InfernalCM().sniff( fname ) True >>> fname = get_test_fname( '2.txt' ) >>> InfernalCM().sniff( fname ) False
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of models and the version of CM file in dataset.
- metadata_spec: MetadataSpecCollection = {'cm_version': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.msa.Hmmer(**kwd)[source]¶
Bases:
Text
- edam_data = 'data_1364'¶
- edam_format = 'format_1370'¶
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- abstract sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.msa.Hmmer2(**kwd)[source]¶
Bases:
Hmmer
- edam_format = 'format_3328'¶
- file_ext = 'hmm2'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
HMMER2 files start with HMMER2.0
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.msa.Hmmer3(**kwd)[source]¶
Bases:
Hmmer
- edam_format = 'format_3329'¶
- file_ext = 'hmm3'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
HMMER3 files start with HMMER3/f
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.msa.HmmerPress(**kwd)[source]¶
Bases:
Binary
Class for hmmpress database files.
- file_ext = 'hmmpress'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.msa.Stockholm_1_0(**kwd)[source]¶
Bases:
Text
- edam_data = 'data_0863'¶
- edam_format = 'format_1961'¶
- file_ext = 'stockholm'¶
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of models in dataset.
- classmethod split(input_datasets: List, subdir_generator_function: Callable, split_params: Dict) None [source]¶
Split the input files by model records.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.msa.MauveXmfa(**kwd)[source]¶
Bases:
Text
- file_ext = 'xmfa'¶
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.msa.Msf(**kwd)[source]¶
Bases:
Text
Multiple sequence alignment format produced by the Accelrys GCG suite and other programs.
- edam_data = 'data_0863'¶
- edam_format = 'format_1947'¶
- file_ext = 'msf'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.neo4j module¶
Neo4j Composite Dataset
- class galaxy.datatypes.neo4j.Neo4j(**kwd)[source]¶
Bases:
Html
base class to use for neostore datatypes derived from html - composite datatype elements stored in extra files path
- generate_primary_file(dataset: HasExtraFilesAndMetadata) str [source]¶
This is called only at upload to write the html file cannot rename the datasets here - they come with the default unfortunately
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.neo4j.Neo4jDB(**kwd)[source]¶
-
Class for neo4jDB database files.
- file_ext = 'neostore'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.neo4j.Neo4jDBzip(**kwd)[source]¶
-
Class for neo4jDB database files.
- file_ext = 'neostore.zip'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'neostore_zip': <galaxy.model.metadata.MetadataElementSpec object>, 'reference_name': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.ngsindex module¶
NGS indexes
- class galaxy.datatypes.ngsindex.BowtieIndex(**kwd)[source]¶
Bases:
Html
base class for BowtieIndex is subclassed by BowtieColorIndex and BowtieBaseIndex
- generate_primary_file(dataset: HasExtraFilesAndMetadata) str [source]¶
This is called only at upload to write the html file cannot rename the datasets here - they come with the default unfortunately
- regenerate_primary_file(dataset: DatasetProtocol) None [source]¶
cannot do this until we are setting metadata
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.ngsindex.BowtieColorIndex(**kwd)[source]¶
Bases:
BowtieIndex
Bowtie color space index
- file_ext = 'bowtie_color_index'¶
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.ngsindex.BowtieBaseIndex(**kwd)[source]¶
Bases:
BowtieIndex
Bowtie base space index
- file_ext = 'bowtie_base_index'¶
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.phylip module¶
Created on January. 05, 2018
@authors: Kenzo-Hugo Hillion and Fabien Mareuil, Institut Pasteur, Paris @contacts: kehillio@pasteur.fr and fabien.mareuil@pasteur.fr @project: galaxy @githuborganization: C3BI Phylip datatype sniffer
- class galaxy.datatypes.phylip.Phylip(**kwd)[source]¶
Bases:
Text
Phylip format stores a multiple sequence alignment
- edam_data = 'data_0863'¶
- edam_format = 'format_1997'¶
- file_ext = 'phylip'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of sequences and the number of data lines in dataset.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
All Phylip files starts with the number of sequences so we can use this to count the following number of sequences in the first ‘stack’
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test_strict_interleaved.phylip') >>> Phylip().sniff(fname) True >>> fname = get_test_fname('test_relaxed_interleaved.phylip') >>> Phylip().sniff(fname) True >>> fname = get_test_fname("not_a_phylip_file.tabular") >>> Phylip().sniff(fname) False
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
galaxy.datatypes.plant_tribes module¶
- class galaxy.datatypes.plant_tribes.Smat(**kwd)[source]¶
Bases:
Text
- file_ext = 'smat'¶
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
The use of ESTScan implies the creation of scores matrices which reflect the codons preferences in the studied organisms. The ESTScan package includes scripts for generating these files. The output of these scripts consists of the matrices, one for each isochor, and which look like this:
FORMAT: hse_4is.conf CODING REGION 6 3 1 s C+G: 0 44 -1 0 2 -2 2 1 -8 0
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test_space.txt') >>> Smat().sniff(fname) False >>> fname = get_test_fname('test_tab.bed') >>> Smat().sniff(fname) False >>> fname = get_test_fname('1.smat') >>> Smat().sniff(fname) True
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.plant_tribes.PlantTribesKsComponents(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'ptkscmp'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of significant components in the Ks distribution. The dataset will always be on the order of less than 10 lines.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- sniff(filename: str) bool [source]¶
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test_tab.bed') >>> PlantTribesKsComponents().sniff(fname) False >>> fname = get_test_fname('1.ptkscmp') >>> PlantTribesKsComponents().sniff(fname) True
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'number_comp': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.proteomics module¶
Proteomics Datatypes
- class galaxy.datatypes.proteomics.Wiff(**kwd)[source]¶
Bases:
Binary
Class for wiff files.
- edam_data = 'data_2536'¶
- edam_format = 'format_3710'¶
- file_ext = 'wiff'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.Wiff2(**kwd)[source]¶
Bases:
Binary
Class for wiff2 files.
- edam_data = 'data_2536'¶
- edam_format = 'format_3710'¶
- file_ext = 'wiff2'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.MzTab(**kwd)[source]¶
Bases:
Text
exchange format for proteomics and metabolomics results
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.mztab') >>> MzTab().sniff(fname) True >>> fname = get_test_fname('test.mztab2') >>> MzTab().sniff(fname) False
- edam_data = 'data_3681'¶
- file_ext = 'mztab'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is the correct type.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.proteomics.MzTab2(**kwd)[source]¶
Bases:
MzTab
exchange format for proteomics and metabolomics results
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.mztab2') >>> MzTab2().sniff(fname) True >>> fname = get_test_fname('test.mztab') >>> MzTab2().sniff(fname) False
- file_ext = 'mztab2'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.Kroenik(**kwd)[source]¶
Bases:
Tabular
Kroenik (HardKloer sibling) files
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.kroenik') >>> Kroenik().sniff(fname) True >>> fname = get_test_fname('test.peplist') >>> Kroenik().sniff(fname) False
- file_ext = 'kroenik'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.proteomics.PepList(**kwd)[source]¶
Bases:
Tabular
Peplist file as used in OpenMS https://github.com/OpenMS/OpenMS/blob/0fc8765670a0ad625c883f328de60f738f7325a4/src/openms/source/FORMAT/FileHandler.cpp#L432
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.peplist') >>> PepList().sniff(fname) True >>> fname = get_test_fname('test.psms') >>> PepList().sniff(fname) False
- file_ext = 'peplist'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.proteomics.PSMS(**kwd)[source]¶
Bases:
Tabular
Percolator tab-delimited output (PSM level, .psms) as used in OpenMS https://github.com/OpenMS/OpenMS/blob/0fc8765670a0ad625c883f328de60f738f7325a4/src/openms/source/FORMAT/FileHandler.cpp#L453 see also http://www.kojak-ms.org/docs/percresults.html
Note that the data rows can have more columns than the header line since ProteinIds are listed tab-separated.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.psms') >>> PSMS().sniff(fname) True >>> fname = get_test_fname('test.kroenik') >>> PSMS().sniff(fname) False
- file_ext = 'psms'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.proteomics.PEFF(**kwd)[source]¶
Bases:
Sequence
PSI Extended FASTA Format https://github.com/HUPO-PSI/PEFF
- file_ext = 'peff'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'test.peff' ) >>> PEFF().sniff( fname ) True >>> fname = get_test_fname( 'sequence.fasta' ) >>> PEFF().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.proteomics.PepXmlReport(**kwd)[source]¶
Bases:
Tabular
pepxml converted to tabular report
- edam_data = 'data_2536'¶
- file_ext = 'pepxml.tsv'¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.ProtXmlReport(**kwd)[source]¶
Bases:
Tabular
protxml converted to tabular report
- edam_data = 'data_2536'¶
- file_ext = 'protxml.tsv'¶
- comment_lines = 1¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.Dta(**kwd)[source]¶
Bases:
TabularData
dta The first line contains the singly protonated peptide mass (MH+) and the peptide charge state separated by a space. Subsequent lines contain space separated pairs of fragment ion m/z and intensity values.
- file_ext = 'dta'¶
- comment_lines = 0¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.Dta2d(**kwd)[source]¶
Bases:
TabularData
dta2d: files with three tab/space-separated columns. The default format is: retention time (seconds) , m/z , intensity. If the first line starts with ‘#’, a different order is defined by the the order of the keywords ‘MIN’ (retention time in minutes) or ‘SEC’ (retention time in seconds), ‘MZ’, and ‘INT’. Example: ‘#MZ MIN INT’ The peaks of one retention time have to be in subsequent lines.
Note: sniffer detects (tab or space separated) dta2d files with correct header, wo header seems to generic
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.dta2d') >>> Dta2d().sniff(fname) True >>> fname = get_test_fname('test.edta') >>> Dta2d().sniff(fname) False
- file_ext = 'dta2d'¶
- comment_lines = 0¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.proteomics.Edta(**kwd)[source]¶
Bases:
TabularData
Input text file containing tab, space or comma separated columns. The separator between columns is checked in the first line in this order.
It supports three variants of this format.
Columns are: RT, MZ, Intensity A header is optional.
Columns are: RT, MZ, Intensity, Charge, <Meta-Data> columns{0,} A header is mandatory.
Columns are: (RT, MZ, Intensity, Charge){1,}, <Meta-Data> columns{0,} Header is mandatory. First quadruplet is the consensus. All following quadruplets describe the sub-features. This variant is discerned from variant #2 by the name of the fifth column, which is required to be RT1 (or rt1). All other column names for sub-features are faithfully ignored.
Note the sniffer only detects files with header.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('test.edta') >>> Edta().sniff(fname) True >>> fname = get_test_fname('test.dta2d') >>> Edta().sniff(fname) False
- file_ext = 'edta'¶
- comment_lines = 0¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.proteomics.ProteomicsXml(**kwd)[source]¶
Bases:
GenericXml
An enhanced XML datatype used to reuse code across several proteomic/mass-spec datatypes.
- edam_data = 'data_2536'¶
- edam_format = 'format_2032'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is the correct XML type.
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.ParamXml(**kwd)[source]¶
Bases:
ProteomicsXml
store Parameters in XML formal
- file_ext = 'paramxml'¶
- blurb = 'parameters in xmls'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.PepXml(**kwd)[source]¶
Bases:
ProteomicsXml
pepXML data
- edam_format = 'format_3655'¶
- file_ext = 'pepxml'¶
- blurb = 'pepXML data'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.MascotXML(**kwd)[source]¶
Bases:
ProteomicsXml
mzXML data
- file_ext = 'mascotxml'¶
- blurb = 'mascot Mass Spectrometry data'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.MzML(**kwd)[source]¶
Bases:
ProteomicsXml
mzML data
- edam_format = 'format_3244'¶
- file_ext = 'mzml'¶
- blurb = 'mzML Mass Spectrometry data'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.NmrML(**kwd)[source]¶
Bases:
ProteomicsXml
nmrML data
- file_ext = 'nmrml'¶
- blurb = 'nmrML NMR data'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.ProtXML(**kwd)[source]¶
Bases:
ProteomicsXml
protXML data
- file_ext = 'protxml'¶
- blurb = 'prot XML Search Results'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.MzXML(**kwd)[source]¶
Bases:
ProteomicsXml
mzXML data
- edam_format = 'format_3654'¶
- file_ext = 'mzxml'¶
- blurb = 'mzXML Mass Spectrometry data'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.MzData(**kwd)[source]¶
Bases:
ProteomicsXml
mzData data
- edam_format = 'format_3245'¶
- file_ext = 'mzdata'¶
- blurb = 'mzData Mass Spectrometry data'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.MzIdentML(**kwd)[source]¶
Bases:
ProteomicsXml
- edam_format = 'format_3247'¶
- file_ext = 'mzid'¶
- blurb = 'XML identified peptides and proteins.'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.TraML(**kwd)[source]¶
Bases:
ProteomicsXml
- edam_format = 'format_3246'¶
- file_ext = 'traml'¶
- blurb = 'TraML transition list'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.TrafoXML(**kwd)[source]¶
Bases:
ProteomicsXml
- file_ext = 'trafoxml'¶
- blurb = 'RT alignment tranformation'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.MzQuantML(**kwd)[source]¶
Bases:
ProteomicsXml
- edam_format = 'format_3248'¶
- file_ext = 'mzq'¶
- blurb = 'XML quantification data'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.ConsensusXML(**kwd)[source]¶
Bases:
ProteomicsXml
- file_ext = 'consensusxml'¶
- blurb = 'OpenMS multiple LC-MS map alignment file'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.FeatureXML(**kwd)[source]¶
Bases:
ProteomicsXml
- file_ext = 'featurexml'¶
- blurb = 'OpenMS feature file'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.IdXML(**kwd)[source]¶
Bases:
ProteomicsXml
- file_ext = 'idxml'¶
- blurb = 'OpenMS identification file'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.TandemXML(**kwd)[source]¶
Bases:
ProteomicsXml
- edam_format = 'format_3711'¶
- file_ext = 'tandem'¶
- blurb = 'X!Tandem search results file'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.UniProtXML(**kwd)[source]¶
Bases:
ProteomicsXml
- file_ext = 'uniprotxml'¶
- blurb = 'UniProt Proteome file'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.XquestXML(**kwd)[source]¶
Bases:
ProteomicsXml
- file_ext = 'xquest.xml'¶
- blurb = 'XQuest XML file'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.XquestSpecXML(**kwd)[source]¶
Bases:
ProteomicsXml
spec.xml
- file_ext = 'spec.xml'¶
- blurb = 'xquest_spectra'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.QCML(**kwd)[source]¶
Bases:
ProteomicsXml
qcml https://github.com/OpenMS/OpenMS/blob/113c49d01677f7f03343ce7cd542d83c99b351ee/share/OpenMS/SCHEMAS/mzQCML_0_0_5.xsd https://github.com/OpenMS/OpenMS/blob/3cfc57ad1788e7ab2bd6dd9862818b2855234c3f/share/OpenMS/SCHEMAS/qcML_0.0.7.xsd
- file_ext = 'qcml'¶
- blurb = 'QualityAssessments to runs'¶
- metadata_spec: metadata.MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.Mgf(**kwd)[source]¶
Bases:
Text
Mascot Generic Format data
- edam_data = 'data_2536'¶
- edam_format = 'format_3651'¶
- file_ext = 'mgf'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.MascotDat(**kwd)[source]¶
Bases:
Text
Mascot search results
- edam_data = 'data_2536'¶
- edam_format = 'format_3713'¶
- file_ext = 'mascotdat'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.ThermoRAW(**kwd)[source]¶
Bases:
Binary
Class describing a Thermo Finnigan binary RAW file
- edam_data = 'data_2536'¶
- edam_format = 'format_3712'¶
- file_ext = 'thermo.raw'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.Msp(**kwd)[source]¶
Bases:
Text
Output of NIST MS Search Program chemdata.nist.gov/mass-spc/ftp/mass-spc/PepLib.pdf
- file_ext = 'msp'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is a NIST MSP output file.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.proteomics.SPLibNoIndex(**kwd)[source]¶
Bases:
Text
SPlib without index file
- file_ext = 'splib_noindex'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.SPLib(**kwd)[source]¶
Bases:
Msp
SpectraST Spectral Library. Closely related to msp format
- file_ext = 'splib'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is a SpectraST generated file.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.proteomics.Ms2(**kwd)[source]¶
Bases:
Text
- file_ext = 'ms2'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is a valid ms2 file.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.proteomics.XHunterAslFormat(**kwd)[source]¶
Bases:
Binary
Annotated Spectra in the HLF format http://www.thegpm.org/HUNTER/format_2006_09_15.html
- file_ext = 'hlf'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.Sf3(**kwd)[source]¶
Bases:
Binary
Class describing a Scaffold SF3 files
- file_ext = 'sf3'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.proteomics.ImzML(**kwd)[source]¶
Bases:
Binary
Class for imzML files. http://www.imzml.org
- edam_format = 'format_3682'¶
- file_ext = 'imzml'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.qiime2 module¶
- class galaxy.datatypes.qiime2.QIIME2Artifact(**kwd)[source]¶
Bases:
_QIIME2ResultBase
- file_ext = 'qza'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'format': <galaxy.model.metadata.MetadataElementSpec object>, 'semantic_type': <galaxy.model.metadata.MetadataElementSpec object>, 'semantic_type_simple': <galaxy.model.metadata.MetadataElementSpec object>, 'uuid': <galaxy.model.metadata.MetadataElementSpec object>, 'version': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.qiime2.QIIME2Visualization(**kwd)[source]¶
Bases:
_QIIME2ResultBase
- file_ext = 'qzv'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'format': <galaxy.model.metadata.MetadataElementSpec object>, 'semantic_type': <galaxy.model.metadata.MetadataElementSpec object>, 'semantic_type_simple': <galaxy.model.metadata.MetadataElementSpec object>, 'uuid': <galaxy.model.metadata.MetadataElementSpec object>, 'version': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.qiime2.QIIME2Metadata(**kwd)[source]¶
Bases:
Tabular
QIIME 2 supports overriding the type of a column to Categorical when a specific directive #q2:types is present under the ID row.
Galaxy already understands column types quite well, however we sometimes want to override its inferred type.
For Galaxy, we are going to require that if a directive occurs, it happens on the second line (after the header). This is the most typical location and interacts best with the current implementation of Tabular.
- file_ext = 'qiime2.tabular'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Let Galaxy’s Tabular format handle most of this. We will just jump in at the last minute to (potentially) override some column types.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
galaxy.datatypes.qualityscore module¶
Qualityscore class
- class galaxy.datatypes.qualityscore.QualityScore(**kwd)[source]¶
Bases:
Text
until we know more about quality score formats
- edam_data = 'data_2048'¶
- edam_format = 'format_3606'¶
- file_ext = 'qual'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.qualityscore.QualityScoreSOLiD(**kwd)[source]¶
Bases:
QualityScore
until we know more about quality score formats
- edam_format = 'format_3610'¶
- file_ext = 'qualsolid'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'sequence.fasta' ) >>> QualityScoreSOLiD().sniff( fname ) False >>> fname = get_test_fname( 'sequence.qualsolid' ) >>> QualityScoreSOLiD().sniff( fname ) True
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.qualityscore.QualityScore454(**kwd)[source]¶
Bases:
QualityScore
until we know more about quality score formats
- edam_format = 'format_3611'¶
- file_ext = 'qual454'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'sequence.fasta' ) >>> QualityScore454().sniff( fname ) False >>> fname = get_test_fname( 'sequence.qual454' ) >>> QualityScore454().sniff( fname ) True
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.qualityscore.QualityScoreSolexa(**kwd)[source]¶
Bases:
QualityScore
until we know more about quality score formats
- edam_format = 'format_3608'¶
- file_ext = 'qualsolexa'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.qualityscore.QualityScoreIllumina(**kwd)[source]¶
Bases:
QualityScore
until we know more about quality score formats
- edam_format = 'format_3609'¶
- file_ext = 'qualillumina'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.registry module¶
Provides mapping between extensions and datatypes, mime-types, etc.
- class galaxy.datatypes.registry.Registry(config=None)[source]¶
Bases:
object
- load_datatypes(root_dir=None, config=None, override=True, use_converters=True, use_display_applications=True, use_build_sites=True)[source]¶
Parse a datatypes XML file located at root_dir/config (if processing the Galaxy distributed config) or contained within an installed Tool Shed repository.
- load_datatype_sniffers(root, override=False, compressed_sniffers=None)[source]¶
Process the sniffers element from a parsed a datatypes XML file located at root_dir/config (if processing the Galaxy distributed config) or contained within an installed Tool Shed repository.
- get_datatype_class_by_name(name)[source]¶
Return the datatype class where the datatype’s type attribute (as defined in the datatype_conf.xml file) contains name.
- get_mimetype_by_extension(ext, default='application/octet-stream')[source]¶
Returns a mimetype based on an extension
- load_datatype_converters(toolbox, use_cached=False)[source]¶
Add datatype converters from self.converters to the calling app’s toolbox.
- load_display_applications(app)[source]¶
Add display applications from self.display_app_containers or to appropriate datatypes.
- reload_display_applications(display_application_ids=None)[source]¶
Reloads display applications: by id, or all if no ids provided Returns tuple( [reloaded_ids], [failed_ids] )
- get_converter_by_target_type(source_ext, target_ext)[source]¶
Returns a converter based on source and target datatypes
- find_conversion_destination_for_dataset_by_extensions(dataset_or_ext: Union[str, DatasetProtocol], accepted_formats: List[str], converter_safe: bool = True) Tuple[bool, Optional[str], Optional[DatasetProtocol]] [source]¶
returns (direct_match, converted_ext, converted_dataset) - direct match is True iff no the data set already has an accepted format - target_ext becomes None if conversion is not possible (or necesary)
- get_upload_metadata_params(context, group, tool)[source]¶
Returns dict of case value:inputs for metadata conditional for upload tool
- property edam_formats¶
- property edam_data¶
galaxy.datatypes.sequence module¶
Sequence classes
- class galaxy.datatypes.sequence.SequenceSplitLocations(**kwd)[source]¶
Bases:
Text
Class storing information about a sequence file composed of multiple gzip files concatenated as one OR an uncompressed file. In the GZIP case, each sub-file’s location is stored in start and end.
The format of the file is JSON:
{ "sections" : [ { "start" : "x", "end" : "y", "sequences" : "z" }, ... ]}
- file_ext = 'fqtoc'¶
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.sequence.Sequence(**kwd)[source]¶
Bases:
Text
Class describing a sequence
- edam_data = 'data_2044'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of sequences and the number of data lines in dataset.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- classmethod do_fast_split(input_datasets, toc_file_datasets, subdir_generator_function, split_params)[source]¶
- classmethod write_split_files(input_datasets, toc_file_datasets, subdir_generator_function, sequences_per_file)[source]¶
- classmethod split(input_datasets: List, subdir_generator_function: Callable, split_params: Dict) None [source]¶
Split a generic sequence file (not sensible or possible, see subclasses).
- static get_split_commands_with_toc(input_name: str, output_name: str, toc_file: Any, start_sequence: int, sequence_count: int) List [source]¶
Uses a Table of Contents dict, parsed from an FQTOC file, to come up with a set of shell commands that will extract the parts necessary >>> three_sections=[dict(start=0, end=74, sequences=10), dict(start=74, end=148, sequences=10), dict(start=148, end=148+76, sequences=10)] >>> Sequence.get_split_commands_with_toc(‘./input.gz’, ‘./output.gz’, dict(sections=three_sections), start_sequence=0, sequence_count=10) [‘dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null >> ./output.gz’] >>> Sequence.get_split_commands_with_toc(‘./input.gz’, ‘./output.gz’, dict(sections=three_sections), start_sequence=1, sequence_count=5) [‘(dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +5 2> /dev/null) | head -20 | gzip -c >> ./output.gz’] >>> Sequence.get_split_commands_with_toc(‘./input.gz’, ‘./output.gz’, dict(sections=three_sections), start_sequence=0, sequence_count=20) [‘dd bs=1 skip=0 count=148 if=./input.gz 2> /dev/null >> ./output.gz’] >>> Sequence.get_split_commands_with_toc(‘./input.gz’, ‘./output.gz’, dict(sections=three_sections), start_sequence=5, sequence_count=10) [‘(dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +21 2> /dev/null) | head -20 | gzip -c >> ./output.gz’, ‘(dd bs=1 skip=74 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +1 2> /dev/null) | head -20 | gzip -c >> ./output.gz’] >>> Sequence.get_split_commands_with_toc(‘./input.gz’, ‘./output.gz’, dict(sections=three_sections), start_sequence=10, sequence_count=10) [‘dd bs=1 skip=74 count=74 if=./input.gz 2> /dev/null >> ./output.gz’] >>> Sequence.get_split_commands_with_toc(‘./input.gz’, ‘./output.gz’, dict(sections=three_sections), start_sequence=5, sequence_count=20) [‘(dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +21 2> /dev/null) | head -20 | gzip -c >> ./output.gz’, ‘dd bs=1 skip=74 count=74 if=./input.gz 2> /dev/null >> ./output.gz’, ‘(dd bs=1 skip=148 count=76 if=./input.gz 2> /dev/null )| zcat | ( tail -n +1 2> /dev/null) | head -20 | gzip -c >> ./output.gz’]
- static get_split_commands_sequential(is_compressed: bool, input_name: str, output_name: str, start_sequence: int, sequence_count: int) List [source]¶
Does a brain-dead sequential scan & extract of certain sequences >>> Sequence.get_split_commands_sequential(True, ‘./input.gz’, ‘./output.gz’, start_sequence=0, sequence_count=10) [‘zcat “./input.gz” | ( tail -n +1 2> /dev/null) | head -40 | gzip -c > “./output.gz”’] >>> Sequence.get_split_commands_sequential(False, ‘./input.fastq’, ‘./output.fastq’, start_sequence=10, sequence_count=10) [‘tail -n +41 “./input.fastq” 2> /dev/null | head -40 > “./output.fastq”’]
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.sequence.Alignment(**kwd)[source]¶
Bases:
Text
Class describing an alignment
- edam_data = 'data_0863'¶
- classmethod split(input_datasets: List, subdir_generator_function: Callable, split_params: Dict) None [source]¶
Split a generic alignment file (not sensible or possible, see subclasses).
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'species': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.sequence.Fasta(**kwd)[source]¶
Bases:
Sequence
Class representing a FASTA sequence
- edam_format = 'format_1929'¶
- file_ext = 'fasta'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in fasta format
A sequence in FASTA format consists of a single-line description, followed by lines of sequence data. The first character of the description line is a greater-than (“>”) symbol in the first column. All lines should be shorter than 80 characters
For complete details see http://www.ncbi.nlm.nih.gov/blast/fasta.shtml
Rules for sniffing as True:
We don’t care about line length (other than empty lines).
The first non-empty line must start with ‘>’ and the Very Next line.strip() must have sequence data and not be a header.
‘sequence data’ here is loosely defined as non-empty lines which do not start with ‘>’
This will cause Color Space FASTA (csfasta) to be detected as True (they are, after all, still FASTA files - they have a header line followed by sequence data)
Previously this method did some checking to determine if the sequence data had integers (presumably to differentiate between fasta and csfasta)
This should be done through sniff order, where csfasta (currently has a null sniff function) is detected for first (stricter definition) followed sometime after by fasta
We will only check that the first purported sequence is correctly formatted.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'sequence.maf' ) >>> Fasta().sniff( fname ) False >>> fname = get_test_fname( 'sequence.fasta' ) >>> Fasta().sniff( fname ) True
- classmethod split(input_datasets: List, subdir_generator_function: Callable, split_params: Dict) None [source]¶
Split a FASTA file sequence by sequence.
Note that even if split_mode=”number_of_parts”, the actual number of sub-files produced may not match that requested by split_size.
If split_mode=”to_size” then split_size is treated as the number of FASTA records to put in each sub-file (not size in bytes).
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.sequence.csFasta(**kwd)[source]¶
Bases:
Sequence
Class representing the SOLID Color-Space sequence ( csfasta )
- edam_format = 'format_3589'¶
- file_ext = 'csfasta'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- Color-space sequence:
>2_15_85_F3 T213021013012303002332212012112221222112212222
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'sequence.fasta' ) >>> csFasta().sniff( fname ) False >>> fname = get_test_fname( 'sequence.csfasta' ) >>> csFasta().sniff( fname ) True
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of sequences and the number of data lines in dataset.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.sequence.Fastg(**kwd)[source]¶
Bases:
Sequence
Class representing a FASTG sequence http://fastg.sourceforge.net/FASTG_Spec_v1.00.pdf
- edam_format = 'format_3823'¶
- file_ext = 'fastg'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- FASTG must begin with lines:
#FASTG:begin; #FASTG:version=*.*; #FASTG:properties;
- Or these can be combined on a line:
#FASTG:begin:version=*.*:properties;
- FASTG must end with line:
#FASTG:end;
- Example FASTG file:
#FASTG:begin; #FASTG:version=1.0:assembly_name=”tiny example”; >chr1:chr1; ACGANNNNN[5:gap:size=(5,4..6)]CAGGC[1:alt:allele|C,G]TATACG >chr2; ACATACGCATATATATATATATATATAT[20:tandem:size=(10,8..12)|AT]TCAGGCA[1:alt|A,T,TT]GGAC #FASTG:end;
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'sequence.fasta' ) >>> Fastg().sniff( fname ) False >>> fname = get_test_fname( 'sequence.fastg' ) >>> Fastg().sniff( fname ) True
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of sequences and the number of data lines in dataset.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'properties': <galaxy.model.metadata.MetadataElementSpec object>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object>, 'version': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.sequence.BaseFastq(**kwd)[source]¶
Bases:
Sequence
Base class for FastQ sequences
- edam_format = 'format_1930'¶
- file_ext = 'fastq'¶
- bases_regexp = re.compile('^[NGTAC 0123\\.]*$', re.IGNORECASE)¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of sequences and the number of data lines in dataset. FIXME: This does not properly handle line wrapping
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in generic fastq format For details, see http://maq.sourceforge.net/fastq.shtml
- Note: There are three kinds of FASTQ files, known as “Sanger” (sometimes called “Standard”), Solexa, and Illumina
These differ in the representation of the quality scores
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('1.fastqsanger') >>> FastqSanger().sniff(fname) True >>> fname = get_test_fname('4.fastqsanger') >>> FastqSanger().sniff(fname) True >>> fname = get_test_fname('3.fastq') >>> FastqSanger().sniff(fname) False >>> Fastq().sniff(fname) True >>> fname = get_test_fname('2.fastq') >>> Fastq().sniff(fname) True >>> FastqSanger().sniff(fname) False >>> fname = get_test_fname('1.fastq') >>> FastqSanger().sniff(fname) False >>> fname = get_test_fname('1.fastqcssanger') >>> FastqSanger().sniff(fname) False >>> Fastq().sniff(fname) True >>> FastqCSSanger().sniff(fname) True
- display_data(trans, dataset: DatasetHasHidProtocol, preview: bool = False, filename: Optional[str] = None, to_ext: Optional[str] = None, **kwd)[source]¶
Displays data in central pane if preview is True, else handles download.
Datatypes should be very careful if overridding this method and this interface between datatypes and Galaxy will likely change.
TOOD: Document alternatives to overridding this method (data providers?).
- classmethod split(input_datasets: List, subdir_generator_function: Callable, split_params: Dict) None [source]¶
FASTQ files are split on cluster boundaries, in increments of 4 lines
- static process_split_file(data: Dict) bool [source]¶
This is called in the context of an external process launched by a Task (possibly not on the Galaxy machine) to create the input files for the Task. The parameters: data - a dict containing the contents of the split file
- classmethod check_first_block(file_prefix: FilePrefix)[source]¶
- validate(dataset: DatasetProtocol, **kwd) DatatypeValidation [source]¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.sequence.Fastq(**kwd)[source]¶
Bases:
BaseFastq
Class representing a generic FASTQ sequence
- edam_format = 'format_1930'¶
- file_ext = 'fastq'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.sequence.FastqSanger(**kwd)[source]¶
Bases:
Fastq
Class representing a FASTQ sequence (the Sanger variant)
phred scored quality values 0:50 represented by ASCII 33:83
- edam_format = 'format_1932'¶
- file_ext = 'fastqsanger'¶
- bases_regexp = re.compile('^[NGTAC]*$', re.IGNORECASE)¶
- static quality_check(lines: Iterable) bool [source]¶
Presuming lines are lines from a fastq file, return True if the qualities are compatible with sanger encoding
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.sequence.FastqSolexa(**kwd)[source]¶
Bases:
Fastq
Class representing a FASTQ sequence ( the Solexa variant )
solexa scored quality values -5:40 represented by ASCII 59:104
- edam_format = 'format_1933'¶
- file_ext = 'fastqsolexa'¶
- static quality_check(lines: Iterable) bool [source]¶
Presuming lines are lines from a fastq file, return True if the qualities are compatible with sanger encoding
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in generic fastq format For details, see http://maq.sourceforge.net/fastq.shtml
- Note: There are three kinds of FASTQ files, known as “Sanger” (sometimes called “Standard”), Solexa, and Illumina
These differ in the representation of the quality scores
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('1.fastqsanger') >>> FastqSanger().sniff(fname) True >>> fname = get_test_fname('4.fastqsanger') >>> FastqSanger().sniff(fname) True >>> fname = get_test_fname('3.fastq') >>> FastqSanger().sniff(fname) False >>> Fastq().sniff(fname) True >>> fname = get_test_fname('2.fastq') >>> Fastq().sniff(fname) True >>> FastqSanger().sniff(fname) False >>> fname = get_test_fname('1.fastq') >>> FastqSanger().sniff(fname) False >>> fname = get_test_fname('1.fastqcssanger') >>> FastqSanger().sniff(fname) False >>> Fastq().sniff(fname) True >>> FastqCSSanger().sniff(fname) True
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.sequence.FastqIllumina(**kwd)[source]¶
Bases:
Fastq
Class representing a FASTQ sequence ( the Illumina 1.3+ variant )
phred scored quality values 0:40 represented by ASCII 64:104
- edam_format = 'format_1931'¶
- file_ext = 'fastqillumina'¶
- static quality_check(lines: Iterable) bool [source]¶
Presuming lines are lines from a fastq file, return True if the qualities are compatible with sanger encoding
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in generic fastq format For details, see http://maq.sourceforge.net/fastq.shtml
- Note: There are three kinds of FASTQ files, known as “Sanger” (sometimes called “Standard”), Solexa, and Illumina
These differ in the representation of the quality scores
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('1.fastqsanger') >>> FastqSanger().sniff(fname) True >>> fname = get_test_fname('4.fastqsanger') >>> FastqSanger().sniff(fname) True >>> fname = get_test_fname('3.fastq') >>> FastqSanger().sniff(fname) False >>> Fastq().sniff(fname) True >>> fname = get_test_fname('2.fastq') >>> Fastq().sniff(fname) True >>> FastqSanger().sniff(fname) False >>> fname = get_test_fname('1.fastq') >>> FastqSanger().sniff(fname) False >>> fname = get_test_fname('1.fastqcssanger') >>> FastqSanger().sniff(fname) False >>> Fastq().sniff(fname) True >>> FastqCSSanger().sniff(fname) True
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.sequence.FastqCSSanger(**kwd)[source]¶
Bases:
Fastq
Class representing a Color Space FASTQ sequence ( e.g a SOLiD variant )
sequence in in color space phred scored quality values 0:93 represented by ASCII 33:126
- file_ext = 'fastqcssanger'¶
- bases_regexp = re.compile('^[NGTAC][0123\\.]*$', re.IGNORECASE)¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.sequence.Maf(**kwd)[source]¶
Bases:
Alignment
Class describing a Maf alignment
- edam_format = 'format_3008'¶
- file_ext = 'maf'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, metadata_tmp_files_dir: Optional[str] = None, **kwd) None [source]¶
Parses and sets species, chromosomes, index from MAF file.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- make_html_table(dataset: DatasetProtocol, skipchars: Optional[List] = None) str [source]¶
Create HTML table, used for displaying peek
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines wether the file is in maf format
The .maf format is line-oriented. Each multiple alignment ends with a blank line. Each sequence in an alignment is on a single line, which can get quite long, but there is no length limit. Words in a line are delimited by any white space. Lines starting with # are considered to be comments. Lines starting with ## can be ignored by most programs, but contain meta-data of one form or another.
The first line of a .maf file begins with ##maf. This word is followed by white-space-separated variable=value pairs. There should be no white space surrounding the “=”.
For complete details see http://genome.ucsc.edu/FAQ/FAQformat#format5
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'sequence.maf' ) >>> Maf().sniff( fname ) True >>> fname = get_test_fname( 'sequence.fasta' ) >>> Maf().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'blocks': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'maf_index': <galaxy.model.metadata.MetadataElementSpec object>, 'species': <galaxy.model.metadata.MetadataElementSpec object>, 'species_chromosomes': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.sequence.MafCustomTrack(**kwd)[source]¶
Bases:
Text
- file_ext = 'mafcustomtrack'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Parses and sets viewport metadata from MAF file.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'vp_chromosome': <galaxy.model.metadata.MetadataElementSpec object>, 'vp_end': <galaxy.model.metadata.MetadataElementSpec object>, 'vp_start': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.sequence.Axt(**kwd)[source]¶
Bases:
Text
Class describing an axt alignment
- edam_data = 'data_0863'¶
- edam_format = 'format_3013'¶
- file_ext = 'axt'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in axt format
axt alignment files are produced from Blastz, an alignment tool available from Webb Miller’s lab at Penn State University.
Each alignment block in an axt file contains three lines: a summary line and 2 sequence lines. Blocks are separated from one another by blank lines.
The summary line contains chromosomal position and size information about the alignment. It consists of 9 required fields.
The sequence lines contain the sequence of the primary assembly (line 2) and aligning assembly (line 3) with inserts. Repeats are indicated by lower-case letters.
For complete details see http://genome.ucsc.edu/goldenPath/help/axt.html
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'alignment.axt' ) >>> Axt().sniff( fname ) True >>> fname = get_test_fname( 'alignment.lav' ) >>> Axt().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.sequence.Lav(**kwd)[source]¶
Bases:
Text
Class describing a LAV alignment
- edam_data = 'data_0863'¶
- edam_format = 'format_3014'¶
- file_ext = 'lav'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in lav format
LAV is an alignment format developed by Webb Miller’s group. It is the primary output format for BLASTZ. The first line of a .lav file begins with #:lav.
For complete details see http://www.bioperl.org/wiki/LAV_alignment_format
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'alignment.lav' ) >>> Lav().sniff( fname ) True >>> fname = get_test_fname( 'alignment.axt' ) >>> Lav().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.sequence.RNADotPlotMatrix(**kwd)[source]¶
Bases:
Data
- edam_format = 'format_3466'¶
- file_ext = 'rna_eps'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.sequence.DotBracket(**kwd)[source]¶
Bases:
Sequence
- edam_data = 'data_0880'¶
- edam_format = 'format_1457'¶
- file_ext = 'dbn'¶
- sequence_regexp = re.compile('^[ACGTURYKMSWBDHVN]+$', re.IGNORECASE)¶
- structure_regexp = re.compile('^[\\(\\)\\.\\[\\]{}]+$')¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of sequences and the number of data lines in dataset.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Galaxy Dbn (Dot-Bracket notation) rules:
The first non-empty line is a header line: no comment lines are allowed.
A header line starts with a ‘>’ symbol and continues with 0 or multiple symbols until the line ends.
The second non-empty line is a sequence line.
A sequence line may only include chars that match the FASTA format (https://en.wikipedia.org/wiki/FASTA_format#Sequence_representation) symbols for nucleotides: ACGTURYKMSWBDHVN, and may thus not include whitespaces.
A sequence line has no prefix and no suffix.
A sequence line is case insensitive.
The third non-empty line is a structure (Dot-Bracket) line and only describes the 2D structure of the sequence above it.
A structure line must consist of the following chars: ‘.{}[]()’.
A structure line must be of the same length as the sequence line, and each char represents the structure of the nucleotide above it.
A structure line has no prefix and no suffix.
A nucleotide pairs with only 1 or 0 other nucleotides.
In a structure line, the number of ‘(’ symbols equals the number of ‘)’ symbols, the number of ‘[’ symbols equals the number of ‘]’ symbols and the number of ‘{’ symbols equals the number of ‘}’ symbols.
The format accepts multiple entries per file, given that each entry is provided as three lines: the header, sequence and structure line.
Sniffing is only applied on the first entry.
Empty lines are allowed.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.sequence.Genbank(**kwd)[source]¶
Bases:
Text
Class representing a Genbank sequence
- edam_format = 'format_1936'¶
- edam_data = 'data_0849'¶
- file_ext = 'genbank'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determine whether the file is in genbank format. Works for compressed files.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( '1.genbank' ) >>> Genbank().sniff( fname ) True
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.sequence.MemePsp(**kwd)[source]¶
Bases:
Sequence
Class representing MEME Position Specific Priors
- file_ext = 'memepsp'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
The format of an entry in a PSP file is:
>ID WIDTH PRIORS
For complete details see http://meme-suite.org/doc/psp-format.html
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('1.memepsp') >>> MemePsp().sniff(fname) True >>> fname = get_test_fname('sequence.fasta') >>> MemePsp().sniff(fname) False
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
galaxy.datatypes.sniff module¶
File format detector
- galaxy.datatypes.sniff.handle_composite_file(datatype, src_path, extra_files, name, is_binary, tmp_dir, tmp_prefix, upload_opts)[source]¶
- class galaxy.datatypes.sniff.ConvertResult(line_count, converted_path, converted_newlines, converted_regex)[source]¶
Bases:
tuple
- property line_count¶
Alias for field number 0
- property converted_path¶
Alias for field number 1
- property converted_newlines¶
Alias for field number 2
- property converted_regex¶
Alias for field number 3
- class galaxy.datatypes.sniff.ConvertFunction(*args, **kwds)[source]¶
Bases:
Protocol
- __init__(*args, **kwargs)¶
- galaxy.datatypes.sniff.convert_newlines(fname: str, in_place: bool = True, tmp_dir: Optional[str] = None, tmp_prefix: Optional[str] = 'gxupload', block_size: int = 131072, regexp=None) ConvertResult [source]¶
Converts in place a file from universal line endings to Posix line endings.
- galaxy.datatypes.sniff.convert_sep2tabs(fname: str, in_place: bool = True, tmp_dir: Optional[str] = None, tmp_prefix: Optional[str] = 'gxupload', block_size: int = 131072)[source]¶
Transforms in place a ‘sep’ separated file to a tab separated one
- galaxy.datatypes.sniff.convert_newlines_sep2tabs(fname: str, in_place: bool = True, tmp_dir: Optional[str] = None, tmp_prefix: Optional[str] = 'gxupload') ConvertResult [source]¶
Converts newlines in a file to posix newlines and replaces spaces with tabs.
- galaxy.datatypes.sniff.iter_headers(fname_or_file_prefix, sep, count=60, comment_designator=None)[source]¶
- galaxy.datatypes.sniff.validate_tabular(fname_or_file_prefix, validate_row, sep, comment_designator=None)[source]¶
- galaxy.datatypes.sniff.get_headers(fname_or_file_prefix, sep, count=60, comment_designator=None)[source]¶
Returns a list with the first ‘count’ lines split by ‘sep’, ignoring lines starting with ‘comment_designator’
>>> fname = get_test_fname('complete.bed') >>> get_headers(fname,'\t') == [['chr7', '127475281', '127491632', 'NM_000230', '0', '+', '127486022', '127488767', '0', '3', '29,172,3225,', '0,10713,13126,'], ['chr7', '127486011', '127488900', 'D49487', '0', '+', '127486022', '127488767', '0', '2', '155,490,', '0,2399']] True >>> fname = get_test_fname('test.gff') >>> get_headers(fname, '\t', count=5, comment_designator='#') == [[''], ['chr7', 'bed2gff', 'AR', '26731313', '26731437', '.', '+', '.', 'score'], ['chr7', 'bed2gff', 'AR', '26731491', '26731536', '.', '+', '.', 'score'], ['chr7', 'bed2gff', 'AR', '26731541', '26731649', '.', '+', '.', 'score'], ['chr7', 'bed2gff', 'AR', '26731659', '26731841', '.', '+', '.', 'score']] True
- galaxy.datatypes.sniff.is_column_based(fname_or_file_prefix, sep='\t', skip=0)[source]¶
Checks whether the file is column based with respect to a separator (defaults to tab separator).
>>> fname = get_test_fname('test.gff') >>> is_column_based(fname) True >>> fname = get_test_fname('test_tab.bed') >>> is_column_based(fname) True >>> is_column_based(fname, sep=' ') False >>> fname = get_test_fname('test_space.txt') >>> is_column_based(fname) False >>> is_column_based(fname, sep=' ') True >>> fname = get_test_fname('test_ensembl.tabular') >>> is_column_based(fname) True >>> fname = get_test_fname('test_tab1.tabular') >>> is_column_based(fname, sep=' ', skip=0) False >>> fname = get_test_fname('test_tab1.tabular') >>> is_column_based(fname) True
- galaxy.datatypes.sniff.guess_ext(fname_or_file_prefix: Union[str, FilePrefix], sniff_order, is_binary=None, auto_decompress=True)[source]¶
Returns an extension that can be used in the datatype factory to generate a data for the ‘fname’ file
>>> from galaxy.datatypes.registry import example_datatype_registry_for_sample >>> datatypes_registry = example_datatype_registry_for_sample() >>> sniff_order = datatypes_registry.sniff_order >>> fname = get_test_fname('empty.txt') >>> guess_ext(fname, sniff_order) 'txt' >>> fname = get_test_fname('megablast_xml_parser_test1.blastxml') >>> guess_ext(fname, sniff_order) 'blastxml' >>> fname = get_test_fname('1.psl') >>> guess_ext(fname, sniff_order) 'psl' >>> fname = get_test_fname('2.psl') >>> guess_ext(fname, sniff_order) 'psl' >>> fname = get_test_fname('interval.interval') >>> guess_ext(fname, sniff_order) 'interval' >>> fname = get_test_fname('interv1.bed') >>> guess_ext(fname, sniff_order) 'bed' >>> fname = get_test_fname('test_tab.bed') >>> guess_ext(fname, sniff_order) 'bed' >>> fname = get_test_fname('sequence.maf') >>> guess_ext(fname, sniff_order) 'maf' >>> fname = get_test_fname('sequence.fasta') >>> guess_ext(fname, sniff_order) 'fasta' >>> fname = get_test_fname('1.genbank') >>> guess_ext(fname, sniff_order) 'genbank' >>> fname = get_test_fname('1.genbank.gz') >>> guess_ext(fname, sniff_order) 'genbank.gz' >>> fname = get_test_fname('file.html') >>> guess_ext(fname, sniff_order) 'html' >>> fname = get_test_fname('test.gtf') >>> guess_ext(fname, sniff_order) 'gtf' >>> fname = get_test_fname('test.gff') >>> guess_ext(fname, sniff_order) 'gff' >>> fname = get_test_fname('gff.gff3') >>> guess_ext(fname, sniff_order) 'gff3' >>> fname = get_test_fname('2.txt') >>> guess_ext(fname, sniff_order) 'txt' >>> fname = get_test_fname('test_tab2.tabular') >>> guess_ext(fname, sniff_order) 'tabular' >>> fname = get_test_fname('3.txt') >>> guess_ext(fname, sniff_order) 'txt' >>> fname = get_test_fname('test_tab1.tabular') >>> guess_ext(fname, sniff_order) 'tabular' >>> fname = get_test_fname('alignment.lav') >>> guess_ext(fname, sniff_order) 'lav' >>> fname = get_test_fname('1.sff') >>> guess_ext(fname, sniff_order) 'sff' >>> fname = get_test_fname('1.bam') >>> guess_ext(fname, sniff_order) 'bam' >>> fname = get_test_fname('3unsorted.bam') >>> guess_ext(fname, sniff_order) 'unsorted.bam' >>> fname = get_test_fname('test.idpdb') >>> guess_ext(fname, sniff_order) 'idpdb' >>> fname = get_test_fname('test.mz5') >>> guess_ext(fname, sniff_order) 'h5' >>> fname = get_test_fname('issue1818.tabular') >>> guess_ext(fname, sniff_order) 'tabular' >>> fname = get_test_fname('drugbank_drugs.cml') >>> guess_ext(fname, sniff_order) 'cml' >>> fname = get_test_fname('q.fps') >>> guess_ext(fname, sniff_order) 'fps' >>> fname = get_test_fname('drugbank_drugs.inchi') >>> guess_ext(fname, sniff_order) 'inchi' >>> fname = get_test_fname('drugbank_drugs.mol2') >>> guess_ext(fname, sniff_order) 'mol2' >>> fname = get_test_fname('drugbank_drugs.sdf') >>> guess_ext(fname, sniff_order) 'sdf' >>> fname = get_test_fname('5e5z.pdb') >>> guess_ext(fname, sniff_order) 'pdb' >>> fname = get_test_fname('Si_uppercase.cell') >>> guess_ext(fname, sniff_order) 'cell' >>> fname = get_test_fname('Si_lowercase.cell') >>> guess_ext(fname, sniff_order) 'cell' >>> fname = get_test_fname('Si.cif') >>> guess_ext(fname, sniff_order) 'cif' >>> fname = get_test_fname('Si.xyz') >>> guess_ext(fname, sniff_order) 'xyz' >>> fname = get_test_fname('Si_multi.xyz') >>> guess_ext(fname, sniff_order) 'xyz' >>> fname = get_test_fname('Si.extxyz') >>> guess_ext(fname, sniff_order) 'extxyz' >>> fname = get_test_fname('Si.castep') >>> guess_ext(fname, sniff_order) 'castep' >>> fname = get_test_fname('test.fits') >>> guess_ext(fname, sniff_order) 'fits' >>> fname = get_test_fname('Si.param') >>> guess_ext(fname, sniff_order) 'param' >>> fname = get_test_fname('Si.den_fmt') >>> guess_ext(fname, sniff_order) 'den_fmt' >>> fname = get_test_fname('mothur_datatypetest_true.mothur.otu') >>> guess_ext(fname, sniff_order) 'mothur.otu' >>> fname = get_test_fname('mothur_datatypetest_true.mothur.lower.dist') >>> guess_ext(fname, sniff_order) 'mothur.lower.dist' >>> fname = get_test_fname('mothur_datatypetest_true.mothur.square.dist') >>> guess_ext(fname, sniff_order) 'mothur.square.dist' >>> fname = get_test_fname('mothur_datatypetest_true.mothur.pair.dist') >>> guess_ext(fname, sniff_order) 'mothur.pair.dist' >>> fname = get_test_fname('mothur_datatypetest_true.mothur.freq') >>> guess_ext(fname, sniff_order) 'mothur.freq' >>> fname = get_test_fname('mothur_datatypetest_true.mothur.quan') >>> guess_ext(fname, sniff_order) 'mothur.quan' >>> fname = get_test_fname('mothur_datatypetest_true.mothur.ref.taxonomy') >>> guess_ext(fname, sniff_order) 'mothur.ref.taxonomy' >>> fname = get_test_fname('mothur_datatypetest_true.mothur.axes') >>> guess_ext(fname, sniff_order) 'mothur.axes' >>> guess_ext(get_test_fname('infernal_model.cm'), sniff_order) 'cm' >>> fname = get_test_fname('1.gg') >>> guess_ext(fname, sniff_order) 'gg' >>> fname = get_test_fname('diamond_db.dmnd') >>> guess_ext(fname, sniff_order) 'dmnd' >>> fname = get_test_fname('1.excel.xls') >>> guess_ext(fname, sniff_order, is_binary=True) 'excel.xls' >>> fname = get_test_fname('biom2_sparse_otu_table_hdf5.biom2') >>> guess_ext(fname, sniff_order) 'biom2' >>> fname = get_test_fname('454Score.pdf') >>> guess_ext(fname, sniff_order) 'pdf' >>> fname = get_test_fname('1.obo') >>> guess_ext(fname, sniff_order) 'obo' >>> fname = get_test_fname('1.arff') >>> guess_ext(fname, sniff_order) 'arff' >>> fname = get_test_fname('1.afg') >>> guess_ext(fname, sniff_order) 'afg' >>> fname = get_test_fname('1.owl') >>> guess_ext(fname, sniff_order) 'owl' >>> fname = get_test_fname('Acanium.snaphmm') >>> guess_ext(fname, sniff_order) 'snaphmm' >>> fname = get_test_fname('wiggle.wig') >>> guess_ext(fname, sniff_order) 'wig' >>> fname = get_test_fname('example.iqtree') >>> guess_ext(fname, sniff_order) 'iqtree' >>> fname = get_test_fname('1.stockholm') >>> guess_ext(fname, sniff_order) 'stockholm' >>> fname = get_test_fname('1.xmfa') >>> guess_ext(fname, sniff_order) 'xmfa' >>> fname = get_test_fname('test.blib') >>> guess_ext(fname, sniff_order) 'blib' >>> fname = get_test_fname('test_strict_interleaved.phylip') >>> guess_ext(fname, sniff_order) 'phylip' >>> fname = get_test_fname('test_relaxed_interleaved.phylip') >>> guess_ext(fname, sniff_order) 'phylip' >>> fname = get_test_fname('1.smat') >>> guess_ext(fname, sniff_order) 'smat' >>> fname = get_test_fname('1.ttl') >>> guess_ext(fname, sniff_order) 'ttl' >>> fname = get_test_fname('1.hdt') >>> guess_ext(fname, sniff_order, is_binary=True) 'hdt' >>> fname = get_test_fname('1.phyloxml') >>> guess_ext(fname, sniff_order) 'phyloxml' >>> fname = get_test_fname('1.dzi') >>> guess_ext(fname, sniff_order) 'dzi' >>> fname = get_test_fname('1.tiff') >>> guess_ext(fname, sniff_order) 'tiff' >>> fname = get_test_fname('1.fastqsanger.gz') >>> guess_ext(fname, sniff_order) # See test_datatype_registry for more compressed type tests. 'fastqsanger.gz' >>> fname = get_test_fname('1.mtx') >>> guess_ext(fname, sniff_order) 'mtx' >>> fname = get_test_fname('mc_preprocess_summ.metacyto_summary.txt') >>> guess_ext(fname, sniff_order) 'metacyto_summary.txt' >>> fname = get_test_fname('Accuri_C6_A01_H2O.fcs') >>> guess_ext(fname, sniff_order) 'fcs' >>> fname = get_test_fname('1imzml') >>> guess_ext(fname, sniff_order) # This test case is ensuring doesn't throw exception, actual value could change if non-utf encoding handling improves. 'data' >>> fname = get_test_fname('too_many_comments_gff3.tabular') >>> guess_ext(fname, sniff_order) # It's a VCF but is sniffed as tabular because of the limit on the number of header lines we read 'tabular'
- class galaxy.datatypes.sniff.FilePrefix(filename, auto_decompress=True)[source]¶
Bases:
object
- property binary¶
- property file_size¶
- galaxy.datatypes.sniff.run_sniffers_raw(file_prefix: FilePrefix, sniff_order)[source]¶
Run through sniffers specified by sniff_order, return None of None match.
- class galaxy.datatypes.sniff.HandleCompressedFileResponse(is_valid, ext, uncompressed_path, compressed_type, is_compressed)[source]¶
Bases:
tuple
- property is_valid¶
Alias for field number 0
- property ext¶
Alias for field number 1
- property uncompressed_path¶
Alias for field number 2
- property compressed_type¶
Alias for field number 3
- property is_compressed¶
Alias for field number 4
- galaxy.datatypes.sniff.handle_compressed_file(file_prefix: FilePrefix, datatypes_registry, ext: str = 'auto', tmp_prefix: Optional[str] = 'sniff_uncompress_', tmp_dir: Optional[str] = None, in_place: bool = False, check_content: bool = True) HandleCompressedFileResponse [source]¶
Check uploaded files for compression, check compressed file contents, and uncompress if necessary.
Supports GZip, BZip2, and the first file in a Zip file.
For performance reasons, the temporary file used for uncompression is located in the same directory as the input/output file. This behavior can be changed with the tmp_dir param.
ext
as returned will only be changed from theext
input param if the param was an autodetect type (auto
) and the file was sniffed as a keep-compressed datatype.is_valid
as returned will only be set if the file is compressed and contains invalid contents (or the first file in the case of a zip file), this is so lengthy decompression can be bypassed if there is invalid content in the first 32KB. Otherwise the caller should be checking content.
- galaxy.datatypes.sniff.handle_uploaded_dataset_file(filename, *args, **kwds) str [source]¶
Legacy wrapper about handle_uploaded_dataset_file_internal for tools using it.
- class galaxy.datatypes.sniff.HandleUploadedDatasetFileInternalResponse(ext, converted_path, compressed_type, converted_newlines, converted_spaces)[source]¶
Bases:
tuple
- property ext¶
Alias for field number 0
- property converted_path¶
Alias for field number 1
- property compressed_type¶
Alias for field number 2
- property converted_newlines¶
Alias for field number 3
- property converted_spaces¶
Alias for field number 4
- galaxy.datatypes.sniff.convert_function(convert_to_posix_lines, convert_spaces_to_tabs) ConvertFunction [source]¶
- galaxy.datatypes.sniff.handle_uploaded_dataset_file_internal(file_prefix: FilePrefix, datatypes_registry, ext: str = 'auto', tmp_prefix: Optional[str] = 'sniff_upload_', tmp_dir: Optional[str] = None, in_place: bool = False, check_content: bool = True, is_binary: Optional[bool] = None, uploaded_file_ext: Optional[str] = None, convert_to_posix_lines: Optional[bool] = None, convert_spaces_to_tabs: Optional[bool] = None) HandleUploadedDatasetFileInternalResponse [source]¶
galaxy.datatypes.spaln module¶
spaln Composite Dataset
- class galaxy.datatypes.spaln.SpalnNuclDb(**kwd)[source]¶
Bases:
_SpalnDb
- file_ext = 'spalndbnp'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'spalndb_name': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.spaln.SpalnProtDb(**kwd)[source]¶
Bases:
_SpalnDb
- file_ext = 'spalndba'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'spalndb_name': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.speech module¶
- class galaxy.datatypes.speech.TextGrid(**kwd)[source]¶
Bases:
Text
Praat Textgrid file for speech annotations
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('1_1119_2_22_001.textgrid') >>> TextGrid().sniff(fname) True >>> fname = get_test_fname('drugbank_drugs.cml') >>> TextGrid().sniff(fname) False
- file_ext = 'textgrid'¶
- header = 'File type = "ooTextFile"\nObject class = "TextGrid"\n'¶
- blurb = 'Praat TextGrid file'¶
- metadata_spec: MetadataSpecCollection = {'annotations': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.speech.BPF(**kwd)[source]¶
Bases:
Text
Munich BPF annotation format https://www.phonetik.uni-muenchen.de/Bas/BasFormatseng.html#Partitur
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('1_1119_2_22_001.par') >>> BPF().sniff(fname) True >>> fname = get_test_fname('1_1119_2_22_001-1.par') >>> BPF().sniff(fname) True >>> fname = get_test_fname('drugbank_drugs.cml') >>> BPF().sniff(fname) False
- file_ext = 'par'¶
- mandatory_headers = ['LHD', 'REP', 'SNB', 'SAM', 'SBF', 'SSB', 'NCH', 'SPN', 'LBD']¶
- optional_headers = ['FIL', 'TYP', 'DBN', 'VOL', 'DIR', 'SRC', 'BEG', 'END', 'RED', 'RET', 'RCC', 'CMT', 'SPI', 'PCF', 'PCN', 'EXP', 'SYS', 'DAT', 'SPA', 'MAO', 'GPO', 'SAO']¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the metadata for this dataset from the file contents
- metadata_spec: MetadataSpecCollection = {'annotations': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.tabular module¶
Tabular datatype
- class galaxy.datatypes.tabular.TabularData(**kwd)[source]¶
Bases:
Text
Generic tabular data
- edam_format = 'format_3475'¶
- CHUNKABLE = True¶
- data_line_offset = 0¶
- max_peek_columns = 50¶
- abstract set_meta(dataset: DatasetProtocol, *, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- get_chunk(trans, dataset: HasFileName, offset: int = 0, ck_size: Optional[int] = None) str [source]¶
- display_data(trans, dataset: DatasetHasHidProtocol, preview: bool = False, filename: Optional[str] = None, to_ext: Optional[str] = None, offset: Optional[int] = None, ck_size: Optional[int] = None, **kwd)[source]¶
Displays data in central pane if preview is True, else handles download.
Datatypes should be very careful if overridding this method and this interface between datatypes and Galaxy will likely change.
TOOD: Document alternatives to overridding this method (data providers?).
- display_as_markdown(dataset_instance: DatasetProtocol) str [source]¶
Prepare for embedding dataset into a basic Markdown document.
This is a somewhat experimental interface and should not be implemented on datatypes not tightly tied to a Galaxy version (e.g. datatypes in the Tool Shed).
Speaking very losely - the datatype should should load a bounded amount of data from the supplied dataset instance and prepare for embedding it into Markdown. This should be relatively vanilla Markdown - the result of this is bleached and it should not contain nested Galaxy Markdown directives.
If the data cannot reasonably be displayed, just indicate this and do not throw an exception.
- make_html_table(dataset: DatasetProtocol, **kwargs) str [source]¶
Create HTML table, used for displaying peek
- make_html_peek_header(dataset: DatasetProtocol, skipchars: Optional[List] = None, column_names: Optional[List] = None, column_number_format: str = '%s', column_parameter_alias: Optional[Dict] = None, **kwargs) str [source]¶
- make_html_peek_rows(dataset: DatasetProtocol, skipchars: Optional[List] = None, **kwargs) str [source]¶
- column_dataprovider(dataset: DatasetProtocol, **settings) ColumnarDataProvider [source]¶
Uses column settings that are passed in
- dataset_column_dataprovider(dataset: DatasetProtocol, **settings) DatasetColumnarDataProvider [source]¶
Attempts to get column settings from dataset.metadata
- dict_dataprovider(dataset: DatasetProtocol, **settings) DictDataProvider [source]¶
Uses column settings that are passed in
- dataset_dict_dataprovider(dataset: DatasetProtocol, **settings) DatasetDictDataProvider [source]¶
Attempts to get column settings from dataset.metadata
- dataproviders: Dict[str, Any] = {'base': <function Data.base_dataprovider>, 'chunk': <function Data.chunk_dataprovider>, 'chunk64': <function Data.chunk64_dataprovider>, 'column': <function TabularData.column_dataprovider>, 'dataset-column': <function TabularData.dataset_column_dataprovider>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider>, 'dict': <function TabularData.dict_dataprovider>, 'line': <function Text.line_dataprovider>, 'regex-line': <function Text.regex_line_dataprovider>}¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.tabular.Tabular(**kwd)[source]¶
Bases:
TabularData
Tab delimited data
- file_ext = 'tabular'¶
- set_meta(dataset: DatasetProtocol, *, overwrite: bool = True, skip: Optional[int] = None, max_data_lines: Optional[int] = 100000, max_guess_type_data_lines: Optional[int] = None, **kwd) None [source]¶
Tries to determine the number of columns as well as those columns that contain numerical values in the dataset. A skip parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many invalid comment lines should be skipped. Using None for skip will cause skip to be zero, but the first line will be processed as a header. A max_data_lines parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many data lines should be processed to ensure that the non-optional metadata parameters are properly set; if used, optional metadata parameters will be set to None, unless the entire file has already been read. Using None for max_data_lines will process all data lines.
Items of interest:
We treat ‘overwrite’ as always True (we always want to set tabular metadata when called).
If a tabular file has no data, it will have one column of type ‘str’.
We used to check only the first 100 lines when setting metadata and this class’s set_peek() method read the entire file to determine the number of lines in the file. Since metadata can now be processed on cluster nodes, we’ve merged the line count portion of the set_peek() processing here, and we now check the entire contents of the file.
- as_gbrowse_display_file(dataset: HasFileName, **kwd) Union[IO[str], TextIOWrapper, GzipFile, BZ2File, IO[bytes], str] [source]¶
- as_ucsc_display_file(dataset: DatasetProtocol, **kwd) Union[IO[str], TextIOWrapper, GzipFile, BZ2File, IO[bytes], str] [source]¶
- dataproviders: Dict[str, Any] = {'base': <function Data.base_dataprovider>, 'chunk': <function Data.chunk_dataprovider>, 'chunk64': <function Data.chunk64_dataprovider>, 'column': <function TabularData.column_dataprovider>, 'dataset-column': <function TabularData.dataset_column_dataprovider>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider>, 'dict': <function TabularData.dict_dataprovider>, 'line': <function Text.line_dataprovider>, 'regex-line': <function Text.regex_line_dataprovider>}¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.tabular.SraManifest(**kwd)[source]¶
Bases:
Tabular
A manifest received from the sra_source tool.
- file_ext = 'sra_manifest.tabular'¶
- data_line_offset = 1¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Tries to determine the number of columns as well as those columns that contain numerical values in the dataset. A skip parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many invalid comment lines should be skipped. Using None for skip will cause skip to be zero, but the first line will be processed as a header. A max_data_lines parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many data lines should be processed to ensure that the non-optional metadata parameters are properly set; if used, optional metadata parameters will be set to None, unless the entire file has already been read. Using None for max_data_lines will process all data lines.
Items of interest:
We treat ‘overwrite’ as always True (we always want to set tabular metadata when called).
If a tabular file has no data, it will have one column of type ‘str’.
We used to check only the first 100 lines when setting metadata and this class’s set_peek() method read the entire file to determine the number of lines in the file. Since metadata can now be processed on cluster nodes, we’ve merged the line count portion of the set_peek() processing here, and we now check the entire contents of the file.
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.tabular.Taxonomy(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'taxonomy'¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.tabular.Sam(**kwd)[source]¶
Bases:
Tabular
,_BamOrSam
- edam_format = 'format_2573'¶
- edam_data = 'data_0863'¶
- file_ext = 'sam'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in SAM format
A file in SAM format consists of lines of tab-separated data. The following header line may be the first line:
@QNAME FLAG RNAME POS MAPQ CIGAR MRNM MPOS ISIZE SEQ QUAL or @QNAME FLAG RNAME POS MAPQ CIGAR MRNM MPOS ISIZE SEQ QUAL OPT
Data in the OPT column is optional and can consist of tab-separated data
For complete details see http://samtools.sourceforge.net/SAM1.pdf
Rules for sniffing as True:
There must be 11 or more columns of data on each line Columns 2 (FLAG), 4(POS), 5 (MAPQ), 8 (MPOS), and 9 (ISIZE) must be numbers (9 can be negative) We will only check that up to the first 5 alignments are correctly formatted.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'sequence.maf' ) >>> Sam().sniff( fname ) False >>> fname = get_test_fname( '1.sam' ) >>> Sam().sniff( fname ) True
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, skip: Optional[int] = None, max_data_lines: Optional[int] = 5, **kwd) None [source]¶
>>> from galaxy.datatypes.sniff import get_test_fname >>> from galaxy.datatypes.registry import example_datatype_registry_for_sample >>> from galaxy.model import Dataset, set_datatypes_registry >>> from galaxy.model import History, HistoryDatasetAssociation >>> from galaxy.model.mapping import init >>> sa_session = init("/tmp", "sqlite:///:memory:", create_tables=True).session >>> hist = History() >>> with sa_session.begin(): ... sa_session.add(hist) >>> set_datatypes_registry(example_datatype_registry_for_sample()) >>> fname = get_test_fname( 'sam_with_header.sam' ) >>> samds = Dataset(external_filename=fname) >>> hda = hist.add_dataset(HistoryDatasetAssociation(id=1, extension='sam', create_dataset=True, sa_session=sa_session, dataset=samds)) >>> Sam().set_meta(hda) >>> hda.metadata.comment_lines 2 >>> hda.metadata.reference_names ['ref', 'ref2']
- static merge(split_files: List[str], output_file: str) None [source]¶
Multiple SAM files may each have headers. Since the headers should all be the same, remove the headers from files 1-n, keeping them in the first file only
- line_dataprovider(dataset: DatasetProtocol, **settings) FilteredLineDataProvider [source]¶
Returns an iterator over the dataset’s lines (that have been stripped) optionally excluding blank lines and lines that start with a comment character.
- regex_line_dataprovider(dataset: DatasetProtocol, **settings) RegexLineDataProvider [source]¶
Returns an iterator over the dataset’s lines optionally including/excluding lines that match one or more regex filters.
- column_dataprovider(dataset: DatasetProtocol, **settings) ColumnarDataProvider [source]¶
Uses column settings that are passed in
- dataset_column_dataprovider(dataset: DatasetProtocol, **settings) DatasetColumnarDataProvider [source]¶
Attempts to get column settings from dataset.metadata
- dict_dataprovider(dataset: DatasetProtocol, **settings) DictDataProvider [source]¶
Uses column settings that are passed in
- dataset_dict_dataprovider(dataset: DatasetProtocol, **settings) DatasetDictDataProvider [source]¶
Attempts to get column settings from dataset.metadata
- header_dataprovider(dataset: DatasetProtocol, **settings) RegexLineDataProvider [source]¶
- id_seq_qual_dataprovider(dataset: DatasetProtocol, **settings) DictDataProvider [source]¶
- genomic_region_dataprovider(dataset: DatasetProtocol, **settings) GenomicRegionDataProvider [source]¶
- genomic_region_dict_dataprovider(dataset: DatasetProtocol, **settings) GenomicRegionDataProvider [source]¶
- dataproviders: Dict[str, Any] = {'base': <function Data.base_dataprovider>, 'chunk': <function Data.chunk_dataprovider>, 'chunk64': <function Data.chunk64_dataprovider>, 'column': <function Sam.column_dataprovider>, 'dataset-column': <function Sam.dataset_column_dataprovider>, 'dataset-dict': <function Sam.dataset_dict_dataprovider>, 'dict': <function Sam.dict_dataprovider>, 'genomic-region': <function Sam.genomic_region_dataprovider>, 'genomic-region-dict': <function Sam.genomic_region_dict_dataprovider>, 'header': <function Sam.header_dataprovider>, 'id-seq-qual': <function Sam.id_seq_qual_dataprovider>, 'line': <function Sam.line_dataprovider>, 'regex-line': <function Sam.regex_line_dataprovider>}¶
- metadata_spec: MetadataSpecCollection = {'bam_header': <galaxy.model.metadata.MetadataElementSpec object>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.tabular.Pileup(**kwd)[source]¶
Bases:
Tabular
Tab delimited data in pileup (6- or 10-column) format
- edam_format = 'format_3015'¶
- file_ext = 'pileup'¶
- line_class = 'genomic coordinate'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Checks for ‘pileup-ness’
There are two main types of pileup: 6-column and 10-column. For both, the first three and last two columns are the same. We only check the first three to allow for some personalization of the format.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'interval.interval' ) >>> Pileup().sniff( fname ) False >>> fname = get_test_fname( '6col.pileup' ) >>> Pileup().sniff( fname ) True >>> fname = get_test_fname( '10col.pileup' ) >>> Pileup().sniff( fname ) True >>> fname = get_test_fname( '1.excel.xls' ) >>> Pileup().sniff( fname ) False >>> fname = get_test_fname( '2.txt' ) >>> Pileup().sniff( fname ) # 2.txt False >>> fname = get_test_fname( 'test_tab2.tabular' ) >>> Pileup().sniff( fname ) False
- genomic_region_dataprovider(dataset: DatasetProtocol, **settings) GenomicRegionDataProvider [source]¶
- genomic_region_dict_dataprovider(dataset: DatasetProtocol, **settings) GenomicRegionDataProvider [source]¶
- dataproviders: Dict[str, Any] = {'base': <function Data.base_dataprovider>, 'chunk': <function Data.chunk_dataprovider>, 'chunk64': <function Data.chunk64_dataprovider>, 'column': <function TabularData.column_dataprovider>, 'dataset-column': <function TabularData.dataset_column_dataprovider>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider>, 'dict': <function TabularData.dict_dataprovider>, 'genomic-region': <function Pileup.genomic_region_dataprovider>, 'genomic-region-dict': <function Pileup.genomic_region_dict_dataprovider>, 'line': <function Text.line_dataprovider>, 'regex-line': <function Text.regex_line_dataprovider>}¶
- metadata_spec: MetadataSpecCollection = {'baseCol': <galaxy.model.metadata.MetadataElementSpec object>, 'chromCol': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.tabular.BaseVcf(**kwd)[source]¶
Bases:
Tabular
Variant Call Format for describing SNPs and other simple genome variations.
- edam_format = 'format_3016'¶
- column_names = ['Chrom', 'Pos', 'ID', 'Ref', 'Alt', 'Qual', 'Filter', 'Info', 'Format', 'data']¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Tries to determine the number of columns as well as those columns that contain numerical values in the dataset. A skip parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many invalid comment lines should be skipped. Using None for skip will cause skip to be zero, but the first line will be processed as a header. A max_data_lines parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many data lines should be processed to ensure that the non-optional metadata parameters are properly set; if used, optional metadata parameters will be set to None, unless the entire file has already been read. Using None for max_data_lines will process all data lines.
Items of interest:
We treat ‘overwrite’ as always True (we always want to set tabular metadata when called).
If a tabular file has no data, it will have one column of type ‘str’.
We used to check only the first 100 lines when setting metadata and this class’s set_peek() method read the entire file to determine the number of lines in the file. Since metadata can now be processed on cluster nodes, we’ve merged the line count portion of the set_peek() processing here, and we now check the entire contents of the file.
- static merge(split_files: List[str], output_file: str) None [source]¶
Merge files with copy.copyfileobj() will not hit the max argument limitation of cat. gz and bz2 files are also working.
- validate(dataset: DatasetProtocol, **kwd) DatatypeValidation [source]¶
- genomic_region_dataprovider(dataset: DatasetProtocol, **settings) GenomicRegionDataProvider [source]¶
- genomic_region_dict_dataprovider(dataset: DatasetProtocol, **settings) GenomicRegionDataProvider [source]¶
- dataproviders: Dict[str, Any] = {'base': <function Data.base_dataprovider>, 'chunk': <function Data.chunk_dataprovider>, 'chunk64': <function Data.chunk64_dataprovider>, 'column': <function TabularData.column_dataprovider>, 'dataset-column': <function TabularData.dataset_column_dataprovider>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider>, 'dict': <function TabularData.dict_dataprovider>, 'genomic-region': <function BaseVcf.genomic_region_dataprovider>, 'genomic-region-dict': <function BaseVcf.genomic_region_dict_dataprovider>, 'line': <function Text.line_dataprovider>, 'regex-line': <function Text.regex_line_dataprovider>}¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'sample_names': <galaxy.model.metadata.MetadataElementSpec object>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.tabular.Vcf(**kwd)[source]¶
Bases:
BaseVcf
- file_ext = 'vcf'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'sample_names': <galaxy.model.metadata.MetadataElementSpec object>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.tabular.VcfGz(**kwd)[source]¶
-
- file_ext = 'vcf_bgzip'¶
- file_ext_export_alias = 'vcf.gz'¶
- compressed = True¶
- compressed_format = 'gzip'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, metadata_tmp_files_dir: Optional[str] = None, **kwd) None [source]¶
Tries to determine the number of columns as well as those columns that contain numerical values in the dataset. A skip parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many invalid comment lines should be skipped. Using None for skip will cause skip to be zero, but the first line will be processed as a header. A max_data_lines parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many data lines should be processed to ensure that the non-optional metadata parameters are properly set; if used, optional metadata parameters will be set to None, unless the entire file has already been read. Using None for max_data_lines will process all data lines.
Items of interest:
We treat ‘overwrite’ as always True (we always want to set tabular metadata when called).
If a tabular file has no data, it will have one column of type ‘str’.
We used to check only the first 100 lines when setting metadata and this class’s set_peek() method read the entire file to determine the number of lines in the file. Since metadata can now be processed on cluster nodes, we’ve merged the line count portion of the set_peek() processing here, and we now check the entire contents of the file.
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'sample_names': <galaxy.model.metadata.MetadataElementSpec object>, 'tabix_index': <galaxy.model.metadata.MetadataElementSpec object>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.tabular.Eland(**kwd)[source]¶
Bases:
Tabular
Support for the export.txt.gz file used by Illumina’s ELANDv2e aligner
- compressed = True¶
- compressed_format = 'gzip'¶
- file_ext = '_export.txt.gz'¶
- make_html_table(dataset: DatasetProtocol, skipchars: Optional[List] = None, peek: Optional[List] = None, **kwargs) str [source]¶
Create HTML table, used for displaying peek
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in ELAND export format
A file in ELAND export format consists of lines of tab-separated data. There is no header.
Rules for sniffing as True:
- There must be 22 columns on each line - LANE, TILEm X, Y, INDEX, READ_NO, SEQ, QUAL, POSITION, *STRAND, FILT must be correct - We will only check that up to the first 5 alignments are correctly formatted.
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, skip: Optional[int] = None, max_data_lines: Optional[int] = 5, **kwd) None [source]¶
Tries to determine the number of columns as well as those columns that contain numerical values in the dataset. A skip parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many invalid comment lines should be skipped. Using None for skip will cause skip to be zero, but the first line will be processed as a header. A max_data_lines parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many data lines should be processed to ensure that the non-optional metadata parameters are properly set; if used, optional metadata parameters will be set to None, unless the entire file has already been read. Using None for max_data_lines will process all data lines.
Items of interest:
We treat ‘overwrite’ as always True (we always want to set tabular metadata when called).
If a tabular file has no data, it will have one column of type ‘str’.
We used to check only the first 100 lines when setting metadata and this class’s set_peek() method read the entire file to determine the number of lines in the file. Since metadata can now be processed on cluster nodes, we’ve merged the line count portion of the set_peek() processing here, and we now check the entire contents of the file.
- metadata_spec: MetadataSpecCollection = {'barcodes': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'lanes': <galaxy.model.metadata.MetadataElementSpec object>, 'reads': <galaxy.model.metadata.MetadataElementSpec object>, 'tiles': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.tabular.ElandMulti(**kwd)[source]¶
Bases:
Tabular
- file_ext = 'elandmulti'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.tabular.FeatureLocationIndex(**kwd)[source]¶
Bases:
Tabular
An index that stores feature locations in tabular format.
- file_ext = 'fli'¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.tabular.BaseCSV(**kwd)[source]¶
Bases:
TabularData
Delimiter-separated table data. This includes CSV, TSV and other dialects understood by the Python ‘csv’ module https://docs.python.org/2/library/csv.html Must be extended to define the dialect to use, strict_width and file_ext. See the Python module csv for documentation of dialect settings
- property dialect¶
- property strict_width¶
- delimiter = ','¶
- peek_size = 1024¶
- big_peek_size = 10240¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- dataproviders: Dict[str, Any] = {'base': <function Data.base_dataprovider>, 'chunk': <function Data.chunk_dataprovider>, 'chunk64': <function Data.chunk64_dataprovider>, 'column': <function TabularData.column_dataprovider>, 'dataset-column': <function TabularData.dataset_column_dataprovider>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider>, 'dict': <function TabularData.dict_dataprovider>, 'line': <function Text.line_dataprovider>, 'regex-line': <function Text.regex_line_dataprovider>}¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.tabular.CSV(**kwd)[source]¶
Bases:
BaseCSV
Comma-separated table data. Only sniffs comma-separated files with at least 2 rows and 2 columns.
- file_ext = 'csv'¶
- strict_width = False¶
- dataproviders: Dict[str, Any] = {'base': <function Data.base_dataprovider>, 'chunk': <function Data.chunk_dataprovider>, 'chunk64': <function Data.chunk64_dataprovider>, 'column': <function TabularData.column_dataprovider>, 'dataset-column': <function TabularData.dataset_column_dataprovider>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider>, 'dict': <function TabularData.dict_dataprovider>, 'line': <function Text.line_dataprovider>, 'regex-line': <function Text.regex_line_dataprovider>}¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.tabular.TSV(**kwd)[source]¶
Bases:
BaseCSV
Tab-separated table data. Only sniff tab-separated files with at least 2 rows and 2 columns.
Note: Use of this datatype is optional as the general tabular datatype will handle most tab-separated files. This datatype is only required for datasets with tabs INSIDE double quotes.
This datatype currently does not support TSV files where the header has one column less to indicate first column is row names. This kind of file is handled fine by the tabular datatype.
- file_ext = 'tsv'¶
- strict_width = True¶
- dataproviders: Dict[str, Any] = {'base': <function Data.base_dataprovider>, 'chunk': <function Data.chunk_dataprovider>, 'chunk64': <function Data.chunk64_dataprovider>, 'column': <function TabularData.column_dataprovider>, 'dataset-column': <function TabularData.dataset_column_dataprovider>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider>, 'dict': <function TabularData.dict_dataprovider>, 'line': <function Text.line_dataprovider>, 'regex-line': <function Text.regex_line_dataprovider>}¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.tabular.ConnectivityTable(**kwd)[source]¶
Bases:
Tabular
- edam_format = 'format_3309'¶
- file_ext = 'ct'¶
- header_regexp = re.compile('^[0-9]+(?:\t|[ ]+).*?(?:ENERGY|energy|dG)[ \t].*?=')¶
- structure_regexp = re.compile('^[0-9]+(?:\t|[ ]+)[ACGTURYKMSWBDHVN]+(?:\t|[ ]+)[^\t]+(?:\t|[ ]+)[^\t]+(?:\t|[ ]+)[^\t]+(?:\t|[ ]+)[^\t]+')¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Tries to determine the number of columns as well as those columns that contain numerical values in the dataset. A skip parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many invalid comment lines should be skipped. Using None for skip will cause skip to be zero, but the first line will be processed as a header. A max_data_lines parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many data lines should be processed to ensure that the non-optional metadata parameters are properly set; if used, optional metadata parameters will be set to None, unless the entire file has already been read. Using None for max_data_lines will process all data lines.
Items of interest:
We treat ‘overwrite’ as always True (we always want to set tabular metadata when called).
If a tabular file has no data, it will have one column of type ‘str’.
We used to check only the first 100 lines when setting metadata and this class’s set_peek() method read the entire file to determine the number of lines in the file. Since metadata can now be processed on cluster nodes, we’ve merged the line count portion of the set_peek() processing here, and we now check the entire contents of the file.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
The ConnectivityTable (CT) is a file format used for describing RNA 2D structures by tools including MFOLD, UNAFOLD and the RNAStructure package. The tabular file format is defined as follows:
5 energy = -12.3 sequence name 1 G 0 2 0 1 2 A 1 3 0 2 3 A 2 4 0 3 4 A 3 5 0 4 5 C 4 6 1 5
The links given at the edam ontology page do not indicate what type of separator is used (space or tab) while different implementations exist. The implementation that uses spaces as separator (implemented in RNAStructure) is as follows:
10 ENERGY = -34.8 seqname 1 G 0 2 9 1 2 G 1 3 8 2 3 G 2 4 7 3 4 a 3 5 0 4 5 a 4 6 0 5 6 a 5 7 0 6 7 C 6 8 3 7 8 C 7 9 2 8 9 C 8 10 1 9 10 a 9 0 0 10
- get_chunk(trans, dataset: HasFileName, offset: int = 0, ck_size: Optional[int] = None) str [source]¶
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.tabular.MatrixMarket(**kwd)[source]¶
Bases:
TabularData
The Matrix Market (MM) exchange formats provide a simple mechanism to facilitate the exchange of matrix data. MM coordinate format is suitable for representing sparse matrices. Only nonzero entries need be encoded, and the coordinates of each are given explicitly.
The tabular file format is defined as follows:
%%MatrixMarket matrix coordinate real general <--- header line % <--+ % comments |-- 0 or more comment lines % <--+ M N L <--- rows, columns, entries I1 J1 A(I1, J1) <--+ I2 J2 A(I2, J2) | I3 J3 A(I3, J3) |-- L lines . . . | IL JL A(IL, JL) <--+
Indices are 1-based, i.e. A(1,1) is the first element.
>>> from galaxy.datatypes.sniff import get_test_fname >>> MatrixMarket().sniff( get_test_fname( 'sequence.maf' ) ) False >>> MatrixMarket().sniff( get_test_fname( '1.mtx' ) ) True >>> MatrixMarket().sniff( get_test_fname( '2.mtx' ) ) True >>> MatrixMarket().sniff( get_test_fname( '3.mtx' ) ) True
- file_ext = 'mtx'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, skip: Optional[int] = None, max_data_lines: Optional[int] = 5, **kwd) None [source]¶
Set the number of lines of data in dataset.
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.tabular.CMAP(**kwd)[source]¶
Bases:
TabularData
# CMAP File Version: 2.0 # Label Channels: 1 # Nickase Recognition Site 1: cttaag;green_01 # Nickase Recognition Site 2: cctcagc;red_01 # Number of Consensus Maps: 459 # Values corresponding to intervals (StdDev, HapDelta) refer to the interval between current site and next site #h CMapId ContigLength NumSites SiteID LabelChannel Position StdDev Coverage Occurrence ChimQuality SegDupL SegDupR FragileL FragileR OutlierFrac ChimNorm Mask #f int float int int int float float float float float float float float float float float Hex 182 58474736.7 10235 1 1 58820.9 35.4 13.5 13.5 -1.00 -1.00 -1.00 3.63 0.00 0.00 -1.00 0 182 58474736.7 10235 1 1 58820.9 35.4 13.5 13.5 -1.00 -1.00 -1.00 3.63 0.00 0.00 -1.00 0 182 58474736.7 10235 1 1 58820.9 35.4 13.5 13.5 -1.00 -1.00 -1.00 3.63 0.00 0.00 -1.00 0
- file_ext = 'cmap'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, skip: Optional[int] = None, max_data_lines: Optional[int] = 7, **kwd) None [source]¶
Set the number of lines of data in dataset.
- metadata_spec: MetadataSpecCollection = {'channel_1_color': <galaxy.model.metadata.MetadataElementSpec object>, 'channel_2_color': <galaxy.model.metadata.MetadataElementSpec object>, 'cmap_version': <galaxy.model.metadata.MetadataElementSpec object>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>, 'label_channels': <galaxy.model.metadata.MetadataElementSpec object>, 'nickase_recognition_site_1': <galaxy.model.metadata.MetadataElementSpec object>, 'nickase_recognition_site_2': <galaxy.model.metadata.MetadataElementSpec object>, 'number_of_consensus_nanomaps': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.tabular.Psl(**kwd)[source]¶
Bases:
Tabular
Tab delimited data in psl format.
- edam_format = 'format_3007'¶
- file_ext = 'psl'¶
- line_class = 'assemblies'¶
- sniff_prefix(file_prefix: FilePrefix)[source]¶
PSL lines represent alignments, and are typically generated by BLAT. Each line consists of 21 required fields, and track lines may optionally be used to provide more information.
Fields are tab-separated, and all 21 are required. Although not part of the formal PSL specification, track lines may be used to further configure sets of features. Track lines are placed at the beginning of the list of features they are to affect.
Rules for sniffing as True:
- There must be 21 columns on each fields line - matches, misMatches repMatches, nCount, qNumInsert, qBaseInsert, tNumInsert, tBaseInsert, strand, qSize, qStart, qEnd, tName, tSize, tStart, tEnd, blockCount, blockSizes, qStarts, tStarts must be correct - We will only check that up to the first 10 alignments are correctly formatted.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( '1.psl' ) >>> Psl().sniff( fname ) True >>> fname = get_test_fname( '2.psl' ) >>> Psl().sniff( fname ) True >>> fname = get_test_fname( 'interval.interval' ) >>> Psl().sniff( fname ) False >>> fname = get_test_fname( '2.txt' ) >>> Psl().sniff( fname ) False >>> fname = get_test_fname( 'test_tab2.tabular' ) >>> Psl().sniff( fname ) False >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.ref.taxonomy' ) >>> Psl().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'column_names': <galaxy.model.metadata.MetadataElementSpec object>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object>, 'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
galaxy.datatypes.text module¶
Clearing house for generic text datatypes that are not XML or tabular.
- class galaxy.datatypes.text.Html(**kwd)[source]¶
Bases:
Text
Class describing an html file
- edam_format = 'format_2331'¶
- file_ext = 'html'¶
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in html format
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'complete.bed' ) >>> Html().sniff( fname ) False >>> fname = get_test_fname( 'file.html' ) >>> Html().sniff( fname ) True
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.Json(**kwd)[source]¶
Bases:
Text
- edam_format = 'format_3464'¶
- file_ext = 'json'¶
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to load the string with the json module. If successful it’s a json file.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.ExpressionJson(**kwd)[source]¶
Bases:
Json
Represents the non-data input or output to a tool or workflow.
- file_ext = 'json'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'json_type': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.text.Ipynb(**kwd)[source]¶
Bases:
Json
- file_ext = 'ipynb'¶
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to load the string with the json module. If successful it’s a json file.
- display_data(trans, dataset: DatasetHasHidProtocol, preview: bool = False, filename: Optional[str] = None, to_ext: Optional[str] = None, **kwd)[source]¶
Displays data in central pane if preview is True, else handles download.
Datatypes should be very careful if overridding this method and this interface between datatypes and Galaxy will likely change.
TOOD: Document alternatives to overridding this method (data providers?).
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of models in dataset.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.Biom1(**kwd)[source]¶
Bases:
Json
BIOM version 1.0 file format description http://biom-format.org/documentation/format_versions/biom-1.0.html
- file_ext = 'biom1'¶
- edam_format = 'format_3746'¶
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to load the string with the json module. If successful it’s a json file.
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Store metadata information from the BIOM file.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'table_column_metadata_headers': <galaxy.model.metadata.MetadataElementSpec object>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object>, 'table_date': <galaxy.model.metadata.MetadataElementSpec object>, 'table_format': <galaxy.model.metadata.MetadataElementSpec object>, 'table_format_url': <galaxy.model.metadata.MetadataElementSpec object>, 'table_generated_by': <galaxy.model.metadata.MetadataElementSpec object>, 'table_id': <galaxy.model.metadata.MetadataElementSpec object>, 'table_matrix_element_type': <galaxy.model.metadata.MetadataElementSpec object>, 'table_matrix_type': <galaxy.model.metadata.MetadataElementSpec object>, 'table_rows': <galaxy.model.metadata.MetadataElementSpec object>, 'table_shape': <galaxy.model.metadata.MetadataElementSpec object>, 'table_type': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.ImgtJson(**kwd)[source]¶
Bases:
Json
https://github.com/repseqio/library-imgt/releases Data coming from IMGT server may be used for academic research only, provided that it is referred to IMGT®, and cited as: “IMGT®, the international ImMunoGeneTics information system® http://www.imgt.org (founder and director: Marie-Paule Lefranc, Montpellier, France).”
- file_ext = 'imgt.json'¶
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in json format with imgt elements
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( '1.json' ) >>> ImgtJson().sniff( fname ) False >>> fname = get_test_fname( 'imgt.json' ) >>> ImgtJson().sniff( fname ) True
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Store metadata information from the imgt file.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'taxon_names': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.GeoJson(**kwd)[source]¶
Bases:
Json
GeoJSON is a geospatial data interchange format based on JavaScript Object Notation (JSON). https://tools.ietf.org/html/rfc7946
- file_ext = 'geojson'¶
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in json format with imgt elements
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( '1.json' ) >>> GeoJson().sniff( fname ) False >>> fname = get_test_fname( 'gis.geojson' ) >>> GeoJson().sniff( fname ) True
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.Obo(**kwd)[source]¶
Bases:
Text
OBO file format description https://owlcollab.github.io/oboformat/doc/GO.format.obo-1_2.html
- edam_data = 'data_0582'¶
- edam_format = 'format_2549'¶
- file_ext = 'obo'¶
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to guess the Obo filetype. It usually starts with a “format-version:” string and has several stanzas which starts with “id:”.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.Arff(**kwd)[source]¶
Bases:
Text
An ARFF (Attribute-Relation File Format) file is an ASCII text file that describes a list of instances sharing a set of attributes. http://weka.wikispaces.com/ARFF
- edam_format = 'format_3581'¶
- file_ext = 'arff'¶
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to guess the Arff filetype. It usually starts with a “format-version:” string and has several stanzas which starts with “id:”.
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Trying to count the comment lines and the number of columns included. A typical ARFF data block looks like this: @DATA 5.1,3.5,1.4,0.2,Iris-setosa 4.9,3.0,1.4,0.2,Iris-setosa
- metadata_spec: MetadataSpecCollection = {'columns': <galaxy.model.metadata.MetadataElementSpec object>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.SnpEffDb(**kwd)[source]¶
Bases:
Text
Class describing a SnpEff genome build
- edam_format = 'format_3624'¶
- file_ext = 'snpeffdb'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- metadata_spec: MetadataSpecCollection = {'annotation': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'genome_version': <galaxy.model.metadata.MetadataElementSpec object>, 'regulation': <galaxy.model.metadata.MetadataElementSpec object>, 'snpeff_version': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.text.SnpSiftDbNSFP(**kwd)[source]¶
Bases:
Text
Class describing a dbNSFP database prepared fpr use by SnpSift dbnsfp
The dbNSFP file is a tabular file with 1 header line. The first 4 columns are required to be: chrom pos ref alt These match columns 1,2,4,5 of the VCF file SnpSift requires the file to be block-gzipped and the indexed with samtools tabix
Example: - Compress using block-gzip algorithm: $ bgzip dbNSFP2.3.txt - Create tabix index $ tabix -s 1 -b 2 -e 2 dbNSFP2.3.txt.gz
- file_ext = 'snpsiftdbnsfp'¶
- generate_primary_file(dataset: HasExtraFilesAndMetadata) str [source]¶
This is called only at upload to write the html file cannot rename the datasets here - they come with the default unfortunately
- regenerate_primary_file(dataset: DatasetProtocol) None [source]¶
cannot do this until we are setting metadata
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'annotation': <galaxy.model.metadata.MetadataElementSpec object>, 'bgzip': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'index': <galaxy.model.metadata.MetadataElementSpec object>, 'reference_name': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.text.IQTree(**kwd)[source]¶
Bases:
Text
IQ-TREE format
- file_ext = 'iqtree'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Detect the IQTree file
Scattered text file containing various headers and data types.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('example.iqtree') >>> IQTree().sniff(fname) True
>>> fname = get_test_fname('temp.txt') >>> IQTree().sniff(fname) False
>>> fname = get_test_fname('test_tab1.tabular') >>> IQTree().sniff(fname) False
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.Paf(**kwd)[source]¶
Bases:
Text
PAF: a Pairwise mApping Format
https://github.com/lh3/miniasm/blob/master/PAF.md
- file_ext = 'paf'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('A-3105.paf') >>> Paf().sniff(fname) True
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.Gfa1(**kwd)[source]¶
Bases:
Text
Graphical Fragment Assembly (GFA) 1.0
http://gfa-spec.github.io/GFA-spec/GFA1.html
- file_ext = 'gfa1'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('big.gfa1') >>> Gfa1().sniff(fname) True >>> Gfa2().sniff(fname) False
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.Gfa2(**kwd)[source]¶
Bases:
Text
Graphical Fragment Assembly (GFA) 2.0
https://github.com/GFA-spec/GFA-spec/blob/master/GFA2.md
- file_ext = 'gfa2'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('sample.gfa2') >>> Gfa2().sniff(fname) True >>> Gfa1().sniff(fname) False
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.Yaml(**kwd)[source]¶
Bases:
Text
Yaml files
- file_ext = 'yaml'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to load the string with the yaml module. If successful it’s a yaml file.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.BCSLmodel(**kwd)[source]¶
Bases:
Text
BioChemical Space Language model file
- file_ext = 'bcsl.model'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in .bcsl.model format
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.BCSLts(**kwd)[source]¶
Bases:
Json
BioChemical Space Language transition system file
- file_ext = 'bcsl.ts'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in .bcsl.ts format
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.StormSample(**kwd)[source]¶
Bases:
Text
Storm PCTL parameter synthesis result file containing probability function of parameters.
- file_ext = 'storm.sample'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in .storm.sample format
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.StormCheck(**kwd)[source]¶
Bases:
Text
Storm PCTL model checking result file containing boolean or numerical result.
- file_ext = 'storm.check'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in .storm.check format
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.CTLresult(**kwd)[source]¶
Bases:
Text
CTL model checking result
- file_ext = 'ctl.result'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in .ctl.result format
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.PithyaProperty(**kwd)[source]¶
Bases:
Text
Pithya CTL property format
- file_ext = 'pithya.property'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in .pithya.property format
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.PithyaModel(**kwd)[source]¶
Bases:
Text
Pithya model format
- file_ext = 'pithya.model'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in .pithya.model format
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.PithyaResult(**kwd)[source]¶
Bases:
Json
Pithya result format
- file_ext = 'pithya.result'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is in .pithya.result format
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.Castep(**kwd)[source]¶
Bases:
Text
Report on a CASTEP calculation
- file_ext = 'castep'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is a CASTEP log
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('Si.castep') >>> Castep().sniff(fname) True >>> fname = get_test_fname('Si.param') >>> Castep().sniff(fname) False
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.Param(**kwd)[source]¶
Bases:
Yaml
CASTEP parameter input file
- file_ext = 'param'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Modified version of the normal Yaml sniff that also checks for a valid CASTEP task key-value pair, which is not case sensitive
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('Si.param') >>> Param().sniff(fname) True >>> fname = get_test_fname('Si.castep') >>> Param().sniff(fname) False
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.text.FormattedDensity(**kwd)[source]¶
Bases:
Text
Final electron density from a CASTEP calculation written to an ASCII file
- file_ext = 'den_fmt'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file contains electron densities in the CASTEP den_fmt format
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname('Si.den_fmt') >>> FormattedDensity().sniff(fname) True >>> fname = get_test_fname('YbCuAs2.den_fmt') >>> FormattedDensity().sniff(fname) True >>> fname = get_test_fname('Si.param') >>> FormattedDensity().sniff(fname) False
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
galaxy.datatypes.tracks module¶
Datatype classes for tracks/track views within galaxy.
- class galaxy.datatypes.tracks.GeneTrack(**kwd)[source]¶
Bases:
Binary
- edam_data = 'data_3002'¶
- edam_format = 'format_2919'¶
- file_ext = 'genetrack'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.tracks.UCSCTrackHub(**kwd)[source]¶
Bases:
Html
Datatype for UCSC TrackHub
- file_ext = 'trackhub'¶
- generate_primary_file(dataset: HasExtraFilesAndMetadata) str [source]¶
This is called only at upload to write the html file cannot rename the datasets here - they come with the default unfortunately
- set_peek(dataset: DatasetProtocol, **kwd) None [source]¶
Set the peek. This method is used by various subclasses of Text.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
galaxy.datatypes.triples module¶
Triple format classes
- class galaxy.datatypes.triples.Triples(**kwd)[source]¶
Bases:
Data
The abstract base class for the file format that can contain triples
- edam_data = 'data_0582'¶
- edam_format = 'format_2376'¶
- file_ext = 'triples'¶
- metadata_spec: MetadataSpecCollection = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.triples.NTriples(**kwd)[source]¶
-
The N-Triples triple data format
- edam_format = 'format_3256'¶
- file_ext = 'nt'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
Returns false and the user must manually set.
- class galaxy.datatypes.triples.N3(**kwd)[source]¶
-
The N3 triple data format
- edam_format = 'format_3257'¶
- file_ext = 'n3'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.triples.Turtle(**kwd)[source]¶
-
The Turtle triple data format
- edam_format = 'format_3255'¶
- file_ext = 'ttl'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
Returns false and the user must manually set.
- class galaxy.datatypes.triples.Rdf(**kwd)[source]¶
Bases:
GenericXml
,Triples
Resource Description Framework format (http://www.w3.org/RDF/).
- edam_format = 'format_3261'¶
- file_ext = 'rdf'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is XML or not
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'megablast_xml_parser_test1.blastxml' ) >>> GenericXml().sniff( fname ) True >>> fname = get_test_fname( 'interval.interval' ) >>> GenericXml().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
Returns false and the user must manually set.
- class galaxy.datatypes.triples.Jsonld(**kwd)[source]¶
-
The JSON-LD data format
- edam_format = 'format_3464'¶
- file_ext = 'jsonld'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Try to load the string with the json module. If successful it’s a json file.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
Returns false and the user must manually set.
galaxy.datatypes.upload_util module¶
- class galaxy.datatypes.upload_util.HandleUploadResponse(stdout, ext, datatype, is_binary, converted_path, converted_newlines, converted_spaces)[source]¶
Bases:
tuple
- property stdout¶
Alias for field number 0
- property ext¶
Alias for field number 1
- property datatype¶
Alias for field number 2
- property is_binary¶
Alias for field number 3
- property converted_path¶
Alias for field number 4
- property converted_newlines¶
Alias for field number 5
- property converted_spaces¶
Alias for field number 6
- galaxy.datatypes.upload_util.handle_upload(registry, path: str, requested_ext: str, name: str, tmp_prefix: Optional[str], tmp_dir: Optional[str], check_content: bool, link_data_only: bool, in_place: bool, auto_decompress: bool, convert_to_posix_lines: bool, convert_spaces_to_tabs: bool) HandleUploadResponse [source]¶
galaxy.datatypes.xml module¶
XML format classes
- class galaxy.datatypes.xml.GenericXml(**kwd)[source]¶
Bases:
Text
Base format class for any XML file.
- edam_format = 'format_2332'¶
- file_ext = 'xml'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Determines whether the file is XML or not
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'megablast_xml_parser_test1.blastxml' ) >>> GenericXml().sniff( fname ) True >>> fname = get_test_fname( 'interval.interval' ) >>> GenericXml().sniff( fname ) False
- static merge(split_files: List[str], output_file: str) None [source]¶
Merging multiple XML files is non-trivial and must be done in subclasses.
- xml_dataprovider(dataset: DatasetProtocol, **settings) XMLDataProvider [source]¶
- dataproviders: Dict[str, Any] = {'base': <function Data.base_dataprovider>, 'chunk': <function Data.chunk_dataprovider>, 'chunk64': <function Data.chunk64_dataprovider>, 'line': <function Text.line_dataprovider>, 'regex-line': <function Text.regex_line_dataprovider>, 'xml': <function GenericXml.xml_dataprovider>}¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- class galaxy.datatypes.xml.MEMEXml(**kwd)[source]¶
Bases:
GenericXml
MEME XML Output data
- file_ext = 'memexml'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- sniff_prefix(file_prefix)¶
Determines whether the file is XML or not
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'megablast_xml_parser_test1.blastxml' ) >>> GenericXml().sniff( fname ) True >>> fname = get_test_fname( 'interval.interval' ) >>> GenericXml().sniff( fname ) False
- class galaxy.datatypes.xml.CisML(**kwd)[source]¶
Bases:
GenericXml
CisML XML data
- file_ext = 'cisml'¶
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- sniff(filename)¶
- sniff_prefix(file_prefix)¶
Determines whether the file is XML or not
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'megablast_xml_parser_test1.blastxml' ) >>> GenericXml().sniff( fname ) True >>> fname = get_test_fname( 'interval.interval' ) >>> GenericXml().sniff( fname ) False
- class galaxy.datatypes.xml.Dzi(**kwd)[source]¶
Bases:
GenericXml
Deep zoom image format, see https://github.com/openseadragon/openseadragon/wiki/The-DZI-File-Format
- file_ext = 'dzi'¶
- set_meta(dataset: DatasetProtocol, overwrite: bool = True, **kwd) None [source]¶
Set the number of lines of data in dataset.
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Checking for keyword - ‘Collection’ or ‘Image’ in the first 200 lines. >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname(‘1.dzi’) >>> Dzi().sniff(fname) True >>> fname = get_test_fname(‘megablast_xml_parser_test1.blastxml’) >>> Dzi().sniff(fname) False
- metadata_spec: MetadataSpecCollection = {'base_name': <galaxy.model.metadata.MetadataElementSpec object>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>, 'format': <galaxy.model.metadata.MetadataElementSpec object>, 'height': <galaxy.model.metadata.MetadataElementSpec object>, 'max_level': <galaxy.model.metadata.MetadataElementSpec object>, 'overlap': <galaxy.model.metadata.MetadataElementSpec object>, 'quality': <galaxy.model.metadata.MetadataElementSpec object>, 'tile_size': <galaxy.model.metadata.MetadataElementSpec object>, 'width': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.xml.Phyloxml(**kwd)[source]¶
Bases:
GenericXml
Format for defining phyloxml data http://www.phyloxml.org/
- edam_data = 'data_0872'¶
- edam_format = 'format_3159'¶
- file_ext = 'phyloxml'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
“Checking for keyword - ‘phyloxml’ always in lowercase in the first few lines.
>>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( '1.phyloxml' ) >>> Phyloxml().sniff( fname ) True >>> fname = get_test_fname( 'interval.interval' ) >>> Phyloxml().sniff( fname ) False >>> fname = get_test_fname( 'megablast_xml_parser_test1.blastxml' ) >>> Phyloxml().sniff( fname ) False
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.xml.Owl(**kwd)[source]¶
Bases:
GenericXml
Web Ontology Language OWL format description http://www.w3.org/TR/owl-ref/
- edam_format = 'format_3262'¶
- file_ext = 'owl'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Checking for keyword - ‘<owl’ in the first 200 lines.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype
- class galaxy.datatypes.xml.Sbml(**kwd)[source]¶
Bases:
GenericXml
System Biology Markup Language http://sbml.org
- file_ext = 'sbml'¶
- edam_data = 'data_2024'¶
- edam_format = 'format_2585'¶
- sniff_prefix(file_prefix: FilePrefix) bool [source]¶
Checking for keyword - ‘<sbml’ in the first 200 lines.
- metadata_spec: MetadataSpecCollection = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object>}¶
Dictionary of metadata fields for this datatype