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galaxy.datatypes package

Subpackages

Submodules

galaxy.datatypes.annotation module

class galaxy.datatypes.annotation.SnapHmm(**kwd)[source]

Bases: galaxy.datatypes.data.Text

file_ext = 'snaphmm'
edam_data = 'data_1364'
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
sniff_prefix(file_prefix)[source]

SNAP model files start with zoeHMM

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029a55518>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
sniff(filename)
class galaxy.datatypes.annotation.Augustus(**kwd)[source]

Bases: galaxy.datatypes.binary.CompressedArchive

Class describing an Augustus prediction model

file_ext = 'augustus'
edam_data = 'data_0950'
compressed = True
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
sniff(filename)[source]

Augustus archives always contain the same files

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029a55470>}

galaxy.datatypes.anvio module

Datatypes for Anvi’o https://github.com/merenlab/anvio

class galaxy.datatypes.anvio.AnvioComposite(**kwd)[source]

Bases: galaxy.datatypes.text.Html

Base class to use for Anvi’o composite datatypes. Generally consist of a sqlite database, plus optional additional files

file_ext = 'anvio_composite'
composite_type = 'auto_primary_file'
generate_primary_file(dataset=None)[source]

This is called only at upload to write the html file cannot rename the datasets here - they come with the default unfortunately

get_mime()[source]

Returns the mime type of the datatype

set_peek(dataset, is_multi_byte=False)[source]

Set the peek and blurb text

display_peek(dataset)[source]

Create HTML content, used for displaying peek.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167cf8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.anvio.AnvioDB(*args, **kwd)[source]

Bases: galaxy.datatypes.anvio.AnvioComposite

Class for AnvioDB database files.

file_ext = 'anvio_db'
composite_type = 'auto_primary_file'
allow_datatype_change = False
__init__(*args, **kwd)[source]
set_meta(dataset, **kwd)[source]

Set the anvio_basename based upon actual extra_files_path contents.

metadata_spec = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027fee240>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167cf8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.anvio.AnvioStructureDB(*args, **kwd)[source]

Bases: galaxy.datatypes.anvio.AnvioDB

Class for Anvio Structure DB database files.

file_ext = 'anvio_structure_db'
composite_type = 'auto_primary_file'
allow_datatype_change = False
metadata_spec = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027fee390>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167cf8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.anvio.AnvioGenomesDB(*args, **kwd)[source]

Bases: galaxy.datatypes.anvio.AnvioDB

Class for Anvio Genomes DB database files.

file_ext = 'anvio_genomes_db'
composite_type = 'auto_primary_file'
allow_datatype_change = False
metadata_spec = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027fee4a8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167cf8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.anvio.AnvioContigsDB(*args, **kwd)[source]

Bases: galaxy.datatypes.anvio.AnvioDB

Class for Anvio Contigs DB database files.

file_ext = 'anvio_contigs_db'
composite_type = 'auto_primary_file'
allow_datatype_change = False
__init__(*args, **kwd)[source]
metadata_spec = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027fee630>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167cf8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.anvio.AnvioProfileDB(*args, **kwd)[source]

Bases: galaxy.datatypes.anvio.AnvioDB

Class for Anvio Profile DB database files.

file_ext = 'anvio_profile_db'
composite_type = 'auto_primary_file'
allow_datatype_change = False
__init__(*args, **kwd)[source]
metadata_spec = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027feee10>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167cf8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.anvio.AnvioPanDB(*args, **kwd)[source]

Bases: galaxy.datatypes.anvio.AnvioDB

Class for Anvio Pan DB database files.

file_ext = 'anvio_pan_db'
composite_type = 'auto_primary_file'
allow_datatype_change = False
metadata_spec = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027feee80>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167cf8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.anvio.AnvioSamplesDB(*args, **kwd)[source]

Bases: galaxy.datatypes.anvio.AnvioDB

Class for Anvio Samples DB database files.

file_ext = 'anvio_samples_db'
composite_type = 'auto_primary_file'
allow_datatype_change = False
metadata_spec = {'anvio_basename': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027feeef0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167cf8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}

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: galaxy.datatypes.data.Text

Class describing the AMOS assembly file

edam_data = 'data_0925'
edam_format = 'format_3582'
file_ext = 'afg'
sniff_prefix(file_prefix)[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 = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604c0f0a58>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
sniff(filename)
class galaxy.datatypes.assembly.Sequences(**kwd)[source]

Bases: galaxy.datatypes.sequence.Fasta

Class describing the Sequences file generated by velveth

edam_data = 'data_0925'
file_ext = 'sequences'
sniff_prefix(file_prefix)[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 = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604b203e10>}
sniff(filename)
class galaxy.datatypes.assembly.Roadmaps(**kwd)[source]

Bases: galaxy.datatypes.data.Text

Class describing the Sequences file generated by velveth

edam_format = 'format_2561'
file_ext = 'roadmaps'
sniff_prefix(file_prefix)[source]
Determines whether the file is a velveth produced RoadMap::
142858 21 1 ROADMAP 1 ROADMAP 2 …
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604b203dd8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
sniff(filename)
class galaxy.datatypes.assembly.Velvet(**kwd)[source]

Bases: galaxy.datatypes.text.Html

composite_type = 'auto_primary_file'
allow_datatype_change = False
file_ext = 'velvet'
__init__(**kwd)[source]
generate_primary_file(dataset=None)[source]
regenerate_primary_file(dataset)[source]

cannot do this until we are setting metadata

set_meta(dataset, **kwd)[source]
metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602c51c4a8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'long_reads': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602c51c1d0>, 'paired_end_reads': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602c51c0f0>, 'short2_reads': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028edeeb8>}

galaxy.datatypes.binary module

Binary classes

class galaxy.datatypes.binary.Binary(**kwd)[source]

Bases: galaxy.datatypes.data.Data

Binary data

edam_format = 'format_2333'
static register_sniffable_binary_format(data_type, ext, type_class)[source]

Deprecated method.

static register_unsniffable_binary_ext(ext)[source]

Deprecated method.

set_peek(dataset, is_multi_byte=False)[source]

Set the peek and blurb text

get_mime()[source]

Returns the mime type of the datatype

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050057cf8>}
class galaxy.datatypes.binary.Ab1(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

Class describing an ab1 binary sequence file

file_ext = 'ab1'
edam_format = 'format_3000'
edam_data = 'data_0924'
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050057d30>}
class galaxy.datatypes.binary.Idat(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

Binary data in idat format

file_ext = 'idat'
edam_format = 'format_2058'
edam_data = 'data_2603'
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050057da0>}
class galaxy.datatypes.binary.Cel(**kwd)[source]

Bases: galaxy.datatypes.binary.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)[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, **kwd)[source]

Set metadata for Cel file.

set_peek(dataset, is_multi_byte=False)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050057cf8>, 'version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050057e10>}
class galaxy.datatypes.binary.MashSketch(**kwd)[source]

Bases: galaxy.datatypes.binary.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 = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b0f0>}
class galaxy.datatypes.binary.CompressedArchive(**kwd)[source]

Bases: galaxy.datatypes.binary.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
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500de4a8>}
class galaxy.datatypes.binary.DynamicCompressedArchive(**kwd)[source]

Bases: galaxy.datatypes.binary.CompressedArchive

matches_any(target_datatypes)[source]

Treat two aspects of compressed datatypes separately.

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b160>}
class galaxy.datatypes.binary.GzDynamicCompressedArchive(**kwd)[source]

Bases: galaxy.datatypes.binary.DynamicCompressedArchive

compressed_format = 'gzip'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b208>}
class galaxy.datatypes.binary.Bz2DynamicCompressedArchive(**kwd)[source]

Bases: galaxy.datatypes.binary.DynamicCompressedArchive

compressed_format = 'bz2'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b240>}
class galaxy.datatypes.binary.CompressedZipArchive(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b2b0>}
class galaxy.datatypes.binary.GenericAsn1Binary(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

Class for generic ASN.1 binary format

file_ext = 'asn1-binary'
edam_format = 'format_1966'
edam_data = 'data_0849'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b320>}
class galaxy.datatypes.binary.BamNative(**kwd)[source]

Bases: galaxy.datatypes.binary.CompressedArchive

Class describing a BAM binary file that is not necessarily sorted

edam_format = 'format_2572'
edam_data = 'data_0863'
file_ext = 'unsorted.bam'
sort_flag = None
static merge(split_files, output_file)[source]

Merges BAM files

Parameters:
  • split_files – List of bam file paths to merge
  • output_file – Write merged bam file to this location
init_meta(dataset, copy_from=None)[source]
sniff(filename)[source]
classmethod is_bam(filename)[source]
set_meta(dataset, overwrite=True, **kwd)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
to_archive(trans, dataset, name='')[source]
groom_dataset_content(file_name)[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, offset=0, ck_size=None)[source]
display_data(trans, dataset, preview=False, filename=None, to_ext=None, offset=None, ck_size=None, **kwd)[source]
validate(dataset, **kwd)[source]
metadata_spec = {'bam_header': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b630>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b400>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b780>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b6a0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500de4a8>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b4e0>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b5c0>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b550>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b470>}
class galaxy.datatypes.binary.Bam(**kwd)[source]

Bases: galaxy.datatypes.binary.BamNative

Class describing a BAM binary file

edam_format = 'format_2572'
edam_data = 'data_0863'
file_ext = 'bam'
track_type = 'ReadTrack'
data_sources = {'data': 'bai', 'index': 'bigwig'}
dataset_content_needs_grooming(file_name)[source]

Check if file_name is a coordinate-sorted BAM file

set_meta(dataset, overwrite=True, **kwd)[source]
sniff(file_name)[source]
line_dataprovider(dataset, **settings)[source]
regex_line_dataprovider(dataset, **settings)[source]
column_dataprovider(dataset, **settings)[source]
dict_dataprovider(dataset, **settings)[source]
header_dataprovider(dataset, **settings)[source]
id_seq_qual_dataprovider(dataset, **settings)[source]
genomic_region_dataprovider(dataset, **settings)[source]
genomic_region_dict_dataprovider(dataset, **settings)[source]
samtools_dataprovider(dataset, **settings)[source]

Generic samtools interface - all options available through settings.

dataproviders = {'base': <function Data.base_dataprovider at 0x7f6052fc9ae8>, 'chunk': <function Data.chunk_dataprovider at 0x7f6052fc9c80>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f6052fc9e18>, 'column': <function Bam.column_dataprovider at 0x7f6050072598>, 'dict': <function Bam.dict_dataprovider at 0x7f6050072730>, 'genomic-region': <function Bam.genomic_region_dataprovider at 0x7f6050072bf8>, 'genomic-region-dict': <function Bam.genomic_region_dict_dataprovider at 0x7f6050072d90>, 'header': <function Bam.header_dataprovider at 0x7f60500728c8>, 'id-seq-qual': <function Bam.id_seq_qual_dataprovider at 0x7f6050072a60>, 'line': <function Bam.line_dataprovider at 0x7f6050072268>, 'regex-line': <function Bam.regex_line_dataprovider at 0x7f6050072400>, 'samtools': <function Bam.samtools_dataprovider at 0x7f6050072f28>}
metadata_spec = {'bam_header': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b630>, 'bam_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006bb38>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b400>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b780>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b6a0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500de4a8>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b4e0>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b5c0>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b550>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b470>}
class galaxy.datatypes.binary.ProBam(**kwd)[source]

Bases: galaxy.datatypes.binary.Bam

Class describing a BAM binary file - extended for proteomics data

edam_format = 'format_3826'
edam_data = 'data_0863'
file_ext = 'probam'
metadata_spec = {'bam_header': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b630>, 'bam_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006bbe0>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b400>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b780>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b6a0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500de4a8>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b4e0>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b5c0>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b550>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006b470>}
class galaxy.datatypes.binary.BamInputSorted(**kwd)[source]

Bases: galaxy.datatypes.binary.BamNative

sort_flag = '-n'
file_ext = 'qname_input_sorted.bam'

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.

sniff(file_name)[source]
dataset_content_needs_grooming(file_name)[source]

Groom if the file is coordinate sorted

metadata_spec = {'bam_header': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006beb8>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006bc88>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075048>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006bf98>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006bf28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500de4a8>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006bd68>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006be48>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006bdd8>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006bcf8>}
class galaxy.datatypes.binary.BamQuerynameSorted(**kwd)[source]

Bases: galaxy.datatypes.binary.BamInputSorted

A class for queryname sorted BAM files.

sort_flag = '-n'
file_ext = 'qname_sorted.bam'
sniff(file_name)[source]
dataset_content_needs_grooming(file_name)[source]

Check if file_name is a queryname-sorted BAM file

metadata_spec = {'bam_header': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075320>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500750f0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075470>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075400>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075390>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500de4a8>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500751d0>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500752b0>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075240>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075160>}
class galaxy.datatypes.binary.CRAM(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

file_ext = 'cram'
edam_format = 'format_3462'
edam_data = 'format_0863'
set_meta(dataset, overwrite=True, **kwd)[source]
get_cram_version(filename)[source]
set_index_file(dataset, index_file)[source]
set_peek(dataset, is_multi_byte=False)[source]
sniff(filename)[source]
metadata_spec = {'cram_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075588>, 'cram_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075518>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050057cf8>}
class galaxy.datatypes.binary.BaseBcf(**kwd)[source]

Bases: galaxy.datatypes.binary.CompressedArchive

edam_format = 'format_3020'
edam_data = 'data_3498'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075630>}
class galaxy.datatypes.binary.Bcf(**kwd)[source]

Bases: galaxy.datatypes.binary.BaseBcf

Class describing a (BGZF-compressed) BCF file

file_ext = 'bcf'
sniff(filename)[source]
set_meta(dataset, overwrite=True, **kwd)[source]

Creates the index for the BCF file.

metadata_spec = {'bcf_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500756d8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075630>}
class galaxy.datatypes.binary.BcfUncompressed(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075780>}
class galaxy.datatypes.binary.H5(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
__init__(**kwd)[source]
sniff(filename)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075828>}
class galaxy.datatypes.binary.Loom(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
sniff(filename)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
set_meta(dataset, overwrite=True, **kwd)[source]
metadata_spec = {'col_attrs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075e48>, 'col_attrs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075eb8>, 'col_graphs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075f28>, 'col_graphs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075f98>, 'creation_date': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075b70>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075828>, 'description': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500759b0>, 'doi': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075a90>, 'layers_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075c50>, 'layers_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075cc0>, 'loom_spec_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075b00>, 'row_attrs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075d68>, 'row_attrs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075dd8>, 'row_graphs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007c048>, 'row_graphs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007c0b8>, 'shape': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075be0>, 'title': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075940>, 'url': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075a20>}
class galaxy.datatypes.binary.Anndata(**kwd)[source]

Bases: galaxy.datatypes.binary.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

file_ext = 'h5ad'
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007c160>}
class galaxy.datatypes.binary.GmxBinary(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

Base class for GROMACS binary files - xtc, trr, cpt

magic_number = None
file_ext = ''
sniff(filename)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007c208>}
class galaxy.datatypes.binary.Trr(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
magic_number = 1993
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007c2b0>}
class galaxy.datatypes.binary.Cpt(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
magic_number = 171817
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007c358>}
class galaxy.datatypes.binary.Xtc(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
magic_number = 1995
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007c400>}
class galaxy.datatypes.binary.Edr(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
magic_number = -55555
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007c4a8>}
class galaxy.datatypes.binary.Biom2(**kwd)[source]

Bases: galaxy.datatypes.binary.H5

Class describing a biom2 file (http://biom-format.org/documentation/biom_format.html)

file_ext = 'biom2'
edam_format = 'format_3746'
sniff(filename)[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, overwrite=True, **kwd)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'creation_date': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007c860>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050075828>, 'format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007c710>, 'format_url': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007c630>, 'format_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007c6a0>, 'generated_by': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007c7f0>, 'id': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007c5c0>, 'nnz': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007c8d0>, 'shape': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007c940>, 'type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007c780>}
class galaxy.datatypes.binary.Cool(**kwd)[source]

Bases: galaxy.datatypes.binary.H5

Class describing the cool format (https://github.com/mirnylab/cooler)

file_ext = 'cool'
sniff(filename)[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
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007ca20>}
class galaxy.datatypes.binary.MCool(**kwd)[source]

Bases: galaxy.datatypes.binary.H5

Class describing the multi-resolution cool format (https://github.com/mirnylab/cooler)

file_ext = 'mcool'
sniff(filename)[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
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007cb00>}
class galaxy.datatypes.binary.Scf(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

Class describing an scf binary sequence file

edam_format = 'format_1632'
edam_data = 'data_0924'
file_ext = 'scf'
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007cba8>}
class galaxy.datatypes.binary.Sff(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

Standard Flowgram Format (SFF)

edam_format = 'format_3284'
edam_data = 'data_0924'
file_ext = 'sff'
sniff(filename)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007cc50>}
class galaxy.datatypes.binary.BigWig(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
track_type = 'LineTrack'
data_sources = {'data_standalone': 'bigwig'}
__init__(**kwd)[source]
sniff(filename)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605006bac8>}
class galaxy.datatypes.binary.BigBed(**kwd)[source]

Bases: galaxy.datatypes.binary.BigWig

BigBed support from UCSC.

edam_format = 'format_3004'
edam_data = 'data_3002'
file_ext = 'bigbed'
data_sources = {'data_standalone': 'bigbed'}
__init__(**kwd)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007cd68>}
class galaxy.datatypes.binary.TwoBit(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

Class describing a TwoBit format nucleotide file

edam_format = 'format_3009'
edam_data = 'data_0848'
file_ext = 'twobit'
sniff(filename)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605007ce80>}
class galaxy.datatypes.binary.SQlite(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

Class describing a Sqlite database

file_ext = 'sqlite'
edam_format = 'format_3621'
init_meta(dataset, copy_from=None)[source]
set_meta(dataset, overwrite=True, **kwd)[source]
sniff(filename)[source]
sniff_table_names(filename, table_names)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
sqlite_dataprovider(dataset, **settings)[source]
sqlite_datatableprovider(dataset, **settings)[source]
sqlite_datadictprovider(dataset, **settings)[source]
dataproviders = {'base': <function Data.base_dataprovider at 0x7f6052fc9ae8>, 'chunk': <function Data.chunk_dataprovider at 0x7f6052fc9c80>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f6052fc9e18>, 'sqlite': <function SQlite.sqlite_dataprovider at 0x7f6050005bf8>, 'sqlite-dict': <function SQlite.sqlite_datadictprovider at 0x7f6050005f28>, 'sqlite-table': <function SQlite.sqlite_datatableprovider at 0x7f6050005d90>}
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050057cf8>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007128>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007198>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500070b8>}
class galaxy.datatypes.binary.GeminiSQLite(**kwd)[source]

Bases: galaxy.datatypes.binary.SQlite

Class describing a Gemini Sqlite database

file_ext = 'gemini.sqlite'
edam_format = 'format_3622'
edam_data = 'data_3498'
set_meta(dataset, overwrite=True, **kwd)[source]
sniff(filename)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050057cf8>, 'gemini_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500072b0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007128>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007198>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500070b8>}
class galaxy.datatypes.binary.CuffDiffSQlite(**kwd)[source]

Bases: galaxy.datatypes.binary.SQlite

Class describing a CuffDiff SQLite database

file_ext = 'cuffdiff.sqlite'
edam_format = 'format_3621'
set_meta(dataset, overwrite=True, **kwd)[source]
sniff(filename)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'cuffdiff_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500073c8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050057cf8>, 'genes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007438>, 'samples': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500074a8>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007128>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007198>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500070b8>}
class galaxy.datatypes.binary.MzSQlite(**kwd)[source]

Bases: galaxy.datatypes.binary.SQlite

Class describing a Proteomics Sqlite database

file_ext = 'mz.sqlite'
set_meta(dataset, overwrite=True, **kwd)[source]
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050057cf8>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007630>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500076a0>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500075c0>}
class galaxy.datatypes.binary.BlibSQlite(**kwd)[source]

Bases: galaxy.datatypes.binary.SQlite

Class describing a Proteomics Spectral Library Sqlite database

file_ext = 'blib'
set_meta(dataset, overwrite=True, **kwd)[source]
sniff(filename)[source]
metadata_spec = {'blib_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500077b8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050057cf8>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007128>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007198>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500070b8>}
class galaxy.datatypes.binary.DlibSQlite(**kwd)[source]

Bases: galaxy.datatypes.binary.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, overwrite=True, **kwd)[source]
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050057cf8>, 'dlib_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500078d0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007128>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007198>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500070b8>}
class galaxy.datatypes.binary.ElibSQlite(**kwd)[source]

Bases: galaxy.datatypes.binary.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, overwrite=True, **kwd)[source]
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050057cf8>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007128>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007198>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500070b8>, 'version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500079e8>}
class galaxy.datatypes.binary.IdpDB(**kwd)[source]

Bases: galaxy.datatypes.binary.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, overwrite=True, **kwd)[source]
sniff(filename)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050057cf8>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007b70>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007be0>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007b00>}
class galaxy.datatypes.binary.GAFASQLite(**kwd)[source]

Bases: galaxy.datatypes.binary.SQlite

Class describing a GAFA SQLite database

file_ext = 'gafa.sqlite'
set_meta(dataset, overwrite=True, **kwd)[source]
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050057cf8>, 'gafa_schema_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007cf8>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007128>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007198>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500070b8>}
class galaxy.datatypes.binary.Xlsx(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

Class for Excel 2007 (xlsx) files

file_ext = 'xlsx'
compressed = True
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007da0>}
class galaxy.datatypes.binary.ExcelXls(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

Class describing an Excel (xls) file

file_ext = 'excel.xls'
edam_format = 'format_3468'
sniff(filename)[source]
get_mime()[source]

Returns the mime type of the datatype

set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007e48>}
class galaxy.datatypes.binary.Sra(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

Sequence Read Archive (SRA) datatype originally from mdshw5/sra-tools-galaxy

file_ext = 'sra'
sniff(filename)[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

set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007ef0>}
class galaxy.datatypes.binary.RData(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

Generic R Data file datatype implementation

file_ext = 'rdata'
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050007f98>}
class galaxy.datatypes.binary.OxliBinary(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500120b8>}
class galaxy.datatypes.binary.OxliCountGraph(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050012160>}
class galaxy.datatypes.binary.OxliNodeGraph(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050012208>}
class galaxy.datatypes.binary.OxliTagSet(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500122b0>}
class galaxy.datatypes.binary.OxliStopTags(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050012358>}
class galaxy.datatypes.binary.OxliSubset(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050012400>}
class galaxy.datatypes.binary.OxliGraphLabels(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500124a8>}
class galaxy.datatypes.binary.PostgresqlArchive(**kwd)[source]

Bases: galaxy.datatypes.binary.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, overwrite=True, **kwd)[source]
sniff(filename)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500de4a8>, 'version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050012588>}
class galaxy.datatypes.binary.Fast5Archive(**kwd)[source]

Bases: galaxy.datatypes.binary.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, overwrite=True, **kwd)[source]
sniff(filename)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500de4a8>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050012668>}
class galaxy.datatypes.binary.Fast5ArchiveGz(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500de4a8>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050012710>}
class galaxy.datatypes.binary.Fast5ArchiveBz2(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500de4a8>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500127b8>}
class galaxy.datatypes.binary.SearchGuiArchive(**kwd)[source]

Bases: galaxy.datatypes.binary.CompressedArchive

Class describing a SearchGUI archive

file_ext = 'searchgui_archive'
set_meta(dataset, overwrite=True, **kwd)[source]
sniff(filename)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500de4a8>, 'searchgui_major_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050012908>, 'searchgui_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050012898>}
class galaxy.datatypes.binary.NetCDF(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

Binary data in netCDF format

file_ext = 'netcdf'
edam_format = 'format_3650'
edam_data = 'data_0943'
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500129b0>}
class galaxy.datatypes.binary.Dcd(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
__init__(**kwd)[source]
sniff(filename)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050012a58>}
class galaxy.datatypes.binary.Vel(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
__init__(**kwd)[source]
sniff(filename)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050012b00>}
class galaxy.datatypes.binary.DAA(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
__init__(**kwd)[source]
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050012ba8>}
class galaxy.datatypes.binary.RMA6(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
__init__(**kwd)[source]
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050012c50>}
class galaxy.datatypes.binary.DMND(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
__init__(**kwd)[source]
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050012cf8>}
class galaxy.datatypes.binary.ICM(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

Class describing an ICM (interpolated context model) file, used by Glimmer

file_ext = 'icm'
edam_data = 'data_0950'
set_peek(dataset, is_multi_byte=False)[source]
sniff(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050012da0>}
class galaxy.datatypes.binary.BafTar(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
get_signature_file()[source]
sniff(filename)[source]
get_type()[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050012e48>}
class galaxy.datatypes.binary.YepTar(**kwd)[source]

Bases: galaxy.datatypes.binary.BafTar

A tar’d up .d directory containing Agilent/Bruker YEP format data

file_ext = 'agilentbrukeryep.d.tar'
get_signature_file()[source]
get_type()[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050012ef0>}
class galaxy.datatypes.binary.TdfTar(**kwd)[source]

Bases: galaxy.datatypes.binary.BafTar

A tar’d up .d directory containing Bruker TDF format data

file_ext = 'brukertdf.d.tar'
get_signature_file()[source]
get_type()[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050012f98>}
class galaxy.datatypes.binary.MassHunterTar(**kwd)[source]

Bases: galaxy.datatypes.binary.BafTar

A tar’d up .d directory containing Agilent MassHunter format data

file_ext = 'agilentmasshunter.d.tar'
get_signature_file()[source]
get_type()[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605001e080>}
class galaxy.datatypes.binary.MassLynxTar(**kwd)[source]

Bases: galaxy.datatypes.binary.BafTar

A tar’d up .d directory containing Waters MassLynx format data

file_ext = 'watersmasslynx.raw.tar'
get_signature_file()[source]
get_type()[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605001e128>}
class galaxy.datatypes.binary.WiffTar(**kwd)[source]

Bases: galaxy.datatypes.binary.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'
sniff(filename)[source]
get_type()[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605001e1d0>}

galaxy.datatypes.blast module

NCBI BLAST datatypes.

Covers the blastxml format and the BLAST databases.

class galaxy.datatypes.blast.BlastXml(**kwd)[source]

Bases: galaxy.datatypes.xml.GenericXml

NCBI Blast XML Output data

file_ext = 'blastxml'
edam_format = 'format_3331'
edam_data = 'data_0857'
set_peek(dataset, is_multi_byte=False)[source]

Set the peek and blurb text

sniff_prefix(file_prefix)[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, output_file)[source]

Merging multiple XML files is non-trivial and must be done in subclasses.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605316e208>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
sniff(filename)
class galaxy.datatypes.blast.BlastNucDb(**kwd)[source]

Bases: galaxy.datatypes.blast._BlastDb, galaxy.datatypes.data.Data

Class for nucleotide BLAST database files.

file_ext = 'blastdbn'
allow_datatype_change = False
composite_type = 'basic'
__init__(**kwd)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028818400>}
class galaxy.datatypes.blast.BlastProtDb(**kwd)[source]

Bases: galaxy.datatypes.blast._BlastDb, galaxy.datatypes.data.Data

Class for protein BLAST database files.

file_ext = 'blastdbp'
allow_datatype_change = False
composite_type = 'basic'
__init__(**kwd)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028818390>}
class galaxy.datatypes.blast.BlastDomainDb(**kwd)[source]

Bases: galaxy.datatypes.blast._BlastDb, galaxy.datatypes.data.Data

Class for domain BLAST database files.

file_ext = 'blastdbd'
allow_datatype_change = False
composite_type = 'basic'
__init__(**kwd)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028818668>}

galaxy.datatypes.checkers module

Module proxies galaxy.util.checkers for backward compatibility.

External datatypes may make use of these functions.

galaxy.datatypes.checkers.check_binary(name, file_path=True)[source]
galaxy.datatypes.checkers.check_bz2(file_path, check_content=True)[source]
galaxy.datatypes.checkers.check_gzip(file_path, check_content=True)[source]
galaxy.datatypes.checkers.check_html(name, file_path=True)[source]

Returns True if the file/string contains HTML code.

galaxy.datatypes.checkers.check_image(file_path)[source]

Simple wrapper around image_type to yield a True/False verdict

galaxy.datatypes.checkers.check_zip(file_path, check_content=True, files=1)[source]
galaxy.datatypes.checkers.is_gzip(file_path)[source]
galaxy.datatypes.checkers.is_bz2(file_path)[source]

galaxy.datatypes.chrominfo module

class galaxy.datatypes.chrominfo.ChromInfo(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'len'
metadata_spec = {'chrom': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028b05ef0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff630f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4ca58>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'length': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029ff1c50>}

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 = ''
__init__(**kwd)[source]
sniff_prefix(file_prefix)[source]

The structure of a typical PLY file: Header, Vertex List, Face List, (lists of other elements)

set_meta(dataset, **kwd)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
sniff(filename)
class galaxy.datatypes.constructive_solid_geometry.PlyAscii(**kwd)[source]

Bases: galaxy.datatypes.constructive_solid_geometry.Ply, galaxy.datatypes.data.Text

file_ext = 'plyascii'
subtype = 'ascii'
__init__(**kwd)[source]
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'face': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028aea7f0>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028aea358>, 'other_elements': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028aea828>, 'vertex': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028aeaf28>}
class galaxy.datatypes.constructive_solid_geometry.PlyBinary(**kwd)[source]

Bases: galaxy.datatypes.constructive_solid_geometry.Ply, galaxy.datatypes.binary.Binary

file_ext = 'plybinary'
subtype = 'binary'
__init__(**kwd)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050057cf8>, 'face': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028d04358>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028d04f60>, 'other_elements': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028d04ac8>, 'vertex': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028d04828>}
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 = ''
__init__(**kwd)[source]
sniff_prefix(file_prefix)[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_meta(dataset, **kwd)[source]
set_initial_metadata(i, line, dataset)[source]
set_structure_metadata(line, dataset, dataset_type)[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.

get_blurb(dataset)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
sniff(filename)
class galaxy.datatypes.constructive_solid_geometry.VtkAscii(**kwd)[source]

Bases: galaxy.datatypes.constructive_solid_geometry.Vtk, galaxy.datatypes.data.Text

file_ext = 'vtkascii'
subtype = 'ASCII'
__init__(**kwd)[source]
metadata_spec = {'cells': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028b05748>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dataset_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028d04518>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'dimensions': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028d040f0>, 'field_components': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028b05550>, 'field_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028b056d8>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028d04c50>, 'lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028ac9b00>, 'origin': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028d048d0>, 'points': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028ac9f60>, 'polygons': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028ac9748>, 'spacing': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028ac92e8>, 'triangle_strips': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028b055c0>, 'vertices': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028ac9cf8>, 'vtk_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028d040b8>}
class galaxy.datatypes.constructive_solid_geometry.VtkBinary(**kwd)[source]

Bases: galaxy.datatypes.constructive_solid_geometry.Vtk, galaxy.datatypes.binary.Binary

file_ext = 'vtkbinary'
subtype = 'BINARY'
__init__(**kwd)[source]
metadata_spec = {'cells': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028c759b0>, 'dataset_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028b053c8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050057cf8>, 'dimensions': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028b05358>, 'field_components': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028c75c18>, 'field_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028c75cc0>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028b05438>, 'lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028a84e48>, 'origin': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028a849e8>, 'points': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028a84a20>, 'polygons': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028a84ba8>, 'spacing': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028a84828>, 'triangle_strips': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028c75208>, 'vertices': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028a84f98>, 'vtk_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028b054a8>}
class galaxy.datatypes.constructive_solid_geometry.STL(**kwd)[source]

Bases: galaxy.datatypes.data.Data

file_ext = 'stl'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028c75588>}
galaxy.datatypes.constructive_solid_geometry.get_next_line(fh)[source]

galaxy.datatypes.coverage module

Coverage datatypes

class galaxy.datatypes.coverage.LastzCoverage(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'coverage'
get_track_resolution(dataset, start, end)[source]
metadata_spec = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60285fe128>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff630f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60285fe400>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'forwardCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60285fe320>, 'positionCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60285fe2b0>, 'reverseCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60285fe390>}

galaxy.datatypes.data module

class galaxy.datatypes.data.DatatypeValidation(state, message)[source]

Bases: object

__init__(state, message)[source]
static validated()[source]
static invalid(message)[source]
static unvalidated()[source]
galaxy.datatypes.data.validate(dataset_instance)[source]
class galaxy.datatypes.data.DataMeta(name, bases, dict_)[source]

Bases: abc.ABCMeta

Metaclass for Data class. Sets up metadata spec.

__init__(name, bases, dict_)[source]
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 = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}

Dictionary of metadata fields for this datatype

copy_safe_peek = True
is_binary = True
allow_datatype_change = True
composite_type = None
primary_file_name = 'index'
track_type = None
data_sources = {}
__init__(**kwd)[source]

Initialize the datatype

supported_display_apps = {}
composite_files = {}
get_raw_data(dataset)[source]

Returns the full data. To stream it open the file_name and read/write as needed

dataset_content_needs_grooming(file_name)[source]

This function is called on an output dataset file after the content is initially generated.

groom_dataset_content(file_name)[source]

This function is called on an output dataset file if dataset_content_needs_grooming returns True.

init_meta(dataset, copy_from=None)[source]
set_meta(dataset, overwrite=True, **kwd)[source]

Unimplemented method, allows guessing of metadata from contents of file

missing_meta(dataset, check=None, skip=None)[source]

Checks for empty metadata values, Returns True if non-optional metadata is missing 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

set_max_optional_metadata_filesize(max_value)[source]
get_max_optional_metadata_filesize()[source]
max_optional_metadata_filesize
set_peek(dataset, is_multi_byte=False)[source]

Set the peek and blurb text

Parameters:is_multi_byte (bool) – deprecated
display_peek(dataset)[source]

Create HTML table, used for displaying peek

to_archive(trans, dataset, name='')[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, data, preview=False, filename=None, to_ext=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, markdown_format_helpers)[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.

display_name(dataset)[source]

Returns formatted html of dataset name

display_info(dataset)[source]

Returns formatted html of dataset info

repair_methods(dataset)[source]

Unimplemented method, returns dict with method/option for repairing errors

get_mime()[source]

Returns the mime type of the datatype

add_display_app(app_id, label, file_function, links_function)[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)

remove_display_app(app_id)[source]

Removes a display app from the datatype

clear_display_apps()[source]
add_display_application(display_application)[source]

New style display applications

get_display_application(key, default=None)[source]
get_display_applications_by_dataset(dataset, trans)[source]
get_display_types()[source]

Returns display types available

get_display_label(type)[source]

Returns primary label for display app

as_display_type(dataset, type, **kwd)[source]

Returns modified file contents for a particular display type

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, datatypes_registry)[source]

Returns available converters by type for this dataset

find_conversion_destination(dataset, accepted_formats, datatypes_registry, **kwd)[source]

Returns ( target_ext, existing converted dataset )

convert_dataset(trans, original_dataset, target_type, return_output=False, visible=True, deps=None, target_context=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)[source]

This function is called on the dataset after metadata is set.

before_setting_metadata(dataset)[source]

This function is called on the dataset before metadata is set.

add_composite_file(name, **kwds)[source]
writable_files
get_composite_files(dataset=None)[source]
generate_primary_file(dataset=None)[source]
has_resolution
matches_any(target_datatypes)[source]

Check if this datatype is of any of the target_datatypes or is a subtype thereof.

static merge(split_files, output_file)[source]

Merge files with copy.copyfileobj() will not hit the max argument limitation of cat. gz and bz2 files are also working.

get_visualizations(dataset)[source]

Returns a list of visualizations for datatype.

has_dataprovider(data_format)[source]

Returns True if data_format is available in dataproviders.

dataprovider(dataset, data_format, **settings)[source]

Base dataprovider factory for all datatypes that returns the proper provider for the given data_format or raises a NoProviderAvailable.

validate(dataset, **kwd)[source]
base_dataprovider(dataset, **settings)[source]
chunk_dataprovider(dataset, **settings)[source]
chunk64_dataprovider(dataset, **settings)[source]
dataproviders = {'base': <function Data.base_dataprovider at 0x7f6052fc9ae8>, 'chunk': <function Data.chunk_dataprovider at 0x7f6052fc9c80>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f6052fc9e18>}
class galaxy.datatypes.data.Text(**kwd)[source]

Bases: galaxy.datatypes.data.Data

edam_format = 'format_2330'
file_ext = 'txt'
line_class = 'line'
is_binary = False
get_mime()[source]

Returns the mime type of the datatype

set_meta(dataset, **kwd)[source]

Set the number of lines of data in dataset.

estimate_file_lines(dataset)[source]

Perform a rough estimate by extrapolating number of lines from a small read.

count_data_lines(dataset)[source]

Count the number of lines of data in dataset, skipping all blank lines and comments.

set_peek(dataset, line_count=None, is_multi_byte=False, WIDTH=256, skipchars=None, line_wrap=True)[source]

Set the peek. This method is used by various subclasses of Text.

classmethod split(input_datasets, subdir_generator_function, split_params)[source]

Split the input files by line.

line_dataprovider(dataset, **settings)[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, **settings)[source]

Returns an iterator over the dataset’s lines optionally including/excluding lines that match one or more regex filters.

dataproviders = {'base': <function Data.base_dataprovider at 0x7f6052fc9ae8>, 'chunk': <function Data.chunk_dataprovider at 0x7f6052fc9c80>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f6052fc9e18>, 'line': <function Text.line_dataprovider at 0x7f6052fcc400>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f6052fcc598>}
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.data.Directory(**kwd)[source]

Bases: galaxy.datatypes.data.Data

Class representing a directory of files.

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5898>}
class galaxy.datatypes.data.GenericAsn1(**kwd)[source]

Bases: galaxy.datatypes.data.Text

Class for generic ASN.1 text format

edam_data = 'data_0849'
edam_format = 'format_1966'
file_ext = 'asn1'
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5940>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.data.LineCount(**kwd)[source]

Bases: galaxy.datatypes.data.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 = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc59e8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.data.Newick(**kwd)[source]

Bases: galaxy.datatypes.data.Text

New Hampshire/Newick Format

edam_data = 'data_0872'
edam_format = 'format_1910'
file_ext = 'newick'
__init__(**kwd)[source]

Initialize foobar datatype

init_meta(dataset, copy_from=None)[source]
sniff(filename)[source]

Returning false as the newick format is too general and cannot be sniffed.

get_visualizations(dataset)[source]

Returns a list of visualizations for datatype.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5a90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.data.Nexus(**kwd)[source]

Bases: galaxy.datatypes.data.Text

Nexus format as used By Paup, Mr Bayes, etc

edam_data = 'data_0872'
edam_format = 'format_1912'
file_ext = 'nex'
__init__(**kwd)[source]

Initialize foobar datatype

init_meta(dataset, copy_from=None)[source]
sniff_prefix(file_prefix)[source]

All Nexus Files Simply puts a ‘#NEXUS’ in its first line

get_visualizations(dataset)[source]

Returns a list of visualizations for datatype.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5b38>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
sniff(filename)
galaxy.datatypes.data.get_test_fname(fname)[source]

Returns test data filename

galaxy.datatypes.data.get_file_peek(file_name, is_multi_byte=False, WIDTH=256, LINE_COUNT=5, skipchars=None, line_wrap=True)[source]

Returns the first LINE_COUNT lines wrapped to WIDTH.

Parameters:is_multi_byte (bool) – deprecated
>>> 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.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: galaxy.datatypes.tabular.Tabular

Tab delimited data containing a marker id and any number of numeric values

file_ext = 'gg'
__init__(**kwd)[source]

Initialize gg datatype, by adding UCSC display apps

set_meta(dataset, **kwd)[source]
as_ucsc_display_file(dataset, **kwd)[source]

Returns file

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, skipchars=[])[source]

Create HTML table, used for displaying peek

validate(dataset, **kwd)[source]

Validate a gg file - all numeric after header row

sniff_prefix(file_prefix)[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
get_mime()[source]

Returns the mime type of the datatype

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029a77748>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029a77940>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'markerCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604bdf01d0>}
sniff(filename)
class galaxy.datatypes.genetics.rgTabList(**kwd)[source]

Bases: galaxy.datatypes.tabular.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'
__init__(**kwd)[source]

Initialize featurelistt datatype

display_peek(dataset)[source]

Returns formated html of peek

get_mime()[source]

Returns the mime type of the datatype

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029a77b00>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029a77400>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029a77358>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029a77550>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029a77630>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602df63b38>}
class galaxy.datatypes.genetics.rgSampleList(**kwd)[source]

Bases: galaxy.datatypes.genetics.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'
__init__(**kwd)[source]

Initialize samplelist datatype

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afc00b8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602e371d68>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602ea0ecf8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029a601d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602af50320>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afc0128>}
class galaxy.datatypes.genetics.rgFeatureList(**kwd)[source]

Bases: galaxy.datatypes.genetics.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'
__init__(**kwd)[source]

Initialize featurelist datatype

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6031b08198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602ad7a0f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602df603c8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602de59c50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602df5a908>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6031b08160>}
class galaxy.datatypes.genetics.Rgenetics(**kwd)[source]

Bases: galaxy.datatypes.text.Html

base class to use for rgenetics datatypes derived from html - composite datatype elements stored in extra files path

composite_type = 'auto_primary_file'
allow_datatype_change = False
file_ext = 'rgenetics'
generate_primary_file(dataset=None)[source]
regenerate_primary_file(dataset)[source]

cannot do this until we are setting metadata

get_mime()[source]

Returns the mime type of the datatype

set_meta(dataset, **kwd)[source]

for lped/pbed eg

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604aec0198>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.genetics.SNPMatrix(**kwd)[source]

Bases: galaxy.datatypes.genetics.Rgenetics

BioC SNPMatrix Rgenetics data collections

file_ext = 'snpmatrix'
set_peek(dataset, **kwd)[source]
sniff(filename)[source]

need to check the file header hex code

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604aec00f0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.genetics.Lped(**kwd)[source]

Bases: galaxy.datatypes.genetics.Rgenetics

linkage pedigree (ped,map) Rgenetics data collections

file_ext = 'lped'
__init__(**kwd)[source]
metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604aec0278>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.genetics.Pphe(**kwd)[source]

Bases: galaxy.datatypes.genetics.Rgenetics

Plink phenotype file - header must have FID IID… Rgenetics data collections

file_ext = 'pphe'
__init__(**kwd)[source]
metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602ea23978>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.genetics.Fphe(**kwd)[source]

Bases: galaxy.datatypes.genetics.Rgenetics

fbat pedigree file - mad format with ! as first char on header row Rgenetics data collections

file_ext = 'fphe'
__init__(**kwd)[source]
metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602ea23780>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.genetics.Phe(**kwd)[source]

Bases: galaxy.datatypes.genetics.Rgenetics

Phenotype file

file_ext = 'phe'
__init__(**kwd)[source]
metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602ea236d8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.genetics.Fped(**kwd)[source]

Bases: galaxy.datatypes.genetics.Rgenetics

FBAT pedigree format - single file, map is header row of rs numbers. Strange. Rgenetics data collections

file_ext = 'fped'
__init__(**kwd)[source]
metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604bd65ef0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.genetics.Pbed(**kwd)[source]

Bases: galaxy.datatypes.genetics.Rgenetics

Plink Binary compressed 2bit/geno Rgenetics data collections

file_ext = 'pbed'
__init__(**kwd)[source]
metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604bd65c88>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.genetics.ldIndep(**kwd)[source]

Bases: galaxy.datatypes.genetics.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'
__init__(**kwd)[source]
metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604bd65fd0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.genetics.Eigenstratgeno(**kwd)[source]

Bases: galaxy.datatypes.genetics.Rgenetics

Eigenstrat format - may be able to get rid of this if we move to shellfish Rgenetics data collections

file_ext = 'eigenstratgeno'
__init__(**kwd)[source]
metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604bd65dd8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.genetics.Eigenstratpca(**kwd)[source]

Bases: galaxy.datatypes.genetics.Rgenetics

Eigenstrat PCA file for case control adjustment Rgenetics data collections

file_ext = 'eigenstratpca'
__init__(**kwd)[source]
metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb42b0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.genetics.Snptest(**kwd)[source]

Bases: galaxy.datatypes.genetics.Rgenetics

BioC snptest Rgenetics data collections

file_ext = 'snptest'
metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4940>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.genetics.IdeasPre(**kwd)[source]

Bases: galaxy.datatypes.text.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.

composite_type = 'auto_primary_file'
allow_datatype_change = False
file_ext = 'ideaspre'
__init__(**kwd)[source]
set_meta(dataset, **kwd)[source]
generate_primary_file(dataset=None)[source]
regenerate_primary_file(dataset)[source]
metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb40b8>, 'chrom_bed': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4da0>, 'chrom_windows': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4748>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'input_config': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb41d0>, 'tmp_archive': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4be0>}
class galaxy.datatypes.genetics.Pheno(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

base class for pheno files

file_ext = 'pheno'
metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4b00>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb46a0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4860>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4908>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb44e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4978>}
class galaxy.datatypes.genetics.RexpBase(**kwd)[source]

Bases: galaxy.datatypes.text.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
composite_type = 'auto_primary_file'
allow_datatype_change = False
__init__(**kwd)[source]
generate_primary_file(dataset=None)[source]

This is called only at upload to write the html file cannot rename the datasets here - they come with the default unfortunately

get_mime()[source]

Returns the mime type of the datatype

get_phecols(phenolist=[], maxConc=20)[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, **kwd)[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_peek(dataset)[source]

expects a .pheno file in the extra_files_dir - ugh

get_file_peek(filename)[source]

can’t really peek at a filename - need the extra_files_path and such?

regenerate_primary_file(dataset)[source]

cannot do this until we are setting metadata

init_meta(dataset, copy_from=None)[source]
set_meta(dataset, **kwd)[source]

NOTE we apply the tabular machinary to the phenodata extracted from a BioC eSet or affybatch.

make_html_table(pp='nothing supplied from peek\n')[source]

Create HTML table, used for displaying peek

display_peek(dataset)[source]

Returns formatted html of peek

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4ba8>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4828>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4470>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb45f8>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4208>}
class galaxy.datatypes.genetics.Affybatch(**kwd)[source]

Bases: galaxy.datatypes.genetics.RexpBase

derived class for BioC data structures in Galaxy

file_ext = 'affybatch'
__init__(**kwd)[source]
metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4a58>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4668>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4160>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4f98>}
class galaxy.datatypes.genetics.Eset(**kwd)[source]

Bases: galaxy.datatypes.genetics.RexpBase

derived class for BioC data structures in Galaxy

file_ext = 'eset'
__init__(**kwd)[source]
metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4630>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4c50>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4588>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4780>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4358>}
class galaxy.datatypes.genetics.MAlist(**kwd)[source]

Bases: galaxy.datatypes.genetics.RexpBase

derived class for BioC data structures in Galaxy

file_ext = 'malist'
__init__(**kwd)[source]
metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604bff27f0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4ac8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602afb4c88>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604bff2940>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604bff2ac8>}
class galaxy.datatypes.genetics.LinkageStudies(**kwd)[source]

Bases: galaxy.datatypes.data.Text

superclass for classical linkage analysis suites

test_files = ['linkstudies.allegro_fparam', 'linkstudies.alohomora_gts', 'linkstudies.linkage_datain', 'linkstudies.linkage_map']
__init__(**kwd)[source]
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604bff2f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.genetics.GenotypeMatrix(**kwd)[source]

Bases: galaxy.datatypes.genetics.LinkageStudies

Sample matrix of genotypes - GTs as columns

file_ext = 'alohomora_gts'
__init__(**kwd)[source]
header_check(fio)[source]
sniff_prefix(file_prefix)[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 = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604bff2748>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
sniff(filename)
class galaxy.datatypes.genetics.MarkerMap(**kwd)[source]

Bases: galaxy.datatypes.genetics.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'
header_check(fio)[source]
sniff_prefix(file_prefix)[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 = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604bff2cc0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
sniff(filename)
class galaxy.datatypes.genetics.DataIn(**kwd)[source]

Bases: galaxy.datatypes.genetics.LinkageStudies

Common linkage input file for intermarker distances and recombination rates

file_ext = 'linkage_datain'
__init__(**kwd)[source]
sniff_prefix(file_prefix)[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 = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604bff2cf8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
sniff(filename)
class galaxy.datatypes.genetics.AllegroLOD(**kwd)[source]

Bases: galaxy.datatypes.genetics.LinkageStudies

Allegro output format for LOD scores

file_ext = 'allegro_fparam'
header_check(fio)[source]
sniff_prefix(file_prefix)[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 = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604bff2b70>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
sniff(filename)

galaxy.datatypes.gis module

GIS classes

class galaxy.datatypes.gis.Shapefile(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

The Shapefile data format: For more information please see http://en.wikipedia.org/wiki/Shapefile

composite_type = 'auto_primary_file'
file_ext = 'shp'
allow_datatype_change = False
__init__(**kwd)[source]
generate_primary_file(dataset=None)[source]
set_peek(dataset, is_multi_byte=False)[source]

Set the peek and blurb text.

display_peek(dataset)[source]

Create HTML content, used for displaying peek.

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027e0d6d8>}

galaxy.datatypes.graph module

Graph content classes.

class galaxy.datatypes.graph.Xgmml(**kwd)[source]

Bases: galaxy.datatypes.xml.GenericXml

XGMML graph format (http://wiki.cytoscape.org/Cytoscape_User_Manual/Network_Formats).

file_ext = 'xgmml'
set_peek(dataset, is_multi_byte=False)[source]

Set the peek and blurb text

sniff(filename)[source]

Returns false and the user must manually set.

static merge(split_files, output_file)[source]

Merging multiple XML files is non-trivial and must be done in subclasses.

node_edge_dataprovider(dataset, **settings)[source]
dataproviders = {'base': <function Data.base_dataprovider at 0x7f6052fc9ae8>, 'chunk': <function Data.chunk_dataprovider at 0x7f6052fc9c80>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f6052fc9e18>, 'line': <function Text.line_dataprovider at 0x7f6052fcc400>, 'node-edge': <function Xgmml.node_edge_dataprovider at 0x7f6029fb3510>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f6052fcc598>, 'xml': <function GenericXml.xml_dataprovider at 0x7f602b6688c8>}
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029fcbac8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.graph.Sif(**kwd)[source]

Bases: galaxy.datatypes.tabular.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'
set_peek(dataset, is_multi_byte=False)[source]

Set the peek and blurb text

sniff(filename)[source]

Returns false and the user must manually set.

static merge(split_files, output_file)[source]
node_edge_dataprovider(dataset, **settings)[source]
dataproviders = {'base': <function Data.base_dataprovider at 0x7f6052fc9ae8>, 'chunk': <function Data.chunk_dataprovider at 0x7f6052fc9c80>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f6052fc9e18>, 'column': <function TabularData.column_dataprovider at 0x7f605001f9d8>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f605001fb70>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f605001fea0>, 'dict': <function TabularData.dict_dataprovider at 0x7f605001fd08>, 'line': <function Text.line_dataprovider at 0x7f6052fcc400>, 'node-edge': <function Sif.node_edge_dataprovider at 0x7f6029fb38c8>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f6052fcc598>}
metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029fcbf98>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029fcbf28>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029fcbeb8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029fcbdd8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029fcbe48>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029fcc048>}
class galaxy.datatypes.graph.XGMMLGraphDataProvider(source, selector=None, max_depth=None, **kwargs)[source]

Bases: galaxy.datatypes.dataproviders.hierarchy.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> }
settings = {'limit': 'int', 'max_depth': 'int', 'offset': 'int', 'selector': 'str'}
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: galaxy.datatypes.dataproviders.column.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 = {'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.images module

Image classes

class galaxy.datatypes.images.Image(**kwd)[source]

Bases: galaxy.datatypes.data.Data

Class describing an image

edam_data = 'data_2968'
edam_format = 'format_3547'
file_ext = ''
__init__(**kwd)[source]
set_peek(dataset, is_multi_byte=False)[source]
sniff(filename)[source]

Determine if the file is in this format

handle_dataset_as_image(hda)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602a227898>}
class galaxy.datatypes.images.Jpg(**kwd)[source]

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3579'
file_ext = 'jpg'
__init__(**kwd)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60295a6470>}
class galaxy.datatypes.images.Png(**kwd)[source]

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3603'
file_ext = 'png'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60295a6550>}
class galaxy.datatypes.images.Tiff(**kwd)[source]

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3591'
file_ext = 'tiff'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60295a6518>}
class galaxy.datatypes.images.Hamamatsu(**kwd)[source]

Bases: galaxy.datatypes.images.Image

file_ext = 'vms'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60295a6b70>}
class galaxy.datatypes.images.Mirax(**kwd)[source]

Bases: galaxy.datatypes.images.Image

file_ext = 'mrxs'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029fc54e0>}
class galaxy.datatypes.images.Sakura(**kwd)[source]

Bases: galaxy.datatypes.images.Image

file_ext = 'svslide'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027774cc0>}
class galaxy.datatypes.images.Nrrd(**kwd)[source]

Bases: galaxy.datatypes.images.Image

file_ext = 'nrrd'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029f9d3c8>}
class galaxy.datatypes.images.Bmp(**kwd)[source]

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3592'
file_ext = 'bmp'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602778a748>}
class galaxy.datatypes.images.Gif(**kwd)[source]

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3467'
file_ext = 'gif'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602778a9e8>}
class galaxy.datatypes.images.Im(**kwd)[source]

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3593'
file_ext = 'im'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602778a588>}
class galaxy.datatypes.images.Pcd(**kwd)[source]

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3594'
file_ext = 'pcd'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60295d0630>}
class galaxy.datatypes.images.Pcx(**kwd)[source]

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3595'
file_ext = 'pcx'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027750358>}
class galaxy.datatypes.images.Ppm(**kwd)[source]

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3596'
file_ext = 'ppm'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604c206ac8>}
class galaxy.datatypes.images.Psd(**kwd)[source]

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3597'
file_ext = 'psd'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027deb898>}
class galaxy.datatypes.images.Xbm(**kwd)[source]

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3598'
file_ext = 'xbm'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602890feb8>}
class galaxy.datatypes.images.Xpm(**kwd)[source]

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3599'
file_ext = 'xpm'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602890fe80>}
class galaxy.datatypes.images.Rgb(**kwd)[source]

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3600'
file_ext = 'rgb'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602890f7b8>}
class galaxy.datatypes.images.Pbm(**kwd)[source]

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3601'
file_ext = 'pbm'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602890ff60>}
class galaxy.datatypes.images.Pgm(**kwd)[source]

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3602'
file_ext = 'pgm'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602890f9b0>}
class galaxy.datatypes.images.Eps(**kwd)[source]

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3466'
file_ext = 'eps'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602890f160>}
class galaxy.datatypes.images.Rast(**kwd)[source]

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3605'
file_ext = 'rast'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602890f940>}
class galaxy.datatypes.images.Pdf(**kwd)[source]

Bases: galaxy.datatypes.images.Image

edam_format = 'format_3508'
file_ext = 'pdf'
sniff(filename)[source]

Determine if the file is in pdf format.

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602890fb00>}
galaxy.datatypes.images.create_applet_tag_peek(class_name, archive, params)[source]
class galaxy.datatypes.images.Gmaj(**kwd)[source]

Bases: galaxy.datatypes.data.Data

Class describing a GMAJ Applet

edam_format = 'format_3547'
file_ext = 'gmaj.zip'
copy_safe_peek = False
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
get_mime()[source]

Returns the mime type of the datatype

sniff(filename)[source]

NOTE: the sniff.convert_newlines() call in the upload utility will keep Gmaj data types from being correctly sniffed, but the files can be uploaded (they’ll be sniffed as ‘txt’). This sniff function is here to provide an example of a sniffer for a zip file.

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602890fa58>}
class galaxy.datatypes.images.Html(**kwd)[source]

Bases: galaxy.datatypes.text.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 = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602890f828>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.images.Laj(**kwd)[source]

Bases: galaxy.datatypes.data.Text

Class describing a LAJ Applet

file_ext = 'laj'
copy_safe_peek = False
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027e0d828>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}

galaxy.datatypes.interval module

Interval datatypes

class galaxy.datatypes.interval.Interval(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

Tab delimited data containing interval information

edam_data = 'data_3002'
edam_format = 'format_3475'
file_ext = 'interval'
line_class = 'region'
track_type = 'FeatureTrack'
data_sources = {'data': 'tabix', 'index': 'bigwig'}

Add metadata elements

__init__(**kwd)[source]

Initialize interval datatype, by adding UCSC display apps

init_meta(dataset, copy_from=None)[source]
set_meta(dataset, overwrite=True, first_line_is_header=False, **kwd)[source]

Tries to guess from the line the location number of the column for the chromosome, region start-end and strand

displayable(dataset)[source]
get_estimated_display_viewport(dataset, chrom_col=None, start_col=None, end_col=None)[source]

Return a chrom, start, stop tuple for viewing a file.

as_ucsc_display_file(dataset, **kwd)[source]

Returns file contents with only the bed data

display_peek(dataset)[source]

Returns formated html of peek

Generate links to UCSC genome browser sites based on the dbkey and content of dataset.

validate(dataset, **kwd)[source]

Validate an interval file using the bx GenomicIntervalReader

repair_methods(dataset)[source]

Return options for removing errors along with a description

sniff_prefix(file_prefix)[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
get_track_resolution(dataset, start, end)[source]
genomic_region_dataprovider(dataset, **settings)[source]
genomic_region_dict_dataprovider(dataset, **settings)[source]
interval_dataprovider(dataset, **settings)[source]
interval_dict_dataprovider(dataset, **settings)[source]
dataproviders = {'base': <function Data.base_dataprovider at 0x7f6052fc9ae8>, 'chunk': <function Data.chunk_dataprovider at 0x7f6052fc9c80>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f6052fc9e18>, 'column': <function TabularData.column_dataprovider at 0x7f605001f9d8>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f605001fb70>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f605001fea0>, 'dict': <function TabularData.dict_dataprovider at 0x7f605001fd08>, 'genomic-region': <function Interval.genomic_region_dataprovider at 0x7f60500338c8>, 'genomic-region-dict': <function Interval.genomic_region_dict_dataprovider at 0x7f6050033a60>, 'interval': <function Interval.interval_dataprovider at 0x7f6050033bf8>, 'interval-dict': <function Interval.interval_dict_dataprovider at 0x7f6050033d90>, 'line': <function Text.line_dataprovider at 0x7f6052fcc400>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f6052fcc598>}
metadata_spec = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605002f940>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff630f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036358>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605002fc50>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605002fcf8>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605002fbe0>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605002fcc0>}
sniff(filename)
class galaxy.datatypes.interval.BedGraph(**kwd)[source]

Bases: galaxy.datatypes.interval.Interval

Tab delimited chrom/start/end/datavalue dataset

edam_format = 'format_3583'
file_ext = 'bedgraph'
track_type = 'LineTrack'
data_sources = {'data': 'bigwig', 'index': 'bigwig'}
as_ucsc_display_file(dataset, **kwd)[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, chrom_col=0, start_col=1, end_col=2)[source]

Set viewport based on dataset’s first 100 lines.

metadata_spec = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036390>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff630f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036630>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500364e0>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500365c0>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036470>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036550>}
class galaxy.datatypes.interval.Bed(**kwd)[source]

Bases: galaxy.datatypes.interval.Interval

Tab delimited data in BED format

edam_format = 'format_3003'
file_ext = 'bed'
data_sources = {'data': 'tabix', 'feature_search': 'fli', 'index': 'bigwig'}
track_type = 'FeatureTrack'
column_names = ['Chrom', 'Start', 'End', 'Name', 'Score', 'Strand', 'ThickStart', 'ThickEnd', 'ItemRGB', 'BlockCount', 'BlockSizes', 'BlockStarts']

Add metadata elements

set_meta(dataset, overwrite=True, **kwd)[source]

Sets the metadata information for datasets previously determined to be in bed format.

as_ucsc_display_file(dataset, **kwd)[source]

Returns file contents with only the bed data. If bed 6+, treat as interval.

sniff_prefix(file_prefix)[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 = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036668>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff630f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036898>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500367b8>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605002fcf8>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036748>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036828>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500368d0>}
class galaxy.datatypes.interval.ProBed(**kwd)[source]

Bases: galaxy.datatypes.interval.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 = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036940>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff630f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036b70>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036a90>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605002fcf8>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036a20>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036b00>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036ba8>}
class galaxy.datatypes.interval.BedStrict(**kwd)[source]

Bases: galaxy.datatypes.interval.Bed

Tab delimited data in strict BED format - no non-standard columns allowed

edam_format = 'format_3584'
file_ext = 'bedstrict'
allow_datatype_change = False
__init__(**kwd)[source]
set_meta(dataset, overwrite=True, **kwd)[source]
sniff(filename)[source]
metadata_spec = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036c18>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff630f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036e48>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036cf8>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036dd8>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036c88>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036d68>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500368d0>}
class galaxy.datatypes.interval.Bed6(**kwd)[source]

Bases: galaxy.datatypes.interval.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 = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036ef0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff630f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d160>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036fd0>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d0f0>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050036f60>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d080>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500368d0>}
class galaxy.datatypes.interval.Bed12(**kwd)[source]

Bases: galaxy.datatypes.interval.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 = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d208>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff630f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d438>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d2e8>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d3c8>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d278>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d358>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60500368d0>}
class galaxy.datatypes.interval.Gff(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular, galaxy.datatypes.interval._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 = {'data': 'interval_index', 'feature_search': 'fli', 'index': 'bigwig'}
track_type = 'FeatureTrack'

Add metadata elements

__init__(**kwd)[source]

Initialize datatype, by adding GBrowse display app

set_attribute_metadata(dataset)[source]

Sets metadata elements for dataset’s attributes.

set_meta(dataset, overwrite=True, **kwd)[source]
display_peek(dataset)[source]

Returns formated html of peek

get_estimated_display_viewport(dataset)[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)[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, **settings)[source]
genomic_region_dict_dataprovider(dataset, **settings)[source]
interval_dataprovider(dataset, **settings)[source]
interval_dict_dataprovider(dataset, **settings)[source]
dataproviders = {'base': <function Data.base_dataprovider at 0x7f6052fc9ae8>, 'chunk': <function Data.chunk_dataprovider at 0x7f6052fc9c80>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f6052fc9e18>, 'column': <function TabularData.column_dataprovider at 0x7f605001f9d8>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f605001fb70>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f605001fea0>, 'dict': <function TabularData.dict_dataprovider at 0x7f605001fd08>, 'genomic-region': <function Gff.genomic_region_dataprovider at 0x7f6050038d08>, 'genomic-region-dict': <function Gff.genomic_region_dict_dataprovider at 0x7f6050038ea0>, 'interval': <function Gff.interval_dataprovider at 0x7f60375d80d0>, 'interval-dict': <function Gff.interval_dict_dataprovider at 0x7f60375d8268>, 'line': <function Text.line_dataprovider at 0x7f6052fcc400>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f6052fcc598>}
metadata_spec = {'attribute_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d7f0>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d780>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d6a0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>}
sniff(filename)
class galaxy.datatypes.interval.Gff3(**kwd)[source]

Bases: galaxy.datatypes.interval.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']
track_type = 'FeatureTrack'

Add metadata elements

__init__(**kwd)[source]

Initialize datatype, by adding GBrowse display app

set_meta(dataset, overwrite=True, **kwd)[source]
sniff_prefix(file_prefix)[source]

Determines whether the file is in GFF version 3 format

GFF 3 format:

  1. adds a mechanism for representing more than one level of hierarchical grouping of features and subfeatures.
  2. separates the ideas of group membership and feature name/id
  3. constrains the feature type field to be taken from a controlled vocabulary.
  4. allows a single feature, such as an exon, to belong to more than one group at a time.
  5. provides an explicit convention for pairwise alignments
  6. 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
metadata_spec = {'attribute_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d7f0>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d780>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d898>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d6a0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>}
class galaxy.datatypes.interval.Gtf(**kwd)[source]

Bases: galaxy.datatypes.interval.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']
track_type = 'FeatureTrack'

Add metadata elements

sniff_prefix(file_prefix)[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
metadata_spec = {'attribute_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d7f0>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d780>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d9b0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003d940>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>}
class galaxy.datatypes.interval.Wiggle(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular, galaxy.datatypes.interval._RemoteCallMixin

Tab delimited data in wiggle format

edam_format = 'format_3005'
file_ext = 'wig'
track_type = 'LineTrack'
data_sources = {'data': 'bigwig', 'index': 'bigwig'}
__init__(**kwd)[source]
get_estimated_display_viewport(dataset)[source]

Return a chrom, start, stop tuple for viewing a file.

display_peek(dataset)[source]

Returns formated html of peek

set_meta(dataset, overwrite=True, **kwd)[source]
sniff_prefix(file_prefix)[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
get_track_resolution(dataset, start, end)[source]
wiggle_dataprovider(dataset, **settings)[source]
wiggle_dict_dataprovider(dataset, **settings)[source]
dataproviders = {'base': <function Data.base_dataprovider at 0x7f6052fc9ae8>, 'chunk': <function Data.chunk_dataprovider at 0x7f6052fc9c80>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f6052fc9e18>, 'column': <function TabularData.column_dataprovider at 0x7f605001f9d8>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f605001fb70>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f605001fea0>, 'dict': <function TabularData.dict_dataprovider at 0x7f605001fd08>, 'line': <function Text.line_dataprovider at 0x7f6052fcc400>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f6052fcc598>, 'wiggle': <function Wiggle.wiggle_dataprovider at 0x7f60375d8c80>, 'wiggle-dict': <function Wiggle.wiggle_dict_dataprovider at 0x7f60375d8e18>}
metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff630f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003db00>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>}
sniff(filename)
class galaxy.datatypes.interval.CustomTrack(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

UCSC CustomTrack

edam_format = 'format_3588'
file_ext = 'customtrack'
__init__(**kwd)[source]

Initialize interval datatype, by adding UCSC display app

set_meta(dataset, overwrite=True, **kwd)[source]
display_peek(dataset)[source]

Returns formated html of peek

get_estimated_display_viewport(dataset, chrom_col=None, start_col=None, end_col=None)[source]

Return a chrom, start, stop tuple for viewing a file.

sniff_prefix(file_prefix)[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 = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003dd68>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003dcf8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003dc88>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003dba8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003dc18>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003ddd8>}
sniff(filename)
class galaxy.datatypes.interval.ENCODEPeak(**kwd)[source]

Bases: galaxy.datatypes.interval.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']
data_sources = {'data': 'tabix', 'index': 'bigwig'}

Add metadata elements

sniff(filename)[source]
metadata_spec = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003de80>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff630f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60375dd0f0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003dfd0>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605002fcf8>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605003df60>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60375dd080>}
class galaxy.datatypes.interval.ChromatinInteractions(**kwd)[source]

Bases: galaxy.datatypes.interval.Interval

Chromatin interactions obtained from 3C/5C/Hi-C experiments.

file_ext = 'chrint'
track_type = 'DiagonalHeatmapTrack'
data_sources = {'data': 'tabix', 'index': 'bigwig'}
column_names = ['Chrom1', 'Start1', 'End1', 'Chrom2', 'Start2', 'End2', 'Value']

Add metadata elements

metadata_spec = {'chrom1Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60375dd160>, 'chrom2Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60375dd320>, 'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605002f940>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff630f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60375dd4e0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'end1Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60375dd2b0>, 'end2Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60375dd400>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605002fc50>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605002fcf8>, 'start1Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60375dd240>, 'start2Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60375dd390>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605002fbe0>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f605002fcc0>, 'valueCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60375dd470>}
sniff(filename)[source]
class galaxy.datatypes.interval.ScIdx(**kwd)[source]

Bases: galaxy.datatypes.tabular.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.

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60375dd588>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60375dd518>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>}
sniff(filename)
file_ext = 'scidx'
__init__(**kwd)[source]

Initialize scidx datatype.

sniff_prefix(file_prefix)[source]

Checks for ‘scidx-ness.’

galaxy.datatypes.isa module

ISA datatype

See https://github.com/ISA-tools

galaxy.datatypes.isa.utf8_text_file_open(path)[source]
class galaxy.datatypes.isa.IsaTab(**kwd)[source]

Bases: galaxy.datatypes.isa._Isa

file_ext = 'isa-tab'
__init__(**kwd)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6026c46080>}
class galaxy.datatypes.isa.IsaJson(**kwd)[source]

Bases: galaxy.datatypes.isa._Isa

file_ext = 'isa-json'
__init__(**kwd)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6026c46630>}

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.

__init__(target)[source]
classmethod process(element)[source]
class galaxy.datatypes.metadata.MetadataCollection(parent)[source]

Bases: object

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)[source]
get_parent()[source]
set_parent(parent)[source]
parent
spec
get(key, default=None)[source]
items()[source]
remove_key(name)[source]
element_is_set(name)[source]
get_metadata_parameter(name, **kwd)[source]
make_dict_copy(to_copy)[source]

Makes a deep copy of input iterable to_copy according to self.spec

requires_dataset_id
from_JSON_dict(filename=None, path_rewriter=None, json_dict=None)[source]
to_JSON_dict(filename=None)[source]
class galaxy.datatypes.metadata.MetadataSpecCollection(*args, **kwds)[source]

Bases: collections.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”.

__init__(*args, **kwds)[source]
append(item)[source]
class galaxy.datatypes.metadata.MetadataParameter(spec)[source]

Bases: object

__init__(spec)[source]
get_field(value=None, context=None, other_values=None, **kwd)[source]
to_string(value)[source]
to_safe_string(value)[source]
make_copy(value, target_context=None, source_context=None)[source]
classmethod marshal(value)[source]

This method should/can be overridden to convert the incoming value to whatever type it is supposed to be.

validate(value)[source]

Throw an exception if the value is invalid.

unwrap(form_value)[source]

Turns a value into its storable form.

wrap(value, session)[source]

Turns a value into its usable form.

from_external_value(value, parent)[source]

Turns a value read from an external dict into its value to be pushed directly into the metadata dict.

to_external_value(value)[source]

Turns a value read from a metadata into its value to be pushed directly into the external dict.

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, **kwargs)[source]

Bases: object

Defines a metadata element and adds it to the metadata_spec (which is a MetadataSpecCollection) of datatype.

__init__(datatype, name=None, desc=None, param=<class 'galaxy.model.metadata.MetadataParameter'>, default=None, no_value=None, visible=True, set_in_upload=False, **kwargs)[source]
get(name, default=None)[source]
wrap(value, session)[source]

Turns a stored value into its usable form.

unwrap(value)[source]

Turns an incoming value into its storable form.

class galaxy.datatypes.metadata.SelectParameter(spec)[source]

Bases: galaxy.model.metadata.MetadataParameter

__init__(spec)[source]
to_string(value)[source]
get_field(value=None, context=None, other_values=None, values=None, **kwd)[source]
wrap(value, session)[source]
classmethod marshal(value)[source]
class galaxy.datatypes.metadata.DBKeyParameter(spec)[source]

Bases: galaxy.model.metadata.SelectParameter

get_field(value=None, context=None, other_values=None, values=None, **kwd)[source]
class galaxy.datatypes.metadata.RangeParameter(spec)[source]

Bases: galaxy.model.metadata.SelectParameter

__init__(spec)[source]
get_field(value=None, context=None, other_values=None, values=None, **kwd)[source]
classmethod marshal(value)[source]
class galaxy.datatypes.metadata.ColumnParameter(spec)[source]

Bases: galaxy.model.metadata.RangeParameter

get_field(value=None, context=None, other_values=None, values=None, **kwd)[source]
class galaxy.datatypes.metadata.ColumnTypesParameter(spec)[source]

Bases: galaxy.model.metadata.MetadataParameter

to_string(value)[source]
class galaxy.datatypes.metadata.ListParameter(spec)[source]

Bases: galaxy.model.metadata.MetadataParameter

to_string(value)[source]
class galaxy.datatypes.metadata.DictParameter(spec)[source]

Bases: galaxy.model.metadata.MetadataParameter

to_string(value)[source]
to_safe_string(value)[source]
class galaxy.datatypes.metadata.PythonObjectParameter(spec)[source]

Bases: galaxy.model.metadata.MetadataParameter

to_string(value)[source]
get_field(value=None, context=None, other_values=None, **kwd)[source]
classmethod marshal(value)[source]
class galaxy.datatypes.metadata.FileParameter(spec)[source]

Bases: galaxy.model.metadata.MetadataParameter

to_string(value)[source]
to_safe_string(value)[source]
get_field(value=None, context=None, other_values=None, **kwd)[source]
wrap(value, session)[source]
make_copy(value, target_context, source_context)[source]
classmethod marshal(value)[source]
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.

to_external_value(value)[source]

Turns a value read from a metadata into its value to be pushed directly into the external dict.

new_file(dataset=None, **kwds)[source]
class galaxy.datatypes.metadata.MetadataTempFile(**kwds)[source]

Bases: object

tmp_dir = 'database/tmp'
__init__(**kwds)[source]
file_name
to_JSON()[source]
classmethod from_JSON(json_dict)[source]
classmethod is_JSONified_value(value)[source]
classmethod cleanup_from_JSON_dict_filename(filename)[source]

galaxy.datatypes.microarrays module

class galaxy.datatypes.microarrays.GenericMicroarrayFile(**kwd)[source]

Bases: galaxy.datatypes.data.Text

Abstract class for most of the microarray files.

set_peek(dataset, is_multi_byte=False)[source]
get_mime()[source]
metadata_spec = {'block_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028f39668>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028f39d30>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028f399e8>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028f39358>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028f397f0>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028f39ac8>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028f39828>}
class galaxy.datatypes.microarrays.Gal(**kwd)[source]

Bases: galaxy.datatypes.microarrays.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)[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, **kwd)[source]

Set metadata for Gal file.

metadata_spec = {'block_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602a613860>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602a613ef0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028f39780>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028f39ef0>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028f395c0>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028f39908>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028f39c88>}
sniff(filename)
class galaxy.datatypes.microarrays.Gpr(**kwd)[source]

Bases: galaxy.datatypes.microarrays.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)[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, **kwd)[source]

Set metadata for Gpr file.

metadata_spec = {'block_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602a613400>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602a6136a0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602a613c88>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602a613390>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602a613320>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602a613d30>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602a613898>}
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: galaxy.datatypes.data.Text

Abstract class for most of the molecule files.

set_peek(dataset, is_multi_byte=False)[source]
get_mime()[source]
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60290dca20>}
class galaxy.datatypes.molecules.MOL(**kwd)[source]

Bases: galaxy.datatypes.molecules.GenericMolFile

file_ext = 'mol'
set_meta(dataset, **kwd)[source]

Set the number molecules, in the case of MOL its always one.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60290dc710>}
class galaxy.datatypes.molecules.SDF(**kwd)[source]

Bases: galaxy.datatypes.molecules.GenericMolFile

file_ext = 'sdf'
sniff_prefix(file_prefix)[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, **kwd)[source]

Set the number of molecules in dataset.

classmethod split(input_datasets, subdir_generator_function, split_params)[source]

Split the input files by molecule records.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602a3c5f60>}
sniff(filename)
class galaxy.datatypes.molecules.MOL2(**kwd)[source]

Bases: galaxy.datatypes.molecules.GenericMolFile

file_ext = 'mol2'
sniff_prefix(file_prefix)[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, **kwd)[source]

Set the number of lines of data in dataset.

classmethod split(input_datasets, subdir_generator_function, split_params)[source]

Split the input files by molecule records.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602a3c53c8>}
sniff(filename)
class galaxy.datatypes.molecules.FPS(**kwd)[source]

Bases: galaxy.datatypes.molecules.GenericMolFile

chemfp fingerprint file: http://code.google.com/p/chem-fingerprints/wiki/FPS

file_ext = 'fps'
sniff_prefix(file_prefix)[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, **kwd)[source]

Set the number of lines of data in dataset.

classmethod split(input_datasets, subdir_generator_function, split_params)[source]

Split the input files by fingerprint records.

static merge(split_files, output_file)[source]

Merging fps files requires merging the header manually. We take the header from the first file.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60293f1dd8>}
sniff(filename)
class galaxy.datatypes.molecules.OBFS(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

OpenBabel Fastsearch format (fs).

file_ext = 'obfs'
composite_type = 'basic'
allow_datatype_change = False
__init__(**kwd)[source]

A Fastsearch Index consists of a binary file with the fingerprints and a pointer the actual molecule file.

set_peek(dataset, is_multi_byte=False)[source]

Set the peek and blurb text.

display_peek(dataset)[source]

Create HTML content, used for displaying peek.

get_mime()[source]

Returns the mime type of the datatype (pretend it is text for peek)

merge(split_files, output_file, extra_merge_args)[source]

Merging Fastsearch indices is not supported.

split(input_datasets, subdir_generator_function, split_params)[source]

Splitting Fastsearch indices is not supported.

metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60293f1668>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6050057cf8>}
class galaxy.datatypes.molecules.DRF(**kwd)[source]

Bases: galaxy.datatypes.molecules.GenericMolFile

file_ext = 'drf'
set_meta(dataset, **kwd)[source]

Set the number of lines of data in dataset.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60293f18d0>}
class galaxy.datatypes.molecules.PHAR(**kwd)[source]

Bases: galaxy.datatypes.molecules.GenericMolFile

Pharmacophore database format from silicos-it.

file_ext = 'phar'
set_peek(dataset, is_multi_byte=False)[source]
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029112a20>}
class galaxy.datatypes.molecules.PDB(**kwd)[source]

Bases: galaxy.datatypes.molecules.GenericMolFile

Protein Databank format. http://www.wwpdb.org/documentation/format33/v3.3.html

file_ext = 'pdb'
sniff_prefix(file_prefix)[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, **kwd)[source]

Find Chain_IDs for metadata.

set_peek(dataset, is_multi_byte=False)[source]
metadata_spec = {'chain_ids': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60297ec978>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60290dca20>}
sniff(filename)
class galaxy.datatypes.molecules.PDBQT(**kwd)[source]

Bases: galaxy.datatypes.molecules.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)[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, is_multi_byte=False)[source]
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60297ec940>}
sniff(filename)
class galaxy.datatypes.molecules.PQR(**kwd)[source]

Bases: galaxy.datatypes.molecules.GenericMolFile

Protein Databank format. https://apbs-pdb2pqr.readthedocs.io/en/latest/formats/pqr.html

file_ext = 'pqr'
get_matcher()[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)[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, **kwd)[source]

Find Optional Chain_IDs for metadata.

set_peek(dataset, is_multi_byte=False)[source]
metadata_spec = {'chain_ids': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60290fd2b0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60290dca20>}
sniff(filename)
class galaxy.datatypes.molecules.grd(**kwd)[source]

Bases: galaxy.datatypes.data.Text

file_ext = 'grd'
set_peek(dataset, is_multi_byte=False)[source]
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60290fdb70>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.molecules.grdtgz(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

file_ext = 'grd.tgz'
set_peek(dataset, is_multi_byte=False)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60290fdc88>}
class galaxy.datatypes.molecules.InChI(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'inchi'
column_names = ['InChI']
set_meta(dataset, **kwd)[source]

Set the number of lines of data in dataset.

set_peek(dataset, is_multi_byte=False)[source]
sniff_prefix(file_prefix)[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 = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60290fd128>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60290fddd8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60297f8400>}
sniff(filename)
class galaxy.datatypes.molecules.SMILES(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'smi'
column_names = ['SMILES', 'TITLE']
set_meta(dataset, **kwd)[source]

Set the number of lines of data in dataset.

set_peek(dataset, is_multi_byte=False)[source]
metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60293f1cc0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60297f8358>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60297ec9b0>}
class galaxy.datatypes.molecules.CML(**kwd)[source]

Bases: galaxy.datatypes.xml.GenericXml

Chemical Markup Language http://cml.sourceforge.net/

file_ext = 'cml'
set_meta(dataset, **kwd)[source]

Set the number of lines of data in dataset.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604c3e45c0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60297ec5f8>}
set_peek(dataset, is_multi_byte=False)[source]
sniff(filename)
sniff_prefix(file_prefix)[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, subdir_generator_function, split_params)[source]

Split the input files by molecule records.

static merge(split_files, output_file)[source]

Merging CML files.

galaxy.datatypes.mothur module

Mothur Metagenomics Datatypes

class galaxy.datatypes.mothur.Otu(**kwd)[source]

Bases: galaxy.datatypes.data.Text

file_ext = 'mothur.otu'
__init__(**kwd)[source]
set_meta(dataset, overwrite=True, **kwd)[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)[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 = {'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027566eb8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'labels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027566f28>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027566fd0>}
sniff(filename)
class galaxy.datatypes.mothur.Sabund(**kwd)[source]

Bases: galaxy.datatypes.mothur.Otu

file_ext = 'mothur.sabund'
__init__(**kwd)[source]

http://www.mothur.org/wiki/Sabund_file

init_meta(dataset, copy_from=None)[source]
sniff_prefix(file_prefix)[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 = {'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728b080>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'labels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728b0f0>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728b160>}
class galaxy.datatypes.mothur.GroupAbund(**kwd)[source]

Bases: galaxy.datatypes.mothur.Otu

file_ext = 'mothur.shared'
__init__(**kwd)[source]
init_meta(dataset, copy_from=None)[source]
set_meta(dataset, overwrite=True, skip=1, **kwd)[source]
sniff_prefix(file_prefix, vals_are_int=False)[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 = {'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027566eb8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728b2b0>, 'labels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027566f28>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027566fd0>}
class galaxy.datatypes.mothur.SecondaryStructureMap(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.map'
__init__(**kwd)[source]

Initialize secondary structure map datatype

sniff_prefix(file_prefix)[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 = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728b668>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728b5f8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728b4e0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728b390>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728b400>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728b780>}
sniff(filename)
class galaxy.datatypes.mothur.AlignCheck(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.align.check'
__init__(**kwd)[source]

Initialize AlignCheck datatype

set_meta(dataset, overwrite=True, **kwd)[source]
metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728ba90>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728ba20>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728b9b0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728b898>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728b940>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728bb38>}
class galaxy.datatypes.mothur.AlignReport(**kwd)[source]

Bases: galaxy.datatypes.tabular.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'
__init__(**kwd)[source]

Initialize AlignCheck datatype

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728bfd0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728bf60>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728bef0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728bc18>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602728bd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751d080>}
class galaxy.datatypes.mothur.DistanceMatrix(**kwd)[source]

Bases: galaxy.datatypes.data.Text

file_ext = 'mothur.dist'

Add metadata elements

init_meta(dataset, copy_from=None)[source]
set_meta(dataset, overwrite=True, skip=0, **kwd)[source]
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751d198>}
class galaxy.datatypes.mothur.LowerTriangleDistanceMatrix(**kwd)[source]

Bases: galaxy.datatypes.mothur.DistanceMatrix

file_ext = 'mothur.lower.dist'
__init__(**kwd)[source]

Initialize secondary structure map datatype

init_meta(dataset, copy_from=None)[source]
sniff_prefix(file_prefix)[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 = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751d2b0>}
sniff(filename)
class galaxy.datatypes.mothur.SquareDistanceMatrix(**kwd)[source]

Bases: galaxy.datatypes.mothur.DistanceMatrix

file_ext = 'mothur.square.dist'
__init__(**kwd)[source]
init_meta(dataset, copy_from=None)[source]
sniff_prefix(file_prefix)[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 = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751d3c8>}
sniff(filename)
class galaxy.datatypes.mothur.PairwiseDistanceMatrix(**kwd)[source]

Bases: galaxy.datatypes.mothur.DistanceMatrix, galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.pair.dist'
__init__(**kwd)[source]

Initialize secondary structure map datatype

set_meta(dataset, overwrite=True, skip=None, **kwd)[source]
sniff_prefix(file_prefix)[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 = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff630f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4ca58>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751d4a8>}
sniff(filename)
class galaxy.datatypes.mothur.Names(**kwd)[source]

Bases: galaxy.datatypes.tabular.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 = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751d748>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751d6d8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751d668>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751d588>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751d5f8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751d7b8>}
class galaxy.datatypes.mothur.Summary(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.summary'
__init__(**kwd)[source]

summarizes the quality of sequences in an unaligned or aligned fasta-formatted sequence file

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751da58>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751d9e8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751d978>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751d898>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751d908>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751dac8>}
class galaxy.datatypes.mothur.Group(**kwd)[source]

Bases: galaxy.datatypes.tabular.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, overwrite=True, skip=None, max_data_lines=None, **kwd)[source]
metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff630f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4ca58>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751dbe0>}
class galaxy.datatypes.mothur.AccNos(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.accnos'
__init__(**kwd)[source]

A list of names

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751de80>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751de10>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751dda0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751dcc0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751dd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751def0>}
class galaxy.datatypes.mothur.Oligos(**kwd)[source]

Bases: galaxy.datatypes.data.Text

file_ext = 'mothur.oligos'
sniff_prefix(file_prefix)[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 = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602751df60>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
sniff(filename)
class galaxy.datatypes.mothur.Frequency(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.freq'
__init__(**kwd)[source]

A list of names

sniff_prefix(file_prefix)[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 = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513240>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60275131d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513160>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513080>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60275130f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60275132b0>}
sniff(filename)
class galaxy.datatypes.mothur.Quantile(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.quan'
__init__(**kwd)[source]

Quantiles for chimera analysis

sniff_prefix(file_prefix)[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 = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff630f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4ca58>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'filtered': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513390>, 'masked': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513400>}
sniff(filename)
class galaxy.datatypes.mothur.LaneMask(**kwd)[source]

Bases: galaxy.datatypes.data.Text

file_ext = 'mothur.filter'
sniff_prefix(file_prefix)[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 = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60275134a8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
sniff(filename)
class galaxy.datatypes.mothur.CountTable(**kwd)[source]

Bases: galaxy.datatypes.tabular.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, overwrite=True, skip=1, max_data_lines=None, **kwd)[source]
metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff630f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4ca58>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60275135c0>}
class galaxy.datatypes.mothur.RefTaxonomy(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.ref.taxonomy'
__init__(**kwd)[source]
sniff_prefix(file_prefix)[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 = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513860>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60275137f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513780>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60275136a0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513710>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60275138d0>}
sniff(filename)
class galaxy.datatypes.mothur.ConsensusTaxonomy(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.cons.taxonomy'
__init__(**kwd)[source]

A list of names

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513b70>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513b00>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513a90>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60275139b0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513be0>}
class galaxy.datatypes.mothur.TaxonomySummary(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.tax.summary'
__init__(**kwd)[source]

A Summary of taxon classification

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513e80>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513e10>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513da0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513cc0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513ef0>}
class galaxy.datatypes.mothur.Axes(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.axes'
__init__(**kwd)[source]

Initialize axes datatype

sniff_prefix(file_prefix)[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. ==> 98_sq_phylip_amazon.fn.unique.pca.axes <==

group axis1 axis2 forest 0.000000 0.145743 pasture 0.145743 0.000000
==> 98_sq_phylip_amazon.nmds.axes <==
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 = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60275151d0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027515160>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60275150f0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027513fd0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027515080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027515240>}
sniff(filename)
class galaxy.datatypes.mothur.SffFlow(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.sff.flow'

http://www.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 …
__init__(**kwd)[source]
metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff630f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4ca58>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'flow_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027515400>, 'flow_values': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6027515390>}
set_meta(dataset, overwrite=True, skip=1, max_data_lines=None, **kwd)[source]
make_html_table(dataset, skipchars=[])[source]

Create HTML table, used for displaying peek

galaxy.datatypes.msa module

class galaxy.datatypes.msa.InfernalCM(**kwd)[source]

Bases: galaxy.datatypes.data.Text

file_ext = 'cm'
set_peek(dataset, is_multi_byte=False)[source]
sniff_prefix(file_prefix)[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, **kwd)[source]

Set the number of models and the version of CM file in dataset.

metadata_spec = {'cm_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60275663c8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6026890be0>}
sniff(filename)
class galaxy.datatypes.msa.Hmmer(**kwd)[source]

Bases: galaxy.datatypes.data.Text

edam_data = 'data_1364'
edam_format = 'format_1370'
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
sniff_prefix(filename)[source]
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60268bc550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
sniff(filename)
class galaxy.datatypes.msa.Hmmer2(**kwd)[source]

Bases: galaxy.datatypes.msa.Hmmer

edam_format = 'format_3328'
file_ext = 'hmm2'
sniff_prefix(file_prefix)[source]

HMMER2 files start with HMMER2.0

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6029fd76d8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.msa.Hmmer3(**kwd)[source]

Bases: galaxy.datatypes.msa.Hmmer

edam_format = 'format_3329'
file_ext = 'hmm3'
sniff_prefix(file_prefix)[source]

HMMER3 files start with HMMER3/f

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60268bc5f8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.msa.HmmerPress(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

Class for hmmpress database files.

file_ext = 'hmmpress'
allow_datatype_change = False
composite_type = 'basic'
set_peek(dataset, is_multi_byte=False)[source]

Set the peek and blurb text.

display_peek(dataset)[source]

Create HTML content, used for displaying peek.

__init__(**kwd)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60268bc668>}
class galaxy.datatypes.msa.Stockholm_1_0(**kwd)[source]

Bases: galaxy.datatypes.data.Text

edam_data = 'data_0863'
edam_format = 'format_1961'
file_ext = 'stockholm'
set_peek(dataset, is_multi_byte=False)[source]
sniff_prefix(file_prefix)[source]
set_meta(dataset, **kwd)[source]

Set the number of models in dataset.

classmethod split(input_datasets, subdir_generator_function, split_params)[source]

Split the input files by model records.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60268bc710>}
sniff(filename)
class galaxy.datatypes.msa.MauveXmfa(**kwd)[source]

Bases: galaxy.datatypes.data.Text

file_ext = 'xmfa'
set_peek(dataset, is_multi_byte=False)[source]
sniff_prefix(file_prefix)[source]
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60268bc780>}
set_meta(dataset, **kwd)[source]
sniff(filename)

galaxy.datatypes.neo4j module

Neo4j Composite Dataset

class galaxy.datatypes.neo4j.Neo4j(**kwd)[source]

Bases: galaxy.datatypes.images.Html

base class to use for neostore datatypes derived from html - composite datatype elements stored in extra files path

generate_primary_file(dataset=None)[source]

This is called only at upload to write the html file cannot rename the datasets here - they come with the default unfortunately

get_mime()[source]

Returns the mime type of the datatype

set_peek(dataset, is_multi_byte=False)[source]

Set the peek and blurb text

display_peek(dataset)[source]

Create HTML content, used for displaying peek.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60268595f8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.neo4j.Neo4jDB(**kwd)[source]

Bases: galaxy.datatypes.neo4j.Neo4j, galaxy.datatypes.data.Data

Class for neo4jDB database files.

file_ext = 'neostore'
composite_type = 'auto_primary_file'
allow_datatype_change = False
__init__(**kwd)[source]
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6026859710>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
class galaxy.datatypes.neo4j.Neo4jDBzip(**kwd)[source]

Bases: galaxy.datatypes.neo4j.Neo4j, galaxy.datatypes.data.Data

Class for neo4jDB database files.

file_ext = 'neostore.zip'
composite_type = 'auto_primary_file'
allow_datatype_change = False
__init__(**kwd)[source]
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60268595f8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'neostore_zip': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6026859390>, 'reference_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60268593c8>}

galaxy.datatypes.ngsindex module

NGS indexes

class galaxy.datatypes.ngsindex.BowtieIndex(**kwd)[source]

Bases: galaxy.datatypes.text.Html

base class for BowtieIndex is subclassed by BowtieColorIndex and BowtieBaseIndex

composite_type = 'auto_primary_file'
allow_datatype_change = False
generate_primary_file(dataset=None)[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)[source]

cannot do this until we are setting metadata

set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60266eb908>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6026867320>}
class galaxy.datatypes.ngsindex.BowtieColorIndex(**kwd)[source]

Bases: galaxy.datatypes.ngsindex.BowtieIndex

Bowtie color space index

file_ext = 'bowtie_color_index'
metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60266eb908>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60268670f0>}
class galaxy.datatypes.ngsindex.BowtieBaseIndex(**kwd)[source]

Bases: galaxy.datatypes.ngsindex.BowtieIndex

Bowtie base space index

file_ext = 'bowtie_base_index'
metadata_spec = {'base_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60266eb908>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6028167278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602669fd68>}

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: galaxy.datatypes.data.Text

Phylip format stores a multiple sequence alignment

edam_data = 'data_0863'
edam_format = 'format_1997'
file_ext = 'phylip'

Add metadata elements

set_meta(dataset, **kwd)[source]

Set the number of sequences and the number of data lines in dataset.

set_peek(dataset, is_multi_byte=False)[source]
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc57f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60265ed320>}
sniff(filename)
sniff_prefix(file_prefix)[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.phylip')
>>> Phylip().sniff(fname)
True

galaxy.datatypes.plant_tribes module

class galaxy.datatypes.plant_tribes.Smat(**kwd)[source]

Bases: galaxy.datatypes.data.Text

file_ext = 'smat'
display_peek(dataset)[source]
set_peek(dataset, is_multi_byte=False)[source]
sniff_prefix(file_prefix)[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 = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602663d550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>}
sniff(filename)
class galaxy.datatypes.plant_tribes.PlantTribesKsComponents(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'ptkscmp'
display_peek(dataset)[source]
set_meta(dataset, **kwd)[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, is_multi_byte=False)[source]
sniff(filename)[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 = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff632b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff630f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4ca58>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff4c550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f604ff634a8>, 'number_comp': <galaxy.model.metadata.MetadataElementSpec object at 0x7f60265bbcc0>}

galaxy.datatypes.proteomics module

Proteomics Datatypes

class galaxy.datatypes.proteomics.Wiff(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

Class for wiff files.

edam_data = 'data_2536'
edam_format = 'format_3710'
file_ext = 'wiff'
allow_datatype_change = False
composite_type = 'auto_primary_file'
__init__(**kwd)[source]
generate_primary_file(dataset=None)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602648e8d0>}
class galaxy.datatypes.proteomics.PepXmlReport(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

pepxml converted to tabular report

edam_data = 'data_2536'
file_ext = 'pepxml.tsv'
__init__(**kwd)[source]
display_peek(dataset)[source]

Returns formated html of peek

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602648edd8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602648ed68>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602648ecf8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602648ec18>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602648ec88>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6052fc5470>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f602649d860>}
class galaxy.datatypes.proteomics.ProtXmlReport(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

protxml converted to tabular report

edam_data = 'data_2536'
file_ext = 'protxml.tsv'
comment_lines = 1
__init__(**kwd)[source]