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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 0x7fc192280490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1922805d0>}

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 0x7fc18eb19110>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc18eb19310>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb19110>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc18eb19550>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb19110>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc18eb19790>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb19110>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc18eb199d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb19110>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc18eb19c10>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb19110>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc18eb19dd0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb19110>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc18eb24050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb19110>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}

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 0x7fc1911dfd50>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc190dcd550>}
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 0x7fc190dcdc90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc190dcd450>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'long_reads': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc190e0e850>, 'paired_end_reads': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc190e0e390>, 'short2_reads': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc190e0e250>}

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 0x7fc1aee1aed0>}
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 0x7fc1aee12190>}
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 0x7fc1aee123d0>}
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 0x7fc1aee1aed0>, 'version': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee12650>}
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 0x7fc1aee12890>}
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 0x7fc1aee12b10>}
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 0x7fc1aee12d50>}
class galaxy.datatypes.binary.GzDynamicCompressedArchive(**kwd)[source]

Bases: galaxy.datatypes.binary.DynamicCompressedArchive

compressed_format = 'gzip'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee12f90>}
class galaxy.datatypes.binary.Bz2DynamicCompressedArchive(**kwd)[source]

Bases: galaxy.datatypes.binary.DynamicCompressedArchive

compressed_format = 'bz2'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1d210>}
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 0x7fc1aee1d490>}
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 0x7fc1aee1d6d0>}
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 0x7fc1aee1dc90>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1da10>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1de10>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1dd90>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1dd10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee12b10>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1db10>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1dc10>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1db90>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1da90>}
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(*args, **kwargs)[source]
regex_line_dataprovider(*args, **kwargs)[source]
column_dataprovider(*args, **kwargs)[source]
dict_dataprovider(*args, **kwargs)[source]
header_dataprovider(*args, **kwargs)[source]
id_seq_qual_dataprovider(*args, **kwargs)[source]
genomic_region_dataprovider(*args, **kwargs)[source]
genomic_region_dict_dataprovider(*args, **kwargs)[source]
samtools_dataprovider(*args, **kwargs)[source]

Generic samtools interface - all options available through settings.

dataproviders = {'base': <function base_dataprovider at 0x7fc1b2122320>, 'chunk': <function chunk_dataprovider at 0x7fc1b2122488>, 'chunk64': <function chunk64_dataprovider at 0x7fc1b21225f0>, 'column': <function column_dataprovider at 0x7fc1aee21de8>, 'dict': <function dict_dataprovider at 0x7fc1aee21f50>, 'genomic-region': <function genomic_region_dataprovider at 0x7fc1aee24410>, 'genomic-region-dict': <function genomic_region_dict_dataprovider at 0x7fc1aee24578>, 'header': <function header_dataprovider at 0x7fc1aee24140>, 'id-seq-qual': <function id_seq_qual_dataprovider at 0x7fc1aee242a8>, 'line': <function line_dataprovider at 0x7fc1aee21b18>, 'regex-line': <function regex_line_dataprovider at 0x7fc1aee21c80>, 'samtools': <function samtools_dataprovider at 0x7fc1aee246e0>}
metadata_spec = {'bam_header': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1dc90>, 'bam_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee23550>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1da10>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1de10>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1dd90>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1dd10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee12b10>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1db10>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1dc10>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1db90>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1da90>}
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 0x7fc1aee1dc90>, 'bam_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee23790>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1da10>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1de10>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1dd90>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1dd10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee12b10>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1db10>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1dc10>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1db90>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1da90>}
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 0x7fc1aee23c50>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee239d0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee23dd0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee23d50>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee23cd0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee12b10>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee23ad0>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee23bd0>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee23b50>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee23a50>}
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 0x7fc1aee272d0>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee27050>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee27450>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee273d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee27350>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee12b10>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee27150>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee27250>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee271d0>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee270d0>}
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 0x7fc1aee27790>, 'cram_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee27710>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1aed0>}
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 0x7fc1aee279d0>}
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 0x7fc1aee27c90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee279d0>}
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 0x7fc1aee27ed0>}
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 0x7fc1aedad190>}
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 0x7fc1aedad9d0>, 'col_attrs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedada50>, 'col_graphs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedadad0>, 'col_graphs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedadb50>, 'creation_date': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedad6d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedad190>, 'description': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedad4d0>, 'doi': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedad5d0>, 'layers_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedad7d0>, 'layers_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedad850>, 'loom_spec_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedad650>, 'row_attrs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedad8d0>, 'row_attrs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedad950>, 'row_graphs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedadbd0>, 'row_graphs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedadc50>, 'shape': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedad750>, 'title': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedad450>, 'url': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedad550>}
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 0x7fc1aedade90>}
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 0x7fc1aedb0190>}
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 0x7fc1aedb03d0>}
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 0x7fc1aedb0610>}
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 0x7fc1aedb0850>}
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 0x7fc1aedb0e10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedad190>, 'format': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedb0c90>, 'format_url': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedb0b90>, 'format_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedb0c10>, 'generated_by': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedb0d90>, 'id': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedb0b10>, 'nnz': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedb0e90>, 'shape': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedb0f10>, 'type': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedb0d10>}
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 0x7fc1aedb61d0>}
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 0x7fc1aedb6450>}
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 0x7fc1aedb66d0>}
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 0x7fc1aedb6950>}
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 0x7fc1aedb6bd0>}
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 0x7fc1aedb6e10>}
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 0x7fc1aedc00d0>}
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]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
sqlite_dataprovider(*args, **kwargs)[source]
sqlite_datatableprovider(*args, **kwargs)[source]
sqlite_datadictprovider(*args, **kwargs)[source]
dataproviders = {'base': <function base_dataprovider at 0x7fc1b2122320>, 'chunk': <function chunk_dataprovider at 0x7fc1b2122488>, 'chunk64': <function chunk64_dataprovider at 0x7fc1b21225f0>, 'sqlite': <function sqlite_dataprovider at 0x7fc1aedc2398>, 'sqlite-dict': <function sqlite_datadictprovider at 0x7fc1aedc2668>, 'sqlite-table': <function sqlite_datatableprovider at 0x7fc1aedc2500>}
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1aed0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc05d0>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc0650>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc0550>}
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 0x7fc1aee1aed0>, 'gemini_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc0910>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc05d0>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc0650>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc0550>}
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 0x7fc1aedc0bd0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1aed0>, 'genes': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc0c50>, 'samples': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc0cd0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc05d0>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc0650>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc0550>}
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 0x7fc1aee1aed0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc0fd0>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedca090>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc0f50>}
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 0x7fc1aedca350>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1aed0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc05d0>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc0650>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc0550>}
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 0x7fc1aee1aed0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedca690>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedca710>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedca610>}
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 0x7fc1aee1aed0>, 'gafa_schema_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedca990>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc05d0>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc0650>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedc0550>}
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 0x7fc1aedcabd0>}
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 0x7fc1aedcae50>}
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 0x7fc1aedd3110>}
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 0x7fc1aedd3350>}
class galaxy.datatypes.binary.OxliBinary(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedd3590>}
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 0x7fc1aedd37d0>}
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 0x7fc1aedd3a10>}
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 0x7fc1aedd3c50>}
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 0x7fc1aedd3e90>}
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 0x7fc1aeddd110>}
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 0x7fc1aeddd350>}
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 0x7fc1aee12b10>, 'version': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aeddd610>}
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 0x7fc1aee12b10>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aeddd8d0>}
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 0x7fc1aee12b10>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedddb10>}
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 0x7fc1aee12b10>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aedddd50>}
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 0x7fc1aee12b10>, 'searchgui_major_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aede70d0>, 'searchgui_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aede7050>}
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 0x7fc1aede7350>}
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 0x7fc1aede75d0>}
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 0x7fc1aede7850>}
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 0x7fc1aede7a90>}
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 0x7fc1aede7cd0>}
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 0x7fc1aede7f10>}
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 0x7fc1aed711d0>}
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 0x7fc1aed71450>}
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 0x7fc1aed71690>}
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 0x7fc1aed71910>}
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 0x7fc1aed71b50>}
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 0x7fc1aed71d90>}
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 0x7fc1aed71fd0>}

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 0x7fc18e43d2d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc18e495e10>}
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 0x7fc18e4d0bd0>}
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 0x7fc18e371190>}

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(file_path, chunk=None)[source]
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 0x7fc18e26e8d0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f690>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'length': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e26e950>}

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 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'face': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e1d2090>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e38a690>, 'other_elements': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e1d2310>, 'vertex': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e1d2490>}
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 0x7fc1aee1aed0>, 'face': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e2997d0>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e299c10>, 'other_elements': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e299810>, 'vertex': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e2995d0>}
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 0x7fc18e2231d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b2124490>, 'dataset_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e299a10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'dimensions': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e299510>, 'field_components': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e223d50>, 'field_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e223950>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e2994d0>, 'lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e28f510>, 'origin': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e299590>, 'points': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e299cd0>, 'polygons': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e28fed0>, 'spacing': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e299dd0>, 'triangle_strips': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e223850>, 'vertices': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e28fa50>, 'vtk_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e299b10>}
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 0x7fc18e29a9d0>, 'dataset_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e205090>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1aed0>, 'dimensions': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e3a8750>, 'field_components': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e29aad0>, 'field_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e29ab10>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e205690>, 'lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e29a190>, 'origin': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e3a8450>, 'points': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e29a090>, 'polygons': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e29a210>, 'spacing': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e3a8050>, 'triangle_strips': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e29aa50>, 'vertices': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e29a1d0>, 'vtk_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e2056d0>}
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 0x7fc18e29a410>}
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 0x7fc18de8ed50>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18de8ef50>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'forwardCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18de8ee50>, 'positionCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18de8edd0>, 'reverseCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18de8eed0>}

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 0x7fc1b21241d0>}

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=[], skip=[])[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_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(*args, **kwargs)[source]
chunk_dataprovider(*args, **kwargs)[source]
chunk64_dataprovider(*args, **kwargs)[source]
dataproviders = {'base': <function base_dataprovider at 0x7fc1b2122320>, 'chunk': <function chunk_dataprovider at 0x7fc1b2122488>, 'chunk64': <function chunk64_dataprovider at 0x7fc1b21225f0>}
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(*args, **kwargs)[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(*args, **kwargs)[source]

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

dataproviders = {'base': <function base_dataprovider at 0x7fc1b2122320>, 'chunk': <function chunk_dataprovider at 0x7fc1b2122488>, 'chunk64': <function chunk64_dataprovider at 0x7fc1b21225f0>, 'line': <function line_dataprovider at 0x7fc1b2122b90>, 'regex-line': <function regex_line_dataprovider at 0x7fc1b2122cf8>}
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1b2124710>}
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 0x7fc1b2124950>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1b2124b90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1b2124e10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1b21330d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc194493290>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc194493590>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'markerCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc194493250>}
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 0x7fc19253cd50>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc19253ce50>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc19253c250>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc19253cd90>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc19253c190>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc19253c1d0>}
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 0x7fc19253cb90>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc19253c150>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc19253ced0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc19253cf90>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc19253c090>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc19253cbd0>}
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 0x7fc192635b90>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1926354d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc192635190>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc192635390>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc192635410>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1926356d0>}
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 0x7fc192635b10>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1926351d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc192635a50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc192635050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1943c1310>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1943c1c10>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1943c1590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1943c11d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc190bcfc90>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1b6f22dd0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1b6f22ad0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1927ab2d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1927ab1d0>, 'chrom_bed': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1927ab290>, 'chrom_windows': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1927ab050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'input_config': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18fa78c90>, 'tmp_archive': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18fa78590>}
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 0x7fc18fa78790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18fa782d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18fa78450>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18fa78510>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18fa78e10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18fa78a50>}
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 0x7fc18fa78910>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18fa78b10>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18fa78390>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18fa784d0>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18fa78d50>}
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 0x7fc191bc3350>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18fa78ed0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18fa78810>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc191bc3b90>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc191bc3d90>}
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 0x7fc191bc3e50>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc191bc3090>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc191bc3150>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc191bc33d0>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc191bc3c50>}
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 0x7fc191bc3650>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc191bc3d10>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc191bc3bd0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc191bc32d0>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc191bc37d0>}
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 0x7fc191bc3910>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc191bc3b10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1b704b790>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1b704b690>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1b704bc50>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc18d822e90>}

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(*args, **kwargs)[source]
dataproviders = {'base': <function base_dataprovider at 0x7fc1b2122320>, 'chunk': <function chunk_dataprovider at 0x7fc1b2122488>, 'chunk64': <function chunk64_dataprovider at 0x7fc1b21225f0>, 'line': <function line_dataprovider at 0x7fc1b2122b90>, 'node-edge': <function node_edge_dataprovider at 0x7fc18e003488>, 'regex-line': <function regex_line_dataprovider at 0x7fc1b2122cf8>, 'xml': <function xml_dataprovider at 0x7fc1b7099578>}
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e01ca10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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(*args, **kwargs)[source]
dataproviders = {'base': <function base_dataprovider at 0x7fc1b2122320>, 'chunk': <function chunk_dataprovider at 0x7fc1b2122488>, 'chunk64': <function chunk64_dataprovider at 0x7fc1b21225f0>, 'column': <function column_dataprovider at 0x7fc1aed7a7d0>, 'dataset-column': <function dataset_column_dataprovider at 0x7fc1aed7a938>, 'dataset-dict': <function dataset_dict_dataprovider at 0x7fc1aed7ac08>, 'dict': <function dict_dataprovider at 0x7fc1aed7aaa0>, 'line': <function line_dataprovider at 0x7fc1b2122b90>, 'node-edge': <function node_edge_dataprovider at 0x7fc18e0038c0>, 'regex-line': <function regex_line_dataprovider at 0x7fc1b2122cf8>}
metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e01cf10>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e01ce90>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e01ce10>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e01cd10>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e01cd90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e01cf90>}
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

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc191c05690>}
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 0x7fc18df9a0d0>}
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 0x7fc18dfb39d0>}
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 0x7fc18fc1f850>}
class galaxy.datatypes.images.Hamamatsu(**kwd)[source]

Bases: galaxy.datatypes.images.Image

file_ext = 'vms'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18fc1f050>}
class galaxy.datatypes.images.Mirax(**kwd)[source]

Bases: galaxy.datatypes.images.Image

file_ext = 'mrxs'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18fc1fdd0>}
class galaxy.datatypes.images.Sakura(**kwd)[source]

Bases: galaxy.datatypes.images.Image

file_ext = 'svslide'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18fc1ffd0>}
class galaxy.datatypes.images.Nrrd(**kwd)[source]

Bases: galaxy.datatypes.images.Image

file_ext = 'nrrd'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18fc10250>}
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 0x7fc18fc10450>}
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 0x7fc18fc10690>}
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 0x7fc18fc10890>}
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 0x7fc18fc10ad0>}
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 0x7fc18fc10cd0>}
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 0x7fc18fc10ed0>}
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 0x7fc18e887110>}
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 0x7fc18e887310>}
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 0x7fc18e887550>}
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 0x7fc18e887790>}
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 0x7fc18e8879d0>}
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 0x7fc18e887c10>}
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 0x7fc18e887e50>}
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 0x7fc18e88d050>}
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 0x7fc18e88d6d0>}
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 0x7fc18e88dc10>}
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 0x7fc18e88d850>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc18e88df90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}

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(*args, **kwargs)[source]
genomic_region_dict_dataprovider(*args, **kwargs)[source]
interval_dataprovider(*args, **kwargs)[source]
interval_dict_dataprovider(*args, **kwargs)[source]
dataproviders = {'base': <function base_dataprovider at 0x7fc1b2122320>, 'chunk': <function chunk_dataprovider at 0x7fc1b2122488>, 'chunk64': <function chunk64_dataprovider at 0x7fc1b21225f0>, 'column': <function column_dataprovider at 0x7fc1aed7a7d0>, 'dataset-column': <function dataset_column_dataprovider at 0x7fc1aed7a938>, 'dataset-dict': <function dataset_dict_dataprovider at 0x7fc1aed7ac08>, 'dict': <function dict_dataprovider at 0x7fc1aed7aaa0>, 'genomic-region': <function genomic_region_dataprovider at 0x7fc1aeca9e60>, 'genomic-region-dict': <function genomic_region_dict_dataprovider at 0x7fc1aecaa050>, 'interval': <function interval_dataprovider at 0x7fc1aecaa1b8>, 'interval-dict': <function interval_dict_dataprovider at 0x7fc1aecaa320>, 'line': <function line_dataprovider at 0x7fc1b2122b90>, 'regex-line': <function regex_line_dataprovider at 0x7fc1b2122cf8>}
metadata_spec = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecab7d0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecaba50>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecab8d0>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecab9d0>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecab850>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecab950>}
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 0x7fc1aecabc90>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecabf10>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecabd90>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecabe90>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecabd10>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecabe10>}
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 0x7fc1aecae1d0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecae3d0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecae2d0>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecab9d0>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecae250>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecae350>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecae450>}
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 0x7fc1aecae690>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecae890>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecae790>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecab9d0>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecae710>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecae810>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecae910>}
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 0x7fc1aecaeb90>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecaee10>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecaec90>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecaed90>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecaec10>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecaed10>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecae450>}
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 0x7fc1aecb5090>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecb5310>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecb5190>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecb5290>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecb5110>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecb5210>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecae450>}
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 0x7fc1aecb5550>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecb57d0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecb5650>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecb5750>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecb55d0>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecb56d0>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecae450>}
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(*args, **kwargs)[source]
genomic_region_dict_dataprovider(*args, **kwargs)[source]
interval_dataprovider(*args, **kwargs)[source]
interval_dict_dataprovider(*args, **kwargs)[source]
dataproviders = {'base': <function base_dataprovider at 0x7fc1b2122320>, 'chunk': <function chunk_dataprovider at 0x7fc1b2122488>, 'chunk64': <function chunk64_dataprovider at 0x7fc1b21225f0>, 'column': <function column_dataprovider at 0x7fc1aed7a7d0>, 'dataset-column': <function dataset_column_dataprovider at 0x7fc1aed7a938>, 'dataset-dict': <function dataset_dict_dataprovider at 0x7fc1aed7ac08>, 'dict': <function dict_dataprovider at 0x7fc1aed7aaa0>, 'genomic-region': <function genomic_region_dataprovider at 0x7fc1aecb8758>, 'genomic-region-dict': <function genomic_region_dict_dataprovider at 0x7fc1aecb88c0>, 'interval': <function interval_dataprovider at 0x7fc1aecb8a28>, 'interval-dict': <function interval_dict_dataprovider at 0x7fc1aecb8b90>, 'line': <function line_dataprovider at 0x7fc1b2122b90>, 'regex-line': <function regex_line_dataprovider at 0x7fc1b2122cf8>}
metadata_spec = {'attribute_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecb5dd0>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecb5d50>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecb5cd0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecb5c50>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>}
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 0x7fc1aecb5dd0>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecb5d50>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc0090>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecb5c50>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>}
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 0x7fc1aecb5dd0>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecb5d50>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc0350>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc02d0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>}
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(*args, **kwargs)[source]
wiggle_dict_dataprovider(*args, **kwargs)[source]
dataproviders = {'base': <function base_dataprovider at 0x7fc1b2122320>, 'chunk': <function chunk_dataprovider at 0x7fc1b2122488>, 'chunk64': <function chunk64_dataprovider at 0x7fc1b21225f0>, 'column': <function column_dataprovider at 0x7fc1aed7a7d0>, 'dataset-column': <function dataset_column_dataprovider at 0x7fc1aed7a938>, 'dataset-dict': <function dataset_dict_dataprovider at 0x7fc1aed7ac08>, 'dict': <function dict_dataprovider at 0x7fc1aed7aaa0>, 'line': <function line_dataprovider at 0x7fc1b2122b90>, 'regex-line': <function regex_line_dataprovider at 0x7fc1b2122cf8>, 'wiggle': <function wiggle_dataprovider at 0x7fc1aecc1848>, 'wiggle-dict': <function wiggle_dict_dataprovider at 0x7fc1aecc19b0>}
metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc0690>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>}
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 0x7fc1aecc0b10>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc0a90>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc0a10>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc0910>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc0990>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc0b90>}
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 0x7fc1aecc0dd0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc0fd0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc0ed0>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecab9d0>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc0e50>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc0f50>}
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 0x7fc1aecc9250>, 'chrom2Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc93d0>, 'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecab7d0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc95d0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'end1Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc9350>, 'end2Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc94d0>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecab8d0>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecab9d0>, 'start1Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc92d0>, 'start2Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc9450>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecab850>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecab950>, 'valueCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc9550>}
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 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc9890>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aecc9810>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>}
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 0x7fc18faabd50>}
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 0x7fc18fd20950>}

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 0x7fc18e90e310>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e90e390>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e90e110>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e90e290>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e90e210>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e90e190>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e90e090>}
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 0x7fc18e90e710>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e90e790>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e90e510>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e90e690>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e90e610>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e90e590>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e90e490>}
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 0x7fc18e90eb50>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e90ebd0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e90e950>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e90ead0>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e90ea50>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e90e9d0>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18e90e8d0>}
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 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18d6b43d0>}
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 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18d6b45d0>}
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 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18d6b4810>}
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 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18d6b4a50>}
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 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18d6b4c90>}
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 0x7fc18d6b4f10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aee1aed0>}
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 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18d699190>}
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 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18d301610>}
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 0x7fc18d310c50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18d6b43d0>}
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 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18d08f3d0>}
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 0x7fc18d08f910>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18d6b43d0>}
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 0x7fc18d08fad0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc18d0995d0>}
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 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18d6993d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18d699350>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18d699450>}
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 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18d699750>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18d6996d0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18d6997d0>}
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 0x7fc1b709d810>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18d699a50>}
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 0x7fc18cb8a810>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'labels': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cb8a890>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cb8a910>}
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 0x7fc18cb8aa50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'labels': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cb8aad0>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cb8ab50>}
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 0x7fc18cb8a810>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cb8ac90>, 'labels': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cb8a890>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cb8a910>}
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 0x7fc18cb8afd0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cb8af50>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cb8aed0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cb8add0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cb8ae50>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccc3090>}
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 0x7fc18ccc33d0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccc3350>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccc32d0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccc31d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccc3250>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccc3450>}
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 0x7fc18ccc3790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccc3710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccc3690>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccc3590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccc3610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccc3810>}
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 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccc3950>}
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 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccc3a90>}
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 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccc3b90>}
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 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f690>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccc3d50>}
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 0x7fc18ccda1d0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccda150>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccda0d0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccc3f90>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccda050>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccda250>}
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 0x7fc18ccda690>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccda610>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccda590>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccda490>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccda510>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccda710>}
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 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f690>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccda950>}
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 0x7fc18ccdad50>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccdacd0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccdac50>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccdab50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccdabd0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccdadd0>}
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 0x7fc18cce2050>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc18cce2490>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cce2410>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cce2390>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cce2290>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cce2310>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cce2510>}
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 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f690>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'filtered': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cce2710>, 'masked': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cce2790>}
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 0x7fc18cce29d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f690>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cce2c10>}
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 0x7fc18cceb090>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cce2fd0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cce2f50>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cce2e50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cce2ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cceb110>}
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 0x7fc18cceb550>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cceb4d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cceb450>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cceb350>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cceb3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cceb5d0>}
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 0x7fc18cceba10>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cceb990>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cceb910>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cceb810>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cceb890>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18cceba90>}
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 0x7fc18ccebed0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccebe50>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccebdd0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccebcd0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccebd50>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccebf50>}
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 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f690>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'flow_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccf5290>, 'flow_values': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18ccf5210>}
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 0x7fc18c602b10>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c602a90>}
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 0x7fc18c602c50>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc18c602d90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc18c602e90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc18c602fd0>}
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 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c61a150>}
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 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c61a290>}
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 0x7fc18c5561d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc18c556310>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc18c5561d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'neostore_zip': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c5564d0>, 'reference_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c556450>}

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 0x7fc18c3dc7d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c3dc850>}
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 0x7fc18c3dc7d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c3dc950>}
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 0x7fc18c3dc7d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18eb02650>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c3dca90>}

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 0x7fc1b2124490>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c367b50>}
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 0x7fc18c399ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
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 0x7fc1aed7f790>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f690>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f590>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1aed7f810>, 'number_comp': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c379110>}

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 0x7fc18c147cd0>}
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 0x7fc18c21d150>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c21d0d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c21d050>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c147f10>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c147f90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c21d1d0>}
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]
display_peek(dataset)[source]

Returns formated html of peek

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c21d590>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c21d510>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c21d490>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c21d390>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c21d410>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c21d610>}
class galaxy.datatypes.proteomics.ProteomicsXml(**kwd)[source]

Bases: galaxy.datatypes.xml.GenericXml

An enhanced XML datatype used to reuse code across several proteomic/mass-spec datatypes.

edam_data = 'data_2536'
edam_format = 'format_2032'
sniff_prefix(file_prefix)[source]

Determines whether the file is the correct XML type.

set_peek(dataset, is_multi_byte=False)[source]

Set the peek and blurb text

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c21d890>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
class galaxy.datatypes.proteomics.PepXml(**kwd)[source]

Bases: galaxy.datatypes.proteomics.ProteomicsXml

pepXML data

edam_format = 'format_3655'
file_ext = 'pepxml'
blurb = 'pepXML data'
root = 'msms_pipeline_analysis'
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c21dad0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
class galaxy.datatypes.proteomics.MzML(**kwd)[source]

Bases: galaxy.datatypes.proteomics.ProteomicsXml

mzML data

edam_format = 'format_3244'
file_ext = 'mzml'
blurb = 'mzML Mass Spectrometry data'
root = '(mzML|indexedmzML)'
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c21dcd0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
class galaxy.datatypes.proteomics.NmrML(**kwd)[source]

Bases: galaxy.datatypes.proteomics.ProteomicsXml

nmrML data

file_ext = 'nmrml'
blurb = 'nmrML NMR data'
root = 'nmrML'
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c21df10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
class galaxy.datatypes.proteomics.ProtXML(**kwd)[source]

Bases: galaxy.datatypes.proteomics.ProteomicsXml

protXML data

file_ext = 'protxml'
blurb = 'prot XML Search Results'
root = 'protein_summary'
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc18c218190>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7fc1b21241d0>}
class galaxy.datatypes.proteomics.MzXML(**kwd)[source]

Bases: galaxy.datatypes.proteomics.ProteomicsXml

mzXML data

edam_format = 'format_3654'