Warning

This document is for an old release of Galaxy. You can alternatively view this page in the latest release if it exists or view the top of the latest release's documentation.

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 0x7f4803804e90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4805722050>}

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 0x7f4802bb0a90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4802bb0c50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802bb0a90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4802bb0e90>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802bb0a90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4802bba0d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802bb0a90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4802bba310>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802bb0a90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4802bba4d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802bb0a90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4802bba710>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802bb0a90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4802bba950>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802bb0a90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}

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 0x7f4803db09d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4805938210>}
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 0x7f4803f6b5d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4803f6bed0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'long_reads': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4803f6bad0>, 'paired_end_reads': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4803f6b2d0>, 'short2_reads': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4803f6b150>}

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 0x7f482a5e1990>}
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 0x7f482a5e1c10>}
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 0x7f482a5e1e50>}
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 0x7f482a5e1990>, 'version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5e6110>}
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 0x7f482a5e6350>}
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 0x7f482a5e65d0>}
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 0x7f482a5e6810>}
class galaxy.datatypes.binary.GzDynamicCompressedArchive(**kwd)[source]

Bases: galaxy.datatypes.binary.DynamicCompressedArchive

compressed_format = 'gzip'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5e6a50>}
class galaxy.datatypes.binary.Bz2DynamicCompressedArchive(**kwd)[source]

Bases: galaxy.datatypes.binary.DynamicCompressedArchive

compressed_format = 'bz2'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5e6c90>}
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 0x7f482a5e6f10>}
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 0x7f482a5f6190>}
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 0x7f482a5f6750>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f64d0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f68d0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f6850>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f67d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5e65d0>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f65d0>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f66d0>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f6650>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f6550>}
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 0x7f482d4d28c0>, 'chunk': <function chunk_dataprovider at 0x7f482d4d2a28>, 'chunk64': <function chunk64_dataprovider at 0x7f482d4d2b90>, 'column': <function column_dataprovider at 0x7f482a5f1f50>, 'dict': <function dict_dataprovider at 0x7f482a5f7140>, 'genomic-region': <function genomic_region_dataprovider at 0x7f482a5f7578>, 'genomic-region-dict': <function genomic_region_dict_dataprovider at 0x7f482a5f76e0>, 'header': <function header_dataprovider at 0x7f482a5f72a8>, 'id-seq-qual': <function id_seq_qual_dataprovider at 0x7f482a5f7410>, 'line': <function line_dataprovider at 0x7f482a5f1c80>, 'regex-line': <function regex_line_dataprovider at 0x7f482a5f1de8>, 'samtools': <function samtools_dataprovider at 0x7f482a5f7848>}
metadata_spec = {'bam_header': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f6750>, 'bam_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f6fd0>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f64d0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f68d0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f6850>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f67d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5e65d0>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f65d0>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f66d0>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f6650>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f6550>}
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 0x7f482a5f6750>, 'bam_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f8250>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f64d0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f68d0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f6850>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f67d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5e65d0>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f65d0>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f66d0>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f6650>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f6550>}
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 0x7f482a5f8710>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f8490>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f8890>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f8810>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f8790>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5e65d0>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f8590>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f8690>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f8610>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f8510>}
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 0x7f482a5f8d50>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f8ad0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f8ed0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f8e50>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f8dd0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5e65d0>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f8bd0>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f8cd0>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f8c50>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5f8b50>}
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 0x7f482a5fc250>, 'cram_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5fc1d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5e1990>}
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 0x7f482a5fc490>}
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 0x7f482a5fc750>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5fc490>}
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 0x7f482a5fc990>}
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 0x7f482a5fcc10>}
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 0x7f482a601490>, 'col_attrs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a601510>, 'col_graphs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a601590>, 'col_graphs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a601610>, 'creation_date': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a601190>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5fcc10>, 'description': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5fcf50>, 'doi': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a601090>, 'layers_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a601290>, 'layers_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a601310>, 'loom_spec_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a601110>, 'row_attrs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a601390>, 'row_attrs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a601410>, 'row_graphs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a601690>, 'row_graphs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a601710>, 'shape': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a601210>, 'title': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5fced0>, 'url': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5fcfd0>}
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 0x7f482a601950>}
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 0x7f482a601c10>}
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 0x7f482a601e50>}
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 0x7f482a6040d0>}
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 0x7f482a604310>}
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 0x7f482a6048d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5fcc10>, 'format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a604750>, 'format_url': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a604650>, 'format_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a6046d0>, 'generated_by': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a604850>, 'id': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a6045d0>, 'nnz': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a604950>, 'shape': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a6049d0>, 'type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a6047d0>}
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 0x7f482a604c50>}
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 0x7f482a604ed0>}
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 0x7f482a58a190>}
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 0x7f482a58a410>}
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 0x7f482a58a690>}
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 0x7f482a58a8d0>}
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 0x7f482a58ab50>}
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 0x7f482d4d28c0>, 'chunk': <function chunk_dataprovider at 0x7f482d4d2a28>, 'chunk64': <function chunk64_dataprovider at 0x7f482d4d2b90>, 'sqlite': <function sqlite_dataprovider at 0x7f482a58d500>, 'sqlite-dict': <function sqlite_datadictprovider at 0x7f482a58d7d0>, 'sqlite-table': <function sqlite_datatableprovider at 0x7f482a58d668>}
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5e1990>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58f090>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58f110>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58afd0>}
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 0x7f482a5e1990>, 'gemini_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58f3d0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58f090>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58f110>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58afd0>}
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 0x7f482a58f690>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5e1990>, 'genes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58f710>, 'samples': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58f790>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58f090>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58f110>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58afd0>}
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 0x7f482a5e1990>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58fa90>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58fb10>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58fa10>}
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 0x7f482a58fdd0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5e1990>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58f090>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58f110>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58afd0>}
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 0x7f482a5e1990>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a594150>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5941d0>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5940d0>}
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 0x7f482a5e1990>, 'gafa_schema_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a594450>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58f090>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58f110>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a58afd0>}
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 0x7f482a594690>}
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 0x7f482a594910>}
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 0x7f482a594b90>}
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 0x7f482a594dd0>}
class galaxy.datatypes.binary.OxliBinary(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a599050>}
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 0x7f482a599290>}
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 0x7f482a5994d0>}
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 0x7f482a599710>}
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 0x7f482a599950>}
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 0x7f482a599b90>}
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 0x7f482a599dd0>}
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 0x7f482a5e65d0>, 'version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5a40d0>}
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 0x7f482a5e65d0>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5a4390>}
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 0x7f482a5e65d0>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5a45d0>}
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 0x7f482a5e65d0>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5a4810>}
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 0x7f482a5e65d0>, 'searchgui_major_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5a4b50>, 'searchgui_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5a4ad0>}
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 0x7f482a5a4dd0>}
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 0x7f482a5ae090>}
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 0x7f482a5ae310>}
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 0x7f482a5ae550>}
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 0x7f482a5ae790>}
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 0x7f482a5ae9d0>}
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 0x7f482a5aec50>}
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 0x7f482a5aeed0>}
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 0x7f482a5b7150>}
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 0x7f482a5b73d0>}
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 0x7f482a5b7610>}
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 0x7f482a5b7850>}
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 0x7f482a5b7a90>}

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 0x7f4802405e50>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f48023a0090>}
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 0x7f48023a0190>}
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 0x7f48023a02d0>}

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 0x7f4802257e90>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c11d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1150>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'length': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802257d10>}

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 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'face': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802257ad0>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802257910>, 'other_elements': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802257850>, 'vertex': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48022571d0>}
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 0x7f482a5e1990>, 'face': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48024c6750>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802257450>, 'other_elements': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802422990>, 'vertex': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802257590>}
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 0x7f4802436b50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf3d0>, 'dataset_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48022b1750>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'dimensions': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48022b1690>, 'field_components': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48023a0ad0>, 'field_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802436410>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48022b12d0>, 'lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802286250>, 'origin': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4803a29f90>, 'points': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48022868d0>, 'polygons': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802286a10>, 'spacing': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802286850>, 'triangle_strips': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48024360d0>, 'vertices': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802286890>, 'vtk_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48022b1310>}
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 0x7f4802219a10>, 'dataset_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48023a0890>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5e1990>, 'dimensions': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48023a0d10>, 'field_components': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802219f50>, 'field_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48022198d0>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48023a07d0>, 'lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802219650>, 'origin': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802219410>, 'points': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802219310>, 'polygons': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802219a90>, 'spacing': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48022196d0>, 'triangle_strips': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802219b90>, 'vertices': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48022193d0>, 'vtk_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48023a0f90>}
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 0x7f48022c8910>}
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 0x7f4801f519d0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c11d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801f514d0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'forwardCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801f51a90>, 'positionCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801f51fd0>, 'reverseCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801f51e50>}

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 0x7f482d4bf110>}

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 0x7f482d4d28c0>, 'chunk': <function chunk_dataprovider at 0x7f482d4d2a28>, 'chunk64': <function chunk64_dataprovider at 0x7f482d4d2b90>}
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 0x7f482d4d28c0>, 'chunk': <function chunk_dataprovider at 0x7f482d4d2a28>, 'chunk64': <function chunk64_dataprovider at 0x7f482d4d2b90>, 'line': <function line_dataprovider at 0x7f482d4c01b8>, 'regex-line': <function regex_line_dataprovider at 0x7f482d4c0320>}
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f482d4bf650>}
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 0x7f482d4bf890>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f482d4bfad0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f482d4bfd50>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f482d4bffd0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4805780fd0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4805780190>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'markerCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4805780210>}
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 0x7f4805780d50>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4805780890>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4805780ed0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48057801d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4805780950>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4805780490>}
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 0x7f4804430d50>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4805780810>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4805780110>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4805780910>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4805780ad0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4804430550>}
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 0x7f48044305d0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4804430690>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4804430e90>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4804430c90>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4804430a90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4804430cd0>}
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 0x7f4804430a10>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4804430b10>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4804f42c50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4804f42510>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4804f42cd0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4804f42050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4804f42190>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4804f42ad0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f48042505d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4804250d10>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4804250a10>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4804250f90>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4804250250>, 'chrom_bed': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4804250710>, 'chrom_windows': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4804250c50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'input_config': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4804250ad0>, 'tmp_archive': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48042503d0>}
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 0x7f4804e7b450>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4804e7bd90>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4804e7b390>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4804e7b9d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4804e7b5d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4804e7b310>}
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 0x7f4804e7b250>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4804e7bb50>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4804e7b7d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4804e7b410>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4804e7bfd0>}
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 0x7f48055db750>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48055db090>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4804e7bf90>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48055dbfd0>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48055dba50>}
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 0x7f48055dbb10>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48055db050>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48055dbbd0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48055dbc50>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48055db450>}
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 0x7f48055db4d0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48055dbe50>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48055db350>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48055dbf10>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4805b8a910>}
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 0x7f4805b8aa90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4805b8af10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4805b8a210>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4805b8a0d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4805b8a990>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f48028dec50>}

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 0x7f482d4d28c0>, 'chunk': <function chunk_dataprovider at 0x7f482d4d2a28>, 'chunk64': <function chunk64_dataprovider at 0x7f482d4d2b90>, 'line': <function line_dataprovider at 0x7f482d4c01b8>, 'node-edge': <function node_edge_dataprovider at 0x7f48028cfc80>, 'regex-line': <function regex_line_dataprovider at 0x7f482d4c0320>, 'xml': <function xml_dataprovider at 0x7f48067fdde8>}
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48018a9f10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f482d4d28c0>, 'chunk': <function chunk_dataprovider at 0x7f482d4d2a28>, 'chunk64': <function chunk64_dataprovider at 0x7f482d4d2b90>, 'column': <function column_dataprovider at 0x7f482a5bc938>, 'dataset-column': <function dataset_column_dataprovider at 0x7f482a5bcaa0>, 'dataset-dict': <function dataset_dict_dataprovider at 0x7f482a5bcd70>, 'dict': <function dict_dataprovider at 0x7f482a5bcc08>, 'line': <function line_dataprovider at 0x7f482d4c01b8>, 'node-edge': <function node_edge_dataprovider at 0x7f48019df140>, 'regex-line': <function regex_line_dataprovider at 0x7f482d4c0320>}
metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48019e5450>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48019e53d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48019e5350>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48019e5250>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48019e52d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48019e54d0>}
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 0x7f480289c790>}
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 0x7f480289ca90>}
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 0x7f480289c7d0>}
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 0x7f480289cf50>}
class galaxy.datatypes.images.Hamamatsu(**kwd)[source]

Bases: galaxy.datatypes.images.Image

file_ext = 'vms'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f480289c990>}
class galaxy.datatypes.images.Mirax(**kwd)[source]

Bases: galaxy.datatypes.images.Image

file_ext = 'mrxs'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f480289c750>}
class galaxy.datatypes.images.Sakura(**kwd)[source]

Bases: galaxy.datatypes.images.Image

file_ext = 'svslide'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48019e5a10>}
class galaxy.datatypes.images.Nrrd(**kwd)[source]

Bases: galaxy.datatypes.images.Image

file_ext = 'nrrd'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48019e5910>}
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 0x7f48019e58d0>}
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 0x7f48019e55d0>}
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 0x7f48019e5dd0>}
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 0x7f4802107d50>}
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 0x7f480189a290>}
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 0x7f480189a150>}
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 0x7f480189a8d0>}
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 0x7f480189d290>}
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 0x7f480189d4d0>}
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 0x7f480189d250>}
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 0x7f48035a1090>}
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 0x7f48035a1290>}
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 0x7f48035a1490>}
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 0x7f48035a1690>}
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 0x7f48035a18d0>}
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 0x7f48035a1ad0>}
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 0x7f48035a1d10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f48035a1f50>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}

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 0x7f482d4d28c0>, 'chunk': <function chunk_dataprovider at 0x7f482d4d2a28>, 'chunk64': <function chunk64_dataprovider at 0x7f482d4d2b90>, 'column': <function column_dataprovider at 0x7f482a5bc938>, 'dataset-column': <function dataset_column_dataprovider at 0x7f482a5bcaa0>, 'dataset-dict': <function dataset_dict_dataprovider at 0x7f482a5bcd70>, 'dict': <function dict_dataprovider at 0x7f482a5bcc08>, 'genomic-region': <function genomic_region_dataprovider at 0x7f482a56c050>, 'genomic-region-dict': <function genomic_region_dict_dataprovider at 0x7f482a56c1b8>, 'interval': <function interval_dataprovider at 0x7f482a56c320>, 'interval-dict': <function interval_dict_dataprovider at 0x7f482a56c488>, 'line': <function line_dataprovider at 0x7f482d4c01b8>, 'regex-line': <function regex_line_dataprovider at 0x7f482d4c0320>}
metadata_spec = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56d290>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c11d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56d510>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56d390>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56d490>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56d310>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56d410>}
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 0x7f482a56d750>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c11d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56d9d0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56d850>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56d950>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56d7d0>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56d8d0>}
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 0x7f482a56dc50>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c11d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56de50>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56dd50>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56d490>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56dcd0>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56ddd0>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56ded0>}
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 0x7f482a571150>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c11d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a571350>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a571250>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56d490>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5711d0>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5712d0>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5713d0>}
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 0x7f482a571650>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c11d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5718d0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a571750>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a571850>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5716d0>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5717d0>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56ded0>}
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 0x7f482a571b10>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c11d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a571d90>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a571c10>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a571d10>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a571b90>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a571c90>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56ded0>}
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 0x7f482a571fd0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c11d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a57a290>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a57a110>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a57a210>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a57a090>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a57a190>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56ded0>}
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 0x7f482d4d28c0>, 'chunk': <function chunk_dataprovider at 0x7f482d4d2a28>, 'chunk64': <function chunk64_dataprovider at 0x7f482d4d2b90>, 'column': <function column_dataprovider at 0x7f482a5bc938>, 'dataset-column': <function dataset_column_dataprovider at 0x7f482a5bcaa0>, 'dataset-dict': <function dataset_dict_dataprovider at 0x7f482a5bcd70>, 'dict': <function dict_dataprovider at 0x7f482a5bcc08>, 'genomic-region': <function genomic_region_dataprovider at 0x7f482a5758c0>, 'genomic-region-dict': <function genomic_region_dict_dataprovider at 0x7f482a575a28>, 'interval': <function interval_dataprovider at 0x7f482a575b90>, 'interval-dict': <function interval_dict_dataprovider at 0x7f482a575cf8>, 'line': <function line_dataprovider at 0x7f482d4c01b8>, 'regex-line': <function regex_line_dataprovider at 0x7f482d4c0320>}
metadata_spec = {'attribute_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a57a890>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a57a810>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a57a790>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a57a710>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>}
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 0x7f482a57a890>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a57a810>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a57ab10>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a57a710>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>}
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 0x7f482a57a890>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a57a810>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a57add0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a57ad50>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>}
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 0x7f482d4d28c0>, 'chunk': <function chunk_dataprovider at 0x7f482d4d2a28>, 'chunk64': <function chunk64_dataprovider at 0x7f482d4d2b90>, 'column': <function column_dataprovider at 0x7f482a5bc938>, 'dataset-column': <function dataset_column_dataprovider at 0x7f482a5bcaa0>, 'dataset-dict': <function dataset_dict_dataprovider at 0x7f482a5bcd70>, 'dict': <function dict_dataprovider at 0x7f482a5bcc08>, 'line': <function line_dataprovider at 0x7f482d4c01b8>, 'regex-line': <function regex_line_dataprovider at 0x7f482d4c0320>, 'wiggle': <function wiggle_dataprovider at 0x7f482a57f9b0>, 'wiggle-dict': <function wiggle_dict_dataprovider at 0x7f482a57fb18>}
metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c11d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a584150>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>}
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 0x7f482a5845d0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a584550>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5844d0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5843d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a584450>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a584650>}
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 0x7f482a584890>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c11d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a584a90>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a584990>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56d490>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a584910>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a584a10>}
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 0x7f482a584cd0>, 'chrom2Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a584e50>, 'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56d290>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c11d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a4ce090>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'end1Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a584dd0>, 'end2Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a584f50>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56d390>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56d490>, 'start1Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a584d50>, 'start2Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a584ed0>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56d310>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a56d410>, 'valueCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a584fd0>}
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 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a4ce350>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a4ce2d0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>}
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 0x7f4803aa5090>}
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 0x7f4803aa5dd0>}

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 0x7f4801490310>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801490390>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801490110>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801490290>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801490210>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801490190>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801490090>}
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 0x7f4801490750>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48014907d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801490550>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48014906d0>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801490650>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48014905d0>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48014904d0>}
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 0x7f4801490b90>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801490c10>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801490990>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801490b10>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801490a90>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801490a10>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801490910>}
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 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801354890>}
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 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48013541d0>}
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 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801381d10>}
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 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801381950>}
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 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801381150>}
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 0x7f4801483d10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5e1990>}
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 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801483610>}
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 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f480171e390>}
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 0x7f480160d2d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801354890>}
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 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f480160d9d0>}
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 0x7f480160dc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4801354890>}
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 0x7f480160ded0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f480138a190>}
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 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f480138a490>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f480138a410>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f480138a510>}
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 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f480138a810>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f480138a790>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f480138a890>}
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 0x7f4831510f10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f480138ab10>}
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 0x7f4800cad4d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'labels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cad550>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cad5d0>}
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 0x7f4800cad710>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'labels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cad790>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cad810>}
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 0x7f4800cad4d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cad950>, 'labels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cad550>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cad5d0>}
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 0x7f4800cadc90>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cadc10>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cadb90>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cada90>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cadb10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cadd10>}
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 0x7f4800cbc090>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cadfd0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cadf50>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cade50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800caded0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cbc110>}
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 0x7f4800cbc450>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cbc3d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cbc350>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cbc250>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cbc2d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cbc4d0>}
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 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cbc610>}
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 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cbc750>}
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 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cbc890>}
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 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c11d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1150>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cbc9d0>}
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 0x7f4800cbcd10>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cbcc90>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cbcc10>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cbcb10>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cbcb90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cbcd90>}
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 0x7f4800cc9150>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cc90d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cc9050>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cbcf10>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cbcf90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cc91d0>}
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 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c11d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1150>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cc9410>}
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 0x7f4800cc9850>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cc97d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cc9750>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cc9650>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cc96d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cc98d0>}
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 0x7f4800cc9b10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4800cc9f50>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cc9ed0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cc9e50>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cc9d50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cc9dd0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800cc9fd0>}
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 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c11d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1150>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'filtered': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b4d210>, 'masked': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b4d290>}
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 0x7f4800b4d4d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c11d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1150>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b4d6d0>}
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 0x7f4800b4db10>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b4da90>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b4da10>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b4d910>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b4d990>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b4db90>}
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 0x7f4800b4dfd0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b4df50>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b4ded0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b4ddd0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b4de50>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b56090>}
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 0x7f4800b564d0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b56450>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b563d0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b562d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b56350>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b56550>}
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 0x7f4800b56990>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b56910>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b56890>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b56790>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b56810>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b56a10>}
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 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c11d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1150>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'flow_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b56d10>, 'flow_values': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800b56c90>}
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 0x7f480059d390>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object at 0x7f480059d310>}
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 0x7f480059d4d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f480059d610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f480059d710>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f480059d850>}
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 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object at 0x7f480059d990>}
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 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object at 0x7f480059db50>}
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 0x7f4800557b50>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4800557c50>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f4800557b50>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'neostore_zip': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800557e10>, 'reference_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800557d90>}

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 0x7f48003d6690>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object at 0x7f480040e850>}
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 0x7f48003d6690>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object at 0x7f480040e2d0>}
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 0x7f48003d6690>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4802c38ed0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800689710>}

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 0x7f482d4bf3d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f4800475410>}
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 0x7f4800320350>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f482a5c1250>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c11d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1150>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c1050>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c10d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482a5c12d0>, 'number_comp': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48003205d0>}

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 0x7f48001d60d0>}
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 0x7f48001d6510>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48001d6490>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48001d6410>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48001d6310>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48001d6390>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48001d6590>}
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 0x7f48001d69d0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48001d6950>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48001d68d0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48001d67d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48001d6850>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f48001d6a50>}
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 0x7f48001d6cd0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f48001d6f10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f48001d8190>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f48001d83d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
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 0x7f48001d8610>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f482d4bf110>}
class galaxy.datatypes.proteomics.MzXML(**kwd)[source]

Bases: galaxy.datatypes.proteomics.ProteomicsXml

mzXML data

edam_format = 'format_3654'