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 0x7f6dc5d6fd68>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dc5d6fdd8>}

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 0x7f6dcd3eb668>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dc182f0f0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3eb668>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dc185b198>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3eb668>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dcd3ca208>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3eb668>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dcd3ca4e0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3eb668>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dcd3ca5c0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3eb668>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dcd3ca828>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3eb668>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dcd3ca9e8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3eb668>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}

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 0x7f6dc137c7f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc137ca58>}
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 0x7f6dc1397630>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dc13976d8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'long_reads': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc13977b8>, 'paired_end_reads': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc1397748>, 'short2_reads': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc1397828>}

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 0x7f6dd76fde80>}
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 0x7f6dd770a198>}
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 0x7f6dd770a2e8>}
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 0x7f6dd76fde80>, 'version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd770a4e0>}
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 0x7f6dd770a6d8>}
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 0x7f6dd770a8d0>}
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 0x7f6dd770aac8>}
class galaxy.datatypes.binary.GzDynamicCompressedArchive(**kwd)[source]

Bases: galaxy.datatypes.binary.DynamicCompressedArchive

compressed_format = 'gzip'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd770acc0>}
class galaxy.datatypes.binary.Bz2DynamicCompressedArchive(**kwd)[source]

Bases: galaxy.datatypes.binary.DynamicCompressedArchive

compressed_format = 'bz2'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd770aeb8>}
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]
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd77150f0>}
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 0x7f6dd77152e8>}
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 0x7f6dd77157b8>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715588>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715908>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715898>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715828>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd770a8d0>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715668>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715748>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd77156d8>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd77155f8>}
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'}
get_index_flag(file_name)[source]

Return pysam flag for bai index (default) or csi index (contig size > (2**29 - 1) )

dataset_content_needs_grooming(file_name)[source]

Check if file_name is a coordinate-sorted BAM file

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

Generic samtools interface - all options available through settings.

dataproviders = {'base': <function Data.base_dataprovider at 0x7f6ddabf0d90>, 'chunk': <function Data.chunk_dataprovider at 0x7f6ddabf0f28>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f6ddabf1158>, 'column': <function Bam.column_dataprovider at 0x7f6dd7719488>, 'dict': <function Bam.dict_dataprovider at 0x7f6dd7719620>, 'genomic-region': <function Bam.genomic_region_dataprovider at 0x7f6dd7719ae8>, 'genomic-region-dict': <function Bam.genomic_region_dict_dataprovider at 0x7f6dd7719c80>, 'header': <function Bam.header_dataprovider at 0x7f6dd77197b8>, 'id-seq-qual': <function Bam.id_seq_qual_dataprovider at 0x7f6dd7719950>, 'line': <function Bam.line_dataprovider at 0x7f6dd7719158>, 'regex-line': <function Bam.regex_line_dataprovider at 0x7f6dd77192f0>, 'samtools': <function Bam.samtools_dataprovider at 0x7f6dd7719e18>}
metadata_spec = {'bam_csi_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715fd0>, 'bam_header': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd77157b8>, 'bam_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715f60>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715588>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715908>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715898>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715828>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd770a8d0>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715668>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715748>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd77156d8>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd77155f8>}
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_csi_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd771c278>, 'bam_header': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd77157b8>, 'bam_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd771c208>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715588>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715908>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715898>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715828>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd770a8d0>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715668>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7715748>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd77156d8>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd77155f8>}
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 0x7f6dd771c6a0>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd771c470>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd771c7f0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd771c780>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd771c710>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd770a8d0>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd771c550>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd771c630>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd771c5c0>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd771c4e0>}
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 0x7f6dd771cc18>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd771c9e8>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd771cd68>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd771ccf8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd771cc88>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd770a8d0>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd771cac8>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd771cba8>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd771cb38>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd771ca58>}
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 0x7f6dd771cfd0>, 'cram_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd771cf60>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76fde80>}
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 0x7f6dd7722208>}
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 0x7f6dd7722400>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7722208>}
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 0x7f6dd77225f8>}
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 0x7f6dd77227f0>}
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 0x7f6dd7722f60>, 'col_attrs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7722fd0>, 'col_graphs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7729080>, 'col_graphs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd77290f0>, 'creation_date': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7722c88>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd77227f0>, 'description': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7722ac8>, 'doi': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7722ba8>, 'layers_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7722d68>, 'layers_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7722dd8>, 'loom_spec_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7722c18>, 'row_attrs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7722e80>, 'row_attrs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7722ef0>, 'row_graphs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7729160>, 'row_graphs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd77291d0>, 'shape': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7722cf8>, 'title': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7722a58>, 'url': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7722b38>}
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 0x7f6dd77293c8>}
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 0x7f6dd77295c0>}
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 0x7f6dd77297b8>}
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 0x7f6dd77299b0>}
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 0x7f6dd7729ba8>}
class galaxy.datatypes.binary.Edr(**kwd)[source]

Bases: galaxy.datatypes.binary.GmxBinary

Class describing an edr file from the GROMACS suite

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('md.edr')
>>> Edr().sniff(fname)
True
>>> fname = get_test_fname('md.trr')
>>> Edr().sniff(fname)
False
file_ext = 'edr'
magic_number = -55555
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7729da0>}
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 0x7f6dd76b72e8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd77227f0>, 'format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76b7198>, 'format_url': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76b70b8>, 'format_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76b7128>, 'generated_by': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76b7278>, 'id': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76b7048>, 'nnz': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76b7358>, 'shape': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76b73c8>, 'type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76b7208>}
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 0x7f6dd76b75f8>}
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 0x7f6dd76b7828>}
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 0x7f6dd76b7a20>}
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 0x7f6dd76b7c18>}
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 0x7f6dd76b7e10>}
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 0x7f6dd76bb048>}
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 0x7f6dd76bb2b0>}
class galaxy.datatypes.binary.SQlite(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

Class describing a Sqlite database

file_ext = 'sqlite'
edam_format = 'format_3621'
init_meta(dataset, copy_from=None)[source]
set_meta(dataset, overwrite=True, **kwd)[source]
sniff(filename)[source]
sniff_table_names(filename, table_names)[source]
set_peek(dataset, is_multi_byte=False)[source]
display_peek(dataset)[source]
sqlite_dataprovider(dataset, **settings)[source]
sqlite_datatableprovider(dataset, **settings)[source]
sqlite_datadictprovider(dataset, **settings)[source]
dataproviders = {'base': <function Data.base_dataprovider at 0x7f6ddabf0d90>, 'chunk': <function Data.chunk_dataprovider at 0x7f6ddabf0f28>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f6ddabf1158>, 'sqlite': <function SQlite.sqlite_dataprovider at 0x7f6dd76be2f0>, 'sqlite-dict': <function SQlite.sqlite_datadictprovider at 0x7f6dd76be620>, 'sqlite-table': <function SQlite.sqlite_datatableprovider at 0x7f6dd76be488>}
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76fde80>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb668>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb6d8>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb5f8>}
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 0x7f6dd76fde80>, 'gemini_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb940>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb668>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb6d8>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb5f8>}
class galaxy.datatypes.binary.ChiraSQLite(**kwd)[source]

Bases: galaxy.datatypes.binary.SQlite

Class describing a ChiRAViz Sqlite database

file_ext = 'chira.sqlite'
set_meta(dataset, overwrite=True, **kwd)[source]
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76fde80>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bbc18>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bbc88>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bbba8>}
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 0x7f6dd76bbef0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76fde80>, 'genes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bbf60>, 'samples': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bbfd0>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb668>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb6d8>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb5f8>}
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 0x7f6dd76fde80>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76c32e8>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76c3358>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76c3278>}
class galaxy.datatypes.binary.PQP(**kwd)[source]

Bases: galaxy.datatypes.binary.SQlite

Class describing a Peptide query parameters file

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.pqp')
>>> PQP().sniff(fname)
True
>>> fname = get_test_fname('test.osw')
>>> PQP().sniff(fname)
False
file_ext = 'pqp'
set_meta(dataset, overwrite=True, **kwd)[source]
sniff(filename)[source]

table definition according to https://github.com/grosenberger/OpenMS/blob/develop/src/openms/source/ANALYSIS/OPENSWATH/TransitionPQPFile.cpp#L264 for now VERSION GENE PEPTIDE_GENE_MAPPING are excluded, since there is test data wo these tables, see also here https://github.com/OpenMS/OpenMS/issues/4365

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76fde80>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76c3630>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76c36a0>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76c35c0>}
class galaxy.datatypes.binary.OSW(**kwd)[source]

Bases: galaxy.datatypes.binary.SQlite

Class describing OpenSwath output

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.osw')
>>> OSW().sniff(fname)
True
>>> fname = get_test_fname('test.sqmass')
>>> OSW().sniff(fname)
False
file_ext = 'osw'
set_meta(dataset, overwrite=True, **kwd)[source]
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76fde80>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76c3978>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76c39e8>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76c3908>}
class galaxy.datatypes.binary.SQmass(**kwd)[source]

Bases: galaxy.datatypes.binary.SQlite

Class describing a Sqmass database

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.sqmass')
>>> SQmass().sniff(fname)
True
>>> fname = get_test_fname('test.pqp')
>>> SQmass().sniff(fname)
False
file_ext = 'sqmass'
set_meta(dataset, overwrite=True, **kwd)[source]
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76fde80>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76c3cc0>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76c3d30>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76c3c50>}
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 0x7f6dd76c3f98>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76fde80>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb668>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb6d8>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb5f8>}
class galaxy.datatypes.binary.DlibSQlite(**kwd)[source]

Bases: galaxy.datatypes.binary.SQlite

Class describing a Proteomics Spectral Library Sqlite database DLIBs only have the “entries”, “metadata”, and “peptidetoprotein” tables populated. ELIBs have the rest of the tables populated too, such as “peptidequants” or “peptidescores”.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.dlib')
>>> DlibSQlite().sniff(fname)
True
>>> fname = get_test_fname('interval.interval')
>>> DlibSQlite().sniff(fname)
False
file_ext = 'dlib'
set_meta(dataset, overwrite=True, **kwd)[source]
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76fde80>, 'dlib_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76ca240>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb668>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb6d8>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb5f8>}
class galaxy.datatypes.binary.ElibSQlite(**kwd)[source]

Bases: galaxy.datatypes.binary.SQlite

Class describing a Proteomics Chromatagram Library Sqlite database DLIBs only have the “entries”, “metadata”, and “peptidetoprotein” tables populated. ELIBs have the rest of the tables populated too, such as “peptidequants” or “peptidescores”.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.elib')
>>> ElibSQlite().sniff(fname)
True
>>> fname = get_test_fname('test.dlib')
>>> ElibSQlite().sniff(fname)
False
file_ext = 'elib'
set_meta(dataset, overwrite=True, **kwd)[source]
sniff(filename)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76fde80>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb668>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb6d8>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb5f8>, 'version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76ca4a8>}
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 0x7f6dd76fde80>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76ca780>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76ca7f0>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76ca710>}
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 0x7f6dd76fde80>, 'gafa_schema_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76caa58>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb668>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb6d8>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76bb5f8>}
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 0x7f6dd76cac50>}
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 0x7f6dd76cae48>}
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 0x7f6dd76d3080>}
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 0x7f6dd76d3278>}
class galaxy.datatypes.binary.OxliBinary(**kwd)[source]

Bases: galaxy.datatypes.binary.Binary

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76d34a8>}
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 0x7f6dd76d36a0>}
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 0x7f6dd76d3898>}
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 0x7f6dd76d3a90>}
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 0x7f6dd76d3c88>}
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 0x7f6dd76d3e80>}
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 0x7f6dd76db0b8>}
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 0x7f6dd770a8d0>, 'version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76db2e8>}
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 0x7f6dd770a8d0>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76db518>}
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 0x7f6dd770a8d0>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76db710>}
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 0x7f6dd770a8d0>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76db908>}
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 0x7f6dd770a8d0>, 'searchgui_major_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76dbba8>, 'searchgui_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76dbb38>}
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 0x7f6dd76dbda0>}
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 0x7f6dd76dbf98>}
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 0x7f6dd76e11d0>}
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 0x7f6dd76e13c8>}
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 0x7f6dd76e15c0>}
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 0x7f6dd76e17b8>}
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 0x7f6dd76e19b0>}
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 0x7f6dd76e1ba8>}
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 0x7f6dd76e1da0>}
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 0x7f6dd76e1f98>}
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 0x7f6dd766c1d0>}
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 0x7f6dd766c3c8>}
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 0x7f6dd766c5c0>}

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 0x7f6dbee828d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dc5a58b00>}
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 0x7f6dc5a585c0>}
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 0x7f6dc5a58e48>}

galaxy.datatypes.checkers module

Module proxies galaxy.util.checkers for backward compatibility.

External datatypes may make use of these functions.

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

Returns True if the file/string contains HTML code.

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

Simple wrapper around image_type to yield a True/False verdict

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

galaxy.datatypes.chrominfo module

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

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'len'
metadata_spec = {'chrom': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbef04e80>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab7f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab2b0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>, 'length': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbef04630>}

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 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'face': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe5eaa58>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe5eada0>, 'other_elements': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe5eaf28>, 'vertex': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe5eaef0>}
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 0x7f6dd76fde80>, 'face': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe700550>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe5eae48>, 'other_elements': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbef14320>, 'vertex': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6da2a47dd8>}
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 0x7f6dc5b8efd0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabefb00>, 'dataset_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc5b8e780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'dimensions': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc5b8ecf8>, 'field_components': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc5b8e748>, 'field_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc5b8ee48>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbef14f60>, 'lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc5b8eda0>, 'origin': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc5b8ec18>, 'points': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc5b8eef0>, 'polygons': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc5b8ef98>, 'spacing': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc5b8ea90>, 'triangle_strips': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc5b8ed68>, 'vertices': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc5b8e7b8>, 'vtk_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbef146d8>}
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 0x7f6dbe56f470>, 'dataset_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe56f198>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76fde80>, 'dimensions': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe56f208>, 'field_components': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe5e06a0>, 'field_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe56f128>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe56f978>, 'lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe56f2b0>, 'origin': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe56f9e8>, 'points': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe56f9b0>, 'polygons': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe56f1d0>, 'spacing': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe56fa20>, 'triangle_strips': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe56fa58>, 'vertices': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe56f240>, 'vtk_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe56f710>}
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 0x7f6dbe5e0a58>}
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 0x7f6dc5bf3240>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab7f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc5bf3e10>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>, 'forwardCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc5bf3f28>, 'positionCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc5bf3160>, 'reverseCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc5bf3f98>}

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 0x7f6ddabef828>}

Dictionary of metadata fields for this datatype

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

Initialize the datatype

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

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

dataset_content_needs_grooming(file_name)[source]

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

groom_dataset_content(file_name)[source]

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

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

Unimplemented method, allows guessing of metadata from contents of file

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

Checks for empty metadata values, Returns True if non-optional metadata is missing Specifying a list of ‘check’ values will only check those names provided; when used, optionality is ignored Specifying a list of ‘skip’ items will return True even when a named metadata value is missing

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

Set the peek and blurb text

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

Create HTML table, used for displaying peek

to_archive(trans, dataset, name='')[source]

Collect archive paths and file handles that need to be exported when archiving dataset.

Parameters:
  • dataset – HistoryDatasetAssociation
  • name – archive name, in collection context corresponds to collection name(s) and element_identifier, joined by ‘/’, e.g ‘fastq_collection/sample1/forward’
display_data(trans, data, preview=False, filename=None, to_ext=None, **kwd)[source]

Displays data in central pane if preview is True, else handles download.

Datatypes should be very careful if overridding this method and this interface between datatypes and Galaxy will likely change.

TOOD: Document alternatives to overridding this method (data providers?).

display_as_markdown(dataset_instance, markdown_format_helpers)[source]

Prepare for embedding dataset into a basic Markdown document.

This is a somewhat experimental interface and should not be implemented on datatypes not tightly tied to a Galaxy version (e.g. datatypes in the Tool Shed).

Speaking very losely - the datatype should should load a bounded amount of data from the supplied dataset instance and prepare for embedding it into Markdown. This should be relatively vanilla Markdown - the result of this is bleached and it should not contain nested Galaxy Markdown directives.

If the data cannot reasonably be displayed, just indicate this and do not throw an exception.

display_name(dataset)[source]

Returns formatted html of dataset name

display_info(dataset)[source]

Returns formatted html of dataset info

repair_methods(dataset)[source]

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

get_mime()[source]

Returns the mime type of the datatype

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

Adds a display app to the datatype. app_id is a unique id label is the primary display label, e.g., display at ‘UCSC’ file_function is a string containing the name of the function that returns a properly formatted display links_function is a string containing the name of the function that returns a list of (link_name,link)

remove_display_app(app_id)[source]

Removes a display app from the datatype

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

New style display applications

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

Returns display types available

get_display_label(type)[source]

Returns primary label for display app

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

Returns modified file contents for a particular display type

Returns a list of tuples of (name, link) for a particular display type. No check on ‘access’ permissions is done here - if you can view the dataset, you can also save it or send it to a destination outside of Galaxy, so Galaxy security restrictions do not apply anyway.

get_converter_types(original_dataset, datatypes_registry)[source]

Returns available converters by type for this dataset

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

Returns ( target_ext, existing converted dataset )

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

This function adds a job to the queue to convert a dataset to another type. Returns a message about success/failure.

after_setting_metadata(dataset)[source]

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

before_setting_metadata(dataset)[source]

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

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

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

static merge(split_files, output_file)[source]

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

get_visualizations(dataset)[source]

Returns a list of visualizations for datatype.

has_dataprovider(data_format)[source]

Returns True if data_format is available in dataproviders.

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

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

validate(dataset, **kwd)[source]
base_dataprovider(dataset, **settings)[source]
chunk_dataprovider(dataset, **settings)[source]
chunk64_dataprovider(dataset, **settings)[source]
dataproviders = {'base': <function Data.base_dataprovider at 0x7f6ddabf0d90>, 'chunk': <function Data.chunk_dataprovider at 0x7f6ddabf0f28>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f6ddabf1158>}
class galaxy.datatypes.data.Text(**kwd)[source]

Bases: galaxy.datatypes.data.Data

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

Returns the mime type of the datatype

set_meta(dataset, **kwd)[source]

Set the number of lines of data in dataset.

estimate_file_lines(dataset)[source]

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

count_data_lines(dataset)[source]

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

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

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

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

Split the input files by line.

line_dataprovider(dataset, **settings)[source]

Returns an iterator over the dataset’s lines (that have been stripped) optionally excluding blank lines and lines that start with a comment character.

regex_line_dataprovider(dataset, **settings)[source]

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

dataproviders = {'base': <function Data.base_dataprovider at 0x7f6ddabf0d90>, 'chunk': <function Data.chunk_dataprovider at 0x7f6ddabf0f28>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f6ddabf1158>, 'line': <function Text.line_dataprovider at 0x7f6ddabf1840>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f6ddabf19d8>}
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6ddabefcf8>}
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 0x7f6ddabefef0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6ddabf5128>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6ddabf5320>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6ddabf5518>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbeb59b00>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbeb59b38>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>, 'markerCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbeb59ef0>}
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 0x7f6dbe848240>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe848400>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe848390>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe848eb8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe848f98>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe848358>}
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 0x7f6dbecba6d8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbecbab38>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dc60ce1d0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe848da0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe848ef0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbecba748>}
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 0x7f6dbeb30eb8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbeb30f28>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbeb307b8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe68ab00>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbeb30630>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbeb30ac8>}
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 0x7f6dbeb30cc0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dbeb30be0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dd251ed30>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dcdff00b8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dcdfc1518>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dbecdbcf8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dbecdb390>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dbecdf160>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dbecdfe80>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dbecdf668>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dbecc4860>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dbecc4a90>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dbe85e400>, 'chrom_bed': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe85e048>, 'chrom_windows': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe85e588>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'input_config': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe85e7f0>, 'tmp_archive': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe85e860>}
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 0x7f6dbe85ebe0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe85eb70>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe85eb00>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe85ea20>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe85ea90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe85ec50>}
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 0x7f6dbe85ef60>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe85ee80>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe85ee10>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe85eef0>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe85efd0>}
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 0x7f6dbebfd2e8>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbebfd208>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbebfd198>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbebfd278>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbebfd358>}
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 0x7f6dbebfd6a0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbebfd5c0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbebfd550>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbebfd630>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbebfd710>}
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 0x7f6dbebfda58>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbebfd978>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbebfd908>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbebfd9e8>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbebfdac8>}
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 0x7f6dbebfdcc0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dbebfdef0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dbec12128>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dbec12320>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dbec12518>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dbe0cab00>}

galaxy.datatypes.graph module

Graph content classes.

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

Bases: galaxy.datatypes.xml.GenericXml

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

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

Set the peek and blurb text

sniff(filename)[source]

Returns false and the user must manually set.

static merge(split_files, output_file)[source]

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

node_edge_dataprovider(dataset, **settings)[source]
dataproviders = {'base': <function Data.base_dataprovider at 0x7f6ddabf0d90>, 'chunk': <function Data.chunk_dataprovider at 0x7f6ddabf0f28>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f6ddabf1158>, 'line': <function Text.line_dataprovider at 0x7f6ddabf1840>, 'node-edge': <function Xgmml.node_edge_dataprovider at 0x7f6dbe2c6bf8>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f6ddabf19d8>, 'xml': <function GenericXml.xml_dataprovider at 0x7f6d9d43dae8>}
metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9d3ed4a8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
class galaxy.datatypes.graph.Sif(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

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

First column: node id Second column: relationship type Third to Nth column: target ids for link

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

Set the peek and blurb text

sniff(filename)[source]

Returns false and the user must manually set.

static merge(split_files, output_file)[source]
node_edge_dataprovider(dataset, **settings)[source]
dataproviders = {'base': <function Data.base_dataprovider at 0x7f6ddabf0d90>, 'chunk': <function Data.chunk_dataprovider at 0x7f6ddabf0f28>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f6ddabf1158>, 'column': <function TabularData.column_dataprovider at 0x7f6dd766fb70>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f6dd766fd08>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f6dd76ad0d0>, 'dict': <function TabularData.dict_dataprovider at 0x7f6dd766fea0>, 'line': <function Text.line_dataprovider at 0x7f6ddabf1840>, 'node-edge': <function Sif.node_edge_dataprovider at 0x7f6d9d3e31e0>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f6ddabf19d8>}
metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe113f60>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe113978>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe113588>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe124a20>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe113b00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe113f28>}
class galaxy.datatypes.graph.XGMMLGraphDataProvider(source, selector=None, max_depth=None, **kwargs)[source]

Bases: galaxy.datatypes.dataproviders.hierarchy.XMLDataProvider

Provide two lists: nodes, edges:

'nodes': contains objects of the form:
    { 'id' : <some string id>, 'data': <any extra data> }
'edges': contains objects of the form:
    { 'source' : <an index into nodes>, 'target': <an index into nodes>, 'data': <any extra data> }
settings = {'limit': 'int', 'max_depth': 'int', 'offset': 'int', 'selector': 'str'}
class galaxy.datatypes.graph.SIFGraphDataProvider(source, indeces=None, column_count=None, column_types=None, parsers=None, parse_columns=True, deliminator='t', filters=None, **kwargs)[source]

Bases: galaxy.datatypes.dataproviders.column.ColumnarDataProvider

Provide two lists: nodes, edges:

'nodes': contains objects of the form:
    { 'id' : <some string id>, 'data': <any extra data> }
'edges': contains objects of the form:
    { 'source' : <an index into nodes>, 'target': <an index into nodes>, 'data': <any extra data> }
settings = {'column_count': 'int', 'column_types': 'list:str', 'comment_char': 'str', 'deliminator': 'str', 'filters': 'list:str', 'indeces': 'list:int', 'invert': 'bool', 'limit': 'int', 'offset': 'int', 'parse_columns': 'bool', 'provide_blank': 'bool', 'regex_list': 'list:escaped', 'strip_lines': 'bool', 'strip_newlines': 'bool'}

galaxy.datatypes.images module

Image classes

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

Bases: galaxy.datatypes.data.Data

Class describing an image

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

Determine if the file is in this format

handle_dataset_as_image(hda)[source]
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9d38a940>}
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 0x7f6d9d38aeb8>}
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 0x7f6d9d3707b8>}
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 0x7f6d9e1841d0>}
class galaxy.datatypes.images.Hamamatsu(**kwd)[source]

Bases: galaxy.datatypes.images.Image

file_ext = 'vms'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9d379668>}
class galaxy.datatypes.images.Mirax(**kwd)[source]

Bases: galaxy.datatypes.images.Image

file_ext = 'mrxs'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9d379ef0>}
class galaxy.datatypes.images.Sakura(**kwd)[source]

Bases: galaxy.datatypes.images.Image

file_ext = 'svslide'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9d330128>}
class galaxy.datatypes.images.Nrrd(**kwd)[source]

Bases: galaxy.datatypes.images.Image

file_ext = 'nrrd'
metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9d330400>}
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 0x7f6d9d330c50>}
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 0x7f6d9d330e10>}
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 0x7f6d9d330fd0>}
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 0x7f6d9d3bc1d0>}
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 0x7f6d9d3bc3c8>}
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 0x7f6d9d3bc5c0>}
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 0x7f6d9d3bc780>}
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 0x7f6d9d3bc978>}
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 0x7f6d9d3bcb38>}
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 0x7f6d9d3bccc0>}
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 0x7f6d9d3bce80>}
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 0x7f6d9d3b1048>}
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 0x7f6d9d3b1208>}
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 0x7f6d9d3b13c8>}
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 0x7f6d9d3b1588>}
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 0x7f6d9d3b1780>}
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 0x7f6d9d3b1978>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6d9d3b1b70>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}

galaxy.datatypes.interval module

Interval datatypes

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

Bases: galaxy.datatypes.tabular.Tabular

Tab delimited data containing interval information

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

Add metadata elements

__init__(**kwd)[source]

Initialize interval datatype, by adding UCSC display apps

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

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

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

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

as_ucsc_display_file(dataset, **kwd)[source]

Returns file contents with only the bed data

display_peek(dataset)[source]

Returns formated html of peek

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

validate(dataset, **kwd)[source]

Validate an interval file using the bx GenomicIntervalReader

repair_methods(dataset)[source]

Return options for removing errors along with a description

sniff_prefix(file_prefix)[source]

Checks for ‘intervalness’

This format is mostly used by galaxy itself. Valid interval files should include a valid header comment, but this seems to be loosely regulated.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'test_space.txt' )
>>> Interval().sniff( fname )
False
>>> fname = get_test_fname( 'interval.interval' )
>>> Interval().sniff( fname )
True
get_track_resolution(dataset, start, end)[source]
genomic_region_dataprovider(dataset, **settings)[source]
genomic_region_dict_dataprovider(dataset, **settings)[source]
interval_dataprovider(dataset, **settings)[source]
interval_dict_dataprovider(dataset, **settings)[source]
dataproviders = {'base': <function Data.base_dataprovider at 0x7f6ddabf0d90>, 'chunk': <function Data.chunk_dataprovider at 0x7f6ddabf0f28>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f6ddabf1158>, 'column': <function TabularData.column_dataprovider at 0x7f6dd766fb70>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f6dd766fd08>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f6dd76ad0d0>, 'dict': <function TabularData.dict_dataprovider at 0x7f6dd766fea0>, 'genomic-region': <function Interval.genomic_region_dataprovider at 0x7f6dd7694840>, 'genomic-region-dict': <function Interval.genomic_region_dict_dataprovider at 0x7f6dd76949d8>, 'interval': <function Interval.interval_dataprovider at 0x7f6dd7694b70>, 'interval-dict': <function Interval.interval_dict_dataprovider at 0x7f6dd7694d08>, 'line': <function Text.line_dataprovider at 0x7f6ddabf1840>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f6ddabf19d8>}
metadata_spec = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd768f7f0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab7f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd768ffd0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd768fe80>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd768ff60>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd768fe10>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd768fef0>}
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 0x7f6dd7695198>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab7f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7695438>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76952e8>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76953c8>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7695278>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7695358>}
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 0x7f6dd76955c0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab7f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76957f0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7695710>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd768ff60>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76956a0>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7695780>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7695828>}
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 0x7f6dd76959e8>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab7f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7695c18>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7695b38>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd768ff60>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7695ac8>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7695ba8>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7695c50>}
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 0x7f6dd7695e10>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab7f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd769f080>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7695ef0>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7695fd0>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7695e80>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7695f60>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7695828>}
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 0x7f6dd769f278>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab7f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd769f4a8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd769f358>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd769f438>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd769f2e8>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd769f3c8>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7695828>}
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 0x7f6dd769f6a0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab7f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd769f8d0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd769f780>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd769f860>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd769f710>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd769f7f0>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd7695828>}
class galaxy.datatypes.interval.Gff(**kwd)[source]

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

Tab delimited data in Gff format

edam_data = 'data_1255'
edam_format = 'format_2305'
file_ext = 'gff'
valid_gff_frame = ['.', '0', '1', '2']
column_names = ['Seqname', 'Source', 'Feature', 'Start', 'End', 'Score', 'Strand', 'Frame', 'Group']
data_sources = {'data': 'interval_index', 'feature_search': 'fli', 'index': 'bigwig'}
track_type = 'FeatureTrack'

Add metadata elements

__init__(**kwd)[source]

Initialize datatype, by adding GBrowse display app

set_attribute_metadata(dataset)[source]

Sets metadata elements for dataset’s attributes.

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

Returns formated html of peek

get_estimated_display_viewport(dataset)[source]

Return a chrom, start, stop tuple for viewing a file. There are slight differences between gff 2 and gff 3 formats. This function should correctly handle both…

sniff_prefix(file_prefix)[source]

Determines whether the file is in gff format

GFF lines have nine required fields that must be tab-separated.

For complete details see http://genome.ucsc.edu/FAQ/FAQformat#format3

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('gff.gff3')
>>> Gff().sniff( fname )
False
>>> fname = get_test_fname('test.gff')
>>> Gff().sniff( fname )
True
genomic_region_dataprovider(dataset, **settings)[source]
genomic_region_dict_dataprovider(dataset, **settings)[source]
interval_dataprovider(dataset, **settings)[source]
interval_dict_dataprovider(dataset, **settings)[source]
dataproviders = {'base': <function Data.base_dataprovider at 0x7f6ddabf0d90>, 'chunk': <function Data.chunk_dataprovider at 0x7f6ddabf0f28>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f6ddabf1158>, 'column': <function TabularData.column_dataprovider at 0x7f6dd766fb70>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f6dd766fd08>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f6dd76ad0d0>, 'dict': <function TabularData.dict_dataprovider at 0x7f6dd766fea0>, 'genomic-region': <function Gff.genomic_region_dataprovider at 0x7f6dd76a0840>, 'genomic-region-dict': <function Gff.genomic_region_dict_dataprovider at 0x7f6dd76a09d8>, 'interval': <function Gff.interval_dataprovider at 0x7f6dd76a0b70>, 'interval-dict': <function Gff.interval_dict_dataprovider at 0x7f6dd76a0d08>, 'line': <function Text.line_dataprovider at 0x7f6ddabf1840>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f6ddabf19d8>}
metadata_spec = {'attribute_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd769fdd8>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd769fd68>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd769fcf8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd769fc88>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>}
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 0x7f6dd769fdd8>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd769fd68>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd769ffd0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd769fc88>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>}
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 0x7f6dd769fdd8>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd769fd68>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76a7278>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76a7208>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>}
class galaxy.datatypes.interval.Wiggle(**kwd)[source]

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

Tab delimited data in wiggle format

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

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

display_peek(dataset)[source]

Returns formated html of peek

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

Determines wether the file is in wiggle format

The .wig format is line-oriented. Wiggle data is preceeded by a track definition line, which adds a number of options for controlling the default display of this track. Following the track definition line is the track data, which can be entered in several different formats.

The track definition line begins with the word ‘track’ followed by the track type. The track type with version is REQUIRED, and it currently must be wiggle_0. For example, track type=wiggle_0…

For complete details see http://genome.ucsc.edu/goldenPath/help/wiggle.html

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'interv1.bed' )
>>> Wiggle().sniff( fname )
False
>>> fname = get_test_fname( 'wiggle.wig' )
>>> Wiggle().sniff( fname )
True
get_track_resolution(dataset, start, end)[source]
wiggle_dataprovider(dataset, **settings)[source]
wiggle_dict_dataprovider(dataset, **settings)[source]
dataproviders = {'base': <function Data.base_dataprovider at 0x7f6ddabf0d90>, 'chunk': <function Data.chunk_dataprovider at 0x7f6ddabf0f28>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f6ddabf1158>, 'column': <function TabularData.column_dataprovider at 0x7f6dd766fb70>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f6dd766fd08>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f6dd76ad0d0>, 'dict': <function TabularData.dict_dataprovider at 0x7f6dd766fea0>, 'line': <function Text.line_dataprovider at 0x7f6ddabf1840>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f6ddabf19d8>, 'wiggle': <function Wiggle.wiggle_dataprovider at 0x7f6dd76a6c80>, 'wiggle-dict': <function Wiggle.wiggle_dict_dataprovider at 0x7f6dd76a6e18>}
metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab7f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76a7518>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>}
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 0x7f6dd76a78d0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76a7860>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76a77f0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76a7710>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76a7780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76a7940>}
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 0x7f6dd76a7b38>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab7f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76a7d68>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76a7c88>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd768ff60>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76a7c18>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76a7cf8>}
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 0x7f6dd76a7f28>, 'chrom2Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6daaa2a128>, 'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd768f7f0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab7f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6daaa2a2e8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>, 'end1Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6daaa2a0b8>, 'end2Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6daaa2a208>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd768fe80>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd768ff60>, 'start1Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76a7fd0>, 'start2Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6daaa2a198>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd768fe10>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd768fef0>, 'valueCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6daaa2a278>}
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 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6daaa2a4e0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6daaa2a470>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>}
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 0x7f6d9d048208>}
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 0x7f6d9d048438>}

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 0x7f6d9cc14710>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cc14780>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cc85748>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cc14b38>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cc14940>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cd014a8>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cc85b38>}
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 0x7f6d9cc330b8>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cc33400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cc14ba8>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cc338d0>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cc33710>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cc14c50>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cc14dd8>}
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 0x7f6d9cce8080>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cce8b38>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cc33860>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cc21e48>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cc218d0>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9ccfce10>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cc33be0>}
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 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c9a19e8>}
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 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c9a1dd8>}
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 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cb80b38>}
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 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9caa77b8>}
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 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9caa7a20>}
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 0x7f6d9caa7c18>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd76fde80>}
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 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9caa7e10>}
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 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9caa2048>}
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 0x7f6d9caa2240>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c9a19e8>}
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 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9caa2438>}
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 0x7f6d9caa2630>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c9a19e8>}
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 0x7f6d9caa2828>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6d9caa2a20>}
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 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9caa2c88>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9caa2c18>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9caa2cf8>}
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 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9caa2ef0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9caa2e80>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9caa2f60>}
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 0x7f6d9d41a4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cab8208>}
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 0x7f6d9c558588>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'labels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c5585f8>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c5674a8>}
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 0x7f6d9c5884a8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'labels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c588940>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c51a9e8>}
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 0x7f6d9c558588>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4fee48>, 'labels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c5585f8>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c5674a8>}
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 0x7f6d9c4fe2b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4fe4a8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4fe7b8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4fe8d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4fe860>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4feef0>}
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 0x7f6d9c498358>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4982e8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c498278>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c498198>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c498208>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4983c8>}
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 0x7f6d9c4987b8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c498748>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4986d8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4985f8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c498668>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c498828>}
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 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c498a90>}
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 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c498cc0>}
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 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c498f28>}
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 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab7f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab2b0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a1198>}
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 0x7f6d9c4a1518>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a14a8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a1438>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a1358>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a13c8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a1588>}
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 0x7f6d9c4a1978>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a1908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a1898>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a17b8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a1828>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a19e8>}
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 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab7f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab2b0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>, 'groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a1c50>}
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 0x7f6d9c4a8048>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a1f98>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a1f28>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a1e48>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a1eb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a80b8>}
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 0x7f6d9c4a82b0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6d9c4a8668>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a85f8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a8588>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a84a8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a8518>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a86d8>}
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 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab7f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab2b0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>, 'filtered': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a88d0>, 'masked': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a8940>}
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 0x7f6d9c4a8ac8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab7f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab2b0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>, 'groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a8cf8>}
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 0x7f6d9c4ae128>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4ae0b8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4ae048>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a8f28>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4a8f98>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4ae198>}
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 0x7f6d9c4ae588>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4ae518>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4ae4a8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4ae3c8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4ae438>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4ae5f8>}
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 0x7f6d9c4ae9b0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4ae940>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4ae8d0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4ae7f0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4ae860>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4aea20>}
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 0x7f6d9c4aee10>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4aeda0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4aed30>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4aec50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4aecc0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4aee80>}
sniff(filename)
class galaxy.datatypes.mothur.SffFlow(**kwd)[source]

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'mothur.sff.flow'

https://mothur.org/wiki/flow_file/ The first line is the total number of flow values - 800 for Titanium data. For GS FLX it would be 400. Following lines contain: - SequenceName - the number of useable flows as defined by 454’s software - the flow intensity for each base going in the order of TACG. Example:

800 GQY1XT001CQL4K 85 1.04 0.00 1.00 0.02 0.03 1.02 0.05 … GQY1XT001CQIRF 84 1.02 0.06 0.98 0.06 0.09 1.05 0.07 … GQY1XT001CF5YW 88 1.02 0.02 1.01 0.04 0.06 1.02 0.03 …
__init__(**kwd)[source]
metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0198>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab7f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab2b0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddaa88d68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75ab080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dd75b0470>, 'flow_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4b8160>, 'flow_values': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c4b80f0>}
set_meta(dataset, overwrite=True, skip=1, max_data_lines=None, **kwd)[source]
make_html_table(dataset, skipchars=None)[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 0x7f6d9bf82358>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c089748>}
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 0x7f6d9bf82128>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6d9bf82668>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6d9bf82860>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6d9bf82a20>}
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 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9bf82c50>}
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 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9bf82dd8>}
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 0x7f6d9ce2aef0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6d9ce2ab70>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
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 0x7f6d9ce2aef0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'neostore_zip': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c06c198>, 'reference_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9c06c4e0>}

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 0x7f6d9cee0e80>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cee0ef0>}
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 0x7f6d9cee0e80>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6d9cee0fd0>}
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 0x7f6d9cee0e80>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcd3d8f28>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dcac60128>}

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 0x7f6ddabefb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6dbe48ceb8>}
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 0x7f6dbe48c320>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f6ddabef828>}
sniff(filename)