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galaxy.datatypes package¶

Subpackages¶

Submodules¶

galaxy.datatypes.annotation module¶

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

Bases: galaxy.datatypes.data.Text

file_ext = 'snaphmm'¶

edam_data = 'data_1364'¶

set_peek(dataset, is_multi_byte=False)[source]¶

display_peek(dataset)[source]¶

sniff_prefix(file_prefix)[source]¶: SNAP model files start with zoeHMM

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53390abba8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f53390ab940>}¶

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 0x7f5313e77e10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5313e77f98>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313e77e10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5314035160>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313e77e10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5314071048>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313e77e10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f53140711d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313e77e10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f53140719b0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313e77e10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5313e77da0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313e77e10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5314071a58>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313e77e10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5312a74c88>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53144f6d30>}¶

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 0x7f53130709e8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5313070da0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'long_reads': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312abeb38>, 'paired_end_reads': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313070dd8>, 'short2_reads': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312a5c5f8>}¶

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 0x7f533d870c88>}¶

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 0x7f533d885c88>}¶

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 0x7f533d885d30>}¶

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 0x7f533d870c88>, 'version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d885d68>}¶

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 0x7f533d885eb8>}¶

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 0x7f533d8950b8>}¶

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 0x7f533d895160>}¶

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

Bases: galaxy.datatypes.binary.DynamicCompressedArchive

compressed_format = 'gzip'¶

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895208>}¶

class galaxy.datatypes.binary.Bz2DynamicCompressedArchive(**kwd)[source]¶

Bases: galaxy.datatypes.binary.DynamicCompressedArchive

compressed_format = 'bz2'¶

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895240>}¶

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 0x7f533d8952b0>}¶

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 0x7f533d895320>}¶

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 0x7f533d895630>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895400>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b62358>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8956a0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8950b8>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8954e0>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8955c0>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895550>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895470>}¶

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 0x7f5340b66730>, 'chunk': <function Data.chunk_dataprovider at 0x7f5340b668c8>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f5340b66a60>, 'column': <function Bam.column_dataprovider at 0x7f533d89b9d8>, 'dict': <function Bam.dict_dataprovider at 0x7f533d89bb70>, 'genomic-region': <function Bam.genomic_region_dataprovider at 0x7f533d89d0d0>, 'genomic-region-dict': <function Bam.genomic_region_dict_dataprovider at 0x7f533d89d268>, 'header': <function Bam.header_dataprovider at 0x7f533d89bd08>, 'id-seq-qual': <function Bam.id_seq_qual_dataprovider at 0x7f533d89bea0>, 'line': <function Bam.line_dataprovider at 0x7f533d89b6a8>, 'regex-line': <function Bam.regex_line_dataprovider at 0x7f533d89b840>, 'samtools': <function Bam.samtools_dataprovider at 0x7f533d89d400>}¶

metadata_spec = {'bam_csi_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895b70>, 'bam_header': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895630>, 'bam_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895b00>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895400>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b62358>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8956a0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8950b8>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8954e0>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8955c0>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895550>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895470>}¶

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 0x7f533d895c88>, 'bam_header': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895630>, 'bam_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895c18>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895400>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b62358>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8956a0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8950b8>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8954e0>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8955c0>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895550>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895470>}¶

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 0x7f533d895f60>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895d30>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8210f0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d821080>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895fd0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8950b8>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895e10>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895ef0>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895e80>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d895da0>}¶

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 0x7f533d8213c8>, 'bam_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d821198>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d821518>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8214a8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d821438>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8950b8>, 'read_groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d821278>, 'reference_lengths': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d821358>, 'reference_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8212e8>, 'sort_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d821208>}¶

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 0x7f533d821630>, 'cram_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8215c0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d870c88>}¶

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 0x7f533d8216d8>}¶

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 0x7f533d821780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8216d8>}¶

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 0x7f533d821828>}¶

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 0x7f533d8218d0>}¶

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 0x7f533d821ef0>, 'col_attrs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d821f60>, 'col_graphs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d821fd0>, 'col_graphs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829080>, 'creation_date': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d821c18>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8218d0>, 'description': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d821a58>, 'doi': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d821b38>, 'layers_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d821cf8>, 'layers_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d821d68>, 'loom_spec_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d821ba8>, 'row_attrs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d821e10>, 'row_attrs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d821e80>, 'row_graphs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8290f0>, 'row_graphs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829160>, 'shape': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d821c88>, 'title': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8219e8>, 'url': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d821ac8>}¶

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 >>> Anndata().sniff(get_test_fname(‘import.loom.krumsiek11.h5ad’)) True >>> Anndata().sniff(get_test_fname(‘adata_0_6_small2.h5ad’)) True >>> Anndata().sniff(get_test_fname(‘adata_0_6_small.h5ad’)) True >>> Anndata().sniff(get_test_fname(‘adata_0_7_4_small2.h5ad’)) True >>> Anndata().sniff(get_test_fname(‘adata_0_7_4_small.h5ad’)) True >>> Anndata().sniff(get_test_fname(‘adata_unk2.h5ad’)) True >>> Anndata().sniff(get_test_fname(‘adata_unk.h5ad’)) True

file_ext = 'h5ad'¶

sniff(filename)[source]¶

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

set_peek(dataset, is_multi_byte=False)[source]¶

display_peek(dataset)[source]¶

metadata_spec = {'anndata_spec_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829400>, 'creation_date': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829470>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8218d0>, 'description': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8292b0>, 'doi': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829390>, 'layers_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8294e0>, 'layers_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829550>, 'obs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829748>, 'obs_layers': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8296d8>, 'obs_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829668>, 'obs_size': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8297b8>, 'obsm_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829898>, 'obsm_layers': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829828>, 'raw_var_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829978>, 'raw_var_layers': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829908>, 'raw_var_size': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8299e8>, 'row_attrs_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8295f8>, 'shape': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829d68>, 'title': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829240>, 'uns_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829cf8>, 'uns_layers': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829c88>, 'url': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829320>, 'var_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829ac8>, 'var_layers': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829a58>, 'var_size': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829b38>, 'varm_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829c18>, 'varm_layers': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d829ba8>}¶

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 0x7f533d829dd8>}¶

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 0x7f533d829e80>}¶

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 0x7f533d829f28>}¶

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 0x7f533d829fd0>}¶

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 0x7f533d82f0b8>}¶

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 0x7f533d82f470>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8218d0>, 'format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82f320>, 'format_url': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82f240>, 'format_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82f2b0>, 'generated_by': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82f400>, 'id': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82f1d0>, 'nnz': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82f4e0>, 'shape': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82f550>, 'type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82f390>}¶

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 0x7f533d82f630>}¶

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 0x7f533d82f710>}¶

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 0x7f533d82f7b8>}¶

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 0x7f533d82f860>}¶

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 0x7f533d82f908>}¶

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 0x7f533d82f9b0>}¶

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 0x7f533d82fac8>}¶

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 0x7f5340b66730>, 'chunk': <function Data.chunk_dataprovider at 0x7f5340b668c8>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f5340b66a60>, 'sqlite': <function SQlite.sqlite_dataprovider at 0x7f533d835268>, 'sqlite-dict': <function SQlite.sqlite_datadictprovider at 0x7f533d835598>, 'sqlite-table': <function SQlite.sqlite_datatableprovider at 0x7f533d835400>}¶

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d870c88>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fd30>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fda0>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fcc0>}¶

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 0x7f533d870c88>, 'gemini_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82feb8>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fd30>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fda0>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fcc0>}¶

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 0x7f533d870c88>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d837080>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8370f0>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82ffd0>}¶

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 0x7f533d837208>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d870c88>, 'genes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d837278>, 'samples': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8372e8>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fd30>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fda0>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fcc0>}¶

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 0x7f533d870c88>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d837470>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8374e0>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d837400>}¶

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 0x7f533d870c88>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d837668>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8376d8>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8375f8>}¶

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 0x7f533d870c88>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d837860>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8378d0>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8377f0>}¶

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 0x7f533d870c88>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d837a58>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d837ac8>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8379e8>}¶

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 0x7f533d837be0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d870c88>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fd30>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fda0>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fcc0>}¶

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 0x7f533d870c88>, 'dlib_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d837cf8>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fd30>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fda0>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fcc0>}¶

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 0x7f533d870c88>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fd30>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fda0>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fcc0>, 'version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d837e10>}¶

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 0x7f533d870c88>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d837f98>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d83e048>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d837f28>}¶

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 0x7f533d870c88>, 'gafa_schema_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d83e160>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fd30>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fda0>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fcc0>}¶

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

Bases: galaxy.datatypes.binary.SQlite

Class describing the NCBI Taxonomy database stored in SQLite as done by rust-ncbitaxonomy

file_ext = 'ncbitaxonomy.sqlite'¶

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 0x7f533d870c88>, 'ncbitaxonomy_schema_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d83e278>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fd30>, 'table_row_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fda0>, 'tables': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d82fcc0>, 'taxon_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d83e2e8>}¶

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 0x7f533d83e390>}¶

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 0x7f533d83e438>}¶

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 0x7f533d83e4e0>}¶

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 0x7f533d83e588>}¶

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

Bases: galaxy.datatypes.binary.Binary

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d83e668>}¶

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 0x7f533d83e710>}¶

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 0x7f533d83e7b8>}¶

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 0x7f533d83e860>}¶

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 0x7f533d83e908>}¶

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 0x7f533d83e9b0>}¶

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 0x7f533d83ea58>}¶

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 0x7f533d8950b8>, 'version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d83eb38>}¶

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 0x7f533d8950b8>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d83ec18>}¶

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 0x7f533d8950b8>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d83ecc0>}¶

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 0x7f533d8950b8>, 'fast5_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d83ed68>}¶

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 0x7f533d8950b8>, 'searchgui_major_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d83eeb8>, 'searchgui_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d83ee48>}¶

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 0x7f533d83ef60>}¶

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 0x7f533d84c048>}¶

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 0x7f533d84c0f0>}¶

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 0x7f533d84c198>}¶

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 0x7f533d84c240>}¶

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 0x7f533d84c2e8>}¶

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 0x7f533d84c390>}¶

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 0x7f533d84c438>}¶

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 0x7f533d8691d0>}¶

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 0x7f533d84c550>}¶

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 0x7f533d84c5f8>}¶

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 0x7f533d84c6a0>}¶

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 0x7f533d84c748>}¶

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 0x7f53133da240>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f531261ac18>}¶

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 0x7f531292af28>}¶

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 0x7f53128b8be0>}¶

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

Bases: galaxy.datatypes.data.Data

Class for LAST database files.

file_ext = 'lastdb'¶

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 0x7f53129e8c50>}¶

class galaxy.datatypes.blast.BlastNucDb5(**kwd)[source]¶

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

Class for nucleotide BLAST database files.

file_ext = 'blastdbn5'¶

allow_datatype_change = False¶

composite_type = 'basic'¶

__init__(**kwd)[source]¶

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53129e8898>}¶

class galaxy.datatypes.blast.BlastProtDb5(**kwd)[source]¶

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

Class for protein BLAST database files.

file_ext = 'blastdbp5'¶

allow_datatype_change = False¶

composite_type = 'basic'¶

__init__(**kwd)[source]¶

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53129e8d68>}¶

class galaxy.datatypes.blast.BlastDomainDb5(**kwd)[source]¶

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

Class for domain BLAST database files.

file_ext = 'blastdbd5'¶

allow_datatype_change = False¶

composite_type = 'basic'¶

__init__(**kwd)[source]¶

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53129e8828>}¶

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 0x7f5315031320>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afef0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'length': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312648390>}¶

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 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'face': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531502a898>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53390cf390>, 'other_elements': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531502a828>, 'vertex': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531502aeb8>}¶

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 0x7f533d870c88>, 'face': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53140aeeb8>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531501db38>, 'other_elements': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53140ae978>, 'vertex': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53397ec908>}¶

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 0x7f5313d468d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dataset_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53144ae128>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'dimensions': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53144a3ac8>, 'field_components': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d469b0>, 'field_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d46940>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53144ae3c8>, 'lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d46780>, 'origin': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53144a3b70>, 'points': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d47a20>, 'polygons': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d467f0>, 'spacing': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d479b0>, 'triangle_strips': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d46860>, 'vertices': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53138346d8>, 'vtk_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53144aeba8>}¶

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 0x7f5313d46ef0>, 'dataset_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d46b00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d870c88>, 'dimensions': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d46b70>, 'field_components': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d46fd0>, 'field_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d46f60>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d46a90>, 'lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d46da0>, 'origin': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d46be0>, 'points': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d46cc0>, 'polygons': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d46e10>, 'spacing': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d46c50>, 'triangle_strips': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d46e80>, 'vertices': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d46d30>, 'vtk_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313d46a20>}¶

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 0x7f5313d67080>}¶

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 0x7f53134a19b0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312dc0fd0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'forwardCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53134a1898>, 'positionCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53134a1a90>, 'reverseCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53134a1828>}¶

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 0x7f5340b696d8>}¶: 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

get_display_links(dataset, type, app, base_url, target_frame='_blank', **kwd)[source]¶: Returns a list of tuples of (name, link) for a particular display type. No check on ‘access’ permissions is done here - if you can view the dataset, you can also save it or send it to a destination outside of Galaxy, so Galaxy security restrictions do not apply anyway.

get_converter_types(original_dataset, datatypes_registry)[source]¶: Returns available converters by type for this dataset

find_conversion_destination(dataset, accepted_formats, datatypes_registry, **kwd)[source]¶: Returns ( direct_match, converted_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 0x7f5340b66730>, 'chunk': <function Data.chunk_dataprovider at 0x7f5340b668c8>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f5340b66a60>}¶

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 0x7f5340b66730>, 'chunk': <function Data.chunk_dataprovider at 0x7f5340b668c8>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f5340b66a60>, 'line': <function Text.line_dataprovider at 0x7f5340b6e0d0>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f5340b6e268>}¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5340b69828>}¶

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 0x7f5340b698d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5340b69978>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5340b69a20>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5340b69ac8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

ucsc_links(dataset, type, app, base_url)[source]¶

from the ever-helpful angie hinrichs angie@soe.ucsc.edu a genome graphs call looks like this

http://genome.ucsc.edu/cgi-bin/hgGenome?clade=mammal&org=Human&db=hg18&hgGenome_dataSetName=dname &hgGenome_dataSetDescription=test&hgGenome_formatType=best%20guess&hgGenome_markerType=best%20guess &hgGenome_columnLabels=best%20guess&hgGenome_maxVal=&hgGenome_labelVals= &hgGenome_maxGapToFill=25000000&hgGenome_uploadFile=http://galaxy.esphealth.org/datasets/333/display/index &hgGenome_doSubmitUpload=submit

Galaxy gives this for an interval file

http://genome.ucsc.edu/cgi-bin/hgTracks?db=hg18&position=chr1:1-1000&hgt.customText= http%3A%2F%2Fgalaxy.esphealth.org%2Fdisplay_as%3Fid%3D339%26display_app%3Ducsc

make_html_table(dataset, 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 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53136d5a20>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312bf6c88>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'markerCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312c3e048>}¶

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 0x7f5313846f28>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313846eb8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53139e97b8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312cf4ba8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53139e9b00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531316c080>}¶

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 0x7f5312c90cc0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312c90518>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313141390>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531316c3c8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313141160>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312c909e8>}¶

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 0x7f53159aa160>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53159aaa20>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531409c6d8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312c90a90>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312c90d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53159aa908>}¶

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 0x7f53159aad30>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f53159aa1d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f53159aa978>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f53159aaf98>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5313237e48>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f531577dbe0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f531577de48>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f531577dba8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f531577dac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f531577dc50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f531577dda0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f531577df98>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f531577da58>, 'chrom_bed': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531577dc88>, 'chrom_windows': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531577dcc0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'input_config': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536b208>, 'tmp_archive': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536bef0>}¶

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 0x7f531536bf28>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536b470>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536bd68>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536bac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536b7b8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536b6a0>}¶

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 0x7f531536bd30>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536beb8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536b940>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536b8d0>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536be10>}¶

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 0x7f531536bb00>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536b9e8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536b550>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536b3c8>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536b4e0>}¶

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 0x7f531536b908>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536b898>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536bc18>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536b2e8>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536b400>}¶

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 0x7f531536bda0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536b358>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536b278>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'pheCols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536b240>, 'pheno_path': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531536b390>}¶

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 0x7f531536bbe0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5339880f98>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f53398809b0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5339880438>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5339880940>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5313718080>}¶

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 0x7f5340b66730>, 'chunk': <function Data.chunk_dataprovider at 0x7f5340b668c8>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f5340b66a60>, 'line': <function Text.line_dataprovider at 0x7f5340b6e0d0>, 'node-edge': <function Xgmml.node_edge_dataprovider at 0x7f53131bfa60>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f5340b6e268>, 'xml': <function GenericXml.xml_dataprovider at 0x7f53161ad620>}¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53143be940>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5340b66730>, 'chunk': <function Data.chunk_dataprovider at 0x7f5340b668c8>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f5340b66a60>, 'column': <function TabularData.column_dataprovider at 0x7f533d852950>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f533d852ae8>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f533d852e18>, 'dict': <function TabularData.dict_dataprovider at 0x7f533d852c80>, 'line': <function Text.line_dataprovider at 0x7f5340b6e0d0>, 'node-edge': <function Sif.node_edge_dataprovider at 0x7f53131bfe18>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f5340b6e268>}¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53143bee10>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53143beda0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53143bed30>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53143bec50>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53143becc0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53143bee80>}¶

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 0x7f53125a3898>}¶

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 0x7f53125a3550>}¶

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 0x7f53143e8748>}¶

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 0x7f5312584d30>}¶

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

Bases: galaxy.datatypes.images.Image

file_ext = 'vms'¶

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53125846a0>}¶

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

Bases: galaxy.datatypes.images.Image

file_ext = 'mrxs'¶

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312584710>}¶

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

Bases: galaxy.datatypes.images.Image

file_ext = 'svslide'¶

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312584898>}¶

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

Bases: galaxy.datatypes.images.Image

file_ext = 'nrrd'¶

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312584780>}¶

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 0x7f53125763c8>}¶

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 0x7f5312576cc0>}¶

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 0x7f5312576c18>}¶

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 0x7f5312576438>}¶

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 0x7f53143ce5c0>}¶

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 0x7f53143ced68>}¶

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 0x7f53143cecf8>}¶

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 0x7f53143cec50>}¶

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 0x7f53143ceba8>}¶

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 0x7f53143ceb00>}¶

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 0x7f53143cea58>}¶

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 0x7f53143b2630>}¶

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 0x7f53143b2d68>}¶

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 0x7f53143b2780>}¶

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 0x7f53143b2668>}¶

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 0x7f53143b2a90>}¶

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 0x7f53143b2a58>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f53143b2b38>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

ucsc_links(dataset, type, app, base_url)[source]¶: 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 0x7f5340b66730>, 'chunk': <function Data.chunk_dataprovider at 0x7f5340b668c8>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f5340b66a60>, 'column': <function TabularData.column_dataprovider at 0x7f533d852950>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f533d852ae8>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f533d852e18>, 'dict': <function TabularData.dict_dataprovider at 0x7f533d852c80>, 'genomic-region': <function Interval.genomic_region_dataprovider at 0x7f533d7eaa60>, 'genomic-region-dict': <function Interval.genomic_region_dict_dataprovider at 0x7f533d7eabf8>, 'interval': <function Interval.interval_dataprovider at 0x7f533d7ead90>, 'interval-dict': <function Interval.interval_dict_dataprovider at 0x7f533d7eaf28>, 'line': <function Text.line_dataprovider at 0x7f5340b6e0d0>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f5340b6e268>}¶

metadata_spec = {'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d787048>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d787a58>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d787358>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7879e8>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7872e8>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7873c8>}¶

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 0x7f533d787a90>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d787d30>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d787be0>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d787cc0>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d787b70>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d787c50>}¶

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 0x7f533d787d68>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d787f98>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d787eb8>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7879e8>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d787e48>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d787f28>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d787fd0>}¶

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 0x7f533d7f2080>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f22b0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f21d0>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7879e8>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2160>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2240>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f22e8>}¶

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 0x7f533d7f2358>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2588>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2438>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2518>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f23c8>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f24a8>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d787fd0>}¶

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 0x7f533d7f2630>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2860>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2710>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f27f0>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f26a0>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2780>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d787fd0>}¶

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 0x7f533d7f2908>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2b38>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f29e8>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2ac8>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2978>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2a58>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d787fd0>}¶

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…

ucsc_links(dataset, type, app, base_url)[source]¶

gbrowse_links(dataset, type, app, base_url)[source]¶

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 0x7f5340b66730>, 'chunk': <function Data.chunk_dataprovider at 0x7f5340b668c8>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f5340b66a60>, 'column': <function TabularData.column_dataprovider at 0x7f533d852950>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f533d852ae8>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f533d852e18>, 'dict': <function TabularData.dict_dataprovider at 0x7f533d852c80>, 'genomic-region': <function Gff.genomic_region_dataprovider at 0x7f533d7ebea0>, 'genomic-region-dict': <function Gff.genomic_region_dict_dataprovider at 0x7f533d7f90d0>, 'interval': <function Gff.interval_dataprovider at 0x7f533d7f9268>, 'interval-dict': <function Gff.interval_dict_dataprovider at 0x7f533d7f9400>, 'line': <function Text.line_dataprovider at 0x7f5340b6e0d0>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f5340b6e268>}¶

metadata_spec = {'attribute_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2ef0>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2e80>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2e10>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2da0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>}¶

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:

adds a mechanism for representing more than one level of hierarchical grouping of features and subfeatures.
separates the ideas of group membership and feature name/id
constrains the feature type field to be taken from a controlled vocabulary.
allows a single feature, such as an exon, to belong to more than one group at a time.
provides an explicit convention for pairwise alignments
provides an explicit convention for features that occupy disjunct regions

The format consists of 9 columns, separated by tabs (NOT spaces).

Undefined fields are replaced with the “.” character, as described in the original GFF spec.

For complete details see http://song.sourceforge.net/gff3.shtml

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'test.gff' )
>>> Gff3().sniff( fname )
False
>>> fname = get_test_fname( 'test.gtf' )
>>> Gff3().sniff( fname )
False
>>> fname = get_test_fname('gff.gff3')
>>> Gff3().sniff( fname )
True
>>> fname = get_test_fname( 'grch37.75.gtf' )
>>> Gff3().sniff( fname )
False

metadata_spec = {'attribute_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2ef0>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2e80>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2f98>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2da0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>}¶

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
>>> fname = get_test_fname( 'grch37.75.gtf' )
>>> Gtf().sniff( fname )
True

metadata_spec = {'attribute_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2ef0>, 'attributes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7f2e80>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fc0f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fc080>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>}¶

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.

gbrowse_links(dataset, type, app, base_url)[source]¶

ucsc_links(dataset, type, app, base_url)[source]¶

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 0x7f5340b66730>, 'chunk': <function Data.chunk_dataprovider at 0x7f5340b668c8>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f5340b66a60>, 'column': <function TabularData.column_dataprovider at 0x7f533d852950>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f533d852ae8>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f533d852e18>, 'dict': <function TabularData.dict_dataprovider at 0x7f533d852c80>, 'line': <function Text.line_dataprovider at 0x7f5340b6e0d0>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f5340b6e268>, 'wiggle': <function Wiggle.wiggle_dataprovider at 0x7f533d7f9e18>, 'wiggle-dict': <function Wiggle.wiggle_dict_dataprovider at 0x7f533d7fe048>}¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fc240>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>}¶

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.

ucsc_links(dataset, type, app, base_url)[source]¶

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 0x7f533d7fc4a8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fc438>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fc3c8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fc2e8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fc358>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fc518>}¶

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 0x7f533d7fc5c0>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fc7f0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fc710>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7879e8>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fc6a0>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fc780>}¶

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 0x7f533d7fc860>, 'chrom2Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fca20>, 'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d787048>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fcbe0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'end1Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fc9b0>, 'end2Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fcb00>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d787358>, 'nameCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7879e8>, 'start1Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fc940>, 'start2Col': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fca90>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7872e8>, 'strandCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7873c8>, 'valueCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fcb70>}¶

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 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fcc88>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7fcc18>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>}¶

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 0x7f531177b438>}¶

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 0x7f53115956d8>}¶

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]¶

check if the meta data with the given name is set, i.e. - if the such a metadata actually exists and - if its value differs from no_value

param name the name of the metadata element return True if the value differes from the no_value

False if its equal of if no metadata with the name is specified

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 0x7f531130a9e8>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531130a4a8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53113a6be0>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531130af28>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531150ecc0>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53112fbbe0>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53112e1be0>}¶

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 0x7f53111af5c0>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53111af630>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531130a1d0>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53111af550>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53111af198>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53111af160>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531130a668>}¶

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 0x7f53111af8d0>, 'block_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53111af940>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'file_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53111af710>, 'file_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53111af860>, 'number_of_data_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53111af7f0>, 'number_of_optional_header_records': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53111af780>, 'version_number': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53111af6a0>}¶

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 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311998550>}¶

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 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311884a20>}¶

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 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311893780>}¶

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 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311851898>}¶

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 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114234e0>}¶

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 0x7f53114230b8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d870c88>}¶

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 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311a8c438>}¶

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 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311a8c9b0>}¶

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 0x7f5311320208>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311998550>}¶

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 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311330c50>}¶

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 0x7f53112df860>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311998550>}¶

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 0x7f5311936908>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f53138de358>}¶

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 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311595710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53118d8390>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53119cda90>}¶

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 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53112df240>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53112df668>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53112dfb70>}¶

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 0x7f53161b1710>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'number_of_molecules': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531121be80>}¶

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 0x7f5313459128>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'labels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53134590f0>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53134592b0>}¶

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 0x7f5313459438>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'labels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53134594a8>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313459518>}¶

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 0x7f5313459128>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313459668>, 'labels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53134590f0>, 'otulabels': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53134592b0>}¶

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 0x7f5313459ac8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53134599b0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313459940>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313459748>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313459828>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313459b38>}¶

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 0x7f5313459e80>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313459dd8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313459d68>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313459c88>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313459cf8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313459ef0>}¶

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 0x7f5315105da0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5315105d30>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5315105cc0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53151050b8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5315105c50>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5315105e10>}¶

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 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5315105f28>}¶

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 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114ea080>}¶

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 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114ea198>}¶

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 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afef0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'sequence_count': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114ea278>}¶

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 0x7f53114ea518>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114ea4a8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114ea438>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114ea358>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114ea3c8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114ea588>}¶

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 0x7f53114ea828>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114ea7b8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114ea748>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114ea668>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114ea6d8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114ea898>}¶

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 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afef0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114ea9b0>}¶

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 0x7f53114eac50>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114eabe0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114eab70>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114eaa90>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114eab00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114eacc0>}¶

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 0x7f5313470cc0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5313470f60>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313470ef0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313470e80>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313470da0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313470e10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313437198>}¶

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 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afef0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'filtered': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314f2e160>, 'masked': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53134389b0>}¶

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 0x7f53114ead68>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afef0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'groups': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114eae80>}¶

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 0x7f53114df160>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114df0f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114df080>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114eaf60>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114eafd0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114df1d0>}¶

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 0x7f53114df470>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114df400>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114df390>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114df2b0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114df320>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114df4e0>}¶

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 0x7f53114df780>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114df710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114df6a0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114df5c0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114df630>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114df7f0>}¶

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 0x7f53114dfa90>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114dfa20>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114df9b0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114df8d0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114df940>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114dfb00>}¶

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 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afef0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'flow_order': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114dfc88>, 'flow_values': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53114dfc18>}¶

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 0x7f5311de5fd0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311d4feb8>}¶

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 0x7f5313438dd8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f53128e4f60>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5313437278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5313437f98>}¶

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 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311d38908>}¶

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 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'number_of_models': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311e49ef0>}¶

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 0x7f5311d98400>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5311d98550>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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 0x7f5311d98400>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'neostore_zip': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311ff6dd8>, 'reference_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311d98710>}¶

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 0x7f5311d0feb8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311d0f128>}¶

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 0x7f5311d0feb8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311d16cf8>}¶

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 0x7f5311d0feb8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequence_space': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312027f98>}¶

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 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311aeaa58>}¶

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 0x7f5311b23d68>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

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

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'ptkscmp'¶

display_peek(dataset)[source]¶

set_meta(dataset, **kwd)[source]¶: Set the number of significant components in the Ks distribution. The dataset will always be on the order of less than 10 lines.

set_peek(dataset, is_multi_byte=False)[source]¶

sniff(filename)[source]¶

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test_tab.bed')
>>> PlantTribesKsComponents().sniff(fname)
False
>>> fname = get_test_fname('1.ptkscmp')
>>> PlantTribesKsComponents().sniff(fname)
True

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afef0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'number_comp': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311aac860>}¶

galaxy.datatypes.proteomics module¶

Proteomics Datatypes

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

Bases: galaxy.datatypes.binary.Binary

Class for wiff files.

edam_data = 'data_2536'¶

edam_format = 'format_3710'¶

file_ext = 'wiff'¶

allow_datatype_change = False¶

composite_type = 'auto_primary_file'¶

__init__(**kwd)[source]¶

generate_primary_file(dataset=None)[source]¶

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100c128>}¶

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

Bases: galaxy.datatypes.data.Text

exchange format for proteomics and metabolomics results

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.mztab')
>>> MzTab().sniff(fname)
True
>>> fname = get_test_fname('test.mztab2')
>>> MzTab().sniff(fname)
False

edam_data = 'data_3681'¶

file_ext = 'mztab'¶

__init__(**kwd)[source]¶

set_peek(dataset, is_multi_byte=False)[source]¶: Set the peek and blurb text

sniff_prefix(file_prefix)[source]¶: Determines whether the file is the correct type.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100c3c8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

class galaxy.datatypes.proteomics.MzTab2(**kwd)[source]¶

Bases: galaxy.datatypes.proteomics.MzTab

exchange format for proteomics and metabolomics results

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.mztab2')
>>> MzTab2().sniff(fname)
True
>>> fname = get_test_fname('test.mztab')
>>> MzTab2().sniff(fname)
False

file_ext = 'mztab2'¶

__init__(**kwd)[source]¶

set_peek(dataset, is_multi_byte=False)[source]¶: Set the peek and blurb text

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100c5c0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.tabular.Tabular

Kroenik (HardKloer sibling) files

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.kroenik')
>>> Kroenik().sniff(fname)
True
>>> fname = get_test_fname('test.peplist')
>>> Kroenik().sniff(fname)
False

file_ext = 'kroenik'¶

__init__(**kwd)[source]¶

display_peek(dataset)[source]¶: Returns formated html of peek

sniff_prefix(file_prefix)[source]¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100cd68>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100ccf8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100cc50>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100c9b0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100cb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100cdd8>}¶

sniff(filename)¶

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

Bases: galaxy.datatypes.tabular.Tabular

Peplist file as used in OpenMS https://github.com/OpenMS/OpenMS/blob/0fc8765670a0ad625c883f328de60f738f7325a4/src/openms/source/FORMAT/FileHandler.cpp#L432

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.peplist')
>>> PepList().sniff(fname)
True
>>> fname = get_test_fname('test.psms')
>>> PepList().sniff(fname)
False

file_ext = 'peplist'¶

__init__(**kwd)[source]¶

display_peek(dataset)[source]¶: Returns formated html of peek

sniff_prefix(file_prefix)[source]¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5310ebf1d0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5310ebf128>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5310ebf0b8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100cf60>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100cfd0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5310ebf2b0>}¶

sniff(filename)¶

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

Bases: galaxy.datatypes.tabular.Tabular

Percolator tab-delimited output (PSM level, .psms) as used in OpenMS https://github.com/OpenMS/OpenMS/blob/0fc8765670a0ad625c883f328de60f738f7325a4/src/openms/source/FORMAT/FileHandler.cpp#L453 see also http://www.kojak-ms.org/docs/percresults.html

Note that the data rows can have more columns than the header line since ProteinIds are listed tab-separated.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.psms')
>>> PSMS().sniff(fname)
True
>>> fname = get_test_fname('test.kroenik')
>>> PSMS().sniff(fname)
False

file_ext = 'psms'¶

__init__(**kwd)[source]¶

display_peek(dataset)[source]¶: Returns formated html of peek

sniff_prefix(file_prefix)[source]¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5310ff2ef0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5310ebff60>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5310ebfe80>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5310ebf828>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5310ebfa90>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5310ff2e80>}¶

sniff(filename)¶

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

Bases: galaxy.datatypes.sequence.Sequence

PSI Extended FASTA Format https://github.com/HUPO-PSI/PEFF

file_ext = 'peff'¶

sniff_prefix(file_prefix)[source]¶

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'test.peff' )
>>> PEFF().sniff( fname )
True
>>> fname = get_test_fname( 'sequence.fasta' )
>>> PEFF().sniff( fname )
False

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5310ff2dd8>}¶

sniff(filename)¶

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

Bases: galaxy.datatypes.tabular.Tabular

pepxml converted to tabular report

edam_data = 'data_2536'¶

file_ext = 'pepxml.tsv'¶

__init__(**kwd)[source]¶

display_peek(dataset)[source]¶: Returns formated html of peek

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531101dc18>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531101dba8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53110793c8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5310ff2588>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5311079278>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531101dc88>}¶

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

Bases: galaxy.datatypes.tabular.Tabular

protxml converted to tabular report

edam_data = 'data_2536'¶

file_ext = 'protxml.tsv'¶

comment_lines = 1¶

__init__(**kwd)[source]¶

display_peek(dataset)[source]¶: Returns formated html of peek

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531101df28>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531101deb8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531101de48>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531101dd68>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531101ddd8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531101df98>}¶

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

Bases: galaxy.datatypes.tabular.TabularData

dta The first line contains the singly protonated peptide mass (MH+) and the peptide charge state separated by a space. Subsequent lines contain space separated pairs of fragment ion m/z and intensity values.

file_ext = 'dta'¶

comment_lines = 0¶

set_meta(dataset, **kwd)[source]¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f240>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f1d0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f160>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f080>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f0f0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f2b0>}¶

class galaxy.datatypes.proteomics.Dta2d(**kwd)[source]¶

Bases: galaxy.datatypes.tabular.TabularData

dta2d: files with three tab/space-separated columns. The default format is: retention time (seconds) , m/z , intensity. If the first line starts with ‘#’, a different order is defined by the the order of the keywords ‘MIN’ (retention time in minutes) or ‘SEC’ (retention time in seconds), ‘MZ’, and ‘INT’. Example: ‘#MZ MIN INT’ The peaks of one retention time have to be in subsequent lines.

Note: sniffer detects (tab or space separated) dta2d files with correct header, wo header seems to generic

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.dta2d')
>>> Dta2d().sniff(fname)
True
>>> fname = get_test_fname('test.edta')
>>> Dta2d().sniff(fname)
False

file_ext = 'dta2d'¶

comment_lines = 0¶

set_meta(dataset, **kwd)[source]¶

sniff_prefix(file_prefix)[source]¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f4a8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f438>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f3c8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313810630>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f358>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f518>}¶

sniff(filename)¶

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

Bases: galaxy.datatypes.tabular.TabularData

Input text file containing tab, space or comma separated columns. The separator between columns is checked in the first line in this order.

It supports three variants of this format.

Columns are: RT, MZ, Intensity A header is optional.
Columns are: RT, MZ, Intensity, Charge, <Meta-Data> columns{0,} A header is mandatory.
Columns are: (RT, MZ, Intensity, Charge){1,}, <Meta-Data> columns{0,} Header is mandatory. First quadruplet is the consensus. All following quadruplets describe the sub-features. This variant is discerned from variant #2 by the name of the fifth column, which is required to be RT1 (or rt1). All other column names for sub-features are faithfully ignored.

Note the sniffer only detects files with header.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('test.edta')
>>> Edta().sniff(fname)
True
>>> fname = get_test_fname('test.dta2d')
>>> Edta().sniff(fname)
False

file_ext = 'edta'¶

comment_lines = 0¶

set_meta(dataset, **kwd)[source]¶

sniff_prefix(file_prefix)[source]¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f780>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f710>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f6a0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f5c0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f630>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f7f0>}¶

sniff(filename)¶

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

Bases: galaxy.datatypes.xml.GenericXml

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

edam_data = 'data_2536'¶

edam_format = 'format_2032'¶

sniff_prefix(file_prefix)[source]¶: Determines whether the file is the correct XML type.

set_peek(dataset, is_multi_byte=False)[source]¶: Set the peek and blurb text

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f860>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

store Parameters in XML formal

file_ext = 'paramxml'¶

blurb = 'parameters in xmls'¶

root = 'parameters|PARAMETERS'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f908>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

pepXML data

edam_format = 'format_3655'¶

file_ext = 'pepxml'¶

blurb = 'pepXML data'¶

root = 'msms_pipeline_analysis'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100f9b0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

mzXML data

file_ext = 'mascotxml'¶

blurb = 'mascot Mass Spectrometry data'¶

root = 'mascot_search_results'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100fa58>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

mzML data

edam_format = 'format_3244'¶

file_ext = 'mzml'¶

blurb = 'mzML Mass Spectrometry data'¶

root = '(mzML|indexedmzML)'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100fb00>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

nmrML data

file_ext = 'nmrml'¶

blurb = 'nmrML NMR data'¶

root = 'nmrML'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100fba8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

protXML data

file_ext = 'protxml'¶

blurb = 'prot XML Search Results'¶

root = 'protein_summary'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100fc50>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

mzXML data

edam_format = 'format_3654'¶

file_ext = 'mzxml'¶

blurb = 'mzXML Mass Spectrometry data'¶

root = 'mzXML'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100fcf8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

mzData data

edam_format = 'format_3245'¶

file_ext = 'mzdata'¶

blurb = 'mzData Mass Spectrometry data'¶

root = 'mzData'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100fda0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

edam_format = 'format_3247'¶

file_ext = 'mzid'¶

blurb = 'XML identified peptides and proteins.'¶

root = 'MzIdentML'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100fe48>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

edam_format = 'format_3246'¶

file_ext = 'traml'¶

blurb = 'TraML transition list'¶

root = 'TraML'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100fef0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

file_ext = 'trafoxml'¶

blurb = 'RT alignment tranformation'¶

root = 'TrafoXML'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531100ff98>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

edam_format = 'format_3248'¶

file_ext = 'mzq'¶

blurb = 'XML quantification data'¶

root = 'MzQuantML'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103d080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

file_ext = 'consensusxml'¶

blurb = 'OpenMS multiple LC-MS map alignment file'¶

root = 'consensusXML'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103d128>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

file_ext = 'featurexml'¶

blurb = 'OpenMS feature file'¶

root = 'featureMap'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103d1d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

file_ext = 'idxml'¶

blurb = 'OpenMS identification file'¶

root = 'IdXML'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103d240>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

edam_format = 'format_3711'¶

file_ext = 'tandem'¶

blurb = 'X!Tandem search results file'¶

root = 'bioml'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103d2e8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

file_ext = 'uniprotxml'¶

blurb = 'UniProt Proteome file'¶

root = 'uniprot'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103d390>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

file_ext = 'xquest.xml'¶

blurb = 'XQuest XML file'¶

root = 'xquest_results'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103d438>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

spec.xml

file_ext = 'spec.xml'¶

blurb = 'xquest_spectra'¶

root = 'xquest_spectra'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103d4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.ProteomicsXml

qcml https://github.com/OpenMS/OpenMS/blob/113c49d01677f7f03343ce7cd542d83c99b351ee/share/OpenMS/SCHEMAS/mzQCML_0_0_5.xsd https://github.com/OpenMS/OpenMS/blob/3cfc57ad1788e7ab2bd6dd9862818b2855234c3f/share/OpenMS/SCHEMAS/qcML_0.0.7.xsd

file_ext = 'qcml'¶

blurb = 'QualityAssessments to runs'¶

root = 'qcML|MzQualityML)'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103d588>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.data.Text

Mascot Generic Format data

edam_data = 'data_2536'¶

edam_format = 'format_3651'¶

file_ext = 'mgf'¶

set_peek(dataset, is_multi_byte=False)[source]¶: Set the peek and blurb text

sniff(filename)[source]¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103d630>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.data.Text

Mascot search results

edam_data = 'data_2536'¶

edam_format = 'format_3713'¶

file_ext = 'mascotdat'¶

set_peek(dataset, is_multi_byte=False)[source]¶: Set the peek and blurb text

sniff(filename)[source]¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103d6d8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.binary.Binary

Class describing a Thermo Finnigan binary RAW file

edam_data = 'data_2536'¶

edam_format = 'format_3712'¶

file_ext = 'thermo.raw'¶

sniff(filename)[source]¶

set_peek(dataset, is_multi_byte=False)[source]¶

display_peek(dataset)[source]¶

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103d780>}¶

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

Bases: galaxy.datatypes.data.Text

Output of NIST MS Search Program chemdata.nist.gov/mass-spc/ftp/mass-spc/PepLib.pdf

file_ext = 'msp'¶

static next_line_starts_with(contents, prefix)[source]¶

sniff_prefix(file_prefix)[source]¶: Determines whether the file is a NIST MSP output file.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103d860>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

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

Bases: galaxy.datatypes.data.Text

SPlib without index file

file_ext = 'splib_noindex'¶

set_peek(dataset, is_multi_byte=False)[source]¶: Set the peek and blurb text

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103d908>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

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

Bases: galaxy.datatypes.proteomics.Msp

SpectraST Spectral Library. Closely related to msp format

file_ext = 'splib'¶

composite_type = 'auto_primary_file'¶

__init__(**kwd)[source]¶

generate_primary_file(dataset=None)[source]¶

set_peek(dataset, is_multi_byte=False)[source]¶: Set the peek and blurb text

sniff_prefix(file_prefix)[source]¶: Determines whether the file is a SpectraST generated file.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103d9b0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

class galaxy.datatypes.proteomics.Ms2(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

file_ext = 'ms2'¶

sniff_prefix(file_prefix)[source]¶: Determines whether the file is a valid ms2 file.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103da58>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

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

Bases: galaxy.datatypes.binary.Binary

Annotated Spectra in the HLF format http://www.thegpm.org/HUNTER/format_2006_09_15.html

file_ext = 'hlf'¶

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103db00>}¶

class galaxy.datatypes.proteomics.Sf3(**kwd)[source]¶

Bases: galaxy.datatypes.binary.Binary

Class describing a Scaffold SF3 files

file_ext = 'sf3'¶

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103dba8>}¶

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

Bases: galaxy.datatypes.binary.Binary

Class for imzML files. http://www.imzml.org

edam_format = 'format_3682'¶

file_ext = 'imzml'¶

allow_datatype_change = False¶

composite_type = 'auto_primary_file'¶

__init__(**kwd)[source]¶

generate_primary_file(dataset=None)[source]¶

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103dc50>}¶

class galaxy.datatypes.proteomics.Analyze75(**kwd)[source]¶

Bases: galaxy.datatypes.binary.Binary

Mayo Analyze 7.5 files http://www.imzml.org

file_ext = 'analyze75'¶

allow_datatype_change = False¶

composite_type = 'auto_primary_file'¶

__init__(**kwd)[source]¶

generate_primary_file(dataset=None)[source]¶

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f531103dcf8>}¶

galaxy.datatypes.qualityscore module¶

Qualityscore class

class galaxy.datatypes.qualityscore.QualityScore(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

until we know more about quality score formats

edam_data = 'data_2048'¶

edam_format = 'format_3606'¶

file_ext = 'qual'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53101a5400>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

class galaxy.datatypes.qualityscore.QualityScoreSOLiD(**kwd)[source]¶

Bases: galaxy.datatypes.qualityscore.QualityScore

until we know more about quality score formats

edam_format = 'format_3610'¶

file_ext = 'qualsolid'¶

sniff_prefix(file_prefix)[source]¶

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'sequence.fasta' )
>>> QualityScoreSOLiD().sniff( fname )
False
>>> fname = get_test_fname( 'sequence.qualsolid' )
>>> QualityScoreSOLiD().sniff( fname )
True

set_meta(dataset, **kwd)[source]¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5310b57cc0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

class galaxy.datatypes.qualityscore.QualityScore454(**kwd)[source]¶

Bases: galaxy.datatypes.qualityscore.QualityScore

until we know more about quality score formats

edam_format = 'format_3611'¶

file_ext = 'qual454'¶

sniff_prefix(file_prefix)[source]¶

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'sequence.fasta' )
>>> QualityScore454().sniff( fname )
False
>>> fname = get_test_fname( 'sequence.qual454' )
>>> QualityScore454().sniff( fname )
True

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53101a55f8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

class galaxy.datatypes.qualityscore.QualityScoreSolexa(**kwd)[source]¶

Bases: galaxy.datatypes.qualityscore.QualityScore

until we know more about quality score formats

edam_format = 'format_3608'¶

file_ext = 'qualsolexa'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53101a5668>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

class galaxy.datatypes.qualityscore.QualityScoreIllumina(**kwd)[source]¶

Bases: galaxy.datatypes.qualityscore.QualityScore

until we know more about quality score formats

edam_format = 'format_3609'¶

file_ext = 'qualillumina'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53101a56d8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

galaxy.datatypes.registry module¶

Provides mapping between extensions and datatypes, mime-types, etc.

exception galaxy.datatypes.registry.ConfigurationError[source]¶: Bases: Exception

class galaxy.datatypes.registry.Registry(config=None)[source]¶

Bases: object

__init__(config=None)[source]¶

load_datatypes(root_dir=None, config=None, deactivate=False, override=True, use_converters=True, use_display_applications=True, use_build_sites=True)[source]¶: Parse a datatypes XML file located at root_dir/config (if processing the Galaxy distributed config) or contained within an installed Tool Shed repository. If deactivate is True, an installed Tool Shed repository that includes custom datatypes is being deactivated or uninstalled, so appropriate loaded datatypes will be removed from the registry. The value of override will be False when a Tool Shed repository is being installed. Since installation is occurring after the datatypes registry has been initialized at server startup, its contents cannot be overridden by newly introduced conflicting data types.

get_legacy_sites_by_build(site_type, build)[source]¶

get_display_sites(site_type)[source]¶

load_datatype_sniffers(root, deactivate=False, handling_proprietary_datatypes=False, override=False, compressed_sniffers=None)[source]¶: Process the sniffers element from a parsed a datatypes XML file located at root_dir/config (if processing the Galaxy distributed config) or contained within an installed Tool Shed repository. If deactivate is True, an installed Tool Shed repository that includes custom sniffers is being deactivated or uninstalled, so appropriate loaded sniffers will be removed from the registry. The value of override will be False when a Tool Shed repository is being installed. Since installation is occurring after the datatypes registry has been initialized at server startup, its contents cannot be overridden by newly introduced conflicting sniffers.

is_extension_unsniffable_binary(ext)[source]¶

get_datatype_class_by_name(name)[source]¶: Return the datatype class where the datatype’s type attribute (as defined in the datatype_conf.xml file) contains name.

get_available_tracks()[source]¶

get_mimetype_by_extension(ext, default='application/octet-stream')[source]¶: Returns a mimetype based on an extension

get_datatype_by_extension(ext)[source]¶: Returns a datatype object based on an extension

change_datatype(data, ext)[source]¶

load_datatype_converters(toolbox, installed_repository_dict=None, deactivate=False, use_cached=False)[source]¶: If deactivate is False, add datatype converters from self.converters or self.proprietary_converters to the calling app’s toolbox. If deactivate is True, eliminates relevant converters from the calling app’s toolbox.

load_display_applications(app, installed_repository_dict=None, deactivate=False)[source]¶: If deactivate is False, add display applications from self.display_app_containers or self.proprietary_display_app_containers to appropriate datatypes. If deactivate is True, eliminates relevant display applications from appropriate datatypes.

reload_display_applications(display_application_ids=None)[source]¶: Reloads display applications: by id, or all if no ids provided Returns tuple( [reloaded_ids], [failed_ids] )

load_external_metadata_tool(toolbox)[source]¶: Adds a tool which is used to set external metadata

set_default_values()[source]¶

get_converters_by_datatype(ext)[source]¶: Returns available converters by source type

get_converter_by_target_type(source_ext, target_ext)[source]¶: Returns a converter based on source and target datatypes

find_conversion_destination_for_dataset_by_extensions(dataset_or_ext, accepted_formats, converter_safe=True)[source]¶: returns (direct_match, converted_ext, converted_dataset) - direct match is True iff no the data set already has an accepted format - target_ext becomes None if conversion is not possible (or necesary)

get_composite_extensions()[source]¶

get_upload_metadata_params(context, group, tool)[source]¶: Returns dict of case value:inputs for metadata conditional for upload tool

edam_formats¶

edam_data¶

to_xml_file(path)[source]¶

get_extension(elem)[source]¶: Function which returns the extension lowercased :param elem: :return extension:

galaxy.datatypes.registry.example_datatype_registry_for_sample(sniff_compressed_dynamic_datatypes_default=True)[source]¶

galaxy.datatypes.sequence module¶

Sequence classes

class galaxy.datatypes.sequence.SequenceSplitLocations(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

Class storing information about a sequence file composed of multiple gzip files concatenated as one OR an uncompressed file. In the GZIP case, each sub-file’s location is stored in start and end.

The format of the file is JSON:

{ "sections" : [
        { "start" : "x", "end" : "y", "sequences" : "z" },
        ...
]}

file_ext = 'fqtoc'¶

set_peek(dataset, is_multi_byte=False)[source]¶

sniff_prefix(file_prefix)[source]¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312ae4b70>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

class galaxy.datatypes.sequence.Sequence(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

Class describing a sequence

edam_data = 'data_2044'¶: 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]¶

static get_sequences_per_file(total_sequences, split_params)[source]¶

classmethod do_slow_split(input_datasets, subdir_generator_function, split_params)[source]¶

classmethod do_fast_split(input_datasets, toc_file_datasets, subdir_generator_function, split_params)[source]¶

classmethod write_split_files(input_datasets, toc_file_datasets, subdir_generator_function, sequences_per_file)[source]¶

split(input_datasets, subdir_generator_function, split_params)[source]¶: Split a generic sequence file (not sensible or possible, see subclasses).

static get_split_commands_with_toc(input_name, output_name, toc_file, start_sequence, sequence_count)[source]¶: Uses a Table of Contents dict, parsed from an FQTOC file, to come up with a set of shell commands that will extract the parts necessary >>> three_sections=[dict(start=0, end=74, sequences=10), dict(start=74, end=148, sequences=10), dict(start=148, end=148+76, sequences=10)] >>> Sequence.get_split_commands_with_toc(‘./input.gz’, ‘./output.gz’, dict(sections=three_sections), start_sequence=0, sequence_count=10) [‘dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null >> ./output.gz’] >>> Sequence.get_split_commands_with_toc(‘./input.gz’, ‘./output.gz’, dict(sections=three_sections), start_sequence=1, sequence_count=5) [‘(dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +5 2> /dev/null) | head -20 | gzip -c >> ./output.gz’] >>> Sequence.get_split_commands_with_toc(‘./input.gz’, ‘./output.gz’, dict(sections=three_sections), start_sequence=0, sequence_count=20) [‘dd bs=1 skip=0 count=148 if=./input.gz 2> /dev/null >> ./output.gz’] >>> Sequence.get_split_commands_with_toc(‘./input.gz’, ‘./output.gz’, dict(sections=three_sections), start_sequence=5, sequence_count=10) [‘(dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +21 2> /dev/null) | head -20 | gzip -c >> ./output.gz’, ‘(dd bs=1 skip=74 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +1 2> /dev/null) | head -20 | gzip -c >> ./output.gz’] >>> Sequence.get_split_commands_with_toc(‘./input.gz’, ‘./output.gz’, dict(sections=three_sections), start_sequence=10, sequence_count=10) [‘dd bs=1 skip=74 count=74 if=./input.gz 2> /dev/null >> ./output.gz’] >>> Sequence.get_split_commands_with_toc(‘./input.gz’, ‘./output.gz’, dict(sections=three_sections), start_sequence=5, sequence_count=20) [‘(dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +21 2> /dev/null) | head -20 | gzip -c >> ./output.gz’, ‘dd bs=1 skip=74 count=74 if=./input.gz 2> /dev/null >> ./output.gz’, ‘(dd bs=1 skip=148 count=76 if=./input.gz 2> /dev/null )| zcat | ( tail -n +1 2> /dev/null) | head -20 | gzip -c >> ./output.gz’]

static get_split_commands_sequential(is_compressed, input_name, output_name, start_sequence, sequence_count)[source]¶: Does a brain-dead sequential scan & extract of certain sequences >>> Sequence.get_split_commands_sequential(True, ‘./input.gz’, ‘./output.gz’, start_sequence=0, sequence_count=10) [‘zcat “./input.gz” | ( tail -n +1 2> /dev/null) | head -40 | gzip -c > “./output.gz”’] >>> Sequence.get_split_commands_sequential(False, ‘./input.fastq’, ‘./output.fastq’, start_sequence=10, sequence_count=10) [‘tail -n +41 “./input.fastq” 2> /dev/null | head -40 > “./output.fastq”’]

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312aff470>}¶

class galaxy.datatypes.sequence.Alignment(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

Class describing an alignment

edam_data = 'data_0863'¶: Add metadata elements

split(input_datasets, subdir_generator_function, split_params)[source]¶: Split a generic alignment file (not sensible or possible, see subclasses).

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'species': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312aff4e0>}¶

class galaxy.datatypes.sequence.Fasta(**kwd)[source]¶

Bases: galaxy.datatypes.sequence.Sequence

Class representing a FASTA sequence

edam_format = 'format_1929'¶

file_ext = 'fasta'¶

sniff_prefix(file_prefix)[source]¶

Determines whether the file is in fasta format

A sequence in FASTA format consists of a single-line description, followed by lines of sequence data. The first character of the description line is a greater-than (“>”) symbol in the first column. All lines should be shorter than 80 characters

For complete details see http://www.ncbi.nlm.nih.gov/blast/fasta.shtml

Rules for sniffing as True:

We don’t care about line length (other than empty lines).

The first non-empty line must start with ‘>’ and the Very Next line.strip() must have sequence data and not be a header.

‘sequence data’ here is loosely defined as non-empty lines which do not start with ‘>’

This will cause Color Space FASTA (csfasta) to be detected as True (they are, after all, still FASTA files - they have a header line followed by sequence data)

Previously this method did some checking to determine if the sequence data had integers (presumably to differentiate between fasta and csfasta)

This should be done through sniff order, where csfasta (currently has a null sniff function) is detected for first (stricter definition) followed sometime after by fasta

We will only check that the first purported sequence is correctly formatted.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'sequence.maf' )
>>> Fasta().sniff( fname )
False
>>> fname = get_test_fname( 'sequence.fasta' )
>>> Fasta().sniff( fname )
True

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

Split a FASTA file sequence by sequence.

Note that even if split_mode=”number_of_parts”, the actual number of sub-files produced may not match that requested by split_size.

If split_mode=”to_size” then split_size is treated as the number of FASTA records to put in each sub-file (not size in bytes).

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53137bf438>}¶

sniff(filename)¶

class galaxy.datatypes.sequence.csFasta(**kwd)[source]¶

Bases: galaxy.datatypes.sequence.Sequence

Class representing the SOLID Color-Space sequence ( csfasta )

edam_format = 'format_3589'¶

file_ext = 'csfasta'¶

sniff_prefix(file_prefix)[source]¶

Color-space sequence:: >2_15_85_F3 T213021013012303002332212012112221222112212222

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'sequence.fasta' )
>>> csFasta().sniff( fname )
False
>>> fname = get_test_fname( 'sequence.csfasta' )
>>> csFasta().sniff( fname )
True

set_meta(dataset, **kwd)[source]¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312affcc0>}¶

sniff(filename)¶

class galaxy.datatypes.sequence.Fastg(**kwd)[source]¶

Bases: galaxy.datatypes.sequence.Sequence

Class representing a FASTG sequence

edam_format = 'format_3823'¶

file_ext = 'fastg'¶

sniff_prefix(file_prefix)[source]¶: FASTG must begin with lines: #FASTG:begin; #FASTG:version=*.*; #FASTG:properties;

set_meta(dataset, **kwd)[source]¶

set_peek(dataset, is_multi_byte=False)[source]¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'properties': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312afff60>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312aff470>, 'version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312aff208>}¶

sniff(filename)¶

class galaxy.datatypes.sequence.BaseFastq(**kwd)[source]¶

Bases: galaxy.datatypes.sequence.Sequence

Base class for FastQ sequences

edam_format = 'format_1930'¶

file_ext = 'fastq'¶

bases_regexp = re.compile('^[NGTAC 0123\\.]*$', re.IGNORECASE)¶

set_meta(dataset, **kwd)[source]¶: Set the number of sequences and the number of data lines in dataset. FIXME: This does not properly handle line wrapping

sniff_prefix(file_prefix)[source]¶

Determines whether the file is in generic fastq format For details, see http://maq.sourceforge.net/fastq.shtml

Note: There are three kinds of FASTQ files, known as “Sanger” (sometimes called “Standard”), Solexa, and Illumina: These differ in the representation of the quality scores

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('1.fastqsanger')
>>> FastqSanger().sniff(fname)
True
>>> fname = get_test_fname('4.fastqsanger')
>>> FastqSanger().sniff(fname)
True
>>> fname = get_test_fname('3.fastq')
>>> FastqSanger().sniff(fname)
False
>>> Fastq().sniff(fname)
True
>>> fname = get_test_fname('2.fastq')
>>> Fastq().sniff(fname)
True
>>> FastqSanger().sniff(fname)
False
>>> fname = get_test_fname('1.fastq')
>>> FastqSanger().sniff(fname)
False
>>> fname = get_test_fname('1.fastqcssanger')
>>> FastqSanger().sniff(fname)
False
>>> Fastq().sniff(fname)
True
>>> FastqCSSanger().sniff(fname)
True

display_data(trans, dataset, preview=False, filename=None, to_ext=None, **kwd)[source]¶

classmethod split(input_datasets, subdir_generator_function, split_params)[source]¶: FASTQ files are split on cluster boundaries, in increments of 4 lines

static process_split_file(data)[source]¶: This is called in the context of an external process launched by a Task (possibly not on the Galaxy machine) to create the input files for the Task. The parameters: data - a dict containing the contents of the split file

static quality_check(lines)[source]¶

classmethod check_first_block(file_prefix)[source]¶

classmethod check_block(block)[source]¶

validate(dataset, **kwd)[source]¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312b112b0>}¶

sniff(filename)¶

class galaxy.datatypes.sequence.Fastq(**kwd)[source]¶

Bases: galaxy.datatypes.sequence.BaseFastq

Class representing a generic FASTQ sequence

edam_format = 'format_1930'¶

file_ext = 'fastq'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312b11240>}¶

class galaxy.datatypes.sequence.FastqSanger(**kwd)[source]¶

Bases: galaxy.datatypes.sequence.Fastq

Class representing a FASTQ sequence ( the Sanger variant )

edam_format = 'format_1932'¶

file_ext = 'fastqsanger'¶

bases_regexp = re.compile('^[NGTAC]*$', re.IGNORECASE)¶

static quality_check(lines)[source]¶: Presuming lines are lines from a fastq file, return True if the qualities are compatible with sanger encoding

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312b11b38>}¶

class galaxy.datatypes.sequence.FastqSolexa(**kwd)[source]¶

Bases: galaxy.datatypes.sequence.Fastq

Class representing a FASTQ sequence ( the Solexa variant )

edam_format = 'format_1933'¶

file_ext = 'fastqsolexa'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312b118d0>}¶

class galaxy.datatypes.sequence.FastqIllumina(**kwd)[source]¶

Bases: galaxy.datatypes.sequence.Fastq

Class representing a FASTQ sequence ( the Illumina 1.3+ variant )

edam_format = 'format_1931'¶

file_ext = 'fastqillumina'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312b11128>}¶

class galaxy.datatypes.sequence.FastqCSSanger(**kwd)[source]¶

Bases: galaxy.datatypes.sequence.Fastq

Class representing a Color Space FASTQ sequence ( e.g a SOLiD variant )

file_ext = 'fastqcssanger'¶

bases_regexp = re.compile('^[NGTAC][0123\\.]*$', re.IGNORECASE)¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312b11048>}¶

class galaxy.datatypes.sequence.Maf(**kwd)[source]¶

Bases: galaxy.datatypes.sequence.Alignment

Class describing a Maf alignment

edam_format = 'format_3008'¶

file_ext = 'maf'¶

init_meta(dataset, copy_from=None)[source]¶

set_meta(dataset, overwrite=True, **kwd)[source]¶: Parses and sets species, chromosomes, index from MAF file.

set_peek(dataset, is_multi_byte=False)[source]¶

display_peek(dataset)[source]¶: Returns formated html of peek

make_html_table(dataset, skipchars=[])[source]¶: Create HTML table, used for displaying peek

sniff_prefix(file_prefix)[source]¶

Determines wether the file is in maf format

The .maf format is line-oriented. Each multiple alignment ends with a blank line. Each sequence in an alignment is on a single line, which can get quite long, but there is no length limit. Words in a line are delimited by any white space. Lines starting with # are considered to be comments. Lines starting with ## can be ignored by most programs, but contain meta-data of one form or another.

The first line of a .maf file begins with ##maf. This word is followed by white-space-separated variable=value pairs. There should be no white space surrounding the “=”.

For complete details see http://genome.ucsc.edu/FAQ/FAQformat#format5

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'sequence.maf' )
>>> Maf().sniff( fname )
True
>>> fname = get_test_fname( 'sequence.fasta' )
>>> Maf().sniff( fname )
False

metadata_spec = {'blocks': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312b11828>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'maf_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312b117f0>, 'species': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312aff4e0>, 'species_chromosomes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312b113c8>}¶

sniff(filename)¶

class galaxy.datatypes.sequence.MafCustomTrack(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

file_ext = 'mafcustomtrack'¶

set_meta(dataset, overwrite=True, **kwd)[source]¶: Parses and sets viewport metadata from MAF file.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'vp_chromosome': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312b11198>, 'vp_end': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312a5c2b0>, 'vp_start': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312a5c240>}¶

class galaxy.datatypes.sequence.Axt(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

Class describing an axt alignment

edam_data = 'data_0863'¶

edam_format = 'format_3013'¶

file_ext = 'axt'¶

sniff_prefix(file_prefix)[source]¶

Determines whether the file is in axt format

axt alignment files are produced from Blastz, an alignment tool available from Webb Miller’s lab at Penn State University.

Each alignment block in an axt file contains three lines: a summary line and 2 sequence lines. Blocks are separated from one another by blank lines.

The summary line contains chromosomal position and size information about the alignment. It consists of 9 required fields.

The sequence lines contain the sequence of the primary assembly (line 2) and aligning assembly (line 3) with inserts. Repeats are indicated by lower-case letters.

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

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'alignment.axt' )
>>> Axt().sniff( fname )
True
>>> fname = get_test_fname( 'alignment.lav' )
>>> Axt().sniff( fname )
False

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312a5c358>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

class galaxy.datatypes.sequence.Lav(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

Class describing a LAV alignment

edam_data = 'data_0863'¶

edam_format = 'format_3014'¶

file_ext = 'lav'¶

sniff_prefix(file_prefix)[source]¶

Determines whether the file is in lav format

LAV is an alignment format developed by Webb Miller’s group. It is the primary output format for BLASTZ. The first line of a .lav file begins with #:lav.

For complete details see http://www.bioperl.org/wiki/LAV_alignment_format

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'alignment.lav' )
>>> Lav().sniff( fname )
True
>>> fname = get_test_fname( 'alignment.axt' )
>>> Lav().sniff( fname )
False

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312a5c3c8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

class galaxy.datatypes.sequence.RNADotPlotMatrix(**kwd)[source]¶

Bases: galaxy.datatypes.data.Data

edam_format = 'format_3466'¶

file_ext = 'rna_eps'¶

set_peek(dataset, is_multi_byte=False)[source]¶

sniff(filename)[source]¶: Determine if the file is in RNA dot plot format.

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312a5c438>}¶

class galaxy.datatypes.sequence.DotBracket(**kwd)[source]¶

Bases: galaxy.datatypes.sequence.Sequence

edam_data = 'data_0880'¶

edam_format = 'format_1457'¶

file_ext = 'dbn'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312a5c518>}¶

sniff(filename)¶

sequence_regexp = re.compile('^[ACGTURYKMSWBDHVN]+$', re.IGNORECASE)¶

structure_regexp = re.compile('^[\$\$\\.\\[\\]{}]+$')¶

set_meta(dataset, **kwd)[source]¶: Set the number of sequences and the number of data lines in dataset.

sniff_prefix(file_prefix)[source]¶

Galaxy Dbn (Dot-Bracket notation) rules:

The first non-empty line is a header line: no comment lines are allowed.
- A header line starts with a ‘>’ symbol and continues with 0 or multiple symbols until the line ends.
The second non-empty line is a sequence line.
- A sequence line may only include chars that match the FASTA format (https://en.wikipedia.org/wiki/FASTA_format#Sequence_representation) symbols for nucleotides: ACGTURYKMSWBDHVN, and may thus not include whitespaces.
- A sequence line has no prefix and no suffix.
- A sequence line is case insensitive.
The third non-empty line is a structure (Dot-Bracket) line and only describes the 2D structure of the sequence above it.
- A structure line must consist of the following chars: ‘.{}[]()’.
- A structure line must be of the same length as the sequence line, and each char represents the structure of the nucleotide above it.
- A structure line has no prefix and no suffix.
- A nucleotide pairs with only 1 or 0 other nucleotides.
  - In a structure line, the number of ‘(‘ symbols equals the number of ‘)’ symbols, the number of ‘[‘ symbols equals the number of ‘]’ symbols and the number of ‘{‘ symbols equals the number of ‘}’ symbols.
The format accepts multiple entries per file, given that each entry is provided as three lines: the header, sequence and structure line.
- Sniffing is only applied on the first entry.
Empty lines are allowed.

class galaxy.datatypes.sequence.Genbank(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

Class representing a Genbank sequence

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312a5c4e0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

edam_format = 'format_1936'¶

edam_data = 'data_0849'¶

file_ext = 'genbank'¶

sniff_prefix(file_prefix)[source]¶

Determine whether the file is in genbank format. Works for compressed files.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( '1.genbank' )
>>> Genbank().sniff( fname )
True

class galaxy.datatypes.sequence.MemePsp(**kwd)[source]¶

Bases: galaxy.datatypes.sequence.Sequence

Class representing MEME Position Specific Priors

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'sequences': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5312a5c630>}¶

sniff(filename)¶

file_ext = 'memepsp'¶

sniff_prefix(file_prefix)[source]¶

The format of an entry in a PSP file is:

>ID WIDTH PRIORS

For complete details see http://meme-suite.org/doc/psp-format.html

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('1.memepsp')
>>> MemePsp().sniff(fname)
True
>>> fname = get_test_fname('sequence.fasta')
>>> MemePsp().sniff(fname)
False

galaxy.datatypes.sniff module¶

File format detector

galaxy.datatypes.sniff.get_test_fname(fname)[source]¶: Returns test data filename

galaxy.datatypes.sniff.sniff_with_cls(cls, fname)[source]¶

galaxy.datatypes.sniff.stream_url_to_file(path, file_sources=None)[source]¶

galaxy.datatypes.sniff.stream_to_open_named_file(stream, fd, filename, source_encoding=None, source_error='strict', target_encoding=None, target_error='strict')[source]¶: Writes a stream to the provided file descriptor, returns the file name. Closes file descriptor

galaxy.datatypes.sniff.stream_to_file(stream, suffix='', prefix='', dir=None, text=False, **kwd)[source]¶: Writes a stream to a temporary file, returns the temporary file’s name

galaxy.datatypes.sniff.handle_composite_file(datatype, src_path, extra_files, name, is_binary, tmp_dir, tmp_prefix, upload_opts)[source]¶

galaxy.datatypes.sniff.convert_newlines(fname, in_place=True, tmp_dir=None, tmp_prefix='gxupload', block_size=131072, regexp=None)[source]¶: Converts in place a file from universal line endings to Posix line endings.

galaxy.datatypes.sniff.convert_newlines_sep2tabs(fname, in_place=True, patt=b'[^\\S\\n]+', tmp_dir=None, tmp_prefix='gxupload')[source]¶

Converts newlines in a file to posix newlines and replaces spaces with tabs.

>>> fname = get_test_fname('temp.txt')
>>> with open(fname, 'wt') as fh:
...     _ = fh.write(u"1 2\r3 4")
>>> convert_newlines_sep2tabs(fname, tmp_prefix="gxtest", tmp_dir=tempfile.gettempdir())
(2, None)
>>> open(fname).read()
'1\t2\n3\t4\n'

galaxy.datatypes.sniff.iter_headers(fname_or_file_prefix, sep, count=60, comment_designator=None)[source]¶

galaxy.datatypes.sniff.validate_tabular(fname_or_file_prefix, validate_row, sep, comment_designator=None)[source]¶

galaxy.datatypes.sniff.get_headers(fname_or_file_prefix, sep, count=60, comment_designator=None)[source]¶

Returns a list with the first ‘count’ lines split by ‘sep’, ignoring lines starting with ‘comment_designator’

>>> fname = get_test_fname('complete.bed')
>>> get_headers(fname,'\t') == [['chr7', '127475281', '127491632', 'NM_000230', '0', '+', '127486022', '127488767', '0', '3', '29,172,3225,', '0,10713,13126,'], ['chr7', '127486011', '127488900', 'D49487', '0', '+', '127486022', '127488767', '0', '2', '155,490,', '0,2399']]
True
>>> fname = get_test_fname('test.gff')
>>> get_headers(fname, '\t', count=5, comment_designator='#') == [[''], ['chr7', 'bed2gff', 'AR', '26731313', '26731437', '.', '+', '.', 'score'], ['chr7', 'bed2gff', 'AR', '26731491', '26731536', '.', '+', '.', 'score'], ['chr7', 'bed2gff', 'AR', '26731541', '26731649', '.', '+', '.', 'score'], ['chr7', 'bed2gff', 'AR', '26731659', '26731841', '.', '+', '.', 'score']]
True

galaxy.datatypes.sniff.is_column_based(fname_or_file_prefix, sep='\t', skip=0)[source]¶

Checks whether the file is column based with respect to a separator (defaults to tab separator).

>>> fname = get_test_fname('test.gff')
>>> is_column_based(fname)
True
>>> fname = get_test_fname('test_tab.bed')
>>> is_column_based(fname)
True
>>> is_column_based(fname, sep=' ')
False
>>> fname = get_test_fname('test_space.txt')
>>> is_column_based(fname)
False
>>> is_column_based(fname, sep=' ')
True
>>> fname = get_test_fname('test_ensembl.tabular')
>>> is_column_based(fname)
True
>>> fname = get_test_fname('test_tab1.tabular')
>>> is_column_based(fname, sep=' ', skip=0)
False
>>> fname = get_test_fname('test_tab1.tabular')
>>> is_column_based(fname)
True

galaxy.datatypes.sniff.guess_ext(fname, sniff_order, is_binary=False)[source]¶

Returns an extension that can be used in the datatype factory to generate a data for the ‘fname’ file

>>> from galaxy.datatypes.registry import example_datatype_registry_for_sample
>>> datatypes_registry = example_datatype_registry_for_sample()
>>> sniff_order = datatypes_registry.sniff_order
>>> fname = get_test_fname('empty.txt')
>>> guess_ext(fname, sniff_order)
'txt'
>>> fname = get_test_fname('megablast_xml_parser_test1.blastxml')
>>> guess_ext(fname, sniff_order)
'blastxml'
>>> fname = get_test_fname('interval.interval')
>>> guess_ext(fname, sniff_order)
'interval'
>>> fname = get_test_fname('interv1.bed')
>>> guess_ext(fname, sniff_order)
'bed'
>>> fname = get_test_fname('test_tab.bed')
>>> guess_ext(fname, sniff_order)
'bed'
>>> fname = get_test_fname('sequence.maf')
>>> guess_ext(fname, sniff_order)
'maf'
>>> fname = get_test_fname('sequence.fasta')
>>> guess_ext(fname, sniff_order)
'fasta'
>>> fname = get_test_fname('1.genbank')
>>> guess_ext(fname, sniff_order)
'genbank'
>>> fname = get_test_fname('1.genbank.gz')
>>> guess_ext(fname, sniff_order)
'genbank.gz'
>>> fname = get_test_fname('file.html')
>>> guess_ext(fname, sniff_order)
'html'
>>> fname = get_test_fname('test.gtf')
>>> guess_ext(fname, sniff_order)
'gtf'
>>> fname = get_test_fname('test.gff')
>>> guess_ext(fname, sniff_order)
'gff'
>>> fname = get_test_fname('gff.gff3')
>>> guess_ext(fname, sniff_order)
'gff3'
>>> fname = get_test_fname('2.txt')
>>> guess_ext(fname, sniff_order)
'txt'
>>> fname = get_test_fname('2.tabular')
>>> guess_ext(fname, sniff_order)
'tabular'
>>> fname = get_test_fname('3.txt')
>>> guess_ext(fname, sniff_order)
'txt'
>>> fname = get_test_fname('test_tab1.tabular')
>>> guess_ext(fname, sniff_order)
'tabular'
>>> fname = get_test_fname('alignment.lav')
>>> guess_ext(fname, sniff_order)
'lav'
>>> fname = get_test_fname('1.sff')
>>> guess_ext(fname, sniff_order)
'sff'
>>> fname = get_test_fname('1.bam')
>>> guess_ext(fname, sniff_order)
'bam'
>>> fname = get_test_fname('3unsorted.bam')
>>> guess_ext(fname, sniff_order)
'unsorted.bam'
>>> fname = get_test_fname('test.idpdb')
>>> guess_ext(fname, sniff_order)
'idpdb'
>>> fname = get_test_fname('test.mz5')
>>> guess_ext(fname, sniff_order)
'h5'
>>> fname = get_test_fname('issue1818.tabular')
>>> guess_ext(fname, sniff_order)
'tabular'
>>> fname = get_test_fname('drugbank_drugs.cml')
>>> guess_ext(fname, sniff_order)
'cml'
>>> fname = get_test_fname('q.fps')
>>> guess_ext(fname, sniff_order)
'fps'
>>> fname = get_test_fname('drugbank_drugs.inchi')
>>> guess_ext(fname, sniff_order)
'inchi'
>>> fname = get_test_fname('drugbank_drugs.mol2')
>>> guess_ext(fname, sniff_order)
'mol2'
>>> fname = get_test_fname('drugbank_drugs.sdf')
>>> guess_ext(fname, sniff_order)
'sdf'
>>> fname = get_test_fname('5e5z.pdb')
>>> guess_ext(fname, sniff_order)
'pdb'
>>> fname = get_test_fname('mothur_datatypetest_true.mothur.otu')
>>> guess_ext(fname, sniff_order)
'mothur.otu'
>>> fname = get_test_fname('mothur_datatypetest_true.mothur.lower.dist')
>>> guess_ext(fname, sniff_order)
'mothur.lower.dist'
>>> fname = get_test_fname('mothur_datatypetest_true.mothur.square.dist')
>>> guess_ext(fname, sniff_order)
'mothur.square.dist'
>>> fname = get_test_fname('mothur_datatypetest_true.mothur.pair.dist')
>>> guess_ext(fname, sniff_order)
'mothur.pair.dist'
>>> fname = get_test_fname('mothur_datatypetest_true.mothur.freq')
>>> guess_ext(fname, sniff_order)
'mothur.freq'
>>> fname = get_test_fname('mothur_datatypetest_true.mothur.quan')
>>> guess_ext(fname, sniff_order)
'mothur.quan'
>>> fname = get_test_fname('mothur_datatypetest_true.mothur.ref.taxonomy')
>>> guess_ext(fname, sniff_order)
'mothur.ref.taxonomy'
>>> fname = get_test_fname('mothur_datatypetest_true.mothur.axes')
>>> guess_ext(fname, sniff_order)
'mothur.axes'
>>> guess_ext(get_test_fname('infernal_model.cm'), sniff_order)
'cm'
>>> fname = get_test_fname('1.gg')
>>> guess_ext(fname, sniff_order)
'gg'
>>> fname = get_test_fname('diamond_db.dmnd')
>>> guess_ext(fname, sniff_order)
'dmnd'
>>> fname = get_test_fname('1.excel.xls')
>>> guess_ext(fname, sniff_order, is_binary=True)
'excel.xls'
>>> fname = get_test_fname('biom2_sparse_otu_table_hdf5.biom2')
>>> guess_ext(fname, sniff_order)
'biom2'
>>> fname = get_test_fname('454Score.pdf')
>>> guess_ext(fname, sniff_order)
'pdf'
>>> fname = get_test_fname('1.obo')
>>> guess_ext(fname, sniff_order)
'obo'
>>> fname = get_test_fname('1.arff')
>>> guess_ext(fname, sniff_order)
'arff'
>>> fname = get_test_fname('1.afg')
>>> guess_ext(fname, sniff_order)
'afg'
>>> fname = get_test_fname('1.owl')
>>> guess_ext(fname, sniff_order)
'owl'
>>> fname = get_test_fname('Acanium.snaphmm')
>>> guess_ext(fname, sniff_order)
'snaphmm'
>>> fname = get_test_fname('wiggle.wig')
>>> guess_ext(fname, sniff_order)
'wig'
>>> fname = get_test_fname('example.iqtree')
>>> guess_ext(fname, sniff_order)
'iqtree'
>>> fname = get_test_fname('1.stockholm')
>>> guess_ext(fname, sniff_order)
'stockholm'
>>> fname = get_test_fname('1.xmfa')
>>> guess_ext(fname, sniff_order)
'xmfa'
>>> fname = get_test_fname('test.blib')
>>> guess_ext(fname, sniff_order)
'blib'
>>> fname = get_test_fname('test.phylip')
>>> guess_ext(fname, sniff_order)
'phylip'
>>> fname = get_test_fname('1.smat')
>>> guess_ext(fname, sniff_order)
'smat'
>>> fname = get_test_fname('1.ttl')
>>> guess_ext(fname, sniff_order)
'ttl'
>>> fname = get_test_fname('1.hdt')
>>> guess_ext(fname, sniff_order, is_binary=True)
'hdt'
>>> fname = get_test_fname('1.phyloxml')
>>> guess_ext(fname, sniff_order)
'phyloxml'
>>> fname = get_test_fname('1.tiff')
>>> guess_ext(fname, sniff_order)
'tiff'
>>> fname = get_test_fname('1.fastqsanger.gz')
>>> guess_ext(fname, sniff_order)  # See test_datatype_registry for more compressed type tests.
'fastqsanger.gz'
>>> fname = get_test_fname('1.mtx')
>>> guess_ext(fname, sniff_order)
'mtx'
>>> fname = get_test_fname('1imzml')
>>> guess_ext(fname, sniff_order)  # This test case is ensuring doesn't throw exception, actual value could change if non-utf encoding handling improves.
'data'
>>> fname = get_test_fname('too_many_comments_gff3.tabular')
>>> guess_ext(fname, sniff_order)  # It's a VCF but is sniffed as tabular because of the limit on the number of header lines we read
'tabular'

galaxy.datatypes.sniff.run_sniffers_raw(filename_or_file_prefix, sniff_order, is_binary=False)[source]¶: Run through sniffers specified by sniff_order, return None of None match.

galaxy.datatypes.sniff.zip_single_fileobj(path)[source]¶

class galaxy.datatypes.sniff.FilePrefix(filename)[source]¶

Bases: object

__init__(filename)[source]¶

file_size¶

string_io()[source]¶

startswith(prefix)[source]¶

line_iterator()[source]¶

search(pattern)[source]¶

search_str(query_str)[source]¶

galaxy.datatypes.sniff.build_sniff_from_prefix(klass)[source]¶

galaxy.datatypes.sniff.disable_parent_class_sniffing(klass)[source]¶

galaxy.datatypes.sniff.handle_compressed_file(filename, datatypes_registry, ext='auto', tmp_prefix='sniff_uncompress_', tmp_dir=None, in_place=False, check_content=True, auto_decompress=True)[source]¶

Check uploaded files for compression, check compressed file contents, and uncompress if necessary.

Supports GZip, BZip2, and the first file in a Zip file.

For performance reasons, the temporary file used for uncompression is located in the same directory as the input/output file. This behavior can be changed with the tmp_dir param.

ext as returned will only be changed from the ext input param if the param was an autodetect type (auto) and the file was sniffed as a keep-compressed datatype.

is_valid as returned will only be set if the file is compressed and contains invalid contents (or the first file in the case of a zip file), this is so lengthy decompression can be bypassed if there is invalid content in the first 32KB. Otherwise the caller should be checking content.

galaxy.datatypes.sniff.handle_uploaded_dataset_file(*args, **kwds)[source]¶: Legacy wrapper about handle_uploaded_dataset_file_internal for tools using it.

galaxy.datatypes.sniff.handle_uploaded_dataset_file_internal(filename, datatypes_registry, ext='auto', tmp_prefix='sniff_upload_', tmp_dir=None, in_place=False, check_content=True, is_binary=None, auto_decompress=True, uploaded_file_ext=None, convert_to_posix_lines=None, convert_spaces_to_tabs=None)[source]¶

exception galaxy.datatypes.sniff.InappropriateDatasetContentError[source]¶: Bases: Exception

galaxy.datatypes.spaln module¶

spaln Composite Dataset

class galaxy.datatypes.spaln.SpalnNuclDb(**kwd)[source]¶

Bases: galaxy.datatypes.spaln._SpalnDb

file_ext = 'spalndbnp'¶

__init__(**kwd)[source]¶

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'spalndb_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5310d092b0>}¶

class galaxy.datatypes.spaln.SpalnProtDb(**kwd)[source]¶

Bases: galaxy.datatypes.spaln._SpalnDb

file_ext = 'spalndba'¶

__init__(**kwd)[source]¶

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'spalndb_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5310d09320>}¶

galaxy.datatypes.tabular module¶

Tabular datatype

class galaxy.datatypes.tabular.TabularData(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

Generic tabular data

edam_format = 'format_3475'¶

CHUNKABLE = True¶

data_line_offset = 0¶: Add metadata elements

set_meta(dataset, **kwd)[source]¶

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

displayable(dataset)[source]¶

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]¶

display_as_markdown(dataset_instance, markdown_format_helpers)[source]¶

make_html_table(dataset, **kwargs)[source]¶: Create HTML table, used for displaying peek

make_html_peek_header(dataset, skipchars=None, column_names=None, column_number_format='%s', column_parameter_alias=None, **kwargs)[source]¶

make_html_peek_rows(dataset, skipchars=None, **kwargs)[source]¶

display_peek(dataset)[source]¶: Returns formatted html of peek

column_dataprovider(dataset, **settings)[source]¶: Uses column settings that are passed in

dataset_column_dataprovider(dataset, **settings)[source]¶: Attempts to get column settings from dataset.metadata

dict_dataprovider(dataset, **settings)[source]¶: Uses column settings that are passed in

dataset_dict_dataprovider(dataset, **settings)[source]¶: Attempts to get column settings from dataset.metadata

dataproviders = {'base': <function Data.base_dataprovider at 0x7f5340b66730>, 'chunk': <function Data.chunk_dataprovider at 0x7f5340b668c8>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f5340b66a60>, 'column': <function TabularData.column_dataprovider at 0x7f533d852950>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f533d852ae8>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f533d852e18>, 'dict': <function TabularData.dict_dataprovider at 0x7f533d852c80>, 'line': <function Text.line_dataprovider at 0x7f5340b6e0d0>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f5340b6e268>}¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6af240>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6af128>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6abef0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69d30>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6abda0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6af278>}¶

class galaxy.datatypes.tabular.Tabular(**kwd)[source]¶

Bases: galaxy.datatypes.tabular.TabularData

Tab delimited data

get_column_names(first_line=None)[source]¶

set_meta(dataset, overwrite=True, skip=None, max_data_lines=100000, max_guess_type_data_lines=None, **kwd)[source]¶

Tries to determine the number of columns as well as those columns that contain numerical values in the dataset. A skip parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many invalid comment lines should be skipped. Using None for skip will cause skip to be zero, but the first line will be processed as a header. A max_data_lines parameter is used because various tabular data types reuse this function, and their data type classes are responsible to determine how many data lines should be processed to ensure that the non-optional metadata parameters are properly set; if used, optional metadata parameters will be set to None, unless the entire file has already been read. Using None for max_data_lines will process all data lines.

Items of interest:

We treat ‘overwrite’ as always True (we always want to set tabular metadata when called).
If a tabular file has no data, it will have one column of type ‘str’.
We used to check only the first 100 lines when setting metadata and this class’s set_peek() method read the entire file to determine the number of lines in the file. Since metadata can now be processed on cluster nodes, we’ve merged the line count portion of the set_peek() processing here, and we now check the entire contents of the file.

as_gbrowse_display_file(dataset, **kwd)[source]¶

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

dataproviders = {'base': <function Data.base_dataprovider at 0x7f5340b66730>, 'chunk': <function Data.chunk_dataprovider at 0x7f5340b668c8>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f5340b66a60>, 'column': <function TabularData.column_dataprovider at 0x7f533d852950>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f533d852ae8>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f533d852e18>, 'dict': <function TabularData.dict_dataprovider at 0x7f533d852c80>, 'line': <function Text.line_dataprovider at 0x7f5340b6e0d0>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f5340b6e268>}¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afef0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>}¶

class galaxy.datatypes.tabular.SraManifest(**kwd)[source]¶

Bases: galaxy.datatypes.tabular.Tabular

A manifest received from the sra_source tool.

ext = 'sra_manifest.tabular'¶

data_line_offset = 1¶

set_meta(dataset, **kwds)[source]¶

get_column_names(first_line)[source]¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6da550>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6da2e8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6cffd0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6cf390>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6cfcf8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6da828>}¶

class galaxy.datatypes.tabular.Taxonomy(**kwd)[source]¶

Bases: galaxy.datatypes.tabular.Tabular

__init__(**kwd)[source]¶: Initialize taxonomy datatype

display_peek(dataset)[source]¶: Returns formated html of peek

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d84c860>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d84c7f0>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6dae10>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6dab00>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6dac50>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d84c8d0>}¶

class galaxy.datatypes.tabular.Sam(**kwd)[source]¶

Bases: galaxy.datatypes.tabular.Tabular

edam_format = 'format_2573'¶

edam_data = 'data_0863'¶

file_ext = 'sam'¶

track_type = 'ReadTrack'¶

data_sources = {'data': 'bam', 'index': 'bigwig'}¶

__init__(**kwd)[source]¶: Initialize sam datatype

display_peek(dataset)[source]¶: Returns formated html of peek

sniff_prefix(file_prefix)[source]¶

Determines whether the file is in SAM format

A file in SAM format consists of lines of tab-separated data. The following header line may be the first line:

@QNAME  FLAG    RNAME   POS     MAPQ    CIGAR   MRNM    MPOS    ISIZE   SEQ     QUAL
or
@QNAME  FLAG    RNAME   POS     MAPQ    CIGAR   MRNM    MPOS    ISIZE   SEQ     QUAL    OPT

Data in the OPT column is optional and can consist of tab-separated data

For complete details see http://samtools.sourceforge.net/SAM1.pdf

Rules for sniffing as True:

There must be 11 or more columns of data on each line
Columns 2 (FLAG), 4(POS), 5 (MAPQ), 8 (MPOS), and 9 (ISIZE) must be numbers (9 can be negative)
We will only check that up to the first 5 alignments are correctly formatted.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'sequence.maf' )
>>> Sam().sniff( fname )
False
>>> fname = get_test_fname( '1.sam' )
>>> Sam().sniff( fname )
True

set_meta(dataset, overwrite=True, skip=None, max_data_lines=5, **kwd)[source]¶

static merge(split_files, output_file)[source]¶: Multiple SAM files may each have headers. Since the headers should all be the same, remove the headers from files 1-n, keeping them in the first file only

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

regex_line_dataprovider(dataset, **settings)[source]¶

column_dataprovider(dataset, **settings)[source]¶

dataset_column_dataprovider(dataset, **settings)[source]¶

dict_dataprovider(dataset, **settings)[source]¶

dataset_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]¶

dataproviders = {'base': <function Data.base_dataprovider at 0x7f5340b66730>, 'chunk': <function Data.chunk_dataprovider at 0x7f5340b668c8>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f5340b66a60>, 'column': <function Sam.column_dataprovider at 0x7f533d84fc80>, 'dataset-column': <function Sam.dataset_column_dataprovider at 0x7f533d84fe18>, 'dataset-dict': <function Sam.dataset_dict_dataprovider at 0x7f533d8551e0>, 'dict': <function Sam.dict_dataprovider at 0x7f533d855048>, 'genomic-region': <function Sam.genomic_region_dataprovider at 0x7f533d8556a8>, 'genomic-region-dict': <function Sam.genomic_region_dict_dataprovider at 0x7f533d855840>, 'header': <function Sam.header_dataprovider at 0x7f533d855378>, 'id-seq-qual': <function Sam.id_seq_qual_dataprovider at 0x7f533d855510>, 'line': <function Sam.line_dataprovider at 0x7f533d84f950>, 'regex-line': <function Sam.regex_line_dataprovider at 0x7f533d84fae8>}¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d84cdd8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d84cd68>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d84ccf8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d84cc18>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d84cc88>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d84ce48>}¶

sniff(filename)¶

class galaxy.datatypes.tabular.Pileup(**kwd)[source]¶

Bases: galaxy.datatypes.tabular.Tabular

Tab delimited data in pileup (6- or 10-column) format

edam_format = 'format_3015'¶

file_ext = 'pileup'¶

line_class = 'genomic coordinate'¶

data_sources = {'data': 'tabix'}¶: Add metadata elements

init_meta(dataset, copy_from=None)[source]¶

display_peek(dataset)[source]¶: Returns formated html of peek

repair_methods(dataset)[source]¶: Return options for removing errors along with a description

sniff_prefix(file_prefix)[source]¶

Checks for ‘pileup-ness’

There are two main types of pileup: 6-column and 10-column. For both, the first three and last two columns are the same. We only check the first three to allow for some personalization of the format.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'interval.interval' )
>>> Pileup().sniff( fname )
False
>>> fname = get_test_fname( '6col.pileup' )
>>> Pileup().sniff( fname )
True
>>> fname = get_test_fname( '10col.pileup' )
>>> Pileup().sniff( fname )
True
>>> fname = get_test_fname( '1.excel.xls' )
>>> Pileup().sniff( fname )
False
>>> fname = get_test_fname( '2.txt' )
>>> Pileup().sniff( fname )  # 2.txt
False
>>> fname = get_test_fname( '2.tabular' )
>>> Pileup().sniff( fname )
False

genomic_region_dataprovider(dataset, **settings)[source]¶

genomic_region_dict_dataprovider(dataset, **settings)[source]¶

dataproviders = {'base': <function Data.base_dataprovider at 0x7f5340b66730>, 'chunk': <function Data.chunk_dataprovider at 0x7f5340b668c8>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f5340b66a60>, 'column': <function TabularData.column_dataprovider at 0x7f533d852950>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f533d852ae8>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f533d852e18>, 'dict': <function TabularData.dict_dataprovider at 0x7f533d852c80>, 'genomic-region': <function Pileup.genomic_region_dataprovider at 0x7f533d855d08>, 'genomic-region-dict': <function Pileup.genomic_region_dict_dataprovider at 0x7f533d855ea0>, 'line': <function Text.line_dataprovider at 0x7f5340b6e0d0>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f5340b6e268>}¶

metadata_spec = {'baseCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857240>, 'chromCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857080>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afef0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'endCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8571d0>, 'startCol': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857160>}¶

sniff(filename)¶

class galaxy.datatypes.tabular.BaseVcf(**kwd)[source]¶

Bases: galaxy.datatypes.tabular.Tabular

Variant Call Format for describing SNPs and other simple genome variations.

edam_format = 'format_3016'¶

track_type = 'VariantTrack'¶

data_sources = {'data': 'tabix', 'index': 'bigwig'}¶

column_names = ['Chrom', 'Pos', 'ID', 'Ref', 'Alt', 'Qual', 'Filter', 'Info', 'Format', 'data']¶

display_peek(dataset)[source]¶: Returns formated html of peek

set_meta(dataset, **kwd)[source]¶

static merge(split_files, output_file)[source]¶

validate(dataset, **kwd)[source]¶

genomic_region_dataprovider(dataset, **settings)[source]¶

genomic_region_dict_dataprovider(dataset, **settings)[source]¶

dataproviders = {'base': <function Data.base_dataprovider at 0x7f5340b66730>, 'chunk': <function Data.chunk_dataprovider at 0x7f5340b668c8>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f5340b66a60>, 'column': <function TabularData.column_dataprovider at 0x7f533d852950>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f533d852ae8>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f533d852e18>, 'dict': <function TabularData.dict_dataprovider at 0x7f533d852c80>, 'genomic-region': <function BaseVcf.genomic_region_dataprovider at 0x7f533d859488>, 'genomic-region-dict': <function BaseVcf.genomic_region_dict_dataprovider at 0x7f533d859620>, 'line': <function Text.line_dataprovider at 0x7f5340b6e0d0>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f5340b6e268>}¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857438>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8573c8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'sample_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857550>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8574a8>}¶

sniff(filename)¶

class galaxy.datatypes.tabular.Vcf(**kwd)[source]¶

Bases: galaxy.datatypes.tabular.BaseVcf

file_ext = 'vcf'¶

sniff_prefix(file_prefix)[source]¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857630>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8575c0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'sample_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857748>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8576a0>}¶

class galaxy.datatypes.tabular.VcfGz(**kwd)[source]¶

Bases: galaxy.datatypes.tabular.BaseVcf, galaxy.datatypes.binary.Binary

file_ext = 'vcf_bgzip'¶

compressed = True¶

compressed_format = 'gzip'¶

sniff(filename)[source]¶

set_meta(dataset, **kwd)[source]¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857438>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8573c8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d870c88>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'sample_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857550>, 'tabix_index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8577b8>, 'viz_filter_cols': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8574a8>}¶

class galaxy.datatypes.tabular.Eland(**kwd)[source]¶

Bases: galaxy.datatypes.tabular.Tabular

Support for the export.txt.gz file used by Illumina’s ELANDv2e aligner

compressed = True¶

compressed_format = 'gzip'¶

file_ext = '_export.txt.gz'¶

__init__(**kwd)[source]¶: Initialize eland datatype

make_html_table(dataset, skipchars=None, peek=None)[source]¶: Create HTML table, used for displaying peek

sniff_prefix(file_prefix)[source]¶

Determines whether the file is in ELAND export format

A file in ELAND export format consists of lines of tab-separated data. There is no header.

Rules for sniffing as True:

- There must be 22 columns on each line
- LANE, TILEm X, Y, INDEX, READ_NO, SEQ, QUAL, POSITION, *STRAND, FILT must be correct
- We will only check that up to the first 5 alignments are correctly formatted.

set_meta(dataset, overwrite=True, skip=None, max_data_lines=5, **kwd)[source]¶

metadata_spec = {'barcodes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857b38>, 'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857908>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857898>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857978>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>, 'lanes': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857ac8>, 'reads': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857a58>, 'tiles': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d8579e8>}¶

sniff(filename)¶

class galaxy.datatypes.tabular.ElandMulti(**kwd)[source]¶

Bases: galaxy.datatypes.tabular.Tabular

file_ext = 'elandmulti'¶

sniff_prefix(file_prefix)[source]¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857da0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857d30>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857cc0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857be0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857c50>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857e10>}¶

sniff(filename)¶

class galaxy.datatypes.tabular.FeatureLocationIndex(**kwd)[source]¶

Bases: galaxy.datatypes.tabular.Tabular

An index that stores feature locations in tabular format.

file_ext = 'fli'¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8be0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857f28>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857eb8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afac8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6afd30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d6c8e10>}¶

class galaxy.datatypes.tabular.BaseCSV(**kwd)[source]¶

Bases: galaxy.datatypes.tabular.TabularData

Delimiter-separated table data. This includes CSV, TSV and other dialects understood by the Python ‘csv’ module https://docs.python.org/2/library/csv.html Must be extended to define the dialect to use, strict_width and file_ext. See the Python module csv for documentation of dialect settings

delimiter = ','¶

peek_size = 1024¶

big_peek_size = 10240¶

is_int(column_text)[source]¶

is_float(column_text)[source]¶

guess_type(text)[source]¶

sniff(filename)[source]¶: Return True if if recognizes dialect and header.

set_meta(dataset, **kwd)[source]¶

dataproviders = {'base': <function Data.base_dataprovider at 0x7f5340b66730>, 'chunk': <function Data.chunk_dataprovider at 0x7f5340b668c8>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f5340b66a60>, 'column': <function TabularData.column_dataprovider at 0x7f533d852950>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f533d852ae8>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f533d852e18>, 'dict': <function TabularData.dict_dataprovider at 0x7f533d852c80>, 'line': <function Text.line_dataprovider at 0x7f5340b6e0d0>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f5340b6e268>}¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e41d0>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4160>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e40f0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d857fd0>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4080>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4240>}¶

class galaxy.datatypes.tabular.CSV(**kwd)[source]¶

Bases: galaxy.datatypes.tabular.BaseCSV

Comma-separated table data. Only sniffs comma-separated files with at least 2 rows and 2 columns.

file_ext = 'csv'¶

dialect¶: alias of csv.excel

strict_width = False¶

dataproviders = {'base': <function Data.base_dataprovider at 0x7f5340b66730>, 'chunk': <function Data.chunk_dataprovider at 0x7f5340b668c8>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f5340b66a60>, 'column': <function TabularData.column_dataprovider at 0x7f533d852950>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f533d852ae8>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f533d852e18>, 'dict': <function TabularData.dict_dataprovider at 0x7f533d852c80>, 'line': <function Text.line_dataprovider at 0x7f5340b6e0d0>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f5340b6e268>}¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e44a8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4438>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e43c8>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e42e8>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4358>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4518>}¶

class galaxy.datatypes.tabular.TSV(**kwd)[source]¶

Bases: galaxy.datatypes.tabular.BaseCSV

Tab-separated table data. Only sniff tab-separated files with at least 2 rows and 2 columns.

Note: Use of this datatype is optional as the general tabular datatype will handle most tab-separated files. This datatype is only required for datasets with tabs INSIDE double quotes.

This datatype currently does not support TSV files where the header has one column less to indicate first column is row names. This kind of file is handled fine by the tabular datatype.

file_ext = 'tsv'¶

dialect¶: alias of csv.excel_tab

strict_width = True¶

dataproviders = {'base': <function Data.base_dataprovider at 0x7f5340b66730>, 'chunk': <function Data.chunk_dataprovider at 0x7f5340b668c8>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f5340b66a60>, 'column': <function TabularData.column_dataprovider at 0x7f533d852950>, 'dataset-column': <function TabularData.dataset_column_dataprovider at 0x7f533d852ae8>, 'dataset-dict': <function TabularData.dataset_dict_dataprovider at 0x7f533d852e18>, 'dict': <function TabularData.dict_dataprovider at 0x7f533d852c80>, 'line': <function Text.line_dataprovider at 0x7f5340b6e0d0>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f5340b6e268>}¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4748>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e46d8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4668>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4588>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e45f8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e47b8>}¶

class galaxy.datatypes.tabular.ConnectivityTable(**kwd)[source]¶

Bases: galaxy.datatypes.tabular.Tabular

edam_format = 'format_3309'¶

file_ext = 'ct'¶

header_regexp = re.compile('^[0-9]+(?:\t|[ ]+).*?(?:ENERGY|energy|dG)[ \t].*?=')¶

structure_regexp = re.compile('^[0-9]+(?:\t|[ ]+)[ACGTURYKMSWBDHVN]+(?:\t|[ ]+)[^\t]+(?:\t|[ ]+)[^\t]+(?:\t|[ ]+)[^\t]+(?:\t|[ ]+)[^\t]+')¶

__init__(**kwd)[source]¶

set_meta(dataset, **kwd)[source]¶

sniff_prefix(file_prefix)[source]¶

The ConnectivityTable (CT) is a file format used for describing RNA 2D structures by tools including MFOLD, UNAFOLD and the RNAStructure package. The tabular file format is defined as follows:

 energy = -12.3  sequence name
 G       0       2       0       1
 A       1       3       0       2
 A       2       4       0       3
 A       3       5       0       4
 C       4       6       1       5

The links given at the edam ontology page do not indicate what type of separator is used (space or tab) while different implementations exist. The implementation that uses spaces as separator (implemented in RNAStructure) is as follows:

  ENERGY = -34.8  seqname
G       0    2    9    1
G       1    3    8    2
G       2    4    7    3
a       3    5    0    4
a       4    6    0    5
a       5    7    0    6
C       6    8    3    7
C       7    9    2    8
C       8   10    1    9
a       9    0    0   10

get_chunk(trans, dataset, chunk)[source]¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4b00>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4ac8>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4a20>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4898>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4940>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4b70>}¶

sniff(filename)¶

class galaxy.datatypes.tabular.MatrixMarket(**kwd)[source]¶

Bases: galaxy.datatypes.tabular.TabularData

The Matrix Market (MM) exchange formats provide a simple mechanism to facilitate the exchange of matrix data. MM coordinate format is suitable for representing sparse matrices. Only nonzero entries need be encoded, and the coordinates of each are given explicitly.

The tabular file format is defined as follows:

%%MatrixMarket matrix coordinate real general <--- header line
%                                             <--+
% comments                                       |-- 0 or more comment lines
%                                             <--+

M N L <— rows, columns, entries I1 J1 A(I1, J1) <–+ I2 J2 A(I2, J2) | I3 J3 A(I3, J3) |– L lines

… |

IL JL A(IL, JL) <–+

Indices are 1-based, i.e. A(1,1) is the first element.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> MatrixMarket().sniff( get_test_fname( 'sequence.maf' ) )
False
>>> MatrixMarket().sniff( get_test_fname( '1.mtx' ) )
True
>>> MatrixMarket().sniff( get_test_fname( '2.mtx' ) )
True
>>> MatrixMarket().sniff( get_test_fname( '3.mtx' ) )
True

file_ext = 'mtx'¶

__init__(**kwd)[source]¶

metadata_spec = {'column_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e49e8>, 'column_types': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4e48>, 'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4da0>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4c88>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'delimiter': <galaxy.model.metadata.MetadataElementSpec object at 0x7f533d7e4cf8>}¶

sniff(filename)¶

sniff_prefix(file_prefix)[source]¶

set_meta(dataset, overwrite=True, skip=None, max_data_lines=5, **kwd)[source]¶

galaxy.datatypes.text module¶

Clearing house for generic text datatypes that are not XML or tabular.

class galaxy.datatypes.text.Html(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

Class describing an html file

edam_format = 'format_2331'¶

file_ext = 'html'¶

set_peek(dataset, is_multi_byte=False)[source]¶

get_mime()[source]¶: Returns the mime type of the datatype

sniff_prefix(file_prefix)[source]¶

Determines whether the file is in html format

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'complete.bed' )
>>> Html().sniff( fname )
False
>>> fname = get_test_fname( 'file.html' )
>>> Html().sniff( fname )
True

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035d30>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

class galaxy.datatypes.text.Json(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

edam_format = 'format_3464'¶

file_ext = 'json'¶

set_peek(dataset, is_multi_byte=False)[source]¶

get_mime()[source]¶: Returns the mime type of the datatype

sniff_prefix(file_prefix)[source]¶: Try to load the string with the json module. If successful it’s a json file.

display_peek(dataset)[source]¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035630>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

class galaxy.datatypes.text.ExpressionJson(**kwd)[source]¶

Bases: galaxy.datatypes.text.Json

Represents the non-data input or output to a tool or workflow.

file_ext = 'json'¶

set_meta(dataset, **kwd)[source]¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035630>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'json_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53140355f8>}¶

class galaxy.datatypes.text.Ipynb(**kwd)[source]¶

Bases: galaxy.datatypes.text.Json

file_ext = 'ipynb'¶

set_peek(dataset, is_multi_byte=False)[source]¶

sniff_prefix(file_prefix)[source]¶: Try to load the string with the json module. If successful it’s a json file.

display_data(trans, dataset, preview=False, filename=None, to_ext=None, **kwd)[source]¶

set_meta(dataset, **kwd)[source]¶: Set the number of models in dataset.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53140356a0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

class galaxy.datatypes.text.Biom1(**kwd)[source]¶

Bases: galaxy.datatypes.text.Json

BIOM version 1.0 file format description http://biom-format.org/documentation/format_versions/biom-1.0.html

file_ext = 'biom1'¶

edam_format = 'format_3746'¶

set_peek(dataset, is_multi_byte=False)[source]¶

sniff_prefix(file_prefix)[source]¶

set_meta(dataset, **kwd)[source]¶: Store metadata information from the BIOM file.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035630>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'table_column_metadata_headers': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313e77a20>, 'table_columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313e77940>, 'table_date': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313e77550>, 'table_format': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313e77198>, 'table_format_url': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313e77390>, 'table_generated_by': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313e77240>, 'table_id': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313e77898>, 'table_matrix_element_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313e77128>, 'table_matrix_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313e772b0>, 'table_rows': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035f60>, 'table_shape': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313e77320>, 'table_type': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313e77748>}¶

sniff(filename)¶

class galaxy.datatypes.text.ImgtJson(**kwd)[source]¶

Bases: galaxy.datatypes.text.Json

file_ext = 'imgt.json'¶

set_peek(dataset, is_multi_byte=False)[source]¶

sniff_prefix(file_prefix)[source]¶

Determines whether the file is in json format with imgt elements

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( '1.json' )
>>> ImgtJson().sniff( fname )
False
>>> fname = get_test_fname( 'imgt.json' )
>>> ImgtJson().sniff( fname )
True

set_meta(dataset, **kwd)[source]¶: Store metadata information from the imgt file.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314035630>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'taxon_names': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5313e77cc0>}¶

sniff(filename)¶

class galaxy.datatypes.text.GeoJson(**kwd)[source]¶

Bases: galaxy.datatypes.text.Json

GeoJSON is a geospatial data interchange format based on JavaScript Object Notation (JSON). https://tools.ietf.org/html/rfc7946

file_ext = 'geojson'¶

set_peek(dataset, is_multi_byte=False)[source]¶

sniff_prefix(file_prefix)[source]¶

Determines whether the file is in json format with imgt elements

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( '1.json' )
>>> GeoJson().sniff( fname )
False
>>> fname = get_test_fname( 'gis.geojson' )
>>> GeoJson().sniff( fname )
True

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314071320>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

class galaxy.datatypes.text.Obo(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

OBO file format description https://owlcollab.github.io/oboformat/doc/GO.format.obo-1_2.html

edam_data = 'data_0582'¶

edam_format = 'format_2549'¶

file_ext = 'obo'¶

set_peek(dataset, is_multi_byte=False)[source]¶

sniff_prefix(file_prefix)[source]¶: Try to guess the Obo filetype. It usually starts with a “format-version:” string and has several stanzas which starts with “id:”.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314071390>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

class galaxy.datatypes.text.Arff(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

An ARFF (Attribute-Relation File Format) file is an ASCII text file that describes a list of instances sharing a set of attributes. http://weka.wikispaces.com/ARFF

edam_format = 'format_3581'¶

file_ext = 'arff'¶: Add metadata elements

set_peek(dataset, is_multi_byte=False)[source]¶

sniff_prefix(file_prefix)[source]¶: Try to guess the Arff filetype. It usually starts with a “format-version:” string and has several stanzas which starts with “id:”.

set_meta(dataset, **kwd)[source]¶: Trying to count the comment lines and the number of columns included. A typical ARFF data block looks like this: @DATA 5.1,3.5,1.4,0.2,Iris-setosa 4.9,3.0,1.4,0.2,Iris-setosa

metadata_spec = {'columns': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314071470>, 'comment_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314071400>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

class galaxy.datatypes.text.SnpEffDb(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

Class describing a SnpEff genome build

edam_format = 'format_3624'¶

file_ext = 'snpeffdb'¶

__init__(**kwd)[source]¶

getSnpeffVersionFromFile(path)[source]¶

set_meta(dataset, **kwd)[source]¶

metadata_spec = {'annotation': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314071630>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'genome_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53140714e0>, 'regulation': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53140715c0>, 'snpeff_version': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314071550>}¶

class galaxy.datatypes.text.SnpSiftDbNSFP(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

Class describing a dbNSFP database prepared fpr use by SnpSift dbnsfp

file_ext = 'snpsiftdbnsfp'¶

composite_type = 'auto_primary_file'¶

allow_datatype_change = False¶: ## The dbNSFP file is a tabular file with 1 header line ## The first 4 columns are required to be: chrom pos ref alt ## These match columns 1,2,4,5 of the VCF file ## SnpSift requires the file to be block-gzipped and the indexed with samtools tabix ## Example: ## Compress using block-gzip algorithm bgzip dbNSFP2.3.txt ## Create tabix index tabix -s 1 -b 2 -e 2 dbNSFP2.3.txt.gz

__init__(**kwd)[source]¶

init_meta(dataset, copy_from=None)[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

regenerate_primary_file(dataset)[source]¶: cannot do this until we are setting metadata

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

metadata_spec = {'annotation': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53140717f0>, 'bgzip': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314071710>, 'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b69780>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>, 'index': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314071780>, 'reference_name': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53140716a0>}¶

class galaxy.datatypes.text.IQTree(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

IQ-TREE format

file_ext = 'iqtree'¶

sniff_prefix(file_prefix)[source]¶

Detect the IQTree file

Scattered text file containing various headers and data types.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('example.iqtree')
>>> IQTree().sniff(fname)
True

>>> fname = get_test_fname('temp.txt')
>>> IQTree().sniff(fname)
False

>>> fname = get_test_fname('test_tab1.tabular')
>>> IQTree().sniff(fname)
False

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314071860>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

class galaxy.datatypes.text.Paf(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

PAF: a Pairwise mApping Format

https://github.com/lh3/miniasm/blob/master/PAF.md

file_ext = 'paf'¶

sniff_prefix(file_prefix)[source]¶

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('A-3105.paf')
>>> Paf().sniff(fname)
True

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53140718d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

class galaxy.datatypes.text.Gfa1(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

Graphical Fragment Assembly (GFA) 1.0

http://gfa-spec.github.io/GFA-spec/GFA1.html

file_ext = 'gfa1'¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5314071940>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

sniff_prefix(file_prefix)[source]¶

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('big.gfa1')
>>> Gfa1().sniff(fname)
True

galaxy.datatypes.tracks module¶

Datatype classes for tracks/track views within galaxy.

class galaxy.datatypes.tracks.GeneTrack(**kwargs)[source]¶

Bases: galaxy.datatypes.binary.Binary

edam_data = 'data_3002'¶

edam_format = 'format_2919'¶

file_ext = 'genetrack'¶

__init__(**kwargs)[source]¶

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5310111278>}¶

class galaxy.datatypes.tracks.UCSCTrackHub(**kwd)[source]¶

Bases: galaxy.datatypes.text.Html

Datatype for UCSC TrackHub

file_ext = 'trackhub'¶

composite_type = 'auto_primary_file'¶

__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

set_peek(dataset, is_multi_byte=False)[source]¶

display_peek(dataset)[source]¶

sniff(filename)[source]¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53101118d0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

galaxy.datatypes.triples module¶

Triple format classes

class galaxy.datatypes.triples.Triples(**kwd)[source]¶

Bases: galaxy.datatypes.data.Data

The abstract base class for the file format that can contain triples

edam_data = 'data_0582'¶

edam_format = 'format_2376'¶

file_ext = 'triples'¶

sniff(filename)[source]¶: Returns false and the user must manually set.

set_peek(dataset, is_multi_byte=False)[source]¶: Set the peek and blurb text

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f530fb62ef0>}¶

class galaxy.datatypes.triples.NTriples(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text, galaxy.datatypes.triples.Triples

The N-Triples triple data format

edam_format = 'format_3256'¶

file_ext = 'nt'¶

sniff_prefix(file_prefix)[source]¶

set_peek(dataset, is_multi_byte=False)[source]¶: Set the peek and blurb text

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f530fb62eb8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f530fb62ef0>}¶

sniff(filename)¶

class galaxy.datatypes.triples.N3(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text, galaxy.datatypes.triples.Triples

The N3 triple data format

edam_format = 'format_3257'¶

file_ext = 'n3'¶

sniff(filename)[source]¶: Returns false and the user must manually set.

set_peek(dataset, is_multi_byte=False)[source]¶: Set the peek and blurb text

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f530fb62fd0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f530fb62ef0>}¶

class galaxy.datatypes.triples.Turtle(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text, galaxy.datatypes.triples.Triples

The Turtle triple data format

edam_format = 'format_3255'¶

file_ext = 'ttl'¶

sniff_prefix(file_prefix)[source]¶

set_peek(dataset, is_multi_byte=False)[source]¶: Set the peek and blurb text

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f530fb62e10>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f530fb62ef0>}¶

sniff(filename)¶

class galaxy.datatypes.triples.Rdf(**kwd)[source]¶

Bases: galaxy.datatypes.xml.GenericXml, galaxy.datatypes.triples.Triples

Resource Description Framework format (http://www.w3.org/RDF/).

edam_format = 'format_3261'¶

file_ext = 'rdf'¶

sniff_prefix(file_prefix)[source]¶

set_peek(dataset, is_multi_byte=False)[source]¶: Set the peek and blurb text

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f530fb62dd8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f530fb62ef0>}¶

sniff(filename)¶

class galaxy.datatypes.triples.Jsonld(**kwd)[source]¶

Bases: galaxy.datatypes.text.Json, galaxy.datatypes.triples.Triples

The JSON-LD data format

edam_format = 'format_3464'¶

file_ext = 'jsonld'¶

sniff_prefix(file_prefix)[source]¶

set_peek(dataset, is_multi_byte=False)[source]¶: Set the peek and blurb text

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f530fb62f98>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f530fb62ef0>}¶

sniff(filename)¶

class galaxy.datatypes.triples.HDT(**kwd)[source]¶

Bases: galaxy.datatypes.binary.Binary, galaxy.datatypes.triples.Triples

The HDT triple data format

edam_format = 'format_2376'¶

file_ext = 'hdt'¶

sniff(filename)[source]¶

set_peek(dataset, is_multi_byte=False)[source]¶: Set the peek and blurb text

metadata_spec = {'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f530fb62be0>}¶

galaxy.datatypes.upload_util module¶

exception galaxy.datatypes.upload_util.UploadProblemException[source]¶: Bases: Exception

galaxy.datatypes.upload_util.handle_upload(registry, path, requested_ext, name, tmp_prefix, tmp_dir, check_content, link_data_only, in_place, auto_decompress, convert_to_posix_lines, convert_spaces_to_tabs)[source]¶

galaxy.datatypes.xml module¶

XML format classes

class galaxy.datatypes.xml.GenericXml(**kwd)[source]¶

Bases: galaxy.datatypes.data.Text

Base format class for any XML file.

edam_format = 'format_2332'¶

file_ext = 'xml'¶

set_peek(dataset, is_multi_byte=False)[source]¶: Set the peek and blurb text

sniff_prefix(file_prefix)[source]¶

Determines whether the file is XML or not

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( 'megablast_xml_parser_test1.blastxml' )
>>> GenericXml().sniff( fname )
True
>>> fname = get_test_fname( 'interval.interval' )
>>> GenericXml().sniff( fname )
False

static merge(split_files, output_file)[source]¶: Merging multiple XML files is non-trivial and must be done in subclasses.

xml_dataprovider(dataset, **settings)[source]¶

dataproviders = {'base': <function Data.base_dataprovider at 0x7f5340b66730>, 'chunk': <function Data.chunk_dataprovider at 0x7f5340b668c8>, 'chunk64': <function Data.chunk64_dataprovider at 0x7f5340b66a60>, 'line': <function Text.line_dataprovider at 0x7f5340b6e0d0>, 'regex-line': <function Text.regex_line_dataprovider at 0x7f5340b6e268>, 'xml': <function GenericXml.xml_dataprovider at 0x7f53161ad620>}¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53161b1710>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

class galaxy.datatypes.xml.MEMEXml(**kwd)[source]¶

Bases: galaxy.datatypes.xml.GenericXml

MEME XML Output data

file_ext = 'memexml'¶

set_peek(dataset, is_multi_byte=False)[source]¶: Set the peek and blurb text

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53161b17b8>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

sniff_prefix(file_prefix)¶

class galaxy.datatypes.xml.CisML(**kwd)[source]¶

Bases: galaxy.datatypes.xml.GenericXml

CisML XML data

file_ext = 'cisml'¶

set_peek(dataset, is_multi_byte=False)[source]¶: Set the peek and blurb text

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53161b1860>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff(filename)¶

sniff_prefix(file_prefix)¶

class galaxy.datatypes.xml.Phyloxml(**kwd)[source]¶

Bases: galaxy.datatypes.xml.GenericXml

Format for defining phyloxml data http://www.phyloxml.org/

edam_data = 'data_0872'¶

edam_format = 'format_3159'¶

file_ext = 'phyloxml'¶

set_peek(dataset, is_multi_byte=False)[source]¶: Set the peek and blurb text

sniff_prefix(file_prefix)[source]¶

“Checking for keyword - ‘phyloxml’ always in lowercase in the first few lines.

>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname( '1.phyloxml' )
>>> Phyloxml().sniff( fname )
True
>>> fname = get_test_fname( 'interval.interval' )
>>> Phyloxml().sniff( fname )
False
>>> fname = get_test_fname( 'megablast_xml_parser_test1.blastxml' )
>>> Phyloxml().sniff( fname )
False

get_visualizations(dataset)[source]¶: Returns a list of visualizations for datatype.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53161b1908>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

class galaxy.datatypes.xml.Owl(**kwd)[source]¶

Bases: galaxy.datatypes.xml.GenericXml

Web Ontology Language OWL format description http://www.w3.org/TR/owl-ref/

edam_format = 'format_3262'¶

file_ext = 'owl'¶

set_peek(dataset, is_multi_byte=False)[source]¶

sniff_prefix(file_prefix)[source]¶: Checking for keyword - ‘<owl’ in the first 200 lines.

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53161b19b0>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

class galaxy.datatypes.xml.Sbml(**kwd)[source]¶

Bases: galaxy.datatypes.xml.GenericXml

System Biology Markup Language http://sbml.org

file_ext = 'sbml'¶

edam_data = 'data_2024'¶

edam_format = 'format_2585'¶

set_peek(dataset, is_multi_byte=False)[source]¶

metadata_spec = {'data_lines': <galaxy.model.metadata.MetadataElementSpec object at 0x7f53161b1a58>, 'dbkey': <galaxy.model.metadata.MetadataElementSpec object at 0x7f5340b696d8>}¶

sniff_prefix(file_prefix)[source]¶: Checking for keyword - ‘<sbml’ in the first 200 lines.