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Source code for galaxy.datatypes.tabular

"""
Tabular datatype
"""
from __future__ import absolute_import

import abc
import csv
import logging
import os
import re
import shutil
import subprocess
import sys
import tempfile
from cgi import escape
from json import dumps

from galaxy import util
from galaxy.datatypes import data, metadata
from galaxy.datatypes.metadata import MetadataElement
from galaxy.datatypes.sniff import get_headers
from galaxy.util import compression_utils

from . import dataproviders

if sys.version_info > (3,):
    long = int

log = logging.getLogger(__name__)


[docs]@dataproviders.decorators.has_dataproviders class TabularData( data.Text ): """Generic tabular data""" edam_format = "format_3475" # All tabular data is chunkable. CHUNKABLE = True """Add metadata elements""" MetadataElement( name="comment_lines", default=0, desc="Number of comment lines", readonly=False, optional=True, no_value=0 ) MetadataElement( name="data_lines", default=0, desc="Number of data lines", readonly=True, visible=False, optional=True, no_value=0 ) MetadataElement( name="columns", default=0, desc="Number of columns", readonly=True, visible=False, no_value=0 ) MetadataElement( name="column_types", default=[], desc="Column types", param=metadata.ColumnTypesParameter, readonly=True, visible=False, no_value=[] ) MetadataElement( name="column_names", default=[], desc="Column names", readonly=True, visible=False, optional=True, no_value=[] ) MetadataElement( name="delimiter", default='\t', desc="Data delimiter", readonly=True, visible=False, optional=True, no_value=[] )
[docs] @abc.abstractmethod def set_meta( self, dataset, **kwd ): raise NotImplementedError
[docs] def set_peek( self, dataset, line_count=None, is_multi_byte=False, WIDTH=256, skipchars=None ): super(TabularData, self).set_peek( dataset, line_count=line_count, is_multi_byte=is_multi_byte, WIDTH=WIDTH, skipchars=skipchars, line_wrap=False ) if dataset.metadata.comment_lines: dataset.blurb = "%s, %s comments" % ( dataset.blurb, util.commaify( str( dataset.metadata.comment_lines ) ) )
[docs] def displayable( self, dataset ): try: return dataset.has_data() \ and dataset.state == dataset.states.OK \ and dataset.metadata.columns > 0 \ and dataset.metadata.data_lines != 0 except: return False
[docs] def get_chunk(self, trans, dataset, offset=0, ck_size=None): with open(dataset.file_name) as f: f.seek(offset) ck_data = f.read(ck_size or trans.app.config.display_chunk_size) if ck_data and ck_data[-1] != '\n': cursor = f.read(1) while cursor and cursor != '\n': ck_data += cursor cursor = f.read(1) last_read = f.tell() return dumps( { 'ck_data': util.unicodify( ck_data ), 'offset': last_read } )
[docs] def display_data(self, trans, dataset, preview=False, filename=None, to_ext=None, offset=None, ck_size=None, **kwd): preview = util.string_as_bool( preview ) if offset is not None: return self.get_chunk(trans, dataset, offset, ck_size) elif to_ext or not preview: to_ext = to_ext or dataset.extension return self._serve_raw(trans, dataset, to_ext, **kwd) elif dataset.metadata.columns > 50: # Fancy tabular display is only suitable for datasets without an incredibly large number of columns. # We should add a new datatype 'matrix', with its own draw method, suitable for this kind of data. # For now, default to the old behavior, ugly as it is. Remove this after adding 'matrix'. max_peek_size = 1000000 # 1 MB if os.stat( dataset.file_name ).st_size < max_peek_size: self._clean_and_set_mime_type( trans, dataset.get_mime() ) return open( dataset.file_name ) else: trans.response.set_content_type( "text/html" ) return trans.stream_template_mako( "/dataset/large_file.mako", truncated_data=open( dataset.file_name ).read(max_peek_size), data=dataset) else: column_names = 'null' if dataset.metadata.column_names: column_names = dataset.metadata.column_names elif hasattr(dataset.datatype, 'column_names'): column_names = dataset.datatype.column_names column_types = dataset.metadata.column_types if not column_types: column_types = [] column_number = dataset.metadata.columns if column_number is None: column_number = 'null' return trans.fill_template( "/dataset/tabular_chunked.mako", dataset=dataset, chunk=self.get_chunk(trans, dataset, 0), column_number=column_number, column_names=column_names, column_types=column_types )
[docs] def make_html_table( self, dataset, **kwargs ): """Create HTML table, used for displaying peek""" out = ['<table cellspacing="0" cellpadding="3">'] try: out.append( self.make_html_peek_header( dataset, **kwargs ) ) out.append( self.make_html_peek_rows( dataset, **kwargs ) ) out.append( '</table>' ) out = "".join( out ) except Exception as exc: out = "Can't create peek %s" % str( exc ) return out
[docs] def make_html_peek_header( self, dataset, skipchars=None, column_names=None, column_number_format='%s', column_parameter_alias=None, **kwargs ): if skipchars is None: skipchars = [] if column_names is None: column_names = [] if column_parameter_alias is None: column_parameter_alias = {} out = [] try: if not column_names and dataset.metadata.column_names: column_names = dataset.metadata.column_names columns = dataset.metadata.columns if columns is None: columns = dataset.metadata.spec.columns.no_value column_headers = [None] * columns # fill in empty headers with data from column_names for i in range( min( columns, len( column_names ) ) ): if column_headers[i] is None and column_names[i] is not None: column_headers[i] = column_names[i] # fill in empty headers from ColumnParameters set in the metadata for name, spec in dataset.metadata.spec.items(): if isinstance( spec.param, metadata.ColumnParameter ): try: i = int( getattr( dataset.metadata, name ) ) - 1 except: i = -1 if 0 <= i < columns and column_headers[i] is None: column_headers[i] = column_parameter_alias.get(name, name) out.append( '<tr>' ) for i, header in enumerate( column_headers ): out.append( '<th>' ) if header is None: out.append( column_number_format % str( i + 1 ) ) else: out.append( '%s.%s' % ( str( i + 1 ), escape( header ) ) ) out.append( '</th>' ) out.append( '</tr>' ) except Exception as exc: log.exception( 'make_html_peek_header failed on HDA %s', dataset.id ) raise Exception( "Can't create peek header %s" % str( exc ) ) return "".join( out )
[docs] def make_html_peek_rows( self, dataset, skipchars=None, **kwargs ): if skipchars is None: skipchars = [] out = [] try: if not dataset.peek: dataset.set_peek() columns = dataset.metadata.columns if columns is None: columns = dataset.metadata.spec.columns.no_value for line in dataset.peek.splitlines(): if line.startswith( tuple( skipchars ) ): out.append( '<tr><td colspan="100%%">%s</td></tr>' % escape( line ) ) elif line: elems = line.split( dataset.metadata.delimiter ) # pad shortened elems, since lines could have been truncated by width if len( elems ) < columns: elems.extend( [''] * ( columns - len( elems ) ) ) # we may have an invalid comment line or invalid data if len( elems ) != columns: out.append( '<tr><td colspan="100%%">%s</td></tr>' % escape( line ) ) else: out.append( '<tr>' ) for elem in elems: out.append( '<td>%s</td>' % escape( elem ) ) out.append( '</tr>' ) except Exception as exc: log.exception( 'make_html_peek_rows failed on HDA %s', dataset.id ) raise Exception( "Can't create peek rows %s" % str( exc ) ) return "".join( out )
[docs] def display_peek( self, dataset ): """Returns formatted html of peek""" return self.make_html_table( dataset )
# ------------- Dataproviders
[docs] @dataproviders.decorators.dataprovider_factory( 'column', dataproviders.column.ColumnarDataProvider.settings ) def column_dataprovider( self, dataset, **settings ): """Uses column settings that are passed in""" dataset_source = dataproviders.dataset.DatasetDataProvider( dataset ) delimiter = dataset.metadata.delimiter return dataproviders.column.ColumnarDataProvider( dataset_source, deliminator=delimiter, **settings )
[docs] @dataproviders.decorators.dataprovider_factory( 'dataset-column', dataproviders.column.ColumnarDataProvider.settings ) def dataset_column_dataprovider( self, dataset, **settings ): """Attempts to get column settings from dataset.metadata""" delimiter = dataset.metadata.delimiter return dataproviders.dataset.DatasetColumnarDataProvider( dataset, deliminator=delimiter, **settings )
[docs] @dataproviders.decorators.dataprovider_factory( 'dict', dataproviders.column.DictDataProvider.settings ) def dict_dataprovider( self, dataset, **settings ): """Uses column settings that are passed in""" dataset_source = dataproviders.dataset.DatasetDataProvider( dataset ) delimiter = dataset.metadata.delimiter return dataproviders.column.DictDataProvider( dataset_source, deliminator=delimiter, **settings )
[docs] @dataproviders.decorators.dataprovider_factory( 'dataset-dict', dataproviders.column.DictDataProvider.settings ) def dataset_dict_dataprovider( self, dataset, **settings ): """Attempts to get column settings from dataset.metadata""" delimiter = dataset.metadata.delimiter return dataproviders.dataset.DatasetDictDataProvider( dataset, deliminator=delimiter, **settings )
[docs]@dataproviders.decorators.has_dataproviders class Tabular( TabularData ): """Tab delimited data"""
[docs] def set_meta( self, dataset, overwrite=True, skip=None, max_data_lines=100000, max_guess_type_data_lines=None, **kwd ): """ 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: 1. We treat 'overwrite' as always True (we always want to set tabular metadata when called). 2. If a tabular file has no data, it will have one column of type 'str'. 3. 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. """ # Store original skip value to check with later requested_skip = skip if skip is None: skip = 0 column_type_set_order = [ 'int', 'float', 'list', 'str' ] # Order to set column types in default_column_type = column_type_set_order[-1] # Default column type is lowest in list column_type_compare_order = list( column_type_set_order ) # Order to compare column types column_type_compare_order.reverse() def type_overrules_type( column_type1, column_type2 ): if column_type1 is None or column_type1 == column_type2: return False if column_type2 is None: return True for column_type in column_type_compare_order: if column_type1 == column_type: return True if column_type2 == column_type: return False # neither column type was found in our ordered list, this cannot happen raise ValueError( "Tried to compare unknown column types: %s and %s" % ( column_type1, column_type2 ) ) def is_int( column_text ): try: int( column_text ) return True except: return False def is_float( column_text ): try: float( column_text ) return True except: if column_text.strip().lower() == 'na': return True # na is special cased to be a float return False def is_list( column_text ): return "," in column_text def is_str( column_text ): # anything, except an empty string, is True if column_text == "": return False return True is_column_type = {} # Dict to store column type string to checking function for column_type in column_type_set_order: is_column_type[column_type] = locals()[ "is_%s" % ( column_type ) ] def guess_column_type( column_text ): for column_type in column_type_set_order: if is_column_type[column_type]( column_text ): return column_type return None data_lines = 0 comment_lines = 0 column_types = [] first_line_column_types = [default_column_type] # default value is one column of type str if dataset.has_data(): # NOTE: if skip > num_check_lines, we won't detect any metadata, and will use default dataset_fh = open( dataset.file_name ) i = 0 while True: line = dataset_fh.readline() if not line: break line = line.rstrip( '\r\n' ) if i < skip or not line or line.startswith( '#' ): # We'll call blank lines comments comment_lines += 1 else: data_lines += 1 if max_guess_type_data_lines is None or data_lines <= max_guess_type_data_lines: fields = line.split( '\t' ) for field_count, field in enumerate( fields ): if field_count >= len( column_types ): # found a previously unknown column, we append None column_types.append( None ) column_type = guess_column_type( field ) if type_overrules_type( column_type, column_types[field_count] ): column_types[field_count] = column_type if i == 0 and requested_skip is None: # This is our first line, people seem to like to upload files that have a header line, but do not # start with '#' (i.e. all column types would then most likely be detected as str). We will assume # that the first line is always a header (this was previous behavior - it was always skipped). When # the requested skip is None, we only use the data from the first line if we have no other data for # a column. This is far from perfect, as # 1,2,3 1.1 2.2 qwerty # 0 0 1,2,3 # will be detected as # "column_types": ["int", "int", "float", "list"] # instead of # "column_types": ["list", "float", "float", "str"] *** would seem to be the 'Truth' by manual # observation that the first line should be included as data. The old method would have detected as # "column_types": ["int", "int", "str", "list"] first_line_column_types = column_types column_types = [ None for col in first_line_column_types ] if max_data_lines is not None and data_lines >= max_data_lines: if dataset_fh.tell() != dataset.get_size(): data_lines = None # Clear optional data_lines metadata value comment_lines = None # Clear optional comment_lines metadata value; additional comment lines could appear below this point break i += 1 dataset_fh.close() # we error on the larger number of columns # first we pad our column_types by using data from first line if len( first_line_column_types ) > len( column_types ): for column_type in first_line_column_types[len( column_types ):]: column_types.append( column_type ) # Now we fill any unknown (None) column_types with data from first line for i in range( len( column_types ) ): if column_types[i] is None: if len( first_line_column_types ) <= i or first_line_column_types[i] is None: column_types[i] = default_column_type else: column_types[i] = first_line_column_types[i] # Set the discovered metadata values for the dataset dataset.metadata.data_lines = data_lines dataset.metadata.comment_lines = comment_lines dataset.metadata.column_types = column_types dataset.metadata.columns = len( column_types ) dataset.metadata.delimiter = '\t'
[docs] def as_gbrowse_display_file( self, dataset, **kwd ): return open( dataset.file_name )
[docs] def as_ucsc_display_file( self, dataset, **kwd ): return open( dataset.file_name )
[docs]class Taxonomy( Tabular ):
[docs] def __init__(self, **kwd): """Initialize taxonomy datatype""" super(Taxonomy, self).__init__( **kwd ) self.column_names = ['Name', 'TaxId', 'Root', 'Superkingdom', 'Kingdom', 'Subkingdom', 'Superphylum', 'Phylum', 'Subphylum', 'Superclass', 'Class', 'Subclass', 'Superorder', 'Order', 'Suborder', 'Superfamily', 'Family', 'Subfamily', 'Tribe', 'Subtribe', 'Genus', 'Subgenus', 'Species', 'Subspecies' ]
[docs] def display_peek( self, dataset ): """Returns formated html of peek""" return self.make_html_table( dataset, column_names=self.column_names )
[docs]@dataproviders.decorators.has_dataproviders class Sam( Tabular ): edam_format = "format_2573" edam_data = "data_0863" file_ext = 'sam' track_type = "ReadTrack" data_sources = { "data": "bam", "index": "bigwig" }
[docs] def __init__(self, **kwd): """Initialize taxonomy datatype""" super( Sam, self ).__init__( **kwd ) self.column_names = ['QNAME', 'FLAG', 'RNAME', 'POS', 'MAPQ', 'CIGAR', 'MRNM', 'MPOS', 'ISIZE', 'SEQ', 'QUAL', 'OPT' ]
[docs] def display_peek( self, dataset ): """Returns formated html of peek""" return self.make_html_table( dataset, column_names=self.column_names )
[docs] def sniff( self, filename ): """ 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 """ try: fh = open( filename ) count = 0 while True: line = fh.readline() line = line.strip() if not line: break # EOF if line: if line[0] != '@': line_pieces = line.split('\t') if len(line_pieces) < 11: return False try: int(line_pieces[1]) int(line_pieces[3]) int(line_pieces[4]) int(line_pieces[7]) int(line_pieces[8]) except ValueError: return False count += 1 if count == 5: return True fh.close() if count < 5 and count > 0: return True except: pass return False
[docs] def set_meta( self, dataset, overwrite=True, skip=None, max_data_lines=5, **kwd ): if dataset.has_data(): dataset_fh = open( dataset.file_name ) comment_lines = 0 if self.max_optional_metadata_filesize >= 0 and dataset.get_size() > self.max_optional_metadata_filesize: # If the dataset is larger than optional_metadata, just count comment lines. for i, l in enumerate(dataset_fh): if l.startswith('@'): comment_lines += 1 else: # No more comments, and the file is too big to look at the whole thing. Give up. dataset.metadata.data_lines = None break else: # Otherwise, read the whole thing and set num data lines. for i, l in enumerate(dataset_fh): if l.startswith('@'): comment_lines += 1 dataset.metadata.data_lines = i + 1 - comment_lines dataset_fh.close() dataset.metadata.comment_lines = comment_lines dataset.metadata.columns = 12 dataset.metadata.column_types = ['str', 'int', 'str', 'int', 'int', 'str', 'str', 'int', 'int', 'str', 'str', 'str']
[docs] def merge( split_files, output_file): """ 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 """ shutil.move(split_files[0], output_file) if len(split_files) > 1: cmd = ['egrep', '-v', '-h', '^@'] + split_files[1:] + ['>>', output_file] subprocess.check_call(cmd, shell=True)
merge = staticmethod(merge) # Dataproviders # sam does not use '#' to indicate comments/headers - we need to strip out those headers from the std. providers # TODO:?? seems like there should be an easier way to do this - metadata.comment_char?
[docs] @dataproviders.decorators.dataprovider_factory( 'line', dataproviders.line.FilteredLineDataProvider.settings ) def line_dataprovider( self, dataset, **settings ): settings[ 'comment_char' ] = '@' return super( Sam, self ).line_dataprovider( dataset, **settings )
[docs] @dataproviders.decorators.dataprovider_factory( 'regex-line', dataproviders.line.RegexLineDataProvider.settings ) def regex_line_dataprovider( self, dataset, **settings ): settings[ 'comment_char' ] = '@' return super( Sam, self ).regex_line_dataprovider( dataset, **settings )
[docs] @dataproviders.decorators.dataprovider_factory( 'column', dataproviders.column.ColumnarDataProvider.settings ) def column_dataprovider( self, dataset, **settings ): settings[ 'comment_char' ] = '@' return super( Sam, self ).column_dataprovider( dataset, **settings )
[docs] @dataproviders.decorators.dataprovider_factory( 'dataset-column', dataproviders.column.ColumnarDataProvider.settings ) def dataset_column_dataprovider( self, dataset, **settings ): settings[ 'comment_char' ] = '@' return super( Sam, self ).dataset_column_dataprovider( dataset, **settings )
[docs] @dataproviders.decorators.dataprovider_factory( 'dict', dataproviders.column.DictDataProvider.settings ) def dict_dataprovider( self, dataset, **settings ): settings[ 'comment_char' ] = '@' return super( Sam, self ).dict_dataprovider( dataset, **settings )
[docs] @dataproviders.decorators.dataprovider_factory( 'dataset-dict', dataproviders.column.DictDataProvider.settings ) def dataset_dict_dataprovider( self, dataset, **settings ): settings[ 'comment_char' ] = '@' return super( Sam, self ).dataset_dict_dataprovider( dataset, **settings )
[docs] @dataproviders.decorators.dataprovider_factory( 'header', dataproviders.line.RegexLineDataProvider.settings ) def header_dataprovider( self, dataset, **settings ): dataset_source = dataproviders.dataset.DatasetDataProvider( dataset ) headers_source = dataproviders.line.RegexLineDataProvider( dataset_source, regex_list=[ '^@' ] ) return dataproviders.line.RegexLineDataProvider( headers_source, **settings )
[docs] @dataproviders.decorators.dataprovider_factory( 'id-seq-qual', dict_dataprovider.settings ) def id_seq_qual_dataprovider( self, dataset, **settings ): # provided as an example of a specified column dict (w/o metadata) settings[ 'indeces' ] = [ 0, 9, 10 ] settings[ 'column_names' ] = [ 'id', 'seq', 'qual' ] return self.dict_dataprovider( dataset, **settings )
[docs] @dataproviders.decorators.dataprovider_factory( 'genomic-region', dataproviders.dataset.GenomicRegionDataProvider.settings ) def genomic_region_dataprovider( self, dataset, **settings ): settings[ 'comment_char' ] = '@' return dataproviders.dataset.GenomicRegionDataProvider( dataset, 2, 3, 3, **settings )
[docs] @dataproviders.decorators.dataprovider_factory( 'genomic-region-dict', dataproviders.dataset.GenomicRegionDataProvider.settings ) def genomic_region_dict_dataprovider( self, dataset, **settings ): settings[ 'comment_char' ] = '@' return dataproviders.dataset.GenomicRegionDataProvider( dataset, 2, 3, 3, True, **settings )
# @dataproviders.decorators.dataprovider_factory( 'samtools' ) # def samtools_dataprovider( self, dataset, **settings ): # dataset_source = dataproviders.dataset.DatasetDataProvider( dataset ) # return dataproviders.dataset.SamtoolsDataProvider( dataset_source, **settings )
[docs]@dataproviders.decorators.has_dataproviders class Pileup( 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""" MetadataElement( name="chromCol", default=1, desc="Chrom column", param=metadata.ColumnParameter ) MetadataElement( name="startCol", default=2, desc="Start column", param=metadata.ColumnParameter ) MetadataElement( name="endCol", default=2, desc="End column", param=metadata.ColumnParameter ) MetadataElement( name="baseCol", default=3, desc="Reference base column", param=metadata.ColumnParameter )
[docs] def init_meta( self, dataset, copy_from=None ): super( Pileup, self ).init_meta( dataset, copy_from=copy_from )
[docs] def display_peek( self, dataset ): """Returns formated html of peek""" return self.make_html_table( dataset, column_parameter_alias={'chromCol': 'Chrom', 'startCol': 'Start', 'baseCol': 'Base'} )
[docs] def repair_methods( self, dataset ): """Return options for removing errors along with a description""" return [ ("lines", "Remove erroneous lines") ]
[docs] def sniff( self, filename ): """ 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 """ headers = get_headers( filename, '\t' ) try: for hdr in headers: if hdr and not hdr[0].startswith( '#' ): if len( hdr ) < 5: return False try: # chrom start in column 1 (with 0-based columns) # and reference base is in column 2 chrom = int( hdr[1] ) assert chrom >= 0 assert hdr[2] in [ 'A', 'C', 'G', 'T', 'N', 'a', 'c', 'g', 't', 'n' ] except: return False return True except: return False
# Dataproviders
[docs] @dataproviders.decorators.dataprovider_factory( 'genomic-region', dataproviders.dataset.GenomicRegionDataProvider.settings ) def genomic_region_dataprovider( self, dataset, **settings ): return dataproviders.dataset.GenomicRegionDataProvider( dataset, **settings )
[docs] @dataproviders.decorators.dataprovider_factory( 'genomic-region-dict', dataproviders.dataset.GenomicRegionDataProvider.settings ) def genomic_region_dict_dataprovider( self, dataset, **settings ): settings[ 'named_columns' ] = True return self.genomic_region_dataprovider( dataset, **settings )
[docs]@dataproviders.decorators.has_dataproviders class Vcf( 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" } file_ext = 'vcf' column_names = [ 'Chrom', 'Pos', 'ID', 'Ref', 'Alt', 'Qual', 'Filter', 'Info', 'Format', 'data' ] MetadataElement( name="columns", default=10, desc="Number of columns", readonly=True, visible=False ) MetadataElement( name="column_types", default=['str', 'int', 'str', 'str', 'str', 'int', 'str', 'list', 'str', 'str'], param=metadata.ColumnTypesParameter, desc="Column types", readonly=True, visible=False ) MetadataElement( name="viz_filter_cols", desc="Score column for visualization", default=[5], param=metadata.ColumnParameter, optional=True, multiple=True, visible=False ) MetadataElement( name="sample_names", default=[], desc="Sample names", readonly=True, visible=False, optional=True, no_value=[] )
[docs] def sniff( self, filename ): headers = get_headers( filename, '\n', count=1 ) return headers[0][0].startswith("##fileformat=VCF")
[docs] def display_peek( self, dataset ): """Returns formated html of peek""" return self.make_html_table( dataset, column_names=self.column_names )
[docs] def set_meta( self, dataset, **kwd ): super( Vcf, self ).set_meta( dataset, **kwd ) source = open( dataset.file_name ) # Skip comments. line = None for line in source: if not line.startswith( '##' ): break if line and line.startswith( '#' ): # Found header line, get sample names. dataset.metadata.sample_names = line.split()[ 9: ]
[docs] @staticmethod def merge(split_files, output_file): stderr_f = tempfile.NamedTemporaryFile(prefix="bam_merge_stderr") stderr_name = stderr_f.name command = ["bcftools", "concat"] + split_files + ["-o", output_file] log.info("Merging vcf files with command [%s]" % " ".join(command)) exit_code = subprocess.call( args=command, stderr=open( stderr_name, 'wb' ) ) with open(stderr_name, "rb") as f: stderr = f.read().strip() # Did merge succeed? if exit_code != 0: raise Exception("Error merging VCF files: %s" % stderr)
# Dataproviders
[docs] @dataproviders.decorators.dataprovider_factory( 'genomic-region', dataproviders.dataset.GenomicRegionDataProvider.settings ) def genomic_region_dataprovider( self, dataset, **settings ): return dataproviders.dataset.GenomicRegionDataProvider( dataset, 0, 1, 1, **settings )
[docs] @dataproviders.decorators.dataprovider_factory( 'genomic-region-dict', dataproviders.dataset.GenomicRegionDataProvider.settings ) def genomic_region_dict_dataprovider( self, dataset, **settings ): settings[ 'named_columns' ] = True return self.genomic_region_dataprovider( dataset, **settings )
[docs]class Eland( Tabular ): """Support for the export.txt.gz file used by Illumina's ELANDv2e aligner""" file_ext = '_export.txt.gz' MetadataElement( name="columns", default=0, desc="Number of columns", readonly=True, visible=False ) MetadataElement( name="column_types", default=[], param=metadata.ColumnTypesParameter, desc="Column types", readonly=True, visible=False, no_value=[] ) MetadataElement( name="comment_lines", default=0, desc="Number of comments", readonly=True, visible=False ) MetadataElement( name="tiles", default=[], param=metadata.ListParameter, desc="Set of tiles", readonly=True, visible=False, no_value=[] ) MetadataElement( name="reads", default=[], param=metadata.ListParameter, desc="Set of reads", readonly=True, visible=False, no_value=[] ) MetadataElement( name="lanes", default=[], param=metadata.ListParameter, desc="Set of lanes", readonly=True, visible=False, no_value=[] ) MetadataElement( name="barcodes", default=[], param=metadata.ListParameter, desc="Set of barcodes", readonly=True, visible=False, no_value=[] )
[docs] def __init__(self, **kwd): """Initialize taxonomy datatype""" super( Eland, self ).__init__( **kwd ) self.column_names = ['MACHINE', 'RUN_NO', 'LANE', 'TILE', 'X', 'Y', 'INDEX', 'READ_NO', 'SEQ', 'QUAL', 'CHROM', 'CONTIG', 'POSITION', 'STRAND', 'DESC', 'SRAS', 'PRAS', 'PART_CHROM' 'PART_CONTIG', 'PART_OFFSET', 'PART_STRAND', 'FILT' ]
[docs] def make_html_table( self, dataset, skipchars=None ): """Create HTML table, used for displaying peek""" if skipchars is None: skipchars = [] out = ['<table cellspacing="0" cellpadding="3">'] try: # Generate column header out.append( '<tr>' ) for i, name in enumerate( self.column_names ): out.append( '<th>%s.%s</th>' % ( str( i + 1 ), name ) ) # This data type requires at least 11 columns in the data if dataset.metadata.columns - len( self.column_names ) > 0: for i in range( len( self.column_names ), dataset.metadata.columns ): out.append( '<th>%s</th>' % str( i + 1 ) ) out.append( '</tr>' ) out.append( self.make_html_peek_rows( dataset, skipchars=skipchars ) ) out.append( '</table>' ) out = "".join( out ) except Exception as exc: out = "Can't create peek %s" % exc return out
[docs] def sniff( self, filename ): """ 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. """ try: fh = compression_utils.get_fileobj(filename, gzip_only=True) count = 0 while True: line = fh.readline() line = line.strip() if not line: break # EOF if line: line_pieces = line.split('\t') if len(line_pieces) != 22: return False try: if long(line_pieces[1]) < 0: raise Exception('Out of range') if long(line_pieces[2]) < 0: raise Exception('Out of range') if long(line_pieces[3]) < 0: raise Exception('Out of range') int(line_pieces[4]) int(line_pieces[5]) # can get a lot more specific except ValueError: fh.close() return False count += 1 if count == 5: break if count > 0: fh.close() return True except: pass fh.close() return False
[docs] def set_meta( self, dataset, overwrite=True, skip=None, max_data_lines=5, **kwd ): if dataset.has_data(): dataset_fh = compression_utils.get_fileobj(dataset.file_name, gzip_only=True) try: lanes = {} tiles = {} barcodes = {} reads = {} # Should always read the entire file (until we devise a more clever way to pass metadata on) # if self.max_optional_metadata_filesize >= 0 and dataset.get_size() > self.max_optional_metadata_filesize: # If the dataset is larger than optional_metadata, just count comment lines. # dataset.metadata.data_lines = None # else: # Otherwise, read the whole thing and set num data lines. for i, line in enumerate(dataset_fh): if line: line_pieces = line.split('\t') if len(line_pieces) != 22: raise Exception('%s:%d:Corrupt line!' % (dataset.file_name, i)) lanes[line_pieces[2]] = 1 tiles[line_pieces[3]] = 1 barcodes[line_pieces[6]] = 1 reads[line_pieces[7]] = 1 pass dataset.metadata.data_lines = i + 1 finally: dataset_fh.close() dataset.metadata.comment_lines = 0 dataset.metadata.columns = 21 dataset.metadata.column_types = ['str', 'int', 'int', 'int', 'int', 'int', 'str', 'int', 'str', 'str', 'str', 'str', 'str', 'str', 'str', 'str', 'str', 'str', 'str', 'str', 'str'] dataset.metadata.lanes = list(lanes.keys()) dataset.metadata.tiles = ["%04d" % int(t) for t in tiles.keys()] dataset.metadata.barcodes = [_ for _ in barcodes.keys() if _ != '0'] + ['NoIndex' for _ in barcodes.keys() if _ == '0'] dataset.metadata.reads = list(reads.keys())
[docs]class ElandMulti( Tabular ): file_ext = 'elandmulti'
[docs] def sniff( self, filename ): return False
[docs]class FeatureLocationIndex( Tabular ): """ An index that stores feature locations in tabular format. """ file_ext = 'fli' MetadataElement( name="columns", default=2, desc="Number of columns", readonly=True, visible=False ) MetadataElement( name="column_types", default=['str', 'str'], param=metadata.ColumnTypesParameter, desc="Column types", readonly=True, visible=False, no_value=[] )
[docs]@dataproviders.decorators.has_dataproviders class BaseCSV( 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 # File chunk used for sniffing CSV dialect big_peek_size = 10240 # Large File chunk used for sniffing CSV dialect
[docs] def is_int( self, column_text ): try: int( column_text ) return True except: return False
[docs] def is_float( self, column_text ): try: float( column_text ) return True except: if column_text.strip().lower() == 'na': return True # na is special cased to be a float return False
[docs] def guess_type( self, text ): if self.is_int(text): return 'int' if self.is_float(text): return 'float' else: return 'str'
[docs] def sniff( self, filename ): """ Return True if if recognizes dialect and header. """ try: # check the dialect works reader = csv.reader(open(filename, 'r'), self.dialect) # Check we can read header and get columns header_row = next(reader) if len(header_row) < 2: # No columns so not separated by this dialect. return False # Check that there is a second row as it is used by set_meta and # that all rows can be read if self.strict_width: num_columns = len(header_row) found_second_line = False for data_row in reader: found_second_line = True # All columns must be the same length if num_columns != len(data_row): return False if not found_second_line: return False else: data_row = next(reader) if len(data_row) < 2: # No columns so not separated by this dialect. return False # ignore the length in the rest for data_row in reader: pass # Optional: Check Python's csv comes up with a similar dialect auto_dialect = csv.Sniffer().sniff(open(filename, 'r').read(self.big_peek_size)) if (auto_dialect.delimiter != self.dialect.delimiter): return False if (auto_dialect.quotechar != self.dialect.quotechar): return False """ Not checking for other dialect options They may be mis detected from just the sample. Or not effect the read such as doublequote Optional: Check for headers as in the past. Note No way around Python's csv calling Sniffer.sniff again. Note Without checking the dialect returned by sniff this test may be checking the wrong dialect. """ if not csv.Sniffer().has_header(open(filename, 'r').read(self.big_peek_size)): return False return True except: # Not readable by Python's csv using this dialect return False
[docs] def set_meta( self, dataset, **kwd ): with open(dataset.file_name, 'r') as csvfile: # Parse file with the correct dialect reader = csv.reader(csvfile, self.dialect) data_row = None header_row = None try: header_row = next(reader) data_row = next(reader) for row in reader: pass except csv.Error as e: raise Exception('CSV reader error - line %d: %s' % (reader.line_num, e)) # Guess column types column_types = [] for cell in data_row: column_types.append(self.guess_type(cell)) # Set metadata dataset.metadata.data_lines = reader.line_num - 1 dataset.metadata.comment_lines = 1 dataset.metadata.column_types = column_types dataset.metadata.columns = max( len( header_row ), len( data_row ) ) dataset.metadata.column_names = header_row dataset.metadata.delimiter = reader.dialect.delimiter
[docs]@dataproviders.decorators.has_dataproviders class CSV( BaseCSV ): """ Comma-separated table data. Only sniffs comma-separated files with at least 2 rows and 2 columns. """ file_ext = 'csv' dialect = csv.excel # This is the default strict_width = False # Previous csv type did not check column width
[docs]@dataproviders.decorators.has_dataproviders class TSV( 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 = csv.excel_tab strict_width = True # Leave files with different width to tabular
[docs]class ConnectivityTable( 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]+")
[docs] def __init__(self, **kwd): super( ConnectivityTable, self ).__init__( **kwd ) self.columns = 6 self.column_names = ['base_index', 'base', 'neighbor_left', 'neighbor_right', 'partner', 'natural_numbering'] self.column_types = ['int', 'str', 'int', 'int', 'int', 'int']
[docs] def set_meta( self, dataset, **kwd ): data_lines = 0 for line in open( dataset.file_name ): data_lines += 1 dataset.metadata.data_lines = data_lines
[docs] def sniff(self, filename): """ 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:: 5 energy = -12.3 sequence name 1 G 0 2 0 1 2 A 1 3 0 2 3 A 2 4 0 3 4 A 3 5 0 4 5 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:: 10 ENERGY = -34.8 seqname 1 G 0 2 9 1 2 G 1 3 8 2 3 G 2 4 7 3 4 a 3 5 0 4 5 a 4 6 0 5 6 a 5 7 0 6 7 C 6 8 3 7 8 C 7 9 2 8 9 C 8 10 1 9 10 a 9 0 0 10 """ i = 0 j = 1 try: with open( filename ) as handle: for line in handle: line = line.strip() if len(line) > 0: if i == 0: if not self.header_regexp.match(line): return False else: length = int(re.split('\W+', line, 1)[0]) else: if not self.structure_regexp.match(line.upper()): return False else: if j != int(re.split('\W+', line, 1)[0]): return False elif j == length: # Last line of first sequence has been recheached return True else: j += 1 i += 1 return False except: return False
[docs] def get_chunk(self, trans, dataset, chunk): ck_index = int(chunk) f = open(dataset.file_name) f.seek(ck_index * trans.app.config.display_chunk_size) # If we aren't at the start of the file, seek to next newline. Do this better eventually. if f.tell() != 0: cursor = f.read(1) while cursor and cursor != '\n': cursor = f.read(1) ck_data = f.read(trans.app.config.display_chunk_size) cursor = f.read(1) while cursor and ck_data[-1] != '\n': ck_data += cursor cursor = f.read(1) # The ConnectivityTable format has several derivatives of which one is delimited by (multiple) spaces. # By converting these spaces back to tabs, chucks can still be interpreted by tab delimited file parsers ck_data_header, ck_data_body = ck_data.split('\n', 1) ck_data_header = re.sub('^([0-9]+)[ ]+', r'\1\t', ck_data_header) ck_data_body = re.sub('\n[ \t]+', '\n', ck_data_body) ck_data_body = re.sub('[ ]+', '\t', ck_data_body) return dumps( { 'ck_data': util.unicodify(ck_data_header + "\n" + ck_data_body ), 'ck_index': ck_index + 1 } )