text: """
  Tabular datatype
  """
  from __future__ import absolute_import
  
  import abc
  import binascii
  import csv
  import logging
  import os
  import re
  import shutil
  import subprocess
  import sys
  import tempfile
  from json import dumps
  
  import pysam
  from markupsafe import escape
  
  from galaxy import util
  from galaxy.datatypes import binary, data, metadata
  from galaxy.datatypes.metadata import MetadataElement
  from galaxy.datatypes.sniff import (
      build_sniff_from_prefix,
      get_headers,
      iter_headers
  )
  from galaxy.util import compression_utils
  from . import dataproviders
  
  if sys.version_info > (3,):
      long = int
  
  log = logging.getLogger(__name__)
  
  
  @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=[])
  
      @abc.abstractmethod
      def set_meta(self, dataset, **kwd):
          raise NotImplementedError
  
      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, 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)))
  
      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 Exception:
              return False
  
      def get_chunk(self, trans, dataset, offset=0, ck_size=None):
          with compression_utils.get_fileobj(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})
  
      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, mode='rb')
              else:
                  trans.response.set_content_type("text/html")
                  return trans.stream_template_mako("/dataset/large_file.mako",
                                                    truncated_data=open(dataset.file_name, mode='r').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)
  
      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" % util.unicodify(exc)
          return out
  
      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 Exception:
                          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" % util.unicodify(exc))
          return "".join(out)
  
      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" % util.unicodify(exc))
          return "".join(out)
  
      def display_peek(self, dataset):
          """Returns formatted html of peek"""
          return self.make_html_table(dataset)
  
      # ------------- Dataproviders
      @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)
  
      @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)
  
      @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)
  
      @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)
  
  
  @dataproviders.decorators.has_dataproviders
  class Tabular(TabularData):
      """Tab delimited data"""
  
      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 ValueError:
                  return False
  
          def is_float(column_text):
              try:
                  float(column_text)
                  return True
              except ValueError:
                  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
              with compression_utils.get_fileobj(dataset.file_name) as dataset_fh:
                  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
  
          # 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'
  
      def as_gbrowse_display_file(self, dataset, **kwd):
          return open(dataset.file_name, 'rb')
  
      def as_ucsc_display_file(self, dataset, **kwd):
          return open(dataset.file_name, 'rb')
  
  
  class Taxonomy(Tabular):
      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'
                               ]
  
      def display_peek(self, dataset):
          """Returns formated html of peek"""
          return self.make_html_table(dataset, column_names=self.column_names)
  
  
  @dataproviders.decorators.has_dataproviders
  @build_sniff_from_prefix
  class Sam(Tabular):
      edam_format = "format_2573"
      edam_data = "data_0863"
      file_ext = 'sam'
      track_type = "ReadTrack"
      data_sources = {"data": "bam", "index": "bigwig"}
  
      def __init__(self, **kwd):
          """Initialize sam datatype"""
          super(Sam, self).__init__(**kwd)
          self.column_names = ['QNAME', 'FLAG', 'RNAME', 'POS', 'MAPQ', 'CIGAR',
                               'MRNM', 'MPOS', 'ISIZE', 'SEQ', 'QUAL', 'OPT'
                               ]
  
      def display_peek(self, dataset):
          """Returns formated html of peek"""
          return self.make_html_table(dataset, column_names=self.column_names)
  
      def sniff_prefix(self, file_prefix):
          """
          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
          """
          fh = file_prefix.string_io()
          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
          if count < 5 and count > 0:
              return True
          return False
  
      def set_meta(self, dataset, overwrite=True, skip=None, max_data_lines=5, **kwd):
          if dataset.has_data():
              with open(dataset.file_name) as dataset_fh:
                  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.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']
  
      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?
      @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)
  
      @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)
  
      @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)
  
      @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)
  
      @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)
  
      @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)
  
      @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)
  
      @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)
  
      @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)
  
      @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 )
  
  
  @dataproviders.decorators.has_dataproviders
  @build_sniff_from_prefix
  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)
  
      def init_meta(self, dataset, copy_from=None):
          super(Pileup, self).init_meta(dataset, copy_from=copy_from)
  
      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'})
  
      def repair_methods(self, dataset):
          """Return options for removing errors along with a description"""
          return [("lines", "Remove erroneous lines")]
  
      def sniff_prefix(self, file_prefix):
          """
          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
          """
          found_non_comment_lines = False
          try:
              headers = iter_headers(file_prefix, '\t')
              for hdr in headers:
                  if hdr and not hdr[0].startswith('#'):
                      if len(hdr) < 5:
                          return False
                      # 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']
                      found_non_comment_lines = True
          except Exception:
              return False
          return found_non_comment_lines
  
      # Dataproviders
      @dataproviders.decorators.dataprovider_factory('genomic-region',
                                                     dataproviders.dataset.GenomicRegionDataProvider.settings)
      def genomic_region_dataprovider(self, dataset, **settings):
          return dataproviders.dataset.GenomicRegionDataProvider(dataset, **settings)
  
      @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)
  
  
  @dataproviders.decorators.has_dataproviders
  @build_sniff_from_prefix
  class BaseVcf(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']
  
      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=[])
  
      def _sniff(self, fname_or_file_prefix):
          # Because this sniffer is run on compressed files that might be BGZF (due to the VcfGz subclass), we should
          # handle unicode decode errors. This should ultimately be done in get_headers(), but guess_ext() currently
          # relies on get_headers() raising this exception.
          headers = get_headers(fname_or_file_prefix, '\n', count=1)
          return headers[0][0].startswith("##fileformat=VCF")
  
      def display_peek(self, dataset):
          """Returns formated html of peek"""
          return self.make_html_table(dataset, column_names=self.column_names)
  
      def set_meta(self, dataset, **kwd):
          super(BaseVcf, 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:]
  
      @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
      @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)
  
      @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)
  
  
  class Vcf(BaseVcf):
      file_ext = 'vcf'
  
      def sniff_prefix(self, file_prefix):
          return self._sniff(file_prefix)
  
  
  class VcfGz(BaseVcf, binary.Binary):
      # This class name is a misnomer, should be VcfBgzip
      file_ext = 'vcf_bgzip'
      compressed = True
      compressed_format = "gzip"
  
      MetadataElement(name="tabix_index", desc="Vcf Index File", param=metadata.FileParameter, file_ext="tbi", readonly=True, no_value=None, visible=False, optional=True)
  
      def sniff(self, filename):
          if not self._sniff(filename):
              return False
          # Check that the file is compressed with bgzip (not gzip), i.e. the
          # compressed format is BGZF, as explained in
          # http://samtools.github.io/hts-specs/SAMv1.pdf
          with open(filename, 'rb') as fh:
              fh.seek(-28, 2)
              last28 = fh.read()
              return binascii.hexlify(last28) == b'1f8b08040000000000ff0600424302001b0003000000000000000000'
  
      def set_meta(self, dataset, **kwd):
          super(BaseVcf, self).set_meta(dataset, **kwd)
          """ Creates the index for the VCF file. """
          # These metadata values are not accessible by users, always overwrite
          index_file = dataset.metadata.tabix_index
          if not index_file:
              index_file = dataset.metadata.spec['tabix_index'].param.new_file(dataset=dataset)
  
          try:
              pysam.tabix_index(dataset.file_name, index=index_file.file_name, preset='vcf', force=True)
          except Exception as e:
              raise Exception('Error setting VCF.gz metadata: %s' % (util.unicodify(e)))
          dataset.metadata.tabix_index = index_file
  
  
  @build_sniff_from_prefix
  class Eland(Tabular):
      """Support for the export.txt.gz file used by Illumina's ELANDv2e aligner"""
      compressed = True
      compressed_format = "gzip"
      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=[])
  
      def __init__(self, **kwd):
          """Initialize eland 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'
                               ]
  
      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
  
      def sniff_prefix(self, file_prefix):
          """
          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.
          """
          fh = file_prefix.string_io()
          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
                  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
                  count += 1
                  if count == 5:
                      break
          if count > 0:
              return True
  
      def set_meta(self, dataset, overwrite=True, skip=None, max_data_lines=5, **kwd):
          if dataset.has_data():
              with compression_utils.get_fileobj(dataset.file_name, compressed_formats=['gzip']) as dataset_fh:
                  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
              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())
  
  
  @build_sniff_from_prefix
  class ElandMulti(Tabular):
      file_ext = 'elandmulti'
  
      def sniff_prefix(self, file_prefix):
          return False
  
  
  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=[])
  
  
  @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
  
      def is_int(self, column_text):
          try:
              int(column_text)
              return True
          except ValueError:
              return False
  
      def is_float(self, column_text):
          try:
              float(column_text)
              return True
          except ValueError:
              if column_text.strip().lower() == 'na':
                  return True  # na is special cased to be a float
              return False
  
      def guess_type(self, text):
          if self.is_int(text):
              return 'int'
          if self.is_float(text):
              return 'float'
          else:
              return 'str'
  
      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 Exception:
              # Not readable by Python's csv using this dialect
              return False
  
      def set_meta(self, dataset, **kwd):
          column_types = []
          header_row = []
          data_row = []
          data_lines = 0
          if dataset.has_data():
              with open(dataset.file_name, 'r') as csvfile:
                  # Parse file with the correct dialect
                  reader = csv.reader(csvfile, self.dialect)
                  try:
                      header_row = next(reader)
                      data_row = next(reader)
                      for row in reader:
                          pass
                  except StopIteration:
                      pass
                  except csv.Error as e:
                      raise Exception('CSV reader error - line %d: %s' % (reader.line_num, e))
                  else:
                      data_lines = reader.line_num - 1
  
          # Guess column types
          for cell in data_row:
              column_types.append(self.guess_type(cell))
  
          # Set metadata
          dataset.metadata.data_lines = data_lines
          dataset.metadata.comment_lines = int(bool(header_row))
          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 = self.dialect.delimiter
  
  
  @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
  
  
  @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
  
  
  @build_sniff_from_prefix
  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]+")
  
      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']
  
      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
  
      def sniff_prefix(self, file_prefix):
          """
          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
  
          handle = file_prefix.string_io()
          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(r'\W+', line, 1)[0])
                  else:
                      if not self.structure_regexp.match(line.upper()):
                          return False
                      else:
                          if j != int(re.split(r'\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
  
      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})
  
  
  @build_sniff_from_prefix
  class MatrixMarket(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"
  
      def __init__(self, **kwd):
          super(MatrixMarket, self).__init__(**kwd)
  
      def sniff_prefix(self, file_prefix):
          return file_prefix.startswith('%%MatrixMarket matrix coordinate')
  
      def set_meta(self, dataset, overwrite=True, skip=None, max_data_lines=5, **kwd):
          if dataset.has_data():
              with open(dataset.file_name) as dataset_fh:
                  comment_lines = 0
                  for i, l in enumerate(dataset_fh):
                      if l.startswith('%'):
                          comment_lines += 1
                      elif 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.
                          # No more comments, and the file is too big to look at the whole thing. Give up.
                          dataset.metadata.data_lines = None
                          break
                  if ' ' in l:
                      dataset.metadata.delimiter = ' '
                  else:
                      dataset.metadata.delimiter = '\t'
                  if not (self.max_optional_metadata_filesize >= 0 and dataset.get_size() > self.max_optional_metadata_filesize):
                      dataset.metadata.data_lines = i + 1 - comment_lines
              dataset.metadata.comment_lines = comment_lines
              dataset.metadata.columns = 3
              dataset.metadata.column_types = ['int', 'int', 'float']
  