text: """
  Interval datatypes
  """
  import logging
  import math
  import sys
  import tempfile
  
  from bx.intervals.io import GenomicIntervalReader, ParseError
  from six.moves.urllib.parse import quote_plus
  
  from galaxy import util
  from galaxy.datatypes import metadata
  from galaxy.datatypes.data import DatatypeValidation
  from galaxy.datatypes.metadata import MetadataElement
  from galaxy.datatypes.sniff import (
      build_sniff_from_prefix,
      get_headers,
      iter_headers
  )
  from galaxy.datatypes.tabular import Tabular
  from galaxy.datatypes.util.gff_util import parse_gff3_attributes, parse_gff_attributes
  from galaxy.util import compression_utils
  from . import (
      data,
      dataproviders
  )
  
  log = logging.getLogger(__name__)
  
  # Contains the meta columns and the words that map to it; list aliases on the
  # right side of the : in decreasing order of priority
  alias_spec = {
      'chromCol'  : ['chrom', 'CHROMOSOME', 'CHROM', 'Chromosome Name'],
      'startCol'  : ['start', 'START', 'chromStart', 'txStart', 'Start Position (bp)'],
      'endCol'    : ['end', 'END', 'STOP', 'chromEnd', 'txEnd', 'End Position (bp)'],
      'strandCol' : ['strand', 'STRAND', 'Strand'],
      'nameCol'   : ['name', 'NAME', 'Name', 'name2', 'NAME2', 'Name2', 'Ensembl Gene ID', 'Ensembl Transcript ID', 'Ensembl Peptide ID']
  }
  
  # a little faster lookup
  alias_helper = {}
  for key, value in alias_spec.items():
      for elem in value:
          alias_helper[elem] = key
  
  # Constants for configuring viewport generation: If a line is greater than
  # VIEWPORT_MAX_READS_PER_LINE * VIEWPORT_READLINE_BUFFER_SIZE bytes in size,
  # then we will not generate a viewport for that dataset
  VIEWPORT_READLINE_BUFFER_SIZE = 1048576  # 1MB
  VIEWPORT_MAX_READS_PER_LINE = 10
  
  
  @dataproviders.decorators.has_dataproviders
  @build_sniff_from_prefix
  class Interval(Tabular):
      """Tab delimited data containing interval information"""
      edam_data = "data_3002"
      edam_format = "format_3475"
      file_ext = "interval"
      line_class = "region"
      track_type = "FeatureTrack"
      data_sources = {"data": "tabix", "index": "bigwig"}
  
      """Add metadata elements"""
      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=3, desc="End column", param=metadata.ColumnParameter)
      MetadataElement(name="strandCol", desc="Strand column (click box & select)", param=metadata.ColumnParameter, optional=True, no_value=0)
      MetadataElement(name="nameCol", desc="Name/Identifier column (click box & select)", param=metadata.ColumnParameter, optional=True, no_value=0)
      MetadataElement(name="columns", default=3, desc="Number of columns", readonly=True, visible=False)
  
      def __init__(self, **kwd):
          """Initialize interval datatype, by adding UCSC display apps"""
          Tabular.__init__(self, **kwd)
          self.add_display_app('ucsc', 'display at UCSC', 'as_ucsc_display_file', 'ucsc_links')
  
      def init_meta(self, dataset, copy_from=None):
          Tabular.init_meta(self, dataset, copy_from=copy_from)
  
      def set_meta(self, dataset, overwrite=True, first_line_is_header=False, **kwd):
          """Tries to guess from the line the location number of the column for the chromosome, region start-end and strand"""
          Tabular.set_meta(self, dataset, overwrite=overwrite, skip=0)
          if dataset.has_data():
              empty_line_count = 0
              num_check_lines = 100  # only check up to this many non empty lines
              with compression_utils.get_fileobj(dataset.file_name) as in_fh:
                  for i, line in enumerate(in_fh):
                      line = line.rstrip('\r\n')
                      if line:
                          if (first_line_is_header or line[0] == '#'):
                              self.init_meta(dataset)
                              line = line.strip('#')
                              elems = line.split('\t')
                              for meta_name, header_list in alias_spec.items():
                                  for header_val in header_list:
                                      if header_val in elems:
                                          # found highest priority header to meta_name
                                          setattr(dataset.metadata, meta_name, elems.index(header_val) + 1)
                                          break  # next meta_name
                              break  # Our metadata is set, so break out of the outer loop
                          else:
                              # Header lines in Interval files are optional. For example, BED is Interval but has no header.
                              # We'll make a best guess at the location of the metadata columns.
                              elems = line.split('\t')
                              if len(elems) > 2:
                                  if overwrite or not dataset.metadata.element_is_set('chromCol'):
                                      dataset.metadata.chromCol = 1
                                  try:
                                      int(elems[1])
                                      if overwrite or not dataset.metadata.element_is_set('startCol'):
                                          dataset.metadata.startCol = 2
                                  except Exception:
                                      pass  # Metadata default will be used
                                  try:
                                      int(elems[2])
                                      if overwrite or not dataset.metadata.element_is_set('endCol'):
                                          dataset.metadata.endCol = 3
                                  except Exception:
                                      pass  # Metadata default will be used
                                  # we no longer want to guess that this column is the 'name', name must now be set manually for interval files
                                  # we will still guess at the strand, as we can make a more educated guess
                                  # if len( elems ) > 3:
                                  #    try:
                                  #        int( elems[3] )
                                  #    except Exception:
                                  #        if overwrite or not dataset.metadata.element_is_set( 'nameCol' ):
                                  #            dataset.metadata.nameCol = 4
                                  if len(elems) < 6 or elems[5] not in data.valid_strand:
                                      if overwrite or not dataset.metadata.element_is_set('strandCol'):
                                          dataset.metadata.strandCol = 0
                                  else:
                                      if overwrite or not dataset.metadata.element_is_set('strandCol'):
                                          dataset.metadata.strandCol = 6
                                  break
                              if (i - empty_line_count) > num_check_lines:
                                  break  # Our metadata is set or we examined 100 non-empty lines, so break out of the outer loop
                      else:
                          empty_line_count += 1
  
      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 \
                  and dataset.metadata.chromCol \
                  and dataset.metadata.startCol \
                  and dataset.metadata.endCol
          except Exception:
              return False
  
      def get_estimated_display_viewport(self, dataset, chrom_col=None, start_col=None, end_col=None):
          """Return a chrom, start, stop tuple for viewing a file."""
          viewport_feature_count = 100  # viewport should check at least 100 features; excludes comment lines
          max_line_count = max(viewport_feature_count, 500)  # maximum number of lines to check; includes comment lines
          if not self.displayable(dataset):
              return (None, None, None)
          try:
              # If column indexes were not passwed, determine from metadata
              if chrom_col is None:
                  chrom_col = int(dataset.metadata.chromCol) - 1
              if start_col is None:
                  start_col = int(dataset.metadata.startCol) - 1
              if end_col is None:
                  end_col = int(dataset.metadata.endCol) - 1
              # Scan lines of file to find a reasonable chromosome and range
              chrom = None
              start = sys.maxsize
              end = 0
              max_col = max(chrom_col, start_col, end_col)
              with compression_utils.get_fileobj(dataset.file_name) as fh:
                  for line in util.iter_start_of_line(fh, VIEWPORT_READLINE_BUFFER_SIZE):
                      # Skip comment lines
                      if not line.startswith('#'):
                          try:
                              fields = line.rstrip().split('\t')
                              if len(fields) > max_col:
                                  if chrom is None or chrom == fields[chrom_col]:
                                      start = min(start, int(fields[start_col]))
                                      end = max(end, int(fields[end_col]))
                                      # Set chrom last, in case start and end are not integers
                                      chrom = fields[chrom_col]
                                  viewport_feature_count -= 1
                          except Exception:
                              # Most likely a non-integer field has been encountered
                              # for start / stop. Just ignore and make sure we finish
                              # reading the line and decrementing the counters.
                              pass
                      # Make sure we are at the next new line
                      readline_count = VIEWPORT_MAX_READS_PER_LINE
                      while line.rstrip('\n\r') == line:
                          assert readline_count > 0, Exception('Viewport readline count exceeded for dataset %s.' % dataset.id)
                          line = fh.readline(VIEWPORT_READLINE_BUFFER_SIZE)
                          if not line:
                              break  # EOF
                          readline_count -= 1
                      max_line_count -= 1
                      if not viewport_feature_count or not max_line_count:
                          # exceeded viewport or total line count to check
                          break
              if chrom is not None:
                  return (chrom, str(start), str(end))  # Necessary to return strings?
          except Exception:
              # Unexpected error, possibly missing metadata
              log.exception("Exception caught attempting to generate viewport for dataset '%d'", dataset.id)
          return (None, None, None)
  
      def as_ucsc_display_file(self, dataset, **kwd):
          """Returns file contents with only the bed data"""
          with tempfile.NamedTemporaryFile(delete=False, mode='w') as fh:
              c, s, e, t, n = dataset.metadata.chromCol, dataset.metadata.startCol, dataset.metadata.endCol, dataset.metadata.strandCol or 0, dataset.metadata.nameCol or 0
              c, s, e, t, n = int(c) - 1, int(s) - 1, int(e) - 1, int(t) - 1, int(n) - 1
              if t >= 0:  # strand column (should) exists
                  for i, elems in enumerate(compression_utils.file_iter(dataset.file_name)):
                      strand = "+"
                      name = "region_%i" % i
                      if n >= 0 and n < len(elems):
                          name = elems[n]
                      if t < len(elems):
                          strand = elems[t]
                      tmp = [elems[c], elems[s], elems[e], name, '0', strand]
                      fh.write('%s\n' % '\t'.join(tmp))
              elif n >= 0:  # name column (should) exists
                  for i, elems in enumerate(compression_utils.file_iter(dataset.file_name)):
                      name = "region_%i" % i
                      if n >= 0 and n < len(elems):
                          name = elems[n]
                      tmp = [elems[c], elems[s], elems[e], name]
                      fh.write('%s\n' % '\t'.join(tmp))
              else:
                  for elems in compression_utils.file_iter(dataset.file_name):
                      tmp = [elems[c], elems[s], elems[e]]
                      fh.write('%s\n' % '\t'.join(tmp))
              return compression_utils.get_fileobj(fh.name, mode='rb')
  
      def display_peek(self, dataset):
          """Returns formated html of peek"""
          return self.make_html_table(dataset, column_parameter_alias={'chromCol': 'Chrom', 'startCol': 'Start', 'endCol': 'End', 'strandCol': 'Strand', 'nameCol': 'Name'})
  
      def ucsc_links(self, dataset, type, app, base_url):
          """
          Generate links to UCSC genome browser sites based on the dbkey
          and content of dataset.
          """
          # Filter UCSC sites to only those that are supported by this build and
          # enabled.
          valid_sites = [(name, url)
                         for name, url in app.datatypes_registry.get_legacy_sites_by_build('ucsc', dataset.dbkey)
                         if name in app.datatypes_registry.get_display_sites('ucsc')]
          if not valid_sites:
              return []
          # If there are any valid sites, we need to generate the estimated
          # viewport
          chrom, start, stop = self.get_estimated_display_viewport(dataset)
          if chrom is None:
              return []
          # Accumulate links for valid sites
          ret_val = []
          for site_name, site_url in valid_sites:
              internal_url = app.url_for(controller='dataset', dataset_id=dataset.id,
                                         action='display_at', filename='ucsc_' + site_name)
              display_url = quote_plus("%s%s/display_as?id=%i&display_app=%s&authz_method=display_at" %
                                       (base_url, app.url_for(controller='root'), dataset.id, type))
              redirect_url = quote_plus("%sdb=%s&position=%s:%s-%s&hgt.customText=%%s" %
                                        (site_url, dataset.dbkey, chrom, start, stop))
              link = '%s?redirect_url=%s&display_url=%s' % (internal_url, redirect_url, display_url)
              ret_val.append((site_name, link))
          return ret_val
  
      def validate(self, dataset, **kwd):
          """Validate an interval file using the bx GenomicIntervalReader"""
          c, s, e, t = dataset.metadata.chromCol, dataset.metadata.startCol, dataset.metadata.endCol, dataset.metadata.strandCol
          c, s, e, t = int(c) - 1, int(s) - 1, int(e) - 1, int(t) - 1
          with compression_utils.get_fileobj(dataset.file_name, "r") as infile:
              reader = GenomicIntervalReader(
                  infile,
                  chrom_col=c,
                  start_col=s,
                  end_col=e,
                  strand_col=t)
  
              while True:
                  try:
                      next(reader)
                  except ParseError as e:
                      return DatatypeValidation.invalid(util.unicodify(e))
                  except StopIteration:
                      return DatatypeValidation.valid()
  
      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 'intervalness'
  
          This format is mostly used by galaxy itself.  Valid interval files should include
          a valid header comment, but this seems to be loosely regulated.
  
          >>> from galaxy.datatypes.sniff import get_test_fname
          >>> fname = get_test_fname( 'test_space.txt' )
          >>> Interval().sniff( fname )
          False
          >>> fname = get_test_fname( 'interval.interval' )
          >>> Interval().sniff( fname )
          True
          """
          found_valid_lines = False
          try:
              headers = iter_headers(file_prefix, '\t', comment_designator='#')
              # If we got here, we already know the file is_column_based and is not bed,
              # so we'll just look for some valid data.
              for hdr in headers:
                  if hdr:
                      if len(hdr) < 3:
                          return False
                      # Assume chrom start and end are in column positions 1 and 2
                      # respectively ( for 0 based columns )
                      int(hdr[1])
                      int(hdr[2])
                      found_valid_lines = True
          except Exception:
              return False
          return found_valid_lines
  
      def get_track_resolution(self, dataset, start, end):
          return None
  
      # ------------- 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.dataprovider_factory('interval',
                                                     dataproviders.dataset.IntervalDataProvider.settings)
      def interval_dataprovider(self, dataset, **settings):
          return dataproviders.dataset.IntervalDataProvider(dataset, **settings)
  
      @dataproviders.decorators.dataprovider_factory('interval-dict',
                                                     dataproviders.dataset.IntervalDataProvider.settings)
      def interval_dict_dataprovider(self, dataset, **settings):
          settings['named_columns'] = True
          return self.interval_dataprovider(dataset, **settings)
  
  
  class BedGraph(Interval):
      """Tab delimited chrom/start/end/datavalue dataset"""
      edam_format = "format_3583"
      file_ext = "bedgraph"
      track_type = "LineTrack"
      data_sources = {"data": "bigwig", "index": "bigwig"}
  
      def as_ucsc_display_file(self, dataset, **kwd):
          """
              Returns file contents as is with no modifications.
              TODO: this is a functional stub and will need to be enhanced moving forward to provide additional support for bedgraph.
          """
          return open(dataset.file_name, 'rb')
  
      def get_estimated_display_viewport(self, dataset, chrom_col=0, start_col=1, end_col=2):
          """
              Set viewport based on dataset's first 100 lines.
          """
          return Interval.get_estimated_display_viewport(self, dataset, chrom_col=chrom_col, start_col=start_col, end_col=end_col)
  
  
  class Bed(Interval):
      """Tab delimited data in BED format"""
      edam_format = "format_3003"
      file_ext = "bed"
      data_sources = {"data": "tabix", "index": "bigwig", "feature_search": "fli"}
      track_type = Interval.track_type
  
      column_names = ['Chrom', 'Start', 'End', 'Name', 'Score', 'Strand', 'ThickStart', 'ThickEnd', 'ItemRGB', 'BlockCount', 'BlockSizes', 'BlockStarts']
  
      """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=3, desc="End column", param=metadata.ColumnParameter)
      MetadataElement(name="strandCol", desc="Strand column (click box & select)", param=metadata.ColumnParameter, optional=True, no_value=0)
      MetadataElement(name="columns", default=3, desc="Number of columns", readonly=True, visible=False)
      MetadataElement(name="viz_filter_cols", desc="Score column for visualization", default=[4], param=metadata.ColumnParameter, optional=True, multiple=True)
      # do we need to repeat these? they are the same as should be inherited from interval type
  
      def set_meta(self, dataset, overwrite=True, **kwd):
          """Sets the metadata information for datasets previously determined to be in bed format."""
          i = 0
          if dataset.has_data():
              for i, line in enumerate(open(dataset.file_name)):
                  line = line.rstrip('\r\n')
                  if line and not line.startswith('#'):
                      elems = line.split('\t')
                      if len(elems) > 2:
                          if len(elems) > 3:
                              if overwrite or not dataset.metadata.element_is_set('nameCol'):
                                  dataset.metadata.nameCol = 4
                          if len(elems) < 6:
                              if overwrite or not dataset.metadata.element_is_set('strandCol'):
                                  dataset.metadata.strandCol = 0
                          else:
                              if overwrite or not dataset.metadata.element_is_set('strandCol'):
                                  dataset.metadata.strandCol = 6
                          break
              Tabular.set_meta(self, dataset, overwrite=overwrite, skip=i)
  
      def as_ucsc_display_file(self, dataset, **kwd):
          """Returns file contents with only the bed data. If bed 6+, treat as interval."""
          for line in open(dataset.file_name):
              line = line.strip()
              if line == "" or line.startswith("#"):
                  continue
              fields = line.split('\t')
              """check to see if this file doesn't conform to strict genome browser accepted bed"""
              try:
                  if len(fields) > 12:
                      return Interval.as_ucsc_display_file(self, dataset)  # too many fields
                  if len(fields) > 6:
                      int(fields[6])
                      if len(fields) > 7:
                          int(fields[7])
                          if len(fields) > 8:
                              if int(fields[8]) != 0:
                                  return Interval.as_ucsc_display_file(self, dataset)
                              if len(fields) > 9:
                                  int(fields[9])
                                  if len(fields) > 10:
                                      fields2 = fields[10].rstrip(",").split(",")  # remove trailing comma and split on comma
                                      for field in fields2:
                                          int(field)
                                      if len(fields) > 11:
                                          fields2 = fields[11].rstrip(",").split(",")  # remove trailing comma and split on comma
                                          for field in fields2:
                                              int(field)
              except Exception:
                  return Interval.as_ucsc_display_file(self, dataset)
              # only check first line for proper form
              break
  
          try:
              return open(dataset.file_name, 'rb')
          except Exception:
              return "This item contains no content"
  
      def sniff_prefix(self, file_prefix):
          """
          Checks for 'bedness'
  
          BED lines have three required fields and nine additional optional fields.
          The number of fields per line must be consistent throughout any single set of data in
          an annotation track.  The order of the optional fields is binding: lower-numbered
          fields must always be populated if higher-numbered fields are used.  The data type of
          all 12 columns is:
          1-str, 2-int, 3-int, 4-str, 5-int, 6-str, 7-int, 8-int, 9-int or list, 10-int, 11-list, 12-list
  
          For complete details see http://genome.ucsc.edu/FAQ/FAQformat#format1
  
          >>> from galaxy.datatypes.sniff import get_test_fname
          >>> fname = get_test_fname( 'test_tab.bed' )
          >>> Bed().sniff( fname )
          True
          >>> fname = get_test_fname( 'interv1.bed' )
          >>> Bed().sniff( fname )
          True
          >>> fname = get_test_fname( 'complete.bed' )
          >>> Bed().sniff( fname )
          True
          """
          if not get_headers(file_prefix, '\t', comment_designator='#', count=1):
              return False
          try:
              headers = iter_headers(file_prefix, '\t', comment_designator='#')
              for hdr in headers:
                  if hdr[0] == '':
                      continue
                  if len(hdr) < 3 or len(hdr) > 12:
                      return False
                  try:
                      int(hdr[1])
                      int(hdr[2])
                  except Exception:
                      return False
                  if len(hdr) > 4:
                      # hdr[3] is a string, 'name', which defines the name of the BED line - difficult to test for this.
                      # hdr[4] is an int, 'score', a score between 0 and 1000.
                      try:
                          if int(hdr[4]) < 0 or int(hdr[4]) > 1000:
                              return False
                      except Exception:
                          return False
                  if len(hdr) > 5:
                      # hdr[5] is strand
                      if hdr[5] not in data.valid_strand:
                          return False
                  if len(hdr) > 6:
                      # hdr[6] is thickStart, the starting position at which the feature is drawn thickly.
                      try:
                          int(hdr[6])
                      except Exception:
                          return False
                  if len(hdr) > 7:
                      # hdr[7] is thickEnd, the ending position at which the feature is drawn thickly
                      try:
                          int(hdr[7])
                      except Exception:
                          return False
                  if len(hdr) > 8:
                      # hdr[8] is itemRgb, an RGB value of the form R,G,B (e.g. 255,0,0).  However, this could also be an int (e.g., 0)
                      try:
                          int(hdr[8])
                      except Exception:
                          try:
                              hdr[8].split(',')
                          except Exception:
                              return False
                  if len(hdr) > 9:
                      # hdr[9] is blockCount, the number of blocks (exons) in the BED line.
                      try:
                          block_count = int(hdr[9])
                      except Exception:
                          return False
                  if len(hdr) > 10:
                      # hdr[10] is blockSizes - A comma-separated list of the block sizes.
                      # Sometimes the blosck_sizes and block_starts lists end in extra commas
                      try:
                          block_sizes = hdr[10].rstrip(',').split(',')
                      except Exception:
                          return False
                  if len(hdr) > 11:
                      # hdr[11] is blockStarts - A comma-separated list of block starts.
                      try:
                          block_starts = hdr[11].rstrip(',').split(',')
                      except Exception:
                          return False
                      if len(block_sizes) != block_count or len(block_starts) != block_count:
                          return False
              return True
          except Exception:
              return False
  
  
  class ProBed(Bed):
      """Tab delimited data in proBED format - adaptation of BED for proteomics data."""
      edam_format = "format_3827"
      file_ext = "probed"
      column_names = ['Chrom', 'Start', 'End', 'Name', 'Score', 'Strand', 'ThickStart', 'ThickEnd', 'ItemRGB', 'BlockCount', 'BlockSizes', 'BlockStarts', 'ProteinAccession', 'PeptideSequence', 'Uniqueness', 'GenomeReferenceVersion', 'PsmScore', 'Fdr', 'Modifications', 'Charge', 'ExpMassToCharge', 'CalcMassToCharge', 'PsmRank', 'DatasetID', 'Uri']
  
  
  class BedStrict(Bed):
      """Tab delimited data in strict BED format - no non-standard columns allowed"""
      edam_format = "format_3584"
      file_ext = "bedstrict"
  
      # no user change of datatype allowed
      allow_datatype_change = False
  
      # Read only metadata elements
      MetadataElement(name="chromCol", default=1, desc="Chrom column", readonly=True, param=metadata.MetadataParameter)
      MetadataElement(name="startCol", default=2, desc="Start column", readonly=True, param=metadata.MetadataParameter)  # TODO: start and end should be able to be set to these or the proper thick[start/end]?
      MetadataElement(name="endCol", default=3, desc="End column", readonly=True, param=metadata.MetadataParameter)
      MetadataElement(name="strandCol", desc="Strand column (click box & select)", readonly=True, param=metadata.MetadataParameter, no_value=0, optional=True)
      MetadataElement(name="nameCol", desc="Name/Identifier column (click box & select)", readonly=True, param=metadata.MetadataParameter, no_value=0, optional=True)
      MetadataElement(name="columns", default=3, desc="Number of columns", readonly=True, visible=False)
  
      def __init__(self, **kwd):
          Tabular.__init__(self, **kwd)
          self.clear_display_apps()  # only new style display applications for this datatype
  
      def set_meta(self, dataset, overwrite=True, **kwd):
          Tabular.set_meta(self, dataset, overwrite=overwrite, **kwd)  # need column count first
          if dataset.metadata.columns >= 4:
              dataset.metadata.nameCol = 4
              if dataset.metadata.columns >= 6:
                  dataset.metadata.strandCol = 6
  
      def sniff(self, filename):
          return False  # NOTE: This would require aggressively validating the entire file
  
  
  class Bed6(BedStrict):
      """Tab delimited data in strict BED format - no non-standard columns allowed; column count forced to 6"""
      edam_format = "format_3585"
      file_ext = "bed6"
  
  
  class Bed12(BedStrict):
      """Tab delimited data in strict BED format - no non-standard columns allowed; column count forced to 12"""
      edam_format = "format_3586"
      file_ext = "bed12"
  
  
  class _RemoteCallMixin(object):
      def _get_remote_call_url(self, redirect_url, site_name, dataset, type, app, base_url):
          """Retrieve the URL to call out to an external site and retrieve data.
          This routes our external URL through a local galaxy instance which makes
          the data available, followed by redirecting to the remote site with a
          link back to the available information.
          """
          internal_url = "%s" % app.url_for(controller='dataset', dataset_id=dataset.id, action='display_at', filename='%s_%s' % (type, site_name))
          base_url = app.config.get("display_at_callback", base_url)
          display_url = quote_plus("%s%s/display_as?id=%i&display_app=%s&authz_method=display_at" %
                                   (base_url, app.url_for(controller='root'), dataset.id, type))
          link = '%s?redirect_url=%s&display_url=%s' % (internal_url, redirect_url, display_url)
          return link
  
  
  @dataproviders.decorators.has_dataproviders
  @build_sniff_from_prefix
  class Gff(Tabular, _RemoteCallMixin):
      """Tab delimited data in Gff format"""
      edam_data = "data_1255"
      edam_format = "format_2305"
      file_ext = "gff"
      valid_gff_frame = ['.', '0', '1', '2']
      column_names = ['Seqname', 'Source', 'Feature', 'Start', 'End', 'Score', 'Strand', 'Frame', 'Group']
      data_sources = {"data": "interval_index", "index": "bigwig", "feature_search": "fli"}
      track_type = Interval.track_type
  
      """Add metadata elements"""
      MetadataElement(name="columns", default=9, desc="Number of columns", readonly=True, visible=False)
      MetadataElement(name="column_types", default=['str', 'str', 'str', 'int', 'int', 'int', 'str', 'str', 'str'],
                      param=metadata.ColumnTypesParameter, desc="Column types", readonly=True, visible=False)
  
      MetadataElement(name="attributes", default=0, desc="Number of attributes", readonly=True, visible=False, no_value=0)
      MetadataElement(name="attribute_types", default={}, desc="Attribute types", param=metadata.DictParameter, readonly=True, visible=False, no_value=[])
  
      def __init__(self, **kwd):
          """Initialize datatype, by adding GBrowse display app"""
          Tabular.__init__(self, **kwd)
          self.add_display_app('ucsc', 'display at UCSC', 'as_ucsc_display_file', 'ucsc_links')
          self.add_display_app('gbrowse', 'display in Gbrowse', 'as_gbrowse_display_file', 'gbrowse_links')
  
      def set_attribute_metadata(self, dataset):
          """
          Sets metadata elements for dataset's attributes.
          """
  
          # Use first N lines to set metadata for dataset attributes. Attributes
          # not found in the first N lines will not have metadata.
          num_lines = 200
          attribute_types = {}
          with compression_utils.get_fileobj(dataset.file_name) as in_fh:
              for i, line in enumerate(in_fh):
                  if line and not line.startswith('#'):
                      elems = line.split('\t')
                      if len(elems) == 9:
                          try:
                              # Loop through attributes to set types.
                              for name, value in parse_gff_attributes(elems[8]).items():
                                  # Default type is string.
                                  value_type = "str"
                                  try:
                                      # Try int.
                                      int(value)
                                      value_type = "int"
                                  except ValueError:
                                      try:
                                          # Try float.
                                          float(value)
                                          value_type = "float"
                                      except ValueError:
                                          pass
                                  attribute_types[name] = value_type
                          except Exception:
                              pass
                      if i + 1 == num_lines:
                          break
  
          # Set attribute metadata and then set additional metadata.
          dataset.metadata.attribute_types = attribute_types
          dataset.metadata.attributes = len(attribute_types)
  
      def set_meta(self, dataset, overwrite=True, **kwd):
          self.set_attribute_metadata(dataset)
  
          i = 0
          with compression_utils.get_fileobj(dataset.file_name) as in_fh:
              for i, line in enumerate(in_fh):
                  line = line.rstrip('\r\n')
                  if line and not line.startswith('#'):
                      elems = line.split('\t')
                      if len(elems) == 9:
                          try:
                              int(elems[3])
                              int(elems[4])
                              break
                          except Exception:
                              pass
          Tabular.set_meta(self, dataset, overwrite=overwrite, skip=i)
  
      def display_peek(self, dataset):
          """Returns formated html of peek"""
          return self.make_html_table(dataset, column_names=self.column_names)
  
      def get_estimated_display_viewport(self, dataset):
          """
          Return a chrom, start, stop tuple for viewing a file.  There are slight differences between gff 2 and gff 3
          formats.  This function should correctly handle both...
          """
          viewport_feature_count = 100  # viewport should check at least 100 features; excludes comment lines
          max_line_count = max(viewport_feature_count, 500)  # maximum number of lines to check; includes comment lines
          if self.displayable(dataset):
              try:
                  seqid = None
                  start = sys.maxsize
                  stop = 0
                  with compression_utils.get_fileobj(dataset.file_name) as fh:
                      for line in util.iter_start_of_line(fh, VIEWPORT_READLINE_BUFFER_SIZE):
                          try:
                              if line.startswith('##sequence-region'):  # ##sequence-region IV 6000000 6030000
                                  elems = line.rstrip('\n\r').split()
                                  if len(elems) > 3:
                                      # line looks like:
                                      # sequence-region   ctg123 1 1497228
                                      seqid = elems[1]  # IV
                                      start = int(elems[2])  # 6000000
                                      stop = int(elems[3])  # 6030000
                                      break  # use location declared in file
                                  elif len(elems) == 2 and elems[1].find('..') > 0:
                                      # line looks like this:
                                      # sequence-region X:120000..140000
                                      elems = elems[1].split(':')
                                      seqid = elems[0]
                                      start = int(elems[1].split('..')[0])
                                      stop = int(elems[1].split('..')[1])
                                      break  # use location declared in file
                                  else:
                                      log.debug("line (%s) uses an unsupported ##sequence-region definition." % str(line))
                                      # break #no break, if bad definition, we try another line
                              elif line.startswith("browser position"):
                                  # Allow UCSC style browser and track info in the GFF file
                                  pos_info = line.split()[-1]
                                  seqid, startend = pos_info.split(":")
                                  start, stop = map(int, startend.split("-"))
                                  break  # use location declared in file
                              elif not line.startswith(('#', 'track', 'browser')):
                                  viewport_feature_count -= 1
                                  elems = line.rstrip('\n\r').split('\t')
                                  if len(elems) > 3:
                                      if not seqid:
                                          # We can only set the viewport for a single chromosome
                                          seqid = elems[0]
                                      if seqid == elems[0]:
                                          # Make sure we have not spanned chromosomes
                                          start = min(start, int(elems[3]))
                                          stop = max(stop, int(elems[4]))
                          except Exception:
                              # most likely start/stop is not an int or not enough fields
                              pass
                          # make sure we are at the next new line
                          readline_count = VIEWPORT_MAX_READS_PER_LINE
                          while line.rstrip('\n\r') == line:
                              assert readline_count > 0, Exception('Viewport readline count exceeded for dataset %s.' % dataset.id)
                              line = fh.readline(VIEWPORT_READLINE_BUFFER_SIZE)
                              if not line:
                                  break  # EOF
                              readline_count -= 1
                          max_line_count -= 1
                          if not viewport_feature_count or not max_line_count:
                              # exceeded viewport or total line count to check
                              break
                      if seqid is not None:
                          return (seqid, str(start), str(stop))  # Necessary to return strings?
              except Exception:
                  log.exception('Unexpected error')
          return (None, None, None)  # could not determine viewport
  
      def ucsc_links(self, dataset, type, app, base_url):
          ret_val = []
          seqid, start, stop = self.get_estimated_display_viewport(dataset)
          if seqid is not None:
              for site_name, site_url in app.datatypes_registry.get_legacy_sites_by_build('ucsc', dataset.dbkey):
                  if site_name in app.datatypes_registry.get_display_sites('ucsc'):
                      redirect_url = quote_plus(
                          "%sdb=%s&position=%s:%s-%s&hgt.customText=%%s" %
                          (site_url, dataset.dbkey, seqid, start, stop))
                      link = self._get_remote_call_url(redirect_url, site_name, dataset, type, app, base_url)
                      ret_val.append((site_name, link))
          return ret_val
  
      def gbrowse_links(self, dataset, type, app, base_url):
          ret_val = []
          seqid, start, stop = self.get_estimated_display_viewport(dataset)
          if seqid is not None:
              for site_name, site_url in app.datatypes_registry.get_legacy_sites_by_build('gbrowse', dataset.dbkey):
                  if site_name in app.datatypes_registry.get_display_sites('gbrowse'):
                      if seqid.startswith('chr') and len(seqid) > 3:
                          seqid = seqid[3:]
                      redirect_url = quote_plus("%s/?q=%s:%s..%s&eurl=%%s" % (site_url, seqid, start, stop))
                      link = self._get_remote_call_url(redirect_url, site_name, dataset, type, app, base_url)
                      ret_val.append((site_name, link))
          return ret_val
  
      def sniff_prefix(self, file_prefix):
          """
          Determines whether the file is in gff format
  
          GFF lines have nine required fields that must be tab-separated.
  
          For complete details see http://genome.ucsc.edu/FAQ/FAQformat#format3
  
          >>> from galaxy.datatypes.sniff import get_test_fname
          >>> fname = get_test_fname('gff.gff3')
          >>> Gff().sniff( fname )
          False
          >>> fname = get_test_fname('test.gff')
          >>> Gff().sniff( fname )
          True
          """
          if len(get_headers(file_prefix, '\t', count=2)) < 2:
              return False
          try:
              headers = iter_headers(file_prefix, '\t')
              for hdr in headers:
                  if hdr and hdr[0].startswith('##gff-version') and hdr[0].find('2') < 0:
                      return False
                  if hdr and hdr[0] and not hdr[0].startswith('#'):
                      if len(hdr) != 9:
                          return False
                      try:
                          int(hdr[3])
                          int(hdr[4])
                      except Exception:
                          return False
                      if hdr[5] != '.':
                          try:
                              float(hdr[5])
                          except Exception:
                              return False
                      if hdr[6] not in data.valid_strand:
                          return False
                      if hdr[7] not in self.valid_gff_frame:
                          return False
              return True
          except Exception:
              return False
  
      # ------------- Dataproviders
      # redefine bc super is Tabular
      @dataproviders.decorators.dataprovider_factory('genomic-region',
                                                     dataproviders.dataset.GenomicRegionDataProvider.settings)
      def genomic_region_dataprovider(self, dataset, **settings):
          return dataproviders.dataset.GenomicRegionDataProvider(dataset, 0, 3, 4, **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.dataprovider_factory('interval',
                                                     dataproviders.dataset.IntervalDataProvider.settings)
      def interval_dataprovider(self, dataset, **settings):
          return dataproviders.dataset.IntervalDataProvider(dataset, 0, 3, 4, 6, 2, **settings)
  
      @dataproviders.decorators.dataprovider_factory('interval-dict',
                                                     dataproviders.dataset.IntervalDataProvider.settings)
      def interval_dict_dataprovider(self, dataset, **settings):
          settings['named_columns'] = True
          return self.interval_dataprovider(dataset, **settings)
  
  
  class Gff3(Gff):
      """Tab delimited data in Gff3 format"""
      edam_format = "format_1975"
      file_ext = "gff3"
      valid_gff3_strand = ['+', '-', '.', '?']
      valid_gff3_phase = Gff.valid_gff_frame
      column_names = ['Seqid', 'Source', 'Type', 'Start', 'End', 'Score', 'Strand', 'Phase', 'Attributes']
      track_type = Interval.track_type
  
      """Add metadata elements"""
      MetadataElement(name="column_types", default=['str', 'str', 'str', 'int', 'int', 'float', 'str', 'int', 'list'],
                      param=metadata.ColumnTypesParameter, desc="Column types", readonly=True, visible=False)
  
      def __init__(self, **kwd):
          """Initialize datatype, by adding GBrowse display app"""
          Gff.__init__(self, **kwd)
  
      def set_meta(self, dataset, overwrite=True, **kwd):
          self.set_attribute_metadata(dataset)
          i = 0
          with compression_utils.get_fileobj(dataset.file_name) as in_fh:
              for i, line in enumerate(in_fh):
                  line = line.rstrip('\r\n')
                  if line and not line.startswith('#'):
                      elems = line.split('\t')
                      valid_start = False
                      valid_end = False
                      if len(elems) == 9:
                          try:
                              start = int(elems[3])
                              valid_start = True
                          except Exception:
                              if elems[3] == '.':
                                  valid_start = True
                          try:
                              end = int(elems[4])
                              valid_end = True
                          except Exception:
                              if elems[4] == '.':
                                  valid_end = True
                          strand = elems[6]
                          phase = elems[7]
                          if valid_start and valid_end and start < end and strand in self.valid_gff3_strand and phase in self.valid_gff3_phase:
                              break
          Tabular.set_meta(self, dataset, overwrite=overwrite, skip=i)
  
      def sniff_prefix(self, file_prefix):
          """
          Determines whether the file is in GFF version 3 format
  
          GFF 3 format:
  
          1) adds a mechanism for representing more than one level
             of hierarchical grouping of features and subfeatures.
          2) separates the ideas of group membership and feature name/id
          3) constrains the feature type field to be taken from a controlled
             vocabulary.
          4) allows a single feature, such as an exon, to belong to more than
             one group at a time.
          5) provides an explicit convention for pairwise alignments
          6) provides an explicit convention for features that occupy disjunct regions
  
          The format consists of 9 columns, separated by tabs (NOT spaces).
  
          Undefined fields are replaced with the "." character, as described in the original GFF spec.
  
          For complete details see http://song.sourceforge.net/gff3.shtml
  
          >>> from galaxy.datatypes.sniff import get_test_fname
          >>> fname = get_test_fname( 'test.gff' )
          >>> Gff3().sniff( fname )
          False
          >>> fname = get_test_fname( 'test.gtf' )
          >>> Gff3().sniff( fname )
          False
          >>> fname = get_test_fname('gff.gff3')
          >>> Gff3().sniff( fname )
          True
          """
          if len(get_headers(file_prefix, '\t', count=2)) < 2:
              return False
          try:
              headers = iter_headers(file_prefix, '\t')
              for hdr in headers:
                  if hdr and hdr[0].startswith('##gff-version') and hdr[0].find('3') >= 0:
                      return True
                  elif hdr and hdr[0].startswith('##gff-version') and hdr[0].find('3') < 0:
                      return False
                  # Header comments may have been stripped, so inspect the data
                  if hdr and hdr[0] and not hdr[0].startswith('#'):
                      if len(hdr) != 9:
                          return False
                      try:
                          int(hdr[3])
                      except Exception:
                          if hdr[3] != '.':
                              return False
                      try:
                          int(hdr[4])
                      except Exception:
                          if hdr[4] != '.':
                              return False
                      if hdr[5] != '.':
                          try:
                              float(hdr[5])
                          except Exception:
                              return False
                      if hdr[6] not in self.valid_gff3_strand:
                          return False
                      if hdr[7] not in self.valid_gff3_phase:
                          return False
                      parse_gff3_attributes(hdr[8])
              return True
          except Exception:
              return False
  
  
  class Gtf(Gff):
      """Tab delimited data in Gtf format"""
      edam_format = "format_2306"
      file_ext = "gtf"
      column_names = ['Seqname', 'Source', 'Feature', 'Start', 'End', 'Score', 'Strand', 'Frame', 'Attributes']
      track_type = Interval.track_type
  
      """Add metadata elements"""
      MetadataElement(name="columns", default=9, desc="Number of columns", readonly=True, visible=False)
      MetadataElement(name="column_types", default=['str', 'str', 'str', 'int', 'int', 'float', 'str', 'int', 'list'],
                      param=metadata.ColumnTypesParameter, desc="Column types", readonly=True, visible=False)
  
      def sniff_prefix(self, file_prefix):
          """
          Determines whether the file is in gtf format
  
          GTF lines have nine required fields that must be tab-separated. The first eight GTF fields are the same as GFF.
          The group field has been expanded into a list of attributes. Each attribute consists of a type/value pair.
          Attributes must end in a semi-colon, and be separated from any following attribute by exactly one space.
          The attribute list must begin with the two mandatory attributes:
  
              gene_id value - A globally unique identifier for the genomic source of the sequence.
              transcript_id value - A globally unique identifier for the predicted transcript.
  
          For complete details see http://genome.ucsc.edu/FAQ/FAQformat#format4
  
          >>> from galaxy.datatypes.sniff import get_test_fname
          >>> fname = get_test_fname( '1.bed' )
          >>> Gtf().sniff( fname )
          False
          >>> fname = get_test_fname( 'test.gff' )
          >>> Gtf().sniff( fname )
          False
          >>> fname = get_test_fname( 'test.gtf' )
          >>> Gtf().sniff( fname )
          True
          """
          if len(get_headers(file_prefix, '\t', count=2)) < 2:
              return False
          try:
              headers = iter_headers(file_prefix, '\t')
              for hdr in headers:
                  if hdr and hdr[0].startswith('##gff-version') and hdr[0].find('2') < 0:
                      return False
                  if hdr and hdr[0] and not hdr[0].startswith('#'):
                      if len(hdr) != 9:
                          return False
                      try:
                          int(hdr[3])
                          int(hdr[4])
                      except Exception:
                          return False
                      if hdr[5] != '.':
                          try:
                              float(hdr[5])
                          except Exception:
                              return False
                      if hdr[6] not in data.valid_strand:
                          return False
                      if hdr[7] not in self.valid_gff_frame:
                          return False
  
                      # Check attributes for gene_id, transcript_id
                      attributes = parse_gff_attributes(hdr[8])
                      if len(attributes) >= 2:
                          if 'gene_id' not in attributes:
                              return False
                          if 'transcript_id' not in attributes:
                              return False
                      else:
                          return False
              return True
          except Exception:
              return False
  
  
  @dataproviders.decorators.has_dataproviders
  @build_sniff_from_prefix
  class Wiggle(Tabular, _RemoteCallMixin):
      """Tab delimited data in wiggle format"""
      edam_format = "format_3005"
      file_ext = "wig"
      track_type = "LineTrack"
      data_sources = {"data": "bigwig", "index": "bigwig"}
  
      MetadataElement(name="columns", default=3, desc="Number of columns", readonly=True, visible=False)
  
      def __init__(self, **kwd):
          Tabular.__init__(self, **kwd)
          self.add_display_app('ucsc', 'display at UCSC', 'as_ucsc_display_file', 'ucsc_links')
          self.add_display_app('gbrowse', 'display in Gbrowse', 'as_gbrowse_display_file', 'gbrowse_links')
  
      def get_estimated_display_viewport(self, dataset):
          """Return a chrom, start, stop tuple for viewing a file."""
          viewport_feature_count = 100  # viewport should check at least 100 features; excludes comment lines
          max_line_count = max(viewport_feature_count, 500)  # maximum number of lines to check; includes comment lines
          if self.displayable(dataset):
              try:
                  chrom = None
                  start = sys.maxsize
                  end = 0
                  span = 1
                  step = None
                  with open(dataset.file_name) as fh:
                      for line in util.iter_start_of_line(fh, VIEWPORT_READLINE_BUFFER_SIZE):
                          try:
                              if line.startswith("browser"):
                                  chr_info = line.rstrip('\n\r').split()[-1]
                                  chrom, coords = chr_info.split(":")
                                  start, end = map(int, coords.split("-"))
                                  break  # use the browser line
                              # variableStep chrom=chr20
                              if line and (line.lower().startswith("variablestep") or line.lower().startswith("fixedstep")):
                                  if chrom is not None:
                                      break  # different chrom or different section of the chrom
                                  chrom = line.rstrip('\n\r').split("chrom=")[1].split()[0]
                                  if 'span=' in line:
                                      span = int(line.rstrip('\n\r').split("span=")[1].split()[0])
                                  if 'step=' in line:
                                      step = int(line.rstrip('\n\r').split("step=")[1].split()[0])
                                      start = int(line.rstrip('\n\r').split("start=")[1].split()[0])
                              else:
                                  fields = line.rstrip('\n\r').split()
                                  if fields:
                                      if step is not None:
                                          if not end:
                                              end = start + span
                                          else:
                                              end += step
                                      else:
                                          start = min(int(fields[0]), start)
                                          end = max(end, int(fields[0]) + span)
                                      viewport_feature_count -= 1
                          except Exception:
                              pass
                          # make sure we are at the next new line
                          readline_count = VIEWPORT_MAX_READS_PER_LINE
                          while line.rstrip('\n\r') == line:
                              assert readline_count > 0, Exception('Viewport readline count exceeded for dataset %s.' % dataset.id)
                              line = fh.readline(VIEWPORT_READLINE_BUFFER_SIZE)
                              if not line:
                                  break  # EOF
                              readline_count -= 1
                          max_line_count -= 1
                          if not viewport_feature_count or not max_line_count:
                              # exceeded viewport or total line count to check
                              break
                  if chrom is not None:
                      return (chrom, str(start), str(end))  # Necessary to return strings?
              except Exception:
                  log.exception('Unexpected error')
          return (None, None, None)  # could not determine viewport
  
      def gbrowse_links(self, dataset, type, app, base_url):
          ret_val = []
          chrom, start, stop = self.get_estimated_display_viewport(dataset)
          if chrom is not None:
              for site_name, site_url in app.datatypes_registry.get_legacy_sites_by_build('gbrowse', dataset.dbkey):
                  if site_name in app.datatypes_registry.get_display_sites('gbrowse'):
                      if chrom.startswith('chr') and len(chrom) > 3:
                          chrom = chrom[3:]
                      redirect_url = quote_plus("%s/?q=%s:%s..%s&eurl=%%s" % (site_url, chrom, start, stop))
                      link = self._get_remote_call_url(redirect_url, site_name, dataset, type, app, base_url)
                      ret_val.append((site_name, link))
          return ret_val
  
      def ucsc_links(self, dataset, type, app, base_url):
          ret_val = []
          chrom, start, stop = self.get_estimated_display_viewport(dataset)
          if chrom is not None:
              for site_name, site_url in app.datatypes_registry.get_legacy_sites_by_build('ucsc', dataset.dbkey):
                  if site_name in app.datatypes_registry.get_display_sites('ucsc'):
                      redirect_url = quote_plus("%sdb=%s&position=%s:%s-%s&hgt.customText=%%s" % (site_url, dataset.dbkey, chrom, start, stop))
                      link = self._get_remote_call_url(redirect_url, site_name, dataset, type, app, base_url)
                      ret_val.append((site_name, link))
          return ret_val
  
      def display_peek(self, dataset):
          """Returns formated html of peek"""
          return self.make_html_table(dataset, skipchars=['track', '#'])
  
      def set_meta(self, dataset, overwrite=True, **kwd):
          max_data_lines = None
          i = 0
          for i, line in enumerate(open(dataset.file_name)):
              line = line.rstrip('\r\n')
              if line and not line.startswith('#'):
                  elems = line.split('\t')
                  try:
                      # variableStep format is nucleotide position\tvalue\n,
                      # fixedStep is value\n
                      # "Wiggle track data values can be integer or real, positive or negative values"
                      float(elems[0])
                      break
                  except Exception:
                      # We are either in the track definition line or in a declaration line
                      pass
          if self.max_optional_metadata_filesize >= 0 and dataset.get_size() > self.max_optional_metadata_filesize:
              # we'll arbitrarily only use the first 100 data lines in this wig file to calculate tabular attributes (column types)
              # this should be sufficient, except when we have mixed wig track types (bed, variable, fixed),
              #    but those cases are not a single table that would have consistant column definitions
              # optional metadata values set in Tabular class will be 'None'
              max_data_lines = 100
          Tabular.set_meta(self, dataset, overwrite=overwrite, skip=i, max_data_lines=max_data_lines)
  
      def sniff_prefix(self, file_prefix):
          """
          Determines wether the file is in wiggle format
  
          The .wig format is line-oriented. Wiggle data is preceeded by a track definition line,
          which adds a number of options for controlling the default display of this track.
          Following the track definition line is the track data, which can be entered in several
          different formats.
  
          The track definition line begins with the word 'track' followed by the track type.
          The track type with version is REQUIRED, and it currently must be wiggle_0.  For example,
          track type=wiggle_0...
  
          For complete details see http://genome.ucsc.edu/goldenPath/help/wiggle.html
  
          >>> from galaxy.datatypes.sniff import get_test_fname
          >>> fname = get_test_fname( 'interv1.bed' )
          >>> Wiggle().sniff( fname )
          False
          >>> fname = get_test_fname( 'wiggle.wig' )
          >>> Wiggle().sniff( fname )
          True
          """
          try:
              headers = iter_headers(file_prefix, None)
              for hdr in headers:
                  if len(hdr) > 1 and hdr[0] == 'track' and hdr[1].startswith('type=wiggle'):
                      return True
              return False
          except Exception:
              return False
  
      def get_track_resolution(self, dataset, start, end):
          range = end - start
          # Determine appropriate resolution to plot ~1000 points
          resolution = math.ceil(10 ** math.ceil(math.log10(range / 1000)))
          # Restrict to valid range
          resolution = min(resolution, 100000)
          resolution = max(resolution, 1)
          return resolution
  
      # ------------- Dataproviders
      @dataproviders.decorators.dataprovider_factory('wiggle', dataproviders.dataset.WiggleDataProvider.settings)
      def wiggle_dataprovider(self, dataset, **settings):
          dataset_source = dataproviders.dataset.DatasetDataProvider(dataset)
          return dataproviders.dataset.WiggleDataProvider(dataset_source, **settings)
  
      @dataproviders.decorators.dataprovider_factory('wiggle-dict', dataproviders.dataset.WiggleDataProvider.settings)
      def wiggle_dict_dataprovider(self, dataset, **settings):
          dataset_source = dataproviders.dataset.DatasetDataProvider(dataset)
          settings['named_columns'] = True
          return dataproviders.dataset.WiggleDataProvider(dataset_source, **settings)
  
  
  @build_sniff_from_prefix
  class CustomTrack(Tabular):
      """UCSC CustomTrack"""
      edam_format = "format_3588"
      file_ext = "customtrack"
  
      def __init__(self, **kwd):
          """Initialize interval datatype, by adding UCSC display app"""
          Tabular.__init__(self, **kwd)
          self.add_display_app('ucsc', 'display at UCSC', 'as_ucsc_display_file', 'ucsc_links')
  
      def set_meta(self, dataset, overwrite=True, **kwd):
          Tabular.set_meta(self, dataset, overwrite=overwrite, skip=1)
  
      def display_peek(self, dataset):
          """Returns formated html of peek"""
          return self.make_html_table(dataset, skipchars=['track', '#'])
  
      def get_estimated_display_viewport(self, dataset, chrom_col=None, start_col=None, end_col=None):
          """Return a chrom, start, stop tuple for viewing a file."""
          # FIXME: only BED and WIG custom tracks are currently supported
          # As per previously existing behavior, viewport will only be over the first intervals
          max_line_count = 100  # maximum number of lines to check; includes comment lines
          variable_step_wig = False
          chrom = None
          span = 1
          if self.displayable(dataset):
              try:
                  with open(dataset.file_name) as fh:
                      for line in util.iter_start_of_line(fh, VIEWPORT_READLINE_BUFFER_SIZE):
                          if not line.startswith('#'):
                              try:
                                  if variable_step_wig:
                                      fields = line.rstrip().split()
                                      if len(fields) == 2:
                                          start = int(fields[0])
                                          return (chrom, str(start), str(start + span))
                                  elif line and (line.lower().startswith("variablestep") or line.lower().startswith("fixedstep")):
                                      chrom = line.rstrip('\n\r').split("chrom=")[1].split()[0]
                                      if 'span=' in line:
                                          span = int(line.rstrip('\n\r').split("span=")[1].split()[0])
                                      if 'start=' in line:
                                          start = int(line.rstrip('\n\r').split("start=")[1].split()[0])
                                          return (chrom, str(start), str(start + span))
                                      else:
                                          variable_step_wig = True
                                  else:
                                      fields = line.rstrip().split('\t')
                                      if len(fields) >= 3:
                                          chrom = fields[0]
                                          start = int(fields[1])
                                          end = int(fields[2])
                                          return (chrom, str(start), str(end))
                              except Exception:
                                  # most likely a non-integer field has been encountered for start / stop
                                  continue
                          # make sure we are at the next new line
                          readline_count = VIEWPORT_MAX_READS_PER_LINE
                          while line.rstrip('\n\r') == line:
                              assert readline_count > 0, Exception('Viewport readline count exceeded for dataset %s.' % dataset.id)
                              line = fh.readline(VIEWPORT_READLINE_BUFFER_SIZE)
                              if not line:
                                  break  # EOF
                              readline_count -= 1
                          max_line_count -= 1
                          if not max_line_count:
                              # exceeded viewport or total line count to check
                              break
              except Exception:
                  log.exception('Unexpected error')
          return (None, None, None)  # could not determine viewport
  
      def ucsc_links(self, dataset, type, app, base_url):
          ret_val = []
          chrom, start, stop = self.get_estimated_display_viewport(dataset)
          if chrom is not None:
              for site_name, site_url in app.datatypes_registry.get_legacy_sites_by_build('ucsc', dataset.dbkey):
                  if site_name in app.datatypes_registry.get_display_sites('ucsc'):
                      internal_url = "%s" % app.url_for(controller='dataset', dataset_id=dataset.id, action='display_at', filename='ucsc_' + site_name)
                      display_url = quote_plus("%s%s/display_as?id=%i&display_app=%s&authz_method=display_at" % (base_url, app.url_for(controller='root'), dataset.id, type))
                      redirect_url = quote_plus("%sdb=%s&position=%s:%s-%s&hgt.customText=%%s" % (site_url, dataset.dbkey, chrom, start, stop))
                      link = '%s?redirect_url=%s&display_url=%s' % (internal_url, redirect_url, display_url)
                      ret_val.append((site_name, link))
          return ret_val
  
      def sniff_prefix(self, file_prefix):
          """
          Determines whether the file is in customtrack format.
  
          CustomTrack files are built within Galaxy and are basically bed or interval files with the first line looking
          something like this.
  
          track name="User Track" description="User Supplied Track (from Galaxy)" color=0,0,0 visibility=1
  
          >>> from galaxy.datatypes.sniff import get_test_fname
          >>> fname = get_test_fname( 'complete.bed' )
          >>> CustomTrack().sniff( fname )
          False
          >>> fname = get_test_fname( 'ucsc.customtrack' )
          >>> CustomTrack().sniff( fname )
          True
          """
          headers = iter_headers(file_prefix, None)
          found_at_least_one_track = False
          first_line = True
          for hdr in headers:
              if first_line:
                  first_line = False
                  try:
                      if hdr[0].startswith('track'):
                          color_found = False
                          visibility_found = False
                          for elem in hdr[1:]:
                              if elem.startswith('color'):
                                  color_found = True
                              if elem.startswith('visibility'):
                                  visibility_found = True
                              if color_found and visibility_found:
                                  break
                          if not color_found or not visibility_found:
                              return False
                      else:
                          return False
                  except Exception:
                      return False
              else:
                  try:
                      if hdr[0] and not hdr[0].startswith('#'):
                          if len(hdr) < 3:
                              return False
                          try:
                              int(hdr[1])
                              int(hdr[2])
                          except Exception:
                              return False
                          found_at_least_one_track = True
                  except Exception:
                      return False
          return found_at_least_one_track
  
  
  class ENCODEPeak(Interval):
      '''
      Human ENCODE peak format. There are both broad and narrow peak formats.
      Formats are very similar; narrow peak has an additional column, though.
  
      Broad peak ( http://genome.ucsc.edu/FAQ/FAQformat#format13 ):
      This format is used to provide called regions of signal enrichment based
      on pooled, normalized (interpreted) data. It is a BED 6+3 format.
  
      Narrow peak http://genome.ucsc.edu/FAQ/FAQformat#format12 and :
      This format is used to provide called peaks of signal enrichment based on
      pooled, normalized (interpreted) data. It is a BED6+4 format.
      '''
      edam_format = "format_3612"
      file_ext = "encodepeak"
      column_names = ['Chrom', 'Start', 'End', 'Name', 'Score', 'Strand', 'SignalValue', 'pValue', 'qValue', 'Peak']
      data_sources = {"data": "tabix", "index": "bigwig"}
  
      """Add metadata elements"""
      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=3, desc="End column", param=metadata.ColumnParameter)
      MetadataElement(name="strandCol", desc="Strand column (click box & select)", param=metadata.ColumnParameter, optional=True, no_value=0)
      MetadataElement(name="columns", default=3, desc="Number of columns", readonly=True, visible=False)
  
      def sniff(self, filename):
          return False
  
  
  class ChromatinInteractions(Interval):
      '''
      Chromatin interactions obtained from 3C/5C/Hi-C experiments.
      '''
      file_ext = "chrint"
      track_type = "DiagonalHeatmapTrack"
      data_sources = {"data": "tabix", "index": "bigwig"}
      column_names = ['Chrom1', 'Start1', 'End1', 'Chrom2', 'Start2', 'End2', 'Value']
  
      """Add metadata elements"""
      MetadataElement(name="chrom1Col", default=1, desc="Chrom1 column", param=metadata.ColumnParameter)
      MetadataElement(name="start1Col", default=2, desc="Start1 column", param=metadata.ColumnParameter)
      MetadataElement(name="end1Col", default=3, desc="End1 column", param=metadata.ColumnParameter)
      MetadataElement(name="chrom2Col", default=4, desc="Chrom2 column", param=metadata.ColumnParameter)
      MetadataElement(name="start2Col", default=5, desc="Start2 column", param=metadata.ColumnParameter)
      MetadataElement(name="end2Col", default=6, desc="End2 column", param=metadata.ColumnParameter)
      MetadataElement(name="valueCol", default=7, desc="Value column", param=metadata.ColumnParameter)
  
      MetadataElement(name="columns", default=7, desc="Number of columns", readonly=True, visible=False)
  
      def sniff(self, filename):
          return False
  
  
  @build_sniff_from_prefix
  class ScIdx(Tabular):
      """
      ScIdx files are 1-based and consist of strand-specific coordinate counts.
      They always have 5 columns, and the first row is the column labels:
      'chrom', 'index', 'forward', 'reverse', 'value'.
      Each line following the first consists of data:
      chromosome name (type str), peak index (type int), Forward strand peak
      count (type int), Reverse strand peak count (type int) and value (type int).
      The value of the 5th 'value' column is the sum of the forward and reverse
      peak count values.
      """
      file_ext = "scidx"
  
      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=[])
  
      def __init__(self, **kwd):
          """
          Initialize scidx datatype.
          """
          Tabular.__init__(self, **kwd)
          # Don't set column names since the first
          # line of the dataset displays them.
          self.column_names = ['chrom', 'index', 'forward', 'reverse', 'value']
  
      def sniff_prefix(self, file_prefix):
          """
          Checks for 'scidx-ness.'
          """
          count = 0
          fh = file_prefix.string_io()
          while True:
              line = fh.readline()
              if not line:
                  # EOF
                  if count > 1:
                      # The second line is always the labels:
                      # chrom index forward reverse value
                      # We need at least the column labels and a data line.
                      return True
                  return False
              line = line.strip()
              # The first line is always a comment like this:
              # 2015-11-23 20:18:56.51;input.bam;READ1
              if count == 0:
                  if line.startswith('#'):
                      count += 1
                      continue
                  else:
                      return False
              # Skip first line.
              if count > 1:
                  items = line.split('\t')
                  if len(items) != 5:
                      return False
                  index = items[1]
                  if not index.isdigit():
                      return False
                  forward = items[2]
                  if not forward.isdigit():
                      return False
                  reverse = items[3]
                  if not reverse.isdigit():
                      return False
                  value = items[4]
                  if not value.isdigit():
                      return False
                  if int(forward) + int(reverse) != int(value):
                      return False
              if count == 100:
                  return True
              count += 1
          if count < 100 and count > 0:
              return True
          return False
  
  
  if __name__ == '__main__':
      import doctest
      doctest.testmod(sys.modules[__name__])
  