File Coverage

blib/lib/Bio/DB/USeq.pm

Criterion	Covered	Total	%
statement	463	932	49.6
branch	151	328	46.0
condition	39	142	27.4
subroutine	62	111	55.8
pod	37	39	94.8
total	752	1552	48.4

package Bio::DB::USeq;

our $VERSION = '0.25';

=head1 NAME

Bio::DB::USeq - Read USeq archive database files

=head1 SYNOPSIS

    use Bio::DB::USeq;
    my $useq = Bio::DB::USeq->new('file.useq') or
        die "unable to open file.useq!\n";

    # sequence IDs
    my @seq_ids = $useq->seq_ids;
    my $length = $useq->length($chr); # approximate, not exact

    ### Retrieving features
    # all features or observations across chromosome I
    my @features = $useq->features(
        -seq_id     => 'chrI',
        -type       => 'region',
    );

    # same thing using a simple form
    # use an array of (chromosome, start, stop, strand)
    my @features = $useq->features('chrI');

    # same thing with a memory efficient iterator
    my $iterator = $useq->get_seq_stream(
        -seq_id     => 'chrI',
    );
    while (my $f = $iterator->next_seq) {
    	# each feature $f supports most SeqFeatureI methods
    }


    ### Retrieving simple scores
    my @scores = $useq->scores(
        -seq_id     => 'chrI',
        -start      => 1000,
        -end        => 2000
    );


    ### Same methods as above after defining an interval first
    my $segment = $useq->segment(
        -seq_id     => 'chrI',
        -start      => 1000,
        -end        => 2000,
    );
    my @scores   = $segment->scores;
    my @features = $segment->features;
    my $iterator = $segment->get_seq_stream;


    ### Efficient retrieval of positioned scores in 100 bins
    # compatible with Bio::Graphics
    my ($wig) = $useq->features(
        # assuming unstranded data here, otherwise two wiggle objects
        # would be returned, one for each strand
        -seq_id     => 'chrI',
        -start      => 1000,
        -end        => 2000,
        -type       => 'wiggle:100',
    );
    my @bins = $wig->wiggle;
    my @bins = $wig->coverage; # same thing
    my ($bins) = $wig->get_tag_values('coverage'); # same thing


    ### Statistical summaries of intervals
    # compatible with Bio::Graphics
    my ($summary) = $useq->features(
        # assuming unstranded data here, otherwise two summaries
        # would be returned, one for each strand
        -seq_id     => 'chrI',
        -start      => 1000,
        -end        => 2000,
        -type       => 'summary',
    );
    my $stats = $summary->statistical_summary(10);
    foreach (@$stats) {
        my $max = $_->{maxVal};
        my $mean = Bio::DB::USeq::binMean($_);
    }


    ### Stranded data using an iterator
    # could be used with either wiggle or summary features
    my $stream = $useq->get_seq_stream(
        -seq_id     => 'chrI',
        -start      => 1000,
        -end        => 2000,
        -type       => 'wiggle:100',
    );
    my ($forward, $reverse);
    if ($useq->stranded) {
        $forward = $stream->next_seq;
        $reverse = $stream->next_seq;
    }
    else {
        $forward = $stream->next_seq;
    }


=head1 DESCRIPTION

Bio::DB::USeq is a B<BioPerl> style adaptor for reading USeq files. USeq files
are compressed, indexed data files supporting modern bioinformatic datasets,
including genomic points, scores, and intervals. More information about the
USeq software package can be found at L<https://github.com/HuntsmanCancerInstitute/USeq>,
including a description of the USeq data
L<archive format|https://github.com/HuntsmanCancerInstitute/USeq/blob/master/Documentation/USeqDocumentation/useqArchiveFormat.html>.

USeq files are typically half the size of corresponding bigBed and bigWig files,
due to a compact internal format and lack of internal zoom data. This adaptor,
however, can still return statistics across different zoom levels in the same
manner as big files, albeit at a cost of calculating these in realtime.

=head2 Generating useq files

USeq files may be generated using tools from the USeq package, available at
L<https://github.com/HuntsmanCancerInstitute/USeq>. They may be generated from
native Bar files, text Wig files, text Bed files, and UCSC bigWig and bigBed file
formats.

=head2 Compatibility

The adaptor follows most conventions of other B<BioPerl>-style Bio::DB
adaptors. Observations or features in the useq file archive are
returned as SeqFeatureI compatible objects.

Coordinates consumed and returned by the adaptor are 1-based, consistent
with B<BioPerl> convention. This is not true of the useq file itself, which
uses the interbase coordinate system.

Unlike wig and bigWig files, useq file archives support stranded data,
which can make data collection much simpler for complex experiments.

See below for GBrowse compatibility.

=head2 Limitations

This adaptor is read only. USeq files, in general, are not modified
or written with data. The exceptions are chromosome or global statistics
are written to the F<archiveReadMe.txt> file inside the zip archive to
cache for future queries.

No support for genomic sequence is included. Users who need access to
genomic sequence should seek an alternative B<BioPerl> adaptor, such as
L<Bio::DB::Fasta>.

Useq files do not have a native concept of type, primary_tag, or source
attributes, as expected with GFF-based database adaptors. The features
method does support special types (see below).

=head2 Requirements

The adaptor is a Perl-only implementation. It only requires the
L<Archive::Zip> module for opening and reading the useq file archive.
B<BioPerl> is required for working with SeqFeatures objects generated
from useq file observations.

=head1 METHODS

=head2 Initializing the Bio::DB::USeq object

These are class methods for creating and working with the Bio::DB::USeq
object itself.

=over 4

=item new

=item new($file)

=item new(-useq => $file)

This will create a new Bio::DB::USeq object. Optionally pass the path
to a useq file archive to open immediately. There is not much to do
unless you open a file.

Named arguments may be used to specify the file. Either -useq or -file
may be used.

=item open($file)

Open a useq file archive. Useq files typically use the F<.useq> file
extension. Returns true if successful.

DO NOT open a subsequent useq file archive when one has already been
opened. Please create a new object to open a new file.

=item clone

Force the object to re-open the useq archive file under a new file
handle to make it clone safe. Do this in the child process before
collecting data.

=item zip

Return the L<Archive::Zip> object representing the useq file archive.
Generally not recommended unless you know what you are doing.

=back

=head2 General information about the useq file

These are class methods for obtaining general information or
metadata attributes regarding the contents of the file.

=over 4

=item stranded

Return true or false (1 or 0) indicating whether the contents of the
useq file archive are recorded in a stranded fashion.

=item attributes

Return an array of available attribute keys that were recorded in the
useq file F<archiveReadMe.txt> member. These are key = value pairs
representing metadata for the useq file.

=item attribute($key)

Return the metadata attribute value for the specified key. These
are recorded in the useq file F<archiveReadMe.txt> member. Returns
undefined if the key does not exist.

=item type

Return the useq file metadata C<dataType> value.

=item genome

Return the useq file metadata C<versionedGenome> value.

=item version

Return the useq file metadata C<useqArchiveVersion> value.

=back

=head2 Chromosome Information

These are methods to obtain information about the chromosomes or reference
sequences represented in the file archive.

B<Note> that generating score statistics across one or more chromosomes may
be computationally expensive. Therefore, chromosome statistics, once
calculated, are cached in the useq file metadata for future reference.
This necessitates writing to the useq zip archive. This is currently the
only supported method for modifying the useq zip archive.

=over 4

=item seq_ids

Return an array of the chromosome or sequence identifiers represented
in the useq file archive. The names are sorted ASCIIbetically before
they are returned.

=item length($seq_id)

Return the length of the provided chromosome or sequence identifier.
Note that this may not be the actual length in the reference genome, but
rather the last recorded position of an observation for that chromosome.
Hence, it should be used only as an approximation.

=item chr_mean($seq_id)

Return the mean score value across the entire chromosome.

=item chr_stdev($seq_id)

Return the score standard deviation across the entire chromosome.

=item chr_stats($seq_id)

Return a statistical summary across the entire chromosome. This
is an anonymous hash with five keys: validCount, sumData,
sumSquares, minVal, and maxVal. These are equivalent to the
statistical summaries generated by the Bio::DB::BigWig adaptor.

=item global_mean

Return the mean score value across all chromosomes.

=item global_stdev

Return the mean score value across all chromosomes.

=item global_stats

Return a statistical summary across all chromosomes.

=back

=head2 Data accession

These are the primary methods for working with data contained in
useq file archive. These should be familiar to most B<BioPerl> users.

=over 4

=item features

Returns an array or array reference of SeqFeatureI compatible
objects overlapping a given genomic interval.

Coordinates of the interrogated regions must be supplied. At a
minimum, the seq_id must be supplied. A start of 1 and an end
corresponding to the length of the seq_id is used if not directly
provided. Coordinates may be specified in two different manners,
either as a list of (seq_id, start, end, strand) or as one or
more keyed values.

    my @features = $useq->features($seq_id, $start, $end);

    @features = $useq->features(
        -seq_id     => $seq_id,
        -start      => $start,
        -end        => $end,
    );

If the -iterator argument is supplied with a true value, then an
iterator object is returned. See get_seq_stream() for details.

Bio::DB::USeq supports four different feature types. Feature
types may be specified using the -type argument.

=over 4

=item region

=item interval

=item observation

The default feature type if the type argument is not specified.
These are SeqFeatureI compatible objects representing observations
in the useq file archive. These are compatible with the iterator.
SeqFeature observations are returned in genomic order.

=item chromosome

Returns SeqFeatureI compatible objects representing the
reference sequences (chromosomes) listed in the useq file
archive. These are not compatibile with the iterator.

=item wiggle

=item wiggle:$bins

=item coverage

=item coverage:$bins

Returns an array of SeqFeatureI compatible objects for each
strand requested. If the useq file contains stranded data,
and no strand is requested, then two objects will be
returned representing each strand.

Each object contains an array representing scores across
the requested coordinates. This object is designed to be
backwards compatible with coverage features from the
L<Bio::DB::Sam> adaptor for use with L<Bio::Graphics> and GBrowse.
Note that only scores are returned, not a true depth coverage
in the sense of the L<Bio::DB::Sam> coverage types.

By default, the wiggle or coverage array is provided at 1
bp resolution. To improve efficiency with large regions,
the wiggle array may be limited by using a bin option,
where the interrogated interval is divided into the number
of bins requested.

To retrieve the scores, call the wiggle() or coverage() method.

For example, to request wiggle scores in 100 equal bins across
the interval, see the following example. The wiggle and
coverage types are synonymous.

  my ($wiggle) = $useq->features(
      -seq_id       => $chromosome,
      -start        => $start,
      -end          => $end,
      -type         => 'wiggle:100',
  );
  my @scores = $wiggle->wiggle;
  @scores    = $wiggle->coverage;

Wiggle objects may also be obtained with a get_seq_stream()
iterator objects.

=item summary

=item summary:$bins

Returns an array of SeqFeatureI compatibile Summary objects
for each strand requested. If the useq file contains stranded
data, and no strand is requested, then two objects will be
returned representing each strand.

Each Summary object can then be used to call statistical
summaries for one or more bins across the interval. Each
statistical summary is an anonymous hash with five keys:
validCount, sumData, sumSquares, minVal, and maxVal. From
these values, a mean and standard deviation may also be
calculated.

  my ($summary) = $useq->features(
      -seq_id       => $chromosome,
      -start        => $start,
      -end          => $end,
      -type         => 'summary',
  );
  my @stats  = $summary->statistical_summary(100);
  foreach my $stat (@stats) {
  	 my $count = $stat->{validCount};
  	 my $sum   = $stat->{sumData};
  	 my $mean  = $sum / $count;
  }

Statistical summaries are equivalent to those generated by the
L<Bio::DB::BigWig> adaptor and may be used interchangeably. They
are compatible with the L<Bio::Graphics> modules.

Summary objects may also be obtained with a get_seq_stream()
iterator object.

=back

=item get_seq_stream

This is a memory efficient data accessor. An iterator object is
returned for an interval specified using coordinate values in the
same manner as features(). Call the method next_seq() on the
iterator object to retrieve the observation SeqFeature objects
one at a time. The iterator is compatible with region, wiggle,
and summary feature types.

    # establish the iterator object
    my $iterator = $useq->get_seq_stream(
        -seq_id     => $seq_id,
        -start      => $start,
        -end        => $end,
        -type       => 'region',
    );

    # retrieve the features one at a time
    while (my $f = $iterator->next_seq) {
    	# each feature $f is either a
    	# Bio::DB::USeq::Feature,
    	# a Bio::DB::USeq::Wiggle, or
    	# a Bio::DB::USeq::Summary object
    }

=item scores

This is a simplified data accessor that only returns the score
values overlapping an interrogated interval, rather than
assembling each observation into a SeqFeature object. The scores
are not associated with genomic coordinates, and are not guaranteed
to be returned in original genomic order.

Provide the interval coordinates in the same manner as the
features() method. Stranded data collection is supported.

    my @scores = $useq->scores(
        -seq_id     => $seq_id,
        -start      => $start,
        -end        => $end,
    );

=item observations

This is a low-level data accessor, similar to features() but returning
an array of references to the original USeq observations. Each USeq
observation is an anonymous array reference of 2-4 elements: start, stop,
score, and text, depending on the stored data type. All coordinates are in
0-based coordinates, unlike high level accessors. Note that while strand is
supported, it is not reported for each observation. If observations exist on
both strands, then each strand should be searched separately. Observations
are not guaranteed to be returned in genomic order.

    my @observations = $useq->observations(
        -seq_id     => $seq_id,
        -start      => $start,
        -end        => $end,
    );
    foreach my $obs (@observations) {
        my $start = $obs->[0] + 1; # convert to 1-based coordinate
        my $stop  = $obs->[1];
        my $score = $obs->[2]; # may not be present
        my $text  = $obs->[3]; # may not be present
    }

=item segment

This returns a L<Bio::DB::USeq::Segment> object, which is a SeqFeatureI
compatible segment object corresponding to the specified coordinates.
From this object, one can call the features(), scores(), or
get_seq_stream() methods directly. Keyed options or location
information need not be provided. Stranded segments are
supported.

    my $segment = $useq->segment(
        -seq_id     => $seq_id,
        -start      => $start,
        -end        => $end,
    );
    my @scores   = $segment->scores;
    my @features = $segment->features('wiggle:100');
    my $iterator = $segment->get_seq_stream('region');

=item get_features_by_location

Convenience method for returning features restricted by location.

=item get_feature_by_id

=item get_feature_by_name

Compatibility methods for returning a specific feature or
observation in the USeq file. Text fields, if present, are not
indexed in the USeq file, preventing efficient searching of names.
As a workaround, an ID or name comprised of "$seq_id:$start:$end"
may be used, although a direct search of coordinates would be
more efficient.

	my $feature = $useq->get_feature_by_id("$seq_id:$start:$end");


=back

=head1 ADDITIONAL CLASSES

These are additional class object that may be returned by various
methods above.

=head2 Bio::DB::USeq::Feature objects

These are SeqFeatureI compliant objects returned by the features()
or next_seq() methods. They support the following methods.

    seq_id
    start
    end
    strand
    score
    type
    source (returns the useq archive filename)
    name (chromosome:start..stop)
    Bio::RangeI methods

Additionally, chromosome and global statistics are also available
from any feature, as well as from Segment, Wiggle, Iterator, and
Summary objects. See the corresponding USeq methods for details.

=head2 Bio::DB::USeq::Segment objects

This is a SeqFeatureI compliant object representing a genomic segment
or interval. These support the following methods.

=over 4

=item features

=item features($type)

=item get_seq_stream

=item get_seq_stream($type)

=item scores

=item observations

Direct methods for returning features or scores. Coordinate information
need not be provided. See the corresponding Bio::DB::USeq methods for
more information.

=item wiggle

=item wiggle($bins)

=item coverage

=item coverage($bins)

=item statistical_summary

=item statistical_summary($bins)

Convenience methods for returning wiggle (coverage) or summary features
over the segment. If desired, the number of bins may be specified. See
the features() method for more information.

=item slices

Returns an array of splice member names that overlap this segment.
See L<USEQ SLICES> for more information.

=back

=head2 Bio::DB::USeq::Iterator objects

This is an iterator object for retrieving useq observation SeqFeatureI
objects in a memory efficient manner by returning one feature at a time.

=over 4

=item next_seq

=item next_feature

Returns the next feature present in the interrogated interval. Features
are generally returned in ascending coordinate order. Returns undefined
when no features are remaining in the interval. Features may include
either region or wiggle types, depending on how the iterator object was
established. See features() and get_seq_stream() methods for more
information.

=back

=head2 Bio::DB::USeq::Wiggle objects

These are SeqFeatureI compliant objects for backwards compatibility with
L<Bio::Graphics> and GBrowse. They support the wiggle() and coverage()
methods, which returns an array of scores over the interrogated region. By
default, the array is equal to the length of the region (1 bp resolution),
or may be limited to a specified number of bins for efficiency. See the
features() method for more information.

=over 4

=item wiggle

=item coverage

The scores are stored as an array in the coverage attribute. For
convenience, the wiggle() and coverage() methods may be used to retrieve
the array or array reference of scores.

=item statistical_summary

Generate a statistical summary hash for the collected wiggle scores
(not the original data). This method is not entirely that useful; best
to use the summary feature type in the first place.

=item chromosome and global statistics

Chromosome and global statistics, including mean and standard deviation,
are available from wiggle objects. See the corresponding USeq methods
for details.

=back

=head2 Bio::DB::USeq::Summary objects

These are SeqFeatureI compliant Summary objects, similar to those
generated by the L<Bio::DB::BigWig> database adaptor. As such, they are
compatible with L<Bio::Graphics> and GBrowse.

Summary objects can generate statistical summaries over a specified
number of bins (default is 1 bin, or the entire requested region).
Each statistical summary is an anonymous hash consisting of five
keys: validCount, sumData, sumSquares, minVal, and maxVal. From
these values, a mean and standard deviation may be calculated.

For convenience, three exported functions are available for calculating
the mean and standard deviation from a statistical summary hash.
See L<EXPORTED FUNCTIONS> for more information.

Use statistical summaries in the following manner.

    my $stats = $summary->statistical_summary(10);
    my $stat  = shift @$stats;
    my $min   = $stat->{minVal};
    my $max   = $stat->{maxVal};
    my $mean  = $stat->{sumData} / $stat->{validCount};

=over 4

=item statistical_summary

=item statistical_summary($bins)

Generate a statistical summary hash for one or more bins across the
interrogated region. Provide the number of bins desired. If a feature
type of "summary:$bins" is requested through the features() or
get_seq_stream() method, then $bins number of bins will be used.
The default number of bins is 1.

=item score

Generate a single statistical summary over the entire region.

=item chromosome and global statistics

Chromosome and global statistics, including mean and standard deviation,
are available from summary objects. See the corresponding USeq methods
for details.

=back

=head1 EXPORTED FUNCTIONS

Three subroutine functions are available for export to assist
in calculating the mean, variance, and standard deviation from
statistical summaries. These functions are identical to those
from the L<Bio::DB::BigWig> adaptor and may be used interchangeably.

They are not exported by default; they must explicitly listed.

    use Bio::DB::USeq qw(binMean binStdev);
    my $stats = $summary->statistical_summary(10);
    my $stat  = shift @$stats;
    my $mean  = binMean($stat);
    my $stdev = binStdev($stat);

=over 4

=item binMean( $stat )

Calculate the mean from a statistical summary anonymous hash.

=item binVariance( $stat )

Calculate the variance from a statistical summary anonymous hash.

=item binStdev( $stat )

Calculate the standard deviation from a statistical summary anonymous hash.

=back

=head1 USEQ SLICES

Genomic observations are recorded in groups, called slices, of
usually 10000 observations at a time. Each slice is a separate
zip file member in the useq file archive. These methods are for
accessing information about each slice. In general, accessing
data through slices is a lower level operation. Users should
preferentially use the main data accessors.

The following are Bio::DB::USeq methods available for working
with slices.

=over 4

=item slices

Returns an array of all the slice member names present in the
useq archive file.

=item slice_feature($slice)

Return a L<Bio::DB::USeq::Segment> object representing the slice interval.
The features(), get_seq_stream(), and scores() methods are supported.

=item slice_seq_id($slice)

Return the chromosome or sequence identifier associated with a slice.

=item slice_start($slice)

Return the start position of the slice interval.

=item slice_end($slice)

Return the end position of the slice interval.

=item slice_strand($slice)

Return the strand of the slice interval.

=item slice_type($slice)

Return the file type of the slice member. This corresponds to the
file extension of the slice zip member and indicates how to parse
the binary member. Each letter in the type corresponds to a data
type, be it integer, short, floating-point, or text. See the
useq file documentation for more details.

=item slice_obs_number($slice)

Return the number of observations recorded in the slice interval.

=back

=head1 GBROWSE COMPATIBILITY

The USeq adaptor has support for L<Bio::Graphics> and GBrowse.
It will work with the segments glyph for intervals,
the wiggle_xyplot glyph for displaying mean scores, and the
wiggle_whiskers glyph for displaying detailed statistics.

Initialize the USeq database adaptor.

    [data1:database]
    db_adaptor    = Bio::DB::USeq
    db_args       = -file /path/to/data1.useq

Displaying simple intervals with the segments glyph.

    [data1_segments]
    database      = data1
    feature       = region
    glyph         = segments
    stranded      = 1

Displaying scores using the wiggle_xyplot glyph.
You may set the bins to whatever number is appropriate (in
this example, 1000), or leave blank (not recommended,
defaults to 1 bp resolution).

    [data1_xyplot]
    database      = data1
    feature       = wiggle:1000
    glyph         = wiggle_xyplot
    graph_type    = histogram
    autoscale     = chromosome

Displaying scores using the wiggle_whiskers glyph. Note that
generating statistical summaries are computationally more expensive
than simple bins of mean values as with the wiggle feature type.

    [data1_whiskers]
    database      = data1
    feature       = summary
    glyph         = wiggle_whiskers
    graph_type    = histogram
    autoscale     = chromosome

=head1 PERFORMANCE

Because the Bio::DB::USeq is implemented as a Perl-only module,
performance is subject to the limitations of Perl execution itself and
the size of the data that needs to be parsed. In general when collecting
score data, requesting scores is the fastest mode of operation, followed
by wiggle feature types, and finally summary feature types.

In comparison to UCSC bigWig files, the USeq format is typically much
faster when viewing intervals where the entire interval is represented by
one or a few internal slices. This is especially true for repeated queries
over the same or neighboring intervals, as the slice contents are retained
in memory. As the number of internal slices that must be loaded into memory
increases, for example querying intervals of hundreds of kilobases in size,
performance will begin to lag as each internal slice must be parsed into
memory. This is where the UCSC bigWig file format with internal zoom levels
of summary statistics can outperform, at the cost of file complexity and size.

=cut


require 5.010000;
use strict;
use Carp qw(carp cluck croak confess);
use Archive::Zip qw( :ERROR_CODES );
use Set::IntervalTree;
use File::Spec;

use base 'Exporter';
our @EXPORT_OK = qw(binMean binVariance binStdev);

1;

#### USeq initialization ####

sub new {
	my $class = shift;
	my $self = {
		'name'            => undef,
		'zip'             => undef,
		'stranded'        => undef,
		'seq_ids'         => {},
		'metadata'        => {},
		'coord2file'      => {},
		'file2attribute'  => {},
		'buffer'          => {},
	};
	bless $self, $class;

	# check for arguments
	my %args;
	if (@_) {
		if ($_[0] =~ /^-/) {
			%args = @_;
		}
		else {
			$args{-file} = shift;
		}
	}

	# open file
	my $file = $args{-file} || $args{-useq} || undef;
	if ($file) {
		return unless ($self->open($file)); # open must return true
	}

	# done
	return $self;
}

sub open {
	my $self = shift;

	# check file
	my $filename = shift;
	unless ($filename) {
		cluck("no file name passed!\n");
		return;
	}
	unless ($filename =~ /\.useq$/i) {
		carp "'$filename' is not a .useq archive file!\n";
		return;
	}
	if ($self->slices) {
		cluck "Only load one useq file per object!\n";
		return;
	}

	# open the archive
	my $zip = Archive::Zip->new();
	my $error = $zip->read($filename);
	unless ($error == AZ_OK) {
		carp " unable to read USeq archive '$filename'! Error $error\n";
		return;
	}
	$self->{'zip'} = $zip;
	(undef, undef, $self->{'name'}) = File::Spec->splitpath($filename);

	# parse the contents
	return unless ($self->_parse_members);
	# we delay parsing metadata unless it is requested

	# success
	return 1;
}

sub clone {
	my $self = shift;
	return unless $self->{'zip'};
	my $file = $self->zip->fileName;
	my $zip = Archive::Zip->new();
	my $error = $zip->read($file);
	unless ($error == AZ_OK) {
		carp " unable to read USeq archive '$file'! Error $error\n";
		return;
	}
	$self->{'zip'} = $zip;
	return 1;
}

sub zip {
	return shift->{'zip'};
}

sub name {
	return shift->{'name'};
}



#### General USeq information ####

sub seq_ids {
	my $self = shift;
	return sort {$a cmp $b} keys %{ $self->{'seq_ids'} };
}

sub length {
	my $self = shift;
	my $seq_id = shift or return;
	if (exists $self->{'seq_ids'}{$seq_id}) {
		return $self->{'seq_ids'}{$seq_id};
	}
}

sub stranded {
	return shift->{'stranded'};
}

sub attributes {
	my $self = shift;
	$self->_parse_metadata unless %{ $self->{'metadata'} };
	return (sort {$a cmp $b} keys %{ $self->{'metadata'} });
}

sub attribute {
	my $self = shift;
	my $key = shift;
	return $self->attributes unless $key;
	$self->_parse_metadata unless %{ $self->{'metadata'} };
	if (exists $self->{'metadata'}{$key}) {
		return $self->{'metadata'}{$key};
	}
	return;
}

sub type {
	return shift->attribute('dataType');
}

sub version {
	return shift->attribute('useqArchiveVersion');
}

sub genome {
	return shift->attribute('versionedGenome');
}

sub chr_stats {
	my $self = shift;
	my $seq_id = shift or return;
	my $delay_write = shift; # option to delay rewriting the metadata
	$self->_parse_metadata unless %{ $self->{'metadata'} };

	# return the chromosome stats
	if (exists $self->{metadata}{"chromStats_$seq_id"}) {
		my @data = split(',', $self->{metadata}{"chromStats_$seq_id"});
		my %stat = (
			'validCount'    => $data[0],
			'sumData'       => $data[1],
			'sumSquares'    => $data[2],
			'minVal'        => $data[3],
			'maxVal'        => $data[4],
		);
		return \%stat;
	}

	# chromosome stats must be generated
	my @slices = $self->_translate_coordinates_to_slices(
		$seq_id, 1, $self->length($seq_id), 0);
	$self->_clear_buffer(\@slices);
	my $stat = $self->_stat_summary(1, $self->length($seq_id), \@slices);

	# then associate with the metadata
	$self->{'metadata'}{"chromStats_$seq_id"} = join(',', map { $stat->{$_} }
		qw(validCount sumData sumSquares minVal maxVal) );
	$self->_rewrite_metadata unless $delay_write;

	return $stat;
}

sub chr_mean {
	my $self = shift;
	my $seq_id = shift or return;
	return Bio::DB::USeq::binMean( $self->chr_stats($seq_id) );
}

sub chr_stdev {
	my $self = shift;
	my $seq_id = shift or return;
	return Bio::DB::USeq::binStdev( $self->chr_stats($seq_id) );
}

sub global_stats {
	# this is an expensive proposition, because it must parse through every
	# slice in the archive
	my $self = shift;
	$self->_parse_metadata unless %{ $self->{'metadata'} };

	# return the chromosome stats
	if (exists $self->{metadata}{globalStats}) {
		my @data = split(',', $self->{metadata}{globalStats});
		my %stat = (
			'validCount'    => $data[0],
			'sumData'       => $data[1],
			'sumSquares'    => $data[2],
			'minVal'        => $data[3],
			'maxVal'        => $data[4],
		);
		return \%stat;
	}

	# calculate new genome-wide statistics from individual chromosome stats
	my $count = 0;
	my $sum;
	my $sum_squares;
	my $min;
	my $max;
	foreach my $seq_id ($self->seq_ids) {
		my $stats = $self->chr_stats($seq_id, 1); # delay writing metadata
		$count       += $stats->{validCount};
		$sum         += $stats->{sumData};
		$sum_squares += $stats->{sumSquares};
		$min          = $stats->{minVal} if (!defined $min or $stats->{minVal} < $min);
		$max          = $stats->{maxVal} if (!defined $max or $stats->{maxVal} < $max);
	}

	# assemble the statistical summary hash
	my %stat = (
		'validCount'    => $count,
		'sumData'       => $sum || 0,
		'sumSquares'    => $sum_squares || 0,
		'minVal'        => $min || 0,
		'maxVal'        => $max || 0,
	);

	# update metadata
	$self->{'metadata'}{'globalStats'} = join(',', map { $stat{$_} }
		qw(validCount sumData sumSquares minVal maxVal) );
	$self->_rewrite_metadata;

	return \%stat;
}

sub global_mean {
	my $self = shift;
	return Bio::DB::USeq::binMean( $self->global_stats );
}

sub global_stdev {
	my $self = shift;
	return Bio::DB::USeq::binStdev( $self->global_stats );
}

#### slice information ####

sub slices {
	my $self = shift;
	return sort {$a cmp $b} keys %{ $self->{'file2attribute'} };
}

sub slice_seq_id {
	my $self = shift;
	my $slice = shift or return;
	return $self->{'file2attribute'}{$slice}->[0];
}

sub slice_start {
	my $self = shift;
	my $slice = shift or return;
	return $self->{'file2attribute'}{$slice}->[1];
}

sub slice_end {
	my $self = shift;
	my $slice = shift or return;
	return $self->{'file2attribute'}{$slice}->[2];
}

sub slice_strand {
	my $self = shift;
	my $slice = shift or return;
	return $self->{'file2attribute'}{$slice}->[3];
}

sub slice_type {
	my $self = shift;
	my $slice = shift or return;
	return $self->{'file2attribute'}{$slice}->[4];
}

sub slice_obs_number {
	my $self = shift;
	my $slice = shift or return;
	return $self->{'file2attribute'}{$slice}->[5];
}

sub slice_feature {
	my $self = shift;
	my $slice = shift or return;
	return Bio::DB::USeq::Segment->new(
		-seq_id     => $self->{'file2attribute'}{$slice}->[0],
		-start      => $self->{'file2attribute'}{$slice}->[1],
		-stop       => $self->{'file2attribute'}{$slice}->[2],
		-strand     => $self->{'file2attribute'}{$slice}->[3],
		-type       => $self->{'file2attribute'}{$slice}->[4],
		-source     => $self->name,
		-name       => $slice,
	);
}





#### Feature and data access ####

sub segment {
	my $self = shift;

	# arguments can be chromo, start, stop, strand
	my ($seq_id, $start, $stop, $strand) = $self->_get_coordinates(@_);
	return unless $self->length($seq_id); # make sure chromosome is represented

	return Bio::DB::USeq::Segment->new(
		-seq_id     => $seq_id,
		-start      => $start,
		-end        => $stop,
		-strand     => $strand,
		-type       => 'segment',
		-source     => $self->name,
		-useq       => $self,
	);
}

sub features {
	my $self = shift;
	my %args = @_;

	# check for type
	my $type;
	$args{-type} ||= $args{-types} || $args{-primary_tag} || 'region';
	if (ref $args{-type} and ref $args{-type} eq 'ARRAY') {
		$type = $args{-type}->[0] || 'region';
		$args{-type} = $type;
	}
	else {
		$type = $args{-type};
	}

	# return an appropriate feature
	if ($type =~ /chromosome/) {
		# compatibility to return chromosome features
		my @chromos = map {
			Bio::DB::USeq::Feature->new(
				-seq_id => $_,
				-start  => 1,
				-end    => $self->length($_),
				-type   => $type,
				-source => $self->name,
				-name   => $_,
			)
		} $self->seq_ids;
		return wantarray ? @chromos : \@chromos;
	}
	elsif ($type =~ /region|interval|observation|coverage|wiggle|summary/) {
		# region or segment are individual observation features
		# coverage or wiggle are for efficient score retrieval with
		# backwards compatibility with Bio::Graphics
		# summary are statistical summaries akin to Bio::DB::BigFile

		# set up an iterator
		my $iterator = $self->get_seq_stream(%args);
		return unless $iterator;

		# if user requested an iterator
		if (exists $args{-iterator} and $args{-iterator}) {
			return $iterator;
		}

		# collect the features
		my @features;
		while (my $f = $iterator->next_seq) {
			push @features, $f;
		}
		return wantarray ? @features : \@features;
	}
	else {
		confess "unknown type request '$type'!\n";
	}
}


sub get_features_by_type {
	# does not work without location information
	cluck "please use features() method instead";
	return;
}


sub get_features_by_location {
	my $self = shift;

	# arguments can be chromo, start, stop, strand
	my ($seq_id, $start, $stop, $strand) = $self->_get_coordinates(@_);

	return $self->features($seq_id, $start, $stop, $strand);
}


sub get_feature_by_id {
	# much as Bio::DB::BigWig fakes the id, so we will too here
	# I don't know how necessary this really is
	my $self = shift;

	# id will be encoded as chr:start:end ?
	my $id = shift;
	my ($seq_id, $start, $end, $type) = split /:/, $id;

	my @list = $self->features(
		-seq_id   => $seq_id,
		-start    => $start,
		-end      => $end,
		-type     => $type || undef,
	);
	return unless @list;
	return $list[0] if scalar @list == 1;
	foreach my $f (@list) {
		return $f if ($f->start == $start and $f->end == $end);
	}
	return;
}


sub get_feature_by_name {
	return shift->get_feature_by_id(@_);
}


sub get_seq_stream {
	my $self = shift;

	# arguments can be chromo, start, stop, strand
	my ($seq_id, $start, $stop, $strand) = $self->_get_coordinates(@_);
	return unless $self->length($seq_id); # make sure chromosome is represented

	# check for type
	my %args = @_;
	my $type;
	$args{-type} ||= $args{-types} || $args{-primary_tag} || 'region';
	if (ref $args{-type} and ref $args{-type} eq 'ARRAY') {
		$type = $args{-type}->[0] || 'region';
	}
	else {
		$type = $args{-type};
	}

	return Bio::DB::USeq::Iterator->new(
		-seq_id     => $seq_id,
		-start      => $start,
		-end        => $stop,
		-strand     => $strand,
		-type       => $type,
		-source     => $self->name,
		-useq       => $self,
	);
}


sub scores {
	my $self = shift;

	# arguments can be chromo, start, stop, strand
	my ($seq_id, $start, $stop, $strand) = $self->_get_coordinates(@_);
	return unless $self->length($seq_id); # make sure chromosome is represented

	# determine which slices to retrieve
	my @slices = $self->_translate_coordinates_to_slices(
		$seq_id, $start, $stop, $strand);
	return unless @slices;
	$self->_clear_buffer(\@slices);

	# collect the scores from each of the requested slices
	my @scores;
	foreach my $slice (@slices) {

		# load and unpack the data
		$self->_load_slice($slice);

		# find the overlapping observations
		my $results = $self->{'buffer'}{$slice}->fetch($start - 1, $stop);

		# record the scores
		foreach my $r (@$results) {
			push @scores, $r->[2] if defined $r->[2];
		}
	}

	return wantarray ? @scores : \@scores;
}

sub observations {
	my $self = shift;

	# arguments can be chromo, start, stop, strand
	my ($seq_id, $start, $stop, $strand) = $self->_get_coordinates(@_);
	return unless $self->length($seq_id); # make sure chromosome is represented

	# determine which slices to retrieve
	my @slices = $self->_translate_coordinates_to_slices(
		$seq_id, $start, $stop, $strand);
	return unless @slices;
	$self->_clear_buffer(\@slices);

	# collect the scores from each of the requested slices
	my @observations;
	foreach my $slice (@slices) {
		# load and unpack the data
		$self->_load_slice($slice);

		# find the overlapping observations
		my $results = $self->{'buffer'}{$slice}->fetch($start - 1, $stop);
		push @observations, @$results;
	}

	return wantarray ? @observations : \@observations;
}




#### Private methods ####

sub _parse_metadata {
	my $self = shift;

	# the metadata file should always be present in a USeq file
	my $readMe = $self->{'zip'}->contents('archiveReadMe.txt');
	unless ($readMe) {
		carp " USeq file is malformed! It does not contain an archiveReadMe.txt file\n";
		return;
	}

	# parse the archive file
	# this is a simple text file, each line is key = value
	$self->{'metadata'}{'comments'} = [];
	foreach (split /\n/, $readMe) {
		if (/^#/) {
			push @{ $self->{'metadata'}{'comments'} }, $_ unless $_ =~ /validCount/;
			next;
		}
		if (/^\s*([^=\s]+)\s*=\s*(.+)\s*$/) {
			# separate key = value pairs tolerating whitespace
			# key may contain anything excluding = and whitespace
			$self->{'metadata'}{$1} = $2;
		}
	}
	return 1;
}


sub _parse_members {
	my $self = shift;

	# there is a lot of information encoded in each zip member file name
	# the chromosome, strand, coordinates of represented interval, and file type
	# this parses and indexes this metadata into a usable format

	my @errors;
	foreach my $member ($self->{'zip'}->memberNames) {

		# archive readMe
		next if ($member eq 'archiveReadMe.txt');

		# data file
		my ($chromo, $strand, $start, $stop, $number, $extension) =
			($member =~ /^([\w\-\.]+)([\+\-\.])(\d+)\-(\d+)\-(\d+)\.(\w+)$/i);

		# check extracted metadata
		unless ($chromo and $strand and defined $start and defined $stop and
			$number and $extension) {
			push @errors, "  data slice $member";
			next;
		}

		# check stranded data
		unless (defined $self->{'stranded'}) {
			if ($strand eq '.') {
				$self->{'stranded'} = 0;
			}
			else {
				$self->{'stranded'} = 1;
			}
		}

		# check seq_ids
		# record the length for each seq_id, which may or may not be entirely
		# accurate, since it is just the last interval position
		if (exists $self->{'seq_ids'}{$chromo} ) {
			if ($stop > $self->{'seq_ids'}{$chromo}) {
				$self->{'seq_ids'}{$chromo} = $stop;
			}
		}
		else {
			$self->{'seq_ids'}{$chromo} = $stop;
		}

		# convert to BioPerl convention
		$strand = $strand eq '+' ? 1 : $strand eq '.' ? 0 : $strand eq '-' ? -1 : 0;
		$start += 1;

		# store the member details for each member slice
		$self->{'file2attribute'}{$member} =
			[$chromo, $start, $stop, $strand, $extension, $number];

		# store the member into a chromosome-strand-specific interval tree
		$self->{'coord2file'}{$chromo}{$strand} ||= Set::IntervalTree->new();
		$self->{'coord2file'}{$chromo}{$strand}->insert($member, $start, $stop);
	}

	# check parsing
	if (@errors) {
		carp "Errors parsing data slice filenames:\n" . join("\n", @errors) . "\n";
	}
	unless (%{ $self->{'coord2file'} }) {
		carp "no data slices present in USeq archive!\n";
		return;
	}

	return 1;
}


sub _get_coordinates {
	my $self = shift;

	my ($seq_id, $start, $stop, $strand);
	if ($_[0] =~ /^\-/) {
		my %args = @_;
		$seq_id = $args{'-seq_id'} || $args{'-chromo'} || undef;
		$start  = $args{'-start'}  || $args{'-pos'}    || undef;
		$stop   = $args{'-end'}    || $args{'-stop'}   || undef;
		$strand = $args{'-strand'} || '0'; # unstranded
	}
	else {
		($seq_id, $start, $stop, $strand) = @_;
	}
	unless ($seq_id) {
		cluck "no sequence ID provided!";
		return;
	}
	$start ||= 1;
	$stop  ||= $self->length($seq_id);
	$strand ||= 0;
	return ($seq_id, $start, $stop, $strand);
}


sub _translate_coordinates_to_slices {
	my $self = shift;
	my ($seq_id, $start, $stop, $strand) = @_;
	return unless (exists $self->{'coord2file'}{$seq_id});

	# check strand request
	my $both = 0;
	if ($strand == 0) {
		# strand was not specified,
		# but collect from both strands if we have stranded data
		$both = 1 if $self->stranded;
	}
	else {
		# strand was specified,
		# but convert it to unstranded if the data is not stranded
		$strand = 0 unless $self->stranded;
	}

	# look for the overlapping slices
	my @slices;
	if ($both) {
		# need to collect from both strands
		# plus strand first, then minus strand
		my $results = $self->{'coord2file'}{$seq_id}{1}->fetch($start, $stop);
		my $results2 = $self->{'coord2file'}{$seq_id}{-1}->fetch($start, $stop);
		push @slices, @$results, @$results2;
	}

	# specific strand
	else {
		my $results = $self->{'coord2file'}{$seq_id}{$strand}->fetch($start, $stop);
		push @slices, @$results;
	}

	return @slices;
}


sub _clear_buffer {
	my $self = shift;

	# make a quick hash of wanted slices
	my %wanted = map {$_ => 1} @{$_[0]};

	# delete the existing buffers of slices we do not want
	foreach (keys %{ $self->{buffer} }) {
		delete $self->{buffer}{$_} unless exists $wanted{$_};
	}
}

sub _load_slice {
	my $self = shift;
	my $slice = shift;
	return if (exists $self->{'buffer'}{$slice});

	# each slice is parsed into observations consisting of start, stop, and
	# optionally score and text depending on type
	# these are stored as anonymous arrays [start, stop, score, text]
	# for quick retrieval each observation interval is stored in a Set::IntervalTree
	# these operations maintain the original 0-based coordinate system

	my $type = $self->slice_type($slice);
	if    ($type eq 's')    { $self->_load_s_slice($slice) }
	elsif ($type eq 'sf')   { $self->_load_sf_slice($slice) }
	elsif ($type eq 'st')   { $self->_load_st_slice($slice) }
	elsif ($type eq 'ss')   { $self->_load_ss_slice($slice) }
	elsif ($type eq 'ssf')  { $self->_load_ssf_slice($slice) }
	elsif ($type eq 'sst')  { $self->_load_sst_slice($slice) }
	elsif ($type eq 'ssft') { $self->_load_ssft_slice($slice) }
	elsif ($type eq 'i')    { $self->_load_i_slice($slice) }
	elsif ($type eq 'if')   { $self->_load_if_slice($slice) }
	elsif ($type eq 'it')   { $self->_load_it_slice($slice) }
	elsif ($type eq 'ii')   { $self->_load_ii_slice($slice) }
	elsif ($type eq 'iif')  { $self->_load_iif_slice($slice) }
	elsif ($type eq 'iit')  { $self->_load_iit_slice($slice) }
	elsif ($type eq 'iift') { $self->_load_iift_slice($slice) }
	elsif ($type eq 'is')   { $self->_load_is_slice($slice) }
	elsif ($type eq 'isf')  { $self->_load_isf_slice($slice) }
	elsif ($type eq 'ist')  { $self->_load_ist_slice($slice) }
	elsif ($type eq 'isft') { $self->_load_isft_slice($slice) }
	else {
		croak "unable to load slice '$slice'! Unsupported slice type $type\n";
	}
}


sub _load_s_slice {
	my $self = shift;
	my $slice = shift;
	my $tree = Set::IntervalTree->new();

	# unpack the data slice zip member
	my $number = $self->slice_obs_number($slice);
	my ($null, @data) = unpack("si>(s>)$number", $self->zip->contents($slice) );

	# convert the unpacked data into start, stop, score
	# first observation
	my $position = shift @data;
	$tree->insert( [$position, $position + 1, undef], $position, $position + 1);

	# remaining observations
	while (@data) {
		$position += (shift @data) + 32768;
		$tree->insert( [$position, $position + 1, undef], $position, $position + 1);
	}

	# store Interval Tree buffer
	$self->{buffer}{$slice} = $tree;
}

sub _load_sf_slice {
	my $self = shift;
	my $slice = shift;
	my $tree = Set::IntervalTree->new();

	# unpack the data slice zip member
	my $number = $self->slice_obs_number($slice);
	my ($null, @data) = unpack("si>f>(s>f>)$number", $self->zip->contents($slice) );

	# convert the unpacked data into start, stop, score
	# first observation
	my $position = shift @data;
	$tree->insert( [$position, $position + 1, shift(@data)], $position, $position + 1);

	# remaining observations
	while (@data) {
		$position += (shift @data) + 32768;
		$tree->insert( [$position, $position + 1, shift(@data)], $position, $position + 1);
	}

	# store Interval Tree buffer
	$self->{buffer}{$slice} = $tree;
}

sub _load_st_slice {
	my $self = shift;
	my $slice = shift;
	my $tree = Set::IntervalTree->new();

	# convert the unpacked data into start, stop, (score), text

	# load the slice contents
	my $contents = $self->zip->contents($slice);

	# first observation
	my $i = 8; # go ahead and set index up to first observation text
	my (undef, $position, $t) = unpack('si>s>', substr($contents, 0, $i));
		# initial null, position, text_length
	my $text = unpack("A$t", substr($contents, $i, $t));
	$tree->insert( [$position, $position + 1, undef, $text], $position, $position + 1);
	$i += $t;

	# remaining observations
	my $p; # position offset
	my $len = CORE::length($contents);
	while ($i < $len) {
		my ($p, $t) = unpack('s>s>', substr($contents, $i, 4));
		$i += 4;
		$position += $p + 32768;
		$text = unpack("A$t", substr($contents, $i, $t));
		$tree->insert( [$position, $position + 1, undef, $text], $position, $position + 1);
		$i += $t;
	}

	# store Interval Tree buffer
	$self->{buffer}{$slice} = $tree;
}

sub _load_ss_slice {
	my $self = shift;
	my $slice = shift;
	my $tree = Set::IntervalTree->new();

	# unpack the data slice zip member
	my $number = $self->slice_obs_number($slice);
	my ($null, @data) = unpack("si>s>(s>s>)$number", $self->zip->contents($slice) );

	# convert the unpacked data into start, stop, no score
	# first observation
	my $position = @data;
	my $position2 = $position + (shift @data) + 32768;
	$tree->insert( [$position, $position2, undef], $position, $position2);

	# remaining observations
	while (@data) {
		$position += (shift @data) + 32768;
		$position2 = $position + (shift @data) + 32768;
		$tree->insert( [$position, $position2, undef], $position, $position2);
	}

	# store Interval Tree buffer
	$self->{buffer}{$slice} = $tree;
}

sub _load_ssf_slice {
	my $self = shift;
	my $slice = shift;
	my $tree = Set::IntervalTree->new();

	# unpack the data slice zip member
	my $number = $self->slice_obs_number($slice);
	my ($null, @data) = unpack("si>s>f>(s>s>f>)$number", $self->zip->contents($slice) );

	# convert the unpacked data into start, stop, score
	# first observation
	my $position = (shift @data);
	my $position2 = $position + (shift @data) + 32768;
	$tree->insert( [$position, $position2, shift(@data)], $position, $position2);

	# remaining observations
	while (@data) {
		$position += (shift @data) + 32768;
		$position2 = $position + (shift @data) + 32768;
		$tree->insert( [$position, $position2, shift(@data)], $position, $position2);
	}

	# store Interval Tree buffer
	$self->{buffer}{$slice} = $tree;
}

sub _load_sst_slice {
	my $self = shift;
	my $slice = shift;
	my $tree = Set::IntervalTree->new();
	my $contents = $self->zip->contents($slice);

	# first observation
	my $i = 10; # go ahead and set index up to first observation text
	my (undef, $position, $l, $t) = unpack('si>s>s>', substr($contents, 0, $i));
		# initial null, position, length, text_length
	my $position2 = $position + $l + 32768;
	my $text = unpack("A$t", substr($contents, $i, $t));
	$tree->insert( [$position, $position2, undef, $text], $position, $position2);
	$i += $t;

	# remaining observations
	my $p; # position offset
	my $len = CORE::length($contents);
	while ($i < $len) {
		($p, $l, $t) = unpack('s>s>s>', substr($contents, $i, 6));
		$i += 6;
		$position += $p + 32768;
		$position2 = $position + $l + 32768;
		$text = unpack("A$t", substr($contents, $i, $t));
		$tree->insert( [$position, $position2, undef, $text], $position, $position2);
		$i += $t;
	}

	# store Interval Tree buffer
	$self->{buffer}{$slice} = $tree;
}

sub _load_ssft_slice {
	my $self = shift;
	my $slice = shift;
	my $tree = Set::IntervalTree->new();
	my $contents = $self->zip->contents($slice);

	# first observation
	my $i = 14; # go ahead and set index up to first observation text
	my (undef, $position, $l, $s, $t) = unpack('si>s>f>s>', substr($contents, 0, $i));
		# initial null, position, length, score, text_length
	my $position2 = $position + $l + 32768;
	my $text = unpack("A$t", substr($contents, $i, $t));
	$tree->insert( [$position, $position2, $s, $text], $position, $position2);
	$i += $t;

	# remaining observations
	my $p; # position offset
	my $len = CORE::length($contents);
	while ($i < $len) {
		($p, $l, $s, $t) = unpack('s>s>f>s>', substr($contents, $i, 10));
		$i += 10;
		$position += $p + 32768;
		$position2 = $position + $l + 32768;
		$text = unpack("A$t", substr($contents, $i, $t));
		$tree->insert( [$position, $position2, $s, $text], $position, $position2);
		$i += $t;
	}

	# store Interval Tree buffer
	$self->{buffer}{$slice} = $tree;
}

sub _load_i_slice {
	my $self = shift;
	my $slice = shift;
	my $tree = Set::IntervalTree->new();

	# unpack the data slice zip member
	my $number = $self->slice_obs_number($slice);
	my ($null, @data) = unpack("si>(i>)$number", $self->zip->contents($slice) );

	# convert the unpacked data into start, stop, score
	# first observation
	my $position = @data;
	$tree->insert( [$position, $position + 1, undef], $position, $position + 1);

	# remaining observations
	while (@data) {
		$position += (shift @data);
		$tree->insert( [$position, $position + 1, undef], $position, $position + 1);
	}

	# store Interval Tree buffer
	$self->{buffer}{$slice} = $tree;
}

sub _load_if_slice {
	my $self = shift;
	my $slice = shift;
	my $tree = Set::IntervalTree->new();

	# unpack the data slice zip member
	my $number = $self->slice_obs_number($slice);
	my ($null, @data) = unpack("si>f>(i>f>)$number", $self->zip->contents($slice) );

	# convert the unpacked data into start, stop, score
	# first observation
	my $position = @data;
	$tree->insert( [$position, $position + 1, shift(@data)], $position, $position + 1);

	# remaining observations
	while (@data) {
		$position += (shift @data);
		$tree->insert( [$position, $position + 1, shift(@data)], $position, $position + 1);
	}

	# store Interval Tree buffer
	$self->{buffer}{$slice} = $tree;
}

sub _load_it_slice {
	my $self = shift;
	my $slice = shift;
	my $tree = Set::IntervalTree->new();

	# convert the unpacked data into start, stop, (score), text

	# load the slice contents
	my $contents = $self->zip->contents($slice);

	# first observation
	my $i = 10; # go ahead and set index up to first observation text
	my (undef, $position, $t) = unpack('si>i>', substr($contents, 0, $i));
		# initial null, position, text_length
	my $text = unpack("A$t", substr($contents, $i, $t));
	$tree->insert( [$position, $position + 1, undef, $text], $position, $position + 1);
	$i += $t;

	# remaining observations
	my $p; # position offset
	my $len = CORE::length($contents);
	while ($i < $len) {
		($p, $t) = unpack('i>s>', substr($contents, $i, 6));
		$i += 6;
		$position += $p + 32768;
		$text = unpack("A$t", substr($contents, $i, $t));
		$tree->insert( [$position, $position + 1, undef, $text], $position, $position + 1);
		$i += $t;
	}

	# store Interval Tree buffer
	$self->{buffer}{$slice} = $tree;
}

sub _load_ii_slice {
	my $self = shift;
	my $slice = shift;
	my $tree = Set::IntervalTree->new();

	# unpack the data slice zip member
	my $number = $self->slice_obs_number($slice);
	my ($null, @data) = unpack("si>i>(i>i>)$number", $self->zip->contents($slice) );

	# convert the unpacked data into start, stop, score
	# first observation
	my $position = @data;
	my $position2 = $position + (shift @data);
	$tree->insert( [$position, $position2, undef], $position, $position2);

	# remaining observations
	while (@data) {
		$position += (shift @data);
		$position2 = $position + (shift @data);
		$tree->insert( [$position, $position2, undef], $position, $position2);
	}

	# store Interval Tree buffer
	$self->{buffer}{$slice} = $tree;
}

sub _load_iif_slice {
	my $self = shift;
	my $slice = shift;
	my $tree = Set::IntervalTree->new();

	# unpack the data slice zip member
	my $number = $self->slice_obs_number($slice);
	my ($null, @data) = unpack("si>i>f>(i>i>f>)$number", $self->zip->contents($slice) );

	# convert the unpacked data into start, stop, score
	# first observation
	my $position = @data;
	my $position2 = $position + (shift @data);
	$tree->insert( [$position, $position2, shift(@data)], $position, $position2);

	# remaining observations
	while (@data) {
		$position += (shift @data);
		$position2 = $position + (shift @data);
		$tree->insert( [$position, $position2, shift(@data)], $position, $position2);
	}

	# store Interval Tree buffer
	$self->{buffer}{$slice} = $tree;
}

sub _load_iit_slice {
	my $self = shift;
	my $slice = shift;
	my $tree = Set::IntervalTree->new();
	my $contents = $self->zip->contents($slice);

	# first observation
	my $i = 12; # go ahead and set index up to first observation text
	my (undef, $position, $l, $t) = unpack('si>i>s>', substr($contents, 0, $i));
		# initial null, position, length, text_length
	my $position2 = $position + $l;
	my $text = unpack("A$t", substr($contents, $i, $t));
	$tree->insert( [$position, $position2, undef, $text], $position, $position2);
	$i += $t;

	# remaining observations
	my $p; # position offset
	my $len = CORE::length($contents);
	while ($i < $len) {
		($p, $l, $t) = unpack('i>i>s>', substr($contents, $i, 10));
		$i += 10;
		$position += $p;
		$position2 = $position + $l;
		$text = unpack("A$t", substr($contents, $i, $t));
		$tree->insert( [$position, $position2, undef, $text], $position, $position2);
		$i += $t;
	}

	# store Interval Tree buffer
	$self->{buffer}{$slice} = $tree;
}

sub _load_iift_slice {
	my $self = shift;
	my $slice = shift;
	my $tree = Set::IntervalTree->new();
	my $contents = $self->zip->contents($slice);

	# first observation
	my $i = 16; # go ahead and set index up to first observation text
	my (undef, $position, $l, $s, $t) = unpack('si>i>f>s>', substr($contents, 0, $i));
		# initial null, position, length, score, text_length
	my $position2 = $position + $l;
	my $text = unpack("A$t", substr($contents, $i, $t));
	$tree->insert( [$position, $position2, $s, $text], $position, $position2);
	$i += $t;

	# remaining observations
	my $p; # position offset
	my $len = CORE::length($contents);
	while ($i < $len) {
		($p, $l, $s, $t) = unpack('i>i>f>s>', substr($contents, $i, 14));
		$i += 14;
		$position += $p;
		$position2 = $position + $l;
		$text = unpack("A$t", substr($contents, $i, $t));
		$tree->insert( [$position, $position2, $s, $text], $position, $position2);
		$i += $t;
	}

	# store Interval Tree buffer
	$self->{buffer}{$slice} = $tree;
}

sub _load_is_slice {
	my $self = shift;
	my $tree = Set::IntervalTree->new();
	my $slice = shift;

	# unpack the data slice zip member
	my $number = $self->slice_obs_number($slice);
	my ($null, @data) = unpack("si>s>(i>s>)$number", $self->zip->contents($slice) );

	# convert the unpacked data into start, stop, score
	# first observation
	my $position = @data;
	my $position2 = $position + (shift @data) + 32768;
	$tree->insert( [$position, $position2, undef], $position, $position2);

	# remaining observations
	while (@data) {
		$position += (shift @data);
		$position2 = $position + (shift @data) + 32768;
		$tree->insert( [$position, $position2, undef], $position, $position2);
	}

	# store Interval Tree buffer
	$self->{buffer}{$slice} = $tree;
}

sub _load_isf_slice {
	my $self = shift;
	my $slice = shift;
	my $tree = Set::IntervalTree->new();

	# unpack the data slice zip member
	my $number = $self->slice_obs_number($slice);
	my ($null, @data) = unpack("si>s>f>(i>s>f>)$number", $self->zip->contents($slice) );

	# convert the unpacked data into start, stop, score
	# first observation
	my $position = @data;
	my $position2 = $position + (shift @data) + 32768;
	$tree->insert( [$position, $position2, shift(@data)], $position, $position2);

	# remaining observations
	while (@data) {
		$position += (shift @data);
		$position2 = $position + (shift @data) + 32768;
		$tree->insert( [$position, $position2, shift(@data)], $position, $position2);
	}

	# store Interval Tree buffer
	$self->{buffer}{$slice} = $tree;
}

sub _load_ist_slice {
	my $self = shift;
	my $slice = shift;
	my $tree = Set::IntervalTree->new();
	my $contents = $self->zip->contents($slice);

	# first observation
	my $i = 10; # go ahead and set index up to first observation text
	my (undef, $position, $l, $t) = unpack('si>s>s>', substr($contents, 0, $i));
		# initial null, position, length, text_length
	my $position2 = $position + $l + 32768;
	my $text = unpack("A$t", substr($contents, $i, $t));
	$tree->insert( [$position, $position2, undef, $text], $position, $position2);
	$i += $t;

	# remaining observations
	my $p; # position offset
	my $len = CORE::length($contents);
	while ($i < $len) {
		($p, $l, $t) = unpack('i>s>s>', substr($contents, $i, 8));
		$i += 8;
		$position += $p;
		$position2 = $position + $l + 32768;
		$text = unpack("A$t", substr($contents, $i, $t));
		$tree->insert( [$position, $position2, undef, $text], $position, $position2);
		$i += $t;
	}

	# store Interval Tree buffer
	$self->{buffer}{$slice} = $tree;
}

sub _load_isft_slice {
	my $self = shift;
	my $slice = shift;
	my $tree = Set::IntervalTree->new();
	my $contents = $self->zip->contents($slice);

	# first observation
	my $i = 14; # go ahead and set index up to first observation text
	my (undef, $position, $l, $s, $t) = unpack('si>s>f>s>', substr($contents, 0, $i));
		# initial null, position, length, score, text_length
	my $position2 = $position + $l + 32768;
	my $text = unpack("A$t", substr($contents, $i, $t));
	$tree->insert( [$position, $position2, $s, $text], $position, $position2);
	$i += $t;

	# remaining observations
	my $p; # position offset
	while ($i < CORE::length($contents)) {
		($p, $l, $s, $t) = unpack('i>s>f>s>', substr($contents, $i, 12));
		$i += 12;
		$position += $p;
		$position2 = $position + $l + 32768;
		$text = unpack("A$t", substr($contents, $i, $t));
		$tree->insert( [$position, $position2, $s, $text], $position, $position2);
		$i += $t;
	}

	# store Interval Tree buffer
	$self->{buffer}{$slice} = $tree;
}

sub _mean_score {
	my $self = shift;
	my ($start, $stop, $slices) = @_;
	return unless @$slices;

	# collect the scores from each of the requested slices
	my $sum;
	my $count = 0;
	foreach my $slice (@$slices) {
		next unless $self->slice_type($slice) =~ /f/;
			# no sense going through if no score present

		# load and unpack the data
		$self->_load_slice($slice);

		# find the overlapping observations and record values
		my $results = $self->{'buffer'}{$slice}->fetch($start - 1, $stop);
		foreach my $r (@$results) {
			$sum += $r->[2] || 0;
			$count++;
		}
	}
	return $count ? $sum / $count : undef;
}

sub _stat_summary {
	my $self = shift;
	my ($start, $stop, $slices) = @_;
	return unless @$slices;

	# initialize the statistical scores
	my $count = 0;
	my $sum;
	my $sum_squares;
	my $min;
	my $max;

	# collect the scores from each of the requested slices
	foreach my $slice (@$slices) {
		next unless $self->slice_type($slice) =~ /f/;
			# no sense going through if no score present

		# load and unpack the data
		$self->_load_slice($slice);

		# find the overlapping observations
		my $results = $self->{'buffer'}{$slice}->fetch($start - 1, $stop);
		foreach my $r (@$results) {
			# each observation is [start, stop, score]
			if (defined $r->[2]) {
				$count++;
				$sum += $r->[2];
				$sum_squares += ($r->[2] * $r->[2]);
				$min = $r->[2] if (!defined $min or $r->[2] < $min);
				$max = $r->[2] if (!defined $max or $r->[2] > $max);
			}
		}
	}

	# assemble the statistical summary hash
	my %summary = (
		'validCount'    => $count,
		'sumData'       => $sum || 0,
		'sumSquares'    => $sum_squares || 0,
		'minVal'        => $min || 0,
		'maxVal'        => $max || 0,
	);
	return \%summary;
}

sub _rewrite_metadata {
	my $self = shift;
	return unless (-w $self->zip->fileName);
	my $md = $self->{'metadata'};

	# generate new metadata as an array
	my @new_md;
	push @new_md, "useqArchiveVersion = " . $md->{useqArchiveVersion};
	push @new_md, @{ $md->{comments} } if exists $md->{comments};
	push @new_md, "dataType = " . $md->{dataType};
	push @new_md, "versionedGenome = " . $md->{versionedGenome};

	# additional keys that may be present
	foreach (keys %$md) {
		next if /useqArchiveVersion|dataType|versionedGenome|comments|chromStats|globalStats/;
		push @new_md, "$_ = $md->{$_}";
	}

	# global and chromosome stats
	push @new_md,
		"# Bio::DB::USeq statistics validCount,sumData,sumSquares,minVal,maxVal";
	push @new_md, "globalStats = $md->{globalStats}" if exists $md->{globalStats};
	foreach (sort {$a cmp $b} keys %$md) {
		push @new_md, "$_ = $md->{$_}" if /chromStats/;
	}

	# write the new metadata to the zip Archive
	$self->{'zip'}->contents('archiveReadMe.txt', join("\n", @new_md));
	$self->{'zip'}->overwrite;
	$self->clone; # not sure if this is necessary, but just in case we reopen the zip
}


######## Exported Functions ########
# these are borrowed from Bio::DB::BigWig from Lincoln Stein

sub binMean {
    my $score = shift;
    return 0 unless $score->{validCount};
    $score->{sumData}/$score->{validCount};
}

sub binVariance {
    my $score = shift;
    return 0 unless $score->{validCount};
    my $var = $score->{sumSquares} - $score->{sumData}**2/$score->{validCount};
    if ($score->{validCount} > 1) {
	$var /= $score->{validCount}-1;
    }
    return 0 if $var < 0;
    return $var;
}

sub binStdev {
    my $score = shift;
    return sqrt(binVariance($score));
}




######## Other Classes #############

package Bio::DB::USeq::Feature;
use base 'Bio::SeqFeature::Lite';

sub new {
    my $class = shift;
    return $class->SUPER::new(@_);
}

sub type {
	# Bio::SeqFeature::Lite mangles the type and returns
	# primary_tag:source if both are set
	# this may wreck havoc with parsers when the type already has a :
	# as in wiggle:1000
	my $self = shift;
	return $self->{type};
}

sub chr_stats {
	my $self = shift;
	return unless exists $self->{useq};
	return $self->{useq}->chr_stats( $self->seq_id );
}

sub chr_mean {
	my $self = shift;
	return unless exists $self->{useq};
	return $self->{useq}->chr_mean( $self->seq_id );
}

sub chr_stdev {
	my $self = shift;
	return unless exists $self->{useq};
	return $self->{useq}->chr_stdev( $self->seq_id );
}

sub global_stats {
	my $self = shift;
	return unless exists $self->{useq};
	return $self->{useq}->global_stats( $self->seq_id );
}

sub global_mean {
	my $self = shift;
	return unless exists $self->{useq};
	return $self->{useq}->global_mean( $self->seq_id );
}

sub global_stdev {
	my $self = shift;
	return unless exists $self->{useq};
	return $self->{useq}->global_stdev( $self->seq_id );
}




package Bio::DB::USeq::Segment;
use base 'Bio::DB::USeq::Feature';

sub new {
    my $class = shift;
    my %args = @_;
    my $segment = $class->SUPER::new(@_);
    $segment->{'useq'} = $args{'-useq'} if exists $args{'-useq'};
    return $segment;
}

sub scores {
	my $self = shift;
	return $self->{'useq'}->scores(
		-seq_id => $self->seq_id,
		-start  => $self->start,
		-end    => $self->end,
		-strand => $self->strand,
	);
}

sub features {
	my $self = shift;
	my $type = shift;
	$type ||= 'region';
	return $self->{'useq'}->features(
		-seq_id => $self->seq_id,
		-start  => $self->start,
		-end    => $self->end,
		-strand => $self->strand,
		-type   => $type,
	);
}

sub get_seq_stream {
	my $self = shift;
	my $type = shift;
	$type ||= 'region';
	return Bio::DB::USeq::Iterator->new(
		-seq_id => $self->seq_id,
		-start  => $self->start,
		-end    => $self->end,
		-strand => $self->strand,
		-source => $self->source,
		-type   => $type,
		-useq   => $self->{useq},
	);
}

sub slices {
	my $self = shift;
	my @slices = $self->{'useq'}->_translate_coordinates_to_slices(
		$self->seq_id, $self->start, $self->end, $self->strand
	);
	return wantarray ? @slices : \@slices;
}

sub coverage {
	my $self = shift;
	my $bins = shift;
	$bins ||= $self->length;
	return $self->features("coverage:$bins");
}

sub wiggle {
	my $self = shift;
	my $bins = shift;
	$bins ||= $self->length;
	return $self->features("wiggle:$bins");
}

sub statistical_summary {
	my $self = shift;
	my $bins = shift;
	$bins ||= 1;
	return $self->features("summary:$bins");
}

sub observations {
	my $self = shift;
	return $self->{'useq'}->observations(
		-seq_id => $self->seq_id,
		-start  => $self->start,
		-end    => $self->end,
		-strand => $self->strand,
	);
}


package Bio::DB::USeq::Iterator;
use base 'Bio::DB::USeq::Feature';

sub new {
    my $class = shift;

    # create object
	my %args = @_;
	my $useq = $args{'-useq'} or
    	die "Bio::DB::USeq::Iterator cannot be created without a -useq argument";
    my $iterator = $class->SUPER::new(@_);
    $iterator->{'useq'} = $useq;

	# determine which members to retrieve
	my @slices = $useq->_translate_coordinates_to_slices(
		$args{-seq_id},
		$args{-start},
		$args{-end},
		$args{-strand},
	);
	$useq->_clear_buffer(\@slices);

	# sort the slices as necessary
	if (scalar(@slices) > 1) {
		@slices = map {$_->[1]}
				sort {$a->[0] <=> $b->[0]}
				map { [$useq->slice_start($_), $_] } @slices;
	}

	# how we set up the iterator depends on the feature type requested
	# we need to add specific information to iterator object

	if ($iterator->type =~ /region|interval|observation/) {
		# useq observation features are simple
		$iterator->{'wiggle'} = 0;

		# prepare specific iterator information
		$iterator->{'slices'} = \@slices;
		$iterator->{'current_results'} = undef;
		$iterator->{'current_strand'} = undef;

		return $iterator;
	}

	# otherwise we work with more complex wiggle or summary features
	# set the type of wiggle feature: 1 = wiggle, 2 = summary
	$iterator->{'wiggle'} = $iterator->type =~ /summary/ ? 2 : 1;

	# normally the iterator will only return one wigggle|summary feature, but
	# will return two if stranded data, per behavior for Bio::Grapics...
	if ($iterator->strand == 0 and $useq->stranded) {
		# we have stranded data, so need to return two features
		# separate the slices into each respective strands for each feature
		my (@f, @r);
		foreach (@slices) {
			if ($useq->slice_strand($_) == 1) {
				push @f, $_;
			}
			else {
				push @r, $_;
			}
		}
		$iterator->{slices} = [\@f, \@r];
	}
	else {
		# only need to return one feature
		$iterator->{slices} = [ \@slices ];
	}

	# check for type and bins
	my ($bin, $step);
	if ($iterator->type =~ /:(\d+)$/i) {
		$bin  = $1;
		my $length = $iterator->length;
		$bin  = $length if $bin > $length;
		$step = $length/$bin;
	}
	$iterator->{bin}  = $bin;
	$iterator->{step} = $step;

	return $iterator;
}

sub next_seq {
	my $self = shift;

	if ($self->{'wiggle'} == 1) {
		return $self->_next_wiggle;
	}
	elsif ($self->{'wiggle'} == 2) {
		return $self->_next_summary;
	}
	else {
		return $self->_next_region;
	}
}

sub next_feature {return shift->next_seq}

sub _next_region {
	# this will keep returning observations as SeqFeature objects until we run out
	# of observations in the requested interval
	my $self = shift;
	my $useq = $self->{'useq'};

	# get current results
	unless ($self->{'current_results'}) {
		my $slice = shift @{ $self->{'slices'} } || undef;
		return unless $slice; # no more slices, we're done

		# collect the observations
		$useq->_load_slice($slice);
		my $result = $useq->{buffer}{$slice}->fetch($self->start - 1, $self->end);

		# sort the observations and store the results
		my @sorted = sort {$a->[0] <=> $b->[0] or $a->[1] <=> $b->[1]} @$result;
		$self->{'current_results'} = \@sorted;
		$self->{'current_strand'} = $useq->slice_strand($slice);
	}

	# return next observation as a Feature object
	my $obs = shift @{ $self->{'current_results'} };
	unless (defined $obs) {
		# no more observations for current slice, try to move to next slice
		undef $self->{'current_results'};
		return $self->_next_region;
	}
	return Bio::DB::USeq::Feature->new(
		-seq_id     => $self->seq_id,
		-start      => $obs->[0] + 1,
		-end        => $obs->[1],
		-strand     => $self->{'current_strand'},
		-score      => $obs->[2],
		-source     => $self->source,
		-type       => $self->type,
		-name       => defined $obs->[3] ? $obs->[3] :
			join(':', $self->seq_id, $obs->[0], $obs->[1]),
	)
}

sub _next_wiggle {
	# this will return one wiggle Feature, two if separate strands are requested

	my $self = shift;
	my $useq = $self->{'useq'};

	# determine which slices to retrieve
	my $slices = shift @{ $self->{slices} };
	return unless $slices;

	# more information
	my $start = $self->start;
	my $stop  = $self->end;
	my $step  = $self->{step};

	# check whether we are working with binned wiggle or not
	my @scores;
	if ($self->{bin} and $step > 1) {
		# we will be collecting the mean score value in bins

		# collect the scores for each bin
		for (my $begin = $start; $begin < $stop; $begin += $step) {

			# round off coordinates to integers
			# beginning point and step may not be integers
			my $s = int($begin + 0.5);
			my $e = int($s + $step - 0.5); # start + step - 1 + 0.5

			# collect the scores from each of the requested slices
			if (scalar @$slices > 1) {
				# more than one slice, identify which subset of slices to collect from
				# may or may not be all of the current slices
				my @sub_slices;
				foreach my $slice (@$slices) {
					next if $s > $useq->slice_end($slice);
					next if $e < $useq->slice_start($slice);
					push @sub_slices, $slice;
				}
				push @scores, $useq->_mean_score($s, $e, \@sub_slices);
			}
			else {
				push @scores, $useq->_mean_score($s, $e, $slices);
			}
		}
	}
	else {
		# otherwise we collect in one step and associate scores at bp resolution
		# collect the scores from each of the requested slices and
		# assemble them into a hash of values

		# correlate scores with position
		my %pos2score;
		foreach my $slice (@$slices) {

			# load and unpack the data
			$useq->_load_slice($slice);

			# fetch the overlapping observations
			# each observation is [start, stop, score]
			my $result = $useq->{'buffer'}{$slice}->fetch($start - 1, $stop);
			foreach my $r (@$result) {
				foreach my $p ( $r->[0] + 1 .. $r->[1] ) {
					$pos2score{$p} = $r->[2];
				}
			}
		}

		# convert positioned scores into an array
		foreach (my $s = $start; $s <= $stop; $s++) {
			push @scores, exists $pos2score{$s} ? $pos2score{$s} : undef;
			# for Bio::Graphics it is better to store undef than 0
			# which can do wonky things with graphs
		}
	}

	# generate the wiggle object
	my $strand = $useq->slice_strand( $slices->[0] ) || 0;
	return Bio::DB::USeq::Wiggle->new(
		-seq_id     => $self->seq_id,
		-start      => $start,
		-end        => $stop,
		-strand     => $strand,
		-type       => $self->type,
		-source     => $self->source,
		-attributes => { 'coverage' => [ \@scores ] },
		-name       => $strand == 1 ? 'Forward' :
			$strand == -1 ? 'Reverse' : q(),
		-useq       => $useq,
	);
}

sub _next_summary {
	# this will return one summary Feature, two if separate strands are requested
	my $self = shift;

	# determine which slices to retrieve
	my $slices = shift @{ $self->{slices} };
	return unless $slices;

	# all of the real statistical work is done elsewhere
	# just return the summary object
	my $strand = $self->{useq}->slice_strand( $slices->[0] ) || 0;
	return Bio::DB::USeq::Summary->new(
		-seq_id     => $self->seq_id,
		-start      => $self->start,
		-end        => $self->end,
		-strand     => $strand,
		-type       => $self->type,
		-source     => $self->source,
		-name       => $strand == 1 ? 'Forward' :
			$strand == -1 ? 'Reverse' : q(),
		-useq       => $self->{'useq'},
		-slices     => $slices,
		-bin        => $self->{bin},
		-step       => $self->{step},
	);
}



package Bio::DB::USeq::Wiggle;
use base 'Bio::DB::USeq::Feature';
# Wiggle scores are stored in the coverage attribute for backwards
# compatibility with Bio::Graphics.

sub new {
    my $class = shift;
    my $wig = $class->SUPER::new(@_);
    my %args = @_;
	my $useq = $args{'-useq'} or
    	die "Bio::DB::USeq::Wiggle cannot be created without a -useq argument";
    $wig->{useq} = $useq;
    return $wig;
}

sub coverage {
	my $self = shift;
	my ($coverage) = $self->get_tag_values('coverage');
	return wantarray ? @$coverage : $coverage;
}

sub wiggle {
	return shift->coverage;
}

# Borrowed from Bio::SeqFeature::Coverage from Bio::DB::Sam
sub gff3_string {
    my $self = shift;
    my $gff3 = $self->SUPER::gff3_string(@_);
    my $coverage = $self->escape(join(',',$self->coverage));
    $gff3 =~ s/coverage=[^;]+/coverage=$coverage/g;
    return $gff3;
}

sub statistical_summary {
	# this is just for the wiggle scores, not original data
		# This is a fake statistical_summary to fool Bio::Graphics into
		# calculating chromosome or global statistics like a BigWig adaptor.
		# A real statistical_summary call would provide the number of bins,
		# so return null if bins is present.
		# We could calculate a real statistical summary, but that would be an
		# expensive circuitous route after we just collected wiggle scores.
		# Better to request the correct feature type in the first place.
	my $self = shift;
	my $bins = shift;
	return if $bins && $bins > 1;

	# initialize the statistical scores
	my $count = 0;
	my $sum;
	my $sum_squares;
	my $min;
	my $max;

	# generate a statistical summary of just the wiggle scores
	foreach my $s ($self->coverage) {
		$count++;
		next unless defined $s;
		$sum += $s;
		$sum_squares += $s * $s;
		$min = $s if (!defined $min or $s < $min);
		$max = $s if (!defined $max or $s > $max);
	}

	# return the statistical hash
	my %stat = (
		'validCount' => $count,
		'sumData'    => $sum || 0,
		'sumSquares' => $sum_squares || 0,
		'minVal'     => $min || 0,
		'maxVal'     => $max || 0,
	);
	return \%stat;
}

sub get_seq_stream {
	my $self = shift;
	return Bio::DB::USeq::Iterator->new(
		-seq_id     => $self->seq_id,
		-start      => $self->start,
		-end        => $self->end,
		-strand     => $self->strand,
		-type       => 'region',
		-source     => $self->source,
		-useq       => $self->{useq},
	);
}




package Bio::DB::USeq::Summary;
use base 'Bio::DB::USeq::Feature';

sub new {
    my $class = shift;
    my $summary = $class->SUPER::new(@_);
    my %args = @_;
	my $useq = $args{'-useq'} or
    	die "Bio::DB::USeq::Summary cannot be created without a -useq argument";
    $summary->{useq} = $useq;
    $summary->{slices} = $args{-slices} if exists $args{-slices};
    $summary->{bin}    = $args{-bin}    if exists $args{-bin};
    $summary->{step}   = $args{-step}   if exists $args{-step};
    return $summary;
}

sub statistical_summary {
	my $self = shift;
	my $useq = $self->{useq};

	# get the number of bins to calculate the statistical summaries
	my $bin = shift;
	$bin ||= $self->{bin} if exists $self->{bin};
	$bin ||= 1;
	my $step = $self->{step} if exists $self->{step};
	$step ||= $self->length / $bin;

	# get the slices
	my $slices = $self->{slices} if exists $self->{slices};
	unless ($slices) {
		# this should already be established, but just in case
		my @a = $useq->_translate_coordinates_to_slices($self->seq_id,
				$self->start, $self->end, $self->strand);
		$useq->_clear_buffer(\@a);
		$slices = \@a;
	}

	# collect the statistical summaries for each bin
	my @summaries;
	for (my $begin = $self->start; $begin < $self->end; $begin += $step) {

		# round off coordinates to integers
		# beginning point and step may not be integers
		my $s = int($begin + 0.5);
		my $e = int($s + $step - 0.5); # start + step - 1 + 0.5

		# collect the scores from each of the requested slices
		if (scalar @$slices > 1) {
			# more than one slice, identify which subset of slices to collect from
			# may or may not be all of the current slices
			my @sub_slices;
			foreach my $slice (@$slices) {
				next if $s > $useq->slice_end($slice);
				next if $e < $useq->slice_start($slice);
				push @sub_slices, $slice;
			}
			push @summaries, $useq->_stat_summary($s, $e, \@sub_slices);
		}
		else {
			push @summaries, $useq->_stat_summary($s, $e, $slices);
		}
	}

	# return the reference to the statistical summaries
	return \@summaries;
}

sub score {
	my $self = shift;
	my $a = $self->statistical_summary(1);
	return $a->[0];
}

sub gff3_string {
	# this is going to be a little convoluted, since what we
	# really want here is coverage, which is easier to calculate with means,
	# rather than doing statistical summaries and calculate means from those
	my $self = shift;
	my ($wig) = $self->{useq}->features(
		# this should only return one feature because we have specific strand
		-seq_id     => $self->seq_id,
		-start      => $self->start,
		-end        => $self->end,
		-strand     => $self->strand,
		-type       => 'coverage:1000',
	);
	return $wig->gff3_string;
}

sub get_seq_stream {
	my $self = shift;
	return Bio::DB::USeq::Iterator->new(
		-seq_id     => $self->seq_id,
		-start      => $self->start,
		-end        => $self->end,
		-strand     => $self->strand,
		-type       => 'region',
		-source     => $self->source,
		-useq       => $self->{useq},
	);
}

=head1 AUTHOR

 Timothy J. Parnell, PhD
 Dept of Oncological Sciences
 Huntsman Cancer Institute
 University of Utah
 Salt Lake City, UT, 84112

This package is free software; you can redistribute it and/or modify
it under the terms of the Artistic License 2.0.