--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/tools/stats/aggregate_scores_in_intervals.py	Fri Mar 09 19:37:19 2012 -0500
@@ -0,0 +1,243 @@
+#!/usr/bin/env python
+# Greg Von Kuster
+"""
+usage: %prog score_file interval_file chrom start stop [out_file] [options] 
+    -b, --binned: 'score_file' is actually a directory of binned array files
+    -m, --mask=FILE: bed file containing regions not to consider valid
+    -c, --chrom_buffer=INT: number of chromosomes (default is 3) to keep in memory when using a user supplied score file
+"""
+
+from __future__ import division
+from galaxy import eggs
+import pkg_resources 
+pkg_resources.require( "bx-python" )
+pkg_resources.require( "lrucache" )
+try:
+    pkg_resources.require( "python-lzo" )
+except:
+    pass
+
+import psyco_full
+import sys
+import os, os.path
+from UserDict import DictMixin
+import bx.wiggle
+from bx.binned_array import BinnedArray, FileBinnedArray
+from bx.bitset import *
+from bx.bitset_builders import *
+from fpconst import isNaN
+from bx.cookbook import doc_optparse
+from galaxy.tools.exception_handling import *
+
+assert sys.version_info[:2] >= ( 2, 4 )
+
+import tempfile, struct
+class PositionalScoresOnDisk:
+    fmt = 'f'
+    fmt_size = struct.calcsize( fmt )
+    default_value = float( 'nan' )
+    
+    def __init__( self ):
+        self.file = tempfile.TemporaryFile( 'w+b' )
+        self.length = 0
+    def __getitem__( self, i ):
+        if i < 0: i = self.length + i
+        if i < 0 or i >= self.length: return self.default_value
+        try:
+            self.file.seek( i * self.fmt_size )
+            return struct.unpack( self.fmt, self.file.read( self.fmt_size ) )[0]
+        except Exception, e:
+            raise IndexError, e
+    def __setitem__( self, i, value ):
+        if i < 0: i = self.length + i
+        if i < 0: raise IndexError, 'Negative assignment index out of range'
+        if i >= self.length:
+            self.file.seek( self.length * self.fmt_size )
+            self.file.write( struct.pack( self.fmt, self.default_value ) * ( i - self.length ) )
+            self.length = i + 1
+        self.file.seek( i * self.fmt_size )
+        self.file.write( struct.pack( self.fmt, value ) )
+    def __len__( self ):
+        return self.length
+    def __repr__( self ):
+        i = 0
+        repr = "[ "
+        for i in xrange( self.length ):
+            repr = "%s %s," % ( repr, self[i] )
+        return "%s ]" % ( repr )
+
+class FileBinnedArrayDir( DictMixin ):
+    """
+    Adapter that makes a directory of FileBinnedArray files look like
+    a regular dict of BinnedArray objects. 
+    """
+    def __init__( self, dir ):
+        self.dir = dir
+        self.cache = dict()
+    def __getitem__( self, key ):
+        value = None
+        if key in self.cache:
+            value = self.cache[key]
+        else:
+            fname = os.path.join( self.dir, "%s.ba" % key )
+            if os.path.exists( fname ):
+                value = FileBinnedArray( open( fname ) )
+                self.cache[key] = value
+        if value is None:
+            raise KeyError( "File does not exist: " + fname )
+        return value
+
+def stop_err(msg):
+    sys.stderr.write(msg)
+    sys.exit()
+    
+def load_scores_wiggle( fname, chrom_buffer_size = 3 ):
+    """
+    Read a wiggle file and return a dict of BinnedArray objects keyed 
+    by chromosome.
+    """ 
+    scores_by_chrom = dict()
+    try:
+        for chrom, pos, val in bx.wiggle.Reader( UCSCOutWrapper( open( fname ) ) ):
+            if chrom not in scores_by_chrom:
+                if chrom_buffer_size:
+                    scores_by_chrom[chrom] = BinnedArray()
+                    chrom_buffer_size -= 1
+                else:
+                    scores_by_chrom[chrom] = PositionalScoresOnDisk()
+            scores_by_chrom[chrom][pos] = val
+    except UCSCLimitException:
+        # Wiggle data was truncated, at the very least need to warn the user.
+        print 'Encountered message from UCSC: "Reached output limit of 100000 data values", so be aware your data was truncated.'
+    except IndexError:
+        stop_err('Data error: one or more column data values is missing in "%s"' %fname)
+    except ValueError:
+        stop_err('Data error: invalid data type for one or more values in "%s".' %fname)
+    return scores_by_chrom
+
+def load_scores_ba_dir( dir ):
+    """
+    Return a dict-like object (keyed by chromosome) that returns 
+    FileBinnedArray objects created from "key.ba" files in `dir`
+    """
+    return FileBinnedArrayDir( dir )
+    
+def main():
+
+    # Parse command line
+    options, args = doc_optparse.parse( __doc__ )
+
+    try:
+        score_fname = args[0]
+        interval_fname = args[1]
+        chrom_col = args[2]
+        start_col = args[3]
+        stop_col = args[4]
+        if len( args ) > 5:
+            out_file = open( args[5], 'w' )
+        else:
+            out_file = sys.stdout
+        binned = bool( options.binned )
+        mask_fname = options.mask
+    except:
+        doc_optparse.exit()
+
+    if score_fname == 'None':
+        stop_err( 'This tool works with data from genome builds hg16, hg17 or hg18.  Click the pencil icon in your history item to set the genome build if appropriate.' )
+    
+    try:
+        chrom_col = int(chrom_col) - 1
+        start_col = int(start_col) - 1
+        stop_col = int(stop_col) - 1
+    except:
+        stop_err( 'Chrom, start & end column not properly set, click the pencil icon in your history item to set these values.' )
+
+    if chrom_col < 0 or start_col < 0 or stop_col < 0:
+        stop_err( 'Chrom, start & end column not properly set, click the pencil icon in your history item to set these values.' )
+        
+    if binned:
+        scores_by_chrom = load_scores_ba_dir( score_fname )
+    else:
+        try:
+            chrom_buffer = int( options.chrom_buffer )
+        except:
+            chrom_buffer = 3
+        scores_by_chrom = load_scores_wiggle( score_fname, chrom_buffer )
+
+    if mask_fname:
+        masks = binned_bitsets_from_file( open( mask_fname ) )
+    else:
+        masks = None
+
+    skipped_lines = 0
+    first_invalid_line = 0
+    invalid_line = ''
+
+    for i, line in enumerate( open( interval_fname )):
+        valid = True
+        line = line.rstrip('\r\n')
+        if line and not line.startswith( '#' ):
+            fields = line.split()
+            
+            try:
+                chrom, start, stop = fields[chrom_col], int( fields[start_col] ), int( fields[stop_col] )
+            except:
+                valid = False
+                skipped_lines += 1
+                if not invalid_line:
+                    first_invalid_line = i + 1
+                    invalid_line = line
+            if valid:
+                total = 0
+                count = 0
+                min_score = 100000000
+                max_score = -100000000
+                for j in range( start, stop ):
+                    if chrom in scores_by_chrom:
+                        try:
+                            # Skip if base is masked
+                            if masks and chrom in masks:
+                                if masks[chrom][j]:
+                                    continue
+                            # Get the score, only count if not 'nan'
+                            score = scores_by_chrom[chrom][j]
+                            if not isNaN( score ):
+                                total += score
+                                count += 1
+                                max_score = max( score, max_score )
+                                min_score = min( score, min_score )
+                        except:
+                            continue
+                if count > 0:
+                    avg = total/count
+                else:
+                    avg = "nan"
+                    min_score = "nan"
+                    max_score = "nan"
+                
+                # Build the resulting line of data
+                out_line = []
+                for k in range(0, len(fields)):
+                    out_line.append(fields[k])
+                out_line.append(avg)
+                out_line.append(min_score)
+                out_line.append(max_score)
+                
+                print >> out_file, "\t".join( map( str, out_line ) )
+            else:
+                skipped_lines += 1
+                if not invalid_line:
+                    first_invalid_line = i + 1
+                    invalid_line = line
+        elif line.startswith( '#' ):
+            # We'll save the original comments
+            print >> out_file, line
+            
+    out_file.close()
+
+    if skipped_lines > 0:
+        print 'Data issue: skipped %d invalid lines starting at line #%d which is "%s"' % ( skipped_lines, first_invalid_line, invalid_line )
+        if skipped_lines == i:
+            print 'Consider changing the metadata for the input dataset by clicking on the pencil icon in the history item.'
+
+if __name__ == "__main__": main()