1 new commit in galaxy-central:

https://bitbucket.org/galaxy/galaxy-central/changeset/565476ce4f03/ changeset: 565476ce4f03 user: jgoecks date: 2012-08-15 23:49:39 summary: Rewrite sampling code for BBI data provider to handle (a) boundary cases during base-level resolution and (b) remainder of region not sampled during first pass. affected #: 1 file

diff -r 2531e085f2625b60135a6b4972f125e22a4fd354 -r 565476ce4f0301d23538d87aeef805edc099badf lib/galaxy/visualization/tracks/data_providers.py --- a/lib/galaxy/visualization/tracks/data_providers.py +++ b/lib/galaxy/visualization/tracks/data_providers.py @@ -947,55 +947,69 @@

return dict( data=dict( min=summary.min_val[0], max=summary.max_val[0], mean=mean, sd=sd ) )

- # The following seems not to work very well, for example it will only return one - # data point if the tile is 1280px wide. Not sure what the intent is. + # Sample from region using approximately this many samples. + N = 1000

- # The first zoom level for BBI files is 640. If too much is requested, it will look at each block instead - # of summaries. The calculation done is: zoom <> (end-start)/num_points/2. - # Thus, the optimal number of points is (end-start)/num_points/2 = 640 - # num_points = (end-start) / 1280 - #num_points = (end-start) / 1280 - #if num_points < 1: - # num_points = end - start - #else: - # num_points = min(num_points, 500) + def summarize_region( bbi, chrom, start, end, num_points ): + ''' + Returns results from summarizing a region using num_points. + NOTE: num_points cannot be greater than end - start or BBI + will return None for all positions.s + ''' + result = []

- # For now, we'll do 1000 data points by default. However, the summaries - # don't seem to work when a summary pixel corresponds to less than one - # datapoint, so we prevent that. + # Get summary; this samples at intervals of length + # (end - start)/num_points -- i.e. drops any fractional component + # of interval length. + summary = bbi.summarize( chrom, start, end, num_points ) + if summary: + #mean = summary.sum_data / summary.valid_count + + ## Standard deviation by bin, not yet used + ## var = summary.sum_squares - mean + ## var /= minimum( valid_count - 1, 1 ) + ## sd = sqrt( var ) + + pos = start + step_size = (end - start) / num_points

- # FIXME: need to choose the number of points to maximize coverage of the area. - # It appears that BBI calculates points using intervals of - # floor( num_points / end - start ) - # In some cases, this prevents sampling near the end of the interval, - # especially when (a) the total interval is small ( < 20-30Kb) and (b) the - # computed interval size has a large fraction, e.g. 14.7 or 35.8 - num_points = min( 1000, end - start ) + for i in range( num_points ): + result.append( (pos, float_nan( summary.sum_data[i] / summary.valid_count[i] ) ) ) + pos += step_size

- # HACK to address the FIXME above; should generalize. - if end - start <= 2000: - num_points = end - start + return result

- summary = bbi.summarize( chrom, start, end, num_points ) + # Approach is different depending on region size. + if end - start < N: + # Get values for individual bases in region, including start and end. + # To do this, need to increase end to next base and request number of points. + num_points = end - start + 1 + end += 1 + + result = summarize_region( bbi, chrom, start, end, num_points ) + else: + # + # The goal is to sample the region between start and end uniformly + # using N data points. The challenge is that the size of sampled + # intervals rarely is full bases, so sampling using N points will + # leave the end of the region unsampled. To recitify this, samples + # beyond N are taken at the end of the interval. + # + + # Do initial summary. + num_points = N + result = summarize_region( bbi, chrom, start, end, num_points ) + + # Do summary of remaining part of region. + step_size = ( end - start ) / num_points + new_start = start + step_size * num_points + new_num_points = min( ( end - new_start ) / step_size, end - start ) + if new_num_points is not 0: + result.extend( summarize_region( bbi, chrom, new_start, end, new_num_points ) ) + #TODO: progressively reduce step_size to generate more datapoints. + + # Cleanup and return. f.close() - - result = [] - - if summary: - #mean = summary.sum_data / summary.valid_count - - ## Standard deviation by bin, not yet used - ## var = summary.sum_squares - mean - ## var /= minimum( valid_count - 1, 1 ) - ## sd = sqrt( var ) - - pos = start - step_size = (end - start) / num_points - - for i in range( num_points ): - result.append( (pos, float_nan( summary.sum_data[i] / summary.valid_count[i] ) ) ) - pos += step_size - return { 'data': result }

class BigBedDataProvider( BBIDataProvider ):

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