def generate(self, Q1, Q2, R1, log_scale=False):
# Module #
from gMiner.operations.genomic_manip.scores import mean_score_by_feature
# Get all the values (fills memory) #
manip = mean_score_by_feature()
x = [feature[3] for chrom in R1 for feature in manip(Q1.read(chrom), R1.read(chrom))]
y = [feature[3] for chrom in R1 for feature in manip(Q2.read(chrom), R1.read(chrom))]
# Graph it #
fig, axes = make_default_figure()
axes.set_title('Scatter plot of "' + Q1.name + '" against "' + Q2.name + '"' + \
' in "' + R1.name + '"')
axes.set_xlabel('Score of "' + Q1.name + '" [unspecified units]')
axes.set_ylabel('Score of "' + Q2.name + '" [unspecified units]')
axes.plot(x,y,'.')
# Logarithmic scale option #
if log_scale:
axes.set_xscale('log')
axes.set_yscale('log')
widen_axis(axes)
# Return a figure #
return fig
def create_bins(X, num_of_bins=10):
for x in X:
length = (x[1] - x[0]) / num_of_bins
for i in xrange(num_of_bins):
yield (x[0]+i*length, x[0]+(i+1)*length, x[2], x[3], x[4])
from bbcflib import track
from gMiner.operations.genomic_manip.scores import mean_score_by_feature
manip = mean_score_by_feature()
with track.load('/scratch/genomic/tracks/pol2.sql') as a:
with track.load('/scratch/genomic/tracks/ribosome_proteins.sql') as b:
with track.new('/tmp/manual.sql') as r:
for chrom in a:
r.write(chrom, manip(a.read(chrom), create_bins(b.read(chrom))))
r.meta_chr = a.meta_chr
r.meta_track = {'datatype': 'qualitative', 'name': 'Mean score per bin', 'created_by': 'gMiner example script'}
