illuminaobscura.py

illuminaobscura.py

#!/usr/bin/env python

from glob import glob
from PIL import Image, ImageDraw
from itertools import defaultdict

# Create matrix
matrix = defaultdict(dict)

lanes = [1, 2, 3, 4, 5, 6, 7]

# Open every QSEQ file and get average quality scores:
# Use only one pair from the paired end sequencing.
for filename in glob('s_*_1_*qseq*'):
  with open(filename) as handle:
    for line in handle:
      line = line.split('\t')[8]
      x, y, qual = int(line[4]), int(line[5]), line[9]
      qual = [ ord(i) for i in qual if i != 'B' ]

      # Get average quality for read x, y
      average_qual = sum(qual)/float(len(qual))

      # Put it in the matrix:

      matrix[x][y] = average


# Optional, save matrix:
# import cPickle as pickle
# with open('quality_matrix.pickle', 'w') as output:
#   pickle_dump(output, matrix)

# Load matrix:
# with open('quality_matrix.pickle') as handle:
#   matrix = pickle_load(handle)

# Draw picture

MEAN = 99
GOOD = "#f00"
BAD = "#0f0"
SIZE = (20000, 20000) # I knew the size beforehand

image = Image.new('RGB', SIZE)
draw = ImageDraw.Draw(image)

for i in matrix:
  for j in matrix[k]:
    if matrix[k][v] > MEAN: draw.point((i, j), GOOD))
    else: draw.point((i, j), BAD))

image.save("IlluminaObscura.png", "png")