def check_column_for_alignment(cluster_strategy, rotation_adjustment, channel, snr, sequencing_chip, um_per_pixel, fia,
end_tiles, column, possible_tile_keys, h5_filename):
base_name = os.path.splitext(h5_filename)[0]
with h5py.File(h5_filename) as h5:
grid = GridImages(h5, channel)
# we assume odd numbers of rows, and good enough for now
if grid.height > 2:
center_row = grid.height / 2
rows_to_check = (center_row, center_row + 1, center_row - 1)
else:
# just one or two rows, might as well try them all
rows_to_check = tuple([i for i in range(grid.height)])
for row in rows_to_check:
image = grid.get(row, column)
if image is None:
log.warn("Could not find an image for %s Row %d Column %d" % (base_name, row, column))
return
log.debug("Aligning %s Row %d Column %d against PhiX" % (base_name, row, column))
fia = process_alignment_image(cluster_strategy, rotation_adjustment, snr, sequencing_chip, base_name, um_per_pixel, image, possible_tile_keys, deepcopy(fia))
if fia.hitting_tiles:
log.debug("%s aligned to at least one tile!" % image.index)
# because of the way we iterate through the images, if we find one that aligns,
# we can just stop because that gives us the outermost column of images and the
# outermost FastQ tile
end_tiles[h5_filename] = [tile.key for tile in fia.hitting_tiles], image.column
break
del fia
gc.collect()
def iterate_all_images(h5_filenames, end_tiles, channel, path_info):
# We need an iterator over all images to feed the parallel processes. Since each image is
# processed independently and in no particular order, we need to return information in addition
# to the image itself that allow files to be written in the correct place and such
for h5_filename in h5_filenames:
base_name = os.path.splitext(h5_filename)[0]
with h5py.File(h5_filename) as h5:
grid = GridImages(h5, channel)
min_column, max_column, tile_map = end_tiles[h5_filename]
for column in range(min_column, max_column):
for row in range(grid._height):
image = grid.get(row, column)
if image is None:
continue
stats_path = os.path.join(
path_info.results_directory, base_name,
'{}_stats.txt'.format(image.index))
alignment_path = os.path.join(
path_info.results_directory, base_name,
'{}_all_read_rcs.txt'.format(image.index))
already_aligned = alignment_is_complete(
stats_path) and os.path.exists(alignment_path)
if already_aligned:
log.debug(
"Image already aligned/checkpointed: {}/{}".format(
h5_filename, image.index))
continue
yield row, column, channel, h5_filename, tile_map[
image.column], base_name
def count_images(h5_filenames, channel):
image_count = 0
for h5_filename in h5_filenames:
with h5py.File(h5_filename, 'r') as h5:
grid = GridImages(h5, channel)
image_count += len(grid)
return image_count
def get_end_tiles(cluster_strategies, rotation_adjustment, h5_filenames,
alignment_channel, snr, metadata, sequencing_chip, fia):
right_end_tiles = {}
left_end_tiles = {}
for cluster_strategy in cluster_strategies:
with h5py.File(h5_filenames[0]) as first_file:
grid = GridImages(first_file, alignment_channel)
# no reason to use all cores yet, since we're IO bound?
num_processes = len(h5_filenames)
pool = multiprocessing.Pool(num_processes)
log.info("Checking Columns")
base_column_checker = functools.partial(
check_column_for_alignment, cluster_strategy,
rotation_adjustment, alignment_channel, snr, sequencing_chip,
metadata['microns_per_pixel'], fia)
# start = time.time()
#FLETCHER log.info("Left Tiles: %s Right Tiles: %s" % (sequencing_chip.left_side_tiles, sequencing_chip.right_side_tiles))
log.info(
"------------------Searching Left End Tile-------------------")
left_end_tiles = dict(
find_bounds(pool, h5_filenames, base_column_checker,
grid.columns, sequencing_chip.left_side_tiles,
sequencing_chip.cluster_size))
log.info("Left End Tiles: %s " % left_end_tiles)
# re_time = time.time()
log.info(
"******************Searching Right End Tile******************")
right_end_tiles = dict(
find_bounds(pool, h5_filenames, base_column_checker,
reversed(grid.columns),
sequencing_chip.miseq_tiles,
sequencing_chip.cluster_size))
log.info("Right End Tiles: %s" % right_end_tiles)
pool.close()
pool.join()
if left_end_tiles and right_end_tiles:
break
if not left_end_tiles and not right_end_tiles:
error.fail(
"End tiles could not be found! Try adjusting the rotation or look at the raw images."
)
log.info("Left End Tiles: %s and Right End Tiles: %s" % left_end_tiles,
right_end_tiles)
default_left_tile, default_left_column, default_left_correlation = decide_default_tiles_and_columns(
left_end_tiles)
default_right_tile, default_right_column, default_right_correlation = decide_default_tiles_and_columns(
right_end_tiles)
end_tiles = build_end_tiles(h5_filenames, sequencing_chip, left_end_tiles,
default_left_tile, default_left_correlation,
right_end_tiles, default_right_tile,
default_left_column, default_right_column,
default_right_correlation)
return end_tiles
def create_fits_files(h5_base_name):
h5_filename = h5_base_name + ".h5"
log.info("Creating fits files for %s" % h5_filename)
h5 = h5py.File(h5_filename)
for channel in h5.keys():
grid = GridImages(h5, channel)
for image in grid:
fits_path = '%s.fits' % os.path.join(h5_base_name, image.index)
# Source Extractor can handle at most 32-bit values, so we have to cast down from our 64-bit images or
# else it will throw an error. We clip to ensure there's no overflow, although this seems improbable
# given that most cameras are 16 bit
clipped_image = np.clip(image, 0, 2**32 - 1).astype(np.uint32)
hdu = fits.PrimaryHDU(clipped_image)
hdu.writeto(fits_path, clobber=True)
log.info("Done creating fits files for %s" % h5_base_name)
def get_end_tiles(cluster_strategies, rotation_adjustment, h5_filenames,
alignment_channel, snr, metadata, sequencing_chip, fia,
floor_alignment):
#print "champ_align, floor = ", floor_alignment
right_end_tiles = {}
left_end_tiles = {}
for cluster_strategy in cluster_strategies:
with h5py.File(h5_filenames[0]) as first_file:
grid = GridImages(first_file, alignment_channel)
# no reason to use all cores yet, since we're IO bound?
num_processes = len(h5_filenames)
pool = multiprocessing.Pool(num_processes)
if floor_alignment:
floor = 1
else:
floor = 0
#print "fia = ", fia
base_column_checker = functools.partial(
check_column_for_alignment, cluster_strategy,
rotation_adjustment, alignment_channel, snr, sequencing_chip,
metadata['microns_per_pixel'], fia, floor)
# print "pass_base_column", floor_alignment, type(floor_alignment)
left_end_tiles = dict(
find_bounds(pool, h5_filenames, base_column_checker,
grid.columns, sequencing_chip.left_side_tiles))
right_end_tiles = dict(
find_bounds(pool, h5_filenames, base_column_checker,
reversed(grid.columns),
sequencing_chip.right_side_tiles))
# print "pass left and right"
pool.close()
pool.join()
if left_end_tiles and right_end_tiles:
break
if not left_end_tiles and not right_end_tiles:
error.fail(
"End tiles could not be found! Try adjusting the rotation or look at the raw images."
)
default_left_tile, default_left_column = decide_default_tiles_and_columns(
left_end_tiles)
default_right_tile, default_right_column = decide_default_tiles_and_columns(
right_end_tiles)
end_tiles = build_end_tiles(h5_filenames, sequencing_chip, left_end_tiles,
default_left_tile, right_end_tiles,
default_right_tile, default_left_column,
default_right_column)
return end_tiles
def otsu_cluster_func(h5_base_name):
h5_filename = h5_base_name + ".h5"
log.info("Finding clusters for %s" % h5_filename)
h5 = h5py.File(h5_filename)
for channel in h5.keys():
grid = GridImages(h5, channel)
for image in grid:
out_filepath = os.path.join(h5_base_name,
image.index + '.clusters.otsu')
threshold = threshold_otsu(image)
mask_pixels = (image > threshold)
mask = ndimage.binary_closing(ndimage.binary_opening(mask_pixels))
label_image, num_labels = ndimage.label(mask)
log.debug("Found %d clusters in %s/%s" %
(num_labels, h5_base_name, image.index))
center_of_masses = ndimage.center_of_mass(image, label_image,
range(num_labels + 1))
write_cluster_locations(center_of_masses, out_filepath)
def load_image(h5_filename, channel, row, column):
with h5py.File(h5_filename) as h5:
grid = GridImages(h5, channel)
return grid.get(row, column)
