def load_chromsizes(chromsizes_filename, assembly=None):
"""
Load a set of chromosomes from a file or using an assembly
identifier. If using just an assembly identifier the chromsizes
will be loaded from the negspy repository.
Parameters:
-----------
chromsizes_filename: string
The file containing the tab-delimited chromosome sizes
assembly: string
Assembly name (e.g. 'hg19'). Not necessary if a chromsizes_filename is passed in
"""
if chromsizes_filename is not None:
chrom_info = nc.get_chrominfo_from_file(chromsizes_filename)
chrom_names = chrom_info.chrom_order
chrom_sizes = [
chrom_info.chrom_lengths[c] for c in chrom_info.chrom_order
]
else:
if assembly is None:
raise ValueError("No assembly or chromsizes specified")
chrom_info = nc.get_chrominfo(assembly)
chrom_names = nc.get_chromorder(assembly)
chrom_sizes = nc.get_chromsizes(assembly)
return (chrom_info, chrom_names, chrom_sizes)
def _bedgraph(filepath, output_file, assembly, chrom_col, from_pos_col,
to_pos_col, value_col, has_header, chromosome, tile_size,
chunk_size, method, nan_value, transform, count_nan,
closed_interval, chromsizes_filename, zoom_step):
last_end = 0
data = []
if output_file is None:
output_file = op.splitext(filepath)[0] + '.hitile'
print("output file:", output_file)
# Override the output file if it existts
if op.exists(output_file):
os.remove(output_file)
f = h5py.File(output_file, 'w')
# get the information about the chromosomes in this assembly
if chromsizes_filename is not None:
chrom_info = nc.get_chrominfo_from_file(chromsizes_filename)
chrom_order = [
a.encode('utf-8')
for a in nc.get_chromorder_from_file(chromsizes_filename)
]
chrom_sizes = nc.get_chromsizes_from_file(chromsizes_filename)
else:
chrom_info = nc.get_chrominfo(assembly)
chrom_order = [a.encode('utf-8') for a in nc.get_chromorder(assembly)]
chrom_sizes = nc.get_chromsizes(assembly)
assembly_size = chrom_info.total_length
print('assembly_size:', assembly_size)
tile_size = tile_size
chunk_size = tile_size * 2**chunk_size # how many values to read in at once while tiling
dsets = [] # data sets at each zoom level
nan_dsets = [] # store nan values
# initialize the arrays which will store the values at each stored zoom level
z = 0
positions = [] # store where we are at the current dataset
data_buffers = [[]]
nan_data_buffers = [[]]
while assembly_size / 2**z > tile_size:
dset_length = math.ceil(assembly_size / 2**z)
dsets += [
f.create_dataset('values_' + str(z), (dset_length, ),
dtype='f',
compression='gzip')
]
nan_dsets += [
f.create_dataset('nan_values_' + str(z), (dset_length, ),
dtype='f',
compression='gzip')
]
data_buffers += [[]]
nan_data_buffers += [[]]
positions += [0]
z += zoom_step
#print("dsets[0][-10:]", dsets[0][-10:])
# load the bigWig file
#print("filepath:", filepath)
# store some meta data
d = f.create_dataset('meta', (1, ), dtype='f')
print("assembly:", assembly)
#print("chrom_info:", nc.get_chromorder(assembly))
d.attrs['zoom-step'] = zoom_step
d.attrs['max-length'] = assembly_size
d.attrs['assembly'] = assembly
d.attrs['chrom-names'] = chrom_order
d.attrs['chrom-sizes'] = chrom_sizes
d.attrs['chrom-order'] = chrom_order
d.attrs['tile-size'] = tile_size
d.attrs['max-zoom'] = max_zoom = math.ceil(
math.log(d.attrs['max-length'] / tile_size) / math.log(2))
d.attrs['max-width'] = tile_size * 2**max_zoom
d.attrs['max-position'] = 0
print("assembly size (max-length)", d.attrs['max-length'])
print("max-width", d.attrs['max-width'])
print("max_zoom:", d.attrs['max-zoom'])
print("chunk-size:", chunk_size)
print("chrom-order", d.attrs['chrom-order'])
t1 = time.time()
# are we reading the input from stdin or from a file?
if filepath == '-':
f = sys.stdin
else:
if filepath.endswith('.gz'):
import gzip
f = gzip.open(filepath, 'rt')
else:
f = open(filepath, 'r')
curr_zoom = 0
def add_values_to_data_buffers(buffers_to_add, nan_buffers_to_add):
curr_zoom = 0
data_buffers[0] += buffers_to_add
nan_data_buffers[0] += nan_buffers_to_add
curr_time = time.time() - t1
percent_progress = (positions[curr_zoom] + 1) / float(assembly_size)
print(
"position: {} progress: {:.2f} elapsed: {:.2f} remaining: {:.2f}".
format(positions[curr_zoom] + 1, percent_progress, curr_time,
curr_time / (percent_progress) - curr_time))
while len(data_buffers[curr_zoom]) >= chunk_size:
# get the current chunk and store it, converting nans to 0
print("len(data_buffers[curr_zoom])", len(data_buffers[curr_zoom]))
curr_chunk = np.array(data_buffers[curr_zoom][:chunk_size])
nan_curr_chunk = np.array(nan_data_buffers[curr_zoom][:chunk_size])
#curr_chunk[np.isnan(curr_chunk)] = 0
'''
print("1cc:", sum(curr_chunk))
print("1db:", data_buffers[curr_zoom][:chunk_size])
print("1curr_chunk:", nan_curr_chunk)
'''
print("positions[curr_zoom]:", positions[curr_zoom])
dsets[curr_zoom][positions[curr_zoom]:positions[curr_zoom] +
chunk_size] = curr_chunk
nan_dsets[curr_zoom][positions[curr_zoom]:positions[curr_zoom] +
chunk_size] = nan_curr_chunk
# aggregate nan values
#nan_curr_chunk[np.isnan(curr_chunk)] = 0
#print("1na_cc:", sum(nan_curr_chunk))
# aggregate and store aggregated values in the next zoom_level's data
data_buffers[curr_zoom + 1] += list(
ct.aggregate(curr_chunk, 2**zoom_step))
nan_data_buffers[curr_zoom + 1] += list(
ct.aggregate(nan_curr_chunk, 2**zoom_step))
data_buffers[curr_zoom] = data_buffers[curr_zoom][chunk_size:]
nan_data_buffers[curr_zoom] = nan_data_buffers[curr_zoom][
chunk_size:]
data = data_buffers[curr_zoom + 1]
nan_data = nan_data_buffers[curr_zoom + 1]
# do the same for the nan values buffers
positions[curr_zoom] += chunk_size
curr_zoom += 1
if curr_zoom * zoom_step >= max_zoom:
break
values = []
nan_values = []
if has_header:
f.readline()
# the genome position up to which we've filled in values
curr_genome_pos = 0
# keep track of the previous value so that we can use it to fill in NAN values
prev_value = 0
for line in f:
# each line should indicate a chromsome, start position and end position
parts = line.strip().split()
start_genome_pos = chrom_info.cum_chrom_lengths[parts[
chrom_col - 1]] + int(parts[from_pos_col - 1])
#print("len(values):", len(values), curr_genome_pos, start_genome_pos)
#print("line:", line)
if start_genome_pos - curr_genome_pos > 1:
values += [np.nan] * (start_genome_pos - curr_genome_pos - 1)
nan_values += [1] * (start_genome_pos - curr_genome_pos - 1)
curr_genome_pos += (start_genome_pos - curr_genome_pos - 1)
# count how many nan values there are in the dataset
nan_count = 1 if parts[value_col - 1] == nan_value else 0
# if the provided values are log2 transformed, we have to un-transform them
if transform == 'exp2':
value = 2**float(
parts[value_col -
1]) if not parts[value_col - 1] == nan_value else np.nan
else:
value = float(
parts[value_col -
1]) if not parts[value_col - 1] == nan_value else np.nan
# print("pos:", int(parts[to_pos_col-1]) - int(parts[from_pos_col-1]))
# we're going to add as many values are as specified in the bedfile line
values_to_add = [value] * (int(parts[to_pos_col - 1]) -
int(parts[from_pos_col - 1]))
nan_counts_to_add = [nan_count] * (int(parts[to_pos_col - 1]) -
int(parts[from_pos_col - 1]))
if closed_interval:
values_to_add += [value]
nan_counts_to_add += [nan_count]
# print("values_to_add", values_to_add)
values += values_to_add
nan_values += nan_counts_to_add
d.attrs['max-position'] = start_genome_pos + len(values_to_add)
#print("values:", values[:30])
curr_genome_pos += len(values_to_add)
while len(values) > chunk_size:
print("len(values):", len(values), chunk_size)
print("line:", line)
add_values_to_data_buffers(values[:chunk_size],
nan_values[:chunk_size])
values = values[chunk_size:]
nan_values = nan_values[chunk_size:]
add_values_to_data_buffers(values, nan_values)
# store the remaining data
while True:
# get the current chunk and store it
chunk_size = len(data_buffers[curr_zoom])
curr_chunk = np.array(data_buffers[curr_zoom][:chunk_size])
nan_curr_chunk = np.array(nan_data_buffers[curr_zoom][:chunk_size])
'''
print("2curr_chunk", curr_chunk)
print("2curr_zoom:", curr_zoom)
print("2db", data_buffers[curr_zoom][:100])
'''
dsets[curr_zoom][positions[curr_zoom]:positions[curr_zoom] +
chunk_size] = curr_chunk
nan_dsets[curr_zoom][positions[curr_zoom]:positions[curr_zoom] +
chunk_size] = nan_curr_chunk
#print("chunk_size:", chunk_size, "len(curr_chunk):", len(curr_chunk), "len(nan_curr_chunk)", len(nan_curr_chunk))
# aggregate and store aggregated values in the next zoom_level's data
data_buffers[curr_zoom + 1] += list(
ct.aggregate(curr_chunk, 2**zoom_step))
nan_data_buffers[curr_zoom + 1] += list(
ct.aggregate(nan_curr_chunk, 2**zoom_step))
data_buffers[curr_zoom] = data_buffers[curr_zoom][chunk_size:]
nan_data_buffers[curr_zoom] = nan_data_buffers[curr_zoom][chunk_size:]
data = data_buffers[curr_zoom + 1]
nan_data = nan_data_buffers[curr_zoom + 1]
positions[curr_zoom] += chunk_size
curr_zoom += 1
# we've created enough tile levels to cover the entire maximum width
if curr_zoom * zoom_step >= max_zoom:
break
def _bedpe(filepath,
output_file,
assembly,
importance_column,
has_header,
max_per_tile,
tile_size,
max_zoom=None,
chromosome=None,
chr1_col=0,
from1_col=1,
to1_col=2,
chr2_col=3,
from2_col=4,
to2_col=5):
print('output_file:', output_file)
if filepath.endswith('.gz'):
print("gzip")
f = gzip.open(filepath, 'rt')
else:
print("plain")
f = open(filepath, 'r')
if output_file is None:
output_file = filepath + ".multires.db"
else:
output_file = output_file
if op.exists(output_file):
os.remove(output_file)
def line_to_dict(line):
parts = line.split()
d = {}
try:
d['xs'] = [
nc.chr_pos_to_genome_pos(parts[chr1_col],
int(parts[from1_col]), assembly),
nc.chr_pos_to_genome_pos(parts[chr1_col], int(parts[to1_col]),
assembly)
]
d['ys'] = [
nc.chr_pos_to_genome_pos(parts[chr2_col],
int(parts[from2_col]), assembly),
nc.chr_pos_to_genome_pos(parts[chr2_col], int(parts[to2_col]),
assembly)
]
except KeyError:
error_str = (
"ERROR converting chromosome position to genome position. "
"Please make sure you've specified the correct assembly "
"using the --assembly option. "
"Current assembly: {}, chromosomes: {},{}".format(
assembly, parts[chr1_col], parts[chr2_col]))
raise (KeyError(error_str))
d['uid'] = slugid.nice().decode('utf-8')
d['chrOffset'] = d['xs'][0] - int(parts[from1_col])
if importance_column is None:
d['importance'] = max(d['xs'][1] - d['xs'][0],
d['ys'][1] - d['ys'][0])
elif importance_column == 'random':
d['importance'] = random.random()
else:
d['importance'] = float(d[importance_column])
d['fields'] = line
return d
entries = []
if has_header:
f.readline()
else:
first_line = f.readline().strip()
try:
parts = first_line.split()
'''
print("chr1_col", chr1_col, "chr2_col", chr2_col,
"from1_col:", from1_col, "from2_col", from2_col,
"to1_col", to1_col, "to2_col", to2_col)
'''
pos = int(parts[from1_col])
pos = int(parts[to1_col])
pos = int(parts[from2_col])
pos = int(parts[to2_col])
except ValueError as ve:
error_str = "Couldn't convert one of the bedpe coordinates to an integer. If the input file contains a header, make sure to indicate that with the --has-header option. Line: {}".format(
first_line)
raise (ValueError(error_str))
entries = [line_to_dict(first_line)]
entries += [line_to_dict(line.strip()) for line in f]
# We neeed chromosome information as well as the assembly size to properly
# tile this data
tile_size = tile_size
chrom_info = nc.get_chrominfo(assembly)
assembly_size = chrom_info.total_length + 1
#max_zoom = int(math.ceil(math.log(assembly_size / min_feature_width) / math.log(2)))
max_zoom = int(math.ceil(
math.log(assembly_size / tile_size) / math.log(2)))
'''
if max_zoom is not None and max_zoom < max_zoom:
max_zoom = max_zoom
'''
# this script stores data in a sqlite database
sqlite3.register_adapter(np.int64, lambda val: int(val))
conn = sqlite3.connect(output_file)
# store some meta data
store_meta_data(conn,
1,
max_length=assembly_size,
assembly=assembly,
chrom_names=nc.get_chromorder(assembly),
chrom_sizes=nc.get_chromsizes(assembly),
tile_size=tile_size,
max_zoom=max_zoom,
max_width=tile_size * 2**max_zoom)
max_width = tile_size * 2**max_zoom
uid_to_entry = {}
c = conn.cursor()
c.execute('''
CREATE TABLE intervals
(
id int PRIMARY KEY,
zoomLevel int,
importance real,
fromX int,
toX int,
fromY int,
toY int,
chrOffset int,
uid text,
fields text
)
''')
print("creating rtree")
c.execute('''
CREATE VIRTUAL TABLE position_index USING rtree(
id,
rFromX, rToX,
rFromY, rToY
)
''')
curr_zoom = 0
counter = 0
max_viewable_zoom = max_zoom
if max_zoom is not None and max_zoom < max_zoom:
max_viewable_zoom = max_zoom
tile_counts = col.defaultdict(
lambda: col.defaultdict(lambda: col.defaultdict(int)))
entries = sorted(entries, key=lambda x: -x['importance'])
counter = 0
for d in entries:
curr_zoom = 0
while curr_zoom <= max_zoom:
tile_width = tile_size * 2**(max_zoom - curr_zoom)
#print("d:", d)
tile_from = list(
map(lambda x: x / tile_width, [d['xs'][0], d['ys'][0]]))
tile_to = list(
map(lambda x: x / tile_width, [d['xs'][1], d['ys'][1]]))
empty_tiles = True
# go through and check if any of the tiles at this zoom level are full
for i in range(int(tile_from[0]), int(tile_to[0]) + 1):
if not empty_tiles:
break
for j in range(int(tile_from[1]), int(tile_to[1]) + 1):
if tile_counts[curr_zoom][i][j] > max_per_tile:
empty_tiles = False
break
if empty_tiles:
# they're all empty so add this interval to this zoom level
for i in range(int(tile_from[0]), int(tile_to[0]) + 1):
for j in range(int(tile_from[1]), int(tile_to[1]) + 1):
tile_counts[curr_zoom][i][j] += 1
#print("adding:", curr_zoom, d)
exec_statement = 'INSERT INTO intervals VALUES (?,?,?,?,?,?,?,?,?,?)'
ret = c.execute(
exec_statement,
(counter, curr_zoom, d['importance'], d['xs'][0],
d['xs'][1], d['ys'][0], d['ys'][1], d['chrOffset'],
d['uid'], d['fields']))
conn.commit()
exec_statement = 'INSERT INTO position_index VALUES (?,?,?,?,?)'
ret = c.execute(
exec_statement,
(counter, d['xs'][0], d['xs'][1], d['ys'][0], d['ys'][1]
) #add counter as a primary key
)
conn.commit()
counter += 1
break
curr_zoom += 1
return
def _bigwig(filepath,
chunk_size=14,
zoom_step=8,
tile_size=1024,
output_file=None,
assembly='hg19',
chromsizes_filename=None,
chromosome=None):
last_end = 0
data = []
if output_file is None:
if chromosome is None:
output_file = op.splitext(filepath)[0] + '.hitile'
else:
output_file = op.splitext(
filepath)[0] + '.' + chromosome + '.hitile'
# Override the output file if it existts
if op.exists(output_file):
os.remove(output_file)
f = h5py.File(output_file, 'w')
if chromsizes_filename is not None:
chrom_info = nc.get_chrominfo_from_file(chromsizes_filename)
chrom_order = [
a for a in nc.get_chromorder_from_file(chromsizes_filename)
]
chrom_sizes = nc.get_chromsizes_from_file(chromsizes_filename)
else:
print("there")
chrom_info = nc.get_chrominfo(assembly)
chrom_order = [a for a in nc.get_chromorder(assembly)]
chrom_sizes = nc.get_chromsizes(assembly)
print("chrom_order:", chrom_order)
assembly_size = chrom_info.total_length
tile_size = tile_size
chunk_size = tile_size * 2**chunk_size # how many values to read in at once while tiling
dsets = [] # data sets at each zoom level
nan_dsets = []
# initialize the arrays which will store the values at each stored zoom level
z = 0
positions = [] # store where we are at the current dataset
data_buffers = [[]]
nan_data_buffers = [[]]
while assembly_size / 2**z > tile_size:
dset_length = math.ceil(assembly_size / 2**z)
dsets += [
f.create_dataset('values_' + str(z), (dset_length, ),
dtype='f',
compression='gzip')
]
nan_dsets += [
f.create_dataset('nan_values_' + str(z), (dset_length, ),
dtype='f',
compression='gzip')
]
data_buffers += [[]]
nan_data_buffers += [[]]
positions += [0]
z += zoom_step
# load the bigWig file
bwf = pbw.open(filepath)
# store some meta data
d = f.create_dataset('meta', (1, ), dtype='f')
if chromosome is not None:
d.attrs['min-pos'] = chrom_info.cum_chrom_lengths[chromosome]
d.attrs['max-pos'] = chrom_info.cum_chrom_lengths[
chromosome] + bwf.chroms()[chromosome]
else:
d.attrs['min-pos'] = 0
d.attrs['max-pos'] = assembly_size
'''
print("chroms.keys:", bwf.chroms().keys())
print("chroms.values:", bwf.chroms().values())
'''
d.attrs['zoom-step'] = zoom_step
d.attrs['max-length'] = assembly_size
d.attrs['assembly'] = assembly
d.attrs['chrom-names'] = [a.encode('utf-8') for a in chrom_order]
d.attrs['chrom-sizes'] = chrom_sizes
d.attrs['chrom-order'] = [a.encode('utf-8') for a in chrom_order]
d.attrs['tile-size'] = tile_size
d.attrs['max-zoom'] = max_zoom = math.ceil(
math.log(d.attrs['max-length'] / tile_size) / math.log(2))
d.attrs['max-width'] = tile_size * 2**max_zoom
d.attrs['max-position'] = 0
print("assembly size (max-length)", d.attrs['max-length'])
print("max-width", d.attrs['max-width'])
print("max_zoom:", d.attrs['max-zoom'])
print("chunk-size:", chunk_size)
print("chrom-order", d.attrs['chrom-order'])
t1 = time.time()
curr_zoom = 0
def add_values_to_data_buffers(buffers_to_add, nan_buffers_to_add):
curr_zoom = 0
data_buffers[0] += buffers_to_add
nan_data_buffers[0] += nan_buffers_to_add
curr_time = time.time() - t1
percent_progress = (positions[curr_zoom] + 1) / float(assembly_size)
print(
"position: {} progress: {:.2f} elapsed: {:.2f} remaining: {:.2f}".
format(positions[curr_zoom] + 1, percent_progress, curr_time,
curr_time / (percent_progress) - curr_time))
while len(data_buffers[curr_zoom]) >= chunk_size:
# get the current chunk and store it, converting nans to 0
print("len(data_buffers[curr_zoom])", len(data_buffers[curr_zoom]))
curr_chunk = np.array(data_buffers[curr_zoom][:chunk_size])
nan_curr_chunk = np.array(nan_data_buffers[curr_zoom][:chunk_size])
#curr_chunk[np.isnan(curr_chunk)] = 0
'''
print("1cc:", sum(curr_chunk))
print("1db:", data_buffers[curr_zoom][:chunk_size])
print("1curr_chunk:", nan_curr_chunk)
'''
print("positions[curr_zoom]:", positions[curr_zoom])
dsets[curr_zoom][positions[curr_zoom]:positions[curr_zoom] +
chunk_size] = curr_chunk
nan_dsets[curr_zoom][positions[curr_zoom]:positions[curr_zoom] +
chunk_size] = nan_curr_chunk
# aggregate nan values
#nan_curr_chunk[np.isnan(curr_chunk)] = 0
#print("1na_cc:", sum(nan_curr_chunk))
# aggregate and store aggregated values in the next zoom_level's data
data_buffers[curr_zoom + 1] += list(
ct.aggregate(curr_chunk, 2**zoom_step))
nan_data_buffers[curr_zoom + 1] += list(
ct.aggregate(nan_curr_chunk, 2**zoom_step))
data_buffers[curr_zoom] = data_buffers[curr_zoom][chunk_size:]
nan_data_buffers[curr_zoom] = nan_data_buffers[curr_zoom][
chunk_size:]
data = data_buffers[curr_zoom + 1]
nan_data = nan_data_buffers[curr_zoom + 1]
# do the same for the nan values buffers
positions[curr_zoom] += chunk_size
curr_zoom += 1
if curr_zoom * zoom_step >= max_zoom:
break
# Do we only want values from a single chromosome?
if chromosome is not None:
chroms_to_use = [chromosome]
else:
chroms_to_use = chrom_order
for chrom in chroms_to_use:
print("chrom:", chrom)
'''
if chrom not in bwf.chroms():
print("skipping chrom (not in bigWig file):",
chrom, chrom_info.chrom_lengths[chrom])
continue
'''
counter = 0
# chrom_size = bwf.chroms()[chrom]
chrom_size = chrom_info.chrom_lengths[chrom]
# print("chrom_size:", chrom_size, bwf.chroms()[chrom])
d.attrs['max-position'] += chrom_size
while counter < chrom_size:
remaining = min(chunk_size, chrom_size - counter)
if chrom not in bwf.chroms():
values = [np.nan] * remaining
nan_values = [1] * remaining
else:
values = bwf.values(chrom, counter, counter + remaining)
nan_values = np.isnan(values).astype('i4')
# print("counter:", counter, "remaining:", remaining,
# "counter + remaining:", counter + remaining)
counter += remaining
curr_zoom = 0
add_values_to_data_buffers(list(values), list(nan_values))
while True:
# get the current chunk and store it
chunk_size = len(data_buffers[curr_zoom])
curr_chunk = np.array(data_buffers[curr_zoom][:chunk_size])
nan_curr_chunk = np.array(nan_data_buffers[curr_zoom][:chunk_size])
dsets[curr_zoom][positions[curr_zoom]:positions[curr_zoom] +
chunk_size] = curr_chunk
nan_dsets[curr_zoom][positions[curr_zoom]:positions[curr_zoom] +
chunk_size] = nan_curr_chunk
# aggregate and store aggregated values in the next zoom_level's data
data_buffers[curr_zoom + 1] += list(
ct.aggregate(curr_chunk, 2**zoom_step))
nan_data_buffers[curr_zoom + 1] += list(
ct.aggregate(nan_curr_chunk, 2**zoom_step))
data_buffers[curr_zoom] = data_buffers[curr_zoom][chunk_size:]
nan_data_buffers[curr_zoom] = nan_data_buffers[curr_zoom][chunk_size:]
data = data_buffers[curr_zoom + 1]
nan_data = nan_data_buffers[curr_zoom + 1]
positions[curr_zoom] += chunk_size
curr_zoom += 1
# we've created enough tile levels to cover the entire maximum width
if curr_zoom * zoom_step >= max_zoom:
break
# still need to take care of the last chunk
data = np.array(data)
t1 = time.time()
pass
def _bedfile(filepath, output_file, assembly, importance_column, has_header,
chromosome, max_per_tile, tile_size, delimiter,
chromsizes_filename, offset):
if output_file is None:
output_file = filepath + ".multires"
else:
output_file = output_file
if op.exists(output_file):
os.remove(output_file)
bed_file = open(filepath, 'r')
if chromsizes_filename is not None:
chrom_info = nc.get_chrominfo_from_file(chromsizes_filename)
chrom_names = chrom_info.chrom_order
chrom_sizes = [
chrom_info.chrom_lengths[c] for c in chrom_info.chrom_order
]
else:
chrom_info = nc.get_chrominfo(assembly)
chrom_names = nc.get_chromorder(assembly)
chrom_sizes = nc.get_chromsizes(assembly)
print("chrom_names:", chrom_info.chrom_order)
print("chrom_sizes:", chrom_sizes)
def line_to_np_array(line):
'''
Convert a bed file line to a numpy array which can later
be used as an entry in an h5py file.
'''
try:
start = int(line[1])
stop = int(line[2])
except ValueError:
raise ValueError(
"Error parsing the position, line: {}".format(line))
chrom = line[0]
if importance_column is None:
importance = stop - start
elif importance_column == 'random':
importance = random.random()
else:
importance = int(line[int(importance_column) - 1])
# convert chromosome coordinates to genome coordinates
genome_start = chrom_info.cum_chrom_lengths[chrom] + start + offset
#nc.chr_pos_to_genome_pos(str(chrom), start, assembly)
genome_end = chrom_info.cum_chrom_lengths[chrom] + stop + offset
#nc.chr_pos_to_genome_pos(chrom, stop, assembly)
pos_offset = genome_start - start
parts = {
'startPos': genome_start,
'endPos': genome_end,
'uid': slugid.nice().decode('utf-8'),
'chrOffset': pos_offset,
'fields': '\t'.join(line),
'importance': importance,
'chromosome': str(chrom)
}
return parts
dset = []
if has_header:
line = bed_file.readline()
header = line.strip().split(delimiter)
else:
line = bed_file.readline().strip()
dset += [line_to_np_array(line.strip().split(delimiter))]
header = map(str, list(range(1,
len(line.strip().split(delimiter)) + 1)))
print("header:", header)
for line in bed_file:
dset += [line_to_np_array(line.strip().split(delimiter))]
if chromosome is not None:
dset = [d for d in dset if d['chromosome'] == chromosome]
# We neeed chromosome information as well as the assembly size to properly
# tile this data
tile_size = tile_size
#if chromosome is None:
assembly_size = chrom_info.total_length + 1
'''
else:
try:
assembly_size = chrom_info.chrom_lengths[chromosome]
except KeyError:
print("ERROR: Chromosome {} not found in assembly {}.".format(chromosome, assembly), file=sys.stderr)
return 1
'''
#max_zoom = int(math.ceil(math.log(assembly_size / min_feature_width) / math.log(2)))
max_zoom = int(math.ceil(
math.log(assembly_size / tile_size) / math.log(2)))
'''
if max_zoom is not None and max_zoom < max_zoom:
max_zoom = max_zoom
'''
# this script stores data in a sqlite database
import sqlite3
sqlite3.register_adapter(np.int64, lambda val: int(val))
print("output_file:", output_file)
conn = sqlite3.connect(output_file)
# store some meta data
store_meta_data(conn,
1,
max_length=assembly_size,
assembly=assembly,
chrom_names=chrom_names,
chrom_sizes=chrom_sizes,
tile_size=tile_size,
max_zoom=max_zoom,
max_width=tile_size * 2**max_zoom,
header=header)
max_width = tile_size * 2**max_zoom
uid_to_entry = {}
intervals = []
# store each bed file entry as an interval
for d in dset:
uid = d['uid']
uid_to_entry[uid] = d
intervals += [(d['startPos'], d['endPos'], uid)]
tile_width = tile_size
removed = set()
c = conn.cursor()
c.execute('''
CREATE TABLE intervals
(
id int PRIMARY KEY,
zoomLevel int,
importance real,
startPos int,
endPos int,
chrOffset int,
uid text,
fields text
)
''')
c.execute('''
CREATE VIRTUAL TABLE position_index USING rtree(
id,
rStartPos, rEndPos
)
''')
curr_zoom = 0
counter = 0
max_viewable_zoom = max_zoom
if max_zoom is not None and max_zoom < max_zoom:
max_viewable_zoom = max_zoom
while curr_zoom <= max_viewable_zoom and len(intervals) > 0:
# at each zoom level, add the top genes
tile_width = tile_size * 2**(max_zoom - curr_zoom)
for tile_num in range(max_width // tile_width):
# go over each tile and distribute the remaining values
#values = interval_tree[tile_num * tile_width: (tile_num+1) * tile_width]
from_value = tile_num * tile_width
to_value = (tile_num + 1) * tile_width
entries = [
i for i in intervals if (i[0] < to_value and i[1] > from_value)
]
values_in_tile = sorted(
entries, key=lambda x: -uid_to_entry[x[-1]]['importance']
)[:max_per_tile] # the importance is always the last column
# take the negative because we want to prioritize
# higher values
if len(values_in_tile) > 0:
for v in values_in_tile:
counter += 1
value = uid_to_entry[v[-1]]
# one extra question mark for the primary key
exec_statement = 'INSERT INTO intervals VALUES (?,?,?,?,?,?,?,?)'
#print("value:", value['startPos'])
ret = c.execute(
exec_statement,
# primary key, zoomLevel, startPos, endPos, chrOffset, line
(counter, curr_zoom, value['importance'],
value['startPos'], value['endPos'],
value['chrOffset'], value['uid'], value['fields']))
conn.commit()
exec_statement = 'INSERT INTO position_index VALUES (?,?,?)'
ret = c.execute(
exec_statement,
(counter, value['startPos'], value['endPos']
) #add counter as a primary key
)
conn.commit()
intervals.remove(v)
#print ("curr_zoom:", curr_zoom, file=sys.stderr)
curr_zoom += 1
conn.commit()
conn.close()
return
def test_chrom_sizes():
assert(nc.get_chromsizes('hg19')[0] == 249250621)
assert(nc.get_chromsizes('hg19')[1] == 243199373)
def test_chrom_sizes():
assert (nc.get_chromsizes('hg19')[0] == 249250621)
assert (nc.get_chromsizes('hg19')[1] == 243199373)
import h5py
import bbi
import negspy.coordinates as nc
import numpy as np
import math
import argparse
import json
import os
import resource
GENOME_BUILD = 'hg19'
GENOME_LENGTH = sum(nc.get_chromsizes(GENOME_BUILD)[:25])
def bigwigs_to_multivec(
input_bigwig_files,
output_file,
starting_resolution
):
f = h5py.File(output_file, 'w')
num_samples = len(input_bigwig_files)
# Create level zero groups
info_group = f.create_group("info")
resolutions_group = f.create_group("resolutions")
chroms_group = f.create_group("chroms")
# Set info attributes
info_group.attrs['tile-size'] = 256
