def cwrap(beta_path, blocks_df, is_nice, min_cov, verbose):
# if verbose:
# eprint('[wt table]', op.splitext(op.basename(beta_path))[0])
if beta_path.endswith('.beta'):
r = collapse_process(beta_path, blocks_df, is_nice)
if r is None:
return
name = op.splitext(op.basename(beta_path))[0]
return {name: beta2vec(r, min_cov)}
else:
return {
op.basename(beta_path)[:-4]:
load_uxm(beta_path, blocks_df, 'U', min_cov)
}
def plot_all(self):
import matplotlib.pyplot as plt
fname_len = min(NR_CHARS_PER_FNAME, max([len(op.basename(op.splitext(f)[0])) for f in self.files]))
ticks = [op.splitext(op.basename(f))[0][:fname_len].ljust(fname_len) for f in self.files]
r = np.concatenate([beta2vec(d).reshape((1, -1)) for d in self.dsets])
plt.imshow(1 - r, cmap='RdYlGn')
# insert borders:
if self.borders is not None:
plt.vlines(self.borders - .5, -.5, len(self.files) - .5)
plt.yticks(np.arange(len(self.files)), ticks)
if self.args.title:
plt.title(self.args.title)
if self.args.output is not None:
plt.savefig(self.args.output)
plt.show()
def run_beta_to_bw(self, beta_path):
eprint('{}'.format(op.basename(beta_path)))
prefix = self.set_prefix(beta_path)
out_bigwig = prefix + BW_EXT
out_bed_graph = prefix + BG_EXT
cov_bigwig = prefix + COV_BW_EXT
cov_bed_graph = prefix + COV_BG_EXT
# Check if the current file should be skipped:
if not delete_or_skip(out_bigwig, self.args.force):
return
# load beta file
barr = self.load_beta(beta_path)
# dump coverage:
if self.args.dump_cov:
eprint('Dumping cov...')
self.ref_dict['cov'] = barr[:, 1]
sort_and_dump_df(
self.ref_dict[self.ref_dict['cov'] >= self.args.min_cov],
cov_bed_graph)
del self.ref_dict['cov']
# convert bedGraph to bigWig:
self.bed_graph_to_bigwig(cov_bed_graph, cov_bigwig)
# dump beta values to bedGraph
eprint('Dumping beta vals...')
self.ref_dict['beta'] = np.round(beta2vec(barr, na=-1), 3)
if self.args.remove_nan:
self.ref_dict = self.ref_dict[self.ref_dict['beta'] != -1]
sort_and_dump_df(self.ref_dict, out_bed_graph)
del self.ref_dict['beta']
# convert bedGraphs to bigWigs:
self.bed_graph_to_bigwig(out_bed_graph, out_bigwig)
def single_beta(beta_path, indices, cov_thresh):
return op.splitext(op.basename(beta_path))[0], \
beta2vec(load_beta_data(beta_path)[indices - 1], min_cov=cov_thresh).astype(np.float16)