def get_expression_profile(expression_level, genes, expression_bins,
input_format, output_format, species, tmp,
symmetric_expression):
df = pd.DataFrame({'genes': genes, 'expression_level': expression_level})
df = df[df.iloc[:, 1].notna()]
df = df.sort_values(by=df.columns[1])
expression_level = np.array(df.iloc[:, 1])
if symmetric_expression:
left = MI.discretize(expression_level[expression_level < 0],
expression_bins // 2)
right = MI.discretize(expression_level[expression_level >= 0],
expression_bins // 2 + expression_bins % 2)
right += expression_bins // 2
expression_profile = np.concatenate((left, right))
else:
expression_profile = MI.discretize(expression_level, expression_bins)
genes = list(df.iloc[:, 0])
genes = [gene.split('.')[0] for gene in genes]
if input_format and output_format and input_format != output_format:
genes = change_accessions(genes, input_format, output_format, species,
tmp)
gene_dict = dict(zip(genes, expression_profile))
expression_profile = np.array(
[gene_dict[gene] for gene in gene_dict.keys() if gene != '-'])
genes = [gene for gene in gene_dict.keys() if gene != '-']
return expression_profile, genes
