def test_combine_map_label_cols(self):
"""combine_map_label_cols: Combine two or more columns from the \
mapping file"""
self.combinecolorby = ['Day', 'Type']
exp=[["Sample-ID","Day","Type","Day&&Type"],\
["Sample1","Day1","Soil","Day1Soil"],\
["Sample2","Day1","Soil","Day1Soil"],\
["Sample3","Day1","Soil","Day1Soil"]]
obs = combine_map_label_cols(self.combinecolorby, self.mapping)
self.assertEqual(obs, exp)
def test_combine_map_label_cols(self):
"""combine_map_label_cols: Combine two or more columns from the \
mapping file"""
self.combinecolorby = ['Day', 'Type']
exp = [["Sample-ID", "Day", "Type", "Day&&Type"],
["Sample1", "Day1", "Soil", "Day1Soil"],
["Sample2", "Day1", "Soil", "Day1Soil"],
["Sample3", "Day1", "Soil", "Day1Soil"]]
obs = combine_map_label_cols(self.combinecolorby, self.mapping)
self.assertEqual(obs, exp)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
mapping_fp = opts.mapping_fp
mapping_category = opts.mapping_category
otu_table_fp = opts.otu_table_fp
output_fp = opts.output_fp
normalize = opts.normalize
# define a function that returns the bin a sample shouldbe placed into
bin_function = lambda id_, sample_metadata:\
sample_metadata[mapping_category]
# parse the sample metadata and add it to the OTU table (we assume that
# sample metadata is not already present in the table)
mapping, headers, comments = parse_mapping_file(open(mapping_fp, 'U'))
# added in ability to combine metadata columns and summarize based on the
# new combined category
if '&&' in mapping_category:
new_mapping = []
new_mapping.append(headers)
for i in range(len(mapping)):
new_mapping.append(mapping[i])
# Create an array using multiple columns from mapping file
combinecolorby = mapping_category.split('&&')
mapping = combine_map_label_cols(combinecolorby, new_mapping)
sample_metadata = mapping_file_to_dict(mapping, headers)
with biom_open(otu_table_fp, 'U') as biom_file:
table = parse_biom_table(biom_file)
table.add_metadata(sample_metadata)
# create a new OTU table where samples are binned based on their return
# value from bin_function
result = table.collapse(bin_function,
norm=False,
min_group_size=1,
axis='sample')
# normalize the result if requested by the user
if normalize:
result.norm(axis='sample', inplace=True)
# write a new BIOM file
write_biom_table(result, output_fp)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
mapping_fp = opts.mapping_fp
mapping_category = opts.mapping_category
otu_table_fp = opts.otu_table_fp
output_fp = opts.output_fp
normalize = opts.normalize
# define a function that returns the bin a sample shouldbe placed into
bin_function = lambda id_, sample_metadata:\
sample_metadata[mapping_category]
# parse the sample metadata and add it to the OTU table (we assume that
# sample metadata is not already present in the table)
mapping, headers, comments = parse_mapping_file(open(mapping_fp, 'U'))
# added in ability to combine metadata columns and summarize based on the
# new combined category
if '&&' in mapping_category:
new_mapping = []
new_mapping.append(headers)
for i in range(len(mapping)):
new_mapping.append(mapping[i])
# Create an array using multiple columns from mapping file
combinecolorby = mapping_category.split('&&')
mapping = combine_map_label_cols(combinecolorby, new_mapping)
sample_metadata = mapping_file_to_dict(mapping, headers)
with biom_open(otu_table_fp, 'U') as biom_file:
table = parse_biom_table(biom_file)
table.add_metadata(sample_metadata)
# create a new OTU table where samples are binned based on their return
# value from bin_function
result = table.collapse(bin_function, norm=False, min_group_size=1,
axis='sample')
# normalize the result if requested by the user
if normalize:
result.norm(axis='sample', inplace=True)
# write a new BIOM file
write_biom_table(result, output_fp)
