Python Table.to_tsv方法代码示例

# 需要导入模块: from biom.table import Table [as 别名]
# 或者: from biom.table.Table import to_tsv [as 别名]
def main(table_loc, otu_list, collapsed_name, output_file, classic=False):
    table = load_table(table_loc)
    f = open(otu_list)
    otus = f.read().strip().split()
    otus = set(otus) & set(table.ids(axis="observation"))
    table1 = table.filter(otus, axis="observation", inplace=False)
    table2 = table.filter(otus, axis="observation", invert=True, inplace=False)
    sums1 = table1.sum(axis='sample')
    sums2 = table2.sum(axis='sample')
    new_table = Table(numpy.array([sums1,sums2]), [collapsed_name, "not_"+collapsed_name], table.ids(axis="sample"), type="otu baptable")
    
    if classic:
        # print to tab delimited biom table
        open(output_file, 'w').write(new_table.to_tsv())
    else:
        # print biom table
        new_table.to_json("predict_reactions.py", open(output_file, 'w'))

# 需要导入模块: from biom.table import Table [as 别名]
# 或者: from biom.table.Table import to_tsv [as 别名]
#			print "index: %i" %index
			ind_taxonomy.append('%s%s' %(syn[levels[index]], taxon[0]['ScientificName']))
       	

#	print ind_taxonomy			
	
	Taxonomy[taxon[0]['ScientificName']]['taxonomy'] = ind_taxonomy

#	print "Taxonomy: %s" %Taxonomy
		
for taxon in observ_ids:
#	print taxon
#	print Taxonomy[taxon]
	observation_metadata.append(Taxonomy[taxon])

#print "observation metadata:\n%s" %observation_metadata
#print len(observation_metadata)

table = Table(data, observ_ids, sample_id, observation_metadata, sample_metadata, table_id='Example Table')
print table

out=open(args.prefix+".biom","w")
table.to_json('pplacer converted by jplace_to_biom.py v.'+VERSION, direct_io=out)
out.close()

out=open(args.prefix+".tsv","w")
out.write(table.to_tsv(header_key='taxonomy', header_value='taxomomy')) #to_json('generaged by test', direct_io=out)
out.close()

print "\n##### DONE! #####\n"

# 需要导入模块: from biom.table import Table [as 别名]
# 或者: from biom.table.Table import to_tsv [as 别名]

#.........这里部分代码省略.........
    upper_percentage = opts.upper_percentage
    otu_table_fp = opts.otu_table_fp
    otu_table = load_table(otu_table_fp)
    delimiter = opts.delimiter
    mapping_fp = opts.mapping
    md_as_string = opts.md_as_string
    md_identifier = opts.md_identifier
    levels = opts.level.split(',')
    suppress_classic_table_output = opts.suppress_classic_table_output
    suppress_biom_table_output = opts.suppress_biom_table_output

    if upper_percentage is not None and lower_percentage is not None:
        raise ValueError(
            "upper_percentage and lower_percentage are mutually exclusive")

    if upper_percentage is not None and lower_percentage is not None and \
            mapping:
        raise ValueError("upper_percentage and lower_percentage can not be "
                         "using with mapping file")

    if upper_percentage is not None and \
            (upper_percentage < 0 or upper_percentage > 1.0):
        raise ValueError('max_otu_percentage should be between 0.0 and 1.0')

    if lower_percentage is not None and \
            (lower_percentage < 0 or lower_percentage > 1.0):
        raise ValueError('lower_percentage should be between 0.0 and 1.0')

    if mapping_fp:
        mapping_file = open(mapping_fp, 'U')
        mapping, header, comments = parse_mapping_file(mapping_file)

        # use the input Mapping file for producing the output filenames
        map_dir_path, map_fname = split(mapping_fp)
        map_basename, map_fname_ext = splitext(map_fname)
    else:
        if suppress_classic_table_output and suppress_biom_table_output:
            option_parser.error("Both classic and BIOM output formats were "
                                "suppressed.")

    if not opts.absolute_abundance:
        otu_table = otu_table.norm(axis='sample', inplace=False)

    # introduced output directory to will allow for multiple outputs
    if opts.output_dir:
        create_dir(opts.output_dir, False)
        output_dir_path = opts.output_dir
    else:
        output_dir_path = './'

    # use the input OTU table to produce the output filenames
    dir_path, fname = split(otu_table_fp)
    basename, fname_ext = splitext(fname)

    # Iterate over the levels and generate a summarized taxonomy for each
    for level in levels:
        if mapping_fp:
            # define output filename
            output_fname = join(output_dir_path,
                                map_basename + '_L%s.txt' % (level))

            summary, tax_order = add_summary_mapping(otu_table,
                                                     mapping,
                                                     int(level),
                                                     md_as_string,
                                                     md_identifier)

            write_add_taxa_summary_mapping(summary, tax_order, mapping,
                                           header, output_fname, delimiter)
        else:
            # define the output filename. The extension will be added to the
            # end depending on the output format
            output_fname = join(output_dir_path, basename + '_L%s' % level)

            summary, header = make_summary(otu_table,
                                           int(level),
                                           upper_percentage,
                                           lower_percentage,
                                           md_as_string,
                                           md_identifier)

            sample_ids = header[1:]

            observation_ids = []
            data = []
            for row in summary:
                # Join taxonomic levels to create an observation ID.
                observation_ids.append(delimiter.join(row[0]))
                data.append(row[1:])

            table = Table(np.asarray(data), observation_ids, sample_ids)
            if opts.transposed_output:
                table = table.transpose()

            if not suppress_classic_table_output:
                with open(output_fname + '.txt', 'w') as outfile:
                    outfile.write(table.to_tsv())

            if not suppress_biom_table_output:
                write_biom_table(table, output_fname + '.biom')