def build_tax_distribution(datafile): distob = Distribution([datafile], 1) distob.file_to_stream_func = my_top_hit_provider #distob.DEBUG = True distob.file_to_stream_func_xargs = [0,7,6] # i.e. pick out first field, then kingdom, comnames distob.interval_locator_funcs = [bin_discrete_value, bin_discrete_value] distdata = build(distob,"singlethread") distob.save("%s.pickle"%datafile) return distdata
def summarise_distributions(distributions, options): measure = "frequency" if options["summary_type"] in ["zipfian","entropy"]: measure = "unsigned_information" kmer_intervals = Distribution.get_intervals(distributions, options["num_processes"]) #print "summarising %s , %s across %s"%(measure, str(kmer_intervals), str(distributions)) print "summarising %s , %d kmers across %s"%(measure, len(kmer_intervals), str(distributions)) sample_measures = Distribution.get_projections(distributions, kmer_intervals, measure, False, options["num_processes"]) zsample_measures = itertools.izip(*sample_measures) sample_name_iter = [tuple([os.path.splitext(os.path.basename(distribution))[0] for distribution in distributions])] zsample_measures = itertools.chain(sample_name_iter, zsample_measures) interval_name_iter = itertools.chain([("kmer_pattern")],kmer_intervals) outfile=open(options["output_filename"], "w") if options["summary_type"] in ["entropy", "frequency"]: zsample_measures_with_rownames = itertools.izip(interval_name_iter, zsample_measures) for interval_measure in zsample_measures_with_rownames: print >> outfile, "%s\t%s"%("%s"%interval_measure[0], string.join((str(item) for item in interval_measure[1]),"\t")) outfile.close() elif options["summary_type"] in ["ranks", "zipfian"]: # duplicate interval_name_iter - needed 3 times interval_name_iter_dup = itertools.tee(interval_name_iter, 3) # triplicate zsample_measures (0 used to get ranks; 1 used to output measures; 3 used to get distances) zsample_measures_dup = itertools.tee(zsample_measures,3) ranks = Distribution.get_rank_iter(zsample_measures_dup[0]) # duplicate ranks (0 used to output; 1 used to get distances) ranks_dup = itertools.tee(ranks, 2) ranks_with_rownames = itertools.izip(interval_name_iter_dup[0], ranks_dup[0]) # output ranks print >> outfile , "*** ranks *** :" for interval_rank in ranks_with_rownames: print >> outfile, "%s\t%s"%("%s"%interval_rank[0], string.join((str(item) for item in interval_rank[1]),"\t")) # output measures print >> outfile , "*** entropies *** :" zsample_measures_with_rownames = itertools.izip(interval_name_iter_dup[1], zsample_measures_dup[1]) for interval_measure in zsample_measures_with_rownames: print >> outfile, "%s\t%s"%("%s"%interval_measure[0], string.join((str(item) for item in interval_measure[1]),"\t")) # get distances print >> outfile , "*** distances *** :" (distance_matrix, point_names_sorted) = Distribution.get_zipfian_distance_matrix(zsample_measures_dup[2], ranks_dup[1]) Distribution.print_distance_matrix(distance_matrix, point_names_sorted, outfile) else: print "warning, unknown summary type %(summary_type)s, no summary available"%options outfile.close()
def get_sample_tax_frequency_distribution(sample_tax_summaries): sample_tax_lists = [ Distribution.load(sample_tax_summary).get_distribution().keys() for sample_tax_summary in sample_tax_summaries ] all_taxa = set( reduce(lambda x,y:x+y, sample_tax_lists)) all_taxa_list = list(all_taxa) all_taxa_list.sort(tax_cmp) #print all_taxa_list sample_tax_frequency_distributions = [["%s\t%s"%item for item in all_taxa_list]] + [ Distribution.load(sample_tax_summary).get_frequency_projection(all_taxa_list) for sample_tax_summary in sample_tax_summaries] #print sample_tax_frequency_distributions fd_iter = itertools.izip(*sample_tax_frequency_distributions) heading = itertools.izip(*[["Kingdom\tFamily"]]+[[re.split("\.",os.path.basename(path.strip()))[0]] for path in sample_tax_summaries]) #print heading fd_iter = itertools.chain(heading, fd_iter) for record in fd_iter: print string.join([str(item) for item in record],"\t")
def use_kmer_prbdf(picklefile): distob = Distribution.load(picklefile) distdata = distob.get_distribution() for (interval, freq) in distdata.items(): print interval, freq
def build_kmer_distribution(datafile, kmer_patterns, sampling_proportion, num_processes, builddir, reverse_complement, pattern_window_length, input_driver_config): if os.path.exists(get_save_filename(datafile, builddir)): print("build_kmer_distribution- skipping %s as already done"%datafile) distob = Distribution.load(get_save_filename(datafile, builddir)) distob.summary() else: filetype = get_file_type(datafile) distob = Distribution([datafile], num_processes) distob.interval_locator_parameters = (None,) distob.interval_locator_funcs = (bin_discrete_value,) distob.assignments_files = ("kmer_binning.txt",) distob.file_to_stream_func = seq_from_sequence_file distob.file_to_stream_func_xargs = [filetype,sampling_proportion] distob.weight_value_provider_func = kmer_count_from_sequence distob.weight_value_provider_func_xargs = [reverse_complement, pattern_window_length, 1] + kmer_patterns if filetype == ".cnt": print "DEBUG setting methods for count file" distob.file_to_stream_func = tag_count_from_tag_count_file distob.file_to_stream_func_xargs = [input_driver_config,sampling_proportion] distob.weight_value_provider_func = kmer_count_from_tag_count #distdata = build(distob, use="singlethread") distdata = build(distob, proc_pool_size=num_processes) distob.save(get_save_filename(datafile, builddir)) print "Distribution %s has %d points distributed over %d intervals, stored in %d parts"%(get_save_filename(datafile, builddir), distob.point_weight, len(distdata), len(distob.part_dict)) return get_save_filename(datafile, builddir)