def aggregate_genotypic_entropy_timeseries( directories, grouping="", subgrouping="", verbose=False, test=False, expected=None ): input_files_glob = ["stats.dat*"] ## this is actually a globbing pattern column = 8 outfile = "genotypic_entropy" return rf.aggregate_timeseries( directories, input_files_glob, outfile, column, grouping=grouping, subgrouping=subgrouping, verbose=verbose, test=test, expected=expected )
def aggregate_timeseries( directories, grouping="", subgrouping="", verbose=False, test=False, expected=None ): input_files_glob = ["tasks.dat*"] ## this is actually a globbing pattern column = 3 outfile = "task_ct" return rf.aggregate_timeseries( directories, input_files_glob, outfile, column, grouping=grouping, subgrouping=subgrouping, verbose=verbose, test=test, expected=expected )
def aggregate_timeseries( directories, grouping="", subgrouping="", verbose=False, test=False, expected=None ): input_files_glob = ["stats.dat*"] ## this is actually a globbing pattern column = 10 outfile = "coalescent_generations" return rf.aggregate_timeseries( directories, input_files_glob, outfile, column, grouping=grouping, subgrouping=subgrouping, verbose=verbose, type=type, test=test, expected=expected )
def aggregate_timeseries( directories, grouping="", subgrouping="", verbose=False, test=False, expected=None ): input_files_glob = ["two_task_functional_modularity__stats__organisms.csv*"] ## this is actually a globbing pattern column = 1 outfile = "functional_modularity" return rf.aggregate_timeseries( directories, input_files_glob, outfile, column, grouping=grouping, subgrouping=subgrouping, verbose=verbose, test=test, expected=expected, separator="," )
def aggregate_noncoding_mutations_timeseries( directories, grouping="", subgrouping="", verbose=False, test=False, expected=None ): input_files_glob = ["mutation_metrics.csv*"] ## this is actually a globbing pattern column = 2 outfile = "noncoding_mutations" return rf.aggregate_timeseries( directories, input_files_glob, outfile, column, grouping=grouping, subgrouping=subgrouping, verbose=verbose, separator=",", header=True, test=test, expected=expected )
def aggregate_timeseries(directories, input_files_glob="", column="", outfile="", grouping="", subgrouping="", verbose=False, test=False, expected=None): input_files_glob_list = [input_files_glob ] ## this is actually a globbing pattern #column = 4 #outfile = "fitness" print "CRAP" print input_files_glob_list print column print outfile print "DONE" return rf.aggregate_timeseries(directories, input_files_glob_list, outfile, int(column), grouping=grouping, subgrouping=subgrouping, verbose=verbose, test=test, expected=expected)
def aggregate_timeseries( directories, input_files_glob="", column="", outfile="", grouping="", subgrouping="", verbose=False, test=False, expected=None ): input_files_glob_list = [input_files_glob] ## this is actually a globbing pattern #column = 4 #outfile = "fitness" print "CRAP" print input_files_glob_list print column print outfile print "DONE" return rf.aggregate_timeseries( directories, input_files_glob_list, outfile, int(column), grouping=grouping, subgrouping=subgrouping, verbose=verbose, test=test, expected=expected )
def aggregate_genotypic_entropy_timeseries(directories, grouping="", subgrouping="", verbose=False, test=False, expected=None): input_files_glob = ["stats.dat*"] ## this is actually a globbing pattern column = 8 outfile = "genotypic_entropy" return rf.aggregate_timeseries(directories, input_files_glob, outfile, column, grouping=grouping, subgrouping=subgrouping, verbose=verbose, test=test, expected=expected)
def aggregate_timeseries(directories, grouping="", subgrouping="", verbose=False, test=False, expected=None): input_files_glob = ["stats.dat*"] ## this is actually a globbing pattern column = 10 outfile = "coalescent_generations" return rf.aggregate_timeseries(directories, input_files_glob, outfile, column, grouping=grouping, subgrouping=subgrouping, verbose=verbose, type=type, test=test, expected=expected)
def aggregate_noncoding_mutations_timeseries(directories, grouping="", subgrouping="", verbose=False, test=False, expected=None): input_files_glob = ["mutation_metrics.csv*" ] ## this is actually a globbing pattern column = 2 outfile = "noncoding_mutations" return rf.aggregate_timeseries(directories, input_files_glob, outfile, column, grouping=grouping, subgrouping=subgrouping, verbose=verbose, separator=",", header=True, test=test, expected=expected)