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)
