for o in [s.replace("correlator","config") for s in toosmall]:
if o in outliercount:
outliercount[o] += 1
else:
outliercount[o] = 1
else:
outlierfile.write("\n")
for config,count in outliercount.items():
outlierfile.write("{} has outliers on {} of {} times\n".format(config,count,len(data.index)))
if count == len(data.index):
logging.error("Config {} has outliers on all times!!".format(config))
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="plot a histogram of a file for a single time")
parser.add_argument("-v", "--verbose", action="store_true",
help="increase output verbosity")
parser.add_argument("-o", "--output-stub", type=str, required=True,
help="stub of name to write output to")
parser.add_argument('datafile', metavar='f', type=str, help='file to plot')
args = parser.parse_args()
if args.verbose:
logging.basicConfig(format='%(levelname)s: %(message)s', level=logging.DEBUG)
logging.debug("Verbose debuging mode activated")
else:
logging.basicConfig(format='%(levelname)s: %(message)s', level=logging.INFO)
data = pandas_reader.read_configcols_paraenformat(args.datafile)
sanity_check(data)
if not ops:
logging.error("Error: no operators found")
parser.print_help()
parser.exit()
args.operators = ops
else:
ops = readops(args.operators)
logging.debug("found operators: {}".format(','.join(ops)))
args.operators = ops
cor_matrix = {}
for snk in args.operators:
for src in args.operators:
filename = args.input_dir + args.filewild.format(snk, src)
logging.info("reading {}".format(filename))
cor_matrix[snk+src] = pandas_reader.read_configcols_paraenformat(filename)
correlator_pannel = pd.Panel(cor_matrix)
length = correlator_pannel.shape[0]
n = int(np.sqrt(length))
B = np.matrix(np.reshape(correlator_pannel.major_xs(args.time).mean().values, (n, n)))
B = hermitionize(B)
evals = LA.eigvalsh(B)
logging.info("eigenvalues are {}".format(",".join(map(str, evals))))
if args.output:
write_eigenvalues(evals)
meval = max(evals)
logging.info("max eigen {}".format(meval))
args.operators = ops
if not args.principle and not args.tstar:
logging.error("tstar required, unless doing princple")
parser.print_help()
parser.exit()
cor_matrix = {}
cor_matrix_multi = {}
cor_matrix_ave = {}
for snk in args.operators:
for src in args.operators:
filename = args.input_dir + args.filewild.format(snk, src)
logging.info("reading {}".format(filename))
try:
tmpcor = pandas_reader.read_configcols_paraenformat(filename)
except:
tmpcor = pandas_reader.read_configcols_normal(filename)
cor_matrix[snk+src] = tmpcor
p = pd.Panel(cor_matrix)
if args.returnmaxeigen:
logging.info("just returning the max eigen")
print diagonalize(p, args.tnaught, args.tstar, generalized=args.generalized)
if args.principle:
diag = principle(p, args.tnaught, generalized=args.generalized)
