def extract(INPUT, xmin, xmax, xstep):
"""Get covariance matrix, coordinates.
This is the meta-extractor. It processes both individual files and
folders.
"""
# result is returned as a named tuple: RESRET
RESRET = namedtuple("ret", ["coord", "covar", "numblocks"])
if os.path.isfile(INPUT):
RESRET = simple_proc_file(INPUT, xmin, xmax, EIGCUT)
COV = RESRET.covar
COORDS = RESRET.coord
# DIMCOV is dimensions of the covariance matrix
DIMCOV = RESRET.numblocks
# then find out domain of files to process
####process individual files in dir 5ab
# error handling, test to see if time value goes out of range,
# i.e. if data isn't available to match the requested time domain
# i,j are new indices, shifting xmin to the origin
# j = 0 # initialized below
# test if directory
elif os.path.isdir(INPUT):
i = 0
# DIMCOV is dimensions of the covariance matrix
DIMCOV = int((xmax - xmin) / xstep + 1)
# cov is the covariance matrix
COV = [[[0] for _ in range(DIMCOV)] for _ in range(DIMCOV)]
# COORDS are the coordinates to be plotted.
# the ith point with the jth value
COORDS = [[[0] for _ in range(2)] for _ in range(DIMCOV)]
for time in np.arange(xmin, xmax + 1, xstep):
COORDS[i][0] = time
j = 0
for time2 in np.arange(xmin, xmax + 1, xstep):
IFILE = proc_folder(INPUT, time)
JFILE = proc_folder(INPUT, time2)
IFILE = INPUT + "/" + IFILE
JFILE = INPUT + "/" + JFILE
try:
TRIAL = open(IFILE, "r")
TRIAL2 = open(JFILE, "r")
except TypeError:
STR1 = "Either domain is invalid,"
print STR1, "or folder is invalid."
print "Double check contents of folder."
print "Offending file(s):"
print IFILE
print JFILE
sys.exit(1)
RESRET = proc_file(IFILE, JFILE)
# fill in the covariance matrix
COV[i][j] = RESRET.covar
# only store coordinates once. each file is read many times
if j == 0:
COORDS[i][1] = RESRET.coord
j += 1
i += 1
return COORDS, COV, DIMCOV
def main():
####set up 1ab
OPTIONS = namedtuple('ops', ['xmin', 'xmax', 'xstep', 'trials'])
###error processing, parameter extractions
INPUT, OPTIONS = procargs(sys.argv[1:])
XMIN, XMAX = xlim_err(OPTIONS.xmin, OPTIONS.xmax)
XSTEP = xstep_err(OPTIONS.xstep, INPUT)
TRIALS = trials_err(OPTIONS.trials)
if TRIALS == -1:
RESULT_MIN, PARAM_ERR, COORDS, COV = singlefit(INPUT,
XMIN, XMAX, XSTEP)
printerr(RESULT_MIN.x, PARAM_ERR)
mkplot(COORDS, COV, RESULT_MIN)
sys.exit(0)
else:
list_fit_params = []
for ctime in range(TRIALS):
IFILE = proc_folder(INPUT, ctime, "blk")
NINPUT = os.path.join(INPUT, IFILE)
RESULT_MIN, PARAM_ERR, COORDS, COV = singlefit(NINPUT,
XMIN, XMAX, XSTEP)
list_fit_params.append(RESULT_MIN.x)
#todo: make plot section
TRANSPOSE = np.array(list_fit_params).T.tolist()
avg_fit_params = [sum(TRANSPOSE[i])/len(TRANSPOSE[i]) for i in range(
len(TRANSPOSE))]
if JACKKNIFE == "YES":
prefactor = (TRIALS-1.0)/(1.0*TRIALS)
elif JACKKNIFE == "NO":
prefactor = (1.0)/((TRIALS-1.0)*(1.0*TRIALS))
else:
print "***ERROR***"
print "JACKKNIFE value should be a string with value either"
print "YES or NO"
print "Please examine the config file."
sys.exit(1)
err_fit_params = [sqrt(sum([(TRANSPOSE[i][j]-avg_fit_params[i])**2
for j in range(len(
TRANSPOSE[i]))])*prefactor)
for i in range(len(TRANSPOSE))]
printerr(avg_fit_params, err_fit_params)
sys.exit(0)