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xsf-method can now handle multiple replika

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jkuhl-uni 2022-12-21 14:57:08 +01:00
parent 6443884c8d
commit 6d7fd3bcfb
1 changed files with 37 additions and 38 deletions
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75
pyerrors/input/openQCD.py
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@ -990,37 +990,28 @@ def read_qtop_sector(path, prefix, c, target=0, **kwargs):
return qtop_projection(qtop, target=target)
def read_ms5_xsf(path, prefix, qc, corr):
"""
Read and process data from files produced by the ms5_xsf method
with a specific prefix and for a given quark combination.
Parameters
----------
path : str
The path to the directory containing the data files.
prefix : str
The prefix of the data files to be processed.
qc : str
The quark combination to be used to filter the data files.
corr : str
The correlator to be extracted from the data files.
Returns
-------
list of list of float
A list of lists containing the real and imaginary parts of the specified correlator for each configuration.
"""
def read_ms5_xsf(path, prefix, qc, corr, sep = "r", **kwargs):
found = []
files = []
names = []
for (dirpath, dirnames, filenames) in os.walk(path + "/"):
found.extend(filenames)
break
for f in found:
if fnmatch.fnmatch(f, prefix + ".ms5_xsf_"+qc+".dat"):
if fnmatch.fnmatch(f, prefix + "*.ms5_xsf_"+qc+".dat"):
files.append(f)
names.append(prefix+"|r"+f.split(".")[0].split(sep)[1])
files = sorted(files)
if "names" in kwargs:
names = kwargs.get("names")
else:
names = sorted(names)
cnfgs = []
realsamples = []
imagsamples = []
repnum = 0
for file in files:
with open(path+"/"+file, "rb") as fp:
@ -1033,14 +1024,11 @@ def read_ms5_xsf(path, prefix, qc, corr):
t = fp.read(8)
zF = struct.unpack('d', t)[0]
print("Parameters:\n kappa:",kappa, "csw:",csw,"dF:", dF,"zF:", zF)
t = fp.read(4)
tmax = struct.unpack('i', t)[0]
t = fp.read(4)
bnd = struct.unpack('i', t)[0]
print("T:", tmax)
placesBI = ["gS", "gP",
"gA", "gV",
@ -1053,14 +1041,14 @@ def read_ms5_xsf(path, prefix, qc, corr):
# the chunks have the following structure:
# confignumber, 10x timedependent complex correlators as doubles, 2x timeindependent complex correlators as doubles
chunksize = 4 +( 8 *2*tmax*10)+( 8 *2*2)
chunksize = 4 +( 8 *2*tmax*10)+( 8 *2*2)
packstr ='=i'+('d'*2*tmax*10)+('d'*2*2)
cnfgs = []
realsamples = []
imagsamples = []
cnfgs.append([])
realsamples.append([])
imagsamples.append([])
for t in range(tmax):
realsamples.append([])
imagsamples.append([])
realsamples[repnum].append([])
imagsamples[repnum].append([])
while True:
cnfgt = fp.read(chunksize)
@ -1068,7 +1056,7 @@ def read_ms5_xsf(path, prefix, qc, corr):
break
asascii=struct.unpack(packstr, cnfgt)
cnfg = asascii[0]
cnfgs.append(cnfg)
cnfgs[repnum].append(cnfg)
if not corr in placesBB:
tmpcorr = asascii[1+2*tmax*placesBI.index(corr):1+2*tmax*placesBI.index(corr)+2*tmax]
@ -1076,13 +1064,24 @@ def read_ms5_xsf(path, prefix, qc, corr):
tmpcorr = asascii[1+2*tmax*len(placesBI)+2*placesBB.index(corr):1+2*tmax*len(placesBI)+2*placesBB.index(corr)+2]
corrres = [[],[]]
for i in range(len(tmpcorr)): corrres[i%2].append(tmpcorr[i])
for t in range(int(len(tmpcorr)/2)): realsamples[t].append(corrres[0][t])
for t in range(int(len(tmpcorr)/2)): imagsamples[t].append(corrres[1][t])
for t in range(int(len(tmpcorr)/2)): realsamples[repnum][t].append(corrres[0][t])
for t in range(int(len(tmpcorr)/2)): imagsamples[repnum][t].append(corrres[1][t])
repnum += 1
s = "Read correlator "+ corr+ " from "+ str(repnum)+ " replika with "+str(len(realsamples[0][t]))
for rep in range(1,repnum):
s+=", "+str(len(realsamples[rep][t]))
s+=" samples"
print(s)
print("Asserted run parameters:\n T:", tmax, "kappa:", kappa, "csw:", csw, "dF:", dF, "zF:", zF)
# we have the data now... but we need to re format the whole thing and put it into Corr objects.
realObs = []
imagObs = []
compObs = []
for t in range(int(len(tmpcorr)/2)): realObs.append(Obs([realsamples[t]], names = [prefix], idl = [cnfgs]))
for t in range(int(len(tmpcorr)/2)): imagObs.append(Obs([imagsamples[t]], names = [prefix], idl = [cnfgs]))
for t in range(int(len(tmpcorr)/2)): realObs.append(Obs([realsamples[rep][t] for rep in range(repnum)], names = names, idl = cnfgs))
for t in range(int(len(tmpcorr)/2)): imagObs.append(Obs([imagsamples[rep][t] for rep in range(repnum)], names = names, idl = cnfgs))
for t in range(int(len(tmpcorr)/2)): compObs.append(CObs(realObs[t], imagObs[t]))
return Corr(compObs)