# Average over bins
mEff_fit_avg = np.average(mEff_fit)
mEff_fit_err = np.std( mEff_fit ) \
* float( binNum - 1 ) / math.sqrt( float( binNum ) )
###########
# Momenta #
###########
# Read momenta list from dataset
# momList[ c, Q ]
momList = rw.getDatasets(threepDir, configList, threep_template,
"Momenta_list")[:, 0, 0, ...]
# Check that momenta agree across configurations
Qsq, Qsq_start, Qsq_end = fncs.processMomList(momList)
# Convert to GeV
Qsq_GeV = pq.convertQsqToGeV(Qsq, latticeSpacing * mEff_fit_avg,
latticeSpacing, latticeDim)
for ts in tsink:
#########################
# Three-point functions #
#########################
configNum = len(configList)
assert configNum % binSize == 0, "Number of configurations " \
+ str( configNum ) + " not evenly divided by bin size " \
+ str( binSize ) + ".\n"
binNum = configNum / binSize
#######################
# Two-point functions #
#######################
# Get the real part of two-point functions
# twop[ c, t ]
twop = rw.getDatasets(twopDir, configList, twop_template, "twop")[:, 0, 0, ...,
0, 0]
print "Read two-point functions from HDF5 files"
# Jackknife
# twop_jk[ b, t ]
twop_jk = fncs.jackknife(twop, binSize)
for ts in tsink:
#########################
# Three-point functions #
#########################
# Get the real part of g5gx, g5gy, and g5gz
configNum = len(configList)
assert configNum % binSize == 0, "Number of configurations " \
+ str( configNum ) + " not evenly divided by bin size " \
+ str( binSize ) + ".\n"
binNum = configNum / binSize
#############################################
# Fill matrices with values from data files #
#############################################
# Get real part of zero-momentum two-point functions
# twop[ c, t ]
twop = rw.getDatasets(twopDir, configList, filename_template,
"twop")[:, 0, 0, :, 0, 0]
print "Read two-point functions from HDF5 files"
#############
# Jackknife #
#############
twop_jk = fncs.jackknife(twop, binSize)
twop_avg = np.average(twop_jk, axis=0)
twop_err = np.std(twop_jk, axis=0) * float(binNum - 1) / math.sqrt(
float(binNum))
############################
print "Fit effective mass" # Average over bins mEff_fit_avg = np.average( mEff_fit ) mEff_fit_err = np.std( mEff_fit ) * float( binNum - 1 ) / np.sqrt( float( binNum ) ) ####################### # Two-point functions # ####################### # Get the real part of two-point functions # twop[ c, t ] twop = rw.getDatasets( twopDir, configList, twop_template, "twop" )[ :, 0, 0, ..., 0, 0 ] print "Read two-point functions from HDF5 files" # Jackknife # twop_jk[ b, t ] twop_jk = fncs.jackknife( twop, binSize ) twop_avg = np.average( twop_jk, axis=0 ) twop_err = np.std( twop_jk, axis=0 ) * float( binNum - 1 ) / np.sqrt( float( binNum ) ) twop_ts = [] threep_jk = []
for ts in tsink:
# Read three-point functions
# threep[ ts ][ curr_type ][ c, src, ds, ti, q, curr, part ]
threep_ts, threepDatasetName_ts = rw.getHDF5File_wNames(
threepDir, configList, threep_template, "threep",
"tsink_" + str(ts))
threep.append(threep_ts)
threepDatasetName.append(threepDatasetName_ts)
momentaList_threep = rw.getDatasets(threepDir, configList,
threep_template,
"Momenta_list_xyz")
# Check that all of the momenta are the same
for c in range(configNum):
momList_0 = momentaList_threep[c, 0].flat
for momList in momentaList_threep[c]:
for i in range(momList.size):
assert momList.flat[ i ] == momList_0[ i ], \
"Momenta lists in configuration " + configList[ c ] \
+ " do not match"
print "Fit effective mass"
# Average over bins
mEff_fit_avg = np.average( mEff_fit )
mEff_fit_err = np.std( mEff_fit ) * float( binNum - 1 ) / np.sqrt( float( binNum ) )
#######################
# Two-point functions #
#######################
# Get the real part of two-point functions
# twop[ c, t ]
twop = rw.getDatasets( twopDir, configList, twop_template, "twop" )[ :, 0, 0, ..., 0, 0 ]
print "Read two-point functions from HDF5 files"
# Jackknife
# twop_jk[ b, t ]
twop_jk = fncs.jackknife( twop, binSize )
for ts in tsink:
#########################
# Three-point functions #
#########################
# Get the real part of gxDx, gyDy, gzDz, and gtDt
twop_jk = fncs.jackknife( twop, binSize )
"""
for ts in tsink:
#########################
# Three-point functions #
#########################
# Get the real part of gxDx, gyDy, gzDz, and gtDt
# three-point functions at zero-momentum
# threep[ c, t ]
if dataFormat == "gpu":
threep_u_gxDx = rw.getDatasets( threepDir, configList, threep_template, \
"tsink_" + str( ts ), "oneD", "dir_00", \
"up", "threep" )[ :, 0, 0, ..., 0, 1, 0 ]
threep_u_gyDy = rw.getDatasets( threepDir, configList, threep_template, \
"tsink_" + str( ts ), "oneD", "dir_01", \
"up", "threep" )[ :, 0, 0, ..., 0, 2, 0 ]
threep_u_gzDz = rw.getDatasets( threepDir, configList, threep_template, \
"tsink_" + str( ts ), "oneD", "dir_02", \
"up", "threep" )[ :, 0, 0, ..., 0, 3, 0 ]
threep_u_gtDt = rw.getDatasets( threepDir, configList, threep_template, \
"tsink_" + str( ts ), "oneD", "dir_03", \
"up", "threep" )[ :, 0, 0, ..., 0, 4, 0 ]
threep_s_gxDx = np.array( [] )
# Fitted effective mass
# mEff_fit_avg
mEff_fit_avg = np.average(mEff_fit)
mEff_fit_err = np.std(mEff_fit) * float(binNum - 1) / math.sqrt(float(binNum))
################
# Momenta list #
################
# Read momenta list from dataset
# momList[ c, Q ]
momList = rw.getDatasets(threepDir, configList, threep_template,
"Momenta_list")[:, 0, 0, ...]
# Check that momenta agree across configurations
Qsq, Qsq_start, Qsq_end = fncs.processMomList(momList)
######################
# Convert Q^2 to GeV #
######################
Qsq_GeV = pq.convertQsqToGeV(Qsq, latticeSpacing * mEff_fit_avg,
latticeSpacing, latticeDim)
#######################
# Two-point functions #
#######################