def get2DoffDiagonal(wave1, wave2, mMin, mMax, intDir):
"""
Get the two dimensional off-diagonal terms for two de-isobarred waves
"""
integrals = getIntegralMatrixAverage(mMin,mMax,intDir,True, False)
waves1=[]
waves2=[]
for key in integrals[0].iterkeys():
if key.startswith(wave1):
mev1 = float(key.split('_')[1])/1000
mev2 = float(key.split('_')[2][:4])/1000
waves1.append([mev1,mev2,integrals[0][key]])
if key.startswith(wave2):
mev1 = float(key.split('_')[1])/1000
mev2 = float(key.split('_')[2][:4])/1000
waves2.append([mev1,mev2,integrals[0][key]])
ints = []
res=[]
ims=[]
for ww1 in waves1:
for ww2 in waves2:
ints.append([ww1[0],ww1[1],ww2[0],ww2[1],abs(integrals[1][ww1[2]][ww2[2]])])
res.append([ww1[0],ww1[1],ww2[0],ww2[1],integrals[1][ww1[2]][ww2[2]].real])
ims.append([ww1[0],ww1[1],ww2[0],ww2[1],integrals[1][ww1[2]][ww2[2]].imag])
return ints,res,ims
def get2DoffDiagonal(wave1, wave2, mMin, mMax, intDir):
"""
Get the two dimensional off-diagonal terms for two de-isobarred waves
"""
integrals = getIntegralMatrixAverage(mMin, mMax, intDir, True, False)
waves1 = []
waves2 = []
for key in integrals[0].iterkeys():
if key.startswith(wave1):
mev1 = float(key.split('_')[1]) / 1000
mev2 = float(key.split('_')[2][:4]) / 1000
waves1.append([mev1, mev2, integrals[0][key]])
if key.startswith(wave2):
mev1 = float(key.split('_')[1]) / 1000
mev2 = float(key.split('_')[2][:4]) / 1000
waves2.append([mev1, mev2, integrals[0][key]])
ints = []
res = []
ims = []
for ww1 in waves1:
for ww2 in waves2:
ints.append([
ww1[0], ww1[1], ww2[0], ww2[1],
abs(integrals[1][ww1[2]][ww2[2]])
])
res.append([
ww1[0], ww1[1], ww2[0], ww2[1],
integrals[1][ww1[2]][ww2[2]].real
])
ims.append([
ww1[0], ww1[1], ww2[0], ww2[1],
integrals[1][ww1[2]][ww2[2]].imag
])
return ints, res, ims
def get_eigenbasis(mlow,mup,integrals,minindex,maxindex): dim = maxindex-minindex+1 # +1 since the maxindex das to be taken as well integral_data = getIntegralMatrixAverage(mlow,mup,integrals,False,False) matrix = [] for i in range(dim): matrix.append([0.+0.j]*dim) for i in range(minindex,maxindex+1): for j in range(minindex,maxindex+1): matrix[i-minindex][j-minindex] = integral_data[1][i-1][j-1] # Indices are counted from 1(fortran style), matrix starts at [0][0](Python style) small_indices = [] smalldim = 0 for i in range(dim): if not matrix[i][i] == 0.j: small_indices.append(i) smalldim+=1 bigeigenbasis = [] for i in range(dim): line = [0.+0.j]*dim bigeigenbasis.append(line) EV_STUFF = False if EV_STUFF: smallmatrix=[] # Only contains nonzero waves... for i in range(smalldim): smallmatrixline=[] ii = small_indices[i] for j in range(smalldim): jj = small_indices[j] smallmatrixline.append(matrix[ii][jj]) smallmatrix.append(smallmatrixline) smalleigenbasis = get_small_eigenbasis(smallmatrix) for i in range(smalldim): ii = small_indices[i] for j in range(smalldim): jj = small_indices[j] bigeigenbasis[ii][jj] = smalleigenbasis[i][j] else: for small_index in small_indices: bigeigenbases[small_index][small_index ] = 1.+0.j try: bigeigenbases[small_index][small_index+1] = -.5+0.j except IndexError: pass try: bigeigenbases[small_index][small_index-1] = -.5+0.j except IndexError: pass return bigeigenbasis
def getMassDependentIntegrals(wave1, wave2, mMin, mMax, intDir, binWidth = 0.01): """ Retruns the mass-dependence of the off diagonal integrals for wave1 and wave2 """ nBins = int((mMax-mMin)/binWidth) ret=[] res=[] ims=[] for i in range(nBins): integrals = getIntegralMatrixAverage(mMin+i*binWidth,mMin+(i+1)*binWidth,intDir, True, False) n1 = integrals[0][wave1] n2 = integrals[0][wave2] integral = integrals[1][n1][n2] ret.append([mMin+(i+.5)*binWidth,abs(integral)]) res.append([mMin+(i+.5)*binWidth,integral.real]) ims.append([mMin+(i+.5)*binWidth,integral.imag]) return ret,res,ims
def getMassDependentIntegrals(wave1, wave2, mMin, mMax, intDir, binWidth=0.01):
"""
Retruns the mass-dependence of the off diagonal integrals for wave1 and wave2
"""
nBins = int((mMax - mMin) / binWidth)
ret = []
res = []
ims = []
for i in range(nBins):
integrals = getIntegralMatrixAverage(mMin + i * binWidth,
mMin + (i + 1) * binWidth, intDir,
True, False)
n1 = integrals[0][wave1]
n2 = integrals[0][wave2]
integral = integrals[1][n1][n2]
ret.append([mMin + (i + .5) * binWidth, abs(integral)])
res.append([mMin + (i + .5) * binWidth, integral.real])
ims.append([mMin + (i + .5) * binWidth, integral.imag])
return ret, res, ims
def get_bad_vector(base1, base2, mMin, mMax, intDir):
"""
Gets the 'bad' vector for the two de-siobarred waves
"""
integrals = getIntegralMatrixAverage(mMin, mMax, intDir, False, False)
nwaves1 = []
nwaves2 = []
binning1 = []
binning2 = []
for key in integrals[0].iterkeys():
if key.startswith(base1):
nwaves1.append(integrals[0][key])
mup = float(key.split('_')[2][:4]) / 1000
mlo = float(key.split('_')[1]) / 1000
if not mup in binning1:
binning1.append(mup)
if not mlo in binning1:
binning1.append(mlo)
if key.startswith(base2):
nwaves2.append(integrals[0][key])
mup = float(key.split('_')[2][:4]) / 1000
mlo = float(key.split('_')[1]) / 1000
if not mup in binning2:
binning2.append(mup)
if not mlo in binning2:
binning2.append(mlo)
binning1.sort()
binning2.sort()
nwaves1.sort()
nwaves2.sort()
excl_bin = 0
dim1 = len(nwaves1)
dim2 = len(nwaves2) - excl_bin
DIM = dim1 + dim2
aa = np.zeros((DIM, DIM), dtype=np.complex128)
for i in range(dim1):
for j in range(dim1):
aa[i, j] = integrals[1][nwaves1[i]][nwaves1[j]]
for i in range(dim1):
for j in range(dim2):
aa[i, j + dim1] = integrals[1][nwaves1[i]][nwaves2[excl_bin + j]]
aa[j + dim1, i] = integrals[1][nwaves2[excl_bin + j]][nwaves1[i]]
for i in range(dim2):
for j in range(dim2):
aa[i + dim1,
j + dim1] = integrals[1][nwaves2[excl_bin +
i]][nwaves2[excl_bin + j]]
for i in range(DIM):
for j in range(DIM):
if i != j and not (aa[i, i] * aa[j, j]) == 0. + 0.j:
aa[i, j] /= (aa[i, i] * aa[j, j])**.5
elif i != j:
aa[i, j] = 0. + 0.j
for i in range(DIM):
if aa[i, i] != 0. + 0.j:
aa[i, i] = 1. + 0.j
val, vec = la.eig(aa)
vec = np.transpose(vec)
mini = 0
maxi = 0
minev = abs(val[0])
maxev = abs(val[0])
valls = [abs(val[i]) for i in range(DIM)]
valls.sort()
outfile = open("GRAM_EV.txt", 'w')
for i in range(DIM):
outfile.write(str(i) + ' ' + str(valls[i]) + '\n')
outfile.close()
for i in range(DIM):
# print ":::",i,abs(val[i]),minev,maxev
if abs(val[i]) < minev and not abs(val[i]) == 0.:
mini = i
minev = abs(val[i])
if abs(val[i]) > maxev:
maxi = i
maxev = abs(val[i])
minval = val[mini].real
# print '------------------------------------------'
# print minev,maxev,mini,maxi
# print '------------------------------------------'
# print vec[mini]
# print
# print vec[maxi]
# with open("GRAM_VEC",'w') as outfile:
# for i in range(DIM):
# outfile.write(str(i)+' '+str(vec[mini][i].real)+' '+str(vec[maxi][i].real)+'\n')
minvec = vector([vec[mini][i].real for i in range(DIM)])
maxvec = vector([vec[maxi][i].real for i in range(DIM)])
# return minvec, maxvec
with open('minvec', 'w') as minnn:
for val in minvec:
minnn.write(str(val) + ' ')
with open('diag_f0', 'w') as diagout:
for nnn in nwaves1:
diagout.write(str(integrals[1][nnn][nnn].real) + ' ')
with open('diag_rho', 'w') as diagout:
for nnn in nwaves2:
diagout.write(str(integrals[1][nnn][nnn].real) + ' ')
return minval
def compare_diag_offdiag(wave1, wave2, base1, base2, mMin, mMax, intDir):
"""
Compares de-isobarred and constant isobar integrals
"""
integrals = getIntegralMatrixAverage(mMin, mMax, intDir, False, False)
nwaves1 = []
nwaves2 = []
binning1 = []
binning2 = []
for key in integrals[0].iterkeys():
print key, wave1, wave2
if key.startswith(base1):
nwaves1.append(integrals[0][key])
mup = float(key.split('_')[2][:4]) / 1000
mlo = float(key.split('_')[1]) / 1000
if not mup in binning1:
binning1.append(mup)
if not mlo in binning1:
binning1.append(mlo)
# print key
if key.startswith(wave1):
nwave1 = integrals[0][key]
print key, '<---- nwave1'
if key.startswith(base2):
nwaves2.append(integrals[0][key])
mup = float(key.split('_')[2][:4]) / 1000
mlo = float(key.split('_')[1]) / 1000
if not mup in binning2:
binning2.append(mup)
if not mlo in binning2:
binning2.append(mlo)
# print key
if key.startswith(wave2):
nwave2 = integrals[0][key]
print key, '<---- nwave2'
binning1.sort()
binning2.sort()
nwaves1.sort()
nwaves2.sort()
diag1 = integrals[1][nwave1][nwave1]
diag2 = integrals[1][nwave2][nwave2]
offd = integrals[1][nwave1][nwave2]
diag1_c = 0.
for nn in nwaves1:
for mm in nwaves1:
diag1_c += integrals[1][nn][mm]
diag2_c = 0.
for nn in nwaves2:
for mm in nwaves2:
diag2_c += integrals[1][nn][mm]
offd_c = 0.
for nn in nwaves1:
for mm in nwaves2:
offd_c += integrals[1][nn][mm]
# print diag1, diag1_c
# print diag2, diag2_c
# print offd, offd_c
# print offd/(diag1*diag2)**.5
# print binning1,binning2
binning1 = numpy.asarray(binning1, dtype=numpy.float64)
binning2 = numpy.asarray(binning2, dtype=numpy.float64)
hist = TH2D('2d', '2d',
len(binning1) - 1, binning1,
len(binning2) - 1, binning2)
mabiwi1 = 0.
for i in range(len(binning1) - 1):
biwi = binning1[i + 1] - binning1[i]
if biwi > mabiwi1:
mabiwi1 = biwi
mabiwi2 = 0.
for i in range(len(binning2) - 1):
biwi = binning2[i + 1] - binning2[i]
if biwi > mabiwi2:
mabiwi2 = biwi
print mabiwi1, mabiwi2
iinntt = 0.
for i in range(len(nwaves1)):
n = nwaves1[i]
bw1 = (binning1[i + 1] - binning1[i]) / mabiwi1
for j in range(len(nwaves2)):
bw2 = (binning2[j + 1] - binning2[j]) / mabiwi2
m = nwaves2[j]
integral_value = abs(integrals[1][n][m] / (diag1 * diag2)**.5)
hist.SetBinContent(i + 1, j + 2, integral_value / bw1 / bw2)
iinntt += integral_value
print 'iinntt: ', iinntt
return hist
def getData(path,compares,WRITE = False):
"""calculates the amplitudes with the correct coefficients for the unphysical modes"""
intFolder = path.replace("/fit/","/integrals/").split("text_fit_")[0]
bestLikeFile = getBestLike(path)
rawData = readTextFile(path+'/'+bestLikeFile)
comparedata = []
for comparepath in compares:
comparedata.append(readTextFile(comparepath+'/'+getBestLike(comparepath)))
mMin = rawData[0]["m3Pi"][0]
mMax = rawData[0]["m3Pi"][1]
rawIntegrals = getIntegralMatrixAverage(mMin,mMax,intFolder,False,False)
isoBases = getDeIsobarredList(rawData)
for key in isoBases.iterkeys(): # Integral indices are usually the same as fit indices, but keep them anyway to avoid later errors
for ppt in isoBases[key]:
ppt.append(rawIntegrals[0][ppt[2]])
keyList = []
for key in isoBases.iterkeys():
keyList.append(key)
if len(keyList) < 2: # If only one wave is de-isobarred, no additional comparefile is needed. To avoid the exception at (*), use the original data as compares
comparedata.append(rawData)
if len(keyList) == 0:
return {}
oneRow=[]
starts=[]
for key in keyList:
starts.append(len(oneRow))
for ppt in isoBases[key]:
oneRow.append(ppt[:])
wave = ppt[2]
for com in comparedata:
try:
aa = com[0][wave]
oneRow[-1].append(aa[0])
oneRow[-1].append(aa[1])
break
except KeyError:
pass
if not len(oneRow[-1]) == 9:
raise IndexError # No compare-vector found. (*)
# print oneRow[-1]
goodMatrix=[]
dim = len(oneRow)
rowResult=[complex(ppt[3],ppt[4])for ppt in oneRow]
comResult=[complex(ppt[7],ppt[8])for ppt in oneRow]
for i in range(len(oneRow)):
goodMatrix.append([0.+0.j]*len(oneRow))
normalizers = []
global_int_norm = 0.
GLOBAL_NORM = False
for i in range(len(oneRow)):
try:
normalizers.append(1./rawIntegrals[1][oneRow[i][6]][oneRow[i][6]])
except ZeroDivisionError:
normalizers.append(0.+0.j)
for j in range(len(oneRow)):
try:
if not GLOBAL_NORM:
goodMatrix[i][j] = rawIntegrals[1][oneRow[i][6]][oneRow[j][6]]/(rawIntegrals[1][oneRow[i][6]][oneRow[i][6]]*rawIntegrals[1][oneRow[j][6]][oneRow[j][6]])**.5
else:
goodMatrix[i][j] = rawIntegrals[1][oneRow[i][6]][oneRow[j][6]]
except ZeroDivisionError:
goodMatrix[i][j] = 0.+0.j
if GLOBAL_NORM:
global_int_norm = 0.
for i in range(len(goodMatrix)):
global_int_norm+=goodMatrix[i][i]
for i in range(len(goodMatrix)):
for j in range(len(goodMatrix)):
goodMatrix[i][j]/=global_int_norm
eigen = eigenBasis(goodMatrix)
coeff = decompose(rowResult,eigen)
normTo = 0 # number of de-isobarred wave to normalize
iiii = []
vecs = []
DONE = True
valolo=[]
for i in range(dim):
# print coeff[i],
valolo.append(eigen[i][0].real)
if eigen[i][0] < 1E-2 and not eigen[i][0] == 0.:
print eigen[i][0]
# print "<<<"
ccc0 = eigen[i][1][starts[normTo]]
rrr0 = rowResult[starts[normTo]]
# print rrr0/ccc0,"<<<<<<<<<<<<<<<<<<<<<<<<<<<"
if not DONE:
print i,'>>>>(',(rrr0/ccc0).real,',',(rrr0/ccc0).imag,')<<<<',i
coeff[i] -= rrr0/ccc0
DONE = True
vecs.append(eigen[i][1][:])
iiii.append(i)
else:
pass
# print " "
# with open("./de_isobarred_corrected/vec",'w') as outoutout:
# for i in range(dim):
# outoutout.write(str(i))
# for j in range(len(vecs)):
# outoutout.write(' '+str(vecs[j][i].real))
# outoutout.write('\n')
if not len(vecs)==0:
coeffs = get_physical_coefficients_intens(rowResult, comResult, vecs, starts)
print coeffs
# coaff = [complex(0.,0.)]*len(coeff)
# coeff[2] -= complex(0.532412199699,0.33230841749)
# coeff[2] -= complex(0.596338098319,0.372208168472)
for i in range(len(iiii)):
coeff[iiii[i]]-=coeffs[i]
reconstruct = [0.+0.j]*dim
for i in range(dim):
for j in range(dim):
reconstruct[i]+=coeff[j]*eigen[j][1][i]
if WRITE:
with open("./de_isobarred_corrected/no_modes",'w') as outoutout:
for i in range(dim):
outoutout.write(str(i)+' '+str((abs(rowResult[i])**2*normalizers[i]).real)+' '+str((abs(reconstruct[i])**2*normalizers[i]).real)+' '+str(abs(comResult[i])**2*normalizers[i].real) +'\n')
# valolo.sort()
# with open("de_isobarred_corrected/val",'w') as outoutout:
# for i in range(len(valolo)):
# outoutout.write(str(i)+' '+str(valolo[i])+'\n')
# print starts
#starts, comResult, keyList
mapResult = {}
actNkey = -1
for i in range(dim):
comResult[i]*=normalizers[i].real**.5
for i in range(len(comResult)):
if i in starts:
actNkey+=1
actKey = keyList[actNkey]
mapResult[actKey] = []
mapResult[actKey].append(comResult[i])
# print mapResult
return mapResult
def compare_diag_offdiag(wave1,wave2,base1,base2,mMin,mMax,intDir):
"""
Compares de-isobarred and constant isobar integrals
"""
integrals = getIntegralMatrixAverage(mMin,mMax,intDir,False, False)
nwaves1 = []
nwaves2 = []
binning1=[]
binning2=[]
for key in integrals[0].iterkeys():
print key, wave1, wave2
if key.startswith(base1):
nwaves1.append(integrals[0][key])
mup = float(key.split('_')[2][:4])/1000
mlo = float(key.split('_')[1])/1000
if not mup in binning1:
binning1.append(mup)
if not mlo in binning1:
binning1.append(mlo)
# print key
if key.startswith(wave1):
nwave1 = integrals[0][key]
print key,'<---- nwave1'
if key.startswith(base2):
nwaves2.append(integrals[0][key])
mup = float(key.split('_')[2][:4])/1000
mlo = float(key.split('_')[1])/1000
if not mup in binning2:
binning2.append(mup)
if not mlo in binning2:
binning2.append(mlo)
# print key
if key.startswith(wave2):
nwave2 = integrals[0][key]
print key,'<---- nwave2'
binning1.sort()
binning2.sort()
nwaves1.sort()
nwaves2.sort()
diag1 = integrals[1][nwave1][nwave1]
diag2 = integrals[1][nwave2][nwave2]
offd = integrals[1][nwave1][nwave2]
diag1_c = 0.
for nn in nwaves1:
for mm in nwaves1:
diag1_c+=integrals[1][nn][mm]
diag2_c = 0.
for nn in nwaves2:
for mm in nwaves2:
diag2_c+=integrals[1][nn][mm]
offd_c = 0.
for nn in nwaves1:
for mm in nwaves2:
offd_c+=integrals[1][nn][mm]
# print diag1, diag1_c
# print diag2, diag2_c
# print offd, offd_c
# print offd/(diag1*diag2)**.5
# print binning1,binning2
binning1 = numpy.asarray(binning1,dtype=numpy.float64)
binning2 = numpy.asarray(binning2,dtype=numpy.float64)
hist= TH2D('2d','2d',len(binning1)-1,binning1, len(binning2)-1, binning2)
mabiwi1 = 0.
for i in range(len(binning1)-1):
biwi = binning1[i+1]-binning1[i]
if biwi > mabiwi1:
mabiwi1 = biwi
mabiwi2 = 0.
for i in range(len(binning2)-1):
biwi = binning2[i+1]-binning2[i]
if biwi > mabiwi2:
mabiwi2=biwi
print mabiwi1,mabiwi2
iinntt=0.
for i in range(len(nwaves1)):
n = nwaves1[i]
bw1 = (binning1[i+1]-binning1[i])/mabiwi1
for j in range(len(nwaves2)):
bw2 = (binning2[j+1]-binning2[j])/mabiwi2
m = nwaves2[j]
integral_value = abs(integrals[1][n][m]/(diag1*diag2)**.5)
hist.SetBinContent(i+1,j+2,integral_value/bw1/bw2)
iinntt += integral_value
print 'iinntt: ',iinntt
return hist
def get_bad_vector(base1,base2,mMin,mMax,intDir):
"""
Gets the 'bad' vector for the two de-siobarred waves
"""
integrals = getIntegralMatrixAverage(mMin,mMax,intDir,False, False)
nwaves1 = []
nwaves2 = []
binning1=[]
binning2=[]
for key in integrals[0].iterkeys():
if key.startswith(base1):
nwaves1.append(integrals[0][key])
mup = float(key.split('_')[2][:4])/1000
mlo = float(key.split('_')[1])/1000
if not mup in binning1:
binning1.append(mup)
if not mlo in binning1:
binning1.append(mlo)
if key.startswith(base2):
nwaves2.append(integrals[0][key])
mup = float(key.split('_')[2][:4])/1000
mlo = float(key.split('_')[1])/1000
if not mup in binning2:
binning2.append(mup)
if not mlo in binning2:
binning2.append(mlo)
binning1.sort()
binning2.sort()
nwaves1.sort()
nwaves2.sort()
excl_bin=0
dim1 = len(nwaves1)
dim2 = len(nwaves2)-excl_bin
DIM = dim1 + dim2
aa = np.zeros((DIM,DIM),dtype = np.complex128)
for i in range(dim1):
for j in range(dim1):
aa[i,j] = integrals[1][nwaves1[i]][nwaves1[j]]
for i in range(dim1):
for j in range(dim2):
aa[i,j+dim1] = integrals[1][nwaves1[i]][nwaves2[excl_bin+j]]
aa[j+dim1,i] = integrals[1][nwaves2[excl_bin+j]][nwaves1[i]]
for i in range(dim2):
for j in range(dim2):
aa[i+dim1,j+dim1] = integrals[1][nwaves2[excl_bin+i]][nwaves2[excl_bin+j]]
for i in range(DIM):
for j in range(DIM):
if i!=j and not (aa[i,i]*aa[j,j]) == 0.+0.j:
aa[i,j]/=(aa[i,i]*aa[j,j])**.5
elif i!= j:
aa[i,j] = 0.+0.j
for i in range(DIM):
if aa[i,i] != 0.+0.j:
aa[i,i]=1.+0.j
val,vec = la.eig(aa)
vec = np.transpose(vec)
mini = 0
maxi = 0
minev = abs(val[0])
maxev = abs(val[0])
valls = [abs(val[i]) for i in range(DIM)]
valls.sort()
outfile = open("GRAM_EV.txt",'w')
for i in range(DIM):
outfile.write(str(i)+' '+str(valls[i])+'\n')
outfile.close()
for i in range(DIM):
# print ":::",i,abs(val[i]),minev,maxev
if abs(val[i]) < minev and not abs(val[i]) == 0.:
mini = i
minev = abs(val[i])
if abs(val[i]) > maxev:
maxi = i
maxev = abs(val[i])
minval = val[mini].real
# print '------------------------------------------'
# print minev,maxev,mini,maxi
# print '------------------------------------------'
# print vec[mini]
# print
# print vec[maxi]
# with open("GRAM_VEC",'w') as outfile:
# for i in range(DIM):
# outfile.write(str(i)+' '+str(vec[mini][i].real)+' '+str(vec[maxi][i].real)+'\n')
minvec = vector([vec[mini][i].real for i in range(DIM)])
maxvec = vector([vec[maxi][i].real for i in range(DIM)])
# return minvec, maxvec
with open('minvec','w') as minnn:
for val in minvec:
minnn.write(str(val)+' ')
with open('diag_f0','w') as diagout:
for nnn in nwaves1:
diagout.write(str(integrals[1][nnn][nnn].real)+' ')
with open('diag_rho','w') as diagout:
for nnn in nwaves2:
diagout.write(str(integrals[1][nnn][nnn].real)+' ')
return minval
def getData(path, compares, WRITE=False):
"""calculates the amplitudes with the correct coefficients for the unphysical modes"""
intFolder = path.replace("/fit/", "/integrals/").split("text_fit_")[0]
bestLikeFile = getBestLike(path)
rawData = readTextFile(path + '/' + bestLikeFile)
comparedata = []
for comparepath in compares:
comparedata.append(
readTextFile(comparepath + '/' + getBestLike(comparepath)))
mMin = rawData[0]["m3Pi"][0]
mMax = rawData[0]["m3Pi"][1]
rawIntegrals = getIntegralMatrixAverage(mMin, mMax, intFolder, False,
False)
isoBases = getDeIsobarredList(rawData)
for key in isoBases.iterkeys(
): # Integral indices are usually the same as fit indices, but keep them anyway to avoid later errors
for ppt in isoBases[key]:
ppt.append(rawIntegrals[0][ppt[2]])
keyList = []
for key in isoBases.iterkeys():
keyList.append(key)
if len(
keyList
) < 2: # If only one wave is de-isobarred, no additional comparefile is needed. To avoid the exception at (*), use the original data as compares
comparedata.append(rawData)
if len(keyList) == 0:
return {}
oneRow = []
starts = []
for key in keyList:
starts.append(len(oneRow))
for ppt in isoBases[key]:
oneRow.append(ppt[:])
wave = ppt[2]
for com in comparedata:
try:
aa = com[0][wave]
oneRow[-1].append(aa[0])
oneRow[-1].append(aa[1])
break
except KeyError:
pass
if not len(oneRow[-1]) == 9:
raise IndexError # No compare-vector found. (*)
# print oneRow[-1]
goodMatrix = []
dim = len(oneRow)
rowResult = [complex(ppt[3], ppt[4]) for ppt in oneRow]
comResult = [complex(ppt[7], ppt[8]) for ppt in oneRow]
for i in range(len(oneRow)):
goodMatrix.append([0. + 0.j] * len(oneRow))
normalizers = []
global_int_norm = 0.
GLOBAL_NORM = False
for i in range(len(oneRow)):
try:
normalizers.append(1. /
rawIntegrals[1][oneRow[i][6]][oneRow[i][6]])
except ZeroDivisionError:
normalizers.append(0. + 0.j)
for j in range(len(oneRow)):
try:
if not GLOBAL_NORM:
goodMatrix[i][j] = rawIntegrals[1][oneRow[i][6]][oneRow[j][
6]] / (rawIntegrals[1][oneRow[i][6]][oneRow[i][6]] *
rawIntegrals[1][oneRow[j][6]][oneRow[j][6]])**.5
else:
goodMatrix[i][j] = rawIntegrals[1][oneRow[i][6]][oneRow[j]
[6]]
except ZeroDivisionError:
goodMatrix[i][j] = 0. + 0.j
if GLOBAL_NORM:
global_int_norm = 0.
for i in range(len(goodMatrix)):
global_int_norm += goodMatrix[i][i]
for i in range(len(goodMatrix)):
for j in range(len(goodMatrix)):
goodMatrix[i][j] /= global_int_norm
eigen = eigenBasis(goodMatrix)
coeff = decompose(rowResult, eigen)
normTo = 0 # number of de-isobarred wave to normalize
iiii = []
vecs = []
DONE = True
valolo = []
for i in range(dim):
# print coeff[i],
valolo.append(eigen[i][0].real)
if eigen[i][0] < 1E-2 and not eigen[i][0] == 0.:
print eigen[i][0]
# print "<<<"
ccc0 = eigen[i][1][starts[normTo]]
rrr0 = rowResult[starts[normTo]]
# print rrr0/ccc0,"<<<<<<<<<<<<<<<<<<<<<<<<<<<"
if not DONE:
print i, '>>>>(', (rrr0 /
ccc0).real, ',', (rrr0 /
ccc0).imag, ')<<<<', i
coeff[i] -= rrr0 / ccc0
DONE = True
vecs.append(eigen[i][1][:])
iiii.append(i)
else:
pass
# print " "
# with open("./de_isobarred_corrected/vec",'w') as outoutout:
# for i in range(dim):
# outoutout.write(str(i))
# for j in range(len(vecs)):
# outoutout.write(' '+str(vecs[j][i].real))
# outoutout.write('\n')
if not len(vecs) == 0:
coeffs = get_physical_coefficients_intens(rowResult, comResult, vecs,
starts)
print coeffs
# coaff = [complex(0.,0.)]*len(coeff)
# coeff[2] -= complex(0.532412199699,0.33230841749)
# coeff[2] -= complex(0.596338098319,0.372208168472)
for i in range(len(iiii)):
coeff[iiii[i]] -= coeffs[i]
reconstruct = [0. + 0.j] * dim
for i in range(dim):
for j in range(dim):
reconstruct[i] += coeff[j] * eigen[j][1][i]
if WRITE:
with open("./de_isobarred_corrected/no_modes", 'w') as outoutout:
for i in range(dim):
outoutout.write(
str(i) + ' ' +
str((abs(rowResult[i])**2 * normalizers[i]).real) + ' ' +
str((abs(reconstruct[i])**2 * normalizers[i]).real) + ' ' +
str(abs(comResult[i])**2 * normalizers[i].real) + '\n')
# valolo.sort()
# with open("de_isobarred_corrected/val",'w') as outoutout:
# for i in range(len(valolo)):
# outoutout.write(str(i)+' '+str(valolo[i])+'\n')
# print starts
#starts, comResult, keyList
mapResult = {}
actNkey = -1
for i in range(dim):
comResult[i] *= normalizers[i].real**.5
for i in range(len(comResult)):
if i in starts:
actNkey += 1
actKey = keyList[actNkey]
mapResult[actKey] = []
mapResult[actKey].append(comResult[i])
# print mapResult
return mapResult