def getVectorEst(type,pimc,outName,reduceFlag,xlab,ylab, skip=0):
''' Return the arrays consisting of the reduec averaged vector
estimators. '''
fileNames = pimc.getFileList(type)
try:
headers = pimchelp.getHeadersFromFile(fileNames[0])
numParams = len(fileNames)
Nx = len(headers)
x = zeros([numParams,Nx],float)
ave = zeros([numParams,Nx],float)
err = zeros([numParams,Nx],float)
for i,fname in enumerate(fileNames):
# Get the estimator data and compute averages
data = loadtxt(fname,ndmin=2)[skip:,:]
ave[i,:],err[i,:] = getStats(data)
# get the headers
x[i,:] = pimchelp.getHeadersFromFile(fname)
# Compute the normalized averages and error for the OBDM
if type == 'obdm':
norm = ave[i,0]
ave[i,:] /= norm
err[i,:] /= norm
# output the vector data to disk
outFile = open('%s-%s' % (type,outName),'w');
# the headers
lab = '%s = %4.2f' % (reduceFlag[0],float(pimc.params[pimc.id[0]][reduceFlag[1]])) # error is happening here. 'List index out of range' cuz id is empty
outFile.write('#%15s%16s%16s' % ('',lab,''))
for j in range(numParams-1):
lab = '%s = %4.2f' % (reduceFlag[0],float(pimc.params[pimc.id[j+1]][reduceFlag[1]]))
outFile.write('%16s%16s%16s' % ('',lab,''))
outFile.write('\n')
outFile.write('#%15s%16s%16s' % (xlab,ylab,'+/-'))
for j in range(numParams-1):
outFile.write('%16s%16s%16s' % (xlab,ylab,'+/-'))
outFile.write('\n')
# the data
for i,h in enumerate(headers):
for j in range(numParams):
outFile.write('%16.8E%16.8E%16.8E' % (x[j,i],ave[j,i],err[j,i]))
outFile.write('\n')
outFile.close()
return x,ave,err
except:
print 'Problem Reducing %s files' % type
return 0,0,0
def getKappa(pimc,outName,reduceFlag,skip=0):
''' Return the arrays containing the reduced averaged compressibility. '''
fileNames = pimc.getFileList('estimator')
headers = pimchelp.getHeadersFromFile(fileNames[0])
aveKappa = zeros([len(fileNames)],float)
errKappa = zeros([len(fileNames)],float)
for i,fname in enumerate(fileNames):
# Now get the temperature, volume and linear system size
ID = pimc.getID(fname)
T = float(pimc.params[ID]['Temperature'])
# We need to get the correct volume, depending on whether or not we are
# looking at the core
if len(glob.glob('../CYLINDER')) > 0:
V = pi*(1.75)**2*float(pimc.params[ID]['Container Length'])
else:
V = float(pimc.params[ID]['Container Volume'])
# Compute the average compressibility and its error
estData = loadtxt(fname,ndmin=2)
N = estData[:,headers.index('N')]
N2 = estData[:,headers.index('N^2')]
N3 = N*N2
numBins = len(N)
# Get the averages
aveN,errN = getStats(N)
aveN2,errN2 = getStats(N2)
aveNN2,errNN2 = getStats(N3)
# Get the covariance
# This is finite, so it must be calculated!
covNN2 = (aveNN2 - aveN*aveN2)/(1.0*numBins-1.0)
# Get the value of rho^2 * kappa and the error
aveKappa[i] = (aveN2-aveN**2)/(T*V)
errKappa[i] = sqrt(errN2**2 + 4.0*errN**2*aveN**2 - 4.0*aveN*covNN2)/(T*V)
# output the estimator data to disk
outFile = open('%s-%s' % ('kappa',outName),'w');
# the headers
outFile.write('#%15s' % reduceFlag[0])
outFile.write('%16s%16s' % ('kappa','+/-'))
outFile.write('\n')
# the data
for i in range(len(fileNames)):
outFile.write('%16.8E' % float(pimc.params[pimc.id[i]][reduceFlag[1]]))
outFile.write('%16.8E%16.8E\n' % (aveKappa[i],errKappa[i]))
outFile.close()
return aveKappa,errKappa
def getScalarEst(type,pimc,outName,reduceFlag, skip=0):
''' Return the arrays containing the reduced averaged scalar
estimators in question.'''
fileNames = pimc.getFileList(type)
headers = pimchelp.getHeadersFromFile(fileNames[0])
ave = zeros([len(fileNames),len(headers)],float)
err = zeros([len(fileNames),len(headers)],float)
for i,fname in enumerate(fileNames):
# Compute the averages and error
data = loadtxt(fname,ndmin=2)[skip:,:]
ave[i,:],err[i,:] = getStats(data)
# compute single centroid virial specific heat if possible
# if 'dEdB' in headers:
# Cv = ave[:,headers.index('EEcv*Beta^2')] - ave[:,headers.index('Ecv*Beta')]**2 - ave[:,headers.index('dEdB')]
# aveNew = zeros([len(fileNames),len(headers)+1],float)
# errNew = zeros([len(fileNames),len(headers)+1],float)
# for i,a in enumerate(ave):
# a = append(a, ave[:,headers.index('EEcv*Beta^2')][i] \
# - ave[:,headers.index('Ecv*Beta')][i]**2 \
# - ave[:,headers.index('dEdB')][i])
# aveNew[i] = a
# for i, e in enumerate(err):
# e = append(e, err[:,headers.index('EEcv*Beta^2')][i] \
# - err[:,headers.index('Ecv*Beta')][i]**2 \
# - err[:,headers.index('dEdB')][i])
# errNew[i] = e
# headers.append('Cv')
# ave = aveNew
# err = errNew
# output the estimator data to disk
outFile = open('%s-%s' % (type,outName),'w');
# the headers
outFile.write('#%15s' % reduceFlag[0])
for head in headers:
outFile.write('%16s%16s' % (head,'+/-'))
outFile.write('\n')
# the data
for i,f in enumerate(fileNames):
outFile.write('%16.8E' % float(pimc.params[pimc.id[i]][reduceFlag[1]]))
for j,h in enumerate(headers):
outFile.write('%16.8E%16.8E' % (ave[i,j],err[i,j]))
outFile.write('\n')
outFile.close()
return headers,ave,err;
def getScalarEst(type,pimc,outName,reduceFlag, skip=0):
''' Return the arrays containing the reduced averaged scalar
estimators in question.'''
# Get the files corresponding to this particular estimator
fileNames = pimc.getFileList(type)
# Grab the headers from the data files
for i in range(len(fileNames)):
fileNames[i]=pimc.baseDir+fileNames[i]
headers = pimchelp.getHeadersFromFile(fileNames[0])
# If the last header is "bins", we are dealing with a MERGED file and must
# handle things appropriately
if headers[-1] == 'bins':
merged = True
# Remove the bins header at the end because it is unecessary in a reduced file
headers.pop(-1)
# Remove all the "variance" headers because these are also unneccessary
headers = filter(lambda string: string != "variance", headers)
else:
merged = False
ave = np.zeros([len(fileNames),len(headers)],float)
err = np.zeros([len(fileNames),len(headers)],float)
if merged:
# Compute the averages and error using appropriate weighting
for i,fname in enumerate(fileNames):
# Grab the file data
filedata = np.loadtxt(fname,ndmin=2,skiprows=skip+2) # Skip number of bins user specified +2 for the two comment rows
# All the data is in the first through 2nd to last columns for a merged file
data = np.array_split(filedata,[filedata.shape[1]-1,filedata.shape[1]],axis=1)[0]
# The weights are the last column
weights = np.array_split(filedata,[filedata.shape[1]-1,filedata.shape[1]],axis=1)[1]
# Pass getStats the data and weights and get the averages and errors
ave[i,:],err[i,:] = getScalarStats(data,weights,merged)
else:
# Compute the averages and errors as usual
for i,fname in enumerate(fileNames):
# Grab the data
data = np.loadtxt(fname,ndmin=2,skiprows=skip+2) # Skip number of bins user specified +2 for the two comment rows
# The file isn't a merged file so give np.average None for weights in getStats and all weights will be 1
weights = None
ave[i,:],err[i,:] = getScalarStats(data,weights,merged)
# output the estimator data to disk
outFile = open(pimc.baseDir+'%s-%s' % (type,outName),'w');
# the headers
outFile.write('#%15s' % reduceFlag[0])
for head in headers:
outFile.write('%16s%16s' % (head,'+/-'))
outFile.write('\n')
# the data
for i,f in enumerate(fileNames):
outFile.write('%16.8E' % float(pimc.params[pimc.id[i]][reduceFlag[1]]))
for j,h in enumerate(headers):
outFile.write('%16.8E%16.8E' % (ave[i,j],err[i,j]))
outFile.write('\n')
outFile.close()
return headers,ave,err;
def getVectorEst(type,pimc,outName,reduceFlag,xlab,ylab, skip=0):
''' Return the arrays consisting of the reduec averaged vector
estimators. '''
fileNames = pimc.getFileList(type)
try:
# Grab the headers from the data files
for i in range(len(fileNames)):
fileNames[i]=pimc.baseDir+fileNames[i]
headers = pimchelp.getHeadersFromFile(fileNames[0])
# If the last header is "bins", we are dealing with a MERGED file and must
# handle things appropriately
if headers[-1] == 'bins':
merged = True
# Remove the bins header at the end because it is unecessary in a reduced file
headers.pop(-1)
else:
merged = False
numParams = len(fileNames)
Nx = len(headers)
x = np.zeros([numParams,Nx],float)
ave = np.zeros([numParams,Nx],float)
err = np.zeros([numParams,Nx],float)
if merged:
# Compute the averages and errors using appropriate weighting
for i,fname in enumerate(fileNames):
# Grab the file data
filedata = np.loadtxt(fname,ndmin=2,skiprows=skip+2) # Skip number of bins user specified +2 for the two column rows
# All the data is in the first through 2nd to last columns for a merged file
data = np.array_split(filedata,[filedata.shape[1]-1,filedata.shape[1]],axis=1)[0]
# The weights are the last column
weights = np.array_split(filedata,[filedata.shape[1]-1,filedata.shape[1]],axis=1)[1]
# Pass getStats the data and weights and get the averages and errors
ave[i,:],err[i,:] = getVectorStats(data,weights,merged)
# get the headers
headers2 = pimchelp.getHeadersFromFile(fname)
headers2.pop(-1)
x[i,:] = headers2
# Compute the normalized averages and error for the OBDM
if type == 'obdm':
norm = ave[i,0]
ave[i,:] /= norm
err[i,:] /= norm
else:
# Compute averages and errors as usual
data = np.loadtxt(fname,ndmin=2)[skip:,:]
ave[i,:],err[i,:] = getVectorStats(data)
# get the headers
x[i,:] = pimchelp.getHeadersFromFile(fname)
# Compute the normalized averages and error for the OBDM
if type == 'obdm':
norm = ave[i,0]
ave[i,:] /= norm
err[i,:] /= norm
# output the vector data to disk
outFile = open(pimc.baseDir+'%s-%s' % (type,outName),'w');
# the headers
lab = '%s = %4.2f' % (reduceFlag[0],float(pimc.params[pimc.id[0]][reduceFlag[1]])) # error is happening here. 'List index out of range' cuz id is empty
outFile.write('#%15s%16s%16s' % ('',lab,''))
for j in range(numParams-1):
lab = '%s = %4.2f' % (reduceFlag[0],float(pimc.params[pimc.id[j+1]][reduceFlag[1]]))
outFile.write('%16s%16s%16s' % ('',lab,''))
outFile.write('\n')
outFile.write('#%15s%16s%16s' % (xlab,ylab,'+/-'))
for j in range(numParams-1):
outFile.write('%16s%16s%16s' % (xlab,ylab,'+/-'))
outFile.write('\n')
# the data
for i,h in enumerate(headers):
for j in range(numParams):
outFile.write('%16.8E%16.8E%16.8E' % (x[j,i],ave[j,i],err[j,i]))
outFile.write('\n')
outFile.close()
return x,ave,err
except:
print 'Problem Reducing %s files' % type
return 0,0,0
def getScalarEst(type, pimc, outName, reduceFlag, skip=0):
''' Return the arrays containing the reduced averaged scalar
estimators in question.'''
# Get the files corresponding to this particular estimator
fileNames = pimc.getFileList(type)
# Grab the headers from the data files
for i in range(len(fileNames)):
fileNames[i] = pimc.baseDir + fileNames[i]
headers = pimchelp.getHeadersFromFile(fileNames[0])
# If the last header is "bins", we are dealing with a MERGED file and must
# handle things appropriately
if headers[-1] == 'bins':
merged = True
# Remove the bins header at the end because it is unecessary in a reduced file
headers.pop(-1)
# Remove all the "variance" headers because these are also unneccessary
headers = filter(lambda string: string != "variance", headers)
else:
merged = False
ave = np.zeros([len(fileNames), len(headers)], float)
err = np.zeros([len(fileNames), len(headers)], float)
if merged:
# Compute the averages and error using appropriate weighting
for i, fname in enumerate(fileNames):
# Grab the file data
filedata = np.loadtxt(
fname, ndmin=2, skiprows=skip + 2
) # Skip number of bins user specified +2 for the two comment rows
# All the data is in the first through 2nd to last columns for a merged file
data = np.array_split(filedata,
[filedata.shape[1] - 1, filedata.shape[1]],
axis=1)[0]
# The weights are the last column
weights = np.array_split(
filedata, [filedata.shape[1] - 1, filedata.shape[1]],
axis=1)[1]
# Pass getStats the data and weights and get the averages and errors
ave[i, :], err[i, :] = getScalarStats(data, weights, merged)
else:
# Compute the averages and errors as usual
for i, fname in enumerate(fileNames):
# Grab the data
data = np.loadtxt(
fname, ndmin=2, skiprows=skip + 2
) # Skip number of bins user specified +2 for the two comment rows
# The file isn't a merged file so give np.average None for weights in getStats and all weights will be 1
weights = None
ave[i, :], err[i, :] = getScalarStats(data, weights, merged)
# output the estimator data to disk
outFile = open(pimc.baseDir + '%s-%s' % (type, outName), 'w')
# the headers
outFile.write('#%15s' % reduceFlag[0])
for head in headers:
outFile.write('%16s%16s' % (head, '+/-'))
outFile.write('\n')
# the data
for i, f in enumerate(fileNames):
outFile.write('%16.8E' % float(pimc.params[pimc.id[i]][reduceFlag[1]]))
for j, h in enumerate(headers):
outFile.write('%16.8E%16.8E' % (ave[i, j], err[i, j]))
outFile.write('\n')
outFile.close()
return headers, ave, err
def getVectorEst(type, pimc, outName, reduceFlag, xlab, ylab, skip=0):
''' Return the arrays consisting of the reduec averaged vector
estimators. '''
fileNames = pimc.getFileList(type)
try:
# Grab the headers from the data files
for i in range(len(fileNames)):
fileNames[i] = pimc.baseDir + fileNames[i]
headers = pimchelp.getHeadersFromFile(fileNames[0])
# If the last header is "bins", we are dealing with a MERGED file and must
# handle things appropriately
if headers[-1] == 'bins':
merged = True
# Remove the bins header at the end because it is unecessary in a reduced file
headers.pop(-1)
else:
merged = False
numParams = len(fileNames)
Nx = len(headers)
x = np.zeros([numParams, Nx], float)
ave = np.zeros([numParams, Nx], float)
err = np.zeros([numParams, Nx], float)
if merged:
# Compute the averages and errors using appropriate weighting
for i, fname in enumerate(fileNames):
# Grab the file data
filedata = np.loadtxt(
fname, ndmin=2, skiprows=skip + 2
) # Skip number of bins user specified +2 for the two column rows
# All the data is in the first through 2nd to last columns for a merged file
data = np.array_split(
filedata, [filedata.shape[1] - 1, filedata.shape[1]],
axis=1)[0]
# The weights are the last column
weights = np.array_split(
filedata, [filedata.shape[1] - 1, filedata.shape[1]],
axis=1)[1]
# Pass getStats the data and weights and get the averages and errors
ave[i, :], err[i, :] = getVectorStats(data, weights, merged)
# get the headers
headers2 = pimchelp.getHeadersFromFile(fname)
headers2.pop(-1)
x[i, :] = headers2
# Compute the normalized averages and error for the OBDM
if type == 'obdm':
norm = ave[i, 0]
ave[i, :] /= norm
err[i, :] /= norm
else:
# Compute averages and errors as usual
data = np.loadtxt(fname, ndmin=2)[skip:, :]
ave[i, :], err[i, :] = getVectorStats(data)
# get the headers
x[i, :] = pimchelp.getHeadersFromFile(fname)
# Compute the normalized averages and error for the OBDM
if type == 'obdm':
norm = ave[i, 0]
ave[i, :] /= norm
err[i, :] /= norm
# output the vector data to disk
outFile = open(pimc.baseDir + '%s-%s' % (type, outName), 'w')
# the headers
lab = '%s = %4.2f' % (
reduceFlag[0], float(pimc.params[pimc.id[0]][reduceFlag[1]])
) # error is happening here. 'List index out of range' cuz id is empty
outFile.write('#%15s%16s%16s' % ('', lab, ''))
for j in range(numParams - 1):
lab = '%s = %4.2f' % (
reduceFlag[0], float(
pimc.params[pimc.id[j + 1]][reduceFlag[1]]))
outFile.write('%16s%16s%16s' % ('', lab, ''))
outFile.write('\n')
outFile.write('#%15s%16s%16s' % (xlab, ylab, '+/-'))
for j in range(numParams - 1):
outFile.write('%16s%16s%16s' % (xlab, ylab, '+/-'))
outFile.write('\n')
# the data
for i, h in enumerate(headers):
for j in range(numParams):
outFile.write('%16.8E%16.8E%16.8E' %
(x[j, i], ave[j, i], err[j, i]))
outFile.write('\n')
outFile.close()
return x, ave, err
except:
print 'Problem Reducing %s files' % type
return 0, 0, 0