corrfitter - Least-Squares Fit to Correlators

Introduction

This module contains tools that facilitate least-squares fits, as functions of time t, of simulation (or other statistical) data for 2-point and 3-point correlators of the form:

Gab(t)    =  <b(t) a(0)>
Gavb(t,T) =  <b(T) V(t) a(0)>

where T > t > 0. Each correlator is modeled using corrfitter.Corr2 for 2-point correlators, or corrfitter.Corr3 for 3-point correlators in terms of amplitudes for each source a, sink b, and vertex V, and the energies associated with each intermediate state. The amplitudes and energies are adjusted in the least-squares fit to reproduce the data; they are defined in a shared prior (typically a dictionary).

An object of type corrfitter.CorrFitter describes a collection of correlators and is used to fit multiple models to data simultaneously. Fitting multiple correlators simultaneously is important if there are statistical correlations between the correlators. Any number of correlators may be described and fit by a single corrfitter.CorrFitter object.

We now review the basic features of corrfitter. These features are also illustrated for real applications in a series of annotated examples following this section. Impatient readers may wish to jump directly to these examples.

About Printing: The examples in this tutorial use the print function as it is used in Python 3. Drop the outermost parenthesis in each print statement if using Python 2; or add

from __future__ import print_function

at the start of your file.

Basic Fits

To illustrate, consider data for two 2-point correlators: Gaa with the same source and sink (a), and Gab which has source a and (different) sink b. The data are contained in a dictionary data, where data['Gaa'] and data['Gab'] are one-dimensional arrays containing values for Gaa(t) and Gab(t), respectively, with t=0,1,2...63. Each array element in data['Gaa'] and data['Gab'] is a Gaussian random variable of type gvar.GVar, and specifies the mean and standard deviation for the corresponding data point:

>>> print(data['Gaa'])
[0.1597910(41) 0.0542088(31) ... ]
>>> print(data['Gab'])
[0.156145(18) 0.102335(15) ... ]

gvar.GVars also capture statistical correlations between different pieces of data, if they exist.

We want to fit this data to the following formulas:

Gaa(t,N) = sum_i=0..N-1  a[i]**2 * exp(-E[i]*t)
Gab(t,N) = sum_i=0..N-1  a[i]*b[i] * exp(-E[i]*t)

Our goal is to find values for the amplitudes, a[i] and b[i], and the energies, E[i], so that these formulas reproduce the average values for Gaa(t,N) and Gab(t,N) that come from the data, to within the data’s statistical errors. We use the same a[i]s and E[i]s in both formulas. The fit parameters used by the fitter are the a[i]s and b[i]s, as well as the differences dE[i]=E[i]-E[i-1] for i>0 and dE[0]=E[0]. The energy differences are usually positive by construction (see below) and are easily converted back to energies using:

E[i] = sum_j=0..i dE[j]

A typical code has the following structure:

import corrfitter as cf

def main():
    data = make_data('mcfile')          # user-supplied routine
    models = make_models()              # user-supplied routine
    N = 4                               # number of terms in fit functions
    prior = make_prior(N)               # user-supplied routine
    fitter = cf.CorrFitter(models=models)
    fit = fitter.lsqfit(data=data, prior=prior)  # do the fit
    print(fit)
    print_results(fit, prior, data)     # user-supplied routine

...

if __name__ == '__main__':
    main()

We discuss each user-supplied routine in turn.

a) make_data

make_data('mcfile') creates the dictionary containing the data that is to be fit. Typically such data comes from a Monte Carlo simulation. Exactly how the data are assembled depends upon how Monte Carlo results are stored.

Imagine, for example, that the simulation creates a file called 'mcfile' with layout

# first correlator: each line has Gaa(t) for t=0,1,2...63
Gaa  0.159774739530e+00 0.541793561501e-01 ...
Gaa  0.159751906801e+00 0.542054488624e-01 ...
Gaa  ...
.
.
.
# second correlator: each line has Gab(t) for t=0,1,2...63
Gab  0.155764170032e+00 0.102268808986e+00 ...
Gab  0.156248435021e+00 0.102341455176e+00 ...
Gab  ...
.
.
.

where each line is one Monte Carlo measurement for one or the other correlator, as indicated by the tags at the start of the line. (Lines for Gab may be interspersed with lines for Gaa since every line has a tag.) A data file in this format can be analyzed using:

import gvar as gv
import corrfitter as cf

def make_data(filename):
    dset = cf.read_dataset(filename)
    return gv.dataset.avg_data(dset)

This reads the data from the file into a dataset, which is a dictionary whose values are two-dimenional arrays where the first index labels the Monte Carlo sample, and the second index labels time: for example,

>>> print(dset['Gaa'])
[ [0.159774739530e+00 0.541793561501e-01 ... ],
  [0.159751906801e+00 0.542054488624e-01 ... ],
  ...]

Function gvar.dataset.avg_data() then averages over the Monte Carlo samples. Thus data = make_data('mcfile') creates a dictionary where data['Gaa'] is a one-dimensional array of gvar.GVars, indexed by time, obtained by averaging over the Gaa data in the 'mcfile', and data['Gab'] is a similar array for the Gab correlator. The correlator values for different ts are typically correlated with each other.

Other data formats are readily adapted to this purpose. For example, the same Monte Carlo data might be stored in an hdf5 file:

import h5py
import gvar as gv

def make_data(filename):
    h5file = h5py.File(filename, 'r')
    dset = dict(
        Gaa=h5file['/run5/Gaa'], Gab=hfile['/run5/Gab']
        )
    return gv.dataset.avg_data(dset)

Here we assume h5file['/run5/Gaa'] and hfile['/run5/Gab'] are hdf5 datasets that have been configured, again, as two-dimensional numpy arrays, where the first index is the Monte Carlo sample (configuration) index, and the second index is time.

Function corrfitter.read_dataset() can read hdf5 files, so this last example could also be handled by

def make_data(filename):
    dset = cf.read_dataset(filename, h5group='/run5')
    return gv.dataset.avg_data(dset)

provided filename ends in '.h5'. This reads in all hdf5 datasets in group /run5.

b) make_models

make_models() identifies which correlators in the fit data are to be fit, and specifies theoretical models (that is, fit functions) for these correlators:

import corrfitter as cf

def make_models():
    tdata = range(64)
    tfit = tdata[2:]
    models = [
        cf.Corr2(datatag='Gaa', tdata=tdata, tfit=tfit, a='a', b='a', dE='dE'),
        cf.Corr2(datatag='Gab', tdata=tdata, tfit=tfit, a='a', b='b', dE='dE'),
        ]
    return models

For each correlator, we specify: the key used in the input data dictionary data for that correlator (datatag); the t values, tdata=[0,1,2...63], associated with each element of the fit data for the correlator; the subset of tdata values, tfit=[2,3,4...63], to be used in the fit; and fit-parameter labels for the source (a) and sink (b) amplitudes, and for the intermediate energy-differences (dE). Fit-parameter labels identify the parts of the prior, discussed below, corresponding to the actual fit parameters (the labels are dictionary keys). Here the two models, for Gaa and Gab, are identical except for the data tags and the sinks. make_models() returns a list of models; the only parts of the input fit data that are fit are those for which a model is specified in make_models().

Note that if there is data for Gba(t,N) in addition to Gab(t,N), and Gba = Gab, then the (weighted) average of the two data sets will be fit if models[1] is replace by:

cf.Corr2(
    datatag='Gab', tmin=1, tmax=63, a='a', b='b', dE='dE',
    otherdata='Gba',
    )

Alternatively one could add a third Corr2 to models for Gba, but it is more efficient to combine it with Gab, before the fit, if they are equivalent.

The arrays tdata and tfit provide more flexibility than is often needed. Here, because there is data for all t values starting with 0, we could have defined the correlator objects more simply, in terms of the minimum and maximum t values used in the fit: for example,

cf.Corr2(datatag='Gaa', tmin=2, tmax=63, a='a', b='a', dE='dE')

corrfitter.Corr2 creates the obvious choices for tdata and tfit from the information given.

c) make_prior

This routine defines the fit parameters that correspond to each fit-parameter label used in make_models() above. It also assigns a priori values to each parameter, expressed in terms of Gaussian random variables (gvar.GVars), with a mean and standard deviation. The prior is built using a Python dictionary (we use gvar.BufferDict but others would work):

import gvar as gv

def make_prior(N):
    prior = gvar.BufferDict()
    prior['a'] = gv.gvar(N * ['0.1(5)'])
    prior['b'] = gv.gvar(N * ['1(5)'])
    prior['dE'] = gv.gvar(N * ['0.25(25)'])
    return prior

make_prior(N) associates arrays of N Gaussian random variables (gvar.GVars) with each fit-parameter label, enough for N terms in the fit function. These are the a priori values for the fit parameters, and they can be retrieved using the label: setting prior=make_prior(N), for example, implies that prior['a'][i], prior['b'][i] and prior['dE'][i] are the a priori values for a[i], b[i] and dE[i] in the fit functions (see above). The a priori value for each a[i] here is set to 0.1±0.5, while that for each b[i] is 1±5:

>>> print(prior['a'])
[0.10(50) 0.10(50) 0.10(50) 0.10(50)]
>>> print(prior['b'])
[1.0(5.0) 1.0(5.0) 1.0(5.0) 1.0(5.0)]

Similarly the a priori value for each energy difference is 0.25±0.25. (See the lsqfit documentation for further information on priors.)

d) print_results

The actual fit is done by fit=fitter.lsqfit(...), and print(fit) right afterwards prints a summary of the fit results. Further results are reported by print_results(fit, prior, data): for example,

def print_results(fit, prior, data):
    print(fit)
    a = fit.p['a']                              # array of a[i]s
    b = fit.p['b']                              # array of b[i]s
    dE = fit.p['dE']                            # array of dE[i]s
    E = np.cumsum(dE)                           # array of E[i]s
    print('Best fit values:)
    print('     a[0] =',a[0])
    print('     b[0] =',b[0])
    print('     E[0] =',E[0])
    print('b[0]/a[0] =',b[0]/a[0])
    outputs = {'E0':E[0], 'a0':a[0], 'b0':b[0], 'b0/a0':b[0]/a[0]}
    inputs = {'a'=prior['a'], 'b'=prior['b'], 'dE'=prior['dE'],
              'data'=[data[k] for k in data])
    print(fit.fmt_errorbudget(outputs, inputs))

The best-fit values from the fit are contained in fit.p and are accessed using the labels defined in the prior and the corrfitter.Corr2 models. Variables like a[0] and E[0] are gvar.GVar objects that contain means and standard deviations, as well as information about any correlations that might exist between different variables (which is relevant for computing functions of the parameters, like b[0]/a[0] in this example).

The last line of print_results(fit,prior,data) prints an error budget for each of the best-fit results for a[0], b[0], E[0] and b[0]/a[0], which are identified in the print output by the labels 'a0', 'b0', 'E0' and 'b0/a0', respectively. The error for any fit result comes from uncertainties in the inputs — in particular, from the fit data and the priors. The error budget breaks the total error for a result down into the components coming from each source. Here the sources are the a priori errors in the priors for the 'a' amplitudes, the 'b' amplitudes, and the 'dE' energy differences, as well as the errors in the fit data data. These sources are labeled in the print output by 'a', 'b', 'dE', and 'data', respectively. (See the gvar/lsqfit tutorial for further details on partial standard deviations and gvar.fmt_errorbudget().)

Plots of the fit data divided by the fit function, for each correlator, are displayed by calling fit.show_plots() provided the matplotlib module is present.

Faster Fits

Good fits often require fit functions with several exponentials and many parameters. Such fits can be costly. One strategy that can speed things up is to use fits with fewer terms to generate estimates for the most important parameters. These estimates are then used as starting values for the full fit. The smaller fit is usually faster, because it has fewer parameters, but the fit is not adequate (because there are too few parameters). Fitting the full fit function is usually faster given reasonable starting estimates, from the smaller fit, for the most important parameters. Continuing with the example from the previous section, the code

data = make_data('mcfile')
fitter = cf.CorrFitter(models=make_models())
p0 = None
for N in [1,2,3,4,5,6,7,8]:
    prior = make_prior(N)
    fit = fitter.lsqfit(data=data, prior=prior, p0=p0)
    print_results(fit, prior, data)
    p0 = fit.pmean

does fits using fit functions with N=1...8 terms. Parameter mean-values fit.pmean from the fit with N exponentials are used as starting values p0 for the fit with N+1 exponentials, hopefully reducing the time required to find the best fit for N+1.

Faster Fits — Postive Parameters

Priors used in corrfitter.CorrFitter assign an a priori Gaussian/normal distribution to each parameter. It is possible instead to assign a log-normal distribution, which forces the corresponding parameter to be positive. Consider, for example, energy parameters labeled by 'dE' in the definition of a model (e.g., Corr2(dE='dE',...)). To assign log-normal distributions to these parameters, include their logarithms in the prior and label the logarithms with 'log(dE)': for example, in make_prior(N) use

prior['log(dE)'] = gv.log(gv.gvar(N * ['0.25(25)']))

instead of prior['dE'] = gv.gvar(N * ['0.25(25)']). The fitter then uses the logarithms as the fit parameters. The original 'dE' parameters are recovered (automatically) inside the fit function from exponentials of the 'log(dE)' fit parameters.

Using log-normal distributions where possible can significantly improve the stability of a fit. This is because otherwise the fit function typically has many symmetries that lead to large numbers of equivalent but different best fits. For example, the fit functions Gaa(t,N) and Gab(t,N) above are unchanged by exchanging a[i], b[i] and E[i] with a[j], b[j] and E[j] for any i and j. We can remove this degeneracy by using a log-normal distribution for the dE[i]s since this guarantees that all dE[i]s are positive, and therefore that E[0],E[1],E[2]... are ordered (in decreasing order of importance to the fit at large t).

Another symmetry of Gaa and Gab, which leaves both fit functions unchanged, is replacing a[i],b[i] by -a[i],-b[i]. Yet another is to add a new term to the fit functions with a[k],b[k],dE[k] where a[k]=0 and the other two have arbitrary values. Both of these symmetries can be removed by using a log-normal distribution for the a[i] priors, thereby forcing all a[i]>0.

The log-normal distributions for the a[i] and dE[i] are introduced into the code example above by changing the corresponding labels in make_prior(N), and taking logarithms of the corresponding prior values:

import gvar as gv
import corrfitter as cf

def make_models():                          # same as before
    models = [
        cf.Corr2(datatag='Gaa', tmin=2, tmax=63, a='a', b='a', dE='dE'),
        cf.Corr2(datatag='Gab', tmin=2, tmax=63, a='a', b='b', dE='dE'),
        ]
    return models

def make_prior(N):
    prior = gv.BufferDict()
    prior['log(a)'] = gv.log(gv.gvar(N * ['0.1(5)']))
    prior['b'] = gv.gvar(N * ['1(5)'])
    prior['log(dE)'] = gv.log(gv.gvar(N * ['0.25(25)']))
    return prior

This replaces the original fit parameters, a[i] and dE[i], by new fit parameters, log(a)[i] and log(dE)[i]. The a priori distributions for the logarithms are Gaussian/normal, with priors of log(0.1±0.5) and log(0.25±0.25) for the log(a)s and log(dE)s respectively.

Note that the labels are unchanged here in make_models(). It is unnecessary to change labels in the models; corrfitter.CorrFitter will automatically connect the modified terms in the prior with the appropriate terms in the models. This allows one to switch back and forth between log-normal and normal distributions without changing the models (or any other code) — only the names in the prior need be changed. corrfitter.CorrFitter also supports “sqrt-normal” distributions, and other distributions, as discussed in the lsqfit documentation.

Finally note that another option for stabilizings fits involving many sources and sinks is to generate priors for the fit amplitudes and energies using corrfitter.EigenBasis.

Faster Fits — Marginalization

Often we care only about parameters in the leading term of the fit function, or just a few of the leading terms. The non-leading terms are needed for a good fit, but we are uninterested in the values of their parameters. In such cases the non-leading terms can be absorbed into the fit data, leaving behind only the leading terms to be fit (to the modified fit data) — non-leading parameters are, in effect, integrated out of the analysis, or marginalized. The errors in the modified data are adjusted to account for uncertainties in the marginalized terms, as specified by their priors. The resulting fit function has many fewer parameters, and so the fit can be much faster.

Continuing with the example in Faster Fits, imagine that Nmax=8 terms are needed to get a good fit, but we only care about parameter values for the first couple of terms. The code from that section can be modified to fit only the leading N terms where N<Nmax, while incorporating (marginalizing) the remaining, non-leading terms as corrections to the data:

Nmax = 8
data = make_data('mcfile')
models = make_models()
fitter = cf.CorrFitter(models=make_models())
prior = make_prior(Nmax)        # build priors for Nmax terms
p0 = None
for N in [1,2,3]:               # fit N terms
    fit = fitter.lsqfit(data=data, prior=prior, p0=p0, nterm=N)
    print_results(fit, prior, data)
    p0 = fit.pmean

Here the nterm parameter in fitter.lsqfit specifies how many terms are used in the fit functions. The prior specifies Nmax terms in all, but only parameters in nterm=N terms are varied in the fit. The remaining terms specified by the prior are automatically incorporated into the fit data by corrfitter.CorrFitter.

Remarkably this method is usually as accurate with N=1 or 2 as a full Nmax-term fit with the original fit data; but it is much faster. If this is not the case, check for singular priors, where the mean is much smaller than the standard deviation. These can lead to singularities in the covariance matrix for the corrected fit data. Such priors are easily fixed: for example, use gvar.gvar('0.1(1.0)') rather than gvar.gvar('0(1)'). In some situations an SVD cut (see below) can also help.

Faster Fits — Chained Fits

Large complicated fits, where lots of models and data are fit simultaneously, can take a very long time. This is especially true if there are strong correlations in the data. Such correlations can also cause problems from numerical roundoff errors when the inverse of the data’s covariance matrix is computed for the \chi^2 function, requiring large SVD cuts which can degrade precision (see below). An alternative approach is to use chained fits. In a chained fit, each model is fit by itself in sequence, but with the best-fit parameters from each fit serving as priors for fit parameters in the next fit. All parameters from one fit become fit parameters in the next, including those parameters that are not explicitly needed by the next fit (since they may be correlated with the input data for the next fit or with its priors). Statistical correlations between data/priors from different models are preserved throughout (approximately).

The results from a chained fit are identical to a standard simultaneous fit in the limit of large statistics (that is, in the Gaussian limit), but a chained fit usually involves fitting only a single correlator at a time. Single-correlator fits are typically much faster than simultaneous multi-correlator fits, and roundoff errors (and therefore SVD cuts) are much less of a problem.

Converting to chained fits is trivial: simply replace fit = fitter.lsqfit(...) by fit = fitter.chained_lsqfit(...). The output from this function comes from the last fit in the chain, whose fit results represent the cummulative results of the entire chain of fits. Results from the different links in the chain — that is, from the fits for individual models — are displayed using print(fit.formatall()).

There are various ways of chaining fits. For example, setting

models = [m1, m2, (m3a, m3b), m4]

causes models m1, m2 and m4 to be fit separately, but fits models m3a and m3b together in a single simultaneous fit:

m1 -> m2 -> (simultaneous fit of m3a, m3b) -> m4

Simultaneous fits make sense when there is lots of overlap between the parameters for the different models.

Another option is

models = [m1, m2, [m3a,m3b], m4]

in fitter.chained_lsqfit which causes the following chain of fits:

m1 -> m2 -> (parallel fit of m3a, m3b) -> m4

Here the output from m1 is used in the prior for fit m2, and the output from m2 is used as the prior for a parallel fit of m3a and m3b together — that is, m3a and m3b are not chained, but rather are fit in parallel with each using a prior from fit m2. The result of the parallel fit of [m3a,m3b] is used as the prior for m4. Parallel fits make sense when there is little overlap between the parameters used by the different fits.

Faster Fits — Faster Fitters

When fits take many iterations to converge (or converge to an obviously wrong result), it is worthwhile trying a different fitter. The lsqfit module, which is used by corrfitter for fitting, offers a variety of alternative fitting algorithms that can sometimes be much faster (2 or 3 times faster). These are deployed by adding extra directives for lsqfit when constructing the fitter or when doing the fit: for example,

import corrfitter as cf

fitter = cf.CorrFitter(
    models=make_models(),
    fitter='gsl_multifit', alg='subspace2D', solver='cholesky'
    )

uses the subspace2D algorithm for subsequent fits with fitter. It is also possible to reset the default algorithms for all fits:

import lsqfit

lsqfit.nonlinear_fit.set(
    fitter='gsl_multifit', alg='subspace2D', solver='cholesky'
    )

The documentation for lsqfit describes many more options.

Faster Fits — Processed Datasets

When fitting very large data sets, it is usually worthwhile to pare the data down to the smallest subset that is needed for the fit. Ideally this is done before the Monte Carlo data are averaged, to keep the size of the covariance matrix down. One way to do this is to process the Monte Carlo data with the models, just before averaging it, by using

import gvar as gv
import corrfitter as cf

def make_pdata(filename, models):
    dset = cf.read_dataset(filename)
    return cf.process_dataset(dset, models)

in place of make_data(filename). Here models is the list of models used by the fitter (fitter.models). Function make_pdata returns processed data which is passed to fitter.lsqfit using the pdata keyword:

import corrfitter as cf

def main():
    N = 4
    models = make_models()
    pdata = make_pdata('mcfile', models)
    prior = make_prior(N)
    fitter = cf.CorrFitter(models=models)
    fit = fitter.lsqfit(pdata=pdata, prior=prior)
    print(fit)
    print_results(fit, prior, pdata)

...

if __name__ == '__main__':
    main()

Processed data can only be used with the models that created it, so parameters in those models should not be changed after the data is processed.

Accurate Fits — SVD Cuts

A key feature of corrfitter is its ability to fit multiple correlators simultaneously, taking account of the statistical correlations between correlators at different times and between different correlators. Information about the correlations typically comes from Monte Carlo samples of the correlators. Problems arise, however, when the number N_s of samples is not much larger than the number N_d of data points being fit. Specifically the smallest eigenvalues of the correlation matrix can be substantially underestimated if N_s is not sufficiently large (10 or 100 times larger than N_d). Indeed there must be N_d-N_s zero eigenvalues when N_s\le N_d. The underestimated (or zero) eigenvalues lead to incorrect and large (or infinite) contributions to the fit’s \chi^2 function, invalidating the fit results.

These problems tend show up as an unexpectedly large \chi^2s, for example, in fits where the \chi^2 per degree of freedom remains substantially larger than one no matter how many fit terms are employed. Such situations are usually improved by introducing an SVD cut:

fit = fitter.lsqfit(data=data, prior=prior, p0=p0, svdcut=1e-2)

This replaces the smallest eigenvalues of the correlation matrix as needed so that no eigenvalue is smaller than svdcut times the largest eigenvalue. Introducing an SVD cut increases the effective errors and so is a conservative move.

The method gvar.dataset.svd_diagnoisis() in module gvar is useful for assessing whether an SVD cut is needed, and for setting its value. One way to use it is in the make_pdata routine when creating processed data (see Faster Fits — Processed Datasets):

import gvar as gv
import corrfitter as cf

GENERATE_SVD = True

def make_pdata(filename, models):
    dset = cf.read_dataset(filename)
    pdata = cf.process_dataset(dset, models)
    if GENERATE_SVD:
        s = gv.dataset.svd_diagnosis(dset, models=models)
        print('suggested svdcut =', s.svdcut)
        s.plot_ratio(show=True)
        svdcut = s.svdcut
    else:
        svdcut = 0.1
    return gv.svd(pdata, svdcut=svdcut)

Here gv.dataset.svd_diagnosis(dset, models) uses a bootstrap simulation (see Bootstrap Analyses) to test the reliability of the eigenvalues determined from the Monte Carlo data in dset. It places the SVD cut at the point where the bootstrapped eigenvalues fall well below the actual values. A plot showing the ratio of bootstrapped to actual eigenvalues is displayed by s.plot_ratio(show=True). The following are sample plots from two otherwise identical simulations of 3 correlators (66 data points in all), one with 100 configurations and the other with 10,000 configurations:

   
_images/svd-bootstrap-1e2.png _images/svd-bootstrap-1e4.png

With only 100 configurations, three quarters of the eigenvalues are too small in the bootstrap simulation, and therefore also likely too small for the real data. Simulated and actual eigenvalues come into agreement around 0.1 (red dashed line), which is the suggested value for svdcut. With 10,000 configurations, all of the eigenvalues are robust and no SVD cut is needed. Both data sets produce good fits (using the appropriate svdcut for each). The fits agree with each other, with uncertainties from the high-statistics case that are 10 times smaller, as expected.

In make_pdata() above, the SVD cut is applied directly to the data (gv.svd(pdata, svdcut=svdcut)) before it is fit, and so need not be supplied to the fitter. This is convenient when using processed data because both the Monte Carlo data (dset) and the corrfitter models are available. Another option is to do the SVD diagnosis just before fitting and pass the value of svdcut to corrfitter.

Variations

A 2-point correlator is turned into a periodic function of t by specifying the period through parameter tp. Doing so causes the replacement (for tp>0)

exp(-E[i]*t)   ->   exp(-E[i]*t) + exp(-E[i]*(tp-t))

in the fit function. If tp is negative, the function is replaced by an anti-periodic function with period abs(tp) and (for tp<0):

exp(-E[i]*t)   ->   exp(-E[i]*t) - exp(-E[i]*(abs(tp)-t))

Also (or alternatively) oscillating terms can be added to the fit by modifying parameter s and by specifying sources, sinks and energies for the oscillating pieces. For example, one might want to replace the sum of exponentials with two sums

sum_i a[i]**2 * exp(-E[i]*t) - sum_i ao[i]**2 (-1)**t * exp(-Eo[i]*t)

in a (nonperiodic) fit function. Then an appropriate model would be, for example,

Corr2(
    datatag='Gaa', tmin=2, tmax=63,
    a=('a','ao'), b=('a','ao'), dE=('dE','dEo'), s=(1,-1)
    )

where ao and dEo refer to additional fit parameters describing the oscillating component. In general parameters for amplitudes and energies can be tuples with two components: the first describing normal states, and the second describing oscillating states. To omit one or the other, put None in place of a label. Parameter s[0] is an overall factor multiplying the non-oscillating terms, and s[1] is the corresponding factor for the oscillating terms.

Very Fast (But Limited) Fits

At large t, two-point correlators are dominated by the term with the smallest E, and often it is only the parameters in that leading term that are needed. In such cases there is a very fast analysis that is often almost as accurate as a full fit. Assuming a non-periodic correlator, for example, we want to calculate energy E[0] and amplitude A[0] where:

G(t) = sum_i=0,N-1 A[i] * exp(-E[i]*t)

This is done using the following code

from corrfitter import fastfit

# Gdata = array containing G(t) for t=0,1,2...
fit = fastfit(Gdata, ampl='0(1)', dE='0.5(5)', tmin=3)
print('E[0] =', fit.E)                  # E[0]
print('A[0] =', fit.ampl)               # A[0]
print('chi2/dof =', fit.E.chi2/fit.dof) # good fit if of order 1 or less
print('Q =', fit.E.Q)                   # good fit if Q > 0.05-0.1

where G is an array containing a two-point correlator, ampl is a prior for the amplitudes A[i], dE is a prior for energy differences E[i]-E[i-1], and tmin is the minimum time used in the analysis.

fastfit is fast because it does not attempt to determine any parameters in G(t) other than E[0] and A[0]. It does this by using the priors for the amplitudes and energy differences to remove (marginalize) all terms from the correlator other than the E[0] term: so the data Gdata(t) for the correlator are replaced by

Gdata(t) - sum_i=1..N-1  A[i] * exp(-E[i]*t)

where A[i] and E[i] for i>0 are replaced by priors given by ampl and (i+1) * dE, respectively. The modified correlator is then fit by a single term, A[0] * exp(-E[0]*t), which means that a fit is not actually necessary since the functional form is so simple. fastfit averages estimates for E[0] and A[0] from all ts larger than tmin. It is important to verify that these estimates agree with each other, by checking the \chi^2 of the average. Try increasing tmin if the \chi^2 is too large; or introduce an SVD cut.

The energies from fastfit are closely related to standard effective masses. The key difference is fastfit’s marginalization of terms from excited states (i>0 above). This allows fastfit to use information from much smaller ts than otherwise, increasing precision. It also quantifies the uncertainty caused by the existence of excited states, and gives a simple criterion for how small tmin can be (the \chi^2). Results are typically as accurate as results obtained from a full multi-exponential fit that uses the same priors for A[i] and E[i], and the same tmin. fastfit can also be used for periodic and anti-periodic correlators, as well as for correlators that contain terms that oscillate in sign from one t to the next.

fastfit is a special case of the more general marginalization strategy discussed in Faster Fits, above.

3-Point Correlators

Correlators Gavb(t,T) = <b(T) V(t) a(0)> can also be included in fits as functions of t. In the illustration above, for example, we might consider additional Monte Carlo data describing a form factor with the same intermediate states before and after V(t). Assuming the data is tagged by aVbT15 and describes T=15, the corresponding entry in the collection of models might then be:

Corr3(datatag='aVbT15', T=15, tdata=range(16), tfit=range(1, 16),
    Vnn='Vnn',                # parameters for V
    a='a', dEa='dE',          # parameters for a->V
    b='b', dEb='dE',          # parameters for V->b
    )

This models the Monte Carlo data for the 3-point function using the following formula:

sum_i,j a[i] * exp(-Ea[i]*t) * Vnn[i,j] * b[j] * exp(-Eb[j]*t)

where the Vnn[i,j]s are new fit parameters related to a->V->b form factors. Obviously multiple values of T can be studied by including multiple corrfitter.Corr3 models, one for each value of T. Either or both of the initial and final states can have oscillating components (include sa and/or sb). If there are oscillating states then additional Vs must be specified: Vno connecting a normal state to an oscillating state, Von connecting oscillating to normal states, and Voo connecting oscillating to oscillating states.

Keywords tdata and tfit need not be specified when there is data for every t=0,1...T: for example,

Corr3(
    datatag='aVbT15', T=15, tmin=1,
    Vnn='Vnn', a='a', dEa='dE', b='b', dEb='dE',
    )

is equivalent to the definition above.

There are two cases that require special treatment. One is when simultaneous fits are made to a->V->b and b->V->a. Then the Vnn, Vno, etc. for b->V->a are the (matrix) transposes of the the same matrices for a->V->b. In this case the models for the two would look something like:

models = [
    ...
    Corr3(
        datatag='aVbT15', T=15, tmin=1,
        Vnn='Vnn', Vno='Vno', Von='Von', Voo='Voo',
        a=('a','ao'), dEa=('dE','dEo'), sa=(1,-1), # a->V
        b=('b','bo'), dEb=('dE','dEo'), sb=(1,-1)  # V->b
        ),
    Corr3(
        datatag='bVaT15', T=15, tmin=1, reverse=True,
        Vnn='Vnn', Vno='Vno', Von='Von', Voo='Voo',
        a=('a','ao'), dEa=('dE','dEo'), sa=(1,-1), # a->V
        b=('b','bo'), dEb=('dE','dEo'), sb=(1,-1)  # V->b
        ),
    ...
    ]

The second Corr3 is identical to the first except for the datatag ('bVaT15'), and the keyword reverse=True, which instructs the model to time-reverse its data, interchanging t=0 with t=T, before fitting. Time-reversing in effect turns b->V->a into a->V->b.

Another way to handle this last situation is to average the data from b->V->a with that from a->V->b for a single fit. This is done using one Corr3 but with the keyword reverseddata to indicate the data to be time-reversed and then averaged with the a->V->b data:

models = [
    ...
    Corr3(
        datatag='aVbT15', T=15, tmin=1, reverseddata='bVaT15',
        Vnn='Vnn', Vno='Vno', Von='Von', Voo='Voo',
        a=('a','ao'), dEa=('dE','dEo'), sa=(1,-1), # a->V
        b=('b','bo'), dEb=('dE','dEo'), sb=(1,-1)  # V->b
        ),
    ...
    ]

The second special case is for fits to a->V->a where the initial and final particles are the same (with the same momentum). In that case, Vnn and Voo are symmetric matrices, and Von is the transpose of Vno. The model for such a case would look like, for example:

Corr3(
    datatag='aVbT15', T=15, tmin=1,
    Vnn='Vnn', Vno='Vno', Voo='Voo', symmetric_V=True,
    a=('a','ao'), dEa=('dE', 'dEo'), sa=(1, -1), # a->V
    b=('a','ao'), dEb=('dE', 'dEo'), sb=(1, -1)  # V->a
    )

Here only Vno is specified, since Von is its transpose. Furthermore Vnn and Voo are (square) symmetric matrices when symmetric_V==True and so only the upper part of each matrix is needed. In this case Vnn and Voo are treated as one-dimensional arrays with N(N+1)/2 elements corresponding to the upper parts of each matrix, where N is the number of exponentials (that is, the number of a[i]s).

Testing Fits with Simulated Data

Large fits are complicated and often involve nontrivial choices about algorithms (e.g., chained fits versus regular fits), priors, and SVD cuts — choices that affect the values and errors for the fit parameters. In such situations it is often a good idea to test the fit protocol that has been selected. This can be done by fitting simulated data. Simulated data looks almost identical to the original fit data but has means that have been adjusted to correspond to fluctuations around a correlator with known (before the fit) parameter values: p=pexact. The corrfitter.CorrFitter iterator simulated_pdata_iter creates any number of different simulated data sets of this kind. Fitting any of these with a particular fit protocol tests the reliability of that protocol since the fit results should agree with pexact to within the (simulated) fit’s errors. One or two fit simulations of this sort are usually enough to establish the validity of a protocol. It is also easy to compare the performance of different fit options by applying these in fits of simulated data, again because we know the correct answers (pexact) ahead of time.

Typically one obtains reasonable values for pexact from a fit to the real data. Assuming these have been dumped into a file named "pexact_file" (using, for example, fit.dump_pmean("pexact_file")), a testing script might look something like:

import gvar as gv
import lsqfit
import corrfitter

def main():
    dataset = gv.dataset.Dataset(...)       # from original fit code
    prior = make_prior(...)
    fitter = corrfitter.CorrFitter(models = make_models(...))
    n = 2                                   # number of simulations
    pexact = lsqfit.nonlinear_fit.load_parameters("pexact_file")
    for spdata in fitter.simulated_pdata_iter(n, dataset, pexact=pexact):
        # sfit = fit to the simulated data sdata
        sfit = fitter.lsqfit(pdata=spdata, p0=pexact, prior=prior...)
        ... check that sfit.p values agree with pexact to within sfit.psdev ...

Bootstrap Analyses

A bootstrap analysis gives more robust error estimates for fit parameters and functions of fit parameters than the conventional fit when errors are large, or fluctuations are non-Gaussian. A typical code looks something like:

import gvar as gv
import gvar.dataset as ds
from corrfitter import CorrFitter
# fit
dset = ds.Dataset('mcfile')
data = ds.avg_data(dset)            # create fit data
fitter = Corrfitter(models=make_models())
N = 4                               # number of terms in fit function
prior = make_prior(N)
fit = fitter.lsqfit(prior=prior, data=data)  # do standard fit
print 'Fit results:'
print 'a', fit.p['a']               # fit results for 'a' amplitudes
print 'dE', fit.p['dE']             # fit results for 'dE' energies
...
...
# bootstrap analysis
print 'Bootstrap fit results:'
nbootstrap = 10                     # number of bootstrap iterations
bs_datalist = (ds.avg_data(d) for d in ds.bootstrap_iter(dset, nbootstrap))
bs = ds.Dataset()                   # bootstrap output stored in bs
for bs_fit in fitter.bootstrap_iter(bs_datalist): # bs_fit = lsqfit output
    p = bs_fit.pmean    # best fit values for current bootstrap iteration
    bs.append('a', p['a']))         # collect bootstrap results for a[i]
    bs.append('dE', p['dE'])        # collect results for dE[i]
    ...                             # include other functions of p
    ...
bs = ds.avg_data(bs, bstrap=True)   # medians + error estimate
print 'a', bs['a']                  # bootstrap result for 'a' amplitudes
print 'dE', bs['dE']                # bootstrap result for 'dE' energies
....

This code first prints out the standard fit results for the 'a' amplitudes and 'dE' energies. It then makes 10 bootstrap copies of the original input data, and fits each using the best-fit parameters from the original fit as the starting point for the bootstrap fit. The variation in the best-fit parameters from fit to fit is an indication of the uncertainty in those parameters. This example uses a gvar.dataset.Dataset object bs to accumulate the results from each bootstrap fit, which are computed using the best-fit values of the parameters (ignoring their standard deviations). Other functions of the fit parameters could be included as well. At the end avg_data(bs, bstrap=True) finds median values for each quantity in bs, as well as a robust estimate of the uncertainty (to within 30% since nbootstrap is only 10).

The list of bootstrap data sets bs_datalist can be omitted in this example in situations where the input data has high statistics. Then the bootstrap copies are generated internally by fitter.bootstrap_iter() from the means and covariance matrix of the input data (assuming Gaussian statistics).

Implementation

Background information on the some of the fitting strategies used by corrfitter.CorrFitter can be found by doing a web searches for “hep-lat/0110175”, “arXiv:1111.1363”, and “:arXiv:1406.2279” (appendix). These are papers by G.P. Lepage and collaborators whose published versions are: G.P. Lepage et al, Nucl.Phys.Proc.Suppl. 106 (2002) 12-20; K. Hornbostel et al, Phys.Rev. D85 (2012) 031504; and C. Bouchard et al, Phys.Rev. D90 (2014) 054506.

Correlator Model Objects

Correlator objects describe theoretical models that are fit to correlator data by varying the models’ parameters.

A model object’s parameters are specified through priors for the fit. A model assigns labels to each of its parameters (or arrays of related parameters), and these labels are used to identify the corresponding parameters in the prior. Parameters can be shared by more than one model object.

A model object also specifies the data that it is to model. The data is identified by the data tag that labels it in the input file or gvar.dataset.Dataset.

class corrfitter.Corr2(datatag, a, b, dE, s=1.0, tp=None, tmin=None, tmax=None, tdata=None, tfit=None, reverse=False, reverseddata=[], otherdata=[])

Two-point correlators Gab(t) = <b(t) a(0)>.

corrfitter.Corr2 models the t dependence of a 2-point correlator Gab(t) using

Gab(t) = sn * sum_i an[i] * bn[i] * fn(En[i], t)
       + so * sum_i ao[i] * bo[i] * fo(Eo[i], t)

where sn and so are typically -1, 0, or 1 and

fn(E, t) =  exp(-E*t) + exp(-E*(tp-t)) # tp>0 -- periodic
       or   exp(-E*t) - exp(-E*(-tp-t))# tp<0 -- anti-periodic
       or   exp(-E*t)                  # if tp is None (nonperiodic)

fo(E, t) = (-1)**t * fn(E, t)

The fit parameters for the non-oscillating piece of Gab (first term) are an[i], bn[i], and dEn[i] where:

dEn[0] = En[0]
dEn[i] = En[i]-En[i-1] > 0     (for i>0)

and therefore En[i] = sum_j=0..i dEn[j]. The fit parameters for the oscillating piece are defined analogously: ao[i], bo[i], and dEo[i].

The fit parameters are specified by the keys corresponding to these parameters in a dictionary of priors supplied to corrfitter.CorrFitter. The keys are strings and are also used to access fit results. A log-normal prior can be specified for a parameter by including an entry for log(c) in the prior, rather than for c itself. See the lsqfit documentation for information about other distributions that are available. Values for both log(c) and c are included in the parameter dictionary. Log-normal distributions are useful for forcing an, bn and/or dE to be positive.

When tp is not None and positive, the correlator is assumed to be symmetrical about tp/2, with Gab(t)=Gab(tp-t). Data from t>tp/2 is averaged with the corresponding data from t<tp/2 before fitting. When tp is negative, the correlator is assumed to be anti-symetrical about -tp/2.

Parameters:
  • datatag (str) – Key used to access correlator data in the input data dictionary (see corrfitter.CorrFitter): data[self.datatag] is a (1-d) array containing the correlator values (gvar.GVars).
  • a (str or tuple) – Key identifying the fit parameters for the source amplitudes an in the dictionary of priors provided to corrfitter.CorrFitter; or a two-tuple of keys for the source amplitudes (an, ao). The corresponding values in the dictionary of priors are (1-d) arrays of prior values with one term for each an[i] or ao[i]. Replacing either key by None causes the corresponding term to be dropped from the fit function. These keys are used to label the corresponding parameter arrays in the fit results as well as in the prior.
  • b (str or tuple) – Same as self.a but for the sinks (bn, bo) instead of the sources (an, ao).
  • dE (str) – Key identifying the fit parameters for the energy differences dEn in the dictionary of priors provided by corrfitter.CorrFitter; or a two-tuple of keys for the energy differences (dEn, dEo). The corresponding values in the dictionary of priors are (1-d) arrays of prior values with one term for each dEn[i] or dEo[i]. Replacing either key by None causes the corresponding term to be dropped from the fit function. These keys are used to label the corresponding parameter arrays in the fit results as well as in the prior.
  • s (float or tuple) – Overall factor sn for non-oscillating part of fit function, or two-tuple of overall factors (sn, so) for both pieces.
  • tdata (list of ints) – The ts corresponding to data entries in the input data. Note that len(self.tdata) should equal len(data[self.datatag]). If tdata is omitted, tdata=numpy.arange(tp) is assumed, or tdata=numpy.arange(tmax) if tp is not specified.
  • tfit (list of ints) – List of ts to use in the fit. Only data with these ts (all of which should be in tdata) is used in the fit. If tfit is omitted, it is assumed to be all t values from tdata that are larger than or equal to tmin (if specified) and smaller than or equal to tmax (if specified).
  • tp (int or None) – If tp is positive, the correlator is assumed to be periodic with Gab(t)=Gab(tp-t). If negative, the correlator is assumed to be anti-periodic with Gab(t)=-Gab(-tp-t). Setting tp=None implies that the correlator is not periodic, but rather continues to fall exponentially as t is increased indefinitely.
  • tmin (int or None) – If tfit is omitted, it is assumed to be all t values from tdata that are larger than or equal to tmin and smaller than or equal to tmax (if specified). tmin is ignored if tfit is specified.
  • tmax (int or None) – If tfit is omitted, it is assumed to be all t values from tdata that are larger than or equal to tmin (if specified) and smaller than or equal to tmax. tmin is ignored if tfit and tdata are specified.
  • ncg (int) – Width of bins used to coarse-grain the correlator before fitting. Each bin of ncg correlator values is replaced by its average. Default is ncg=1 (ie, no coarse-graining).
  • reverse (bool) – If True, the data associated with self.datatag is time-reversed (data -> [data[0], data[-1], data[-2]...data[1]]). Ignored otherwise.
  • otherdata (str or list or None) – Data tag or list of data tags for additional data that are averaged with the self.datatag data before fitting. This is useful including data from correlators with the source and sink interchanged. Default is None.
  • reverseddata (str or list or None) – Data tag or list of data tags for data that is time-reversed and then averaged with the self.datatag data before fitting. Default is None.
builddata(data)

Assemble fit data from dictionary data.

builddataset(dataset)

Assemble fit data from data set dictionary dataset.

buildprior(prior, nterm=None, mopt=None, extend=None)

Create fit prior by extracting relevant pieces from prior.

This routine selects the entries in dictionary prior corresponding to the model’s fit parameters. If nterm is not None, it also adjusts the number of terms that are retained.

Parameters:
  • prior (dictionary) – Dictionary containing priors for fit parameters.
  • nterm (None or int or two-tuple) – Setting nterm=(n,no) restricts the number of terms to n in the non-oscillating part and no in the oscillating part of the fit function. Replacing either or both by None keeps all terms, as does setting nterm=None. This optional argument is used to implement marginalization.
fitfcn(p, t=None)

Return fit function for parameters p.

class corrfitter.Corr3(datatag, T, Vnn, a, b, dEa, dEb, sa=1.0, sb=1.0, Vno=None, Von=None, Voo=None, tdata=None, tfit=None, tmin=None. reverse=False, symmetric_V=False, reverseddata=[], otherdata=[])

Three-point correlators Gavb(t, T) = <b(T) V(t) a(0)>.

corrfitter.Corr3 models the t dependence of a 3-point correlator Gavb(t, T) using

Gavb(t, T) =
 sum_i,j san * an[i] * fn(Ean[i],t) * Vnn[i,j] * sbn * bn[j] * fn(Ebn[j],T-t)
+sum_i,j san * an[i] * fn(Ean[i],t) * Vno[i,j] * sbo * bo[j] * fo(Ebo[j],T-t)
+sum_i,j sao * ao[i] * fo(Eao[i],t) * Von[i,j] * sbn * bn[j] * fn(Ebn[j],T-t)
+sum_i,j sao * ao[i] * fo(Eao[i],t) * Voo[i,j] * sbo * bo[j] * fo(Ebo[j],T-t)

where

fn(E, t) =  exp(-E*t)
fo(E, t) = (-1)**t * exp(-E*t)

The fit parameters for the non-oscillating piece of Gavb (first term) are Vnn[i,j], an[i], bn[j], dEan[i] and dEbn[j] where, for example:

dEan[0] = Ean[0]
dEan[i] = Ean[i] - Ean[i-1] > 0     (for i>0)

and therefore Ean[i] = sum_j=0..i dEan[j]. The parameters for the other terms are similarly defined.

Parameters:
  • datatag (str) – Tag used to label correlator in the input data.
  • a (str or tuple) – Key identifying the fit parameters for the source amplitudes an, for a->V, in the dictionary of priors provided to corrfitter.CorrFitter; or a two-tuple of keys for the source amplitudes (an, ao). The corresponding values in the dictionary of priors are (1-d) arrays of prior values with one term for each an[i] or ao[i]. Replacing either key by None causes the corresponding term to be dropped from the fit function. These keys are used to label the corresponding parameter arrays in the fit results as well as in the prior.
  • b (str or tuple) – Same as self.a but for the V->b sink amplitudes (bn, bo).
  • dEa (str or tuple) – Fit-parameter label for a->V intermediate-state energy differences dEan, or two-tuple of labels for the differences (dEan, dEao). Each label represents an array of energy differences. Replacing either label by None causes the corresponding term in the correlator function to be dropped. These keys are used to label the corresponding parameter arrays in the fit results as well as in the prior.
  • dEb (str or tuple) – Same as self.dEa but for V->b sink energies (dEbn, dEbo).
  • sa (float or tuple) – Overall factor san for the non-oscillating a->V terms in the correlator, or two-tuple containing the overall factors (san, sao) for the non-oscillating and oscillating terms. Default is (1,-1).
  • sb (float or tuple) – Same as self.sa but for V->b sink overall factors (sbn, sbo).
  • Vnn (str or None) –

    Fit-parameter label for the matrix of current matrix elements Vnn[i,j] connecting non-oscillating states. The matrix must be square and symmetric if symmetric_V=True, and only the elements V[i,j] for j>=i are specified, using a 1-d array V_sym with the following layout:

    [V[0,0],V[0,1],V[0,2]...V[0,N],
        V[1,1],V[1,2]...V[1,N],
               V[2,2]...V[2,N],
                     .
                      .
                       .
                        V[N,N]]

    Note that V[i,j] = V_symm[i*N + j - i * (i+1) / 2] for j>=i. Set Vnn=None to omit it.

  • Vno (str or None) – Fit-parameter label for the matrix of current matrix elements Vno[i,j] connecting non-oscillating to oscillating states. Only one of Von and Vno can be specified if symmetric_V=True; the other is defined to be its transform. Set Vno=None to omit it.
  • Von (str or None) – Fit-parameter label for the matrix of current matrix elements Vno[i,j] connecting oscillating to non- oscillating states. Only one of Von and Vno can be specified if symmetric_V=True; the other is defined to be its transform. Set Von=None to omit it.
  • Voo (str or None) –

    Fit-parameter label for the matrix of current matrix elements Voo[i,j] connecting oscillating states. The matrix must be square and symmetric if symmetric_V=True, and only the elements V[i,j] for j>=i are specified, using a 1-d array V_sym with the following layout:

    [V[0,0],V[0,1],V[0,2]...V[0,N],
        V[1,1],V[1,2]...V[1,N],
               V[2,2]...V[2,N],
                     .
                      .
                       .
                        V[N,N]]

    Note that V[i,j] = V_symm[i*N + j - i * (i+1) / 2] for j>=i. Set Voo=None to omit it.

  • reverse (bool) –

    If True, the data associated with self.datatag is time-reversed before fitting (interchanging t=0 with t=T). This is useful for doing simultaneous fits to a->V->b and b->V->a, where one is time-reversed relative to the other: e.g.,

    models = [ ...
    Corr3(
        datatag='a->V->b', tmin=3, T=15,
        a=('a', 'ao'), dEa=('dEa', 'dEao'),
        b=('b', 'bo'), dEb=('dEb', 'dEbo'),
        Vnn='Vnn', Vno='Vno', Von='Von', Voo='Voo',
        ),
    Corr3(
        datatag='b->V->a', tmin=3, T=15,
        a=('a', 'ao'), dEa=('dEa', 'dEao'),
        b=('b', 'bo'), dEb=('dEb', 'dEbo'),
        Vnn='Vnn', Vno='Vno', Von='Von', Voo='Voo',
        reverse=True,
        ),
    ...
    ]

    Another (faster) strategy for such situations is to average data from the second process with that from the first, before fitting, using keyword reverseddata. Default is False.

  • symmetric_V (bool) – If True, the fit function for a->V->b is unchanged (symmetrical) under the the interchange of a and b. Then Vnn and Voo are square, symmetric matrices and their priors are one-dimensional arrays containing only elements V[i,j] with j>=i, as discussed above. Only one of Von and Vno can be specified if symmetric_V=True; the other is defined to be its transform.
  • T (int) – Separation between source and sink.
  • tdata (list of ints) – The ts corresponding to data entries in the input data. If omitted, is assumed equal to numpy.arange(T + 1).
  • tfit (list of ints) – List of ts to use in the fit. Only data with these ts (all of which should be in tdata) is used in the fit. If omitted, is assumed equal to numpy.arange(tmin, T - tmin + 1).
  • tmin (int or None) – If tfit is omitted, it is set equal to numpy.arange(tmin, T - tmin + 1). tmin is ignored if tfit is specified.
  • ncg (int) – Width of bins used to coarse-grain the correlator before fitting. Each bin of ncg correlator values is replaced by its average. Default is ncg=1 (ie, no coarse-graining).
  • reverseddata (str or list or None) –

    Data tag or list of data tags for additional data that are time-reversed and then averaged with the self.datatag data before fitting. This is useful for folding data from b->V->a into a fit for a->V->b: e.g.,

    Corr3(
    datatag='a->V->b',
    a=('a', 'ao'), dEa=('dEa', 'dEao'),
    b=('b', 'bo'), dEb=('dEb', 'dEbo'),
    Vnn='Vnn', Vno='Vno', Von='Von', Voo='Voo',
    tmin=3, T=15, reverseddata='b->V->a'
    ),

    This is faster than using a separate model with transpose_V=True. Default is None.

  • otherdata (str or list or None) – Data tag or list of data tags for additional data that are averaged with the self.datatag data before fitting. Default is None.

corrfitter.CorrFitter Objects

corrfitter.CorrFitter objects are wrappers for lsqfit.nonlinear_fit() which is used to fit a collection of models to a collection of Monte Carlo data.

class corrfitter.CorrFitter(models, nterm=None, ratio=False, fast=True, **fitterargs)

Nonlinear least-squares fitter for a collection of correlator models.

Parameters:
  • models – List of models, derived from lsqfit.MultiFitterModel, to be fit to the data. Individual models in the list can be replaced by lists of models or tuples of models; see below.
  • nterm (tuple or int or None) – Number of terms fit in the non-oscillating part of fit functions; or a two-tuple of numbers indicating how many terms to fit in each of the non-oscillating and oscillating parts. Terms omitted from the fit are marginalized (i.e., included as corrections to the fit data). If set to None, all parameters in the prior are fit, and none are marginalized.
  • ratio (bool) – If True, implement marginalization using ratios: data_marg = data * fitfcn(prior_marg) / fitfcn(prior). If False (default), implement using differences: data_marg = data + (fitfcn(prior_marg) - fitfcn(prior)).
  • fast (bool) – Setting fast=True (default) strips any variable not required by the fit from the prior. This speeds fits but loses information about correlations between variables in the fit and those that are not. The information can be restored using lsqfit.wavg after the fit.
  • fitterargs – Additional arguments for the lsqfit.nonlinear_fit, such as tol, maxit, svdcut, fitter, etc., as needed.
bootstrap_fit_iter(datalist=None, n=None)

Iterator that creates bootstrap copies of a corrfitter.CorrFitter fit using bootstrap data from list data_list.

A bootstrap analysis is a robust technique for estimating means and standard deviations of arbitrary functions of the fit parameters. This method creates an interator that implements such an analysis of list (or iterator) datalist, which contains bootstrap copies of the original data set. Each data_list[i] is a different data input for self.lsqfit() (that is, a dictionary containing fit data). The iterator works its way through the data sets in data_list, fitting the next data set on each iteration and returning the resulting lsqfit.LSQFit fit object. Typical usage, for an corrfitter.CorrFitter object named fitter, would be:

for fit in fitter.bootstrap_iter(datalist):
    ... analyze fit parameters in fit.p ...
Parameters:
  • data_list (sequence or iterator or None) – Collection of bootstrap data sets for fitter. If None, the data_list is generated internally using the means and standard deviations of the fit data (assuming gaussian statistics).
  • n (integer) – Maximum number of iterations if n is not None; otherwise there is no maximum.
Returns:

Iterator that returns a lsqfit.LSQFit object containing results from the fit to the next data set in data_list.
bootstrap_iter(datalist=None, n=None)

Iterator that creates bootstrap copies of a corrfitter.CorrFitter fit using bootstrap data from list data_list.

A bootstrap analysis is a robust technique for estimating means and standard deviations of arbitrary functions of the fit parameters. This method creates an interator that implements such an analysis of list (or iterator) datalist, which contains bootstrap copies of the original data set. Each data_list[i] is a different data input for self.lsqfit() (that is, a dictionary containing fit data). The iterator works its way through the data sets in data_list, fitting the next data set on each iteration and returning the resulting lsqfit.LSQFit fit object. Typical usage, for an corrfitter.CorrFitter object named fitter, would be:

for fit in fitter.bootstrap_iter(datalist):
    ... analyze fit parameters in fit.p ...
Parameters:
  • data_list (sequence or iterator or None) – Collection of bootstrap data sets for fitter. If None, the data_list is generated internally using the means and standard deviations of the fit data (assuming gaussian statistics).
  • n (integer) – Maximum number of iterations if n is not None; otherwise there is no maximum.
Returns:

Iterator that returns a lsqfit.LSQFit object containing results from the fit to the next data set in data_list.
static read_dataset(inputfiles, grep=None, keys=None, h5group='/', binsize=1, tcol=0, Gcol=1)

Read correlator Monte Carlo data from files into a gvar.dataset.Dataset.

Three files formats are supported by read_dataset(), depending upon inputfiles.

If inputfiles is a string ending in '.h5', it is assumed to be the name of a file in hpf5 format. The file is opened as h5file and all hpf5 datasets in h5file[h5group] are collected into a dictionary and returned.

The second file format is the text-file format supported by gvar.dataset.Dataset: each line consists of a tag or key identifying a correlator followed by data corresponding to a single Monte Carlo measurement of the correlator. This format is assumed if inputfiles is a filename or a list of filenames. It allows a single file to contain an arbitrary number of measurements for an arbitrary number of different correlators. The data can also be spread over multiple files. A typical file might look like

# this is a comment; it is ignored
aa 1.237 0.912 0.471
bb 3.214 0.535 0.125
aa 1.035 0.851 0.426
bb 2.951 0.625 0.091
...

which describes two correlators, aa and bb, each having three different t values.

The third file format is assumed when inputfiles is a dictionary. The dictionary’s keys and values identify the (one-dimensional) correlators and the files containing their Monte Carlo data, respectively. So the data for correlators aa and bb above are in separate files:

fileinputs = dict(aa='aafile', bb='bbfile')

Each line in these data files consists of an index t value followed by the corresponding value for correlator G(t). The ts increase from line to line up to their maximum value, at which point they repeat. The aafile file for correlator aa above would look like:

# this is a comment; it is ignored
1 1.237
2 0.912
3 0.471
1 1.035
2 0.851
3 0.426
...

The columns in these files containing t and G(t) are assumed to be columns 0 and 1, respectively. These can be changed by setting arguments tcol and Gcol, respectively.

corrfitter.process_dataset supports keywords binsize, grep and keys. If binsize is greater than one, random samples are binned with bins of size binsize. If grep is not None, only keys that match or partially match regular expression grep are retained; others are ignored. If keys is not None, only keys that are in list keys are retained; others are discarded.

simulated_pdata_iter(n, dataset, pexact=None, rescale=1.0)

Create iterator that returns simulated fit pdata from dataset.

Simulated fit data has the same covariance matrix as pdata=self.process_dataset(dataset), but mean values that fluctuate randomly, from copy to copy, around the value of the fitter’s fit function evaluated at p=pexact. The fluctuations are generated from bootstrap copies of dataset.

The best-fit results from a fit to such simulated copies of pdata should agree with the numbers in pexact to within the errors specified by the fits (to the simulated data) — pexact gives the “correct” values for the parameters. Knowing the correct value for each fit parameter ahead of a fit allows us to test the reliability of the fit’s error estimates and to explore the impact of various fit options (e.g., fitter.chained_fit versus fitter.lsqfit, choice of SVD cuts, omission of select models, etc.)

Typically one need examine only a few simulated fits in order to evaluate fit reliability, since we know the correct values for the parameters ahead of time. Consequently this method is much faster than traditional bootstrap analyses.

pexact is usually taken from the last fit done by the fitter (self.fit.pmean) unless overridden in the function call. Typical usage is as follows:

dataset = gvar.dataset.Dataset(...)
data = gvar.dataset.avg_data(dataset)
...
fit = fitter.lsqfit(data=data, ...)
...
for spdata in fitter.simulated_pdata_iter(n=4, dataset):
    # redo fit 4 times with different simulated data each time
    # here pexact=fit.pmean is set implicitly
    sfit = fitter.lsqfit(pdata=spdata, ...)
    ... check that sfit.p (or functions of it) agrees ...
    ... with pexact=fit.pmean to within sfit.p's errors      ...
Parameters:
  • n (int) – Maximum number of simulated data sets made by iterator.
  • dataset (dictionary) – Dataset containing Monte Carlo copies of the correlators. dataset[datatag] is a two-dimensional array for the correlator corresponding to datatag, where the first index labels the Monte Carlo copy and the second index labels time.
  • pexact (dictionary or None) – Correct parameter values for fits to the simulated data — fit results should agree with pexact to within errors. If None, uses self.fit.pmean from the last fit.
  • rescale (float) – Rescale errors in simulated data by rescale (i.e., multiply covariance matrix by rescale ** 2). Default is one, which implies no rescaling.

corrfitter.EigenBasis Objects

corrfitter.EigenBasis objects are useful for analyzing two-point and three-point correlators with multiplle sources and sinks. The current interface for EigenBasis is experimental. It may change in the near future, as experience accumulates from its use.

class corrfitter.EigenBasis(data, srcs, t, keyfmt='{s1}.{s2}', tdata=None, osc=False)

Eigen-basis of correlator sources/sinks.

Given N sources/sinks and the N \times N matrix G_{ij}(t) of 2-point correlators created from every combination of source and sink, we can define a new basis of sources that makes the matrix correlator approximately diagonal. Each source in the new basis is associated with an eigenvector v^{(a)} defined by the matrix equation

G(t_1) v^{(a)} = \lambda^{(a)}(t_1-t_0) G(t_0) v^{(a)},

for some t_0, t_1. As t_0, t_1 increase, fewer and fewer states couple to G(t). In the limit where only N states couple, the correlator

\overline{G}_{ab}(t) \equiv v^{(a)T} G(t) v^{(b)}

becomes diagonal, and each diagonal element couples to only a single state.

In practice, this condition is only approximate: that is, \overline G(t) is approximately diagonal, with diagonal elements that overlap strongly with the lowest lying states, but somewhat with other states. These new sources are nevertheless useful for fits because there is an obvious prior for their amplitudes: prior[a][b] approximately equal to one when b==a, approximately zero when b!=a and b<N, and order one otherwise.

Such a prior can significantly enhance the stability of a multi-source fit, making it easier to extract reliable results for excited states. It encodes the fact that only a small number of states couple strongly to G(t) by time t_0, without being overly prescriptive about what their energies are. We can easilty project our correlator onto the new eigen-basis (using EigenBasis.apply()) in order to use this prior, but this is unnecessary. EigenBasis.make_prior() creates a prior of this type in the eigen-basis and then transforms it back to the original basis, thereby creating an equivalent prior for the amplitudes corresponding to the original sources.

Typical usage is straightforward. For example,

basis = EigenBasis(
    data,                           # data dictionary
    keyfmt='G.{s1}.{s2}',           # key format for dictionary entries
    srcs=['local', 'smeared'],      # names of sources/sinks
    t=(5, 7),                       # t0, t1 used for diagonalization
    )
prior = basis.make_prior(nterm=4, keyfmt='m.{s1}')

creates an eigen-prior that is optimized for fitting the 2-by-2 matrix correlator given by

[[data['G.local.local'],     data['G.local.smeared']]
 [data['G.smeared.local'],   data['G.smeared.smeared']]]

where data is a dictionary containing all the correlators. Parameter t specifies the times used for the diagonalization: t_0=5 and t_1=7. Parameter nterm specifies the number of terms in the fit. basis.make_prior(...) creates priors prior['m.local'] and prior['m.smeared'] for the amplitudes corresponding to the local and smeared source, and a prior prior[log(m.dE)] for the logarithm of the energy differences between successive levels.

The amplitudes prior['m.local'] and prior['m.smeared'] are complicated, with strong correlations between local and smeared entries for the same state. Projecting the prior unto the eigen-basis, however, reveals its underlying structure:

p_eig = basis.apply(prior)

implies

p_eig['m.0'] = [1.0(3), 0.0(1), 0(1), 0(1)]
p_eig['m.1'] = [0.0(1), 1.0(3), 0(1), 0(1)]

where the different entries are now uncorrelated. This structure registers our expectation that the 'm.0' source in the eigen-basis overlaps strongly with the ground state, but almost not at all with the first excited state; and vice versa for the 'm.1' source. Amplitude p_eig is noncommittal about higher states. This structure is built into prior['m.local'] and prior['smeared'].

It is easy to check that fit results are consistent with the underlying prior. This can be done by projecting the best-fit parameters unto the eigen-basis using p_eig = basis.apply(fit.p). Alternatively, a table listing the amplitudes in the new eigen-basis, together with the energies, is printed by:

print(basis.tabulate(fit.p, keyfmt='m.{s1}', eig_srcs=True))

The prior can be adjusted, if needed, using the dEfac, ampl, and states arguments in EigenBasis.make_prior().

EigenBasis.tabulate() is also useful for printing the amplitudes for the original sources:

print(basis.tabulate(fit.p, keyfmt='m.{s1}'))

corrfitter.EigenBasis requires the scipy library in Python.

Parameters:
  • data – Dictionary containing the matrix correlator using the original basis of sources and sinks.
  • keyfmt – Format string used to generate the keys in dictionary data corresponding to different components of the matrix of correlators. The key for G_{ij} is assumed to be keyfmt.format(s1=i, s2=j) where i and j are drawn from the list of sources, srcs.
  • srcs – List of source names used with keyfmt to create the keys for finding correlator components G_{ij} in the data dictionary.
  • t – t=(t0, t1) specifies the t values used to diagonalize the correlation function. Larger t values are better than smaller ones, but only if the statistics are adequate. When fitting staggered-quark correlators, with oscillating components, choose t values where the oscillating pieces are positive (typically odd t). If only one t is given, t=t0, then t1=t0+2 is used with it. Fits that use corrfitter.EigenBasis typically depend only weakly on the choice of t.
  • tdata – Array containing the times for which there is correlator data. tdata is set equal to numpy.arange(len(G_ij)) if it is not specified (or equals None).

The interface for EigenBasis is experimental. It may change in the near future, as experience accumulates from its use.

In addition to keyfmt, srcs, t and tdata above, the main attributes are:

E

Array of approximate energies obtained from the eigenanalysis.

eig_srcs

List of labels for the sources in the eigen-basis: '0', '1' …

svdcorrection

The sum of the SVD corrections added to the data by the last call to EigenBasis.svd().

svdn

The number of degrees of freedom modified by the SVD correction in the last call to EigenBasis.svd().

v

v[a] is the eigenvector corresponding to source a in the new basis, where a=0,1....

v_inv

v_inv[i] is the inverse-eigenvector for transforming from the new basis back to the original basis.

The main methods are:

apply(data, keyfmt='{s1}')

Transform data to the eigen-basis.

The data to be transformed is data[k] where key k equals keyfmt.format(s1=s1) for vector data, or keyfmt.format(s1=s1, s2=s2) for matrix data with sources s1 and s2 drawn from self.srcs. A dictionary containing the transformed data is returned using the same keys but with the sources replaced by '0', '1' ... (from basis.eig_srcs).

If keyfmt is an array of formats, the transformation is applied for each format and a dictionary containing all of the results is returned. This is useful when the same sources and sinks are used for different types of correlators (e.g., in both two-point and three-point correlators).

make_prior(nterm, keyfmt='{s1}', dEfac='1(1)', ampl=('1.0(3)', '0.03(10)', '0.2(1.0)'), states=None, eig_srcs=False)

Create prior from eigen-basis.

Parameters:
  • nterm (int) – Number of terms in fit function.
  • keyfmt (str) – Format string usded to generate keys for amplitudes and energies in the prior (a dictionary): keys are obtained from keyfmt.format(s1=a) where a is one of the original sources, self.srcs, if eig_srcs=False (default), or one of the eigen-sources, self.eig_srcs, if eig_srcs=True. The key for the energy differences is generated by 'log({})'.format(keyfmt.format(s1='dE')). The default is keyfmt={s1}.
  • dEfac (str or gvar.GVar) – A string or gvar.GVar from which the priors for energy differences dE[i] are constructed. The mean value for dE[0] is set equal to the lowest energy obtained from the diagonalization. The mean values for the other dE[i]s are set equal to the difference between the lowest two energies from the diagonalization (or to the lowest energy if there is only one). These central values are then multiplied by gvar.gvar(dEfac). The default value, 1(1), sets the width equal to the mean value. The prior is the logarithm of the resulting values.
  • ampl (tuple) – A 3-tuple of strings or gvar.GVars from which priors are contructed for amplitudes corresponding to the eigen-sources. gvar.gvar(ampl[0]) is used for for source components where the overlap with a particular state is expected to be large; 1.0(3) is the default value. gvar.gvar(ampl[1]) is used for states that are expected to have little overlap with the source; 0.03(10) is the default value. gvar.gvar(ampl[2]) is used where there is nothing known about the overlap of a state with the source; 0(1) is the default value.
  • states (list) – A list of the states in the correlator corresponding to successive eigen-sources, where states[i] is the state corresponding to i-th source. The correspondence between sources and states is strong for the first sources, but can decay for subsequent sources, depending upon the quality of the data being used and the t values used in the diagonalization. In such situations one might specify fewer states than there are sources by making the length of states smaller than the number of sources. Setting states=[] assigns broad priors to the every component of every source. Parameter states can also be used to deal with situations where the order of later sources is not aligned with that of the actual states: for example, states=[0,1,3] connects the eigen-sources with the first, second and fourth states in the correlator. The default value, states=[0, 1 ... N-1] where N is the number of sources, assumes that sources and states are aligned.
  • eig_srcs (bool) – Amplitudes for the eigen-sources are tabulated if eig_srcs=True; otherwise amplitudes for the original basis of sources are tabulated (default).
svd(data, keyfmt=None, svdcut=1e-15)

Apply SVD cut to data in the eigen-basis.

The SVD cut is applied to data[k] where key k equals keyfmt.format(s1=s1) for vector data, or keyfmt.format(s1=s1, s2=s2) for matrix data with sources s1 and s2 drawn from self.srcs. The data are transformed to the eigen-basis of sources/sinks before the cut is applied and then transformed back to the original basis of sources. Results are returned in a dictionary containing the modified correlators.

If keyfmt is a list of formats, the SVD cut is applied to the collection of data formed from each format. The defaul value for keyfmt is self.keyfmt.

tabulate(p, keyfmt='{s1}', nterm=None, nsrcs=None, eig_srcs=False, indent=' ')

Create table containing energies and amplitudes for nterm states.

Given a correlator-fit result fit and a corresponding EigenBasis object basis, a table listing the energies and amplitudes for the first N states in correlators can be printed using

print basis.tabulate(fit.p)

where N is the number of sources and basis is an EigenBasis object. The amplitudes are tabulated for the original sources unless parameter eig_srcs=True, in which case the amplitudes are projected onto the the eigen-basis defined by basis.

Parameters:
  • p – Dictionary containing parameters values.
  • keyfmt – Parameters are p[k] where keys k are obtained from keyfmt.format(s1=s) where s is one of the original sources (basis.srcs) or one of the eigen-sources (basis.eig_srcs). The default definition is '{s1}'.
  • nterm – The number of states from the fit tabulated. The default sets nterm equal to the number of sources in the basis.
  • nsrcs – The number of sources tabulated. The default causes all sources to be tabulated.
  • eig_srcs – Amplitudes for the eigen-sources are tabulated if eig_srcs=True; otherwise amplitudes for the original basis of sources are tabulated (default).
  • indent – A string prepended to each line of the table. Default is 4 * ' '.
unapply(data, keyfmt='{s1}')

Transform data from the eigen-basis to the original basis.

The data to be transformed is data[k] where key k equals keyfmt.format(s1=s1) for vector data, or keyfmt.format(s1=s1, s2=s2) for matrix data with sources s1 and s2 drawn from self.eig_srcs. A dictionary containing the transformed data is returned using the same keys but with the original sources (from self.srcs).

If keyfmt is an array of formats, the transformation is applied for each format and a dictionary containing all of the results is returned. This is useful when the same sources and sinks are used for different types of correlators (e.g., in both two-point and three-point correlators).

Fast Fit Objects

class corrfitter.fastfit(G, ampl='0(1)', dE='1(1)', E=None, s=(1, -1), tp=None, tmin=6, svdcut=1e-06, osc=False, nterm=10)

Fast fit of a two-point correlator.

This function class estimates E=En[0] and ampl=an[0]*bn[0] for a two-point correlator modeled by

Gab(t) = sn * sum_i an[i]*bn[i] * fn(En[i], t)
       + so * sum_i ao[i]*bo[i] * fo(Eo[i], t)

where (sn, so) is typically (1, -1) and

fn(E, t) =  exp(-E*t) + exp(-E*(tp-t)) # tp>0 -- periodic
       or   exp(-E*t) - exp(-E*(-tp-t))# tp<0 -- anti-periodic
       or   exp(-E*t)                  # if tp is None (nonperiodic)

fo(E, t) = (-1)**t * fn(E, t)

Prior estimates for the amplitudes and energies of excited states are used to remove (that is, marginalize) their contributions to give a corrected correlator Gc(t) that includes uncertainties due to the terms removed. Estimates of E are given by:

Eeff(t) = arccosh(0.5 * (Gc(t+1) + Gc(t-1)) / Gc(t)),

The final estimate is the weighted average Eeff_avg of the Eeff(t)s for different ts. Similarly, an estimate for the amplitude ampl is obtained from the weighted average of

Aeff(t) = Gc(t) / fn(Eeff_avg, t).

If osc=True, an estimate is returned for Eo[0] rather than En[0], and ao[0]*bo[0] rather than an[0]*bn[0]. These estimates are reliable when Eo[0] is smaller than En[0] (and so dominates at large t), but probably not otherwise.

Examples

The following code examines a periodic correlator (period 64) at large times (t >= tmin), where estimates for excited states don’t matter much:

>>> import corrfitter as cf
>>> print(G)
[0.305808(29) 0.079613(24) ... ]
>>> fit = cf.fastfit(G, tmin=24, tp=64)
>>> print('E =', fit.E, ' ampl =', fit.ampl)
E = 0.41618(13)  ampl = 0.047686(95)

Smaller tmin values can be used if (somewhat) realistic priors are provided for the amplitudes and energy gaps:

>>> fit = cf.fastfit(G, ampl='0(1)', dE='0.5(5)', tmin=3, tp=64)
>>> print('E =', fit.E, ' ampl =', fit.ampl)
E = 0.41624(11)  ampl = 0.047704(71)

The result here is roughly the same as from the larger tmin, but this would not be true for a correlator whose signal to noise ratio falls quickly with increasing time.

corrfitter.fastfit estimates the amplitude and energy at all times larger than tmin and then averages to get its final results. The chi-squared of the average (e.g., fit.E.chi2) gives an indication of the consistency of the estimates from different times. The chi-squared per degree of freedom is printed out for both the energy and the amplitude using

>>> print(fit)
E: 0.41624(11) ampl: 0.047704(71) chi2/dof [dof]: 0.9 0.8 [57] Q: 0.8 0.9

Large values for chi2/dof indicate an unreliable results. In such cases the priors should be adjusted, and/or tmin increased, and/or an SVD cut introduced. The averages in the example above have good values for chi2/dof.

Parameters:
  • G – An array of gvar.GVars containing the two-point correlator. G[j] is assumed to correspond to time t=j, where j=0....
  • ampl – A gvar.GVar or its string representation giving an estimate for the amplitudes of the ground state and the excited states. Use ampl=(ampln, amplo) when the correlator contains oscillating states; ampln is the estimate for non-oscillating states, and amplo for oscillating states; setting one or the other to None causes the corresponding terms to be dropped. Default value is '0(1)'.
  • dE – A gvar.GVar or its string representation giving an estimate for the energy separation between successive states. This estimate is also used to provide an estimate for the lowest energy when parameter E is not specified. Use dE=(dEn, dEo) when the correlator contains oscillating states: dEn is the estimate for non-oscillating states, and dEo for oscillating states; setting one or the other to None causes the corresponding terms to be dropped. Default value is '1(1)'.
  • E – A gvar.GVar or its string representation giving an estimate for the energy of the lowest-lying state. Use E=(En, Eo) when the correlator contains oscillating states: En is the estimate for the lowest non-oscillating state, and Eo for lowest oscillating state. Setting E=None causes E to be set equal to dE. Default value is None.
  • s – A tuple containing overall factors (sn, so) multiplying contributions from the normal and oscillating states. Default is (1,-1).
  • tp (int or None) – When not None, the correlator is periodic with period tp when tp>0, or anti-periodic with period -tp when tp<0. Setting tp=None implies that the correlator is neither periodic nor anti-periodic. Default is None.
  • tmin (int) – Only G(t) with t >= tmin are used. Default value is 6.
  • svdcut (float or None) – SVD cut used in the weighted average of results from different times. (See the corrfitter.CorrFitter documentation for a discussion of SVD cuts.) Default is 1e-6.
  • osc (bool) – Set osc=True if the lowest-lying state is an oscillating state. Default is False.

Note that specifying a single gvar.GVar g (as opposed to a tuple) for any of parameters ampl, dE, or E is equivalent to specifying the tuple (g, None) when osc=False, or the tuple (None, g) when osc=True. A similar rule applies to parameter s.

corrfitter.fastfit objects have the following attributes:

E

Energy of the lowest-lying state (gvar.GVar).

ampl

Amplitude of the lowest-lying state (gvar.GVar).

Both E and ampl are obtained by averaging results calculated for each time larger than tmin. These are averaged to produce a final result. The consistency among results from different times is measured by the chi-squared of the average. Each of E and ampl has the following extra attributes:

chi2

chi-squared for the weighted average.

dof

The effective number of degrees of freedom in the weighted average.

Q

The probability that the chi-squared could have been larger, by chance, assuming that the data are all Gaussain and consistent with each other. Values smaller than 0.05 or 0.1 suggest inconsistency. (Also called the p-factor.)

An easy way to inspect these attributes is to print the fit object fit using print(fit), which lists the values of the energy and amplitude, the chi2/dof for each of these, the number of degrees of freedom, and the Q for each.