This python library aims to offer analysis tools for QCD Data
- Computation of effective mass from 2-point correlation function
def effective_mass(t_correlator,t_initial_guess, t_analysis_type, **analysis_kwargs)
- First order energy by
C(t) ~ A cosh((t-Nt/2)*E )
def energy_firstorder( t_correlator, t_meff, t_A0, t_E0, t_fitter, t_fitter_params = dict(), t_plateau_est_acc = 1e-4, t_plateau_area = 2)
- Jackknife (with and without blocking)
def jackknife(t_data, t_n = 1, t_random_leaveout = False, t_num_ran_indices=None, t_blocked = False, t_num_blocks = None)
- Bootstrap (with and without blocking)
def bootstrap(t_data,t_num_leave_outs, t_blocked = False, t_num_blocks = None)
- Blocking
def blocking(t_data, t_num_blocks = 2) # estimator and variances
def var_per_num_blocks(t_data,t_num_blocks_range = None) # block number analysis
- Fitting
- Least Square Diagonal Approximation
class DiagonalLeastSquare(t_model,t_abscissa,t_data=None,t_ordinate=None,t_ordinate_var=None,t_analysis_params=None)
* fit: `Fit_Results = DiagonalLeastSquare.fit()` * print: `DiagonalLeastSquare.print_result(*args,**kwargs)`
- Least Square Correltated
class CorrelatedLeastSquare(t_model,t_abscissa,t_data=None,t_ordinate=None,t_ordinate_cov=None,t_analysis_params=None, t_inv_acc=1e-8)
* fit: `Fit_Results = DiagonalLeastSquare.fit()` * print: `DiagonalLeastSquare.print_result(*args,**kwargs)`
- Sampled Least Square Diagonal Approximation
class Sampled_DiagonalLeastSquare(t_model,t_abscissa,t_data,t_analysis_params)
* fit: `Fit_Results = DiagonalLeastSquare.fit()` * print: `DiagonalLeastSquare.print_result(*args,**kwargs)`
- Sampled Least Square Correltated
class Sampled_CorrelatedLeastSquare(t_model,t_abscissa,t_data,t_analysis_params,t_frozen_cov_flag=False,t_inv_acc=1e-8)
* set `t_frozen_cov_flag = True` to compute the convariance matrix once over full data and do not recompute it on every sample. Might increase stability and greatly increases speed. * fit: `Fit_Results = DiagonalLeastSquare.fit()` * print: `DiagonalLeastSquare.print_result(*args,**kwargs)`
- Akaike information criterion
def AIC_chisq(t_dof, t_chisq)
def AICc_chisq(t_dof, t_datasize, *AIC_args, **AIC_kwargs)
def AIC_weights(t_AICs)
- dimension transformations
- mass:
def massMeV(t_mlat, t_a, t_mlat_err = None, t_a_err = None)
- Note: Either both or no err parameter must be given.
- mass:
scipy
itertools
warnings
pathlb
numpy
matplotlib.pyplot
aff
USQCDs python aff library (Optional)- only required if
qcdanalysistools.fileIO.aff
is used. - needs to be installed beforehand
- only required if
python setup.py install --user
or
sudo python setup.py install
This software package is accessible under the MIT license.