All notable changes to this project will be documented in this file.
- Allow providing lower triangular matrix constructed from a Cholesky decomposition in least squares function for correlated fits.
- Corrected bug that prevented combined fits with multiple x-obs in some cases.
- Support for numpy 2 was added via a new autograd release
- Support for python<3.9 was dropped and dependencies were updated.
- Minor bug fixes in input.sfcf
- Fixed a bug in error computation when combining two Obs from the same ensemble and fluctuations on one replicum are not part of one of the Obs.
- New special function module.
- Various bug fixes in input module.
- More efficient implementation of read_sfcf
- added support for addition and multiplication of complex numbers to Corr objects
- the Corr.GEVP method can now also propagate the errors for the eigenvectors
- Fixed bug in combined fit with multiple independent variables
- Check for invalid set of configuration numbers added when initializing an Obs object.
- Fixed a bug in hadrons.read_hdf5
- Vectorized
gamma_method
added which can be applied to lists or arrays of pyerrors objects. - Wrapper for numerical integration of
Obs
valued functions withObs
valued intervals. - Bootstrap import and export added.
matmul
overloaded forCorr
class.- More options for initializing
Corr
objects added. - General Hadrons hdf5 reader added.
- New variant of second_derivative.
- CObs formatting added.
- Comparisons for
Corr
class added.
- support for python<3.8 was dropped and dependencies were updated.
- Another bug appearing in an edge case of
_compute_drho
fixed.
input.pandas
can now deal with columns that only haveNone
entries.- Bug in f-string conversion of
Obs
fixed. - Bug in edge case of
_compute_drho
fixed. - Several numpy 1.25 deprecations fixed.
pyerrors
can now deal with replica with different gapsizes.- String formatting method for
Obs
added. t0
can now be extracted from hadrons files.w0
can now be extracted from openQCD files.pandas
SQL export can now deal withNone
entries in columns withpyerrors
datatypes.
dobs
submodule is now correctly imported.- Bug in merging of
Obs
fixed. - Bug in
rapidjson
dict output fixed. - String conversion of
Obs
can now handle specialdvalue
s - Bug in sfcf name sorting fixed.
- Alternative way of specifying priors in
least_squares
added. - Correlated fits now also work with priors.
- Lists of
Obs
can now be serialized and deserialized in pandas.to_sql print_config
function for debugging purposes added.Corr.show
can now visualize results of combined fits.
- Fit routines refactored and simplified.
- sfcf input routines refactored.
- drho is not automatically computed for all windows in the automatic windowing procedure. This change speeds up the
gamma_method
for very long Monte Carlo histories. __slots__
added toCorr
class.
- The fit module now has a new interface to deal with combined fits.
pyerrors
wrapper for matplotliberrorbar
method added forObs
valued lists/arrays.- roots module can now determine roots of multi parameter
Obs
valued functions.
- Bug in treatment of error propagation of non-overlapping configurations fixed.
Corr.symmetric
can now deal withNone
entries.- Fix in
ms5_xsf
input routines. - Bug in
dobs
output format fixed.
- Alias
gm
forObs.gamma_method
added. - Hotelling t-squared p-value added for correlated fits.
- String conversion of numpy arrays containing
Obs
improved. - Input routine for xSF measurement program added.
- Complex valued
Corr
objects fixed. - Small bug in
qtop_projection
fixed. - Bug in
Corr.spaghetti_plot
fixed which appeared in connection with replica separators.
- Merged
Obs
are no longer filtered as this lead to inconsistentidl
s in some edge cases. Error estimates are unaffected up to filter precision.
- Removed the
Obs
attributeis_merged
as this information was only needed for the filtering. The change results in a ~1.15x speed up in the multiplication of twoObs
.
- Log-derivatives and symmetric log-effective mass added.
- Covariance for irregular Monte Carlo chains sped up.
- Additional checks in
Corr.GEVP
added.
- Bug in
Obs.details
fixed which appeared when tau had zero error. - Bug in
input.json
export in connection withnumpy.int64
fixed. - Small bug fixes in
input.openQCD
.
- Integrated autocorrelation times are now correctly estimated for gapped irregular Monte Carlo chains.
- The output of
Obs.details
was improved and now contains information about the stepsize in configurations for which the integrated autocorrelation time was estimated.
least_squares
andtotal_least_squares
fits now have an optional keyword argumentnum_grad
. If this argument is set toTrue
the error propagation of the fit is performed via numerical instead of automatic differentiation. This options allows for fits functions which contain special functions or which are not analytically known.
- Bug in
Corr.show
comp
option fixed.
- New submodule
input.pandas
added which adds the possibility to read and write pandas DataFrames containingObs
orCorr
objects to csv files or SQLite databases. hash
method forObs
objects added.Obs.reweight
method added in analogy toCorr.reweight
which allows for a more convenient reweighting of individual observables.Corr.show
now has the additional argumenttitle
which allows to add a title to the figure. Figures are now saved withbbox_inches='tight'
.- Function for the extraction of the gradient flow coupling added (see 1607.06423 for details).
Corr.is_matrix_symmetric
added which efficiently checks whether a correlator matrix is symmetric. This is used to speed up the GEVP method.
Corr.m_eff
can now deal with correlator entries which are exactly zero.- Minor bugs in
input.dobs
fixed.
- Further bugs in connection with correlator objects which have arrays with None entries as content fixed.
- Bug in
Corr.matrix_symmetric
fixed which appeared when a time slice contained an array withNone
entries.
- Bug in error propagation of correlated least square fits fixed.
Fit_result.gamma_method
can now be called with kwargs.
obs.covariance
now has the option to smooth small eigenvalues of the matrix with the method described in hep-lat/9412087.Corr.prune
was added which can reduce the size of a correlator matrix before solving the GEVP.Corr.show
has two additional optional arguments.hide_sigma
to hide data points with large errors andreferences
to display reference values as horizontal lines.- I/O routines for ALPHA dobs format added.
input.hadrons
functionality extended.
- The standard behavior of the
Corr.GEVP
method has changed. It now returns all eigenvectors of the system instead of only the specified ones as default. The standard way of sorting the eigenvectors was changed toEigenvalue
. The argumentsorted_list
was deprecated in favor ofsort
. - Before performing a correlated fit the routine first runs an uncorrelated one to obtain a better guess for the initial parameters.
obs.covariance
now also gives correct estimators if data defined on non-identical configurations is passed to the function.- Rounding errors in estimating derivatives of fit parameters with respect to input data from the inverse hessian reduced. This should only make a difference when the magnitude of the errors of different fit parameters vary vastly.
- Bug in json.gz format fixed which did not properly store the replica mean values. Format version bumped to 1.1.
- The GEVP matrix is now symmetrized before solving the system for all sorting options not only the one with fixed
ts
. - Automatic range estimation improved in
fits.residual_plot
. - Bugs in
input.bdio
fixed.
- The possibility to work with Monte Carlo histories which are evenly or unevenly spaced was added.
cov_Obs
added as a possibility to propagate the error of non Monte Carlo data together with Monte Carlo data.CObs
class added which can handle complex valued Markov chain Monte Carlo data and the corresponding error propagation.- Matrix to matrix operations like the matrix inverse now also work for complex matrices and matrices containing entries that are not
Obs
butfloat
orint
. - Support for a new
json.gz
file format was added. - The Corr class now has additional methods like
reverse
,T_symmetry
,correlate
andreweight
. Corr.m_eff
can now cope with periodic and anti-periodic correlation functions.- Forward, backward and improved variants of the first and second derivative were added to the
Corr
class. GEVP
functionality of theCorr
class was reworked and improved.- The
linalg
module now has explicit functionsinv
,cholesky
anddet
. Obs
objects now have methodsis_zero
andis_zero_within_error
as well as overloaded comparison operations.- Functions to convert
Obs
data to or from jackknife was added. - Alternative matrix multiplication routines
einsum
andjack_matmul
were added tolinalg
module which make use of the jackknife approximation and are much faster for large matrices. - Additional input routines for npr data added to
input.hadrons
. - The
sfcf
andopenQCD
input modules can now handle all recent file type versions. extract_t0
can now visualize the extraction on the fly.- Module added which provides the Dirac gamma matrices in the Grid convention.
- Version number added.
- The internal bookkeeping system for ensembles/replica was changed. The separator for replica is now
|
. - The fit functions were renamed to
least_squares
andtotal_least_squares
. - The output of the fit functions is now a dedicated results class which keeps track of all relevant information.
- The fit functions can now deal with provided covariance matrices.
covariance
can now operate on a list or array ofObs
and returns a matrix. The covariance estimate by pyerrors is now always positive semi-definite (within machine precision. Various warnings and exceptions were added for cases in which estimated covariances are close to singular.- The convention for the fit range in the Corr class has been changed.
- Various method of the
Corr
class were renamed. Obs.print
was renamed toObs.details
and the output was improved.- The default value for
Corr.prange
is nowNone
. - The
input
module was restructured to contain one submodule per data source. - Performance of Obs.init improved.
- The function
plot_corrs
was deprecated as all its functionality is now contained withinCorr.show
. fits.covariance_matrix
was removed as it is now redundant with the functionality ofcovariance
.- The kwarg
bias_correction
inderived_observable
was removed. - Obs no longer have an attribute
e_Q
. - Removed
fits.fit_exp
. - Removed jackknife module.
Corr
class addedroots
module added which can find the roots of a function that depends on Monte Carlo data via pyerrorsObs
input/hadrons
module added which can read hdf5 files written by Hadronsread_rwms
can now read reweighting factors in the format used by openQCD-2.0.
- Bug in
pyerrors.covariance
fixed that appeared when working with several replica of different length.
- Compatibility with the BDIO Native format outlined here. Read and write function added to input.bdio
- new function
input.bdio.read_dSdm
which can read the bdio output of the programdSdm
by Tomasz Korzec - Expected chisquare implemented for fits with xerrors
- New implementation of the covariance of two observables which employs the arithmetic mean of the integrated autocorrelation times of the two observables. This new procedure has proven to be less biased in simulated data and is also much faster to compute as the computation time is of O(N) whereas the evaluation of the full correlation function is of O(Nlog(N)).
- Added function
gen_correlated_data
tomisc
which generates a set of observables with given covariance and autocorrelation.
- Bias correction hep-lat/0306017 eq. (49) is no longer applied to the exponential tail in the critical slowing down analysis, but only to the part which is directly estimated from rho. This can lead to slightly smaller errors when using the critical slowing down analysis. The values for the integrated autocorrelation time tauint now include this bias correction (up to now the bias correction was applied after estimating tauint). The errors resulting from the automatic windowing procedure are unchanged.
- Bug in
fits.standard_fit
fixed which occurred when attempting a fit with zero degrees of freedom.
merge_obs
function added which allows to merge Obs which describe different replica of the same observable and have been read in separately. Use with care as there is no safeguard implemented which prevent you from merging unrelated Obs.standard fit
andodr_fit
can now treat fits with several x-values via tuples.- Fit functions have a new kwarg
dict_output
which allows to change the output to a dictionary containing additional information. S_dict
andtau_exp_dict
added to Obs in which global values for individual ensembles can be stored.- new function
read_pbp
added which reads dS/dm_q from pbp.dat files. - new function
extract_t0
added which can extract the value of t0 from .ms.dat files of openQCD v 1.2
- When creating an Obs object defined for multiple replica/ensembles, the given names are now sorted alphabetically before assigning the internal dictionaries. This makes sure that
my_Obs
has the same dictionaries asmy_Obs * 1
(derived_observable
always sorted the names). WARNING:Obs
created with previous versions of pyerrors may not be completely identical to new ones (The internal dictionaries may have different ordering). However, this should not affect the inner workings of the error analysis.
- Bug in
covariance
fixed which appeared when different ensemble contents were used.
- New fit functions for fitting with and without x-errors added which use automatic differentiation and should be more reliable than the old ones.
- Fitting with Bayesian priors added.
- New functions for visualization of fits which can be activated via the kwargs resplot and qqplot.
- chisquare/expected_chisquared which takes into account correlations in the data and non-linearities in the fit function can now be activated with the kwarg expected_chisquare.
- Silent mode added to fit functions.
- Examples reworked.
- Changed default function to compute covariances.
- output of input.bdio.read_mesons is now a dictionary instead of a list.
- The function
fit_general
which is based on numerical differentiation will be removed in future versions as new fit functions based on automatic differentiation are now available.
- mesons bdio functionality improved and accelerated, progress report added.
- added the possibility to manually supply a jacobian to derived_observable via the kwarg
man_grad
. This feature was not implemented for the user, but for internal optimization of most basic arithmetic operations which now do not require a call to the autograd package anymore. This results in a speed up of 2 to 3, especially relevant for the multiplication of large matrices.
- input.py and bdio.py moved into submodule input. This should not affect the user API.
- autograd.numpy was replaced by pure numpy wherever it was possible. This should result in a slight speed up.
- fixed bias_correction which broke as a result of the vectorized derived_observable.
- linalg.eig does not give an error anymore if the eigenvalues are complex by just truncating the imaginary part.
- Matrix pencil method for algebraic extraction of energy levels implemented according to Y. Hua, T. K. Sarkar, IEEE Trans. Acoust. 38, 814-824 (1990) in module
mpm.py
. - Import API simplified. After
import pyerrors as pe
, some submodules can be accessed viape.fits
etc. derived_observable
now supports functions which have single- or multi-dimensional numpy arrays as input and/or output (Works only with automatic differentiation).- Matrix functions accelerated by using the new version of
derived_observable
. - New matrix functions: Moore-Penrose Pseudoinverse, Singular Value Decomposition, eigenvalue determination of a general matrix (automatic differentiation included from autograd master).
- Obs can now be compared with < or >, a list of Obs can now be sorted.
- Numerical differentiation can now be controlled via the kwargs of numdifftools.step_generators.MaxStepGenerator.
- Tuned standard parameters for numerical derivative to
base_step=0.1
andstep_ratio=2.5
.
- Matrix functions moved to new module
linalg.py
. - Kolmogorov-Smirnov test moved to new module
misc.py
.
- Numerical differentiation is now based on the package numdifftools which should be more reliable.
- kwarg
h_num_grad
changed tonum_grad
which takes boolean values (default False). - Speed up of rfft calculation of the autocorrelation by reducing the zero padding.