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27
CHANGELOG.md
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@ -4,20 +4,33 @@ All notable changes to this project will be documented in this file.
## [2.0.0] - 2021-??-??
### Added
- `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` but `float` or `int`
- `Obs` objects now have methods `is_zero` and `is_zero_within_error`
- `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` but `float` or `int`.
- The possibility to work with Monte Carlo histories which are evenly or unevenly spaced was added.
- The Corr class now has additional methods like `reverse`, `T_symmetry`, `correlate` and `reweight`.
- `linalg` module now has explicit functions `inv` and `cholesky`.
- `Obs` objects now have methods `is_zero` and `is_zero_within_error` as well as overloaded comparison operations.
- Functions to convert Obs data to or from jackknife was added.
- Alternative matrix multiplication routine `jack_matmul` was added to `linalg` module which makes use of the jackknife approximation and is much faster for large matrices.
- Additional input routines for npr data added to `input.hadrons`.
- Version number added.
### Changed
- Additional attributes can no longer be added to existing `Obs`. This makes it no longer possible to import `Obs` created with previous versions of pyerrors
- The default value for `Corr.prange` is now `None`
- The `input` module was restructured to contain one submodule per data source
- The internal bookkeeping system for ensembles/replica was changed. The separator for replica is now `|`.
- The fit functions were renamed to `least_squares` and `total_least_squares`.
- The fit functions can now deal with provided covariance matrices.
- The convention for the fit range in the Corr class has been changed.
- Obs.print was renamed to Obs.details and the output was improved.
- The default value for `Corr.prange` is now `None`.
- The `input` module was restructured to contain one submodule per data source.
- Performance of Obs.__init__ improved.
### Deprecated
- The function `plot_corrs` was deprecated as all its functionality is now contained within `Corr.show`
- The kwarg `bias_correction` in `derived_observable` was removed
- Obs no longer have an attribute `e_Q`
- Removed `fits.fit_exp`
- Removed jackknife module
## [1.1.0] - 2021-10-11
### Added
@ -77,7 +90,7 @@ All notable changes to this project will be documented in this file.
## [0.7.0] - 2020-03-10
### Added
- New fit funtions for fitting with and without x-errors added which use automatic differentiation and should be more reliable than the old ones.
- 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.

8
README.md
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@ -3,7 +3,7 @@
`pyerrors` is a python package for error computation and propagation of Markov chain Monte Carlo data.
- **Documentation:** https://fjosw.github.io/pyerrors/pyerrors.html
- **Examples**: https://github.com/fjosw/pyerrors/tree/develop/examples
- **Examples**: https://github.com/fjosw/pyerrors/tree/develop/examples (Do not work properly at the moment)
- **Contributing:** https://github.com/fjosw/pyerrors/blob/develop/CONTRIBUTING.md
- **Bug reports:** https://github.com/fjosw/pyerrors/issues
@ -16,9 +16,3 @@ to install the most recent release run
```bash
pip install git+https://github.com/fjosw/pyerrors.git@master
```
## Other implementations
There exist similar publicly available implementations of gamma method error analysis suites in
- [Fortran](https://gitlab.ift.uam-csic.es/alberto/aderrors)
- [Julia](https://gitlab.ift.uam-csic.es/alberto/aderrors.jl)
- [Python](https://github.com/mbruno46/pyobs)

5
pyerrors/__init__.py
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@ -8,6 +8,11 @@ It is based on the **gamma method** [arXiv:hep-lat/0306017](https://arxiv.org/ab
- **non-linear fits with x- and y-errors** and exact linear error propagation based on automatic differentiation as introduced in [arXiv:1809.01289](https://arxiv.org/abs/1809.01289)
- **real and complex matrix operations** and their error propagation based on automatic differentiation (Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...)
There exist similar publicly available implementations of gamma method error analysis suites in
- [Fortran](https://gitlab.ift.uam-csic.es/alberto/aderrors)
- [Julia](https://gitlab.ift.uam-csic.es/alberto/aderrors.jl)
- [Python](https://github.com/mbruno46/pyobs)
## Basic example
```python