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Fabian Joswig 2021-10-11 17:12:57 +01:00
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README.md
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@ -10,7 +10,7 @@ It is based on the gamma method [arXiv:hep-lat/0306017](https://arxiv.org/abs/he
* implementation of the matrix-pencil-method [IEEE Trans. Acoust. 38, 814-824 (1990)](https://ieeexplore.ieee.org/document/56027) for the extraction of energy levels, especially suited for noisy data and excited states
There exist similar implementations of gamma method error analysis suites in
- [Fortran](https://gitlab.ift.uam-csic.es/alberto/aderrors).
- [Fortran](https://gitlab.ift.uam-csic.es/alberto/aderrors)
- [Julia](https://gitlab.ift.uam-csic.es/alberto/aderrors.jl)
- [Python 3](https://github.com/mbruno46/pyobs)
@ -33,15 +33,15 @@ The basic objects of a pyerrors analysis are instances of the class `Obs`. They
import numpy as np
import pyerrors as pe
observable1 = pe.Obs([samples1], ['ensemble1'])
observable1.gamma_method()
observable1.print()
obs1 = pe.Obs([samples1], ['ensemble1'])
obs1.gamma_method()
obs1.print()
```
Often one is interested in secondary observables which can be arbitrary functions of primary observables. `pyerrors` overloads most basic math operations and numpy functions such that the user can work with `Obs` objects as if they were floats
```python
observable3 = 12.0 / observable1 ** 2 - np.exp(-1.0 / observable2)
observable3.gamma_method()
observable3.print()
obs3 = 12.0 / obs1 ** 2 - np.exp(-1.0 / obs2)
obs3.gamma_method()
obs3.print()
```
More detailed examples can be found in the `/examples` folder: