pyerrors
What is pyerrors?
pyerrors
is a python package for error computation and propagation of Markov chain Monte Carlo data.
It is based on the gamma method arXiv:hep-lat/0306017. Some of its features are:
- automatic differentiation as suggested in arXiv:1809.01289 (partly based on the autograd package)
- treatment of slow modes in the simulation as suggested in arXiv:1009.5228
- coherent error propagation for data from different Markov chains
- non-linear fits with x- and y-errors and exact linear error propagation based on automatic differentiation as introduced in [arXiv: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...)
Getting started
import numpy as np
import pyerrors as pe
my_obs = pe.Obs([samples], ['ensemble_name'])
my_new_obs = 2 * np.log(my_obs) / my_obs
my_new_obs.gamma_method()
my_new_obs.details()
print(my_new_obs)
The Obs
class
import pyerrors as pe
my_obs = pe.Obs([samples], ['ensemble_name'])
Multiple ensembles/replica
Irregular Monte Carlo chains
Error propagation
Automatic differentiation, cite Alberto,
numpy overloaded
import numpy as np
import pyerrors as pe
my_obs = pe.Obs([samples], ['ensemble_name'])
my_new_obs = 2 * np.log(my_obs) / my_obs
my_new_obs.gamma_method()
my_new_obs.details()
Error estimation
$\delta_i\delta_j$
Exponential tails
Covariance
Optimization / fits / roots
Complex observables
Matrix operations
Input
View Source
r''' # What is pyerrors? `pyerrors` is a python package for error computation and propagation of Markov chain Monte Carlo data. It is based on the **gamma method** [arXiv:hep-lat/0306017](https://arxiv.org/abs/hep-lat/0306017). Some of its features are: - **automatic differentiation** as suggested in [arXiv:1809.01289](https://arxiv.org/abs/1809.01289) (partly based on the [autograd](https://github.com/HIPS/autograd) package) - **treatment of slow modes** in the simulation as suggested in [arXiv:1009.5228](https://arxiv.org/abs/1009.5228) - coherent **error propagation** for data from **different Markov chains** - **non-linear fits with x- and y-errors** and exact linear error propagation based on automatic differentiation as introduced in [arXiv: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...) ## Getting started ```python import numpy as np import pyerrors as pe my_obs = pe.Obs([samples], ['ensemble_name']) my_new_obs = 2 * np.log(my_obs) / my_obs my_new_obs.gamma_method() my_new_obs.details() print(my_new_obs) ``` # The `Obs` class `pyerrors.obs.Obs` ```python import pyerrors as pe my_obs = pe.Obs([samples], ['ensemble_name']) ``` ## Multiple ensembles/replica ## Irregular Monte Carlo chains # Error propagation Automatic differentiation, cite Alberto, numpy overloaded ```python import numpy as np import pyerrors as pe my_obs = pe.Obs([samples], ['ensemble_name']) my_new_obs = 2 * np.log(my_obs) / my_obs my_new_obs.gamma_method() my_new_obs.details() ``` # Error estimation `pyerrors.obs.Obs.gamma_method` $\delta_i\delta_j$ ## Exponential tails ## Covariance # Optimization / fits / roots # Complex observables # Matrix operations # Input ''' from .obs import * from .correlators import * from .fits import * from . import dirac from . import linalg from . import misc from . import mpm from . import npr from . import roots from .version import __version__