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 for exact liner error propagation 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 (Matrix inverse, Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...).

More detailed examples can found in the GitHub repository badge.

If you use pyerrors for research that leads to a publication please consider citing:

  • Ulli Wolff, Monte Carlo errors with less errors. Comput.Phys.Commun. 156 (2004) 143-153, Comput.Phys.Commun. 176 (2007) 383 (erratum).
  • Alberto Ramos, Automatic differentiation for error analysis of Monte Carlo data. Comput.Phys.Commun. 238 (2019) 19-35.

and

  • Stefan Schaefer, Rainer Sommer, Francesco Virotta, Critical slowing down and error analysis in lattice QCD simulations. Nucl.Phys.B 845 (2011) 93-119.

where applicable.

There exist similar publicly available implementations of gamma method error analysis suites in Fortran, Julia and Python.

Basic example

import numpy as np
import pyerrors as pe

my_obs = pe.Obs([samples], ['ensemble_name']) # Initialize an Obs object
my_new_obs = 2 * np.log(my_obs) / my_obs ** 2 # Construct derived Obs object
my_new_obs.gamma_method()                     # Estimate the statistical error
print(my_new_obs)                             # Print the result to stdout
> 0.31498(72)

The Obs class

pyerrors introduces a new datatype, Obs, which simplifies error propagation and estimation for auto- and cross-correlated data. An Obs object can be initialized with two arguments, the first is a list containing the samples for an observable from a Monte Carlo chain. The samples can either be provided as python list or as numpy array. The second argument is a list containing the names of the respective Monte Carlo chains as strings. These strings uniquely identify a Monte Carlo chain/ensemble.

import pyerrors as pe

my_obs = pe.Obs([samples], ['ensemble_name'])

Error propagation

When performing mathematical operations on Obs objects the correct error propagation is intrinsically taken care of using a first order Taylor expansion $$\delta_f^i=\sum_\alpha \bar{f}_\alpha \delta_\alpha^i\,,\quad \delta_\alpha^i=a_\alpha^i-\bar{a}_\alpha\,,$$ as introduced in arXiv:hep-lat/0306017. The required derivatives $\bar{f}_\alpha$ are evaluated up to machine precision via automatic differentiation as suggested in arXiv:1809.01289.

The Obs class is designed such that mathematical numpy functions can be used on Obs just as for regular floats.

import numpy as np
import pyerrors as pe

my_obs1 = pe.Obs([samples1], ['ensemble_name'])
my_obs2 = pe.Obs([samples2], ['ensemble_name'])

my_sum = my_obs1 + my_obs2

my_m_eff = np.log(my_obs1 / my_obs2)

iamzero = my_m_eff - my_m_eff
# Check that value and fluctuations are zero within machine precision
print(iamzero == 0.0)
> True

Error estimation

The error estimation within pyerrors is based on the gamma method introduced in arXiv:hep-lat/0306017. After having arrived at the derived quantity of interest the gamma_method can be called as detailed in the following example.

my_sum.gamma_method()
print(my_sum)
> 1.70(57)
my_sum.details()
> Result         1.70000000e+00 +/- 5.72046658e-01 +/- 7.56746598e-02 (33.650%)
>  t_int         2.71422900e+00 +/- 6.40320983e-01 S = 2.00
> 1000 samples in 1 ensemble:
>   · Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)

We use the following definition of the integrated autocorrelation time established in Madras & Sokal 1988 $$\tau_\mathrm{int}=\frac{1}{2}+\sum_{t=1}^{W}\rho(t)\geq \frac{1}{2}\,.$$ The window $W$ is determined via the automatic windowing procedure described in arXiv:hep-lat/0306017. The standard value for the parameter $S$ of this automatic windowing procedure is $S=2$. Other values for $S$ can be passed to the gamma_method as parameter.

my_sum.gamma_method(S=3.0)
my_sum.details()
> Result         1.70000000e+00 +/- 6.30675201e-01 +/- 1.04585650e-01 (37.099%)
>  t_int         3.29909703e+00 +/- 9.77310102e-01 S = 3.00
> 1000 samples in 1 ensemble:
>   · Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)

The integrated autocorrelation time $\tau_\mathrm{int}$ and the autocorrelation function $\rho(W)$ can be monitored via the methods pyerrors.obs.Obs.plot_tauint and pyerrors.obs.Obs.plot_tauint.

If the parameter $S$ is set to zero it is assumed that the dataset does not exhibit any autocorrelation and the windowsize is chosen to be zero. In this case the error estimate is identical to the sample standard error.

Exponential tails

Slow modes in the Monte Carlo history can be accounted for by attaching an exponential tail to the autocorrelation function $\rho$ as suggested in arXiv:1009.5228. The longest autocorrelation time in the history, $\tau_\mathrm{exp}$, can be passed to the gamma_method as parameter. In this case the automatic windowing procedure is vacated and the parameter $S$ does not affect the error estimate.

my_sum.gamma_method(tau_exp=7.2)
my_sum.details()
> Result         1.70000000e+00 +/- 6.28097762e-01 +/- 5.79077524e-02 (36.947%)
>  t_int         3.27218667e+00 +/- 7.99583654e-01 tau_exp = 7.20,  N_sigma = 1
> 1000 samples in 1 ensemble:
>   · Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)

For the full API see pyerrors.obs.Obs.gamma_method.

Multiple ensembles/replica

Error propagation for multiple ensembles (Markov chains with different simulation parameters) is handled automatically. Ensembles are uniquely identified by their name.

obs1 = pe.Obs([samples1], ['ensemble1'])
obs2 = pe.Obs([samples2], ['ensemble2'])

my_sum = obs1 + obs2
my_sum.details()
> Result   2.00697958e+00
> 1500 samples in 2 ensembles:
>   · Ensemble 'ensemble1' : 1000 configurations (from 1 to 1000)
>   · Ensemble 'ensemble2' : 500 configurations (from 1 to 500)

pyerrors identifies multiple replica (independent Markov chains with identical simulation parameters) by the vertical bar | in the name of the data set.

obs1 = pe.Obs([samples1], ['ensemble1|r01'])
obs2 = pe.Obs([samples2], ['ensemble1|r02'])

> my_sum = obs1 + obs2
> my_sum.details()
> Result   2.00697958e+00
> 1500 samples in 1 ensemble:
>   · Ensemble 'ensemble1'
>     · Replicum 'r01' : 1000 configurations (from 1 to 1000)
>     · Replicum 'r02' : 500 configurations (from 1 to 500)

Error estimation for multiple ensembles

In order to keep track of different error analysis parameters for different ensembles one can make use of global dictionaries as detailed in the following example.

pe.Obs.S_dict['ensemble1'] = 2.5
pe.Obs.tau_exp_dict['ensemble2'] = 8.0
pe.Obs.tau_exp_dict['ensemble3'] = 2.0

In case the gamma_method is called without any parameters it will use the values specified in the dictionaries for the respective ensembles. Passing arguments to the gamma_method still dominates over the dictionaries.

Irregular Monte Carlo chains

Obs objects defined on irregular Monte Carlo chains can be initialized with the parameter idl.

# Observable defined on configurations 20 to 519
obs1 = pe.Obs([samples1], ['ensemble1'], idl=[range(20, 520)])
obs1.details()
> Result         9.98319881e-01
> 500 samples in 1 ensemble:
>   · Ensemble 'ensemble1' : 500 configurations (from 20 to 519)

# Observable defined on every second configuration between 5 and 1003
obs2 = pe.Obs([samples2], ['ensemble1'], idl=[range(5, 1005, 2)])
obs2.details()
> Result         9.99100712e-01
> 500 samples in 1 ensemble:
>   · Ensemble 'ensemble1' : 500 configurations (from 5 to 1003 in steps of 2)

# Observable defined on configurations 2, 9, 28, 29 and 501
obs3 = pe.Obs([samples3], ['ensemble1'], idl=[[2, 9, 28, 29, 501]])
obs3.details()
> Result         1.01718064e+00
> 5 samples in 1 ensemble:
>   · Ensemble 'ensemble1' : 5 configurations (irregular range)

Obs objects defined on regular and irregular histories of the same ensemble can be combined with each other and the correct error propagation and estimation is automatically taken care of.

Warning: Irregular Monte Carlo chains can result in odd patterns in the autocorrelation functions. Make sure to check the autocorrelation time with e.g. pyerrors.obs.Obs.plot_rho or pyerrors.obs.Obs.plot_tauint.

For the full API see pyerrors.obs.Obs.

Correlators

When one is not interested in single observables but correlation functions, pyerrors offers the Corr class which simplifies the corresponding error propagation and provides the user with a set of standard methods. In order to initialize a Corr objects one needs to arrange the data as a list of Obs

my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3])
print(my_corr)
> x0/a  Corr(x0/a)
> ------------------
> 0      0.7957(80)
> 1      0.5156(51)
> 2      0.3227(33)
> 3      0.2041(21)

In case the correlation functions are not defined on the outermost timeslices, for example because of fixed boundary conditions, a padding can be introduced.

my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3], padding=[1, 1])
print(my_corr)
> x0/a  Corr(x0/a)
> ------------------
> 0
> 1      0.7957(80)
> 2      0.5156(51)
> 3      0.3227(33)
> 4      0.2041(21)
> 5

The individual entries of a correlator can be accessed via slicing

print(my_corr[3])
> 0.3227(33)

Error propagation with the Corr class works very similar to Obs objects. Mathematical operations are overloaded and Corr objects can be computed together with other Corr objects, Obs objects or real numbers and integers.

my_new_corr = 0.3 * my_corr[2] * my_corr * my_corr + 12 / my_corr

pyerrors provides the user with a set of regularly used methods for the manipulation of correlator objects:

  • Corr.gamma_method applies the gamma method to all entries of the correlator.
  • Corr.m_eff to construct effective masses. Various variants for periodic and fixed temporal boundary conditions are available.
  • Corr.deriv returns the first derivative of the correlator as Corr. Different discretizations of the numerical derivative are available.
  • Corr.second_deriv returns the second derivative of the correlator as Corr. Different discretizations of the numerical derivative are available.
  • Corr.symmetric symmetrizes parity even correlations functions, assuming periodic boundary conditions.
  • Corr.anti_symmetric anti-symmetrizes parity odd correlations functions, assuming periodic boundary conditions.
  • Corr.T_symmetry averages a correlator with its time symmetry partner, assuming fixed boundary conditions.
  • Corr.plateau extracts a plateau value from the correlator in a given range.
  • Corr.roll periodically shifts the correlator.
  • Corr.reverse reverses the time ordering of the correlator.
  • Corr.correlate constructs a disconnected correlation function from the correlator and another Corr or Obs object.
  • Corr.reweight reweights the correlator.

pyerrors can also handle matrices of correlation functions and extract energy states from these matrices via a generalized eigenvalue problem (see pyerrors.correlators.Corr.GEVP).

For the full API see pyerrors.correlators.Corr.

Complex valued observables

pyerrors can handle complex valued observables via the class pyerrors.obs.CObs. CObs are initialized with a real and an imaginary part which both can be Obs valued.

my_real_part = pe.Obs([samples1], ['ensemble1'])
my_imag_part = pe.Obs([samples2], ['ensemble1'])

my_cobs = pe.CObs(my_real_part, my_imag_part)
my_cobs.gamma_method()
print(my_cobs)
> (0.9959(91)+0.659(28)j)

Elementary mathematical operations are overloaded and samples are properly propagated as for the Obs class.

my_derived_cobs = (my_cobs + my_cobs.conjugate()) / np.abs(my_cobs)
my_derived_cobs.gamma_method()
print(my_derived_cobs)
> (1.668(23)+0.0j)

The Covobs class

In many projects, auxiliary data that is not based on Monte Carlo chains enters. Examples are experimentally determined mesons masses which are used to set the scale or renormalization constants. These numbers come with an error that has to be propagated through the analysis. The Covobs class allows to define such quantities in pyerrors. Furthermore, external input might consist of correlated quantities. An example are the parameters of an interpolation formula, which are defined via mean values and a covariance matrix between all parameters. The contribution of the interpolation formula to the error of a derived quantity therefore might depend on the complete covariance matrix.

This concept is built into the definition of Covobs. In pyerrors, external input is defined by $M$ mean values, a $M\times M$ covariance matrix, where $M=1$ is permissible, and a name that uniquely identifies the covariance matrix. Below, we define the pion mass, based on its mean value and error, 134.9768(5). Note, that the square of the error enters cov_Obs, since the second argument of this function is the covariance matrix of the Covobs.

import pyerrors.obs as pe

mpi = pe.cov_Obs(134.9768, 0.0005**2, 'pi^0 mass')
mpi.gamma_method()
mpi.details()
> Result         1.34976800e+02 +/- 5.00000000e-04 +/- 0.00000000e+00 (0.000%)
>  pi^0 mass     5.00000000e-04
> 0 samples in 1 ensemble:
>   · Covobs   'pi^0 mass'

The resulting object mpi is an Obs that contains a Covobs. In the following, it may be handled as any other Obs. The contribution of the covariance matrix to the error of an Obs is determined from the $M \times M$ covariance matrix $\Sigma$ and the gradient of the Obs with respect to the external quantities, which is the $1\times M$ Jacobian matrix $J$, via $$s = \sqrt{J^T \Sigma J}\,,$$ where the Jacobian is computed for each derived quantity via automatic differentiation.

Correlated auxiliary data is defined similarly to above, e.g., via

RAP = pe.cov_Obs([16.7457, -19.0475], [[3.49591, -6.07560], [-6.07560, 10.5834]], 'R_AP, 1906.03445, (5.3a)')
print(RAP)
> [Obs[16.7(1.9)], Obs[-19.0(3.3)]]

where RAP now is a list of two Obs that contains the two correlated parameters.

Since the gradient of a derived observable with respect to an external covariance matrix is propagated through the entire analysis, the Covobs class allows to quote the derivative of a result with respect to the external quantities. If these derivatives are published together with the result, small shifts in the definition of external quantities, e.g., the definition of the physical point, can be performed a posteriori based on the published information. This may help to compare results of different groups. The gradient of an Obs o with respect to a covariance matrix with the identifying string k may be accessed via

o.covobs[k].grad

Error propagation in iterative algorithms

pyerrors supports exact linear error propagation for iterative algorithms like various variants of non-linear least sqaures fits or root finding. The derivatives required for the error propagation are calculated as described in arXiv:1809.01289.

Least squares fits

Standard non-linear least square fits with errors on the dependent but not the independent variables can be performed with pyerrors.fits.least_squares. As default solver the Levenberg-Marquardt algorithm implemented in scipy is used.

Fit functions have to be of the following form

import autograd.numpy as anp

def func(a, x):
    return a[1] * anp.exp(-a[0] * x)

It is important that numerical functions refer to autograd.numpy instead of numpy for the automatic differentiation in iterative algorithms to work properly.

Fits can then be performed via

fit_result = pe.fits.least_squares(x, y, func)
print("\n", fit_result)
> Fit with 2 parameters
> Method: Levenberg-Marquardt
> `ftol` termination condition is satisfied.
> chisquare/d.o.f.: 0.9593035785160936

>  Goodness of fit:
> χ²/d.o.f. = 0.959304
> p-value   = 0.5673
> Fit parameters:
> 0      0.0548(28)
> 1      1.933(64)

where x is a list or numpy.array of floats and y is a list or numpy.array of Obs.

Data stored in Corr objects can be fitted directly using the Corr.fit method.

my_corr = pe.Corr(y)
fit_result = my_corr.fit(func, fitrange=[12, 25])

this can simplify working with absolute fit ranges and takes care of gaps in the data automatically.

For fit functions with multiple independent variables the fit function can be of the form

def func(a, x):
    (x1, x2) = x
    return a[0] * x1 ** 2 + a[1] * x2

pyerrors also supports correlated fits which can be triggered via the parameter correlated_fit=True. Details about how the required covariance matrix is estimated can be found in pyerrors.obs.covariance.

Direct visualizations of the performed fits can be triggered via resplot=True or qqplot=True. For all available options see pyerrors.fits.least_squares.

Total least squares fits

pyerrors can also fit data with errors on both the dependent and independent variables using the total least squares method also referred to orthogonal distance regression as implemented in scipy, see pyerrors.fits.least_squares. The syntax is identical to the standard least squares case, the only diffrence being that x also has to be a list or numpy.array of Obs.

For the full API see pyerrors.fits for fits and pyerrors.roots for finding roots of functions.

Matrix operations

pyerrors provides wrappers for Obs- and CObs-valued matrix operations based on numpy.linalg. The supported functions include:

  • inv for the matrix inverse.
  • cholseky for the Cholesky decomposition.
  • det for the matrix determinant.
  • eigh for eigenvalues and eigenvectors of hermitean matrices.
  • eig for eigenvalues of general matrices.
  • pinv for the Moore-Penrose pseudoinverse.
  • svd for the singular-value-decomposition.

For the full API see pyerrors.linalg.

Export data

The preferred exported file format within pyerrors is json.gz. Files written to this format are valid JSON files that have been compressed using gzip. The structure of the content is inspired by the dobs format of the ALPHA collaboration. The aim of the format is to facilitate the storage of data in a self-contained way such that, even years after the creation of the file, it is possible to extract all necessary information:

  • What observables are stored? Possibly: How exactly are they defined.
  • How does each single ensemble or external quantity contribute to the error of the observable?
  • Who did write the file when and on which machine?

This can be achieved by storing all information in one single file. The export routines of pyerrors are written such that as much information as possible is written automatically as described in the following example

my_obs = pe.Obs([samples], ["test_ensemble"])
my_obs.tag = "My observable"

pe.input.json.dump_to_json(my_obs, "test_output_file", description="This file contains a test observable")
# For a single observable one can equivalently use the class method dump
my_obs.dump("test_output_file", description="This file contains a test observable")

check = pe.input.json.load_json("test_output_file")

print(my_obs == check)
> True

The format also allows to directly write out the content of Corr objects or lists and arrays of Obs objects by passing the desired data to pyerrors.input.json.dump_to_json.

json.gz format specification

The first entries of the file provide optional auxiliary information:

  • program is a string that indicates which program was used to write the file.
  • version is a string that specifies the version of the format.
  • who is a string that specifies the user name of the creator of the file.
  • date is a string and contains the creation date of the file.
  • host is a string and contains the hostname of the machine where the file has been written.
  • description contains information on the content of the file. This field is not filled automatically in pyerrors. The user is advised to provide as detailed information as possible in this field. Examples are: Input files of measurements or simulations, LaTeX formulae or references to publications to specify how the observables have been computed, details on the analysis strategy, ... This field may be any valid JSON type. Strings, arrays or objects (equivalent to dicts in python) are well suited to provide information.

The only necessary entry of the file is the field -obsdata, an array that contains the actual data.

Each entry of the array belongs to a single structure of observables. Currently, these structures can be either of Obs, list, numpy.ndarray, Corr. All Obs inside a structure (with dimension > 0) have to be defined on the same set of configurations. Different structures, that are represented by entries of the array obsdata, are treated independently. Each entry of the array obsdata has the following required entries:

  • type is a string that specifies the type of the structure. This allows to parse the content to the correct form after reading the file. It is always possible to interpret the content as list of Obs.
  • value is an array that contains the mean values of the Obs inside the structure. The following entries are optional:
  • layout is a string that specifies the layout of multi-dimensional structures. Examples are "2, 2" for a 2x2 dimensional matrix or "64, 4, 4" for a Corr with $T=64$ and 4x4 matrices on each time slices. "1" denotes a single Obs. Multi-dimensional structures are stored in row-major format (see below).
  • tag is any JSON type. It contains additional information concerning the structure. The tag of an Obs in pyerrors is written here.
  • reweighted is a Bool that may be used to specify, whether the Obs in the structure have been reweighted.
  • data is an array that contains the data from MC chains. We will define it below.
  • cdata is an array that contains the data from external quantities with an error (Covobs in pyerrors). We will define it below.

The array data contains the data from MC chains. Each entry of the array corresponds to one ensemble and contains:

  • id, a string that contains the name of the ensemble
  • replica, an array that contains an entry per replica of the ensemble.

Each entry of replica contains name, a string that contains the name of the replica deltas, an array that contains the actual data.

Each entry in deltas corresponds to one configuration of the replica and has $1+N$ many entries. The first entry is an integer that specifies the configuration number that, together with ensemble and replica name, may be used to uniquely identify the configuration on which the data has been obtained. The following N entries specify the deltas, i.e., the deviation of the observable from the mean value on this configuration, of each Obs inside the structure. Multi-dimensional structures are stored in a row-major format. For primary observables, such as correlation functions, $value + delta_i$ matches the primary data obtained on the configuration.

The array cdata contains information about the contribution of auxiliary observables, represented by Covobs in pyerrors, to the total error of the observables. Each entry of the array belongs to one auxiliary covariance matrix and contains:

  • id, a string that identifies the covariance matrix
  • layout, a string that defines the dimensions of the $M\times M$ covariance matrix (has to be "M, M" or "1").
  • cov, an array that contains the $M\times M$ many entries of the covariance matrix, stored in row-major format.
  • grad, an array that contains N entries, one for each Obs inside the structure. Each entry itself is an array, that contains the M gradients of the Nth observable with respect to the quantity that corresponds to the Mth diagonal entry of the covariance matrix.

A JSON schema that may be used to verify the correctness of a file with respect to the format definition is stored in ./examples/json_schema.json. The schema is a self-descriptive format definition and contains an exemplary file.

Julia I/O routines for the json.gz format, compatible with ADerrors.jl, can be found here.

  1r'''
  2# What is pyerrors?
  3`pyerrors` is a python package for error computation and propagation of Markov chain Monte Carlo data.
  4It is based on the gamma method [arXiv:hep-lat/0306017](https://arxiv.org/abs/hep-lat/0306017). Some of its features are:
  5- automatic differentiation for exact liner error propagation as suggested in [arXiv:1809.01289](https://arxiv.org/abs/1809.01289) (partly based on the [autograd](https://github.com/HIPS/autograd) package).
  6- treatment of slow modes in the simulation as suggested in [arXiv:1009.5228](https://arxiv.org/abs/1009.5228).
  7- coherent error propagation for data from different Markov chains.
  8- 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).
  9- real and complex matrix operations and their error propagation based on automatic differentiation (Matrix inverse, Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...).
 10
 11More detailed examples can found in the [GitHub repository](https://github.com/fjosw/pyerrors/tree/develop/examples) [![badge](https://img.shields.io/badge/-try%20it%20out-579ACA.svg?logo=data:image/png;base64,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)](https://mybinder.org/v2/gh/fjosw/pyerrors/HEAD?labpath=examples).
 12
 13If you use `pyerrors` for research that leads to a publication please consider citing:
 14- Ulli Wolff, *Monte Carlo errors with less errors*. Comput.Phys.Commun. 156 (2004) 143-153, Comput.Phys.Commun. 176 (2007) 383 (erratum).
 15- Alberto Ramos, *Automatic differentiation for error analysis of Monte Carlo data*. Comput.Phys.Commun. 238 (2019) 19-35.
 16
 17and
 18
 19- Stefan Schaefer, Rainer Sommer, Francesco Virotta, *Critical slowing down and error analysis in lattice QCD simulations*. Nucl.Phys.B 845 (2011) 93-119.
 20
 21where applicable.
 22
 23There 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) and [Python](https://github.com/mbruno46/pyobs).
 24
 25## Basic example
 26
 27```python
 28import numpy as np
 29import pyerrors as pe
 30
 31my_obs = pe.Obs([samples], ['ensemble_name']) # Initialize an Obs object
 32my_new_obs = 2 * np.log(my_obs) / my_obs ** 2 # Construct derived Obs object
 33my_new_obs.gamma_method()                     # Estimate the statistical error
 34print(my_new_obs)                             # Print the result to stdout
 35> 0.31498(72)
 36```
 37
 38# The `Obs` class
 39
 40`pyerrors` introduces a new datatype, `Obs`, which simplifies error propagation and estimation for auto- and cross-correlated data.
 41An `Obs` object can be initialized with two arguments, the first is a list containing the samples for an observable from a Monte Carlo chain.
 42The samples can either be provided as python list or as numpy array.
 43The second argument is a list containing the names of the respective Monte Carlo chains as strings. These strings uniquely identify a Monte Carlo chain/ensemble.
 44
 45```python
 46import pyerrors as pe
 47
 48my_obs = pe.Obs([samples], ['ensemble_name'])
 49```
 50
 51## Error propagation
 52
 53When performing mathematical operations on `Obs` objects the correct error propagation is intrinsically taken care of using a first order Taylor expansion
 54$$\delta_f^i=\sum_\alpha \bar{f}_\alpha \delta_\alpha^i\,,\quad \delta_\alpha^i=a_\alpha^i-\bar{a}_\alpha\,,$$
 55as introduced in [arXiv:hep-lat/0306017](https://arxiv.org/abs/hep-lat/0306017).
 56The required derivatives $\bar{f}_\alpha$ are evaluated up to machine precision via automatic differentiation as suggested in [arXiv:1809.01289](https://arxiv.org/abs/1809.01289).
 57
 58The `Obs` class is designed such that mathematical numpy functions can be used on `Obs` just as for regular floats.
 59
 60```python
 61import numpy as np
 62import pyerrors as pe
 63
 64my_obs1 = pe.Obs([samples1], ['ensemble_name'])
 65my_obs2 = pe.Obs([samples2], ['ensemble_name'])
 66
 67my_sum = my_obs1 + my_obs2
 68
 69my_m_eff = np.log(my_obs1 / my_obs2)
 70
 71iamzero = my_m_eff - my_m_eff
 72# Check that value and fluctuations are zero within machine precision
 73print(iamzero == 0.0)
 74> True
 75```
 76
 77## Error estimation
 78
 79The error estimation within `pyerrors` is based on the gamma method introduced in [arXiv:hep-lat/0306017](https://arxiv.org/abs/hep-lat/0306017).
 80After having arrived at the derived quantity of interest the `gamma_method` can be called as detailed in the following example.
 81
 82```python
 83my_sum.gamma_method()
 84print(my_sum)
 85> 1.70(57)
 86my_sum.details()
 87> Result	 1.70000000e+00 +/- 5.72046658e-01 +/- 7.56746598e-02 (33.650%)
 88>  t_int	 2.71422900e+00 +/- 6.40320983e-01 S = 2.00
 89> 1000 samples in 1 ensemble:
 90>   · Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)
 91
 92```
 93
 94We use the following definition of the integrated autocorrelation time established in [Madras & Sokal 1988](https://link.springer.com/article/10.1007/BF01022990)
 95$$\tau_\mathrm{int}=\frac{1}{2}+\sum_{t=1}^{W}\rho(t)\geq \frac{1}{2}\,.$$
 96The window $W$ is determined via the automatic windowing procedure described in [arXiv:hep-lat/0306017](https://arxiv.org/abs/hep-lat/0306017).
 97The standard value for the parameter $S$ of this automatic windowing procedure is $S=2$. Other values for $S$ can be passed to the `gamma_method` as parameter.
 98
 99```python
100my_sum.gamma_method(S=3.0)
101my_sum.details()
102> Result	 1.70000000e+00 +/- 6.30675201e-01 +/- 1.04585650e-01 (37.099%)
103>  t_int	 3.29909703e+00 +/- 9.77310102e-01 S = 3.00
104> 1000 samples in 1 ensemble:
105>   · Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)
106
107```
108
109The integrated autocorrelation time $\tau_\mathrm{int}$ and the autocorrelation function $\rho(W)$ can be monitored via the methods `pyerrors.obs.Obs.plot_tauint` and `pyerrors.obs.Obs.plot_tauint`.
110
111If the parameter $S$ is set to zero it is assumed that the dataset does not exhibit any autocorrelation and the windowsize is chosen to be zero.
112In this case the error estimate is identical to the sample standard error.
113
114### Exponential tails
115
116Slow modes in the Monte Carlo history can be accounted for by attaching an exponential tail to the autocorrelation function $\rho$ as suggested in [arXiv:1009.5228](https://arxiv.org/abs/1009.5228). The longest autocorrelation time in the history, $\tau_\mathrm{exp}$, can be passed to the `gamma_method` as parameter. In this case the automatic windowing procedure is vacated and the parameter $S$ does not affect the error estimate.
117
118```python
119my_sum.gamma_method(tau_exp=7.2)
120my_sum.details()
121> Result	 1.70000000e+00 +/- 6.28097762e-01 +/- 5.79077524e-02 (36.947%)
122>  t_int	 3.27218667e+00 +/- 7.99583654e-01 tau_exp = 7.20,  N_sigma = 1
123> 1000 samples in 1 ensemble:
124>   · Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)
125```
126
127For the full API see `pyerrors.obs.Obs.gamma_method`.
128
129## Multiple ensembles/replica
130
131Error propagation for multiple ensembles (Markov chains with different simulation parameters) is handled automatically. Ensembles are uniquely identified by their `name`.
132
133```python
134obs1 = pe.Obs([samples1], ['ensemble1'])
135obs2 = pe.Obs([samples2], ['ensemble2'])
136
137my_sum = obs1 + obs2
138my_sum.details()
139> Result   2.00697958e+00
140> 1500 samples in 2 ensembles:
141>   · Ensemble 'ensemble1' : 1000 configurations (from 1 to 1000)
142>   · Ensemble 'ensemble2' : 500 configurations (from 1 to 500)
143```
144
145`pyerrors` identifies multiple replica (independent Markov chains with identical simulation parameters) by the vertical bar `|` in the name of the data set.
146
147```python
148obs1 = pe.Obs([samples1], ['ensemble1|r01'])
149obs2 = pe.Obs([samples2], ['ensemble1|r02'])
150
151> my_sum = obs1 + obs2
152> my_sum.details()
153> Result   2.00697958e+00
154> 1500 samples in 1 ensemble:
155>   · Ensemble 'ensemble1'
156>     · Replicum 'r01' : 1000 configurations (from 1 to 1000)
157>     · Replicum 'r02' : 500 configurations (from 1 to 500)
158```
159
160### Error estimation for multiple ensembles
161
162In order to keep track of different error analysis parameters for different ensembles one can make use of global dictionaries as detailed in the following example.
163
164```python
165pe.Obs.S_dict['ensemble1'] = 2.5
166pe.Obs.tau_exp_dict['ensemble2'] = 8.0
167pe.Obs.tau_exp_dict['ensemble3'] = 2.0
168```
169
170In case the `gamma_method` is called without any parameters it will use the values specified in the dictionaries for the respective ensembles.
171Passing arguments to the `gamma_method` still dominates over the dictionaries.
172
173
174## Irregular Monte Carlo chains
175
176`Obs` objects defined on irregular Monte Carlo chains can be initialized with the parameter `idl`.
177
178```python
179# Observable defined on configurations 20 to 519
180obs1 = pe.Obs([samples1], ['ensemble1'], idl=[range(20, 520)])
181obs1.details()
182> Result	 9.98319881e-01
183> 500 samples in 1 ensemble:
184>   · Ensemble 'ensemble1' : 500 configurations (from 20 to 519)
185
186# Observable defined on every second configuration between 5 and 1003
187obs2 = pe.Obs([samples2], ['ensemble1'], idl=[range(5, 1005, 2)])
188obs2.details()
189> Result	 9.99100712e-01
190> 500 samples in 1 ensemble:
191>   · Ensemble 'ensemble1' : 500 configurations (from 5 to 1003 in steps of 2)
192
193# Observable defined on configurations 2, 9, 28, 29 and 501
194obs3 = pe.Obs([samples3], ['ensemble1'], idl=[[2, 9, 28, 29, 501]])
195obs3.details()
196> Result	 1.01718064e+00
197> 5 samples in 1 ensemble:
198>   · Ensemble 'ensemble1' : 5 configurations (irregular range)
199
200```
201
202`Obs` objects defined on regular and irregular histories of the same ensemble can be combined with each other and the correct error propagation and estimation is automatically taken care of.
203
204**Warning:** Irregular Monte Carlo chains can result in odd patterns in the autocorrelation functions.
205Make sure to check the autocorrelation time with e.g. `pyerrors.obs.Obs.plot_rho` or `pyerrors.obs.Obs.plot_tauint`.
206
207For the full API see `pyerrors.obs.Obs`.
208
209# Correlators
210When one is not interested in single observables but correlation functions, `pyerrors` offers the `Corr` class which simplifies the corresponding error propagation and provides the user with a set of standard methods. In order to initialize a `Corr` objects one needs to arrange the data as a list of `Obs`
211```python
212my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3])
213print(my_corr)
214> x0/a	Corr(x0/a)
215> ------------------
216> 0	 0.7957(80)
217> 1	 0.5156(51)
218> 2	 0.3227(33)
219> 3	 0.2041(21)
220```
221In case the correlation functions are not defined on the outermost timeslices, for example because of fixed boundary conditions, a padding can be introduced.
222```python
223my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3], padding=[1, 1])
224print(my_corr)
225> x0/a	Corr(x0/a)
226> ------------------
227> 0
228> 1	 0.7957(80)
229> 2	 0.5156(51)
230> 3	 0.3227(33)
231> 4	 0.2041(21)
232> 5
233```
234The individual entries of a correlator can be accessed via slicing
235```python
236print(my_corr[3])
237> 0.3227(33)
238```
239Error propagation with the `Corr` class works very similar to `Obs` objects. Mathematical operations are overloaded and `Corr` objects can be computed together with other `Corr` objects, `Obs` objects or real numbers and integers.
240```python
241my_new_corr = 0.3 * my_corr[2] * my_corr * my_corr + 12 / my_corr
242```
243
244`pyerrors` provides the user with a set of regularly used methods for the manipulation of correlator objects:
245- `Corr.gamma_method` applies the gamma method to all entries of the correlator.
246- `Corr.m_eff` to construct effective masses. Various variants for periodic and fixed temporal boundary conditions are available.
247- `Corr.deriv` returns the first derivative of the correlator as `Corr`. Different discretizations of the numerical derivative are available.
248- `Corr.second_deriv` returns the second derivative of the correlator as `Corr`. Different discretizations of the numerical derivative are available.
249- `Corr.symmetric` symmetrizes parity even correlations functions, assuming periodic boundary conditions.
250- `Corr.anti_symmetric` anti-symmetrizes parity odd correlations functions, assuming periodic boundary conditions.
251- `Corr.T_symmetry` averages a correlator with its time symmetry partner, assuming fixed boundary conditions.
252- `Corr.plateau` extracts a plateau value from the correlator in a given range.
253- `Corr.roll` periodically shifts the correlator.
254- `Corr.reverse` reverses the time ordering of the correlator.
255- `Corr.correlate` constructs a disconnected correlation function from the correlator and another `Corr` or `Obs` object.
256- `Corr.reweight` reweights the correlator.
257
258`pyerrors` can also handle matrices of correlation functions and extract energy states from these matrices via a generalized eigenvalue problem (see `pyerrors.correlators.Corr.GEVP`).
259
260For the full API see `pyerrors.correlators.Corr`.
261
262# Complex valued observables
263
264`pyerrors` can handle complex valued observables via the class `pyerrors.obs.CObs`.
265`CObs` are initialized with a real and an imaginary part which both can be `Obs` valued.
266
267```python
268my_real_part = pe.Obs([samples1], ['ensemble1'])
269my_imag_part = pe.Obs([samples2], ['ensemble1'])
270
271my_cobs = pe.CObs(my_real_part, my_imag_part)
272my_cobs.gamma_method()
273print(my_cobs)
274> (0.9959(91)+0.659(28)j)
275```
276
277Elementary mathematical operations are overloaded and samples are properly propagated as for the `Obs` class.
278```python
279my_derived_cobs = (my_cobs + my_cobs.conjugate()) / np.abs(my_cobs)
280my_derived_cobs.gamma_method()
281print(my_derived_cobs)
282> (1.668(23)+0.0j)
283```
284
285# The `Covobs` class
286In many projects, auxiliary data that is not based on Monte Carlo chains enters. Examples are experimentally determined mesons masses which are used to set the scale or renormalization constants. These numbers come with an error that has to be propagated through the analysis. The `Covobs` class allows to define such quantities in `pyerrors`. Furthermore, external input might consist of correlated quantities. An example are the parameters of an interpolation formula, which are defined via mean values and a covariance matrix between all parameters. The contribution of the interpolation formula to the error of a derived quantity therefore might depend on the complete covariance matrix.
287
288This concept is built into the definition of `Covobs`. In `pyerrors`, external input is defined by $M$ mean values, a $M\times M$ covariance matrix, where $M=1$ is permissible, and a name that uniquely identifies the covariance matrix. Below, we define the pion mass, based on its mean value and error, 134.9768(5). Note, that the square of the error enters `cov_Obs`, since the second argument of this function is the covariance matrix of the `Covobs`.
289
290```python
291import pyerrors.obs as pe
292
293mpi = pe.cov_Obs(134.9768, 0.0005**2, 'pi^0 mass')
294mpi.gamma_method()
295mpi.details()
296> Result	 1.34976800e+02 +/- 5.00000000e-04 +/- 0.00000000e+00 (0.000%)
297>  pi^0 mass 	 5.00000000e-04
298> 0 samples in 1 ensemble:
299>   · Covobs   'pi^0 mass'
300```
301The resulting object `mpi` is an `Obs` that contains a `Covobs`. In the following, it may be handled as any other `Obs`. The contribution of the covariance matrix to the error of an `Obs` is determined from the $M \times M$ covariance matrix $\Sigma$ and the gradient of the `Obs` with respect to the external quantities, which is the $1\times M$ Jacobian matrix $J$, via
302$$s = \sqrt{J^T \Sigma J}\,,$$
303where the Jacobian is computed for each derived quantity via automatic differentiation.
304
305Correlated auxiliary data is defined similarly to above, e.g., via
306```python
307RAP = pe.cov_Obs([16.7457, -19.0475], [[3.49591, -6.07560], [-6.07560, 10.5834]], 'R_AP, 1906.03445, (5.3a)')
308print(RAP)
309> [Obs[16.7(1.9)], Obs[-19.0(3.3)]]
310```
311where `RAP` now is a list of two `Obs` that contains the two correlated parameters.
312
313Since the gradient of a derived observable with respect to an external covariance matrix is propagated through the entire analysis, the `Covobs` class allows to quote the derivative of a result with respect to the external quantities. If these derivatives are published together with the result, small shifts in the definition of external quantities, e.g., the definition of the physical point, can be performed a posteriori based on the published information. This may help to compare results of different groups. The gradient of an `Obs` `o` with respect to a covariance matrix with the identifying string `k` may be accessed via
314```python
315o.covobs[k].grad
316```
317
318# Error propagation in iterative algorithms
319
320`pyerrors` supports exact linear error propagation for iterative algorithms like various variants of non-linear least sqaures fits or root finding. The derivatives required for the error propagation are calculated as described in [arXiv:1809.01289](https://arxiv.org/abs/1809.01289).
321
322## Least squares fits
323
324Standard non-linear least square fits with errors on the dependent but not the independent variables can be performed with `pyerrors.fits.least_squares`. As default solver the Levenberg-Marquardt algorithm implemented in [scipy](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.least_squares.html) is used.
325
326Fit functions have to be of the following form
327```python
328import autograd.numpy as anp
329
330def func(a, x):
331    return a[1] * anp.exp(-a[0] * x)
332```
333**It is important that numerical functions refer to `autograd.numpy` instead of `numpy` for the automatic differentiation in iterative algorithms to work properly.**
334
335Fits can then be performed via
336```python
337fit_result = pe.fits.least_squares(x, y, func)
338print("\n", fit_result)
339> Fit with 2 parameters
340> Method: Levenberg-Marquardt
341> `ftol` termination condition is satisfied.
342> chisquare/d.o.f.: 0.9593035785160936
343
344>  Goodness of fit:
345> χ²/d.o.f. = 0.959304
346> p-value   = 0.5673
347> Fit parameters:
348> 0	 0.0548(28)
349> 1	 1.933(64)
350```
351where x is a `list` or `numpy.array` of `floats` and y is a `list` or `numpy.array` of `Obs`.
352
353Data stored in `Corr` objects can be fitted directly using the `Corr.fit` method.
354```python
355my_corr = pe.Corr(y)
356fit_result = my_corr.fit(func, fitrange=[12, 25])
357```
358this can simplify working with absolute fit ranges and takes care of gaps in the data automatically.
359
360For fit functions with multiple independent variables the fit function can be of the form
361
362```python
363def func(a, x):
364    (x1, x2) = x
365    return a[0] * x1 ** 2 + a[1] * x2
366```
367
368`pyerrors` also supports correlated fits which can be triggered via the parameter `correlated_fit=True`.
369Details about how the required covariance matrix is estimated can be found in `pyerrors.obs.covariance`.
370
371Direct visualizations of the performed fits can be triggered via `resplot=True` or `qqplot=True`. For all available options see `pyerrors.fits.least_squares`.
372
373## Total least squares fits
374`pyerrors` can also fit data with errors on both the dependent and independent variables using the total least squares method also referred to orthogonal distance regression as implemented in [scipy](https://docs.scipy.org/doc/scipy/reference/odr.html), see `pyerrors.fits.least_squares`. The syntax is identical to the standard least squares case, the only diffrence being that `x` also has to be a `list` or `numpy.array` of `Obs`.
375
376For the full API see `pyerrors.fits` for fits and `pyerrors.roots` for finding roots of functions.
377
378# Matrix operations
379`pyerrors` provides wrappers for `Obs`- and `CObs`-valued matrix operations based on `numpy.linalg`. The supported functions include:
380- `inv` for the matrix inverse.
381- `cholseky` for the Cholesky decomposition.
382- `det` for the matrix determinant.
383- `eigh` for eigenvalues and eigenvectors of hermitean matrices.
384- `eig` for eigenvalues of general matrices.
385- `pinv` for the Moore-Penrose pseudoinverse.
386- `svd` for the singular-value-decomposition.
387
388For the full API see `pyerrors.linalg`.
389
390# Export data
391
392The preferred exported file format within `pyerrors` is json.gz. Files written to this format are valid JSON files that have been compressed using gzip. The structure of the content is inspired by the dobs format of the ALPHA collaboration. The aim of the format is to facilitate the storage of data in a self-contained way such that, even years after the creation of the file, it is possible to extract all necessary information:
393- What observables are stored? Possibly: How exactly are they defined.
394- How does each single ensemble or external quantity contribute to the error of the observable?
395- Who did write the file when and on which machine?
396
397This can be achieved by storing all information in one single file. The export routines of `pyerrors` are written such that as much information as possible is written automatically as described in the following example
398```python
399my_obs = pe.Obs([samples], ["test_ensemble"])
400my_obs.tag = "My observable"
401
402pe.input.json.dump_to_json(my_obs, "test_output_file", description="This file contains a test observable")
403# For a single observable one can equivalently use the class method dump
404my_obs.dump("test_output_file", description="This file contains a test observable")
405
406check = pe.input.json.load_json("test_output_file")
407
408print(my_obs == check)
409> True
410```
411The format also allows to directly write out the content of `Corr` objects or lists and arrays of `Obs` objects by passing the desired data to `pyerrors.input.json.dump_to_json`.
412
413## json.gz format specification
414The first entries of the file provide optional auxiliary information:
415- `program` is a string that indicates which program was used to write the file.
416- `version` is a string that specifies the version of the format.
417- `who` is a string that specifies the user name of the creator of the file.
418- `date` is a string and contains the creation date of the file.
419- `host` is a string and contains the hostname of the machine where the file has been written.
420- `description` contains information on the content of the file. This field is not filled automatically in `pyerrors`. The user is advised to provide as detailed information as possible in this field. Examples are: Input files of measurements or simulations, LaTeX formulae or references to publications to specify how the observables have been computed, details on the analysis strategy, ... This field may be any valid JSON type. Strings, arrays or objects (equivalent to dicts in python) are well suited to provide information.
421
422The only necessary entry of the file is the field
423-`obsdata`, an array that contains the actual data.
424
425Each entry of the array belongs to a single structure of observables. Currently, these structures can be either of `Obs`, `list`, `numpy.ndarray`, `Corr`. All `Obs` inside a structure (with dimension > 0) have to be defined on the same set of configurations. Different structures, that are represented by entries of the array `obsdata`, are treated independently. Each entry of the array `obsdata` has the following required entries:
426- `type` is a string that specifies the type of the structure. This allows to parse the content to the correct form after reading the file. It is always possible to interpret the content as list of Obs.
427- `value` is an array that contains the mean values of the Obs inside the structure.
428The following entries are optional:
429- `layout` is a string that specifies the layout of multi-dimensional structures. Examples are "2, 2" for a 2x2 dimensional matrix or  "64, 4, 4" for a Corr with $T=64$ and 4x4 matrices on each time slices. "1" denotes a single Obs. Multi-dimensional structures are stored in row-major format (see below).
430- `tag` is any JSON type. It contains additional information concerning the structure. The `tag` of an `Obs` in `pyerrors` is written here.
431- `reweighted` is a Bool that may be used to specify, whether the `Obs` in the structure have been reweighted.
432- `data` is an array that contains the data from MC chains. We will define it below.
433- `cdata` is an array that contains the data from external quantities with an error (`Covobs` in `pyerrors`). We will define it below.
434
435The array `data` contains the data from MC chains. Each entry of the array corresponds to one ensemble and contains:
436- `id`, a string that contains the name of the ensemble
437- `replica`, an array that contains an entry per replica of the ensemble.
438
439Each entry of `replica` contains
440`name`, a string that contains the name of the replica
441`deltas`, an array that contains the actual data.
442
443Each entry in `deltas` corresponds to one configuration of the replica and has $1+N$ many entries. The first entry is an integer that specifies the configuration number that, together with ensemble and replica name, may be used to uniquely identify the configuration on which the data has been obtained. The following N entries specify the deltas, i.e., the deviation of the observable from the mean value on this configuration, of each `Obs` inside the structure. Multi-dimensional structures are stored in a row-major format. For primary observables, such as correlation functions, $value + delta_i$ matches the primary data obtained on the configuration.
444
445The array `cdata` contains information about the contribution of auxiliary observables, represented by `Covobs` in `pyerrors`, to the total error of the observables. Each entry of the array belongs to one auxiliary covariance matrix and contains:
446- `id`, a string that identifies the covariance matrix
447- `layout`, a string that defines the dimensions of the $M\times M$ covariance matrix (has to be "M, M" or "1").
448- `cov`, an array that contains the $M\times M$ many entries of the covariance matrix, stored in row-major format.
449- `grad`, an array that contains N entries, one for each `Obs` inside the structure. Each entry itself is an array, that contains the M gradients of the Nth observable  with respect to the quantity that corresponds to the Mth diagonal entry of the covariance matrix.
450
451A JSON schema that may be used to verify the correctness of a file with respect to the format definition is stored in ./examples/json_schema.json. The schema is a self-descriptive format definition and contains an exemplary file.
452
453Julia I/O routines for the json.gz format, compatible with [ADerrors.jl](https://gitlab.ift.uam-csic.es/alberto/aderrors.jl), can be found [here](https://github.com/fjosw/ADjson.jl).
454'''
455from .obs import *
456from .correlators import *
457from .fits import *
458from .misc import *
459from . import dirac
460from . import input
461from . import linalg
462from . import mpm
463from . import roots
464
465from .version import __version__