From 94cbd87271f866aee4d3ea8ea825646c0a2d4012 Mon Sep 17 00:00:00 2001 From: fjosw Date: Sun, 7 Nov 2021 21:09:48 +0000 Subject: [PATCH] Documentation updated --- docs/pyerrors.html | 23 +++++++++++++++++++++++ docs/search.js | 2 +- 2 files changed, 24 insertions(+), 1 deletion(-) diff --git a/docs/pyerrors.html b/docs/pyerrors.html index 7791e7d0..3dbd6e19 100644 --- a/docs/pyerrors.html +++ b/docs/pyerrors.html @@ -62,6 +62,7 @@
  • Exponential tails
  • Covariance
    • +
  • Correlators
  • Optimization / fits / roots
  • Complex observables
  • Matrix operations
    • @@ -162,13 +163,26 @@ It is based on the gamma method Covariance +

    Correlators

    + +

    pyerrors.correlators.Corr

    +

    Optimization / fits / roots

    +

    pyerrors.fits +pyerrors.roots

    +

    Complex observables

    +

    pyerrors.obs.CObs

    +

    Matrix operations

    +

    pyerrors.linalg

    +

    Input

    + +

    pyerrors.input

    @@ -230,13 +244,22 @@ It is based on the gamma method ## Covariance +# Correlators +`pyerrors.correlators.Corr` + # Optimization / fits / roots +`pyerrors.fits` +`pyerrors.roots` + # Complex observables +`pyerrors.obs.CObs` # Matrix operations +`pyerrors.linalg` # Input +`pyerrors.input` ''' from .obs import * from .correlators import * diff --git a/docs/search.js b/docs/search.js index 116ffeda..8e2e79e9 100644 --- a/docs/search.js +++ b/docs/search.js @@ -1,6 +1,6 @@ window.pdocSearch = (function(){ /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oWhat is pyerrors?\n\n

    pyerrors is a python package for error computation and propagation of Markov chain Monte Carlo data.\nIt is based on the gamma method arXiv:hep-lat/0306017. Some of its features are:

    \n\n\n\n

    Getting started

    \n\n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name'])\nmy_new_obs = 2 * np.log(my_obs) / my_obs\nmy_new_obs.gamma_method()\nmy_new_obs.details()\nprint(my_new_obs)\n
    \n\n

    The Obs class

    \n\n

    pyerrors.obs.Obs

    \n\n
    import pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name'])\n
    \n\n

    Multiple ensembles/replica

    \n\n

    Irregular Monte Carlo chains

    \n\n

    Error propagation

    \n\n

    Automatic differentiation, cite Alberto,

    \n\n

    numpy overloaded

    \n\n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name'])\nmy_new_obs = 2 * np.log(my_obs) / my_obs\nmy_new_obs.gamma_method()\nmy_new_obs.details()\n
    \n\n

    Error estimation

    \n\n

    pyerrors.obs.Obs.gamma_method

    \n\n

    $\\delta_i\\delta_j$

    \n\n

    Exponential tails

    \n\n

    Covariance

    \n\n

    Optimization / fits / roots

    \n\n

    Complex observables

    \n\n

    Matrix operations

    \n\n

    Input

    \n"}, "pyerrors.correlators": {"fullname": "pyerrors.correlators", "modulename": "pyerrors.correlators", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.correlators.Corr": {"fullname": "pyerrors.correlators.Corr", "modulename": "pyerrors.correlators", "qualname": "Corr", "type": "class", "doc": "

    The class for a correlator (time dependent sequence of pe.Obs).

    \n\n

    Everything, this class does, can be achieved using lists or arrays of Obs.\nBut it is simply more convenient to have a dedicated object for correlators.\nOne often wants to add or multiply correlators of the same length at every timeslice and it is inconvinient\nto iterate over all timeslices for every operation. This is especially true, when dealing with smearing matrices.

    \n\n

    The correlator can have two types of content: An Obs at every timeslice OR a GEVP\nsmearing matrix at every timeslice. Other dependency (eg. spacial) are not supported.

    \n"}, "pyerrors.correlators.Corr.__init__": {"fullname": "pyerrors.correlators.Corr.__init__", "modulename": "pyerrors.correlators", "qualname": "Corr.__init__", "type": "function", "doc": "

    \n", "parameters": ["self", "data_input", "padding_front", "padding_back", "prange"], "funcdef": "def"}, "pyerrors.correlators.Corr.reweighted": {"fullname": "pyerrors.correlators.Corr.reweighted", "modulename": "pyerrors.correlators", "qualname": "Corr.reweighted", "type": "variable", "doc": "

    \n"}, "pyerrors.correlators.Corr.gamma_method": {"fullname": "pyerrors.correlators.Corr.gamma_method", "modulename": "pyerrors.correlators", "qualname": "Corr.gamma_method", "type": "function", "doc": "

    Apply the gamma method to the content of the Corr.

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.projected": {"fullname": "pyerrors.correlators.Corr.projected", "modulename": "pyerrors.correlators", "qualname": "Corr.projected", "type": "function", "doc": "

    \n", "parameters": ["self", "vector_l", "vector_r"], "funcdef": "def"}, "pyerrors.correlators.Corr.sum": {"fullname": "pyerrors.correlators.Corr.sum", "modulename": "pyerrors.correlators", "qualname": "Corr.sum", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.smearing": {"fullname": "pyerrors.correlators.Corr.smearing", "modulename": "pyerrors.correlators", "qualname": "Corr.smearing", "type": "function", "doc": "

    \n", "parameters": ["self", "i", "j"], "funcdef": "def"}, "pyerrors.correlators.Corr.plottable": {"fullname": "pyerrors.correlators.Corr.plottable", "modulename": "pyerrors.correlators", "qualname": "Corr.plottable", "type": "function", "doc": "

    Outputs the correlator in a plotable format.

    \n\n

    Outputs three lists containing the timeslice index, the value on each\ntimeslice and the error on each timeslice.

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.symmetric": {"fullname": "pyerrors.correlators.Corr.symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.symmetric", "type": "function", "doc": "

    Symmetrize the correlator around x0=0.

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.anti_symmetric": {"fullname": "pyerrors.correlators.Corr.anti_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.anti_symmetric", "type": "function", "doc": "

    Anti-symmetrize the correlator around x0=0.

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.smearing_symmetric": {"fullname": "pyerrors.correlators.Corr.smearing_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.smearing_symmetric", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.GEVP": {"fullname": "pyerrors.correlators.Corr.GEVP", "modulename": "pyerrors.correlators", "qualname": "Corr.GEVP", "type": "function", "doc": "

    \n", "parameters": ["self", "t0", "ts", "state"], "funcdef": "def"}, "pyerrors.correlators.Corr.Eigenvalue": {"fullname": "pyerrors.correlators.Corr.Eigenvalue", "modulename": "pyerrors.correlators", "qualname": "Corr.Eigenvalue", "type": "function", "doc": "

    \n", "parameters": ["self", "t0", "state"], "funcdef": "def"}, "pyerrors.correlators.Corr.roll": {"fullname": "pyerrors.correlators.Corr.roll", "modulename": "pyerrors.correlators", "qualname": "Corr.roll", "type": "function", "doc": "

    Periodically shift the correlator by dt timeslices

    \n\n

    Attributes:

    \n\n

    dt : int\n number of timeslices

    \n", "parameters": ["self", "dt"], "funcdef": "def"}, "pyerrors.correlators.Corr.reverse": {"fullname": "pyerrors.correlators.Corr.reverse", "modulename": "pyerrors.correlators", "qualname": "Corr.reverse", "type": "function", "doc": "

    Reverse the time ordering of the Corr

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.correlate": {"fullname": "pyerrors.correlators.Corr.correlate", "modulename": "pyerrors.correlators", "qualname": "Corr.correlate", "type": "function", "doc": "

    Correlate the correlator with another correlator or Obs

    \n", "parameters": ["self", "partner"], "funcdef": "def"}, "pyerrors.correlators.Corr.reweight": {"fullname": "pyerrors.correlators.Corr.reweight", "modulename": "pyerrors.correlators", "qualname": "Corr.reweight", "type": "function", "doc": "

    Reweight the correlator.

    \n\n
    Parameters
    \n\n\n\n
    Keyword arguments
    \n\n

    all_configs : bool\n if True, the reweighted observables are normalized by the average of\n the reweighting factor on all configurations in weight.idl and not\n on the configurations in obs[i].idl.

    \n", "parameters": ["self", "weight", "kwargs"], "funcdef": "def"}, "pyerrors.correlators.Corr.T_symmetry": {"fullname": "pyerrors.correlators.Corr.T_symmetry", "modulename": "pyerrors.correlators", "qualname": "Corr.T_symmetry", "type": "function", "doc": "

    Return the time symmetry average of the correlator and its partner

    \n\n

    Attributes:

    \n\n

    partner : Corr\n Time symmetry partner of the Corr\npartity : int\n Parity quantum number of the correlator, can be +1 or -1

    \n", "parameters": ["self", "partner", "parity"], "funcdef": "def"}, "pyerrors.correlators.Corr.deriv": {"fullname": "pyerrors.correlators.Corr.deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.deriv", "type": "function", "doc": "

    Return the first derivative of the correlator with respect to x0.

    \n\n

    Attributes:

    \n\n

    symmetric : bool\n decides whether symmertic of simple finite differences are used. Default: True

    \n", "parameters": ["self", "symmetric"], "funcdef": "def"}, "pyerrors.correlators.Corr.second_deriv": {"fullname": "pyerrors.correlators.Corr.second_deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.second_deriv", "type": "function", "doc": "

    Return the second derivative of the correlator with respect to x0.

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.m_eff": {"fullname": "pyerrors.correlators.Corr.m_eff", "modulename": "pyerrors.correlators", "qualname": "Corr.m_eff", "type": "function", "doc": "

    Returns the effective mass of the correlator as correlator object

    \n\n
    Parameters
    \n\n\n", "parameters": ["self", "variant", "guess"], "funcdef": "def"}, "pyerrors.correlators.Corr.fit": {"fullname": "pyerrors.correlators.Corr.fit", "modulename": "pyerrors.correlators", "qualname": "Corr.fit", "type": "function", "doc": "

    Fits function to the data

    \n\n

    Attributes:

    \n\n

    function : obj\n function to fit to the data. See fits.least_squares for details.\nfitrange : list\n Range in which the function is to be fitted to the data.\n If not specified, self.prange or all timeslices are used.\nsilent : bool\n Decides whether output is printed to the standard output.

    \n", "parameters": ["self", "function", "fitrange", "silent", "kwargs"], "funcdef": "def"}, "pyerrors.correlators.Corr.plateau": {"fullname": "pyerrors.correlators.Corr.plateau", "modulename": "pyerrors.correlators", "qualname": "Corr.plateau", "type": "function", "doc": "

    Extract a plateu value from a Corr object

    \n\n

    Attributes:

    \n\n

    plateau_range : list\n list with two entries, indicating the first and the last timeslice\n of the plateau region.\nmethod : str\n method to extract the plateau.\n 'fit' fits a constant to the plateau region\n 'avg', 'average' or 'mean' just average over the given timeslices.

    \n", "parameters": ["self", "plateau_range", "method"], "funcdef": "def"}, "pyerrors.correlators.Corr.set_prange": {"fullname": "pyerrors.correlators.Corr.set_prange", "modulename": "pyerrors.correlators", "qualname": "Corr.set_prange", "type": "function", "doc": "

    Sets the attribute prange of the Corr object.

    \n", "parameters": ["self", "prange"], "funcdef": "def"}, "pyerrors.correlators.Corr.show": {"fullname": "pyerrors.correlators.Corr.show", "modulename": "pyerrors.correlators", "qualname": "Corr.show", "type": "function", "doc": "

    Plots the correlator, uses tag as label if available.

    \n\n
    Parameters
    \n\n\n", "parameters": ["self", "x_range", "comp", "y_range", "logscale", "plateau", "fit_res", "ylabel", "save"], "funcdef": "def"}, "pyerrors.correlators.Corr.dump": {"fullname": "pyerrors.correlators.Corr.dump", "modulename": "pyerrors.correlators", "qualname": "Corr.dump", "type": "function", "doc": "

    Dumps the Corr into a pickel file

    \n\n

    Attributes:

    \n\n

    filename : str\n Name of the file

    \n", "parameters": ["self", "filename"], "funcdef": "def"}, "pyerrors.correlators.Corr.print": {"fullname": "pyerrors.correlators.Corr.print", "modulename": "pyerrors.correlators", "qualname": "Corr.print", "type": "function", "doc": "

    \n", "parameters": ["self", "range"], "funcdef": "def"}, "pyerrors.correlators.Corr.sqrt": {"fullname": "pyerrors.correlators.Corr.sqrt", "modulename": "pyerrors.correlators", "qualname": "Corr.sqrt", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.log": {"fullname": "pyerrors.correlators.Corr.log", "modulename": "pyerrors.correlators", "qualname": "Corr.log", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.exp": {"fullname": "pyerrors.correlators.Corr.exp", "modulename": "pyerrors.correlators", "qualname": "Corr.exp", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.sin": {"fullname": "pyerrors.correlators.Corr.sin", "modulename": "pyerrors.correlators", "qualname": "Corr.sin", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.cos": {"fullname": "pyerrors.correlators.Corr.cos", "modulename": "pyerrors.correlators", "qualname": "Corr.cos", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.tan": {"fullname": "pyerrors.correlators.Corr.tan", "modulename": "pyerrors.correlators", "qualname": "Corr.tan", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.sinh": {"fullname": "pyerrors.correlators.Corr.sinh", "modulename": "pyerrors.correlators", "qualname": "Corr.sinh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.cosh": {"fullname": "pyerrors.correlators.Corr.cosh", "modulename": "pyerrors.correlators", "qualname": "Corr.cosh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.tanh": {"fullname": "pyerrors.correlators.Corr.tanh", "modulename": "pyerrors.correlators", "qualname": "Corr.tanh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.arcsin": {"fullname": "pyerrors.correlators.Corr.arcsin", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsin", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.arccos": {"fullname": "pyerrors.correlators.Corr.arccos", "modulename": "pyerrors.correlators", "qualname": "Corr.arccos", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.arctan": {"fullname": "pyerrors.correlators.Corr.arctan", "modulename": "pyerrors.correlators", "qualname": "Corr.arctan", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.arcsinh": {"fullname": "pyerrors.correlators.Corr.arcsinh", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsinh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.arccosh": {"fullname": "pyerrors.correlators.Corr.arccosh", "modulename": "pyerrors.correlators", "qualname": "Corr.arccosh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.arctanh": {"fullname": "pyerrors.correlators.Corr.arctanh", "modulename": "pyerrors.correlators", "qualname": "Corr.arctanh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.dirac": {"fullname": "pyerrors.dirac", "modulename": "pyerrors.dirac", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.dirac.Grid_gamma": {"fullname": "pyerrors.dirac.Grid_gamma", "modulename": "pyerrors.dirac", "qualname": "Grid_gamma", "type": "function", "doc": "

    Returns gamma matrix in Grid labeling.

    \n", "parameters": ["gamma_tag"], "funcdef": "def"}, "pyerrors.fits": {"fullname": "pyerrors.fits", "modulename": "pyerrors.fits", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.fits.Fit_result": {"fullname": "pyerrors.fits.Fit_result", "modulename": "pyerrors.fits", "qualname": "Fit_result", "type": "class", "doc": "

    Represents fit results.

    \n\n
    Attributes
    \n\n\n"}, "pyerrors.fits.Fit_result.__init__": {"fullname": "pyerrors.fits.Fit_result.__init__", "modulename": "pyerrors.fits", "qualname": "Fit_result.__init__", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.fits.Fit_result.gamma_method": {"fullname": "pyerrors.fits.Fit_result.gamma_method", "modulename": "pyerrors.fits", "qualname": "Fit_result.gamma_method", "type": "function", "doc": "

    Apply the gamma method to all fit parameters

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.fits.least_squares": {"fullname": "pyerrors.fits.least_squares", "modulename": "pyerrors.fits", "qualname": "least_squares", "type": "function", "doc": "

    Performs a non-linear fit to y = func(x).

    \n\n

    Arguments:

    \n\n

    x : list\n list of floats.\ny : list\n list of Obs.\nfunc : object\n fit function, has to be of the form

    \n\n
    def func(a, x):\n    return a[0] + a[1] * x + a[2] * anp.sinh(x)\n\nFor multiple x values func can be of the form\n\ndef func(a, x):\n    (x1, x2) = x\n    return a[0] * x1 ** 2 + a[1] * x2\n\nIt is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work\n
    \n\n

    priors : list, optional\n priors has to be a list with an entry for every parameter in the fit. The entries can either be\n Obs (e.g. results from a previous fit) or strings containing a value and an error formatted like\n 0.548(23), 500(40) or 0.5(0.4)\n It is important for the subsequent error estimation that the e_tag for the gamma method is large\n enough.\nsilent : bool, optional\n If true all output to the console is omitted (default False).

    \n\n
    Keyword arguments
    \n\n

    initial_guess -- can provide an initial guess for the input parameters. Relevant for\n non-linear fits with many parameters.\nmethod -- can be used to choose an alternative method for the minimization of chisquare.\n The possible methods are the ones which can be used for scipy.optimize.minimize and\n migrad of iminuit. If no method is specified, Levenberg-Marquard is used.\n Reliable alternatives are migrad, Powell and Nelder-Mead.\nresplot -- If true, a plot which displays fit, data and residuals is generated (default False).\nqqplot -- If true, a quantile-quantile plot of the fit result is generated (default False).\nexpected_chisquare -- If true prints the expected chisquare which is\n corrected by effects caused by correlated input data.\n This can take a while as the full correlation matrix\n has to be calculated (default False).

    \n", "parameters": ["x", "y", "func", "priors", "silent", "kwargs"], "funcdef": "def"}, "pyerrors.fits.standard_fit": {"fullname": "pyerrors.fits.standard_fit", "modulename": "pyerrors.fits", "qualname": "standard_fit", "type": "function", "doc": "

    \n", "parameters": ["x", "y", "func", "silent", "kwargs"], "funcdef": "def"}, "pyerrors.fits.odr_fit": {"fullname": "pyerrors.fits.odr_fit", "modulename": "pyerrors.fits", "qualname": "odr_fit", "type": "function", "doc": "

    \n", "parameters": ["x", "y", "func", "silent", "kwargs"], "funcdef": "def"}, "pyerrors.fits.total_least_squares": {"fullname": "pyerrors.fits.total_least_squares", "modulename": "pyerrors.fits", "qualname": "total_least_squares", "type": "function", "doc": "

    Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.

    \n\n

    x : list\n list of Obs, or a tuple of lists of Obs\ny : list\n list of Obs. The dvalues of the Obs are used as x- and yerror for the fit.\nfunc : object\n func has to be of the form

    \n\n
    def func(a, x):\n    y = a[0] + a[1] * x + a[2] * anp.sinh(x)\n    return y\n\nFor multiple x values func can be of the form\n\ndef func(a, x):\n    (x1, x2) = x\n    return a[0] * x1 ** 2 + a[1] * x2\n\nIt is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.\n
    \n\n

    silent : bool, optional\n If true all output to the console is omitted (default False).\nBased on the orthogonal distance regression module of scipy

    \n\n
    Keyword arguments
    \n\n

    initial_guess -- can provide an initial guess for the input parameters. Relevant for non-linear\n fits with many parameters.\nexpected_chisquare -- If true prints the expected chisquare which is\n corrected by effects caused by correlated input data.\n This can take a while as the full correlation matrix\n has to be calculated (default False).

    \n", "parameters": ["x", "y", "func", "silent", "kwargs"], "funcdef": "def"}, "pyerrors.fits.prior_fit": {"fullname": "pyerrors.fits.prior_fit", "modulename": "pyerrors.fits", "qualname": "prior_fit", "type": "function", "doc": "

    \n", "parameters": ["x", "y", "func", "priors", "silent", "kwargs"], "funcdef": "def"}, "pyerrors.fits.fit_lin": {"fullname": "pyerrors.fits.fit_lin", "modulename": "pyerrors.fits", "qualname": "fit_lin", "type": "function", "doc": "

    Performs a linear fit to y = n + m * x and returns two Obs n, m.

    \n\n

    y has to be a list of Obs, the dvalues of the Obs are used as yerror for the fit.\nx can either be a list of floats in which case no xerror is assumed, or\na list of Obs, where the dvalues of the Obs are used as xerror for the fit.

    \n", "parameters": ["x", "y", "kwargs"], "funcdef": "def"}, "pyerrors.fits.qqplot": {"fullname": "pyerrors.fits.qqplot", "modulename": "pyerrors.fits", "qualname": "qqplot", "type": "function", "doc": "

    Generates a quantile-quantile plot of the fit result which can be used to\ncheck if the residuals of the fit are gaussian distributed.

    \n", "parameters": ["x", "o_y", "func", "p"], "funcdef": "def"}, "pyerrors.fits.residual_plot": {"fullname": "pyerrors.fits.residual_plot", "modulename": "pyerrors.fits", "qualname": "residual_plot", "type": "function", "doc": "

    Generates a plot which compares the fit to the data and displays the corresponding residuals

    \n", "parameters": ["x", "y", "func", "fit_res"], "funcdef": "def"}, "pyerrors.fits.covariance_matrix": {"fullname": "pyerrors.fits.covariance_matrix", "modulename": "pyerrors.fits", "qualname": "covariance_matrix", "type": "function", "doc": "

    Returns the covariance matrix of y.

    \n", "parameters": ["y"], "funcdef": "def"}, "pyerrors.fits.error_band": {"fullname": "pyerrors.fits.error_band", "modulename": "pyerrors.fits", "qualname": "error_band", "type": "function", "doc": "

    Returns the error band for an array of sample values x, for given fit function func with optimized parameters beta.

    \n", "parameters": ["x", "func", "beta"], "funcdef": "def"}, "pyerrors.fits.ks_test": {"fullname": "pyerrors.fits.ks_test", "modulename": "pyerrors.fits", "qualname": "ks_test", "type": "function", "doc": "

    Performs a Kolmogorov\u2013Smirnov test for the Q-values of all fit object.

    \n\n

    If no list is given all Obs in memory are used.

    \n\n

    Disclaimer: The determination of the individual Q-values as well as this function have not been tested yet.

    \n", "parameters": ["obs"], "funcdef": "def"}, "pyerrors.fits.fit_general": {"fullname": "pyerrors.fits.fit_general", "modulename": "pyerrors.fits", "qualname": "fit_general", "type": "function", "doc": "

    Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.

    \n\n

    Plausibility of the results should be checked. To control the numerical differentiation\nthe kwargs of numdifftools.step_generators.MaxStepGenerator can be used.

    \n\n

    func has to be of the form

    \n\n

    def func(a, x):\n y = a[0] + a[1] * x + a[2] * np.sinh(x)\n return y

    \n\n

    y has to be a list of Obs, the dvalues of the Obs are used as yerror for the fit.\nx can either be a list of floats in which case no xerror is assumed, or\na list of Obs, where the dvalues of the Obs are used as xerror for the fit.

    \n\n
    Keyword arguments
    \n\n

    silent -- If true all output to the console is omitted (default False).\ninitial_guess -- can provide an initial guess for the input parameters. Relevant for non-linear fits\n with many parameters.

    \n", "parameters": ["x", "y", "func", "silent", "kwargs"], "funcdef": "def"}, "pyerrors.input": {"fullname": "pyerrors.input", "modulename": "pyerrors.input", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.input.bdio": {"fullname": "pyerrors.input.bdio", "modulename": "pyerrors.input.bdio", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.input.bdio.read_ADerrors": {"fullname": "pyerrors.input.bdio.read_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "read_ADerrors", "type": "function", "doc": "

    Extract generic MCMC data from a bdio file

    \n\n

    read_ADerrors requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n\n", "parameters": ["file_path", "bdio_path", "kwargs"], "funcdef": "def"}, "pyerrors.input.bdio.write_ADerrors": {"fullname": "pyerrors.input.bdio.write_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "write_ADerrors", "type": "function", "doc": "

    Write Obs to a bdio file according to ADerrors conventions

    \n\n

    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n\n", "parameters": ["obs_list", "file_path", "bdio_path", "kwargs"], "funcdef": "def"}, "pyerrors.input.bdio.read_mesons": {"fullname": "pyerrors.input.bdio.read_mesons", "modulename": "pyerrors.input.bdio", "qualname": "read_mesons", "type": "function", "doc": "

    Extract mesons data from a bdio file and return it as a dictionary

    \n\n

    The dictionary can be accessed with a tuple consisting of (type, source_position, kappa1, kappa2)

    \n\n

    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n\n", "parameters": ["file_path", "bdio_path", "kwargs"], "funcdef": "def"}, "pyerrors.input.bdio.read_dSdm": {"fullname": "pyerrors.input.bdio.read_dSdm", "modulename": "pyerrors.input.bdio", "qualname": "read_dSdm", "type": "function", "doc": "

    Extract dSdm data from a bdio file and return it as a dictionary

    \n\n

    The dictionary can be accessed with a tuple consisting of (type, kappa)

    \n\n

    read_dSdm requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n\n", "parameters": ["file_path", "bdio_path", "kwargs"], "funcdef": "def"}, "pyerrors.input.hadrons": {"fullname": "pyerrors.input.hadrons", "modulename": "pyerrors.input.hadrons", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.input.hadrons.read_meson_hd5": {"fullname": "pyerrors.input.hadrons.read_meson_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_meson_hd5", "type": "function", "doc": "

    Read hadrons meson hdf5 file and extract the meson labeled 'meson'

    \n\n
    Parameters
    \n\n\n", "parameters": ["path", "filestem", "ens_id", "meson", "tree"], "funcdef": "def"}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"fullname": "pyerrors.input.hadrons.read_ExternalLeg_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_ExternalLeg_hd5", "type": "function", "doc": "

    Read hadrons ExternalLeg hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n\n", "parameters": ["path", "filestem", "ens_id", "order"], "funcdef": "def"}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"fullname": "pyerrors.input.hadrons.read_Bilinear_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Bilinear_hd5", "type": "function", "doc": "

    Read hadrons Bilinear hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n\n", "parameters": ["path", "filestem", "ens_id", "order"], "funcdef": "def"}, "pyerrors.input.misc": {"fullname": "pyerrors.input.misc", "modulename": "pyerrors.input.misc", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.input.misc.read_pbp": {"fullname": "pyerrors.input.misc.read_pbp", "modulename": "pyerrors.input.misc", "qualname": "read_pbp", "type": "function", "doc": "

    Read pbp format from given folder structure. Returns a list of length nrw

    \n\n
    Keyword arguments
    \n\n

    r_start -- list which contains the first config to be read for each replicum\nr_stop -- list which contains the last config to be read for each replicum

    \n", "parameters": ["path", "prefix", "kwargs"], "funcdef": "def"}, "pyerrors.input.openQCD": {"fullname": "pyerrors.input.openQCD", "modulename": "pyerrors.input.openQCD", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.input.openQCD.read_rwms": {"fullname": "pyerrors.input.openQCD.read_rwms", "modulename": "pyerrors.input.openQCD", "qualname": "read_rwms", "type": "function", "doc": "

    Read rwms format from given folder structure. Returns a list of length nrw

    \n\n
    Attributes
    \n\n\n\n
    Keyword arguments
    \n\n

    r_start -- list which contains the first config to be read for each replicum\nr_stop -- list which contains the last config to be read for each replicum\npostfix -- postfix of the file to read, e.g. '.ms1' for openQCD-files

    \n", "parameters": ["path", "prefix", "version", "names", "kwargs"], "funcdef": "def"}, "pyerrors.input.openQCD.extract_t0": {"fullname": "pyerrors.input.openQCD.extract_t0", "modulename": "pyerrors.input.openQCD", "qualname": "extract_t0", "type": "function", "doc": "

    Extract t0 from given .ms.dat files. Returns t0 as Obs.

    \n\n

    It is assumed that all boundary effects have sufficiently decayed at x0=xmin.\nThe data around the zero crossing of t^2 - 0.3 is fitted with a linear function\nfrom which the exact root is extracted.\nOnly works with openQCD v 1.2.

    \n\n
    Parameters
    \n\n\n\n
    Keyword arguments
    \n\n

    r_start -- list which contains the first config to be read for each replicum.\nr_stop -- list which contains the last config to be read for each replicum.\nplaquette -- If true extract the plaquette estimate of t0 instead.

    \n", "parameters": ["path", "prefix", "dtr_read", "xmin", "spatial_extent", "fit_range", "kwargs"], "funcdef": "def"}, "pyerrors.input.sfcf": {"fullname": "pyerrors.input.sfcf", "modulename": "pyerrors.input.sfcf", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.input.sfcf.read_sfcf": {"fullname": "pyerrors.input.sfcf.read_sfcf", "modulename": "pyerrors.input.sfcf", "qualname": "read_sfcf", "type": "function", "doc": "

    Read sfcf C format from given folder structure.

    \n\n
    Keyword arguments
    \n\n

    im -- if True, read imaginary instead of real part of the correlation function.\nsingle -- if True, read a boundary-to-boundary correlation function with a single value\nb2b -- if True, read a time-dependent boundary-to-boundary correlation function\nnames -- Alternative labeling for replicas/ensembles. Has to have the appropriate length

    \n", "parameters": ["path", "prefix", "name", "kwargs"], "funcdef": "def"}, "pyerrors.input.sfcf.read_sfcf_c": {"fullname": "pyerrors.input.sfcf.read_sfcf_c", "modulename": "pyerrors.input.sfcf", "qualname": "read_sfcf_c", "type": "function", "doc": "

    Read sfcf c format from given folder structure.

    \n\n
    Arguments
    \n\n

    quarks -- Label of the quarks used in the sfcf input file\nnoffset -- Offset of the source (only relevant when wavefunctions are used)\nwf -- ID of wave function\nwf2 -- ID of the second wavefunction (only relevant for boundary-to-boundary correlation functions)

    \n\n
    Keyword arguments
    \n\n

    im -- if True, read imaginary instead of real part of the correlation function.\nb2b -- if True, read a time-dependent boundary-to-boundary correlation function\nnames -- Alternative labeling for replicas/ensembles. Has to have the appropriate length\nens_name : str\n replaces the name of the ensemble

    \n", "parameters": ["path", "prefix", "name", "quarks", "noffset", "wf", "wf2", "kwargs"], "funcdef": "def"}, "pyerrors.input.sfcf.read_qtop": {"fullname": "pyerrors.input.sfcf.read_qtop", "modulename": "pyerrors.input.sfcf", "qualname": "read_qtop", "type": "function", "doc": "

    Read qtop format from given folder structure.

    \n\n
    Keyword arguments
    \n\n

    target -- specifies the topological sector to be reweighted to (default 0)\nfull -- if true read the charge instead of the reweighting factor.

    \n", "parameters": ["path", "prefix", "kwargs"], "funcdef": "def"}, "pyerrors.jackknifing": {"fullname": "pyerrors.jackknifing", "modulename": "pyerrors.jackknifing", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.jackknifing.Jack": {"fullname": "pyerrors.jackknifing.Jack", "modulename": "pyerrors.jackknifing", "qualname": "Jack", "type": "class", "doc": "

    \n"}, "pyerrors.jackknifing.Jack.__init__": {"fullname": "pyerrors.jackknifing.Jack.__init__", "modulename": "pyerrors.jackknifing", "qualname": "Jack.__init__", "type": "function", "doc": "

    \n", "parameters": ["self", "value", "jacks"], "funcdef": "def"}, "pyerrors.jackknifing.Jack.print": {"fullname": "pyerrors.jackknifing.Jack.print", "modulename": "pyerrors.jackknifing", "qualname": "Jack.print", "type": "function", "doc": "

    Print basic properties of the Jack.

    \n", "parameters": ["self", "kwargs"], "funcdef": "def"}, "pyerrors.jackknifing.Jack.plot_tauint": {"fullname": "pyerrors.jackknifing.Jack.plot_tauint", "modulename": "pyerrors.jackknifing", "qualname": "Jack.plot_tauint", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.jackknifing.Jack.plot_history": {"fullname": "pyerrors.jackknifing.Jack.plot_history", "modulename": "pyerrors.jackknifing", "qualname": "Jack.plot_history", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.jackknifing.Jack.dump": {"fullname": "pyerrors.jackknifing.Jack.dump", "modulename": "pyerrors.jackknifing", "qualname": "Jack.dump", "type": "function", "doc": "

    Dump the Jack to a pickle file 'name'.

    \n\n

    Keyword arguments:\npath -- specifies a custom path for the file (default '.')

    \n", "parameters": ["self", "name", "kwargs"], "funcdef": "def"}, "pyerrors.jackknifing.generate_jack": {"fullname": "pyerrors.jackknifing.generate_jack", "modulename": "pyerrors.jackknifing", "qualname": "generate_jack", "type": "function", "doc": "

    \n", "parameters": ["obs", "kwargs"], "funcdef": "def"}, "pyerrors.jackknifing.derived_jack": {"fullname": "pyerrors.jackknifing.derived_jack", "modulename": "pyerrors.jackknifing", "qualname": "derived_jack", "type": "function", "doc": "

    Construct a derived Jack according to func(data, **kwargs).

    \n\n
    Parameters
    \n\n\n\n
    Notes
    \n\n

    For simple mathematical operations it can be practical to use anonymous functions.\nFor the ratio of two jacks one can e.g. use

    \n\n

    new_jack = derived_jack(lambda x : x[0] / x[1], [jack1, jack2])

    \n", "parameters": ["func", "data", "kwargs"], "funcdef": "def"}, "pyerrors.linalg": {"fullname": "pyerrors.linalg", "modulename": "pyerrors.linalg", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.linalg.derived_array": {"fullname": "pyerrors.linalg.derived_array", "modulename": "pyerrors.linalg", "qualname": "derived_array", "type": "function", "doc": "

    Construct a derived Obs according to func(data, **kwargs) of matrix value data\nusing automatic differentiation.

    \n\n
    Parameters
    \n\n\n\n
    Keyword arguments
    \n\n

    man_grad -- manually supply a list or an array which contains the jacobian\n of func. Use cautiously, supplying the wrong derivative will\n not be intercepted.

    \n", "parameters": ["func", "data", "kwargs"], "funcdef": "def"}, "pyerrors.linalg.matmul": {"fullname": "pyerrors.linalg.matmul", "modulename": "pyerrors.linalg", "qualname": "matmul", "type": "function", "doc": "

    Matrix multiply all operands.

    \n\n

    Supports real and complex valued matrices and is faster compared to\nstandard multiplication via the @ operator.

    \n", "parameters": ["operands"], "funcdef": "def"}, "pyerrors.linalg.inv": {"fullname": "pyerrors.linalg.inv", "modulename": "pyerrors.linalg", "qualname": "inv", "type": "function", "doc": "

    Inverse of Obs or CObs valued matrices.

    \n", "parameters": ["x"], "funcdef": "def"}, "pyerrors.linalg.cholesky": {"fullname": "pyerrors.linalg.cholesky", "modulename": "pyerrors.linalg", "qualname": "cholesky", "type": "function", "doc": "

    Cholesky decompostion of Obs or CObs valued matrices.

    \n", "parameters": ["x"], "funcdef": "def"}, "pyerrors.linalg.scalar_mat_op": {"fullname": "pyerrors.linalg.scalar_mat_op", "modulename": "pyerrors.linalg", "qualname": "scalar_mat_op", "type": "function", "doc": "

    Computes the matrix to scalar operation op to a given matrix of Obs.

    \n", "parameters": ["op", "obs", "kwargs"], "funcdef": "def"}, "pyerrors.linalg.eigh": {"fullname": "pyerrors.linalg.eigh", "modulename": "pyerrors.linalg", "qualname": "eigh", "type": "function", "doc": "

    Computes the eigenvalues and eigenvectors of a given hermitian matrix of Obs according to np.linalg.eigh.

    \n", "parameters": ["obs", "kwargs"], "funcdef": "def"}, "pyerrors.linalg.eig": {"fullname": "pyerrors.linalg.eig", "modulename": "pyerrors.linalg", "qualname": "eig", "type": "function", "doc": "

    Computes the eigenvalues of a given matrix of Obs according to np.linalg.eig.

    \n", "parameters": ["obs", "kwargs"], "funcdef": "def"}, "pyerrors.linalg.pinv": {"fullname": "pyerrors.linalg.pinv", "modulename": "pyerrors.linalg", "qualname": "pinv", "type": "function", "doc": "

    Computes the Moore-Penrose pseudoinverse of a matrix of Obs.

    \n", "parameters": ["obs", "kwargs"], "funcdef": "def"}, "pyerrors.linalg.svd": {"fullname": "pyerrors.linalg.svd", "modulename": "pyerrors.linalg", "qualname": "svd", "type": "function", "doc": "

    Computes the singular value decomposition of a matrix of Obs.

    \n", "parameters": ["obs", "kwargs"], "funcdef": "def"}, "pyerrors.linalg.slogdet": {"fullname": "pyerrors.linalg.slogdet", "modulename": "pyerrors.linalg", "qualname": "slogdet", "type": "function", "doc": "

    Computes the determinant of a matrix of Obs via np.linalg.slogdet.

    \n", "parameters": ["obs", "kwargs"], "funcdef": "def"}, "pyerrors.linalg.grad_eig": {"fullname": "pyerrors.linalg.grad_eig", "modulename": "pyerrors.linalg", "qualname": "grad_eig", "type": "function", "doc": "

    Gradient of a general square (complex valued) matrix

    \n", "parameters": ["ans", "x"], "funcdef": "def"}, "pyerrors.misc": {"fullname": "pyerrors.misc", "modulename": "pyerrors.misc", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.misc.gen_correlated_data": {"fullname": "pyerrors.misc.gen_correlated_data", "modulename": "pyerrors.misc", "qualname": "gen_correlated_data", "type": "function", "doc": "

    Generate observables with given covariance and autocorrelation times.

    \n\n
    Arguments
    \n\n

    means -- list containing the mean value of each observable.\ncov -- covariance matrix for the data to be geneated.\nname -- ensemble name for the data to be geneated.\ntau -- can either be a real number or a list with an entry for\n every dataset.\nsamples -- number of samples to be generated for each observable.

    \n", "parameters": ["means", "cov", "name", "tau", "samples"], "funcdef": "def"}, "pyerrors.mpm": {"fullname": "pyerrors.mpm", "modulename": "pyerrors.mpm", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.mpm.matrix_pencil_method": {"fullname": "pyerrors.mpm.matrix_pencil_method", "modulename": "pyerrors.mpm", "qualname": "matrix_pencil_method", "type": "function", "doc": "

    Matrix pencil method to extract k energy levels from data

    \n\n

    Implementation of the matrix pencil method based on\neq. (2.17) of Y. Hua, T. K. Sarkar, IEEE Trans. Acoust. 38, 814-824 (1990)

    \n\n
    Parameters
    \n\n\n", "parameters": ["corrs", "k", "p", "kwargs"], "funcdef": "def"}, "pyerrors.mpm.matrix_pencil_method_old": {"fullname": "pyerrors.mpm.matrix_pencil_method_old", "modulename": "pyerrors.mpm", "qualname": "matrix_pencil_method_old", "type": "function", "doc": "

    Older impleentation of the matrix pencil method with pencil p on given data to\n extract energy levels.

    \n\n
    Parameters
    \n\n\n", "parameters": ["data", "p", "noise_level", "verbose", "kwargs"], "funcdef": "def"}, "pyerrors.npr": {"fullname": "pyerrors.npr", "modulename": "pyerrors.npr", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.npr.Npr_matrix": {"fullname": "pyerrors.npr.Npr_matrix", "modulename": "pyerrors.npr", "qualname": "Npr_matrix", "type": "class", "doc": "

    ndarray(shape, dtype=float, buffer=None, offset=0,\n strides=None, order=None)

    \n\n

    An array object represents a multidimensional, homogeneous array\nof fixed-size items. An associated data-type object describes the\nformat of each element in the array (its byte-order, how many bytes it\noccupies in memory, whether it is an integer, a floating point number,\nor something else, etc.)

    \n\n

    Arrays should be constructed using array, zeros or empty (refer\nto the See Also section below). The parameters given here refer to\na low-level method (ndarray(...)) for instantiating an array.

    \n\n

    For more information, refer to the numpy module and examine the\nmethods and attributes of an array.

    \n\n
    Parameters
    \n\n\n\n
    Attributes
    \n\n\n\n
    See Also
    \n\n

    array: Construct an array.
    \nzeros: Create an array, each element of which is zero.
    \nempty: Create an array, but leave its allocated memory unchanged (i.e.,\nit contains \"garbage\").
    \ndtype: Create a data-type.
    \nnumpy.typing.NDArray: A :term:generic <generic type> version\nof ndarray.

    \n\n
    Notes
    \n\n

    There are two modes of creating an array using __new__:

    \n\n
      \n
    1. If buffer is None, then only shape, dtype, and order\nare used.
      2. \n
    2. If buffer is an object exposing the buffer interface, then\nall keywords are interpreted.
      3. \n
    \n\n

    No __init__ method is needed because the array is fully initialized\nafter the __new__ method.

    \n\n
    Examples
    \n\n

    These examples illustrate the low-level ndarray constructor. Refer\nto the See Also section above for easier ways of constructing an\nndarray.

    \n\n

    First mode, buffer is None:

    \n\n
    >>> np.ndarray(shape=(2,2), dtype=float, order='F')\narray([[0.0e+000, 0.0e+000], # random\n       [     nan, 2.5e-323]])\n
    \n\n

    Second mode:

    \n\n
    >>> np.ndarray((2,), buffer=np.array([1,2,3]),\n...            offset=np.int_().itemsize,\n...            dtype=int) # offset = 1*itemsize, i.e. skip first element\narray([2, 3])\n
    \n"}, "pyerrors.npr.Npr_matrix.__init__": {"fullname": "pyerrors.npr.Npr_matrix.__init__", "modulename": "pyerrors.npr", "qualname": "Npr_matrix.__init__", "type": "function", "doc": "

    \n", "parameters": [], "funcdef": "def"}, "pyerrors.npr.Npr_matrix.g5H": {"fullname": "pyerrors.npr.Npr_matrix.g5H", "modulename": "pyerrors.npr", "qualname": "Npr_matrix.g5H", "type": "variable", "doc": "

    Gamma_5 hermitean conjugate

    \n\n

    Returns gamma_5 @ M.T.conj() @ gamma_5 and exchanges in and out going\nmomenta. Works only for 12x12 matrices.

    \n"}, "pyerrors.npr.inv_propagator": {"fullname": "pyerrors.npr.inv_propagator", "modulename": "pyerrors.npr", "qualname": "inv_propagator", "type": "function", "doc": "

    Inverts a 12x12 quark propagator

    \n", "parameters": ["prop"], "funcdef": "def"}, "pyerrors.npr.Zq": {"fullname": "pyerrors.npr.Zq", "modulename": "pyerrors.npr", "qualname": "Zq", "type": "function", "doc": "

    Calculates the quark field renormalization constant Zq

    \n\n

    Attributes:\ninv_prop -- Inverted 12x12 quark propagator\nfermion -- Fermion type for which the tree-level propagator is used\n in the calculation of Zq. Default Wilson.

    \n", "parameters": ["inv_prop", "fermion"], "funcdef": "def"}, "pyerrors.obs": {"fullname": "pyerrors.obs", "modulename": "pyerrors.obs", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.obs.Obs": {"fullname": "pyerrors.obs.Obs", "modulename": "pyerrors.obs", "qualname": "Obs", "type": "class", "doc": "

    Class for a general observable.

    \n\n

    Instances of Obs are the basic objects of a pyerrors error analysis.\nThey are initialized with a list which contains arrays of samples for\ndifferent ensembles/replica and another list of same length which contains\nthe names of the ensembles/replica. Mathematical operations can be\nperformed on instances. The result is another instance of Obs. The error of\nan instance can be computed with the gamma_method. Also contains additional\nmethods for output and visualization of the error calculation.

    \n\n
    Attributes
    \n\n\n"}, "pyerrors.obs.Obs.__init__": {"fullname": "pyerrors.obs.Obs.__init__", "modulename": "pyerrors.obs", "qualname": "Obs.__init__", "type": "function", "doc": "

    Initialize Obs object.

    \n\n
    Attributes
    \n\n\n", "parameters": ["self", "samples", "names", "idl", "means", "kwargs"], "funcdef": "def"}, "pyerrors.obs.Obs.S_global": {"fullname": "pyerrors.obs.Obs.S_global", "modulename": "pyerrors.obs", "qualname": "Obs.S_global", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.S_dict": {"fullname": "pyerrors.obs.Obs.S_dict", "modulename": "pyerrors.obs", "qualname": "Obs.S_dict", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.tau_exp_global": {"fullname": "pyerrors.obs.Obs.tau_exp_global", "modulename": "pyerrors.obs", "qualname": "Obs.tau_exp_global", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.tau_exp_dict": {"fullname": "pyerrors.obs.Obs.tau_exp_dict", "modulename": "pyerrors.obs", "qualname": "Obs.tau_exp_dict", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.N_sigma_global": {"fullname": "pyerrors.obs.Obs.N_sigma_global", "modulename": "pyerrors.obs", "qualname": "Obs.N_sigma_global", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.filter_eps": {"fullname": "pyerrors.obs.Obs.filter_eps", "modulename": "pyerrors.obs", "qualname": "Obs.filter_eps", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.names": {"fullname": "pyerrors.obs.Obs.names", "modulename": "pyerrors.obs", "qualname": "Obs.names", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.shape": {"fullname": "pyerrors.obs.Obs.shape", "modulename": "pyerrors.obs", "qualname": "Obs.shape", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.r_values": {"fullname": "pyerrors.obs.Obs.r_values", "modulename": "pyerrors.obs", "qualname": "Obs.r_values", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.deltas": {"fullname": "pyerrors.obs.Obs.deltas", "modulename": "pyerrors.obs", "qualname": "Obs.deltas", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.idl": {"fullname": "pyerrors.obs.Obs.idl", "modulename": "pyerrors.obs", "qualname": "Obs.idl", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.is_merged": {"fullname": "pyerrors.obs.Obs.is_merged", "modulename": "pyerrors.obs", "qualname": "Obs.is_merged", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.N": {"fullname": "pyerrors.obs.Obs.N", "modulename": "pyerrors.obs", "qualname": "Obs.N", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.ddvalue": {"fullname": "pyerrors.obs.Obs.ddvalue", "modulename": "pyerrors.obs", "qualname": "Obs.ddvalue", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.reweighted": {"fullname": "pyerrors.obs.Obs.reweighted", "modulename": "pyerrors.obs", "qualname": "Obs.reweighted", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.tag": {"fullname": "pyerrors.obs.Obs.tag", "modulename": "pyerrors.obs", "qualname": "Obs.tag", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.value": {"fullname": "pyerrors.obs.Obs.value", "modulename": "pyerrors.obs", "qualname": "Obs.value", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.dvalue": {"fullname": "pyerrors.obs.Obs.dvalue", "modulename": "pyerrors.obs", "qualname": "Obs.dvalue", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_names": {"fullname": "pyerrors.obs.Obs.e_names", "modulename": "pyerrors.obs", "qualname": "Obs.e_names", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_content": {"fullname": "pyerrors.obs.Obs.e_content", "modulename": "pyerrors.obs", "qualname": "Obs.e_content", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.expand_deltas": {"fullname": "pyerrors.obs.Obs.expand_deltas", "modulename": "pyerrors.obs", "qualname": "Obs.expand_deltas", "type": "function", "doc": "

    Expand deltas defined on idx to a regular, contiguous range, where holes are filled by 0.\n If idx is of type range, the deltas are not changed

    \n\n
    Parameters
    \n\n\n", "parameters": ["self", "deltas", "idx", "shape"], "funcdef": "def"}, "pyerrors.obs.Obs.calc_gamma": {"fullname": "pyerrors.obs.Obs.calc_gamma", "modulename": "pyerrors.obs", "qualname": "Obs.calc_gamma", "type": "function", "doc": "

    Calculate Gamma_{AA} from the deltas, which are defined on idx.\n idx is assumed to be a contiguous range (possibly with a stepsize != 1)

    \n\n
    Parameters
    \n\n\n", "parameters": ["self", "deltas", "idx", "shape", "w_max", "fft"], "funcdef": "def"}, "pyerrors.obs.Obs.gamma_method": {"fullname": "pyerrors.obs.Obs.gamma_method", "modulename": "pyerrors.obs", "qualname": "Obs.gamma_method", "type": "function", "doc": "

    Calculate the error and related properties of the Obs.

    \n\n
    Keyword arguments
    \n\n

    S : float\n specifies a custom value for the parameter S (default 2.0), can be\n a float or an array of floats for different ensembles\ntau_exp : float\n positive value triggers the critical slowing down analysis\n (default 0.0), can be a float or an array of floats for different\n ensembles\nN_sigma : float\n number of standard deviations from zero until the tail is\n attached to the autocorrelation function (default 1)\nfft : bool\n determines whether the fft algorithm is used for the computation\n of the autocorrelation function (default True)

    \n", "parameters": ["self", "kwargs"], "funcdef": "def"}, "pyerrors.obs.Obs.print": {"fullname": "pyerrors.obs.Obs.print", "modulename": "pyerrors.obs", "qualname": "Obs.print", "type": "function", "doc": "

    \n", "parameters": ["self", "level"], "funcdef": "def"}, "pyerrors.obs.Obs.details": {"fullname": "pyerrors.obs.Obs.details", "modulename": "pyerrors.obs", "qualname": "Obs.details", "type": "function", "doc": "

    Output detailed properties of the Obs.

    \n", "parameters": ["self", "ens_content"], "funcdef": "def"}, "pyerrors.obs.Obs.is_zero_within_error": {"fullname": "pyerrors.obs.Obs.is_zero_within_error", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero_within_error", "type": "function", "doc": "

    Checks whether the observable is zero within 'sigma' standard errors.

    \n\n

    Works only properly when the gamma method was run.

    \n", "parameters": ["self", "sigma"], "funcdef": "def"}, "pyerrors.obs.Obs.is_zero": {"fullname": "pyerrors.obs.Obs.is_zero", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero", "type": "function", "doc": "

    Checks whether the observable is zero within machine precision.

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.plot_tauint": {"fullname": "pyerrors.obs.Obs.plot_tauint", "modulename": "pyerrors.obs", "qualname": "Obs.plot_tauint", "type": "function", "doc": "

    Plot integrated autocorrelation time for each ensemble.

    \n", "parameters": ["self", "save"], "funcdef": "def"}, "pyerrors.obs.Obs.plot_rho": {"fullname": "pyerrors.obs.Obs.plot_rho", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rho", "type": "function", "doc": "

    Plot normalized autocorrelation function time for each ensemble.

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.plot_rep_dist": {"fullname": "pyerrors.obs.Obs.plot_rep_dist", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rep_dist", "type": "function", "doc": "

    Plot replica distribution for each ensemble with more than one replicum.

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.plot_history": {"fullname": "pyerrors.obs.Obs.plot_history", "modulename": "pyerrors.obs", "qualname": "Obs.plot_history", "type": "function", "doc": "

    Plot derived Monte Carlo history for each ensemble.

    \n", "parameters": ["self", "expand"], "funcdef": "def"}, "pyerrors.obs.Obs.plot_piechart": {"fullname": "pyerrors.obs.Obs.plot_piechart", "modulename": "pyerrors.obs", "qualname": "Obs.plot_piechart", "type": "function", "doc": "

    Plot piechart which shows the fractional contribution of each\nensemble to the error and returns a dictionary containing the fractions.

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.dump": {"fullname": "pyerrors.obs.Obs.dump", "modulename": "pyerrors.obs", "qualname": "Obs.dump", "type": "function", "doc": "

    Dump the Obs to a pickle file 'name'.

    \n\n
    Keyword arguments
    \n\n

    path -- specifies a custom path for the file (default '.')

    \n", "parameters": ["self", "name", "kwargs"], "funcdef": "def"}, "pyerrors.obs.Obs.sqrt": {"fullname": "pyerrors.obs.Obs.sqrt", "modulename": "pyerrors.obs", "qualname": "Obs.sqrt", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.log": {"fullname": "pyerrors.obs.Obs.log", "modulename": "pyerrors.obs", "qualname": "Obs.log", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.exp": {"fullname": "pyerrors.obs.Obs.exp", "modulename": "pyerrors.obs", "qualname": "Obs.exp", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.sin": {"fullname": "pyerrors.obs.Obs.sin", "modulename": "pyerrors.obs", "qualname": "Obs.sin", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.cos": {"fullname": "pyerrors.obs.Obs.cos", "modulename": "pyerrors.obs", "qualname": "Obs.cos", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.tan": {"fullname": "pyerrors.obs.Obs.tan", "modulename": "pyerrors.obs", "qualname": "Obs.tan", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.arcsin": {"fullname": "pyerrors.obs.Obs.arcsin", "modulename": "pyerrors.obs", "qualname": "Obs.arcsin", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.arccos": {"fullname": "pyerrors.obs.Obs.arccos", "modulename": "pyerrors.obs", "qualname": "Obs.arccos", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.arctan": {"fullname": "pyerrors.obs.Obs.arctan", "modulename": "pyerrors.obs", "qualname": "Obs.arctan", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.sinh": {"fullname": "pyerrors.obs.Obs.sinh", "modulename": "pyerrors.obs", "qualname": "Obs.sinh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.cosh": {"fullname": "pyerrors.obs.Obs.cosh", "modulename": "pyerrors.obs", "qualname": "Obs.cosh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.tanh": {"fullname": "pyerrors.obs.Obs.tanh", "modulename": "pyerrors.obs", "qualname": "Obs.tanh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.arcsinh": {"fullname": "pyerrors.obs.Obs.arcsinh", "modulename": "pyerrors.obs", "qualname": "Obs.arcsinh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.arccosh": {"fullname": "pyerrors.obs.Obs.arccosh", "modulename": "pyerrors.obs", "qualname": "Obs.arccosh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.arctanh": {"fullname": "pyerrors.obs.Obs.arctanh", "modulename": "pyerrors.obs", "qualname": "Obs.arctanh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.sinc": {"fullname": "pyerrors.obs.Obs.sinc", "modulename": "pyerrors.obs", "qualname": "Obs.sinc", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.N_sigma": {"fullname": "pyerrors.obs.Obs.N_sigma", "modulename": "pyerrors.obs", "qualname": "Obs.N_sigma", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.S": {"fullname": "pyerrors.obs.Obs.S", "modulename": "pyerrors.obs", "qualname": "Obs.S", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_ddvalue": {"fullname": "pyerrors.obs.Obs.e_ddvalue", "modulename": "pyerrors.obs", "qualname": "Obs.e_ddvalue", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_drho": {"fullname": "pyerrors.obs.Obs.e_drho", "modulename": "pyerrors.obs", "qualname": "Obs.e_drho", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_dtauint": {"fullname": "pyerrors.obs.Obs.e_dtauint", "modulename": "pyerrors.obs", "qualname": "Obs.e_dtauint", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_dvalue": {"fullname": "pyerrors.obs.Obs.e_dvalue", "modulename": "pyerrors.obs", "qualname": "Obs.e_dvalue", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_n_dtauint": {"fullname": "pyerrors.obs.Obs.e_n_dtauint", "modulename": "pyerrors.obs", "qualname": "Obs.e_n_dtauint", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_n_tauint": {"fullname": "pyerrors.obs.Obs.e_n_tauint", "modulename": "pyerrors.obs", "qualname": "Obs.e_n_tauint", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_rho": {"fullname": "pyerrors.obs.Obs.e_rho", "modulename": "pyerrors.obs", "qualname": "Obs.e_rho", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_tauint": {"fullname": "pyerrors.obs.Obs.e_tauint", "modulename": "pyerrors.obs", "qualname": "Obs.e_tauint", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_windowsize": {"fullname": "pyerrors.obs.Obs.e_windowsize", "modulename": "pyerrors.obs", "qualname": "Obs.e_windowsize", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.tau_exp": {"fullname": "pyerrors.obs.Obs.tau_exp", "modulename": "pyerrors.obs", "qualname": "Obs.tau_exp", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.CObs": {"fullname": "pyerrors.obs.CObs", "modulename": "pyerrors.obs", "qualname": "CObs", "type": "class", "doc": "

    Class for a complex valued observable.

    \n"}, "pyerrors.obs.CObs.__init__": {"fullname": "pyerrors.obs.CObs.__init__", "modulename": "pyerrors.obs", "qualname": "CObs.__init__", "type": "function", "doc": "

    \n", "parameters": ["self", "real", "imag"], "funcdef": "def"}, "pyerrors.obs.CObs.tag": {"fullname": "pyerrors.obs.CObs.tag", "modulename": "pyerrors.obs", "qualname": "CObs.tag", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.CObs.real": {"fullname": "pyerrors.obs.CObs.real", "modulename": "pyerrors.obs", "qualname": "CObs.real", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.CObs.imag": {"fullname": "pyerrors.obs.CObs.imag", "modulename": "pyerrors.obs", "qualname": "CObs.imag", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.CObs.gamma_method": {"fullname": "pyerrors.obs.CObs.gamma_method", "modulename": "pyerrors.obs", "qualname": "CObs.gamma_method", "type": "function", "doc": "

    Executes the gamma_method for the real and the imaginary part.

    \n", "parameters": ["self", "kwargs"], "funcdef": "def"}, "pyerrors.obs.CObs.is_zero": {"fullname": "pyerrors.obs.CObs.is_zero", "modulename": "pyerrors.obs", "qualname": "CObs.is_zero", "type": "function", "doc": "

    Checks whether both real and imaginary part are zero within machine precision.

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.CObs.conjugate": {"fullname": "pyerrors.obs.CObs.conjugate", "modulename": "pyerrors.obs", "qualname": "CObs.conjugate", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.merge_idx": {"fullname": "pyerrors.obs.merge_idx", "modulename": "pyerrors.obs", "qualname": "merge_idx", "type": "function", "doc": "

    Returns the union of all lists in idl

    \n\n
    Parameters
    \n\n\n", "parameters": ["idl"], "funcdef": "def"}, "pyerrors.obs.expand_deltas_for_merge": {"fullname": "pyerrors.obs.expand_deltas_for_merge", "modulename": "pyerrors.obs", "qualname": "expand_deltas_for_merge", "type": "function", "doc": "

    Expand deltas defined on idx to the list of configs that is defined by new_idx.\n New, empy entries are filled by 0. If idx and new_idx are of type range, the smallest\n common divisor of the step sizes is used as new step size.

    \n\n
    Parameters
    \n\n\n", "parameters": ["deltas", "idx", "shape", "new_idx"], "funcdef": "def"}, "pyerrors.obs.filter_zeroes": {"fullname": "pyerrors.obs.filter_zeroes", "modulename": "pyerrors.obs", "qualname": "filter_zeroes", "type": "function", "doc": "

    Filter out all configurations with vanishing fluctuation such that they do not\n contribute to the error estimate anymore. Returns the new names, deltas and\n idl according to the filtering.\n A fluctuation is considered to be vanishing, if it is smaller than eps times\n the mean of the absolute values of all deltas in one list.

    \n\n
    Parameters
    \n\n\n\n
    Optional parameters
    \n\n

    eps -- Prefactor that enters the filter criterion.

    \n", "parameters": ["names", "deltas", "idl", "eps"], "funcdef": "def"}, "pyerrors.obs.derived_observable": {"fullname": "pyerrors.obs.derived_observable", "modulename": "pyerrors.obs", "qualname": "derived_observable", "type": "function", "doc": "

    Construct a derived Obs according to func(data, **kwargs) using automatic differentiation.

    \n\n
    Parameters
    \n\n\n\n
    Keyword arguments
    \n\n

    num_grad : bool\n if True, numerical derivatives are used instead of autograd\n (default False). To control the numerical differentiation the\n kwargs of numdifftools.step_generators.MaxStepGenerator\n can be used.\nman_grad : list\n manually supply a list or an array which contains the jacobian\n of func. Use cautiously, supplying the wrong derivative will\n not be intercepted.

    \n\n
    Notes
    \n\n

    For simple mathematical operations it can be practical to use anonymous\nfunctions. For the ratio of two observables one can e.g. use

    \n\n

    new_obs = derived_observable(lambda x: x[0] / x[1], [obs1, obs2])

    \n", "parameters": ["func", "data", "kwargs"], "funcdef": "def"}, "pyerrors.obs.reduce_deltas": {"fullname": "pyerrors.obs.reduce_deltas", "modulename": "pyerrors.obs", "qualname": "reduce_deltas", "type": "function", "doc": "

    Extract deltas defined on idx_old on all configs of idx_new.

    \n\n
    Parameters
    \n\n\n", "parameters": ["deltas", "idx_old", "idx_new"], "funcdef": "def"}, "pyerrors.obs.reweight": {"fullname": "pyerrors.obs.reweight", "modulename": "pyerrors.obs", "qualname": "reweight", "type": "function", "doc": "

    Reweight a list of observables.

    \n\n
    Parameters
    \n\n\n\n
    Keyword arguments
    \n\n

    all_configs : bool\n if True, the reweighted observables are normalized by the average of\n the reweighting factor on all configurations in weight.idl and not\n on the configurations in obs[i].idl.

    \n", "parameters": ["weight", "obs", "kwargs"], "funcdef": "def"}, "pyerrors.obs.correlate": {"fullname": "pyerrors.obs.correlate", "modulename": "pyerrors.obs", "qualname": "correlate", "type": "function", "doc": "

    Correlate two observables.

    \n\n

    Attributes:

    \n\n

    obs_a : Obs\n First observable\nobs_b : Obs\n Second observable

    \n\n

    Keep in mind to only correlate primary observables which have not been reweighted\nyet. The reweighting has to be applied after correlating the observables.\nCurrently only works if ensembles are identical. This is not really necessary.

    \n", "parameters": ["obs_a", "obs_b"], "funcdef": "def"}, "pyerrors.obs.covariance": {"fullname": "pyerrors.obs.covariance", "modulename": "pyerrors.obs", "qualname": "covariance", "type": "function", "doc": "

    Calculates the covariance of two observables.

    \n\n

    covariance(obs, obs) is equal to obs.dvalue ** 2\nThe gamma method has to be applied first to both observables.

    \n\n

    If abs(covariance(obs1, obs2)) > obs1.dvalue * obs2.dvalue, the covariance\nis constrained to the maximum value in order to make sure that covariance\nmatrices are positive semidefinite.

    \n\n
    Keyword arguments
    \n\n

    correlation -- if true the correlation instead of the covariance is\n returned (default False)

    \n", "parameters": ["obs1", "obs2", "correlation", "kwargs"], "funcdef": "def"}, "pyerrors.obs.covariance2": {"fullname": "pyerrors.obs.covariance2", "modulename": "pyerrors.obs", "qualname": "covariance2", "type": "function", "doc": "

    Alternative implementation of the covariance of two observables.

    \n\n

    covariance(obs, obs) is equal to obs.dvalue ** 2\nThe gamma method has to be applied first to both observables.

    \n\n

    If abs(covariance(obs1, obs2)) > obs1.dvalue * obs2.dvalue, the covariance\nis constrained to the maximum value in order to make sure that covariance\nmatrices are positive semidefinite.

    \n\n
    Keyword arguments
    \n\n

    correlation -- if true the correlation instead of the covariance is\n returned (default False)

    \n", "parameters": ["obs1", "obs2", "correlation", "kwargs"], "funcdef": "def"}, "pyerrors.obs.covariance3": {"fullname": "pyerrors.obs.covariance3", "modulename": "pyerrors.obs", "qualname": "covariance3", "type": "function", "doc": "

    Another alternative implementation of the covariance of two observables.

    \n\n

    covariance2(obs, obs) is equal to obs.dvalue ** 2\nCurrently only works if ensembles are identical.\nThe gamma method has to be applied first to both observables.

    \n\n

    If abs(covariance2(obs1, obs2)) > obs1.dvalue * obs2.dvalue, the covariance\nis constrained to the maximum value in order to make sure that covariance\nmatrices are positive semidefinite.

    \n\n
    Keyword arguments
    \n\n

    correlation -- if true the correlation instead of the covariance is\n returned (default False)\nplot -- if true, the integrated autocorrelation time for each ensemble is\n plotted.

    \n", "parameters": ["obs1", "obs2", "correlation", "kwargs"], "funcdef": "def"}, "pyerrors.obs.pseudo_Obs": {"fullname": "pyerrors.obs.pseudo_Obs", "modulename": "pyerrors.obs", "qualname": "pseudo_Obs", "type": "function", "doc": "

    Generate a pseudo Obs with given value, dvalue and name

    \n\n

    The standard number of samples is a 1000. This can be adjusted.

    \n", "parameters": ["value", "dvalue", "name", "samples"], "funcdef": "def"}, "pyerrors.obs.dump_object": {"fullname": "pyerrors.obs.dump_object", "modulename": "pyerrors.obs", "qualname": "dump_object", "type": "function", "doc": "

    Dump object into pickle file.

    \n\n
    Keyword arguments
    \n\n

    path -- specifies a custom path for the file (default '.')

    \n", "parameters": ["obj", "name", "kwargs"], "funcdef": "def"}, "pyerrors.obs.load_object": {"fullname": "pyerrors.obs.load_object", "modulename": "pyerrors.obs", "qualname": "load_object", "type": "function", "doc": "

    Load object from pickle file.

    \n", "parameters": ["path"], "funcdef": "def"}, "pyerrors.obs.merge_obs": {"fullname": "pyerrors.obs.merge_obs", "modulename": "pyerrors.obs", "qualname": "merge_obs", "type": "function", "doc": "

    Combine all observables in list_of_obs into one new observable

    \n\n

    It is not possible to combine obs which are based on the same replicum

    \n", "parameters": ["list_of_obs"], "funcdef": "def"}, "pyerrors.roots": {"fullname": "pyerrors.roots", "modulename": "pyerrors.roots", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.roots.find_root": {"fullname": "pyerrors.roots.find_root", "modulename": "pyerrors.roots", "qualname": "find_root", "type": "function", "doc": "

    Finds the root of the function func(x, d) where d is an Obs.

    \n\n
    Parameters
    \n\n\n", "parameters": ["d", "func", "guess", "kwargs"], "funcdef": "def"}, "pyerrors.version": {"fullname": "pyerrors.version", "modulename": "pyerrors.version", "qualname": "", "type": "module", "doc": "

    \n"}}, "docInfo": {"pyerrors": {"qualname": 0, "fullname": 1, "doc": 171}, "pyerrors.correlators": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.correlators.Corr": {"qualname": 1, "fullname": 3, "doc": 51}, "pyerrors.correlators.Corr.__init__": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.reweighted": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.gamma_method": {"qualname": 2, "fullname": 4, "doc": 5}, "pyerrors.correlators.Corr.projected": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.sum": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.smearing": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.plottable": {"qualname": 2, "fullname": 4, "doc": 16}, "pyerrors.correlators.Corr.symmetric": {"qualname": 2, "fullname": 4, "doc": 4}, "pyerrors.correlators.Corr.anti_symmetric": {"qualname": 2, "fullname": 4, "doc": 5}, "pyerrors.correlators.Corr.smearing_symmetric": 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"doc"], "ref": "fullname", "documentStore": {"docs": {"pyerrors": {"fullname": "pyerrors", "modulename": "pyerrors", "qualname": "", "type": "module", "doc": "

    What is pyerrors?

    \n\n

    pyerrors is a python package for error computation and propagation of Markov chain Monte Carlo data.\nIt is based on the gamma method arXiv:hep-lat/0306017. Some of its features are:

    \n\n\n\n

    Getting started

    \n\n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name'])\nmy_new_obs = 2 * np.log(my_obs) / my_obs\nmy_new_obs.gamma_method()\nmy_new_obs.details()\nprint(my_new_obs)\n
    \n\n

    The Obs class

    \n\n

    pyerrors.obs.Obs

    \n\n
    import pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name'])\n
    \n\n

    Multiple ensembles/replica

    \n\n

    Irregular Monte Carlo chains

    \n\n

    Error propagation

    \n\n

    Automatic differentiation, cite Alberto,

    \n\n

    numpy overloaded

    \n\n
    import numpy as np\nimport pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name'])\nmy_new_obs = 2 * np.log(my_obs) / my_obs\nmy_new_obs.gamma_method()\nmy_new_obs.details()\n
    \n\n

    Error estimation

    \n\n

    pyerrors.obs.Obs.gamma_method

    \n\n

    $\\delta_i\\delta_j$

    \n\n

    Exponential tails

    \n\n

    Covariance

    \n\n

    Correlators

    \n\n

    pyerrors.correlators.Corr

    \n\n

    Optimization / fits / roots

    \n\n

    pyerrors.fits\npyerrors.roots

    \n\n

    Complex observables

    \n\n

    pyerrors.obs.CObs

    \n\n

    Matrix operations

    \n\n

    pyerrors.linalg

    \n\n

    Input

    \n\n

    pyerrors.input

    \n"}, "pyerrors.correlators": {"fullname": "pyerrors.correlators", "modulename": "pyerrors.correlators", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.correlators.Corr": {"fullname": "pyerrors.correlators.Corr", "modulename": "pyerrors.correlators", "qualname": "Corr", "type": "class", "doc": "

    The class for a correlator (time dependent sequence of pe.Obs).

    \n\n

    Everything, this class does, can be achieved using lists or arrays of Obs.\nBut it is simply more convenient to have a dedicated object for correlators.\nOne often wants to add or multiply correlators of the same length at every timeslice and it is inconvinient\nto iterate over all timeslices for every operation. This is especially true, when dealing with smearing matrices.

    \n\n

    The correlator can have two types of content: An Obs at every timeslice OR a GEVP\nsmearing matrix at every timeslice. Other dependency (eg. spacial) are not supported.

    \n"}, "pyerrors.correlators.Corr.__init__": {"fullname": "pyerrors.correlators.Corr.__init__", "modulename": "pyerrors.correlators", "qualname": "Corr.__init__", "type": "function", "doc": "

    \n", "parameters": ["self", "data_input", "padding_front", "padding_back", "prange"], "funcdef": "def"}, "pyerrors.correlators.Corr.reweighted": {"fullname": "pyerrors.correlators.Corr.reweighted", "modulename": "pyerrors.correlators", "qualname": "Corr.reweighted", "type": "variable", "doc": "

    \n"}, "pyerrors.correlators.Corr.gamma_method": {"fullname": "pyerrors.correlators.Corr.gamma_method", "modulename": "pyerrors.correlators", "qualname": "Corr.gamma_method", "type": "function", "doc": "

    Apply the gamma method to the content of the Corr.

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.projected": {"fullname": "pyerrors.correlators.Corr.projected", "modulename": "pyerrors.correlators", "qualname": "Corr.projected", "type": "function", "doc": "

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    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.smearing": {"fullname": "pyerrors.correlators.Corr.smearing", "modulename": "pyerrors.correlators", "qualname": "Corr.smearing", "type": "function", "doc": "

    \n", "parameters": ["self", "i", "j"], "funcdef": "def"}, "pyerrors.correlators.Corr.plottable": {"fullname": "pyerrors.correlators.Corr.plottable", "modulename": "pyerrors.correlators", "qualname": "Corr.plottable", "type": "function", "doc": "

    Outputs the correlator in a plotable format.

    \n\n

    Outputs three lists containing the timeslice index, the value on each\ntimeslice and the error on each timeslice.

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    Symmetrize the correlator around x0=0.

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    Anti-symmetrize the correlator around x0=0.

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.smearing_symmetric": {"fullname": "pyerrors.correlators.Corr.smearing_symmetric", "modulename": "pyerrors.correlators", "qualname": "Corr.smearing_symmetric", "type": "function", "doc": "

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    Periodically shift the correlator by dt timeslices

    \n\n

    Attributes:

    \n\n

    dt : int\n number of timeslices

    \n", "parameters": ["self", "dt"], "funcdef": "def"}, "pyerrors.correlators.Corr.reverse": {"fullname": "pyerrors.correlators.Corr.reverse", "modulename": "pyerrors.correlators", "qualname": "Corr.reverse", "type": "function", "doc": "

    Reverse the time ordering of the Corr

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    Correlate the correlator with another correlator or Obs

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    Reweight the correlator.

    \n\n
    Parameters
    \n\n\n\n
    Keyword arguments
    \n\n

    all_configs : bool\n if True, the reweighted observables are normalized by the average of\n the reweighting factor on all configurations in weight.idl and not\n on the configurations in obs[i].idl.

    \n", "parameters": ["self", "weight", "kwargs"], "funcdef": "def"}, "pyerrors.correlators.Corr.T_symmetry": {"fullname": "pyerrors.correlators.Corr.T_symmetry", "modulename": "pyerrors.correlators", "qualname": "Corr.T_symmetry", "type": "function", "doc": "

    Return the time symmetry average of the correlator and its partner

    \n\n

    Attributes:

    \n\n

    partner : Corr\n Time symmetry partner of the Corr\npartity : int\n Parity quantum number of the correlator, can be +1 or -1

    \n", "parameters": ["self", "partner", "parity"], "funcdef": "def"}, "pyerrors.correlators.Corr.deriv": {"fullname": "pyerrors.correlators.Corr.deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.deriv", "type": "function", "doc": "

    Return the first derivative of the correlator with respect to x0.

    \n\n

    Attributes:

    \n\n

    symmetric : bool\n decides whether symmertic of simple finite differences are used. Default: True

    \n", "parameters": ["self", "symmetric"], "funcdef": "def"}, "pyerrors.correlators.Corr.second_deriv": {"fullname": "pyerrors.correlators.Corr.second_deriv", "modulename": "pyerrors.correlators", "qualname": "Corr.second_deriv", "type": "function", "doc": "

    Return the second derivative of the correlator with respect to x0.

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.m_eff": {"fullname": "pyerrors.correlators.Corr.m_eff", "modulename": "pyerrors.correlators", "qualname": "Corr.m_eff", "type": "function", "doc": "

    Returns the effective mass of the correlator as correlator object

    \n\n
    Parameters
    \n\n\n", "parameters": ["self", "variant", "guess"], "funcdef": "def"}, "pyerrors.correlators.Corr.fit": {"fullname": "pyerrors.correlators.Corr.fit", "modulename": "pyerrors.correlators", "qualname": "Corr.fit", "type": "function", "doc": "

    Fits function to the data

    \n\n

    Attributes:

    \n\n

    function : obj\n function to fit to the data. See fits.least_squares for details.\nfitrange : list\n Range in which the function is to be fitted to the data.\n If not specified, self.prange or all timeslices are used.\nsilent : bool\n Decides whether output is printed to the standard output.

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    Extract a plateu value from a Corr object

    \n\n

    Attributes:

    \n\n

    plateau_range : list\n list with two entries, indicating the first and the last timeslice\n of the plateau region.\nmethod : str\n method to extract the plateau.\n 'fit' fits a constant to the plateau region\n 'avg', 'average' or 'mean' just average over the given timeslices.

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    Sets the attribute prange of the Corr object.

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    Plots the correlator, uses tag as label if available.

    \n\n
    Parameters
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    Dumps the Corr into a pickel file

    \n\n

    Attributes:

    \n\n

    filename : str\n Name of the file

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    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.log": {"fullname": "pyerrors.correlators.Corr.log", "modulename": "pyerrors.correlators", "qualname": "Corr.log", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.exp": {"fullname": "pyerrors.correlators.Corr.exp", "modulename": "pyerrors.correlators", "qualname": "Corr.exp", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.sin": {"fullname": "pyerrors.correlators.Corr.sin", "modulename": "pyerrors.correlators", "qualname": "Corr.sin", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.cos": {"fullname": "pyerrors.correlators.Corr.cos", "modulename": "pyerrors.correlators", "qualname": "Corr.cos", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.tan": {"fullname": "pyerrors.correlators.Corr.tan", "modulename": "pyerrors.correlators", "qualname": "Corr.tan", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.sinh": {"fullname": "pyerrors.correlators.Corr.sinh", "modulename": "pyerrors.correlators", "qualname": "Corr.sinh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.cosh": {"fullname": "pyerrors.correlators.Corr.cosh", "modulename": "pyerrors.correlators", "qualname": "Corr.cosh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.tanh": {"fullname": "pyerrors.correlators.Corr.tanh", "modulename": "pyerrors.correlators", "qualname": "Corr.tanh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.arcsin": {"fullname": "pyerrors.correlators.Corr.arcsin", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsin", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.arccos": {"fullname": "pyerrors.correlators.Corr.arccos", "modulename": "pyerrors.correlators", "qualname": "Corr.arccos", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.arctan": {"fullname": "pyerrors.correlators.Corr.arctan", "modulename": "pyerrors.correlators", "qualname": "Corr.arctan", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.arcsinh": {"fullname": "pyerrors.correlators.Corr.arcsinh", "modulename": "pyerrors.correlators", "qualname": "Corr.arcsinh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.arccosh": {"fullname": "pyerrors.correlators.Corr.arccosh", "modulename": "pyerrors.correlators", "qualname": "Corr.arccosh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.correlators.Corr.arctanh": {"fullname": "pyerrors.correlators.Corr.arctanh", "modulename": "pyerrors.correlators", "qualname": "Corr.arctanh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.dirac": {"fullname": "pyerrors.dirac", "modulename": "pyerrors.dirac", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.dirac.Grid_gamma": {"fullname": "pyerrors.dirac.Grid_gamma", "modulename": "pyerrors.dirac", "qualname": "Grid_gamma", "type": "function", "doc": "

    Returns gamma matrix in Grid labeling.

    \n", "parameters": ["gamma_tag"], "funcdef": "def"}, "pyerrors.fits": {"fullname": "pyerrors.fits", "modulename": "pyerrors.fits", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.fits.Fit_result": {"fullname": "pyerrors.fits.Fit_result", "modulename": "pyerrors.fits", "qualname": "Fit_result", "type": "class", "doc": "

    Represents fit results.

    \n\n
    Attributes
    \n\n\n"}, "pyerrors.fits.Fit_result.__init__": {"fullname": "pyerrors.fits.Fit_result.__init__", "modulename": "pyerrors.fits", "qualname": "Fit_result.__init__", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.fits.Fit_result.gamma_method": {"fullname": "pyerrors.fits.Fit_result.gamma_method", "modulename": "pyerrors.fits", "qualname": "Fit_result.gamma_method", "type": "function", "doc": "

    Apply the gamma method to all fit parameters

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.fits.least_squares": {"fullname": "pyerrors.fits.least_squares", "modulename": "pyerrors.fits", "qualname": "least_squares", "type": "function", "doc": "

    Performs a non-linear fit to y = func(x).

    \n\n

    Arguments:

    \n\n

    x : list\n list of floats.\ny : list\n list of Obs.\nfunc : object\n fit function, has to be of the form

    \n\n
    def func(a, x):\n    return a[0] + a[1] * x + a[2] * anp.sinh(x)\n\nFor multiple x values func can be of the form\n\ndef func(a, x):\n    (x1, x2) = x\n    return a[0] * x1 ** 2 + a[1] * x2\n\nIt is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work\n
    \n\n

    priors : list, optional\n priors has to be a list with an entry for every parameter in the fit. The entries can either be\n Obs (e.g. results from a previous fit) or strings containing a value and an error formatted like\n 0.548(23), 500(40) or 0.5(0.4)\n It is important for the subsequent error estimation that the e_tag for the gamma method is large\n enough.\nsilent : bool, optional\n If true all output to the console is omitted (default False).

    \n\n
    Keyword arguments
    \n\n

    initial_guess -- can provide an initial guess for the input parameters. Relevant for\n non-linear fits with many parameters.\nmethod -- can be used to choose an alternative method for the minimization of chisquare.\n The possible methods are the ones which can be used for scipy.optimize.minimize and\n migrad of iminuit. If no method is specified, Levenberg-Marquard is used.\n Reliable alternatives are migrad, Powell and Nelder-Mead.\nresplot -- If true, a plot which displays fit, data and residuals is generated (default False).\nqqplot -- If true, a quantile-quantile plot of the fit result is generated (default False).\nexpected_chisquare -- If true prints the expected chisquare which is\n corrected by effects caused by correlated input data.\n This can take a while as the full correlation matrix\n has to be calculated (default False).

    \n", "parameters": ["x", "y", "func", "priors", "silent", "kwargs"], "funcdef": "def"}, "pyerrors.fits.standard_fit": {"fullname": "pyerrors.fits.standard_fit", "modulename": "pyerrors.fits", "qualname": "standard_fit", "type": "function", "doc": "

    \n", "parameters": ["x", "y", "func", "silent", "kwargs"], "funcdef": "def"}, "pyerrors.fits.odr_fit": {"fullname": "pyerrors.fits.odr_fit", "modulename": "pyerrors.fits", "qualname": "odr_fit", "type": "function", "doc": "

    \n", "parameters": ["x", "y", "func", "silent", "kwargs"], "funcdef": "def"}, "pyerrors.fits.total_least_squares": {"fullname": "pyerrors.fits.total_least_squares", "modulename": "pyerrors.fits", "qualname": "total_least_squares", "type": "function", "doc": "

    Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.

    \n\n

    x : list\n list of Obs, or a tuple of lists of Obs\ny : list\n list of Obs. The dvalues of the Obs are used as x- and yerror for the fit.\nfunc : object\n func has to be of the form

    \n\n
    def func(a, x):\n    y = a[0] + a[1] * x + a[2] * anp.sinh(x)\n    return y\n\nFor multiple x values func can be of the form\n\ndef func(a, x):\n    (x1, x2) = x\n    return a[0] * x1 ** 2 + a[1] * x2\n\nIt is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.\n
    \n\n

    silent : bool, optional\n If true all output to the console is omitted (default False).\nBased on the orthogonal distance regression module of scipy

    \n\n
    Keyword arguments
    \n\n

    initial_guess -- can provide an initial guess for the input parameters. Relevant for non-linear\n fits with many parameters.\nexpected_chisquare -- If true prints the expected chisquare which is\n corrected by effects caused by correlated input data.\n This can take a while as the full correlation matrix\n has to be calculated (default False).

    \n", "parameters": ["x", "y", "func", "silent", "kwargs"], "funcdef": "def"}, "pyerrors.fits.prior_fit": {"fullname": "pyerrors.fits.prior_fit", "modulename": "pyerrors.fits", "qualname": "prior_fit", "type": "function", "doc": "

    \n", "parameters": ["x", "y", "func", "priors", "silent", "kwargs"], "funcdef": "def"}, "pyerrors.fits.fit_lin": {"fullname": "pyerrors.fits.fit_lin", "modulename": "pyerrors.fits", "qualname": "fit_lin", "type": "function", "doc": "

    Performs a linear fit to y = n + m * x and returns two Obs n, m.

    \n\n

    y has to be a list of Obs, the dvalues of the Obs are used as yerror for the fit.\nx can either be a list of floats in which case no xerror is assumed, or\na list of Obs, where the dvalues of the Obs are used as xerror for the fit.

    \n", "parameters": ["x", "y", "kwargs"], "funcdef": "def"}, "pyerrors.fits.qqplot": {"fullname": "pyerrors.fits.qqplot", "modulename": "pyerrors.fits", "qualname": "qqplot", "type": "function", "doc": "

    Generates a quantile-quantile plot of the fit result which can be used to\ncheck if the residuals of the fit are gaussian distributed.

    \n", "parameters": ["x", "o_y", "func", "p"], "funcdef": "def"}, "pyerrors.fits.residual_plot": {"fullname": "pyerrors.fits.residual_plot", "modulename": "pyerrors.fits", "qualname": "residual_plot", "type": "function", "doc": "

    Generates a plot which compares the fit to the data and displays the corresponding residuals

    \n", "parameters": ["x", "y", "func", "fit_res"], "funcdef": "def"}, "pyerrors.fits.covariance_matrix": {"fullname": "pyerrors.fits.covariance_matrix", "modulename": "pyerrors.fits", "qualname": "covariance_matrix", "type": "function", "doc": "

    Returns the covariance matrix of y.

    \n", "parameters": ["y"], "funcdef": "def"}, "pyerrors.fits.error_band": {"fullname": "pyerrors.fits.error_band", "modulename": "pyerrors.fits", "qualname": "error_band", "type": "function", "doc": "

    Returns the error band for an array of sample values x, for given fit function func with optimized parameters beta.

    \n", "parameters": ["x", "func", "beta"], "funcdef": "def"}, "pyerrors.fits.ks_test": {"fullname": "pyerrors.fits.ks_test", "modulename": "pyerrors.fits", "qualname": "ks_test", "type": "function", "doc": "

    Performs a Kolmogorov\u2013Smirnov test for the Q-values of all fit object.

    \n\n

    If no list is given all Obs in memory are used.

    \n\n

    Disclaimer: The determination of the individual Q-values as well as this function have not been tested yet.

    \n", "parameters": ["obs"], "funcdef": "def"}, "pyerrors.fits.fit_general": {"fullname": "pyerrors.fits.fit_general", "modulename": "pyerrors.fits", "qualname": "fit_general", "type": "function", "doc": "

    Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.

    \n\n

    Plausibility of the results should be checked. To control the numerical differentiation\nthe kwargs of numdifftools.step_generators.MaxStepGenerator can be used.

    \n\n

    func has to be of the form

    \n\n

    def func(a, x):\n y = a[0] + a[1] * x + a[2] * np.sinh(x)\n return y

    \n\n

    y has to be a list of Obs, the dvalues of the Obs are used as yerror for the fit.\nx can either be a list of floats in which case no xerror is assumed, or\na list of Obs, where the dvalues of the Obs are used as xerror for the fit.

    \n\n
    Keyword arguments
    \n\n

    silent -- If true all output to the console is omitted (default False).\ninitial_guess -- can provide an initial guess for the input parameters. Relevant for non-linear fits\n with many parameters.

    \n", "parameters": ["x", "y", "func", "silent", "kwargs"], "funcdef": "def"}, "pyerrors.input": {"fullname": "pyerrors.input", "modulename": "pyerrors.input", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.input.bdio": {"fullname": "pyerrors.input.bdio", "modulename": "pyerrors.input.bdio", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.input.bdio.read_ADerrors": {"fullname": "pyerrors.input.bdio.read_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "read_ADerrors", "type": "function", "doc": "

    Extract generic MCMC data from a bdio file

    \n\n

    read_ADerrors requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n\n", "parameters": ["file_path", "bdio_path", "kwargs"], "funcdef": "def"}, "pyerrors.input.bdio.write_ADerrors": {"fullname": "pyerrors.input.bdio.write_ADerrors", "modulename": "pyerrors.input.bdio", "qualname": "write_ADerrors", "type": "function", "doc": "

    Write Obs to a bdio file according to ADerrors conventions

    \n\n

    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n\n", "parameters": ["obs_list", "file_path", "bdio_path", "kwargs"], "funcdef": "def"}, "pyerrors.input.bdio.read_mesons": {"fullname": "pyerrors.input.bdio.read_mesons", "modulename": "pyerrors.input.bdio", "qualname": "read_mesons", "type": "function", "doc": "

    Extract mesons data from a bdio file and return it as a dictionary

    \n\n

    The dictionary can be accessed with a tuple consisting of (type, source_position, kappa1, kappa2)

    \n\n

    read_mesons requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n\n", "parameters": ["file_path", "bdio_path", "kwargs"], "funcdef": "def"}, "pyerrors.input.bdio.read_dSdm": {"fullname": "pyerrors.input.bdio.read_dSdm", "modulename": "pyerrors.input.bdio", "qualname": "read_dSdm", "type": "function", "doc": "

    Extract dSdm data from a bdio file and return it as a dictionary

    \n\n

    The dictionary can be accessed with a tuple consisting of (type, kappa)

    \n\n

    read_dSdm requires bdio to be compiled into a shared library. This can be achieved by\nadding the flag -fPIC to CC and changing the all target to

    \n\n

    all: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/

    \n\n
    Parameters
    \n\n\n", "parameters": ["file_path", "bdio_path", "kwargs"], "funcdef": "def"}, "pyerrors.input.hadrons": {"fullname": "pyerrors.input.hadrons", "modulename": "pyerrors.input.hadrons", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.input.hadrons.read_meson_hd5": {"fullname": "pyerrors.input.hadrons.read_meson_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_meson_hd5", "type": "function", "doc": "

    Read hadrons meson hdf5 file and extract the meson labeled 'meson'

    \n\n
    Parameters
    \n\n\n", "parameters": ["path", "filestem", "ens_id", "meson", "tree"], "funcdef": "def"}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"fullname": "pyerrors.input.hadrons.read_ExternalLeg_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_ExternalLeg_hd5", "type": "function", "doc": "

    Read hadrons ExternalLeg hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n\n", "parameters": ["path", "filestem", "ens_id", "order"], "funcdef": "def"}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"fullname": "pyerrors.input.hadrons.read_Bilinear_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Bilinear_hd5", "type": "function", "doc": "

    Read hadrons Bilinear hdf5 file and output an array of CObs

    \n\n
    Parameters
    \n\n\n", "parameters": ["path", "filestem", "ens_id", "order"], "funcdef": "def"}, "pyerrors.input.misc": {"fullname": "pyerrors.input.misc", "modulename": "pyerrors.input.misc", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.input.misc.read_pbp": {"fullname": "pyerrors.input.misc.read_pbp", "modulename": "pyerrors.input.misc", "qualname": "read_pbp", "type": "function", "doc": "

    Read pbp format from given folder structure. Returns a list of length nrw

    \n\n
    Keyword arguments
    \n\n

    r_start -- list which contains the first config to be read for each replicum\nr_stop -- list which contains the last config to be read for each replicum

    \n", "parameters": ["path", "prefix", "kwargs"], "funcdef": "def"}, "pyerrors.input.openQCD": {"fullname": "pyerrors.input.openQCD", "modulename": "pyerrors.input.openQCD", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.input.openQCD.read_rwms": {"fullname": "pyerrors.input.openQCD.read_rwms", "modulename": "pyerrors.input.openQCD", "qualname": "read_rwms", "type": "function", "doc": "

    Read rwms format from given folder structure. Returns a list of length nrw

    \n\n
    Attributes
    \n\n\n\n
    Keyword arguments
    \n\n

    r_start -- list which contains the first config to be read for each replicum\nr_stop -- list which contains the last config to be read for each replicum\npostfix -- postfix of the file to read, e.g. '.ms1' for openQCD-files

    \n", "parameters": ["path", "prefix", "version", "names", "kwargs"], "funcdef": "def"}, "pyerrors.input.openQCD.extract_t0": {"fullname": "pyerrors.input.openQCD.extract_t0", "modulename": "pyerrors.input.openQCD", "qualname": "extract_t0", "type": "function", "doc": "

    Extract t0 from given .ms.dat files. Returns t0 as Obs.

    \n\n

    It is assumed that all boundary effects have sufficiently decayed at x0=xmin.\nThe data around the zero crossing of t^2 - 0.3 is fitted with a linear function\nfrom which the exact root is extracted.\nOnly works with openQCD v 1.2.

    \n\n
    Parameters
    \n\n\n\n
    Keyword arguments
    \n\n

    r_start -- list which contains the first config to be read for each replicum.\nr_stop -- list which contains the last config to be read for each replicum.\nplaquette -- If true extract the plaquette estimate of t0 instead.

    \n", "parameters": ["path", "prefix", "dtr_read", "xmin", "spatial_extent", "fit_range", "kwargs"], "funcdef": "def"}, "pyerrors.input.sfcf": {"fullname": "pyerrors.input.sfcf", "modulename": "pyerrors.input.sfcf", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.input.sfcf.read_sfcf": {"fullname": "pyerrors.input.sfcf.read_sfcf", "modulename": "pyerrors.input.sfcf", "qualname": "read_sfcf", "type": "function", "doc": "

    Read sfcf C format from given folder structure.

    \n\n
    Keyword arguments
    \n\n

    im -- if True, read imaginary instead of real part of the correlation function.\nsingle -- if True, read a boundary-to-boundary correlation function with a single value\nb2b -- if True, read a time-dependent boundary-to-boundary correlation function\nnames -- Alternative labeling for replicas/ensembles. Has to have the appropriate length

    \n", "parameters": ["path", "prefix", "name", "kwargs"], "funcdef": "def"}, "pyerrors.input.sfcf.read_sfcf_c": {"fullname": "pyerrors.input.sfcf.read_sfcf_c", "modulename": "pyerrors.input.sfcf", "qualname": "read_sfcf_c", "type": "function", "doc": "

    Read sfcf c format from given folder structure.

    \n\n
    Arguments
    \n\n

    quarks -- Label of the quarks used in the sfcf input file\nnoffset -- Offset of the source (only relevant when wavefunctions are used)\nwf -- ID of wave function\nwf2 -- ID of the second wavefunction (only relevant for boundary-to-boundary correlation functions)

    \n\n
    Keyword arguments
    \n\n

    im -- if True, read imaginary instead of real part of the correlation function.\nb2b -- if True, read a time-dependent boundary-to-boundary correlation function\nnames -- Alternative labeling for replicas/ensembles. Has to have the appropriate length\nens_name : str\n replaces the name of the ensemble

    \n", "parameters": ["path", "prefix", "name", "quarks", "noffset", "wf", "wf2", "kwargs"], "funcdef": "def"}, "pyerrors.input.sfcf.read_qtop": {"fullname": "pyerrors.input.sfcf.read_qtop", "modulename": "pyerrors.input.sfcf", "qualname": "read_qtop", "type": "function", "doc": "

    Read qtop format from given folder structure.

    \n\n
    Keyword arguments
    \n\n

    target -- specifies the topological sector to be reweighted to (default 0)\nfull -- if true read the charge instead of the reweighting factor.

    \n", "parameters": ["path", "prefix", "kwargs"], "funcdef": "def"}, "pyerrors.jackknifing": {"fullname": "pyerrors.jackknifing", "modulename": "pyerrors.jackknifing", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.jackknifing.Jack": {"fullname": "pyerrors.jackknifing.Jack", "modulename": "pyerrors.jackknifing", "qualname": "Jack", "type": "class", "doc": "

    \n"}, "pyerrors.jackknifing.Jack.__init__": {"fullname": "pyerrors.jackknifing.Jack.__init__", "modulename": "pyerrors.jackknifing", "qualname": "Jack.__init__", "type": "function", "doc": "

    \n", "parameters": ["self", "value", "jacks"], "funcdef": "def"}, "pyerrors.jackknifing.Jack.print": {"fullname": "pyerrors.jackknifing.Jack.print", "modulename": "pyerrors.jackknifing", "qualname": "Jack.print", "type": "function", "doc": "

    Print basic properties of the Jack.

    \n", "parameters": ["self", "kwargs"], "funcdef": "def"}, "pyerrors.jackknifing.Jack.plot_tauint": {"fullname": "pyerrors.jackknifing.Jack.plot_tauint", "modulename": "pyerrors.jackknifing", "qualname": "Jack.plot_tauint", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.jackknifing.Jack.plot_history": {"fullname": "pyerrors.jackknifing.Jack.plot_history", "modulename": "pyerrors.jackknifing", "qualname": "Jack.plot_history", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.jackknifing.Jack.dump": {"fullname": "pyerrors.jackknifing.Jack.dump", "modulename": "pyerrors.jackknifing", "qualname": "Jack.dump", "type": "function", "doc": "

    Dump the Jack to a pickle file 'name'.

    \n\n

    Keyword arguments:\npath -- specifies a custom path for the file (default '.')

    \n", "parameters": ["self", "name", "kwargs"], "funcdef": "def"}, "pyerrors.jackknifing.generate_jack": {"fullname": "pyerrors.jackknifing.generate_jack", "modulename": "pyerrors.jackknifing", "qualname": "generate_jack", "type": "function", "doc": "

    \n", "parameters": ["obs", "kwargs"], "funcdef": "def"}, "pyerrors.jackknifing.derived_jack": {"fullname": "pyerrors.jackknifing.derived_jack", "modulename": "pyerrors.jackknifing", "qualname": "derived_jack", "type": "function", "doc": "

    Construct a derived Jack according to func(data, **kwargs).

    \n\n
    Parameters
    \n\n\n\n
    Notes
    \n\n

    For simple mathematical operations it can be practical to use anonymous functions.\nFor the ratio of two jacks one can e.g. use

    \n\n

    new_jack = derived_jack(lambda x : x[0] / x[1], [jack1, jack2])

    \n", "parameters": ["func", "data", "kwargs"], "funcdef": "def"}, "pyerrors.linalg": {"fullname": "pyerrors.linalg", "modulename": "pyerrors.linalg", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.linalg.derived_array": {"fullname": "pyerrors.linalg.derived_array", "modulename": "pyerrors.linalg", "qualname": "derived_array", "type": "function", "doc": "

    Construct a derived Obs according to func(data, **kwargs) of matrix value data\nusing automatic differentiation.

    \n\n
    Parameters
    \n\n\n\n
    Keyword arguments
    \n\n

    man_grad -- manually supply a list or an array which contains the jacobian\n of func. Use cautiously, supplying the wrong derivative will\n not be intercepted.

    \n", "parameters": ["func", "data", "kwargs"], "funcdef": "def"}, "pyerrors.linalg.matmul": {"fullname": "pyerrors.linalg.matmul", "modulename": "pyerrors.linalg", "qualname": "matmul", "type": "function", "doc": "

    Matrix multiply all operands.

    \n\n

    Supports real and complex valued matrices and is faster compared to\nstandard multiplication via the @ operator.

    \n", "parameters": ["operands"], "funcdef": "def"}, "pyerrors.linalg.inv": {"fullname": "pyerrors.linalg.inv", "modulename": "pyerrors.linalg", "qualname": "inv", "type": "function", "doc": "

    Inverse of Obs or CObs valued matrices.

    \n", "parameters": ["x"], "funcdef": "def"}, "pyerrors.linalg.cholesky": {"fullname": "pyerrors.linalg.cholesky", "modulename": "pyerrors.linalg", "qualname": "cholesky", "type": "function", "doc": "

    Cholesky decompostion of Obs or CObs valued matrices.

    \n", "parameters": ["x"], "funcdef": "def"}, "pyerrors.linalg.scalar_mat_op": {"fullname": "pyerrors.linalg.scalar_mat_op", "modulename": "pyerrors.linalg", "qualname": "scalar_mat_op", "type": "function", "doc": "

    Computes the matrix to scalar operation op to a given matrix of Obs.

    \n", "parameters": ["op", "obs", "kwargs"], "funcdef": "def"}, "pyerrors.linalg.eigh": {"fullname": "pyerrors.linalg.eigh", "modulename": "pyerrors.linalg", "qualname": "eigh", "type": "function", "doc": "

    Computes the eigenvalues and eigenvectors of a given hermitian matrix of Obs according to np.linalg.eigh.

    \n", "parameters": ["obs", "kwargs"], "funcdef": "def"}, "pyerrors.linalg.eig": {"fullname": "pyerrors.linalg.eig", "modulename": "pyerrors.linalg", "qualname": "eig", "type": "function", "doc": "

    Computes the eigenvalues of a given matrix of Obs according to np.linalg.eig.

    \n", "parameters": ["obs", "kwargs"], "funcdef": "def"}, "pyerrors.linalg.pinv": {"fullname": "pyerrors.linalg.pinv", "modulename": "pyerrors.linalg", "qualname": "pinv", "type": "function", "doc": "

    Computes the Moore-Penrose pseudoinverse of a matrix of Obs.

    \n", "parameters": ["obs", "kwargs"], "funcdef": "def"}, "pyerrors.linalg.svd": {"fullname": "pyerrors.linalg.svd", "modulename": "pyerrors.linalg", "qualname": "svd", "type": "function", "doc": "

    Computes the singular value decomposition of a matrix of Obs.

    \n", "parameters": ["obs", "kwargs"], "funcdef": "def"}, "pyerrors.linalg.slogdet": {"fullname": "pyerrors.linalg.slogdet", "modulename": "pyerrors.linalg", "qualname": "slogdet", "type": "function", "doc": "

    Computes the determinant of a matrix of Obs via np.linalg.slogdet.

    \n", "parameters": ["obs", "kwargs"], "funcdef": "def"}, "pyerrors.linalg.grad_eig": {"fullname": "pyerrors.linalg.grad_eig", "modulename": "pyerrors.linalg", "qualname": "grad_eig", "type": "function", "doc": "

    Gradient of a general square (complex valued) matrix

    \n", "parameters": ["ans", "x"], "funcdef": "def"}, "pyerrors.misc": {"fullname": "pyerrors.misc", "modulename": "pyerrors.misc", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.misc.gen_correlated_data": {"fullname": "pyerrors.misc.gen_correlated_data", "modulename": "pyerrors.misc", "qualname": "gen_correlated_data", "type": "function", "doc": "

    Generate observables with given covariance and autocorrelation times.

    \n\n
    Arguments
    \n\n

    means -- list containing the mean value of each observable.\ncov -- covariance matrix for the data to be geneated.\nname -- ensemble name for the data to be geneated.\ntau -- can either be a real number or a list with an entry for\n every dataset.\nsamples -- number of samples to be generated for each observable.

    \n", "parameters": ["means", "cov", "name", "tau", "samples"], "funcdef": "def"}, "pyerrors.mpm": {"fullname": "pyerrors.mpm", "modulename": "pyerrors.mpm", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.mpm.matrix_pencil_method": {"fullname": "pyerrors.mpm.matrix_pencil_method", "modulename": "pyerrors.mpm", "qualname": "matrix_pencil_method", "type": "function", "doc": "

    Matrix pencil method to extract k energy levels from data

    \n\n

    Implementation of the matrix pencil method based on\neq. (2.17) of Y. Hua, T. K. Sarkar, IEEE Trans. Acoust. 38, 814-824 (1990)

    \n\n
    Parameters
    \n\n\n", "parameters": ["corrs", "k", "p", "kwargs"], "funcdef": "def"}, "pyerrors.mpm.matrix_pencil_method_old": {"fullname": "pyerrors.mpm.matrix_pencil_method_old", "modulename": "pyerrors.mpm", "qualname": "matrix_pencil_method_old", "type": "function", "doc": "

    Older impleentation of the matrix pencil method with pencil p on given data to\n extract energy levels.

    \n\n
    Parameters
    \n\n\n", "parameters": ["data", "p", "noise_level", "verbose", "kwargs"], "funcdef": "def"}, "pyerrors.npr": {"fullname": "pyerrors.npr", "modulename": "pyerrors.npr", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.npr.Npr_matrix": {"fullname": "pyerrors.npr.Npr_matrix", "modulename": "pyerrors.npr", "qualname": "Npr_matrix", "type": "class", "doc": "

    ndarray(shape, dtype=float, buffer=None, offset=0,\n strides=None, order=None)

    \n\n

    An array object represents a multidimensional, homogeneous array\nof fixed-size items. An associated data-type object describes the\nformat of each element in the array (its byte-order, how many bytes it\noccupies in memory, whether it is an integer, a floating point number,\nor something else, etc.)

    \n\n

    Arrays should be constructed using array, zeros or empty (refer\nto the See Also section below). The parameters given here refer to\na low-level method (ndarray(...)) for instantiating an array.

    \n\n

    For more information, refer to the numpy module and examine the\nmethods and attributes of an array.

    \n\n
    Parameters
    \n\n\n\n
    Attributes
    \n\n\n\n
    See Also
    \n\n

    array: Construct an array.
    \nzeros: Create an array, each element of which is zero.
    \nempty: Create an array, but leave its allocated memory unchanged (i.e.,\nit contains \"garbage\").
    \ndtype: Create a data-type.
    \nnumpy.typing.NDArray: A :term:generic <generic type> version\nof ndarray.

    \n\n
    Notes
    \n\n

    There are two modes of creating an array using __new__:

    \n\n
      \n
    1. If buffer is None, then only shape, dtype, and order\nare used.
      2. \n
    2. If buffer is an object exposing the buffer interface, then\nall keywords are interpreted.
      3. \n
    \n\n

    No __init__ method is needed because the array is fully initialized\nafter the __new__ method.

    \n\n
    Examples
    \n\n

    These examples illustrate the low-level ndarray constructor. Refer\nto the See Also section above for easier ways of constructing an\nndarray.

    \n\n

    First mode, buffer is None:

    \n\n
    >>> np.ndarray(shape=(2,2), dtype=float, order='F')\narray([[0.0e+000, 0.0e+000], # random\n       [     nan, 2.5e-323]])\n
    \n\n

    Second mode:

    \n\n
    >>> np.ndarray((2,), buffer=np.array([1,2,3]),\n...            offset=np.int_().itemsize,\n...            dtype=int) # offset = 1*itemsize, i.e. skip first element\narray([2, 3])\n
    \n"}, "pyerrors.npr.Npr_matrix.__init__": {"fullname": "pyerrors.npr.Npr_matrix.__init__", "modulename": "pyerrors.npr", "qualname": "Npr_matrix.__init__", "type": "function", "doc": "

    \n", "parameters": [], "funcdef": "def"}, "pyerrors.npr.Npr_matrix.g5H": {"fullname": "pyerrors.npr.Npr_matrix.g5H", "modulename": "pyerrors.npr", "qualname": "Npr_matrix.g5H", "type": "variable", "doc": "

    Gamma_5 hermitean conjugate

    \n\n

    Returns gamma_5 @ M.T.conj() @ gamma_5 and exchanges in and out going\nmomenta. Works only for 12x12 matrices.

    \n"}, "pyerrors.npr.inv_propagator": {"fullname": "pyerrors.npr.inv_propagator", "modulename": "pyerrors.npr", "qualname": "inv_propagator", "type": "function", "doc": "

    Inverts a 12x12 quark propagator

    \n", "parameters": ["prop"], "funcdef": "def"}, "pyerrors.npr.Zq": {"fullname": "pyerrors.npr.Zq", "modulename": "pyerrors.npr", "qualname": "Zq", "type": "function", "doc": "

    Calculates the quark field renormalization constant Zq

    \n\n

    Attributes:\ninv_prop -- Inverted 12x12 quark propagator\nfermion -- Fermion type for which the tree-level propagator is used\n in the calculation of Zq. Default Wilson.

    \n", "parameters": ["inv_prop", "fermion"], "funcdef": "def"}, "pyerrors.obs": {"fullname": "pyerrors.obs", "modulename": "pyerrors.obs", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.obs.Obs": {"fullname": "pyerrors.obs.Obs", "modulename": "pyerrors.obs", "qualname": "Obs", "type": "class", "doc": "

    Class for a general observable.

    \n\n

    Instances of Obs are the basic objects of a pyerrors error analysis.\nThey are initialized with a list which contains arrays of samples for\ndifferent ensembles/replica and another list of same length which contains\nthe names of the ensembles/replica. Mathematical operations can be\nperformed on instances. The result is another instance of Obs. The error of\nan instance can be computed with the gamma_method. Also contains additional\nmethods for output and visualization of the error calculation.

    \n\n
    Attributes
    \n\n\n"}, "pyerrors.obs.Obs.__init__": {"fullname": "pyerrors.obs.Obs.__init__", "modulename": "pyerrors.obs", "qualname": "Obs.__init__", "type": "function", "doc": "

    Initialize Obs object.

    \n\n
    Attributes
    \n\n\n", "parameters": ["self", "samples", "names", "idl", "means", "kwargs"], "funcdef": "def"}, "pyerrors.obs.Obs.S_global": {"fullname": "pyerrors.obs.Obs.S_global", "modulename": "pyerrors.obs", "qualname": "Obs.S_global", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.S_dict": {"fullname": "pyerrors.obs.Obs.S_dict", "modulename": "pyerrors.obs", "qualname": "Obs.S_dict", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.tau_exp_global": {"fullname": "pyerrors.obs.Obs.tau_exp_global", "modulename": "pyerrors.obs", "qualname": "Obs.tau_exp_global", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.tau_exp_dict": {"fullname": "pyerrors.obs.Obs.tau_exp_dict", "modulename": "pyerrors.obs", "qualname": "Obs.tau_exp_dict", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.N_sigma_global": {"fullname": "pyerrors.obs.Obs.N_sigma_global", "modulename": "pyerrors.obs", "qualname": "Obs.N_sigma_global", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.filter_eps": {"fullname": "pyerrors.obs.Obs.filter_eps", "modulename": "pyerrors.obs", "qualname": "Obs.filter_eps", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.names": {"fullname": "pyerrors.obs.Obs.names", "modulename": "pyerrors.obs", "qualname": "Obs.names", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.shape": {"fullname": "pyerrors.obs.Obs.shape", "modulename": "pyerrors.obs", "qualname": "Obs.shape", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.r_values": {"fullname": "pyerrors.obs.Obs.r_values", "modulename": "pyerrors.obs", "qualname": "Obs.r_values", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.deltas": {"fullname": "pyerrors.obs.Obs.deltas", "modulename": "pyerrors.obs", "qualname": "Obs.deltas", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.idl": {"fullname": "pyerrors.obs.Obs.idl", "modulename": "pyerrors.obs", "qualname": "Obs.idl", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.is_merged": {"fullname": "pyerrors.obs.Obs.is_merged", "modulename": "pyerrors.obs", "qualname": "Obs.is_merged", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.N": {"fullname": "pyerrors.obs.Obs.N", "modulename": "pyerrors.obs", "qualname": "Obs.N", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.ddvalue": {"fullname": "pyerrors.obs.Obs.ddvalue", "modulename": "pyerrors.obs", "qualname": "Obs.ddvalue", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.reweighted": {"fullname": "pyerrors.obs.Obs.reweighted", "modulename": "pyerrors.obs", "qualname": "Obs.reweighted", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.tag": {"fullname": "pyerrors.obs.Obs.tag", "modulename": "pyerrors.obs", "qualname": "Obs.tag", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.value": {"fullname": "pyerrors.obs.Obs.value", "modulename": "pyerrors.obs", "qualname": "Obs.value", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.dvalue": {"fullname": "pyerrors.obs.Obs.dvalue", "modulename": "pyerrors.obs", "qualname": "Obs.dvalue", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_names": {"fullname": "pyerrors.obs.Obs.e_names", "modulename": "pyerrors.obs", "qualname": "Obs.e_names", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_content": {"fullname": "pyerrors.obs.Obs.e_content", "modulename": "pyerrors.obs", "qualname": "Obs.e_content", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.expand_deltas": {"fullname": "pyerrors.obs.Obs.expand_deltas", "modulename": "pyerrors.obs", "qualname": "Obs.expand_deltas", "type": "function", "doc": "

    Expand deltas defined on idx to a regular, contiguous range, where holes are filled by 0.\n If idx is of type range, the deltas are not changed

    \n\n
    Parameters
    \n\n\n", "parameters": ["self", "deltas", "idx", "shape"], "funcdef": "def"}, "pyerrors.obs.Obs.calc_gamma": {"fullname": "pyerrors.obs.Obs.calc_gamma", "modulename": "pyerrors.obs", "qualname": "Obs.calc_gamma", "type": "function", "doc": "

    Calculate Gamma_{AA} from the deltas, which are defined on idx.\n idx is assumed to be a contiguous range (possibly with a stepsize != 1)

    \n\n
    Parameters
    \n\n\n", "parameters": ["self", "deltas", "idx", "shape", "w_max", "fft"], "funcdef": "def"}, "pyerrors.obs.Obs.gamma_method": {"fullname": "pyerrors.obs.Obs.gamma_method", "modulename": "pyerrors.obs", "qualname": "Obs.gamma_method", "type": "function", "doc": "

    Calculate the error and related properties of the Obs.

    \n\n
    Keyword arguments
    \n\n

    S : float\n specifies a custom value for the parameter S (default 2.0), can be\n a float or an array of floats for different ensembles\ntau_exp : float\n positive value triggers the critical slowing down analysis\n (default 0.0), can be a float or an array of floats for different\n ensembles\nN_sigma : float\n number of standard deviations from zero until the tail is\n attached to the autocorrelation function (default 1)\nfft : bool\n determines whether the fft algorithm is used for the computation\n of the autocorrelation function (default True)

    \n", "parameters": ["self", "kwargs"], "funcdef": "def"}, "pyerrors.obs.Obs.print": {"fullname": "pyerrors.obs.Obs.print", "modulename": "pyerrors.obs", "qualname": "Obs.print", "type": "function", "doc": "

    \n", "parameters": ["self", "level"], "funcdef": "def"}, "pyerrors.obs.Obs.details": {"fullname": "pyerrors.obs.Obs.details", "modulename": "pyerrors.obs", "qualname": "Obs.details", "type": "function", "doc": "

    Output detailed properties of the Obs.

    \n", "parameters": ["self", "ens_content"], "funcdef": "def"}, "pyerrors.obs.Obs.is_zero_within_error": {"fullname": "pyerrors.obs.Obs.is_zero_within_error", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero_within_error", "type": "function", "doc": "

    Checks whether the observable is zero within 'sigma' standard errors.

    \n\n

    Works only properly when the gamma method was run.

    \n", "parameters": ["self", "sigma"], "funcdef": "def"}, "pyerrors.obs.Obs.is_zero": {"fullname": "pyerrors.obs.Obs.is_zero", "modulename": "pyerrors.obs", "qualname": "Obs.is_zero", "type": "function", "doc": "

    Checks whether the observable is zero within machine precision.

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.plot_tauint": {"fullname": "pyerrors.obs.Obs.plot_tauint", "modulename": "pyerrors.obs", "qualname": "Obs.plot_tauint", "type": "function", "doc": "

    Plot integrated autocorrelation time for each ensemble.

    \n", "parameters": ["self", "save"], "funcdef": "def"}, "pyerrors.obs.Obs.plot_rho": {"fullname": "pyerrors.obs.Obs.plot_rho", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rho", "type": "function", "doc": "

    Plot normalized autocorrelation function time for each ensemble.

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.plot_rep_dist": {"fullname": "pyerrors.obs.Obs.plot_rep_dist", "modulename": "pyerrors.obs", "qualname": "Obs.plot_rep_dist", "type": "function", "doc": "

    Plot replica distribution for each ensemble with more than one replicum.

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.plot_history": {"fullname": "pyerrors.obs.Obs.plot_history", "modulename": "pyerrors.obs", "qualname": "Obs.plot_history", "type": "function", "doc": "

    Plot derived Monte Carlo history for each ensemble.

    \n", "parameters": ["self", "expand"], "funcdef": "def"}, "pyerrors.obs.Obs.plot_piechart": {"fullname": "pyerrors.obs.Obs.plot_piechart", "modulename": "pyerrors.obs", "qualname": "Obs.plot_piechart", "type": "function", "doc": "

    Plot piechart which shows the fractional contribution of each\nensemble to the error and returns a dictionary containing the fractions.

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.dump": {"fullname": "pyerrors.obs.Obs.dump", "modulename": "pyerrors.obs", "qualname": "Obs.dump", "type": "function", "doc": "

    Dump the Obs to a pickle file 'name'.

    \n\n
    Keyword arguments
    \n\n

    path -- specifies a custom path for the file (default '.')

    \n", "parameters": ["self", "name", "kwargs"], "funcdef": "def"}, "pyerrors.obs.Obs.sqrt": {"fullname": "pyerrors.obs.Obs.sqrt", "modulename": "pyerrors.obs", "qualname": "Obs.sqrt", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.log": {"fullname": "pyerrors.obs.Obs.log", "modulename": "pyerrors.obs", "qualname": "Obs.log", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.exp": {"fullname": "pyerrors.obs.Obs.exp", "modulename": "pyerrors.obs", "qualname": "Obs.exp", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.sin": {"fullname": "pyerrors.obs.Obs.sin", "modulename": "pyerrors.obs", "qualname": "Obs.sin", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.cos": {"fullname": "pyerrors.obs.Obs.cos", "modulename": "pyerrors.obs", "qualname": "Obs.cos", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.tan": {"fullname": "pyerrors.obs.Obs.tan", "modulename": "pyerrors.obs", "qualname": "Obs.tan", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.arcsin": {"fullname": "pyerrors.obs.Obs.arcsin", "modulename": "pyerrors.obs", "qualname": "Obs.arcsin", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.arccos": {"fullname": "pyerrors.obs.Obs.arccos", "modulename": "pyerrors.obs", "qualname": "Obs.arccos", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.arctan": {"fullname": "pyerrors.obs.Obs.arctan", "modulename": "pyerrors.obs", "qualname": "Obs.arctan", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.sinh": {"fullname": "pyerrors.obs.Obs.sinh", "modulename": "pyerrors.obs", "qualname": "Obs.sinh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.cosh": {"fullname": "pyerrors.obs.Obs.cosh", "modulename": "pyerrors.obs", "qualname": "Obs.cosh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.tanh": {"fullname": "pyerrors.obs.Obs.tanh", "modulename": "pyerrors.obs", "qualname": "Obs.tanh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.arcsinh": {"fullname": "pyerrors.obs.Obs.arcsinh", "modulename": "pyerrors.obs", "qualname": "Obs.arcsinh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.arccosh": {"fullname": "pyerrors.obs.Obs.arccosh", "modulename": "pyerrors.obs", "qualname": "Obs.arccosh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.arctanh": {"fullname": "pyerrors.obs.Obs.arctanh", "modulename": "pyerrors.obs", "qualname": "Obs.arctanh", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.sinc": {"fullname": "pyerrors.obs.Obs.sinc", "modulename": "pyerrors.obs", "qualname": "Obs.sinc", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.Obs.N_sigma": {"fullname": "pyerrors.obs.Obs.N_sigma", "modulename": "pyerrors.obs", "qualname": "Obs.N_sigma", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.S": {"fullname": "pyerrors.obs.Obs.S", "modulename": "pyerrors.obs", "qualname": "Obs.S", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_ddvalue": {"fullname": "pyerrors.obs.Obs.e_ddvalue", "modulename": "pyerrors.obs", "qualname": "Obs.e_ddvalue", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_drho": {"fullname": "pyerrors.obs.Obs.e_drho", "modulename": "pyerrors.obs", "qualname": "Obs.e_drho", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_dtauint": {"fullname": "pyerrors.obs.Obs.e_dtauint", "modulename": "pyerrors.obs", "qualname": "Obs.e_dtauint", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_dvalue": {"fullname": "pyerrors.obs.Obs.e_dvalue", "modulename": "pyerrors.obs", "qualname": "Obs.e_dvalue", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_n_dtauint": {"fullname": "pyerrors.obs.Obs.e_n_dtauint", "modulename": "pyerrors.obs", "qualname": "Obs.e_n_dtauint", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_n_tauint": {"fullname": "pyerrors.obs.Obs.e_n_tauint", "modulename": "pyerrors.obs", "qualname": "Obs.e_n_tauint", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_rho": {"fullname": "pyerrors.obs.Obs.e_rho", "modulename": "pyerrors.obs", "qualname": "Obs.e_rho", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_tauint": {"fullname": "pyerrors.obs.Obs.e_tauint", "modulename": "pyerrors.obs", "qualname": "Obs.e_tauint", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.e_windowsize": {"fullname": "pyerrors.obs.Obs.e_windowsize", "modulename": "pyerrors.obs", "qualname": "Obs.e_windowsize", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.Obs.tau_exp": {"fullname": "pyerrors.obs.Obs.tau_exp", "modulename": "pyerrors.obs", "qualname": "Obs.tau_exp", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.CObs": {"fullname": "pyerrors.obs.CObs", "modulename": "pyerrors.obs", "qualname": "CObs", "type": "class", "doc": "

    Class for a complex valued observable.

    \n"}, "pyerrors.obs.CObs.__init__": {"fullname": "pyerrors.obs.CObs.__init__", "modulename": "pyerrors.obs", "qualname": "CObs.__init__", "type": "function", "doc": "

    \n", "parameters": ["self", "real", "imag"], "funcdef": "def"}, "pyerrors.obs.CObs.tag": {"fullname": "pyerrors.obs.CObs.tag", "modulename": "pyerrors.obs", "qualname": "CObs.tag", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.CObs.real": {"fullname": "pyerrors.obs.CObs.real", "modulename": "pyerrors.obs", "qualname": "CObs.real", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.CObs.imag": {"fullname": "pyerrors.obs.CObs.imag", "modulename": "pyerrors.obs", "qualname": "CObs.imag", "type": "variable", "doc": "

    \n"}, "pyerrors.obs.CObs.gamma_method": {"fullname": "pyerrors.obs.CObs.gamma_method", "modulename": "pyerrors.obs", "qualname": "CObs.gamma_method", "type": "function", "doc": "

    Executes the gamma_method for the real and the imaginary part.

    \n", "parameters": ["self", "kwargs"], "funcdef": "def"}, "pyerrors.obs.CObs.is_zero": {"fullname": "pyerrors.obs.CObs.is_zero", "modulename": "pyerrors.obs", "qualname": "CObs.is_zero", "type": "function", "doc": "

    Checks whether both real and imaginary part are zero within machine precision.

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.CObs.conjugate": {"fullname": "pyerrors.obs.CObs.conjugate", "modulename": "pyerrors.obs", "qualname": "CObs.conjugate", "type": "function", "doc": "

    \n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.obs.merge_idx": {"fullname": "pyerrors.obs.merge_idx", "modulename": "pyerrors.obs", "qualname": "merge_idx", "type": "function", "doc": "

    Returns the union of all lists in idl

    \n\n
    Parameters
    \n\n\n", "parameters": ["idl"], "funcdef": "def"}, "pyerrors.obs.expand_deltas_for_merge": {"fullname": "pyerrors.obs.expand_deltas_for_merge", "modulename": "pyerrors.obs", "qualname": "expand_deltas_for_merge", "type": "function", "doc": "

    Expand deltas defined on idx to the list of configs that is defined by new_idx.\n New, empy entries are filled by 0. If idx and new_idx are of type range, the smallest\n common divisor of the step sizes is used as new step size.

    \n\n
    Parameters
    \n\n\n", "parameters": ["deltas", "idx", "shape", "new_idx"], "funcdef": "def"}, "pyerrors.obs.filter_zeroes": {"fullname": "pyerrors.obs.filter_zeroes", "modulename": "pyerrors.obs", "qualname": "filter_zeroes", "type": "function", "doc": "

    Filter out all configurations with vanishing fluctuation such that they do not\n contribute to the error estimate anymore. Returns the new names, deltas and\n idl according to the filtering.\n A fluctuation is considered to be vanishing, if it is smaller than eps times\n the mean of the absolute values of all deltas in one list.

    \n\n
    Parameters
    \n\n\n\n
    Optional parameters
    \n\n

    eps -- Prefactor that enters the filter criterion.

    \n", "parameters": ["names", "deltas", "idl", "eps"], "funcdef": "def"}, "pyerrors.obs.derived_observable": {"fullname": "pyerrors.obs.derived_observable", "modulename": "pyerrors.obs", "qualname": "derived_observable", "type": "function", "doc": "

    Construct a derived Obs according to func(data, **kwargs) using automatic differentiation.

    \n\n
    Parameters
    \n\n\n\n
    Keyword arguments
    \n\n

    num_grad : bool\n if True, numerical derivatives are used instead of autograd\n (default False). To control the numerical differentiation the\n kwargs of numdifftools.step_generators.MaxStepGenerator\n can be used.\nman_grad : list\n manually supply a list or an array which contains the jacobian\n of func. Use cautiously, supplying the wrong derivative will\n not be intercepted.

    \n\n
    Notes
    \n\n

    For simple mathematical operations it can be practical to use anonymous\nfunctions. For the ratio of two observables one can e.g. use

    \n\n

    new_obs = derived_observable(lambda x: x[0] / x[1], [obs1, obs2])

    \n", "parameters": ["func", "data", "kwargs"], "funcdef": "def"}, "pyerrors.obs.reduce_deltas": {"fullname": "pyerrors.obs.reduce_deltas", "modulename": "pyerrors.obs", "qualname": "reduce_deltas", "type": "function", "doc": "

    Extract deltas defined on idx_old on all configs of idx_new.

    \n\n
    Parameters
    \n\n\n", "parameters": ["deltas", "idx_old", "idx_new"], "funcdef": "def"}, "pyerrors.obs.reweight": {"fullname": "pyerrors.obs.reweight", "modulename": "pyerrors.obs", "qualname": "reweight", "type": "function", "doc": "

    Reweight a list of observables.

    \n\n
    Parameters
    \n\n\n\n
    Keyword arguments
    \n\n

    all_configs : bool\n if True, the reweighted observables are normalized by the average of\n the reweighting factor on all configurations in weight.idl and not\n on the configurations in obs[i].idl.

    \n", "parameters": ["weight", "obs", "kwargs"], "funcdef": "def"}, "pyerrors.obs.correlate": {"fullname": "pyerrors.obs.correlate", "modulename": "pyerrors.obs", "qualname": "correlate", "type": "function", "doc": "

    Correlate two observables.

    \n\n

    Attributes:

    \n\n

    obs_a : Obs\n First observable\nobs_b : Obs\n Second observable

    \n\n

    Keep in mind to only correlate primary observables which have not been reweighted\nyet. The reweighting has to be applied after correlating the observables.\nCurrently only works if ensembles are identical. This is not really necessary.

    \n", "parameters": ["obs_a", "obs_b"], "funcdef": "def"}, "pyerrors.obs.covariance": {"fullname": "pyerrors.obs.covariance", "modulename": "pyerrors.obs", "qualname": "covariance", "type": "function", "doc": "

    Calculates the covariance of two observables.

    \n\n

    covariance(obs, obs) is equal to obs.dvalue ** 2\nThe gamma method has to be applied first to both observables.

    \n\n

    If abs(covariance(obs1, obs2)) > obs1.dvalue * obs2.dvalue, the covariance\nis constrained to the maximum value in order to make sure that covariance\nmatrices are positive semidefinite.

    \n\n
    Keyword arguments
    \n\n

    correlation -- if true the correlation instead of the covariance is\n returned (default False)

    \n", "parameters": ["obs1", "obs2", "correlation", "kwargs"], "funcdef": "def"}, "pyerrors.obs.covariance2": {"fullname": "pyerrors.obs.covariance2", "modulename": "pyerrors.obs", "qualname": "covariance2", "type": "function", "doc": "

    Alternative implementation of the covariance of two observables.

    \n\n

    covariance(obs, obs) is equal to obs.dvalue ** 2\nThe gamma method has to be applied first to both observables.

    \n\n

    If abs(covariance(obs1, obs2)) > obs1.dvalue * obs2.dvalue, the covariance\nis constrained to the maximum value in order to make sure that covariance\nmatrices are positive semidefinite.

    \n\n
    Keyword arguments
    \n\n

    correlation -- if true the correlation instead of the covariance is\n returned (default False)

    \n", "parameters": ["obs1", "obs2", "correlation", "kwargs"], "funcdef": "def"}, "pyerrors.obs.covariance3": {"fullname": "pyerrors.obs.covariance3", "modulename": "pyerrors.obs", "qualname": "covariance3", "type": "function", "doc": "

    Another alternative implementation of the covariance of two observables.

    \n\n

    covariance2(obs, obs) is equal to obs.dvalue ** 2\nCurrently only works if ensembles are identical.\nThe gamma method has to be applied first to both observables.

    \n\n

    If abs(covariance2(obs1, obs2)) > obs1.dvalue * obs2.dvalue, the covariance\nis constrained to the maximum value in order to make sure that covariance\nmatrices are positive semidefinite.

    \n\n
    Keyword arguments
    \n\n

    correlation -- if true the correlation instead of the covariance is\n returned (default False)\nplot -- if true, the integrated autocorrelation time for each ensemble is\n plotted.

    \n", "parameters": ["obs1", "obs2", "correlation", "kwargs"], "funcdef": "def"}, "pyerrors.obs.pseudo_Obs": {"fullname": "pyerrors.obs.pseudo_Obs", "modulename": "pyerrors.obs", "qualname": "pseudo_Obs", "type": "function", "doc": "

    Generate a pseudo Obs with given value, dvalue and name

    \n\n

    The standard number of samples is a 1000. This can be adjusted.

    \n", "parameters": ["value", "dvalue", "name", "samples"], "funcdef": "def"}, "pyerrors.obs.dump_object": {"fullname": "pyerrors.obs.dump_object", "modulename": "pyerrors.obs", "qualname": "dump_object", "type": "function", "doc": "

    Dump object into pickle file.

    \n\n
    Keyword arguments
    \n\n

    path -- specifies a custom path for the file (default '.')

    \n", "parameters": ["obj", "name", "kwargs"], "funcdef": "def"}, "pyerrors.obs.load_object": {"fullname": "pyerrors.obs.load_object", "modulename": "pyerrors.obs", "qualname": "load_object", "type": "function", "doc": "

    Load object from pickle file.

    \n", "parameters": ["path"], "funcdef": "def"}, "pyerrors.obs.merge_obs": {"fullname": "pyerrors.obs.merge_obs", "modulename": "pyerrors.obs", "qualname": "merge_obs", "type": "function", "doc": "

    Combine all observables in list_of_obs into one new observable

    \n\n

    It is not possible to combine obs which are based on the same replicum

    \n", "parameters": ["list_of_obs"], "funcdef": "def"}, "pyerrors.roots": {"fullname": "pyerrors.roots", "modulename": "pyerrors.roots", "qualname": "", "type": "module", "doc": "

    \n"}, "pyerrors.roots.find_root": {"fullname": "pyerrors.roots.find_root", "modulename": "pyerrors.roots", "qualname": "find_root", "type": "function", "doc": "

    Finds the root of the function func(x, d) where d is an Obs.

    \n\n
    Parameters
    \n\n\n", "parameters": ["d", "func", "guess", "kwargs"], "funcdef": "def"}, "pyerrors.version": {"fullname": "pyerrors.version", "modulename": "pyerrors.version", "qualname": "", "type": "module", "doc": "

    \n"}}, "docInfo": {"pyerrors": {"qualname": 0, "fullname": 1, "doc": 186}, "pyerrors.correlators": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.correlators.Corr": {"qualname": 1, "fullname": 3, "doc": 51}, "pyerrors.correlators.Corr.__init__": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.reweighted": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.gamma_method": {"qualname": 2, "fullname": 4, "doc": 5}, "pyerrors.correlators.Corr.projected": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.sum": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.smearing": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.plottable": {"qualname": 2, "fullname": 4, "doc": 16}, "pyerrors.correlators.Corr.symmetric": {"qualname": 2, "fullname": 4, "doc": 4}, "pyerrors.correlators.Corr.anti_symmetric": {"qualname": 2, "fullname": 4, "doc": 5}, "pyerrors.correlators.Corr.smearing_symmetric": 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but good enough.