What is pyerrors?\n\npyerrors 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- automatic differentiation for exact liner error propagation as suggested in arXiv:1809.01289 (partly based on the autograd package).
\n- treatment of slow modes in the simulation as suggested in arXiv:1009.5228.
\n- coherent error propagation for data from different Markov chains.
\n- non-linear fits with x- and y-errors and exact linear error propagation based on automatic differentiation as introduced in arXiv:1809.01289.
\n- real and complex matrix operations and their error propagation based on automatic differentiation (Matrix inverse, Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...).
\n
\n\nThere exist similar publicly available implementations of gamma method error analysis suites in Fortran, Julia and Python.
\n\nBasic example
\n\nimport numpy as np\nimport pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name']) # Initialize an Obs object\nmy_new_obs = 2 * np.log(my_obs) / my_obs ** 2 # Construct derived Obs object\nmy_new_obs.gamma_method() # Estimate the statistical error\nprint(my_new_obs) # Print the result to stdout\n> 0.31498(72)\n
The Obs class
\n\npyerrors introduces a new datatype, Obs, which simplifies error propagation and estimation for auto- and cross-correlated data.\nAn Obs object can be initialized with two arguments, the first is a list containing the samples for an Observable from a Monte Carlo chain.\nThe samples can either be provided as python list or as numpy array.\nThe second argument is a list containing the names of the respective Monte Carlo chains as strings. These strings uniquely identify a Monte Carlo chain/ensemble.
\n\nimport pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name'])\n
Error propagation
\n\nWhen performing mathematical operations on Obs objects the correct error propagation is intrinsically taken care using a first order Taylor expansion\n$$\\delta_f^i=\\sum_\\alpha \\bar{f}_\\alpha \\delta_\\alpha^i\\,,\\quad \\delta_\\alpha^i=a_\\alpha^i-\\bar{a}_\\alpha\\,,$$\nas introduced in arXiv:hep-lat/0306017.\nThe required derivatives $\\bar{f}_\\alpha$ are evaluated up to machine precision via automatic differentiation as suggested in arXiv:1809.01289.
\n\nThe Obs class is designed such that mathematical numpy functions can be used on Obs just as for regular floats.
\n\nimport numpy as np\nimport pyerrors as pe\n\nmy_obs1 = pe.Obs([samples1], ['ensemble_name'])\nmy_obs2 = pe.Obs([samples2], ['ensemble_name'])\n\nmy_sum = my_obs1 + my_obs2\n\nmy_m_eff = np.log(my_obs1 / my_obs2)\n\niamzero = my_m_eff - my_m_eff\n# Check that value and fluctuations are zero within machine precision\nprint(iamzero == 0.0)\n> True\n
Error estimation
\n\nThe error estimation within pyerrors is based on the gamma method introduced in arXiv:hep-lat/0306017.\nAfter having arrived at the derived quantity of interest the gamma_method can be called as detailed in the following example.
\n\nmy_sum.gamma_method()\nprint(my_sum)\n> 1.70(57)\nmy_sum.details()\n> Result 1.70000000e+00 +/- 5.72046658e-01 +/- 7.56746598e-02 (33.650%)\n> t_int 2.71422900e+00 +/- 6.40320983e-01 S = 2.00\n> 1000 samples in 1 ensemble:\n> \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
We use the following definition of the integrated autocorrelation time established in Madras & Sokal 1988\n$$\\tau_\\mathrm{int}=\\frac{1}{2}+\\sum_{t=1}^{W}\\rho(t)\\geq \\frac{1}{2}\\,.$$\nThe window $W$ is determined via the automatic windowing procedure described in arXiv:hep-lat/0306017.\nThe standard value for the parameter $S$ of this automatic windowing procedure is $S=2$. Other values for $S$ can be passed to the gamma_method as parameter.
\n\nmy_sum.gamma_method(S=3.0)\nmy_sum.details()\n> Result 1.70000000e+00 +/- 6.30675201e-01 +/- 1.04585650e-01 (37.099%)\n> t_int 3.29909703e+00 +/- 9.77310102e-01 S = 3.00\n> 1000 samples in 1 ensemble:\n> \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
The integrated autocorrelation time $\\tau_\\mathrm{int}$ and the autocorrelation function $\\rho(W)$ can be monitored via the methods pyerrors.obs.Obs.plot_tauint and pyerrors.obs.Obs.plot_tauint.
\n\nIf the parameter $S$ is set to zero it is assumed that dataset does not exhibit any autocorrelation and the windowsize is chosen to be zero.\nIn this case the error estimate is identical to the sample standard error.
\n\nExponential tails
\n\nSlow modes in the Monte Carlo history can be accounted for by attaching an exponential tail to the autocorrelation function $\\rho$ as suggested in arXiv:1009.5228. The longest autocorrelation time in the history, $\\tau_\\mathrm{exp}$, can be passed to the gamma_method as parameter. In this case the automatic windowing procedure is vacated and the parameter $S$ does not affect the error estimate.
\n\nmy_sum.gamma_method(tau_exp=7.2)\nmy_sum.details()\n> Result 1.70000000e+00 +/- 6.28097762e-01 +/- 5.79077524e-02 (36.947%)\n> t_int 3.27218667e+00 +/- 7.99583654e-01 tau_exp = 7.20, N_sigma = 1\n> 1000 samples in 1 ensemble:\n> \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
For the full API see pyerrors.obs.Obs.gamma_method.
\n\nMultiple ensembles/replica
\n\nError propagation for multiple ensembles (Markov chains with different simulation parameters) is handled automatically. Ensembles are uniquely identified by their name.
\n\nobs1 = pe.Obs([samples1], ['ensemble1'])\nobs2 = pe.Obs([samples2], ['ensemble2'])\n\nmy_sum = obs1 + obs2\nmy_sum.details()\n> Result 2.00697958e+00\n> 1500 samples in 2 ensembles:\n> \u00b7 Ensemble 'ensemble1' : 1000 configurations (from 1 to 1000)\n> \u00b7 Ensemble 'ensemble2' : 500 configurations (from 1 to 500)\n
pyerrors identifies multiple replica (independent Markov chains with identical simulation parameters) by the vertical bar | in the name of the data set.
\n\nobs1 = pe.Obs([samples1], ['ensemble1|r01'])\nobs2 = pe.Obs([samples2], ['ensemble1|r02'])\n\n> my_sum = obs1 + obs2\n> my_sum.details()\n> Result 2.00697958e+00\n> 1500 samples in 1 ensemble:\n> \u00b7 Ensemble 'ensemble1'\n> \u00b7 Replicum 'r01' : 1000 configurations (from 1 to 1000)\n> \u00b7 Replicum 'r02' : 500 configurations (from 1 to 500)\n
Error estimation for multiple ensembles
\n\nIn order to keep track of different error analysis parameters for different ensembles one can make use of global dictionaries as detailed in the following example.
\n\npe.Obs.S_dict['ensemble1'] = 2.5\npe.Obs.tau_exp_dict['ensemble2'] = 8.0\npe.Obs.tau_exp_dict['ensemble3'] = 2.0\n
In case the gamma_method is called without any parameters it will use the values specified in the dictionaries for the respective ensembles.\nPassing arguments to the gamma_method still dominates over the dictionaries.
\n\nIrregular Monte Carlo chains
\n\nIrregular Monte Carlo chains can be initialized with the parameter idl.
\n\n# Observable defined on configurations 20 to 519\nobs1 = pe.Obs([samples1], ['ensemble1'], idl=[range(20, 520)])\nobs1.details()\n> Result 9.98319881e-01\n> 500 samples in 1 ensemble:\n> \u00b7 Ensemble 'ensemble1' : 500 configurations (from 20 to 519)\n\n# Observable defined on every second configuration between 5 and 1003\nobs2 = pe.Obs([samples2], ['ensemble1'], idl=[range(5, 1005, 2)])\nobs2.details()\n> Result 9.99100712e-01\n> 500 samples in 1 ensemble:\n> \u00b7 Ensemble 'ensemble1' : 500 configurations (from 5 to 1003 in steps of 2)\n\n# Observable defined on configurations 2, 9, 28, 29 and 501\nobs3 = pe.Obs([samples3], ['ensemble1'], idl=[[2, 9, 28, 29, 501]])\nobs3.details()\n> Result 1.01718064e+00\n> 5 samples in 1 ensemble:\n> \u00b7 Ensemble 'ensemble1' : 5 configurations (irregular range)\n
Warning: Irregular Monte Carlo chains can result in odd patterns in the autocorrelation functions.\nMake sure to check the autocorrelation time with e.g. pyerrors.obs.Obs.plot_rho or pyerrors.obs.Obs.plot_tauint.
\n\nFor the full API see pyerrors.obs.Obs.
\n\nCorrelators
\n\nFor the full API see pyerrors.correlators.Corr.
\n\nComplex observables
\n\npyerrors can handle complex valued observables via the class pyerrors.obs.CObs.\nCObs are initialized with a real and an imaginary part which both can be Obs valued.
\n\nmy_real_part = pe.Obs([samples1], ['ensemble1'])\nmy_imag_part = pe.Obs([samples2], ['ensemble1'])\n\nmy_cobs = pe.CObs(my_real_part, my_imag_part)\nmy_cobs.gamma_method()\nprint(my_cobs)\n> (0.9959(91)+0.659(28)j)\n
Elementary mathematical operations are overloaded and samples are properly propagated as for the Obs class.
\n\nmy_derived_cobs = (my_cobs + my_cobs.conjugate()) / np.abs(my_cobs)\nmy_derived_cobs.gamma_method()\nprint(my_derived_cobs)\n> (1.668(23)+0.0j)\n
Optimization / fits / roots
\n\npyerrors.fits\npyerrors.roots
\n\nMatrix operations
\n\npyerrors.linalg
\n\nExport data
\n\nThe preferred exported file format within pyerrors is
\n\nJackknife samples
\n\nFor comparison with other analysis workflows pyerrors can generate jackknife samples from an Obs object or import jackknife samples into an Obs object.\nSee pyerrors.obs.Obs.export_jackknife and pyerrors.obs.import_jackknife for details.
\n\n\n\npyerrors.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\nEverything, 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 inconvenient\nto iterate over all timeslices for every operation. This is especially true, when dealing with smearing matrices.
\n\nThe 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", "kwargs"], "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\nOutputs 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\nParameters
\n\n\n- dt (int):\nnumber of timeslices
\n
\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\nParameters
\n\n\n- partner (Obs or Corr):\npartner to correlate the correlator with.\nCan either be an Obs which is correlated with all entries of the\ncorrelator or a Corr of same length.
\n
\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\nParameters
\n\n\n- weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
\n- all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl.
\n
\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\nParameters
\n\n\n- partner (Corr):\nTime symmetry partner of the Corr
\n- partity (int):\nParity quantum number of the correlator, can be +1 or -1
\n
\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\nParameters
\n\n\n- symmetric (bool):\ndecides whether symmetric of simple finite differences are used. Default: True
\n
\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\nParameters
\n\n\n- variant (str):\nlog : uses the standard effective mass log(C(t) / C(t+1))\ncosh, periodic : Use periodicitiy of the correlator by solving C(t) / C(t+1) = cosh(m * (t - T/2)) / cosh(m * (t + 1 - T/2)) for m.\nsinh : Use anti-periodicitiy of the correlator by solving C(t) / C(t+1) = sinh(m * (t - T/2)) / sinh(m * (t + 1 - T/2)) for m.\nSee, e.g., arXiv:1205.5380\narccosh : Uses the explicit form of the symmetrized correlator (not recommended)
\n- guess (float):\nguess for the root finder, only relevant for the root variant
\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\nParameters
\n\n\n- function (obj):\nfunction to fit to the data. See fits.least_squares for details.
\n- fitrange (list):\nRange in which the function is to be fitted to the data.\nIf not specified, self.prange or all timeslices are used.
\n- silent (bool):\nDecides whether output is printed to the standard output.
\n
\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 plateau value from a Corr object
\n\nParameters
\n\n\n- plateau_range (list):\nlist with two entries, indicating the first and the last timeslice\nof the plateau region.
\n- method (str):\nmethod 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
\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\nParameters
\n\n\n- x_range (list):\nlist of two values, determining the range of the x-axis e.g. [4, 8]
\n- comp (Corr or list of Corr):\nCorrelator or list of correlators which are plotted for comparison.
\n- logscale (bool):\nSets y-axis to logscale
\n- plateau (Obs):\nplateau to be visualized in the figure
\n- fit_res (Fit_result):\nFit_result object to be visualized
\n- ylabel (str):\nLabel for the y-axis
\n- save (str):\npath to file in which the figure should be saved
\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 pickle file
\n\nParameters
\n\n\n- filename (str):\nName of the file
\n
\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.covobs": {"fullname": "pyerrors.covobs", "modulename": "pyerrors.covobs", "qualname": "", "type": "module", "doc": "\n"}, "pyerrors.covobs.Covobs": {"fullname": "pyerrors.covobs.Covobs", "modulename": "pyerrors.covobs", "qualname": "Covobs", "type": "class", "doc": "\n"}, "pyerrors.covobs.Covobs.__init__": {"fullname": "pyerrors.covobs.Covobs.__init__", "modulename": "pyerrors.covobs", "qualname": "Covobs.__init__", "type": "function", "doc": "Initialize Covobs object.
\n\nParameters
\n\n\n- mean (float):\nMean value of the new Obs
\n- cov (list or array):\n2d Covariance matrix or 1d diagonal entries
\n- name (str):\nidentifier for the covariance matrix
\n- pos (int):\nPosition of the variance belonging to mean in cov.\nIs taken to be 1 if cov is 0-dimensional
\n- grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
\n
\n", "parameters": ["self", "mean", "cov", "name", "pos", "grad"], "funcdef": "def"}, "pyerrors.covobs.Covobs.errsq": {"fullname": "pyerrors.covobs.Covobs.errsq", "modulename": "pyerrors.covobs", "qualname": "Covobs.errsq", "type": "function", "doc": "Return the variance (= square of the error) of the Covobs
\n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.covobs.Covobs.cov": {"fullname": "pyerrors.covobs.Covobs.cov", "modulename": "pyerrors.covobs", "qualname": "Covobs.cov", "type": "variable", "doc": "\n"}, "pyerrors.covobs.Covobs.grad": {"fullname": "pyerrors.covobs.Covobs.grad", "modulename": "pyerrors.covobs", "qualname": "Covobs.grad", "type": "variable", "doc": "\n"}, "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\nAttributes
\n\n\n- fit_parameters (list):\nresults for the individual fit parameters,\nalso accessible via indices.
\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\nParameters
\n\n\n- x (list):\nlist of floats.
\n- y (list):\nlist of Obs.
\nfunc (object):\nfit function, has to be of the form
\n\ndef func(a, x):\n y = a[0] + a[1] * x + a[2] * anp.sinh(x)\n return y\n
For 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
It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work
- priors (list, optional):\npriors has to be a list with an entry for every parameter in the fit. The entries can either be\nObs (e.g. results from a previous fit) or strings containing a value and an error formatted like\n0.548(23), 500(40) or 0.5(0.4)
\n- silent (bool, optional):\nIf true all output to the console is omitted (default False).
\n- initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for\n non-linear fits with many parameters.
\n- method (str):\ncan be used to choose an alternative method for the minimization of chisquare.\nThe possible methods are the ones which can be used for scipy.optimize.minimize and\nmigrad of iminuit. If no method is specified, Levenberg-Marquard is used.\nReliable alternatives are migrad, Powell and Nelder-Mead.
\n- resplot (bool):\nIf true, a plot which displays fit, data and residuals is generated (default False).
\n- qqplot (bool):\nIf true, a quantile-quantile plot of the fit result is generated (default False).
\n- expected_chisquare (bool):\nIf true prints the expected chisquare which is\ncorrected by effects caused by correlated input data.\nThis can take a while as the full correlation matrix\nhas to be calculated (default False).
\n- correlated_fit (bool):\nIf true, use the full correlation matrix in the definition of the chisquare\n(only works for prior==None and when no method is given, at the moment).
\n- const_par (list, optional):\nList of N Obs that are used to constrain the last N fit parameters of func.
\n
\n", "parameters": ["x", "y", "func", "priors", "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\nParameters
\n\n\n- x (list):\nlist of Obs, or a tuple of lists of Obs
\n- y (list):\nlist of Obs. The dvalues of the Obs are used as x- and yerror for the fit.
\nfunc (object):\nfunc has to be of the form
\n\ndef func(a, x):\n y = a[0] + a[1] * x + a[2] * anp.sinh(x)\n return y\n
For 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
It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.
- silent (bool, optional):\nIf true all output to the console is omitted (default False).
\n- initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for non-linear\nfits with many parameters.
\n- expected_chisquare (bool):\nIf true prints the expected chisquare which is\ncorrected by effects caused by correlated input data.\nThis can take a while as the full correlation matrix\nhas to be calculated (default False).
\n- const_par (list, optional):\nList of N Obs that are used to constrain the last N fit parameters of func.
\n- Based on the orthogonal distance regression module of scipy
\n
\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.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.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\ny 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.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\nread_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\nall: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
\n\nParameters
\n\n\n- file_path -- path to the bdio file
\n- bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
\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\nread_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\nall: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
\n\nParameters
\n\n\n- file_path -- path to the bdio file
\n- bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
\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\nThe dictionary can be accessed with a tuple consisting of (type, source_position, kappa1, kappa2)
\n\nread_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\nall: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
\n\nParameters
\n\n\n- file_path -- path to the bdio file
\n- bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
\n- stop -- stops reading at given configuration number (default None)
\n- alternative_ensemble_name -- Manually overwrite ensemble name
\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\nThe dictionary can be accessed with a tuple consisting of (type, kappa)
\n\nread_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\nall: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
\n\nParameters
\n\n\n- file_path -- path to the bdio file
\n- bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
\n- stop -- stops reading at given configuration number (default None)
\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\nParameters
\n\n\n- path (str):\npath to the files to read
\n- filestem (str):\nnamestem of the files to read
\n- ens_id (str):\nname of the ensemble, required for internal bookkeeping
\n- meson (str):\nlabel of the meson to be extracted, standard value meson_0 which\ncorresponds to the pseudoscalar pseudoscalar two-point function.
\n- tree (str):\nLabel of the upmost directory in the hdf5 file, default 'meson'\nfor outputs of the Meson module. Can be altered to read input\nfrom other modules with similar structures.
\n- idl (range):\nIf specified only configurations in the given range are read in.
\n
\n", "parameters": ["path", "filestem", "ens_id", "meson", "tree", "idl"], "funcdef": "def"}, "pyerrors.input.hadrons.Npr_matrix": {"fullname": "pyerrors.input.hadrons.Npr_matrix", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix", "type": "class", "doc": "ndarray(shape, dtype=float, buffer=None, offset=0,\n strides=None, order=None)
\n\nAn 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\nArrays 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\nFor more information, refer to the numpy module and examine the\nmethods and attributes of an array.
\n\nParameters
\n\n\n- (for the __new__ method; see Notes below)
\n- shape (tuple of ints):\nShape of created array.
\n- dtype (data-type, optional):\nAny object that can be interpreted as a numpy data type.
\n- buffer (object exposing buffer interface, optional):\nUsed to fill the array with data.
\n- offset (int, optional):\nOffset of array data in buffer.
\n- strides (tuple of ints, optional):\nStrides of data in memory.
\n- order ({'C', 'F'}, optional):\nRow-major (C-style) or column-major (Fortran-style) order.
\n
\n\nAttributes
\n\n\n- T (ndarray):\nTranspose of the array.
\n- data (buffer):\nThe array's elements, in memory.
\n- dtype (dtype object):\nDescribes the format of the elements in the array.
\n- flags (dict):\nDictionary containing information related to memory use, e.g.,\n'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
\n- flat (numpy.flatiter object):\nFlattened version of the array as an iterator. The iterator\nallows assignments, e.g.,
x.flat = 3 (See ndarray.flat for\nassignment examples; TODO). \n- imag (ndarray):\nImaginary part of the array.
\n- real (ndarray):\nReal part of the array.
\n- size (int):\nNumber of elements in the array.
\n- itemsize (int):\nThe memory use of each array element in bytes.
\n- nbytes (int):\nThe total number of bytes required to store the array data,\ni.e.,
itemsize * size. \n- ndim (int):\nThe array's number of dimensions.
\n- shape (tuple of ints):\nShape of the array.
\n- strides (tuple of ints):\nThe step-size required to move from one element to the next in\nmemory. For example, a contiguous
(3, 4) array of type\nint16 in C-order has strides (8, 2). This implies that\nto move from element to element in memory requires jumps of 2 bytes.\nTo move from row-to-row, one needs to jump 8 bytes at a time\n(2 * 4). \n- ctypes (ctypes object):\nClass containing properties of the array needed for interaction\nwith ctypes.
\n- base (ndarray):\nIf the array is a view into another array, that array is its
base\n(unless that array is also a view). The base array is where the\narray data is actually stored. \n
\n\nSee Also
\n\narray: 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\nNotes
\n\nThere are two modes of creating an array using __new__:
\n\n\n- If
buffer is None, then only shape, dtype, and order\nare used. \n- If
buffer is an object exposing the buffer interface, then\nall keywords are interpreted. \n
\n\nNo __init__ method is needed because the array is fully initialized\nafter the __new__ method.
\n\nExamples
\n\nThese examples illustrate the low-level ndarray constructor. Refer\nto the See Also section above for easier ways of constructing an\nndarray.
\n\nFirst 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
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
Gamma_5 hermitean conjugate
\n\nUses the fact that the propagator is gamma5 hermitean, so just the\nin and out momenta of the propagator are exchanged.
\n"}, "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\nParameters
\n\n\n- path (str):\npath to the files to read
\n- filestem (str):\nnamestem of the files to read
\n- ens_id (str):\nname of the ensemble, required for internal bookkeeping
\n- idl (range):\nIf specified only configurations in the given range are read in.
\n
\n", "parameters": ["path", "filestem", "ens_id", "idl"], "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\nParameters
\n\n\n- path (str):\npath to the files to read
\n- filestem (str):\nnamestem of the files to read
\n- ens_id (str):\nname of the ensemble, required for internal bookkeeping
\n- idl (range):\nIf specified only configurations in the given range are read in.
\n
\n", "parameters": ["path", "filestem", "ens_id", "idl"], "funcdef": "def"}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"fullname": "pyerrors.input.hadrons.read_Fourquark_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Fourquark_hd5", "type": "function", "doc": "Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs
\n\nParameters
\n\n\n- path (str):\npath to the files to read
\n- filestem (str):\nnamestem of the files to read
\n- ens_id (str):\nname of the ensemble, required for internal bookkeeping
\n- idl (range):\nIf specified only configurations in the given range are read in.
\n- vertices (list):\nVertex functions to be extracted.
\n
\n", "parameters": ["path", "filestem", "ens_id", "idl", "vertices"], "funcdef": "def"}, "pyerrors.input.json": {"fullname": "pyerrors.input.json", "modulename": "pyerrors.input.json", "qualname": "", "type": "module", "doc": "\n"}, "pyerrors.input.json.create_json_string": {"fullname": "pyerrors.input.json.create_json_string", "modulename": "pyerrors.input.json", "qualname": "create_json_string", "type": "function", "doc": "Generate the string for the export of a list of Obs or structures containing Obs\nto a .json(.gz) file
\n\nParameters
\n\n\n- ol (list):\nList of objects that will be exported. At the moments, these objects can be\neither of: Obs, list, numpy.ndarray.\nAll Obs inside a structure have to be defined on the same set of configurations.
\n- description (str):\nOptional string that describes the contents of the json file.
\n- indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
\n
\n", "parameters": ["ol", "description", "indent"], "funcdef": "def"}, "pyerrors.input.json.dump_to_json": {"fullname": "pyerrors.input.json.dump_to_json", "modulename": "pyerrors.input.json", "qualname": "dump_to_json", "type": "function", "doc": "Export a list of Obs or structures containing Obs to a .json(.gz) file
\n\nParameters
\n\n\n- ol (list):\nList of objects that will be exported. At the moments, these objects can be\neither of: Obs, list, numpy.ndarray.\nAll Obs inside a structure have to be defined on the same set of configurations.
\n- fname (str):\nFilename of the output file.
\n- description (str):\nOptional string that describes the contents of the json file.
\n- indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
\n- gz (bool):\nIf True, the output is a gzipped json. If False, the output is a json file.
\n
\n", "parameters": ["ol", "fname", "description", "indent", "gz"], "funcdef": "def"}, "pyerrors.input.json.import_json_string": {"fullname": "pyerrors.input.json.import_json_string", "modulename": "pyerrors.input.json", "qualname": "import_json_string", "type": "function", "doc": "Reconstruct a list of Obs or structures containing Obs from a json string.
\n\nThe following structures are supported: Obs, list, numpy.ndarray\nIf the list contains only one element, it is unpacked from the list.
\n\nParameters
\n\n\n- json_string (str):\njson string containing the data.
\n- verbose (bool):\nPrint additional information that was written to the file.
\n- full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
\n
\n", "parameters": ["json_string", "verbose", "full_output"], "funcdef": "def"}, "pyerrors.input.json.load_json": {"fullname": "pyerrors.input.json.load_json", "modulename": "pyerrors.input.json", "qualname": "load_json", "type": "function", "doc": "Import a list of Obs or structures containing Obs from a .json.gz file.
\n\nThe following structures are supported: Obs, list, numpy.ndarray\nIf the list contains only one element, it is unpacked from the list.
\n\nParameters
\n\n\n- fname (str):\nFilename of the input file.
\n- verbose (bool):\nPrint additional information that was written to the file.
\n- gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
\n- full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
\n
\n", "parameters": ["fname", "verbose", "gz", "full_output"], "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\nKeyword arguments
\n\nr_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\nParameters
\n\n\n- version (str):\nversion of openQCD, default 2.0
\n- r_start (list):\nlist which contains the first config to be read for each replicum
\n- r_stop (list):\nlist which contains the last config to be read for each replicum
\n- postfix (str):\npostfix of the file to read, e.g. '.ms1' for openQCD-files
\n
\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\nIt 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\nParameters
\n\n\n- path (str):\nPath to .ms.dat files
\n- prefix (str):\nEnsemble prefix
\n- dtr_read (int):\nDetermines how many trajectories should be skipped when reading the ms.dat files.\nCorresponds to dtr_cnfg / dtr_ms in the openQCD input file.
\n- xmin (int):\nFirst timeslice where the boundary effects have sufficiently decayed.
\n- spatial_extent (int):\nspatial extent of the lattice, required for normalization.
\n- fit_range (int):\nNumber of data points left and right of the zero crossing to be included in the linear fit. (Default: 5)
\n- r_start (list):\nlist which contains the first config to be read for each replicum.
\n- r_stop (list):\nlist which contains the last config to be read for each replicum.
\n- plaquette (bool):\nIf true extract the plaquette estimate of t0 instead.
\n
\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\nParameters
\n\n\n- im -- if True, read imaginary instead of real part of the correlation function.
\n- single -- if True, read a boundary-to-boundary correlation function with a single value
\n- b2b -- if True, read a time-dependent boundary-to-boundary correlation function
\n- names -- Alternative labeling for replicas/ensembles. Has to have the appropriate length
\n
\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\nParameters
\n\n\n- quarks -- Label of the quarks used in the sfcf input file
\n- noffset -- Offset of the source (only relevant when wavefunctions are used)
\n- wf -- ID of wave function
\n- wf2 -- ID of the second wavefunction (only relevant for boundary-to-boundary correlation functions)
\n- im -- if True, read imaginary instead of real part of the correlation function.
\n- b2b -- if True, read a time-dependent boundary-to-boundary correlation function
\n- names -- Alternative labeling for replicas/ensembles. Has to have the appropriate length
\n- ens_name (str):\nreplaces the name of the ensemble
\n
\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\nParameters
\n\n\n- target -- specifies the topological sector to be reweighted to (default 0)
\n- full -- if true read the charge instead of the reweighting factor.
\n
\n", "parameters": ["path", "prefix", "kwargs"], "funcdef": "def"}, "pyerrors.linalg": {"fullname": "pyerrors.linalg", "modulename": "pyerrors.linalg", "qualname": "", "type": "module", "doc": "\n"}, "pyerrors.linalg.matmul": {"fullname": "pyerrors.linalg.matmul", "modulename": "pyerrors.linalg", "qualname": "matmul", "type": "function", "doc": "Matrix multiply all operands.
\n\nParameters
\n\n\n- operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
\n- This implementation is faster compared to standard multiplication via the @ operator.
\n
\n", "parameters": ["operands"], "funcdef": "def"}, "pyerrors.linalg.jack_matmul": {"fullname": "pyerrors.linalg.jack_matmul", "modulename": "pyerrors.linalg", "qualname": "jack_matmul", "type": "function", "doc": "Matrix multiply both operands making use of the jackknife approximation.
\n\nParameters
\n\n\n- operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
\n- For large matrices this is considerably faster compared to matmul.
\n
\n", "parameters": ["operands"], "funcdef": "def"}, "pyerrors.linalg.einsum": {"fullname": "pyerrors.linalg.einsum", "modulename": "pyerrors.linalg", "qualname": "einsum", "type": "function", "doc": "Wrapper for numpy.einsum
\n\nParameters
\n\n\n- subscripts (str):\nSubscripts for summation (see numpy documentation for details)
\n- operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
\n
\n", "parameters": ["subscripts", "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 decomposition of Obs or CObs valued matrices.
\n", "parameters": ["x"], "funcdef": "def"}, "pyerrors.linalg.det": {"fullname": "pyerrors.linalg.det", "modulename": "pyerrors.linalg", "qualname": "det", "type": "function", "doc": "Determinant of Obs valued matrices.
\n", "parameters": ["x"], "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.misc": {"fullname": "pyerrors.misc", "modulename": "pyerrors.misc", "qualname": "", "type": "module", "doc": "\n"}, "pyerrors.misc.dump_object": {"fullname": "pyerrors.misc.dump_object", "modulename": "pyerrors.misc", "qualname": "dump_object", "type": "function", "doc": "Dump object into pickle file.
\n\nParameters
\n\n\n- obj (object):\nobject to be saved in the pickle file
\n- name (str):\nname of the file
\n- path (str):\nspecifies a custom path for the file (default '.')
\n
\n", "parameters": ["obj", "name", "kwargs"], "funcdef": "def"}, "pyerrors.misc.load_object": {"fullname": "pyerrors.misc.load_object", "modulename": "pyerrors.misc", "qualname": "load_object", "type": "function", "doc": "Load object from pickle file.
\n\nParameters
\n\n\n- path (str):\npath to the file
\n
\n", "parameters": ["path"], "funcdef": "def"}, "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\nParameters
\n\n\n- means (list):\nlist containing the mean value of each observable.
\n- cov (numpy.ndarray):\ncovariance matrix for the data to be generated.
\n- name (str):\nensemble name for the data to be geneated.
\n- tau (float or list):\ncan either be a real number or a list with an entry for\nevery dataset.
\n- samples (int):\nnumber of samples to be generated for each observable.
\n
\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\nImplementation of the matrix pencil method based on\neq. (2.17) of Y. Hua, T. K. Sarkar, IEEE Trans. Acoust. 38, 814-824 (1990)
\n\nParameters
\n\n\n- data (list):\ncan be a list of Obs for the analysis of a single correlator, or a list of lists\nof Obs if several correlators are to analyzed at once.
\n- k (int):\nNumber of states to extract (default 1).
\n- p (int):\nmatrix pencil parameter which filters noise. The optimal value is expected between\nlen(data)/3 and 2*len(data)/3. The computation is more expensive the closer p is\nto len(data)/2 but could possibly suppress more noise (default len(data)//2).
\n
\n", "parameters": ["corrs", "k", "p", "kwargs"], "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\nInstances 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\nAttributes
\n\n\n- S_global (float):\nStandard value for S (default 2.0)
\n- S_dict (dict):\nDictionary for S values. If an entry for a given ensemble\nexists this overwrites the standard value for that ensemble.
\n- tau_exp_global (float):\nStandard value for tau_exp (default 0.0)
\n- tau_exp_dict (dict):\nDictionary for tau_exp values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
\n- N_sigma_global (float):\nStandard value for N_sigma (default 1.0)
\n- N_sigma_dict (dict):\nDictionary for N_sigma values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
\n
\n"}, "pyerrors.obs.Obs.__init__": {"fullname": "pyerrors.obs.Obs.__init__", "modulename": "pyerrors.obs", "qualname": "Obs.__init__", "type": "function", "doc": "Initialize Obs object.
\n\nParameters
\n\n\n- samples (list):\nlist of numpy arrays containing the Monte Carlo samples
\n- names (list):\nlist of strings labeling the individual samples
\n- idl (list, optional):\nlist of ranges or lists on which the samples are defined
\n- means (list, optional):\nlist of mean values for the case that the mean values were\nalready subtracted from the samples
\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.N_sigma_dict": {"fullname": "pyerrors.obs.Obs.N_sigma_dict", "modulename": "pyerrors.obs", "qualname": "Obs.N_sigma_dict", "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.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.cov_names": {"fullname": "pyerrors.obs.Obs.cov_names", "modulename": "pyerrors.obs", "qualname": "Obs.cov_names", "type": "variable", "doc": "\n"}, "pyerrors.obs.Obs.mc_names": {"fullname": "pyerrors.obs.Obs.mc_names", "modulename": "pyerrors.obs", "qualname": "Obs.mc_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.covobs": {"fullname": "pyerrors.obs.Obs.covobs", "modulename": "pyerrors.obs", "qualname": "Obs.covobs", "type": "variable", "doc": "\n"}, "pyerrors.obs.Obs.gamma_method": {"fullname": "pyerrors.obs.Obs.gamma_method", "modulename": "pyerrors.obs", "qualname": "Obs.gamma_method", "type": "function", "doc": "Estimate the error and related properties of the Obs.
\n\nParameters
\n\n\n- S (float):\nspecifies a custom value for the parameter S (default 2.0).\nIf set to 0 it is assumed that the data exhibits no\nautocorrelation. In this case the error estimates coincides\nwith the sample standard error.
\n- tau_exp (float):\npositive value triggers the critical slowing down analysis\n(default 0.0).
\n- N_sigma (float):\nnumber of standard deviations from zero until the tail is\nattached to the autocorrelation function (default 1).
\n- fft (bool):\ndetermines whether the fft algorithm is used for the computation\nof the autocorrelation function (default True)
\n
\n", "parameters": ["self", "kwargs"], "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\nParameters
\n\n\n- ens_content (bool):\nprint details about the ensembles and replica if true.
\n
\n", "parameters": ["self", "ens_content"], "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.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\nParameters
\n\n\n- sigma (int):\nNumber of standard errors used for the check.
\n- Works only properly when the gamma method was run.
\n
\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 a given tolerance.
\n\nParameters
\n\n\n- rtol (float):\nRelative tolerance (for details see numpy documentation).
\n- atol (float):\nAbsolute tolerance (for details see numpy documentation).
\n
\n", "parameters": ["self", "rtol", "atol"], "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\nParameters
\n\n\n- save (str):\nsaves the figure to a file named 'save' if.
\n
\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\nParameters
\n\n\n- expand (bool):\nshow expanded history for irregular Monte Carlo chains (default: True).
\n
\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\nParameters
\n\n\n- name (str):\nname of the file to be saved.
\n- path (str):\nspecifies a custom path for the file (default '.')
\n
\n", "parameters": ["self", "name", "kwargs"], "funcdef": "def"}, "pyerrors.obs.Obs.export_jackknife": {"fullname": "pyerrors.obs.Obs.export_jackknife", "modulename": "pyerrors.obs", "qualname": "Obs.export_jackknife", "type": "function", "doc": "Export jackknife samples from the Obs
\n\nReturns
\n\n\n- numpy.ndarray: Returns a numpy array of length N + 1 where N is the number of samples\nfor the given ensemble and replicum. The zeroth entry of the array contains\nthe mean value of the Obs, entries 1 to N contain the N jackknife samples\nderived from the Obs. The current implementation only works for observables\ndefined on exactly one ensemble and replicum. The derived jackknife samples\nshould agree with samples from a full jackknife analysis up to O(1/N).
\n
\n", "parameters": ["self"], "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": {"fullname": "pyerrors.obs.Obs.N", "modulename": "pyerrors.obs", "qualname": "Obs.N", "type": "variable", "doc": "\n"}, "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.is_merged": {"fullname": "pyerrors.obs.Obs.is_merged", "modulename": "pyerrors.obs", "qualname": "Obs.is_merged", "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.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\nParameters
\n\n\n- func (object):\narbitrary function of the form func(data, **kwargs). For the\nautomatic differentiation to work, all numpy functions have to have\nthe autograd wrapper (use 'import autograd.numpy as anp').
\n- data (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
\n- num_grad (bool):\nif True, numerical derivatives are used instead of autograd\n(default False). To control the numerical differentiation the\nkwargs of numdifftools.step_generators.MaxStepGenerator\ncan be used.
\n- man_grad (list):\nmanually supply a list or an array which contains the jacobian\nof func. Use cautiously, supplying the wrong derivative will\nnot be intercepted.
\n
\n\nNotes
\n\nFor simple mathematical operations it can be practical to use anonymous\nfunctions. For the ratio of two observables one can e.g. use
\n\nnew_obs = derived_observable(lambda x: x[0] / x[1], [obs1, obs2])
\n", "parameters": ["func", "data", "array_mode", "kwargs"], "funcdef": "def"}, "pyerrors.obs.reweight": {"fullname": "pyerrors.obs.reweight", "modulename": "pyerrors.obs", "qualname": "reweight", "type": "function", "doc": "Reweight a list of observables.
\n\nParameters
\n\n\n- weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
\n- obs (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
\n- all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl.
\n
\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\nParameters
\n\n\n- obs_a (Obs):\nFirst observable
\n- obs_b (Obs):\nSecond observable
\n- Keep in mind to only correlate primary observables which have not been reweighted
\n- yet. The reweighting has to be applied after correlating the observables.
\n- Currently only works if ensembles are identical. This is not really necessary.
\n
\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\ncovariance(obs, obs) is equal to obs.dvalue ** 2\nThe gamma method has to be applied first to both observables.
\n\nIf abs(covariance(obs1, obs2)) > obs1.dvalue * obs2.dvalue, the covariance\nis constrained to the maximum value.
\n\nKeyword arguments
\n\ncorrelation -- if true the correlation instead of the covariance is\n returned (default False)
\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\nParameters
\n\n\n- value (float):\ncentral value of the Obs to be generated.
\n- dvalue (float):\nerror of the Obs to be generated.
\n- name (str):\nname of the ensemble for which the Obs is to be generated.
\n- samples (int):\nnumber of samples for the Obs (default 1000).
\n
\n", "parameters": ["value", "dvalue", "name", "samples"], "funcdef": "def"}, "pyerrors.obs.import_jackknife": {"fullname": "pyerrors.obs.import_jackknife", "modulename": "pyerrors.obs", "qualname": "import_jackknife", "type": "function", "doc": "Imports jackknife samples and returns an Obs
\n\nParameters
\n\n\n- jacks (numpy.ndarray):\nnumpy array containing the mean value as zeroth entry and\nthe N jackknife samples as first to Nth entry.
\n- name (str):\nname of the ensemble the samples are defined on.
\n
\n", "parameters": ["jacks", "name", "idl"], "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\nParameters
\n\n\n- list_of_obs (list):\nlist of the Obs object to be combined
\n- It is not possible to combine obs which are based on the same replicum
\n
\n", "parameters": ["list_of_obs"], "funcdef": "def"}, "pyerrors.obs.cov_Obs": {"fullname": "pyerrors.obs.cov_Obs", "modulename": "pyerrors.obs", "qualname": "cov_Obs", "type": "function", "doc": "Create an Obs based on mean(s) and a covariance matrix
\n\nParameters
\n\n\n- mean (list of floats or float):\nN mean value(s) of the new Obs
\n- cov (list or array):\n2d (NxN) Covariance matrix, 1d diagonal entries or 0d covariance
\n- name (str):\nidentifier for the covariance matrix
\n- grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
\n
\n", "parameters": ["means", "cov", "name", "grad"], "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\nParameters
\n\n\n- d (Obs):\nObs passed to the function.
\n- func (object):\nFunction to be minimized. Any numpy functions have to use the autograd.numpy wrapper.\nExample:\n
python\nimport autograd.numpy as anp\ndef root_func(x, d):\n return anp.exp(-x ** 2) - d\n \n- guess (float):\nInitial guess for the minimization.
\n
\n\nReturns
\n\n\n- Obs:
Obs valued root of the function. \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": 1048}, "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": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.GEVP": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.Eigenvalue": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.roll": {"qualname": 2, "fullname": 4, "doc": 10}, "pyerrors.correlators.Corr.reverse": {"qualname": 2, "fullname": 4, "doc": 4}, "pyerrors.correlators.Corr.correlate": {"qualname": 2, "fullname": 4, "doc": 19}, "pyerrors.correlators.Corr.reweight": {"qualname": 2, "fullname": 4, "doc": 28}, "pyerrors.correlators.Corr.T_symmetry": {"qualname": 2, "fullname": 4, "doc": 21}, "pyerrors.correlators.Corr.deriv": {"qualname": 2, "fullname": 4, "doc": 18}, "pyerrors.correlators.Corr.second_deriv": {"qualname": 2, "fullname": 4, "doc": 6}, "pyerrors.correlators.Corr.m_eff": {"qualname": 2, "fullname": 4, "doc": 68}, "pyerrors.correlators.Corr.fit": {"qualname": 2, "fullname": 4, "doc": 32}, "pyerrors.correlators.Corr.plateau": {"qualname": 2, "fullname": 4, "doc": 34}, "pyerrors.correlators.Corr.set_prange": {"qualname": 2, "fullname": 4, "doc": 5}, "pyerrors.correlators.Corr.show": {"qualname": 2, "fullname": 4, "doc": 56}, "pyerrors.correlators.Corr.dump": {"qualname": 2, "fullname": 4, "doc": 9}, "pyerrors.correlators.Corr.print": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.sqrt": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.log": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.exp": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.sin": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.cos": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.tan": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.sinh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.cosh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.tanh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.arcsin": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.arccos": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.arctan": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.arcsinh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.arccosh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.arctanh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.covobs": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.covobs.Covobs": {"qualname": 1, "fullname": 3, "doc": 0}, "pyerrors.covobs.Covobs.__init__": {"qualname": 2, "fullname": 4, "doc": 45}, "pyerrors.covobs.Covobs.errsq": {"qualname": 2, "fullname": 4, "doc": 5}, "pyerrors.covobs.Covobs.cov": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.covobs.Covobs.grad": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.dirac": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.dirac.Grid_gamma": {"qualname": 1, "fullname": 3, "doc": 5}, "pyerrors.fits": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.fits.Fit_result": {"qualname": 1, "fullname": 3, "doc": 13}, "pyerrors.fits.Fit_result.__init__": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.fits.Fit_result.gamma_method": {"qualname": 2, "fullname": 4, "doc": 5}, "pyerrors.fits.least_squares": {"qualname": 1, "fullname": 3, "doc": 203}, "pyerrors.fits.total_least_squares": {"qualname": 1, "fullname": 3, "doc": 133}, "pyerrors.fits.prior_fit": {"qualname": 1, "fullname": 3, "doc": 0}, "pyerrors.fits.standard_fit": {"qualname": 1, "fullname": 3, "doc": 0}, "pyerrors.fits.odr_fit": {"qualname": 1, "fullname": 3, "doc": 0}, "pyerrors.fits.fit_lin": {"qualname": 1, "fullname": 3, "doc": 33}, "pyerrors.fits.qqplot": {"qualname": 1, "fullname": 3, "doc": 12}, "pyerrors.fits.residual_plot": {"qualname": 1, "fullname": 3, "doc": 8}, "pyerrors.fits.covariance_matrix": {"qualname": 1, "fullname": 3, "doc": 4}, "pyerrors.fits.error_band": {"qualname": 1, "fullname": 3, "doc": 14}, "pyerrors.input": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.input.bdio": {"qualname": 0, "fullname": 3, "doc": 0}, "pyerrors.input.bdio.read_ADerrors": {"qualname": 1, "fullname": 4, "doc": 46}, "pyerrors.input.bdio.write_ADerrors": {"qualname": 1, "fullname": 4, "doc": 47}, "pyerrors.input.bdio.read_mesons": {"qualname": 1, "fullname": 4, "doc": 68}, "pyerrors.input.bdio.read_dSdm": {"qualname": 1, "fullname": 4, "doc": 61}, "pyerrors.input.hadrons": {"qualname": 0, "fullname": 3, "doc": 0}, "pyerrors.input.hadrons.read_meson_hd5": {"qualname": 1, "fullname": 4, "doc": 66}, "pyerrors.input.hadrons.Npr_matrix": {"qualname": 1, "fullname": 4, "doc": 425}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"qualname": 2, "fullname": 5, "doc": 0}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"qualname": 2, "fullname": 5, "doc": 12}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"qualname": 1, "fullname": 4, "doc": 33}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"qualname": 1, "fullname": 4, "doc": 33}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"qualname": 1, "fullname": 4, "doc": 38}, "pyerrors.input.json": {"qualname": 0, "fullname": 3, "doc": 0}, "pyerrors.input.json.create_json_string": {"qualname": 1, "fullname": 4, "doc": 51}, "pyerrors.input.json.dump_to_json": {"qualname": 1, "fullname": 4, "doc": 64}, "pyerrors.input.json.import_json_string": {"qualname": 1, "fullname": 4, "doc": 47}, "pyerrors.input.json.load_json": {"qualname": 1, "fullname": 4, "doc": 57}, "pyerrors.input.misc": {"qualname": 0, "fullname": 3, "doc": 0}, "pyerrors.input.misc.read_pbp": {"qualname": 1, "fullname": 4, "doc": 28}, "pyerrors.input.openQCD": {"qualname": 0, "fullname": 3, "doc": 0}, "pyerrors.input.openQCD.read_rwms": {"qualname": 1, "fullname": 4, "doc": 46}, "pyerrors.input.openQCD.extract_t0": {"qualname": 1, "fullname": 4, "doc": 115}, "pyerrors.input.sfcf": {"qualname": 0, "fullname": 3, "doc": 0}, "pyerrors.input.sfcf.read_sfcf": {"qualname": 1, "fullname": 4, "doc": 41}, "pyerrors.input.sfcf.read_sfcf_c": {"qualname": 1, "fullname": 4, "doc": 63}, "pyerrors.input.sfcf.read_qtop": {"qualname": 1, "fullname": 4, "doc": 21}, "pyerrors.linalg": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.linalg.matmul": {"qualname": 1, "fullname": 3, "doc": 23}, "pyerrors.linalg.jack_matmul": {"qualname": 1, "fullname": 3, "doc": 27}, "pyerrors.linalg.einsum": {"qualname": 1, "fullname": 3, "doc": 24}, "pyerrors.linalg.inv": {"qualname": 1, "fullname": 3, "doc": 5}, "pyerrors.linalg.cholesky": {"qualname": 1, "fullname": 3, "doc": 6}, "pyerrors.linalg.det": {"qualname": 1, "fullname": 3, "doc": 4}, "pyerrors.linalg.eigh": {"qualname": 1, "fullname": 3, "doc": 11}, "pyerrors.linalg.eig": {"qualname": 1, "fullname": 3, "doc": 9}, "pyerrors.linalg.pinv": {"qualname": 1, "fullname": 3, "doc": 6}, "pyerrors.linalg.svd": {"qualname": 1, "fullname": 3, "doc": 6}, "pyerrors.misc": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.misc.dump_object": {"qualname": 1, "fullname": 3, "doc": 22}, "pyerrors.misc.load_object": {"qualname": 1, "fullname": 3, "doc": 9}, "pyerrors.misc.gen_correlated_data": {"qualname": 1, "fullname": 3, "doc": 43}, "pyerrors.mpm": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.mpm.matrix_pencil_method": {"qualname": 1, "fullname": 3, "doc": 75}, "pyerrors.obs": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.obs.Obs": {"qualname": 1, "fullname": 3, "doc": 107}, "pyerrors.obs.Obs.__init__": {"qualname": 2, "fullname": 4, "doc": 40}, "pyerrors.obs.Obs.S_global": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.S_dict": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.tau_exp_global": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.tau_exp_dict": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.N_sigma_global": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.N_sigma_dict": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.filter_eps": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.names": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.shape": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.r_values": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.deltas": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.idl": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.ddvalue": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.reweighted": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.tag": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.value": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.dvalue": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_names": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.cov_names": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.mc_names": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_content": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.covobs": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.gamma_method": {"qualname": 2, "fullname": 4, "doc": 66}, "pyerrors.obs.Obs.details": {"qualname": 2, "fullname": 4, "doc": 12}, "pyerrors.obs.Obs.print": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.is_zero_within_error": {"qualname": 2, "fullname": 4, "doc": 21}, "pyerrors.obs.Obs.is_zero": {"qualname": 2, "fullname": 4, "doc": 24}, "pyerrors.obs.Obs.plot_tauint": {"qualname": 2, "fullname": 4, "doc": 14}, "pyerrors.obs.Obs.plot_rho": {"qualname": 2, "fullname": 4, "doc": 7}, "pyerrors.obs.Obs.plot_rep_dist": {"qualname": 2, "fullname": 4, "doc": 8}, "pyerrors.obs.Obs.plot_history": {"qualname": 2, "fullname": 4, "doc": 19}, "pyerrors.obs.Obs.plot_piechart": {"qualname": 2, "fullname": 4, "doc": 12}, "pyerrors.obs.Obs.dump": {"qualname": 2, "fullname": 4, "doc": 18}, "pyerrors.obs.Obs.export_jackknife": {"qualname": 2, "fullname": 4, "doc": 54}, "pyerrors.obs.Obs.sqrt": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.log": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.exp": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.sin": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.cos": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.tan": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.arcsin": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.arccos": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.arctan": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.sinh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.cosh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.tanh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.arcsinh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.arccosh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.arctanh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.sinc": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.N": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.N_sigma": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.S": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_ddvalue": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_drho": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_dtauint": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_dvalue": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_n_dtauint": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_n_tauint": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_rho": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_tauint": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_windowsize": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.is_merged": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.tau_exp": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.CObs": {"qualname": 1, "fullname": 3, "doc": 4}, "pyerrors.obs.CObs.__init__": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.CObs.tag": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.CObs.real": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.CObs.imag": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.CObs.gamma_method": {"qualname": 2, "fullname": 4, "doc": 5}, "pyerrors.obs.CObs.is_zero": {"qualname": 2, "fullname": 4, "doc": 10}, "pyerrors.obs.CObs.conjugate": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.derived_observable": {"qualname": 1, "fullname": 3, "doc": 93}, "pyerrors.obs.reweight": {"qualname": 1, "fullname": 3, "doc": 38}, "pyerrors.obs.correlate": {"qualname": 1, "fullname": 3, "doc": 28}, "pyerrors.obs.covariance": {"qualname": 1, "fullname": 3, "doc": 37}, "pyerrors.obs.pseudo_Obs": {"qualname": 1, "fullname": 3, "doc": 32}, "pyerrors.obs.import_jackknife": {"qualname": 1, "fullname": 3, "doc": 28}, "pyerrors.obs.merge_obs": {"qualname": 1, "fullname": 3, "doc": 19}, "pyerrors.obs.cov_Obs": {"qualname": 1, "fullname": 3, "doc": 42}, "pyerrors.roots": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.roots.find_root": {"qualname": 1, "fullname": 3, "doc": 49}, "pyerrors.version": {"qualname": 0, "fullname": 2, "doc": 0}}, "length": 197, "save": true}, "index": {"qualname": {"root": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.obs.Obs.cos": {"tf": 1}}, "df": 2, "r": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.sum": {"tf": 1}, "pyerrors.correlators.Corr.smearing": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.smearing_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.correlators.Corr.arctanh": {"tf": 1}}, "df": 42, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.obs.Obs.cosh": {"tf": 1}}, "df": 2}}, "v": {"docs": {"pyerrors.covobs.Covobs.cov": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.covobs.Covobs": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.covobs.Covobs.cov": {"tf": 1}, "pyerrors.covobs.Covobs.grad": {"tf": 1}, "pyerrors.obs.Obs.covobs": {"tf": 1}}, "df": 6}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.fits.covariance_matrix": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}, "_": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.Obs.cov_names": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 1}}}}, "b": {"docs": {"pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.conjugate": {"tf": 1}}, "df": 8}, "n": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.CObs.conjugate": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.linalg.cholesky": {"tf": 1}}, "df": 1}}}}}}}}, "_": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "_": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}}, "df": 6}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.Obs.reweighted": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 4}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.reverse": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.residual_plot": {"tf": 1}}, "df": 1}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}}, "df": 1}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}, "d": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}}}}}}, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}}}}, "p": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 1}}}}}}, "q": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {"pyerrors.obs.CObs.real": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}}, "df": 1}}}, "_": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.r_values": {"tf": 1}}, "df": 1}}}}}}, "g": {"5": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}}, "df": 4}}}}}}}}}}}, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.covobs.Covobs.grad": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 1}}}}}}}}}}, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.obs.Obs.print": {"tf": 1}}, "df": 2}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.prior_fit": {"tf": 1}}, "df": 1}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1}}, "df": 1}}}}, "_": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_tauint": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.plot_rho": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 1}}}}}}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 1}}}}}}}, "p": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.linalg.pinv": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 1}}}}}}}}}, "s": {"docs": {"pyerrors.obs.Obs.S": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.sum": {"tf": 1}}, "df": 1}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.smearing": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.smearing_symmetric": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.symmetric": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 1}}}}}}}}}}, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 1}}}}}}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.shape": {"tf": 1}}, "df": 1}}}}, "q": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.obs.Obs.sqrt": {"tf": 1}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.obs.Obs.sin": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.obs.Obs.sinh": {"tf": 1}}, "df": 2}, "c": {"docs": {"pyerrors.obs.Obs.sinc": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.standard_fit": {"tf": 1}}, "df": 1}}}}}}}}}}}, "v": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.linalg.svd": {"tf": 1}}, "df": 1}}, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.S_global": {"tf": 1}}, "df": 1}}}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.S_dict": {"tf": 1}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}}, "df": 1}}}}}}}}}}}, "r": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.obs.Obs.arcsin": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.obs.Obs.arcsinh": {"tf": 1}}, "df": 2}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.obs.Obs.arccos": {"tf": 1}}, "df": 2, "s": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.obs.Obs.arccosh": {"tf": 1}}, "df": 2}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.obs.Obs.arctan": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.obs.Obs.arctanh": {"tf": 1}}, "df": 2}}}}}}}, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.linalg.eig": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 1}}}}}}, "h": {"docs": {"pyerrors.linalg.eigh": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}}, "df": 1}}}}}, "x": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.obs.Obs.exp": {"tf": 1}}, "df": 2, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "t": {"0": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {"pyerrors.covobs.Covobs.errsq": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}}}}}}}}, "_": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.Obs.e_names": {"tf": 1}}, "df": 1}}, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}}, "df": 1}}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_n_tauint": {"tf": 1}}, "df": 1}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_content": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.e_ddvalue": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.e_drho": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_dtauint": {"tf": 1}}, "df": 1}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.e_dvalue": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.e_rho": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_tauint": {"tf": 1}}, "df": 1}}}}}}, "w": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.e_windowsize": {"tf": 1}}, "df": 1}}}}}}}}}, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.obs.Obs.tan": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.obs.Obs.tanh": {"tf": 1}}, "df": 2}}, "u": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.obs.Obs.tau_exp": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.Obs.tau_exp_global": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}}, "df": 1}}}}}}}}}}, "g": {"docs": {"pyerrors.obs.Obs.tag": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}}, "df": 2}}, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}}}}}, "t": {"docs": {"pyerrors.linalg.det": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.details": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.Obs.deltas": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 2, "_": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 1}}}}}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.misc.dump_object": {"tf": 1}}, "df": 1}}}}}}}}}}, "d": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.ddvalue": {"tf": 1}}, "df": 1}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.dvalue": {"tf": 1}}, "df": 1}}}}}, "m": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}}, "c": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.mc_names": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.merge_obs": {"tf": 1}}, "df": 1}}}}}}}}, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}}, "df": 3}}}}}}, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.fit_lin": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.obs.Obs.filter_eps": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1}}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.obs.Obs.log": {"tf": 1}}, "df": 2}, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json.load_json": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.misc.load_object": {"tf": 1}}, "df": 1}}}}}}}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}}}}}}, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.odr_fit": {"tf": 1}}, "df": 1}}}}}}, "b": {"docs": {"pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.S_dict": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}, "pyerrors.obs.Obs.filter_eps": {"tf": 1}, "pyerrors.obs.Obs.names": {"tf": 1}, "pyerrors.obs.Obs.shape": {"tf": 1}, "pyerrors.obs.Obs.r_values": {"tf": 1}, "pyerrors.obs.Obs.deltas": {"tf": 1}, "pyerrors.obs.Obs.idl": {"tf": 1}, "pyerrors.obs.Obs.ddvalue": {"tf": 1}, "pyerrors.obs.Obs.reweighted": {"tf": 1}, "pyerrors.obs.Obs.tag": {"tf": 1}, "pyerrors.obs.Obs.value": {"tf": 1}, "pyerrors.obs.Obs.dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_names": {"tf": 1}, "pyerrors.obs.Obs.cov_names": {"tf": 1}, "pyerrors.obs.Obs.mc_names": {"tf": 1}, "pyerrors.obs.Obs.e_content": {"tf": 1}, "pyerrors.obs.Obs.covobs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.print": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.Obs.sqrt": {"tf": 1}, "pyerrors.obs.Obs.log": {"tf": 1}, "pyerrors.obs.Obs.exp": {"tf": 1}, "pyerrors.obs.Obs.sin": {"tf": 1}, "pyerrors.obs.Obs.cos": {"tf": 1}, "pyerrors.obs.Obs.tan": {"tf": 1}, "pyerrors.obs.Obs.arcsin": {"tf": 1}, "pyerrors.obs.Obs.arccos": {"tf": 1}, "pyerrors.obs.Obs.arctan": {"tf": 1}, "pyerrors.obs.Obs.sinh": {"tf": 1}, "pyerrors.obs.Obs.cosh": {"tf": 1}, "pyerrors.obs.Obs.tanh": {"tf": 1}, "pyerrors.obs.Obs.arcsinh": {"tf": 1}, "pyerrors.obs.Obs.arccosh": {"tf": 1}, "pyerrors.obs.Obs.arctanh": {"tf": 1}, "pyerrors.obs.Obs.sinc": {"tf": 1}, "pyerrors.obs.Obs.N": {"tf": 1}, "pyerrors.obs.Obs.N_sigma": {"tf": 1}, "pyerrors.obs.Obs.S": {"tf": 1}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1}, "pyerrors.obs.Obs.e_drho": {"tf": 1}, "pyerrors.obs.Obs.e_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_rho": {"tf": 1}, "pyerrors.obs.Obs.e_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_windowsize": {"tf": 1}, "pyerrors.obs.Obs.is_merged": {"tf": 1}, "pyerrors.obs.Obs.tau_exp": {"tf": 1}}, "df": 66}}, "q": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}}, "df": 1}}}}}}, "w": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "n": {"docs": {"pyerrors.obs.Obs.N": {"tf": 1}}, "df": 1, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 3}}}}}}}}}, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.Obs.N_sigma": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.Obs.N_sigma_global": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}}, "df": 1}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.names": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.CObs.imag": {"tf": 1}}, "df": 1}}}, "n": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.linalg.inv": {"tf": 1}}, "df": 1}}, "d": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.idl": {"tf": 1}}, "df": 1}}, "s": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 2, "_": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.Obs.is_merged": {"tf": 1}}, "df": 1}}}}}}}, "j": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1}}}}}}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.value": {"tf": 1}}, "df": 1}}}}}}, "fullname": {"root": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.sum": {"tf": 1}, "pyerrors.correlators.Corr.smearing": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.smearing_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.covobs": {"tf": 1}, "pyerrors.covobs.Covobs": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.covobs.Covobs.cov": {"tf": 1}, "pyerrors.covobs.Covobs.grad": {"tf": 1}, "pyerrors.dirac": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.prior_fit": {"tf": 1}, "pyerrors.fits.standard_fit": {"tf": 1}, "pyerrors.fits.odr_fit": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.covariance_matrix": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.bdio": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.misc": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.linalg": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.S_dict": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}, "pyerrors.obs.Obs.filter_eps": {"tf": 1}, "pyerrors.obs.Obs.names": {"tf": 1}, "pyerrors.obs.Obs.shape": {"tf": 1}, "pyerrors.obs.Obs.r_values": {"tf": 1}, "pyerrors.obs.Obs.deltas": {"tf": 1}, "pyerrors.obs.Obs.idl": {"tf": 1}, "pyerrors.obs.Obs.ddvalue": {"tf": 1}, "pyerrors.obs.Obs.reweighted": {"tf": 1}, "pyerrors.obs.Obs.tag": {"tf": 1}, "pyerrors.obs.Obs.value": {"tf": 1}, "pyerrors.obs.Obs.dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_names": {"tf": 1}, "pyerrors.obs.Obs.cov_names": {"tf": 1}, "pyerrors.obs.Obs.mc_names": {"tf": 1}, "pyerrors.obs.Obs.e_content": {"tf": 1}, "pyerrors.obs.Obs.covobs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.print": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.Obs.sqrt": {"tf": 1}, "pyerrors.obs.Obs.log": {"tf": 1}, "pyerrors.obs.Obs.exp": {"tf": 1}, "pyerrors.obs.Obs.sin": {"tf": 1}, "pyerrors.obs.Obs.cos": {"tf": 1}, "pyerrors.obs.Obs.tan": {"tf": 1}, "pyerrors.obs.Obs.arcsin": {"tf": 1}, "pyerrors.obs.Obs.arccos": {"tf": 1}, "pyerrors.obs.Obs.arctan": {"tf": 1}, "pyerrors.obs.Obs.sinh": {"tf": 1}, "pyerrors.obs.Obs.cosh": {"tf": 1}, "pyerrors.obs.Obs.tanh": {"tf": 1}, "pyerrors.obs.Obs.arcsinh": {"tf": 1}, "pyerrors.obs.Obs.arccosh": {"tf": 1}, "pyerrors.obs.Obs.arctanh": {"tf": 1}, "pyerrors.obs.Obs.sinc": {"tf": 1}, "pyerrors.obs.Obs.N": {"tf": 1}, "pyerrors.obs.Obs.N_sigma": {"tf": 1}, "pyerrors.obs.Obs.S": {"tf": 1}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1}, "pyerrors.obs.Obs.e_drho": {"tf": 1}, "pyerrors.obs.Obs.e_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_rho": {"tf": 1}, "pyerrors.obs.Obs.e_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_windowsize": {"tf": 1}, "pyerrors.obs.Obs.is_merged": {"tf": 1}, "pyerrors.obs.Obs.tau_exp": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.conjugate": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}, "pyerrors.roots": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}, "pyerrors.version": {"tf": 1}}, "df": 197}}}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.obs.Obs.print": {"tf": 1}}, "df": 2}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.prior_fit": {"tf": 1}}, "df": 1}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1}}, "df": 1}}}}, "_": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_tauint": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.plot_rho": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 1}}}}}}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 1}}}}}}}, "p": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.linalg.pinv": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 1}}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.obs.Obs.cos": {"tf": 1}}, "df": 2, "r": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.sum": {"tf": 1}, "pyerrors.correlators.Corr.smearing": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.smearing_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.correlators.Corr.arctanh": {"tf": 1}}, "df": 42, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.sum": {"tf": 1}, "pyerrors.correlators.Corr.smearing": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.smearing_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 44}}}}, "s": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.obs.Obs.cosh": {"tf": 1}}, "df": 2}}, "v": {"docs": {"pyerrors.covobs.Covobs.cov": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.covobs": {"tf": 1}, "pyerrors.covobs.Covobs": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.errsq": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.cov": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.grad": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.covobs": {"tf": 1}}, "df": 7}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.fits.covariance_matrix": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}, "_": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.Obs.cov_names": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 1}}}}, "b": {"docs": {"pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.conjugate": {"tf": 1}}, "df": 8}, "n": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.CObs.conjugate": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.linalg.cholesky": {"tf": 1}}, "df": 1}}}}}}}}, "_": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "_": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}}, "df": 6}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.Obs.reweighted": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 4}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.reverse": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.residual_plot": {"tf": 1}}, "df": 1}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}}, "df": 1}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}, "d": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}}}}}}, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}}}}, "p": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 1}}}}}}, "q": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {"pyerrors.obs.CObs.real": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.roots": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 2}}}, "_": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.r_values": {"tf": 1}}, "df": 1}}}}}}, "g": {"5": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}}, "df": 4}}}}}}}}}}}, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.covobs.Covobs.grad": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 1}}}}}}}}}}, "s": {"docs": {"pyerrors.obs.Obs.S": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.sum": {"tf": 1}}, "df": 1}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.smearing": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.smearing_symmetric": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.symmetric": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 1}}}}}}}}}}, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 1}}}}}}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.shape": {"tf": 1}}, "df": 1}}}}, "q": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.obs.Obs.sqrt": {"tf": 1}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.obs.Obs.sin": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.obs.Obs.sinh": {"tf": 1}}, "df": 2}, "c": {"docs": {"pyerrors.obs.Obs.sinc": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.standard_fit": {"tf": 1}}, "df": 1}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.input.sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 4}}}, "v": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.linalg.svd": {"tf": 1}}, "df": 1}}, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.S_global": {"tf": 1}}, "df": 1}}}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.S_dict": {"tf": 1}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}}, "df": 1}}}}}}}}}}}, "r": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.obs.Obs.arcsin": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.obs.Obs.arcsinh": {"tf": 1}}, "df": 2}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.obs.Obs.arccos": {"tf": 1}}, "df": 2, "s": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.obs.Obs.arccosh": {"tf": 1}}, "df": 2}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.obs.Obs.arctan": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.obs.Obs.arctanh": {"tf": 1}}, "df": 2}}}}}}}, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.linalg.eig": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 1}}}}}}, "h": {"docs": {"pyerrors.linalg.eigh": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}}, "df": 1}}}}}, "x": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.obs.Obs.exp": {"tf": 1}}, "df": 2, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "t": {"0": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {"pyerrors.covobs.Covobs.errsq": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}}}}}}}}, "_": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.Obs.e_names": {"tf": 1}}, "df": 1}}, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}}, "df": 1}}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_n_tauint": {"tf": 1}}, "df": 1}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_content": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.e_ddvalue": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.e_drho": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_dtauint": {"tf": 1}}, "df": 1}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.e_dvalue": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.e_rho": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_tauint": {"tf": 1}}, "df": 1}}}}}}, "w": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.e_windowsize": {"tf": 1}}, "df": 1}}}}}}}}}, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.obs.Obs.tan": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.obs.Obs.tanh": {"tf": 1}}, "df": 2}}, "u": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.obs.Obs.tau_exp": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.Obs.tau_exp_global": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}}, "df": 1}}}}}}}}}}, "g": {"docs": {"pyerrors.obs.Obs.tag": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}}, "df": 2}}, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}}}}}, "t": {"docs": {"pyerrors.linalg.det": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.details": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.Obs.deltas": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 2, "_": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 1}}}}}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.misc.dump_object": {"tf": 1}}, "df": 1}}}}}}}}}}, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.dirac": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 2}}}}, "d": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.ddvalue": {"tf": 1}}, "df": 1}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.dvalue": {"tf": 1}}, "df": 1}}}}}, "m": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.misc": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.misc": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 6}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}}, "p": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.mpm": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 2}}, "c": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.mc_names": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.merge_obs": {"tf": 1}}, "df": 1}}}}}}}}, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.prior_fit": {"tf": 1}, "pyerrors.fits.standard_fit": {"tf": 1}, "pyerrors.fits.odr_fit": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.covariance_matrix": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}}, "df": 15, "_": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}}, "df": 3}}}}}}, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.fit_lin": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.obs.Obs.filter_eps": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1}}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.obs.Obs.log": {"tf": 1}}, "df": 2}, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json.load_json": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.misc.load_object": {"tf": 1}}, "df": 1}}}}}}}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.linalg": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}}, "df": 11}}}}}}, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.odr_fit": {"tf": 1}}, "df": 1}}}}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}}}}}, "b": {"docs": {"pyerrors.obs": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.S_global": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.S_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.filter_eps": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.names": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.shape": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.r_values": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.deltas": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.idl": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.ddvalue": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.reweighted": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tag": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.value": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dvalue": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_names": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.cov_names": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.mc_names": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_content": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.covobs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.details": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.print": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rho": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.sqrt": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.log": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.exp": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.sin": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.cos": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tan": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arcsin": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arccos": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arctan": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.sinh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.cosh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tanh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arcsinh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arccosh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arctanh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.sinc": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N_sigma": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.S": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_drho": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_dtauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_rho": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_tauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_windowsize": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_merged": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tau_exp": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.conjugate": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 83}}, "q": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input": {"tf": 1}, "pyerrors.input.bdio": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.misc": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 28}}}, "v": {"docs": {"pyerrors.linalg.inv": {"tf": 1}}, "df": 1}}, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.CObs.imag": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.idl": {"tf": 1}}, "df": 1}}, "s": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 2, "_": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.Obs.is_merged": {"tf": 1}}, "df": 1}}}}}}}, "b": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 5}}}}, "w": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 8}}}}}}, "n": {"docs": {"pyerrors.obs.Obs.N": {"tf": 1}}, "df": 1, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 3}}}}}}}}}, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.Obs.N_sigma": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.Obs.N_sigma_global": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}}, "df": 1}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.names": {"tf": 1}}, "df": 1}}}}, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 5}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1}}}}}}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.value": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.version": {"tf": 1}}, "df": 1}}}}}}}}}, "doc": {"root": {"0": {"0": {"6": {"9": {"7": {"9": {"5": {"8": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "1": {"2": {"8": {"9": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"1": {"8": {"0": {"6": {"4": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 3}}, "df": 1}, "2": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "4": {"5": {"8": {"5": {"6": {"5": {"0": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"9": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 2}, "pyerrors.obs.Obs.gamma_method": {"tf": 2}}, "df": 12, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"0": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}, "d": {"docs": {"pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 1}}, "1": {"0": {"0": {"0": {"docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 2}, "3": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "5": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "5": {"0": {"0": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "9": {"8": {"8": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "9": {"0": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 4.69041575982343}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}}, "df": 12, "d": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}, "*": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}, "2": {"0": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}, "3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "7": {"2": {"1": {"8": {"6": {"6": {"7": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "8": {"0": {"9": {"7": {"7": {"6": {"2": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "9": {"9": {"0": {"9": {"7": {"0": {"3": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {"pyerrors": {"tf": 3.7416573867739413}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.8284271247461903}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 11, "d": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 5}, "*": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "(": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, ")": {"docs": {}, "df": 0, "/": {"3": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}}}}, "3": {"0": {"6": {"7": {"5": {"2": {"0": {"1": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "1": {"4": {"9": {"8": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "2": {"3": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "6": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "7": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "8": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "9": {"docs": {"pyerrors": {"tf": 7.3484692283495345}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 2}, "docs": {"pyerrors": {"tf": 2}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}, "4": {"0": {"3": {"2": {"0": {"9": {"8": {"3": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 3}, "5": {"0": {"0": {"docs": {"pyerrors": {"tf": 2.8284271247461903}}, "df": 1, "(": {"4": {"0": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}, "1": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "1": {"9": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "2": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "2": {"8": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "3": {"8": {"0": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "4": {"8": {"docs": {}, "df": 0, "(": {"2": {"3": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}, "docs": {}, "df": 0}, "6": {"7": {"4": {"6": {"5": {"9": {"8": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2, "(": {"0": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "6": {"5": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "9": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "6": {"8": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "7": {"0": {"0": {"0": {"0": {"0": {"0": {"0": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1}}, "df": 1}, "1": {"4": {"2": {"2": {"9": {"0": {"0": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "2": {"0": {"4": {"6": {"6": {"5": {"8": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1}}, "df": 1}, "7": {"3": {"1": {"0": {"1": {"0": {"2": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"0": {"7": {"7": {"5": {"2": {"4": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 2}}, "df": 1}, "8": {"1": {"4": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "2": {"4": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 3}, "9": {"1": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "4": {"7": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "8": {"3": {"1": {"9": {"8": {"8": {"1": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"1": {"0": {"0": {"7": {"1": {"2": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "5": {"8": {"3": {"6": {"5": {"4": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}, "docs": {}, "df": 0, "p": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}}, "df": 1, "y": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 5}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2}}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 2}}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 6, "l": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.correlate": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.7320508075688772}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 58}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 2}}, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "h": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.misc.dump_object": {"tf": 1.4142135623730951}, "pyerrors.misc.load_object": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}}, "df": 13}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1.4142135623730951}}, "df": 2}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 2}}, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 3}}}}}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 3}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 7}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}}, "df": 1, "=": {"docs": {}, "df": 0, "=": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}}, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 2}}}, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 2}}, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}, "u": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "e": {"docs": {"pyerrors": {"tf": 4.47213595499958}, "pyerrors.correlators.Corr": {"tf": 1}}, "df": 2, "r": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 5}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 2, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 2}}}}}, "n": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.linalg.pinv": {"tf": 1}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 9, "_": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 2}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}}, "df": 2, "_": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}}}}}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1.4142135623730951}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 4}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}}}}}}, "o": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}}, "df": 1, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 3}}}}, "t": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 1, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.linalg.pinv": {"tf": 1}}, "df": 1}}}}}}}}}}}, "b": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}}, "df": 1}}}, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 8, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 4.358898943540674}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 10}}}}, "x": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2, "l": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1}}}}, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3}}, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}}, "df": 2}}}, "p": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "d": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 4, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 3, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}}}}}}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}}, "df": 8}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.CObs.gamma_method": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.linalg.eig": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}}, "df": 3}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}}, "df": 2}}}}}}}}, "h": {"docs": {"pyerrors.linalg.eigh": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}}, "df": 1}}}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 4.69041575982343}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 2.449489742783178}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 20, "e": {"1": {"docs": {"pyerrors": {"tf": 3.4641016151377544}}, "df": 1, "|": {"docs": {}, "df": 0, "r": {"0": {"1": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "2": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "2": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 2.6457513110645907}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}}, "df": 2}}}}}}}}}}}}}}, "_": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.details": {"tf": 1}}, "df": 1}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 9}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}}, "df": 3}}, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 3.1622776601683795}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 3, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}, "g": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 11}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 4}}}}}, "t": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}, "q": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}, "c": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 3, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 8}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}}, "df": 5}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 3, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.merge_obs": {"tf": 1.7320508075688772}}, "df": 1}}}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 3, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": null}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}}, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 2}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 2}, "pyerrors.input.json.load_json": {"tf": 1.7320508075688772}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 18}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 4}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 3, "u": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.correlators.Corr.reweight": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}}, "df": 11}}}}}, "j": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 2}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}, "t": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.dump": {"tf": 1}}, "df": 9, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr": {"tf": 2}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.show": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.7320508075688772}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1.7320508075688772}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 20, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 3}}, "s": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 4}}}}}}}}, "b": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}}, "df": 6}, "s": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1, "(": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 1}}}}, "v": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}}, "df": 3, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 3}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.fits.covariance_matrix": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}, "pyerrors.obs.cov_Obs": {"tf": 2}}, "df": 5, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "(": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}}}}, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}}, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 2, "/": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}, "r": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}}, "df": 2}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 5}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}}, "df": 3}}, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "l": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 5}}}, "l": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 4}}, "u": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}}, "df": 5}}}, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 2}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}, "(": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 1, "+": {"1": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.7320508075688772}}, "df": 1}, "docs": {}, "df": 0}}}, "c": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}, "p": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}, "_": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}, "t": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 3}}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2}}}}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 1}}}}}}}, "m": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}}, "df": 2, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.covariance_matrix": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}, "pyerrors.obs.cov_Obs": {"tf": 1.7320508075688772}}, "df": 16}, "c": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}}, "df": 5}}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 3}}}}}, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 2}}}}, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "k": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 2}}, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 1}}, "n": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 4}, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 2}}}, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}, "j": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}, "x": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}}, "df": 3}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 2}, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 3}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 4}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 3, "a": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.linalg.pinv": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 10}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1.7320508075688772}}, "df": 7, "(": {"docs": {"pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 1}}, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 2.6457513110645907}}, "df": 2, "_": {"0": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2.8284271247461903}}, "df": 1}}}}}, "y": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors": {"tf": 2}}, "df": 1, "s": {"1": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "2": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "docs": {}, "df": 0}}}, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}}, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 3.4641016151377544}}, "df": 1}}}, "m": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors": {"tf": 2.449489742783178}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}}}}}}}, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}}, "df": 4, "i": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 3}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 2}}, "d": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "c": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}}, "df": 1}}}, "s": {"1": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}}, "df": 1}}, "d": {"docs": {"pyerrors.roots.find_root": {"tf": 2.23606797749979}}, "df": 1, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}}, "df": 1, "a": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.fit": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3.1622776601683795}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 16, "t": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 3, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}}, "df": 4}}}}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}}, "df": 4, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 6}}}}}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}}, "df": 1}}}}}}}, "s": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}}, "df": 2}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 2}}}}}}, "k": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 2}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1}}}}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}}, "df": 3}}}}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 2}}, "a": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}}, "df": 6, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "(": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}}}}}}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "^": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "=": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}, "\\": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "^": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, ",": {"docs": {}, "df": 0, ",": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}, "=": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "^": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}}}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 4}, "p": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 2}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 3.3166247903554}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero": {"tf": 1.4142135623730951}}, "df": 5}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 5}}}}}}, "f": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 8, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}}, "df": 2}}}}, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 2}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 20}}}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 3}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1.4142135623730951}}, "df": 2}}}}}}, "w": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}, "t": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1.4142135623730951}}, "df": 1, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2.6457513110645907}}, "df": 1, "=": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "m": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 3}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}, "pyerrors.obs.pseudo_Obs": {"tf": 1.4142135623730951}}, "df": 4}}}}, "s": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 1}}}}, "b": {"2": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 2}}, "docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 6}, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2}}}, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "{": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}, "a": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 2}}}}, "a": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}}, "w": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4}}, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 15}, "k": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 2}}, "df": 3}}}}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 2.23606797749979}, "pyerrors.input.bdio.write_ADerrors": {"tf": 2.23606797749979}, "pyerrors.input.bdio.read_mesons": {"tf": 2.23606797749979}, "pyerrors.input.bdio.read_dSdm": {"tf": 2.23606797749979}}, "df": 4, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}}}}}}, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, ")": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}}, "df": 4}}}}}}}, "b": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 3}}, "df": 1, "=": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}, "y": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2.449489742783178}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}}, "df": 1}}}}}}}}, "g": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 8, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"5": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}, "docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 6, "_": {"5": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 3.4641016151377544}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}}, "df": 3}}}}}}}}}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {"pyerrors": {"tf": 6.082762530298219}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.6457513110645907}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 2}}, "df": 10}, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}}}, "v": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 4}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 23}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}, "z": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}}, "df": 3, "i": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 2}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, ":": {"1": {"0": {"0": {"9": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "2": {"0": {"5": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "8": {"0": {"9": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}}}}}}, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 5.830951894845301}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}}, "df": 17, "'": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}, "(": {"docs": {}, "df": 0, "[": {"2": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0, "[": {"0": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}}}, "c": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}}, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 4}}}}}}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}}, "df": 2}}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 5}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}}, "df": 5}}}}}}}}, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 2}}}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 2}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 4}}}}, "g": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 5}}, "z": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 2}}, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}}, "df": 3}}, "n": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 4}}, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 5}}, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "c": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 4}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 3}}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 5}}}}, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 4}}}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 1}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 2}}}, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1}}}}}}}, "b": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 1}}}}, "(": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "(": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"1": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}}}}}}}}}, "d": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4, "d": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 3}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 2}}}}}}}, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "c": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1, "n": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 3, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 1}}}}}, "g": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}}}}, "x": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.7320508075688772}}, "df": 1}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "/": {"0": {"3": {"0": {"6": {"0": {"1": {"7": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 6}}, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 6}}}, "r": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 5}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}}, "df": 3}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 2}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 3}, "pyerrors.fits.total_least_squares": {"tf": 3}, "pyerrors.fits.fit_lin": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 2}, "pyerrors.input.json.dump_to_json": {"tf": 2}, "pyerrors.input.json.import_json_string": {"tf": 2}, "pyerrors.input.json.load_json": {"tf": 2}, "pyerrors.input.misc.read_pbp": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_rwms": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 2}, "pyerrors.mpm.matrix_pencil_method": {"tf": 2}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 3}, "pyerrors.obs.derived_observable": {"tf": 2}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}, "pyerrors.obs.merge_obs": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1.7320508075688772}}, "df": 27, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.merge_obs": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}, "b": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}}, "df": 4}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}}, "df": 4}}}, "b": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}}, "df": 4}}}}}}, "o": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 2, "(": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "(": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "w": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.misc.load_object": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 8}}}, "(": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, ")": {"docs": {}, "df": 0, "/": {"2": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "3": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0, "/": {"2": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}}}}}}}}, "v": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 4}}}, "f": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "f": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.fit": {"tf": 2}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.fits.Fit_result": {"tf": 1.4142135623730951}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.8284271247461903}, "pyerrors.fits.total_least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.fit_lin": {"tf": 1.7320508075688772}, "pyerrors.fits.qqplot": {"tf": 1.4142135623730951}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 12, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}}}}, "_": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}}, "df": 1}}}}}}}}}, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 10}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 2}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_to_json": {"tf": 2.23606797749979}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 2}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 2}, "pyerrors.misc.load_object": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1.7320508075688772}}, "df": 22, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 3}}}, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}}}, "l": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}}, "df": 1}}, "d": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}}, "df": 2}}}, "x": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}}}, "m": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 4, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 9}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 3}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 5}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 2.449489742783178}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 5, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.fit": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 2}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}, "pyerrors.roots.find_root": {"tf": 2.23606797749979}}, "df": 14}}}}, "(": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 5, "i": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 2}}}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_zero": {"tf": 1.4142135623730951}, "pyerrors.obs.pseudo_Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 13}}}, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 5}, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "{": {"1": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "{": {"2": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}}, "df": 3}}}}, "l": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 7}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 2}}}}}, "p": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 2}}}}, "f": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}}, "df": 1}}}, "s": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}}, "df": 3, "u": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "c": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 3}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 2}}}}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "b": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.linalg.einsum": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 1}}}}}}, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 2}}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 2, "i": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1, "f": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}}, "df": 2}}}}, "l": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 2}}, "h": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 3, "(": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 3}}}}, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2}}, "df": 1}}, "g": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 4.123105625617661}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 2.23606797749979}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 2.23606797749979}, "pyerrors.obs.pseudo_Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.import_jackknife": {"tf": 1.7320508075688772}}, "df": 9, "e": {"docs": {}, "df": 0, "s": {"1": {"docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}, "2": {"docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}, "3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}, "e": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 6}}, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 6}}, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "e": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 2.449489742783178}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}}, "df": 8}}}}}}, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1.4142135623730951}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}, "pyerrors.obs.pseudo_Obs": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 26, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 6}}, "d": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2}}, "df": 1, "s": {"docs": {}, "df": 0, "=": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 10}}}}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}, "o": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}}, "df": 2}, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}, "y": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 5}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 6}, "e": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1.4142135623730951}}, "df": 6}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "l": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 1}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, ",": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}}}}}}}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1}}}}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "=": {"2": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2}}}}, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs": {"tf": 1}}, "df": 1}}}}}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 13}}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}, "e": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 2}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr": {"tf": 1.4142135623730951}}, "df": 1}}}}, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 4, "i": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_mesons": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.7320508075688772}}, "df": 4}}, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2.449489742783178}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 2}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.covobs.Covobs.errsq": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}, "k": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}}}, "f": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.4142135623730951}}, "df": 2}}}}, "t": {"0": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}}, "df": 1}, "docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 2}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 3, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1}}, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 19}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "n": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1, "s": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}}, "w": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 10}}, "a": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}}, "df": 2}}}, "y": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "{": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "}": {"docs": {}, "df": 0, "=": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "{": {"1": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "{": {"2": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "+": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "{": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "=": {"1": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "^": {"docs": {}, "df": 0, "{": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "(": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, ")": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "q": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}, "docs": {}, "df": 0}}}}}}}}}}}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}}}}}}}}}}}, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 3, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2}}}}, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.Obs": {"tf": 1}}, "df": 1}}}}}}}}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}, "g": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 5}}}}}, "_": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}}, "df": 10, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr": {"tf": 2}, "pyerrors.correlators.Corr.plottable": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.roll": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 6}}}}}}}, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.6457513110645907}}, "df": 4}}}, "h": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1}}, "df": 1}}}}, "/": {"2": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 2}}, "df": 1}, "docs": {}, "df": 0}, "u": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}}, "df": 4}}}, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1.7320508075688772}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "^": {"2": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}, "n": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 2}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 6, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}}, "df": 3, "e": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 5}}, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 4}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 2}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.jack_matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.einsum": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1.7320508075688772}}, "df": 18}}, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 15}}}, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}}, "df": 1}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}}}, "p": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}}, "df": 4}, "e": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 4, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}, "x": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}}}}}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.4142135623730951}, "pyerrors.misc.dump_object": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1.7320508075688772}, "pyerrors.obs.pseudo_Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 19, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}}}}, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 3, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.Obs": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs": {"tf": 1}}, "df": 1}}}}}}}}}}}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 3.4641016151377544}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 11, "(": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "b": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 2}}, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 1}}, "x": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 1}}}, "x": {"0": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 2, "=": {"0": {"docs": {"pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}}, "df": 2}, "docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}, "1": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}}, "df": 2}, "2": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}}, "df": 2}, "docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.6457513110645907}, "pyerrors.fits.total_least_squares": {"tf": 2.8284271247461903}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 9, "_": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}, "[": {"0": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}, "1": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.fits.total_least_squares": {"tf": 2}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.fits.covariance_matrix": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 7, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"1": {"6": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3.1622776601683795}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 11, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "n": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}, "f": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "r": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}}, "df": 2}}}, "_": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}}, "df": 2}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 8, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "x": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1}}, "df": 1}, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}}, "df": 2}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 2}}}}}}, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 7}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "c": {"docs": {"pyerrors.obs.Obs": {"tf": 2}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 6}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 2}}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}}, "df": 3}}}}}, "m": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 2, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 4}}}}}, "i": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 7, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 6}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}, "d": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.4142135623730951}}, "df": 1, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 3}}}}}}, "l": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.reweight": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}}, "df": 8}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 2}}}}}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 2}, "m": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}, "r": {"0": {"1": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "2": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 10, "l": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}}}, "d": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 2}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 2}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 2}, "pyerrors.input.misc.read_pbp": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_rwms": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_qtop": {"tf": 1.4142135623730951}}, "df": 12, "_": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}}, "df": 1}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 2}}}}}, "d": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 1}}}}}}}, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.fits.Fit_result": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 5}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 3}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}}, "df": 3}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 11}}}}, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 3, "s": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 2}}}}}}}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 7}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.reverse": {"tf": 1}}, "df": 1}}}}, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 2}, "pyerrors.input.sfcf.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 2}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}}, "df": 4}}}}}}, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.covariance_matrix": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 23}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}}, "df": 1}}}}}}}}}, "l": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.4142135623730951}}, "df": 4}}, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}}, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}}, "df": 3}}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "(": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 8}, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 4, "_": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "(": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1}}}}}}}}}, "w": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1}}, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}}, "o": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}}}}, "w": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 1}}, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_mesons": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.7320508075688772}}, "df": 4, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 5, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.linalg.matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.jack_matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.einsum": {"tf": 1}}, "df": 3}}}}, "n": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 2}}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 4}, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1.4142135623730951}}, "df": 6}}}}}, "b": {"docs": {"pyerrors": {"tf": 6.6332495807108}, "pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.correlate": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 2.449489742783178}, "pyerrors.fits.fit_lin": {"tf": 2.23606797749979}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 2}, "pyerrors.input.json.dump_to_json": {"tf": 2}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.7320508075688772}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}, "pyerrors.obs.pseudo_Obs": {"tf": 2.23606797749979}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 2.23606797749979}}, "df": 41, "j": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.8284271247461903}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.misc.dump_object": {"tf": 1.7320508075688772}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 19}}}}, "s": {"1": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4}, "2": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 4}, "3": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 3}, "docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}, "pyerrors.obs.correlate": {"tf": 2.23606797749979}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}, "pyerrors.obs.merge_obs": {"tf": 1.4142135623730951}}, "df": 13}}}, "[": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}}, "df": 2}}, "_": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}, "b": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.449489742783178}}, "df": 3, "=": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 10, "c": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 3, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "w": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}}, "df": 2}}}}}}}, "d": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 11}}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 2, "=": {"0": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}, "c": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "w": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 2}, "(": {"1": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1}}}, "docs": {}, "df": 0}}, "v": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 3}, "pyerrors.obs.Obs.__init__": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 28, "e": {"docs": {}, "df": 0, "(": {"docs": {"pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 1}, "c": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}}, "df": 3}, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2}}}}, "e": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 2}}, "e": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}}, "df": 2}}}}, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 2}}}}}}, "j": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 1, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 2}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}}, "df": 4}}}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_to_json": {"tf": 2.23606797749979}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}}, "df": 4, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}}, "df": 1}}}}}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 2}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 2}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.449489742783178}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.4142135623730951}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 2.6457513110645907}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 18}, "p": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 2, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1}}}}}}, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 7, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 2}}}}}}, "w": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 4}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 2}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "y": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 1, "f": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 7, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}}, "df": 2}}}}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 7}}}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 2}}}}}, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}, "f": {"2": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 1}}, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}}, "df": 2}}}}}}, "d": {"docs": {}, "df": 0, "f": {"5": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}, "docs": {}, "df": 0}}, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1.4142135623730951}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.linalg.eigh": {"tf": 1}}, "df": 1}}}}}}}}, "u": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "l": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1.4142135623730951}}, "df": 2}}, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.4142135623730951}}, "df": 1}}}}, "q": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 1}}}}, "k": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}}, "df": 1, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2}}, "y": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3}}}}}}, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"1": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1}, "2": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 1}}}}, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "_": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "_": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "_": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}}}, "pipeline": ["trimmer", "stopWordFilter", "stemmer"], "_isPrebuiltIndex": true};
+ /** pdoc search index */const docs = {"version": "0.9.5", "fields": ["qualname", "fullname", "doc"], "ref": "fullname", "documentStore": {"docs": {"pyerrors": {"fullname": "pyerrors", "modulename": "pyerrors", "qualname": "", "type": "module", "doc": "What is pyerrors?
\n\npyerrors 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- automatic differentiation for exact liner error propagation as suggested in arXiv:1809.01289 (partly based on the autograd package).
\n- treatment of slow modes in the simulation as suggested in arXiv:1009.5228.
\n- coherent error propagation for data from different Markov chains.
\n- non-linear fits with x- and y-errors and exact linear error propagation based on automatic differentiation as introduced in arXiv:1809.01289.
\n- real and complex matrix operations and their error propagation based on automatic differentiation (Matrix inverse, Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...).
\n
\n\nThere exist similar publicly available implementations of gamma method error analysis suites in Fortran, Julia and Python.
\n\nBasic example
\n\nimport numpy as np\nimport pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name']) # Initialize an Obs object\nmy_new_obs = 2 * np.log(my_obs) / my_obs ** 2 # Construct derived Obs object\nmy_new_obs.gamma_method() # Estimate the statistical error\nprint(my_new_obs) # Print the result to stdout\n> 0.31498(72)\n
The Obs class
\n\npyerrors introduces a new datatype, Obs, which simplifies error propagation and estimation for auto- and cross-correlated data.\nAn Obs object can be initialized with two arguments, the first is a list containing the samples for an Observable from a Monte Carlo chain.\nThe samples can either be provided as python list or as numpy array.\nThe second argument is a list containing the names of the respective Monte Carlo chains as strings. These strings uniquely identify a Monte Carlo chain/ensemble.
\n\nimport pyerrors as pe\n\nmy_obs = pe.Obs([samples], ['ensemble_name'])\n
Error propagation
\n\nWhen performing mathematical operations on Obs objects the correct error propagation is intrinsically taken care using a first order Taylor expansion\n$$\\delta_f^i=\\sum_\\alpha \\bar{f}_\\alpha \\delta_\\alpha^i\\,,\\quad \\delta_\\alpha^i=a_\\alpha^i-\\bar{a}_\\alpha\\,,$$\nas introduced in arXiv:hep-lat/0306017.\nThe required derivatives $\\bar{f}_\\alpha$ are evaluated up to machine precision via automatic differentiation as suggested in arXiv:1809.01289.
\n\nThe Obs class is designed such that mathematical numpy functions can be used on Obs just as for regular floats.
\n\nimport numpy as np\nimport pyerrors as pe\n\nmy_obs1 = pe.Obs([samples1], ['ensemble_name'])\nmy_obs2 = pe.Obs([samples2], ['ensemble_name'])\n\nmy_sum = my_obs1 + my_obs2\n\nmy_m_eff = np.log(my_obs1 / my_obs2)\n\niamzero = my_m_eff - my_m_eff\n# Check that value and fluctuations are zero within machine precision\nprint(iamzero == 0.0)\n> True\n
Error estimation
\n\nThe error estimation within pyerrors is based on the gamma method introduced in arXiv:hep-lat/0306017.\nAfter having arrived at the derived quantity of interest the gamma_method can be called as detailed in the following example.
\n\nmy_sum.gamma_method()\nprint(my_sum)\n> 1.70(57)\nmy_sum.details()\n> Result 1.70000000e+00 +/- 5.72046658e-01 +/- 7.56746598e-02 (33.650%)\n> t_int 2.71422900e+00 +/- 6.40320983e-01 S = 2.00\n> 1000 samples in 1 ensemble:\n> \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
We use the following definition of the integrated autocorrelation time established in Madras & Sokal 1988\n$$\\tau_\\mathrm{int}=\\frac{1}{2}+\\sum_{t=1}^{W}\\rho(t)\\geq \\frac{1}{2}\\,.$$\nThe window $W$ is determined via the automatic windowing procedure described in arXiv:hep-lat/0306017.\nThe standard value for the parameter $S$ of this automatic windowing procedure is $S=2$. Other values for $S$ can be passed to the gamma_method as parameter.
\n\nmy_sum.gamma_method(S=3.0)\nmy_sum.details()\n> Result 1.70000000e+00 +/- 6.30675201e-01 +/- 1.04585650e-01 (37.099%)\n> t_int 3.29909703e+00 +/- 9.77310102e-01 S = 3.00\n> 1000 samples in 1 ensemble:\n> \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
The integrated autocorrelation time $\\tau_\\mathrm{int}$ and the autocorrelation function $\\rho(W)$ can be monitored via the methods pyerrors.obs.Obs.plot_tauint and pyerrors.obs.Obs.plot_tauint.
\n\nIf the parameter $S$ is set to zero it is assumed that dataset does not exhibit any autocorrelation and the windowsize is chosen to be zero.\nIn this case the error estimate is identical to the sample standard error.
\n\nExponential tails
\n\nSlow modes in the Monte Carlo history can be accounted for by attaching an exponential tail to the autocorrelation function $\\rho$ as suggested in arXiv:1009.5228. The longest autocorrelation time in the history, $\\tau_\\mathrm{exp}$, can be passed to the gamma_method as parameter. In this case the automatic windowing procedure is vacated and the parameter $S$ does not affect the error estimate.
\n\nmy_sum.gamma_method(tau_exp=7.2)\nmy_sum.details()\n> Result 1.70000000e+00 +/- 6.28097762e-01 +/- 5.79077524e-02 (36.947%)\n> t_int 3.27218667e+00 +/- 7.99583654e-01 tau_exp = 7.20, N_sigma = 1\n> 1000 samples in 1 ensemble:\n> \u00b7 Ensemble 'ensemble_name' : 1000 configurations (from 1 to 1000)\n
For the full API see pyerrors.obs.Obs.gamma_method.
\n\nMultiple ensembles/replica
\n\nError propagation for multiple ensembles (Markov chains with different simulation parameters) is handled automatically. Ensembles are uniquely identified by their name.
\n\nobs1 = pe.Obs([samples1], ['ensemble1'])\nobs2 = pe.Obs([samples2], ['ensemble2'])\n\nmy_sum = obs1 + obs2\nmy_sum.details()\n> Result 2.00697958e+00\n> 1500 samples in 2 ensembles:\n> \u00b7 Ensemble 'ensemble1' : 1000 configurations (from 1 to 1000)\n> \u00b7 Ensemble 'ensemble2' : 500 configurations (from 1 to 500)\n
pyerrors identifies multiple replica (independent Markov chains with identical simulation parameters) by the vertical bar | in the name of the data set.
\n\nobs1 = pe.Obs([samples1], ['ensemble1|r01'])\nobs2 = pe.Obs([samples2], ['ensemble1|r02'])\n\n> my_sum = obs1 + obs2\n> my_sum.details()\n> Result 2.00697958e+00\n> 1500 samples in 1 ensemble:\n> \u00b7 Ensemble 'ensemble1'\n> \u00b7 Replicum 'r01' : 1000 configurations (from 1 to 1000)\n> \u00b7 Replicum 'r02' : 500 configurations (from 1 to 500)\n
Error estimation for multiple ensembles
\n\nIn order to keep track of different error analysis parameters for different ensembles one can make use of global dictionaries as detailed in the following example.
\n\npe.Obs.S_dict['ensemble1'] = 2.5\npe.Obs.tau_exp_dict['ensemble2'] = 8.0\npe.Obs.tau_exp_dict['ensemble3'] = 2.0\n
In case the gamma_method is called without any parameters it will use the values specified in the dictionaries for the respective ensembles.\nPassing arguments to the gamma_method still dominates over the dictionaries.
\n\nIrregular Monte Carlo chains
\n\nIrregular Monte Carlo chains can be initialized with the parameter idl.
\n\n# Observable defined on configurations 20 to 519\nobs1 = pe.Obs([samples1], ['ensemble1'], idl=[range(20, 520)])\nobs1.details()\n> Result 9.98319881e-01\n> 500 samples in 1 ensemble:\n> \u00b7 Ensemble 'ensemble1' : 500 configurations (from 20 to 519)\n\n# Observable defined on every second configuration between 5 and 1003\nobs2 = pe.Obs([samples2], ['ensemble1'], idl=[range(5, 1005, 2)])\nobs2.details()\n> Result 9.99100712e-01\n> 500 samples in 1 ensemble:\n> \u00b7 Ensemble 'ensemble1' : 500 configurations (from 5 to 1003 in steps of 2)\n\n# Observable defined on configurations 2, 9, 28, 29 and 501\nobs3 = pe.Obs([samples3], ['ensemble1'], idl=[[2, 9, 28, 29, 501]])\nobs3.details()\n> Result 1.01718064e+00\n> 5 samples in 1 ensemble:\n> \u00b7 Ensemble 'ensemble1' : 5 configurations (irregular range)\n
Warning: Irregular Monte Carlo chains can result in odd patterns in the autocorrelation functions.\nMake sure to check the autocorrelation time with e.g. pyerrors.obs.Obs.plot_rho or pyerrors.obs.Obs.plot_tauint.
\n\nFor the full API see pyerrors.obs.Obs.
\n\nCorrelators
\n\nFor the full API see pyerrors.correlators.Corr.
\n\nComplex observables
\n\npyerrors can handle complex valued observables via the class pyerrors.obs.CObs.\nCObs are initialized with a real and an imaginary part which both can be Obs valued.
\n\nmy_real_part = pe.Obs([samples1], ['ensemble1'])\nmy_imag_part = pe.Obs([samples2], ['ensemble1'])\n\nmy_cobs = pe.CObs(my_real_part, my_imag_part)\nmy_cobs.gamma_method()\nprint(my_cobs)\n> (0.9959(91)+0.659(28)j)\n
Elementary mathematical operations are overloaded and samples are properly propagated as for the Obs class.
\n\nmy_derived_cobs = (my_cobs + my_cobs.conjugate()) / np.abs(my_cobs)\nmy_derived_cobs.gamma_method()\nprint(my_derived_cobs)\n> (1.668(23)+0.0j)\n
Optimization / fits / roots
\n\npyerrors.fits\npyerrors.roots
\n\nMatrix operations
\n\npyerrors.linalg
\n\nExport data
\n\nThe preferred exported file format within pyerrors is
\n\nJackknife samples
\n\nFor comparison with other analysis workflows pyerrors can generate jackknife samples from an Obs object or import jackknife samples into an Obs object.\nSee pyerrors.obs.Obs.export_jackknife and pyerrors.obs.import_jackknife for details.
\n\n\n\npyerrors.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\nEverything, 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 inconvenient\nto iterate over all timeslices for every operation. This is especially true, when dealing with smearing matrices.
\n\nThe 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", "kwargs"], "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\nOutputs 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\nParameters
\n\n\n- dt (int):\nnumber of timeslices
\n
\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\nParameters
\n\n\n- partner (Obs or Corr):\npartner to correlate the correlator with.\nCan either be an Obs which is correlated with all entries of the\ncorrelator or a Corr of same length.
\n
\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\nParameters
\n\n\n- weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
\n- all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl.
\n
\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\nParameters
\n\n\n- partner (Corr):\nTime symmetry partner of the Corr
\n- partity (int):\nParity quantum number of the correlator, can be +1 or -1
\n
\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\nParameters
\n\n\n- symmetric (bool):\ndecides whether symmetric of simple finite differences are used. Default: True
\n
\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\nParameters
\n\n\n- variant (str):\nlog : uses the standard effective mass log(C(t) / C(t+1))\ncosh, periodic : Use periodicitiy of the correlator by solving C(t) / C(t+1) = cosh(m * (t - T/2)) / cosh(m * (t + 1 - T/2)) for m.\nsinh : Use anti-periodicitiy of the correlator by solving C(t) / C(t+1) = sinh(m * (t - T/2)) / sinh(m * (t + 1 - T/2)) for m.\nSee, e.g., arXiv:1205.5380\narccosh : Uses the explicit form of the symmetrized correlator (not recommended)
\n- guess (float):\nguess for the root finder, only relevant for the root variant
\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\nParameters
\n\n\n- function (obj):\nfunction to fit to the data. See fits.least_squares for details.
\n- fitrange (list):\nRange in which the function is to be fitted to the data.\nIf not specified, self.prange or all timeslices are used.
\n- silent (bool):\nDecides whether output is printed to the standard output.
\n
\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 plateau value from a Corr object
\n\nParameters
\n\n\n- plateau_range (list):\nlist with two entries, indicating the first and the last timeslice\nof the plateau region.
\n- method (str):\nmethod 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
\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\nParameters
\n\n\n- x_range (list):\nlist of two values, determining the range of the x-axis e.g. [4, 8]
\n- comp (Corr or list of Corr):\nCorrelator or list of correlators which are plotted for comparison.
\n- logscale (bool):\nSets y-axis to logscale
\n- plateau (Obs):\nplateau to be visualized in the figure
\n- fit_res (Fit_result):\nFit_result object to be visualized
\n- ylabel (str):\nLabel for the y-axis
\n- save (str):\npath to file in which the figure should be saved
\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 pickle file
\n\nParameters
\n\n\n- filename (str):\nName of the file
\n
\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.covobs": {"fullname": "pyerrors.covobs", "modulename": "pyerrors.covobs", "qualname": "", "type": "module", "doc": "\n"}, "pyerrors.covobs.Covobs": {"fullname": "pyerrors.covobs.Covobs", "modulename": "pyerrors.covobs", "qualname": "Covobs", "type": "class", "doc": "\n"}, "pyerrors.covobs.Covobs.__init__": {"fullname": "pyerrors.covobs.Covobs.__init__", "modulename": "pyerrors.covobs", "qualname": "Covobs.__init__", "type": "function", "doc": "Initialize Covobs object.
\n\nParameters
\n\n\n- mean (float):\nMean value of the new Obs
\n- cov (list or array):\n2d Covariance matrix or 1d diagonal entries
\n- name (str):\nidentifier for the covariance matrix
\n- pos (int):\nPosition of the variance belonging to mean in cov.\nIs taken to be 1 if cov is 0-dimensional
\n- grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
\n
\n", "parameters": ["self", "mean", "cov", "name", "pos", "grad"], "funcdef": "def"}, "pyerrors.covobs.Covobs.errsq": {"fullname": "pyerrors.covobs.Covobs.errsq", "modulename": "pyerrors.covobs", "qualname": "Covobs.errsq", "type": "function", "doc": "Return the variance (= square of the error) of the Covobs
\n", "parameters": ["self"], "funcdef": "def"}, "pyerrors.covobs.Covobs.cov": {"fullname": "pyerrors.covobs.Covobs.cov", "modulename": "pyerrors.covobs", "qualname": "Covobs.cov", "type": "variable", "doc": "\n"}, "pyerrors.covobs.Covobs.grad": {"fullname": "pyerrors.covobs.Covobs.grad", "modulename": "pyerrors.covobs", "qualname": "Covobs.grad", "type": "variable", "doc": "\n"}, "pyerrors.dirac": {"fullname": "pyerrors.dirac", "modulename": "pyerrors.dirac", "qualname": "", "type": "module", "doc": "\n"}, "pyerrors.dirac.epsilon_tensor": {"fullname": "pyerrors.dirac.epsilon_tensor", "modulename": "pyerrors.dirac", "qualname": "epsilon_tensor", "type": "function", "doc": "Rank-3 epsilon tensor
\n\nBased on https://codegolf.stackexchange.com/a/160375
\n", "parameters": ["i", "j", "k"], "funcdef": "def"}, "pyerrors.dirac.epsilon_tensor_rank4": {"fullname": "pyerrors.dirac.epsilon_tensor_rank4", "modulename": "pyerrors.dirac", "qualname": "epsilon_tensor_rank4", "type": "function", "doc": "Rank-4 epsilon tensor
\n\nExtension of https://codegolf.stackexchange.com/a/160375
\n", "parameters": ["i", "j", "k", "o"], "funcdef": "def"}, "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\nAttributes
\n\n\n- fit_parameters (list):\nresults for the individual fit parameters,\nalso accessible via indices.
\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\nParameters
\n\n\n- x (list):\nlist of floats.
\n- y (list):\nlist of Obs.
\nfunc (object):\nfit function, has to be of the form
\n\ndef func(a, x):\n y = a[0] + a[1] * x + a[2] * anp.sinh(x)\n return y\n
For 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
It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work
- priors (list, optional):\npriors has to be a list with an entry for every parameter in the fit. The entries can either be\nObs (e.g. results from a previous fit) or strings containing a value and an error formatted like\n0.548(23), 500(40) or 0.5(0.4)
\n- silent (bool, optional):\nIf true all output to the console is omitted (default False).
\n- initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for\n non-linear fits with many parameters.
\n- method (str):\ncan be used to choose an alternative method for the minimization of chisquare.\nThe possible methods are the ones which can be used for scipy.optimize.minimize and\nmigrad of iminuit. If no method is specified, Levenberg-Marquard is used.\nReliable alternatives are migrad, Powell and Nelder-Mead.
\n- resplot (bool):\nIf true, a plot which displays fit, data and residuals is generated (default False).
\n- qqplot (bool):\nIf true, a quantile-quantile plot of the fit result is generated (default False).
\n- expected_chisquare (bool):\nIf true prints the expected chisquare which is\ncorrected by effects caused by correlated input data.\nThis can take a while as the full correlation matrix\nhas to be calculated (default False).
\n- correlated_fit (bool):\nIf true, use the full correlation matrix in the definition of the chisquare\n(only works for prior==None and when no method is given, at the moment).
\n- const_par (list, optional):\nList of N Obs that are used to constrain the last N fit parameters of func.
\n
\n", "parameters": ["x", "y", "func", "priors", "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\nParameters
\n\n\n- x (list):\nlist of Obs, or a tuple of lists of Obs
\n- y (list):\nlist of Obs. The dvalues of the Obs are used as x- and yerror for the fit.
\nfunc (object):\nfunc has to be of the form
\n\ndef func(a, x):\n y = a[0] + a[1] * x + a[2] * anp.sinh(x)\n return y\n
For 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
It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation\nwill not work.
- silent (bool, optional):\nIf true all output to the console is omitted (default False).
\n- initial_guess (list):\ncan provide an initial guess for the input parameters. Relevant for non-linear\nfits with many parameters.
\n- expected_chisquare (bool):\nIf true prints the expected chisquare which is\ncorrected by effects caused by correlated input data.\nThis can take a while as the full correlation matrix\nhas to be calculated (default False).
\n- const_par (list, optional):\nList of N Obs that are used to constrain the last N fit parameters of func.
\n- Based on the orthogonal distance regression module of scipy
\n
\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.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.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\ny 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.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\nread_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\nall: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
\n\nParameters
\n\n\n- file_path -- path to the bdio file
\n- bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
\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\nread_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\nall: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
\n\nParameters
\n\n\n- file_path -- path to the bdio file
\n- bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
\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\nThe dictionary can be accessed with a tuple consisting of (type, source_position, kappa1, kappa2)
\n\nread_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\nall: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
\n\nParameters
\n\n\n- file_path -- path to the bdio file
\n- bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
\n- stop -- stops reading at given configuration number (default None)
\n- alternative_ensemble_name -- Manually overwrite ensemble name
\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\nThe dictionary can be accessed with a tuple consisting of (type, kappa)
\n\nread_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\nall: bdio.o $(LIBDIR)\n gcc -shared -Wl,-soname,libbdio.so -o $(BUILDDIR)/libbdio.so $(BUILDDIR)/bdio.o\n cp $(BUILDDIR)/libbdio.so $(LIBDIR)/
\n\nParameters
\n\n\n- file_path -- path to the bdio file
\n- bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
\n- stop -- stops reading at given configuration number (default None)
\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\nParameters
\n\n\n- path (str):\npath to the files to read
\n- filestem (str):\nnamestem of the files to read
\n- ens_id (str):\nname of the ensemble, required for internal bookkeeping
\n- meson (str):\nlabel of the meson to be extracted, standard value meson_0 which\ncorresponds to the pseudoscalar pseudoscalar two-point function.
\n- tree (str):\nLabel of the upmost directory in the hdf5 file, default 'meson'\nfor outputs of the Meson module. Can be altered to read input\nfrom other modules with similar structures.
\n- idl (range):\nIf specified only configurations in the given range are read in.
\n
\n", "parameters": ["path", "filestem", "ens_id", "meson", "tree", "idl"], "funcdef": "def"}, "pyerrors.input.hadrons.Npr_matrix": {"fullname": "pyerrors.input.hadrons.Npr_matrix", "modulename": "pyerrors.input.hadrons", "qualname": "Npr_matrix", "type": "class", "doc": "ndarray(shape, dtype=float, buffer=None, offset=0,\n strides=None, order=None)
\n\nAn 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\nArrays 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\nFor more information, refer to the numpy module and examine the\nmethods and attributes of an array.
\n\nParameters
\n\n\n- (for the __new__ method; see Notes below)
\n- shape (tuple of ints):\nShape of created array.
\n- dtype (data-type, optional):\nAny object that can be interpreted as a numpy data type.
\n- buffer (object exposing buffer interface, optional):\nUsed to fill the array with data.
\n- offset (int, optional):\nOffset of array data in buffer.
\n- strides (tuple of ints, optional):\nStrides of data in memory.
\n- order ({'C', 'F'}, optional):\nRow-major (C-style) or column-major (Fortran-style) order.
\n
\n\nAttributes
\n\n\n- T (ndarray):\nTranspose of the array.
\n- data (buffer):\nThe array's elements, in memory.
\n- dtype (dtype object):\nDescribes the format of the elements in the array.
\n- flags (dict):\nDictionary containing information related to memory use, e.g.,\n'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
\n- flat (numpy.flatiter object):\nFlattened version of the array as an iterator. The iterator\nallows assignments, e.g.,
x.flat = 3 (See ndarray.flat for\nassignment examples; TODO). \n- imag (ndarray):\nImaginary part of the array.
\n- real (ndarray):\nReal part of the array.
\n- size (int):\nNumber of elements in the array.
\n- itemsize (int):\nThe memory use of each array element in bytes.
\n- nbytes (int):\nThe total number of bytes required to store the array data,\ni.e.,
itemsize * size. \n- ndim (int):\nThe array's number of dimensions.
\n- shape (tuple of ints):\nShape of the array.
\n- strides (tuple of ints):\nThe step-size required to move from one element to the next in\nmemory. For example, a contiguous
(3, 4) array of type\nint16 in C-order has strides (8, 2). This implies that\nto move from element to element in memory requires jumps of 2 bytes.\nTo move from row-to-row, one needs to jump 8 bytes at a time\n(2 * 4). \n- ctypes (ctypes object):\nClass containing properties of the array needed for interaction\nwith ctypes.
\n- base (ndarray):\nIf the array is a view into another array, that array is its
base\n(unless that array is also a view). The base array is where the\narray data is actually stored. \n
\n\nSee Also
\n\narray: 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\nNotes
\n\nThere are two modes of creating an array using __new__:
\n\n\n- If
buffer is None, then only shape, dtype, and order\nare used. \n- If
buffer is an object exposing the buffer interface, then\nall keywords are interpreted. \n
\n\nNo __init__ method is needed because the array is fully initialized\nafter the __new__ method.
\n\nExamples
\n\nThese examples illustrate the low-level ndarray constructor. Refer\nto the See Also section above for easier ways of constructing an\nndarray.
\n\nFirst 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
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
Gamma_5 hermitean conjugate
\n\nUses the fact that the propagator is gamma5 hermitean, so just the\nin and out momenta of the propagator are exchanged.
\n"}, "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\nParameters
\n\n\n- path (str):\npath to the files to read
\n- filestem (str):\nnamestem of the files to read
\n- ens_id (str):\nname of the ensemble, required for internal bookkeeping
\n- idl (range):\nIf specified only configurations in the given range are read in.
\n
\n", "parameters": ["path", "filestem", "ens_id", "idl"], "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\nParameters
\n\n\n- path (str):\npath to the files to read
\n- filestem (str):\nnamestem of the files to read
\n- ens_id (str):\nname of the ensemble, required for internal bookkeeping
\n- idl (range):\nIf specified only configurations in the given range are read in.
\n
\n", "parameters": ["path", "filestem", "ens_id", "idl"], "funcdef": "def"}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"fullname": "pyerrors.input.hadrons.read_Fourquark_hd5", "modulename": "pyerrors.input.hadrons", "qualname": "read_Fourquark_hd5", "type": "function", "doc": "Read hadrons FourquarkFullyConnected hdf5 file and output an array of CObs
\n\nParameters
\n\n\n- path (str):\npath to the files to read
\n- filestem (str):\nnamestem of the files to read
\n- ens_id (str):\nname of the ensemble, required for internal bookkeeping
\n- idl (range):\nIf specified only configurations in the given range are read in.
\n- vertices (list):\nVertex functions to be extracted.
\n
\n", "parameters": ["path", "filestem", "ens_id", "idl", "vertices"], "funcdef": "def"}, "pyerrors.input.json": {"fullname": "pyerrors.input.json", "modulename": "pyerrors.input.json", "qualname": "", "type": "module", "doc": "\n"}, "pyerrors.input.json.create_json_string": {"fullname": "pyerrors.input.json.create_json_string", "modulename": "pyerrors.input.json", "qualname": "create_json_string", "type": "function", "doc": "Generate the string for the export of a list of Obs or structures containing Obs\nto a .json(.gz) file
\n\nParameters
\n\n\n- ol (list):\nList of objects that will be exported. At the moments, these objects can be\neither of: Obs, list, numpy.ndarray.\nAll Obs inside a structure have to be defined on the same set of configurations.
\n- description (str):\nOptional string that describes the contents of the json file.
\n- indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
\n
\n", "parameters": ["ol", "description", "indent"], "funcdef": "def"}, "pyerrors.input.json.dump_to_json": {"fullname": "pyerrors.input.json.dump_to_json", "modulename": "pyerrors.input.json", "qualname": "dump_to_json", "type": "function", "doc": "Export a list of Obs or structures containing Obs to a .json(.gz) file
\n\nParameters
\n\n\n- ol (list):\nList of objects that will be exported. At the moments, these objects can be\neither of: Obs, list, numpy.ndarray.\nAll Obs inside a structure have to be defined on the same set of configurations.
\n- fname (str):\nFilename of the output file.
\n- description (str):\nOptional string that describes the contents of the json file.
\n- indent (int):\nSpecify the indentation level of the json file. None or 0 is permissible and\nsaves disk space.
\n- gz (bool):\nIf True, the output is a gzipped json. If False, the output is a json file.
\n
\n", "parameters": ["ol", "fname", "description", "indent", "gz"], "funcdef": "def"}, "pyerrors.input.json.import_json_string": {"fullname": "pyerrors.input.json.import_json_string", "modulename": "pyerrors.input.json", "qualname": "import_json_string", "type": "function", "doc": "Reconstruct a list of Obs or structures containing Obs from a json string.
\n\nThe following structures are supported: Obs, list, numpy.ndarray\nIf the list contains only one element, it is unpacked from the list.
\n\nParameters
\n\n\n- json_string (str):\njson string containing the data.
\n- verbose (bool):\nPrint additional information that was written to the file.
\n- full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
\n
\n", "parameters": ["json_string", "verbose", "full_output"], "funcdef": "def"}, "pyerrors.input.json.load_json": {"fullname": "pyerrors.input.json.load_json", "modulename": "pyerrors.input.json", "qualname": "load_json", "type": "function", "doc": "Import a list of Obs or structures containing Obs from a .json.gz file.
\n\nThe following structures are supported: Obs, list, numpy.ndarray\nIf the list contains only one element, it is unpacked from the list.
\n\nParameters
\n\n\n- fname (str):\nFilename of the input file.
\n- verbose (bool):\nPrint additional information that was written to the file.
\n- gz (bool):\nIf True, assumes that data is gzipped. If False, assumes JSON file.
\n- full_output (bool):\nIf True, a dict containing auxiliary information and the data is returned.\nIf False, only the data is returned.
\n
\n", "parameters": ["fname", "verbose", "gz", "full_output"], "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\nKeyword arguments
\n\nr_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\nParameters
\n\n\n- version (str):\nversion of openQCD, default 2.0
\n- r_start (list):\nlist which contains the first config to be read for each replicum
\n- r_stop (list):\nlist which contains the last config to be read for each replicum
\n- postfix (str):\npostfix of the file to read, e.g. '.ms1' for openQCD-files
\n
\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\nIt 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\nParameters
\n\n\n- path (str):\nPath to .ms.dat files
\n- prefix (str):\nEnsemble prefix
\n- dtr_read (int):\nDetermines how many trajectories should be skipped when reading the ms.dat files.\nCorresponds to dtr_cnfg / dtr_ms in the openQCD input file.
\n- xmin (int):\nFirst timeslice where the boundary effects have sufficiently decayed.
\n- spatial_extent (int):\nspatial extent of the lattice, required for normalization.
\n- fit_range (int):\nNumber of data points left and right of the zero crossing to be included in the linear fit. (Default: 5)
\n- r_start (list):\nlist which contains the first config to be read for each replicum.
\n- r_stop (list):\nlist which contains the last config to be read for each replicum.
\n- plaquette (bool):\nIf true extract the plaquette estimate of t0 instead.
\n
\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\nParameters
\n\n\n- im -- if True, read imaginary instead of real part of the correlation function.
\n- single -- if True, read a boundary-to-boundary correlation function with a single value
\n- b2b -- if True, read a time-dependent boundary-to-boundary correlation function
\n- names -- Alternative labeling for replicas/ensembles. Has to have the appropriate length
\n
\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\nParameters
\n\n\n- quarks -- Label of the quarks used in the sfcf input file
\n- noffset -- Offset of the source (only relevant when wavefunctions are used)
\n- wf -- ID of wave function
\n- wf2 -- ID of the second wavefunction (only relevant for boundary-to-boundary correlation functions)
\n- im -- if True, read imaginary instead of real part of the correlation function.
\n- b2b -- if True, read a time-dependent boundary-to-boundary correlation function
\n- names -- Alternative labeling for replicas/ensembles. Has to have the appropriate length
\n- ens_name (str):\nreplaces the name of the ensemble
\n
\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\nParameters
\n\n\n- target -- specifies the topological sector to be reweighted to (default 0)
\n- full -- if true read the charge instead of the reweighting factor.
\n
\n", "parameters": ["path", "prefix", "kwargs"], "funcdef": "def"}, "pyerrors.linalg": {"fullname": "pyerrors.linalg", "modulename": "pyerrors.linalg", "qualname": "", "type": "module", "doc": "\n"}, "pyerrors.linalg.matmul": {"fullname": "pyerrors.linalg.matmul", "modulename": "pyerrors.linalg", "qualname": "matmul", "type": "function", "doc": "Matrix multiply all operands.
\n\nParameters
\n\n\n- operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
\n- This implementation is faster compared to standard multiplication via the @ operator.
\n
\n", "parameters": ["operands"], "funcdef": "def"}, "pyerrors.linalg.jack_matmul": {"fullname": "pyerrors.linalg.jack_matmul", "modulename": "pyerrors.linalg", "qualname": "jack_matmul", "type": "function", "doc": "Matrix multiply both operands making use of the jackknife approximation.
\n\nParameters
\n\n\n- operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
\n- For large matrices this is considerably faster compared to matmul.
\n
\n", "parameters": ["operands"], "funcdef": "def"}, "pyerrors.linalg.einsum": {"fullname": "pyerrors.linalg.einsum", "modulename": "pyerrors.linalg", "qualname": "einsum", "type": "function", "doc": "Wrapper for numpy.einsum
\n\nParameters
\n\n\n- subscripts (str):\nSubscripts for summation (see numpy documentation for details)
\n- operands (numpy.ndarray):\nArbitrary number of 2d-numpy arrays which can be real or complex\nObs valued.
\n
\n", "parameters": ["subscripts", "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 decomposition of Obs or CObs valued matrices.
\n", "parameters": ["x"], "funcdef": "def"}, "pyerrors.linalg.det": {"fullname": "pyerrors.linalg.det", "modulename": "pyerrors.linalg", "qualname": "det", "type": "function", "doc": "Determinant of Obs valued matrices.
\n", "parameters": ["x"], "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.misc": {"fullname": "pyerrors.misc", "modulename": "pyerrors.misc", "qualname": "", "type": "module", "doc": "\n"}, "pyerrors.misc.dump_object": {"fullname": "pyerrors.misc.dump_object", "modulename": "pyerrors.misc", "qualname": "dump_object", "type": "function", "doc": "Dump object into pickle file.
\n\nParameters
\n\n\n- obj (object):\nobject to be saved in the pickle file
\n- name (str):\nname of the file
\n- path (str):\nspecifies a custom path for the file (default '.')
\n
\n", "parameters": ["obj", "name", "kwargs"], "funcdef": "def"}, "pyerrors.misc.load_object": {"fullname": "pyerrors.misc.load_object", "modulename": "pyerrors.misc", "qualname": "load_object", "type": "function", "doc": "Load object from pickle file.
\n\nParameters
\n\n\n- path (str):\npath to the file
\n
\n", "parameters": ["path"], "funcdef": "def"}, "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\nParameters
\n\n\n- means (list):\nlist containing the mean value of each observable.
\n- cov (numpy.ndarray):\ncovariance matrix for the data to be generated.
\n- name (str):\nensemble name for the data to be geneated.
\n- tau (float or list):\ncan either be a real number or a list with an entry for\nevery dataset.
\n- samples (int):\nnumber of samples to be generated for each observable.
\n
\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\nImplementation of the matrix pencil method based on\neq. (2.17) of Y. Hua, T. K. Sarkar, IEEE Trans. Acoust. 38, 814-824 (1990)
\n\nParameters
\n\n\n- data (list):\ncan be a list of Obs for the analysis of a single correlator, or a list of lists\nof Obs if several correlators are to analyzed at once.
\n- k (int):\nNumber of states to extract (default 1).
\n- p (int):\nmatrix pencil parameter which filters noise. The optimal value is expected between\nlen(data)/3 and 2*len(data)/3. The computation is more expensive the closer p is\nto len(data)/2 but could possibly suppress more noise (default len(data)//2).
\n
\n", "parameters": ["corrs", "k", "p", "kwargs"], "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\nInstances 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\nAttributes
\n\n\n- S_global (float):\nStandard value for S (default 2.0)
\n- S_dict (dict):\nDictionary for S values. If an entry for a given ensemble\nexists this overwrites the standard value for that ensemble.
\n- tau_exp_global (float):\nStandard value for tau_exp (default 0.0)
\n- tau_exp_dict (dict):\nDictionary for tau_exp values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
\n- N_sigma_global (float):\nStandard value for N_sigma (default 1.0)
\n- N_sigma_dict (dict):\nDictionary for N_sigma values. If an entry for a given ensemble exists\nthis overwrites the standard value for that ensemble.
\n
\n"}, "pyerrors.obs.Obs.__init__": {"fullname": "pyerrors.obs.Obs.__init__", "modulename": "pyerrors.obs", "qualname": "Obs.__init__", "type": "function", "doc": "Initialize Obs object.
\n\nParameters
\n\n\n- samples (list):\nlist of numpy arrays containing the Monte Carlo samples
\n- names (list):\nlist of strings labeling the individual samples
\n- idl (list, optional):\nlist of ranges or lists on which the samples are defined
\n- means (list, optional):\nlist of mean values for the case that the mean values were\nalready subtracted from the samples
\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.N_sigma_dict": {"fullname": "pyerrors.obs.Obs.N_sigma_dict", "modulename": "pyerrors.obs", "qualname": "Obs.N_sigma_dict", "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.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.cov_names": {"fullname": "pyerrors.obs.Obs.cov_names", "modulename": "pyerrors.obs", "qualname": "Obs.cov_names", "type": "variable", "doc": "\n"}, "pyerrors.obs.Obs.mc_names": {"fullname": "pyerrors.obs.Obs.mc_names", "modulename": "pyerrors.obs", "qualname": "Obs.mc_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.covobs": {"fullname": "pyerrors.obs.Obs.covobs", "modulename": "pyerrors.obs", "qualname": "Obs.covobs", "type": "variable", "doc": "\n"}, "pyerrors.obs.Obs.gamma_method": {"fullname": "pyerrors.obs.Obs.gamma_method", "modulename": "pyerrors.obs", "qualname": "Obs.gamma_method", "type": "function", "doc": "Estimate the error and related properties of the Obs.
\n\nParameters
\n\n\n- S (float):\nspecifies a custom value for the parameter S (default 2.0).\nIf set to 0 it is assumed that the data exhibits no\nautocorrelation. In this case the error estimates coincides\nwith the sample standard error.
\n- tau_exp (float):\npositive value triggers the critical slowing down analysis\n(default 0.0).
\n- N_sigma (float):\nnumber of standard deviations from zero until the tail is\nattached to the autocorrelation function (default 1).
\n- fft (bool):\ndetermines whether the fft algorithm is used for the computation\nof the autocorrelation function (default True)
\n
\n", "parameters": ["self", "kwargs"], "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\nParameters
\n\n\n- ens_content (bool):\nprint details about the ensembles and replica if true.
\n
\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\nParameters
\n\n\n- sigma (int):\nNumber of standard errors used for the check.
\n- Works only properly when the gamma method was run.
\n
\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 a given tolerance.
\n\nParameters
\n\n\n- rtol (float):\nRelative tolerance (for details see numpy documentation).
\n- atol (float):\nAbsolute tolerance (for details see numpy documentation).
\n
\n", "parameters": ["self", "rtol", "atol"], "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\nParameters
\n\n\n- save (str):\nsaves the figure to a file named 'save' if.
\n
\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\nParameters
\n\n\n- expand (bool):\nshow expanded history for irregular Monte Carlo chains (default: True).
\n
\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\nParameters
\n\n\n- name (str):\nname of the file to be saved.
\n- path (str):\nspecifies a custom path for the file (default '.')
\n
\n", "parameters": ["self", "name", "kwargs"], "funcdef": "def"}, "pyerrors.obs.Obs.export_jackknife": {"fullname": "pyerrors.obs.Obs.export_jackknife", "modulename": "pyerrors.obs", "qualname": "Obs.export_jackknife", "type": "function", "doc": "Export jackknife samples from the Obs
\n\nReturns
\n\n\n- numpy.ndarray: Returns a numpy array of length N + 1 where N is the number of samples\nfor the given ensemble and replicum. The zeroth entry of the array contains\nthe mean value of the Obs, entries 1 to N contain the N jackknife samples\nderived from the Obs. The current implementation only works for observables\ndefined on exactly one ensemble and replicum. The derived jackknife samples\nshould agree with samples from a full jackknife analysis up to O(1/N).
\n
\n", "parameters": ["self"], "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.N": {"fullname": "pyerrors.obs.Obs.N", "modulename": "pyerrors.obs", "qualname": "Obs.N", "type": "variable", "doc": "\n"}, "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.is_merged": {"fullname": "pyerrors.obs.Obs.is_merged", "modulename": "pyerrors.obs", "qualname": "Obs.is_merged", "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.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\nParameters
\n\n\n- func (object):\narbitrary function of the form func(data, **kwargs). For the\nautomatic differentiation to work, all numpy functions have to have\nthe autograd wrapper (use 'import autograd.numpy as anp').
\n- data (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
\n- num_grad (bool):\nif True, numerical derivatives are used instead of autograd\n(default False). To control the numerical differentiation the\nkwargs of numdifftools.step_generators.MaxStepGenerator\ncan be used.
\n- man_grad (list):\nmanually supply a list or an array which contains the jacobian\nof func. Use cautiously, supplying the wrong derivative will\nnot be intercepted.
\n
\n\nNotes
\n\nFor simple mathematical operations it can be practical to use anonymous\nfunctions. For the ratio of two observables one can e.g. use
\n\nnew_obs = derived_observable(lambda x: x[0] / x[1], [obs1, obs2])
\n", "parameters": ["func", "data", "array_mode", "kwargs"], "funcdef": "def"}, "pyerrors.obs.reweight": {"fullname": "pyerrors.obs.reweight", "modulename": "pyerrors.obs", "qualname": "reweight", "type": "function", "doc": "Reweight a list of observables.
\n\nParameters
\n\n\n- weight (Obs):\nReweighting factor. An Observable that has to be defined on a superset of the\nconfigurations in obs[i].idl for all i.
\n- obs (list):\nlist of Obs, e.g. [obs1, obs2, obs3].
\n- all_configs (bool):\nif True, the reweighted observables are normalized by the average of\nthe reweighting factor on all configurations in weight.idl and not\non the configurations in obs[i].idl.
\n
\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\nParameters
\n\n\n- obs_a (Obs):\nFirst observable
\n- obs_b (Obs):\nSecond observable
\n- Keep in mind to only correlate primary observables which have not been reweighted
\n- yet. The reweighting has to be applied after correlating the observables.
\n- Currently only works if ensembles are identical. This is not really necessary.
\n
\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\ncovariance(obs, obs) is equal to obs.dvalue ** 2\nThe gamma method has to be applied first to both observables.
\n\nIf abs(covariance(obs1, obs2)) > obs1.dvalue * obs2.dvalue, the covariance\nis constrained to the maximum value.
\n\nKeyword arguments
\n\ncorrelation -- if true the correlation instead of the covariance is\n returned (default False)
\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\nParameters
\n\n\n- value (float):\ncentral value of the Obs to be generated.
\n- dvalue (float):\nerror of the Obs to be generated.
\n- name (str):\nname of the ensemble for which the Obs is to be generated.
\n- samples (int):\nnumber of samples for the Obs (default 1000).
\n
\n", "parameters": ["value", "dvalue", "name", "samples"], "funcdef": "def"}, "pyerrors.obs.import_jackknife": {"fullname": "pyerrors.obs.import_jackknife", "modulename": "pyerrors.obs", "qualname": "import_jackknife", "type": "function", "doc": "Imports jackknife samples and returns an Obs
\n\nParameters
\n\n\n- jacks (numpy.ndarray):\nnumpy array containing the mean value as zeroth entry and\nthe N jackknife samples as first to Nth entry.
\n- name (str):\nname of the ensemble the samples are defined on.
\n
\n", "parameters": ["jacks", "name", "idl"], "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\nParameters
\n\n\n- list_of_obs (list):\nlist of the Obs object to be combined
\n- It is not possible to combine obs which are based on the same replicum
\n
\n", "parameters": ["list_of_obs"], "funcdef": "def"}, "pyerrors.obs.cov_Obs": {"fullname": "pyerrors.obs.cov_Obs", "modulename": "pyerrors.obs", "qualname": "cov_Obs", "type": "function", "doc": "Create an Obs based on mean(s) and a covariance matrix
\n\nParameters
\n\n\n- mean (list of floats or float):\nN mean value(s) of the new Obs
\n- cov (list or array):\n2d (NxN) Covariance matrix, 1d diagonal entries or 0d covariance
\n- name (str):\nidentifier for the covariance matrix
\n- grad (list or array):\nGradient of the Covobs wrt. the means belonging to cov.
\n
\n", "parameters": ["means", "cov", "name", "grad"], "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\nParameters
\n\n\n- d (Obs):\nObs passed to the function.
\n- func (object):\nFunction to be minimized. Any numpy functions have to use the autograd.numpy wrapper.\nExample:\n
python\nimport autograd.numpy as anp\ndef root_func(x, d):\n return anp.exp(-x ** 2) - d\n \n- guess (float):\nInitial guess for the minimization.
\n
\n\nReturns
\n\n\n- Obs:
Obs valued root of the function. \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": 1048}, "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": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.GEVP": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.Eigenvalue": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.roll": {"qualname": 2, "fullname": 4, "doc": 10}, "pyerrors.correlators.Corr.reverse": {"qualname": 2, "fullname": 4, "doc": 4}, "pyerrors.correlators.Corr.correlate": {"qualname": 2, "fullname": 4, "doc": 19}, "pyerrors.correlators.Corr.reweight": {"qualname": 2, "fullname": 4, "doc": 28}, "pyerrors.correlators.Corr.T_symmetry": {"qualname": 2, "fullname": 4, "doc": 21}, "pyerrors.correlators.Corr.deriv": {"qualname": 2, "fullname": 4, "doc": 18}, "pyerrors.correlators.Corr.second_deriv": {"qualname": 2, "fullname": 4, "doc": 6}, "pyerrors.correlators.Corr.m_eff": {"qualname": 2, "fullname": 4, "doc": 68}, "pyerrors.correlators.Corr.fit": {"qualname": 2, "fullname": 4, "doc": 32}, "pyerrors.correlators.Corr.plateau": {"qualname": 2, "fullname": 4, "doc": 34}, "pyerrors.correlators.Corr.set_prange": {"qualname": 2, "fullname": 4, "doc": 5}, "pyerrors.correlators.Corr.show": {"qualname": 2, "fullname": 4, "doc": 56}, "pyerrors.correlators.Corr.dump": {"qualname": 2, "fullname": 4, "doc": 9}, "pyerrors.correlators.Corr.print": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.sqrt": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.log": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.exp": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.sin": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.cos": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.tan": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.sinh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.cosh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.tanh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.arcsin": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.arccos": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.arctan": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.arcsinh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.arccosh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.correlators.Corr.arctanh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.covobs": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.covobs.Covobs": {"qualname": 1, "fullname": 3, "doc": 0}, "pyerrors.covobs.Covobs.__init__": {"qualname": 2, "fullname": 4, "doc": 45}, "pyerrors.covobs.Covobs.errsq": {"qualname": 2, "fullname": 4, "doc": 5}, "pyerrors.covobs.Covobs.cov": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.covobs.Covobs.grad": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.dirac": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.dirac.epsilon_tensor": {"qualname": 1, "fullname": 3, "doc": 8}, "pyerrors.dirac.epsilon_tensor_rank4": {"qualname": 1, "fullname": 3, "doc": 8}, "pyerrors.dirac.Grid_gamma": {"qualname": 1, "fullname": 3, "doc": 5}, "pyerrors.fits": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.fits.Fit_result": {"qualname": 1, "fullname": 3, "doc": 13}, "pyerrors.fits.Fit_result.__init__": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.fits.Fit_result.gamma_method": {"qualname": 2, "fullname": 4, "doc": 5}, "pyerrors.fits.least_squares": {"qualname": 1, "fullname": 3, "doc": 203}, "pyerrors.fits.total_least_squares": {"qualname": 1, "fullname": 3, "doc": 133}, "pyerrors.fits.prior_fit": {"qualname": 1, "fullname": 3, "doc": 0}, "pyerrors.fits.standard_fit": {"qualname": 1, "fullname": 3, "doc": 0}, "pyerrors.fits.odr_fit": {"qualname": 1, "fullname": 3, "doc": 0}, "pyerrors.fits.fit_lin": {"qualname": 1, "fullname": 3, "doc": 33}, "pyerrors.fits.qqplot": {"qualname": 1, "fullname": 3, "doc": 12}, "pyerrors.fits.residual_plot": {"qualname": 1, "fullname": 3, "doc": 8}, "pyerrors.fits.covariance_matrix": {"qualname": 1, "fullname": 3, "doc": 4}, "pyerrors.fits.error_band": {"qualname": 1, "fullname": 3, "doc": 14}, "pyerrors.input": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.input.bdio": {"qualname": 0, "fullname": 3, "doc": 0}, "pyerrors.input.bdio.read_ADerrors": {"qualname": 1, "fullname": 4, "doc": 46}, "pyerrors.input.bdio.write_ADerrors": {"qualname": 1, "fullname": 4, "doc": 47}, "pyerrors.input.bdio.read_mesons": {"qualname": 1, "fullname": 4, "doc": 68}, "pyerrors.input.bdio.read_dSdm": {"qualname": 1, "fullname": 4, "doc": 61}, "pyerrors.input.hadrons": {"qualname": 0, "fullname": 3, "doc": 0}, "pyerrors.input.hadrons.read_meson_hd5": {"qualname": 1, "fullname": 4, "doc": 66}, "pyerrors.input.hadrons.Npr_matrix": {"qualname": 1, "fullname": 4, "doc": 425}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"qualname": 2, "fullname": 5, "doc": 0}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"qualname": 2, "fullname": 5, "doc": 12}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"qualname": 1, "fullname": 4, "doc": 33}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"qualname": 1, "fullname": 4, "doc": 33}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"qualname": 1, "fullname": 4, "doc": 38}, "pyerrors.input.json": {"qualname": 0, "fullname": 3, "doc": 0}, "pyerrors.input.json.create_json_string": {"qualname": 1, "fullname": 4, "doc": 51}, "pyerrors.input.json.dump_to_json": {"qualname": 1, "fullname": 4, "doc": 64}, "pyerrors.input.json.import_json_string": {"qualname": 1, "fullname": 4, "doc": 47}, "pyerrors.input.json.load_json": {"qualname": 1, "fullname": 4, "doc": 57}, "pyerrors.input.misc": {"qualname": 0, "fullname": 3, "doc": 0}, "pyerrors.input.misc.read_pbp": {"qualname": 1, "fullname": 4, "doc": 28}, "pyerrors.input.openQCD": {"qualname": 0, "fullname": 3, "doc": 0}, "pyerrors.input.openQCD.read_rwms": {"qualname": 1, "fullname": 4, "doc": 46}, "pyerrors.input.openQCD.extract_t0": {"qualname": 1, "fullname": 4, "doc": 115}, "pyerrors.input.sfcf": {"qualname": 0, "fullname": 3, "doc": 0}, "pyerrors.input.sfcf.read_sfcf": {"qualname": 1, "fullname": 4, "doc": 41}, "pyerrors.input.sfcf.read_sfcf_c": {"qualname": 1, "fullname": 4, "doc": 63}, "pyerrors.input.sfcf.read_qtop": {"qualname": 1, "fullname": 4, "doc": 21}, "pyerrors.linalg": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.linalg.matmul": {"qualname": 1, "fullname": 3, "doc": 23}, "pyerrors.linalg.jack_matmul": {"qualname": 1, "fullname": 3, "doc": 27}, "pyerrors.linalg.einsum": {"qualname": 1, "fullname": 3, "doc": 24}, "pyerrors.linalg.inv": {"qualname": 1, "fullname": 3, "doc": 5}, "pyerrors.linalg.cholesky": {"qualname": 1, "fullname": 3, "doc": 6}, "pyerrors.linalg.det": {"qualname": 1, "fullname": 3, "doc": 4}, "pyerrors.linalg.eigh": {"qualname": 1, "fullname": 3, "doc": 11}, "pyerrors.linalg.eig": {"qualname": 1, "fullname": 3, "doc": 9}, "pyerrors.linalg.pinv": {"qualname": 1, "fullname": 3, "doc": 6}, "pyerrors.linalg.svd": {"qualname": 1, "fullname": 3, "doc": 6}, "pyerrors.misc": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.misc.dump_object": {"qualname": 1, "fullname": 3, "doc": 22}, "pyerrors.misc.load_object": {"qualname": 1, "fullname": 3, "doc": 9}, "pyerrors.misc.gen_correlated_data": {"qualname": 1, "fullname": 3, "doc": 43}, "pyerrors.mpm": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.mpm.matrix_pencil_method": {"qualname": 1, "fullname": 3, "doc": 75}, "pyerrors.obs": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.obs.Obs": {"qualname": 1, "fullname": 3, "doc": 107}, "pyerrors.obs.Obs.__init__": {"qualname": 2, "fullname": 4, "doc": 40}, "pyerrors.obs.Obs.S_global": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.S_dict": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.tau_exp_global": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.tau_exp_dict": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.N_sigma_global": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.N_sigma_dict": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.filter_eps": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.names": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.shape": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.r_values": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.deltas": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.idl": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.ddvalue": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.reweighted": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.tag": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.value": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.dvalue": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_names": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.cov_names": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.mc_names": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_content": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.covobs": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.gamma_method": {"qualname": 2, "fullname": 4, "doc": 66}, "pyerrors.obs.Obs.details": {"qualname": 2, "fullname": 4, "doc": 12}, "pyerrors.obs.Obs.is_zero_within_error": {"qualname": 2, "fullname": 4, "doc": 21}, "pyerrors.obs.Obs.is_zero": {"qualname": 2, "fullname": 4, "doc": 24}, "pyerrors.obs.Obs.plot_tauint": {"qualname": 2, "fullname": 4, "doc": 14}, "pyerrors.obs.Obs.plot_rho": {"qualname": 2, "fullname": 4, "doc": 7}, "pyerrors.obs.Obs.plot_rep_dist": {"qualname": 2, "fullname": 4, "doc": 8}, "pyerrors.obs.Obs.plot_history": {"qualname": 2, "fullname": 4, "doc": 19}, "pyerrors.obs.Obs.plot_piechart": {"qualname": 2, "fullname": 4, "doc": 12}, "pyerrors.obs.Obs.dump": {"qualname": 2, "fullname": 4, "doc": 18}, "pyerrors.obs.Obs.export_jackknife": {"qualname": 2, "fullname": 4, "doc": 54}, "pyerrors.obs.Obs.sqrt": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.log": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.exp": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.sin": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.cos": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.tan": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.arcsin": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.arccos": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.arctan": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.sinh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.cosh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.tanh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.arcsinh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.arccosh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.arctanh": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.N": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.N_sigma": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.S": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_ddvalue": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_drho": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_dtauint": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_dvalue": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_n_dtauint": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_n_tauint": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_rho": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_tauint": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.e_windowsize": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.is_merged": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.Obs.tau_exp": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.CObs": {"qualname": 1, "fullname": 3, "doc": 4}, "pyerrors.obs.CObs.__init__": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.CObs.tag": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.CObs.real": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.CObs.imag": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.CObs.gamma_method": {"qualname": 2, "fullname": 4, "doc": 5}, "pyerrors.obs.CObs.is_zero": {"qualname": 2, "fullname": 4, "doc": 10}, "pyerrors.obs.CObs.conjugate": {"qualname": 2, "fullname": 4, "doc": 0}, "pyerrors.obs.derived_observable": {"qualname": 1, "fullname": 3, "doc": 93}, "pyerrors.obs.reweight": {"qualname": 1, "fullname": 3, "doc": 38}, "pyerrors.obs.correlate": {"qualname": 1, "fullname": 3, "doc": 28}, "pyerrors.obs.covariance": {"qualname": 1, "fullname": 3, "doc": 37}, "pyerrors.obs.pseudo_Obs": {"qualname": 1, "fullname": 3, "doc": 32}, "pyerrors.obs.import_jackknife": {"qualname": 1, "fullname": 3, "doc": 28}, "pyerrors.obs.merge_obs": {"qualname": 1, "fullname": 3, "doc": 19}, "pyerrors.obs.cov_Obs": {"qualname": 1, "fullname": 3, "doc": 42}, "pyerrors.roots": {"qualname": 0, "fullname": 2, "doc": 0}, "pyerrors.roots.find_root": {"qualname": 1, "fullname": 3, "doc": 49}, "pyerrors.version": {"qualname": 0, "fullname": 2, "doc": 0}}, "length": 197, "save": true}, "index": {"qualname": {"root": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.obs.Obs.cos": {"tf": 1}}, "df": 2, "r": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.sum": {"tf": 1}, "pyerrors.correlators.Corr.smearing": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.smearing_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.correlators.Corr.arctanh": {"tf": 1}}, "df": 42, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.obs.Obs.cosh": {"tf": 1}}, "df": 2}}, "v": {"docs": {"pyerrors.covobs.Covobs.cov": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.covobs.Covobs": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.covobs.Covobs.cov": {"tf": 1}, "pyerrors.covobs.Covobs.grad": {"tf": 1}, "pyerrors.obs.Obs.covobs": {"tf": 1}}, "df": 6}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.fits.covariance_matrix": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}, "_": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.Obs.cov_names": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 1}}}}, "b": {"docs": {"pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.conjugate": {"tf": 1}}, "df": 8}, "n": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.CObs.conjugate": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.linalg.cholesky": {"tf": 1}}, "df": 1}}}}}}}}, "_": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "_": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}}, "df": 6}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.Obs.reweighted": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 4}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.reverse": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.residual_plot": {"tf": 1}}, "df": 1}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}}, "df": 1}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}, "d": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}}}}}}, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}}}}, "p": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 1}}}}}}, "q": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {"pyerrors.obs.CObs.real": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}}, "df": 1}}}, "_": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.r_values": {"tf": 1}}, "df": 1}}}}}}, "g": {"5": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}}, "df": 4}}}}}}}}}}}, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.covobs.Covobs.grad": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 1}}}}}}}}}}, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.print": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.prior_fit": {"tf": 1}}, "df": 1}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1}}, "df": 1}}}}, "_": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_tauint": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.plot_rho": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 1}}}}}}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 1}}}}}}}, "p": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.linalg.pinv": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 1}}}}}}}}}, "s": {"docs": {"pyerrors.obs.Obs.S": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.sum": {"tf": 1}}, "df": 1}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.smearing": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.smearing_symmetric": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.symmetric": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 1}}}}}}}}}}, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 1}}}}}}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.shape": {"tf": 1}}, "df": 1}}}}, "q": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.obs.Obs.sqrt": {"tf": 1}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.obs.Obs.sin": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.obs.Obs.sinh": {"tf": 1}}, "df": 2}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.standard_fit": {"tf": 1}}, "df": 1}}}}}}}}}}}, "v": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.linalg.svd": {"tf": 1}}, "df": 1}}, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.S_global": {"tf": 1}}, "df": 1}}}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.S_dict": {"tf": 1}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}}, "df": 1}}}}}}}}}}}, "r": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.obs.Obs.arcsin": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.obs.Obs.arcsinh": {"tf": 1}}, "df": 2}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.obs.Obs.arccos": {"tf": 1}}, "df": 2, "s": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.obs.Obs.arccosh": {"tf": 1}}, "df": 2}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.obs.Obs.arctan": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.obs.Obs.arctanh": {"tf": 1}}, "df": 2}}}}}}}, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.linalg.eig": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 1}}}}}}, "h": {"docs": {"pyerrors.linalg.eigh": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}}, "df": 1}}}}}, "x": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.obs.Obs.exp": {"tf": 1}}, "df": 2, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "t": {"0": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {"pyerrors.covobs.Covobs.errsq": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}}}}}}}}, "p": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "k": {"4": {"docs": {"pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}}}}}}}}}}, "_": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.Obs.e_names": {"tf": 1}}, "df": 1}}, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}}, "df": 1}}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_n_tauint": {"tf": 1}}, "df": 1}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_content": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.e_ddvalue": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.e_drho": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_dtauint": {"tf": 1}}, "df": 1}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.e_dvalue": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.e_rho": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_tauint": {"tf": 1}}, "df": 1}}}}}}, "w": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.e_windowsize": {"tf": 1}}, "df": 1}}}}}}}}}, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.obs.Obs.tan": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.obs.Obs.tanh": {"tf": 1}}, "df": 2}}, "u": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.obs.Obs.tau_exp": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.Obs.tau_exp_global": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}}, "df": 1}}}}}}}}}}, "g": {"docs": {"pyerrors.obs.Obs.tag": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}}, "df": 2}}, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}}}}}, "t": {"docs": {"pyerrors.linalg.det": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.details": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.Obs.deltas": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 2, "_": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 1}}}}}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.misc.dump_object": {"tf": 1}}, "df": 1}}}}}}}}}}, "d": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.ddvalue": {"tf": 1}}, "df": 1}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.dvalue": {"tf": 1}}, "df": 1}}}}}, "m": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}}, "c": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.mc_names": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.merge_obs": {"tf": 1}}, "df": 1}}}}}}}}, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}}, "df": 3}}}}}}, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.fit_lin": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.obs.Obs.filter_eps": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1}}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.obs.Obs.log": {"tf": 1}}, "df": 2}, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json.load_json": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.misc.load_object": {"tf": 1}}, "df": 1}}}}}}}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}}}}}}, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.odr_fit": {"tf": 1}}, "df": 1}}}}}}, "b": {"docs": {"pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.S_dict": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}, "pyerrors.obs.Obs.filter_eps": {"tf": 1}, "pyerrors.obs.Obs.names": {"tf": 1}, "pyerrors.obs.Obs.shape": {"tf": 1}, "pyerrors.obs.Obs.r_values": {"tf": 1}, "pyerrors.obs.Obs.deltas": {"tf": 1}, "pyerrors.obs.Obs.idl": {"tf": 1}, "pyerrors.obs.Obs.ddvalue": {"tf": 1}, "pyerrors.obs.Obs.reweighted": {"tf": 1}, "pyerrors.obs.Obs.tag": {"tf": 1}, "pyerrors.obs.Obs.value": {"tf": 1}, "pyerrors.obs.Obs.dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_names": {"tf": 1}, "pyerrors.obs.Obs.cov_names": {"tf": 1}, "pyerrors.obs.Obs.mc_names": {"tf": 1}, "pyerrors.obs.Obs.e_content": {"tf": 1}, "pyerrors.obs.Obs.covobs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.Obs.sqrt": {"tf": 1}, "pyerrors.obs.Obs.log": {"tf": 1}, "pyerrors.obs.Obs.exp": {"tf": 1}, "pyerrors.obs.Obs.sin": {"tf": 1}, "pyerrors.obs.Obs.cos": {"tf": 1}, "pyerrors.obs.Obs.tan": {"tf": 1}, "pyerrors.obs.Obs.arcsin": {"tf": 1}, "pyerrors.obs.Obs.arccos": {"tf": 1}, "pyerrors.obs.Obs.arctan": {"tf": 1}, "pyerrors.obs.Obs.sinh": {"tf": 1}, "pyerrors.obs.Obs.cosh": {"tf": 1}, "pyerrors.obs.Obs.tanh": {"tf": 1}, "pyerrors.obs.Obs.arcsinh": {"tf": 1}, "pyerrors.obs.Obs.arccosh": {"tf": 1}, "pyerrors.obs.Obs.arctanh": {"tf": 1}, "pyerrors.obs.Obs.N": {"tf": 1}, "pyerrors.obs.Obs.N_sigma": {"tf": 1}, "pyerrors.obs.Obs.S": {"tf": 1}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1}, "pyerrors.obs.Obs.e_drho": {"tf": 1}, "pyerrors.obs.Obs.e_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_rho": {"tf": 1}, "pyerrors.obs.Obs.e_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_windowsize": {"tf": 1}, "pyerrors.obs.Obs.is_merged": {"tf": 1}, "pyerrors.obs.Obs.tau_exp": {"tf": 1}}, "df": 64}}, "q": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}}, "df": 1}}}}}}, "w": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "n": {"docs": {"pyerrors.obs.Obs.N": {"tf": 1}}, "df": 1, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 3}}}}}}}}}, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.Obs.N_sigma": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.Obs.N_sigma_global": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}}, "df": 1}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.names": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.CObs.imag": {"tf": 1}}, "df": 1}}}, "n": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.linalg.inv": {"tf": 1}}, "df": 1}}, "d": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.idl": {"tf": 1}}, "df": 1}}, "s": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 2, "_": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.Obs.is_merged": {"tf": 1}}, "df": 1}}}}}}}, "j": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1}}}}}}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.value": {"tf": 1}}, "df": 1}}}}}}, "fullname": {"root": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.sum": {"tf": 1}, "pyerrors.correlators.Corr.smearing": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.smearing_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.covobs": {"tf": 1}, "pyerrors.covobs.Covobs": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.covobs.Covobs.cov": {"tf": 1}, "pyerrors.covobs.Covobs.grad": {"tf": 1}, "pyerrors.dirac": {"tf": 1}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.prior_fit": {"tf": 1}, "pyerrors.fits.standard_fit": {"tf": 1}, "pyerrors.fits.odr_fit": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.covariance_matrix": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input": {"tf": 1}, "pyerrors.input.bdio": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.misc": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.linalg": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.S_global": {"tf": 1}, "pyerrors.obs.Obs.S_dict": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}, "pyerrors.obs.Obs.filter_eps": {"tf": 1}, "pyerrors.obs.Obs.names": {"tf": 1}, "pyerrors.obs.Obs.shape": {"tf": 1}, "pyerrors.obs.Obs.r_values": {"tf": 1}, "pyerrors.obs.Obs.deltas": {"tf": 1}, "pyerrors.obs.Obs.idl": {"tf": 1}, "pyerrors.obs.Obs.ddvalue": {"tf": 1}, "pyerrors.obs.Obs.reweighted": {"tf": 1}, "pyerrors.obs.Obs.tag": {"tf": 1}, "pyerrors.obs.Obs.value": {"tf": 1}, "pyerrors.obs.Obs.dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_names": {"tf": 1}, "pyerrors.obs.Obs.cov_names": {"tf": 1}, "pyerrors.obs.Obs.mc_names": {"tf": 1}, "pyerrors.obs.Obs.e_content": {"tf": 1}, "pyerrors.obs.Obs.covobs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.Obs.sqrt": {"tf": 1}, "pyerrors.obs.Obs.log": {"tf": 1}, "pyerrors.obs.Obs.exp": {"tf": 1}, "pyerrors.obs.Obs.sin": {"tf": 1}, "pyerrors.obs.Obs.cos": {"tf": 1}, "pyerrors.obs.Obs.tan": {"tf": 1}, "pyerrors.obs.Obs.arcsin": {"tf": 1}, "pyerrors.obs.Obs.arccos": {"tf": 1}, "pyerrors.obs.Obs.arctan": {"tf": 1}, "pyerrors.obs.Obs.sinh": {"tf": 1}, "pyerrors.obs.Obs.cosh": {"tf": 1}, "pyerrors.obs.Obs.tanh": {"tf": 1}, "pyerrors.obs.Obs.arcsinh": {"tf": 1}, "pyerrors.obs.Obs.arccosh": {"tf": 1}, "pyerrors.obs.Obs.arctanh": {"tf": 1}, "pyerrors.obs.Obs.N": {"tf": 1}, "pyerrors.obs.Obs.N_sigma": {"tf": 1}, "pyerrors.obs.Obs.S": {"tf": 1}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1}, "pyerrors.obs.Obs.e_drho": {"tf": 1}, "pyerrors.obs.Obs.e_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_rho": {"tf": 1}, "pyerrors.obs.Obs.e_tauint": {"tf": 1}, "pyerrors.obs.Obs.e_windowsize": {"tf": 1}, "pyerrors.obs.Obs.is_merged": {"tf": 1}, "pyerrors.obs.Obs.tau_exp": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.conjugate": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}, "pyerrors.roots": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}, "pyerrors.version": {"tf": 1}}, "df": 197}}}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.projected": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.print": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.prior_fit": {"tf": 1}}, "df": 1}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1}}, "df": 1}}}}, "_": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_tauint": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.plot_rho": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 1}}}}}}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 1}}}}}}}, "p": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.linalg.pinv": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 1}}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.obs.Obs.cos": {"tf": 1}}, "df": 2, "r": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.sum": {"tf": 1}, "pyerrors.correlators.Corr.smearing": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.smearing_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.correlators.Corr.arctanh": {"tf": 1}}, "df": 42, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.projected": {"tf": 1}, "pyerrors.correlators.Corr.sum": {"tf": 1}, "pyerrors.correlators.Corr.smearing": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.smearing_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.GEVP": {"tf": 1}, "pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.correlators.Corr.print": {"tf": 1}, "pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.correlators.Corr.cos": {"tf": 1}, "pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 44}}}}, "s": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.cosh": {"tf": 1}, "pyerrors.obs.Obs.cosh": {"tf": 1}}, "df": 2}}, "v": {"docs": {"pyerrors.covobs.Covobs.cov": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.covobs": {"tf": 1}, "pyerrors.covobs.Covobs": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.errsq": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.cov": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.grad": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.covobs": {"tf": 1}}, "df": 7}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.fits.covariance_matrix": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}, "_": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.Obs.cov_names": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 1}}}}, "b": {"docs": {"pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.conjugate": {"tf": 1}}, "df": 8}, "n": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.CObs.conjugate": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.linalg.cholesky": {"tf": 1}}, "df": 1}}}}}}}}, "_": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "_": {"docs": {"pyerrors.correlators.Corr.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}}, "df": 6}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweighted": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.Obs.reweighted": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 4}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.reverse": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.residual_plot": {"tf": 1}}, "df": 1}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}}, "df": 1}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}, "d": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}}}}}}, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "d": {"5": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}}}}, "p": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 1}}}}}}, "q": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {"pyerrors.obs.CObs.real": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.roots": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 2}}}, "_": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.r_values": {"tf": 1}}, "df": 1}}}}}}, "g": {"5": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}}, "df": 4}}}}}}}}}}}, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.GEVP": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.covobs.Covobs.grad": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 1}}}}}}}}}}, "s": {"docs": {"pyerrors.obs.Obs.S": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.sum": {"tf": 1}}, "df": 1}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.smearing": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.smearing_symmetric": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.symmetric": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 1}}}}}}}}}}, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 1}}}}}}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.shape": {"tf": 1}}, "df": 1}}}}, "q": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.sqrt": {"tf": 1}, "pyerrors.obs.Obs.sqrt": {"tf": 1}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.sin": {"tf": 1}, "pyerrors.obs.Obs.sin": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.sinh": {"tf": 1}, "pyerrors.obs.Obs.sinh": {"tf": 1}}, "df": 2}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.standard_fit": {"tf": 1}}, "df": 1}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.input.sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 4}}}, "v": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.linalg.svd": {"tf": 1}}, "df": 1}}, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.S_global": {"tf": 1}}, "df": 1}}}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.S_dict": {"tf": 1}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}}, "df": 1}}}}}}}}}}}, "r": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.arcsin": {"tf": 1}, "pyerrors.obs.Obs.arcsin": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arcsinh": {"tf": 1}, "pyerrors.obs.Obs.arcsinh": {"tf": 1}}, "df": 2}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.correlators.Corr.arccos": {"tf": 1}, "pyerrors.obs.Obs.arccos": {"tf": 1}}, "df": 2, "s": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.arccosh": {"tf": 1}, "pyerrors.obs.Obs.arccosh": {"tf": 1}}, "df": 2}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.arctan": {"tf": 1}, "pyerrors.obs.Obs.arctan": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.arctanh": {"tf": 1}, "pyerrors.obs.Obs.arctanh": {"tf": 1}}, "df": 2}}}}}}}, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.linalg.eig": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.correlators.Corr.Eigenvalue": {"tf": 1}}, "df": 1}}}}}}, "h": {"docs": {"pyerrors.linalg.eigh": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}}, "df": 1}}}}}, "x": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.exp": {"tf": 1}, "pyerrors.obs.Obs.exp": {"tf": 1}}, "df": 2, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "t": {"0": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {"pyerrors.covobs.Covobs.errsq": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}}}}}}}}, "p": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "k": {"4": {"docs": {"pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}}}}}}}}}}, "_": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.Obs.e_names": {"tf": 1}}, "df": 1}}, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_n_dtauint": {"tf": 1}}, "df": 1}}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_n_tauint": {"tf": 1}}, "df": 1}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_content": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.e_ddvalue": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.e_drho": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_dtauint": {"tf": 1}}, "df": 1}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.e_dvalue": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.e_rho": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.e_tauint": {"tf": 1}}, "df": 1}}}}}}, "w": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.Obs.e_windowsize": {"tf": 1}}, "df": 1}}}}}}}}}, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.tan": {"tf": 1}, "pyerrors.obs.Obs.tan": {"tf": 1}}, "df": 2, "h": {"docs": {"pyerrors.correlators.Corr.tanh": {"tf": 1}, "pyerrors.obs.Obs.tanh": {"tf": 1}}, "df": 2}}, "u": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.obs.Obs.tau_exp": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.Obs.tau_exp_global": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.tau_exp_dict": {"tf": 1}}, "df": 1}}}}}}}}}}, "g": {"docs": {"pyerrors.obs.Obs.tag": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}}, "df": 2}}, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}}}}}, "t": {"docs": {"pyerrors.linalg.det": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.details": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.Obs.deltas": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 2, "_": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 1}}}}}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.misc.dump_object": {"tf": 1}}, "df": 1}}}}}}}}}}, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.dirac": {"tf": 1}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 4}}}}, "d": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.ddvalue": {"tf": 1}}, "df": 1}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.dvalue": {"tf": 1}}, "df": 1}}}}}, "m": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.misc": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.misc": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 6}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}}, "p": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.mpm": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 2}}, "c": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.mc_names": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.merge_obs": {"tf": 1}}, "df": 1}}}}}}}}, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.prior_fit": {"tf": 1}, "pyerrors.fits.standard_fit": {"tf": 1}, "pyerrors.fits.odr_fit": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.covariance_matrix": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}}, "df": 15, "_": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.__init__": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}}, "df": 3}}}}}}, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.fit_lin": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.obs.Obs.filter_eps": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1}}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.log": {"tf": 1}, "pyerrors.obs.Obs.log": {"tf": 1}}, "df": 2}, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json.load_json": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.misc.load_object": {"tf": 1}}, "df": 1}}}}}}}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.linalg": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}}, "df": 11}}}}}}, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.odr_fit": {"tf": 1}}, "df": 1}}}}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}}}}}, "b": {"docs": {"pyerrors.obs": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.S_global": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.S_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tau_exp_global": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tau_exp_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N_sigma_global": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N_sigma_dict": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.filter_eps": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.names": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.shape": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.r_values": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.deltas": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.idl": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.ddvalue": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.reweighted": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tag": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.value": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dvalue": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_names": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.cov_names": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.mc_names": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_content": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.covobs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.details": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rho": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.sqrt": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.log": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.exp": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.sin": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.cos": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tan": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arcsin": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arccos": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arctan": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.sinh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.cosh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tanh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arcsinh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arccosh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.arctanh": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.N_sigma": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.S": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_ddvalue": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_drho": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_dtauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_dvalue": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_n_dtauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_n_tauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_rho": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_tauint": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.e_windowsize": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_merged": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.tau_exp": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.CObs.__init__": {"tf": 1}, "pyerrors.obs.CObs.tag": {"tf": 1}, "pyerrors.obs.CObs.real": {"tf": 1}, "pyerrors.obs.CObs.imag": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.conjugate": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 81}}, "q": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input": {"tf": 1}, "pyerrors.input.bdio": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.misc": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 28}}}, "v": {"docs": {"pyerrors.linalg.inv": {"tf": 1}}, "df": 1}}, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.CObs.imag": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.idl": {"tf": 1}}, "df": 1}}, "s": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 2, "_": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.Obs.is_merged": {"tf": 1}}, "df": 1}}}}}}}, "b": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 5}}}}, "w": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 8}}}}}}, "n": {"docs": {"pyerrors.obs.Obs.N": {"tf": 1}}, "df": 1, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 3}}}}}}}}}, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.Obs.N_sigma": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.Obs.N_sigma_global": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.N_sigma_dict": {"tf": 1}}, "df": 1}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.names": {"tf": 1}}, "df": 1}}}}, "j": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 5}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1}}}}}}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.obs.Obs.value": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.version": {"tf": 1}}, "df": 1}}}}}}}}}, "doc": {"root": {"0": {"0": {"6": {"9": {"7": {"9": {"5": {"8": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "1": {"2": {"8": {"9": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"1": {"8": {"0": {"6": {"4": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 3}}, "df": 1}, "2": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "4": {"5": {"8": {"5": {"6": {"5": {"0": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"9": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 2}, "pyerrors.obs.Obs.gamma_method": {"tf": 2}}, "df": 12, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"0": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}, "d": {"docs": {"pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 1}}, "1": {"0": {"0": {"0": {"docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 2}, "3": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "5": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "5": {"0": {"0": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "9": {"8": {"8": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "9": {"0": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 4.69041575982343}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}}, "df": 12, "d": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}, "*": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}, "2": {"0": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}, "3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "7": {"2": {"1": {"8": {"6": {"6": {"7": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "8": {"0": {"9": {"7": {"7": {"6": {"2": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "9": {"9": {"0": {"9": {"7": {"0": {"3": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {"pyerrors": {"tf": 3.7416573867739413}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.8284271247461903}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 11, "d": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 5}, "*": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "(": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, ")": {"docs": {}, "df": 0, "/": {"3": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}}}}, "3": {"0": {"6": {"7": {"5": {"2": {"0": {"1": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "1": {"4": {"9": {"8": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "2": {"3": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "6": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "7": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "8": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "9": {"docs": {"pyerrors": {"tf": 7.3484692283495345}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 2}, "docs": {"pyerrors": {"tf": 2}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 4}, "4": {"0": {"3": {"2": {"0": {"9": {"8": {"3": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 4}, "5": {"0": {"0": {"docs": {"pyerrors": {"tf": 2.8284271247461903}}, "df": 1, "(": {"4": {"0": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}, "1": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "1": {"9": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "2": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "2": {"8": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "3": {"8": {"0": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "4": {"8": {"docs": {}, "df": 0, "(": {"2": {"3": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}, "docs": {}, "df": 0}, "6": {"7": {"4": {"6": {"5": {"9": {"8": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2, "(": {"0": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "6": {"5": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "9": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "6": {"8": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "7": {"0": {"0": {"0": {"0": {"0": {"0": {"0": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1}}, "df": 1}, "1": {"4": {"2": {"2": {"9": {"0": {"0": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "+": {"0": {"0": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "2": {"0": {"4": {"6": {"6": {"5": {"8": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1}}, "df": 1}, "7": {"3": {"1": {"0": {"1": {"0": {"2": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"0": {"7": {"7": {"5": {"2": {"4": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 2}}, "df": 1}, "8": {"1": {"4": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "2": {"4": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 3}, "9": {"1": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "4": {"7": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "8": {"3": {"1": {"9": {"8": {"8": {"1": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"1": {"0": {"0": {"7": {"1": {"2": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "5": {"8": {"3": {"6": {"5": {"4": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}, "docs": {}, "df": 0, "p": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}}, "df": 1, "y": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 5}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2}}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 2}}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 6, "l": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.correlate": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.7320508075688772}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 58}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 2}}, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "h": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.misc.dump_object": {"tf": 1.4142135623730951}, "pyerrors.misc.load_object": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}}, "df": 13}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1.4142135623730951}}, "df": 2}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 2}}, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 3}}}}}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 3}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 7}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}}, "df": 1, "=": {"docs": {}, "df": 0, "=": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}}, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 2}}}, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}}, "df": 2}}, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}, "u": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "e": {"docs": {"pyerrors": {"tf": 4.47213595499958}, "pyerrors.correlators.Corr": {"tf": 1}}, "df": 2, "r": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 5}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 2, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 2}}}}}, "n": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.linalg.pinv": {"tf": 1}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 9, "_": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 2}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}}, "df": 2, "_": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}}}}}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1.4142135623730951}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 4}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}}}}}}, "o": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}}, "df": 1, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 3}}}}, "t": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 1, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.linalg.pinv": {"tf": 1}}, "df": 1}}}}}}}}}}}, "b": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}}, "df": 1}}}, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 8, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 4.358898943540674}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 10}}}}, "x": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2, "l": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1}}}}, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3}}, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}}, "df": 2}}}, "p": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "d": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 4, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 3, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}}}}}}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}}, "df": 8}}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 1}, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}}, "df": 1}}}}}}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.CObs.gamma_method": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.linalg.eig": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}}, "df": 3}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}}, "df": 2}}}}}}}}, "h": {"docs": {"pyerrors.linalg.eigh": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}}, "df": 1}}}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 4.69041575982343}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 2.449489742783178}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 20, "e": {"1": {"docs": {"pyerrors": {"tf": 3.4641016151377544}}, "df": 1, "|": {"docs": {}, "df": 0, "r": {"0": {"1": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "2": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "2": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 2.6457513110645907}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}}, "df": 2}}}}}}}}}}}}}}, "_": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.details": {"tf": 1}}, "df": 1}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 9}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}}, "df": 3}}, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 3.1622776601683795}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 3, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}, "g": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 11}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 4}}}}}, "p": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}}}, "t": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}, "q": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}, "c": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 3, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 8}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}}, "df": 5}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 3, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}, "/": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "/": {"1": {"6": {"0": {"3": {"7": {"5": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.merge_obs": {"tf": 1.7320508075688772}}, "df": 1}}}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 3, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": null}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}}, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 2}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 2}, "pyerrors.input.json.load_json": {"tf": 1.7320508075688772}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 18}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 4}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 3, "u": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 3.605551275463989}, "pyerrors.correlators.Corr.reweight": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}}, "df": 11}}}}}, "j": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 2}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}, "t": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.dump": {"tf": 1}}, "df": 9, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr": {"tf": 2}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.show": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.7320508075688772}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1.7320508075688772}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 20, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 3}}, "s": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 4}}}}}}}}, "b": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}}, "df": 6}, "s": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1, "(": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 1}}}}, "v": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}}, "df": 3, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 3}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.fits.covariance_matrix": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}, "pyerrors.obs.cov_Obs": {"tf": 2}}, "df": 5, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "(": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}}}}}}, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}}, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 3}, "pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 2, "/": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}, "r": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}}, "df": 2}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 5}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}}, "df": 3}}, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "l": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 5}}}, "l": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 4}}, "u": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}}, "df": 5}}}, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 2}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}, "(": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 1, "+": {"1": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.7320508075688772}}, "df": 1}, "docs": {}, "df": 0}}}, "c": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}, "p": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}, "_": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}, "t": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 3}}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2}}}}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 1}}}}}}}, "m": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}}, "df": 2, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.covariance_matrix": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}, "pyerrors.obs.cov_Obs": {"tf": 1.7320508075688772}}, "df": 16}, "c": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}}, "df": 5}}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 3}}}}}, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 2}}}}, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "k": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 2}}, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 1}}, "n": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 4}, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 2}}}, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}, "j": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}, "x": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}}, "df": 3}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 2}, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 3}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 4}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 3, "a": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.linalg.pinv": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 10}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1.7320508075688772}}, "df": 7, "(": {"docs": {"pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 1}}, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 2.6457513110645907}}, "df": 2, "_": {"0": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2.8284271247461903}}, "df": 1}}}}}, "y": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors": {"tf": 2}}, "df": 1, "s": {"1": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "2": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "docs": {}, "df": 0}}}, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}}, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 3.4641016151377544}}, "df": 1}}}, "m": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors": {"tf": 2.449489742783178}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}}}}}}}, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}}, "df": 4, "i": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 3}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 2}}, "d": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "c": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}}, "df": 1}}}, "s": {"1": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}}, "df": 1}}, "d": {"docs": {"pyerrors.roots.find_root": {"tf": 2.23606797749979}}, "df": 1, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}}, "df": 1, "a": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr.fit": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3.1622776601683795}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 16, "t": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 3, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}}, "df": 4}}}}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}}, "df": 4, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 6}}}}}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}}, "df": 1}}}}}}}, "s": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}}, "df": 2}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 2}}}}}}, "k": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 2}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1}}}}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}}, "df": 3}}}}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 2}}, "a": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}}, "df": 6, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "(": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}}}}}}, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "^": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "=": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}, "\\": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "^": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, ",": {"docs": {}, "df": 0, ",": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}, "=": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "^": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}}}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 4}, "p": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 2}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 3.3166247903554}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero": {"tf": 1.4142135623730951}}, "df": 5}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 5}}}}}}, "f": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 8, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}}, "df": 2}}}}, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 2}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 20}}}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 3}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1.4142135623730951}}, "df": 2}}}}}}, "w": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}, "t": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1.4142135623730951}}, "df": 1, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2.6457513110645907}}, "df": 1, "=": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}, "r": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "m": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 3}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1.7320508075688772}, "pyerrors.obs.pseudo_Obs": {"tf": 1.4142135623730951}}, "df": 4}}}}, "s": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 1}}}}, "b": {"2": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 2}}, "docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 7}, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2}}}, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "{": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}, "a": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 2}}}}, "a": {"docs": {"pyerrors.fits.error_band": {"tf": 1}}, "df": 1}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}}, "w": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4}}, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 15}, "k": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 2}}, "df": 3}}}}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 2.23606797749979}, "pyerrors.input.bdio.write_ADerrors": {"tf": 2.23606797749979}, "pyerrors.input.bdio.read_mesons": {"tf": 2.23606797749979}, "pyerrors.input.bdio.read_dSdm": {"tf": 2.23606797749979}}, "df": 4, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}}}}}}, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, ")": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}}, "df": 4}}}}}}}, "b": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 3}}, "df": 1, "=": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}, "y": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2.449489742783178}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}}, "df": 1}}}}}}}}, "g": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 8, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"5": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}, "docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 6, "_": {"5": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 3.4641016151377544}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}}, "df": 3}}}}}}}}}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.qqplot": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {"pyerrors": {"tf": 6.082762530298219}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.6457513110645907}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 2}}, "df": 10}, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1}}}}, "v": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 4}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 23}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.dirac.Grid_gamma": {"tf": 1}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}, "z": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}}, "df": 3, "i": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 2}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, ":": {"1": {"0": {"0": {"9": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "2": {"0": {"5": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "8": {"0": {"9": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}}}}}}, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 5.830951894845301}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}}, "df": 17, "'": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}, "(": {"docs": {}, "df": 0, "[": {"2": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0, "[": {"0": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}}}, "c": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}}, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 4}}}}}}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}}, "df": 2}}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 5}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 2.8284271247461903}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}}, "df": 5}}}}}}}}, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 2}}}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 2}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 4}}}}, "g": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 5}}, "z": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 2}}, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}}, "df": 3}}, "n": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 4}}, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 5}}, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "c": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 4}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 3}}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 5}}}}, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 4}}}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 1}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.gamma_method": {"tf": 1}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 2}}}, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1}}}}}}}, "b": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 1}}}}, "(": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "(": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"1": {"docs": {"pyerrors.obs.covariance": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}}}}}}}}}, "d": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4, "d": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 3}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 2}}}}}}}, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "c": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1, "n": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 3, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 1}}}}}, "g": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}}}}, "x": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.7320508075688772}}, "df": 1}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "/": {"0": {"3": {"0": {"6": {"0": {"1": {"7": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 6}}, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 6}}}, "r": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 5}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}}, "df": 3}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.show": {"tf": 2}, "pyerrors.covobs.Covobs.__init__": {"tf": 1.4142135623730951}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 3}, "pyerrors.fits.total_least_squares": {"tf": 3}, "pyerrors.fits.fit_lin": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 2}, "pyerrors.input.json.dump_to_json": {"tf": 2}, "pyerrors.input.json.import_json_string": {"tf": 2}, "pyerrors.input.json.load_json": {"tf": 2}, "pyerrors.input.misc.read_pbp": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_rwms": {"tf": 2.23606797749979}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 2}, "pyerrors.mpm.matrix_pencil_method": {"tf": 2}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 3}, "pyerrors.obs.derived_observable": {"tf": 2}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}, "pyerrors.obs.merge_obs": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1.7320508075688772}}, "df": 27, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.merge_obs": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}, "b": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}}, "df": 4}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}}, "df": 4}}}, "b": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}}, "df": 4}}}}}}, "o": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 2, "(": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "(": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "w": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.misc.load_object": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 8}}}, "(": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, ")": {"docs": {}, "df": 0, "/": {"2": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "3": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0, "/": {"2": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}}}}, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}}}}}}}}, "v": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 4}}}, "f": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "f": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.fit": {"tf": 2}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.fits.Fit_result": {"tf": 1.4142135623730951}, "pyerrors.fits.Fit_result.gamma_method": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.8284271247461903}, "pyerrors.fits.total_least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.fit_lin": {"tf": 1.7320508075688772}, "pyerrors.fits.qqplot": {"tf": 1.4142135623730951}, "pyerrors.fits.residual_plot": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 12, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}}}}, "_": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}}, "df": 1}}}}}}}}}, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 10}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.correlators.Corr.dump": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 2}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_to_json": {"tf": 2.23606797749979}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 2}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 2}, "pyerrors.misc.load_object": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1.7320508075688772}}, "df": 22, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 3}}}, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}}}, "l": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}}, "df": 1}}, "d": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}}, "df": 2}}}, "x": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}}}, "m": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 4, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 9}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 3}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 5}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 2.449489742783178}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 5, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.fit": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 2}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}, "pyerrors.roots.find_root": {"tf": 2.23606797749979}}, "df": 14}}}}, "(": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 5, "i": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 2}}}}}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_zero": {"tf": 1.4142135623730951}, "pyerrors.obs.pseudo_Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 13}}}, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 5}, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "{": {"1": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "{": {"2": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.plot_piechart": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}}, "df": 3}}}}, "l": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 7}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}}, "df": 2}}}}}, "p": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 2}}}}, "f": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}}, "df": 1}}}, "s": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}}, "df": 3, "u": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "c": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 3}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 2}}}}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "b": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.linalg.einsum": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 1}}}}}}, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 2}}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 2, "i": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1, "f": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}}, "df": 2}}}}, "l": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 2}}, "h": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 3, "(": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 3}}}}, "z": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2}}, "df": 1}}, "g": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 4.123105625617661}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 2.23606797749979}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 2.23606797749979}, "pyerrors.obs.pseudo_Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.import_jackknife": {"tf": 1.7320508075688772}}, "df": 9, "e": {"docs": {}, "df": 0, "s": {"1": {"docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}, "2": {"docs": {"pyerrors": {"tf": 2.23606797749979}}, "df": 1}, "3": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}, "e": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.correlate": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 6}}, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 6}}, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "e": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 2.449489742783178}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1.4142135623730951}}, "df": 8}}}}}, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}}}}}}}, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.7320508075688772}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1.4142135623730951}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1.4142135623730951}, "pyerrors.obs.pseudo_Obs": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 26, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 6}}, "d": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2}}, "df": 1, "s": {"docs": {}, "df": 0, "=": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 10}}}}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}, "o": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1.4142135623730951}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.4142135623730951}}, "df": 2}, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}, "y": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 5}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 6}, "e": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1.4142135623730951}}, "df": 6}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}}, "df": 1}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "l": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 1}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, ",": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}}}}}}}}}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1}}}}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "=": {"2": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2}}}}, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs": {"tf": 1}}, "df": 1}}}}}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}}, "df": 13}}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}, "e": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 2}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr": {"tf": 1.4142135623730951}}, "df": 1}}}}, "y": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 4, "i": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_mesons": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.7320508075688772}}, "df": 4}}, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2.449489742783178}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}}, "df": 2}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.covobs.Covobs.errsq": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}, "k": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 2}}}, "f": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.4142135623730951}}, "df": 2}}}}, "t": {"0": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}}, "df": 1}, "docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 2}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 3, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1}}, "u": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_history": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 19}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "n": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1, "s": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}}}, "w": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 10}}, "a": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}}, "df": 2}}}, "y": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {"pyerrors.misc.gen_correlated_data": {"tf": 1}}, "df": 1, "_": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "{": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "}": {"docs": {}, "df": 0, "=": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "{": {"1": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "{": {"2": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "+": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "{": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "=": {"1": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "^": {"docs": {}, "df": 0, "{": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "}": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "(": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, ")": {"docs": {}, "df": 0, "\\": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "q": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}, "docs": {}, "df": 0}}}}}}}}}}}, "docs": {}, "df": 0}}}, "docs": {}, "df": 0}}}}}}}}}}}, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 3, "_": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2}}}}, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.Obs": {"tf": 1}}, "df": 1}}}}}}}}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}, "g": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 5}}}}}, "_": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.7320508075688772}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}}, "df": 10, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr": {"tf": 2}, "pyerrors.correlators.Corr.plottable": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.roll": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 6}}}}}}}, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.6457513110645907}}, "df": 4}}}, "h": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1}}, "df": 1}}}}, "/": {"2": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 2}}, "df": 1}, "docs": {}, "df": 0}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}, "r": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}}, "df": 4}}}, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1.7320508075688772}}, "df": 1}}}}, "^": {"2": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}, "n": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 2}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 6, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}}, "df": 3, "e": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 5}}, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.plot_rho": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 4}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 2}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.jack_matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.einsum": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 1.7320508075688772}}, "df": 18}}, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 15}}}, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}}, "df": 1}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}}}}, "p": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}}, "df": 4}, "e": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 4, "_": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}, "x": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}}}}}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.dump": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.4142135623730951}, "pyerrors.misc.dump_object": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1.7320508075688772}, "pyerrors.obs.pseudo_Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 19, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}}}}, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 3, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {"pyerrors.obs.Obs": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.Obs": {"tf": 1}}, "df": 1}}}}}}}}}}}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 3.4641016151377544}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 11, "(": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}}, "i": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "b": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 2}}, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 1}}, "x": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 1}}}, "x": {"0": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 2, "=": {"0": {"docs": {"pyerrors.correlators.Corr.symmetric": {"tf": 1}, "pyerrors.correlators.Corr.anti_symmetric": {"tf": 1}}, "df": 2}, "docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}}}, "1": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}}, "df": 2}, "2": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}}, "df": 2}, "docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.6457513110645907}, "pyerrors.fits.total_least_squares": {"tf": 2.8284271247461903}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 9, "_": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}, "[": {"0": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}, "1": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}, "y": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 2}, "pyerrors.fits.total_least_squares": {"tf": 2}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.fits.covariance_matrix": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 7, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.fit_lin": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"1": {"6": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"pyerrors.correlators.Corr.roll": {"tf": 1}, "pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 3.1622776601683795}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 2}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1}}, "df": 11, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors": {"tf": 2}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "n": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}}, "f": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "r": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_tauint": {"tf": 1}}, "df": 2}}}, "_": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}}, "df": 2}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 8, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}}}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "x": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1}}, "df": 1}, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.fits.Fit_result": {"tf": 1}}, "df": 2}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 2}}}}}}, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 7}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "c": {"docs": {"pyerrors.obs.Obs": {"tf": 2}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.input.sfcf.read_qtop": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 6}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 2}}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}}, "df": 3}}}}}, "m": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 2, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 4}}}}}, "i": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 7, "_": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 6}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}}, "d": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.4142135623730951}}, "df": 1, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 3}}}}}}, "l": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr.reweight": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}}, "df": 8}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.obs.Obs.plot_history": {"tf": 1}}, "df": 2}}}}}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 2}, "m": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}}, "r": {"0": {"1": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "2": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.obs.CObs.gamma_method": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 10, "l": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}}}, "d": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 2.23606797749979}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 2}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 2}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 2}, "pyerrors.input.misc.read_pbp": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.read_rwms": {"tf": 2}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_sfcf": {"tf": 2}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.7320508075688772}, "pyerrors.input.sfcf.read_qtop": {"tf": 1.4142135623730951}}, "df": 12, "_": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}}, "df": 1}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 2}}}}}, "d": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 1}}}}}}}, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 3.1622776601683795}, "pyerrors.fits.Fit_result": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 5}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}}, "df": 3}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.fits.residual_plot": {"tf": 1}}, "df": 3}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 11}}}}, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.plateau": {"tf": 1.4142135623730951}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}}, "df": 3, "s": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 2}}}}}}}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 7}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 2}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.correlators.Corr.reverse": {"tf": 1}}, "df": 1}}}}, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 2}, "pyerrors.input.sfcf.read_qtop": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 2}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}}, "df": 4}}}}}}, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.second_deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}, "pyerrors.dirac.Grid_gamma": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.fit_lin": {"tf": 1}, "pyerrors.fits.covariance_matrix": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.plot_piechart": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 23}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}}, "df": 1}}}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}}, "df": 1}}}}}}}}}, "l": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.4142135623730951}}, "df": 4}}, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 2}}}, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2}}, "df": 3}}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "(": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1}}, "df": 8}, "k": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1.4142135623730951}}, "df": 4, "_": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "(": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.roots.find_root": {"tf": 1}}, "df": 1}}}}}}}}}, "w": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1}}, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}}, "o": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.input.openQCD.read_rwms": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 3}}}}}, "w": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}}, "df": 1}}, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.is_zero": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_mesons": {"tf": 1.7320508075688772}, "pyerrors.input.bdio.read_dSdm": {"tf": 1.7320508075688772}}, "df": 4, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}}, "df": 5, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.linalg.matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.jack_matmul": {"tf": 1.4142135623730951}, "pyerrors.linalg.einsum": {"tf": 1}}, "df": 3}}}}, "n": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}}, "df": 2}}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 4}, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.23606797749979}, "pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1.4142135623730951}}, "df": 6}}}}}, "b": {"docs": {"pyerrors": {"tf": 6.6332495807108}, "pyerrors.correlators.Corr": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.correlate": {"tf": 1.7320508075688772}, "pyerrors.correlators.Corr.reweight": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.7320508075688772}, "pyerrors.fits.total_least_squares": {"tf": 2.449489742783178}, "pyerrors.fits.fit_lin": {"tf": 2.23606797749979}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.json.create_json_string": {"tf": 2}, "pyerrors.input.json.dump_to_json": {"tf": 2}, "pyerrors.input.json.import_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.load_json": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.linalg.eigh": {"tf": 1}, "pyerrors.linalg.eig": {"tf": 1}, "pyerrors.linalg.pinv": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}, "pyerrors.obs.Obs.dump": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1.7320508075688772}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}, "pyerrors.obs.correlate": {"tf": 1.4142135623730951}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}, "pyerrors.obs.pseudo_Obs": {"tf": 2.23606797749979}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1.4142135623730951}, "pyerrors.obs.cov_Obs": {"tf": 1.4142135623730951}, "pyerrors.roots.find_root": {"tf": 2.23606797749979}}, "df": 41, "j": {"docs": {"pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.misc.dump_object": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.set_prange": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.8284271247461903}, "pyerrors.input.json.create_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.dump_to_json": {"tf": 1.4142135623730951}, "pyerrors.misc.dump_object": {"tf": 1.7320508075688772}, "pyerrors.misc.load_object": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.__init__": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 19}}}}, "s": {"1": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 4}, "2": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.obs.derived_observable": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}}, "df": 4}, "3": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1}}, "df": 3}, "docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {"pyerrors": {"tf": 2.449489742783178}, "pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.misc.gen_correlated_data": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.reweight": {"tf": 1.7320508075688772}, "pyerrors.obs.correlate": {"tf": 2.23606797749979}, "pyerrors.obs.covariance": {"tf": 1.4142135623730951}, "pyerrors.obs.merge_obs": {"tf": 1.4142135623730951}}, "df": 13}}}, "[": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}}, "df": 2}}, "_": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}, "b": {"docs": {"pyerrors.obs.correlate": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.reverse": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.449489742783178}}, "df": 3, "=": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}, "pyerrors.obs.Obs.plot_rep_dist": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.merge_obs": {"tf": 1}}, "df": 10, "c": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}}, "df": 3, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}, "w": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 1.7320508075688772}}, "df": 2}}}}}}}, "d": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}}, "df": 1, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.plottable": {"tf": 1.4142135623730951}, "pyerrors.correlators.Corr.fit": {"tf": 1.4142135623730951}, "pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs": {"tf": 1}, "pyerrors.obs.Obs.details": {"tf": 1}}, "df": 11}}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}}}}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}, "pyerrors.fits.total_least_squares": {"tf": 1}}, "df": 2}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 2}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 2, "=": {"0": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}}}}}, "c": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "w": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1}, "pyerrors.input.json.dump_to_json": {"tf": 1}}, "df": 2}, "(": {"1": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 1}}}, "docs": {}, "df": 0}}, "v": {"docs": {"pyerrors.input.openQCD.extract_t0": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors": {"tf": 2.6457513110645907}, "pyerrors.correlators.Corr.plottable": {"tf": 1}, "pyerrors.correlators.Corr.plateau": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.fits.error_band": {"tf": 1}, "pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.linalg.inv": {"tf": 1}, "pyerrors.linalg.cholesky": {"tf": 1}, "pyerrors.linalg.det": {"tf": 1}, "pyerrors.linalg.svd": {"tf": 1}, "pyerrors.misc.gen_correlated_data": {"tf": 1}, "pyerrors.mpm.matrix_pencil_method": {"tf": 1}, "pyerrors.obs.Obs": {"tf": 3}, "pyerrors.obs.Obs.__init__": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.CObs": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}, "pyerrors.obs.pseudo_Obs": {"tf": 1.7320508075688772}, "pyerrors.obs.import_jackknife": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 28, "e": {"docs": {}, "df": 0, "(": {"docs": {"pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.m_eff": {"tf": 1.4142135623730951}}, "df": 1}, "c": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.covobs.Covobs.errsq": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 2}, "pyerrors.fits.Fit_result": {"tf": 1}, "pyerrors.linalg.matmul": {"tf": 1}}, "df": 3}, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.correlators.Corr.show": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs": {"tf": 1}}, "df": 2}}}}, "e": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 2}}, "e": {"docs": {}, "df": 0, "x": {"docs": {"pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}, "pyerrors.input.openQCD.read_rwms": {"tf": 1.4142135623730951}}, "df": 2}}}}, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 2}}}}}}, "j": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}, "m": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.4142135623730951}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 1, "k": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 2}, "pyerrors.obs.import_jackknife": {"tf": 1.4142135623730951}}, "df": 4}}}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}}}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json.create_json_string": {"tf": 1.7320508075688772}, "pyerrors.input.json.dump_to_json": {"tf": 2.23606797749979}, "pyerrors.input.json.import_json_string": {"tf": 1.4142135623730951}, "pyerrors.input.json.load_json": {"tf": 1.4142135623730951}}, "df": 4, "_": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}}, "df": 1}}}}}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 2}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.obs.Obs.gamma_method": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {"pyerrors": {"tf": 2.23606797749979}, "pyerrors.correlators.Corr": {"tf": 1}, "pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.m_eff": {"tf": 2}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.correlators.Corr.show": {"tf": 1}, "pyerrors.fits.least_squares": {"tf": 2.23606797749979}, "pyerrors.fits.total_least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.fit_lin": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 2.449489742783178}, "pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1}, "pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.4142135623730951}, "pyerrors.linalg.jack_matmul": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 2.6457513110645907}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 18}, "p": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}}, "df": 2, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}}, "df": 1}}}}}}, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}, "pyerrors.input.openQCD.extract_t0": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 7, "t": {"docs": {}, "df": 0, "h": {"docs": {"pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.import_jackknife": {"tf": 1}}, "df": 2}}}}}}, "w": {"docs": {"pyerrors": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1.7320508075688772}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 4}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 2}}, "df": 1, "s": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "y": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 1, "f": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.total_least_squares": {"tf": 1}, "pyerrors.input.openQCD.extract_t0": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.export_jackknife": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 7, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}}}}}}, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.correlators.Corr.reweight": {"tf": 1.4142135623730951}, "pyerrors.obs.reweight": {"tf": 1.4142135623730951}}, "df": 2}}}}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.correlators.Corr.deriv": {"tf": 1}, "pyerrors.correlators.Corr.fit": {"tf": 1}, "pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.obs.Obs.gamma_method": {"tf": 1}, "pyerrors.obs.Obs.is_zero_within_error": {"tf": 1}, "pyerrors.obs.Obs.is_zero": {"tf": 1}, "pyerrors.obs.CObs.is_zero": {"tf": 1}}, "df": 7}}}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.covobs.Covobs.__init__": {"tf": 1}, "pyerrors.obs.cov_Obs": {"tf": 1}}, "df": 2}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.bdio.write_ADerrors": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.json.import_json_string": {"tf": 1}, "pyerrors.input.json.load_json": {"tf": 1}}, "df": 2}}}}}, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"pyerrors.linalg.einsum": {"tf": 1}, "pyerrors.obs.derived_observable": {"tf": 1}, "pyerrors.roots.find_root": {"tf": 1}}, "df": 3}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {"pyerrors.input.bdio.read_ADerrors": {"tf": 1}, "pyerrors.input.bdio.write_ADerrors": {"tf": 1}, "pyerrors.input.bdio.read_mesons": {"tf": 1}, "pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 4}, "f": {"2": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1}}, "df": 1}}, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "l": {"docs": {"pyerrors": {"tf": 1.4142135623730951}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1.4142135623730951}, "pyerrors.obs.Obs.plot_history": {"tf": 1.4142135623730951}}, "df": 2}}}}}}, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, ":": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "f": {"docs": {"pyerrors.dirac.epsilon_tensor": {"tf": 1}, "pyerrors.dirac.epsilon_tensor_rank4": {"tf": 1}}, "df": 2}}}}}}}}}}}}}}}, "d": {"docs": {}, "df": 0, "f": {"5": {"docs": {"pyerrors.input.hadrons.read_meson_hd5": {"tf": 1.4142135623730951}, "pyerrors.input.hadrons.read_ExternalLeg_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Bilinear_hd5": {"tf": 1}, "pyerrors.input.hadrons.read_Fourquark_hd5": {"tf": 1}}, "df": 4}, "docs": {}, "df": 0}}, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.input.hadrons.Npr_matrix.g5H": {"tf": 1.4142135623730951}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"pyerrors.linalg.eigh": {"tf": 1}}, "df": 1}}}}}}}}, "u": {"docs": {}, "df": 0, "a": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1}}, "df": 1}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"pyerrors": {"tf": 1}}, "df": 1}}, "l": {"docs": {"pyerrors.fits.least_squares": {"tf": 1.4142135623730951}, "pyerrors.fits.qqplot": {"tf": 1.4142135623730951}}, "df": 2}}, "u": {"docs": {}, "df": 0, "m": {"docs": {"pyerrors.correlators.Corr.T_symmetry": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "k": {"docs": {"pyerrors.input.sfcf.read_sfcf_c": {"tf": 1.4142135623730951}}, "df": 1}}}}, "q": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"pyerrors.fits.least_squares": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors.input.sfcf.read_qtop": {"tf": 1}}, "df": 1}}}}, "k": {"docs": {"pyerrors.mpm.matrix_pencil_method": {"tf": 1.7320508075688772}}, "df": 1, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {"pyerrors": {"tf": 1}, "pyerrors.obs.correlate": {"tf": 1}}, "df": 2}}, "y": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}, "pyerrors.input.misc.read_pbp": {"tf": 1}, "pyerrors.obs.covariance": {"tf": 1}}, "df": 3}}}}}}, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"1": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1}, "2": {"docs": {"pyerrors.input.bdio.read_mesons": {"tf": 1}}, "df": 1}, "docs": {"pyerrors.input.bdio.read_dSdm": {"tf": 1}}, "df": 1}}}}, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {"pyerrors.obs.derived_observable": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "_": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "_": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "_": {"docs": {}, "df": 0, "_": {"docs": {"pyerrors.input.hadrons.Npr_matrix": {"tf": 1}}, "df": 1}}}}}}}}}}}, "pipeline": ["trimmer", "stopWordFilter", "stemmer"], "_isPrebuiltIndex": true};
// mirrored in build-search-index.js (part 1)
// Also split on html tags. this is a cheap heuristic, but good enough.