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docstrings extended
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Fabian Joswig
parent
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2 changed files with 82 additions and 40 deletions
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@ -15,6 +15,14 @@ from .pyerrors import Obs, derived_observable, covariance, pseudo_Obs
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class Fit_result(Sequence):
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"""Represents fit results.
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Attributes
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----------
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fit_parameters : list
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results for the individual fit parameters,
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also accesible via indices.
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"""
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def __init__(self):
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self.fit_parameters = None
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@ -24,16 +24,21 @@ class Obs:
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Attributes
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----------
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e_tag_global -- Integer which determines which part of the name belongs
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to the ensemble and which to the replicum.
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S_global -- Standard value for S (default 2.0)
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S_dict -- Dictionary for S values. If an entry for a given ensemble
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exists this overwrites the standard value for that ensemble.
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tau_exp_global -- Standard value for tau_exp (default 0.0)
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tau_exp_dict -- Dictionary for tau_exp values. If an entry for a given
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ensemble exists this overwrites the standard value for that
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ensemble.
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N_sigma_global -- Standard value for N_sigma (default 1.0)
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e_tag_global : int
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Integer which determines which part of the name belongs
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to the ensemble and which to the replicum.
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S_global : float
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Standard value for S (default 2.0)
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S_dict : dict
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Dictionary for S values. If an entry for a given ensemble
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exists this overwrites the standard value for that ensemble.
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tau_exp_global : float
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Standard value for tau_exp (default 0.0)
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tau_exp_dict :dict
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Dictionary for tau_exp values. If an entry for a given ensemble exists
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this overwrites the standard value for that ensemble.
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N_sigma_global : float
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Standard value for N_sigma (default 1.0)
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"""
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__slots__ = ['names', 'shape', 'r_values', 'deltas', 'N', '_value', '_dvalue',
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'ddvalue', 'reweighted', 'S', 'tau_exp', 'N_sigma', 'e_names',
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@ -50,6 +55,20 @@ class Obs:
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filter_eps = 1e-10
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def __init__(self, samples, names, idl=None, means=None, **kwargs):
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""" Initialize Obs object.
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Attributes
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----------
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samples : list
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list of numpy arrays containing the Monte Carlo samples
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names : list
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list of strings labeling the indivdual samples
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idl : list, optional
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list of ranges or lists on which the samples are defined
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means : list, optional
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list of mean values for the case that the mean values were
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already subtracted from the samples
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"""
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if means is None:
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if len(samples) != len(names):
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@ -173,17 +192,22 @@ class Obs:
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Keyword arguments
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-----------------
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S -- specifies a custom value for the parameter S (default 2.0), can be
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a float or an array of floats for different ensembles
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tau_exp -- positive value triggers the critical slowing down analysis
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(default 0.0), can be a float or an array of floats for
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different ensembles
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N_sigma -- number of standard deviations from zero until the tail is
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attached to the autocorrelation function (default 1)
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e_tag -- number of characters which label the ensemble. The remaining
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ones label replica (default 0)
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fft -- boolean, which determines whether the fft algorithm is used for
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the computation of the autocorrelation function (default True)
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S : float
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specifies a custom value for the parameter S (default 2.0), can be
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a float or an array of floats for different ensembles
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tau_exp : float
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positive value triggers the critical slowing down analysis
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(default 0.0), can be a float or an array of floats for different
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ensembles
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N_sigma : float
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number of standard deviations from zero until the tail is
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attached to the autocorrelation function (default 1)
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e_tag : int
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number of characters which label the ensemble. The remaining
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ones label replica (default 0)
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fft : bool
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determines whether the fft algorithm is used for the computation
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of the autocorrelation function (default True)
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"""
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if 'e_tag' in kwargs:
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@ -846,11 +870,15 @@ def expand_deltas_for_merge(deltas, idx, shape, new_idx):
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Parameters
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----------
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deltas -- List of fluctuations
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idx -- List or range of configs on which the deltas are defined.
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Has to be a subset of new_idx.
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shape -- Number of configs in idx.
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new_idx -- List of configs that defines the new range.
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deltas : list
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List of fluctuations
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idx : list
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List or range of configs on which the deltas are defined.
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Has to be a subset of new_idx.
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shape : list
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Number of configs in idx.
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new_idx : list
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List of configs that defines the new range.
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"""
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if type(idx) is range and type(new_idx) is range:
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@ -903,20 +931,24 @@ def derived_observable(func, data, **kwargs):
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Parameters
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----------
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func -- arbitrary function of the form func(data, **kwargs). For the
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automatic differentiation to work, all numpy functions have to have
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the autograd wrapper (use 'import autograd.numpy as anp').
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data -- list of Obs, e.g. [obs1, obs2, obs3].
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func : object
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arbitrary function of the form func(data, **kwargs). For the
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automatic differentiation to work, all numpy functions have to have
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the autograd wrapper (use 'import autograd.numpy as anp').
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data : list
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list of Obs, e.g. [obs1, obs2, obs3].
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Keyword arguments
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-----------------
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num_grad -- if True, numerical derivatives are used instead of autograd
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(default False). To control the numerical differentiation the
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kwargs of numdifftools.step_generators.MaxStepGenerator
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can be used.
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man_grad -- manually supply a list or an array which contains the jacobian
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of func. Use cautiously, supplying the wrong derivative will
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not be intercepted.
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num_grad : bool
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if True, numerical derivatives are used instead of autograd
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(default False). To control the numerical differentiation the
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kwargs of numdifftools.step_generators.MaxStepGenerator
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can be used.
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man_grad : list
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manually supply a list or an array which contains the jacobian
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of func. Use cautiously, supplying the wrong derivative will
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not be intercepted.
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Notes
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-----
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@ -1077,9 +1109,11 @@ def reweight(weight, obs, **kwargs):
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Parameters
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----------
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weight -- Reweighting factor. An Observable that has to be defined on a superset of the
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configurations in obs[i].idl for all i.
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obs -- list of Obs, e.g. [obs1, obs2, obs3].
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weight : Obs
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Reweighting factor. An Observable that has to be defined on a superset of the
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configurations in obs[i].idl for all i.
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obs : list
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list of Obs, e.g. [obs1, obs2, obs3].
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Keyword arguments
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-----------------
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