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docstrings updated
This commit is contained in:
Fabian Joswig
parent
a23a97aed1
commit
effccb1cc8
8 changed files with 173 additions and 159 deletions
171
pyerrors/obs.py
171
pyerrors/obs.py
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@ -53,7 +53,7 @@ class Obs:
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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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Parameters
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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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@ -150,57 +150,11 @@ class Obs:
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res[e_name].append(e_name)
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return res
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def expand_deltas(self, deltas, idx, shape):
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"""Expand deltas defined on idx to a regular, contiguous range, where holes are filled by 0.
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If idx is of type range, the deltas are not changed
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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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shape -- Number of configs in idx.
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"""
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if type(idx) is range:
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return deltas
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else:
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ret = np.zeros(idx[-1] - idx[0] + 1)
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for i in range(shape):
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ret[idx[i] - idx[0]] = deltas[i]
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return ret
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def calc_gamma(self, deltas, idx, shape, w_max, fft):
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"""Calculate Gamma_{AA} from the deltas, which are defined on idx.
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idx is assumed to be a contiguous range (possibly with a stepsize != 1)
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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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shape -- Number of configs in idx.
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w_max -- Upper bound for the summation window
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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
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"""
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gamma = np.zeros(w_max)
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deltas = self.expand_deltas(deltas, idx, shape)
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new_shape = len(deltas)
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if fft:
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max_gamma = min(new_shape, w_max)
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# The padding for the fft has to be even
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padding = new_shape + max_gamma + (new_shape + max_gamma) % 2
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gamma[:max_gamma] += np.fft.irfft(np.abs(np.fft.rfft(deltas, padding)) ** 2)[:max_gamma]
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else:
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for n in range(w_max):
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if new_shape - n >= 0:
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gamma[n] += deltas[0:new_shape - n].dot(deltas[n:new_shape])
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return gamma
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def gamma_method(self, **kwargs):
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"""Calculate the error and related properties of the Obs.
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Keyword arguments
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-----------------
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Parameters
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----------
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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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@ -378,6 +332,52 @@ class Obs:
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self.ddvalue = np.sqrt(self.ddvalue) / self.dvalue
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return
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def expand_deltas(self, deltas, idx, shape):
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"""Expand deltas defined on idx to a regular, contiguous range, where holes are filled by 0.
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If idx is of type range, the deltas are not changed
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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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shape -- Number of configs in idx.
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"""
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if type(idx) is range:
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return deltas
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else:
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ret = np.zeros(idx[-1] - idx[0] + 1)
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for i in range(shape):
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ret[idx[i] - idx[0]] = deltas[i]
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return ret
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def calc_gamma(self, deltas, idx, shape, w_max, fft):
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"""Calculate Gamma_{AA} from the deltas, which are defined on idx.
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idx is assumed to be a contiguous range (possibly with a stepsize != 1)
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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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shape -- Number of configs in idx.
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w_max -- Upper bound for the summation window
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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
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"""
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gamma = np.zeros(w_max)
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deltas = self.expand_deltas(deltas, idx, shape)
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new_shape = len(deltas)
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if fft:
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max_gamma = min(new_shape, w_max)
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# The padding for the fft has to be even
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padding = new_shape + max_gamma + (new_shape + max_gamma) % 2
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gamma[:max_gamma] += np.fft.irfft(np.abs(np.fft.rfft(deltas, padding)) ** 2)[:max_gamma]
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else:
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for n in range(w_max):
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if new_shape - n >= 0:
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gamma[n] += deltas[0:new_shape - n].dot(deltas[n:new_shape])
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return gamma
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def print(self, level=1):
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warnings.warn("Method 'print' renamed to 'details'", DeprecationWarning)
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self.details(level > 1)
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@ -539,9 +539,10 @@ class Obs:
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def dump(self, name, **kwargs):
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"""Dump the Obs to a pickle file 'name'.
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Keyword arguments
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-----------------
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path -- specifies a custom path for the file (default '.')
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Parameters
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----------
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path : str
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specifies a custom path for the file (default '.')
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"""
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if 'path' in kwargs:
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file_name = kwargs.get('path') + '/' + name + '.p'
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@ -836,7 +837,8 @@ def merge_idx(idl):
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Parameters
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----------
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idl -- List of lists or ranges.
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idl : list
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List of lists or ranges.
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"""
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# Use groupby to efficiently check whether all elements of idl are identical
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@ -893,14 +895,15 @@ def filter_zeroes(names, deltas, idl, eps=Obs.filter_eps):
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Parameters
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----------
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names -- List of names
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deltas -- Dict lists of fluctuations
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idx -- Dict of lists or ranges of configs on which the deltas are defined.
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Has to be a subset of new_idx.
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Optional parameters
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----------
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eps -- Prefactor that enters the filter criterion.
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names : list
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List of names
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deltas : dict
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Dict lists of fluctuations
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idx : dict
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Dict of lists or ranges of configs on which the deltas are defined.
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Has to be a subset of new_idx.
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eps : float
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Prefactor that enters the filter criterion.
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"""
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new_names = []
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new_deltas = {}
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@ -931,9 +934,6 @@ def derived_observable(func, data, **kwargs):
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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 : 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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@ -1072,10 +1072,13 @@ def reduce_deltas(deltas, idx_old, idx_new):
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Parameters
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----------
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deltas -- List of fluctuations
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idx_old -- List or range of configs on which the deltas are defined
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idx_new -- List of configs for which we want to extract the deltas.
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Has to be a subset of idx_old.
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deltas : list
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List of fluctuations
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idx_old : list
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List or range of configs on which the deltas are defined
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idx_new : list
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List of configs for which we want to extract the deltas.
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Has to be a subset of idx_old.
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"""
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if not len(deltas) == len(idx_old):
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raise Exception('Lenght of deltas and idx_old have to be the same: %d != %d' % (len(deltas), len(idx_old)))
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@ -1109,9 +1112,6 @@ def reweight(weight, obs, **kwargs):
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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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all_configs : bool
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if True, the reweighted observables are normalized by the average of
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the reweighting factor on all configurations in weight.idl and not
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@ -1146,8 +1146,8 @@ def reweight(weight, obs, **kwargs):
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def correlate(obs_a, obs_b):
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"""Correlate two observables.
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Attributes:
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-----------
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Parameters
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----------
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obs_a : Obs
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First observable
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obs_b : Obs
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@ -1193,10 +1193,11 @@ def covariance(obs1, obs2, correlation=False, **kwargs):
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is constrained to the maximum value in order to make sure that covariance
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matrices are positive semidefinite.
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Keyword arguments
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-----------------
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correlation -- if true the correlation instead of the covariance is
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returned (default False)
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Parameters
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----------
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correlation : bool
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if true the correlation instead of the covariance is
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returned (default False)
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"""
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for name in sorted(set(obs1.names + obs2.names)):
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@ -1450,9 +1451,14 @@ def pseudo_Obs(value, dvalue, name, samples=1000):
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def dump_object(obj, name, **kwargs):
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"""Dump object into pickle file.
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Keyword arguments
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-----------------
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path -- specifies a custom path for the file (default '.')
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Parameters
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----------
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obj : object
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object to be saved in the pickle file
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name : str
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name of the file
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path : str
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specifies a custom path for the file (default '.')
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"""
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if 'path' in kwargs:
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file_name = kwargs.get('path') + '/' + name + '.p'
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@ -1471,6 +1477,11 @@ def load_object(path):
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def merge_obs(list_of_obs):
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"""Combine all observables in list_of_obs into one new observable
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Parameters
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----------
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list_of_obs : list
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list of the Obs object to be combined
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It is not possible to combine obs which are based on the same replicum
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"""
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replist = [item for obs in list_of_obs for item in obs.names]
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