mirror of
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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
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@ -226,8 +226,8 @@ class Corr:
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def roll(self, dt):
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"""Periodically shift the correlator by dt timeslices
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Attributes:
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-----------
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Parameters
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----------
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dt : int
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number of timeslices
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"""
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@ -264,9 +264,6 @@ class Corr:
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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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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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@ -283,8 +280,8 @@ class Corr:
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def T_symmetry(self, partner, parity=+1):
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"""Return the time symmetry average of the correlator and its partner
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Attributes:
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-----------
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Parameters
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----------
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partner : Corr
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Time symmetry partner of the Corr
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partity : int
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@ -309,8 +306,8 @@ class Corr:
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def deriv(self, symmetric=True):
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"""Return the first derivative of the correlator with respect to x0.
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Attributes:
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-----------
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Parameters
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----------
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symmetric : bool
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decides whether symmertic of simple finite differences are used. Default: True
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"""
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@ -414,8 +411,8 @@ class Corr:
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def fit(self, function, fitrange=None, silent=False, **kwargs):
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"""Fits function to the data
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Attributes:
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-----------
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Parameters
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----------
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function : obj
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function to fit to the data. See fits.least_squares for details.
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fitrange : list
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@ -447,8 +444,8 @@ class Corr:
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def plateau(self, plateau_range=None, method="fit"):
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""" Extract a plateu value from a Corr object
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Attributes:
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-----------
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Parameters
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----------
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plateau_range : list
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list with two entries, indicating the first and the last timeslice
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of the plateau region.
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@ -578,8 +575,8 @@ class Corr:
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def dump(self, filename):
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"""Dumps the Corr into a pickel file
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Attributes:
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-----------
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Parameters
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----------
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filename : str
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Name of the file
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"""
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@ -59,7 +59,7 @@ class Fit_result(Sequence):
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def least_squares(x, y, func, priors=None, silent=False, **kwargs):
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"""Performs a non-linear fit to y = func(x).
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Arguments:
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Parameters
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----------
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x : list
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list of floats.
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@ -87,22 +87,23 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
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enough.
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silent : bool, optional
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If true all output to the console is omitted (default False).
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Keyword arguments
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-----------------
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initial_guess -- can provide an initial guess for the input parameters. Relevant for
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initial_guess : list
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can provide an initial guess for the input parameters. Relevant for
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non-linear fits with many parameters.
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method -- can be used to choose an alternative method for the minimization of chisquare.
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The possible methods are the ones which can be used for scipy.optimize.minimize and
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migrad of iminuit. If no method is specified, Levenberg-Marquard is used.
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Reliable alternatives are migrad, Powell and Nelder-Mead.
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resplot -- If true, a plot which displays fit, data and residuals is generated (default False).
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qqplot -- If true, a quantile-quantile plot of the fit result is generated (default False).
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expected_chisquare -- If true prints the expected chisquare which is
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corrected by effects caused by correlated input data.
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This can take a while as the full correlation matrix
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has to be calculated (default False).
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method : str
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can be used to choose an alternative method for the minimization of chisquare.
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The possible methods are the ones which can be used for scipy.optimize.minimize and
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migrad of iminuit. If no method is specified, Levenberg-Marquard is used.
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Reliable alternatives are migrad, Powell and Nelder-Mead.
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resplot : bool
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If true, a plot which displays fit, data and residuals is generated (default False).
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qqplot : bool
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If true, a quantile-quantile plot of the fit result is generated (default False).
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expected_chisquare : bool
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If true prints the expected chisquare which is
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corrected by effects caused by correlated input data.
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This can take a while as the full correlation matrix
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has to be calculated (default False).
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"""
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if priors is not None:
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return _prior_fit(x, y, func, priors, silent=silent, **kwargs)
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@ -254,6 +255,8 @@ def odr_fit(x, y, func, silent=False, **kwargs):
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def total_least_squares(x, y, func, silent=False, **kwargs):
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"""Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
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Parameters
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----------
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x : list
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list of Obs, or a tuple of lists of Obs
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y : list
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@ -276,15 +279,14 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
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silent : bool, optional
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If true all output to the console is omitted (default False).
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Based on the orthogonal distance regression module of scipy
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Keyword arguments
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-----------------
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initial_guess -- can provide an initial guess for the input parameters. Relevant for non-linear
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fits with many parameters.
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expected_chisquare -- If true prints the expected chisquare which is
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corrected by effects caused by correlated input data.
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This can take a while as the full correlation matrix
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has to be calculated (default False).
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initial_guess : list
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can provide an initial guess for the input parameters. Relevant for non-linear
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fits with many parameters.
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expected_chisquare : bool
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If true prints the expected chisquare which is
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corrected by effects caused by correlated input data.
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This can take a while as the full correlation matrix
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has to be calculated (default False).
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"""
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output = Fit_result()
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@ -13,15 +13,16 @@ from ..fits import fit_lin
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def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
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"""Read rwms format from given folder structure. Returns a list of length nrw
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Attributes
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-----------------
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version -- version of openQCD, default 2.0
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Keyword arguments
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-----------------
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r_start -- list which contains the first config to be read for each replicum
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r_stop -- list which contains the last config to be read for each replicum
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postfix -- postfix of the file to read, e.g. '.ms1' for openQCD-files
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Parameters
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----------
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version : str
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version of openQCD, default 2.0
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r_start : list
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list which contains the first config to be read for each replicum
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r_stop : list
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list which contains the last config to be read for each replicum
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postfix : str
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postfix of the file to read, e.g. '.ms1' for openQCD-files
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"""
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known_oqcd_versions = ['1.4', '1.6', '2.0']
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if not (version in known_oqcd_versions):
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@ -174,19 +175,25 @@ def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwar
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Parameters
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----------
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path -- Path to .ms.dat files
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prefix -- Ensemble prefix
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dtr_read -- Determines how many trajectories should be skipped when reading the ms.dat files.
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Corresponds to dtr_cnfg / dtr_ms in the openQCD input file.
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xmin -- First timeslice where the boundary effects have sufficiently decayed.
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spatial_extent -- spatial extent of the lattice, required for normalization.
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fit_range -- Number of data points left and right of the zero crossing to be included in the linear fit. (Default: 5)
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Keyword arguments
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-----------------
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r_start -- list which contains the first config to be read for each replicum.
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r_stop -- list which contains the last config to be read for each replicum.
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plaquette -- If true extract the plaquette estimate of t0 instead.
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path : str
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Path to .ms.dat files
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prefix : str
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Ensemble prefix
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dtr_read : int
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Determines how many trajectories should be skipped when reading the ms.dat files.
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Corresponds to dtr_cnfg / dtr_ms in the openQCD input file.
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xmin : int
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First timeslice where the boundary effects have sufficiently decayed.
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spatial_extent : int
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spatial extent of the lattice, required for normalization.
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fit_range : int
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Number of data points left and right of the zero crossing to be included in the linear fit. (Default: 5)
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r_start : list
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list which contains the first config to be read for each replicum.
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r_stop: list
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list which contains the last config to be read for each replicum.
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plaquette : bool
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If true extract the plaquette estimate of t0 instead.
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"""
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ls = []
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@ -11,8 +11,8 @@ from ..obs import Obs
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def read_sfcf(path, prefix, name, **kwargs):
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"""Read sfcf C format from given folder structure.
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Keyword arguments
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-----------------
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Parameters
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----------
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im -- if True, read imaginary instead of real part of the correlation function.
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single -- if True, read a boundary-to-boundary correlation function with a single value
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b2b -- if True, read a time-dependent boundary-to-boundary correlation function
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@ -113,15 +113,12 @@ def read_sfcf(path, prefix, name, **kwargs):
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def read_sfcf_c(path, prefix, name, quarks='.*', noffset=0, wf=0, wf2=0, **kwargs):
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"""Read sfcf c format from given folder structure.
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Arguments
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-----------------
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Parameters
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----------
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quarks -- Label of the quarks used in the sfcf input file
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noffset -- Offset of the source (only relevant when wavefunctions are used)
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wf -- ID of wave function
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wf2 -- ID of the second wavefunction (only relevant for boundary-to-boundary correlation functions)
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Keyword arguments
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-----------------
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im -- if True, read imaginary instead of real part of the correlation function.
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b2b -- if True, read a time-dependent boundary-to-boundary correlation function
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names -- Alternative labeling for replicas/ensembles. Has to have the appropriate length
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@ -236,8 +233,8 @@ def read_sfcf_c(path, prefix, name, quarks='.*', noffset=0, wf=0, wf2=0, **kwarg
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def read_qtop(path, prefix, **kwargs):
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"""Read qtop format from given folder structure.
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Keyword arguments
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-----------------
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Parameters
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----------
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target -- specifies the topological sector to be reweighted to (default 0)
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full -- if true read the charge instead of the reweighting factor.
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"""
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@ -20,9 +20,6 @@ def derived_array(func, data, **kwargs):
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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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Keyword arguments
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-----------------
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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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@ -8,8 +8,8 @@ from .obs import Obs
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def gen_correlated_data(means, cov, name, tau=0.5, samples=1000):
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""" Generate observables with given covariance and autocorrelation times.
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Arguments
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-----------------
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Parameters
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----------
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means -- list containing the mean value of each observable.
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cov -- covariance matrix for the data to be geneated.
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name -- ensemble name for the data to be geneated.
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@ -73,9 +73,12 @@ def inv_propagator(prop):
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def Zq(inv_prop, fermion='Wilson'):
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""" Calculates the quark field renormalization constant Zq
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Attributes:
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inv_prop -- Inverted 12x12 quark propagator
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fermion -- Fermion type for which the tree-level propagator is used
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Parameters
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----------
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inv_prop : array
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Inverted 12x12 quark propagator
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fermion : str
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Fermion type for which the tree-level propagator is used
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in the calculation of Zq. Default Wilson.
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"""
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_check_geometry()
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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
|
||||
|
|
@ -893,14 +895,15 @@ def filter_zeroes(names, deltas, idl, eps=Obs.filter_eps):
|
|||
|
||||
Parameters
|
||||
----------
|
||||
names -- List of names
|
||||
deltas -- Dict lists of fluctuations
|
||||
idx -- Dict of lists or ranges of configs on which the deltas are defined.
|
||||
Has to be a subset of new_idx.
|
||||
|
||||
Optional parameters
|
||||
----------
|
||||
eps -- Prefactor that enters the filter criterion.
|
||||
names : list
|
||||
List of names
|
||||
deltas : dict
|
||||
Dict lists of fluctuations
|
||||
idx : dict
|
||||
Dict of lists or ranges of configs on which the deltas are defined.
|
||||
Has to be a subset of new_idx.
|
||||
eps : float
|
||||
Prefactor that enters the filter criterion.
|
||||
"""
|
||||
new_names = []
|
||||
new_deltas = {}
|
||||
|
|
@ -931,9 +934,6 @@ def derived_observable(func, data, **kwargs):
|
|||
the autograd wrapper (use 'import autograd.numpy as anp').
|
||||
data : list
|
||||
list of Obs, e.g. [obs1, obs2, obs3].
|
||||
|
||||
Keyword arguments
|
||||
-----------------
|
||||
num_grad : bool
|
||||
if True, numerical derivatives are used instead of autograd
|
||||
(default False). To control the numerical differentiation the
|
||||
|
|
@ -1072,10 +1072,13 @@ def reduce_deltas(deltas, idx_old, idx_new):
|
|||
|
||||
Parameters
|
||||
----------
|
||||
deltas -- List of fluctuations
|
||||
idx_old -- List or range of configs on which the deltas are defined
|
||||
idx_new -- List of configs for which we want to extract the deltas.
|
||||
Has to be a subset of idx_old.
|
||||
deltas : list
|
||||
List of fluctuations
|
||||
idx_old : list
|
||||
List or range of configs on which the deltas are defined
|
||||
idx_new : list
|
||||
List of configs for which we want to extract the deltas.
|
||||
Has to be a subset of idx_old.
|
||||
"""
|
||||
if not len(deltas) == len(idx_old):
|
||||
raise Exception('Lenght of deltas and idx_old have to be the same: %d != %d' % (len(deltas), len(idx_old)))
|
||||
|
|
@ -1109,9 +1112,6 @@ def reweight(weight, obs, **kwargs):
|
|||
configurations in obs[i].idl for all i.
|
||||
obs : list
|
||||
list of Obs, e.g. [obs1, obs2, obs3].
|
||||
|
||||
Keyword arguments
|
||||
-----------------
|
||||
all_configs : bool
|
||||
if True, the reweighted observables are normalized by the average of
|
||||
the reweighting factor on all configurations in weight.idl and not
|
||||
|
|
@ -1146,8 +1146,8 @@ def reweight(weight, obs, **kwargs):
|
|||
def correlate(obs_a, obs_b):
|
||||
"""Correlate two observables.
|
||||
|
||||
Attributes:
|
||||
-----------
|
||||
Parameters
|
||||
----------
|
||||
obs_a : Obs
|
||||
First observable
|
||||
obs_b : Obs
|
||||
|
|
@ -1193,10 +1193,11 @@ def covariance(obs1, obs2, correlation=False, **kwargs):
|
|||
is constrained to the maximum value in order to make sure that covariance
|
||||
matrices are positive semidefinite.
|
||||
|
||||
Keyword arguments
|
||||
-----------------
|
||||
correlation -- if true the correlation instead of the covariance is
|
||||
returned (default False)
|
||||
Parameters
|
||||
----------
|
||||
correlation : bool
|
||||
if true the correlation instead of the covariance is
|
||||
returned (default False)
|
||||
"""
|
||||
|
||||
for name in sorted(set(obs1.names + obs2.names)):
|
||||
|
|
@ -1450,9 +1451,14 @@ def pseudo_Obs(value, dvalue, name, samples=1000):
|
|||
def dump_object(obj, name, **kwargs):
|
||||
"""Dump object into pickle file.
|
||||
|
||||
Keyword arguments
|
||||
-----------------
|
||||
path -- specifies a custom path for the file (default '.')
|
||||
Parameters
|
||||
----------
|
||||
obj : object
|
||||
object to be saved in the pickle file
|
||||
name : str
|
||||
name of the file
|
||||
path : str
|
||||
specifies a custom path for the file (default '.')
|
||||
"""
|
||||
if 'path' in kwargs:
|
||||
file_name = kwargs.get('path') + '/' + name + '.p'
|
||||
|
|
@ -1471,6 +1477,11 @@ def load_object(path):
|
|||
def merge_obs(list_of_obs):
|
||||
"""Combine all observables in list_of_obs into one new observable
|
||||
|
||||
Parameters
|
||||
----------
|
||||
list_of_obs : list
|
||||
list of the Obs object to be combined
|
||||
|
||||
It is not possible to combine obs which are based on the same replicum
|
||||
"""
|
||||
replist = [item for obs in list_of_obs for item in obs.names]
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue