mirror of
https://github.com/fjosw/pyerrors.git
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better docstrings (#144)
* first example of returns statement in docstring * added a some return statements for pandas API * last return statements in pandas input * added returns to bdio docstrings * few returns statements added to docstring * finished docstrings for hadrons submodule * also finished docstrings for json submodule * finished misc submodule * added returns in docstrings in openqQCD * made some cosmetic chanes to dostrings * added return nad return statement in docstring * linting * Improved docstrings of mpm, fits, roots, misc to have return statements returns added for misc.py returns added for mpm.py reutrns added for fits.py * linting... * Some polishing of docstrings
This commit is contained in:
Justus Kuhlmann
GitHub
parent
b9cdebd442
commit
26447d658c
14 changed files with 276 additions and 6 deletions
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@ -26,6 +26,11 @@ def epsilon_tensor(i, j, k):
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"""Rank-3 epsilon tensor
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Based on https://codegolf.stackexchange.com/a/160375
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Returns
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-------
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elem : int
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Element (i,j,k) of the epsilon tensor of rank 3
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"""
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test_set = set((i, j, k))
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if not (test_set <= set((1, 2, 3)) or test_set <= set((0, 1, 2))):
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@ -38,6 +43,12 @@ def epsilon_tensor_rank4(i, j, k, o):
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"""Rank-4 epsilon tensor
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Extension of https://codegolf.stackexchange.com/a/160375
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Returns
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-------
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elem : int
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Element (i,j,k,o) of the epsilon tensor of rank 4
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"""
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test_set = set((i, j, k, o))
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if not (test_set <= set((1, 2, 3, 4)) or test_set <= set((0, 1, 2, 3))):
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@ -129,6 +129,11 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
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If True, a quantile-quantile plot of the fit result is generated (default False).
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num_grad : bool
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Use numerical differentation instead of automatic differentiation to perform the error propagation (default False).
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Returns
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-------
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output : Fit_result
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Parameters and information on the fitted result.
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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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@ -180,7 +185,12 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
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Notes
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-----
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Based on the orthogonal distance regression module of scipy
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Based on the orthogonal distance regression module of scipy.
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Returns
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-------
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output : Fit_result
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Parameters and information on the fitted result.
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'''
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output = Fit_result()
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@ -668,6 +678,11 @@ def fit_lin(x, y, **kwargs):
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a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
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y : list
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List of Obs, the dvalues of the Obs are used as yerror for the fit.
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Returns
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-------
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fit_parameters : list[Obs]
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LIist of fitted observables.
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"""
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def f(a, x):
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@ -687,6 +702,10 @@ def fit_lin(x, y, **kwargs):
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def qqplot(x, o_y, func, p):
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"""Generates a quantile-quantile plot of the fit result which can be used to
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check if the residuals of the fit are gaussian distributed.
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Returns
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-------
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None
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"""
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residuals = []
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@ -711,7 +730,12 @@ def qqplot(x, o_y, func, p):
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def residual_plot(x, y, func, fit_res):
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""" Generates a plot which compares the fit to the data and displays the corresponding residuals"""
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"""Generates a plot which compares the fit to the data and displays the corresponding residuals
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Returns
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-------
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None
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"""
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sorted_x = sorted(x)
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xstart = sorted_x[0] - 0.5 * (sorted_x[1] - sorted_x[0])
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xstop = sorted_x[-1] + 0.5 * (sorted_x[-1] - sorted_x[-2])
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@ -741,7 +765,13 @@ def residual_plot(x, y, func, fit_res):
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def error_band(x, func, beta):
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"""Returns the error band for an array of sample values x, for given fit function func with optimized parameters beta."""
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"""Calculate the error band for an array of sample values x, for given fit function func with optimized parameters beta.
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Returns
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-------
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err : np.array(Obs)
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Error band for an array of sample values x
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"""
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cov = covariance(beta)
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if np.any(np.abs(cov - cov.T) > 1000 * np.finfo(np.float64).eps):
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warnings.warn("Covariance matrix is not symmetric within floating point precision", RuntimeWarning)
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@ -765,6 +795,10 @@ def ks_test(objects=None):
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----------
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objects : list
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List of fit results to include in the analysis (optional).
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Returns
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-------
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None
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"""
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if objects is None:
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@ -18,6 +18,11 @@ def read_ADerrors(file_path, bdio_path='./libbdio.so', **kwargs):
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----------
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file_path -- path to the bdio file
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bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
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Returns
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-------
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data : List[Obs]
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Extracted data
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"""
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bdio = ctypes.cdll.LoadLibrary(bdio_path)
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@ -169,6 +174,11 @@ def write_ADerrors(obs_list, file_path, bdio_path='./libbdio.so', **kwargs):
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----------
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file_path -- path to the bdio file
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bdio_path -- path to the shared bdio library libbdio.so (default ./libbdio.so)
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Returns
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-------
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success : int
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returns 0 is successful
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"""
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for obs in obs_list:
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@ -314,6 +324,11 @@ def read_mesons(file_path, bdio_path='./libbdio.so', **kwargs):
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Fixed step size between two measurements (default 1)
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alternative_ensemble_name : str
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Manually overwrite ensemble name
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Returns
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-------
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data : dict
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Extracted meson data
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"""
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start = kwargs.get('start', 1)
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@ -106,6 +106,11 @@ def create_pobs_string(obsl, name, spec='', origin='', symbol=[], enstag=None):
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A list of symbols that describe the observables to be written. May be empty.
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enstag : str
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Enstag that is written to pobs. If None, the ensemble name is used.
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Returns
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-------
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xml_str : str
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XML formatted string of the input data
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"""
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od = {}
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@ -196,6 +201,10 @@ def write_pobs(obsl, fname, name, spec='', origin='', symbol=[], enstag=None, gz
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Enstag that is written to pobs. If None, the ensemble name is used.
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gz : bool
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If True, the output is a gzipped xml. If False, the output is an xml file.
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Returns
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-------
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None
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"""
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pobsstring = create_pobs_string(obsl, name, spec, origin, symbol, enstag)
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@ -305,6 +314,14 @@ def read_pobs(fname, full_output=False, gz=True, separator_insertion=None):
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by "|%s" % (separator_insertion) when constructing the names of the replica.
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int: Insert the separator "|" at the position given by separator_insertion.
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None (default): Replica names remain unchanged.
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Returns
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-------
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res : list[Obs]
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Imported data
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or
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res : dict
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Imported data and meta-data
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"""
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if not fname.endswith('.xml') and not fname.endswith('.gz'):
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@ -402,6 +419,14 @@ def import_dobs_string(content, noempty=False, full_output=False, separator_inse
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True (default): separator "|" is inserted after len(ensname), assuming that the
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ensemble name is a prefix to the replica name.
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None or False: No separator is inserted.
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Returns
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-------
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res : list[Obs]
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Imported data
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or
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res : dict
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Imported data and meta-data
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"""
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root = et.fromstring(content)
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@ -567,6 +592,14 @@ def read_dobs(fname, noempty=False, full_output=False, gz=True, separator_insert
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True (default): separator "|" is inserted after len(ensname), assuming that the
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ensemble name is a prefix to the replica name.
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None or False: No separator is inserted.
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Returns
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-------
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res : list[Obs]
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Imported data
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or
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res : dict
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Imported data and meta-data
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"""
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if not fname.endswith('.xml') and not fname.endswith('.gz'):
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@ -669,6 +702,11 @@ def create_dobs_string(obsl, name, spec='dobs v1.0', origin='', symbol=[], who=N
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enstags : dict
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Provide alternative enstag for ensembles in the form enstags = {ename: enstag}
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Otherwise, the ensemble name is used.
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Returns
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-------
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xml_str : str
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XML string generated from the data
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"""
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if enstags is None:
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enstags = {}
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@ -857,6 +895,10 @@ def write_dobs(obsl, fname, name, spec='dobs v1.0', origin='', symbol=[], who=No
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Otherwise, the ensemble name is used.
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gz : bool
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If True, the output is a gzipped XML. If False, the output is a XML file.
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Returns
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-------
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None
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"""
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if enstags is None:
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enstags = {}
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@ -74,6 +74,11 @@ def read_meson_hd5(path, filestem, ens_id, meson='meson_0', idl=None, gammas=Non
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two-point function. The gammas argument dominateds over meson.
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idl : range
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If specified only configurations in the given range are read in.
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Returns
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-------
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corr : Corr
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Correlator of the source sink combination in question.
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'''
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files, idx = _get_files(path, filestem, idl)
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@ -129,6 +134,11 @@ def read_DistillationContraction_hd5(path, ens_id, diagrams=["direct"], idl=None
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List of strings of the diagrams to extract, e.g. ["direct", "box", "cross"].
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idl : range
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If specified only configurations in the given range are read in.
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Returns
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-------
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result : dict
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extracted DistillationContration data
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"""
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res_dict = {}
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@ -258,6 +268,11 @@ def read_ExternalLeg_hd5(path, filestem, ens_id, idl=None):
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name of the ensemble, required for internal bookkeeping
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idl : range
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If specified only configurations in the given range are read in.
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Returns
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-------
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result : Npr_matrix
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read Cobs-matrix
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"""
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files, idx = _get_files(path, filestem, idl)
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@ -298,6 +313,11 @@ def read_Bilinear_hd5(path, filestem, ens_id, idl=None):
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name of the ensemble, required for internal bookkeeping
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idl : range
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If specified only configurations in the given range are read in.
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Returns
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-------
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result_dict: dict[Npr_matrix]
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extracted Bilinears
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"""
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files, idx = _get_files(path, filestem, idl)
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@ -354,6 +374,11 @@ def read_Fourquark_hd5(path, filestem, ens_id, idl=None, vertices=["VA", "AV"]):
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If specified only configurations in the given range are read in.
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vertices : list
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Vertex functions to be extracted.
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Returns
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-------
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result_dict : dict
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extracted fourquark matrizes
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"""
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files, idx = _get_files(path, filestem, idl)
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@ -29,6 +29,11 @@ def create_json_string(ol, description='', indent=1):
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indent : int
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Specify the indentation level of the json file. None or 0 is permissible and
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saves disk space.
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Returns
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-------
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json_string : str
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String for export to .json(.gz) file
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"""
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def _gen_data_d_from_list(ol):
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@ -212,6 +217,10 @@ def dump_to_json(ol, fname, description='', indent=1, gz=True):
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saves disk space.
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gz : bool
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If True, the output is a gzipped json. If False, the output is a json file.
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Returns
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-------
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Null
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"""
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jsonstring = create_json_string(ol, description, indent)
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@ -247,6 +256,17 @@ def _parse_json_dict(json_dict, verbose=True, full_output=False):
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full_output : bool
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If True, a dict containing auxiliary information and the data is returned.
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If False, only the data is returned.
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Returns
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-------
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result : list[Obs]
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reconstructed list of observables from the json string
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or
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result : Obs
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only one observable if the list only has one entry
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or
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result : dict
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if full_output=True
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"""
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def _gen_obsd_from_datad(d):
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@ -447,6 +467,17 @@ def import_json_string(json_string, verbose=True, full_output=False):
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full_output : bool
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If True, a dict containing auxiliary information and the data is returned.
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If False, only the data is returned.
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Returns
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-------
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result : list[Obs]
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reconstructed list of observables from the json string
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or
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result : Obs
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only one observable if the list only has one entry
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or
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result : dict
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if full_output=True
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"""
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return _parse_json_dict(json.loads(json_string), verbose, full_output)
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@ -469,6 +500,17 @@ def load_json(fname, verbose=True, gz=True, full_output=False):
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full_output : bool
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If True, a dict containing auxiliary information and the data is returned.
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If False, only the data is returned.
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Returns
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-------
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result : list[Obs]
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reconstructed list of observables from the json string
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or
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result : Obs
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only one observable if the list only has one entry
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or
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result : dict
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if full_output=True
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"""
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if not fname.endswith('.json') and not fname.endswith('.gz'):
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fname += '.json'
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@ -586,6 +628,10 @@ def dump_dict_to_json(od, fname, description='', indent=1, reps='DICTOBS', gz=Tr
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Specify the structure of the placeholder in exported dict to be reps[0-9]+.
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gz : bool
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If True, the output is a gzipped json. If False, the output is a json file.
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Returns
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-------
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None
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"""
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if not isinstance(od, dict):
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@ -683,6 +729,14 @@ def load_json_dict(fname, verbose=True, gz=True, full_output=False, reps='DICTOB
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If False, only the data is returned.
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reps : str
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Specify the structure of the placeholder in imported dict to be reps[0-9]+.
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Returns
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-------
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data : Obs / list / Corr
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Read data
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or
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data : dict
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Read data and meta-data
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"""
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indata = load_json(fname, verbose=verbose, gz=gz, full_output=True)
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description = indata['description']['description']
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@ -7,7 +7,7 @@ from ..obs import Obs
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def read_pbp(path, prefix, **kwargs):
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"""Read pbp format from given folder structure. Returns a list of length nrw
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"""Read pbp format from given folder structure.
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Parameters
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----------
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@ -15,6 +15,11 @@ def read_pbp(path, prefix, **kwargs):
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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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Returns
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-------
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result : list[Obs]
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list of observables read
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"""
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ls = []
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@ -42,6 +42,11 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
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files performed if given.
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print_err : bool
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Print additional information that is useful for debugging.
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Returns
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-------
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rwms : Obs
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Reweighting factors read
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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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@ -285,6 +290,11 @@ def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwar
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1, it is assumed that this is due to thermalization and the first measurement belongs
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to the first config (default).
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If False: The config numbers are assumed to be traj_number // difference
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Returns
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-------
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t0 : Obs
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Extracted t0
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"""
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ls = []
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@ -565,6 +575,11 @@ def read_qtop(path, prefix, c, dtr_cnfg=1, version="openQCD", **kwargs):
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for version=='sfqcd' If False, the Wilson flow is used.
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integer_charge : bool
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If True, the charge is rounded towards the nearest integer on each config.
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Returns
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-------
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result : Obs
|
||||
Read topological charge
|
||||
"""
|
||||
|
||||
return _read_flow_obs(path, prefix, c, dtr_cnfg=dtr_cnfg, version=version, obspos=0, **kwargs)
|
||||
|
|
@ -689,6 +704,11 @@ def _read_flow_obs(path, prefix, c, dtr_cnfg=1, version="openQCD", obspos=0, sum
|
|||
for version=='sfqcd' If False, the Wilson flow is used.
|
||||
integer_charge : bool
|
||||
If True, the charge is rounded towards the nearest integer on each config.
|
||||
|
||||
Returns
|
||||
-------
|
||||
result : Obs
|
||||
flow observable specified
|
||||
"""
|
||||
known_versions = ["openQCD", "sfqcd"]
|
||||
|
||||
|
|
@ -923,6 +943,11 @@ def qtop_projection(qtop, target=0):
|
|||
Topological charge.
|
||||
target : int
|
||||
Specifies the topological sector to be reweighted to (default 0)
|
||||
|
||||
Returns
|
||||
-------
|
||||
reto : Obs
|
||||
projection to the topological charge sector defined by target
|
||||
"""
|
||||
if qtop.reweighted:
|
||||
raise Exception('You can not use a reweighted observable for reweighting!')
|
||||
|
|
@ -977,6 +1002,11 @@ def read_qtop_sector(path, prefix, c, target=0, **kwargs):
|
|||
Zeuthen_flow : bool
|
||||
(optional) If True, the Zeuthen flow is used for Qtop. Only possible
|
||||
for version=='sfqcd' If False, the Wilson flow is used.
|
||||
|
||||
Returns
|
||||
-------
|
||||
reto : Obs
|
||||
projection to the topological charge sector defined by target
|
||||
"""
|
||||
|
||||
if not isinstance(target, int):
|
||||
|
|
|
|||
|
|
@ -22,6 +22,10 @@ def to_sql(df, table_name, db, if_exists='fail', gz=True, **kwargs):
|
|||
How to behave if table already exists. Options 'fail', 'replace', 'append'.
|
||||
gz : bool
|
||||
If True the json strings are gzipped.
|
||||
|
||||
Returns
|
||||
-------
|
||||
None
|
||||
"""
|
||||
se_df = _serialize_df(df, gz=gz)
|
||||
con = sqlite3.connect(db)
|
||||
|
|
@ -41,6 +45,11 @@ def read_sql(sql, db, auto_gamma=False, **kwargs):
|
|||
auto_gamma : bool
|
||||
If True applies the gamma_method to all imported Obs objects with the default parameters for
|
||||
the error analysis. Default False.
|
||||
|
||||
Returns
|
||||
-------
|
||||
data : pandas.DataFrame
|
||||
Dataframe with the content of the sqlite database.
|
||||
"""
|
||||
con = sqlite3.connect(db)
|
||||
extract_df = pd.read_sql(sql, con, **kwargs)
|
||||
|
|
@ -62,6 +71,10 @@ def dump_df(df, fname, gz=True):
|
|||
Filename of the output file.
|
||||
gz : bool
|
||||
If True, the output is a gzipped csv file. If False, the output is a csv file.
|
||||
|
||||
Returns
|
||||
-------
|
||||
None
|
||||
"""
|
||||
out = _serialize_df(df, gz=False)
|
||||
|
||||
|
|
@ -88,6 +101,11 @@ def load_df(fname, auto_gamma=False, gz=True):
|
|||
the error analysis. Default False.
|
||||
gz : bool
|
||||
If True, assumes that data is gzipped. If False, assumes JSON file.
|
||||
|
||||
Returns
|
||||
-------
|
||||
data : pandas.DataFrame
|
||||
Dataframe with the content of the sqlite database.
|
||||
"""
|
||||
if not fname.endswith('.csv') and not fname.endswith('.gz'):
|
||||
fname += '.csv'
|
||||
|
|
|
|||
|
|
@ -55,9 +55,15 @@ def read_sfcf(path, prefix, name, quarks='.*', corr_type='bi', noffset=0, wf=0,
|
|||
files: list
|
||||
list of files to be read per replica, default is all.
|
||||
for non-compact output format, hand the folders to be read here.
|
||||
check_configs:
|
||||
check_configs: list[list[int]]
|
||||
list of list of supposed configs, eg. [range(1,1000)]
|
||||
for one replicum with 1000 configs
|
||||
|
||||
Returns
|
||||
-------
|
||||
result: list[Obs]
|
||||
list of Observables with length T, observable per timeslice.
|
||||
bb-type correlators have length 1.
|
||||
"""
|
||||
if kwargs.get('im'):
|
||||
im = 1
|
||||
|
|
|
|||
|
|
@ -10,6 +10,11 @@ def check_idl(idl, che):
|
|||
idl of the current replicum
|
||||
che : list
|
||||
list of configurations to be checked against
|
||||
|
||||
Returns
|
||||
-------
|
||||
miss_str : str
|
||||
string with integers of which idls are missing
|
||||
"""
|
||||
missing = []
|
||||
for c in che:
|
||||
|
|
@ -22,3 +27,4 @@ def check_idl(idl, che):
|
|||
for i in missing[1:]:
|
||||
miss_str += "," + str(i)
|
||||
print(miss_str)
|
||||
return miss_str
|
||||
|
|
|
|||
|
|
@ -14,6 +14,10 @@ def dump_object(obj, name, **kwargs):
|
|||
name of the file
|
||||
path : str
|
||||
specifies a custom path for the file (default '.')
|
||||
|
||||
Returns
|
||||
-------
|
||||
None
|
||||
"""
|
||||
if 'path' in kwargs:
|
||||
file_name = kwargs.get('path') + '/' + name + '.p'
|
||||
|
|
@ -30,6 +34,11 @@ def load_object(path):
|
|||
----------
|
||||
path : str
|
||||
path to the file
|
||||
|
||||
Returns
|
||||
-------
|
||||
object : Obs
|
||||
Loaded Object
|
||||
"""
|
||||
with open(path, 'rb') as file:
|
||||
return pickle.load(file)
|
||||
|
|
@ -48,6 +57,11 @@ def pseudo_Obs(value, dvalue, name, samples=1000):
|
|||
name of the ensemble for which the Obs is to be generated.
|
||||
samples: int
|
||||
number of samples for the Obs (default 1000).
|
||||
|
||||
Returns
|
||||
-------
|
||||
res : Obs
|
||||
Generated Observable
|
||||
"""
|
||||
if dvalue <= 0.0:
|
||||
return Obs([np.zeros(samples) + value], [name])
|
||||
|
|
@ -83,6 +97,11 @@ def gen_correlated_data(means, cov, name, tau=0.5, samples=1000):
|
|||
every dataset.
|
||||
samples : int
|
||||
number of samples to be generated for each observable.
|
||||
|
||||
Returns
|
||||
-------
|
||||
corr_obs : list[Obs]
|
||||
Generated observable list
|
||||
"""
|
||||
|
||||
assert len(means) == cov.shape[-1]
|
||||
|
|
|
|||
|
|
@ -21,6 +21,11 @@ def matrix_pencil_method(corrs, k=1, p=None, **kwargs):
|
|||
matrix pencil parameter which filters noise. The optimal value is expected between
|
||||
len(data)/3 and 2*len(data)/3. The computation is more expensive the closer p is
|
||||
to len(data)/2 but could possibly suppress more noise (default len(data)//2).
|
||||
|
||||
Returns
|
||||
-------
|
||||
energy_levels : list[Obs]
|
||||
Extracted energy levels
|
||||
"""
|
||||
if isinstance(corrs[0], Obs):
|
||||
data = [corrs]
|
||||
|
|
|
|||
|
|
@ -24,7 +24,7 @@ def find_root(d, func, guess=1.0, **kwargs):
|
|||
|
||||
Returns
|
||||
-------
|
||||
Obs
|
||||
res : Obs
|
||||
`Obs` valued root of the function.
|
||||
'''
|
||||
d_val = np.vectorize(lambda x: x.value)(np.array(d))
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue