High-level analysis functions

High-level analysis functions

py_alf.analysis(directory, symmetry=None, custom_obs=None, do_tau=True, always=False)

Perform analysis in the given directory.

Results are written to the pickled dictionary res.pkl and in plain text in the folder res/.

Parameters
directorypath-like object

Directory containing Monte Carlo bins.

symmetrylist of functions, optional

List of functions reppresenting symmetry operations on lattice, including unity. It is used to symmetrize lattice-type observables.

custom_obsdict, default={}

Defines additional observables derived from existing observables. The key of each entry is the observable name and the value is a dictionary with the format:

{'needs': some_list,
 'kwargs': some_dict,
 'function': some_function,}

some_list contains observable names to be read by py_alf.ana.ReadObs. Jackknife bins and kwargs from some_dict are handed to some_function with a separate call for each bin.

do_taubool, default=True

Analyze time-displaced correlation functions. Setting this to False speeds up analysis and makes result files much smaller.

alwaysbool, default=False

Do not skip if parameters and bins are older than results.

py_alf.check_warmup(*args, gui='tk', **kwargs)

Plot bins to determine n_skip.

Calls either py_alf.check_warmup_tk() or py_alf.check_warmup_ipy().

Parameters
*args
gui{“tk”, “ipy”}
**kwargs
py_alf.check_warmup_tk(directories, names, custom_obs={})

Plot bins to determine n_skip. Opens a new window.

Parameters
directorieslist of path-like objects

Directories with bins to check.

nameslist of str

Names of observables to check.

custom_obsdict, default={}

Defines additional observables derived from existing observables. See py_alf.analysis().

py_alf.check_warmup_ipy(directories, names, custom_obs={}, ncols=3)

Plot bins to determine n_skip in a Jupyter Widget.

Parameters
directorieslist of path-like objects

Directories with bins to check.

nameslist of str

Names of observables to check.

custom_obsdict, default={}

Defines additional observables derived from existing observables. See py_alf.analysis().

Returns
Jupyter Widget

A graphical user interface based on ipywidgets

py_alf.check_rebin(*args, gui='tk', **kwargs)

Plot error vs n_rebin in a Jupyter Widget.

Calls either py_alf.check_rebin_tk() or py_alf.check_rebin_ipy().

Parameters
*args
gui{“tk”, “ipy”}
**kwargs
py_alf.check_rebin_tk(directories, names, Nmax0=100, custom_obs={})

Plot error vs n_rebin. Opens a new window.

Parameters
directorieslist of path-like objects

Directories with bins to check.

nameslist of str

Names of observables to check.

Nmax0int, default=100

Biggest n_rebin to consider. The default is 100.

custom_obsdict, default={}

Defines additional observables derived from existing observables. See py_alf.analysis().

py_alf.check_rebin_ipy(directories, names, custom_obs={}, Nmax0=100, ncols=3)

Plot error vs n_rebin in a Jupyter Widget.

Parameters
directorieslist of path-like objects

Directories with bins to check.

nameslist of str

Names of observables to check.

Nmax0int, default=100

Biggest n_rebin to consider. The default is 100.

custom_obsdict, default={}

Defines additional observables derived from existing observables. See py_alf.analysis().

Returns
Jupyter Widget

A graphical user interface based on ipywidgets