Utility functions for ENCORE

class mdaencore.utils.ParallelCalculation(n_jobs, function, args=None, kwargs=None)

Generic parallel calculation class. Can use arbitrary functions, arguments to functions and kwargs to functions.

Variables:

• n_jobs (int) – Number of cores to be used for parallel calculation. If -1 use all available cores.

• function (callable object) – Function to be run in parallel.

• args (list of tuples) – Each tuple contains the arguments that will be passed to function(). This means that a call to function() is performed for each tuple. function is called as function(*args, **kwargs). Runs are distributed on the requested numbers of cores.

• kwargs (list of dicts) – Each tuple contains the named arguments that will be passed to function, similarly as described for the args attribute.

• nruns (int) – Number of runs to be performed. Must be equal to len(args) and len(kwargs).

run()

Run parallel calculation.

Returns:

results – int is the number of the calculation corresponding to a certain argument in the args list, and object is the result of corresponding calculation. For instance, in (3, output), output is the return of function(*args[3], **kwargs[3]).

Return type:

tuple of ordered tuples (int, object)

worker(q, results)

Generic worker. Will run function with the prescribed args and kwargs.

Parameters:

• q (multiprocessing.Manager.Queue object) – work queue, from which the worker fetches arguments and messages

• results (multiprocessing.Manager.Queue object) – results queue, where results are put after each calculation is finished

class mdaencore.utils.TriangularMatrix(size, metadata=None, loadfile=None)

Triangular matrix class. This class is designed to provide a memory-efficient representation of a triangular matrix that still behaves as a square symmetric one. The class wraps a numpy.array object, in which data are memorized in row-major order. It also has few additional facilities to conveniently load/write a matrix from/to file. It can be accessed using the [] and () operators, similarly to a normal numpy array.

as_array()

Return standard numpy array equivalent

loadz(fname)

Load matrix from the npz compressed numpy format.

Parameters:

fname (str) – Name of the file to be loaded.

savez(fname)

Save matrix in the npz compressed numpy format. Save metadata and data as well.

Parameters:

fname (str) – Name of the file to be saved.

mdaencore.utils.merge_universes(universes)

Merge list of universes into one

Parameters:

universes (list of Universe objects)

Return type:

Universe object

mdaencore.utils.trm_indices(a, b)

Generate (i,j) indeces of a triangular matrix, between elements a and b. The matrix size is automatically determined from the number of elements. For instance: trm_indices((0,0),(2,1)) yields (0,0) (1,0) (1,1) (2,0) (2,1).

Parameters:

• a ((int i, int j) tuple) – starting matrix element.

• b ((int i, int j) tuple) – final matrix element.

mdaencore.utils.trm_indices_diag(n)

generate (i,j) indeces of a triangular matrix of n rows (or columns), with diagonal

Parameters:

n (int) – Matrix size

mdaencore.utils.trm_indices_nodiag(n)

generate (i,j) indeces of a triangular matrix of n rows (or columns), without diagonal (e.g. no elements (0,0),(1,1),…,(n,n))

Parameters:

n (int) – Matrix size

mdaencore.cutils.PureRMSD(coordsi, coordsj, atomsn, masses, summasses)