pyemma.coordinates.clustering.RegularSpaceClustering¶

class pyemma.coordinates.clustering.RegularSpaceClustering(dmin, max_centers=1000, metric='euclidean')¶

Clusters data objects in such a way, that cluster centers are at least in distance of dmin to each other according to the given metric. The assignment of data objects to cluster centers is performed by Voronoi partioning.

Regular space clustering [Prinz_2011] is very similar to Hartigan’s leader algorithm [Hartigan_1975]. It consists of two passes through the data. Initially, the first data point is added to the list of centers. For every subsequent data point, if it has a greater distance than dmin from every center, it also becomes a center. In the second pass, a Voronoi discretization with the computed centers is used to partition the data.

Parameters:	dmin (float) – minimum distance between all clusters. metric (str) – metric to use during clustering (‘euclidean’, ‘minRMSD’) max_centers (int) – if this cutoff is hit during finding the centers, the algorithm will abort.

References

[Prinz_2011]

Prinz J-H, Wu H, Sarich M, Keller B, Senne M, Held M, Chodera JD, Schuette Ch and Noe F. 2011. Markov models of molecular kinetics: Generation and Validation. J. Chem. Phys. 134, 174105.

[Hartigan_1975]

Hartigan J. Clustering algorithms. New York: Wiley; 1975.

__init__(dmin, max_centers=1000, metric='euclidean')¶

Methods

`__init__`(dmin[, max_centers, metric])
`assign`([X, stride])	Assigns the given trajectory or list of trajectories to cluster centers by using the discretization defined by this clustering method (usually a Voronoi tesselation).
`describe`(args, *kwargs)	Get a descriptive string representation of this class.
`dimension`()	output dimension of clustering algorithm (always 1).
`get_output`([dimensions, stride])	Maps all input data of this transformer and returns it as an array or list of arrays.
`iterator`([stride, lag])	Returns an iterator that allows to access the transformed data.
`map`(X)	Maps the input data through the transformer to correspondingly shaped output data array/list.
`n_frames_total`([stride])	Returns total number of frames.
`number_of_trajectories`()	Returns the number of trajectories.
`output_type`()
`parametrize`([stride])	Parametrize this Transformer
`save_dtrajs`([trajfiles, prefix, output_dir, ...])	saves calculated discrete trajectories. Filenames are taken from
`trajectory_length`(itraj[, stride])	Returns the length of trajectory of the requested index.
`trajectory_lengths`([stride])	Returns the length of each trajectory.

Attributes

`chunksize`	chunksize defines how much data is being processed at once.
`data_producer`	where the transformer obtains its data.
`dmin`	Minimum distance between cluster centers.
`dtrajs`	Discrete trajectories (assigned data to cluster centers).
`in_memory`	are results stored in memory?
`max_centers`	Cutoff during clustering.
`overwrite_dtrajs`	Should existing dtraj files be overwritten.

assign(X=None, stride=1)¶

Assigns the given trajectory or list of trajectories to cluster centers by using the discretization defined by this clustering method (usually a Voronoi tesselation).

You can assign multiple times with different strides. The last result of assign will be saved and is available as the attribute dtrajs().

Parameters:	X (ndarray(T, n) or list of ndarray(T_i, n), optional, default = None) – Optional input data to map, where T is the number of time steps and n is the number of dimensions. When a list is provided they can have differently many time steps, but the number of dimensions need to be consistent. When X is not provided, the result of assign is identical to get_output(), i.e. the data used for clustering will be assigned. If X is given, the stride argument is not accepted. stride (int, optional, default = 1) – If set to 1, all frames of the input data will be assigned. Note that this could cause this calculation to be very slow for large data sets. Since molecular dynamics data is usually correlated at short timescales, it is often sufficient to obtain the discretization at a longer stride. Note that the stride option used to conduct the clustering is independent of the assign stride. This argument is only accepted if X is not given.
Returns:	Y – The discretized trajectory: int-array with the indexes of the assigned clusters, or list of such int-arrays. If called with a list of trajectories, Y will also be a corresponding list of discrete trajectories
Return type:	ndarray(T, dtype=int) or list of ndarray(T_i, dtype=int)

chunksize¶: chunksize defines how much data is being processed at once.

data_producer¶: where the transformer obtains its data.

describe(*args, **kwargs)¶: Get a descriptive string representation of this class.

dimension()¶: output dimension of clustering algorithm (always 1).

dmin¶: Minimum distance between cluster centers.

dtrajs¶: Discrete trajectories (assigned data to cluster centers).

get_output(dimensions=slice(0, None, None), stride=1)¶

Maps all input data of this transformer and returns it as an array or list of arrays.

Parameters:	dimensions (list-like of indexes or slice) – indices of dimensions you like to keep, default = all stride (int) – only take every n’th frame, default = 1
Returns:	output – the mapped data, where T is the number of time steps of the input data, or if stride > 1, floor(T_in / stride). d is the output dimension of this transformer. If the input consists of a list of trajectories, Y will also be a corresponding list of trajectories
Return type:	ndarray(T, d) or list of ndarray(T_i, d)

Notes

This function may be RAM intensive if stride is too large or too many dimensions are selected.
if in_memory attribute is True, then results of this methods are cached.

Example

plotting trajectories

>>> import pyemma.coordinates as coor
>>> import matplotlib.pyplot as plt
>>> %matplotlib inline # only for ipython notebook
>>>
>>> tica = coor.tica() # fill with some actual data!
>>> trajs = tica.get_output(dimensions=(0,), stride=100)
>>> for traj in trajs:
>>>     plt.figure()
>>>     plt.plot(traj[:, 0])

in_memory¶: are results stored in memory?

iterator(stride=1, lag=0)¶

Returns an iterator that allows to access the transformed data.

Parameters:

stride (int) – Only transform every N’th frame, default = 1
lag (int) – Configure the iterator such that it will return time-lagged data with a lag time of lag. If lag is used together with stride the operation will work as if the striding operation is applied before the time-lagged trajectory is shifted by lag steps. Therefore the effective lag time will be stride*lag.

Returns:

iterator – If lag = 0, a call to the .next() method of this iterator will return the pair (itraj, X) : (int, ndarray(n, m)), where itraj corresponds to input sequence number (eg. trajectory index) and X is the transformed data, n = chunksize or n < chunksize at end of input.

If lag > 0, a call to the .next() method of this iterator will return the tuple (itraj, X, Y) : (int, ndarray(n, m), ndarray(p, m)) where itraj and X are the same as above and Y contain the time-lagged data.

Return type:

a pyemma.coordinates.transfrom.TransformerIterator transformer iterator

map(X)¶

Maps the input data through the transformer to correspondingly shaped output data array/list.

Parameters:	X (ndarray(T, n) or list of ndarray(T_i, n)) – The input data, where T is the number of time steps and n is the number of dimensions. If a list is provided, the number of time steps is allowed to vary, but the number of dimensions are required to be to be consistent. required to be to be consistent.
Returns:	Y – The mapped data, where T is the number of time steps of the input data and d is the output dimension of this transformer. If called with a list of trajectories, Y will also be a corresponding list of trajectories
Return type:	ndarray(T, d) or list of ndarray(T_i, d)

max_centers¶: Cutoff during clustering. If reached no more data is taken into account. You might then consider a larger value or a larger dmin value.

n_frames_total(stride=1)¶

Returns total number of frames.

Parameters:	stride (int) – return value is the number of frames in trajectories when running through them with a step size of stride.
Returns:	int
Return type:	n_frames_total

number_of_trajectories()¶

Returns the number of trajectories.

Returns:	int
Return type:	number of trajectories

overwrite_dtrajs¶: Should existing dtraj files be overwritten. Set this property to True to overwrite.

parametrize(stride=1)¶: Parametrize this Transformer

save_dtrajs(trajfiles=None, prefix='', output_dir='.', output_format='ascii', extension='.dtraj')¶

saves calculated discrete trajectories. Filenames are taken from given reader. If data comes from memory dtrajs are written to a default filename.

Parameters:

trajfiles (list of str (optional)) – names of input trajectory files, will be used generate output files.
prefix (str) – prepend prefix to filenames.
output_dir (str) – save files to this directory.
output_format (str) – if format is ‘ascii’ dtrajs will be written as csv files, otherwise they will be written as NumPy .npy files.
extension (str) – file extension to append (eg. ‘.itraj’)

trajectory_length(itraj, stride=1)¶

Returns the length of trajectory of the requested index.

Parameters:	itraj (int) – trajectory index stride (int) – return value is the number of frames in the trajectory when running through it with a step size of stride.
Returns:	int
Return type:	length of trajectory

trajectory_lengths(stride=1)¶

Returns the length of each trajectory.

Parameters:	stride (int) – return value is the number of frames of the trajectories when running through them with a step size of stride.
Returns:	int
Return type:	length of each trajectory