pyemma.coordinates.data.DataInMemory

class pyemma.coordinates.data.DataInMemory(data, chunksize=5000)

multi-dimensional data fully stored in memory.

Used to pass arbitrary coordinates to pipeline. Data is being flattened to two dimensions to ensure it is compatible.

Parameters:data (ndarray (nframe, ndim) or list of ndarrays (nframe, ndim)) – Data has to be either one 2d array which stores amount of frames in first dimension and coordinates/features in second dimension or a list of this arrays.
__init__(data, chunksize=5000)

Methods

__init__(data[, chunksize])
describe()
dimension() Returns the number of output dimensions
fit(X, **kwargs) For compatibility with sklearn
fit_transform(X, **kwargs) For compatibility with sklearn
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.
load_from_files(files) construct this by loading all files into memory
map(X) Deprecated: use transform(X)
n_frames_total([stride]) Returns the total number of frames, over all trajectories
number_of_trajectories() Returns the number of trajectories
output_type() By default transformers return single precision floats.
parametrize([stride]) Parametrize this Transformer
register_progress_callback(call_back[, stage]) Registers the progress reporter.
trajectory_length(itraj[, stride]) Returns the length of trajectory
trajectory_lengths([stride]) Returns the length of each trajectory
transform(X)

Attributes

chunksize chunksize defines how much data is being processed at once.
data_producer where the transformer obtains its data.
in_memory are results stored in memory?
logger The logger for this class instance
name The name of this instance
ntraj
chunksize

chunksize defines how much data is being processed at once.

data_producer

where the transformer obtains its data.

dimension()

Returns the number of output dimensions

Returns:
fit(X, **kwargs)

For compatibility with sklearn

fit_transform(X, **kwargs)

For compatibility with sklearn

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

Fill with some actual data!

>>> tica = coor.tica() 
>>> 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 TransformerIterator
classmethod load_from_files(files)

construct this by loading all files into memory

Parameters:files (str or list of str) – filenames to read from
logger

The logger for this class instance

map(X)

Deprecated: use transform(X)

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

n_frames_total(stride=1)

Returns the total number of frames, over all trajectories

Parameters:stride – return value is the number of frames in trajectories when running through them with a step size of stride
Returns:the total number of frames, over all trajectories
name

The name of this instance

number_of_trajectories()

Returns the number of trajectories

Returns:number of trajectories
output_type()

By default transformers return single precision floats.

parametrize(stride=1)

Parametrize this Transformer

register_progress_callback(call_back, stage=0)

Registers the progress reporter.

Parameters:
  • call_back (function) –

    This function will be called with the following arguments:

    1. stage (int)
    2. instance of pyemma.utils.progressbar.ProgressBar
    3. optional *args and named keywords (**kw), for future changes
  • stage (int, optional, default=0) – The stage you want the given call back function to be fired.
trajectory_length(itraj, stride=1)

Returns the length of trajectory

Parameters:
  • itraj – trajectory index
  • stride – return value is the number of frames in trajectory when running through it with a step size of stride
Returns:

length of trajectory
trajectory_lengths(stride=1)

Returns the length of each trajectory

Parameters:stride – return value is the number of frames in trajectories when running through them with a step size of stride
Returns:numpy array containing length of each trajectory