pyemma.coordinates.data.DataInMemory¶

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

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, **kw)¶

Methods

`__init__`(data[, chunksize])
`describe`()
`dimension`()
`get_output`([dimensions, stride, skip, chunk])
`iterator`([stride, lag, chunk, ...])
`load_from_files`(files)	construct this by loading all files into memory
`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.
`register_progress_callback`(call_back[, stage])	Registers the progress reporter.
`trajectory_length`(itraj[, stride, skip])	Returns the length of trajectory
`trajectory_lengths`([stride, skip])	Returns the length of each trajectory
`write_to_csv`([filename, extension, ...])	write all data to csv with numpy.savetxt

Attributes

`IN_MEMORY_FILENAME`
`chunksize`
`data`	Property that returns the data that was hold in storage (data in memory mode).
`data_producer`	The data producer for this data source object (can be another data source object).
`default_chunksize`
`filenames`	Property which returns a list of filenames the data is originally from.
`in_memory`	are results stored in memory?
`is_random_accessible`	Check if self._is_random_accessible is set to true and if all the random access strategies are implemented.
`is_reader`	Property telling if this data source is a reader or not.
`logger`	The logger for this class instance
`name`	The name of this instance
`ndim`
`ntraj`
`ra_itraj_cuboid`	Implementation of random access with slicing that can be up to 3-dimensional, where the first dimension corresponds to the trajectory index, the second dimension corresponds to the frames and the third dimension corresponds to the dimensions of the frames.
`ra_itraj_jagged`	Behaves like ra_itraj_cuboid just that the trajectories are not truncated and returned as a list.
`ra_itraj_linear`	Implementation of random access that takes arguments as the default random access (i.e., up to three dimensions with trajs, frames and dims, respectively), but which considers the frame indexing to be contiguous.
`ra_linear`	Implementation of random access that takes a (maximal) two-dimensional slice where the first component corresponds to the frames and the second component corresponds to the dimensions.
`show_progress`

data¶: Property that returns the data that was hold in storage (data in memory mode). :returns: list :rtype: The stored data.

data_producer¶: The data producer for this data source object (can be another data source object). :returns: :rtype: This data source’s data producer.

filenames¶: Property which returns a list of filenames the data is originally from. :returns: list of str :rtype: list of filenames if data is originating from a file based reader

in_memory¶: are results stored in memory?

is_random_accessible¶: Check if self._is_random_accessible is set to true and if all the random access strategies are implemented. :returns: bool :rtype: Returns True if random accessible via strategies and False otherwise.

is_reader¶: Property telling if this data source is a reader or not. :returns: bool :rtype: True if this data source is a reader and False otherwise

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

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.

ra_itraj_cuboid¶

Implementation of random access with slicing that can be up to 3-dimensional, where the first dimension corresponds to the trajectory index, the second dimension corresponds to the frames and the third dimension corresponds to the dimensions of the frames.

The with the frame slice selected frames will be loaded from each in the trajectory-slice selected trajectories and then sliced with the dimension slice. For example: The data consists out of three trajectories with length 10, 20, 10, respectively. The slice data[:, :15, :3] returns a 3D array of shape (3, 10, 3), where the first component corresponds to the three trajectories, the second component corresponds to 10 frames (note that the last 5 frames are being truncated as the other two trajectories only have 10 frames) and the third component corresponds to the selected first three dimensions.

Returns:	Returns an object that allows access by slices in the described manner.

ra_itraj_jagged¶

Behaves like ra_itraj_cuboid just that the trajectories are not truncated and returned as a list.

Returns:	Returns an object that allows access by slices in the described manner.

ra_itraj_linear¶

Implementation of random access that takes arguments as the default random access (i.e., up to three dimensions with trajs, frames and dims, respectively), but which considers the frame indexing to be contiguous. Therefore, it returns a simple 2D array.

Returns:	A 2D array of the sliced data containing [frames, dims].

ra_linear¶

Implementation of random access that takes a (maximal) two-dimensional slice where the first component corresponds to the frames and the second component corresponds to the dimensions. Here it is assumed that the frame indexing is contiguous, i.e., the first frame of the second trajectory has the index of the last frame of the first trajectory plus one.

Returns:	Returns an object that allows access by slices in the described manner.

register_progress_callback(call_back, stage=0)¶

Registers the progress reporter.

Parameters:	call_back (function) – This function will be called with the following arguments: stage (int) instance of pyemma.utils.progressbar.ProgressBar 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, skip=None)¶

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 skip – skip parameter
Returns:	length of trajectory

trajectory_lengths(stride=1, skip=0)¶

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 skip – return value is the number of frames in trajectories when skipping the first “skip” frames (plus stride)
Returns:	numpy array containing length of each trajectory

write_to_csv(filename=None, extension='.dat', overwrite=False, stride=1, chunksize=100, **kw)¶

write all data to csv with numpy.savetxt

Parameters:

filename (str, optional) –
filename string, which may contain placeholders {itraj} and {stride}:
- itraj will be replaced by trajetory index
- stride is stride argument of this method
If filename is not given, it is being tried to obtain the filenames from the data source of this iterator.
extension (str, optional, default='.dat') – filename extension of created files
overwrite (bool, optional, default=False) – shall existing files be overwritten? If a file exists, this method will raise.
stride (int) – omit every n’th frame
chunksize (int) – how many frames to process at once
kw (dict) – named arguments passed into numpy.savetxt (header, seperator etc.)

Example

Assume you want to save features calculated by some FeatureReader to ASCII: >>> import numpy as np, pyemma >>> from pyemma.util.files import TemporaryDirectory >>> import os >>> data = [np.random.random((10,3))] * 3 >>> reader = pyemma.coordinates.source(data) >>> filename = “distances_{itraj}.dat” >>> with TemporaryDirectory() as td: ... os.chdir(td) ... reader.write_to_csv(filename, header=’‘, delim=’;’) ... print(os.listdir(‘.’)) [‘distances_2.dat’, ‘distances_1.dat’, ‘distances_0.dat’]