Coordinates package (pyemma.coordinates)¶
The coordinates package contains tools to select features from MD-trajectories. It also assigns them to a discrete state space, which will be later used in Markov modeling.
It supports reading from MD-trajectories, comma separated value ASCII files and NumPy arrays. The discretized trajectories are being stored as NumPy arrays of integers.
User API¶
Data handling and IO
featurizer(topfile) |
Featurizer to select features from MD data. |
load(trajfiles[, features, top, stride, ...]) |
Loads coordinate features into memory. |
source(inp[, features, top, chunk_size]) |
Wraps input as data source for pipeline. |
pipeline(stages[, run, stride, chunksize]) |
Data analysis pipeline. |
discretizer(reader[, transform, cluster, ...]) |
Specialized pipeline: From trajectories to clustering. |
save_traj(traj_inp, indexes, outfile[, ...]) |
Saves a sequence of frames as a single trajectory. |
save_trajs(traj_inp, indexes[, prefix, fmt, ...]) |
Saves sequences of frames as multiple trajectories. |
Transformations
pca([data, dim, stride]) |
Principal Component Analysis (PCA). |
tica([data, lag, dim, stride, ...]) |
Time-lagged independent component analysis (TICA). |
Clustering Algorithms
cluster_kmeans([data, k, max_iter, stride, ...]) |
k-means clustering |
cluster_regspace([data, dmin, max_centers, ...]) |
Regular space clustering |
cluster_uniform_time([data, k, stride, metric]) |
Uniform time clustering |
assign_to_centers([data, centers, stride, ...]) |
Assigns data to the nearest cluster centers |
Classes¶
Coordinate classes encapsulating complex functionality. You don’t need to construct these classes yourself, as this is done by the user API functions above. Find here a documentation how to extract features from them.
pipelines.Pipeline(chain[, chunksize, ...]) |
Data processing pipeline. |
transform.PCA(output_dimension) |
Principal component analysis. |
transform.TICA(lag, output_dimension[, ...]) |
Time-lagged independent component analysis (TICA) |