Changelog

2.0.3 (29-1-2016)

New features:

  • msm: added keyword “count_mode” to estimate_markov_model, to specify the way of counting during creation of a count matrix. It defaults to the same behaviour like prior versions (sliding window). New options:

    • ‘effective’: Uses an estimate of the transition counts that are

      statistically uncorrelated. Recommended when used with a Bayesian MSM.

    • ‘sample’: A trajectory of length T will have T/tau counts at time indices

      0 -> tau, tau -> 2 tau, ..., T/tau - 1 -> T

  • msm: added possibility to constrain the stationary distribution for BayesianMSM

  • coordinates: added “periodic” keyword to features in Featurizer to indicate a unit cell with periodic boundary conditions.

  • coordinates: added “count_contacts” keyword to Featurizer.add_contacts() method to count formed contacts instead of dimension of all possible contacts.

  • logging: pyemma.log file will be rotated after reaching a size of 1 MB

Fixes:

  • logging: do not replace existing loggers anymore. Use hierarchical logging (all loggers “derive” from ‘pyemma’ logger. So log levels etc. can be manipulated by changing this new ‘pyemma’ root logger.
  • some deprecation warnings have been fixed (IPython and Python-3.5 related).

2.0.2 (11-9-2015)

New features:

  • coordinates: added Sparsifier, which detects constant features in data stream and removes them for further processing.
  • coordinates: cache lengths of NumPy arrays
  • coordinates: clustering.interface new methods index_clusters and sample_indexes_by_cluster
  • coordinates: featurizer.add_contacts has new threshold value of .3 nm
  • coordinates: featurizer.pairs gets opt arg excluded_neighbors (default (=0) is unchanged)
  • coordinates: featurizer.describe uses resSeq instead of residue.index
  • plots: network plots gets new arg state_labels, arg state_colors extended, textkwargs added
  • plots: timescale plot accepts different units for x,y axes
  • logging: full-feature access to Python logging system (edit logging.yml in .pyemma dir)

Fixes:

  • Upon import no deprecation warning (about acf function) is shown.
  • coordinates: chunksize attribute moved to readers (no consequence for user-scripts)
  • coordinates: fixed bug in parallel evaluation of Estimators, when they have active loggers.
  • documentation fixes

2.0.1 (9-3-2015)

Urgent bug fix: reading other formats than XTC was not possible in coordinates pipeline. This bug has been introduced into 2.0, prior versions were not affected.

2.0 (9-1-2015)

2.0 is a major release offering several new features and a major internal reorganization of the code.

New features:

  • coordinates: Featurizer new features: ResidueMinDistanceFeature and GroupMinDistanceFeature.
  • coordinates: PCA and TICA use a default variance cutoff of 95%.
  • coordinates: TICA is scaled to produce a kinetic map by default.
  • coordinates: TICA eigenvalues can be used to calculate timescales.
  • coordinates: new MiniBatchKmeans implementation.
  • coordinates: Early termination of pipeline possible (eg. max_clusters reached).
  • coordinates: random access of input through pipeline via indices.
  • msm: Estimator for Bayesian Markov state models.
  • msm: MSMs can be systematically coarse-grained to few-state models
  • msm: Estimators for discrete Hidden Markov Models (HMMs) and Bayesian Hidden Markov models (BHMMs).
  • msm: SampledModels, e.g. generated from BayesianMSM or BayesianHMM allow statistics (means, variances, confidence intervals) to be computed for all properties of MSMs and HMMs.
  • msm: Generalized Chapman-Kolmogorov test for both MSM and HMM models
  • plots: plotting functions for Chapman-Kolmogorov tests and 2D free energy surfaces.
  • plots: more flexible network plots.

Documentation:

  • One new application-based ipython notebooks and three new methodological ipython notebooks are provided. All Notebooks and most of the data are provided for download at pyemma.org.
  • Many improvements in API documentation.

Code architecture:

  • Object structure is more clear, general and extensible. We have three main class types: Estimators, Transformers and Models. Estimators (e.g. MaximumLikelihoodMSM) read data and produce a Transformer or a Model. Transformers (e.g. TICA) can be employed in order to transform input data into output data (e.g. dimension reduction). Models (e.g. MSM) can be analyzed in order to compute molecular quantities of interest, such as equilibrium probabilities or transition rates.
  • Estimators and Transformers have basic compatibility with scikit-learn objects.
  • Code for low-level msm functions (msm.analysis, msm.estimation, msm.generation, msm.flux) has been relocated to the subsidiary package msmtools (github.com/markovmodel/msmtools). msmtools is part of the PyEMMA distribution but can be separately installed without depending on PyEMMA in order to facilitate further method development.
  • Removed deprecated functions from 1.1 that were kept during 1.2

1.2.2 (7-27-2015)

  • msm estimation: new fast transition matrix sampler
  • msm estimation: new feature “auto-sparse”: automatically decide which datatype to use for transition matrix estimation.
  • coordinates package: kinetic map feature for TICA (arXiv:1506.06259 [physics.comp-ph])
  • coordinates package: better examples for API functions.
  • coordinates package: cluster assignment bugfix in parallel environments (OpenMP).
  • coordinates package: added cos/sin transformations for angle based features to featurizer. This is recommended for PCA/TICA transformations.
  • coordinates package: added minimum RMSD feature to featurizer.
  • coordinates package: Regular space clustering terminates early now, when it reaches max_clusters cutoff.
  • plots package: use Fruchterman Reingold spring algorithm to calculate positions in network plots.
  • ipython notebooks: new real-world examples, which show the complete workflow
  • general: made all example codes in documentation work.

1.2.1 (5-28-2015)

  • general: Time consuming algorithms now display progressbars (optional).

  • general: removed scikit-learn dependency (due to new kmeans impl. Thanks @clonker)

  • coordinates package: new and faster implementation of Kmeans (10x faster than scikit-learn).

  • coordinates package: allow metrics to be passed to cluster algorithms.

  • coordinates package: cache trajectory lengths by default

    (uncached led to 1 pass of reading for non indexed (XTC) formats).

    This avoids re-reading e.g XTC files to determine their lengths.

  • coordinates package: enable passing chunk size to readers and pipelines in API.

  • coordinates package: assign_to_centers now allows all supported file formats as centers input.

  • coordinates package: save_traj(s) now handles stride parameter.

  • coordinates package: save_traj now accepts also lists of files as an input In this case, an extra parameter topfile has to be parsed as well.

  • plots package: added functions to plot flux and msm models.

  • Bugfixes:

    • [msm.MSM.pcca]: coarse-grained transition matrix corrected
    • [msm.generation]: stopping states option fixed
    • [coordinates.NumPyReader]: during gathering of shapes of all files, none of them were closed.

1.2 (4-14-2015)

1.2 is a major new release which offers a load of new and useful functionalities for coordinate loading, data processing and Markov model estimation and analysis. In a few places we had to change existing API functions, but we encourage everyone to update to 1.2.

  • coordinate package: featurizer can be constructed separately
  • coordinate package: new functions for loading data and creating file readers for large trajectories
  • coordinate package: all clustering functions were renamed (e.g.: kmeans -> cluster_kmeans). Old function names do still work, but are deprecated
  • coordinate package: new pipeline() function for generic data processing pipelines. Using pipelines you can go from data loading, over transformation via TICA or PCA, to clustered data all via stream processing. This avoids having to load large datasets into memory.
  • msm package: markov_model() function creates a MSM object that offers a lot of analysis functions such as spectral analysis, mean first passage times, pcca, calculation of experimental observables, etc.
  • msm package: estimate_markov_model() function creates a EstimatedMSM object from data. Offers all functionalities of MSM plus additional functions related to trajectories, such as drawing representative smaples for MSM states
  • msm package: Chapman-Kolmogorow test and implied timescales calculation are more robust
  • msm package: cktest() and tpt() functions now accept MSM objects as inputs
  • various bug fixes

1.1.2 (3-18-2015)

  • PCCA++ now produces correct memberships (fixes a problem from nonorthonormal eigenvectors)
  • Improved Coordinates API documentation (Examples, examples, EXAMPLES)