dylightful package

Submodules

dylightful.bar_plot module

dylightful.bar_plot.make_barplot(time_ser, ylabel, yticks, prefix=None, save_path=None)

Craeates and saves bar plot

Args:

time_ser ([type]): by paser.get_timeseries processed pml time series of features path ([type], optional): Where to save. Defaults to None. prefix ([type], optional): Name of the file

dylightful.discretizer module

dylightful.discretizer.find_states_gaussian(proj, prefix, save_path, num_cluster=15, tol=0.01)

Cluster the projection to get realy discretized values necessary for the MSM

Args:

proj ([type]): [description] num_cluster (int, optional): [description]. Defaults to 15. tol (float, optional): [description]. Defaults to 0.01.

dylightful.discretizer.find_states_kmeans(proj, prefix, save_path, num_cluster=15, tol=0.01, plotting=True)

Cluster the projection to get realy discretized values necessary for the MSM

Args:

proj ([type]): [description] num_cluster (int, optional): [description]. Defaults to 15. tol (float, optional): [description]. Defaults to 0.01.

dylightful.discretizer.plot_ellbow_kmeans(metric, prefix=None, save_path=None, name='_ellbow_kMeans.png', ylabel='Sum of squared distances $R$')

Plots the sum of squared distances for K-Means to do the ellbow method visually

Args:

sum_of_squared_distances ([type]): [description] file_name ([type], optional): [description]. Defaults to None. save_path ([type], optional): [description]. Defaults to None.

Returns:

None

dylightful.discretizer.plot_scores_kmeans(metric, prefix=None, save_path=None, name='_scores_kmeans.png')

Plots the scores of the k_means finder

Args:

sum_of_squared_distances ([type]): [description] file_name ([type], optional): [description]. Defaults to None. save_path ([type], optional): [description]. Defaults to None.

Returns:

None

dylightful.discretizer.plot_tae_training(tae_model, prefix=None, save_path=None)

Plots the loss function for the trainig of the TAE model.

Args:

tae_model ([type]): [description] file_name ([type], optional): [description]. Defaults to None. save_path ([type], optional): [description]. Defaults to None.

dylightful.discretizer.plot_tae_transform(proj, prefix=None, save_path=None)

Plots the transformation obtained by the TAE model.

Args:

proj ([type]): [description] num_steps (int, optional): [description]. Defaults to 1000. file_name ([type], optional): [description]. Defaults to None. save_path ([type], optional): [description]. Defaults to None.

dylightful.discretizer.smooth_projection_gaussian(arr, num_cluster)

Clusters an array with k_means according to num_cluster

Args:

proj ([type]): [description] num_cluster ([type]): [description]

Returns:

[type]: [description]

dylightful.discretizer.smooth_projection_k_means(arr, num_cluster)

Clusters an array with k_means according to num_cluster

Args:

proj ([type]): [description] num_cluster ([type]): [description]

Returns:

[type]: [description]

dylightful.discretizer.tae_discretizer(time_ser, num_states, clustering=None, size=3, prefix=None, save_path=None, num_cluster=15, tol=0.01, plotting=True)

Test MSM with time lagged autoencoders according to Noé et al.

Args:

time_ser ([type]): Dynophore trajectory converted by the parser size (int, optional): 10*size is the autoencoder size Defaults to 3. file_name (str, optional): Name the output file. Defaults to None. save_path (str, optional): Path to output folder. Defaults to None. num_cluster (int, optional): Maximal number of MSM states to fit the analysis to. Defaults to 15. tol (float, optional): Tolerrance when to stop the clustering to find optimal states. Defaults to 0.01.

dylightful.dylightful module

dylightful.mdanalysis module

dylightful.mdanalysis.write_dcd(labels, topology, coordinates, base, prefix=None)

dylightful.mdanalysis.write_state(labels, topology, coordinates, base, prefix=None, selection_string="protein or resname '*'")

write out multipdb for markov state of given perspective

Args:

labels ([type]): markov state trajectory topology ([type]): [description] coordinates ([type]): [description] base ([type]): [description]

dylightful.metrics module

dylightful.metrics.calculate_aic(scores)

Plot aic of the HMM analysis

returns:

None

dylightful.metrics.calculate_bic(scores)

Plot aic of the HMM analysis

returns:

None

dylightful.metrics.calculate_max_probas(time_ser, model)

Calculate the metric to evaluate based on max probabilities

Args:

time_ser (np.ndarray): dynophore time series model (HMM): Fitted HMM

Returns:

np.float: Probability of prediting the given time series based on the fitted model

Model

dylightful.metrics.calculate_mean_probas(time_ser, model)

Calculate the metric to evaluate based on average probabilities

Args:

time_ser (np.ndarray): dynophore time series model (HMM): Fitted HMM

Returns:

np.float: Probability of prediting the given time series based on the fitted model

Model

dylightful.metrics.calculate_min_probas(time_ser, model)

Calculate the metric to evaluate based on min probabilities

Args:

time_ser (np.ndarray): dynophore time series model (HMM): Fitted HMM

Returns:

np.float: Probability of prediting the given time series based on the fitted model

Model

dylightful.msm module

dylightful.msm.build_tae_msm(traj_path, time_ser, num_states, prefix=None)

does the tae analysis of a dynophore trajectory

Args:

traj_path (string): path to trajectory to be discretized num_states (int): assumed states

dylightful.msm.fit_msm(trajectory, prefix=None, save_path=None)

Function to fit the msm to a given trajectory and save the vizualization

Args:

trajectory ([type]): Time series to be discretized prefix ([type], optional): Name to save a file. save_path ([type], optional): Wheree to save a file.

Returns:

[type]: msm

dylightful.msm.map_pharmacophore_states(labels, parsed_states)

dylightful.msm.model_msm(trajectory)

Fits the estimator for the Markovian analysis

Args:

trajectory (_type_): _description_

Returns:

_type_: _description_

dylightful.msm.plot_clustered_traj(trajectory, prefix=None, save_path=None)

plots the discretized trajectory of the Markov Model

Args:

labels ([type]): [description]

dylightful.parser module

dylightful.parser.get_atom_serials(pml_path)

dylightful.parser.get_time_series(pml_path)

gets the time_series of the dynophore from the pml file

Args:

pml_path (str): path to the pml file containing the Dynophore trajectory

Returns:

[dictionary, JSON]: returns the time series for each superfeature as a JSON file

dylightful.parser.load_env_partners(json_path)

Generates the env_partners with occurences from the corresponding json

Args:

json_path ([type]): [description]

dylightful.parser.load_env_partners_mixed(json_path)

Generates the env_partners with occurences from the corresponding json

Args:

json_path ([type]): [description]

dylightful.parser.rewrites_time_series(feature_series)

Convertes to a sparse time series to be ready for the HMM processing

Args:

feature_series (np.array):

Returns:

dictionionary, JSON: JSON with the time series per superfeature

dylightful.plot_hmm module

dylightful.plot_hmm.plot_score(scores, file_name=None, save_path=None)

Plots the score for the HMM analysis, as well as AIC and BIC scores

Args:

scores ([type]): [description] file_name ([type], optional): [description]. Defaults to None. save_path ([type], optional): [description]. Defaults to None.

dylightful.plot_hmm.plot_state_diagram(probabilities, max_states=15, file_name=None, save_path=None)

Plots the state diagram

Args:

probabilities ([type]): array of time series max_states (int): maximum number of hidden states filename (str): filename of the parent dynophore trajectory

dylightful.plot_hmm.plot_transmat_graph(trans_mat, file_name=None, save_path=None)

transition state matrix visualized as a directed graph

Args:

trans_mat ([type]): [description] file_name ([type], optional): [description]. Defaults to None. save_path ([type], optional): [description]. Defaults to None.

dylightful.plot_hmm.plot_transmat_map(trans_mat, file_name=None, save_path=None)

State transition matrix visualised as a heatmap

Args:

trans_mat ([type]): [description] file_name ([type], optional): [description]. Defaults to None. save_path ([type], optional): [description]. Defaults to None.

dylightful.postprocess module

dylightful.postprocess.generate_state_map(time_ser_superf, labels_states, state_data)

Generates a map foar each time point of a Markov Sate to a Superfeature tuple such that the time series is the (0,1,2), (0,1,3), (0,1,2) etc…

Args:

time_ser_superf ([type]): loaded combined array of superfeature occurences such that shape is ( len_md_traj, num_superfeatures,) labels_states ([type]): time series of the labels of the different Markov states

Returns:

[np.ndarray]: array of

dylightful.postprocess.get_distinct_superfeatures(unique_pharmc)

[summary]

Args:

unique_pharmc ([type]): [description]

Returns:

[type]: [description]

dylightful.postprocess.get_env_partners(frame_indices, env_partners)

Args:

frameIndices_state ([type]): [description] env_partners ([type]): [description]

Returns:

[type]: [description]

dylightful.postprocess.get_information_mstates(labels_states, state_data)

caclulates some general information such as occurences of the Markov states and unique pharmacophores

dylightful.postprocess.get_information_pc(pharmacophore_traj)

Calculates information about the pharcophore/superfeature patterns

Args:

state_data ([type]): [description]

Returns:

[(list, float)]:

dylightful.postprocess.get_unique_env_partner(partner_traj)

Counts env_partners from the env_partner trajectory

Args:

partner_traj ([type]): specific to a state returns the trajectory of the env partners

Returns:

[(unique_partners, their_counts)]:

dylightful.postprocess.load_time_ser_superfeat(path_to_processed_dynp)

dylightful.postprocess.load_validation()

[summary]

Returns:

[type]: [description]

dylightful.postprocess.postprocessing_msm(labels_states, dynophore_json, processed_dyn, save_path)

Postprocessing of the msm for validation purposes of the Markov model.

dylightful.postprocess.sort_markov_matrix(markov_matrix)

Takes in random markov matrix returns sorted markov matrix

Args:

markov_matrix (np.array): unsorted matrix

Returns:

(np.array): sorted Markov matrix

dylightful.preanalysis module

dylightful.preanalysis.autocorr()

dylightful.utilities module

dylightful.utilities.get_dir(path)

Automatically extracts the path to the dynophore trajectory

Args:

path (str): File path to the dynophore trajectory

Returns:

str: /some/file/path

dylightful.utilities.get_name(path)

Gets the name of the dynophore trajectory without the .pml extension

Args:

path (str): File path to the dynophore trajectory

Returns:

str: dynophore_pml

dylightful.utilities.load_parsed_dyno(traj_path)

Loads the parsed trajectory from the parser

Args:

traj_path ([type]): Path to the parsed traj

Returns:

[type]: trajectory as pd.DataFrame, number of observations as int

dylightful.utilities.make_name(prefix, name, dir='/home/docs/checkouts/readthedocs.org/user_builds/dylightful/checkouts/latest/dylightful')

Merge a prefix and a name

Args:

prefix ([type]): [description] name ([type]): [description]

Returns:

[type]: [description]

dylightful.utilities.parse_file_path(path)

Automatically generates an output path for the time trajectory

Args:

path ([type]): Dynophore input path

dylightful.utilities.save_dict(d, save_path, name=None)

Module contents

Dylightful