API Overview

Modules

• cobalt
• dataset
• dca
• fasta
• functional
• io: The io module provides the Python interfaces to stream handling. The
• plot
• resampling
• sampling
• statmech
• stats
• utils

Classes

• dataset.DatasetDCA: Dataset class for handling multi-sequence alignments data.

Functions

• cobalt.prune_redundant_sequences: Prunes sequences from X such that no sequence has more than 'seqid_th' fraction of its residues identical to any other sequence in the set.
• cobalt.run_cobalt: Runs the Cobalt algorithm to split the input MSA into training and test sets.
• cobalt.split_train_test: Splits X into two sets, T and S, such that no sequence in S has more than
• dca.get_contact_map: Computes the contact map from the model coupling matrix.
• dca.get_mf_contact_map: Computes the contact map from the model coupling matrix.
• dca.get_seqid: When average is True:
• dca.set_zerosum_gauge: Sets the zero-sum gauge on the coupling matrix.
• fasta.compute_weights: Computes the weight to be assigned to each sequence 's' in 'data' as 1 / n_clust, where 'n_clust' is the number of sequences
• fasta.decode_sequence: Takes a numeric sequence or list of seqences in input an returns the corresponding string encoding.
• fasta.encode_sequence: Encodes a sequence or a list of sequences into a numeric format.
• fasta.get_tokens: Converts the alphabet into the corresponding tokens.
• fasta.import_from_fasta: Import sequences from a fasta file. The following operations are performed:
• fasta.validate_alphabet: Check if the chosen alphabet is compatible with the input sequences.
• fasta.write_fasta: Generate a fasta file with the input sequences.
• functional.one_hot: A fast one-hot encoding function faster than the PyTorch one working with torch.int32 and returning a float Tensor.
• io.load_chains: Loads the sequences from a fasta file and returns the numeric-encoded version.
• io.load_params: Import the parameters of the model from a file.
• io.load_params_oldformat: Import the parameters of the model from a file. Assumes the old DCA format.
• io.save_chains: Saves the chains in a fasta file.
• io.save_params: Saves the parameters of the model in a file.
• io.save_params_oldformat: Saves the parameters of the model in a file. Assumes the old DCA format.
• plot.plot_PCA: Makes the scatter plot of the components (pc1, pc2) of the input data and shows the histograms of the components.
• plot.plot_autocorrelation: Plots the time-autocorrelation curve of the sequence identity and the generated and data sequence identities.
• plot.plot_contact_map: Plots the contact map.
• plot.plot_pearson_sampling: Plots the Pearson correlation coefficient over sampling time.
• plot.plot_scatter_correlations: Plots the scatter plot of the data and generated Cij and Cijk values.
• sampling.get_sampler: Returns the sampling function corresponding to the chosen method.
• sampling.gibbs_mutate: Attempts to perform num_mut mutations using the Gibbs sampler.
• sampling.gibbs_sampling: Gibbs sampling.
• sampling.metropolis: Metropolis sampling.
• sampling.metropolis_mutate: Attempts to perform num_mut mutations using the Metropolis sampler.
• statmech.compute_energy: Compute the DCA energy of the sequences in X.
• statmech.compute_entropy: Compute the entropy of the DCA model.
• statmech.compute_logZ_exact: Compute the log-partition function of the model.
• statmech.compute_log_likelihood: Compute the log-likelihood of the model.
• statmech.enumerate_states: Enumerate all possible states of a system of L sites and q states.
• statmech.iterate_tap: Iterates the TAP equations until convergence.
• stats.extract_Cij_from_freq: Extracts the lower triangular part of the covariance matrices of the data and chains starting from the frequencies.
• stats.extract_Cij_from_seqs: Extracts the lower triangular part of the covariance matrices of the data and chains starting from the sequences.
• stats.generate_unique_triplets: Generates a set of unique triplets of positions. Used to compute the 3-points statistics.
• stats.get_correlation_two_points: Computes the Pearson coefficient and the slope between the two-point frequencies of data and chains.
• stats.get_covariance_matrix: Computes the weighted covariance matrix of the input multi sequence alignment.
• stats.get_freq_single_point: Computes the single point frequencies of the input MSA.
• stats.get_freq_three_points: Computes the 3-body connected correlation statistics of the input MSAs.
• stats.get_freq_two_points: Computes the 2-points statistics of the input MSA.
• utils.get_device: Returns the device where to store the tensors.
• utils.get_dtype: Returns the data type of the tensors.
• utils.get_mask_save: Returns the mask to save the upper-triangular part of the coupling matrix.
• utils.init_chains: Initialize the chains of the DCA model. If 'fi' is provided, the chains are sampled from the
• utils.init_parameters: Initialize the parameters of the DCA model.
• utils.resample_sequences: Extracts nextract sequences from data with replacement according to the weights.

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