Compute 95% bootstrap confidence intervals for the mean of 'series'.

Compute the data coefficient along the path

Compute the covalent ligands from a list of connections. Parameters ---------- connections: List[gemmi.Connection] The connection

Compute the ratio between d_alpha and alpha

Compute the noise coefficient along the path

Crop the data to a maximum number of tokens. Parameters ---------- data : Tokenized The tokenized data. m

Crop the data to a maximum number of tokens. Parameters ---------- data : Tokenized The tokenized data. m

returns x if it exists, otherwise y

Dump the object to an NPZ file. Parameters ---------- path : Path The path to the file.

Prompts user to input tags from command line if no tags are provided in config. :param cfg: A DictConfig composed by Hydra. :param save_to_fi

Filter a chains based on their length. Parameters ---------- structure : Structure The structure to filter chains

Filter proteins with all unknown residues. Parameters ---------- structure : Structure The structure to filter ch

Filter protein if consecutive CA atoms above a threshold. Parameters ---------- structure : Structure The structu

Filter out clashing chains. Parameters ---------- structure : Structure The structure to filter chains from.

Filter chains in a structure. Parameters ---------- structure : Structure The structure to filter chains from.

Filter excluded ligands. Parameters ---------- structure : Structure The structure to filter chains from.

Filter a data record. Parameters ---------- record : Record The object to consider filtering in / out. R

Filter a data record. Parameters ---------- record : Record The object to consider filtering in / out. R

Filter complexes based on their resolution. Parameters ---------- record : Record The record to filter.

Filter structures based on their resolution. Parameters ---------- record : Record The record to filter.

Filter a record based on its date. Parameters ---------- record : Record The record to filter. Returns

Get the positional encoding for each coordinate. Args: pos: (*, in_dim) Returns: out:

xq: [B, H, N, D] xk: [B, H, N, D] pos: [B, N, in_dim]

Create a dictionary for the ligand symmetries. Parameters ---------- path : str The path to the ligand symmetries. Returns

Get the unknown token for a given entity type. Parameters ---------- dtype : gemmi.EntityType The entity type. Returns -

Load the object from an NPZ file. Parameters ---------- path : Path The path to the file. Returns

Load the object from a JSON file. Parameters ---------- path : Path The path to the file. Returns

Load a structure from an NPZ file. Parameters ---------- path : Path The path to the file. Returns

compute upper and lower bounds for bonds to assess violations.

Print the number of failed examples.

Process an A3M file. Parameters ---------- path : Path The path to the a3m(.gz) file. taxonomy : Redis The taxonomy d

Process an A3M file. Parameters ---------- path : Path The path to the a3m(.gz) file. max_seqs : int, optional The ma

Parse a fasta file. The name of the fasta file is used as the name of this job. We rely on the fasta record id to determine the entity type.

Parse a Boltz input yaml / json. The input file should be a yaml file with the following format: sequences: - protein: i

Pick a random token from an interface. Parameters ---------- tokens : np.ndarray The token data. interface : int The

Maps the given sequence into a one-hot encoded matrix. Args: sequence: An amino acid sequence. mapping: A dictionary mapping amino ac

Lightning hook that is called at the beginning of fit (train + validate), validate, test, or predict. This is a good hook when you ne

Run the setup for the DataModule. Parameters ---------- stage : str, optional The stage, one of 'fit', 'validate'

Run the setup for the DataModule. Parameters ---------- stage : str, optional The stage, one of 'fit', 'validate'

Optional decorator that controls the failure behavior when executing the task function. This wrapper can be used to: - make sure loggers

Calculate diversity among pairs of modeled protein structures for each score. Returns: dict of scores('rmsd': float, 'tm': float, 'gd

Tokenize the input data. Parameters ---------- data : Input The input data. Returns -------

Get the validation dataloader. Returns ------- DataLoader The validation dataloader.

Write the predictions to disk.