ample.util.spicker module¶

class Spickerer(spicker_exe=None, run_dir=None)[source]¶

Bases: object

Methods

`cluster`(models[, num_clusters, …])	Cluster decoys using spicker
`create_input_files`(models[, score_type, …])	jmht
`process_log`([logfile])	Read the spicker str.txt file and return a list of SpickerResults for each cluster.
`results_summary`()	Summarise the spicker results

get_length

cluster(models, num_clusters=10, max_cluster_size=200, run_dir=None, score_type=’rmsd’, score_matrix=None, nproc=1)[source]¶

Cluster decoys using spicker

Parameters:	models : list A list containing structure decoys cluster_dir : str The directory to store the cluster data in cluster_method_type : str The method to be used to cluster the decoys score_type : str The scoring metric for clustering num_clusters : int The number of clusters to produce max_cluster_size : int The maximum number of decoys per cluster cluster_exe : str The path to the spicker executable nproc : int The number of processors to use score_matrix : str, optional The path to the score matrix to be used
Returns:	list A list containing the clusters
Raises:	RuntimeError No clusters returned by SPICKER

create_input_files(models, score_type=’rmsd’, score_matrix=None)[source]¶: jmht Create the input files required to run spicker (See notes in spicker.f FORTRAN file for a description of the required files)

process_log(logfile=None)[source]¶

Read the spicker str.txt file and return a list of SpickerResults for each cluster.

We use the R_nat value to order the files in the cluster