def prepare(args: dict, overwriting: bool):
"""Setup run by creating directories and log files.
Args:
args (dict): argparser arguments
overwriting (bool): overwriting flag
Returns:
Tuple(DataFrame, DataFrame): Path to output and mapping_table subdirectories.
"""
output_dir_lsh = make_dir(args, "results_tmp", "lsh_folding", overwriting)
mapping_table_dir = make_dir(args, "mapping_table", None, overwriting)
create_log_files(output_dir_lsh)
create_log_files(mapping_table_dir)
load_config(args)
load_key(args)
method_params_fp = ConfigDict.get_parameters()["fingerprint"]
method_params_lsh = ConfigDict.get_parameters()["lsh"]
method_params = {**method_params_fp, **method_params_lsh}
key = SecretDict.get_secrets()["key"]
lshf = LSHFoldingCalculator.from_param_dict(
secret=key, method_param_dict=method_params, verbosity=0)
outcols = ["fp_feat", "fp_val", "fold_id", "success", "error_message"]
out_types = ["object", "object", "object", "bool", "object"]
dt = DfTransformer(
lshf,
input_columns={"canonical_smiles": "smiles"},
output_columns=outcols,
output_types=out_types,
success_column="success",
nproc=args["number_cpu"],
verbosity=0,
)
return output_dir_lsh, mapping_table_dir, dt
def prepare(args: dict, overwriting: bool) -> Path:
"""Load config and key file,create output directories and setup log files.
Args:
args (dict): argparser dictionary
Returns:
Path: output directory path
"""
output_dir = make_dir(args, "results_tmp", "thresholding", overwriting)
create_log_files(output_dir)
return output_dir
def prepare(args: dict, overwriting: bool):
"""Load config and key file,create output directories and setup log files.
Args:
args (dict): argparser dictionary
Returns:
Path: output directory path
"""
output_dir = make_dir(args, "results_tmp", "activity_formatting",
overwriting)
mapping_table_dir = make_dir(args, "mapping_table", None, overwriting)
create_log_files(output_dir)
return output_dir, mapping_table_dir
def prepare(args: dict, overwriting: bool) -> Path:
"""
Create output folder for matrices
Args:
args (dict): argparser dictionary
overwriting (bool): overwriting argument
Returns:
Path: output path
"""
output_dir = make_dir(args, "matrices", None, overwriting)
results_dir = make_dir(args, "results", None, overwriting)
create_log_files(output_dir)
return output_dir, results_dir
def prepare(args):
"""
Prepare output directories and instantiate df tansformer object for scaffold based folding
Args:
args (dict): argparser arguments
Returns:
Tuple(Path, DfTransformer): Path to output directory and instatitaed DfTranfomer for sccaffold folding
"""
output_dir = make_dir(args, "results_tmp", "folding", args["non_interactive"])
mapping_table_dir = make_dir(args, "mapping_table", None, args["non_interactive"])
create_log_files(output_dir)
create_log_files(mapping_table_dir)
load_config(args)
load_key(args)
key = SecretDict.get_secrets()["key"]
method_params = ConfigDict.get_parameters()["scaffold_folding"]
sa = ScaffoldFoldAssign.from_param_dict(
secret=key, method_param_dict=method_params, verbosity=0
)
outcols = ["murcko_smiles", "sn_smiles", "fold_id", "success", "error_message"]
out_types = ["object", "object", "int", "bool", "object"]
dt = DfTransformer(
sa,
input_columns={"canonical_smiles": "smiles"},
output_columns=outcols,
output_types=out_types,
success_column="success",
nproc=args["number_cpu"],
verbosity=0,
)
return output_dir, mapping_table_dir, dt