Source code for peptdeep.settings

import os
import collections
import numpy as np

from alphabase.yaml_utils import load_yaml
from alphabase.constants.modification import (
    load_mod_df,
    keep_modloss_by_importance,
    add_new_modifications,
    MOD_DF,
)

from peptdeep.constants._const import CONST_FOLDER

global_settings = load_yaml(os.path.join(CONST_FOLDER, "default_settings.yaml"))
"""
Global settings in peptdeep,
it controls all functionalities of PeptDeep.
"""

model_const = load_yaml(os.path.join(CONST_FOLDER, "model_const.yaml"))

### MOD_TO_FEATURE
mod_elements = model_const["mod_elements"]
mod_feature_size = len(mod_elements)

mod_elem_to_idx = dict(zip(mod_elements, range(mod_feature_size)))


def _parse_mod_formula(formula):
    """
    Parse a modification formula to a feature vector
    """
    feature = np.zeros(mod_feature_size)
    elems = formula.strip(")").split(")")
    for elem in elems:
        chem, num = elem.split("(")
        num = int(num)
        if chem in mod_elem_to_idx:
            feature[mod_elem_to_idx[chem]] = num
        else:
            feature[-1] += num
    return feature


MOD_TO_FEATURE = {}




def update_all_mod_features():
    for modname, formula in MOD_DF[["mod_name", "composition"]].values:
        MOD_TO_FEATURE[modname] = _parse_mod_formula(formula)



update_all_mod_features()




def add_user_defined_modifications(user_mods: dict = None):
    """
    Add user-defined modifications into the system,
    this is userful for isotope labeling.

    Parameters
    ----------
    user_mods : dict, optional
        Example:
        ```
        {
        "Dimethyl2@Any_N-term": {
        "composition": "H(2)2H(2)C(2)",
        "modloss_composition": ""
        }, ...
        }
        ```
        Set as `global_settings["user_defined_modifications"]` if it is None.
        By default None.
    """
    if user_mods is None:
        user_mods = global_settings["common"]["user_defined_modifications"]
    add_new_modifications(user_mods)

    update_all_mod_features()



def _refine_global_settings():
    global_settings["thread_num"] = min(
        global_settings["thread_num"], global_settings["MAX_THREADS"]
    )
    global_settings["PEPTDEEP_HOME"] = os.path.expanduser(
        global_settings["PEPTDEEP_HOME"]
    )
    global_settings["library"]["output_folder"] = global_settings["library"][
        "output_folder"
    ].format(PEPTDEEP_HOME=global_settings["PEPTDEEP_HOME"])
    global_settings["model_mgr"]["transfer"]["model_output_folder"] = global_settings[
        "model_mgr"
    ]["transfer"]["model_output_folder"].format(
        PEPTDEEP_HOME=global_settings["PEPTDEEP_HOME"]
    )
    # global_settings['percolator']['output_folder']=(
    #     global_settings['percolator']['output_folder'].format(
    #         PEPTDEEP_HOME=global_settings['PEPTDEEP_HOME']
    #     )
    # )
    for key, val in list(global_settings["model_mgr"]["instrument_group"].items()):
        global_settings["model_mgr"]["instrument_group"][key.upper()] = val

    add_user_defined_modifications()


_refine_global_settings()




def update_settings(dict_, new_dict):
    for k, v in new_dict.items():
        if isinstance(v, collections.abc.Mapping):
            dict_[k] = update_settings(dict_.get(k, {}), v)
        else:
            dict_[k] = v
    return dict_





def update_global_settings(new_settings):
    update_settings(global_settings, new_settings)
    _refine_global_settings()





def load_global_settings(yaml: str):
    d = load_yaml(yaml)
    update_global_settings(d)





def update_modifications(tsv: str = "", modloss_importance_level: float = 1.0):
    """
    Load modification tsv either from alphabase default
    `modification.tsv <https://github.com/MannLabs/alphabase/blob/main/alphabase/constants/const_files/modification.tsv>`_
    or an external tsv file.

    Parameters
    ----------
    tsv : str, optional
        External modification tsv file, "" refers to the default alphabase tsv,
        by default "".
    modloss_importance_level : float, optional
        Only keep the important modification losses, by default 1.0
    """
    if os.path.isfile(tsv):
        load_mod_df(tsv, modloss_importance_level=modloss_importance_level)
    else:
        keep_modloss_by_importance(modloss_importance_level)

    add_user_defined_modifications()



update_modifications()