Source code for pimmslearn.utils

import pathlib
from typing import Union

import numpy as np
import pandas as pd

from pimmslearn.io.datasplits import long_format




def append_to_filepath(
    filepath: Union[pathlib.Path, str],
    to_append: str,
    sep: str = "_",
    new_suffix: str = None,
) -> pathlib.Path:
    """Append filepath with specified to_append using a seperator.

    Example: `data.csv` to data_processed.csv
    """
    filepath = pathlib.Path(filepath)
    suffix = filepath.suffix
    if new_suffix:
        suffix = f".{new_suffix}"
    new_fp = filepath.parent / f"{filepath.stem}{sep}{to_append}{suffix}"
    return new_fp





def create_random_missing_data(
    N, M, mean: float = 25.0, std_dev: float = 2.0, prop_missing: float = 0.15
):
    data = np.random.normal(loc=mean, scale=std_dev, size=(N, M))
    prop_missing = float(prop_missing)
    if prop_missing > 0.0 and prop_missing < 1.0:
        mask = np.random.choice(
            [False, True], size=data.shape, p=[prop_missing, 1 - prop_missing]
        )
        data = np.where(mask, data, np.nan)
    return data





def create_random_missing_data_long(N: int, M: int, prop_missing=0.1):
    """Build example long"""
    data = create_random_missing_data(N=N, M=M, prop_missing=prop_missing)
    df_long = long_format(pd.DataFrame(data))
    df_long.index.names = ("Sample ID", "peptide")
    df_long.reset_index(inplace=True)
    return df_long





def create_random_df(
    N: int,
    M: int,
    scaling_factor: float = 30.0,
    prop_na: float = 0.0,
    start_idx: int = 0,
    name_index="Sample ID",
    name_columns="peptide",
):
    X = np.random.rand(N, M)

    if prop_na > 0.0 and prop_na < 1.0:
        mask = ~(X < prop_na)
        X = np.where(mask, X, np.nan)

    X *= scaling_factor

    X = pd.DataFrame(
        X,
        index=[f"sample_{i:0{len(str(N))}}" for i in range(start_idx, start_idx + N)],
        columns=(f"feat_{i:0{len(str(M))}}" for i in range(M)),
    )
    X.index.name = name_index
    X.columns.name = name_columns
    return X