comparison table_scipy_interpolate.py @ 0:0112f08c95ed draft default tip

planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/tables commit d0ff40eb2b536fec6c973c3a9ea8e7f31cd9a0d6

0:0112f08c95ed

import argparse
import logging
from typing import Callable, Tuple


import numpy as np
import pandas as pd
from scipy.interpolate import Akima1DInterpolator, CubicSpline, PchipInterpolator
from utils import LoadDataAction, StoreOutputAction


class InterpolationModelAction(argparse.Action):
    def __call__(
        self,
        parser: argparse.ArgumentParser,
        namespace: argparse.Namespace,
        values: str,
        option_string: str = None,
    ) -> None:
        """
        Custom argparse action to map interpolation method names to their corresponding functions.

        Parameters:
        parser (argparse.ArgumentParser): The argument parser instance.
        namespace (argparse.Namespace): The namespace to hold the parsed values.
        values (str): The interpolation method name.
        option_string (str): The option string.
        """
        interpolators = {
            "linear": np.interp,
            "cubic": CubicSpline,
            "pchip": PchipInterpolator,
            "akima": Akima1DInterpolator,
        }
        if values not in interpolators:
            raise ValueError(f"Unknown interpolation method: {values}")
        setattr(namespace, self.dest, interpolators[values])


def interpolate_data(
    reference: pd.DataFrame,
    query: pd.DataFrame,
    x_col: int,
    y_col: int,
    xnew_col: int,
    model: Callable,
    output_dataset: Tuple[Callable[[pd.DataFrame, str], None], str],
) -> None:
    """
    Interpolate data using the specified model.

    Parameters:
    reference (pd.DataFrame): The reference dataset.
    query (pd.DataFrame): The query dataset.
    x_col (int): The 1-based index of the x column in the reference dataset.
    y_col (int): The 1-based index of the y column in the reference dataset.
    xnew_col (int): The 1-based index of the x column in the query dataset.
    model (Callable): The interpolation model to use.
    output_dataset (Tuple[Callable[[pd.DataFrame, str], None], str]): The output dataset and its file extension.
    """
    try:
        # Convert 1-based indices to 0-based indices
        x_col_name = reference.columns[x_col - 1]
        y_col_name = reference.columns[y_col - 1]
        xnew_col_name = query.columns[xnew_col - 1]

        # Check if y_col already exists in the query dataset
        if y_col_name in query.columns:
            raise ValueError(
                f"Column '{y_col_name}' already exists in the query dataset."
            )

        if model == np.interp:
            query[y_col_name] = model(
                query[xnew_col_name], reference[x_col_name], reference[y_col_name]
            )
        else:
            model_instance = model(reference[x_col_name], reference[y_col_name])
            query[y_col_name] = model_instance(query[xnew_col_name]).astype(float)

        write_func, file_path = output_dataset
        write_func(query, file_path)
    except Exception as e:
        logging.error(f"Error in interpolate_data function: {e}")
        raise


def main(
    reference_dataset: pd.DataFrame,
    query_dataset: pd.DataFrame,
    x_col: int,
    y_col: int,
    xnew_col: int,
    model: Callable,
    output_dataset: Tuple[Callable[[pd.DataFrame, str], None], str],
) -> None:
    """
    Main function to load the datasets, perform interpolation, and save the result.

    Parameters:
    reference_dataset (Tuple[pd.DataFrame, str]): The reference dataset and its file extension.
    query_dataset (Tuple[pd.DataFrame, str]): The query dataset and its file extension.
    x_col (int): The 1-based index of the x column in the reference dataset.
    y_col (int): The 1-based index of the y column in the reference dataset.
    xnew_col (int): The 1-based index of the x column in the query dataset.
    model (Callable): The interpolation model to use.
    output_dataset (Tuple[Callable[[pd.DataFrame, str], None], str]): The output dataset and its file extension.
    """
    try:
        interpolate_data(reference_dataset, query_dataset, x_col, y_col, xnew_col, model, output_dataset)
    except Exception as e:
        logging.error(f"Error in main function: {e}")
        raise


if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    parser = argparse.ArgumentParser(
        description="Interpolate data using various methods."
    )
    parser.add_argument(
        "--reference_dataset",
        nargs=2,
        action=LoadDataAction,
        required=True,
        help="Path to the reference dataset and its file extension (csv, tsv, parquet)",
    )
    parser.add_argument(
        "--query_dataset",
        nargs=2,
        action=LoadDataAction,
        required=True,
        help="Path to the query dataset and its file extension (csv, tsv, parquet)",
    )
    parser.add_argument(
        "--x_col",
        type=int,
        required=True,
        help="1-based index of the x column in the reference dataset",
    )
    parser.add_argument(
        "--y_col",
        type=int,
        required=True,
        help="1-based index of the y column in the reference dataset",
    )
    parser.add_argument(
        "--xnew_col",
        type=int,
        required=True,
        help="1-based index of the x column in the query dataset",
    )
    parser.add_argument(
        "--model",
        type=str,
        action=InterpolationModelAction,
        required=True,
        help="Interpolation model to use (linear, cubic, pchip, akima)",
    )
    parser.add_argument(
        "--output_dataset",
        nargs=2,
        action=StoreOutputAction,
        required=True,
        help="Path to the output dataset and its file extension (csv, tsv, parquet)",
    )

    args = parser.parse_args()
    main(
        args.reference_dataset,
        args.query_dataset,
        args.x_col,
        args.y_col,
        args.xnew_col,
        args.model,
        args.output_dataset,
    )