"""SpectraFit, the command line tool for fitting."""
import argparse

from datetime import datetime
from getpass import getuser
from socket import gethostname
from typing import Any
from typing import Dict
from typing import Tuple
from uuid import uuid4

import pandas as pd

from spectrafit import __version__
from spectrafit.models import SolverModels
from spectrafit.plotting import PlotSpectra
from spectrafit.report import PrintingResults
from spectrafit.report import PrintingStatus
from spectrafit.tools import PostProcessing
from spectrafit.tools import PreProcessing
from spectrafit.tools import SaveResult
from spectrafit.tools import load_data
from spectrafit.tools import read_input_file


__status__ = PrintingStatus()


def get_args() -> Dict[str, Any]:
    """Get the arguments from the command line.

    Returns:
        Dict[str, Any]: Return the input file arguments as a dictionary without
             additional information beyond the command line arguments.
    """
    parser = argparse.ArgumentParser(
        description="Fast Fitting Program for ascii txt files."
    )
    parser.add_argument("infile", type=str, help="Filename of the spectra data")
    parser.add_argument(
        "-o",
        "--outfile",
        default="spectrafit_results",
        type=str,
        help="Filename for the export, default to set to 'spectrafit_results'.",
    )
    parser.add_argument(
        "-i",
        "--input",
        type=str,
        default="fitting_input.toml",
        help=(
            "Filename for the input parameter, default to set to 'fitting_input.toml'."
            "Supported fileformats are: '*.json', '*.yml', '*.yaml', and '*.toml'"
        ),
    )
    parser.add_argument(
        "-ov",
        "--oversampling",
        action="store_true",
        default=False,
        help="Oversampling the spectra by using factor of 5; default to False.",
    )
    parser.add_argument(
        "-e0",
        "--energy_start",
        type=float,
        default=None,
        help="Starting energy in eV; default to start of energy.",
    )
    parser.add_argument(
        "-e1",
        "--energy_stop",
        type=float,
        default=None,
        help="Ending energy in eV; default to end of energy.",
    )
    parser.add_argument(
        "-s",
        "--smooth",
        type=int,
        default=None,
        help="Number of smooth points for lmfit; default to 0.",
    )
    parser.add_argument(
        "-sh",
        "--shift",
        type=float,
        default=None,
        help="Constant applied energy shift; default to 0.0.",
    )
    parser.add_argument(
        "-c",
        "--column",
        nargs=2,
        type=int,
        default=[0, 1],
        help=(
            "Selected columns for the energy- and intensity-values; default to 0 for"
            " energy (x-axis) and 1 for intensity (y-axis)."
        ),
    )
    parser.add_argument(
        "-sep",
        "--separator",
        type=str,
        default="\t",
        choices=["\t", ",", ";", ":", "|", " ", "s+"],
        help="Redefine the type of separator; default to '\t'.",
    )
    parser.add_argument(
        "-dec",
        "--decimal",
        type=str,
        default=".",
        choices=[".", ","],
        help="Type of decimal separator; default to '.'.",
    )
    parser.add_argument(
        "-hd",
        "--header",
        type=int,
        default=None,
        help="Selected the header for the dataframe; default to None.",
    )
    parser.add_argument(
        "-g",
        "--global",
        type=int,
        default=0,
        choices=[0, 1, 2],
        help=(
            "Perform a global fit over the complete dataframe. The options are '0' "
            "for classic fit (default). The option '1' for global fitting with "
            "auto-definition of the peaks depending on the column size and '2' for "
            "self-defined global fitting routines."
        ),
    )
    parser.add_argument(
        "-auto",
        "--autopeak",
        help=(
            "Auto detection of peaks in the spectra based on `SciPy`. The position, "
            "height, and width are used as estimation for the `Gaussian` models."
            "The default option is 'False' for manual peak definition."
        ),
        action="store_true",
        default=False,
    )

    parser.add_argument(
        "-np",
        "--noplot",
        help="No plotting the spectra and the fit of `SpectraFit`.",
        action="store_true",
        default=False,
    )
    parser.add_argument(
        "-v",
        "--version",
        help="Display the current version of `SpectraFit`.",
        action="store_true",
        default=False,
    )
    parser.add_argument(
        "-vb",
        "--verbose",
        help="Display the initial configuration parameters as a dictionary.",
        action="store_true",
        default=False,
    )
    return vars(parser.parse_args())


def command_line_runner(args: Dict[str, Any] = None) -> None:
    """Run spectrafit from the command line.

    Args:
        args (Dict[str, Any], optional): The input file arguments as a
             dictionary with additional information beyond the command line arguments.
             Defaults to None.
    """
    while True:
        if not args:
            args = extracted_from_command_line_runner()
        if args["version"]:
            __status__.version
            return

        __status__.start

        df_result, args = fitting_routine(args=args)
        PlotSpectra(df=df_result, args=args)()
        SaveResult(df=df_result, args=args)()
        args = None

        __status__.end

        again = input("Would you like to fit again ...? Enter y/n: ").lower()
        if again == "n":
            __status__.thanks
            __status__.credits
            return
        elif again == "y":
            continue
        else:
            __status__.yes_no


def extracted_from_command_line_runner() -> Dict[str, Any]:
    """Extract the input commands from the terminal.

    Raises:
        KeyError: Missing key `minimizer` in `parameters`.
        KeyError: Missing key `optimizer` in `parameters`.

    Returns:
        Dict[str, Any]: The input file arguments as a dictionary with additional
             information beyond the command line arguments.
    """
    result = get_args()
    _args = read_input_file(result["input"])
    if "settings" in _args.keys():
        for key in _args["settings"].keys():
            result[key] = _args["settings"][key]
    if "description" in _args["fitting"].keys():
        result["description"] = _args["fitting"]["description"]
    if "parameters" in _args["fitting"].keys():
        if "minimizer" in _args["fitting"]["parameters"].keys():
            result["minimizer"] = _args["fitting"]["parameters"]["minimizer"]
        else:
            raise SystemExit("Missing 'minimizer' in 'parameters'!")
        if "optimizer" in _args["fitting"]["parameters"].keys():
            result["optimizer"] = _args["fitting"]["parameters"]["optimizer"]
        else:
            raise SystemExit("Missing key 'optimizer' in 'parameters'!")
        if "report" in _args["fitting"]["parameters"].keys():
            result["report"] = _args["fitting"]["parameters"]["report"]
        else:
            result["report"] = {
                "show_correl": True,
                "min_correl": 0.1,
                "sort_pars": False,
            }
        if "conf_interval" in _args["fitting"]["parameters"].keys():
            result["conf_interval"] = _args["fitting"]["parameters"]["conf_interval"]
        else:
            result["conf_interval"] = None
    if "peaks" in _args["fitting"].keys():
        result["peaks"] = _args["fitting"]["peaks"]
    result["timestamp"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    result["ID"] = str(uuid4())
    result["user_name"] = getuser()
    result["user_system"] = gethostname()
    result["used_version"] = __version__
    return result


def fitting_routine(args: Dict[str, Any]) -> Tuple[pd.DataFrame, dict]:
    """Run the fitting algorithm.

    Args:
        args (Dict[str, Any]): The input file arguments as a dictionary with
             additional information beyond the command line arguments.

    Returns:
        Tuple[pd.DataFrame, dict]: Returns a DataFrame and a dictionary, which is
             containing the input data (`x` and `data`), as well as the best fit, single
             contributions of each peak and the corresponding residuum. The dictionary
             contains the raw input data, the best fit, the single contributions and the
             corresponding residuum. Furthermore, the dictionary is extended by advanced
             statistical information of the fit.
    """
    df = load_data(args)
    df, args = PreProcessing(df=df, args=args)()

    minimizer, result = SolverModels(df=df, args=args)()

    df, args = PostProcessing(df=df, args=args, minimizer=minimizer, result=result)()
    PrintingResults(args=args, minimizer=minimizer, result=result)()

    return df, args


if __name__ == "__main__":
    command_line_runner()
