# Path: freesas/autorg.py
# def auto_gpa(data, Rg_min=1.0, qRg_max=1.3, qRg_min=0.5):
#     def curate_data(data):
# def auto_guinier(data, Rg_min=1.0, qRg_max=1.3, relax=1.2):
#         I = data.T[1]
#         I = I[range0]
#         I0 = I[p1] * numpy.exp(q2[p1] * Rg ** 2 / 3.0)
#         I1 = I[range1]
# 
# Path: freesas/sasio.py
# def load_scattering_data(filename: PathType) -> ndarray:
#     """
#     Load scattering data q, I, err into a numpy array.
# 
#     :param filename: ASCII file, 3 column (q,I,err)
#     :return: numpy array with 3 column (q,I,err)
#     """
#     try:
#         data = loadtxt(filename)
#     except OSError:
#         raise OSError("File could not be read.")
#     except ValueError:
#         try:
#             with open(filename) as data_file:
#                 text = data_file.readlines()
#         except OSError:
#             raise OSError("File could not be read.")
#         else:
#             try:
#                 data = parse_ascii_data(text, number_of_columns=3)
#             except ValueError:
#                 raise ValueError(
#                     "File does not seem to be " "in the format q, I, err. "
#                 )
#     return data
# 
# def convert_inverse_angstrom_to_nanometer(
#     data_in_inverse_angstrom: ndarray,
# ) -> ndarray:
#     """
#     Convert data with q in 1/Å to 1/nm.
# 
#     :param data_in_inverse_angstrom: numpy array in format
#                                      (q_in_inverse_Angstrom,I,err)
#     :return: numpy array with 3 column (q_in_inverse_nm,I,err)
#     """
#     q_in_angstrom, intensity, err = data_in_inverse_angstrom.T
#     return array([q_in_angstrom * 10.0, intensity, err]).T
# 
# Path: freesas/sas_argparser.py
# class GuinierParser:
#     """
#     Wrapper class for argparse ArgumentParser that provides predefined
#     arguments for auto_rg like programs.
#     """
# 
#     usage = ""
# 
#     def __init__(self, prog: str, description: str, epilog: str, **kwargs):
#         """
#         Create parser argparse ArgumentParser with argument
#         - standardized usage text
#         - standardized version text
#         - verbose and version args added by default
# 
#         :param prog:         name of the executable
#         :param description:  description param of argparse ArgumentParser
#         :param epilog:       epilog param of argparse ArgumentParser
#         :param kwargs:       additional kwargs for argparse ArgumentParser
#         """
# 
#         file_help_text = "dat files of the scattering curves"
#         self.parser = SASParser(
#             prog=prog, description=description, epilog=epilog, **kwargs
#         )
#         self.parser.add_file_argument(help_text=file_help_text)
#         self.parser.add_output_filename_argument()
#         self.parser.add_output_data_format(
#             "native", "csv", "ssf", default="native"
#         )
#         self.parser.add_q_unit_argument()
#         self.usage = self.parser.usage
# 
#     def parse_args(self, *args, **kwargs):
#         """ Wrapper for SASParser parse_args() """
#         return self.parser.parse_args(*args, **kwargs)
# 
#     def add_argument(self, *args, **kwargs):
#         """ Wrapper for SASParser add_argument() """
#         self.parser.add_argument(*args, **kwargs)


import sys
import logging
import platform
from os import linesep as os_linesep
from pathlib import Path
from contextlib import contextmanager
from typing import Callable, List, Optional, IO, Generator
from numpy import ndarray
from .autorg import (
    RG_RESULT,
    InsufficientDataError,
    NoGuinierRegionError,
)
from .sasio import (
    load_scattering_data,
    convert_inverse_angstrom_to_nanometer,
)
from .sas_argparser import GuinierParser
                    f"{rg_result.start_point:3}",
                    f"{rg_result.end_point:3}",
                    f"{rg_result.quality:6.4f}",
                    f"{rg_result.aggregated:6.4f}",
                ]
            )
            + linesep
        )
    elif output_format == "ssv":
        return (
            " ".join(
                [
                    f"{rg_result.Rg:6.4f}",
                    f"{rg_result.sigma_Rg:6.4f}",
                    f"{rg_result.I0:6.4f}",
                    f"{rg_result.sigma_I0:6.4f}",
                    f"{rg_result.start_point:3}",
                    f"{rg_result.end_point:3}",
                    f"{rg_result.quality:6.4f}",
                    f"{rg_result.aggregated:6.4f}",
                    f"{afile}",
                ]
            )
            + linesep
        )
    else:
        return f"{afile} {rg_result}{linesep}"


def run_guinier_fit(

# Path: freesas/autorg.py
# def auto_gpa(data, Rg_min=1.0, qRg_max=1.3, qRg_min=0.5):
#     def curate_data(data):
# def auto_guinier(data, Rg_min=1.0, qRg_max=1.3, relax=1.2):
#         I = data.T[1]
#         I = I[range0]
#         I0 = I[p1] * numpy.exp(q2[p1] * Rg ** 2 / 3.0)
#         I1 = I[range1]
# 
# Path: freesas/sasio.py
# def load_scattering_data(filename: PathType) -> ndarray:
#     """
#     Load scattering data q, I, err into a numpy array.
# 
#     :param filename: ASCII file, 3 column (q,I,err)
#     :return: numpy array with 3 column (q,I,err)
#     """
#     try:
#         data = loadtxt(filename)
#     except OSError:
#         raise OSError("File could not be read.")
#     except ValueError:
#         try:
#             with open(filename) as data_file:
#                 text = data_file.readlines()
#         except OSError:
#             raise OSError("File could not be read.")
#         else:
#             try:
#                 data = parse_ascii_data(text, number_of_columns=3)
#             except ValueError:
#                 raise ValueError(
#                     "File does not seem to be " "in the format q, I, err. "
#                 )
#     return data
# 
# def convert_inverse_angstrom_to_nanometer(
#     data_in_inverse_angstrom: ndarray,
# ) -> ndarray:
#     """
#     Convert data with q in 1/Å to 1/nm.
# 
#     :param data_in_inverse_angstrom: numpy array in format
#                                      (q_in_inverse_Angstrom,I,err)
#     :return: numpy array with 3 column (q_in_inverse_nm,I,err)
#     """
#     q_in_angstrom, intensity, err = data_in_inverse_angstrom.T
#     return array([q_in_angstrom * 10.0, intensity, err]).T
# 
# Path: freesas/sas_argparser.py
# class GuinierParser:
#     """
#     Wrapper class for argparse ArgumentParser that provides predefined
#     arguments for auto_rg like programs.
#     """
# 
#     usage = ""
# 
#     def __init__(self, prog: str, description: str, epilog: str, **kwargs):
#         """
#         Create parser argparse ArgumentParser with argument
#         - standardized usage text
#         - standardized version text
#         - verbose and version args added by default
# 
#         :param prog:         name of the executable
#         :param description:  description param of argparse ArgumentParser
#         :param epilog:       epilog param of argparse ArgumentParser
#         :param kwargs:       additional kwargs for argparse ArgumentParser
#         """
# 
#         file_help_text = "dat files of the scattering curves"
#         self.parser = SASParser(
#             prog=prog, description=description, epilog=epilog, **kwargs
#         )
#         self.parser.add_file_argument(help_text=file_help_text)
#         self.parser.add_output_filename_argument()
#         self.parser.add_output_data_format(
#             "native", "csv", "ssf", default="native"
#         )
#         self.parser.add_q_unit_argument()
#         self.usage = self.parser.usage
# 
#     def parse_args(self, *args, **kwargs):
#         """ Wrapper for SASParser parse_args() """
#         return self.parser.parse_args(*args, **kwargs)
# 
#     def add_argument(self, *args, **kwargs):
#         """ Wrapper for SASParser add_argument() """
#         self.parser.add_argument(*args, **kwargs)


# Path: freesas/fitting.py
import sys
import logging
import platform
from os import linesep as os_linesep
from pathlib import Path
from contextlib import contextmanager
from typing import Callable, List, Optional, IO, Generator
from numpy import ndarray
from .autorg import (
    RG_RESULT,
    InsufficientDataError,
    NoGuinierRegionError,
)
from .sasio import (
    load_scattering_data,
    convert_inverse_angstrom_to_nanometer,
)
from .sas_argparser import GuinierParser

                    f"{rg_result.start_point:3}",
                    f"{rg_result.end_point:3}",
                    f"{rg_result.quality:6.4f}",
                    f"{rg_result.aggregated:6.4f}",
                ]
            )
            + linesep
        )
    elif output_format == "ssv":
        return (
            " ".join(
                [
                    f"{rg_result.Rg:6.4f}",
                    f"{rg_result.sigma_Rg:6.4f}",
                    f"{rg_result.I0:6.4f}",
                    f"{rg_result.sigma_I0:6.4f}",
                    f"{rg_result.start_point:3}",
                    f"{rg_result.end_point:3}",
                    f"{rg_result.quality:6.4f}",
                    f"{rg_result.aggregated:6.4f}",
                    f"{afile}",
                ]
            )
            + linesep
        )
    else:
        return f"{afile} {rg_result}{linesep}"


def run_guinier_fit(

    fit_function: Callable[[ndarray], RG_RESULT],