"""Utilities for loading datasets that are unapproved of in the main repo.

Old code, delete.
"""

__author__ = [
    "Emiliathewolf",
    "TonyBagnall",
    "jasonlines",
]

import numpy as np
import pandas as pd

from sktime.datatypes._panel._convert import (
    _make_column_names,
    from_long_to_nested,
    from_nested_to_2d_np_array,
    from_nested_to_3d_numpy,
)


def _read_header(file, full_file_path_and_name):
    """Read the header information, returning the meta information."""
    # Meta data for data information
    meta_data = {
        "is_univariate": True,
        "is_equally_spaced": True,
        "is_equal_length": True,
        "has_nans": False,
        "has_timestamps": False,
        "has_class_labels": True,
    }
    # Read header until @data tag met
    for line in file:
        line = line.strip().lower()
        if line:
            if line.startswith("@problemname"):
                tokens = line.split(" ")
                token_len = len(tokens)
            elif line.startswith("@timestamps"):
                tokens = line.split(" ")
                if tokens[1] == "true":
                    meta_data["has_timestamps"] = True
                elif tokens[1] != "false":
                    raise OSError(
                        f"invalid timestamps tag value {tokens[1]} value in file "
                        f"{full_file_path_and_name}"
                    )
            elif line.startswith("@univariate"):
                tokens = line.split(" ")
                token_len = len(tokens)
                if tokens[1] == "false":
                    meta_data["is_univariate"] = False
                elif tokens[1] != "true":
                    raise OSError(
                        f"invalid univariate tag value {tokens[1]} in file "
                        f"{full_file_path_and_name}"
                    )
            elif line.startswith("@equallength"):
                tokens = line.split(" ")
                if tokens[1] == "false":
                    meta_data["is_equal_length"] = False
                elif tokens[1] != "true":
                    raise OSError(
                        f"invalid unequal tag value {tokens[1]} in file "
                        f"{full_file_path_and_name}"
                    )
            elif line.startswith("@classlabel"):
                tokens = line.split(" ")
                token_len = len(tokens)
                if tokens[1] == "false":
                    meta_data["class_labels"] = False
                elif tokens[1] != "true":
                    raise OSError(
                        "invalid classLabel value in file " f"{full_file_path_and_name}"
                    )
                if token_len == 2 and meta_data["class_labels"]:
                    raise OSError(
                        f"if the classlabel tag is true then class values must be "
                        f"supplied in file{full_file_path_and_name} but read {tokens}"
                    )
            elif line.startswith("@data"):
                return meta_data
    raise OSError(
        f"End of file reached for {full_file_path_and_name} but no indicated start of "
        f"data with the tag @data"
    )


def load_from_tsfile(
    full_file_path_and_name,
    replace_missing_vals_with="NaN",
    return_y=True,
):
    """Load time series data into X and (optionally) y.

    Data from a .ts file into a an 2D (univariate) or 3D (multivariate) if equal
    length or Pandas DataFrame if unequal length. If present, y is loaded into a 1D
    array.

    Parameters
    ----------
    full_file_path_and_name: str
        The full pathname of the .ts file to read.
    replace_missing_vals_with: str, default NaN
       The value that missing values in the text file should be replaced
       with prior to parsing.
    return_y: boolean default True
       whether to return the y variable, if it is present.

    Returns
    -------
    X: DataFrame or ndarray
    y (optional): ndarray.
    """
    # Initialize flags and variables used when parsing the file
    is_first_case = True
    instance_list = []
    class_val_list = []
    line_num = 0
    num_dimensions = 0
    num_cases = 0
    # equal_length = True
    with open(full_file_path_and_name, encoding="utf-8") as file:
        _meta_data = _read_header(file, full_file_path_and_name)
        for line in file:
            num_cases += 1
            line = line.replace("?", replace_missing_vals_with)
            dimensions = line.split(":")
            # If first instance then note the number of dimensions.
            # This must be the same for all cases.
            if is_first_case:
                num_dimensions = len(dimensions)
                if _meta_data["has_class_labels"]:
                    num_dimensions -= 1
                instance_list = [[] for _ in range(num_dimensions)]
                is_first_case = False
                _meta_data["num_dimensions"] = num_dimensions
            # See how many dimensions a case has
            this_line_num_dim = len(dimensions)
            if _meta_data["has_class_labels"]:
                this_line_num_dim -= 1
            if this_line_num_dim != _meta_data["num_dimensions"]:
                raise OSError(
                    f"Error input {full_file_path_and_name} all cases must "
                    f"have the {num_dimensions} dimensions. Case "
                    f"{num_cases} has {this_line_num_dim}"
                )
            # Process the data for each dimension
            for dim in range(0, _meta_data["num_dimensions"]):
                dimension = dimensions[dim].strip()
                if dimension:
                    data_series = dimension.split(",")
                    data_series = [float(i) for i in data_series]
                    instance_list[dim].append(pd.Series(data_series))
                else:
                    instance_list[dim].append(pd.Series(dtype="object"))
            if _meta_data["has_class_labels"]:
                class_val_list.append(dimensions[_meta_data["num_dimensions"]].strip())
                line_num += 1
    # Check that the file was not empty
    if line_num:
        # Create a DataFrame from the data parsed
        data = pd.DataFrame(dtype=np.float32)
        for dim in range(0, _meta_data["num_dimensions"]):
            data["dim_" + str(dim)] = instance_list[dim]
        if not _meta_data["has_timestamps"] and _meta_data["is_equal_length"]:
            if _meta_data["is_univariate"]:
                data = from_nested_to_2d_np_array(data)
            else:
                data = from_nested_to_3d_numpy(data)
        if return_y and not _meta_data["has_class_labels"]:
            raise OSError(
                f"class labels have been requested, but they "
                f"are not present in the file "
                f"{full_file_path_and_name}"
            )
        if _meta_data["has_class_labels"] and return_y:
            return data, np.asarray(class_val_list)
        else:
            return data
    else:
        raise OSError(
            f"Empty file {full_file_path_and_name} with header info but no " f"cases"
        )
