# -*- coding: utf-8 -*-
# Copyright 2007-2023 The HyperSpy developers
#
# This file is part of RosettaSciIO.
#
# RosettaSciIO is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# RosettaSciIO is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with RosettaSciIO.  If not, see <https://www.gnu.org/licenses/#GPL>.


import logging
import os
import warnings
from datetime import datetime as dt

import numpy as np

from rsciio._docstrings import FILENAME_DOC, LAZY_UNSUPPORTED_DOC, RETURNS_DOC

_logger = logging.getLogger(__name__)


# At some point, if there is another readerw, whith also use csv file, it will
# be necessary to mention the other reader in this message (and to add an
# argument in the load function to specify the correct reader)
invalid_file_error = (
    "The Protochips csv reader can't import the file, please"
    " make sure, this is a valid Protochips log file."
)


def file_reader(filename, lazy=False):
    """
    Read a Protochips ``.csv`` logfile containing data for heater, biasing or gas
    cell experiments using an in-situ holder.

    Parameters
    ----------
    %s
    %s

    %s
    """
    if lazy is not False:
        raise NotImplementedError("Lazy loading is not supported.")

    csv_file = ProtochipsCSV(filename)
    return _protochips_log_reader(csv_file)


file_reader.__doc__ %= (FILENAME_DOC, LAZY_UNSUPPORTED_DOC, RETURNS_DOC)


def _protochips_log_reader(csv_file):
    csvs = []
    for key in csv_file.logged_quantity_name_list:
        try:
            csvs.append(csv_file.get_dictionary(key))
        except Exception:
            raise IOError(invalid_file_error)
    return csvs


class ProtochipsCSV(object):
    def __init__(
        self,
        filename,
    ):
        self.filename = filename
        self._parse_header()
        self._read_data()

    def _parse_header(self):
        with open(self.filename, "r") as f:
            s = f.readline()
            self.column_name = s.replace(", ", ",").replace("\n", "").split(",")
            if not self._is_protochips_csv_file():
                raise IOError(invalid_file_error)
            self._read_all_metadata_header(f)
        self.logged_quantity_name_list = self.column_name[2:]

    def _is_protochips_csv_file(self):
        # This check is not great, but it's better than nothing...
        if (
            "Time" in self.column_name
            and "Notes" in self.column_name
            and len(self.column_name) >= 3
        ):
            return True
        else:
            return False

    def get_dictionary(self, quantity):
        return {
            "data": self._data_dictionary[quantity],
            "axes": self._get_axes(),
            "metadata": self._get_metadata(quantity),
            "mapping": self._get_mapping(),
            "original_metadata": {"Protochips_header": self._get_original_metadata()},
        }

    def _get_original_metadata(self):
        d = {"Start time": self.start_datetime}
        d["Time units"] = self.time_units
        for quantity in self.logged_quantity_name_list:
            d["%s_units" % quantity] = self._parse_quantity_units(quantity)
        if self.user:
            d["User"] = self.user
        d["Calibration file path"] = self._parse_calibration_filepath()
        d["Time axis"] = self._get_metadata_time_axis()
        # Add the notes here, because there are not well formatted enough to
        # go in metadata
        d["Original notes"] = self._parse_notes()
        return d

    def _get_metadata(self, quantity):
        date, time = np.datetime_as_string(self.start_datetime).split("T")
        return {
            "General": {
                "original_filename": os.path.split(self.filename)[1],
                "title": "%s (%s)" % (quantity, self._parse_quantity_units(quantity)),
                "date": date,
                "time": time,
            },
            "Signal": {"signal_type": "", "quantity": self._parse_quantity(quantity)},
        }

    def _get_mapping(self):
        mapping = {
            "Protochips_header.Calibration file path": (
                "General.notes",
                self._parse_calibration_file_name,
            ),
            "Protochips_header.User": ("General.authors", None),
        }
        return mapping

    def _get_metadata_time_axis(self):
        return {"value": self.time_axis, "units": self.time_units}

    def _read_data(self):
        names = [name.replace(" ", "_") for name in self.column_name]
        data = np.genfromtxt(
            self.filename,
            delimiter=",",
            dtype=None,
            names=names,
            skip_header=self.header_last_line_number,
            encoding="latin1",
        )

        self._data_dictionary = dict()
        for i, name, name_dtype in zip(range(len(names)), self.column_name, names):
            if name == "Notes":
                self.notes = data[name_dtype].astype(str)
            elif name == "Time":
                self.time_axis = data[name_dtype]
            else:
                self._data_dictionary[name] = data[name_dtype]

    def _parse_notes(self):
        arr = np.vstack((self.time_axis, self.notes))
        return np.compress(arr[1] != "", arr, axis=1)

    def _parse_calibration_filepath(self):
        # for the gas cell, the calibration is saved in the notes colunm
        if hasattr(self, "calibration_file"):
            calibration_file = self.calibration_file
        else:
            calibration_file = (
                "The calibration files names are saved in the"
                " 'Original notes' array of the original metadata."
            )
        return calibration_file

    def _parse_calibration_file_name(self, path):
        basename = os.path.basename(path)
        return "Calibration file name: %s" % basename.split("\\")[-1]

    def _get_axes(self):
        scale = np.diff(self.time_axis[1:-1]).mean()
        max_diff = np.diff(self.time_axis[1:-1]).max()
        units = "s"
        offset = 0
        if self.time_units == "Milliseconds":
            scale /= 1000
            max_diff /= 1000
            # Once we support non-uniform axis, don't forgot to update the
            # documentation of the protochips reader
            _logger.warning(
                "The time axis is not uniform, the time step is "
                "thus extrapolated to {0} {1}. The maximal step in time step is {2} {1}".format(
                    scale, units, max_diff
                )
            )
        else:
            warnings.warn("Time units not recognised, assuming second.")

        return [
            {
                "size": self.time_axis.shape[0],
                "index_in_array": 0,
                "name": "Time",
                "scale": scale,
                "offset": offset,
                "units": units,
                "navigate": False,
            }
        ]

    def _parse_quantity(self, quantity):
        quantity_name = quantity.split(" ")[-1]
        return "%s (%s)" % (quantity_name, self._parse_quantity_units(quantity))

    def _parse_quantity_units(self, quantity):
        quantity = quantity.split(" ")[-1].lower()
        return self.__dict__["%s_units" % quantity]

    def _read_all_metadata_header(self, f):
        param, value = self._parse_metadata_header(f.readline())
        i = 2
        while "User" not in param:  # user should be the last of the header
            if "Calibration file" in param:
                self.calibration_file = value
            elif "Date (yyyy.mm.dd)" in param:
                date = value
            elif "Time (hh:mm:ss.ms)" in param:
                time = value
            else:
                attr_name = param.replace(" ", "_").lower()
                self.__dict__[attr_name] = value
            i += 1
            try:
                param, value = self._parse_metadata_header(f.readline())
            except ValueError:
                # when the last line of header does not contain 'User',
                # possibly some old file.
                self.user = None
                break
            except IndexError:
                _logger.warning("The metadata may not be parsed properly.")
                break
        else:
            self.user = value
        self.header_last_line_number = i
        self.start_datetime = np.datetime64(
            dt.strptime(date + time, "%Y.%m.%d%H:%M:%S.%f")
        )

    def _parse_metadata_header(self, line):
        return line.replace(", ", ",").split(",")[1].split(" = ")