"""Generic gaze data and class definitions."""

"""
This program 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.
This program 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
this program. If not, see <https://www.gnu.org/licenses/>.
"""

__author__ = "Théo de la Hogue"
__credits__ = []
__copyright__ = "Copyright 2023, Ecole Nationale de l'Aviation Civile (ENAC)"
__license__ = "GPLv3"

import json
import math
from typing import Self

import cv2
import numpy
import pandas

from argaze import DataFeatures
from argaze.AreaOfInterest import AOIFeatures

@DataFeatures.timestamp
class GazePosition(tuple):
	"""Define gaze position as a tuple of coordinates with precision.

	Parameters:
		precision: the radius of a circle around value where other same gaze position measurements could be.
		message: a string to describe why the position is what it is.
	"""

	def __new__(cls, position: tuple = (), **kwargs):

		return tuple.__new__(cls, position)

	def __init__(self, position: tuple = (), precision: int | float = None, message: str = None):

		self.__precision = precision
		self.__message = message

	@property
	def value(self):
		"""Get position's tuple value."""
		return tuple(self)

	@property
	def precision(self):
		"""Get position's precision."""
		return self.__precision

	@property
	def message(self):
		"""Get position's message."""
		return self.__message

	@classmethod
	def from_dict(cls, position_data: dict) -> Self:

		if 'value' in position_data.keys():

			value = position_data.pop('value')
			return GazePosition(value, **position_data)

		else:

			return GazePosition(**position_data)

	def __bool__(self) -> bool:
		"""Is the position value valid?"""
		return len(self) > 0

	def __repr__(self):
		"""String representation"""

		return json.dumps(DataFeatures.as_dict(self))

	def __add__(self, position: Self) -> Self:
		"""Add position.

		!!! note
			The returned position precision is the maximal precision.

		!!! note
			The returned position timestamp is the self object timestamp.
		"""
		if self.__precision is not None and position.precision is not None:

			return GazePosition(tuple(numpy.array(self) + numpy.array(position)),
								precision=max(self.__precision, position.precision), timestamp=self.timestamp)

		else:

			return GazePosition(tuple(numpy.array(self) + numpy.array(position)), timestamp=self.timestamp)

	__radd__ = __add__

	def __sub__(self, position: Self) -> Self:
		"""Subtract position.

		!!! note
			The returned position precision is the maximal precision.

		!!! note
			The returned position timestamp is the self object timestamp.
		"""
		if self.__precision is not None and position.precision is not None:

			return GazePosition(tuple(numpy.array(self) - numpy.array(position)),
								precision=max(self.__precision, position.precision), timestamp=self.timestamp)

		else:

			return GazePosition(tuple(numpy.array(self) - numpy.array(position)), timestamp=self.timestamp)

	def __rsub__(self, position: Self) -> Self:
		"""Reversed subtract position.

		!!! note
			The returned position precision is the maximal precision.

		!!! note
			The returned position timestamp is the self object timestamp.
		"""
		if self.__precision is not None and position.precision is not None:

			return GazePosition(tuple(numpy.array(position) - numpy.array(self)),
								precision=max(self.__precision, position.precision), timestamp=self.timestamp)

		else:

			return GazePosition(tuple(numpy.array(position) - numpy.array(self)), timestamp=self.timestamp)

	def __mul__(self, factor: int | float | tuple) -> Self:
		"""Multiply position by a factor.

		!!! note
			The returned position precision is also multiplied by the factor.

		!!! note
			The returned position timestamp is the self object timestamp.
		"""
		return GazePosition(tuple(numpy.array(self) * factor), precision=self.__precision * factor if self.__precision is not None else None, timestamp=self.timestamp)
	
	def __truediv__(self, factor: int | float | tuple) -> Self:
		"""divide position by a factor.

		!!! note
			The returned position precision is also divided by the factor.

		!!! note
			The returned position timestamp is the self object timestamp.
		"""
		return GazePosition(tuple(numpy.array(self) / factor), precision=self.__precision / factor if self.__precision is not None else None, timestamp=self.timestamp)

	def __pow__(self, factor: int | float) -> Self:
		"""Power position by a factor.

		!!! note
			The returned position precision is also powered by the factor.

		!!! note
			The returned position timestamp is the self object timestamp.
		"""
		return GazePosition(tuple(numpy.array(self) ** factor),
							precision=self.__precision ** factor if self.__precision is not None else None,
							timestamp=self.timestamp)

	def distance(self, gaze_position) -> float:
		"""Distance to another gaze positions."""

		distance = (self[0] - gaze_position[0]) ** 2 + (self[1] - gaze_position[1]) ** 2
		distance = numpy.sqrt(distance)

		return distance

	def overlap(self, gaze_position, both=False) -> float:
		"""Does this gaze position overlap another gaze position considering its precision?
		Set both to True to test if the other gaze position overlaps this one too."""

		distance = numpy.sqrt(numpy.sum((self - gaze_position) ** 2))

		if both:
			return distance < min(self.__precision, gaze_position.precision)
		else:
			return distance < self.__precision

	def draw(self, image: numpy.array, color: tuple = None, size: int = None, draw_precision=True):
		"""Draw gaze position point and precision circle."""

		if self:

			int_value = (int(self[0]), int(self[1]))

			# Draw point at position if required
			if color is not None:
				cv2.circle(image, int_value, size, color, -1)

			# Draw precision circle
			if self.__precision is not None and draw_precision:
				cv2.circle(image, int_value, round(self.__precision), color, 1)


class TimeStampedGazePositions(DataFeatures.TimestampedObjectsList):
	"""Handle timestamped gaze positions into a list."""

	def __init__(self, gaze_positions=None):

		if gaze_positions is None:
			gaze_positions = []

		DataFeatures.TimestampedObjectsList.__init__(self, GazePosition, gaze_positions)

	def values(self) -> list:
		"""Get all timestamped position values as list of tuple."""
		return [tuple(ts_position) for ts_position in self]

	''' Is it still needed as there is a TimestampedObjectsList.from_json method?
	@classmethod
	def from_json(self, json_filepath: str) -> TimeStampedGazePositions:
		"""Create a TimeStampedGazePositions from .json file."""

		with open(json_filepath, encoding='utf-8') as ts_positions_file:

			json_positions = json.load(ts_positions_file)

			return TimeStampedGazePositions({ast.literal_eval(ts_str): json_positions[ts_str] for ts_str in json_positions})
	'''

	def centroid(self) -> numpy.array:
		"""Calculate positions' centroid.

		Returns:
			centroid: centroid of all positions.
		"""

		positions_array = numpy.asarray(self.values())
		centroid = numpy.mean(positions_array, axis=0)

		return (centroid[0], centroid[1])

	def distances(self, point: numpy.array) -> numpy.array:
		"""Calculate all positions' distances to a point.

		Returns:
			distances: array with all distances to the point.
		"""

		positions_array = numpy.asarray(self.values())
		distances_array = numpy.sqrt(numpy.sum((positions_array - point)**2, axis=1))

		return distances_array

	@classmethod
	def from_dataframe(cls, dataframe: pandas.DataFrame, timestamp: str, x: str, y: str, precision: str = None,
					   message: str = None) -> Self:
		"""Create a TimeStampedGazePositions from [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html).

		Parameters:
			dataframe:
			timestamp: specific timestamp column label.
			x: specific x column label.
			y: specific y column label.
			precision: specific precision column label if exist.
			message: specific message column label if exist.
		"""

		# Copy columns
		columns = (timestamp, x, y)

		if precision is not None:
			columns += (precision,)

		if message is not None:
			columns += (message,)

		df = dataframe.loc[:, columns]

		# Merge x and y columns into one 'value' column
		df['value'] = tuple(zip(df[x], df[y]))
		df.drop(columns=[x, y], inplace=True, axis=1)

		# Replace tuple values containing NaN values by ()
		df['value'] = df.apply(lambda row: () if pandas.isnull(list(row.value)).any() else row.value, axis=True)

		# Handle precision data
		if precision:

			# Rename precision column into 'precision' column
			df.rename(columns={precision: 'precision'}, inplace=True)

		else:

			# Append a None precision column
			df['precision'] = df.apply(lambda row: None, axis=True)

		# Handle message data
		if message:

			# Rename message column into 'message' column
			df.rename(columns={precision: 'message'}, inplace=True)

		else:

			# Append a None message column
			df['message'] = df.apply(lambda row: None, axis=True)

		# Rename timestamp column into 'timestamp' column
		df.rename(columns={timestamp: 'timestamp'}, inplace=True)

		# Filter duplicate timestamps
		df = df[df.timestamp.duplicated() == False]

		# Create timestamped gaze positions
		return TimeStampedGazePositions(df.apply(
			lambda row: GazePosition(row.value, precision=row.precision, message=row.message, timestamp=row.timestamp),
			axis=True))


class GazePositionCalibrationFailed(Exception):
	"""Exception raised by GazePositionCalibrator."""

	def __init__(self, message):
		super().__init__(message)


class GazePositionCalibrator(DataFeatures.PipelineStepObject):
	"""Abstract class to define what should provide a gaze position calibrator algorithm."""

	# noinspection PyMissingConstructor
	@DataFeatures.PipelineStepInit
	def __init__(self, **kwargs):
		pass

	def store(self, observed_gaze_position: GazePosition, expected_gaze_position: GazePosition):
		"""Store observed and expected gaze positions.

		Parameters:
			observed_gaze_position: where gaze position actually is
			expected_gaze_position: where gaze position should be
		"""

		raise NotImplementedError('calibrate() method not implemented')

	def reset(self):
		"""Reset observed and expected gaze positions."""

		raise NotImplementedError('reset() method not implemented')

	def calibrate(self) -> any:
		"""Process calibration from observed and expected gaze positions.

		Returns:
			calibration outputs: any data returned to assess calibration
		"""

		raise NotImplementedError('terminate() method not implemented')

	def apply(self, observed_gaze_position: GazePosition) -> GazePosition:
		"""Apply calibration onto observed gaze position.

		Parameters:
			observed_gaze_position: where gaze position actually is

		Returns:
			expected_gaze_position: where gaze position should be if the calibrator is ready else, observed gaze position
		"""

		raise NotImplementedError('apply() method not implemented')

	def draw(self, image: numpy.array, **kwargs):
		"""Draw calibration into image.
		
		Parameters:
			image: where to draw
		"""

		raise NotImplementedError('draw() method not implemented')

	def is_calibrating(self) -> bool:
		"""Is the calibration running?"""

		raise NotImplementedError('ready getter not implemented')

@DataFeatures.timestamp
class GazeMovement(TimeStampedGazePositions):
	"""Define abstract gaze movement class as timestamped gaze positions list.

	!!! note
		Gaze movement timestamp is always equal to its first position timestamp.

	Parameters:
		positions: timestamp gaze positions.
		finished: is the movement finished?
		message: a string to describe why the movement is what it is.
	"""

	def __new__(cls, positions: TimeStampedGazePositions = None, **kwargs):

		# noinspection PyArgumentList
		return TimeStampedGazePositions.__new__(cls, positions)

	def __init__(self, positions: TimeStampedGazePositions = None, finished: bool = False, message: str = None):
		"""Initialize GazeMovement"""

		TimeStampedGazePositions.__init__(self, positions)

		self.__finished = finished
		self.__message = message

	def is_finished(self) -> bool:
		"""Is the movement finished?"""
		return self.__finished

	def finish(self) -> Self:
		"""Set gaze movement as finished"""
		self.__finished = True
		return self

	@property
	def message(self):
		"""Get movement's message."""
		return self.__message

	@property
	def amplitude(self):
		"""Get inferred amplitude from first and last positions."""
		if self:

			return numpy.linalg.norm(self[0] - self[-1])

		else:

			return 0

	def __str__(self) -> str:
		"""String display"""

		if self:

			output = f'{type(self)}:\n\tduration={self.duration}\n\tsize={len(self)}\n\tfinished={self.is_finished()}'

			for position in self:
				output += f'\n\t{position.timestamp}:\n\t\tvalue={position},\n\t\tprecision={position.precision}'

		else:

			output = f'{type(self)}'

		return output

	def draw_positions(self, image: numpy.array, position_color: tuple = None, line_color: tuple = None):
		"""Draw gaze movement positions with line between each position.
		
		Parameters:
			image: where to draw
			position_color: color of position point
			line_color: color of line between each position
		"""

		positions = self.copy()

		while len(positions) >= 2:

			start_gaze_position = positions.pop(0)
			next_gaze_position = positions[0]

			# Draw line between positions if required
			if line_color is not None:
				cv2.line(image, (int(start_gaze_position[0]), int(start_gaze_position[1])),
						 (int(next_gaze_position[0]), int(next_gaze_position[1])), line_color, 1)

			# Draw position if required
			if position_color is not None:
				start_gaze_position.draw(image, position_color, draw_precision=False)

	def draw(self, image: numpy.array, **kwargs):
		"""Draw gaze movement into image."""

		raise NotImplementedError('draw() method not implemented')


class Fixation(GazeMovement):
	"""Define abstract fixation as gaze movement."""

	def __init__(self, positions: TimeStampedGazePositions = TimeStampedGazePositions(), finished: bool = False, message: str = None, **kwargs):
		
		super().__init__(positions, finished, message, **kwargs)

		self._focus = ()

	@property
	def focus(self) -> tuple:
		"""Get representative position of the fixation."""
		return self._focus

	def merge(self, fixation) -> Self:
		"""Merge another fixation into this fixation."""

		raise NotImplementedError('merge() method not implemented')


def is_fixation(gaze_movement):
	"""Is a gaze movement a fixation?"""

	return type(gaze_movement).__bases__[0] == Fixation or type(gaze_movement) == Fixation


class Saccade(GazeMovement):
	"""Define abstract saccade as gaze movement."""

	def __init__(self, positions: TimeStampedGazePositions = TimeStampedGazePositions(), finished: bool = False,
				 message: str = None, **kwargs):
		super().__init__(positions, finished, message, **kwargs)


def is_saccade(gaze_movement):
	"""Is a gaze movement a saccade?"""

	return type(gaze_movement).__bases__[0] == Saccade or type(gaze_movement) == Saccade


class TimeStampedGazeMovements(DataFeatures.TimestampedObjectsList):
	"""Handle timestamped gaze movements into a list"""

	def __init__(self, gaze_movements: list = []):
		DataFeatures.TimestampedObjectsList.__init__(self, GazeMovement, gaze_movements)

@DataFeatures.timestamp
class GazeStatus(list):
	"""Define gaze status as a list of 1 or 2 (index, GazeMovement) tuples.

	Parameters:
		position: the position that the status represents.
	"""

	def __init__(self, position: GazePosition):
		
		self.timestamp = position.timestamp
		self.__position = position

	@property
	def position(self) -> GazePosition:
		"""Get gaze status position."""
		return self.__position

	def append(self, movement_index: int, movement_type: type):
		"""Append movement index and type."""

		super().append((movement_index, movement_type))


class TimeStampedGazeStatus(DataFeatures.TimestampedObjectsList):
	"""Handle timestamped gaze status into a list."""

	def __init__(self):
		super().__init__(GazeStatus)


class GazeMovementIdentifier(DataFeatures.PipelineStepObject):
	"""Abstract class to define what should provide a gaze movement identifier."""

	# noinspection PyMissingConstructor
	@DataFeatures.PipelineStepInit
	def __init__(self, **kwargs):

		pass

	@DataFeatures.PipelineStepMethod
	def identify(self, timestamped_gaze_position: GazePosition, terminate: bool = False) -> GazeMovement:
		"""Identify gaze movement from successive timestamped gaze positions.

		!!! warning "Mandatory"
			Each identified gaze movement have to share its first/last gaze position with previous/next gaze movement.

		Parameters:
			timestamped_gaze_position: new gaze position from where identification have to be done considering former gaze positions.
			terminate: allows to notify identification algorithm that given gaze position will be the last one.
		
		Returns:
			gaze_movement: identified gaze movement once it is finished otherwise it returns empty gaze movement at least.
		"""

		raise NotImplementedError('identify() method not implemented')

	def current_gaze_movement(self) -> GazeMovement:
		"""Get the current identified gaze movement (finished or in progress) if it exists otherwise, an empty gaze movement."""

		raise NotImplementedError('current_gaze_movement getter not implemented')

	def current_fixation(self) -> Fixation:
		"""Get the current identified fixation (finished or in progress) if it exists otherwise, an empty gaze movement."""

		raise NotImplementedError('current_fixation getter not implemented')

	def current_saccade(self) -> Saccade:
		"""Get the current identified saccade (finished or in progress) if it exists otherwise, an empty gaze movement."""

		raise NotImplementedError('current_saccade getter not implemented')

	def browse(self, ts_gaze_positions: TimeStampedGazePositions) -> tuple[
		TimeStampedGazeMovements, TimeStampedGazeMovements, TimeStampedGazeStatus]:
		"""Identify fixations and saccades browsing timestamped gaze positions.

		Returns:
			timestamped_fixations: all fixations stored by timestamped.
			timestamped_saccades: all saccades stored by timestamped.
			timestamped_gaze_status: all gaze status stored by timestamped.
		"""

		assert (type(ts_gaze_positions) == TimeStampedGazePositions)

		ts_fixations = TimeStampedGazeMovements()
		ts_saccades = TimeStampedGazeMovements()
		ts_status = TimeStampedGazeStatus()

		# Get last ts to terminate identification on last gaze position
		last_ts = ts_gaze_positions[-1].timestamp

		# Iterate on gaze positions
		for gaze_position in ts_gaze_positions:

			gaze_movement = self.identify(gaze_position, terminate=(gaze_position.timestamp == last_ts))

			if gaze_movement:

				# First gaze movement position is always shared with previous gaze movement
				for movement_position in gaze_movement:

					# Is a status already exist for this position?
					gaze_status = ts_status.look_for(movement_position.timestamp)

					if not gaze_status:
						gaze_status = GazeStatus(movement_position)
						ts_status.append(gaze_status)

					gaze_status.append(len(ts_fixations), type(gaze_movement))

				# Store gaze movement into the appropriate list
				if is_fixation(gaze_movement):

					ts_fixations.append(gaze_movement)

				elif is_saccade(gaze_movement):

					ts_saccades.append(gaze_movement)

		return ts_fixations, ts_saccades, ts_status

	def __call__(self, ts_gaze_positions: TimeStampedGazePositions) -> tuple[int | float, GazeMovement]:
		"""GazeMovement generator.

		Parameters:
			ts_gaze_positions: timestamped gaze positions to process.

		Returns:
			timestamp: first gaze position date of identified gaze movement
			gaze_movement: identified gaze movement once it is finished
		"""

		assert (type(ts_gaze_positions) == TimeStampedGazePositions)

		# Get last ts to terminate identification on last gaze position
		last_ts = ts_gaze_positions[-1]

		# Iterate on gaze positions
		for gaze_position in ts_gaze_positions:

			gaze_movement = self.identify(gaze_position, terminate=(gaze_position.timestamp == last_ts))

			if gaze_movement:
				yield gaze_movement


class ScanStepError(Exception):
	"""Exception raised at ScanStep creation if a aoi scan step doesn't start by a fixation or doesn't end by a saccade."""

	def __init__(self, message):
		super().__init__(message)


class ScanStep():
	"""Define a scan step as a fixation and a consecutive saccade.

	Parameters:
		first_fixation: a fixation that comes before the next saccade.
		last_saccade: a saccade that comes after the previous fixation.
	
	!!! warning
		Scan step have to start by a fixation and then end by a saccade.
	"""

	def __init__(self, first_fixation: Fixation, last_saccade: Saccade):

		self.__first_fixation = first_fixation
		self.__last_saccade = last_saccade

		# First movement have to be a fixation
		if not is_fixation(self.__first_fixation):
			raise ScanStepError('First step movement is not a fixation')

		# Last movement have to be a saccade
		if not is_saccade(self.__last_saccade):
			raise ScanStepError('Last step movement is not a saccade')

	@property
	def first_fixation(self):
		"""Get scan step first fixation."""
		return self.__first_fixation

	@property
	def last_saccade(self):
		"""Get scan step last saccade."""
		return self.__last_saccade

	@property
	def fixation_duration(self) -> int | float:
		"""Time spent on AOI

		Returns:
			fixation duration
		"""

		return self.__first_fixation.duration

	@property
	def duration(self) -> int | float:
		"""Time spent on AOI and time spent to go to next AOI

		Returns:
			duration
		"""

		return self.__first_fixation.duration + self.__last_saccade.duration


class ScanPath(list):
	"""List of scan steps."""

	def __init__(self, duration_max: int | float = 0):

		super().__init__()

		self.__duration_max = duration_max
		self.__last_fixation = None
		self.__duration = 0

	@property
	def duration_max(self) -> float:
		"""Duration from which older scan steps are removed each time new scan steps are added. 0 means no maximal duration."""
		return self.__duration_max

	@duration_max.setter
	def duration_max(self, duration_max: float):

		self.__duration_max = duration_max

	@property
	def duration(self) -> int | float:
		"""Sum of all scan steps duration

		Returns:
			duration
		"""

		return self.__duration

	def __check_duration(self):
		"""Constrain path duration to maximal duration."""

		if self.__duration_max > 0:

			while self.__duration > self.__duration_max:
				oldest_step = self.pop(0)

				self.__duration -= oldest_step.duration

	def append_saccade(self, saccade) -> ScanStep:
		"""Append new saccade to scan path and return last new scan step if one have been created."""

		# Ignore saccade if no fixation came before
		if self.__last_fixation != None:

			try:

				# Edit new step
				new_step = ScanStep(self.__last_fixation, saccade)

				# Append new step
				super().append(new_step)

				# Update duration
				self.__duration += new_step.duration

				# Constrain path duration to maximal duration
				self.__check_duration()

				# Return new step
				return new_step

			finally:

				# Clear last fixation
				self.__last_fixation = None

	def append_fixation(self, fixation):
		"""Append new fixation to scan path.
		!!! warning
			Consecutive fixations are ignored keeping the last fixation"""

		self.__last_fixation = fixation

	def draw(self, image: numpy.array, draw_fixations: dict = None, draw_saccades: dict = None, deepness: int = 0):
		"""Draw scan path into image.

		Parameters:
			image: where to draw
			draw_fixations: Fixation.draw parameters (which depends on the loaded gaze movement identifier module,
				if None, no fixation is drawn)
			draw_saccades: Saccade.draw parameters (which depends on the loaded gaze movement identifier module,
				if None, no saccade is drawn)
			deepness: number of steps back to draw
		"""

		for step in self[-deepness:]:

			# Draw fixation if required
			if draw_fixations is not None:
				step.first_fixation.draw(image, **draw_fixations)

			# Draw saccade if required
			if draw_saccades is not None:
				step.last_saccade.draw(image, **draw_saccades)


class ScanPathAnalyzer(DataFeatures.PipelineStepObject):
	"""Abstract class to define what should provide a scan path analyzer."""

	# noinspection PyMissingConstructor
	@DataFeatures.PipelineStepInit
	def __init__(self, **kwargs):
		self.__analysis = [name for (name, value) in self.__class__.__dict__.items() if
						   isinstance(value, property) and value.fset is None]

	def analysis(self) -> DataFeatures.DataDictionary:
		"""Get all scan path analyzer analysis as data dictionary."""

		return DataFeatures.DataDictionary({a: getattr(self, a) for a in self.__analysis})

	@DataFeatures.PipelineStepMethod
	def analyze(self, scan_path: ScanPath):
		"""Analyze scan path."""

		raise NotImplementedError('analyze() method not implemented')


class AOIMatcher(DataFeatures.PipelineStepObject):
	"""Abstract class to define what should provide an AOI matcher algorithm."""

	# noinspection PyMissingConstructor
	@DataFeatures.PipelineStepInit
	def __init__(self, **kwargs):
		self.__exclude = []

	@property
	def exclude(self):
		"""List of AOI to exclude from matching."""
		return self.__exclude

	@exclude.setter
	def exclude(self, exclude: list[str]):
		self.__exclude = exclude

	def match(self, gaze_movement: GazeMovement, aoi_scene: AOIFeatures.AOIScene) -> tuple[
		str, AOIFeatures.AreaOfInterest]:
		"""Which AOI is looked in the scene?"""

		raise NotImplementedError('match() method not implemented')

	def draw(self, image: numpy.array, aoi_scene: AOIFeatures.AOIScene):
		"""Draw matching into image.
		
		Parameters:
			image: where to draw
			aoi_scene: to refresh looked aoi if required
		"""

		raise NotImplementedError('draw() method not implemented')

	def looked_aoi(self) -> AOIFeatures.AreaOfInterest:
		"""Get most likely looked aoi."""

		raise NotImplementedError('looked_aoi() method not implemented')

	def looked_aoi_name(self) -> str:
		"""Get most likely looked aoi name."""
		raise NotImplementedError('looked_aoi_name() method not implemented')


class AOIScanStepError(Exception):
	"""
	Exception raised at AOIScanStep creation if an aoi scan step doesn't start by a fixation or
	doesn't end by a saccade.
	"""

	def __init__(self, message, aoi=''):
		super().__init__(message)

		self.aoi = aoi


class AOIScanStep():
	"""Define an aoi scan step as a set of successive gaze movements onto a same AOI.

	Parameters:
		movements: all movements over an AOI and the last saccade that comes out.
		aoi: AOI name
		letter: AOI unique letter to ease sequence analysis.

	!!! warning
		Aoi scan step have to start by a fixation and then end by a saccade.
	"""

	def __init__(self, movements: TimeStampedGazeMovements, aoi: str = '', letter: str = ''):

		self.__movements = movements
		self.__aoi = aoi
		self.__letter = letter

		# First movement have to be a fixation
		if not is_fixation(self.first_fixation):
			raise AOIScanStepError('First step movement is not a fixation', self.aoi)

		# Last movement have to be a saccade
		if not is_saccade(self.last_saccade):
			raise AOIScanStepError('Last step movement is not a saccade', self.aoi)

	@property
	def movements(self):
		"""Get AOI scan step movements."""
		return self.__movements

	@property
	def aoi(self):
		"""Get AOI scan step aoi."""
		return self.__aoi

	@property
	def letter(self):
		"""Get AOI scan step letter."""
		return self.__letter

	@property
	def first_fixation(self):
		"""First fixation on AOI."""
		return self.movements[0]

	@property
	def last_saccade(self):
		"""Last saccade that comes out AOI."""
		return self.movements[-1]

	@property
	def fixation_duration(self) -> int | float:
		"""Time spent on AOI

		Returns:
			fixation duration
		"""
		return self.last_saccade[0].timestamp - self.first_fixation[0].timestamp

	@property
	def duration(self) -> int | float:
		"""Time spent on AOI and time spent to go to next AOI

		Returns:
			duration
		"""
		return self.last_saccade[-1].timestamp - self.first_fixation[0].timestamp


# Define strings for outside AOI case
OutsideAOI = 'GazeFeatures.OutsideAOI'


class AOIScanPath(list):
	"""List of aoi scan steps over successive aoi."""

	def __init__(self, expected_aoi: list[str] = [], duration_max: int | float = 0):

		super().__init__()

		self.__expected_aoi = expected_aoi
		self.__duration_max = duration_max
		self.__duration = 0

		self.clear()

	@property
	def expected_aoi(self):
		"""List of all expected aoi."""

		return self.__expected_aoi

	@expected_aoi.setter
	def expected_aoi(self, expected_aoi: list[str] = []):
		"""Edit list of all expected aoi.

		!!! warning
			This will clear the AOIScanPath
		"""

		# Check expected aoi are not the same as previous ones
		if len(expected_aoi) == len(self.__expected_aoi[1:]):

			equal = [a == b for a, b in zip(expected_aoi, self.__expected_aoi[1:])]

			if all(equal):
				return

		# Otherwise, update expected aoi
		self.__expected_aoi = [OutsideAOI]
		self.__expected_aoi += expected_aoi

		self.clear()

	@property
	def duration_max(self) -> float:
		"""Duration from which older aoi scan steps are removed each time new aoi scan steps are added. 0 means no maximal duration."""
		return self.__duration_max

	@duration_max.setter
	def duration_max(self, duration_max: float):

		self.__duration_max = duration_max

	@property
	def duration(self) -> float:
		"""Sum of all scan steps duration"""

		return self.__duration

	def __check_duration(self):
		"""Constrain path duration to maximal duration."""

		if self.__duration_max > 0:

			while self.__duration > self.__duration_max:

				oldest_step = self.pop(0)

				self.__duration -= oldest_step.duration

				# Edit transition matrix
				if len(self) > 0:
					# Decrement [index: source, columns: destination] value
					self.__transition_matrix.loc[oldest_step.aoi, self[0].aoi,] -= 1

	def clear(self):
		"""Clear aoi scan steps list, letter sequence and transition matrix."""

		super().clear()

		# noinspection PyAttributeOutsideInit
		self.__movements = TimeStampedGazeMovements()
		# noinspection PyAttributeOutsideInit
		self.__current_aoi = ''
		# noinspection PyAttributeOutsideInit
		self.__index = ord('A')
		# noinspection PyAttributeOutsideInit
		self.__aoi_letter = {}
		# noinspection PyAttributeOutsideInit
		self.__letter_aoi = {}

		size = len(self.__expected_aoi)
		# noinspection PyAttributeOutsideInit
		self.__transition_matrix = pandas.DataFrame(numpy.zeros((size, size)), index=self.__expected_aoi, columns=self.__expected_aoi)

	def __get_aoi_letter(self, aoi):

		try:

			return self.__aoi_letter[aoi]

		except KeyError:

			letter = chr(self.__index)
			self.__aoi_letter[aoi] = letter
			self.__index += 1
			return letter

	def get_letter_aoi(self, letter):
		"""Get which aoi is related to a unique letter."""

		return self.__letter_aoi[letter]

	@property
	def letter_sequence(self) -> str:
		"""Convert aoi scan path into a string with unique letter per aoi step."""

		sequence = ''
		for step in self:
			sequence += step.letter

		return sequence

	@property
	def current_aoi(self):
		"""AOI name of aoi scan step under construction"""

		return self.__current_aoi

	@property
	def transition_matrix(self) -> pandas.DataFrame:
		"""[Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) where indexes are transition departures and columns are transition destinations."""

		return self.__transition_matrix

	def append_saccade(self, saccade):
		"""Append new saccade to aoi scan path."""

		# Ignore saccade if no fixation have been stored before
		if len(self.__movements) > 0:
			self.__movements.append(saccade)

	def append_fixation(self, fixation, looked_aoi: str):
		"""Append new fixation to aoi scan path and return last new aoi scan step if one have been created.

		!!! warning
			It could raise AOIScanStepError
		"""

		# Replace None aoi by generic OutsideAOI name
		if looked_aoi is None:

			looked_aoi = OutsideAOI

		# Raise error when aoi is not expected
		elif looked_aoi not in self.__expected_aoi:

			raise AOIScanStepError('AOI not expected', looked_aoi)

		# Is it fixation onto a new aoi?
		if looked_aoi != self.__current_aoi and len(self.__movements) > 0:

			try:

				# Edit unique letter per aoi
				letter = self.__get_aoi_letter(self.__current_aoi)

				# Remember which letter identify which aoi
				self.__letter_aoi[letter] = self.__current_aoi

				# Edit new step
				new_step = AOIScanStep(self.__movements, self.__current_aoi, letter)

				# Edit transition matrix
				if len(self) > 0:
					# Increment [index: source, columns: destination] value
					self.__transition_matrix.loc[self[-1].aoi, self.__current_aoi,] += 1

				# Append new step
				super().append(new_step)

				# Update duration
				self.__duration += new_step.duration

				# Constrain path duration to maximal duration
				self.__check_duration()

				# Return new step
				return new_step

			finally:

				# Clear movements
				# noinspection PyAttributeOutsideInit
				self.__movements = TimeStampedGazeMovements()

				# Append new fixation
				self.__movements.append(fixation)

				# Remember new aoi
				self.__current_aoi = looked_aoi
		else:

			# Append new fixation
			self.__movements.append(fixation)

			# Remember aoi
			# noinspection PyAttributeOutsideInit
			self.__current_aoi = looked_aoi

			return None

	def fixations_count(self):
		"""Get how many fixations are there in the scan path and how many fixation are there in each aoi."""

		scan_fixations_count = 0
		aoi_fixations_count = {aoi: 0 for aoi in self.__expected_aoi}

		for aoi_scan_step in self:
			step_fixations_count = len(aoi_scan_step.movements) - 1  # -1: to ignore last saccade

			scan_fixations_count += step_fixations_count
			aoi_fixations_count[aoi_scan_step.aoi] += step_fixations_count

		return scan_fixations_count, aoi_fixations_count


class AOIScanPathAnalyzer(DataFeatures.PipelineStepObject):
	"""Abstract class to define what should provide an aoi scan path analyzer."""

	# noinspection PyMissingConstructor
	@DataFeatures.PipelineStepInit
	def __init__(self, **kwargs):
		self.__analysis = [name for (name, value) in self.__class__.__dict__.items() if
						   isinstance(value, property) and value.fset is None]

	def analysis(self) -> DataFeatures.DataDictionary:
		"""Get all aoi scan path analyzer analysis as data dictionary."""

		return DataFeatures.DataDictionary({a: getattr(self, a) for a in self.__analysis})

	@DataFeatures.PipelineStepMethod
	def analyze(self, aoi_scan_path: AOIScanPath):
		"""Analyze aoi scan path."""

		raise NotImplementedError('analyze() method not implemented')