<reponame>Margon01/DeepTrack-2.0_old
"""Tools to manage the properties of a feature

Classes
-------
Property
    The class `Property`, which represents the values of a property of a feature.
    A Property be:
    * A constant (initialization with, e.g., a number, a tuple)
    * A sequence of variables (initialization with, e.g., an iterator)
    * A random variable (initialization with, e.g., a function)
SequentialProperty
    The class `SequentialProperty`, which extends `Property` to sample one value
    for each step in a sequence.
PropertyDict
    The class `PropertyDict`, which is a dictionary with each element a Property.
    The class provides utility functions to update, sample, clear and retrieve
    properties.
"""

import numpy as np
from .utils import (
    isiterable,
    get_kwarg_names,
    kwarg_has_default,
)

import typing


from . import features

import copy
import collections


class Property:
    """Represents a property of a feature

    The class Property` wraps an input, which is treated
    internally as a sampling rule. This sampling rule is used
    to update the value of the property.
    The sampling rule can be, for example:
    * A constant (initialization with, e.g., a number, a tuple)
    * A sequence of variables (initialization with, e.g., a generator)
    * A discrete random variable (initialization with, e.g., a list, a dictionary)
    * A continuous random variable (initialization with, e.g., a function)

    Parameters
    ----------
    sampling_rule : any
        Defines the sampling rule to update the value of the feature property.
        See method `sample()` for how different sampling rules are sampled.

    Attributes
    ----------
    sampling_rule : any
        The sampling rule to update the value of the feature property.
    current_value : any
        The current value obtained from the last call to the sampling rule.

    """

    def __init__(self, sampling_rule: any):
        self.sampling_rule = sampling_rule
        self.parent = None

    @property
    def current_value(self):
        """Current value of the property of the feature

        `current_value` is the result of the latest `update()` call.
        Note that any randomization only occurs when the method `update()` is called
        and, therefore, the current value does not change between calls.

        The method getter calls the method `update()` if `current_value`
        has not been set yet.

        """

        self._current_value

    @current_value.setter
    def current_value(self, updated_current_value):
        self._current_value = updated_current_value
        if id(self) not in features.UPDATE_MEMO["memoization"]:
            # Some values work, some don't. self, updated_current_value and self._current_value work
            # Best guess is an error in the gc reference counter causing it to dereference
            # But then again, I don't think it should be the same reference anyway
            features.UPDATE_MEMO["memoization"][id(self)] = updated_current_value

    @current_value.getter
    def current_value(self):

        if not hasattr(self, "_current_value"):
            self.update()

        return self._current_value

    def update(self, **kwargs) -> "Property":
        """Updates the current value

        The method `update()` sets the property `current_value`
        as the output of the method `sample()`. Will only update
        once per resolve.

        Any object that implements the method `update()` will have it called.

        Returns
        -------
        Property
            Returns itself.

        """

        # If currently updated through a call to feature and deeptrack.UPDATE_MEMO
        my_id = id(self)

        # if my_id in deeptrack.UPDATE_MEMO["memoization"] and not hasattr(self, '_current_value'):
        #     a = 1+1

        if (
            features.UPDATE_LOCK.locked()
            and my_id in features.UPDATE_MEMO["memoization"]
        ):
            return self

        if self.parent:
            kwargs.update(self.parent)

        kwargs.update(features.UPDATE_MEMO["user_arguments"])
        self.current_value = self.sample(self.sampling_rule, **kwargs)

        return self

    def sample(self, sampling_rule, **kwargs):
        """Samples the sampling rule

        Returns a sampled instance of the `sampling_rule` field.
        The logic behind the sampling depends on the type of
        `sampling_rule`. These are checked in the following order of
        priority:

        1. Any object with a callable `sample()` method has this
            method called and returned.
        2. If the rule is a ``dict``, sample each value and combine the
            result into a new ``dict`` using the original keys.
        3. If the rule is a ``list``, sample each element of the list and
            combine the result into a ne ``list``.
        4. If the rule is an ``iterable``, return the next output.
        5. If the rule is callable, call it with its accepted arguments.
            Example arguments can be the value of some other property.
        6. If none of the above apply, return the rule itself.

        Parameters
        ----------
        sampling_rule : any
            The rule to sample values from.
        **kwargs
            Arguments that will be passed on to functions that accepts them.

        Returns
        -------
        any
            A sampled instance of the `sampling_rule`.

        """

        if isinstance(sampling_rule, features.Feature):
            # I am worried passing kwargs may lead to name clash
            sampling_rule._update(**kwargs)
            return sampling_rule

        if isinstance(sampling_rule, Property):
            sampling_rule.parent.update_item(sampling_rule)
            return sampling_rule.current_value

        elif isinstance(sampling_rule, dict):
            # If the ruleset is a dict, return a new dict with each
            # element being sampled from the original dict.
            out = {}
            for key, val in sampling_rule.items():
                out[key] = self.sample(val, **kwargs)
            return out

        elif isinstance(sampling_rule, list):
            return [self.sample(item, **kwargs) for item in sampling_rule]

        elif isinstance(sampling_rule, (tuple, np.ndarray)):
            # tuple and ndarrays are elementary
            return sampling_rule

        elif isiterable(sampling_rule):
            # If it's iterable, return the next value
            try:
                return next(sampling_rule)
            except StopIteration:
                return self.current_value
        elif isinstance(sampling_rule, slice):
            return slice(
                self.sample(sampling_rule.start, **kwargs),
                self.sample(sampling_rule.stop, **kwargs),
                self.sample(sampling_rule.step, **kwargs)
            )

        elif callable(sampling_rule):
            # If it's a function, extract the arguments it accepts.
            function_input = {}

            # Get the kwarg arguments the function accepts
            knames = get_kwarg_names(sampling_rule)
            for i in range(len(knames)):
                key = knames[i]
                # If that name is among passed kwarg arguments
                if key in kwargs:
                    if isinstance(kwargs[key], Property):
                        # If it is a property, update it and pass the current value
                        if not kwargs[key] is self:
                            if kwargs[key].parent:
                                kwargs[key].parent.update_item(kwargs[key])
                            else:
                                kwargs[key].update(**kwargs)

                        if (
                            isinstance(kwargs[key], SequentialProperty)
                            and "sequence_step" in kwargs
                        ):
                            function_input[key] = kwargs[key].current_value[
                                kwargs["sequence_step"]
                            ]
                        else:
                            function_input[key] = kwargs[key].current_value

                    else:
                        function_input[key] = kwargs[key]

                elif not kwarg_has_default(sampling_rule, key):
                    function_input[key] = None

            new_value = sampling_rule(**function_input)
            while isinstance(new_value, Property):
                new_value = self.sample(new_value, **kwargs)
            return new_value
        else:
            # Else, assume it's elementary.
            return sampling_rule

    def __deepcopy__(self, memo):
        is_in = id(self) in features.UPDATE_MEMO["memoization"]
        if is_in:
            return self
        else:
            cls = self.__class__  # Extract the class of the object
            result = cls.__new__(
                cls
            )  # Create a new instance of the object based on extracted class
            memo[id(self)] = result
            for k, v in self.__dict__.items():
                setattr(
                    result, k, copy.deepcopy(v, memo)
                )  # Copy over attributes by copying directly or in case of complex objects like lists for exaample calling the `__deepcopy()__` method defined by them. Thus recursively copying the whole tree of objects.
            return result


class SequentialProperty(Property):
    """Property that has multiple sequential values

    Extends standard `Property` to resolve one value for each step
    in a sequence of images. This is often used when creating movies.

    Parameters
    ----------
    initializer : any
        Sampling rule for the first step of the sequence.
    sampling_rule : any
        Sampling rule for each step after the first.

    Attributes
    ----------
    initializer : any
        Sampling rule for the first step of the sequence.
    sampling_rule : any
        Sampling rule for each step after the first.
    has_updated_since_last_resolve : bool
        Whether the property has been updated since the last resolve.

    """

    def __init__(self, sampling_rule, initializer=None):
        super().__init__(sampling_rule)
        if initializer is None:
            self.initializer = sampling_rule
        else:
            self.initializer = initializer

        self.sampling_rule = sampling_rule

        # Deprecated
        self.has_updated_since_last_resolve = False

    def update(self, sequence_length=0, **kwargs):
        """Updates current_value

        For each step in the sequence, sample `self.sampling_rule`.
        `self.initializer` is used for the first step. These rules
        should output one value per step. Sampling rules
        that are functions can optionally accept the following keyword
        arguments:

        * sequence_step : the current position in the sequence.
        * sequence_length : the length of the sequence.
        * previous_value : the value of the property at the previous
          step in the sequence.
        * previous_values : the value of the property at all previous
          steps in the sequence.

        Parameters
        ----------
        sequence_length : int, optional
            length of the sequence

        Returns
        -------
        self
            returns self
        """
        my_id = id(self)
        if (
            features.UPDATE_LOCK.locked()
            and my_id in features.UPDATE_MEMO["memoization"]
        ):
            return self

        kwargs.update(features.UPDATE_MEMO["user_arguments"])

        new_current_value = []

        for step in range(sequence_length):
            # Use initializer for first time step
            ruleset = self.sampling_rule

            # Elements inserted here can be passed to property functions
            kwargs.update(
                sequence_step=step,
                sequence_length=sequence_length,
                previous_values=new_current_value,
            )
            if step == 0:
                kwargs.update(previous_value=self.sample(self.initializer, **kwargs))
            else:
                kwargs.update(previous_value=new_current_value[-1])

            next_value = self.sample(ruleset, **kwargs)

            new_current_value.append(next_value)

        self.current_value = new_current_value
        features.UPDATE_MEMO["memoization"][my_id] = new_current_value
        return self


class PropertyDict(collections.OrderedDict):
    """Dictionary with Property elements

    A dictionary of properties. It provides utility functions to update,
    sample, reset and retrieve properties.

    Parameters
    ----------
    *args, **kwargs
        Arguments used to initialize a dict

    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        for k, v in self.items():
            if hasattr(v, "parent") and not v.parent:
                v.parent = self

    def current_value_dict(self, **kwargs) -> dict:
        """Retrieves the current value of all properties as a dictionary

        Returns
        -------
        dict
            A dictionary with the current value of all properties

        """
        current_value_dict = {}
        for key, property in self.items():

            property_value = property.current_value

            # If the property is sequential, retrieve the value
            # of the current timestep
            if isinstance(property, SequentialProperty):
                sequence_step = kwargs.get("sequence_step", None)
                if sequence_step is not None:
                    property_value = property_value[sequence_step]

            current_value_dict[key] = property_value

        return current_value_dict

    def update(self, **kwargs) -> "PropertyDict":
        """Updates all properties

        Calls the method `update()` on each property in the dictionary.

        Returns
        -------
        Properties
            Returns itself

        """
        property_arguments = collections.OrderedDict(self)
        property_arguments.update(kwargs)
        property_arguments.update(features.UPDATE_MEMO["user_arguments"])
        for key, prop in self.items():
            if isinstance(property_arguments[key], Property):
                prop.update(**property_arguments)
            elif id(prop) not in features.UPDATE_MEMO["memoization"]:
                prop.current_value = property_arguments[key]

        return self

    def update_item(self, item: Property, **kwargs):
        """Updates a single property.

        Parameters
        ----------
        item: Property
            The item to update.

        Returns
        -------
        self
        """
        property_arguments = collections.OrderedDict(self)
        property_arguments.update(kwargs)
        for key, prop in self.items():
            if prop == item:
                prop.update(**property_arguments)
                break

        return self

    def sample(self, **kwargs) -> dict:
        """Samples all properties

        Returns
        -------
        dict
            A dictionary with each key-value pair the result of a
            `sample()` call on the property with the same key.

        """

        sample_dict = {}
        for key, property in self.items():
            sample_dict[key] = property.sample(**kwargs)

        return sample_dict

"""Tools to manage the properties of a feature

Classes
-------
Property
    The class `Property`, which represents the values of a property of a feature.
    A Property be:
    * A constant (initialization with, e.g., a number, a tuple)
    * A sequence of variables (initialization with, e.g., an iterator)
    * A random variable (initialization with, e.g., a function)
SequentialProperty
    The class `SequentialProperty`, which extends `Property` to sample one value
    for each step in a sequence.
PropertyDict
    The class `PropertyDict`, which is a dictionary with each element a Property.
    The class provides utility functions to update, sample, clear and retrieve
    properties.
"""

import numpy as np
from .utils import (
    isiterable,
    get_kwarg_names,
    kwarg_has_default,
)

import typing


from . import features

import copy
import collections


class Property:
    """Represents a property of a feature

    The class Property` wraps an input, which is treated
    internally as a sampling rule. This sampling rule is used
    to update the value of the property.
    The sampling rule can be, for example:
    * A constant (initialization with, e.g., a number, a tuple)
    * A sequence of variables (initialization with, e.g., a generator)
    * A discrete random variable (initialization with, e.g., a list, a dictionary)
    * A continuous random variable (initialization with, e.g., a function)

    Parameters
    ----------
    sampling_rule : any
        Defines the sampling rule to update the value of the feature property.
        See method `sample()` for how different sampling rules are sampled.

    Attributes
    ----------
    sampling_rule : any
        The sampling rule to update the value of the feature property.
    current_value : any
        The current value obtained from the last call to the sampling rule.

    """

    def __init__(self, sampling_rule: any):
        self.sampling_rule = sampling_rule
        self.parent = None

    @property
    def current_value(self):
        """Current value of the property of the feature

        `current_value` is the result of the latest `update()` call.
        Note that any randomization only occurs when the method `update()` is called
        and, therefore, the current value does not change between calls.

        The method getter calls the method `update()` if `current_value`
        has not been set yet.

        """

        self._current_value

    @current_value.setter
    def current_value(self, updated_current_value):
        self._current_value = updated_current_value
        if id(self) not in features.UPDATE_MEMO["memoization"]:
            # Some values work, some don't. self, updated_current_value and self._current_value work
            # Best guess is an error in the gc reference counter causing it to dereference
            # But then again, I don't think it should be the same reference anyway
            features.UPDATE_MEMO["memoization"][id(self)] = updated_current_value

    @current_value.getter
    def current_value(self):

        if not hasattr(self, "_current_value"):
            self.update()

        return self._current_value

    def update(self, **kwargs) -> "Property":
        """Updates the current value

        The method `update()` sets the property `current_value`
        as the output of the method `sample()`. Will only update
        once per resolve.

        Any object that implements the method `update()` will have it called.

        Returns
        -------
        Property
            Returns itself.

        """

        # If currently updated through a call to feature and deeptrack.UPDATE_MEMO
        my_id = id(self)

        # if my_id in deeptrack.UPDATE_MEMO["memoization"] and not hasattr(self, '_current_value'):
        #     a = 1+1

        if (
            features.UPDATE_LOCK.locked()
            and my_id in features.UPDATE_MEMO["memoization"]
        ):
            return self

        if self.parent:
            kwargs.update(self.parent)

        kwargs.update(features.UPDATE_MEMO["user_arguments"])
        self.current_value = self.sample(self.sampling_rule, **kwargs)

        return self

    def sample(self, sampling_rule, **kwargs):
        """Samples the sampling rule

        Returns a sampled instance of the `sampling_rule` field.
        The logic behind the sampling depends on the type of
        `sampling_rule`. These are checked in the following order of
        priority:

        1. Any object with a callable `sample()` method has this
            method called and returned.
        2. If the rule is a ``dict``, sample each value and combine the
            result into a new ``dict`` using the original keys.
        3. If the rule is a ``list``, sample each element of the list and
            combine the result into a ne ``list``.
        4. If the rule is an ``iterable``, return the next output.
        5. If the rule is callable, call it with its accepted arguments.
            Example arguments can be the value of some other property.
        6. If none of the above apply, return the rule itself.

        Parameters
        ----------
        sampling_rule : any
            The rule to sample values from.
        **kwargs
            Arguments that will be passed on to functions that accepts them.

        Returns
        -------
        any
            A sampled instance of the `sampling_rule`.

        """

        if isinstance(sampling_rule, features.Feature):
            # I am worried passing kwargs may lead to name clash
            sampling_rule._update(**kwargs)
            return sampling_rule

        if isinstance(sampling_rule, Property):
            sampling_rule.parent.update_item(sampling_rule)
            return sampling_rule.current_value

        elif isinstance(sampling_rule, dict):
            # If the ruleset is a dict, return a new dict with each
            # element being sampled from the original dict.
            out = {}
            for key, val in sampling_rule.items():
                out[key] = self.sample(val, **kwargs)
            return out

        elif isinstance(sampling_rule, list):
            return [self.sample(item, **kwargs) for item in sampling_rule]

        elif isinstance(sampling_rule, (tuple, np.ndarray)):
            # tuple and ndarrays are elementary
            return sampling_rule

        elif isiterable(sampling_rule):
            # If it's iterable, return the next value
            try:
                return next(sampling_rule)
            except StopIteration:
                return self.current_value
        elif isinstance(sampling_rule, slice):
            return slice(
                self.sample(sampling_rule.start, **kwargs),
                self.sample(sampling_rule.stop, **kwargs),
                self.sample(sampling_rule.step, **kwargs)
            )

        elif callable(sampling_rule):
            # If it's a function, extract the arguments it accepts.
            function_input = {}

            # Get the kwarg arguments the function accepts
            knames = get_kwarg_names(sampling_rule)
            for i in range(len(knames)):
                key = knames[i]
                # If that name is among passed kwarg arguments
                if key in kwargs:
                    if isinstance(kwargs[key], Property):
                        # If it is a property, update it and pass the current value
                        if not kwargs[key] is self:
                            if kwargs[key].parent:
                                kwargs[key].parent.update_item(kwargs[key])
                            else:
                                kwargs[key].update(**kwargs)

                        if (
                            isinstance(kwargs[key], SequentialProperty)
                            and "sequence_step" in kwargs
                        ):
                            function_input[key] = kwargs[key].current_value[
                                kwargs["sequence_step"]
                            ]
                        else:
                            function_input[key] = kwargs[key].current_value

                    else:
                        function_input[key] = kwargs[key]

                elif not kwarg_has_default(sampling_rule, key):
                    function_input[key] = None

            new_value = sampling_rule(**function_input)
            while isinstance(new_value, Property):
                new_value = self.sample(new_value, **kwargs)
            return new_value
        else:
            # Else, assume it's elementary.
            return sampling_rule

    def __deepcopy__(self, memo):
        is_in = id(self) in features.UPDATE_MEMO["memoization"]
        if is_in:
            return self
        else:
            cls = self.__class__  # Extract the class of the object
            result = cls.__new__(
                cls
            )  # Create a new instance of the object based on extracted class
            memo[id(self)] = result
            for k, v in self.__dict__.items():
                setattr(
                    result, k, copy.deepcopy(v, memo)
                )  # Copy over attributes by copying directly or in case of complex objects like lists for exaample calling the `__deepcopy()__` method defined by them. Thus recursively copying the whole tree of objects.
            return result


class SequentialProperty(Property):
    """Property that has multiple sequential values

    Extends standard `Property` to resolve one value for each step
    in a sequence of images. This is often used when creating movies.

    Parameters
    ----------
    initializer : any
        Sampling rule for the first step of the sequence.
    sampling_rule : any
        Sampling rule for each step after the first.

    Attributes
    ----------
    initializer : any
        Sampling rule for the first step of the sequence.
    sampling_rule : any
        Sampling rule for each step after the first.
    has_updated_since_last_resolve : bool
        Whether the property has been updated since the last resolve.

    """

    def __init__(self, sampling_rule, initializer=None):
        super().__init__(sampling_rule)
        if initializer is None:
            self.initializer = sampling_rule
        else:
            self.initializer = initializer

        self.sampling_rule = sampling_rule

        # Deprecated
        self.has_updated_since_last_resolve = False

    def update(self, sequence_length=0, **kwargs):
        """Updates current_value

        For each step in the sequence, sample `self.sampling_rule`.
        `self.initializer` is used for the first step. These rules
        should output one value per step. Sampling rules
        that are functions can optionally accept the following keyword
        arguments:

        * sequence_step : the current position in the sequence.
        * sequence_length : the length of the sequence.
        * previous_value : the value of the property at the previous
          step in the sequence.
        * previous_values : the value of the property at all previous
          steps in the sequence.

        Parameters
        ----------
        sequence_length : int, optional
            length of the sequence

        Returns
        -------
        self
            returns self
        """
        my_id = id(self)
        if (
            features.UPDATE_LOCK.locked()
            and my_id in features.UPDATE_MEMO["memoization"]
        ):
            return self

        kwargs.update(features.UPDATE_MEMO["user_arguments"])

        new_current_value = []

        for step in range(sequence_length):
            # Use initializer for first time step
            ruleset = self.sampling_rule

            # Elements inserted here can be passed to property functions
            kwargs.update(
                sequence_step=step,
                sequence_length=sequence_length,
                previous_values=new_current_value,
            )
            if step == 0:
                kwargs.update(previous_value=self.sample(self.initializer, **kwargs))
            else:
                kwargs.update(previous_value=new_current_value[-1])

            next_value = self.sample(ruleset, **kwargs)

            new_current_value.append(next_value)

        self.current_value = new_current_value
        features.UPDATE_MEMO["memoization"][my_id] = new_current_value
        return self


class PropertyDict(collections.OrderedDict):
    """Dictionary with Property elements

    A dictionary of properties. It provides utility functions to update,
    sample, reset and retrieve properties.

    Parameters
    ----------
    *args, **kwargs
        Arguments used to initialize a dict

    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        for k, v in self.items():
            if hasattr(v, "parent") and not v.parent:
                v.parent = self

    def current_value_dict(self, **kwargs) -> dict:
        """Retrieves the current value of all properties as a dictionary

        Returns
        -------
        dict
            A dictionary with the current value of all properties

        """
        current_value_dict = {}
        for key, property in self.items():

            property_value = property.current_value

            # If the property is sequential, retrieve the value
            # of the current timestep
            if isinstance(property, SequentialProperty):
                sequence_step = kwargs.get("sequence_step", None)
                if sequence_step is not None:
                    property_value = property_value[sequence_step]

            current_value_dict[key] = property_value

        return current_value_dict

    def update(self, **kwargs) -> "PropertyDict":
        """Updates all properties

        Calls the method `update()` on each property in the dictionary.

        Returns
        -------
        Properties
            Returns itself

        """
        property_arguments = collections.OrderedDict(self)
        property_arguments.update(kwargs)
        property_arguments.update(features.UPDATE_MEMO["user_arguments"])
        for key, prop in self.items():
            if isinstance(property_arguments[key], Property):
                prop.update(**property_arguments)
            elif id(prop) not in features.UPDATE_MEMO["memoization"]:
                prop.current_value = property_arguments[key]

        return self

    def update_item(self, item: Property, **kwargs):
        """Updates a single property.

        Parameters
        ----------
        item: Property
            The item to update.

        Returns
        -------
        self
        """
        property_arguments = collections.OrderedDict(self)
        property_arguments.update(kwargs)
        for key, prop in self.items():
            if prop == item:
                prop.update(**property_arguments)
                break

        return self

    def sample(self, **kwargs) -> dict:
        """Samples all properties

        Returns
        -------
        dict
            A dictionary with each key-value pair the result of a
            `sample()` call on the property with the same key.

        """

        sample_dict = {}
        for key, property in self.items():
            sample_dict[key] = property.sample(**kwargs)

        return sample_dict
Tools to manage the properties of a feature  Classes ------- Property     The class `Property`, which represents the values of a property of a feature.     A Property be:     * A constant (initialization with, e.g., a number, a tuple)     * A sequence of variables (initialization with, e.g., an iterator)     * A random variable (initialization with, e.g., a function) SequentialProperty     The class `SequentialProperty`, which extends `Property` to sample one value     for each step in a sequence. PropertyDict     The class `PropertyDict`, which is a dictionary with each element a Property.     The class provides utility functions to update, sample, clear and retrieve     properties. Represents a property of a feature      The class Property` wraps an input, which is treated     internally as a sampling rule. This sampling rule is used     to update the value of the property.     The sampling rule can be, for example:     * A constant (initialization with, e.g., a number, a tuple)     * A sequence of variables (initialization with, e.g., a generator)     * A discrete random variable (initialization with, e.g., a list, a dictionary)     * A continuous random variable (initialization with, e.g., a function)      Parameters     ----------     sampling_rule : any         Defines the sampling rule to update the value of the feature property.         See method `sample()` for how different sampling rules are sampled.      Attributes     ----------     sampling_rule : any         The sampling rule to update the value of the feature property.     current_value : any         The current value obtained from the last call to the sampling rule. Current value of the property of the feature          `current_value` is the result of the latest `update()` call.         Note that any randomization only occurs when the method `update()` is called         and, therefore, the current value does not change between calls.          The method getter calls the method `update()` if `current_value`         has not been set yet. # Some values work, some don't. self, updated_current_value and self._current_value work # Best guess is an error in the gc reference counter causing it to dereference # But then again, I don't think it should be the same reference anyway Updates the current value          The method `update()` sets the property `current_value`         as the output of the method `sample()`. Will only update         once per resolve.          Any object that implements the method `update()` will have it called.          Returns         -------         Property             Returns itself. # If currently updated through a call to feature and deeptrack.UPDATE_MEMO # if my_id in deeptrack.UPDATE_MEMO["memoization"] and not hasattr(self, '_current_value'): #     a = 1+1 Samples the sampling rule          Returns a sampled instance of the `sampling_rule` field.         The logic behind the sampling depends on the type of         `sampling_rule`. These are checked in the following order of         priority:          1. Any object with a callable `sample()` method has this             method called and returned.         2. If the rule is a ``dict``, sample each value and combine the             result into a new ``dict`` using the original keys.         3. If the rule is a ``list``, sample each element of the list and             combine the result into a ne ``list``.         4. If the rule is an ``iterable``, return the next output.         5. If the rule is callable, call it with its accepted arguments.             Example arguments can be the value of some other property.         6. If none of the above apply, return the rule itself.          Parameters         ----------         sampling_rule : any             The rule to sample values from.         **kwargs             Arguments that will be passed on to functions that accepts them.          Returns         -------         any             A sampled instance of the `sampling_rule`. # I am worried passing kwargs may lead to name clash # If the ruleset is a dict, return a new dict with each # element being sampled from the original dict. # tuple and ndarrays are elementary # If it's iterable, return the next value # If it's a function, extract the arguments it accepts. # Get the kwarg arguments the function accepts # If that name is among passed kwarg arguments # If it is a property, update it and pass the current value # Else, assume it's elementary. # Extract the class of the object # Create a new instance of the object based on extracted class # Copy over attributes by copying directly or in case of complex objects like lists for exaample calling the `__deepcopy()__` method defined by them. Thus recursively copying the whole tree of objects. Property that has multiple sequential values      Extends standard `Property` to resolve one value for each step     in a sequence of images. This is often used when creating movies.      Parameters     ----------     initializer : any         Sampling rule for the first step of the sequence.     sampling_rule : any         Sampling rule for each step after the first.      Attributes     ----------     initializer : any         Sampling rule for the first step of the sequence.     sampling_rule : any         Sampling rule for each step after the first.     has_updated_since_last_resolve : bool         Whether the property has been updated since the last resolve. # Deprecated Updates current_value          For each step in the sequence, sample `self.sampling_rule`.         `self.initializer` is used for the first step. These rules         should output one value per step. Sampling rules         that are functions can optionally accept the following keyword         arguments:          * sequence_step : the current position in the sequence.         * sequence_length : the length of the sequence.         * previous_value : the value of the property at the previous           step in the sequence.         * previous_values : the value of the property at all previous           steps in the sequence.          Parameters         ----------         sequence_length : int, optional             length of the sequence          Returns         -------         self             returns self # Use initializer for first time step # Elements inserted here can be passed to property functions Dictionary with Property elements      A dictionary of properties. It provides utility functions to update,     sample, reset and retrieve properties.      Parameters     ----------     *args, **kwargs         Arguments used to initialize a dict Retrieves the current value of all properties as a dictionary          Returns         -------         dict             A dictionary with the current value of all properties # If the property is sequential, retrieve the value # of the current timestep Updates all properties          Calls the method `update()` on each property in the dictionary.          Returns         -------         Properties             Returns itself Updates a single property.          Parameters         ----------         item: Property             The item to update.          Returns         -------         self Samples all properties          Returns         -------         dict             A dictionary with each key-value pair the result of a             `sample()` call on the property with the same key.