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settings

ConvolutionSettings

Bases: EasyDynamicsBase

Settings for numerical convolutions.

Source code in src/easydynamics/settings/convolution_settings.py 
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class ConvolutionSettings(EasyDynamicsBase):
    """
    Settings for numerical convolutions.
    """

    def __init__(
        self,
        upsample_factor: Numeric | None = 5,
        extension_factor: Numeric | None = 0.2,
        suppress_warnings: bool = False,
        display_name: str | None = 'MyConvolutionSettings',
        unique_name: str | None = None,
    ) -> None:
        """
        Initialize the ConvolutionSettings.

        Parameters
        ----------
        upsample_factor : Numeric | None, default=5
            The factor by which to upsample the input data before convolution.
        extension_factor : Numeric | None, default=0.2
            The factor by which to extend the input data range before convolution.
        suppress_warnings : bool, default=False
            Whether to suppress warnings about wide or narrow peaks in the models.
        display_name : str | None, default='MyConvolutionSettings'
            Display name of the model.
        unique_name : str | None, default=None
            Unique name of the model. If None, a unique name will be generated.

        Raises
        ------
        TypeError
            If upsample_factor is not a number or None. If extension_factor is not a number or
            None. If suppress_warnings is not a boolean.
        ValueError
            If upsample_factor is not greater than 1. If extension_factor is negative.
        """
        super().__init__(
            display_name=display_name,
            unique_name=unique_name,
        )

        if extension_factor is not None:
            if not isinstance(extension_factor, Numeric):
                raise TypeError('Extension factor must be a number.')
            extension_factor = float(extension_factor)
            if extension_factor < 0.0:
                raise ValueError('Extension factor must be non-negative.')
        self._extension_factor = extension_factor

        if upsample_factor is not None:
            if not isinstance(upsample_factor, Numeric):
                raise TypeError('Upsample factor must be a numerical value or None.')
            upsample_factor = float(upsample_factor)
            if upsample_factor <= 1.0:
                raise ValueError('Upsample factor must be greater than 1.')
        self._upsample_factor = upsample_factor

        if not isinstance(suppress_warnings, bool):
            raise TypeError('suppress_warnings must be True or False.')
        self._suppress_warnings = suppress_warnings

        self._convolution_plan_is_valid = False

    @property
    def upsample_factor(self) -> Numeric | None:
        """
        Get the upsample factor.

        Returns
        -------
        Numeric | None
            The upsample factor.
        """

        return self._upsample_factor

    @upsample_factor.setter
    def upsample_factor(self, factor: Numeric | None) -> None:
        """
        Set the upsample factor and recreate the dense grid.

        Parameters
        ----------
        factor : Numeric | None
            The new upsample factor.

        Raises
        ------
        TypeError
            If factor is not a number or None.
        ValueError
            If factor is not greater than 1.
        """
        if factor is None:
            self._upsample_factor = factor
            self.convolution_plan_is_valid = False
            return

        if not isinstance(factor, Numeric):
            raise TypeError('Upsample factor must be a numerical value or None.')
        factor = float(factor)
        if factor <= 1.0:
            raise ValueError('Upsample factor must be greater than 1.')

        self._upsample_factor = factor

        self.convolution_plan_is_valid = False

    @property
    def extension_factor(self) -> float:
        """
        Get the extension factor.

        The extension factor determines how much the energy range is extended on both sides before
        convolution. 0.2 means extending by 20% of the original energy span on each side

        Returns
        -------
        float
            The extension factor.
        """

        return self._extension_factor

    @extension_factor.setter
    def extension_factor(self, factor: Numeric) -> None:
        """
        Set the extension factor and recreate the dense grid.

        The extension factor determines how much the energy range is extended on both sides before
        convolution. 0.2 means extending by 20% of the original energy span on each side.

        Parameters
        ----------
        factor : Numeric
            The new extension factor.

        Raises
        ------
        TypeError
            If factor is not a number.
        ValueError
            If factor is negative.
        """

        if not isinstance(factor, Numeric):
            raise TypeError('Extension factor must be a number.')
        if factor < 0.0:
            raise ValueError('Extension factor must be non-negative.')

        self._extension_factor = float(factor)
        self.convolution_plan_is_valid = False

    @property
    def convolution_plan_is_valid(self) -> bool:
        """
        Get whether the convolution plan is valid.

        Returns
        -------
        bool
            Whether the convolution plan is valid.
        """
        return self._convolution_plan_is_valid

    @convolution_plan_is_valid.setter
    def convolution_plan_is_valid(self, is_valid: bool) -> None:
        """
        Set whether the convolution plan is valid.

        Parameters
        ----------
        is_valid : bool
            Whether the convolution plan is valid.

        Raises
        ------
        TypeError
            If is_valid is not a bool.
        """
        if not isinstance(is_valid, bool):
            raise TypeError('convolution_plan_is_valid must be True or False.')
        self._convolution_plan_is_valid = is_valid

    @property
    def suppress_warnings(self) -> bool:
        """
        Get whether to suppress warnings.

        Returns
        -------
        bool
            Whether to suppress warnings.
        """
        return self._suppress_warnings

    @suppress_warnings.setter
    def suppress_warnings(self, suppress: bool) -> None:
        """
        Set whether to suppress warnings.

        Parameters
        ----------
        suppress : bool
            Whether to suppress warnings.

        Raises
        ------
        TypeError
            If suppress is not a bool.
        """
        if not isinstance(suppress, bool):
            raise TypeError('suppress_warnings must be True or False.')
        self._suppress_warnings = suppress

    def __repr__(self) -> str:
        """
        Return a string representation of the ConvolutionSettings.

        Returns
        -------
        str
            A string representation of the ConvolutionSettings.
        """
        return (
            f'{self.__class__.__name__}('
            f'upsample_factor={self.upsample_factor}, '
            f'extension_factor={self.extension_factor}, '
            f'suppress_warnings={self.suppress_warnings})'
        )

upsample_factor property writable

Get the upsample factor.

Returns:

Type Description
Numeric | None

The upsample factor.

extension_factor property writable

Get the extension factor.

The extension factor determines how much the energy range is extended on both sides before convolution. 0.2 means extending by 20% of the original energy span on each side

Returns:

Type Description
float

The extension factor.

convolution_plan_is_valid property writable

Get whether the convolution plan is valid.

Returns:

Type Description
bool

Whether the convolution plan is valid.

suppress_warnings property writable

Get whether to suppress warnings.

Returns:

Type Description
bool

Whether to suppress warnings.

__init__(upsample_factor=5, extension_factor=0.2, suppress_warnings=False, display_name='MyConvolutionSettings', unique_name=None)

Initialize the ConvolutionSettings.

Parameters:

Name Type Description Default
upsample_factor Numeric | None

The factor by which to upsample the input data before convolution.

 5
extension_factor Numeric | None

The factor by which to extend the input data range before convolution.

 0.2
suppress_warnings bool

Whether to suppress warnings about wide or narrow peaks in the models.

 False
display_name str | None

Display name of the model.

 'MyConvolutionSettings'
unique_name str | None

Unique name of the model. If None, a unique name will be generated.

 None

Raises:

Type Description
TypeError

If upsample_factor is not a number or None. If extension_factor is not a number or None. If suppress_warnings is not a boolean.
ValueError

If upsample_factor is not greater than 1. If extension_factor is negative.
Source code in src/easydynamics/settings/convolution_settings.py 
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def __init__(
    self,
    upsample_factor: Numeric | None = 5,
    extension_factor: Numeric | None = 0.2,
    suppress_warnings: bool = False,
    display_name: str | None = 'MyConvolutionSettings',
    unique_name: str | None = None,
) -> None:
    """
    Initialize the ConvolutionSettings.

    Parameters
    ----------
    upsample_factor : Numeric | None, default=5
        The factor by which to upsample the input data before convolution.
    extension_factor : Numeric | None, default=0.2
        The factor by which to extend the input data range before convolution.
    suppress_warnings : bool, default=False
        Whether to suppress warnings about wide or narrow peaks in the models.
    display_name : str | None, default='MyConvolutionSettings'
        Display name of the model.
    unique_name : str | None, default=None
        Unique name of the model. If None, a unique name will be generated.

    Raises
    ------
    TypeError
        If upsample_factor is not a number or None. If extension_factor is not a number or
        None. If suppress_warnings is not a boolean.
    ValueError
        If upsample_factor is not greater than 1. If extension_factor is negative.
    """
    super().__init__(
        display_name=display_name,
        unique_name=unique_name,
    )

    if extension_factor is not None:
        if not isinstance(extension_factor, Numeric):
            raise TypeError('Extension factor must be a number.')
        extension_factor = float(extension_factor)
        if extension_factor < 0.0:
            raise ValueError('Extension factor must be non-negative.')
    self._extension_factor = extension_factor

    if upsample_factor is not None:
        if not isinstance(upsample_factor, Numeric):
            raise TypeError('Upsample factor must be a numerical value or None.')
        upsample_factor = float(upsample_factor)
        if upsample_factor <= 1.0:
            raise ValueError('Upsample factor must be greater than 1.')
    self._upsample_factor = upsample_factor

    if not isinstance(suppress_warnings, bool):
        raise TypeError('suppress_warnings must be True or False.')
    self._suppress_warnings = suppress_warnings

    self._convolution_plan_is_valid = False

__repr__()

Return a string representation of the ConvolutionSettings.

Returns:

Type Description
str

A string representation of the ConvolutionSettings.
Source code in src/easydynamics/settings/convolution_settings.py 
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def __repr__(self) -> str:
    """
    Return a string representation of the ConvolutionSettings.

    Returns
    -------
    str
        A string representation of the ConvolutionSettings.
    """
    return (
        f'{self.__class__.__name__}('
        f'upsample_factor={self.upsample_factor}, '
        f'extension_factor={self.extension_factor}, '
        f'suppress_warnings={self.suppress_warnings})'
    )

DetailedBalanceSettings

Bases: EasyDynamicsBase

Class to manage detailed balance settings for a SampleModel or Analysis.

Source code in src/easydynamics/settings/detailed_balance_settings.py 
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class DetailedBalanceSettings(EasyDynamicsBase):
    """
    Class to manage detailed balance settings for a SampleModel or Analysis.
    """

    def __init__(
        self,
        use_detailed_balance: bool = True,
        normalize_detailed_balance: bool = True,
        display_name: str = 'DetailedBalanceSettings',
        unique_name: str | None = None,
    ) -> None:
        """
        Initialize the DetailedBalanceSettings.

        Parameters
        ----------
        use_detailed_balance : bool, default=True
            Whether to apply detailed balance to the model. If False, no detailed balance is
            applied.
        normalize_detailed_balance : bool, default=True
            Whether to normalize the detailed balance factor by dividing with temperature.
        display_name : str, default='DetailedBalanceSettings'
            Display name of the model.
        unique_name : str | None, default=None
            Unique name of the model. If None, a unique name will be generated.


        Raises
        ------
        TypeError
            If use_detailed_balance or normalize_detailed_balance is not a bool.
        """
        if not isinstance(use_detailed_balance, bool):
            raise TypeError('use_detailed_balance must be True or False')
        self._use_detailed_balance = use_detailed_balance

        if not isinstance(normalize_detailed_balance, bool):
            raise TypeError('normalize_detailed_balance must be True or False')
        self._normalize_detailed_balance = normalize_detailed_balance

        super().__init__(
            display_name=display_name,
            unique_name=unique_name,
        )

    # ------------------------------------------------------------------
    # Properties
    # ------------------------------------------------------------------

    @property
    def use_detailed_balance(self) -> bool:
        """
        Get whether to apply detailed balance to the model.

        Returns
        -------
        bool
            True if detailed balance is applied, False otherwise.
        """
        return self._use_detailed_balance

    @use_detailed_balance.setter
    def use_detailed_balance(self, value: bool) -> None:
        """
        Set whether to apply detailed balance to the model.

        Parameters
        ----------
        value : bool
            True to apply detailed balance, False otherwise.

        Raises
        ------
        TypeError
            If value is not a bool.
        """
        if not isinstance(value, bool):
            raise TypeError('use_detailed_balance must be True or False')
        self._use_detailed_balance = value

    @property
    def normalize_detailed_balance(self) -> bool:
        """
        Get whether to divide the detailed balance factor by temperature.

        Returns
        -------
        bool
            True if the detailed balance factor should be normalized by dividing with temperature,
            False otherwise.
        """
        return self._normalize_detailed_balance

    @normalize_detailed_balance.setter
    def normalize_detailed_balance(self, value: bool) -> None:
        """
        Set whether to normalize the detailed balance factor by dividing with temperature.

        Parameters
        ----------
        value : bool
            True to normalize the detailed balance factor by dividing with temperature, False
            otherwise.

        Raises
        ------
        TypeError
            If value is not a bool.
        """
        if not isinstance(value, bool):
            raise TypeError('normalize_detailed_balance must be True or False')
        self._normalize_detailed_balance = value

    def __repr__(self) -> str:
        """
        Return a string representation of the DetailedBalanceSettings.

        Returns
        -------
        str
            A string representation of the DetailedBalanceSettings.
        """
        return (
            f'DetailedBalanceSettings(use_detailed_balance={self.use_detailed_balance}, '
            f'normalize_detailed_balance={self.normalize_detailed_balance})'
        )

use_detailed_balance property writable

Get whether to apply detailed balance to the model.

Returns:

Type Description
bool

True if detailed balance is applied, False otherwise.

normalize_detailed_balance property writable

Get whether to divide the detailed balance factor by temperature.

Returns:

Type Description
bool

True if the detailed balance factor should be normalized by dividing with temperature, False otherwise.

__init__(use_detailed_balance=True, normalize_detailed_balance=True, display_name='DetailedBalanceSettings', unique_name=None)

Initialize the DetailedBalanceSettings.

Parameters:

Name Type Description Default
use_detailed_balance bool

Whether to apply detailed balance to the model. If False, no detailed balance is applied.

 True
normalize_detailed_balance bool

Whether to normalize the detailed balance factor by dividing with temperature.

 True
display_name str

Display name of the model.

 'DetailedBalanceSettings'
unique_name str | None

Unique name of the model. If None, a unique name will be generated.

 None

Raises:

Type Description
TypeError

If use_detailed_balance or normalize_detailed_balance is not a bool.
Source code in src/easydynamics/settings/detailed_balance_settings.py 
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def __init__(
    self,
    use_detailed_balance: bool = True,
    normalize_detailed_balance: bool = True,
    display_name: str = 'DetailedBalanceSettings',
    unique_name: str | None = None,
) -> None:
    """
    Initialize the DetailedBalanceSettings.

    Parameters
    ----------
    use_detailed_balance : bool, default=True
        Whether to apply detailed balance to the model. If False, no detailed balance is
        applied.
    normalize_detailed_balance : bool, default=True
        Whether to normalize the detailed balance factor by dividing with temperature.
    display_name : str, default='DetailedBalanceSettings'
        Display name of the model.
    unique_name : str | None, default=None
        Unique name of the model. If None, a unique name will be generated.


    Raises
    ------
    TypeError
        If use_detailed_balance or normalize_detailed_balance is not a bool.
    """
    if not isinstance(use_detailed_balance, bool):
        raise TypeError('use_detailed_balance must be True or False')
    self._use_detailed_balance = use_detailed_balance

    if not isinstance(normalize_detailed_balance, bool):
        raise TypeError('normalize_detailed_balance must be True or False')
    self._normalize_detailed_balance = normalize_detailed_balance

    super().__init__(
        display_name=display_name,
        unique_name=unique_name,
    )

__repr__()

Return a string representation of the DetailedBalanceSettings.

Returns:

Type Description
str

A string representation of the DetailedBalanceSettings.
Source code in src/easydynamics/settings/detailed_balance_settings.py 
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def __repr__(self) -> str:
    """
    Return a string representation of the DetailedBalanceSettings.

    Returns
    -------
    str
        A string representation of the DetailedBalanceSettings.
    """
    return (
        f'DetailedBalanceSettings(use_detailed_balance={self.use_detailed_balance}, '
        f'normalize_detailed_balance={self.normalize_detailed_balance})'
    )

convolution_settings

ConvolutionSettings

Bases: EasyDynamicsBase

Settings for numerical convolutions.

Source code in src/easydynamics/settings/convolution_settings.py 
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class ConvolutionSettings(EasyDynamicsBase):
    """
    Settings for numerical convolutions.
    """

    def __init__(
        self,
        upsample_factor: Numeric | None = 5,
        extension_factor: Numeric | None = 0.2,
        suppress_warnings: bool = False,
        display_name: str | None = 'MyConvolutionSettings',
        unique_name: str | None = None,
    ) -> None:
        """
        Initialize the ConvolutionSettings.

        Parameters
        ----------
        upsample_factor : Numeric | None, default=5
            The factor by which to upsample the input data before convolution.
        extension_factor : Numeric | None, default=0.2
            The factor by which to extend the input data range before convolution.
        suppress_warnings : bool, default=False
            Whether to suppress warnings about wide or narrow peaks in the models.
        display_name : str | None, default='MyConvolutionSettings'
            Display name of the model.
        unique_name : str | None, default=None
            Unique name of the model. If None, a unique name will be generated.

        Raises
        ------
        TypeError
            If upsample_factor is not a number or None. If extension_factor is not a number or
            None. If suppress_warnings is not a boolean.
        ValueError
            If upsample_factor is not greater than 1. If extension_factor is negative.
        """
        super().__init__(
            display_name=display_name,
            unique_name=unique_name,
        )

        if extension_factor is not None:
            if not isinstance(extension_factor, Numeric):
                raise TypeError('Extension factor must be a number.')
            extension_factor = float(extension_factor)
            if extension_factor < 0.0:
                raise ValueError('Extension factor must be non-negative.')
        self._extension_factor = extension_factor

        if upsample_factor is not None:
            if not isinstance(upsample_factor, Numeric):
                raise TypeError('Upsample factor must be a numerical value or None.')
            upsample_factor = float(upsample_factor)
            if upsample_factor <= 1.0:
                raise ValueError('Upsample factor must be greater than 1.')
        self._upsample_factor = upsample_factor

        if not isinstance(suppress_warnings, bool):
            raise TypeError('suppress_warnings must be True or False.')
        self._suppress_warnings = suppress_warnings

        self._convolution_plan_is_valid = False

    @property
    def upsample_factor(self) -> Numeric | None:
        """
        Get the upsample factor.

        Returns
        -------
        Numeric | None
            The upsample factor.
        """

        return self._upsample_factor

    @upsample_factor.setter
    def upsample_factor(self, factor: Numeric | None) -> None:
        """
        Set the upsample factor and recreate the dense grid.

        Parameters
        ----------
        factor : Numeric | None
            The new upsample factor.

        Raises
        ------
        TypeError
            If factor is not a number or None.
        ValueError
            If factor is not greater than 1.
        """
        if factor is None:
            self._upsample_factor = factor
            self.convolution_plan_is_valid = False
            return

        if not isinstance(factor, Numeric):
            raise TypeError('Upsample factor must be a numerical value or None.')
        factor = float(factor)
        if factor <= 1.0:
            raise ValueError('Upsample factor must be greater than 1.')

        self._upsample_factor = factor

        self.convolution_plan_is_valid = False

    @property
    def extension_factor(self) -> float:
        """
        Get the extension factor.

        The extension factor determines how much the energy range is extended on both sides before
        convolution. 0.2 means extending by 20% of the original energy span on each side

        Returns
        -------
        float
            The extension factor.
        """

        return self._extension_factor

    @extension_factor.setter
    def extension_factor(self, factor: Numeric) -> None:
        """
        Set the extension factor and recreate the dense grid.

        The extension factor determines how much the energy range is extended on both sides before
        convolution. 0.2 means extending by 20% of the original energy span on each side.

        Parameters
        ----------
        factor : Numeric
            The new extension factor.

        Raises
        ------
        TypeError
            If factor is not a number.
        ValueError
            If factor is negative.
        """

        if not isinstance(factor, Numeric):
            raise TypeError('Extension factor must be a number.')
        if factor < 0.0:
            raise ValueError('Extension factor must be non-negative.')

        self._extension_factor = float(factor)
        self.convolution_plan_is_valid = False

    @property
    def convolution_plan_is_valid(self) -> bool:
        """
        Get whether the convolution plan is valid.

        Returns
        -------
        bool
            Whether the convolution plan is valid.
        """
        return self._convolution_plan_is_valid

    @convolution_plan_is_valid.setter
    def convolution_plan_is_valid(self, is_valid: bool) -> None:
        """
        Set whether the convolution plan is valid.

        Parameters
        ----------
        is_valid : bool
            Whether the convolution plan is valid.

        Raises
        ------
        TypeError
            If is_valid is not a bool.
        """
        if not isinstance(is_valid, bool):
            raise TypeError('convolution_plan_is_valid must be True or False.')
        self._convolution_plan_is_valid = is_valid

    @property
    def suppress_warnings(self) -> bool:
        """
        Get whether to suppress warnings.

        Returns
        -------
        bool
            Whether to suppress warnings.
        """
        return self._suppress_warnings

    @suppress_warnings.setter
    def suppress_warnings(self, suppress: bool) -> None:
        """
        Set whether to suppress warnings.

        Parameters
        ----------
        suppress : bool
            Whether to suppress warnings.

        Raises
        ------
        TypeError
            If suppress is not a bool.
        """
        if not isinstance(suppress, bool):
            raise TypeError('suppress_warnings must be True or False.')
        self._suppress_warnings = suppress

    def __repr__(self) -> str:
        """
        Return a string representation of the ConvolutionSettings.

        Returns
        -------
        str
            A string representation of the ConvolutionSettings.
        """
        return (
            f'{self.__class__.__name__}('
            f'upsample_factor={self.upsample_factor}, '
            f'extension_factor={self.extension_factor}, '
            f'suppress_warnings={self.suppress_warnings})'
        )

upsample_factor property writable

Get the upsample factor.

Returns:

Type Description
Numeric | None

The upsample factor.

extension_factor property writable

Get the extension factor.

The extension factor determines how much the energy range is extended on both sides before convolution. 0.2 means extending by 20% of the original energy span on each side

Returns:

Type Description
float

The extension factor.

convolution_plan_is_valid property writable

Get whether the convolution plan is valid.

Returns:

Type Description
bool

Whether the convolution plan is valid.

suppress_warnings property writable

Get whether to suppress warnings.

Returns:

Type Description
bool

Whether to suppress warnings.

__init__(upsample_factor=5, extension_factor=0.2, suppress_warnings=False, display_name='MyConvolutionSettings', unique_name=None)

Initialize the ConvolutionSettings.

Parameters:

Name Type Description Default
upsample_factor Numeric | None

The factor by which to upsample the input data before convolution.

 5
extension_factor Numeric | None

The factor by which to extend the input data range before convolution.

 0.2
suppress_warnings bool

Whether to suppress warnings about wide or narrow peaks in the models.

 False
display_name str | None

Display name of the model.

 'MyConvolutionSettings'
unique_name str | None

Unique name of the model. If None, a unique name will be generated.

 None

Raises:

Type Description
TypeError

If upsample_factor is not a number or None. If extension_factor is not a number or None. If suppress_warnings is not a boolean.
ValueError

If upsample_factor is not greater than 1. If extension_factor is negative.
Source code in src/easydynamics/settings/convolution_settings.py 
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def __init__(
    self,
    upsample_factor: Numeric | None = 5,
    extension_factor: Numeric | None = 0.2,
    suppress_warnings: bool = False,
    display_name: str | None = 'MyConvolutionSettings',
    unique_name: str | None = None,
) -> None:
    """
    Initialize the ConvolutionSettings.

    Parameters
    ----------
    upsample_factor : Numeric | None, default=5
        The factor by which to upsample the input data before convolution.
    extension_factor : Numeric | None, default=0.2
        The factor by which to extend the input data range before convolution.
    suppress_warnings : bool, default=False
        Whether to suppress warnings about wide or narrow peaks in the models.
    display_name : str | None, default='MyConvolutionSettings'
        Display name of the model.
    unique_name : str | None, default=None
        Unique name of the model. If None, a unique name will be generated.

    Raises
    ------
    TypeError
        If upsample_factor is not a number or None. If extension_factor is not a number or
        None. If suppress_warnings is not a boolean.
    ValueError
        If upsample_factor is not greater than 1. If extension_factor is negative.
    """
    super().__init__(
        display_name=display_name,
        unique_name=unique_name,
    )

    if extension_factor is not None:
        if not isinstance(extension_factor, Numeric):
            raise TypeError('Extension factor must be a number.')
        extension_factor = float(extension_factor)
        if extension_factor < 0.0:
            raise ValueError('Extension factor must be non-negative.')
    self._extension_factor = extension_factor

    if upsample_factor is not None:
        if not isinstance(upsample_factor, Numeric):
            raise TypeError('Upsample factor must be a numerical value or None.')
        upsample_factor = float(upsample_factor)
        if upsample_factor <= 1.0:
            raise ValueError('Upsample factor must be greater than 1.')
    self._upsample_factor = upsample_factor

    if not isinstance(suppress_warnings, bool):
        raise TypeError('suppress_warnings must be True or False.')
    self._suppress_warnings = suppress_warnings

    self._convolution_plan_is_valid = False

__repr__()

Return a string representation of the ConvolutionSettings.

Returns:

Type Description
str

A string representation of the ConvolutionSettings.
Source code in src/easydynamics/settings/convolution_settings.py 
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def __repr__(self) -> str:
    """
    Return a string representation of the ConvolutionSettings.

    Returns
    -------
    str
        A string representation of the ConvolutionSettings.
    """
    return (
        f'{self.__class__.__name__}('
        f'upsample_factor={self.upsample_factor}, '
        f'extension_factor={self.extension_factor}, '
        f'suppress_warnings={self.suppress_warnings})'
    )

detailed_balance_settings

DetailedBalanceSettings

Bases: EasyDynamicsBase

Class to manage detailed balance settings for a SampleModel or Analysis.

Source code in src/easydynamics/settings/detailed_balance_settings.py 
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class DetailedBalanceSettings(EasyDynamicsBase):
    """
    Class to manage detailed balance settings for a SampleModel or Analysis.
    """

    def __init__(
        self,
        use_detailed_balance: bool = True,
        normalize_detailed_balance: bool = True,
        display_name: str = 'DetailedBalanceSettings',
        unique_name: str | None = None,
    ) -> None:
        """
        Initialize the DetailedBalanceSettings.

        Parameters
        ----------
        use_detailed_balance : bool, default=True
            Whether to apply detailed balance to the model. If False, no detailed balance is
            applied.
        normalize_detailed_balance : bool, default=True
            Whether to normalize the detailed balance factor by dividing with temperature.
        display_name : str, default='DetailedBalanceSettings'
            Display name of the model.
        unique_name : str | None, default=None
            Unique name of the model. If None, a unique name will be generated.


        Raises
        ------
        TypeError
            If use_detailed_balance or normalize_detailed_balance is not a bool.
        """
        if not isinstance(use_detailed_balance, bool):
            raise TypeError('use_detailed_balance must be True or False')
        self._use_detailed_balance = use_detailed_balance

        if not isinstance(normalize_detailed_balance, bool):
            raise TypeError('normalize_detailed_balance must be True or False')
        self._normalize_detailed_balance = normalize_detailed_balance

        super().__init__(
            display_name=display_name,
            unique_name=unique_name,
        )

    # ------------------------------------------------------------------
    # Properties
    # ------------------------------------------------------------------

    @property
    def use_detailed_balance(self) -> bool:
        """
        Get whether to apply detailed balance to the model.

        Returns
        -------
        bool
            True if detailed balance is applied, False otherwise.
        """
        return self._use_detailed_balance

    @use_detailed_balance.setter
    def use_detailed_balance(self, value: bool) -> None:
        """
        Set whether to apply detailed balance to the model.

        Parameters
        ----------
        value : bool
            True to apply detailed balance, False otherwise.

        Raises
        ------
        TypeError
            If value is not a bool.
        """
        if not isinstance(value, bool):
            raise TypeError('use_detailed_balance must be True or False')
        self._use_detailed_balance = value

    @property
    def normalize_detailed_balance(self) -> bool:
        """
        Get whether to divide the detailed balance factor by temperature.

        Returns
        -------
        bool
            True if the detailed balance factor should be normalized by dividing with temperature,
            False otherwise.
        """
        return self._normalize_detailed_balance

    @normalize_detailed_balance.setter
    def normalize_detailed_balance(self, value: bool) -> None:
        """
        Set whether to normalize the detailed balance factor by dividing with temperature.

        Parameters
        ----------
        value : bool
            True to normalize the detailed balance factor by dividing with temperature, False
            otherwise.

        Raises
        ------
        TypeError
            If value is not a bool.
        """
        if not isinstance(value, bool):
            raise TypeError('normalize_detailed_balance must be True or False')
        self._normalize_detailed_balance = value

    def __repr__(self) -> str:
        """
        Return a string representation of the DetailedBalanceSettings.

        Returns
        -------
        str
            A string representation of the DetailedBalanceSettings.
        """
        return (
            f'DetailedBalanceSettings(use_detailed_balance={self.use_detailed_balance}, '
            f'normalize_detailed_balance={self.normalize_detailed_balance})'
        )

use_detailed_balance property writable

Get whether to apply detailed balance to the model.

Returns:

Type Description
bool

True if detailed balance is applied, False otherwise.

normalize_detailed_balance property writable

Get whether to divide the detailed balance factor by temperature.

Returns:

Type Description
bool

True if the detailed balance factor should be normalized by dividing with temperature, False otherwise.

__init__(use_detailed_balance=True, normalize_detailed_balance=True, display_name='DetailedBalanceSettings', unique_name=None)

Initialize the DetailedBalanceSettings.

Parameters:

Name Type Description Default
use_detailed_balance bool

Whether to apply detailed balance to the model. If False, no detailed balance is applied.

 True
normalize_detailed_balance bool

Whether to normalize the detailed balance factor by dividing with temperature.

 True
display_name str

Display name of the model.

 'DetailedBalanceSettings'
unique_name str | None

Unique name of the model. If None, a unique name will be generated.

 None

Raises:

Type Description
TypeError

If use_detailed_balance or normalize_detailed_balance is not a bool.
Source code in src/easydynamics/settings/detailed_balance_settings.py 
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def __init__(
    self,
    use_detailed_balance: bool = True,
    normalize_detailed_balance: bool = True,
    display_name: str = 'DetailedBalanceSettings',
    unique_name: str | None = None,
) -> None:
    """
    Initialize the DetailedBalanceSettings.

    Parameters
    ----------
    use_detailed_balance : bool, default=True
        Whether to apply detailed balance to the model. If False, no detailed balance is
        applied.
    normalize_detailed_balance : bool, default=True
        Whether to normalize the detailed balance factor by dividing with temperature.
    display_name : str, default='DetailedBalanceSettings'
        Display name of the model.
    unique_name : str | None, default=None
        Unique name of the model. If None, a unique name will be generated.


    Raises
    ------
    TypeError
        If use_detailed_balance or normalize_detailed_balance is not a bool.
    """
    if not isinstance(use_detailed_balance, bool):
        raise TypeError('use_detailed_balance must be True or False')
    self._use_detailed_balance = use_detailed_balance

    if not isinstance(normalize_detailed_balance, bool):
        raise TypeError('normalize_detailed_balance must be True or False')
    self._normalize_detailed_balance = normalize_detailed_balance

    super().__init__(
        display_name=display_name,
        unique_name=unique_name,
    )

__repr__()

Return a string representation of the DetailedBalanceSettings.

Returns:

Type Description
str

A string representation of the DetailedBalanceSettings.
Source code in src/easydynamics/settings/detailed_balance_settings.py 
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def __repr__(self) -> str:
    """
    Return a string representation of the DetailedBalanceSettings.

    Returns
    -------
    str
        A string representation of the DetailedBalanceSettings.
    """
    return (
        f'DetailedBalanceSettings(use_detailed_balance={self.use_detailed_balance}, '
        f'normalize_detailed_balance={self.normalize_detailed_balance})'
    )