TCC_Curve.py

# Copyright (c) 2021-2024 Paulo Meira
# Copyright (c) 2021-2024 DSS-Extensions contributors
from __future__ import annotations
from typing import Union, List, AnyStr, Optional, Iterator, TYPE_CHECKING
from typing_extensions import TypedDict, Unpack
from .types import Float64Array, Int32Array
from . import enums
from .DSSObj import IDSSObj, DSSObj
from .Batch import DSSBatch
from .ArrayProxy import BatchInt32ArrayProxy

class TCC_Curve(DSSObj):
    __slots__ = DSSObj._extra_slots
    _cls_name = 'TCC_Curve'
    _cls_idx = 7
    _cls_int_idx = {
        1,
    }
    _cls_float_idx = {
    }
    _cls_prop_idx = {
        'npts': 1,
        'c_array': 2,
        't_array': 3,
        'like': 4,
    }


    def edit(self, **kwargs: Unpack[TCC_CurveProperties]) -> TCC_Curve:
        """
        Edit this TCC_Curve.

        This method will try to open a new edit context (if not already open), 
        edit the properties, and finalize the edit context. 
        It can be seen as a shortcut to manually setting each property, or a Pythonic 
        analogous (but extended) to the DSS `Edit` command.

        :param **kwargs: Pass keyword arguments equivalent to the DSS properties of the object.
        :return: Returns itself to allow call chaining.
        """

        self._edit(props=kwargs)
        return self


    def _get_NPts(self) -> int:
        return self._lib.Obj_GetInt32(self._ptr, 1)

    def _set_NPts(self, value: int, flags: enums.SetterFlags = 0):
        self._lib.Obj_SetInt32(self._ptr, 1, value, flags)

    NPts = property(_get_NPts, _set_NPts) # type: int
    """
    Number of points to expect in time-current arrays.

    DSS property name: `NPts`, DSS property index: 1.
    """

    def _get_C_Array(self) -> Float64Array:
        return self._get_float64_array(self._lib.Obj_GetFloat64Array, self._ptr, 2)

    def _set_C_Array(self, value: Float64Array, flags: enums.SetterFlags = 0):
        self._set_float64_array_o(2, value, flags)

    C_Array = property(_get_C_Array, _set_C_Array) # type: Float64Array
    """
    Array of current (or voltage) values corresponding to time values (see help on T_Array).

    DSS property name: `C_Array`, DSS property index: 2.
    """

    def _get_T_Array(self) -> Float64Array:
        return self._get_float64_array(self._lib.Obj_GetFloat64Array, self._ptr, 3)

    def _set_T_Array(self, value: Float64Array, flags: enums.SetterFlags = 0):
        self._set_float64_array_o(3, value, flags)

    T_Array = property(_get_T_Array, _set_T_Array) # type: Float64Array
    """
    Array of time values in sec. Typical array syntax: 
    t_array = (1, 2, 3, 4, ...)

    Can also substitute a file designation: 
    t_array =  (file=filename)

    The specified file has one value per line.

    DSS property name: `T_Array`, DSS property index: 3.
    """

    def Like(self, value: AnyStr):
        """
        Make like another object, e.g.:

        New Capacitor.C2 like=c1  ...

        DSS property name: `Like`, DSS property index: 4.
        """
        self._set_string_o(4, value)


class TCC_CurveProperties(TypedDict):
    NPts: int
    C_Array: Float64Array
    T_Array: Float64Array
    Like: AnyStr

class TCC_CurveBatch(DSSBatch):
    _cls_name = 'TCC_Curve'
    _obj_cls = TCC_Curve
    _cls_idx = 7
    __slots__ = []


    def edit(self, **kwargs: Unpack[TCC_CurveBatchProperties]) -> TCC_CurveBatch:
        """
        Edit this TCC_Curve batch.

        This method will try to open a new edit context (if not already open), 
        edit the properties, and finalize the edit context for objects in the batch.
        It can be seen as a shortcut to manually setting each property, or a Pythonic
        analogous (but extended) to the DSS `BatchEdit` command.

        :param **kwargs: Pass keyword arguments equivalent to the DSS properties of the objects.
        :return: Returns itself to allow call chaining.
        """

        self._edit(props=kwargs)
        return self


    if TYPE_CHECKING:
        def __iter__(self) -> Iterator[TCC_Curve]:
            yield from DSSBatch.__iter__(self)

    def _get_NPts(self) -> BatchInt32ArrayProxy:
        return BatchInt32ArrayProxy(self, 1)

    def _set_NPts(self, value: Union[int, Int32Array], flags: enums.SetterFlags = 0):
        self._set_batch_int32_array(1, value, flags)

    NPts = property(_get_NPts, _set_NPts) # type: BatchInt32ArrayProxy
    """
    Number of points to expect in time-current arrays.

    DSS property name: `NPts`, DSS property index: 1.
    """

    def _get_C_Array(self) -> List[Float64Array]:
        return [
            self._get_float64_array(self._lib.Obj_GetFloat64Array, x, 2)
            for x in self._unpack()
        ]

    def _set_C_Array(self, value: Union[Float64Array, List[Float64Array]], flags: enums.SetterFlags = 0):
        self._set_batch_float64_array_prop(2, value, flags)

    C_Array = property(_get_C_Array, _set_C_Array) # type: List[Float64Array]
    """
    Array of current (or voltage) values corresponding to time values (see help on T_Array).

    DSS property name: `C_Array`, DSS property index: 2.
    """

    def _get_T_Array(self) -> List[Float64Array]:
        return [
            self._get_float64_array(self._lib.Obj_GetFloat64Array, x, 3)
            for x in self._unpack()
        ]

    def _set_T_Array(self, value: Union[Float64Array, List[Float64Array]], flags: enums.SetterFlags = 0):
        self._set_batch_float64_array_prop(3, value, flags)

    T_Array = property(_get_T_Array, _set_T_Array) # type: List[Float64Array]
    """
    Array of time values in sec. Typical array syntax: 
    t_array = (1, 2, 3, 4, ...)

    Can also substitute a file designation: 
    t_array =  (file=filename)

    The specified file has one value per line.

    DSS property name: `T_Array`, DSS property index: 3.
    """

    def Like(self, value: AnyStr, flags: enums.SetterFlags = 0):
        """
        Make like another object, e.g.:

        New Capacitor.C2 like=c1  ...

        DSS property name: `Like`, DSS property index: 4.
        """
        self._set_batch_string(4, value, flags)

class TCC_CurveBatchProperties(TypedDict):
    NPts: Union[int, Int32Array]
    C_Array: Float64Array
    T_Array: Float64Array
    Like: AnyStr

class ITCC_Curve(IDSSObj, TCC_CurveBatch):
    __slots__ = IDSSObj._extra_slots

    def __init__(self, iobj):
        IDSSObj.__init__(self, iobj, TCC_Curve, TCC_CurveBatch)
        TCC_CurveBatch.__init__(self, self._api_util, sync_cls_idx=TCC_Curve._cls_idx)

    if TYPE_CHECKING:
        def __getitem__(self, name_or_idx: Union[AnyStr, int]) -> TCC_Curve:
            return self.find(name_or_idx)

        def batch(self, **kwargs) -> TCC_CurveBatch: #TODO: add annotation to kwargs (specialized typed dict)
            """
            Creates a new batch handler of (existing) TCC_Curve objects
            """
            return self._batch_cls(self._api_util, **kwargs)

        def __iter__(self) -> Iterator[TCC_Curve]:
            yield from TCC_CurveBatch.__iter__(self)

        
    def new(self, name: AnyStr, *, begin_edit: Optional[bool] = None, activate=False, **kwargs: Unpack[TCC_CurveProperties]) -> TCC_Curve:
        """
        Creates a new TCC_Curve.

        :param name: The object's name is a required positional argument.

        :param activate: Activation (setting `activate` to true) is useful for integration with the classic API, and some internal OpenDSS commands.
        If you interact with this object only via the Alt API, no need to activate it (due to performance costs).

        :param begin_edit: This controls how the edit context is left after the object creation:
        - `True`: The object will be left in the edit state, requiring an `end_edit` call or equivalent.
        - `False`: No edit context is started.
        - `None`: If no properties are passed as keyword arguments, the object will be left in the edit state (assumes the user will fill the properties from Python attributes). Otherwise, the internal edit context will be finalized.

        :param **kwargs: Pass keyword arguments equivalent to the DSS properties of the object.
        :return: Returns the new DSS object, wrapped in Python.

        Note that, to make it easier for new users where the edit context might not be too relevant, AltDSS automatically opens/closes edit contexts for single properties if the object is not in the edit state already.
        """
        return self._new(name, begin_edit=begin_edit, activate=activate, props=kwargs)

    def batch_new(self, names: Optional[List[AnyStr]] = None, *, df = None, count: Optional[int] = None, begin_edit: Optional[bool] = None, **kwargs: Unpack[TCC_CurveBatchProperties]) -> TCC_CurveBatch:
        """
        Creates a new batch of TCC_Curve objects

        Either `names`, `count` or `df` is required. 

        :param begin_edit: The argument `begin_edit` indicates if the user want to leave the elements in the edit state, and requires a call to `end_edit()` or equivalent. The default `begin_edit` is set to `None`. With `None`, the behavior will be adjusted according the default of how the batch is created.
        :param **kwargs: Pass keyword arguments equivalent to the DSS properties of the object.
        :param names: When using a list of names, each new object will match the names from this list. `begin_edit` defaults to `True` if no arguments for properties were passed, `False` otherwise.
        :param count: When using `count`, new objects will be created with based on a random prefix, with an increasing integer up to `count`. `begin_edit` defaults to `True` if no arguments for properties were passed, `False` otherwise.
        :param df: Currently **EXPERIMENTAL AND LIMITED**, tries to get the columns from a dataframe to populate the names and the DSS properties. `begin_edit` defaults to `False`.
        :return: Returns the new batch of DSS objects, wrapped in Python.

        Note that, to make it easier for new users where the edit context might not be too relevant, AltDSS automatically opens/closes edit contexts for single properties if the object is not in the edit state already.
        """
        return self._batch_new_aux(names=names, df=df, count=count, begin_edit=begin_edit, props=kwargs)