"""
accumulate_min_over_label_fc
============================
Autogenerated DPF operator classes.
"""
from warnings import warn
from ansys.dpf.core.dpf_operator import Operator
from ansys.dpf.core.inputs import Input, _Inputs
from ansys.dpf.core.outputs import Output, _Outputs
from ansys.dpf.core.operators.specification import PinSpecification, Specification


class accumulate_min_over_label_fc(Operator):
    """Compute the component-wise sum over all the fields having the same id
    for the label set in input in the fields container and take its
    opposite. This computation can be incremental, if the input fields
    container is connected and the operator is ran several time, the
    output field will be on all the inputs connected

    Parameters
    ----------
    fields_container : FieldsContainer


    Examples
    --------
    >>> from ansys.dpf import core as dpf

    >>> # Instantiate operator
    >>> op = dpf.operators.math.accumulate_min_over_label_fc()

    >>> # Make input connections
    >>> my_fields_container = dpf.FieldsContainer()
    >>> op.inputs.fields_container.connect(my_fields_container)

    >>> # Instantiate operator and connect inputs in one line
    >>> op = dpf.operators.math.accumulate_min_over_label_fc(
    ...     fields_container=my_fields_container,
    ... )

    >>> # Get output data
    >>> result_field = op.outputs.field()
    """

    def __init__(self, fields_container=None, config=None, server=None):
        super().__init__(
            name="accumulate_min_over_label_fc", config=config, server=server
        )
        self._inputs = InputsAccumulateMinOverLabelFc(self)
        self._outputs = OutputsAccumulateMinOverLabelFc(self)
        if fields_container is not None:
            self.inputs.fields_container.connect(fields_container)

    @staticmethod
    def _spec():
        description = """Compute the component-wise sum over all the fields having the same id
            for the label set in input in the fields container and
            take its opposite. This computation can be incremental, if
            the input fields container is connected and the operator
            is ran several time, the output field will be on all the
            inputs connected"""
        spec = Specification(
            description=description,
            map_input_pin_spec={
                0: PinSpecification(
                    name="fields_container",
                    type_names=["fields_container"],
                    optional=False,
                    document="""""",
                ),
            },
            map_output_pin_spec={
                0: PinSpecification(
                    name="field",
                    type_names=["field"],
                    optional=False,
                    document="""""",
                ),
            },
        )
        return spec

    @staticmethod
    def default_config(server=None):
        """Returns the default config of the operator.

        This config can then be changed to the user needs and be used to
        instantiate the operator. The Configuration allows to customize
        how the operation will be processed by the operator.

        Parameters
        ----------
        server : server.DPFServer, optional
            Server with channel connected to the remote or local instance. When
            ``None``, attempts to use the global server.
        """
        return Operator.default_config(
            name="accumulate_min_over_label_fc", server=server
        )

    @property
    def inputs(self):
        """Enables to connect inputs to the operator

        Returns
        --------
        inputs : InputsAccumulateMinOverLabelFc
        """
        return super().inputs

    @property
    def outputs(self):
        """Enables to get outputs of the operator by evaluationg it

        Returns
        --------
        outputs : OutputsAccumulateMinOverLabelFc
        """
        return super().outputs


class InputsAccumulateMinOverLabelFc(_Inputs):
    """Intermediate class used to connect user inputs to
    accumulate_min_over_label_fc operator.

    Examples
    --------
    >>> from ansys.dpf import core as dpf
    >>> op = dpf.operators.math.accumulate_min_over_label_fc()
    >>> my_fields_container = dpf.FieldsContainer()
    >>> op.inputs.fields_container.connect(my_fields_container)
    """

    def __init__(self, op: Operator):
        super().__init__(accumulate_min_over_label_fc._spec().inputs, op)
        self._fields_container = Input(
            accumulate_min_over_label_fc._spec().input_pin(0), 0, op, -1
        )
        self._inputs.append(self._fields_container)

    @property
    def fields_container(self):
        """Allows to connect fields_container input to the operator.

        Parameters
        ----------
        my_fields_container : FieldsContainer

        Examples
        --------
        >>> from ansys.dpf import core as dpf
        >>> op = dpf.operators.math.accumulate_min_over_label_fc()
        >>> op.inputs.fields_container.connect(my_fields_container)
        >>> # or
        >>> op.inputs.fields_container(my_fields_container)
        """
        return self._fields_container


class OutputsAccumulateMinOverLabelFc(_Outputs):
    """Intermediate class used to get outputs from
    accumulate_min_over_label_fc operator.

    Examples
    --------
    >>> from ansys.dpf import core as dpf
    >>> op = dpf.operators.math.accumulate_min_over_label_fc()
    >>> # Connect inputs : op.inputs. ...
    >>> result_field = op.outputs.field()
    """

    def __init__(self, op: Operator):
        super().__init__(accumulate_min_over_label_fc._spec().outputs, op)
        self._field = Output(accumulate_min_over_label_fc._spec().output_pin(0), 0, op)
        self._outputs.append(self._field)

    @property
    def field(self):
        """Allows to get field output of the operator

        Returns
        ----------
        my_field : Field

        Examples
        --------
        >>> from ansys.dpf import core as dpf
        >>> op = dpf.operators.math.accumulate_min_over_label_fc()
        >>> # Connect inputs : op.inputs. ...
        >>> result_field = op.outputs.field()
        """  # noqa: E501
        return self._field
