How to use the brainflow.board_shim.BrainFlowError function in brainflow

:param order: filter order
        :type order: int
        :param filter_type: filter type from special enum
        :type filter_type: int
        :param ripple: ripple value for Chebyshev filter
        :type ripple: float
        """
        if not isinstance (sampling_rate, int):
            raise BrainFlowError ('wrong type for sampling rate', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if not isinstance (filter_type, int):
            raise BrainFlowError ('wrong type for filter type', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if len (data.shape) != 1:
            raise BrainFlowError ('wrong shape for filter data array, it should be 1d array', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        res = DataHandlerDLL.get_instance ().perform_bandstop (data, data.shape[0], sampling_rate, center_freq, band_width, order, filter_type, ripple)
        if res != BrainflowExitCodes.STATUS_OK.value:
            raise BrainFlowError ('unable to apply band stop filter', res)

:type data: 1d numpy array
        :param period: downsampling period
        :type period: int
        :param operation: int value from AggOperation enum
        :type operation: int
        :return: downsampled data
        :rtype: 1d numpy array
        """
        if not isinstance (period, int):
            raise BrainFlowError ('wrong type for period', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if not isinstance (operation, int):
            raise BrainFlowError ('wrong type for operation', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if len (data.shape) != 1:
            raise BrainFlowError ('wrong shape for filter data array, it should be 1d array', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if period <= 0:
            raise BrainFlowError ('Invalid value for period', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)

        downsampled_data = numpy.zeros (int (data.shape[0] / period)).astype (numpy.float64)
        res = DataHandlerDLL.get_instance ().perform_downsampling (data, data.shape[0], period, operation, downsampled_data)
        if res != BrainflowExitCodes.STATUS_OK.value:
            raise BrainFlowError ('unable to perform downsampling', res)

        return downsampled_data

def perform_downsampling (cls, data, period, operation):
        """perform data downsampling, it doesnt apply lowpass filter for you, it just aggregates several data points

        :param data: initial data
        :type data: 1d numpy array
        :param period: downsampling period
        :type period: int
        :param operation: int value from AggOperation enum
        :type operation: int
        :return: downsampled data
        :rtype: 1d numpy array
        """
        if not isinstance (period, int):
            raise BrainFlowError ('wrong type for period', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if not isinstance (operation, int):
            raise BrainFlowError ('wrong type for operation', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if len (data.shape) != 1:
            raise BrainFlowError ('wrong shape for filter data array, it should be 1d array', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if period <= 0:
            raise BrainFlowError ('Invalid value for period', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)

        downsampled_data = numpy.zeros (int (data.shape[0] / period)).astype (numpy.float64)
        res = DataHandlerDLL.get_instance ().perform_downsampling (data, data.shape[0], period, operation, downsampled_data)
        if res != BrainflowExitCodes.STATUS_OK.value:
            raise BrainFlowError ('unable to perform downsampling', res)

        return downsampled_data

:type data: 1d numpy array
        :param sampling_rate: board's sampling rate
        :type sampling_rate: float
        :param cutoff: cutoff frequency
        :type cutoff: float
        :param order: filter order
        :type order: int
        :param filter_type: filter type from special enum
        :type filter_type: int
        :param ripple: ripple value for Chebyshev filter
        :type ripple: float
        """
        if not isinstance (sampling_rate, int):
            raise BrainFlowError ('wrong type for sampling rate', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if not isinstance (filter_type, int):
            raise BrainFlowError ('wrong type for filter type', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if len (data.shape) != 1:
            raise BrainFlowError ('wrong shape for filter data array, it should be 1d array', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        res = DataHandlerDLL.get_instance ().perform_highpass (data, data.shape[0], sampling_rate, cutoff, order, filter_type, ripple)
        if res != BrainflowExitCodes.STATUS_OK.value:
            raise BrainFlowError ('unable to apply high pass filter', res)

:type wavelet: str
        :param decomposition_level: level of decomposition
        :type decomposition_level: int
        :return: tuple of wavelet coeffs in format [A(J) D(J) D(J-1) ..... D(1)] where J is decomposition level, A - app coeffs, D - detailed coeffs, and array with lengths for each block
        :rtype: tuple
        """
        try:
            wavelet_func = wavelet.encode ()
        except:
            wavelet_func = wavelet

        wavelet_coeffs = numpy.zeros (data.shape[0] + 2 * (40 + 1)).astype (numpy.float64)
        lengths = numpy.zeros (decomposition_level + 1).astype (numpy.int32)
        res = DataHandlerDLL.get_instance ().perform_wavelet_transform (data, data.shape[0], wavelet_func, decomposition_level, wavelet_coeffs, lengths)
        if res != BrainflowExitCodes.STATUS_OK.value:
            raise BrainFlowError ('unable to perform wavelet transform', res)

        # we could return a tuple here but lets keep it like in other bindings
        return wavelet_coeffs[0: sum (lengths)], lengths

:type data: 1d numpy array
        :param sampling_rate: board's sampling rate
        :type sampling_rate: float
        :param cutoff: cutoff frequency
        :type cutoff: float
        :param order: filter order
        :type order: int
        :param filter_type: filter type from special enum
        :type filter_type: int
        :param ripple: ripple value for Chebyshev filter
        :type ripple: float
        """
        if not isinstance (sampling_rate, int):
            raise BrainFlowError ('wrong type for sampling rate', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if not isinstance (filter_type, int):
            raise BrainFlowError ('wrong type for filter type', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if len (data.shape) != 1:
            raise BrainFlowError ('wrong shape for filter data array, it should be 1d array', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        res = DataHandlerDLL.get_instance ().perform_lowpass (data, data.shape[0], sampling_rate, cutoff, order, filter_type, ripple)
        if res != BrainflowExitCodes.STATUS_OK.value:
            raise BrainFlowError ('unable to perform low pass filter', res)

def perform_rolling_filter (cls, data, period, operation):
        """smooth data using moving average or median

        :param data: data to smooth, it works in-place
        :type data: 1d numpy array
        :param period: window size
        :type period: int
        :param operation: int value from AggOperation enum
        :type operation: int
        """
        if not isinstance (period, int):
            raise BrainFlowError ('wrong type for period', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if not isinstance (operation, int):
            raise BrainFlowError ('wrong type for operation', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if len (data.shape) != 1:
            raise BrainFlowError ('wrong shape for filter data array, it should be 1d array', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        res = DataHandlerDLL.get_instance ().perform_rolling_filter (data, data.shape[0], period, operation)
        if res != BrainflowExitCodes.STATUS_OK.value:
            raise BrainFlowError ('unable to smooth data', res)

:param order: filter order
        :type order: int
        :param filter_type: filter type from special enum
        :type filter_type: int
        :param ripple: ripple value for Chebyshev filter
        :type ripple: float
        """
        if not isinstance (sampling_rate, int):
            raise BrainFlowError ('wrong type for sampling rate', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if not isinstance (filter_type, int):
            raise BrainFlowError ('wrong type for filter type', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if len (data.shape) != 1:
            raise BrainFlowError ('wrong shape for filter data array, it should be 1d array', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        res = DataHandlerDLL.get_instance ().perform_bandpass (data, data.shape[0], sampling_rate, center_freq, band_width, order, filter_type, ripple)
        if res != BrainflowExitCodes.STATUS_OK.value:
            raise BrainFlowError ('unable to apply band pass filter', res)

:type data: 1d numpy array
        :param sampling_rate: board's sampling rate
        :type sampling_rate: float
        :param center_freq: center frequency
        :type center_freq: float
        :param band_width: band width
        :type band_width: float
        :param order: filter order
        :type order: int
        :param filter_type: filter type from special enum
        :type filter_type: int
        :param ripple: ripple value for Chebyshev filter
        :type ripple: float
        """
        if not isinstance (sampling_rate, int):
            raise BrainFlowError ('wrong type for sampling rate', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if not isinstance (filter_type, int):
            raise BrainFlowError ('wrong type for filter type', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if len (data.shape) != 1:
            raise BrainFlowError ('wrong shape for filter data array, it should be 1d array', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        res = DataHandlerDLL.get_instance ().perform_bandstop (data, data.shape[0], sampling_rate, center_freq, band_width, order, filter_type, ripple)
        if res != BrainflowExitCodes.STATUS_OK.value:
            raise BrainFlowError ('unable to apply band stop filter', res)

:type data: 1d numpy array
        :param sampling_rate: board's sampling rate
        :type sampling_rate: float
        :param center_freq: center frequency
        :type center_freq: float
        :param band_width: band width
        :type band_width: float
        :param order: filter order
        :type order: int
        :param filter_type: filter type from special enum
        :type filter_type: int
        :param ripple: ripple value for Chebyshev filter
        :type ripple: float
        """
        if not isinstance (sampling_rate, int):
            raise BrainFlowError ('wrong type for sampling rate', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if not isinstance (filter_type, int):
            raise BrainFlowError ('wrong type for filter type', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        if len (data.shape) != 1:
            raise BrainFlowError ('wrong shape for filter data array, it should be 1d array', BrainflowExitCodes.INVALID_ARGUMENTS_ERROR.value)
        res = DataHandlerDLL.get_instance ().perform_bandpass (data, data.shape[0], sampling_rate, center_freq, band_width, order, filter_type, ripple)
        if res != BrainflowExitCodes.STATUS_OK.value:
            raise BrainFlowError ('unable to apply band pass filter', res)