Method add_energy

python/simulation.py:3122–3146

`add_energy(fcen, df, nfreq, freq, EnergyRegions, decimation_factor=0)` ##sig Add a bunch of `EnergyRegion`s to the current simulation (initializing the fields if they have not yet been initialized), telling Meep to accumulate the appropriate field Fourier transfor

(self, *args, **kwargs)

        )

    def add_energy(self, *args, **kwargs):
        """
        `add_energy(fcen, df, nfreq, freq, EnergyRegions, decimation_factor=0)`  ##sig

        Add a bunch of `EnergyRegion`s to the current simulation (initializing the fields
        if they have not yet been initialized), telling Meep to accumulate the appropriate
        field Fourier transforms for `nfreq` equally spaced frequencies covering the
        frequency range `fcen-df/2` to `fcen+df/2` or an array/list `freq` for arbitrarily
        spaced frequencies. Return an *energy object*, which you can pass to the functions
        below to get the energy spectrum, etcetera. To reduce the memory-bandwidth burden of
        accumulating DFT fields, an integer `decimation_factor` can be specified for updating the DFT
        fields at every `decimation_factor` timesteps. If `decimation_factor` is 0 (the default),
        this value is automatically determined from the
        [Nyquist rate](https://en.wikipedia.org/wiki/Nyquist_rate) of the bandwidth-limited
        sources and this DFT monitor. It can be turned off by setting it to 1. Use this feature
        with care, as the decimated timeseries may be corrupted by
        [aliasing](https://en.wikipedia.org/wiki/Aliasing) of high frequencies.
        """
        args = fix_dft_args(args, 0)
        freq = args[0]
        energys = args[1:]
        decimation_factor = kwargs.get("decimation_factor", 0)
        en = DftEnergy(self._add_energy, [freq, energys, decimation_factor])
        self.dft_objects.append(en)
        return en

    def _add_energy(self, freq, energys, decimation_factor):
        if self.fields is None: