Пример #1
0
    def transfer_function(nu: np.ndarray, center_nu: float, nu_bw: float,
                          nu_offset: float = 0.):
        """The transfer function of the flat top window.

        Parameters
        ----------
        nu :
            The frequency components. :math:`[ps^{-1}]`
        center_nu :
            The center frequency. :math:`[ps^{-1}]`
        nu_bw :
            The spectral bandwith. :math:`[ps^{-1}]`
        nu_offset :
            The offset frequency. :math:`[ps^{-1}]`

        """
        window = FlatTopFilter.amplitude_transfer_function(nu, center_nu,
                                                           nu_bw, nu_offset)

        return Field.temporal_power(window)
Пример #2
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    def transfer_function(time: np.ndarray, v_pi: List[float],
                          v_bias: List[float],
                          v_mod: List[Union[float, Callable]]) -> np.ndarray:

        v_pi = util.make_list(v_pi, 2)
        v_bias = util.make_list(v_bias, 2)
        v_mod = util.make_list(v_mod, 2)
        v_mod_: List[Callable] = []
        for v in v_mod:
            if (callable(v)):
                v_mod_.append(v)
            else:
                v_mod_.append(lambda t: v)
        print(v_pi, v_bias, v_mod_)
        phase_shift = [
            lambda t: cst.PI * (v_bias[0] + v_mod_[0](t)) / v_pi[0],
            lambda t: cst.PI * (v_bias[1] + v_mod_[1](t)) / v_pi[1]
        ]
        tf = np.cos((phase_shift[0](time) - phase_shift[1](time)) / 2.)

        return Field.temporal_power(tf)
Пример #3
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    def __call__(self, domain: Domain, ports: List[int], fields: List[Field]
                 ) -> Tuple[List[int], List[Field]]:

        output_fields: List[Field] = []

        for field in fields:
            # Channels
            for i in range(len(field)):
                window = np.zeros(field[i].shape, dtype=complex)
                window = self.window(field.nu[i], self.center_nu, self.nu_bw,
                                     self.nu_offset)
                window_shift = FFT.ifftshift(window)
                field[i] = FFT.ifft(window_shift * FFT.fft(field[i]))
            # Noise
            if (self.NOISE):
                field.noise *= Field.temporal_power(self.window(
                                    domain.noise_nu, self.center_nu,
                                    self.nu_bw, self.nu_offset))
            output_fields.append(field)

        return self.output_ports(ports), output_fields
    def _calc_power_for_ratio(self, array: np.ndarray):

        return np.sum(Field.temporal_power(array), axis=1).reshape((-1, 1))
Пример #5
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    def get_criterion(self, waves_f, waves_b):
        criterion = np.sum(Field.temporal_power(waves_f))
        criterion += np.sum(Field.temporal_power(waves_b))

        return criterion
Пример #6
0
                               RS=False,
                               XPM=False,
                               ASYM=True,
                               COUP=True,
                               approx_type=1,
                               nlse_method='ssfm_super_sym',
                               steps=steps,
                               ode_method=method,
                               save=True,
                               wait=False,
                               NOISE=True)
        lt.add_link(pulse[0], coupler[0])
        lt.run(pulse)
        lt.reset()
        # Plot parameters and get waves
        x_datas.append(coupler[2][0].time)
        y_datas.append(Field.temporal_power(coupler[2][0].channels))
        plot_groups.append(0)

    line_labels.extend(ode_methods)
    plot_titles.extend(["ODE solvers test with n={}".format(str(steps))])
    # -------------------- Plotting results ------------------------
    plot.plot2d(x_datas,
                y_datas,
                plot_groups=plot_groups,
                plot_titles=plot_titles,
                x_labels=['t'],
                y_labels=['P_t'],
                line_labels=line_labels,
                line_opacities=[0.1])