diff --git a/metaprop_fibermode_doublet_sym.py b/metaprop_fibermode_doublet_sym.py index 1287107..602ef31 100644 --- a/metaprop_fibermode_doublet_sym.py +++ b/metaprop_fibermode_doublet_sym.py @@ -73,9 +73,9 @@ rho = np.sqrt(X**2 + Y**2) sampling_period = xs[1] - xs[0] -d0 = 535e-6 #propagation distance: source - MS1 [m] +d0 = 353e-6 #propagation distance: source - MS1 [m] d_source = d0 - lda0 / 2 #propagation distance: source - MS1 used for FDTD simulations [m] -d1 = 415e-6 #propagation distance: MS1 - MS2 [m] +d1 = 218e-6 #propagation distance: MS1 - MS2 [m] d2 = d0 #propagation distance: MS2 - target [m] phase_min = 0 @@ -539,8 +539,8 @@ def forward_propagate() -> None: intermediate_fields[1] = np.fft.fftshift(ifft_operator.output_array.copy()) end_time = time.time() - #make_2Dplot_of(intermediate_fields[0], choose_quantity="amplitude", save_name="intermediate_field_1_amplitude", plot_zoom_x=full_view_x, plot_zoom_y=full_view_y) - #make_2Dplot_of(intermediate_fields[1], choose_quantity="amplitude", save_name="intermediate_field_2_amplitude", plot_zoom_x=full_view_x, plot_zoom_y=full_view_y) + make_2Dplot_of(intermediate_fields[0], choose_quantity="amplitude", save_name="intermediate_field_1_amplitude", plot_zoom_x=zoom_output_fiber_radius, plot_zoom_y=zoom_output_fiber_radius) + make_2Dplot_of(intermediate_fields[1], choose_quantity="amplitude", save_name="intermediate_field_2_amplitude", plot_zoom_x=zoom_output_fiber_radius, plot_zoom_y=zoom_output_fiber_radius) print("Propagation finished in {:.6f} seconds.".format(end_time - start_time)) diff --git a/metasurface/full_meta_meep.ipynb b/metasurface/full_meta_meep.ipynb index 979d812..d2d55f6 100644 --- a/metasurface/full_meta_meep.ipynb +++ b/metasurface/full_meta_meep.ipynb @@ -369,21 +369,21 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "38796b96", "metadata": {}, "outputs": [], "source": [ "#far field points for projection monitor\n", "ff_resolution = 1\n", - "ff_plane_size = 30 #[um]\n", + "ff_plane_size = 19 #[um]\n", "\n", "xs_far = np.linspace(-ff_plane_size/2, ff_plane_size/2, ff_resolution * ff_plane_size)\n", "ys_far = np.linspace(-ff_plane_size/2, ff_plane_size/2, ff_resolution * ff_plane_size)\n", "\n", "X_far, Y_far = np.meshgrid(xs_far, ys_far)\n", "\n", - "projection_distance = h + d1 - lda0 / 2\n", + "projection_distance = d1 - np.abs(source_position.z)\n", "\n", "near_region_monitor = mp.Near2FarRegion(\n", " center=mp.Vector3(0, 0, 0),\n", @@ -502,19 +502,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "1fb2e049", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -524,7 +534,14 @@ "ax.set_ylabel('Y [um]')\n", "fig.colorbar(im, ax=ax)\n", "fig.show()\n", - "fig.savefig(\"magnitude_field_before_MS2.pdf\")" + "fig.savefig(\"magnitude_field_before_MS2.pdf\")\n", + "\n", + "#make a line plot that is a slice along the y=0 axis\n", + "fig, az = plt.subplots()\n", + "im = az.plot(xs_far, np.abs(ff_Ex[ff_resolution * ff_plane_size//2, :]))\n", + "az.set_xlabel('X [um]')\n", + "az.set_ylabel('Magnitude of E-field')\n", + "fig.show()" ] } ], diff --git a/metasurface/meep_fullmeta_propagation.py b/metasurface/meep_fullmeta_propagation.py index 856b1af..d4f530b 100644 --- a/metasurface/meep_fullmeta_propagation.py +++ b/metasurface/meep_fullmeta_propagation.py @@ -230,7 +230,7 @@ ys_far = np.linspace(-ff_plane_size/2, ff_plane_size/2, ff_resolution * ff_plane X_far, Y_far = np.meshgrid(xs_far, ys_far) -projection_distance = h + d1 - lda0 / 2 +projection_distance = d1 - np.abs(source_position.z) near_region_monitor = mp.Near2FarRegion( center=mp.Vector3(0, 0, 0), diff --git a/unit_cell/unit_cell_param_sweep_lineplot.pdf b/unit_cell/unit_cell_param_sweep_lineplot.pdf index ab53eae..57d3c22 100644 Binary files a/unit_cell/unit_cell_param_sweep_lineplot.pdf and b/unit_cell/unit_cell_param_sweep_lineplot.pdf differ diff --git a/unit_cell/unit_cell_plots.py b/unit_cell/unit_cell_plots.py index cc7ec1d..addb74e 100644 --- a/unit_cell/unit_cell_plots.py +++ b/unit_cell/unit_cell_plots.py @@ -2,15 +2,16 @@ import numpy as np import matplotlib.pyplot as plt #import the parameter sweep arrays for the central wavelength -diameters = np.load("unit_cell_center_1550_h900nm_res50_wvl_diameters.npy") -mode_wvl = np.load("unit_cell_center_1550_h900nm_res50_wvl_mode_wvl.npy") -mode_tran_arr = np.load("unit_cell_center_1550_h900nm_res50_wvl_mode_tran.npy") +diameters = np.load("unit_cell/unit_cell_center_1550_h900nm_res50_wvl_diameters.npy") +print(diameters) +mode_wvl = np.load("unit_cell/unit_cell_center_1550_h900nm_res50_wvl_mode_wvl.npy") +mode_tran_arr = np.load("unit_cell/unit_cell_center_1550_h900nm_res50_wvl_mode_tran.npy") print(mode_tran_arr) -mode_phase_arr = np.load("unit_cell_center_1550_h900nm_res50_wvl_mode_phase.npy") +mode_phase_arr = np.load("unit_cell/unit_cell_center_1550_h900nm_res50_wvl_mode_phase.npy") print(mode_wvl) unwrap_mode_phase_arr = np.abs(np.unwrap(mode_phase_arr - mode_phase_arr[0], period=2*np.pi)) #import the reference value array -ref_results = np.loadtxt("results_S21_p700nm_h900nm.txt", skiprows=5,dtype=complex) +ref_results = np.loadtxt("unit_cell/results_S21_p700nm_h900nm.txt", skiprows=5,dtype=complex) R_ref = np.real(ref_results[:,1]) S21_ref = ref_results[:,3] S21_ref_mag = np.abs(S21_ref) @@ -22,7 +23,7 @@ plt.figure() plt.subplot(1,2,1) plt.plot(1e3*diameters/2,mode_tran_arr, '^r-.', label='FDTD') plt.plot(R_ref,S21_ref_mag, '*k--',label='FEM') -plt.xlabel("Pillar Radius (um)") +plt.xlabel("Pillar Radius (nm)") plt.xticks([t for t in np.linspace(1e3*diameters[0]/2, 1e3*diameters[-1]/2, 5)]) plt.gca().set_xticklabels([f"{t:.0f}" for t in np.linspace(1e3*diameters[0]/2, 1e3*diameters[-1]/2, 5)]) plt.ylabel("Transmission") @@ -31,11 +32,11 @@ plt.legend() plt.subplot(1,2,2) plt.plot(1e3*diameters/2,unwrap_mode_phase_arr, '^r-.', label='FDTD') plt.plot(R_ref,S21_ref_phase, '*k--', label='FEM') -plt.xlabel("Pillar Radius (um)") +plt.xlabel("Pillar Radius (nm)") plt.xticks([t for t in np.linspace(1e3*diameters[0]/2, 1e3*diameters[-1]/2, 5)]) plt.gca().set_xticklabels([f"{t:.0f}" for t in np.linspace(1e3*diameters[0]/2, 1e3*diameters[-1]/2, 5)]) plt.ylabel("Phase (rad)") plt.legend() plt.subplots_adjust(wspace=0.5) -plt.savefig("unit_cell_param_sweep_lineplot.pdf") \ No newline at end of file +plt.savefig("unit_cell/unit_cell_param_sweep_lineplot.pdf") \ No newline at end of file