diff --git a/.github/workflows/lint-notebooks.yml b/.github/workflows/lint-notebooks.yml new file mode 100644 index 00000000..7220984c --- /dev/null +++ b/.github/workflows/lint-notebooks.yml @@ -0,0 +1,27 @@ +name: "notebooks-linting" + +on: + workflow_dispatch: + push: + branches: [ main, develop ] + pull_request: + branches: [ main, develop ] + +jobs: + lint: + name: Run notebook linting + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 1 + + - uses: astral-sh/ruff-action@v3 + with: + version: 0.5.5 + + - name: Run ruff format + run: ruff format --check --diff . + + - name: Run ruff check + run: ruff check . diff --git a/8ChannelDemultiplexer.ipynb b/8ChannelDemultiplexer.ipynb index 603de6c4..ae17ce22 100644 --- a/8ChannelDemultiplexer.ipynb +++ b/8ChannelDemultiplexer.ipynb @@ -41,14 +41,13 @@ }, "outputs": [], "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.gridspec import GridSpec\n", "import gdstk\n", - "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "import tidy3d as td\n", "import tidy3d.web as web\n", - "from tidy3d.plugins.mode import ModeSolver\n" + "from matplotlib.gridspec import GridSpec\n", + "from tidy3d.plugins.mode import ModeSolver" ] }, { @@ -70,7 +69,7 @@ }, "outputs": [], "source": [ - "td.config.logging_level = \"ERROR\"\n" + "td.config.logging_level = \"ERROR\"" ] }, { @@ -108,7 +107,7 @@ "freq0 = td.C_0 / lda0 # corresponding central frequency\n", "freqs = td.C_0 / ldas # corresponding frequency range\n", "\n", - "fwidth = 0.5 * (np.max(freqs) - np.min(freqs)) # width of the excitation spectrum\n" + "fwidth = 0.5 * (np.max(freqs) - np.min(freqs)) # width of the excitation spectrum" ] }, { @@ -132,7 +131,7 @@ "si = td.Medium(permittivity=n_si**2)\n", "\n", "n_sio2 = 1.44 # silicon oxide refractive index\n", - "sio2 = td.Medium(permittivity=n_sio2**2)\n" + "sio2 = td.Medium(permittivity=n_sio2**2)" ] }, { @@ -155,7 +154,7 @@ "outputs": [], "source": [ "h = 0.22 # waveguide thickness\n", - "ws = np.linspace(0.3, 2.5, 30) # range of waveguide width\n" + "ws = np.linspace(0.3, 2.5, 30) # range of waveguide width" ] }, { @@ -184,7 +183,7 @@ "grid_spec = td.GridSpec.auto(min_steps_per_wvl=30, wavelength=lda0)\n", "\n", "# define boundary spec\n", - "bound_spec = td.BoundarySpec.all_sides(boundary=td.PML())\n" + "bound_spec = td.BoundarySpec.all_sides(boundary=td.PML())" ] }, { @@ -210,11 +209,8 @@ "\n", "# loop over the waveguide width and compute the effective indices at each iteration\n", "for i, w in enumerate(ws):\n", - "\n", " # define the waveguide structure\n", - " waveguide = td.Structure(\n", - " geometry=td.Box(center=(0, 0, 0), size=(w, td.inf, h)), medium=si\n", - " )\n", + " waveguide = td.Structure(geometry=td.Box(center=(0, 0, 0), size=(w, td.inf, h)), medium=si)\n", "\n", " sim_size = (6 * w, 1, 8 * h) # simulation domain size\n", " mode_size = (6 * w, 0, 8 * h) # mode plane size\n", @@ -244,7 +240,7 @@ " mode_data = mode_solver.solve()\n", "\n", " # obtain the effective indices\n", - " n_eff[i] = mode_data.n_eff.values\n" + " n_eff[i] = mode_data.n_eff.values" ] }, { @@ -281,9 +277,9 @@ "\n", "plt.ylim(1.6, 3)\n", "plt.legend((\"TE0\", \"TE1\", \"TE2\", \"TE3\"))\n", - "plt.xlabel(\"Waveguide width ($\\mu m$)\")\n", + "plt.xlabel(r\"Waveguide width ($\\mu m$)\")\n", "plt.ylabel(\"Effective index\")\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -304,11 +300,8 @@ "outputs": [], "source": [ "for i, w in enumerate(ws):\n", + " waveguide = td.Structure(geometry=td.Box(center=(0, 0, 0), size=(w, td.inf, h)), medium=si)\n", "\n", - " waveguide = td.Structure(\n", - " geometry=td.Box(center=(0, 0, 0), size=(w, td.inf, h)), medium=si\n", - " )\n", - " \n", " sim_size = (6 * w, 1, 8 * h) # simulation domain size\n", " mode_size = (6 * w, 0, 8 * h) # mode plane size\n", "\n", @@ -333,7 +326,7 @@ "\n", " mode_data = mode_solver.solve()\n", "\n", - " n_eff[i] = mode_data.n_eff.values\n" + " n_eff[i] = mode_data.n_eff.values" ] }, { @@ -361,9 +354,9 @@ "\n", "plt.ylim(1.6, 2.2)\n", "plt.legend((\"TM0\", \"TM1\", \"TM2\", \"TM3\"))\n", - "plt.xlabel(\"Waveguide width ($\\mu m$)\")\n", + "plt.xlabel(r\"Waveguide width ($\\mu m$)\")\n", "plt.ylabel(\"Effective index\")\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -409,7 +402,7 @@ "metadata": {}, "outputs": [], "source": [ - "td.config.logging_level = \"WARNING\"\n" + "td.config.logging_level = \"WARNING\"" ] }, { @@ -438,7 +431,7 @@ "outputs": [], "source": [ "Bx = 10 # horizontal length of the waveguide bend\n", - "By = 1 # verticel length of the waveguide bend\n" + "By = 1 # verticel length of the waveguide bend" ] }, { @@ -459,16 +452,13 @@ "outputs": [], "source": [ "def make_sim(pol, w_access, w_bus, gap, l_couple):\n", - "\n", " # construct the access waveguide including the bends\n", " y = By + (w_access + w_bus) / 2 + gap\n", " access_wg = gdstk.RobustPath(\n", " (-3 * l_couple, y), w_access, simple_path=True, layer=1, datatype=0\n", " )\n", " access_wg.segment((-l_couple / 2 - Bx, y))\n", - " access_wg.segment(\n", - " (-l_couple / 2, y), offset=lambda u: (np.cos(u * np.pi) - 1) * By / 2\n", - " )\n", + " access_wg.segment((-l_couple / 2, y), offset=lambda u: (np.cos(u * np.pi) - 1) * By / 2)\n", " access_wg.segment((l_couple / 2, y))\n", " access_wg.segment(\n", " (l_couple / 2 + Bx, y), offset=lambda u: (np.cos((1 - u) * np.pi) - 1) * By / 2\n", @@ -476,9 +466,7 @@ " access_wg.segment((3 * l_couple, y))\n", "\n", " # construct the bus waveguide\n", - " bus_wg = gdstk.FlexPath(\n", - " [(-3 * l_couple, 0), (3 * l_couple, 0)], w_bus, layer=1, datatype=1\n", - " )\n", + " bus_wg = gdstk.FlexPath([(-3 * l_couple, 0), (3 * l_couple, 0)], w_bus, layer=1, datatype=1)\n", "\n", " # define a cell\n", " cell = gdstk.Cell(\"directional_coupler\")\n", @@ -513,7 +501,7 @@ " else:\n", " symmetry = symmetry = (0, 0, 0)\n", "\n", - " # define a mode source to lauch either te0 or tm0 mode to the access waveguide\n", + " # define a mode source to launch either te0 or tm0 mode to the access waveguide\n", " mode_source = td.ModeSource(\n", " center=(-Lx / 2 + lda0 / 2, y_in, 0),\n", " size=(0, 6 * w_access, 8 * h),\n", @@ -562,7 +550,7 @@ " symmetry=symmetry,\n", " )\n", "\n", - " return sim\n" + " return sim" ] }, { @@ -589,7 +577,7 @@ " \"TE1\": {\"w_access\": 0.4, \"w_bus\": 0.835, \"gap\": 0.2, \"l_couple\": 15.5},\n", " \"TE2\": {\"w_access\": 0.406, \"w_bus\": 1.29, \"gap\": 0.2, \"l_couple\": 21.3},\n", " \"TE3\": {\"w_access\": 0.379, \"w_bus\": 1.631, \"gap\": 0.2, \"l_couple\": 17.6},\n", - "}\n" + "}" ] }, { @@ -597,7 +585,7 @@ "id": "7342b080", "metadata": {}, "source": [ - "### TE0 to TE3 Convertion " + "### TE0 to TE3 Conversion " ] }, { @@ -647,7 +635,7 @@ "sim = make_sim(\"TE\", **design_params[\"TE3\"])\n", "\n", "ax = sim.plot(z=0)\n", - "ax.set_aspect(\"auto\")\n" + "ax.set_aspect(\"auto\")" ] }, { @@ -788,7 +776,7 @@ ")\n", "\n", "mode_data = mode_solver.solve()\n", - "mode_data.to_dataframe()\n" + "mode_data.to_dataframe()" ] }, { @@ -824,10 +812,8 @@ "f, ax = plt.subplots(4, 1, tight_layout=True, figsize=(5, 8))\n", "\n", "for i, mode_index in enumerate(mode_indices):\n", - " abs(mode_data.Ey.isel(mode_index=mode_index)).plot(\n", - " x=\"y\", y=\"z\", ax=ax[i], cmap=\"magma\"\n", - " )\n", - " ax[i].set_title(f\"|Ey(x, y)| of the TE{i} mode\")\n" + " abs(mode_data.Ey.isel(mode_index=mode_index)).plot(x=\"y\", y=\"z\", ax=ax[i], cmap=\"magma\")\n", + " ax[i].set_title(f\"|Ey(x, y)| of the TE{i} mode\")" ] }, { @@ -1198,7 +1184,7 @@ ], "source": [ "job = web.Job(simulation=sim, task_name=\"evanescent_coupler_te3\", verbose=True)\n", - "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n" + "sim_data = job.run(path=\"data/simulation_data.hdf5\")" ] }, { @@ -1234,9 +1220,7 @@ " ax2 = fig.add_subplot(gs[1, 0])\n", " ax3 = fig.add_subplot(gs[1, 1])\n", "\n", - " sim_data.plot_field(\n", - " field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\", f=freq0, ax=ax1\n", - " )\n", + " sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\", f=freq0, ax=ax1)\n", " ax1.set_aspect(\"auto\")\n", "\n", " T_bus = sim_data[\"bus_flux\"].flux\n", @@ -1244,16 +1228,16 @@ " ax2.plot(ldas, T_bus)\n", " ax2.set_xlim(1.5, 1.6)\n", " ax2.set_ylim(0, 1)\n", - " ax2.set_xlabel(\"Wavelength ($\\mu$m)\")\n", + " ax2.set_xlabel(r\"Wavelength ($\\mu$m)\")\n", " ax2.set_ylabel(\"Transmission to bus waveguide\")\n", "\n", " mode_amp = sim_data[\"bus_mode\"].amps.sel(direction=\"+\")\n", " mode_power = np.abs(mode_amp) ** 2 / T_bus\n", " ax3.plot(ldas, mode_power)\n", " ax3.set_xlim(1.5, 1.6)\n", - " ax3.set_xlabel(\"Wavelength ($\\mu$m)\")\n", + " ax3.set_xlabel(r\"Wavelength ($\\mu$m)\")\n", " ax3.set_ylabel(\"Mode fraction\")\n", - " ax3.legend((f\"{pol}0\", f\"{pol}1\", f\"{pol}2\", f\"{pol}3\"))\n" + " ax3.legend((f\"{pol}0\", f\"{pol}1\", f\"{pol}2\", f\"{pol}3\"))" ] }, { @@ -1284,7 +1268,7 @@ } ], "source": [ - "postprocess(sim_data, \"TE\")\n" + "postprocess(sim_data, \"TE\")" ] }, { @@ -1292,7 +1276,7 @@ "id": "d17da4e0", "metadata": {}, "source": [ - "### TE0 to TE2 Convertion " + "### TE0 to TE2 Conversion " ] }, { @@ -1644,7 +1628,7 @@ "sim = make_sim(\"TE\", **design_params[\"TE2\"])\n", "\n", "job = web.Job(simulation=sim, task_name=\"evanescent_coupler_te2\", verbose=True)\n", - "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n" + "sim_data = job.run(path=\"data/simulation_data.hdf5\")" ] }, { @@ -1665,7 +1649,7 @@ } ], "source": [ - "postprocess(sim_data, \"TE\")\n" + "postprocess(sim_data, \"TE\")" ] }, { @@ -1673,7 +1657,7 @@ "id": "d2936650", "metadata": {}, "source": [ - "### TE0 to TE1 Convertion " + "### TE0 to TE1 Conversion " ] }, { @@ -2004,7 +1988,7 @@ "sim = make_sim(\"TE\", **design_params[\"TE1\"])\n", "\n", "job = web.Job(simulation=sim, task_name=\"evanescent_coupler_te1\", verbose=True)\n", - "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n" + "sim_data = job.run(path=\"data/simulation_data.hdf5\")" ] }, { @@ -2025,7 +2009,7 @@ } ], "source": [ - "postprocess(sim_data, \"TE\")\n" + "postprocess(sim_data, \"TE\")" ] }, { @@ -2033,7 +2017,7 @@ "id": "2c969833", "metadata": {}, "source": [ - "### TM0 to TM3 Convertion " + "### TM0 to TM3 Conversion " ] }, { @@ -2364,7 +2348,7 @@ "sim = make_sim(\"TM\", **design_params[\"TM3\"])\n", "\n", "job = web.Job(simulation=sim, task_name=\"evanescent_coupler_tm3\", verbose=True)\n", - "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n" + "sim_data = job.run(path=\"data/simulation_data.hdf5\")" ] }, { @@ -2385,7 +2369,7 @@ } ], "source": [ - "postprocess(sim_data, \"TM\")\n" + "postprocess(sim_data, \"TM\")" ] }, { @@ -2393,7 +2377,7 @@ "id": "27391958", "metadata": {}, "source": [ - "### TM0 to TM2 Convertion " + "### TM0 to TM2 Conversion " ] }, { @@ -2737,7 +2721,7 @@ "sim = make_sim(\"TM\", **design_params[\"TM2\"])\n", "\n", "job = web.Job(simulation=sim, task_name=\"evanescent_coupler_tm2\", verbose=True)\n", - "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n" + "sim_data = job.run(path=\"data/simulation_data.hdf5\")" ] }, { @@ -2758,7 +2742,7 @@ } ], "source": [ - "postprocess(sim_data, \"TM\")\n" + "postprocess(sim_data, \"TM\")" ] }, { @@ -2766,7 +2750,7 @@ "id": "e901c1cd", "metadata": {}, "source": [ - "### TM0 to TM1 Convertion " + "### TM0 to TM1 Conversion " ] }, { @@ -3110,7 +3094,7 @@ "sim = make_sim(\"TM\", **design_params[\"TM1\"])\n", "\n", "job = web.Job(simulation=sim, task_name=\"evanescent_coupler_tm1\", verbose=True)\n", - "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n" + "sim_data = job.run(path=\"data/simulation_data.hdf5\")" ] }, { @@ -3131,7 +3115,7 @@ } ], "source": [ - "postprocess(sim_data, \"TM\")\n" + "postprocess(sim_data, \"TM\")" ] }, { @@ -3166,7 +3150,7 @@ "gds_path = \"misc/8ChannelDemultiplexer.gds\" # path of the gds file\n", "\n", "lib = gdstk.read_gds(infile=gds_path) # import gds file\n", - "cell = lib.cells[0] # read cell\n" + "cell = lib.cells[0] # read cell" ] }, { @@ -3189,7 +3173,7 @@ " gds_layer=0,\n", " axis=2,\n", " slab_bounds=(-h / 2, h / 2),\n", - ")\n" + ")" ] }, { @@ -3214,7 +3198,7 @@ " geometry=s,\n", " medium=si,\n", " )\n", - " )\n" + " )" ] }, { @@ -3248,7 +3232,7 @@ " s.plot(z=0, ax=ax)\n", "ax.set_ylim(-20, 20)\n", "ax.set_xlim(-194, 6)\n", - "ax.set_aspect(\"auto\")\n" + "ax.set_aspect(\"auto\")" ] }, { @@ -3306,7 +3290,7 @@ " boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n", " medium=sio2,\n", " symmetry=(0, 0, -1),\n", - ")\n" + ")" ] }, { @@ -3336,7 +3320,7 @@ ], "source": [ "ax = sim.plot(z=0)\n", - "ax.set_aspect(\"auto\")\n" + "ax.set_aspect(\"auto\")" ] }, { @@ -3467,8 +3451,7 @@ ], "source": [ "job = web.Job(simulation=sim, task_name=\"8_channel_demultiplexer_I7\", verbose=True)\n", - "estimated_cost = web.estimate_cost(job.task_id)\n", - "\n" + "estimated_cost = web.estimate_cost(job.task_id)" ] }, { @@ -3751,7 +3734,7 @@ "ax = sim_data.plot_field(\n", " field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\", f=freq0, vmin=0, vmax=1000\n", ")\n", - "ax.set_aspect(\"auto\")\n" + "ax.set_aspect(\"auto\")" ] }, { @@ -3782,14 +3765,14 @@ "source": [ "for index in range(4):\n", " amp = sim_data[\"bus_mode\"].amps.sel(mode_index=index, direction=\"+\")\n", - " T = np.abs(amp)**2\n", - " plt.plot(ldas, T, label=f'TM{index}')\n", + " T = np.abs(amp) ** 2\n", + " plt.plot(ldas, T, label=f\"TM{index}\")\n", " plt.xlim(1.5, 1.6)\n", " plt.ylim(0, 1)\n", - " plt.xlabel(\"Wavelength ($\\mu$m)\")\n", + " plt.xlabel(r\"Wavelength ($\\mu$m)\")\n", " plt.ylabel(\"Transmission to bus waveguide\")\n", "plt.legend()\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -3829,16 +3812,18 @@ ")\n", "\n", "# define a simulation by copying the previous simulation and updating a few things\n", - "sim = sim.copy(update={\n", - " 'size':(200, 15, 10 * h),\n", - " 'center':(-94, 2.5, 0),\n", - " 'symmetry':(0, 0, 1),\n", - " 'sources':[mode_source],\n", - " })\n", + "sim = sim.copy(\n", + " update={\n", + " \"size\": (200, 15, 10 * h),\n", + " \"center\": (-94, 2.5, 0),\n", + " \"symmetry\": (0, 0, 1),\n", + " \"sources\": [mode_source],\n", + " }\n", + ")\n", "\n", "\n", "ax = sim.plot(z=0)\n", - "ax.set_aspect(\"auto\")\n" + "ax.set_aspect(\"auto\")" ] }, { @@ -4180,7 +4165,7 @@ ], "source": [ "job = web.Job(simulation=sim, task_name=\"8_channel_demultiplexer_I3\", verbose=True)\n", - "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n" + "sim_data = job.run(path=\"data/simulation_data.hdf5\")" ] }, { @@ -4212,7 +4197,7 @@ "ax = sim_data.plot_field(\n", " field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\", f=freq0, vmin=0, vmax=2000\n", ")\n", - "ax.set_aspect(\"auto\")\n" + "ax.set_aspect(\"auto\")" ] }, { @@ -4243,11 +4228,11 @@ "source": [ "for index in range(4):\n", " amp = sim_data[\"bus_mode\"].amps.sel(mode_index=index, direction=\"+\")\n", - " T = np.abs(amp)**2\n", - " plt.plot(ldas, T, label=f'TE{index}')\n", + " T = np.abs(amp) ** 2\n", + " plt.plot(ldas, T, label=f\"TE{index}\")\n", " plt.xlim(1.5, 1.6)\n", " plt.ylim(0, 1)\n", - " plt.xlabel(\"Wavelength ($\\mu$m)\")\n", + " plt.xlabel(r\"Wavelength ($\\mu$m)\")\n", " plt.ylabel(\"Transmission to bus waveguide\")\n", "plt.legend()\n", "plt.show()" diff --git a/90BendPolarizationSplitterRotator.ipynb b/90BendPolarizationSplitterRotator.ipynb index 0698ea06..737badcf 100644 --- a/90BendPolarizationSplitterRotator.ipynb +++ b/90BendPolarizationSplitterRotator.ipynb @@ -29,10 +29,9 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", "import gdstk\n", - "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "import tidy3d as td\n", "import tidy3d.web as web\n", "from tidy3d.plugins.mode import ModeSolver\n", @@ -317,7 +316,7 @@ "id": "a01abde4", "metadata": {}, "source": [ - "Define a Tidy3D simulation. Rememeber to set `medium=sio2`, which makes the background medium silicon oxide instead of air. To ensure good accuracy, we use an automatic nonuniform grid with `min_steps_per_wvl=20`." + "Define a Tidy3D simulation. Remember to set `medium=sio2`, which makes the background medium silicon oxide instead of air. To ensure good accuracy, we use an automatic nonuniform grid with `min_steps_per_wvl=20`." ] }, { @@ -11968,7 +11967,7 @@ "plt.plot(ldas, OPL_inner[:, 1], linewidth=3, label=\"TM mode on inner bend\")\n", "plt.legend()\n", "plt.ylim(16, 30)\n", - "plt.xlabel(\"Wavelength ($\\mu m$)\")\n", + "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n", "plt.ylabel(\"OPL (a.u.)\")\n", "plt.show()" ] @@ -12442,7 +12441,7 @@ " ldas, 10 * np.log10(T_through_tm), linewidth=3, linestyle=\"--\", label=f\"{pol}->TM (through)\"\n", " )\n", " plt.legend()\n", - " plt.xlabel(\"Wavelength ($\\mu m$)\")\n", + " plt.xlabel(r\"Wavelength ($\\mu m$)\")\n", " plt.ylabel(\"Mode conversion efficiency (dB)\")\n", " plt.title(f\"Mode conversion efficiency for {pol} mode\")\n", " plt.show()\n", @@ -12917,7 +12916,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.11.0" }, "title": "PSR based on 90 degree bends | Flexcompute" }, diff --git a/90OpticalHybrid.ipynb b/90OpticalHybrid.ipynb index 15e3f365..47f5992b 100644 --- a/90OpticalHybrid.ipynb +++ b/90OpticalHybrid.ipynb @@ -40,11 +40,10 @@ }, "outputs": [], "source": [ - "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "\n", + "import numpy as np\n", "import tidy3d as td\n", - "import tidy3d.web as web\n" + "import tidy3d.web as web" ] }, { @@ -85,7 +84,7 @@ "freq0 = td.C_0 / lda0 # central frequency\n", "ldas = np.linspace(1.53, 1.56, 101) # wavelength range\n", "freqs = td.C_0 / ldas # frequency range\n", - "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))\n" + "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))" ] }, { @@ -114,7 +113,7 @@ "si = td.Medium(permittivity=n_si**2)\n", "\n", "n_sio2 = 1.45 # silicon oxide refractive index\n", - "sio2 = td.Medium(permittivity=n_sio2**2)\n" + "sio2 = td.Medium(permittivity=n_sio2**2)" ] }, { @@ -140,7 +139,7 @@ "outputs": [], "source": [ "thickness = 0.22 # si layer thickness\n", - "width = 0.5 # waveguide width\n" + "width = 0.5 # waveguide width" ] }, { @@ -171,7 +170,7 @@ ")\n", "\n", "# define mmi structure\n", - "mmi = td.Structure(geometry=mmi_geometry, medium=si)\n" + "mmi = td.Structure(geometry=mmi_geometry, medium=si)" ] }, { @@ -245,7 +244,7 @@ " boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n", " medium=sio2,\n", " symmetry=(0, 0, 1),\n", - ")\n" + ")" ] }, { @@ -281,7 +280,7 @@ } ], "source": [ - "ax = sim.plot(z=0)\n" + "ax = sim.plot(z=0)" ] }, { @@ -654,7 +653,7 @@ ], "source": [ "job = web.Job(simulation=sim, task_name=\"mmi\", verbose=True)\n", - "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n" + "sim_data = job.run(path=\"data/simulation_data.hdf5\")" ] }, { @@ -691,7 +690,7 @@ ], "source": [ "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\", vmax=2000)\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -737,11 +736,11 @@ "# plot loss\n", "plt.plot(ldas, -10 * np.log10(P_through), label=\"through port\")\n", "plt.plot(ldas, -10 * np.log10(P_cross), label=\"cross port\")\n", - "plt.xlabel(\"Wavelength ($\\mu m$)\")\n", + "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n", "plt.ylabel(\"Insertion loss (dB)\")\n", "plt.ylim(2.5, 4)\n", "plt.legend()\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -786,10 +785,10 @@ "\n", "# plot phase difference\n", "plt.plot(ldas, delta_phase)\n", - "plt.xlabel(\"Wavelength ($\\mu m$)\")\n", + "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n", "plt.ylabel(\"Phase difference\")\n", "plt.ylim(85, 95)\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -828,7 +827,7 @@ ")\n", "\n", "# define optical hybrid structure\n", - "optical_hybrid = td.Structure(geometry=optical_hybrid_geometry, medium=si)\n" + "optical_hybrid = td.Structure(geometry=optical_hybrid_geometry, medium=si)" ] }, { @@ -896,7 +895,7 @@ " freqs=freqs,\n", " name=\"port_Ip\",\n", " mode_spec=mode_spec,\n", - ")\n" + ")" ] }, { @@ -941,7 +940,7 @@ " boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n", " medium=sio2,\n", " symmetry=(0, 0, 1),\n", - ")\n" + ")" ] }, { @@ -978,7 +977,7 @@ ], "source": [ "sim.plot(z=0)\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -1351,7 +1350,7 @@ ], "source": [ "job = web.Job(simulation=sim, task_name=\"optical_hybrid_lo_input\", verbose=True)\n", - "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n" + "sim_data = job.run(path=\"data/simulation_data.hdf5\")" ] }, { @@ -1388,7 +1387,7 @@ ], "source": [ "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\", vmax=1500)\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -1434,7 +1433,7 @@ " # compute and plot insertion loss\n", " loss = -10 * np.log10(P_Ip + P_In + P_Qp + P_Qn)\n", " ax[0].plot(ldas, loss)\n", - " ax[0].set_xlabel(\"Wavelength ($\\mu m$)\")\n", + " ax[0].set_xlabel(r\"Wavelength ($\\mu m$)\")\n", " ax[0].set_ylabel(\"Insertion loss (dB)\")\n", " ax[0].set_ylim(0, 1)\n", "\n", @@ -1443,7 +1442,7 @@ " CMRR_Q = -20 * np.log10(np.abs((P_Qp - P_Qn) / (P_Qp + P_Qn)))\n", " ax[1].plot(ldas, CMRR_I, label=\"CMRR$_I$\")\n", " ax[1].plot(ldas, CMRR_Q, label=\"CMRR$_Q$\")\n", - " ax[1].set_xlabel(\"Wavelength ($\\mu m$)\")\n", + " ax[1].set_xlabel(r\"Wavelength ($\\mu m$)\")\n", " ax[1].set_ylabel(\"CMRR (dB)\")\n", " ax[1].set_ylim(25, 45)\n", " ax[1].legend()\n", @@ -1453,10 +1452,10 @@ " Imbalance_Q = -10 * np.log10(P_Qp / P_Qn)\n", " ax[2].plot(ldas, Imbalance_I, label=\"Imbalance$_I$\")\n", " ax[2].plot(ldas, Imbalance_Q, label=\"Imbalance$_Q$\")\n", - " ax[2].set_xlabel(\"Wavelength ($\\mu m$)\")\n", + " ax[2].set_xlabel(r\"Wavelength ($\\mu m$)\")\n", " ax[2].set_ylabel(\"Imbalance (dB)\")\n", " ax[2].set_ylim(-0.5, 0.5)\n", - " ax[2].legend()\n" + " ax[2].legend()" ] }, { @@ -1494,7 +1493,7 @@ } ], "source": [ - "calculate_FOM(sim_data)\n" + "calculate_FOM(sim_data)" ] }, { @@ -1551,7 +1550,7 @@ "# copy simulation and change the source\n", "sim = sim.copy(update={\"sources\": [mode_source_signal]})\n", "sim.plot(z=0)\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -1916,7 +1915,7 @@ ], "source": [ "job = web.Job(simulation=sim, task_name=\"optical_hybrid_signal_input\", verbose=True)\n", - "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n" + "sim_data = job.run(path=\"data/simulation_data.hdf5\")" ] }, { @@ -1953,7 +1952,7 @@ ], "source": [ "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\", vmax=1500)\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -1989,7 +1988,7 @@ } ], "source": [ - "calculate_FOM(sim_data)\n" + "calculate_FOM(sim_data)" ] }, { diff --git a/AbsorbingBoundaryReflection.ipynb b/AbsorbingBoundaryReflection.ipynb index a0fe8373..33223571 100644 --- a/AbsorbingBoundaryReflection.ipynb +++ b/AbsorbingBoundaryReflection.ipynb @@ -23,9 +23,8 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "\n", + "import numpy as np\n", "import tidy3d as td\n", "import tidy3d.web as web" ] @@ -458,7 +457,6 @@ "\n", "# Iterate over each number of absorber layers in the list\n", "for num_layers in num_layers_list:\n", - "\n", " # Calculate the reflection values by taking the absolute square of the mode amplitude values from the simulation results\n", " reflection = (\n", " np.abs(batch_results[f\"{num_layers} layers\"][\"modemonitor\"].amps.sel(direction=\"-\").values)\n", @@ -539,7 +537,7 @@ "source": [ "### Reflection as a Function of Grid Resolution\n", "\n", - "Next we are going to test how the reflection depends on the grid resolution and thus the physical layer thickness of the absorber region. Simiarly we perform a parameter sweep from 10 to 30 minimum steps per wavelength. " + "Next we are going to test how the reflection depends on the grid resolution and thus the physical layer thickness of the absorber region. Similarly we perform a parameter sweep from 10 to 30 minimum steps per wavelength. " ] }, { @@ -730,7 +728,6 @@ "\n", "# Iterate over each grid_resolution in the list\n", "for grid_resolution in grid_resolution_list:\n", - "\n", " # Calculate the reflection values by taking the absolute square of the mode amplitude values from the simulation results\n", " reflection = (\n", " np.abs(\n", @@ -950,7 +947,6 @@ "colormap = cm.viridis # Use the 'viridis' colormap for plotting\n", "\n", "for num_layers in num_layers_list:\n", - "\n", " reflection = (\n", " np.abs(batch_results[f\"{num_layers} layers\"][\"modemonitor\"].amps.sel(direction=\"-\").values)\n", " ** 2\n", @@ -971,7 +967,7 @@ "id": "cc3f7c92-c409-437e-8285-bda71af615f3", "metadata": {}, "source": [ - "For PML, the reflection shouldn't depend noticably on the grid resolution so we can skip the corresponding test here." + "For PML, the reflection shouldn't depend noticeably on the grid resolution so we can skip the corresponding test here." ] }, { diff --git a/AdiabaticCouplerLN.ipynb b/AdiabaticCouplerLN.ipynb index d3b4051f..d929dde3 100644 --- a/AdiabaticCouplerLN.ipynb +++ b/AdiabaticCouplerLN.ipynb @@ -33,12 +33,11 @@ "metadata": {}, "outputs": [], "source": [ - "import tidy3d as td\n", - "import tidy3d.web as web\n", - "\n", - "import numpy as np\n", + "import gdstk\n", "import matplotlib.pyplot as plt\n", - "import gdstk" + "import numpy as np\n", + "import tidy3d as td\n", + "import tidy3d.web as web" ] }, { @@ -904,7 +903,7 @@ "\n", "plt.plot(ldas, T_te, c=\"blue\", label=\"TE\", linewidth=3)\n", "plt.plot(ldas, T_tm, \"--\", c=\"red\", label=\"TM\", linewidth=3)\n", - "plt.xlabel(\"Wavelength ($\\mu m$)\")\n", + "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n", "plt.ylabel(\"Coupling efficiency\")\n", "plt.ylim(0.5, 1.05)\n", "plt.legend()\n", diff --git a/AdjointPlugin0Quickstart.ipynb b/AdjointPlugin0Quickstart.ipynb index b15ed495..b35571c1 100644 --- a/AdjointPlugin0Quickstart.ipynb +++ b/AdjointPlugin0Quickstart.ipynb @@ -47,13 +47,12 @@ }, "outputs": [], "source": [ - "import tidy3d as td\n", - "import tidy3d.plugins.adjoint as tda\n", - "import matplotlib.pylab as plt\n", - "\n", "import jax\n", "import jax.numpy as jnp\n", - "import optax" + "import matplotlib.pylab as plt\n", + "import optax\n", + "import tidy3d as td\n", + "import tidy3d.plugins.adjoint as tda" ] }, { @@ -83,7 +82,7 @@ "eps_box = 2\n", "\n", "# size of sim in x,y,z\n", - "L = 10 * wavelength \n", + "L = 10 * wavelength\n", "\n", "# spc between sources, monitors, and PML / box\n", "buffer = 1.0 * wavelength" @@ -100,7 +99,7 @@ "source": [ "# create a source to the left of sim\n", "source = td.PointDipole(\n", - " center=(-L/2 + buffer, 0, 0),\n", + " center=(-L / 2 + buffer, 0, 0),\n", " source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 10.0),\n", " polarization=\"Ez\",\n", ")" @@ -117,12 +116,12 @@ "source": [ "# create a monitor to right of sim for measuring intensity\n", "monitor = td.FieldMonitor(\n", - " center=(+L/2 - buffer, 0, 0),\n", + " center=(+L / 2 - buffer, 0, 0),\n", " size=(0, 0, 0),\n", " freqs=[freq0],\n", " name=\"point\",\n", " colocate=False,\n", - ")\n" + ")" ] }, { @@ -142,7 +141,7 @@ " sources=[source],\n", " output_monitors=[monitor],\n", " monitors=[],\n", - " run_time=120/freq0,\n", + " run_time=120 / freq0,\n", ")" ] }, @@ -170,6 +169,7 @@ "size_min = 0\n", "size_max = L - 4 * buffer\n", "\n", + "\n", "def get_size(param: float):\n", " \"\"\"Size of box as function of parameter, smoothly maps (-inf, inf) to (size_min, size_max).\"\"\"\n", " param_01 = 0.5 * (jnp.tanh(param) + 1)\n", @@ -187,6 +187,7 @@ "source": [ "# function to construct the simulation as a function of the design parameter\n", "\n", + "\n", "def make_sim(param: float) -> float:\n", " \"\"\"Make simulation with a Box added, as given by the design parameter.\"\"\"\n", "\n", @@ -198,11 +199,8 @@ " size_box = get_size(param)\n", "\n", " box = tda.JaxStructure(\n", - " geometry=tda.JaxBox(\n", - " center=(0, 0, 0),\n", - " size=(size_box, size_box, size_box)\n", - " ), \n", - " medium=tda.JaxMedium(permittivity=eps_box)\n", + " geometry=tda.JaxBox(center=(0, 0, 0), size=(size_box, size_box, size_box)),\n", + " medium=tda.JaxMedium(permittivity=eps_box),\n", " )\n", "\n", " # add the box to the simulation\n", @@ -220,18 +218,18 @@ "source": [ "# function to compute and measure intensity as function of the design paramater\n", "\n", + "\n", "def intensity(param: float) -> float:\n", " \"\"\"Intensity measured at monitor as function of parameter.\"\"\"\n", - " \n", + "\n", " # make the sim using the paramter value\n", " sim_with_square = make_sim(param)\n", - " \n", + "\n", " # run sim through tidy3d web API\n", " data = tda.web.run_local(sim_with_square, task_name=\"adjoint_quickstart\", verbose=False)\n", - " \n", + "\n", " # evaluate the intensity at the measurement position\n", - " return jnp.sum(jnp.array(data.get_intensity(monitor.name).values))\n", - " " + " return jnp.sum(jnp.array(data.get_intensity(monitor.name).values))" ] }, { @@ -362,8 +360,8 @@ "opt_state = optimizer.init(param)\n", "\n", "# store history\n", - "objective_history = [1.0] # the normalized objective function with no box\n", - "param_history = [-100, param] # -100 is approximately \"no box\" (size=0)\n", + "objective_history = [1.0] # the normalized objective function with no box\n", + "param_history = [-100, param] # -100 is approximately \"no box\" (size=0)\n", "\n", "for i in range(num_steps):\n", " print(f\"step = {i + 1}\")\n", @@ -375,7 +373,7 @@ "\n", " # outputs\n", " print(f\"\\tintensity = {value:.4e}\")\n", - " print(f\"\\tgrad_norm = {jnp.linalg.norm(gradient):.4e}\") \n", + " print(f\"\\tgrad_norm = {jnp.linalg.norm(gradient):.4e}\")\n", "\n", " # compute and apply updates to the optimizer based on gradient (-1 sign to maximize obj_fn)\n", " updates, opt_state = optimizer.update(-gradient, opt_state, param)\n", @@ -383,7 +381,7 @@ "\n", " # save history\n", " objective_history.append(value)\n", - " param_history.append(param) \n" + " param_history.append(param)" ] }, { @@ -435,7 +433,7 @@ "\n", "# add a field monitor for plotting\n", "fld_mnt = td.FieldMonitor(\n", - " center=(+L/2 - buffer, 0, 0),\n", + " center=(+L / 2 - buffer, 0, 0),\n", " size=(td.inf, td.inf, 0),\n", " freqs=[freq0],\n", " name=\"fields\",\n", @@ -480,7 +478,9 @@ ], "source": [ "# plot intensity distribution\n", - "ax = data_final.plot_field(field_monitor_name=\"fields\", field_name=\"E\", val=\"abs^2\", vmax=intensity_final)\n", + "ax = data_final.plot_field(\n", + " field_monitor_name=\"fields\", field_name=\"E\", val=\"abs^2\", vmax=intensity_final\n", + ")\n", "\n", "ax.plot(source.center[0], 0, marker=\"o\", mfc=\"limegreen\", mec=\"black\", ms=10)\n", "ax.plot(monitor.center[0], 0, marker=\"o\", mfc=\"orange\", mec=\"black\", ms=10)\n", @@ -512,9 +512,9 @@ "objective_history = objective_history\n", "_ = plt.scatter(sizes, objective_history)\n", "ax = plt.gca()\n", - "ax.set_xlabel('box size (um)')\n", - "ax.set_ylabel('intensity enhancement (unitless)')\n", - "plt.title('intensity enhancement vs. box size')\n", + "ax.set_xlabel(\"box size (um)\")\n", + "ax.set_ylabel(\"intensity enhancement (unitless)\")\n", + "plt.title(\"intensity enhancement vs. box size\")\n", "plt.show()" ] }, diff --git a/AdjointPlugin10YBranchLevelSet.ipynb b/AdjointPlugin10YBranchLevelSet.ipynb index ea3aea03..92879752 100644 --- a/AdjointPlugin10YBranchLevelSet.ipynb +++ b/AdjointPlugin10YBranchLevelSet.ipynb @@ -29,26 +29,27 @@ "outputs": [], "source": [ "# Standard python imports.\n", + "import pickle\n", "from typing import List\n", - "import numpy as np\n", - "import matplotlib.pylab as plt\n", + "\n", + "import gdstk\n", "\n", "# Import jax to be able to use automatic differentiation.\n", "import jax.numpy as jnp\n", - "from jax import value_and_grad\n", + "import matplotlib.pylab as plt\n", + "import numpy as np\n", "import optax\n", - "import pickle\n", - "import gdstk\n", "\n", "# Import regular tidy3d.\n", "import tidy3d as td\n", - "import tidy3d.web as web\n", "\n", "# Import the components we need from the adjoint plugin.\n", "import tidy3d.plugins.adjoint as tda\n", + "import tidy3d.web as web\n", + "from jax import value_and_grad\n", "from tidy3d.plugins.adjoint.web import run\n", "\n", - "plt.rcParams['font.size'] = '12'" + "plt.rcParams[\"font.size\"] = \"12\"" ] }, { @@ -84,7 +85,9 @@ "# Inverse design set up parameters.\n", "grid_size = 0.016 # Simulation grid size on design region (um).\n", "ls_grid_size = 0.004 # Discretization size of the level set function (um).\n", - "ls_down_sample = 20 # The spacing between the level set control knots is given by ls_grid_size*ls_down_sample.\n", + "ls_down_sample = (\n", + " 20 # The spacing between the level set control knots is given by ls_grid_size*ls_down_sample.\n", + ")\n", "fom_name_1 = \"fom_field1\" # Name of the monitor used to compute the objective function.\n", "min_feature_size = 0.14 # Minimum fabrication feature size (um).\n", "gap_par = 1.0 # Parameter to minimum gap fabrication constraint.\n", @@ -114,7 +117,7 @@ "outputs": [], "source": [ "# Minimum and maximum values for the permittivities.\n", - "eps_max = nSi ** 2\n", + "eps_max = nSi**2\n", "eps_min = 1.0\n", "\n", "# Material definition.\n", @@ -179,8 +182,9 @@ "metadata": {}, "outputs": [], "source": [ - "class LevelSetInterp(object):\n", + "class LevelSetInterp:\n", " \"\"\"This class implements the level set surface using Gaussian radial basis functions.\"\"\"\n", + "\n", " def __init__(\n", " self,\n", " x0: jnp.ndarray = None,\n", @@ -203,7 +207,7 @@ " (xyi[:, 1].reshape(-1, 1) - xyj[:, 1].reshape(1, -1)) ** 2\n", " + (xyi[:, 0].reshape(-1, 1) - xyj[:, 0].reshape(1, -1)) ** 2\n", " )\n", - " return jnp.exp(-(dist ** 2) / (2 * self.sig ** 2))\n", + " return jnp.exp(-(dist**2) / (2 * self.sig**2))\n", "\n", " def get_ls(self, x1, y1):\n", " xx, yy = jnp.meshgrid(y1, x1)\n", @@ -216,34 +220,33 @@ "def plot_level_set(x0, y0, rho, x1, y1, phi):\n", " y, x = np.meshgrid(y0, x0)\n", " yy, xx = np.meshgrid(y1, x1)\n", - " \n", + "\n", " fig = plt.figure(figsize=(12, 6), tight_layout=True)\n", - " ax1 = fig.add_subplot(1, 2, 1, projection='3d')\n", + " ax1 = fig.add_subplot(1, 2, 1, projection=\"3d\")\n", " ax1.view_init(elev=45, azim=-45, roll=0)\n", " ax1.plot_surface(xx, yy, phi, cmap=\"RdBu\", alpha=0.8)\n", - " ax1.contourf(xx, yy, phi, levels=[np.amin(phi), 0],\n", - " zdir ='z',\n", - " offset = 0,\n", - " colors=[\"k\",\"w\"], alpha=0.5)\n", + " ax1.contourf(\n", + " xx, yy, phi, levels=[np.amin(phi), 0], zdir=\"z\", offset=0, colors=[\"k\", \"w\"], alpha=0.5\n", + " )\n", " ax1.contour3D(xx, yy, phi, 1, cmap=\"binary\", linewidths=[2])\n", " ax1.scatter(x, y, rho, color=\"black\", linewidth=1.0)\n", " ax1.set_title(\"Level set surface\")\n", - " ax1.set_xlabel(\"x ($\\mu m$)\")\n", - " ax1.set_ylabel(\"y ($\\mu m$)\")\n", + " ax1.set_xlabel(r\"x ($\\mu m$)\")\n", + " ax1.set_ylabel(r\"y ($\\mu m$)\")\n", " ax1.xaxis.pane.fill = False\n", " ax1.yaxis.pane.fill = False\n", " ax1.zaxis.pane.fill = False\n", - " ax1.xaxis.pane.set_edgecolor('w')\n", - " ax1.yaxis.pane.set_edgecolor('w')\n", - " ax1.zaxis.pane.set_edgecolor('w')\n", - " \n", - " ax2 = fig.add_subplot(1, 2, 2) \n", - " ax2.contourf(xx, yy, phi, levels=[0, np.amax(phi)], colors=[[0,0,0]])\n", + " ax1.xaxis.pane.set_edgecolor(\"w\")\n", + " ax1.yaxis.pane.set_edgecolor(\"w\")\n", + " ax1.zaxis.pane.set_edgecolor(\"w\")\n", + "\n", + " ax2 = fig.add_subplot(1, 2, 2)\n", + " ax2.contourf(xx, yy, phi, levels=[0, np.amax(phi)], colors=[[0, 0, 0]])\n", " ax2.set_title(\"Zero level set contour\")\n", - " ax2.set_xlabel(\"x ($\\mu m$)\")\n", - " ax2.set_ylabel(\"y ($\\mu m$)\")\n", + " ax2.set_xlabel(r\"x ($\\mu m$)\")\n", + " ax2.set_ylabel(r\"y ($\\mu m$)\")\n", " ax2.set_aspect(\"equal\")\n", - " plt.show() \n" + " plt.show()" ] }, { @@ -263,7 +266,8 @@ " param = jnp.array(design_param).reshape((nx_rho, int(ny_rho / 2)))\n", " return jnp.concatenate((jnp.fliplr(jnp.copy(param)), param), axis=1).flatten()\n", "\n", - "def get_eps(design_param, sharpness = 10, plot_levelset=False) -> np.ndarray:\n", + "\n", + "def get_eps(design_param, sharpness=10, plot_levelset=False) -> np.ndarray:\n", " \"\"\"Returns the permittivities defined by the zero level set isocontour\"\"\"\n", " phi_model = LevelSetInterp(x0=x_rho, y0=y_rho, z0=design_param, sigma=rho_size)\n", " phi = phi_model.get_ls(x1=x_phi, y1=y_phi)\n", @@ -304,17 +308,21 @@ " eps_val = jnp.array(eps).reshape((nx_phi, ny_phi, 1, 1))\n", " coords_x = [(dr_center_x - dr_size_x / 2) + ix * ls_grid_size for ix in range(nx_phi)]\n", "\n", - " if unfold == False:\n", + " if not unfold:\n", " # Creation of a JaxCustomMedium using the values of the design parameters.\n", " coords_yp = [0 + iy * ls_grid_size for iy in range(int(ny_phi / 2))]\n", " coords = dict(x=coords_x, y=coords_yp, z=[0], f=[freq])\n", " eps_jax = {\n", - " f\"eps_{dim}{dim}\": tda.JaxDataArray(values=eps_val[:,int(ny_phi / 2):, :, :], coords=coords)\n", + " f\"eps_{dim}{dim}\": tda.JaxDataArray(\n", + " values=eps_val[:, int(ny_phi / 2) :, :, :], coords=coords\n", + " )\n", " for dim in \"xyz\"\n", " }\n", " eps_dataset = tda.JaxPermittivityDataset(**eps_jax)\n", " eps_medium = tda.JaxCustomMedium(eps_dataset=eps_dataset, interp_method=\"linear\")\n", - " box = tda.JaxBox(center=(dr_center_x, dr_size_y / 4, 0), size=(dr_size_x, dr_size_y / 2, w_thick))\n", + " box = tda.JaxBox(\n", + " center=(dr_center_x, dr_size_y / 4, 0), size=(dr_size_x, dr_size_y / 2, w_thick)\n", + " )\n", " structure = [tda.JaxStructure(geometry=box, medium=eps_medium)]\n", "\n", " else:\n", @@ -322,16 +330,11 @@ " coords_y = [-dr_size_y / 2 + iy * ls_grid_size for iy in range(ny_phi)]\n", " coords = dict(x=coords_x, y=coords_y, z=[0], f=[freq])\n", " eps_jax = {\n", - " f\"eps_{dim}{dim}\": tda.JaxDataArray(values=eps_val, coords=coords)\n", - " for dim in \"xyz\"\n", + " f\"eps_{dim}{dim}\": tda.JaxDataArray(values=eps_val, coords=coords) for dim in \"xyz\"\n", " }\n", " eps_dataset = tda.JaxPermittivityDataset(**eps_jax)\n", - " eps_medium = tda.JaxCustomMedium(\n", - " eps_dataset=eps_dataset, interp_method=\"linear\"\n", - " )\n", - " box = tda.JaxBox(\n", - " center=(dr_center_x, 0, 0), size=(dr_size_x, dr_size_y, w_thick)\n", - " )\n", + " eps_medium = tda.JaxCustomMedium(eps_dataset=eps_dataset, interp_method=\"linear\")\n", + " box = tda.JaxBox(center=(dr_center_x, 0, 0), size=(dr_size_x, dr_size_y, w_thick))\n", " structure = [tda.JaxStructure(geometry=box, medium=eps_medium)]\n", " return structure" ] @@ -366,40 +369,54 @@ " [\n", " (-size_x / 2 + w_length, w_width / 2),\n", " (-size_x / 2 + w_length + 0.5, w_width / 2),\n", - " (-size_x / 2 + w_length + 0.75, w_gap / 2 + w_width), \n", + " (-size_x / 2 + w_length + 0.75, w_gap / 2 + w_width),\n", " (-size_x / 2 + w_length + dr_size_x, w_gap / 2 + w_width),\n", " (-size_x / 2 + w_length + dr_size_x, w_gap / 2),\n", - " (-size_x / 2 + w_length + 2.5*dr_size_x / 3, w_gap / 2), \n", - " (-size_x / 2 + w_length + 2.3*dr_size_x / 3, w_gap / 6), \n", - " (-size_x / 2 + w_length + 1.8*dr_size_x / 3, w_gap / 6), \n", - " (-size_x / 2 + w_length + 1.8*dr_size_x / 3, -w_gap / 6), \n", - " (-size_x / 2 + w_length + 2.3*dr_size_x / 3, -w_gap / 6), \n", - " (-size_x / 2 + w_length + 2.5*dr_size_x / 3, -w_gap / 2), \n", - " (-size_x / 2 + w_length + dr_size_x, -w_gap / 2), \n", + " (-size_x / 2 + w_length + 2.5 * dr_size_x / 3, w_gap / 2),\n", + " (-size_x / 2 + w_length + 2.3 * dr_size_x / 3, w_gap / 6),\n", + " (-size_x / 2 + w_length + 1.8 * dr_size_x / 3, w_gap / 6),\n", + " (-size_x / 2 + w_length + 1.8 * dr_size_x / 3, -w_gap / 6),\n", + " (-size_x / 2 + w_length + 2.3 * dr_size_x / 3, -w_gap / 6),\n", + " (-size_x / 2 + w_length + 2.5 * dr_size_x / 3, -w_gap / 2),\n", + " (-size_x / 2 + w_length + dr_size_x, -w_gap / 2),\n", " (-size_x / 2 + w_length + dr_size_x, -w_gap / 2 - w_width),\n", - " (-size_x / 2 + w_length + 0.75, -w_gap / 2 - w_width), \n", - " (-size_x / 2 + w_length + 0.5, -w_width / 2), \n", + " (-size_x / 2 + w_length + 0.75, -w_gap / 2 - w_width),\n", + " (-size_x / 2 + w_length + 0.5, -w_width / 2),\n", " (-size_x / 2 + w_length, -w_width / 2),\n", " ]\n", ")\n", "\n", - "y_poly = td.PolySlab(\n", - " vertices=vertices, axis=2, slab_bounds=(-w_thick / 2, w_thick / 2)\n", + "y_poly = td.PolySlab(vertices=vertices, axis=2, slab_bounds=(-w_thick / 2, w_thick / 2))\n", + "y_hole1 = td.Cylinder(\n", + " center=(-size_x / 2 + w_length + 1.7 * dr_size_x / 3, w_gap / 2 + w_width / 1.75, 0),\n", + " radius=min_feature_size / 3,\n", + " length=w_thick,\n", + " axis=2,\n", + ")\n", + "y_hole2 = td.Cylinder(\n", + " center=(-size_x / 2 + w_length + 1.7 * dr_size_x / 3, -w_gap / 2 - w_width / 1.75, 0),\n", + " radius=min_feature_size / 3,\n", + " length=w_thick,\n", + " axis=2,\n", + ")\n", + "y_hole3 = td.Cylinder(\n", + " center=(-size_x / 2 + w_length + 2.3 * dr_size_x / 3, w_gap / 2 + w_width / 1.75, 0),\n", + " radius=min_feature_size / 1.5,\n", + " length=w_thick,\n", + " axis=2,\n", + ")\n", + "y_hole4 = td.Cylinder(\n", + " center=(-size_x / 2 + w_length + 2.3 * dr_size_x / 3, -w_gap / 2 - w_width / 1.75, 0),\n", + " radius=min_feature_size / 1.5,\n", + " length=w_thick,\n", + " axis=2,\n", ")\n", - "y_hole1 = td.Cylinder(center=(-size_x / 2 + w_length + 1.7*dr_size_x / 3, w_gap / 2 + w_width / 1.75, 0), \n", - " radius=min_feature_size/3, length=w_thick, axis=2)\n", - "y_hole2 = td.Cylinder(center=(-size_x / 2 + w_length + 1.7*dr_size_x / 3, -w_gap / 2 - w_width / 1.75, 0), \n", - " radius=min_feature_size/3, length=w_thick, axis=2)\n", - "y_hole3 = td.Cylinder(center=(-size_x / 2 + w_length + 2.3*dr_size_x / 3, w_gap / 2 + w_width / 1.75, 0), \n", - " radius=min_feature_size/1.5, length=w_thick, axis=2)\n", - "y_hole4 = td.Cylinder(center=(-size_x / 2 + w_length + 2.3*dr_size_x / 3, -w_gap / 2 - w_width / 1.75, 0), \n", - " radius=min_feature_size/1.5, length=w_thick, axis=2)\n", "init_design = td.ClipOperation(operation=\"difference\", geometry_a=y_poly, geometry_b=y_hole1)\n", "init_design = td.ClipOperation(operation=\"difference\", geometry_a=init_design, geometry_b=y_hole2)\n", "init_design = td.ClipOperation(operation=\"difference\", geometry_a=init_design, geometry_b=y_hole3)\n", "init_design = td.ClipOperation(operation=\"difference\", geometry_a=init_design, geometry_b=y_hole4)\n", "\n", - "init_eps = init_design.inside_meshgrid(x=x_phi, y=y_phi, z=np.zeros((1)))\n", + "init_eps = init_design.inside_meshgrid(x=x_phi, y=y_phi, z=np.zeros(1))\n", "init_eps = np.squeeze(init_eps) * eps_max\n", "\n", "init_design.plot(z=0)\n", @@ -424,12 +441,14 @@ " \"\"\"Calculate the L2 norm between eps_ref and eps.\"\"\"\n", " return jnp.mean(jnp.abs(eps_ref - eps) ** 2)\n", "\n", + "\n", "# Objective function to be passed to the optimization algorithm.\n", "def obj_eps(design_param, eps_ref) -> float:\n", " param = mirror_param(design_param)\n", " eps = get_eps(param)\n", " return fom_eps(eps_ref, eps)\n", "\n", + "\n", "# Function to calculate the objective function value and its\n", "# gradient with respect to the design parameters.\n", "obj_grad_eps = value_and_grad(obj_eps)" @@ -613,8 +632,8 @@ ], "source": [ "# Initialize adam optimizer with starting parameters.\n", - "start_par = np.zeros((npar))\n", - "optimizer = optax.adam(learning_rate=learning_rate*10)\n", + "start_par = np.zeros(npar)\n", + "optimizer = optax.adam(learning_rate=learning_rate * 10)\n", "opt_state = optimizer.init(start_par)\n", "\n", "# Store history\n", @@ -623,7 +642,6 @@ "params_history_eps = [start_par]\n", "\n", "for i in range(50):\n", - "\n", " # Compute gradient and current objective funciton value.\n", " value, gradient = obj_grad_eps(params_eps, init_eps)\n", "\n", @@ -737,10 +755,10 @@ ")\n", "\n", "# Output bends.\n", - "x_start = -size_x / 2 + w_length + dr_size_x - grid_size # x-coordinate of the starting point of the waveguide bends.\n", - "x = np.linspace(\n", - " x_start, x_start + bend_length, 100\n", - ") # x-coordinates of the top edge vertices.\n", + "x_start = (\n", + " -size_x / 2 + w_length + dr_size_x - grid_size\n", + ") # x-coordinate of the starting point of the waveguide bends.\n", + "x = np.linspace(x_start, x_start + bend_length, 100) # x-coordinates of the top edge vertices.\n", "y = (\n", " (x - x_start) * bend_offset / bend_length\n", " - bend_offset * np.sin(2 * np.pi * (x - x_start) / bend_length) / (np.pi * 2)\n", @@ -762,9 +780,9 @@ " cell,\n", " gds_layer=1,\n", " axis=2,\n", - " slab_bounds=(-w_thick/2, w_thick/2),\n", + " slab_bounds=(-w_thick / 2, w_thick / 2),\n", " )[1],\n", - " medium=mat_si\n", + " medium=mat_si,\n", ")\n", "\n", "# Define bottom waveguide bend structure.\n", @@ -773,9 +791,9 @@ " cell,\n", " gds_layer=1,\n", " axis=2,\n", - " slab_bounds=(-w_thick/2, w_thick/2),\n", + " slab_bounds=(-w_thick / 2, w_thick / 2),\n", " )[0],\n", - " medium=mat_si\n", + " medium=mat_si,\n", ")" ] }, @@ -850,9 +868,7 @@ "\n", " # Creates a uniform mesh for the design region.\n", " adjoint_dr_mesh = td.MeshOverrideStructure(\n", - " geometry=td.Box(\n", - " center=(dr_center_x, 0, 0), size=(dr_size_x, dr_size_y, w_thick)\n", - " ),\n", + " geometry=td.Box(center=(dr_center_x, 0, 0), size=(dr_size_x, dr_size_y, w_thick)),\n", " dl=[grid_size, grid_size, grid_size],\n", " enforce=True,\n", " )\n", @@ -1267,9 +1283,7 @@ } ], "source": [ - "sim_init = init_design.to_simulation()[0].copy(\n", - " update=dict(monitors=(field_xy, fom_final_1))\n", - ")\n", + "sim_init = init_design.to_simulation()[0].copy(update=dict(monitors=(field_xy, fom_final_1)))\n", "sim_data = web.run(sim_init, task_name=\"initial y-branch\")" ] }, @@ -1338,76 +1352,76 @@ " phi_xx = phi_2x[0]\n", " phi_xy = phi_2x[1]\n", " phi_yy = phi_2y[1]\n", - " return phi_x, phi_y, phi_xx, phi_xy, phi_yy \n", + " return phi_x, phi_y, phi_xx, phi_xy, phi_yy\n", + "\n", "\n", - "# Minimum gap size fabrication constraint integrand calculation. \n", + "# Minimum gap size fabrication constraint integrand calculation.\n", "# The \"beta\" parameter relax the constraint near the zero plane.\n", - "def fab_penalty_ls_gap(params, \n", - " beta=1, \n", - " min_feature_size=min_feature_size, \n", - " grid_size=ls_grid_size):\n", - " \n", + "def fab_penalty_ls_gap(params, beta=1, min_feature_size=min_feature_size, grid_size=ls_grid_size):\n", " # Get the level set surface.\n", " phi_model = LevelSetInterp(x0=x_rho, y0=y_rho, z0=params, sigma=rho_size)\n", " phi = phi_model.get_ls(x1=x_phi, y1=y_phi)\n", " phi = jnp.reshape(phi, (nx_phi, ny_phi))\n", - " \n", + "\n", " # Calculates their derivatives.\n", - " phi_x, phi_y, phi_xx, phi_xy, phi_yy = ls_derivatives(phi, grid_size) \n", + " phi_x, phi_y, phi_xx, phi_xy, phi_yy = ls_derivatives(phi, grid_size)\n", "\n", " # Calculates the gap penalty over the level set grid.\n", " pi_d = np.pi / (1.3 * min_feature_size)\n", - " phi_v = jnp.maximum(jnp.power(phi_x ** 2 + phi_y ** 2, 0.5), jnp.power(1e-32, 1/4))\n", - " phi_vv = (phi_x ** 2 * phi_xx + 2 * phi_x * phi_y * phi_xy + phi_y ** 2 * phi_yy) / phi_v ** 2 \n", - " return jnp.maximum((jnp.abs(phi_vv) / \n", - " (pi_d * jnp.abs(phi) + beta * phi_v) - pi_d) , 0) * grid_size ** 2\n", + " phi_v = jnp.maximum(jnp.power(phi_x**2 + phi_y**2, 0.5), jnp.power(1e-32, 1 / 4))\n", + " phi_vv = (phi_x**2 * phi_xx + 2 * phi_x * phi_y * phi_xy + phi_y**2 * phi_yy) / phi_v**2\n", + " return (\n", + " jnp.maximum((jnp.abs(phi_vv) / (pi_d * jnp.abs(phi) + beta * phi_v) - pi_d), 0)\n", + " * grid_size**2\n", + " )\n", + "\n", "\n", "# Minimum radius of curvature fabrication constraint integrand calculation.\n", "# The \"alpha\" parameter controls its relative weight to the gap penalty.\n", "# The \"sharpness\" parameter controls the smoothness of the surface near the zero-contour.\n", - "def fab_penalty_ls_curve(params, \n", - " alpha=1,\n", - " sharpness = 1, \n", - " min_feature_size=min_feature_size, \n", - " grid_size=ls_grid_size):\n", - " \n", + "def fab_penalty_ls_curve(\n", + " params, alpha=1, sharpness=1, min_feature_size=min_feature_size, grid_size=ls_grid_size\n", + "):\n", " # Get the permittivity surface and calculates their derivatives.\n", - " eps = get_eps(params, sharpness = sharpness)\n", - " eps_x, eps_y, eps_xx, eps_xy, eps_yy = ls_derivatives(eps, grid_size) \n", + " eps = get_eps(params, sharpness=sharpness)\n", + " eps_x, eps_y, eps_xx, eps_xy, eps_yy = ls_derivatives(eps, grid_size)\n", "\n", - " # Calculates the curvature penalty over the permittivity grid. \n", + " # Calculates the curvature penalty over the permittivity grid.\n", " pi_d = np.pi / (1.1 * min_feature_size)\n", - " eps_v = jnp.maximum(jnp.sqrt(eps_x ** 2 + eps_y ** 2), jnp.power(1e-32, 1/6))\n", + " eps_v = jnp.maximum(jnp.sqrt(eps_x**2 + eps_y**2), jnp.power(1e-32, 1 / 6))\n", " k = (eps_x**2 * eps_yy - 2 * eps_x * eps_y * eps_xy + eps_y**2 * eps_xx) / eps_v**3\n", " curve_const = jnp.abs(k * jnp.arctan(eps_v / eps)) - pi_d\n", - " return alpha * jnp.maximum(curve_const , 0) * grid_size ** 2\n", + " return alpha * jnp.maximum(curve_const, 0) * grid_size**2\n", + "\n", "\n", "# Gap and curvature fabrication constraints calculation.\n", "# Penalty values are normalized by \"norm_gap\" and \"norm_curve\".\n", - "def fab_penalty_ls(params, \n", - " beta=gap_par, \n", - " alpha=curve_par,\n", - " sharpness = 4,\n", - " min_feature_size=min_feature_size, \n", - " grid_size=ls_grid_size,\n", - " norm_gap=1,\n", - " norm_curve=1):\n", - " \n", + "def fab_penalty_ls(\n", + " params,\n", + " beta=gap_par,\n", + " alpha=curve_par,\n", + " sharpness=4,\n", + " min_feature_size=min_feature_size,\n", + " grid_size=ls_grid_size,\n", + " norm_gap=1,\n", + " norm_curve=1,\n", + "):\n", " # Get the gap penalty fabrication constraint value.\n", - " gap_penalty_int = fab_penalty_ls_gap(params=params, \n", - " beta=beta, \n", - " min_feature_size=min_feature_size, \n", - " grid_size=grid_size)\n", + " gap_penalty_int = fab_penalty_ls_gap(\n", + " params=params, beta=beta, min_feature_size=min_feature_size, grid_size=grid_size\n", + " )\n", " gap_penalty = jnp.nansum(gap_penalty_int) / norm_gap\n", "\n", " # Get the curvature penalty fabrication constraint value.\n", - " curve_penalty_int = fab_penalty_ls_curve(params=params, \n", - " alpha=alpha,\n", - " sharpness=sharpness, \n", - " min_feature_size=min_feature_size, \n", - " grid_size=grid_size)\n", + " curve_penalty_int = fab_penalty_ls_curve(\n", + " params=params,\n", + " alpha=alpha,\n", + " sharpness=sharpness,\n", + " min_feature_size=min_feature_size,\n", + " grid_size=grid_size,\n", + " )\n", " curve_penalty = jnp.nansum(curve_penalty_int) / norm_curve\n", - " \n", + "\n", " return gap_penalty, curve_penalty" ] }, @@ -1443,22 +1457,32 @@ "curve_penalty_int = fab_penalty_ls_curve(mirror_param(init_rho), alpha=curve_par, sharpness=4)\n", "\n", "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8), tight_layout=True)\n", - "yy, xx = np.meshgrid(y_phi, x_phi) \n", - "\n", - "im = ax1.imshow(np.flipud(gap_penalty_int.T), extent=[x_phi[0], x_phi[-1], y_phi[0], y_phi[-1]], interpolation='none', cmap='gnuplot2_r')\n", - "ax1.contour(xx, yy, eps_fit, [(eps_min + eps_max) / 2], colors='k', linewidths=0.5)\n", - "ax1.set_title(f\"Gap Penalty = {init_fab_gap:.3f}\") \n", - "ax1.set_xlabel(\"x ($\\mu m$)\")\n", - "ax1.set_ylabel(\"y ($\\mu m$)\")\n", - "fig.colorbar(im, ax = ax1, shrink = 0.3) \n", - "\n", - "im = ax2.imshow(jnp.flipud(curve_penalty_int.T), extent=[x_phi[0], x_phi[-1], y_phi[0], y_phi[-1]], interpolation='none', cmap='gnuplot2_r')\n", - "ax2.contour(xx, yy, eps_fit, [(eps_min + eps_max) / 2], colors='k', linewidths=0.5)\n", - "ax2.set_title(f\"Curve Penalty = {init_fab_curve:.3f}\") \n", - "ax2.set_xlabel(\"x ($\\mu m$)\")\n", - "ax2.set_ylabel(\"y ($\\mu m$)\")\n", - "fig.colorbar(im, ax = ax2, shrink = 0.3)\n", - "plt.show()\n" + "yy, xx = np.meshgrid(y_phi, x_phi)\n", + "\n", + "im = ax1.imshow(\n", + " np.flipud(gap_penalty_int.T),\n", + " extent=[x_phi[0], x_phi[-1], y_phi[0], y_phi[-1]],\n", + " interpolation=\"none\",\n", + " cmap=\"gnuplot2_r\",\n", + ")\n", + "ax1.contour(xx, yy, eps_fit, [(eps_min + eps_max) / 2], colors=\"k\", linewidths=0.5)\n", + "ax1.set_title(f\"Gap Penalty = {init_fab_gap:.3f}\")\n", + "ax1.set_xlabel(r\"x ($\\mu m$)\")\n", + "ax1.set_ylabel(r\"y ($\\mu m$)\")\n", + "fig.colorbar(im, ax=ax1, shrink=0.3)\n", + "\n", + "im = ax2.imshow(\n", + " jnp.flipud(curve_penalty_int.T),\n", + " extent=[x_phi[0], x_phi[-1], y_phi[0], y_phi[-1]],\n", + " interpolation=\"none\",\n", + " cmap=\"gnuplot2_r\",\n", + ")\n", + "ax2.contour(xx, yy, eps_fit, [(eps_min + eps_max) / 2], colors=\"k\", linewidths=0.5)\n", + "ax2.set_title(f\"Curve Penalty = {init_fab_curve:.3f}\")\n", + "ax2.set_xlabel(r\"x ($\\mu m$)\")\n", + "ax2.set_ylabel(r\"y ($\\mu m$)\")\n", + "fig.colorbar(im, ax=ax2, shrink=0.3)\n", + "plt.show()" ] }, { @@ -1484,16 +1508,20 @@ " eta1 = jnp.sum(jnp.abs(amp1)) ** 2\n", " return jnp.abs(0.5 - eta1), eta1\n", "\n", + "\n", "# Objective function to be passed to the optimization algorithm.\n", - "def obj(design_param, fab_const: float = 0.0, norm_gap=1.0, norm_curve=1.0, verbose: bool = False) -> float:\n", + "def obj(\n", + " design_param, fab_const: float = 0.0, norm_gap=1.0, norm_curve=1.0, verbose: bool = False\n", + ") -> float:\n", " param = mirror_param(design_param)\n", " sim = make_adjoint_sim(param)\n", " sim_data = run(sim, task_name=\"inv_des_ybranch\", verbose=verbose)\n", " fom_val, eta1 = fom(sim_data)\n", - " fab_gap, fab_curve = fab_penalty_ls(param, norm_gap=norm_gap, norm_curve=norm_curve) \n", - " J = fom_val + fab_const * (fab_gap + fab_curve) \n", + " fab_gap, fab_curve = fab_penalty_ls(param, norm_gap=norm_gap, norm_curve=norm_curve)\n", + " J = fom_val + fab_const * (fab_gap + fab_curve)\n", " return J, [sim_data, eta1, fab_gap, fab_curve]\n", "\n", + "\n", "# Function to calculate the objective function value and its\n", "# gradient with respect to the design parameters.\n", "obj_grad = value_and_grad(obj, has_aux=True)" @@ -1515,6 +1543,7 @@ "# where to store history\n", "history_fname = \"./misc/y_branch_fab.pkl\"\n", "\n", + "\n", "def save_history(history_dict: dict) -> None:\n", " \"\"\"Convenience function to save the history to file.\"\"\"\n", " with open(history_fname, \"wb\") as file:\n", @@ -1552,7 +1581,7 @@ ], "source": [ "# Initialize adam optimizer with starting parameters.\n", - "optimizer = optax.adam(learning_rate=learning_rate*8)\n", + "optimizer = optax.adam(learning_rate=learning_rate * 8)\n", "\n", "try:\n", " history_dict = load_history()\n", @@ -1562,14 +1591,12 @@ " params = history_dict[\"params\"][-1]\n", " else:\n", " params = np.array(init_rho)\n", - " opt_state = optimizer.init(params) \n", + " opt_state = optimizer.init(params)\n", " num_iters_completed = len(history_dict[\"params\"])\n", " print(\"Loaded optimization checkpoint from file.\")\n", - " print(\n", - " f\"Found {num_iters_completed} iterations previously completed out of {iterations} total.\"\n", - " )\n", + " print(f\"Found {num_iters_completed} iterations previously completed out of {iterations} total.\")\n", " if num_iters_completed < iterations:\n", - " print(f\"Will resume optimization.\")\n", + " print(\"Will resume optimization.\")\n", " else:\n", " print(\"Optimization completed, will return results.\")\n", "\n", @@ -1607,11 +1634,12 @@ "\n", "if iter_done < iterations:\n", " for i in range(iter_done, iterations):\n", - "\n", " # Compute gradient and current objective function value.\n", - " (value, data), gradient = obj_grad(params, fab_const=0.05, norm_gap=init_fab_gap, norm_curve=init_fab_curve)\n", + " (value, data), gradient = obj_grad(\n", + " params, fab_const=0.05, norm_gap=init_fab_gap, norm_curve=init_fab_curve\n", + " )\n", " sim_data_i, eta1, penalty_gap, penalty_curve = data\n", - " \n", + "\n", " # outputs\n", " print(f\"Step = {i + 1}\")\n", " print(f\"\\tobj_val = {value:.4e}\")\n", @@ -1628,7 +1656,7 @@ " history_dict[\"values\"].append(value)\n", " history_dict[\"eta1\"].append(eta1)\n", " history_dict[\"penalty_gap\"].append(penalty_gap)\n", - " history_dict[\"penalty_curve\"].append(penalty_curve) \n", + " history_dict[\"penalty_curve\"].append(penalty_curve)\n", " history_dict[\"params\"].append(params)\n", " history_dict[\"gradients\"].append(gradient)\n", " history_dict[\"opt_states\"].append(opt_state)\n", @@ -1747,21 +1775,31 @@ "curve_penalty_int = fab_penalty_ls_curve(mirror_param(final_par), alpha=curve_par, sharpness=4)\n", "\n", "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8), tight_layout=True)\n", - "yy, xx = np.meshgrid(y_phi, x_phi) \n", - "\n", - "im = ax1.imshow(np.flipud(gap_penalty_int.T), extent=[x_phi[0], x_phi[-1], y_phi[0], y_phi[-1]], interpolation='none', cmap='gnuplot2_r')\n", - "ax1.contour(xx, yy, eps_final, [(eps_min + eps_max) / 2], colors='k', linewidths=0.5)\n", - "ax1.set_title(f\"Gap Penalty = {final_fab_gap:.3f}\") \n", - "ax1.set_xlabel(\"x ($\\mu m$)\")\n", - "ax1.set_ylabel(\"y ($\\mu m$)\")\n", - "fig.colorbar(im, ax = ax1, shrink = 0.3) \n", - "\n", - "im = ax2.imshow(jnp.flipud(curve_penalty_int.T), extent=[x_phi[0], x_phi[-1], y_phi[0], y_phi[-1]], interpolation='none', cmap='gnuplot2_r')\n", - "ax2.contour(xx, yy, eps_final, [(eps_min + eps_max) / 2], colors='k', linewidths=0.5)\n", - "ax2.set_title(f\"Curve Penalty = {final_fab_curve:.3f}\") \n", - "ax2.set_xlabel(\"x ($\\mu m$)\")\n", - "ax2.set_ylabel(\"y ($\\mu m$)\")\n", - "fig.colorbar(im, ax = ax2, shrink = 0.3)\n", + "yy, xx = np.meshgrid(y_phi, x_phi)\n", + "\n", + "im = ax1.imshow(\n", + " np.flipud(gap_penalty_int.T),\n", + " extent=[x_phi[0], x_phi[-1], y_phi[0], y_phi[-1]],\n", + " interpolation=\"none\",\n", + " cmap=\"gnuplot2_r\",\n", + ")\n", + "ax1.contour(xx, yy, eps_final, [(eps_min + eps_max) / 2], colors=\"k\", linewidths=0.5)\n", + "ax1.set_title(f\"Gap Penalty = {final_fab_gap:.3f}\")\n", + "ax1.set_xlabel(r\"x ($\\mu m$)\")\n", + "ax1.set_ylabel(r\"y ($\\mu m$)\")\n", + "fig.colorbar(im, ax=ax1, shrink=0.3)\n", + "\n", + "im = ax2.imshow(\n", + " jnp.flipud(curve_penalty_int.T),\n", + " extent=[x_phi[0], x_phi[-1], y_phi[0], y_phi[-1]],\n", + " interpolation=\"none\",\n", + " cmap=\"gnuplot2_r\",\n", + ")\n", + "ax2.contour(xx, yy, eps_final, [(eps_min + eps_max) / 2], colors=\"k\", linewidths=0.5)\n", + "ax2.set_title(f\"Curve Penalty = {final_fab_curve:.3f}\")\n", + "ax2.set_xlabel(r\"x ($\\mu m$)\")\n", + "ax2.set_ylabel(r\"y ($\\mu m$)\")\n", + "fig.colorbar(im, ax=ax2, shrink=0.3)\n", "plt.show()" ] }, diff --git a/AdjointPlugin11CircuitMZI.ipynb b/AdjointPlugin11CircuitMZI.ipynb index ae410b39..8caf348e 100644 --- a/AdjointPlugin11CircuitMZI.ipynb +++ b/AdjointPlugin11CircuitMZI.ipynb @@ -41,15 +41,13 @@ } ], "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", "import functools\n", "\n", "import jax\n", "import jax.numpy as jnp\n", - "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "import sax\n", - "\n", "import tidy3d as td\n", "import tidy3d.plugins.adjoint as tda\n", "\n", @@ -133,58 +131,58 @@ "source": [ "big_number = Lx * 10\n", "\n", - "dy = (ly - 2 * wg_width) / 4 + wg_width/2\n", + "dy = (ly - 2 * wg_width) / 4 + wg_width / 2\n", "\n", "# all of the possible input and output waveguides\n", "waveguide_in_center = td.Structure(\n", " geometry=td.Box(\n", " size=(big_number, wg_width, lz),\n", - " center=(-big_number/2, 0, 0),\n", + " center=(-big_number / 2, 0, 0),\n", " ),\n", - " medium=td.Medium(permittivity=eps_wg)\n", + " medium=td.Medium(permittivity=eps_wg),\n", ")\n", "\n", "waveguide_in_top = td.Structure(\n", " geometry=td.Box(\n", " size=(big_number, wg_width, lz),\n", - " center=(-big_number/2, +dy, 0),\n", + " center=(-big_number / 2, +dy, 0),\n", " ),\n", - " medium=td.Medium(permittivity=eps_wg)\n", + " medium=td.Medium(permittivity=eps_wg),\n", ")\n", "\n", "waveguide_in_bot = td.Structure(\n", " geometry=td.Box(\n", " size=(big_number, wg_width, lz),\n", - " center=(-big_number/2, -dy, 0),\n", + " center=(-big_number / 2, -dy, 0),\n", " ),\n", - " medium=td.Medium(permittivity=eps_wg)\n", + " medium=td.Medium(permittivity=eps_wg),\n", ")\n", "\n", "waveguide_out_center = td.Structure(\n", " geometry=td.Box(\n", " size=(big_number, wg_width, lz),\n", - " center=(+big_number/2, 0, 0),\n", + " center=(+big_number / 2, 0, 0),\n", " ),\n", " medium=td.Medium(permittivity=eps_wg),\n", - " name=\"center\"\n", + " name=\"center\",\n", ")\n", "\n", "waveguide_out_top = td.Structure(\n", " geometry=td.Box(\n", " size=(big_number, wg_width, lz),\n", - " center=(+big_number/2, +dy, 0),\n", + " center=(+big_number / 2, +dy, 0),\n", " ),\n", " medium=td.Medium(permittivity=eps_wg),\n", - " name=\"top\"\n", + " name=\"top\",\n", ")\n", "\n", "waveguide_out_bot = td.Structure(\n", " geometry=td.Box(\n", " size=(big_number, wg_width, lz),\n", - " center=(+big_number/2, -dy, 0),\n", + " center=(+big_number / 2, -dy, 0),\n", " ),\n", " medium=td.Medium(permittivity=eps_wg),\n", - " name=\"bot\"\n", + " name=\"bot\",\n", ")" ] }, @@ -212,10 +210,12 @@ " size=mode_size,\n", ")\n", "\n", + "\n", "def get_source_plane(waveguide: td.Structure) -> td.Box:\n", " \"\"\"SOurce plane with y position moved to cover a specific waveguide\"\"\"\n", " return source_plane_base.updated_copy(center=(source_x, waveguide.geometry.center[1], 0))\n", "\n", + "\n", "measure_plane = td.Box(\n", " center=[meas_x, 0, 0],\n", " size=mode_size,\n", @@ -238,47 +238,49 @@ "metadata": {}, "outputs": [], "source": [ - "from tidy3d.plugins.adjoint.utils.filter import ConicFilter\n", "from typing import List\n", "\n", - "radius = .120\n", + "from tidy3d.plugins.adjoint.utils.filter import ConicFilter\n", + "\n", + "radius = 0.120\n", "beta = 50\n", "design_region_dl = float(lx) / nx\n", "conic_filter = ConicFilter(radius=radius, design_region_dl=design_region_dl)\n", "\n", + "\n", "def tanh_projection(x, beta, eta=0.5):\n", " tanhbn = jnp.tanh(beta * eta)\n", " num = tanhbn + jnp.tanh(beta * (x - eta))\n", " den = tanhbn + jnp.tanh(beta * (1 - eta))\n", - " return num / den \n", + " return num / den\n", + "\n", "\n", "def filter_project(x, beta, eta=0.5):\n", " x = conic_filter.evaluate(x)\n", " return tanh_projection(x, beta=beta, eta=eta)\n", "\n", + "\n", "def pre_process(params, beta):\n", " \"\"\"Get the permittivity values (1, eps_wg) array as a function of the parameters (0,1)\"\"\"\n", " params1 = filter_project(params, beta=beta)\n", " params2 = filter_project(params1, beta=beta)\n", " return params2\n", "\n", + "\n", "def get_eps(params, beta):\n", " params = pre_process(params, beta=beta)\n", " eps_min = 1.0001\n", " eps_values = eps_min + (eps_wg - eps_min) * params\n", " return eps_values\n", - " \n", - "def make_input_structures(params, beta) -> List[tda.JaxStructure]:\n", "\n", + "\n", + "def make_input_structures(params, beta) -> List[tda.JaxStructure]:\n", " size_box_x = float(lx) / nx\n", " size_box_y = float(ly) / ny\n", - " size_box = (size_box_x, size_box_y, lz)\n", "\n", " x0_min = -lx / 2 + size_box_x / 2\n", " y0_min = -ly / 2 + size_box_y / 2\n", "\n", - " input_structures = []\n", - "\n", " coords_x = [x0_min + index_x * size_box_x - 1e-5 for index_x in range(nx)]\n", " coords_y = [y0_min + index_y * size_box_y - 1e-5 for index_y in range(ny)]\n", "\n", @@ -293,7 +295,7 @@ " custom_medium = tda.JaxCustomMedium(eps_dataset=eps_dataset)\n", " box = tda.JaxBox(center=(0, 0, 0), size=(lx, ly, lz))\n", " custom_structure = tda.JaxStructure(geometry=box, medium=custom_medium)\n", - " return [custom_structure]\n" + " return [custom_structure]" ] }, { @@ -313,7 +315,6 @@ "outputs": [], "source": [ "def make_sim_base(params, beta, shape) -> tda.JaxSimulation:\n", - "\n", " input_structures = make_input_structures(params, beta=beta)\n", "\n", " num_wg_in, num_wg_out = shape\n", @@ -326,7 +327,7 @@ " wgs_out = [waveguide_out_center]\n", " else:\n", " wgs_out = [waveguide_out_top, waveguide_out_bot]\n", - " \n", + "\n", " return tda.JaxSimulation(\n", " size=[Lx, Ly, Lz],\n", " grid_spec=td.GridSpec.auto(min_steps_per_wvl=steps_per_wvl, wavelength=wavelength),\n", @@ -340,7 +341,7 @@ " boundary_spec=td.BoundarySpec.pml(x=True, y=True, z=False),\n", " shutoff=1e-8,\n", " courant=0.9,\n", - " )\n" + " )" ] }, { @@ -369,16 +370,18 @@ } ], "source": [ - "f, ((ax1, ax2), (ax3, ax4)) = f, (axtop, axbot) = f, axes = plt.subplots(2, 2, tight_layout=True, figsize=(10,8))\n", + "f, ((ax1, ax2), (ax3, ax4)) = f, (axtop, axbot) = f, axes = plt.subplots(\n", + " 2, 2, tight_layout=True, figsize=(10, 8)\n", + ")\n", "\n", - "for num_in in (1,2):\n", - " for num_out in (1,2):\n", - " ax = axes[num_in - 1, num_out-1]\n", + "for num_in in (1, 2):\n", + " for num_out in (1, 2):\n", + " ax = axes[num_in - 1, num_out - 1]\n", " shape = (num_in, num_out)\n", " sim = make_sim_base(params0, beta=5.0, shape=shape)\n", " _ = sim.plot_eps(z=0, ax=ax)\n", " ax.set_title(f\"sim for shape={shape}\")\n", - " \n", + "\n", "plt.show()" ] }, @@ -568,16 +571,17 @@ "source": [ "from tidy3d.plugins.mode import ModeSolver\n", "from tidy3d.plugins.mode.web import run as run_mode_solver\n", + "\n", "num_modes = 4\n", "mode_spec = td.ModeSpec(num_modes=num_modes)\n", "\n", - "sim_start = make_sim_base(params0, beta=5.0, shape=(1,1))\n", + "sim_start = make_sim_base(params0, beta=5.0, shape=(1, 1))\n", "\n", "mode_solver = ModeSolver(\n", " simulation=sim_start.to_simulation()[0],\n", " plane=get_source_plane(sim_start.structures[0]),\n", " mode_spec=td.ModeSpec(num_modes=num_modes),\n", - " freqs=[freq0]\n", + " freqs=[freq0],\n", ")\n", "modes = run_mode_solver(mode_solver, reduce_simulation=True)" ] @@ -623,7 +627,7 @@ " field = modes.field_components[field_name].sel(mode_index=mode_ind)\n", " ax = axs[mode_ind, field_ind]\n", " field.real.plot(ax=ax)\n", - " ax.set_title(f'index={mode_ind}, {field_name}(y)')" + " ax.set_title(f\"index={mode_ind}, {field_name}(y)\")" ] }, { @@ -665,7 +669,6 @@ "outputs": [], "source": [ "def make_source(waveguide):\n", - "\n", " # source seeding the simulation\n", " return td.ModeSource(\n", " source_time=td.GaussianPulse(freq0=freq0, fwidth=freqw),\n", @@ -676,12 +679,11 @@ " direction=\"+\",\n", " )\n", "\n", - "def make_output_monitors(waveguides):\n", "\n", + "def make_output_monitors(waveguides):\n", " monitors = []\n", "\n", " for waveguide in waveguides:\n", - "\n", " # monitor where we compute the objective function from\n", " measurement_monitor = td.ModeMonitor(\n", " center=[meas_x, waveguide.geometry.center[1], 0],\n", @@ -692,7 +694,7 @@ " )\n", " monitors.append(measurement_monitor)\n", "\n", - " return monitors\n" + " return monitors" ] }, { @@ -719,13 +721,10 @@ " wg_in = sim.structures[source_index]\n", " forward_source_in = make_source(wg_in)\n", "\n", - " wgs_out = list(sim.structures)[int(num_wgs_in):]\n", + " wgs_out = list(sim.structures)[int(num_wgs_in) :]\n", " output_monitors = make_output_monitors(wgs_out)\n", "\n", - " return sim.updated_copy(\n", - " sources=[forward_source_in],\n", - " output_monitors=output_monitors\n", - " )" + " return sim.updated_copy(sources=[forward_source_in], output_monitors=output_monitors)" ] }, { @@ -754,7 +753,7 @@ } ], "source": [ - "ax = make_sim(params0, shape=(2,1), beta=1, source_index=0).plot(z=0)" + "ax = make_sim(params0, shape=(2, 1), beta=1, source_index=0).plot(z=0)" ] }, { @@ -778,14 +777,13 @@ "metadata": {}, "outputs": [], "source": [ - "def component(params=params0, beta=5, shape=(2,2)):\n", - "\n", + "def component(params=params0, beta=5, shape=(2, 2)):\n", " num_in, num_out = shape\n", " num_in = int(num_in)\n", " num_out = int(num_out)\n", - " \n", + "\n", " def get_S_column(sim_data):\n", - " \"\"\"Compute a column of the scattering matrix for a single dataset.\"\"\" \n", + " \"\"\"Compute a column of the scattering matrix for a single dataset.\"\"\"\n", " outputs = []\n", " for out_mnt in sim_data.simulation.output_monitors:\n", " amps = sim_data[out_mnt.name].amps\n", @@ -793,11 +791,14 @@ " outputs.append(amp)\n", " return outputs\n", "\n", - " sims = [make_sim(params, shape=shape, beta=beta, source_index=source_index) for source_index in range(num_in)]\n", + " sims = [\n", + " make_sim(params, shape=shape, beta=beta, source_index=source_index)\n", + " for source_index in range(num_in)\n", + " ]\n", " sim_datas = tda.web.run_async(sims, verbose=False, path_dir=\"data\")\n", "\n", " s_columns = [get_S_column(sim_data) for sim_data in sim_datas]\n", - " \n", + "\n", " # assemble the scattering matrix\n", " s_dict = {}\n", " for index_in in range(num_in):\n", @@ -808,7 +809,7 @@ " s_element = s_col[index_out]\n", " s_dict[(label_in, label_out)] = s_element\n", "\n", - " return sax.reciprocal(s_dict)\n" + " return sax.reciprocal(s_dict)" ] }, { @@ -844,7 +845,7 @@ } ], "source": [ - "component_sdict = component(params0, beta=1, shape=(1,2))\n", + "component_sdict = component(params0, beta=1, shape=(1, 2))\n", "component_sdict" ] }, @@ -924,10 +925,12 @@ ], "source": [ "def component1x2(params=params0, beta=1.0):\n", - " return component(params=params, beta=beta, shape=(1,2))\n", - " \n", + " return component(params=params, beta=beta, shape=(1, 2))\n", + "\n", + "\n", "def component2x2(params=params0, beta=1.0):\n", - " return component(params=params, beta=beta, shape=(2,2))\n", + " return component(params=params, beta=beta, shape=(2, 2))\n", + "\n", "\n", "circuit_fn, _ = sax.circuit(\n", " netlist={\n", @@ -972,7 +975,12 @@ "outputs": [], "source": [ "# how to pass specific parmaeters to each of the sub-functions for the instances\n", - "s = circuit_fn(splitter={\"params\": params0}, combiner={\"params\": 0 * params0}, beta=3, phase_sifter=dict(phi=2.0))\n" + "s = circuit_fn(\n", + " splitter={\"params\": params0},\n", + " combiner={\"params\": 0 * params0},\n", + " beta=3,\n", + " phase_sifter=dict(phi=2.0),\n", + ")" ] }, { @@ -1023,10 +1031,11 @@ "source": [ "from tidy3d.plugins.adjoint.utils.penalty import ErosionDilationPenalty\n", "\n", + "\n", "def penalty(params, beta) -> float:\n", " processed_params = pre_process(params, beta=beta)\n", " ed_penalty = ErosionDilationPenalty(length_scale=radius, pixel_size=design_region_dl, beta=100)\n", - " return ed_penalty.evaluate(processed_params)\n" + " return ed_penalty.evaluate(processed_params)" ] }, { @@ -1050,36 +1059,38 @@ " \"\"\"Circuit-level objective function.\"\"\"\n", "\n", " params1, params2 = params\n", - " \n", - " circuit_function = functools.partial(circuit_fn, splitter={\"params\": params1}, combiner={\"params\": params2}, beta=beta)\n", - " \n", + "\n", + " circuit_function = functools.partial(\n", + " circuit_fn, splitter={\"params\": params1}, combiner={\"params\": params2}, beta=beta\n", + " )\n", + "\n", " def top_minus_bot(phi: float) -> float:\n", " \"\"\"Power in top port minus power in bottom port.\"\"\"\n", "\n", - " #evaluate the circuit at phi\n", + " # evaluate the circuit at phi\n", " sdict = circuit_function(phase_shifter={\"phi\": phi})\n", - " \n", + "\n", " # S-parameters for the whole circuit\n", " s_00 = sdict[\"in\", \"out0\"]\n", " s_01 = sdict[\"in\", \"out1\"]\n", "\n", " # power at ports\n", - " power_top = jnp.sum(jnp.abs(s_00)**2)\n", - " power_bot = jnp.sum(jnp.abs(s_01)**2)\n", - " \n", + " power_top = jnp.sum(jnp.abs(s_00) ** 2)\n", + " power_bot = jnp.sum(jnp.abs(s_01) ** 2)\n", + "\n", " # top power minus bottom power\n", " return power_top - power_bot\n", "\n", " # combine objectives together: at worst, it will be -1, at best + 1.\n", " objective = (top_minus_bot(0.0) - top_minus_bot(np.pi)) / 2.0\n", - " \n", + "\n", " # combined penalty for both devices\n", " penalty_weight = 0.5\n", " feature_penalty1 = penalty(params=params1, beta=beta)\n", " feature_penalty2 = penalty(params=params2, beta=beta)\n", " feature_penalty = penalty_weight * (feature_penalty1 + feature_penalty2) / 2.0\n", "\n", - " return objective - feature_penalty\n" + " return objective - feature_penalty" ] }, { @@ -1097,7 +1108,7 @@ "metadata": {}, "outputs": [], "source": [ - "dJ_fn = jax.value_and_grad(J)\n" + "dJ_fn = jax.value_and_grad(J)" ] }, { @@ -1117,7 +1128,7 @@ "source": [ "params0_combined = np.stack((params0, params0), axis=0)\n", "\n", - "val, grad = dJ_fn(params0_combined, beta=1)\n" + "val, grad = dJ_fn(params0_combined, beta=1)" ] }, { @@ -1414,7 +1425,6 @@ "beta_final = 20\n", "\n", "for i in range(num_steps):\n", - "\n", " # compute gradient and current objective function value\n", "\n", " perc_done = i / num_steps\n", @@ -1425,7 +1435,7 @@ " print(f\"step = {i + 1}\")\n", " print(f\"\\tbeta = {beta:.4e}\")\n", " print(f\"\\tJ = {value:.4e}\")\n", - " print(f\"\\tgrad_norm = {np.linalg.norm(gradient):.4e}\") \n", + " print(f\"\\tgrad_norm = {np.linalg.norm(gradient):.4e}\")\n", "\n", " # compute and apply updates to the optimizer based on gradient (-1 sign to maximize obj_fn)\n", " updates, opt_state = optimizer.update(-gradient, opt_state, params)\n", @@ -1437,7 +1447,7 @@ "\n", " # save history\n", " Js.append(value)\n", - " params_history.append(params) \n", + " params_history.append(params)\n", " beta_history.append(beta)\n", "\n", "power = J(params_history[-1], beta=beta)\n", @@ -1451,7 +1461,7 @@ "source": [ "## Results\n", "\n", - "Finally, we can inpect the results.\n", + "Finally, we can inspect the results.\n", "\n", "First we plot the objective function over iteration number and note that it steadily increases." ] @@ -1478,7 +1488,7 @@ "plt.xlabel(\"iterations\")\n", "plt.ylabel(\"objective function\")\n", "plt.ylim(-1.5, 1)\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -1515,9 +1525,9 @@ "metadata": {}, "outputs": [], "source": [ - "sim1_final = make_sim(params1_final, beta=beta_final, source_index=0, shape=(1,2))\n", - "sim2_final = make_sim(params2_final, beta=beta_final, source_index=0, shape=(2,2))\n", - "sim3_final = make_sim(params2_final, beta=beta_final, source_index=1, shape=(2,2))\n" + "sim1_final = make_sim(params1_final, beta=beta_final, source_index=0, shape=(1, 2))\n", + "sim2_final = make_sim(params2_final, beta=beta_final, source_index=0, shape=(2, 2))\n", + "sim3_final = make_sim(params2_final, beta=beta_final, source_index=1, shape=(2, 2))" ] }, { @@ -1546,17 +1556,17 @@ } ], "source": [ - "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, tight_layout=True, figsize=(10,6))\n", + "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, tight_layout=True, figsize=(10, 6))\n", "\n", "sim1_final.plot_eps(z=0, ax=ax1)\n", "sim2_final.plot_eps(z=0, ax=ax2)\n", "sim3_final.plot_eps(z=0, ax=ax3)\n", "\n", - "ax1.set_title('first component (splitter)')\n", - "ax2.set_title('second component (combiner)')\n", - "ax3.set_title('second component (combiner)')\n", + "ax1.set_title(\"first component (splitter)\")\n", + "ax2.set_title(\"second component (combiner)\")\n", + "ax3.set_title(\"second component (combiner)\")\n", "\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -1583,7 +1593,7 @@ "\n", "sim1_final = sim1_final.copy(update=dict(monitors=(field_mnt,)))\n", "sim2_final = sim2_final.copy(update=dict(monitors=(field_mnt,)))\n", - "sim3_final = sim3_final.copy(update=dict(monitors=(field_mnt,)))\n" + "sim3_final = sim3_final.copy(update=dict(monitors=(field_mnt,)))" ] }, { @@ -1603,7 +1613,9 @@ "source": [ "sims_final = (sim1_final, sim2_final, sim3_final)\n", "\n", - "sim_data1_final, sim_data2_final, sim_data3_final = tda.web.run_async(sims_final, path_dir=\"data\", verbose=False)\n" + "sim_data1_final, sim_data2_final, sim_data3_final = tda.web.run_async(\n", + " sims_final, path_dir=\"data\", verbose=False\n", + ")" ] }, { @@ -1637,7 +1649,7 @@ "for sim_data_final, ax_eps, ax_fld, ax_int in zip(sim_datas, axes_eps, axes_fld, axes_int):\n", " sim_data_final.simulation.plot_eps(z=0.01, ax=ax_eps)\n", " sim_data_final.plot_field(\"field_mnt\", \"Ez\", z=0, ax=ax_fld)\n", - " sim_data_final.plot_field(\"field_mnt\", \"E\", \"abs^2\", z=0, ax=ax_int)\n" + " sim_data_final.plot_field(\"field_mnt\", \"E\", \"abs^2\", z=0, ax=ax_int)" ] }, { @@ -1656,7 +1668,6 @@ "outputs": [], "source": [ "def get_sim_data_right(phi):\n", - "\n", " out_top_1 = sim_data1_final[\"top\"].amps.sel(direction=\"+\", f=freq0, mode_index=0)\n", " out_bot_1 = sim_data1_final[\"bot\"].amps.sel(direction=\"+\", f=freq0, mode_index=0)\n", "\n", @@ -1666,10 +1677,10 @@ "\n", " src_top = sim2_final.sources[0]\n", " src_bot = sim3_final.sources[0]\n", - " \n", + "\n", " src_time_top = src_top.source_time.updated_copy(amplitude=abs(out_top_1), phase=phase_top)\n", " src_time_bot = src_bot.source_time.updated_copy(amplitude=abs(out_bot_1), phase=phase_bot)\n", - " \n", + "\n", " src_top = src_top.updated_copy(source_time=src_time_top)\n", " src_bot = src_bot.updated_copy(source_time=src_time_bot)\n", "\n", @@ -2418,16 +2429,18 @@ "alpha = 0.0\n", "f, (axes_eps, axes_fld, axes_int) = plt.subplots(3, 3, figsize=(10, 8), tight_layout=True)\n", "sim_datas = [sim_data1_final, sim_data_right_p0, sim_data_right_pi]\n", - "for sim_data_final, ax_eps, ax_fld, ax_int, phi in zip(sim_datas, axes_eps, axes_fld, axes_int, (None, \"0\", \"π\")):\n", + "for sim_data_final, ax_eps, ax_fld, ax_int, phi in zip(\n", + " sim_datas, axes_eps, axes_fld, axes_int, (None, \"0\", \"π\")\n", + "):\n", " sim_data_final.simulation.plot_eps(z=0.01, ax=ax_eps, source_alpha=alpha, monitor_alpha=0)\n", " sim_data_final.plot_field(\"field_mnt\", \"Ez\", z=0, ax=ax_fld)\n", " sim_data_final.plot_field(\"field_mnt\", \"E\", \"abs^2\", z=0, ax=ax_int)\n", "\n", " for ax in (ax_eps, ax_fld, ax_int):\n", " if phi is not None:\n", - " ax.set_title(rf'output sim (phi={phi})')\n", + " ax.set_title(rf\"output sim (phi={phi})\")\n", " else:\n", - " ax.set_title(\"input sim\")\n" + " ax.set_title(\"input sim\")" ] }, { @@ -2447,10 +2460,10 @@ "metadata": {}, "outputs": [], "source": [ - "power_top_p0 = jnp.sum(jnp.abs(jnp.array(sim_data_right_p0.output_data[0].amps.values))**2)\n", - "power_bot_p0 = jnp.sum(jnp.abs(jnp.array(sim_data_right_p0.output_data[1].amps.values))**2)\n", - "power_top_pi = jnp.sum(jnp.abs(jnp.array(sim_data_right_pi.output_data[0].amps.values))**2)\n", - "power_bot_pi = jnp.sum(jnp.abs(jnp.array(sim_data_right_pi.output_data[1].amps.values))**2)" + "power_top_p0 = jnp.sum(jnp.abs(jnp.array(sim_data_right_p0.output_data[0].amps.values)) ** 2)\n", + "power_bot_p0 = jnp.sum(jnp.abs(jnp.array(sim_data_right_p0.output_data[1].amps.values)) ** 2)\n", + "power_top_pi = jnp.sum(jnp.abs(jnp.array(sim_data_right_pi.output_data[0].amps.values)) ** 2)\n", + "power_bot_pi = jnp.sum(jnp.abs(jnp.array(sim_data_right_pi.output_data[1].amps.values)) ** 2)" ] }, { @@ -2473,13 +2486,13 @@ } ], "source": [ - "print('phi = 0')\n", - "print(f' Transmission_top = {100 * power_top_p0:.2f} %')\n", - "print(f' Transmission_bot = {100 * power_bot_p0:.2f} %')\n", + "print(\"phi = 0\")\n", + "print(f\" Transmission_top = {100 * power_top_p0:.2f} %\")\n", + "print(f\" Transmission_bot = {100 * power_bot_p0:.2f} %\")\n", "\n", - "print('phi = pi')\n", - "print(f' Transmission_top = {100 * power_top_pi:.2f} %')\n", - "print(f' Transmission_bot = {100 * power_bot_pi:.2f} %')" + "print(\"phi = pi\")\n", + "print(f\" Transmission_top = {100 * power_top_pi:.2f} %\")\n", + "print(f\" Transmission_bot = {100 * power_bot_pi:.2f} %\")" ] }, { @@ -2510,7 +2523,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.11.0" }, "nbdime-conflicts": { "local_diff": [ diff --git a/AdjointPlugin12LightExtractor.ipynb b/AdjointPlugin12LightExtractor.ipynb index e09b75f1..25399d55 100644 --- a/AdjointPlugin12LightExtractor.ipynb +++ b/AdjointPlugin12LightExtractor.ipynb @@ -26,24 +26,24 @@ "outputs": [], "source": [ "# Standard python imports.\n", - "from typing import List\n", - "import numpy as np\n", - "import matplotlib.pylab as plt\n", - "import scipy as sp\n", - "import optax\n", "import pickle\n", + "from typing import List\n", "\n", "# Import jax to be able to use automatic differentiation.\n", "import jax\n", "import jax.numpy as jnp\n", - "from jax import value_and_grad\n", + "import matplotlib.pylab as plt\n", + "import numpy as np\n", + "import optax\n", + "import scipy as sp\n", "\n", "# Import regular tidy3d.\n", "import tidy3d as td\n", - "import tidy3d.web as web\n", "\n", "# Import the components we need from the adjoint plugin.\n", "import tidy3d.plugins.adjoint as tda\n", + "import tidy3d.web as web\n", + "from jax import value_and_grad\n", "from tidy3d.plugins.adjoint.utils.filter import ConicFilter\n", "from tidy3d.plugins.adjoint.utils.penalty import ErosionDilationPenalty\n", "from tidy3d.plugins.adjoint.web import run" @@ -159,20 +159,24 @@ "source": [ "conic_filter = ConicFilter(radius=min_feature, design_region_dl=grid_size)\n", "\n", + "\n", "def tanh_projection(x, beta, eta=0.5):\n", " tanhbn = jnp.tanh(beta * eta)\n", " num = tanhbn + jnp.tanh(beta * (x - eta))\n", " den = tanhbn + jnp.tanh(beta * (1 - eta))\n", " return num / den\n", "\n", + "\n", "def filter_project(x, beta, eta=0.5):\n", " x = conic_filter.evaluate(x)\n", " return tanh_projection(x, beta=beta, eta=eta)\n", "\n", + "\n", "def pre_process(params, beta):\n", " params1 = filter_project(params, beta=beta)\n", " return params1\n", "\n", + "\n", "def get_eps(params, beta: float = 1.00) -> jnp.ndarray:\n", " \"\"\"Returns the permittivities after filter and projection transformations\"\"\"\n", " params1 = pre_process(params, beta=beta)\n", @@ -198,12 +202,15 @@ "def include_constant_regions(eps, circ_center=[0, 0], circ_radius=1.0) -> jnp.ndarray:\n", " # Build the geometric mask.\n", " yv, xv = jnp.meshgrid(y_grid, x_grid)\n", - " geo_mask = jnp.where(\n", - " jnp.abs((xv - circ_center[0]) ** 2 + (yv - circ_center[1]) ** 2)\n", - " <= (2 * circ_radius) ** 2,\n", - " 1,\n", - " 0,\n", - " ) * eps_max\n", + " geo_mask = (\n", + " jnp.where(\n", + " jnp.abs((xv - circ_center[0]) ** 2 + (yv - circ_center[1]) ** 2)\n", + " <= (2 * circ_radius) ** 2,\n", + " 1,\n", + " 0,\n", + " )\n", + " * eps_max\n", + " )\n", " eps = jnp.maximum(geo_mask, eps)\n", " return eps" ] @@ -226,13 +233,12 @@ " eps_val = jnp.array(eps).reshape((nx_grid, ny_grid, 1, 1))\n", " coords_x = [(cr_center_x - cr_l / 2) + ix * grid_size for ix in range(nx_grid)]\n", "\n", - " if unfold == False:\n", + " if not unfold:\n", " # Creation of a JaxCustomMedium using the values of the design parameters.\n", " coords_yp = [0 + iy * grid_size for iy in range(ny_grid)]\n", " coords = dict(x=coords_x, y=coords_yp, z=[0], f=[freq])\n", " eps_jax = {\n", - " f\"eps_{dim}{dim}\": tda.JaxDataArray(values=eps_val, coords=coords)\n", - " for dim in \"xyz\"\n", + " f\"eps_{dim}{dim}\": tda.JaxDataArray(values=eps_val, coords=coords) for dim in \"xyz\"\n", " }\n", " eps_dataset = tda.JaxPermittivityDataset(**eps_jax)\n", " eps_medium = tda.JaxCustomMedium(eps_dataset=eps_dataset, interp_method=\"linear\")\n", @@ -245,17 +251,14 @@ " coords = dict(x=coords_x, y=coords_y, z=[0], f=[freq])\n", " eps_jax = {\n", " f\"eps_{dim}{dim}\": tda.JaxDataArray(\n", - " values=jnp.concatenate((jnp.fliplr(jnp.copy(eps_val)), eps_val), axis=1), coords=coords\n", + " values=jnp.concatenate((jnp.fliplr(jnp.copy(eps_val)), eps_val), axis=1),\n", + " coords=coords,\n", " )\n", " for dim in \"xyz\"\n", " }\n", " eps_dataset = tda.JaxPermittivityDataset(**eps_jax)\n", - " eps_medium = tda.JaxCustomMedium(\n", - " eps_dataset=eps_dataset, interp_method=\"linear\"\n", - " )\n", - " box = tda.JaxBox(\n", - " center=(cr_center_x, 0, 0), size=(cr_l, cr_w, wg_thick)\n", - " )\n", + " eps_medium = tda.JaxCustomMedium(eps_dataset=eps_dataset, interp_method=\"linear\")\n", + " box = tda.JaxBox(center=(cr_center_x, 0, 0), size=(cr_l, cr_w, wg_thick))\n", " structure = [tda.JaxStructure(geometry=box, medium=eps_medium)]\n", " return structure" ] @@ -1097,12 +1100,14 @@ " dip_power += jnp.abs(field_mon.flux)\n", " return mode_power, dip_power\n", "\n", + "\n", "def penalty(params, beta) -> float:\n", " \"\"\"Penalize changes in structure after erosion and dilation to enforce larger feature sizes.\"\"\"\n", " params_processed = pre_process(params, beta=beta)\n", " ed_penalty = ErosionDilationPenalty(length_scale=min_feature, pixel_size=grid_size)\n", " return ed_penalty.evaluate(params_processed)\n", "\n", + "\n", "# Objective function to be passed to the optimization algorithm.\n", "def obj(param, beta: float = 1.0, step_num: int = None, verbose: bool = False) -> float:\n", " sim = make_adjoint_sim(param, beta)\n", @@ -1117,6 +1122,7 @@ " J = fom_val - penalty_weight * penalty_val\n", " return J, [sim_data, mode_power, dip_power, penalty_val]\n", "\n", + "\n", "# Function to calculate the objective function value and its\n", "# gradient with respect to the design parameters.\n", "obj_grad = value_and_grad(obj, has_aux=True)" @@ -1138,11 +1144,13 @@ "# where to store history\n", "history_fname = \"./misc/qe_light_coupler.pkl\"\n", "\n", + "\n", "def save_history(history_dict: dict) -> None:\n", " \"\"\"Convenience function to save the history to file.\"\"\"\n", " with open(history_fname, \"wb\") as file:\n", " pickle.dump(history_dict, file)\n", "\n", + "\n", "def load_history() -> dict:\n", " \"\"\"Convenience method to load the history from file.\"\"\"\n", " with open(history_fname, \"rb\") as file:\n", @@ -1180,14 +1188,12 @@ " history_dict = load_history()\n", " opt_state = history_dict[\"opt_states\"][-1]\n", " params = history_dict[\"params\"][-1]\n", - " opt_state = optimizer.init(params) \n", + " opt_state = optimizer.init(params)\n", " num_iters_completed = len(history_dict[\"params\"])\n", " print(\"Loaded optimization checkpoint from file.\")\n", - " print(\n", - " f\"Found {num_iters_completed} iterations previously completed out of {max_iter} total.\"\n", - " )\n", + " print(f\"Found {num_iters_completed} iterations previously completed out of {max_iter} total.\")\n", " if num_iters_completed < max_iter:\n", - " print(f\"Will resume optimization.\")\n", + " print(\"Will resume optimization.\")\n", " else:\n", " print(\"Optimization completed, will return results.\")\n", "\n", @@ -1226,7 +1232,7 @@ " print(f\"Iteration = ({i + 1} / {max_iter})\")\n", "\n", " # Compute gradient and current objective function value.\n", - " beta_i = i//iter_steps + beta_min\n", + " beta_i = i // iter_steps + beta_min\n", " (value, data), gradient = obj_grad(params, beta=beta_i, step_num=(i + 1))\n", " sim_data_i, mode_power_i, dip_power_i, penalty_val_i = data\n", " # Outputs.\n", @@ -1254,7 +1260,7 @@ " history_dict[\"beta\"].append(beta_i)\n", " history_dict[\"gradients\"].append(gradient)\n", " history_dict[\"opt_states\"].append(opt_state)\n", - " #history_dict[\"data\"].append(sim_data_i) # Uncomment to store data, can create large files.\n", + " # history_dict[\"data\"].append(sim_data_i) # Uncomment to store data, can create large files.\n", " save_history(history_dict)" ] }, @@ -1805,8 +1811,9 @@ "source": [ "# make the misc/ directory to store the GDS file if it doesnt exist already\n", "import os\n", - "if not os.path.exists('./misc/'):\n", - " os.mkdir('./misc/')\n", + "\n", + "if not os.path.exists(\"./misc/\"):\n", + " os.mkdir(\"./misc/\")\n", "\n", "sim_final.to_gds_file(\n", " fname=\"./misc/inv_des_light_extractor.gds\",\n", diff --git a/AdjointPlugin13Metasurface.ipynb b/AdjointPlugin13Metasurface.ipynb index bd8cdc2b..e1a73050 100644 --- a/AdjointPlugin13Metasurface.ipynb +++ b/AdjointPlugin13Metasurface.ipynb @@ -27,12 +27,10 @@ }, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", "import jax\n", "import jax.numpy as jnp\n", - "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "import tidy3d as td\n", "import tidy3d.plugins.adjoint as tda" ] @@ -97,7 +95,7 @@ "source": [ "# total z size and the center of the slab\n", "Lz = buffer + dist_src + thickness + dist_mnt + buffer\n", - "z_center_slab = -Lz/2 + buffer + dist_src + thickness / 2.0" + "z_center_slab = -Lz / 2 + buffer + dist_src + thickness / 2.0" ] }, { @@ -144,15 +142,14 @@ "# substrate of the same permittivity as the mask\n", "substrate = td.Structure(\n", " geometry=td.Box.from_bounds(\n", - " rmin=(-td.inf, -td.inf, -1000),\n", - " rmax=(+td.inf, +td.inf, z_center_slab-thickness/2)\n", + " rmin=(-td.inf, -td.inf, -1000), rmax=(+td.inf, +td.inf, z_center_slab - thickness / 2)\n", " ),\n", - " medium=td.Medium(permittivity=permittivity)\n", + " medium=td.Medium(permittivity=permittivity),\n", ")\n", "\n", "# plane wave\n", "src = td.PlaneWave(\n", - " center=(0, 0, -Lz/2 + buffer),\n", + " center=(0, 0, -Lz / 2 + buffer),\n", " size=(td.inf, td.inf, 0),\n", " source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n", " direction=\"+\",\n", @@ -160,7 +157,7 @@ "\n", "# monitor we use to measure the intensity pattern above the device\n", "mnt_out = td.FieldMonitor(\n", - " center=(0, 0, +Lz/2 - buffer),\n", + " center=(0, 0, +Lz / 2 - buffer),\n", " size=(td.inf, td.inf, 0),\n", " freqs=[freq0],\n", " colocate=False,\n", @@ -173,7 +170,7 @@ " size=(td.inf, 0, td.inf),\n", " freqs=[freq0],\n", " name=\"side\",\n", - ")\n" + ")" ] }, { @@ -193,13 +190,14 @@ }, "outputs": [], "source": [ - "from tidy3d.plugins.adjoint.utils.filter import ConicFilter, BinaryProjector\n", + "from tidy3d.plugins.adjoint.utils.filter import BinaryProjector, ConicFilter\n", "\n", "radius = 0.20\n", "beta = 50\n", "\n", "conic_filter = ConicFilter(radius=radius, design_region_dl=dl_design_region)\n", "\n", + "\n", "def filter_project(params: jnp.ndarray, beta: float, eta=0.5) -> jnp.ndarray:\n", " \"\"\"Apply conic filter and binarization to the raw params.\"\"\"\n", " params_smooth = conic_filter.evaluate(params)\n", @@ -207,12 +205,14 @@ " params_smooth_binarized = binary_projector.evaluate(params_smooth)\n", " return params_smooth_binarized\n", "\n", - "def get_eps(params: jnp.ndarray, beta: float) -> jnp.ndarray: \n", + "\n", + "def get_eps(params: jnp.ndarray, beta: float) -> jnp.ndarray:\n", " \"\"\"Get the permittivity values (1, permittivity) array as a funciton of the parameters (0, 1)\"\"\"\n", " mask = filter_project(params, beta)\n", " eps = 1 + mask * (permittivity - 1)\n", " return eps.reshape((nx, ny, 1, 1))\n", - " \n", + "\n", + "\n", "def make_slab(params: jnp.ndarray, beta: float) -> tda.JaxStructure:\n", " \"\"\"make the phase mask as a function of the parameters for a given `beta` value.\"\"\"\n", "\n", @@ -246,11 +246,11 @@ }, "outputs": [], "source": [ - "def make_sim(params: jnp.ndarray, beta: float, pml_xy:bool=False) -> tda.JaxSimulation:\n", + "def make_sim(params: jnp.ndarray, beta: float, pml_xy: bool = False) -> tda.JaxSimulation:\n", " \"\"\"The `JaxSimulation` as a function of the design parameters.\"\"\"\n", "\n", " slab = make_slab(params, beta)\n", - " \n", + "\n", " # put a mesh override structure to ensure uniform dl across the slab\n", " design_region_mesh = td.MeshOverrideStructure(\n", " geometry=slab.geometry,\n", @@ -261,8 +261,7 @@ " return tda.JaxSimulation(\n", " size=(length, length, Lz),\n", " grid_spec=td.GridSpec.auto(\n", - " min_steps_per_wvl=min_steps_per_wvl,\n", - " override_structures=[design_region_mesh]\n", + " min_steps_per_wvl=min_steps_per_wvl, override_structures=[design_region_mesh]\n", " ),\n", " boundary_spec=td.BoundarySpec.pml(x=pml_xy, y=pml_xy, z=True),\n", " input_structures=[slab],\n", @@ -279,7 +278,7 @@ "id": "bcb0c957-b748-4dd4-9fbc-9311d0fd487d", "metadata": {}, "source": [ - "Let's make a simulation with some random starting parameters to inpsect our setup." + "Let's make a simulation with some random starting parameters to inspect our setup." ] }, { @@ -359,6 +358,7 @@ "\n", "logo_fname = \"misc/logo.png\"\n", "\n", + "\n", "def get_logo() -> np.ndarray:\n", " \"\"\"Get the Flexcompute logo from file, load it into a numpy array, rescale it to (0, 1).\"\"\"\n", " im = cv2.imread(logo_fname, cv2.IMREAD_GRAYSCALE).astype(float)\n", @@ -366,7 +366,8 @@ " im /= np.max(im)\n", " return im\n", "\n", - "def intensity_desired_fn_logo(xs:list, ys:list, rescale:float=0.5) -> np.ndarray:\n", + "\n", + "def intensity_desired_fn_logo(xs: list, ys: list, rescale: float = 0.5) -> np.ndarray:\n", " \"\"\"Return the 'value' of the flexcompute logo as a function of (x,y) with some rescaling.\"\"\"\n", " logo_values = get_logo()\n", "\n", @@ -376,12 +377,12 @@ " # re-interpolate the logo data at the supplied x,y points using xarray\n", " nx, ny = logo_values.shape\n", " xs_logo = np.linspace(rescale * min(xs), rescale * max(xs), nx)\n", - " ys_logo = np.linspace(rescale * min(ys), rescale * max(ys), ny) \n", + " ys_logo = np.linspace(rescale * min(ys), rescale * max(ys), ny)\n", " logo_dataarray = xr.DataArray(logo_values, coords=dict(x=xs_logo, y=ys_logo))\n", " logo_interp = logo_dataarray.interp(x=xs, y=ys)\n", "\n", " # handle any nans for out of bounds (replace with 0)\n", - " return np.nan_to_num(logo_interp.values, nan=np.min(logo_interp))\n" + " return np.nan_to_num(logo_interp.values, nan=np.min(logo_interp))" ] }, { @@ -401,7 +402,7 @@ }, "outputs": [], "source": [ - "xs = ys = np.linspace(-length/2, length/2, nx)\n", + "xs = ys = np.linspace(-length / 2, length / 2, nx)\n", "intensity_desired = intensity_desired_fn_logo(xs, ys)" ] }, @@ -425,11 +426,11 @@ } ], "source": [ - "plt.pcolormesh(xs, ys, intensity_desired.T, cmap='magma')\n", + "plt.pcolormesh(xs, ys, intensity_desired.T, cmap=\"magma\")\n", "plt.gca().set_aspect(\"equal\")\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.title('desired intensity pattern')\n", + "plt.xlabel(\"x\")\n", + "plt.ylabel(\"y\")\n", + "plt.title(\"desired intensity pattern\")\n", "plt.colorbar()\n", "plt.show()" ] @@ -643,7 +644,6 @@ " \"\"\"Convenience function to grab the (unnormalized) intensity patterns from the data.\"\"\"\n", "\n", " # first, grab the dataset storing the intensity values and coordinates\n", - " mnt_out_name = mnt_out.name\n", " intensity_dataset = sim_data.get_intensity(mnt_out.name)\n", " xs = intensity_dataset.coords[\"x\"]\n", " ys = intensity_dataset.coords[\"y\"]\n", @@ -656,15 +656,17 @@ "\n", " return intensity_measured, intensity_desired\n", "\n", + "\n", "# range within which to consider intensity as part of the objective function\n", "# eg. if the measured intensity is above int_max, we just consider it at the target value of 1.0\n", "intensity_range = int_min, int_max = (0.0, 1.0)\n", "\n", + "\n", "def intensity_diff_fn(sim_data: tda.JaxSimulationData) -> float:\n", " \"\"\"Returns a measure for the amount of difference between desired and target intensity patterns.\"\"\"\n", "\n", " intensity_measured, intensity_desired = get_intensities(sim_data)\n", - " \n", + "\n", " # normalize the measured intensity such that there's the same \"power\" in the signal as expected in the logo\n", " intensity_measured *= np.mean(intensity_desired) / intensity_norm_mean\n", "\n", @@ -673,15 +675,15 @@ " intensity_measured = jnp.minimum(intensity_measured, int_max)\n", " intensity_measured = jnp.maximum(intensity_measured, int_min)\n", " intensity_desired = int_range_magnitude * intensity_desired + int_min\n", - " \n", + "\n", " # take the elementwise difference\n", " difference = intensity_measured - intensity_desired\n", - " \n", + "\n", " # normalized by the 'worst case' (difference if measured was exact inverse of the target)\n", " difference_denominator = int_range_magnitude * np.ones_like(intensity_desired)\n", - " \n", + "\n", " # return the normalized norm of the difference\n", - " return jnp.linalg.norm(difference) / jnp.linalg.norm(difference_denominator)\n" + " return jnp.linalg.norm(difference) / jnp.linalg.norm(difference_denominator)" ] }, { @@ -707,9 +709,10 @@ "source": [ "from tidy3d.plugins.adjoint.utils.penalty import ErosionDilationPenalty\n", "\n", + "\n", "def penalty_fn(params, beta):\n", " processed_params = filter_project(params, beta=beta)\n", - " \n", + "\n", " penalty = ErosionDilationPenalty(pixel_size=dl_design_region, length_scale=radius)\n", " return penalty.evaluate(processed_params)" ] @@ -737,16 +740,17 @@ "source": [ "penalty_weight = 0.1\n", "\n", - "#let's ignore the penalty for the demo, but set to True to explore how it changes the final device\n", + "# let's ignore the penalty for the demo, but set to True to explore how it changes the final device\n", "include_penalty = False\n", "\n", + "\n", "def loss_fn(params: jnp.ndarray, beta: float) -> tuple[float, dict]:\n", " \"\"\"Loss function for the design, the difference in intensity + the feature size penalty.\"\"\"\n", - " \n", + "\n", " # construct and run the simulation\n", " sim = make_sim(params, beta=beta)\n", " sim_data = tda.web.run(sim, task_name=\"phase_mask_example\", verbose=False)\n", - " \n", + "\n", " # grab the respective and total losses\n", " intensity_diff_loss = intensity_diff_fn(sim_data)\n", " if include_penalty:\n", @@ -758,7 +762,7 @@ "\n", " # dictionary to stash results if we want to use them in the optimization loop\n", " aux_data = dict(intensity_diff=intensity_diff_loss, penalty=penalty_loss, sim_data=sim_data)\n", - " \n", + "\n", " return total_loss, aux_data" ] }, @@ -1118,7 +1122,7 @@ " (loss, aux_data), gradient = loss_fn_val_grad(params, beta=beta)\n", " penalty = aux_data[\"penalty\"]\n", " intensity_diff = aux_data[\"intensity_diff\"]\n", - " \n", + "\n", " # save history\n", " history[\"loss\"].append(loss)\n", " history[\"params\"].append(params)\n", @@ -1126,14 +1130,14 @@ " history[\"penalty\"].append(penalty)\n", " history[\"intensity_diff\"].append(intensity_diff)\n", " history[\"sim_data\"].append(aux_data[\"sim_data\"])\n", - " \n", + "\n", " # log some output\n", " print(f\"\\tloss = {loss:.3e}\")\n", " print(f\"\\t\\tpenalty = {penalty:.3e}\")\n", " print(f\"\\t\\tintensity difference = {intensity_diff:.3e}\")\n", " print(f\"\\tbeta = {beta:.2f}\")\n", - " print(f\"\\t|gradient| = {np.linalg.norm(gradient):.3e}\") \n", - " \n", + " print(f\"\\t|gradient| = {np.linalg.norm(gradient):.3e}\")\n", + "\n", " # compute and apply updates to the optimizer based on gradient (+1 sign to minimize loss_fn)\n", " updates, opt_state = optimizer.update(+gradient, opt_state, params)\n", " params = optax.apply_updates(params, updates)\n", @@ -1141,9 +1145,9 @@ " # cap the parameters between their bounds\n", " params = jnp.minimum(params, 1.0)\n", " params = jnp.maximum(params, 0.0)\n", - " \n", + "\n", " # update the beta value\n", - " beta += beta_increment\n" + " beta += beta_increment" ] }, { @@ -1601,7 +1605,7 @@ } ], "source": [ - "sim_data_final = td.web.run(sim_final, task_name='Inspect')" + "sim_data_final = td.web.run(sim_final, task_name=\"Inspect\")" ] }, { @@ -1626,7 +1630,7 @@ "source": [ "f, axes = plt.subplots(1, 2, figsize=(10, 4), tight_layout=True)\n", "\n", - "for (ax, name) in zip(axes, (\"output\", \"side\")):\n", + "for ax, name in zip(axes, (\"output\", \"side\")):\n", " sim_data_final.plot_field(field_monitor_name=name, field_name=\"E\", val=\"abs^2\", ax=ax)" ] }, @@ -1665,9 +1669,9 @@ "# target intensity\n", "im = ax0.imshow(np.rot90(intensity_desired), cmap=\"magma\")\n", "ax0.set_aspect(\"equal\")\n", - "ax0.set_xlabel('x')\n", - "ax0.set_ylabel('y')\n", - "ax0.set_title('target intensity (normalized)')\n", + "ax0.set_xlabel(\"x\")\n", + "ax0.set_ylabel(\"y\")\n", + "ax0.set_title(\"target intensity (normalized)\")\n", "plt.colorbar(im, ax=ax0)\n", "\n", "# optimization progress\n", @@ -1694,8 +1698,10 @@ "# final fields\n", "vmin = None\n", "vmax = None\n", - "for (ax, name) in zip((ax4, ax5), (\"output\", \"side\")):\n", - " sim_data_final.plot_field(field_monitor_name=name, field_name=\"E\", val=\"abs^2\", vmin=vmin, vmax=vmax, ax=ax)\n", + "for ax, name in zip((ax4, ax5), (\"output\", \"side\")):\n", + " sim_data_final.plot_field(\n", + " field_monitor_name=name, field_name=\"E\", val=\"abs^2\", vmin=vmin, vmax=vmax, ax=ax\n", + " )\n", "\n", "# plt.savefig('phase_mask.png', dpi=300)\n", "plt.show()" @@ -1737,7 +1743,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.11.0" }, "title": "Metasurface Inverse Design with Topology Optimization" }, diff --git a/AdjointPlugin14PreFab.ipynb b/AdjointPlugin14PreFab.ipynb index 50fe8802..4ff01db1 100644 --- a/AdjointPlugin14PreFab.ipynb +++ b/AdjointPlugin14PreFab.ipynb @@ -33,8 +33,8 @@ "outputs": [], "source": [ "# Standard python imports.\n", - "import numpy as np\n", "import matplotlib.pylab as plt\n", + "import numpy as np\n", "\n", "# Import regular tidy3d.\n", "import tidy3d as td\n", @@ -368,9 +368,7 @@ " X = np.linspace(-dev_width / 2, dev_width / 2, Nx)\n", " Y = np.linspace(-dev_height / 2, dev_height / 2, Ny)\n", " Z = np.array([0])\n", - " eps_array = td.SpatialDataArray(\n", - " np.expand_dims(eps_values, axis=-1), coords=dict(x=X, y=Y, z=Z)\n", - " )\n", + " eps_array = td.SpatialDataArray(np.expand_dims(eps_values, axis=-1), coords=dict(x=X, y=Y, z=Z))\n", " gc = td.Structure(\n", " geometry=td.Box(center=(0, 0, 0), size=(td.inf, td.inf, w_thick)),\n", " medium=td.CustomMedium.from_eps_raw(eps_array),\n", @@ -505,9 +503,7 @@ "zoom_bounds = ((xs, ys), (xs + zoom_size, ys + zoom_size))\n", "titles = [\"Device\", \"Prediction\", \"Correction\", \"Outcome\"]\n", "fig, axs = plt.subplots(2, 4, figsize=(20, 10))\n", - "for i, (title, data) in enumerate(\n", - " zip(titles, [device, prediction, correction, outcome])\n", - "):\n", + "for i, (title, data) in enumerate(zip(titles, [device, prediction, correction, outcome])):\n", " data.plot(ax=axs[0, i])\n", " axs[0, i].set_title(title)\n", " data.plot(bounds=zoom_bounds, ax=axs[1, i])\n", @@ -598,9 +594,7 @@ "metadata": {}, "outputs": [], "source": [ - "def run_simulations(\n", - " devices: list[np.ndarray], task_names: list[str]\n", - ") -> td.web.BatchData:\n", + "def run_simulations(devices: list[np.ndarray], task_names: list[str]) -> td.web.BatchData:\n", " \"\"\"Construct and run a set of simulations in a batch.\"\"\"\n", " sims = {\n", " task_name: make_simulation(device.to_ndarray())\n", @@ -2478,9 +2472,7 @@ "gds_library = gdstk.read_gds(infile=GDS_FILE)\n", "\n", "device_cell = device.to_gdstk(cell_name=\"gc_device\", gds_layer=(1, 0))\n", - "prediction_cell = prediction.binarize().to_gdstk(\n", - " cell_name=\"gc_prediction\", gds_layer=(9, 0)\n", - ")\n", + "prediction_cell = prediction.binarize().to_gdstk(cell_name=\"gc_prediction\", gds_layer=(9, 0))\n", "correction_cell = correction.to_gdstk(\n", " cell_name=\"gc_correction\", gds_layer=(90, 0), contour_approx_mode=3\n", ")\n", diff --git a/AdjointPlugin1Intro.ipynb b/AdjointPlugin1Intro.ipynb index d450218a..fc29578e 100644 --- a/AdjointPlugin1Intro.ipynb +++ b/AdjointPlugin1Intro.ipynb @@ -135,7 +135,7 @@ "source": [ "import jax\n", "import jax.numpy as jnp\n", - "import matplotlib.pylab as plt\n" + "import matplotlib.pylab as plt" ] }, { @@ -167,7 +167,7 @@ "\n", "\n", "def df(x):\n", - " return 5 * jnp.cos(x) - 2 * x + 1\n" + " return 5 * jnp.cos(x) - 2 * x + 1" ] }, { @@ -215,7 +215,7 @@ "plt.plot(xs, df(xs), label=\"df/dx(x)\")\n", "plt.xlabel(\"x\")\n", "plt.legend()\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -267,7 +267,7 @@ "plt.plot(xs_jax, df_jax_eval, \"k.\", label=\"df/dx(x) [using jax]\")\n", "plt.xlabel(\"x\")\n", "plt.legend()\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -314,7 +314,7 @@ "plt.plot(xs_jax, df_jax_eval, \"k.\", label=\"df/dx(x) [using jax]\")\n", "plt.xlabel(\"x\")\n", "plt.legend()\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -360,7 +360,7 @@ "plt.plot(xs, df_jax_eval, \"k-.\", label=\"df/dx(x) [using jax]\")\n", "plt.xlabel(\"x\")\n", "plt.legend()\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -403,7 +403,7 @@ "try:\n", " df_jax(1)\n", "except TypeError as e:\n", - " print(repr(e))\n" + " print(repr(e))" ] }, { @@ -449,7 +449,7 @@ "# gives derivative w.r.t. all three args\n", "dg_all = jax.grad(g, argnums=(0, 1, 2))\n", "dgdx, dgdy, dgdz = dg_all(1.0, 1.0, 1.0)\n", - "print(f\"dgdx={dgdx}, dgdy={dgdy}, dgdz={dgdz}\")\n" + "print(f\"dgdx={dgdx}, dgdy={dgdy}, dgdz={dgdz}\")" ] }, { @@ -508,7 +508,7 @@ "outputs": [], "source": [ "import tidy3d as td\n", - "from tidy3d.plugins.adjoint import JaxSimulation, JaxStructure, JaxMedium, JaxBox\n" + "from tidy3d.plugins.adjoint import JaxBox, JaxMedium, JaxSimulation, JaxStructure" ] }, { @@ -593,7 +593,7 @@ " output_monitors=[mode_mnt],\n", " boundary_spec=td.BoundarySpec.all_sides(td.PML()),\n", " grid_spec=td.GridSpec.uniform(dl=dl),\n", - " )\n" + " )" ] }, { @@ -642,7 +642,7 @@ "# sim, _ = jax_sim.to_simulation()\n", "for ax, dim in zip(axes, \"xyz\"):\n", " jax_sim.plot(**{dim: 0}, ax=ax)\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -683,7 +683,7 @@ " jax_mode_data = jax_sim_data.output_monitor_data[\"mode\"]\n", " mode_amps = jax_mode_data.amps\n", " amp = mode_amps.sel(direction=\"+\", f=freq0, mode_index=0)\n", - " return abs(amp) ** 2\n" + " return abs(amp) ** 2" ] }, { @@ -711,7 +711,7 @@ }, "outputs": [], "source": [ - "from tidy3d.plugins.adjoint.web import run as run_adjoint\n" + "from tidy3d.plugins.adjoint.web import run as run_adjoint" ] }, { @@ -732,7 +732,7 @@ " \"\"\"Compute power transmitted into 0th order mode given a set of scatterer parameters.\"\"\"\n", " jax_sim = make_simulation(center=center, size=size, eps=eps)\n", " jax_sim_data = run_adjoint(jax_sim, task_name=\"adjoint_power\", verbose=True)\n", - " return compute_power(jax_sim_data)\n" + " return compute_power(jax_sim_data)" ] }, { @@ -761,7 +761,7 @@ }, "outputs": [], "source": [ - "d_power = jax.value_and_grad(power, argnums=(0, 1, 2))\n" + "d_power = jax.value_and_grad(power, argnums=(0, 1, 2))" ] }, { @@ -1492,7 +1492,7 @@ } ], "source": [ - "power_value, (dp_center, dp_dsize, dp_deps) = d_power(center0, size0, eps0)\n" + "power_value, (dp_center, dp_dsize, dp_deps) = d_power(center0, size0, eps0)" ] }, { @@ -1533,7 +1533,7 @@ "print(f\"power = {power_value:.3f}\")\n", "print(f\"d_power/d_center = {dp_center}\")\n", "print(f\"d_power/d_size = {dp_dsize}\")\n", - "print(f\"d_power/d_eps = {dp_deps}\")\n" + "print(f\"d_power/d_eps = {dp_deps}\")" ] }, { diff --git a/AdjointPlugin2GradientChecking.ipynb b/AdjointPlugin2GradientChecking.ipynb index 8419bc7d..38c39c18 100644 --- a/AdjointPlugin2GradientChecking.ipynb +++ b/AdjointPlugin2GradientChecking.ipynb @@ -28,23 +28,23 @@ }, "outputs": [], "source": [ - "import numpy as np\n", - "import jax.numpy as jnp\n", + "from typing import List, Tuple\n", + "\n", "import jax\n", - "import tmm\n", + "import jax.numpy as jnp\n", "import matplotlib.pyplot as plt\n", - "from typing import Tuple, List\n", - "\n", + "import numpy as np\n", "import tidy3d as td\n", - "from tidy3d.web import run as run_sim\n", + "import tmm\n", "from tidy3d.plugins.adjoint import (\n", - " JaxSimulation,\n", " JaxBox,\n", " JaxMedium,\n", - " JaxStructure,\n", + " JaxSimulation,\n", " JaxSimulationData,\n", + " JaxStructure,\n", ")\n", - "from tidy3d.plugins.adjoint.web import run as run_adjoint\n" + "from tidy3d.plugins.adjoint.web import run as run_adjoint\n", + "from tidy3d.web import run as run_sim" ] }, { @@ -93,7 +93,7 @@ "theta = 0 * np.pi / 8\n", "\n", "# resolution\n", - "dl = 0.01\n" + "dl = 0.01" ] }, { @@ -144,7 +144,7 @@ " d_list = [np.inf] + slab_ds + [np.inf]\n", "\n", " # compute transmission with TMM\n", - " return tmm.coh_tmm(\"p\", n_list, d_list, theta, wavelength)[\"T\"]\n" + " return tmm.coh_tmm(\"p\", n_list, d_list, theta, wavelength)[\"T\"]" ] }, { @@ -178,7 +178,7 @@ ], "source": [ "T_tmm = compute_T_tmm(slab_eps=slab_eps0, slab_ds=slab_ds0)\n", - "print(f\"T (tmm) = {T_tmm:.3f}\")\n" + "print(f\"T (tmm) = {T_tmm:.3f}\")" ] }, { @@ -217,9 +217,7 @@ }, "outputs": [], "source": [ - "def compute_grad_tmm(\n", - " slab_eps=slab_eps0, slab_ds=slab_ds0\n", - ") -> Tuple[List[float], List[float]]:\n", + "def compute_grad_tmm(slab_eps=slab_eps0, slab_ds=slab_ds0) -> Tuple[List[float], List[float]]:\n", " \"\"\"Compute numerical gradient of transmission w.r.t. each of the slab permittivities and thicknesses using TMM.\"\"\"\n", "\n", " delta = 1e-4\n", @@ -252,7 +250,7 @@ "\n", " grad_tmm[arg_index][slab_index] = grad\n", " grad_eps, grad_ds = grad_tmm\n", - " return grad_eps, grad_ds\n" + " return grad_eps, grad_ds" ] }, { @@ -288,7 +286,7 @@ "source": [ "grad_eps_tmm, grad_ds_tmm = compute_grad_tmm()\n", "print(f\"gradient w.r.t. eps (tmm) = {grad_eps_tmm}\")\n", - "print(f\"gradient w.r.t. ds (tmm) = {grad_ds_tmm}\")\n" + "print(f\"gradient w.r.t. ds (tmm) = {grad_ds_tmm}\")" ] }, { @@ -329,7 +327,6 @@ " \"\"\"Create a JaxSimulation given the slab permittivities and thicknesses.\"\"\"\n", "\n", " # frequency setup\n", - " wavelength = td.C_0 / freq0\n", " fwidth = freq0 / 10.0\n", " freqs = [freq0]\n", "\n", @@ -347,8 +344,7 @@ " # make structures\n", " slabs = []\n", " z_start = -length_z / 2 + space_below\n", - " for (d, eps) in zip(slab_ds, slab_eps):\n", - "\n", + " for d, eps in zip(slab_ds, slab_eps):\n", " # dont track the gradient through the center of each slab\n", " # as tidy3d doesn't have enough information to properly process the interface between touching JaxBox objects\n", " z_center = jax.lax.stop_gradient(z_start + d / 2)\n", @@ -380,9 +376,7 @@ " boundary_y = td.Boundary.bloch_from_source(\n", " source=source, domain_size=sim_size[1], axis=1, medium=bck_medium\n", " )\n", - " boundary_spec = td.BoundarySpec(\n", - " x=boundary_x, y=boundary_y, z=td.Boundary.pml(num_layers=40)\n", - " )\n", + " boundary_spec = td.BoundarySpec(x=boundary_x, y=boundary_y, z=td.Boundary.pml(num_layers=40))\n", "\n", " # monitors\n", " mnt_z = length_z / 2 - space_above / 2.0\n", @@ -406,7 +400,7 @@ " medium=bck_medium,\n", " subpixel=True,\n", " shutoff=1e-8,\n", - " )\n" + " )" ] }, { @@ -479,7 +473,7 @@ "sim = make_sim()\n", "f, ax = plt.subplots(1, 1, figsize=(10, 10))\n", "sim.plot(y=0, ax=ax)\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -507,7 +501,7 @@ "def post_process_T(sim_data: JaxSimulationData) -> float:\n", " \"\"\"Given some JaxSimulationData from the run, return the transmission of \"p\" polarized light.\"\"\"\n", " amps = sim_data.output_monitor_data[\"diffraction\"].amps.sel(polarization=\"p\")\n", - " return jnp.sum(abs(amps.values) ** 2)\n" + " return jnp.sum(abs(amps.values) ** 2)" ] }, { @@ -536,7 +530,7 @@ " \"\"\"Given the slab permittivities and thicknesses, compute T, making sure to use `tidy3d.plugins.adjoint.web.run_adjoint`.\"\"\"\n", " sim = make_sim(slab_eps=slab_eps, slab_ds=slab_ds)\n", " sim_data = run_adjoint(sim, task_name=\"slab\", verbose=True)\n", - " return post_process_T(sim_data)\n" + " return post_process_T(sim_data)" ] }, { @@ -565,7 +559,7 @@ }, "outputs": [], "source": [ - "compute_T_and_grad_fdtd = jax.value_and_grad(compute_T_fdtd, argnums=(0, 1))\n" + "compute_T_and_grad_fdtd = jax.value_and_grad(compute_T_fdtd, argnums=(0, 1))" ] }, { @@ -1323,7 +1317,7 @@ "source": [ "# set logging level to ERROR to avoid redundant warnings from adjoint run\n", "td.config.logging_level = \"ERROR\"\n", - "T_fdtd, (grad_eps_fdtd, grad_ds_fdtd) = compute_T_and_grad_fdtd(slab_eps0, slab_ds0)\n" + "T_fdtd, (grad_eps_fdtd, grad_ds_fdtd) = compute_T_and_grad_fdtd(slab_eps0, slab_ds0)" ] }, { @@ -1351,7 +1345,7 @@ "outputs": [], "source": [ "grad_eps_fdtd = np.array(grad_eps_fdtd)\n", - "grad_ds_fdtd = np.array(grad_ds_fdtd)\n" + "grad_ds_fdtd = np.array(grad_ds_fdtd)" ] }, { @@ -1378,7 +1372,7 @@ ], "source": [ "print(f\"T (tmm) = {T_tmm:.5f}\")\n", - "print(f\"T (FDTD) = {T_fdtd:.5f}\")\n" + "print(f\"T (FDTD) = {T_fdtd:.5f}\")" ] }, { @@ -1432,7 +1426,7 @@ "rms_ds = np.linalg.norm(grad_ds_tmm - grad_ds_fdtd) / np.linalg.norm(grad_ds_tmm)\n", "\n", "print(f\"RMS error = {rms_eps * 100} %\")\n", - "print(f\"RMS error = {rms_ds * 100} %\")\n" + "print(f\"RMS error = {rms_ds * 100} %\")" ] }, { @@ -1468,12 +1462,8 @@ "grad_eps_fdtd_norm = normalize(grad_eps_fdtd)\n", "grad_ds_fdtd_norm = normalize(grad_ds_fdtd)\n", "\n", - "rms_eps = np.linalg.norm(grad_eps_tmm_norm - grad_eps_fdtd_norm) / np.linalg.norm(\n", - " grad_eps_tmm_norm\n", - ")\n", - "rms_ds = np.linalg.norm(grad_ds_tmm_norm - grad_ds_fdtd_norm) / np.linalg.norm(\n", - " grad_ds_tmm_norm\n", - ")\n" + "rms_eps = np.linalg.norm(grad_eps_tmm_norm - grad_eps_fdtd_norm) / np.linalg.norm(grad_eps_tmm_norm)\n", + "rms_ds = np.linalg.norm(grad_ds_tmm_norm - grad_ds_fdtd_norm) / np.linalg.norm(grad_ds_tmm_norm)" ] }, { @@ -1512,7 +1502,7 @@ "print(80 * \"-\")\n", "print(f\"\\tgrad_ds (tmm) = {grad_ds_tmm_norm}\")\n", "print(f\"\\tgrad_ds (FDTD) = {grad_ds_fdtd_norm}\")\n", - "print(f\"\\tRMS error = {rms_ds * 100} %\")\n" + "print(f\"\\tRMS error = {rms_ds * 100} %\")" ] }, { diff --git a/AdjointPlugin3InverseDesign.ipynb b/AdjointPlugin3InverseDesign.ipynb index ba5a33ae..5a4eedd6 100644 --- a/AdjointPlugin3InverseDesign.ipynb +++ b/AdjointPlugin3InverseDesign.ipynb @@ -24,22 +24,30 @@ "outputs": [], "source": [ "from typing import List\n", - "import numpy as np\n", - "import matplotlib.pylab as plt\n", "\n", "# import jax to be able to use automatic differentiation\n", "import jax.numpy as jnp\n", - "from jax import grad, value_and_grad\n", + "import matplotlib.pylab as plt\n", + "import numpy as np\n", "\n", "# import regular tidy3d\n", "import tidy3d as td\n", "import tidy3d.web as web\n", - "from tidy3d.plugins.mode import ModeSolver\n", + "from jax import value_and_grad\n", "\n", "# import the components we need from the adjoint plugin\n", - "from tidy3d.plugins.adjoint import JaxSimulation, JaxBox, JaxCustomMedium, JaxStructure, JaxStructureStaticGeometry\n", - "from tidy3d.plugins.adjoint import JaxSimulationData, JaxDataArray, JaxPermittivityDataset\n", + "from tidy3d.plugins.adjoint import (\n", + " JaxBox,\n", + " JaxCustomMedium,\n", + " JaxDataArray,\n", + " JaxPermittivityDataset,\n", + " JaxSimulation,\n", + " JaxSimulationData,\n", + " JaxStructure,\n", + " JaxStructureStaticGeometry,\n", + ")\n", "from tidy3d.plugins.adjoint.web import run\n", + "from tidy3d.plugins.mode import ModeSolver\n", "\n", "# set random seed to get same results\n", "np.random.seed(2)" @@ -85,7 +93,7 @@ "min_steps_per_wvl = 16\n", "# in the design region, we set uniform grid resolution,\n", "# and define the design parameters on the same grid\n", - "dl_design_region = 0.01 \n", + "dl_design_region = 0.01\n", "\n", "# space between boxes and PML\n", "buffer = 1.0 * wavelength\n", @@ -138,7 +146,7 @@ "outputs": [], "source": [ "waveguide = td.Structure(\n", - " geometry=td.Box(size=(2*Lx, wg_width, lz)), medium=td.Medium(permittivity=eps_wg)\n", + " geometry=td.Box(size=(2 * Lx, wg_width, lz)), medium=td.Medium(permittivity=eps_wg)\n", ")\n", "\n", "mode_size = (0, wg_width * 3, lz)\n", @@ -177,13 +185,14 @@ }, "outputs": [], "source": [ - "from tidy3d.plugins.adjoint.utils.filter import ConicFilter, BinaryProjector\n", + "from tidy3d.plugins.adjoint.utils.filter import BinaryProjector, ConicFilter\n", "\n", - "radius = .120\n", + "radius = 0.120\n", "beta = 50\n", "\n", "conic_filter = ConicFilter(radius=radius, design_region_dl=float(lx) / nx)\n", "\n", + "\n", "def filter_project(params, beta, eta=0.5):\n", " \"\"\"Apply conic filter and binarization to the raw params.\"\"\"\n", " params_smooth = conic_filter.evaluate(params)\n", @@ -191,18 +200,17 @@ " params_smooth_binarized = binary_projector.evaluate(params_smooth)\n", " return params_smooth_binarized\n", "\n", + "\n", "def get_eps(params, beta):\n", " \"\"\"Get the permittivity values (1, eps_wg) array as a funciton of the parameters (0, 1)\"\"\"\n", " processed_params = filter_project(params, beta)\n", " return 1 + processed_params * (eps_wg - 1)\n", "\n", - "def make_input_structures(params, beta) -> List[JaxStructure]:\n", "\n", + "def make_input_structures(params, beta) -> List[JaxStructure]:\n", " x0_min = -lx / 2 + dl_design_region / 2\n", " y0_min = -ly / 2 + dl_design_region / 2\n", "\n", - " input_structures = []\n", - "\n", " coords_x = [x0_min + dl_design_region * ix for ix in range(nx)]\n", " coords_y = [y0_min + dl_design_region * iy for iy in range(ny)]\n", "\n", @@ -210,9 +218,7 @@ "\n", " eps = get_eps(params, beta=beta).reshape((nx, ny, 1, 1))\n", "\n", - " field_components = {\n", - " f\"eps_{dim}{dim}\": JaxDataArray(values=eps, coords=coords) for dim in \"xyz\"\n", - " }\n", + " field_components = {f\"eps_{dim}{dim}\": JaxDataArray(values=eps, coords=coords) for dim in \"xyz\"}\n", " eps_dataset = JaxPermittivityDataset(**field_components)\n", " custom_medium = JaxCustomMedium(eps_dataset=eps_dataset)\n", " box = td.Box(center=(0, 0, 0), size=(lx, ly, lz))\n", @@ -241,14 +247,13 @@ "outputs": [], "source": [ "def make_sim_base(params, beta) -> JaxSimulation:\n", - "\n", " input_structures = make_input_structures(params, beta=beta)\n", " design_region_mesh = td.MeshOverrideStructure(\n", " geometry=td.Box(size=(lx, ly, lz)),\n", " dl=[dl_design_region] * 3,\n", " enforce=True,\n", " )\n", - " grid_spec=td.GridSpec.auto(\n", + " grid_spec = td.GridSpec.auto(\n", " wavelength=wavelength,\n", " min_steps_per_wvl=16,\n", " override_structures=[design_region_mesh],\n", @@ -521,6 +526,7 @@ ], "source": [ "from tidy3d.plugins.mode.web import run as run_mode_solver\n", + "\n", "num_modes = 4\n", "mode_spec = td.ModeSpec(num_modes=num_modes)\n", "\n", @@ -528,7 +534,7 @@ " simulation=sim_start.to_simulation()[0],\n", " plane=source_plane,\n", " mode_spec=td.ModeSpec(num_modes=num_modes),\n", - " freqs=[freq0]\n", + " freqs=[freq0],\n", ")\n", "modes = run_mode_solver(mode_solver, reduce_simulation=True)" ] @@ -570,12 +576,14 @@ "source": [ "fig, axs = plt.subplots(num_modes, 3, figsize=(12, 12), tight_layout=True)\n", "for mode_index in range(num_modes):\n", - " vmax = 1.1 * max(abs(modes.field_components[n].sel(mode_index=mode_index)).max() for n in (\"Ex\", \"Ey\", \"Ez\"))\n", + " vmax = 1.1 * max(\n", + " abs(modes.field_components[n].sel(mode_index=mode_index)).max() for n in (\"Ex\", \"Ey\", \"Ez\")\n", + " )\n", " for field_name, ax in zip((\"Ex\", \"Ey\", \"Ez\"), axs[mode_index]):\n", " field = modes.field_components[field_name].sel(mode_index=mode_index)\n", " field.real.plot(label=\"Real\", ax=ax)\n", " field.imag.plot(ls=\"--\", label=\"Imag\", ax=ax)\n", - " ax.set_title(f'index={mode_index}, {field_name}')\n", + " ax.set_title(f\"index={mode_index}, {field_name}\")\n", " ax.set_ylim(-vmax, vmax)\n", "\n", "axs[0, 0].legend()\n", @@ -711,6 +719,7 @@ "source": [ "from tidy3d.plugins.adjoint.utils.penalty import ErosionDilationPenalty\n", "\n", + "\n", "def penalty(params, beta):\n", " processed_params = filter_project(params, beta=beta)\n", "\n", @@ -743,7 +752,7 @@ " if step_num:\n", " task_name += f\"_step_{step_num}\"\n", " sim_data = run(sim, task_name=task_name, verbose=verbose)\n", - " penalty_weight = np.minimum(1, beta/25)\n", + " penalty_weight = np.minimum(1, beta / 25)\n", " return measure_power(sim_data) - penalty_weight * penalty(params, beta)" ] }, @@ -1710,17 +1719,16 @@ "beta_increment = 1\n", "\n", "for i in range(num_steps):\n", - "\n", " # compute gradient and current objective funciton value\n", "\n", " beta = beta0 + i * beta_increment\n", - " value, gradient = dJ_fn(params, step_num=i+1, beta=beta)\n", + " value, gradient = dJ_fn(params, step_num=i + 1, beta=beta)\n", "\n", " # outputs\n", " print(f\"step = {i + 1}\")\n", " print(f\"\\tbeta = {beta:.4e}\")\n", " print(f\"\\tJ = {value:.4e}\")\n", - " print(f\"\\tgrad_norm = {np.linalg.norm(gradient):.4e}\") \n", + " print(f\"\\tgrad_norm = {np.linalg.norm(gradient):.4e}\")\n", "\n", " # compute and apply updates to the optimizer based on gradient (-1 sign to maximize obj_fn)\n", " updates, opt_state = optimizer.update(-gradient, opt_state, params)\n", @@ -1732,7 +1740,7 @@ "\n", " # save history\n", " Js.append(value)\n", - " params_history.append(params) \n", + " params_history.append(params)\n", " beta_history.append(beta)\n", "\n", "power = J(params_history[-1], beta=beta)\n", @@ -2290,7 +2298,10 @@ } ], "source": [ - "final_power = sim_data_final[\"measurement\"].amps.sel(direction=\"+\", f=freq0, mode_index=mode_index_out).abs**2\n", + "final_power = (\n", + " sim_data_final[\"measurement\"].amps.sel(direction=\"+\", f=freq0, mode_index=mode_index_out).abs\n", + " ** 2\n", + ")\n", "print(f\"Final power conversion = {final_power*100:.2f}%\")" ] }, @@ -2311,10 +2322,7 @@ "outputs": [], "source": [ "sim_final.to_gds_file(\n", - " fname=\"./misc/inv_des_mode_conv.gds\", \n", - " z=0, \n", - " frequency=freq0, \n", - " permittivity_threshold=2.6\n", + " fname=\"./misc/inv_des_mode_conv.gds\", z=0, frequency=freq0, permittivity_threshold=2.6\n", ")" ] } diff --git a/AdjointPlugin4MultiObjective.ipynb b/AdjointPlugin4MultiObjective.ipynb index 470ebd7f..6bede27a 100644 --- a/AdjointPlugin4MultiObjective.ipynb +++ b/AdjointPlugin4MultiObjective.ipynb @@ -23,11 +23,10 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", - "import jax.numpy as jnp\n", "import jax\n", + "import jax.numpy as jnp\n", "import matplotlib.pylab as plt\n", - "\n", + "import numpy as np\n", "import tidy3d as td\n", "import tidy3d.plugins.adjoint as tda" ] @@ -94,7 +93,7 @@ "\n", "\n", "src_pos_x = -Lx / 2 + buffer / 2\n", - "mnt_pos_x = +Lx / 2 - buffer / 2\n" + "mnt_pos_x = +Lx / 2 - buffer / 2" ] }, { @@ -108,12 +107,9 @@ " \"\"\"Make a simulation as a function of the box permittivity and the frequency.\"\"\"\n", "\n", " box_size = ly + dy_sign * dy\n", - " \n", + "\n", " box = tda.JaxStructure(\n", - " geometry=tda.JaxBox(\n", - " center=(0.0, 0.0, 0.0),\n", - " size=(lx, box_size, lz)\n", - " ),\n", + " geometry=tda.JaxBox(center=(0.0, 0.0, 0.0), size=(lx, box_size, lz)),\n", " medium=tda.JaxMedium(permittivity=permittivity),\n", " )\n", "\n", @@ -122,7 +118,7 @@ " polarization=\"Ey\",\n", " source_time=td.GaussianPulse(\n", " freq0=freq0,\n", - " fwidth=freq0/10,\n", + " fwidth=freq0 / 10,\n", " ),\n", " )\n", "\n", @@ -143,7 +139,7 @@ " x=td.Boundary.pml(), y=td.Boundary.periodic(), z=td.Boundary.periodic()\n", " ),\n", " run_time=200 / src.source_time.fwidth,\n", - " )\n" + " )" ] }, { @@ -172,12 +168,12 @@ } ], "source": [ - "f, axes = plt.subplots(1,3, tight_layout=True, figsize=(10, 4))\n", + "f, axes = plt.subplots(1, 3, tight_layout=True, figsize=(10, 4))\n", "\n", "for ax, dy_sign in zip(axes, (-1, 0, 1)):\n", " jax_sim = make_sim(permittivity=permittivity_val, dy_sign=dy_sign)\n", " ax = jax_sim.plot(z=0, ax=ax)\n", - " ax.set_title(f\"size[y]={jax_sim.input_structures[0].geometry.size[1]:.2f}\")\n" + " ax.set_title(f\"size[y]={jax_sim.input_structures[0].geometry.size[1]:.2f}\")" ] }, { @@ -200,7 +196,7 @@ "def post_process(sim_data: tda.JaxSimulationData) -> float:\n", " \"\"\"O-th order diffracted power.\"\"\"\n", " amp = sim_data[\"diffraction\"].amps.sel(orders_x=0, orders_y=0)\n", - " return jnp.sum(jnp.abs(amp.values) ** 2)\n" + " return jnp.sum(jnp.abs(amp.values) ** 2)" ] }, { @@ -223,7 +219,7 @@ " sims = [make_sim(permittivity, dy_sign=dy_sign) for dy_sign in (-1, 0, 1)]\n", " sim_data_list = tda.web.run_async(sims, path_dir=\"data\", verbose=True)\n", " powers = [post_process(sim_data) for sim_data in sim_data_list]\n", - " return jnp.mean(jnp.array(powers))\n" + " return jnp.mean(jnp.array(powers))" ] }, { @@ -1142,7 +1138,7 @@ "\n", "power_average, grad_power_min = grad_objective(permittivity_val)\n", "print(f\"average power = {power_average:.2e}\")\n", - "print(f\"derivative of average power wrt permittivity = {grad_power_min:.2e}\")\n" + "print(f\"derivative of average power wrt permittivity = {grad_power_min:.2e}\")" ] }, { @@ -1170,17 +1166,17 @@ " for dy_sign in (-1, 0, 1):\n", " print(f\"working on dy_sign = {dy_sign}\")\n", "\n", - " def objective_fn(p):\n", + " def objective_fn(p, dy_sign=dy_sign):\n", " sims = make_sim(p, dy_sign=dy_sign)\n", " sim_data = tda.web.run(sims, task_name=f\"dy_sign={dy_sign}\", verbose=False)\n", " return post_process(sim_data)\n", - " \n", + "\n", " grad_fn = jax.grad(objective_fn)\n", - " \n", + "\n", " gradient = grad_fn(permittivity)\n", " grad_avg += gradient / 3.0\n", - " \n", - " return grad_avg\n" + "\n", + " return grad_avg" ] }, { @@ -1200,7 +1196,7 @@ } ], "source": [ - "grad_man = grad_manual(permittivity_val)\n" + "grad_man = grad_manual(permittivity_val)" ] }, { @@ -1228,7 +1224,7 @@ ], "source": [ "print(f\"gradient (batched) = {grad_power_min:.4e}\")\n", - "print(f\"gradient (looped) = {grad_man:.4e}\")\n" + "print(f\"gradient (looped) = {grad_man:.4e}\")" ] }, { diff --git a/AdjointPlugin5BoundaryGradients.ipynb b/AdjointPlugin5BoundaryGradients.ipynb index 8d45aa87..494d4c4d 100644 --- a/AdjointPlugin5BoundaryGradients.ipynb +++ b/AdjointPlugin5BoundaryGradients.ipynb @@ -27,14 +27,13 @@ "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pylab as plt\n", - "import numpy as np\n", "import jax\n", "import jax.numpy as jnp\n", - "\n", + "import matplotlib.pylab as plt\n", + "import numpy as np\n", "import tidy3d as td\n", - "import tidy3d.web as web\n", "import tidy3d.plugins.adjoint as tda\n", + "import tidy3d.web as web\n", "from tidy3d.plugins.adjoint.web import run" ] }, @@ -65,7 +64,7 @@ "wg_jax_medium = td.material_library[\"Si3N4\"][\"Philipp1973Sellmeier\"]\n", "\n", "wg_length = 1 * wavelength\n", - "taper_length = 10.\n", + "taper_length = 10.0\n", "\n", "spc_pml = 1.5 * wavelength\n", "\n", @@ -93,7 +92,7 @@ "x_end = +taper_length / 2\n", "xs = np.linspace(x_start, x_end, num_points)\n", "\n", - "ys0 = (wg_width_in + (wg_width_out - wg_width_in) * (xs - x_start) / (x_end - x_start)) / 2.0\n" + "ys0 = (wg_width_in + (wg_width_out - wg_width_in) * (xs - x_start) / (x_end - x_start)) / 2.0" ] }, { @@ -122,11 +121,11 @@ } ], "source": [ - "plt.plot(xs, +ys0, 'ko-')\n", - "plt.plot(xs, -ys0, 'ko-')\n", - "plt.xlabel('x')\n", - "plt.ylabel('y')\n", - "plt.title('taper points')\n", + "plt.plot(xs, +ys0, \"ko-\")\n", + "plt.plot(xs, -ys0, \"ko-\")\n", + "plt.xlabel(\"x\")\n", + "plt.ylabel(\"y\")\n", + "plt.title(\"taper points\")\n", "plt.show()" ] }, @@ -147,20 +146,16 @@ "source": [ "def make_taper(ys) -> tda.JaxPolySlab:\n", " \"\"\"Create a JaxPolySlab for the taper based on the supplied y values.\"\"\"\n", - " \n", + "\n", " # note, jax doesnt work well with concatenating, so we just construct these vertices for Tidy3D in a loop.\n", - " \n", + "\n", " vertices = []\n", " for x, y in zip(xs, ys):\n", " vertices.append((x, y))\n", " for x, y in zip(xs[::-1], ys[::-1]):\n", " vertices.append((x, -y))\n", - " \n", - " return tda.JaxPolySlab(\n", - " vertices=vertices,\n", - " slab_bounds=(-1, 1),\n", - " axis=2\n", - " )" + "\n", + " return tda.JaxPolySlab(vertices=vertices, slab_bounds=(-1, 1), axis=2)" ] }, { @@ -213,46 +208,43 @@ " \"\"\"Make a JaxSimulation for the taper.\"\"\"\n", "\n", " wg_in_box = td.Box.from_bounds(\n", - " rmin=(-Lx, -wg_width_in/2, -td.inf),\n", - " rmax=(-Lx/2 + wg_length + 0.01, +wg_width_in/2, +td.inf)\n", + " rmin=(-Lx, -wg_width_in / 2, -td.inf),\n", + " rmax=(-Lx / 2 + wg_length + 0.01, +wg_width_in / 2, +td.inf),\n", " )\n", "\n", " wg_out_box = td.Box.from_bounds(\n", - " rmin=(+Lx/2 - wg_length - 0.01, -wg_width_out/2, -td.inf),\n", - " rmax=(+Lx, +wg_width_out/2, +td.inf)\n", + " rmin=(+Lx / 2 - wg_length - 0.01, -wg_width_out / 2, -td.inf),\n", + " rmax=(+Lx, +wg_width_out / 2, +td.inf),\n", " )\n", - " \n", + "\n", " taper_geo = make_taper(ys)\n", - " \n", + "\n", " wg_in = td.Structure(geometry=wg_in_box, medium=wg_medium)\n", " wg_out = td.Structure(geometry=wg_out_box, medium=wg_medium)\n", - " taper = tda.JaxStructureStaticMedium(\n", - " geometry=taper_geo, medium=wg_jax_medium\n", - " )\n", - " \n", - " mode_source=td.ModeSource(\n", - " center=(-Lx/2 + wg_length/2, 0, 0),\n", + " taper = tda.JaxStructureStaticMedium(geometry=taper_geo, medium=wg_jax_medium)\n", + "\n", + " mode_source = td.ModeSource(\n", + " center=(-Lx / 2 + wg_length / 2, 0, 0),\n", " size=(0, td.inf, td.inf),\n", - " source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0/10),\n", + " source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 10),\n", " direction=\"+\",\n", " )\n", - " \n", - " mode_monitor=td.ModeMonitor(\n", - " center=(+Lx/2 - wg_length/2, 0, 0),\n", + "\n", + " mode_monitor = td.ModeMonitor(\n", + " center=(+Lx / 2 - wg_length / 2, 0, 0),\n", " size=(0, td.inf, td.inf),\n", " freqs=[freq0],\n", " mode_spec=td.ModeSpec(),\n", - " name='mode',\n", + " name=\"mode\",\n", " )\n", - " \n", - " field_monitor=td.FieldMonitor(\n", + "\n", + " field_monitor = td.FieldMonitor(\n", " center=(0, 0, 0),\n", " size=(td.inf, td.inf, 0),\n", " freqs=[freq0],\n", - " name='field',\n", + " name=\"field\",\n", " )\n", "\n", - "\n", " return tda.JaxSimulation(\n", " size=(Lx, Ly, 0),\n", " structures=[wg_in, wg_out],\n", @@ -260,11 +252,10 @@ " output_monitors=[mode_monitor],\n", " monitors=[field_monitor] if include_field_mnt else [],\n", " sources=[mode_source],\n", - " run_time=100/freq0,\n", + " run_time=100 / freq0,\n", " grid_spec=td.GridSpec.auto(min_steps_per_wvl=30),\n", " boundary_spec=td.BoundarySpec.pml(x=True, y=True, z=False),\n", - " )\n", - " " + " )" ] }, { @@ -352,7 +343,7 @@ "source": [ "def measure_transmission(sim_data: tda.JaxSimulationData) -> float:\n", " \"\"\"Measure the first order transmission.\"\"\"\n", - " amp_data = sim_data['mode'].amps\n", + " amp_data = sim_data[\"mode\"].amps\n", " amp = amp_data.sel(f=freq0, direction=\"+\", mode_index=0)\n", " return abs(amp) ** 2" ] @@ -380,13 +371,14 @@ "source": [ "def get_ys(parameters: np.ndarray) -> np.ndarray:\n", " \"\"\"Convert arbitrary parameters to y values for the vertices (parameter (-inf, inf) -> wg width of (wg_width_out, wg_width_in).\"\"\"\n", - " \n", + "\n", " params_between_0_1 = (jnp.tanh(np.pi * parameters) + 1.0) / 2.0\n", - " \n", + "\n", " params_scaled = params_between_0_1 * (wg_width_out - wg_width_in) / 2.0\n", " ys = params_scaled + wg_width_in / 2\n", " return ys\n", "\n", + "\n", "def get_params(ys: np.ndarray) -> np.ndarray:\n", " \"\"\"inverse of above, get parameters from ys\"\"\"\n", " params_scaled = ys - wg_width_in / 2\n", @@ -395,12 +387,11 @@ " params = np.arctanh(tanh_pi_params) / np.pi\n", " return params\n", "\n", + "\n", "# assert that the inverse function works properly\n", "params_test = 2 * (np.random.random((10,)) - 0.5)\n", "ys_test = get_ys(params_test)\n", - "assert np.allclose(get_params(ys_test), params_test)\n", - " \n", - " " + "assert np.allclose(get_params(ys_test), params_test)" ] }, { @@ -418,7 +409,7 @@ "metadata": {}, "outputs": [], "source": [ - "def make_sim_params(parameters: np.ndarray, include_field_mnt: bool=False) -> tda.JaxSimulation:\n", + "def make_sim_params(parameters: np.ndarray, include_field_mnt: bool = False) -> tda.JaxSimulation:\n", " \"\"\"Make the simulation out of raw parameters.\"\"\"\n", " ys = get_ys(parameters)\n", " return make_sim(ys, include_field_mnt=include_field_mnt)" @@ -447,28 +438,30 @@ "metadata": {}, "outputs": [], "source": [ - "def smooth_penalty(parameters: np.ndarray, min_radius: float = .150, alpha: float = 1.0, kappa: float = 10.0) -> float:\n", + "def smooth_penalty(\n", + " parameters: np.ndarray, min_radius: float = 0.150, alpha: float = 1.0, kappa: float = 10.0\n", + ") -> float:\n", " \"\"\"Penalty between 0 and alpha based on radius of curvature.\"\"\"\n", "\n", " def quad_fit(p0, pc, p2):\n", " \"\"\"Quadratic bezier fit (and derivatives) for three points.\n", - " (x(t), y(t)) = P(t) = P0*t^2 + P1*2*t*(1-t) + P2*(1-t)^2\n", - " t in [0, 1]\n", + " (x(t), y(t)) = P(t) = P0*t^2 + P1*2*t*(1-t) + P2*(1-t)^2\n", + " t in [0, 1]\n", " \"\"\"\n", "\n", " # ensure curve goes through (x1, y1) at t=0.5\n", - " p1 = 2 * pc - p0/2 - p2/2\n", - " \n", + " p1 = 2 * pc - p0 / 2 - p2 / 2\n", + "\n", " def p(t):\n", " \"\"\"Bezier curve parameterization.\"\"\"\n", - " term0 = (1-t)**2 * (p0 - p1)\n", + " term0 = (1 - t) ** 2 * (p0 - p1)\n", " term1 = p1\n", " term2 = t**2 * (p2 - p1)\n", " return term0 + term1 + term2\n", "\n", " def d_p(t):\n", " \"\"\"First derivative function.\"\"\"\n", - " d_term0 = 2 * (1-t) * (p1 - p0)\n", + " d_term0 = 2 * (1 - t) * (p1 - p0)\n", " d_term2 = 2 * t * (p2 - p1)\n", " return d_term0 + d_term2\n", "\n", @@ -477,21 +470,20 @@ " d2_term0 = 2 * p0\n", " d2_term1 = -4 * p1\n", " d2_term2 = 2 * p2\n", - " return d2_term0 + d2_term1 + d2_term2 \n", - " \n", + " return d2_term0 + d2_term1 + d2_term2\n", "\n", " return p, d_p, d2_p\n", "\n", " def get_fit_vals(xs, ys):\n", - " \"\"\" Get the values of the bezier curve and its derivatives at t=0.5 along the taper.\"\"\"\n", - " \n", + " \"\"\"Get the values of the bezier curve and its derivatives at t=0.5 along the taper.\"\"\"\n", + "\n", " ps = jnp.stack((xs, ys), axis=1)\n", " p0 = ps[:-2]\n", " pc = ps[1:-1]\n", " p2 = ps[2:]\n", - " \n", + "\n", " p, d_p, d_2p = quad_fit(p0, pc, p2)\n", - " \n", + "\n", " ps = p(0.5)\n", " dps = d_p(0.5)\n", " d2ps = d_2p(0.5)\n", @@ -502,7 +494,7 @@ " ps, dps, d2ps = get_fit_vals(xs, ys)\n", " xp, yp = dps\n", " xp2, yp2 = d2ps\n", - " num = (xp**2 + yp**2) ** (3.0/2.0)\n", + " num = (xp**2 + yp**2) ** (3.0 / 2.0)\n", " den = abs(xp * yp2 - yp * xp2) + 1e-2\n", " return num / den\n", "\n", @@ -515,7 +507,7 @@ " rs = get_radii_curvature(xs, ys)\n", "\n", " # return the average penalty over the points\n", - " return jnp.sum(penalty_fn(rs)) / len(rs)\n" + " return jnp.sum(penalty_fn(rs)) / len(rs)" ] }, { @@ -539,9 +531,9 @@ "source": [ "from tidy3d.plugins.adjoint.utils.penalty import RadiusPenalty\n", "\n", - "penalty = RadiusPenalty(min_radius=.150, alpha=1.0, kappa=10.0)\n", + "penalty = RadiusPenalty(min_radius=0.150, alpha=1.0, kappa=10.0)\n", "vertices0 = jnp.array(make_taper(ys0).vertices)\n", - "penalty_value = penalty.evaluate(vertices0)\n" + "penalty_value = penalty.evaluate(vertices0)" ] }, { @@ -614,7 +606,7 @@ ], "source": [ "# desired ys\n", - "ys0 = np.linspace(wg_width_in/2 + 0.001, wg_width_out/2 - 0.001, num_points)\n", + "ys0 = np.linspace(wg_width_in / 2 + 0.001, wg_width_out / 2 - 0.001, num_points)\n", "\n", "# corresponding parameters\n", "params0 = get_params(ys0)\n", @@ -655,7 +647,7 @@ ], "source": [ "penalty_value = penalty.evaluate(vertices0)\n", - "print(f'starting penalty = {float(penalty_value):.2e}')" + "print(f\"starting penalty = {float(penalty_value):.2e}\")" ] }, { @@ -1027,7 +1019,7 @@ } ], "source": [ - "sim_data = web.run(sim.to_simulation()[0], task_name='taper fields')" + "sim_data = web.run(sim.to_simulation()[0], task_name=\"taper fields\")" ] }, { @@ -1056,9 +1048,9 @@ ], "source": [ "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 3))\n", - "sim_data.plot_field(field_monitor_name='field', field_name='Ez', val='real', ax=ax1)\n", - "sim_data.plot_field(field_monitor_name='field', field_name='E', val='abs', ax=ax2)\n", - "plt.show()\n" + "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"Ez\", val=\"real\", ax=ax1)\n", + "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs\", ax=ax2)\n", + "plt.show()" ] }, { @@ -1095,8 +1087,10 @@ "def objective(parameters: np.ndarray, verbose: bool = False) -> float:\n", " \"\"\"Construct simulation, run, and measure transmission.\"\"\"\n", " sim = make_sim_params(parameters, include_field_mnt=False)\n", - " sim_data = run(sim, task_name='adjoint_taper', path='data/simulation.hdf5', verbose=verbose)\n", - " return measure_transmission(sim_data) - penalty.evaluate(sim.input_structures[0].geometry.vertices_jax)" + " sim_data = run(sim, task_name=\"adjoint_taper\", path=\"data/simulation.hdf5\", verbose=verbose)\n", + " return measure_transmission(sim_data) - penalty.evaluate(\n", + " sim.input_structures[0].geometry.vertices_jax\n", + " )" ] }, { @@ -2147,8 +2141,8 @@ } ], "source": [ - "print(f'objective = {val:.2e}')\n", - "print(f'gradient = {np.nan_to_num(grad)}')" + "print(f\"objective = {val:.2e}\")\n", + "print(f\"gradient = {np.nan_to_num(grad)}\")" ] }, { @@ -2306,7 +2300,7 @@ "import optax\n", "\n", "# turn off warnings to reduce verbosity\n", - "td.config.logging_level='ERROR'\n", + "td.config.logging_level = \"ERROR\"\n", "\n", "# hyperparameters\n", "num_steps = 41\n", @@ -2322,17 +2316,16 @@ "param_history = [params]\n", "\n", "for i in range(num_steps):\n", - "\n", " # compute gradient and current objective funciton value\n", " value, gradient = grad_fn(params, verbose=False)\n", - " \n", + "\n", " # convert nan to 0 (infinite radius of curvature) and multiply all by -1 to maximize obj_fn.\n", " gradient = -1 * np.nan_to_num(np.array(gradient.copy()))\n", "\n", " # outputs\n", " print(f\"step = {i + 1}\")\n", " print(f\"\\tJ = {value:.4e}\")\n", - " print(f\"\\tgrad_norm = {np.linalg.norm(gradient):.4e}\") \n", + " print(f\"\\tgrad_norm = {np.linalg.norm(gradient):.4e}\")\n", "\n", " # compute and apply updates to the optimizer based on gradient\n", " updates, opt_state = optimizer.update(gradient, opt_state, params)\n", @@ -2340,7 +2333,7 @@ "\n", " # save history\n", " objective_history.append(value)\n", - " param_history.append(params) \n" + " param_history.append(params)" ] }, { @@ -2394,8 +2387,8 @@ ], "source": [ "ax = plt.plot(objective_history)\n", - "plt.xlabel('iteration number')\n", - "plt.ylabel('objective function')\n", + "plt.xlabel(\"iteration number\")\n", + "plt.ylabel(\"objective function\")\n", "plt.show()" ] }, @@ -2797,7 +2790,7 @@ } ], "source": [ - "sim_data_best = td.web.run(sim_best.to_simulation()[0], task_name='taper final')\n" + "sim_data_best = td.web.run(sim_best.to_simulation()[0], task_name=\"taper final\")" ] }, { @@ -2836,14 +2829,14 @@ "f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, tight_layout=True, figsize=(11, 7))\n", "\n", "# plot original\n", - "sim_data.plot_field(field_monitor_name='field', field_name='Ez', val='real', ax=ax1)\n", - "sim_data.plot_field(field_monitor_name='field', field_name='E', val='abs', ax=ax2)\n", + "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"Ez\", val=\"real\", ax=ax1)\n", + "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs\", ax=ax2)\n", "\n", "# plot optimized\n", - "sim_data_best.plot_field(field_monitor_name='field', field_name='Ez', val='real', ax=ax3)\n", - "sim_data_best.plot_field(field_monitor_name='field', field_name='E', val='abs', ax=ax4)\n", + "sim_data_best.plot_field(field_monitor_name=\"field\", field_name=\"Ez\", val=\"real\", ax=ax3)\n", + "sim_data_best.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs\", ax=ax4)\n", "\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -2870,7 +2863,9 @@ "source": [ "transmission_start = float(measure_transmission(sim_data))\n", "transmission_end = float(measure_transmission(sim_data_best))\n", - "print(f'Transmission improved from {(transmission_start * 100):.2f}% to {(transmission_end * 100):.2f}%')" + "print(\n", + " f\"Transmission improved from {(transmission_start * 100):.2f}% to {(transmission_end * 100):.2f}%\"\n", + ")" ] } ], diff --git a/AdjointPlugin6GratingCoupler.ipynb b/AdjointPlugin6GratingCoupler.ipynb index 28ca025a..5f6ac052 100644 --- a/AdjointPlugin6GratingCoupler.ipynb +++ b/AdjointPlugin6GratingCoupler.ipynb @@ -28,36 +28,35 @@ "outputs": [], "source": [ "# Standard python imports.\n", - "from typing import List\n", - "import numpy as np\n", - "import scipy as sp\n", - "import matplotlib.pylab as plt\n", - "import os\n", "import json\n", - "import pydantic as pd\n", - "from typing import Callable\n", + "import os\n", + "from typing import Callable, List\n", "\n", "# Import jax to be able to use automatic differentiation.\n", "import jax.numpy as jnp\n", "import jax.scipy as jsp\n", - "from jax import value_and_grad\n", + "import matplotlib.pylab as plt\n", + "import numpy as np\n", + "import pydantic as pd\n", + "import scipy as sp\n", "\n", "# Import regular tidy3d.\n", "import tidy3d as td\n", "import tidy3d.web as web\n", + "from jax import value_and_grad\n", "\n", "# Import the components we need from the adjoint plugin.\n", "from tidy3d.plugins.adjoint import (\n", - " JaxSimulation,\n", " JaxBox,\n", " JaxCustomMedium,\n", - " JaxStructure,\n", - " JaxSimulationData,\n", " JaxDataArray,\n", " JaxPermittivityDataset,\n", + " JaxSimulation,\n", + " JaxSimulationData,\n", + " JaxStructure,\n", ")\n", - "from tidy3d.plugins.adjoint.web import run\n", - "from tidy3d.plugins.adjoint.utils.penalty import ErosionDilationPenalty" + "from tidy3d.plugins.adjoint.utils.penalty import ErosionDilationPenalty\n", + "from tidy3d.plugins.adjoint.web import run" ] }, { @@ -295,7 +294,7 @@ "metadata": {}, "outputs": [], "source": [ - "from tidy3d.plugins.adjoint.utils.filter import ConicFilter, BinaryProjector\n", + "from tidy3d.plugins.adjoint.utils.filter import BinaryProjector, ConicFilter\n", "\n", "conic_filter = ConicFilter(radius=filter_radius, design_region_dl=dr_grid_size)\n", "\n", @@ -433,9 +432,7 @@ "\n", " # Creates a uniform mesh for the design region.\n", " adjoint_dr_mesh = td.MeshOverrideStructure(\n", - " geometry=td.Box(\n", - " center=(dr_center_x, 0, 0), size=(dr_size_x, dr_size_y, w_thick)\n", - " ),\n", + " geometry=td.Box(center=(dr_center_x, 0, 0), size=(dr_size_x, dr_size_y, w_thick)),\n", " dl=[dr_grid_size, dr_grid_size, dr_grid_size],\n", " enforce=True,\n", " )\n", @@ -524,9 +521,7 @@ "\n", "\n", "# Objective function to be passed to the optimization algorithm.\n", - "def obj(\n", - " design_param, beta: float = 1.0, step_num: int = None, verbose: bool = False\n", - ") -> float:\n", + "def obj(design_param, beta: float = 1.0, step_num: int = None, verbose: bool = False) -> float:\n", " sim = make_adjoint_sim(design_param, beta)\n", " task_name = \"inv_des\"\n", " if step_num:\n", @@ -558,9 +553,10 @@ }, "outputs": [], "source": [ - "import optax\n", "import pickle\n", "\n", + "import optax\n", + "\n", "# hyperparameters\n", "learning_rate = 0.3\n", "optimizer = optax.adam(learning_rate=learning_rate)\n", @@ -617,16 +613,13 @@ " params = history_dict[\"params\"][-1]\n", " num_iters_completed = len(history_dict[\"params\"])\n", " print(\"Loaded optimization checkpoint from file.\")\n", - " print(\n", - " f\"Found {num_iters_completed} iterations previously completed out of {total_iter} total.\"\n", - " )\n", + " print(f\"Found {num_iters_completed} iterations previously completed out of {total_iter} total.\")\n", " if num_iters_completed < total_iter:\n", - " print(f\"Will resume optimization.\")\n", + " print(\"Will resume optimization.\")\n", " else:\n", " print(\"Optimization completed, will return results.\")\n", "\n", "except FileNotFoundError:\n", - "\n", " params = np.array(init_par)\n", " opt_state = optimizer.init(params)\n", " history_dict = dict(\n", @@ -1112,9 +1105,7 @@ "power_0 = np.abs(coeffs_f.sel(mode_index=0)) ** 2\n", "power_0_db = 10 * np.log10(power_0)\n", "\n", - "sim_plot = sim_final.updated_copy(\n", - " symmetry=(0, 0, 0), monitors=(field_xy, field_xz, gc_efficiency)\n", - ")\n", + "sim_plot = sim_final.updated_copy(symmetry=(0, 0, 0), monitors=(field_xy, field_xz, gc_efficiency))\n", "sim_data_plot = sim_data_final.updated_copy(simulation=sim_plot)\n", "\n", "f, ax = plt.subplots(2, 2, figsize=(8, 6), tight_layout=True)\n", diff --git a/AdjointPlugin7Metalens.ipynb b/AdjointPlugin7Metalens.ipynb index 94a02a4c..42004246 100644 --- a/AdjointPlugin7Metalens.ipynb +++ b/AdjointPlugin7Metalens.ipynb @@ -39,17 +39,15 @@ "outputs": [], "source": [ "# standard python imports\n", - "import numpy as np\n", - "from numpy import random\n", + "import jax\n", + "import jax.numpy as jnp\n", "import matplotlib.pyplot as plt\n", - "\n", + "import numpy as np\n", "import tidy3d as td\n", - "from tidy3d import web\n", - "\n", "import tidy3d.plugins.adjoint as tda\n", - "from tidy3d.plugins.adjoint.web import run as run_adj\n", - "import jax\n", - "import jax.numpy as jnp" + "from numpy import random\n", + "from tidy3d import web\n", + "from tidy3d.plugins.adjoint.web import run as run_adj" ] }, { @@ -158,7 +156,7 @@ "length_z = space_below_sub + thickness_sub + H + 1.7 * focal_length\n", "\n", "# construct simulation size array\n", - "sim_size = (length_xy, length_xy, length_z)\n" + "sim_size = (length_xy, length_xy, length_z)" ] }, { @@ -188,7 +186,7 @@ "substrate = td.Structure(\n", " geometry=td.Box.from_bounds(\n", " rmin=(-td.inf, -td.inf, -1000),\n", - " rmax=(+td.inf, +td.inf, -length_z / 2 + space_below_sub + thickness_sub)\n", + " rmax=(+td.inf, +td.inf, -length_z / 2 + space_below_sub + thickness_sub),\n", " ),\n", " medium=SiO2,\n", ")" @@ -237,20 +235,23 @@ "xs = x_centers.flatten()\n", "ys = y_centers.flatten()\n", "\n", + "\n", "def get_sizes(params):\n", " \"\"\"Returns the actual side lengths of the boxes as a function of design parameters from (-inf, +inf).\"\"\"\n", " return S * (jnp.tanh(params) + 1.0) / 2.0\n", "\n", + "\n", "# initially, start with parameters of 0 (all boxes have side length S/2)\n", "params0 = 0 * np.ones(x_centers.shape)\n", "\n", + "\n", "def make_structures(params, apply_symmetry: bool = True):\n", " \"\"\"Make the JaxStructure objects that will be used as .input_structures.\"\"\"\n", - " \n", + "\n", " sizes = get_sizes(params)\n", " nx, ny = sizes.shape\n", " geometries = []\n", - " \n", + "\n", " for i in range(nx):\n", " i_quad = max(i, nx - 1 - i)\n", " for j in range(ny):\n", @@ -259,21 +260,19 @@ " x0 = x_centers[i, j]\n", " y0 = y_centers[i, j]\n", "\n", - " if apply_symmetry and symmetry[0] != 0 and x0 < -S/2:\n", + " if apply_symmetry and symmetry[0] != 0 and x0 < -S / 2:\n", + " continue\n", + "\n", + " if apply_symmetry and symmetry[1] != 0 and y0 < -S / 2:\n", " continue\n", "\n", - " if apply_symmetry and symmetry[1] != 0 and y0 < -S/2:\n", - " continue \n", + " geometry = tda.JaxBox(center=(x0, y0, center_z), size=(size, size, H))\n", "\n", - " geometry = tda.JaxBox(\n", - " center=(x0, y0, center_z),\n", - " size=(size, size, H)\n", - " )\n", - " \n", " geometries.append(geometry)\n", " medium = tda.JaxMedium(permittivity=n_Si**2)\n", " return [tda.JaxStructure(medium=medium, geometry=geo) for geo in geometries]\n", "\n", + "\n", "structures = make_structures(params0)" ] }, @@ -311,9 +310,7 @@ "grid_z = td.AutoGrid(min_steps_per_wvl=grids_per_unit_length)\n", "\n", "# we need to supply the wavelength because of the automatic mesh in z\n", - "grid_spec = td.GridSpec(\n", - " wavelength=wavelength, grid_x=grid_x, grid_y=grid_y, grid_z=grid_z\n", - ")\n", + "grid_spec = td.GridSpec(wavelength=wavelength, grid_x=grid_x, grid_y=grid_y, grid_z=grid_z)\n", "\n", "# put an override box over the pillars to avoid parsing a large amount of structures in the mesher\n", "grid_spec = grid_spec.copy(\n", @@ -328,7 +325,7 @@ " )\n", " ]\n", " )\n", - ")\n" + ")" ] }, { @@ -368,7 +365,7 @@ " pol_angle=0,\n", ")\n", "\n", - "run_time = 50 / fwidth\n" + "run_time = 50 / fwidth" ] }, { @@ -476,7 +473,7 @@ } ], "source": [ - "def make_sim(angles, apply_symmetry: bool=True):\n", + "def make_sim(angles, apply_symmetry: bool = True):\n", " metalens = make_structures(angles, apply_symmetry=apply_symmetry)\n", " sim = tda.JaxSimulation(\n", " size=sim_size,\n", @@ -494,6 +491,7 @@ " )\n", " return sim\n", "\n", + "\n", "sim = make_sim(params0)" ] }, @@ -743,7 +741,7 @@ "sim.plot(x=0.1, ax=ax1)\n", "sim.plot(y=0.1, ax=ax2)\n", "sim.plot(z=-length_z / 2 + space_below_sub + thickness_sub + H / 2, ax=ax3)\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -774,16 +772,17 @@ "# turn off warnings as we understand they are just about AutoGrid and can be ignored in our case\n", "td.config.logging_level = \"ERROR\"\n", "\n", + "\n", "def measure_focal_intensity(sim_data: tda.JaxSimulationData) -> float:\n", " \"\"\"Measures electric intensity at focal point.\"\"\"\n", - " return jnp.sum(sim_data.get_intensity('focal_point').values)\n", + " return jnp.sum(sim_data.get_intensity(\"focal_point\").values)\n", + "\n", "\n", "def J(params) -> float:\n", " \"\"\"Objective function, returns intensity at focal point as a function of params.\"\"\"\n", " sim = make_sim(params)\n", - " sim_data = run_adj(sim, task_name='metalens_adj', verbose=False)\n", - " return measure_focal_intensity(sim_data)\n", - " " + " sim_data = run_adj(sim, task_name=\"metalens_adj\", verbose=False)\n", + " return measure_focal_intensity(sim_data)" ] }, { @@ -943,9 +942,11 @@ "params_empty = -1e5 * np.ones_like(params0)\n", "J_empty = np.array(J(params_empty))\n", "\n", + "\n", "def J_normalized(params):\n", " return J(params) / J_empty\n", "\n", + "\n", "val_normalized = val / J_empty\n", "\n", "dJ_normalized = jax.value_and_grad(J_normalized)\n", @@ -1047,14 +1048,13 @@ "params_history = [params0]\n", "\n", "for i in range(num_steps):\n", - "\n", " # compute gradient and current objective funciton value\n", " value, gradient = dJ_normalized(params)\n", "\n", " # outputs\n", " print(f\"step = {i + 1}\")\n", " print(f\"\\tJ = {value:.4e}\")\n", - " print(f\"\\tgrad_norm = {np.linalg.norm(gradient):.4e}\") \n", + " print(f\"\\tgrad_norm = {np.linalg.norm(gradient):.4e}\")\n", "\n", " # compute and apply updates to the optimizer based on gradient (-1 sign to maximize obj_fn)\n", " updates, opt_state = optimizer.update(-gradient, opt_state, params)\n", @@ -1062,7 +1062,7 @@ "\n", " # save history\n", " J_history.append(value)\n", - " params_history.append(params) \n" + " params_history.append(params)" ] }, { @@ -1094,8 +1094,8 @@ ], "source": [ "plt.plot(J_history)\n", - "plt.xlabel('iterations')\n", - "plt.ylabel('objective function (focusing intensity enhancement)')\n", + "plt.xlabel(\"iterations\")\n", + "plt.ylabel(\"objective function (focusing intensity enhancement)\")\n", "plt.show()" ] }, @@ -1106,7 +1106,7 @@ "outputs": [], "source": [ "sim_before = make_sim(0 * params_after, apply_symmetry=False).to_simulation()[0]\n", - "sim_after = make_sim(params_after, apply_symmetry=False).to_simulation()[0]\n" + "sim_after = make_sim(params_after, apply_symmetry=False).to_simulation()[0]" ] }, { @@ -1142,21 +1142,23 @@ "metadata": {}, "outputs": [], "source": [ - "sim_after_mnt = sim_after.updated_copy(monitors=list(sim_after.monitors) + \n", - "[\n", - " td.FieldMonitor(\n", - " size=(0, td.inf, td.inf),\n", - " center=(0,0,0),\n", - " freqs=[f0],\n", - " name='fields_yz',\n", - " ),\n", - " td.FieldMonitor(\n", - " size=(td.inf, td.inf, 0),\n", - " center=(0,0,focal_z),\n", - " freqs=[f0],\n", - " name='far_field',\n", - " ), \n", - "])" + "sim_after_mnt = sim_after.updated_copy(\n", + " monitors=list(sim_after.monitors)\n", + " + [\n", + " td.FieldMonitor(\n", + " size=(0, td.inf, td.inf),\n", + " center=(0, 0, 0),\n", + " freqs=[f0],\n", + " name=\"fields_yz\",\n", + " ),\n", + " td.FieldMonitor(\n", + " size=(td.inf, td.inf, 0),\n", + " center=(0, 0, focal_z),\n", + " freqs=[f0],\n", + " name=\"far_field\",\n", + " ),\n", + " ]\n", + ")" ] }, { @@ -1497,7 +1499,7 @@ } ], "source": [ - "sim_data_after_mnt = web.run(sim_after_mnt, task_name='meta_near_field_after')" + "sim_data_after_mnt = web.run(sim_after_mnt, task_name=\"meta_near_field_after\")" ] }, { @@ -1520,8 +1522,8 @@ ], "source": [ "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 4))\n", - "sim_data_after_mnt.plot_field('far_field', 'int', vmax=105, ax=ax1)\n", - "sim_data_after_mnt.plot_field('fields_yz', 'int', vmax=180, ax=ax2)\n", + "sim_data_after_mnt.plot_field(\"far_field\", \"int\", vmax=105, ax=ax1)\n", + "sim_data_after_mnt.plot_field(\"fields_yz\", \"int\", vmax=180, ax=ax2)\n", "plt.show()" ] }, diff --git a/AdjointPlugin8WaveguideBend.ipynb b/AdjointPlugin8WaveguideBend.ipynb index 067a7bc3..235b6009 100644 --- a/AdjointPlugin8WaveguideBend.ipynb +++ b/AdjointPlugin8WaveguideBend.ipynb @@ -59,8 +59,8 @@ }, "outputs": [], "source": [ - "import numpy as np\n", - "import matplotlib.pylab as plt" + "import matplotlib.pylab as plt\n", + "import numpy as np" ] }, { @@ -110,11 +110,11 @@ "# note: we only optimize results at the central frequency for now.\n", "fwidth = freq0 / 10\n", "num_freqs = 10\n", - "freqs = np.linspace(freq0 - fwidth/2, freq0 + fwidth/2, num_freqs)\n", + "freqs = np.linspace(freq0 - fwidth / 2, freq0 + fwidth / 2, num_freqs)\n", "\n", "# define the discretization of the bend polygon in angle\n", "num_pts = 60\n", - "angles = np.linspace(0, np.pi/2, num_pts + 2)[1:-1]\n", + "angles = np.linspace(0, np.pi / 2, num_pts + 2)[1:-1]\n", "\n", "# refractive indices of waveguide and substrate (air above)\n", "n_wg = 2.0\n", @@ -239,24 +239,24 @@ "def make_vertices(params: np.ndarray) -> list:\n", " \"\"\"Make bend polygon vertices as a function of design parameters.\"\"\"\n", " vertices = []\n", - " vertices.append((-Lx/2 + 1e-2, -Ly/2 + t + radius))\n", - " vertices.append((-Lx/2 + t, -Ly/2 + t + radius + wmid/2))\n", + " vertices.append((-Lx / 2 + 1e-2, -Ly / 2 + t + radius))\n", + " vertices.append((-Lx / 2 + t, -Ly / 2 + t + radius + wmid / 2))\n", " for angle, param in zip(angles, params):\n", " thickness_i = thickness(param)\n", - " radius_i = radius + thickness_i/2.0\n", - " x = radius_i * np.sin(angle) -Lx/2 + t\n", - " y = radius_i * np.cos(angle) -Ly/2 + t\n", + " radius_i = radius + thickness_i / 2.0\n", + " x = radius_i * np.sin(angle) - Lx / 2 + t\n", + " y = radius_i * np.cos(angle) - Ly / 2 + t\n", " vertices.append((x, y))\n", - " vertices.append((-Lx/2 + t + radius + wmid/2, -Ly/2 + t))\n", - " vertices.append((-Lx/2 + t + radius, -Ly/2 + 1e-2))\n", - " vertices.append((-Lx/2 + t + radius - wmid/2, -Ly/2 + t))\n", + " vertices.append((-Lx / 2 + t + radius + wmid / 2, -Ly / 2 + t))\n", + " vertices.append((-Lx / 2 + t + radius, -Ly / 2 + 1e-2))\n", + " vertices.append((-Lx / 2 + t + radius - wmid / 2, -Ly / 2 + t))\n", " for angle, param in zip(angles[::-1], params[::-1]):\n", " thickness_i = thickness(param)\n", - " radius_i = radius - thickness_i/2.0\n", - " x = radius_i * np.sin(angle) -Lx/2 + t\n", - " y = radius_i * np.cos(angle) -Ly/2 + t\n", - " vertices.append((x, y)) \n", - " vertices.append((-Lx/2 + t, -Ly/2 + t + radius - wmid/2))\n", + " radius_i = radius - thickness_i / 2.0\n", + " x = radius_i * np.sin(angle) - Lx / 2 + t\n", + " y = radius_i * np.cos(angle) - Ly / 2 + t\n", + " vertices.append((x, y))\n", + " vertices.append((-Lx / 2 + t, -Ly / 2 + t + radius - wmid / 2))\n", " return vertices" ] }, @@ -353,9 +353,9 @@ " vertices = make_vertices(params)\n", " return tda.JaxPolySlab(\n", " vertices=vertices,\n", - " slab_bounds=(-h/2, h/2),\n", + " slab_bounds=(-h / 2, h / 2),\n", " axis=2,\n", - " ) " + " )" ] }, { @@ -475,17 +475,17 @@ "outputs": [], "source": [ "box_in = td.Box.from_bounds(\n", - " rmin=(-Lx/2 - 1, -Ly/2 + t + radius - wmid/2, -h/2),\n", - " rmax=(-Lx/2 + t + 1e-3, -Ly/2 + t + radius + wmid/2, +h/2),\n", + " rmin=(-Lx / 2 - 1, -Ly / 2 + t + radius - wmid / 2, -h / 2),\n", + " rmax=(-Lx / 2 + t + 1e-3, -Ly / 2 + t + radius + wmid / 2, +h / 2),\n", ")\n", "box_out = td.Box.from_bounds(\n", - " rmin=(-Lx/2 + t + radius - wmid/2, -Ly/2 - 1, -h/2),\n", - " rmax=(-Lx/2 + t + radius + wmid/2, -Ly/2 + t, +h/2),\n", + " rmin=(-Lx / 2 + t + radius - wmid / 2, -Ly / 2 - 1, -h / 2),\n", + " rmax=(-Lx / 2 + t + radius + wmid / 2, -Ly / 2 + t, +h / 2),\n", ")\n", "\n", "geo_sub = td.Box.from_bounds(\n", " rmin=(-td.inf, -td.inf, -10000),\n", - " rmax=(+td.inf, +td.inf, -h/2),\n", + " rmax=(+td.inf, +td.inf, -h / 2),\n", ")\n", "\n", "wg_in = td.Structure(geometry=box_in, medium=td.Medium(permittivity=n_wg**2))\n", @@ -547,9 +547,9 @@ "def eval_penalty(params):\n", " \"\"\"Evaluate penalty on a set of params looking at radius of curvature.\"\"\"\n", " vertices = make_vertices(params)\n", - " _vertices = jnp.array(vertices)\n", - " vertices_top = _vertices[1:num_pts+3] # select outer set of points along bend\n", - " vertices_bot = _vertices[num_pts+4:] # select inner set of points along bend\n", + " _vertices = jnp.array(vertices)\n", + " vertices_top = _vertices[1 : num_pts + 3] # select outer set of points along bend\n", + " vertices_bot = _vertices[num_pts + 4 :] # select inner set of points along bend\n", " penalty_top = penalty.evaluate(vertices_top)\n", " penalty_bot = penalty.evaluate(vertices_bot)\n", " return (penalty_top + penalty_bot) / 2.0" @@ -622,12 +622,12 @@ "\n", "mode_src = td.ModeSource(\n", " size=(0, mode_width, mode_height),\n", - " center=(-Lx/2 + t/2, -Ly/2 + t + radius, 0),\n", + " center=(-Lx / 2 + t / 2, -Ly / 2 + t + radius, 0),\n", " direction=\"+\",\n", " source_time=td.GaussianPulse(\n", " freq0=freq0,\n", " fwidth=fwidth,\n", - " )\n", + " ),\n", ")" ] }, @@ -663,7 +663,7 @@ "source": [ "mode_mnt = td.ModeMonitor(\n", " size=(mode_width, 0, mode_height),\n", - " center=(-Lx/2 + t + radius, -Ly/2 + t/2, 0),\n", + " center=(-Lx / 2 + t + radius, -Ly / 2 + t / 2, 0),\n", " name=monitor_name,\n", " freqs=[freq0],\n", " mode_spec=mode_spec,\n", @@ -671,14 +671,14 @@ "\n", "flux_mnt = td.FluxMonitor(\n", " size=(mode_width, 0, mode_height),\n", - " center=(-Lx/2 + t + radius, -Ly/2 + t/2, 0),\n", + " center=(-Lx / 2 + t + radius, -Ly / 2 + t / 2, 0),\n", " name=\"flux\",\n", " freqs=[freq0],\n", ")\n", "\n", "mode_mnt_bb = td.ModeMonitor(\n", " size=(mode_width, 0, mode_height),\n", - " center=(-Lx/2 + t + radius, -Ly/2 + t/2, 0),\n", + " center=(-Lx / 2 + t + radius, -Ly / 2 + t / 2, 0),\n", " name=\"mode_bb\",\n", " freqs=freqs.tolist(),\n", " mode_spec=mode_spec,\n", @@ -727,8 +727,8 @@ " grid_spec=td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl),\n", " boundary_spec=td.BoundarySpec.pml(x=True, y=True, z=True),\n", " monitors=monitors,\n", - " run_time = 10/fwidth,\n", - ")" + " run_time=10 / fwidth,\n", + " )" ] }, { @@ -864,9 +864,9 @@ "source": [ "sim = make_sim(params)\n", "\n", - "f, (ax1, ax2) = plt.subplots(1,2,tight_layout=True, figsize=(10,4))\n", + "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 4))\n", "ax = sim.plot(z=0.01, ax=ax1)\n", - "ax = sim.plot(x=-Lx/2+t/2, ax=ax2)" + "ax = sim.plot(x=-Lx / 2 + t / 2, ax=ax2)" ] }, { @@ -938,7 +938,9 @@ "source": [ "from tidy3d.plugins.mode import ModeSolver\n", "\n", - "ms = ModeSolver(simulation=sim.to_simulation()[0], plane=mode_src, mode_spec=mode_spec, freqs=mode_mnt.freqs)\n", + "ms = ModeSolver(\n", + " simulation=sim.to_simulation()[0], plane=mode_src, mode_spec=mode_spec, freqs=mode_mnt.freqs\n", + ")\n", "data = ms.solve()\n", "\n", "print(\"Effective index of computed modes: \", np.array(data.n_eff))\n", @@ -948,7 +950,7 @@ " for field_ind, field_name in enumerate((\"Ex\", \"Ey\", \"Ez\")):\n", " field = data.field_components[field_name].sel(mode_index=mode_ind)\n", " ax = axs[mode_ind, field_ind]\n", - " field.real.plot(x='y', y='z', ax=ax, cmap='RdBu')\n", + " field.real.plot(x=\"y\", y=\"z\", ax=ax, cmap=\"RdBu\")\n", " ax.set_title(f\"{field_name}, mode_ind={mode_ind}\")" ] }, @@ -978,7 +980,9 @@ "mode_index = 0\n", "\n", "# make the mode source with appropriate mode index\n", - "mode_src = ms.to_source(mode_index=mode_index, source_time=mode_src.source_time, direction=mode_src.direction)" + "mode_src = ms.to_source(\n", + " mode_index=mode_index, source_time=mode_src.source_time, direction=mode_src.direction\n", + ")" ] }, { @@ -1007,11 +1011,11 @@ }, "outputs": [], "source": [ - "def objective(params, use_fld_mnt:bool = True):\n", + "def objective(params, use_fld_mnt: bool = True):\n", " sim = make_sim(params, use_fld_mnt=use_fld_mnt)\n", - " sim_data = run(sim, task_name='bend', verbose=False)\n", + " sim_data = run(sim, task_name=\"bend\", verbose=False)\n", " amps = sim_data[monitor_name].amps.sel(direction=\"-\", mode_index=mode_index).values\n", - " transmission = jnp.abs(jnp.array(amps))**2\n", + " transmission = jnp.abs(jnp.array(amps)) ** 2\n", " J = jnp.sum(transmission) - eval_penalty(params)\n", " return J, sim_data" ] @@ -1279,17 +1283,16 @@ "data_history = []\n", "\n", "for i in range(num_steps):\n", - "\n", " # compute gradient and current objective funciton value\n", " (value, sim_data), gradient = val_grad(params)\n", - " \n", + "\n", " # multiply all by -1 to maximize obj_fn\n", " gradient = -np.array(gradient.copy())\n", "\n", " # outputs\n", " print(f\"step = {i + 1}\")\n", " print(f\"\\tJ = {value:.4e}\")\n", - " print(f\"\\tgrad_norm = {np.linalg.norm(gradient):.4e}\") \n", + " print(f\"\\tgrad_norm = {np.linalg.norm(gradient):.4e}\")\n", "\n", " # compute and apply updates to the optimizer based on gradient\n", " updates, opt_state = optimizer.update(gradient, opt_state, params)\n", @@ -1298,7 +1301,7 @@ " # save history\n", " objective_history.append(value)\n", " param_history.append(params)\n", - " data_history.append(sim_data)\n" + " data_history.append(sim_data)" ] }, { @@ -1331,9 +1334,9 @@ "source": [ "_ = plt.plot(objective_history)\n", "ax = plt.gca()\n", - "ax.set_xlabel('iteration number')\n", - "ax.set_ylabel('objective function')\n", - "ax.set_title('optimization progress')\n", + "ax.set_xlabel(\"iteration number\")\n", + "ax.set_ylabel(\"objective function\")\n", + "ax.set_title(\"optimization progress\")\n", "plt.show()" ] }, @@ -1356,7 +1359,7 @@ "data_start = data_history[0]\n", "\n", "sim_final = make_sim(param_history[-1])\n", - "data_final = data_history[-1]\n" + "data_final = data_history[-1]" ] }, { @@ -1416,15 +1419,15 @@ "source": [ "f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, tight_layout=True, figsize=(10, 6))\n", "\n", - "_ = data_start.plot_field('field', 'E', 'abs^2', ax=ax1)\n", + "_ = data_start.plot_field(\"field\", \"E\", \"abs^2\", ax=ax1)\n", "_ = sim_start.plot(z=0, ax=ax2)\n", - "ax1.set_title('starting device')\n", - "ax2.set_title('starting device')\n", + "ax1.set_title(\"starting device\")\n", + "ax2.set_title(\"starting device\")\n", "\n", - "_ = data_final.plot_field('field', 'E', 'abs^2', ax=ax3)\n", + "_ = data_final.plot_field(\"field\", \"E\", \"abs^2\", ax=ax3)\n", "_ = sim_final.plot(z=0, ax=ax4)\n", - "ax3.set_title('final device')\n", - "ax4.set_title('final device')\n", + "ax3.set_title(\"final device\")\n", + "ax4.set_title(\"final device\")\n", "\n", "plt.show()" ] @@ -1446,7 +1449,7 @@ "metadata": {}, "outputs": [], "source": [ - "amps = sim_data['mode_bb'].amps.sel(direction=\"-\", mode_index=mode_index)" + "amps = sim_data[\"mode_bb\"].amps.sel(direction=\"-\", mode_index=mode_index)" ] }, { @@ -1467,12 +1470,12 @@ } ], "source": [ - "transmission = abs(amps)**2 \n", + "transmission = abs(amps) ** 2\n", "transmission_percent = 100 * transmission\n", "transmission_percent.plot(x=\"f\")\n", "ax = plt.gca()\n", - "ax.set_title('mode_index=0, transmitted power %')\n", - "ax.set_ylabel('T (%)')\n", + "ax.set_title(\"mode_index=0, transmitted power %\")\n", + "ax.set_ylabel(\"T (%)\")\n", "plt.show()" ] }, @@ -1505,7 +1508,7 @@ "loss = 1 - transmission\n", "loss_db = 10 * np.log10(transmission)\n", "loss_db.plot(x=\"f\")\n", - "plt.ylabel('loss (dB)')\n", + "plt.ylabel(\"loss (dB)\")\n", "plt.show()" ] }, @@ -40464,8 +40467,8 @@ "\n", "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=False, figsize=(8, 4))\n", "\n", - "def animate(i):\n", "\n", + "def animate(i):\n", " # grab data at iteration \"i\"\n", " sim_data_i = data_history[i]\n", "\n", @@ -40473,18 +40476,19 @@ " sim_i = sim_data_i.simulation\n", " sim_i.plot_eps(z=0, monitor_alpha=0.0, source_alpha=0.0, ax=ax1)\n", " # ax1.set_aspect('equal')\n", - " \n", + "\n", " # plot intensity\n", " int_i = sim_data_i.get_intensity(\"field\")\n", - " int_i.squeeze().plot.pcolormesh(x='x', y='y', ax=ax2, add_colorbar=False, cmap=\"magma\")\n", + " int_i.squeeze().plot.pcolormesh(x=\"x\", y=\"y\", ax=ax2, add_colorbar=False, cmap=\"magma\")\n", " # ax2.set_aspect('equal')\n", - " \n", + "\n", + "\n", "# create animation\n", - "ani = animation.FuncAnimation(fig, animate, frames=len(data_history));\n", + "ani = animation.FuncAnimation(fig, animate, frames=len(data_history))\n", "plt.close()\n", "\n", "# display the animation (press \"play\" to start)\n", - "HTML(ani.to_jshtml())\n" + "HTML(ani.to_jshtml())" ] }, { diff --git a/AdjointPlugin9WDM.ipynb b/AdjointPlugin9WDM.ipynb index 256a1a19..3aca170c 100644 --- a/AdjointPlugin9WDM.ipynb +++ b/AdjointPlugin9WDM.ipynb @@ -25,14 +25,14 @@ "metadata": {}, "outputs": [], "source": [ + "import jax\n", + "import jax.numpy as jnp\n", "import matplotlib.pylab as plt\n", + "import numpy as np\n", "\n", "# first import tidy3d, its adjoint plugin, numpy, and jax.\n", "import tidy3d as td\n", "import tidy3d.plugins.adjoint as tda\n", - "import numpy as np\n", - "import jax.numpy as jnp\n", - "import jax\n", "\n", "np.random.seed(2)" ] @@ -62,7 +62,7 @@ "source": [ "# material information\n", "n_si = 3.49\n", - "n_sio2 = 1.45 # not used in 2D\n", + "n_sio2 = 1.45 # not used in 2D\n", "n_air = 1\n", "\n", "# design output wavelengths\n", @@ -83,7 +83,7 @@ "# size of design region\n", "lx = 2.8\n", "ly = 2.8\n", - "lz = td.inf # in 2D, we say the size of components is inf but the size of simulation is 0.\n", + "lz = td.inf # in 2D, we say the size of components is inf but the size of simulation is 0.\n", "\n", "# size of waveguides\n", "wg_width = 0.3\n", @@ -117,31 +117,31 @@ "\n", "wg_in = td.Structure(\n", " geometry=td.Box(\n", - " center=(-Lx/2, 0, 0),\n", + " center=(-Lx / 2, 0, 0),\n", " size=(wg_length * 2, wg_width, lz),\n", " ),\n", - " medium=td.Medium(permittivity=n_si**2)\n", + " medium=td.Medium(permittivity=n_si**2),\n", ")\n", "\n", "wg_top = td.Structure(\n", " geometry=td.Box(\n", - " center=(+Lx/2, +wg_width/2+wg_spacing/2, 0),\n", + " center=(+Lx / 2, +wg_width / 2 + wg_spacing / 2, 0),\n", " size=(wg_length * 2, wg_width, lz),\n", " ),\n", - " medium=td.Medium(permittivity=n_si**2)\n", + " medium=td.Medium(permittivity=n_si**2),\n", ")\n", "\n", "wg_bot = td.Structure(\n", " geometry=td.Box(\n", - " center=(+Lx/2, -wg_width/2-wg_spacing/2, 0),\n", + " center=(+Lx / 2, -wg_width / 2 - wg_spacing / 2, 0),\n", " size=(wg_length * 2, wg_width, lz),\n", " ),\n", - " medium=td.Medium(permittivity=n_si**2)\n", + " medium=td.Medium(permittivity=n_si**2),\n", ")\n", "\n", "# and a field monitor that measures fields on the z=0 plane\n", "fld_mnt = td.FieldMonitor(\n", - " center=(0,0,0),\n", + " center=(0, 0, 0),\n", " size=(td.inf, td.inf, 0),\n", " freqs=[freq_top, freq_bot],\n", " name=\"field\",\n", @@ -172,11 +172,8 @@ "nx = 55\n", "ny = 55\n", "\n", - "design_region_geo = tda.JaxBox(\n", - " size=(lx, ly, lz),\n", - " center=(0,0,0)\n", - ")\n", - "design_region_dl=lx/nx" + "design_region_geo = tda.JaxBox(size=(lx, ly, lz), center=(0, 0, 0))\n", + "design_region_dl = lx / nx" ] }, { @@ -202,44 +199,50 @@ "\n", "# note: params is an array of shape (nx, ny) that stores values between -inf (air) and +inf (silicon)\n", "\n", + "\n", "def tanh_projection(x, beta, eta=0.5):\n", " tanhbn = jnp.tanh(beta * eta)\n", " num = tanhbn + jnp.tanh(beta * (x - eta))\n", " den = tanhbn + jnp.tanh(beta * (1 - eta))\n", - " return num / den \n", + " return num / den\n", + "\n", "\n", "def filter_project(x, beta, eta=0.5):\n", " x = conic_filter.evaluate(x)\n", " return tanh_projection(x, beta=beta, eta=eta)\n", "\n", + "\n", "# number of times to filter -> project. Two times with a lower beta (~30) seems to give decent results.\n", "num_projections = 2\n", "\n", + "\n", "def pre_process(params, beta):\n", " \"\"\"Get the permittivity values (1, eps_wg) array as a funciton of the parameters (0,1)\"\"\"\n", " for _ in range(num_projections):\n", " params = filter_project(params, beta=beta)\n", " return params\n", "\n", + "\n", "def make_eps(params, beta):\n", " params = pre_process(params, beta=beta)\n", " eps_values = 1 + (n_si**2 - 1) * params\n", " return eps_values\n", "\n", + "\n", "def make_custom_medium(params, beta):\n", " \"\"\"Make JaxCustomMedium as a function of provided parameters.\"\"\"\n", " eps = make_eps(params, beta).reshape((nx, ny, 1, 1))\n", " eps = jnp.where(eps < 1, 1, eps)\n", " eps = jnp.where(eps > n_si**2, n_si**2, eps)\n", "\n", - " xs = list(jnp.linspace(-lx/2, lx/2, nx))\n", - " ys = list(jnp.linspace(-ly/2, ly/2, ny))\n", + " xs = list(jnp.linspace(-lx / 2, lx / 2, nx))\n", + " ys = list(jnp.linspace(-ly / 2, ly / 2, ny))\n", " zs = [0]\n", " freqs = [freq0]\n", " coords = dict(x=xs, y=ys, z=zs, f=freqs)\n", "\n", " eps_dataset = tda.JaxDataArray(values=eps, coords=coords)\n", - " \n", + "\n", " medium = tda.JaxCustomMedium(\n", " eps_dataset=tda.JaxPermittivityDataset(\n", " eps_xx=eps_dataset,\n", @@ -248,12 +251,9 @@ " )\n", " )\n", "\n", - " struct = tda.JaxStructure(\n", - " geometry=design_region_geo,\n", - " medium=medium\n", - " )\n", + " struct = tda.JaxStructure(geometry=design_region_geo, medium=medium)\n", "\n", - " return struct " + " return struct" ] }, { @@ -276,9 +276,8 @@ "outputs": [], "source": [ "def make_sim_base(params, beta):\n", - "\n", " input_struct = make_custom_medium(params, beta=beta)\n", - " \n", + "\n", " return tda.JaxSimulation(\n", " size=(Lx, Ly, Lz),\n", " grid_spec=td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl, wavelength=wavelength_top),\n", @@ -372,7 +371,7 @@ "\n", "# make a plane corresponding to where we wish to measure the input mode\n", "plane_in = td.Box(\n", - " center=(-Lx/2 + space_fraction * wg_length, 0, 0),\n", + " center=(-Lx / 2 + space_fraction * wg_length, 0, 0),\n", " size=mode_size,\n", ")\n", "\n", @@ -381,7 +380,7 @@ " simulation=sim_base.to_simulation()[0],\n", " plane=plane_in,\n", " freqs=[freq_top],\n", - " mode_spec=td.ModeSpec(num_modes=num_modes)\n", + " mode_spec=td.ModeSpec(num_modes=num_modes),\n", ")" ] }, @@ -606,12 +605,15 @@ "source": [ "fig, axs = plt.subplots(num_modes, 3, figsize=(12, 12), tight_layout=True)\n", "for mode_index in range(num_modes):\n", - " vmax = 1.1 * max(abs(mode_data.field_components[n].sel(mode_index=mode_index)).max() for n in (\"Ex\", \"Ey\", \"Ez\"))\n", + " vmax = 1.1 * max(\n", + " abs(mode_data.field_components[n].sel(mode_index=mode_index)).max()\n", + " for n in (\"Ex\", \"Ey\", \"Ez\")\n", + " )\n", " for field_name, ax in zip((\"Ex\", \"Ey\", \"Ez\"), axs[mode_index]):\n", " field = mode_data.field_components[field_name].sel(mode_index=mode_index)\n", " field.real.plot(label=\"Real\", ax=ax)\n", " field.imag.plot(ls=\"--\", label=\"Imag\", ax=ax)\n", - " ax.set_title(f'index={mode_index}, {field_name}')\n", + " ax.set_title(f\"index={mode_index}, {field_name}\")\n", " ax.set_ylim(-vmax, vmax)\n", "\n", "axs[0, 0].legend()\n", @@ -637,7 +639,7 @@ "outputs": [], "source": [ "mode_index = 0\n", - "mode_spec = td.ModeSpec(num_modes=mode_index+1)\n", + "mode_spec = td.ModeSpec(num_modes=mode_index + 1)\n", "mode_solver = mode_solver.updated_copy(mode_spec=mode_spec)" ] }, @@ -670,10 +672,7 @@ ")\n", "\n", "# make a basic monitor\n", - "mode_mnt = mode_solver.to_monitor(\n", - " freqs=[freq0],\n", - " name=\"_\"\n", - ")\n", + "mode_mnt = mode_solver.to_monitor(freqs=[freq0], name=\"_\")\n", "\n", "# construct the proper centers for the monitors at the 'top' and 'bot' ports\n", "mnt_center_top = list(plane_in.center)\n", @@ -688,8 +687,12 @@ "mnt_freqs = dict(top=freq_top, bot=freq_bot)\n", "\n", "# make two updated copies of the mode monitor with the proper frequencies, centers, and names\n", - "mode_mnt_top = mode_mnt.updated_copy(freqs=[mnt_freqs[\"top\"]], center=mnt_center_top, name=mnt_names[\"top\"])\n", - "mode_mnt_bot = mode_mnt.updated_copy(freqs=[mnt_freqs[\"bot\"]], center=mnt_center_bot, name=mnt_names[\"bot\"])\n", + "mode_mnt_top = mode_mnt.updated_copy(\n", + " freqs=[mnt_freqs[\"top\"]], center=mnt_center_top, name=mnt_names[\"top\"]\n", + ")\n", + "mode_mnt_bot = mode_mnt.updated_copy(\n", + " freqs=[mnt_freqs[\"bot\"]], center=mnt_center_bot, name=mnt_names[\"bot\"]\n", + ")\n", "\n", "# make another dictionary mapping the keys to the monitors\n", "mode_mnts = dict(top=mode_mnt_top, bot=mode_mnt_bot)" @@ -713,7 +716,7 @@ "outputs": [], "source": [ "Nf = 121\n", - "freqs_flux = np.linspace(freq_bot - fwidth/10, freq_top + fwidth/10, Nf)\n", + "freqs_flux = np.linspace(freq_bot - fwidth / 10, freq_top + fwidth / 10, Nf)\n", "\n", "flux_mnt_names = dict(top=\"flux_top\", bot=\"flux_bot\")\n", "\n", @@ -750,14 +753,13 @@ "outputs": [], "source": [ "def make_sim(params, beta):\n", - "\n", " output_monitors = [mode_mnts[\"top\"], mode_mnts[\"bot\"]]\n", "\n", " sim_base = make_sim_base(params, beta=beta)\n", " return sim_base.updated_copy(\n", " output_monitors=output_monitors,\n", " sources=[mode_src],\n", - " monitors=tuple(list(sim_base.monitors) + [flux_mnt_top, flux_mnt_bot])\n", + " monitors=tuple(list(sim_base.monitors) + [flux_mnt_top, flux_mnt_bot]),\n", " )" ] }, @@ -838,7 +840,7 @@ " mnt_name = mnt_names[mnt_key]\n", " freq = freqs[freq_key]\n", " mnt_data = sim_data[mnt_name]\n", - " amp = mnt_data.amps.sel(direction=\"+\", mode_index=0, f=freq) \n", + " amp = mnt_data.amps.sel(direction=\"+\", mode_index=0, f=freq)\n", " return jnp.abs(amp) ** 2\n", "\n", " power_max = get_power(\"top\", \"top\") + get_power(\"bot\", \"bot\")\n", @@ -862,6 +864,7 @@ "source": [ "from tidy3d.plugins.adjoint.utils.penalty import ErosionDilationPenalty\n", "\n", + "\n", "def penalty(params, beta) -> float:\n", " \"\"\"Penalty based on the amount of change after erosion and dilation of structures.\"\"\"\n", " params = pre_process(params, beta=beta)\n", @@ -1753,10 +1756,9 @@ "beta_history = []\n", "\n", "for i in range(num_steps):\n", - "\n", " perc_done = i / (num_steps - 1)\n", " beta_i = beta_min * (1 - perc_done) + beta_max * perc_done\n", - " \n", + "\n", " # compute gradient and current objective funciton value\n", " (value, data), gradient = grad_fn(params, beta=beta_i)\n", "\n", @@ -1764,7 +1766,7 @@ " print(f\"step = {i + 1}\")\n", " print(f\"\\tJ = {value:.4e}\")\n", " print(f\"\\tbeta = {beta_i:.2f}\")\n", - " print(f\"\\tgrad_norm = {np.linalg.norm(gradient):.4e}\") \n", + " print(f\"\\tgrad_norm = {np.linalg.norm(gradient):.4e}\")\n", "\n", " # compute and apply updates to the optimizer based on gradient (-1 sign to maximize obj_fn)\n", " updates, opt_state = optimizer.update(-gradient, opt_state, params)\n", @@ -1816,8 +1818,8 @@ ], "source": [ "plt.plot(Js)\n", - "plt.xlabel('iteration number')\n", - "plt.ylabel('objective function')\n", + "plt.xlabel(\"iteration number\")\n", + "plt.ylabel(\"objective function\")\n", "plt.show()" ] }, @@ -1902,7 +1904,7 @@ ], "source": [ "# plot flux\n", - "for key, color in zip((\"top\", \"bot\"), ('royalblue', 'firebrick')):\n", + "for key, color in zip((\"top\", \"bot\"), (\"royalblue\", \"firebrick\")):\n", " freq = freqs[key]\n", " flux_data = sim_data_final[flux_mnt_names[key]]\n", " wvl_nm = 1000 * td.C_0 / freq\n", @@ -1912,8 +1914,8 @@ " label = f\"{key} ({int(wvl_nm)} nm)\"\n", " plt.plot(wavelengths_nm, flux_db, label=label, color=color)\n", " plt.scatter([wvl_nm], [0], 100, marker=\"*\", color=color)\n", - " plt.xlabel('wavelength (nm)')\n", - " plt.ylabel('transmission (dB)')\n", + " plt.xlabel(\"wavelength (nm)\")\n", + " plt.ylabel(\"transmission (dB)\")\n", " plt.legend()\n", "\n", "plt.show()" @@ -1982,28 +1984,30 @@ "\n", "fig, (ax1, ax2, ax3) = fig, axes = plt.subplots(1, 3, tight_layout=False, figsize=(9, 4))\n", "\n", - "def animate(i):\n", "\n", + "def animate(i):\n", " sim_data_i = data_history[i]\n", "\n", " sim_i = sim_data_i.simulation\n", " sim_i.plot_eps(z=0.01, monitor_alpha=0, source_alpha=0, ax=ax1)\n", - " ax1.set_aspect('equal')\n", + " ax1.set_aspect(\"equal\")\n", "\n", " for key, ax in zip((\"top\", \"bot\"), (ax2, ax3)):\n", - "\n", " freq = freqs[key]\n", " wvl = 1000 * td.C_0 / freq\n", "\n", " int_i = sim_data_i.get_intensity(\"field\").sel(f=freq)\n", - " int_i.squeeze().plot.pcolormesh(x='x', y='y', ax=ax, add_colorbar=False, cmap=\"magma\", vmax=1000)\n", - " \n", - " ax.set_aspect('equal')\n", + " int_i.squeeze().plot.pcolormesh(\n", + " x=\"x\", y=\"y\", ax=ax, add_colorbar=False, cmap=\"magma\", vmax=1000\n", + " )\n", + "\n", + " ax.set_aspect(\"equal\")\n", " ax.set_title(f\"wavelength = {int(wvl)} nm\")\n", - " \n", + "\n", + "\n", "# create animation\n", "ani = animation.FuncAnimation(fig, animate, frames=len(data_history))\n", - "plt.close()\n" + "plt.close()" ] }, { @@ -68823,7 +68827,7 @@ ], "source": [ "# display the animation (press \"play\" to start)\n", - "HTML(ani.to_jshtml())\n" + "HTML(ani.to_jshtml())" ] }, { diff --git a/AllDielectricStructuralColor.ipynb b/AllDielectricStructuralColor.ipynb index 5b31b264..8895f679 100644 --- a/AllDielectricStructuralColor.ipynb +++ b/AllDielectricStructuralColor.ipynb @@ -32,14 +32,14 @@ "outputs": [], "source": [ "# Standard python imports.\n", - "import numpy as np\n", "import matplotlib.pylab as plt\n", - "from scipy.signal import find_peaks\n", + "import numpy as np\n", "import pandas as pd\n", "\n", "# Import regular tidy3d.\n", "import tidy3d as td\n", - "import tidy3d.plugins.design as tdd" + "import tidy3d.plugins.design as tdd\n", + "from scipy.signal import find_peaks" ] }, { @@ -143,7 +143,6 @@ " FDTD simulation object.\n", " \"\"\"\n", "\n", - "\n", " _inf = 10\n", "\n", " # Dielectric stack width (um).\n", @@ -222,9 +221,7 @@ " sim = td.Simulation(\n", " size=(size_x, size_y, size_z),\n", " center=(0, 0, 0),\n", - " grid_spec=td.GridSpec.auto(\n", - " min_steps_per_wvl=40, wavelength=(wl_min + wl_max) / 2\n", - " ),\n", + " grid_spec=td.GridSpec.auto(min_steps_per_wvl=40, wavelength=(wl_min + wl_max) / 2),\n", " structures=[substrate, si3n4_layer, tio2_layer, sio2_layer],\n", " sources=[plane_wave],\n", " monitors=[ref_monitor],\n", @@ -399,7 +396,9 @@ "outputs": [], "source": [ "method = tdd.MethodGrid()\n", - "design_space = tdd.DesignSpace(parameters=[param_p], method=method, task_name=\"GridSearch_Notebook\", path_dir=\"./data\")" + "design_space = tdd.DesignSpace(\n", + " parameters=[param_p], method=method, task_name=\"GridSearch_Notebook\", path_dir=\"./data\"\n", + ")" ] }, { @@ -419,7 +418,7 @@ "source": [ "# Post-processing function.\n", "def fn_post(sim_data: td.SimulationData) -> dict:\n", - " \"\"\"Pos-processing function to calculate and return the reflectance peak \n", + " \"\"\"Pos-processing function to calculate and return the reflectance peak\n", " in addition to the reflectance FluxMonitor.\n", " Parameters:\n", " sim_data: tidy3d.SimulationData\n", @@ -428,7 +427,7 @@ " dict:\n", " A dictionary containing the reflectance monitor,\n", " the reflectance peak and wavelength.\n", - " \"\"\" \n", + " \"\"\"\n", " R_data = sim_data[\"R\"]\n", " R = R_data.flux.values\n", " wavelength = td.C_0 / R_data.flux.f.values\n", @@ -846,9 +845,7 @@ ], "source": [ "df_wl = pd.DataFrame({\"Wavelength\": td.C_0 / df.loc[0, \"Reflectance\"].flux.f.values})\n", - "df_R = pd.DataFrame(\n", - " {df.loc[i, \"period\"]: df.loc[i, \"Reflectance\"].flux.values for i in df.index}\n", - ")\n", + "df_R = pd.DataFrame({df.loc[i, \"period\"]: df.loc[i, \"Reflectance\"].flux.values for i in df.index})\n", "df_R_wl = pd.concat([df_wl, df_R], axis=1)\n", "df_R_wl.set_index(\"Wavelength\", inplace=True)\n", "df_R_wl.head()" @@ -876,9 +873,11 @@ " xlabel=\"Wavelength (um)\",\n", " ylabel=\"R\",\n", ")\n", - "ax.legend([f\"period = {p:.2f} $\\\\mu m$\" for p in df_R_wl.columns],\n", - " bbox_to_anchor=(1.02, 1.02), \n", - " loc='upper left')\n", + "ax.legend(\n", + " [f\"period = {p:.2f} $\\\\mu m$\" for p in df_R_wl.columns],\n", + " bbox_to_anchor=(1.02, 1.02),\n", + " loc=\"upper left\",\n", + ")\n", "plt.show()" ] } diff --git a/AndersonLocalization.ipynb b/AndersonLocalization.ipynb index f0bd1480..b9b94e02 100644 --- a/AndersonLocalization.ipynb +++ b/AndersonLocalization.ipynb @@ -36,14 +36,13 @@ "from dataclasses import dataclass\n", "from typing import List, Tuple\n", "\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "import scipy.io as sio\n", - "\n", "import tidy3d as td\n", "from tidy3d import web\n", "\n", - "td.config.logging_level = 'ERROR'" + "td.config.logging_level = \"ERROR\"" ] }, { @@ -71,7 +70,7 @@ "# Define grid spec\n", "dl = wavelength / grids_pw\n", "grid_spec = td.GridSpec.uniform(dl=dl)\n", - " \n", + "\n", "# Define PML layers, for this we have no PML in x, y but `npml` cells in z\n", "periodic_bc = td.Boundary(plus=td.Periodic(), minus=td.Periodic())\n", "pml = td.Boundary(plus=td.Absorber(num_layers=npml), minus=td.Absorber(num_layers=npml))\n", @@ -87,22 +86,23 @@ "@dataclass\n", "class SimulationParameters:\n", " \"\"\"Stores parameters for a given simulation.\"\"\"\n", - " f0a : List[float] # Array of frequencies to scan via FFT (in units of freq0)\n", - " Lx : float # Length of slab in x\n", - " Ly : float # Length of slab in y\n", - " Lz : float # Length of slab in z\n", - " space : float # Space between PML and slab\n", - " fwidth : float # Bandwidth of the excitation pulse in Hz\n", - " offset : float # Gaussian source offset; the source peak is at time t = offset/fwidth\n", - " run_time : float # Run time of simulation (sec)\n", - " ff0 : float # Nominal volume filling fraction, actual filling fraction is lower due to overlap between spheres\n", - " radius : float # Radius of spheres (um)\n", - " material : str # type of material to use for spheres. \"dielectric\" or \"PEC\"\n", - " subpixel : td.SubpixelSpec # subpixel smoothening spec to be used\n", - " sim_mode : str # Mode of simulation (\"transmission\" or \"beam_spreading\")\n", - " task_name : str # Name of the task in tidy3d\n", - " ref_ind : float = None # Refractive index of the spheres, needed if material == 'dielectric' \n", - " Nt: int = 1 # Number of snapshots in the field time dependence monitor" + "\n", + " f0a: List[float] # Array of frequencies to scan via FFT (in units of freq0)\n", + " Lx: float # Length of slab in x\n", + " Ly: float # Length of slab in y\n", + " Lz: float # Length of slab in z\n", + " space: float # Space between PML and slab\n", + " fwidth: float # Bandwidth of the excitation pulse in Hz\n", + " offset: float # Gaussian source offset; the source peak is at time t = offset/fwidth\n", + " run_time: float # Run time of simulation (sec)\n", + " ff0: float # Nominal volume filling fraction, actual filling fraction is lower due to overlap between spheres\n", + " radius: float # Radius of spheres (um)\n", + " material: str # type of material to use for spheres. \"dielectric\" or \"PEC\"\n", + " subpixel: td.SubpixelSpec # subpixel smoothening spec to be used\n", + " sim_mode: str # Mode of simulation (\"transmission\" or \"beam_spreading\")\n", + " task_name: str # Name of the task in tidy3d\n", + " ref_ind: float = None # Refractive index of the spheres, needed if material == 'dielectric'\n", + " Nt: int = 1 # Number of snapshots in the field time dependence monitor" ] }, { @@ -113,21 +113,21 @@ "source": [ "# 1. Transmittance simulation (dielectric spheres)\n", "sim_params_1 = SimulationParameters(\n", - " Lx = 5 * wavelength,\n", - " Ly = 5 * wavelength,\n", - " Lz = 5 * wavelength,\n", - " space = 2 * wavelength,\n", - " radius = 0.1,\n", - " ff0 = 0.35,\n", - " fwidth = freq0 / 20.0,\n", - " offset = 10.0,\n", - " run_time = 8e-12,\n", - " f0a = np.linspace(0.9, 1.1, 201).tolist(),\n", - " material = \"dielectric\",\n", - " ref_ind = 3.5,\n", - " subpixel = td.SubpixelSpec(),\n", - " sim_mode = \"transmission\",\n", - " task_name = \"dielectric_transmission\",\n", + " Lx=5 * wavelength,\n", + " Ly=5 * wavelength,\n", + " Lz=5 * wavelength,\n", + " space=2 * wavelength,\n", + " radius=0.1,\n", + " ff0=0.35,\n", + " fwidth=freq0 / 20.0,\n", + " offset=10.0,\n", + " run_time=8e-12,\n", + " f0a=np.linspace(0.9, 1.1, 201).tolist(),\n", + " material=\"dielectric\",\n", + " ref_ind=3.5,\n", + " subpixel=td.SubpixelSpec(),\n", + " sim_mode=\"transmission\",\n", + " task_name=\"dielectric_transmission\",\n", ")" ] }, @@ -139,21 +139,21 @@ "source": [ "# 2. Transmittance simulation (PEC spheres)\n", "sim_params_2 = SimulationParameters(\n", - " Lx = 6 * wavelength,\n", - " Ly = 6 * wavelength,\n", - " Lz = 2 * wavelength,\n", - " space = 2 * wavelength,\n", - " radius = 0.05,\n", - " ff0 = 0.80,\n", - " fwidth = freq0 / 7.0,\n", - " offset = 10.0,\n", - " run_time = 10e-12,\n", - " f0a = np.linspace(0.8, 1.2, 201).tolist(),\n", - " material = \"PEC\",\n", - " ref_ind = None,\n", - " subpixel = td.SubpixelSpec(),\n", - " sim_mode = \"transmission\",\n", - " task_name = \"PEC_transmission\",\n", + " Lx=6 * wavelength,\n", + " Ly=6 * wavelength,\n", + " Lz=2 * wavelength,\n", + " space=2 * wavelength,\n", + " radius=0.05,\n", + " ff0=0.80,\n", + " fwidth=freq0 / 7.0,\n", + " offset=10.0,\n", + " run_time=10e-12,\n", + " f0a=np.linspace(0.8, 1.2, 201).tolist(),\n", + " material=\"PEC\",\n", + " ref_ind=None,\n", + " subpixel=td.SubpixelSpec(),\n", + " sim_mode=\"transmission\",\n", + " task_name=\"PEC_transmission\",\n", ")" ] }, @@ -165,22 +165,22 @@ "source": [ "# 3. Transverse spreading simulation (PEC spheres)\n", "sim_params_3 = SimulationParameters(\n", - " Lx = 20 * wavelength,\n", - " Ly = 20 * wavelength,\n", - " Lz = 2 * wavelength,\n", - " space = 2 * wavelength,\n", - " radius = 0.05,\n", - " ff0 = 0.80,\n", - " fwidth = freq0 / 7.0,\n", - " offset = 10.0,\n", - " run_time = 2e-12,\n", - " Nt = 2,\n", - " f0a = np.linspace(0.8, 1.2, 201).tolist(),\n", - " material = \"PEC\",\n", - " ref_ind = None,\n", - " subpixel = td.SubpixelSpec(),\n", - " sim_mode = \"beam_spreading\",\n", - " task_name = \"PEC_beam_spreading\",\n", + " Lx=20 * wavelength,\n", + " Ly=20 * wavelength,\n", + " Lz=2 * wavelength,\n", + " space=2 * wavelength,\n", + " radius=0.05,\n", + " ff0=0.80,\n", + " fwidth=freq0 / 7.0,\n", + " offset=10.0,\n", + " run_time=2e-12,\n", + " Nt=2,\n", + " f0a=np.linspace(0.8, 1.2, 201).tolist(),\n", + " material=\"PEC\",\n", + " ref_ind=None,\n", + " subpixel=td.SubpixelSpec(),\n", + " sim_mode=\"beam_spreading\",\n", + " task_name=\"PEC_beam_spreading\",\n", ")" ] }, @@ -203,19 +203,19 @@ "\n", " if sim_params.material == \"dielectric\":\n", " if sim_params.ref_ind is None:\n", - " raise ValueError(\"must specify SimulationParameters.ref_ind\") \n", + " raise ValueError(\"must specify SimulationParameters.ref_ind\")\n", " ff_appx = 1 - np.exp(-sim_params.ff0)\n", " medium_spheres = td.Medium(permittivity=sim_params.ref_ind**2)\n", " medium_out = td.Medium(permittivity=1 + (sim_params.ref_ind**2 - 1) * ff_appx)\n", - " \n", + "\n", " elif sim_params.material == \"PEC\":\n", " medium_spheres = td.PEC\n", - " medium_out = td.Medium(permittivity=1) \n", + " medium_out = td.Medium(permittivity=1)\n", "\n", " else:\n", " raise ValueError(f\"unrecognized 'material' of {sim_params.material}\")\n", "\n", - " return medium_spheres, medium_out " + " return medium_spheres, medium_out" ] }, { @@ -253,15 +253,11 @@ " position_x = np.random.uniform(-Lx / 2 - radius, Lx / 2 + radius)\n", " position_y = np.random.uniform(-Ly / 2 - radius, Ly / 2 + radius)\n", " position_z = np.random.uniform(-Lz / 2 - radius, Lz / 2 + radius)\n", - " sphere_i = td.Sphere(\n", - " center=[position_x, position_y, position_z],\n", - " radius=radius\n", - " )\n", + " sphere_i = td.Sphere(center=[position_x, position_y, position_z], radius=radius)\n", " sphere_geometries.append(sphere_i)\n", - " \n", + "\n", " spheres = td.Structure(\n", - " geometry=td.GeometryGroup(geometries=sphere_geometries),\n", - " medium=medium_spheres\n", + " geometry=td.GeometryGroup(geometries=sphere_geometries), medium=medium_spheres\n", " )\n", "\n", " # Define effective medium around the slab\n", @@ -274,12 +270,9 @@ "\n", " # Define incident plane wave\n", " gaussian = td.GaussianPulse(\n", - " freq0=freq0,\n", - " fwidth=sim_params.fwidth,\n", - " offset=sim_params.offset,\n", - " phase=0\n", + " freq0=freq0, fwidth=sim_params.fwidth, offset=sim_params.offset, phase=0\n", " )\n", - " \n", + "\n", " if sim_params.sim_mode == \"transmission\":\n", " source_size = (td.inf, td.inf, 0)\n", " elif sim_params.sim_mode == \"beam_spreading\":\n", @@ -345,7 +338,7 @@ " fields=[\"Ex\", \"Ey\", \"Ez\"],\n", " name=\"spread_monitor\",\n", " )\n", - " \n", + "\n", " # Records permittivity throughout simulation volume\n", " eps_monitor = td.PermittivityMonitor(\n", " center=[0.0, 0.0, 0.0],\n", @@ -355,7 +348,7 @@ " )\n", "\n", " monitors = [freq_monitorT, time_monitorT, eps_monitor]\n", - " \n", + "\n", " if sim_params.sim_mode == \"transmission\":\n", " monitors.append(time_monitorZH)\n", " monitors.append(time_monitorZ)\n", @@ -364,7 +357,7 @@ " else:\n", " raise ValueError(f\"sim_mode of {sim_params.sim_mode} not recognized.\")\n", "\n", - " # Define simulation parameters \n", + " # Define simulation parameters\n", " sim = td.Simulation(\n", " size=sim_size,\n", " grid_spec=grid_spec,\n", @@ -377,7 +370,7 @@ " subpixel=sim_params.subpixel,\n", " )\n", "\n", - " return sim\n" + " return sim" ] }, { @@ -398,13 +391,18 @@ "@dataclass\n", "class SimulationOutputs:\n", " \"\"\"Stores outputs of a given simulation.\"\"\"\n", - " flux_f : td.FluxDataArray # Flux transmission as a function of frequency\n", - " flux_t : td.FluxTimeDataArray # Flux transmission as a function of time\n", - " eps : td.ScalarFieldDataArray # Relative permattivity at the Ex positions in the yee lattice\n", - " ff_realized : float # Sphere filling fraction as computed from the raw permittivity distribution\n", - " int_half : td.ScalarFieldTimeDataArray = None # Field intensity at the time halfway through simulation\n", - " int_end : td.ScalarFieldTimeDataArray = None # Field intensity at the final time in simulation\n", - " int_spread : td.ScalarFieldTimeDataArray = None # Fiend intensity cross section as function of time" + "\n", + " flux_f: td.FluxDataArray # Flux transmission as a function of frequency\n", + " flux_t: td.FluxTimeDataArray # Flux transmission as a function of time\n", + " eps: td.ScalarFieldDataArray # Relative permattivity at the Ex positions in the yee lattice\n", + " ff_realized: float # Sphere filling fraction as computed from the raw permittivity distribution\n", + " int_half: td.ScalarFieldTimeDataArray = (\n", + " None # Field intensity at the time halfway through simulation\n", + " )\n", + " int_end: td.ScalarFieldTimeDataArray = None # Field intensity at the final time in simulation\n", + " int_spread: td.ScalarFieldTimeDataArray = (\n", + " None # Fiend intensity cross section as function of time\n", + " )" ] }, { @@ -417,9 +415,9 @@ " \"\"\"Compute effective filling fraction from data.\"\"\"\n", " Lz = sim_params.Lz\n", " eps_in_slab = np.real(eps)\n", - " eps_in_slab = eps_in_slab.where(eps_in_slab.z < Lz/2, drop=True)\n", - " eps_in_slab = eps_in_slab.where(eps_in_slab.z > -Lz/2, drop=True)\n", - " \n", + " eps_in_slab = eps_in_slab.where(eps_in_slab.z < Lz / 2, drop=True)\n", + " eps_in_slab = eps_in_slab.where(eps_in_slab.z > -Lz / 2, drop=True)\n", + "\n", " if sim_params.material == \"dielectric\":\n", " eps_max = sim_params.ref_ind**2\n", " ff = (np.mean(eps_in_slab.values) - 1) / (eps_max - 1)\n", @@ -437,38 +435,36 @@ "metadata": {}, "outputs": [], "source": [ - "def post_process_results(sim_params: SimulationParameters, sim_data: td.SimulationData) -> SimulationOutputs:\n", + "def post_process_results(\n", + " sim_params: SimulationParameters, sim_data: td.SimulationData\n", + ") -> SimulationOutputs:\n", " \"\"\"Process the results of a simulation run into SimulationOutputs.\"\"\"\n", "\n", - " flux_f = sim_data[\"freq_monitorT\"].flux\n", - " flux_t = sim_data[\"time_monitorT\"].flux\n", " eps = sim_data[\"eps_monitor\"].eps_xx\n", " ff_realized = compute_ff(sim_params, eps)\n", - " \n", + "\n", " if sim_params.sim_mode == \"transmission\":\n", - " int_half = sim_data.get_intensity(\"time_monitorZH\")\n", - " int_end = sim_data.get_intensity(\"time_monitorZ\")\n", " sim_outputs = SimulationOutputs(\n", - " flux_f = sim_data[\"freq_monitorT\"].flux,\n", - " flux_t = sim_data[\"time_monitorT\"].flux,\n", - " int_half = sim_data.get_intensity(\"time_monitorZH\"),\n", - " int_end = sim_data.get_intensity(\"time_monitorZ\"),\n", - " eps = sim_data[\"eps_monitor\"].eps_xx,\n", - " ff_realized = ff_realized,\n", - " ) \n", + " flux_f=sim_data[\"freq_monitorT\"].flux,\n", + " flux_t=sim_data[\"time_monitorT\"].flux,\n", + " int_half=sim_data.get_intensity(\"time_monitorZH\"),\n", + " int_end=sim_data.get_intensity(\"time_monitorZ\"),\n", + " eps=sim_data[\"eps_monitor\"].eps_xx,\n", + " ff_realized=ff_realized,\n", + " )\n", "\n", " elif sim_params.sim_mode == \"beam_spreading\":\n", " int_spread = sim_data.get_intensity(\"spread_monitor\")\n", " sim_outputs = SimulationOutputs(\n", - " flux_f = sim_data[\"freq_monitorT\"].flux,\n", - " flux_t = sim_data[\"time_monitorT\"].flux,\n", - " eps = sim_data[\"eps_monitor\"].eps_xx,\n", - " ff_realized = ff_realized,\n", - " int_spread = int_spread,\n", + " flux_f=sim_data[\"freq_monitorT\"].flux,\n", + " flux_t=sim_data[\"time_monitorT\"].flux,\n", + " eps=sim_data[\"eps_monitor\"].eps_xx,\n", + " ff_realized=ff_realized,\n", + " int_spread=int_spread,\n", " )\n", " else:\n", - " raise ValueError(f\"sim_mode of {sim_mode} not recognized.\")\n", - " \n", + " raise ValueError(f\"sim_mode of {sim_params.sim_mode} not recognized.\")\n", + "\n", " return sim_outputs" ] }, @@ -487,7 +483,7 @@ "metadata": {}, "outputs": [], "source": [ - "def run(sim_params : SimulationParameters) -> SimulationOutputs:\n", + "def run(sim_params: SimulationParameters) -> SimulationOutputs:\n", " \"\"\"process simulation parameters into simulation outputs through a tidy3d simulation.\"\"\"\n", " sim = make_sim(sim_params)\n", " sim_data = web.run(sim, task_name=sim_params.task_name)\n", @@ -510,15 +506,14 @@ "outputs": [], "source": [ "def plot_transmission(sim_params: SimulationParameters, sim_outputs: SimulationOutputs) -> None:\n", - "\n", " f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, tight_layout=True)\n", - " \n", + "\n", " sim_outputs.flux_f.plot(ax=ax1)\n", " sim_outputs.flux_t.plot(ax=ax2)\n", "\n", " def take_log(intensity):\n", " return np.log10(intensity / intensity.max() + 1e-6)\n", - " \n", + "\n", " if sim_outputs.int_half is not None:\n", " # Intensity section I(0,y,z;tmax/2)\n", " take_log(sim_outputs.int_half).interp(x=0).squeeze().plot.pcolormesh(ax=ax3)\n", @@ -532,7 +527,7 @@ " np.log10(sim_outputs.int_spread).isel(t=-1).squeeze().plot.pcolormesh(ax=ax4)\n", " ax2.set_yscale(\"log\")\n", " ax3.set_aspect(1)\n", - " ax4.set_aspect(1) \n", + " ax4.set_aspect(1)\n", " plt.show()" ] }, @@ -558,17 +553,17 @@ " sim_outputs = run(sim_params)\n", "\n", " # display the fill fraction\n", - " print(f\"ff_realized = {(sim_outputs.ff_realized*100):.2f} %\") \n", + " print(f\"ff_realized = {(sim_outputs.ff_realized*100):.2f} %\")\n", "\n", " # plot data\n", " plot_transmission(sim_params, sim_outputs)\n", - " \n", + "\n", " # plot the simulation\n", " sim = make_sim(sim_params)\n", " f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True)\n", " sim.plot(x=0, monitor_alpha=0, ax=ax1)\n", " sim.plot(z=0, monitor_alpha=0, ax=ax2)\n", - " plt.show() \n", + " plt.show()\n", "\n", " return sim_outputs" ] @@ -583,7 +578,7 @@ "\n", "### Dielectric Sphere Transmission\n", "\n", - "First, we simulate a pulse transmission through a slab containing randomly distributed dielectric spheres. It shows that transmited flux decays mono-exponentially with time, as expected for diffusive transport. y-z cross-sections of the spatial intensity distribution, obtained at long times show sine-like dependence with the depth coordinate z, also expected in diffusion. We also show y-z and x-y slices of dielectric permeability of the simulated system for illustration." + "First, we simulate a pulse transmission through a slab containing randomly distributed dielectric spheres. It shows that transmitted flux decays mono-exponentially with time, as expected for diffusive transport. y-z cross-sections of the spatial intensity distribution, obtained at long times show sine-like dependence with the depth coordinate z, also expected in diffusion. We also show y-z and x-y slices of dielectric permeability of the simulated system for illustration." ] }, { @@ -967,7 +962,7 @@ } ], "source": [ - "sim_outputs_1 = workflow(sim_params_1)\n" + "sim_outputs_1 = workflow(sim_params_1)" ] }, { @@ -1360,7 +1355,7 @@ } ], "source": [ - "sim_outputs_2 = workflow(sim_params_2)\n" + "sim_outputs_2 = workflow(sim_params_2)" ] }, { @@ -1743,7 +1738,7 @@ } ], "source": [ - "sim_outputs_3 = workflow(sim_params_3)\n" + "sim_outputs_3 = workflow(sim_params_3)" ] }, { @@ -1777,7 +1772,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.0" }, "nbdime-conflicts": { "local_diff": [ diff --git a/AnimationTutorial.ipynb b/AnimationTutorial.ipynb index de70ec10..84b23496 100644 --- a/AnimationTutorial.ipynb +++ b/AnimationTutorial.ipynb @@ -17,7 +17,7 @@ "\n", "This tutorial demonstrates how to create animations in a waveguide taper simulation and display them directly in a Jupyter Notebook. We will show how to use Tidy3D convenience features to animate time-domain and frequency-domain fields.\n", "\n", - "We also provide a conprehensive list of other tutorials such as [how to define boundary conditions](https://www.flexcompute.com/tidy3d/examples/notebooks/BoundaryConditions/), [how to defining spatially-varying sources](https://www.flexcompute.com/tidy3d/examples/notebooks/CustomFieldSource/) and [structures](https://www.flexcompute.com/tidy3d/examples/notebooks/CustomMediumTutorial/), [how to model dispersive materials](https://www.flexcompute.com/tidy3d/examples/notebooks/Dispersion/), and [how to visualize simulation setups](https://www.flexcompute.com/tidy3d/examples/notebooks/VizSimulation/) and [results](https://www.flexcompute.com/tidy3d/examples/notebooks/VizData/).\n", + "We also provide a comprehensive list of other tutorials such as [how to define boundary conditions](https://www.flexcompute.com/tidy3d/examples/notebooks/BoundaryConditions/), [how to defining spatially-varying sources](https://www.flexcompute.com/tidy3d/examples/notebooks/CustomFieldSource/) and [structures](https://www.flexcompute.com/tidy3d/examples/notebooks/CustomMediumTutorial/), [how to model dispersive materials](https://www.flexcompute.com/tidy3d/examples/notebooks/Dispersion/), and [how to visualize simulation setups](https://www.flexcompute.com/tidy3d/examples/notebooks/VizSimulation/) and [results](https://www.flexcompute.com/tidy3d/examples/notebooks/VizData/).\n", "\n", "If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. " ] @@ -29,13 +29,12 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", "import matplotlib.animation as animation\n", - "from IPython.display import HTML\n", - "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "import tidy3d as td\n", - "import tidy3d.web as web\n" + "import tidy3d.web as web\n", + "from IPython.display import HTML" ] }, { @@ -65,7 +64,7 @@ "freq0 = td.C_0 / lda0 # central frequency\n", "ldas = np.linspace(1.5, 1.6, 101) # wavelength range\n", "freqs = td.C_0 / ldas # frequency range\n", - "fwidth = 0.5 * (np.max(freqs) - np.min(freqs)) # width of the frequency distribution\n" + "fwidth = 0.5 * (np.max(freqs) - np.min(freqs)) # width of the frequency distribution" ] }, { @@ -79,7 +78,7 @@ "si = td.Medium(permittivity=n_si**2)\n", "\n", "n_sio2 = 1.44 # silicon oxide refractive index\n", - "sio2 = td.Medium(permittivity=n_sio2**2)\n" + "sio2 = td.Medium(permittivity=n_sio2**2)" ] }, { @@ -97,9 +96,7 @@ "\n", "# define the substrate structure\n", "sub = td.Structure(\n", - " geometry=td.Box.from_bounds(\n", - " rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, 0)\n", - " ),\n", + " geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, 0)),\n", " medium=sio2,\n", ")\n", "\n", @@ -119,7 +116,7 @@ "linear_taper = td.Structure(\n", " geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(0, t_wg)),\n", " medium=si,\n", - ")\n" + ")" ] }, { @@ -141,14 +138,14 @@ "\n", "# define a field time monitor to record time-domain animation frames\n", "time_monitor = td.FieldTimeMonitor(\n", - " center=(0, 0, t_wg/2),\n", + " center=(0, 0, t_wg / 2),\n", " size=(td.inf, td.inf, 0),\n", - " fields=['Hz'],\n", + " fields=[\"Hz\"],\n", " start=0,\n", " stop=run_time,\n", " interval=100,\n", - " interval_space=(3,3,1),\n", - " name='time_movie_monitor'\n", + " interval_space=(3, 3, 1),\n", + " name=\"time_movie_monitor\",\n", ")" ] }, @@ -168,11 +165,11 @@ "outputs": [], "source": [ "frequency_monitor = td.FieldMonitor(\n", - " center=(0, 0, t_wg/2),\n", + " center=(0, 0, t_wg / 2),\n", " size=(td.inf, td.inf, 0),\n", " freqs=[freq0],\n", - " fields=['Hz'],\n", - " name='frequency_monitor'\n", + " fields=[\"Hz\"],\n", + " name=\"frequency_monitor\",\n", ")" ] }, @@ -216,9 +213,7 @@ " sources=[mode_source],\n", " monitors=[time_monitor, frequency_monitor],\n", " run_time=run_time,\n", - " boundary_spec=td.BoundarySpec.all_sides(\n", - " boundary=td.PML()\n", - " ), # pml is used in all boundaries\n", + " boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()), # pml is used in all boundaries\n", " symmetry=(0, -1, 0),\n", ") # a pec symmetry plane at y=0 can be used to reduce the grid points of the simulation" ] @@ -249,7 +244,7 @@ } ], "source": [ - "sim.plot(z=t_wg/2)\n", + "sim.plot(z=t_wg / 2)\n", "plt.show()" ] }, @@ -438,11 +433,11 @@ } ], "source": [ - "fig, ax = plt.subplots(1,2, figsize=(10,5), tight_layout=True)\n", + "fig, ax = plt.subplots(1, 2, figsize=(10, 5), tight_layout=True)\n", "\n", - "sim_data['time_movie_monitor'].Hz.sel(t=1e-13, method='nearest').plot(x='x', y='y', ax=ax[0])\n", + "sim_data[\"time_movie_monitor\"].Hz.sel(t=1e-13, method=\"nearest\").plot(x=\"x\", y=\"y\", ax=ax[0])\n", "\n", - "sim_data['time_movie_monitor'].Hz.sel(t=2e-13, method='nearest').plot(x='x', y='y', ax=ax[1])\n", + "sim_data[\"time_movie_monitor\"].Hz.sel(t=2e-13, method=\"nearest\").plot(x=\"x\", y=\"y\", ax=ax[1])\n", "plt.show()" ] }, @@ -19760,21 +19755,25 @@ } ], "source": [ - "t_end = sim_data['time_movie_monitor'].Hz.coords['t'][-1] # end time of the animation\n", + "t_end = sim_data[\"time_movie_monitor\"].Hz.coords[\"t\"][-1] # end time of the animation\n", "frames = 50 # number of frames\n", "\n", "fig, ax = plt.subplots()\n", "\n", + "\n", "def animate(i):\n", - " t = t_end*i/frames # time at each frame\n", - " sim_data['time_movie_monitor'].Hz.sel(t=t, method='nearest').plot(x='x', y='y', ax=ax, vmin=-0.1, vmax=0.1, add_colorbar=False, cmap='seismic')\n", + " t = t_end * i / frames # time at each frame\n", + " sim_data[\"time_movie_monitor\"].Hz.sel(t=t, method=\"nearest\").plot(\n", + " x=\"x\", y=\"y\", ax=ax, vmin=-0.1, vmax=0.1, add_colorbar=False, cmap=\"seismic\"\n", + " )\n", + "\n", "\n", "# create animation\n", - "ani = animation.FuncAnimation(fig, animate, frames=frames);\n", + "ani = animation.FuncAnimation(fig, animate, frames=frames)\n", "plt.close()\n", "\n", "# display the animation\n", - "HTML(ani.to_jshtml())\n" + "HTML(ani.to_jshtml())" ] }, { @@ -19805,7 +19804,7 @@ } ], "source": [ - "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,5), tight_layout=True)\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5), tight_layout=True)\n", "\n", "sim_data.plot_field(\"frequency_monitor\", \"Hz\", \"real\", f=freq0, ax=ax1)\n", "ax1.set_xlim(6, 11)\n", @@ -64410,35 +64409,43 @@ "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "\n", "frames = 36\n", - "phi_range = np.linspace(0, -2*np.pi, frames)\n", - "vmax = np.amax(np.abs(sim_data['frequency_monitor'].Hz.values))*0.7\n", + "phi_range = np.linspace(0, -2 * np.pi, frames)\n", + "vmax = np.amax(np.abs(sim_data[\"frequency_monitor\"].Hz.values)) * 0.7\n", "fig, ax1 = plt.subplots(1, 1, figsize=(6, 4), tight_layout=True)\n", "\n", + "\n", "def animate_ii(i):\n", - " sim_data_p = sim_data['frequency_monitor']\n", + " sim_data_p = sim_data[\"frequency_monitor\"]\n", " sim_data_p = sim_data_p.apply_phase(phi_range[i])\n", " Hz = sim_data_p.Hz\n", " x = Hz[\"x\"]\n", - " y = Hz[\"y\"] \n", - " Hz = np.squeeze(np.real(np.flipud(np.rot90(Hz[:,:,0]))))\n", + " y = Hz[\"y\"]\n", + " Hz = np.squeeze(np.real(np.flipud(np.rot90(Hz[:, :, 0]))))\n", " divnorm = mcolors.TwoSlopeNorm(vmin=-vmax, vcenter=0, vmax=vmax)\n", - " image = ax1.pcolormesh(x, y, Hz, cmap='bwr', shading='gouraud', norm=divnorm)\n", - " sim_data.simulation.plot_structures_eps(z=0, alpha=0.25, cbar=False, \n", - " hlim=[np.amin(x), np.amax(x)], vlim=[np.amin(y), np.amax(y)], ax=ax1)\n", - " \n", + " image = ax1.pcolormesh(x, y, Hz, cmap=\"bwr\", shading=\"gouraud\", norm=divnorm)\n", + " sim_data.simulation.plot_structures_eps(\n", + " z=0,\n", + " alpha=0.25,\n", + " cbar=False,\n", + " hlim=[np.amin(x), np.amax(x)],\n", + " vlim=[np.amin(y), np.amax(y)],\n", + " ax=ax1,\n", + " )\n", + "\n", " divider = make_axes_locatable(ax1)\n", - " cax = divider.new_horizontal(size = \"2%\", pad = 0.05)\n", + " cax = divider.new_horizontal(size=\"2%\", pad=0.05)\n", " fig.add_axes(cax)\n", - " fig.colorbar(image, cax = cax, orientation = \"vertical\", label='$H_z$')\n", + " fig.colorbar(image, cax=cax, orientation=\"vertical\", label=\"$H_z$\")\n", "\n", " ax1.set_xlim([np.amin(x), np.amax(x)])\n", - " ax1.set_ylim([np.amin(y), np.amax(y)]) \n", + " ax1.set_ylim([np.amin(y), np.amax(y)])\n", " ax1.set_xlabel(\"x ($\\\\mu$m)\")\n", " ax1.set_ylabel(\"y ($\\\\mu$m)\")\n", " ax1.set_aspect(\"equal\")\n", "\n", + "\n", "# create animation\n", - "ani = animation.FuncAnimation(fig, animate_ii, frames=frames);\n", + "ani = animation.FuncAnimation(fig, animate_ii, frames=frames)\n", "plt.close()\n", "\n", "# display the animation\n", @@ -64473,7 +64480,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" + "version": "3.11.0" }, "title": "Creating FDTD Animations in Tidy3D | Flexcompute", "widgets": { diff --git a/AnisotropicMetamaterialBroadbandPBS.ipynb b/AnisotropicMetamaterialBroadbandPBS.ipynb index 93033055..2f579ad1 100644 --- a/AnisotropicMetamaterialBroadbandPBS.ipynb +++ b/AnisotropicMetamaterialBroadbandPBS.ipynb @@ -25,13 +25,12 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "\n", + "import numpy as np\n", "import tidy3d as td\n", "import tidy3d.web as web\n", "from tidy3d.plugins.mode import ModeSolver\n", - "from tidy3d.plugins.mode.web import run as run_mode_solver\n" + "from tidy3d.plugins.mode.web import run as run_mode_solver" ] }, { @@ -53,7 +52,7 @@ "freq0 = td.C_0 / lda0 # central frequency\n", "ldas = np.linspace(1.45, 1.65, 101) # wavelength range\n", "freqs = td.C_0 / ldas # frequency range\n", - "fwidth = 0.5 * (np.max(freqs) - np.min(freqs)) # width of the source frequency range\n" + "fwidth = 0.5 * (np.max(freqs) - np.min(freqs)) # width of the source frequency range" ] }, { @@ -73,7 +72,7 @@ "sio2 = td.material_library[\"SiO2\"][\"Palik_Lossless\"]\n", "\n", "n_su8 = 1.58\n", - "su8 = td.Medium(permittivity=n_su8**2)\n" + "su8 = td.Medium(permittivity=n_su8**2)" ] }, { @@ -82,7 +81,7 @@ "source": [ "Determine the geometric parameters. The thickness of the silicon layer is recorded to be 250 nm. The input and output single mode waveguides each have a width $w_{in}$ of 450 nm. The width of the [100] anisotropic metamaterial (AM[100]) $w_{100}$ is 650 nm. The junction between the single mode strip waveguide and the AM region takes on a taper structure, incorporating $n_{tp}=$10 grating periods. The periods $p$ for both the AM[100] and AM[010] are set at 250 nm. The duty cycles $f_{100}$ and $f_{010}$ are set to 0.7 for both metamaterials, with an option for fine-tuning later.\n", "\n", - "\"Top" + "\"Top" ] }, { @@ -98,7 +97,7 @@ "n_tp = 10 # number of periods in the taper region\n", "f_0 = 0.7 # initial duty cycle for the anisotropic metamaterial\n", "inf_eff = 1e3 # effective infinity\n", - "buffer = 0.6 * lda0 # buffer spacing\n" + "buffer = 0.6 * lda0 # buffer spacing" ] }, { @@ -165,7 +164,7 @@ "am_010_medium = td.AnisotropicMedium(xx=n_o_medium, yy=n_e_medium, zz=n_o_medium)\n", "\n", "# create waveguide structures\n", - "n_010_example = 5 # use 5 periods for the AM[010] region width as an example\n", + "n_010_example = 5 # use 5 periods for the AM[010] region width as an example\n", "am_010_waveguide = td.Structure(\n", " geometry=td.Box.from_bounds(\n", " rmin=(-inf_eff, -n_010_example * p / 2, 0), rmax=(inf_eff, n_010_example * p / 2, t)\n", @@ -215,7 +214,7 @@ "\n", "# plot the mode solving cross section\n", "mode_solver.plot()\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -365,7 +364,7 @@ ], "source": [ "mode_data = run_mode_solver(mode_solver, verbose=False)\n", - "mode_data.to_dataframe()\n" + "mode_data.to_dataframe()" ] }, { @@ -402,7 +401,7 @@ "axes[1, 1].set_title(\"Ey of the odd TE mode\")\n", "mode_solver.plot_field(mode_index=0, field_name=\"Ey\", val=\"real\", vmin=-30, vmax=30, ax=axes[1, 0])\n", "axes[1, 0].set_title(\"Ey of the even TE mode\")\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -435,7 +434,7 @@ "# calculate TE mode coupling length\n", "n_te0 = mode_data.n_eff.sel(mode_index=0).values[0]\n", "n_te1 = mode_data.n_eff.sel(mode_index=1).values[0]\n", - "print(f\"The coupling length for the TE mode is {lda0/(2*(n_te0-n_te1)) :.2f} μm.\")\n" + "print(f\"The coupling length for the TE mode is {lda0/(2*(n_te0-n_te1)) :.2f} μm.\")" ] }, { @@ -469,10 +468,12 @@ " Returns:\n", " td.Simulation\n", " \"\"\"\n", - " \n", - " l_c = p * n_100 - (1 - f_100) * p # coupling region length\n", - " l_tp = p * n_tp # taper length\n", - " y_0 = n_010 * p / 2 + (1 - f_010) * p / 2 + w_100 / 2 # y coordinate of the input waveguide center\n", + "\n", + " l_c = p * n_100 - (1 - f_100) * p # coupling region length\n", + " l_tp = p * n_tp # taper length\n", + " y_0 = (\n", + " n_010 * p / 2 + (1 - f_010) * p / 2 + w_100 / 2\n", + " ) # y coordinate of the input waveguide center\n", "\n", " # create the swg structures in the coupling region\n", " am_geos = 0\n", @@ -486,7 +487,7 @@ " am_geos += td.Box(center=(f_100 * p / 2 + i * p, -y_0, t / 2), size=(p * f_100, w_100, t))\n", "\n", " am_structure = td.Structure(geometry=am_geos, medium=si)\n", - " \n", + "\n", " # create the waveguide taper structures\n", " vertices = [\n", " (-inf_eff, y_0 + w_in / 2),\n", @@ -528,7 +529,7 @@ " direction=\"+\",\n", " mode_spec=mode_spec,\n", " mode_index=mode_index,\n", - " num_freqs=7, # use 7 frequency points since the simulation bandwidth is large\n", + " num_freqs=7, # use 7 frequency points since the simulation bandwidth is large\n", " )\n", "\n", " # add a mode monitor to measure transmission at the through port\n", @@ -539,7 +540,7 @@ " mode_spec=mode_spec,\n", " name=\"through\",\n", " )\n", - " \n", + "\n", " # add a mode monitor to measure transmission at the cross port\n", " mode_cross = td.ModeMonitor(\n", " center=(l_c + l_tp + buffer / 2, -y_0, t / 2),\n", @@ -573,7 +574,7 @@ " medium=su8,\n", " )\n", "\n", - " return sim\n" + " return sim" ] }, { @@ -698,7 +699,7 @@ ], "source": [ "sim = make_sim(mode_index=1, n_100=30, n_010=5)\n", - "sim.plot_3d()\n" + "sim.plot_3d()" ] }, { @@ -729,7 +730,7 @@ "sim.plot_grid(z=t / 2, ax=ax)\n", "ax.set_xlim(0, 1)\n", "ax.set_ylim(0, 1)\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -777,7 +778,7 @@ "\n", "# create batch\n", "batch = web.Batch(simulations=sims, verbose=True)\n", - "_ = batch.estimate_cost()\n" + "_ = batch.estimate_cost()" ] }, { @@ -936,7 +937,7 @@ ], "source": [ "batch_results = batch.run(path_dir=\"data\")\n", - "_ = batch.real_cost()\n" + "_ = batch.real_cost()" ] }, { @@ -956,7 +957,7 @@ "source": [ "def cal_transmission_at_freq0(mode_index, n_010):\n", " \"\"\"\n", - " Calculates the transmission at the central frequency as a function of n_100 for a given mode index \n", + " Calculates the transmission at the central frequency as a function of n_100 for a given mode index\n", " and n_010\n", "\n", " Args:\n", @@ -990,7 +991,7 @@ " for n_100 in n_100_list\n", " ]\n", "\n", - " return cross, through\n" + " return cross, through" ] }, { @@ -1027,7 +1028,7 @@ " ax.set_xlabel(\"n_100\")\n", " ax.set_ylabel(\"Transmission\")\n", " ax.legend()\n", - " ax.grid()\n" + " ax.grid()" ] }, { @@ -1076,7 +1077,7 @@ " plot_transmission_at_freq0(cross_te, through_te, n_010, \"TE\", axes[1])\n", "\n", " plt.tight_layout()\n", - " plt.show()\n" + " plt.show()" ] }, { @@ -1094,7 +1095,7 @@ "source": [ "def get_spectra(sim_data):\n", " \"\"\"\n", - " Calculates the power spectra for the through and cross ports \n", + " Calculates the power spectra for the through and cross ports\n", " from simulation data.\n", "\n", " Args:\n", @@ -1169,7 +1170,7 @@ "\n", " # adjust layout and display\n", " plt.tight_layout()\n", - " plt.show()\n" + " plt.show()" ] }, { @@ -1204,7 +1205,7 @@ " field_component=\"Hz\",\n", " field_vmin=-0.5,\n", " field_vmax=0.5,\n", - ")\n" + ")" ] }, { @@ -1232,7 +1233,7 @@ " field_component=\"Ez\",\n", " field_vmin=-50,\n", " field_vmax=50,\n", - ")\n" + ")" ] }, { @@ -1269,7 +1270,7 @@ "plt.xlabel(\"Wavelength (μm)\")\n", "plt.ylabel(\"PER_TM (dB)\")\n", "plt.grid()\n", - "plt.show()\n" + "plt.show()" ] }, { diff --git a/AntennaCharacteristics.ipynb b/AntennaCharacteristics.ipynb index 352c6062..d7a1543a 100644 --- a/AntennaCharacteristics.ipynb +++ b/AntennaCharacteristics.ipynb @@ -31,16 +31,16 @@ }, "outputs": [], "source": [ - "# Tidy3d import \n", + "# Tidy3d import\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# External modules needed for this notebook\n", + "import numpy as np\n", "import tidy3d as td\n", - "from tidy3d.web import run\n", "\n", "# Tidy3d plugin import\n", "import tidy3d.plugins.smatrix as smatrix\n", - "\n", - "# External modules needed for this notebook\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" + "from tidy3d.web import run" ] }, { @@ -59,17 +59,17 @@ "outputs": [], "source": [ "# Scaling used for millimeters\n", - "mm = 1e3 \n", + "mm = 1e3\n", "\n", "# Frequency range\n", "freq_start = 0.5e9\n", "freq_stop = 20e9\n", "\n", - "freq0 = (freq_start + freq_stop) / 2 # Centeral frequency\n", + "freq0 = (freq_start + freq_stop) / 2 # Centeral frequency\n", "freqs_target = [7.5e9, 10e9] # Target frequencies (in Hz) for computing directivity and axial ratio\n", - "fwidth = freq_stop-freq_start # Bandwidth\n", + "fwidth = freq_stop - freq_start # Bandwidth\n", "\n", - "# Wavelength of centeral frequency in Vaccum\n", + "# Wavelength of centeral frequency in vacuum\n", "wavelength0 = td.C_0 / freq0\n", "\n", "# Frequency sweep for S-parameters\n", @@ -95,7 +95,7 @@ "air = td.Medium()\n", "eps_r = 2.2\n", "sub_medium = td.Medium(permittivity=eps_r)\n", - "PEC = td.PEC2D # Thickness-free PEC medium" + "PEC = td.PEC2D # Thickness-free PEC medium" ] }, { @@ -114,18 +114,18 @@ "outputs": [], "source": [ "# Substrate parameters\n", - "sub_x = 23.34*mm \n", - "sub_y = 40*mm \n", - "sub_z = 0.794*mm \n", + "sub_x = 23.34 * mm\n", + "sub_y = 40 * mm\n", + "sub_z = 0.794 * mm\n", "\n", "# Patch parameters\n", - "patch_x = 12.45*mm \n", - "patch_y = 16*mm\n", + "patch_x = 12.45 * mm\n", + "patch_y = 16 * mm\n", "\n", "# Feedline parameters\n", - "feed_x = 2.46*mm \n", - "feed_y = 20*mm \n", - "feed_offset = 2.09*mm\n", + "feed_x = 2.46 * mm\n", + "feed_y = 20 * mm\n", + "feed_offset = 2.09 * mm\n", "\n", "# Define substrate structure\n", "substrate = td.Structure(\n", @@ -135,27 +135,27 @@ "\n", "# Define ground plane structure and assign the material by PEC\n", "ground_plane = td.Structure(\n", - " geometry=td.Box.from_bounds([-sub_x/2, -sub_y/2, -sub_z/2], [sub_x/2, sub_y/2, -sub_z/2]),\n", + " geometry=td.Box.from_bounds(\n", + " [-sub_x / 2, -sub_y / 2, -sub_z / 2], [sub_x / 2, sub_y / 2, -sub_z / 2]\n", + " ),\n", " medium=PEC,\n", ")\n", "\n", "# Define patch geometry\n", "patch_geometry = td.Box.from_bounds(\n", - " [-patch_x/2, -sub_y/2 + feed_y, sub_z/2],\n", - " [patch_x/2, -sub_y/2 + feed_y+patch_y, sub_z/2] \n", + " [-patch_x / 2, -sub_y / 2 + feed_y, sub_z / 2],\n", + " [patch_x / 2, -sub_y / 2 + feed_y + patch_y, sub_z / 2],\n", ")\n", "\n", "# Define feedline geometry\n", "feedline_geometry = td.Box.from_bounds(\n", - " [-patch_x/2+feed_offset, -sub_y/2, sub_z/2],\n", - " [-patch_x/2+feed_offset+feed_x, -sub_y/2+feed_y, sub_z/2]\n", + " [-patch_x / 2 + feed_offset, -sub_y / 2, sub_z / 2],\n", + " [-patch_x / 2 + feed_offset + feed_x, -sub_y / 2 + feed_y, sub_z / 2],\n", ")\n", "\n", "# Unionize the patch and feedline\n", "radiating_geometry = td.ClipOperation(\n", - " operation='union',\n", - " geometry_a=patch_geometry,\n", - " geometry_b=feedline_geometry\n", + " operation=\"union\", geometry_a=patch_geometry, geometry_b=feedline_geometry\n", ")\n", "\n", "# Define radiating structure and assign conductive patch by PEC\n", @@ -164,9 +164,9 @@ " medium=PEC,\n", ")\n", "\n", - "# List of structures for the simulation. \n", - "# Arrange structures in the following order: dielectric first, followed by PEC. \n", - "# This ensures that PEC override dielectric at the interfaces. \n", + "# List of structures for the simulation.\n", + "# Arrange structures in the following order: dielectric first, followed by PEC.\n", + "# This ensures that PEC override dielectric at the interfaces.\n", "# Reversing the order may lead to inaccurate solutions.\n", "structures_list = [substrate, ground_plane, radiating_structure]" ] @@ -190,9 +190,9 @@ "source": [ "# Field monitor to view the electromagnetic fields in the patch plane\n", "monitor_field = td.FieldMonitor(\n", - " center=(0, 0, sub_z/2),\n", + " center=(0, 0, sub_z / 2),\n", " size=(td.inf, td.inf, 0),\n", - " freqs=freqs_target, \n", + " freqs=freqs_target,\n", " name=\"field\",\n", ")\n", "\n", @@ -200,15 +200,19 @@ "theta = np.linspace(-np.pi, np.pi, 200)\n", "phi = np.linspace(0, np.pi, 100)\n", "\n", - "# We define a DirectivityMonitor to obatin radiation characteritics such as directivity, axial ratio and polarized far fields.\n", + "# We define a DirectivityMonitor to obtain radiation characteristics such as directivity, axial ratio and polarized far fields.\n", "monitor_directivity = td.DirectivityMonitor(\n", " center=[0, 0, 0],\n", - " size=(30*mm, 45*mm, 4*mm), # We introduce a monitor box to enclose the whole structure of interest\n", - " freqs=freqs_target, \n", + " size=(\n", + " 30 * mm,\n", + " 45 * mm,\n", + " 4 * mm,\n", + " ), # We introduce a monitor box to enclose the whole structure of interest\n", + " freqs=freqs_target,\n", " name=\"radiation\",\n", " phi=list(phi),\n", " theta=list(theta),\n", - " far_field_approx=True, # Far-field approximation is suitable for most cases. For more accurate far-field projections, set far_field_approx=False.\n", + " far_field_approx=True, # Far-field approximation is suitable for most cases. For more accurate far-field projections, set far_field_approx=False.\n", ")" ] }, @@ -261,17 +265,17 @@ "# Create the simulation object\n", "sim = td.Simulation(\n", " center=[0, 0, 0],\n", - " size=[sim_x,sim_y,sim_z],\n", + " size=[sim_x, sim_y, sim_z],\n", " grid_spec=td.GridSpec.auto(\n", - " min_steps_per_wvl=20, # The largest cell size is set to 20 cells per smallest wavelength.\n", - " wavelength=td.C_0 / freq_stop, # Smallest wavelength to resolve \n", + " min_steps_per_wvl=20, # The largest cell size is set to 20 cells per smallest wavelength.\n", + " wavelength=td.C_0 / freq_stop, # Smallest wavelength to resolve\n", " ),\n", - " structures=structures_list, \n", + " structures=structures_list,\n", " sources=[], # Sources will be added by TerminalComponentModeler\n", " monitors=[monitor_directivity, monitor_field],\n", - " run_time= 70*(sub_y/td.C_0), \n", + " run_time=70 * (sub_y / td.C_0),\n", " boundary_spec=boundary_spec,\n", - " plot_length_units=\"mm\", # This option will make plots default to units of millimeters.\n", + " plot_length_units=\"mm\", # This option will make plots default to units of millimeters.\n", ")" ] }, @@ -304,14 +308,14 @@ " impedance=port_impedance,\n", ")\n", "\n", - "# We integrate the base simulation with the LumpedPort using the TerminalComponentModeler. \n", + "# We integrate the base simulation with the LumpedPort using the TerminalComponentModeler.\n", "# This allows us to compute scattering parameters and extract any additional data needed from the simulation.\n", "modeler = smatrix.TerminalComponentModeler(\n", " simulation=sim,\n", " ports=[port],\n", " freqs=freqs,\n", " verbose=True,\n", - " remove_dc_component=False, # Include DC component for more accuracy at low frequencies\n", + " remove_dc_component=False, # Include DC component for more accuracy at low frequencies\n", ")" ] }, @@ -357,12 +361,24 @@ "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n", "\n", "# Examine the structure and mesh in the x-y plane\n", - "sim_temp.plot(z=sub_z / 2, ax=ax1, hlim=[-sim_x/2, sim_x/2], vlim=[-sim_y/2, sim_y/2], monitor_alpha=0.2)\n", - "sim_temp.plot_grid(z=sub_z / 2, ax=ax1, hlim=[-sim_x/2, sim_x/2], vlim=[-sim_y/2, sim_y/2])\n", + "sim_temp.plot(\n", + " z=sub_z / 2,\n", + " ax=ax1,\n", + " hlim=[-sim_x / 2, sim_x / 2],\n", + " vlim=[-sim_y / 2, sim_y / 2],\n", + " monitor_alpha=0.2,\n", + ")\n", + "sim_temp.plot_grid(z=sub_z / 2, ax=ax1, hlim=[-sim_x / 2, sim_x / 2], vlim=[-sim_y / 2, sim_y / 2])\n", "\n", "# Examine the structure and mesh in the x-z plane\n", - "sim_temp.plot(y=-sub_y / 2+2*mm, ax=ax2, hlim=[-sim_x/2, sim_x/2], vlim=[-sim_z/2, sim_z/2], monitor_alpha=0.2)\n", - "sim_temp.plot_grid(y=-sub_y / 2, ax=ax2, hlim=[-sim_x/2, sim_x/2], vlim=[-sim_z/2, sim_z/2])\n", + "sim_temp.plot(\n", + " y=-sub_y / 2 + 2 * mm,\n", + " ax=ax2,\n", + " hlim=[-sim_x / 2, sim_x / 2],\n", + " vlim=[-sim_z / 2, sim_z / 2],\n", + " monitor_alpha=0.2,\n", + ")\n", + "sim_temp.plot_grid(y=-sub_y / 2, ax=ax2, hlim=[-sim_x / 2, sim_x / 2], vlim=[-sim_z / 2, sim_z / 2])\n", "\n", "# Show the plots\n", "plt.show()" @@ -382,7 +398,8 @@ "outputs": [], "source": [ "batch_data = {\n", - " task_name: run(simulation = simulation, task_name=\"AntennaCharacteristics\") for task_name, simulation in modeler.sim_dict.items()\n", + " task_name: run(simulation=simulation, task_name=\"AntennaCharacteristics\")\n", + " for task_name, simulation in modeler.sim_dict.items()\n", "}" ] }, @@ -412,11 +429,15 @@ "source": [ "s_matrix = modeler._construct_smatrix()\n", "\n", - "plt.plot(s_matrix.f / 1e9, 20 * np.log10(np.abs(s_matrix.isel(port_out=0, port_in=0).values.flatten())),'-b');\n", + "plt.plot(\n", + " s_matrix.f / 1e9,\n", + " 20 * np.log10(np.abs(s_matrix.isel(port_out=0, port_in=0).values.flatten())),\n", + " \"-b\",\n", + ")\n", "\n", - "plt.xlabel('Frequency (GHz)')\n", - "plt.ylabel('$|S_{11}|$ (dB)')\n", - "plt.ylim(-25, 2) \n", + "plt.xlabel(\"Frequency (GHz)\")\n", + "plt.ylabel(\"$|S_{11}|$ (dB)\")\n", + "plt.ylim(-25, 2)\n", "plt.grid(True)\n", "plt.show()" ] @@ -448,14 +469,18 @@ "sim_data = batch_data[\"smatrix_lumped_port\"]\n", "\n", "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(11, 4))\n", - "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"Ez\", val=\"abs\", f=freqs_target[0], ax=ax1)\n", - "ax1.set_xlim([-10*mm, 10*mm])\n", - "ax1.set_ylim([-20*mm, 20*mm])\n", + "sim_data.plot_field(\n", + " field_monitor_name=\"field\", field_name=\"Ez\", val=\"abs\", f=freqs_target[0], ax=ax1\n", + ")\n", + "ax1.set_xlim([-10 * mm, 10 * mm])\n", + "ax1.set_ylim([-20 * mm, 20 * mm])\n", "ax1.set_title(\"Electric field distribution at 7.5 GHz\")\n", "\n", - "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"Ez\", val=\"abs\", f=freqs_target[1], ax=ax2)\n", - "ax2.set_xlim([-10*mm, 10*mm])\n", - "ax2.set_ylim([-20*mm, 20*mm])\n", + "sim_data.plot_field(\n", + " field_monitor_name=\"field\", field_name=\"Ez\", val=\"abs\", f=freqs_target[1], ax=ax2\n", + ")\n", + "ax2.set_xlim([-10 * mm, 10 * mm])\n", + "ax2.set_ylim([-20 * mm, 20 * mm])\n", "ax2.set_title(\"Electric field distribution at 10 GHz\");" ] }, @@ -518,7 +543,8 @@ ], "source": [ "def log_value(values):\n", - " return 20 * np.log10(values) \n", + " return 20 * np.log10(values)\n", + "\n", "\n", "# Extract directivity\n", "D_directivity = sim_data[\"radiation\"].directivity.sel(f=freqs_target[0], phi=0, method=\"nearest\")\n", @@ -531,8 +557,8 @@ "# Plot directivity\n", "ax.set_theta_direction(-1)\n", "ax.set_theta_offset(np.pi / 2.0)\n", - "ax.plot(theta, D_directivity, '-b', label=\"Tidy3D\")\n", - "ax.set_title(\"Directivity Pattern\", va='bottom')\n", + "ax.plot(theta, D_directivity, \"-b\", label=\"Tidy3D\")\n", + "ax.set_title(\"Directivity Pattern\", va=\"bottom\")\n", "\n", "plt.show()" ] @@ -584,8 +610,8 @@ "# Plot axial ratio\n", "ax.set_theta_direction(-1)\n", "ax.set_theta_offset(np.pi / 2.0)\n", - "ax.plot(theta, log_value(AxialRatio), '-b', label=\"Tidy3D\")\n", - "ax.set_title(\"Axial Ratio Pattern\", va='bottom')\n", + "ax.plot(theta, log_value(AxialRatio), \"-b\", label=\"Tidy3D\")\n", + "ax.set_title(\"Axial Ratio Pattern\", va=\"bottom\")\n", "\n", "plt.show()" ] @@ -627,26 +653,32 @@ ], "source": [ "# Extract left polarization\n", - "left_polarization = sim_data[\"radiation\"].left_polarization.sel(f=freqs_target[0], phi=0, method=\"nearest\").abs\n", + "left_polarization = (\n", + " sim_data[\"radiation\"].left_polarization.sel(f=freqs_target[0], phi=0, method=\"nearest\").abs\n", + ")\n", "left_polarization = np.squeeze(left_polarization)\n", "\n", "# Extract right polarization\n", - "right_polarization = sim_data[\"radiation\"].right_polarization.sel(f=freqs_target[0], phi=0, method=\"nearest\").abs\n", + "right_polarization = (\n", + " sim_data[\"radiation\"].right_polarization.sel(f=freqs_target[0], phi=0, method=\"nearest\").abs\n", + ")\n", "right_polarization = np.squeeze(right_polarization)\n", "\n", - "fig, (ax1, ax2) = plt.subplots(1, 2, subplot_kw={\"projection\": \"polar\"}, figsize=(12, 6), tight_layout=True)\n", + "fig, (ax1, ax2) = plt.subplots(\n", + " 1, 2, subplot_kw={\"projection\": \"polar\"}, figsize=(12, 6), tight_layout=True\n", + ")\n", "\n", "# Plot left polarization\n", "ax1.set_theta_direction(-1)\n", "ax1.set_theta_offset(np.pi / 2.0)\n", - "ax1.plot(theta, abs(left_polarization), '-b', label=\"Tidy3D\")\n", - "ax1.set_title(\"Electric far field for left-hand circular polarization (Abs) [V/m]\", va='bottom')\n", + "ax1.plot(theta, abs(left_polarization), \"-b\", label=\"Tidy3D\")\n", + "ax1.set_title(\"Electric far field for left-hand circular polarization (Abs) [V/m]\", va=\"bottom\")\n", "\n", "# Plot right polarization\n", "ax2.set_theta_direction(-1)\n", "ax2.set_theta_offset(np.pi / 2.0)\n", - "ax2.plot(theta, abs(right_polarization), '-b', label=\"Tidy3D\")\n", - "ax2.set_title(\"Electric far field for right-hand circular polarization (Abs) [V/m]\", va='bottom')\n", + "ax2.plot(theta, abs(right_polarization), \"-b\", label=\"Tidy3D\")\n", + "ax2.set_title(\"Electric far field for right-hand circular polarization (Abs) [V/m]\", va=\"bottom\")\n", "\n", "plt.show()" ] @@ -665,7 +697,7 @@ "description": "Patch antennas are widely used in wireless communication applications due to their simple design, ease of fabrication, and low profile. In this notebook, we will demonstrate how to use Tidy3D to simulate a rectangular patch antenna and compute key performance metrics. These include S-parameters using the TerminalComponentModeler, as well as directivity, axial ratio, and polarized far-field components using the DirectivityMonitor.", "feature_image": "./img/PatchAntenna.png", "kernelspec": { - "display_name": "photon_forge", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -680,7 +712,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.11.0" }, "title": "How to compute directivity and S-parameters of patch antenna using Tidy3D FDTD" }, diff --git a/AntiResonantHollowCoreFiber.ipynb b/AntiResonantHollowCoreFiber.ipynb index 93df31ec..f08cf064 100644 --- a/AntiResonantHollowCoreFiber.ipynb +++ b/AntiResonantHollowCoreFiber.ipynb @@ -33,7 +33,6 @@ "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "\n", "import tidy3d as td\n", "from tidy3d.plugins.mode import ModeSolver\n", "from tidy3d.plugins.mode.web import run as run_mode_solver" @@ -100,7 +99,7 @@ "id": "328e0ec2-24a4-4d35-ad1f-784ac285b7b7", "metadata": {}, "source": [ - "To create the hollow-core fiber structures, we first create a core region made of air with radius `D/2 + d`. The tubes are constructred by two concentric cylinders, one larger cylinder made of silica with radius `d/2` and one smaller cylinder made of air with radius `d/2 - t`. The combination of these structures with the background medium set to silica will result in the desired fiber cross section." + "To create the hollow-core fiber structures, we first create a core region made of air with radius `D/2 + d`. The tubes are constructed by two concentric cylinders, one larger cylinder made of silica with radius `d/2` and one smaller cylinder made of air with radius `d/2 - t`. The combination of these structures with the background medium set to silica will result in the desired fiber cross section." ] }, { @@ -113,9 +112,7 @@ "lz = 1 # dummy length in the z direction\n", "\n", "# create a core region made of air\n", - "core = td.Structure(\n", - " geometry=td.Cylinder(center=(0, 0, 0), radius=D / 2 + d, length=lz), medium=air\n", - ")\n", + "core = td.Structure(geometry=td.Cylinder(center=(0, 0, 0), radius=D / 2 + d, length=lz), medium=air)\n", "\n", "# create outer cylinders made of silica\n", "tubes_outer = [\n", @@ -222,9 +219,7 @@ "outputs": [], "source": [ "# define the mode spec\n", - "mode_spec = td.ModeSpec(\n", - " num_modes=5, target_neff=1, precision=\"double\", num_pml=(15, 15)\n", - ")\n", + "mode_spec = td.ModeSpec(num_modes=5, target_neff=1, precision=\"double\", num_pml=(15, 15))\n", "\n", "# define a mode solver\n", "mode_solver = ModeSolver(\n", @@ -438,7 +433,7 @@ "id": "cb537e39-ab8b-4d21-9d07-1e01596bf1be", "metadata": {}, "source": [ - "Plot the first 4 mode profiles. We see that the first mode is the HE$_{11}$ mode. The second and third modes are the HE$_{31}$ modes. The forth mode has the field concentrated inside the tubes and is commonly refered to as the \"tube mode\"." + "Plot the first 4 mode profiles. We see that the first mode is the HE$_{11}$ mode. The second and third modes are the HE$_{31}$ modes. The forth mode has the field concentrated inside the tubes and is commonly referred to as the \"tube mode\"." ] }, { @@ -684,7 +679,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.0" }, "title": "Mode analysis of an anti-resonant hollow-core fiber in Tidy3D | Flexcompute" }, diff --git a/AutoGrid.ipynb b/AutoGrid.ipynb index 5bfc73e6..306126e1 100644 --- a/AutoGrid.ipynb +++ b/AutoGrid.ipynb @@ -13,24 +13,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# basic imports\n", - "import numpy as np\n", "import matplotlib.pylab as plt\n", + "import numpy as np\n", "\n", "# tidy3d imports\n", "import tidy3d as td\n", - "from tidy3d.plugins.mode import ModeSolver\n" + "from tidy3d.plugins.mode import ModeSolver" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "scrolled": true }, @@ -67,19 +67,19 @@ ")\n", "\n", "# boundaries\n", - "boundary_spec = td.BoundarySpec.all_sides(boundary=td.PML())\n" + "boundary_spec = td.BoundarySpec.all_sides(boundary=td.PML())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Define a helper function to show us the various grids as we go along this exmaple." + "Define a helper function to show us the various grids as we go along this example." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "scrolled": true }, @@ -98,7 +98,7 @@ " ax[1].set_ylim(-0.4, 0.4)\n", " print(f\"Total number of grid points (millions): {sim.num_cells / 1e6:1.2}\")\n", "\n", - " return ax\n" + " return ax" ] }, { @@ -112,9 +112,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of grid points (millions): 8.3\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sim_uniform = td.Simulation(\n", " size=[5, 3, 3],\n", @@ -125,7 +143,7 @@ " run_time=1e-12,\n", ")\n", "\n", - "ax = plot_sim_grid(sim_uniform)\n" + "ax = plot_sim_grid(sim_uniform)" ] }, { @@ -146,9 +164,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of grid points (millions): 8.4\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sim_quasiuniform = td.Simulation(\n", " size=[5, 3, 3],\n", @@ -159,7 +195,7 @@ " run_time=1e-12,\n", ")\n", "\n", - "ax = plot_sim_grid(sim_quasiuniform)\n" + "ax = plot_sim_grid(sim_quasiuniform)" ] }, { @@ -182,9 +218,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of grid points (millions): 0.53\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# provide wavelength directly\n", "sim_nonuniform_10 = td.Simulation(\n", @@ -213,7 +267,7 @@ " sources=[source],\n", ")\n", "\n", - "ax = plot_sim_grid(sim_nonuniform_10)\n" + "ax = plot_sim_grid(sim_nonuniform_10)" ] }, { @@ -225,9 +279,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of grid points (millions): 2.3\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sim_nonuniform_20 = td.Simulation(\n", " size=[5, 3, 3],\n", @@ -240,7 +312,7 @@ " boundary_spec=boundary_spec,\n", " run_time=1e-12,\n", ")\n", - "ax = plot_sim_grid(sim_nonuniform_20)\n" + "ax = plot_sim_grid(sim_nonuniform_20)" ] }, { @@ -257,13 +329,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Minimal grid size along y-direction = 16.67nm\n" + ] + } + ], "source": [ - "print(\n", - " f\"Minimal grid size along y-direction = {min(sim_nonuniform_20.grid.sizes.y)*1e3:1.2f}nm\"\n", - ")\n" + "print(f\"Minimal grid size along y-direction = {min(sim_nonuniform_20.grid.sizes.y)*1e3:1.2f}nm\")" ] }, { @@ -275,9 +353,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of grid points (millions): 1.9\n", + "Minimal grid size along y-direction = 24.57nm\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sim_nonuniform_20 = td.Simulation(\n", " size=[5, 3, 3],\n", @@ -292,9 +389,7 @@ " run_time=1e-12,\n", ")\n", "ax = plot_sim_grid(sim_nonuniform_20)\n", - "print(\n", - " f\"Minimal grid size along y-direction = {min(sim_nonuniform_20.grid.sizes.y)*1e3:1.2f}nm\"\n", - ")\n" + "print(f\"Minimal grid size along y-direction = {min(sim_nonuniform_20.grid.sizes.y)*1e3:1.2f}nm\")" ] }, { @@ -317,9 +412,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of grid points (millions): 2.0\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sim_nonuniform_20 = td.Simulation(\n", " size=[5, 3, 3],\n", @@ -327,7 +440,9 @@ " wavelength=wavelength,\n", " min_steps_per_wvl=20,\n", " dl_min=25 * nm,\n", - " snapping_points=[(0, -0.4, 0.2),]\n", + " snapping_points=[\n", + " (0, -0.4, 0.2),\n", + " ],\n", " ),\n", " medium=sub_med,\n", " structures=[box1, box2],\n", @@ -359,9 +474,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of grid points (millions): 4.0\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Define a \"dummy\" box with refractive index 5 around the central location of the waveguide\n", "refine_box = td.Structure(\n", @@ -383,7 +516,7 @@ " run_time=1e-12,\n", ")\n", "\n", - "ax = plot_sim_grid(sim_nonuniform_20_refine)\n" + "ax = plot_sim_grid(sim_nonuniform_20_refine)" ] }, { @@ -395,9 +528,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of grid points (millions): 3.0\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Redefine the box with size 0 along x\n", "refine_box = td.Structure(\n", @@ -419,7 +570,7 @@ " run_time=1e-12,\n", ")\n", "\n", - "ax = plot_sim_grid(sim_nonuniform_20_refine)\n" + "ax = plot_sim_grid(sim_nonuniform_20_refine)" ] }, { @@ -432,9 +583,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of grid points (millions): 2.9\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "refine_box = td.MeshOverrideStructure(\n", " geometry=td.Box(center=(0, 0, 0), size=(td.inf, 0.4, 0.4)),\n", @@ -455,7 +624,7 @@ " run_time=1e-12,\n", ")\n", "\n", - "ax = plot_sim_grid(sim_nonuniform_20_refine)\n" + "ax = plot_sim_grid(sim_nonuniform_20_refine)" ] }, { @@ -467,11 +636,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
20:39:04 Eastern Daylight Time WARNING: Use the remote mode solver with subpixel\n",
+       "                               averaging for better accuracy through            \n",
+       "                               'tidy3d.web.run(...)' or the deprecated          \n",
+       "                               'tidy3d.plugins.mode.web.run(...)'.              \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[2;36m20:39:04 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Use the remote mode solver with subpixel\u001b[0m\n", + "\u001b[2;36m \u001b[0m\u001b[31maveraging for better accuracy through \u001b[0m\n", + "\u001b[2;36m \u001b[0m\u001b[32m'tidy3d.web.run\u001b[0m\u001b[32m(\u001b[0m\u001b[32m...\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m or the deprecated \u001b[0m\n", + "\u001b[2;36m \u001b[0m\u001b[32m'tidy3d.plugins.mode.web.run\u001b[0m\u001b[32m(\u001b[0m\u001b[32m...\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m. \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "num_modes = 3\n", "plane = td.Box(center=(0, 0, 0), size=(0, 2, 2))\n", @@ -482,13 +681,9 @@ "modes = ms.solve()\n", "fig, axs = plt.subplots(num_modes, 2, figsize=(12, 14))\n", "for mode_ind in range(num_modes):\n", - " ms.plot_field(\n", - " \"Ey\", \"abs\", f=freq0, mode_index=mode_ind, ax=axs[mode_ind, 0], robust=False\n", - " )\n", - " ms.plot_field(\n", - " \"Ez\", \"abs\", f=freq0, mode_index=mode_ind, ax=axs[mode_ind, 1], robust=False\n", - " )\n", - "plt.show()\n" + " ms.plot_field(\"Ey\", \"abs\", f=freq0, mode_index=mode_ind, ax=axs[mode_ind, 0], robust=False)\n", + " ms.plot_field(\"Ez\", \"abs\", f=freq0, mode_index=mode_ind, ax=axs[mode_ind, 1], robust=False)\n", + "plt.show()" ] }, { @@ -503,9 +698,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of grid points (millions): 2.9\n", + "Minimal grid size along x-direction = 22.22nm\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "refine_box = td.MeshOverrideStructure(\n", " geometry=td.Box(center=(0, 0, 0), size=(td.inf, 0.4, 0.4)),\n", @@ -529,7 +743,7 @@ "ax = plot_sim_grid(sim_nonuniform_20_coarser)\n", "print(\n", " f\"Minimal grid size along x-direction = {min(sim_nonuniform_20_coarser.grid.sizes.x)*1e3:1.2f}nm\"\n", - ")\n" + ")" ] }, { @@ -541,9 +755,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of grid points (millions): 1.4\n", + "Minimal grid size along x-direction = 40.00nm\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "refine_box = td.MeshOverrideStructure(\n", " geometry=td.Box(center=(0, 0, 0), size=(td.inf, 0.4, 0.4)),\n", @@ -568,7 +801,7 @@ "ax = plot_sim_grid(sim_nonuniform_20_coarser)\n", "print(\n", " f\"Minimal grid size along x-direction = {min(sim_nonuniform_20_coarser.grid.sizes.x)*1e3:1.2f}nm\"\n", - ")\n" + ")" ] }, { @@ -582,16 +815,53 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Combination of uniform and nonuiform grid\n", + "## Combination of uniform and nonuniform grid\n", "\n", "Finally, we note that `Tidy3D` allows another easy way to handle the `x` direction in our example, namely applying different grid specifications along each of the three simulation directions. For example, we can set a fixed grid size of `40nm` along the propagation direction, where we expect the field dependence to be smooth, while still using the refined nonuniform mesh in the other two directions. This achieves the same effect as in the plot above." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
20:39:06 Eastern Daylight Time WARNING: Override structures take no effect along\n",
+       "                               x-axis. If intending to apply override structures\n",
+       "                               to this axis, use 'AutoGrid' or                  \n",
+       "                               'QuasiUniformGrid'.                              \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[2;36m20:39:06 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Override structures take no effect along\u001b[0m\n", + "\u001b[2;36m \u001b[0m\u001b[31mx-axis. If intending to apply override structures\u001b[0m\n", + "\u001b[2;36m \u001b[0m\u001b[31mto this axis, use \u001b[0m\u001b[32m'AutoGrid'\u001b[0m\u001b[31m or \u001b[0m\n", + "\u001b[2;36m \u001b[0m\u001b[32m'QuasiUniformGrid'\u001b[0m\u001b[31m. \u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of grid points (millions): 1.4\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "grid_spec = td.GridSpec(\n", " grid_x=td.UniformGrid(dl=0.04),\n", @@ -610,7 +880,7 @@ " run_time=1e-12,\n", ")\n", "\n", - "ax = plot_sim_grid(sim_nonuniform_yz_uniform_x)\n" + "ax = plot_sim_grid(sim_nonuniform_yz_uniform_x)" ] }, { @@ -647,7 +917,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.0" }, "title": "Automatic Nonuniform Meshing in Tidy3D | Flexcompute" }, diff --git a/Autograd0Quickstart.ipynb b/Autograd0Quickstart.ipynb index da6484a5..de418e43 100644 --- a/Autograd0Quickstart.ipynb +++ b/Autograd0Quickstart.ipynb @@ -7,13 +7,13 @@ "source": [ "# Inverse design quickstart\n", "\n", - "This notebook will get users up and running with a very simple inverse design optimization with `tidy3d`. Inverse design uses the \"adjoint method\" to compute gradients of a figure of merit with respect to design parameters using only 2 simulations, no matter how many design parameters are present. This gradient is then used to do high-dimensional gradient-based optimization of the system.\n", + "This notebook will get users up and running with a very simple [inverse design optimization](https://www.flexcompute.com/tidy3d/inverse-design/) with `tidy3d`. Inverse design uses the **adjoint method** to compute gradients of a figure of merit with respect to design parameters using only 2 simulations, no matter how many design parameters are present. This gradient is then used to do high-dimensional gradient-based optimization of the system.\n", "\n", "The setup we'll demonstrate here involves a point dipole source and a point field monitor on either side of a dielectric box. We use gradient-based optimization to maximize the intensity enhancement at the measurement spot with respect to the box size in all 3 dimensions.\n", "\n", "\"Schematic\n", "\n", - "For more detailed notebooks, see these\n", + "For more advanced examples and tutorial notebooks, check out\n", "\n", "* [Tidy3D Autograd Tutorial](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd1Intro/).\n", "\n", @@ -21,38 +21,25 @@ "\n", "* [Shape Optimization](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd5BoundaryGradients/).\n", "\n", - "* [Grating Coupler Inverse Design](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd6GratingCoupler/).\n" + "* [Grating Coupler Inverse Design](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd6GratingCoupler/).\n", + "\n", + "To see all Tidy3D [examples](https://www.flexcompute.com/tidy3d/learning-center/example-library/) and [tutorials](https://www.flexcompute.com/tidy3d/learning-center/tidy3d-python/), as well as other learning materials, please visit our [Learning Center](https://www.flexcompute.com/tidy3d/learning-center/).\n" ] }, { "cell_type": "code", "execution_count": 1, - "id": "b876c5fa-c839-46fa-8199-3318f08e49c7", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# To install optax that provides the optimizer, uncomment the lines below.\n", - "# !pip install optax" - ] - }, - { - "cell_type": "code", - "execution_count": 2, "id": "19017d86-5994-4740-8913-a76f4a994a2d", "metadata": { "tags": [] }, "outputs": [], "source": [ - "import tidy3d as td\n", - "from tidy3d.web import run\n", - "import matplotlib.pylab as plt\n", - "\n", "import autograd as ag\n", "import autograd.numpy as anp\n", - "import optax" + "import matplotlib.pylab as plt\n", + "import tidy3d as td\n", + "from tidy3d.web import run" ] }, { @@ -67,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "f043dfbf-4fe1-458c-9227-0d953b3b12ef", "metadata": { "tags": [] @@ -98,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "98e99e56-9ade-441f-8128-e94e1eaf538e", "metadata": { "tags": [] @@ -123,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "9051ee45-42db-40cb-8d10-618e7ab49950", "metadata": { "tags": [] @@ -141,22 +128,31 @@ }, { "cell_type": "markdown", - "id": "f9de2970-f755-48cb-8bb2-33eb50c8741f", + "id": "c041608f-0554-4ece-8038-3a9143219d4d", "metadata": {}, "source": [ - "## Define Objective Function\n", + "Create a function that takes the input parameter (size of the box) and returns a Tidy3D [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html) objective with the source and monitor defined above.\n", "\n", - "Now we construct our objective function. Our objective function measures the intensity at the measurement point as a function of the box size." + "To visualize the simulation setup, we create an initial simulation with a box of size 2.5 μm and visualize it." ] }, { "cell_type": "code", - "execution_count": 6, - "id": "84f53e4a-fb12-4a69-9639-bd1e02ab61bb", - "metadata": { - "tags": [] - }, - "outputs": [], + "execution_count": 5, + "id": "04d3111f-6cbb-49bb-82f0-f8d535e994a6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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6QlnP4/GIG264QahUKnH77beLRYsWiYULF4qxY8eK5s2b+/1es2bNEgBE//79xYsvvigWLVokRo4cKaZPn+4rc//99wuVSiWefPJJ8a9//cs3w02gcarS3y4nJ0csXLhQzJgxQyQmJooOHTr4jeWVxqkG+/s2xGKxiNTUVNG+fXsxb948MX/+fNGrVy9x2WWX1avP888/LwCIyZMni5UrV4oPPvigwc+WdO/eXQAQXbp0Cal8qBYvXiwAiNtuu0289tprYuTIkQKAePrpp/3KSX+HumOAhw4d6huD+re//U30799faLVasXnzZr9yu3fvFgaDwW9GJaPRGPJY2wkTJojWrVvXGx8KQCQkJIg//vGPYsmSJWLWrFnCZDIJs9ksLrroIrFy5UohRPD/h6Tfq27v9WD7Q/fu3cWdd94ZUp0pNAxVUtzRo0fFyJEjRYsWLYTBYBC/+93vxPjx4+tN/hAoVIWoHkLTuXNnodPpREZGhhg3bpxfYPz888/ir3/9q+jYsaMwGo2iefPm4pprrhGfffaZr0xhYaG46aabRFZWltDr9SIrK0vccccd4tChQw3WPdT1nE6neO6550S3bt2EwWAQqampok+fPuKJJ56oN3nAG2+8IXr16uUrN2DAALFhwwbf+0VFRWLQoEEiJSUlpMkfVq9e7fu85s2bNzj5Q12hhKoQQnz55ZfiiiuuEAkJCSIrK0tMmzZNfPLJJ/XqU15eLv7yl7+IZs2aNTr5Q21SGD/zzDMhlZdj2bJl4pJLLhF6vV507NhRLFiwoF54BQvVs2fPiocfflhkZmYKg8Eg+vXrJ9avXx9wO1u2bBH9+/cXRqNRtGjRQowfP17Y7faQ6rhnzx7f5A21ARBTpkwRQ4cOFQkJCaJVq1bilVdeEUuXLhWJiYni3nvvFUIoE6oHDhwQAPz+v6HIqYRogmsKREQNeOmllzB58mT88ssv9XqwXigGDhyIrKwsvP32275lKpUKc+bMweOPP97k2580aRK++OIL7N69m71/FcR7qkQUVUIIvP766xgwYMAFG6gA8Mwzz2D16tUxe/Tb3//+dzz11FMMVIVxSA0RRUVFRQU++OADbNy4Ed9++y3ef//9WFcppnJycoKOq21qaWlpsh5TR6FjqBJRVJw5cwZ/+ctf0KxZMzz66KMYPHhwrKtEpDjeUyUiIlII76kSEREphKFKRESkEN5TDZHX68XJkyeRkpLC3nJERBcQIQTKysqQlZXV4AM5AIZqyE6ePIlFixZBq1XuTyaEQElJCYQQ0Ov1vsbyer0x6RWo1Wr9fj+Hw9EkU6M1RKVS+T1Sy+1215uSMBrYHtXYHjXYHjXivT1UKhVSU1MVPQF65plncPz4cbRp06bheim2xfNcSkoKtFotEhMTG/2mEiohBDp06ACNRgOn0+mbQ9VsNgd9LmRT8nq9vmc96vV632Okos1ms/n+J01LS1Ps7y0H26MG26Ma26NGvLeHx+OBxWJRLFSlubtTUlIaLctQDZHUOGq1GjqdLuLP83q98Hq9vm+dpaWlvkehVVZWIjExMao7qrSD6nQ6GAwGVFVVwePxhLQTKamsrAxerxfJyclwOBw4e/Zs1A8cTqeT7fE/bI9qbI8a50J7OJ1OqNVq3ytSLpcLQGhPZWJHpRhzuVywWCzQ6XRIT09Heno6dDodLBZL1C6pSDuoy+VCeno60tLSYDKZYLfbAz6GqqmUlZXBbrfDZDIhLS0N6enpcLlcsFqtET3DUw6n08n2+B+2RzW2Rw22R+MYqjHk9XpRUlICnU7n+7apVquRlpYWtR217g4qfdtMSUmJ6o5a+4AhfdvU6/VRPXDUPmCwPdgeANujNrZHaBiqMeLxeOB2u6HVautdvonWjhpsB5VEa0cNdMCQROvAEeiAIWF71GB71GB7VIvH9pCeaRwLDNUYcLvdcDqdUKlUaNasWcBr/k29oza2g0qa+sDR0AFD0tQHjoYOGBK2Rw22Rw22R7V4a4/y8vKY9IoGGKpRVztQNRpNgzfRm2pHDXUHlTTVgSOUA4akqQ4coRwwJGyPGmyPGmyPavHUHsnJyTEb6sNQjaLagWowGELqlab0jip3B5UofeCQc8CQKH3gkHPAkLA9arA9arA9qsVLeyQlJUGtVsPj8cDhcERUBwCyznoZqjJoNJqwB3vXDVQ546eU2lHD3UElSh04wjlgSJQ6cIRzwJCwPWqwPWqwParFS3totVpoNBq43e6IgtXhcMj6OzJUZXK5XLJ31HDOUOuKdEeNdAeVRHrgiOSAIYn0wBHJAUPC9qjB9qjB9qgWL+2h1+uh1WrDDlaHwwG32y3vS4XsrTQhj8eDWbNmITs7GwkJCejYsSOefPLJRs8ON23ahN69e8NgMOCiiy7C8uXL65VZvHgxOnToAKPRiJycHHz11Vey6yftnHK+uSgRqJJwd1SldlBJuAcOJQ4YknAPHEocMCRsjxpsjxpsj2rx0h4GgyGsYJUCte50iI2Jq1B97rnnsGTJErzyyis4cOAAnnvuOTz//PNYtGhR0HWOHDmCQYMG4ZprrsG+ffswadIk3Hvvvfjkk098ZVavXo0pU6Zgzpw52LNnDy677DLk5+fj9OnTsuonhIBWq4UQIqRgVTJQJXJ3VKV3UIncA4eSBwyJ3AOHkgcMCdujBtujBtujWry0h9xgrR2otedaDkVcheq2bdtw0003YdCgQejQoQNuu+02XHfddQ2eVS5duhTZ2dmYN28eunTpggkTJuC2227DggULfGXmz5+P0aNH4+6770bXrl2xdOlSJCYm4o033pBdR7VaDb1e32iwNkWg1q5DKDtqU+2gklAPHE1xwJCEeuBoigOGhO1Rg+1Rg+1RLV7aI9RgjSRQgTgL1f79+6OwsBCHDh0CAPz3v//F1q1b8cc//jHoOtu3b0deXp7fsvz8fGzfvh1A9c6ye/duvzJqtRp5eXm+MoE4HA7Y7Xa/l0Sr1TYYrE0ZqLV/h4Z21KbeQSWNHTia8oAhaezA0ZQHDAnbowbbowbbo1q8tEdjwRppoAJxFqrTp0/H7bffjs6dO0On06FXr16YNGkShg8fHnSdoqIiZGRk+C3LyMiA3W7H2bNnYbFY4PF4ApYpKioK+rkFBQUwm82+V9u2bf3eDxas0QhUSbAdNVo7qCTYgSMaBwxJsANHNA4YErZHDbZHDbZHtXhpj2DBqkSgAnEWqu+88w5WrFiBlStXYs+ePXjrrbfw4osv4q233op6XWbMmAGbzeZ7HT9+vF6ZusEazUCV1N1RHQ5HVHdQSd0DRzQPGJK6Bw6HwxG1A4aE7VGD7VGD7VEtXtqjbrAqFahAnD36berUqb6zVQDo0aMHjh49ioKCAowaNSrgOpmZmSguLvZbVlxcDJPJhISEBGg0Gmg0moBlMjMzg9bFYDCE9MeVeoVJDRPNQJVIO6rFYoHFYgEAtGjRIurPOJQODtKl8mgeMCTSgePMmTNRP2BI2B412B412B7V4qU9pOO7NLGDEoEKxNmZamVlZb3G1Wg0DfZay83NRWFhod+yDRs2IDc3F0D1TtSnTx+/Ml6vF4WFhb4yRERESoirUP3Tn/6Ep59+GuvWrcMvv/yC9957D/Pnz8fNN9/sKzNjxgyMHDnS9/PYsWPx888/Y9q0afjhhx/w6quv4p133sHkyZN9ZaZMmYLXXnsNb731Fg4cOIBx48ahoqICd999d8R1li75qtVq37ccuTNwREq6J+F2u32XUKL5fENJ7UtasXoMk3SPSPpG7na7o/q8SYDtURvbowbbo1q8tEfdcaiRzrwkiavLv4sWLcKsWbNw//334/Tp08jKysJ9992H2bNn+8qcOnUKx44d8/2cnZ2NdevWYfLkyXjppZfQpk0b/P3vf0d+fr6vzLBhw3DmzBnMnj0bRUVF6NmzJ9avX1+v81IohBC+HdDj8fjuoep0OqjVauh0OjidTlRVVUGv1we9rOL1eiGE8D1RPlxCCJSUlMDtdiM1NRUqlQomkwklJSU4ffo0UlNTodPpItpGKCoqKlBeXo7k5GTfl4vExESUlpbC7XYjKSmpyevgcrlQUlICrVYLk8kElUoFs9ns97eQMz1kONgeNdgeNdge1aLVHi6Xy+9YXZfT6YTH44FGo/FtTwgBt9sNIUS9S9FypqdViXAns73A2O12PPvss0hOToZarYbX64Xb7YZKpYJWq/XbGRt6r3YZIQSaNWsma7aO2oQQcDqdvp2gdoA39J7S3G43XC4XdDpdvd+lofeUJD2RQqVSQa/X12uPYO8pie1Rg+1Rg+1RLZrt4Xa7UVpaCpVKVe+z3G43vF4v1Gp1wPYI9J7X68Vjjz0Gm80Gk8nU4Lbj6kw13pWWlqJ9+/bQaDQoKSlBQkJC0G93tb8VBirjcrngcDhwww03wGw2R+tXICI679lsNnz88ccwGAx+Z761rxgEuzoQqExVVVXI22aoyiBdSrDZbDAYDA32mtPr9b5u43a7PWBZj8cDs9mM5s2bN3ndiYguJFqtFjqdzncpt6ysDJWVlWjWrFmDva6lSfjtdju0Wi1SUlL46LemolKpYLPZQu6G3lQPDiYiotDJHRccydOGGKoyGAwGaDQaWeO6GKxERLET7kQbtYO1srIy5PUYqjJ4vV6YTCbZN9EZrERE0VdRURHRzFVSsFZUVIS8DkNVBmk8ajjqBis7XRMRNR23243y8vKIZ65KSUmRNeSJoRpFtYO1pKSEwUpE1ERcLheSk5MVmQoyMTEx5LIM1SirPZNJtGcQISK6UOh0uqhMqlEXQzUG9Ho9UlNTeaZKRNREmnIyjYYwVGOk9vgpIiI6PzBUYyiaj1siIqKmx6M6ERGRQhiqRERECmGoEhERKYShSkREpBCGagxxSA0R0fmFoRoj0kN5iYjo/MFQjQGv18tpComImlCsHlzCUI0yr9cLq9UKt9vNyR+IiJqI0+mEy+WK+nYZqlEkBarL5UJqaionfyAiaiIqlQolJSWK3GZzu90hl+VRXYZI5pKsHajp6enQ6XQK1oyIiGrT6/XQarWwWCwRBavT6YTNZgu5PENVBq1WK+sJ8JK6gcrLvkRETUulUiE1NRU6nS7sYHU6nbBYLNBoNCGvw1CVwe12o6KiAmVlZSGvw0AlIooNlUqFtLS0sIJVClSdTgeTyRTyegxVGdxuN5KSkmC320MKVgYqEVFsqdVq2cFaO1DT0tJk9X9hqMqUmJgIk8nUaLAyUImI4oOcYI0kUAGGalhSUlIaDFYGKhFRfAklWCMNVIChGrZgwcpAJSKKTw0FqxKBCjBUI1I3WBmoRETxLVCwKhWoABD+wEsCUB2sAGC322G326FSqRioRERxTApWq9WKM2fOAKge1xppoAI8U1VEUlKS798Gg4GBSkQU59Rqtd9QGZPJpMgsdwzVCEmXfFUqFYxGI6qqqmSNYyUiouhzOp2wWq3Q6XTQ6XSwWq2KTGnIy78RCHQPtaysDHa7HUDNpWEiIoofde+hAoDVaoXFYon49h3PVMMUrFNSY8NtiIgodgJ1Sgpngohg4i5UT5w4gTvvvBNpaWlISEhAjx49sGvXrgbX2bRpE3r37g2DwYCLLroIy5cvr1dm8eLF6NChA4xGI3JycvDVV1+FXcfGevkyWImI4k9DvXyVCta4CtWSkhJceeWV0Ol0+M9//oP9+/dj3rx5SE1NDbrOkSNHMGjQIFxzzTXYt28fJk2ahHvvvReffPKJr8zq1asxZcoUzJkzB3v27MFll12G/Px8nD59WnYdQx02w2AlIoofoQybUSJY4+qe6nPPPYe2bdvizTff9C3Lzs5ucJ2lS5ciOzsb8+bNAwB06dIFW7duxYIFC5Cfnw8AmD9/PkaPHo27777bt866devwxhtvYPr06bLqKN0vDeW6e+3hNrV/JiKi6JEzDrX2cBvpHqsccXWm+sEHH6Bv374YOnQoWrZsiV69euG1115rcJ3t27cjLy/Pb1l+fj62b98OoPqPuXv3br8yarUaeXl5vjKh0uv18Hg8sm5k84yViCh2wpnYoe4Z6zn7kPKff/4ZS5YsQadOnfDJJ59g3LhxePDBB/HWW28FXaeoqAgZGRl+yzIyMmC323H27FlYLBZ4PJ6AZYqKioJ+rsPh8E3oIL3UajXMZrPsnmEMViKi6HO5XOE/baZWsJ6zDyn3er3o3bs3nnnmGfTq1QtjxozB6NGjsXTp0qjXpaCgAGaz2fdq27YtHA4HtNrwrpgzWImIosfr9aKkpCSyyfH/F6zn7EPKW7Vqha5du/ot69KlC44dOxZ0nczMTBQXF/stKy4uhslkQkJCAtLT06HRaAKWyczMDPq5M2bMgM1m872OHz8OIUQYv1WN2sFaUVER0WcREVFwTqcTWq024qkH68681Gj5sLfUBK688kocPHjQb9mhQ4fQvn37oOvk5uaisLDQb9mGDRuQm5sLoPo+aJ8+ffzKeL1eFBYW+soEYjAYYDKZ/F5KkIK1vLxc1nV6IiIKnUqlQmpqqjJTD56rDymfPHkyduzYgWeeeQY//vgjVq5ciWXLlmH8+PG+MjNmzMDIkSN9P48dOxY///wzpk2bhh9++AGvvvoq3nnnHUyePNlXZsqUKXjttdfw1ltv4cCBAxg3bhwqKip8vYGjLSUlBcnJyXC5XDHZPhHR+U6v10OlUkV9u3E1pKZfv3547733MGPGDMydOxfZ2dlYuHAhhg8f7itz6tQpv8vB2dnZWLduHSZPnoyXXnoJbdq0wd///nffcBoAGDZsGM6cOYPZs2ejqKgIPXv2xPr16+t1XoqmpKSkiC8nExFRYLEIVABQCR7ZQ2K32zFt2jT069cPiYmJEX+e0+lEVVUVhg4diubNmytQQyIiAoDffvsNa9asgdFoVOSpYZWVlbj33nths9kavRUYV5d/iYiIzmUMVSIiIoUwVImIiBTCUCUiIlIIQ5WIiEghDNUY4uQPRETnF4ZqjFRUVHDyByKiJhKr0aIM1RgoKytDeXk5dDpdrKtCRHRecjqdMQlWhmqUlZWVwW63Izk5Oewn3hARUcOEECgpKYHX6434s+R8BkNVhkinvZIC1WQyISkpSaFaERFRXXq9Hm63G1arNaJg9Xq9sNvtIZdnqMpgMBjC7lxUO1BTUlIUrhkREdWmVquRmpoKl8sVdrB6vV5YrVZ4PJ7Qtyt7Kxcwr9cLm80Gp9Mpaz0GKhFR9Ol0OqSnp4cVrFKgulwumM3mkNdjqMrgdDqh0WhgsVhCDlYGKhFR7Oj1etnBWjtQ09PTZfV/YajKZDKZoNPpQgpWBioRUezJCda6gSr3KTcMVZnUajXS0tIaDVYGKhFR/AglWCMNVIChGpbGgpWBSkQUfxoKViUCFWCohi1YsDJQiYjiV6BgVSpQAYCzD0RAClar1QqLxQKDwYCqqioGKhFRHJOC1WKxwGKxAKieiz3SQAV4phoxKViFEKiqqoLRaGSgEhHFOb1ej7S0NLhcLrhcLqSlpUUcqABDVREVFRW+fzscDtnjWImIKLrqzpRkt9sVmdKQoRqh2vdQW7VqFfJwGyIiio3a91BbtGiBFi1aRDTzUm0M1QjU7ZQU6nAbIiKKjUCdksKZICIYhmqYgvXyZbASEcWnhnr5KhWsDNUwNDZshsFKRBRfQhk2o0SwMlRlqqysDGkcKoOViCg+yBmHGmmwMlRl0Gq1qKioCHkcKoOViCi2wpnYIZJgZajKoNVqkZSUJGscKoOViCg2hBBhz5RUO1j5kPIm4na7kZiYKHs9BisRUXQJIVBSUhLR1INSsPIh5U3E7XaHvW7dYHW5XArWjIiIanM6nYpMPajX6/mQ8nhVO1hLSkoUmb2DiIjqE0IgNTVVkakH+ZDyOCYFq1ar5WVgIqImotfrodPpor5dhmoMqNVqpKamQqVSxboqRETnJbU6NvHGUI0RlUqlyGUJIiKKH3Edqs8++yxUKhUmTZrUYLk1a9agc+fOMBqN6NGjBz7++GO/94UQmD17Nlq1aoWEhATk5eXh8OHDTVjz0PBMlYjo/BK3ofr111/jb3/7Gy699NIGy23btg133HEH7rnnHuzduxdDhgzBkCFD8N133/nKPP/883j55ZexdOlS7Ny5E0lJScjPz0dVVVVT/xpERHQBictQLS8vx/Dhw/Haa68hNTW1wbIvvfQSrr/+ekydOhVdunTBk08+id69e+OVV14BUH2WunDhQsycORM33XQTLr30UvzjH//AyZMnsXbt2ij8NkREdKGIy1AdP348Bg0ahLy8vEbLbt++vV65/Px8bN++HQBw5MgRFBUV+ZUxm83IycnxlSEiIlJC6INvomTVqlXYs2cPvv7665DKFxUVISMjw29ZRkYGioqKfO9Ly4KVCcThcMDhcPh+ljNNFRERXZji6kz1+PHjmDhxIlasWAGj0RjTuhQUFMBsNvtebdu2VXwbnPyBiOj8Elehunv3bpw+fRq9e/eGVquFVqvF5s2b8fLLL0Or1QacfzEzMxPFxcV+y4qLi5GZmel7X1oWrEwgM2bMgM1m872OHz8e6a/nx+VycfIHIqLzTFyF6sCBA/Htt99i3759vlffvn0xfPhw7Nu3DxqNpt46ubm5KCws9Fu2YcMG5ObmAgCys7ORmZnpV8Zut2Pnzp2+MoEYDAaYTCa/l1KcTidKSko4pIaIqIlEMld7JOLqnmpKSgq6d+/utywpKQlpaWm+5SNHjkTr1q1RUFAAAJg4cSIGDBiAefPmYdCgQVi1ahV27dqFZcuWAYBvnOtTTz2FTp06ITs7G7NmzUJWVhaGDBkS1d8PqA5Ui8UCrVbLyR+IiJqIy+VCRUVF1I+zcRWqoTh27Jjf9FP9+/fHypUrMXPmTDz66KPo1KkT1q5d6xfO06ZNQ0VFBcaMGYPS0lJcddVVWL9+fdTv20qBqtPpYDKZ/DpCERGRcnQ6HcrLy6HVamU9AzuQysrKkMvGfahu2rSpwZ8BYOjQoRg6dGjQz1CpVJg7dy7mzp0bUV30en3YnYtqB2paWlrMLk0QEV0ItFotkpOTfSM3wg3WsrIyVFRUhFw+ru6pxju1Wg273S47WOsGaqwmeiYiupAkJSXBZDLBbrejrKxM9vplZWWw2+1ISkoKeZ24P1ONJw6HAx6PB1arNeRwZKASEcWOdIYq94xVClSTyRSwk2wwPMLLIISA2WyGy+WC1Wpt9IyVgUpEFHspKSmyzlhrB6rcy8Y8ysuk1WqRnp7eaLAyUImI4keowRpJoAIM1bDo9foGg5WBSkQUfxoL1kgDFWCohi1YsDJQiYjiV7BgVSJQAXZUiogUrBaLBVarFSaTCVarlYFKRBTH6nZekv4daaACDNWIScF65swZnqESEZ0j6garEoEK8PIvERGRYhiqEZLuoUpnrG63O6ThNkREFDu176FGMkFEXQzVCNTtlGQwGEIabkNERLFTt1OS3HGsDWGohilYL9/GhtsQEVHsBOvlq1SwMlTD0NiwGQYrEVH8aWzYjBLBylCVye12h9TLl8FKRBQ/Qh2HGmmwMlRlUKlUsNlsIQ+bYbASEcWe3IkdIglWhqoMBoMBGo1G1jhUBisRUeyEO1NS7WCV85ByhqoMXq8XJpNJ9sQODFYiouirqKiIaKYkKVj5kPIm4nQ6w54pqW6wCiEUrh0REUncbjfKy8sjnikpJSVF1kPKGapRVDtYS0pKGKxERE3E5XIhOTlZkakHExMTQy7LUI2y2jMvOZ3OWFeHiOi8pNPpZJ1hKoWhGgN6vR6pqak8UyUiaiJabWyeF8NQjRGdTge9Xh/rahARkYIYqjHEx8MREZ1feFQnIiJSCEOViIhIIQxVIiIihTBUiYiIFMJQjSEOqSEiOr8wVGNECMHJH4iIzjMM1Rjwer2cppCIqAnF6sElDNUo83q9sFqtcLvdnPyBiKiJOJ1OuFyuqG+XoRpFUqC6XC6kpqZy8gcioiaiUqlQUlKiyG02t9sdclke1WWIZC7J2oGanp4OnU6nYM2IiKg2vV4PrVYLi8USUbA6nU7YbLaQyzNUZdBqtbKeAC+pG6i87EtE1LRUKhVSU1Oh0+nCDlan0wmLxQKNRhPyOnEXqgUFBejXrx9SUlLQsmVLDBkyBAcPHmx0vTVr1qBz584wGo3o0aMHPv74Y7/3hRCYPXs2WrVqhYSEBOTl5eHw4cOy6uZ2u1FRUYGysrKQ12GgEhHFhkqlQlpaWljBKgWqTqeDyWQKeb24C9XNmzdj/Pjx2LFjBzZs2ACXy4XrrrsOFRUVQdfZtm0b7rjjDtxzzz3Yu3cvhgwZgiFDhuC7777zlXn++efx8ssvY+nSpdi5cyeSkpKQn5+PqqqqkOvmdruRlJQEu90eUrAyUImIYkutVssO1tqBmpaWJqv/i0rE+biOM2fOoGXLlti8eTOuvvrqgGWGDRuGiooKfPTRR75lV1xxBXr27ImlS5dCCIGsrCw89NBDePjhhwEANpsNGRkZWL58OW6//fZG62G32zFt2jT069cPHo8HdrsdJpMp6FPlGwtUp9OJqqoqDB06FM2bNw/1z0FERI347bffsGbNGhiNRt+xN9STnECBWllZiXvvvRc2m63Rs9a4O1OtS7pB3FDwbN++HXl5eX7L8vPzsX37dgDAkSNHUFRU5FfGbDYjJyfHV6Yuh8MBu93u95KkpKTAZDIFPWPlGSoRUXwJ5Yw1kjNU33aUqGxT8Xq9mDRpEq688kp07949aLmioiJkZGT4LcvIyEBRUZHvfWlZsDJ1FRQUwGw2+15t27b1ez9YsDJQiYjiU0PBqkSgAnEequPHj8d3332HVatWRX3bM2bMgM1m872OHz9er0zdYGWgEhHFt0DBqlSgAkD4Ay+b2IQJE/DRRx/hiy++QJs2bRosm5mZieLiYr9lxcXFyMzM9L0vLWvVqpVfmZ49ewb8TIPBAIPB0Gg9pXuq0iVilUrFQCUiimNSsFqtVpw5cwZA9bjWSAMViMMzVSEEJkyYgPfeew+ff/45srOzG10nNzcXhYWFfss2bNiA3NxcAEB2djYyMzP9ytjtduzcudNXJhJJSUm+fxsMBgYqEVGcU6vVfp2OTCaTIrPcxd2Z6vjx47Fy5Uq8//77SElJ8d3zNJvNSEhIAACMHDkSrVu3RkFBAQBg4sSJGDBgAObNm4dBgwZh1apV2LVrF5YtWwageqzSpEmT8NRTT6FTp07Izs7GrFmzkJWVhSFDhkRUX+mSr0qlgsFgQFVVFcrKyoL2CiYiothzOp2wWq2+2e2sVqsiVxnjLlSXLFkCAPjDH/7gt/zNN9/EXXfdBQA4duyY3zeK/v37Y+XKlZg5cyYeffRRdOrUCWvXrvXr3DRt2jRUVFRgzJgxKC0txVVXXYX169fDaDSGXddA91DLysp8PYUZrERE8afuPVSgOlQtFkvEwRp3oRrKsNlNmzbVWzZ06FAMHTo06DoqlQpz587F3LlzI6meT7BOSbXvsdb+mYiIYi9YpyTpHmukwRp391TPBY318m1sHCsREUVfQ718w5l5KRCGqkyhDpthsBIRxY9Qhs0oEawMVZnsdnvI41AZrEREsSdnHGqkwcpQlUGv18Pj8ci63s5gJSKKnXAmdqgbrHIeUh53HZXimVqthtlsln0Dm52XiIiiz+VywWazhTVTUu0JIviQ8ibicDig1Yb3PYRnrERE0eP1elFSUhLZ5Pj/C1Y5DynnmaoMkT4lr/YZa2JioqyGIiKi0DmdTmi12oinHqw781JjGKpRJgVraWmp3/SGRESkHJVKhdTUVEWmHpTzGQzVGEhJSYHb7Q57HBQRETVMr9dDpVJFfbu8pxojSUlJvjknY+23/fvhrqyMdTWIiBQTi0AFeKYaU+F2elJSldWK7Y89hotuvRWX3HlnrKtDMox7o7DxQuewJX8dGOsqEMnGM9UL3MkvvkDVmTM49umn8Ho8sa4OEdE5jaF6ARNC4Ninn0J4vag4cQLWb76JdZWIiM5pDNULmO3wYdh+/BHG5s3hdbnw6+efx7pKRETnNIbqBezXjRvhcTigSUiAxmjEyc2b4Sovj3W1iIjOWQzVC5TH6cSvhYVQ/6/buS45GQ6bDae+/DLWVSMiOmcxVGNIziTNSjv99dc4e+YMdP+bjEL9v57Ixz/7LGZ1IiI618ka0+H1erF582Zs2bIFR48eRWVlJVq0aIFevXohLy8Pbdu2bap6nncqKirgcrlitv3jhYUQHg80tcbK6pKSYPnmG5QfP45ktiURncMinVY2XCGdqZ49exZPPfUU2rZtixtuuAH/+c9/UFpaCo1Ggx9//BFz5sxBdnY2brjhBuzYsaOp63zOKysrQ3l5ecwmf6iyWlG8Ywe0iYl+y7WJifCcPYsTmzfHpF5EREpxOp0xCdaQzlQvvvhi5Obm4rXXXsP//d//BQyDo0ePYuXKlbj99tvx2GOPYfTo0YpX9nxQVlYGu92O5OTkmE2of/KLL+AqL0dCy5Z+y1VqNVRaLY59+ik63XEH1Jzwn4jOUUIIlJSUoGXLlhHP/+v1ekMuG1Kofvrpp+jSpUuDZdq3b48ZM2bg4YcfxrFjx0KuwLkk0mmvpEA1mUwwGAyoqqpSqGahk8amQq2GKsCOpk9J8Y1ZbdGrV9TrR0SkBL1ej/Lyclit1oieVOP1en3Pwg5FSFtpLFBr0+l06NixY8jlzyUGgyHszkW1AzWWDymXxqbqk5MDvq/W6zlmlYjOeWq1GqmpqXC5XLBarbLONiVerxdWqxUeGbPNhTX5bFVVFb755hucPn26XkUHDx4czkeeE7xeL2w2G4xGI/R6fcjrxUugAjVjU/XNmgV8X6VS+casdr/vPuiChC8RUbzT6XRIT0+HxWKRfcYqBarL5YLZbA55m7JDdf369Rg5ciQsFku991QqlaxEP9c4nU5oNBpYLBakp6eHFKzxFKh1x6YGo0tORpXVilNffol2+flRrCERkbL0er3sYK0dqOnp6bKuUMq+yPzAAw9g6NChOHXqFLxer9/rfA5Uiclkgk6ng8ViafR5qPEUqED9sanBcMwqEZ1PpGAN5VJw3UCVc1USCCNUi4uLMWXKFGRkZMhd9bygVquRlpbWaLDGW6ACgcemBlN7zCoR0bkulGCNNFCBMEL1tttuw6ZNm2Rv6HzSWLDGY6A6SktRvHMnvC4XKouLUVFUVG+eX+H1orK4GJXFxXCWl8NVXo6TW7fGqMZERMpqKFiVCFQgjHuqr7zyCoYOHYotW7agR48e9casPvjgg2FV5FwjBavVavW7xxqPgQoAGoMB2X/6E9yVlQAAyzffwH7kiF9HJK/LBbVOh9ZXX+1bntq5c0zqS0TUFALdYwWgSKACYYTqv/71L3z66acwGo3YtGmTX4cXlUp1wYQqUD9YpbGn8RaoAKBNSED3sWN9P+947DHYjxwJWPbiO++E+Xe/i1bViIiiqnawSp1u3W53xIEKhBGqjz32GJ544glMnz494lkqzgdSsJ46dQpVVVUwGo1xF6hERORPr9cjLS3NF6pKBCoQxj1Vp9OJYcOGMVBrqaio8P3b4XA02iuYiIhiq+5MSXa7PawJIuqSnYyjRo3C6tWrI97w+aL2PdRWrVqFPNyGiIhio3anpBYtWqBFixYRzbxUm+zLvx6PB88//zw++eQTXHrppfU6Ks2fPz+iCp1LAnVKCtR5iYiI4kOwXr7hzrxUl+y1vv32W/Tq1QtqtRrfffcd9u7d6/dSwuLFi9GhQwcYjUbk5OTgq6++arD8mjVr0LlzZxiNRvTo0QMff/yx3/tCCMyePRutWrVCQkIC8vLycPjw4YjqGKyXb6jjWOOFEAIVngrY3DZUeipjXR0iOhd5PUDxJuCXf1X/1xufEwE1NGxGzgQRDZF9prpx48awNhSq1atXY8qUKVi6dClycnKwcOFC5Ofn4+DBg2hZ51FlALBt2zbccccdKCgowI033oiVK1diyJAh2LNnD7p37w4AeP755/Hyyy/jrbfeQnZ2NmbNmoX8/Hzs378fRqNRdh0bGzYTbLhNPFFpNHC4q1By4gd4UL3zqATgSdBiZ9lXuA7s/UtEITj+LrB7IlD5a82yxDZAn5eAtrfErl51hDIONZwpDetSrLfR0aNHMWHChIg/Z/78+Rg9ejTuvvtudO3aFUuXLkViYiLeeOONgOVfeuklXH/99Zg6dSq6dOmCJ598Er1798Yrr7wCoPpMbOHChZg5cyZuuukmXHrppfjHP/6BkydPYu3atbLrV1lZGdI41Hg/Y7UNuQQf3eHA53/RYPNfdNj8Fx02Dddh4+0qzHA9j89L+JQaImrE8XeBLbf5ByoAVJ6oXn783djUqw45EztEesYq+0z1mmuuCTgZ+6lTp3Dq1ClfmIXD6XRi9+7dmDFjhm+ZWq1GXl4etm/fHnCd7du3Y8qUKX7L8vPzfYF55MgRFBUVIS8vz/e+2WxGTk4Otm/fjttvvz3k+mm1WlRUVKB58+YhDZuJ1zNWj/Bgge6fON0/+EPIX/z1RQxoNgAaFR9UTkQBeD3VZ6gQAd4UAFTA7klA65sAdeyOI+HMlFT3jDUhISHk7ckO1Z49e/r97PF48PPPP+PHH3/E8uXL5X6cH4vFAo/HU29e4YyMDPzwww8B1ykqKgpYvqioyPe+tCxYmUAcDgccDofvZ7vdDq1Wi6SkJFnjUOMxWPeW78Vp1+kGyxS7irG3fC/6pvSNUq2I6JxyZkv9M1Q/Aqg8Xl0u4w/RqpV/DYQIe6ak2sHqcrlCXk92qC5YsCDg8r///e945ZVXMHz4cLkfGZcKCgrwxBNP+C0bP348EhMTZX9WoGCNJYur/mP7IilHRBegs6eULacwIQRKSkrg9XrDPpmRgrW4uDjkdRS7pzpw4EDs27cvos9IT0+HRqOp9wsUFxcjMzMz4DqZmZkNlpf+K+czAWDGjBmw2Wy+1/Hjx2U9U6+uuvdY5XzzUVq6LrRQD7UcEV2AElopW05hTqdTkakH9Xq9rIeUKxaqn3/+Oa655pqIPkOv16NPnz4oLCz0LfN6vSgsLERubm7AdXJzc/3KA8CGDRt85bOzs5GZmelXxm63Y+fOnUE/EwAMBgNMJpPfK1K1g1X6BhULvZJ7oaWufk/q2jJ0GeiV3CtKNSKic06L31f38kX9PjbVVEBi2+pyMSCEQGpqqiK327Ta0C/qyr78e8st9btIFxcXY+fOnbjmmmv83n/3Xfk9v6ZMmYJRo0ahb9++uPzyy7Fw4UJUVFTg7rvvBgCMHDkSrVu3RkFBAQBg4sSJGDBgAObNm4dBgwZh1apV2LVrF5YtWwagepL/SZMm4amnnkKnTp18Q2qysrIwZMgQ2fWLlBSsp0+fjlmPYI1Kg6ltpmLqkakB31dBhYfbPMxOSkQUnFpTPWxmy22oDtbaHZb+F7R9Fsask5Jer683OVE0yA7VQKfBZrMZF198sSIVGjZsGM6cOYPZs2ejqKgIPXv2xPr1630djY4dO+Y3bqh///5YuXIlZs6ciUcffRSdOnXC2rVrfWNUAWDatGmoqKjAmDFjUFpaiquuugrr168Pa4yqEtRqNVJTU/3mnYy2a1OvxQt4AS/8+oJfp6UMXQYebvMwrk29NmZ1I6JzRNtbgN//O8g41YUxHacaq/npVUKIQP2hqQ673Y5p06ahX79+YXVWqsvpdOLs2bP485//jObNmytQw/B4hAd7y/fC4rIgXZeOXsm9eIZ6jhj3RmHjhc5hS/46MNZVoFB5PdW9fM+eqr6H2uL3MR1G89tvv2HNmjUwGo2KXP6trKzEvffeC5vN1uitwJDOVIUQAcemUmTi4W+qUWk4bIaIIqPWxGzYTLwJ6fy4W7duWLVqVaP3AA8fPoxx48bh2WefVaRyRERE55KQzlQXLVqERx55BPfffz/+7//+D3379kVWVhaMRiNKSkqwf/9+bN26Fd9//z0mTJiAcePGNXW9iYiI4k5IoTpw4EDs2rULW7duxerVq7FixQocPXoUZ8+eRXp6Onr16oWRI0di+PDhSE1Nbeo6ExERxSVZvX+vuuoqXHXVVU1VFyIionNabPocEwDEbPIHIiJqGgzVGHG5XHH3ODgiIooMQzUGnE4nSkpK4mJIDRHR+SiSudojwVCNMqfTCYvFAq1WG/NHwBERna9cLhcqKiqivl2GahRJgarT6ZCamsozVSKiJqLT6VBeXo6ysrKIP6uysjLksrJD9dprr633nFEAKCkpwbXXnt/zxer1+rA7F9UO1LS0NAYqEVET0mq1SE5Oht1ujyhYy8rKZJ3xyp5Qf9OmTfj222+xd+9erFixAklJSQCqQ2Pz5s1yP+6colarYbfbkZiYKGuy5rqBGquJnomILiRJSUnQarW+h5ekpKTIWr+srAx2u92Xc6EI6+j+2WefoaioCFdccQV++eWXcD7inORwOODxeGC1WkM+Y2WgEhHFTkpKCkwmk+wzVilQTSaTrIeohHWEb9WqFTZv3owePXqgX79+2LRpUzgfc84RQsBsNsPlcoUUrAxUIqLYkxustQNV7tmt7KO8dC/QYDBg5cqVmDhxIq6//nq8+uqrcj/qnKTVapGent5osDJQiYjiR6jBGkmgAmHcU637+NWZM2eiS5cuGDVqlOyNn6v0ej3S09NhsVhgtVrrhSYDlYgo/kghGewea6SBCoQRqkeOHEGLFi38lt16663o3Lkzdu3aFVYlzkXBgpWBSkQUv4IFqxKBCoQRqu3btw+4vFu3bujWrVvYFTkX1Q1Wk8kEq9XKQCUiimN1g1X6d6SBCoQRquRPCtYzZ87wDJWI6BxRN1iVCFSAMyoREREphqEaIekeqnTG6na7ZY1jJSKi6Kt9DzWccazB8PJvBAJ1SmqoVzCRkpb8dWCsq0B0TgrWKSncmZdq4xE/TMF6+UpnrKFOEEFERNETLFDDnXmpLoZqGBobNsNgJSKKP40Nm1EiWBmqMrnd7pB6+TJYiYjiR6jjUCMNVoaqDCqVCjabLeRhMwxWIqLYkzuxQyTBylCVwWAwQKPRyOqAxGAlIoqdcGdKqh2sTfqQ8guZ1+uFyWSS3aOXwUpEFH0VFRURzZQkBauch5QzVGVwOp1hD5GpG6x1H0xARETKcbvdKC8vj3impJSUlKZ/SDmFp3awlpSUMFiJiJqIy+VCcnKyIlMPNvlDyil8tWdecjqdsa4OEdF5SafTyTrDVApDNQb0ej1SU1N5pkpE1ES02thMGMhQjRGdTge9Xh/rahARkYLiJlRdLhceeeQR9OjRA0lJScjKysLIkSNx8uTJRtddvHgxOnToAKPRiJycHHz11Vd+71dVVWH8+PFIS0tDcnIybr31VhQXFzfVrxIyzgtMRHR+iZujemVlJfbs2YNZs2Zhz549ePfdd3Hw4EEMHjy4wfVWr16NKVOmYM6cOdizZw8uu+wy5Ofn4/Tp074ykydPxocffog1a9Zg8+bNOHnyJG655Zam/pWIiOgCEzdPqTGbzdiwYYPfsldeeQWXX345jh07hnbt2gVcb/78+Rg9ejTuvvtuAMDSpUuxbt06vPHGG5g+fTpsNhtef/11rFy5Etdeey0A4M0330SXLl2wY8cOXHHFFU37ixER0QUjbs5UA7HZbFCpVGjWrFnA951OJ3bv3o28vDzfMrVajby8PGzfvh0AsHv3brhcLr8ynTt3Rrt27XxliIiIlBA3Z6p1VVVV4ZFHHsEdd9wBk8kUsIzFYoHH40FGRobf8oyMDPzwww8AgKKiIuj1+nrBnJGRgaKioqDbdzgccDgcvp+l5+wREREFE7Mz1RUrViA5Odn32rJli+89l8uFP//5zxBCYMmSJTGpX0FBAcxms+/Vtm1bxbfBITVEROeXmIXq4MGDsW/fPt+rb9++AGoC9ejRo9iwYUPQs1QASE9Ph0ajqdeTt7i4GJmZmQCAzMxMOJ1OlJaWBi0TyIwZM2Cz2Xyv48ePh/mbBiaE4OQPRETnmZiFakpKCi666CLfKyEhwReohw8fxmeffYa0tLQGP0Ov16NPnz4oLCz0LfN6vSgsLERubi4AoE+fPtDpdH5lDh48iGPHjvnKBGIwGGAymfxeSvF6vZymkIioCcXqwSVxc0/V5XLhtttuw549e/DRRx/B4/H47nk2b97cN1HCwIEDcfPNN2PChAkAgClTpmDUqFHo27cvLr/8cixcuBAVFRW+3sBmsxn33HMPpkyZgubNm8NkMuGBBx5Abm5uTHr+er1eWK1WuN1uJCcnR337REQXAqfTGZNJduImVE+cOIEPPvgAANCzZ0+/9zZu3Ig//OEPAICffvoJFovF996wYcNw5swZzJ49G0VFRejZsyfWr1/v13lpwYIFUKvVuPXWW+FwOJCfn49XX321yX+nuqRAdblcSE1NhcfjiXodiIguBCqVCiUlJYoEq9vtDrls3IRqhw4dQroc+ssvv9RbNmHCBN+ZayBGoxGLFy/G4sWLI6liRHNJ1g7U9PR0AGCoEhE1Eb1eD4/HA4vFgvT09LCD1el0wmazhVw+rsepxhutVivrCfCSuoHKOX+JiJqWSqVCamoqdDodLBZLWB1DnU4nLBYLNBpNyOswVGVwu92oqKhAWVlZyOswUImIYkOlUiEtLS2sYJUCVafTyeqoylCVwe12IykpCXa7PaRgZaASEcWWWq2WHay1AzUtLU3Ww08YqjIlJibCZDI1GqwMVCKi+CAnWCMJVIChGpaUlJQGg5WBSkQUX0IJ1kgDFWCohi1YsDJQiYjiU0PBqkSgAgzViNQNVgYqEVF8CxSsSgUqEEfjVM9VKSkpAKqfYmO326FSqRioRERxTApWq9WKM2fOAKge1xppoAI8U1VEUlKS798Gg4GBSkQU59Rqtd9QGZPJFHGgAgzViEmXfFUqFYxGI6qqqmSNYyUiouhzOp2wWq3Q6XTQ6XSwWq2KPDmMl38jEOgeallZme+B5tKlYSIiih9176ECgNVqjXhKQ4BnqmEL1impseE2REQUO4E6JYUzQUQwDNUwNNbLl8FKRBR/Gurlq1SwMlRlCnXYDIOViCh+hDJsRolgZajKZLfbQx6HymAlIoo9OeNQIw1WhqoM0vP55NzIZrASEcVOOBM71A3Wc/Ih5ecCtVoNs9ksu2dY7Qkiav9MRERNx+VywWazhfe0mVoTRPAh5U3E4XBAqw3vewjPWImIosfr9aKkpCSyyfH/F6xyHlLOM1UZhBARrV/7jDUxMVFWQxERUeicTie0Wm3EUw/WnXmpMQzVKJOCtbS01G96QyIiUo5KpUJqaqoyUw/K+AyGagykpKTA7XYrMiUWERHVp9froVKpor5dhmqMJCUlRXw5mYiIAotFoALsqBRT4XZ6IiKi+MRQJSIiUghDlYiISCEMVSIiIoUwVImIiBTCUCUiIlIIQzWG5EzSTERE8Y+hGiMVFRVwuVyxrgYR0XkpVvMAMFRjoKysDOXl5dDpdLGuChHRecnpdMYkWBmqUVZWVga73Y7k5GRO/kBE1ESEECgpKYHX6434s+R8BkNVhkinvZIC1WQycTJ9IqImpNfr4Xa7YbVaIwpWr9frexZ2KBiqMhgMhrA7F9UOVD6knIioaanVaqSmpsLlcoUdrF6vF1arFR6PJ/Ttyt5KlIwdOxYqlQoLFy5stOzixYvRoUMHGI1G5OTk4KuvvvJ7v6qqCuPHj0daWhqSk5Nx6623ori4WHadvF4vbDab7KfLMFCJiKJPp9MhPT09rGCVAtXlcsFsNoe8XlyG6nvvvYcdO3YgKyur0bKrV6/GlClTMGfOHOzZsweXXXYZ8vPzcfr0aV+ZyZMn48MPP8SaNWuwefNmnDx5ErfccovsejmdTmg0GlgslpCDlYFKRBQ7er1edrDWDtT09HRZ/V/iLlRPnDiBBx54ACtWrAipd+z8+fMxevRo3H333ejatSuWLl2KxMREvPHGGwAAm82G119/HfPnz8e1116LPn364M0338S2bduwY8cO2fUzmUzQ6XQhBSsDlYgo9uQEa91A1ev1srYVV6Hq9XoxYsQITJ06Fd26dWu0vNPpxO7du5GXl+dbplarkZeXh+3btwMAdu/eDZfL5Vemc+fOaNeuna9MIA6HA3a73e8lfX5aWlqjwcpAJSKKH6EEa6SBCsRZqD733HPQarV48MEHQypvsVjg8XiQkZHhtzwjIwNFRUUAgKKiIuj1ejRr1ixomUAKCgpgNpt9r7Zt2/reayxYGahERPGnoWBVIlCBGIbqihUrkJyc7Htt3rwZL730EpYvXx6zJ7bXNmPGDNhsNt/r+PHjfu8HC1YGKhFR/AoUrEoFKgDEbPaBwYMHIycnx/fzmjVrcPr0abRr1863zOPx4KGHHsLChQvxyy+/1PuM9PR0aDSaej15i4uLkZmZCQDIzMyE0+lEaWmp39lq7TKBGAwGGAyGBn8HKVitVissFgsMBgOqqqoYqEREcUwKVovFAovFAqB6LvZIAxWI4ZlqSkoKLrroIt9rzJgx+Oabb7Bv3z7fKysrC1OnTsUnn3wS8DP0ej369OmDwsJC3zKv14vCwkLk5uYCAPr06QOdTudX5uDBgzh27JivTCSkYBVCoKqqCkajkYFKRBTn9Ho90tLS4HK54HK5kJaWFnGgAjE8U60rLS0NaWlpfst0Oh0yMzNxySWX+JYNHDgQN998MyZMmAAAmDJlCkaNGoW+ffvi8ssvx8KFC1FRUYG7774bAGA2m3HPPfdgypQpaN68OUwmEx544AHk5ubiiiuuUKTuFRUVvn87HA44nU5FGoeIiJpG3ZmS7HY70tLSoFZHdq4ZN6Eaqp9++sl3ug4Aw4YNw5kzZzB79mwUFRWhZ8+eWL9+vV/npQULFkCtVuPWW2+Fw+FAfn4+Xn31VUXqU3fqQelSsBKXEYiISHm176G2aNECQHXHV6vVGnGwxnWoBrqPGmjZhAkTfGeugRiNRixevBiLFy9WsHaBOyXVvsfKYCUiii/BOiVJ91gjDda4GlJzLgnWyzfUcaxERBRdDfXyDWfmpUAYqmFobNgMg5WIKL6EMmxGiWBlqMpUWVkZ0jhUBisRUXyQMw410mBlqMqg1WpRUVER8jhUBisRUWyFM7FDJMHKUJVBq9UiKSlJ1jhUBisRUWwIIcKeKal2sPIh5U3E7XYjMTFR9noMViKi6BJCoKSkJKKpB6VgPS8eUh6P3G532OvWDVaXy6VgzYiIqDan06nI1IN6vf7cf0j5+ap2sJaUlITdZZuIiBomhEBqaqoyUw+eyw8pP99JwarVankZmIioiej1euh0uqhvl6EaA2q1GqmpqXHxiDsiovNRpHP4hr3dmGyVoFKpOIUhEdF5hqEaQzxTJSI6vzBUiYiIFMJQJSIiUghDlYiISCEMVSIiIoUwVGOIkz8QEZ1fGKox4nK5OPkDEdF5hqEaA06nEyUlJRxSQ0TURCKZqz0SDNUoczqdsFgs0Gq1nPyBiKiJuFwuVFRURH27DNUokgJVp9NxmkIioiak0+lQXl6OsrKyiD+rsrIy5LIMVRn0en3YnYtqB2paWhoDlYioCWm1WiQnJ8Nut0cUrGVlZbLOeBmqMqjVatjtdtnBWjdQYzXRMxHRhSQpKQkmkynsYC0rK4PdbkdSUlLI64T+kDiCw+GAx+OB1WoNORwZqEREsZOSkgIAsNvtfj83RgpUk8kEjUYT8vZ4hJdBCAGz2QyXywWr1droGSsDlYgo9lJSUmSdsdYO1FBDWMKjvExarRbp6emNBisDlYgofoQarJEEKsBQDYter28wWBmoRETxp7FgjTRQAYZq2IIFKwOViCh+BQtWJQIVYEeliEjBarFYYLVaYTKZYLVaGahERHGsbucl6d+RBirAUI2YFKxnzpzhGSoR0TmibrAqEagAL/8SEREphqEaIekeqnTG6na7QxpuQ0REsVP7HmokE0TUxVCNQN1OSQaDIaThNkREFDt1OyXJHcfakLgL1QMHDmDw4MEwm81ISkpCv379cOzYsQbXWbNmDTp37gyj0YgePXrg448/9ntfCIHZs2ejVatWSEhIQF5eHg4fPhxRPYP18m1suA0REcVOsF6+SgVrXIXqTz/9hKuuugqdO3fGpk2b8M0332DWrFkwGo1B19m2bRvuuOMO3HPPPdi7dy+GDBmCIUOG4LvvvvOVef755/Hyyy9j6dKl2LlzJ5KSkpCfn4+qqqqw6tnYsBkGKxFR/Gls2IwSwaoSQohIK6qU22+/HTqdDm+//XbI6wwbNgwVFRX46KOPfMuuuOIK9OzZE0uXLoUQAllZWXjooYfw8MMPAwBsNhsyMjKwfPly3H777SFtx263Y9q0aejVqxfKy8tD6uXbUPg6nU5UVVVh6NChaN68eci/LxERNey3337DmjVrYDQafc+tljMOtW7ZyspK3HvvvbDZbDCZTA2uGzdnql6vF+vWrcPFF1+M/Px8tGzZEjk5OVi7dm2D623fvh15eXl+y/Lz87F9+3YAwJEjR1BUVORXxmw2Iycnx1cmEIfDAbvd7vdSqVSw2WwhD5vhGSsRUezJndghkjPWuAnV06dPo7y8HM8++yyuv/56fPrpp7j55ptxyy23YPPmzUHXKyoqQkZGht+yjIwMFBUV+d6XlgUrE0hBQQHMZrPv1bZtWxgMBmg0GlnjUBmsRESxE+5MSbWD9Zx4SPmKFSuQnJzsex08eBAAcNNNN2Hy5Mno2bMnpk+fjhtvvBFLly6Nev1mzJgBm83mex0/fhxerxcmk0n2xA4MViKi6KuoqIhopiQpWOU8pDxmMyoNHjwYOTk5vp9btGgBrVaLrl27+pXr0qULtm7dGvRzMjMzUVxc7LesuLgYmZmZvvelZa1atfIr07Nnz6CfazAYYDAY/JY5nc6wZ0oKNKUhERE1DbfbjYqKCjRr1iyimZJSUlLgcDhCLh+zM9WUlBRcdNFFvpfZbEa/fv18Z6ySQ4cOoX379kE/Jzc3F4WFhX7LNmzYgNzcXABAdnY2MjMz/crY7Xbs3LnTVyZaap+xlpSUII76iBERnVdcLheSk5MVmXowMTEx5LJxNffv1KlTMWzYMFx99dW45pprsH79enz44YfYtGmTr8zIkSPRunVrFBQUAAAmTpyIAQMGYN68eRg0aBBWrVqFXbt2YdmyZQAAlUqFSZMm4amnnkKnTp2QnZ2NWbNmISsrC0OGDIn67ygF6+nTp+F0OqO+fSKiC4FOp0NSUlLUtxtXoXrzzTdj6dKlKCgowIMPPohLLrkE/+///T9cddVVvjLHjh3zuwTbv39/rFy5EjNnzsSjjz6KTp06Ye3atejevbuvzLRp01BRUYExY8agtLQUV111FdavX9/g+NempNfrkZqaKus6PRERhU6rjU28xdU41XgmjVPt16+frEsBwTidTlRWVmLYsGEcp0pEpKBA41QjcU6OU70Q8fFwRETnFx7ViYiIFMJQJSIiUghDlYiISCEMVSIiIoUwVGOIHa+JiM4vDNUYEUJw8gciovMMQzUGvF4vpykkImpCsXpwCUM1yrxeL6xWK9xutyKDkomIqD6n0wmXyxX17TJUo0gKVJfLhdTUVE7+QETURFQqFUpKShS5zeZ2u0Muy6O6DJHMJVk7UNPT06HT6RSsGRER1abX66HVamGxWCIKVqfTCZvNFnJ5hqoMWq1W1hPgJXUDlZd9iYialkqlQmpqKnQ6XdjB6nQ6YbFYoNFoQl6HoSqD9NDbsrKykNdhoBIRxYZKpUJaWlpYwSoFqk6na3QS/doYqjK43W4kJSXBbreHFKwMVCKi2FKr1bKDtXagpqWlyer/wlCVKTExESaTqdFgZaASEcUHOcEaSaACDNWwpKSkNBisDFQiovgSSrBGGqgAQzVswYKVgUpEFJ8aClYlAhVgqEakbrAyUImI4lugYFUqUAEg/IGXBKA6WAHAbrfDbrdDpVIxUImI4pgUrFarFWfOnAFQPa410kAFeKaqiKSkJN+/DQYDA5WIKM6p1Wq/oTImk0mRWe4YqhGSLvmqVCoYjUZUVVXJGsdKRETR53Q6YbVaodPpoNPpYLVaFZnSkJd/IxDoHmpZWRnsdjuAmkvDREQUP+reQwUAq9UKi8US8e07nqmGKVinpMaG2xARUewE6pQUzgQRwTBUw9BYL18GKxFR/Gmol69SwcpQlSnUYTMMViKi+BHKsBklgpWhKpPdbg95HCqDlYgo9uSMQ400WBmqMuj1eng8Hlk3shmsRESxE87EDnWDVc5Dytn7Vwa1Wg2z2Sy7Z1jtCSJq/0xERE3H5XLBZrOF97SZWhNE8CHlTcThcECrDe97CM9YiYiix+v1oqSkJLLJ8f8XrHIeUs4zVRmEEBGtX/uMNTExUVZDERFR6JxOJ7RabcRTD9adeakxDNUok4K1tLTUb3pDIiJSjkqlQmpqqjJTD8r4DIaqDGq1Gm63O+KprAwGAxISElBVVSXrWj0RETXOZrP5jtcqlSriz2NHpSbSrFkzWK1WRb75eL1eeDwevPfeezAYDPXu1brdbrhcLuh0urDv44ZaD6fTCZVKBb1e77cDNvSekoQQcDqdEEJAr9f7/X0bek9pDf3N2R6Nv6c0tkc1tkeNUNtDo9HAZrPBbrcrdrwOVVyFanl5OaZPn461a9fCarUiOzsbDz74IMaOHdvgemvWrMGsWbPwyy+/oFOnTnjuuedwww03+N4XQmDOnDl47bXXUFpaiiuvvBJLlixBp06dZNVPakCldlyVSgWdToeKigokJyf7LgdXVFTUW9aUdDodSkpK4PF4kJqaCpVKBZfLBbvdDq1W61vWlIxGI0pKSlBeXo7U1FTodDoIIVBSUgK32+1b1tQqKipQXl7O9mB7+LA9apwr7eF2u6FWq6FSqS7sUJ0yZQo+//xz/POf/0SHDh3w6aef4v7770dWVhYGDx4ccJ1t27bhjjvuQEFBAW688UasXLkSQ4YMwZ49e9C9e3cAwPPPP4+XX34Zb731FrKzszFr1izk5+dj//79MBqNsuqoVCMB1Q1lMplgMBh8OwQAVFZWolmzZlEbeqPX633jsex2O0wmE2w2GwwGgyLPFwxVy5Ytfd3X09LSYLfb4fV60bJly6g9Tk+v10Or1bI9wPaoXQe2R7VzpT1UKpXvWK1E+8j50qQSkXZpVVD37t0xbNgwzJo1y7esT58++OMf/4innnoq4DrDhg1DRUUFPvroI9+yK664Aj179sTSpUshhEBWVhYeeughPPzwwwCqr7dnZGRg+fLluP3220Oqm91ux3PPPYfk5GRFvhF6vV54vV60atWq3tNtTCZTTMayOp1O3wN7dTod0tPTo3bAkHi9XlgsFrhcLgBAixYtYvJ8WrZHNbZHDbZHjXhvD6fTiVOnTikWqi6XC48++ihsNlujPYHjapxq//798cEHH+DEiRMQQmDjxo04dOgQrrvuuqDrbN++HXl5eX7L8vPzsX37dgDAkSNHUFRU5FfGbDYjJyfHV4aIiEgJcRWqixYtQteuXdGmTRvo9Xpcf/31WLx4Ma6++uqg6xQVFSEjI8NvWUZGBoqKinzvS8uClQnE4XDAbrf7vZqK9K3PZDLFbIIIaSovvV6P9PR0uN1uWK1WWfcSIiU9rMDtdvumgoz0MUzhYHtUY3vUYHvUYHs0LGahumLFCiQnJ/teW7ZswaJFi7Bjxw588MEH2L17N+bNm4fx48fjs88+i3r9CgoKYDabfa+2bds2yXYqKip8O2hKSkpMZl6qOzemwWBAeno6XC5X1HbUuk//ke6PKPF8QzlqHzDYHmwPgO1RG9ujcTEL1cGDB2Pfvn2+V9++ffHoo49i/vz5+NOf/oRLL70UEyZMwLBhw/Diiy8G/ZzMzEwUFxf7LSsuLkZmZqbvfWlZsDKBzJgxAzabzfc6fvx4uL9qUG63G+Xl5fXuSURzRw022bT0DTAaO2qwx+kp+eDgUNQ9YEjYHmwPtgfbI1QxC9WUlBRcdNFFvpfL5YLL5ap3U1mj0TT4B8rNzUVhYaHfsg0bNiA3NxcAkJ2djczMTL8ydrsdO3fu9JUJxGAw+C5vSC8lOZ1OeL1eJCcnB7zJH40dtbGnN0RjR23s+bTROnAEO2BI2B7V2B412B414q09SktLYxascXNP1WQyYcCAAZg6dSo2bdqEI0eOYPny5fjHP/6Bm2++2Vdu5MiRmDFjhu/niRMnYv369Zg3bx5++OEHPP7449i1axcmTJgAoLor9KRJk/DUU0/hgw8+wLfffouRI0ciKysLQ4YMifavCaD6fq3H44FarW5wXFdT7qihPg6pKQ8coT7wvakPHI0dMCRsj2psjxpsjxrx1B5utxsejyfi+drDETehCgCrVq1Cv379MHz4cHTt2hXPPvssnn76ab/JH44dO4ZTp075fu7fvz9WrlyJZcuW4bLLLsO///1vrF271jdGFQCmTZuGBx54AGPGjEG/fv1QXl6O9evXyx6jqgSHwwG32w2NRhPSzCNNsaPKfb5gUxw4Qj1gSJrqwBHqAUPC9qjG9qjB9qgRL+2RmpoKIQQcDoci7SEnnONqnGo8s9vteP7555GUlBT2OFUpULVaLXQ6nd841cbI3bmDCeeBvUqsW5vcA4ZS69YVyd+U7RH5unWxPaqxPWqE+zd1Op04ceKE75aiwWCIqD3Onj2Lxx9//Nwbpxrv1Gp12N96ageqwWCQvb4S3wAj/Z9eiW/kkf5Pr9Q38kj/p2d7VGN71GB71IiH9lCr1dDr9RGdsXq9XjgcDnnblb2VC5zb7Zb1xAIg8kCVRLKjKvUtOpIDh1LfoiM9cCj1LZrtUY3tUYPtUSMe2kOj0YQdrFKgCiFkPSSAoSqDx+MBUN3goQarUoEqCWdHVWoHlYRz4FDyshQQ/oFDqQOGhO1Rje1Rg+1RIx7aQ6vVyg7W2oEq9+k/DFWZdDodVCpVSMGqdKBK5OyoSu+gEjkHDqUPGBK5Bw6lDxgStkc1tkcNtkeNeGgPOcFaN1DlPsqOoSqTSqWCwWBoNFibKlAloeyoTbWDSkI5cDTVAUMS6oGjqQ4YErZHNbZHDbZHjXhoj1CCNdJABRiqYZF6kwUL1qYOVElDO2pT76CShg4cTX3AkDR24GjqA4aE7VGN7VGD7VEjHtqjoWBVIlABhmrYggVrtAJVEmhHjdYOKgl04IjWAUMS7MARrQOGhO1Rje1Rg+1RIx7aI1CwKhWoQJw9pPxcIwWrw+GA0+mEy+WC1+uNWqBKpP8Z7HY7nE4nHA5H1HZQiXTgsFgssFgsAOD3NI1okA4cVqsVFosFBoMBVVVVUX/eI9ujGtujBtujRjy0hxSaTqcTVVVVvuWRBirAUA2ZNEeG1+v1PSBYotFo4HK5fGXUanW9MsE+TzqzjZRGo4FarUZ5eTmA6mkfa+8s0ZKYmAibzQag+rm14QxBilRCQgIqKyvhcrmg1+uh0WhQWVkZ1TqwPWqwPWqwPao1dXt4PB54vV4IIXyjNgJRq9W+9zUaDYQQAY/d0mXiUOZK4oxKIfr111+b7PFvREQU/44fP442bdo0WIahGiKv14uTJ08iJSUFKpVK1rp2ux1t27bF8ePHFX/aTVNgfZveuVZn1rfpnWt1vpDqK4RAWVkZsrKyGr1Ezcu/IVKr1Y1+Q2lMUzxCrimxvk3vXKsz69v0zrU6Xyj1NZvNIZVj718iIiKFMFSJiIgUwlCNAoPBgDlz5kR1mE0kWN+md67VmfVteudanVnfwNhRiYiISCE8UyUiIlIIQ5WIiEghDFUiIiKFMFTDVF5ejgkTJqBNmzZISEhA165dsXTp0kbXW7NmDTp37gyj0YgePXrg448/9ntfCIHZs2ejVatWSEhIQF5eHg4fPqxInQ8cOIDBgwfDbDYjKSkJ/fr1w7Fjx+K2vpKxY8dCpVJh4cKFjZZdvHgxOnToAKPRiJycHHz11Vd+71dVVWH8+PFIS0tDcnIybr31VhQXF0dcR5fLhUceeQQ9evRAUlISsrKyMHLkSJw8eTJu69zYduuK1b5QUFCAfv36ISUlBS1btsSQIUNw8ODBRteLh30XAJ599lmoVCpMmjQprut74sQJ3HnnnUhLS0NCQgJ69OiBXbt2NbjOpk2b0Lt3bxgMBlx00UVYvnx5vTJy97NQeDwezJo1C9nZ2UhISEDHjh3x5JNPNjqNYFTqKygso0ePFh07dhQbN24UR44cEX/729+ERqMR77//ftB1vvzyS6HRaMTzzz8v9u/fL2bOnCl0Op349ttvfWWeffZZYTabxdq1a8V///tfMXjwYJGdnS3Onj0bUX1//PFH0bx5czF16lSxZ88e8eOPP4r3339fFBcXx2V9Je+++6647LLLRFZWlliwYEGDZVetWiX0er144403xPfffy9Gjx4tmjVr5vc7jh07VrRt21YUFhaKXbt2iSuuuEL0798/4nqWlpaKvLw8sXr1avHDDz+I7du3i8svv1z06dMnLuscynZri+W+kJ+fL958803x3XffiX379okbbrhBtGvXTpSXlwddJx72XSGE+Oqrr0SHDh3EpZdeKiZOnBi39f3tt99E+/btxV133SV27twpfv75Z/HJJ5+IH3/8Meg6P//8s0hMTBRTpkwR+/fvF4sWLRIajUasX7/eV0bufhaqp59+WqSlpYmPPvpIHDlyRKxZs0YkJyeLl156Keb1ZaiGqVu3bmLu3Ll+y3r37i0ee+yxoOv8+c9/FoMGDfJblpOTI+677z4hhBBer1dkZmaKF154wfd+aWmpMBgM4l//+ldE9R02bJi48847Za0Ty/oKIcSvv/4qWrduLb777jvRvn37RkP18ssvF+PHj/f97PF4RFZWligoKPDVTafTiTVr1vjKHDhwQAAQ27dvj7i+dX311VcCgDh69Gjc1bmx7dYV632httOnTwsAYvPmzUHLxEN9y8rKRKdOncSGDRvEgAEDGgzVWNf3kUceEVdddZWsdaZNmya6devmt2zYsGEiPz/f97Pc/SxUgwYNEn/961/9lt1yyy1i+PDhMa8vL/+GqX///vjggw9w4sQJCCGwceNGHDp0CNddd13QdbZv3468vDy/Zfn5+di+fTsA4MiRIygqKvIrYzabkZOT4ysTDq/Xi3Xr1uHiiy9Gfn4+WrZsiZycHKxdu7bB9WJVX6nOI0aMwNSpU9GtW7dGyzudTuzevduvLmq1Gnl5eb667N69Gy6Xy69M586d0a5du4jrG4jNZoNKpUKzZs3iqs6hbLeuWO4LdUlPeWnevHnQMvFQ3/Hjx2PQoEH16hGP9f3ggw/Qt29fDB06FC1btkSvXr3w2muvRVTncPazUPXv3x+FhYU4dOgQAOC///0vtm7dij/+8Y8xry9DNUyLFi1C165d0aZNG+j1elx//fVYvHgxrr766qDrFBUVISMjw29ZRkYGioqKfO9Ly4KVCcfp06dRXl6OZ599Ftdffz0+/fRT3HzzzbjllluwefPmuKsvADz33HPQarV48MEHQypvsVjg8Xgara9er68XckrUt66qqio88sgjuOOOO4LOMxqrOoey3bpiuS/U5vV6MWnSJFx55ZXo3r170HKxru+qVauwZ88eFBQUhFQ+1vX9+eefsWTJEnTq1AmffPIJxo0bhwcffBBvvfWW7Drb7XacPXs2rP0sVNOnT8ftt9+Ozp07Q6fToVevXpg0aRKGDx8e8/oyVEOwYsUKJCcn+15btmzBokWLsGPHDnzwwQfYvXs35s2bh/Hjx+Ozzz6LdXXr1Vfq1HHTTTdh8uTJ6NmzJ6ZPn44bb7wxpM5VTa1ufTdv3oyXXnoJy5cvl/1EoGgJtE9IXC4X/vznP0MIgSVLlsSwluef8ePH47vvvsOqVatiXZWgjh8/jokTJ2LFihUwGo2xrk5IvF4vevfujWeeeQa9evXCmDFjMHr06Lg4PgTyzjvvYMWKFVi5ciX27NmDt956Cy+++GKDXwKihU+pCcHgwYORk5Pj+7l169YYOHAg3nvvPQwaNAgAcOmll2Lfvn148cUXg17uyczMrNdrs7i4GJmZmb73pWWtWrXyK9OzZ8+w69uiRQtotVp07drVr1yXLl2wdevWoJ8Tq/quWbMGp0+fRrt27XzLPB4PHnroISxcuBC//PJLvc9IT0+HRqNptL5OpxOlpaV+Z361y4Rb59atWwOoCdSjR4/i888/b/BpGNGus5zt1hWtfaEhEyZMwEcffYQvvvii0SdGxbK+u3fvxunTp9G7d2/fMo/Hgy+++AKvvPIKHA4HNBpN3NQXAFq1ahXw+PD//t//C7pOsDqbTCYkJCRAo9HI3s9CNXXqVN/ZKgD06NEDR48eRUFBAUaNGhXT+vJMNQQpKSm46KKLfC+XywWXy1XvuXoajcb3hPhAcnNzUVhY6Ldsw4YNyM3NBQBkZ2cjMzPTr4zdbsfOnTt9ZcKpr9lsRr9+/eoNQzh06BDat28fd/UdM2YMvvnmG+zbt8/3ysrKwtSpU/HJJ58E/Ay9Xo8+ffr41cXr9aKwsNBXlz59+kCn0/mVOXjwII4dOyarvoHqnJCQ4AvUw4cP47PPPkNaWlqDnxHtOsvZbl3R2hcCEUJgwoQJeO+99/D5558jOzu70XViWd+BAwfi22+/9dt/+/bti+HDh2Pfvn31AjXW9QWAK6+8UvHjQzj7WagqKysVP/4qVt+QuzSRnwEDBohu3bqJjRs3ip9//lm8+eabwmg0ildffdVXZsSIEWL69Om+n7/88kuh1WrFiy++KA4cOCDmzJkTsNt8s2bNxPvvvy+++eYbcdNNNynSbf7dd98VOp1OLFu2TBw+fNjXnXzLli1xWd+6AvX+vfbaa8WiRYt8P69atUoYDAaxfPlysX//fjFmzBjRrFkzUVRU5CszduxY0a5dO/H555+LXbt2idzcXJGbmxtx/ZxOpxg8eLBo06aN2Ldvnzh16pTv5XA44q7OjW03nvaFcePGCbPZLDZt2uT3d62srPSViaf6BlK392+81ferr74SWq1WPP300+Lw4cNixYoVIjExUfzzn//0lZk+fboYMWKE72dpiMrUqVPFgQMHxOLFiwMOUWls/w7HqFGjROvWrX1Dat59912Rnp4upk2bFvP6MlTDdOrUKXHXXXeJrKwsYTQaxSWXXCLmzZsnvF6vr8yAAQPEqFGj/NZ75513xMUXXyz0er3o1q2bWLdund/7Xq9XzJo1S2RkZAiDwSAGDhwoDh48qEidX3/9dXHRRRcJo9EoLrvsMrF27Vq/9+OtvrUFCtX27duLOXPm+C1btGiRaNeundDr9eLyyy8XO3bs8Hv/7Nmz4v777xepqakiMTFR3HzzzeLUqVMR1+/IkSMCQMDXxo0b47LODW03nvaFYH/XN998My7rG0jdUI3H+n744Yeie/fuwmAwiM6dO4tly5b5vT9q1CgxYMAAv2UbN24UPXv2FHq9Xvzud7/zaxNJY/t3OOx2u5g4caJo166dMBqN4ne/+5147LHH/L7Axqq+fEoNERGRQnhPlYiISCEMVSIiIoUwVImIiBTCUCUiIlIIQ5WIiEghDFUiIiKFMFSJiIgUwlAlIiJSCEOViAAAr7/+eoPPA1bK+vXr0bNnzwbnaSU6VzFUiQhVVVWYNWsW5syZ0+Tbuv7666HT6bBixYom3xZRtDFUiQj//ve/YTKZcOWVV0Zle3fddRdefvnlqGyLKJoYqkTnkTNnziAzMxPPPPOMb9m2bdug1+vrPfaqtlWrVuFPf/qT37I//OEPmDRpkt+yIUOG4K677vL93KFDBzz11FMYOXIkkpOT0b59e3zwwQc4c+YMbrrpJiQnJ+PSSy/Frl27/D7nT3/6E3bt2oWffvop/F+WKA4xVInOIy1atMAbb7yBxx9/HLt27UJZWRlGjBiBCRMmYODAgUHX27p1K/r27RvWNhcsWIArr7wSe/fuxaBBgzBixAiMHDkSd955J/bs2YOOHTti5MiRqP3sjnbt2iEjIwNbtmwJa5tE8YqhSnSeueGGGzB69GgMHz4cY8eORVJSEgoKCoKWLy0thc1mQ1ZWVtjbu++++9CpUyfMnj0bdrsd/fr1w9ChQ3HxxRfjkUcewYEDB1BcXOy3XlZWFo4ePRrWNoniFUOV6Dz04osvwu12Y82aNVixYgUMBkPQsmfPngUAGI3GsLZ16aWX+v6dkZEBAOjRo0e9ZadPn/ZbLyEhAZWVlWFtkyheMVSJzkM//fQTTp48Ca/Xi19++aXBsmlpaVCpVCgpKWn0cz0eT71lOp3O92+VShV0Wd0hNL/99htatGjR6DaJziUMVaLzjNPpxJ133olhw4bhySefxL333lvvLLE2vV6Prl27Yv/+/fXeq3vJ9ueff1akjlVVVfjpp5/Qq1cvRT6PKF4wVInOM4899hhsNhtefvllPPLII7j44ovx17/+tcF18vPzsXXr1nrL33//fbz77rv46aef8PTTT2P//v04evQoTpw4EVEdd+zYAYPBgNzc3Ig+hyjeMFSJziObNm3CwoUL8fbbb8NkMkGtVuPtt9/Gli1bsGTJkqDr3XPPPfj4449hs9n8lg8aNAjPP/88unbtii+++AKvvvoqvvrqK7z99tsR1fNf//oXhg8fjsTExIg+hyjeqETtfu5EdMEaOnQoevfujRkzZgCoHqfas2dPLFy4UNHtWCwWXHLJJdi1axeys7MV/WyiWOOZKhEBAF544QUkJyc3+XZ++eUXvPrqqwxUOi/xTJWIAmqqM1Wi8xlDlYiISCG8/EtERKQQhioREZFCGKpEREQKYagSEREphKFKRESkEIYqERGRQhiqRERECmGoEhERKYShSkREpJD/D4WLTqG3dKYSAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "def make_sim(size_box: float) -> td.Simulation:\n", " \"\"\"Create the simulation given a box size.\"\"\"\n", @@ -178,6 +174,33 @@ " return sim\n", "\n", "\n", + "size_box = 2.5 # initial box size\n", + "\n", + "# create a simulation and visualize the setup\n", + "sim_0 = make_sim(size_box)\n", + "sim_0.plot(y=0)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f9de2970-f755-48cb-8bb2-33eb50c8741f", + "metadata": {}, + "source": [ + "## Define Objective Function\n", + "\n", + "The crucial step in inverse design is to define the objective function. Now we can construct our objective function, which is simply the intensity at the measurement point as a function of the box size." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "84f53e4a-fb12-4a69-9639-bd1e02ab61bb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ "def objective_fn(size_box: float) -> float:\n", " \"\"\"Calculate the intensity at the monitor position given a box size.\"\"\"\n", "\n", @@ -201,32 +224,6 @@ "To visualize the simulation setup, we create an initial simulation with a box of size 2.5 μm and visualize it." ] }, - { - "cell_type": "code", - "execution_count": 7, - "id": "20390a0f-a94a-4db5-b421-298cfb12500e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "size_box = 2.5 # initial box size\n", - "\n", - "# create a simulation and visualize the setup\n", - "sim_0 = make_sim(size_box)\n", - "sim_0.plot(y=0)\n", - "plt.show()" - ] - }, { "cell_type": "markdown", "id": "edb2d3c1-6065-4b77-a525-cfbe1d19cacc", @@ -239,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "27726803-439a-4bbe-a143-40846a11be97", "metadata": { "tags": [] @@ -252,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "11a25a43-795c-4202-b9e5-dcad237625c1", "metadata": { "tags": [] @@ -266,52 +263,47 @@ "\tsize_box = 2.50\n", "\tintensity = 7.45e+02\n", "step = 2\n", - "\tsize_box = 2.70\n", - "\tintensity = 9.55e+02\n", + "\tsize_box = 2.65\n", + "\tintensity = 8.93e+02\n", "step = 3\n", - "\tsize_box = 2.90\n", - "\tintensity = 1.10e+03\n", + "\tsize_box = 2.84\n", + "\tintensity = 1.07e+03\n", "step = 4\n", - "\tsize_box = 3.10\n", - "\tintensity = 1.24e+03\n", + "\tsize_box = 2.99\n", + "\tintensity = 1.14e+03\n", "step = 5\n", - "\tsize_box = 3.30\n", - "\tintensity = 1.54e+03\n", + "\tsize_box = 3.12\n", + "\tintensity = 1.26e+03\n", "step = 6\n", - "\tsize_box = 3.50\n", - "\tintensity = 1.75e+03\n", + "\tsize_box = 3.32\n", + "\tintensity = 1.61e+03\n", "step = 7\n", - "\tsize_box = 3.69\n", - "\tintensity = 1.96e+03\n", + "\tsize_box = 3.59\n", + "\tintensity = 1.81e+03\n", "step = 8\n", - "\tsize_box = 3.89\n", - "\tintensity = 2.17e+03\n" + "\tsize_box = 3.85\n", + "\tintensity = 2.15e+03\n", + "step = 9\n", + "\tsize_box = 4.13\n", + "\tintensity = 2.20e+03\n" ] } ], "source": [ - "# hyperparameters\n", - "num_steps = 8 # number of iterations\n", - "learning_rate = 0.2\n", - "\n", - "# initialize adam optimizer with starting parameter\n", - "optimizer = optax.adam(learning_rate=learning_rate)\n", - "opt_state = optimizer.init(size_box)\n", - "\n", "# store history\n", "objective_history = [] # list to store the history of the objective function\n", "param_history = [size_box] # list to store the history of the box size\n", "\n", - "for i in range(num_steps):\n", + "# we will run the optimization for 9 iterations\n", + "for i in range(9):\n", " print(f\"step = {i + 1}\\n\\tsize_box = {size_box:.2f}\", end=\"\")\n", "\n", " # compute gradient and current objective function value\n", " value, gradient = val_and_grad_fn(size_box)\n", " print(f\"\\n\\tintensity = {value:.2e}\")\n", "\n", - " # compute and apply updates to the optimizer based on gradient (-1 sign to maximize objective_fn)\n", - " updates, opt_state = optimizer.update(-gradient, opt_state, size_box)\n", - " size_box = float(optax.apply_updates(size_box, updates))\n", + " # update the parameter with the gradient and a learning rate of 2e-4\n", + " size_box = size_box + gradient * 2e-4\n", "\n", " # save history\n", " objective_history.append(value)\n", @@ -332,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "4ae5c5bc-254d-4d9b-833b-9854e3da4962", "metadata": { "tags": [] @@ -340,7 +332,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -368,21 +360,21 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "9d9439d6-86bd-4e77-b5e5-e3e25f74c784", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
21:55:37 Eastern Daylight Time Created task 'quickstart_final' with task_id     \n",
-       "                               'fdve-32c7caf5-71ef-4932-bc45-a778c9d49872' and  \n",
+       "
10:38:15 Eastern Daylight Time Created task 'quickstart_final' with task_id     \n",
+       "                               'fdve-47894fc9-b5e1-45ea-801a-b7af1298406a' and  \n",
        "                               task_type 'FDTD'.                                \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m21:55:37 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'quickstart_final'\u001b[0m with task_id     \n",
-       "\u001b[2;36m                               \u001b[0m\u001b[32m'fdve-32c7caf5-71ef-4932-bc45-a778c9d49872'\u001b[0m and  \n",
+       "\u001b[2;36m10:38:15 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'quickstart_final'\u001b[0m with task_id     \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'fdve-47894fc9-b5e1-45ea-801a-b7af1298406a'\u001b[0m and  \n",
        "\u001b[2;36m                               \u001b[0mtask_type \u001b[32m'FDTD'\u001b[0m.                                \n"
       ]
      },
@@ -393,14 +385,14 @@
      "data": {
       "text/html": [
        "
                               View task using web UI at                        \n",
-       "                               'https://tidy3d.simulation.cloud/workbench?taskId\n",
-       "                               =fdve-32c7caf5-71ef-4932-bc45-a778c9d49872'.     \n",
+       "                               'https://tidy3d.simulation.cloud/workbench?taskId\n",
+       "                               =fdve-47894fc9-b5e1-45ea-801a-b7af1298406a'.     \n",
        "
\n"
       ],
       "text/plain": [
        "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                        \n",
-       "\u001b[2;36m                               \u001b[0m\u001b]8;id=15824;https://tidy3d.simulation.cloud/workbench?taskId=fdve-32c7caf5-71ef-4932-bc45-a778c9d49872\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=889496;https://tidy3d.simulation.cloud/workbench?taskId=fdve-32c7caf5-71ef-4932-bc45-a778c9d49872\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[2;36m                               \u001b[0m\u001b]8;id=15824;https://tidy3d.simulation.cloud/workbench?taskId=fdve-32c7caf5-71ef-4932-bc45-a778c9d49872\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=996128;https://tidy3d.simulation.cloud/workbench?taskId=fdve-32c7caf5-71ef-4932-bc45-a778c9d49872\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=15824;https://tidy3d.simulation.cloud/workbench?taskId=fdve-32c7caf5-71ef-4932-bc45-a778c9d49872\u001b\\\u001b[32m-32c7caf5-71ef-4932-bc45-a778c9d49872'\u001b[0m\u001b]8;;\u001b\\.     \n"
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=484200;https://tidy3d.simulation.cloud/workbench?taskId=fdve-47894fc9-b5e1-45ea-801a-b7af1298406a\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=74293;https://tidy3d.simulation.cloud/workbench?taskId=fdve-47894fc9-b5e1-45ea-801a-b7af1298406a\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=484200;https://tidy3d.simulation.cloud/workbench?taskId=fdve-47894fc9-b5e1-45ea-801a-b7af1298406a\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=348126;https://tidy3d.simulation.cloud/workbench?taskId=fdve-47894fc9-b5e1-45ea-801a-b7af1298406a\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=484200;https://tidy3d.simulation.cloud/workbench?taskId=fdve-47894fc9-b5e1-45ea-801a-b7af1298406a\u001b\\\u001b[32m-47894fc9-b5e1-45ea-801a-b7af1298406a'\u001b[0m\u001b]8;;\u001b\\.     \n"
       ]
      },
      "metadata": {},
@@ -413,7 +405,7 @@
        "
\n" ], "text/plain": [ - "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=590404;https://tidy3d.simulation.cloud/folders/639eb096-a602-4b56-a502-cac1f18f9557\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\. \n" + "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=726947;https://tidy3d.simulation.cloud/folders/639eb096-a602-4b56-a502-cac1f18f9557\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\. \n" ] }, "metadata": {}, @@ -422,7 +414,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7116f87294e3463da8a0b97987228986", + "model_id": "4a6da30052414022a1f3c12df2bbbdee", "version_major": 2, "version_minor": 0 }, @@ -446,14 +438,14 @@ { "data": { "text/html": [ - "
21:55:38 Eastern Daylight Time Maximum FlexCredit cost: 0.025. Minimum cost     \n",
+       "
10:38:16 Eastern Daylight Time Maximum FlexCredit cost: 0.025. Minimum cost     \n",
        "                               depends on task execution details. Use           \n",
        "                               'web.real_cost(task_id)' to get the billed       \n",
        "                               FlexCredit cost after a simulation run.          \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m21:55:38 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost     \n",
+       "\u001b[2;36m10:38:16 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost     \n",
        "\u001b[2;36m                               \u001b[0mdepends on task execution details. Use           \n",
        "\u001b[2;36m                               \u001b[0m\u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed       \n",
        "\u001b[2;36m                               \u001b[0mFlexCredit cost after a simulation run.          \n"
@@ -465,11 +457,199 @@
     {
      "data": {
       "text/html": [
-       "
                               status = success                                 \n",
+       "
10:38:17 Eastern Daylight Time status = queued                                  \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:38:17 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               To cancel the simulation, use                    \n",
+       "                               'web.abort(task_id)' or 'web.delete(task_id)' or \n",
+       "                               abort/delete the task in the web UI. Terminating \n",
+       "                               the Python script will not stop the job running  \n",
+       "                               on the cloud.                                    \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use                    \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or \u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or \n",
+       "\u001b[2;36m                               \u001b[0mabort/delete the task in the web UI. Terminating \n",
+       "\u001b[2;36m                               \u001b[0mthe Python script will not stop the job running  \n",
+       "\u001b[2;36m                               \u001b[0mon the cloud.                                    \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
10:38:22 Eastern Daylight Time status = preprocess                              \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:38:22 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                              \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
10:38:26 Eastern Daylight Time starting up solver                               \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:38:26 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                               \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               running solver                                   \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7f1165a4ec2b41c284aca675b9e533a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
10:38:29 Eastern Daylight Time early shutoff detected at 32%, exiting.          \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:38:29 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m32\u001b[0m%, exiting.          \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               status = postprocess                             \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                             \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
10:38:31 Eastern Daylight Time status = success                                 \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mstatus = success                                 \n"
+       "\u001b[2;36m10:38:31 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                 \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
10:38:33 Eastern Daylight Time View simulation result at                        \n",
+       "                               'https://tidy3d.simulation.cloud/workbench?taskId\n",
+       "                               =fdve-47894fc9-b5e1-45ea-801a-b7af1298406a'.     \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:38:33 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                        \n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=796591;https://tidy3d.simulation.cloud/workbench?taskId=fdve-47894fc9-b5e1-45ea-801a-b7af1298406a\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=118104;https://tidy3d.simulation.cloud/workbench?taskId=fdve-47894fc9-b5e1-45ea-801a-b7af1298406a\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=796591;https://tidy3d.simulation.cloud/workbench?taskId=fdve-47894fc9-b5e1-45ea-801a-b7af1298406a\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=930827;https://tidy3d.simulation.cloud/workbench?taskId=fdve-47894fc9-b5e1-45ea-801a-b7af1298406a\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=796591;https://tidy3d.simulation.cloud/workbench?taskId=fdve-47894fc9-b5e1-45ea-801a-b7af1298406a\u001b\\\u001b[4;34m-47894fc9-b5e1-45ea-801a-b7af1298406a'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m     \n"
       ]
      },
      "metadata": {},
@@ -478,7 +658,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7e69dbe1139d4cc2982fe0ba7c381ccc",
+       "model_id": "102d053235b4491a9957aabb196134f1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -502,11 +682,11 @@
     {
      "data": {
       "text/html": [
-       "
21:55:40 Eastern Daylight Time loading simulation from simulation_data.hdf5     \n",
+       "
10:38:35 Eastern Daylight Time loading simulation from simulation_data.hdf5     \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m21:55:40 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5     \n"
+       "\u001b[2;36m10:38:35 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5     \n"
       ]
      },
      "metadata": {},
@@ -540,13 +720,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "e48e1432-a162-46eb-9776-8100de2d7848",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
diff --git a/Autograd10YBranchLevelSet.ipynb b/Autograd10YBranchLevelSet.ipynb
index e4c98f67..9a54429c 100644
--- a/Autograd10YBranchLevelSet.ipynb
+++ b/Autograd10YBranchLevelSet.ipynb
@@ -29,21 +29,21 @@
    "outputs": [],
    "source": [
     "# Standard python imports.\n",
+    "import pickle\n",
     "from typing import List\n",
-    "import numpy as np\n",
-    "import matplotlib.pylab as plt\n",
     "\n",
     "# Import autograd to be able to use automatic differentiation.\n",
     "import autograd.numpy as anp\n",
-    "from autograd import grad\n",
-    "from autograd.tracer import getval\n",
-    "import optax\n",
-    "import pickle\n",
     "import gdstk\n",
+    "import matplotlib.pylab as plt\n",
+    "import numpy as np\n",
+    "import optax\n",
     "\n",
     "# Import regular tidy3d.\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
+    "from autograd import grad\n",
+    "from autograd.tracer import getval\n",
     "from tidy3d.plugins.autograd import value_and_grad\n",
     "\n",
     "plt.rcParams[\"font.size\"] = \"12\""
@@ -82,7 +82,9 @@
     "# Inverse design set up parameters.\n",
     "grid_size = 0.016  # Simulation grid size on design region (um).\n",
     "ls_grid_size = 0.004  # Discretization size of the level set function (um).\n",
-    "ls_down_sample = 20  # The spacing between the level set control knots is given by ls_grid_size*ls_down_sample.\n",
+    "ls_down_sample = (\n",
+    "    20  # The spacing between the level set control knots is given by ls_grid_size*ls_down_sample.\n",
+    ")\n",
     "fom_name_1 = \"fom_field1\"  # Name of the monitor used to compute the objective function.\n",
     "min_feature_size = 0.14  # Minimum fabrication feature size (um).\n",
     "gap_par = 1.0  # Parameter to minimum gap fabrication constraint.\n",
@@ -314,9 +316,7 @@
     "def update_design(eps, unfold=False) -> List[td.Structure]:\n",
     "    # Reflects the structure about the x-axis.\n",
     "    eps_val = anp.array(eps).reshape((nx_phi, ny_phi, 1))\n",
-    "    coords_x = [\n",
-    "        (dr_center_x - dr_size_x / 2) + ix * ls_grid_size for ix in range(nx_phi)\n",
-    "    ]\n",
+    "    coords_x = [(dr_center_x - dr_size_x / 2) + ix * ls_grid_size for ix in range(nx_phi)]\n",
     "\n",
     "    if not unfold:\n",
     "        # Creation of a CustomMedium using the values of the design parameters.\n",
@@ -429,18 +429,10 @@
     "    length=w_thick,\n",
     "    axis=2,\n",
     ")\n",
-    "init_design = td.ClipOperation(\n",
-    "    operation=\"difference\", geometry_a=y_poly, geometry_b=y_hole1\n",
-    ")\n",
-    "init_design = td.ClipOperation(\n",
-    "    operation=\"difference\", geometry_a=init_design, geometry_b=y_hole2\n",
-    ")\n",
-    "init_design = td.ClipOperation(\n",
-    "    operation=\"difference\", geometry_a=init_design, geometry_b=y_hole3\n",
-    ")\n",
-    "init_design = td.ClipOperation(\n",
-    "    operation=\"difference\", geometry_a=init_design, geometry_b=y_hole4\n",
-    ")\n",
+    "init_design = td.ClipOperation(operation=\"difference\", geometry_a=y_poly, geometry_b=y_hole1)\n",
+    "init_design = td.ClipOperation(operation=\"difference\", geometry_a=init_design, geometry_b=y_hole2)\n",
+    "init_design = td.ClipOperation(operation=\"difference\", geometry_a=init_design, geometry_b=y_hole3)\n",
+    "init_design = td.ClipOperation(operation=\"difference\", geometry_a=init_design, geometry_b=y_hole4)\n",
     "\n",
     "init_eps = init_design.inside_meshgrid(x=x_phi, y=y_phi, z=np.zeros(1))\n",
     "init_eps = np.squeeze(init_eps) * eps_max\n",
@@ -465,7 +457,7 @@
     "# Figure of Merit (FOM) calculation.\n",
     "def fom_eps(eps_ref: anp.ndarray, eps: anp.ndarray) -> float:\n",
     "    \"\"\"Calculate the L2 norm between eps_ref and eps.\"\"\"\n",
-    "    return anp.mean((anp.abs(eps_ref - eps) ** 2))\n",
+    "    return anp.mean(anp.abs(eps_ref - eps) ** 2)\n",
     "\n",
     "\n",
     "# Objective function to be passed to the optimization algorithm.\n",
@@ -660,7 +652,7 @@
     "obj_eps = []\n",
     "\n",
     "for i in range(50):\n",
-    "    # Compute gradient and current objective funciton value.\n",
+    "    # Compute gradient and current objective function value.\n",
     "    value, gradient = obj_grad_eps(params_eps, init_eps)\n",
     "\n",
     "    # outputs\n",
@@ -775,9 +767,7 @@
     "x_start = (\n",
     "    -size_x / 2 + w_length + dr_size_x - grid_size\n",
     ")  # x-coordinate of the starting point of the waveguide bends.\n",
-    "x = np.linspace(\n",
-    "    x_start, x_start + bend_length, 100\n",
-    ")  # x-coordinates of the top edge vertices.\n",
+    "x = np.linspace(x_start, x_start + bend_length, 100)  # x-coordinates of the top edge vertices.\n",
     "y = (\n",
     "    (x - x_start) * bend_offset / bend_length\n",
     "    - bend_offset * np.sin(2 * np.pi * (x - x_start) / bend_length) / (np.pi * 2)\n",
@@ -790,12 +780,8 @@
     "\n",
     "# add path to the cell\n",
     "cell = gdstk.Cell(\"bend\")\n",
-    "cell.add(\n",
-    "    gdstk.FlexPath(x + 1j * y, w_width, layer=1, datatype=0)\n",
-    ")  # Top waveguide bend.\n",
-    "cell.add(\n",
-    "    gdstk.FlexPath(x - 1j * y, w_width, layer=1, datatype=0)\n",
-    ")  # Bottom waveguide bend.\n",
+    "cell.add(gdstk.FlexPath(x + 1j * y, w_width, layer=1, datatype=0))  # Top waveguide bend.\n",
+    "cell.add(gdstk.FlexPath(x - 1j * y, w_width, layer=1, datatype=0))  # Bottom waveguide bend.\n",
     "\n",
     "# Define top waveguide bend structure.\n",
     "wg_bend_top = td.Structure(\n",
@@ -891,9 +877,7 @@
     "\n",
     "    # Creates a uniform mesh for the design region.\n",
     "    adjoint_dr_mesh = td.MeshOverrideStructure(\n",
-    "        geometry=td.Box(\n",
-    "            center=(dr_center_x, 0, 0), size=(dr_size_x, dr_size_y, w_thick)\n",
-    "        ),\n",
+    "        geometry=td.Box(center=(dr_center_x, 0, 0), size=(dr_size_x, dr_size_y, w_thick)),\n",
     "        dl=[grid_size, grid_size, grid_size],\n",
     "        enforce=True,\n",
     "    )\n",
@@ -1344,9 +1328,7 @@
     "\n",
     "# Minimum gap size fabrication constraint integrand calculation.\n",
     "# The \"beta\" parameter relax the constraint near the zero plane.\n",
-    "def fab_penalty_ls_gap(\n",
-    "    params, beta=1, min_feature_size=min_feature_size, grid_size=ls_grid_size\n",
-    "):\n",
+    "def fab_penalty_ls_gap(params, beta=1, min_feature_size=min_feature_size, grid_size=ls_grid_size):\n",
     "    # Get the level set surface.\n",
     "    phi_model = LevelSetInterp(x0=x_rho, y0=y_rho, z0=params, sigma=rho_size)\n",
     "    phi = phi_model.get_ls(x1=x_phi, y1=y_phi)\n",
@@ -1358,9 +1340,7 @@
     "    # Calculates the gap penalty over the level set grid.\n",
     "    pi_d = np.pi / (1.3 * min_feature_size)\n",
     "    phi_v = anp.maximum(anp.power(phi_x**2 + phi_y**2, 0.5), anp.power(1e-32, 1 / 4))\n",
-    "    phi_vv = (\n",
-    "        phi_x**2 * phi_xx + 2 * phi_x * phi_y * phi_xy + phi_y**2 * phi_yy\n",
-    "    ) / phi_v**2\n",
+    "    phi_vv = (phi_x**2 * phi_xx + 2 * phi_x * phi_y * phi_xy + phi_y**2 * phi_yy) / phi_v**2\n",
     "    return (\n",
     "        anp.maximum((anp.abs(phi_vv) / (pi_d * anp.abs(phi) + beta * phi_v) - pi_d), 0)\n",
     "        * grid_size**2\n",
@@ -1456,9 +1436,7 @@
     "\n",
     "# Visualization of gap and curvature fabrication constraints values.\n",
     "gap_penalty_int = fab_penalty_ls_gap(mirror_param(init_rho), beta=gap_par)\n",
-    "curve_penalty_int = fab_penalty_ls_curve(\n",
-    "    mirror_param(init_rho), alpha=curve_par, sharpness=4\n",
-    ")\n",
+    "curve_penalty_int = fab_penalty_ls_curve(mirror_param(init_rho), alpha=curve_par, sharpness=4)\n",
     "\n",
     "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8), tight_layout=True)\n",
     "yy, xx = np.meshgrid(y_phi, x_phi)\n",
@@ -1599,11 +1577,9 @@
     "    params = history_dict[\"params\"][-1]\n",
     "    num_iters_completed = len(history_dict[\"params\"])\n",
     "    print(\"Loaded optimization checkpoint from file.\")\n",
-    "    print(\n",
-    "        f\"Found {num_iters_completed} iterations previously completed out of {iterations} total.\"\n",
-    "    )\n",
+    "    print(f\"Found {num_iters_completed} iterations previously completed out of {iterations} total.\")\n",
     "    if num_iters_completed < iterations:\n",
-    "        print(f\"Will resume optimization.\")\n",
+    "        print(\"Will resume optimization.\")\n",
     "    else:\n",
     "        print(\"Optimization completed, will return results.\")\n",
     "\n",
@@ -2341,9 +2317,7 @@
     "\n",
     "# Visualization of gap and curvature fabrication constraints values.\n",
     "gap_penalty_int = fab_penalty_ls_gap(mirror_param(final_par), beta=gap_par)\n",
-    "curve_penalty_int = fab_penalty_ls_curve(\n",
-    "    mirror_param(final_par), alpha=curve_par, sharpness=4\n",
-    ")\n",
+    "curve_penalty_int = fab_penalty_ls_curve(mirror_param(final_par), alpha=curve_par, sharpness=4)\n",
     "\n",
     "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8), tight_layout=True)\n",
     "yy, xx = np.meshgrid(y_phi, x_phi)\n",
@@ -2801,7 +2775,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.8"
+   "version": "3.11.0"
   },
   "title": "How to perform the inverse design of a y-branch using level set and the adjoint plugin in Tidy3D FDTD"
  },
diff --git a/Autograd12LightExtractor.ipynb b/Autograd12LightExtractor.ipynb
index b80f0087..9267f928 100644
--- a/Autograd12LightExtractor.ipynb
+++ b/Autograd12LightExtractor.ipynb
@@ -24,21 +24,26 @@
    "outputs": [],
    "source": [
     "# Standard python imports.\n",
-    "from typing import List\n",
-    "import numpy as np\n",
-    "import matplotlib.pylab as plt\n",
-    "import scipy as sp\n",
-    "import optax\n",
     "import pickle\n",
+    "from typing import List\n",
     "\n",
     "# Import autograd for automatic differentiation.\n",
     "import autograd as ag\n",
     "import autograd.numpy as anp\n",
-    "from tidy3d.plugins.autograd import rescale, make_filter_and_project, make_erosion_dilation_penalty, value_and_grad\n",
+    "import matplotlib.pylab as plt\n",
+    "import numpy as np\n",
+    "import optax\n",
+    "import scipy as sp\n",
     "\n",
     "# Import regular tidy3d.\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web"
+    "import tidy3d.web as web\n",
+    "from tidy3d.plugins.autograd import (\n",
+    "    make_erosion_dilation_penalty,\n",
+    "    make_filter_and_project,\n",
+    "    rescale,\n",
+    "    value_and_grad,\n",
+    ")"
    ]
   },
   {
@@ -150,10 +155,13 @@
    "outputs": [],
    "source": [
     "def pre_process(params, beta):\n",
-    "    filter_project = make_filter_and_project(radius=min_feature, dl=grid_size, beta=beta, eta=0.5, filter_type='conic')\n",
+    "    filter_project = make_filter_and_project(\n",
+    "        radius=min_feature, dl=grid_size, beta=beta, eta=0.5, filter_type=\"conic\"\n",
+    "    )\n",
     "    params1 = filter_project(params, beta)\n",
     "    return params1\n",
     "\n",
+    "\n",
     "def get_eps(params, beta: float = 1.00):\n",
     "    \"\"\"Returns the permittivities after filter and projection transformations\"\"\"\n",
     "    params1 = pre_process(params, beta=beta)\n",
@@ -181,11 +189,14 @@
     "    yv, xv = anp.meshgrid(y_grid, x_grid)\n",
     "\n",
     "    # Shouldn't this be --> |x-x0|^2 + |y-y0|^2 <= r*2\n",
-    "    geo_mask = anp.where(\n",
-    "        anp.abs((xv - circ_center[0]) ** 2 + (yv - circ_center[1]) ** 2) <= (circ_radius ** 2),\n",
-    "        1,\n",
-    "        0,\n",
-    "    ) * eps_max\n",
+    "    geo_mask = (\n",
+    "        anp.where(\n",
+    "            anp.abs((xv - circ_center[0]) ** 2 + (yv - circ_center[1]) ** 2) <= (circ_radius**2),\n",
+    "            1,\n",
+    "            0,\n",
+    "        )\n",
+    "        * eps_max\n",
+    "    )\n",
     "    eps = anp.maximum(geo_mask, eps)\n",
     "    return eps"
    ]
@@ -208,7 +219,7 @@
     "    coords_x = [(cr_center_x - cr_l / 2) + ix * grid_size for ix in range(nx_grid)]\n",
     "    eps_val = anp.array(eps).reshape((nx_grid, ny_grid, 1))\n",
     "\n",
-    "    if unfold == False:\n",
+    "    if not unfold:\n",
     "        coords_yp = [0 + iy * grid_size for iy in range(ny_grid)]\n",
     "        coords = dict(x=coords_x, y=coords_yp, z=[0])\n",
     "        eps1 = td.SpatialDataArray(eps_val, coords)\n",
@@ -219,9 +230,11 @@
     "    # VJP for one of anp.copy(), anp.concatenate(), or anp.fliplr() not defined,\n",
     "    # so the optimization should only be run with `unfold=False` for now\n",
     "    else:\n",
-    "        coords_y = [-cr_w / 2 + iy * grid_size for iy in range(2*ny_grid)]\n",
+    "        coords_y = [-cr_w / 2 + iy * grid_size for iy in range(2 * ny_grid)]\n",
     "        coords = dict(x=coords_x, y=coords_y, z=[0])\n",
-    "        eps1 = td.SpatialDataArray(anp.concatenate((anp.fliplr(anp.copy(eps_val)), eps_val), axis=1), coords)\n",
+    "        eps1 = td.SpatialDataArray(\n",
+    "            anp.concatenate((anp.fliplr(anp.copy(eps_val)), eps_val), axis=1), coords\n",
+    "        )\n",
     "        eps_medium = td.CustomMedium(permittivity=eps1)\n",
     "        box = td.Box(center=(cr_center_x, 0, 0), size=(cr_l, cr_w, wg_thick))\n",
     "        structure = [td.Structure(geometry=box, medium=eps_medium)]\n",
@@ -339,14 +352,14 @@
     "    adjoint_dr_mesh = td.MeshOverrideStructure(\n",
     "        geometry=td.Box(center=(cr_center_x, 0, 0), size=(cr_w, cr_l, wg_thick)),\n",
     "        dl=[grid_size, grid_size, grid_size],\n",
-    "        enforce=True)\n",
-    "\n",
-    "    grid_spec=td.GridSpec.auto(\n",
-    "                wavelength=wl_max,\n",
-    "                min_steps_per_wvl=15,\n",
-    "                override_structures=[adjoint_dr_mesh],\n",
-    "            )\n",
+    "        enforce=True,\n",
+    "    )\n",
     "\n",
+    "    grid_spec = td.GridSpec.auto(\n",
+    "        wavelength=wl_max,\n",
+    "        min_steps_per_wvl=15,\n",
+    "        override_structures=[adjoint_dr_mesh],\n",
+    "    )\n",
     "\n",
     "    return td.Simulation(\n",
     "        size=[size_x, size_y, size_z],\n",
@@ -386,7 +399,7 @@
     }
    ],
    "source": [
-    "init_par = np.ones((nx_grid, ny_grid))*0.5\n",
+    "init_par = np.ones((nx_grid, ny_grid)) * 0.5\n",
     "init_design = make_adjoint_sim(init_par, beta=beta_min, unfold=True)\n",
     "\n",
     "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(12, 4))\n",
@@ -553,9 +566,7 @@
     "from tidy3d.plugins.mode import ModeSolver\n",
     "from tidy3d.plugins.mode.web import run as run_mode_solver\n",
     "\n",
-    "sim_init = init_design.updated_copy(\n",
-    "    monitors=[field_monitor_xy, mode_monitor] + field_monitor\n",
-    ")\n",
+    "sim_init = init_design.updated_copy(monitors=[field_monitor_xy, mode_monitor] + field_monitor)\n",
     "\n",
     "mode_solver = ModeSolver(\n",
     "    simulation=sim_init,\n",
@@ -808,15 +819,16 @@
     "def fom(sim_data: td.SimulationData) -> float:\n",
     "    \"\"\"Return the coupling efficiency.\"\"\"\n",
     "    # best to use autograd-wrapped numpy functions for differentiation\n",
-    "    mode_amps = sim_data['mode_monitor_fom'].amps.sel(direction=\"-\", f=freq, mode_index=0).data\n",
-    "    mode_power = anp.sum(anp.abs(mode_amps) ** 2)   \n",
+    "    mode_amps = sim_data[\"mode_monitor_fom\"].amps.sel(direction=\"-\", f=freq, mode_index=0).data\n",
+    "    mode_power = anp.sum(anp.abs(mode_amps) ** 2)\n",
     "\n",
     "    # unlike Jax version, should avoid in-place operators (e.g, `+=`), use numpy when possible\n",
-    "    field_mon_list = [sim_data[f'field_monitor_fom_{i}'] for i in range(0,6)]\n",
+    "    field_mon_list = [sim_data[f\"field_monitor_fom_{i}\"] for i in range(0, 6)]\n",
     "    dip_power = anp.sum([anp.abs(mon.flux.data) for mon in field_mon_list])\n",
     "\n",
     "    return mode_power, dip_power\n",
     "\n",
+    "\n",
     "def penalty(params, beta) -> float:\n",
     "    \"\"\"Penalize changes in structure after erosion and dilation to enforce larger feature sizes.\"\"\"\n",
     "    params_processed = pre_process(params, beta=beta)\n",
@@ -824,9 +836,10 @@
     "    ed_penalty = erode_dilate_penalty(params_processed)\n",
     "    return ed_penalty\n",
     "\n",
+    "\n",
     "# Objective function to be passed to the optimization algorithm.\n",
     "def obj(param, beta: float = 1.0, step_num: int = None, verbose: bool = False) -> float:\n",
-    "    sim = make_adjoint_sim(param, beta, unfold=False)   # non-differentiable if `unfold=True`\n",
+    "    sim = make_adjoint_sim(param, beta, unfold=False)  # non-differentiable if `unfold=True`\n",
     "    task_name = \"inv_des\"\n",
     "    if step_num:\n",
     "        task_name += f\"_step_{step_num}\"\n",
@@ -838,6 +851,7 @@
     "    J = fom_val - penalty_weight * penalty_val\n",
     "    return J, [sim_data, mode_power, dip_power, penalty_val]\n",
     "\n",
+    "\n",
     "# Function to calculate the objective function value and its gradient with respect to the design parameters.\n",
     "# Use tidy3d's wrapped ag.value_and_grad() for it's auxiliary data functionality\n",
     "obj_grad = value_and_grad(obj, has_aux=True)"
@@ -859,11 +873,13 @@
     "# where to store history\n",
     "history_fname = \"misc/qe_light_coupler_autograd.pkl\"\n",
     "\n",
+    "\n",
     "def save_history(history_dict: dict) -> None:\n",
     "    \"\"\"Convenience function to save the history to file.\"\"\"\n",
     "    with open(history_fname, \"wb\") as file:\n",
     "        pickle.dump(history_dict, file)\n",
     "\n",
+    "\n",
     "def load_history() -> dict:\n",
     "    \"\"\"Convenience method to load the history from file.\"\"\"\n",
     "    with open(history_fname, \"rb\") as file:\n",
@@ -901,14 +917,12 @@
     "    history_dict = load_history()\n",
     "    opt_state = history_dict[\"opt_states\"][-1]\n",
     "    params = history_dict[\"params\"][-1]\n",
-    "    opt_state = optimizer.init(params)    \n",
+    "    opt_state = optimizer.init(params)\n",
     "    num_iters_completed = len(history_dict[\"params\"])\n",
     "    print(\"Loaded optimization checkpoint from file.\")\n",
-    "    print(\n",
-    "        f\"Found {num_iters_completed} iterations previously completed out of {max_iter} total.\"\n",
-    "    )\n",
+    "    print(f\"Found {num_iters_completed} iterations previously completed out of {max_iter} total.\")\n",
     "    if num_iters_completed < max_iter:\n",
-    "        print(f\"Will resume optimization.\")\n",
+    "        print(\"Will resume optimization.\")\n",
     "    else:\n",
     "        print(\"Optimization completed, will return results.\")\n",
     "\n",
@@ -945,15 +959,17 @@
     "    # small # of iters for quick testing\n",
     "    for i in range(iter_done, max_iter):\n",
     "        print(f\"Iteration = ({i + 1} / {max_iter})\")\n",
-    "        plt.subplots(1,1, figsize=(3,2))\n",
-    "        plt.imshow(np.flipud(1-params.T), cmap='gray', vmin=0, vmax=1)\n",
-    "        plt.axis('off')\n",
+    "        plt.subplots(1, 1, figsize=(3, 2))\n",
+    "        plt.imshow(np.flipud(1 - params.T), cmap=\"gray\", vmin=0, vmax=1)\n",
+    "        plt.axis(\"off\")\n",
     "        plt.show()\n",
     "\n",
     "        # Compute gradient and current objective function value.\n",
-    "        beta_i = i//iter_steps + beta_min\n",
+    "        beta_i = i // iter_steps + beta_min\n",
     "        (value, gradient), data = obj_grad(params, beta=beta_i, step_num=(i + 1))\n",
-    "        sim_data_i, mode_power_i, dip_power_i, penalty_val_i = [data[0]]+[dat._value for dat in data[1:]]\n",
+    "        sim_data_i, mode_power_i, dip_power_i, penalty_val_i = [data[0]] + [\n",
+    "            dat._value for dat in data[1:]\n",
+    "        ]\n",
     "        # Outputs.\n",
     "        print(f\"\\tbeta = {beta_i}\")\n",
     "        print(f\"\\tJ = {value:.4e}\")\n",
@@ -979,7 +995,7 @@
     "        history_dict[\"beta\"].append(beta_i)\n",
     "        history_dict[\"gradients\"].append(gradient)\n",
     "        history_dict[\"opt_states\"].append(opt_state)\n",
-    "        #history_dict[\"data\"].append(sim_data_i)  # Uncomment to store data, can create large files.\n",
+    "        # history_dict[\"data\"].append(sim_data_i)  # Uncomment to store data, can create large files.\n",
     "        save_history(history_dict)"
    ]
   },
@@ -1014,16 +1030,19 @@
     "    params = np.concatenate((np.fliplr(np.copy(params)), params), axis=1)\n",
     "    return params\n",
     "\n",
-    "params1 = history_dict['params'][32]\n",
+    "\n",
+    "params1 = history_dict[\"params\"][32]\n",
     "params1_full = pre_process(params1, beta=final_beta)\n",
     "params1_full = include_constant_regions(\n",
-    "        params1_full, circ_center=[qe_pos.center[0], qe_pos.center[1]], circ_radius=non_etch_r)\n",
+    "    params1_full, circ_center=[qe_pos.center[0], qe_pos.center[1]], circ_radius=non_etch_r\n",
+    ")\n",
     "params1_full = unfold_params(params1_full)\n",
     "\n",
-    "params2 = history_dict['params'][-1]\n",
+    "params2 = history_dict[\"params\"][-1]\n",
     "params2_full = pre_process(params2, beta=final_beta)\n",
     "params2_full = include_constant_regions(\n",
-    "        params2_full, circ_center=[qe_pos.center[0], qe_pos.center[1]], circ_radius=non_etch_r)\n",
+    "    params2_full, circ_center=[qe_pos.center[0], qe_pos.center[1]], circ_radius=non_etch_r\n",
+    ")\n",
     "params2_full = unfold_params(params2_full)"
    ]
   },
@@ -1044,8 +1063,14 @@
     }
    ],
    "source": [
-    "plt.imshow(1-np.flipud(params1_full.T), cmap='gray', vmin=0, vmax=1, extent=[wg_length, cr_l+wg_length, -cr_w/2, cr_w/2])\n",
-    "plt.plot(dp_source.center[0], 0, 'r*')\n",
+    "plt.imshow(\n",
+    "    1 - np.flipud(params1_full.T),\n",
+    "    cmap=\"gray\",\n",
+    "    vmin=0,\n",
+    "    vmax=1,\n",
+    "    extent=[wg_length, cr_l + wg_length, -cr_w / 2, cr_w / 2],\n",
+    ")\n",
+    "plt.plot(dp_source.center[0], 0, \"r*\")\n",
     "plt.show()"
    ]
   },
@@ -1066,8 +1091,14 @@
     }
    ],
    "source": [
-    "plt.imshow(1-np.flipud(params2_full.T), cmap='gray', vmin=0, vmax=1, extent=[wg_length, cr_l+wg_length, -cr_w/2, cr_w/2])\n",
-    "plt.plot(dp_source.center[0], 0, 'r*')\n",
+    "plt.imshow(\n",
+    "    1 - np.flipud(params2_full.T),\n",
+    "    cmap=\"gray\",\n",
+    "    vmin=0,\n",
+    "    vmax=1,\n",
+    "    extent=[wg_length, cr_l + wg_length, -cr_w / 2, cr_w / 2],\n",
+    ")\n",
+    "plt.plot(dp_source.center[0], 0, \"r*\")\n",
     "plt.show()"
    ]
   },
@@ -1099,11 +1130,11 @@
     "fig, ax = plt.subplots(1, 1, figsize=(7, 5))\n",
     "ax.plot(ce_vals, \"ko\", label=\"C. Efficiency\")\n",
     "ax.plot(pen_vals, \"bs\", label=\"Fab. Penalty\")\n",
-    "ax.plot(history_dict['values'], \"ro\", label='J')\n",
+    "ax.plot(history_dict[\"values\"], \"ro\", label=\"J\")\n",
     "ax.set_xlabel(\"iterations\")\n",
     "ax.set_ylabel(\"objective function\")\n",
     "ax.set_title(f\"Final Coupling Efficiency: {ce_vals[-1]:.2f}\")\n",
-    "ax.axvline(x=32, color='r', linestyle='--')\n",
+    "ax.axvline(x=32, color=\"r\", linestyle=\"--\")\n",
     "\n",
     "ax.legend()\n",
     "plt.show()"
@@ -1143,9 +1174,9 @@
     }
    ],
    "source": [
-    "plt.plot([np.linalg.norm(grad) for grad in history_dict['gradients']])\n",
-    "plt.ylabel('norm(grad)')\n",
-    "plt.xlabel('iteration')"
+    "plt.plot([np.linalg.norm(grad) for grad in history_dict[\"gradients\"]])\n",
+    "plt.ylabel(\"norm(grad)\")\n",
+    "plt.xlabel(\"iteration\")"
    ]
   },
   {
@@ -1177,29 +1208,33 @@
     "\n",
     "fig, axs = plt.subplots(nrows=2, ncols=1)\n",
     "\n",
-    "gradients = history_dict['gradients']\n",
-    "params = history_dict['params']\n",
+    "gradients = history_dict[\"gradients\"]\n",
+    "params = history_dict[\"params\"]\n",
     "gradients = [unfold_params(grad).T for grad in gradients]\n",
-    "params = [unfold_params(init_par).T]+[unfold_params(1.0-p).T for p in params]\n",
+    "params = [unfold_params(init_par).T] + [unfold_params(1.0 - p).T for p in params]\n",
     "\n",
     "div = make_axes_locatable(axs[1])\n",
     "div0 = make_axes_locatable(axs[0])\n",
-    "cax = div.append_axes('top', size='5%', pad=0.05)\n",
-    "cax0 = div0.append_axes('bottom', size='5%', pad=0.05)\n",
-    "cax0.axis('off')\n",
+    "cax = div.append_axes(\"top\", size=\"5%\", pad=0.05)\n",
+    "cax0 = div0.append_axes(\"bottom\", size=\"5%\", pad=0.05)\n",
+    "cax0.axis(\"off\")\n",
+    "\n",
     "\n",
     "def animate(i):\n",
-    "    im_g = axs[1].imshow(gradients[i], interpolation='none', vmin=np.min(gradients[i]), vmax=np.max(gradients[i]))\n",
-    "    im_p = axs[0].imshow(params[i], interpolation='none',cmap='gray', vmin=0, vmax=1)    \n",
-    "    axs[1].axis('off')\n",
-    "    axs[0].axis('off')\n",
+    "    im_g = axs[1].imshow(\n",
+    "        gradients[i], interpolation=\"none\", vmin=np.min(gradients[i]), vmax=np.max(gradients[i])\n",
+    "    )\n",
+    "    axs[0].imshow(params[i], interpolation=\"none\", cmap=\"gray\", vmin=0, vmax=1)\n",
+    "    axs[1].axis(\"off\")\n",
+    "    axs[0].axis(\"off\")\n",
     "    cax.cla()\n",
-    "    fig.colorbar(im_g, cax=cax, orientation='horizontal').ax.xaxis.set_ticks_position('top')\n",
-    "    \n",
-    "    axs[0].set_title(f'iteration {i}')\n",
+    "    fig.colorbar(im_g, cax=cax, orientation=\"horizontal\").ax.xaxis.set_ticks_position(\"top\")\n",
+    "\n",
+    "    axs[0].set_title(f\"iteration {i}\")\n",
+    "\n",
     "\n",
     "anim = animation.FuncAnimation(fig, animate, frames=100, blit=False, interval=500)\n",
-    "anim.save('autograd_anim.mp4', fps=2.0)"
+    "anim.save(\"autograd_anim.mp4\", fps=2.0)"
    ]
   },
   {
@@ -1238,7 +1273,12 @@
     ")\n",
     "sim_final = make_adjoint_sim(params2, beta=final_beta, unfold=True)\n",
     "sim_final = sim_final.updated_copy(monitors=[field_monitor_xy, mode_monitor] + field_monitor)\n",
-    "sim_final.plot_eps(z=0, source_alpha=0, monitor_alpha=0, ax=ax, )\n",
+    "sim_final.plot_eps(\n",
+    "    z=0,\n",
+    "    source_alpha=0,\n",
+    "    monitor_alpha=0,\n",
+    "    ax=ax,\n",
+    ")\n",
     "plt.show()"
    ]
   },
@@ -1504,10 +1544,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# make the misc/ directory to store the GDS file if it doesnt exist already\n",
+    "# make the misc/ directory to store the GDS file if it doesn't exist already\n",
     "import os\n",
-    "if not os.path.exists('./misc/'):\n",
-    "    os.mkdir('./misc/')\n",
+    "\n",
+    "if not os.path.exists(\"./misc/\"):\n",
+    "    os.mkdir(\"./misc/\")\n",
     "\n",
     "sim_final.to_gds_file(\n",
     "    fname=\"./misc/inv_des_light_extractor_autograd.gds\",\n",
diff --git a/Autograd13Metasurface.ipynb b/Autograd13Metasurface.ipynb
index 64da0386..bb333472 100644
--- a/Autograd13Metasurface.ipynb
+++ b/Autograd13Metasurface.ipynb
@@ -25,13 +25,10 @@
    },
    "outputs": [],
    "source": [
+    "import autograd.numpy as anp\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "\n",
-    "import autograd.numpy as anp\n",
-    "\n",
     "import tidy3d as td\n",
-    "\n",
     "from tidy3d.plugins.autograd import value_and_grad"
    ]
   },
@@ -191,7 +188,7 @@
    },
    "outputs": [],
    "source": [
-    "from tidy3d.plugins.autograd import rescale, make_filter_and_project\n",
+    "from tidy3d.plugins.autograd import make_filter_and_project, rescale\n",
     "\n",
     "radius = 0.120\n",
     "beta = 50\n",
@@ -200,7 +197,7 @@
     "\n",
     "\n",
     "def get_eps(params: anp.ndarray, beta: float) -> anp.ndarray:\n",
-    "    \"\"\"Get the permittivity values (1, permittivity) array as a funciton of the parameters (0, 1)\"\"\"\n",
+    "    \"\"\"Get the permittivity values (1, permittivity) array as a function of the parameters (0, 1)\"\"\"\n",
     "    density = filter_project(params, beta)\n",
     "    eps = rescale(density, 1, permittivity)\n",
     "    return eps.reshape((nx, ny, 1, 1))\n",
@@ -270,7 +267,7 @@
    "id": "bcb0c957-b748-4dd4-9fbc-9311d0fd487d",
    "metadata": {},
    "source": [
-    "Let's make a simulation with some random starting parameters to inpsect our setup."
+    "Let's make a simulation with some random starting parameters to inspect our setup."
    ]
   },
   {
@@ -345,8 +342,8 @@
    },
    "outputs": [],
    "source": [
-    "from PIL import Image\n",
     "import xarray as xr\n",
+    "from PIL import Image\n",
     "\n",
     "logo_fname = \"misc/logo.png\"\n",
     "\n",
@@ -790,9 +787,7 @@
     }
    ],
    "source": [
-    "print(\n",
-    "    f\"Average intensity of '{intensity_norm_mean:.2f}' (a.u.) measured without any device.\"\n",
-    ")"
+    "print(f\"Average intensity of '{intensity_norm_mean:.2f}' (a.u.) measured without any device.\")"
    ]
   },
   {
@@ -840,9 +835,7 @@
     "    intensity_measured, intensity_desired = get_intensities(sim_data)\n",
     "\n",
     "    # normalize the measured intensity such that there's the same \"power\" in the signal as expected in the logo\n",
-    "    intensity_measured = (\n",
-    "        intensity_measured * anp.mean(intensity_desired) / intensity_norm_mean\n",
-    "    )\n",
+    "    intensity_measured = intensity_measured * anp.mean(intensity_desired) / intensity_norm_mean\n",
     "\n",
     "    # apply the \"capping\" within intensity_range (optional)\n",
     "    int_range_magnitude = anp.abs(int_max - int_min)\n",
@@ -908,10 +901,10 @@
    },
    "outputs": [],
    "source": [
-    "# construct a funciton of `params` and `beta` that returns the loss value, gradient, and the aux_data\n",
+    "# construct a function of `params` and `beta` that returns the loss value, gradient, and the aux_data\n",
     "loss_fn_val_grad = value_and_grad(loss_fn, has_aux=True)\n",
     "\n",
-    "# call this on our initial parmaeters\n",
+    "# call this on our initial parameters\n",
     "(val, grad), sim_data = loss_fn_val_grad(params0, beta=beta0)"
    ]
   },
@@ -1630,9 +1623,7 @@
     "f, axes = plt.subplots(1, 2, figsize=(10, 4), tight_layout=True)\n",
     "\n",
     "for ax, name in zip(axes, (\"output\", \"side\")):\n",
-    "    sim_data_final.plot_field(\n",
-    "        field_monitor_name=name, field_name=\"E\", val=\"abs^2\", ax=ax\n",
-    "    )"
+    "    sim_data_final.plot_field(field_monitor_name=name, field_name=\"E\", val=\"abs^2\", ax=ax)"
    ]
   },
   {
@@ -1665,9 +1656,7 @@
     }
    ],
    "source": [
-    "f, ((ax0, ax1), (ax2, ax3), (ax4, ax5)) = plt.subplots(\n",
-    "    3, 2, figsize=(9, 10), tight_layout=True\n",
-    ")\n",
+    "f, ((ax0, ax1), (ax2, ax3), (ax4, ax5)) = plt.subplots(3, 2, figsize=(9, 10), tight_layout=True)\n",
     "\n",
     "# target intensity\n",
     "im = ax0.imshow(np.rot90(intensity_desired), cmap=\"magma\")\n",
@@ -1735,7 +1724,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.11.0"
   },
   "title": "Metasurface Inverse Design with Topology Optimization"
  },
diff --git a/Autograd15Antenna.ipynb b/Autograd15Antenna.ipynb
index 3e081c35..7bafefc4 100644
--- a/Autograd15Antenna.ipynb
+++ b/Autograd15Antenna.ipynb
@@ -39,11 +39,9 @@
    },
    "outputs": [],
    "source": [
-    "import matplotlib.pylab as plt\n",
-    "\n",
     "import autograd\n",
     "import autograd.numpy as np\n",
-    "\n",
+    "import matplotlib.pylab as plt\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web"
    ]
@@ -213,9 +211,9 @@
    "id": "d8543e97-047b-499b-9ed6-a1bb44d4d818",
    "metadata": {},
    "source": [
-    "Next we define the geometric parmaeters describing our device.\n",
+    "Next we define the geometric parameters describing our device.\n",
     "\n",
-    "We have a metal rectanglular slab sitting on top of a SiO2 substrate with air on top. \n",
+    "We have a rectangular metal slab sitting on top of a SiO2 substrate with air on top. \n",
     "\n",
     "The rectangular slab has a hole in the center, where we'll be measuring the field intensity enhancement."
    ]
@@ -484,13 +482,10 @@
     "\n",
     "    density_masked = density * mask.values\n",
     "\n",
-    "    eps_inf_scaled_array = eps_background + density_masked * (\n",
-    "        medium_gold.eps_inf - eps_background\n",
-    "    )\n",
+    "    eps_inf_scaled_array = eps_background + density_masked * (medium_gold.eps_inf - eps_background)\n",
     "\n",
     "    poles_arrays_scaled = [\n",
-    "        (a * np.ones_like(density_masked), c * density_masked)\n",
-    "        for (a, c) in medium_gold.poles\n",
+    "        (a * np.ones_like(density_masked), c * density_masked) for (a, c) in medium_gold.poles\n",
     "    ]\n",
     "\n",
     "    eps_inf_scaled = td.ScalarFieldDataArray(\n",
@@ -537,7 +532,7 @@
    "id": "7ce00266-df6b-4aa6-a237-a49c637708e0",
    "metadata": {},
    "source": [
-    "We also write a function that generates a `td.Simulation` with the structure added to our original simulation, along with a mesh override structure to ensure even meshing in the design region."
+    "We also write a function that generates a `td.Simulation` with the structure added to our original simulation."
    ]
   },
   {
@@ -554,13 +549,6 @@
     "\n",
     "    antenna = make_antenna(params, beta=beta)\n",
     "\n",
-    "    # add uniform mesh override structures to simulation (if desired)\n",
-    "    design_region_mesh = td.MeshOverrideStructure(\n",
-    "        geometry=antenna.geometry.updated_copy(size=(td.inf, td.inf, thick_metal)),\n",
-    "        dl=[dl] * 3,\n",
-    "        enforce=True,\n",
-    "    )\n",
-    "\n",
     "    sim = sim_no_antenna.updated_copy(\n",
     "        structures=list(sim_no_antenna.structures) + [antenna],\n",
     "    )\n",
@@ -592,7 +580,7 @@
     "ny = int(size_design_y // pixel_size)\n",
     "\n",
     "\n",
-    "# some intial parameters to test with\n",
+    "# some initial parameters to test with\n",
     "def make_symmetric_x(arr: np.ndarray) -> np.ndarray:\n",
     "    \"\"\"make an array symmetric in x.\"\"\"\n",
     "    return (arr + np.flipud(arr)) / 2.0\n",
@@ -1044,9 +1032,7 @@
     "def intensity_enhancement(\n",
     "    params: np.ndarray, task_name: str = \"antenna_intensity\", verbose=False\n",
     ") -> float:\n",
-    "    intensity_with_params = measure_intensity(\n",
-    "        params, task_name=task_name, verbose=verbose\n",
-    "    )\n",
+    "    intensity_with_params = measure_intensity(params, task_name=task_name, verbose=verbose)\n",
     "    return intensity_with_params / intensity0\n",
     "\n",
     "\n",
@@ -1059,9 +1045,7 @@
     "    if penalty_only:\n",
     "        enhancement_factor = 0.0\n",
     "    else:\n",
-    "        enhancement_factor = intensity_enhancement(\n",
-    "            params, verbose=verbose, task_name=\"antenna\"\n",
-    "        )\n",
+    "        enhancement_factor = intensity_enhancement(params, verbose=verbose, task_name=\"antenna\")\n",
     "        print(f\"\\tenhancement = {getval(enhancement_factor):.2e}\")\n",
     "\n",
     "    # penalty_value = 0\n",
@@ -1400,9 +1384,7 @@
     "    val, grad = val_grad_fn(params0, verbose=True)\n",
     "    print(f\"starting objective function value = {val}\")\n",
     "    vmag1 = np.max(abs(grad))\n",
-    "    im1 = plt.imshow(\n",
-    "        np.flipud(np.squeeze(grad)).T, cmap=\"PiYG\", vmax=vmag1, vmin=-vmag1\n",
-    "    )\n",
+    "    im1 = plt.imshow(np.flipud(np.squeeze(grad)).T, cmap=\"PiYG\", vmax=vmag1, vmin=-vmag1)\n",
     "    plt.colorbar(im1)\n",
     "    plt.title(\"gradient w.r.t. parameters\")"
    ]
@@ -2429,7 +2411,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.0"
   },
   "title": "Inverse Design of a Plasmonic Nanoantenna Metasurface in Tidy3D | Flexcompute"
  },
diff --git a/Autograd16BilayerCoupler.ipynb b/Autograd16BilayerCoupler.ipynb
index 1f83fa8b..e744bc28 100644
--- a/Autograd16BilayerCoupler.ipynb
+++ b/Autograd16BilayerCoupler.ipynb
@@ -110,12 +110,7 @@
     "size_sim_x = length_waveguide + size_design_x + space_design_edge\n",
     "size_sim_y = space_design_edge + size_design_y + space_design_edge\n",
     "size_sim_z = (\n",
-    "    thick_Si_substrate\n",
-    "    + thick_substrate\n",
-    "    + thick_Si\n",
-    "    + space_SiSiN\n",
-    "    + thick_SiN\n",
-    "    + space_cladding_top\n",
+    "    thick_Si_substrate + thick_substrate + thick_Si + space_SiSiN + thick_SiN + space_cladding_top\n",
     ")\n",
     "size_sim = (size_sim_x, size_sim_y, size_sim_z)\n",
     "\n",
@@ -124,9 +119,7 @@
     "sim_bot_z = -size_sim_z / 2.0\n",
     "center_SiN_z = sim_top_z - space_cladding_top - thick_SiN / 2.0\n",
     "center_Si_z = sim_top_z - space_cladding_top - thick_SiN - space_SiSiN - thick_Si / 2.0\n",
-    "center_Si_etch_z = (\n",
-    "    sim_top_z - space_cladding_top - thick_SiN - space_SiSiN - thick_etch / 2.0\n",
-    ")\n",
+    "center_Si_etch_z = sim_top_z - space_cladding_top - thick_SiN - space_SiSiN - thick_etch / 2.0\n",
     "center_source_z = sim_top_z - space_cladding_top / 2.0\n",
     "center_Si_substrate_z = sim_bot_z + thick_Si_substrate / 2\n",
     "\n",
@@ -309,7 +302,7 @@
    "source": [
     "### Inspect waveguide modes\n",
     "\n",
-    "We will run the mode solver to determine the proper `mode_index` correponding to the fundamental TE mode."
+    "We will run the mode solver to determine the proper `mode_index` corresponding to the fundamental TE mode."
    ]
   },
   {
@@ -564,7 +557,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from tidy3d.plugins.autograd import rescale, make_filter_and_project\n",
+    "from tidy3d.plugins.autograd import make_filter_and_project, rescale\n",
     "\n",
     "# resolution of the design region pixels\n",
     "pixel_size_Si = 15 * nm\n",
@@ -590,9 +583,7 @@
     ")\n",
     "\n",
     "\n",
-    "def get_density(\n",
-    "    params: np.ndarray, radius: float, beta: float, pixel_size: float\n",
-    ") -> np.ndarray:\n",
+    "def get_density(params: np.ndarray, radius: float, beta: float, pixel_size: float) -> np.ndarray:\n",
     "    \"\"\"Generic function to get the etch density as a function of design parameters, using filter and projection.\"\"\"\n",
     "    filter_project = make_filter_and_project(radius, pixel_size, beta=beta)\n",
     "    return filter_project(params)\n",
@@ -600,16 +591,12 @@
     "\n",
     "def get_density_Si(params: np.ndarray) -> td.Structure:\n",
     "    \"\"\"Get the density of the Si etch as a function of its design parameters.\"\"\"\n",
-    "    return get_density(\n",
-    "        params=params, radius=radius_Si, beta=beta_Si, pixel_size=pixel_size_Si\n",
-    "    )\n",
+    "    return get_density(params=params, radius=radius_Si, beta=beta_Si, pixel_size=pixel_size_Si)\n",
     "\n",
     "\n",
     "def get_density_SiN(params: np.ndarray) -> td.Structure:\n",
     "    \"\"\"Get the density of the SiN etch as a function of its design parameters.\"\"\"\n",
-    "    return get_density(\n",
-    "        params=params, radius=radius_SiN, beta=beta_SiN, pixel_size=pixel_size_SiN\n",
-    "    )\n",
+    "    return get_density(params=params, radius=radius_SiN, beta=beta_SiN, pixel_size=pixel_size_SiN)\n",
     "\n",
     "\n",
     "def get_permittivity(density: np.ndarray, eps_max: float) -> np.ndarray:\n",
@@ -617,9 +604,7 @@
     "    return rescale(density, eps_SiO2, eps_max)\n",
     "\n",
     "\n",
-    "def make_etch_structure(\n",
-    "    density: np.ndarray, eps_max: float, geometry: td.Box\n",
-    ") -> td.Structure:\n",
+    "def make_etch_structure(density: np.ndarray, eps_max: float, geometry: td.Box) -> td.Structure:\n",
     "    \"\"\"Make a `td.Structure` containing a `td.CustomMedium` corresponding to this density array, given a geometry.\"\"\"\n",
     "\n",
     "    permittivity_data = get_permittivity(density, eps_max=eps_max)\n",
@@ -644,17 +629,13 @@
     "def get_etch_Si(params: np.ndarray) -> td.Structure:\n",
     "    \"\"\"Get the etch region for Si, using the Si parameters.\"\"\"\n",
     "    density = get_density_Si(params)\n",
-    "    return make_etch_structure(\n",
-    "        density=density, eps_max=eps_Si, geometry=etch_Si_geometry\n",
-    "    )\n",
+    "    return make_etch_structure(density=density, eps_max=eps_Si, geometry=etch_Si_geometry)\n",
     "\n",
     "\n",
     "def get_etch_SiN(params: np.ndarray) -> td.Structure:\n",
     "    \"\"\"Get the etch region for SiN, using the SiN parameters.\"\"\"\n",
     "    density = get_density_SiN(params)\n",
-    "    return make_etch_structure(\n",
-    "        density=density, eps_max=eps_SiN, geometry=slab_SiN.geometry\n",
-    "    )\n"
+    "    return make_etch_structure(density=density, eps_max=eps_SiN, geometry=slab_SiN.geometry)"
    ]
   },
   {
@@ -1162,12 +1143,8 @@
    "source": [
     "from tidy3d.plugins.autograd import make_erosion_dilation_penalty\n",
     "\n",
-    "penalty_fn_Si = make_erosion_dilation_penalty(\n",
-    "    radius_Si, pixel_size_Si, beta=beta_penalty\n",
-    ")\n",
-    "penalty_fn_SiN = make_erosion_dilation_penalty(\n",
-    "    radius_SiN, pixel_size_SiN, beta=beta_penalty\n",
-    ")\n",
+    "penalty_fn_Si = make_erosion_dilation_penalty(radius_Si, pixel_size_Si, beta=beta_penalty)\n",
+    "penalty_fn_SiN = make_erosion_dilation_penalty(radius_SiN, pixel_size_SiN, beta=beta_penalty)\n",
     "\n",
     "\n",
     "def penalty_Si(params: np.ndarray) -> float:\n",
@@ -1987,9 +1964,7 @@
     "ax1.set_title(\"gradient w.r.t. Si parameters\")\n",
     "\n",
     "vmag2 = np.max(abs(grad_SiN))\n",
-    "im2 = ax2.imshow(\n",
-    "    np.flipud(np.squeeze(grad_SiN)).T, cmap=\"PiYG\", vmax=vmag2, vmin=-vmag2\n",
-    ")\n",
+    "im2 = ax2.imshow(np.flipud(np.squeeze(grad_SiN)).T, cmap=\"PiYG\", vmax=vmag2, vmin=-vmag2)\n",
     "plt.colorbar(im2, ax=ax2)\n",
     "ax2.set_title(\"gradient w.r.t. SiN parameters\")\n",
     "\n",
@@ -3198,7 +3173,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.0"
   },
   "title": "Inverse Design of a Bilayer Grating Coupler in Tidy3D | Flexcompute"
  },
diff --git a/Autograd17BandPassFilter.ipynb b/Autograd17BandPassFilter.ipynb
index 710babff..4aef1a1c 100644
--- a/Autograd17BandPassFilter.ipynb
+++ b/Autograd17BandPassFilter.ipynb
@@ -39,9 +39,7 @@
    "source": [
     "import autograd as ag\n",
     "import autograd.numpy as np\n",
-    "\n",
     "import matplotlib.pylab as plt\n",
-    "\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web"
    ]
@@ -325,7 +323,7 @@
     "\n",
     "Next, we define the functions that allow us to generate our design region using topology optimization.\n",
     "\n",
-    "We'll apply several functions that enable us to filter and project our optimization parameters into a permittivity grid, and then turn this permittivity grid into a structure that can be added to our base simnulation as the optimization progresses."
+    "We'll apply several functions that enable us to filter and project our optimization parameters into a permittivity grid, and then turn this permittivity grid into a structure that can be added to our base simulation as the optimization progresses."
    ]
   },
   {
@@ -335,7 +333,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from tidy3d.plugins.autograd import rescale, make_filter_and_project\n",
+    "from tidy3d.plugins.autograd import make_filter_and_project, rescale\n",
     "\n",
     "# radius of the circular filter (um) and the threshold strength\n",
     "radius = 0.120  # <= larger radius = bigger feature sizes\n",
@@ -358,7 +356,7 @@
     "\n",
     "\n",
     "def get_design_region(params, beta) -> td.Structure:\n",
-    "    \"\"\"Get the design region evaluted at a set of parameters.\"\"\"\n",
+    "    \"\"\"Get the design region evaluated at a set of parameters.\"\"\"\n",
     "\n",
     "    xmin = center_design_x - size_design_x / 2.0\n",
     "    xmax = center_design_x + size_design_x / 2.0\n",
@@ -380,7 +378,7 @@
     "\n",
     "\n",
     "def get_sim(params, beta) -> td.Simulation:\n",
-    "    \"\"\"Get the design region evaluted at a set of parameters.\"\"\"\n",
+    "    \"\"\"Get the design region evaluated at a set of parameters.\"\"\"\n",
     "    design_region = get_design_region(params=params, beta=beta)\n",
     "    new_structures = list(sim_base.structures)\n",
     "    new_structures[-1] = design_region\n",
@@ -442,9 +440,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def get_transmission(\n",
-    "    params, beta, step_num: int, verbose: bool, use_broadband: bool\n",
-    ") -> np.ndarray:\n",
+    "def get_transmission(params, beta, step_num: int, verbose: bool, use_broadband: bool) -> np.ndarray:\n",
     "    \"\"\"Compute transmission amplitudes as function of frequency.\"\"\"\n",
     "    sim = get_sim(params=params, beta=beta)\n",
     "    task_name = \"bend_transmission_mf\"\n",
@@ -522,7 +518,7 @@
    "source": [
     "### Fabrication Penalty\n",
     "\n",
-    "Next, we introduce a builtin fabrication penalty function, that evaluates our design region to reduce our objective function if any small feature sizes are measured compared to the `radius` defined earlier."
+    "Next, we introduce a built-in fabrication penalty function that evaluates our design region to reduce our objective function if any small feature sizes are measured compared to the `radius` defined earlier."
    ]
   },
   {
@@ -2072,7 +2068,7 @@
     "beta_max = 50\n",
     "\n",
     "for i in range(num_steps):\n",
-    "    # compute gradient and current objective funciton value\n",
+    "    # compute gradient and current objective function value\n",
     "    density = get_density(params, beta)\n",
     "\n",
     "    plt.subplots(1, 1, figsize=(2, 2))\n",
@@ -2933,7 +2929,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.11.0"
   },
   "title": "Inverse Design of an Integrated Bandpass Filter in Tidy3D | Flexcompute"
  },
diff --git a/Autograd18TopologyBend.ipynb b/Autograd18TopologyBend.ipynb
index 3626a9ac..127a9fb6 100644
--- a/Autograd18TopologyBend.ipynb
+++ b/Autograd18TopologyBend.ipynb
@@ -34,12 +34,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tidy3d as td\n",
-    "import tidy3d.web as web\n",
-    "\n",
-    "import matplotlib.pylab as plt\n",
     "import autograd\n",
-    "import autograd.numpy as np"
+    "import autograd.numpy as np\n",
+    "import matplotlib.pylab as plt\n",
+    "import tidy3d as td\n",
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -142,9 +141,7 @@
     "    grid_spec=td.GridSpec.auto(\n",
     "        min_steps_per_wvl=min_steps_per_wvl,\n",
     "        override_structures=[\n",
-    "            td.MeshOverrideStructure(\n",
-    "                geometry=design_region_geometry, dl=3 * [pixel_size]\n",
-    "            )\n",
+    "            td.MeshOverrideStructure(geometry=design_region_geometry, dl=3 * [pixel_size])\n",
     "        ],\n",
     "    ),\n",
     ")"
@@ -155,7 +152,7 @@
    "id": "2b3ce430-dfc0-44d0-8bc4-eb28b2411aea",
    "metadata": {},
    "source": [
-    "Let's vizualize the base simulation to verify that it looks correct."
+    "Let's visualize the base simulation to verify that it looks correct."
    ]
   },
   {
@@ -219,9 +216,7 @@
     "    \"\"\"Get design region structure as a function of optimization parameters.\"\"\"\n",
     "    density = get_density(params, beta=beta)\n",
     "    eps_data = 1 + (eps_mat - 1) * density\n",
-    "    return td.Structure.from_permittivity_array(\n",
-    "        eps_data=eps_data, geometry=design_region_geometry\n",
-    "    )"
+    "    return td.Structure.from_permittivity_array(eps_data=eps_data, geometry=design_region_geometry)"
    ]
   },
   {
@@ -229,7 +224,7 @@
    "id": "90110e4f-9ea4-48fc-b311-c5b568540333",
    "metadata": {},
    "source": [
-    "Next, it is very convenient to wrap this in a function that returns an updated copy of the base simulation with the design region added. We'll be calling this in our objective function. We'll also add some logic to exlude field monitors if they aren't needed, for example during the optimization."
+    "Next, it is very convenient to wrap this in a function that returns an updated copy of the base simulation with the design region added. We'll be calling this in our objective function. We'll also add some logic to exclude field monitors if they aren't needed, for example during the optimization."
    ]
   },
   {
@@ -239,9 +234,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def get_sim(\n",
-    "    params: np.ndarray, beta: float, with_fld_mnt: bool = False\n",
-    ") -> td.Simulation:\n",
+    "def get_sim(params: np.ndarray, beta: float, with_fld_mnt: bool = False) -> td.Simulation:\n",
     "    \"\"\"Get simulation as a function of optimization parameters.\"\"\"\n",
     "    design_region = get_design_region(params, beta=beta)\n",
     "    sim = sim_base.updated_copy(structures=sim_base.structures + (design_region,))\n",
@@ -436,9 +429,7 @@
     }
    ],
    "source": [
-    "sim_data_init = web.run(\n",
-    "    sim0.updated_copy(monitors=[field_monitor]), task_name=\"initial_bend\"\n",
-    ")"
+    "sim_data_init = web.run(sim0.updated_copy(monitors=[field_monitor]), task_name=\"initial_bend\")"
    ]
   },
   {
@@ -538,9 +529,9 @@
    "source": [
     "## Optimization\n",
     "\n",
-    "Getting the gradient of the `objective` function is easy using `autograd`. Calling `g = autograd.value_and_grad(f)` returns a function `g` that when evaluated returns the objetive function value and its gradient.\n",
+    "Getting the gradient of the `objective` function is easy using `autograd`. Calling `g = autograd.value_and_grad(f)` returns a function `g` that when evaluated returns the objective function value and its gradient.\n",
     "\n",
-    "We use this as it's more efficient and we dont have to re-compute the objective during the gradient calculation step if we want to store the value too.\n",
+    "We use this as it's more efficient and we don't have to re-compute the objective during the gradient calculation step if we want to store the value too.\n",
     "\n",
     "Let's construct this function now and have it ready to use in the main optimization loop."
    ]
@@ -1139,7 +1130,7 @@
     "    plt.axis(\"off\")\n",
     "    plt.show()\n",
     "\n",
-    "    # re-compute gradient and current objective funciton value\n",
+    "    # re-compute gradient and current objective function value\n",
     "    value, gradient = val_grad_fn(params, beta=beta)\n",
     "\n",
     "    # outputs\n",
@@ -1231,9 +1222,9 @@
    "id": "2f0f78ae-7e1a-4abc-82bd-107fa69c05e6",
    "metadata": {},
    "source": [
-    "It seems to exhibit large feature sizes, which is promising! There are some discontinuities with the waveguide, which could be rectified with a more advanced approach, but is beyond the scope of this notebook.\n",
+    "It seems to exhibit large feature sizes, which is promising! There are some discontinuities with the waveguide, which could be rectified with a more advanced approach but it is beyond the scope of this notebook.\n",
     "\n",
-    "Let's add a multi-frequency mode monitor and a field monitor to inpsect the performance."
+    "Let's add a multi-frequency mode monitor and a field monitor to inspect the performance."
    ]
   },
   {
diff --git a/Autograd19ApodizedCoupler.ipynb b/Autograd19ApodizedCoupler.ipynb
index da05a1fe..868ab9ca 100644
--- a/Autograd19ApodizedCoupler.ipynb
+++ b/Autograd19ApodizedCoupler.ipynb
@@ -27,17 +27,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tidy3d as td\n",
-    "import tidy3d.web as web\n",
-    "\n",
     "# as we are using autograd to perform gradient calculations, we need to import numpy from autograd's wrapper\n",
     "import autograd as ag\n",
     "import autograd.numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
     "# we will use optax to perform the optimization using gradient descent\n",
     "import optax\n",
-    "\n",
-    "import matplotlib.pyplot as plt"
+    "import tidy3d as td\n",
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -144,9 +142,7 @@
     "def get_periodicity(fill_fraction: float) -> float:\n",
     "    \"\"\"periodicity (bragg condition) as function of fill fraction, angle, wavelength, and etching parameters.\"\"\"\n",
     "    return lda0 / (\n",
-    "        fill_fraction * neff_unetch\n",
-    "        + (1 - fill_fraction) * neff_etch\n",
-    "        - n_c * np.sin(theta_c)\n",
+    "        fill_fraction * neff_unetch + (1 - fill_fraction) * neff_etch - n_c * np.sin(theta_c)\n",
     "    )\n",
     "\n",
     "\n",
@@ -183,9 +179,7 @@
    "source": [
     "def get_widths(p_list, f_list):\n",
     "    # calculate the widths of air gaps and silicon teeth\n",
-    "    return np.array(\n",
-    "        [item for p, f in zip(p_list, f_list) for item in [p * (1 - f), p * f]]\n",
-    "    )\n",
+    "    return np.array([item for p, f in zip(p_list, f_list) for item in [p * (1 - f), p * f]])\n",
     "\n",
     "\n",
     "widths = get_widths(p_list, f_list)\n",
@@ -222,9 +216,7 @@
     "\n",
     "# function to inversely project a design parameter between min_width and max_width to between -inf to inf\n",
     "def inverse_project(y):\n",
-    "    return np.arctanh(\n",
-    "        (2 * (y - 0.5 * (max_width + min_width))) / (max_width - min_width)\n",
-    "    )\n",
+    "    return np.arctanh((2 * (y - 0.5 * (max_width + min_width))) / (max_width - min_width))\n",
     "\n",
     "\n",
     "# project the widths to parameters between -inf to inf\n",
@@ -255,9 +247,7 @@
     "\n",
     "# create the top oxide layer\n",
     "tox = td.Structure(\n",
-    "    geometry=td.Box.from_bounds(\n",
-    "        rmin=(-inf_eff, -inf_eff, 0), rmax=(inf_eff, inf_eff, t_tox)\n",
-    "    ),\n",
+    "    geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, 0), rmax=(inf_eff, inf_eff, t_tox)),\n",
     "    medium=sio2,\n",
     ")\n",
     "\n",
@@ -269,17 +259,13 @@
     "\n",
     "# create the unetched waveguide\n",
     "unetched_waveguide = td.Structure(\n",
-    "    geometry=td.Box.from_bounds(\n",
-    "        rmin=(0, -inf_eff, 0), rmax=(inf_eff, inf_eff, t_si - etch_depth)\n",
-    "    ),\n",
+    "    geometry=td.Box.from_bounds(rmin=(0, -inf_eff, 0), rmax=(inf_eff, inf_eff, t_si - etch_depth)),\n",
     "    medium=si,\n",
     ")\n",
     "\n",
     "# create the bottom oxide layer\n",
     "box = td.Structure(\n",
-    "    geometry=td.Box.from_bounds(\n",
-    "        rmin=(-inf_eff, -inf_eff, -t_box), rmax=(inf_eff, inf_eff, 0)\n",
-    "    ),\n",
+    "    geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, -t_box), rmax=(inf_eff, inf_eff, 0)),\n",
     "    medium=sio2,\n",
     ")\n",
     "\n",
@@ -327,7 +313,7 @@
     "    freqs=freqs,\n",
     "    mode_spec=td.ModeSpec(num_modes=1, target_neff=3),\n",
     "    name=\"mode\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1366,7 +1352,7 @@
    "id": "1dbb83d5-8db8-4fff-b80b-1501d9a8b125",
    "metadata": {},
    "source": [
-    "We see that the optimized and apodized designs both exceed that of the uniform with -2dB efficiency. However, the optimized design far ouperforms in terms of bandwidth. We can also confirm that the minimal feature size is maintained above 85 nm."
+    "We see that the optimized and apodized designs both exceed that of the uniform with -2dB efficiency. However, the optimized design far outperforms in terms of bandwidth. We can also confirm that the minimal feature size is maintained above 85 nm."
    ]
   },
   {
@@ -1410,7 +1396,7 @@
     "print(\"Optimized:\")\n",
     "print(f\"  The 1dB bandwidth is {bandwidth(ldas, ce_opt):.1f} nm\")\n",
     "print(f\"  The maximum coupling efficiency is {np.max(dB(ce_opt)):.2f} dB.\")\n",
-    "print(f\"  The minimal feature size is {1e3 * np.min(project(params_opt)):.2f} nm.\")\n"
+    "print(f\"  The minimal feature size is {1e3 * np.min(project(params_opt)):.2f} nm.\")"
    ]
   },
   {
diff --git a/Autograd1Intro.ipynb b/Autograd1Intro.ipynb
index d614826d..902153cd 100644
--- a/Autograd1Intro.ipynb
+++ b/Autograd1Intro.ipynb
@@ -81,9 +81,9 @@
     "\n",
     "Using `autograd`, we may write a function $f$ using most of the fundamental operations in python and `numpy`. In `autograd` both the operations and their derivatives are tracked when the function is called. Thus, `autograd` gives the option to apply `autograd.grad` to this function, which uses all of the derivative information and the chain rule to construct a new function that gives the derivative of the function with respect to its input arguments.\n",
     "\n",
-    "In `tidy3d`, we can track functions **that involve Tidy3D simulations** in their computational graph. In essence, we privode the \"derivative\" of the `tidy3d.web.run()` function, using the adjoint method, to tell `autograd` how to differentiate functions that might involve both the setting up and postprocessing of a tidy3d simulation and its data. The end result is a framework where users can set up modeling and optimizations and utilize `autograd` automatic differentiation for optimization and sensitivity analysis efficiently and without needing to derive a single derivative rule.\n",
+    "In `tidy3d`, we can track functions **that involve Tidy3D simulations** in their computational graph. In essence, we provide the \"derivative\" of the `tidy3d.web.run()` function, using the adjoint method, to tell `autograd` how to differentiate functions that might involve both the setting up and postprocessing of a tidy3d simulation and its data. The end result is a framework where users can set up modeling and optimizations and utilize `autograd` automatic differentiation for optimization and sensitivity analysis efficiently and without needing to derive a single derivative rule.\n",
     "\n",
-    "In this notebook, we will give an overview of how `autograd` works for beginners and provide simple example of the plugin. More complex case studies and examples will be provided in other notebooks, linked here:\n",
+    "In this notebook, we will give an overview of how `autograd` works for beginners and provide a simple example of the plugin. More complex case studies and examples will be provided in other notebooks, linked here:\n",
     "\n",
     "* [Gradient Checking Notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd2GradientChecking/).\n",
     "\n",
@@ -400,7 +400,7 @@
     "        geometry=td.Box(size=(td.inf, 0.3, 0.2)), medium=td.Medium(permittivity=2.0)\n",
     "    )\n",
     "\n",
-    "    # our \"forward\" soruce\n",
+    "    # our \"forward\" source\n",
     "    mode_src = td.ModeSource(\n",
     "        size=(0, 1.5, 1.5),\n",
     "        center=(-0.9, 0, 0),\n",
diff --git a/Autograd20MetalensWaveguideTaper.ipynb b/Autograd20MetalensWaveguideTaper.ipynb
index bdf62cbd..3898ce84 100644
--- a/Autograd20MetalensWaveguideTaper.ipynb
+++ b/Autograd20MetalensWaveguideTaper.ipynb
@@ -23,11 +23,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import autograd.numpy as np\n",
     "import autograd as ag\n",
-    "import optax\n",
+    "import autograd.numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import optax\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web"
    ]
@@ -122,9 +121,7 @@
    "outputs": [],
    "source": [
     "# define the slab waveguide structure\n",
-    "slab = td.Structure(\n",
-    "    geometry=td.Box(center=(0, 0, 0), size=(td.inf, td.inf, t_si)), medium=si\n",
-    ")\n",
+    "slab = td.Structure(geometry=td.Box(center=(0, 0, 0), size=(td.inf, td.inf, t_si)), medium=si)\n",
     "\n",
     "# add a mode source as excitation\n",
     "mode_spec = td.ModeSpec(num_modes=1, target_neff=3.5)\n",
@@ -479,11 +476,7 @@
     "\n",
     "# extract the transmission power and phase shift\n",
     "for i, l_slot in enumerate(l_slot_list):\n",
-    "    amp = (\n",
-    "        batch_results[f\"l_slot={l_slot:.2f}\"][\"mode\"]\n",
-    "        .amps.sel(direction=\"+\")\n",
-    "        .values[0][0]\n",
-    "    )\n",
+    "    amp = batch_results[f\"l_slot={l_slot:.2f}\"][\"mode\"].amps.sel(direction=\"+\").values[0][0]\n",
     "    T[i] = np.abs(amp) ** 2\n",
     "    phase[i] = np.angle(amp)"
    ]
@@ -516,7 +509,7 @@
    "source": [
     "unwrapped_phase = np.unwrap(phase - phase[-1]) / (2 * np.pi) + 1\n",
     "plt.plot(l_slot_list, T, c=\"red\", linewidth=2, label=\"Transmission\")\n",
-    "plt.plot(l_slot_list, unwrapped_phase, c=\"blue\", linewidth=2, label=f\"Phase (2$\\pi$)\")\n",
+    "plt.plot(l_slot_list, unwrapped_phase, c=\"blue\", linewidth=2, label=r\"Phase (2$\\pi$)\")\n",
     "plt.xlabel(\"Slot length (μm)\")\n",
     "plt.xlim(l_slot_min, l_slot_max)\n",
     "plt.ylim(0, 1)\n",
@@ -575,15 +568,12 @@
     "plt.scatter(ys, desired_phases, c=\"red\")\n",
     "plt.plot(ys, desired_phases, c=\"red\", linewidth=2)\n",
     "plt.xlabel(\"Position (μm)\")\n",
-    "plt.ylabel(\"Desired phase shift (2$\\pi$)\")\n",
+    "plt.ylabel(r\"Desired phase shift (2$\\pi$)\")\n",
     "plt.show()\n",
     "\n",
     "# desired slot lengths\n",
     "desired_l_slots = np.array(\n",
-    "    [\n",
-    "        np.interp(desired_phases[i], unwrapped_phase[::-1], l_slot_list[::-1])\n",
-    "        for i in range(len(ys))\n",
-    "    ]\n",
+    "    [np.interp(desired_phases[i], unwrapped_phase[::-1], l_slot_list[::-1]) for i in range(len(ys))]\n",
     ")"
    ]
   },
@@ -652,15 +642,11 @@
     "def make_sim(l_slots):\n",
     "    # create the slot geometries\n",
     "    slots_geo = []\n",
-    "    for y, l_slots in zip(ys, l_slots):\n",
-    "        slots_geo.append(\n",
-    "            td.Box(center=(l_slots / 2, y, 0), size=(l_slots, w_slot, t_si))\n",
-    "        )\n",
+    "    for y, l_slot in zip(ys, l_slots):\n",
+    "        slots_geo.append(td.Box(center=(l_slot / 2, y, 0), size=(l_slot, w_slot, t_si)))\n",
     "\n",
     "        if y != 0:\n",
-    "            slots_geo.append(\n",
-    "                td.Box(center=(l_slots / 2, -y, 0), size=(l_slots, w_slot, t_si))\n",
-    "            )\n",
+    "            slots_geo.append(td.Box(center=(l_slot / 2, -y, 0), size=(l_slot, w_slot, t_si)))\n",
     "\n",
     "    # create the slot structures\n",
     "    slots = [td.Structure(geometry=s, medium=sio2) for s in slots_geo]\n",
@@ -1203,9 +1189,7 @@
    ],
    "source": [
     "sim_taper = sim_meta.copy(update={\"structures\": [taper]})\n",
-    "sim_data_taper = web.run(\n",
-    "    simulation=sim_taper, task_name=\"linear taper\", path=\"data/sim_taper.hdf5\"\n",
-    ")"
+    "sim_data_taper = web.run(simulation=sim_taper, task_name=\"linear taper\", path=\"data/sim_taper.hdf5\")"
    ]
   },
   {
@@ -1325,13 +1309,11 @@
    "source": [
     "def J(l_slots):\n",
     "    sim = make_sim(l_slots)\n",
-    "    sim_data = web.run(\n",
-    "        simulation=sim, task_name=\"invdes\", verbose=False, local_gradient=False\n",
-    "    )\n",
+    "    sim_data = web.run(simulation=sim, task_name=\"invdes\", verbose=False, local_gradient=False)\n",
     "\n",
-    "    return np.sum(\n",
-    "        np.abs(sim_data[\"mode\"].amps.sel(mode_index=0, direction=\"+\").values) ** 2\n",
-    "    ) / len(ldas)"
+    "    return np.sum(np.abs(sim_data[\"mode\"].amps.sel(mode_index=0, direction=\"+\").values) ** 2) / len(\n",
+    "        ldas\n",
+    "    )"
    ]
   },
   {
@@ -1954,9 +1936,7 @@
    ],
    "source": [
     "T_opt = np.abs(sim_data_opt[\"mode\"].amps.sel(direction=\"+\").values) ** 2\n",
-    "plt.plot(\n",
-    "    ldas, 10 * np.log10(T_opt), c=\"red\", linewidth=2, label=\"Optimized metalens taper\"\n",
-    ")\n",
+    "plt.plot(ldas, 10 * np.log10(T_opt), c=\"red\", linewidth=2, label=\"Optimized metalens taper\")\n",
     "plt.plot(ldas, 10 * np.log10(T_meta), c=\"blue\", linewidth=2, label=\"Metalens taper\")\n",
     "plt.plot(ldas, 10 * np.log10(T_taper), c=\"black\", linewidth=2, label=\"Linear taper\")\n",
     "plt.xlabel(\"Wavelength (μm)\")\n",
diff --git a/Autograd21GaPLightExtractor.ipynb b/Autograd21GaPLightExtractor.ipynb
index 7d79aa04..9dae71ff 100644
--- a/Autograd21GaPLightExtractor.ipynb
+++ b/Autograd21GaPLightExtractor.ipynb
@@ -23,11 +23,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import autograd.numpy as np\n",
     "import autograd as ag\n",
-    "import optax\n",
-    "import matplotlib.pyplot as plt\n",
+    "import autograd.numpy as np\n",
     "import gdstk\n",
+    "import matplotlib.pyplot as plt\n",
+    "import optax\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web"
    ]
@@ -125,9 +125,7 @@
    "source": [
     "# define the diamond substrate\n",
     "substrate = td.Structure(\n",
-    "    geometry=td.Box.from_bounds(\n",
-    "        rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, 0)\n",
-    "    ),\n",
+    "    geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, 0)),\n",
     "    medium=diamond,\n",
     ")\n",
     "\n",
@@ -161,7 +159,7 @@
    "source": [
     "## Design Region\n",
     "\n",
-    "The design region is a pixellated array of permittivity values between $\\varepsilon=1$ and $\\varepsilon=\\varepsilon_{GaP}$. To define it, we use a density array whose element values are between 0 to 1, apply a conic smoothing filter, a tanh projection function, and finally scale it linearly to $\\varepsilon=1$ and $\\varepsilon=\\varepsilon_{GaP}$."
+    "The design region is a pixelated array of permittivity values between $\\varepsilon=1$ and $\\varepsilon=\\varepsilon_{GaP}$. To define it, we use a density array whose element values are between 0 to 1, apply a conic smoothing filter, a tanh projection function, and finally scale it linearly to $\\varepsilon=1$ and $\\varepsilon=\\varepsilon_{GaP}$."
    ]
   },
   {
@@ -171,7 +169,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from tidy3d.plugins.autograd import rescale, make_filter_and_project\n",
+    "from tidy3d.plugins.autograd import make_filter_and_project, rescale\n",
     "\n",
     "# define the conic filter and tanh projection\n",
     "filter_project_fn = make_filter_and_project(radius=min_feature, dl=pixel_size)\n",
@@ -194,9 +192,7 @@
     "    \"\"\"Get design region structure as a function of optimization parameters.\"\"\"\n",
     "    density = get_density(params, beta=beta)\n",
     "    eps_data = rescale(density, 1, n_GaP**2)\n",
-    "    return td.Structure.from_permittivity_array(\n",
-    "        eps_data=eps_data, geometry=design_region\n",
-    "    )"
+    "    return td.Structure.from_permittivity_array(eps_data=eps_data, geometry=design_region)"
    ]
   },
   {
@@ -427,9 +423,7 @@
    ],
    "source": [
     "# create simulations without the extractor structure\n",
-    "sims_ref = {\n",
-    "    axis: sims0[axis].updated_copy(structures=[substrate]) for axis in [\"x\", \"y\", \"z\"]\n",
-    "}\n",
+    "sims_ref = {axis: sims0[axis].updated_copy(structures=[substrate]) for axis in [\"x\", \"y\", \"z\"]}\n",
     "\n",
     "# run the simulations\n",
     "ref_results = web.run_async(simulations=sims_ref, path_dir=\"data\")"
@@ -2802,7 +2796,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.11.0"
   },
   "title": "Inverse Design of a Photon Extractor for NV Centers in Diamond"
  },
diff --git a/Autograd22PhotonicCrystal.ipynb b/Autograd22PhotonicCrystal.ipynb
index 0ab66c60..8af20177 100644
--- a/Autograd22PhotonicCrystal.ipynb
+++ b/Autograd22PhotonicCrystal.ipynb
@@ -27,15 +27,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import tidy3d as td\n",
-    "import tidy3d.web as web\n",
+    "import autograd\n",
     "\n",
     "# we'll use autograd for automatic differentiation, so derivative-traced numpy operations will use autograd.numpy\n",
     "import autograd.numpy as anp\n",
-    "import autograd"
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import tidy3d as td\n",
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -291,7 +290,7 @@
     "    size=(0, td.inf, td.inf),\n",
     "    freqs=[freq0],\n",
     "    name=\"flux\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1169,7 +1168,7 @@
    "id": "33a6f960-db52-47b9-a5c0-245f128950a9",
    "metadata": {},
    "source": [
-    "And then we can use this funtion in our gradient-ascent optimizer using `optax`.\n",
+    "And then we can use this function in our gradient-ascent optimizer using `optax`.\n",
     "\n",
     "We first set up the optimizer parameters."
    ]
@@ -1259,7 +1258,7 @@
     "for i in range(num_steps):\n",
     "    print(f\"step = {i + 1}\")\n",
     "\n",
-    "    # compute gradient and current objective funciton value\n",
+    "    # compute gradient and current objective function value\n",
     "    value, gradient = val_grad(params)\n",
     "\n",
     "    gradient = np.array(gradient)\n",
diff --git a/Autograd23FabricationAwareInvdes.ipynb b/Autograd23FabricationAwareInvdes.ipynb
index 98246dc5..56a25821 100644
--- a/Autograd23FabricationAwareInvdes.ipynb
+++ b/Autograd23FabricationAwareInvdes.ipynb
@@ -219,9 +219,7 @@
     "\n",
     "# we average the metrics over the channels with some frequency width\n",
     "channel_fwidth = df_design / 2.0\n",
-    "channel_bounds = [\n",
-    "    (f - channel_fwidth / 2, f + channel_fwidth / 2) for f in freqs_design\n",
-    "]\n",
+    "channel_bounds = [(f - channel_fwidth / 2, f + channel_fwidth / 2) for f in freqs_design]\n",
     "num_freqs_channel = 5\n",
     "channel_freqs = []\n",
     "for fmin, fmax in channel_bounds:\n",
@@ -282,9 +280,7 @@
     "    medium=td.Medium(permittivity=n_si**2),\n",
     ")\n",
     "\n",
-    "centers_y = np.linspace(\n",
-    "    -ly / 2.0 + ly_single / 2.0, +ly / 2.0 - ly_single / 2.0, num_freqs_design\n",
-    ")\n",
+    "centers_y = np.linspace(-ly / 2.0 + ly_single / 2.0, +ly / 2.0 - ly_single / 2.0, num_freqs_design)\n",
     "mode_size = (0, 0.9 * ly_single, td.inf)\n",
     "\n",
     "wgs_out = []\n",
@@ -626,9 +622,7 @@
     "    for field_name, ax in zip((\"Ex\", \"Ey\", \"Ez\"), axs[mode_index]):\n",
     "        for freq in freqs_design:\n",
     "            key = f\"{td.C_0 / freq * 1000:.0f} nm\"\n",
-    "            field = mode_data.field_components[field_name].sel(\n",
-    "                mode_index=mode_index, f=freq\n",
-    "            )\n",
+    "            field = mode_data.field_components[field_name].sel(mode_index=mode_index, f=freq)\n",
     "            field.real.plot(label=f\"Real ({key})\", ax=ax)\n",
     "            field.imag.plot(ls=\"--\", label=f\"Imag ({key})\", ax=ax)\n",
     "        ax.set_title(f\"index={mode_index}, {field_name}\")\n",
@@ -749,9 +743,7 @@
     "\n",
     "def add_prediction_buffer(params):\n",
     "    \"\"\"Add a buffer layer to the design region. This is useful for predicting the interface between the design region and the waveguides.\"\"\"\n",
-    "    params = anp.pad(\n",
-    "        params, pad_width=n_prediction_buffer, mode=\"constant\", constant_values=0\n",
-    "    )\n",
+    "    params = anp.pad(params, pad_width=n_prediction_buffer, mode=\"constant\", constant_values=0)\n",
     "    wg_mask = anp.zeros_like(params)\n",
     "    center_y_mask = wg_mask.shape[1] // 2\n",
     "\n",
@@ -767,17 +759,13 @@
     "        wg_center = int(center_y / dl_design_region)\n",
     "        wg_mask[\n",
     "            -n_prediction_buffer:,\n",
-    "            center_y_mask + wg_center - wg_half_width : center_y_mask\n",
-    "            + wg_center\n",
-    "            + wg_half_width,\n",
+    "            center_y_mask + wg_center - wg_half_width : center_y_mask + wg_center + wg_half_width,\n",
     "        ] = 1\n",
     "\n",
     "    return params * (1 - wg_mask) + wg_mask\n",
     "\n",
     "\n",
-    "def params_to_device_array(\n",
-    "    params: np.ndarray, scale: float, pad_width: int = 100\n",
-    ") -> np.ndarray:\n",
+    "def params_to_device_array(params: np.ndarray, scale: float, pad_width: int = 100) -> np.ndarray:\n",
     "    \"\"\"Convert params to a device array at 1 nm resolution.\"\"\"\n",
     "    params = add_prediction_buffer(params)\n",
     "    device_array = resize_array(params, scale)\n",
@@ -833,9 +821,7 @@
     "    if use_predict:\n",
     "        device_array = params_to_device_array(density, dl_design_region * 1000)\n",
     "        prediction_array = pf.predict.predict_array_with_grad(device_array, FAB_MODEL)\n",
-    "        predicted_density = device_array_to_params(\n",
-    "            prediction_array, dl_design_region * 1000\n",
-    "        )\n",
+    "        predicted_density = device_array_to_params(prediction_array, dl_design_region * 1000)\n",
     "        return rescale(predicted_density, 1, n_si**2)\n",
     "    # ********** END OF ADDED PREFAB CODE **********\n",
     "\n",
@@ -843,9 +829,7 @@
     "        return rescale(density, 1, n_si**2)\n",
     "\n",
     "\n",
-    "def make_custom_medium(\n",
-    "    params: np.ndarray, beta: float, use_predict: bool = False\n",
-    ") -> td.Structure:\n",
+    "def make_custom_medium(params: np.ndarray, beta: float, use_predict: bool = False) -> td.Structure:\n",
     "    \"\"\"Make td.Structure containing custom medium with the permittivity data as a function of parameters.\"\"\"\n",
     "    eps = make_eps(params, beta, use_predict).reshape((nx, ny, 1))\n",
     "    xs = anp.linspace(-lx / 2, lx / 2, nx)\n",
@@ -885,8 +869,7 @@
     "        dl=[dl_design_region, dl_design_region, dl_design_region],\n",
     "    )\n",
     "    grid_spec = sim_base.grid_spec.updated_copy(\n",
-    "        override_structures=list(sim_base.grid_spec.override_structures)\n",
-    "        + [design_override]\n",
+    "        override_structures=list(sim_base.grid_spec.override_structures) + [design_override]\n",
     "    )\n",
     "\n",
     "    update_dict = dict(\n",
@@ -968,9 +951,7 @@
     "import xarray as xr\n",
     "\n",
     "\n",
-    "def average_over_channel(\n",
-    "    spectrum: xr.DataArray, fmin: float, fmax: float\n",
-    ") -> xr.DataArray:\n",
+    "def average_over_channel(spectrum: xr.DataArray, fmin: float, fmax: float) -> xr.DataArray:\n",
     "    \"\"\"Get average of the spectrum within the frequency range between fmin and fmax.\"\"\"\n",
     "    freqs = spectrum.f\n",
     "    freqs_in_channel = np.logical_and(freqs >= fmin, freqs <= fmax).values\n",
@@ -989,9 +970,7 @@
     "    return average_over_channel(power_spectrum, fmin=fmin_channel, fmax=fmax_channel)\n",
     "\n",
     "\n",
-    "def get_metric(\n",
-    "    sim_data: td.SimulationData, mnt_index: int, leak_weight: float = 1.0\n",
-    ") -> float:\n",
+    "def get_metric(sim_data: td.SimulationData, mnt_index: int, leak_weight: float = 1.0) -> float:\n",
     "    \"\"\"measure of how well this channel (`mnt_index`) performs. With an adjustable weight to reduce cross talk influence.\"\"\"\n",
     "\n",
     "    power_all = [\n",
@@ -1057,16 +1036,12 @@
     "use_predict = True\n",
     "\n",
     "\n",
-    "def objective(\n",
-    "    params, beta: float, penalty_weight: float = 1.0, leak_weight: float = 0.0\n",
-    ") -> float:\n",
+    "def objective(params, beta: float, penalty_weight: float = 1.0, leak_weight: float = 0.0) -> float:\n",
     "    metric = 0.0\n",
     "    penalty_value = 0.0\n",
     "\n",
     "    if use_metric:\n",
-    "        sim = get_sim(\n",
-    "            params, beta=beta, include_extra_mnts=False, use_predict=use_predict\n",
-    "        )\n",
+    "        sim = get_sim(params, beta=beta, include_extra_mnts=False, use_predict=use_predict)\n",
     "        simulations = {f\"WDM_invdes_{key}\": sim for key in keys}\n",
     "        batch_data = web.run_async(simulations, verbose=False, path_dir=\"data/\")\n",
     "        metric = 0.0\n",
@@ -1317,19 +1292,13 @@
     "iter_final = -1\n",
     "\n",
     "num_freqs_measure = 151\n",
-    "freqs_measure = np.linspace(\n",
-    "    freq_min - df_design, freq_max + df_design, num_freqs_measure\n",
-    ")\n",
+    "freqs_measure = np.linspace(freq_min - df_design, freq_max + df_design, num_freqs_measure)\n",
     "\n",
-    "sim_final = get_sim(\n",
-    "    params_history[iter_final], beta=beta_history[iter_final], use_predict=False\n",
-    ")\n",
+    "sim_final = get_sim(params_history[iter_final], beta=beta_history[iter_final], use_predict=False)\n",
     "\n",
     "for i in range(num_freqs_design):\n",
     "    sim_final = sim_final.updated_copy(freqs=freqs_measure, path=f\"monitors/{i}\")\n",
-    "    sim_final = sim_final.updated_copy(\n",
-    "        freqs=freqs_measure, path=f\"monitors/{i + num_freqs_design}\"\n",
-    "    )"
+    "    sim_final = sim_final.updated_copy(freqs=freqs_measure, path=f\"monitors/{i + num_freqs_design}\")"
    ]
   },
   {
@@ -1520,7 +1489,7 @@
     "\n",
     "\n",
     "plot_flux(sim_data_final_predicted, \"total flux (with fabrication prediction)\")\n",
-    "plot_flux(sim_data_final, \"total flux (without fabrication prediction)\", linestyle=\"--\")\n"
+    "plot_flux(sim_data_final, \"total flux (without fabrication prediction)\", linestyle=\"--\")"
    ]
   },
   {
@@ -1574,14 +1543,12 @@
     "    plt.show()\n",
     "\n",
     "\n",
-    "plot_power(\n",
-    "    sim_data_final_predicted, \"power at mode_index=0 (with fabrication prediction)\"\n",
-    ")\n",
+    "plot_power(sim_data_final_predicted, \"power at mode_index=0 (with fabrication prediction)\")\n",
     "plot_power(\n",
     "    sim_data_final,\n",
     "    \"power at mode_index=0 (without fabrication prediction)\",\n",
     "    linestyle=\"--\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
diff --git a/Autograd24DigitalSplitter.ipynb b/Autograd24DigitalSplitter.ipynb
index 265be708..4925c19a 100644
--- a/Autograd24DigitalSplitter.ipynb
+++ b/Autograd24DigitalSplitter.ipynb
@@ -27,13 +27,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import autograd.numpy as anp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import autograd.numpy as anp\n",
     "from autograd import value_and_grad"
    ]
   },
@@ -539,7 +537,6 @@
     "params_history = []\n",
     "\n",
     "for i in range(n_iter):\n",
-    "\n",
     "    # show a permittivity plot every 5 iterations\n",
     "    if i % 5 == 0:\n",
     "        make_sim(design_params).plot_eps(z=h_si / 2)\n",
@@ -884,9 +881,9 @@
     "plt.plot(fom_history, c=\"red\", linewidth=2)\n",
     "plt.xlabel(\"Iterations\")\n",
     "plt.ylabel(\"Figure of merit\")\n",
-    "plt.axvline(x=25, color='black', linestyle='--')\n",
-    "plt.text(12, 0.3, 'Stage 1', fontsize=12, horizontalalignment='center')\n",
-    "plt.text(38, 0.3, 'Stage 2', fontsize=12, horizontalalignment='center')\n",
+    "plt.axvline(x=25, color=\"black\", linestyle=\"--\")\n",
+    "plt.text(12, 0.3, \"Stage 1\", fontsize=12, horizontalalignment=\"center\")\n",
+    "plt.text(38, 0.3, \"Stage 2\", fontsize=12, horizontalalignment=\"center\")\n",
     "plt.show()"
    ]
   },
@@ -990,7 +987,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Create an arbitray simulation to visualize. We can see that cylinders with a high permittivity disappear. Cylinders with a low permittivity becomes air holes. Cylinders with an intermedium permittivity becomes smaller holes. "
+    "Create an arbitrary simulation to visualize. We can see that cylinders with a high permittivity disappear. Cylinders with a low permittivity become air holes. Cylinders with an intermedium permittivity become smaller holes. "
    ]
   },
   {
diff --git a/Autograd2GradientChecking.ipynb b/Autograd2GradientChecking.ipynb
index caa0e5e0..84d69321 100644
--- a/Autograd2GradientChecking.ipynb
+++ b/Autograd2GradientChecking.ipynb
@@ -20,15 +20,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
+    "from typing import List, Tuple\n",
+    "\n",
     "import autograd as ag\n",
     "import autograd.numpy as anp\n",
-    "import tmm\n",
     "import matplotlib.pyplot as plt\n",
-    "from typing import Tuple, List\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web"
+    "import tidy3d.web as web\n",
+    "import tmm"
    ]
   },
   {
@@ -151,7 +151,7 @@
    "source": [
     "### Numerical Gradient with TMM\n",
     "\n",
-    "Next, we will use our `compute_T_tmm()` function to compute the \"numerical\" gradient to use as comparison against our adjoint results with FDTD.\n",
+    "Next, we will use our `compute_T_tmm()` function to compute the \"numerical\" gradient to use as a comparison against our adjoint results with FDTD.\n",
     "\n",
     "The derivative of a function $f(x)$ w.r.t. $x$ can be approximated using finite differences as\n",
     "\n",
@@ -161,7 +161,7 @@
     "\n",
     "To compute the gradient of our transmission with respect to each of the slab thicknesses and permittivities, we need to repeat this step for each of the values. Luckily, since TMM is very fast, we can compute these quantities quite quickly compared to if we were using FDTD.\n",
     "\n",
-    "> Important note: We assume in our TMM numerical gradient that when the slabs are touching (`spc=0`) and a slab thickness is modified, that the thicknesses of the neighboring slabs adjust to accomidate this change. For example, if slab `i` increases by `dt`, slab `i-1` and `i+1` each decrease by `dt/2`. We also account for this in our FDTD set up by keeping the centers of all boxes constant and not tracking the gradient through these quantities. The reason this is required is that `tidy3d` does not recognize the space between touching `td.Box` objects as a single interface and will instead \"double count\" the gradient contribution of the interface if they are placed right next to each other. One must therefore be careful about overlapping or touching two `td.Box` or other geometries when computing gradients.\n",
+    "> Important note: We assume in our TMM numerical gradient that when the slabs are touching (`spc=0`) and a slab thickness is modified, the thicknesses of the neighboring slabs adjust to accommodate this change. For example, if slab `i` increases by `dt`, slab `i-1` and `i+1` each decrease by `dt/2`. We also account for this in our FDTD set up by keeping the centers of all boxes constant and not tracking the gradient through these quantities. The reason this is required is that `tidy3d` does not recognize the space between touching `td.Box` objects as a single interface and will instead \"double count\" the gradient contribution of the interface if they are placed right next to each other. One must therefore be careful about overlapping or touching two `td.Box` or other geometries when computing gradients.\n",
     "\n",
     "Here we write the function to return the numerical gradient."
    ]
@@ -254,18 +254,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 7,
    "id": "4d12f133-a8d8-4e59-938f-fca4d2e2fa0b",
    "metadata": {},
    "outputs": [],
    "source": [
     "from autograd.tracer import getval\n",
     "\n",
+    "\n",
     "def make_sim(slab_eps=slab_eps0, slab_ds=slab_ds0) -> td.Simulation:\n",
     "    \"\"\"Create a Simulation given the slab permittivities and thicknesses.\"\"\"\n",
     "\n",
     "    # frequency setup\n",
-    "    wavelength = td.C_0 / freq0\n",
     "    fwidth = freq0 / 10.0\n",
     "    freqs = [freq0]\n",
     "\n",
@@ -284,7 +284,6 @@
     "    slabs = []\n",
     "    z_start = -length_z / 2 + space_below\n",
     "    for d, eps in zip(slab_ds, slab_eps):\n",
-    "\n",
     "        # dont track the gradient through the center of each slab\n",
     "        # as tidy3d doesn't have enough information to properly process the interface between touching Box objects\n",
     "        z_center = z_start + d / 2\n",
@@ -356,13 +355,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 8,
    "id": "bc0dc456-e742-4299-ae5e-22daa49e3997",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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lNpvVoEGD1Mcff+z363z++edq6NChymKxqPz8fDVt2jRlt9tDGqtBKf7eV5Lh2WefxQMPPICffvoJnTp10ns4umGUJIJSCv3790dubq73mG6iEndIhBJLVVUV3n//fWzevBl79+7FmjVr9B6S7rikJF399NNPOO+885CdnY27774b8+fP13tIumOURMLwNw8QCcMoiYRJqB09brcbx48fR2ZmZlhXZFB8UUqhoqICHTp0CHjBgTQJFeXx48fRuVNnuNUvp7elJKfgdzf+Dnl5ed5p6uvrm5yupwWTyeSdcdxud7NXNsRScnIykpP/NUs4HI4WT4OMNoPB4HP5U6jvR0VFBVa9vQp19XXe+4qLi9GxY8eojjOWEirKzMxMuJUbC8c8AQB4+OMZ6Ny5M3Jzc70R5Obm6vJT1el0es8XtVqtzV4DGEtutxulpaUAfvkh4bnkTWs2my3s9+OHH35AXX2dz3ucmZkZk3HGSkJF6Vll7dqmq/c+l8sFt9uNjIwMOBwO1NTUaB6m0+lEeXm597K16upqpKWlaRqmJ8iUlBSYzWbU1tbC5XJpPkNXVFRE9H54vmcN3+N421SJnxXtGKmpqUFWVhZyc3ORl5eHuro6lJaWRnR9YCicTidKSkqQkpKCvLw85OXlISUlBSUlJZqtwnqCrKurQ15eHnJzc5GVlQW73e5zxUOsVVRUwG636/p+SJDwUaampnqXBiaTSdMZoWGQnqWB0WhEbm6uZmE2DtKzpMnMzNQ0zIZB6vV+SMEoG/2mA61mBH9BemgVZnNBemgVpr8gPRIxzISP0p9YzwiBgvSIdZgtBekR6zADBemRaGEyymbEakYIJkiPWIUZbJAesQozmCA9EilMRhlAtGeEUIL0iHaYoQbpEe0wQwnSI1HCZJQtiNaMEE6QHtEKM9wgPaIVZjhBeiRCmIwyCJHOCJEE6RFpmJEG6RFpmJEE6dHaw2SUQQp3RohGkB7hhhmtID3CDTMaQXq05jAZZQhCnRGiGaRHqGFGO0iPUMOMZpAerTVMRhmiYGeEWATpEWyYsQrSI9gwYxGkR+P3ozVcs88ow9BSmLEM0qOlMGMdpEdLYcYySI+G74eWpwXGCqMMU3NhahGkR3NhahWkR3NhahGkh+f90OOyu2hjlBFoHKbD4dAsSI/GYTocDk2D9GgcppZBephMpiZ/3DUeJdSlW7HgCfPMmTOaB+nhCdPzNzEBID8/X/NrMj3xef5MnpZBejS8SDtecUlJJAyjjJBnG7LhNo3Wu+c925D19fXeVVYtr8f0aLjKqsf1mAC4TZnoGu/UMZvNmh83a7xTx2w2a3o9pkfjbUitr8cEfnk/Gv+F6XjEKMPU3F5WLQ9oN7eXVesLpZvbqaNlmJ73g9uUCaqlwx5ahNnSYQ+twmxpL6sWYTZ8P+Ltl2T5wyhDFOxxyFiGGexxyFiHGexhj1iG2fj9iLdfkuUPowxBqCcGxCLMUE8MiFWYoR6HjEWYWp6ooaXW8So0EO4MEM0wwz1TJ9phhntiQDTDbK1BAowyKJHOANEIM9JT56IVZqRn6kQjzNYcJMAoWxStGSCSMKN1LmukYUbr1LlIwmztQQJxFOXixYvRr18/74HpoqIifPTRRzH9mtGeAcIJM9onl4cbZrTPZQ0nzEQIEoijKDt27Ii//OUv2L17N3bt2oVRo0Zh3Lhx2LdvX0y+XqxmgFDCjNXVHqGGGauTy0MJM1GCBOIoyquvvhpXXXUVzj//fHTv3h3z589HRkYGtm/fHvWvFesZIJgwY335VbBhxvpqj2DCTKQggTiKsiGXy4W33noLVVVVKCoqanY6h8MBu93uc2uspqbG52OtZoBAYWp1PWRLYWp1+VWgMBMtSCDOoty7dy8yMjJgNptx11134b333sMFF1zQ7PQLFiyA1Wr13goLC5tMU1NT450RtJ4B/IWp9QXKzYWp9fWQ/sJMxCCBOIuyR48e+Prrr7Fjxw5MnToVkyZNwrffftvs9LNmzYLNZvPeiouLm0yTmpoKu92O0tJSXWaAhmF6rofU+gLlxmGWlpZqfoEy4BumXu+HBHF19q7JZEK3bt0AAAMHDsTOnTvx7LPPYsmSJX6nN5vNPn8V2J/U1FRYLBbU1tYC0OePxppMJu9FygA0DdLDE+aJEydQW1sLi8Wiy3mkmZmZcDqdur4feovrV+t2u+FwOCJ6jvr6ep/nqKqqinRYIXO73T7bu3a7XZdfl9jwtTscDl3+xLvT6dT9/dBb3CwpZ82ahSuvvBKdOnVCRUUFVqxYgS1btmDdunURPa/dbkd+fj5yc3NRVVXljUOrpUTDbcj8/HwA8K5CarmUaLgNmZ6e7l191HKp3XgbUo/3Q4K4ifL06dOYOHEiTpw4AavVin79+mHdunX4t3/7t4ieNzk52TvzN/4dM7GeEZrbqZOXl6dpmP526uTm5moapr+dOlq/H1LETZSvvPJKTJ43MzPTZ6bXakYItJfVs/NHizCb28vq2cbUIsxAe1kTMcy43qaMBn/X38X6wtxgDntocaF0S4c9tLhQOpjDHnr8ahE9JXyUzYnVjBDKcchYhhnscchYhhnKcchECpNRBhDtGSGcEwNiEWaoJwbEIsxwTgxIlDAZZQuiNSNEcqZONMMM90ydaIYZyZk6iRAmowxCpDNCNE6di0aYkZ46F40wo3HqXGsPk1EGKdwZIZrnskYSZrTOZY0kzGiey9qaw2SUIQh1RojFyeXhhBntk8vDCTMWJ5e31jDj5jhlrLhcrpB+2pvNZqSlpaG8vBz19fVIT0/3O51SCmVlZaivr0dOTg4ARHXPpdVqRVlZGU6fPo2cnJxmf7ViVVUVKisrvVfXRHMMWVlZPmNISUnxO11dXR3KysqQnJyMrKysqP5pgcbvR2v4swWM0uXynvwcrKSkJKSnp8PpdEIp1eS3ciul4HQ6YTQakZGRAZfLBZfLFc1hAwAyMjK8v6rfZDI1CbO+vh51dXVIT09HUlJSyK8zGKmpqXA6naiqqoLJZGqyBHS73XA6nTCbzTCZTBGfq+xPw/ejrq4u6s+vtYSPcvTo0ejfv7/ew6Ao+ec//4knnnhC72FEJOGjtFqtaNOmjd7DoCixWq16DyFi3NFDJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJIGEZJJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJIGEZJJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJIGEZJJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJImLiJcsGCBRg0aBAyMzPRtm1bjB8/HgcOHNB7WERRFzdRfvrpp5g2bRq2b9+O9evXo66uDpdffjmqqqr0HhpRVCXrPYBgffzxxz4fL1u2DG3btsXu3bsxbNgwnUZFFH1xE2VjNpsNANCmTZtmp3E4HHA4HN6P7XZ7zMdFFKm4WX1tyO124/7778fFF1+MPn36NDvdggULYLVavbfCwkINR0kUnriMctq0afjmm2/w1ltvBZxu1qxZsNls3ltxcbFGIyQKX9ytvt5zzz344IMP8Nlnn6Fjx44BpzWbzTCbzRqNjCg64iZKpRTuvfdevPfee9iyZQvOO+88vYdEFBNxE+W0adOwYsUKrFmzBpmZmTh58iQAwGq1IjU1VefREUVP3GxTLl68GDabDSNGjED79u29t5UrV+o9NKKoipslpVJK7yEQaSJulpREiYJREgnDKImEYZREwsTNjp6YqbMDzjK9R0HRUhf/5zczypMbgCMH9R4FRcvJI3qPIGJcfSUShlESCcMoiYThNmXBaKBTf71HQdFS+k8A8/UeRUQYZUoWYMrRexQULSlZeo8gYlx9JRKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJIGEZJJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJIGEZJJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJIGEZJJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJImLiK8rPPPsPVV1+NDh06wGAwYPXq1XoPiSjq4irKqqoq9O/fHy+88ILeQyGKmWS9BxCKK6+8EldeeaXewyCKqbiKMlQOhwMOh8P7sd1u13E0RMGJq9XXUC1YsABWq9V7Kyws1HtIRC1q1VHOmjULNpvNeysuLtZ7SEQtatWrr2azGWazWe9hEIWkVS8pieJRXC0pKysrcejQIe/HP/74I77++mu0adMGnTp10nFkRNETV1Hu2rULI0eO9H784IMPAgAmTZqEZcuW6TQqouiKqyhHjBgBpZTewyCKKW5TEgnDKImEYZREwjBKImEYJZEwjJJIGEZJJAyjJBImrk4eiIX/u+YBOMzZeg+DouSAo1zvIUSMS0oiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJIGEZJJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJIGEZJJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwyXoPQG/vXHo1ctt20XsYFCWlp38A3vpM72FEhEtKImEYJZEwjJJIGEZJJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJIGEZJJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYSJuyhfeOEFnHvuubBYLBgyZAj+8Y9/6D0koqiKqyhXrlyJBx98EHPmzMFXX32F/v3744orrsDp06f1HhpR1IQV5ahRozB37twm95eVlWHUqFERD6o5Tz/9NKZMmYJbb70VF1xwAV566SWkpaXh1VdfjdnXJNJaWFFu2bIFzz//PMaPH4+qqirv/U6nE59++mnUBteQ0+nE7t27MXr0aO99RqMRo0ePxrZt2/w+xuFwwG63+9yIpAt79XXDhg04efIkfv3rX+Onn36K4pD8KykpgcvlQrt27Xzub9euHU6ePOn3MQsWLIDVavXeCgsLYz5OokiFHWX79u3x6aefom/fvhg0aBC2bNkSxWFFx6xZs2Cz2by34uJivYdE1KLkcB5kMBgAAGazGStWrMB//ud/YsyYMZgxY0ZUB9dQXl4ekpKScOrUKZ/7T506hYKCAr+PMZvNMJvNMRsTUSyEtaRUSvl8/Oijj+KNN97AU089FZVB+WMymTBw4EBs3LjRe5/b7cbGjRtRVFQUs69LpLWwlpQ//vgj8vPzfe677rrr0LNnT+zatSsqA/PnwQcfxKRJk3DRRRdh8ODBWLRoEaqqqnDrrbfG7GsSaS2sKDt37uz3/t69e6N3794RDSiQG2+8EWfOnMHs2bNx8uRJXHjhhfj444+b7PwhimdhRamne+65B/fcc4/ewyCKmbg6o4coETBKImEYJZEwjJJIGEZJJAyjJBKGURIJE3fHKaPtD/Un0KsuSe9hUJR8V38Ca/UeRIS4pCQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJIGEZJJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRL+r269c8H5yO98vt7DoCg583Oq3kOIGJeURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJIGEZJJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJIGEZJJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYZL1HoDe/r9xg/GrX/1K72FQlHz1VQ7enaf3KCLDJSWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTBxE+X8+fMxdOhQpKWlITs7W+/hEMVM3ETpdDpxww03YOrUqXoPhSim4ubc17lz5wIAli1bpu9AiGIsbqIMh8PhgMPh8H5st9t1HA1RcOJm9TUcCxYsgNVq9d4KCwv1HhJRi3SNcubMmTAYDAFv+/fvD/v5Z82aBZvN5r0VFxdHcfREsaHr6utDDz2EyZMnB5ymS5cuYT+/2WyG2WwO+/FEetA1yvz8fOTn5+s5BCJx4mZHz5EjR3D27FkcOXIELpcLX3/9NQCgW7duyMjI0HdwRFEUN1HOnj0b//3f/+39eMCAAQCAzZs3Y8SIETqNiij64mbv67Jly6CUanJjkNTaxE2URImCURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJIGEZJJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJIGEZJJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjDKBPDKV6/go+8/0nsYFKRkvQdAsWWrteHpbU+jQ1YHXNHtChgN/DksHd+hVm7d4XUorSnFwdKD2HNij97DoSAwylbu3W/fhcFggKPegfcPvK/3cCgIjLIVO3T2EHYe34ksUxYsyRa8f+B9VNdV6z0sagGjbMU+OPgBauprkGHKgNVsxemq09j4w0a9h0UtYJStVL27Hu98+w6SDckwGAxISUqBW7nx3v739B4atYBRtlLbirfhZ9vPsFqs3vsyTBn47OfPcMx+TMeRUUsYZSu15sAa1LnqYEm2eO/LNGeiqq4Ka79fq+PIqCWMshWy1drw4fcfIi0lzed+o8EII4xY9e0quJVbp9FRSxhlK7Tu8DqU1ZYhy5zV5HNWi5XHLIVjlK3Qu9++CwBINjY9YSs1OZXHLIVjlK1Mw2OT/hgMBliSLVhzYA2PWQrFKFuZDw5+gOq6amSYMpqdhscsZWOUrYjn2GSKMQUGg6HZ6TzHLP++/+8ajo6CxShbEX/HJpuTacrE5z9/zmOWAjHKVmT1gdVw1DuQZEhCnavO72EPt3J7j19WOCt4zFIgRtmKfFn8JUxJJpTWlKK0phQnKk40mabYVozSmlKU1ZYhxZiCrUe26jBSCoQXObcib1z7Bk5VngIAvPvdu3jzmzebTGM0GvFg0YMo6lgEADg3+1wth0hBYJStSJecLuiS0wUAsO3otman65rTFUWFRVoNi0LE1VciYRgledVVVmLX/Pn4YfVq1JaW6j2chMXVV/KqOn4cRzdvxs8ff4x9S5eioKgIHUeNQttBg5BkMuk9vITBKMmXUjDn5MDtdKJ4wwYc3bQJqW3bouNll6HjyJGwdusW8MQEihyjbMXq3fWwO+w+97U/mwTXPw/hZFnTHUFVJ345hGIwGmHKyoIpKwsupxO1paU4+PrrOLxqFaznn49Ol1+ODpdeCkturiavI9EkfJSV9ZWw19tbnjDOpJvTkWXOgku5fO6f+EU2HJ+9g61Ja/w+zl1Xh6SsDMDzuJQkpORYoZSCq7YWpfu+Qek3e7F36UvIGzIIBSOHIXfQr2BMSYn1SwpKZX2l3kOIWMJH+Y+Kf6CkvETvYURdfqd8zG0/F0opn/sNa1+Ccik4MpqLyIR6QzXg8vspINcM5XbDUVWJqg2f4OdN62HIy4Lx0l5IGtoThvPa6rp6+0PFD7p97WhJ+ChbM0uKpcl9DoMRyuCCwRj+jneD0QhkpgKZqVB19VBnK+F6Zxtc7++CsUtbJP3210gacn4kQ09oPCRCkUlOAlJNgNEA1DqhjpTAfbjp6X0UPC4pKSyq3gVU1gJOF2BKgvHcdjCO6I2koT1gyPN/gTUFJ+GjHJw5GBdmX6j3MDSzOell1FXYYahz+J/ArWDOz/O7eqvcbtRXVaO+uhoGgwHm3LZoN3IYCkZciqwe50e0ShwtX2d+rfcQIpbwUWYkZyArOXF+sve5fQqqT570+zmn3Y7i9ethcCkkJSUBgHeva11FBZRSSE5NRcdhw1E4ejTaDRmC5NRULYffoozk5n/jQrxI+CgTTddrr232c+UHD6J4/XoAvxwacVZWwu1wwGgywdqt2y/HJ0eMQFrbtloNNyExSmqitqQEhuRkWHJz0XHUKHQcORI5PXuKWD1NBIySvExWK0xWK9pccIHY1dNEwCjJK61dO1z5zjtcIuqM333ywSD1x3eASBhGSSRMXET5008/4fbbb8d5552H1NRUdO3aFXPmzIHT6dR7aERRFxc7evbv3w+3240lS5agW7du+OabbzBlyhRUVVVh4cKFeg+PKKriIsoxY8ZgzJgx3o+7dOmCAwcOYPHixYySWp24iNIfm82GNm3aBJzG4XDA4fjXOZ52e+u7mJlan7jYpmzs0KFDeO6553DnnXcGnG7BggWwWq3eW2FhoUYjJAqfrlHOnDkTBoMh4G3//v0+jzl27BjGjBmDG264AVOmTAn4/LNmzYLNZvPeiouLY/lyiKJC19XXhx56CJMnTw44TZcuXbz/P378OEaOHImhQ4di6dKlLT6/2WyG2WyOdJhEmtI1yvz8fOTn5wc17bFjxzBy5EgMHDgQr732Gow884RaqbjY0XPs2DGMGDECnTt3xsKFC3HmzBnv5woKCnQcGVH0xUWU69evx6FDh3Do0CF07NjR53ONf1sbUbyLi3XAyZMnQynl90bU2sRFlESJhFESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJIGEZJJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJIGEZJJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJIGEZJJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSBhGSSQMoyQShlESCcMoiYRhlETCMEoiYRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJIGEZJJAyjJBKGURIJwyiJhGGURMIwSiJhGCWRMIySSJhkvQegN5vNhrNnz+o9DIoSm82m9xAilvBRbtiwAQcPHgx6erfbDafTCYPBAJPJBIPBEJVpQ6GUgtPphFIKJpMJRmPzKzyhTBuq+vp61NXVISUlBcnJgWelUKYNRePvcXFxcdSeWy8JH2VSUhIsFktQ09bV1cFutyM5ORk5OTlBRZaSkoKysjK4XK6gHxOIUgplZWWor69HTk4OUlJSWnyMxWJBWVkZKisrg35MS6qqqlBVVYWMjAykp6cH/ZjKysqQHhOIv/cjKSkp4ufVG6NMSoLJZGpxOqfTCZvNBrPZjNzc3KCXOCaTCSkpKSgpKYHdbg/psY253W6UlpbC7Xajbdu2QY3bo23btigtLYXNZkNeXl5Ij22soqIC1dXVyM7ORmZmZtCPM5lMSE5O9oYUymMba+79aA1RckdPEJxOJ0pKSpCSkhJWVCaTCXl5eairq/NGFSpPkHV1dWFFZTQakZub6/0B4XQ6Qx4D8EuQdrsdWVlZYUWVmZmJrKws2O12VFRUhDWGSN8P6VrXq4mBaM0AkYQZaZAekYYZaZAekYTZ2oMEGGVA0Z4BwgkzWkF6hBtmtIL0CCfMRAgSYJTNitUMEEqY0Q7SI9Qwox2kRyhhJkqQAKP0K9YzQDBhxipIj2DDjFWQHsGEmUhBAowSNTU1Ph9rNQMECjPWQXq0FGasg/QIFGaiBQkwStTU1HhnBK1nAH9hahWkR3NhahWkh78wEzFIgMcpkZqaCrvdDqfTCYfDofkM4AmzpKQEJSUlAH45+0WLID08YZaWlqKkpARmsxm1tbWaBenh+Vp6vh8SxM0rveaaa9CpUydYLBa0b98eEyZMwPHjxyN+3tTUVFgsFtTW1kIppcsMYDKZkJubi7q6OtTV1SE3N1ezID08YSqlUFtbC4vFommQHpmZmbq/H3qLm1c7cuRIvP322zhw4ADeffddHD58GNdff33Ez1tfXw+Hw+H9uKqqKuLnDJXb7Ybdbvd+bLfbwzrBIFINX7vD4Qj7BINIeJaQ/saUKOJm9fWBBx7w/r9z586YOXMmxo8f7z3J2R+Hw+HzBjec8Rvel5+fj9zcXFRVVXmn0Wop0XAbMj8/HwBQUlKC0tJSTZcSDbch09PTvauyWq5GN96G1OP9kCBulpQNnT17Fm+88QaGDh0a8OTqBQsWwGq1em+FhYVNpklOTvbO/NE4BSwU/nbqROOUvFA13qkTrVPyQuFvp47W74cUcRXljBkzkJ6ejtzcXBw5cgRr1qwJOP2sWbNgs9m8N3+X9XhmwoYfazEjBNrLqmWYze1l1TLMQHtZEzFMXaOcOXMmDAZDwNv+/fu900+fPh179uzBJ598gqSkJEycOBFKqWaf32w2Iysry+fWmL9LqWI9IwRz2EOLMFs67KFFmMEc9ki0MHXdpnzooYcwefLkgNN06dLF+/+8vDzk5eWhe/fu6NWrFwoLC7F9+3YUFRVFfWwNd883/DhSoRyHbHi4JNrbmMEeh2x8uCSa25ihHIeM1fshka5R5ufne3duhMqz5Gi4Iyfaoj0jhHNiQCzCDPXEgFiEGc6JAYkSZlzsfd2xYwd27tyJSy65BDk5OTh8+DD+9Kc/oWvXrjFZSjYUrRkhkjN1ohlmuGfqRDPMSM7USYQw42JHT1paGv7+97/jsssuQ48ePXD77bejX79++PTTT2E2m2P+9SPdponGqXPR2MaM9NS5aGxjRuPUuda+jRkXS8q+ffti06ZNuo4h3J/Q0TyXNZIlZrTOZY1kiRnNc1lb8xIzLpaUUoT6EzoWJ5eHs8SM9snl4SwxY3FyeWtdYjLKEAU7I8Tyao9QwozV1R6hhBnLqz1aY5iMMgwtzQhaXH4VTJixvvwqmDC1uPyq4fvR+PrYeMQow9RcmFpeDxkoTK2uhwwUppbXQ3reD0aZ4BqHqfUFyoD/MLW+QNlfmHpcoJyZmYnU1NSYf51Yi4u9r5I13Atot9thMBg0vbIC8N0re+LECQDQ/ALlhntlz5w54x2X1tdDtoYouaSMgoa/gt9sNmt+gTLwSwANj9lG488ChMpoNPqcX5yVlZVwFyhHA79jEfKsshoMBu8V83rsBayoqPD+xgCDwaDZZV8NOZ1OlJaWIiUlBSkpKSgtLdX8QulAFyjEC0YZgcbbkLm5ubrsnm+4DZmbm6v59ZiA704dz4UDWl6PCcC7PR3vGGWYmtupo/VxM387dbS+UNrfTh2tL5T2vB/19fUx/TpaYJRhaGkvq1ZhBtrLqlWYgfayahVmw/fD3zWz8YZRhijYwx6xDjOYwx6xDjOYwx6xDrPx+xHNP0irF0YZglCPQ8YqzFCOQ8YqzFCOQ8YqTD2OC2uBUQYp3Bkg2mGGc2JAtMMM58SAaIfZWoMEGGVQIp0BohVmJGfqRCvMSM7UiVaYrTlIgFG2KFozQKRhRuPUuUjDjMapc5GG2dqDBBhlQNGeAcINM5rnsoYbZjTPZQ03zEQIEmCUzYrVDBBqmLE4uTzUMGNxcnmoYSZKkACj9CvWM0CwYcbyao9gw4zl1R7BhplIQQKMsskZIFrNAC2FqcXlVy2FqcXlVy2FmWhBAozS+7cQAe1ngObC1PJ6yObC1PJ6yObCTMQgAUaJ5ORklJSUwOFw6DIDNA5T6wuUgaZhOhwOzS9QbhymXu+HBPF/TlKEMjMzvWECv/zWdq1ngMa/LlHrC5SBf4V55swZ3f6kuSfMhn/VWo/3Q28Jv6QkkiahlpSeC2APnz3sva+8vBxpaWnIzMxEdXU1Tp06BavVqumJzdXV1aiqqvL+toCzZ8/C4XAgLS1NszHU19fDZrMhKSkJaWlpqKiowMmTJzX97QGev2jtcrnCfj88f1um4Xscbxc+G1S8jTgCR48e9fuHY6l1Ky4uRseOHfUeRtASKkq3243jx48jMzMTFRUVKCwsRHFxcau4Bi8Qu92ekK/V8z536NAhrn5XUEKtvhqNRu9PTM8fi23uj8m2Ron4Wq1Wq95DCVn8/PggShCMkkiYhI3SbDZjzpw5mvx9S73xtcaXhNrRQxQPEnZJSSQVoyQShlESCcMoiYRJqCjPnj2LW265BVlZWcjOzsbtt9+OysrKgI8ZMWIEDAaDz+2uu+7SaMTBe+GFF3DuuefCYrFgyJAh+Mc//hFw+lWrVqFnz56wWCzo27cvPvzwQ41GGrlQXuuyZcuavH8Wi0XD0YZBJZAxY8ao/v37q+3bt6vPP/9cdevWTd18880BHzN8+HA1ZcoUdeLECe/NZrNpNOLgvPXWW8pkMqlXX31V7du3T02ZMkVlZ2erU6dO+Z1+69atKikpSf31r39V3377rXr00UdVSkqK2rt3r8YjD12or/W1115TWVlZPu/fyZMnNR51aBImym+//VYBUDt37vTe99FHHymDwaCOHTvW7OOGDx+u/vjHP2owwvANHjxYTZs2zfuxy+VSHTp0UAsWLPA7/e9+9zs1duxYn/uGDBmi7rzzzpiOMxpCfa2vvfaaslqtGo0uOhJm9XXbtm3Izs7GRRdd5L1v9OjRMBqN2LFjR8DHvvHGG8jLy0OfPn0wa9YsVFdXx3q4QXM6ndi9ezdGjx7tvc9oNGL06NHYtm2b38ds27bNZ3oAuOKKK5qdXopwXisAVFZWonPnzigsLMS4ceOwb98+LYYbtoQ5If3kyZNo27atz33Jyclo06YNTp482ezjfv/736Nz587o0KED/u///g8zZszAgQMH8Pe//z3WQw5KSUkJXC4X2rVr53N/u3btsH//fr+POXnypN/pA30fJAjntfbo0QOvvvoq+vXrB5vNhoULF2Lo0KHYt2+f2Mu54j7KmTNn4oknngg4zXfffRf2899xxx3e//ft2xft27fHZZddhsOHD6Nr165hPy9po6ioCEVFRd6Phw4dil69emHJkiX485//rOPImhf3UT700EOYPHlywGm6dOmCgoICnD592uf++vp6nD17FgUFBUF/vSFDhgAADh06JCLKvLw8JCUl4dSpUz73nzp1qtnXVVBQENL0UoTzWhtLSUnBgAEDcOjQoVgMMSrifpsyPz8fPXv2DHgzmUwoKipCeXk5du/e7X3spk2b4Ha7vaEF4+uvvwYAtG/fPtovJSwmkwkDBw7Exo0bvfe53W5s3LjRZwnRUFFRkc/0ALB+/fpmp5cinNfamMvlwt69e8W8f37pvadJS2PGjFEDBgxQO3bsUF988YU6//zzfQ6JHD16VPXo0UPt2LFDKaXUoUOH1Lx589SuXbvUjz/+qNasWaO6dOmihg0bptdL8Outt95SZrNZLVu2TH377bfqjjvuUNnZ2d5d/xMmTFAzZ870Tr9161aVnJysFi5cqL777js1Z86cuDokEsprnTt3rlq3bp06fPiw2r17t7rpppuUxWJR+/bt0+sltCihoiwtLVU333yzysjIUFlZWerWW29VFRUV3s//+OOPCoDavHmzUkqpI0eOqGHDhqk2bdoos9msunXrpqZPny7uOKVSSj333HOqU6dOymQyqcGDB6vt27d7Pzd8+HA1adIkn+nffvtt1b17d2UymVTv3r3V2rVrNR5x+EJ5rffff7932nbt2qmrrrpKffXVVzqMOni8dItImLjfpiRqbRglkTCMkkgYRkkkDKMkEoZREgnDKImEYZREwjBKImEYJZEwjJJIGEb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",
       "text/plain": [
        "
"
       ]
@@ -388,7 +387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 9,
    "id": "1917a8b6-8ba9-4767-90ca-06cd6daadcd3",
    "metadata": {},
    "outputs": [],
@@ -409,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 10,
    "id": "f64ac290-b67d-4a9a-88e8-504de931fef8",
    "metadata": {},
    "outputs": [],
@@ -423,18 +422,256 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 11,
    "id": "4a11ca07-e177-4e15-9d60-bf31bbed0939",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
↓ simulation_data.hdf5.gz ━━━━━━━━━━━━━━━━━━━━ 100.0% • 2.9/2.9 kB • ? • 0:00:00\n",
+       "
11:02:39 Eastern Daylight Time Created task 'slab' with task_id                 \n",
+       "                               'fdve-a5cb239b-7212-4ce3-b5dc-88d53ca94fe0' and  \n",
+       "                               task_type 'FDTD'.                                \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m11:02:39 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'slab'\u001b[0m with task_id                 \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'fdve-a5cb239b-7212-4ce3-b5dc-88d53ca94fe0'\u001b[0m and  \n",
+       "\u001b[2;36m                               \u001b[0mtask_type \u001b[32m'FDTD'\u001b[0m.                                \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               View task using web UI at                        \n",
+       "                               'https://tidy3d.simulation.cloud/workbench?taskId\n",
+       "                               =fdve-a5cb239b-7212-4ce3-b5dc-88d53ca94fe0'.     \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                        \n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=724624;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a5cb239b-7212-4ce3-b5dc-88d53ca94fe0\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=150323;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a5cb239b-7212-4ce3-b5dc-88d53ca94fe0\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=724624;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a5cb239b-7212-4ce3-b5dc-88d53ca94fe0\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=625914;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a5cb239b-7212-4ce3-b5dc-88d53ca94fe0\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=724624;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a5cb239b-7212-4ce3-b5dc-88d53ca94fe0\u001b\\\u001b[32m-a5cb239b-7212-4ce3-b5dc-88d53ca94fe0'\u001b[0m\u001b]8;;\u001b\\.     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               Task folder: 'default'.                          \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=537812;https://tidy3d.simulation.cloud/folders/639eb096-a602-4b56-a502-cac1f18f9557\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                          \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "205e101a22b64c89b13b4ec071a986b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
11:02:41 Eastern Daylight Time Maximum FlexCredit cost: 0.107. Minimum cost     \n",
+       "                               depends on task execution details. Use           \n",
+       "                               'web.real_cost(task_id)' to get the billed       \n",
+       "                               FlexCredit cost after a simulation run.          \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m11:02:41 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.107\u001b[0m. Minimum cost     \n",
+       "\u001b[2;36m                               \u001b[0mdepends on task execution details. Use           \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed       \n",
+       "\u001b[2;36m                               \u001b[0mFlexCredit cost after a simulation run.          \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               status = queued                                  \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               To cancel the simulation, use                    \n",
+       "                               'web.abort(task_id)' or 'web.delete(task_id)' or \n",
+       "                               abort/delete the task in the web UI. Terminating \n",
+       "                               the Python script will not stop the job running  \n",
+       "                               on the cloud.                                    \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use                    \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or \u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or \n",
+       "\u001b[2;36m                               \u001b[0mabort/delete the task in the web UI. Terminating \n",
+       "\u001b[2;36m                               \u001b[0mthe Python script will not stop the job running  \n",
+       "\u001b[2;36m                               \u001b[0mon the cloud.                                    \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
11:02:50 Eastern Daylight Time starting up solver                               \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m11:02:50 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                               \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               running solver                                   \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ddd994cfd89d4c6dafc981861ef322d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
11:03:08 Eastern Daylight Time early shutoff detected at 32%, exiting.          \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m11:03:08 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m32\u001b[0m%, exiting.          \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
11:03:09 Eastern Daylight Time status = postprocess                             \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1;32m↓\u001b[0m \u001b[1;34msimulation_data.hdf5.gz\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m2.9/2.9 kB\u001b[0m • \u001b[31m?\u001b[0m • \u001b[36m0:00:00\u001b[0m\n"
+       "\u001b[2;36m11:03:09 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                             \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
11:03:11 Eastern Daylight Time status = success                                 \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m11:03:11 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                 \n"
       ]
      },
      "metadata": {},
@@ -453,11 +690,29 @@
     {
      "data": {
       "text/html": [
-       "
\n",
+       "
11:03:13 Eastern Daylight Time View simulation result at                        \n",
+       "                               'https://tidy3d.simulation.cloud/workbench?taskId\n",
+       "                               =fdve-a5cb239b-7212-4ce3-b5dc-88d53ca94fe0'.     \n",
        "
\n"
       ],
       "text/plain": [
-       "\n"
+       "\u001b[2;36m11:03:13 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                        \n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=623879;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a5cb239b-7212-4ce3-b5dc-88d53ca94fe0\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=850228;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a5cb239b-7212-4ce3-b5dc-88d53ca94fe0\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=623879;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a5cb239b-7212-4ce3-b5dc-88d53ca94fe0\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=379991;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a5cb239b-7212-4ce3-b5dc-88d53ca94fe0\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=623879;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a5cb239b-7212-4ce3-b5dc-88d53ca94fe0\u001b\\\u001b[4;34m-a5cb239b-7212-4ce3-b5dc-88d53ca94fe0'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c5119d1e1dac474b8b704677cd2b3a36",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
       ]
      },
      "metadata": {},
@@ -466,11 +721,21 @@
     {
      "data": {
       "text/html": [
-       "
             loading simulation from simulation_data.hdf5                       \n",
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
11:03:14 Eastern Daylight Time loading simulation from simulation_data.hdf5     \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
+       "\u001b[2;36m11:03:14 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5     \n"
       ]
      },
      "metadata": {},
@@ -480,7 +745,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.7851624129038903\n"
+      "0.7851633658072134\n"
      ]
     }
    ],
@@ -503,7 +768,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 12,
    "id": "59647cdf-8c2a-40d6-bbc9-b7537616938a",
    "metadata": {},
    "outputs": [],
@@ -521,20 +786,334 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 13,
    "id": "ef6716b3-79a3-40cc-ac2a-5056b5ed2ae9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
↓ simulation_data.hdf5.gz ━━━━━━━━━━━━ 100.0% • 530.7/530.7 • 1.6 MB/s • 0:00:00\n",
-       "                                                kB                              \n",
+       "
                               Created task 'slab' with task_id                 \n",
+       "                               'fdve-6327c101-ebea-4ec1-8ed9-ca51ea65ea59' and  \n",
+       "                               task_type 'FDTD'.                                \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'slab'\u001b[0m with task_id                 \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'fdve-6327c101-ebea-4ec1-8ed9-ca51ea65ea59'\u001b[0m and  \n",
+       "\u001b[2;36m                               \u001b[0mtask_type \u001b[32m'FDTD'\u001b[0m.                                \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               View task using web UI at                        \n",
+       "                               'https://tidy3d.simulation.cloud/workbench?taskId\n",
+       "                               =fdve-6327c101-ebea-4ec1-8ed9-ca51ea65ea59'.     \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                        \n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=381942;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6327c101-ebea-4ec1-8ed9-ca51ea65ea59\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=119266;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6327c101-ebea-4ec1-8ed9-ca51ea65ea59\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=381942;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6327c101-ebea-4ec1-8ed9-ca51ea65ea59\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=837630;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6327c101-ebea-4ec1-8ed9-ca51ea65ea59\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=381942;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6327c101-ebea-4ec1-8ed9-ca51ea65ea59\u001b\\\u001b[32m-6327c101-ebea-4ec1-8ed9-ca51ea65ea59'\u001b[0m\u001b]8;;\u001b\\.     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               Task folder: 'default'.                          \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=972212;https://tidy3d.simulation.cloud/folders/639eb096-a602-4b56-a502-cac1f18f9557\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                          \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b76858e5bd704fb78b6934756404e6d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
11:03:15 Eastern Daylight Time Maximum FlexCredit cost: 0.107. Minimum cost     \n",
+       "                               depends on task execution details. Use           \n",
+       "                               'web.real_cost(task_id)' to get the billed       \n",
+       "                               FlexCredit cost after a simulation run.          \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m11:03:15 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.107\u001b[0m. Minimum cost     \n",
+       "\u001b[2;36m                               \u001b[0mdepends on task execution details. Use           \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed       \n",
+       "\u001b[2;36m                               \u001b[0mFlexCredit cost after a simulation run.          \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63c3a46e13b447a69816b6f186d366f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
11:03:17 Eastern Daylight Time status = queued                                  \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m11:03:17 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               To cancel the simulation, use                    \n",
+       "                               'web.abort(task_id)' or 'web.delete(task_id)' or \n",
+       "                               abort/delete the task in the web UI. Terminating \n",
+       "                               the Python script will not stop the job running  \n",
+       "                               on the cloud.                                    \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use                    \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or \u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or \n",
+       "\u001b[2;36m                               \u001b[0mabort/delete the task in the web UI. Terminating \n",
+       "\u001b[2;36m                               \u001b[0mthe Python script will not stop the job running  \n",
+       "\u001b[2;36m                               \u001b[0mon the cloud.                                    \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
11:03:23 Eastern Daylight Time status = preprocess                              \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m11:03:23 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                              \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
11:03:27 Eastern Daylight Time starting up solver                               \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m11:03:27 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                               \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
11:03:28 Eastern Daylight Time running solver                                   \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m11:03:28 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mrunning solver                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7fd942e798764251a640ef9d51c09a1d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
11:03:36 Eastern Daylight Time early shutoff detected at 32%, exiting.          \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m11:03:36 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m32\u001b[0m%, exiting.          \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               status = postprocess                             \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                             \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
11:03:40 Eastern Daylight Time status = success                                 \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m11:03:40 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                 \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
11:03:42 Eastern Daylight Time View simulation result at                        \n",
+       "                               'https://tidy3d.simulation.cloud/workbench?taskId\n",
+       "                               =fdve-6327c101-ebea-4ec1-8ed9-ca51ea65ea59'.     \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1;32m↓\u001b[0m \u001b[1;34msimulation_data.hdf5.gz\u001b[0m \u001b[38;2;114;156;31m━━━━━━━━━━━━\u001b[0m \u001b[35m100.0%\u001b[0m • \u001b[32m530.7/530.7\u001b[0m • \u001b[31m1.6 MB/s\u001b[0m • \u001b[36m0:00:00\u001b[0m\n",
-       "                                                \u001b[32mkB         \u001b[0m                     \n"
+       "\u001b[2;36m11:03:42 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                        \n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=512484;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6327c101-ebea-4ec1-8ed9-ca51ea65ea59\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=746443;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6327c101-ebea-4ec1-8ed9-ca51ea65ea59\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=512484;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6327c101-ebea-4ec1-8ed9-ca51ea65ea59\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=94708;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6327c101-ebea-4ec1-8ed9-ca51ea65ea59\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=512484;https://tidy3d.simulation.cloud/workbench?taskId=fdve-6327c101-ebea-4ec1-8ed9-ca51ea65ea59\u001b\\\u001b[4;34m-6327c101-ebea-4ec1-8ed9-ca51ea65ea59'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "566d17963ef249f6996b0e9f02302bd5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
       ]
      },
      "metadata": {},
@@ -553,11 +1132,68 @@
     {
      "data": {
       "text/html": [
-       "
\n",
+       "
11:03:43 Eastern Daylight Time loading simulation from simulation_data.hdf5     \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m11:03:43 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               Started working on Batch containing 1 tasks.     \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m1\u001b[0m tasks.     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
11:03:46 Eastern Daylight Time Maximum FlexCredit cost: 0.108 for the whole     \n",
+       "                               batch.                                           \n",
        "
\n"
       ],
       "text/plain": [
-       "\n"
+       "\u001b[2;36m11:03:46 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.108\u001b[0m for the whole     \n",
+       "\u001b[2;36m                               \u001b[0mbatch.                                           \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               Use 'Batch.real_cost()' to get the billed        \n",
+       "                               FlexCredit cost after the Batch has completed.   \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed        \n",
+       "\u001b[2;36m                               \u001b[0mFlexCredit cost after the Batch has completed.   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "578ea6abaa99482a9250a6791e9222f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
       ]
      },
      "metadata": {},
@@ -566,15 +1202,49 @@
     {
      "data": {
       "text/html": [
-       "
15:31:34 EDT loading simulation from simulation_data.hdf5                       \n",
+       "
11:04:13 Eastern Daylight Time Batch complete.                                  \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m15:31:34 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
+       "\u001b[2;36m11:04:13 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                  \n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "882da8e401234e45acf7844c2e645294",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -593,7 +1263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 14,
    "id": "41e66722-bb8c-41d9-a0a7-85009ff46df3",
    "metadata": {},
    "outputs": [],
@@ -604,7 +1274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 15,
    "id": "953d2497-240a-4295-9fcd-5342942983d0",
    "metadata": {},
    "outputs": [
@@ -634,7 +1304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 16,
    "id": "d0e496be-a967-427d-9ed6-8eea5a96fd70",
    "metadata": {},
    "outputs": [
@@ -644,12 +1314,12 @@
      "text": [
       "un-normalized:\n",
       "\tgrad_eps (tmm)  = [-0.2766323   0.01377339 -0.2032054  -0.28999361]\n",
-      "\tgrad_eps (FDTD)  = [-0.28169015  0.01413355 -0.2066128  -0.29536529]\n",
+      "\tgrad_eps (FDTD)  = [-0.2816894   0.01413387 -0.20661187 -0.29536459]\n",
       "--------------------------------------------------------------------------------\n",
       "\tgrad_ds  (tmm)  = [-1.75199732 -0.21552416  1.00729645 -2.08209951]\n",
-      "\tgrad_ds  (FDTD)  = [-1.78417513 -0.21968112  1.02736547 -2.12168249]\n",
-      "RMS error = 1.8095298024231286 %\n",
-      "RMS error = 1.8894452951610317 %\n"
+      "\tgrad_ds  (FDTD)  = [-1.78417308 -0.21968072  1.0273637  -2.12167783]\n",
+      "RMS error = 1.8092412465199876 %\n",
+      "RMS error = 1.8892656820215306 %\n"
      ]
     }
    ],
@@ -679,7 +1349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 17,
    "id": "2993410c-0c1d-412e-9c2c-1c43664ae20c",
    "metadata": {},
    "outputs": [],
@@ -687,6 +1357,7 @@
     "def normalize(arr):\n",
     "    return arr / np.linalg.norm(arr)\n",
     "\n",
+    "\n",
     "grad_eps_tmm_norm = normalize(grad_eps_tmm)\n",
     "grad_ds_tmm_norm = normalize(grad_ds_tmm)\n",
     "grad_eps_fdtd_norm = normalize(grad_eps_fdtd)\n",
@@ -698,7 +1369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 18,
    "id": "8a375cb8-79ae-4b57-86bd-26af66bba8b4",
    "metadata": {},
    "outputs": [
@@ -708,12 +1379,12 @@
      "text": [
       "normalized:\n",
       "\tgrad_eps (tmm)  = [-0.61534061  0.03063751 -0.45200988 -0.64506151]\n",
-      "\tgrad_eps (FDTD)  = [-0.61546283  0.03088029 -0.45142685 -0.64534154]\n",
-      "\tRMS error = 0.07015884074927643 %\n",
+      "\tgrad_eps (FDTD)  = [-0.61546297  0.03088108 -0.45142613 -0.64534188]\n",
+      "\tRMS error = 0.07026239057079954 %\n",
       "--------------------------------------------------------------------------------\n",
       "\tgrad_ds  (tmm)  = [-0.60214521 -0.07407365  0.34619844 -0.71559827]\n",
-      "\tgrad_ds  (FDTD)  = [-0.60183674 -0.07410269  0.34655022 -0.71568449]\n",
-      "\tRMS error = 0.047663670213745624 %\n"
+      "\tgrad_ds  (FDTD)  = [-0.6018371  -0.07410268  0.34655023 -0.71568417]\n",
+      "\tRMS error = 0.0476352958864505 %\n"
      ]
     }
    ],
@@ -764,7 +1435,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.0"
   },
   "title": "Adjoint Analysis: Multi-layer Slab | Flexcompute",
   "vscode": {
diff --git a/Autograd3InverseDesign.ipynb b/Autograd3InverseDesign.ipynb
index 3ee75458..280cd6c4 100644
--- a/Autograd3InverseDesign.ipynb
+++ b/Autograd3InverseDesign.ipynb
@@ -28,15 +28,15 @@
    "outputs": [],
    "source": [
     "from typing import List\n",
-    "import numpy as np\n",
-    "import matplotlib.pylab as plt\n",
     "\n",
     "import autograd.numpy as anp\n",
-    "from autograd import value_and_grad\n",
+    "import matplotlib.pylab as plt\n",
+    "import numpy as np\n",
     "\n",
     "# import regular tidy3d\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
+    "from autograd import value_and_grad\n",
     "from tidy3d.plugins.mode import ModeSolver\n",
     "\n",
     "# set random seed to get same results\n",
@@ -54,7 +54,7 @@
     "\n",
     "\"Schematic\n",
     "\n",
-    "A mode source is injected into a waveguide on the left-hand side. The light propagates through a rectangular region with pixellated permittivity with the value of each pixel independently tunable between 1 (vacuum) and some maximum permittivity. Finally, we measure the transmission of the light into a waveguide on the right-hand side.\n",
+    "A mode source is injected into a waveguide on the left-hand side. The light propagates through a rectangular region with pixelated permittivity with the value of each pixel independently tunable between 1 (vacuum) and some maximum permittivity. Finally, we measure the transmission of the light into a waveguide on the right-hand side.\n",
     "\n",
     "The goal of the inverse design exercise is to find the best distribution of permittivities ($\\epsilon_{ij}$) in the coupling region to maximize the power conversion between the input mode and the output mode.\n",
     "\n",
@@ -159,7 +159,7 @@
    "source": [
     "### Input Structures\n",
     "\n",
-    "Next, we write a function to return the pixellated array given our flattened tuple of permittivity values $\\epsilon_{ij}$ using the `tidy3d.plugins.autograd` plugin.\n",
+    "Next, we write a function to return the pixelated array given our flattened tuple of permittivity values $\\epsilon_{ij}$ using the `tidy3d.plugins.autograd` plugin.\n",
     "\n",
     "We start with an array of parameters between 0 and 1, apply a conic filter and tanh projection to compute smooth, well-binarized features."
    ]
@@ -173,7 +173,7 @@
    },
    "outputs": [],
    "source": [
-    "from tidy3d.plugins.autograd import rescale, make_filter_and_project\n",
+    "from tidy3d.plugins.autograd import make_filter_and_project, rescale\n",
     "\n",
     "# radius of the circular filter (um) and the threshold strength\n",
     "radius = 0.120\n",
@@ -192,9 +192,7 @@
     "def make_input_structures(params, beta) -> List[td.Structure]:\n",
     "    box = td.Box(center=(0, 0, 0), size=(lx, ly, lz))\n",
     "    eps_data = get_eps(params, beta=beta).reshape((nx, ny, 1))\n",
-    "    custom_structure = td.Structure.from_permittivity_array(\n",
-    "        geometry=box, eps_data=eps_data\n",
-    "    )\n",
+    "    custom_structure = td.Structure.from_permittivity_array(geometry=box, eps_data=eps_data)\n",
     "\n",
     "    return [custom_structure]"
    ]
@@ -207,7 +205,7 @@
     "### Making the Simulation\n",
     "Next, we write a function to return a basic `td.Simulation` as a function of our parameter values.\n",
     "\n",
-    "We make sure to add the pixellated `td.Structure` list to `input_structures` but leave out the sources and monitors for now as we'll want to add those after the mode solver is run so we can inspect them."
+    "We make sure to add the pixelated `td.Structure` list to `input_structures` but leave out the sources and monitors for now as we'll want to add those after the mode solver is run so we can inspect them."
    ]
   },
   {
@@ -520,8 +518,7 @@
     "fig, axs = plt.subplots(num_modes, 3, figsize=(12, 12), tight_layout=True)\n",
     "for mode_index in range(num_modes):\n",
     "    vmax = 1.1 * max(\n",
-    "        abs(modes.field_components[n].sel(mode_index=mode_index)).max()\n",
-    "        for n in (\"Ex\", \"Ey\", \"Ez\")\n",
+    "        abs(modes.field_components[n].sel(mode_index=mode_index)).max() for n in (\"Ex\", \"Ey\", \"Ez\")\n",
     "    )\n",
     "    for field_name, ax in zip((\"Ex\", \"Ey\", \"Ez\"), axs[mode_index]):\n",
     "        field = modes.field_components[field_name].sel(mode_index=mode_index)\n",
@@ -710,9 +707,7 @@
    },
    "outputs": [],
    "source": [
-    "def J(\n",
-    "    params: np.ndarray, beta: float, step_num: int = None, verbose: bool = False\n",
-    ") -> float:\n",
+    "def J(params: np.ndarray, beta: float, step_num: int = None, verbose: bool = False) -> float:\n",
     "    sim = make_sim(params, beta=beta)\n",
     "    task_name = \"inv_des\"\n",
     "    if step_num:\n",
@@ -1511,7 +1506,7 @@
     "beta_increment = 1\n",
     "\n",
     "for i in range(num_steps):\n",
-    "    # compute gradient and current objective funciton value\n",
+    "    # compute gradient and current objective function value\n",
     "\n",
     "    density = filter_project(params, beta)\n",
     "    plt.subplots(figsize=(2, 2))\n",
@@ -2095,9 +2090,7 @@
    ],
    "source": [
     "final_power = (\n",
-    "    sim_data_final[\"measurement\"]\n",
-    "    .amps.sel(direction=\"+\", f=freq0, mode_index=mode_index_out)\n",
-    "    .abs\n",
+    "    sim_data_final[\"measurement\"].amps.sel(direction=\"+\", f=freq0, mode_index=mode_index_out).abs\n",
     "    ** 2\n",
     ")\n",
     "print(f\"Final power conversion = {final_power * 100:.2f}%\")"
@@ -2162,7 +2155,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.0"
   },
   "nbdime-conflicts": {
    "local_diff": [
diff --git a/Autograd4MultiObjective.ipynb b/Autograd4MultiObjective.ipynb
index ad69ef2a..18d126cd 100644
--- a/Autograd4MultiObjective.ipynb
+++ b/Autograd4MultiObjective.ipynb
@@ -21,13 +21,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "\n",
     "import autograd as ag\n",
     "import autograd.numpy as anp\n",
-    "\n",
     "import matplotlib.pylab as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web"
    ]
@@ -319,7 +316,7 @@
     "    for dy_sign in (-1, 0, 1):\n",
     "        print(f\"working on dy_sign = {dy_sign}\")\n",
     "\n",
-    "        def objective_fn(p):\n",
+    "        def objective_fn(p, dy_sign=dy_sign):\n",
     "            sim = make_sim(p, dy_sign=dy_sign)\n",
     "            sim_data = td.web.run(sim, task_name=f\"dy_sign={dy_sign}\", verbose=False)\n",
     "            return post_process(sim_data)\n",
diff --git a/Autograd5BoundaryGradients.ipynb b/Autograd5BoundaryGradients.ipynb
index 1c07e6f5..847e0058 100644
--- a/Autograd5BoundaryGradients.ipynb
+++ b/Autograd5BoundaryGradients.ipynb
@@ -25,12 +25,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.pylab as plt\n",
-    "import numpy as np\n",
-    "\n",
     "import autograd as ag\n",
     "import autograd.numpy as anp\n",
-    "\n",
+    "import matplotlib.pylab as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web"
    ]
@@ -89,9 +87,7 @@
     "x_end = +taper_length / 2\n",
     "xs = np.linspace(x_start, x_end, num_points)\n",
     "\n",
-    "ys0 = (\n",
-    "    wg_width_in + (wg_width_out - wg_width_in) * (xs - x_start) / (x_end - x_start)\n",
-    ") / 2.0"
+    "ys0 = (wg_width_in + (wg_width_out - wg_width_in) * (xs - x_start) / (x_end - x_start)) / 2.0"
    ]
   },
   {
@@ -362,9 +358,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def make_sim_params(\n",
-    "    parameters: np.ndarray, include_field_mnt: bool = False\n",
-    ") -> td.Simulation:\n",
+    "def make_sim_params(parameters: np.ndarray, include_field_mnt: bool = False) -> td.Simulation:\n",
     "    \"\"\"Make the simulation out of raw parameters.\"\"\"\n",
     "    ys = get_ys(parameters)\n",
     "    return make_sim(ys, include_field_mnt=include_field_mnt)"
@@ -1841,7 +1835,7 @@
     "param_history = [params]\n",
     "\n",
     "for i in range(num_steps):\n",
-    "    # compute gradient and current objective funciton value\n",
+    "    # compute gradient and current objective function value\n",
     "    value, gradient = grad_fn(params, verbose=False)\n",
     "\n",
     "    # convert nan to 0 (infinite radius of curvature) and multiply all by -1 to maximize obj_fn.\n",
@@ -2322,9 +2316,7 @@
     "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs\", ax=ax2)\n",
     "\n",
     "# plot optimized\n",
-    "sim_data_best.plot_field(\n",
-    "    field_monitor_name=\"field\", field_name=\"Ez\", val=\"real\", ax=ax3\n",
-    ")\n",
+    "sim_data_best.plot_field(field_monitor_name=\"field\", field_name=\"Ez\", val=\"real\", ax=ax3)\n",
     "sim_data_best.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs\", ax=ax4)\n",
     "\n",
     "plt.show()"
@@ -2438,7 +2430,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.0"
   },
   "title": "Inverse Design of a Waveguide Taper in Tidy3D | Flexcompute",
   "widgets": {
diff --git a/Autograd6GratingCoupler.ipynb b/Autograd6GratingCoupler.ipynb
index 06f40373..8b5096b5 100644
--- a/Autograd6GratingCoupler.ipynb
+++ b/Autograd6GratingCoupler.ipynb
@@ -27,17 +27,17 @@
    "source": [
     "# Standard python imports.\n",
     "from typing import List\n",
-    "import numpy as np\n",
-    "import scipy as sp\n",
-    "import matplotlib.pylab as plt\n",
     "\n",
     "# Import autograd to be able to use automatic differentiation.\n",
     "import autograd.numpy as anp\n",
-    "from autograd import value_and_grad\n",
+    "import matplotlib.pylab as plt\n",
+    "import numpy as np\n",
+    "import scipy as sp\n",
     "\n",
     "# Import regular tidy3d.\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web"
+    "import tidy3d.web as web\n",
+    "from autograd import value_and_grad"
    ]
   },
   {
@@ -50,7 +50,7 @@
     "\n",
     "We are considering a full-etched grating structure, so a $SiO_{2}$ BOX layer is included. To reduce backreflection, we adjusted the fiber tilt angle to $10^{\\circ}$ [[1](https://doi.org/10.1364/OE.23.022628), [2](https://doi.org/10.3390/mi11070666)].\n",
     "\n",
-    "In the following block of code, you can find the parameters that can be modified to configure the grating coupler structure, optimization, and simulation setup. Special care should be devoted to the `it_per_step` and `opt_steps` variables bellow."
+    "In the following block of code, you can find the parameters that can be modified to configure the grating coupler structure, optimization, and simulation setup. Special care should be devoted to the `it_per_step` and `opt_steps` variables below."
    ]
   },
   {
@@ -77,7 +77,7 @@
     "gc_length = 4.0  # Grating coupler length (um).\n",
     "dr_grid_size = 0.02  # Grid size within the design region (um).\n",
     "\n",
-    "# Inverse design set up parameters.\n",
+    "# Inverse design setup parameters.\n",
     "#################################################################\n",
     "# Total number of iterations = opt_steps x it_per_step.\n",
     "it_per_step = 1  # Number of iterations per optimization step.\n",
@@ -275,9 +275,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def get_eps(\n",
-    "    design_param: np.ndarray, beta: float = 1.00, binarize: bool = False\n",
-    ") -> np.ndarray:\n",
+    "def get_eps(design_param: np.ndarray, beta: float = 1.00, binarize: bool = False) -> np.ndarray:\n",
     "    \"\"\"Returns the permittivities after applying a conic density filter on design parameters\n",
     "    to enforce fabrication constraints, followed by a binarization projection function\n",
     "    which reduces grayscale.\n",
@@ -309,11 +307,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from tidy3d.plugins.autograd import rescale, make_filter_and_project\n",
+    "from tidy3d.plugins.autograd import make_filter_and_project, rescale\n",
     "\n",
-    "filter_project = make_filter_and_project(\n",
-    "    filter_radius, dr_grid_size, padding=\"constant\"\n",
-    ")\n",
+    "filter_project = make_filter_and_project(filter_radius, dr_grid_size, padding=\"constant\")\n",
     "\n",
     "\n",
     "def interface_buffer(params):\n",
@@ -328,7 +324,7 @@
     "\n",
     "\n",
     "def pre_process(params, beta):\n",
-    "    \"\"\"Get the permittivity values (1, eps_wg) array as a funciton of the parameters (0,1)\"\"\"\n",
+    "    \"\"\"Get the permittivity values (1, eps_wg) array as a function of the parameters (0,1)\"\"\"\n",
     "    params1 = interface_buffer(params)\n",
     "    params2 = filter_project(params1, beta=beta)\n",
     "    params3 = filter_project(params2, beta=beta)\n",
@@ -407,9 +403,7 @@
     "\n",
     "    # Creates a uniform mesh for the design region.\n",
     "    adjoint_dr_mesh = td.MeshOverrideStructure(\n",
-    "        geometry=td.Box(\n",
-    "            center=(dr_center_x, 0, 0), size=(dr_size_x, dr_size_y, w_thick)\n",
-    "        ),\n",
+    "        geometry=td.Box(center=(dr_center_x, 0, 0), size=(dr_size_x, dr_size_y, w_thick)),\n",
     "        dl=[dr_grid_size, dr_grid_size, dr_grid_size],\n",
     "        enforce=True,\n",
     "    )\n",
@@ -489,9 +483,7 @@
     "\n",
     "\n",
     "# Objective function to be passed to the optimization algorithm.\n",
-    "def obj(\n",
-    "    design_param, beta: float = 1.0, step_num: int = None, verbose: bool = False\n",
-    ") -> float:\n",
+    "def obj(design_param, beta: float = 1.0, step_num: int = None, verbose: bool = False) -> float:\n",
     "    sim = make_adjoint_sim(design_param, beta)\n",
     "    task_name = \"inv_des\"\n",
     "    if step_num:\n",
@@ -534,9 +526,10 @@
    },
    "outputs": [],
    "source": [
-    "import optax\n",
     "import pickle\n",
     "\n",
+    "import optax\n",
+    "\n",
     "# hyperparameters\n",
     "learning_rate = 0.2\n",
     "optimizer = optax.adam(learning_rate=learning_rate)\n",
@@ -593,11 +586,9 @@
     "    params = history_dict[\"params\"][-1]\n",
     "    num_iters_completed = len(history_dict[\"params\"])\n",
     "    print(\"Loaded optimization checkpoint from file.\")\n",
-    "    print(\n",
-    "        f\"Found {num_iters_completed} iterations previously completed out of {total_iter} total.\"\n",
-    "    )\n",
+    "    print(f\"Found {num_iters_completed} iterations previously completed out of {total_iter} total.\")\n",
     "    if num_iters_completed < total_iter:\n",
-    "        print(f\"Will resume optimization.\")\n",
+    "        print(\"Will resume optimization.\")\n",
     "    else:\n",
     "        print(\"Optimization completed, will return results.\")\n",
     "\n",
@@ -925,7 +916,7 @@
     "for i in range(iter_done, total_iter):\n",
     "    print(f\"iteration = ({i + 1} / {total_iter})\")\n",
     "\n",
-    "    # compute gradient and current objective funciton value\n",
+    "    # compute gradient and current objective function value\n",
     "    perc_done = i / (total_iter - 1)\n",
     "    beta_i = beta_min * (1 - perc_done) + beta_max * perc_done\n",
     "    value, gradient = obj_grad(params, beta=beta_i)\n",
@@ -1112,7 +1103,6 @@
     "    mode_spec=mode_spec,\n",
     "    name=\"gc_efficiency\",\n",
     ")\n",
-    "import tidy3d.web as web\n",
     "\n",
     "sim_final = sim_final.copy(update=dict(monitors=(field_xy, field_xz, gc_efficiency)))\n",
     "sim_data_final = web.run(sim_final, task_name=\"inv_des_final\")"
@@ -1140,9 +1130,7 @@
     "power_0 = np.abs(coeffs_f.sel(mode_index=0)) ** 2\n",
     "power_0_db = 10 * np.log10(power_0)\n",
     "\n",
-    "sim_plot = sim_final.updated_copy(\n",
-    "    symmetry=(0, 0, 0), monitors=(field_xy, field_xz, gc_efficiency)\n",
-    ")\n",
+    "sim_plot = sim_final.updated_copy(symmetry=(0, 0, 0), monitors=(field_xy, field_xz, gc_efficiency))\n",
     "sim_data_plot = sim_data_final.updated_copy(simulation=sim_plot)\n",
     "\n",
     "f, ax = plt.subplots(2, 2, figsize=(8, 6), tight_layout=True)\n",
@@ -1235,7 +1223,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.0"
   },
   "nbdime-conflicts": {
    "local_diff": [
diff --git a/Autograd7Metalens.ipynb b/Autograd7Metalens.ipynb
index fab2a5a4..921b1961 100644
--- a/Autograd7Metalens.ipynb
+++ b/Autograd7Metalens.ipynb
@@ -34,14 +34,12 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import tidy3d as td\n",
-    "from tidy3d import web\n",
-    "\n",
+    "import numpy as np\n",
     "import optax\n",
-    "from autograd import value_and_grad"
+    "import tidy3d as td\n",
+    "from autograd import value_and_grad\n",
+    "from tidy3d import web"
    ]
   },
   {
diff --git a/Autograd8WaveguideBend.ipynb b/Autograd8WaveguideBend.ipynb
index 81ff6d4e..04efacdb 100644
--- a/Autograd8WaveguideBend.ipynb
+++ b/Autograd8WaveguideBend.ipynb
@@ -33,12 +33,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tidy3d as td\n",
-    "import tidy3d.web as web\n",
-    "import numpy as np\n",
-    "import matplotlib.pylab as plt\n",
     "import autograd as ag\n",
-    "import autograd.numpy as anp"
+    "import autograd.numpy as anp\n",
+    "import matplotlib.pylab as plt\n",
+    "import numpy as np\n",
+    "import tidy3d as td\n",
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -670,9 +670,7 @@
    "source": [
     "from tidy3d.plugins.mode import ModeSolver\n",
     "\n",
-    "ms = ModeSolver(\n",
-    "    simulation=sim, plane=mode_src, mode_spec=mode_spec, freqs=mode_mnt.freqs\n",
-    ")\n",
+    "ms = ModeSolver(simulation=sim, plane=mode_src, mode_spec=mode_spec, freqs=mode_mnt.freqs)\n",
     "data = ms.solve()\n",
     "\n",
     "print(\"Effective index of computed modes: \", np.array(data.n_eff))\n",
@@ -975,7 +973,7 @@
     "data_history = []\n",
     "\n",
     "for i in range(num_steps):\n",
-    "    # compute gradient and current objective funciton value\n",
+    "    # compute gradient and current objective function value\n",
     "    value, gradient = val_grad(params)\n",
     "\n",
     "    # outputs\n",
@@ -989,7 +987,7 @@
     "\n",
     "    # save history\n",
     "    objective_history.append(value)\n",
-    "    param_history.append(params)\n"
+    "    param_history.append(params)"
    ]
   },
   {
@@ -1736,8 +1734,6 @@
     }
    ],
    "source": [
-    "import tidy3d.web as web\n",
-    "\n",
     "sim_start = make_sim(param_history[0])\n",
     "data_start = web.run(sim_start, task_name=\"start\")\n",
     "\n",
@@ -1904,7 +1900,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.0"
   },
   "title": "Adjoint Optimization of a Waveguide Bend in Tidy3D | Flexcompute"
  },
diff --git a/Autograd9WDM.ipynb b/Autograd9WDM.ipynb
index a5411de4..fc7fd70a 100644
--- a/Autograd9WDM.ipynb
+++ b/Autograd9WDM.ipynb
@@ -31,7 +31,6 @@
     "import autograd.numpy as anp\n",
     "import matplotlib.pylab as plt\n",
     "import numpy as np\n",
-    "\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
     "\n",
@@ -84,9 +83,7 @@
     "\n",
     "# we average the metrics over the channels with some frequency width\n",
     "channel_fwidth = df_design / 2.0\n",
-    "channel_bounds = [\n",
-    "    (f - channel_fwidth / 2, f + channel_fwidth / 2) for f in freqs_design\n",
-    "]\n",
+    "channel_bounds = [(f - channel_fwidth / 2, f + channel_fwidth / 2) for f in freqs_design]\n",
     "num_freqs_channel = 5\n",
     "channel_freqs = []\n",
     "for fmin, fmax in channel_bounds:\n",
@@ -128,7 +125,7 @@
    "source": [
     "### Static Simulation\n",
     "\n",
-    "First, we'll define the simulation without any design region using the \"static\" components that dont change over the optimization."
+    "First, we'll define the simulation without any design region using the \"static\" components that don't change over the optimization."
    ]
   },
   {
@@ -147,9 +144,7 @@
     "    medium=td.Medium(permittivity=n_si**2),\n",
     ")\n",
     "\n",
-    "centers_y = np.linspace(\n",
-    "    -ly / 2.0 + ly_single / 2.0, +ly / 2.0 - ly_single / 2.0, num_freqs_design\n",
-    ")\n",
+    "centers_y = np.linspace(-ly / 2.0 + ly_single / 2.0, +ly / 2.0 - ly_single / 2.0, num_freqs_design)\n",
     "mode_size = (0, 0.9 * ly_single, td.inf)\n",
     "\n",
     "wgs_out = []\n",
@@ -450,9 +445,7 @@
     "    for field_name, ax in zip((\"Ex\", \"Ey\", \"Ez\"), axs[mode_index]):\n",
     "        for freq in freqs_design:\n",
     "            key = f\"{td.C_0 / freq * 1000:.0f} nm\"\n",
-    "            field = mode_data.field_components[field_name].sel(\n",
-    "                mode_index=mode_index, f=freq\n",
-    "            )\n",
+    "            field = mode_data.field_components[field_name].sel(mode_index=mode_index, f=freq)\n",
     "            field.real.plot(label=f\"Real ({key})\", ax=ax)\n",
     "            field.imag.plot(ls=\"--\", label=f\"Imag ({key})\", ax=ax)\n",
     "        ax.set_title(f\"index={mode_index}, {field_name}\")\n",
@@ -469,7 +462,7 @@
    "source": [
     "We identify `mode_index=0` as the first order mode that is out of plane of the device. Let's choose to optimize our device with respect to this as the mode of interest for both the input and output.\n",
     "\n",
-    "We'll update or static simulation with the new mode index and mode specificiation, in case these are different from the original ones."
+    "We'll update or static simulation with the new mode index and mode specification, in case these are different from the original ones."
    ]
   },
   {
@@ -586,8 +579,7 @@
     "        dl=[dl_design_region, dl_design_region, dl_design_region],\n",
     "    )\n",
     "    grid_spec = sim_static.grid_spec.updated_copy(\n",
-    "        override_structures=list(sim_static.grid_spec.override_structures)\n",
-    "        + [design_override]\n",
+    "        override_structures=list(sim_static.grid_spec.override_structures) + [design_override]\n",
     "    )\n",
     "\n",
     "    update_dict = dict(\n",
@@ -654,7 +646,7 @@
     "\n",
     "In this case, it is quite simple, we simply measure the transmitted power in our `n=4` output waveguide modes for each of the `n=4` design frequencies.\n",
     "\n",
-    "Our objective whenn looking at waveguide `i` will be to maximize power transmission at frequency `i`. To reduce cross talk between waveguide `i` and frequency `j != i`, we will subtract the average of the power transmissions for all of the other ports.\n",
+    "Our objective when looking at waveguide `i` will be to maximize power transmission at frequency `i`. To reduce cross talk between waveguide `i` and frequency `j != i`, we will subtract the average of the power transmissions for all of the other ports.\n",
     "\n",
     "Our overall metric will then be the average"
    ]
@@ -669,9 +661,7 @@
     "import xarray as xr\n",
     "\n",
     "\n",
-    "def average_over_channel(\n",
-    "    spectrum: xr.DataArray, fmin: float, fmax: float\n",
-    ") -> xr.DataArray:\n",
+    "def average_over_channel(spectrum: xr.DataArray, fmin: float, fmax: float) -> xr.DataArray:\n",
     "    \"\"\"Get average of the spectrum within the frequency range between fmin and fmax.\"\"\"\n",
     "    freqs = spectrum.f\n",
     "    freqs_in_channel = np.logical_and(freqs >= fmin, freqs <= fmax).values\n",
@@ -690,9 +680,7 @@
     "    return average_over_channel(power_spectrum, fmin=fmin_channel, fmax=fmax_channel)\n",
     "\n",
     "\n",
-    "def get_metric(\n",
-    "    sim_data: td.SimulationData, mnt_index: int, leak_weight: float = 1.0\n",
-    ") -> float:\n",
+    "def get_metric(sim_data: td.SimulationData, mnt_index: int, leak_weight: float = 1.0) -> float:\n",
     "    \"\"\"measure of how well this channel (`mnt_index`) performs. With an adjustable weight to reduce cross talk influence.\"\"\"\n",
     "\n",
     "    power_all = [\n",
@@ -753,9 +741,7 @@
     "use_metric = True\n",
     "\n",
     "\n",
-    "def objective(\n",
-    "    params, beta: float, penalty_weight: float = 1.0, leak_weight: float = 0.0\n",
-    ") -> float:\n",
+    "def objective(params, beta: float, penalty_weight: float = 1.0, leak_weight: float = 0.0) -> float:\n",
     "    metric = 0.0\n",
     "    penalty_value = 0.0\n",
     "\n",
@@ -2014,17 +2000,13 @@
     "# we'll sample the modes at a finer frequency resolution for this final evaluation, for smoother plots\n",
     "\n",
     "num_freqs_measure = 151\n",
-    "freqs_measure = np.linspace(\n",
-    "    freq_min - df_design, freq_max + df_design, num_freqs_measure\n",
-    ")\n",
+    "freqs_measure = np.linspace(freq_min - df_design, freq_max + df_design, num_freqs_measure)\n",
     "\n",
     "sim_final = get_sim(params_history[-1], beta=beta_history[-1])\n",
     "\n",
     "for i in range(num_freqs_design):\n",
     "    sim_final = sim_final.updated_copy(freqs=freqs_measure, path=f\"monitors/{i}\")\n",
-    "    sim_final = sim_final.updated_copy(\n",
-    "        freqs=freqs_measure, path=f\"monitors/{i + num_freqs_design}\"\n",
-    "    )"
+    "    sim_final = sim_final.updated_copy(freqs=freqs_measure, path=f\"monitors/{i + num_freqs_design}\")"
    ]
   },
   {
@@ -2409,7 +2391,7 @@
     "\n",
     "Let's inspect the flux over each of the output ports as a function of wavelength.\n",
     "\n",
-    "We notice that the ports have peaks in transmission at their corresponding design wavelengths, and are supressed at the other wavelengths, as expected!"
+    "We notice that the ports have peaks in transmission at their corresponding design wavelengths, and are suppressed at the other wavelengths, as expected!"
    ]
   },
   {
@@ -35586,6 +35568,18 @@
    "name": "python3"
   },
   "keywords": "inverse design, WDM, design optimization, adjoint, Tidy3D, FDTD",
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  },
   "title": "Adjoint Optimization of a WDM in Tidy3D | Flexcompute"
  },
  "nbformat": 4,
diff --git a/Bandstructure.ipynb b/Bandstructure.ipynb
index 1a45fe82..b1434455 100644
--- a/Bandstructure.ipynb
+++ b/Bandstructure.ipynb
@@ -34,13 +34,12 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "import xarray as xr\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
+    "import xarray as xr\n",
     "from tidy3d import web\n",
-    "from tidy3d.plugins.resonance import ResonanceFinder\n"
+    "from tidy3d.plugins.resonance import ResonanceFinder"
    ]
   },
   {
@@ -66,7 +65,7 @@
    },
    "outputs": [],
    "source": [
-    "rng = np.random.default_rng(12345)\n"
+    "rng = np.random.default_rng(12345)"
    ]
   },
   {
@@ -104,7 +103,9 @@
    "source": [
     "# Simulation parameters\n",
     "runtime_fwidth = 200.0  # in units of 1/frequency bandwidth of the source\n",
-    "t_start_fwidth = 5.0  # time to start monitoring after source has decayed, units of 1/frequency bandwidth\n",
+    "t_start_fwidth = (\n",
+    "    5.0  # time to start monitoring after source has decayed, units of 1/frequency bandwidth\n",
+    ")\n",
     "dPML = 1.0  # space between PhC slabs and PML, in unit of longest wavelength of interest\n",
     "\n",
     "# Structure parameters (um)\n",
@@ -116,9 +117,7 @@
     "\n",
     "# Frequency range of interest (Hz)\n",
     "freq_range_unitless = np.array((0.1, 0.43))  # in units of c/a\n",
-    "freq_scale = (\n",
-    "    td.constants.C_0 / a_lattice\n",
-    ")  # frequency scale determined by the lattice constant\n",
+    "freq_scale = td.constants.C_0 / a_lattice  # frequency scale determined by the lattice constant\n",
     "freq_range = freq_range_unitless * freq_scale\n",
     "lambda_range = (td.constants.C_0 / freq_range[1], td.constants.C_0 / freq_range[0])\n",
     "\n",
@@ -144,7 +143,7 @@
     "\n",
     "# Dipole polarization and symmetry\n",
     "polarization = \"Hz\"\n",
-    "symmetry = (0, 0, 1)\n"
+    "symmetry = (0, 0, 1)"
    ]
   },
   {
@@ -193,7 +192,7 @@
     "    name=\"hole\",\n",
     ")\n",
     "\n",
-    "structures = [slab, hole]\n"
+    "structures = [slab, hole]"
    ]
   },
   {
@@ -219,9 +218,7 @@
    },
    "outputs": [],
    "source": [
-    "dipole_positions = rng.uniform(\n",
-    "    [-Lx / 2, -Ly / 2, 0], [Lx / 2, Ly / 2, 0], [num_dipoles, 3]\n",
-    ")\n",
+    "dipole_positions = rng.uniform([-Lx / 2, -Ly / 2, 0], [Lx / 2, Ly / 2, 0], [num_dipoles, 3])\n",
     "\n",
     "dipole_phases = rng.uniform(0, 2 * np.pi, num_dipoles)\n",
     "\n",
@@ -237,7 +234,7 @@
     "            polarization=polarization,\n",
     "            name=\"dipole_\" + str(i),\n",
     "        )\n",
-    "    )\n"
+    "    )"
    ]
   },
   {
@@ -263,9 +260,7 @@
    },
    "outputs": [],
    "source": [
-    "monitor_positions = rng.uniform(\n",
-    "    [-Lx / 2, -Ly / 2, 0], [Lx / 2, Ly / 2, 0], [num_monitors, 3]\n",
-    ")\n",
+    "monitor_positions = rng.uniform([-Lx / 2, -Ly / 2, 0], [Lx / 2, Ly / 2, 0], [num_monitors, 3])\n",
     "\n",
     "monitors_time = []\n",
     "for i in range(num_monitors):\n",
@@ -277,7 +272,7 @@
     "            start=t_start,\n",
     "            name=\"monitor_time_\" + str(i),\n",
     "        )\n",
-    "    )\n"
+    "    )"
    ]
   },
   {
@@ -330,7 +325,7 @@
     "            z=td.Boundary.pml(),\n",
     "        )\n",
     "    )\n",
-    "bspecs = bspecs_gammax + bspecs_xm + bspecs_mgamma\n"
+    "bspecs = bspecs_gammax + bspecs_xm + bspecs_mgamma"
    ]
   },
   {
@@ -370,7 +365,7 @@
     "        boundary_spec=bspecs[i],\n",
     "        normalize_index=None,\n",
     "        symmetry=symmetry,\n",
-    "    )\n"
+    "    )"
    ]
   },
   {
@@ -439,7 +434,7 @@
     ")\n",
     "ax2.hlines(1.5e-15, freq_range[0], freq_range[1], linewidth=10, color=\"g\", alpha=0.4)\n",
     "ax2.legend((\"source spectrum\", \"measurement\"))\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -636,7 +631,7 @@
     "batch = td.web.Batch(simulations=sims, verbose=True)\n",
     "\n",
     "# run the batch and store all of the data in the `data/` dir.\n",
-    "batch_data = batch.run(path_dir=\"data\")\n"
+    "batch_data = batch.run(path_dir=\"data\")"
    ]
   },
   {
@@ -679,7 +674,7 @@
     "plt.title(\"FieldTimeMonitor data\")\n",
     "plt.xlabel(\"t\")\n",
     "plt.ylabel(\"Hz\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -867,7 +862,7 @@
    "source": [
     "resonance_finder = ResonanceFinder(freq_window=tuple(freq_range))\n",
     "resonance_data = resonance_finder.run(signals=batch_data[\"sim_1\"].data)\n",
-    "resonance_data.to_dataframe()\n"
+    "resonance_data.to_dataframe()"
    ]
   },
   {
@@ -898,7 +893,7 @@
     "resonance_datas = []\n",
     "for i in range(3 * Nk):\n",
     "    sim_data = batch_data[f\"sim_{i}\"]\n",
-    "    resonance_datas.append(resonance_finder.run(signals=sim_data.data))\n"
+    "    resonance_datas.append(resonance_finder.run(signals=sim_data.data))"
    ]
   },
   {
@@ -927,7 +922,7 @@
     "    resonance_data = resonance_data.where(abs(resonance_data.Q) > minQ, drop=True)\n",
     "    resonance_data = resonance_data.where(resonance_data.amplitude > minamp, drop=True)\n",
     "    resonance_data = resonance_data.where(resonance_data.error < maxerr, drop=True)\n",
-    "    return resonance_data\n"
+    "    return resonance_data"
    ]
   },
   {
@@ -984,9 +979,9 @@
     "plt.title(\"Band diagram\")\n",
     "plt.ylabel(\"Frequency (c/a)\")\n",
     "plt.xlabel(\"Wavevector\")\n",
-    "plt.xticks([0, 0.5, 1, 1.5], [\"$\\Gamma$\", \"X\", \"M\", \"$\\Gamma$\"])\n",
+    "plt.xticks([0, 0.5, 1, 1.5], [r\"$\\Gamma$\", \"X\", \"M\", r\"$\\Gamma$\"])\n",
     "plt.xlim(0, 1.5)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
diff --git a/BatchModeSolver.ipynb b/BatchModeSolver.ipynb
index 38e3e04a..e7419ce0 100644
--- a/BatchModeSolver.ipynb
+++ b/BatchModeSolver.ipynb
@@ -27,9 +27,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
     "from tidy3d.plugins import waveguide"
diff --git a/BayesianOptimizationYJunction.ipynb b/BayesianOptimizationYJunction.ipynb
index 7c50e746..07f0f894 100644
--- a/BayesianOptimizationYJunction.ipynb
+++ b/BayesianOptimizationYJunction.ipynb
@@ -53,14 +53,13 @@
     "# Uncomment the following line to install the package\n",
     "# pip install bayesian-optimization==1.5.1\n",
     "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "import gdstk\n",
-    "from scipy.interpolate import make_interp_spline\n",
-    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
+    "import tidy3d.plugins.design as tdd\n",
     "import tidy3d.web as web\n",
-    "import tidy3d.plugins.design as tdd"
+    "from scipy.interpolate import make_interp_spline"
    ]
   },
   {
@@ -85,7 +84,7 @@
    "source": [
     "lda0 = 1.55  # central wavelength\n",
     "freq0 = td.C_0 / lda0  # central frequency\n",
-    "n_wav = 100 # Number of wavelengths to sample in the range\n",
+    "n_wav = 100  # Number of wavelengths to sample in the range\n",
     "ldas = np.linspace(1.5, 1.6, n_wav)  # wavelength range\n",
     "freqs = td.C_0 / ldas  # frequency range\n",
     "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))  # width of the source frequency range\n",
@@ -108,16 +107,16 @@
    "outputs": [],
    "source": [
     "t = 0.22  # thickness of the silicon layer\n",
-    "num_d = 13 # dimensional space of the design region\n",
+    "num_d = 13  # dimensional space of the design region\n",
     "\n",
     "l_in = 1  # input waveguide length\n",
     "l_junction = 2  # length of the junction\n",
     "l_bend = 6  # horizontal length of the waveguide bend\n",
     "h_bend = 2  # vertical offset of the waveguide bend\n",
     "l_out = 1  # output waveguide length\n",
-    "branch_width = 0.5 # width of one Y branch\n",
-    "branch_sep = 0.2 # distance between y branches at the junction\n",
-    "inf_eff = 100  # effective infinity\n"
+    "branch_width = 0.5  # width of one Y branch\n",
+    "branch_sep = 0.2  # distance between y branches at the junction\n",
+    "inf_eff = 100  # effective infinity"
    ]
   },
   {
@@ -146,21 +145,25 @@
    "source": [
     "def fn_pre(**w_params: dict) -> td.Simulation:\n",
     "    \"\"\"Create a Simulation of a Y splitter from a series of junction widths.\n",
-    "    \n",
+    "\n",
     "    Includes mode monitors to measure the power transmitted and reflected to source.\n",
     "    \"\"\"\n",
     "    w_start = 0.5\n",
     "    w_end = branch_width * 2 + branch_sep\n",
-    "    \n",
-    "    widths = [w_start] # Ensures input waveguide is included in spline for first point of junction\n",
+    "\n",
+    "    widths = [w_start]  # Ensures input waveguide is included in spline for first point of junction\n",
     "    widths.extend(list(w_params.values()))\n",
-    "    widths.append(w_end) # Ensures final point of junction smoothly converts to the branches\n",
-    "    \n",
-    "    x_junction = np.linspace(l_in, l_in + l_junction, num_d + 2)  # x coordinates of the top edge vertices\n",
+    "    widths.append(w_end)  # Ensures final point of junction smoothly converts to the branches\n",
+    "\n",
+    "    x_junction = np.linspace(\n",
+    "        l_in, l_in + l_junction, num_d + 2\n",
+    "    )  # x coordinates of the top edge vertices\n",
     "    y_junction = np.array(widths)  # y coordinates of the top edge vertices\n",
     "\n",
     "    # pass vertices through spline and increase sampling to smooth the geometry\n",
-    "    new_x_junction = np.linspace(l_in, l_in + l_junction, 100)  # x coordinates of the top edge vertices\n",
+    "    new_x_junction = np.linspace(\n",
+    "        l_in, l_in + l_junction, 100\n",
+    "    )  # x coordinates of the top edge vertices\n",
     "    spline = make_interp_spline(x_junction, y_junction, k=2)\n",
     "    spline_yjunction = spline(new_x_junction)\n",
     "\n",
@@ -175,7 +178,7 @@
     "        geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(0, t)), medium=si\n",
     "    )\n",
     "\n",
-    "    x_start = l_in + l_junction # x coordinate of the starting point of the waveguide bends\n",
+    "    x_start = l_in + l_junction  # x coordinate of the starting point of the waveguide bends\n",
     "\n",
     "    x_bend = np.linspace(x_start, x_start + l_bend, 100)  # x coordinates of the top edge vertices\n",
     "\n",
@@ -192,8 +195,12 @@
     "\n",
     "    # add path to the cell\n",
     "    cell = gdstk.Cell(\"bends\")\n",
-    "    cell.add(gdstk.FlexPath(x_bend + 1j * y_bend, branch_width, layer=1, datatype=0))  # top waveguide bend\n",
-    "    cell.add(gdstk.FlexPath(x_bend - 1j * y_bend, branch_width, layer=1, datatype=0))  # bottom waveguide bend\n",
+    "    cell.add(\n",
+    "        gdstk.FlexPath(x_bend + 1j * y_bend, branch_width, layer=1, datatype=0)\n",
+    "    )  # top waveguide bend\n",
+    "    cell.add(\n",
+    "        gdstk.FlexPath(x_bend - 1j * y_bend, branch_width, layer=1, datatype=0)\n",
+    "    )  # bottom waveguide bend\n",
     "\n",
     "    # define top waveguide bend structure\n",
     "    wg_bend_1 = td.Structure(\n",
@@ -244,13 +251,13 @@
     "\n",
     "    # add a mode monitor to measure transmission at the output waveguide\n",
     "    mode_monitor_11 = td.ModeMonitor(\n",
-    "        center=(l_in / 3 , 0, t / 2),\n",
+    "        center=(l_in / 3, 0, t / 2),\n",
     "        size=(0, 4 * w_start, 6 * t),\n",
     "        freqs=freqs,\n",
     "        mode_spec=mode_spec,\n",
     "        name=\"mode_11\",\n",
     "    )\n",
-    "    \n",
+    "\n",
     "    mode_monitor_12 = td.ModeMonitor(\n",
     "        center=(l_in + l_junction + l_bend + l_out / 2, w_end / 2 - w_start / 2 + h_bend, t / 2),\n",
     "        size=(0, 4 * w_start, 6 * t),\n",
@@ -278,19 +285,24 @@
     "        boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
     "        medium=sio2,\n",
     "    )\n",
-    "    \n",
+    "\n",
     "    return sim\n",
     "\n",
+    "\n",
     "def fn_post(sim_data: td.SimulationData) -> float:\n",
     "    \"\"\"Calculate the loss function from the power at the mode monitors in the SimulationData.\"\"\"\n",
     "    # Calculate the power reflected back to source and transmitted to one branch\n",
-    "    power_reflected = np.squeeze(np.abs(sim_data[\"mode_11\"].amps.sel(direction=\"-\", mode_index=0)) ** 2)\n",
-    "    power_transmitted = np.squeeze(np.abs(sim_data[\"mode_12\"].amps.sel(direction=\"+\", mode_index=0)) ** 2)\n",
-    "    \n",
+    "    power_reflected = np.squeeze(\n",
+    "        np.abs(sim_data[\"mode_11\"].amps.sel(direction=\"-\", mode_index=0)) ** 2\n",
+    "    )\n",
+    "    power_transmitted = np.squeeze(\n",
+    "        np.abs(sim_data[\"mode_12\"].amps.sel(direction=\"+\", mode_index=0)) ** 2\n",
+    "    )\n",
+    "\n",
     "    # Loss function proposed by Gao et al. which takes advantage of branch symmetry\n",
-    "    loss_fn = 1 / 3 * n_wav * np.sum(power_reflected ** 2 + 2 * (power_transmitted - 0.5) ** 2)\n",
-    "    output = -float(loss_fn.values) # Negative value as this is a minimizing loss function\n",
-    "    \n",
+    "    loss_fn = 1 / 3 * n_wav * np.sum(power_reflected**2 + 2 * (power_transmitted - 0.5) ** 2)\n",
+    "    output = -float(loss_fn.values)  # Negative value as this is a minimizing loss function\n",
+    "\n",
     "    return output"
    ]
   },
@@ -358,14 +370,16 @@
     "method = tdd.MethodBayOpt(\n",
     "    initial_iter=30,\n",
     "    n_iter=70,\n",
-    "    acq_func='ucb',\n",
+    "    acq_func=\"ucb\",\n",
     "    kappa=0.3,\n",
     "    seed=1,\n",
     ")\n",
     "\n",
     "parameters = [tdd.ParameterFloat(name=f\"w_{i}\", span=(0.5, 1.6)) for i in range(num_d)]\n",
     "\n",
-    "design_space = tdd.DesignSpace(method=method, parameters=parameters, task_name=\"bay_opt_notebook\", path_dir=\"./data\")"
+    "design_space = tdd.DesignSpace(\n",
+    "    method=method, parameters=parameters, task_name=\"bay_opt_notebook\", path_dir=\"./data\"\n",
+    ")"
    ]
   },
   {
@@ -1971,7 +1985,7 @@
     }
    ],
    "source": [
-    "ax=df[\"output\"].plot(xlabel=\"Simulation Number\", ylabel=\"Fitness\")"
+    "ax = df[\"output\"].plot(xlabel=\"Simulation Number\", ylabel=\"Fitness\")"
    ]
   },
   {
@@ -2000,11 +2014,15 @@
     "best_sim_filename = results.task_paths[idx_best_result]\n",
     "best_sim = td.SimulationData.from_file(best_sim_filename)\n",
     "\n",
-    "power_reflected = np.array(np.squeeze(np.abs(best_sim[\"mode_11\"].amps.sel(direction=\"-\", mode_index=0)) ** 2)).mean()\n",
-    "power_transmitted = np.array(np.squeeze(np.abs(best_sim[\"mode_12\"].amps.sel(direction=\"+\", mode_index=0)) ** 2)).mean()\n",
+    "power_reflected = np.array(\n",
+    "    np.squeeze(np.abs(best_sim[\"mode_11\"].amps.sel(direction=\"-\", mode_index=0)) ** 2)\n",
+    ").mean()\n",
+    "power_transmitted = np.array(\n",
+    "    np.squeeze(np.abs(best_sim[\"mode_12\"].amps.sel(direction=\"+\", mode_index=0)) ** 2)\n",
+    ").mean()\n",
     "\n",
     "print(f\"Mean Reflected Power: {round(power_reflected, 3)} (Paper: 0.004)\")\n",
-    "print(f\"Mean Transmitted Power: {round(power_transmitted, 3)} (Paper: 0.460)\")\n"
+    "print(f\"Mean Transmitted Power: {round(power_transmitted, 3)} (Paper: 0.460)\")"
    ]
   },
   {
@@ -2348,9 +2366,7 @@
     "\n",
     "mode_12 = final_sim.monitors[1]\n",
     "\n",
-    "final_sim = final_sim.copy(\n",
-    "    update={\"monitors\": (field_monitor, mode_12)}\n",
-    ")\n",
+    "final_sim = final_sim.copy(update={\"monitors\": (field_monitor, mode_12)})\n",
     "\n",
     "final_sim_data = web.run(final_sim, task_name=\"BO_notebook_final_sim\")"
    ]
@@ -2407,7 +2423,9 @@
     }
    ],
    "source": [
-    "power_transmitted = np.squeeze(np.abs(final_sim_data[\"mode_12\"].amps.sel(direction=\"+\", mode_index=0)) ** 2)\n",
+    "power_transmitted = np.squeeze(\n",
+    "    np.abs(final_sim_data[\"mode_12\"].amps.sel(direction=\"+\", mode_index=0)) ** 2\n",
+    ")\n",
     "plt.plot(freqs, power_transmitted)\n",
     "plt.title(\"Power transmitted across frequency range\")\n",
     "plt.xlabel(\"Frequency / Hz\")\n",
diff --git a/BilayerSiNEdgeCoupler.ipynb b/BilayerSiNEdgeCoupler.ipynb
index 69ded76c..a09cedf2 100644
--- a/BilayerSiNEdgeCoupler.ipynb
+++ b/BilayerSiNEdgeCoupler.ipynb
@@ -31,9 +31,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web"
    ]
diff --git a/BilevelPSR.ipynb b/BilevelPSR.ipynb
index 13477037..44be5708 100644
--- a/BilevelPSR.ipynb
+++ b/BilevelPSR.ipynb
@@ -37,10 +37,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "import gdstk\n",
-    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
     "from tidy3d.plugins.mode import ModeSolver"
@@ -190,15 +189,15 @@
     "theta = np.pi / 30  # s bend angle\n",
     "\n",
     "# define a gds cell\n",
-    "cell = gdstk.Cell(\"device\") \n",
+    "cell = gdstk.Cell(\"device\")\n",
     "\n",
     "# define the top waveguide using a path\n",
-    "top_wg = gdstk.RobustPath((-inf_eff, 0), w_1,layer=1, datatype=0)\n",
+    "top_wg = gdstk.RobustPath((-inf_eff, 0), w_1, layer=1, datatype=0)\n",
     "top_wg.horizontal(0)\n",
     "top_wg.horizontal(L_blt / 2, w_2)\n",
     "top_wg.horizontal(L_blt, w_3)\n",
     "top_wg.horizontal(L_blt + L_s)\n",
-    "top_wg.segment((L_blt + L_s + L_ac, (w_5-w_3)/2), w_5)\n",
+    "top_wg.segment((L_blt + L_s + L_ac, (w_5 - w_3) / 2), w_5)\n",
     "top_wg.horizontal(L_blt + L_s + L_ac + L_t)\n",
     "top_wg.arc(R, -np.pi / 2, -np.pi / 2 + theta)\n",
     "top_wg.arc(R, np.pi / 2 + theta, np.pi / 2)\n",
@@ -206,12 +205,12 @@
     "cell.add(top_wg)\n",
     "\n",
     "# define the bottom waveguide using a path\n",
-    "bottom_wg = gdstk.RobustPath((L_blt + L_s, (-w_4-w_3-2*gap)/2), w_4, layer=1, datatype=0)\n",
-    "bottom_wg.segment((L_blt + L_s + L_ac, (-w_3-2*gap-w_6)/2), w_6)\n",
+    "bottom_wg = gdstk.RobustPath((L_blt + L_s, (-w_4 - w_3 - 2 * gap) / 2), w_4, layer=1, datatype=0)\n",
+    "bottom_wg.segment((L_blt + L_s + L_ac, (-w_3 - 2 * gap - w_6) / 2), w_6)\n",
     "bottom_wg.arc(R, np.pi / 2, np.pi / 2 - theta)\n",
     "bottom_wg.arc(R, -np.pi / 2 - theta, -np.pi / 2)\n",
     "bottom_wg.horizontal(inf_eff)\n",
-    "cell.add(bottom_wg)   \n",
+    "cell.add(bottom_wg)\n",
     "\n",
     "# define the waveguide tidy3d geometries\n",
     "wg_geos = td.PolySlab.from_gds(\n",
@@ -514,15 +513,15 @@
    "source": [
     "mode_solver = ModeSolver(\n",
     "    simulation=sim_te,\n",
-    "    plane=td.Box(center=(0, 0, t_si/2), size=(0, 4*w_1, 5*t_si)),\n",
+    "    plane=td.Box(center=(0, 0, t_si / 2), size=(0, 4 * w_1, 5 * t_si)),\n",
     "    mode_spec=mode_spec,\n",
     "    freqs=[freq0],\n",
     ")\n",
     "mode_data = mode_solver.solve()\n",
     "\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8,3), tight_layout=True)\n",
-    "mode_data.intensity.sel(mode_index=0).plot(x='y', y='z', cmap='magma', ax=ax1)\n",
-    "mode_data.intensity.sel(mode_index=1).plot(x='y', y='z', cmap='magma', ax=ax2)\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3), tight_layout=True)\n",
+    "mode_data.intensity.sel(mode_index=0).plot(x=\"y\", y=\"z\", cmap=\"magma\", ax=ax1)\n",
+    "mode_data.intensity.sel(mode_index=1).plot(x=\"y\", y=\"z\", cmap=\"magma\", ax=ax2)\n",
     "\n",
     "mode_data.to_dataframe()"
    ]
@@ -639,12 +638,14 @@
     }
    ],
    "source": [
-    "mode_solver = mode_solver.copy(update={\"plane\": td.Box(center=(L_blt/2, 0, t_si/2), size=(0, 2*w_pes, 5*t_si))})\n",
+    "mode_solver = mode_solver.copy(\n",
+    "    update={\"plane\": td.Box(center=(L_blt / 2, 0, t_si / 2), size=(0, 2 * w_pes, 5 * t_si))}\n",
+    ")\n",
     "mode_data = mode_solver.solve()\n",
     "\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8,3), tight_layout=True)\n",
-    "mode_data.intensity.sel(mode_index=0).plot(x='y', y='z', cmap='magma', ax=ax1)\n",
-    "mode_data.intensity.sel(mode_index=1).plot(x='y', y='z', cmap='magma', ax=ax2)\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3), tight_layout=True)\n",
+    "mode_data.intensity.sel(mode_index=0).plot(x=\"y\", y=\"z\", cmap=\"magma\", ax=ax1)\n",
+    "mode_data.intensity.sel(mode_index=1).plot(x=\"y\", y=\"z\", cmap=\"magma\", ax=ax2)\n",
     "\n",
     "mode_data.to_dataframe()"
    ]
@@ -761,12 +762,14 @@
     }
    ],
    "source": [
-    "mode_solver = mode_solver.copy(update={\"plane\": td.Box(center=(L_blt + L_s, 0, t_si/2), size=(0, 3*w_3, 5*t_si))})\n",
+    "mode_solver = mode_solver.copy(\n",
+    "    update={\"plane\": td.Box(center=(L_blt + L_s, 0, t_si / 2), size=(0, 3 * w_3, 5 * t_si))}\n",
+    ")\n",
     "mode_data = mode_solver.solve()\n",
     "\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8,3), tight_layout=True)\n",
-    "mode_data.intensity.sel(mode_index=0).plot(x='y', y='z', cmap='magma', ax=ax1)\n",
-    "mode_data.intensity.sel(mode_index=1).plot(x='y', y='z', cmap='magma', ax=ax2)\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3), tight_layout=True)\n",
+    "mode_data.intensity.sel(mode_index=0).plot(x=\"y\", y=\"z\", cmap=\"magma\", ax=ax1)\n",
+    "mode_data.intensity.sel(mode_index=1).plot(x=\"y\", y=\"z\", cmap=\"magma\", ax=ax2)\n",
     "\n",
     "mode_data.to_dataframe()"
    ]
@@ -885,12 +888,14 @@
     }
    ],
    "source": [
-    "mode_solver = mode_solver.copy(update={\"plane\": td.Box(center=(L_blt + L_s + L_ac, 0, t_si/2), size=(0, 5*w_5, 5*t_si))})\n",
+    "mode_solver = mode_solver.copy(\n",
+    "    update={\"plane\": td.Box(center=(L_blt + L_s + L_ac, 0, t_si / 2), size=(0, 5 * w_5, 5 * t_si))}\n",
+    ")\n",
     "mode_data = mode_solver.solve()\n",
     "\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8,3), tight_layout=True)\n",
-    "mode_data.intensity.sel(mode_index=0).plot(x='y', y='z', cmap='magma', ax=ax1)\n",
-    "mode_data.intensity.sel(mode_index=1).plot(x='y', y='z', cmap='magma', ax=ax2)\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3), tight_layout=True)\n",
+    "mode_data.intensity.sel(mode_index=0).plot(x=\"y\", y=\"z\", cmap=\"magma\", ax=ax1)\n",
+    "mode_data.intensity.sel(mode_index=1).plot(x=\"y\", y=\"z\", cmap=\"magma\", ax=ax2)\n",
     "\n",
     "mode_data.to_dataframe()"
    ]
@@ -1178,7 +1183,6 @@
    "outputs": [],
    "source": [
     "def plot_transmission(sim_data):\n",
-    "\n",
     "    # get te mode amplitude in the top waveguide\n",
     "    amp_top_te = sim_data[\"mode_top\"].amps.sel(mode_index=0, direction=\"+\")\n",
     "    # calculate te mode transmission in the top waveguide\n",
@@ -1205,7 +1209,7 @@
     "    # formatting the plot\n",
     "    plt.xlim(1.5, 1.58)\n",
     "    plt.ylim(-60, 1)\n",
-    "    plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "    plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "    plt.ylabel(\"Transmission (dB)\")\n",
     "    plt.legend()\n",
     "    plt.show()"
diff --git a/BiosensorGrating.ipynb b/BiosensorGrating.ipynb
index 63e7f368..60bd97cf 100644
--- a/BiosensorGrating.ipynb
+++ b/BiosensorGrating.ipynb
@@ -6,11 +6,11 @@
    "source": [
     "# Biosensor grating simulation\n",
     "\n",
-    "Bragg gratings are structures which involve a periodic variation in the refractive index or geometry of waveguide, so that certain frequencies of light are reflected off the grating while others are transmitted.\n",
+    "Bragg gratings are structures that involve a periodic variation in the refractive index or geometry of waveguide, so that certain frequencies of light are reflected off the grating while others are transmitted.\n",
     "\n",
-    "Since gratings can be designed to be extremely sentitive, one possible application they have is to detect the presence of foreign molecules. If particles such as biomolecules are deposited on the device, it will no longer have the same reflective properties in the band of frequencies for which it was designed. Therefore, carefully-designed Bragg gratings can be used as biosensors.\n",
+    "Since gratings can be designed to be extremely sensitive, one possible application they have is to detect the presence of foreign molecules. If particles such as biomolecules are deposited on the device, it will no longer have the same reflective properties in the band of frequencies for which it was designed. Therefore, carefully designed Bragg gratings can be used as biosensors.\n",
     "\n",
-    "In this example, an optical biosensor grating is modeled to detect the presence of biomolecules. The grating is designed to be reflective over a narrow band around its resonant frequency which is modified by the presence of a biomolecule.\n",
+    "In this example, an optical biosensor grating is modeled to detect the presence of biomolecules. The grating is designed to be reflective over a narrow band around its resonant frequency, which is modified by the presence of a biomolecule.\n",
     "\n",
     "Reference:  `Brian Cunningham, Bo Lin, Jean Qiu, Peter Li, Jane Pepper, Brenda Hugh, \"A plastic colorimetric resonant optical biosensor for multiparallel detection of label-free biochemical interactions,\" Sensors and Actuators B 85 (2002)`, DOI: [10.1016/S0925-4005(02)00111-9]().\n",
     "\n",
@@ -32,11 +32,11 @@
    "outputs": [],
    "source": [
     "# basic imports\n",
-    "import numpy as np\n",
     "import matplotlib.pylab as plt\n",
+    "import numpy as np\n",
     "\n",
     "# Tidy3D imports\n",
-    "import tidy3d as td\n"
+    "import tidy3d as td"
    ]
   },
   {
@@ -146,7 +146,7 @@
     ")\n",
     "\n",
     "# grid specification\n",
-    "grid_spec = td.GridSpec.auto(min_steps_per_wvl=30)\n"
+    "grid_spec = td.GridSpec.auto(min_steps_per_wvl=30)"
    ]
   },
   {
@@ -179,7 +179,7 @@
     "    source_time=source_time,\n",
     "    pol_angle=0,\n",
     "    direction=\"+\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -250,7 +250,7 @@
     "    name=\"flux_tran\",\n",
     ")\n",
     "\n",
-    "monitors = [monitor_xz, monitor_flux_refl, monitor_flux_tran]\n"
+    "monitors = [monitor_xz, monitor_flux_refl, monitor_flux_tran]"
    ]
   },
   {
@@ -303,7 +303,7 @@
     "\n",
     "# plot the simulation domain\n",
     "sim.plot(y=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -662,7 +662,7 @@
     "# run simulation\n",
     "import tidy3d.web as web\n",
     "\n",
-    "sim_data = web.run(sim, task_name=\"biosensor\", path=\"data/biosensor.hdf5\", verbose=True)\n"
+    "sim_data = web.run(sim, task_name=\"biosensor\", path=\"data/biosensor.hdf5\", verbose=True)"
    ]
   },
   {
@@ -704,7 +704,7 @@
     "sim_data.plot_field(\"fields_xz\", field_name=\"Ex\", val=\"abs\", f=freq0, ax=ax[0])\n",
     "sim_data.plot_field(\"fields_xz\", field_name=\"Sz\", val=\"real\", f=freq0, ax=ax[1])\n",
     "sim_data.plot_field(\"fields_xz\", field_name=\"Sx\", val=\"real\", f=freq0, ax=ax[2])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -759,7 +759,7 @@
     ")\n",
     "ax.legend()\n",
     "ax.grid()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   }
  ],
@@ -787,7 +787,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.0"
   },
   "title": "Biosensor grating Modeling in Tidy3D | Flexcompute",
   "widgets": {
diff --git a/BistablePCCavity.ipynb b/BistablePCCavity.ipynb
index 4ccf4448..ab30e2f6 100644
--- a/BistablePCCavity.ipynb
+++ b/BistablePCCavity.ipynb
@@ -34,15 +34,15 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
+    "import gdstk\n",
     "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import scipy\n",
     "\n",
     "# tidy3D import\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n",
-    "import gdstk\n",
-    "import scipy"
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -50,11 +50,11 @@
    "id": "173272c1",
    "metadata": {},
    "source": [
-    "The waveguide-cavity system we examine is given by a square lattice (with lattice constant a) of high dielectric rods ($n$ = 3.5) of radius $.2a$. The waveguide is given by removing a row of these rods, and the cavity is given by point defect in the crystal - instead of a rod, the defect is an ellipse with major and minor axes of $a$ and $2a$, respectively. Here we will set $a$ = 1.\n",
+    "The waveguide-cavity system we examine is given by a square lattice (with lattice constant a) of high dielectric rods ($n$ = 3.5) of radius $.2a$. The waveguide is given by removing a row of these rods, and the cavity is given by a point defect in the crystal - instead of a rod, the defect is an ellipse with major and minor axes of $a$ and $2a$, respectively. Here we will set $a$ = 1.\n",
     "\n",
     "To build this in Tidy3D, we take advantage of the \"square_cylinder_array\" method in the [common photonic crystal structures](https://www.flexcompute.com/tidy3d/examples/notebooks/PhotonicCrystalsComponents/) page in the Tidy3D learning center.\n",
     "\n",
-    "Since we are dealing with a photonic crystal with discrete translational symmetry, we can get spurious reflections if we simply use a PML boundary condition. Thus instead we use the custom abosrbing condition given in Mekis et al.'s [Absorbing boundary conditions for FDTD simulations of photonic crystal waveguides](https://ieeexplore.ieee.org/document/819374). This is a $k$-matched distributed Bragg reflector waveguide consisting of a periodic array of alternating periodic slabs, with the waveguide given by a single line defect of a single slab with larger thickness. Since this waveguide has continuous translational symmetry, we can now feed it into a standard PML boundary. For simplicity, in defining the parameters for this waveguide, we use the parameters given in Mekis et al."
+    "Since we are dealing with a photonic crystal with discrete translational symmetry, we can get spurious reflections if we simply use a PML boundary condition. Thus, instead we use the custom absorbing condition given in Mekis et al.'s [Absorbing boundary conditions for FDTD simulations of photonic crystal waveguides](https://ieeexplore.ieee.org/document/819374). This is a $k$-matched distributed Bragg reflector waveguide consisting of a periodic array of alternating periodic slabs, with the waveguide given by a single line defect of a single slab with larger thickness. Since this waveguide has continuous translational symmetry, we can now feed it into a standard PML boundary. For simplicity, in defining the parameters for this waveguide, we use the parameters given in Mekis et al."
    ]
   },
   {
@@ -112,6 +112,7 @@
     "    structure = td.Structure(geometry=td.GeometryGroup(geometries=cylinder_group), medium=medium)\n",
     "    return structure\n",
     "\n",
+    "\n",
     "def DBR(\n",
     "    x0,\n",
     "    y0,\n",
@@ -121,7 +122,7 @@
     "    spacing,\n",
     "    num_layers,\n",
     "    medium,\n",
-    "    direction='+',\n",
+    "    direction=\"+\",\n",
     "):\n",
     "    # parameters\n",
     "    # ------------------------------------------------------------\n",
@@ -137,15 +138,12 @@
     "\n",
     "    slab_group = []\n",
     "    orientation = 1\n",
-    "    if direction != '+':\n",
+    "    if direction != \"+\":\n",
     "        orientation = -1\n",
-    "    start_x, start_y = x0 + orientation*length/2, y0 - num_layers/2*a\n",
-    "    for i in range(0, num_layers+1):\n",
-    "        if i != num_layers//2:\n",
-    "            s = td.Box(\n",
-    "                center=(start_x, start_y+i*a, 0),\n",
-    "                size=(length, thickness, td.inf)\n",
-    "            )\n",
+    "    start_x, start_y = x0 + orientation * length / 2, y0 - num_layers / 2 * a\n",
+    "    for i in range(0, num_layers + 1):\n",
+    "        if i != num_layers // 2:\n",
+    "            s = td.Box(center=(start_x, start_y + i * a, 0), size=(length, thickness, td.inf))\n",
     "            slab_group.append(s)\n",
     "    structure = td.Structure(geometry=td.GeometryGroup(geometries=slab_group), medium=medium)\n",
     "    return structure"
@@ -176,17 +174,18 @@
     "air = td.Medium(permittivity=n_air)\n",
     "\n",
     "# Kerr coefficient\n",
-    "n2 = 1.5e-17 # m^2/W, from paper\n",
-    "n2 *= 1e12 # convert to um^2/W\n",
+    "n2 = 1.5e-17  # m^2/W, from paper\n",
+    "n2 *= 1e12  # convert to um^2/W\n",
     "\n",
     "a = 1\n",
-    "radius = 0.2*a\n",
+    "radius = 0.2 * a\n",
     "block_rows = 21\n",
     "block_cols = 17\n",
     "\n",
     "######################### SIMPLE CRYSTAL ##########################\n",
-    "block = square_cylinder_array(0, 0, -a, radius, a, a,\n",
-    "                                     block_cols, block_rows, td.inf, rod, reference_plane='middle')\n",
+    "block = square_cylinder_array(\n",
+    "    0, 0, -a, radius, a, a, block_cols, block_rows, td.inf, rod, reference_plane=\"middle\"\n",
+    ")\n",
     "######################### WAVEGUIDE WITHOUT CAVITY ##########################\n",
     "waveguide = td.Structure(\n",
     "    geometry=td.Box(\n",
@@ -199,7 +198,7 @@
     "########################### WAVEGUIDE WITH CAVITY ###########################\n",
     "cavity_air = td.Structure(\n",
     "    geometry=td.Box(\n",
-    "        center=[0, -3*a, 0],\n",
+    "        center=[0, -3 * a, 0],\n",
     "        size=[a, a, td.inf],\n",
     "    ),\n",
     "    medium=air,\n",
@@ -207,23 +206,23 @@
     ")\n",
     "air_block = td.Structure(\n",
     "    geometry=td.Box(\n",
-    "        center=[-22*a, 0, 0],\n",
-    "        size=[30*a - a/2, td.inf, td.inf],\n",
+    "        center=[-22 * a, 0, 0],\n",
+    "        size=[30 * a - a / 2, td.inf, td.inf],\n",
     "    ),\n",
     "    medium=air,\n",
     "    name=\"air block\",\n",
     ")\n",
     "\n",
     "################### DISTRIBUTED BRAGG REFLECTOR BOUNDARY ###################\n",
-    "thickness = 0.25*a\n",
+    "thickness = 0.25 * a\n",
     "n_DBR = 10.2\n",
     "dbr_med = td.Medium(permittivity=n_DBR**2)\n",
-    "dbr_boundary = DBR(block_cols*a/2, 0, 0, 20, thickness, a, block_rows-1, dbr_med)\n",
+    "dbr_boundary = DBR(block_cols * a / 2, 0, 0, 20, thickness, a, block_rows - 1, dbr_med)\n",
     "\n",
     "################################## CAVITY ##################################\n",
     "major = a\n",
-    "minor = 0.2*a\n",
-    "cavity_gdstk = gdstk.ellipse((0, -3*a), (minor/2, major/2))\n",
+    "minor = 0.2 * a\n",
+    "cavity_gdstk = gdstk.ellipse((0, -3 * a), (minor / 2, major / 2))\n",
     "cavity_cell = gdstk.Cell(\"Ellipse\")\n",
     "cavity_cell.add(cavity_gdstk)\n",
     "cavity = td.PolySlab.from_gds(\n",
@@ -232,7 +231,7 @@
     "    gds_dtype=0,\n",
     "    axis=2,\n",
     "    slab_bounds=(-td.inf, td.inf),\n",
-    "    reference_plane='bottom',\n",
+    "    reference_plane=\"bottom\",\n",
     ")[0]\n",
     "\n",
     "cavity_structure = td.Structure(\n",
@@ -249,27 +248,27 @@
    "outputs": [],
    "source": [
     "freqs = np.linspace(1.08e14, 1.1e14, 1000)\n",
-    "freq0 = freqs[len(freqs)//2]\n",
-    "fwidth = freqs[-1]-freqs[0]\n",
+    "freq0 = freqs[len(freqs) // 2]\n",
+    "fwidth = freqs[-1] - freqs[0]\n",
     "\n",
-    "run_time = 10000/freq0\n",
+    "run_time = 10000 / freq0\n",
     "\n",
     "# simulation parameters\n",
-    "x_span = 27*a\n",
-    "y_span = 17*a\n",
+    "x_span = 27 * a\n",
+    "y_span = 17 * a\n",
     "\n",
     "min_steps_per_wvl = 30\n",
-    "gridSpecUniform = td.GridSpec.uniform(dl=a/24)\n",
+    "gridSpecUniform = td.GridSpec.uniform(dl=a / 24)\n",
     "\n",
     "pulse = td.GaussianPulse(freq0=freq0, fwidth=fwidth)\n",
     "source_distance = 9\n",
     "gaussianBeam = td.GaussianBeam(\n",
-    "    center=[-source_distance*a, 0, 0],\n",
-    "    size=[0,td.inf,td.inf],\n",
+    "    center=[-source_distance * a, 0, 0],\n",
+    "    size=[0, td.inf, td.inf],\n",
     "    source_time=pulse,\n",
-    "    direction='+',\n",
-    "    pol_angle=np.pi/2,\n",
-    "    waist_radius=2.0*a\n",
+    "    direction=\"+\",\n",
+    "    pol_angle=np.pi / 2,\n",
+    "    waist_radius=2.0 * a,\n",
     ")"
    ]
   },
@@ -307,38 +306,40 @@
    "source": [
     "cavity_monitor_size = 7\n",
     "field_monitor_cavity = td.FieldTimeMonitor(\n",
-    "            fields=[\"Ez\"],\n",
-    "            center=[0, -3*a, 0],\n",
-    "            size=[cavity_monitor_size*a, cavity_monitor_size*a, 0],\n",
-    "            start=run_time*8/10,\n",
-    "            stop=run_time*8/10,\n",
-    "            name='field cavity',\n",
-    "        )\n",
+    "    fields=[\"Ez\"],\n",
+    "    center=[0, -3 * a, 0],\n",
+    "    size=[cavity_monitor_size * a, cavity_monitor_size * a, 0],\n",
+    "    start=run_time * 8 / 10,\n",
+    "    stop=run_time * 8 / 10,\n",
+    "    name=\"field cavity\",\n",
+    ")\n",
     "permittivity_monitor_cavity = td.PermittivityMonitor(\n",
-    "            center=[0, -3*a, 0],\n",
-    "            size=[cavity_monitor_size*a, cavity_monitor_size*a, 0],\n",
-    "            freqs=freqs[0],\n",
-    "            name='permittivity cavity',\n",
-    "        )\n",
+    "    center=[0, -3 * a, 0],\n",
+    "    size=[cavity_monitor_size * a, cavity_monitor_size * a, 0],\n",
+    "    freqs=freqs[0],\n",
+    "    name=\"permittivity cavity\",\n",
+    ")\n",
     "\n",
     "flux_distance = 4.5\n",
     "\n",
     "field_time_monitor_wg = td.FieldTimeMonitor(\n",
-    "            fields=[\"Ez\"],\n",
-    "            center=[flux_distance*a, 0, 0],\n",
-    "            size=(0, 0, 0),\n",
-    "            start=run_time*8/10, # time to start monitoring after source has decayed, units of 1/frequency bandwidth\n",
-    "            name='field time wg',\n",
-    "        )\n",
+    "    fields=[\"Ez\"],\n",
+    "    center=[flux_distance * a, 0, 0],\n",
+    "    size=(0, 0, 0),\n",
+    "    start=run_time\n",
+    "    * 8\n",
+    "    / 10,  # time to start monitoring after source has decayed, units of 1/frequency bandwidth\n",
+    "    name=\"field time wg\",\n",
+    ")\n",
     "flux_monitor = td.FluxMonitor(\n",
-    "    center=[flux_distance*a, 0, 0],\n",
-    "    size=[0,td.inf,td.inf],\n",
+    "    center=[flux_distance * a, 0, 0],\n",
+    "    size=[0, td.inf, td.inf],\n",
     "    freqs=freqs,\n",
-    "    normal_dir='+',\n",
+    "    normal_dir=\"+\",\n",
     "    name=\"flux\",\n",
     ")\n",
     "\n",
-    "x_boundary = td.Boundary(minus=td.PML(num_layers=80),plus=td.PML(num_layers=80))\n",
+    "x_boundary = td.Boundary(minus=td.PML(num_layers=80), plus=td.PML(num_layers=80))\n",
     "\n",
     "sim_linear = td.Simulation(\n",
     "    center=(0, -a, 0),\n",
@@ -347,12 +348,15 @@
     "    grid_spec=gridSpecUniform,\n",
     "    structures=[block, waveguide, cavity_air, cavity_structure, dbr_boundary, air_block],\n",
     "    sources=[gaussianBeam],\n",
-    "    monitors=[field_monitor_cavity, permittivity_monitor_cavity, field_time_monitor_wg, flux_monitor],\n",
+    "    monitors=[\n",
+    "        field_monitor_cavity,\n",
+    "        permittivity_monitor_cavity,\n",
+    "        field_time_monitor_wg,\n",
+    "        flux_monitor,\n",
+    "    ],\n",
     "    run_time=run_time,\n",
-    "    boundary_spec=td.BoundarySpec(\n",
-    "        x=x_boundary, y=td.Boundary.periodic(), z=td.Boundary.periodic()\n",
-    "    ),\n",
-    "    shutoff=False\n",
+    "    boundary_spec=td.BoundarySpec(x=x_boundary, y=td.Boundary.periodic(), z=td.Boundary.periodic()),\n",
+    "    shutoff=False,\n",
     ")"
    ]
   },
@@ -932,8 +936,8 @@
    "source": [
     "Q = res_data.Q[0].item()\n",
     "fres = res_data.freq[0].item()\n",
-    "w_res = fres*2*np.pi\n",
-    "gamma = w_res/2/Q"
+    "w_res = fres * 2 * np.pi\n",
+    "gamma = w_res / 2 / Q"
    ]
   },
   {
@@ -971,34 +975,40 @@
     }
    ],
    "source": [
-    "n_r = np.where(sim_linear_data.monitor_data['permittivity cavity'].eps_zz[1:,1:,0,0] != 1.+0.j, n_rods, 1)\n",
+    "n_r = np.where(\n",
+    "    sim_linear_data.monitor_data[\"permittivity cavity\"].eps_zz[1:, 1:, 0, 0] != 1.0 + 0.0j,\n",
+    "    n_rods,\n",
+    "    1,\n",
+    ")\n",
     "n2_r = np.zeros(n_r.shape)\n",
     "x, y = n_r.shape\n",
     "\n",
     "# compute length between entries in monitor data\n",
-    "entry_length_x, entry_length_y = cavity_monitor_size*a/x, cavity_monitor_size*a/y\n",
+    "entry_length_x, entry_length_y = cavity_monitor_size * a / x, cavity_monitor_size * a / y\n",
     "# compute number of entries that span a/2\n",
-    "wx, wy = int(a/entry_length_x/2), int(3*a/entry_length_y/4)\n",
+    "wx, wy = int(a / entry_length_x / 2), int(3 * a / entry_length_y / 4)\n",
     "# coordinates in entries of the monitor\n",
-    "rod_x, rod_y = x//2, y//2 #+ wy//2\n",
+    "rod_x, rod_y = x // 2, y // 2  # + wy//2\n",
     "\n",
     "# isolate rod within window for n_2(r)\n",
-    "n2_r[rod_x-wx:rod_x+wx, rod_y-wy:rod_y+wy] = n_r[rod_x-wx:rod_x+wx, rod_y-wy:rod_y+wy]\n",
+    "n2_r[rod_x - wx : rod_x + wx, rod_y - wy : rod_y + wy] = n_r[\n",
+    "    rod_x - wx : rod_x + wx, rod_y - wy : rod_y + wy\n",
+    "]\n",
     "n2_r = np.where(n2_r > 1, n2, 0)\n",
     "\n",
     "# get field components at resonant frequency\n",
-    "Ez = sim_linear_data.monitor_data['field cavity'].Ez[:,:,0,0]\n",
+    "Ez = sim_linear_data.monitor_data[\"field cavity\"].Ez[:, :, 0, 0]\n",
     "\n",
-    "integrand_num = (np.abs(Ez*Ez)**2 + 2*np.abs(Ez*np.conjugate(Ez))**2) * n_r**2 * n2_r\n",
-    "integrand_den = (np.abs(Ez)**2 * n_r**2)\n",
+    "integrand_num = (np.abs(Ez * Ez) ** 2 + 2 * np.abs(Ez * np.conjugate(Ez)) ** 2) * n_r**2 * n2_r\n",
+    "integrand_den = np.abs(Ez) ** 2 * n_r**2\n",
     "\n",
     "int_num = integrand_num.integrate(coord=(\"x\", \"y\")).item()\n",
     "int_den = integrand_den.integrate(coord=(\"x\", \"y\")).item()\n",
     "\n",
-    "kappa = (td.C_0/w_res)**2 * int_num/ ((int_den)**2 * n2)\n",
-    "print(\"kappa = \",kappa)\n",
+    "kappa = (td.C_0 / w_res) ** 2 * int_num / ((int_den) ** 2 * n2)\n",
+    "print(\"kappa = \", kappa)\n",
     "\n",
-    "f, ax = plt.subplots(1,2) \n",
+    "fig, ax = plt.subplots(1, 2)\n",
     "ax[0].imshow(n2_r.T)\n",
     "ax[0].set_title(\"$n_2(r)$\")\n",
     "ax[1].imshow(np.abs(Ez).T)\n",
@@ -1021,7 +1031,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "P_0 = td.C_0/(kappa*Q**2*w_res*n2)"
+    "P_0 = td.C_0 / (kappa * Q**2 * w_res * n2)"
    ]
   },
   {
@@ -1053,8 +1063,8 @@
     }
    ],
    "source": [
-    "detuning = 2*np.sqrt(3)\n",
-    "detuned_freq = (w_res-detuning*gamma)/(2*np.pi)\n",
+    "detuning = 2 * np.sqrt(3)\n",
+    "detuned_freq = (w_res - detuning * gamma) / (2 * np.pi)\n",
     "print(detuned_freq)"
    ]
   },
@@ -1083,8 +1093,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sim_waveguide = sim_linear.updated_copy(monitors=[flux_monitor],\n",
-    "                                        structures=[block, waveguide, dbr_boundary, air_block])"
+    "sim_waveguide = sim_linear.updated_copy(\n",
+    "    monitors=[flux_monitor], structures=[block, waveguide, dbr_boundary, air_block]\n",
+    ")"
    ]
   },
   {
@@ -1502,32 +1513,34 @@
    ],
    "source": [
     "# ramp up to CW\n",
-    "muramp = 3/gamma # central time for ramp up\n",
-    "sigmaramp = 1/gamma # time width for ramp up\n",
+    "muramp = 3 / gamma  # central time for ramp up\n",
+    "sigmaramp = 1 / gamma  # time width for ramp up\n",
     "\n",
-    "PCW = 3.95*P_0\n",
+    "PCW = 3.95 * P_0\n",
     "# switching pulse\n",
-    "mupulse = 12.5/gamma\n",
-    "sigmapulse = 1.5/gamma\n",
-    "Ppeak = 20.85*P_0\n",
+    "mupulse = 12.5 / gamma\n",
+    "sigmapulse = 1.5 / gamma\n",
+    "Ppeak = 20.85 * P_0\n",
+    "\n",
     "\n",
     "def power_curve(t):\n",
     "    t /= gamma\n",
-    "    sig = np.sqrt(PCW)*(1 + scipy.special.erf((t - muramp)/(sigmaramp*np.sqrt(2))))/2\n",
-    "    sig += (np.sqrt(Ppeak) - np.sqrt(PCW))*np.exp(-((t - mupulse)/sigmapulse)**2/2)\n",
+    "    sig = np.sqrt(PCW) * (1 + scipy.special.erf((t - muramp) / (sigmaramp * np.sqrt(2)))) / 2\n",
+    "    sig += (np.sqrt(Ppeak) - np.sqrt(PCW)) * np.exp(-(((t - mupulse) / sigmapulse) ** 2) / 2)\n",
     "    return sig\n",
     "\n",
+    "\n",
     "nt = 2000\n",
-    "tmax = 19/gamma\n",
+    "tmax = 19 / gamma\n",
     "times = np.linspace(0, tmax, nt)\n",
     "pulse_run_time = tmax\n",
     "\n",
     "fig, ax = plt.subplots(1, 1, figsize=(6, 5))\n",
-    "ax.set_xlabel('t$\\gamma$')\n",
-    "ax.set_ylabel('$P/P_0$')\n",
+    "ax.set_xlabel(r\"t$\\gamma$\")\n",
+    "ax.set_ylabel(\"$P/P_0$\")\n",
     "\n",
-    "plt.plot(times*gamma, np.abs(power_curve(times*gamma))**2/P_0)\n",
-    "plt.plot(times*gamma, np.abs(power_curve(times*gamma))**2/P_0/detuned_transmission)\n",
+    "plt.plot(times * gamma, np.abs(power_curve(times * gamma)) ** 2 / P_0)\n",
+    "plt.plot(times * gamma, np.abs(power_curve(times * gamma)) ** 2 / P_0 / detuned_transmission)\n",
     "plt.show()"
    ]
   },
@@ -1561,11 +1574,15 @@
     }
    ],
    "source": [
-    "source_time = td.CustomSourceTime.from_values(freq0=detuned_freq,\n",
-    "                                              fwidth=fwidth,\n",
-    "                                              values=power_curve(times*gamma)/np.sqrt(2*detuned_transmission),\n",
-    "                                              dt=times[1]-times[0])\n",
-    "nonlinear_source = gaussianBeam.updated_copy(center=[-source_distance*a, 0, 0], source_time=source_time)\n",
+    "source_time = td.CustomSourceTime.from_values(\n",
+    "    freq0=detuned_freq,\n",
+    "    fwidth=fwidth,\n",
+    "    values=power_curve(times * gamma) / np.sqrt(2 * detuned_transmission),\n",
+    "    dt=times[1] - times[0],\n",
+    ")\n",
+    "nonlinear_source = gaussianBeam.updated_copy(\n",
+    "    center=[-source_distance * a, 0, 0], source_time=source_time\n",
+    ")\n",
     "fig, ax = plt.subplots(1, 1, figsize=(5, 4))\n",
     "source_time.plot(np.linspace(0, pulse_run_time, 20000), ax=ax)\n",
     "plt.show()"
@@ -1595,11 +1612,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "chi3 = n2*n_rods*np.real(n_rods)/283\n",
+    "chi3 = n2 * n_rods * np.real(n_rods) / 283\n",
     "\n",
-    "cavityMedium = td.Medium(permittivity=n_rods**2,\n",
-    "                        nonlinear_spec=td.NonlinearSpec(models=[td.NonlinearSusceptibility(chi3=chi3)],\n",
-    "                                                        num_iters=10))\n",
+    "cavityMedium = td.Medium(\n",
+    "    permittivity=n_rods**2,\n",
+    "    nonlinear_spec=td.NonlinearSpec(models=[td.NonlinearSusceptibility(chi3=chi3)], num_iters=10),\n",
+    ")\n",
     "\n",
     "cavity_structure_nonlinear = td.Structure(\n",
     "    geometry=cavity,\n",
@@ -1657,27 +1675,27 @@
    ],
    "source": [
     "field_before_pulse = td.FieldTimeMonitor(\n",
-    "            center=[0, 0, 0],\n",
-    "            size=[td.inf,td.inf,0],\n",
-    "            name='field before pulse',\n",
-    "            start=6/gamma,\n",
-    "            stop=6/gamma,\n",
-    "            fields=['Ez']\n",
-    "        )\n",
+    "    center=[0, 0, 0],\n",
+    "    size=[td.inf, td.inf, 0],\n",
+    "    name=\"field before pulse\",\n",
+    "    start=6 / gamma,\n",
+    "    stop=6 / gamma,\n",
+    "    fields=[\"Ez\"],\n",
+    ")\n",
     "field_after_pulse = td.FieldTimeMonitor(\n",
-    "            center=[0, 0, 0],\n",
-    "            size=[td.inf,td.inf,0],\n",
-    "            name='field after pulse',\n",
-    "            start=19/gamma,\n",
-    "            stop=19/gamma,\n",
-    "            fields=['Ez']\n",
-    "        )\n",
+    "    center=[0, 0, 0],\n",
+    "    size=[td.inf, td.inf, 0],\n",
+    "    name=\"field after pulse\",\n",
+    "    start=19 / gamma,\n",
+    "    stop=19 / gamma,\n",
+    "    fields=[\"Ez\"],\n",
+    ")\n",
     "flux_t = td.FluxTimeMonitor(\n",
-    "            center=[flux_distance*a, 0, 0],\n",
-    "            size=[0,td.inf,td.inf],\n",
-    "            name=\"flux t\",\n",
-    "            normal_dir='+',\n",
-    "        )"
+    "    center=[flux_distance * a, 0, 0],\n",
+    "    size=[0, td.inf, td.inf],\n",
+    "    name=\"flux t\",\n",
+    "    normal_dir=\"+\",\n",
+    ")"
    ]
   },
   {
@@ -1713,12 +1731,13 @@
     }
    ],
    "source": [
-    "sim_nonlinear = sim_linear.updated_copy(run_time=pulse_run_time,\n",
-    "                                        medium=air,\n",
-    "                                        sources=[nonlinear_source],\n",
-    "                                        monitors=[field_before_pulse, field_after_pulse, flux_t],\n",
-    "                                        structures=[block, waveguide, cavity_air, cavity_structure_nonlinear, air_block, dbr_boundary],\n",
-    "                                       )"
+    "sim_nonlinear = sim_linear.updated_copy(\n",
+    "    run_time=pulse_run_time,\n",
+    "    medium=air,\n",
+    "    sources=[nonlinear_source],\n",
+    "    monitors=[field_before_pulse, field_after_pulse, flux_t],\n",
+    "    structures=[block, waveguide, cavity_air, cavity_structure_nonlinear, air_block, dbr_boundary],\n",
+    ")"
    ]
   },
   {
@@ -2156,7 +2175,9 @@
     }
    ],
    "source": [
-    "sim_nonlinear_data.plot_field(field_monitor_name='field before pulse', field_name=\"Ez\", vmin=-10, vmax=10)\n",
+    "sim_nonlinear_data.plot_field(\n",
+    "    field_monitor_name=\"field before pulse\", field_name=\"Ez\", vmin=-10, vmax=10\n",
+    ")\n",
     "plt.show()"
    ]
   },
@@ -2178,7 +2199,9 @@
     }
    ],
    "source": [
-    "sim_nonlinear_data.plot_field(field_monitor_name='field after pulse', field_name=\"Ez\", vmin=-10, vmax=10)\n",
+    "sim_nonlinear_data.plot_field(\n",
+    "    field_monitor_name=\"field after pulse\", field_name=\"Ez\", vmin=-10, vmax=10\n",
+    ")\n",
     "plt.show()"
    ]
   },
@@ -2208,10 +2231,10 @@
    "outputs": [],
    "source": [
     "def rolling_max(sig, freq, dt):\n",
-    "    interval = int(1/(dt * freq))\n",
+    "    interval = int(1 / (dt * freq))\n",
     "    N = len(sig) // interval\n",
-    "    times = np.linspace(0, (len(sig)-interval)*dt, N-1)\n",
-    "    sig_max = np.zeros(N-1)\n",
+    "    times = np.linspace(0, (len(sig) - interval) * dt, N - 1)\n",
+    "    sig_max = np.zeros(N - 1)\n",
     "    for i, t in enumerate(times):\n",
     "        start = int(t / dt)\n",
     "        stop = start + interval\n",
@@ -2245,7 +2268,10 @@
     }
    ],
    "source": [
-    "times_nonlinear, nonlinear_transmission = rolling_max(sim_nonlinear_data[\"flux t\"].flux.data, detuned_freq, sim_nonlinear.dt)\n",
+    "times_nonlinear, nonlinear_transmission = rolling_max(\n",
+    "    sim_nonlinear_data[\"flux t\"].flux.data, detuned_freq, sim_nonlinear.dt\n",
+    ")\n",
+    "\n",
     "\n",
     "# integrate a complex velocity f(x, t) using forward euler\n",
     "def integrate(f, num_steps, T):\n",
@@ -2254,17 +2280,19 @@
     "    time = 0\n",
     "    for time_step in range(1, num_steps):\n",
     "        x0 = integral[time_step - 1]\n",
-    "        integral[time_step] = x0 + dt*f(x0, time)\n",
+    "        integral[time_step] = x0 + dt * f(x0, time)\n",
     "        time += dt\n",
     "    return integral\n",
     "\n",
-    "interp_values = np.abs(source_time.amp_time(times_nonlinear))*np.sqrt(2*detuned_transmission)\n",
+    "\n",
+    "interp_values = np.abs(source_time.amp_time(times_nonlinear)) * np.sqrt(2 * detuned_transmission)\n",
     "\n",
     "power_input = scipy.interpolate.interp1d(times_nonlinear, interp_values)\n",
     "\n",
+    "\n",
     "def f(x, t):\n",
-    "    Sin = power_input(t)\n",
-    "    return 1j*gamma*(detuning - np.abs(x)**2/P_0)*x - gamma*x - gamma*power_input(t)\n",
+    "    return 1j * gamma * (detuning - np.abs(x) ** 2 / P_0) * x - gamma * x - gamma * power_input(t)\n",
+    "\n",
     "\n",
     "nt = len(times_nonlinear)\n",
     "tmax = times_nonlinear[-1]\n",
@@ -2274,16 +2302,16 @@
     "Sin = power_input(times_nonlinear)\n",
     "Sref = res\n",
     "\n",
-    "Pin = np.abs(Sin)**2\n",
-    "Pout = np.abs(Sin + Sref)**2\n",
+    "Pin = np.abs(Sin) ** 2\n",
+    "Pout = np.abs(Sin + Sref) ** 2\n",
     "\n",
     "fig, ax = plt.subplots(1, 1, figsize=(6, 5))\n",
-    "ax.set_xlabel('t$\\gamma$')\n",
-    "ax.set_ylabel('$P/P_0$')\n",
+    "ax.set_xlabel(r\"t$\\gamma$\")\n",
+    "ax.set_ylabel(\"$P/P_0$\")\n",
     "\n",
-    "plt.plot(times_nonlinear*gamma, Pin/P_0, label='Input power')\n",
-    "plt.plot(times_nonlinear*gamma, Pout/P_0, label='Analytic')\n",
-    "plt.plot(times_nonlinear*gamma, nonlinear_transmission/P_0, label='Tidy3D')\n",
+    "plt.plot(times_nonlinear * gamma, Pin / P_0, label=\"Input power\")\n",
+    "plt.plot(times_nonlinear * gamma, Pout / P_0, label=\"Analytic\")\n",
+    "plt.plot(times_nonlinear * gamma, nonlinear_transmission / P_0, label=\"Tidy3D\")\n",
     "\n",
     "ax.legend()\n",
     "plt.show()"
@@ -2324,7 +2352,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.0"
   },
   "title": "Bistability through Nonlinearity in Tidy3D | Flexcompute"
  },
diff --git a/BoundaryConditions.ipynb b/BoundaryConditions.ipynb
index bea622a9..3e1b6e39 100644
--- a/BoundaryConditions.ipynb
+++ b/BoundaryConditions.ipynb
@@ -27,12 +27,12 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3d imports\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n"
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -81,7 +81,7 @@
     "source_time = td.GaussianPulse(freq0=f0, fwidth=fwidth, offset=offset)\n",
     "\n",
     "# Simulation run time past the source decay (around t=2*offset/fwidth)\n",
-    "run_time = 50 / fwidth\n"
+    "run_time = 50 / fwidth"
    ]
   },
   {
@@ -211,7 +211,7 @@
     "# this monitor will be used to plot fields on a plane through the middle of the domain in the time domain\n",
     "monitor_xz_time = td.FieldTimeMonitor(\n",
     "    center=(0, 0, 0), size=(domain_size, 0, domain_size), interval=50, name=\"xz_time\"\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -268,7 +268,7 @@
     "# Visualize the geometry\n",
     "fig, ax1 = plt.subplots(figsize=(4, 4))\n",
     "sim.plot(y=0, ax=ax1)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -317,7 +317,7 @@
     "# Visualize the geometry\n",
     "fig, ax1 = plt.subplots(figsize=(4, 4))\n",
     "sim.plot(y=0, ax=ax1)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -682,15 +682,11 @@
     }
    ],
    "source": [
-    "sim_data = web.run(\n",
-    "    sim, task_name=\"bc_example1\", path=\"data/bc_example1.hdf5\", verbose=True\n",
-    ")\n",
+    "sim_data = web.run(sim, task_name=\"bc_example1\", path=\"data/bc_example1.hdf5\", verbose=True)\n",
     "fig, ax = plt.subplots(tight_layout=True, figsize=(5, 4))\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"xz_time\", field_name=\"Ex\", y=0, val=\"abs\", t=2e-13, ax=ax\n",
-    ")\n",
+    "sim_data.plot_field(field_monitor_name=\"xz_time\", field_name=\"Ex\", y=0, val=\"abs\", t=2e-13, ax=ax)\n",
     "\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -738,7 +734,7 @@
     ")\n",
     "\n",
     "ax = sim.plot(y=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -775,9 +771,7 @@
     "\n",
     "\n",
     "# create the Gaussian beam source\n",
-    "buffer_source = (\n",
-    "    domain_size / 10\n",
-    ")  # distance between the source and the bottom of the domain\n",
+    "buffer_source = domain_size / 10  # distance between the source and the bottom of the domain\n",
     "gaussian_beam = td.GaussianBeam(\n",
     "    center=(0, 0, -domain_size / 2 + buffer_source),\n",
     "    size=(td.inf, td.inf, 0),\n",
@@ -797,7 +791,7 @@
     "    monitors=[monitor_xz_time],\n",
     "    run_time=run_time,\n",
     "    boundary_spec=bspec_pml_pec,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -834,7 +828,7 @@
     "# Visualize the geometry\n",
     "fig, ax1 = plt.subplots(figsize=(4, 4))\n",
     "sim.plot(y=0, ax=ax1)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1189,9 +1183,7 @@
     }
    ],
    "source": [
-    "sim_data = web.run(\n",
-    "    sim, task_name=\"bc_example2\", path=\"data/bc_example2.hdf5\", verbose=True\n",
-    ")\n"
+    "sim_data = web.run(sim, task_name=\"bc_example2\", path=\"data/bc_example2.hdf5\", verbose=True)"
    ]
   },
   {
@@ -1227,13 +1219,9 @@
    ],
    "source": [
     "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 4))\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"xz_time\", field_name=\"Ex\", y=0, val=\"abs\", t=2e-13, ax=ax1\n",
-    ")\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"xz_time\", field_name=\"Ez\", y=0, val=\"abs\", t=2e-13, ax=ax2\n",
-    ")\n",
-    "plt.show()\n"
+    "sim_data.plot_field(field_monitor_name=\"xz_time\", field_name=\"Ex\", y=0, val=\"abs\", t=2e-13, ax=ax1)\n",
+    "sim_data.plot_field(field_monitor_name=\"xz_time\", field_name=\"Ez\", y=0, val=\"abs\", t=2e-13, ax=ax2)\n",
+    "plt.show()"
    ]
   },
   {
@@ -1317,7 +1305,7 @@
     "sim.plot(x=0, ax=ax1)\n",
     "sim.plot(y=0, ax=ax2)\n",
     "sim.plot(z=0, ax=ax3)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1672,9 +1660,7 @@
     }
    ],
    "source": [
-    "sim_data = web.run(\n",
-    "    sim, task_name=\"bc_example3\", path=\"data/bc_example3.hdf5\", verbose=True\n",
-    ")\n"
+    "sim_data = web.run(sim, task_name=\"bc_example3\", path=\"data/bc_example3.hdf5\", verbose=True)"
    ]
   },
   {
@@ -1740,36 +1726,20 @@
    ],
    "source": [
     "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 4))\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"xz_freq\", field_name=\"Ey\", y=0, val=\"abs\", f=f0, ax=ax1\n",
-    ")\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"xz_freq\", field_name=\"Ez\", y=0, val=\"abs\", f=f0, ax=ax2\n",
-    ")\n",
+    "sim_data.plot_field(field_monitor_name=\"xz_freq\", field_name=\"Ey\", y=0, val=\"abs\", f=f0, ax=ax1)\n",
+    "sim_data.plot_field(field_monitor_name=\"xz_freq\", field_name=\"Ez\", y=0, val=\"abs\", f=f0, ax=ax2)\n",
     "fig.suptitle(\"Notice that both Ey and Ez go to zero on both sides\", fontsize=16)\n",
     "\n",
     "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 4))\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"yz_freq\", field_name=\"Ex\", x=0, val=\"abs\", f=f0, ax=ax1\n",
-    ")\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"yz_freq\", field_name=\"Ez\", x=0, val=\"abs\", f=f0, ax=ax2\n",
-    ")\n",
-    "fig.suptitle(\n",
-    "    \"Notice that both Ex and Ez go to zero on the left (PEC) side\", fontsize=16\n",
-    ")\n",
+    "sim_data.plot_field(field_monitor_name=\"yz_freq\", field_name=\"Ex\", x=0, val=\"abs\", f=f0, ax=ax1)\n",
+    "sim_data.plot_field(field_monitor_name=\"yz_freq\", field_name=\"Ez\", x=0, val=\"abs\", f=f0, ax=ax2)\n",
+    "fig.suptitle(\"Notice that both Ex and Ez go to zero on the left (PEC) side\", fontsize=16)\n",
     "\n",
     "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 4))\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"yz_freq\", field_name=\"Hx\", x=0, val=\"abs\", f=f0, ax=ax1\n",
-    ")\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"yz_freq\", field_name=\"Hz\", x=0, val=\"abs\", f=f0, ax=ax2\n",
-    ")\n",
-    "fig.suptitle(\n",
-    "    \"Notice that both Hx and Hz go to zero on the right (PMC) side\", fontsize=16\n",
-    ")\n",
-    "plt.show()\n"
+    "sim_data.plot_field(field_monitor_name=\"yz_freq\", field_name=\"Hx\", x=0, val=\"abs\", f=f0, ax=ax1)\n",
+    "sim_data.plot_field(field_monitor_name=\"yz_freq\", field_name=\"Hz\", x=0, val=\"abs\", f=f0, ax=ax2)\n",
+    "fig.suptitle(\"Notice that both Hx and Hz go to zero on the right (PMC) side\", fontsize=16)\n",
+    "plt.show()"
    ]
   },
   {
@@ -1809,9 +1779,7 @@
     "# First, define the plane wave source, since it is needed to define the Bloch boundary in this case.\n",
     "# Note that in general, the Bloch boundary can also be defined by just providing a bandstructure-normalized Bloch vector.\n",
     "\n",
-    "buffer_source = (\n",
-    "    domain_size / 10\n",
-    ")  # distance between the source and the bottom of the domain\n",
+    "buffer_source = domain_size / 10  # distance between the source and the bottom of the domain\n",
     "plane_wave = td.PlaneWave(\n",
     "    center=(0, 0, -domain_size / 2 + buffer_source),\n",
     "    size=(td.inf, td.inf, 0),\n",
@@ -1847,7 +1815,7 @@
     "# Visualize the geometry\n",
     "fig, ax1 = plt.subplots(figsize=(4, 4))\n",
     "sim.plot(y=0, ax=ax1)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2202,7 +2170,7 @@
     }
    ],
    "source": [
-    "sim_data = web.run(sim, task_name=\"bc_example4\", path=\"data/bc_example4.hdf5\")\n"
+    "sim_data = web.run(sim, task_name=\"bc_example4\", path=\"data/bc_example4.hdf5\")"
    ]
   },
   {
@@ -2258,40 +2226,22 @@
    ],
    "source": [
     "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, tight_layout=True, figsize=(13, 4))\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"xz_freq\", field_name=\"Ey\", y=0, val=\"real\", f=f0, ax=ax1\n",
-    ")\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"xz_freq\", field_name=\"Ey\", y=0, val=\"real\", f=f0, ax=ax2\n",
-    ")\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"xz_freq\", field_name=\"Ez\", y=0, val=\"real\", f=f0, ax=ax3\n",
-    ")\n",
+    "sim_data.plot_field(field_monitor_name=\"xz_freq\", field_name=\"Ey\", y=0, val=\"real\", f=f0, ax=ax1)\n",
+    "sim_data.plot_field(field_monitor_name=\"xz_freq\", field_name=\"Ey\", y=0, val=\"real\", f=f0, ax=ax2)\n",
+    "sim_data.plot_field(field_monitor_name=\"xz_freq\", field_name=\"Ez\", y=0, val=\"real\", f=f0, ax=ax3)\n",
     "\n",
     "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, tight_layout=True, figsize=(13, 4))\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"yz_freq\", field_name=\"Ex\", x=0, val=\"real\", f=f0, ax=ax1\n",
-    ")\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"yz_freq\", field_name=\"Ey\", x=0, val=\"real\", f=f0, ax=ax2\n",
-    ")\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"yz_freq\", field_name=\"Ez\", x=0, val=\"real\", f=f0, ax=ax3\n",
-    ")\n",
+    "sim_data.plot_field(field_monitor_name=\"yz_freq\", field_name=\"Ex\", x=0, val=\"real\", f=f0, ax=ax1)\n",
+    "sim_data.plot_field(field_monitor_name=\"yz_freq\", field_name=\"Ey\", x=0, val=\"real\", f=f0, ax=ax2)\n",
+    "sim_data.plot_field(field_monitor_name=\"yz_freq\", field_name=\"Ez\", x=0, val=\"real\", f=f0, ax=ax3)\n",
     "\n",
     "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, tight_layout=True, figsize=(13, 4))\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"xy_freq\", field_name=\"Ex\", z=0, val=\"real\", f=f0, ax=ax1\n",
-    ")\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"xy_freq\", field_name=\"Ey\", z=0, val=\"real\", f=f0, ax=ax2\n",
-    ")\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"xy_freq\", field_name=\"Ez\", z=0, val=\"real\", f=f0, ax=ax3\n",
-    ")\n",
+    "sim_data.plot_field(field_monitor_name=\"xy_freq\", field_name=\"Ex\", z=0, val=\"real\", f=f0, ax=ax1)\n",
+    "sim_data.plot_field(field_monitor_name=\"xy_freq\", field_name=\"Ey\", z=0, val=\"real\", f=f0, ax=ax2)\n",
+    "sim_data.plot_field(field_monitor_name=\"xy_freq\", field_name=\"Ez\", z=0, val=\"real\", f=f0, ax=ax3)\n",
     "fig.suptitle(\"Observe the phi=pi/6 angle w.r.t. the x axis\", fontsize=16)\n",
     "\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
diff --git a/BraggGratings.ipynb b/BraggGratings.ipynb
index 2a9b4bd5..1cef3b19 100644
--- a/BraggGratings.ipynb
+++ b/BraggGratings.ipynb
@@ -34,8 +34,8 @@
    "outputs": [],
    "source": [
     "# basic imports\n",
-    "import numpy as np\n",
     "import matplotlib.pylab as plt\n",
+    "import numpy as np\n",
     "\n",
     "# Tidy3D imports\n",
     "import tidy3d as td"
diff --git a/BroadbandDirectionalCoupler.ipynb b/BroadbandDirectionalCoupler.ipynb
index 942f8566..8a1cc453 100644
--- a/BroadbandDirectionalCoupler.ipynb
+++ b/BroadbandDirectionalCoupler.ipynb
@@ -38,10 +38,9 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "import gdstk\n",
-    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
     "from tidy3d.plugins import waveguide"
@@ -898,7 +897,7 @@
     "theta_t1 = phase_2 - phase_1\n",
     "\n",
     "plt.plot(ldas, np.unwrap(theta_t1))\n",
-    "plt.xlabel(\"$\\lambda (\\mu m)$\")\n",
+    "plt.xlabel(r\"$\\lambda (\\mu m)$\")\n",
     "plt.ylabel(\"Phase shift\")\n",
     "plt.xlim(1.5, 1.6)\n",
     "plt.show()"
@@ -1350,7 +1349,7 @@
     "theta_t2 = phase_2 - phase_1\n",
     "\n",
     "plt.plot(ldas, np.unwrap(theta_t2))\n",
-    "plt.xlabel(\"$\\lambda (\\mu m)$\")\n",
+    "plt.xlabel(r\"$\\lambda (\\mu m)$\")\n",
     "plt.ylabel(\"Phase shift\")\n",
     "plt.xlim(1.5, 1.6)\n",
     "plt.show()"
@@ -1393,7 +1392,6 @@
     "\n",
     "for i, L_1 in enumerate(L_1_array):\n",
     "    for j, L_2 in enumerate(L_2_array):\n",
-    "\n",
     "        # compute the transmission coefficient\n",
     "        t = np.cos(np.pi * del_n * L_1 / lda0)\n",
     "        # compute the coupling coefficient\n",
@@ -1411,9 +1409,9 @@
     "        E_out = np.dot(C, np.dot(P_t, np.dot(P, np.dot(P_t, np.dot(C, E_in)))))\n",
     "\n",
     "        # total transmission\n",
-    "        T = np.abs(E_out[0,0]) ** 2 + np.abs(E_out[1,0]) ** 2\n",
+    "        T = np.abs(E_out[0, 0]) ** 2 + np.abs(E_out[1, 0]) ** 2\n",
     "        # power transmitted to the cross port\n",
-    "        eta_cross_1550[i, j] = np.abs(E_out[0,0]) ** 2 / T"
+    "        eta_cross_1550[i, j] = np.abs(E_out[0, 0]) ** 2 / T"
    ]
   },
   {
@@ -1461,8 +1459,8 @@
     ")\n",
     "plt.clabel(cp, inline=1, fontsize=10)\n",
     "cp = plt.contourf(L_2_array, L_1_array, eta_cross_1550, levels=100, vmin=0, vmax=1, cmap=\"bwr\")\n",
-    "plt.xlabel(\"$L_2 (\\mu m)$\")\n",
-    "plt.ylabel(\"$L_1 (\\mu m)$\")\n",
+    "plt.xlabel(r\"$L_2 (\\mu m)$\")\n",
+    "plt.ylabel(r\"$L_1 (\\mu m)$\")\n",
     "plt.title(\"Transmission to cross port\")\n",
     "plt.colorbar()\n",
     "plt.show()"
@@ -1511,8 +1509,8 @@
     "\n",
     "    E_out = np.dot(C, np.dot(P_t, np.dot(P, np.dot(P_t, np.dot(C, E_in)))))\n",
     "\n",
-    "    T = np.abs(E_out[0,0]) ** 2 + np.abs(E_out[1,0]) ** 2\n",
-    "    eta_cross_ldas[i] = np.abs(E_out[0,0]) ** 2 / T"
+    "    T = np.abs(E_out[0, 0]) ** 2 + np.abs(E_out[1, 0]) ** 2\n",
+    "    eta_cross_ldas[i] = np.abs(E_out[0, 0]) ** 2 / T"
    ]
   },
   {
@@ -1554,7 +1552,7 @@
     "plt.legend()\n",
     "plt.xlim(1.5, 1.6)\n",
     "plt.ylim(-6, 0)\n",
-    "plt.xlabel(\"$\\lambda (\\mu m)$\")\n",
+    "plt.xlabel(r\"$\\lambda (\\mu m)$\")\n",
     "plt.ylabel(\"Transmission to cross port (dB)\")\n",
     "plt.show()"
    ]
@@ -1910,7 +1908,7 @@
    "id": "219d6dc6",
    "metadata": {},
    "source": [
-    "The cost is reasonaly so we can run the simulation."
+    "The cost is reasonably so we can run the simulation."
    ]
   },
   {
@@ -2257,7 +2255,7 @@
     "\n",
     "plt.axhline(y=-3, color=\"r\", linestyle=\"--\", linewidth=3, label=\"Ideal\")\n",
     "plt.plot(ldas, 10 * np.log10(T_cross / T_total), linewidth=3, label=\"FDTD\")\n",
-    "plt.xlabel(\"$\\lambda (\\mu m)$\")\n",
+    "plt.xlabel(r\"$\\lambda (\\mu m)$\")\n",
     "plt.ylabel(\"Transmission to cross port (dB)\")\n",
     "plt.xlim(1.5, 1.6)\n",
     "plt.ylim(-6, 0)\n",
@@ -2338,7 +2336,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.0"
   },
   "title": "Broadband Directional Coupler Modeling | Flexcompute",
   "widgets": {
diff --git a/BroadbandPlaneWaveWithConstantObliqueIncidentAngle.ipynb b/BroadbandPlaneWaveWithConstantObliqueIncidentAngle.ipynb
index adac90c4..c4041266 100644
--- a/BroadbandPlaneWaveWithConstantObliqueIncidentAngle.ipynb
+++ b/BroadbandPlaneWaveWithConstantObliqueIncidentAngle.ipynb
@@ -48,16 +48,18 @@
     "def plot_theta_actual(theta_deg):\n",
     "    import numpy as np\n",
     "    from matplotlib import pyplot as plt\n",
+    "\n",
     "    theta = theta_deg / 180 * np.pi\n",
     "    freq_ratio = np.linspace(0.5, 1.5, 100)\n",
     "    theta_actual = 180 / np.pi * np.arcsin(np.sin(theta) / freq_ratio)\n",
-    "    plt.axhline(y=theta * 180 / np.pi, color='k', ls=\"--\")\n",
+    "    plt.axhline(y=theta * 180 / np.pi, color=\"k\", ls=\"--\")\n",
     "    plt.plot(freq_ratio, theta_actual)\n",
     "    plt.legend([\"desired\", \"actual\"])\n",
     "    plt.ylabel(\"Angle, deg.\")\n",
-    "    plt.xlabel(f\"Relative frequency $f/f_0$\")\n",
+    "    plt.xlabel(\"Relative frequency $f/f_0$\")\n",
     "    plt.show()\n",
     "\n",
+    "\n",
     "plot_theta_actual(theta_deg=30)"
    ]
   },
@@ -134,8 +136,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tidy3d as td\n",
     "import numpy as np\n",
+    "import tidy3d as td\n",
     "from matplotlib import pyplot as plt\n",
     "from tidy3d import web"
    ]
@@ -311,10 +313,7 @@
     "\n",
     "field_freqs = [freq_range[0], freq0, freq_range[1]]\n",
     "field_yz = td.FieldMonitor(\n",
-    "    center=(0, 0, 0),\n",
-    "    size=(0, td.inf, td.inf),\n",
-    "    freqs=field_freqs,\n",
-    "    name=\"field_yz\"\n",
+    "    center=(0, 0, 0), size=(0, td.inf, td.inf), freqs=field_freqs, name=\"field_yz\"\n",
     ")"
    ]
   },
@@ -351,8 +350,12 @@
     "\n",
     "# Boundary conditions\n",
     "boundary_spec_fixed_k = td.BoundarySpec(\n",
-    "    x=td.Boundary.bloch_from_source(axis=0, source=source_fixed_k, domain_size=sim_size[0], medium=mat0),\n",
-    "    y=td.Boundary.bloch_from_source(axis=1, source=source_fixed_k, domain_size=sim_size[1], medium=mat0),\n",
+    "    x=td.Boundary.bloch_from_source(\n",
+    "        axis=0, source=source_fixed_k, domain_size=sim_size[0], medium=mat0\n",
+    "    ),\n",
+    "    y=td.Boundary.bloch_from_source(\n",
+    "        axis=1, source=source_fixed_k, domain_size=sim_size[1], medium=mat0\n",
+    "    ),\n",
     "    z=td.Boundary.pml(),\n",
     ")"
    ]
@@ -426,7 +429,9 @@
     "    shutoff=1e-7,\n",
     ")\n",
     "\n",
-    "sim_fixed_angle = sim_fixed_k.updated_copy(sources=[source_fixed_angle], boundary_spec=boundary_spec_fixed_angle)"
+    "sim_fixed_angle = sim_fixed_k.updated_copy(\n",
+    "    sources=[source_fixed_angle], boundary_spec=boundary_spec_fixed_angle\n",
+    ")"
    ]
   },
   {
@@ -646,7 +651,7 @@
    ],
    "source": [
     "cost_fixed_angle = web.estimate_cost(task_id=task_fixed_angle)\n",
-    "cost_fixed_k = web.estimate_cost(task_id=task_fixed_k) "
+    "cost_fixed_k = web.estimate_cost(task_id=task_fixed_k)"
    ]
   },
   {
@@ -1336,7 +1341,7 @@
    "id": "4c4b5d79-fbec-4830-a0c7-2722ed0929ec",
    "metadata": {},
    "source": [
-    "We start analysis of simulation resutls with a visual inspection of field distributions recorded at three different frequencies. In the code below we plot the fixed in-plane k simulation results in the top row and the fixed angle simulation results in the second row."
+    "We start the analysis of simulation results with a visual inspection of field distributions recorded at three different frequencies. In the code below, we plot the fixed in-plane k simulation results in the top row and the fixed angle simulation results in the second row."
    ]
   },
   {
@@ -1363,12 +1368,14 @@
     "\n",
     "for ind in range(num_plots):\n",
     "    sim_data_fixed_k.plot_field(\"field_yz\", \"Ex\", \"real\", f=float(field_freqs[ind]), ax=ax[0, ind])\n",
-    "    sim_data_fixed_angle.plot_field(\"field_yz\", \"Ex\", \"real\", f=float(field_freqs[ind]), ax=ax[1, ind])\n",
-    "    ax[0, ind].set_title(fr\"Fixed in-plane k, $f/f_0 = {field_freqs[ind] / freq0}$\")\n",
-    "    ax[1, ind].set_title(fr\"Fixed angle, $f/f_0 = {field_freqs[ind] / freq0}$\")\n",
-    "    \n",
+    "    sim_data_fixed_angle.plot_field(\n",
+    "        \"field_yz\", \"Ex\", \"real\", f=float(field_freqs[ind]), ax=ax[1, ind]\n",
+    "    )\n",
+    "    ax[0, ind].set_title(rf\"Fixed in-plane k, $f/f_0 = {field_freqs[ind] / freq0}$\")\n",
+    "    ax[1, ind].set_title(rf\"Fixed angle, $f/f_0 = {field_freqs[ind] / freq0}$\")\n",
+    "\n",
     "plt.tight_layout()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1417,11 +1424,17 @@
     "    n_list = [n_list0[i], n_list1[i], n_list2[i], n_list3[i], n_list4[i], n_list0[i]]\n",
     "\n",
     "    # get transmission for fixed angle\n",
-    "    T = tmm.coh_tmm(\"p\", n_list, d_list, theta, lam)[\"T\"] * np.cos(pol) ** 2 + tmm.coh_tmm(\"s\", n_list, d_list, theta, lam)[\"T\"] * np.sin(pol) ** 2\n",
+    "    T = (\n",
+    "        tmm.coh_tmm(\"p\", n_list, d_list, theta, lam)[\"T\"] * np.cos(pol) ** 2\n",
+    "        + tmm.coh_tmm(\"s\", n_list, d_list, theta, lam)[\"T\"] * np.sin(pol) ** 2\n",
+    "    )\n",
     "    transmission_tmm_fixed_angle.append(T)\n",
-    "    \n",
+    "\n",
     "    # get transmission for fixed in-plane k\n",
-    "    T = tmm.coh_tmm(\"p\", n_list, d_list, theta_fixed_k, lam)[\"T\"] * np.cos(pol) ** 2 + tmm.coh_tmm(\"s\", n_list, d_list, theta_fixed_k, lam)[\"T\"] * np.sin(pol) ** 2\n",
+    "    T = (\n",
+    "        tmm.coh_tmm(\"p\", n_list, d_list, theta_fixed_k, lam)[\"T\"] * np.cos(pol) ** 2\n",
+    "        + tmm.coh_tmm(\"s\", n_list, d_list, theta_fixed_k, lam)[\"T\"] * np.sin(pol) ** 2\n",
+    "    )\n",
     "    transmission_tmm_fixed_k.append(T)"
    ]
   },
@@ -1454,13 +1467,39 @@
     "_, ax = plt.subplots(1, 1, figsize=(10, 5))\n",
     "\n",
     "ax.axvline(x=td.C_0 / freq0, label=\"source central frequency\", color=\"k\", ls=\"--\", lw=0.5)\n",
-    "ax.plot(monitor_lambdas, transmission_tmm_fixed_angle, label=r\"TMM $\\theta = \\theta_0$\", lw=3, color=\"skyblue\")\n",
-    "ax.plot(monitor_lambdas, transmission_tmm_fixed_k, label=r\"TMM $\\theta = \\arcsin\\left(\\frac{\\lambda}{\\lambda_0} \\sin(\\theta_0) \\right)$\", lw=3, color=\"lightcoral\")\n",
-    "ax.plot(monitor_lambdas, sim_data_fixed_angle[\"flux\"].flux, \":\" , lw=2, label=\"Tidy3D fixed angle\" , color=\"darkblue\")\n",
-    "ax.plot(monitor_lambdas, sim_data_fixed_k[\"flux\"].flux, \"--\", lw=2, label=\"Tidy3D fixed in-plane k (Bloch BC)\", color=\"darkred\")\n",
+    "ax.plot(\n",
+    "    monitor_lambdas,\n",
+    "    transmission_tmm_fixed_angle,\n",
+    "    label=r\"TMM $\\theta = \\theta_0$\",\n",
+    "    lw=3,\n",
+    "    color=\"skyblue\",\n",
+    ")\n",
+    "ax.plot(\n",
+    "    monitor_lambdas,\n",
+    "    transmission_tmm_fixed_k,\n",
+    "    label=r\"TMM $\\theta = \\arcsin\\left(\\frac{\\lambda}{\\lambda_0} \\sin(\\theta_0) \\right)$\",\n",
+    "    lw=3,\n",
+    "    color=\"lightcoral\",\n",
+    ")\n",
+    "ax.plot(\n",
+    "    monitor_lambdas,\n",
+    "    sim_data_fixed_angle[\"flux\"].flux,\n",
+    "    \":\",\n",
+    "    lw=2,\n",
+    "    label=\"Tidy3D fixed angle\",\n",
+    "    color=\"darkblue\",\n",
+    ")\n",
+    "ax.plot(\n",
+    "    monitor_lambdas,\n",
+    "    sim_data_fixed_k[\"flux\"].flux,\n",
+    "    \"--\",\n",
+    "    lw=2,\n",
+    "    label=\"Tidy3D fixed in-plane k (Bloch BC)\",\n",
+    "    color=\"darkred\",\n",
+    ")\n",
     "ax.set_xlabel(\"wavelength ($\\\\mu m$)\")\n",
     "ax.set_ylabel(\"Transmitted\")\n",
-    "ax.set_title(fr\"$\\theta = {theta_deg}$, $\\phi = {phi_deg}$, $\\alpha_p = {pol_deg}$\")\n",
+    "ax.set_title(rf\"$\\theta = {theta_deg}$, $\\phi = {phi_deg}$, $\\alpha_p = {pol_deg}$\")\n",
     "ax.legend()\n",
     "plt.show()"
    ]
@@ -1483,7 +1522,7 @@
  "metadata": {
   "description": "This notebook demonstrate how to simulate a broadband plane wave with a constant oblique incident angle in Tidy3D.",
   "kernelspec": {
-   "display_name": "flex",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1498,7 +1537,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.11.0"
   },
   "title": "Broadband plane wave with a constant oblique incident angle in Tidy3D | Flexcompute"
  },
diff --git a/BullseyeCavityPSO.ipynb b/BullseyeCavityPSO.ipynb
index 2abb30b2..589c619f 100644
--- a/BullseyeCavityPSO.ipynb
+++ b/BullseyeCavityPSO.ipynb
@@ -25,13 +25,13 @@
     "# pip install pyswarms\n",
     "\n",
     "# Standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pylab as plt\n",
+    "import numpy as np\n",
     "\n",
     "# Import regular tidy3d\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n",
     "import tidy3d.plugins.design as tdd\n",
+    "import tidy3d.web as web\n",
     "from tidy3d.plugins.mode import ModeSolver\n",
     "from tidy3d.plugins.mode.web import run as run_mode_solver"
    ]
@@ -104,11 +104,11 @@
     "run_time = 5e-12  # Simulation run time.\n",
     "\n",
     "# Material definition.\n",
-    "mat_tio2 = td.Medium(permittivity=n_tio2 ** 2)  # TiO2 medium.\n",
-    "mat_sio2 = td.Medium(permittivity=n_sio2 ** 2)  # SiO2 medium.\n",
+    "mat_tio2 = td.Medium(permittivity=n_tio2**2)  # TiO2 medium.\n",
+    "mat_sio2 = td.Medium(permittivity=n_sio2**2)  # SiO2 medium.\n",
     "mat_etch = td.Medium(permittivity=1)  # Etch medium.\n",
-    "mat_core = td.Medium(permittivity=n_core ** 2)  # Fiber core medium.\n",
-    "mat_clad = td.Medium(permittivity=n_clad ** 2)  # Fiber cladding medium.\n",
+    "mat_core = td.Medium(permittivity=n_core**2)  # Fiber core medium.\n",
+    "mat_clad = td.Medium(permittivity=n_clad**2)  # Fiber cladding medium.\n",
     "\n",
     "# Computational domain size.\n",
     "pml_spacing = 0.6 * wl\n",
@@ -184,7 +184,7 @@
     "    # Flux monitor to get the total dipole power.\n",
     "    flux_dip = td.FluxMonitor(\n",
     "        center=(0, 0, size_z / 2),\n",
-    "        size=(0.8*size_x, 0.8*size_y, size_z),\n",
+    "        size=(0.8 * size_x, 0.8 * size_y, size_z),\n",
     "        freqs=freqs,\n",
     "        exclude_surfaces=(\"z-\",),\n",
     "        name=\"flux_dip\",\n",
@@ -618,6 +618,7 @@
     "    sim = get_simulation(r, p, w, h, t_sio2, dcf)\n",
     "    return sim\n",
     "\n",
+    "\n",
     "def fn_post(sim_data: td.SimulationData) -> float:\n",
     "    \"\"\"Analyze SimulationData from a bullseye cavity simulation and return coupling efficiency.\"\"\"\n",
     "    ce = np.max(get_coupling_eff(sim_data))\n",
@@ -660,8 +661,8 @@
     "init_par[:, 4] = init_par[:, 4] * t_sio2\n",
     "init_par[:, 5] = init_par[:, 5] * d_cf\n",
     "\n",
-    "particle_swarm=tdd.MethodParticleSwarm(\n",
-    "    n_particles=n_particle, \n",
+    "particle_swarm = tdd.MethodParticleSwarm(\n",
+    "    n_particles=n_particle,\n",
     "    n_iter=iterations,\n",
     "    cognitive_coeff=0.5,\n",
     "    social_coeff=0.3,\n",
@@ -669,7 +670,9 @@
     "    init_pos=init_par,\n",
     ")\n",
     "\n",
-    "design_space = tdd.DesignSpace(method=particle_swarm, parameters=parameters, task_name=\"PSO_Notebook\", path_dir=\"./data\")"
+    "design_space = tdd.DesignSpace(\n",
+    "    method=particle_swarm, parameters=parameters, task_name=\"PSO_Notebook\", path_dir=\"./data\"\n",
+    ")"
    ]
   },
   {
@@ -1064,7 +1067,7 @@
     "best_par = results.optimizer.swarm.best_pos\n",
     "cost_history = results.optimizer.cost_history\n",
     "\n",
-    "print(f\"Best parameters:\")\n",
+    "print(\"Best parameters:\")\n",
     "print(f\"r_cav = {best_par[0]:.3f} um\")\n",
     "print(f\"p_bragg = {best_par[1]:.3f} um\")\n",
     "print(f\"w_bragg = {best_par[2]:.3f} um\")\n",
@@ -1116,11 +1119,11 @@
     ")\n",
     "\n",
     "# Field monitor to visualize the fields at the peak coupling efficiency.\n",
-    "wl_mon = 0.725 # Wavelength for field monitor.\n",
+    "wl_mon = 0.725  # Wavelength for field monitor.\n",
     "field_monitor_xz_f = td.FieldMonitor(\n",
-    "    center=(0, 0.05, final_design.center[2]/2),\n",
+    "    center=(0, 0.05, final_design.center[2] / 2),\n",
     "    size=(final_design.size[0], 0, final_design.size[2]),\n",
-    "    freqs=[td.C_0/wl_mon],\n",
+    "    freqs=[td.C_0 / wl_mon],\n",
     "    name=\"field_xz_f\",\n",
     ")\n",
     "\n",
@@ -1547,7 +1550,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "p_bulk_a = ((2 * np.pi * freqs) ** 2 / (12 * np.pi)) * (td.MU_0 * n_tio2 / td.C_0)\n"
+    "p_bulk_a = ((2 * np.pi * freqs) ** 2 / (12 * np.pi)) * (td.MU_0 * n_tio2 / td.C_0)"
    ]
   },
   {
diff --git a/CMOSRGBSensor.ipynb b/CMOSRGBSensor.ipynb
index f9892b51..94fc6513 100644
--- a/CMOSRGBSensor.ipynb
+++ b/CMOSRGBSensor.ipynb
@@ -24,13 +24,13 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# Tidy3D imports\n",
     "import tidy3d as td\n",
     "from tidy3d import web\n",
-    "from tidy3d.plugins.dispersion import FastDispersionFitter, AdvancedFastFitterParam"
+    "from tidy3d.plugins.dispersion import AdvancedFastFitterParam, FastDispersionFitter"
    ]
   },
   {
@@ -75,12 +75,12 @@
     }
    ],
    "source": [
-    "width_len = Lx/2  # Lens and color pixel width.\n",
-    "radius_len = 1.0 # Lens radius.\n",
+    "width_len = Lx / 2  # Lens and color pixel width.\n",
+    "radius_len = 1.0  # Lens radius.\n",
     "\n",
     "# A focal distance = R*n/(n-1)~3um is sufficient.\n",
-    "height_len = (1-np.cos(np.arcsin(width_len/radius_len/2)))*radius_len\n",
-    "print(f'The height of the lens is {height_len:.3f} microns.')"
+    "height_len = (1 - np.cos(np.arcsin(width_len / radius_len / 2))) * radius_len\n",
+    "print(f\"The height of the lens is {height_len:.3f} microns.\")"
    ]
   },
   {
@@ -96,23 +96,27 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "d_len_to_filter = 0.2 # Distance from the base of the lens to the top of the filter.\n",
-    "t_filter = 1.0 # Filter thickness.\n",
-    "d_filter_to_shield = 0.5 # Distance from the base of filter to the top of metallic shields.\n",
+    "d_len_to_filter = 0.2  # Distance from the base of the lens to the top of the filter.\n",
+    "t_filter = 1.0  # Filter thickness.\n",
+    "d_filter_to_shield = 0.5  # Distance from the base of filter to the top of metallic shields.\n",
     "\n",
     "# Metal shields are simplified to be metallic layer with holes.\n",
-    "t_shield = 0.10 # Shield thickness.\n",
-    "radius_shield = 0.95 * width_len # Radius of hole.\n",
-    "d_shield_to_connect = 0.5 # Distance from the base of shield to the top of metallic interconnect.\n",
+    "t_shield = 0.10  # Shield thickness.\n",
+    "radius_shield = 0.95 * width_len  # Radius of hole.\n",
+    "d_shield_to_connect = 0.5  # Distance from the base of shield to the top of metallic interconnect.\n",
     "\n",
     "# Metallic interconnects are assumed to be rectangular rods.\n",
-    "t_connect = 0.10 # Thickness.\n",
-    "width_connect = 0.15 #Width of each rod.\n",
-    "gap_connect = 0.2 #Gap between adjacent interconnection planes.\n",
-    "distance_connect = 1.8*width_len # Distance between the center of rods parallel to the sides of unit cell. \n",
+    "t_connect = 0.10  # Thickness.\n",
+    "width_connect = 0.15  # Width of each rod.\n",
+    "gap_connect = 0.2  # Gap between adjacent interconnection planes.\n",
+    "distance_connect = (\n",
+    "    1.8 * width_len\n",
+    ")  # Distance between the center of rods parallel to the sides of unit cell.\n",
     "\n",
-    "d_connect_to_AR = 0.5 # Distance from the base of the interconnects to the top of the anti-reflection layer.\n",
-    "t_silicon = 2 # Thickness of silicon photon-detector."
+    "d_connect_to_AR = (\n",
+    "    0.5  # Distance from the base of the interconnects to the top of the anti-reflection layer.\n",
+    ")\n",
+    "t_silicon = 2  # Thickness of silicon photon-detector."
    ]
   },
   {
@@ -156,10 +160,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "SiN = td.material_library['SiN']['Horiba']\n",
-    "Silicon = td.material_library['aSi']['Horiba']\n",
-    "Silica = td.material_library['SiO2']['Palik_Lossless']\n",
-    "metal = td.material_library['Al']['Rakic1995']"
+    "SiN = td.material_library[\"SiN\"][\"Horiba\"]\n",
+    "Silicon = td.material_library[\"aSi\"][\"Horiba\"]\n",
+    "Silica = td.material_library[\"SiO2\"][\"Palik_Lossless\"]\n",
+    "metal = td.material_library[\"Al\"][\"Rakic1995\"]"
    ]
   },
   {
@@ -302,19 +306,25 @@
     "fname_r = \"./misc/red_eps.csv\"\n",
     "fitter_r = FastDispersionFitter.from_file(fname_r, delimiter=\",\")\n",
     "fitter_r = fitter_r.updated_copy(k_data=fitter_r.k_data + 1e-2)\n",
-    "mat_red, _ = fitter_r.fit(max_num_poles=9, eps_inf=1.0, advanced_param=advanced_param, tolerance_rms=2e-2)\n",
+    "mat_red, _ = fitter_r.fit(\n",
+    "    max_num_poles=9, eps_inf=1.0, advanced_param=advanced_param, tolerance_rms=2e-2\n",
+    ")\n",
     "\n",
     "# Green material\n",
     "fname_g = \"./misc/green_eps.csv\"\n",
     "fitter_g = FastDispersionFitter.from_file(fname_g, delimiter=\",\")\n",
     "fitter_g = fitter_g.updated_copy(k_data=fitter_g.k_data + 1e-2)\n",
-    "mat_green, _ = fitter_g.fit(max_num_poles=9, eps_inf=1.0, advanced_param=advanced_param, tolerance_rms=2e-2)\n",
+    "mat_green, _ = fitter_g.fit(\n",
+    "    max_num_poles=9, eps_inf=1.0, advanced_param=advanced_param, tolerance_rms=2e-2\n",
+    ")\n",
     "\n",
     "# Blue material\n",
     "fname_b = \"./misc/blue_eps.csv\"\n",
     "fitter_b = FastDispersionFitter.from_file(fname_b, delimiter=\",\")\n",
     "fitter_b = fitter_b.updated_copy(k_data=fitter_b.k_data + 1e-2)\n",
-    "mat_blue, _ = fitter_b.fit(max_num_poles=9, eps_inf=1.0, advanced_param=advanced_param, tolerance_rms=2e-2)"
+    "mat_blue, _ = fitter_b.fit(\n",
+    "    max_num_poles=9, eps_inf=1.0, advanced_param=advanced_param, tolerance_rms=2e-2\n",
+    ")"
    ]
   },
   {
@@ -365,24 +375,36 @@
    "outputs": [],
    "source": [
     "# Compute the thickness of anti-reflection layer so that it is reflectiveless for the green light.\n",
-    "n_SiN, _ = SiN.eps_complex_to_nk(SiN.eps_model(td.C_0/lam_green))\n",
-    "t_AR = lam_green/4/n_SiN\n",
+    "n_SiN, _ = SiN.eps_complex_to_nk(SiN.eps_model(td.C_0 / lam_green))\n",
+    "t_AR = lam_green / 4 / n_SiN\n",
     "\n",
     "# Total size of computational domain along z-direction.\n",
-    "Lz = dPML + height_len + d_len_to_filter + t_filter + d_filter_to_shield + \\\n",
-    "    t_shield + d_shield_to_connect + 2*t_connect + gap_connect + d_connect_to_AR + t_AR + t_silicon\n",
+    "Lz = (\n",
+    "    dPML\n",
+    "    + height_len\n",
+    "    + d_len_to_filter\n",
+    "    + t_filter\n",
+    "    + d_filter_to_shield\n",
+    "    + t_shield\n",
+    "    + d_shield_to_connect\n",
+    "    + 2 * t_connect\n",
+    "    + gap_connect\n",
+    "    + d_connect_to_AR\n",
+    "    + t_AR\n",
+    "    + t_silicon\n",
+    ")\n",
     "\n",
     "# Wavelength and frequency range for Gaussian source.\n",
-    "freq_range = (td.C_0/lam_range[1], td.C_0/lam_range[0])\n",
+    "freq_range = (td.C_0 / lam_range[1], td.C_0 / lam_range[0])\n",
     "freq0 = np.mean(np.array(freq_range))\n",
-    "fwidth = freq0/3\n",
+    "fwidth = freq0 / 3\n",
     "\n",
     "# Maximum simulation runtime.\n",
-    "runtime = 100/fwidth\n",
+    "runtime = 100 / fwidth\n",
     "\n",
     "# Monitor frequency defined at the central wavelength of RGB colors.\n",
-    "monitor_field_freqs = td.C_0/np.array([lam_red, lam_green, lam_blue])\n",
-    "monitor_flux_freqs = np.linspace(td.C_0/lam_range[1], td.C_0/lam_range[0], 200)"
+    "monitor_field_freqs = td.C_0 / np.array([lam_red, lam_green, lam_blue])\n",
+    "monitor_flux_freqs = np.linspace(td.C_0 / lam_range[1], td.C_0 / lam_range[0], 200)"
    ]
   },
   {
@@ -403,21 +425,21 @@
     "structs = []\n",
     "\n",
     "# Center of the sphere.\n",
-    "z0_len = Lz/2 - dPML - radius_len\n",
+    "z0_len = Lz / 2 - dPML - radius_len\n",
     "x0_len = width_len / 2\n",
-    "y0_len = width_len /2\n",
+    "y0_len = width_len / 2\n",
     "\n",
     "# Names of the lens structures.\n",
     "len_names = [\"len blue\", \"len red\", \"len green1\", \"len green2\"]\n",
     "# Signs of their x,y center positions on the Bayer unit cell.\n",
-    "len_sign = [[1,1], [-1,-1], [-1,1], [1, -1]]\n",
+    "len_sign = [[1, 1], [-1, -1], [-1, 1], [1, -1]]\n",
     "\n",
     "# Now we include the lens structures.\n",
     "for l_name, l_sign in zip(len_names, len_sign):\n",
     "    structs.append(\n",
     "        td.Structure(\n",
     "            geometry=td.Sphere(\n",
-    "                center=(l_sign[0]*x0_len, l_sign[1]*y0_len, z0_len),\n",
+    "                center=(l_sign[0] * x0_len, l_sign[1] * y0_len, z0_len),\n",
     "                radius=radius_len,\n",
     "            ),\n",
     "            medium=Silica,\n",
@@ -440,17 +462,17 @@
    "outputs": [],
    "source": [
     "# Lower and upper boundary of the silica layer.\n",
-    "z_lower_silica = -Lz/2+t_silicon\n",
-    "z_upper_silica = Lz/2-dPML-height_len\n",
+    "z_lower_silica = -Lz / 2 + t_silicon\n",
+    "z_upper_silica = Lz / 2 - dPML - height_len\n",
     "\n",
     "structs.append(\n",
     "    td.Structure(\n",
     "        geometry=td.Box.from_bounds(\n",
-    "            rmin = (-td.inf,-td.inf,z_lower_silica),\n",
-    "            rmax = (td.inf,td.inf,z_upper_silica),\n",
+    "            rmin=(-td.inf, -td.inf, z_lower_silica),\n",
+    "            rmax=(td.inf, td.inf, z_upper_silica),\n",
     "        ),\n",
     "        medium=Silica,\n",
-    "        name='silica layer',\n",
+    "        name=\"silica layer\",\n",
     "    )\n",
     ")"
    ]
@@ -472,23 +494,23 @@
     "z_upper_filter = z_upper_silica - d_len_to_filter\n",
     "z_lower_filter = z_upper_filter - t_filter\n",
     "\n",
-    "ePML = 100 #um, extra length to extend into PML \n",
+    "ePML = 100  # um, extra length to extend into PML\n",
     "\n",
     "# Color filter names.\n",
     "cf_names = [\"blue filter\", \"red filter\", \"green1 filter\", \"green2 filter\"]\n",
     "# Color filter materials.\n",
-    "cf_materials = [mat_blue, mat_red, mat_green, mat_green] \n",
+    "cf_materials = [mat_blue, mat_red, mat_green, mat_green]\n",
     "# Minimum and maximum filter bound coordinates.\n",
-    "cf_rmin = [[0,0], [-ePML,-ePML], [-ePML,0], [0,-ePML]]\n",
-    "cf_rmax = [[ePML,ePML], [0,0], [0,ePML], [ePML,0]]\n",
+    "cf_rmin = [[0, 0], [-ePML, -ePML], [-ePML, 0], [0, -ePML]]\n",
+    "cf_rmax = [[ePML, ePML], [0, 0], [0, ePML], [ePML, 0]]\n",
     "\n",
     "# Now we include the color filter structures.\n",
     "for cf_name, cf_mat, r_min, r_max in zip(cf_names, cf_materials, cf_rmin, cf_rmax):\n",
     "    structs.append(\n",
     "        td.Structure(\n",
     "            geometry=td.Box.from_bounds(\n",
-    "                rmin = (r_min[0],r_min[1],z_lower_filter),\n",
-    "                rmax = (r_max[0],r_max[1],z_upper_filter),\n",
+    "                rmin=(r_min[0], r_min[1], z_lower_filter),\n",
+    "                rmax=(r_max[0], r_max[1], z_upper_filter),\n",
     "            ),\n",
     "            medium=cf_mat,\n",
     "            name=cf_name,\n",
@@ -510,32 +532,32 @@
    "outputs": [],
    "source": [
     "# A-coordinate of the center of metal shield.\n",
-    "z0_shield = z_lower_filter - d_filter_to_shield - t_shield/2\n",
+    "z0_shield = z_lower_filter - d_filter_to_shield - t_shield / 2\n",
     "\n",
     "# First, a metallic layer:\n",
     "structs.append(\n",
-    "   td.Structure(\n",
-    "       geometry=td.Box(\n",
-    "           size = (td.inf, td.inf, t_shield),\n",
-    "           center = (0,0,z0_shield),\n",
-    "       ),\n",
-    "       medium=metal,\n",
-    "       name='metal shield',\n",
-    "   )\n",
+    "    td.Structure(\n",
+    "        geometry=td.Box(\n",
+    "            size=(td.inf, td.inf, t_shield),\n",
+    "            center=(0, 0, z0_shield),\n",
+    "        ),\n",
+    "        medium=metal,\n",
+    "        name=\"metal shield\",\n",
+    "    )\n",
     ")\n",
     "\n",
     "# Then, make a hole:\n",
     "structs.append(\n",
-    "   td.Structure(\n",
-    "       geometry=td.Cylinder(\n",
-    "           center = (0,0,z0_shield),\n",
-    "           axis = 2,\n",
-    "           length = t_shield,\n",
-    "           radius = radius_shield,\n",
-    "       ),\n",
-    "       medium=Silica,\n",
-    "       name='metal shield hole',\n",
-    "   )\n",
+    "    td.Structure(\n",
+    "        geometry=td.Cylinder(\n",
+    "            center=(0, 0, z0_shield),\n",
+    "            axis=2,\n",
+    "            length=t_shield,\n",
+    "            radius=radius_shield,\n",
+    "        ),\n",
+    "        medium=Silica,\n",
+    "        name=\"metal shield hole\",\n",
+    "    )\n",
     ")"
    ]
   },
@@ -553,48 +575,48 @@
    "outputs": [],
    "source": [
     "# Z-coordinate of the center of the interconnection planes.\n",
-    "z0_connect = z0_shield - t_shield/2 - d_shield_to_connect - t_connect/2\n",
-    "z1_connect = z0_connect - gap_connect  - t_connect\n",
+    "z0_connect = z0_shield - t_shield / 2 - d_shield_to_connect - t_connect / 2\n",
+    "z1_connect = z0_connect - gap_connect - t_connect\n",
     "\n",
     "# Add the interconnections parallel to the sides of the unit cell.\n",
     "for i in range(4):\n",
     "    # Locate the the interconnections at x-, y- directions in different planes.\n",
-    "    zc = z0_connect if (i%2) else z1_connect\n",
+    "    zc = z0_connect if (i % 2) else z1_connect\n",
     "\n",
     "    center = [0, zc]\n",
-    "    center.insert(i%2, distance_connect/2 * (-1) ** (i//2))\n",
-    "    \n",
-    "    size = [td.inf,t_connect]\n",
-    "    size.insert(i%2, width_connect)\n",
+    "    center.insert(i % 2, distance_connect / 2 * (-1) ** (i // 2))\n",
+    "\n",
+    "    size = [td.inf, t_connect]\n",
+    "    size.insert(i % 2, width_connect)\n",
     "    structs.append(\n",
     "        td.Structure(\n",
     "            geometry=td.Box(\n",
-    "                center = center,\n",
-    "                size = size,\n",
+    "                center=center,\n",
+    "                size=size,\n",
     "            ),\n",
     "            medium=metal,\n",
-    "            name='interconnect ' + str(i),\n",
+    "            name=\"interconnect \" + str(i),\n",
     "        )\n",
     "    )\n",
     "\n",
     "# Add the interconnections at the center of the unit cell.\n",
     "for i in range(2):\n",
     "    # Locate the the interconnections at x-, y- directions in different planes.\n",
-    "    zc = z0_connect if (i%2) else z1_connect\n",
-    "    \n",
-    "    center = [0, 0, zc]    \n",
+    "    zc = z0_connect if (i % 2) else z1_connect\n",
+    "\n",
+    "    center = [0, 0, zc]\n",
     "    size = [td.inf, t_connect]\n",
-    "    size.insert(i%2, 2*width_connect)\n",
+    "    size.insert(i % 2, 2 * width_connect)\n",
     "    structs.append(\n",
     "        td.Structure(\n",
     "            geometry=td.Box(\n",
-    "                center = center,\n",
-    "                size = size,\n",
+    "                center=center,\n",
+    "                size=size,\n",
     "            ),\n",
     "            medium=metal,\n",
-    "            name='interconnect_ground ' + str(i),\n",
+    "            name=\"interconnect_ground \" + str(i),\n",
     "        )\n",
-    "    )    "
+    "    )"
    ]
   },
   {
@@ -611,16 +633,16 @@
    "outputs": [],
    "source": [
     "# z-coordinate of the center\n",
-    "z0_AR = z1_connect - t_connect/2 - d_connect_to_AR - t_AR/2\n",
+    "z0_AR = z1_connect - t_connect / 2 - d_connect_to_AR - t_AR / 2\n",
     "\n",
     "structs.append(\n",
     "    td.Structure(\n",
     "        geometry=td.Box(\n",
-    "            center = (0,0, z0_AR),\n",
-    "            size = (td.inf,td.inf,t_AR),\n",
+    "            center=(0, 0, z0_AR),\n",
+    "            size=(td.inf, td.inf, t_AR),\n",
     "        ),\n",
     "        medium=SiN,\n",
-    "        name='anti-reflection',\n",
+    "        name=\"anti-reflection\",\n",
     "    )\n",
     ")"
    ]
@@ -640,16 +662,16 @@
    "source": [
     "# Z-coordinate of the silicon photodetector center. Add substantial thickness to extend into PML\n",
     "\n",
-    "z0_silica = -Lz/2 - ePML\n",
+    "z0_silica = -Lz / 2 - ePML\n",
     "\n",
     "structs.append(\n",
     "    td.Structure(\n",
     "        geometry=td.Box(\n",
-    "            center = (0,0, z0_silica),\n",
-    "            size = (td.inf,td.inf,(t_silicon+ePML)*2),\n",
+    "            center=(0, 0, z0_silica),\n",
+    "            size=(td.inf, td.inf, (t_silicon + ePML) * 2),\n",
     "        ),\n",
     "        medium=Silicon,\n",
-    "        name='silicon layer',\n",
+    "        name=\"silicon layer\",\n",
     "    )\n",
     ")"
    ]
@@ -672,7 +694,7 @@
    "source": [
     "# Boundary condition\n",
     "absorber = td.Boundary.absorber()\n",
-    "periodic =  td.Boundary.periodic()\n",
+    "periodic = td.Boundary.periodic()\n",
     "bspec = td.BoundarySpec(x=periodic, y=periodic, z=absorber)\n",
     "\n",
     "# Grid specs\n",
@@ -694,14 +716,14 @@
    "outputs": [],
    "source": [
     "source = td.PlaneWave(\n",
-    "    center=(0,0,(Lz-dPML)/2),\n",
+    "    center=(0, 0, (Lz - dPML) / 2),\n",
     "    size=(td.inf, td.inf, 0),\n",
-    "    source_time = td.GaussianPulse(\n",
+    "    source_time=td.GaussianPulse(\n",
     "        freq0=freq0,\n",
     "        fwidth=fwidth,\n",
     "    ),\n",
-    "    direction='-',\n",
-    "    name='planewave',\n",
+    "    direction=\"-\",\n",
+    "    name=\"planewave\",\n",
     ")"
    ]
   },
@@ -716,7 +738,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will include field monitors to observe the field propagating from the source to the silicon photodetectors. In addition, four flux monitors will be added to calculate the pixel power at the dielectric-silicon interface. A fill factor of 50% will be used to simulate the active region area of the phototetectors."
+    "We will include field monitors to observe the field propagating from the source to the silicon photodetectors. In addition, four flux monitors will be added to calculate the pixel power at the dielectric-silicon interface. A fill factor of 50% will be used to simulate the active region area of the photodetectors."
    ]
   },
   {
@@ -726,48 +748,58 @@
    "outputs": [],
    "source": [
     "monitors = []\n",
-    "z_silicon_top = -Lz/2+t_silicon\n",
+    "z_silicon_top = -Lz / 2 + t_silicon\n",
     "\n",
     "# Field monitor in xy plane just below antireflection layer.\n",
     "monitors.append(\n",
     "    td.FieldMonitor(\n",
-    "        center = (0, 0, -Lz/2+t_silicon-0.001),\n",
-    "        size = (td.inf, td.inf, 0),\n",
-    "        freqs = monitor_field_freqs,\n",
-    "        name='field_xy_silicon',\n",
+    "        center=(0, 0, -Lz / 2 + t_silicon - 0.001),\n",
+    "        size=(td.inf, td.inf, 0),\n",
+    "        freqs=monitor_field_freqs,\n",
+    "        name=\"field_xy_silicon\",\n",
     "    )\n",
     ")\n",
     "\n",
     "# Field monitor in yz plane crossing the center blue and green filter.\n",
     "monitors.append(\n",
     "    td.FieldMonitor(\n",
-    "        center = (width_len/2, 0, 0),\n",
-    "        size = (0, td.inf, td.inf),\n",
-    "        freqs = monitor_field_freqs,\n",
-    "        name='field_yz_BG',\n",
+    "        center=(width_len / 2, 0, 0),\n",
+    "        size=(0, td.inf, td.inf),\n",
+    "        freqs=monitor_field_freqs,\n",
+    "        name=\"field_yz_BG\",\n",
     "    )\n",
     ")\n",
     "\n",
     "# Field monitor in yz plane crossing the center red and green filter.\n",
     "monitors.append(\n",
     "    td.FieldMonitor(\n",
-    "        center = (-width_len/2, 0, 0),\n",
-    "        size = (0, td.inf, td.inf),\n",
-    "        freqs = monitor_field_freqs,\n",
-    "        name='field_yz_GR',\n",
+    "        center=(-width_len / 2, 0, 0),\n",
+    "        size=(0, td.inf, td.inf),\n",
+    "        freqs=monitor_field_freqs,\n",
+    "        name=\"field_yz_GR\",\n",
     "    )\n",
     ")\n",
     "\n",
     "# Photodetector flux monitors.\n",
-    "fmon_names = [\"flux_si_blue\", \"flux_si_red\", \"flux_si_green1\", \"flux_si_green2\"]  # Names of the RGGB flux monitors.\n",
-    "fmon_sign = [[1,1], [-1,-1], [-1,1], [1,-1]]  # Signs of their x,y center positions on the Bayer unit cell.\n",
+    "fmon_names = [\n",
+    "    \"flux_si_blue\",\n",
+    "    \"flux_si_red\",\n",
+    "    \"flux_si_green1\",\n",
+    "    \"flux_si_green2\",\n",
+    "]  # Names of the RGGB flux monitors.\n",
+    "fmon_sign = [\n",
+    "    [1, 1],\n",
+    "    [-1, -1],\n",
+    "    [-1, 1],\n",
+    "    [1, -1],\n",
+    "]  # Signs of their x,y center positions on the Bayer unit cell.\n",
     "\n",
     "for f_name, f_sign in zip(fmon_names, fmon_sign):\n",
     "    monitors.append(\n",
     "        td.FluxMonitor(\n",
-    "            center = (f_sign[0]*width_len/2, f_sign[1]*width_len/2, z_silicon_top),\n",
-    "            size = (0.7*width_len, 0.7*width_len, 0),\n",
-    "            freqs = monitor_flux_freqs,\n",
+    "            center=(f_sign[0] * width_len / 2, f_sign[1] * width_len / 2, z_silicon_top),\n",
+    "            size=(0.7 * width_len, 0.7 * width_len, 0),\n",
+    "            freqs=monitor_flux_freqs,\n",
     "            name=f_name,\n",
     "        )\n",
     "    )"
@@ -787,13 +819,13 @@
    "outputs": [],
    "source": [
     "sim = td.Simulation(\n",
-    "    size = (Lx,Ly,Lz),\n",
-    "    grid_spec = grid_spec,\n",
-    "    structures = structs,\n",
-    "    sources = [source],\n",
-    "    monitors = monitors,\n",
-    "    run_time = runtime,\n",
-    "    boundary_spec = bspec,\n",
+    "    size=(Lx, Ly, Lz),\n",
+    "    grid_spec=grid_spec,\n",
+    "    structures=structs,\n",
+    "    sources=[source],\n",
+    "    monitors=monitors,\n",
+    "    run_time=runtime,\n",
+    "    boundary_spec=bspec,\n",
     ")"
    ]
   },
@@ -947,11 +979,11 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(1,4,figsize=(10,5),tight_layout=True)\n",
-    "sim.plot(x=width_len/1.99,ax=ax[0])\n",
-    "sim.plot(y=width_len/2,ax=ax[1])\n",
-    "sim.plot(x=-width_len/1.99,ax=ax[2])\n",
-    "sim.plot(y=-width_len/2,ax=ax[3])\n",
+    "fig, ax = plt.subplots(1, 4, figsize=(10, 5), tight_layout=True)\n",
+    "sim.plot(x=width_len / 1.99, ax=ax[0])\n",
+    "sim.plot(y=width_len / 2, ax=ax[1])\n",
+    "sim.plot(x=-width_len / 1.99, ax=ax[2])\n",
+    "sim.plot(y=-width_len / 2, ax=ax[3])\n",
     "ax[0].set_title(\"G1   B\")\n",
     "ax[1].set_title(\"G2   B\")\n",
     "ax[2].set_title(\"R   G2\")\n",
@@ -1383,14 +1415,14 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(1,3, figsize=(12, 4))\n",
-    "sim_data.plot_field('field_yz_BG', 'E', val='abs^2', ax=ax[0], f=td.C_0/lam_red)\n",
-    "sim_data.plot_field('field_yz_BG', 'E', val='abs^2', ax=ax[1], f=td.C_0/lam_green)\n",
-    "sim_data.plot_field('field_yz_BG', 'E', val='abs^2', ax=ax[2], f=td.C_0/lam_blue)\n",
-    "ax[0].set_title('$\\\\lambda = 650$ nm')\n",
-    "ax[1].set_title('$\\\\lambda = 550$ nm')\n",
-    "ax[2].set_title('$\\\\lambda = 450$ nm')\n",
-    "fig.suptitle('G1 B sensor cross-section')\n",
+    "fig, ax = plt.subplots(1, 3, figsize=(12, 4))\n",
+    "sim_data.plot_field(\"field_yz_BG\", \"E\", val=\"abs^2\", ax=ax[0], f=td.C_0 / lam_red)\n",
+    "sim_data.plot_field(\"field_yz_BG\", \"E\", val=\"abs^2\", ax=ax[1], f=td.C_0 / lam_green)\n",
+    "sim_data.plot_field(\"field_yz_BG\", \"E\", val=\"abs^2\", ax=ax[2], f=td.C_0 / lam_blue)\n",
+    "ax[0].set_title(\"$\\\\lambda = 650$ nm\")\n",
+    "ax[1].set_title(\"$\\\\lambda = 550$ nm\")\n",
+    "ax[2].set_title(\"$\\\\lambda = 450$ nm\")\n",
+    "fig.suptitle(\"G1 B sensor cross-section\")\n",
     "plt.show()"
    ]
   },
@@ -1418,14 +1450,14 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(1,3, figsize=(12, 4))\n",
-    "sim_data.plot_field('field_yz_GR', 'E', val='abs^2', ax=ax[0], f=td.C_0/lam_red)\n",
-    "sim_data.plot_field('field_yz_GR', 'E', val='abs^2', ax=ax[1], f=td.C_0/lam_green)\n",
-    "sim_data.plot_field('field_yz_GR', 'E', val='abs^2', ax=ax[2], f=td.C_0/lam_blue)\n",
-    "ax[0].set_title('$\\\\lambda = 650$ nm')\n",
-    "ax[1].set_title('$\\\\lambda = 550$ nm')\n",
-    "ax[2].set_title('$\\\\lambda = 450$ nm')\n",
-    "fig.suptitle('R G2 sensor cross-section')\n",
+    "fig, ax = plt.subplots(1, 3, figsize=(12, 4))\n",
+    "sim_data.plot_field(\"field_yz_GR\", \"E\", val=\"abs^2\", ax=ax[0], f=td.C_0 / lam_red)\n",
+    "sim_data.plot_field(\"field_yz_GR\", \"E\", val=\"abs^2\", ax=ax[1], f=td.C_0 / lam_green)\n",
+    "sim_data.plot_field(\"field_yz_GR\", \"E\", val=\"abs^2\", ax=ax[2], f=td.C_0 / lam_blue)\n",
+    "ax[0].set_title(\"$\\\\lambda = 650$ nm\")\n",
+    "ax[1].set_title(\"$\\\\lambda = 550$ nm\")\n",
+    "ax[2].set_title(\"$\\\\lambda = 450$ nm\")\n",
+    "fig.suptitle(\"R G2 sensor cross-section\")\n",
     "plt.show()"
    ]
   },
@@ -1453,13 +1485,13 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(1,3, tight_layout=True, figsize=(14, 4))\n",
-    "sim_data.plot_field('field_xy_silicon', 'E', val='abs^2', ax=ax[0], f=td.C_0/lam_red)\n",
-    "sim_data.plot_field('field_xy_silicon', 'E', val='abs^2', ax=ax[1], f=td.C_0/lam_green)\n",
-    "sim_data.plot_field('field_xy_silicon', 'E', val='abs^2', ax=ax[2], f=td.C_0/lam_blue)\n",
-    "ax[0].set_title('R')\n",
-    "ax[1].set_title('G')\n",
-    "ax[2].set_title('B')\n",
+    "fig, ax = plt.subplots(1, 3, tight_layout=True, figsize=(14, 4))\n",
+    "sim_data.plot_field(\"field_xy_silicon\", \"E\", val=\"abs^2\", ax=ax[0], f=td.C_0 / lam_red)\n",
+    "sim_data.plot_field(\"field_xy_silicon\", \"E\", val=\"abs^2\", ax=ax[1], f=td.C_0 / lam_green)\n",
+    "sim_data.plot_field(\"field_xy_silicon\", \"E\", val=\"abs^2\", ax=ax[2], f=td.C_0 / lam_blue)\n",
+    "ax[0].set_title(\"R\")\n",
+    "ax[1].set_title(\"G\")\n",
+    "ax[2].set_title(\"B\")\n",
     "plt.show()"
    ]
   },
@@ -1500,25 +1532,25 @@
     }
    ],
    "source": [
-    "flux_red = -sim_data['flux_si_red'].flux\n",
-    "flux_blue = -sim_data['flux_si_blue'].flux\n",
-    "flux_green1 = -sim_data['flux_si_green1'].flux\n",
-    "flux_green2 = -sim_data['flux_si_green2'].flux\n",
+    "flux_red = -sim_data[\"flux_si_red\"].flux\n",
+    "flux_blue = -sim_data[\"flux_si_blue\"].flux\n",
+    "flux_green1 = -sim_data[\"flux_si_green1\"].flux\n",
+    "flux_green2 = -sim_data[\"flux_si_green2\"].flux\n",
     "flux_green = flux_green1 + flux_green2\n",
     "\n",
     "fig, ax = plt.subplots(1, figsize=(5, 4))\n",
-    "lambda_flux = td.C_0/monitor_flux_freqs\n",
-    "ax.plot(lambda_flux, flux_green,'g')\n",
-    "ax.plot(lambda_flux, flux_red,'r')\n",
-    "ax.plot(lambda_flux, flux_blue,'b')\n",
-    "ax.set_xlabel('Wavelength ($\\\\mu m$)')\n",
-    "ax.set_ylabel('OE')\n",
+    "lambda_flux = td.C_0 / monitor_flux_freqs\n",
+    "ax.plot(lambda_flux, flux_green, \"g\")\n",
+    "ax.plot(lambda_flux, flux_red, \"r\")\n",
+    "ax.plot(lambda_flux, flux_blue, \"b\")\n",
+    "ax.set_xlabel(\"Wavelength ($\\\\mu m$)\")\n",
+    "ax.set_ylabel(\"OE\")\n",
     "ax.set_xlim(0.4, 0.7)\n",
     "ax.set_ylim(0, 0.25)\n",
     "\n",
-    "ax.vlines(0.45, 0, 0.25, linestyles='dashed', colors='b')\n",
-    "ax.vlines(0.55, 0, 0.25, linestyles='dashed', colors='g')\n",
-    "ax.vlines(0.65, 0, 0.25, linestyles='dashed', colors='r')\n",
+    "ax.vlines(0.45, 0, 0.25, linestyles=\"dashed\", colors=\"b\")\n",
+    "ax.vlines(0.55, 0, 0.25, linestyles=\"dashed\", colors=\"g\")\n",
+    "ax.vlines(0.65, 0, 0.25, linestyles=\"dashed\", colors=\"r\")\n",
     "plt.show()"
    ]
   },
@@ -1556,7 +1588,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.11.0"
   },
   "title": "How to perform the simulation of a RGB CMOS image sensor in Tidy3D FDTD"
  },
diff --git a/CavityFOM.ipynb b/CavityFOM.ipynb
index e9ff473a..1d6b44a9 100644
--- a/CavityFOM.ipynb
+++ b/CavityFOM.ipynb
@@ -29,8 +29,8 @@
    "outputs": [],
    "source": [
     "# Standard python imports.\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# Tidy3D imports.\n",
     "import tidy3d as td\n",
@@ -87,7 +87,9 @@
     "\n",
     "# Simulation runtime.\n",
     "runtime_fwidth = 20.0  # In units of 1/frequency bandwidth of the source.\n",
-    "t_start_fwidth = 2.0  # Time to start monitoring after source has decayed, units of 1/frequency bandwidth.\n",
+    "t_start_fwidth = (\n",
+    "    2.0  # Time to start monitoring after source has decayed, units of 1/frequency bandwidth.\n",
+    ")\n",
     "run_time = runtime_fwidth / freq_bw\n",
     "t_start = t_start_fwidth / freq_bw\n",
     "print(f\"Total runtime = {(run_time*1e12):.2f} ps\")\n",
@@ -152,9 +154,7 @@
     "            if i != 0 or abs(j) > l_number:  # don't populate cavity with cylinders\n",
     "                var_radius = R\n",
     "                shift_x = 0\n",
-    "                if i == 0 and (\n",
-    "                    abs(j) == l_number + 1\n",
-    "                ):  # checks if cylinder is on side of cavity\n",
+    "                if i == 0 and (abs(j) == l_number + 1):  # checks if cylinder is on side of cavity\n",
     "                    var_radius = side_R\n",
     "                    shift_x = del_R * np.sign(j)\n",
     "                c = td.Cylinder(\n",
@@ -166,9 +166,7 @@
     "                    length=height,\n",
     "                )\n",
     "                cylinders.append(c)\n",
-    "    structure = td.Structure(\n",
-    "        geometry=td.GeometryGroup(geometries=cylinders), medium=mat_hole\n",
-    "    )\n",
+    "    structure = td.Structure(geometry=td.GeometryGroup(geometries=cylinders), medium=mat_hole)\n",
     "    return structure"
    ]
   },
@@ -268,7 +266,7 @@
     "mode_freq = freq_c\n",
     "\n",
     "# Apodization to exclude the source pulse from the frequency-domain monitors.\n",
-    "apod = td.ApodizationSpec(start=t_start, width=t_start/10)\n",
+    "apod = td.ApodizationSpec(start=t_start, width=t_start / 10)\n",
     "\n",
     "# Field monitor.\n",
     "mon_box = td.Box(center=(0, 0, 0), size=(size_x / 2, size_y / 2, 4 * t))\n",
@@ -281,11 +279,9 @@
     "    apodization=apod,\n",
     ")\n",
     "\n",
-    "eps_mon = td.PermittivityMonitor(center = mon_box.center,\n",
-    "                                 size = mon_box.size,\n",
-    "                                 freqs = mode_freq,\n",
-    "                                 name = 'eps_mon',\n",
-    "                                 colocate=False)"
+    "eps_mon = td.PermittivityMonitor(\n",
+    "    center=mon_box.center, size=mon_box.size, freqs=mode_freq, name=\"eps_mon\", colocate=False\n",
+    ")"
    ]
   },
   {
@@ -447,7 +443,7 @@
     "    grid_spec=td.GridSpec.auto(wavelength=wl_c, min_steps_per_wvl=20),\n",
     "    structures=[phc_slab, phc_holes],\n",
     "    sources=[dip_source],\n",
-    "    monitors=[mon_fieldtime, mon_field,eps_mon],\n",
+    "    monitors=[mon_fieldtime, mon_field, eps_mon],\n",
     "    run_time=run_time,\n",
     "    shutoff=0,\n",
     "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
@@ -486,9 +482,7 @@
     "sim.plot(z=0.0, ax=ax1)\n",
     "\n",
     "plot_time = 3 / freq_bw\n",
-    "sim.sources[0].source_time.plot(\n",
-    "    times=np.linspace(0, plot_time, 1001), val=\"abs\", ax=ax2\n",
-    ")\n",
+    "sim.sources[0].source_time.plot(times=np.linspace(0, plot_time, 1001), val=\"abs\", ax=ax2)\n",
     "ax2.set_xlim(0, plot_time)\n",
     "ax2.vlines(t_start, 0, 1, linewidth=2, color=\"g\", alpha=0.4)\n",
     "ax2.legend([\"source\", \"start time\"])\n",
@@ -1084,7 +1078,7 @@
     "e_2 = np.abs(e_x) ** 2 + np.abs(e_y) ** 2 + np.abs(e_z) ** 2\n",
     "\n",
     "# Permittivity distribution.\n",
-    "eps = abs(sim_data['eps_mon'].eps_xx).isel(f=0)\n",
+    "eps = abs(sim_data[\"eps_mon\"].eps_xx).isel(f=0)\n",
     "\n",
     "# Calculation of effective mode volume.\n",
     "e_eps = eps * e_2\n",
@@ -1180,7 +1174,7 @@
     "fig, ax1 = plt.subplots(1, 1, tight_layout=True, figsize=(7, 4))\n",
     "\n",
     "ax1.plot(del_w * 1e-9 / (2 * np.pi), F_p_w)\n",
-    "ax1.set_xlabel(\"$(\\omega_{c} - \\omega)/2 \\pi$ (GHz)\")\n",
+    "ax1.set_xlabel(r\"$(\\omega_{c} - \\omega)/2 \\pi$ (GHz)\")\n",
     "ax1.set_ylabel(\"$F_{p}$\")\n",
     "ax1.set_xlim(-detuning * 1e-9, detuning * 1e-9)\n",
     "plt.show()"
diff --git a/CharacteristicImpedanceCalculator.ipynb b/CharacteristicImpedanceCalculator.ipynb
index ac9bdb72..22e6bff5 100644
--- a/CharacteristicImpedanceCalculator.ipynb
+++ b/CharacteristicImpedanceCalculator.ipynb
@@ -32,14 +32,14 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.ticker as ticker\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3D imports\n",
     "import tidy3d as td\n",
-    "from tidy3d import web\n",
     "import tidy3d.plugins.microwave as microwave\n",
+    "from tidy3d import web\n",
     "\n",
     "# We set the logging level to \"ERROR\". Otherwise there are numerous warnings due to the proximity of the structure to PML boundaries.\n",
     "td.config.logging_level = \"ERROR\""
@@ -66,7 +66,7 @@
    },
    "outputs": [],
    "source": [
-    "#Frequency range of interest is from 0.1 GHz to 10 GHz\n",
+    "# Frequency range of interest is from 0.1 GHz to 10 GHz\n",
     "freq_start = 1e8\n",
     "freq_stop = 100e8\n",
     "freq0 = (freq_stop) / 2\n",
@@ -100,7 +100,7 @@
     "diel = td.Medium(permittivity=4.4)\n",
     "\n",
     "# Create a structure representing a microstrip along the x axis\n",
-    "strip_center = (0, 0, height+thickness/2)\n",
+    "strip_center = (0, 0, height + thickness / 2)\n",
     "strip = td.Structure(\n",
     "    geometry=td.Box(\n",
     "        center=strip_center,\n",
@@ -113,7 +113,7 @@
     "substrate = td.Structure(\n",
     "    geometry=td.Box(\n",
     "        center=[0, 0, 0],\n",
-    "        size=[td.inf, td.inf, 2*height],\n",
+    "        size=[td.inf, td.inf, 2 * height],\n",
     "    ),\n",
     "    medium=diel,\n",
     ")\n",
@@ -1420,7 +1420,9 @@
    "source": [
     "# Plot Ez field in the xy plane\n",
     "f, ax1 = plt.subplots(1, 1, tight_layout=True, figsize=(8, 3))\n",
-    "sim_data_with_load.plot_field(field_monitor_name=\"propagate\", field_name=\"Ez\", val=\"abs\", f=freq0, ax=ax1)\n",
+    "sim_data_with_load.plot_field(\n",
+    "    field_monitor_name=\"propagate\", field_name=\"Ez\", val=\"abs\", f=freq0, ax=ax1\n",
+    ")\n",
     "update_axis_with_format(ax1, formatter, \"x (mm)\", \"y (mm)\")\n",
     "ax1.set_title(\"Cross section at z = 0.5 mm\")\n",
     "plt.show()"
diff --git a/ChargeSolver.ipynb b/ChargeSolver.ipynb
index 75698d46..1c54e9e9 100644
--- a/ChargeSolver.ipynb
+++ b/ChargeSolver.ipynb
@@ -92,11 +92,11 @@
     "h_side = 0.22\n",
     "\n",
     "w_contact = 1.2\n",
-    "h_contact = 1.\n",
+    "h_contact = 1.0\n",
     "\n",
-    "z_size = h_clearance/5\n",
+    "z_size = h_clearance / 5\n",
     "\n",
-    "res = h_clearance/10"
+    "res = h_clearance / 10"
    ]
   },
   {
@@ -152,10 +152,10 @@
     }
    ],
    "source": [
-    "SiO2_optic = td.material_library['SiO2']['Palik_Lossless']\n",
+    "SiO2_optic = td.material_library[\"SiO2\"][\"Palik_Lossless\"]\n",
     "SiO2 = td.MultiPhysicsMedium(\n",
     "    optical=SiO2_optic,\n",
-    "    charge=td.ChargeInsulatorMedium(permittivity=SiO2_optic.eps_model(frequency=0)), \n",
+    "    charge=td.ChargeInsulatorMedium(permittivity=SiO2_optic.eps_model(frequency=0)),\n",
     "    name=\"SiO2\",\n",
     ")"
    ]
@@ -178,7 +178,7 @@
    "source": [
     "SiO2 = td.MultiPhysicsMedium(\n",
     "    optical=td.Medium(permittivity=3.9),\n",
-    "    charge=td.ChargeInsulatorMedium(permittivity=3.9), # redefining permittivity\n",
+    "    charge=td.ChargeInsulatorMedium(permittivity=3.9),  # redefining permittivity\n",
     "    name=\"SiO2\",\n",
     ")"
    ]
@@ -198,15 +198,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "aux = td.MultiPhysicsMedium(\n",
-    "    charge=td.ChargeConductorMedium(conductivity=1),\n",
-    "    name=\"aux\"\n",
-    ")\n",
+    "aux = td.MultiPhysicsMedium(charge=td.ChargeConductorMedium(conductivity=1), name=\"aux\")\n",
     "\n",
-    "air = td.MultiPhysicsMedium(\n",
-    "    heat=td.FluidSpec(),\n",
-    "    name=\"air\"\n",
-    ")"
+    "air = td.MultiPhysicsMedium(heat=td.FluidSpec(), name=\"air\")"
    ]
   },
   {
@@ -287,7 +281,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "intrinsic_si = td.material_library['cSi'].variants['Si_MultiPhysics'].medium.charge"
+    "intrinsic_si = td.material_library[\"cSi\"].variants[\"Si_MultiPhysics\"].medium.charge"
    ]
   },
   {
@@ -390,33 +384,30 @@
     "donor_boxes = []\n",
     "\n",
     "acceptor_boxes.append(\n",
-    "    td.ConstantDoping.from_bounds(\n",
-    "        rmin=[-5, 0, -np.inf],\n",
-    "        rmax=[5, 0.22, np.inf],\n",
-    "        concentration=1e15\n",
-    "    )\n",
+    "    td.ConstantDoping.from_bounds(rmin=[-5, 0, -np.inf], rmax=[5, 0.22, np.inf], concentration=1e15)\n",
     ")\n",
     "\n",
     "# p implant\n",
     "acceptor_boxes.append(\n",
     "    td.GaussianDoping.from_bounds(\n",
-    "    rmin=[-6, -0.3, -np.inf],\n",
-    "    rmax=[-0.15, 0.098, np.inf], \n",
-    "    concentration=7e17,\n",
-    "    ref_con=1e6,\n",
-    "    width=0.1,\n",
-    "    source=\"ymax\")\n",
+    "        rmin=[-6, -0.3, -np.inf],\n",
+    "        rmax=[-0.15, 0.098, np.inf],\n",
+    "        concentration=7e17,\n",
+    "        ref_con=1e6,\n",
+    "        width=0.1,\n",
+    "        source=\"ymax\",\n",
+    "    )\n",
     ")\n",
     "\n",
     "# n implant\n",
     "donor_boxes.append(\n",
     "    td.GaussianDoping.from_bounds(\n",
     "        rmin=[0.15, -0.3, -np.inf],\n",
-    "        rmax=[6, 0.098, np.inf], \n",
+    "        rmax=[6, 0.098, np.inf],\n",
     "        concentration=5e17,\n",
     "        ref_con=1e6,\n",
     "        width=0.1,\n",
-    "        source=\"ymax\"\n",
+    "        source=\"ymax\",\n",
     "    )\n",
     ")\n",
     "\n",
@@ -424,11 +415,11 @@
     "acceptor_boxes.append(\n",
     "    td.GaussianDoping.from_bounds(\n",
     "        rmin=[-6, -0.3, -np.inf],\n",
-    "        rmax=[-2, 0.22, np.inf], \n",
+    "        rmax=[-2, 0.22, np.inf],\n",
     "        concentration=1e19,\n",
     "        ref_con=1e6,\n",
     "        width=0.1,\n",
-    "        source=\"ymax\"\n",
+    "        source=\"ymax\",\n",
     "    )\n",
     ")\n",
     "\n",
@@ -436,11 +427,12 @@
     "donor_boxes.append(\n",
     "    td.GaussianDoping.from_bounds(\n",
     "        rmin=[2, -0.3, -np.inf],\n",
-    "        rmax=[6, 0.22, np.inf], \n",
+    "        rmax=[6, 0.22, np.inf],\n",
     "        concentration=1e19,\n",
     "        ref_con=1e6,\n",
     "        width=0.1,\n",
-    "        source=\"ymax\")\n",
+    "        source=\"ymax\",\n",
+    "    )\n",
     ")\n",
     "\n",
     "\n",
@@ -452,7 +444,7 @@
     "        concentration=5e17,\n",
     "        ref_con=1e6,\n",
     "        width=0.12,\n",
-    "        source=\"xmin\"\n",
+    "        source=\"xmin\",\n",
     "    )\n",
     ")\n",
     "\n",
@@ -460,11 +452,12 @@
     "donor_boxes.append(\n",
     "    td.GaussianDoping.from_bounds(\n",
     "        rmin=[-0.06, 0.02, -np.inf],\n",
-    "        rmax=[0.25, 0.26, np.inf], \n",
+    "        rmax=[0.25, 0.26, np.inf],\n",
     "        concentration=7e17,\n",
     "        ref_con=1e6,\n",
     "        width=0.11,\n",
-    "        source=\"xmax\")\n",
+    "        source=\"xmax\",\n",
+    "    )\n",
     ")"
    ]
   },
@@ -516,58 +509,72 @@
     "overlap_factor = 1.0001\n",
     "\n",
     "oxide = td.Structure(\n",
-    "    geometry=td.Box(center=(0, h_core, 0), size=(10, 5, td.inf)),\n",
-    "    medium=SiO2,\n",
-    "    name=\"oxide\"\n",
+    "    geometry=td.Box(center=(0, h_core, 0), size=(10, 5, td.inf)), medium=SiO2, name=\"oxide\"\n",
     ")\n",
     "\n",
     "core_p = td.Structure(\n",
-    "    geometry=td.Box(center=(-w_core/2, h_core/2, 0), size=(w_core, h_core, td.inf)),\n",
+    "    geometry=td.Box(center=(-w_core / 2, h_core / 2, 0), size=(w_core, h_core, td.inf)),\n",
     "    medium=Si_2D_doping,\n",
-    "    name=\"core_p\"\n",
+    "    name=\"core_p\",\n",
     ")\n",
     "\n",
     "core_n = td.Structure(\n",
-    "    geometry=td.Box(center=(w_core/2, h_core/2, 0), size=(w_core, h_core, td.inf)),\n",
+    "    geometry=td.Box(center=(w_core / 2, h_core / 2, 0), size=(w_core, h_core, td.inf)),\n",
     "    medium=Si_2D_doping,\n",
-    "    name=\"core_n\"\n",
+    "    name=\"core_n\",\n",
     ")\n",
     "\n",
     "clearance_p = td.Structure(\n",
-    "    geometry=td.Box(center=(-w_core - w_clearance/2, h_clearance/2, 0), size=(w_clearance, h_clearance, td.inf)),\n",
+    "    geometry=td.Box(\n",
+    "        center=(-w_core - w_clearance / 2, h_clearance / 2, 0),\n",
+    "        size=(w_clearance, h_clearance, td.inf),\n",
+    "    ),\n",
     "    medium=Si_2D_doping,\n",
-    "    name=\"clearance_p\"\n",
+    "    name=\"clearance_p\",\n",
     ")\n",
     "\n",
     "clearance_n = td.Structure(\n",
-    "    geometry=td.Box(center=(w_core + w_clearance/2, h_clearance/2, 0), size=(w_clearance, h_clearance, td.inf)),\n",
+    "    geometry=td.Box(\n",
+    "        center=(w_core + w_clearance / 2, h_clearance / 2, 0),\n",
+    "        size=(w_clearance, h_clearance, td.inf),\n",
+    "    ),\n",
     "    medium=Si_2D_doping,\n",
-    "    name=\"clearance_n\"\n",
+    "    name=\"clearance_n\",\n",
     ")\n",
     "\n",
     "side_p = td.Structure(\n",
-    "    geometry=td.Box(center=(-w_core - w_clearance - w_side/2, h_side/2, 0), size=(w_side, h_side, td.inf)),\n",
+    "    geometry=td.Box(\n",
+    "        center=(-w_core - w_clearance - w_side / 2, h_side / 2, 0), size=(w_side, h_side, td.inf)\n",
+    "    ),\n",
     "    medium=Si_2D_doping,\n",
-    "    name=\"side_p\"\n",
+    "    name=\"side_p\",\n",
     ")\n",
     "\n",
     "side_n = td.Structure(\n",
-    "    geometry=td.Box(center=(w_core + w_clearance + w_side/2, h_side/2, 0), size=(w_side, h_side, td.inf)),\n",
+    "    geometry=td.Box(\n",
+    "        center=(w_core + w_clearance + w_side / 2, h_side / 2, 0), size=(w_side, h_side, td.inf)\n",
+    "    ),\n",
     "    medium=Si_2D_doping,\n",
-    "    name=\"side_n\"\n",
+    "    name=\"side_n\",\n",
     ")\n",
     "\n",
     "# create a couple structs to define the contacts\n",
     "contact_p = td.Structure(\n",
-    "    geometry=td.Box(center=(-w_core - w_clearance - w_side + w_contact/2, h_side + h_contact/2, 0), size=(w_contact, h_contact, td.inf)),\n",
+    "    geometry=td.Box(\n",
+    "        center=(-w_core - w_clearance - w_side + w_contact / 2, h_side + h_contact / 2, 0),\n",
+    "        size=(w_contact, h_contact, td.inf),\n",
+    "    ),\n",
     "    medium=aux,\n",
-    "    name=\"contact_p\"\n",
+    "    name=\"contact_p\",\n",
     ")\n",
     "\n",
     "contact_n = td.Structure(\n",
-    "    geometry=td.Box(center=(w_core + w_clearance + w_side - w_contact/2, h_side + h_contact/2, 0), size=(w_contact, h_contact, td.inf)),\n",
+    "    geometry=td.Box(\n",
+    "        center=(w_core + w_clearance + w_side - w_contact / 2, h_side + h_contact / 2, 0),\n",
+    "        size=(w_contact, h_contact, td.inf),\n",
+    "    ),\n",
     "    medium=aux,\n",
-    "    name=\"contact_n\"\n",
+    "    name=\"contact_n\",\n",
     ")"
    ]
   },
@@ -598,7 +605,17 @@
    ],
    "source": [
     "# create a scene with the previous structures\n",
-    "all_structures = [oxide, core_p, core_n, clearance_n, clearance_p, side_p, side_n, contact_p, contact_n]\n",
+    "all_structures = [\n",
+    "    oxide,\n",
+    "    core_p,\n",
+    "    core_n,\n",
+    "    clearance_n,\n",
+    "    clearance_p,\n",
+    "    side_p,\n",
+    "    side_n,\n",
+    "    contact_p,\n",
+    "    contact_n,\n",
+    "]\n",
     "\n",
     "scene = td.Scene(\n",
     "    medium=air,\n",
@@ -609,7 +626,7 @@
     "_, ax = plt.subplots(2, 1, figsize=(6, 6))\n",
     "\n",
     "scene.plot(z=0, ax=ax[0])\n",
-    "scene.plot(y=h_core/2, ax=ax[1])\n",
+    "scene.plot(y=h_core / 2, ax=ax[1])\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -619,7 +636,7 @@
    "id": "1e922e67-7ec7-4439-b415-9266a9c6f73c",
    "metadata": {},
    "source": [
-    "And to make sure our doping concentration regions are correct, we can visualize them with the convencience function [`plot_structures_property`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Scene.html#tidy3d.Scene.plot_structures_property). \n",
+    "And to make sure our doping concentration regions are correct, we can visualize them with the convenience function [`plot_structures_property`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Scene.html#tidy3d.Scene.plot_structures_property). \n",
     "\n",
     "In the plots below, we have used `property=\"doping\"`, but we can also use `property=\"acceptors\"` or `property=\"donors\"` to check individual doping concentration regions. "
    ]
@@ -653,9 +670,11 @@
    ],
    "source": [
     "# plot doping from scene\n",
-    "_, ax = plt.subplots(1,2, figsize=(15, 4))\n",
-    "scene.plot_structures_property(z=0, property=\"doping\", ax=ax[0], limits=[-1e18,1e18])\n",
-    "scene.plot_structures_property(z=0, property=\"doping\", ax=ax[1], hlim=[-0.5,0.5], vlim=[-0.5,0.5], limits=[-5e17,5e17])\n"
+    "_, ax = plt.subplots(1, 2, figsize=(15, 4))\n",
+    "scene.plot_structures_property(z=0, property=\"doping\", ax=ax[0], limits=[-1e18, 1e18])\n",
+    "scene.plot_structures_property(\n",
+    "    z=0, property=\"doping\", ax=ax[1], hlim=[-0.5, 0.5], vlim=[-0.5, 0.5], limits=[-5e17, 5e17]\n",
+    ")"
    ]
   },
   {
@@ -727,12 +746,16 @@
    "source": [
     "# capacitance monitors\n",
     "capacitance_global_mnt = td.SteadyCapacitanceMonitor(\n",
-    "    center=(0, 0.14, 0), size=(td.inf, td.inf, 0), name=\"capacitance_global_mnt\",\n",
+    "    center=(0, 0.14, 0),\n",
+    "    size=(td.inf, td.inf, 0),\n",
+    "    name=\"capacitance_global_mnt\",\n",
     ")\n",
     "\n",
     "# charge monitor around the waveguide\n",
     "capacitance_wvg_mnt = td.SteadyCapacitanceMonitor(\n",
-    "    center=(0, 0.14, 0), size=(0.6, 0.3, 0), name=\"capcitance_wvg_mnt\",\n",
+    "    center=(0, 0.14, 0),\n",
+    "    size=(0.6, 0.3, 0),\n",
+    "    name=\"capcitance_wvg_mnt\",\n",
     ")\n",
     "\n",
     "# charge monitor around the waveguide\n",
@@ -741,17 +764,27 @@
     ")\n",
     "\n",
     "charge_monitor_z0 = td.SteadyFreeCarrierMonitor(\n",
-    "    center=(0, 0.14, 0), size=(0.6, 0.3, 0), name=\"charge_z0\", unstructured=True, \n",
+    "    center=(0, 0.14, 0),\n",
+    "    size=(0.6, 0.3, 0),\n",
+    "    name=\"charge_z0\",\n",
+    "    unstructured=True,\n",
     ")\n",
     "\n",
     "# voltage monitor around waveguide\n",
     "voltage_monitor_z0 = td.SteadyPotentialMonitor(\n",
-    "    center=(0, 0.14, 0), size=(0.6, 0.3, 0), name=\"voltage_z0\", unstructured=True, \n",
+    "    center=(0, 0.14, 0),\n",
+    "    size=(0.6, 0.3, 0),\n",
+    "    name=\"voltage_z0\",\n",
+    "    unstructured=True,\n",
     ")\n",
     "\n",
     "# Will be used later for the mode simulations\n",
     "charge_monitor_z0_big = td.SteadyFreeCarrierMonitor(\n",
-    "    center=(0, 0.14, 0), size=(td.inf, td.inf, 0), name=\"charge_z0_big\", unstructured=True, conformal=True\n",
+    "    center=(0, 0.14, 0),\n",
+    "    size=(td.inf, td.inf, 0),\n",
+    "    name=\"charge_z0_big\",\n",
+    "    unstructured=True,\n",
+    "    conformal=True,\n",
     ")"
    ]
   },
@@ -772,17 +805,25 @@
    "outputs": [],
    "source": [
     "# mesh\n",
-    "mesh = td.DistanceUnstructuredGrid(dl_interface=res*1.14, dl_bulk=res*4, distance_interface=0.02*h_side,\n",
-    "    distance_bulk=1*h_side, relative_min_dl=0,\n",
+    "mesh = td.DistanceUnstructuredGrid(\n",
+    "    dl_interface=res * 1.14,\n",
+    "    dl_bulk=res * 4,\n",
+    "    distance_interface=0.02 * h_side,\n",
+    "    distance_bulk=1 * h_side,\n",
+    "    relative_min_dl=0,\n",
     "    uniform_grid_mediums=[Si_2D_doping.name],\n",
-    "    non_refined_structures=[oxide.name]\n",
+    "    non_refined_structures=[oxide.name],\n",
     ")\n",
     "\n",
-    "# devsim setting \n",
-    "convergence_settings = td.ChargeToleranceSpec(rel_tol=1e-5, abs_tol=1e5, max_iters=400, ramp_up_iters=1)\n",
+    "# devsim setting\n",
+    "convergence_settings = td.ChargeToleranceSpec(\n",
+    "    rel_tol=1e-5, abs_tol=1e5, max_iters=400, ramp_up_iters=1\n",
+    ")\n",
     "\n",
     "# currently we support Isothermal cases only. Temperature in K\n",
-    "analysis_type = td.IsothermalSteadyChargeDCAnalysis(temperature=300, convergence_dv=10, tolerance_settings=convergence_settings)"
+    "analysis_type = td.IsothermalSteadyChargeDCAnalysis(\n",
+    "    temperature=300, convergence_dv=10, tolerance_settings=convergence_settings\n",
+    ")"
    ]
   },
   {
@@ -815,16 +856,23 @@
     "# build heat simulation object\n",
     "charge_sim = td.HeatChargeSimulation(\n",
     "    sources=[],\n",
-    "    monitors=[capacitance_global_mnt, capacitance_wvg_mnt, charge_3D_mnt, charge_monitor_z0, voltage_monitor_z0, charge_monitor_z0_big],\n",
+    "    monitors=[\n",
+    "        capacitance_global_mnt,\n",
+    "        capacitance_wvg_mnt,\n",
+    "        charge_3D_mnt,\n",
+    "        charge_monitor_z0,\n",
+    "        voltage_monitor_z0,\n",
+    "        charge_monitor_z0_big,\n",
+    "    ],\n",
     "    analysis_spec=analysis_type,\n",
-    "    center=(0,0,0),\n",
-    "    size=(10.5,10,z_size),\n",
+    "    center=(0, 0, 0),\n",
+    "    size=(10.5, 10, z_size),\n",
     "    structures=all_structures,\n",
     "    medium=air,\n",
     "    boundary_spec=boundary_conditions,\n",
     "    grid_spec=mesh,\n",
-    "    symmetry=(0, 0, 0)\n",
-    "    )\n",
+    "    symmetry=(0, 0, 0),\n",
+    ")\n",
     "\n",
     "# plot simulation\n",
     "fig, ax = plt.subplots(1, 2, figsize=(10, 15))\n",
@@ -1124,9 +1172,15 @@
     }
    ],
    "source": [
-    "#%matplotlib inline\n",
+    "# %matplotlib inline\n",
     "from tidy3d import web\n",
-    "charge_data=web.run(charge_sim, task_name=\"charge_junction\", path=\"charge_junction.hdf5\", solver_version=\"release-25.2.1\")"
+    "\n",
+    "charge_data = web.run(\n",
+    "    charge_sim,\n",
+    "    task_name=\"charge_junction\",\n",
+    "    path=\"charge_junction.hdf5\",\n",
+    "    solver_version=\"release-25.2.1\",\n",
+    ")"
    ]
   },
   {
@@ -1134,7 +1188,7 @@
    "id": "43917eb8-5125-45c8-bff0-a7d81c29ad51",
    "metadata": {},
    "source": [
-    "### Post-process Charge simmulation\n",
+    "### Post-process Charge simulation\n",
     "Let's begin by visualizing the potential and electron fields at three different biases to make sure the solution looks reasonable. As it can be seen in the figure, as the (reverse) bias increases, the depletion region widens, as it is expected. The electric potential field also adapts to the applied biases and the amount of charge present in the waveguide area, which is also to be expected. "
    ]
   },
@@ -1167,13 +1221,17 @@
     "voltages = charge_data[charge_3D_mnt.name].holes.values.voltage.data\n",
     "\n",
     "fig, ax = plt.subplots(2, 3)\n",
-    "for n, index in enumerate([0,2,5]):\n",
+    "for n, index in enumerate([0, 2, 5]):\n",
     "    # let's read voltage first\n",
-    "    charge_data[voltage_monitor_z0.name].potential.sel(voltage=voltages[index]).plot(ax=ax[0][n], grid=False)\n",
+    "    charge_data[voltage_monitor_z0.name].potential.sel(voltage=voltages[index]).plot(\n",
+    "        ax=ax[0][n], grid=False\n",
+    "    )\n",
     "\n",
     "    # now let's plot some electrons\n",
-    "    np.log10(charge_data[charge_3D_mnt.name].electrons.sel(z=0, voltage=voltages[index])).plot(ax=ax[1][n], grid=False)\n",
-    "    ax[1][n].set_title(\"Bias {0:0.2f}V\".format(voltages[index]))\n",
+    "    np.log10(charge_data[charge_3D_mnt.name].electrons.sel(z=0, voltage=voltages[index])).plot(\n",
+    "        ax=ax[1][n], grid=False\n",
+    "    )\n",
+    "    ax[1][n].set_title(f\"Bias {voltages[index]:0.2f}V\")\n",
     "\n",
     "plt.tight_layout()"
    ]
@@ -1213,15 +1271,15 @@
     "# capacitance from monitor - waveguide area\n",
     "CV_baehrjones = [\n",
     "    [-0.4, -0.25, 0, 0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4],\n",
-    "    [0.261, 0.248, 0.223, 0.208, 0.198, 0.190, 0.184, 0.175, 0.168, 0.157, 0.150]\n",
+    "    [0.261, 0.248, 0.223, 0.208, 0.198, 0.190, 0.184, 0.175, 0.168, 0.157, 0.150],\n",
     "]\n",
     "\n",
     "mnt_v = np.array(charge_data[capacitance_global_mnt.name].electron_capacitance.coords[\"v\"].data)\n",
     "mnt_ce = np.array(charge_data[capacitance_global_mnt.name].electron_capacitance.data)\n",
     "mnt_ch = np.array(charge_data[capacitance_global_mnt.name].hole_capacitance.data)\n",
     "\n",
-    "plt.plot(mnt_v, -0.5*(mnt_ce+mnt_ch) * 10 , 'k.-', label=\"Simulation Data\")\n",
-    "plt.plot(CV_baehrjones[0], np.array(CV_baehrjones[1])*10, 'r-', label=\"Experimental Data\")\n",
+    "plt.plot(mnt_v, -0.5 * (mnt_ce + mnt_ch) * 10, \"k.-\", label=\"Simulation Data\")\n",
+    "plt.plot(CV_baehrjones[0], np.array(CV_baehrjones[1]) * 10, \"r-\", label=\"Experimental Data\")\n",
     "\n",
     "plt.xlabel(\"Reverse bias (V)\")\n",
     "plt.ylabel(\"pF/cm\")\n",
@@ -1282,7 +1340,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "si = td.material_library['cSi']['Palik_Lossless']\n",
+    "si = td.material_library[\"cSi\"][\"Palik_Lossless\"]\n",
     "n_si, k_si = si.nk_model(frequency=td.C_0 / wvl_um)\n",
     "si_non_perturb = td.Medium.from_nk(n=n_si, k=k_si, freq=freq0)"
    ]
@@ -1339,10 +1397,10 @@
     "Ne_range = np.concatenate(([0], np.logspace(-6, 20, num=200)))\n",
     "Nh_range = np.concatenate(([0], np.logspace(-6, 20, num=200)))\n",
     "\n",
-    "Ne_mesh, Nh_mesh = np.meshgrid(Ne_range, Nh_range, indexing='ij')\n",
+    "Ne_mesh, Nh_mesh = np.meshgrid(Ne_range, Nh_range, indexing=\"ij\")\n",
     "\n",
-    "dn_mesh = ne_coeff * Ne_mesh ** ne_pow + nh_coeff * Nh_mesh ** nh_pow\n",
-    "dk_mesh = ke_coeff * Ne_mesh ** ke_pow + kh_coeff * Nh_mesh ** kh_pow"
+    "dn_mesh = ne_coeff * Ne_mesh**ne_pow + nh_coeff * Nh_mesh**nh_pow\n",
+    "dk_mesh = ke_coeff * Ne_mesh**ke_pow + kh_coeff * Nh_mesh**kh_pow"
    ]
   },
   {
@@ -1390,7 +1448,7 @@
     "        delta_n=n_si_perturbation,\n",
     "        delta_k=k_si_perturbation,\n",
     "        freq=freq0,\n",
-    "    )\n",
+    "    ),\n",
     ")\n",
     "\n",
     "new_structs = []\n",
@@ -1400,9 +1458,9 @@
     "        new_structs.append(struct.updated_copy(medium=si_perturb))\n",
     "\n",
     "scene = td.Scene(\n",
-    "    medium=SiO2.optical, # currently td.Simulation cannot accpet a MultiphysicsMedium\n",
+    "    medium=SiO2.optical,  # currently td.Simulation cannot accepet a MultiphysicsMedium\n",
     "    structures=new_structs,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1433,22 +1491,22 @@
     }
    ],
    "source": [
-    "span = 2*wvl_um\n",
+    "span = 2 * wvl_um\n",
     "\n",
-    "port_center = (0,h_core,-span/2)\n",
+    "port_center = (0, h_core, -span / 2)\n",
     "port_size = (5, 5, 0)\n",
     "\n",
-    "buffer = 1*wvl_um\n",
-    "sim_size = (13+buffer, 10+buffer, span)\n",
+    "buffer = 1 * wvl_um\n",
+    "sim_size = (13 + buffer, 10 + buffer, span)\n",
     "\n",
     "bc_spec = td.BoundarySpec(\n",
-    "    x = td.Boundary.pml(num_layers=20),\n",
-    "    y = td.Boundary.pml(num_layers=30),\n",
-    "    z = td.Boundary.periodic(),\n",
+    "    x=td.Boundary.pml(num_layers=20),\n",
+    "    y=td.Boundary.pml(num_layers=30),\n",
+    "    z=td.Boundary.periodic(),\n",
     ")\n",
     "\n",
     "sim = td.Simulation(\n",
-    "    center=(0,0,0),\n",
+    "    center=(0, 0, 0),\n",
     "    size=sim_size,\n",
     "    medium=scene.medium,\n",
     "    structures=scene.structures,\n",
@@ -1493,12 +1551,13 @@
     "        h_data = charge_data[charge_monitor_z0_big.name].holes.sel(voltage=v)\n",
     "        perturbed_sims.append(\n",
     "            sim.perturbed_mediums_copy(\n",
-    "                electron_density=e_data, \n",
+    "                electron_density=e_data,\n",
     "                hole_density=h_data,\n",
     "            )\n",
     "        )\n",
     "    return perturbed_sims\n",
     "\n",
+    "\n",
     "perturbed_sims = apply_charge(charge_data)"
    ]
   },
@@ -1530,15 +1589,15 @@
    "source": [
     "_, ax = plt.subplots(1, 2, figsize=(15, 5))\n",
     "\n",
-    "sampling_region = td.Box(center=(0, h_core/2, 0), size=(6, 5, 1))\n",
+    "sampling_region = td.Box(center=(0, h_core / 2, 0), size=(6, 5, 1))\n",
     "eps_undoped = sim.epsilon(box=sampling_region).sel(z=0, method=\"nearest\")\n",
     "\n",
-    "for ax_ind, ind in enumerate([0, len(voltages)-1]):\n",
+    "for ax_ind, ind in enumerate([0, len(voltages) - 1]):\n",
     "    eps_doped = perturbed_sims[ind].epsilon(box=sampling_region).sel(z=0, method=\"nearest\")\n",
     "    eps_doped = eps_doped.interp(x=eps_undoped.x, y=eps_undoped.y)\n",
     "    eps_diff = np.abs(np.real(eps_doped - eps_undoped))\n",
     "    eps_diff.plot(x=\"x\", ax=ax[ax_ind])\n",
-    "    \n",
+    "\n",
     "    ax[ax_ind].set_aspect(\"equal\")\n",
     "    ax[ax_ind].set_title(f\"Bias: {voltages[ind]:1.1f} V\")\n",
     "    ax[ax_ind].set_xlabel(\"x (um)\")\n",
@@ -1592,7 +1651,7 @@
     "mode_plane = td.Box(size=port_size)\n",
     "\n",
     "# visualize\n",
-    "ax = sim.plot(z = 0)\n",
+    "ax = sim.plot(z=0)\n",
     "mode_plane.plot(z=0, ax=ax, alpha=0.5)\n",
     "plt.show()"
    ]
@@ -1614,7 +1673,7 @@
     "        mode_spec=td.ModeSpec(num_modes=1, precision=\"double\"),\n",
     "    )\n",
     "    mode_solvers[i] = ms\n",
-    "    i +=1"
+    "    i += 1"
    ]
   },
   {
@@ -1804,24 +1863,24 @@
     "\n",
     "ind_V0 = 1\n",
     "delta_neff = np.real(n_eff_freq0 - n_eff_freq0[ind_V0])\n",
-    "rel_phase_change = 2*np.pi*delta_neff/wvl_um * 1e4\n",
-    "alpha_dB_cm = 10*4*np.pi*np.imag(n_eff_freq0)/wvl_um*1e4*np.log10(np.exp(1))\n",
+    "rel_phase_change = 2 * np.pi * delta_neff / wvl_um * 1e4\n",
+    "alpha_dB_cm = 10 * 4 * np.pi * np.imag(n_eff_freq0) / wvl_um * 1e4 * np.log10(np.exp(1))\n",
     "\n",
     "# other results\n",
     "v_other = [-0.5, 0, 0.4, 0.7, 1.5, 2.4, 3.8]\n",
-    "pc_other=[-0.91, 0, 0.6, 1,   1.8, 2.7, 3.6]\n",
+    "pc_other = [-0.91, 0, 0.6, 1, 1.8, 2.7, 3.6]\n",
     "loss_other = [4.9, 4.55, 4.34, 4.15, 3.77, 3.41, 2.95]\n",
     "\n",
     "_, ax = plt.subplots(1, 2, figsize=(15, 4))\n",
-    "ax[0].plot(voltages, rel_phase_change, 'k.-', label=\"Simulation Data\")\n",
-    "ax[0].plot(v_other, pc_other, 'r-', label=\"Other Software\")\n",
+    "ax[0].plot(voltages, rel_phase_change, \"k.-\", label=\"Simulation Data\")\n",
+    "ax[0].plot(v_other, pc_other, \"r-\", label=\"Other Software\")\n",
     "ax[0].set_xlabel(\"Reverse bias (V)\")\n",
     "ax[0].set_ylabel(\"Relative phase (rad/cm)\")\n",
     "ax[0].grid()\n",
     "ax[0].legend()\n",
     "\n",
-    "ax[1].plot(voltages, alpha_dB_cm, 'k.-', label=\"Simulation Data\")\n",
-    "ax[1].plot(v_other, loss_other, 'r-', label=\"Other Software\")\n",
+    "ax[1].plot(voltages, alpha_dB_cm, \"k.-\", label=\"Simulation Data\")\n",
+    "ax[1].plot(v_other, loss_other, \"r-\", label=\"Other Software\")\n",
     "ax[1].set_xlabel(\"Reverse bias (V)\")\n",
     "ax[1].set_ylabel(\"Loss (dB/cm)\")\n",
     "ax[1].grid()\n",
@@ -1866,7 +1925,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.11.0"
   },
   "title": "Charge Solver: a silicon electro-optic phase modulator | Flexcompute"
  },
diff --git a/CircularlyPolarizedPatchAntenna.ipynb b/CircularlyPolarizedPatchAntenna.ipynb
index cb5a6621..093c4642 100644
--- a/CircularlyPolarizedPatchAntenna.ipynb
+++ b/CircularlyPolarizedPatchAntenna.ipynb
@@ -36,19 +36,17 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3d imports\n",
     "import tidy3d as td\n",
+    "\n",
     "# Import a few plugins which contain some of the tools we will use in this tutorial\n",
     "import tidy3d.plugins.smatrix as smatrix\n",
-    "from tidy3d.plugins.dispersion import (\n",
-    "    FastDispersionFitter\n",
-    ")\n",
-    "from tidy3d.plugins.microwave import (\n",
-    "    LobeMeasurer\n",
-    ")\n",
+    "from tidy3d.plugins.dispersion import FastDispersionFitter\n",
+    "from tidy3d.plugins.microwave import LobeMeasurer\n",
+    "\n",
     "td.config.logging_level = \"ERROR\""
    ]
   },
@@ -212,9 +210,7 @@
     "    )\n",
     "    # A ground plane is connected to the bottom of the pins, where a small air gap is introduced for exciting the\n",
     "    # feeding pin\n",
-    "    ground_box = td.Box(\n",
-    "        center=[0, 0, -H - ground_thickness / 2], size=[G, G, ground_thickness]\n",
-    "    )\n",
+    "    ground_box = td.Box(center=[0, 0, -H - ground_thickness / 2], size=[G, G, ground_thickness])\n",
     "    # A cylindrical hole is created in the ground plane to allow for the coaxial cable connection at the feeding pin.\n",
     "    feed_gap = td.Cylinder(\n",
     "        center=(fx, 0, -H - ground_thickness / 2),\n",
@@ -231,7 +227,7 @@
     "            axis=2,\n",
     "        )\n",
     "        feed_gap = td.GeometryGroup(geometries=(feed_gap, left_feed_gap))\n",
-    "    \n",
+    "\n",
     "    # The air gap is removed from the ground box\n",
     "    ground_with_holes = td.ClipOperation(\n",
     "        operation=\"difference\", geometry_a=ground_box, geometry_b=feed_gap\n",
@@ -285,16 +281,14 @@
     "    if differential_feed:\n",
     "        shorted_disc = td.Cylinder(\n",
     "            center=(-fx, 0, -patch_thickness / 2), radius=r2, length=patch_thickness, axis=2\n",
-    "        )   \n",
+    "        )\n",
     "    else:\n",
     "        shorted_disc = td.Cylinder(\n",
     "            center=(0, 0, -patch_thickness / 2), radius=r1, length=patch_thickness, axis=2\n",
     "        )\n",
     "    # All patch structures are collected into a list and assigned the same material\n",
     "    patch_geoms = [patch_L, patch_R, patch_B, patch_T, feed_disc, shorted_disc]\n",
-    "    patch_structures = [\n",
-    "        td.Structure(geometry=patch, medium=metal) for patch in patch_geoms\n",
-    "    ]\n",
+    "    patch_structures = [td.Structure(geometry=patch, medium=metal) for patch in patch_geoms]\n",
     "\n",
     "    # Next, the shorting and feeding pins are added to the simulation as cylinders.\n",
     "    if differential_feed:\n",
@@ -336,6 +330,7 @@
     "        main_patch,\n",
     "    ] + patch_structures\n",
     "\n",
+    "\n",
     "structures_list = make_structures(differential_feed=False)\n",
     "# Setting up the scene for visualization, with all structures included\n",
     "# and units set to millimeters for the plot.\n",
@@ -390,20 +385,20 @@
     "    if use_differential_feed:\n",
     "        short_x_center = -fx\n",
     "    mesh_overrides = [\n",
-    "            td.MeshOverrideStructure(\n",
-    "                geometry=td.Box(\n",
-    "                    center=[fx, 0, middle_ground],\n",
-    "                    size=[port_diameter, port_diameter, ground_thickness],\n",
-    "                ),\n",
-    "                dl=[dl_pins, dl_pins, dl_background],\n",
+    "        td.MeshOverrideStructure(\n",
+    "            geometry=td.Box(\n",
+    "                center=[fx, 0, middle_ground],\n",
+    "                size=[port_diameter, port_diameter, ground_thickness],\n",
     "            ),\n",
-    "            td.MeshOverrideStructure(\n",
-    "                geometry=td.Box(\n",
-    "                    center=[short_x_center, 0, middle_ground],\n",
-    "                    size=[port_diameter, port_diameter, ground_thickness],\n",
-    "                ),\n",
-    "                dl=[dl_pins, dl_pins, dl_background],\n",
+    "            dl=[dl_pins, dl_pins, dl_background],\n",
+    "        ),\n",
+    "        td.MeshOverrideStructure(\n",
+    "            geometry=td.Box(\n",
+    "                center=[short_x_center, 0, middle_ground],\n",
+    "                size=[port_diameter, port_diameter, ground_thickness],\n",
     "            ),\n",
+    "            dl=[dl_pins, dl_pins, dl_background],\n",
+    "        ),\n",
     "    ]\n",
     "\n",
     "    top_substrate_spec = td.LayerRefinementSpec.from_layer_bounds(\n",
@@ -411,13 +406,13 @@
     "        axis=2,\n",
     "        bounds_snapping=\"lower\",\n",
     "        bounds_refinement=td.GridRefinement(dl=dz_metal),\n",
-    "        corner_refinement=td.GridRefinement(dl=dl_plane/2)\n",
+    "        corner_refinement=td.GridRefinement(dl=dl_plane / 2),\n",
     "    )\n",
     "    bottom_substrate_spec = td.LayerRefinementSpec.from_layer_bounds(\n",
     "        bounds=(bottom_substrate - patch_thickness, bottom_substrate),\n",
     "        axis=2,\n",
     "        bounds_snapping=\"upper\",\n",
-    "        bounds_refinement=td.GridRefinement(dl=dz_metal)\n",
+    "        bounds_refinement=td.GridRefinement(dl=dz_metal),\n",
     "    )\n",
     "    ground_spec = td.LayerRefinementSpec.from_layer_bounds(\n",
     "        min_steps_along_axis=1,\n",
@@ -432,6 +427,7 @@
     "        override_structures=mesh_overrides,\n",
     "    )\n",
     "\n",
+    "\n",
     "grid_spec = make_grid_spec(use_differential_feed=False)"
    ]
   },
@@ -483,7 +479,7 @@
     "    grid_spec=grid_spec,\n",
     "    structures=structures_list,\n",
     "    run_time=run_time,\n",
-    "    plot_length_units=\"mm\"\n",
+    "    plot_length_units=\"mm\",\n",
     ")\n",
     "\n",
     "# Display information about the simulation grid, including the number of cells along each axis and the smallest cell size,\n",
@@ -584,7 +580,8 @@
     "    center=coax_center,\n",
     "    outer_diameter=port_diameter,\n",
     "    # Defines the boundary for the outer conductor, which in this case is the ground plane.\n",
-    "    inner_diameter=2 * rfeed,  # Defines the boundary for the inner conductor, corresponding to the feed pin diameter.\n",
+    "    inner_diameter=2\n",
+    "    * rfeed,  # Defines the boundary for the inner conductor, corresponding to the feed pin diameter.\n",
     "    normal_axis=2,\n",
     "    direction=\"+\",  # Indicates that the current will flow in the positive direction along the feed pin.\n",
     "    name=\"coax_port\",\n",
@@ -835,10 +832,10 @@
    ],
    "source": [
     "xz_plane_lobe_measurer = LobeMeasurer(angle=theta_proj, radiation_pattern=Urad_RH_Phi_0)\n",
-    "main_lobe_phi_0 = xz_plane_lobe_measurer.main_lobe[\"direction\"]*180/np.pi\n",
+    "main_lobe_phi_0 = xz_plane_lobe_measurer.main_lobe[\"direction\"] * 180 / np.pi\n",
     "\n",
     "yz_plane_lobe_measurer = LobeMeasurer(angle=theta_proj, radiation_pattern=Urad_RH_Phi_90)\n",
-    "main_lobe_phi_90 = yz_plane_lobe_measurer.main_lobe[\"direction\"]*180/np.pi -360\n",
+    "main_lobe_phi_90 = yz_plane_lobe_measurer.main_lobe[\"direction\"] * 180 / np.pi - 360\n",
     "\n",
     "print(f\"The main lobe direction in the xz-plane is {main_lobe_phi_0:.2f} degrees.\")\n",
     "print(f\"The main lobe direction in the yz-plane is {main_lobe_phi_90:.2f} degrees.\")"
@@ -922,10 +919,10 @@
     "\n",
     "freq = axial_ratio.f.values.squeeze() / 1e9\n",
     "axial_ratio_dB = 20 * np.log10(np.abs(axial_ratio.values.squeeze()))\n",
-    "plt.plot(freq, axial_ratio_dB, '-b')\n",
-    "plt.xlabel('Frequency (GHz)')\n",
-    "plt.ylabel('Axial ratio (dB)')\n",
-    "plt.title('Axial ratio')\n",
+    "plt.plot(freq, axial_ratio_dB, \"-b\")\n",
+    "plt.xlabel(\"Frequency (GHz)\")\n",
+    "plt.ylabel(\"Axial ratio (dB)\")\n",
+    "plt.title(\"Axial ratio\")\n",
     "# Make the plot identical to [1].\n",
     "plt.xlim(2.1, 3.1)\n",
     "plt.ylim(0, 12)\n",
@@ -963,10 +960,10 @@
     "freq = antenna_parameters_freq.f.squeeze() / 1e9\n",
     "gain = antenna_parameters_freq.partial_gain(pol_basis=\"circular\").Gright.values.squeeze()\n",
     "gain_dB = 10 * np.log10(gain)\n",
-    "plt.plot(freq, gain_dB, '-b')\n",
-    "plt.xlabel('Frequency (GHz)')\n",
-    "plt.ylabel('Gain (dB)')\n",
-    "plt.title('Gain')\n",
+    "plt.plot(freq, gain_dB, \"-b\")\n",
+    "plt.xlabel(\"Frequency (GHz)\")\n",
+    "plt.ylabel(\"Gain (dB)\")\n",
+    "plt.title(\"Gain\")\n",
     "plt.xlim(2.1, 3.1)\n",
     "plt.ylim(5, 10)\n",
     "plt.yticks([6, 7, 8, 9])\n",
@@ -1002,13 +999,13 @@
     }
    ],
    "source": [
-    "#Create simulation in the differential feed configuration\n",
+    "# Create simulation in the differential feed configuration\n",
     "structures_list = make_structures(True)\n",
     "grid_spec = make_grid_spec(True)\n",
     "\n",
-    "#Create a new monitor that samples entire spherical surface\n",
+    "# Create a new monitor that samples entire spherical surface\n",
     "theta_proj2 = np.linspace(0, np.pi, 101)\n",
-    "phi_proj2 = np.linspace(0, 2*np.pi, 200)\n",
+    "phi_proj2 = np.linspace(0, 2 * np.pi, 200)\n",
     "\n",
     "# First, create a DirectivityMonitor that will be used to plot the radiation pattern in the xz and yz planes\n",
     "mon_rad_spatial = td.DirectivityMonitor(\n",
@@ -1054,7 +1051,9 @@
     "diff_center = [-fx, 0, port_location]\n",
     "diff_port = coax_port.updated_copy(center=diff_center, name=\"diff_port\")\n",
     "\n",
-    "modeler_diff_feed = modeler.updated_copy(simulation=sim,ports=[coax_port, diff_port], radiation_monitors=[mon_rad_spatial])"
+    "modeler_diff_feed = modeler.updated_copy(\n",
+    "    simulation=sim, ports=[coax_port, diff_port], radiation_monitors=[mon_rad_spatial]\n",
+    ")"
    ]
   },
   {
@@ -1227,12 +1226,16 @@
     "\n",
     "# Create a differential excitation by setting the amplitudes of the two ports to be equal in magnitude but opposite in phase.\n",
     "port_amplitudes = {\"coax_port\": 1.0, \"diff_port\": -1.0}\n",
-    "antenna_parameters_shell = modeler_diff_feed.get_antenna_metrics_data(port_amplitudes, monitor_name=\"rad_spatial\")\n",
-    "D_right_diff = antenna_parameters_shell.partial_directivity(pol_basis=\"circular\").Dright.sel(f=freq0)\n",
+    "antenna_parameters_shell = modeler_diff_feed.get_antenna_metrics_data(\n",
+    "    port_amplitudes, monitor_name=\"rad_spatial\"\n",
+    ")\n",
+    "D_right_diff = antenna_parameters_shell.partial_directivity(pol_basis=\"circular\").Dright.sel(\n",
+    "    f=freq0\n",
+    ")\n",
     "\n",
-    "#These helper functions are used to extract the fields along theta for phi=0 and phi=pi/2, so that theta is in the range [0, 2*pi).\n",
+    "# These helper functions are used to extract the fields along theta for phi=0 and phi=pi/2, so that theta is in the range [0, 2*pi).\n",
     "D_Phi_0_diff = antenna_parameters_shell.get_phi_slice(D_right_diff, 0.0)\n",
-    "D_Phi_90_diff = antenna_parameters_shell.get_phi_slice(D_right_diff, np.pi/2)\n",
+    "D_Phi_90_diff = antenna_parameters_shell.get_phi_slice(D_right_diff, np.pi / 2)\n",
     "theta_plot = D_Phi_90_diff.theta.values\n",
     "# Convert to numpy arrays for plotting\n",
     "D_Phi_0_diff = D_Phi_0_diff.squeeze().values\n",
@@ -1289,13 +1292,17 @@
    ],
    "source": [
     "freq = antenna_parameters_shell.f.squeeze() / 1e9\n",
-    "gain_diff = antenna_parameters_shell.partial_gain(pol_basis=\"circular\").Gright.sel(theta=0,phi=0).values.squeeze()\n",
+    "gain_diff = (\n",
+    "    antenna_parameters_shell.partial_gain(pol_basis=\"circular\")\n",
+    "    .Gright.sel(theta=0, phi=0)\n",
+    "    .values.squeeze()\n",
+    ")\n",
     "gain_diff_dB = 10 * np.log10(gain_diff)\n",
-    "plt.plot(freq, gain_diff_dB, '-.b', label=\"Differential feed\")\n",
-    "plt.plot(freq, gain_dB, '-k', label=\"Proposed feed [1]\")\n",
-    "plt.xlabel('Frequency (GHz)')\n",
-    "plt.ylabel('Gain (dB)')\n",
-    "plt.title('Gain')\n",
+    "plt.plot(freq, gain_diff_dB, \"-.b\", label=\"Differential feed\")\n",
+    "plt.plot(freq, gain_dB, \"-k\", label=\"Proposed feed [1]\")\n",
+    "plt.xlabel(\"Frequency (GHz)\")\n",
+    "plt.ylabel(\"Gain (dB)\")\n",
+    "plt.title(\"Gain\")\n",
     "plt.xlim(2.1, 3.1)\n",
     "plt.ylim(7, 11)\n",
     "plt.yticks([7, 8, 9, 10, 11])\n",
diff --git a/CoupledLineBandpassFilter.ipynb b/CoupledLineBandpassFilter.ipynb
index e9698fe1..1d68654f 100644
--- a/CoupledLineBandpassFilter.ipynb
+++ b/CoupledLineBandpassFilter.ipynb
@@ -24,23 +24,23 @@
    "outputs": [],
    "source": [
     "# Tidy3d imports\n",
-    "import tidy3d as td\n",
-    "\n",
-    "# Tidy3d plugin imports\n",
-    "import tidy3d.plugins.smatrix as smatrix\n",
-    "import tidy3d.plugins.microwave as mw\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.ticker as ticker\n",
     "\n",
     "# External modules needed for this notebook\n",
     "import numpy as np\n",
-    "import matplotlib.ticker as ticker\n",
-    "import matplotlib.pyplot as plt\n",
     "\n",
     "# uncomment the following line to install scikit-rf if it's not installed in your environment already\n",
     "# pip install scikit-rf\n",
     "import skrf as rf  # For validation using circuit models of the filter\n",
+    "import tidy3d as td\n",
+    "import tidy3d.plugins.microwave as mw\n",
+    "\n",
+    "# Tidy3d plugin imports\n",
+    "import tidy3d.plugins.smatrix as smatrix\n",
     "from scipy import (\n",
     "    optimize as opt,\n",
-    ")  # Used to optimize microstrip parameters for the filter\n",
+    ")\n",
     "\n",
     "# We set the logging level to \"ERROR\". Otherwise there are numerous warnings due to the proximity of the structure to PML boundaries.\n",
     "td.config.logging_level = \"ERROR\""
@@ -174,9 +174,7 @@
     "def generate_coupled_lines(lengths, widths, gaps, xstarts, ystarts):\n",
     "    coupled_lines = []\n",
     "    # Each segment is composed of two microstrips\n",
-    "    for length, width, gap, xstart, ystart in zip(\n",
-    "        lengths, widths, gaps, xstarts, ystarts\n",
-    "    ):\n",
+    "    for length, width, gap, xstart, ystart in zip(lengths, widths, gaps, xstarts, ystarts):\n",
     "        coupled_lines.append(\n",
     "            td.Structure(\n",
     "                geometry=td.Box.from_bounds(\n",
@@ -242,7 +240,7 @@
     "for geo in coupled_lines:\n",
     "    geo.plot(z=0, ax=ax)\n",
     "# Formatter to help plotting in units of millimeters\n",
-    "formatter = ticker.FuncFormatter(lambda y, _: \"{:g}\".format((1e-3) * y))\n",
+    "formatter = ticker.FuncFormatter(lambda y, _: f\"{(1e-3) * y:g}\")\n",
     "xlbl = \"x (mm)\"\n",
     "ylbl = \"y (mm)\"\n",
     "# Update plot labels\n",
@@ -1093,9 +1091,7 @@
     "    er_geo_mean = np.sqrt(er_eff_even * er_eff_odd)\n",
     "    length = (lda0) / 4 / (np.sqrt(er_geo_mean))\n",
     "    # Microstrips terminated by an open circuit have fringing fields that can be modelled as a slight extension to the microstrip\n",
-    "    dL = mw.models.microstrip.compute_end_effect_length(\n",
-    "        eps_sub, er_geo_mean, width, h_sub\n",
-    "    )\n",
+    "    dL = mw.models.microstrip.compute_end_effect_length(eps_sub, er_geo_mean, width, h_sub)\n",
     "    # As a result, we need a slightly shorter section\n",
     "    length -= dL\n",
     "    strips_L.append(length)\n",
diff --git a/CreatingGeometryUsingTrimesh.ipynb b/CreatingGeometryUsingTrimesh.ipynb
index 1831881a..9ff09cda 100644
--- a/CreatingGeometryUsingTrimesh.ipynb
+++ b/CreatingGeometryUsingTrimesh.ipynb
@@ -29,10 +29,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tidy3d as td\n",
-    "import trimesh\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
+    "import tidy3d as td\n",
+    "import trimesh"
    ]
   },
   {
@@ -1320,7 +1320,7 @@
     }
    ],
    "source": [
-    "n_sections = 100 # how many sections to discretize the mesh\n",
+    "n_sections = 100  # how many sections to discretize the mesh\n",
     "\n",
     "# create a ring mesh\n",
     "ring_mesh = trimesh.creation.annulus(r_min=9, r_max=10, height=1, sections=n_sections)\n",
@@ -1334,7 +1334,7 @@
    "id": "06e28d19",
    "metadata": {},
    "source": [
-    "To use this geometry in a Tidy3D simulation, we need to convert the mesh into a Tidy3D [TriangleMesh](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.TriangleMesh.html) geometry. The `from_trimesh()` method conviently converts a mesh to a Tidy3D geometry. From there, you can further define Tidy3D [Structure](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Structure.html) and put it into a [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html)."
+    "To use this geometry in a Tidy3D simulation, we need to convert the mesh into a Tidy3D [TriangleMesh](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.TriangleMesh.html) geometry. The `from_trimesh()` method conveniently converts a mesh to a Tidy3D geometry. From there, you can further define Tidy3D [Structure](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Structure.html) and put it into a [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html)."
    ]
   },
   {
@@ -1432,7 +1432,7 @@
    ],
    "source": [
     "# translate the ring\n",
-    "ring_mesh.apply_translation([5,10,0])\n",
+    "ring_mesh.apply_translation([5, 10, 0])\n",
     "\n",
     "# define a tidy3d geometry from a mesh\n",
     "ring_geo = td.TriangleMesh.from_trimesh(ring_mesh)\n",
@@ -2719,8 +2719,8 @@
     "cylinder_mesh = trimesh.creation.cylinder(radius=2, height=10, sections=n_sections)\n",
     "\n",
     "# apply rotation\n",
-    "rot_x = trimesh.transformations.rotation_matrix(np.radians(45), [1,0,0])\n",
-    "cylinder_mesh.apply_transform(rot_x)  \n",
+    "rot_x = trimesh.transformations.rotation_matrix(np.radians(45), [1, 0, 0])\n",
+    "cylinder_mesh.apply_transform(rot_x)\n",
     "\n",
     "cylinder_mesh.show()"
    ]
@@ -2795,7 +2795,7 @@
    "source": [
     "cylinder_geo = td.TriangleMesh.from_trimesh(cylinder_mesh)\n",
     "\n",
-    "fig, (ax1, ax2) = plt.subplots(1,2,tight_layout=True)\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True)\n",
     "cylinder_geo.plot(x=0, ax=ax1)\n",
     "cylinder_geo.plot(z=0, ax=ax2)\n",
     "plt.show()"
@@ -2835,38 +2835,39 @@
    "outputs": [],
    "source": [
     "def create_box(width, height, depth):\n",
-    "    \n",
     "    # create vertices\n",
-    "    vertices = np.array([\n",
-    "        [+width/2, +height/2, +depth/2], # front top right\n",
-    "        [-width/2, +height/2, +depth/2], # front top left\n",
-    "        [-width/2, -height/2, +depth/2], # front bottom left\n",
-    "        [+width/2, -height/2, +depth/2], # front bottom right\n",
-    "        [+width/2, +height/2, -depth/2], # back top right\n",
-    "        [-width/2, +height/2, -depth/2], # back top left \n",
-    "        [-width/2, -height/2, -depth/2], # back bottom left\n",
-    "        [+width/2, -height/2, -depth/2]  # back bottom right\n",
-    "    ])\n",
+    "    vertices = np.array(\n",
+    "        [\n",
+    "            [+width / 2, +height / 2, +depth / 2],  # front top right\n",
+    "            [-width / 2, +height / 2, +depth / 2],  # front top left\n",
+    "            [-width / 2, -height / 2, +depth / 2],  # front bottom left\n",
+    "            [+width / 2, -height / 2, +depth / 2],  # front bottom right\n",
+    "            [+width / 2, +height / 2, -depth / 2],  # back top right\n",
+    "            [-width / 2, +height / 2, -depth / 2],  # back top left\n",
+    "            [-width / 2, -height / 2, -depth / 2],  # back bottom left\n",
+    "            [+width / 2, -height / 2, -depth / 2],  # back bottom right\n",
+    "        ]\n",
+    "    )\n",
     "\n",
     "    # define faces\n",
-    "    faces = np.array([\n",
-    "        [0,1,2], # front face\n",
-    "        [0,2,3], \n",
-    "        [4,6,5], # back face\n",
-    "        [4,7,6],\n",
-    "        [0,4,5], # left face\n",
-    "        [0,5,1],\n",
-    "        [3,2,6], # right face\n",
-    "        [3,6,7],\n",
-    "        [1,5,6], # top face \n",
-    "        [1,6,2],\n",
-    "        [0,3,7], # bottom face\n",
-    "        [0,7,4]\n",
-    "    ])\n",
-    "    \n",
-    "    return trimesh.Trimesh(vertices=vertices, \n",
-    "                           faces=faces, \n",
-    "                           process=False)"
+    "    faces = np.array(\n",
+    "        [\n",
+    "            [0, 1, 2],  # front face\n",
+    "            [0, 2, 3],\n",
+    "            [4, 6, 5],  # back face\n",
+    "            [4, 7, 6],\n",
+    "            [0, 4, 5],  # left face\n",
+    "            [0, 5, 1],\n",
+    "            [3, 2, 6],  # right face\n",
+    "            [3, 6, 7],\n",
+    "            [1, 5, 6],  # top face\n",
+    "            [1, 6, 2],\n",
+    "            [0, 3, 7],  # bottom face\n",
+    "            [0, 7, 4],\n",
+    "        ]\n",
+    "    )\n",
+    "\n",
+    "    return trimesh.Trimesh(vertices=vertices, faces=faces, process=False)"
    ]
   },
   {
@@ -4195,7 +4196,7 @@
     "    for i in range(major_segments):\n",
     "        # calculate the angle for the current major segment\n",
     "        theta = i * 2 * np.pi / major_segments\n",
-    "        \n",
+    "\n",
     "        # loop over the minor circle (the \"cross-section\" of the doughnut)\n",
     "        for j in range(minor_segments):\n",
     "            # calculate the angle for the current minor segment\n",
@@ -4232,7 +4233,7 @@
     "            faces.append([v2, v3, v4])\n",
     "\n",
     "    # create mesh using the generated vertices and faces\n",
-    "    return trimesh.Trimesh(vertices=vertices, faces=faces)\n"
+    "    return trimesh.Trimesh(vertices=vertices, faces=faces)"
    ]
   },
   {
@@ -5510,7 +5511,9 @@
     }
    ],
    "source": [
-    "torus_mesh = create_torus(major_radius=2, minor_radius=0.3, major_segments=n_sections, minor_segments=n_sections)\n",
+    "torus_mesh = create_torus(\n",
+    "    major_radius=2, minor_radius=0.3, major_segments=n_sections, minor_segments=n_sections\n",
+    ")\n",
     "torus_mesh.show()"
    ]
   },
@@ -5582,7 +5585,7 @@
     "            faces.append([v1, v3, v4])\n",
     "\n",
     "    # create mesh using the generated vertices and faces\n",
-    "    return trimesh.Trimesh(vertices=vertices, faces=faces)\n"
+    "    return trimesh.Trimesh(vertices=vertices, faces=faces)"
    ]
   },
   {
@@ -6901,15 +6904,15 @@
     "torus_structure = td.Structure(geometry=torus_geo, medium=td.Medium(permittivity=2))\n",
     "ellipsoid_structure = td.Structure(geometry=ellipsoid_geo, medium=td.Medium(permittivity=2))\n",
     "\n",
-    "# put the strcutures into a simulation\n",
+    "# put the structures into a simulation\n",
     "sim = td.Simulation(\n",
-    "    size=(5,5,5),\n",
-    "    structures=[torus_structure,ellipsoid_structure],\n",
+    "    size=(5, 5, 5),\n",
+    "    structures=[torus_structure, ellipsoid_structure],\n",
     "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=25, wavelength=1),\n",
-    "    run_time=1e-12\n",
-    "                   )\n",
+    "    run_time=1e-12,\n",
+    ")\n",
     "\n",
-    "# plot the cross section of the simualtion\n",
+    "# plot the cross section of the simulation\n",
     "sim.plot(z=0)\n",
     "plt.show()"
    ]
@@ -6942,7 +6945,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.0"
   },
   "title": "Defining complex geometries using trimesh | Flexcompute"
  },
diff --git a/CustomFieldSource.ipynb b/CustomFieldSource.ipynb
index 61d1e741..1a063426 100644
--- a/CustomFieldSource.ipynb
+++ b/CustomFieldSource.ipynb
@@ -31,12 +31,12 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3D import\n",
     "import tidy3d as td\n",
-    "from tidy3d import web\n"
+    "from tidy3d import web"
    ]
   },
   {
@@ -86,7 +86,7 @@
     "    waist_radius=waist_radius,\n",
     "    waist_distance=-8,\n",
     "    direction=\"+\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -134,9 +134,7 @@
    ],
    "source": [
     "# Monitor for propagation\n",
-    "mnt_xy = td.FieldMonitor(\n",
-    "    center=(0, 0, 0), size=(10, 10, 0), freqs=[freq0], name=\"field_xy\"\n",
-    ")\n",
+    "mnt_xy = td.FieldMonitor(center=(0, 0, 0), size=(10, 10, 0), freqs=[freq0], name=\"field_xy\")\n",
     "\n",
     "# Monitors for forward and backward flux\n",
     "mnt_flux_pos = src_pos - 0.5\n",
@@ -154,8 +152,12 @@
     "\n",
     "# Monitor to be used as custom source, small nonzero along x\n",
     "mnt_yz_2 = td.FieldMonitor(\n",
-    "    center=(-src_pos, 0, 0), size=(1e-5, 8, 8), freqs=[freq0], colocate=False, name=\"yz_nonzero_size_x\"\n",
-    ")\n"
+    "    center=(-src_pos, 0, 0),\n",
+    "    size=(1e-5, 8, 8),\n",
+    "    freqs=[freq0],\n",
+    "    colocate=False,\n",
+    "    name=\"yz_nonzero_size_x\",\n",
+    ")"
    ]
   },
   {
@@ -192,7 +194,7 @@
     "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
     ")\n",
     "sim.plot(z=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -541,7 +543,7 @@
     }
    ],
    "source": [
-    "sim_data = web.run(sim, task_name=\"free space gaussian\", verbose=True)\n"
+    "sim_data = web.run(sim, task_name=\"free space gaussian\", verbose=True)"
    ]
   },
   {
@@ -576,7 +578,7 @@
     "ax.set_title(\n",
     "    f\"Flux fwd={float(sim_data['flux_f'].flux):1.2e}, bck={float(sim_data['flux_b'].flux):1.2e}\"\n",
     ")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -608,7 +610,7 @@
     "    source_time=pulse,\n",
     "    center=(0, 1, 0),\n",
     "    # size is by default taken from the data, but it must be reset to zero along x\n",
-    "    size=(0, 8, 8),  \n",
+    "    size=(0, 8, 8),\n",
     ")\n",
     "sim_1 = td.Simulation(\n",
     "    size=sim_size,\n",
@@ -625,7 +627,7 @@
     "    monitors=[mnt_xy, mnt_flux_f, mnt_flux_b],\n",
     "    run_time=run_time,\n",
     "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -862,7 +864,7 @@
    ],
    "source": [
     "batch = web.Batch(simulations={\"zero_x\": sim_1, \"nonzero_x\": sim_2}, verbose=True)\n",
-    "batch_results = batch.run(path_dir=\"data\")\n"
+    "batch_results = batch.run(path_dir=\"data\")"
    ]
   },
   {
@@ -870,7 +872,7 @@
    "id": "4f22fc0f",
    "metadata": {},
    "source": [
-    "Below we plot the injected field in the two simulations. As can be seen, there is a bit of backwards ppower injected in the simulation in which we used a `FieldMonitor` with size `0` along x, while the injection is extremely clean in the case where the size of the monitor was slightly larger than zero. As mentioned above, this is because the fields from the original simulation are captured exactly as they are on the numerical grid."
+    "Below we plot the injected field in the two simulations. As can be seen, there is a bit of backwards power injected in the simulation in which we used a `FieldMonitor` with size `0` along x, while the injection is extremely clean in the case where the size of the monitor was slightly larger than zero. As mentioned above, this is because the fields from the original simulation are captured exactly as they are on the numerical grid."
    ]
   },
   {
@@ -1007,17 +1009,21 @@
     "sim_data.plot_field(\"field_xy\", field_name=\"Ey\", val=\"abs\", ax=ax[0])\n",
     "ax[0].set_xlim([-sim.size[0] / 2, sim.size[0] / 2])\n",
     "ax[0].set_ylim([-sim.size[1] / 2, sim.size[1] / 2])\n",
-    "title = f\"Flux: fwd={float(sim_data['flux_f'].flux):1.2e}, bck={float(sim_data['flux_b'].flux):1.2e}\"\n",
+    "title = (\n",
+    "    f\"Flux: fwd={float(sim_data['flux_f'].flux):1.2e}, bck={float(sim_data['flux_b'].flux):1.2e}\"\n",
+    ")\n",
     "title += \"\\nzero-size FieldData along x\"\n",
     "ax[0].set_title(title)\n",
     "sim_data = batch_results[\"nonzero_x\"]\n",
     "sim_data.plot_field(\"field_xy\", field_name=\"Ey\", val=\"abs\", ax=ax[1])\n",
     "ax[1].set_xlim([-sim.size[0] / 2, sim.size[0] / 2])\n",
     "ax[1].set_ylim([-sim.size[1] / 2, sim.size[1] / 2])\n",
-    "title = f\"Flux: fwd={float(sim_data['flux_f'].flux):1.2e}, bck={float(sim_data['flux_b'].flux):1.2e}\"\n",
+    "title = (\n",
+    "    f\"Flux: fwd={float(sim_data['flux_f'].flux):1.2e}, bck={float(sim_data['flux_b'].flux):1.2e}\"\n",
+    ")\n",
     "title += \"\\nnonzero-size FieldData along x\"\n",
     "ax[1].set_title(title)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1113,7 +1119,7 @@
     "    monitors=[mnt_xy, mnt_flux_f, mnt_flux_b],\n",
     "    run_time=run_time,\n",
     "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1350,7 +1356,7 @@
    ],
    "source": [
     "batch = web.Batch(simulations={\"custom_E\": sim_3, \"custom_EH\": sim_4}, verbose=True)\n",
-    "batch_results = batch.run(path_dir=\"data\")\n"
+    "batch_results = batch.run(path_dir=\"data\")"
    ]
   },
   {
@@ -1497,17 +1503,21 @@
     "sim_data.plot_field(\"field_xy\", field_name=\"Ey\", val=\"abs\", ax=ax[0])\n",
     "ax[0].set_xlim([-sim.size[0] / 2, sim.size[0] / 2])\n",
     "ax[0].set_ylim([-sim.size[1] / 2, sim.size[1] / 2])\n",
-    "title = f\"Flux: fwd={float(sim_data['flux_f'].flux):1.2e}, bck={float(sim_data['flux_b'].flux):1.2e}\"\n",
+    "title = (\n",
+    "    f\"Flux: fwd={float(sim_data['flux_f'].flux):1.2e}, bck={float(sim_data['flux_b'].flux):1.2e}\"\n",
+    ")\n",
     "title += \"\\nOnly E field supplied\"\n",
     "ax[0].set_title(title)\n",
     "sim_data = batch_results[\"custom_EH\"]\n",
     "sim_data.plot_field(\"field_xy\", field_name=\"Ey\", val=\"abs\", ax=ax[1])\n",
     "ax[1].set_xlim([-sim.size[0] / 2, sim.size[0] / 2])\n",
     "ax[1].set_ylim([-sim.size[1] / 2, sim.size[1] / 2])\n",
-    "title = f\"Flux: fwd={float(sim_data['flux_f'].flux):1.2e}, bck={float(sim_data['flux_b'].flux):1.2e}\"\n",
+    "title = (\n",
+    "    f\"Flux: fwd={float(sim_data['flux_f'].flux):1.2e}, bck={float(sim_data['flux_b'].flux):1.2e}\"\n",
+    ")\n",
     "title += \"\\nE and H fields supplied\"\n",
     "ax[1].set_title(title)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1938,7 +1948,7 @@
     }
    ],
    "source": [
-    "sim_data_JM = web.run(sim_JM, task_name='custom current')\n"
+    "sim_data_JM = web.run(sim_JM, task_name=\"custom current\")"
    ]
   },
   {
@@ -1973,7 +1983,7 @@
     "ax.set_title(\n",
     "    f\"Flux fwd={float(sim_data['flux_f'].flux):1.2e}, bck={float(sim_data['flux_b'].flux):1.2e}\"\n",
     ")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2004,7 +2014,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.0"
   },
   "nbdime-conflicts": {
    "local_diff": [
diff --git a/CustomMediumTutorial.ipynb b/CustomMediumTutorial.ipynb
index fd6ec6d8..f830e9b8 100644
--- a/CustomMediumTutorial.ipynb
+++ b/CustomMediumTutorial.ipynb
@@ -40,13 +40,11 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "import xarray as xr\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
-    "from tidy3d import web\n",
-    "from tidy3d import SpatialDataArray\n"
+    "import xarray as xr\n",
+    "from tidy3d import SpatialDataArray, web"
    ]
   },
   {
@@ -73,7 +71,7 @@
    "source": [
     "lda0 = 1  # central wavelength\n",
     "\n",
-    "freq0 = td.C_0 / lda0  # central frequency\n"
+    "freq0 = td.C_0 / lda0  # central frequency"
    ]
   },
   {
@@ -108,7 +106,7 @@
    },
    "outputs": [],
    "source": [
-    "Nx, Ny, Nz = 100, 100, 1 # number of grid points along each dimension\n",
+    "Nx, Ny, Nz = 100, 100, 1  # number of grid points along each dimension\n",
     "\n",
     "r = 20  # radius of the lens, unit: micron\n",
     "t = 10  # thickness of the lens, unit: micron\n",
@@ -117,7 +115,7 @@
     "# Note: when only one coordinate is supplied along an axis, it means the medium is uniform along this axis.\n",
     "X = np.linspace(-r, r, Nx)  # x grid\n",
     "Y = np.linspace(-r, r, Ny)  # y grid\n",
-    "Z = [0]  # z grid\n"
+    "Z = [0]  # z grid"
    ]
   },
   {
@@ -151,7 +149,7 @@
     "n_data = np.ones((Nx, Ny, Nz))\n",
     "n0 = 2\n",
     "A = 1e-3\n",
-    "n_data[r_mesh <= r] = n0 * (1 - A * r_mesh[r_mesh <= r] ** 2)\n"
+    "n_data[r_mesh <= r] = n0 * (1 - A * r_mesh[r_mesh <= r] ** 2)"
    ]
   },
   {
@@ -177,7 +175,7 @@
    "outputs": [],
    "source": [
     "# convert to dataset array\n",
-    "n_dataset = SpatialDataArray(n_data, coords=dict(x=X, y=Y, z=Z))\n"
+    "n_dataset = SpatialDataArray(n_data, coords=dict(x=X, y=Y, z=Z))"
    ]
   },
   {
@@ -220,7 +218,7 @@
     "mat_custom2 = td.CustomMedium.from_eps_raw(eps_dataset, interp_method=\"nearest\")\n",
     "\n",
     "# define permittivity directly in the class\n",
-    "mat_custom3 = td.CustomMedium(permittivity=eps_dataset, interp_method=\"nearest\")\n"
+    "mat_custom3 = td.CustomMedium(permittivity=eps_dataset, interp_method=\"nearest\")"
    ]
   },
   {
@@ -266,7 +264,7 @@
     "# define the lens structure as a box\n",
     "lens = td.Structure(\n",
     "    geometry=td.Box(center=(0, 0, t / 2), size=(td.inf, td.inf, t)), medium=mat_custom1\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -304,7 +302,7 @@
     "# define a field monitor in the xz plane at y=0\n",
     "monitor_field_xz = td.FieldMonitor(\n",
     "    center=[0, 0, 0], size=[td.inf, 0, td.inf], freqs=[freq0], name=\"field_xz\"\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -344,15 +342,13 @@
     "    sources=[plane_wave],\n",
     "    monitors=[monitor_field_xz],\n",
     "    run_time=run_time,\n",
-    "    boundary_spec=td.BoundarySpec.all_sides(\n",
-    "        boundary=td.PML()\n",
-    "    ),  # pml is applied in all boundaries\n",
+    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),  # pml is applied in all boundaries\n",
     "    symmetry=(\n",
     "        -1,\n",
     "        1,\n",
     "        0,\n",
     "    ),  # symmetry is used such that only a quarter of the structure needs to be modeled.\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -399,7 +395,7 @@
    ],
    "source": [
     "sim.plot_eps(x=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -436,7 +432,7 @@
    ],
    "source": [
     "sim.plot_eps(z=t / 2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -821,7 +817,7 @@
     "    task_name=\"gradient_index_lens\",\n",
     "    path=\"data/simulation.hdf5\",\n",
     "    verbose=True,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -866,7 +862,7 @@
    ],
    "source": [
     "sim_data.plot_field(\"field_xz\", \"Ex\", vmin=-15, vmax=15)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -903,7 +899,7 @@
    ],
    "source": [
     "sim_data.plot_field(\"field_xz\", \"E\", \"abs^2\", vmin=0, vmax=300)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -934,11 +930,13 @@
    "source": [
     "# spatially varying Lorentz model that has the same permittivity profile as\n",
     "# the custom non-dispersive medium at `freq0`.\n",
-    "eps_inf_dataset = xr.ones_like(eps_dataset) # uniform eps_inf\n",
-    "f0_dataset = xr.ones_like(eps_inf_dataset)*2*freq0 # uniform oscillator frequency as well\n",
-    "gamma_dataset = xr.zeros_like(eps_inf_dataset) # lossless oscillator\n",
-    "delep_dataset = (eps_dataset-1)*3/4 # non-uniform oscillator strength\n",
-    "mat_lorentz = td.CustomLorentz(eps_inf=eps_inf_dataset, coeffs=((delep_dataset, f0_dataset, gamma_dataset),))\n"
+    "eps_inf_dataset = xr.ones_like(eps_dataset)  # uniform eps_inf\n",
+    "f0_dataset = xr.ones_like(eps_inf_dataset) * 2 * freq0  # uniform oscillator frequency as well\n",
+    "gamma_dataset = xr.zeros_like(eps_inf_dataset)  # lossless oscillator\n",
+    "delep_dataset = (eps_dataset - 1) * 3 / 4  # non-uniform oscillator strength\n",
+    "mat_lorentz = td.CustomLorentz(\n",
+    "    eps_inf=eps_inf_dataset, coeffs=((delep_dataset, f0_dataset, gamma_dataset),)\n",
+    ")"
    ]
   },
   {
@@ -971,8 +969,8 @@
    },
    "outputs": [],
    "source": [
-    "lens_lorentz = lens.copy(update={\"medium\":mat_lorentz})\n",
-    "sim_lorentz = sim.copy(update={\"structures\":[lens_lorentz]})"
+    "lens_lorentz = lens.copy(update={\"medium\": mat_lorentz})\n",
+    "sim_lorentz = sim.copy(update={\"structures\": [lens_lorentz]})"
    ]
   },
   {
@@ -1011,7 +1009,7 @@
    ],
    "source": [
     "sim_lorentz.plot_eps(x=0, freq=freq0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1040,7 +1038,7 @@
    ],
    "source": [
     "sim_lorentz.plot_eps(z=t / 2, freq=freq0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1419,7 +1417,7 @@
     "    task_name=\"gradient_index_lens_lorentz\",\n",
     "    path=\"data/simulation_lorentz.hdf5\",\n",
     "    verbose=True,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1458,7 +1456,7 @@
    ],
    "source": [
     "sim_data_lorentz.plot_field(\"field_xz\", \"Ex\", vmin=-15, vmax=15)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1487,7 +1485,7 @@
    ],
    "source": [
     "sim_data_lorentz.plot_field(\"field_xz\", \"E\", \"abs^2\", vmin=0, vmax=300)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1530,7 +1528,7 @@
    },
    "outputs": [],
    "source": [
-    "mat_anisotropic = td.CustomAnisotropicMedium(xx=mat_custom1, yy=mat_custom2, zz=mat_lorentz)\n"
+    "mat_anisotropic = td.CustomAnisotropicMedium(xx=mat_custom1, yy=mat_custom2, zz=mat_lorentz)"
    ]
   },
   {
@@ -1547,8 +1545,8 @@
    },
    "outputs": [],
    "source": [
-    "lens_anisotropic = lens.copy(update={\"medium\":mat_anisotropic})\n",
-    "sim_anisotropic = sim.copy(update={\"structures\":[lens_anisotropic]})\n"
+    "lens_anisotropic = lens.copy(update={\"medium\": mat_anisotropic})\n",
+    "sim_anisotropic = sim.copy(update={\"structures\": [lens_anisotropic]})"
    ]
   },
   {
@@ -1587,7 +1585,7 @@
    ],
    "source": [
     "sim_anisotropic.plot_eps(x=0, freq=freq0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1616,7 +1614,7 @@
    ],
    "source": [
     "sim_anisotropic.plot_eps(z=t / 2, freq=freq0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1995,7 +1993,7 @@
     "    task_name=\"gradient_index_lens_anisotropic\",\n",
     "    path=\"data/simulation_anisotropic.hdf5\",\n",
     "    verbose=True,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -2034,7 +2032,7 @@
    ],
    "source": [
     "sim_data_anisotropic.plot_field(\"field_xz\", \"Ex\", vmin=-15, vmax=15)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2063,7 +2061,7 @@
    ],
    "source": [
     "sim_data_anisotropic.plot_field(\"field_xz\", \"E\", \"abs^2\", vmin=0, vmax=300)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
diff --git a/Design.ipynb b/Design.ipynb
index f1f2ac4e..2ada47e2 100644
--- a/Design.ipynb
+++ b/Design.ipynb
@@ -37,15 +37,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "7d44f419-15d4-41a7-b699-5c7351f3a952",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import matplotlib.pylab as plt\n",
     "import typing\n",
     "\n",
+    "import matplotlib.pylab as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.plugins.design as tdd"
    ]
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "41b9d573-6386-4f17-bae5-82d45dc0e56b",
    "metadata": {
     "tags": []
@@ -72,7 +72,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -83,8 +83,8 @@
    ],
    "source": [
     "# Define your design space (parameters (x,y) within certain ranges)\n",
-    "param_x = tdd.ParameterFloat(name=\"x\", span=(-15,15))\n",
-    "param_y = tdd.ParameterFloat(name=\"y\", span=(-15,15))\n",
+    "param_x = tdd.ParameterFloat(name=\"x\", span=(-15, 15))\n",
+    "param_y = tdd.ParameterFloat(name=\"y\", span=(-15, 15))\n",
     "\n",
     "# Define your sampling method, Monte Carlo method with 10,000 points\n",
     "method = tdd.MethodMonteCarlo(num_points=10000)\n",
@@ -94,26 +94,32 @@
     "\n",
     "# Define your fitness function / figure of merit. Here we compute a gaussian as a function of x and y with different values for the width.\n",
     "sigmas = {f\"sigma={s}\": s for s in [5, 10, 20]}\n",
-    "def f(x:float, y:float) -> typing.Dict[str, float]:\n",
-    "    \"\"\"gaussian distribution as a function of x and y.\"\"\"\n",
+    "\n",
+    "\n",
+    "def f(x: float, y: float) -> typing.Dict[str, float]:\n",
+    "    \"\"\"Gaussian distribution as a function of x and y.\"\"\"\n",
     "    r2 = x**2 + y**2\n",
-    "    gaussian = lambda sigma: np.exp(-r2 / sigma ** 2)\n",
+    "\n",
+    "    def gaussian(sigma: float) -> float:\n",
+    "        return np.exp(-r2 / sigma**2)\n",
+    "\n",
     "    # return a dictionary, where the key is used to label the output names\n",
     "    return {key: gaussian(sigma) for key, sigma in sigmas.items()}\n",
     "\n",
+    "\n",
     "# Call .run on the DesignSpace with our function and convert the results to a pandas.DataFrame\n",
     "df = design_space.run(f).to_dataframe()\n",
     "\n",
     "# Plot the results using pandas.DataFrame builtins\n",
     "f, axes = plt.subplots(1, 3, figsize=(10, 3), tight_layout=True)\n",
     "for ax, C in zip(axes, sigmas.keys()):\n",
-    "    _ = df.plot.hexbin(x='x', y='y', gridsize=20, C=C, cmap=\"magma\", ax=ax)\n",
-    "    ax.set_title(C)\n"
+    "    _ = df.plot.hexbin(x=\"x\", y=\"y\", gridsize=20, C=C, cmap=\"magma\", ax=ax)\n",
+    "    ax.set_title(C)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "29c56a39-ca58-4870-a65d-5e9842586c24",
    "metadata": {
     "tags": []
@@ -150,58 +156,58 @@
        "  \n",
        "    \n",
        "      0\n",
-       "      -9.287730\n",
-       "      -3.498281\n",
-       "      1.944830e-02\n",
-       "      0.373440\n",
-       "      0.781727\n",
+       "      -11.734928\n",
+       "      -10.201184\n",
+       "      0.000063\n",
+       "      0.089124\n",
+       "      0.546385\n",
        "    \n",
        "    \n",
        "      1\n",
-       "      7.280786\n",
-       "      -1.283115\n",
-       "      1.123373e-01\n",
-       "      0.578937\n",
-       "      0.872284\n",
+       "      13.090192\n",
+       "      12.839524\n",
+       "      0.000001\n",
+       "      0.034664\n",
+       "      0.431488\n",
        "    \n",
        "    \n",
        "      2\n",
-       "      -14.875910\n",
-       "      -11.875533\n",
-       "      5.079723e-07\n",
-       "      0.026697\n",
-       "      0.404217\n",
+       "      -9.923979\n",
+       "      2.477132\n",
+       "      0.015224\n",
+       "      0.351265\n",
+       "      0.769854\n",
        "    \n",
        "    \n",
        "      3\n",
-       "      6.986373\n",
-       "      -4.965107\n",
-       "      5.294678e-02\n",
-       "      0.479689\n",
-       "      0.832223\n",
+       "      12.402777\n",
+       "      3.761279\n",
+       "      0.001208\n",
+       "      0.186419\n",
+       "      0.657086\n",
        "    \n",
        "    \n",
        "      4\n",
-       "      4.749807\n",
-       "      6.478849\n",
-       "      7.566453e-02\n",
-       "      0.524473\n",
-       "      0.851002\n",
+       "      6.023827\n",
+       "      -2.162388\n",
+       "      0.194272\n",
+       "      0.663900\n",
+       "      0.902663\n",
        "    \n",
        "  \n",
        "\n",
        ""
       ],
       "text/plain": [
-       "           x          y       sigma=5  sigma=10  sigma=20\n",
-       "0  -9.287730  -3.498281  1.944830e-02  0.373440  0.781727\n",
-       "1   7.280786  -1.283115  1.123373e-01  0.578937  0.872284\n",
-       "2 -14.875910 -11.875533  5.079723e-07  0.026697  0.404217\n",
-       "3   6.986373  -4.965107  5.294678e-02  0.479689  0.832223\n",
-       "4   4.749807   6.478849  7.566453e-02  0.524473  0.851002"
+       "           x          y   sigma=5  sigma=10  sigma=20\n",
+       "0 -11.734928 -10.201184  0.000063  0.089124  0.546385\n",
+       "1  13.090192  12.839524  0.000001  0.034664  0.431488\n",
+       "2  -9.923979   2.477132  0.015224  0.351265  0.769854\n",
+       "3  12.402777   3.761279  0.001208  0.186419  0.657086\n",
+       "4   6.023827  -2.162388  0.194272  0.663900  0.902663"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -225,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "86300207-941f-4ccb-9ccb-f815cee5e7e6",
    "metadata": {},
    "outputs": [],
@@ -249,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "fcf092e4-9d87-476b-a0a4-fe137bbf92c9",
    "metadata": {},
    "outputs": [],
@@ -261,7 +267,7 @@
     "    for i in range(num):\n",
     "        n = refractive_indices[i % len(refractive_indices)]\n",
     "        thickness = t * lambda0 / n\n",
-    "        medium = td.Medium(permittivity = n ** 2)\n",
+    "        medium = td.Medium(permittivity=n**2)\n",
     "        geometry = td.Box(center=(0, 0, z + thickness / 2), size=(td.inf, td.inf, thickness))\n",
     "        layers.append(td.Structure(geometry=geometry, medium=medium))\n",
     "        z += thickness\n",
@@ -270,26 +276,31 @@
     "        size=(0, 0, Lz),\n",
     "        center=(0, 0, z / 2),\n",
     "        structures=layers,\n",
-    "        sources=[td.PlaneWave(\n",
-    "            size=(td.inf, td.inf, 0),\n",
-    "            center=(0, 0, -buffer * 0.75),\n",
-    "            source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0/10),\n",
-    "            direction=\"+\",\n",
-    "        )],\n",
-    "        monitors=[td.FluxMonitor(\n",
-    "            size=(td.inf, td.inf, 0),\n",
-    "            center=(0, 0, z + buffer * 0.75),\n",
-    "            freqs=[freq0],\n",
-    "            name=mnt_name,\n",
-    "        )],\n",
+    "        sources=[\n",
+    "            td.PlaneWave(\n",
+    "                size=(td.inf, td.inf, 0),\n",
+    "                center=(0, 0, -buffer * 0.75),\n",
+    "                source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 10),\n",
+    "                direction=\"+\",\n",
+    "            )\n",
+    "        ],\n",
+    "        monitors=[\n",
+    "            td.FluxMonitor(\n",
+    "                size=(td.inf, td.inf, 0),\n",
+    "                center=(0, 0, z + buffer * 0.75),\n",
+    "                freqs=[freq0],\n",
+    "                name=mnt_name,\n",
+    "            )\n",
+    "        ],\n",
     "        boundary_spec=td.BoundarySpec.pml(x=False, y=False, z=True),\n",
     "        run_time=100 / freq0,\n",
     "    )\n",
     "    return sim\n",
     "\n",
+    "\n",
     "def post(data: td.SimulationData) -> dict:\n",
     "    \"\"\"Post-processing function, which processes the tidy3d simulation data to return the function output.\"\"\"\n",
-    "    flux = np.sum(data['flux'].flux.values)\n",
+    "    flux = np.sum(data[\"flux\"].flux.values)\n",
     "    return {\"flux\": flux}"
    ]
   },
@@ -334,13 +345,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "6e0cc1d2-0a15-4343-97ed-05f2686498c8",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -373,13 +384,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "3c94ab9c-3b96-4431-8b35-d257b911d2e3",
    "metadata": {},
    "outputs": [],
    "source": [
-    "param_num = tdd.ParameterInt(name='num', span=(1, 5))\n",
-    "param_t = tdd.ParameterFloat(name='t', span=(0.1, 0.5), num_points=5)"
+    "param_num = tdd.ParameterInt(name=\"num\", span=(1, 5))\n",
+    "param_t = tdd.ParameterFloat(name=\"t\", span=(0.1, 0.5), num_points=5)"
    ]
   },
   {
@@ -394,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "646f08e9-b6c8-4a33-8d8d-919e972ea4c1",
    "metadata": {},
    "outputs": [],
@@ -424,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "efce81e5-b648-4788-91bc-57beec8a7dee",
    "metadata": {},
    "outputs": [],
@@ -442,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "6563ba39-7280-4c1b-aefa-86ce7c26c67d",
    "metadata": {},
    "outputs": [],
@@ -462,12 +473,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 11,
    "id": "46e97289-edec-4ede-b109-e2bbf96e2264",
    "metadata": {},
    "outputs": [],
    "source": [
-    "design_space = tdd.DesignSpace(parameters=[param_num, param_t], method=method, path_dir=\"./data\", folder_name=\"Design_Tutorial\", task_name=\"Design_notebook\")"
+    "design_space = tdd.DesignSpace(\n",
+    "    parameters=[param_num, param_t],\n",
+    "    method=method,\n",
+    "    path_dir=\"./data\",\n",
+    "    folder_name=\"Design_Tutorial\",\n",
+    "    task_name=\"Design_notebook\",\n",
+    ")"
    ]
   },
   {
@@ -477,7 +494,7 @@
    "source": [
     "We can provide a `path_dir` to specify the local directory location where files should be stored, and the `folder_name` which is the location on `Tidy3D` cloud where the `Simulations` will be kept.\n",
     "\n",
-    "The `task_name` argument asigns the root of task name for `Tidy3D` cloud. This is combined with the index of the `Simulation` output by the `Pre` function (often 0) and a counter for the simulations run by the `DesignSpace` in this format: \n",
+    "The `task_name` argument assigns the root of task name for `Tidy3D` cloud. This is combined with the index of the `Simulation` output by the `Pre` function (often 0) and a counter for the simulations run by the `DesignSpace` in this format: \n",
     "\n",
     "`{task_name}_{sim_index}_{counter}` \n",
     "\n",
@@ -485,7 +502,7 @@
     "\n",
     "`{task_name}_{dict_key}_{counter}`\n",
     "\n",
-    "### Working with Pre and Post functions\n",
+    "### Working with Pre and Post Functions\n",
     "\n",
     "Now we need to pass our `Pre` and `Post` functions to the `DesignSpace` object to get our results.\n",
     "\n",
@@ -518,7 +535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 12,
    "id": "6d3b23d1-32ae-4460-9944-14919a990ba4",
    "metadata": {
     "scrolled": true,
@@ -528,115 +545,11 @@
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      "data": {
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08:56:24 BST Running 40 Simulations                                             \n",
-       "
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-      "text/plain": [
-       "\u001b[2;36m08:56:24 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m40\u001b[0m Simulations                                             \n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
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-       "\u001b[2;36m08:56:25 BST\u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR: The name already exists.                                    \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR: The name already exists.                                    \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR: The name already exists.                                    \u001b[0m\n"
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-     "metadata": {},
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-     "data": {
-      "text/html": [
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             ERROR: The name already exists.                                    \n",
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-      "text/plain": [
-       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR: The name already exists.                                    \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-       "
             ERROR: The name already exists.                                    \n",
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-      "text/plain": [
-       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR: The name already exists.                                    \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
             ERROR: The name already exists.                                    \n",
+       "
11:09:43 Eastern Daylight Time Running 40 Simulations                           \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR: The name already exists.                                    \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
             ERROR: The name already exists.                                    \n",
-       "
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-      ],
-      "text/plain": [
-       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[1;31mERROR: The name already exists.                                    \u001b[0m\n"
+       "\u001b[2;36m11:09:43 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m40\u001b[0m Simulations                           \n"
       ]
      },
      "metadata": {},
@@ -661,9 +574,9 @@
    "metadata": {},
    "source": [
     "\n",
-    "`Results` contains three main related datastructures.\n",
+    "`Results` contains three main related data structures.\n",
     "\n",
-    "* `dims`, which correpsond to the `kwargs` of the pre-processing function, `('num', 't')` here.\n",
+    "* `dims`, which correspond to the `kwargs` of the pre-processing function, `('num', 't')` here.\n",
     "\n",
     "* `coords`, which is a tuple containing the values passed in for each of the dims. `coords[i]` is a tuple of `n`, and `t` values for the `i`th function call.\n",
     "\n",
@@ -674,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "82f5590d-fe19-4c57-ba19-aa80d2c2a60d",
    "metadata": {},
    "outputs": [
@@ -707,33 +620,33 @@
        "  \n",
        "    \n",
        "      0\n",
-       "      3\n",
-       "      0.231423\n",
-       "      0.712001\n",
+       "      4\n",
+       "      0.119511\n",
+       "      0.999964\n",
        "    \n",
        "    \n",
        "      1\n",
        "      3\n",
-       "      0.327886\n",
-       "      0.952495\n",
+       "      0.454268\n",
+       "      0.967664\n",
        "    \n",
        "    \n",
        "      2\n",
-       "      3\n",
-       "      0.306500\n",
-       "      0.862998\n",
+       "      1\n",
+       "      0.466081\n",
+       "      0.997644\n",
        "    \n",
        "    \n",
        "      3\n",
-       "      4\n",
-       "      0.366405\n",
-       "      0.998939\n",
+       "      3\n",
+       "      0.308095\n",
+       "      0.870236\n",
        "    \n",
        "    \n",
        "      4\n",
        "      1\n",
-       "      0.128428\n",
-       "      0.913065\n",
+       "      0.153233\n",
+       "      0.895102\n",
        "    \n",
        "  \n",
        "\n",
@@ -741,14 +654,14 @@
       ],
       "text/plain": [
        "   num         t      flux\n",
-       "0    3  0.231423  0.712001\n",
-       "1    3  0.327886  0.952495\n",
-       "2    3  0.306500  0.862998\n",
-       "3    4  0.366405  0.998939\n",
-       "4    1  0.128428  0.913065"
+       "0    4  0.119511  0.999964\n",
+       "1    3  0.454268  0.967664\n",
+       "2    1  0.466081  0.997644\n",
+       "3    3  0.308095  0.870236\n",
+       "4    1  0.153233  0.895102"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -769,13 +682,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "22546073-b479-4b72-bf4e-5c0cb5e14afa",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -789,11 +702,11 @@
     "\n",
     "# plot a hexagonal binning of the results\n",
     "im = df.plot.hexbin(x=\"t\", y=\"num\", gridsize=10, C=\"flux\", cmap=\"magma\", ax=ax1)\n",
-    "ax1.set_title('SCS (hexagonal binning)')\n",
+    "ax1.set_title(\"SCS (hexagonal binning)\")\n",
     "\n",
     "# scatterplot the raw results with flux on the y axis\n",
     "im = df.plot.scatter(x=\"t\", y=\"flux\", s=50, c=\"num\", cmap=\"magma\", ax=ax2)\n",
-    "ax2.set_title('SCS (data scatter plot)')\n",
+    "ax2.set_title(\"SCS (data scatter plot)\")\n",
     "\n",
     "plt.show()"
    ]
@@ -826,7 +739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "75f3ae9e-ebfe-476d-ad6b-d0f3df8cfafa",
    "metadata": {
     "scrolled": true
@@ -835,11 +748,11 @@
     {
      "data": {
       "text/html": [
-       "
08:34:22 BST Running 40 Simulations                                             \n",
+       "
11:11:21 Eastern Daylight Time Running 40 Simulations                           \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m08:34:22 BST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m40\u001b[0m Simulations                                             \n"
+       "\u001b[2;36m11:11:21 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m40\u001b[0m Simulations                           \n"
       ]
      },
      "metadata": {},
@@ -847,7 +760,7 @@
     }
    ],
    "source": [
-    "param_t2 = tdd.ParameterFloat(name='t', span=(0.2, 0.3), num_points=5)\n",
+    "param_t2 = tdd.ParameterFloat(name=\"t\", span=(0.2, 0.3), num_points=5)\n",
     "design_space2 = design_space.updated_copy(parameters=[param_num, param_t2])\n",
     "results2 = design_space2.run(pre, post)"
    ]
@@ -862,13 +775,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "1da98b32-0c37-4235-9244-d6630ed733e2",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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Hh2XLljFw4EAiIiL4+eefWbp0KS+//DLBwcE5Pr6rcdDT05M333yTf/3rX9xxxx306dOHI0eO8Pnnn7s1Rywj77zzDr/99hsREREMGzaMhg0bcuHCBbZv387KlSu5cOECYItboaGhdOjQgZCQEP755x8++ugjunfvbi+28vDDD/PSSy/Ru3dvnn76aa5cucL06dOpW7euvehHZsLDw7FYLLz77rvExsbi7e3NHXfcwbx585g2bRq9e/emVq1aXL58mU8//ZTAwMBsXWQVIlflc5VGIZz6+eef9WOPPabr16+v/f39tZeXl65du7Z+6qmndExMTLr2ixYt0rfccosuVaqULlWqlK5fv74ePny43rdvn0O7devW6S5duuiAgABdqlQp3bRpUz116lSHNitXrtQdOnTQvr6+OjAwUPfo0UPv2bPHoU1aGd0bSw9rnXHp+KtXr+qnn35alytXTpcqVUr36NFDnzhxwq3y9QsXLszw+W3btumIiAjt5eWlq1atqidNmpRp+fru3bun279Tp04ulSwWQoiiYP/+/XrYsGG6evXq2svLSwcEBOgOHTroqVOn6sTERHu7lJQUPWHCBF2jRg3t6empq1SposeMGePQRuvMPzsBPXz4cIdtR44c0YB+77337NsGDhyoS5UqpQ8dOqTvuusu7efnp0NCQvS4ceO01WpNd8yMyte7Eme0dj0OTps2TdeoUUN7e3vrVq1a6bVr17ocCzJ63WliYmL08OHDdZUqVbSnp6cODQ3Vd955p545c6a9zSeffKI7duyoy5Urp729vXWtWrX0Cy+8oGNjYx2O9csvv+jGjRtrLy8vXa9ePT137lyXytdrrfWnn36qa9asqS0Wi72U/fbt23Xfvn111apVtbe3t65QoYK+55579NatW52+ZiHymtJaZusLIYQQQgghRH6SOWJCCCGEEEIIkc8kERNCCCGEEEKIfCaJmBBCCCGEEELkM0nEhBBCuOydd95BKcUzzzyTZbuFCxdSv359fHx8aNKkiX35CSGEECK3FdXYJImYEEIIl2zZsoVPPvmEpk2bZtlu/fr19O3blyFDhrBjxw569epFr169MlxnSAghhMiJohybpGqiEEIIp+Lj42nRogXTpk3jzTffJDw8PNPFyfv06UNCQgJLliyxb2vbti3h4eHMmDEjn3oshBCiuCvqsUkWdM6AaZqcPn2agIAAlFIF3R0hRDGkteby5ctUqlQJw8j+4ITExESSk5Ozdf6bP9+8vb3x9vbOsP3w4cPp3r07nTt35s0338zy2Bs2bGDUqFEO2yIjI/nuu+/c7qe4TmKTECKvSWzKX5KIZeD06dNUqVKloLshhCgBTpw4QeXKlbO1b2JiIjVqhBEdfcHtff39/YmPj3fYNm7cOMaPH5+u7fz589m+fTtbtmxx6djR0dGEhIQ4bAsJCSE6OtrtforrJDYJIfKLxKb8IYlYBgICAgDbmzAwMLCAeyOEKI7i4uKoUqWK/fMmO5KTk4mOvsDRI/MJDPRz49xXqF7j4XSfcRldcTxx4gQjR45kxYoV+Pj4ZLuvIuckNgkh8prEpvwliVgG0m6JBgYGSrATQuSp3BhiFujvQ6C/r+s7mKZtPxc+47Zt28aZM2do0aKFfZvVamXt2rV89NFHJCUlYbFYHPYJDQ0lJibGYVtMTAyhoaGu91GkI7FJCJFfJDblD6maKIQQRZ1puv9w0Z133snff//Nzp077Y9WrVrxyCOPsHPnznSBDqBdu3asWrXKYduKFSto165djl+qEEKIIkJik1NyR0wIIYo6rW0Pd9q7KCAggMaNGztsK1WqFOXKlbNvHzBgAGFhYUycOBGAkSNH0qlTJz744AO6d+/O/Pnz2bp1KzNnznS9j0IIIYo2iU1OyR0xIYQo6kzt5lXH3F215Pjx40RFRdl/bt++PfPmzWPmzJk0a9aMb775hu+++y5d0BRCCFGMSWxyStYRy0BcXBylS5cmNjZWxuGXIFprkuJtpVa9/b3cGh+ttSbhwhVSElPxL1cKTx+52SyylhufM2nHuHD6G7cnRJet9IB8xhUxuRmbtDaBZEAB7n3eCSGKL4lN+Uu+LYoSL+lKMhu+2sa6LzZz8WQsAGUql+bWQW1o268l3n5eme5rmpqti/5k7axNRP1jmwDq4W2hZe+m3DasHRVqlXerL2cPn+evn//hStxV/MuWIvyeRpQJK539FydKBjfH1rvVVhQrWiej9Uk0p4HUa1u9UYTZHir9vAoh8pPWVjQxaH0auIpt8FZZDBWGUiXjy3l2WaNOk7xqFSkb/oDERDAMVOkgPNu3x6tLJEYOKiFmi8Qmp2RooijREi5eYep9s1jy9kounoq1b794KpYf31rB1PtmkXDxSob7mlaTuU8vYsELPxC993oVntQkK1sW/smkez7l0KZjLvfjv4Pn8c4dH/PzB7/x+2ebWPruKt66ZQpzRiwiMT4p032vXLrKmv9uZN6z3zJv1Hf8PnszV2MTXfwNiGIhDydEl0Rr166lR48eVKpUCaWUS4t9rl69mhYtWuDt7U3t2rWZPXt2ujYff/wx1atXx8fHh4iICDZv3pz7nc+C1lcx9TY0x7mehAEkofVhTL0DrVPytU9CaK3R+gKmPorVPICpN6H1fiAesAIpQAym3o5pHinYzuaTq1evcvToUWJiYnB14FryH+tIeGUMKatWwJUrts/51FT0+XMk//gD8c8+TcrGDXnc85tIbHJKEjFRos19ejExB87aPuhu/Ky7Nr805sBZ5j69OMN9f53xB38u2WNrftPnpGk1SU1K5bPH/seVS1ez7EPi5SQ+fugL9q09ZDuWqbGmmmhTozX8+dMeZg74itSkVIf9tNas+e9GxreexA9v/cL2H3ax4/u/+W7CMsa3mcQfc7aka3902wl+fv9Xvn99OWv+u5HLZx0XTRRFlAS7XJWQkECzZs34+OOPXWp/5MgRunfvzu23387OnTt55plnGDp0KMuXL7e3WbBgAaNGjWLcuHFs376dZs2aERkZyZkzZ/LqZTjQWmPqXUDmF3UgHlPvy5f+CAGg9UVMvQlT/4XWx4BT2IbMZtKeY5j6dL71L7/t3buXIUMep0yZYGrUqENoaGWaNm3OZ5/Nwsziczt1714SP/s062IXVitXP5lOyl9/5kHPMyGxySkZmihyVWpSKn8t28ve1QdIvpJCmbDStH4wnEoNQpzvnM+i959h/++Hs2xjWjX7fz9M9IGzhNYJtm+3plhZ+9mmLPfVpib5SjJbvtlJp6GZl0b9ffYmzhw6h85kkqo2Nce2n2Tzwp20f7TV9f0+38wPb/5yvZ1V23PJ1KRUFr/6M4bFoF2/lpw5dI45IxZx+p8YDA8DpWyvbcnElbR7tCU9x96FxdNxSJJpNbl8LgFtagLKl0r3fH5LTbby18//sOuXvSTFJxEYGkir+5pSs01Vmd+i3QxguuQFO3d07dqVrl27utx+xowZ1KhRgw8++ACABg0asG7dOj788EMiIyMBmDRpEsOGDWPw4MH2fZYuXcqsWbMYPXp07r+IdC4BCS60O4fWV1HKjbV/hMgGWxL2F9evgrp250frY2gqFrvP/TVr1tK16z2kpKSQmnr9wuvu3XsYOvRf/PzzchYsmJdhWfakpT+4fJ6khV/j0aRp/vz+JDY5JYmYyDWHtxzniye+Jv78FQyLwjQ1hmGwdtYmGnWpyyOT78O7VObzrfLbjh93Y1gMTGvWf/iGxWDHD7vo+tzt9m2Htxwn4ULGQxZvpIHt3+/KNBEzrSZ/fLkl0yTMTsG6L7bYE7GrcYksfXdV1vsAP761ghqtq/Dxg1+QeNk2XNFMvf56NZo/vtzClYtXeGTKfSilSEpI5vfZm/jjy63ExVwGwC/Il3aPtKTjkLb4l3V94m1uOf7nKWYNmc/lcwkoQ6FNjWEx2LxgB9WaV+ax//bBv1ypfO9XYaG0iXIjgLnTVji3YcMGOnfu7LAtMjKSZ555BoDk5GS2bdvGmDFj7M8bhkHnzp3ZsCHzoUJJSUkkJV2/gxUXF5ftPmp9FlthDudfdjXnUFTJ9rmEcMZ2h3YfriZfjpKwXVgok6t9KkgXLlygR49eJCUlpbvzlTY0cfHib5k48V3Gjn0Zra+gdQyQghmbiHXXLpfPZZ48gXnsKJbqNXLzJWRIYpNzBT400Z0x8ykpKbz++uvUqlULHx8fmjVrxrJlyxzajB8/HqWUw6N+/fp5/TJKvJO7ovjk0bkkXLQNwzOttqF+aUnOnlUH+PzxBU6TnvyUcP6K7XuJM4p0SZez4YZ2GuKzSNhiYy5z+awLV6mvDZNMSbRdJdv23d+kJqc62QmSEpKZ//wPJF5OtP03yeTYO37YzcENR7ly6SpT75/Fsg9W25MwsL3eX2f8wYfdZ3Lh5CXn/c1FZw6dY3rfOfbfY1rSmvZeOvHXKWY8MoeUxBI8t0WGfxSo6OhoQkIc7/qHhIQQFxfH1atXOXfuHFarNcM20dHRmR534sSJlC5d2v6oUiUnyVEKrn3pVSDzxESeuwhkfy6zznKIbdEze/YXxMfHZzn8UGvNZ599Smrqn5h6M5pjaKIwLx1w+3xmFp87uUpik1MFmoi5O2Z+7NixfPLJJ0ydOpU9e/bwxBNP0Lt3b3bs2OHQrlGjRkRFRdkf69aty4+XU6L9/N6vmKnWLIfXHfjjCHvXHMznnmXOr4yva99LtO2O0I1cviukIKB85ndqnN4Ju7n9tStjUf/EYFic//kaHgYn/jqdeRKW1s5i8MeXW/nf89/b5sxl0C9t1cSdiefzxxe4PHk4N/zyn7WkJqVm+rsyrZqovWfY/r3rVwSLHVO7/xCF3pgxY4iNjbU/Tpw4kYOjeeLalScNyjMH5xHCOU0crr0fM6YoXtU9582b7zSuent78r//vQTqwg1bNconG3+vHvk0IE5ik1MFmojdOGa+YcOGzJgxAz8/P2bNmpVh+zlz5vDyyy/TrVs3atasyZNPPkm3bt3s4/LTeHh4EBoaan+UL+9eCXHhngsnL7F37SEXvuwr/vhyaz71yrnw7g1dukNnWk3C72nksK16q6oEBPu7dJ5W9zXN9LnSIQH4lvZxfhBlK6mftj6ZMlwMYDcXIcmEaTU5vPkYe1buz/K/o2k1Ob0nhiNbjrt2/hy6cukqfy7d4/S/k1Kw7ov8rUBXqMhVxwIVGhpKTEyMw7aYmBgCAwPx9fWlfPnyWCyWDNuEhoZmelxvb28CAwMdHtmlVAiuDgNTVMj2eYRwSY4u5hlAUC51pHA4d+680zYDB0XSslW9dHO7VIUAVKgby9wYBpY6dd3tYvZIbHKqwBKxtDHzN46rdzZmPikpCR8fxy+tvr6+6e54HThwgEqVKlGzZk0eeeQRjh/Pny+NhYE1xUrMwXNE7TuTZcnz3BRz4KyLX/Y1p/fk0+1wF1RqGEqtttUwLJknNYZFUbtd9XTFRiweBrf/K/MCHGBLlvwCfWjZO/NEzOJpoV2/lqgs+gC264a3Dmpj/wCuGh7mMNcrM86S4xulJKW4lOAZHgY7r1WLzGvnjl5w6XVqbRvCWGJJsCtQ7dq1Y9UqxzmbK1asoF0722eEl5cXLVu2dGhjmiarVq2yt8l7gUAAzu9CVEAp73zojyjJlCpF9uaHgSIUVczu2oaEVHBaPGP48J5k9DtTSuF5V6P0O2REKTxat8EonU/rk0pscqrAErHsjJmPjIxk0qRJHDhwANM0WbFiBYsXLyYqKsreJiIigtmzZ7Ns2TKmT5/OkSNHuPXWW7l8+XKGxwRbghcXF+fwKGqSEpJZ/uFqJkR8yL87T+P9yBm81uJ9Frz4A+eOXXB+gBxwZYhcdtrmh/5T76dc1bIZJiDKUJSrWpZHp96f4b63PtaWiIeb29relEgZFoV3KS+GffkIvoFZ3/HqNLQtgRUCMk0IDYtBcM3yRDzcwr6teY9G+AR4Z/2dSl0bUunCzTPDoggo559lUppGm9r1OXI5ZHgU/vfW5XMJ7Pv9EHvXHCQ2uoA+O7S2VZty+VHyhn+4Iz4+np07d7Jz507AVp5+586d9ot6Y8aMYcCAAfb2TzzxBIcPH+bFF19k7969TJs2ja+//ppnn33W3mbUqFF8+umnfPHFF/zzzz88+eSTJCQk2Kso5jWlFIZqDGT1eRSIofLpSrko4cpjGy7rLn+UqpnbnSlwAwY8muXznp4eNGpUHcPIOM553FoPS9taWZ9EKVRQGXwe7pfdbrpPYpNThetbsRNTpkyhTp061K9fHy8vL0aMGMHgwYMd3phdu3blwQcfpGnTpkRGRvLTTz9x6dIlvv7660yPm7sTovPf1bhEPnrwc1ZM/d2hqIQ12crWRX8xqfunnNwVlcURciascUWXvjAbFoOabavlWT+yIyDYn5HfDyHy2U4OQw0DK/hz96jbGPn9kEzneBmG4sGJ9zBwxoPUaHX9PeMT4E3HIW15ftkTVG0W5rQP/uVKMWLhIELr2oYDGR4GyqLsiUW15mH834KB+Phfv0rt6ePJA291z/yCorLlX33+fS+N76rvNMEyrZra7aq7dAdNKZVvFQpDagfbEk4nDIuiZpv8fW9dOHGJOSO+4fWID5nZ/ys+HTiPN9pP4fNhC4g5eDZf+yJXHXPX1q1bad68Oc2b2y60jBo1iubNm/Paa68BEBUV5TDSokaNGixdupQVK1bQrFkzPvjgA/773//aS9cD9OnTh/fff5/XXnuN8PBwdu7cybJly9JdjMxLSnljqJbXvsje+Hflh1J1MFQ4SkkxZZH3lDJQykni4MCCogqGal4s36P9+z9K2bJlMyxN7wplKLyHdMSrX1tU+YynTXiEN6fUq+MwgoJy0FM3SWxySun8nHV/g+TkZPz8/Pjmm2/o1auXffvAgQO5dOkS33//fab7JiYmcv78eSpVqsTo0aNZsmQJu3fvzrR969at6dy5MxMnTszw+YxKBFepUoXY2NgcjcnPL1+NXMyOJbvRmXyJVobti/PYP0bi4ZU3E1y/euZbdv642+lcnqcXP0a1FpXzpA85ZVpN4s/bEln/cn5u32FJTUolJSkVb39vDFfncN1Aa82RLcfZuXQPV2MT8S/rR/OejbNM5v78aQ+LXv2JhPNX7MmwmWoSEOzPgxO706hzPWIOnmNKz/+SnJiS4XtEGYr6t9Wm94SuTOz4H5cuSI38bghVw50nmbnhx7dXsOazjZm+v9MM/bwvDW6vky99Onv4PP+5bxaJl5PSvecNi8LD25PhXw+kcuOKmR4jLi6O0qVL5+hzJu0YF/+cTmCA6+s+xV2+SplmTxaZzzhhkxvvmTS20G8FFEoVr8IHougwdRRaHwAy+u4QhqI8ShnY7oQV7/epbbpOJJcvx2O1Wu3bLRYLVquVkycXEVrR+d+9Ni3o01Uw9+5FJyZilC2HR4OGGOXKudQPiU35q8AuK9w4Zj4tEUsbMz9ixIgs9/Xx8SEsLIyUlBQWLVrEQw89lGnb+Ph4Dh06RP/+/TNt4+3tjbd30RwTH3fmMjt+3J1l9T1tai6fjefvZf/Q/N7GedKP7i/dyYF1h0m4eCXTuyrtHm1ZaJMwsN2xC6zgWgGOjHh4e+Dhnf0/KaVsd3XcubPTrFtDGnepx+6V+zm123bXs0rTMBrcUQfLtcQspHZ5hn89iNlPfM2FE5dsCZsG0JimpkXPxjw4sQeePh40696IP3/ak+n7ybAYVGlWiSrNKmX7dbqr8/Bb2b1yP+ePXcj4vaWgRc8m1L+tdr70R2vNlyO+yXRJANOqSUlKYfYTX/Py2qezlZS7zd0riSXwqqNwZJuPUvzuLIiixVAV0QSjiQYdh61qp/+1eWBF83tZdrVs2ZK//97J1KkfM3Pmf7l06RIAHTveytNPj6BixRZonFeeNowwjKrVoWr1PO2vSyQ2OVWgn8KjRo1i4MCBtGrVijZt2jB58mSHMfMDBgwgLCzMfidr06ZNnDp1ivDwcE6dOsX48eMxTZMXX3zRfsznn3+eHj16UK1aNU6fPs24ceOwWCz07du3QF5jXtu9cr9LJdCVofgrDxOxoIqBPP3tEP733Hcc3nwcZSiUoTBTTTx9Pbn98XZ0GdkpT85d0lk8LTTt2oCmXRtk2iasUShj1jzF/rWH2PPrAZKvphBUMZDWDzSjXNXri2I+9G4PLp6O5diOk7YNN7y1lFKUq1aGQTMecjqpODf5lvZhxDeDWThmCbt/2YvGVtjHtJp4+njQcUhb7h51W7716fiOU5zeE5NlG23VXDwZy741B/PnLp12M9iVwEUzhRCFk1IeKCrnpJp9sVG5cmXefXci77zzNvHx8Xh7e+Pl5QWA1la0jgbiM9lbAd4oVYim10hscqpAE7E+ffpw9uxZXnvtNaKjowkPD3cYM3/8+HGH+V+JiYmMHTuWw4cP4+/vT7du3ZgzZw5BN4x3PXnyJH379uX8+fMEBwdzyy23sHHjRoKDg/P75eWLxMtJKItyOmxLm5qrsdlfPNEVZasEMfzrQUTvP8M/vx0k+UoyZcKCaNqtgcP8JlEwjGtDELO6c+Rdyov/+98ANn+9k9+/2MyZg7ZKhEGVArllUBva9W3p0pyt3OZf1o/BnzzExVOx7F61n6T4JEqHBNA4sn6+v7f2/HYAw2I4HYZreBjs+fVAviRiyjRRbgQ7d9oKIYTIX0opAgICbtpmwaAZpv4HuIBj5qqBAAzVqFBVlJTY5FyBj0sYMWJEpkMRV69e7fBzp06d2LMn67LZ8+fPz62uFQkBwf5OkzC4NuwuJMBpu9wQWreCvfCEKHo8vD1o378V7fu3IvlqCtrUePl55utdsMyUCSvNLQNaF2gfkq8kowxs02uyoiH5Skp+dOlaZSo3pvuWwMpUQghR1CnliUU1ResEtD4DJAMeKBWMUoVwTpXEJqcKPBETOdO4Sz08fTxISUzNsp1pNWnZu0k+9UoUF16+hefKWmFRplJpF9dn05QJy6fAKOPwhRCixFCqFErVKOhuOCexyakiVb5epOcT4M0tg9pkObbasBhUahBCnQ7Fb+0NIfJb855NXFr42jQ1rR8Iz/sO2U4mJYKFEEIULhKbnJJErBjo+vwdNOvWEMDxC6KyPcpWDWLo533zp3qbEMVcQPlSdBjQOsuLH8pQtOzVxKEQSp4ytfsPIYQQIi9JbHJKhiYWAxYPg0en3k/zHo35ffYmjmw9gbZqylcvQ4cBrWn9YLgUyxAiF/V4uQsJFxLY/t0uh8IdhkVhWjX1b6vNgxN7FHAvhRBCCFGYSSJWTBiGosnd9Wlyd33S1uguDMUVhCiOLB4G/T7sTUSfFvwxZytHtx1Ha6jSpBIdBrSi7q218vcOtIzDF0IIUdhIbHJKErFiSBIwIfKeUora7apTu131gu7KtSEd7gS7kjf8QwghRD6T2OSUJGJCCFHUSYlgIYQQhY3EJqckERNCiKJOhn8IIYQobCQ2OSWJmBBCFHXazWpTJfCqoxBCiHwmsckpScSEEKKok6uOQgghChuJTU5JIiaEEEWdBDshhMg2rTVXr17Fy8sLDw/5apxrJDY5JQs6CyFEUSeLZgohhNuioqJ45ZVXCQ6uSKlSpfHy8uPOO+/i++9/sC8FlNu01qQeOEDy72tJXv8H5vlzeXKeQkFik1OS9gshRFGnTdvDnfZCCFGC7dy5kzvuuIu4uDisVitgS5LWrFnLr7/+xuOPD2PGjI9zdUmglJ07SFowHzM66vpGpbA0bYbvo/0xygfn2rkKBYlNTskdMSGEKOrkqqMQQrgsPj6eyMjuDklYmrSfZ878lMmTp+TaOZPX/8HVKR9ixkQ7PqE11r//IuH18Zhnz+ba+QoFiU1OSSImhBBFXdo4fHceQghRQn311TzOnj2bLgm72b///T6pqak5Pp95+TKJn39m+yGjIY+miU5I4OoXn+f4XIWKxCanJBETQoiiTq46CiGEy+bM+cqldtHRMaxd+3uOz5eybi04SfowTay7d2HGxOT4fIWGxCanZI6YEEIUdaZ2szJVyQt2QgiRJioq2uViHGfOnMnx+VJ373Z5jazUf/bgFRKS43MWChKbnJI7YkIIUdTJVUchhHBZuXJlXW5btqzrbTOVkuxaO6UgF4ZCFhoSm5ySREwIIYo883p1KlceuDcOf/r06TRt2pTAwEACAwNp164dP//8c6btZ8+ejVLK4eHj45PD1yiEELmjb98+LlVDLFOmDJ06dczx+YzQimC48JVba4zicjcMyMvYVFzikiRiQghR1OXxVcfKlSvzzjvvsG3bNrZu3codd9xBz5492b17d6b7BAYGEhUVZX8cO3Ysp69SCFHIaW1F61i0vojWiQXdnUwNGjQQf39/jCySI6UUTz89Am9v7xyfz6vTbS4N0VNBQVgaNc7x+QqNPIxNxSUuyRwxIYQo6txNrtxMxHr06OHw81tvvcX06dPZuHEjjRo1ynAfpRShoaFunUcIUTRpnYrWx9CcBm4oSqGDMFQNlCpdYH3LSJkyZfjxx+/o2vUeUlJSHCojpt0pu/feHowd+3KunM+oUROPZuGk/vVnlnPFvB94COXKnbOiIg9jU3GJS8Xov7YQQpRQ2SwRHBcX5/BISkpyeiqr1cr8+fNJSEigXbt2mbaLj4+nWrVqVKlSxelVSiFE0aV1KqbegeYEDkkYAJcw9U60Ppfn/di6dStPPjmcLl3uplev+5g2bTpxcXGZtu/UqSPbtm3i0UcfwcvLy769bt26TJs2lYULF3D1YiJxZy5jWnNWVl0phe+Tw7E0aWrbcGOyZRigFN59H8Grwy05Ok+hk0+xqSjHJbkjJoQQRV02rzpWqVLFYfO4ceMYP358hrv8/ffftGvXjsTERPz9/fn2229p2LBhhm3r1avHrFmzaNq0KbGxsbz//vu0b9+e3bt3U7lyZdf7KYQo9Ex9EEjIooXG1HswaIdSnrl+/vj4ePr2fZQlS5bi4eFBamoqSil++GEJL744mq++mkPPnvc69khfReto6tVTfPbZS0yd+hrR0cn4+flRNrAc6+dsZeKtHxEbbUvkSpX1o/2jrej4WAR+Qb7Z6qfy9sbvmVFYD+wnefVvmKdOoTw8sDRshNdtt2OUK5fj30Whk8exqTjEJaVdrd9ZgsTFxVG6dGliY2MJDAws6O4IIYqh3PicSTvGpbnPEOjn+jyGuCtJBD06mRMnTjic29vbO9P5EMnJyRw/fpzY2Fi++eYb/vvf/7JmzZpMg96NUlJSaNCgAX379uWNN95wuZ/CkcSmwkVrzYE/DrPyo3VcPHkJi6eFKs0qUapsKc4dvYBhKKqGh9HmoeakJKYQdzYe3wBvQuoEu1QooijQOgVTrwecf5VUqhaGquK0nXvn19x9d3dWrfo1w8WZlVIYhsGKFcu4/fbb0NqKqfcBaSXp0/47aMCH5ITafPzQIqL3nUHflEAoQxFUKZARCwcTVLH4/v0VpdhUHOKS3BETQoiiLptXHdOqTbnCy8uL2rVrA9CyZUu2bNnClClT+OSTT5zu6+npSfPmzTl48KDrfRSiEIs7c5mPHpjN+eMXHbafPXLB4efdq/bz8/u/OWwzLAZefp5UahhCRJ/mtOjVFMMoqonZRVxJwgC0Pgu5nIitWvUrv/yyIotzarTWvPTSGDZtWo+pdwM3/je6se+JYPyFNeVyuiQMQJua2KjLfP74Ap75YWixSabzVB7HpuIQlyQRE0KIou6GsfUut8/xKU2X5pSBbfz+33//Tbdu3XJ8XiHyy9kj51k/Zyvbv/+bxMtJ+AX50vK+prTo2YRPHp1D/Pkrzg+SwXdQ02qSeDmJw5uOc3jTcRaOXkLju+rRaVg7qjYLy/0Xkod0ujlhWXGnrWtmzJhpH46YGdM02bJlK0eObKNa9fgsj2fxhLueqcbcEf9kfCyrycm/ozi6/SQ1WuZuUlks5XNsKopxSRIxIYQo6rTOshJXhu3dMGbMGLp27UrVqlW5fPky8+bNY/Xq1SxfvhyAAQMGEBYWxsSJEwF4/fXXadu2LbVr1+bSpUu89957HDt2jKFDh7p1XiHy29W4RLYu/ot1X2zm3E13t+LOxLP60w2snrkhwzsm2ZWabGXnkj3sXLKHslVKc/dzt9O8R2MMy/WCDqap2b/2EOvnbiVq3xksHga129egQ/9WVKxfcOtOKbxdvB8GkPtrNu3atSvLJOxGXl7ngayHyVk8DBpHlicg2IvLZzNehNnwMPjzx92SiLkiD2NTcYlLkogJIURRl8fl68+cOcOAAQOIioqidOnSNG3alOXLl9OlSxcAjh8/7rAez8WLFxk2bBjR0dGUKVOGli1bsn79epfG7QtRUA5vOc5nj/2PxPikTEfbaWveTqu/cCKWec98xw9vrqDL0x3p0L8VyVdS+HzYfA6sP4phUZjX+nD++CU2fLWNO4ffQtfnby+goXJBgBeQcdJyI0PlftlwDw/Xv8aWKetaoRDDogip65dpIqa1JuHS1Uz3N+Pi0OfPgYcnRqVKKIvF5T4WO3kYm4pLXJJETAghiro8TsQ+++yzLJ9fvXq1w88ffvghH374oVvnKGw+/vhj3nvvPaKjo2nWrBlTp06lTZs2GbZNSUlh4sSJfPHFF5w6dYp69erx7rvvcvfdd9vbjB8/ngkTJjjsV69ePfbu3Zunr0O45uzh88wc8BWpSamuTnnKU/HnEvj2tZ85vPkYCeevcGiTbeFZ84ZEMK2k+qqP1+Ff1o+OQ9rmez+VMlBUQ+sDWbUCfIHcrwp45513sG/fPlJTsx72aLFY8PT0JDeGRyql8C/rl2679dhRkn78gdTt2+x3dlRgIF53dsarazeUp1e6fYq9PIxNxSUuyTpiQghR1Gk312nROZ8jVpwtWLCAUaNGMW7cOLZv306zZs2IjIzkzJkzGbYfO3Ysn3zyCVOnTmXPnj088cQT9O7dmx07dji0a9SoEVFRUfbHunXr8uPlCBf89sl6rMnWXB1ymBv+XLKHgxuOOu3XL1PW2pLIAqCohKJaFi18MFQzlMr9r5xPPvkvp0mYh4cHvXv3xGIpw/UqiZmzpphE/ZN5OX4z1aT5vY0dtqXu3kXCm6+TumO7w/A6HRdH0nffcuXf76BdnLtUrEhsckoSMSGEKOrSrjq68xCZmjRpEsOGDWPw4ME0bNiQGTNm4Ofnx6xZszJsP2fOHF5++WW6detGzZo1efLJJ+nWrRsffPCBQzsPDw9CQ0Ptj/Lly+fHyynWYmMus3zyGt7v+glv3fofpj38Bdu+/SvLpCTpSjInd0Vx4u/TJMYnkXw1hW3f/pXjRXsL0tW4RHav2l8g57aViK+BoVoAIYAntgFXAShVD0O1QinXS5i7o379+kyYMC7T5z08PChbtiwffPAehgrD2e1O06r56+dzJFxIyfB5w6Ko1qIyVZpVsm/TCQlcmToFrNaMi01ojfXQIZK+WejSaypWJDY5JUMThRCiqDNxc/hHnvWkyEtOTmbbtm2MGTPGvs0wDDp37syGDRsy3CcpKQkfH8dCBL6+vunueB04cIBKlSrh4+NDu3btmDhxIlWrVs20L0lJSQ4VwOLi4rLzkoqtHT/u5n+jvsW0avsdo4unYjm08RjLJ6/hibn9KVslyN4+7kw8Kz/6nc0Ld5Jy1fZF28Pbg8Z31SM1Ofcr+uUnw6LspfSj9p3hwB9HsKZYKV+9LA3vqIPFM+/nKSkViEXl//par776CuXKlWXcuNc5f/48FosF0zTRWtOpU0c++2wmVatWxbZsbjBwNtNjKcOTv5ZccVxezP6colzVsgya8ZDDfLzkdb9DcnLWhSa0Jnntarzvux/lm70FoYskiU1OSSImhBBFXR7PEStJzp07h9VqJSTEsRJdSEhIpvO5IiMjmTRpEh07dqRWrVqsWrWKxYsXOywwGxERwezZs6lXrx5RUVFMmDCBW2+9lV27dhEQEJDhcSdOnJhuXpmwObjhKF+NXGz7cn3D29mekJ2MZXq/L3l++RN4+3lx4eQlpt7/OfHn4h3mWaUmpfLXT3vyu/u5TpuapPgkpt7/OUe3nUAphTJsd3hKlfWj2wt30LZvi4LuZp5QSjF8+P8xbNhQli79icOHj+Dj402XLp2pW7euQzuDBpjaCziN7Y1zY8ZVCovRiP4fRbBx/g7Wzd7MuaO2ypmlQwO5ZWBr2j3SEt9Ax4suqVs3u1btLzmZ1D278WzZKjdedtEgsckpScSEEKKI06Z2a25LYZsHU9RNmTKFYcOGUb9+fZRS1KpVi8GDBzsMZezatav9302bNiUiIoJq1arx9ddfM2TIkAyPO2bMGEaNGmX/OS4ujipVpGQ2wPLJa2z/yOStbFpNLpy4xPbv/qZdv5bMGbGI+HMJDknY9bbZ+3uoULs8Zw6ey/A5pRQanW+FP7SG3z/fTEpiyrWfNfradYCEC1dYOGYJVy8ncvvj7XP5vCZwGVsRDG+UKpWrx3eHl5cXvXv3yrKNUgYWVQetq6GJAZ0EGChVDghEKYWnD9w6qA23DGxNUkIy2tT4BHhnWpVSX8m8gmK6tlddb1scSGxyThIxIYQo6vJ4HbGSpHz58lgsFmJiYhy2x8TEEBqacfnt4OBgvvvuOxITEzl//jyVKlVi9OjR1KxZM9PzBAUFUbduXQ4ePJhpG29vb7y982ZuTVF2/sRFDl+rIpglBRu+2kblxhU5vvNU7pxcARoin+3EXSM7cebwOX6d8QdnDp7H08eDJpH1KVe1DNH7z4KC8tXKcPqfGP74cisJF1xYADobDIuBt78XSfFJWSaVSyeupFm3hpStHJTjc2ptojmO1ieBG+bjaX8MVR2lCvf8R6W8UFTJsnaHUgoff+d/f6psGYg67dLnqhEU5EYviwGJTU5JIiaEEEWdDP/INV5eXrRs2ZJVq1bRq1cvAEzTZNWqVYwYMSLLfX18fAgLCyMlJYVFixbx0EMPZdo2Pj6eQ4cO0b9//9zsfolw8cQl1xpqOH/iErt+2YthMZwX47iWZGUlrGEod/7fLTTrblt7qELN8jz8757p2jW4vY79300iGxD5zG1ordm7+iAb/7edQ5uOcTU20bXXkQXDYuBb2se1JE8pNs7bRrcX78zRObU2MfXfwMUMno3H1LtQ1MZQlXN0nsLMPHuW5N9+JWXTRvTlOJcSCBUYiKVBCVtLUWKTU5KICSFEUSfBLleNGjWKgQMH0qpVK9q0acPkyZNJSEhg8ODBAAwYMICwsDAmTpwIwKZNmzh16hTh4eGcOnWK8ePHY5omL774ov2Yzz//PD169KBatWqcPn2acePGYbFY6Nu3b4G8xqJIa01SfLJbF809vT1IjE/GlbWODcMgtF4wF05cIvHy9SIpfqV9aH5vY259LILgGtlfC0spRYPb69iTtLNHzrPsw9X8tXRPtoZHGhaDpt0aULVZGD+8+YvT9trUHNzowp1EZ8fhOBknYTe00QfRBKGUf47PV9ikbNnM1U+m25KvjKokZsLrnntL3uLOEpuckkRMCCGKOgl2uapPnz6cPXuW1157jejoaMLDw1m2bJm9gMfx48cxjOurvyQmJjJ27FgOHz6Mv78/3bp1Y86cOQTdMAzp5MmT9O3bl/PnzxMcHMwtt9zCxo0bCQ4Ozu+XV+Rcib3K+jlb+ePLLcSdiQdsFeyczScxLAaNOtclsII/pkvveU3DO+vSefgtHNxwlCuxifiX9aN2u+p5UnUwuEY5+v/nfhImdGXLN3/yx5ytXDieeYJjWAwCQwLoMeZOPHw8qRYeRkCwP5sW7Mh0n5tZU3JWHVJrE61dG+ap9WmUquu8YRGSeuggV2dMcz0BMwwwTbzuisSrc5e87VxhJLHJKUnEhBCiqJNgl+tGjBiR6VDE1atXO/zcqVMn9uzJuvLe/Pnzc6trJcrFU7F83Gc2l07HOSRerkzqN60m7R9thV+QLz+/96sL7TUtezXB08fTYWhhXitVxo/bhrXjtmHtWPnR7/z8/m+OQymvDZkMqhTIk/MGOJTkB1vREFcYFoNKDUKcN8xSPJDxGls305wDilcilrzkR9caKgVeXng0boLXnZ3xKGlDEtNIbHJKEjEhhCjitHazMlUJnBAtih6tNZ8NmU9sVFy2qql1e+EOwhrZCqy06N2U7d/9nelxlKFo1KUuFWoVbJGJziNupf5ttfnjyy3sWXWA1KRUylYNov0jrWjRuwnefl7p9qneojIVapXjzOHzWc5xM60m7fq1zGEP3bmjVrTXZruZGX+Z1D93ujYfzD+AgP98lPedKuQkNjlnOG+Stz7++GOqV6+Oj48PERERbN68OdO2KSkpvP7669SqVQsfHx+aNWvGsmXLcnRMIYQo8tKuOrrzEKKQO7jhKFF7Y9yeP1Wuahn6TurFncNvsW978O3u1OlQA7AlXWnS/l29ZWX6ftAr553OBZUbV6TPv+9lwrbneGvXSzz3079o90jLDJMwsM09u3fsXbYCgJnMhVOGomnXBlRpVimHvfNx3sSueFX81BcvuVzVT1+OQ7sxf6zYktjkVIHeEVuwYAGjRo1ixowZREREMHnyZCIjI9m3bx8VKlRI137s2LHMnTuXTz/9lPr167N8+XJ69+7N+vXrad68ebaOKYQQRZ4M/xDF0I4fdmF4GJipzr/Qtnu0JXXa1aB0aADVWlROt+aTp48nQz/vx9/L/mHdF1s48ZdtQd+whhW5ZVBrmnVrmCfzwPJLg9vr8OjU+5n//PekJF0rJ6+xD3Fs1r0hD7/XM9O1sFyllC/oQCDOhbY5TfoKF+XjRhLq4YEyCvxeR8GT2ORUgSZikyZNYtiwYfZKVDNmzGDp0qXMmjWL0aNHp2s/Z84cXnnlFbp16wbAk08+ycqVK/nggw+YO3duto4phBBFngQ7UQwlXLjiUhJmeBgElCtlLymfGYuHQfg9jQi/p1FudbFQCb+nEfU61mLr4r848MdhUpOtVKhRjoiHm1Oxfk7nhl1nqOqY+i8nrbxQ5N45CwNVvjxGaEXM6KisGxoGHs1b5E+nCjuJTU4VWCKWnJzMtm3bGDNmjH2bYRh07tyZDRs2ZLhPUlISPjddkfD19WXdunXZPqYQQgghCh+/IF+X1v/SVhO/Mn751KvCzTfQh1sHteHWQW3y7BxKlUVRD633ZdLCC0M1QynPPOuDaWr2/naAdV+m3d2EauFhdBjQmnqdamMYObvzlxGlFF53RZL45WxnnSuZFRJFthRYInbu3DmsVqu9HHCakJAQ9u7dm+E+kZGRTJo0iY4dO1KrVi1WrVrF4sWLsVqt2T4m2BK8pKTra4bExTm/5S6EEIWG1i7PXbC3F6KQMC9cIGX9Oszz51FeXng0aYqlYSOadW/I5q93Oj+AUjS9u36e91NcZ6iKaEqj9Wk0Z7EV5vBGqUooQlAq775epiSm8sWTC/nntwMYFmWfQ7hv7SH++e0gjbvUo/9H9+Phnft98Ox0G6n79pK6aWP6J5UCrfHufT8edevl+rmLJIlNThWpqolTpkxh2LBh1K9fH6UUtWrVYvDgwcyaNStHx504cSITJkzIpV4KIUT+0qbt4U57UbzFxMSwcOE3xMScISgoiPvu60WNGjUKulsOdGoqiV/NIWXNatuGa3Nqkn9ZjqpQgVpPjqBCrXKcO3oh04IdylCE39OI0qGB+dRrkUYpP5SqDdTO1/N+88oS9q45CODwvkj79+5V+1n82s889G6PXD+3Mgx8H3+C5Bo1SV6+DH3xgv05o1IY3j3uxTOiba6ft6iS2ORcgSVi5cuXx2KxEBMT47A9JiaG0NDQDPcJDg7mu+++IzExkfPnz1OpUiVGjx5NzZo1s31MgDFjxjBq1Cj7z3FxcVSpUiW7L00IIfKXjMMX1yQmJvLUUyOZPftLTNPEYrFgtVp54YWXuPfee5g167+ULVs23/tlK0ud9i3LlnBd/fQTUrdsvn4V3Hq93Lk+d46r777N4HdGMX34T1w+n4C+KRlThqJy44o88Hb3fHgFojC4cOISWxf/lWWZfm1qNi/cSeSznfIkQVeGgXfk3Xh1uQvz2FH01auowNIYYWE5LoZS7EhscqrASrp4eXnRsmVLVq1aZd9mmiarVq2iXbt2We7r4+NDWFgYqampLFq0iJ49e+bomN7e3gQGBjo8hBCiyJASwQJITU2lR49ezJo1m9TUVEzTJCUlBdM00VqzZMlPdOx4O5cvX863PmltxdQnMfUmTP37tcdmUvb+TurmTZkPRTJNSE7Gf/3PjFr6OHf8qz2+pa/PES9TuTT3jOnM8K8H4uNfvMqki8xt+/Yvh+UHsm77d572RRkGlho18WjYCEvl9JU6BRKbXFCgQxNHjRrFwIEDadWqFW3atGHy5MkkJCTYKx4OGDCAsLAwJk6cCMCmTZs4deoU4eHhnDp1ivHjx2OaJi+++KLLxxRCiOJGhn8IgHnz/sfKlasyfd5qtbJ37z4mTZrMuHGv5nl/tE7B1DuBhJueuUrKr7+CobL+4mWapP65E//+yXR78U4iR91O/Ll4lGHgX75UnhRkKOnizyew+eudHN5yHDPVpGL9CrR9uAXBNcsVdNcAuBQVh1IKndUtMcAwFJeiZL5/QZPY5FyBJmJ9+vTh7NmzvPbaa0RHRxMeHs6yZcvsxTaOHz+OccM6DImJiYwdO5bDhw/j7+9Pt27dmDNnDkFBQS4fUwghih3t5pXEEjghuiSYOvVjDMPAzGIhWavVyrRpM3jllTF4eOTtVwBT/0P6JOzac4fPuvae1RrriRMYZcti8TBkLlgeWj93K99NWIaZqq8NJYUDfxxm9cwNtHu0Jb3Hd8XiUbBrY3n5uVaJUQNevnlXtVG4SGKTUwVerGPEiBGMGDEiw+dWr17t8HOnTp3Ys2dPjo4phBDFjsn16TeuthfFitVqZevWbS61PXPmDMePH7fPr84LWicAF5y2E4XD1kV/smjsT+m2pxXA2PDVNhSK+9/slt9dc9Dozrqs/WyT03ZmqkmjLnXzoUciSxKbnCrwREwIIQqjqKgoli79idjYOCpWDOXee3vg7+9f0N3KkDY12o2rju60FUVDVnfBMmK9oTBGXtA6BlBkVlXBqBmM9dIV51fLlcJSVYpn5SVripUf316ZdSMN67/aSqdhbSlfLf+LvaSp1a6600qahkVRoXZ5qreU901Bk9jkXMHeYxZCiELm0qVL9O37KFWq1GDYsCd46aUxPPLIAEJDK/Pqq+Py/AtstpjZeIhixdPTk9q1a7lUMMDf3z8fKgOnZPms5x0NnCdhhoFHeHOMMgX3xb8k2PPrAeLPZzyE9EaGodg0f4fbxz906BArV67ijz/+IDExMTtdtFNKMXDGQ3j7e2NY0r/XDYuBT4APA6c9KMUzCgOJTU5JIiaEKLH27t3LM8+MokmTcOrXb0SvXvfTokUbvv56oT3hSvv/hIQE3nprIoMGPWafP1Fo6Gw8RLEzYsT/OW1jsVgYMmQwPj4+TtvmTNbzc4y6oVja1LTdNMuwgQFe3ng/2Cf3uyYcnDl4DsPi/OugadXEHDjr8nF/+201HTveTu3a9enS5W5uueU2QkPDePHF0SQkOE/8MhNaJ5hnfxxGeI/GGDfMWbN4GITf24hnfxxGhVrls318kYskNjklQxOFECWO1pq33prIq6+Ow8PDg9TUVAD27z+QZZKltWbu3Hk8/HAfuncv2LkSN5LhHwJgyJDHmDHjUw4ePGh/T9/Iw8ODsmXL8sILz+V5X5QKRuvjWTyv8B7SkeRS3qSu2Wv7ApZWnMtqxagQgu//jcBSsWKe97WkMzwMly4uKQUWT4tLx1yw4Gv69eufbntsbByTJk3m119/Y/XqVdke7l2uahkemdybnq9FErP/DACh9SpQqoxfto4n8obEJuckERNClDjTp8/g1VfHATh8YXXly4jFYmHq1I8LVSImE6IF2IYcrl69kp4972PTps14eHhgtVqxWCykpqZSo0Z1liz5nrCwsDzvi1IBoAOBzEuIKw8L3o/ejc+9T5Ky4Q/Mc+dQXl54NG2GpX4DGVqWT2q2rurSF2AN1Ghd1Wm7qKgo+vcfhNY6w89Uq9XKzp1/MmbMK0ydOiU7XbbzL+uHf9vqOTqGyEMSm5ySREwIUaIkJyfz2msTsr2/1WplzZq1udijnJO1WkSakJAQNmxYx4YNG5g7dx5nzpwlKKg0Dz74AF26dHZYEiavGaoRpt4BZDYvyAdDNUIFeeHdtXu+9Us4qto8jIr1KxC9/2yWCZmHlwet72/m9HiffvoZVqs1ywtbVquVzz77nLfffpOAgIBs9VsUfhKbnJNETAhRZB04cICPP57OV1/N49KlWMqUCeLRRx9h+PAnqVWrVob7LF36E+fPn8/Rea3W9MO+CpRcdRQ3UErRvn172rdvX8D98MagJVqfRHOa6wU8PFEqDEUYSslaTwVNKUWf9+7l4wdnk5pszTQZe+Ct7viWdj638Ntvv3OpiufVq1dZvXoNPXrc43afRREhsckpKdYhhCiSFi1aTKNGTfn442mcO3ee1NRUzp49x3/+8xENGzblhx9+zHC/w4ePYLG4Ns8hI0op6tSpk+3980LaVUd3HkLkB6U8MYwaGKodhmp77dEOQ1WXJKwQqdKkEsMXDqJSgxAAlKFQhm1oaOnQQAZ8/ACtH3B+Nwzg8uV4l8+bk6IdovCT2OSc3BETQhQ5O3bs4OGHH8lw+IvVasU0TR54oA/bt2+mcePGDs/7+vq4vebSzYYPfzJH++c6jXtXEkvefGhRwJQygLyu1ChyokqTSoxa+jjH/zzF0a0nsFpNQusEU69jLZeqKqapXr0aR44ccelztkqVyjnpsijsJDY5JYmYEKLAxcTE8OWXczly5Ag+Pj7cdVcX7rqrS6bzWT74YDKQeXGNtEniH374Hz77bKbDc50735nt8vMeHh7UqlWLgQMHZGv/vKK17eFOeyEALp+NZ8O8bWxasIO4M/F4+XrStGsDOgxoTeXGUrGwJKraLIyqzbJf0GXIkMGsWvWr03Y1a9agXbt22T6PKPwkNjknQxOFEAUmNTWVZ599nsqVqzN69Mv897+z+OijaXTteg+1atVly5Yt6fa5evUqX3/9dYbluW8+9ldfzSMlxXFh2bp163LnnXe4NDwxrWpbWtvGjRvz228rKFWqlKsvMV/I8A+RHSf+Os27d07jlylruXQ6DjPVJPFyElsX/cWHPT7l9883FXQXRRF0//33UbduXTw8sr7WP378a/laPEbkP4lNzskdMSFEgfnXv/6Pzz+fbb9DdeNQlhMnTtKp051s2PA7zZpdn5tw8eJFUlJcK5aRlJREbGws5cs7Lu752WcziYjowPnz59MldIZhoJTi6adHcOzYMS5diiUsrBIDBvTnzjvvKJwltWVCtHBT/PkEPnl0LonxSemKM5hWE601017+jKmLphBvXiYgIIAePbrzwAP358Ni0KIo8/LyYsWKn7nzzkgOHjyIYRj2z/a0dRsnTnyL/v0fLeCeijwnsckpuRQhhMg1hw8f5s0332b48Kd4+eWxbN26NdO2W7ZsYdaszzMdJmi1WklOTub5519y2O7OAqBKqQzbV6tWjc2b19Or173p7oy1aNGcX375mUmT3mfRooWsWvULX345m86d7yycSVg+mD59Ok2bNiUwMJDAwEDatWvHzz//nOU+CxcupH79+vj4+NCkSRN++umnfOqtcMWm+TsyTMIArlqvsjBmDvOjP2fRT4v4+edlfPPNIvr3H0SVKjX4448/CqDHoiipWrUqf/65jc8+m0nLli0oU6YMlSpVZMiQwezcuZXRo18s6C6KIq64xCW5IyaEyLH4+HiGDBnGwoWLMAwDwzDQWjNx4ru0adOahQvnU7Wq40Kg06Z9Yr86mhmr1crKlas4dOiQvRx9YGAgt93Wid9/X4fVas10X4vFQpcunTO9el+1alUWLlzA6dOn2bBhI6mpqTRoUJ+mTZtm4zdQsPJ6rZbKlSvzzjvvUKdOHbTWfPHFF/Ts2ZMdO3bQqFGjdO3Xr19P3759mThxIvfccw/z5s2jV69ebN++PV3xFFEwNn29I8MkzKqtLD7zFWeSowEwr71Z0u5oXLhwgS5durJx47oi+bci8o+fnx+PPTaYxx4bXNBdEQUkL2NTcYlLSmd31noxFhcXR+nSpYmNjSUwMLCguyNEoZaSkkLnzpH88cf6DBMjDw8PQkND2bp1IyEhIfbtDRs24Z9/9rp0jq+//h8PPviA/ecff1zCvff2drrf8uU/cdddXVw6R37Ljc+ZtGOcHPAQgV5eru+XnEzlL7/O0bnLli3Le++9x5AhQ9I916dPHxISEliyZIl9W9u2bQkPD2fGjBnZOp/I3dg0psFEkq+mpNv+T8Lf/Hzuuyz3tVgs9OhxD99++02O+iCEKHyKcmwqinFJhiYKIXJk/vwFrF37e6Z3p1JTU4mKiuKdd/6da+fs0eMexoyxDVm8ebJ32s/jxr1aaJOwXGcq9x/YguWNj6SkJKenslqtzJ8/n4SEhEwrnm3YsIHOnTs7bIuMjGTDhg05f60iV3iXyvjL0Z+Xt6LIegiu1Wrlhx9+JCoqKi+6JoQoLvIpNhXluCSJmBAiR6ZOnea08pXVauWzz2Zx9epV+7aIiAinVbXStGjRPN22t99+k//9by7NmjkOj2rZsgULF85n/PjXXDp2cZDdylRVqlShdOnS9sfEiRMzPcfff/+Nv78/3t7ePPHEE3z77bc0bNgww7bR0dEOdz8BQkJCiI6OzrXXLHKm2T2NMCzpE67zyWfRLizmY5om69bJXDEhRObyOjYVh7gkc8SEENmmtWbHjh0uLdx5+XI8hw4dso/F/r//+xezZ3+R5T4Wi4Xbb7/NPj/sZg8/3IeHH+7D4cOHOX/+PMHBwVSvXt3NV1H0aa3Q2vVCImltT5w44TD8w9vbO9N96tWrx86dO4mNjeWbb75h4MCBrFmzJtOgJwq39o+24o8v0y8PoZTh8qKqDz3Ul1at3uf550fRp89DudxDIURRl9exqTjEJbkjJoQoEK1bt+axxwZnWonQYrHg5eXFBx84H9JYs2ZNWrduXSKTMMj+Vce0alNpj6wSMS8vL2rXrk3Lli2ZOHEizZo1Y8qUKRm2DQ0NJSYmxmFbTEwMoaGhufaaRc6E1C7PQ+/0AAXKuP43WMW7GsqNrwbbt+/g4YcfYfTol/Oim0KIIiyvY1NxiEuSiAlRRCQmJvLzz8uYO/crli1b7tJ8nrymlKJZs2YuLcrp7+9PzZo1HbZ98sk0Ro58Gg8PDwzDwNPT0z5csUqVyqxd+6tUZnOB1m4Gu1wo0WSaZqbvwXbt2rFq1SqHbStWrMh07L4oGG0eCueJr/pTK6KafVuzwNZoNxbzSbsb/u6777F48be53kchRNGV37GpKMYlScSEKORSU1OZMOENKlasQrduPejffxBdu95DxYpVePPNt7Ms4Z4fnnrq/5wOTbRYLAwZMhg/Pz+H7R4eHnz44fucPHmUd9+dyNChj/HUU8NZtmwphw7tp1WrVnnZ9WIjbfiHOw93jBkzhrVr13L06FH+/vtvxowZw+rVq3nkkUcAGDBgAGPGjLG3HzlyJMuWLeODDz5g7969jB8/nq1btzJixIhcfd156eOPP6Z69er4+PgQERHB5s2bM22bkpLC66+/Tq1atfDx8aFZs2YsW7YsR8fML3Xa1+DJ/w3g1Q3PMPK7Ifxn7TsMGfKY28exWCxMmjQ59zsohCiy8jI2FZe4JHPEhCjErFYrDz/8CIsXf5tu4eOLFy/y2mvj2b17D1999aVLd6XyQt++D/Ppp5+xceOmTMvXV6hQIcsFPENCQnj++VF52c3izVRo043kyp22wJkzZxgwYABRUVGULl2apk2bsnz5crp0sVWlPH78uMP7r3379sybN4+xY8fy8ssvU6dOHb777rsis4bYggULGDVqFDNmzCAiIoLJkycTGRnJvn37qFChQrr2Y8eOZe7cuXz66afUr1+f5cuX07t3b9avX0/z5s2zdcz8FlQxkKCKtjkZM2dOp3LlMN5/fxIJCQku7W+1Wvnjj/WcO3eO8uXL52VXhRBFRR7GpuISl2QdsQzkxzpiiYmJ7N69m9TUVGrWrElwcHCenEcUbbNnf8HgwUOdtvvqqy/p169vPvQoY5cvX2bgwMF8++33WCwW+7yv1NRUmjcPZ/HihSV2/lZmcnOtlsP39SPA0/W1Wi6nJFNz8TxZKzETERERtG7dmo8++giwDXepUqUKTz31FKNHj07XvlKlSrzyyisMHz7cvu3+++/H19eXuXPnZuuYGcnvNS7j4+P53//m8/jjT7q8z8GDewkLC7P3Nat5h0KIwkdiU/6SoYn5LC4ujpdeGkNoaGVatWpL27a3ULFiFR56qC+7du0q6O6JQmbKlKlO73QZhsGUKVPzqUcZCwgIYPHib9i/fw+vvDKGwYMH8swzT7Nhw+9s27ZZkrA8ltdDE0uS5ORktm3b5rDejGEYdO7cOdP1ZpKSkvDx8XHY5uvry7p167J9zLTj3ryeTn7y9/dn6NAh+Pr6utReKcUzzzxHqVKlCQkJo1Sp0jz4YJ8CX6dHCFEwJDY5J4lYPrp48SLt2t3KBx98SGxsrH271Wrl22+/o02b9vzxh6zLImwuXrzIzp1/Op1/ZZommzdvIT4+Pp96lrk6deowYcI4Zs6cwXvvvUvbtm0zrYooco++NvzDnYfI2Llz57BarW6tNxMZGcmkSZM4cOAApmmyYsUKFi9ebF/wODvHBJg4caLDWjpVqlTJ4atzn1KKgQP7O13zzzAMtNYsW7bc/plltVr57rsf6NChE599Nis/uitygWma6YbCC5EdEpuck0QsH/3f/z3Fvn37MpxHk5qaSlJSEvfe29th0VuRsatXr3Ly5EkuXbpU0F3JM4mJiW61LwxVFEXB0Nr9h8g9U6ZMoU6dOtSvXx8vLy9GjBjB4MGDczxvc8yYMcTGxtofJ06cyKUeu2fkyKcwDCPLiyppyVdqaqrD9tTUVLTWDBv2BJs2bcrTforsu3TpEu+/P4lateri4eGDt7cfXbrczY8/LpGkTGSbxCbnJBHLJ1FRUSxc+E2WFe5M0+TChYssWPB1PvbMdampqezbt4/du3dz+fLlXD32zp07eemlMQwePITnnnuBjRs3Zvjhv3PnTh59dCClS5elSpUalCkTTMeOt/Ptt98Vu2BRvnx5SpUq5VLbwMAAgoKC8rZDAgCtEzDN/VjNjVjN9VjNHZg6Bq1dL/md+32S4R+5pXz58lgsFrfWmwkODua7774jISGBY8eOsXfvXoflGrJzTLAtYnrzejoFoX79+ixa9LXD8hJp0n52lnRaLBY+/DDj9X1EwTpy5AjNmrXkpZfGcPjwEbTWpKSk8ttvq7n33t489tgwpyMzhMiIxCbnJBHLJz/99LNLZcaVUnz77ff50CPXJSQkMGHCG4SFVaN+/cY0bhxOcHBFhgx5nEOHDuXo2OfOnePOO++iefPWTJo0mblz5/Gf/3xEu3a3EhHRnpMnT9rbfvfd97Ru3Y4FC74mJeX6Vdf16zdw330P8txzLxarZMzT05PBgwc6HRJksVgYNmwoFosln3pWcpn6OKbegiYKSASSgVi0/gdTb0Pr5ILpl6ncfoiMeXl50bJlS4f1ZkzTZNWqVU7Xm/Hx8SEsLIzU1FQWLVpEz549c3zMwuKee7qza9dOhg9/0n7RJyDAn0cffQSllNMv6rbfybckJxfM34jIWGpqKpGR3Th9+nS6/4Zp31lmz/6Ct99+pyC6J4o4iU3OSSKWT+Li4lwapqK1LlTD7WJjY7nlltt4/fU3OXPmjH17UlISX345h5Yt27B9+/ZsHTshIYHbb+/CmjVrAVtASHsA7Nixk1tuuY3z589z+PBhHnqoL1arNd3Ql7Rg8eGHk5kzZ262+lJYjRr1DH5+fpkmWRaLhYAAf0aOfCqfe1bymDoarQ9f+ymjhP8Kpv6rQC4GyPCP3DVq1Cg+/fRTvvjiC/755x+efPJJEhISGDx4MJB+fZpNmzaxePFiDh8+zO+//87dd9+NaZq8+OKLLh+zKKhTpw6TJ0/i4sWzWK1JxMVd5M03J7j8nk9NTc33giMiaz/88CMHDhxMF1dv9v77k9weLi+ExCbnJBHLJ2FhYS7d2vfw8KBq1fyfkJ2ZESOe5u+//86w76mpqcTHJ9C9e89sXeX89NP/snv37kzvFKampnLy5EkmT/4P06d/4nQCsVKKd999r1jdFatRowYrVvxMUFBplFL2ORpp/y5TpgyrVv1SIJP4SxKtNVofcdYKiAfO50OPbjqzDP/IVX369OH999/ntddeIzw8nJ07d7Js2TJ7sY3jx4/bC3GAbT7n2LFjadiwIb179yYsLIx169Y5DBd2dsyiJu3CYlBQkMtz4SwWS4kpSZ2f4uPjmT59BuHhLQkKKk9ISBhDhjzOjh07nO47Z85cl0ZTxMbGsmzZ8tzorihBJDY5J+uIZSAv1mq5cuUKFStWJi7O+dyqFSuW0bnznbly3pyIjo6mcuXqLg2pnD//K/r0ecjlY2utqV27HkeOHHWaOJUrVw5PTw+io2OybJfmn3/+pn79+i73pSi4fPkyX301j6+++h9nz54lJCSERx/tR79+fV2eRyayT+uLmPpPF1uXw2I0cdoqN9dq2X33ILfXamm0bHaJWqulOMjvdcRc1aNHL5YtW57lXRUPDw/uu683CxbMy8eeFX8HDx6kQ4eOnDlz1mG7h4cHqampvP32m4wZ81Km+7dp044tW7a6dK7p0z/iiSf+laP+isJPYlP+kjti+cTPz4/nn38uyzYeHh60aNGCO+64PZ96lbUff1ziUhJmGAbffLPYrWNfvXrVPinYmfPnz3Px4kWXj33hwgW3+lIUBAQE8MQT/+L331ezd+9u1qz5lWHDhkoSlk807lSkzP/hO6ZWbj+EyC3PPz/K6dA2q9XKs88+nU89KhlWrFhJ/fqN0yVhcL165csvj2Xu3K8yPUaZMmVcXmKkdOnS2euoKLEkNjkniVg+euWVMQwd+hiAQwGGtGEddevWZenS73Nc8ji3xMbGuTRkwVbt0b3kx921pdypCFi+fHm3ji2EM8qtj8rC8fcrRH7p1KkjU6dOBsiwqqJSihkzPqZt27YF0Lvi6Y8//qBr13ucXixVSjF+/OuZXvR88MH7Xbog6u3tzd13R2arr0KIzMk3hnxkGAYzZ85gxYpldOvWlYCAAHx9fWnatAmffjqDLVs2ZFnKOL+FhFRw6Y6YxWKhUqWKbh3b19eXevXqupSQhYSEMGDAAKdJoWEYNG3alDp16rjVFyGcCwJcu3igVP5fCJBFM0VBGzFiOGvW/Er37t3sFxMNw+Dee+/h999/4/HHhxVwD4uPo0eP8eCDfV2Kz1prDh06zMaNGzN8vm/fhylbtkyWF4ANw2DQoIGUKVMm230WJZPEJueyrostcp1Sis6d7ywUc8Cc6dnzXnx9fZ0uMG21Wunf/1G3j//00yMYMWJklm0sFoP/+78nGDiwP1OnfpRlwQ7TNBkz5kW377YJ4YxSXqArAM7mKSoU7l2UyA3uVpuSmcEiL3TseCsdO97KlStXiI2NJSgoCF9f34LuVrGxc+dOHn74Efbt2+/2vidPnspwe6lSpViy5Hs6d76b5OTkdENMDcMgIqINkya9l60+i5JNYpNzckdMZCowMJCnnx6RZWLj4eFB06ZNspVYPvbYYFq3bpVlafbateswcuRTVKtWjcWLF+Ll5ZVu6Eva/mPHvszDD/dxux9CuMJQtQG/LNso1cCWtOUzEzfH4bt4d0+I7PDz86NixYqShOWi9evX07JlRLaSMAB/f/9Mn2vXrh3btm2iX7++eHp62rdXqlSRN998nVWrfsHPL+vPPiEyIrHJOUnERJbefPN1HnjgPgCHhCmtfHr16tVZuvSHbM1r8/HxYcWKZfTs2QOlFBaLBU9PT/t57rjjdn7//Tf7BOGuXe9mx44tDBkyGF9fH3s/unS5k2XLlvLGGxNy+nKFyJRSnhiqOYpKpP/oDMRQzTBUhYLompQIFqIYO3bsGHfccZdLS+BkpFSpUnTseGuWberXr88XX8zi3Llo/v57B/v27eb48SOMGfOSJNQi2yQ2OSdDE0WWPDw8mD9/Hg8//D1Tp37MunV/YLVaqV27FsOHP8ngwYNyVGI0MDCQRYsWcvjwYebP/5ozZ85QpkwZHnzwfho2bJiufYMGDZgxYxofffQf4uLi8PPzw8fHJycvUQiXKeWJUnXRuiYQB5iAL0oVbPVK7Wa1qZIY7IQoai5evMiwYU+waJF7VYlvpJTi8cddr7AbGBhI48aNs30+IW4ksck5ScSEU4ZhcN99vbnvvt7XFrbVuV7ZsWbNmrz88miX23t4eFC2bNlc7YMQrlLKAyg87z93rySWxGAnRFFy+fJlOna8g3/++SdHx2nWrClvvvl6LvVKCPdIbHJOhiYKtyilCk15fSGEjZmNhxCi8ImPj2fatOnUqlWPXbt2uVQZMTNt20awfv3vMr9LFBiJTc7JHTEhhCji5KqjEEXfyZMnueOOLhw4cDBHx7FYLLz44vO8+ebrcuFUFCiJTc4V+F/oxx9/TPXq1fHx8SEiIoLNmzdn2X7y5MnUq1cPX19fqlSpwrPPPktiYqL9+fHjx9sLSaQ96tevn9cvQwghCoypca8yVQksESxEYWa1Wuna9R6OHDmao+P07/8oUVEnePvtNyUJEwVOYpNzBXpHbMGCBYwaNYoZM2YQERHB5MmTiYyMZN++fVSokL762Lx58xg9ejSzZs2iffv27N+/n0GDBqGUYtKkSfZ2jRo1YuXKlfafby53LoQQxYlcdRSiaFu2bDm7du3O0TFCQkL44otZspamKDQkNjlXoBnKpEmTGDZsGIMHDwZgxowZLF26lFmzZjF6dPrCDevXr6dDhw7069cPgOrVq9O3b182bdrk0M7Dw4PQ0NC8fwFCCFEI2K46utdeCFF4zJ79JRaLJdtzwkqXLs327ZslCROFisQm5wrsvnVycjLbtm2jc+fO1ztjGHTu3JkNGzZkuE/79u3Ztm2bffji4cOH+emnn+jWrZtDuwMHDlCpUiVq1qzJI488wvHjx7PsS1JSEnFxcQ4PIYQoKmStFiGKthMnTmQrCfP19eW5554lJuYUlSpVyrKtefEiSd99S8IH/ybhvXdJnP8/rNHR2e2yEE5JbHKuwO6InTt3DqvVSkhIiMP2kJAQ9u7dm+E+/fr149y5c9xyyy1orUlNTeWJJ57g5ZdftreJiIhg9uzZ1KtXj6ioKCZMmMCtt97Krl27CAgIyPC4EydOZMIEWQxYCFE0mShMXA9g7rQVQuTc4cOHmT79ExYuXMTly5cJC6vEY48NYtCggQQFBREUFIRSCq1duyXg4WGhVq1abNmyMdPvNjdK+vknkr75GrS2PQDr3n9IXv4znnd2waffIyiZUyZymcQm54rUX93q1at5++23mTZtGtu3b2fx4sUsXbqUN954w96ma9euPPjggzRt2pTIyEh++uknLl26xNdff53pcceMGUNsbKz9ceLEifx4OUKUaFqnYOrjWM2tWM2NWM3tmPo0WqcWdNeKnLTvVu48hBD5Y968/1GvXiM+/HAKx44d48KFC+zatZtRo16gbt2G/PXXX9x3Xy+3jtm8eQtWr17lUhKW/OtKkr6eD6bp+Mdv2oqFp6xaYXteiFwmscm5ArsjVr58eSwWCzExMQ7bY2JiMp3f9eqrr9K/f3+GDh0KQJMmTUhISODxxx/nlVdeybBCUFBQEHXr1uXgwczLwXp7e+Pt7Z2DVyOEcIfWFzH13ziuGpKI1nFojmDQDKX8C6p7RU5axSl32gtR3FmtVrZu3cqlS7GEhFSgWbNm+T6Has2atTz66MB0d7rSfr5w4QJ33hnJ1q2bKF26NHFxcZhm5qsp1a1bl//+9xNuuaWDS69FJyeT+M1Cp+2Sf1mO1113Y5QtPAvVi6JPYpNzBXZHzMvLi5YtW7Jq1Sr7NtM0WbVqFe3atctwnytXrqRLtiwWC0C6D7k08fHxHDp0iIoVK+ZSz4UQOaF1fAZJ2I1SMPWfaJ2cn90q0vS14R+uPnQJHP4hSg7TNJk06UOqVq1B27a3cPfd3WnevDUNGjRmzpy5+dqXN998K8sy8larlQsXLjB//gK+/34xXl5eGVZ6tlgshIeHs2XLBm699RaXE8rUbVvh6lUXWipSfl/r0jGFcJXEJucKdGjiqFGj+PTTT/niiy/4559/ePLJJ0lISLBXURwwYABjxoyxt+/RowfTp09n/vz5HDlyhBUrVvDqq6/So0cPe0L2/PPPs2bNGo4ePcr69evp3bs3FouFvn37FshrFEI4MvVxwNn4gxS0PpUf3SkWZPiHEDamafLIIwN4/vmXOH06yuG5/fsPMGDAYF59dVy+9OXUqVOsXPmr0yIcpmkyc+andOx4K5s3r+e++3rZv9MAlCtXjjFjXuL3338jMDDQrT5YT5+GG46VKQXWqNNuHVsIZyQ2Oef20MTExER8fHwyfC4qKsqtO099+vTh7NmzvPbaa0RHRxMeHs6yZcvsBTyOHz/ucCVp7NixKKUYO3Ysp06dIjg4mB49evDWW2/Z25w8eZK+ffty/vx5goODueWWW9i4cSPBwcHuvlQhsk1rEziHqaOARMCCUuVRVESpkjsM1jb/6yzOEzHQRAE18rpLxUJxHP6RmJjI1KlT+e233zhz5ky64Vrbt29P1z63YpMoumbN+pz58xdk+FzayJk333yb22+/jTvuuN2lY546dYqDBw9x9OhRtm7dTkxMDAEB/vTseS/du3dzSJpudPLkSZf7feqULQlq0qQJCxb8j3PnznHkyBG8vLxo0KABXl5eLh1H62Q054AUwBPl6+Hyt1vlSsImhBskNjnndiLWokUL5s2bR3h4uMP2RYsW8cQTT3D27Fm3jjdixAhGjBiR4XOrV692+NnDw4Nx48YxblzmV7Pmz5cJpyJ3aH0VrU+jiQFSAQ8UIShVCaV8s9gvEVP/BVy5aXs8mmMoGmCo9AuWlwxJuJKE2SSjtYlSRaqmUIHQbg7pKArDP4YMGcIvv/zCAw88QJs2bZwOxcrt2CSKHq01H344xWn1QQ8PD/7zn4/siZjVamXJkqVMn/4Ju3btwsPDg9tvv53bb+/EggUL+emnnx32V0phsViYNWs2VatW5fvvF6V73wGUKlXK5b77+jrGlPLly1O+fHmX99faiqkPADHYPmMVoLFEBuHlHUHy/M2QmsWdOdPEUq++y+cTwhUSm5xzOxG77bbbaNu2LRMmTOCll14iISGB4cOH8/XXXzvcmRKiKNP6PKbehWPSkIzmBFqfxKARSqUPkrZg+CeQ2Zh8jdZ70HiiVJk86Hlh5+4V18L/oSzyxpIlS/jpp5/o0KGDS+0lNomTJ0+yZ88/TtulpqaydOlPaK25fPky99zTk99/X+ewoPKcOXOZPfuLDPdPWz4HbHfLOnW6k61bN1KnTh2Hdg0aNKBKlcqcOJH1nTEPDw969brXlZeYSX/S4s6Na6DaYpdS4NGpPiqkNEmTl4M1kwTVxwfPiLbZ7oMQJYW7sckZtxOxadOm0b17d4YOHcqSJUuIiorC39+fzZs307hx41zplBAFSeuEDJIwhxaYejcGLdNV9tOcIfMk7DpTH8FSIhMxb8AH23BNZ8rme4WzosrUtoc77Qu7sLAwl0pzp5HYJBISElxum5qaSkpKCg8++DDr128AcJjL5eriylarlStXrjBhwpvMneuYuFksFkaOfJoXXngpyzt0qampDB/+pMt9v5ltGHdcps8rQ2FpUBGPDnVJXbsvgwYK3yHDUFI9WuQyiU3OZWvMT9euXbnvvvv4448/OH78OO+++64EOlFsmNqVdeR0hu20jsqgbUbi0PqK82bFjFIKpSq71NZQYXncm+IjbRy+O4/C7oMPPuCll17i2LFjLu8jsalkCw0NzXS+1s3KlSvHzp07+eWXFS4nXZlJTU1lwYKvOX/+fLrnRo58iu7du2V4USlt23vvvUurVq2ydW6tNVo7n4umUHje3TTtxLYHoIIr4DtyFJ6tWmfr/EJkRWKTc27fETt06BD9+vUjOjqa5cuXs2bNGu69915GjhzJW2+9haenZ650TIiCYCuykTbG3pkzaF3vpjlMrpQJTpMI+LnTvWJBUQnNBeBCFq3CAFnPxlXFcRx+q1atSExMpGbNmvj5+aWLLRcuOL5/JDaJoKAgevfuyXff/WAfOpgRi8XCsGFDmDVrNh4eHlm2dVVqair79+9Pt/yOh4cHixcv5L33PmDKlKmcOXPG/lzjxo147bWxPPDA/Tk4cwoujTBQYIQEUOq117AeOow2NZYqVbDUbyAjD0SekdjknNuJWHh4ON27d2f58uUEBQXRpUsXunXrxoABA1ixYgU7duxw95BFmtZX0DoKzRVAoVQQilCUKrC1skWOpOJ6MQmNLQjeOJzDnZvMJbMIhVIGBo3R+hiaU9h+52m8UKoqijD5cuCG4jj8o2/fvpw6dYq3336bkJAQp+8HiU0CYPToF/nuux8yLdhhGAb+/qUYPvxJhgwZlitJ2I3HzoinpycvvzyaF154jq1bt3L5cjyVKlWkUaNGufA5594fs1G9OpYatXN4TiFcI7HJuWzNEevfv7/Dtvbt27Njxw6eeeaZHHWmKNHaxNT7geibtp9DcxhFPQwVUjCdEzngbjEJx/aKctfG6zv7NLEAuTfGuKhRykCpGmhdFYhFk4rCCygtCVg25PVVx4kTJ7J48WL27t2Lr68v7du3591336VevXqZ7jN79mz7mpBpvL29SUx0ZX4grF+/ng0bNtCsWTOX2ktsEgAtW7bkm28W8NBDfbFarQ7DDg3DIDAwgGXLllK5cmX8/QOcVlh0lZ+fH40aNcqyjaenZ7o7ZjnniS2euDK8Mq2tEPkjL2NTQcQlcD82OeP2JfmbA12agIAAPvvssxx3qKjIKAm74Vm0/getzwGgdRJan0frC2idnG99FO5TygIEudg6KN2dT6Uq4coVStt6YhIQlbKgVFkMVcF2N1mSsGxJu+rozsMda9asYfjw4WzcuJEVK1aQkpLCXXfd5bQ4QmBgIFFRUfaHO2Pq69evz9Wrrg/1ldgk0vTseS8HD+5l9OgXqVmzJmXLlqVhwwa8887bHDy4j4iICAC6dbs7V5Iwi8XC4MED8ff3d944lylloKjktJ02NSSXl89Yka/yMjYVRFwC92OTM27fEfvyyy8zfU4plWkwLE60jifzJOw6U+8HHQXcOIFXgS6PoWpmuRaVKDiGqoKpL7nQLn3RCaX8UdRE68NZ7FkKpapnu39C3CyvF81ctmyZw8+zZ8+mQoUKbNu2jY4dO2a6n1KK0NBQt86V5p133uG5557jrbfeokmTJunG4QcGBjr8LLFJ3KhKlSq8+ebrvPnm65m26dv3YZ577kUuX76cblFWV3l4eFCxYkVee21sdruaY0pVRpvRaDMJZUl/fV1bTfSlKyTO/IpSz72MymThcyFyW17GpoKIS+B+bHLG7URs5MiRDj+npKRw5coVvLy88PPzKxHBTuvTpC2WmLVkHJMwru1zFlNfxKAFSpW8Yg2FnVLlULoqmuOZt6FKhuuIARiqKiZeaH0Ux0nUxrX5gzVlDqHIVRr3ZoqktY2Lcyx57e3tjbcLJaxjY2MBKFs264Iq8fHxVKtWDdM0adGiBW+//bbT4Vtp7r77bgDuvPNOx75rjVIqXaU7iU3CXX5+fnzzzQK6deuR4XsqK2lDGm+99RbmzLF9ASwoSnmTuvYiRgMTVSEQbTVRFuP6/8fEkTjlF/T5BJJ/+xXvrt0KrK+iZMnP2JQfcQncj03OuP1t8OLFi+m2HThwgCeffJIXXnjB3cMVSZoE3J0gm17qtbWoWslQgULIMGpi6lJofRy48Ta337ViElnP/zNUKJoQbPOfklDYhjxKAibygsa9q45p4/CrVKnisH3cuHGMHz8+y31N0+SZZ56hQ4cOWZaGr1evHrNmzaJp06bExsby/vvv0759e3bv3k3lys6XMPjtt9+cv5AbSGwS2dG58538/vtvvPzyq/z66/X3nJeXFw899CAtWzZn3rz57N9/AIvFQs2aNWjUqCENGjSgR4/uNGzYsAB7b6NNk+QffkHPicXStDIerWuAvw/6ciLWTYew7j5l/8qSvHIFXnd3le8dIl/kV2zKr7gE7scmZ5TOjQHSwNatW3n00UfZu3dvbhyuQMXFxVG6dGliY2MzvMVoNXcCl3LlXIZqgVLu3cYU6dnKzp+7Xr2S0uRG4Qfbn8dVbNURPQFfCWAiVzj7nHHnGAtaPoefxfXFWK9Yk+iz7QNOnDjhcG5Xrjo++eST/Pzzz6xbt87lwAW2O1QNGjSgb9++vPHGGy7vl1MlKTaJnDl06BB79+7Dw8ODVq1aUq5cuYLukkvMuDjiR45wuX3AR9NRpUrlYY9EUVYUY1NRi0s3yrXL8x4eHpw+fTq3DleoKRWEdmEOkQtHQuvz2UrEtLaiOQs6DtCgAlBUKBZ3XLROQROF1tHYhnd6XHttlVAq/dh2U0eh9SFsZdBtQ0ZtVxd8MWiQo0TXlnTJ8FFRuGmt0O5cdbzWNjAw0K1AO2LECJYsWcLatWvdCnZgqxjXvHlzDh486FL7tWvXZvl8VnMAbpTd2PTxxx/z3nvvER0dTbNmzZg6dSpt2rTJtP3kyZOZPn06x48fp3z58jzwwANMnDgRn2vzccaPH8+ECRMc9qlXr16xSBCLi1q1alGrVq2C7oYQxUZ+xKb8jEuQe7Epjdvf2n/44QeHn7XWREVF8dFHH9GhQwd3D1ckKSqiOZpLR3NvLCmAqWPQev+1fa+9wXUUmoMoamGoMNsmnYomGq2jsM1VsqAoj1JhKFU4r4ZpHYep/8JxbalUNMfR+gQGDVEq2P6MqU+h9YEbj3DDv69i6h0YNJe7jqJYM6893GnvDq01Tz31FN9++y2rV6+mRo0abh4BrFYrf//9N926uTY/5bbbbku37ca70TePw8/N2LRgwQJGjRrFjBkziIiIYPLkyURGRrJv374M5wLNmzeP0aNHM2vWLNq3b8/+/fsZNGgQSikmTZpkb9eoUSNWrlxp/9nDo+hfOBMFT/n7o4KC0JcuOW9bthz4ycVFkT/yMjYVRFwC92OTM25HgV69eqU7eXBwMHfccQcffPCBu4crkpTyRlEbrV3PoDOmIYM7PFkx9Rm0/sfxGNefResDmGgUZTD1n9juKKWxojmN1qdR1M6w6l9B0jrpWp8zexNrTL3HnlhpnezCfwONqffJXDxRrOX1opnDhw9n3rx5fP/99wQEBBAdbasaW7p0aXx9bdVfBwwYQFhYGBMnTgTg9ddfp23bttSuXZtLly7x3nvvcezYMYYOHerSOW+e85WSksKOHTt49dVXeeutt9K1z83YNGnSJIYNG2Zfb2bGjBksXbqUWbNmMXr06HTt169fT4cOHejXrx8A1atXp2/fvmzatMmhnYeHR46qdQmREWUYeN3ZhaTF30BWs02UwqtzF4mFIt/kZWwqiLgE7scmZ9xOxLJb4rW4MVRlTCzXypSn4FhF0Q+44sJRFArXKy1pbV67E+as3SE0Htf6lVmbg2h8Mq38VxC0PoXzO4QaUx/Hohqjica1oikJQBxQOqddFKJQyusFnadPnw6kvxL4+eefM2jQIACOHz+OYVwvnX3x4kWGDRtGdHQ0ZcqUoWXLlqxfv97l4galS6f/e+3SpQteXl6MGjWKbdu2OTyXW7EpOTmZbdu2MWbMGPs2wzDo3LkzGzZsyHCf9u3bM3fuXDZv3kybNm04fPgwP/30U7pKjQcOHKBSpUr4+PjQrl07Jk6cSNWqVTPtS1JSEklJSfafb64kJkQarzs7k7J+HWZMDGT0t2AYGBUr4nX7HfnfOVFi5WVsKoi4BO7HJmdkXEQOGKritcp4568ViTBQBKFUAFZzD3DGyREqoZSXy+fTnMNxyF7mLbNKwtKY+iiWwpSIEeViy3O2eWQ61o1jx10r4CFE8ZPXd8Rcqem0evVqh58//PBDPvzwQ/dO5IKQkBD27duX68dNc+7cOaxWKyEhjpVRQ0JCMp3P1a9fP86dO8ctt9yC1prU1FSeeOIJXn75ZXubiIgIZs+eTb169YiKimLChAnceuut7Nq1i4CAgAyPO3HixHTzykTJonWi7aKjthWiQgVdmzNtcWinfH3xG/0KV2d8jPWff8AwQCnbHTLTxNKgIb7/elLWEBP5Ki9jU2GKS5D92ORSIjZq1CiXD3jjePiSQCkDCE6XwxuqHqa2kn4dsTQhGMrNScE6HtfWL3NVPFonFIr5YrY/KOfJ43XJZG91CiGKn7y+I1YQ/vrrL4ef0+Z8vfPOO4SHhwOFJzatXr2at99+m2nTphEREcHBgwcZOXIkb7zxBq+++ioAXbt2tbdv2rQpERERVKtWja+//pohQ4ZkeNwxY8Y4vMa4uLh0ZZ1F8aS1xtQHgVM3PRGD5uC1QlSOF1KNwEBKvTgG64njpGzejE6IR/n74xnRFktY4ZqKIEqGkhqb3OFSIvb555/TuHFjPDw87IsYZkTGHV+nlAWDxsBFTH0KuIwtiQq8Vkwj56XVc0cSUPCJmI07SaaBwh/NBRePXFheoxC5L6/viBWE8PDwDONN27ZtmTVrFpA3sal8+fJYLBZiYmIctsfExGQ6v+vVV1+lf//+9nkGTZo0ISEhgccff5xXXnnFYWhMmqCgIOrWrZtltS5XF9gWxY+pDwCZVfu0YupdGDRFqfSL11qqVMVSJfMhr0Lkl5Iam9zhUiIWGxvLokWLqFChAjVr1mTLli1FZn2NgmQL/mWxZPBBmb3jBbh0K9Y96b8gFASlFOhywDkXWvsCPihV8dqCy854A7nz30CIwqg4BrsjR444/GwYBsHBwfZy8JA3scnLy4uWLVuyatUqewEQ0zRZtWoVI0ZkvFbTlStX0iVbFott6Fhmn9nx8fEcOnQo3TwyUTzY1raMwzbv2Rsole6CgNaJaH36hmkHtriG9ifzJOw6Ux/AoE0huagrRHolNTa5w6VErEyZMhw5coQKFSpw9OhRKdhRYMphW1TYnSF8WfEACk9Zd0NVxtTOEzGlqlwLPL6gw0g3dCPdcWtLoBLFWnEc/lGtWjVWrVrFqlWrOHPmTLq4M2vWrDyLTaNGjWLgwIG0atWKNm3aMHnyZBISEuxVFG+uxNWjRw8mTZpE8+bN7UMTX331VXr06GFPyJ5//nl69OhBtWrVOH36NOPGjcNisdC3b99c6bMoWFpfQXMGrVOwFYi6jGPxqVIYVLcvv2JbhmYvjqNAktE6DnCc/5W5q0AsEJTT7guRJ0pqbHKHS4nY/fffT8eOHalUqRJKKVq1amUPLjc7fPiwWx0QrlPKwKAupt6ddTuqu7TOmSLs2hy3wkGpIJSuhuZYFq2CUVS0/2So2rYFAzmZQVsDpeo5rDsmhCgaJkyYwOuvv06rVq2oWLFihhdT8io29enTh7Nnz/Laa68RHR1NeHg4y5YtsxfwuLkS19ixY1FKMXbsWE6dOkVwcDA9evRwKGV88uRJ+vbty/nz5wkODuaWW25h48aNBAfL51NRpnUKpv4HnA6TT8DUu1HURlHqpmVobub6OkSaBJQkYkLkG1dikztcSsRmzpzJfffdx8GDB3n66acZNmxYplWeRN5SKhiDRph6H7ahDGlvAA1YUKoWhqqEqb3ROqvqLUEoVS3P++suw6iBqf3Q+hiOSwB42e6EUdnhTa+UQqnaaF0ZraPQXEGhQJVGEYJSUhhUFH/azeEfuT7COQ/MmDGD2bNnZzl0Ly9j04gRIzIdinhzJS4PDw/GjRvHuHHjMj3e/Pnzc6VfovDQOhVT78R2B8zVfQ6i8c+zPglRmJTU2OQOl7+l3n333QBs27aNkSNHSiJWgGzJWDk0Z0FfxrYwtL9DSVtbaX1vTH0U2zj1NJ4oFYaiaqG6G3YjQ4WgqYAtuCVje5sGZHnVQSkflHJ/VXUhigPz2sOd9oVdcnIy7du3d9pOYpMoKJpTuJOEXRefa31QhWh6gRA3K8mxyVVufxP//PPPJdAVAkoZGCoEw6iNYdTBUBXTryuiymIxWmCoNhiqGYZqgaHaYajqhTYJS2O70+WPUmVRKlDmeAmRBa2V24/CbujQocybN8/l9hKbBNiKZGh99doj777Waa3ROuv5yXkvAKXkPS8KL4lNzsm4rRJAKT/Ar6C7IYTII8XxqmNiYiIzZ85k5cqVNG3aFE9PT4fnS9qalSJrWqei9Qk0p7le0MqC0pWuFXjyyuUzJl97FBQDQ9UtwPML4ZzEJuckERNCiCKuOJYI/uuvv+yLY+7atcvhOblDLm6kdTKm3oGtiuCNrGhOoHU0Bi1QyrcgupcDAdjmSltxnA/ui6EayN0wUehJbHJOEjEhRLGjtcaMOo2Ou4zy88OoXBmVwaK6xYXG9aXQ09oXdr/99ltBd0EUEbaqhTcnYTdKwdTbULoVhpG9tX7S8yT7y8mUAS46bWW74+V3bT74FVDGtQqJpeVihCgSJDY5J4mYEKJYSdm4gaQlP2Keur6kgQoOxvvubnjefkex/AJju+ro+usqClcdhXCF1gm4ktRAKpqtaN3i2nD9nFHKQOlKTpZbyYgXivpo9mBbAyyz49e03/FShFIEllcSIh2JTc4V30vEQogSJ/G7xVz9ZDrmacdJ9PrsWRLnfEHi55+hi0J9XDfpbDyEKA4059xobSs3b5qJuXJupSoD7txh88JQ4RiGN4ZqiqIK6Rdv9kWpBhiqaq70UYiCJLHJObkjJoQoFlL37CH5++9sP2SSbKX8vhZL/QZ4te+Qfx3LB8VxHL4QLtFp62m6+qZORrMRq1kaRU0Mo7TzU2gNXMDUCdjmbMVzfShkqWv/n5hBPwxsiZY3SlV0WNtSKdu6n1pXx3ZnzAS8cLZUixBFicQm5yQRE0IUC8krloNhgJlF3SWlSF6+rPglYhS/ylRCuER5ZnMV2Fg0O7CaPkBFDBWKUt7pWpk6Bq0PkXmFxLR1OgOBABSp2NbrDHGpmIZt2Zmy2ei/EIWfxCbnJBETQhR52jRJ/evPrJMwAK0xjx/DvHQJIygoX/qWH7R277toMRydKUooRQU0h3NwhETgCKY+gtJVrs3Nst2RMvVptN7v4nHiAD8Mo0EO+iJE8SKxyTmZIyaEKPpSU50nYTfQibkzR6Sw0ChMNx5aZv6LYkIpHyA4V46lOYGpD9r+rZPR+oCbR4hG66Rc6YsQxYHEJuckERNCFH2enuDr4hpBhoERGJi3/clnaVcd3XkIUVwYqh7gn0tHO4XWCWiiyE7pAE1MLvVDiKJPYpNzkogJIYo8pRRet3a0zRHLimHg0aIlyi/n5asLEzMbDyGKC6U8MFQLwHnhDVdofRqtL2WnJyB3xISwk9jknCRiQohiwavzXeDhAU4qjnl3655PPco/aZWp3HkIUZwoZWCoptgWS84ZzWWyX0j75nL0QpRcEpuck0RMCFEsGMHB+D37PHh5pU/GDAMsFnyfHI6lRs2C6WAekrVahLBVIDRUE5Sqx/Wy8tk8FqVwfxVljVLlc3ReIYoTiU3OSdVEIUSx4VG/Pv7vvEfKmtUk//E7Oi4O5eeHZ9v2eN1+B0Zw7kzqL2xkrRYhbJQyUFQEVRGreQpwt+AGKAJRKhStTzlvfMNeUAqlitf8UyFyQmKTc5KICSGKFSMoCO+evfDu2augu5JvpESwEOkZqhKmTgROuLWfUpVQyg90CLhUfEMBHhiqUTZ6KUTxJbHJOUnEhBBCCFHsKKWwqFqYZik0+3Bl4JOiqi0Jw1aN0XaFPgZbspXR/gaKEJSqnuGC0EIIkZUCnyP28ccfU716dXx8fIiIiGDz5s1Ztp88eTL16tXD19eXKlWq8Oyzz5J405pA7h5TCCGKMqlMJUTmDCMUQ7VDqRqAZ2atUKrGtTY2ShlYjAYYqhWKSkAQUBaohqIJhmqOodpjGPUkCRMiAxKbnCvQO2ILFixg1KhRzJgxg4iICCZPnkxkZCT79u2jQoUK6drPmzeP0aNHM2vWLNq3b8/+/fsZNGgQSikmTZqUrWMKIURRJ+PwhciaUl4oqoGqhmmaKHUJrS8CGpTvtbtaGX8lUsofperkb4eFKAYkNjlXoHfEJk2axLBhwxg8eDANGzZkxowZ+Pn5MWvWrAzbr1+/ng4dOtCvXz+qV6/OXXfdRd++fR3ueLl7TCGEKOqkMpUQrjMMA6XKYhi1MIzaGCos0ySssNJWK6n795Gycwephw6izZJ4L0EUdhKbnCuwT57k5GS2bdvGmDFj7NsMw6Bz585s2LAhw33at2/P3Llz2bx5M23atOHw4cP89NNP9O/fP9vHBEhKSiIp6foijHFxcTl9eUIIkW/kqqMQJYM2TZKX/0zy8mXo2Fj7dlW2HN7d78Hz9jtQTtZSFCK/SGxyrsASsXPnzmG1WgkJCXHYHhISwt69ezPcp1+/fpw7d45bbrkFrTWpqak88cQTvPzyy9k+JsDEiROZMGFCDl+REEIUDI1Cu7HmkTtthRCFgzZNrs6cQeqmjemfu3CexDlfYI06jU+/RyUZE4WCxCbnCrxYhztWr17N22+/zbRp09i+fTuLFy9m6dKlvPHGGzk67pgxY4iNjbU/Tpxwr9StEEIUJM31K4+uPErgRUchiryUP9ZlmIQ5tFm5gtQ/d+ZPh4RwQmKTcwV2R6x8+fJYLBZiYhzX6IiJiSE0NDTDfV599VX69+/P0KFDAWjSpAkJCQk8/vjjvPLKK9k6JoC3tzfe3lLxSAhRNMnwDyGKN601yb8sB6WyXmzJMEhe8Que4c3zr3NCZEJik3MFdkfMy8uLli1bsmrVKvs20zRZtWoV7dq1y3CfK1euYBiOXbZYLIDtQyo7xxRCiKJOJkQLUbzpuDjMkyecr3hrmlj37EanpuZPx4TIgsQm5wq0TNCoUaMYOHAgrVq1ok2bNkyePJmEhAQGDx4MwIABAwgLC2PixIkA9OjRg0mTJtG8eXMiIiI4ePAgr776Kj169LAnZM6OKYQQxY1cdRSimEtOdq99Sgp4FK1KkKL4kdjkXIHOEevTpw/vv/8+r732GuHh4ezcuZNly5bZi20cP36cqKgoe/uxY8fy3HPPMXbsWBo2bMiQIUOIjIzkk08+cfmYQghR3Ohs/M8dEydOpHXr1gQEBFChQgV69erFvn37nO63cOFC6tevj4+PD02aNOGnn37K7ksUokRTgYFw7YKzU76+4OOTtx0SwgV5GZuKS1wq8GIdI0aM4NixYyQlJbFp0yYiIiLsz61evZrZs2fbf/bw8GDcuHEcPHiQq1evcvz4cT7++GOCgoJcPqYQQhQ37kyGdvcKJcCaNWsYPnw4GzduZMWKFaSkpHDXXXeRkJCQ6T7r16+nb9++DBkyhB07dtCrVy969erFrl27cvhqhSh5lLc3Hm3bgeHka5th4NXpNqmaKAqFvIxNxSUuKa2dDTgueeLi4ihdujSxsbEEBgYWdHeEEMVQbnzOpB3j6Wpj8DZcvwKeZCbyn2MTs33us2fPUqFCBdasWUPHjh0zbNOnTx8SEhJYsmSJfVvbtm0JDw9nxowZbp9TSGwq6aynTpIwYRykpmY8V0wp8PbG/823McqVz/8OimKhqMamohqXCvyOmBBCiJzJ6ztiN4u9tpBs2bJlM22zYcMGOnfu7LAtMjKSDRs25OzkQpRQlrDK+I18Fjw9bUnXjZQCX1/8nn9RkjBRaORnbCqqcUlmcgohRBGntfNiaje3B9tVyxu5spSHaZo888wzdOjQgcaNG2faLjo6Ot3c3JCQEKKjo13vqBDCgUejxvi/N4mUtWtI2bAenRCPCgzEs8MteHW4FeXvX9BdFMIuv2JTUY5LckdMCCGKODMbD4AqVapQunRp+yOtQm1Whg8fzq5du5g/f37uv5BC5OOPP6Z69er4+PgQERHB5s2bs2w/efJk6tWrh6+vL1WqVOHZZ58lMTExR8cUIiNGYCDe9/TA/62JBEyeiv/rb+Ed2VWSMFHo5FdsKspxSe6ICSFEEZfdEsEnTpxwGIfv7G7YiBEjWLJkCWvXrqVy5cpZtg0NDSUmJsZhW0xMDKGhoa53tIAsWLCAUaNGMWPGDCIiIpg8eTKRkZHs27ePChUqpGs/b948Ro8ezaxZs2jfvj379+9n0KBBKKWYNGlSto4phBBFXX7EpqIel+SOmBBCFHX6+hAQVx5pFYIDAwMdHpkFO601I0aM4Ntvv+XXX3+lRo0aTrvUrl07Vq1a5bBtxYoVtGvXLqevNs9NmjSJYcOGMXjwYBo2bMiMGTPw8/Nj1qxZGbZfv349HTp0oF+/flSvXp277rqLvn37OtzxcveYQghR5OVhbCoucUkSMSGEKOKyO/zDVcOHD2fu3LnMmzePgIAAoqOjiY6O5urVq/Y2AwYMYMyYMfafR44cybJly/jggw/Yu3cv48ePZ+vWrYwYMSIHrzTvJScns23bNocJ3YZh0Llz50wndLdv355t27bZE6/Dhw/z00//396dx0dRpXsD/1VVb0kgCRBICLsgYRHCJkiuSNAgIAM47npF8HUZHR1FRBQVUFBBR5S5XBSVzeUdccaL4B0cECJhURaBRFYRYpA1YREI2Xqrc/9oEmiyVHfSVZ3u/n399Ee7+nTVc8qknjxVp059g1tuuaXW6wQAu92OwsJCrxcRUajQMzeFS15iIUZEFOL8OePo783TAPD+++/j/PnzSE9PR/PmzSteX3zxRUWbw4cP48SJExXv09LS8Pe//x0ffvghUlNT8eWXX2LZsmU13khdH5w+fRput9uvG7rvu+8+TJs2Dddffz3MZjPat2+P9PR0vPjii7VeJ+B5YOnl90m0atWqjr0jIjKOnrkpXPIS7xEjIqIa+fK4yaysrErL7rzzTtx55506RFS/ZGVl4Y033sB7772Hfv364eDBg3j66acxffp0TJ48udbrnTRpEsaPH1/xvrCwkMUYERHCJy+xECMiCnH+Dunwd2hiJElISICiKH7d0D158mSMHj0aDz/8MACgW7duKC4uxqOPPoqXXnqpVusEfHucABFRfcXcpI1DE4mIQpwQwu8XVc1isaB3795eN3SrqorMzMxqb+guKSmBLHunU0VRAHj+39RmnUREoY65SRuviBERhbjaThFMVRs/fjzGjBmDPn36oG/fvpg9ezaKi4vx4IMPAvDcAN6iRYuKZ9uMGDEC77zzDnr27FkxNHHy5MkYMWJERUGmtU4ionDD3KSNhRgRUYi7bNZfn9tT9e6++26cOnUKU6ZMQX5+Pnr06IGVK1dWTLZx+PBhrytgL7/8MiRJwssvv4xjx46hadOmGDFiBF5//XWf10lEFG6Ym7RJIhKvA2ooLCxEXFwczp8/7/VAOSKiQAnEcaZ8HfcnvgCLbPP5ew61DJ8VzOQxLsQwNxGR3pibjMUrYkRkCPfxY3AfOgQAUNq0gdKiZXADCiMc/kFERPUNc5M2FmJEpCv3b4dQ9vfP4P7lF6/lSocOsN17P5SrrgpSZOHDM/zD9wwWgbmOiIgMxtykjbMmEpFuXLm5KH59OtwHD1b6zJ2bi+IZr8F14Jcqvkn+KD/r6M+LiIhIT8xN2liIEZEuhKqidN5cwOUC1CqeDiIE4Haj9P25EFV9Tj4Twv8XERGRnpibtHFoYj0h7HY4f/gejrWZUAsKAEWBqUtXWG7KgNKpMyRJCnaIRH5x79kNcfp0zY2EgDh7Fq6fcmDu2cuYwMKQgIDq1/CPCMx2RERkKOYmbSzE6gH17FmUvDUTav4JQJIqTgm4snfAtX0bzINugm30AyzGKKS49uwBFAVwu2tuqChw793DQqwO/D2TGIlnHYmIyFjMTdo4NDHIhKqi5J23oZ4suLjgsp/Ci8O1nGsz4fhmRRCiI6o94XL63tbp0jGS8KfW4kVERKQn5iZtLMSCzLVrF9SjR6q+h+Yy9m/+BeH0/Q9bomCTmyVq/lwDAFQVcmIz/QMKY0IIv19ERER6Ym7SxkIsyJzfbwBkH/43lJTAtWun/gERBYg5Lc23n21Jgjntev0DCmOcmYqIiOob5iZtLMSCTJw549tVAwDi7FmdoyEKHLlBQ1iG3qLZzjz4ZshxcQZEFL7UizdE+/MiIiLSE3OTNk7WEWzR0V4TdNTIZtM/HqIAst52O0RZGZyZqz1Xx8pPOlz8b3P6INjuuie4QYYBAT9viNYtEiIKBKGqcO3eBdePWyFKSiA1bAjzdf2hpHTixF0UMpibtLEQCzJzr95w796l3VBRYOrWXf+AiAJIkmVE3T8aloHpcKzNhDvX82Bn5ar2sAy6EUrrNkGOMDz4eyYxEs86EoUKd34+Sme/A7Ug/9IJLFmGc10W5NZtED3uGciNGgc7TCJNzE3aWIgFmbl/Gsr++QVQVlb9aQNZhqnfdZBjY40NjihAlFatEPXA2GCHEbaE8O9MYgTeD00UEtRz51Ay4zWIoqKLC1Svf6tHj6B45htoMHUapOjoIEVJ5BvmJm0sxIJMstkQ/ZenUfLuLM/zlq68X0ySISe3QNR/jg5OgERU7/GsI1FlQgi4D/wCx9rvoB454hlZ0rkzzOk3QklKCnZ4VXKs/LenCKvu3nFVhTh1Co51a2EdNtzY4Ij8xNykjYVYPWDq3AUxL0+FfflXcGXvuHRKIDoalvQbYf3DCEhRUcENkoiIKEQIhwOl78+FKyfb6/5Ux9EjcKxaCettd8DyhxH16n4r4XLBsT5LewIvIeBYs4aFGFEYYCFWTyitWyP6L09DPX8e6qmTkEwmyC1aQjKbgx0aEdVzqvDzrGMkjv+giFL64Ty4fsrxvLm8sLn43/alX0KKiYblxgzjg6uGOHcOKC31re3vZyDsdkhWq75BEdUBc5M2FmL1jBwXx6m8icgv4uI//rQnClfuw4fh2r5Ns13Z0v+B+YZ0SKZ68qeQv1fnfHlOI1EQMTdp428xEVGIEwBUP16Rl+ookjg3rPOtSCku9gxdrCekRo0gNWrkQ0MJcqvWHDFD9R5zkzYWYkREIY4PzSS6xJ2fr32fFQDIMtSTJ/UPyEeSLMNyU4b2lTEhYMkYbExQRHXA3KSNhRgRUYgTQvj9IgpXksXi2zA/IYB6dlXJknEz5Natq7+iJ0lQOneBOe0/jA2MqBaYm7SxECMiCnE860h0ianrNb49kEgImLp01T8gP0hWK2ImToKpdx9PMSlJgKJ4/i3LMA+4AdHjxtef+9qIasDcpI2/yUREIY7PaiG6xNw/DWX/WALY7dU3kmUoHa6G0qKFcYH5SIqORvSfn4R65gycO7ZDFBdBjo2FqU9fyLGxwQ6PyGfMTdpYiBFRlYSqwrVrJ9TffvMMh2nXDkqXrpA4U1e9U34u0Z/2ROFKiopC1KOPofS//8uz4MqrY7IMKToaUQ89bHxwfpCbNIF18M3BDoOo1pibtLEQI6JKnDnZKPtkMcTZs5fuVVBVSAkJiBrzIEzXdAtugOSFZx2JvJl79YY0YSLKvvgc6uHDlz6QJJi6p8J2739CbtYseAESRQDmJm0sxIjIi3PbjyidO+fSgstmHxNnzqDk3VmIHjcepm7dgxAdVYXJjqgyU5euaPDqa3AfOgT38WOQFAVKhw6QmyQEOzSiiMDcpI2FGBFVEE4HShctqKGB5yBZuuAjNJg1G5KiGBQZ1US9+I8/7YkihdK2LZS2bYMdBlHEYW7SVi9u9pg7dy7atm0Lm82Gfv36YevWrdW2TU9PhyRJlV7Dhw+vaDN27NhKnw8dOtSIrhCFNOePPwIlJTU3EgLi/Pl69SDUSCckASGpfrwi76wjEREZi7lJW9CviH3xxRcYP3485s2bh379+mH27NkYMmQI9u/fj2ZVjN9eunQpHA5HxfszZ84gNTUVd955p1e7oUOHYtGiRRXvrVarfp0gChPuA794pkp2u2tuqChwHzgAc+8+xgRGNRJ+Dv8QETj8g4iIjMXcpC3ohdg777yDRx55BA8++CAAYN68eVixYgUWLlyIF154oVL7xo0be71fsmQJoqOjKxViVqsVSUlJ+gVOFI6EgM/HQRF5QwjqKxUqJA7/IApZQlXh/jUX4tw5SFFRUDp2hGS2BDssojphbtIW1KGJDocD27dvR0ZGRsUyWZaRkZGBTZs2+bSOBQsW4J577kFMTIzX8qysLDRr1gwpKSl4/PHHcebMmWrXYbfbUVhY6PUiikRycgvfCiy3G3KLlvoHRD7x/5GZ/iW79evXY8SIEUhOToYkSVi2bFmN7bOysqocQp6fn1+HXhKFHyEEHOvXoej5CSh5fTpK585Bydtv4cLTT6Hsy39COJ3BDpGo1pibtAW1EDt9+jTcbjcSExO9licmJvq0U7Zu3Yrdu3fj4Ye9nwUydOhQfPLJJ8jMzMSbb76JdevWYdiwYXBXM9xqxowZiIuLq3i1atWq9p0iCmGWtP/wDE3UYrXC3O86/QMin6iS6vfLH8XFxUhNTcXcuXP9+t7+/ftx4sSJildVw82JgkG4XBAuV7DDgH3ZUpQtWgBx+rT3B6UlcHzzL5TMnlUv4iSqDeYmbUEfmlgXCxYsQLdu3dC3b1+v5ffcc0/Ff3fr1g3du3dH+/btkZWVhZtuuqnSeiZNmoTx48dXvC8sLGQxRhFJatAA1ltvg/3Lf9TYznbHXZB432W9offwj2HDhmHYsGH+hoVmzZohPj7e7+8R6UHY7XBuWA9H5mqoF0/2yi1bwpIxGOa06yGZzYbG4zrwCxxfL6++gRBw79sHx6qVsA7/g3GBEQUIc5O2oF4RS0hIgKIoKCgo8FpeUFCgeX9XcXExlixZgoceekhzO1dddRUSEhJw8ODBKj+3Wq2IjY31ehFFKsstw2G97Q7Pg5wlyfulKLDecx8sGYODHSZdRq3FP0bo0aMHmjdvjsGDB+P77783ZJuBwtl8w4tadAHFr09D2f//tKIIAwD12DGULV6Ekr/OhCgrMzQmx5rVnuNsTYSAI3M1hBp5985Q6GNu0hbUK2IWiwW9e/dGZmYmbr31VgCAqqrIzMzEk08+WeN3//nPf8Jut+P+++/X3M7Ro0dx5swZNG/ePBBhE4U1SZJgHTES5htugHPDerh/+w0AoLS7CubrB0DmiYp6x9+x9eVtr7wf1mq1BmSG2ebNm2PevHno06cP7HY75s+fj/T0dGzZsgW9evWq8/r1xtl8w0/pe3OhHjtW+YOLz0Z05+aidOF8RP+55r89Asm18yfAhwJLnD0L9cRxKLwvl0IMc5O2oA9NHD9+PMaMGYM+ffqgb9++mD17NoqLiytmUXzggQfQokULzJgxw+t7CxYswK233oomTZp4LS8qKsKrr76K22+/HUlJScjNzcXEiRPRoUMHDBkyxLB+EYU6OS4e1j+MDHYYpKMrh2BPnToVr7zySp3Xm5KSgpSUlIr3aWlpyM3NxbvvvotPP/20zuvXG2fzDS/uw7/BvW9vzY1UFa4ft0I9fQpyQlNjAvNnIg67Q7sNUZiIpNwU9ELs7rvvxqlTpzBlyhTk5+ejR48eWLlyZcUEHocPH4Z8xaX7/fv3Y+PGjfj2228rrU9RFOzcuRMff/wxzp07h+TkZNx8882YPn06zz4SUVhS4YYEjWe/XdEeAI4cOeI1FFvPY2Tfvn2xceNG3dYfKOWz+U6aNKliWaBn823UqBFuvPFGvPbaa5VOJl7ObrfDbrdXvOeMvrXj3LzJMwRQ6+qTLMO5ZTOsw0cYEpfUuAnEqZM+NJQg1fBzQlRfMTdpC3ohBgBPPvlktUMRs7KyKi1LSUmBEFU/7CgqKgqrVq0KZHhEIUuUlsK5eRMc69dBnP0dki0Kpmv7wpKeDrlJQrDDowARFx+b6U97AIbeE5uTkxMSw8Nrms33559/1vx++Wy+CxYs8Fo+dOhQ3HbbbWjXrh1yc3Px4osvYtiwYdi0aROUamYqnTFjBl599dXad4YAAMLXAlaSfG8bAJb0QZ6Jkar5ewYAIMswdesOOS7OsLiIAoW5SVu9KMQoONSCAjh/ygEcdkiNm8Dcqzckmy3YYVGAuI8dQ8lf34Q4f65imTh/Ho5v/gXHv1cg6pE/cQr6MKFKKiQ/pv3194booqIir8mO8vLykJOTg8aNG6N169aYNGkSjh07hk8++QQAMHv2bLRr1w5du3ZFWVkZ5s+fj++++67KUQzhJlCz+QKc0TdQpOhoAJJ2QyEgRcdotwsQyw0D4fh2JcSFCzVcrZNgGTHKsJiIAom5SRsLsQiknj2L0oXz4d6969JseKqKMqsV1qG3wDJyFCStmZyoXhNFRSh5ayZE0YXKH15M+KUfvA+pUSOYOqZUbkMhxTP8w/ffWdWPoSIAsG3bNgwaNKjifXlxMGbMGCxevBgnTpzA4cOHKz53OBx49tlncezYMURHR6N79+5Ys2aN1zrqq0DM5jtt2jTN7Vw+m291hVigblCPdKY+18Kx2oc/tFQVpj7X6h/QRVKDBoie+AJK3n4L4uxZTy4uvzp2cZbaqMefgKl9e8NiIgok5iZtLMQijHr+PIpfmwZx7qxngRCXDvx2O+zLv4J6/hxsD3imWqbQ5NiwHuJCYc1DXiQJ9q+XwzRhonGBkU78m5kKfp51TE9Pr3Y4OAAsXrzY6/3EiRMxcWJo/lxxNt/wo1zdEXLr1lCPHq3+ypMsQ+nUCUqLFsbGltwCDWa8BeeWzXBuWA/17O+QoqJhvrYvzAMHQo6LNzQeosBibtLCQizC2L9a6inCarhp2Zm1Fub+abxSEsIcWWtrLsIAQFXh3rMb6tnfITdqXHNbqtdU4YY/j4X0tKfqcDbf8CJJEqL/Mg7Fb0yHOH++cv6TZUhNmyLqT382JB5x8djryFwD96E8AIDSvgOsf7wdSufOPAlKYYO5SRsLsQgiSkrg/GGjTzNHOTLXsBALYeLs7z63VX9nIRbqavusFqoaZ/MNP3JCAmJemQ7Hyn97TlSVlng+MJshxcdDbtoMzu83wjxgAOQGDXWLQzgdKJ3733D9lOM1k6MrJxuuHdth6ncdoh5+FJKJf55R6GNu0sbf9AjiPnzYt+eWqCrcP+/TPyDSj9ns8zNqJLNF52BIbwJuCD/OOgo/x+FHIs7mG37k2FjY7roblpGjULZwPlw/bgXcbohTp+A+dQruPbthX/olbKPHwHLDQF1iKF200PMgZ8D7pGh5QbZ1C8oaNETU/aN12T6RkZibtHFGhkjidvncVBQVofi1V1Ey/0O4ftlf4xhcqn9MPXp6zrZqkOLjIbdsaUBEpCe1Fv8QRSr73z+Da9uPnjeXF0NCAC4XyhYtgHPL5oBvVz1ZANemH2oeNi4EnGszofKZcRQGmJu0sRCLIHJysmcmJl+oKty5uXBt+gElM173zOpUWqpvgBQw1psytIegShIsGYM5Q2YYKH9Wi+8vnlihyOQ+fgzODes176Et++JzCK1jqJ8c33/v0wkyCAHnph8Cum2iYGBu0sa/wCKI3KgxTN1TfUsE5S4mIvfP+1DyX7MDnphIH8pV7WG9/Y7qG0gSlM5dYBkyzLigSDdCuP1+EUUi57osn3KgOHsWrt27Arpt8fsZ3xrKsu9tieox5iZtLMQijPW22wFF8f3KWLmL94259+3VJzAKOOsfRiLq0ccgJ10xPXZMDCwjRiL6mWd5Q3iY4PAPIt+4jx/XHi0AAJIE9fjxwG7cYvUt9wrhaUsU4pibtPGvsAijtG6D6AkTUTrnbxBFRd4PkNQiy3BkrYWp6zX6BkkBY+6fBtN1/aH+dgjquXOQbDYo7TtAMpuDHRoFkOeGaN9PrkTiDdFEAADFx/PPQkA9dy6gmzalpsL53RrthqoKU48eAd02UTAwN2ljIRaBTB1T0OCdv8G17Uc4c7Lhzj8BcdmTx6ulqlCPH9M/QAooSZKgtG0HJdiBkG6E8HOKYBF5Zx2JAMCU0gnunTt9OgHpXJ8F6y3DIcfGBmbb13SDlNDUM+ywhgdLyy1aQrmqfUC2SRRMzE3aODQxQklmM8z90xD9+BOw/WGk71808UoKUX3D4R9EvjFff4Pv90mXlcG5fl3Ati3JMqL/8hRgsVQdgywDUVGI+vMTfKgzhQXmJm0sxAjK1R19S0yyDFPXrvoHRER+4Q3RRL6RGzb03CvtCyHgWJ8V0O0rrdsgZsorMPXs5X2/mCzDdG1fNHhlGpQr7+slClHMTdo4NJEgx8fD1Ks3XDu213wTsxCwpN9oXGBE5JPyKYL9aU8UqZSOKT63FQG+TwwAlObJiH7yKajnzkE9dhQAILdqHbAhkET1BXOTNhZiBACw3fefKD54AKKwsNpizHr3PZCbNTM4MiLS4hmH78cN0RE4Dp+onGSL8r2x2aJbHHJ8POT4eN3WTxRszE3aODSRAHieMRYz+RXPc8auGJsuNWoE20OPwMpnThHVU+6Ls1P59kIEzkxFVE5OTobUuLF2Q0mCktJR/4CIwhZzkxZeEaMKcuPGiH76GainT8O1by/gdEJu1gxKl66Q/HkINHlRCwvhXL8OrpxsCHsZ5KbNYL5hIEzdU7lfiYgMJskyLDcPgX3J5zU3FALu7GyUfvYJbPfdz+M1EQUcCzGqRE5IgGXADcEOIyw4f9yK0g/nAW53xXTJ6vHjcGXvgNy6DaLHT4AcFxfkKCnUeYZzcPgHka8sGTfDvX8/XNk7NNs6M9dAimkA2x9v83n96pnTcKzLgjsvDwCgtGsHy8B0yE0Sah0zUahhbtLG0ztEOnHt3YvS9+cCLpf3M2su3oOnHj2CkllvQbhcQYqQwoUQqt8vokgmKQqinvgLLMOG+9Te8e8VECUlmu2EECj76n9Q9NyzcKz4F9y7d8G9exccK/6FoueeRdlX/wPhwzPMiMIBc5M2FmJEOrEv/bLmBqoK9cgRuLZvMyYgClt8VguR/yRFARS50n3RVXI64fxxi2Yzx4r/hePr5Z6Tb5dPfKWqnunwv14Ox4r/rUPURKGDuUkbCzEiHbhPHIc796D3lbCqSBIca78zJigKWzzrSFQ76qlTvhViigL15Kkam4jiYtiXL9NclX35MoiSYh8jJApdzE3aeI8YhT317O9wrMuCKzsbsNshNW0Ky8B0mHr0hGTS51dALSjwraEQUAvydYmBIoe/D8GMxIdmElVFsvg4Pb0QXm1FWRmcW7dAPX4cUGQoKZ08x3K3D79bLhecP/wAS8bgWkZNFBqYm7SxECPDCSEgLlwAnA5IsXGQzGbdtuX44XuULZxfMSwEAHDqJEp374LcogWin50IuVGjgG/Xrz7pVAxS5PA8BJMPzSTyl6lbdzg3rNduqKowdesOIQQc367yDD13OABF8Xz+zQrAavVcXdMaCaEocJ84Xvfgieo55iZt/AuQDCPcbjjXr4Nj9bdQy5OQxQLz9QNgHTIs4A+Ldu3ehbL5H1ZOiuWTZZw4gZK330LMK9MCXgwqV7UHLBZPoq6JLMPUrXtAt02Rx9/hHJE4/IOoKqaevSDFxkFcKNQsoOz/uxxSbCyc69ddWnj5FTC73eftSrLib6hEIYe5SRvvESNDCJcLpXP+hrJPFkPNP3HpA4cDzqy1KJr6Mty/5gZ0m2W+TJZx/Bhc234M6HYBQIqKgvn6AYDWc2dUFZYbMwK+fYosHIdPVDuSyYSoJ570XNnSOF67fsrxLsJqy+2GkpJS9/UQ1XPMTdpYiJEh7Mu+gmvnT543VV2hcjhQ/M4siLKygGzPfewo1Lw83ybLyFobkG1eyXb7HZATE2tM7tY77oTSsqUu26fIIaD6/SIiD1PHFMS8NBlKR43iKBDTzksSpNhYmHr2qvu6iOo55iZtLMRId8JuhyNzdc1JTFWB4iI4t2wKyDbVUzXPbnUpOAH1pI8Ta/hJio5BzEtTYOqfduk+gvLPGjeG7aFHYB0+QpdtU2ThWUeiulHatkPM85Mgd7jat1kUa0OSAElC1CN/8kydTxTmmJu08R4x0p1rz27AlytdkgTn5s2wDBxU5236dc+XjpOFSDExiH74Uah33wv3vr0QDgfkJk2gpHSCpDVskchHHIdPVHeipATqwQN1X5EkQWrQ0HPfWXlRJwTkxCTYHhgDU+cudd8GUQhgbtLGQox0J4p9fF5K+WyKAeDPZBnm1J4B2WaNm2nYEHLffrpvhyKVv8kr8pIdkRZRUhKgFQnYHv8zJEWB+1AeAM8VN+XqjpD0utpGVC8xN2lhIUa6kxo29LGhZ+x8QLYZFQXLDQPh+C6zYpbEKqkqzDfeGJBtEgULzzoS1Z0UE+Pb9PM1rkSC1LQpTJ06Q5IkmLTuOyMKY8xN2jg2inRn6toViI7WbigEzGn/EbDtWm+7A3Lz5Jony7jnXijNkwO2TaJg4A3RRHUnRUXB1D1Ve7ZbDVGjx/DKFxGYm3zBQox0J5ktsN48tOZGsgwpLg7mAA7fk6KiEPPiyzAPuKHSQ5OlZs0Q9afHYR0yLGDbIwoWIYSfN0RH3kMziXxhGXpLzaMoysmy5+qZLAPlzwSLiUHUU+NguqabvkEShQjmJm0cmkiGsIwYCfeJ43Bt2exJXJcnOlmGFB2N6GcnQrJYArpdKToaUWP/H2x33Q3Xzz8DDgekJk2gdLiaZywpjLgB+PPzHHnJjsgXpk6dYBs9BmWfflxlrgKAqIcfhdL1Gji/3wD1+HFAUWBK6QRTn2v9myiKKOwxN2lhIUaGkGQZUY8+BlfPXnCsXgV37sWHN8fEwJJ+IywZgyHHx+u3/egYmHv11m39RMHkGVfve7KLxLOORL6y3HgT5Nat4fh2FVzbt3mKMUWBqd91sA4eAqVtWwCAddjw4AZKVM8xN2ljIUaGkWQZ5n7XwdzvOginA3C5AauV07gT1Zl/yS4SzzoS+cPU4WqYOlwN4XIBDjtgtfHZX0R+Y27SwkKMgkIyWwCO4CAKDD/POtZpVjiiCCKZTJXuMSYiHzE3aeKlCCIiqtH69esxYsQIJCcnQ5IkLFu2TPM7WVlZ6NWrF6xWKzp06IDFixfrHicREUWOcMhNLMSIiEKcqMU//iguLkZqairmzp3rU/u8vDwMHz4cgwYNQk5ODsaNG4eHH34Yq1atqk33iIgoBDE3aeP1diKikKfvOPxhw4Zh2DDfH/Uwb948tGvXDrNmzQIAdO7cGRs3bsS7776LIUOG+LVtIiIKVcxNWnhFjIgo5AnP2HpfXzrfEL1p0yZkZGR4LRsyZAg2bdqk63aJiKg+YW7SwitiVSifPrOwsDDIkRBRuCo/vgRmul7/h3RcHkM5q9UKq9Va52jy8/ORmJjotSwxMRGFhYUoLS1FVFRUnbcRiZibiEhvzE3GYiFWhQsXLgAAWrVqFeRIiCjcXbhwAXFxcbX6rsViQVJSEvLz8/3+boMGDSod46ZOnYpXXnmlVrGQ/pibiMgozE3GYCFWheTkZBw5cgQNGzaEJGmPbS0sLESrVq1w5MgRxMbGGhChvsKtPwD7FArCrT9AzX0SQuDChQtITk6u9fptNhvy8vLgcDj8/q4QotLxLRBnHAEgKSkJBQUFXssKCgoQGxvLq2F1UFNuCsffn9rivriE++IS7gsPrf3A3GQsFmJVkGUZLVu29Pt7sbGxYfXLHW79AdinUBBu/QGq71NtzzZezmazwWaz1Xk9gdS/f3988803XstWr16N/v37Bymi8OBLbgrH35/a4r64hPviEu4Lj5r2A3OTcThZBxER1aioqAg5OTnIyckB4JkCOCcnB4cPHwYATJo0CQ888EBF+8ceewy//vorJk6ciJ9//hnvvfce/vGPf+CZZ54JRvhERBSGwiE3sRAjIqIabdu2DT179kTPnj0BAOPHj0fPnj0xZcoUAMCJEycqEh8AtGvXDitWrMDq1auRmpqKWbNmYf78+Zy6noiIAiYcchOHJgaA1WrF1KlTAzaGNdjCrT8A+xQKwq0/QPj0KT09vcYZtBYvXlzld7Kzs3WMii4XLj9rgcB9cQn3xSXcFx7htB/CITdJIjDzUxIREREREZGPODSRiIiIiIjIYCzEiIiIiIiIDMZCjIiIiIiIyGAsxIiIiIiIiAzGQqwKc+fORdu2bWGz2dCvXz9s3bq12rYfffQRBgwYgEaNGqFRo0bIyMio1F4IgSlTpqB58+aIiopCRkYGDhw4oHc3vASyT06nE88//zy6deuGmJgYJCcn44EHHsDx48eN6EqFQP9/utxjjz0GSZIwe/ZsHSKvmh792bdvH0aOHIm4uDjExMTg2muv9ZrKVW+B7lNRURGefPJJtGzZElFRUejSpQvmzZundzcq+NOfpUuXok+fPoiPj0dMTAx69OiBTz/91KtNfTg2UOjw5+dvz549uP3229G2bVvDj2VG0PP4H2oCfVwKVf7sh8stWbIEkiTh1ltv1TdAA/mzLxYvXgxJkrxe9e1BzGFNkJclS5YIi8UiFi5cKPbs2SMeeeQRER8fLwoKCqpsf99994m5c+eK7OxssW/fPjF27FgRFxcnjh49WtFm5syZIi4uTixbtkz89NNPYuTIkaJdu3aitLQ0JPt07tw5kZGRIb744gvx888/i02bNom+ffuK3r17G9IfPfp0uaVLl4rU1FSRnJws3n33XZ174qFHfw4ePCgaN24snnvuObFjxw5x8OBBsXz58mrXGQp9euSRR0T79u3F2rVrRV5envjggw+Eoihi+fLl9a4/a9euFUuXLhV79+4VBw8eFLNnzxaKooiVK1dWtAn2sYFCh78/f1u3bhUTJkwQn3/+uUhKSjLsWGYEPY//oUaP41Io8nc/lMvLyxMtWrQQAwYMEKNGjTImWJ35uy8WLVokYmNjxYkTJype+fn5BkcduViIXaFv377iiSeeqHjvdrtFcnKymDFjhk/fd7lcomHDhuLjjz8WQgihqqpISkoSf/3rXyvanDt3TlitVvH5558HNvhqBLpPVdm6dasAIH777bc6x+sLvfp09OhR0aJFC7F7927Rpk0bw/540aM/d999t7j//vsDHquv9OhT165dxbRp07za9erVS7z00kuBCboGde2PEEL07NlTvPzyy0KI+nFsoNBRl58/I49lRjAip4WKQB+XQlVt9oPL5RJpaWli/vz5YsyYMWFTiPm7LxYtWiTi4uIMio6uxKGJl3E4HNi+fTsyMjIqlsmyjIyMDGzatMmndZSUlMDpdKJx48YAgLy8POTn53utMy4uDv369fN5nXWhR5+qcv78eUiShPj4+LqGrEmvPqmqitGjR+O5555D165dAx53dfToj6qqWLFiBTp27IghQ4agWbNm6NevH5YtW6ZHFyrR6/9RWloavv76axw7dgxCCKxduxa//PILbr755oD34XJ17Y8QApmZmdi/fz9uuOEGAME/NlDoCMTvU7gwKqeFAj2OS6Gotvth2rRpaNasGR566CEjwjREbfdFUVER2rRpg1atWmHUqFHYs2ePEeESeI+Yl9OnT8PtdiMxMdFreWJiIvLz831ax/PPP4/k5OSKX4Ly79VlnXWhR5+uVFZWhueffx733nsvYmNj6xyzFr369Oabb8JkMuGpp54KaLxa9OjPyZMnUVRUhJkzZ2Lo0KH49ttv8cc//hG33XYb1q1bF/A+XEmv/0dz5sxBly5d0LJlS1gsFgwdOhRz587V/Y+I2vbn/PnzaNCgASwWC4YPH445c+Zg8ODBAIJ/bKDQEYjfp3BhRE4LFXocl0JRbfbDxo0bsWDBAnz00UdGhGiY2uyLlJQULFy4EMuXL8dnn30GVVWRlpaGo0ePGhFyxDMFO4BwMnPmTCxZsgRZWVlhc6OjVp+cTifuuusuCCHw/vvvByFC/1XVp+3bt+Nvf/sbduzYAUmSghyhf6rqj6qqAIBRo0bhmWeeAQD06NEDP/zwA+bNm4eBAwcGLV5fVPdzN2fOHGzevBlff/012rRpg/Xr1+OJJ56ot39UNWzYEDk5OSgqKkJmZibGjx+Pq666Cunp6cEOjSgihWOe9lekH5cuXLiA0aNH46OPPkJCQkKwwwm6/v37o3///hXv09LS0LlzZ3zwwQeYPn16ECOLDCzELpOQkABFUVBQUOC1vKCgAElJSTV+9+2338bMmTOxZs0adO/evWJ5+fcKCgrQvHlzr3X26NEjcMFXQ48+lSsvwn777Td89913hlwNA/Tp04YNG3Dy5Em0bt26Ypnb7cazzz6L2bNn49ChQwHtw+X06E9CQgJMJhO6dOni1b5z587YuHFj4IKvhh59Ki0txYsvvoivvvoKw4cPBwB0794dOTk5ePvtt3UtxGrbH1mW0aFDBwCeQnjfvn2YMWMG0tPTg35soNBRl9+ncKNnTgs1ehyXQpG/+yE3NxeHDh3CiBEjKpaVn7w0mUzYv38/2rdvr2/QOgnEscJsNqNnz544ePCgHiHSFTg08TIWiwW9e/dGZmZmxTJVVZGZmel1tuBKb731FqZPn46VK1eiT58+Xp+1a9cOSUlJXussLCzEli1balxnoOjRJ+BSEXbgwAGsWbMGTZo00SX+qujRp9GjR2Pnzp3IycmpeCUnJ+O5557DqlWrdOsLoE9/LBYLrr32Wuzfv99r+S+//II2bdoEtgNV0KNPTqcTTqcTsux92FIUpSKJ6qW2/bmSqqqw2+0Agn9soNARqJ+/cKBXTgtFehyXQpG/+6FTp07YtWuXV74fOXIkBg0ahJycHLRq1crI8AMqED8Tbrcbu3bt8jpBSDoK7lwh9c+SJUuE1WoVixcvFnv37hWPPvqoiI+Pr5jKc/To0eKFF16oaD9z5kxhsVjEl19+6TX154ULF7zaxMfHi+XLl4udO3eKUaNGGT59fSD75HA4xMiRI0XLli1FTk6OVxu73R6SfaqKkTON6dGfpUuXCrPZLD788ENx4MABMWfOHKEoitiwYUPI9mngwIGia9euYu3ateLXX38VixYtEjabTbz33nv1rj9vvPGG+Pbbb0Vubq7Yu3evePvtt4XJZBIfffSRV5+DeWyg0OHvz5/dbhfZ2dkiOztbNG/eXEyYMEFkZ2eLAwcOBKsLAWPE8T9U6HFcCkX+7ocrhdOsif7ui1dffVWsWrVK5Obmiu3bt4t77rlH2Gw2sWfPnmB1IaKwEKvCnDlzROvWrYXFYhF9+/YVmzdvrvhs4MCBYsyYMRXv27RpIwBUek2dOrWijaqqYvLkySIxMVFYrVZx0003if379xvYo8D2KS8vr8rPAYi1a9eGZJ+qYvSUz3r0Z8GCBaJDhw7CZrOJ1NRUsWzZMoN64xHoPp04cUKMHTtWJCcnC5vNJlJSUsSsWbOEqqr1rj8vvfRSxb5v1KiR6N+/v1iyZInX+urDsYFChz8/f9UdpwcOHGh84DrQ+/gfSgJ9XApV/uyHK4VTISaEf/ti3LhxFW0TExPFLbfcInbs2BGEqCOTJIQQul1uIyIiIiIiokp4jxgREREREZHBWIgREREREREZjIUYERERERGRwViIERERERERGYyFGBERERERkcFYiBERERERERmMhRgREREREZHBWIgREREREREZjIUYUQCkp6dj3LhxwQ6DiIgIAPMSUShgIUZERERERGQwSQghgh0EUSgbO3YsPv74Y69leXl5aNu2bXACIiKiiMa8RBQaWIgR1dH58+cxbNgwXHPNNZg2bRoAoGnTplAUJciRERFRJGJeIgoNpmAHQBTq4uLiYLFYEB0djaSkpGCHQ0REEY55iSg08B4xIiIiIiIig7EQIyIiIiIiMhgLMaIAsFgscLvdwQ6DiIgIAPMSUShgIUYUAG3btsWWLVtw6NAhnD59GqqqBjskIiKKYMxLRPUfCzGiAJgwYQIURUGXLl3QtGlTHD58ONghERFRBGNeIqr/OH09ERERERGRwXhFjIiIiIiIyGAsxIiIiIiIiAzGQoyIiIiIiMhgLMSIiIiIiIgMxkKMiIiIiIjIYCzEiIiIiIiIDMZCjIiIiIiIyGAsxIiIiIiIiAzGQoyIiIiIiMhgLMSIiIiIiIgMxkKMiIiIiIjIYCzEiIiIiIiIDPZ/F3OPcMqhc/8AAAAASUVORK5CYII=",
       "text/plain": [
        "
"
       ]
@@ -886,10 +799,10 @@
     "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3.5))\n",
     "\n",
     "im = df2.plot.scatter(x=\"t\", y=\"flux\", s=50, c=\"num\", cmap=\"magma\", ax=ax1)\n",
-    "ax1.set_title('Second run')\n",
+    "ax1.set_title(\"Second run\")\n",
     "\n",
     "im = df_combined.plot.scatter(x=\"t\", y=\"flux\", s=50, c=\"num\", cmap=\"magma\", ax=ax2)\n",
-    "ax2.set_title('Combined results')\n",
+    "ax2.set_title(\"Combined results\")\n",
     "\n",
     "plt.show()"
    ]
@@ -908,7 +821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 17,
    "id": "20de0ff1-1bcc-4d48-a40d-83f52d3c1a08",
    "metadata": {},
    "outputs": [],
@@ -928,7 +841,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 18,
    "id": "85939227-dee3-491d-89cf-37768dd3dde9",
    "metadata": {},
    "outputs": [
@@ -961,27 +874,27 @@
        "  \n",
        "    \n",
        "      36\n",
-       "      3\n",
-       "      0.477944\n",
-       "      0.995562\n",
+       "      4\n",
+       "      0.364913\n",
+       "      0.998095\n",
        "    \n",
        "    \n",
        "      37\n",
        "      2\n",
-       "      0.383136\n",
-       "      0.929275\n",
+       "      0.196359\n",
+       "      0.946069\n",
        "    \n",
        "    \n",
        "      38\n",
-       "      2\n",
-       "      0.267950\n",
-       "      0.950784\n",
+       "      4\n",
+       "      0.209707\n",
+       "      0.827698\n",
        "    \n",
        "    \n",
        "      39\n",
        "      3\n",
-       "      0.277211\n",
-       "      0.773291\n",
+       "      0.298262\n",
+       "      0.854926\n",
        "    \n",
        "    \n",
        "      40\n",
@@ -995,14 +908,14 @@
       ],
       "text/plain": [
        "    num         t      flux\n",
-       "36    3  0.477944  0.995562\n",
-       "37    2  0.383136  0.929275\n",
-       "38    2  0.267950  0.950784\n",
-       "39    3  0.277211  0.773291\n",
+       "36    4  0.364913  0.998095\n",
+       "37    2  0.196359  0.946069\n",
+       "38    4  0.209707  0.827698\n",
+       "39    3  0.298262  0.854926\n",
        "40    5  1.200000  1.900000"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1023,7 +936,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 19,
    "id": "8d09ce82-4498-4758-96ff-a07c363a07ed",
    "metadata": {},
    "outputs": [
@@ -1049,7 +962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 20,
    "id": "d2d3b12e-18a6-4d90-afcb-5ad59e1ce308",
    "metadata": {},
    "outputs": [
@@ -1082,33 +995,33 @@
        "  \n",
        "    \n",
        "      35\n",
-       "      1\n",
-       "      0.362959\n",
-       "      0.930869\n",
+       "      4\n",
+       "      0.266642\n",
+       "      0.840108\n",
        "    \n",
        "    \n",
        "      36\n",
-       "      3\n",
-       "      0.477944\n",
-       "      0.995562\n",
+       "      4\n",
+       "      0.364913\n",
+       "      0.998095\n",
        "    \n",
        "    \n",
        "      37\n",
        "      2\n",
-       "      0.383136\n",
-       "      0.929275\n",
+       "      0.196359\n",
+       "      0.946069\n",
        "    \n",
        "    \n",
        "      38\n",
-       "      2\n",
-       "      0.267950\n",
-       "      0.950784\n",
+       "      4\n",
+       "      0.209707\n",
+       "      0.827698\n",
        "    \n",
        "    \n",
        "      39\n",
        "      3\n",
-       "      0.277211\n",
-       "      0.773291\n",
+       "      0.298262\n",
+       "      0.854926\n",
        "    \n",
        "  \n",
        "\n",
@@ -1116,14 +1029,14 @@
       ],
       "text/plain": [
        "    num         t      flux\n",
-       "35    1  0.362959  0.930869\n",
-       "36    3  0.477944  0.995562\n",
-       "37    2  0.383136  0.929275\n",
-       "38    2  0.267950  0.950784\n",
-       "39    3  0.277211  0.773291"
+       "35    4  0.266642  0.840108\n",
+       "36    4  0.364913  0.998095\n",
+       "37    2  0.196359  0.946069\n",
+       "38    4  0.209707  0.827698\n",
+       "39    3  0.298262  0.854926"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1146,7 +1059,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 21,
    "id": "45c2027f-d912-432a-bb91-904c078fe0e0",
    "metadata": {},
    "outputs": [
@@ -1180,32 +1093,32 @@
        "    \n",
        "      0\n",
        "      4\n",
-       "      0.151689\n",
-       "      0.946493\n",
+       "      0.119511\n",
+       "      0.999964\n",
        "    \n",
        "    \n",
        "      1\n",
-       "      4\n",
-       "      0.427012\n",
-       "      0.937147\n",
+       "      3\n",
+       "      0.454268\n",
+       "      0.967664\n",
        "    \n",
        "    \n",
        "      2\n",
-       "      2\n",
-       "      0.244466\n",
-       "      0.954050\n",
+       "      1\n",
+       "      0.466081\n",
+       "      0.997644\n",
        "    \n",
        "    \n",
        "      3\n",
-       "      2\n",
-       "      0.326142\n",
-       "      0.923577\n",
+       "      3\n",
+       "      0.308095\n",
+       "      0.870236\n",
        "    \n",
        "    \n",
        "      4\n",
        "      1\n",
-       "      0.484898\n",
-       "      0.999998\n",
+       "      0.153233\n",
+       "      0.895102\n",
        "    \n",
        "  \n",
        "\n",
@@ -1213,14 +1126,14 @@
       ],
       "text/plain": [
        "   num         t      flux\n",
-       "0    4  0.151689  0.946493\n",
-       "1    4  0.427012  0.937147\n",
-       "2    2  0.244466  0.954050\n",
-       "3    2  0.326142  0.923577\n",
-       "4    1  0.484898  0.999998"
+       "0    4  0.119511  0.999964\n",
+       "1    3  0.454268  0.967664\n",
+       "2    1  0.466081  0.997644\n",
+       "3    3  0.308095  0.870236\n",
+       "4    1  0.153233  0.895102"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1232,7 +1145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 22,
    "id": "95b601c7-043b-4566-aeca-e40d6c70e92e",
    "metadata": {},
    "outputs": [
@@ -1266,32 +1179,32 @@
        "    \n",
        "      0\n",
        "      4\n",
-       "      0.151689\n",
-       "      0.473247\n",
+       "      0.119511\n",
+       "      0.499982\n",
        "    \n",
        "    \n",
        "      1\n",
-       "      4\n",
-       "      0.427012\n",
-       "      0.468574\n",
+       "      3\n",
+       "      0.454268\n",
+       "      0.483832\n",
        "    \n",
        "    \n",
        "      2\n",
-       "      2\n",
-       "      0.244466\n",
-       "      0.477025\n",
+       "      1\n",
+       "      0.466081\n",
+       "      0.498822\n",
        "    \n",
        "    \n",
        "      3\n",
-       "      2\n",
-       "      0.326142\n",
-       "      0.461788\n",
+       "      3\n",
+       "      0.308095\n",
+       "      0.435118\n",
        "    \n",
        "    \n",
        "      4\n",
        "      1\n",
-       "      0.484898\n",
-       "      0.499999\n",
+       "      0.153233\n",
+       "      0.447551\n",
        "    \n",
        "  \n",
        "\n",
@@ -1299,14 +1212,14 @@
       ],
       "text/plain": [
        "   num         t      flux\n",
-       "0    4  0.151689  0.473247\n",
-       "1    4  0.427012  0.468574\n",
-       "2    2  0.244466  0.477025\n",
-       "3    2  0.326142  0.461788\n",
-       "4    1  0.484898  0.499999"
+       "0    4  0.119511  0.499982\n",
+       "1    3  0.454268  0.483832\n",
+       "2    1  0.466081  0.498822\n",
+       "3    3  0.308095  0.435118\n",
+       "4    1  0.153233  0.447551"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1328,7 +1241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 23,
    "id": "b926506c-d54e-4b06-83dd-d1dd047b11e5",
    "metadata": {},
    "outputs": [],
@@ -1357,7 +1270,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['./data/fdve-ba077ef4-b5e0-47ad-bb00-7d991abb089e.hdf5', './data/fdve-e9887d59-1261-433a-a878-f6498c1c3ac4.hdf5', './data/fdve-358eb0f9-f1b9-4362-83a5-16489b5dd7e8.hdf5']\n"
+      "['./data\\\\fdve-da1e6864-80c0-4e4b-879e-26d3a83070e0.hdf5', './data\\\\fdve-c0278de8-32b1-4b81-895f-a95824f68dec.hdf5', './data\\\\fdve-10711290-c029-4698-88ef-cf0ab0f30773.hdf5']\n"
      ]
     }
    ],
@@ -1384,7 +1297,7 @@
    "source": [
     "## Working with Optimizers\n",
     "\n",
-    "Using a grid search or Monte Carlo are good ways to sample a large design space. However, the `Design` plugin can go further and enable efficient optimization of a design problem using a range of different optimization techniques. `Method` objects such as Bayesian optimization, genetic algorithms, and partical swarm optimization can all be used in the same way as the above examples. These methods use a `float` value output by the post function to evaluate the effectiveness of a solution, and then propose new solutions that should prove better. More detailed examples can be found in the notebooks associated with each technique.\n",
+    "Using a grid search or Monte Carlo is a good way to sample a large design space. However, the `Design` plugin can go further and enable efficient optimization of a design problem using a range of different optimization techniques. `Method` objects such as Bayesian optimization, genetic algorithms, and particle swarm optimization can all be used in the same way as the above examples. These methods use a `float` value output by the post function to evaluate the effectiveness of a solution, and then propose new solutions that should prove better. More detailed examples can be found in the notebooks associated with each technique.\n",
     "\n",
     "1. [Bayesian Optimization of Y-Junction](https://www.flexcompute.com/tidy3d/examples/notebooks/BayesianOptimizationYJunction/)\n",
     "\n",
@@ -1418,7 +1331,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.0"
   }
  },
  "nbformat": 4,
diff --git a/DielectricMetasurfaceAbsorber.ipynb b/DielectricMetasurfaceAbsorber.ipynb
index 512dcb3b..57cc209a 100644
--- a/DielectricMetasurfaceAbsorber.ipynb
+++ b/DielectricMetasurfaceAbsorber.ipynb
@@ -38,11 +38,10 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n"
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -75,11 +74,11 @@
    },
    "outputs": [],
    "source": [
-    "THz = 1e12  # convertion factor from Hz to THz\n",
+    "THz = 1e12  # conversion factor from Hz to THz\n",
     "freqs = np.linspace(0.4 * THz, 0.8 * THz, 100)  # freqeucny range of the simulation\n",
     "freq0 = 0.6 * THz  # central frequency\n",
     "freqw = 0.4 * THz  # width of the frequency range\n",
-    "lda0 = td.C_0 / freq0  # central wavelength\n"
+    "lda0 = td.C_0 / freq0  # central wavelength"
    ]
   },
   {
@@ -115,7 +114,7 @@
     "n_pdms = np.sqrt(eps_pdms)  # refractive index of PDMS\n",
     "PDMS = td.Medium.from_nk(\n",
     "    n=np.real(n_pdms), k=np.imag(n_pdms), freq=freq0\n",
-    ")  # define PDMS with the complex refractive index\n"
+    ")  # define PDMS with the complex refractive index"
    ]
   },
   {
@@ -143,7 +142,7 @@
     "p = 330  # unit cell size\n",
     "h = 85  # height of the cylinder\n",
     "r = 106  # radius of the cylinder\n",
-    "t = 8  # thickness of the substrate\n"
+    "t = 8  # thickness of the substrate"
    ]
   },
   {
@@ -182,7 +181,7 @@
     "# construct the silicon resonator\n",
     "cylinder = td.Structure(\n",
     "    geometry=td.Cylinder(center=[0, 0, h / 2], radius=r, length=h, axis=2), medium=Si\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -244,14 +243,12 @@
     ")\n",
     "\n",
     "# add a flux monitor to detect reflection\n",
-    "monitor_r = td.FluxMonitor(\n",
-    "    center=[0, 0, 0.4 * Lz], size=[td.inf, td.inf, 0], freqs=freqs, name=\"R\"\n",
-    ")\n",
+    "monitor_r = td.FluxMonitor(center=[0, 0, 0.4 * Lz], size=[td.inf, td.inf, 0], freqs=freqs, name=\"R\")\n",
     "\n",
     "# add a field monitor to see the field profile at the absorption peak frequency\n",
     "monitor_field = td.FieldMonitor(\n",
     "    center=[0, 0, 0], size=[td.inf, 0, lda0], freqs=[freq0], name=\"field\"\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -290,7 +287,7 @@
     "        x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
     "    ),\n",
     "    symmetry=(-1, 1, 0),\n",
-    ")  # symmetry can be used to greatly reduce the computational cost\n"
+    ")  # symmetry can be used to greatly reduce the computational cost"
    ]
   },
   {
@@ -327,10 +324,8 @@
     }
    ],
    "source": [
-    "sim.sources[0].source_time.plot_spectrum(\n",
-    "    times=np.linspace(0, sim.run_time, 2000), val=\"abs\"\n",
-    ")\n",
-    "plt.show()\n"
+    "sim.sources[0].source_time.plot_spectrum(times=np.linspace(0, sim.run_time, 2000), val=\"abs\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -372,7 +367,7 @@
     "ax1 = sim.plot_grid(y=0, ax=ax1)\n",
     "\n",
     "ax2 = sim.plot(z=h, ax=ax2)\n",
-    "ax2 = sim.plot_grid(z=h, ax=ax2)\n"
+    "ax2 = sim.plot_grid(z=h, ax=ax2)"
    ]
   },
   {
@@ -1306,7 +1301,7 @@
    "source": [
     "sim_data = web.run(\n",
     "    sim, task_name=\"all_dielectric_metasurface_absorber\", path=\"data/simulation.hdf5\"\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1358,7 +1353,7 @@
     "plt.xlabel(\"Frequency (THz)\")\n",
     "plt.ylim(0, 1)\n",
     "plt.legend((\"R\", \"T\", \"A\"))\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1395,7 +1390,7 @@
    ],
    "source": [
     "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1475,9 +1470,7 @@
     "for i in range(Nx):\n",
     "    for j in range(Ny):\n",
     "        cylinder = td.Structure(\n",
-    "            geometry=td.Cylinder(\n",
-    "                center=[i * p, j * p, h / 2], radius=r, length=h, axis=2\n",
-    "            ),\n",
+    "            geometry=td.Cylinder(center=[i * p, j * p, h / 2], radius=r, length=h, axis=2),\n",
     "            medium=Si,\n",
     "        )\n",
     "        metasurface.append(cylinder)\n",
@@ -1508,14 +1501,12 @@
     "        monitor_field,\n",
     "    ],  # we will reuse the flux monitors defined earlier\n",
     "    run_time=run_time,\n",
-    "    boundary_spec=td.BoundarySpec.all_sides(\n",
-    "        boundary=td.PML()\n",
-    "    ),  # pml is applied in all boundaries\n",
+    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),  # pml is applied in all boundaries\n",
     "    symmetry=(-1, 1, 0),\n",
     ")  # the same symmetry can be used\n",
     "\n",
     "sim.plot(z=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1869,7 +1860,7 @@
     "    task_name=\"all_dielectric_metasurface_absorber\",\n",
     "    path=\"data/simulation.hdf5\",\n",
     "    verbose=True,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1913,7 +1904,7 @@
     "plt.xlabel(\"Frequency (THz)\")\n",
     "plt.ylim(0, 1)\n",
     "plt.legend((\"R\", \"T\", \"A\"))\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1950,7 +1941,7 @@
    ],
    "source": [
     "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1985,7 +1976,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.11.0"
   },
   "nbdime-conflicts": {
    "local_diff": [
diff --git a/DirectionalCouplerSurrogate.ipynb b/DirectionalCouplerSurrogate.ipynb
index 32b82d30..a56740ed 100644
--- a/DirectionalCouplerSurrogate.ipynb
+++ b/DirectionalCouplerSurrogate.ipynb
@@ -27,28 +27,29 @@
    "outputs": [],
    "source": [
     "# Standard python and external package imports\n",
+    "import pickle\n",
+    "from pathlib import Path\n",
+    "\n",
     "import gdstk\n",
-    "import matplotlib.pyplot as plt\n",
     "import matplotlib.colors as mcolors\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from pathlib import Path\n",
-    "import pickle\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "from torch.utils.data import DataLoader\n",
     "\n",
     "# tidy3D imports\n",
     "import tidy3d as td\n",
     "import tidy3d.plugins.design as tdd\n",
-    "from tidy3d import web"
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "from tidy3d import web\n",
+    "from torch.utils.data import DataLoader"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The first step in the design proces will be to generate a dataset of simulation data for randomly sampled geometric parameters for the directional coupler. This part sets up a place to save the simulation data and the option to reuse it if you've already run through the notebook."
+    "The first step in the design process will be to generate a dataset of simulation data for randomly sampled geometric parameters for the directional coupler. This part sets up a place to save the simulation data and the option to reuse it if you've already run through the notebook."
    ]
   },
   {
@@ -71,7 +72,7 @@
    "source": [
     "## Simulation Setup\n",
     "\n",
-    "Before running simulatinons, we need to set up general parameters that will remain unchanged for all of the simulations. We set up the desired frequency range to use, optical constants for the materials in the coupler, and parameters controlling fixed aspects of the coupler geometry. Finally, we specify the bounds for the parameters we will sample to create the training dataset."
+    "Before running simulations, we need to set up general parameters that will remain unchanged for all of the simulations. We set up the desired frequency range to use, optical constants for the materials in the coupler, and parameters controlling fixed aspects of the coupler geometry. Finally, we specify the bounds for the parameters we will sample to create the training dataset."
    ]
   },
   {
@@ -423,7 +424,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here, we run the Monte Carlo simulations or load previous simulation data from a past run thruogh the notebook. If the simulations are run again, we overwrite the simulation data file to be loaded again in the future. By setting the `use_previous_simulation_data` flag, you can opt to use past data or rerun the simulations. If any of the parameters controlling the general geometry or number of simulations are changed, you should rerun the simulations to ensure they are accurate for the result of the notebook."
+    "Here, we run the Monte Carlo simulations or load previous simulation data from a past run through the notebook. If the simulations are run again, we overwrite the simulation data file to be loaded again in the future. By setting the `use_previous_simulation_data` flag, you can opt to use past data or rerun the simulations. If any of the parameters controlling the general geometry or number of simulations are changed, you should rerun the simulations to ensure they are accurate for the result of the notebook."
    ]
   },
   {
@@ -491,9 +492,7 @@
    ],
    "source": [
     "top_center_freq = abs(df[\"top\"].apply(lambda x: x.sel(f=freq0, method=\"nearest\"))) ** 2\n",
-    "bot_center_freq = (\n",
-    "    abs(df[\"bottom\"].apply(lambda x: x.sel(f=freq0, method=\"nearest\"))) ** 2\n",
-    ")\n",
+    "bot_center_freq = abs(df[\"bottom\"].apply(lambda x: x.sel(f=freq0, method=\"nearest\"))) ** 2\n",
     "\n",
     "\n",
     "# Compute MSE error function for fitness\n",
@@ -583,9 +582,7 @@
     "\n",
     "# Read the dataframe back into a Result that we can use as a pytorch Dataset\n",
     "expanded_results = tdd.Result()\n",
-    "expanded_results = expanded_results.from_dataframe(\n",
-    "    expanded_df, dims=list(results.dims) + [\"freq\"]\n",
-    ")"
+    "expanded_results = expanded_results.from_dataframe(expanded_df, dims=list(results.dims) + [\"freq\"])"
    ]
   },
   {
@@ -615,9 +612,7 @@
     ")\n",
     "\n",
     "training_dataloader = DataLoader(training_dataset, batch_size=batch_size, shuffle=True)\n",
-    "validation_dataloader = DataLoader(\n",
-    "    validation_dataset, batch_size=batch_size, shuffle=False\n",
-    ")\n",
+    "validation_dataloader = DataLoader(validation_dataset, batch_size=batch_size, shuffle=False)\n",
     "testing_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
    ]
   },
@@ -713,9 +708,7 @@
     "    if loss_fn_user is None:\n",
     "        loss_fn_user = loss_fn\n",
     "\n",
-    "    loss = loss_fn_user(\n",
-    "        network(inputs.type(torch.FloatTensor)), labels.type(torch.FloatTensor)\n",
-    "    )\n",
+    "    loss = loss_fn_user(network(inputs.type(torch.FloatTensor)), labels.type(torch.FloatTensor))\n",
     "\n",
     "    return loss\n",
     "\n",
@@ -735,9 +728,7 @@
     "    \"\"\"Train a network according to hyperparameters specification for a given number of epochs.\"\"\"\n",
     "\n",
     "    # build network and set to training mode\n",
-    "    network = VariableANN(\n",
-    "        network_input_size, hyperparameters[\"hidden\"], hyperparameters[\"dropout\"]\n",
-    "    )\n",
+    "    network = VariableANN(network_input_size, hyperparameters[\"hidden\"], hyperparameters[\"dropout\"])\n",
     "    network.train()\n",
     "    opt = optim.Adam(\n",
     "        network.parameters(),\n",
@@ -754,8 +745,6 @@
     "    training_loss_by_epoch[0] = avg_training_loss\n",
     "    validation_loss_by_epoch[0] = avg_validation_loss\n",
     "\n",
-    "    final_training_loss = np.inf\n",
-    "    final_validation_loss = np.inf\n",
     "    for epoch_idx in range(0, epochs):\n",
     "        training_loss_by_iteration = []\n",
     "\n",
@@ -801,12 +790,9 @@
     "num_trial_networks = 75\n",
     "\n",
     "\n",
-    "def hyperparemeter_fn(\n",
-    "    dropout, learning_rate_log, weight_decay_log, **hidden_layer_sizes\n",
-    "):\n",
+    "def hyperparemeter_fn(dropout, learning_rate_log, weight_decay_log, **hidden_layer_sizes):\n",
     "    hidden_layer_sizes = [\n",
-    "        hidden_layer_sizes[f\"hidden_layer_size_{idx}\"]\n",
-    "        for idx in range(0, len(hidden_layer_sizes))\n",
+    "        hidden_layer_sizes[f\"hidden_layer_size_{idx}\"] for idx in range(0, len(hidden_layer_sizes))\n",
     "    ]\n",
     "\n",
     "    hyperparam = {\n",
@@ -822,12 +808,8 @@
     "\n",
     "\n",
     "param_dropout = tdd.ParameterAny(name=\"dropout\", allowed_values=tuple(allowed_dropout))\n",
-    "param_lr_log = tdd.ParameterFloat(\n",
-    "    name=\"learning_rate_log\", span=allowed_learning_rate_log_range\n",
-    ")\n",
-    "param_wd_log = tdd.ParameterFloat(\n",
-    "    name=\"weight_decay_log\", span=allowed_weight_decay_log_range\n",
-    ")\n",
+    "param_lr_log = tdd.ParameterFloat(name=\"learning_rate_log\", span=allowed_learning_rate_log_range)\n",
+    "param_wd_log = tdd.ParameterFloat(name=\"weight_decay_log\", span=allowed_weight_decay_log_range)\n",
     "params_hyperparameter = [\n",
     "    tdd.ParameterAny(\n",
     "        name=f\"hidden_layer_size_{idx}\",\n",
@@ -891,20 +873,15 @@
     }
    ],
    "source": [
-    "# Extract the best parameters from the design space result and formulate the hyperparameter dictionary for the optimziation\n",
+    "# Extract the best parameters from the design space result and formulate the hyperparameter dictionary for the optimization\n",
     "best_parameters = dict(\n",
     "    zip(\n",
     "        network_hyperparameter_results.dims,\n",
-    "        network_hyperparameter_results.coords[\n",
-    "            np.argmax(network_hyperparameter_results.values)\n",
-    "        ],\n",
+    "        network_hyperparameter_results.coords[np.argmax(network_hyperparameter_results.values)],\n",
     "    )\n",
     ")\n",
     "best_hyperparam = {\n",
-    "    \"hidden\": [\n",
-    "        best_parameters[f\"hidden_layer_size_{idx}\"]\n",
-    "        for idx in range(0, num_hidden_layers)\n",
-    "    ],\n",
+    "    \"hidden\": [best_parameters[f\"hidden_layer_size_{idx}\"] for idx in range(0, num_hidden_layers)],\n",
     "    \"dropout\": best_parameters[\"dropout\"],\n",
     "    \"learning_rate\": 10 ** best_parameters[\"learning_rate_log\"],\n",
     "    \"weight_decay\": 10 ** best_parameters[\"weight_decay_log\"],\n",
@@ -929,9 +906,7 @@
     "avg_l1_validation_loss = evaluate_loss_from_loader(\n",
     "    validation_dataloader, optimized_network, l1_loss_fn\n",
     ")\n",
-    "avg_l1_test_loss = evaluate_loss_from_loader(\n",
-    "    testing_dataloader, optimized_network, l1_loss_fn\n",
-    ")\n",
+    "avg_l1_test_loss = evaluate_loss_from_loader(testing_dataloader, optimized_network, l1_loss_fn)\n",
     "\n",
     "print(f\"Average L1 validation loss: {avg_l1_validation_loss}\")\n",
     "print(f\"Average L1 test loss: {avg_l1_test_loss}\")\n",
@@ -987,23 +962,15 @@
    "source": [
     "# Set up coupler geometry in a pytorch tensor and initialize to the mean values\n",
     "# for both the coupling length and the waveguide spacing\n",
-    "coupler_geometry = torch.Tensor(\n",
-    "    [np.mean(wg_spacing_coup_range), np.mean(coup_length_range)]\n",
-    ")\n",
+    "coupler_geometry = torch.Tensor([np.mean(wg_spacing_coup_range), np.mean(coup_length_range)])\n",
     "coupler_geometry.requires_grad = True  # we will track the gradient in the optimizer\n",
     "\n",
-    "freq_tensor = torch.Tensor([freq0]).unsqueeze(\n",
-    "    dim=0\n",
-    ")  # design coupler at center frequency\n",
+    "freq_tensor = torch.Tensor([freq0]).unsqueeze(dim=0)  # design coupler at center frequency\n",
     "\n",
     "# Set up bounds to clamp the design parameters between so we stay in the parameter\n",
     "# space learned by the neural network surrogate\n",
-    "min_design_tensor = torch.Tensor(\n",
-    "    [np.min(wg_spacing_coup_range), np.min(coup_length_range)]\n",
-    ")\n",
-    "max_design_tensor = torch.Tensor(\n",
-    "    [np.max(wg_spacing_coup_range), np.max(coup_length_range)]\n",
-    ")\n",
+    "min_design_tensor = torch.Tensor([np.min(wg_spacing_coup_range), np.min(coup_length_range)])\n",
+    "max_design_tensor = torch.Tensor([np.max(wg_spacing_coup_range), np.max(coup_length_range)])\n",
     "\n",
     "# Set up the transmission goal we can use in the loss function and define an MSE loss\n",
     "desired_transmission = torch.Tensor(\n",
@@ -1044,9 +1011,7 @@
     "\n",
     "final_geometry = torch.clamp(coupler_geometry, min_design_tensor, max_design_tensor)\n",
     "final_geometry_numpy = final_geometry.detach().numpy()\n",
-    "final_geometry_numpy_display = [\n",
-    "    float(np.round(float(num), 4)) for num in final_geometry_numpy\n",
-    "]\n",
+    "final_geometry_numpy_display = [float(np.round(float(num), 4)) for num in final_geometry_numpy]\n",
     "\n",
     "plt.plot(coupler_loss_curve, linewidth=2, color=\"g\")\n",
     "plt.xlabel(\"Iteration\")\n",
@@ -1418,9 +1383,7 @@
    "source": [
     "def make_prediction(network, wg_spacing_coup, coup_length, freq):\n",
     "    \"\"\"Use the neural network surrogate to estimate the output coupling power fractions.\"\"\"\n",
-    "    feature_array = np.expand_dims(\n",
-    "        np.array([wg_spacing_coup, coup_length, freq]), axis=0\n",
-    "    )\n",
+    "    feature_array = np.expand_dims(np.array([wg_spacing_coup, coup_length, freq]), axis=0)\n",
     "    input_tensor = torch.Tensor(feature_array)\n",
     "    prediction = np.squeeze(network(input_tensor).detach().numpy())\n",
     "\n",
@@ -1459,7 +1422,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Since the neural network was trained to predict output powers for multiple frequencies, we can compare the spectrum preduction for our final design to the ground truth simulation data. We also summarize the mean squared and mean absolute error across the spectrum."
+    "Since the neural network was trained to predict output powers for multiple frequencies, we can compare the spectrum prediction for our final design to the ground truth simulation data. We also summarize the mean squared and mean absolute error across the spectrum."
    ]
   },
   {
@@ -1558,7 +1521,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.8"
+   "version": "3.11.0"
   }
  },
  "nbformat": 4,
diff --git a/Dispersion.ipynb b/Dispersion.ipynb
index 01324aa6..410cdc6c 100644
--- a/Dispersion.ipynb
+++ b/Dispersion.ipynb
@@ -27,11 +27,10 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
-    "from tidy3d import web\n"
+    "from tidy3d import web"
    ]
   },
   {
@@ -74,7 +73,7 @@
     "spacing = 1 * lambda_range[-1]\n",
     "\n",
     "# simulation size\n",
-    "sim_size = Lx, Ly, Lz = (1.0, 1.0, 4 * spacing + sum(t_slabs))\n"
+    "sim_size = Lx, Ly, Lz = (1.0, 1.0, 4 * spacing + sum(t_slabs))"
    ]
   },
   {
@@ -105,8 +104,8 @@
     "mat1 = td.Medium(permittivity=4.0, conductivity=0.005)\n",
     "\n",
     "# active material with n & k values at a specified frequency or wavelength\n",
-    "# note: negative k value corresponds to a gain medium; it is only allowed \n",
-    "#       when `allow_gain` is set to be True \n",
+    "# note: negative k value corresponds to a gain medium; it is only allowed\n",
+    "#       when `allow_gain` is set to be True\n",
     "mat2 = td.Medium.from_nk(n=3.0, k=-0.1, freq=freq0, allow_gain=True)\n",
     "\n",
     "# weakly dispersive material defined by dn_dwvl at a given frequency\n",
@@ -116,7 +115,7 @@
     "mat4 = td.material_library[\"BK7\"][\"Zemax\"]\n",
     "\n",
     "# put all together\n",
-    "mat_slabs = [mat1, mat2, mat3, mat4]\n"
+    "mat_slabs = [mat1, mat2, mat3, mat4]"
    ]
   },
   {
@@ -153,14 +152,14 @@
     "        medium=mat,\n",
     "    )\n",
     "    slabs.append(slab)\n",
-    "    slab_position += t\n"
+    "    slab_position += t"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We must now define the excitation conditions and field monitors. We will excite the slab using a normally incident (along z) planewave, polarized along the x direciton."
+    "We must now define the excitation conditions and field monitors. We will excite the slab using a normally incident (along z) planewave, polarized along the x direction."
    ]
   },
   {
@@ -178,7 +177,7 @@
     "    center=(0, 0, -Lz / 2 + spacing),\n",
     "    direction=\"+\",\n",
     "    pol_angle=0,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -202,7 +201,7 @@
     "    size=(td.inf, td.inf, 0),\n",
     "    freqs=monitor_freqs,\n",
     "    name=\"flux\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -222,7 +221,7 @@
    "source": [
     "boundary_spec = td.BoundarySpec(\n",
     "    x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -249,7 +248,7 @@
     "    monitors=[monitor],\n",
     "    run_time=t_stop,\n",
     "    boundary_spec=boundary_spec,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -272,18 +271,12 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T17:40:39.117056Z",
-     "iopub.status.busy": "2023-08-18T17:40:39.116912Z",
-     "iopub.status.idle": "2023-08-18T17:40:39.357256Z",
-     "shell.execute_reply": "2023-08-18T17:40:39.356745Z"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -294,32 +287,26 @@
    ],
    "source": [
     "sim.plot(x=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next, we use `Simulation.plot_eps()` to vizualize the permittivity of the stack. However, because the stack contains dispersive materials, we need to specify the `freq` of interest as an argument to the plotting tool.  Here we show the permittivity at the lowest and highest frequences in the range of interest.  Note that in this case, the real part of the permittivity (being plotted) only changes slightly between the two frequencies on the dispersive material.  However, for other materials with more dispersion, the effect can be much more prominent."
+    "Next, we use `Simulation.plot_eps()` to visualize the permittivity of the stack. However, because the stack contains dispersive materials, we need to specify the `freq` of interest as an argument to the plotting tool.  Here we show the permittivity at the lowest and highest frequencies in the range of interest.  Note that in this case, the real part of the permittivity (being plotted) only changes slightly between the two frequencies on the dispersive material.  However, for other materials with more dispersion, the effect can be much more prominent."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T17:40:39.359274Z",
-     "iopub.status.busy": "2023-08-18T17:40:39.359098Z",
-     "iopub.status.idle": "2023-08-18T17:40:39.824781Z",
-     "shell.execute_reply": "2023-08-18T17:40:39.824295Z"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -334,32 +321,26 @@
     "fig, axes = plt.subplots(1, len(freqs_plot), tight_layout=True, figsize=(12, 4))\n",
     "for ax, freq_plot in zip(axes, freqs_plot):\n",
     "    sim.plot_eps(x=0, freq=freq_plot, ax=ax)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can also take a look at the source to make sure it's defined correctly over our frequency range of interst."
+    "We can also take a look at the source to make sure it's defined correctly over our frequency range of interest."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T17:40:39.826859Z",
-     "iopub.status.busy": "2023-08-18T17:40:39.826683Z",
-     "iopub.status.idle": "2023-08-18T17:40:40.281674Z",
-     "shell.execute_reply": "2023-08-18T17:40:40.281177Z"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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ycnL01VdfqVevXpowYYLX65cvX660tDR98cUX6tevX9N8iAigYhdlNZWG128AABB5gwcPtv798ccf6+2331b79u2tnz59+kiSNdz69ddf64orrlC3bt2UlJSk3NxcSdLu3bujfu/hoGIXZS4z2B0l2AEATm6x7Wz6n42nNtt7hyMxMdH6d1lZmS6++GLdd999fueZ+7pefPHF6tKlix577DFlZ2fL7XarX79+Lb7xgmAXZWalrvoYwQ4AcHKz2WxhDYe2FGeddZb+9a9/KTc3VzEx/lHo4MGD2rp1qx577DGdd955kqR333032rfZKAzFRhlDsQAANK/Zs2fr0KFDuuKKK/T+++9r+/btev311zVt2jS5XC517NhRp5xyih599FFt27ZNb731lubMmdPct90gBLsosyp2DMUCANAssrOztW7dOrlcLo0aNUr9+/fXNddco5SUFNntdtntdq1cuVKbNm1Sv379dO211+qBBx5o7ttuEIZio6ymdgi2hqFYAACiYu3atX7HevbsqVWrVgV9TX5+vr744guvY/WXWMnNzQ17yZVooGIXZTVVDMUCAIDIiEqwW7p0qXJzcxUfH6+8vDxt3Lgx6LkjRoyQzWbz+xk3bpx1ztSpU/2eHzNmTDQ+ygkzK3XVx1r2AocAAODkE/Gh2H/+85+aM2eOli1bpry8PC1ZskSjR4/W1q1blZ6e7nf+qlWrvFqJDx48qDPPPFOXXnqp13ljxozRihUrrMdxcXGR+xBNyKrYMccOAAA0sYhX7BYvXqwZM2Zo2rRp6tu3r5YtW6aEhAQtX7484PmdOnVSZmam9bN69WolJCT4Bbu4uDiv8zp27Bjpj9IkzIqdq1pyuwh3AACg6UQ02FVVVWnTpk3Kz8+ve0O7Xfn5+Vq/fn2DrvH4449r4sSJXgsLSp6JkOnp6erdu7d++9vf6uDBg0GvUVlZqdLSUq+f5lJ/bh3z7AAAQFOKaLA7cOCAXC6XMjIyvI5nZGSoqKjouK/fuHGjPvvsM/3qV7/yOj5mzBg99dRTWrNmje677z79+9//1oUXXiiXyxXwOosWLVJycrL1k5OT0/gPdYK8gh2dsQCAk1BL7AZtidzu6M+nb9HLnTz++OPq37+/hg4d6nV84sSJ1r/79++vAQMGqHv37lq7dq1Gjhzpd525c+d6LSxYWlrabOGufrBj9wkAwMkkNjZWNptN+/fvV1pammy2k2/XiWgwDENVVVXav3+/7Ha7nE5n1N47osEuNTVVDodDxcXFXseLi4uVmZkZ8rXl5eVauXKlFixYcNz36datm1JTU7Vt27aAwS4uLq7FNFe46lfsaKAAAJxEHA6HTjvtNH333XfatWtXc99Oi5eQkKDOnTvLbo/e6nIRDXZOp1ODBw/WmjVrNH78eEmesuSaNWtUUFAQ8rXPPvusKisrddVVVx33fb777jsdPHjQ2ri3pXK7DLmq6x4zxw4AcLJp3769evbsqerq6uOf3IY5HA7FxMREvaoZ8aHYOXPmaMqUKRoyZIiGDh2qJUuWqLy8XNOmTZMkTZ48WaeeeqoWLVrk9brHH39c48eP1ymnnOJ1vKysTHfeeacmTJigzMxMbd++XTfeeKN69Oih0aNHR/rjnBDfIMdQLADgZORwOORwOJr7NhBAxIPd5Zdfrv3792vevHkqKirSwIEDVVhYaDVU7N69269EuXXrVr377rt64403/K7ncDj0ySef6Mknn1RJSYmys7M1atQoLVy4sMUMtwbjqvIJdgzFAgCAJmQz2mBrS2lpqZKTk3X48GElJSVF7X2PFNVoWf5e6/H4h1LV84J2UXt/AADQMkQqi7BXbBTV+FTsathWDAAANCGCXRT5rlvHUCwAAGhKBLso8m2eoCsWAAA0JYJdFNEVCwAAIolgF0V+FTuGYgEAQBMi2EVRTaXvY4IdAABoOgS7KPJrnmAoFgAANCGCXRT5LlDMUCwAAGhKBLso8q3QMRQLAACaEsEuiuiKBQAAkUSwiyJXbbBzxHoe+865AwAAOBEEuygyK3bxyZ4/ezVbigEAgCZEsIsiM9i1S/H82anYAQCApkSwi6K6ip3D85hgBwAAmhDBLorMIFc3FEuwAwAATYdgF0U1tevYtUtmKBYAADQ9gl0UUbEDAACRRLCLIrNiF9fB82f33YkCAADgRBDsosis2DkTbJIkt6thrys/6FL1UZZGAQAAoRHsosjsio2tDXYyJMMdumpXtt+lvwz/Xk9MKI707QEAgJMcwS6KzGDnTKz7sx+varfj30clSSW7ayJ2XwAAoHUg2EWROacuNt5mHTOOM8JacYghWAAA0DAEuyhyVXuCXUy9YOd2hR6KrThUV9I73rkAAKBtI9hFkVGb0RzOxlXs6KIFAAChEOyiyFVTW7FzNrxiV16vYldDsAMAACEQ7KKormLnfyyY8n11J7gqI3BTAACg1SDYRZG7trHVEWuTaot27hBDsYZhqLSoXsWukoodAAAIjmAXReawq81hk93hOWaEGIqtLDVUXVH3PMEOAACEQrCLIrNiZ4+R7I7j7z5Rutd77TqCHQAACIVgF0Vmxc4RY5Ot9i8fao5d/WFYiWAHAABCI9hFiWEYVsXO5vD8SKG3FPOt2LHcCQAACIVgFyX1K3MNHYotK6ZiBwAAGo5gFyX1A5y9XvNEqHXszJ0qTKxjBwAAQiHYRUn9AGePUYPm2Lm9R2JVc4xgBwAAgiPYRYm7uu7fdodNttqh2FBbirlrvIOci6FYAAAQAsEuSnwrdg0ZivWdf8dQLAAACIVgFyVmSLM5JJutbrmTUM0TvhU7micAAEAoBLsoMUOaWamzN2go1vsxy50AAIBQohLsli5dqtzcXMXHxysvL08bN24Meu4TTzzhqWjV+4mPj/c6xzAMzZs3T1lZWWrXrp3y8/P19ddfR/pjnBBr14naQFfXPBFiKNavYheRWwMAAK1ExIPdP//5T82ZM0fz58/Xhx9+qDPPPFOjR4/Wvn37gr4mKSlJe/futX6++eYbr+fvv/9+PfTQQ1q2bJk2bNigxMREjR49WseOHYv0x2k0cy6dPdbz2JpjF6piVztMG5vgCYM1lSFOBgAAbV7Eg93ixYs1Y8YMTZs2TX379tWyZcuUkJCg5cuXB32NzWZTZmam9ZORkWE9ZxiGlixZottuu02XXHKJBgwYoKeeekrff/+9XnjhhUh/nEYzQ5pVsTMXKK4J9oq6ip0z0Qx2kbs/AABw8otosKuqqtKmTZuUn59f94Z2u/Lz87V+/fqgrysrK1OXLl2Uk5OjSy65RJ9//rn13M6dO1VUVOR1zeTkZOXl5QW9ZmVlpUpLS71+os1d7TvHzvM71JZiZuhzJni+JpY7AQAAoUQ02B04cEAul8ur4iZJGRkZKioqCvia3r17a/ny5fp//+//6e9//7vcbrfOOeccfffdd5JkvS6cay5atEjJycnWT05Ozol+tLBZFbsYc45dbfNEiK5Yl2/FjuYJAAAQQovrih02bJgmT56sgQMHavjw4Vq1apXS0tL017/+tdHXnDt3rg4fPmz9fPvtt014xw1jzbGL8Ty2NWCOnRn6zIody50AAIBQIhrsUlNT5XA4VFxc7HW8uLhYmZmZDbpGbGysBg0apG3btkmS9bpwrhkXF6ekpCSvn2jz7Ypt0F6xPhU7hmIBAEAoEQ12TqdTgwcP1po1a6xjbrdba9as0bBhwxp0DZfLpU8//VRZWVmSpK5duyozM9PrmqWlpdqwYUODr9kc/Naxa8BQbF1XLBU7AABwfDGRfoM5c+ZoypQpGjJkiIYOHaolS5aovLxc06ZNkyRNnjxZp556qhYtWiRJWrBggc4++2z16NFDJSUleuCBB/TNN9/oV7/6lSRPx+w111yju+66Sz179lTXrl11++23Kzs7W+PHj4/0x2k0vzl2DWieMPy6Ygl2AAAguIgHu8svv1z79+/XvHnzVFRUpIEDB6qwsNBqfti9e7fs9rrC4Q8//KAZM2aoqKhIHTt21ODBg/Xee++pb9++1jk33nijysvLNXPmTJWUlOjcc89VYWGh30LGLYlVsTPn2DVgSzFX7fCttY4dzRMAACCEiAc7SSooKFBBQUHA59auXev1+I9//KP++Mc/hryezWbTggULtGDBgqa6xYjzXcfOrNyFGoo1d6VguRMAANAQLa4rtrXyrdjZrYpdqOYJz2+WOwEAAA1BsIsSI8jOE0aoLcWsOXY0TwAAgOMj2EWJuXSJLYw5dnU7T9A8AQAAjo9gFyVmSHPEeK9jZ4QYijWHaWOtOXaRuz8AAHDyI9hFiRng6vaK9QS8UDtPuH3n2FUaMgyqdgAAIDCCXZSYjRDm3DpzKLYhFTtzKFaSXNWRuT8AAHDyI9hFSWP2inXXhjizeUJiyRMAABAcwS5KDL85djav44FYc+za2aTaoh0NFAAAIBiCXZRYXbG1lbq6il2Iodja0GePsSkmjs5YAAAQGsEuSnzXsbPbvY/7ne82rDXu7A4pxkmwAwAAoRHsosSaYxfreXy8BYrrr29nj7HJEef5N7tPAACAYAh2UWINqzq817Ezd5fwP7/uuD1G1lAszRMAACAYgl2UWHvFmnPs7KHXsfOt2DHHDgAAHA/BLkrMoGb33XkiSPOEV8XOIYIdAAA4LoJdlPitY3ec5glz6FY2z/CtPbZ2KJYFigEAQBAEuygxFxu25tjVVu7cQYOd99CtozbYBZuTBwAAQLCLkmAVO3eQLcXMwGcGOkft61zVBDsAABAYwS5K3L7r2Flz7IKc77OgsTkU62YoFgAABEGwixJraNWq2NWuY3ecOXZmEHRYc+yo2AEAgMAIdlFSF9S8fwcbinX5BEFHrPdxAAAAXwS7KPFd7sTceSJY84ThuzxKDEOxAAAgNIJdlPgOxVp7xQaZY2dW5hy+FTuGYgEAQBAEuyjxbZ6wHXeBYtWeV1uxY44dAAA4DoJdlPhV7Myh2JrQ59dV7BiKBQAAoRHsoiTsil3t+TarK9bzmOYJAAAQDMEuSoLNsQvePFFbsasNdI4YhmIBAEBoBLsoCdYVG7x5ovZ8nzl2DMUCAIBgCHZR4rv3q7WOXZChVd8KH12xAADgeAh2UeK7k4S180TQLcW8z7cqdsyxAwAAQRDsosTcYaKuK7b2eLBg5/Kt2Jlz7CJ2iwAA4CRHsIsSqwJnzrEzFygOsqWY7/lmwGMoFgAABEOwixK/OXYxobcU859jZzZPEOwAAEBgBLso8V2XrsEVO2sdu9qh2CALGgMAABDsosR3Xbrw59h5fjMUCwAAgiHYRUnQrtigQ7G151tz7FjHDgAAhEawixJzKzCbNcfO89sddCjWe05eXVcsFTsAABAYwS5KzMqcw7cr9njr2MX47BVLsAMAAEEQ7KLEd86cORQbtCvW5dNFay1QHLl7BAAAJ7eoBLulS5cqNzdX8fHxysvL08aNG4Oe+9hjj+m8885Tx44d1bFjR+Xn5/udP3XqVNlsNq+fMWPGRPpjnBAzkJldsWZgM9xBhmLNvWVjfbpiqdgBAIAgIh7s/vnPf2rOnDmaP3++PvzwQ5155pkaPXq09u3bF/D8tWvX6oorrtDbb7+t9evXKycnR6NGjdKePXu8zhszZoz27t1r/fzjH/+I9Ec5IeacOYe188Rxmieqfde98z4OAADgK+LBbvHixZoxY4amTZumvn37atmyZUpISNDy5csDnv/0009r1qxZGjhwoPr06aO//e1vcrvdWrNmjdd5cXFxyszMtH46duwY6Y9yQqx17Mw5dg7v48HOr5tjx5ZiAAAgtIgGu6qqKm3atEn5+fl1b2i3Kz8/X+vXr2/QNSoqKlRdXa1OnTp5HV+7dq3S09PVu3dv/fa3v9XBgweb9N6bkuE2rCYJswJnNk+E2xVrHgcAAPAVE8mLHzhwQC6XSxkZGV7HMzIy9OWXXzboGjfddJOys7O9wuGYMWP085//XF27dtX27dt1yy236MILL9T69evlcDj8rlFZWanKykrrcWlpaSM/UePUr8rZrTl2tUOxQRcorj2frlgAANBAEQ12J+ree+/VypUrtXbtWsXHx1vHJ06caP27f//+GjBggLp37661a9dq5MiRftdZtGiR7rzzzqjccyD1q2x2a46d53ewOXYun71izYDHUCwAAAgmokOxqampcjgcKi4u9jpeXFyszMzMkK998MEHde+99+qNN97QgAEDQp7brVs3paamatu2bQGfnzt3rg4fPmz9fPvtt+F9kBNUf4kSu98cu8AVOKP2NQ6/OXZU7AAAQGARDXZOp1ODBw/2anwwGyGGDRsW9HX333+/Fi5cqMLCQg0ZMuS47/Pdd9/p4MGDysrKCvh8XFyckpKSvH6iqX54q5tjF3oo1m+nitqhWMMVfIkUAADQtkW8K3bOnDl67LHH9OSTT2rLli367W9/q/Lyck2bNk2SNHnyZM2dO9c6/7777tPtt9+u5cuXKzc3V0VFRSoqKlJZWZkkqaysTDfccIP++9//ateuXVqzZo0uueQS9ejRQ6NHj470x2kUq2Jnqz/Hrva5YBW7IF2xkuRikWIAABBAxOfYXX755dq/f7/mzZunoqIiDRw4UIWFhVZDxe7du2W31+XLRx55RFVVVfrFL37hdZ358+frjjvukMPh0CeffKInn3xSJSUlys7O1qhRo7Rw4ULFxcVF+uM0iu8uEp5/N6xiVzfHrt71qg3JaQvwKgAA0JZFpXmioKBABQUFAZ9bu3at1+Ndu3aFvFa7du30+uuvN9GdRYfvvq9SveVOglTfrNc4AlTsmGcHAAACYK/YKPDdJ1aqmzsnBZ4zF2inCjMM0hkLAAACIdhFgW/1TZLs9rp/B9p9wtqpot5rWKQYAACEQrCLAt9dJCTv6l2geXZmlc9cmFiq64xlKBYAAARCsIsC310kpLo5dp7nAw3F1r6mfsWORYoBAEAIBLsoCFSxqz/EGmj3CXeN/7w8KnYAACAUgl0UBOqKtdev2AVsnqg9L9AcO4IdAAAIgGAXBYGqb15dsQGbJ2pfU2+OXd22Yk1+iwAAoBUg2EWBK9A6dra65UsCdsUG6qSNMZ+jYgcAAPwR7KLAWpMu1vu4GeyMgM0T/lW+uoodwQ4AAPgj2EWB1RXr8N4GzHzsDrTcScg5dk1/jwAA4ORHsIsCs9nB7rOBmznPLuQcO7piAQBAAxHsoiBQV6xUt/xJyHXsYljHDgAANAzBLgpcAebLSZKtdluxUOvY1V/I2M4cOwAAEALBLgqs5olgFbsA69iZ24x5Vexqh2JZxw4AAARCsIuCui3FvI+HnGNXG/bqL2RsdcXWNPUdAgCA1oBgFwV1S5f4VOxqh2IDrWNnhr36Cxmz8wQAAAiFYBcF5vIkvsHOqtiFGIo15+F5Xu/5zRw7AAAQCMEuCoI3T3h+B9x5wlzuJEDFjq5YAAAQCMEuCsylS/yaJ2ofGwEWKK6r2NU73xyKZUsxAAAQAMEuCgJtDybVNUYECmpua46df1csQ7EAACAQgl0U1HXF+s6xC1yxMwxDqs1u9bti7SxQDAAAQiDYRUHQLcWCzLGrv/wJXbEAAKChCHZR4DrOlmK+XbHuehU8uz3AUCxz7AAAQAAEuyio23nC+7jdEXgdO6Pe3rGBK3ZNf48AAODkR7CLgmALFJtDsfWDnOQ9586rK5Z17AAAQAgEuyg43pZibnfg86W6qp4kOZxm8wTBDgAA+CPYRUHQLcXMrlifvV/rz7mrX7GLMYNdFcEOAAD4I9hFget4W4oZPs0T9bti631DZsWuprLJbxEAALQCBLsT9OmqMv1r1n6V7Q+wL1itYM0Tttqc59c8UW/XCZutLgzGxNUGOyp2AAAgAILdCTDchgrn/aAd7xzTc7/Z71d5M7mDLncSZIHi2mYKm8+346gNdq5Kgh0AAPBHsDsB+7+qW3dk/9Zqffqv8oDnuWuDmt3hfTxYV6zZTFG/cUKqm2NXQ7ADAAABEOxOwK71x7web1t7NOB5VsUuNtgCxd7nmztP+FbszKFYumIBAEAgBLsTsOs9T7DrPDROklRx0B3wPFdN4C3FzEl2vsudmF2xNp8Kn8Pp+U3FDgAABEKwa6SaSkN7PqySJPW9KEGSVH4wcAOFWbFzNHRLMXPdO7vPUGwcQ7EAACA4gl0j7f2kUjWVhhLT7MoZGi/JU7EL1EDhrg5csaubY+d93OqK9anYWUOxdMUCAIAACHaN9F1tte60s+KUmOr5M9ZUGqoqCxDsjrdAsd/OE0G6YmmeAAAAIfjO+kID7fnIs0rwaWfFKTbeLmeiTVXlhsoPuhTXwTuRBdtSTOY6dn5z7GrP9+mKtbYUq/Isalx/jTvP+xj69+ISlXzrUlKWQ8OvS7E6aQEAQOtHsGsEt8vQns2eYHfqWZ7GiYRTHKoqr1HFQbc65fqcH7Ri5/ntu9zJ8bpiJU+4i4nzfv6b/1bqgyfLrMftMxzK+2VSQz8WAAA4yTEU2wgHvq5WVZkhZ6JNaT1jJUmJp3j+lIEaKFxBmids9iBDse7A697VD3aBhmO//9gTNhM6ee7lv4+WquJQ8B0xAABA6xKVYLd06VLl5uYqPj5eeXl52rhxY8jzn332WfXp00fx8fHq37+/Xn31Va/nDcPQvHnzlJWVpXbt2ik/P19ff/11JD+Cl+8+9ASo7DPjrCpcwimeFBYo2LmDLHdy/HXsfCp8MbKGbwM1UHz/sWfe37BfJym9T6yqygy9t6y0QZ8JAACc/CIe7P75z39qzpw5mj9/vj788EOdeeaZGj16tPbt2xfw/Pfee09XXHGFpk+fro8++kjjx4/X+PHj9dlnn1nn3H///XrooYe0bNkybdiwQYmJiRo9erSOHTsW8JpNbevrnoWIO+fVjYUmpnpSWsUB/7Xs3LUbVPgOxZp/fbfPcifBumJtNlvQ/WINt6G9n9YODw+K04gbUiRJH//fMh3aWS0AAND6RTzYLV68WDNmzNC0adPUt29fLVu2TAkJCVq+fHnA8//0pz9pzJgxuuGGG3T66adr4cKFOuuss/TnP/9Zkqdat2TJEt1222265JJLNGDAAD311FP6/vvv9cILL0T64+jQrmp9t6lSNnvd+nVS6KHYYFuK2YMsd2KdH+DbMZshfPeLPbSzRpWlhmLibUrtGasuefHqPjxe7hrprftLrKohAABovSLaPFFVVaVNmzZp7ty51jG73a78/HytX78+4GvWr1+vOXPmeB0bPXq0Fdp27typoqIi5efnW88nJycrLy9P69ev18SJE/2uWVlZqcrKSutxaalnePKFaw8oIbZShlG7SLDn/0luz/ZfydkOnTY4Tt1+3E7xSZ6U9dkLnv1gc38Urw4ZdX8+cyi2IuBQbO1n99lSzBZkuZO6ip1/R6ujtkjoO8fu+088ny+zn1OO2vcZPidFO9cVaed/jun53x/QwMvby3B7wmfFQZcqDrpVftClqoqmC31N2oNLQy8AoJWqqDoSketGNNgdOHBALpdLGRkZXsczMjL05ZdfBnxNUVFRwPOLioqs581jwc7xtWjRIt15551+x79575jiHbEhP8PHz5YrJt6mvhclKKVzjDb93dN12v9niV7n1VXs/IdizS3FHL5z7MyKXZCdJ3y7YiUFHYot3uIZbs3q57SOndI9Vj/9wyl66YaD2vHOMe14JzpD1QAAILRjrsj8b3KbWO5k7ty5XlXA0tJS5eTk6Ce3d1T7RM9yIDZ77datNs/vmmOGDu2q0fa1R3VwR40+ea7cen3X8+LV4/x2Xu/RoIpd0Dl23oeNIEO3kqxqnO9Q7JEiz/smn+b9lfYcmaArnnTow6fLtG9rtWKcnntNPMWhhFPsSjzF4Vl3L5zqWJgFvkC7cTT1ewAAcDIpq3Bo3tVNf92IBrvU1FQ5HA4VFxd7HS8uLlZmZmbA12RmZoY83/xdXFysrKwsr3MGDhwY8JpxcXGKi4vzO973okQlJSUGeEWdH1+brG/fr9SXhRU6tKNGnc+O09kzkvwWD040u2IPuP0WDw7aFWsud+I7x84cirX7p61gFbvyA56LmLtg1JfVP07j7vX//AAAoHmUlvqP8DWFiDZPOJ1ODR48WGvWrLGOud1urVmzRsOGDQv4mmHDhnmdL0mrV6+2zu/atasyMzO9ziktLdWGDRuCXvNE2Gw2dR4ar1HzOmniE+k65zfJfqFOkhJOqdtWrPpoXegyDKOuYufwnWPn+e3XFRuqYmcGu0rv4+X7PcGufVqAFwEAgDYh4kOxc+bM0ZQpUzRkyBANHTpUS5YsUXl5uaZNmyZJmjx5sk499VQtWrRIkvQ///M/Gj58uP7whz9o3LhxWrlypT744AM9+uijkjxB65prrtFdd92lnj17qmvXrrr99tuVnZ2t8ePHR/rjBBUbb/MMZxpSdYUhZ23DbP1qnN1nOp/V9eq3QLHnt+9yJ1K9rtgq7/BoVewIdgAAtFkRD3aXX3659u/fr3nz5qmoqEgDBw5UYWGh1fywe/du2eut63HOOefomWee0W233aZbbrlFPXv21AsvvKB+/fpZ59x4440qLy/XzJkzVVJSonPPPVeFhYWKj4+P9McJyma3KbadTdUVhqoqDJkDvK56y4wE23nCf46d57c91FBsvTl2xw675apdqs5cTw8AALQ9UWmeKCgoUEFBQcDn1q5d63fs0ksv1aWXXhr0ejabTQsWLNCCBQua6habhDPBE+yqK+qSmjkMK/k3T9isdez8FxuWAlfsHAEqduW1iyLHJ9utih4AAGh72Cu2CcUmeP6c9deFq78wsG/zRN0cO+/jDVrupF7FrsyaX8fXCQBAW0YSaELORE/oql+xc9Wr2PkGNWuoNcgCxYGaNGLMBYrrV+z2mx2xDMMCANCWEeyakDNAxc6ot9RJ/SVQpLqg5/ZZ7sQcmg1UsXME2FKsjMYJAAAggl2Tik3whK6qcv+KnSPWv/pmC7bzhFWx838Pcw5d/aFYljoBAAASwa5JORPMoVj/OXa+8+ukur1g/St2tc8H6Ip1BFigmKFYAAAgEeyaVKCh2KDbianeOnY+22e5Q3TF1q1jV3esrLYrlmAHAEDbRrBrQoGHYkNU7Kw5dr47T3h+20N0xboCDsXydQIA0JaRBJqQM9Hz56z2ap7w/A7U4WpW5IwgXbG2AK8JOBRL8wQAABDBrkmZc+zqD8WGrtgFnmPnDtEV69s8UVNlqKrc8++EjgQ7AADaMoJdE7KGYr12nvCErkBdsXVz7Hx3nqh9PtAcO5+h2GMlnpNtDimuA7tOAADQlhHsmlCgodhQzRPB17Eznw8wFOv0Hoqt+MFzcrtke8DzAQBA20Gwa0KhKnahljvxnWMXsivW3HmitmJ3tLZi164jXyUAAG0daaAJBV7HzvM7YPNEI7pirZ0nait2R38wgx3z6wAAaOsIdk3IWseuvGHNE+YcOp8pdiG7Yq05dmawK6kdik3hqwQAoK0jDTShwEOxnt+OQHPsaveONYJ0xYaq2NVUeh5bFTuCHQAAbR5poAmZFbuGbynm+R18HTv/18T4rGNHsAMAACbSQBNyJtbOsTtqyKhtgLDm2AVY7iTYHDt3iK5Ya0sxs3niMHPsAACAB8GuCZlDsZIn3En15tgFyF1mQ4X/HLsQQ7Fx3gsUU7EDAAAm0kATiomzWVU4c/eJkOvY1R7ynWNnrWMXonmixqd5IoHlTgAAaPNIA03IZrNZw7FV5Z5KmrXzRMg5dj5DseYcuxBbihkuyVVt1FvuhK8SAIC2jjTQxGJ9GijqmicCzbELvFes4Qo+fOtsX1cVPHrYbS1QHJ/MHDsAANo6gl0Tc/oseRJqKNZ+vK7YAM0TdodNCZ08X9vhb2usuXwMxQIAANJAE4ttZw7F+jRPxPqfa1be/IZizZ0nghThElM9TxzYVu05L8ZTyQMAAG0bwa6JORN9h2I9x0NtKeZfsTO8nvdlBbuvPcGuXYrdWuwYAAC0XQS7JuY/FBuieSLIHDt3iK5YSUpM9XxtZsWONewAAIBEsGtyZvNEQ5Y7sQfpijUreIHWsZOkxFM8L9z3pSfYmXPuAABA20YiaGLW7hM+FbuAW4odbyg22By7NM8Tx0o9L+zYJcDFAQBAm0Owa2Kx1lCs2TzhOR5quRPfBYrNKl+grlipbo6dqVPXAJ0ZAACgzSHYNTGnORRb2xVbc8xTVTO7ZeszK3Juv6HY4OvYSXVz7EydcqnYAQAAgl2TM5snzKFYc5252PgAc+yCDMU2dLkTU8cuVOwAAADBrsn5Nk9YwS5Qxc4cig22QHHQrti6YOeIlZJPpSsWAAAQ7JqctdxJuXfFLibe/09tNU/4LXcSeh07Z6JNMbUVwJTOMQHXyAMAAG0Pwa6J1XXFmnPsait2CcGXO/GfY+f9vC+bzWbNs2MYFgAAmAh2Tcx/KDZE80SwoViX9/OBmGvZdepK4wQAAPAg2DUxv+aJY8GbJ4KtY+c+zjp2knRKN0+lLrOf80RuFwAAtCKUe5qYuVesVbGrCNU84fltuHyGYs2u2BCx+/wbU3T6uAR1Hhp3gncMAABaC4JdE4tN8J5jF6p5wmx6cIfZFStJcR3s6nJ2/IneLgAAaEUYim1i5lBsTaUhV7VRNxQboHki6FBsbQUvVMUOAADAV0Sjw6FDhzRp0iQlJSUpJSVF06dPV1lZWcjzf/e736l3795q166dOnfurN///vc6fPiw13k2m83vZ+XKlZH8KA1mDsVK0tESt1Q7yuoMMRQrQzKMuuHYuopdpO4SAAC0RhEdip00aZL27t2r1atXq7q6WtOmTdPMmTP1zDPPBDz/+++/1/fff68HH3xQffv21TfffKPf/OY3+v777/Xcc895nbtixQqNGTPGepySkhLJj9Jgjlib7DGe/V4rDtYtUBcTqHmiXnAzXJKt9ttwN6ArFgAAwFfEgt2WLVtUWFio999/X0OGDJEkPfzwwxo7dqwefPBBZWdn+72mX79++te//mU97t69u+6++25dddVVqqmpUUxM3e2mpKQoMzMzUrd/QpwJdh0rdav8oKf05nAq4CLC9nrBze2uK58eb69YAACAQCI2FLt+/XqlpKRYoU6S8vPzZbfbtWHDhgZf5/Dhw0pKSvIKdZI0e/ZspaamaujQoVq+fLnXUGZzMxcpLj/gKb3FBmickHwqdu4AQ7HMsQMAAGGIWMWuqKhI6enp3m8WE6NOnTqpqKioQdc4cOCAFi5cqJkzZ3odX7BggS644AIlJCTojTfe0KxZs1RWVqbf//73Aa9TWVmpyspK63FpaWmYnyY8ZqNExaHaYBdgfp0k2eodrt9AwVAsAABojLCD3c0336z77rsv5Dlbtmxp9A2ZSktLNW7cOPXt21d33HGH13O333679e9BgwapvLxcDzzwQNBgt2jRIt15550nfE8N5azdfaKidig2Jliwqzc8W3+/WIZiAQBAY4Qd7K677jpNnTo15DndunVTZmam9u3b53W8pqZGhw4dOu7cuCNHjmjMmDHq0KGDnn/+ecXGht4PNS8vTwsXLlRlZaXi4vwX7J07d67mzJljPS4tLVVOTk7Ia54Is2JXfjB0xa7+cib194ut21IsMvcHAABap7CDXVpamtLS0o573rBhw1RSUqJNmzZp8ODBkqS33npLbrdbeXl5QV9XWlqq0aNHKy4uTi+++KLi44+/CO/mzZvVsWPHgKFOkuLi4oI+FwnmkidmxS7oUKzXHLu6f5sLFgdquAAAAAgmYnPsTj/9dI0ZM0YzZszQsmXLVF1drYKCAk2cONHqiN2zZ49Gjhypp556SkOHDlVpaalGjRqliooK/f3vf1dpaak1Hy4tLU0Oh0MvvfSSiouLdfbZZys+Pl6rV6/WPffco+uvvz5SHyVsZpCzKnbBmidsQYZiaxcopmIHAADCEdF17J5++mkVFBRo5MiRstvtmjBhgh566CHr+erqam3dulUVFRWSpA8//NDqmO3Ro4fXtXbu3Knc3FzFxsZq6dKluvbaa2UYhnr06KHFixdrxowZkfwoYTF3n6g4zlCs5KnaGS7vih0LFAMAgMaIaLDr1KlT0MWIJSk3N9drmZIRI0Ycd9mSMWPGeC1M3BKZQ7HmOnaBFic22e2Sy1W3jZhU1xVrpysWAACEgcG+CLD2hTV8HgdgdsbWz7NmVywVOwAAEA6CXQQ4fYJcyKFYMwPWm2PHOnYAAKAxCHYRYA7FmoI1T0h1VblAO0+wjh0AAAgHwS4CfIdeQ1XszHl0brpiAQDACSI6REBCJ+9SW2yI5gmrYldvjh3r2AEAgMYg2EVAWk/vnTIaNseOnScAAMCJITpEQGKqQwmd6v60obtiPb/NKl39uXZ0xQIAgHAQ7CIkrVdd1S4mRPOEOcfOrNLVn2vHOnYAACAcBLsISevttP59vJ0npLpKXf0dKKjYAQCAcBDsIqR+xS5k80TtN2AGuvo7UDDHDgAAhIPoECFpvesNxYaq2Pksd1K/YkdXLAAACAfBLkJO6VYX7EItNGz3GYqlYgcAABorprlvoLWKcdp0wc0pKvm2Rqk9YoOe5zsUW39rMXaeAAAA4SDYRdDgqzoc9xxzKNaaY2cOxdrYKxYAAISHwb5mZlbsrDl2tUOxVOsAAEC4CHbNzHeOnVm5Y34dAAAIF/GhmfkNxdZW7uiIBQAA4SLYNTO/5onayh0VOwAAEC7iQzOrm2NXOxRbW7Fj1wkAABAugl0zM4dcfbti2ScWAACEi2DXzPzXsWMoFgAANA7xoZnVBbvanSfMrliGYgEAQJgIds3Mfx07z2+GYgEAQLgIds3Md46d1RVLxQ4AAISJYNfM/IZiXd7HAQAAGor40MysYGcOxZpdsSxQDAAAwkSwa2bmzhNuumIBAMAJIj40M9+9Yq117JhjBwAAwkSwa2b+69iZxxmKBQAA4SHYNTMzwNXNsfNU7uwxzXVHAADgZEWwa2bWOnbmlmI1nt80TwAAgHAR7JqZNcfOZS53wjp2AACgcQh2zcwaivXkOWsdO3sMFTsAABAegl0z813HzhqK5ZsBAABhIj40M3PI1e1mKBYAAJwYgl0zswfbeYKhWAAAECaCXTOzOXzm2NXULndCxQ4AAISJYNfMbLWFubquWM9jljsBAADhItg1s7o5drW/mWMHAAAaKaLB7tChQ5o0aZKSkpKUkpKi6dOnq6ysLORrRowYIZvN5vXzm9/8xuuc3bt3a9y4cUpISFB6erpuuOEG1dTURPKjRIzdd+cJFigGAACNFNGNqyZNmqS9e/dq9erVqq6u1rRp0zRz5kw988wzIV83Y8YMLViwwHqckJBg/dvlcmncuHHKzMzUe++9p71792ry5MmKjY3VPffcE7HPEilmZc6onWRnVu6YYwcAAMIVsWC3ZcsWFRYW6v3339eQIUMkSQ8//LDGjh2rBx98UNnZ2UFfm5CQoMzMzIDPvfHGG/riiy/05ptvKiMjQwMHDtTChQt100036Y477pDT6YzI54mUujl2nt/mUCx7xQIAgHBFbCh2/fr1SklJsUKdJOXn58tut2vDhg0hX/v0008rNTVV/fr109y5c1VRUeF13f79+ysjI8M6Nnr0aJWWlurzzz8PeL3KykqVlpZ6/bQU5pCrWaljKBYAADRWxOpCRUVFSk9P936zmBh16tRJRUVFQV935ZVXqkuXLsrOztYnn3yim266SVu3btWqVaus69YPdZKsx8Guu2jRIt15550n8nEipm7nCRYoBgAAJybsYHfzzTfrvvvuC3nOli1bGn1DM2fOtP7dv39/ZWVlaeTIkdq+fbu6d+/eqGvOnTtXc+bMsR6XlpYqJyen0ffYlKw5dlZXrOc3FTsAABCusIPdddddp6lTp4Y8p1u3bsrMzNS+ffu8jtfU1OjQoUNB588FkpeXJ0natm2bunfvrszMTG3cuNHrnOLiYkkKet24uDjFxcU1+D2jyVY7yc4MdmbljuYJAAAQrrCDXVpamtLS0o573rBhw1RSUqJNmzZp8ODBkqS33npLbrfbCmsNsXnzZklSVlaWdd27775b+/bts4Z6V69eraSkJPXt2zfMT9P8zADndnn/tlGxAwAAYYpY88Tpp5+uMWPGaMaMGdq4caPWrVungoICTZw40eqI3bNnj/r06WNV4LZv366FCxdq06ZN2rVrl1588UVNnjxZP/7xjzVgwABJ0qhRo9S3b19dffXV+vjjj/X666/rtttu0+zZs1tsVS4Ua46d23uOHV2xAAAgXBFdoPjpp59Wnz59NHLkSI0dO1bnnnuuHn30Uev56upqbd261ep6dTqdevPNNzVq1Cj16dNH1113nSZMmKCXXnrJeo3D4dDLL78sh8OhYcOG6aqrrtLkyZO91r07mVh7xZpz7OiKBQAAjRTRulCnTp1CLkacm5trLcwrSTk5Ofr3v/993Ot26dJFr776apPcY3Orq9h5fltDsWz2BgAAwkR8aGb22m/A7WIoFgAAnBiCXTPzXe7EYLkTAADQSAS7Zmaz+8yxY7kTAADQSAS7ZubXFWs2T8RQsQMAAOEh2DWzujl2nt/mAsU0TwAAgHARH5qZudyJ7wLFNE8AAIBwEeyamRngDKsrtvY4zRMAACBMBLtmZgY4c24de8UCAIDGItg1M7Ni5/ap2LFXLAAACBfBrpmZ3a9mxc5dQ8UOAAA0DsGumTmCVOyYYwcAAMJFsGtm5pCrq9rz2Fyo2EZXLAAACBPBrpk5fLtiraFYKnYAACA8BLtmZlXszDl21lBsM90QAAA4aRHsmpkj1vO7bh272oodQ7EAACBMBLtmZvet2NX+ttkZigUAAOEh2DUzax272rl1LFAMAAAai2DXzHx3nnDXdsWa69sBAAA0FMGumdlr59i5/bpim+uOAADAyYpg18z894r1Pg4AANBQBLtm5jvHrm6v2Ga6IQAAcNIi2DWz+hU7wzAYigUAAI1GsGtm5hw7ybOdmLmlGEOxAAAgXAS7ZlY/wLlrWKAYAAA0HsGumdUPcG6XUbdAMRU7AAAQJoJdM/Oq2FXXq9gxxw4AAISJYNfM/Cp2LHcCAAAaiWDXzGw2m7W0ibumbh07ljsBAADhIti1AI7a7cNqqgzrGM0TAAAgXAS7FsCsztVU1gt2DMUCAIAwEexaALM656oX7Gx8MwAAIEzEhxbAHIqtPlZ/KJaKHQAACA/BrgUwh2Jd9efY0TwBAADCRLBrAazmCYZiAQDACSA+tABW80RV3XZiNhtDsQAAIDwEuxbAEVtbsTtm7jpBqAMAAOEj2LUA5nw6syuWYVgAANAYRIgWwO6zQDGLEwMAgMaIaLA7dOiQJk2apKSkJKWkpGj69OkqKysLev6uXbs8W2wF+Hn22Wet8wI9v3Llykh+lIiy+yxQzFAsAABojIjWhiZNmqS9e/dq9erVqq6u1rRp0zRz5kw988wzAc/PycnR3r17vY49+uijeuCBB3ThhRd6HV+xYoXGjBljPU5JSWny+48Wu09XLPvEAgCAxohYsNuyZYsKCwv1/vvva8iQIZKkhx9+WGPHjtWDDz6o7Oxsv9c4HA5lZmZ6HXv++ed12WWXqX379l7HU1JS/M49WZnBzpxjx+LEAACgMSI2FLt+/XqlpKRYoU6S8vPzZbfbtWHDhgZdY9OmTdq8ebOmT5/u99zs2bOVmpqqoUOHavny5TIMI8AVTg7mnLpqM9gx8xEAADRCxCp2RUVFSk9P936zmBh16tRJRUVFDbrG448/rtNPP13nnHOO1/EFCxboggsuUEJCgt544w3NmjVLZWVl+v3vfx/wOpWVlaqsrLQel5aWhvlpIsucU+diKBYAAJyAsGtDN998c9AGB/Pnyy+/POEbO3r0qJ555pmA1brbb79dP/rRjzRo0CDddNNNuvHGG/XAAw8EvdaiRYuUnJxs/eTk5Jzw/TUls2JXU2k+ZigWAACEL+yK3XXXXaepU6eGPKdbt27KzMzUvn37vI7X1NTo0KFDDZob99xzz6miokKTJ08+7rl5eXlauHChKisrFRcX5/f83LlzNWfOHOtxaWlpiwp31hw7c7kTKnYAAKARwg52aWlpSktLO+55w4YNU0lJiTZt2qTBgwdLkt566y253W7l5eUd9/WPP/64fvrTnzbovTZv3qyOHTsGDHWSFBcXF/S5lsCaY8fOEwAA4AREbI7d6aefrjFjxmjGjBlatmyZqqurVVBQoIkTJ1odsXv27NHIkSP11FNPaejQodZrt23bpnfeeUevvvqq33VfeuklFRcX6+yzz1Z8fLxWr16te+65R9dff32kPkrEWXPsqphjBwAAGi+i69g9/fTTKigo0MiRI2W32zVhwgQ99NBD1vPV1dXaunWrKioqvF63fPlynXbaaRo1apTfNWNjY7V06VJde+21MgxDPXr00OLFizVjxoxIfpSIsubYUbEDAAAnwGaczOuENFJpaamSk5N1+PBhJSUlNfft6LXbDumzF8qVNcCpvZ9UKftMpyY9ndHctwUAACIkUlmEFdNaAHus53cNCxQDAIATQLBrAXzn2NEVCwAAGoNg1wI4fObY0TwBAAAag2DXAthqK3bWUCzNEwAAoBEIdi2AVbFjKBYAAJwAgl0LYO08Ye0VS8UOAACEj2DXApjr2LmqvR8DAACEg2DXAvjOqWOOHQAAaAyCXQvgW6Gz8a0AAIBGIEK0AL4LEjMUCwAAGoNg1wL4BjmGYgEAQGMQ7FoA/zl2zXQjAADgpEawawH8KnbsFQsAABqBYNcC+AY5micAAEBjECFaAP+KXfPcBwAAOLkR7FoA1rEDAABNgWDXAvh3xTbPfQAAgJMbwa4F8JtjR8UOAAA0AsGuBXBQsQMAAE2AYNcC+FbomGMHAAAag2DXAvhW6Gx0xQIAgEYg2LUAvnPsYpxU7AAAQPgIdi2Ab1ds+zQm2QEAgPAR7FoAh0/Frn06wQ4AAISPYNcC2HxyXIdMgh0AAAgfwa4F8KrY2aTEVIIdAAAIH8GuBag/xy6hk12OWJonAABA+Ah2LUD9rtgOGVTrAABA4xDsWoD669jROAEAABqLYNcC1K/YEewAAEBjEexagPpz7GicAAAAjUWwawHqV+wSOhHsAABA4xDsWoD6c+wST+ErAQAAjUOKaAG8KnanULEDAACNQ7BrAerPsUvrGdt8NwIAAE5qMcc/BZFms9n027ez5a4xFNeBrA0AABqHYNdCtE9jCBYAAJwYykMAAACtBMEOAACglYhYsLv77rt1zjnnKCEhQSkpKQ16jWEYmjdvnrKystSuXTvl5+fr66+/9jrn0KFDmjRpkpKSkpSSkqLp06errKwsAp8AAADg5BKxYFdVVaVLL71Uv/3tbxv8mvvvv18PPfSQli1bpg0bNigxMVGjR4/WsWPHrHMmTZqkzz//XKtXr9bLL7+sd955RzNnzozERwAAADip2AzDMCL5Bk888YSuueYalZSUhDzPMAxlZ2fruuuu0/XXXy9JOnz4sDIyMvTEE09o4sSJ2rJli/r27av3339fQ4YMkSQVFhZq7Nix+u6775Sdnd2geyotLVVycrIOHz6spKSkE/p8AAAA4YpUFmkxc+x27typoqIi5efnW8eSk5OVl5en9evXS5LWr1+vlJQUK9RJUn5+vux2uzZs2BD02pWVlSotLfX6AQAAaG1aTLArKiqSJGVkZHgdz8jIsJ4rKipSenq61/MxMTHq1KmTdU4gixYtUnJysvWTk5PTxHcPAADQ/MIKdjfffLNsNlvIny+//DJS99poc+fO1eHDh62fb7/9trlvCQAAoMmFtUDxddddp6lTp4Y8p1u3bo26kczMTElScXGxsrKyrOPFxcUaOHCgdc6+ffu8XldTU6NDhw5Zrw8kLi5OcXFxjbovAACAk0VYwS4tLU1paWkRuZGuXbsqMzNTa9assYJcaWmpNmzYYHXWDhs2TCUlJdq0aZMGDx4sSXrrrbfkdruVl5cXkfsCAAA4WURsjt3u3bu1efNm7d69Wy6XS5s3b9bmzZu91pzr06ePnn/+eUme/VKvueYa3XXXXXrxxRf16aefavLkycrOztb48eMlSaeffrrGjBmjGTNmaOPGjVq3bp0KCgo0ceLEBnfEAgAAtFYR2yt23rx5evLJJ63HgwYNkiS9/fbbGjFihCRp69atOnz4sHXOjTfeqPLycs2cOVMlJSU699xzVVhYqPj4eOucp59+WgUFBRo5cqTsdrsmTJighx56KFIfAwAA4KQR8XXsWiLWsQMAAM2p1a9jBwAAgBMTsaHYlswsUrJQMQAAaA5mBmnqgdM2GewOHjwoSSxUDAAAmtXBgweVnJzcZNdrk8GuU6dOkjydu035x0TklJaWKicnR99++y3zIk8ifG8nH76zkxPf28nn8OHD6ty5s5VJmkqbDHZ2u2dqYXJyMv8BnGSSkpL4zk5CfG8nH76zkxPf28nHzCRNdr0mvRoAAACaDcEOAACglWiTwS4uLk7z589n/9iTCN/ZyYnv7eTDd3Zy4ns7+UTqO2uTCxQDAAC0Rm2yYgcAANAaEewAAABaCYIdAABAK9Fqg93SpUuVm5ur+Ph45eXlaePGjSHPf/bZZ9WnTx/Fx8erf//+evXVV6N0pzCF85099thjOu+889SxY0d17NhR+fn5x/2OERnh/rdmWrlypWw2m8aPHx/ZG4SfcL+zkpISzZ49W1lZWYqLi1OvXr34v5FRFu53tmTJEvXu3Vvt2rVTTk6Orr32Wh07dixKdwtJeuedd3TxxRcrOztbNptNL7zwwnFfs3btWp111lmKi4tTjx499MQTT4T/xkYrtHLlSsPpdBrLly83Pv/8c2PGjBlGSkqKUVxcHPD8devWGQ6Hw7j//vuNL774wrjtttuM2NhY49NPP43ynbdd4X5nV155pbF06VLjo48+MrZs2WJMnTrVSE5ONr777rso33nbFu73Ztq5c6dx6qmnGuedd55xySWXROdmYRhG+N9ZZWWlMWTIEGPs2LHGu+++a+zcudNYu3atsXnz5ijfedsV7nf29NNPG3FxccbTTz9t7Ny503j99deNrKws49prr43ynbdtr776qnHrrbcaq1atMiQZzz//fMjzd+zYYSQkJBhz5swxvvjiC+Phhx82HA6HUVhYGNb7tspgN3ToUGP27NnWY5fLZWRnZxuLFi0KeP5ll11mjBs3zutYXl6e8etf/zqi94k64X5nvmpqaowOHToYTz75ZKRuEQE05nurqakxzjnnHONvf/ubMWXKFIJdlIX7nT3yyCNGt27djKqqqmjdInyE+53Nnj3buOCCC7yOzZkzx/jRj34U0ftEcA0JdjfeeKNxxhlneB27/PLLjdGjR4f1Xq1uKLaqqkqbNm1Sfn6+dcxutys/P1/r168P+Jr169d7nS9Jo0ePDno+mlZjvjNfFRUVqq6ubvI99xBcY7+3BQsWKD09XdOnT4/GbaKexnxnL774ooYNG6bZs2crIyND/fr10z333COXyxWt227TGvOdnXPOOdq0aZM1XLtjxw69+uqrGjt2bFTuGY3TVFmk1e0Ve+DAAblcLmVkZHgdz8jI0JdffhnwNUVFRQHPLyoqith9ok5jvjNfN910k7Kzs/3+o0DkNOZ7e/fdd/X4449r8+bNUbhD+GrMd7Zjxw699dZbmjRpkl599VVt27ZNs2bNUnV1tebPnx+N227TGvOdXXnllTpw4IDOPfdcGYahmpoa/eY3v9Ett9wSjVtGIwXLIqWlpTp69KjatWvXoOu0uood2p57771XK1eu1PPPP6/4+Pjmvh0EceTIEV199dV67LHHlJqa2ty3gwZyu91KT0/Xo48+qsGDB+vyyy/XrbfeqmXLljX3rSGItWvX6p577tFf/vIXffjhh1q1apVeeeUVLVy4sLlvDVHQ6ip2qampcjgcKi4u9jpeXFyszMzMgK/JzMwM63w0rcZ8Z6YHH3xQ9957r958800NGDAgkrcJH+F+b9u3b9euXbt08cUXW8fcbrckKSYmRlu3blX37t0je9NtXGP+W8vKylJsbKwcDod17PTTT1dRUZGqqqrkdDojes9tXWO+s9tvv11XX321fvWrX0mS+vfvr/Lycs2cOVO33nqr7HZqOi1RsCySlJTU4Gqd1Aordk6nU4MHD9aaNWusY263W2vWrNGwYcMCvmbYsGFe50vS6tWrg56PptWY70yS7r//fi1cuFCFhYUaMmRING4V9YT7vfXp00effvqpNm/ebP389Kc/1fnnn6/NmzcrJycnmrffJjXmv7Uf/ehH2rZtmxXCJemrr75SVlYWoS4KGvOdVVRU+IU3M5gb7CLaYjVZFgmvr+PksHLlSiMuLs544oknjC+++MKYOXOmkZKSYhQVFRmGYRhXX321cfPNN1vnr1u3zoiJiTEefPBBY8uWLcb8+fNZ7iTKwv3O7r33XsPpdBrPPfecsXfvXuvnyJEjzfUR2qRwvzdfdMVGX7jf2e7du40OHToYBQUFxtatW42XX37ZSE9PN+66667m+ghtTrjf2fz5840OHToY//jHP4wdO3YYb7zxhtG9e3fjsssua66P0CYdOXLE+Oijj4yPPvrIkGQsXrzY+Oijj4xvvvnGMAzDuPnmm42rr77aOt9c7uSGG24wtmzZYixdupTlTup7+OGHjc6dOxtOp9MYOnSo8d///td6bvjw4caUKVO8zv+///f/Gr169TKcTqdxxhlnGK+88kqU7xjhfGddunQxJPn9zJ8/P/o33saF+99afQS75hHud/bee+8ZeXl5RlxcnNGtWzfj7rvvNmpqaqJ8121bON9ZdXW1cccddxjdu3c34uPjjZycHGPWrFnGDz/8EP0bb8PefvvtgP87ZX5XU6ZMMYYPH+73moEDBxpOp9Po1q2bsWLFirDf12YY1GUBAABag1Y3xw4AAKCtItgBAAC0EgQ7AACAVoJgBwAA0EoQ7AAAAFoJgh0AAEArQbADAABoJQh2AAAArQTBDkCbsHbtWtlsNpWUlDTL+69Zs0ann366XC7Xcc8tLCzUwIEDvfZnBRDaO++8o4svvljZ2dmy2Wx64YUXmv397rjjDvXp00eJiYnq2LGj8vPztWHDhojeF8EOQKszYsQIXXPNNV7HzjnnHO3du1fJycnNck833nijbrvtNmsz9lDGjBmj2NhYPf3001G4M6B1KC8v15lnnqmlS5e2mPfr1auX/vznP+vTTz/Vu+++q9zcXI0aNUr79++P2H2xpRiAVmfEiBEaOHCglixZ0ty3Ikl69913ddFFF6moqEjx8fENes3SpUv1xBNP6P3334/w3QGtj81m0/PPP6/x48dbxyorK3XrrbfqH//4h0pKStSvXz/dd999GjFiRETeL5DS0lIlJyfrzTff1MiRI0/4fQOhYgegVZk6dar+/e9/609/+pNsNptsNpt27drlNxT7xBNPKCUlRS+//LJ69+6thIQE/eIXv1BFRYWefPJJ5ebmqmPHjvr973/vNXxaWVmp66+/XqeeeqoSExOVl5entWvXhrynlStX6ic/+YlXqPv44491/vnnq0OHDkpKStLgwYP1wQcfWM9ffPHF+uCDD7R9+/Ym/fsAbVVBQYHWr1+vlStX6pNPPtGll16qMWPG6Ouvv47K+1dVVenRRx9VcnKyzjzzzIi9T0zErgwAzeBPf/qTvvrqK/Xr108LFiyQJKWlpWnXrl1+51ZUVOihhx7SypUrdeTIEf385z/Xz372M6WkpOjVV1/Vjh07NGHCBP3oRz/S5ZdfLsnzPw5ffPGFVq5cqezsbD3//PMaM2aMPv30U/Xs2TPgPf3nP//RlVde6XVs0qRJGjRokB555BE5HA5t3rxZsbGx1vOdO3dWRkaG/vOf/6h79+5N9NcB2qbdu3drxYoV2r17t7KzsyVJ119/vQoLC7VixQrdc889EXvvl19+WRMnTlRFRYWysrK0evVqpaamRuz9CHYAWpXk5GQ5nU4lJCQoMzMz5LnV1dV65JFHrOD0i1/8Qv/7v/+r4uJitW/fXn379tX555+vt99+W5dffnmj/8fhm2++sc437d69WzfccIP69OkjSQFDYXZ2tr755puw/wYAvH366adyuVzq1auX1/HKykqdcsopkqQvv/xSp59+esjr3HTTTbr33nvDeu/zzz9fmzdv1oEDB/TYY4/psssu04YNG5Senh7eh2gggh2ANishIcGrGpaRkaHc3Fy1b9/e69i+ffskNex/HAI5evSo39y6OXPm6Fe/+pX+93//V/n5+br00kv9KnPt2rVTRUVFoz8fAI+ysjI5HA5t2rTJr4HJ/O+9W7du2rJlS8jrhPrvPJjExET16NFDPXr00Nlnn62ePXvq8ccf19y5c8O+VkMQ7AC0WfWHPiXPBOhAx8xlRxryPw6BpKam6ocffvA6dscdd+jKK6/UK6+8otdee03z58/XypUr9bOf/cw659ChQ0pLS2vUZwNQZ9CgQXK5XNq3b5/OO++8gOc4nU6rgh5JbrdblZWVEbs+wQ5Aq+N0Ohu0Xly4GvI/DsFe98UXX/gd79Wrl3r16qVrr71WV1xxhVasWGEFu2PHjmn79u0aNGhQk90/0JqVlZVp27Zt1uOdO3dq8+bN6tSpk3r16qVJkyZp8uTJ+sMf/qBBgwZp//79WrNmjQYMGKBx48Y16ft17txZ5eXluvvuu/XTn/5UWVlZOnDggJYuXao9e/bo0ksvbZLPHAhdsQBandzcXG3YsEG7du3SgQMHmmyh3/r/47Bq1Srt3LlTGzdu1KJFi/TKK68Efd3o0aP17rvvWo+PHj2qgoICrV27Vt98843WrVun999/32t+z3//+1/FxcVp2LBhTXLvQGv3wQcfaNCgQdb/Z2jOnDkaNGiQ5s2bJ0lasWKFJk+erOuuu069e/fW+PHj9f7776tz584ReT+Hw6Evv/xSEyZMUK9evXTxxRfr4MGD+s9//qMzzjijCT5xYFTsALQ6119/vaZMmaK+ffvq6NGj2rlzZ5Nde8WKFbrrrrt03XXXac+ePUpNTdXZZ5+tiy66KOhrJk2apBtvvFFbt25V79695XA4dPDgQU2ePFnFxcVKTU3Vz3/+c915553Wa/7xj39o0qRJSkhIaLJ7B1qzESNGKNTSvLGxsbrzzju9/juL5PvFx8dr1apVTfJe4WCBYgCIghtuuEGlpaX661//etxzDxw4oN69e+uDDz5Q165do3B3AFoLhmIBIApuvfVWdenSpUHDwrt27dJf/vIXQh2AsFGxAwAAaCWo2AEAALQSBDsAAIBWgmAHAADQShDsAAAAWgmCHQAAQCtBsAMAAGglCHYAAACtBMEOAACglSDYAQAAtBIEOwAAgFbi/wOvWujnK8SW7AAAAABJRU5ErkJggg==",
       "text/plain": [
        "
"
       ]
@@ -369,7 +350,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "
"
       ]
@@ -384,10 +365,10 @@
     "ax1.set_xlim(0, 1e-13)\n",
     "ax2 = sim.sources[0].source_time.plot_spectrum(times=np.linspace(0, sim.run_time, 1001))\n",
     "ax2.fill_between(\n",
-    "    freq_range, [-8e-16, -8e-16], [8e-16, 8e-16], alpha=0.4, color=\"g\", label=\"mesure\"\n",
+    "    freq_range, [-8e-16, -8e-16], [8e-16, 8e-16], alpha=0.4, color=\"g\", label=\"measure\"\n",
     ")\n",
     "ax2.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -403,25 +384,21 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T17:40:40.283644Z",
-     "iopub.status.busy": "2023-08-18T17:40:40.283488Z",
-     "iopub.status.idle": "2023-08-18T17:41:23.122184Z",
-     "shell.execute_reply": "2023-08-18T17:41:23.121440Z"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
[10:40:40] Created task 'dispersion' with task_id                  webapi.py:188\n",
-       "           'fdve-856a276d-7d85-4f17-b50c-be3e3f57777bv1'.                       \n",
+       "
10:27:54 Eastern Daylight Time Created task 'dispersion' with task_id           \n",
+       "                               'fdve-12657b1d-2873-4588-a14c-f505cb802247' and  \n",
+       "                               task_type 'FDTD'.                                \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[10:40:40]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'dispersion'\u001b[0m with task_id                  \u001b]8;id=696507;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=515177;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[2;36m           \u001b[0m\u001b[32m'fdve-856a276d-7d85-4f17-b50c-be3e3f57777bv1'\u001b[0m.          \u001b[2m             \u001b[0m\n"
+       "\u001b[2;36m10:27:54 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'dispersion'\u001b[0m with task_id           \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'fdve-12657b1d-2873-4588-a14c-f505cb802247'\u001b[0m and  \n",
+       "\u001b[2;36m                               \u001b[0mtask_type \u001b[32m'FDTD'\u001b[0m.                                \n"
       ]
      },
      "metadata": {},
@@ -430,29 +407,15 @@
     {
      "data": {
       "text/html": [
-       "
           View task using web UI at                               webapi.py:190\n",
-       "           'https://tidy3d.simulation.cloud/workbench?taskId=fdve-              \n",
-       "           856a276d-7d85-4f17-b50c-be3e3f57777bv1'.                             \n",
+       "
                               View task using web UI at                        \n",
+       "                               'https://tidy3d.simulation.cloud/workbench?taskId\n",
+       "                               =fdve-12657b1d-2873-4588-a14c-f505cb802247'.     \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                               \u001b]8;id=467527;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=611785;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#190\u001b\\\u001b[2m190\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[2;36m           \u001b[0m\u001b]8;id=19615;https://tidy3d.simulation.cloud/workbench?taskId=fdve-856a276d-7d85-4f17-b50c-be3e3f57777bv1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=658103;https://tidy3d.simulation.cloud/workbench?taskId=fdve-856a276d-7d85-4f17-b50c-be3e3f57777bv1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=19615;https://tidy3d.simulation.cloud/workbench?taskId=fdve-856a276d-7d85-4f17-b50c-be3e3f57777bv1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=929960;https://tidy3d.simulation.cloud/workbench?taskId=fdve-856a276d-7d85-4f17-b50c-be3e3f57777bv1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=19615;https://tidy3d.simulation.cloud/workbench?taskId=fdve-856a276d-7d85-4f17-b50c-be3e3f57777bv1\u001b\\\u001b[32m-\u001b[0m\u001b]8;;\u001b\\ \u001b[2m             \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m\u001b]8;id=19615;https://tidy3d.simulation.cloud/workbench?taskId=fdve-856a276d-7d85-4f17-b50c-be3e3f57777bv1\u001b\\\u001b[32m856a276d-7d85-4f17-b50c-be3e3f57777bv1'\u001b[0m\u001b]8;;\u001b\\.                \u001b[2m             \u001b[0m\n"
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-    },
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-      "text/plain": [
-       "Output()"
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                        \n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=343017;https://tidy3d.simulation.cloud/workbench?taskId=fdve-12657b1d-2873-4588-a14c-f505cb802247\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=920609;https://tidy3d.simulation.cloud/workbench?taskId=fdve-12657b1d-2873-4588-a14c-f505cb802247\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=343017;https://tidy3d.simulation.cloud/workbench?taskId=fdve-12657b1d-2873-4588-a14c-f505cb802247\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=616090;https://tidy3d.simulation.cloud/workbench?taskId=fdve-12657b1d-2873-4588-a14c-f505cb802247\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=343017;https://tidy3d.simulation.cloud/workbench?taskId=fdve-12657b1d-2873-4588-a14c-f505cb802247\u001b\\\u001b[32m-12657b1d-2873-4588-a14c-f505cb802247'\u001b[0m\u001b]8;;\u001b\\.     \n"
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      },
      "metadata": {},
@@ -461,34 +424,11 @@
     {
      "data": {
       "text/html": [
-       "
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-     "output_type": "display_data"
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-    {
-     "data": {
-      "text/html": [
-       "
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-       "
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-      "text/plain": [
-       "\n"
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
[10:40:41] status = queued                                         webapi.py:361\n",
+       "
                               Task folder: 'default'.                          \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[10:40:41]\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                         \u001b]8;id=990468;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=879973;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#361\u001b\\\u001b[2m361\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=901973;https://tidy3d.simulation.cloud/folders/639eb096-a602-4b56-a502-cac1f18f9557\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                          \n"
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      },
      "metadata": {},
@@ -497,7 +437,7 @@
     {
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@@ -508,19 +448,6 @@
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      "output_type": "display_data"
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-    {
-     "data": {
-      "text/html": [
-       "
[10:40:50] status = preprocess                                     webapi.py:355\n",
-       "
\n"
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-      "text/plain": [
-       "\u001b[2;36m[10:40:50]\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                     \u001b]8;id=946281;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=877706;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#355\u001b\\\u001b[2m355\u001b[0m\u001b]8;;\u001b\\\n"
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-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
       "text/html": [
@@ -534,41 +461,17 @@
     {
      "data": {
       "text/html": [
-       "
[10:40:54] Maximum FlexCredit cost: 0.106. Use                     webapi.py:341\n",
-       "           'web.real_cost(task_id)' to get the billed FlexCredit                \n",
-       "           cost after a simulation run.                                         \n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m[10:40:54]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.106\u001b[0m. Use                     \u001b]8;id=644139;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=144210;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#341\u001b\\\u001b[2m341\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[2;36m           \u001b[0m\u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit   \u001b[2m             \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0mcost after a simulation run.                            \u001b[2m             \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
           starting up solver                                      webapi.py:377\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                      \u001b]8;id=345975;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=347335;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#377\u001b\\\u001b[2m377\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
           running solver                                          webapi.py:386\n",
+       "
10:27:55 Eastern Daylight Time Maximum FlexCredit cost: 0.104. Minimum cost     \n",
+       "                               depends on task execution details. Use           \n",
+       "                               'web.real_cost(task_id)' to get the billed       \n",
+       "                               FlexCredit cost after a simulation run.          \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                          \u001b]8;id=883190;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=17186;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#386\u001b\\\u001b[2m386\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m10:27:55 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.104\u001b[0m. Minimum cost     \n",
+       "\u001b[2;36m                               \u001b[0mdepends on task execution details. Use           \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed       \n",
+       "\u001b[2;36m                               \u001b[0mFlexCredit cost after a simulation run.          \n"
       ]
      },
      "metadata": {},
@@ -577,17 +480,11 @@
     {
      "data": {
       "text/html": [
-       "
           To cancel the simulation, use 'web.abort(task_id)' or   webapi.py:387\n",
-       "           'web.delete(task_id)' or abort/delete the task in the                \n",
-       "           web UI. Terminating the Python script will not stop the              \n",
-       "           job running on the cloud.                                            \n",
+       "
10:27:56 Eastern Daylight Time status = success                                 \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or   \u001b]8;id=941165;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=167474;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[2;36m           \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the   \u001b[2m             \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0mweb UI. Terminating the Python script will not stop the \u001b[2m             \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0mjob running on the cloud.                               \u001b[2m             \u001b[0m\n"
+       "\u001b[2;36m10:27:56 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                 \n"
       ]
      },
      "metadata": {},
@@ -596,7 +493,7 @@
     {
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       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1acd96318c374957b0d98e1dcc21c3bb",
+       "model_id": "f1ad7939f78743aca0d017313191eb08",
        "version_major": 2,
        "version_minor": 0
       },
@@ -607,19 +504,6 @@
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "data": {
-      "text/html": [
-       "
[10:41:18] early shutoff detected, exiting.                        webapi.py:404\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m[10:41:18]\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected, exiting.                        \u001b]8;id=44549;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=101605;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#404\u001b\\\u001b[2m404\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
       "text/html": [
@@ -633,111 +517,11 @@
     {
      "data": {
       "text/html": [
-       "
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+       "
10:27:57 Eastern Daylight Time loading simulation from data/sim_data.hdf5       \n",
        "
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       ],
       "text/plain": [
-       "\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
           status = postprocess                                    webapi.py:419\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                    \u001b]8;id=206095;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=143274;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#419\u001b\\\u001b[2m419\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
[10:41:22] status = success                                        webapi.py:426\n",
-       "
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-      ],
-      "text/plain": [
-       "\u001b[2;36m[10:41:22]\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                        \u001b]8;id=521402;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=699626;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#426\u001b\\\u001b[2m426\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
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-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-       "version_major": 2,
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-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
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-      ],
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-    {
-     "data": {
-      "text/html": [
-       "
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-       "
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-      "text/plain": [
-       "\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
[10:41:23] loading SimulationData from data/sim_data.hdf5          webapi.py:590\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m[10:41:23]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/sim_data.hdf5          \u001b]8;id=748573;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=582633;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#590\u001b\\\u001b[2m590\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m10:27:57 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/sim_data.hdf5       \n"
       ]
      },
      "metadata": {},
@@ -745,7 +529,7 @@
     }
    ],
    "source": [
-    "sim_data = web.run(sim, task_name=\"dispersion\", path=\"data/sim_data.hdf5\", verbose=True)\n"
+    "sim_data = web.run(sim, task_name=\"dispersion\", path=\"data/sim_data.hdf5\", verbose=True)"
    ]
   },
   {
@@ -768,18 +552,12 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T17:41:23.991057Z",
-     "iopub.status.busy": "2023-08-18T17:41:23.990818Z",
-     "iopub.status.idle": "2023-08-18T17:41:24.100299Z",
-     "shell.execute_reply": "2023-08-18T17:41:24.099680Z"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
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",
+      "image/png": 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",
       "text/plain": [
        "
"
       ]
@@ -794,7 +572,7 @@
     "plt.plot(monitor_lambdas, transmission, color=\"k\")\n",
     "plt.xlabel(\"wavelength (um)\")\n",
     "plt.ylabel(\"transmitted flux\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -808,25 +586,21 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T17:41:24.103592Z",
-     "iopub.status.busy": "2023-08-18T17:41:24.103401Z",
-     "iopub.status.idle": "2023-08-18T17:41:53.436273Z",
-     "shell.execute_reply": "2023-08-18T17:41:53.435426Z"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
[10:41:24] Created task 'docs_dispersion_norm' with task_id        webapi.py:188\n",
-       "           'fdve-66335a77-705d-42a5-be83-4aac692b3f9fv1'.                       \n",
+       "
                               Created task 'docs_dispersion_norm' with task_id \n",
+       "                               'fdve-0eb95b51-1792-4d79-8760-9bf898e2e855' and  \n",
+       "                               task_type 'FDTD'.                                \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[10:41:24]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'docs_dispersion_norm'\u001b[0m with task_id        \u001b]8;id=547994;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=657414;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#188\u001b\\\u001b[2m188\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[2;36m           \u001b[0m\u001b[32m'fdve-66335a77-705d-42a5-be83-4aac692b3f9fv1'\u001b[0m.          \u001b[2m             \u001b[0m\n"
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'docs_dispersion_norm'\u001b[0m with task_id \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'fdve-0eb95b51-1792-4d79-8760-9bf898e2e855'\u001b[0m and  \n",
+       "\u001b[2;36m                               \u001b[0mtask_type \u001b[32m'FDTD'\u001b[0m.                                \n"
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      },
      "metadata": {},
@@ -835,145 +609,15 @@
     {
      "data": {
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-       "
           View task using web UI at                               webapi.py:190\n",
-       "           'https://tidy3d.simulation.cloud/workbench?taskId=fdve-              \n",
-       "           66335a77-705d-42a5-be83-4aac692b3f9fv1'.                             \n",
+       "
                               View task using web UI at                        \n",
+       "                               'https://tidy3d.simulation.cloud/workbench?taskId\n",
+       "                               =fdve-0eb95b51-1792-4d79-8760-9bf898e2e855'.     \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                               \u001b]8;id=823276;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=318320;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#190\u001b\\\u001b[2m190\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[2;36m           \u001b[0m\u001b]8;id=570233;https://tidy3d.simulation.cloud/workbench?taskId=fdve-66335a77-705d-42a5-be83-4aac692b3f9fv1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=4942;https://tidy3d.simulation.cloud/workbench?taskId=fdve-66335a77-705d-42a5-be83-4aac692b3f9fv1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=570233;https://tidy3d.simulation.cloud/workbench?taskId=fdve-66335a77-705d-42a5-be83-4aac692b3f9fv1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=513962;https://tidy3d.simulation.cloud/workbench?taskId=fdve-66335a77-705d-42a5-be83-4aac692b3f9fv1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=570233;https://tidy3d.simulation.cloud/workbench?taskId=fdve-66335a77-705d-42a5-be83-4aac692b3f9fv1\u001b\\\u001b[32m-\u001b[0m\u001b]8;;\u001b\\ \u001b[2m             \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m\u001b]8;id=570233;https://tidy3d.simulation.cloud/workbench?taskId=fdve-66335a77-705d-42a5-be83-4aac692b3f9fv1\u001b\\\u001b[32m66335a77-705d-42a5-be83-4aac692b3f9fv1'\u001b[0m\u001b]8;;\u001b\\.                \u001b[2m             \u001b[0m\n"
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-       "
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-    {
-     "data": {
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-       "
[10:41:25] status = queued                                         webapi.py:361\n",
-       "
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-      "text/plain": [
-       "\u001b[2;36m[10:41:25]\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                         \u001b]8;id=550319;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=673364;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#361\u001b\\\u001b[2m361\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-      "text/plain": [
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-     "output_type": "display_data"
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-    {
-     "data": {
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-       "
[10:41:33] status = preprocess                                     webapi.py:355\n",
-       "
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-      "text/plain": [
-       "\u001b[2;36m[10:41:33]\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                     \u001b]8;id=165196;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=566202;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#355\u001b\\\u001b[2m355\u001b[0m\u001b]8;;\u001b\\\n"
-      ]
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
[10:41:38] Maximum FlexCredit cost: 0.025. Use                     webapi.py:341\n",
-       "           'web.real_cost(task_id)' to get the billed FlexCredit                \n",
-       "           cost after a simulation run.                                         \n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m[10:41:38]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Use                     \u001b]8;id=337606;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=356289;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#341\u001b\\\u001b[2m341\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[2;36m           \u001b[0m\u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit   \u001b[2m             \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0mcost after a simulation run.                            \u001b[2m             \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
           starting up solver                                      webapi.py:377\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                      \u001b]8;id=435774;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=734489;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#377\u001b\\\u001b[2m377\u001b[0m\u001b]8;;\u001b\\\n"
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-     "metadata": {},
-     "output_type": "display_data"
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-    {
-     "data": {
-      "text/html": [
-       "
           running solver                                          webapi.py:386\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                          \u001b]8;id=233769;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=10535;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#386\u001b\\\u001b[2m386\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                        \n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=447744;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0eb95b51-1792-4d79-8760-9bf898e2e855\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=358008;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0eb95b51-1792-4d79-8760-9bf898e2e855\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=447744;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0eb95b51-1792-4d79-8760-9bf898e2e855\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=996562;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0eb95b51-1792-4d79-8760-9bf898e2e855\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=447744;https://tidy3d.simulation.cloud/workbench?taskId=fdve-0eb95b51-1792-4d79-8760-9bf898e2e855\u001b\\\u001b[32m-0eb95b51-1792-4d79-8760-9bf898e2e855'\u001b[0m\u001b]8;;\u001b\\.     \n"
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      },
      "metadata": {},
@@ -982,17 +626,11 @@
     {
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-       "
           To cancel the simulation, use 'web.abort(task_id)' or   webapi.py:387\n",
-       "           'web.delete(task_id)' or abort/delete the task in the                \n",
-       "           web UI. Terminating the Python script will not stop the              \n",
-       "           job running on the cloud.                                            \n",
+       "
                               Task folder: 'default'.                          \n",
        "
\n"
       ],
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-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or   \u001b]8;id=539889;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=393880;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#387\u001b\\\u001b[2m387\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[2;36m           \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the   \u001b[2m             \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0mweb UI. Terminating the Python script will not stop the \u001b[2m             \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0mjob running on the cloud.                               \u001b[2m             \u001b[0m\n"
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=485531;https://tidy3d.simulation.cloud/folders/639eb096-a602-4b56-a502-cac1f18f9557\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                          \n"
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      "metadata": {},
@@ -1001,7 +639,7 @@
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        "version_minor": 0
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@@ -1012,19 +650,6 @@
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-    {
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[10:41:43] early shutoff detected, exiting.                        webapi.py:404\n",
-       "
\n"
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-       "\u001b[2;36m[10:41:43]\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected, exiting.                        \u001b]8;id=336770;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=36869;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#404\u001b\\\u001b[2m404\u001b[0m\u001b]8;;\u001b\\\n"
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@@ -1038,38 +663,17 @@
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-    {
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-      "text/html": [
-       "
[10:41:44] status = postprocess                                    webapi.py:419\n",
+       "
10:27:58 Eastern Daylight Time Maximum FlexCredit cost: 0.025. Minimum cost     \n",
+       "                               depends on task execution details. Use           \n",
+       "                               'web.real_cost(task_id)' to get the billed       \n",
+       "                               FlexCredit cost after a simulation run.          \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[10:41:44]\u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                    \u001b]8;id=210915;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=866464;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#419\u001b\\\u001b[2m419\u001b[0m\u001b]8;;\u001b\\\n"
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-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
+       "\u001b[2;36m10:27:58 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost     \n",
+       "\u001b[2;36m                               \u001b[0mdepends on task execution details. Use           \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed       \n",
+       "\u001b[2;36m                               \u001b[0mFlexCredit cost after a simulation run.          \n"
       ]
      },
      "metadata": {},
@@ -1078,30 +682,20 @@
     {
      "data": {
       "text/html": [
-       "
[10:41:47] status = success                                        webapi.py:426\n",
+       "
10:27:59 Eastern Daylight Time status = success                                 \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[10:41:47]\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                        \u001b]8;id=371728;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=195904;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#426\u001b\\\u001b[2m426\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m10:27:59 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                 \n"
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-    {
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@@ -1125,24 +719,11 @@
     {
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-    {
-     "data": {
-      "text/html": [
-       "
[10:41:53] loading SimulationData from data/sim_data.hdf5          webapi.py:590\n",
+       "
10:28:00 Eastern Daylight Time loading simulation from data/sim_data.hdf5       \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[10:41:53]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/sim_data.hdf5          \u001b]8;id=125162;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=459159;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#590\u001b\\\u001b[2m590\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m10:28:00 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/sim_data.hdf5       \n"
       ]
      },
      "metadata": {},
@@ -1153,27 +734,24 @@
     "sim_norm = sim.copy(update={\"structures\": []})\n",
     "\n",
     "sim_data_norm = web.run(\n",
-    "    sim_norm, task_name=\"docs_dispersion_norm\", path=\"data/sim_data.hdf5\", verbose=True,\n",
+    "    sim_norm,\n",
+    "    task_name=\"docs_dispersion_norm\",\n",
+    "    path=\"data/sim_data.hdf5\",\n",
+    "    verbose=True,\n",
     ")\n",
-    "transmission_norm = sim_data_norm[\"flux\"].flux\n"
+    "transmission_norm = sim_data_norm[\"flux\"].flux"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T17:41:53.902482Z",
-     "iopub.status.busy": "2023-08-18T17:41:53.902352Z",
-     "iopub.status.idle": "2023-08-18T17:41:54.037260Z",
-     "shell.execute_reply": "2023-08-18T17:41:54.036745Z"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -1189,7 +767,7 @@
     "plt.legend()\n",
     "plt.xlabel(\"wavelength (um)\")\n",
     "plt.ylabel(\"fraction of transmitted power (normalized)\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1212,31 +790,18 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T17:41:54.040749Z",
-     "iopub.status.busy": "2023-08-18T17:41:54.040565Z",
-     "iopub.status.idle": "2023-08-18T17:41:54.067256Z",
-     "shell.execute_reply": "2023-08-18T17:41:54.066662Z"
-    },
     "tags": []
    },
    "outputs": [],
    "source": [
     "# import TMM package\n",
-    "import tmm\n"
+    "import tmm"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T17:41:54.069314Z",
-     "iopub.status.busy": "2023-08-18T17:41:54.069175Z",
-     "iopub.status.idle": "2023-08-18T17:41:54.137198Z",
-     "shell.execute_reply": "2023-08-18T17:41:54.136623Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# prepare list of thicknesses including air boundaries\n",
@@ -1251,31 +816,24 @@
     "# loop through wavelength and record TMM computed transmission\n",
     "transmission_tmm = []\n",
     "for i, lam in enumerate(monitor_lambdas):\n",
-    "\n",
     "    # create list of refractive index at this wavelength including outer material (air)\n",
     "    n_list = [1, n_list1[i], n_list2[i], n_list3[i], n_list4[i], 1]\n",
     "\n",
     "    # get transmission at normal incidence\n",
     "    T = tmm.coh_tmm(\"s\", n_list, d_list, 0, lam)[\"T\"]\n",
-    "    transmission_tmm.append(T)\n"
+    "    transmission_tmm.append(T)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T17:41:54.139089Z",
-     "iopub.status.busy": "2023-08-18T17:41:54.138949Z",
-     "iopub.status.idle": "2023-08-18T17:41:54.261167Z",
-     "shell.execute_reply": "2023-08-18T17:41:54.260584Z"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -1288,10 +846,10 @@
     "plt.figure()\n",
     "plt.plot(monitor_lambdas, transmission_tmm, label=\"TMM\")\n",
     "plt.plot(monitor_lambdas, transmission / transmission_norm, \"k--\", label=\"Tidy3D\")\n",
-    "plt.xlabel(\"wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Transmitted\")\n",
     "plt.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1322,7 +880,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.0"
   },
   "title": "Modeling Dispersive Materials in Tidy3D | Flexcompute",
   "widgets": {
diff --git a/DistributedBraggReflectorCavity.ipynb b/DistributedBraggReflectorCavity.ipynb
index 5470e9e0..4833e432 100644
--- a/DistributedBraggReflectorCavity.ipynb
+++ b/DistributedBraggReflectorCavity.ipynb
@@ -38,11 +38,10 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n"
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -85,7 +84,7 @@
    "source": [
     "lda0 = 0.63  # central wavelength\n",
     "freq0 = td.C_0 / lda0  # central frequency\n",
-    "freqs = freq0 * np.linspace(0.5, 1.5, 1001)  # frequency range of interest\n"
+    "freqs = freq0 * np.linspace(0.5, 1.5, 1001)  # frequency range of interest"
    ]
   },
   {
@@ -105,7 +104,7 @@
     "n_tio2 = 2.5  # refractive index of TiO2\n",
     "n_sio2 = 1.5  # refractive index of SiO2\n",
     "n_s = 1.5  # refractive index of the substrate material. It's set to SiO2 in this case\n",
-    "inf_eff = 10  # effective infinity in this model\n"
+    "inf_eff = 10  # effective infinity in this model"
    ]
   },
   {
@@ -145,7 +144,7 @@
    ],
    "source": [
     "df = 4 * np.arcsin((n_tio2 - n_sio2) / (n_tio2 + n_sio2)) / np.pi\n",
-    "print(f\"The normalized bandwidth of the reflection band is {df:1.2f}\")\n"
+    "print(f\"The normalized bandwidth of the reflection band is {df:1.2f}\")"
    ]
   },
   {
@@ -206,7 +205,7 @@
     "            )\n",
     "            z_0 = z_0 + t_2\n",
     "\n",
-    "    return layers\n"
+    "    return layers"
    ]
   },
   {
@@ -232,13 +231,10 @@
    "outputs": [],
    "source": [
     "def make_DBR(N):\n",
-    "\n",
     "    # build the DBR layers using the previously defined function\n",
     "    DBR = build_layers(n_tio2, n_sio2, N, 0)\n",
     "\n",
-    "    thickness = N * (\n",
-    "        lda0 / (4 * n_tio2) + lda0 / (4 * n_sio2)\n",
-    "    )  # total thickness of the DBR layers\n",
+    "    thickness = N * (lda0 / (4 * n_tio2) + lda0 / (4 * n_sio2))  # total thickness of the DBR layers\n",
     "\n",
     "    # build the substrate structure\n",
     "    sub = td.Structure(\n",
@@ -285,7 +281,7 @@
     "        ),  # pml is applied in the z direction\n",
     "        shutoff=1e-7,\n",
     "    )  # early shutoff level is decreased to 1e-7 to increase the simulation accuracy\n",
-    "    return sim\n"
+    "    return sim"
    ]
   },
   {
@@ -312,9 +308,7 @@
    "outputs": [],
    "source": [
     "Ns = np.array([2, 3, 4, 5, 10])  # collection of N for the parameter sweep\n",
-    "sims = {\n",
-    "    f\"N={N:.2f}\": make_DBR(N) for N in Ns\n",
-    "}  # construct simulations for each N from Ns\n"
+    "sims = {f\"N={N:.2f}\": make_DBR(N) for N in Ns}  # construct simulations for each N from Ns"
    ]
   },
   {
@@ -382,7 +376,7 @@
    ],
    "source": [
     "batch = web.Batch(simulations=sims, verbose=True)\n",
-    "batch_results = batch.run(path_dir=\"data\")\n"
+    "batch_results = batch.run(path_dir=\"data\")"
    ]
   },
   {
@@ -715,7 +709,7 @@
     "plt.xlabel(\"Normalized frequency\")\n",
     "plt.ylabel(\"Reflectance\")\n",
     "plt.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -780,7 +774,7 @@
     "freq0 = td.C_0 / lda0  # central frequency\n",
     "freqs = freq0 * np.linspace(\n",
     "    0.9, 1.1, 1001\n",
-    ")  # frequency range of interest. The range is narrowed compared to previous simulations\n"
+    ")  # frequency range of interest. The range is narrowed compared to previous simulations"
    ]
   },
   {
@@ -863,9 +857,7 @@
     "\n",
     "# construct the substrate\n",
     "sub = td.Structure(\n",
-    "    geometry=td.Box.from_bounds(\n",
-    "        rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, 0)\n",
-    "    ),\n",
+    "    geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, 0)),\n",
     "    medium=td.Medium(permittivity=n_s**2),\n",
     ")\n",
     "\n",
@@ -874,9 +866,7 @@
     "DBR_cavity.append(cavity)\n",
     "DBR_cavity.append(sub)\n",
     "\n",
-    "thickness = (\n",
-    "    thickness_bottom + thickness_cavity + thickness_top\n",
-    ")  # total thickness of the device\n",
+    "thickness = thickness_bottom + thickness_cavity + thickness_top  # total thickness of the device\n",
     "\n",
     "fwidth = 0.1 * freq0  # width of the frequency range\n",
     "\n",
@@ -919,9 +909,9 @@
     "    shutoff=1e-7,\n",
     ")  # early shutoff level is decreased to 1e-7 to increase the simulation accuracy\n",
     "\n",
-    "# visulize the simulation setup\n",
+    "# visualize the simulation setup\n",
     "ax = sim.plot(y=0)\n",
-    "ax.get_xaxis().set_visible(False)\n"
+    "ax.get_xaxis().set_visible(False)"
    ]
   },
   {
@@ -1278,9 +1268,7 @@
     }
    ],
    "source": [
-    "sim_data = web.run(\n",
-    "    sim, task_name=\"dbr_cavity\", path=\"data/simulation.hdf5\", verbose=True\n",
-    ")\n"
+    "sim_data = web.run(sim, task_name=\"dbr_cavity\", path=\"data/simulation.hdf5\", verbose=True)"
    ]
   },
   {
@@ -1328,7 +1316,7 @@
     "plt.plot(freqs / freq0, R)\n",
     "plt.xlabel(\"Normalized frequency\")\n",
     "plt.ylabel(\"Reflectance\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1375,7 +1363,7 @@
     "# plot the field distribution at the off-resonance frequency\n",
     "np.squeeze(sim_data[\"field\"].Ex.sel(f=0.9 * freq0)).abs.plot(ax=ax2)\n",
     "ax2.set_title(\"|Ex(x, y)| at $0.9f_0$\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1413,7 +1401,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.11.0"
   },
   "title": "Distributed Bragg Reflector and Cavity | Flexcompute",
   "widgets": {
diff --git a/EMEBends.ipynb b/EMEBends.ipynb
index 9df4f7e7..88e729e3 100644
--- a/EMEBends.ipynb
+++ b/EMEBends.ipynb
@@ -32,13 +32,14 @@
    "outputs": [],
    "source": [
     "# import libraries\n",
-    "import tidy3d as td\n",
-    "import numpy as np\n",
+    "# suppress warnings during calculations\n",
+    "import warnings\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import tidy3d as td\n",
     "from tidy3d import web\n",
     "\n",
-    "# suppress warnings during calculations\n",
-    "import warnings\n",
     "warnings.filterwarnings(\"ignore\")"
    ]
   },
@@ -126,7 +127,6 @@
     "\n",
     "    # iterate through each section and create each mode spec\n",
     "    for i, bend in enumerate(bends):\n",
-    "\n",
     "        length, radius = bend\n",
     "\n",
     "        mode_specs.append(mode_spec.updated_copy(bend_radius=radius))\n",
@@ -138,9 +138,7 @@
     "\n",
     "        size += length\n",
     "\n",
-    "    eme_grid_spec = td.EMEExplicitGrid(\n",
-    "        boundaries=boundaries[:-1], mode_specs=mode_specs\n",
-    "    )\n",
+    "    eme_grid_spec = td.EMEExplicitGrid(boundaries=boundaries[:-1], mode_specs=mode_specs)\n",
     "\n",
     "    # monitor for visualizing fields\n",
     "    eme_field_mon = td.EMEFieldMonitor(name=\"field\", size=(td.inf, 0, td.inf))\n",
@@ -231,7 +229,6 @@
     "\n",
     "# function for creating a list of points representing the bend waveguide from an EME simulation object\n",
     "def eme2Curve(sim):\n",
-    "\n",
     "    mode_specs = sim.eme_grid_spec.mode_specs\n",
     "    boundaries = sim.eme_grid_spec.boundaries\n",
     "    boundaries = np.append(0, boundaries)\n",
@@ -272,7 +269,6 @@
    "outputs": [],
    "source": [
     "def get_fdtd(x, y, sim, width=0.6, thickness=0.4):\n",
-    "\n",
     "    # output angle\n",
     "    theta = np.arctan2(y[-1] - y[-2], x[-1] - x[-2])\n",
     "\n",
@@ -344,7 +340,6 @@
     "\n",
     "    # adjusting the theta angle of the output monitor\n",
     "    if abs(theta - np.pi / 2) < 0.05 or abs(theta - np.pi) < 0.05:\n",
-    "\n",
     "        modeMon = td.ModeMonitor(\n",
     "            name=\"modeMon\",\n",
     "            size=mon_size,\n",
@@ -590,7 +585,7 @@
    ],
    "source": [
     "# running EME simulation\n",
-    "eme_data = web.run(eme_sim, task_name='eme',verbose = True)"
+    "eme_data = web.run(eme_sim, task_name=\"eme\", verbose=True)"
    ]
   },
   {
@@ -609,7 +604,7 @@
    "source": [
     "# analyzing the transmittance\n",
     "t = float(eme_data.smatrix.S21.isel(mode_index_in=0, mode_index_out=0, f=0).abs ** 2)\n",
-    "print(\"Transmittance: %f\" % t)"
+    "print(f\"Transmittance: {t}\")"
    ]
   },
   {
@@ -798,7 +793,7 @@
     }
    ],
    "source": [
-    "eme_data = web.run(eme_sim, task_name='eme',verbose = True)"
+    "eme_data = web.run(eme_sim, task_name=\"eme\", verbose=True)"
    ]
   },
   {
@@ -912,7 +907,7 @@
    "source": [
     "x, y = eme2Curve(eme_sim)\n",
     "fdtd_sim = get_fdtd(x, y, eme_sim)\n",
-    "fdtd_data = web.run(fdtd_sim, task_name='fdtd',verbose = True)"
+    "fdtd_data = web.run(fdtd_sim, task_name=\"fdtd\", verbose=True)"
    ]
   },
   {
@@ -931,10 +926,10 @@
    ],
    "source": [
     "eme = float(eme_data.smatrix.S21.isel(mode_index_in=0, mode_index_out=0, f=0).abs ** 2)\n",
-    "print(\"EME transmittance:  %f\" % eme)\n",
+    "print(f\"EME transmittance:  {eme}\")\n",
     "\n",
     "fdtd = float(fdtd_data[\"modeMon\"].amps.sel(direction=\"+\", mode_index=0).abs ** 2)\n",
-    "print(\"FDTD transmittance: %f\" % fdtd)"
+    "print(f\"FDTD transmittance: {fdtd}\")"
    ]
   },
   {
@@ -973,15 +968,13 @@
     }
    ],
    "source": [
-    "fig,ax = plt.subplots(figsize = (18,8))\n",
-    "eme_data.plot_field(\"field\", \"Hz\", \"real\", mode_index=0, eme_port_index=0, robust=False,\n",
-    "                    ax=ax)\n",
-    "ax.set_title('EME')\n",
+    "fig, ax = plt.subplots(figsize=(18, 8))\n",
+    "eme_data.plot_field(\"field\", \"Hz\", \"real\", mode_index=0, eme_port_index=0, robust=False, ax=ax)\n",
+    "ax.set_title(\"EME\")\n",
     "\n",
-    "fig,ax = plt.subplots()\n",
-    "fdtd_data.plot_field(\"field\", \"Hz\", \"real\", robust=False,\n",
-    "                    ax=ax)\n",
-    "ax.set_title('FDTD')\n",
+    "fig, ax = plt.subplots()\n",
+    "fdtd_data.plot_field(\"field\", \"Hz\", \"real\", robust=False, ax=ax)\n",
+    "ax.set_title(\"FDTD\")\n",
     "\n",
     "plt.show()"
    ]
@@ -2154,15 +2147,13 @@
     "    x, y = eme2Curve(eme_sim)\n",
     "    fdtd_sim = get_fdtd(x, y, eme_sim)\n",
     "\n",
-    "    eme_data = web.run(eme_sim, task_name='eme',verbose = True)\n",
-    "    fdtd_data = web.run(fdtd_sim, task_name='fdtd',verbose = True)\n",
+    "    eme_data = web.run(eme_sim, task_name=\"eme\", verbose=True)\n",
+    "    fdtd_data = web.run(fdtd_sim, task_name=\"fdtd\", verbose=True)\n",
     "\n",
     "    fdtd = float(fdtd_data[\"modeMon\"].amps.sel(direction=\"+\", mode_index=0).abs ** 2)\n",
     "    transmittance_fdtd.append(fdtd)\n",
     "\n",
-    "    eme = float(\n",
-    "        eme_data.smatrix.S21.isel(mode_index_in=0, mode_index_out=0, f=0).abs ** 2\n",
-    "    )\n",
+    "    eme = float(eme_data.smatrix.S21.isel(mode_index_in=0, mode_index_out=0, f=0).abs ** 2)\n",
     "    transmittance_eme.append(eme)"
    ]
   },
@@ -2193,8 +2184,8 @@
     "fig, ax = plt.subplots()\n",
     "ax.plot([20, 40, 60, 80, 100], transmittance_fdtd, \"o\", label=\"FDTD\")\n",
     "ax.plot([20, 40, 60, 80, 100], transmittance_eme, \"*\", label=\"EME\")\n",
-    "ax.set_ylabel('transmittance')\n",
-    "ax.set_xlabel('bend length')\n",
+    "ax.set_ylabel(\"transmittance\")\n",
+    "ax.set_xlabel(\"bend length\")\n",
     "\n",
     "ax.legend()\n",
     "\n",
@@ -2455,8 +2446,8 @@
     }
    ],
    "source": [
-    "eme_data = web.run(eme_sim, task_name='eme',verbose = True)\n",
-    "fdtd_data = web.run(fdtd_sim, task_name='fdtd',verbose = True)\n",
+    "eme_data = web.run(eme_sim, task_name=\"eme\", verbose=True)\n",
+    "fdtd_data = web.run(fdtd_sim, task_name=\"fdtd\", verbose=True)\n",
     "\n",
     "\n",
     "fdtd = float(fdtd_data[\"modeMon\"].amps.sel(direction=\"+\", mode_index=0).abs ** 2)\n",
@@ -2487,7 +2478,7 @@
     }
    ],
    "source": [
-    "print(\"fdtd transmittance: %f\\neme transmittance: %f\" % (fdtd, eme))"
+    "print(f\"fdtd transmittance: {fdtd}\\neme transmittance: {eme}\")"
    ]
   },
   {
@@ -2537,9 +2528,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots(figsize=(60, 16))\n",
-    "eme_data.plot_field(\n",
-    "    \"field\", \"Hz\", \"real\", mode_index=0, eme_port_index=0, robust=False, ax=ax\n",
-    ")\n",
+    "eme_data.plot_field(\"field\", \"Hz\", \"real\", mode_index=0, eme_port_index=0, robust=False, ax=ax)\n",
     "plt.show()"
    ]
   },
@@ -2563,7 +2552,6 @@
    "outputs": [],
    "source": [
     "def euler_curve(A=2.4, L=10, num_points=1000):\n",
-    "\n",
     "    from scipy import integrate\n",
     "\n",
     "    Ls = np.linspace(0, L, num_points)  # L at (x1,y1)\n",
@@ -2572,24 +2560,13 @@
     "\n",
     "    # compute x1 and y1 using the above integral equations\n",
     "\n",
-    "    y1 = np.array(\n",
-    "        [\n",
-    "            integrate.quad(lambda theta: A * np.sin(theta**2 / 2), 0, L / A)[0]\n",
-    "            for L in Ls\n",
-    "        ]\n",
-    "    )\n",
-    "    x1 = np.array(\n",
-    "        [\n",
-    "            integrate.quad(lambda theta: A * np.cos(theta**2 / 2), 0, L / A)[0]\n",
-    "            for L in Ls\n",
-    "        ]\n",
-    "    )\n",
+    "    y1 = np.array([integrate.quad(lambda theta: A * np.sin(theta**2 / 2), 0, L / A)[0] for L in Ls])\n",
+    "    x1 = np.array([integrate.quad(lambda theta: A * np.cos(theta**2 / 2), 0, L / A)[0] for L in Ls])\n",
     "\n",
     "    return x1, y1\n",
     "\n",
     "\n",
     "def euler_bend(offset, length, num_points=1000):\n",
-    "\n",
     "    from scipy.optimize import fsolve\n",
     "\n",
     "    def get_params(params):\n",
@@ -2622,7 +2599,6 @@
    "outputs": [],
    "source": [
     "def get_local_radius(x, y):\n",
-    "\n",
     "    yd = np.gradient(y, x, edge_order=2)\n",
     "    ydd = np.gradient(yd, x, edge_order=2)\n",
     "    R = ((1 + yd**2) ** (3 / 2)) / ydd\n",
@@ -2658,7 +2634,7 @@
     "local_radius = get_local_radius(x_euler, y_euler)\n",
     "\n",
     "fig, ax = plt.subplots()\n",
-    "ax.set_title('Euler S-bend')\n",
+    "ax.set_title(\"Euler S-bend\")\n",
     "ax.plot(x_euler, y_euler)\n",
     "\n",
     "plt.show()"
@@ -2682,7 +2658,7 @@
    ],
    "source": [
     "fig, ax = plt.subplots()\n",
-    "ax.set_title('Local curvature radius')\n",
+    "ax.set_title(\"Local curvature radius\")\n",
     "ax.plot(x_euler, local_radius)\n",
     "\n",
     "plt.show()"
@@ -2714,7 +2690,7 @@
    "source": [
     "dR = np.gradient(local_radius, x_euler, edge_order=2)\n",
     "fig, ax = plt.subplots()\n",
-    "ax.set_title('Local radius derivative')\n",
+    "ax.set_title(\"Local radius derivative\")\n",
     "ax.plot(x_euler, abs(dR))\n",
     "\n",
     "plt.show()"
@@ -2744,7 +2720,6 @@
     "\n",
     "\n",
     "def get_bends(x_euler, y_euler, grid_per_section=5, plot=False):\n",
-    "\n",
     "    # local radius and its derivatives\n",
     "    local_radius = get_local_radius(x_euler, y_euler)\n",
     "    dR = np.gradient(local_radius, x_euler, edge_order=2)\n",
@@ -2764,9 +2739,7 @@
     "    # discontinuity points in the derivative that are continuous in the local radius curve\n",
     "    discontinuity = x_euler[np.isnan(dR)][~np.isnan(local_radius[np.isnan(dR)])]\n",
     "    discontinuity_y = y_euler[np.isnan(dR)][~np.isnan(local_radius[np.isnan(dR)])]\n",
-    "    discontinuity_radius = local_radius[np.isnan(dR)][\n",
-    "        ~np.isnan(local_radius[np.isnan(dR)])\n",
-    "    ]\n",
+    "    discontinuity_radius = local_radius[np.isnan(dR)][~np.isnan(local_radius[np.isnan(dR)])]\n",
     "\n",
     "    # retrieving the x position and local radius of the EME grid\n",
     "    x_positions = np.concatenate(\n",
@@ -2802,21 +2775,25 @@
     "        fig, ax = plt.subplots()\n",
     "        ax.plot(x_euler, y_euler)\n",
     "        ax.plot(\n",
-    "            x_euler[section1][section1_points], y_euler[section1][section1_points], \"o\",\n",
-    "            label = 'section 1'\n",
+    "            x_euler[section1][section1_points],\n",
+    "            y_euler[section1][section1_points],\n",
+    "            \"o\",\n",
+    "            label=\"section 1\",\n",
     "        )\n",
     "        ax.plot(\n",
     "            x_euler[section2][section2_points],\n",
     "            y_euler[section2][section2_points],\n",
     "            \"o\",\n",
-    "            label = 'section 2'\n",
+    "            label=\"section 2\",\n",
     "        )\n",
     "        ax.plot(\n",
-    "            x_euler[section3][section3_points], y_euler[section3][section3_points], \"o\",\n",
-    "            label = 'section 3'\n",
+    "            x_euler[section3][section3_points],\n",
+    "            y_euler[section3][section3_points],\n",
+    "            \"o\",\n",
+    "            label=\"section 3\",\n",
     "        )\n",
-    "        ax.plot(discontinuity, discontinuity_y, \"*\",label = 'discontinuity')\n",
-    "        \n",
+    "        ax.plot(discontinuity, discontinuity_y, \"*\", label=\"discontinuity\")\n",
+    "\n",
     "        ax.legend()\n",
     "\n",
     "        plt.show()\n",
@@ -3623,23 +3600,17 @@
     "\n",
     "grids = 10\n",
     "for l in [30, 50, 80, 100]:\n",
-    "\n",
     "    x_euler, y_euler = euler_bend(offset=5, length=l)\n",
     "    bends = get_bends(x_euler, y_euler, grid_per_section=grids, plot=False)\n",
     "\n",
-    "    eme_sim = get_bend_eme(\n",
-    "        wvgIn=2, wvgOut=2, bends=bends, plane_size=(15, 15), num_modes=5\n",
-    "    )\n",
+    "    eme_sim = get_bend_eme(wvgIn=2, wvgOut=2, bends=bends, plane_size=(15, 15), num_modes=5)\n",
     "    fdtd_sim = get_fdtd(x_euler, y_euler, eme_sim)\n",
     "\n",
-    "\n",
-    "    eme_data = web.run(eme_sim, task_name='eme',verbose = True)\n",
-    "    fdtd_data = web.run(fdtd_sim, task_name='fdtd',verbose = True)\n",
+    "    eme_data = web.run(eme_sim, task_name=\"eme\", verbose=True)\n",
+    "    fdtd_data = web.run(fdtd_sim, task_name=\"fdtd\", verbose=True)\n",
     "\n",
     "    fdtd = float(fdtd_data[\"modeMon\"].amps.sel(direction=\"+\", mode_index=0).abs ** 2)\n",
-    "    eme = float(\n",
-    "        eme_data.smatrix.S21.isel(mode_index_in=0, mode_index_out=0, f=0).abs ** 2\n",
-    "    )\n",
+    "    eme = float(eme_data.smatrix.S21.isel(mode_index_in=0, mode_index_out=0, f=0).abs ** 2)\n",
     "\n",
     "    transmittance_fdtd.append(fdtd)\n",
     "    transmittance_eme.append(eme)"
@@ -3665,10 +3636,10 @@
     "# plot results\n",
     "fig, ax = plt.subplots()\n",
     "\n",
-    "ax.plot([30, 50, 80, 100], transmittance_fdtd, 'o',label = 'FDTD')\n",
-    "ax.plot([30, 50, 80, 100], transmittance_eme, '*',label = 'EME')\n",
-    "ax.set_ylabel('transmittance')\n",
-    "ax.set_xlabel('bend length')\n",
+    "ax.plot([30, 50, 80, 100], transmittance_fdtd, \"o\", label=\"FDTD\")\n",
+    "ax.plot([30, 50, 80, 100], transmittance_eme, \"*\", label=\"EME\")\n",
+    "ax.set_ylabel(\"transmittance\")\n",
+    "ax.set_xlabel(\"bend length\")\n",
     "ax.legend()\n",
     "\n",
     "plt.show()"
@@ -3885,8 +3856,8 @@
     "fdtd_sim = get_fdtd(x_euler, y_euler, eme_sim)\n",
     "\n",
     "\n",
-    "eme_data = web.run(eme_sim, task_name='eme',verbose = True)\n",
-    "fdtd_data = web.run(fdtd_sim, task_name='fdtd',verbose = True)\n"
+    "eme_data = web.run(eme_sim, task_name=\"eme\", verbose=True)\n",
+    "fdtd_data = web.run(fdtd_sim, task_name=\"fdtd\", verbose=True)"
    ]
   },
   {
@@ -3914,7 +3885,7 @@
     "fdtd = float(fdtd_data[\"modeMon\"].amps.sel(direction=\"+\", mode_index=0).abs ** 2)\n",
     "eme = float(eme_data.smatrix.S21.isel(mode_index_in=0, mode_index_out=0, f=0).abs ** 2)\n",
     "\n",
-    "print(\"fdtd transmittance: %f\\neme transmittance: %f\" % (fdtd, eme))"
+    "print(f\"fdtd transmittance: {fdtd}\\neme transmittance: {eme}\")"
    ]
   },
   {
diff --git a/EMESolver.ipynb b/EMESolver.ipynb
index 169fe55b..82e2249b 100644
--- a/EMESolver.ipynb
+++ b/EMESolver.ipynb
@@ -28,11 +28,10 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
     "import tidy3d as td\n",
-    "from tidy3d.plugins import waveguide\n",
-    "import tidy3d.web as web"
+    "import tidy3d.web as web\n",
+    "from matplotlib import pyplot as plt\n",
+    "from tidy3d.plugins import waveguide"
    ]
   },
   {
@@ -117,16 +116,13 @@
     "    mode_specs=[\n",
     "        td.EMEModeSpec(num_modes=2),\n",
     "        td.EMEModeSpec(num_modes=4),\n",
-    "        td.EMEModeSpec(num_modes=2)\n",
-    "    ]\n",
+    "        td.EMEModeSpec(num_modes=2),\n",
+    "    ],\n",
     ")\n",
     "\n",
-    "eme_field_mon = td.EMEFieldMonitor(\n",
-    "    name=\"field\", \n",
-    "    size=(td.inf, 0, td.inf)\n",
-    ")\n",
+    "eme_field_mon = td.EMEFieldMonitor(name=\"field\", size=(td.inf, 0, td.inf))\n",
     "\n",
-    "lengths = np.linspace(l, 6*l, 100)\n",
+    "lengths = np.linspace(l, 6 * l, 100)\n",
     "\n",
     "eme_sim = td.EMESimulation(\n",
     "    center=(0, 0, l / 2),\n",
@@ -140,7 +136,7 @@
     "    freqs=[td.C_0 / 1.55],\n",
     "    axis=2,\n",
     "    port_offsets=(w, w),\n",
-    "    sweep_spec=td.EMELengthSweep(scale_factors=list(lengths / l))\n",
+    "    sweep_spec=td.EMELengthSweep(scale_factors=list(lengths / l)),\n",
     ")\n",
     "\n",
     "_ = eme_sim.plot(y=0, monitor_alpha=0)\n",
@@ -498,10 +494,10 @@
     }
    ],
    "source": [
-    "transmission = eme_sim_data.smatrix.S21.isel(mode_index_in=0, mode_index_out=0, f=0).abs**2\n",
+    "transmission = eme_sim_data.smatrix.S21.isel(mode_index_in=0, mode_index_out=0, f=0).abs ** 2\n",
     "plt.plot(lengths, transmission)\n",
-    "plt.xlabel('Coupling region length')\n",
-    "plt.ylabel('Transmission')\n",
+    "plt.xlabel(\"Coupling region length\")\n",
+    "plt.ylabel(\"Transmission\")\n",
     "plt.show()"
    ]
   },
@@ -554,6 +550,7 @@
     "# Large number to be used in replacement of td.inf when necessary.\n",
     "_inf = 1e3\n",
     "\n",
+    "\n",
     "def get_taper(\n",
     "    taper_shape=\"linear\",\n",
     "    init_coord=[pad_x, taper_w_in / 2],\n",
@@ -598,15 +595,14 @@
     "    )\n",
     "    return taper\n",
     "\n",
+    "\n",
     "size_x = taper_l + 2 * pad_x\n",
     "size_y = taper_w_out + 2 * pad_y\n",
     "size_z = box_thick + clad_thick + taper_t\n",
     "\n",
     "# Silicon dioxide box + cladding layers\n",
     "sio2_medium = td.Structure(\n",
-    "    geometry=td.Box.from_bounds(\n",
-    "        rmin=(pad_x, -_inf, -_inf), rmax=(_inf, _inf, _inf)\n",
-    "    ),\n",
+    "    geometry=td.Box.from_bounds(rmin=(pad_x, -_inf, -_inf), rmax=(_inf, _inf, _inf)),\n",
     "    medium=mat_sio2,\n",
     ")\n",
     "\n",
@@ -648,27 +644,20 @@
    "source": [
     "eme_num_modes = 30\n",
     "\n",
-    "eme_grid_spec = td.EMEUniformGrid(\n",
-    "    num_cells=40,\n",
-    "    mode_spec=td.EMEModeSpec(num_modes=eme_num_modes)\n",
-    ")\n",
+    "eme_grid_spec = td.EMEUniformGrid(num_cells=40, mode_spec=td.EMEModeSpec(num_modes=eme_num_modes))\n",
     "\n",
-    "eme_field_mon = td.EMEFieldMonitor(\n",
-    "    name=\"field\", \n",
-    "    size=(td.inf, td.inf, 0),\n",
-    "    num_modes=1\n",
-    ")\n",
+    "eme_field_mon = td.EMEFieldMonitor(name=\"field\", size=(td.inf, td.inf, 0), num_modes=1)\n",
     "\n",
     "eme_mode_mon = td.EMEModeSolverMonitor(\n",
-    "    name=\"modes\", \n",
+    "    name=\"modes\",\n",
     "    size=(td.inf, td.inf, td.inf),\n",
     "    center=(pad_x, 0, 0),\n",
     "    num_modes=9,\n",
-    "    eme_cell_interval_space=40\n",
+    "    eme_cell_interval_space=40,\n",
     ")\n",
     "\n",
     "eme_coeff_mon = td.EMECoefficientMonitor(\n",
-    "    name=\"coeffs\", \n",
+    "    name=\"coeffs\",\n",
     "    size=(td.inf, td.inf, td.inf),\n",
     ")\n",
     "\n",
@@ -687,7 +676,7 @@
     "    eme_grid_spec=eme_grid_spec,\n",
     "    port_offsets=(pad_x, pad_x),\n",
     "    store_port_modes=False,\n",
-    "    sweep_spec=td.EMELengthSweep(scale_factors=list(lengths / taper_l))\n",
+    "    sweep_spec=td.EMELengthSweep(scale_factors=list(lengths / taper_l)),\n",
     ")\n",
     "\n",
     "eme_sim.plot(z=0, monitor_alpha=0)\n",
@@ -1050,7 +1039,9 @@
     }
    ],
    "source": [
-    "eff = 20 * np.log10((eme_sim_data.smatrix.S21.isel(mode_index_in=0, mode_index_out=0)).abs.squeeze())\n",
+    "eff = 20 * np.log10(\n",
+    "    (eme_sim_data.smatrix.S21.isel(mode_index_in=0, mode_index_out=0)).abs.squeeze()\n",
+    ")\n",
     "plt.plot(lengths, eff)\n",
     "plt.xlabel(\"Length (um)\")\n",
     "plt.ylabel(\"Power (dB)\")\n",
@@ -1088,7 +1079,9 @@
     "for mode_index in range(9):\n",
     "    i = mode_index // 3\n",
     "    j = mode_index % 3\n",
-    "    eme_sim_data.plot_field(\"modes\", \"Ey\", val=\"abs\", eme_cell_index=0, mode_index=mode_index, ax=ax[i][j])\n",
+    "    eme_sim_data.plot_field(\n",
+    "        \"modes\", \"Ey\", val=\"abs\", eme_cell_index=0, mode_index=mode_index, ax=ax[i][j]\n",
+    "    )\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -1098,7 +1091,7 @@
    "id": "e76e36f3-e142-4d68-bffa-fc917ab70e8f",
    "metadata": {},
    "source": [
-    "We can also make a 2D plot of the scattering matrix as a function of cell index and mode index (although note that the coefficients are not normalized). This shows us that the scattering is occuring towards the beginning of the taper. Note that once again the default behavior of the monitor is to record at `sweep_index=0` (length 1 um)."
+    "We can also make a 2D plot of the scattering matrix as a function of cell index and mode index (although note that the coefficients are not normalized). This shows us that the scattering is occurring towards the beginning of the taper. Note that once again, the default behavior of the monitor is to record at `sweep_index=0` (length 1 um)."
    ]
   },
   {
@@ -1166,10 +1159,7 @@
    "source": [
     "sweep_spec = td.EMEModeSweep(num_modes=list(np.arange(1, eme_num_modes)))\n",
     "\n",
-    "eme_sim = eme_sim.updated_copy(\n",
-    "    sweep_spec=sweep_spec,\n",
-    "    monitors=[]\n",
-    ")"
+    "eme_sim = eme_sim.updated_copy(sweep_spec=sweep_spec, monitors=[])"
    ]
   },
   {
@@ -1520,7 +1510,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.0"
   }
  },
  "nbformat": 4,
diff --git a/EdgeCoupler.ipynb b/EdgeCoupler.ipynb
index 4849924a..c4ec821e 100644
--- a/EdgeCoupler.ipynb
+++ b/EdgeCoupler.ipynb
@@ -41,12 +41,12 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# Tidy3D imports\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n"
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -86,7 +86,7 @@
     "\n",
     "mat_si = td.Medium(permittivity=3.48**2)  # Taper and substrate material.\n",
     "mat_sio2 = td.Medium(permittivity=1.44**2)  # Box and cladding material.\n",
-    "mat_air = td.Medium(permittivity=1.00)  # External medium material.\n"
+    "mat_air = td.Medium(permittivity=1.00)  # External medium material."
    ]
   },
   {
@@ -118,7 +118,7 @@
     "spot_size = 2.5\n",
     "\n",
     "box_thick = 3.0  # Silicon dioxide box layer.\n",
-    "clad_thick = 3.0  # Silicon dioxide layer covering the taper.\n"
+    "clad_thick = 3.0  # Silicon dioxide layer covering the taper."
    ]
   },
   {
@@ -153,7 +153,7 @@
     "run_time = 30 / freq_width\n",
     "\n",
     "# Large number to be used in replacement of td.inf when necessary.\n",
-    "_inf = 1e3\n"
+    "_inf = 1e3"
    ]
   },
   {
@@ -222,7 +222,7 @@
     "        medium=mat_si,\n",
     "        name=tap_name,\n",
     "    )\n",
-    "    return taper\n"
+    "    return taper"
    ]
   },
   {
@@ -306,9 +306,7 @@
     "\n",
     "    # Silicon dioxide box + cladding layers\n",
     "    sio2_medium = td.Structure(\n",
-    "        geometry=td.Box.from_bounds(\n",
-    "            rmin=(pad_x, -_inf, -_inf), rmax=(_inf, _inf, _inf)\n",
-    "        ),\n",
+    "        geometry=td.Box.from_bounds(rmin=(pad_x, -_inf, -_inf), rmax=(_inf, _inf, _inf)),\n",
     "        medium=mat_sio2,\n",
     "    )\n",
     "\n",
@@ -336,8 +334,8 @@
     "                run_time=run_time,\n",
     "            )\n",
     "        )\n",
-    "    sims = {sim_name: sim for sim_name, sim in zip(tap_names, sim_tap)}\n",
-    "    return sims\n"
+    "    sims = dict(zip(tap_names, sim_tap))\n",
+    "    return sims"
    ]
   },
   {
@@ -421,7 +419,7 @@
    ],
    "source": [
     "# Get the list of simulation objects.\n",
-    "sim_tap = get_simulations(tap_length=taper_l)\n"
+    "sim_tap = get_simulations(tap_length=taper_l)"
    ]
   },
   {
@@ -438,7 +436,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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kqXMMOJIkqXMMOJK2rCTPaL5A8IjmCdvXJXlq2+2S1D4f9CdpS0vyG8ARwDcAB6rqt1pukqRNwIAjaUtrvntuH/Ag8ANVNddykyRtAk5RSdrqjgWOBI5iMJIjSY7gSNrakuwFrgBOAU6sqvNbbpKkTWC67QZI0uFK8lLgkap6R5Ie8A9JfrCqPth22yS1yxEcSZLUOa7BkSRJnWPAkSRJnWPAkSRJnWPAkSRJnWPAkSRJnWPAkSRJnWPAkSRJnWPAkSRJnWPAkSRJnWPAkSRJnWPAkSRJnWPAkSRJnWPAkbTlJbk5yRnr+H7/Nsn16/V+kibPgCNp20tSSb51fr+q/q6qvr3NNklaGwOOpE5LMt12GyRNngFH0oZJ8vQkH0/yQJIrk1yR5DeSnJvk70fqLoyiJPmRJP+U5MtJbk1y0Ujdf5/kliT3Jrlw5NhFSa5O8rYkXwbOTXJakn9Mcn+S25P8lyQ7mvofbk79ZJKvJHlBktOTHBh6z5OT/EWSu5tr/pcN6C5J68iAI2lDNAHiL4G3AscA7wR+apWnfxV4KfBNwI8Ar0jy4837PgV4A/DvgccDxwJPGDn/LODq5vy3A3PA/wYcB3w/8BzgFwCq6lnNOd9VVUdW1ZUjv0cP+CvgFmA3cBJwxSp/D0ktMeBI2ijfB8wAf1hVj1TV1cC+1ZxYVX9bVZ+uqn5VfQr4M+DZzeGfBv6qqj5cVQ8B/yfQH3mLf6yqv2zO/3pVXVtVH62q2aq6GfjjofdbyWkMgtR/rKqvVtWDVfX3K50kqV3OTUvaKI8HbquqGiq7ZTUnJvle4GLgqcAOYCeDEaD59711vm5VfTXJvSNvcevwTpJvA34f2AN8I4N/+65d5e9xMnBLVc2usr6kTcARHEkb5XbgpCQZKtvVvH6VQdAAIMm3jJz7DmAvcHJVPQ54IzD/PrczCB3z534jg2mqYTWy/wbgvwOnVtVjgV8ber+V3ArscrGytLUYcCRtlH8EZoH/kGQmyU8ymO4B+CTwHUmeluQI4KKRc48C7quqB5OcBrx46NjVwPOTPLNZ5/NaVv637Cjgy8BXkvwb4BUjx+8EnrTEuf+NQai6OMljkhyR5H9c4XqSWmbAkbQhquph4CeBc4H7gBcAf9Ecu4FBMPkb4EZgdE3LLwCvTfIA8GrgqqH3vQ74RQajPLcDXwQOsLxfZRCSHgDeBFw5cvwi4PLmU1Znj/wec8CPAt8KfKG51gtWuJ6klmXx9LgkbZwkfwocqKpfb7stkrrNERxJktQ5BhxJktQ5TlFJkqTOcQRHkiR1Tuee63DcccfV7t27J3rNL9zzwESvJ0mSBu6+5YZ7qur40fLOBZzdu3ezf//+iV7zFZd9YKLXkyRJA2982Rljn5DuFJUkSeocA44kSeocA44kSeocA44kSeocA44kSeocA44kSeocA44kSeocA44kSeocA44kSeocA44kSeocA44kSeqczn0XlSRJ2mBV7Jjrc8TsLEfMznHE7Cw75+bYOTtH2m5bw4AjSdI21usPgsrO2TmOmJ1j59zc0P7sorKFMDM7t+mngAw4kiRtdVVM94udc4uDys5FAWWwf3B7EFamq9pu/YZoNeAkORP4I6AH/ElVXTxy/FeAlwOzwN3Az1bV2K9FlyRpq0sVOxfCycGQMr9/xJiy+deuBpXD1VrASdIDXg88FzgA7Euyt6o+O1Ttn4A9VfW1JK8ALgFeMPnWSpK0OlP9YsfcXDOa0l8IJDvm+kuGk/nXHf1+283vjDZHcE4DbqqqzwMkuQI4C1gIOFX1oaH6HwVeMtEWSpK2n2YB7c65uUFQmW1eR7Z3zPVHgssg1Mz0HUnZDNoMOCcBtw7tHwC+d5n6LwP+etyBJOcB5wHs2rVrvdonSdqCev0+O5oAMhxQFvbn5vfnj42W9zfNJ4F0+LbEIuMkLwH2AM8ed7yqLgUuBdizZ4/RWZK2omah7I65xYHk0WzvnJuj51oU0W7AuQ04eWj/CU3ZIknOAC4Enl1VD02obZKk1apiut9nx1yfmf7B0DEz118UQGZGRlZm5kb2+3P0zCZaJ20GnH3AqUlOYRBsXgi8eLhCkqcDfwycWVV3Tb6JktRRVU3g6DPThI+ZhaBxMIAstT1ab7M/E0XbT2sBp6pmk5wPvJfBx8Qvq6rrkrwW2F9Ve4HfAY4E3pkE4AtV9WNttVmSWlHFVBNI5oPGcCg5+DocRPpMN6MpM0MjKwdHWvy0jrqt1TU4VXUNcM1I2auHts+YeKMk6XBV0atipgkS88Fjun/o/uLtuUVBZfj4TH8QVJy6kR6dLbHIWJLWRRNApkeCx3S/FkLFQhgZCSbDdYdDyGhdp2qkzcGAI6ldzSdnejUID72hIDEoOxhApvt9ekPb8+Xz504vCiKHlvl8Emn7MOBIWhjZ6DWhorcocCxfNggYtRA+RoPK8LHh/YU6fqRX0gYw4EiTVMVUwVQVUzX4Yz/VBIupoZAx2O4vKh/U64/UWVzWm3/f/qGBZWpoemZxaCmfGyKpcww42ryqCIMvn5sqCIM/0lkICNWUDe3XyH5zznDZUnXH/fRWqDMfHMafNya8GCQkaSIMOGv09X+9h2+754tDJYv/gC31uO8sVKtD6mToLdK833xZ5s9ZZj/NH9FDtw9eLwvh4eA1purge2Xhdbju0q9TzfXnQ8V8CDm03mB7PqgMh5ZDAswSfSdJ0koMOGv0wGf+hR+8+daVK0qSpInxP5IlSVLnGHAkSVLnGHAkSVLnGHAkSVLnGHAkSVLnGHAkSVLntBpwkpyZ5PokNyW5YMzxnUmubI5/LMnuFpopSZK2mNYCTpIe8HrgecBTgBclecpItZcBX6yqbwX+APjtybZSkiRtRSsGnCS/lOToDbj2acBNVfX5qnoYuAI4a6TOWcDlzfbVwHOSLPVwYEmSJGB1IzgnAPuSXNVMKa1XwDgJGH4E8IGmbGydqpoFvgQcO/pGSc5Lsj/J/rvvvnudmidJkraqFQNOVf06cCrwZuBc4MYkv5nkyRvctlWrqkurak9V7Tn++OPbbo4kSWrZqtbgVFUBdzQ/s8DRwNVJLlnDtW8DTh7af0JTNrZOkmngccC9a7imJEnaBlazBueVSa4FLgE+AnxnVb0C+B7gp9Zw7X3AqUlOSbIDeCGwd6TOXuCcZvungQ82YUuSJGlJq/k28WOAn6yqW4YLq6qf5PmHe+Gqmk1yPvBeoAdcVlXXJXktsL+q9jKYFntrkpuA+xiEIEmSpGWtGHCq6jXLHPvcWi5eVdcA14yUvXpo+0HgZ9ZyDUmStP34JGNJktQ5BhxJktQ5BhxJktQ5BhxJktQ5q/kUlZbxDbtO4FPffNzCflj5U+wZqjJ4LHQtcawWlWWhTq24P7o9eI/h7cH7p4Zfh99rcdm416mm7lSN1h3sT1WZoCVJrTDgrNFR/2YX/7Dr8W03Y3OrYmooSE0NhaBQCwFpvnyqBkFpaihILXV8uM7Cfi3eH1dnqmnToWXjfmCq+ofU7/UX1+sNBUtJUrsMONp4Cf0A2+DPf5YIQL2R8l71F9XpjZzTq6LX7x9ybLDdH9oe1OsNHZ/fn6piun/wOtM+I1PSNmLAkdZRJcwlzAGP9NpuzYgaDk+DkDUainr9g6FoelFw6i8cGy6b7h88d7q/uM78/qDeoG73I66kzcKAI20XCf2EPi2Fr4VRpYPhaBCChrbr0LLh8un++PNGj033+45YSducAUfSZDQB6+EpGHw7ywZrwtTiALQ4BM2MKZ+ZW1x/uM7gWC2U9wxR0qZlwJHUTQmzvTDb27jP8qVqIRDNLApBB8uGjy9Vd6Y/x8zcwToz/b7TedIaGXAk6TBVwsPTPR5e7xGpodGnQeiZWxSQdswdDEUH9/tM9+cG23OHnrej31/fNkqbnAFHkjabodGnB9frPasWjRDNzM0tBKP5IDQIR3OLAtS47R1zTs9p82sl4CQ5BrgS2A3cDJxdVV8cqfM04A3AY4E54HVVdeVEGypJXZHwSK/HI731GW2a6i8OPDvm5kPQfFg6tHy0/nzAkjZCWyM4FwAfqKqLk1zQ7L9qpM7XgJdW1Y1JHg9cm+S9VXX/hNsqSRrRn5riwakpHlzjn5H5dUyDwDMcfh7dtk9N16i2As5ZwOnN9uXA3zIScKrqhqHtf01yF3A8cP9EWihJ2nAL65im1zCy1DxGYOei4HMw/OycPbi/c+j4oLw5x5Gkzmkr4JxQVbc323cAJyxXOclpwA7gn5c4fh5wHsCuXbvWsZmSpE0vYbbXY7bX46vMHN5bVB0SfHYO7e9cdGx+v79wzKm2zWfDAk6SvwG+ZcyhC4d3qqqSLLlaLcmJwFuBc6pq7B1UVZcClwLs2bPHlW+SpEelEh6anuah6Wke2Pnoz59fk7RzKBDtnJ0dKptdFIqOWNifY+ec4WgjbFjAqaozljqW5M4kJ1bV7U2AuWuJeo8F3gNcWFUf3aCmSpK0JgtrkmYe/Z/V+dGjg8Fo9HUQho6YHT02y0zf/6ZfSltTVHuBc4CLm9d3j1ZIsgN4F/CWqrp6ss2TJGkyhkePHq2pfn8k/MwuCkKD7aZs7uD2dlhz1FbAuRi4KsnLgFuAswGS7AF+vqpe3pQ9Czg2ybnNeedW1Scm31xJkjaf/tQUX5+a4uszj27t0VR/MGp0xEL4ORiMDt0/WGcrjRi1EnCq6l7gOWPK9wMvb7bfBrxtwk2TJKnz+lPhwanpRz2l1uv3DwaeofBzxNAoEWyOEOSTjCVJ0qrMTU3x1R07+OqOtluyMp+NJEmSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOidVm+NbP9dLkgeA69tuxzZxHHBP243YJuzrybCfJ8e+nozt0M9PrKrjRwu7+G3i11fVnrYbsR0k2W9fT4Z9PRn28+TY15OxnfvZKSpJktQ5BhxJktQ5XQw4l7bdgG3Evp4c+3oy7OfJsa8nY9v2c+cWGUuSJHVxBEeSJG1zBhxJktQ5nQo4Sc5Mcn2Sm5Jc0HZ7uizJzUk+neQTSfa33Z6uSHJZkruSfGao7Jgk709yY/N6dJtt7Iol+vqiJLc19/Unkvxwm23sgiQnJ/lQks8muS7JK5ty7+t1tkxfb8v7ujNrcJL0gBuA5wIHgH3Ai6rqs602rKOS3AzsqaquP0BqopI8C/gK8JaqempTdglwX1Vd3AT3o6vqVW22swuW6OuLgK9U1e+22bYuSXIicGJVfTzJUcC1wI8D5+J9va6W6euz2Yb3dZdGcE4Dbqqqz1fVw8AVwFktt0l6VKrqw8B9I8VnAZc325cz+AdLa7REX2udVdXtVfXxZvsB4HPASXhfr7tl+npb6lLAOQm4dWj/ANv4f9gJKOB9Sa5Ncl7bjem4E6rq9mb7DuCENhuzDZyf5FPNFJbTJusoyW7g6cDH8L7eUCN9Ddvwvu5SwNFkPbOqvht4HvCLzXC/NlgN5pS7Ma+8Ob0BeDLwNOB24PdabU2HJDkS+HPgl6vqy8PHvK/X15i+3pb3dZcCzm3AyUP7T2jKtAGq6rbm9S7gXQymCLUx7mzm1ufn2O9quT2dVVV3VtVcVfWBN+F9vS6SzDD4g/v2qvqLptj7egOM6+vtel93KeDsA05NckqSHcALgb0tt6mTkjymWcBGkscAPwR8ZvmztAZ7gXOa7XOAd7fYlk6b/4Pb+Am8r9csSYA3A5+rqt8fOuR9vc6W6uvtel935lNUAM1H3/4Q6AGXVdXr2m1RNyV5EoNRGxh8I/077Ov1keTPgNOB44A7gdcAfwlcBewCbgHOrioXx67REn19OoNh/AJuBn5uaJ2IDkOSZwJ/B3wa6DfFv8ZgbYj39Tpapq9fxDa8rzsVcCRJkqBbU1SSJEmAAUeSJHWQAUeSJHWOAUeSJHWOAUeSJHWOAUeSJHWOAUeSJHWOAUfSlpXkGc0XCB7RPGH7uiRPbbtdktrng/4kbWlJfgM4AvgG4EBV/VbLTZK0CRhwJG1pzXfP7QMeBH6gquZabpKkTcApKklb3bHAkcBRDEZyJMkRHElbW5K9wBXAKcCJVXV+y02StAlMt90ASTpcSV4KPFJV70jSA/4hyQ9W1QfbbpukdjmCI0mSOsc1OJIkqXMMOJIkqXMMOJIkqXMMOJIkqXMMOJIkqXMMOJIkqXMMOJIkqXMMOJIkqXMMOJIkqXMMOJIkqXMMOJIkqXMMOJIkqXMMOJK0hCT/Lsn7Vln3oiRv2+g2SVodA44kAUl2J6kk0/NlVfX2qvqhNtsl6fAYcCRJUucYcCStSZLHJ/nzJHcn+Zck/yHJMUkOJPnRps6RSW5K8tJm/0+TvDHJ+5M8kOT/S/LEoff8gST7knypef2BoWN/m+Q/J/lIc+77khw3dPz7kvxDkvuTfDLJ6as898PN6/1JvpLk+5Ocm+Tvh87/oyS3JvlykmuT/NuN6FNJa2fAkXTYkkwB/w/wSeAk4DnALwPPAH4WeFOSbwb+APhEVb1l6PR/B/xn4DjgE8Dbm/c8BngP8H8BxwK/D7wnybFD574Y+F+AbwZ2AL/anHtSc+5vAMc05X+e5PiVzgWe1bx+U1UdWVX/OOZX3gc8rXnvdwDvTHLEih0laeIMOJLW4hnA8VX12qp6uKo+D7wJeGFVvQ94J/AB4IeBnxs59z1V9eGqegi4EPj+JCcDPwLcWFVvrarZqvoz4L8DPzp07n+tqhuq6uvAVQxCB8BLgGuq6pqq6lfV+4H9zfVXOndFVfW2qrq3adfvATuBb1/t+ZImx4AjaS2eCDy+mQ66P8n9wK8BJzTHLwWeCvxpVd07cu6t8xtV9RXgPuDxzc8tI3VvYTBCNO+Ooe2vAUcOtednRtrzTODEVZy7oiS/muRzzdTZ/cDjGIxASdpkpleuIklLuhX4l6o6dfRAkh6DgPMW4BeS/NeqummoyslDdY9kMO3zr83PE1lsF/D/rrI9b62q//VR/RYDtdzBZr3N/8FgGu66quon+SKQw7iWpA3mCI6ktfhvwANJXpXkG5L0kjw1yTMYjOQUg7U4vwO8pQk98344yTOT7GCwFuejVXUrcA3wbUlenGQ6yQuApwB/tYr2vA340ST/c9OWI5KcnuQJqzj3bqAPPGmJ40cBs0296SSvBh67iveV1AIDjqTDVlVzwPMZrGP5F+Ae4E+AHwR+BXhpU+e3GYSdC4ZOfwfwGgZTU9/DYP0MzVTW84H/HbiXwajJ86vqnlW051bgLAbh6m4GIzr/kVX8W1dVXwNeB3ykmd76vpEq72UwinQDgymzBxmaZpO0uaRq2VFZSVp3Sf4UOFBVv952WyR1kyM4kiSpcww4kiSpc5yikiRJneMIjiRJ6pzOPQfnuOOOq927d0/0ml+454GJXk+SJA3cfcsN91TV8aPlnQs4u3fvZv/+/RO95isu+8BErydJkgbe+LIzRp98DjhFJUmSOsiAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOseAI0mSOqdz30UlSZJaUEWvqu1WLDDgSJK03VUx3S92zM01P/2h7YP7Ow85trher4V88/olyg04kiRtYVP9/iGhY6Y/HD76zMzNsbN5PTSYDLa7tmal1YCT5Ezgj4Ae8CdVdfHI8V8BXg7MAncDP1tVY78WXZKkLaOKmf4gWMzMHQwoi8sO7u9YCCeHlm+maaHNpLWAk6THYGTpucABYF+SvVX12aFq/wTsqaqvJXkFcAnwgsm3VpK03U31i5n+IHwMB5FxoWSp7YV6/X7bv07ntTmCcxpwU1V9HiDJFcBZwELAqaoPDdX/KPCSibZQkrRlTfX7CwFj8DoY8ZhupnSWCiszQ8eG6007UrKltBlwTgJuHdo/AHzvMvVfBvz1uANJzgPOA9i1a9d6tU+SNAlVTDVTNuNCxkx/EEqGg8pydedfnbrZ3rbEIuMkLwH2AM8ed7yqLgUuBdizZ493tCRthOZjwDPNKMh8mJjfnl4IHzW0PRpOFp87ON7Op2/UbW0GnNuAk4f2n9CULZLkDOBC4NlV9dCE2iZJW1Pzcd/FoWPwupqy0eOj+137pI26q82Asw84NckpDILNC4EXD1dI8nTgj4Ezq+quyTdRktbXVBM+Rn8GQWLxscUhpMaWD4eQ+XqSWgw4VTWb5HzgvQw+Jn5ZVV2X5LXA/qraC/wOcCTwziQAX6iqH2urzZK6KzUcLkZDyEr7qy9zKkaajFbX4FTVNcA1I2WvHto+Y+KNkrQ5NOs9hoNCb3i7xpdPN4tLR0PGaJ3pfjXvYfCQumhLLDKW1K5U0RsKBL2FkFFj90fLlg4mh4aShTI/ASNpDQw40haRoaDQq34TOA6Ggvng0BsJC/N1D4aI1dQZeU/DhqQtxoAjraSZKumNBIvewujEoYFjalFIWKrufCAZ/76j4cNPr0jS6hlwtLlUMVUwVYv/6E8tCgB1yPH5IDC1sD0aKkbfY/Gx3kIoGX0f12ZI0lZkwNmOaj4g1KJAMLWw3190bL7ufAgYPW++/GC95ULJ4vcZF1bSdv9IkrY8A856qdGQUIv+6I+WTY2tf2iwWOo9xl1rYSpjhXNcTyFJ6joDzhrd9f79nLf/U66PkCRpE/Hv8lqViz8lSdps/NssSZI6x4AjSZI6x4AjSZI6x4AjSZI6x4AjSZI6p9WAk+TMJNcnuSnJBWOO70xyZXP8Y0l2t9BMSZK0xbQWcJL0gNcDzwOeArwoyVNGqr0M+GJVfSvwB8BvT7aVkiRpK1ox4CT5pSRHb8C1TwNuqqrPV9XDwBXAWSN1zgIub7avBp6TxCf5S5KkZa1mBOcEYF+Sq5oppfUKGCcBtw7tH2jKxtapqlngS8Cx63R9SZLUUSsGnKr6deBU4M3AucCNSX4zyZM3uG2rluS8JPuT7L/77rvbbo4kSWrZqtbgVFUBdzQ/s8DRwNVJLlnDtW8DTh7af0JTNrZOkmngccC9Y9p3aVXtqao9xx9//BqaJEmSumA1a3BemeRa4BLgI8B3VtUrgO8BfmoN194HnJrklCQ7gBcCe0fq7AXOabZ/GvhgE7YkSZKWtJpvEz8G+MmqumW4sKr6SZ5/uBeuqtkk5wPvBXrAZVV1XZLXAvurai+DabG3JrkJuI9BCJIkSVrWigGnql6zzLHPreXiVXUNcM1I2auHth8EfmYt15AkSduPTzKWJEmdY8CRJEmdY8CRJEmdY8CRJEmdY8BZq6kp5vzyCEmSNpXVfExcy/jmM76H13zhfqhiqvnpzb/2Dy1bTfmgrL+o3rj3PXS/P/b4aFnPRwlJkjrOgLNeEvoJfQaPet7URkLToYGpf8ix4f1xQWqw3V/y/XojQWzwPgfD16JQ13b/SJK2PAPOdrTJw1jGBKbemODVGwpP44PU4rLRYDXd748NXaN1HfGSpK3HgKNNpxJme2HTLBEbGc2aXghN48PTcPl0E8oOvq6u7vD7TvebcNd2P0jSFmLAkVaSMJcw13LCyJjQMx+ShoPTaACbr7s4XPXHlk+Pec/pvqNYkrYeA460RcyPbM0yxUMTv3gtDjwL4Wk4GA2OLS6b318cmIbLppd4r5m+oUrS4TPgSFpZG+FqaB3VfBCaHh1ZGiqbPz4flhbqjqkztp6jVFKnGHAkbU4LU4NTPDyJy40EnvlRppmhkaXR4/M/M4ccP7SOQUqarFYCTpJjgCuB3cDNwNlV9cWROk8D3gA8FpgDXldVV060oZK2jUp4pNfjkV5vQ68ztTAC1Wdm7tAwNDMcnOaWDlHDx4bPc2pPGmhrBOcC4ANVdXGSC5r9V43U+Rrw0qq6McnjgWuTvLeq7p9wWyVp3fSnQp9BkPr6zAZcoFkvtVRYWjpAHVpnZu7Q+n6aT1tFWwHnLOD0Zvty4G8ZCThVdcPQ9r8muQs4Hrh/Ii2UpK1ofr1Ub4oH1/u9m3VRo+FnOBiN3547pHxxiJqj58CT1llbAeeEqrq92b4DOGG5yklOA3YA/7zRDZMkLWFoXdRD6/zXY2po5GjH3KHhaDgYzYeiQ8vnFr2H6522tw0LOEn+BviWMYcuHN6pqkqy5F2Y5ETgrcA5VdVfos55wHkAu3btOuw2S5La0Z+a4qGpwaf0vrpO7zk1HJjGBKMdc6MhabC/Y4l60wamLWXDAk5VnbHUsSR3Jjmxqm5vAsxdS9R7LPAe4MKq+ugy17oUuBRgz5493oGSJPpTUzw8NcXD6/SXbqpfTPfnhgLTSttzC6NRw9s7mpGmrE+ztIS2pqj2AucAFzev7x6tkGQH8C7gLVV19WSbJ0nSYv2p8PDU9PoEpqpmdGhxIJoPQDuGgtDBOouDkyNLy2sr4FwMXJXkZcAtwNkASfYAP19VL2/KngUcm+Tc5rxzq+oTk2+uJEnraNFjCdb2cbqpfv+Q4HPw9dDQtHh7/rzuLfRuJeBU1b3Ac8aU7wde3my/DXjbhJsmSdKW0p+a4sGpKR5cy5/05vECg1GjOXbOjgtD47d3Du1vpoXdPslYkqTtbujxAoc9olTFVNWmWVtkwJEkSWuX0M9miTf4UEpJktQ9BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5qU301ebrIckDwPVtt2ObOA64p+1GbBP29WTYz5NjX0/GdujnJ1bV8aOFXfw28eurak/bjdgOkuy3ryfDvp4M+3ly7OvJ2M797BSVJEnqHAOOJEnqnC4GnEvbbsA2Yl9Pjn09Gfbz5NjXk7Ft+7lzi4wlSZK6OIIjSZK2OQOOJEnqnE4FnCRnJrk+yU1JLmi7PV2W5OYkn07yiST7225PVyS5LMldST4zVHZMkvcnubF5PbrNNnbFEn19UZLbmvv6E0l+uM02dkGSk5N8KMlnk1yX5JVNuff1Olumr7flfd2ZNThJesANwHOBA8A+4EVV9dlWG9ZRSW4G9lRV1x8gNVFJngV8BXhLVT21KbsEuK+qLm6C+9FV9ao229kFS/T1RcBXqup322xblyQ5ETixqj6e5CjgWuDHgXPxvl5Xy/T12WzD+7pLIzinATdV1eer6mHgCuCsltskPSpV9WHgvpHis4DLm+3LGfyDpTVaoq+1zqrq9qr6eLP9APA54CS8r9fdMn29LXUp4JwE3Dq0f4Bt/D/sBBTwviTXJjmv7cZ03AlVdXuzfQdwQpuN2QbOT/KpZgrLaZN1lGQ38HTgY3hfb6iRvoZteF93KeBosp5ZVd8NPA/4xWa4XxusBnPK3ZhX3pzeADwZeBpwO/B7rbamQ5IcCfw58MtV9eXhY97X62tMX2/L+7pLAec24OSh/Sc0ZdoAVXVb83oX8C4GU4TaGHc2c+vzc+x3tdyezqqqO6tqrqr6wJvwvl4XSWYY/MF9e1X9RVPsfb0BxvX1dr2vuxRw9gGnJjklyQ7ghcDeltvUSUke0yxgI8ljgB8CPrP8WVqDvcA5zfY5wLtbbEunzf/BbfwE3tdrliTAm4HPVdXvDx3yvl5nS/X1dr2vO/MpKoDmo29/CPSAy6rqde22qJuSPInBqA0MvpH+Hfb1+kjyZ8DpwHHAncBrgL8ErgJ2AbcAZ1eVi2PXaIm+Pp3BMH4BNwM/N7RORIchyTOBvwM+DfSb4l9jsDbE+3odLdPXL2Ib3tedCjiSJEnQrSkqSZIkwIAjSZI6yIAjSZI6x4AjSZI6x4AjSZI6x4AjSZI6x4AjSZI6x4AjactK8ozmCwSPaJ6wfV2Sp7bdLknt80F/kra0JL8BHAF8A3Cgqn6r5SZJ2gQMOJK2tOa75/YBDwI/UFVzLTdJ0ibgFJWkre5Y4EjgKAYjOZLkCI6krS3JXuAK4BTgxKo6v+UmSdoEpttugCQdriQvBR6pqnck6QH/kOQHq+qDbbdNUrscwZEkSZ3jGhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5BhxJktQ5/z820QnRkc/7pAAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
        "
"
       ]
@@ -458,7 +456,7 @@
     "    ax.set_ylim(-1.5 * taper_w_out / 2, 1.5 * taper_w_out / 2)\n",
     "    ax.set_aspect(\"auto\")  # Used to better visualize the shapes.\n",
     "    ax.set_title(tap_n)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -475,7 +473,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "
"
       ]
@@ -496,7 +494,7 @@
     "sim_tap[\"linear\"].plot(z=0, ax=ax1)\n",
     "sim_tap[\"linear\"].plot(x=pad_x, ax=ax2)\n",
     "sim_tap[\"linear\"].plot(x=pad_x + taper_l + pad_x / 2, ax=ax3)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -741,7 +739,7 @@
     "    cost = web.estimate_cost(bat.taskId, verbose=False)\n",
     "    tot_cost += cost\n",
     "    print(f\"Maximum FlexCredit cost for {sim_name} = {cost:.2f}\")\n",
-    "print(f\"Maximum FlexCredit cost for batch = {tot_cost:.2f}\")\n"
+    "print(f\"Maximum FlexCredit cost for batch = {tot_cost:.2f}\")"
    ]
   },
   {
@@ -856,7 +854,7 @@
     }
    ],
    "source": [
-    "batch_taper = batch.run(path_dir=\"data/data_taper\")\n"
+    "batch_taper = batch.run(path_dir=\"data/data_taper\")"
    ]
   },
   {
@@ -1037,7 +1035,7 @@
     }
    ],
    "source": [
-    "sim_tap_results = {tl: sn for tl, sn in batch_taper.items()}\n"
+    "sim_tap_results = dict(batch_taper.items())"
    ]
   },
   {
@@ -1054,7 +1052,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "
"
       ]
@@ -1071,14 +1069,14 @@
     "    sim_data.plot_field(\"field_xy\", \"Ey\", f=freq_c, val=\"abs\", ax=ax)\n",
     "    ax.set_title(tap_n)\n",
     "    ax.set_aspect(\"auto\")  # Used to better visualize the shapes.\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Bellow, we have the $|E_{y}|$ field recorded by the monitors positioned at the taper tip and taper output of the quadratic structure. We can clearly visualize the Gaussian profile corresponding to the fields launched by a lensed fiber at the taper tip. In contrast, the output field corresponds to the transverse electric polarization of the fundamental output waveguide mode."
+    "Below, we have the $|E_{y}|$ field recorded by the monitors positioned at the taper tip and taper output of the quadratic structure. We can clearly visualize the Gaussian profile corresponding to the fields launched by a lensed fiber at the taper tip. In contrast, the output field corresponds to the transverse electric polarization of the fundamental output waveguide mode."
    ]
   },
   {
@@ -1095,7 +1093,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "
"
       ]
@@ -1108,22 +1106,18 @@
    ],
    "source": [
     "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(8, 4))\n",
-    "sim_tap_results[\"quadratic\"].plot_field(\n",
-    "    \"field_input\", \"Ey\", f=freq_c, val=\"abs\", ax=ax1\n",
-    ")\n",
+    "sim_tap_results[\"quadratic\"].plot_field(\"field_input\", \"Ey\", f=freq_c, val=\"abs\", ax=ax1)\n",
     "ax1.set_title(\"|Ey| at taper tip\")\n",
     "ax1.collections[-1].set_clim(0, 20)\n",
     "ax1.set_xlim(-2, 2)\n",
     "ax1.set_ylim(-2, 2)\n",
     "\n",
-    "sim_tap_results[\"quadratic\"].plot_field(\n",
-    "    \"field_output\", \"Ey\", f=freq_c, val=\"abs\", ax=ax2\n",
-    ")\n",
+    "sim_tap_results[\"quadratic\"].plot_field(\"field_output\", \"Ey\", f=freq_c, val=\"abs\", ax=ax2)\n",
     "ax2.set_title(\"|Ey| at taper output\")\n",
     "ax2.collections[-1].set_clim(0, 50)\n",
     "ax2.set_xlim(-2, 2)\n",
     "ax2.set_ylim(-2, 2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1147,7 +1141,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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4KD4+reJPGTu9caNLAMqXd+OlU0rv3q7ir13b22/8Vyofhmx+R9WNTImLcxV/XJwrhw65b/4BAa596tFH3f1rr/U2XmOMyYNSJk+tX++OKbcPHHDf9lMmTw0YAC+95Cr/gvbn1JKC/CYpyaWrsbGwcqU7xsVBqVLQvLlro7rvPvjgA/ftv0gRryM2xpg85fDhtDHT6SdPxcenTZxq0sTtet64sRvgV1gaUi0pyMsSEtxvbUyMq/xjY113QLVqLgFo3hxeftkdK1f2OlpjjMlTjh1Lq/xTxk6vW+fWUkuZONWkCfTr5442hMqSgrwjfQIQHe2O69a5kSotW0KLFm54arNmOT8nxRhj8pFz51wf/+rVaZOo1q51LQIp46ebNnX9/U2bWuWfGUsKvJCU5DqroqPTypo1aQlAy5auC6BZMyhTxutojTEmT1B1e6FdOHlq61bXxH/zza786U/uWKeO9aBeLksKcpqqm7OyfDmsWOHKypVuo5/AwLTlqJo3L3gjVowx5gqdPev6+VPGT69a5RKAokXd2OlbboHu3eGFF1xrQKlSXkdcMFhSkN2OHIGoqLQEYMUKl6q2agXBwTBkiGsJKFfO60iNMSZPOHz4/IlTcXFuul+9eq7BNCDANf0HBNjwqZxmScHVOHvW/famtAIsX+4WAWrZ0iUADz8MX37pVqqwDixjTCGnCrt2ucbS9OXoUVfhN2sGnTrBs8+6vn8/P68jLnwsKciqlN/mZctg6VJX1qxxE1WDg6FLF3jlFdeOVVjmrhhjzEUkJ7u+/vSzp1eudAv6tGjhekwfeAD+9S+35K/1/ecNlhRczNmz7rd46VJYssQdExKgTRtX3n/fjQm45hqvIzXGGE8lJbmlfWNi0mZQr1zpVvtr3tw1nv7lL+521apeR2syY0lBir170yr/JUtcB1fDhi4BuO02lwTUrWvdAMaYQi052SUAKTOno6NdL2qlSu57UosW8MYbLgG44QavozWXq3AmBYmJbhLr4sUuAViyBI4fdwlA27bw7rtuVoBNBzTGFGLJyW6nv/Szp1euhAoVXALQsiUMHeoSARs7XTAUjqTgxAk3CHDRIpcILF/uBv+1bQtdu7q0tkED69QyxhRaqrBzp6v4o6LSWgKuv94lAIGB8PrrLhEoX97raE1OKZhJQUICjBsHCxe6RGDTJteW1b69G9bapo21axljCrWDB8+fPR0VBcWLu0bSwEB48UV3rFjR60hNbhJV9TqGbBdYrJhG9+rlkoAOHVxqW7Kk12EZY4wn4uPd4L+Uyn/FCrc2QFCQmzwVHOxuV6/udaTmSolIjKoGXvX7FMikIDBQo6OjvQ7DGGNyXWKiWwkwZemUFSvcQkD+/mkJQHCw9ZgWNNmVFBTM7gNjjCkEVGH37vPXT4uNdd/4UxZRffxxtySwNZaarPAsKRCR8kAYUBvYAdytqr9lcN4/gT5AEWAm8KwWxOYNY4y5hJMn3QDA5cvdOmrLl7uWgVatXHn9ddcNcP31Xkdq8isvWwpeAWar6nsi8orv/svpTxCRtkA74BbfQ4uATsC8XIzTGGNyXXKyGyO9bFla2brVfetv1QruuQc++ghq17blU0z28TIpGACE+G6PxFX0L19wjgJ+QAlAgOLA/twJzxhjcs/hw64LICUBWL7cTf1r08YlAYMGuf0BrBvA5CQvk4LKqrrXd3sf8Lu9r1R1qYjMBfbikoJ/q+qGXIzRGGOyXWKi2zolfSvA3r1uCmCbNjB4MHz3nVsl0JjclKNJgYjMAqpk8NRr6e+oqorI78YJiMhNQGOghu+hmSLSQVUXZnDu48DjADVr1rza0I0xJtscOuQq/pSV1KOj3fppbdq4mdMvvghNmtheasZ7OZoUqGq3iz0nIvtFpKqq7hWRqsCBDE67HVimqid9r5kKtAF+lxSo6tfA1+CmJGZH/MYYc7lSVlFP2Ux12TK3o3pwsEsCXnoJWre2ZYFN3uRl90Ek8BDwnu84IYNzfgH+JCL/wHUfdAI+ya0AjTHmUo4cOb8VICoKqlVLawX461+tFcDkH14mBe8BP4rIIGAncDeAiAQCT6rqY8A4oAuwBjfocJqqTvQoXmNMIZeyQ2DKPmpLlrh1AoKCXBLwwguuFcD2BjD5la1oaIwxF3HqlPvmn7Kh6tKlcO210K6d20+tbVu4+WYoZsvAGY/ZiobGGJPNfv01LQFYvNgtF3zzzS4JeOQR+Ppr1zVgTEFlSYExplBKSnIDAhcvTisnTrgEoF07+Phjt5daqVJeR2pM7rGkwBhTKJw65RYHWrTIlWXL3DoA7dtDly7wxhvQsKGtDmgKN0sKjDEF0oED7tt/ShKwdq3rCmjfHp54Ar791hYHMuZClhQYY/I9Vdi2zVX+Cxe64/79biBgu3bwz3+6GQKlS3sdqTF5myUFxph8JykJVq9OSwIWLnTrAHTo4FoCnn0Wmja1tQGMuVyWFBhj8ryzZ93SwAsWuARgyRKoWtUlAH37wvvv226BxmQHSwqMMXlOfLxbE2DBAleio90gwI4d4bHHYMQIGw9gTE6wpMAY47mjR92gwPnzXRKwZg00b+6SgFdecWMDrrvO6yiNKfgsKTDG5LojR1zlP3++K1u2uA2DOnaEf/zDLRVs6wMYk7nExEQ2bdrEqlWrsu09LSkwxuS4Q4fSkoB582D7drdXQKdO8PnnbmZAiRJeR2lM3nX8+HFWr15NXFxcatmwYQPVq1cnICAg2z7HkgJjTLZLSQLmzXNl5043NbBTJ7dUcIsWULy411Eak/eoKr/++ut5lX9cXBz79u3D39+fZs2aERgYyGOPPYa/vz9lypQBQLJplK0lBcaYq3bkSForwLx5sGOHSwJCQmDYMJcE2KZBxpwvKSmJzZs3s3LlytQSFxdHkSJFaN68Oc2aNeOuu+7i7bffpkGDBhTNhTm29t/UGHPZTpxwUwPnzHFl61Y3GLBzZ9cS0LKlJQHGpHfmzBnWrl17XgKwZs0aqlSpkpoAPP/88zRr1oyqVatm2zf/y2X/bY0xl3TqlFsbYO5clwSsWePGAXTpYmMCjLlQfHw8q1atIjY2NrVs2rSJm266iRYtWtC8eXPuvfdeAgICKFu2rNfhnseSAmPM7yQmQlQUzJ7tSlQUBAS4JODdd90gQZsdYAwcO3bsvMo/NjaWX375hSZNmtC8eXNatWrFn//8Z/z9/SmVD/7TWFJgjEEV1q2DWbNcErBwIdSqBV27wosvuqmC117rdZTGeOu3335j5cqVxMTEpJa9e/cSEBBAixYt6N69Oy+//DKNGzemeD4dSWtJgTGF1K5dLglISQRKl4Zu3eD++yE01FYMNIXbb7/9RmxsLDExMURHRxMTE8OBAwcICAigZcuW9O7dmzfeeINGjRrlygDA3GJJgTGFxPHjbkzAzJkuETh0yLUEdOsGb78Ndet6HaEx3jh+/DixsbFER0enlv3799OsWTNatmxJv379+Pvf/55rMwC8ZEmBMQVUQgKsWOGSgJkz3a6CrVtD9+4wZowbI1CkiNdRGpO74uPjiYuLIyoqKjUB2L17NwEBAQQGBtKnTx+GDBlCw4YNC3wCkBFLCowpIFTd1MAZM1wSMG8e1KnjkoChQ92OgvlgnJMx2ebs2bOsXr2a6Ojo1CRg69atNG3alKCgILp06cJLL71EkyZNKGZzaIEsJgUiUgQIAKoBp4G1qnogJwMzxlzasWNuPMCMGTB9Opw7B7feCnff7dYLsHEBprBISkpi06ZNrFixgqioKKKioli7di033XQTQUFBBAcH89RTT3HzzTdTsmRJr8PNszJNCkSkHvAy0A3YAhwE/IAGInIK+AoYqarJOR2oMQaSkyEmBqZNc0nAqlVu0aAePWDwYGjaFDxa88SYXKOq7N69m6ioKFasWMGKFSuIjo6mYsWKBAcHExwczMCBA2nevDnXXHON1+HmK6KqF39SZAzwH2ChXnCiiFQC/gj8pqojczTKyxQYGKjR0dFeh2FMtti3z7UETJvmugUqVXJJQM+e0KGDdQmYgu/o0aNER0ezYsUKli9fzooVK0hKSiIoKIhWrVoRHBxMUFAQN9xwg9ehekZEYlQ18KrfJ7OkIL+ypMDkZwkJsGwZTJ3qEoHt290MgR49XLnxRq8jNCbnJCYmsmbNGpYtW8by5ctZvnw5u3btSl0IKKUloFatWp4tBZwXZVdScMkxBSJSC4hX1UMi0hpoD2xT1Yir/XBjjPPrry4BmDrVjRGoW9e1BHz2mZsxYGOgTEG1e/fu1ARg2bJlrFy5klq1atGqVSvatGnDc889h7+/vw0EzCWXGlMwBHgIUBH5ATe2YB7QR0Q6qepzOR6hMQVQQoLbS2DKFJcI/PqrmyXQrx/8+99QpYrXERqT/c6cOUNMTAzLli1LLWfOnKF169a0bt2aN998k6CgoDy3H0BhcqnU616gMVAa+AWooqqnRKQYEJfDsRlToOzZ41oDpkxxiwfVrw+9esFXX0FwMBTCKdGmAFNVfvnlF5YuXcrSpUtZtmwZa9eupXHjxrRu3ZrbbruN9957j7p161o3QB5yqaTgjKqeA86JyDZVPQWgqokici7nwzMm/0pKcosHTZ7sEoEdO9x0wf794YsvoHJlryM0JvucPXuWlStXsmTJEpYsWcLSpUtJTEykTZs2tG3blg8++IDAwEBKly7tdagmE5dKCq4XkTsAAa7z3cZ339p3jLnA0aNuquDkya5boEoV6NMHPv3U7Sxo3aKmoDh48GBqArB48WJWrlxJgwYNaNu2Lbfffjv//Oc/qVOnjrUC5DOXmpL4TWYvVtVHsj2ibGCzD0xuUYX1610SMHkyrFzpdhTs08eVmjW9jtCYq5ecnMzGjRtZvHhxahKwf/9+WrduTbt27Wjbti2tWrXiWttK0zO5Mvsgr1b6xnjpzBm3hPCkSa6oQt++8PLL0LmzrRtg8r8zZ84QHR3NokWLUhOBsmXL0q5dO9q1a8df/vIXmjZtWij3BijoLjX74PnMnlfVj7I3HGPypr17XUvApEkwZw7ccotLBCZOBH9/W0XQ5G9Hjx5lyZIlLFy4kEWLFhEbG0ujRo3o0KEDDz74IF999RXVqlXzOkyTCy7Vw5nSFtQQCAIifff7AStyKihjvKYKcXGu0p840W00dOutcOedMGwYVKjgdYTGXLn9+/ezcOFCFixYwIIFC9i2bRtBQUF06NCBIUOG0Lp1a+sKKKQu1X3wdwARWQC0UNUTvvtDgck5Hp0xuej0adcKMHGiaxEoVcqtG/DPf7odBosX9zpCY67ML7/8kpoAzJ8/n4MHD9KuXTs6duzIf//7X1q0aEGJEiW8DtPkAVkdC10ZSD8F8ZzvsSsiIn8AhuLWQAhW1QxHBYpIT+BToCgwTFXfu9LPNCYj+/e7BGDiRJg7F5o1c4nA7NnQoIF1C5j8R1XZtm0b8+fPT00CTp8+TceOHenUqRODBw/G39+fIkWKeB2qyYOymhR8C6wQkZSljW8DRlzF564F7sDtspghESkKfAF0B3YDUSISqarrr+JzTSGnCuvWuSQgMhI2bHD7Cdx1F4SGQiHeT8XkU6rKli1bmDdvHvPnz2fevHmICJ06daJTp0787W9/o2HDhjY10GRJlpICVX1XRKYCHXwPPaKqK6/0Q1V1A3CpX9JgYKuq/uw79wdgAGBJgbksCQmwcKFLAiIj3fbD/frB3/8OISFgraYmP1FVtm7dyrx585g7dy7z5s2jWLFihISE0LVrV9566y1bJdBcsUvNPiijqicBVDUWiM3snGxWHdiV7v5uoNXFThaRx4HHAWra5PBC7+hRt6RwZKQ73nSTW0lw/Hi4+WbrFjD5y44dO5gzZw5z585l7ty5AHTu3Jlu3brxzjvv2CJBJttcqqVggojEAROAGFWNBxCRukBn4G7gf8C4C18oIrOAjLZ1eU1VJ1xN0BlR1a+Br8EtXpTd72/yvp07XRIwYYJbXrhjR5cIfPgh2Gwqk5/s2bOHuXPnMmfOHObMmcPp06fp3LkzXbp0YciQIdx0002WBJgccanZB11FpDfwBNBORMoDCcAm3OyDh1R130Ve2+0qY/sVSL9zfA3fY8YAadMGJ0xwZfdut4rg00+7FoEyZbyO0JisOXLkCPPmzWP27NnMmTOH/fv3ExISQufOnXnhhRdo3LixJQEmV1xyTIGqTgGm5EIsF4oC6otIHVwycC/wRw/iMHlIQgIsWJCWCBQvDgMGuL0F2ra1vQVM/nDq1CkWLVrErFmzmD17Nlu2bKFdu3Z06dKF0aNHExAQYKsFGk948idURG4HPgcqApNFJE5Ve4hINdzUw96+nRgHA9NxUxKHq+o6L+I13jp+3I0LmDDBbTJUv75LBKZMgSZNbHyAyfsSExOJiYlh1qxZzJo1i6ioKJo1a0a3bt345JNPaNWqla0TYPKETDdEyq9sQ6T8b8+etPEBixdDu3YuEejf38YHmLwvZYbArFmzmDlzJnPnzqVGjRp0796drl270rFjR1sx0GSrXNkQyZjctHGjGwswfjxs2gS9e8Mjj0BYGFx3ndfRGZO5I0eOMHv2bGbOnMnMmTM5d+4c3bt358477+TLL7+kSpWMxl0bk7dcMinwLSK0TlUb5UI8phBJTnazBFISgfh41xrwzjtu5oC1ppq8LCEhgWXLljF9+nRmzJjBxo0b6dChA7feeivPPfecDQ40+VJWBhomicgmEampqr/kRlCm4Dp71i0nPH686x4oXx5uuw1GjYKWLW18gMnbtm7dyowZM5g+fTrz58/npptu4tZbb+X999+nbdu2lCxZ0usQjbkqWe0+KAesE5EVQHzKg6raP0eiMgXK8eNuUOD48W7AYNOmLhGYP98NGjQmrzpx4gRz5sxh+vTpTJ8+ndOnT9OjRw8GDhzIsGHDqFixotchGpOtspoUvJGjUZgCZ98+1xIQEeEGCnbo4LoGPvkErGvV5FXJycnExcUxffp0pk2bRmxsLK1bt6Znz56MHz8ef39/6xIwBVpW9z6YLyK1gPqqOktESuOmCRqTassW1xoQEeE2GurVywYKmrzv0KFDzJgxg2nTpjF9+nTKli1Lr169eOWVV+jUqROlS5f2OkRjck2WkgIR+RNuX4HyQD3cvgT/BbrmXGgmr1OF2FiXBIwfD0eOuNaAN9+Ezp1toKDJm5KSkoiOjmbq1KlMnTqVjRs3EhISQs+ePRk6dCh169b1OkRjPJPV7oOncbsWLgdQ1S0iUinHojJ5VlISLFoE4eEuEShRAm6/Hf73P2jVCmyLdpMXHTx4kOnTpzN16lRmzJhB5cqV6dWrF//4xz9o3769LRxkjE9Wk4KzqnoupS9NRIoBBW/VI5Ohs2dh1izXIhAZCTVquERg8mQ3aNC6WE1ek741YMqUKWzatIkuXbqkJgK2k6oxGctqUjBfRF4FSolId+ApYGLOhWW8duKEmzEQEeFmDNx8M9xxB7z2GtSp43V0xvze4cOHmTFjBlOmTGHatGlUqlSJ3r178/7779OuXTtrDTAmC7KaFLwCDALW4HZMnAIMy6mgjDcOHnQtAeHhsHChW1r49tvdZkOVK3sdnTHnU1VWrVrF5MmTmTJlCmvWrCEkJITevXvzzjvvUKtWLa9DNCbfyWpS0BkYpar/y8lgTO7btcu1BkREuEGDPXrA/ffD999D2bJeR2fM+U6ePMmsWbNSE4HSpUvTp08f3nzzTTp27Iifn5/XIRqTr2U1KXgQ+I+IHAEWAguARar6W45FZnLM5s2uNSA8HLZtc5sMPf88dOsGpUp5HZ0x59u2bRuTJk1i8uTJLF26lNatW9OnTx9eeukl6tvqV8Zkq6yuU/AQgG9r47uAL4BqWX298ZYqrFqVlggcOeK6Bf7xD7fHQPHiXkdoTJqEhAQWLVqUmggcO3aM3r178+STT/LTTz/Z7oLG5KCsrlNwP9ABuBk4BPwb12Jg8qiUzYbCw+Gnn1xicOed8PXX0Lq1TR00ecuhQ4eYOnUqkyZNYsaMGdSvX58+ffowevRomjdvThH7hTUmV2T1m/4nwDbcgkVzVXVHTgVkrlxiohsgGB7uxghcd51LBH76CQICbOqgyTtUlXXr1jFx4kQmTZrE2rVr6dq1K/369ePTTz+1bYaN8UhWuw8qiEhToCPwrojUBzap6gM5Gp25pHPnYPZsV/FPmAC1armpg7NmQSPb7NrkIWfPnmXevHmpiYCI0K9fP4YMGUJISIjtMGhMHpDV7oPrgJpALaA2UBZIzrmwTGZOnYLp010iMGUKNGniEoHXX4fatb2Ozpg0Bw4cYMqUKURGRjJ79mz8/f3p168fkydPpkmTJra5kDF5TFa7DxalK/9W1d05F5LJyIkTbgXBn36CGTMgMNB1DXzwAVSt6nV0xjjpuwUmTpzI+vXr6datGwMGDOCrr76yrYaNyeOy2n1wC4CIlMnZcEx6x47BxIkwbhzMmQPt27tE4D//gQoVvI7OGCchIYEFCxYQGRlJZGQkqkq/fv0YOnQonTp1sm4BY/KRrHYf+APf4XZJFBE5CDykqmtzMrjC6MgRt6rguHGwYIHbbfCuu2DECLj+eq+jM8Y5evQoU6dOJTIykmnTptGgQQP69+9PZGQk/v7+1i1gTD6V1e6Dr4HnVXUugIiE+B5rmzNhFS4HD7pBguPGwdKl0LUr/PGPblXB667zOjpjnB07dqS2BqxYsYKOHTsyYMAAPvroI6paH5YxBUJWk4JrUhICAFWdJyLX5FBMhcL+/W7a4NixEBPjlhceNMglBmWsk8bkAapKbGwsEyZMYMKECezdu5e+ffsyePBgunfvzjXX2J8AYwqarCYFP4vIG7guBID7gZ9zJqSCa98+N1Bw3DhYuRJ694bBg11CULq019EZA+fOnWP+/PmMHz+eyMhISpUqxYABA/jiiy9o06YNRYsW9TpEY0wOympS8CjwdyAcUNxqho/mVFAFSUoiMHasW2q4Tx947jmXCNjeLSYvOH78OFOnTmXChAlMnTqVhg0bMmDAAGbMmEGjRo1sfIAxhUimSYGI+AFPAjfhtk1+QVUTciOw/CyjROAvf7FEwOQde/fuJTIykvHjx7N48WLat2/Pbbfdxr/+9S8bH2BMIXaploKRQAKuZaAX0Bh4Lodjypf27k1LBFavhr593c6Dt95qiYDJGzZv3kxERATjx49n48aN9O7dm0cffZQff/zRNhkyxgCXTgqaqOrNACISCqzI+ZDyj/37XSLw449pLQIvvGCJgMkbUgYKRkREEBERwW+//cZtt93G3//+d0JCQihRooTXIRpj8phLJQWpXQWqmmh9i276YHi4SwRiYqxrwOQtSUlJLFq0iPDwcMaPH0/JkiW5/fbbCQ0NJTg42HYbNMZk6lJJQYCIHPfdFqCU774AqqqFYhb94cNu+mBYmNuOuFcvN2ugZ08oVcrr6Exhd/bsWWbPnk14eDiRkZHUqFGDO+64gylTptj+AsaYy5JpUqCqhXb+0bFjMH68SwQWL3ZdAo8/7hYZsumDxmvx8fFMnTqV8PBwpk6dir+/P3fccQevvfYaderU8To8Y0w+ldUpiYXCiRNur4GwMJg3zy0x/MADrqvAFhQyXjt27BiTJk3ip59+Yvbs2bRu3Zo77riDjz/+mMqVK3sdnjGmACj0ScHp0273wR9+gJkz3aZD99wD334LZct6HZ0p7A4fPkxkZCTjxo1j4cKFdOrUibvuuothw4ZRvnx5r8MzxhQwhTIpOHfOJQBjxsCkSRAU5BKBr78G+ztrvHbw4EEiIiIYN24cy5cvp3v37tx///2MGTOG62wzDGNMDio0SUFSEsyf7xKBiAho1AgGDoR//Qus5dV4bf/+/URERDB27FhiYmLo2bMnjz/+OBEREbbHgDEm13iSFIjIH4ChuMWQglU1OoNzbgS+BSrjllb+WlU/vZzPUYVly1wiMHYsVKvmEoHYWKhZ8+p/DmOuxr59+wgPD2fs2LGsXLmS3r17M3jwYHr27Ekpm9ZijPGAVy0Fa4E7gK8yOScRt6xyrIhcC8SIyExVXX+pNz99Gl591SUDfn4uEZg/Hxo0yKbojblC+/bt46effmLs2LHExcXRp08fnn32WXr06GGJgDHGc54kBaq6Ach0/rSq7gX2+m6fEJENQHXgkknBli2uuyAiAgICwKZpGy8dOHCAn376iR9//JGVK1fSt29f/vKXv9CjRw/8bMUrY0weki/GFIhIbaA5sDyTcx4HHgeoWbMm77+fO7EZk5EjR44QHh5OWFgYUVFR9O7d21oEjDF5Xo4lBSIyC6iSwVOvqeqEy3ifMsBPwHOqevxi56nq18DXAIGBgXqZ4Rpz1Y4fP86ECRMICwtj4cKFdO/enSeeeIIJEyZQ2la8MsbkAzmWFKhqt6t9DxEpjksIRqtq+NVHZUz2OnXqFJMmTeKHH35g9uzZdOrUiYEDBzJmzBjbedAYk+/k2e4DcQMOQoENqvqR1/EYk+LcuXPMmDGDMWPGMHnyZFq1asW9995LaGgo5cqV8zo8Y4y5Yl5NSbwd+ByoCEwWkThV7SEi1YBhqtobaAc8AKwRkTjfS19V1SlexGwKt+TkZBYsWMD3339PeHg4jRs3ZuDAgXz88cdUqlTJ6/CMMSZbeDX7IAKIyODxPUBv3+1FuN0YjfGEqhIbG8v3339PWFgYFStWZODAgcTGxlLTFrowxhRAebb7wBivbN26le+//57Ro0eTlJTEwIEDmTFjBk2aNPE6NGOMyVGWFBiDW2Y4LCyM0aNHs2PHDu655x6+/fZbgoODM11PwxhjChJLCkyhFR8fz4QJExg1ahRLliyhX79+vPXWW3Tt2pVixey/hjGm8LG/fKZQSUpKYvbs2YwaNYrIyEjatm3L/fffz9ixY23jIWNMoWdJgSnwVJW4uDhGjRrFmDFjqF69Ovfffz8ffPABlW2LTGOMSWVJgSmwfv31V0aPHs23335LfHw8999/P3PmzKFRo0Zeh2aMMXmSJQWmQImPjyc8PJxvv/2WmJgY7rzzTr788kvat29PkSJFvA7PGGPyNEsKTL6XnJzM/PnzGTlyJOPHj6ddu3YMGjSIyMhI23zIGGMugyUFJt/asmULI0eO5LvvvqNcuXI8+OCDvPfee1SpktE+XMYYYy7FkgKTrxw9epQff/yRkSNHsm3bNv74xz8SGRlJQECA16EZY0y+Z0mByfOSkpKYNWsWI0aMYOrUqXTv3p2//e1v9OjRg+LFi3sdnjHGFBiWFJg8a9OmTYwYMYLvvvuOqlWr8sgjj/DFF19Qvnx5r0MzxpgCyZICk6ccO3aMsLAwRowYwc8//8z999/PtGnT8Pf39zo0Y4wp8CwpMJ5LTk5m7ty5DB8+nEmTJtGtWzdeffVV6x4wxphcZkmB8cyOHTsYMWIEI0aMoGzZsjz66KN8+umnVKhQwevQjDGXkJCQwO7duzlz5ozXoRQqfn5+1KhRI8e+MFlSYHLV6dOnCQ8PZ/jw4axatYqBAwcSHh5O8+bNbTdCY/KR3bt3c+2111K7dm37v5tLVJXDhw+ze/du6tSpkyOfYUmByXGqSlRUFMOHD+fHH38kODiYJ554gv79++Pn5+d1eMaYK3DmzBlLCHKZiHDDDTdw8ODBHPsMSwpMjjl48CDfffcdw4cP58yZMzzyyCOsXr2aGjVqeB2aMSYbWEKQ+3L6mltSYLJVYmIi06dPZ/jw4cyePZsBAwbw5Zdf0qFDB/sDYowxeZztEGOyxZYtW3j11VepVasW77zzDj169OCXX35h5MiRdOzY0RICY0y2K1OmDAB79uzhrrvu8jiagsFaCswVO3XqFOPGjSM0NJSNGzfywAMPMGPGDJo2bep1aMaYQqRatWqMGzcuRz8jMTGRYsUKfpVpLQXmsqgq0dHRPPnkk9SoUYOwsDCeffZZdu3axYcffmgJgTEm1+3YsSN1gbMRI0Zwxx130LNnT+rXr89LL72Uet6MGTNo06YNLVq04A9/+AMnT54E4K233iIoKAh/f38ef/xxVBWAkJAQnnvuOQIDA/n0009z/wfzgCUFJkuOHDnCZ599RrNmzbj77rupUaMGq1evZvLkydxxxx2UKFHC6xCNMR4SkWwvVyouLo6wsDDWrFlDWFgYu3bt4tChQ7zzzjvMmjWL2NhYAgMD+eijjwAYPHgwUVFRrF27ltOnTzNp0qTU9zp37hzR0dG88MILV32N8oOC3xZirlhycjJz5swhNDSUqVOn0rt3bz7++GNCQkIoUsTySWNMmpRv13lB165dKVu2LABNmjRh586dHD16lPXr19OuXTvAVfZt2rQBYO7cufzzn//k1KlTHDlyhKZNm9KvXz8A7rnnHm9+CI9YUmB+Z9euXXzzzTd88803lC1blkGDBtlGRMaYfKNkyZKpt4sWLUpiYiKqSvfu3RkzZsx55545c4annnqK6OhobrzxRoYOHXreKo3XXHNNrsWdF9jXPQO4rHncuHH06tWLgIAA9u3bx7hx41i5ciXPPPOMJQTGmHytdevWLF68mK1btwIQHx/P5s2bUxOAChUqcPLkyRwfsJjXWUtBIbdhwwZCQ0P57rvvaNy4MY899hg//fQTpUuX9jo0Y4zJNhUrVmTEiBEMHDiQs2fPAvDOO+/QoEED/vSnP+Hv70+VKlUICgryOFJvSV7qB8ougYGBGh0d7XUYedbJkyf58ccfCQ0NZfv27Tz00EM8+uij1K9f3+vQjDH5xIYNG2jcuLHXYRRKGV17EYlR1cCrfW9rKSgkVJUVK1YwbNgwxo0bR8eOHXn55Zfp3bt3oZh7a4wx5tKsNijgDh06xKhRowgNDeX06dMMGjSIdevWUa1aNa9DM8YYk8dYUlAAJScnM3v2bEJDQ5k2bRr9+vXj888/p2PHjjaV0BhjzEVZUlCA/PLLL6lTCcuXL8+gQYP4z3/+Q7ly5bwOzRhjTD5gSUE+d/bsWSIjIwkNDSUqKop7772X8PBwWrRo4XVoxhhj8hlLCvKpdevWERoayqhRo/D392fQoEFERERQqlQpr0MzxhiTT1kHcz5y4sQJhg0bRuvWrbn11lspVaoUS5cuZc6cOdx3332WEBhjTDZ4+OGHL3sRo/Hjx7N+/frU+0OGDGHWrFnZHVqO8yQpEJE/iMg6EUkWkUznVYpIURFZKSKTMjuvoFJVli5dymOPPUbNmjWZPHkyr7/+Ojt37uTdd9+lXr16XodojDGFQlJS0kWfuzApeOutt+jWrVtuhJWtvGopWAvcASzIwrnPAhtyNpy85+DBg3z00Uc0bdqUhx9+mPr167NhwwYiIiLo27evrS1gjDHAu+++S4MGDWjfvj0DBw7kww8/JCQkhJQF7A4dOkTt2rUBt8Vyhw4daNGiBS1atGDJkiWA+/I1ePBgGjZsSLdu3Thw4EDq+9euXZuXX36ZFi1aMHbsWP73v/8RFBREQEAAd955J6dOnWLJkiVERkby17/+lWbNmrFt27bzWhuioqJo27YtAQEBBAcHc+LEidy9SJfBk5pFVTcAl9waU0RqAH2Ad4Hncz4ybyUlJTFjxgxCQ0OZPXs2AwYM4KuvvqJ9+/ZXtY2oMcbkuJz4G3WJFXdjYmL44YcfiIuLIzExkRYtWtCyZcuLnl+pUiVmzpyJn58fW7ZsYeDAgURHRxMREcGmTZtYv349+/fvp0mTJjz66KOpr7vhhhuIjY0F4PDhw/zpT38C4PXXXyc0NJRnnnmG/v3707dvX+66667zPvPcuXPcc889hIWFERQUxPHjx/N0V29e/7r5CfAScO2lThSRx4HHAWrWrJmzUWWz7du3p04lrFq1KoMGDWL48OFcd911XodmjDFZ48GS+QsXLuT2229P3aulf//+mZ6fkJDA4MGDiYuLo2jRomzevBmABQsWMHDgQIoWLUq1atXo0qXLea9Lv33y2rVref311zl69CgnT56kR48emX7mpk2bqFq1auqeCnn973qOJQUiMguoksFTr6nqhCy8vi9wQFVjRCTkUuer6tfA1+D2Pri8aHPfmTNniIiIIDQ0lFWrVnHfffcxZcoUbr75Zq9DM8aYfK1YsWIkJycDnLcN8scff0zlypVZtWoVycnJ+Pn5Zen90m+f/PDDDzN+/HgCAgIYMWIE8+bNy9bYvZZjYwpUtZuq+mdQLpkQ+LQD+ovIDuAHoIuIjMqpeHNLXFwczzzzDDVq1OCbb77h8ccfZ/fu3XzyySeWEBhjzGXo2LEj48eP5/Tp05w4cYKJEycCbhxATEwMwHmzCI4dO0bVqlUpUqQI3333XerAwY4dOxIWFkZSUhJ79+5l7ty5F/3MEydOULVqVRISEhg9enTq49dee22GYwUaNmzI3r17iYqKSn19YmLi1f/wOSTPTklU1b+pag1VrQ3cC8xR1fs9DuuKHD16lC+//JKWLVvSv39/brjhBmJiYpgxYwZ33303JUuW9DpEY4zJd1q0aME999xDQEAAvXr1Sm2if/HFF/nPf/5D8+bNOXToUOr5Tz31FCNHjiQgIICNGzemtgDcfvvt1K9fnyZNmvDggw/Spk2bi37m22+/TatWrWjXrh2NGjVKffzee+/lgw8+oHnz5mzbti318RIlShAWFsYzzzxDQEAA3bt3P6/1Iq/xZOtkEbkd+ByoCBwF4lS1h4hUA4apau8Lzg8BXlTVvll5/7ywdbKqMn/+fEJDQ5k4cSK33norgwYNolu3bhQtWtTT2Iwx5mrlxa2Thw4dSpkyZXjxxRe9DiVHFbitk1U1AojI4PE9QO8MHp8HzMvxwLLBr7/+ysiRIxk+fDh+fn4MGjSIjz76iIoVK3odmjHGGJOpvD77IF9ISEhg0qRJhIaGsnjxYv7whz8wevRogoODbSqhMcbkkqFDh3odQr5nScFV2LRpE6GhoXz77bfUr1+fQYMGERYWdt5IVWOMMSa/sKTgMsXHxzN27FhCQ0PZsmULDz74IPPmzTtvwIkxxhiTH1lSkAWqSlRUFKGhoYwdO5Z27drxwgsv0KdPH4oXL+51eMYYY0y2sKQgE4cPH2bUqFGEhoZy6tQpHn30UdasWUP16tW9Ds0YY4zJdnl2nQKvJCcnM2vWLO69917q1atHVFQUn332GZs3b+bVV1+1hMAYY8xlSVmrJsWePXt+t0fChXbs2IG/v39Oh/Y7lhT47N69m7fffpt69erx4osv0r59e7Zv386oUaMICQmhSBG7VMYYYy7fhUlBtWrVzltpMS8p1DVdQkICERER9OnTh1tuuYW9e/cybtw4Vq5cyeDBgylXrpzXIRpjjMnEqFGjCA4OplmzZjzxxBMsX76cW265hTNnzhAfH0/Tpk1Zu3Yt8+bNo2PHjvTp04eGDRvy5JNPpu6PMGbMGG6++Wb8/f15+eWXU9+7TJkyvPbaawQEBNC6dWv2798PuK3t77zzToKCgggKCmLx4sWAmxL56KOPEhISQt26dfnss88AeOWVV9i2bRvNmjXjr3/963mtABfbztkzqlrgSsuWLTUzW7Zs0ZdfflmrVKmi7du315EjR2p8fHymrzHGGJNm/fr159132yRmb8lKDH379tVz586pquqf//xnHTlypL722mv6wgsv6FNPPaX/93//p6qqc+fO1ZIlS+q2bds0MTFRu3XrpmPHjtVff/1Vb7zxRj1w4IAmJCRo586dNSIiwvczoZGRkaqq+te//lXffvttVVUdOHCgLly4UFVVd+7cqY0aNVJV1TfffFPbtGmjZ86c0YMHD2r58uX13Llzun37dm3atGlq3Onvx8fH6+nTp1VVdfPmzZpSf134msyuvS/WaM2G+rPQDDQ8ffo04eHhDBs2jHXr1vHggw8yd+5cm0pojDHZwIMV85k9ezYxMTGpex6cPn2aSpUqMWTIEIKCgvDz80v9tg4QHBxM3bp1ARg4cCCLFi2iePHihISEpK46e99997FgwQJuu+02SpQoQd++bnX9li1bMnPmTABmzZrF+vXrU9/3+PHjnDx5EoA+ffpQsmRJSpYsSaVKlVJbFy7mYts5e6XAJwWrVq1i2LBhfP/99wQFBfH000/Tv39/SpQo4XVoxhhjroKq8tBDD/GPf/zjvMf37t3LyZMnSUhI4MyZM6kLyl24wuylVpwtXrx46jlFixZN3d0wOTmZZcuWZbj1cvoN7tK/5mKudDvnnFIgxxQkJyczbNgwWrVqRd++fSlXrhwxMTFMmzaNu+66yxICY4wpALp27cq4ceM4cOAAAEeOHGHnzp088cQTvP3229x3333njRFYsWIF27dvJzk5mbCwMNq3b09wcDDz58/n0KFDJCUlMWbMGDp16pTp59566618/vnnqffj4uIyPf9i2yrDxbdz9kqBbClYvXo1EydOZMiQIfTs2dN2JTTGmAKoSZMmvPPOO9x6660kJydTvHhxBgwYQPHixfnjH/9IUlISbdu2Zc6cORQpUoSgoCAGDx7M1q1b6dy5M7fffjtFihThvffeo3Pnzqgqffr0YcCAAZl+7meffcbTTz/NLbfcQmJiIh07duS///3vRc+/4YYbaNeuHf7+/vTq1Yunn3469bmnnnqKO++8k2+//ZaePXt6vky+J1sn57SAgABdtWqV12EYY0yBlRe3Ts7MvHnz+PDDD5k0aZLXoVy1nNw6uUB2H9jSw8YYY8zlK5DdB8YYY0x6ISEhhISEeB1GnlcgWwqMMcbkvILY/ZzX5fQ1t6TAGGPMZfPz8+Pw4cOWGOQiVeXw4cM5Om3Rug+MMcZctho1arB7924OHjzodSiFip+fHzVq1Mix97ekwBhjzGUrXrw4derU8ToMk82s+8AYY4wxgCUFxhhjjPGxpMAYY4wxQAFd0VBETgCbvI6jgKsAHPI6iALOrnHOs2ucO+w657yGqnrt1b5JQR1ouCk7lns0Fyci0XaNc5Zd45xn1zh32HXOeSISnR3vY90HxhhjjAEsKTDGGGOMT0FNCr72OoBCwK5xzrNrnPPsGucOu845L1uucYEcaGiMMcaYy1dQWwqMMcYYc5ksKTDGGGMMkM+SAhEZLiIHRGTtRZ4PEZFjIhLnK0PSPddTRDaJyFYReSX3os5frvQai8iNIjJXRNaLyDoReTZ3I88/rub32Pd8URFZKSKTcifi/Ocq/1ZcLyLjRGSjiGwQkTa5F3n+cpXX+S++vxVrRWSMiOTc1n/52KWuse+cEN/1XSci89M9fvn1nqrmmwJ0BFoAay/yfAgwKYPHiwLbgLpACWAV0MTrnycvlqu4xlWBFr7b1wKb7Rpn7zVO9/zzwPeZnVPYy9VcY2Ak8Jjvdgngeq9/nrxaruLvRXVgO1DKd/9H4GGvf568WLJwja8H1gM1ffcr+Y5XVO/lq5YCVV0AHLmClwYDW1X1Z1U9B/wADMjW4AqIK73GqrpXVWN9t08AG3D/8c0FruL3GBGpAfQBhmVrUAXMlV5jESmL+yMc6nufc6p6NHujKziu5ncZt3heKREpBpQG9mRbYAVIFq7xH4FwVf3Fd/4B3+NXVO/lq6Qgi9qIyCoRmSoiTX2PVQd2pTtnN1ZhXY2MrnEqEakNNAeW53pkBcfFrvEnwEtAsjdhFSgZXeM6wEHgG18XzTARucbDGAuC311nVf0V+BD4BdgLHFPVGV4GmY81AMqJyDwRiRGRB32PX1G9V9CSgliglqoGAJ8D470Np0DK9BqLSBngJ+A5VT2e++EVCBleYxHpCxxQ1RgPYysoLvZ7XAzXVPsfVW0OxAM2BunKXex3uRzuW2sdoBpwjYjc71WQ+VwxoCWuBbEH8IaINLjSNytQSYGqHlfVk77bU4DiIlIB+BW4Md2pNXyPmcuUyTVGRIrjEoLRqhruYZj5WibXuB3QX0R24JoCu4jIKO8izb8yuca7gd2qmtLKNQ6XJJgrkMl17gZsV9WDqpoAhANtPQw1P9sNTFfVeFU9BCwAArjCeq9AJQUiUkVExHc7GPfzHQaigPoiUkdESgD3ApHeRZp/Xewa+x4LBTao6kdexpjfXewaq+rfVLWGqtbG/Q7PUVX7dnUFMrnG+4BdItLQd2pX3CAucwUy+Zv8C9BaREr7nu+KG4dkLt8EoL2IFBOR0kAr3LW8onovX+2SKCJjcKNZK4jIbuBNoDiAqv4XuAv4s4gkAqeBe9UNw0wUkcHAdNyIzOGqus6DHyHPu9JrLCLtgQeANSIS53u7V33fDkw6V/F7bLLoKq/xM8Bo3x/Sn4FHcjn8fOMqrvNyERmH615IBFZiSyFn6FLXWFU3iMg0YDVurNEwVV3re+1l13u2zLExxhhjgALWfWCMMcaYK2dJgTHGGGMASwqMMcYY42NJgTHGGGMASwqMMcYY42NJgTF5nIh8LCLPpbs/XUSGpbv/LxF5Phs/b4SI3JVd75fufV9Nd7t2Zru+XfC659It3Xq1MXwoIl2y472MKYgsKTAm71uMb7U3ESkCVADS74fQFljiQVyX69VLn3I+32Y5j+J2hcwOn2PLFhtzUZYUGJP3LQHa+G43BdYCJ0SknIiUBBoDsSIyRESixO1P/7U4jURkRcob+b6hr/Hdbiki832bqEwXkaoXfvDFzvFtvvK+iKwQkc0i0sH3eGkR+VFE1otIhIgsF5FAEXkPtyNenIiM9r19URH5n7g94GeISKkMfvYuQKyqJqb73EDf7Qq+JZ8RkYdFZLyIzBSRHSIyWESeF7ep0TIRKQ+gqjuBG0SkytX8gxhTUFlSYEwep6p7cKty1sS1CizF7UDZBggE1vi2Rv23qgapqj9QCuirqhuBEiJSx/d29wBh4vap+By4S1VbAsOBd9N/bhbOKaaqwcBzuFXWAJ4CflPVJsAbuI1aUNVXgNOq2kxV7/OdWx/4QlWbAkeBOzP48dsBWd0Ayh+4AwjyxXnKt6nRUiB990Os732NMRfIV8scG1OILcElBG2Bj3BboLYFjuG6FwA6i8hLuL3pywPrgInAj7hk4D3f8R6gIa4Snelbmr4obgvb9C51TsqmVzFAbd/t9sCnAKq6VkRWZ/IzbVfVuAzeI72qZH1N/LmqegLXinIM97MDrAFuSXfeAdzOfMaYC1hSYEz+kDKu4GZc98Eu4AXgOPCNiPgBXwKBqrpLRIYCfr7XhgFjRSQcUFXdIiI3A+tUtQ0XJ5c456zvmMSV/S05m+52Eq5140KnSfs5wK2Tn9LC6XfBuenfLznd/eQL4vPzva8x5gLWfWBM/rAE6AscUdUkVT0CXI/rQlhCWgV5SETK4DaiAUBVt+Eq3TdwCQLAJqCiiLQB11UgIukHL2b1nAstBu72nd8El8SkSPB1SVyODcBN6e7vwNclQbqf8TI1wCVWxpgLWFJgTP6wBjfrYNkFjx1T1UOqehT4H66ym47bNjW9MOB+XFcCvjEIdwHvi8gqII4L9rPPyjkZ+BKXSKwH3sF1YRzzPfc1sDrdQMOsmAp0THf/Q9yueytx1+Oy+JKSm4Doy32tMYWB7ZJojMk2IlIUKK6qZ0SkHjALaOhLMK70PSOAl1R1SzbEdzvQQlXfuNr3MqYgsjEFxpjsVBqY6/tGLsBTV5MQ+LyCG3B41UkB7m/ev7LhfYwpkKylwBhjjDGAjSkwxhhjjI8lBcYYY4wBLCkwxhhjjI8lBcYYY4wBLCkwxhhjjM//B1wTHXS4lzKoAAAAAElFTkSuQmCC\n",
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",
       "text/plain": [
        "
"
       ]
@@ -1183,7 +1177,7 @@
     "ax1.set_ylabel(\"Power (dB)\")\n",
     "ax1.set_title(\"Coupling Efficiency\")\n",
     "plt.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1757,7 +1751,7 @@
     "    cost = web.estimate_cost(bat.taskId, verbose=False)\n",
     "    tot_cost += cost\n",
     "    print(f\"Maximum FlexCredit cost for {sim_name} = {cost:.2f}\")\n",
-    "print(f\"Maximum FlexCredit cost for batch = {tot_cost:.2f}\")\n"
+    "print(f\"Maximum FlexCredit cost for batch = {tot_cost:.2f}\")"
    ]
   },
   {
@@ -1872,7 +1866,7 @@
     }
    ],
    "source": [
-    "batch_sweep = batch.run(path_dir=\"data/data_sweep\")\n"
+    "batch_sweep = batch.run(path_dir=\"data/data_sweep\")"
    ]
   },
   {
@@ -1982,7 +1976,7 @@
     }
    ],
    "source": [
-    "print(batch_sweep[\"sim_100_quadratic\"].log)\n"
+    "print(batch_sweep[\"sim_100_quadratic\"].log)"
    ]
   },
   {
@@ -2300,7 +2294,7 @@
     "        coeffs_f = mode_amps.amps.sel(direction=\"+\", f=freq_c)\n",
     "        power_0 = np.abs(coeffs_f.sel(mode_index=0)) ** 2\n",
     "        power_0_db = 10 * np.log10(power_0)\n",
-    "        coup_eff_tl[i, j + 1] = power_0_db\n"
+    "        coup_eff_tl[i, j + 1] = power_0_db"
    ]
   },
   {
@@ -2324,7 +2318,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "
"
       ]
@@ -2342,10 +2336,10 @@
     "for ce, ls, lab in zip(coup_eff_tl, lin_style, tap_names):\n",
     "    ax.plot(taper_lengths, ce, ls, label=lab)\n",
     "ax.set_xlim([taper_lengths[0] - 2, taper_lengths[-1] + 2])\n",
-    "ax.set_xlabel(\"Taper Length ($\\mu m$)\")\n",
+    "ax.set_xlabel(r\"Taper Length ($\\mu m$)\")\n",
     "ax.set_ylabel(\"Coupling Efficiency (dB)\")\n",
     "plt.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2414,7 +2408,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "
"
       ]
@@ -2437,7 +2431,7 @@
     "    sim_data.plot_field(\"field_xy\", \"Ey\", f=freq_c, val=\"abs\", ax=ax)\n",
     "    ax.set_title(tap_n)\n",
     "    ax.set_aspect(\"auto\")  # Used to better visualize the shapes.\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2455,7 +2449,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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rVo3x+Pz587Ny5UqyZMnCoUOH6NixI15eXsyfP58DBw7g6+vLuXPncHNz4913373zvDx58rBjxw4ALl26RNeuXQHo168fEydOpGfPnrRs2TLaJofg4GA6dOjA7NmzqV69OtevX0/2f8BKKQgMhG3bYONG2LTJJgGPPgq1a9vStatNCNLKoj6JzRjDyZMn2bx5Mx4eHmzevBlfX18qVqxIvXr16N69O1OnTk2yfgAJKW22EiVGavyAURwbNmygTZs2ZMuWDYCWLe8/D1NISAg9evTA29ub9OnTc/DgQQDWr19Px44dSZ8+PYUKFaJRo0Z3PS/q8sk+Pj7069ePq1evEhAQwAsvvHDf9zxw4AAFCxa8M7zlkUceue/xSqmkZ4xd+GfLlsiaAB8f2/Gvbl3o0gX++AMej66bt4qT0NBQdu3axaZNm+6UkJAQ6tSpQ506dRgxYgRVq1ZNFT+i0mZSkIzWe8iQIQPh4eEAdy2DPGLECAoUKMCuXbsIDw8nS5YssXq9qMsnv/322yxYsIBKlSoxadIk1q5dm6CxK6USX1iYHQ64bh2sXw+bN9vfNbVrQ61ath9A9eqQCq5HyUZISAheXl6sW7eOtWvX4uHhwZNPPkndunV56aWXGDx4cLLrC5BQtDtJEqlfvz4LFizg1q1b3Lhxg8WLFwO2H8D27dsBmDt37p3jr127RsGCBUmXLh1Tp04lLCzszuvMnj2bsLAw/P39WbNmTYzveePGDQoWLEhISAjTp0+/c3/OnDm5cePG/xxfpkwZ/P392bZt253nh4aGxv/DK6ViLSTEdgQcNgxatIC8eeH11+2iQG3b2tqBM2dg3jz48ku7EqAmBPFz+/ZtNm7cyODBg2natCl58uThgw8+4OzZs3Tr1o2jR4+yd+9exo0bR+fOnSlZsmSqTAggrdYUOKBKlSp06NCBSpUqkT9//jtV9J9//jnt27dn3LhxNG/e/M7xH374IW3btmXKlCm8+OKLd2oA2rRpw+rVq3Fzc6NIkSLUrl07xvf89ttvqVmz5p3OLhGJwGuvvUbXrl0ZNWrUXYlIpkyZmD17Nj179uTWrVtkzZqVVatWkUMHIyuVaIKD7UiAtWtt2bLFrvj33HPQuTNMnAgFCjgdZeoSHBzMtm3bWLNmDWvWrMHT05MyZcpQv359PvroI2bNmsVjjz3mdJiO0KWTHTJo0CBy5MjB559/7nQoiSo5nnulnBQSYucDWLPGJgEeHlCyJDRoYEu9epBGr0eJJjg4GC8vL9avX8/atWvZvHkzJUuWpFGjRjRs2JB69erxaApffjHZz2iolFLK9gnw9obVq20isGmTnQq4YUO7LsCsWZA7t9NRpi63bt1i69atbNiwgXXr1rF161ZKlizJc889R7du3ZgxY0aarQl4EE0KHDJo0CCnQ1BKJYLwcLss8Jo1NhFYtw4KFrRJwHvvwZQptp+ASjhBQUFs2rQJd3d31q9fz86dO6lQoQLPPvssvXv3pl69euTWzCtWNClQSql4MMZODLR6dWTJmRMaN4YOHWDsWB0emNDCwsLYs2cPq1atYuXKlWzevJny5cvTpEkTBg0aRK1atbQvVBxpUqCUUg/pwgVwd4dVq2wJDrZJwAsvwA8/QNGiTkeYukQMEVy/fj3r169n06ZNFCxYkAYNGtC9e3dmz55Nrly5nA4zVdCkQCmlHuDWLTtb4MqVthw7ZkcHNGkCn30GTz+t0wUnpPDwcHbt2sXq1atxd3dn48aNlChRgueee4733nuPP//8k/z58zsdZqqkSYFSSt3DGDth0IoVtmzZAhUrwvPPw+jRUKMGZMzodJSpi5+fHytXrmTVqlW4u7uTJ08eGjduzHvvvcfUqVPJkyeP0yGmCZoUqLtcvXqVGTNm8OGHHwJw5swZevXqddd8Bvfy8/OjRYsW+Pj4JFWYSiW48+dh+XKbBKxcCY88YpOAjz6CuXPtWgIPFB4OQUF2MYKgIFvFEHV7+7Zta4jY3ltCQ+1whbAw+1oR+xElNDT6GVlFIku6dJElffrIbYYM/1syZry7ZMtmZ0KKbpstm108IYGqRC5cuMCaNWtwd3fH3d2dgIAAmjRpQrNmzRg+fDiFCxdOkPdRD0eTAnWXq1ev8uuvv95JCgoVKnTfhECplCosDDw9YdniUJYtMxw6ko7GVa/xQqWzfNPfj+JZ/OHaNfC+DhsD7DrDAVG2N2/abcR+RCKQOXPkhTRLFrufJYstmTPbC+u920yZ7EU5QwZ7AY8oERf0TJkiH4tuXWNjbBJx7zYiuQgJsQlFRIm4HRISuX/7tk1ebt2yn+Xe/cBA+3o5ctielI88Yrc5ckD27JHbnDkhVy6bReXKdacEZc3K1oMHWe7pydI1a/A7fpz69evTuHFjevToQfny5VPtLIEpiSYFSWjatGmMGjWK4OBgatasybvvvkvXrl3x9PQkLCyMGjVqMHv2bC5evMiAAQPImTMnhw8fpmHDhvz666+kS5eOmTNnMmTIEIwxNG/enB9++AGAHDly0Lt3b5YsWULWrFlZuHAhBQoU4MKFC3Tv3p0TJ04AMHLkSOrWrcugQYM4ceIER48e5cSJE3z88cf06tWLvn37cuTIESpXrszzzz/PRx99dKcWIKblnJVKNgIDbS/A8+ft9uJFuHTpzvbS6SCWHynJ0jPPsPxGbQqZ0zST5QzPuYE6+Q+S8UZ28MkFJx6xF7VHH7UXvwIF7AxDERfBnDkjL4Q5ctgEIHt2e+GP7qKdWoSG2qToxg24fv1/E6SbN+39165hDhzg6vHjXDl2jFtnzpDu+nXKpU9PHWP4TgTJmxfx84MFC2yHjbx5IV++u0uBArbkyWMTIpXoNClIIvv27WP27Nls2rSJjBkz8uGHH3LgwAFatmxJv379uHXrFm+++Sbly5dn7dq1eHp64uvrS9GiRXnxxReZN28ederUoU+fPmzfvp3cuXPTtGlTFixYQOvWrbl58ya1atVi8ODBfPnll4wfP55+/frRu3dvPvnkE+rVq8eJEyd44YUX2LdvHwD79+9nzZo13LhxgzJlyvDBBx8wdOhQfHx88Pb2BmzTQISYlnNWKlGFhMDZs+Dvf3c5exbOnYss58/bi1b+/LbkzYvJmw9fKcfic41ZfKQsPv55aFD5Gs3bBjG0RRCFyz8F2T4DSd0ziyaYDBnsTEsxjPk/duwYy5cvZ7VrCuHcuXPTqFEjmjZtSqNGjSJHCNy6ZZO1iHLxoi0XLsDBg3aGpwsXIv9tr12ziUHBglCokC0FC/5vefxxXR86ntJkUuDAysm4u7uzffv2O2se3Lp1i/z58zNgwACqV69OlixZGDVq1J3ja9SowVNPPQVAx44d2bhxIxkzZqRBgwZ31uh+4403WL9+Pa1btyZTpky0aNECgKpVq7Jy5UoAVq1aha+v753XvX79OgEBAQA0b96czJkzkzlzZvLnz8+5c+fu+xliWs5ZqTi7fRtOnYKTJ+8up09HlsuX7a/Gey8AlStH/pKMKDlyEBwirFsHixfDkiW2xvvll2HAZ3Ya4cyZU94a98lVcHAwmzZtYunSpSxdupTLly/zwgsv0Lx5c3766aeY+wVkzQpPPmlLbISE2CTB39+uBuXvb/82vL1h2bLIRPH8edtcEfHaTzxht4UL2xJxv64gFaM0mRQ4sdyDMYbOnTvz/fff33W/v78/AQEBhISEEBQUdGfho3vb1h7U1pYxY8Y7x6RPn/7O6obh4eFs2bIl2qWXM2fOfGc/6nNiEtflnFUaduOGHb937Bj4+dntiRORF//Ll+2vvogv7cKFoVw5O+D/iSdsKVDggVXHFy/CP/NtIrByJZQtaxOBhQuhfHkdLpiQTp8+zb///ss///yDu7s7pUuXpnnz5kyZMoWqVauSLjGaTzJmjKwhqFo15uPCwmxiEJFQnjply4oVkcnn6dO2+SciSShc2K5A9dRTkdsUvg5CfKTJpMAJjRs3plWrVnzyySfkz5+fy5cvc+PGDXr27Mm3337LsWPH6NOnD6NHjwbA09OTY8eOUbRoUWbPns37779PjRo16NWrFxcvXiR37tzMnDmTnj173vd9mzZtyi+//MIXX3wBgLe3N5UrV47x+JiWVQa7nPOTTz5JunTpmDx58p3lnFUaFh5uv2QPH7bl6FF74Y/YBgbaif6LF7elWDGoW9d+ERcpEqsLfkwOHoRFi+yFf/duO3nQyy/DmDG29UAljMDAQDZu3MiqVatYsWIFJ0+e5Pnnn6dVq1b8+uuvFEhOSzimTx9Zk1QthrWBwsNtrUPUmik/P9uv4ehRWzJlivx7jfr3W6KEvZ2KfxBpUpBE3Nzc+O6772jatCnh4eFkzJiRVq1akTFjRl5//XXCwsKoU6cOq1evJl26dFSvXp0ePXrc6WjYpk0b0qVLx9ChQ2nYsOGdjoatWrW67/uOGjWKjz76iIoVKxIaGkr9+vUZO3ZsjMfnyZOHunXrUr58eZo1a8ZHH31057GYlnNWqZwxtsr24EE4dMiWgwcjk4DHHrOd8EqUsL+yWraM/BItUCDBfqaHh8O2bTB/vu2bdv26fauvvoJGjVL193SSMsawd+9eFi9ezMqVK9m2bRuVK1emcePGjBkzhpo1a5IhQwq+dKRLF9ncFF3iYIxNGo4fj6zh2rvXtkUdOWJruvLli/x7L1EispQsmeKXuNSlk5OhtWvXMnz4cJYsWeJ0KPGW0s59mhYQYC/2+/fDgQN2P2KbPTuUKQOlStlSunRkIpCIyWFIiF1eeP58WyPw6KPQurUt1aql7o7+Sen27dts2LCBxYsXs2jRIgBefvllXnjhBerXr0/OnDkdjjAZCQuzTRFHjthy9Gjk/qFDtqmjZElbIv6vRJRHHkm0sHTpZKXUw4v4FbRvH/j62u2+fTYRuHTJfomVKWNL8+bw6af2viScV/7mTTuJ0Pz5sHSp/S5t3douNFSmTJKFkaoZY9i/fz8rVqxgxYoVbNiwATc3N1q2bMmiRYt0zoD7SZ/eLm5RtKitoooq4v/X4cORtWoLFkTWsuXMaefEjihly9pt4cLJJsPVmgKVqPTcO+jyZfDxsWXvXlt8fOwvHTc3+4UUsX36advG79BY8MuXbSfB+fPtxb9GDXjlFWjVyvY1VPF39epV3N3d+ffff/n3339Jly4dTZs2pWnTpjRu3JjHUni1d7JnjO1/s39/ZNm3z9bGXblis9+I/4tubraUKhXr+bS1puAhGWM0801iqS3hTLZu37ZfLrt327Jnj73437hhe/KXL2+3bdrY7eOPJ4vu+P7+NgmYN8/OLNi4sU0E/vgjxTfLJgvGGHx8fO4MF/T29qZu3bq8+OKLfPrppzz99NP6nZiURCKHRDZpcvdj16/b5GD/fluDN22aTeJPnrRNdOXK3V1Klky0xTfSRFKQJUsWLl26RJ48efQ/QRIxxnDp0iUdtpiQjLFX0l27Isvu3bZNs0QJu2JPhQrQs6fdFimSLC7+UZ08CX//bYuPj22h+PBDO4ogWzano0v5AgMDWbNmDUuWLGHp0qWkT5+e5s2b89VXX9GgQQOy6UlOnh55BKpXtyWqW7dssuDra5OEadPsf5zTp23NQoUKtlSsmGChpInmg5CQEE6dOkVQUJBDUaVNWbJk4cknnySjLif38MLDbbvkzp2wY4fd7txpH6tUyZaKFW1xc7Nz6CdTJ0/aBYX++ss2q7ZsCe3a2ZqBZBx2inH8+PE7tQEbNmzgmWeeoUWLFjRv3pyyZcvqD6HUKDDQJgp79twpsmpVgjQfpImkQKlkLSzMVv/v2BFZvL1tHXqVKvDMM3ZbubKdvCUFfMmfOGFrA+bMsT90WreG9u1tvyzNEeMnPDwcLy+vOyMFzpw5Q7NmzWjevDlNmzYldwxTEKvUTfsUKJUSRdQAeHnZQfdeXjYBKFjQztRWpYqdgeeZZ1Jcw/rx47ZGYM4c+xFbtYL+/W2NgE5HHz+3b99m9erVLFiwgEWLFpE7d25efvllfv31V2rVqkV6XSxIJRBNCpRKTKdOwZYtkUnA9u12sH1E++GgQTYZSMIhfwnpzBmbBMyaZROBNm3gm2+gYUOtEYivq1evsmzZMhYsWMDy5cupUKECrVu3Zv369ZQqVcrp8FQqpUmBUgklKMhW/Xt42ERgyxZ7X82adozd55/bBCCFz8F76ZJtGpg1y3ZzaNUKBg60NQKaCMSPn58fixYtYtGiRXh6elK/fn3atGnDL7/8Qv4U/nejUgZNCpSKq7Nn7RKvmzfbsnu3HWNcu7ZtRB861E6DmgL6ADxIQICdUXDmTNiwAV580Q5yaNZMpxeOr8OHDzNnzhz++usvzpw5Q4sWLejZsydNmjTRqcRVktOkQKnYMMb2mNuwwS6csmmT/clcp45d4Of7721zQCr6Eg8NBXd3mDLFzixYty507GgTA531Nn7279/P/PnzmTNnDmfOnKFdu3aMHDmSevXqaf8A5ShNCpSKTni4HQ+8fj2sW2e3WbPCs89CvXrw5Zd29rFkMjVpQjHGTn8wbRrMmGHnWenUCUaOtGvAqLgxxrBt2zYWLFjA/PnzuXHjBq1bt2bEiBGaCKhkRZMCpcAmAXv22NV31q61ScBjj0GDBnZg/fDhdq7zVOr4cZsETJ9umwreeMNON/z0005HlnKFhYWxceNG5s2bx7x588iePTtt2rRh8uTJVKtWjXSpLKFUqYMmBSptMsYuUrJ6ta0jX7s2Mgl49VUYPTrVT7p/7ZodQjhlip0srV07GDvWtojo9SpuQkJCWLt2LXPnzmXBggUULFiQtm3bsnz5ctzc3JwOT6kH0qRApR3nz9sEYMUKWLXK3te4sa0JGDnS1pWncqGhsHKlTQT++cd+/I8/hpde0tkF4yo4OBh3d3fmzp3LwoULeeqpp2jbti2bNm2iZMmSToen1ENxJCkQkceA2UAxwA9ob4y5Es1xRYAJQGHAAC8ZY/ySLFCVsgUH21EB//5r1+I9dszWBDz/PHz1lV2BLBWMDIiN3bttIjB9ul0SoXNnWxmSJ4/TkaVMwcHBrFy5kjlz5rB48WLKlCnDq6++yoABAyiaipuZVOrnVE1BX8DdGDNURPq6bveJ5rgpwGBjzEoRyQGEJ2WQKgU6ccL+BP73X1izBsqUsePnRo+28wVkSDuVYxcu2CRg8mQ7UKJTJ3tKtJ9A3Ny+ffuuRMDNzY1XX32V7777jifTQC2TShuc+oZsBTRw7U8G1nJPUiAibkAGY8xKAGNMQBLGp1KKkBBbG7B0qU0Gzp2zScCrr8L48Wmuy3xoqG0d+eMP20Ly8su2j2TDhtpPIC6CgoJYvnw5c+bMYenSpVSoUIF27doxZMgQnkjlfU5U2uRUUlDAGOPv2j8LFIjmmNLAVRGZBxQHVgF9jTFh9x4oIu8D7wMUKVIkcSJWycelS7YmYMkS2yxQvDi0aAETJ0K1apAGh3ft22drBKZNs/0j333Xno5HH3U6spQnNDSU1atXM3PmTBYuXEjFihV59dVX+fHHHylYsKDT4SmVqBItKRCRVcDj0Tz0ddQbxhgjItEt1ZgBeBZ4BjiB7YPwNjDx3gONMeOAcWBXSYxX4Cp5OngQFi2yZdcu+9O3RQv46Se7cmAadOWKnUho8mS7PPEbb9hcqXx5pyNLecLDw/Hw8GDmzJnMmTOHokWL0rFjR7777jutEVBpSqIlBcaYJjE9JiLnRKSgMcZfRAoC56M57BTgbYw56nrOAqAW0SQFKhUKD4etW2H+fJsIXL9uRwn07WsTgqxZnY7QEcbYuZTGj7ctJi+8YNdUev75NNVdIkEYY9izZw8zZsxg1qxZZMuWjddff11HDag0zamvkUVAZ2Coa7swmmO2AblEJJ8x5gLQCPBKuhBVkgsNtZMGzZtnk4Fcueyye9Om2SWF03Cj+NmzMGmSbRLIkgW6doVRo3T0QFz4+fkxY8YMZsyYQUBAAK+99tqdZgJJI6NRlIqJU0nBUOAvEXkPOA60BxCRakB3Y0wXY0yYiHwOuIv9n7odGO9QvCqxhITYCYTmzoUFC+ysgW3b2vkE0ng3+fBw21lw3Dh7Otq2tflRjRppZiRlgrl48SJ//fUX06dP5+DBg7z66qv8/vvv1K5dW2cWVCoKR5ICY8wloHE093sBXaLcXglUTMLQVFIIDrZXub/+sk0DpUvb6fS2bYNixZyOznFnzthagQkTbEfBbt3saIJHHnE6spQlKCiIxYsXM3XqVNavX0+zZs34v//7P5o2bUpGXeNZqWhpK6RKGiEhkYnAwoW2FuDVV+Gbb6BwYaejc1xoqO0kOH68bUF59VWYPdsOptBagdgzxrB582YmT57M3LlzqVq1Km+++SbTpk3jEc2qlHogTQpU4gkNtWsKzJ5t+wiULAkdOsB//qOJgMvJk7afwMSJdihh1652wqEcOZyOLGXx8/NjypQpTJkyhUyZMtG5c2f27NmjIweUekiaFKiEFR4OHh52yb25c+3Fv0MH2L49Va8y+DDCwmytwO+/w8aN8PrrdiRBRW0oeyiBgYHMmzePP//8k127dvHaa68xc+ZMqlWrph0GlYojTQpUwvDxsT9xZ86E7NntlW7TJls7oAC7HtPEiTYZyJ8funePPF0qdowxbN26lT/++IO5c+dSq1YtunfvTsuWLcmsKzopFW+aFKi4O3nS1ghMn25n0unY0fYXqFhRG8JdjLEVJ7/+amsD2raFv/+GqlWdjixlOXfuHNOmTeOPP/4gODiYd955R5sHlEoEmhSoh3PtGsyZY8fG7dljr3K//ALPPpum5xG4V2CgzZfGjIGAAPjwQ3uacud2OrKUIzg4mH/++YdJkyaxbt06WrduzdixY6lXr542DyiVSDQpUA8WscrOlCm2MbxxY/j4Y2jWDLTK9i5HjthEYMoUqFMHhg61sw1qvhR73t7eTJo0iRkzZvD000/z9ttvM3XqVHLmzOl0aEqlepoUqJjt2WMHzE+fbhcd6tzZ1oM/9pjTkSUrxtj5l37+2TYVvPuunXKheHGnI0s5rl27xowZM5gwYQIXL17k7bffxsPDgxIlSjgdmlJpiiYF6m4XL9reb5Mm2Z5xb71lB86XLu10ZMlOYKDNl0aNsoMueveGWbMgWzanI0sZjDFs2rSJCRMmsHDhQp5//nm+//57GjduTPo0uNKlUsmBJgXKTiz077/w5592gqGXX4YffrALD+mX8/84ccJWmEycCLVqwYgRtkVFm7lj58KFC0yZMoUJEyYA8N577zFs2DDy58/vcGRKKU0K0rKozQMlSsA779jE4NFHnY4s2TEGNm+GkSNtU8Fbb8GWLfa0qQcLDw/H3d2d8ePHs2LFClq3bs2ECROoU6eOdhpUKhmJVVIgIumASkAh4BbgY4yJbrljldxduWKbB/78E/z9tXngAUJC7GCLESPg6lXbRPDHH6B93mLnzJkz/Pnnn0ycOJFHH32Url27Mm7cOHLlyuV0aEqpaNw3KRCREkAfoAlwCLgAZAFKi0gg8Dsw2RgTntiBqngID7fTDU+YAP/8Ay+8AN9+a7vFa/NAtC5ftqsTjh5t86UBA6B5cx1FEBvh4eGsWrWKsWPHsmbNGtq3b89ff/1F1apVtVZAqWTuQTUF3wG/Ad2MMSbqAyKSH3gd6ARMTpzwVLycPm2bByZOtD9tu3Sxg+Xz5HE6smTr8GHbRDBjhu1asWQJVK7sdFQpw6VLl/jzzz/5/fffyZ49Ox988AGTJ0/WoYRKpSD3TQqMMR3v89h5YGRCB6TiKTQUli2zy+1t3Ajt29uVCatW1Z5wMTDGzsj800/2lHXrBnv3QsGCTkeW/Blj8PDwYOzYsSxatIiWLVsyZcoUatWqpbUCSqVAD+xTICJFgZvGmIsiUguoBxwxxsxP9OhU7B0/bmsE/vjDLkLUtatOrP8AYWGwYAEMHw4XLsCnn9qJGvWUPVhAQADTpk3jt99+IzAwkO7duzNixAjyaC2UUinag/oUDAA6A0ZEZmH7FqwFmovIc8aYjxM9QhWz0FDbR+D3321X+Ndft7UEFSo4HVmyFhhoW1X++1/Ilw+++AJatdLuFbFx8OBBxowZw9SpU2nQoAE//fQTjRo1Ip12tlAqVXhQTcFrQFkgG3ACeNwYEygiGQDvRI5NxeTUKdtpcOJEWyvw/vu2i7zOmnNfFy/aKYjHjIHatW1iULeutqo8SHh4OMuWLeOXX35hx44ddOnSBW9vb4oUKeJ0aEqpBPagpCDIGBMMBIvIEWNMIIAxJlREghM/PHVHeLgdIP/rr3YkQceOdtm9ihWdjizZO3rU1grMmGHXb1q/Hp5+2umokr+rV6/yxx9/MGbMGHLlykXPnj1ZsGABWbJkcTo0pVQieVBSkEtEXgEEeMS1j+u2znCTFC5fhsmT4bffIGtW+OADe1t7dD/Qjh3w44+wcqUdeKGdB2Nn7969/PLLL8yePZtmzZoxbdo07TioVBrxoKRgHfCya399lP2I2yoxGGNX1PntN9sTrnlzO9lQnTpa1/0AxtiZmn/4Afbtg08+sV0uHnnE6ciSt7CwMJYsWcKoUaPw9fWlW7du+Pr6UlCzKKXSlAcNSXwnqQJRQFCQXVFn9GhbQ9C9OwwbZnvDqfsKC4P58+1SxYGB8OWXtt9lpkxOR5a8Xb16lYkTJzJ69GgKFChAr169aNeuHZn0xCmVJj1o9MGn93vcGPPfhA0njTpxwtYKTJwI1arBN9/Aiy/q9HmxEBwMU6bY3Omxx6B/fzvpkJ66+zt06BCjRo1i+vTpvPjii8yaNYuaNWs6HZZSymEPaj6IaLguA1QHFrluvwx4JlZQaYIxsGGDXXd3zRro1MnOoFOqlNORpQhBQXYAxrBhULasnaupfn1tXbkfYwyrV69m5MiRbNmyhffff589e/bwxBNPOB2aUiqZeFDzwX8ARGQ9UMUYc8N1exCwNNGjS41u3bLd4EeNgtu3oVcvOzYuRw6nI0sRbt60fQSGD7eVKnPnQo0aTkeVvAUGBjJt2jRGjRqFMYbevXsze/ZssukQVqXUPWK7dHIBIOoQxGDXfSq2zpyxA+THj7dXsR9/hCZNtJ47lq5ds6MxR46EZ5+1czbpmgT3d/r0aUaPHs2ECROoVasWI0eOpHHjxjqKQCkVo9gmBVMATxGJmNq4NTApMQJKdby87JVs6VJ44w1tInhIly7Bzz/bhKBZM9vS4ubmdFTJm7e3N//9739ZsmQJb775Jh4eHpQsWdLpsJRSKUCsfqYaYwYD7wBXXOUdY8z3iRlYihbRFb5+fXjlFahUyc6gM3q0JgSxdOkS/N//2WWLz56FrVth6lRNCGISHh7O0qVLady4MS1atKBcuXIcOXKEUaNGaUKglIq1B40+yGGMCQAwxuwAdtzvmDQvIMDOJzByJOTNa1fYadsWMsS2QkZduWJnH/z1V2jXDnbuBJ1NN2ZBQUFMnTqVESNGkDlzZj777DPat2+vQwqVUnHyoKvVQhHxBhYC240xNwFE5CmgIdAeGA/MTcwgk72zZ23HwXHjoEED+5O2dm3tCv8QrlyxudSYMdC6tW11KV7c6aiSr0uXLjFmzBh+/fVXqlatyujRo2nYsKH2F1BKxct9mw+MMY0Bd6AbsFdErovIJWAa8DjQ2RiTdhOC/fvtEsVubnD9uq3jnjtXZx58CJcv27kFSpWCkyftKZwwQROCmPj5+dGrVy9KlSrF8ePHWb16NUuXLqVRo0aaECil4u2B9drGmH+Af5IglpRjyxY7dZ6HB3z4IRw8aJsLVKxdumSbCcaOhTZtwNMTnnrK6aiSrx07djB8+HCWL19O165d8fHxoVChQk6HpZRKZbSxO7aMgWXL7KT6J07AZ5/Z+QZ0rPdDiZoMvPIKbN8OxYo5HVXyZIzh33//Zfjw4Rw8eJDevXszduxYHtGFHJRSiUSTggcJDYU5c2zNgDHQty+0b6+dBx9S1GSgbVtNBu4nODiYmTNn8uOPP5IhQwa++OIL2rdvT8aMGZ0OTSmVyumVLSZBQXamwR9/hEKF4Pvv7UB5bbd9KFeuwIgRtgOh1gzc382bN5kwYQI//fQTZcqUYcSIETRp0kT7CiilkswD5ykQkfQisj8h31REHhORlSJyyLXNHc0xDUXEO0oJEpHWCRlHtAIC7By6Tz0FS5bA5Ml2jYKXXtKE4CFcu2bXdSpVCk6ftqMJxo/XhCA6ly9f5ptvvqF48eJs2LCBv//+m5UrV/L8889rQqCUSlIPTAqMMWHAARFJyNHifQF3Y0wp7OiGvtG87xpjTGVjTGWgERAIrEjAGO527RoMHmyTgW3bbP+BJUugXr1Ee8vU6OZN2+2iVCk4fNj2xZw4UUcTROfs2bN8+eWXlCpVCj8/P9avX8/cuXOpXr2606EppdKo2DYf5MYOSfQEbkbcaYxpGcf3bQU0cO1PBtYCfe5zfDtgmTEmMI7vF7PLl+08umPG2NqAdevssnvqody+bWsChgyBunX1NN7P8ePHGTZsGDNnzuTNN99k586dFNEZmpRSyUBsk4L+Cfy+BYwx/q79szx4caXXgP/G9KCIvA+8D8T+y/XiRdvz7fff7Zi4LVtAp4N9aKGhdq6m//wHypWzSzw884zTUSVPBw8eZOjQoSxcuJD333+f/fv3kz9/fqfDUkqpO2KVFBhj1olIUaCUMWaViGQD0t/vOSKyCjvB0b2+vue1jYiY+7xOQaACsPw+8Y0DxgFUq1YtxtcC4Px522dg4kQ7ikB7vsVJeDjMng0DB9p+mNOmaUtLTHx8fBgyZAgrV66kZ8+eHD58mNy5/6cbjVJKOS5WSYGIdMX+En8MKAE8AYwFGsf0HGNMk/u83jkRKWiM8Xdd9M/f5+3bA/ONMSGxiTVGFy/akQQTJkDHjuDtDYULx+sl0yJjYMECGDAAsmeH336DRo20D2Z0vL29+fbbb9m0aROffvopv//+Ozlz5nQ6LKWUilGsVkkEPgLqAtcBjDGHgPjUey4COrv2O2PXVohJR2BmnN8pYrm9MmXgxg2bDIwerQnBQzIG/vkHqlWzowq+/952ImzcWBOCe3l5edGqVSteeukl6tWrx9GjR/nyyy81IVBKJXux7VNw2xgTHDE8SkQyAPevpr+/ocBfIvIecBxbG4CIVAO6G2O6uG4XAwoD6x76Ha5evXuA/I4dULRoPEJOm4yB1auhXz+bU/3nP7YLRrrYppNphDGGdevWMXToUHx8fOjbty+zZs0ia9asToemlFKxFtukYJ2I/B+QVUSeBz4EFsf1TY0xl4im6cEY4wV0iXLbD9tUEXvh4bYL/IgR0KKFTqofDzt3whdf2FmdBw2CDh0g/X17kqQ94eHhLF68mKFDh3Lp0iX69OnDwoULyZw5s9OhKaXUQ4ttUtAXeA/Yg10x8R9gQmIFFS979sDTT8PGjbbJQD2048dtzcCqVbbvQJcuoDPs3s0Yw7x58xg4cCCZM2fmq6++ok2bNqTXrEkplYLFNiloCEwzxoxPzGASRNmydqEi9dCuXLF9BSZOhB497OKP2gx+N2MMy5Yto1+/fgD88MMPvPTSSzrzoFIqVYhtUvAW8JuIXAY2AOuBjcaYK4kWWVxlyuR0BClOUJDtezlsmO0vsGePHWaoIhljWL16Nf379+f69et88803tGnTRpMBpVSqEtt5CjoDiEgh7OyCY4BCsX2+Sp7CwmD6dOjfH6pUgfXrbcuLihSRDAwaNIjz588zYMAAXnvtNW0mUEqlSrGdp+BN4FnsJEIXgdHYGgOVAkUML/zqK9s8MGOGnZpYRdJkQCmVFsX2l/5I4Ah2wqI1rlEBKgXy8IA+fez0Dd9/Dy+/rPMMRGWMYc2aNQwaNIizZ88ycOBATQaUUmlGbJsP8opIOaA+MFhESgEHjDGdEjU6lWCOHrXDC7dts3MNvPWWDi+819q1axk4cCBnzpxhwIABdOzYkQwZtIVMKZV2xGoKGhF5BCgCFAWKAY8C4YkXlkooN25A375QowZUrQoHDsA772hCENX69etp2LAhXbp04d1332Xfvn106tRJEwKlVJoT22+9jVHKaGPMqcQLSSWEsDCYMgW+/hqaNoXdu3VEwb02bdrEwIEDOXr0KP3799dEQCmV5sW2+aAigIjkSNxwVHwZA8uX234DOXLYxYtq1HA6quRly5YtDBw4kAMHDtC/f3/eeustMursTEopFevRB+WBqdhVEkVELgCdjTE+iRmcejjbt8OXX8Lp07YTYevW2okwqu3btzNgwAD27NlDv379ePvtt8mk81oopdQdsV3WZhzwqTGmqDGmCPCZ6z6VDBw/Dq+/bkcStG8PPj52EiJNCKzdu3fTunVrWrZsyUsvvcShQ4d4//33NSFQSql7xDYpyG6MWRNxwxizFsieKBGpWLt+3a4KXaUKlC5tpyXu1g20Wdw6ePAgHTt2pGnTpjRo0IDDhw/z0Ucf6WJFSikVg9gmBUdFpL+IFHOVfsDRxAxMxSwsDMaNs+s9nTljOxEOGmT7ECg4fvw47733HnXr1qVChQocPnyYjz/+WJcxVkqpB4jtb8p3gf8A8wCDnc3w3cQKSsVs7Vro3Rty5YIlS+wwQ2VdvHiRwYMHM2XKFD744AMOHTpErly5nA5LKaVSjPsmBSKSBegOlMQum/yZMSYkKQJTdzt2zE4+5OUFw4dD27baZyBCQEAAI0aM4Oeff+a1117D19eXAgUKOB2WUkqlOA9qPpgMVMMmBM2AHxM9InWXgADo1w+qVYPKlWHfPmjXThMCgODgYMaMGUOpUqXYt28fW7duZfTo0ZoQKKVUHD2o+cDNGFMBQEQmAp6JH5ICCA+PnHyoUSPYtQuefNLpqJKH8PBwZs6cSf/+/SlTpgz//PMPzzzzjNNhKaVUivegpOBOU4ExJlTXjk8aGzfCxx9Dxowwbx7UrOl0RMmDMYZly5bx1VdfkTVrVv744w8aNGjgdFhKKZVqPCgpqCQi1137AmR13RbAGGMeSdTo0pgTJ+zkQ5s2wQ8/QMeO2kwQYevWrfTp04dz584xZMgQWrdujSapSimVsO7bp8AYk94Y84ir5DTGZIiyrwlBArl5EwYMgGeegaefhv377WREes2D/fv307ZtW9q1a0enTp3Ys2cPbdq00YRAKaUSQWznKVCJwBiYPt0mAocOwc6ddr6B7DotFGfPnqV79+48++yz1KxZk4MHD/Lee+/pgkVKKZWI9BvWIdu22fkGgoNh5kyoV8/piJKHgIAAfvrpJ0aNGsU777zDgQMHeOyxx5wOSyml0gStKUhi/v7wzjvQqhV07QqenpoQAISEhDB27FhKly7NwYMH2b59O8OHD9eEQCmlkpDWFCSRkBAYNcquXvjuu7bfwCPaKwNjDHPmzKFfv34ULVqUxYsXU1WnaVRKKUdoUpAE3N2hZ08oUgQ2b7aLFylwd3enb9++hIeHM2bMGJ5//nmnQ1JKqTRNk4JEdPIkfPaZbSIYOdI2GWinefD29qZPnz4cPnyYIUOG8Oqrr5IunbZkKaWU0/SbOBHcvg1DhkQOMfT1hdatNSHw8/OjU6dOvPjii7z88svs27ePDh06aEKglFLJhH4bJ7ClS6F8eVs74OkJ33wD2bI5HZWzLl++zGeffUbVqlUpUaIEhw4dokePHmTKlMnp0JRSSkWhzQcJ5Ngx6NULDhywHQqbNXM6IucFBQUxZswYfvjhB9q2bcvevXt5/PHHnQ5LKaVUDLSmIJ6CguDbb6F6dahTB/bs0YQgYsGismXLsm7dOtatW8dvv/2mCYFSSiVzWlMQD8uXQ48etrlg+3YoWtTpiJy3ceNGPv30U8LDw5k0aRLPPfec0yEppZSKJU0K4uD0afjkE5sI/PILvPSS0xE579ChQ/Tt2xcvLy+GDBlCx44dtQOhUkqlMPqt/RBCQ+Hnn6FSJTuqwMdHE4JLly7x8ccfU7t2bapXr87+/ft54403NCFQSqkUSGsKYsnTE7p3h1y5YONGmxSkZbdv32b06NEMHTqU9u3b4+vrS/78+Z0OSymlVDw48nNORB4TkZUicsi1zR3DccNEZK+I7BORUeLAerlXrsAHH9iJhz791M5OmJYTAmMMf/31151OhBs2bGDMmDGaECilVCrgVB1vX8DdGFMKcHfdvouI1AHqAhWB8kB1IMl6rRkD06aBm5uddMjXF958M21PQOTh4UHdunUZOnQoEydOZNGiRTydljMkpZRKZZxqPmgFNHDtTwbWAn3uOcYAWYBMgAAZgXNJEdyBA7Z24OpVWLgQatRIindNvvz8/Ojbty8bN25k8ODBdOrUSfsMKKVUKuTUN3sBY4y/a/8sUODeA4wxHsAawN9Vlhtj9kX3YiLyvoh4iYjXhQsX4hzU7dt2BsK6dW1zgadn2k4IAgIC+Prrr6latSpubm4cOHCAzp07a0KglFKpVKLVFIjIKiC62Wq+jnrDGGNExETz/JJAWeBJ110rReRZY8yGe481xowDxgFUq1btf14rNjZuhPffh5IlYedOKFw4Lq+SOhhjmDVrFl9++SXPPfccu3fv5oknnnA6LKWUUoks0ZICY0yTmB4TkXMiUtAY4y8iBYHz0RzWBthijAlwPWcZUBv4n6QgPq5cgb59YckSOz3xK6+k7X4Du3btolevXly/fp2ZM2dSr149p0NSSimVRJyqB14EdHbtdwYWRnPMCeA5EckgIhmxnQyjbT6IC2Ng1iwoVw7Sp4e9e6Ft27SbEFy8eJEPP/yQpk2b0rFjR7y8vDQhUEqpNMappGAo8LyIHAKauG4jItVEZILrmLnAEWAPsAvYZYxZnBBvfuyYnXRoyBD4+2/49Vc7/0BaFBoayi+//IKbmxsZMmRg3759dO/enfTp0zsdmlJKqSTmyOgDY8wloHE093sBXVz7YUC3hHzfkBAYMQKGDYMvvrDzDmTMmJDvkLK4u7vTu3dvHn/8cVavXk358uWdDkkppZSD0syMhp6e0LUrFCxo9596yumInOPn58fnn3/O9u3b+emnn2jTpg0OzAullFIqmUn1Y8uuX4devewQw759YdmytJsQBAYGMnDgQKpWrUqlSpXw9fXllVde0YRAKaUUkMqTgoULbUfCmzdtR8KOHdNmR0JjDPPmzcPNzY19+/axc+dO+vfvT9asWZ0OTSmlVDKSKpsPzpyxtQN79sDUqdCggdMROWffvn306tULf39//vzzTxo2bOh0SEoppZKpVFdTcOGCXdq4bFnYtSvtJgTXr1/niy++oH79+jRv3pydO3dqQqCUUuq+Ul1NwbVrdnbCtNqRPjw8nMmTJ/P111/zwgsv4OPjQ4EC/zOLtFJKKfU/Ul1SULJk2k0IPDw86NWrFxkyZGDhwoVUr17d6ZCUUkqlIKkuKUiL/P396dOnD6tXr2bo0KG8/vrrumiRUkqph6ZXjhQsODiYH3/8kQoVKvDEE0+wf/9+3nzzTU0IlFJKxYnWFKRQ//77L71796ZkyZJ4eHhQqlQpp0NSSimVwmlSkMIcO3aMTz75BB8fH0aOHEmLFi2cDkkppVQqofXMKcStW7cYNGgQ1apVo0aNGvj4+GhCoJRSKkFpTUEyZ4xh0aJFfPzxx1SrVo2dO3dSpEgRp8NSSimVCmlSkIwdOnSIXr164efnx/jx42nSpInTISmllErFtPkgGbp58yZff/01tWvXpnHjxuzatUsTAqWUUolOk4JkxBjD/PnzcXNzw8/Pj927d/P555+TKVMmp0NTSimVBmjzQTJx7NgxevbsydGjR5k8eTIN0uqiDUoppRyjNQUOCw4O5vvvv6d69erUrVsXb29vTQiUUko5QmsKHLRx40a6detG8eLF2bZtG8WLF3c6JKWUUmmYJgUOuHz5Mn369GHZsmX8/PPPvPLKK4iI02EppZRK47T5IAkZY5g2bRrlypUja9as+Pr60rZtW00IlFJKJQtaU5BEfH19+eijj7h27RoLFy6kRo0aToeklFJK3UVrChJZQEAAffr04bnnnqNt27Zs27ZNEwKllFLJkiYFiWjRokWUK1cOf39/fHx86NGjB+nTp3c6LKWUUipa2nyQCPz9/enVqxe7du3SOQeUUkqlGFpTkICMMUyYMIFKlSpRunRpdu/erQmBUkqpFENrChLIgQMH6NatG4GBgaxatYqKFSs6HZJSSin1ULSmIJ5u377Nf/7zH+rWrUubNm3w8PDQhEAppVSKpDUF8bBu3Tq6devG008/zc6dOylcuLDTISmllFJxpklBHFy+fJkvvviCFStWMGrUKNq0aeN0SEoppVS8afPBQzDGMH36dMqVK0e2bNnYu3evJgRKKaVSDa0piKWjR4/ywQcfcPbsWRYsWEDNmjWdDkkppZRKUFpT8AAhISEMGzaMGjVq0LhxY7y8vDQhUEoplSppTcF9eHl50aVLF/Lnz4+npydPPfWU0yEppZRSiUZrCqIREBDAp59+SosWLfj8889Zvny5JgRKKaVSPUeSAhF5TERWisgh1zZ3DMf9ICI+rtIhKWJbuXIlFSpU4OLFi/j4+PDmm2/q0sZKKaXSBKdqCvoC7saYUoC76/ZdRKQ5UAWoDNQEPheRRxIroCtXrvDuu+/SpUsXfvvtN6ZMmULevHkT6+2UUkqpZMeppKAVMNm1PxloHc0xbsB6Y0yoMeYmsBt4MTGCmT9/PuXLlydbtmz4+Pjw4ouJ8jZKKaVUsuZUR8MCxhh/1/5ZoEA0x+wCBorIT0A2oCHgG92Licj7wPsARYoUiXUQ58+fp0ePHnh7ezNr1iyeffbZh/gISimlVOqSaDUFIrIqSn+AqKVV1OOMMQYw9z7fGLMC+AfYDMwEPICw6N7LGDPOGFPNGFMtX758D4zNGMOMGTOoUKECxYsXZ9euXZoQKKWUSvMSrabAGNMkpsdE5JyIFDTG+ItIQeB8DK8xGBjses4M4GB84zp9+jQffPABR48eZcmSJVSvXj2+L6mUUkqlCk71KVgEdHbtdwYW3nuAiKQXkTyu/YpARWBFXN/QGMP48eOpXLkyzzzzDNu3b9eEQCmllIrCqT4FQ4G/ROQ94DjQHkBEqgHdjTFdgIzABtdwwOvAm8aY0Li82dGjR+natSvXr1/H3d1dlzZWSimlouFITYEx5pIxprExppQxpokx5rLrfi9XQoAxJsgY4+YqtYwx3g/7PuHh4fz888/UqFGDZs2a4eHhoQmBUkopFYNUO83xsWPHeOeddwgJCcHDw4NSpUo5HZJSSimVrKXKaY5///13atSoQYsWLVi/fr0mBEoppVQsiB0RmHo8+uijpnTp0kyePBk3Nzenw1FKKaUSnYhsN8ZUi+/rpLrmg9y5c+Ph4UGGDKnuoymllFKJKtU1H+TNm1cTAqWUUioOUl1SoJRSSqm40aRAKaWUUoAmBUoppZRy0aRAKaWUUoAmBUoppZRy0aRAKaWUUoAmBUoppZRy0aRAKaWUUkAqnOZYRG4AB5yOIw3IC1x0OohUTs9x4tNznDT0PCe+MsaYnPF9kdQ49d+BhJj/Wd2fiHjpeU5ceo4Tn57jpKHnOfGJiFdCvI42HyillFIK0KRAKaWUUi6pMSkY53QAaYSe58Sn5zjx6TlOGnqeE1+CnONU19FQKaWUUnGTGmsKlFJKKRUHmhQopZRSCkhBSYGI/CEi50XEJ4bHG4jINRHxdpUBUR57UUQOiMhhEembdFGnPHE9zyJSWETWiIiviOwVkd5JG3nKEZ+/Zdfj6UVkp4gsSZqIU554fl/kEpG5IrJfRPaJSO2kizxlied5/sT1XeEjIjNFJEvSRZ5yPOgcu45p4Dq/e0VkXZT7H/7aZ4xJEQWoD1QBfGJ4vAGwJJr70wNHgKeATMAuwM3pz5NcSzzOc0Ggims/J3BQz3PCnuMoj38KzLjfMWm9xOccA5OBLq79TEAupz9Pci3x+L54AjgGZHXd/gt42+nPkxxLLM5xLsAXKOK6nd+1jdO1L8XUFBhj1gOX4/DUGsBhY8xRY0wwMAtolaDBpSJxPc/GGH9jzA7X/g1gH/Y/vrpHPP6WEZEngebAhAQNKpWJ6zkWkUexX8ITXa8TbIy5mrDRpR7x+VvGTp6XVUQyANmAMwkWWCoSi3P8OjDPGHPCdfx51/1xuvalmKQglmqLyC4RWSYi5Vz3PQGcjHLMKfRiFV/Rnec7RKQY8AywNckjSz1iOscjgS+BcGfCSlWiO8fFgQvAn64mmgkikt3BGFOD/znPxpjTwHDgBOAPXDPGrHAyyBSsNJBbRNaKyHYRect1f5yufakpKdgBFDXGVAJ+ARY4G06qdd/zLCI5gL+Bj40x15M+vFQh2nMsIi2A88aY7Q7GllrE9HecAVtV+5sx5hngJqD9kOIupr/l3NhfrcWBQkB2EXnTqSBTuAxAVWwN4gtAfxEpHdcXSzVJgTHmujEmwLX/D5BRRPICp4HCUQ590nWfioP7nGdEJCM2IZhujJnnYJgp2n3OcV2gpYj4YasCG4nINOciTbnuc45PAaeMMRG1XHOxSYKKg/uc5ybAMWPMBWNMCDAPqONgqCnZKWC5MeamMeYisB6oRByvfakmKRCRx0VEXPs1sJ/tErANKCUixUUkE/AasMi5SFO2mM6z676JwD5jzH+djDGli+kcG2O+MsY8aYwphv07Xm2M0V9XcXCfc3wWOCkiZVyHNsZ24lJxcJ/v5RNALRHJ5nq8MbYfknp4C4F6IpJBRLIBNbHnMk7XvhSzSqKIzMT2ZM0rIqeAgUBGAGPMWKAd8IGIhAK3gNeM7YIZKiI9gOXY3ph/GGP2OvARUoS4nmcRqQd0AvaIiLfr5f7P9etARRGPv2UVS/E8xz2B6a4v0qPAO0kcfooRj/O8VUTmYpsXQoGd6FTI0XrQOTbG7BORf4Hd2L5GE4wxPq7nPvS1T6c5VkoppRSQipoPlFJKKRU/mhQopZRSCtCkQCmllFIumhQopZRSCtCkQCmllFIumhQolcyJyAgR+TjK7eUiMiHK7Z9E5NMEfL9JItIuoV4vyuv+X5T9Yvdb9e2e530cZerW+MYwXEQaJcRrKZUaaVKgVPK3CddsbyKSDsgLRF0PoQ6w2YG4Htb/PfiQu7kWy3kXuypkQvgFnbZYqRhpUqBU8rcZqO3aLwf4ADdEJLeIZAbKAjtEZICIbBO7Pv04sZ4WEc+IF3L9Qt/j2q8qIutci6gsF5GC975xTMe4Fl/5QUQ8ReSgiDzruj+biPwlIr4iMl9EtopINREZil0Rz1tEprtePr2IjBe7BvwKEckazWdvBOwwxoRGed9qrv28rimfEZG3RWSBiKwUET8R6SEin4pd1GiLiDwGYIw5DuQRkcfj8w+iVGqlSYFSyZwx5gx2Zs4i2FoBD+wKlLWBasAe19Koo40x1Y0x5YGsQAtjzH4gk4gUd71cB2C22HUqfgHaGWOqAn8Ag6O+byyOyWCMqQF8jJ1lDeBD4Ioxxg3oj12oBWNMX+CWMaayMeYN17GlgDHGmHLAVaBtNB+/LhDbBaDKA68A1V1xBroWNfIAojY/7HC9rlLqHilmmmOl0rjN2ISgDvBf7BKodYBr2OYFgIYi8iV2bfrHgL3AYuAvbDIw1LXtAJTBXkRXuqamT49dwjaqBx0TsejVdqCYa78e8DOAMcZHRHbf5zMdM8Z4R/MaURUk9nPirzHG3MDWolzDfnaAPUDFKMedx67Mp5S6hyYFSqUMEf0KKmCbD04CnwHXgT9FJAvwK1DNGHNSRAYBWVzPnQ3MEZF5gDHGHBKRCsBeY0xtYiYPOOa2axtG3L5LbkfZD8PWbtzrFpGfA+w8+RE1nFnuOTbq64VHuR1+T3xZXK+rlLqHNh8olTJsBloAl40xYcaYy0AubBPCZiIvkBdFJAd2IRoAjDFHsBfd/tgEAeAAkE9EaoNtKhCRqJ0XY3vMvTYB7V3Hu2GTmAghriaJh7EPKBnlth+uJgmifMaHVBqbWCml7qFJgVIpwx7sqIMt99x3zRhz0RhzFRiPvdgtxy6bGtVs4E1sUwKuPgjtgB9EZBfgzT3r2cfmmGj8ik0kfIHvsE0Y11yPjQN2R+loGBvLgPpRbg/Hrrq3E3s+HoorKSkJeD3sc5VKC3SVRKVUghGR9EBGY0yQiJQAVgFlXAlGXF9zPvClMeZQAsTXBqhijOkf39dSKjXSPgVKqYSUDVjj+kUuwIfxSQhc+mI7HMY7KcB+5/2UAK+jVKqkNQVKKaWUArRPgVJKKaVcNClQSimlFKBJgVJKKaVcNClQSimlFKBJgVJKKaVc/h+dKUwQqOrBuwAAAABJRU5ErkJggg==",
       "text/plain": [
        "
"
       ]
@@ -2488,7 +2482,7 @@
     "ax1.set_ylabel(\"Power (dB)\")\n",
     "ax1.set_title(\"Coupling Efficiency\")\n",
     "plt.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   }
  ],
@@ -2517,7 +2511,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.93.9.16"
+   "version": "3.11.0"
   },
   "nbdime-conflicts": {
    "local_diff": [
diff --git a/EffectiveIndexApproximation.ipynb b/EffectiveIndexApproximation.ipynb
index 7ecc54f2..cffa68c1 100644
--- a/EffectiveIndexApproximation.ipynb
+++ b/EffectiveIndexApproximation.ipynb
@@ -25,13 +25,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "import gdstk\n",
-    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
-    "from tidy3d.plugins.dispersion import FastDispersionFitter, AdvancedFastFitterParam"
+    "from tidy3d.plugins.dispersion import AdvancedFastFitterParam, FastDispersionFitter"
    ]
   },
   {
@@ -3315,7 +3314,7 @@
     "plt.xlim(1.5, 1.6)\n",
     "plt.ylim(0, 1)\n",
     "plt.title(\"Strip-to-slot Coupling Efficiency\")\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Transmission\")\n",
     "plt.legend()\n",
     "plt.show()"
diff --git a/EulerWaveguideBend.ipynb b/EulerWaveguideBend.ipynb
index cef2d6b1..86add796 100644
--- a/EulerWaveguideBend.ipynb
+++ b/EulerWaveguideBend.ipynb
@@ -36,14 +36,13 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
+    "import gdstk\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import scipy.integrate as integrate\n",
-    "from scipy.optimize import fsolve\n",
-    "import gdstk\n",
-    "\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n"
+    "import tidy3d.web as web\n",
+    "from scipy.optimize import fsolve"
    ]
   },
   {
@@ -91,7 +90,7 @@
    "outputs": [],
    "source": [
     "R_eff = 4  # effective radius of the bend\n",
-    "A = 2.4  # clothoid parameter\n"
+    "A = 2.4  # clothoid parameter"
    ]
   },
   {
@@ -145,7 +144,7 @@
     "        break\n",
     "\n",
     "# after L_max is determined, R_min is also determined\n",
-    "R_min = A**2 / L_max\n"
+    "R_min = A**2 / L_max"
    ]
   },
   {
@@ -172,7 +171,7 @@
    "source": [
     "# getting the coordinates of the second clothoid curve by mirroring the first curve with respect to y=-x+R_eff\n",
     "x3 = np.flipud(R_eff - y1)\n",
-    "y3 = np.flipud(R_eff - x1)\n"
+    "y3 = np.flipud(R_eff - x1)"
    ]
   },
   {
@@ -201,7 +200,7 @@
     "def circle(var):\n",
     "    a = var[0]\n",
     "    b = var[1]\n",
-    "    Func = np.empty((2))\n",
+    "    Func = np.empty(2)\n",
     "    Func[0] = (xp - a) ** 2 + (yp - b) ** 2 - R_min**2\n",
     "    Func[1] = (R_eff - yp - a) ** 2 + (R_eff - xp - b) ** 2 - R_min**2\n",
     "    return Func\n",
@@ -211,7 +210,7 @@
     "\n",
     "# calculate the coordinates of the circular curve\n",
     "x2 = np.linspace(xp + 0.01, R_eff - yp - 0.01, 50)\n",
-    "y2 = -np.sqrt(R_min**2 - (x2 - a) ** 2) + b\n"
+    "y2 = -np.sqrt(R_min**2 - (x2 - a) ** 2) + b"
    ]
   },
   {
@@ -261,7 +260,7 @@
     "plt.axis(\"equal\")\n",
     "plt.ylim(-1, R_eff + 1)\n",
     "plt.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -298,7 +297,7 @@
     "freq0 = td.C_0 / lda0  # central frequency\n",
     "ldas = np.linspace(1.5, 1.6, 100)  # simulation wavelength range\n",
     "freqs = td.C_0 / ldas  # simulation wavelength range\n",
-    "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))  # frequency width of the souce\n"
+    "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))  # frequency width of the source"
    ]
   },
   {
@@ -318,7 +317,7 @@
     "t = 0.21  # thickness of the waveguide\n",
     "w = 0.4  # width of the waveguide\n",
     "inf_eff = 100  # effective infinity of the simulation\n",
-    "buffer = 1  # buffer distance\n"
+    "buffer = 1  # buffer distance"
    ]
   },
   {
@@ -347,7 +346,7 @@
     "si = td.Medium(permittivity=n_si**2)\n",
     "\n",
     "n_sio2 = 1.444  # silicon oxide refractive index\n",
-    "sio2 = td.Medium(permittivity=n_sio2**2)\n"
+    "sio2 = td.Medium(permittivity=n_sio2**2)"
    ]
   },
   {
@@ -374,17 +373,16 @@
    "source": [
     "# function that takes the x and y coordinates of a curve and returns a waveguide bend structure with a given width and thickness\n",
     "def line_to_structure(x, y, w, t):\n",
-    "   \n",
-    "    cell = gdstk.Cell(\"bend\") # define a gds cell\n",
-    "    \n",
+    "    cell = gdstk.Cell(\"bend\")  # define a gds cell\n",
+    "\n",
     "    # add points to include the input and output straght waveguides\n",
     "    x = np.insert(x, 0, -inf_eff)\n",
     "    x = np.append(x, R_eff)\n",
-    "    y = np.insert(y,0,0)\n",
+    "    y = np.insert(y, 0, 0)\n",
     "    y = np.append(y, inf_eff)\n",
     "\n",
-    "    cell.add(gdstk.FlexPath(x + 1j * y, w, layer=1, datatype=0)) # add path to cell\n",
-    "    \n",
+    "    cell.add(gdstk.FlexPath(x + 1j * y, w, layer=1, datatype=0))  # add path to cell\n",
+    "\n",
     "    # define structure from cell\n",
     "    bend = td.Structure(\n",
     "        geometry=td.PolySlab.from_gds(\n",
@@ -392,11 +390,11 @@
     "            gds_layer=1,\n",
     "            axis=2,\n",
     "            slab_bounds=(0, t),\n",
-    "        )[0], \n",
-    "        medium=si\n",
+    "        )[0],\n",
+    "        medium=si,\n",
     "    )\n",
     "\n",
-    "    return bend\n"
+    "    return bend"
    ]
   },
   {
@@ -421,7 +419,7 @@
    },
    "outputs": [],
    "source": [
-    "circular_bend = line_to_structure(x_circle, y_circle, w, t)\n"
+    "circular_bend = line_to_structure(x_circle, y_circle, w, t)"
    ]
   },
   {
@@ -499,15 +497,13 @@
     "    sources=[mode_source],\n",
     "    monitors=[mode_monitor, field_monitor],\n",
     "    run_time=run_time,\n",
-    "    boundary_spec=td.BoundarySpec.all_sides(\n",
-    "        boundary=td.PML()\n",
-    "    ),  # pml is applied in all boundaries\n",
+    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),  # pml is applied in all boundaries\n",
     "    medium=sio2,\n",
     ")  # background medium is set to sio2 because of the substrate and upper cladding\n",
     "\n",
     "# visualize the circular bend structure\n",
     "sim.plot(z=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -867,7 +863,7 @@
    ],
    "source": [
     "job = web.Job(simulation=sim, task_name=\"circular_bend\")\n",
-    "sim_data_circular = job.run(path=\"data/simulation_data_circular.hdf5\")\n"
+    "sim_data_circular = job.run(path=\"data/simulation_data_circular.hdf5\")"
    ]
   },
   {
@@ -905,15 +901,15 @@
    "source": [
     "# extract the transmission data from the mode monitor\n",
     "amp = sim_data_circular[\"mode\"].amps.sel(mode_index=0, direction=\"+\")\n",
-    "T_circular = np.abs(amp)**2 \n",
+    "T_circular = np.abs(amp) ** 2\n",
     "\n",
     "# plot the bending loss as a function of wavelength\n",
     "plt.plot(ldas, -10 * np.log10(T_circular))\n",
     "plt.xlim(1.5, 1.6)\n",
     "plt.ylim(0, 0.03)\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Bending loss (dB)\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -967,7 +963,7 @@
     "    vmin=-20,\n",
     "    vmax=30,\n",
     ")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1019,7 +1015,7 @@
     "\n",
     "# visualize the euler bend structure\n",
     "sim.plot(z=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1379,7 +1375,7 @@
    ],
    "source": [
     "job = web.Job(simulation=sim, task_name=\"circular_bend\")\n",
-    "sim_data_euler = job.run(path=\"data/simulation_data_euler.hdf5\")\n"
+    "sim_data_euler = job.run(path=\"data/simulation_data_euler.hdf5\")"
    ]
   },
   {
@@ -1417,17 +1413,17 @@
    "source": [
     "# extract the transmission data from the mode monitor\n",
     "amp = sim_data_euler[\"mode\"].amps.sel(mode_index=0, direction=\"+\")\n",
-    "T_euler = np.abs(amp)**2 \n",
+    "T_euler = np.abs(amp) ** 2\n",
     "\n",
     "# plotting the losses\n",
     "plt.plot(ldas, -10 * np.log10(T_circular), label=\"Circular bend\")\n",
     "plt.plot(ldas, -10 * np.log10(T_euler), label=\"Euler bend\")\n",
     "plt.xlim(1.5, 1.6)\n",
     "plt.ylim(0, 0.03)\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Bending loss (dB)\")\n",
     "plt.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1479,7 +1475,7 @@
     "    vmin=-20,\n",
     "    vmax=30,\n",
     ")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1516,7 +1512,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.0"
   },
   "title": "Euler Waveguide Bend Modeling in Tidy3D | Flexcompute",
   "widgets": {
diff --git a/FieldProjections.ipynb b/FieldProjections.ipynb
index 0497deb5..2df66dfb 100644
--- a/FieldProjections.ipynb
+++ b/FieldProjections.ipynb
@@ -44,12 +44,12 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3d imports\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n"
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -98,9 +98,7 @@
     "min_cells_per_wvl = 30\n",
     "\n",
     "# create the PEC plate\n",
-    "plate = td.Structure(\n",
-    "    geometry=td.Box(size=[td.inf, thick, td.inf], center=[0, 0, 0]), medium=pec\n",
-    ")\n",
+    "plate = td.Structure(geometry=td.Box(size=[td.inf, thick, td.inf], center=[0, 0, 0]), medium=pec)\n",
     "\n",
     "# create the aperture in the plate\n",
     "aperture = td.Structure(\n",
@@ -114,7 +112,7 @@
     "boundary_spec = td.BoundarySpec.all_sides(boundary=td.PML())\n",
     "\n",
     "# set the total domain size in x, y, and z\n",
-    "sim_size = [width * 2, 2, height * 2]\n"
+    "sim_size = [width * 2, 2, height * 2]"
    ]
   },
   {
@@ -148,7 +146,7 @@
     ")\n",
     "\n",
     "# Simulation run time\n",
-    "run_time = 50 / fwidth\n"
+    "run_time = 50 / fwidth"
    ]
   },
   {
@@ -172,8 +170,12 @@
    "source": [
     "offset_mon = 0.3\n",
     "monitor_near = td.FieldMonitor(\n",
-    "    center=[0, offset_mon, 0], size=[td.inf, 0, td.inf], freqs=[f0], name=\"near_field\", colocate=False\n",
-    ")\n"
+    "    center=[0, offset_mon, 0],\n",
+    "    size=[td.inf, 0, td.inf],\n",
+    "    freqs=[f0],\n",
+    "    name=\"near_field\",\n",
+    "    colocate=False,\n",
+    ")"
    ]
   },
   {
@@ -218,7 +220,7 @@
     "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 3))\n",
     "sim.plot(x=0, ax=ax1)\n",
     "sim.plot(y=0, ax=ax2)\n",
-    "plt.show();\n"
+    "plt.show();"
    ]
   },
   {
@@ -577,9 +579,7 @@
     }
    ],
    "source": [
-    "sim_data = web.run(\n",
-    "    sim, task_name=\"aperture_1\", path=\"data/aperture_1.hdf5\", verbose=True\n",
-    ")\n"
+    "sim_data = web.run(sim, task_name=\"aperture_1\", path=\"data/aperture_1.hdf5\", verbose=True)"
    ]
   },
   {
@@ -601,7 +601,7 @@
     "\n",
     "# theta and phi angles at which to observe fields - part of the half-space to the right\n",
     "theta_proj = np.linspace(np.pi / 10, np.pi - np.pi / 10, 100)\n",
-    "phi_proj = np.linspace(np.pi / 10, np.pi - np.pi / 10, 100)\n"
+    "phi_proj = np.linspace(np.pi / 10, np.pi - np.pi / 10, 100)"
    ]
   },
   {
@@ -674,7 +674,8 @@
     "    # far away that geometric far field approximations can be invoked to speed up the calculation\n",
     ")\n",
     "\n",
-    "# helper functin to call the projector\n",
+    "\n",
+    "# helper function to call the projector\n",
     "def get_proj_fields(sim_data, monitor_near, monitor_far, pts_per_wavelength=10):\n",
     "    # object that does projections is constructed using the near-field monitor, because those are the fields to be projected\n",
     "    projector = td.FieldProjector.from_near_field_monitors(\n",
@@ -694,7 +695,7 @@
     "t0 = time.perf_counter()\n",
     "projected_field_data = get_proj_fields(sim_data, monitor_near, monitor_far)\n",
     "t1 = time.perf_counter()\n",
-    "proj_time = t1 - t0\n"
+    "proj_time = t1 - t0"
    ]
   },
   {
@@ -711,9 +712,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def analytic_fields_aperture(\n",
-    "    proj_monitor, sim_size, aperture_height, aperture_width, r_proj\n",
-    "):\n",
+    "def analytic_fields_aperture(proj_monitor, sim_size, aperture_height, aperture_width, r_proj):\n",
     "    \"\"\"Compute the far fields analytically.\"\"\"\n",
     "    # in Tidy3D, the plane wave source is normalized so that a total flux of 1 is injected into the simulation domain,\n",
     "    # which corresponds to an electric field strength that is inversely proportional to the square root of the in-plane domain area\n",
@@ -766,9 +765,7 @@
     "    )\n",
     "\n",
     "\n",
-    "analytic_field_data = analytic_fields_aperture(\n",
-    "    monitor_far, sim_size, height, width, r_proj\n",
-    ")\n"
+    "analytic_field_data = analytic_fields_aperture(monitor_far, sim_size, height, width, r_proj)"
    ]
   },
   {
@@ -827,7 +824,7 @@
     "    ax[0].set_title(\"Analytic\")\n",
     "    ax[1].set_title(\"Field projection\")\n",
     "    for _ax in ax:\n",
-    "        _ax.set_xlabel(\"$\\phi$ (deg)\")\n",
+    "        _ax.set_xlabel(r\"$\\phi$ (deg)\")\n",
     "        _ax.set_ylabel(\"$\\\\theta$ (deg)\")\n",
     "\n",
     "\n",
@@ -849,7 +846,7 @@
     "    f\"Normalized root mean squared error: {rmse(Etheta_analytic.values, Etheta_proj.values) * 100:.2f} %\"\n",
     ")\n",
     "\n",
-    "plt.show();\n"
+    "plt.show();"
    ]
   },
   {
@@ -884,7 +881,7 @@
     "    ],  # just provide the far field FieldProjectionAngleMonitor as the input monitor\n",
     "    run_time=run_time,\n",
     "    boundary_spec=boundary_spec,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1243,9 +1240,7 @@
     }
    ],
    "source": [
-    "sim_data2 = web.run(\n",
-    "    sim2, task_name=\"aperture_2\", path=\"data/aperture_2.hdf5\", verbose=True\n",
-    ")\n"
+    "sim_data2 = web.run(sim2, task_name=\"aperture_2\", path=\"data/aperture_2.hdf5\", verbose=True)"
    ]
   },
   {
@@ -1293,7 +1288,7 @@
     "    f\"Normalized root mean squared error: {rmse(Etheta_analytic.values, Etheta_proj_server.values) * 100:.2f} %\"\n",
     ")\n",
     "\n",
-    "plt.show();\n"
+    "plt.show();"
    ]
   },
   {
@@ -1321,11 +1316,9 @@
    ],
    "source": [
     "# use the simulation log to find the time taken for server-side computations\n",
-    "server_time = float(\n",
-    "    sim_data2.log.split(\"Field projection time (s):    \", 1)[1].split(\"\\n\", 1)[0]\n",
-    ")\n",
+    "server_time = float(sim_data2.log.split(\"Field projection time (s):    \", 1)[1].split(\"\\n\", 1)[0])\n",
     "print(f\"Client-side field projection took {proj_time:.2f} s\")\n",
-    "print(f\"Server-side field projection took {server_time:.2f} s\")\n"
+    "print(f\"Server-side field projection took {server_time:.2f} s\")"
    ]
   },
   {
@@ -1353,10 +1346,12 @@
    "outputs": [],
    "source": [
     "# make an updated copy of the projection monitor, this time requesting downsampling\n",
-    "monitor_far = monitor_far.copy(update={\"interval_space\": (4, 1, 3)}) # downsample by a factor of 4 along x, and 3 along y\n",
+    "monitor_far = monitor_far.copy(\n",
+    "    update={\"interval_space\": (4, 1, 3)}\n",
+    ")  # downsample by a factor of 4 along x, and 3 along y\n",
     "\n",
     "# update the simulation object with this new monitor\n",
-    "sim2 = sim2.copy(update={\"monitors\": [monitor_far]})\n"
+    "sim2 = sim2.copy(update={\"monitors\": [monitor_far]})"
    ]
   },
   {
@@ -1715,9 +1710,7 @@
     }
    ],
    "source": [
-    "sim_data2 = web.run(\n",
-    "    sim2, task_name=\"aperture_2\", path=\"data/aperture_2.hdf5\", verbose=True\n",
-    ")\n"
+    "sim_data2 = web.run(sim2, task_name=\"aperture_2\", path=\"data/aperture_2.hdf5\", verbose=True)"
    ]
   },
   {
@@ -1765,7 +1758,7 @@
     "    f\"Normalized root mean squared error: {rmse(Etheta_analytic.values, Etheta_proj_server.values) * 100:.2f} %\"\n",
     ")\n",
     "\n",
-    "plt.show();\n"
+    "plt.show();"
    ]
   },
   {
@@ -1791,11 +1784,9 @@
    ],
    "source": [
     "# use the simulation log to find the time taken for server-side computations\n",
-    "server_time = float(\n",
-    "    sim_data2.log.split(\"Field projection time (s):    \", 1)[1].split(\"\\n\", 1)[0]\n",
-    ")\n",
+    "server_time = float(sim_data2.log.split(\"Field projection time (s):    \", 1)[1].split(\"\\n\", 1)[0])\n",
     "print(f\"Client-side field projection took {proj_time:.2f} s\")\n",
-    "print(f\"Server-side field projection took {server_time:.2f} s\")\n"
+    "print(f\"Server-side field projection took {server_time:.2f} s\")"
    ]
   },
   {
@@ -1831,10 +1822,12 @@
     "new_monitor_far = monitor_far.copy(update={\"window_size\": window_size})\n",
     "\n",
     "# new simulation object without a physical aperture, but with windowing enabled in the projection monitor\n",
-    "sim_window = sim.copy(update={\n",
-    "    \"monitors\": [new_monitor_far],\n",
-    "    \"structures\": [],\n",
-    "})\n"
+    "sim_window = sim.copy(\n",
+    "    update={\n",
+    "        \"monitors\": [new_monitor_far],\n",
+    "        \"structures\": [],\n",
+    "    }\n",
+    ")"
    ]
   },
   {
@@ -2195,7 +2188,7 @@
    "source": [
     "sim_data_window = web.run(\n",
     "    sim_window, task_name=\"aperture_2\", path=\"data/aperture_2.hdf5\", verbose=True\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -2258,13 +2251,13 @@
     "im1 = ax.plot(\n",
     "    Etheta_analytic.theta * 180 / np.pi,\n",
     "    np.real(Etheta_analytic.sel(phi=np.pi / 2, method=\"nearest\")),\n",
-    "    '-k',\n",
+    "    \"-k\",\n",
     "    label=\"Analytic\",\n",
     ")\n",
     "im2 = ax.plot(\n",
     "    Etheta_proj.theta * 180 / np.pi,\n",
     "    np.real(Etheta_proj.sel(phi=np.pi / 2, method=\"nearest\")),\n",
-    "    '-r',\n",
+    "    \"-r\",\n",
     "    label=\"Projection\",\n",
     ")\n",
     "ax.legend()\n",
@@ -2276,7 +2269,7 @@
     "    f\"Normalized root mean squared error: {rmse(Etheta_analytic.values, Etheta_proj.values) * 100:.2f} %\"\n",
     ")\n",
     "\n",
-    "plt.show();\n"
+    "plt.show();"
    ]
   },
   {
@@ -2349,7 +2342,7 @@
     "    ax[1].set_title(\"Ey\")\n",
     "    ax[2].set_title(\"Ez\")\n",
     "    for _ax in ax:\n",
-    "        _ax.set_xlabel(\"$\\phi$ (deg)\")\n",
+    "        _ax.set_xlabel(r\"$\\phi$ (deg)\")\n",
     "        _ax.set_ylabel(\"$\\\\theta$ (deg)\")\n",
     "\n",
     "\n",
@@ -2357,9 +2350,7 @@
     "fields_cartesian = projected_field_data.fields_cartesian.isel(f=0, r=0)\n",
     "\n",
     "# plot Ex, Ey, Ez\n",
-    "make_cart_plot(\n",
-    "    phi_proj, theta_proj, fields_cartesian.Ex, fields_cartesian.Ey, fields_cartesian.Ez\n",
-    ")\n",
+    "make_cart_plot(phi_proj, theta_proj, fields_cartesian.Ex, fields_cartesian.Ey, fields_cartesian.Ez)\n",
     "\n",
     "# get the power\n",
     "power = projected_field_data.power.isel(f=0, r=0)\n",
@@ -2375,10 +2366,10 @@
     ")\n",
     "fig.colorbar(im, ax=ax)\n",
     "_ = ax.set_title(\"Power\")\n",
-    "_ = ax.set_xlabel(\"$\\phi$ (deg)\")\n",
+    "_ = ax.set_xlabel(r\"$\\phi$ (deg)\")\n",
     "_ = ax.set_ylabel(\"$\\\\theta$ (deg)\")\n",
     "\n",
-    "plt.show();\n"
+    "plt.show();"
    ]
   },
   {
@@ -2425,9 +2416,7 @@
     "\n",
     "# now all the fields stored in 'projected_field_data' correspond to this new distance\n",
     "# compare to the analytical fields at this new distance\n",
-    "analytic_field_data_new = analytic_fields_aperture(\n",
-    "    monitor_far, sim_size, height, width, r_proj_new\n",
-    ")\n",
+    "analytic_field_data_new = analytic_fields_aperture(monitor_far, sim_size, height, width, r_proj_new)\n",
     "\n",
     "# plot Etheta\n",
     "Etheta_analytic = analytic_field_data_new.Etheta.isel(f=0, r=0)\n",
@@ -2439,7 +2428,7 @@
     "    f\"Normalized root mean squared error: {rmse(Etheta_analytic.values, Etheta_proj.values) * 100:.2f} %\"\n",
     ")\n",
     "\n",
-    "plt.show();\n"
+    "plt.show();"
    ]
   },
   {
@@ -2513,7 +2502,7 @@
     "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 3))\n",
     "sim3.plot(x=0, ax=ax1)\n",
     "sim3.plot(y=0, ax=ax2)\n",
-    "plt.show();\n"
+    "plt.show();"
    ]
   },
   {
@@ -2872,9 +2861,7 @@
     }
    ],
    "source": [
-    "sim_data3 = web.run(\n",
-    "    sim3, task_name=\"aperture_3\", path=\"data/aperture_3.hdf5\", verbose=True\n",
-    ")\n"
+    "sim_data3 = web.run(sim3, task_name=\"aperture_3\", path=\"data/aperture_3.hdf5\", verbose=True)"
    ]
   },
   {
@@ -2958,11 +2945,9 @@
     "import time\n",
     "\n",
     "t0 = time.perf_counter()\n",
-    "projected_field_data_noapprox = get_proj_fields(\n",
-    "    sim_data3, monitor_near, monitor_intermediate_proj\n",
-    ")\n",
+    "projected_field_data_noapprox = get_proj_fields(sim_data3, monitor_near, monitor_intermediate_proj)\n",
     "t1 = time.perf_counter()\n",
-    "proj_time_new = t1 - t0\n"
+    "proj_time_new = t1 - t0"
    ]
   },
   {
@@ -3046,7 +3031,7 @@
     "    sim_data3, monitor_near, monitor_intermediate_proj_approx\n",
     ")\n",
     "t1 = time.perf_counter()\n",
-    "proj_time_new_approx = t1 - t0\n"
+    "proj_time_new_approx = t1 - t0"
    ]
   },
   {
@@ -3065,12 +3050,8 @@
    ],
    "source": [
     "# let's see how long this took compared to the previous case when the approximations were turned on\n",
-    "print(\n",
-    "    f\"Client-side field projection *with approximations on* took {proj_time_new_approx:.2f} s\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Client-side field projection *with approximations off* took {proj_time_new:.2f} s\"\n",
-    ")\n"
+    "print(f\"Client-side field projection *with approximations on* took {proj_time_new_approx:.2f} s\")\n",
+    "print(f\"Client-side field projection *with approximations off* took {proj_time_new:.2f} s\")"
    ]
   },
   {
@@ -3101,7 +3082,7 @@
     "    ax[2].set_title(\"Ez\")\n",
     "    for _ax in ax:\n",
     "        _ax.set_xlabel(\"$y$ (micron)\")\n",
-    "        _ax.set_ylabel(\"$x$ (micron)\")\n"
+    "        _ax.set_ylabel(\"$x$ (micron)\")"
    ]
   },
   {
@@ -3211,9 +3192,7 @@
     "\n",
     "# RMSE\n",
     "Emag_meas = np.sqrt(\n",
-    "    np.abs(fields_meas.Ex) ** 2\n",
-    "    + np.abs(fields_meas.Ey) ** 2\n",
-    "    + np.abs(fields_meas.Ez) ** 2\n",
+    "    np.abs(fields_meas.Ex) ** 2 + np.abs(fields_meas.Ey) ** 2 + np.abs(fields_meas.Ez) ** 2\n",
     ")\n",
     "Emag_proj_noapprox = np.sqrt(\n",
     "    np.abs(fields_proj_noapprox.Ex) ** 2\n",
@@ -3232,7 +3211,7 @@
     "    f\"Normalized RMSE for |E|, with far field approximation: {rmse(Emag_meas.values, Emag_proj_approx.values) * 100:.2f} %\"\n",
     ")\n",
     "\n",
-    "plt.show();\n"
+    "plt.show();"
    ]
   },
   {
@@ -3277,7 +3256,7 @@
     "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 3))\n",
     "sim4.plot(x=0, ax=ax1)\n",
     "sim4.plot(y=0, ax=ax2)\n",
-    "plt.show();\n"
+    "plt.show();"
    ]
   },
   {
@@ -3630,9 +3609,7 @@
    ],
    "source": [
     "# run the simulation\n",
-    "sim_data4 = web.run(\n",
-    "    sim4, task_name=\"aperture_4\", path=\"data/aperture_4.hdf5\", verbose=True\n",
-    ")\n"
+    "sim_data4 = web.run(sim4, task_name=\"aperture_4\", path=\"data/aperture_4.hdf5\", verbose=True)"
    ]
   },
   {
@@ -3733,15 +3710,11 @@
     ")\n",
     "\n",
     "# use the simulation log to find the time taken for server-side computations\n",
-    "server_time = float(\n",
-    "    sim_data4.log.split(\"Field projection time (s):    \", 1)[1].split(\"\\n\", 1)[0]\n",
-    ")\n",
-    "print(\n",
-    "    f\"Client-side field projection *without approximations* took {proj_time_new:.2f} s\"\n",
-    ")\n",
+    "server_time = float(sim_data4.log.split(\"Field projection time (s):    \", 1)[1].split(\"\\n\", 1)[0])\n",
+    "print(f\"Client-side field projection *without approximations* took {proj_time_new:.2f} s\")\n",
     "print(f\"Server-side field projection *without approximations* took {server_time:.2f} s\")\n",
     "\n",
-    "plt.show();\n"
+    "plt.show();"
    ]
   },
   {
@@ -3779,7 +3752,7 @@
     "    angle_phi=np.pi / 4,  # angles are with respect to the source plane's normal axis\n",
     "    waist_radius=2 * wavelength,\n",
     "    waist_distance=-wavelength * 4,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -3828,7 +3801,7 @@
     "\n",
     "fig, (ax) = plt.subplots(1, 1, figsize=(7, 3))\n",
     "sim5.plot(y=0, ax=ax)\n",
-    "plt.show();\n"
+    "plt.show();"
    ]
   },
   {
@@ -4187,9 +4160,7 @@
     }
    ],
    "source": [
-    "sim_data5 = web.run(\n",
-    "    sim5, task_name=\"kspace_monitor\", path=\"data/kspace_monitor.hdf5\", verbose=True\n",
-    ")\n"
+    "sim_data5 = web.run(sim5, task_name=\"kspace_monitor\", path=\"data/kspace_monitor.hdf5\", verbose=True)"
    ]
   },
   {
@@ -4237,9 +4208,7 @@
     "\n",
     "# plot\n",
     "Etheta = far_data.Etheta.isel(f=0, r=0)\n",
-    "fig, ax = plt.subplots(\n",
-    "    1, 1, tight_layout=True, figsize=(7, 5), subplot_kw={\"projection\": \"polar\"}\n",
-    ")\n",
+    "fig, ax = plt.subplots(1, 1, tight_layout=True, figsize=(7, 5), subplot_kw={\"projection\": \"polar\"})\n",
     "ax.grid(False)\n",
     "# im = ax.pcolormesh(np.squeeze(phi), np.squeeze(theta) * 180 / np.pi, np.abs(Etheta), cmap='RdBu', shading='auto')\n",
     "im = ax.pcolormesh(\n",
@@ -4250,7 +4219,7 @@
     "    shading=\"auto\",\n",
     ")\n",
     "fig.colorbar(im, ax=ax)\n",
-    "_ = ax.set_xlabel(\"$\\phi$ (deg)\")\n",
+    "_ = ax.set_xlabel(r\"$\\phi$ (deg)\")\n",
     "\n",
     "label_position = ax.get_rlabel_position()\n",
     "_ = ax.text(\n",
@@ -4262,7 +4231,7 @@
     "    va=\"center\",\n",
     ")\n",
     "\n",
-    "plt.show();\n"
+    "plt.show();"
    ]
   },
   {
@@ -4278,9 +4247,9 @@
    "metadata": {},
    "source": [
     "### Notes \n",
-    "* Since field projections rely on the surface equivalence principle, we have assumed that the tangential near fields recorded on the near field monitor serve as equivalent sources which generate the correct far fields. However, this requires that the field strength decays nearly to zero near the edges of the near-field monitor, which may not always be the case. For example, if we had used a larger aperture compared to the full simulation size in the transverse direction, we may expect a degradation in accuracy of the field projections.\n",
+    "* Since field projections rely on the surface equivalence principle, we have assumed that the tangential near fields recorded on the near field monitor serve as equivalent sources which generate the correct far fields. However, this requires that the field strength decays nearly to zero near the edges of the near-field monitor, which may not always be the case. For example, if we had used a larger aperture compared to the full simulation size in the transverse direction, we may expect a degradation in the accuracy of the field projections.\n",
     "Despite this limitation, the field projections are still remarkably accurate in realistic scenarios. For realistic case studies further demonstrating the accuracy of the field projections, see our [metalens](https://www.flexcompute.com/tidy3d/examples/notebooks/Metalens/) and [zone plate](https://www.flexcompute.com/tidy3d/examples/notebooks/ZonePlateFieldProjection/) case studies.\n",
-    "* The field projections make use of the analytical homogeneous medium Green's function, which assumes that the fields are propagating in a homogeneous medium. Therefore, one should use PMLs / absorbers as boundary conditions in the part of the domain where fields are projected. For far field projections in the context of perdiodic boundary conditions, see the [diffraction efficiency example](https://www.flexcompute.com/tidy3d/examples/notebooks/GratingEfficiency/) which demonstrates the use of a [DiffractionMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.DiffractionMonitor.html).\n",
+    "* The field projections make use of the analytical homogeneous medium Green's function, which assumes that the fields are propagating in a homogeneous medium. Therefore, one should use PMLs / absorbers as boundary conditions in the part of the domain where fields are projected. For far field projections in the context of periodic boundary conditions, see the [diffraction efficiency example](https://www.flexcompute.com/tidy3d/examples/notebooks/GratingEfficiency/), which demonstrates the use of a [DiffractionMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.DiffractionMonitor.html).\n",
     "* Server-side field projections will add to the monetary cost of the simulation. However, typically the far field projections have a very small computation cost compared to the FDTD simulation itself, so the increase in monetary cost should be negligibly small in most cases."
    ]
   },
@@ -4311,7 +4280,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.11.0"
   },
   "title": "Performing Far-field Projections in Tidy3D | Flexcompute",
   "widgets": {
diff --git a/Fitting.ipynb b/Fitting.ipynb
index 9b6d5e0c..49cc37a1 100644
--- a/Fitting.ipynb
+++ b/Fitting.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "Here we show how to fit optical measurement data and use the results to create dispersion material models for `Tidy3D`.\n",
     "\n",
-    "`Tidy3D`'s dispersion fitting tool peforms an optimization to find a medium defined as a dispersive [PoleResidue](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PoleResidue.html) model that minimizes the RMS error between the model results and the data. This can then be directly used as a material in simulations.\n",
+    "`Tidy3D`'s dispersion fitting tool performs an optimization to find a medium defined as a dispersive [PoleResidue](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PoleResidue.html) model that minimizes the RMS error between the model results and the data. This can then be directly used as a material in simulations.\n",
     "\n",
     "If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. For simulation examples, please visit our [examples page](https://www.flexcompute.com/tidy3d/examples/). If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. FDTD simulations can diverge due to various reasons. If you run into any simulation divergence issues, please follow the steps outlined in our [troubleshooting guide](https://www.flexcompute.com/tidy3d/examples/notebooks/DivergedFDTDSimulation/) to resolve it.\n",
     "\n",
@@ -26,7 +26,6 @@
     "# first import packages\n",
     "import matplotlib.pylab as plt\n",
     "import numpy as np\n",
-    "\n",
     "import tidy3d as td"
    ]
   },
@@ -70,7 +69,7 @@
     }
    ],
    "source": [
-    "from tidy3d.plugins.dispersion import FastDispersionFitter, AdvancedFastFitterParam\n",
+    "from tidy3d.plugins.dispersion import AdvancedFastFitterParam, FastDispersionFitter\n",
     "\n",
     "fname = \"misc/nk_data.csv\"\n",
     "\n",
@@ -79,7 +78,7 @@
     "\n",
     "# lets plot the data\n",
     "fitter.plot()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -88,11 +87,11 @@
    "source": [
     "## Fitting the data\n",
     "\n",
-    "The fitting tool fit a dispersion model to the data by minimizing the root mean squared (RMS) error between the model n,k prediciton and the data at the given wavelengths.\n",
+    "The fitting tool fit a dispersion model to the data by minimizing the root mean squared (RMS) error between the model n,k prediction and the data at the given wavelengths.\n",
     "\n",
     "There are various fitting parameters that can be set, but the most important is the number of \"poles\" in the model.\n",
     "\n",
-    "For each pole, there are 4 degrees of freedom in the model.  Adding more poles can produce a closer fit, but each additional pole added will make the fit harder to obtain and will slow down the FDTD.  Therefore, it is best to try the fit with few numbers of poles and increase until the results look good.\n",
+    "For each pole, there are 4 degrees of freedom in the model.  Adding more poles can produce a closer fit, but each additional pole added will make the fit harder to obtain and will slow down the FDTD.  Therefore, it is best to try the fit with few poles and increase until the results look good.\n",
     "\n",
     "Here, we will first try fitting the data with 1 pole and specify the RMS value that we are happy with (`tolerance_rms`).\n",
     "\n",
@@ -169,10 +168,10 @@
     }
    ],
    "source": [
-    "advanced_param = AdvancedFastFitterParam(weights=(1,1))\n",
+    "advanced_param = AdvancedFastFitterParam(weights=(1, 1))\n",
     "medium, rms_error = fitter.fit(max_num_poles=1, advanced_param=advanced_param, tolerance_rms=2e-2)\n",
     "fitter.plot(medium)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -241,7 +240,7 @@
    "source": [
     "medium, rms_error = fitter.fit(max_num_poles=3, advanced_param=advanced_param, tolerance_rms=2e-2)\n",
     "fitter.plot(medium)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -306,7 +305,7 @@
    ],
    "source": [
     "fitter = fitter.copy(update={\"wvl_range\": (3, 20)})\n",
-    "medium, rms_error = fitter.fit(max_num_poles=1, tolerance_rms=2e-2)\n"
+    "medium, rms_error = fitter.fit(max_num_poles=1, tolerance_rms=2e-2)"
    ]
   },
   {
@@ -329,7 +328,7 @@
    ],
    "source": [
     "fitter.plot(medium)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -358,7 +357,7 @@
    },
    "outputs": [],
    "source": [
-    "b = td.Structure(geometry=td.Box(size=(1, 1, 1)), medium=medium)\n"
+    "b = td.Structure(geometry=td.Box(size=(1, 1, 1)), medium=medium)"
    ]
   },
   {
@@ -369,7 +368,7 @@
     "\n",
     "In many cases, one may want to perform the fit once and then hardcode the result in their tidy3d script.\n",
     "\n",
-    "For a quick and easy way to do this, just `print()` the medium and the output can be copied and pasted into your main svript"
+    "For a quick and easy way to do this, just `print()` the medium and the output can be copied and pasted into your main script"
    ]
   },
   {
@@ -391,7 +390,7 @@
     }
    ],
    "source": [
-    "print(medium)\n"
+    "print(medium)"
    ]
   },
   {
@@ -403,8 +402,8 @@
    "outputs": [],
    "source": [
     "# medium = td.PoleResidue(\n",
-    "# \teps_inf=3.394619381557077, \n",
-    "# \tpoles=(((-1667817350156741.8-206849778477574.28j), (1.004708275108508e+16-2.307684527443524e+16j)),), \n",
+    "# \teps_inf=3.394619381557077,\n",
+    "# \tpoles=(((-1667817350156741.8-206849778477574.28j), (1.004708275108508e+16-2.307684527443524e+16j)),),\n",
     "# \tfrequency_range=None)"
    ]
   },
@@ -430,7 +429,7 @@
     "medium.to_file(fname)\n",
     "\n",
     "# load the file in your script\n",
-    "medium = td.PoleResidue.from_file(fname)\n"
+    "medium = td.PoleResidue.from_file(fname)"
    ]
   },
   {
@@ -479,7 +478,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.0"
   },
   "title": "Fitting Dispersive Material Models in Tidy3D | Flexcompute",
   "widgets": {
diff --git a/FocusedApodGC.ipynb b/FocusedApodGC.ipynb
index c83d80b0..142942d2 100644
--- a/FocusedApodGC.ipynb
+++ b/FocusedApodGC.ipynb
@@ -42,14 +42,14 @@
    "outputs": [],
    "source": [
     "# Standard python imports.\n",
-    "import numpy as np\n",
-    "import matplotlib.pylab as plt\n",
     "import gdstk\n",
+    "import matplotlib.pylab as plt\n",
+    "import numpy as np\n",
     "\n",
     "# Import regular tidy3d.\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
-    "from tidy3d.plugins import waveguide\n"
+    "from tidy3d.plugins import waveguide"
    ]
   },
   {
@@ -138,7 +138,7 @@
     "wl = 1.55  # Center simulation wavelength (um).\n",
     "bw = 0.06  # Simulation wavelength bandwidth (um).\n",
     "n_wl = 61  # Number of wavelength points in monitors.\n",
-    "run_time = 2e-12  # Run time parameter for simulation (s).\n"
+    "run_time = 2e-12  # Run time parameter for simulation (s)."
    ]
   },
   {
@@ -177,7 +177,7 @@
     "wl_range = np.linspace(wl_min, wl_max, n_wl)\n",
     "freq = td.C_0 / wl\n",
     "freqs = td.C_0 / wl_range\n",
-    "freqw = 0.5 * (freqs[0] - freqs[-1])\n"
+    "freqw = 0.5 * (freqs[0] - freqs[-1])"
    ]
   },
   {
@@ -217,8 +217,8 @@
     "    R: float = 0.025,\n",
     "    min_feature: float = 0.140,\n",
     "):\n",
-    "    del_x = np.zeros((N))\n",
-    "    f_x = np.zeros((N))\n",
+    "    del_x = np.zeros(N)\n",
+    "    f_x = np.zeros(N)\n",
     "    theta_rad = theta * np.pi / 180\n",
     "    del_0 = lamb / (no - nc * np.sin(theta_rad))\n",
     "    f_0 = (del_0 - min_feature) / del_0\n",
@@ -319,9 +319,7 @@
     "    gc_non_etch = td.PolySlab.from_gds(\n",
     "        gc_cell, gds_layer=2, axis=2, slab_bounds=(-wg_t / 2, wg_t / 2 - etch_d)\n",
     "    )[0]\n",
-    "    wg = td.PolySlab.from_gds(\n",
-    "        gc_cell, gds_layer=3, axis=2, slab_bounds=(-wg_t / 2, wg_t / 2)\n",
-    "    )[0]\n",
+    "    wg = td.PolySlab.from_gds(gc_cell, gds_layer=3, axis=2, slab_bounds=(-wg_t / 2, wg_t / 2))[0]\n",
     "    gc_struct = td.Structure(\n",
     "        geometry=td.GeometryGroup(geometries=(gc_non_etch, *gc_etch, wg)), medium=mat_si\n",
     "    )\n",
@@ -330,7 +328,7 @@
     "    if gds_file:\n",
     "        lib.write_gds(gds_file)\n",
     "\n",
-    "    return gc_struct\n"
+    "    return gc_struct"
    ]
   },
   {
@@ -443,9 +441,7 @@
     "            center=(gc_0 + src_pos, 0, h_dev / 2 + h_clad + src_offset),\n",
     "            size=(\n",
     "                1.2 * spot_size,\n",
-    "                1.2 * spot_size\n",
-    "                if sim_dim == \"3D\"\n",
-    "                else td.inf,  # Make it infinity in 2D.\n",
+    "                1.2 * spot_size if sim_dim == \"3D\" else td.inf,  # Make it infinity in 2D.\n",
     "                0,\n",
     "            ),\n",
     "            source_time=td.GaussianPulse(freq0=freq, fwidth=freqw),\n",
@@ -559,7 +555,7 @@
     "        symmetry=(0, -1 if sim_dim == \"3D\" else 0, 0),\n",
     "        run_time=run_time,\n",
     "    )\n",
-    "    return sim\n"
+    "    return sim"
    ]
   },
   {
@@ -602,7 +598,7 @@
    ],
    "source": [
     "# Definition of wide non-etched and etched waveguides.\n",
-    "wg_non_etch, wg_etch = [\n",
+    "wg_non_etch, wg_etch = (\n",
     "    waveguide.RectangularDielectric(\n",
     "        wavelength=wl,\n",
     "        core_width=2 * spot_size,\n",
@@ -612,7 +608,7 @@
     "        clad_medium=mat_clad,\n",
     "    )\n",
     "    for t in [h_dev, h_dev - etch_d]\n",
-    "]\n",
+    ")\n",
     "\n",
     "# Take a look at the waveguide cross-sections.\n",
     "fig, ax = plt.subplots(1, 2, figsize=(10, 4), tight_layout=True)\n",
@@ -741,7 +737,7 @@
     "sim_2d.plot(y=0, ax=ax2)\n",
     "ax2.set_aspect(\"auto\")\n",
     "ax2.set_title(\"2D\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -779,7 +775,7 @@
     "etch_d_vals = np.linspace(0.07, 0.14, 8)\n",
     "R_vals = np.linspace(0.015, 0.035, 6)\n",
     "src_pos_vals = np.linspace(4.0, 6.0, 5)\n",
-    "print(f\"Number of simulations: {len(etch_d_vals)*len(R_vals)*len(src_pos_vals):d}\")\n"
+    "print(f\"Number of simulations: {len(etch_d_vals)*len(R_vals)*len(src_pos_vals):d}\")"
    ]
   },
   {
@@ -829,9 +825,9 @@
     "\n",
     "fig, ax = plt.subplots(1, figsize=(5, 3))\n",
     "ax.plot(etch_d_vals, n_e_vals, \"o\", color=\"black\")\n",
-    "ax.set_xlabel(\"Etch Depth ($\\mu m$)\")\n",
+    "ax.set_xlabel(r\"Etch Depth ($\\mu m$)\")\n",
     "ax.set_ylabel(\"Effective Index\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -875,7 +871,7 @@
     "    for sp in src_pos_vals\n",
     "}\n",
     "\n",
-    "batch_data = web.run_async(simulations=sim_sweep, path_dir=\"data\", verbose=False)\n"
+    "batch_data = web.run_async(simulations=sim_sweep, path_dir=\"data\", verbose=False)"
    ]
   },
   {
@@ -913,7 +909,7 @@
     "            power_db = np.asarray(np.amax(10 * np.log10(power)))\n",
     "            if ce_vals[j, k] < power_db:\n",
     "                ce_vals[j, k] = power_db\n",
-    "                src_vals[j, k] = sp\n"
+    "                src_vals[j, k] = sp"
    ]
   },
   {
@@ -981,10 +977,10 @@
     "ax.set_xticks(etch_d_vals)\n",
     "ax.set_yticks(R_vals)\n",
     "ax.set_title(f\"Maximum CE: {ce_2d:.3f} dB\")\n",
-    "ax.set_xlabel(\"Etch Depth ($\\mu m$)\")\n",
-    "ax.set_ylabel(\"R ($\\mu m^{-1}$)\")\n",
+    "ax.set_xlabel(r\"Etch Depth ($\\mu m$)\")\n",
+    "ax.set_ylabel(r\"R ($\\mu m^{-1}$)\")\n",
     "fig.colorbar(pcm, ax=ax, label=\"Coupling Efficiency (dB)\", pad=0.01)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1040,7 +1036,7 @@
     "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3.0))\n",
     "sim_3d.plot(z=h_dev / 2 - etch_d / 2, ax=ax1)\n",
     "sim_3d.plot(y=0, ax=ax2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1107,7 +1103,7 @@
     "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 4))\n",
     "ax1.plot(wl_range, power_db, color=\"black\", linestyle=\"solid\", linewidth=1.0)\n",
     "ax1.set_xlim([wl_range[0], wl_range[-1]])\n",
-    "ax1.set_xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "ax1.set_xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "ax1.set_ylabel(\"Power (dB)\")\n",
     "ax1.set_title(f\"Maximum CE: {ce_3d:.3f} dB\")\n",
     "\n",
@@ -1128,10 +1124,10 @@
     "    label=\"reflected\",\n",
     ")\n",
     "ax2.set_xlim([wl_range[0], wl_range[-1]])\n",
-    "ax2.set_xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "ax2.set_xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "ax2.set_ylabel(\"Power (W)\")\n",
     "ax2.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1163,7 +1159,7 @@
     "sim_3d_in.plot_field(\"field_xy\", \"E\", f=freq, val=\"abs\", ax=ax1)\n",
     "sim_3d_in.plot_field(\"field_xz\", \"E\", f=freq, val=\"abs\", ax=ax2)\n",
     "ax2.set_aspect(\"auto\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1206,7 +1202,7 @@
     ")\n",
     "\n",
     "job = web.Job(simulation=sim_3d_o, task_name=\"gc_out_coupling_3d\", verbose=False)\n",
-    "sim_3d_out = job.run(path=\"data/gc3d_out_data.hdf5\")\n"
+    "sim_3d_out = job.run(path=\"data/gc3d_out_data.hdf5\")"
    ]
   },
   {
@@ -1254,9 +1250,9 @@
     "sim_3d_out.plot_field(\"near_field\", \"E\", f=freq, val=\"abs\", ax=ax2)\n",
     "ax3.plot(wl_range, power_back, color=\"black\", linestyle=\"solid\", linewidth=1.0)\n",
     "ax3.set_xlim([wl_range[0], wl_range[-1]])\n",
-    "ax3.set_xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "ax3.set_xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "ax3.set_ylabel(\"Power (W)\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1360,16 +1356,14 @@
     ")\n",
     "\n",
     "# Make a near field to far field projector with the near field monitor data\n",
-    "near_field_surface = td.FieldProjectionSurface(\n",
-    "    monitor=sim_3d_o.monitors[0], normal_dir=\"+\"\n",
-    ")\n",
+    "near_field_surface = td.FieldProjectionSurface(monitor=sim_3d_o.monitors[0], normal_dir=\"+\")\n",
     "n2f = td.FieldProjector(sim_data=sim_3d_out, surfaces=[near_field_surface])\n",
     "\n",
     "# Compute the far_fields\n",
     "far_fields = n2f.project_fields(monitor_n2f)\n",
     "\n",
     "# Compute the scattered cross section\n",
-    "ps = np.abs(far_fields.radar_cross_section.sel(f=freq).values[0, ...])\n"
+    "ps = np.abs(far_fields.radar_cross_section.sel(f=freq).values[0, ...])"
    ]
   },
   {
@@ -1420,7 +1414,7 @@
     "ax.set_yticklabels([])\n",
     "ax.set_title(\"Scattered Cross-section (arb. units)\", va=\"bottom\")\n",
     "plt.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   }
  ],
diff --git a/FreeFormCoupler.ipynb b/FreeFormCoupler.ipynb
index 470a0afa..9da16913 100644
--- a/FreeFormCoupler.ipynb
+++ b/FreeFormCoupler.ipynb
@@ -27,9 +27,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web"
    ]
@@ -125,7 +124,6 @@
    "outputs": [],
    "source": [
     "def make_structures(adjustable_spacing):\n",
-    "\n",
     "    # import coupler geometry from a stl file\n",
     "    coupler_geometry = td.TriangleMesh.from_stl(\n",
     "        filename=\"misc/chip_to_chip_coupler.stl\",\n",
@@ -208,7 +206,6 @@
    "outputs": [],
    "source": [
     "def make_sim(adjustable_spacing):\n",
-    "\n",
     "    # add a mode source as excitation\n",
     "    mode_spec = td.ModeSpec(num_modes=1, target_neff=n_core)\n",
     "    mode_source = td.ModeSource(\n",
diff --git a/FresnelLens.ipynb b/FresnelLens.ipynb
index a7814294..ebb0f0e8 100644
--- a/FresnelLens.ipynb
+++ b/FresnelLens.ipynb
@@ -46,9 +46,8 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web"
    ]
@@ -85,11 +84,11 @@
    "source": [
     "lda0 = 1  # operation wavelength of the lens is 1 um\n",
     "r = 50  # radius of the lens is set to 50 um\n",
-    "x = np.linspace(0, r, 1000)  \n",
+    "x = np.linspace(0, r, 1000)\n",
     "R = 60  # radius of the corresponding sphere\n",
     "n = 1.5  # index of refraction of glass\n",
-    "O = np.sqrt(R**2-r**2)  # origin of the spherical lens\n",
-    "y_spherical = np.sqrt(R**2-x**2)-O   # boundary of the spherical lens"
+    "O = np.sqrt(R**2 - r**2)  # origin of the spherical lens\n",
+    "y_spherical = np.sqrt(R**2 - x**2) - O  # boundary of the spherical lens"
    ]
   },
   {
@@ -128,12 +127,12 @@
     }
    ],
    "source": [
-    "h = lda0/(n-1)   # fresnel lens thickness\n",
-    "y_fresnel = y_spherical % h   # boundary of the fresnel lens\n",
-    "plt.plot(x, y_spherical, x, y_fresnel)   \n",
-    "plt.xlabel(\"x ($\\mu m$)\")\n",
-    "plt.ylabel(\"z ($\\mu m$)\")\n",
-    "plt.legend(['Spherical lens','Fresnel lens'])\n",
+    "h = lda0 / (n - 1)  # fresnel lens thickness\n",
+    "y_fresnel = y_spherical % h  # boundary of the fresnel lens\n",
+    "plt.plot(x, y_spherical, x, y_fresnel)\n",
+    "plt.xlabel(r\"x ($\\mu m$)\")\n",
+    "plt.ylabel(r\"z ($\\mu m$)\")\n",
+    "plt.legend([\"Spherical lens\", \"Fresnel lens\"])\n",
     "plt.show()"
    ]
   },
@@ -172,12 +171,12 @@
     }
    ],
    "source": [
-    "H = h/10  # fresnel lens discretization level\n",
-    "y_discretized = H*(y_fresnel // H)   # boundary of the discretized fresnel lens\n",
-    "plt.plot(x,y_discretized)\n",
-    "plt.legend(['Discretized Fresnel lens'])\n",
-    "plt.xlabel(\"x ($\\mu m$)\")\n",
-    "plt.ylabel(\"z ($\\mu m$)\")\n",
+    "H = h / 10  # fresnel lens discretization level\n",
+    "y_discretized = H * (y_fresnel // H)  # boundary of the discretized fresnel lens\n",
+    "plt.plot(x, y_discretized)\n",
+    "plt.legend([\"Discretized Fresnel lens\"])\n",
+    "plt.xlabel(r\"x ($\\mu m$)\")\n",
+    "plt.ylabel(r\"z ($\\mu m$)\")\n",
     "plt.show()"
    ]
   },
@@ -207,18 +206,39 @@
     "glass = td.Medium(permittivity=n**2)\n",
     "\n",
     "# create the fresnel lens by using cylinders\n",
-    "fresnel_lens = [] \n",
-    "for i in range(len(y_discretized)-1):\n",
-    "    if y_discretized[-i] != y_discretized[-i-1]:\n",
-    "        if y_discretized[-i-1] == 0:\n",
-    "            air_domain = td.Structure(geometry=td.Cylinder(center=(0,0,y_discretized[-i]/2), radius=x[-i], length=y_discretized[-i], axis=2),medium=air)\n",
+    "fresnel_lens = []\n",
+    "for i in range(len(y_discretized) - 1):\n",
+    "    if y_discretized[-i] != y_discretized[-i - 1]:\n",
+    "        if y_discretized[-i - 1] == 0:\n",
+    "            air_domain = td.Structure(\n",
+    "                geometry=td.Cylinder(\n",
+    "                    center=(0, 0, y_discretized[-i] / 2),\n",
+    "                    radius=x[-i],\n",
+    "                    length=y_discretized[-i],\n",
+    "                    axis=2,\n",
+    "                ),\n",
+    "                medium=air,\n",
+    "            )\n",
     "            fresnel_lens.append(air_domain)\n",
-    "        lens_domain = td.Structure(geometry=td.Cylinder(center=(0,0,y_discretized[-i-1]/2), radius=x[-i-1], length=y_discretized[-i-1], axis=2),medium=glass)\n",
+    "        lens_domain = td.Structure(\n",
+    "            geometry=td.Cylinder(\n",
+    "                center=(0, 0, y_discretized[-i - 1] / 2),\n",
+    "                radius=x[-i - 1],\n",
+    "                length=y_discretized[-i - 1],\n",
+    "                axis=2,\n",
+    "            ),\n",
+    "            medium=glass,\n",
+    "        )\n",
     "        fresnel_lens.append(lens_domain)\n",
     "\n",
     "# create a 0.5 um thick substrate\n",
     "sub_thickness = 0.5\n",
-    "substrate = td.Structure(geometry=td.Cylinder(center=(0,0,-sub_thickness/2), radius=max(x), length=sub_thickness, axis=2), medium=glass)\n",
+    "substrate = td.Structure(\n",
+    "    geometry=td.Cylinder(\n",
+    "        center=(0, 0, -sub_thickness / 2), radius=max(x), length=sub_thickness, axis=2\n",
+    "    ),\n",
+    "    medium=glass,\n",
+    ")\n",
     "fresnel_lens.append(substrate)"
    ]
   },
@@ -287,28 +307,24 @@
     }
    ],
    "source": [
-    "freq0 = td.C_0/lda0  # central frequency of the source\n",
-    "fwidth = freq0/10  # frequency width of the source\n",
+    "freq0 = td.C_0 / lda0  # central frequency of the source\n",
+    "fwidth = freq0 / 10  # frequency width of the source\n",
     "\n",
     "# define a plane wave source\n",
-    "source_z = 3  #z position of the source\n",
+    "source_z = 3  # z position of the source\n",
     "pulse = td.GaussianPulse(freq0=freq0, fwidth=fwidth)\n",
-    "source = td.PlaneWave(size=(td.inf,td.inf,0), center=(0,0,source_z), source_time=pulse, direction='-')\n",
+    "source = td.PlaneWave(\n",
+    "    size=(td.inf, td.inf, 0), center=(0, 0, source_z), source_time=pulse, direction=\"-\"\n",
+    ")\n",
     "\n",
     "# define a field monitor in the yz plane\n",
     "field_monitor_yz = td.FieldMonitor(\n",
-    "    center=(0, 0, 0), \n",
-    "    size=(0, td.inf, td.inf), \n",
-    "    freqs=[freq0], \n",
-    "    name=\"field_yz\"\n",
+    "    center=(0, 0, 0), size=(0, td.inf, td.inf), freqs=[freq0], name=\"field_yz\"\n",
     ")\n",
     "\n",
     "# define a field monitor in the xz plane\n",
     "field_monitor_xz = td.FieldMonitor(\n",
-    "    center=(0, 0, 0), \n",
-    "    size=(td.inf, 0, td.inf), \n",
-    "    freqs=[freq0], \n",
-    "    name=\"field_xz\"\n",
+    "    center=(0, 0, 0), size=(td.inf, 0, td.inf), freqs=[freq0], name=\"field_xz\"\n",
     ")"
    ]
   },
@@ -350,7 +366,7 @@
     "# define simulation domain size\n",
     "buffer_xy = 2\n",
     "Lz = 120\n",
-    "sim_size = (2*r+buffer_xy, 2*r+buffer_xy, Lz)    \n",
+    "sim_size = (2 * r + buffer_xy, 2 * r + buffer_xy, Lz)\n",
     "\n",
     "# define simulation run time\n",
     "run_time = 1e-12\n",
@@ -358,16 +374,16 @@
     "# define simulation\n",
     "offset_z = 5\n",
     "sim = td.Simulation(\n",
-    "    center=(0,0,-Lz/2+offset_z),\n",
+    "    center=(0, 0, -Lz / 2 + offset_z),\n",
     "    size=sim_size,\n",
-    "    grid_spec = td.GridSpec.auto(min_steps_per_wvl=15),\n",
+    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=15),\n",
     "    sources=[source],\n",
     "    monitors=[field_monitor_yz, field_monitor_xz],\n",
     "    structures=fresnel_lens,\n",
     "    run_time=run_time,\n",
     "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
-    "    symmetry=(-1, 1, 0)  # symmetry is applied to reduce the computational load\n",
-    "    )\n",
+    "    symmetry=(-1, 1, 0),  # symmetry is applied to reduce the computational load\n",
+    ")\n",
     "\n",
     "sim.plot_eps(y=0)\n",
     "plt.show()"
@@ -799,10 +815,14 @@
     }
    ],
    "source": [
-    "fig, (ax1,ax2) = plt.subplots(1,2, figsize=(12,6))\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n",
     "\n",
-    "sim_data.plot_field(field_monitor_name='field_xz', field_name='E', val='abs', vmin=0, vmax=6, ax=ax1)\n",
-    "sim_data.plot_field(field_monitor_name='field_yz', field_name='E', val='abs', vmin=0, vmax=6, ax=ax2)\n",
+    "sim_data.plot_field(\n",
+    "    field_monitor_name=\"field_xz\", field_name=\"E\", val=\"abs\", vmin=0, vmax=6, ax=ax1\n",
+    ")\n",
+    "sim_data.plot_field(\n",
+    "    field_monitor_name=\"field_yz\", field_name=\"E\", val=\"abs\", vmin=0, vmax=6, ax=ax2\n",
+    ")\n",
     "plt.show()"
    ]
   },
@@ -841,17 +861,21 @@
     }
    ],
    "source": [
-    "focal_z = -102 # z position of the focal spot\n",
+    "focal_z = -102  # z position of the focal spot\n",
     "\n",
     "# plot field intensity at the focus\n",
-    "fig, (ax1,ax2) = plt.subplots(1,2, figsize=(16,6))\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n",
     "\n",
-    "sim_data.get_intensity(field_monitor_name='field_xz').sel(y=0, z=focal_z, f=freq0, method='nearest').plot(ax=ax1)\n",
-    "ax1.set_xlim([-10,10])\n",
+    "sim_data.get_intensity(field_monitor_name=\"field_xz\").sel(\n",
+    "    y=0, z=focal_z, f=freq0, method=\"nearest\"\n",
+    ").plot(ax=ax1)\n",
+    "ax1.set_xlim([-10, 10])\n",
     "\n",
-    "sim_data.get_intensity(field_monitor_name='field_yz').sel(x=0, z=focal_z, f=freq0, method='nearest').plot(ax=ax2)\n",
-    "ax2.set_xlim([-10,10])\n",
-    "plt.show()\n"
+    "sim_data.get_intensity(field_monitor_name=\"field_yz\").sel(\n",
+    "    x=0, z=focal_z, f=freq0, method=\"nearest\"\n",
+    ").plot(ax=ax2)\n",
+    "ax2.set_xlim([-10, 10])\n",
+    "plt.show()"
    ]
   },
   {
@@ -899,15 +923,15 @@
     }
    ],
    "source": [
-    "Lz = 10 # new simulation domain size in z\n",
+    "Lz = 10  # new simulation domain size in z\n",
     "offset_z = 4\n",
     "\n",
-    "pos_monitor_z = -5 # z position of the field projection monitor\n",
+    "pos_monitor_z = -5  # z position of the field projection monitor\n",
     "\n",
     "# grids of the projected field position\n",
-    "xs_far = np.linspace(-30,30,301) \n",
-    "ys_far = np.linspace(-110,-10,301)\n",
-    "    \n",
+    "xs_far = np.linspace(-30, 30, 301)\n",
+    "ys_far = np.linspace(-110, -10, 301)\n",
+    "\n",
     "# define a field projection monitor\n",
     "monitor_proj = td.FieldProjectionCartesianMonitor(\n",
     "    center=[0, 0, pos_monitor_z],\n",
@@ -918,13 +942,17 @@
     "    proj_distance=0,  # distance from this monitor to where fields are projected\n",
     "    x=xs_far,\n",
     "    y=ys_far,\n",
-    "    far_field_approx=False\n",
+    "    far_field_approx=False,\n",
     ")\n",
     "\n",
     "# define a new simulation by copying the previous simulation and updating the information\n",
-    "sim_new = sim.copy(update={\"center\":(0,0,-Lz/2+offset_z),\n",
-    "    \"size\":(2*r+buffer_xy,2*r+buffer_xy,Lz),\n",
-    "    \"monitors\":[monitor_proj]})\n",
+    "sim_new = sim.copy(\n",
+    "    update={\n",
+    "        \"center\": (0, 0, -Lz / 2 + offset_z),\n",
+    "        \"size\": (2 * r + buffer_xy, 2 * r + buffer_xy, Lz),\n",
+    "        \"monitors\": [monitor_proj],\n",
+    "    }\n",
+    ")\n",
     "sim_new.plot_eps(y=0)\n",
     "plt.show()"
    ]
@@ -1033,7 +1061,7 @@
     "job = web.Job(simulation=sim_new, task_name=\"fresnel_lens_field_projection\")\n",
     "estimated_cost = web.estimate_cost(job.task_id)\n",
     "\n",
-    "print(f'The estimated maximum cost is {estimated_cost:.3f} Flex Credits.')"
+    "print(f\"The estimated maximum cost is {estimated_cost:.3f} Flex Credits.\")"
    ]
   },
   {
@@ -1354,10 +1382,10 @@
     "proj_fields = sim_data_new[\"focal_plane_proj\"].fields_cartesian.sel(f=freq0)\n",
     "\n",
     "# compute norm of the field\n",
-    "E = np.sqrt(np.abs(proj_fields.Ex)**2+np.abs(proj_fields.Ey)**2+np.abs(proj_fields.Ez)**2)\n",
+    "E = np.sqrt(np.abs(proj_fields.Ex) ** 2 + np.abs(proj_fields.Ey) ** 2 + np.abs(proj_fields.Ez) ** 2)\n",
     "\n",
     "# plot field distribution\n",
-    "E.plot(x='y', y='z', vmin=0, vmax=7, cmap='magma')\n",
+    "E.plot(x=\"y\", y=\"z\", vmin=0, vmax=7, cmap=\"magma\")\n",
     "plt.show()"
    ]
   },
@@ -1399,17 +1427,19 @@
     "fig, ax = plt.subplots()\n",
     "\n",
     "# plot field intensity at the focus from the exact simulation\n",
-    "sim_data.get_intensity(field_monitor_name='field_xz').sel(y=0, z=focal_z, f=freq0, method='nearest').plot(ax=ax)\n",
+    "sim_data.get_intensity(field_monitor_name=\"field_xz\").sel(\n",
+    "    y=0, z=focal_z, f=freq0, method=\"nearest\"\n",
+    ").plot(ax=ax)\n",
     "\n",
     "# plot field intensity at the focus from the field projection\n",
     "I = E**2\n",
-    "I.sel(z=focal_z-pos_monitor_z, method='nearest').plot(ax=ax)\n",
+    "I.sel(z=focal_z - pos_monitor_z, method=\"nearest\").plot(ax=ax)\n",
     "\n",
     "# formatting the plot\n",
-    "ax.set_xlim([-10,10])\n",
-    "ax.set_title('Field intensity comparison')\n",
-    "ax.set_ylabel('Field intensity')\n",
-    "ax.legend(('Exact simulation', 'Field projection'))\n",
+    "ax.set_xlim([-10, 10])\n",
+    "ax.set_title(\"Field intensity comparison\")\n",
+    "ax.set_ylabel(\"Field intensity\")\n",
+    "ax.legend((\"Exact simulation\", \"Field projection\"))\n",
     "plt.show()"
    ]
   },
diff --git a/FullyAnisotropic.ipynb b/FullyAnisotropic.ipynb
index 61b6daa6..c2764cb5 100644
--- a/FullyAnisotropic.ipynb
+++ b/FullyAnisotropic.ipynb
@@ -35,14 +35,14 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
-    "import xarray as xr\n",
     "import matplotlib.pylab as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3D import\n",
     "import tidy3d as td\n",
-    "from tidy3d.constants import C_0\n",
-    "import tidy3d.web as web"
+    "import tidy3d.web as web\n",
+    "import xarray as xr\n",
+    "from tidy3d.constants import C_0"
    ]
   },
   {
@@ -172,9 +172,7 @@
     "conductivity_phi = rotation_around_x.rotate_tensor(conductivity_phi)\n",
     "\n",
     "# define a fully anisotropic medium\n",
-    "medium_phi = td.FullyAnisotropicMedium(\n",
-    "    permittivity=permittivity_phi, conductivity=conductivity_phi\n",
-    ")"
+    "medium_phi = td.FullyAnisotropicMedium(permittivity=permittivity_phi, conductivity=conductivity_phi)"
    ]
   },
   {
@@ -386,8 +384,12 @@
    ],
    "source": [
     "mnt_flux = td.FluxMonitor(center=(0, 0, 0), size=(1.3, 1.3, 1.3), freqs=freqs, name=\"flux\")\n",
-    "mnt_field_yz = td.FieldMonitor(center=(0, 0, 0), size=(0, td.inf, td.inf), freqs=freqs, name=\"field_yz\")\n",
-    "mnt_field_xy = td.FieldMonitor(center=(0, 0, 0), size=(td.inf, td.inf, 0), freqs=freqs, name=\"field_xy\")"
+    "mnt_field_yz = td.FieldMonitor(\n",
+    "    center=(0, 0, 0), size=(0, td.inf, td.inf), freqs=freqs, name=\"field_yz\"\n",
+    ")\n",
+    "mnt_field_xy = td.FieldMonitor(\n",
+    "    center=(0, 0, 0), size=(td.inf, td.inf, 0), freqs=freqs, name=\"field_xy\"\n",
+    ")"
    ]
   },
   {
@@ -1526,9 +1528,24 @@
     }
    ],
    "source": [
-    "sim_data_diag = web.run(simulation=sim_diag, task_name=\"fully_anisotropic_diag\", path=\"data/simulation_data_diag.hdf5\", verbose=True)\n",
-    "sim_data_phi = web.run(simulation=sim_phi, task_name=\"fully_anisotropic_phi\", path=\"data/simulation_data_phi.hdf5\", verbose=True)\n",
-    "sim_data_theta = web.run(simulation=sim_theta, task_name=\"fully_anisotropic_theta\", path=\"data/simulation_data_theta.hdf5\", verbose=True)"
+    "sim_data_diag = web.run(\n",
+    "    simulation=sim_diag,\n",
+    "    task_name=\"fully_anisotropic_diag\",\n",
+    "    path=\"data/simulation_data_diag.hdf5\",\n",
+    "    verbose=True,\n",
+    ")\n",
+    "sim_data_phi = web.run(\n",
+    "    simulation=sim_phi,\n",
+    "    task_name=\"fully_anisotropic_phi\",\n",
+    "    path=\"data/simulation_data_phi.hdf5\",\n",
+    "    verbose=True,\n",
+    ")\n",
+    "sim_data_theta = web.run(\n",
+    "    simulation=sim_theta,\n",
+    "    task_name=\"fully_anisotropic_theta\",\n",
+    "    path=\"data/simulation_data_theta.hdf5\",\n",
+    "    verbose=True,\n",
+    ")"
    ]
   },
   {
@@ -1564,7 +1581,7 @@
     }
    ],
    "source": [
-    "# retrive flux values from flux monitor\n",
+    "# retrieve flux values from flux monitor\n",
     "flux_diag = sim_data_diag[\"flux\"].flux\n",
     "flux_phi = sim_data_phi[\"flux\"].flux\n",
     "# scale power by injection angle\n",
@@ -1769,7 +1786,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.0"
   },
   "title": "Defining Fully Anisotropic Materials in Tidy3D | Flexcompute",
   "widgets": {
diff --git a/GDSExport.ipynb b/GDSExport.ipynb
index c58966c8..e83a1ec5 100644
--- a/GDSExport.ipynb
+++ b/GDSExport.ipynb
@@ -16,7 +16,7 @@
     "\n",
     "Tidy3D [geometries](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/index.html#geometry), [structures](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Structure.html#tidy3d.Structure), and [simulations](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html#tidy3d.Simulation) can be exported to GDS format via the third-party [gdstk](https://heitzmann.github.io/gdstk/) or [gdspy](https://gdspy.readthedocs.io/en/stable/) packages.\n",
     "\n",
-    "There are 4 methods available on those objects to export them to GDS. All of them take an axis coordinate (either `x`, `y`, or `z`) that defines the cross-section plane from which to export the GDS polygons. Geometry and structure methods also take the `layer` and `datatype` numbers to export, whereas simulations require defining a dictionary maping structure media to `layer` and `datatype`. This dictionary allows us to export each medium to a separate GDS layer/datatype. Structure and simulation methods also accept a `permittivity_threshold` and `frequency` arguments, which can be used to sample custom medium when exporting geometry, for example, from an inverse designed structure.\n",
+    "There are 4 methods available on those objects to export them to GDS. All of them take an axis coordinate (either `x`, `y`, or `z`) that defines the cross-section plane from which to export the GDS polygons. Geometry and structure methods also take the `layer` and `datatype` numbers to export, whereas simulations require defining a dictionary mapping structure media to `layer` and `datatype`. This dictionary allows us to export each medium to a separate GDS layer/datatype. Structure and simulation methods also accept a `permittivity_threshold` and `frequency` arguments, which can be used to sample custom medium when exporting geometry, for example, from an inverse designed structure.\n",
     "\n",
     "- [Geometry.to_gds_file](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Geometry.html#tidy3d.Geometry.to_gds_file), [Structure.to_gds_file](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Structure.html#tidy3d.Structure.to_gds_file), [Simulation.to_gds_file](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html#tidy3d.Simulation.to_gds_file): export the cross-section polygons directly to a GDS file.\n",
     "\n",
@@ -45,11 +45,12 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import gdstk\n",
     "import os\n",
     "\n",
+    "import gdstk\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
     "# tidy3d import\n",
     "import tidy3d as td\n",
     "from tidy3d import web"
@@ -101,16 +102,15 @@
     }
    ],
    "source": [
-    "core_medium = td.Medium(permittivity=3.48 ** 2)\n",
+    "core_medium = td.Medium(permittivity=3.48**2)\n",
     "wg = td.Structure(geometry=td.Box(size=(30, 0.45, 0.22)), medium=core_medium)\n",
     "\n",
-    "ring_geo = (\n",
-    "    td.Cylinder(radius=5, length=0.22, axis=2, center=(-4, 5.3, 0))\n",
-    "    - td.Cylinder(radius=4.5, length=0.22, axis=2, center=(-4, 5.3, 0))\n",
+    "ring_geo = td.Cylinder(radius=5, length=0.22, axis=2, center=(-4, 5.3, 0)) - td.Cylinder(\n",
+    "    radius=4.5, length=0.22, axis=2, center=(-4, 5.3, 0)\n",
     ")\n",
     "ring = td.Structure(geometry=ring_geo, medium=core_medium)\n",
     "\n",
-    "alternative_medium = td.Medium(permittivity=2.0 ** 2)\n",
+    "alternative_medium = td.Medium(permittivity=2.0**2)\n",
     "wg2 = td.Structure(\n",
     "    geometry=td.Box(center=(0, 10.7, 0.2 - 0.11), size=(30, 0.6, 0.4)),\n",
     "    medium=alternative_medium,\n",
@@ -158,7 +158,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ring_geo.to_gds_file(\"./misc/gds_export_ring.gds\", z=0, gds_layer=1, gds_dtype=2, gds_cell_name=\"RING\")"
+    "ring_geo.to_gds_file(\n",
+    "    \"./misc/gds_export_ring.gds\", z=0, gds_layer=1, gds_dtype=2, gds_cell_name=\"RING\"\n",
+    ")"
    ]
   },
   {
@@ -201,7 +203,7 @@
     "for polygon in polygons:\n",
     "    vertices = polygon.points\n",
     "    vertices = np.vstack((vertices, vertices[0]))\n",
-    "    ax.plot(vertices[:,0], vertices[:, 1])"
+    "    ax.plot(vertices[:, 0], vertices[:, 1])"
    ]
   },
   {
@@ -232,9 +234,7 @@
     "    alternative_medium: (2, 2),\n",
     "    custom_medium: (3, 3),\n",
     "}\n",
-    "sim.to_gds(\n",
-    "    cell, z=0, permittivity_threshold=2.0**2, frequency=freq0, gds_layer_dtype_map=layer_map\n",
-    ")\n",
+    "sim.to_gds(cell, z=0, permittivity_threshold=2.0**2, frequency=freq0, gds_layer_dtype_map=layer_map)\n",
     "\n",
     "print(cell)"
    ]
@@ -313,7 +313,6 @@
     "    medium,\n",
     "    sidewall_angle=0,\n",
     "):\n",
-    "\n",
     "    \"\"\"\n",
     "    This function defines a directional coupler and returns the tidy3d structure of it.\n",
     "\n",
@@ -358,9 +357,7 @@
     "        (bend_length + coup_length / 2 + x0, wg_spacing_in / 2 + y0),\n",
     "        offset=lambda u: -A * np.cos(np.pi * u) - A,\n",
     "    )\n",
-    "    coup.segment(\n",
-    "        (wg_length + bend_length + coup_length / 2 + x0, wg_spacing_in / 2 + y0)\n",
-    "    )\n",
+    "    coup.segment((wg_length + bend_length + coup_length / 2 + x0, wg_spacing_in / 2 + y0))\n",
     "\n",
     "    # add path to the cell\n",
     "    cell.add(coup)\n",
@@ -560,7 +557,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finaly, let's see the generated GDS in Klayout.\n",
+    "Finally, let's see the generated GDS in Klayout.\n",
     "\n",
     ""
    ]
@@ -586,7 +583,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.0"
   },
   "title": "Export to GDS file in Tidy3D | Flexcompute"
  },
diff --git a/GDSImport.ipynb b/GDSImport.ipynb
index b1194604..f29431a1 100644
--- a/GDSImport.ipynb
+++ b/GDSImport.ipynb
@@ -10,13 +10,13 @@
    "source": [
     "# Importing GDS files\n",
     "\n",
-    "In Tidy3D, complex structures can be defined or imported from GDSII files via the third-party [gdstk](https://heitzmann.github.io/gdstk/) package. In this tutorial, we will first illustrate how to use the package to define a structure, then we will save this to file, and then we will read that file and import the structures in a simulation.\n",
+    "In Tidy3D, complex structures can be defined or imported from GDSII files via the third-party [gdstk](https://heitzmann.github.io/gdstk/) package. In this tutorial, we will first illustrate how to use the package to define a structure. Then we will save this to file, and then we will read that file and import the structures in a simulation.\n",
     "\n",
     "\"Schematic\n",
     "\n",
     "Note that this tutorial requires gdstk, so grab it with `pip install gdstk` before running the tutorial or uncomment the cell line below.\n",
     "\n",
-    "We also provide a conprehensive list of other tutorials such as [how to define boundary conditions](https://www.flexcompute.com/tidy3d/examples/notebooks/BoundaryConditions/), [how to compute the S-matrix of a device](https://www.flexcompute.com/tidy3d/examples/notebooks/SMatrix/), [how to interact with tidy3d's web API](https://www.flexcompute.com/tidy3d/examples/notebooks/WebAPI/), and [how to define self-intersecting polygons](https://www.flexcompute.com/tidy3d/examples/notebooks/SelfIntersectingPolyslab/).\n",
+    "We also provide a comprehensive list of other tutorials such as [how to define boundary conditions](https://www.flexcompute.com/tidy3d/examples/notebooks/BoundaryConditions/), [how to compute the S-matrix of a device](https://www.flexcompute.com/tidy3d/examples/notebooks/SMatrix/), [how to interact with tidy3d's web API](https://www.flexcompute.com/tidy3d/examples/notebooks/WebAPI/), and [how to define self-intersecting polygons](https://www.flexcompute.com/tidy3d/examples/notebooks/SelfIntersectingPolyslab/).\n",
     "\n",
     "If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. "
    ]
@@ -30,11 +30,12 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import gdstk\n",
     "import os\n",
     "\n",
+    "import gdstk\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
     "# tidy3d import\n",
     "import tidy3d as td\n",
     "from tidy3d import web"
@@ -48,7 +49,7 @@
     "\n",
     "In this section, we will construct an integrated adiabatic coupler as in the title image in this notebook using `gdstk`. If you are only interested in importing an already existing GDSII file, just jump ahead to the next section.\n",
     "\n",
-    "We create a function that generates the coupler and returns the correcponding cell. The geometry is built by defining the cross-section of the device in the x-y plane. The cross section can be supplied at the `top`, `bottom`, or the `middle` of the device, specified by the parameter `reference_plane`. Here we choose to define the cross section on the base. There, the two arms of the device start at a distance `wg_spacing_in` apart, then come together at a coupling distance `wg_spacing_coup` for a certain length `coup_length`, and then split again into separate ports. In the coupling region, the field overlap results in energy exchange between the two waveguides. The cell also includes a rectangle around the whole device, which we will use as substrate later. The waveguides use GDS layer 1 and the rectangle layer 0.\n",
+    "We create a function that generates the coupler and returns the corresponding cell. The geometry is built by defining the cross-section of the device in the x-y plane. The cross section can be supplied at the `top`, `bottom`, or the `middle` of the device, specified by the parameter `reference_plane`. Here we choose to define the cross section on the base. There, the two arms of the device start at a distance `wg_spacing_in` apart, then come together at a coupling distance `wg_spacing_coup` for a certain length `coup_length`, and then split again into separate ports. In the coupling region, the field overlap results in energy exchange between the two waveguides. The cell also includes a rectangle around the whole device, which we will use as substrate later. The waveguides use GDS layer 1 and the rectangle layer 0.\n",
     "\n",
     "Here, we will only see how to define, export, and import such a device using `gdstk`. When importing the device, we can optionally dilate or erode its cross section via `dilation`. In a later example we will simulate the device and study the frequency dependence of the transmission into each of the ports."
    ]
@@ -77,16 +78,17 @@
     "    \"\"\"Make an integrated coupler using the gdstk RobustPath object.\"\"\"\n",
     "    input_length = length / 2 - coup_length / 2 - bend_length\n",
     "    if input_length <= 0:\n",
-    "        raise ValueError(\n",
-    "            \"Device length must be larger than coupling length plus bending regions.\"\n",
-    "        )\n",
+    "        raise ValueError(\"Device length must be larger than coupling length plus bending regions.\")\n",
     "\n",
     "    delta = (wg_spacing_in - (wg_spacing_coup + wg_width)) / 2\n",
     "    turn_angle = 2 * np.arctan(delta / bend_length)\n",
     "    bend_radius = bend_length / 2 / np.sin(turn_angle)\n",
-    "        \n",
+    "\n",
     "    coup = gdstk.RobustPath(\n",
-    "        (-0.5 * length, wg_spacing_in / 2), wg_width, simple_path=True, layer=1,\n",
+    "        (-0.5 * length, wg_spacing_in / 2),\n",
+    "        wg_width,\n",
+    "        simple_path=True,\n",
+    "        layer=1,\n",
     "    )\n",
     "    coup.segment((input_length, 0), relative=True)\n",
     "    coup.turn(bend_radius, -turn_angle)\n",
@@ -109,7 +111,7 @@
     "        layer=0,\n",
     "    )\n",
     "    cell.add(substrate)\n",
-    "    return cell\n"
+    "    return cell"
    ]
   },
   {
@@ -154,7 +156,7 @@
     "wg_spacing_side = 4.0\n",
     "\n",
     "# Total device length along propagation direction\n",
-    "device_length = 25\n"
+    "device_length = 25"
    ]
   },
   {
@@ -169,7 +171,7 @@
    "source": [
     "### Saving the geometry to a GDS file\n",
     "\n",
-    "Next, we construct the coupler and add the cell (including its dependencies) to a GDS library. The library is saved to a file, so that we can demosntrate how to load the geometry straight from a gds file. Alternatively, we could use the created cell directly."
+    "Next, we construct the coupler and add the cell (including its dependencies) to a GDS library. The library is saved to a file, so that we can demonstrate how to load the geometry straight from a gds file. Alternatively, we could use the created cell directly."
    ]
   },
   {
@@ -182,7 +184,13 @@
    "source": [
     "# Create a gds cell to add our structures to\n",
     "coup_cell = make_coupler(\n",
-    "    device_length, wg_spacing_in, wg_width, wg_spacing_coup, wg_spacing_side, coup_length, bend_length\n",
+    "    device_length,\n",
+    "    wg_spacing_in,\n",
+    "    wg_width,\n",
+    "    wg_spacing_coup,\n",
+    "    wg_spacing_side,\n",
+    "    coup_length,\n",
+    "    bend_length,\n",
     ")\n",
     "\n",
     "# Create a library for the cell and save it, just so that we can demosntrate loading\n",
@@ -194,7 +202,7 @@
     "\n",
     "lib = gdstk.Library()\n",
     "lib.add(coup_cell, *coup_cell.dependencies(True))\n",
-    "lib.write_gds(gds_path)\n"
+    "lib.write_gds(gds_path)"
    ]
   },
   {
@@ -206,7 +214,7 @@
     "## Loading a GDS file into Tidy3D\n",
     "\n",
     "To load the geometry from a GDSII file, we use gdstk to load the library and select the cell with the geometry we want.\n",
-    "It is usualy esier to create a dictionary of all the cells in the library to verify that we can find the correct one by name:"
+    "It is usually easier to create a dictionary of all the cells in the library to verify that we can find the correct one by name:"
    ]
   },
   {
@@ -231,7 +239,7 @@
     "# Create a cell dictionary with all the cells in the file\n",
     "all_cells = {c.name: c for c in lib_loaded.cells}\n",
     "\n",
-    "print(\"Cell names: \" + \", \".join(all_cells.keys()))\n"
+    "print(\"Cell names: \" + \", \".join(all_cells.keys()))"
    ]
   },
   {
@@ -259,7 +267,7 @@
    "source": [
     "coup_cell_loaded = all_cells[\"COUPLER\"]\n",
     "\n",
-    "print(coup_cell_loaded)\n"
+    "print(coup_cell_loaded)"
    ]
   },
   {
@@ -303,7 +311,7 @@
     "    sidewall_angle=sidewall_angle,\n",
     "    dilation=dilation,\n",
     "    reference_plane=reference_plane,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -369,7 +377,7 @@
     "# Waveguides\n",
     "arms = td.Structure(geometry=arms_geo, medium=medium_wg)\n",
     "\n",
-    "structures = [substrate, arms]\n"
+    "structures = [substrate, arms]"
    ]
   },
   {
@@ -412,7 +420,7 @@
     "    structures=structures,\n",
     "    run_time=2e-12,\n",
     "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -460,7 +468,7 @@
     "sim.plot(x=0.1, lw=1, edgecolor=\"k\", ax=ax2)\n",
     "\n",
     "ax2.set_xlim([-3, 3])\n",
-    "_ = ax2.set_ylim([-1, 1])\n"
+    "_ = ax2.set_ylim([-1, 1])"
    ]
   }
  ],
@@ -469,9 +477,9 @@
   "feature_imag": "",
   "feature_image": "N/A",
   "kernelspec": {
-   "display_name": "tidy3d",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "tidy3d"
+   "name": "python3"
   },
   "keywords": "gds, import, Tidy3D, FDTD",
   "language_info": {
@@ -484,7 +492,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.11.0"
   },
   "title": "GDS File Import in Tidy3D | Flexcompute"
  },
diff --git a/GeneticAlgorithmReflector.ipynb b/GeneticAlgorithmReflector.ipynb
index e938f227..9ef04486 100644
--- a/GeneticAlgorithmReflector.ipynb
+++ b/GeneticAlgorithmReflector.ipynb
@@ -68,12 +68,11 @@
     "# Uncomment the following line to install pygad if it's not installed in your environment already\n",
     "# pip install pygad\n",
     "\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n",
-    "import tidy3d.plugins.design as tdd"
+    "import tidy3d.plugins.design as tdd\n",
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -145,10 +144,10 @@
     "l = 1  # length of the waveguide in the simulation\n",
     "Px = Py = 0.12  # pixel sizes in the x and y directions\n",
     "Nx = 18  # number of pixels in the x direction\n",
-    "Ny = 9  # numbre of pixels in the y direction\n",
+    "Ny = 9  # number of pixels in the y direction\n",
     "buffer = 0.8  # buffer spacing\n",
-    "res = 15 # overall resolution setting (steps per wavelength)\n",
-    "gsx = gsy = 5 # number of grid steps per pixel in override region"
+    "res = 15  # overall resolution setting (steps per wavelength)\n",
+    "gsx = gsy = 5  # number of grid steps per pixel in override region"
    ]
   },
   {
@@ -156,7 +155,7 @@
    "id": "7f6e1510",
    "metadata": {},
    "source": [
-    "We will create an array of length 162 to represent a design. Each element corresponds to each pixel in the design region. An element value of 1 means the pixel is silicon while an element value of 0 means the pixel is void. To facilitate the optimization, we define a helper function `create_design(pixels)` that takes the pixel array and creates the [Structures](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Structure.html) of the simulation, inlcuding the waveguide and the design region. "
+    "We will create an array of length 162 to represent a design. Each element corresponds to each pixel in the design region. An element value of 1 means the pixel is silicon while an element value of 0 means the pixel is void. To facilitate the optimization, we define a helper function `create_design(pixels)` that takes the pixel array and creates the [Structures](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Structure.html) of the simulation, including the waveguide and the design region. "
    ]
   },
   {
@@ -233,7 +232,6 @@
    "outputs": [],
    "source": [
     "def make_sim(pixels):\n",
-    "\n",
     "    design = create_design(pixels)\n",
     "\n",
     "    # Add a mode source as excitation\n",
@@ -279,14 +277,20 @@
     "    # Mesh override structure over the pixel region to ensure grid that conforms to the pixels\n",
     "    mesh_override = td.MeshOverrideStructure(\n",
     "        geometry=td.Box.from_bounds(rmin=(l, -Py * Ny, 0), rmax=(l + Px * Nx, Py * Ny, 0)),\n",
-    "        dl=(Px / gsx, Py / gsy, 1), # the z-direction dl doesn't matter as the box size is 0 along z\n",
+    "        dl=(\n",
+    "            Px / gsx,\n",
+    "            Py / gsy,\n",
+    "            1,\n",
+    "        ),  # the z-direction dl doesn't matter as the box size is 0 along z\n",
     "    )\n",
     "\n",
     "    # Define simulation\n",
     "    sim = td.Simulation(\n",
     "        center=sim_box.center,\n",
     "        size=sim_box.size,\n",
-    "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=res, wavelength=lda0, override_structures=[mesh_override]),\n",
+    "        grid_spec=td.GridSpec.auto(\n",
+    "            min_steps_per_wvl=res, wavelength=lda0, override_structures=[mesh_override]\n",
+    "        ),\n",
     "        structures=[design, substrate],\n",
     "        sources=[mode_source],\n",
     "        monitors=[mode_monitor],\n",
@@ -453,7 +457,7 @@
    "outputs": [],
    "source": [
     "method = tdd.MethodGenAlg(\n",
-    "    solutions_per_pop=30, \n",
+    "    solutions_per_pop=30,\n",
     "    n_generations=25,\n",
     "    n_parents_mating=10,\n",
     "    keep_elitism=1,\n",
@@ -469,7 +473,9 @@
     "\n",
     "parameters = [tdd.ParameterInt(name=i, span=(0, 1)) for i in range(Nx * Ny)]\n",
     "\n",
-    "design_space = tdd.DesignSpace(method=method, parameters=parameters, task_name=\"GA_Notebook\", path_dir=\"./data\")"
+    "design_space = tdd.DesignSpace(\n",
+    "    method=method, parameters=parameters, task_name=\"GA_Notebook\", path_dir=\"./data\"\n",
+    ")"
    ]
   },
   {
@@ -477,7 +483,7 @@
    "id": "68b26822",
    "metadata": {},
    "source": [
-    "We can optionally summarise the design space to check the setup."
+    "We can optionally summarize the design space to check the setup."
    ]
   },
   {
@@ -495,7 +501,7 @@
    "id": "9be63767",
    "metadata": {},
    "source": [
-    "To run the GA optimization we need to define the `fn_pre` and `fn_post` functions for the optmizer. The pre function creates a `Simulation` from the parameters suggested by GA as a solution to the reflector. The post function defines how the `SimulationData` should be evaluated to create a single float value which is then fed back into the GA. By evaluating of the float values of the current generation the GA can then assemble the population for the next generation, based on our hyperparameters."
+    "To run the GA optimization we need to define the `fn_pre` and `fn_post` functions for the optimizer. The pre function creates a `Simulation` from the parameters suggested by GA as a solution to the reflector. The post function defines how the `SimulationData` should be evaluated to create a single float value which is then fed back into the GA. By evaluating of the float values of the current generation the GA can then assemble the population for the next generation, based on our hyperparameters."
    ]
   },
   {
@@ -508,9 +514,10 @@
     "def fn_pre(**params):\n",
     "    pixels = np.array(list(params.values()))\n",
     "    sim = make_sim(pixels)\n",
-    "    \n",
+    "\n",
     "    return sim\n",
     "\n",
+    "\n",
     "def fn_post(sim_data):\n",
     "    return abs(sim_data[\"mode\"].amps.sel(direction=\"-\").squeeze(drop=True).values) ** 2"
    ]
@@ -1258,12 +1265,16 @@
     "pixels_final, solution_fitness, solution_idx = ga_instance.best_solution()\n",
     "print(f\"Fitness value of the best solution = {solution_fitness:.3f}\")\n",
     "\n",
+    "\n",
     "def plot_fitness_evolution(data_frame, solutions_per_pop, fitness_column_name=\"output\"):\n",
     "    \"\"\"Plot the best, worst, and average fitness across generations.\"\"\"\n",
     "\n",
     "    # Divide the solutions into generations\n",
     "    fitness_values = data_frame[fitness_column_name]\n",
-    "    generation_fitness = [fitness_values[i: i + solutions_per_pop] for i in range(0, len(fitness_values), solutions_per_pop)]\n",
+    "    generation_fitness = [\n",
+    "        fitness_values[i : i + solutions_per_pop]\n",
+    "        for i in range(0, len(fitness_values), solutions_per_pop)\n",
+    "    ]\n",
     "\n",
     "    # Get min, max, and mean values per generation\n",
     "    min_fitness = [generation.min() for generation in generation_fitness]\n",
@@ -1278,6 +1289,7 @@
     "    plt.legend()\n",
     "    plt.show()\n",
     "\n",
+    "\n",
     "plot_fitness_evolution(df, 40)"
    ]
   },
@@ -1741,11 +1753,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Make the misc/ directory to store the GDS file if it doesnt exist already\n",
+    "# Make the misc/ directory to store the GDS file if it doesn't exist already\n",
     "import os\n",
-    "if not os.path.exists('./misc/'):\n",
-    "    os.mkdir('./misc/')\n",
-    "    \n",
+    "\n",
+    "if not os.path.exists(\"./misc/\"):\n",
+    "    os.mkdir(\"./misc/\")\n",
+    "\n",
     "sim_final.to_gds_file(fname=\"misc/optimized_reflector.gds\", z=t / 2)"
    ]
   }
@@ -1775,7 +1788,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.0"
   },
   "title": "Genetic Algorithm Optimization of a Reflector | Flexcompute"
  },
diff --git a/GeometryTransformations.ipynb b/GeometryTransformations.ipynb
index 7a3618aa..c390e058 100644
--- a/GeometryTransformations.ipynb
+++ b/GeometryTransformations.ipynb
@@ -7,13 +7,13 @@
    "source": [
     "# Geometry transformations\n",
     "\n",
-    "Tidy3D has a rich set of classes and functions for the creation complex geometries. The set of primitive solids supported is:\n",
+    "Tidy3D has a rich set of classes and functions for the creation of complex geometries. The set of primitive solids supported is:\n",
     "\n",
     "- [Box](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Box.html): rectangular prism.\n",
     "\n",
     "- [Sphere](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Sphere.html): regular sphere.\n",
     "\n",
-    "- [Cylinder](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Cylinder.html): cylinder with optinal angled sidewalls along the main axis.\n",
+    "- [Cylinder](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Cylinder.html): cylinder with optional angled sidewalls along the main axis.\n",
     "\n",
     "- [PolySlab](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PolySlab.html): polygon extruded along its normal axis with optional angled sidewalls.\n",
     "\n",
@@ -37,8 +37,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tidy3d as td\n",
     "import numpy as np\n",
+    "import tidy3d as td\n",
     "from matplotlib import pyplot as plt"
    ]
   },
@@ -76,7 +76,7 @@
     "\n",
     "rotated_box = box.rotated(np.pi / 6, axis=2)\n",
     "\n",
-    "_ = rotated_box.plot(z = 0)"
+    "_ = rotated_box.plot(z=0)"
    ]
   },
   {
@@ -107,7 +107,9 @@
     }
    ],
    "source": [
-    "ellipsoid = td.Cylinder(radius=0.5, length=0.5, axis=1).scaled(x=2, y=1, z=1).rotated(np.pi / 4, axis=1)\n",
+    "ellipsoid = (\n",
+    "    td.Cylinder(radius=0.5, length=0.5, axis=1).scaled(x=2, y=1, z=1).rotated(np.pi / 4, axis=1)\n",
+    ")\n",
     "\n",
     "_ = ellipsoid.plot(y=0)"
    ]
@@ -118,7 +120,7 @@
    "metadata": {},
    "source": [
     "By composing rotations and translations, we can create a cylindrical array of boxes.\n",
-    "In this example, we're also using the `GeometryGroup` class to group the indididual geometries into a single object.\n",
+    "In this example, we're also using the `GeometryGroup` class to group the individual geometries into a single object.\n",
     "That group can also be used as any other geometrical object and freely transformed."
    ]
   },
@@ -222,7 +224,7 @@
    "source": [
     "A `Transformed` object contains an inner geometry and a transformation, written as a [4×4 matrix](https://en.wikipedia.org/wiki/Transformation_matrix) and applied to the (homogeneous) coordinates of the inner geometry.\n",
     "It is possible to define a `Transformed` object directly from the inner geometry and the transformation.\n",
-    "To help create the most ususal transformation matrices, the `Transformed` class has 3 static methods for `translation`, `rotation`, and `scaling` that can be used and combined (the `@` operator can be used for matrix multiplication with numpy arrays)."
+    "To help create the most usual transformation matrices, the `Transformed` class has 3 static methods for `translation`, `rotation`, and `scaling` that can be used and combined (the `@` operator can be used for matrix multiplication with numpy arrays)."
    ]
   },
   {
@@ -501,7 +503,7 @@
    "source": [
     "### Angled waveguide\n",
     "\n",
-    "When we want to create a waveguide with angled sidewalls, we don't allways want the end facets to be angled as well.\n",
+    "When we want to create a waveguide with angled sidewalls, we don't always want the end facets to be angled as well.\n",
     "We can use an extra box to trim down the facet angle:"
    ]
   },
@@ -546,7 +548,7 @@
    "source": [
     "### Pattern intersection\n",
     "\n",
-    "Overlapping patterns can be usefull, for example, when combining diffraction gratings or creating complex phase plates.\n",
+    "Overlapping patterns can be useful, for example, when combining diffraction gratings or creating complex phase plates.\n",
     "The symmetric difference operation (XOR) can be useful in such cases:"
    ]
   },
@@ -573,7 +575,10 @@
     "    inner = td.Cylinder(center=(-2, 0, 0), radius=radius - 0.05, length=0.1, axis=2)\n",
     "    return outer - inner\n",
     "\n",
-    "pattern1 = td.GeometryGroup(geometries=[ring(0.2 * i) for i in range(5, 16)]) * td.Box(size=(2, 2, 0.1))\n",
+    "\n",
+    "pattern1 = td.GeometryGroup(geometries=[ring(0.2 * i) for i in range(5, 16)]) * td.Box(\n",
+    "    size=(2, 2, 0.1)\n",
+    ")\n",
     "\n",
     "_ = pattern1.plot(z=0)"
    ]
@@ -634,11 +639,11 @@
     }
    ],
    "source": [
-    "a = 1.0            # Out-of-plane lattice period\n",
-    "c = a * 2 ** -0.5  # In-plane lattice period\n",
-    "r = a * 0.24       # Pore radius\n",
-    "r_pert = r * 0.5   # Perturbed pore radius\n",
-    "n = 3              # Number of periods (odd for a centered cavity)\n",
+    "a = 1.0  # Out-of-plane lattice period\n",
+    "c = a * 2**-0.5  # In-plane lattice period\n",
+    "r = a * 0.24  # Pore radius\n",
+    "r_pert = r * 0.5  # Perturbed pore radius\n",
+    "n = 3  # Number of periods (odd for a centered cavity)\n",
     "\n",
     "pores = []\n",
     "\n",
@@ -648,7 +653,7 @@
     "    for i in range(n + 1):\n",
     "        p = (i - n * 0.5) * c\n",
     "        r_ik = r_pert if k == n // 2 and i == n // 2 else r\n",
-    "        \n",
+    "\n",
     "        # z-aligned pores\n",
     "        pore = td.Cylinder(center=(p, y, 0), length=length, radius=r, axis=2)\n",
     "        pores.append(pore)\n",
@@ -657,7 +662,7 @@
     "                center=(p + 0.5 * c, y + 0.5 * a, 0), length=length, radius=r_ik, axis=2\n",
     "            )\n",
     "            pores.append(pore)\n",
-    "        \n",
+    "\n",
     "        # x-aligned pores\n",
     "        pore = td.Cylinder(center=(0, y + 0.25 * a, p), length=length, radius=r, axis=0)\n",
     "        pores.append(pore)\n",
@@ -697,7 +702,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.0"
   },
   "title": "Apply Geometry Transformations in Tidy3D | Flexcompute"
  },
diff --git a/GradientMetasurfaceReflector.ipynb b/GradientMetasurfaceReflector.ipynb
index be0a8c69..45d2efcc 100644
--- a/GradientMetasurfaceReflector.ipynb
+++ b/GradientMetasurfaceReflector.ipynb
@@ -36,11 +36,10 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n"
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -84,7 +83,7 @@
    "outputs": [],
    "source": [
     "lda0 = 0.85  # central wavelength\n",
-    "freq0 = td.C_0 / lda0  # central frequency\n"
+    "freq0 = td.C_0 / lda0  # central frequency"
    ]
   },
   {
@@ -115,7 +114,7 @@
     "d2 = 0.05  # spacer thickness\n",
     "d3 = 0.13  # gold layer thickness\n",
     "w = 0.09  # antenna width\n",
-    "inf_eff = 1e2  # effective infinity\n"
+    "inf_eff = 1e2  # effective infinity"
    ]
   },
   {
@@ -149,7 +148,7 @@
     "au = td.material_library[\"Au\"][\"Olmon2012evaporated\"]\n",
     "\n",
     "# define SiO2 material for the substrate\n",
-    "sio2 = td.Medium(permittivity=2.25)\n"
+    "sio2 = td.Medium(permittivity=2.25)"
    ]
   },
   {
@@ -176,27 +175,21 @@
    "source": [
     "# define SiO2 substrate\n",
     "sub = td.Structure(\n",
-    "    geometry=td.Box.from_bounds(\n",
-    "        rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, -d3)\n",
-    "    ),\n",
+    "    geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, -d3)),\n",
     "    medium=sio2,\n",
     ")\n",
     "\n",
     "# define gold layer\n",
     "gold_layer = td.Structure(\n",
-    "    geometry=td.Box.from_bounds(\n",
-    "        rmin=(-inf_eff, -inf_eff, -d3), rmax=(inf_eff, inf_eff, 0)\n",
-    "    ),\n",
+    "    geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, -d3), rmax=(inf_eff, inf_eff, 0)),\n",
     "    medium=au,\n",
     ")\n",
     "\n",
     "# define MgF2 spacer layer\n",
     "spacer = td.Structure(\n",
-    "    geometry=td.Box.from_bounds(\n",
-    "        rmin=(-inf_eff, -inf_eff, 0), rmax=(inf_eff, inf_eff, d2)\n",
-    "    ),\n",
+    "    geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, 0), rmax=(inf_eff, inf_eff, d2)),\n",
     "    medium=mgf2,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -235,7 +228,7 @@
     "# define a diffraction monitor to calculate the reflection coefficient\n",
     "monitor_r = td.DiffractionMonitor(\n",
     "    center=[0, 0, 0.6 * lda0], size=[td.inf, td.inf, 0], freqs=[freq0], name=\"R\"\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -271,13 +264,12 @@
     "    z=td.Boundary(minus=td.PML(), plus=td.PML()),\n",
     ")\n",
     "\n",
+    "\n",
     "# define a function to build simulation given antenna length L\n",
     "def make_sim(L):\n",
     "    # define the gold antenna\n",
     "    antenna = td.Structure(\n",
-    "        geometry=td.Box.from_bounds(\n",
-    "            rmin=(-w / 2, -L / 2, d2), rmax=(w / 2, L / 2, d2 + d1)\n",
-    "        ),\n",
+    "        geometry=td.Box.from_bounds(rmin=(-w / 2, -L / 2, d2), rmax=(w / 2, L / 2, d2 + d1)),\n",
     "        medium=au,\n",
     "    )\n",
     "    unit_cell = [sub, gold_layer, spacer, antenna]\n",
@@ -292,7 +284,7 @@
     "        run_time=run_time,\n",
     "        boundary_spec=boundary_spec,  # pml is applied to z direction. x and y directions are periodic\n",
     "    )\n",
-    "    return sim\n"
+    "    return sim"
    ]
   },
   {
@@ -332,7 +324,7 @@
     "sim = make_sim(0.15)\n",
     "# visualize the simulation\n",
     "sim.plot(x=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -774,7 +766,7 @@
    "source": [
     "Ls = np.linspace(0.04, 0.28, 14)  # antenna lengths for parameter sweep\n",
     "\n",
-    "sims = {f\"L={L:.2f}\": make_sim(L) for L in Ls}  # construct simulation batch\n"
+    "sims = {f\"L={L:.2f}\": make_sim(L) for L in Ls}  # construct simulation batch"
    ]
   },
   {
@@ -1849,7 +1841,7 @@
    "source": [
     "# submit simulation batch to the server\n",
     "batch = web.Batch(simulations=sims, verbose=True)\n",
-    "batch_results = batch.run(path_dir=\"data\")\n"
+    "batch_results = batch.run(path_dir=\"data\")"
    ]
   },
   {
@@ -2615,7 +2607,7 @@
     "r = np.zeros(len(Ls), dtype=\"complex\")\n",
     "for i, L in enumerate(Ls):\n",
     "    sim_data = batch_results[f\"L={L:.2f}\"]\n",
-    "    r[i] = np.array(sim_data[\"R\"].amps.sel(f=freq0, polarization=\"s\"))[0][0]\n"
+    "    r[i] = np.array(sim_data[\"R\"].amps.sel(f=freq0, polarization=\"s\"))[0][0]"
    ]
   },
   {
@@ -2656,14 +2648,14 @@
     "theta = np.unwrap(np.angle(r)) * 180 / np.pi\n",
     "theta = theta - theta[0]\n",
     "ax1.plot(Ls, theta)\n",
-    "ax1.set_xlabel(\"L ($\\mu m$)\")\n",
+    "ax1.set_xlabel(r\"L ($\\mu m$)\")\n",
     "ax1.set_ylabel(\"Reflection phase (degree)\")\n",
     "\n",
     "# plot the reflectivity\n",
     "ax2.plot(Ls, np.abs(r) ** 2)\n",
-    "ax2.set_xlabel(\"L ($\\mu m$)\")\n",
+    "ax2.set_xlabel(r\"L ($\\mu m$)\")\n",
     "ax2.set_ylabel(\"Reflectivity\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2730,7 +2722,7 @@
     "plt.scatter(np.linspace(1, N_sample, N_sample), theta_design)\n",
     "plt.xlabel(\"Site\")\n",
     "plt.ylabel(\"Reflection phase (degree)\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2774,7 +2766,7 @@
     "            )\n",
     "        )\n",
     "\n",
-    "super_cell = antennas + [spacer, sub, gold_layer]\n"
+    "super_cell = antennas + [spacer, sub, gold_layer]"
    ]
   },
   {
@@ -2840,7 +2832,7 @@
     "    monitors=[monitor_r, field_monitor],\n",
     "    run_time=run_time,\n",
     "    boundary_spec=boundary_spec,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -2878,7 +2870,7 @@
    "source": [
     "# visualize the antennas in the super cell\n",
     "sim.plot(z=d2 + d1)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2916,7 +2908,7 @@
    "source": [
     "# visualize the xz plane\n",
     "sim.plot(y=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -4266,7 +4258,7 @@
    ],
    "source": [
     "job = web.Job(simulation=sim, task_name=\"beam_steering_metasurface\", verbose=True)\n",
-    "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n"
+    "sim_data = job.run(path=\"data/simulation_data.hdf5\")"
    ]
   },
   {
@@ -4319,7 +4311,7 @@
     "plt.scatter(phi, theta * 180 / np.pi, s=60, c=power, vmin=0, vmax=1, cmap=\"bwr\")\n",
     "ax.set_rlim(0, 90)\n",
     "plt.colorbar()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -4358,7 +4350,7 @@
     "plt.scatter(sim_data[\"R\"].orders_x, sim_data[\"R\"].power.values[:, 0])\n",
     "plt.xlabel(\"Diffraction order\")\n",
     "plt.ylabel(\"Power\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -4400,7 +4392,7 @@
     "fig, ax = plt.subplots()\n",
     "Ey = sim_data[\"field\"].Ey.sel(f=freq0).real\n",
     "Ey.plot(x=\"x\", y=\"z\", ax=ax, vmin=-100, vmax=100, cmap=\"bwr\")\n",
-    "ax.set_aspect(\"equal\")\n"
+    "ax.set_aspect(\"equal\")"
    ]
   },
   {
diff --git a/GrapheneMetamaterial.ipynb b/GrapheneMetamaterial.ipynb
index ec77c7a3..1d8e41e7 100644
--- a/GrapheneMetamaterial.ipynb
+++ b/GrapheneMetamaterial.ipynb
@@ -39,11 +39,10 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n"
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -91,7 +90,7 @@
     "freq0 = 2.5 * THz  # central frequency\n",
     "freqs = np.linspace(0.1, 5, 300) * THz  # frequency range of interest\n",
     "\n",
-    "lda0 = td.C_0 / freq0  # central wavelength\n"
+    "lda0 = td.C_0 / freq0  # central wavelength"
    ]
   },
   {
@@ -117,7 +116,7 @@
    "outputs": [],
    "source": [
     "n_topas = 1.53  # refractive index of the polymer\n",
-    "topas = td.Medium(permittivity=n_topas**2)\n"
+    "topas = td.Medium(permittivity=n_topas**2)"
    ]
   },
   {
@@ -144,7 +143,7 @@
    "source": [
     "gamma = 0.0033  # scattering rate\n",
     "temp = 300  # temperature\n",
-    "scaling = 2  # number of layers.\n"
+    "scaling = 2  # number of layers."
    ]
   },
   {
@@ -171,7 +170,7 @@
    "source": [
     "a = 15  # unit cell size\n",
     "h = 35.9  # distance between graphene and the ground plate\n",
-    "offset = lda0 / 2  # distance between the flux monitor and the graphene\n"
+    "offset = lda0 / 2  # distance between the flux monitor and the graphene"
    ]
   },
   {
@@ -207,7 +206,7 @@
     "grid_spec = td.GridSpec.auto(min_steps_per_wvl=200, wavelength=lda0)\n",
     "\n",
     "# simulation run time\n",
-    "run_time = 1e-11\n"
+    "run_time = 1e-11"
    ]
   },
   {
@@ -233,7 +232,6 @@
    "outputs": [],
    "source": [
     "def make_sim_uniform(mu_c):\n",
-    "\n",
     "    # define graphene\n",
     "    graphene = td.material_library[\"graphene\"](\n",
     "        gamma=gamma, mu_c=mu_c, temp=temp, scaling=scaling\n",
@@ -278,7 +276,7 @@
     "        symmetry=(-1, 1, 0),  # symmetry is used to reduce the computational load\n",
     "    )\n",
     "\n",
-    "    return sim\n"
+    "    return sim"
    ]
   },
   {
@@ -375,7 +373,7 @@
     "mu_cs = [0, 0.1, 0.2, 0.5]  # values of mu_c to be simulated\n",
     "\n",
     "# define a simulation batch\n",
-    "sims = {f\"mu_c={mu_c:.2f}\": make_sim_uniform(mu_c) for mu_c in mu_cs}\n"
+    "sims = {f\"mu_c={mu_c:.2f}\": make_sim_uniform(mu_c) for mu_c in mu_cs}"
    ]
   },
   {
@@ -758,7 +756,7 @@
    ],
    "source": [
     "batch = web.Batch(simulations=sims, folder_name=\"default\")\n",
-    "batch_results = batch.run(path_dir=\"data\")\n"
+    "batch_results = batch.run(path_dir=\"data\")"
    ]
   },
   {
@@ -792,7 +790,7 @@
     "        plt.ylim(0, 1)\n",
     "        plt.xlabel(\"Frequency (THz)\")\n",
     "        plt.ylabel(\"Absorption\")\n",
-    "        plt.legend()\n"
+    "        plt.legend()"
    ]
   },
   {
@@ -1036,7 +1034,7 @@
     }
    ],
    "source": [
-    "plot_absorption(batch_results)\n"
+    "plot_absorption(batch_results)"
    ]
   },
   {
@@ -1137,7 +1135,7 @@
     "        symmetry=(-1, 1, 0),\n",
     "    )\n",
     "\n",
-    "    return sim\n"
+    "    return sim"
    ]
   },
   {
@@ -1208,7 +1206,7 @@
    ],
    "source": [
     "sim = make_sim_fishnet(0.5)\n",
-    "ax = sim.plot(z=h)\n"
+    "ax = sim.plot(z=h)"
    ]
   },
   {
@@ -1665,7 +1663,7 @@
     "\n",
     "# submit the batch to the server\n",
     "batch = web.Batch(simulations=sims, folder_name=\"default\")\n",
-    "batch_results = batch.run(path_dir=\"data\")\n"
+    "batch_results = batch.run(path_dir=\"data\")"
    ]
   },
   {
@@ -1909,7 +1907,7 @@
     }
    ],
    "source": [
-    "plot_absorption(batch_results)\n"
+    "plot_absorption(batch_results)"
    ]
   },
   {
@@ -1962,12 +1960,12 @@
     "freqs = np.linspace(10, 250, 100) * THz\n",
     "graphene = td.material_library[\"graphene\"](gamma=gamma, mu_c=mu_c, temp=temp)\n",
     "sigma_analytical = graphene.numerical_conductivity(freqs)\n",
-    "plt.plot(freqs / THz, np.real(sigma_analytical * 1e6), label=\"Re($\\sigma$) ($\\mu$S)\")\n",
-    "plt.plot(freqs / THz, np.imag(sigma_analytical * 1e6), label=\"Im($\\sigma$) ($\\mu$S)\")\n",
+    "plt.plot(freqs / THz, np.real(sigma_analytical * 1e6), label=r\"Re($\\sigma$) ($\\mu$S)\")\n",
+    "plt.plot(freqs / THz, np.imag(sigma_analytical * 1e6), label=r\"Im($\\sigma$) ($\\mu$S)\")\n",
     "plt.xlabel(\"frequency (THz)\")\n",
     "plt.title(\"analytically calculated surface conductivity\")\n",
     "plt.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2004,7 +2002,7 @@
    ],
    "source": [
     "graphene.medium.plot_sigma(freqs)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
diff --git a/GratingCoupler.ipynb b/GratingCoupler.ipynb
index cacb4fec..4a942572 100644
--- a/GratingCoupler.ipynb
+++ b/GratingCoupler.ipynb
@@ -23,11 +23,10 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pylab as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n"
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -91,7 +90,7 @@
    "source": [
     "t_si = 0.26  # thickness of the silicon layer\n",
     "etch_depth = 0.16  # etching depth\n",
-    "ff = 0.8 # filling fraction of the grating\n",
+    "ff = 0.8  # filling fraction of the grating\n",
     "t_tox = 0.68  # top oxide layer thickness\n",
     "t_box = 2  # bottom oxide layer thickness\n",
     "n = 20  # number of grating teeth to create"
@@ -132,9 +131,7 @@
    "source": [
     "# create the top oxide layer\n",
     "tox = td.Structure(\n",
-    "    geometry=td.Box.from_bounds(\n",
-    "        rmin=(-inf_eff, -inf_eff, 0), rmax=(inf_eff, inf_eff, t_tox)\n",
-    "    ),\n",
+    "    geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, 0), rmax=(inf_eff, inf_eff, t_tox)),\n",
     "    medium=sio2,\n",
     ")\n",
     "\n",
@@ -146,17 +143,13 @@
     "\n",
     "# create the etched waveguide\n",
     "etched_waveguide = td.Structure(\n",
-    "    geometry=td.Box.from_bounds(\n",
-    "        rmin=(0, -inf_eff, 0), rmax=(inf_eff, inf_eff, t_si - etch_depth)\n",
-    "    ),\n",
+    "    geometry=td.Box.from_bounds(rmin=(0, -inf_eff, 0), rmax=(inf_eff, inf_eff, t_si - etch_depth)),\n",
     "    medium=si,\n",
     ")\n",
     "\n",
     "# create the bottom oxide layer\n",
     "box = td.Structure(\n",
-    "    geometry=td.Box.from_bounds(\n",
-    "        rmin=(-inf_eff, -inf_eff, -t_box), rmax=(inf_eff, inf_eff, 0)\n",
-    "    ),\n",
+    "    geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, -t_box), rmax=(inf_eff, inf_eff, 0)),\n",
     "    medium=sio2,\n",
     ")\n",
     "\n",
@@ -186,9 +179,9 @@
    "source": [
     "def make_2d_sim(p: float, source_x: float) -> td.Simulation:\n",
     "    \"\"\"Function to create a 2D simulation given the grating period and source position\"\"\"\n",
-    "    \n",
-    "    source_gap = 0.5 # gap distance between the source and the top oxide\n",
-    "    \n",
+    "\n",
+    "    source_gap = 0.5  # gap distance between the source and the top oxide\n",
+    "\n",
     "    # define a gaussian beam source\n",
     "    source = td.GaussianBeam(\n",
     "        size=(2 * mfd, td.inf, 0),\n",
@@ -209,14 +202,13 @@
     "        name=\"mode\",\n",
     "    )\n",
     "\n",
-    "    l_grating = n*p # length of the grating region\n",
+    "    l_grating = n * p  # length of the grating region\n",
     "\n",
     "    # create the grating geometries\n",
     "    gratings = 0\n",
     "    for i in range(n):\n",
-    "        \n",
     "        gratings += td.Box(\n",
-    "            center=(ff*p/2+i*p, 0, t_si - etch_depth / 2), size=(p*ff, td.inf, etch_depth)\n",
+    "            center=(ff * p / 2 + i * p, 0, t_si - etch_depth / 2), size=(p * ff, td.inf, etch_depth)\n",
     "        )\n",
     "\n",
     "    # create the grating structure\n",
@@ -225,7 +217,7 @@
     "    # create a box to represent the simulation domain box\n",
     "    sim_box = td.Box.from_bounds(\n",
     "        rmin=(-buffer, 0, -t_box - buffer),\n",
-    "        rmax=(l_grating + buffer, 0, t_si + t_tox+buffer),\n",
+    "        rmax=(l_grating + buffer, 0, t_si + t_tox + buffer),\n",
     "    )\n",
     "\n",
     "    run_time = 1e-12  # simulation run time\n",
@@ -284,7 +276,7 @@
    "source": [
     "sim0 = make_2d_sim(p=0.5, source_x=5)\n",
     "sim0.plot_eps(y=0, freq=freq0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -592,12 +584,17 @@
     }
    ],
    "source": [
-    "mode_unetched = td.ModeSimulation.from_simulation(sim0, mode_spec=td.ModeSpec(num_modes=1, target_neff=3.47), plane=td.Box(center=(-0.1,0,0), \n",
-    "                                                                                          size=(0,td.inf, 5*t_si)),\n",
-    "                                             freqs=[freq0])\n",
+    "mode_unetched = td.ModeSimulation.from_simulation(\n",
+    "    sim0,\n",
+    "    mode_spec=td.ModeSpec(num_modes=1, target_neff=3.47),\n",
+    "    plane=td.Box(center=(-0.1, 0, 0), size=(0, td.inf, 5 * t_si)),\n",
+    "    freqs=[freq0],\n",
+    ")\n",
     "data_unetched = web.run(simulation=mode_unetched, task_name=\"unetched\")\n",
     "\n",
-    "mode_etched = mode_unetched.updated_copy(plane=td.Box(center=(n*0.5+0.1,0,0), size=(0,td.inf, 5*t_si)))\n",
+    "mode_etched = mode_unetched.updated_copy(\n",
+    "    plane=td.Box(center=(n * 0.5 + 0.1, 0, 0), size=(0, td.inf, 5 * t_si))\n",
+    ")\n",
     "data_etched = web.run(simulation=mode_etched, task_name=\"etched\")"
    ]
   },
@@ -622,17 +619,17 @@
     }
    ],
    "source": [
-    "neff_unetch = data_unetched.modes.n_eff.values.item()  # effective index of the slab mode of the unetched waveguide\n",
-    "neff_etch = data_etched.modes.n_eff.values.item()  # effective index of the slab mode of the etched waveguide\n",
+    "neff_unetch = (\n",
+    "    data_unetched.modes.n_eff.values.item()\n",
+    ")  # effective index of the slab mode of the unetched waveguide\n",
+    "neff_etch = (\n",
+    "    data_etched.modes.n_eff.values.item()\n",
+    ")  # effective index of the slab mode of the etched waveguide\n",
     "theta_c = np.sin(theta) / n_sio2  # incident angle in the cladding\n",
-    "ff = 0.8 # filling fraction\n",
+    "ff = 0.8  # filling fraction\n",
     "\n",
     "# calculate the grating period\n",
-    "p = lda0 / (\n",
-    "        ff * neff_unetch\n",
-    "        + (1 - ff) * neff_etch\n",
-    "        - n_sio2 * np.sin(theta_c)\n",
-    "    )\n",
+    "p = lda0 / (ff * neff_unetch + (1 - ff) * neff_etch - n_sio2 * np.sin(theta_c))\n",
     "\n",
     "print(f\"Calculated grating period is {p*1e3:.0f} nm\")"
    ]
@@ -826,7 +823,7 @@
     "    for p in p_list\n",
     "    for source_x in source_x_list\n",
     "}\n",
-    "        \n",
+    "\n",
     "batch = web.Batch(simulations=sims_2d)\n",
     "batch_results = batch.run(path_dir=\"data\")"
    ]
@@ -844,13 +841,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ce = np.array([\n",
+    "ce = np.array(\n",
     "    [\n",
-    "        np.abs(batch_results[f\"p={p:.2f};source_x={source_x:.2f}\"][\"mode\"].amps.sel(f=freq0, direction=\"-\").values.item())**2\n",
-    "        for source_x in source_x_list\n",
+    "        [\n",
+    "            np.abs(\n",
+    "                batch_results[f\"p={p:.2f};source_x={source_x:.2f}\"][\"mode\"]\n",
+    "                .amps.sel(f=freq0, direction=\"-\")\n",
+    "                .values.item()\n",
+    "            )\n",
+    "            ** 2\n",
+    "            for source_x in source_x_list\n",
+    "        ]\n",
+    "        for p in p_list\n",
     "    ]\n",
-    "    for p in p_list\n",
-    "])"
+    ")"
    ]
   },
   {
@@ -934,15 +938,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "w_wg = 0.5 # waveguide width\n",
-    "w_grating = mfd # grating width is set to the mode field diameter\n",
-    "l_taper = 50 # length of the linear taper\n",
+    "w_wg = 0.5  # waveguide width\n",
+    "w_grating = mfd  # grating width is set to the mode field diameter\n",
+    "l_taper = 50  # length of the linear taper\n",
+    "\n",
     "\n",
     "def make_3d_sim(p: float, source_x: float) -> td.Simulation:\n",
     "    \"\"\"Function to create a 2D simulation given the grating period and source position\"\"\"\n",
-    "    \n",
+    "\n",
     "    source_gap = 0.5\n",
-    "    \n",
+    "\n",
     "    # define a gaussian beam source\n",
     "    source = td.GaussianBeam(\n",
     "        size=(2 * mfd, 2 * mfd, 0),\n",
@@ -956,41 +961,41 @@
     "\n",
     "    # define a mode monitor\n",
     "    mode_monitor = td.ModeMonitor(\n",
-    "        center=(-l_taper-buffer / 2, 0, t_si / 2),\n",
-    "        size=(0, 4*w_wg, 6 * t_si),\n",
+    "        center=(-l_taper - buffer / 2, 0, t_si / 2),\n",
+    "        size=(0, 4 * w_wg, 6 * t_si),\n",
     "        freqs=freqs,\n",
     "        mode_spec=td.ModeSpec(num_modes=1, target_neff=3.47),\n",
     "        name=\"mode\",\n",
     "    )\n",
     "\n",
-    "    l_grating = n*p # length of the grating region\n",
-    "    \n",
+    "    l_grating = n * p  # length of the grating region\n",
+    "\n",
     "    gratings = 0\n",
     "    for i in range(n):\n",
-    "        \n",
     "        gratings += td.Box(\n",
-    "            center=(ff*p/2+i*p, 0, t_si - etch_depth / 2), size=(p*ff, w_grating, etch_depth)\n",
+    "            center=(ff * p / 2 + i * p, 0, t_si - etch_depth / 2),\n",
+    "            size=(p * ff, w_grating, etch_depth),\n",
     "        )\n",
     "\n",
     "    # create the grating structure\n",
     "    gratings = td.Structure(geometry=gratings, medium=si)\n",
     "\n",
     "    vertices = [\n",
-    "        (0, w_grating/2),\n",
-    "        (0, -w_grating/2),\n",
-    "        (-l_taper, -w_wg/2),\n",
-    "        (-l_taper-2*buffer, -w_wg/2),\n",
-    "        (-l_taper-2*buffer, w_wg/2),\n",
-    "        (-l_taper, w_wg/2)\n",
+    "        (0, w_grating / 2),\n",
+    "        (0, -w_grating / 2),\n",
+    "        (-l_taper, -w_wg / 2),\n",
+    "        (-l_taper - 2 * buffer, -w_wg / 2),\n",
+    "        (-l_taper - 2 * buffer, w_wg / 2),\n",
+    "        (-l_taper, w_wg / 2),\n",
     "    ]\n",
     "    taper = td.Structure(\n",
-    "    geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(0, t_si)), medium=si\n",
-    ")\n",
-    "    \n",
+    "        geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(0, t_si)), medium=si\n",
+    "    )\n",
+    "\n",
     "    # create a box to represent the simulation domain box\n",
     "    sim_box = td.Box.from_bounds(\n",
-    "        rmin=(-buffer-l_taper, -w_grating/2-buffer, -t_box - buffer),\n",
-    "        rmax=(l_grating + buffer, w_grating/2+buffer, t_si + t_tox+buffer),\n",
+    "        rmin=(-buffer - l_taper, -w_grating / 2 - buffer, -t_box - buffer),\n",
+    "        rmax=(l_grating + buffer, w_grating / 2 + buffer, t_si + t_tox + buffer),\n",
     "    )\n",
     "\n",
     "    run_time = 2e-12  # simulation run time\n",
@@ -1013,7 +1018,7 @@
     "        sources=[source],\n",
     "        monitors=[mode_monitor],\n",
     "        run_time=run_time,\n",
-    "        symmetry=(0, -1, 0)\n",
+    "        symmetry=(0, -1, 0),\n",
     "    )\n",
     "\n",
     "    return sim"
@@ -1235,9 +1240,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Plot the coupling efficiency as a function of wavelength. Compared to 2D, the coupling efficiency of -4 dB is slightly lower in 3D. This is because of two factors: 1. the taper introduces some loss during the mode size convertion down to the single-mode waveguide; 2. The optimal parameters in 2D can differ slightly from the optimal parameters in 3D. \n",
+    "Plot the coupling efficiency as a function of wavelength. Compared to 2D, the coupling efficiency of -4 dB is slightly lower in 3D. This is because of two factors: 1. the taper introduces some loss during the mode size conversion down to the single-mode waveguide; 2. The optimal parameters in 2D can differ slightly from the optimal parameters in 3D. \n",
     "\n",
-    "One can perform parameter sweep similar to the 2D case to further optimize the design. For simplicity we skip this part in this notebook."
+    "One can perform a parameter sweep similar to the 2D case to further optimize the design. For simplicity, we skip this part in this notebook."
    ]
   },
   {
@@ -1257,9 +1262,9 @@
     }
    ],
    "source": [
-    "ce_3d = np.abs(sim_data[\"mode\"].amps.sel(direction=\"-\"))**2\n",
+    "ce_3d = np.abs(sim_data[\"mode\"].amps.sel(direction=\"-\")) ** 2\n",
     "\n",
-    "plt.plot(ldas, 10*np.log10(ce_3d), c=\"red\")\n",
+    "plt.plot(ldas, 10 * np.log10(ce_3d), c=\"red\")\n",
     "plt.ylim(-12, 0)\n",
     "plt.xlabel(\"Wavelength (μm)\")\n",
     "plt.ylabel(\"Coupling efficiency (dB)\")\n",
@@ -1312,7 +1317,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.11.0"
   },
   "title": "Uniform Grating Coupler Modeling in Tidy3D | Flexcompute",
   "widgets": {
diff --git a/GratingEfficiency.ipynb b/GratingEfficiency.ipynb
index 12a23ba7..b8bc10da 100644
--- a/GratingEfficiency.ipynb
+++ b/GratingEfficiency.ipynb
@@ -32,12 +32,12 @@
    "outputs": [],
    "source": [
     "# basic python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# Tidy3D import\n",
     "import tidy3d as td\n",
-    "from tidy3d import web\n"
+    "from tidy3d import web"
    ]
   },
   {
@@ -134,7 +134,7 @@
     ")\n",
     "\n",
     "# Collect all structures\n",
-    "structures = [substrate, grating_L1, grating_L2]\n"
+    "structures = [substrate, grating_L1, grating_L2]"
    ]
   },
   {
@@ -184,7 +184,7 @@
     "    pol_angle=0,\n",
     "    angle_theta=0,\n",
     "    angle_phi=0,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -255,7 +255,7 @@
     "    normal_dir=\"-\",\n",
     ")\n",
     "\n",
-    "monitors = [monitor_xz, monitor_r, monitor_t]\n"
+    "monitors = [monitor_xz, monitor_r, monitor_t]"
    ]
   },
   {
@@ -263,7 +263,7 @@
    "id": "383d61f0-c458-486d-9573-e52847faa88a",
    "metadata": {},
    "source": [
-    "### Set up boundary conditions and initialize simualtion\n",
+    "### Set up boundary conditions and initialize simulation\n",
     "\n",
     "For normal incidence, we can use periodic boundary conditions along the `x` and `y` directions. More generally, we need to use Bloch boundary conditions as will be illustrated below. We can also use Bloch boundaries with zero Bloch vector for normal incidence, but a simulation with Bloch boundaries uses complex fields and is twice more computationally expensive than a simulation with periodic boundaries, while the results for `bloch_vec = 0` are equivalent.\n",
     "\n",
@@ -308,9 +308,7 @@
     "boundary_spec = td.BoundarySpec(\n",
     "    x=td.Boundary.periodic(),\n",
     "    y=td.Boundary.periodic(),\n",
-    "    z=td.Boundary(\n",
-    "        minus=td.PML(num_layers=num_pml_layers), plus=td.PML(num_layers=num_pml_layers)\n",
-    "    ),\n",
+    "    z=td.Boundary(minus=td.PML(num_layers=num_pml_layers), plus=td.PML(num_layers=num_pml_layers)),\n",
     ")\n",
     "\n",
     "# Simulation\n",
@@ -326,7 +324,7 @@
     "\n",
     "fig, ax = plt.subplots(1, 1, figsize=(5, 8))\n",
     "simulation.plot(y=0, ax=ax)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -689,7 +687,7 @@
     "    task_name=\"GratingEfficiency\",\n",
     "    path=\"data/GratingEfficiency.hdf5\",\n",
     "    verbose=True,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -698,7 +696,7 @@
    "metadata": {},
    "source": [
     "### Diffraction data\n",
-    "Now we can extract the diffracted power from the output data structures and verfify that the sum across all reflection and transmission orders is close to 1. We can also access the diffraction angles and the complex power amplitudes for each order and polarization."
+    "Now we can extract the diffracted power from the output data structures and verify that the sum across all reflection and transmission orders is close to 1. We can also access the diffraction angles and the complex power amplitudes for each order and polarization."
    ]
   },
   {
@@ -850,7 +848,6 @@
     "    eps_grid_L1 = np.ones((num_x, num_y)) * eps_diel\n",
     "    eps_grid_L2 = np.ones((num_x, num_y)) * eps_background\n",
     "else:\n",
-    "\n",
     "    eps_grid_substrate = np.ones((num_x, num_y)) * eps_diel\n",
     "    eps_grid_L1 = np.ones((num_x, num_y)) * eps_diel\n",
     "    eps_grid_L2 = np.ones((num_x, num_y)) * eps_diel\n",
@@ -944,7 +941,7 @@
     "    data_t.orders_y,\n",
     "    obj,\n",
     "    Ti,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -998,7 +995,7 @@
     "ax[1].set_xlabel(\"Order along x\")\n",
     "ax[1].legend()\n",
     "\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1075,9 +1072,7 @@
     "coords[\"orders_y\"] = np.atleast_1d(data_t.orders_y)\n",
     "coords[\"f\"] = np.array(data_t.f)\n",
     "coords[\"polarization\"] = [\"s\", \"p\"]\n",
-    "amps_grcwa_sp = xr.DataArray(\n",
-    "    np.stack([amps_grcwa_tp[2], amps_grcwa_tp[1]], axis=3), coords=coords\n",
-    ")\n",
+    "amps_grcwa_sp = xr.DataArray(np.stack([amps_grcwa_tp[2], amps_grcwa_tp[1]], axis=3), coords=coords)\n",
     "\n",
     "# finally, we can compare the complex amplitudes for the y=0 order, as a function of orders along x\n",
     "pol = \"p\"\n",
@@ -1100,7 +1095,7 @@
     "ax[1].set_xlabel(\"Order along x\")\n",
     "ax[1].legend()\n",
     "\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1144,7 +1139,7 @@
     "sim_data.plot_field(\"field_xz\", field_name=\"Ex\", val=\"abs\", ax=axs[1])\n",
     "sim_data.plot_field(\"field_xz\", field_name=\"Ey\", val=\"abs\", ax=axs[2])\n",
     "sim_data.plot_field(\"field_xz\", field_name=\"Ez\", val=\"abs\", ax=axs[3])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1212,9 +1207,7 @@
     "boundary_spec = td.BoundarySpec(\n",
     "    x=bloch_x,\n",
     "    y=bloch_y,\n",
-    "    z=td.Boundary(\n",
-    "        minus=td.PML(num_layers=num_pml_layers), plus=td.PML(num_layers=num_pml_layers)\n",
-    "    ),\n",
+    "    z=td.Boundary(minus=td.PML(num_layers=num_pml_layers), plus=td.PML(num_layers=num_pml_layers)),\n",
     ")\n",
     "\n",
     "# Simulation\n",
@@ -1234,7 +1227,7 @@
     "\n",
     "fig, ax = plt.subplots(1, 1, figsize=(5, 8))\n",
     "simulation.plot(y=0, ax=ax)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1606,7 +1599,7 @@
     "    task_name=\"GratingEfficiency\",\n",
     "    path=\"data/GratingEfficiency.hdf5\",\n",
     "    verbose=True,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1680,7 +1673,7 @@
     "\n",
     "print(f\"Total power: {total_power.values}\")\n",
     "print(\"Theta (degrees):\", \", \".join(f\"{t:.2f}\" for t in theta * 180 / np.pi))\n",
-    "print(f\"Amplitude data: \\n{data_t.amps}\")\n"
+    "print(f\"Amplitude data: \\n{data_t.amps}\")"
    ]
   },
   {
@@ -1688,7 +1681,7 @@
    "id": "6c21f383",
    "metadata": {},
    "source": [
-    "We still used a P-polarized source (`pol_angle = 0` in the source defition), but now we also get nonzero `polarization = \"s\"` amplitudes in the data. This is because the input polarization is at an angle with respect to the `x`-axis, and is not preserved by the translational invariance along `y`. Similarly, now there is a nonzero `Ey` field component in the recorded near fields."
+    "We still used a P-polarized source (`pol_angle = 0` in the source definition), but now we also get nonzero `polarization = \"s\"` amplitudes in the data. This is because the input polarization is at an angle with respect to the `x`-axis, and is not preserved by the translational invariance along `y`. Similarly, now there is a nonzero `Ey` field component in the recorded near fields."
    ]
   },
   {
@@ -1723,7 +1716,7 @@
     "sim_data.plot_field(\"field_xz\", field_name=\"Ex\", val=\"abs\", ax=axs[1])\n",
     "sim_data.plot_field(\"field_xz\", field_name=\"Ey\", val=\"abs\", ax=axs[2])\n",
     "sim_data.plot_field(\"field_xz\", field_name=\"Ez\", val=\"abs\", ax=axs[3])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1799,7 +1792,6 @@
     "    eps_grid_L1 = np.ones((num_x, num_y)) * eps_diel\n",
     "    eps_grid_L2 = np.ones((num_x, num_y)) * eps_background\n",
     "else:\n",
-    "\n",
     "    eps_grid_substrate = np.ones((num_x, num_y)) * eps_diel\n",
     "    eps_grid_L1 = np.ones((num_x, num_y)) * eps_diel\n",
     "    eps_grid_L2 = np.ones((num_x, num_y)) * eps_diel\n",
@@ -1893,7 +1885,7 @@
     "    data_t.orders_y,\n",
     "    obj,\n",
     "    Ti,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1938,7 +1930,7 @@
     "ax[1].set_xlabel(\"Order along x\")\n",
     "ax[1].legend()\n",
     "\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   }
  ],
@@ -1961,7 +1953,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.0"
+   "version": "3.11.0"
   },
   "title": "Multilevel Blazed Diffraction Grating | Flexcompute",
   "widgets": {
diff --git a/Gyrotropic.ipynb b/Gyrotropic.ipynb
index b023a24b..cad61935 100644
--- a/Gyrotropic.ipynb
+++ b/Gyrotropic.ipynb
@@ -50,8 +50,8 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pylab as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3D import\n",
     "import tidy3d as td\n",
@@ -194,7 +194,9 @@
    "outputs": [],
    "source": [
     "medium_unperturbed = td.Medium(permittivity=eps0)\n",
-    "medium_gyrotropic = td.FullyAnisotropicMedium(permittivity=permittivity_unperturbed, conductivity=conductivity)"
+    "medium_gyrotropic = td.FullyAnisotropicMedium(\n",
+    "    permittivity=permittivity_unperturbed, conductivity=conductivity\n",
+    ")"
    ]
   },
   {
@@ -301,7 +303,7 @@
    "outputs": [],
    "source": [
     "source = td.UniformCurrentSource(\n",
-    "    center=(0, 0, -sim_length/2.5),\n",
+    "    center=(0, 0, -sim_length / 2.5),\n",
     "    size=(td.inf, td.inf, 0),\n",
     "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
     "    polarization=\"Ex\",\n",
@@ -372,7 +374,7 @@
     "    grid_spec=grid_spec,\n",
     "    medium=medium_gyrotropic,\n",
     "    sources=[source],\n",
-    "    monitors=[ mnt_xz],\n",
+    "    monitors=[mnt_xz],\n",
     "    run_time=run_time,\n",
     "    boundary_spec=boundary_spec,\n",
     ")"
@@ -1114,8 +1116,18 @@
     }
    ],
    "source": [
-    "data_unperturbed = web.run(simulation=sim_unperturbed, task_name=\"gyrotropic_reference\", path=\"data/simulation_data_gyrotropic_ref.hdf5\", verbose=True)\n",
-    "data_gyrotropic = web.run(simulation=sim_gyrotropic, task_name=\"gyrotropic\", path=\"data/simulation_data_gyrotropic.hdf5\", verbose=True)"
+    "data_unperturbed = web.run(\n",
+    "    simulation=sim_unperturbed,\n",
+    "    task_name=\"gyrotropic_reference\",\n",
+    "    path=\"data/simulation_data_gyrotropic_ref.hdf5\",\n",
+    "    verbose=True,\n",
+    ")\n",
+    "data_gyrotropic = web.run(\n",
+    "    simulation=sim_gyrotropic,\n",
+    "    task_name=\"gyrotropic\",\n",
+    "    path=\"data/simulation_data_gyrotropic.hdf5\",\n",
+    "    verbose=True,\n",
+    ")"
    ]
   },
   {
@@ -1178,9 +1190,7 @@
    "source": [
     "# reference (constant polarization) wave propagation\n",
     "Ex_ref = (\n",
-    "    data_unperturbed[\"freq_mnt_xz\"]\n",
-    "    .field_components[\"Ex\"]\n",
-    "    .isel({\"f\": freq_ind, \"x\": 0, \"y\": 0})\n",
+    "    data_unperturbed[\"freq_mnt_xz\"].field_components[\"Ex\"].isel({\"f\": freq_ind, \"x\": 0, \"y\": 0})\n",
     ")\n",
     "\n",
     "# predicted rotatory power at chosen wavelength\n",
@@ -1223,17 +1233,9 @@
    },
    "outputs": [],
    "source": [
-    "Ex_num = (\n",
-    "    data_gyrotropic[\"freq_mnt_xz\"]\n",
-    "    .field_components[\"Ex\"]\n",
-    "    .isel({\"f\": freq_ind, \"x\": 0, \"y\": 0})\n",
-    ")\n",
+    "Ex_num = data_gyrotropic[\"freq_mnt_xz\"].field_components[\"Ex\"].isel({\"f\": freq_ind, \"x\": 0, \"y\": 0})\n",
     "\n",
-    "Ey_num = (\n",
-    "    data_gyrotropic[\"freq_mnt_xz\"]\n",
-    "    .field_components[\"Ey\"]\n",
-    "    .isel({\"f\": freq_ind, \"x\": 0, \"y\": 0})\n",
-    ")"
+    "Ey_num = data_gyrotropic[\"freq_mnt_xz\"].field_components[\"Ey\"].isel({\"f\": freq_ind, \"x\": 0, \"y\": 0})"
    ]
   },
   {
@@ -1269,7 +1271,7 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(2, 1, figsize=(8,6))\n",
+    "fig, ax = plt.subplots(2, 1, figsize=(8, 6))\n",
     "Ex_num.real.plot(ax=ax[0])\n",
     "Ex_theory.real.plot(ax=ax[0], ls=\"--\")\n",
     "ax[0].set_ylabel(\"Re[Ex]\")\n",
@@ -1322,9 +1324,7 @@
     "\n",
     "# reference (constant polarization) wave propagation\n",
     "Ex_ref = (\n",
-    "    data_unperturbed[\"freq_mnt_xz\"]\n",
-    "    .field_components[\"Ex\"]\n",
-    "    .isel({\"f\": freq_ind, \"x\": 0, \"y\": 0})\n",
+    "    data_unperturbed[\"freq_mnt_xz\"].field_components[\"Ex\"].isel({\"f\": freq_ind, \"x\": 0, \"y\": 0})\n",
     ")\n",
     "\n",
     "# predicted rotatory power at chosen wavelength\n",
@@ -1345,20 +1345,12 @@
     "Ey_theory = Ex_ref * factor_y\n",
     "\n",
     "# extract simulation data at the chosen frequency\n",
-    "Ex_num = (\n",
-    "    data_gyrotropic[\"freq_mnt_xz\"]\n",
-    "    .field_components[\"Ex\"]\n",
-    "    .isel({\"f\": freq_ind, \"x\": 0, \"y\": 0})\n",
-    ")\n",
+    "Ex_num = data_gyrotropic[\"freq_mnt_xz\"].field_components[\"Ex\"].isel({\"f\": freq_ind, \"x\": 0, \"y\": 0})\n",
     "\n",
-    "Ey_num = (\n",
-    "    data_gyrotropic[\"freq_mnt_xz\"]\n",
-    "    .field_components[\"Ey\"]\n",
-    "    .isel({\"f\": freq_ind, \"x\": 0, \"y\": 0})\n",
-    ")\n",
+    "Ey_num = data_gyrotropic[\"freq_mnt_xz\"].field_components[\"Ey\"].isel({\"f\": freq_ind, \"x\": 0, \"y\": 0})\n",
     "\n",
     "# plot comparison\n",
-    "fig, ax = plt.subplots(2, 1, figsize=(8,6))\n",
+    "fig, ax = plt.subplots(2, 1, figsize=(8, 6))\n",
     "Ex_num.real.plot(ax=ax[0])\n",
     "Ex_theory.real.plot(ax=ax[0], ls=\"--\")\n",
     "ax[0].set_ylabel(\"Re[Ex]\")\n",
diff --git a/HeatDissipationSOI.ipynb b/HeatDissipationSOI.ipynb
index 43596513..87b43e1d 100644
--- a/HeatDissipationSOI.ipynb
+++ b/HeatDissipationSOI.ipynb
@@ -30,12 +30,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import gdstk\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import tidy3d as td\n",
-    "import gdstk\n",
-    "\n",
-    "import tidy3d.web as web\n",
-    "import matplotlib.pyplot as plt"
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -72,7 +71,7 @@
     "oxide_bottom_thickness = 2\n",
     "oxide_top_thickness = 1\n",
     "\n",
-    "inf = 1000 # large number for making structures extend effectively to infinity"
+    "inf = 1000  # large number for making structures extend effectively to infinity"
    ]
   },
   {
@@ -85,17 +84,20 @@
     "# create geometries\n",
     "arm_geo = td.Box(size=(heat_length, arm_width, arm_thickness))\n",
     "\n",
-    "heat_geo = td.Box(center=(0, 0, heat_height + heat_thickness/2),\n",
-    "                     size=(heat_length, heat_width, heat_thickness))\n",
+    "heat_geo = td.Box(\n",
+    "    center=(0, 0, heat_height + heat_thickness / 2), size=(heat_length, heat_width, heat_thickness)\n",
+    ")\n",
     "# translate copies of each heater along their corresponding distance\n",
     "heat_geos = [heat_geo]\n",
-    "for i in range(len(heat_offsets)-1):\n",
-    "    dif = heat_offsets[i]-heat_offsets[i+1]\n",
+    "for i in range(len(heat_offsets) - 1):\n",
+    "    dif = heat_offsets[i] - heat_offsets[i + 1]\n",
     "    heat_geos.append(heat_geos[i].translated(x=0, y=dif, z=0))\n",
     "\n",
-    "oxide_geo = td.Box.from_bounds(rmin=(-inf, -inf, -2-arm_thickness/2), rmax=(inf, inf, heat_height))\n",
+    "oxide_geo = td.Box.from_bounds(\n",
+    "    rmin=(-inf, -inf, -2 - arm_thickness / 2), rmax=(inf, inf, heat_height)\n",
+    ")\n",
     "\n",
-    "si_bottom_geo = td.Box.from_bounds(rmin=(-inf, -inf, -inf), rmax=(inf, inf, -2-arm_thickness/2))"
+    "si_bottom_geo = td.Box.from_bounds(rmin=(-inf, -inf, -inf), rmax=(inf, inf, -2 - arm_thickness / 2))"
    ]
   },
   {
@@ -120,7 +122,7 @@
     "# material properties\n",
     "Si_n = 3.4777  # Si refraction index\n",
     "Si_dn_dT = 1.86e-4  # Si thermo-optic coefficient dn/dT, 1/K\n",
-    "Si_s = 0.711 # Si specific heat, J / (kg * K)\n",
+    "Si_s = 0.711  # Si specific heat, J / (kg * K)\n",
     "Si_k = 148e-6  # Si thermal conductivity, W / (um * K)\n",
     "\n",
     "SiO2_n = 1.444  # SiO2 refraction index\n",
@@ -132,7 +134,7 @@
     "TiN_k = 28e-6  # TiN thermal conductivity W/(um*K)\n",
     "\n",
     "Si = td.PerturbationMedium(\n",
-    "    permittivity=Si_n ** 2,\n",
+    "    permittivity=Si_n**2,\n",
     "    perturbation_spec=td.IndexPerturbation(\n",
     "        delta_n=td.ParameterPerturbation(\n",
     "            heat=td.LinearHeatPerturbation(coeff=Si_dn_dT, temperature_ref=300)\n",
@@ -147,7 +149,7 @@
     ")\n",
     "\n",
     "SiO2 = td.PerturbationMedium(\n",
-    "    permittivity=SiO2_n ** 2,\n",
+    "    permittivity=SiO2_n**2,\n",
     "    perturbation_spec=td.IndexPerturbation(\n",
     "        delta_n=td.ParameterPerturbation(\n",
     "            heat=td.LinearHeatPerturbation(coeff=SiO2_dn_dT, temperature_ref=300)\n",
@@ -200,7 +202,9 @@
    "source": [
     "oxide = td.Structure(geometry=oxide_geo, medium=SiO2, name=\"BOX\")\n",
     "arm = td.Structure(geometry=arm_geo, medium=Si, name=\"MZI\")\n",
-    "heaters = [td.Structure(geometry=heat_geo, medium=TiN, name=str(i)) for i, heat_geo in enumerate(heat_geos)]\n",
+    "heaters = [\n",
+    "    td.Structure(geometry=heat_geo, medium=TiN, name=str(i)) for i, heat_geo in enumerate(heat_geos)\n",
+    "]\n",
     "si_bottom = td.Structure(geometry=si_bottom_geo, medium=Si, name=\"Si_bottom\")\n",
     "\n",
     "scene = td.Scene(\n",
@@ -236,7 +240,7 @@
    "source": [
     "heater_volume = heat_thickness * heat_width * heat_length\n",
     "\n",
-    "heat_watts = 13e-3 # heat given in paper as 13 mW\n",
+    "heat_watts = 13e-3  # heat given in paper as 13 mW\n",
     "heat_rate = heat_watts / heater_volume\n",
     "heat_sources = [td.HeatSource(rate=heat_rate, structures=[heater.name]) for heater in heaters]"
    ]
@@ -256,10 +260,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "temp = td.TemperatureMonitor(size=(0, 0, 0),\n",
-    "                             name=\"t\",\n",
-    "                             unstructured=True,\n",
-    "                             conformal=True)"
+    "temp = td.TemperatureMonitor(size=(0, 0, 0), name=\"t\", unstructured=True, conformal=True)"
    ]
   },
   {
@@ -303,7 +304,7 @@
     "    dl_interface=dl_min,\n",
     "    dl_bulk=4 * dl_min,\n",
     "    distance_interface=3 * dl_min,\n",
-    "    distance_bulk=2 * heat_height\n",
+    "    distance_bulk=2 * heat_height,\n",
     ")"
    ]
   },
@@ -332,14 +333,14 @@
    "source": [
     "def create_sim(heat_index, symmetry):\n",
     "    heat_sim = td.HeatChargeSimulation(\n",
-    "        size=(0, 6 + heat_index*heat_width, 6),\n",
+    "        size=(0, 6 + heat_index * heat_width, 6),\n",
     "        boundary_spec=[bc_bottom],\n",
     "        structures=[oxide, arm, heaters[heat_index], si_bottom],\n",
     "        sources=[heat_sources[heat_index]],\n",
     "        monitors=[temp],\n",
     "        symmetry=symmetry,\n",
     "        grid_spec=grid_spec,\n",
-    "        medium=air\n",
+    "        medium=air,\n",
     "    )\n",
     "    return heat_sim"
    ]
@@ -353,9 +354,9 @@
    "source": [
     "heat_sims_1 = {}\n",
     "for i in range(len(heat_sources)):\n",
-    "    symmetry = (0,0,0)\n",
+    "    symmetry = (0, 0, 0)\n",
     "    if i == 0:\n",
-    "        symmetry = (0,1,0)\n",
+    "        symmetry = (0, 1, 0)\n",
     "    heat_sims_1[str(i)] = create_sim(i, symmetry)"
    ]
   },
@@ -378,7 +379,7 @@
    ],
    "source": [
     "# check simulations\n",
-    "fig, ax = plt.subplots(1, 2, tight_layout=True, figsize=(8,6))\n",
+    "fig, ax = plt.subplots(1, 2, tight_layout=True, figsize=(8, 6))\n",
     "\n",
     "heat_sims_1[\"0\"].plot(x=0, ax=ax[0])\n",
     "ax[0].set_title(\"0-offset simulation\")\n",
@@ -595,7 +596,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "temps_1 = np.squeeze([(batch_1_data[str(i)][\"t\"].temperature - 300) for i in range(len(heat_sources))])"
+    "temps_1 = np.squeeze(\n",
+    "    [(batch_1_data[str(i)][\"t\"].temperature - 300) for i in range(len(heat_sources))]\n",
+    ")"
    ]
   },
   {
@@ -662,6 +665,7 @@
     "    condition=td.TemperatureBC(temperature=300),\n",
     ")\n",
     "\n",
+    "\n",
     "def make_stack(dS, si_thick, box_thick, top_thick):\n",
     "    # define stack geometry\n",
     "    inf = 1000\n",
@@ -670,37 +674,41 @@
     "    heat_width = 2\n",
     "\n",
     "    # define silicon waveguide and surrounding silicon geometries based on separation distances\n",
-    "    si_geos = [td.Box(center=(-dS/2, 0, 0), size=(wg_width, inf, si_thick)),\n",
-    "               td.Box.from_bounds(\n",
-    "                   rmin=((dS + wg_width)/2 + si_spacing, 0, -si_thick/2),\n",
-    "                   rmax=(inf, inf, si_thick/2)\n",
-    "               )]\n",
+    "    si_geos = [\n",
+    "        td.Box(center=(-dS / 2, 0, 0), size=(wg_width, inf, si_thick)),\n",
+    "        td.Box.from_bounds(\n",
+    "            rmin=((dS + wg_width) / 2 + si_spacing, 0, -si_thick / 2), rmax=(inf, inf, si_thick / 2)\n",
+    "        ),\n",
+    "    ]\n",
     "    geo_group_R = td.GeometryGroup(geometries=si_geos)\n",
     "    geo_group = [geo_group_R.rotated(np.pi, axis=1), geo_group_R]\n",
     "    if dS > 2 * si_spacing + wg_width:\n",
-    "        geo_group.append(td.Box(size=(dS - wg_width - 2*si_spacing, inf, si_thick)))\n",
+    "        geo_group.append(td.Box(size=(dS - wg_width - 2 * si_spacing, inf, si_thick)))\n",
     "    si_geo = td.GeometryGroup(geometries=geo_group)\n",
     "\n",
     "    # geometry of the buried oxide layer\n",
     "    oxide_geo = td.Box.from_bounds(\n",
-    "        rmin=(-inf, -inf, -si_thick/2 - box_thick),\n",
-    "        rmax=(inf, inf, si_thick/2 + top_thick)\n",
+    "        rmin=(-inf, -inf, -si_thick / 2 - box_thick), rmax=(inf, inf, si_thick / 2 + top_thick)\n",
     "    )\n",
-    "    \n",
+    "\n",
     "    # geometry of the silicon substrate\n",
-    "    si_bottom_geo = td.Box.from_bounds(rmin=(-inf, -inf, -inf), rmax=(inf, inf, si_thick/2 - box_thick))\n",
-    "    \n",
+    "    si_bottom_geo = td.Box.from_bounds(\n",
+    "        rmin=(-inf, -inf, -inf), rmax=(inf, inf, si_thick / 2 - box_thick)\n",
+    "    )\n",
+    "\n",
     "    # geometry of the heating element\n",
     "    heat_geo = td.Box(\n",
-    "        center=(-dS/2, 0, si_thick/2 + top_thick + heat_thickness/2),\n",
-    "        size=(heat_width, heat_length, heat_thickness)\n",
+    "        center=(-dS / 2, 0, si_thick / 2 + top_thick + heat_thickness / 2),\n",
+    "        size=(heat_width, heat_length, heat_thickness),\n",
     "    )\n",
     "\n",
     "    # add materials to corresponding layer geometries\n",
-    "    si = td.Structure(geometry=si_geo.translated(x=-dS/2, y=0, z=0), medium=Si, name=\"si\")\n",
+    "    si = td.Structure(geometry=si_geo.translated(x=-dS / 2, y=0, z=0), medium=Si, name=\"si\")\n",
     "    oxide = td.Structure(geometry=oxide_geo, medium=SiO2, name=\"oxide\")\n",
     "    si_bottom = td.Structure(geometry=si_bottom_geo, medium=Si, name=\"si bottom\")\n",
-    "    heater = td.Structure(geometry=heat_geo.translated(x=-dS/2, y=0, z=0), medium=TiN, name=\"heater\")\n",
+    "    heater = td.Structure(\n",
+    "        geometry=heat_geo.translated(x=-dS / 2, y=0, z=0), medium=TiN, name=\"heater\"\n",
+    "    )\n",
     "\n",
     "    # Add mesh\n",
     "    dl_min = heat_thickness / 3\n",
@@ -708,13 +716,13 @@
     "        dl_interface=dl_min,\n",
     "        dl_bulk=4 * dl_min,\n",
     "        distance_interface=3 * dl_min,\n",
-    "        distance_bulk=2 * top_thick\n",
+    "        distance_bulk=2 * top_thick,\n",
     "    )\n",
     "\n",
     "    # add symmetry if there is no offset (the reference and heated waveguide are the same)\n",
-    "    symmetry = (0,0,0)\n",
+    "    symmetry = (0, 0, 0)\n",
     "    if dS == 0:\n",
-    "        symmetry=(1,0,0)\n",
+    "        symmetry = (1, 0, 0)\n",
     "\n",
     "    # record temperature at cross-section through the waveguide's center\n",
     "    sim_center_x = -5\n",
@@ -726,21 +734,21 @@
     "        size=(sim_size_x, 0, 0),\n",
     "        name=\"temp_line_profile\",\n",
     "        unstructured=True,\n",
-    "        conformal=True\n",
+    "        conformal=True,\n",
     "    )\n",
     "\n",
     "    # create corresponding heat simulation\n",
     "    sim_padding_z = 1\n",
     "    sim = td.HeatChargeSimulation(\n",
     "        center=(sim_center_x, 0, 0),\n",
-    "        size=(sim_size_x, 0, si_thick+box_thick+top_thick+2*sim_padding_z),\n",
+    "        size=(sim_size_x, 0, si_thick + box_thick + top_thick + 2 * sim_padding_z),\n",
     "        medium=air,\n",
     "        structures=[si_bottom, oxide, si, heater],\n",
     "        sources=[heat_source],\n",
     "        boundary_spec=[bc_bottom],\n",
     "        monitors=[line_monitor],\n",
     "        grid_spec=grid_spec,\n",
-    "        symmetry=symmetry\n",
+    "        symmetry=symmetry,\n",
     "    )\n",
     "    return sim"
    ]
@@ -797,7 +805,7 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(2, 1, tight_layout=True, figsize=(8,4))\n",
+    "fig, ax = plt.subplots(2, 1, tight_layout=True, figsize=(8, 4))\n",
     "\n",
     "SOIs[\"0.22-3\"].plot(y=0, ax=ax[0])\n",
     "ax[0].set_title(\"220nm thick Si, 3 μm distance\")\n",
@@ -1012,15 +1020,22 @@
    ],
    "source": [
     "import pandas as pd\n",
-    "paper_data = pd.read_excel('misc/heat_dissipation_SOI.xlsx')\n",
+    "\n",
+    "paper_data = pd.read_excel(\"misc/heat_dissipation_SOI.xlsx\")\n",
     "\n",
     "fig, ax = plt.subplots()\n",
     "for dS in dSs:\n",
     "    SOI_data = np.squeeze(batch_2_data[f\"0.22-{dS}\"][\"temp_line_profile\"].temperature - 300)\n",
-    "    line, = plt.plot(SOI_data.x, SOI_data, label=\"Tidy3D, \"+str(dS)+\"μm-offset\")\n",
+    "    (line,) = plt.plot(SOI_data.x, SOI_data, label=\"Tidy3D, \" + str(dS) + \"μm-offset\")\n",
     "    coords = paper_data[f\"Distance (um) {dS}um separation\"]\n",
     "    temps = paper_data[f\"Temp. Change (°C) {dS}um separation\"]\n",
-    "    plt.scatter(coords[::5]*1e6, temps[::5], label=\"Paper, \"+str(dS)+\"μm-offset\", marker='.', color=line.get_color())\n",
+    "    plt.scatter(\n",
+    "        coords[::5] * 1e6,\n",
+    "        temps[::5],\n",
+    "        label=\"Paper, \" + str(dS) + \"μm-offset\",\n",
+    "        marker=\".\",\n",
+    "        color=line.get_color(),\n",
+    "    )\n",
     "plt.legend()\n",
     "plt.xlabel(\"Coordinates\")\n",
     "plt.ylabel(\"Heat change (°C)\")\n",
@@ -1061,9 +1076,9 @@
     "        temp_data = batch_2_data[f\"{si_thickness[0]}-{dS}\"][\"temp_line_profile\"].temperature\n",
     "        t_ref = np.squeeze(temp_data.sel(x=0, method=\"nearest\") - 300)\n",
     "        t_heat = np.squeeze(temp_data.sel(x=-dS, method=\"nearest\") - 300)\n",
-    "        thickness_plot.append((t_ref-t_heat)/t_heat*100)\n",
-    "    plt.plot(dSs, thickness_plot, label=str(si_thickness[0])+\"μm thickness\")\n",
-    "plt.axhline(y=-90, color='gray', linestyle='--', label=\"-90% cutoff\")\n",
+    "        thickness_plot.append((t_ref - t_heat) / t_heat * 100)\n",
+    "    plt.plot(dSs, thickness_plot, label=str(si_thickness[0]) + \"μm thickness\")\n",
+    "plt.axhline(y=-90, color=\"gray\", linestyle=\"--\", label=\"-90% cutoff\")\n",
     "plt.xlabel(\"Separation of waveguides (μm)\")\n",
     "plt.ylabel(\"Temperature change (%)\")\n",
     "plt.legend()\n",
@@ -1104,7 +1119,7 @@
    "language": "python",
    "name": "python3"
   },
-  "keyworkds": "silicon, SOI, Tidy3D, heat",
+  "keywords": "silicon, SOI, Tidy3D, heat",
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -1115,7 +1130,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.11.0"
   },
   "title": "How to Model Heat Dissipation and Thermal Crosstalk in SOI Devices in Tidy3D"
  },
diff --git a/HeatSolver.ipynb b/HeatSolver.ipynb
index 582f2132..ffb3f1c5 100644
--- a/HeatSolver.ipynb
+++ b/HeatSolver.ipynb
@@ -37,12 +37,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "import xarray as xr\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n"
+    "import tidy3d.web as web\n",
+    "import xarray as xr"
    ]
   },
   {
@@ -73,7 +72,7 @@
     "source1 = 1000  # J / (um^3 * K)\n",
     "source2 = 300  # J / (um^3 * K)\n",
     "temperature1 = 400  # K\n",
-    "temperature2 = 300  # K\n"
+    "temperature2 = 300  # K"
    ]
   },
   {
@@ -110,7 +109,7 @@
     "background_medium = td.Medium(\n",
     "    heat_spec=td.FluidSpec(),\n",
     "    name=\"fluid\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -144,7 +143,7 @@
     "    geometry=td.Box(center=(0, 0, 0), size=(2 * r1, td.inf, td.inf)),\n",
     "    medium=background_medium,\n",
     "    name=\"core\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -165,7 +164,7 @@
     "scene_planar = td.Scene(\n",
     "    structures=[planar_top_layer, planar_bottom_layer, planar_core],\n",
     "    medium=background_medium,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -195,7 +194,7 @@
    ],
    "source": [
     "scene_planar.plot(z=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -225,7 +224,7 @@
    ],
    "source": [
     "scene_planar.plot(z=0, vlim=[-0.1, 0.1], hlim=[-1, 1])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -251,8 +250,9 @@
     "\n",
     "# create a cylinder in STL representation using trimesh\n",
     "import trimesh\n",
+    "\n",
     "# number of sections along circumference corresponding to resolution 0.2\n",
-    "num_sections = int(np.ceil(2 * np.pi * r0 / 0.02))  \n",
+    "num_sections = int(np.ceil(2 * np.pi * r0 / 0.02))\n",
     "cylinder_mesh = trimesh.creation.cylinder(radius=r0, height=1, sections=num_sections)\n",
     "\n",
     "cyl_bottom_layer = td.Structure(\n",
@@ -270,7 +270,7 @@
     "scene_cyl = td.Scene(\n",
     "    structures=[cyl_top_layer, cyl_bottom_layer, cyl_core],\n",
     "    medium=background_medium,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -301,7 +301,7 @@
     "scene_sphere = td.Scene(\n",
     "    structures=[sphere_top_layer, sphere_bottom_layer, sphere_core],\n",
     "    medium=background_medium,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -351,7 +351,7 @@
     "scene_cyl.plot(z=0, ax=ax[0])\n",
     "scene_sphere.plot_heat_conductivity(z=0, ax=ax[1])\n",
     "plt.tight_layout()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -386,7 +386,7 @@
     "cyl_sim_size = (2, 2, 0.1)\n",
     "\n",
     "sphere_sim_center = (0, 0, 0)\n",
-    "sphere_sim_size = (2, 2, 2)\n"
+    "sphere_sim_size = (2, 2, 2)"
    ]
   },
   {
@@ -427,7 +427,7 @@
     "bc_bottom = td.HeatBoundarySpec(\n",
     "    condition=td.TemperatureBC(temperature=temperature1),\n",
     "    placement=td.StructureStructureInterface(structures=[\"core\", \"bottom_layer\"]),\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -456,7 +456,7 @@
    "outputs": [],
    "source": [
     "source_bottom = td.HeatSource(structures=[\"bottom_layer\"], rate=source1)\n",
-    "source_top = td.HeatSource(structures=[\"top_layer\"], rate=source2)\n"
+    "source_top = td.HeatSource(structures=[\"top_layer\"], rate=source2)"
    ]
   },
   {
@@ -478,7 +478,7 @@
    "source": [
     "temp_mnt = td.TemperatureMonitor(\n",
     "    center=(0, 0, 0), size=(td.inf, td.inf, td.inf), name=\"temperature\"\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -530,7 +530,7 @@
     "    grid_spec=td.UniformUnstructuredGrid(dl=0.02),\n",
     "    monitors=[temp_mnt],\n",
     "    symmetry=(1, 1, 1),\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -874,7 +874,7 @@
     }
    ],
    "source": [
-    "heat_sim_data_planar = web.run(simulation=heat_sim_planar, task_name=\"heat_sim_planar\")\n"
+    "heat_sim_data_planar = web.run(simulation=heat_sim_planar, task_name=\"heat_sim_planar\")"
    ]
   },
   {
@@ -1199,7 +1199,7 @@
    "source": [
     "job = web.Job(simulation=heat_sim_cyl, task_name=\"heat_sim_cyl\")\n",
     "estimate_cost = job.estimate_cost()\n",
-    "heat_sim_data_cyl = job.run()\n"
+    "heat_sim_data_cyl = job.run()"
    ]
   },
   {
@@ -1219,10 +1219,11 @@
    "source": [
     "dl_refine = [0.04, 0.02, 0.01, 0.005]\n",
     "heat_sim_sphere_refine = {\n",
-    "    \"heat_sim_sphere_\"\n",
-    "    + str(dl): heat_sim_sphere.updated_copy(grid_spec=td.UniformUnstructuredGrid(dl=dl))\n",
+    "    \"heat_sim_sphere_\" + str(dl): heat_sim_sphere.updated_copy(\n",
+    "        grid_spec=td.UniformUnstructuredGrid(dl=dl)\n",
+    "    )\n",
     "    for dl in dl_refine\n",
-    "}\n"
+    "}"
    ]
   },
   {
@@ -1361,7 +1362,7 @@
    "source": [
     "batch = web.Batch(simulations=heat_sim_sphere_refine)\n",
     "batch_data = batch.run()\n",
-    "heat_sim_data_sphere = batch_data[\"heat_sim_sphere_0.02\"]\n"
+    "heat_sim_data_sphere = batch_data[\"heat_sim_sphere_0.02\"]"
    ]
   },
   {
@@ -1411,7 +1412,7 @@
     "heat_sim_data_sphere.plot_field(\"temperature\", z=0, ax=ax[2])\n",
     "\n",
     "plt.tight_layout()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1473,7 +1474,7 @@
     "    lambda r: 1,\n",
     "    lambda r: 1.0 / r,\n",
     "    lambda r: 1.0 / r**2,\n",
-    "]\n"
+    "]"
    ]
   },
   {
@@ -1512,7 +1513,7 @@
     "\n",
     "    A1, B1, B2, A2 = np.linalg.solve(mat, rhs)\n",
     "\n",
-    "    return A1, B1, B2, A2\n"
+    "    return A1, B1, B2, A2"
    ]
   },
   {
@@ -1544,7 +1545,7 @@
     "    T1 = -0.5 / N * source1 / conductivity1 * x1**2 + A1 * u[N - 1](np.abs(x1)) + B1\n",
     "    T2 = -0.5 / N * source2 / conductivity2 * x2**2 + A2 * u[N - 1](np.abs(x2)) + B2\n",
     "\n",
-    "    return np.concatenate((x1, x2)), np.concatenate((T1, T2))\n"
+    "    return np.concatenate((x1, x2)), np.concatenate((T1, T2))"
    ]
   },
   {
@@ -1569,7 +1570,7 @@
     "x_exact_cyl, temperature_exact_cyl = get_analytical(2, x)\n",
     "\n",
     "x = heat_sim_data_sphere[\"temperature\"].temperature.coords[\"x\"]\n",
-    "x_exact_sphere, temperature_exact_sphere = get_analytical(3, x)\n"
+    "x_exact_sphere, temperature_exact_sphere = get_analytical(3, x)"
    ]
   },
   {
@@ -1601,28 +1602,22 @@
     "fig, ax = plt.subplots(1, 3, figsize=(10, 3))\n",
     "\n",
     "ax[0].plot(x_exact_planar, temperature_exact_planar, \".\")\n",
-    "heat_sim_data_planar[\"temperature\"].temperature.sel(z=0, y=0, method=\"nearest\").plot(\n",
-    "    ax=ax[0]\n",
-    ")\n",
+    "heat_sim_data_planar[\"temperature\"].temperature.sel(z=0, y=0, method=\"nearest\").plot(ax=ax[0])\n",
     "ax[0].legend([\"numerical\", \"analytical\"])\n",
     "ax[0].set_ylim([300, 400])\n",
     "\n",
     "ax[1].plot(x_exact_cyl, temperature_exact_cyl, \".\")\n",
-    "heat_sim_data_cyl[\"temperature\"].temperature.sel(z=0, y=0, method=\"nearest\").plot(\n",
-    "    ax=ax[1]\n",
-    ")\n",
+    "heat_sim_data_cyl[\"temperature\"].temperature.sel(z=0, y=0, method=\"nearest\").plot(ax=ax[1])\n",
     "ax[1].legend([\"numerical\", \"analytical\"])\n",
     "ax[1].set_ylim([300, 400])\n",
     "\n",
     "ax[2].plot(x_exact_sphere, temperature_exact_sphere, \".\")\n",
-    "heat_sim_data_sphere[\"temperature\"].temperature.sel(z=0, y=0, method=\"nearest\").plot(\n",
-    "    ax=ax[2]\n",
-    ")\n",
+    "heat_sim_data_sphere[\"temperature\"].temperature.sel(z=0, y=0, method=\"nearest\").plot(ax=ax[2])\n",
     "ax[2].legend([\"numerical\", \"analytical\"])\n",
     "ax[2].set_ylim([300, 400])\n",
     "\n",
     "plt.tight_layout()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1656,9 +1651,7 @@
     "for ind, (_, sim_data) in enumerate(batch_data.items()):\n",
     "    x = sim_data[\"temperature\"].temperature.coords[\"x\"]\n",
     "    x_exact_sphere, temperature_exact_sphere = get_analytical(3, x)\n",
-    "    temperature_numerical = sim_data[\"temperature\"].temperature.interp(\n",
-    "        z=0, y=0, x=x_exact_sphere\n",
-    "    )\n",
+    "    temperature_numerical = sim_data[\"temperature\"].temperature.interp(z=0, y=0, x=x_exact_sphere)\n",
     "    error = (temperature_numerical - temperature_exact_sphere).abs\n",
     "    errors.append(np.max(error))\n",
     "    ax[0].scatter(x_exact_sphere, error, label=f\"dl = {dl_refine[ind]}\")\n",
@@ -1680,7 +1673,7 @@
     "ax[1].legend()\n",
     "\n",
     "plt.tight_layout()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
diff --git a/HighQGe.ipynb b/HighQGe.ipynb
index 58fe87aa..2bf02e43 100644
--- a/HighQGe.ipynb
+++ b/HighQGe.ipynb
@@ -41,12 +41,12 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3D import\n",
     "import tidy3d as td\n",
-    "from tidy3d import web\n"
+    "from tidy3d import web"
    ]
   },
   {
@@ -81,7 +81,7 @@
     "n_BaF2 = 1.45\n",
     "n_Ge = 4\n",
     "BaF2 = td.Medium(permittivity=n_BaF2**2)\n",
-    "Ge = td.Medium(permittivity=n_Ge**2)\n"
+    "Ge = td.Medium(permittivity=n_Ge**2)"
    ]
   },
   {
@@ -110,7 +110,7 @@
     "w1 = w2 = w = 1.265\n",
     "\n",
     "# resolution (should be commensurate with periodicity)\n",
-    "dl = P / 32\n"
+    "dl = P / 32"
    ]
   },
   {
@@ -160,7 +160,7 @@
     "    ),\n",
     "    medium=Ge,\n",
     "    name=\"cell2\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -191,7 +191,7 @@
     ")\n",
     "\n",
     "# Simulation run time.  Note you need to run a long time to calculate high Q resonances.\n",
-    "run_time = 8e-11\n"
+    "run_time = 8e-11"
    ]
   },
   {
@@ -215,7 +215,7 @@
     "    size=[td.inf, td.inf, 0],\n",
     "    freqs=freqs,\n",
     "    name=\"flux\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -280,7 +280,7 @@
     "    boundary_spec=td.BoundarySpec(\n",
     "        x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
     "    ),\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -320,7 +320,7 @@
     "sim_actual.plot_eps(x=0, ax=ax1)\n",
     "sim_actual.plot_eps(y=-0.1, ax=ax2)\n",
     "sim_actual.plot_eps(z=0.1, ax=ax3)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -570,7 +570,7 @@
    "source": [
     "# run all simulations, take about 2-3 minutes each with some download time\n",
     "batch = web.Batch(simulations={\"norm\": sim_empty, \"actual\": sim_actual}, verbose=True)\n",
-    "batch_data = batch.run(path_dir=\"data\")\n"
+    "batch_data = batch.run(path_dir=\"data\")"
    ]
   },
   {
@@ -702,7 +702,7 @@
    "source": [
     "batch_data = batch.load(path_dir=\"data\")\n",
     "transmission = batch_data[\"actual\"][\"flux\"].flux / batch_data[\"norm\"][\"flux\"].flux\n",
-    "reflection = 1 - transmission\n"
+    "reflection = 1 - transmission"
    ]
   },
   {
@@ -740,12 +740,12 @@
     "fig, ax = plt.subplots(1, 1, figsize=(6, 4.5))\n",
     "plt.plot(wavelengths, reflection, \"k\", label=\"R\")\n",
     "plt.plot(wavelengths, transmission, \"r--\", label=\"T\")\n",
-    "plt.xlabel(\"wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Magnitude\")\n",
     "plt.xlim([8.8, 12])\n",
     "plt.ylim([0.0, 1.0])\n",
     "plt.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
diff --git a/HighQSi.ipynb b/HighQSi.ipynb
index 4fd8f366..2fbb4cb8 100644
--- a/HighQSi.ipynb
+++ b/HighQSi.ipynb
@@ -38,12 +38,12 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3D import\n",
     "import tidy3d as td\n",
-    "from tidy3d import web\n"
+    "from tidy3d import web"
    ]
   },
   {
@@ -82,7 +82,7 @@
     "n_SiO2 = 1.46\n",
     "n_Si = 3.52\n",
     "SiO2 = td.Medium(permittivity=n_SiO2**2)\n",
-    "Si = td.Medium(permittivity=n_Si**2)\n"
+    "Si = td.Medium(permittivity=n_Si**2)"
    ]
   },
   {
@@ -112,6 +112,7 @@
     "# resolution (should be commensurate with periodicity)\n",
     "dl = P / 32\n",
     "\n",
+    "\n",
     "# computes widths in y (w1 and w2) given the difference in lengths in y and the sum of lengths\n",
     "def calc_ws(delta):\n",
     "    \"\"\"delta is a tunable parameter used to break symmetry.\n",
@@ -121,7 +122,7 @@
     "    \"\"\"\n",
     "    w1 = (w_sum + delta) / 2\n",
     "    w2 = w_sum - w1\n",
-    "    return w1, w2\n"
+    "    return w1, w2"
    ]
   },
   {
@@ -152,6 +153,7 @@
     "    name=\"substrate\",\n",
     ")\n",
     "\n",
+    "\n",
     "# creates a list of structures given a value of 'delta'\n",
     "def geometry(delta):\n",
     "    w1, w2 = calc_ws(delta)\n",
@@ -175,7 +177,7 @@
     "        name=\"cell2\",\n",
     "    )\n",
     "\n",
-    "    return [substrate, cell1, cell2]\n"
+    "    return [substrate, cell1, cell2]"
    ]
   },
   {
@@ -205,7 +207,7 @@
     ")\n",
     "\n",
     "# Simulation run time.  Note you need to run a long time to calculate high Q resonances.\n",
-    "run_time = 7e-12\n"
+    "run_time = 7e-12"
    ]
   },
   {
@@ -228,7 +230,7 @@
     "    size=[td.inf, td.inf, 0],\n",
     "    freqs=freqs,\n",
     "    name=\"flux\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -302,7 +304,7 @@
     "    boundary_spec=td.BoundarySpec(\n",
     "        x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
     "    ),\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -336,7 +338,7 @@
     "sim_d0.plot_eps(x=0, ax=ax1)\n",
     "sim_d0.plot_eps(y=g, ax=ax2)\n",
     "sim_d0.plot_eps(z=0, ax=ax3)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -657,7 +659,7 @@
     "    verbose=True,\n",
     ")\n",
     "\n",
-    "results = batch.run(path_dir=\"data\")\n"
+    "results = batch.run(path_dir=\"data\")"
    ]
   },
   {
@@ -859,7 +861,7 @@
     "batch_data = batch.load(path_dir=\"data\")\n",
     "flux_norm = batch_data[\"normalization\"][\"flux\"].flux\n",
     "trans_g0 = batch_data[\"Si-resonator-delta-0\"][\"flux\"].flux / flux_norm\n",
-    "trans_g20 = batch_data[\"Si-resonator-delta-20\"][\"flux\"].flux / flux_norm\n"
+    "trans_g20 = batch_data[\"Si-resonator-delta-20\"][\"flux\"].flux / flux_norm"
    ]
   },
   {
@@ -899,14 +901,14 @@
     "# plot transmission, compare to paper results, look similar\n",
     "fig, ax = plt.subplots(1, 1, figsize=(6, 4.5))\n",
     "wavelengths_nm = td.C_0 / trans_g0.f / nm\n",
-    "plt.plot(wavelengths_nm, trans_g0.values, color=\"red\", label=\"$\\delta=0$\")\n",
-    "plt.plot(wavelengths_nm, trans_g20.values, color=\"blue\", label=\"$\\delta=20~nm$\")\n",
+    "plt.plot(wavelengths_nm, trans_g0.values, color=\"red\", label=r\"$\\delta=0$\")\n",
+    "plt.plot(wavelengths_nm, trans_g20.values, color=\"blue\", label=r\"$\\delta=20~nm$\")\n",
     "plt.xlabel(\"wavelength ($nm$)\")\n",
     "plt.ylabel(\"Transmission\")\n",
     "plt.xlim([1050, 1400])\n",
     "plt.ylim([0, 1])\n",
     "plt.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
diff --git a/IntegratedVivaldiAntenna.ipynb b/IntegratedVivaldiAntenna.ipynb
index 7f9e48da..324adec7 100644
--- a/IntegratedVivaldiAntenna.ipynb
+++ b/IntegratedVivaldiAntenna.ipynb
@@ -37,14 +37,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
-    "\n",
-    "from tidy3d.plugins.mode import ModeSolver\n",
-    "from tidy3d.plugins import waveguide"
+    "from tidy3d.plugins import waveguide\n",
+    "from tidy3d.plugins.mode import ModeSolver"
    ]
   },
   {
@@ -136,7 +134,6 @@
    "source": [
     "# define a function to create silicon waveguide mode solver\n",
     "def silicon_waveguide_mode_solver(w):\n",
-    "\n",
     "    silicon_waveguide = waveguide.RectangularDielectric(\n",
     "        wavelength=lda0,\n",
     "        core_width=w,\n",
@@ -431,7 +428,6 @@
    "source": [
     "# define a function to create plasmonic waveguide mode solver\n",
     "def plasmonic_waveguide_mode_solver(p):\n",
-    "\n",
     "    # define the waveguide left to the slot\n",
     "    slot_left = td.Structure(\n",
     "        geometry=td.Box(center=(-(s + p) / 2, 0, 0), size=(p, t, td.inf)), medium=ag\n",
@@ -751,7 +747,6 @@
    "source": [
     "# define a function to create hybrid waveguide mode solver\n",
     "def hybrid_waveguide_mode_solver(n_eff):\n",
-    "\n",
     "    w = np.interp(\n",
     "        n_eff, n_eff_silicon, w_list\n",
     "    )  # determine the silicon waveguide width given the effective index\n",
@@ -1665,7 +1660,7 @@
     "plt.plot(ldas, 10 * np.log10(T), c=\"red\", linewidth=3, label=\"Coupling efficiency\")\n",
     "plt.plot(ldas, 10 * np.log10(R), c=\"blue\", linewidth=3, label=\"Return loss\")\n",
     "plt.ylim(-45, 0)\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.legend()\n",
     "plt.ylabel(\"dB\")\n",
     "plt.show()"
@@ -1748,6 +1743,7 @@
     "# solve the system of equations\n",
     "A, B = np.linalg.solve(matrix, vector)\n",
     "\n",
+    "\n",
     "# function to compute the x coordinate of the exponential curve given the z coordinate\n",
     "def compute_x(z):\n",
     "    return A * np.exp(R * z) + B\n",
diff --git a/InverseDesign.ipynb b/InverseDesign.ipynb
index 19bb0ea3..55827623 100644
--- a/InverseDesign.ipynb
+++ b/InverseDesign.ipynb
@@ -27,7 +27,6 @@
    "source": [
     "import matplotlib.pylab as plt\n",
     "import numpy as np\n",
-    "\n",
     "import tidy3d as td\n",
     "import tidy3d.plugins.invdes as tdi"
    ]
@@ -107,7 +106,7 @@
     "Ly = buffer + ly_des + buffer\n",
     "\n",
     "# source and monitor locations\n",
-    "x_src = -lx_des/2 - buffer\n",
+    "x_src = -lx_des / 2 - buffer\n",
     "x_mnt = -x_src\n",
     "\n",
     "# material Medium\n",
@@ -116,10 +115,12 @@
     "# grid spec\n",
     "grid_spec = td.GridSpec.auto(wavelength=wavelength, min_steps_per_wvl=min_steps_per_wvl)\n",
     "\n",
+    "\n",
     "# monitor names\n",
     "def output_monitor_name(i: int) -> str:\n",
     "    return f\"MNT_{i}\"\n",
     "\n",
+    "\n",
     "field_mnt_name = \"field\"\n",
     "\n",
     "# mode spec\n",
@@ -168,7 +169,6 @@
     "waveguides_out = []\n",
     "monitors_out = []\n",
     "for i, wg_y_center in enumerate(wg_y_centers_out):\n",
-    "\n",
     "    wg_out = td.Structure(\n",
     "        geometry=td.Box(\n",
     "            size=(Lx, ly_wg, td.inf),\n",
@@ -207,7 +207,7 @@
     "\n",
     "# used to visualize fields in the plane, not for optimization\n",
     "fld_mnt = td.FieldMonitor(\n",
-    "    center=(0,0,0),\n",
+    "    center=(0, 0, 0),\n",
     "    size=(td.inf, td.inf, 0),\n",
     "    freqs=[freq0],\n",
     "    name=field_mnt_name,\n",
@@ -263,7 +263,10 @@
     "ax = simulation.plot(z=0)\n",
     "\n",
     "import matplotlib\n",
-    "rect = matplotlib.patches.Rectangle(xy=(-lx_des/2, -ly_des/2), width=lx_des, height=ly_des, fill=None)\n",
+    "\n",
+    "rect = matplotlib.patches.Rectangle(\n",
+    "    xy=(-lx_des / 2, -ly_des / 2), width=lx_des, height=ly_des, fill=None\n",
+    ")\n",
     "ax.add_patch(rect)\n",
     "plt.show()"
    ]
@@ -334,13 +337,13 @@
     "penalty = tdi.ErosionDilationPenalty(weight=weight, length_scale=length_scale)\n",
     "\n",
     "design_region = tdi.TopologyDesignRegion(\n",
-    "        size=(lx_des, ly_des, td.inf),\n",
-    "        center=(0, 0, 0),\n",
-    "        eps_bounds=(1.0, eps_mat), # the minimum and maximum permittivity values in the final grid\n",
-    "        transformations=[filter_project],\n",
-    "        penalties=[penalty],\n",
-    "        pixel_size=pixel_size,\n",
-    "    )"
+    "    size=(lx_des, ly_des, td.inf),\n",
+    "    center=(0, 0, 0),\n",
+    "    eps_bounds=(1.0, eps_mat),  # the minimum and maximum permittivity values in the final grid\n",
+    "    transformations=[filter_project],\n",
+    "    penalties=[penalty],\n",
+    "    pixel_size=pixel_size,\n",
+    ")"
    ]
   },
   {
@@ -437,7 +440,7 @@
     "eps_arr = structure.medium.permittivity.values\n",
     "im = plt.imshow(eps_arr.squeeze().T, cmap=\"binary\")\n",
     "plt.colorbar(im)\n",
-    "plt.title('relative permittivity of design region')\n",
+    "plt.title(\"relative permittivity of design region\")\n",
     "plt.show()"
    ]
   },
@@ -588,19 +591,22 @@
    "source": [
     "import autograd.numpy as npa\n",
     "\n",
+    "\n",
     "def post_process_fn(sim_data: td.SimulationData, **kwargs) -> float:\n",
     "    \"\"\"Function called internally to compute contribution to the objective function from the data.\"\"\"\n",
     "\n",
     "    # grab the amplitudes for each of the output waveguide monitors\n",
-    "    amps = [tdi.utils.get_amps(sim_data, monitor_name=mnt.name, direction=\"+\") for mnt in monitors_out]\n",
+    "    amps = [\n",
+    "        tdi.utils.get_amps(sim_data, monitor_name=mnt.name, direction=\"+\") for mnt in monitors_out\n",
+    "    ]\n",
     "\n",
     "    # compute the power at each of the output waveguides\n",
     "    powers = [tdi.utils.sum_abs_squared(amp) for amp in amps]\n",
     "\n",
-    "    # # or, when written in more low-level syntax        \n",
+    "    # # or, when written in more low-level syntax\n",
     "    # amps = [sim_data[mnt.name].amps.sel(direction=\"+\") for mnt in monitors_out]\n",
     "    # powers = [npa.sum(abs(npa.array(amp.values))**2) for amp in amps]\n",
-    "    \n",
+    "\n",
     "    powers = npa.array(powers)\n",
     "\n",
     "    # get a set of weights picking out which powers are the lowest of the three\n",
@@ -832,7 +838,7 @@
    ],
    "source": [
     "result.plot_optimization()\n",
-    "_ = plt.gca().set_title('optimization history')"
+    "_ = plt.gca().set_title(\"optimization history\")"
    ]
   },
   {
@@ -865,7 +871,7 @@
    ],
    "source": [
     "history_keys = result.keys\n",
-    "history_penalty = result.history.get('penalty')\n",
+    "history_penalty = result.history.get(\"penalty\")\n",
     "final_objective = result.get(\"objective_fn_val\", index=-1)\n",
     "\n",
     "print(f\"result contains '.history' for: {tuple(history_keys)}\")\n",
@@ -960,10 +966,7 @@
    "outputs": [],
    "source": [
     "sim_last.to_gds_file(\n",
-    "    fname=\"./misc/inv_des_demo.gds\", \n",
-    "    z=0, \n",
-    "    frequency=freq0, \n",
-    "    permittivity_threshold=2.5\n",
+    "    fname=\"./misc/inv_des_demo.gds\", z=0, frequency=freq0, permittivity_threshold=2.5\n",
     ")"
    ]
   },
@@ -993,11 +996,7 @@
     "mnt_name_left = \"mode\"\n",
     "\n",
     "mnt_left = td.ModeMonitor(\n",
-    "    size=source.size,\n",
-    "    center=source.center,\n",
-    "    mode_spec=mode_spec,\n",
-    "    name=mnt_name_left,\n",
-    "    freqs=[freq0]\n",
+    "    size=source.size, center=source.center, mode_spec=mode_spec, name=mnt_name_left, freqs=[freq0]\n",
     ")\n",
     "\n",
     "srcs_right = []\n",
@@ -1027,8 +1026,7 @@
    "outputs": [],
    "source": [
     "simulations = [\n",
-    "    simulation.updated_copy(sources=[src], monitors=[fld_mnt, mnt_left])\n",
-    "    for src in srcs_right\n",
+    "    simulation.updated_copy(sources=[src], monitors=[fld_mnt, mnt_left]) for src in srcs_right\n",
     "]"
    ]
   },
@@ -1050,7 +1048,7 @@
     }
    ],
    "source": [
-    "f, axes = plt.subplots(1,3,figsize=(10,4), tight_layout=True)\n",
+    "f, axes = plt.subplots(1, 3, figsize=(10, 4), tight_layout=True)\n",
     "for ax, sim in zip(axes, simulations):\n",
     "    sim.plot_eps(z=0, ax=ax)\n",
     "plt.show()"
@@ -1081,7 +1079,7 @@
     "        # # or, when written in more low-level syntax\n",
     "        # amp = sim_data[mnt_name_left].amps.sel(direction=\"-\")\n",
     "        # power = abs(jnp.sum(jnp.array(amp.values)))**2\n",
-    "        \n",
+    "\n",
     "        power_left += power\n",
     "    return power_left"
    ]
diff --git a/KerrSidebands.ipynb b/KerrSidebands.ipynb
index da7886e6..80378cb3 100644
--- a/KerrSidebands.ipynb
+++ b/KerrSidebands.ipynb
@@ -185,13 +185,13 @@
    ],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
-    "from numpy import random\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import tidy3d as td\n",
     "\n",
     "# tidy3D import\n",
     "import tidy3d.web as web\n",
-    "import tidy3d as td\n",
+    "from numpy import random\n",
     "\n",
     "# define geometry\n",
     "wg_width = 0.25\n",
@@ -201,7 +201,7 @@
     "\n",
     "# compute quantities based on geometry parameters\n",
     "x_span = 2 * wg_spacing + 2 * wg_length + 2 * buffer\n",
-    "y_span = wg_width  + 2 * buffer\n",
+    "y_span = wg_width + 2 * buffer\n",
     "wg_insert_x = wg_length + wg_spacing"
    ]
   },
@@ -264,13 +264,9 @@
     "\n",
     "# define the nonlinear parameters\n",
     "n_kerr_2 = 2e-8\n",
-    "kerr_chi3 = (\n",
-    "    4 * (n_solid**2) * td.constants.EPSILON_0 * td.constants.C_0 * n_kerr_2 / 3\n",
-    ")\n",
+    "kerr_chi3 = 4 * (n_solid**2) * td.constants.EPSILON_0 * td.constants.C_0 * n_kerr_2 / 3\n",
     "amp = 400\n",
-    "chi3_model = td.NonlinearSpec(\n",
-    "    models=[td.NonlinearSusceptibility(chi3=kerr_chi3)], num_iters=10\n",
-    ")\n",
+    "chi3_model = td.NonlinearSpec(models=[td.NonlinearSusceptibility(chi3=kerr_chi3)], num_iters=10)\n",
     "kerr_solid = td.Medium(permittivity=n_solid**2, nonlinear_spec=chi3_model)"
    ]
   },
@@ -463,9 +459,7 @@
     "sim_modesolver = td.Simulation(\n",
     "    center=[0, 0, 0],\n",
     "    size=[x_span, y_span, 3],\n",
-    "    grid_spec=td.GridSpec.auto(\n",
-    "        min_steps_per_wvl=min_steps_per_wvl, wavelength=td.C_0 / freq0\n",
-    "    ),\n",
+    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl, wavelength=td.C_0 / freq0),\n",
     "    structures=[waveguide],\n",
     "    run_time=1e-12,\n",
     "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.Periodic()),\n",
@@ -580,9 +574,7 @@
     }
    ],
    "source": [
-    "f, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(\n",
-    "    2, 3, tight_layout=True, figsize=(10, 6)\n",
-    ")\n",
+    "f, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(2, 3, tight_layout=True, figsize=(10, 6))\n",
     "\n",
     "mode_solver.plot_field(\"Ex\", \"abs\", mode_index=0, ax=ax1)\n",
     "mode_solver.plot_field(\"Ey\", \"abs\", mode_index=0, ax=ax2)\n",
@@ -699,9 +691,7 @@
     "    normalize_index=None,\n",
     "    center=[0, 0, 0],\n",
     "    size=[x_span, y_span, 0],\n",
-    "    grid_spec=td.GridSpec.auto(\n",
-    "        min_steps_per_wvl=min_steps_per_wvl, wavelength=td.C_0 / freq0\n",
-    "    ),\n",
+    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl, wavelength=td.C_0 / freq0),\n",
     "    structures=[waveguide],\n",
     "    sources=[mode_source_p, mode_source_s],\n",
     "    monitors=[field_monitor, mode_monitor, flux_monitor],\n",
@@ -890,9 +880,7 @@
     }
    ],
    "source": [
-    "sim_data = web.run(\n",
-    "    sim, task_name=\"kerr_sideband\", path=\"data/simulation_data.hdf5\", verbose=True\n",
-    ")"
+    "sim_data = web.run(sim, task_name=\"kerr_sideband\", path=\"data/simulation_data.hdf5\", verbose=True)"
    ]
   },
   {
@@ -1201,9 +1189,7 @@
     "    band1 = sim.sources[0].source_time.freq0 + (\n",
     "        sim.sources[0].source_time.freq0 - sim.sources[1].source_time.freq0\n",
     "    )\n",
-    "    bm = (sim_data[\"fluxMon\"].flux.f > band1 * 0.99) & (\n",
-    "        sim_data[\"fluxMon\"].flux.f < band1 * 1.01\n",
-    "    )\n",
+    "    bm = (sim_data[\"fluxMon\"].flux.f > band1 * 0.99) & (sim_data[\"fluxMon\"].flux.f < band1 * 1.01)\n",
     "    max = sim_data[\"fluxMon\"].flux[bm].max()\n",
     "\n",
     "    Amps.append(amplitude)\n",
@@ -1299,23 +1285,17 @@
    "outputs": [],
    "source": [
     "mode_source_p = mode_source_p.copy(\n",
-    "    update=dict(\n",
-    "        source_time=td.ContinuousWave(freq0=freqp, fwidth=freq0 / 10, amplitude=amp)\n",
-    "    )\n",
+    "    update=dict(source_time=td.ContinuousWave(freq0=freqp, fwidth=freq0 / 10, amplitude=amp))\n",
     ")\n",
     "mode_source_s = mode_source_s.copy(\n",
-    "    update=dict(\n",
-    "        source_time=td.ContinuousWave(freq0=freqs, fwidth=freq0 / 10, amplitude=amp / 2)\n",
-    "    )\n",
+    "    update=dict(source_time=td.ContinuousWave(freq0=freqs, fwidth=freq0 / 10, amplitude=amp / 2))\n",
     ")\n",
     "\n",
     "sim_1 = td.Simulation(\n",
     "    normalize_index=None,\n",
     "    center=[0, 0, 0],\n",
     "    size=[x_span, y_span, 0],\n",
-    "    grid_spec=td.GridSpec.auto(\n",
-    "        min_steps_per_wvl=min_steps_per_wvl, wavelength=td.C_0 / freq0\n",
-    "    ),\n",
+    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl, wavelength=td.C_0 / freq0),\n",
     "    structures=[waveguide],\n",
     "    sources=[mode_source_p, mode_source_s],\n",
     "    monitors=[field_monitor, mode_monitor],\n",
@@ -1328,22 +1308,16 @@
     ")\n",
     "\n",
     "mode_source_p = mode_source_p.copy(\n",
-    "    update=dict(\n",
-    "        source_time=td.ContinuousWave(freq0=freqp, fwidth=freq0 / 10, amplitude=amp / 2)\n",
-    "    )\n",
+    "    update=dict(source_time=td.ContinuousWave(freq0=freqp, fwidth=freq0 / 10, amplitude=amp / 2))\n",
     ")\n",
     "mode_source_s = mode_source_s.copy(\n",
-    "    update=dict(\n",
-    "        source_time=td.ContinuousWave(freq0=freqs, fwidth=freq0 / 10, amplitude=amp)\n",
-    "    )\n",
+    "    update=dict(source_time=td.ContinuousWave(freq0=freqs, fwidth=freq0 / 10, amplitude=amp))\n",
     ")\n",
     "sim_2 = td.Simulation(\n",
     "    normalize_index=None,\n",
     "    center=[0, 0, 0],\n",
     "    size=[x_span, y_span, 0],\n",
-    "    grid_spec=td.GridSpec.auto(\n",
-    "        min_steps_per_wvl=min_steps_per_wvl, wavelength=td.C_0 / freq0\n",
-    "    ),\n",
+    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl, wavelength=td.C_0 / freq0),\n",
     "    structures=[waveguide],\n",
     "    sources=[mode_source_p, mode_source_s],\n",
     "    monitors=[field_monitor, mode_monitor],\n",
@@ -1541,7 +1515,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Visualize the output spectrum of two further simualtions."
+    "Visualize the output spectrum of two further simulations."
    ]
   },
   {
@@ -1620,7 +1594,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.0"
   },
   "title": "How to model Kerr sidebands in waveguides using Tidy3D | Flexcompute"
  },
diff --git a/LNOIPolarizationSplitterRotator.ipynb b/LNOIPolarizationSplitterRotator.ipynb
index 46274d3c..68b91717 100644
--- a/LNOIPolarizationSplitterRotator.ipynb
+++ b/LNOIPolarizationSplitterRotator.ipynb
@@ -33,9 +33,9 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "from matplotlib import pyplot as plt\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
+    "from matplotlib import pyplot as plt\n",
     "from tidy3d import material_library\n",
     "from tidy3d.plugins.mode import ModeSolver"
    ]
@@ -177,7 +177,7 @@
     "width_test_length = 10  # length of taper\n",
     "\n",
     "\"\"\"\n",
-    "Define points that take the shape of a taper. Note that we extend the inital and final\n",
+    "Define points that take the shape of a taper. Note that we extend the initial and final\n",
     "widths past the boundaries to avoid the sidewall angles from interfering with the ends\n",
     "of the taper.\n",
     "\"\"\"\n",
@@ -235,7 +235,6 @@
     "mode_solvers = {}\n",
     "\n",
     "for i, m in enumerate(plane_xs):\n",
-    "\n",
     "    # define plane at x coordinate\n",
     "    plane = td.Box(center=(m, 0, 0), size=(0, td.inf, td.inf))\n",
     "\n",
@@ -423,8 +422,8 @@
     }
    ],
    "source": [
-    "from matplotlib.patches import Ellipse\n",
     "import matplotlib.colors as mcolors\n",
+    "from matplotlib.patches import Ellipse\n",
     "\n",
     "ridge_waveguide_widths = (\n",
     "    width_test_w1 - width_test_w0\n",
@@ -550,9 +549,9 @@
     "\n",
     "l2_scales = np.linspace(1 / l2_sweep, 500 / l2_sweep, 51)\n",
     "l2_scale_factors = np.ones((len(l2_scales), 2 * l1_grid_spec.num_cells + l2_grid_spec.num_cells))\n",
-    "l2_scale_factors[\n",
-    "    :, l1_grid_spec.num_cells : l1_grid_spec.num_cells + l2_grid_spec.num_cells\n",
-    "] = l2_scales[:, None]\n",
+    "l2_scale_factors[:, l1_grid_spec.num_cells : l1_grid_spec.num_cells + l2_grid_spec.num_cells] = (\n",
+    "    l2_scales[:, None]\n",
+    ")\n",
     "\n",
     "# define EME simulation\n",
     "l2_sweep_sim = td.EMESimulation(\n",
@@ -836,7 +835,6 @@
    "outputs": [],
    "source": [
     "def make_l2_FDTD_sim(l2_sweep):\n",
-    "\n",
     "    # create taper structure\n",
     "    l2_test_pts = [\n",
     "        (-10, -w0 / 2),\n",
@@ -1564,7 +1562,7 @@
     "    ),\n",
     ")\n",
     "\n",
-    "# again owing to the adiabatic nature of this problem, we need only use a unifrom grid throughout the simulation\n",
+    "# again owing to the adiabatic nature of this problem, we need only use a uniform grid throughout the simulation\n",
     "l3_grid_spec = td.EMEUniformGrid(num_cells=16, mode_spec=td.EMEModeSpec(num_modes=num_modes))\n",
     "l4_grid_spec = td.EMEUniformGrid(num_cells=41, mode_spec=td.EMEModeSpec(num_modes=num_modes))\n",
     "l4_total_grid = td.EMECompositeGrid(subgrids=[l3_grid_spec, l4_grid_spec], subgrid_boundaries=[0])\n",
@@ -1578,9 +1576,9 @@
     "\n",
     "l4_scales = np.linspace(1 / l4_sweep, 200 / l4_sweep, 61)\n",
     "l4_scale_factors = np.ones((len(l4_scales), l3_grid_spec.num_cells + l4_grid_spec.num_cells))\n",
-    "l4_scale_factors[\n",
-    "    :, l3_grid_spec.num_cells : l3_grid_spec.num_cells + l4_grid_spec.num_cells\n",
-    "] = l4_scales[:, None]\n",
+    "l4_scale_factors[:, l3_grid_spec.num_cells : l3_grid_spec.num_cells + l4_grid_spec.num_cells] = (\n",
+    "    l4_scales[:, None]\n",
+    ")\n",
     "\n",
     "l4_EME = td.EMESimulation(\n",
     "    center=((l4_sweep - l3) / 2, 0, 0),\n",
diff --git a/LayerRefinement.ipynb b/LayerRefinement.ipynb
index f0ef34e3..a6935d55 100644
--- a/LayerRefinement.ipynb
+++ b/LayerRefinement.ipynb
@@ -20,9 +20,10 @@
    "outputs": [],
    "source": [
     "# basic imports\n",
-    "import numpy as np\n",
     "import pprint\n",
+    "\n",
     "import matplotlib.pylab as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3d imports\n",
     "import tidy3d as td"
@@ -101,7 +102,9 @@
     "    length=patch_thickness,\n",
     ")\n",
     "\n",
-    "patch_geometry = patch_geometry_original - slot_geometry + tranmission_line_geometry - cutout_geometry\n",
+    "patch_geometry = (\n",
+    "    patch_geometry_original - slot_geometry + tranmission_line_geometry - cutout_geometry\n",
+    ")\n",
     "\n",
     "# define ground plane geometry\n",
     "ground_geometry = td.Box(\n",
@@ -248,7 +251,7 @@
     "    sim.plot_grid(y=0, ax=ax[1], override_structures_alpha=0)\n",
     "    ax[1].set_xlim(-5 * mm, 5 * mm)\n",
     "    ax[1].set_ylim(-5 * mm, 5 * mm)\n",
-    "    \n",
+    "\n",
     "    pprint.pp(sim.grid_info)"
    ]
   },
diff --git a/LinearLumpedElements.ipynb b/LinearLumpedElements.ipynb
index 7b5f5260..3fb27ad7 100644
--- a/LinearLumpedElements.ipynb
+++ b/LinearLumpedElements.ipynb
@@ -20,8 +20,8 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3d imports\n",
     "import tidy3d as td\n",
@@ -70,21 +70,28 @@
    "source": [
     "# Here we create the main transmission line structure along with a substrate structure that is sandwiched between the two strips.\n",
     "strip = td.Structure(\n",
-    "    geometry=td.Box(center=(0, strip_height+strip_thickness/2, 0), size=(tline_length, strip_thickness, strip_width)),\n",
+    "    geometry=td.Box(\n",
+    "        center=(0, strip_height + strip_thickness / 2, 0),\n",
+    "        size=(tline_length, strip_thickness, strip_width),\n",
+    "    ),\n",
     "    medium=pec,\n",
-    "    name=\"strip\"\n",
+    "    name=\"strip\",\n",
     ")\n",
     "\n",
     "strip2 = td.Structure(\n",
-    "    geometry=td.Box(center=(0, -strip_thickness/2, 0), size=(tline_length, strip_thickness, strip_width)),\n",
+    "    geometry=td.Box(\n",
+    "        center=(0, -strip_thickness / 2, 0), size=(tline_length, strip_thickness, strip_width)\n",
+    "    ),\n",
     "    medium=pec,\n",
-    "    name=\"strip2\"\n",
+    "    name=\"strip2\",\n",
     ")\n",
     "\n",
     "substrate = td.Structure(\n",
-    "    geometry=td.Box(center=(0, strip_height/2, 0), size=(tline_length, strip_height, strip_width)),\n",
+    "    geometry=td.Box(\n",
+    "        center=(0, strip_height / 2, 0), size=(tline_length, strip_height, strip_width)\n",
+    "    ),\n",
     "    medium=substrate_med,\n",
-    "    name=\"substrate\"\n",
+    "    name=\"substrate\",\n",
     ")\n",
     "\n",
     "# PML wavelength at 25 GHz\n",
@@ -102,10 +109,7 @@
     ")\n",
     "# This monitor will be used to record the fields along the propagation axis of the transmission line.\n",
     "prop_mon = td.FieldMonitor(\n",
-    "    center = (0, strip_height/2,0),\n",
-    "    size = (simx, 0, simz),\n",
-    "    freqs=[fstart, freq0, fstop],\n",
-    "    name=\"prop\"\n",
+    "    center=(0, strip_height / 2, 0), size=(simx, 0, simz), freqs=[fstart, freq0, fstop], name=\"prop\"\n",
     ")"
    ]
   },
@@ -132,17 +136,17 @@
     "Rval = 75\n",
     "Lval = 1e-9\n",
     "\n",
-    "# The RLCNetwork can model either a single resistor, inductor, or capacitor, as well as, \n",
+    "# The RLCNetwork can model either a single resistor, inductor, or capacitor, as well as,\n",
     "# simple series or parallel combinations of these circuit elements.\n",
     "RLC = td.RLCNetwork(\n",
-    "    resistance=Rval, #units are Ohms\n",
-    "    inductance=Lval, #units are Henries\n",
-    "    network_topology=\"series\"\n",
+    "    resistance=Rval,  # units are Ohms\n",
+    "    inductance=Lval,  # units are Henries\n",
+    "    network_topology=\"series\",\n",
     ")\n",
     "# The LinearLumpedElement models the network as a thin sheet with an equivalent medium that\n",
     "# results in the same voltage-current relationship as the desired network.\n",
     "# In this case, we are using the element to terminate a transmission line, so we ensure that it\n",
-    "# is located at the end of the transmission line, with the proper width and height \n",
+    "# is located at the end of the transmission line, with the proper width and height\n",
     "lumped_element = td.LinearLumpedElement(\n",
     "    network=RLC,\n",
     "    center=(tline_length / 2, strip_height / 2, 0),\n",
@@ -178,7 +182,7 @@
    "source": [
     "# Now we create the simulation and include the definition of the lumped element.\n",
     "sim = td.Simulation(\n",
-    "    center=(0, strip_height/2, 0),\n",
+    "    center=(0, strip_height / 2, 0),\n",
     "    size=(simx, simy, simz),\n",
     "    boundary_spec=boundary_spec,\n",
     "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=80, wavelength=3000),\n",
@@ -188,39 +192,52 @@
     "    monitors=[prop_mon, lumped_element.to_monitor(freqs=freqs)],\n",
     "    # The source will be added by the TerminalComponentModeler\n",
     "    sources=[],\n",
-    "    run_time= 50*(tline_length/td.C_0),\n",
-    "    plot_length_units=\"mm\", # This option will make plots default to units of millimeters.\n",
+    "    run_time=50 * (tline_length / td.C_0),\n",
+    "    plot_length_units=\"mm\",  # This option will make plots default to units of millimeters.\n",
     ")\n",
     "# The next step is to setup a LumpedPort for the TerminalComponentModeler, which is needed for extracting scattering parameters\n",
     "# as well as providing a source for the simulation.\n",
     "port1 = smatrix.LumpedPort(\n",
     "    voltage_axis=1,\n",
-    "    size=(0,strip_height,strip_width),\n",
-    "    center=(-tline_length/2, strip_height/2, 0),\n",
+    "    size=(0, strip_height, strip_width),\n",
+    "    center=(-tline_length / 2, strip_height / 2, 0),\n",
     "    num_grid_cells=5,\n",
     "    name=\"port_1\",\n",
     ")\n",
     "# Finally, we combine the base simulation with the LumpedPort in the TerminalComponentModeler, which will enable use\n",
     "# to compute scattering parameters as well as retrieve any other desired data from the simulation.\n",
     "modeler = smatrix.TerminalComponentModeler(\n",
-    "    simulation = sim,\n",
-    "    freqs = freqs,\n",
-    "    ports = [port1],\n",
-    "    verbose = True,\n",
-    "    remove_dc_component=False, # Include DC component for more accuracy at low frequencies\n",
+    "    simulation=sim,\n",
+    "    freqs=freqs,\n",
+    "    ports=[port1],\n",
+    "    verbose=True,\n",
+    "    remove_dc_component=False,  # Include DC component for more accuracy at low frequencies\n",
     ")\n",
-    "# Before running the solver, we plot the created structure along its propagation direction. We also plot the cross-section \n",
+    "# Before running the solver, we plot the created structure along its propagation direction. We also plot the cross-section\n",
     "# of the structure at the terminated end of the transmission line, which includes the location of the lumped element.\n",
     "sim_temp = list(modeler.sim_dict.values())[0]\n",
     "f, (ax1, ax2) = plt.subplots(2, 1, tight_layout=True, figsize=(15, 12))\n",
-    "sim_temp.plot(z = 0*mill, ax=ax1)\n",
-    "sim_temp.plot(x = tline_length/2, ax=ax2)\n",
-    "sim_temp.plot_grid(x = tline_length/2, ax=ax2, vlim=[-2*strip_width, 2*strip_width], hlim=[-strip_height, 2*strip_height])\n",
+    "sim_temp.plot(z=0 * mill, ax=ax1)\n",
+    "sim_temp.plot(x=tline_length / 2, ax=ax2)\n",
+    "sim_temp.plot_grid(\n",
+    "    x=tline_length / 2,\n",
+    "    ax=ax2,\n",
+    "    vlim=[-2 * strip_width, 2 * strip_width],\n",
+    "    hlim=[-strip_height, 2 * strip_height],\n",
+    ")\n",
     "# Add arrows that indicate locations of the Port and the load\n",
-    "ax1.annotate(\"Port 1\", xy=(-tline_length/2 - 10*mill, strip_height + 10*mill), xytext=(-tline_length/1.2,strip_width*10),\n",
-    "            arrowprops=dict(facecolor='black', shrink=0.05))\n",
-    "ax1.annotate(\"Load\", xy=(tline_length/2 + 10*mill, strip_height + 10*mill), xytext=(tline_length/1.5,strip_width*10),\n",
-    "            arrowprops=dict(facecolor='black', shrink=0.05))\n",
+    "ax1.annotate(\n",
+    "    \"Port 1\",\n",
+    "    xy=(-tline_length / 2 - 10 * mill, strip_height + 10 * mill),\n",
+    "    xytext=(-tline_length / 1.2, strip_width * 10),\n",
+    "    arrowprops=dict(facecolor=\"black\", shrink=0.05),\n",
+    ")\n",
+    "ax1.annotate(\n",
+    "    \"Load\",\n",
+    "    xy=(tline_length / 2 + 10 * mill, strip_height + 10 * mill),\n",
+    "    xytext=(tline_length / 1.5, strip_width * 10),\n",
+    "    arrowprops=dict(facecolor=\"black\", shrink=0.05),\n",
+    ")\n",
     "plt.show()"
    ]
   },
@@ -395,12 +412,8 @@
     "sim_data = batch_data[\"smatrix_port_1\"]\n",
     "\n",
     "f, (ax1, ax2) = plt.subplots(2, 1, tight_layout=True, figsize=(6, 6))\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"prop\", field_name=\"Ey\", val=\"abs\", f=freq0, ax=ax1\n",
-    ")\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"prop\", field_name=\"Hz\", val=\"abs\", f=freq0, ax=ax2\n",
-    ")\n",
+    "sim_data.plot_field(field_monitor_name=\"prop\", field_name=\"Ey\", val=\"abs\", f=freq0, ax=ax1)\n",
+    "sim_data.plot_field(field_monitor_name=\"prop\", field_name=\"Hz\", val=\"abs\", f=freq0, ax=ax2)\n",
     "plt.show()"
    ]
   },
@@ -550,15 +563,17 @@
     }
    ],
    "source": [
-    "# The eigenmode associated with the tranmission line is computed at the central frequency 25 GHz\n",
+    "# The eigenmode associated with the transmission line is computed at the central frequency 25 GHz\n",
     "mode_spec = td.ModeSpec(num_modes=1, target_neff=2.2)\n",
-    "mode_plane = td.Box(center = sim.center, size = (0, simy, simz))\n",
+    "mode_plane = td.Box(center=sim.center, size=(0, simy, simz))\n",
     "\n",
-    "mode_solver = mode.ModeSolver(simulation=sim_temp, plane=mode_plane, mode_spec=mode_spec, freqs=[freq0])\n",
+    "mode_solver = mode.ModeSolver(\n",
+    "    simulation=sim_temp, plane=mode_plane, mode_spec=mode_spec, freqs=[freq0]\n",
+    ")\n",
     "mode_data = web.run(mode_solver)\n",
     "\n",
     "# Convert the effective refractive index into an effective permittivity\n",
-    "er_eff = (np.real(mode_data.n_eff.values[0]))**2"
+    "er_eff = (np.real(mode_data.n_eff.values[0])) ** 2"
    ]
   },
   {
@@ -591,23 +606,26 @@
     "    sign=\"+\",\n",
     ")\n",
     "# Pass these definitions to the ImpedanceCalculator\n",
-    "impedance_calc = mw.ImpedanceCalculator(\n",
-    "    voltage_integral=V_integral, current_integral=I_integral\n",
-    ")\n",
+    "impedance_calc = mw.ImpedanceCalculator(voltage_integral=V_integral, current_integral=I_integral)\n",
     "characteristic_impedance_data = impedance_calc.compute_impedance(mode_data)\n",
     "# Select the real part of the only value in the array\n",
     "characteristic_impedance = np.real(characteristic_impedance_data.values[0])\n",
     "\n",
+    "\n",
     "# Define a helper function that computes the reflection coefficient using transmission line theory.\n",
     "def compute_Gamma_from_transmission_line_theory(ZL, Z0, er_eff):\n",
-    "    beta = 2*np.pi*freqs/td.C_0*np.sqrt(er_eff) # Phase constant of the transmission line.\n",
+    "    beta = 2 * np.pi * freqs / td.C_0 * np.sqrt(er_eff)  # Phase constant of the transmission line.\n",
     "    # Calculate impedance seen at the input of the transmission line\n",
     "    # Equation 2.44 from [2]\n",
-    "    Zin = Z0*(ZL + complex(0,1)*Z0*np.tan(beta*tline_length))/(Z0 + complex(0,1)*ZL*np.tan(beta*tline_length))\n",
+    "    Zin = (\n",
+    "        Z0\n",
+    "        * (ZL + complex(0, 1) * Z0 * np.tan(beta * tline_length))\n",
+    "        / (Z0 + complex(0, 1) * ZL * np.tan(beta * tline_length))\n",
+    "    )\n",
     "    # Calculate the reflection coefficient using the reference impedance associated with the port.\n",
     "    Zport = port1.impedance\n",
     "    # Equation 2.72 from [2] to account for generator and load mismatch.\n",
-    "    Gamma = (Zin-Zport)/(Zin+Zport)\n",
+    "    Gamma = (Zin - Zport) / (Zin + Zport)\n",
     "    return Gamma"
    ]
   },
@@ -639,7 +657,9 @@
     "# Tidy3D uses the physics convention for time-harmonic fields,\n",
     "# so we take the conjugate in order to switch to the engineering convention.\n",
     "load_impedance = np.conj(lumped_element.impedance(freqs))\n",
-    "Gamma = compute_Gamma_from_transmission_line_theory(load_impedance, characteristic_impedance, er_eff)\n",
+    "Gamma = compute_Gamma_from_transmission_line_theory(\n",
+    "    load_impedance, characteristic_impedance, er_eff\n",
+    ")\n",
     "\n",
     "# Get S11 data\n",
     "freq = s_matrix.f / 1e9\n",
@@ -647,18 +667,8 @@
     "\n",
     "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 3))\n",
     "fig.suptitle(\"Transmission line terminated by RL series network\")\n",
-    "ax1.plot(\n",
-    "    s_matrix.f / 1e9,\n",
-    "    20 * np.log10(np.abs(S11_RL_term)),\n",
-    "    \"--b\",\n",
-    "    label=\"Tidy3D\"\n",
-    ")\n",
-    "ax1.plot(\n",
-    "    freqs / 1e9,\n",
-    "    20 * np.log10(np.abs(Gamma)),\n",
-    "    \"-k\",\n",
-    "    label=\"Transmission line theory\"\n",
-    ")\n",
+    "ax1.plot(s_matrix.f / 1e9, 20 * np.log10(np.abs(S11_RL_term)), \"--b\", label=\"Tidy3D\")\n",
+    "ax1.plot(freqs / 1e9, 20 * np.log10(np.abs(Gamma)), \"-k\", label=\"Transmission line theory\")\n",
     "ax1.set_xlabel(\"Frequency (GHz)\")\n",
     "ax1.set_ylabel(r\"$|S_{11}|$ (dB)\")\n",
     "ax1.set_xlim([0, 50])\n",
@@ -667,16 +677,11 @@
     "\n",
     "ax2.plot(\n",
     "    s_matrix.f / 1e9,\n",
-    "    -np.angle(S11_RL_term), #Follow engineering convention exp(j omega t)\n",
+    "    -np.angle(S11_RL_term),  # Follow engineering convention exp(j omega t)\n",
     "    \"--b\",\n",
-    "    label=\"Tidy3D\"\n",
-    ")\n",
-    "ax2.plot(\n",
-    "    freqs / 1e9,\n",
-    "    np.angle(Gamma),\n",
-    "    \"-k\",\n",
-    "    label=\"Transmission line theory\"\n",
+    "    label=\"Tidy3D\",\n",
     ")\n",
+    "ax2.plot(freqs / 1e9, np.angle(Gamma), \"-k\", label=\"Transmission line theory\")\n",
     "ax2.set_xlabel(\"Frequency (GHz)\")\n",
     "ax2.set_ylabel(r\"$\\angle~S_{11}$ (rad)\")\n",
     "ax2.set_xlim([0, 50])\n",
@@ -713,32 +718,38 @@
    "source": [
     "# Get the monitor data associated the lumped element\n",
     "vi_mon_data = sim_data[lumped_element.monitor_name]\n",
-    "# Setup voltage and current path integrals automatically around the lumped element. \n",
-    "voltage_integral, current_integral = mw.path_integrals_from_lumped_element(lumped_element, sim_data.simulation.grid)\n",
+    "# Setup voltage and current path integrals automatically around the lumped element.\n",
+    "voltage_integral, current_integral = mw.path_integrals_from_lumped_element(\n",
+    "    lumped_element, sim_data.simulation.grid\n",
+    ")\n",
     "# Compute the voltage and current using the path integral tools and the frequency-domain field data.\n",
     "V = voltage_integral.compute_voltage(vi_mon_data)\n",
     "I = current_integral.compute_current(vi_mon_data)\n",
     "# Apply Ohm's law to retrieve impedance\n",
-    "impedance = V/I\n",
+    "impedance = V / I\n",
     "resistance = np.real(impedance).values\n",
-    "reactance = -np.imag(impedance).values # Change of sign, since Tidy3D uses the physics convention for time-harmonic fields (exp(-j omega t))\n",
+    "reactance = -np.imag(\n",
+    "    impedance\n",
+    ").values  # Change of sign, since Tidy3D uses the physics convention for time-harmonic fields (exp(-j omega t))\n",
     "# Here we use the convenience methods from the lumped element to compute the exact impedance of the modeled network.\n",
     "ideal_impedance = lumped_element.impedance(freqs=freqs)\n",
     "ideal_resistance = np.real(ideal_impedance)\n",
-    "ideal_reactance = -np.imag(ideal_impedance) # Change of sign, since Tidy3D uses the physics convention for time-harmonic fields (exp(-j omega t))\n",
+    "ideal_reactance = -np.imag(\n",
+    "    ideal_impedance\n",
+    ")  # Change of sign, since Tidy3D uses the physics convention for time-harmonic fields (exp(-j omega t))\n",
     "\n",
     "# Plot the results for visual comparison\n",
     "f, (ax1, ax2) = plt.subplots(2, 1, tight_layout=True, figsize=(8, 6))\n",
     "f.suptitle(\"RL series network\")\n",
     "ax1.plot(freqs / 1e9, resistance, label=\"Computed\")\n",
-    "ax1.plot(freqs / 1e9, ideal_resistance,\"--\", label=\"Desired\")\n",
+    "ax1.plot(freqs / 1e9, ideal_resistance, \"--\", label=\"Desired\")\n",
     "ax1.set_ylabel(r\"Resistance ($\\Omega$)\")\n",
     "ax1.set_xlabel(\"Frequency (GHz)\")\n",
     "ax1.set_xlim(0, 50)\n",
     "ax1.set_ylim(0, 100)\n",
     "ax1.legend(loc=\"lower right\")\n",
     "ax2.plot(freqs / 1e9, reactance, label=\"Computed\")\n",
-    "ax2.plot(freqs / 1e9, ideal_reactance,\"--\", label=\"Desired\")\n",
+    "ax2.plot(freqs / 1e9, ideal_reactance, \"--\", label=\"Desired\")\n",
     "ax2.set_ylabel(r\"Reactance ($\\Omega$)\")\n",
     "ax2.set_xlabel(\"Frequency (GHz)\")\n",
     "ax2.set_xlim(0, 50)\n",
@@ -778,7 +789,7 @@
     }
    ],
    "source": [
-    "# Example network from Sec 15.9.6 [1] \n",
+    "# Example network from Sec 15.9.6 [1]\n",
     "L1 = 1e-9\n",
     "L2 = 1.5e-9\n",
     "C1 = 0.2e-12\n",
@@ -790,7 +801,8 @@
     "# If you have installed lcapy, the transfer function will be automatically generated using a simple description of the network.\n",
     "# Otherwise, the precomputed transfer function coefficients will be used.\n",
     "try:\n",
-    "    from lcapy import R, C, L, s\n",
+    "    from lcapy import C, L, R, s\n",
+    "\n",
     "    print(\"Generating transfer function using 'lcapy' ...\")\n",
     "    # Create the 4th order network using 'lcapy'\n",
     "    # The network is described by adding each of the elements in series and parallel\n",
@@ -816,8 +828,12 @@
     "    b = [\n",
     "        R1 + R2 + R3,\n",
     "        C1 * R1 * R3 + C1 * R2 * R3 + C2 * R1 * R2 + C2 * R2 * R3 + L1 + L2,\n",
-    "        C1 * C2 * R1 * R2 * R3 + C1 * L1 * R1 + C1 * L1 * R2\n",
-    "        + C1 * L2 * R3 + C2 * L1 * R2 + C2 * L2 * R2,\n",
+    "        C1 * C2 * R1 * R2 * R3\n",
+    "        + C1 * L1 * R1\n",
+    "        + C1 * L1 * R2\n",
+    "        + C1 * L2 * R3\n",
+    "        + C2 * L1 * R2\n",
+    "        + C2 * L2 * R2,\n",
     "        C1 * C2 * L1 * R1 * R2 + C1 * C2 * L2 * R2 * R3 + C1 * L1 * L2,\n",
     "        C1 * C2 * L1 * L2 * R2,\n",
     "    ]\n",
@@ -1002,7 +1018,9 @@
     "# Tidy3D uses the physics convention for time-harmonic fields,\n",
     "# so we take the conjugate in order to switch to the engineering convention.\n",
     "load_impedance = np.conj(lumped_element.impedance(freqs))\n",
-    "Gamma = compute_Gamma_from_transmission_line_theory(load_impedance, characteristic_impedance, er_eff)\n",
+    "Gamma = compute_Gamma_from_transmission_line_theory(\n",
+    "    load_impedance, characteristic_impedance, er_eff\n",
+    ")\n",
     "\n",
     "# Get the S11 data\n",
     "freq = s_matrix.f / 1e9\n",
@@ -1010,18 +1028,8 @@
     "\n",
     "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 3))\n",
     "fig.suptitle(\"Transmission line terminated by the seven-element 4th-order network\")\n",
-    "ax1.plot(\n",
-    "    s_matrix.f / 1e9,\n",
-    "    20 * np.log10(np.abs(S11_ntwk)),\n",
-    "    \"--b\",\n",
-    "    label=\"Tidy3D\"\n",
-    ")\n",
-    "ax1.plot(\n",
-    "    freqs / 1e9,\n",
-    "    20 * np.log10(np.abs(Gamma)),\n",
-    "    \"-k\",\n",
-    "    label=\"Transmission line theory\"\n",
-    ")\n",
+    "ax1.plot(s_matrix.f / 1e9, 20 * np.log10(np.abs(S11_ntwk)), \"--b\", label=\"Tidy3D\")\n",
+    "ax1.plot(freqs / 1e9, 20 * np.log10(np.abs(Gamma)), \"-k\", label=\"Transmission line theory\")\n",
     "ax1.set_xlabel(\"Frequency (GHz)\")\n",
     "ax1.set_ylabel(r\"$|S_{11}|$ (dB)\")\n",
     "ax1.set_xlim([0, 50])\n",
@@ -1029,16 +1037,11 @@
     "ax1.legend()\n",
     "ax2.plot(\n",
     "    s_matrix.f / 1e9,\n",
-    "    -np.angle(S11_ntwk), #Follow engineering convention exp(j omega t)\n",
+    "    -np.angle(S11_ntwk),  # Follow engineering convention exp(j omega t)\n",
     "    \"--b\",\n",
-    "    label=\"Tidy3D\"\n",
-    ")\n",
-    "ax2.plot(\n",
-    "    freqs / 1e9,\n",
-    "    np.angle(Gamma),\n",
-    "    \"-k\",\n",
-    "    label=\"Transmission line theory\"\n",
+    "    label=\"Tidy3D\",\n",
     ")\n",
+    "ax2.plot(freqs / 1e9, np.angle(Gamma), \"-k\", label=\"Transmission line theory\")\n",
     "ax2.set_xlabel(\"Frequency (GHz)\")\n",
     "ax2.set_ylabel(r\"$\\angle~S_{11}$ (rad)\")\n",
     "ax2.set_xlim([0, 50])\n",
@@ -1077,32 +1080,38 @@
     "sim_data = batch_data[\"smatrix_port_1\"]\n",
     "# Get the monitor data associated the lumped element\n",
     "vi_mon_data = sim_data[lumped_element.monitor_name]\n",
-    "# Setup voltage and current path integrals automatically around the lumped element. \n",
-    "voltage_integral, current_integral = mw.path_integrals_from_lumped_element(lumped_element, sim_data.simulation.grid)\n",
+    "# Setup voltage and current path integrals automatically around the lumped element.\n",
+    "voltage_integral, current_integral = mw.path_integrals_from_lumped_element(\n",
+    "    lumped_element, sim_data.simulation.grid\n",
+    ")\n",
     "# Compute the voltage and current using the path integral tools and the frequency-domain field data.\n",
     "V = voltage_integral.compute_voltage(vi_mon_data)\n",
     "I = current_integral.compute_current(vi_mon_data)\n",
     "# Apply Ohm's law to retrieve impedance\n",
-    "impedance = V/I\n",
+    "impedance = V / I\n",
     "resistance = np.real(impedance).values\n",
-    "reactance = -np.imag(impedance).values # Change of sign, since Tidy3D uses the physics convention for time-harmonic fields (exp(-j omega t))\n",
+    "reactance = -np.imag(\n",
+    "    impedance\n",
+    ").values  # Change of sign, since Tidy3D uses the physics convention for time-harmonic fields (exp(-j omega t))\n",
     "# Here we use the convenience methods from the lumped element to compute the intended impedance of the modeled network.\n",
     "ideal_impedance = lumped_element.impedance(freqs=freqs)\n",
     "ideal_resistance = np.real(ideal_impedance)\n",
-    "ideal_reactance = -np.imag(ideal_impedance) # Change of sign, since Tidy3D uses the physics convention for time-harmonic fields (exp(-j omega t))\n",
+    "ideal_reactance = -np.imag(\n",
+    "    ideal_impedance\n",
+    ")  # Change of sign, since Tidy3D uses the physics convention for time-harmonic fields (exp(-j omega t))\n",
     "\n",
     "# Plot the results for visual comparison\n",
     "f, (ax1, ax2) = plt.subplots(2, 1, tight_layout=True, figsize=(8, 6))\n",
     "f.suptitle(\"Seven-element 4th-order network\")\n",
     "ax1.plot(freqs / 1e9, resistance, label=\"Computed\")\n",
-    "ax1.plot(freqs / 1e9, ideal_resistance,\"--\", label=\"Desired\")\n",
+    "ax1.plot(freqs / 1e9, ideal_resistance, \"--\", label=\"Desired\")\n",
     "ax1.set_ylabel(r\"Resistance ($\\Omega$)\")\n",
     "ax1.set_xlabel(\"Frequency (GHz)\")\n",
     "ax1.set_xlim(0, 50)\n",
     "ax1.set_ylim(0, 250)\n",
     "ax1.legend(loc=\"upper right\")\n",
     "ax2.plot(freqs / 1e9, reactance, label=\"Computed\")\n",
-    "ax2.plot(freqs / 1e9, ideal_reactance,\"--\", label=\"Desired\")\n",
+    "ax2.plot(freqs / 1e9, ideal_reactance, \"--\", label=\"Desired\")\n",
     "ax2.set_ylabel(r\"Reactance ($\\Omega$)\")\n",
     "ax2.set_xlabel(\"Frequency (GHz)\")\n",
     "ax2.set_xlim(0, 50)\n",
@@ -1398,11 +1407,9 @@
     "lumped_element = lumped_element.updated_copy(\n",
     "    dist_type=\"off\", size=(0, strip_height, 0), network=RLC\n",
     ")\n",
-    "# Use a convenience method for estimating the parasitic inductance and capacitance \n",
+    "# Use a convenience method for estimating the parasitic inductance and capacitance\n",
     "# associated with the wire connections, which are used in the lumped element.\n",
-    "Lp, Cp = lumped_element.estimate_parasitic_elements(\n",
-    "    grid=modeler.sim_dict[\"smatrix_port_1\"].grid\n",
-    ")\n",
+    "Lp, Cp = lumped_element.estimate_parasitic_elements(grid=modeler.sim_dict[\"smatrix_port_1\"].grid)\n",
     "print(\n",
     "    f\"The parasitic inductance of the wire connections is {Lp*1e9:.2f} nH, \"\n",
     "    f\"while the parasitic capacitance is {Cp*1e15:.2f} fF.\"\n",
@@ -1443,24 +1450,9 @@
     "\n",
     "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 3))\n",
     "fig.suptitle(\"Transmission line terminated by RL series network\")\n",
-    "ax1.plot(\n",
-    "    s_matrix.f / 1e9,\n",
-    "    20 * np.log10(np.abs(S11_RL_term)),\n",
-    "    \"-b\",\n",
-    "    label=\"Distributed\"\n",
-    ")\n",
-    "ax1.plot(\n",
-    "    s_matrix.f / 1e9,\n",
-    "    20 * np.log10(np.abs(S11_not_comp)),\n",
-    "    \"--r\",\n",
-    "    label=\"Single-cell\"\n",
-    ")\n",
-    "ax1.plot(\n",
-    "    s_matrix.f / 1e9,\n",
-    "    20 * np.log10(np.abs(S11_comp)),\n",
-    "    \"--g\",\n",
-    "    label=\"Compensated-single-cell\"\n",
-    ")\n",
+    "ax1.plot(s_matrix.f / 1e9, 20 * np.log10(np.abs(S11_RL_term)), \"-b\", label=\"Distributed\")\n",
+    "ax1.plot(s_matrix.f / 1e9, 20 * np.log10(np.abs(S11_not_comp)), \"--r\", label=\"Single-cell\")\n",
+    "ax1.plot(s_matrix.f / 1e9, 20 * np.log10(np.abs(S11_comp)), \"--g\", label=\"Compensated-single-cell\")\n",
     "ax1.set_xlabel(\"Frequency (GHz)\")\n",
     "ax1.set_ylabel(r\"$|S_{11}|$ (dB)\")\n",
     "ax1.set_xlim([0, 50])\n",
@@ -1469,21 +1461,21 @@
     "\n",
     "ax2.plot(\n",
     "    s_matrix.f / 1e9,\n",
-    "    -np.angle(S11_RL_term), #Follow engineering convention exp(j omega t)\n",
+    "    -np.angle(S11_RL_term),  # Follow engineering convention exp(j omega t)\n",
     "    \"-b\",\n",
-    "    label=\"Distributed\"\n",
+    "    label=\"Distributed\",\n",
     ")\n",
     "ax2.plot(\n",
     "    s_matrix.f / 1e9,\n",
-    "    -np.angle(S11_not_comp), #Follow engineering convention exp(j omega t)\n",
+    "    -np.angle(S11_not_comp),  # Follow engineering convention exp(j omega t)\n",
     "    \"--r\",\n",
-    "    label=\"Single-cell\"\n",
+    "    label=\"Single-cell\",\n",
     ")\n",
     "ax2.plot(\n",
     "    s_matrix.f / 1e9,\n",
-    "    -np.angle(S11_comp), #Follow engineering convention exp(j omega t)\n",
+    "    -np.angle(S11_comp),  # Follow engineering convention exp(j omega t)\n",
     "    \"--g\",\n",
-    "    label=\"Compensated-single-cell\"\n",
+    "    label=\"Compensated-single-cell\",\n",
     ")\n",
     "ax2.set_xlabel(\"Frequency (GHz)\")\n",
     "ax2.set_ylabel(r\"$\\angle~S_{11}$ (rad)\")\n",
@@ -1531,7 +1523,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.0"
+   "version": "3.11.0"
   },
   "title": "How to use lumped elements in Tidy3D FDTD simulations"
  },
diff --git a/MIMResonator.ipynb b/MIMResonator.ipynb
index 6be9c750..8c46aa5e 100644
--- a/MIMResonator.ipynb
+++ b/MIMResonator.ipynb
@@ -29,8 +29,8 @@
    "outputs": [],
    "source": [
     "# Standard python imports.\n",
-    "import numpy as np\n",
     "import matplotlib.pylab as plt\n",
+    "import numpy as np\n",
     "\n",
     "# Import regular tidy3d.\n",
     "import tidy3d as td\n",
@@ -109,9 +109,7 @@
     "freq_bw = 0.5 * (freq_range[0] - freq_range[-1])  # Source bandwidth (Hz).\n",
     "freq_res = td.C_0 / wl_res\n",
     "\n",
-    "size_z = (\n",
-    "    2 * wl_max + mgf2_t + 2 * al_t + znse_t\n",
-    ")  # Simulation size in the z-direction (um)."
+    "size_z = 2 * wl_max + mgf2_t + 2 * al_t + znse_t  # Simulation size in the z-direction (um)."
    ]
   },
   {
@@ -222,9 +220,7 @@
     "    sim = td.Simulation(\n",
     "        size=(size_x, size_y, size_z),\n",
     "        center=(0, 0, 0),\n",
-    "        grid_spec=td.GridSpec.auto(\n",
-    "            min_steps_per_wvl=40, wavelength=(wl_min + wl_max) / 2\n",
-    "        ),\n",
+    "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=40, wavelength=(wl_min + wl_max) / 2),\n",
     "        structures=[substrate, mgf2_layer, al_bot, znse_layer, al_top],\n",
     "        sources=[plane_wave],\n",
     "        monitors=[trans_monitor, field_xz],\n",
diff --git a/MMI1x4.ipynb b/MMI1x4.ipynb
index ca7b4305..5619ab1b 100644
--- a/MMI1x4.ipynb
+++ b/MMI1x4.ipynb
@@ -15,11 +15,11 @@
    "source": [
     "Note: the cost of running the entire notebook is larger than 1 FlexCredit.\n",
     "\n",
-    "Optical power splitters are essential components in integrated photonics. Power splitters based on multimode interference (MMI) device are easy to fabricate and can achieve low excess loss as well as large bandwidth. Although the design of a MMI power splitter is based on the self-imaging principle, fine-tuning the geometric parameters with accurate and fast numerical simulations is crucial to achieving optimal device performance. \n",
+    "Optical power splitters are essential components in integrated photonics. Power splitters based on multimode interference (MMI) device are easy to fabricate and can achieve low excess loss as well as large bandwidth. Although the design of an MMI power splitter is based on the self-imaging principle, fine-tuning the geometric parameters with accurate and fast numerical simulations is crucial to achieving optimal device performance. \n",
     "\n",
     "This example aims to demonstrate the design and optimization of 1 to 4 MMI device at telecom wavelength for power splitting applications. The initial design is adapted from [D. Malka, Y. Danan, Y. Ramon, Z. Zalevsky, A Photonic 1 x 4 Power Splitter Based on Multimode Interference in Silicon–Gallium-Nitride Slot Waveguide Structures. Materials. 9, 516 (2016)](https://www.mdpi.com/1996-1944/9/7/516).\n",
     "\n",
-    "The device uses a Si-GaN-Si slot waveguide strcuture as schematically shown below.\n",
+    "The device uses a Si-GaN-Si slot waveguide structure as schematically shown below.\n",
     "\n",
     "\"Schematic\n",
     "\n",
@@ -50,12 +50,11 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
-    "from tidy3d.plugins.mode import ModeSolver\n"
+    "from tidy3d.plugins.mode import ModeSolver"
    ]
   },
   {
@@ -88,7 +87,7 @@
     "# define media\n",
     "si = td.Medium(permittivity=n_si**2)\n",
     "gan = td.Medium(permittivity=n_gan**2)\n",
-    "sio2 = td.Medium(permittivity=n_sio2**2)\n"
+    "sio2 = td.Medium(permittivity=n_sio2**2)"
    ]
   },
   {
@@ -96,7 +95,7 @@
    "id": "c8f745a8",
    "metadata": {},
    "source": [
-    "Define initial design parameters and wrap simulation setup in a function. The arguments of the function are the paremeters we want to optimize later. In this example, we aim to optimize the length and width of the MMI section."
+    "Define initial design parameters and wrap simulation setup in a function. The arguments of the function are the parameters we want to optimize later. In this example, we aim to optimize the length and width of the MMI section."
    ]
   },
   {
@@ -122,7 +121,7 @@
     "L2 = 5  # length of the output tapper\n",
     "H_Si = 0.3  # thickness of the Si layer\n",
     "H_GaN = 0.1  # thickness of the GaN layer\n",
-    "g3 = (W2 - W1) / 2  # auxilary parameter defined for easier geometry building\n",
+    "g3 = (W2 - W1) / 2  # auxiliary parameter defined for easier geometry building\n",
     "g2 = g1 - 2 * g3  # gap between the output tapers\n",
     "lda0 = 1.55  # central wavelength\n",
     "freq0 = td.C_0 / lda0  # central frequency\n",
@@ -134,6 +133,7 @@
     "buffer_x = 1\n",
     "buffer_y = 1.5\n",
     "\n",
+    "\n",
     "# define a function that takes the geometric parameters as input arguments and return a Simulation object\n",
     "def make_sim(L_MMI, W_MMI):\n",
     "    # the whole device is defined as a PolySlab with vertices given by the following\n",
@@ -185,9 +185,7 @@
     "        medium=si,\n",
     "    )\n",
     "    mmi_layer2 = td.Structure(\n",
-    "        geometry=td.PolySlab(\n",
-    "            vertices=vertices, axis=2, slab_bounds=(-0.5 * H_GaN, 0.5 * H_GaN)\n",
-    "        ),\n",
+    "        geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(-0.5 * H_GaN, 0.5 * H_GaN)),\n",
     "        medium=gan,\n",
     "    )\n",
     "\n",
@@ -265,7 +263,7 @@
     "        medium=sio2,\n",
     "        symmetry=(1, 0, -1),\n",
     "    )\n",
-    "    return sim\n"
+    "    return sim"
    ]
   },
   {
@@ -313,7 +311,7 @@
     "sim = make_sim(L_MMI, W_MMI)\n",
     "sim.plot(z=0, ax=ax1)\n",
     "sim.plot(y=-buffer_y / 3, ax=ax2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -360,7 +358,7 @@
     "    mode_spec=sim.sources[0].mode_spec,\n",
     "    freqs=[freq0],\n",
     ")\n",
-    "mode_data = mode_solver.solve()\n"
+    "mode_data = mode_solver.solve()"
    ]
   },
   {
@@ -409,7 +407,7 @@
     "ax2.set_aspect(\"equal\")\n",
     "ax3.set_title(\"|Ez(x, y)|\")\n",
     "ax3.set_aspect(\"equal\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -782,7 +780,7 @@
    ],
    "source": [
     "job = web.Job(simulation=sim, task_name=\"mmi\", verbose=True)\n",
-    "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n"
+    "sim_data = job.run(path=\"data/simulation_data.hdf5\")"
    ]
   },
   {
@@ -819,11 +817,9 @@
    ],
    "source": [
     "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 8))\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\", ax=ax1, f=freq0\n",
-    ")\n",
+    "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\", ax=ax1, f=freq0)\n",
     "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"Ez\", ax=ax2, f=freq0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -831,7 +827,7 @@
    "id": "53bb4a23",
    "metadata": {},
    "source": [
-    "Plot transmission on each output waveguide as well as the total excess loss. At the central wavelength of 1550 nm, the transimission power at the inner waveguide and outer waveguide differs by about 2%. The excess loss is about 0.4 dB."
+    "Plot transmission on each output waveguide as well as the total excess loss. At the central wavelength of 1550 nm, the transmission power at the inner waveguide and outer waveguide differs by about 2%. The excess loss is about 0.4 dB."
    ]
   },
   {
@@ -866,7 +862,7 @@
     "plt.sca(ax1)\n",
     "plt.plot(ldas, T1, ldas, T2)\n",
     "plt.vlines(x=1.55, ymin=0.1, ymax=0.3, colors=\"black\", ls=\"--\")\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Transmission to output waveguide\")\n",
     "plt.legend((\"Inner waveguide\", \"Outer waveguide\"))\n",
     "\n",
@@ -875,9 +871,9 @@
     "plt.plot(ldas, excess_loss)\n",
     "plt.vlines(x=1.55, ymin=0, ymax=1, colors=\"black\", ls=\"--\")\n",
     "plt.hlines(y=excess_loss[50], xmin=1.5, xmax=1.6, colors=\"black\", ls=\"--\")\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Excess loss (dB)\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -916,10 +912,10 @@
     "mode_amp = sim_data[\"mode2\"].amps.sel(direction=\"+\")\n",
     "mode_power = np.abs(mode_amp) ** 2 / T2\n",
     "plt.plot(ldas, mode_power)\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Power share (%)\")\n",
     "plt.legend([\"Mode 0\", \"Mode 1\"])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1129,7 +1125,7 @@
     "    for W_MMI in W_MMIs\n",
     "}\n",
     "batch = web.Batch(simulations=sims, verbose=True)\n",
-    "batch_results = batch.run(path_dir=\"data\")\n"
+    "batch_results = batch.run(path_dir=\"data\")"
    ]
   },
   {
@@ -1162,7 +1158,7 @@
     "        t1 = sim_data[\"flux1\"].flux\n",
     "        T1[i, j] = t1[50]  # the index 50 corresponds to the wavelength of 1550 nm\n",
     "        t2 = sim_data[\"flux2\"].flux\n",
-    "        T2[i, j] = t2[50]\n"
+    "        T2[i, j] = t2[50]"
    ]
   },
   {
@@ -1203,17 +1199,15 @@
     "plt.pcolor(W_MMIs, L_MMIs, np.abs(T1 - T2), vmin=0, vmax=0.02, cmap=\"binary\")\n",
     "plt.colorbar()\n",
     "plt.title(\"Power difference between inner and outer waveguides\")\n",
-    "plt.xlabel(\"W_MMI ($\\mu m$)\")\n",
-    "plt.ylabel(\"L_MMI ($\\mu m$)\")\n",
+    "plt.xlabel(r\"W_MMI ($\\mu m$)\")\n",
+    "plt.ylabel(r\"L_MMI ($\\mu m$)\")\n",
     "plt.sca(ax2)\n",
-    "plt.pcolor(\n",
-    "    W_MMIs, L_MMIs, -10 * np.log10(2 * (T1 + T2)), vmin=0, vmax=0.25, cmap=\"binary\"\n",
-    ")\n",
+    "plt.pcolor(W_MMIs, L_MMIs, -10 * np.log10(2 * (T1 + T2)), vmin=0, vmax=0.25, cmap=\"binary\")\n",
     "plt.colorbar()\n",
     "plt.title(\"Excess loss (dB)\")\n",
-    "plt.xlabel(\"W_MMI ($\\mu m$)\")\n",
-    "plt.ylabel(\"L_MMI ($\\mu m$)\")\n",
-    "plt.show()\n"
+    "plt.xlabel(r\"W_MMI ($\\mu m$)\")\n",
+    "plt.ylabel(r\"L_MMI ($\\mu m$)\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -1251,10 +1245,8 @@
    "source": [
     "sim_data = batch_results[\"L_MMI=11.15;W_MMI=4.90\"]\n",
     "f, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\", ax=ax, f=freq0\n",
-    ")\n",
-    "plt.show()\n"
+    "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\", ax=ax, f=freq0)\n",
+    "plt.show()"
    ]
   },
   {
@@ -1297,7 +1289,7 @@
     "plt.sca(ax1)\n",
     "plt.plot(ldas, T1, ldas, T2)\n",
     "plt.vlines(x=1.55, ymin=0.1, ymax=0.3, colors=\"black\", ls=\"--\")\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Transmission to output\")\n",
     "plt.legend((\"Inner output\", \"Outer outport\"))\n",
     "\n",
@@ -1306,9 +1298,9 @@
     "plt.plot(ldas, excess_loss)\n",
     "plt.vlines(x=1.55, ymin=0, ymax=1, colors=\"black\", ls=\"--\")\n",
     "plt.hlines(y=excess_loss[50], xmin=1.5, xmax=1.6, colors=\"black\", ls=\"--\")\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Excess loss (dB)\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1348,10 +1340,10 @@
     "mode_amp = sim_data[\"mode2\"].amps.sel(direction=\"+\")\n",
     "mode_power = np.abs(mode_amp) ** 2 / T2\n",
     "plt.plot(ldas, mode_power)\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Power fraction of the modes (%)\")\n",
     "plt.legend([\"Mode 0\", \"Mode 1\"])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1388,7 +1380,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.0"
   },
   "title": "MMI Power Splitter Modeling in Tidy3D | Flexcompute",
   "widgets": {
diff --git a/MMIMeepBenchmark.ipynb b/MMIMeepBenchmark.ipynb
index 7d0899f5..7095bafd 100644
--- a/MMIMeepBenchmark.ipynb
+++ b/MMIMeepBenchmark.ipynb
@@ -17,10 +17,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tidy3d as td\n",
-    "from tidy3d import web\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
+    "import tidy3d as td\n",
+    "from tidy3d import web"
    ]
   },
   {
@@ -223,9 +223,7 @@
     "        Output_straight_waveguide__top_,\n",
     "        Output_straight_waveguide__bottom_,\n",
     "    ],\n",
-    "    boundary_spec=td.BoundarySpec(\n",
-    "        x=td.Boundary.pml(), y=td.Boundary.pml(), z=td.Boundary.pml()\n",
-    "    ),\n",
+    "    boundary_spec=td.BoundarySpec(x=td.Boundary.pml(), y=td.Boundary.pml(), z=td.Boundary.pml()),\n",
     ")"
    ]
   },
@@ -452,7 +450,7 @@
     "ax.set_ylim(0.425, 0.525)\n",
     "\n",
     "ax.set_ylabel(\"Transmittance\")\n",
-    "ax.set_xlabel(\"Wavelength ($\\mu$m)\")\n",
+    "ax.set_xlabel(r\"Wavelength ($\\mu$m)\")\n",
     "\n",
     "plt.show()"
    ]
diff --git a/MachZehnderModulator.ipynb b/MachZehnderModulator.ipynb
index 231b96d2..c6b8e44b 100644
--- a/MachZehnderModulator.ipynb
+++ b/MachZehnderModulator.ipynb
@@ -33,12 +33,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import gdstk\n",
     "import numpy as np\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
     "import tidy3d as td\n",
-    "from tidy3d import web\n",
-    "import gdstk"
+    "from matplotlib import pyplot as plt\n",
+    "from tidy3d import web"
    ]
   },
   {
@@ -88,7 +87,7 @@
     "y_length_bend = 10\n",
     "wg_spacing = 3.5\n",
     "taper_length = 10\n",
-    "pin_length = 200\n"
+    "pin_length = 200"
    ]
   },
   {
@@ -122,7 +121,7 @@
     "\n",
     "Before we can start the Charge simulation, let us create a semiconductor medium. Semiconductor mediums are constructed with the class [`SemiconductorMedium`](url=https://docs.flexcompute.com/projects/tidy3d/en/v2.8.0rc2/api/_autosummary/tidy3d.SemiconductorMedium.html#tidy3d.SemiconductorMedium). \n",
     "\n",
-    "Since semiconductor mediums accept doping, let's create those first. Doping is defined here in terms of boxes. Among the different types of doping boxes available we'll be using here the [`ConstantDoping`](https://docs.flexcompute.com/projects/tidy3d/en/v2.8.0rc2/api/_autosummary/tidy3d.ConstantDoping.html#tidy3d.ConstantDoping) which applies a constant doping to the doping box. One thing to note is that these doping boxes are additive, i.e., if two donor doping boxes overlap the total concentration in the overlap region will be sum of these two overlaping doping boxes. To following further demonstrate this concept"
+    "Since semiconductor mediums accept doping, let's create those first. Doping is defined here in terms of boxes. Among the different types of doping boxes available we'll be using here the [`ConstantDoping`](https://docs.flexcompute.com/projects/tidy3d/en/v2.8.0rc2/api/_autosummary/tidy3d.ConstantDoping.html#tidy3d.ConstantDoping), which applies a constant doping to the doping box. One thing to note is that these doping boxes are additive, i.e., if two donor doping boxes overlap, the total concentration in the overlap region will be the sum of these two overlapping doping boxes. To further demonstrate this concept:"
    ]
   },
   {
@@ -136,17 +135,33 @@
     "donors = []\n",
     "\n",
     "acceptors.append(\n",
-    "    td.ConstantDoping.from_bounds(rmin=[-td.inf, wg_spacing/2-x_total, -1], rmax=[td.inf, wg_spacing/2-x_p, h_side+1], concentration=conc_pp)\n",
+    "    td.ConstantDoping.from_bounds(\n",
+    "        rmin=[-td.inf, wg_spacing / 2 - x_total, -1],\n",
+    "        rmax=[td.inf, wg_spacing / 2 - x_p, h_side + 1],\n",
+    "        concentration=conc_pp,\n",
+    "    )\n",
     ")\n",
     "acceptors.append(\n",
-    "    td.ConstantDoping.from_bounds(rmin=[-td.inf, wg_spacing/2-x_total, -1], rmax=[td.inf, wg_spacing/2-x_i, h_side+1], concentration=conc_p)\n",
+    "    td.ConstantDoping.from_bounds(\n",
+    "        rmin=[-td.inf, wg_spacing / 2 - x_total, -1],\n",
+    "        rmax=[td.inf, wg_spacing / 2 - x_i, h_side + 1],\n",
+    "        concentration=conc_p,\n",
+    "    )\n",
     ")\n",
     "\n",
     "donors.append(\n",
-    "    td.ConstantDoping.from_bounds(rmin=[-td.inf, wg_spacing/2+x_p, -1], rmax=[td.inf, wg_spacing/2+x_total, h_side+1], concentration=conc_nn)\n",
+    "    td.ConstantDoping.from_bounds(\n",
+    "        rmin=[-td.inf, wg_spacing / 2 + x_p, -1],\n",
+    "        rmax=[td.inf, wg_spacing / 2 + x_total, h_side + 1],\n",
+    "        concentration=conc_nn,\n",
+    "    )\n",
     ")\n",
     "donors.append(\n",
-    "    td.ConstantDoping.from_bounds(rmin=[-td.inf, wg_spacing/2+x_i, -1], rmax=[td.inf, wg_spacing/2+x_total, h_side+1], concentration=conc_n)\n",
+    "    td.ConstantDoping.from_bounds(\n",
+    "        rmin=[-td.inf, wg_spacing / 2 + x_i, -1],\n",
+    "        rmax=[td.inf, wg_spacing / 2 + x_total, h_side + 1],\n",
+    "        concentration=conc_n,\n",
+    "    )\n",
     ")"
    ]
   },
@@ -159,7 +174,7 @@
    "source": [
     "# let's define a material here for our Charge simulations\n",
     "si_doped = td.MultiPhysicsMedium(\n",
-    "    optical=td.material_library['cSi']['Li1993_293K'],\n",
+    "    optical=td.material_library[\"cSi\"][\"Li1993_293K\"],\n",
     "    charge=td.SemiconductorMedium(\n",
     "        permittivity=11.1,\n",
     "        N_c=2.86e19,\n",
@@ -198,7 +213,7 @@
     "\n",
     "si_non_perturb = td.Medium.from_nk(n=n_si, k=k_si, freq=freq0)\n",
     "\n",
-    "sio2 = td.Medium(permittivity=1.444 ** 2)"
+    "sio2 = td.Medium(permittivity=1.444**2)"
    ]
   },
   {
@@ -267,7 +282,19 @@
    "outputs": [],
    "source": [
     "def make_rib_waveguide(\n",
-    "    x0, y0, z0, x1, y1, core_width, slab_width, side_width, core_thickness, slab_thickness, side_thickness, medium, sidewall_angle=0\n",
+    "    x0,\n",
+    "    y0,\n",
+    "    z0,\n",
+    "    x1,\n",
+    "    y1,\n",
+    "    core_width,\n",
+    "    slab_width,\n",
+    "    side_width,\n",
+    "    core_thickness,\n",
+    "    slab_thickness,\n",
+    "    side_thickness,\n",
+    "    medium,\n",
+    "    sidewall_angle=0,\n",
     "):\n",
     "    \"\"\"\n",
     "    This function defines a linear waveguide taper and returns the tidy3d structure of it.\n",
@@ -288,59 +315,59 @@
     "    medium: medium of the waveguide\n",
     "    sidewall_angle: side wall angle of the waveguide (rad)\n",
     "    \"\"\"\n",
-    "    \n",
+    "\n",
     "    # modulator\n",
     "    slab = make_waveguide(\n",
-    "        x0=x0, \n",
-    "        y0=y0, \n",
-    "        z0=z0, \n",
-    "        x1=x1, \n",
-    "        y1=y1, \n",
-    "        wg_width_0=slab_width, \n",
-    "        wg_width_1=slab_width, \n",
+    "        x0=x0,\n",
+    "        y0=y0,\n",
+    "        z0=z0,\n",
+    "        x1=x1,\n",
+    "        y1=y1,\n",
+    "        wg_width_0=slab_width,\n",
+    "        wg_width_1=slab_width,\n",
     "        wg_thickness=slab_thickness,\n",
     "        medium=medium,\n",
     "        sidewall_angle=sidewall_angle,\n",
     "    )\n",
-    "    \n",
+    "\n",
     "    core = make_waveguide(\n",
-    "        x0=x0, \n",
-    "        y0=y0, \n",
-    "        z0=z0, \n",
-    "        x1=x1, \n",
-    "        y1=y1, \n",
-    "        wg_width_0=core_width, \n",
-    "        wg_width_1=core_width, \n",
+    "        x0=x0,\n",
+    "        y0=y0,\n",
+    "        z0=z0,\n",
+    "        x1=x1,\n",
+    "        y1=y1,\n",
+    "        wg_width_0=core_width,\n",
+    "        wg_width_1=core_width,\n",
     "        wg_thickness=core_thickness,\n",
     "        medium=medium,\n",
     "        sidewall_angle=sidewall_angle,\n",
     "    )\n",
-    "    \n",
+    "\n",
     "    y_side_top = y0 + (slab_width / 2 - side_width / 2)\n",
     "    y_side_bottom = y0 - (slab_width / 2 - side_width / 2)\n",
-    "    \n",
+    "\n",
     "    side_top = make_waveguide(\n",
-    "        x0=x0, \n",
-    "        y0=y_side_top, \n",
-    "        z0=z0, \n",
-    "        x1=x1, \n",
+    "        x0=x0,\n",
+    "        y0=y_side_top,\n",
+    "        z0=z0,\n",
+    "        x1=x1,\n",
     "        y1=y_side_top,\n",
-    "        wg_width_0=side_width, \n",
-    "        wg_width_1=side_width, \n",
-    "        wg_thickness=side_thickness, \n",
+    "        wg_width_0=side_width,\n",
+    "        wg_width_1=side_width,\n",
+    "        wg_thickness=side_thickness,\n",
     "        medium=medium,\n",
     "        sidewall_angle=sidewall_angle,\n",
     "    )\n",
-    "    \n",
+    "\n",
     "    side_bottom = make_waveguide(\n",
-    "        x0=x0, \n",
-    "        y0=y_side_bottom, \n",
-    "        z0=z0, \n",
-    "        x1=x1, \n",
+    "        x0=x0,\n",
+    "        y0=y_side_bottom,\n",
+    "        z0=z0,\n",
+    "        x1=x1,\n",
     "        y1=y_side_bottom,\n",
-    "        wg_width_0=side_width, \n",
-    "        wg_width_1=side_width, \n",
-    "        wg_thickness=side_thickness, \n",
+    "        wg_width_0=side_width,\n",
+    "        wg_width_1=side_width,\n",
+    "        wg_thickness=side_thickness,\n",
     "        medium=medium,\n",
     "        sidewall_angle=sidewall_angle,\n",
     "    )\n",
@@ -366,16 +393,16 @@
    "source": [
     "# top arm: PIN region\n",
     "pin_wg = make_rib_waveguide(\n",
-    "    x0=-pin_length / 2, \n",
-    "    y0=wg_spacing / 2, \n",
-    "    z0=0, \n",
-    "    x1=pin_length / 2, \n",
-    "    y1=wg_spacing / 2, \n",
-    "    core_width=w_core, \n",
+    "    x0=-pin_length / 2,\n",
+    "    y0=wg_spacing / 2,\n",
+    "    z0=0,\n",
+    "    x1=pin_length / 2,\n",
+    "    y1=wg_spacing / 2,\n",
+    "    core_width=w_core,\n",
     "    slab_width=2 * x_total,\n",
     "    side_width=x_total - x_side,\n",
-    "    core_thickness=h_core, \n",
-    "    slab_thickness=h_slab, \n",
+    "    core_thickness=h_core,\n",
+    "    slab_thickness=h_slab,\n",
     "    side_thickness=h_side,\n",
     "    medium=si_doped,\n",
     "    sidewall_angle=0,\n",
@@ -383,21 +410,26 @@
     "\n",
     "# auxiliary materials we use to define BCs\n",
     "aux_medium = td.MultiPhysicsMedium(\n",
-    "    charge=td.ChargeConductorMedium(conductivity=1),\n",
-    "    name=\"aux_medium\"\n",
+    "    charge=td.ChargeConductorMedium(conductivity=1), name=\"aux_medium\"\n",
     ")\n",
     "\n",
     "# create a couple structs to define the contacts\n",
     "contact_p = td.Structure(\n",
-    "    geometry=td.Box(center=(0, wg_spacing/2 - x_total + w_contact/2, h_side + h_contact/2), size=(td.inf, w_contact, h_contact)),\n",
+    "    geometry=td.Box(\n",
+    "        center=(0, wg_spacing / 2 - x_total + w_contact / 2, h_side + h_contact / 2),\n",
+    "        size=(td.inf, w_contact, h_contact),\n",
+    "    ),\n",
     "    medium=aux_medium,\n",
-    "    name=\"contact_p\"\n",
+    "    name=\"contact_p\",\n",
     ")\n",
     "\n",
     "contact_n = td.Structure(\n",
-    "    geometry=td.Box(center=(0, wg_spacing/2 + x_total - w_contact/2, h_side + h_contact/2), size=(td.inf, w_contact, h_contact)),\n",
+    "    geometry=td.Box(\n",
+    "        center=(0, wg_spacing / 2 + x_total - w_contact / 2, h_side + h_contact / 2),\n",
+    "        size=(td.inf, w_contact, h_contact),\n",
+    "    ),\n",
     "    medium=aux_medium,\n",
-    "    name=\"contact_n\"\n",
+    "    name=\"contact_n\",\n",
     ")"
    ]
   },
@@ -429,7 +461,7 @@
    ],
    "source": [
     "scene_charge = td.Scene(\n",
-    "    structures=pin_wg + [contact_p, contact_n], \n",
+    "    structures=pin_wg + [contact_p, contact_n],\n",
     "    medium=sio2,\n",
     ")\n",
     "\n",
@@ -475,8 +507,8 @@
    "metadata": {},
    "source": [
     "### Boundary conditions\n",
-    "Since we're interested in the response of the system for different applied voltages we'll need to solve the charge problem at each of these voltages. \n",
-    "In Charge this can readily be done since the [`VoltageBC`](https://docs.flexcompute.com/projects/tidy3d/en/v2.8.0rc2/api/_autosummary/tidy3d.VoltageBC.html#tidy3d.VoltageBC) can accpet an array of voltages as source through [`DCVoltageSource`](https://docs.flexcompute.com/projects/tidy3d/en/v2.8.0rc2/api/_autosummary/tidy3d.DCVoltageSource.html#tidy3d.DCVoltageSource). A parameter scan will be run and the returned data will have the provided voltage values as a separate dimension.\n",
+    "Since we're interested in the response of the system for different applied voltages, we'll need to solve the charge problem at each of these voltages. \n",
+    "In Charge this can readily be done since the [`VoltageBC`](https://docs.flexcompute.com/projects/tidy3d/en/v2.8.0rc2/api/_autosummary/tidy3d.VoltageBC.html#tidy3d.VoltageBC) can accept an array of voltages as source through [`DCVoltageSource`](https://docs.flexcompute.com/projects/tidy3d/en/v2.8.0rc2/api/_autosummary/tidy3d.DCVoltageSource.html#tidy3d.DCVoltageSource). A parameter scan will be run and the returned data will have the provided voltage values as a separate dimension.\n",
     " \n",
     "\n",
     "Let's define forward bias values up to 1.2 V with a step of 0.1 V."
@@ -522,7 +554,10 @@
    "outputs": [],
    "source": [
     "charge_mnt = td.SteadyFreeCarrierMonitor(\n",
-    "    center=(0, 0, 0), size=(0, td.inf, td.inf), name=\"charge_mnt\", unstructured=True, \n",
+    "    center=(0, 0, 0),\n",
+    "    size=(0, td.inf, td.inf),\n",
+    "    name=\"charge_mnt\",\n",
+    "    unstructured=True,\n",
     ")"
    ]
   },
@@ -548,7 +583,9 @@
    "source": [
     "convergence_settings = td.ChargeToleranceSpec(rel_tol=1e-5, abs_tol=5e10, max_iters=400)\n",
     "\n",
-    "analysis_type = td.IsothermalSteadyChargeDCAnalysis(temperature=300, tolerance_settings=convergence_settings, convergence_dv=0.1)\n",
+    "analysis_type = td.IsothermalSteadyChargeDCAnalysis(\n",
+    "    temperature=300, tolerance_settings=convergence_settings, convergence_dv=0.1\n",
+    ")\n",
     "\n",
     "res = 0.005\n",
     "mesh = td.UniformUnstructuredGrid(dl=res, relative_min_dl=0)"
@@ -630,13 +667,13 @@
     "    sources=[],\n",
     "    monitors=[charge_mnt],\n",
     "    analysis_spec=analysis_type,\n",
-    "    center=(0,wg_spacing/2, (h_side+h_contact)/2),\n",
-    "    size=(0, 2*x_total, h_side+h_contact),\n",
+    "    center=(0, wg_spacing / 2, (h_side + h_contact) / 2),\n",
+    "    size=(0, 2 * x_total, h_side + h_contact),\n",
     "    structures=scene_charge.structures,\n",
     "    medium=scene_charge.medium,\n",
     "    boundary_spec=boundary_conditions,\n",
     "    grid_spec=mesh,\n",
-    "    symmetry=(0, 0, 0)\n",
+    "    symmetry=(0, 0, 0),\n",
     ")"
    ]
   },
@@ -1025,7 +1062,7 @@
     }
    ],
    "source": [
-    "charge_data=web.run(charge_sim, task_name=\"mzi_pin\", path=\"charge_mzi_pin.hdf5\")"
+    "charge_data = web.run(charge_sim, task_name=\"mzi_pin\", path=\"charge_mzi_pin.hdf5\")"
    ]
   },
   {
@@ -1133,10 +1170,10 @@
     "Ne_range = np.concatenate(([0], np.logspace(15, 20, 20)))\n",
     "Nh_range = np.concatenate(([0], np.logspace(15, 20, 21)))\n",
     "\n",
-    "Ne_mesh, Nh_mesh = np.meshgrid(Ne_range, Nh_range, indexing='ij')\n",
+    "Ne_mesh, Nh_mesh = np.meshgrid(Ne_range, Nh_range, indexing=\"ij\")\n",
     "\n",
-    "dn_mesh = ne_coeff * Ne_mesh ** ne_pow + nh_coeff * Nh_mesh ** nh_pow\n",
-    "dk_mesh = ke_coeff * Ne_mesh ** ke_pow + kh_coeff * Nh_mesh ** kh_pow"
+    "dn_mesh = ne_coeff * Ne_mesh**ne_pow + nh_coeff * Nh_mesh**nh_pow\n",
+    "dk_mesh = ke_coeff * Ne_mesh**ke_pow + kh_coeff * Nh_mesh**kh_pow"
    ]
   },
   {
@@ -1174,7 +1211,7 @@
     "        delta_n=n_si_perturbation,\n",
     "        delta_k=k_si_perturbation,\n",
     "        freq=freq0,\n",
-    "    )\n",
+    "    ),\n",
     ")"
    ]
   },
@@ -1195,8 +1232,18 @@
    "outputs": [],
    "source": [
     "def make_y_junction(\n",
-    "    x0, y0, z0, wg_thickness, wg_spacing, wg_length_in, wg_length_out, bend_length, direction, medium, sidewall_angle=0,\n",
-    "):    \n",
+    "    x0,\n",
+    "    y0,\n",
+    "    z0,\n",
+    "    wg_thickness,\n",
+    "    wg_spacing,\n",
+    "    wg_length_in,\n",
+    "    wg_length_out,\n",
+    "    bend_length,\n",
+    "    direction,\n",
+    "    medium,\n",
+    "    sidewall_angle=0,\n",
+    "):\n",
     "    \"\"\"\n",
     "    This function defines a waveguide y junction and returns the tidy3d structure of it.\n",
     "    The width of waveguides are set to 500 nm.\n",
@@ -1216,7 +1263,7 @@
     "    sidewall_angle: side wall angle of the waveguide (rad)\n",
     "    \"\"\"\n",
     "    # parameters of y junction\n",
-    "    \n",
+    "\n",
     "    # width of the 13 segments\n",
     "    w1 = 0.5\n",
     "    w2 = 0.5\n",
@@ -1231,9 +1278,9 @@
     "    w11 = 1.31\n",
     "    w12 = 1.2\n",
     "    w13 = 1.2\n",
-    "    \n",
+    "\n",
     "    l_junction = 2  # length of the junction\n",
-    "    \n",
+    "\n",
     "    if wg_length_in < l_junction:\n",
     "        raise ValueError(f\"'wg_length_in' cannot be less than {l_junction}.\")\n",
     "\n",
@@ -1244,15 +1291,15 @@
     "    x1 = x0 - direction * wg_length_out\n",
     "    x2 = x1 - direction * bend_length\n",
     "    x3 = x2 - direction * l_junction\n",
-    "    x4 = x2 - direction * wg_length_in    \n",
+    "    x4 = x2 - direction * wg_length_in\n",
     "\n",
     "    slab_bounds = (z0, z0 + wg_thickness)\n",
     "\n",
     "    # straight input waveguide\n",
     "    wg_in = td.PolySlab(\n",
-    "        vertices=[(x4, y0 - w1 / 2), (x3, y0 - w1 / 2), (x3, y0 + w1 / 2), (x4, y0 + w1 / 2)], \n",
-    "        axis=2, \n",
-    "        slab_bounds=slab_bounds, \n",
+    "        vertices=[(x4, y0 - w1 / 2), (x3, y0 - w1 / 2), (x3, y0 + w1 / 2), (x4, y0 + w1 / 2)],\n",
+    "        axis=2,\n",
+    "        slab_bounds=slab_bounds,\n",
     "        sidewall_angle=sidewall_angle,\n",
     "    )\n",
     "\n",
@@ -1261,36 +1308,37 @@
     "    y = np.array(\n",
     "        [w1, w2, w3, w4, w5, w6, w7, w8, w9, w10, w11, w12, w13]\n",
     "    )  # y coordinates of the top edge vertices\n",
-    "    \n",
+    "\n",
     "    # using concatenate to include bottom edge vertices\n",
     "    x = np.concatenate((x, np.flipud(x)))\n",
     "    y = y0 + np.concatenate((y / 2, -np.flipud(y / 2)))\n",
-    "    \n",
+    "\n",
     "    # stacking x and y coordinates to form vertices pairs\n",
     "    vertices = np.transpose(np.vstack((x, y)))\n",
-    "    \n",
-    "    junction = td.PolySlab(vertices=vertices, axis=2, slab_bounds=slab_bounds, sidewall_angle=sidewall_angle)\n",
+    "\n",
+    "    junction = td.PolySlab(\n",
+    "        vertices=vertices, axis=2, slab_bounds=slab_bounds, sidewall_angle=sidewall_angle\n",
+    "    )\n",
     "\n",
     "    # bends and output waveguides\n",
-    "    x = np.linspace(\n",
-    "        x2, x1, 100\n",
-    "    )  # x coordinates of the top edge vertices\n",
-    "    \n",
+    "    x = np.linspace(x2, x1, 100)  # x coordinates of the top edge vertices\n",
+    "\n",
     "    y = (\n",
     "        np.abs(x - x2) * h_bend / bend_length\n",
     "        - h_bend * np.sin(2 * np.pi * np.abs(x - x2) / bend_length) / (np.pi * 2)\n",
-    "        + w13 / 2 - w1 / 2\n",
+    "        + w13 / 2\n",
+    "        - w1 / 2\n",
     "    )  # y coordinates of the top edge vertices\n",
-    "    \n",
+    "\n",
     "    # adding the last point to include the straight waveguide at the output\n",
     "    x = np.append(x, x0)\n",
     "    y = np.append(y, y[-1])\n",
-    "    \n",
+    "\n",
     "    # add path to the cell\n",
     "    cell = gdstk.Cell(\"bends\")\n",
     "    cell.add(gdstk.FlexPath(x + 1j * (y + y0), w1, layer=1, datatype=0))  # top waveguide bend\n",
     "    cell.add(gdstk.FlexPath(x - 1j * (y - y0), w1, layer=1, datatype=0))  # bottom waveguide bend\n",
-    "    \n",
+    "\n",
     "    wg_bends = td.PolySlab.from_gds(\n",
     "        cell,\n",
     "        gds_layer=1,\n",
@@ -1299,9 +1347,9 @@
     "        sidewall_angle=sidewall_angle,\n",
     "    )\n",
     "\n",
-    "    # combine all components into a single struture using GeometryGroup\n",
+    "    # combine all components into a single structure using GeometryGroup\n",
     "    y_junction = td.Structure(\n",
-    "        geometry=td.GeometryGroup(geometries=[wg_in, junction] + wg_bends), \n",
+    "        geometry=td.GeometryGroup(geometries=[wg_in, junction] + wg_bends),\n",
     "        medium=medium,\n",
     "    )\n",
     "\n",
@@ -1324,7 +1372,17 @@
    "outputs": [],
    "source": [
     "def make_strip_rib_taper(\n",
-    "    x0, y0, z0, x1, y1, core_width, slab_width, core_thickness, slab_thickness, medium, sidewall_angle=0\n",
+    "    x0,\n",
+    "    y0,\n",
+    "    z0,\n",
+    "    x1,\n",
+    "    y1,\n",
+    "    core_width,\n",
+    "    slab_width,\n",
+    "    core_thickness,\n",
+    "    slab_thickness,\n",
+    "    medium,\n",
+    "    sidewall_angle=0,\n",
     "):\n",
     "    \"\"\"\n",
     "    This function defines a linear waveguide taper and returns the tidy3d structure of it.\n",
@@ -1346,28 +1404,28 @@
     "\n",
     "    # core\n",
     "    core = make_waveguide(\n",
-    "        x0=x0, \n",
-    "        y0=y0, \n",
-    "        z0=z0, \n",
-    "        x1=x1, \n",
-    "        y1=y1, \n",
-    "        wg_width_0=core_width, \n",
-    "        wg_width_1=core_width, \n",
-    "        wg_thickness=core_thickness, \n",
+    "        x0=x0,\n",
+    "        y0=y0,\n",
+    "        z0=z0,\n",
+    "        x1=x1,\n",
+    "        y1=y1,\n",
+    "        wg_width_0=core_width,\n",
+    "        wg_width_1=core_width,\n",
+    "        wg_thickness=core_thickness,\n",
     "        medium=medium,\n",
     "        sidewall_angle=sidewall_angle,\n",
     "    )\n",
     "\n",
     "    # slab\n",
     "    slab = make_waveguide(\n",
-    "        x0=x0, \n",
-    "        y0=y0, \n",
-    "        z0=z0, \n",
-    "        x1=x1, \n",
-    "        y1=y1, \n",
-    "        wg_width_0=core_width, \n",
-    "        wg_width_1=slab_width, \n",
-    "        wg_thickness=slab_thickness, \n",
+    "        x0=x0,\n",
+    "        y0=y0,\n",
+    "        z0=z0,\n",
+    "        x1=x1,\n",
+    "        y1=y1,\n",
+    "        wg_width_0=core_width,\n",
+    "        wg_width_1=slab_width,\n",
+    "        wg_thickness=slab_thickness,\n",
     "        medium=medium,\n",
     "        sidewall_angle=sidewall_angle,\n",
     "    )\n",
@@ -1422,45 +1480,45 @@
     "\n",
     "# bottom arm\n",
     "bot_arm = make_waveguide(\n",
-    "    x0=-mzi_length / 2, \n",
-    "    y0=-wg_spacing / 2, \n",
-    "    z0=0, \n",
-    "    x1=mzi_length / 2, \n",
-    "    y1=-wg_spacing / 2, \n",
-    "    wg_width_0=w_core, \n",
-    "    wg_width_1=w_core, \n",
-    "    wg_thickness=h_core, \n",
+    "    x0=-mzi_length / 2,\n",
+    "    y0=-wg_spacing / 2,\n",
+    "    z0=0,\n",
+    "    x1=mzi_length / 2,\n",
+    "    y1=-wg_spacing / 2,\n",
+    "    wg_width_0=w_core,\n",
+    "    wg_width_1=w_core,\n",
+    "    wg_thickness=h_core,\n",
     "    medium=si_non_perturb,\n",
     "    sidewall_angle=0,\n",
     ")\n",
     "\n",
     "# top arm: taper in\n",
     "taper_in = make_strip_rib_taper(\n",
-    "    x0=-mzi_length / 2, \n",
-    "    y0=wg_spacing / 2, \n",
-    "    z0=0, \n",
-    "    x1=-pin_length / 2, \n",
-    "    y1=wg_spacing / 2, \n",
-    "    core_width=w_core, \n",
-    "    slab_width=2 * x_side, \n",
-    "    core_thickness=h_core, \n",
-    "    slab_thickness=h_slab, \n",
+    "    x0=-mzi_length / 2,\n",
+    "    y0=wg_spacing / 2,\n",
+    "    z0=0,\n",
+    "    x1=-pin_length / 2,\n",
+    "    y1=wg_spacing / 2,\n",
+    "    core_width=w_core,\n",
+    "    slab_width=2 * x_side,\n",
+    "    core_thickness=h_core,\n",
+    "    slab_thickness=h_slab,\n",
     "    medium=si_non_perturb,\n",
     "    sidewall_angle=0,\n",
     ")\n",
     "\n",
     "# top arm: PIN region\n",
     "pin_wg = make_rib_waveguide(\n",
-    "    x0=-pin_length / 2, \n",
-    "    y0=wg_spacing / 2, \n",
-    "    z0=0, \n",
-    "    x1=pin_length / 2, \n",
-    "    y1=wg_spacing / 2, \n",
-    "    core_width=w_core, \n",
+    "    x0=-pin_length / 2,\n",
+    "    y0=wg_spacing / 2,\n",
+    "    z0=0,\n",
+    "    x1=pin_length / 2,\n",
+    "    y1=wg_spacing / 2,\n",
+    "    core_width=w_core,\n",
     "    slab_width=2 * x_total,\n",
     "    side_width=x_total - x_side,\n",
-    "    core_thickness=h_core, \n",
-    "    slab_thickness=h_slab, \n",
+    "    core_thickness=h_core,\n",
+    "    slab_thickness=h_slab,\n",
     "    side_thickness=h_side,\n",
     "    medium=si_perturb,\n",
     "    sidewall_angle=0,\n",
@@ -1469,15 +1527,15 @@
     "\n",
     "# top arm: taper out\n",
     "taper_out = make_strip_rib_taper(\n",
-    "    x0=mzi_length / 2, \n",
-    "    y0=wg_spacing / 2, \n",
-    "    z0=0, \n",
-    "    x1=pin_length / 2, \n",
-    "    y1=wg_spacing / 2, \n",
-    "    core_width=w_core, \n",
-    "    slab_width=2 * x_side, \n",
-    "    core_thickness=h_core, \n",
-    "    slab_thickness=h_slab, \n",
+    "    x0=mzi_length / 2,\n",
+    "    y0=wg_spacing / 2,\n",
+    "    z0=0,\n",
+    "    x1=pin_length / 2,\n",
+    "    y1=wg_spacing / 2,\n",
+    "    core_width=w_core,\n",
+    "    slab_width=2 * x_side,\n",
+    "    core_thickness=h_core,\n",
+    "    slab_thickness=h_slab,\n",
     "    medium=si_non_perturb,\n",
     "    sidewall_angle=0,\n",
     ")"
@@ -1510,7 +1568,7 @@
    ],
    "source": [
     "scene = td.Scene(\n",
-    "    structures=[coupler_in, coupler_out, bot_arm] + taper_in + pin_wg + taper_out, \n",
+    "    structures=[coupler_in, coupler_out, bot_arm] + taper_in + pin_wg + taper_out,\n",
     "    medium=sio2,\n",
     ")\n",
     "\n",
@@ -1520,7 +1578,7 @@
     "scene.plot(x=0, ax=ax[2])\n",
     "\n",
     "plt.tight_layout()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1574,10 +1632,10 @@
    "outputs": [],
    "source": [
     "src = td.ModeSource(\n",
-    "    center=(-port_x - 0.5, port_y, port_z), \n",
-    "    size=port_size, \n",
-    "    direction=\"+\", \n",
-    "    mode_index=0, \n",
+    "    center=(-port_x - 0.5, port_y, port_z),\n",
+    "    size=port_size,\n",
+    "    direction=\"+\",\n",
+    "    mode_index=0,\n",
     "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
     ")"
    ]
@@ -1597,9 +1655,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mnt_in = td.ModeMonitor(center=(-port_x, port_y, port_z), size=port_size, freqs=freqs, mode_spec=td.ModeSpec(num_modes=3), name=\"in\")\n",
-    "mnt_out = td.ModeMonitor(center=(port_x, port_y, port_z), size=port_size, freqs=freqs, mode_spec=td.ModeSpec(num_modes=3), name=\"out\")\n",
-    "mnt_field = td.FieldMonitor(center=(0, 0, z_core), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\")"
+    "mnt_in = td.ModeMonitor(\n",
+    "    center=(-port_x, port_y, port_z),\n",
+    "    size=port_size,\n",
+    "    freqs=freqs,\n",
+    "    mode_spec=td.ModeSpec(num_modes=3),\n",
+    "    name=\"in\",\n",
+    ")\n",
+    "mnt_out = td.ModeMonitor(\n",
+    "    center=(port_x, port_y, port_z),\n",
+    "    size=port_size,\n",
+    "    freqs=freqs,\n",
+    "    mode_spec=td.ModeSpec(num_modes=3),\n",
+    "    name=\"out\",\n",
+    ")\n",
+    "mnt_field = td.FieldMonitor(\n",
+    "    center=(0, 0, z_core), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\"\n",
+    ")"
    ]
   },
   {
@@ -1819,12 +1891,13 @@
     "        h_data = charge_data[\"charge_mnt\"].holes.sel(voltage=v)\n",
     "        perturbed_sims.append(\n",
     "            sim.perturbed_mediums_copy(\n",
-    "                electron_density=e_data, \n",
+    "                electron_density=e_data,\n",
     "                hole_density=h_data,\n",
     "            )\n",
     "        )\n",
     "    return perturbed_sims\n",
     "\n",
+    "\n",
     "perturbed_sims = apply_charge(charge_data)"
    ]
   },
@@ -1864,7 +1937,7 @@
     "    eps_doped = eps_doped.interp(y=eps_undoped.y, z=eps_undoped.z)\n",
     "    eps_diff = np.abs(np.real(eps_doped - eps_undoped))\n",
     "    eps_diff.plot(x=\"y\", ax=ax[ax_ind])\n",
-    "    \n",
+    "\n",
     "    ax[ax_ind].set_aspect(\"equal\")\n",
     "    ax[ax_ind].set_title(f\"Bias: {voltages[ind]:1.1f} V\")\n",
     "    ax[ax_ind].set_xlabel(\"y (um)\")\n",
@@ -2184,7 +2257,7 @@
     "ax[0].axhline(y=-1, color=\"k\", linestyle=\"--\")\n",
     "\n",
     "ax[0].set_xlabel(\"Bias (V)\")\n",
-    "ax[0].set_ylabel(\"Phase shift ($\\pi$)\")\n",
+    "ax[0].set_ylabel(r\"Phase shift ($\\pi$)\")\n",
     "\n",
     "ax[1].plot(voltages, 10 * np.log10(intensity), \".-\")\n",
     "\n",
@@ -2641,7 +2714,7 @@
     "\n",
     "boundary_conditions = [bc_v1, bc_v2]\n",
     "\n",
-    "charge_sim_95 = charge_sim.updated_copy(boundary_spec=boundary_conditions) \n",
+    "charge_sim_95 = charge_sim.updated_copy(boundary_spec=boundary_conditions)\n",
     "\n",
     "charge_data_95 = web.run(charge_sim_95, task_name=\"mzi_pin_Vpi\", path=\"charge_mzi_pin_Vpi.hdf5\")"
    ]
@@ -2686,7 +2759,7 @@
     "    eps_doped = eps_doped.interp(y=eps_undoped.y, z=eps_undoped.z)\n",
     "    eps_diff = np.abs(np.real(eps_doped - eps_undoped))\n",
     "    eps_diff.plot(x=\"y\", ax=ax[ax_ind])\n",
-    "    \n",
+    "\n",
     "    ax[ax_ind].set_aspect(\"equal\")\n",
     "    ax[ax_ind].set_title(f\"Bias: {[0, 0.95][ax_ind]:1.2f} V\")\n",
     "    ax[ax_ind].set_xlabel(\"y (um)\")\n",
@@ -2838,7 +2911,9 @@
     }
    ],
    "source": [
-    "batch = web.Batch(simulations={f\"Bias: 0 V\": perturbed_sims[0], f\"Bias: 0.95 V\": perturbed_sim_0_95V[-1]})\n",
+    "batch = web.Batch(\n",
+    "    simulations={\"Bias: 0 V\": perturbed_sims[0], \"Bias: 0.95 V\": perturbed_sim_0_95V[-1]}\n",
+    ")\n",
     "batch_data = batch.run()"
    ]
   },
@@ -2958,7 +3033,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.11.0"
   },
   "title": "Modeling electro-optic Mach-Zehnder modulator using Tidy3D | Flexcompute"
  },
diff --git a/MetalHeaterPhaseShifter.ipynb b/MetalHeaterPhaseShifter.ipynb
index 5abb7d78..a3d0df16 100644
--- a/MetalHeaterPhaseShifter.ipynb
+++ b/MetalHeaterPhaseShifter.ipynb
@@ -20,10 +20,9 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n"
+    "import tidy3d.web as web\n",
+    "from matplotlib import pyplot as plt"
    ]
   },
   {
@@ -78,7 +77,7 @@
     "# heater parameters\n",
     "h_heater = 0.14\n",
     "w_heater = 2\n",
-    "d_heater = 2  # distance between heater and waveguide\n"
+    "d_heater = 2  # distance between heater and waveguide"
    ]
   },
   {
@@ -108,7 +107,7 @@
     "z_box_clad = (box_clad_max + box_clad_min) / 2\n",
     "\n",
     "z_sim = (h_box + h_clad - h_wafer) / 2\n",
-    "h_sim = h_box + h_clad + h_wafer + 2 * h_buffer\n"
+    "h_sim = h_box + h_clad + h_wafer + 2 * h_buffer"
    ]
   },
   {
@@ -138,7 +137,7 @@
     "\n",
     "TiN_k = 28e-6  # TiN thermal conductivity W/(um*K)\n",
     "TiN_s = 0.598 * 5240e-12  # TiN volumetric heat capacity, J / (um^3 * K)\n",
-    "TiN_sigma = 2.3  # Electric conductivity of TiN, S/um\n"
+    "TiN_sigma = 2.3  # Electric conductivity of TiN, S/um"
    ]
   },
   {
@@ -173,7 +172,7 @@
     "\n",
     "# an alternative but equivalent way of defining a medium specification\n",
     "SiO2 = td.PerturbationMedium(\n",
-    "    permittivity=SiO2_n ** 2,\n",
+    "    permittivity=SiO2_n**2,\n",
     "    perturbation_spec=td.IndexPerturbation(\n",
     "        delta_n=td.ParameterPerturbation(\n",
     "            heat=td.LinearHeatPerturbation(coeff=SiO2_n_slope, temperature_ref=300)\n",
@@ -195,7 +194,7 @@
     "    name=\"TiN\",\n",
     ")\n",
     "\n",
-    "air = td.Medium(heat_spec=td.FluidSpec(), name=\"air\")\n"
+    "air = td.Medium(heat_spec=td.FluidSpec(), name=\"air\")"
    ]
   },
   {
@@ -233,7 +232,7 @@
     "    geometry=td.Box(center=(0, 0, z_wafer), size=(td.inf, td.inf, h_wafer)),\n",
     "    medium=Si,\n",
     "    name=\"wafer\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -254,7 +253,7 @@
     "scene = td.Scene(\n",
     "    medium=air,\n",
     "    structures=[box_clad, core, heater, wafer],\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -283,10 +282,8 @@
     }
    ],
    "source": [
-    "scene.plot(\n",
-    "    y=0, hlim=[-w_sim / 2, w_sim / 2], vlim=[z_sim - h_sim / 2, z_sim + h_sim / 2]\n",
-    ")\n",
-    "plt.show()\n"
+    "scene.plot(y=0, hlim=[-w_sim / 2, w_sim / 2], vlim=[z_sim - h_sim / 2, z_sim + h_sim / 2])\n",
+    "plt.show()"
    ]
   },
   {
@@ -316,7 +313,7 @@
     "bc_top = td.HeatBoundarySpec(\n",
     "    placement=td.MediumMediumInterface(mediums=[\"SiO2\", \"air\"]),\n",
     "    condition=td.HeatFluxBC(flux=0),\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -336,7 +333,7 @@
    "source": [
     "current = 7.4e-3  # A\n",
     "heat_rate = (current / h_heater / w_heater) ** 2 / TiN_sigma  # convert into power\n",
-    "heater_source = td.UniformHeatSource(rate=heat_rate, structures=[heater.name])\n"
+    "heater_source = td.UniformHeatSource(rate=heat_rate, structures=[heater.name])"
    ]
   },
   {
@@ -354,7 +351,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "temp_mnt = td.TemperatureMonitor(size=(td.inf, 0, td.inf), name=\"temperature\", unstructured=True, conformal=True)\n"
+    "temp_mnt = td.TemperatureMonitor(\n",
+    "    size=(td.inf, 0, td.inf), name=\"temperature\", unstructured=True, conformal=True\n",
+    ")"
    ]
   },
   {
@@ -382,7 +381,7 @@
     "    distance_interface=3 * dl_min,\n",
     "    distance_bulk=2 * d_heater,\n",
     "    non_refined_structures=[\"wafer\"],  # do not refine near wafer\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -419,7 +418,7 @@
     "    monitors=[temp_mnt],\n",
     "    symmetry=(1, 0, 0),\n",
     "    grid_spec=grid_spec,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -441,7 +440,7 @@
    ],
    "source": [
     "heat_sim.plot(y=0.0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -610,7 +609,7 @@
    ],
    "source": [
     "job = web.Job(simulation=heat_sim, task_name=\"heat_sim_check_mesh\")\n",
-    "heat_sim_data = job.run()\n"
+    "heat_sim_data = job.run()"
    ]
   },
   {
@@ -679,7 +678,7 @@
     "    heater_source = td.UniformHeatSource(rate=heat_rate, structures=[heater.name])\n",
     "\n",
     "    heat_sim_new = heat_sim.updated_copy(sources=[heater_source])\n",
-    "    heat_sims[f\"heat_wg_current_{ind}\"] = heat_sim_new\n"
+    "    heat_sims[f\"heat_wg_current_{ind}\"] = heat_sim_new"
    ]
   },
   {
@@ -697,7 +696,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "batch = web.Batch(simulations=heat_sims)\n"
+    "batch = web.Batch(simulations=heat_sims)"
    ]
   },
   {
@@ -873,7 +872,7 @@
     }
    ],
    "source": [
-    "batch_data = batch.run()\n"
+    "batch_data = batch.run()"
    ]
   },
   {
@@ -916,7 +915,7 @@
     "batch_data[\"heat_wg_current_9\"].plot_field(\"temperature\", ax=ax[2], vmax=temp_max_plot)\n",
     "ax[2].set_title(f\"Current I = {currents[9]:1.3} A\")\n",
     "\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -970,7 +969,7 @@
     "\n",
     "for _, hs_data in batch_data.items():\n",
     "    psim = optic_sim.perturbed_mediums_copy(temperature=hs_data[\"temperature\"].temperature)\n",
-    "    perturb_sims.append(psim)\n"
+    "    perturb_sims.append(psim)"
    ]
   },
   {
@@ -988,7 +987,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mode_plane = td.Box(center=(0, 0, z_core), size=(4, 0, 3))\n"
+    "mode_plane = td.Box(center=(0, 0, z_core), size=(4, 0, 3))"
    ]
   },
   {
@@ -1020,7 +1019,7 @@
     "fig, ax = plt.subplots(1, 1)\n",
     "perturb_sims[0].plot(y=0, ax=ax)\n",
     "mode_plane.plot(y=0, ax=ax, alpha=0.5)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1038,8 +1037,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from tidy3d.plugins.mode.web import run_batch as run_mode_batch\n",
-    "from tidy3d.plugins.mode import ModeSolver\n"
+    "from tidy3d.plugins.mode import ModeSolver\n",
+    "from tidy3d.plugins.mode.web import run_batch as run_mode_batch"
    ]
   },
   {
@@ -1067,7 +1066,7 @@
     "        freqs=[freq0],\n",
     "    )\n",
     "\n",
-    "    mode_solvers.append(ms)\n"
+    "    mode_solvers.append(ms)"
    ]
   },
   {
@@ -1345,7 +1344,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "n_eff = np.array([md.n_eff.isel(f=0, mode_index=0) for md in mode_data])\n"
+    "n_eff = np.array([md.n_eff.isel(f=0, mode_index=0) for md in mode_data])"
    ]
   },
   {
@@ -1375,7 +1374,7 @@
     "    2.4418833363425283,\n",
     "    2.4424252490914293,\n",
     "    2.443039460394046,\n",
-    "]\n"
+    "]"
    ]
   },
   {
@@ -1413,15 +1412,13 @@
    ],
    "source": [
     "print(f\"Phase shift over 320um: {2 * np.pi / 1.55 * (n_eff[-1] - n_eff[0]) * 320}\")\n",
-    "print(\n",
-    "    f\"Phase shift over 320um (ref): {2 * np.pi / 1.55 * (neff_ref[-1] - neff_ref[0]) * 320}\"\n",
-    ")\n",
+    "print(f\"Phase shift over 320um (ref): {2 * np.pi / 1.55 * (neff_ref[-1] - neff_ref[0]) * 320}\")\n",
     "plt.plot(currents * 1e3, n_eff[:] - n_eff[0], \"o:\")\n",
     "plt.plot(currents_ref * 1e3, np.array(neff_ref) - neff_ref[0])\n",
     "plt.xlabel(\"Current / mA\")\n",
     "plt.ylabel(\"Change in effective refractive index $n_{eff}$\")\n",
     "plt.legend([\"tidy3d\", \"reference\"])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
diff --git a/MetalOxideSunscreen.ipynb b/MetalOxideSunscreen.ipynb
index 10c7442d..64ee3420 100644
--- a/MetalOxideSunscreen.ipynb
+++ b/MetalOxideSunscreen.ipynb
@@ -41,9 +41,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tidy3d as td\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import tidy3d as td\n",
     "from tidy3d import web\n",
     "\n",
     "# defining a random seed for reproducibility\n",
@@ -122,10 +122,10 @@
    "source": [
     "from tidy3d.plugins.dispersion import FastDispersionFitter\n",
     "\n",
-    "url = 'https://refractiveindex.info/tmp/database/data-nk/main/TiO2/Zhukovsky.txt'\n",
+    "url = \"https://refractiveindex.info/tmp/database/data-nk/main/TiO2/Zhukovsky.txt\"\n",
     "\n",
     "# creating the fitter object loading that from the url\n",
-    "fitter = FastDispersionFitter.from_url(url,delimiter= '\\t')\n",
+    "fitter = FastDispersionFitter.from_url(url, delimiter=\"\\t\")\n",
     "\n",
     "# fitting the data\n",
     "medium, error = fitter.fit(max_num_poles=5)"
@@ -160,15 +160,15 @@
     "\n",
     "# original dataset\n",
     "freq = td.C_0 / fitter.wvl_um * 10**-12\n",
-    "ax.plot(freq, fitter.n_data, 'o', alpha=0.2, color=\"black\", label = 'n')\n",
-    "ax.plot(freq, fitter.k_data, 'o', alpha=0.2, color=\"black\", label = 'k')\n",
+    "ax.plot(freq, fitter.n_data, \"o\", alpha=0.2, color=\"black\", label=\"n\")\n",
+    "ax.plot(freq, fitter.k_data, \"o\", alpha=0.2, color=\"black\", label=\"k\")\n",
     "\n",
     "\n",
     "medium.plot(np.linspace(td.C_0 / 0.215, td.C_0 / 1.5, 1000), ax=ax)\n",
     "ax.lines[-1].set_ls(\"--\")\n",
-    "ax.lines[-1].set_label('Fitted n')\n",
+    "ax.lines[-1].set_label(\"Fitted n\")\n",
     "ax.lines[-2].set_ls(\"--\")\n",
-    "ax.lines[-2].set_label('Fitted k')"
+    "ax.lines[-2].set_label(\"Fitted k\")"
    ]
   },
   {
@@ -212,7 +212,7 @@
     "Lx = lx\n",
     "Ly = ly\n",
     "\n",
-    "# souce and monitor position\n",
+    "# source and monitor position\n",
     "monitor_z = (Lz / 2 - lz / 2) / 3\n",
     "source_z = -Lz / 2 + 2 * (Lz / 2 - lz / 2) / 3\n",
     "\n",
@@ -263,9 +263,7 @@
     "    )\n",
     "\n",
     "    # create a first geometry object and sum the others to form the agglomerate\n",
-    "    particles = td.Sphere(\n",
-    "        center=(positions_x[0], position_y[0], positions_z[0]), radius=size\n",
-    "    )\n",
+    "    particles = td.Sphere(center=(positions_x[0], position_y[0], positions_z[0]), radius=size)\n",
     "\n",
     "    for pos in zip(positions_x[1:], position_y[1:], positions_z[1:]):\n",
     "        particles += td.Sphere(center=pos, radius=size)\n",
@@ -412,19 +410,18 @@
     ")\n",
     "\n",
     "monitor_bottom = td.FluxMonitor(\n",
-    "        center=(0, 0, -Lz / 2 + monitor_z),\n",
-    "        size=(td.inf, td.inf, 0),\n",
-    "        freqs=freqs,\n",
-    "        name=\"z-\",\n",
-    "    )\n",
+    "    center=(0, 0, -Lz / 2 + monitor_z),\n",
+    "    size=(td.inf, td.inf, 0),\n",
+    "    freqs=freqs,\n",
+    "    name=\"z-\",\n",
+    ")\n",
     "\n",
     "monitor_top = td.FluxMonitor(\n",
-    "        center=(0, 0, Lz / 2 - monitor_z),\n",
-    "        size=(td.inf, td.inf, 0),\n",
-    "        freqs=freqs,\n",
-    "        name=\"z\",\n",
-    "    )\n",
-    "\n",
+    "    center=(0, 0, Lz / 2 - monitor_z),\n",
+    "    size=(td.inf, td.inf, 0),\n",
+    "    freqs=freqs,\n",
+    "    name=\"z\",\n",
+    ")\n",
     "\n",
     "\n",
     "mesh_override = td.MeshOverrideStructure(\n",
@@ -437,7 +434,7 @@
     "    size=(Lx, Ly, Lz),\n",
     "    structures=[],\n",
     "    sources=[source],\n",
-    "    monitors=[monitor_bottom,monitor_top],\n",
+    "    monitors=[monitor_bottom, monitor_top],\n",
     "    run_time=run_time,\n",
     "    boundary_spec=boundary_spec,\n",
     "    grid_spec=grid_spec,\n",
@@ -826,9 +823,9 @@
     "    R = abs(sim_data[\"z-\"].flux)\n",
     "    A = 1 - (T + R)\n",
     "\n",
-    "    df[\"Transmitted %s\" % sim.split()[-1]] = T\n",
-    "    df[\"Reflected %s\" % sim.split()[-1]] = R\n",
-    "    df[\"Absorbed %s\" % sim.split()[-1]] = A\n",
+    "    df[f\"Transmitted {sim.split()[-1]}\"] = T\n",
+    "    df[f\"Reflected {sim.split()[-1]}\"] = R\n",
+    "    df[f\"Absorbed {sim.split()[-1]}\"] = A\n",
     "\n",
     "df.head(10)"
    ]
@@ -883,24 +880,20 @@
     "ax = df[\"Transmitted 10%\"].plot()\n",
     "ax = df[\"Reflected 10%\"].plot(ax=ax)\n",
     "ax = df[\"Absorbed 10%\"].plot(ax=ax)\n",
-    "ax.set_xlabel('wavelength ($\\mu$m)')\n",
+    "ax.set_xlabel(r\"wavelength ($\\mu$m)\")\n",
     "ax.legend()\n",
     "\n",
     "fig, ax = plt.subplots()\n",
     "ax = df[\"Transmitted 20%\"].plot()\n",
     "ax = df[\"Reflected 20%\"].plot(ax=ax)\n",
     "ax = df[\"Absorbed 20%\"].plot(ax=ax)\n",
-    "ax.set_xlabel('wavelength ($\\mu$m)')\n",
+    "ax.set_xlabel(r\"wavelength ($\\mu$m)\")\n",
     "ax.legend()\n",
     "\n",
     "plt.show()\n",
     "\n",
-    "print(\n",
-    "    \"Proportion of scattered light at 10%s: %.2f\" % (\"%\", df[\"Reflected 10%\"].mean())\n",
-    ")\n",
-    "print(\n",
-    "    \"Proportion of scattered light at 20%s: %.2f\" % (\"%\", df[\"Reflected 20%\"].mean())\n",
-    ")"
+    "print(f\"Proportion of scattered light at 10%: {df['Reflected 10%'].mean():.2f}\")\n",
+    "print(f\"Proportion of scattered light at 20%: {df['Reflected 20%'].mean():.2f}\")"
    ]
   }
  ],
@@ -930,7 +923,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.0"
   },
   "title": "Study of the UV blocking mechanism of metal oxide-based sunscreens using Tidy3D | Flexcompute"
  },
diff --git a/Metalens.ipynb b/Metalens.ipynb
index 5b7e63c5..cd7028ec 100644
--- a/Metalens.ipynb
+++ b/Metalens.ipynb
@@ -46,12 +46,11 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
-    "from numpy import random\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
-    "from tidy3d import web\n"
+    "from numpy import random\n",
+    "from tidy3d import web"
    ]
   },
   {
@@ -123,7 +122,7 @@
     "n_SiO2 = 1.46\n",
     "air = td.Medium(permittivity=1.0)\n",
     "SiO2 = td.Medium(permittivity=n_SiO2**2)\n",
-    "TiO2 = td.Medium(permittivity=n_TiO2**2)\n"
+    "TiO2 = td.Medium(permittivity=n_TiO2**2)"
    ]
   },
   {
@@ -150,6 +149,7 @@
     "# focal length given diameter and numerical aperture\n",
     "f = length_xy / 2 / NA * np.sqrt(1 - NA**2)\n",
     "\n",
+    "\n",
     "# Function describing the theoretical best angle of each box at position (x,y).  see paper for details\n",
     "def theta(x, y):\n",
     "    return np.pi / wavelength * (f - np.sqrt(x**2 + y**2 + f**2))\n",
@@ -159,7 +159,7 @@
     "length_z = space_below_sub + thickness_sub + H + 1.7 * f\n",
     "\n",
     "# construct simulation size array\n",
-    "sim_size = (length_xy, length_xy, length_z)\n"
+    "sim_size = (length_xy, length_xy, length_z)"
    ]
   },
   {
@@ -204,9 +204,7 @@
     "center_z = -length_z / 2 + space_below_sub + thickness_sub + H / 2.0\n",
     "\n",
     "# x, y vertices of box of size (L, W) centered at the origin\n",
-    "vertices_origin = np.array(\n",
-    "    [[+L / 2, +W / 2], [-L / 2, +W / 2], [-L / 2, -W / 2], [+L / 2, -W / 2]]\n",
-    ")\n",
+    "vertices_origin = np.array([[+L / 2, +W / 2], [-L / 2, +W / 2], [-L / 2, -W / 2], [+L / 2, -W / 2]])\n",
     "\n",
     "\n",
     "xs, ys = np.meshgrid(centers_x, centers_y, indexing=\"ij\")\n",
@@ -216,9 +214,7 @@
     "angles = theta(xs, ys)\n",
     "\n",
     "# 2x2 rotation matrix angle `angle` with respect to x axis\n",
-    "rotation_matrix = np.array(\n",
-    "    [[+np.cos(angles), -np.sin(angles)], [+np.sin(angles), +np.cos(angles)]]\n",
-    ")\n",
+    "rotation_matrix = np.array([[+np.cos(angles), -np.sin(angles)], [+np.sin(angles), +np.cos(angles)]])\n",
     "\n",
     "# rotate the origin vertices by this angle\n",
     "vertices_rotated = np.einsum(\"ij, jkn -> nik\", vertices_origin, rotation_matrix)\n",
@@ -237,9 +233,7 @@
     "        ),\n",
     "    )\n",
     "\n",
-    "metalens = td.Structure(\n",
-    "    geometry=td.GeometryGroup(geometries=metalens_geometry), medium=TiO2\n",
-    ")\n"
+    "metalens = td.Structure(geometry=td.GeometryGroup(geometries=metalens_geometry), medium=TiO2)"
    ]
   },
   {
@@ -276,9 +270,7 @@
     "grid_z = td.AutoGrid(min_steps_per_wvl=grids_per_unit_length)\n",
     "\n",
     "# we need to supply the wavelength because of the automatic mesh in z\n",
-    "grid_spec = td.GridSpec(\n",
-    "    wavelength=wavelength, grid_x=grid_x, grid_y=grid_y, grid_z=grid_z\n",
-    ")\n",
+    "grid_spec = td.GridSpec(wavelength=wavelength, grid_x=grid_x, grid_y=grid_y, grid_z=grid_z)\n",
     "\n",
     "# put an override box over the pillars to avoid parsing a large amount of structures in the mesher\n",
     "grid_spec = grid_spec.copy(\n",
@@ -293,7 +285,7 @@
     "            )\n",
     "        ]\n",
     "    )\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -334,10 +326,9 @@
     ")\n",
     "\n",
     "gaussian_y = td.GaussianPulse(freq0=f0, fwidth=fwidth, phase=-np.pi / 2)\n",
-    "source_y = source_x.updated_copy(source_time = gaussian_y,\n",
-    "                                pol_angle=np.pi / 2)\n",
+    "source_y = source_x.updated_copy(source_time=gaussian_y, pol_angle=np.pi / 2)\n",
     "\n",
-    "run_time = 50 / fwidth\n"
+    "run_time = 50 / fwidth"
    ]
   },
   {
@@ -404,7 +395,7 @@
     ")\n",
     "\n",
     "# put them into a single list\n",
-    "monitors = [monitor_center, monitor_xz, monitor_yz, monitor_xy]\n"
+    "monitors = [monitor_center, monitor_xz, monitor_yz, monitor_xy]"
    ]
   },
   {
@@ -421,7 +412,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We get a number of warnings about structures being too close to the PML. In FDTD simulations, this can result in instability, as PML are absorbing for propagating fields, but can be amplifying for evanescent fields. This particular simulation runs without any issues even with PML on the sides, but it is best to heed these warnings to avoid problems. There are two ways that we can fix the simulation: one is to just put some space between the last of the metalens boxes and the PML. The other is to use adiabatic absorbers on the sides, which are always stable. The only downside of the absorbers is that they are slightly thicker than the PML, making the overall simulation size slighlty larger. This is why we only put them along x and y, while we leave the PML in z."
+    "We get a number of warnings about structures being too close to the PML. In FDTD simulations, this can result in instability, as PML are absorbing for propagating fields, but can be amplifying for evanescent fields. This particular simulation runs without any issues even with PML on the sides, but it is best to heed these warnings to avoid problems. There are two ways that we can fix the simulation: one is to just put some space between the last of the metalens boxes and the PML. The other is to use adiabatic absorbers on the sides, which are always stable. The only downside of the absorbers is that they are slightly thicker than the PML, making the overall simulation size slightly larger. This is why we only put them along x and y, while we leave the PML in z."
    ]
   },
   {
@@ -477,13 +468,13 @@
     "    size=sim_size,\n",
     "    grid_spec=grid_spec,\n",
     "    structures=[substrate, metalens],\n",
-    "    sources=[source_x,source_y],\n",
+    "    sources=[source_x, source_y],\n",
     "    monitors=monitors,\n",
     "    run_time=run_time,\n",
     "    boundary_spec=td.BoundarySpec(\n",
     "        x=td.Boundary.absorber(), y=td.Boundary.absorber(), z=td.Boundary.pml()\n",
     "    ),\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -532,7 +523,7 @@
     "sim.plot(x=0.1, ax=ax1)\n",
     "sim.plot(y=0.1, ax=ax2)\n",
     "sim.plot(z=-length_z / 2 + space_below_sub + thickness_sub + H / 2, ax=ax3)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -908,7 +899,7 @@
    ],
    "source": [
     "job = web.Job(simulation=sim, task_name=\"metalens\", verbose=True)\n",
-    "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n"
+    "sim_data = job.run(path=\"data/simulation_data.hdf5\")"
    ]
   },
   {
@@ -975,7 +966,7 @@
     }
    ],
    "source": [
-    "print(sim_data.log)\n"
+    "print(sim_data.log)"
    ]
   },
   {
@@ -1023,21 +1014,17 @@
    "source": [
     "focal_z = center_z + H / 2 + f\n",
     "data_center_line = sim_data[\"center\"]\n",
-    "I = (\n",
-    "    abs(data_center_line.Ex) ** 2\n",
-    "    + abs(data_center_line.Ey) ** 2\n",
-    "    + abs(data_center_line.Ez) ** 2\n",
-    ")\n",
+    "I = abs(data_center_line.Ex) ** 2 + abs(data_center_line.Ey) ** 2 + abs(data_center_line.Ez) ** 2\n",
     "I.plot()\n",
     "plt.title(\"intensity(z)\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We now can inspect the field patterns on the area monitors using the Tidy3D visualization nethods."
+    "We now can inspect the field patterns on the area monitors using the Tidy3D visualization methods."
    ]
   },
   {
@@ -1072,7 +1059,7 @@
     "ax1.set_title(\"x-z plane\")\n",
     "ax2.set_title(\"y-z plane\")\n",
     "ax3.set_title(\"x-y (focal) plane\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1108,7 +1095,7 @@
     "ax1.set_title(\"x-z plane\")\n",
     "ax2.set_title(\"y-z plane\")\n",
     "ax3.set_title(\"x-y (focal) plane\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1144,7 +1131,7 @@
     "ax1.set_title(\"x-z plane\")\n",
     "ax2.set_title(\"y-z plane\")\n",
     "ax3.set_title(\"x-y (focal) plane\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1267,7 +1254,7 @@
     "sim_new.plot(x=0.1, ax=ax1)\n",
     "sim_new.plot(y=0.1, ax=ax2)\n",
     "sim_new.plot(z=-length_z / 2 + space_below_sub + thickness_sub + H / 2, ax=ax3)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1561,7 +1548,7 @@
    "source": [
     "job = web.Job(simulation=sim_new, task_name=\"metalens\", verbose=True)\n",
     "sim_data_new = job.run(path=\"data/simulation_data_new.hdf5\")\n",
-    "print(sim_data_new.log)\n"
+    "print(sim_data_new.log)"
    ]
   },
   {
@@ -1632,19 +1619,13 @@
     "    f=f0, z=monitor_proj.proj_distance\n",
     ")\n",
     "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14.3, 3.6))\n",
-    "im1 = ax1.pcolormesh(\n",
-    "    ys_far, xs_far, np.real(proj_fields.Ex), cmap=\"RdBu\", shading=\"auto\"\n",
-    ")\n",
-    "im2 = ax2.pcolormesh(\n",
-    "    ys_far, xs_far, np.real(proj_fields.Ey), cmap=\"RdBu\", shading=\"auto\"\n",
-    ")\n",
-    "im3 = ax3.pcolormesh(\n",
-    "    ys_far, xs_far, np.real(proj_fields.Ez), cmap=\"RdBu\", shading=\"auto\"\n",
-    ")\n",
+    "im1 = ax1.pcolormesh(ys_far, xs_far, np.real(proj_fields.Ex), cmap=\"RdBu\", shading=\"auto\")\n",
+    "im2 = ax2.pcolormesh(ys_far, xs_far, np.real(proj_fields.Ey), cmap=\"RdBu\", shading=\"auto\")\n",
+    "im3 = ax3.pcolormesh(ys_far, xs_far, np.real(proj_fields.Ez), cmap=\"RdBu\", shading=\"auto\")\n",
     "fig.colorbar(im1, ax=ax1)\n",
     "fig.colorbar(im2, ax=ax2)\n",
     "fig.colorbar(im3, ax=ax3)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1666,7 +1647,7 @@
    "Far field projection"
   ],
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1681,7 +1662,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.0"
   },
   "nbdime-conflicts": {
    "local_diff": [
diff --git a/MetasurfaceBIC.ipynb b/MetasurfaceBIC.ipynb
index afd8ad66..422016d0 100644
--- a/MetasurfaceBIC.ipynb
+++ b/MetasurfaceBIC.ipynb
@@ -28,12 +28,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
-    "from tidy3d.plugins.dispersion import FastDispersionFitter\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
+    "from tidy3d.plugins.dispersion import FastDispersionFitter"
    ]
   },
   {
@@ -162,8 +161,8 @@
     "fitter = FastDispersionFitter.from_file(\"misc/amorphous_silicon_from_paper.txt\")\n",
     "\n",
     "# alternatively one can create the fitter object from a refractiveindex.io url\n",
-    "#url = \"https://refractiveindex.info/tmp/database/data-nk/main/Si/Pierce.txt\"\n",
-    "#fitter = FastDispersionFitter.from_url(url, delimiter=\"\\t\")\n",
+    "# url = \"https://refractiveindex.info/tmp/database/data-nk/main/Si/Pierce.txt\"\n",
+    "# fitter = FastDispersionFitter.from_url(url, delimiter=\"\\t\")\n",
     "\n",
     "# fit the data\n",
     "silicon, error = fitter.fit(max_num_poles=2)\n",
@@ -370,7 +369,7 @@
    "outputs": [],
    "source": [
     "# create a flux monitor to measure the transmission spectrum\n",
-    "monitor_z = -1 # monitor z position\n",
+    "monitor_z = -1  # monitor z position\n",
     "flux_monitor = td.FluxMonitor(\n",
     "    center=[0, 0, monitor_z], size=[td.inf, td.inf, 0], freqs=freqs, name=\"flux\"\n",
     ")"
@@ -524,7 +523,7 @@
     "    \"\"\"\n",
     "\n",
     "    # create a plane wave source\n",
-    "    source_z = 1 # source z position\n",
+    "    source_z = 1  # source z position\n",
     "\n",
     "    # create an oblique plane wave source\n",
     "    plane_wave = td.PlaneWave(\n",
@@ -738,7 +737,7 @@
     }
    ],
    "source": [
-    "theta_list = np.linspace(0, 16, 17) # theta values for the parameter sweep\n",
+    "theta_list = np.linspace(0, 16, 17)  # theta values for the parameter sweep\n",
     "\n",
     "# create a dictionary of simulations\n",
     "sims = {f\"theta={theta:.1f}\": make_sim(np.deg2rad(theta)) for theta in theta_list}\n",
diff --git a/MicrowaveFrequencySelectiveSurface.ipynb b/MicrowaveFrequencySelectiveSurface.ipynb
index 7e087c8a..62c7029a 100644
--- a/MicrowaveFrequencySelectiveSurface.ipynb
+++ b/MicrowaveFrequencySelectiveSurface.ipynb
@@ -36,11 +36,10 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n"
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -80,7 +79,7 @@
     "\n",
     "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))  # width of the source spectrum\n",
     "\n",
-    "lda0 = td.C_0 / freq0  # central wavelength\n"
+    "lda0 = td.C_0 / freq0  # central wavelength"
    ]
   },
   {
@@ -110,7 +109,7 @@
     "L = 9.4 * mm  # length of the cross\n",
     "W = 2 * mm  # width of the cross\n",
     "t_sub = 2.2 * mm  # thickness of the substrate\n",
-    "t_copper = 0.1 * mm  # thickness of the copper layer\n"
+    "t_copper = 0.1 * mm  # thickness of the copper layer"
    ]
   },
   {
@@ -141,7 +140,7 @@
     ")  # define copper as a Medium2D\n",
     "\n",
     "eps_sub = 2.5  # permittivity of the substrate\n",
-    "sub_medium = td.Medium(permittivity=eps_sub)  # define substrate medium\n"
+    "sub_medium = td.Medium(permittivity=eps_sub)  # define substrate medium"
    ]
   },
   {
@@ -169,17 +168,13 @@
     "cross = []\n",
     "cross.append(\n",
     "    td.Structure(\n",
-    "        geometry=td.Box.from_bounds(\n",
-    "            rmin=(-L / 2, -W / 2, t_sub), rmax=(L / 2, W / 2, t_sub)\n",
-    "        ),\n",
+    "        geometry=td.Box.from_bounds(rmin=(-L / 2, -W / 2, t_sub), rmax=(L / 2, W / 2, t_sub)),\n",
     "        medium=copper,\n",
     "    )\n",
     ")\n",
     "cross.append(\n",
     "    td.Structure(\n",
-    "        geometry=td.Box.from_bounds(\n",
-    "            rmin=(-W / 2, -L / 2, t_sub), rmax=(W / 2, L / 2, t_sub)\n",
-    "        ),\n",
+    "        geometry=td.Box.from_bounds(rmin=(-W / 2, -L / 2, t_sub), rmax=(W / 2, L / 2, t_sub)),\n",
     "        medium=copper,\n",
     "    )\n",
     ")\n",
@@ -187,7 +182,7 @@
     "substrate = td.Structure(\n",
     "    geometry=td.Box(center=(0, 0, t_sub / 2), size=(td.inf, td.inf, t_sub)),\n",
     "    medium=sub_medium,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -264,7 +259,7 @@
     "# define a field monitor to visualize field distribution\n",
     "field_monitor = td.FieldMonitor(\n",
     "    center=(0, 0, t_sub), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\"\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -324,7 +319,7 @@
     "    run_time=run_time,\n",
     "    boundary_spec=boundary_spec,\n",
     "    symmetry=(-1, 1, 0),  # symmetry is used to reduce the computational load\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -370,7 +365,7 @@
     "sim.plot_grid(x=0, ax=ax[1])\n",
     "ax[1].set_xlim(0, 5000)\n",
     "ax[1].set_ylim(-2000, 5000)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -728,7 +723,7 @@
    ],
    "source": [
     "job = web.Job(simulation=sim, task_name=\"frequency_selective_surface\")\n",
-    "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n"
+    "sim_data = job.run(path=\"data/simulation_data.hdf5\")"
    ]
   },
   {
@@ -782,7 +777,7 @@
     "plt.xlabel(\"Frequency (GHz)\")\n",
     "plt.ylabel(\"S-parameters (dB)\")\n",
     "plt.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -834,7 +829,7 @@
    ],
    "source": [
     "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
diff --git a/MidIRMetalens.ipynb b/MidIRMetalens.ipynb
index 8fe137f7..c9c81de2 100644
--- a/MidIRMetalens.ipynb
+++ b/MidIRMetalens.ipynb
@@ -33,9 +33,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web"
    ]
@@ -145,6 +144,7 @@
     "Lz = h + 1.2 * lda0  # simulation domain size in z direction\n",
     "min_steps_per_wvl = 12  # minimum steps per wavelength for the grid\n",
     "\n",
+    "\n",
     "# define a function to create unit cell simulation given pillar diameter\n",
     "def make_unit_cell_sim(D):\n",
     "    sim = td.Simulation(\n",
@@ -442,14 +442,14 @@
     "ax1.plot(D_list, theta / (2 * np.pi), linewidth=3, c=\"blue\")\n",
     "ax1.set_xlim(np.min(D_list), np.max(D_list))\n",
     "ax1.set_ylim(0, 1)\n",
-    "ax1.set_xlabel(\"D ($\\mu m$)\")\n",
-    "ax1.set_ylabel(\"Transmission phase ($2\\pi$)\")\n",
+    "ax1.set_xlabel(r\"D ($\\mu m$)\")\n",
+    "ax1.set_ylabel(r\"Transmission phase ($2\\pi$)\")\n",
     "\n",
     "# plot the transmittance\n",
     "ax2.plot(D_list, np.abs(t), linewidth=3, c=\"red\")\n",
     "ax2.set_xlim(np.min(D_list), np.max(D_list))\n",
     "ax2.set_ylim(0, 1)\n",
-    "ax2.set_xlabel(\"D ($\\mu m$)\")\n",
+    "ax2.set_xlabel(r\"D ($\\mu m$)\")\n",
     "ax2.set_ylabel(\"Transmittance\")\n",
     "plt.show()"
    ]
@@ -1135,13 +1135,13 @@
     "fig, ax = plt.subplots()\n",
     "I_x.plot(linewidth=3, c=\"red\", ax=ax)\n",
     "ax.set_ylabel(\"Intensity\")\n",
-    "ax.set_xlabel(\"x ($\\mu m$)\")\n",
+    "ax.set_xlabel(r\"x ($\\mu m$)\")\n",
     "ax.set_title(\"Field intensity at focus\")\n",
     "plt.show()\n",
     "\n",
+    "\n",
     "# function for determining the fwhm\n",
     "def cal_fwhm(x, y):\n",
-    "\n",
     "    # filter the data\n",
     "    y = y[x > 0]\n",
     "    x = x[x > 0]\n",
@@ -1156,6 +1156,7 @@
     "\n",
     "    return fwhm_value\n",
     "\n",
+    "\n",
     "# calculate fwhm\n",
     "fwhm = cal_fwhm(I_x.x.values, I_x.values)\n",
     "print(f\"The FWHM is {fwhm:.3f} um.\")"
diff --git a/MoS2Waveguide.ipynb b/MoS2Waveguide.ipynb
index 1cad3a37..0f7453f1 100644
--- a/MoS2Waveguide.ipynb
+++ b/MoS2Waveguide.ipynb
@@ -29,12 +29,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
-    "from tidy3d.plugins.dispersion import FastDispersionFitter, AdvancedFastFitterParam\n",
+    "from tidy3d.plugins.dispersion import AdvancedFastFitterParam, FastDispersionFitter\n",
     "from tidy3d.plugins.mode import ModeSolver"
    ]
   },
@@ -124,8 +123,8 @@
     }
    ],
    "source": [
-    "fitter = FastDispersionFitter.from_file('misc/MoS2_nk.csv', skiprows=0, delimiter=\",\")\n",
-    "advanced_param = AdvancedFastFitterParam(weights=(1,1))\n",
+    "fitter = FastDispersionFitter.from_file(\"misc/MoS2_nk.csv\", skiprows=0, delimiter=\",\")\n",
+    "advanced_param = AdvancedFastFitterParam(weights=(1, 1))\n",
     "MoS2, rms_error = fitter.fit(max_num_poles=4, advanced_param=advanced_param, tolerance_rms=2e-2)\n",
     "fitter.plot(MoS2)\n",
     "plt.show()"
@@ -146,13 +145,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "t = 0.00065 # thickness of the MoS2 monolayer\n",
+    "t = 0.00065  # thickness of the MoS2 monolayer\n",
     "\n",
     "# define MoS2 as Medium2D\n",
     "MoS2_2D = td.Medium2D.from_medium(medium=MoS2, thickness=t)\n",
     "\n",
-    "n_env = 1.46 # refractive index of the environment\n",
-    "env_medium = td.Medium(permittivity=n_env**2) # define environment medium"
+    "n_env = 1.46  # refractive index of the environment\n",
+    "env_medium = td.Medium(permittivity=n_env**2)  # define environment medium"
    ]
   },
   {
@@ -174,7 +173,7 @@
     "freq0 = td.C_0 / lda0  # central frequency\n",
     "ldas = np.linspace(0.5, 0.9, 50)  # wavelength range\n",
     "freqs = td.C_0 / ldas  # frequency range\n",
-    "fwidth = 0.4*(np.max(freqs) - np.min(freqs)) # width of the source frequency range"
+    "fwidth = 0.4 * (np.max(freqs) - np.min(freqs))  # width of the source frequency range"
    ]
   },
   {
@@ -192,11 +191,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "l = 150 # length of the waveguide\n",
+    "l = 150  # length of the waveguide\n",
     "\n",
     "# define the waveguide structure\n",
-    "MoS2_monolayer = td.Structure(geometry=td.Box(center=(0,0,0), size=(l, 0, td.inf)),\n",
-    "                medium=MoS2_2D)\n"
+    "MoS2_monolayer = td.Structure(\n",
+    "    geometry=td.Box(center=(0, 0, 0), size=(l, 0, td.inf)), medium=MoS2_2D\n",
+    ")"
    ]
   },
   {
@@ -204,7 +204,7 @@
    "id": "17b5cb58",
    "metadata": {},
    "source": [
-    "As discussed in the [paper](https://www.science.org/doi/10.1126/science.adi2322), the waveguide mode can be excited by a [PlaneWave](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PlaneWave.html) or a [PointDipole](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PointDipole.html). However, for simulations with a waveguide, the more natural source is a [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html). We will define a [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html) at the begining of the waveguide here and later use the [ModeSolver](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.mode.ModeSolver.html) to investigate the waveguide mode profile and effective index.\n",
+    "As discussed in the [paper](https://www.science.org/doi/10.1126/science.adi2322), the waveguide mode can be excited by a [PlaneWave](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PlaneWave.html) or a [PointDipole](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PointDipole.html). However, for simulations with a waveguide, the more natural source is a [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html). We will define a [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html) at the beginning of the waveguide here and later use the [ModeSolver](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.mode.ModeSolver.html) to investigate the waveguide mode profile and effective index.\n",
     "\n",
     "In addition, we also defined a [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) to help visualize the waveguide mode propagation and out-couple into free space."
    ]
@@ -242,7 +242,7 @@
     "mode_spec = td.ModeSpec(num_modes=1)\n",
     "mode_source = td.ModeSource(\n",
     "    size=(0, td.inf, td.inf),\n",
-    "    center=(-l/2+lda0, 0, 0),\n",
+    "    center=(-l / 2 + lda0, 0, 0),\n",
     "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
     "    mode_spec=mode_spec,\n",
     "    mode_index=0,\n",
@@ -252,7 +252,12 @@
     "\n",
     "# define a field monitor\n",
     "field_monitor = td.FieldMonitor(\n",
-    "    center=(0, 0, 0), size=(td.inf, 30, 0), freqs=[freq0], fields=['Ex','Ey','Ez'], interval_space=(1,2,1), name=\"field\"\n",
+    "    center=(0, 0, 0),\n",
+    "    size=(td.inf, 30, 0),\n",
+    "    freqs=[freq0],\n",
+    "    fields=[\"Ex\", \"Ey\", \"Ez\"],\n",
+    "    interval_space=(1, 2, 1),\n",
+    "    name=\"field\",\n",
     ")"
    ]
   },
@@ -271,14 +276,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "Lx = 200 # simulation domain size in the x direction\n",
-    "Ly = 80 # simulation domain size in the y direction\n",
-    "run_time = 1.5e-12 # simulation run time\n",
+    "Lx = 200  # simulation domain size in the x direction\n",
+    "Ly = 80  # simulation domain size in the y direction\n",
+    "run_time = 1.5e-12  # simulation run time\n",
     "\n",
     "# construct simulation\n",
     "sim = td.Simulation(\n",
-    "    center=((Lx-l)/2-lda0, 0, 0),\n",
-    "    size=(Lx,Ly,0),\n",
+    "    center=((Lx - l) / 2 - lda0, 0, 0),\n",
+    "    size=(Lx, Ly, 0),\n",
     "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=30, wavelength=lda0),\n",
     "    structures=[MoS2_monolayer],\n",
     "    sources=[mode_source],\n",
@@ -288,7 +293,7 @@
     "        x=td.Boundary.pml(), y=td.Boundary.pml(), z=td.Boundary.periodic()\n",
     "    ),\n",
     "    medium=env_medium,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -318,7 +323,7 @@
    ],
    "source": [
     "ax = sim.plot(z=0)\n",
-    "ax.set_aspect('auto')"
+    "ax.set_aspect(\"auto\")"
    ]
   },
   {
@@ -334,7 +339,7 @@
    "id": "9dc47caf",
    "metadata": {},
    "source": [
-    "Before running the simulation, we use the [ModeSolver](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.mode.ModeSolver.html) to inspect the waveguide mode profile. The mode solving is performed on the same plane as the [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html) defined ealier."
+    "Before running the simulation, we use the [ModeSolver](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.mode.ModeSolver.html) to inspect the waveguide mode profile. The mode solving is performed on the same plane as the [ModeSource](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeSource.html) defined earlier."
    ]
   },
   {
@@ -369,9 +374,7 @@
     ")\n",
     "\n",
     "# define mode solver\n",
-    "mode_solver = ModeSolver(\n",
-    "    simulation=sim, plane=mode_plane, mode_spec=mode_spec, freqs=freqs\n",
-    ")\n",
+    "mode_solver = ModeSolver(simulation=sim, plane=mode_plane, mode_spec=mode_spec, freqs=freqs)\n",
     "\n",
     "# solving for the mode\n",
     "mode_data = mode_solver.solve()"
@@ -382,7 +385,7 @@
    "id": "b8584b16",
    "metadata": {},
    "source": [
-    "After running the mode solving, let's plot the real part of the effective index, $n_{eff}$, as a function of wavelength. It shows that $n_{eff}$ is constant and equal to the refractive index of the environment medium. This is not suprissing since the MoS$_2$ waveguide is atomically thin so most of the mode field is in the environment medium."
+    "After running the mode solving, let's plot the real part of the effective index, $n_{eff}$, as a function of wavelength. It shows that $n_{eff}$ is constant and equal to the refractive index of the environment medium. This is not surprising since the MoS$_2$ waveguide is atomically thin so most of the mode field is in the environment medium."
    ]
   },
   {
@@ -404,10 +407,10 @@
    ],
    "source": [
     "n_eff = np.real(mode_data.n_complex.values)\n",
-    "plt.plot(ldas*1e3, n_eff)\n",
-    "plt.xlabel('wavelength (nm)')\n",
-    "plt.ylabel('n_eff')\n",
-    "plt.ylim(1,2)\n",
+    "plt.plot(ldas * 1e3, n_eff)\n",
+    "plt.xlabel(\"wavelength (nm)\")\n",
+    "plt.ylabel(\"n_eff\")\n",
+    "plt.ylim(1, 2)\n",
     "plt.show()"
    ]
   },
@@ -438,9 +441,9 @@
    ],
    "source": [
     "k_eff = np.imag(mode_data.n_complex.values)\n",
-    "plt.plot(ldas*1e3, k_eff)\n",
-    "plt.xlabel('wavelength (nm)')\n",
-    "plt.ylabel('k_eff')\n",
+    "plt.plot(ldas * 1e3, k_eff)\n",
+    "plt.xlabel(\"wavelength (nm)\")\n",
+    "plt.ylabel(\"k_eff\")\n",
     "plt.show()"
    ]
   },
@@ -470,15 +473,13 @@
     }
    ],
    "source": [
-    "f, (ax1, ax2, ax3) = plt.subplots(\n",
-    "    1, 3, tight_layout=True, figsize=(8, 3)\n",
-    ")\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, tight_layout=True, figsize=(8, 3))\n",
     "mode_data.Ez.sel(mode_index=0, f=freqs[0]).abs.plot(ax=ax1)\n",
-    "ax1.set_title(f'$\\lambda$={ldas[0]} $\\mu m$')\n",
-    "mode_data.Ez.sel(mode_index=0, f=freq0, method='nearest').abs.plot(ax=ax2)\n",
-    "ax2.set_title(f'$\\lambda$={lda0} $\\mu m$')\n",
+    "ax1.set_title(rf\"$\\lambda$={ldas[0]} $\\mu m$\")\n",
+    "mode_data.Ez.sel(mode_index=0, f=freq0, method=\"nearest\").abs.plot(ax=ax2)\n",
+    "ax2.set_title(rf\"$\\lambda$={lda0} $\\mu m$\")\n",
     "mode_data.Ez.sel(mode_index=0, f=freqs[-1]).abs.plot(ax=ax3)\n",
-    "ax3.set_title(f'$\\lambda$={ldas[-1]} $\\mu m$')\n",
+    "ax3.set_title(rf\"$\\lambda$={ldas[-1]} $\\mu m$\")\n",
     "plt.show()"
    ]
   },
@@ -837,9 +838,7 @@
     }
    ],
    "source": [
-    "sim_data = web.run(\n",
-    "    sim, task_name=\"mos2_wg\", path=\"data/simulation.hdf5\", verbose=True\n",
-    ")"
+    "sim_data = web.run(sim, task_name=\"mos2_wg\", path=\"data/simulation.hdf5\", verbose=True)"
    ]
   },
   {
@@ -891,9 +890,9 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(figsize=(10,3))\n",
-    "sim_data.plot_field(field_monitor_name='field', field_name='E', val='abs^2', ax=ax)\n",
-    "ax.set_aspect('auto')\n",
+    "fig, ax = plt.subplots(figsize=(10, 3))\n",
+    "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\", ax=ax)\n",
+    "ax.set_aspect(\"auto\")\n",
     "plt.show()"
    ]
   },
@@ -940,7 +939,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.11.0"
   },
   "title": "Monolayer MoS2 waveguide | Flexcompute"
  },
diff --git a/ModalSourcesMonitors.ipynb b/ModalSourcesMonitors.ipynb
index cff2a5c9..45b68a82 100644
--- a/ModalSourcesMonitors.ipynb
+++ b/ModalSourcesMonitors.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "Here, we look at a simple demonstration of how to excite a specific waveguide mode, and how to decompose the fields recorded in a monitor on the basis of waveguide modes, i.e. how to compute the power carried in each mode.\n",
     "\n",
-    "We also provide a conprehensive list of other tutorials such as [how to define boundary conditions](https://www.flexcompute.com/tidy3d/examples/notebooks/BoundaryConditions/), [how to defining spatially-varying sources](https://www.flexcompute.com/tidy3d/examples/notebooks/CustomFieldSource/) and [structures](https://www.flexcompute.com/tidy3d/examples/notebooks/CustomMediumTutorial/), [how to model dispersive materials](https://www.flexcompute.com/tidy3d/examples/notebooks/Dispersion/), and [how to visualize simulation setups](https://www.flexcompute.com/tidy3d/examples/notebooks/VizSimulation/) and [results](https://www.flexcompute.com/tidy3d/examples/notebooks/VizData/).\n",
+    "We also provide a comprehensive list of other tutorials such as [how to define boundary conditions](https://www.flexcompute.com/tidy3d/examples/notebooks/BoundaryConditions/), [how to defining spatially-varying sources](https://www.flexcompute.com/tidy3d/examples/notebooks/CustomFieldSource/) and [structures](https://www.flexcompute.com/tidy3d/examples/notebooks/CustomMediumTutorial/), [how to model dispersive materials](https://www.flexcompute.com/tidy3d/examples/notebooks/Dispersion/), and [how to visualize simulation setups](https://www.flexcompute.com/tidy3d/examples/notebooks/VizSimulation/) and [results](https://www.flexcompute.com/tidy3d/examples/notebooks/VizData/).\n",
     "\n",
     "If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. "
    ]
@@ -22,14 +22,14 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3D import\n",
     "import tidy3d as td\n",
     "from tidy3d import web\n",
-    "from tidy3d.plugins.mode import ModeSolver\n"
+    "from tidy3d.plugins.mode import ModeSolver"
    ]
   },
   {
@@ -70,7 +70,7 @@
     "run_time = 20 / fwidth\n",
     "\n",
     "# Grid specification\n",
-    "grid_spec = td.GridSpec.auto(min_steps_per_wvl=20, wavelength=lambda0)\n"
+    "grid_spec = td.GridSpec.auto(min_steps_per_wvl=20, wavelength=lambda0)"
    ]
   },
   {
@@ -160,7 +160,7 @@
     "    freqs=list(freqs),\n",
     "    mode_spec=td.ModeSpec(num_modes=3),\n",
     "    name=\"mode\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -205,7 +205,7 @@
     "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(11, 4))\n",
     "sim.plot(z=0, ax=ax1)\n",
     "sim.plot(y=0, ax=ax2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -416,7 +416,7 @@
    "source": [
     "source_time = td.GaussianPulse(freq0=freq0, fwidth=fwidth)\n",
     "mode_source = ms.to_source(mode_index=0, direction=\"+\", source_time=source_time)\n",
-    "sim = sim.copy(update=dict(sources=[mode_source]))\n"
+    "sim = sim.copy(update=dict(sources=[mode_source]))"
    ]
   },
   {
@@ -789,7 +789,7 @@
    ],
    "source": [
     "job = web.Job(simulation=sim, task_name=\"mode_tutorial\", verbose=True)\n",
-    "sim_data = job.run(path=\"data/simulation.hdf5\")\n"
+    "sim_data = job.run(path=\"data/simulation.hdf5\")"
    ]
   },
   {
@@ -825,7 +825,7 @@
    ],
    "source": [
     "sim_data.plot_field(\"field\", \"Ey\", z=wg_height / 2, f=freq0, val=\"real\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -917,7 +917,7 @@
     "fig, axs = plt.subplots(3, 2, figsize=(12, 12))\n",
     "for mode_ind in range(3):\n",
     "    ms.plot_field(\"Ey\", \"abs\", f=freq0, mode_index=mode_ind, ax=axs[mode_ind, 0])\n",
-    "    ms.plot_field(\"Ez\", \"abs\", f=freq0, mode_index=mode_ind, ax=axs[mode_ind, 1])\n"
+    "    ms.plot_field(\"Ez\", \"abs\", f=freq0, mode_index=mode_ind, ax=axs[mode_ind, 1])"
    ]
   },
   {
@@ -951,7 +951,7 @@
    "source": [
     "# Flux in the mode monitor (total power through the cross-section)\n",
     "flux_wg = sim_data[\"flux\"].flux\n",
-    "print(\"Flux at central frequency: \", flux_wg.isel(f=fcent_ind).values)\n"
+    "print(\"Flux at central frequency: \", flux_wg.isel(f=fcent_ind).values)"
    ]
   },
   {
@@ -1016,7 +1016,7 @@
     "ax.set_ylabel(\"Power in mode (W)\")\n",
     "ax.set_title(\"Mode decomposition (forward-propagating)\")\n",
     "ax.legend([\"Mode 0\", \"Mode 1\", \"Mode 2\"])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1061,7 +1061,7 @@
     "ax.set_title(\"Power in mode monitor\")\n",
     "ax.legend([\"Total\", \"Mode 0\"])\n",
     "\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1102,7 +1102,7 @@
     "        size=[4, wgout_width, wg_height],\n",
     "    ),\n",
     "    medium=mat_wg,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1126,7 +1126,7 @@
     "    monitors=[freq_mnt, mode_mnt, flux_mnt],\n",
     "    run_time=run_time,\n",
     "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1159,7 +1159,7 @@
     "ax2 = fig.add_subplot(gs[0, 1])\n",
     "sim_jct.plot(z=0.1, ax=ax1)\n",
     "sim_jct.plot(y=0.1, ax=ax2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1509,7 +1509,7 @@
    ],
    "source": [
     "job = web.Job(simulation=sim_jct, task_name=\"mode_tutorial\", verbose=True)\n",
-    "sim_data_jct = job.run(path=\"data/mode_converter.hdf5\")\n"
+    "sim_data_jct = job.run(path=\"data/mode_converter.hdf5\")"
    ]
   },
   {
@@ -1537,7 +1537,7 @@
    ],
    "source": [
     "sim_data_jct.plot_field(\"field\", \"Ey\", z=wg_height / 2, f=freq0, val=\"real\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1584,7 +1584,7 @@
     "ax.set_ylabel(\"Power in mode (W)\")\n",
     "ax.set_title(\"Mode decomposition (+ propagating)\")\n",
     "ax.legend([\"Mode 0 + 1 + 2\", \"Mode 0\", \"Mode 1\", \"Mode 2\"])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1616,7 +1616,7 @@
    },
    "outputs": [],
    "source": [
-    "broadband_mode_source = mode_source.copy(update={\"num_freqs\": 7})\n"
+    "broadband_mode_source = mode_source.copy(update={\"num_freqs\": 7})"
    ]
   },
   {
@@ -1640,7 +1640,7 @@
    "outputs": [],
    "source": [
     "sim_bb = sim.copy(update={\"sources\": [broadband_mode_source]})\n",
-    "sim_jct_bb = sim_jct.copy(update={\"sources\": [broadband_mode_source]})\n"
+    "sim_jct_bb = sim_jct.copy(update={\"sources\": [broadband_mode_source]})"
    ]
   },
   {
@@ -1997,7 +1997,7 @@
    ],
    "source": [
     "job = web.Job(simulation=sim_bb, task_name=\"mode_tutorial\", verbose=True)\n",
-    "sim_data_bb = job.run(path=\"data/simulation_bb.hdf5\")\n"
+    "sim_data_bb = job.run(path=\"data/simulation_bb.hdf5\")"
    ]
   },
   {
@@ -2067,7 +2067,7 @@
     "    ]\n",
     ")\n",
     "\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2424,14 +2424,14 @@
    ],
    "source": [
     "job = web.Job(simulation=sim_jct_bb, task_name=\"mode_tutorial\", verbose=True)\n",
-    "sim_data_jct_bb = job.run(path=\"data/mode_converter_bb.hdf5\")\n"
+    "sim_data_jct_bb = job.run(path=\"data/mode_converter_bb.hdf5\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here we plot the comparison for mode mixing results between three options: (1) the simulation containing a mode source that uses only one frequency point, (2) the same as first one, but normalized by flux derived from straight waveguide simulation, and (3) the simulation containing a broadband mode source that uses mutliple frequency points. As one can see, using the broadband source feature allows to obtain results of the same accuracy without running an additional normalization simulation."
+    "Here we plot the comparison for mode mixing results between three options: (1) the simulation containing a mode source that uses only one frequency point, (2) the same as first one, but normalized by flux derived from straight waveguide simulation, and (3) the simulation containing a broadband mode source that uses multiple frequency points. As one can see, using the broadband source feature allows one to obtain results of the same accuracy without running an additional normalization simulation."
    ]
   },
   {
@@ -2469,15 +2469,9 @@
     "\n",
     "fig, ax = plt.subplots(3, 1, figsize=(6, 7), tight_layout=True)\n",
     "for mode_index in range(3):\n",
-    "    ax[mode_index].plot(\n",
-    "        lambdas, np.abs(coeffs_f_jct_nonorm.sel(mode_index=mode_index)) ** 2, \"x:\"\n",
-    "    )\n",
-    "    ax[mode_index].plot(\n",
-    "        lambdas, np.abs(coeffs_f_jct.sel(mode_index=mode_index)) ** 2, \".--\"\n",
-    "    )\n",
-    "    ax[mode_index].plot(\n",
-    "        lambdas, np.abs(coeffs_f_jct_bb.sel(mode_index=mode_index)) ** 2\n",
-    "    )\n",
+    "    ax[mode_index].plot(lambdas, np.abs(coeffs_f_jct_nonorm.sel(mode_index=mode_index)) ** 2, \"x:\")\n",
+    "    ax[mode_index].plot(lambdas, np.abs(coeffs_f_jct.sel(mode_index=mode_index)) ** 2, \".--\")\n",
+    "    ax[mode_index].plot(lambdas, np.abs(coeffs_f_jct_bb.sel(mode_index=mode_index)) ** 2)\n",
     "    ax[mode_index].set_xlabel(\"Wavelength (um)\")\n",
     "    ax[mode_index].set_xlim([lambdas[-1], lambdas[0]])\n",
     "    ax[mode_index].set_ylabel(\"Power in mode (W)\")\n",
@@ -2488,10 +2482,8 @@
     "        facecolor=\"k\",\n",
     "        alpha=0.1,\n",
     "    )\n",
-    "    ax[mode_index].legend(\n",
-    "        [\"Single freq\", \"Single freq + norm\", \"Broadband\", \"+/- 1.5 * fwidth\"]\n",
-    "    )\n",
-    "plt.show()\n"
+    "    ax[mode_index].legend([\"Single freq\", \"Single freq + norm\", \"Broadband\", \"+/- 1.5 * fwidth\"])\n",
+    "plt.show()"
    ]
   },
   {
@@ -2521,7 +2513,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.11.0"
   },
   "nbdime-conflicts": {
    "local_diff": [
diff --git a/ModeOverlap.ipynb b/ModeOverlap.ipynb
index deec88da..81e7d504 100644
--- a/ModeOverlap.ipynb
+++ b/ModeOverlap.ipynb
@@ -19,12 +19,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
-    "from tidy3d.plugins.mode import ModeSolver\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n"
+    "from tidy3d.plugins.mode import ModeSolver"
    ]
   },
   {
@@ -81,7 +80,7 @@
     "lda0 = 1.55  # wavelength of interest\n",
     "freq0 = td.C_0 / lda0  # frequency of interest\n",
     "r_beam = 5.25  # beam radius\n",
-    "size = 6 * r_beam  # plane size for the beam profile\n"
+    "size = 6 * r_beam  # plane size for the beam profile"
    ]
   },
   {
@@ -108,7 +107,7 @@
     "    size=(size, size, 0),\n",
     "    resolution=200,\n",
     "    freqs=[freq0],\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -135,8 +134,8 @@
     }
    ],
    "source": [
-    "gaussian_beam.field_data.Ey.abs.plot(cmap=\"hot\", x='x', y='y')\n",
-    "plt.show()\n"
+    "gaussian_beam.field_data.Ey.abs.plot(cmap=\"hot\", x=\"x\", y=\"y\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -145,7 +144,7 @@
    "source": [
     "## Conducting Mode Analysis on the Waveguide\n",
     "\n",
-    "We will proceed by performing a mode analysis on the waveguide structure at the end facet of the edge coupler. Our aim is to sweep a range of waveguide widths to identtify the optimal value, one which would result in the highest coupling efficiency. To accomplish this, we first define a function `make_mode_solver`. This function takes the waveguide width as an input parameter and returns a [ModeSolver]() instance."
+    "We will proceed by performing a mode analysis on the waveguide structure at the end facet of the edge coupler. Our aim is to sweep a range of waveguide widths to identify the optimal value, one which would result in the highest coupling efficiency. To accomplish this, we first define a function `make_mode_solver`. This function takes the waveguide width as an input parameter and returns a [ModeSolver]() instance."
    ]
   },
   {
@@ -183,7 +182,7 @@
     "        freqs=[freq0],\n",
     "    )\n",
     "\n",
-    "    return mode_solver\n"
+    "    return mode_solver"
    ]
   },
   {
@@ -332,7 +331,7 @@
     "mode_solvers = {f\"w={w:.2f}\": make_mode_solver(w) for w in w_list}\n",
     "\n",
     "batch = web.Batch(simulations=mode_solvers, verbose=True)\n",
-    "batch_results = batch.run(path_dir=\"data\")\n"
+    "batch_results = batch.run(path_dir=\"data\")"
    ]
   },
   {
@@ -381,7 +380,7 @@
     "plt.plot(1e3 * w_list, np.abs(overlap) ** 2, \"r\", linewidth=2)\n",
     "plt.xlabel(\"Waveguide width (nm)\")\n",
     "plt.ylabel(\"Coupling efficiency\")\n",
-    "plt.grid()\n"
+    "plt.grid()"
    ]
   },
   {
@@ -410,7 +409,7 @@
    "source": [
     "optimal_mode = batch_results[\"w=0.13\"]\n",
     "optimal_mode.Ey.abs.plot(vmax=4, cmap=\"hot\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -461,7 +460,7 @@
     "plt.ylabel(\"Shift in y (μm)\")\n",
     "plt.title(\"Coupling Efficiency\")\n",
     "plt.colorbar()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -500,7 +499,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.0"
+   "version": "3.11.0"
   },
   "title": "Mode Overlap Integral between a Waveguide Mode and a Gaussian Mode | Flexcompute"
  },
diff --git a/ModeSolver.ipynb b/ModeSolver.ipynb
index 42d5e7dc..2435b22a 100644
--- a/ModeSolver.ipynb
+++ b/ModeSolver.ipynb
@@ -20,13 +20,13 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pylab as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3D import\n",
     "import tidy3d as td\n",
-    "from tidy3d.constants import C_0\n",
     "import tidy3d.web as web\n",
+    "from tidy3d.constants import C_0\n",
     "from tidy3d.plugins.mode import ModeSolver"
    ]
   },
@@ -67,7 +67,7 @@
     "run_time = 1e-12\n",
     "\n",
     "# automatic grid specification\n",
-    "grid_spec = td.GridSpec.auto(min_steps_per_wvl=20, wavelength=wvl_um)\n"
+    "grid_spec = td.GridSpec.auto(min_steps_per_wvl=20, wavelength=wvl_um)"
    ]
   },
   {
@@ -88,14 +88,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "
"
+       "
"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -114,7 +112,7 @@
     ")\n",
     "\n",
     "ax = sim.plot(z=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -136,7 +134,7 @@
    },
    "outputs": [],
    "source": [
-    "plane = td.Box(center=(0, 0, 0), size=(4, 0, 3.5))\n"
+    "plane = td.Box(center=(0, 0, 0), size=(4, 0, 3.5))"
    ]
   },
   {
@@ -165,7 +163,7 @@
     "mode_spec = td.ModeSpec(\n",
     "    num_modes=3,\n",
     "    target_neff=2.0,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -187,7 +185,7 @@
    "source": [
     "num_freqs = 11\n",
     "f0_ind = num_freqs // 2\n",
-    "freqs = np.linspace(freq0 - fwidth / 2, freq0 + fwidth / 2, num_freqs)\n"
+    "freqs = np.linspace(freq0 - fwidth / 2, freq0 + fwidth / 2, num_freqs)"
    ]
   },
   {
@@ -209,13 +207,17 @@
     {
      "data": {
       "text/html": [
-       "
[15:52:36] WARNING: Use the remote mode solver with subpixel averaging for      \n",
-       "           better accuracy through 'tidy3d.plugins.mode.web.run(...)'.          \n",
+       "
10:40:25 Eastern Daylight Time WARNING: Use the remote mode solver with subpixel\n",
+       "                               averaging for better accuracy through            \n",
+       "                               'tidy3d.web.run(...)' or the deprecated          \n",
+       "                               'tidy3d.plugins.mode.web.run(...)'.              \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[15:52:36]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Use the remote mode solver with subpixel averaging for      \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m\u001b[31mbetter accuracy through \u001b[0m\u001b[32m'tidy3d.plugins.mode.web.run\u001b[0m\u001b[32m(\u001b[0m\u001b[32m...\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.          \u001b[0m\n"
+       "\u001b[2;36m10:40:25 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Use the remote mode solver with subpixel\u001b[0m\n",
+       "\u001b[2;36m                               \u001b[0m\u001b[31maveraging for better accuracy through            \u001b[0m\n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'tidy3d.web.run\u001b[0m\u001b[32m(\u001b[0m\u001b[32m...\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m or the deprecated          \u001b[0m\n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'tidy3d.plugins.mode.web.run\u001b[0m\u001b[32m(\u001b[0m\u001b[32m...\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.              \u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -229,7 +231,7 @@
     "    mode_spec=mode_spec,\n",
     "    freqs=freqs,\n",
     ")\n",
-    "mode_data = mode_solver.solve()\n"
+    "mode_data = mode_solver.solve()"
    ]
   },
   {
@@ -303,7 +305,7 @@
        "    \n",
        "      1\n",
        "      2.400000\n",
-       "      1.610272\n",
+       "      1.610273\n",
        "      0.0\n",
        "      0.010245\n",
        "      0.747363\n",
@@ -344,7 +346,7 @@
        "    \n",
        "      2\n",
        "      2.307692\n",
-       "      1.339798\n",
+       "      1.339799\n",
        "      0.0\n",
        "      0.203097\n",
        "      0.888992\n",
@@ -380,7 +382,7 @@
        "      0.180304\n",
        "      0.883673\n",
        "      0.670054\n",
-       "      2.257858\n",
+       "      2.257857\n",
        "    \n",
        "    \n",
        "      1.399031e+14\n",
@@ -408,10 +410,10 @@
        "      2.142857\n",
        "      1.419700\n",
        "      0.0\n",
-       "      0.159137\n",
+       "      0.159136\n",
        "      0.880026\n",
        "      0.691309\n",
-       "      2.090148\n",
+       "      2.090147\n",
        "    \n",
        "    \n",
        "      1.448997e+14\n",
@@ -470,7 +472,7 @@
        "      2.000000\n",
        "      1.487099\n",
        "      0.0\n",
-       "      0.122765\n",
+       "      0.122766\n",
        "      0.877010\n",
        "      0.728694\n",
        "      1.827302\n",
@@ -501,10 +503,10 @@
        "      1.935484\n",
        "      1.516732\n",
        "      0.0\n",
-       "      0.107622\n",
-       "      0.877209\n",
+       "      0.107621\n",
+       "      0.877208\n",
        "      0.744987\n",
-       "      1.723899\n",
+       "      1.723898\n",
        "    \n",
        "    \n",
        "      1.598893e+14\n",
@@ -530,12 +532,12 @@
        "    \n",
        "      2\n",
        "      1.875000\n",
-       "      1.543974\n",
+       "      1.543973\n",
        "      0.0\n",
        "      0.094364\n",
        "      0.878263\n",
        "      0.759842\n",
-       "      1.634964\n",
+       "      1.634963\n",
        "    \n",
        "    \n",
        "      1.648859e+14\n",
@@ -561,10 +563,10 @@
        "    \n",
        "      2\n",
        "      1.818182\n",
-       "      1.569047\n",
+       "      1.569048\n",
        "      0.0\n",
        "      0.082819\n",
-       "      0.879989\n",
+       "      0.879990\n",
        "      0.773393\n",
        "      1.558063\n",
        "    \n",
@@ -597,7 +599,7 @@
        "      0.072798\n",
        "      0.882226\n",
        "      0.785770\n",
-       "      1.491208\n",
+       "      1.491207\n",
        "    \n",
        "    \n",
        "      1.748789e+14\n",
@@ -638,32 +640,32 @@
        "                         wavelength     n eff  k eff  TE (Ex) fraction  \\\n",
        "f            mode_index                                                  \n",
        "1.249135e+14 0             2.400000  1.686130    0.0          0.995963   \n",
-       "             1             2.400000  1.610272    0.0          0.010245   \n",
+       "             1             2.400000  1.610273    0.0          0.010245   \n",
        "             2             2.400000  1.294575    0.0          0.226875   \n",
        "1.299101e+14 0             2.307692  1.706504    0.0          0.996620   \n",
        "             1             2.307692  1.637271    0.0          0.008747   \n",
-       "             2             2.307692  1.339798    0.0          0.203097   \n",
+       "             2             2.307692  1.339799    0.0          0.203097   \n",
        "1.349066e+14 0             2.222222  1.724923    0.0          0.997149   \n",
        "             1             2.222222  1.661620    0.0          0.007504   \n",
        "             2             2.222222  1.381435    0.0          0.180304   \n",
        "1.399031e+14 0             2.142857  1.741628    0.0          0.997578   \n",
        "             1             2.142857  1.683633    0.0          0.006470   \n",
-       "             2             2.142857  1.419700    0.0          0.159137   \n",
+       "             2             2.142857  1.419700    0.0          0.159136   \n",
        "1.448997e+14 0             2.068966  1.756829    0.0          0.997929   \n",
        "             1             2.068966  1.703585    0.0          0.005608   \n",
        "             2             2.068966  1.454838    0.0          0.139923   \n",
        "1.498962e+14 0             2.000000  1.770701    0.0          0.998217   \n",
        "             1             2.000000  1.721718    0.0          0.004884   \n",
-       "             2             2.000000  1.487099    0.0          0.122765   \n",
+       "             2             2.000000  1.487099    0.0          0.122766   \n",
        "1.548928e+14 0             1.935484  1.783397    0.0          0.998457   \n",
        "             1             1.935484  1.738240    0.0          0.004275   \n",
-       "             2             1.935484  1.516732    0.0          0.107622   \n",
+       "             2             1.935484  1.516732    0.0          0.107621   \n",
        "1.598893e+14 0             1.875000  1.795048    0.0          0.998656   \n",
        "             1             1.875000  1.753333    0.0          0.003760   \n",
-       "             2             1.875000  1.543974    0.0          0.094364   \n",
+       "             2             1.875000  1.543973    0.0          0.094364   \n",
        "1.648859e+14 0             1.818182  1.805767    0.0          0.998824   \n",
        "             1             1.818182  1.767156    0.0          0.003321   \n",
-       "             2             1.818182  1.569047    0.0          0.082819   \n",
+       "             2             1.818182  1.569048    0.0          0.082819   \n",
        "1.698824e+14 0             1.764706  1.815652    0.0          0.998965   \n",
        "             1             1.764706  1.779845    0.0          0.002946   \n",
        "             2             1.764706  1.592156    0.0          0.072798   \n",
@@ -681,10 +683,10 @@
        "             2                 0.888992        0.647088   2.457125  \n",
        "1.349066e+14 0                 0.871672        0.899721   1.473565  \n",
        "             1                 0.772128        0.934256   1.778317  \n",
-       "             2                 0.883673        0.670054   2.257858  \n",
+       "             2                 0.883673        0.670054   2.257857  \n",
        "1.399031e+14 0                 0.880228        0.903585   1.416463  \n",
        "             1                 0.783911        0.937626   1.687731  \n",
-       "             2                 0.880026        0.691309   2.090148  \n",
+       "             2                 0.880026        0.691309   2.090147  \n",
        "1.448997e+14 0                 0.888036        0.907257   1.366859  \n",
        "             1                 0.795151        0.940743   1.608796  \n",
        "             2                 0.877877        0.710836   1.948158  \n",
@@ -693,16 +695,16 @@
        "             2                 0.877010        0.728694   1.827302  \n",
        "1.548928e+14 0                 0.901682        0.914064   1.285192  \n",
        "             1                 0.815850        0.946311   1.478919  \n",
-       "             2                 0.877209        0.744987   1.723899  \n",
+       "             2                 0.877208        0.744987   1.723898  \n",
        "1.598893e+14 0                 0.907645        0.917216   1.251271  \n",
        "             1                 0.825298        0.948805   1.425223  \n",
-       "             2                 0.878263        0.759842   1.634964  \n",
+       "             2                 0.878263        0.759842   1.634963  \n",
        "1.648859e+14 0                 0.913108        0.920214   1.221021  \n",
        "             1                 0.834165        0.951128   1.377568  \n",
-       "             2                 0.879989        0.773393   1.558063  \n",
+       "             2                 0.879990        0.773393   1.558063  \n",
        "1.698824e+14 0                 0.918121        0.923064   1.193899  \n",
        "             1                 0.842474        0.953296   1.335090  \n",
-       "             2                 0.882226        0.785770   1.491208  \n",
+       "             2                 0.882226        0.785770   1.491207  \n",
        "1.748789e+14 0                 0.922729        0.925776   1.169463  \n",
        "             1                 0.850255        0.955322   1.297071  \n",
        "             2                 0.884840        0.797097   1.432774  "
@@ -739,14 +741,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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9r7X2x1hr3wQcyzI+VlbTyOf5VawrqGLd3gNsLTnUdoXusH7RzMlKclr7A+MZnGjDN72uttyd9NdA4T9h3xdH59S31r4xgA8LgYi8AFwOlKlq9gme/yFwS7s4MoG+qlrlq5g6w/6aRj7Pq+TzvCpW51eSV+50MUT2CmZ8em++O2sIEwbGMyEt3ubi8TaXy+nb3/v50a6eAwXOc62t/QnznORvrX1j2vjyiOAl4Cng9yd6UlV/AfwCQESuAH7QHYvAMYk/r5K8Cifxx4SFMHlQAjdNSmfq4D5kJsfYuH1va2l2Zt3c8xkUrIS9nx2di6ettT/fWvvGnILPCoGqLheRDA83vwl43VexeNPRxF/J6ryqryf+yU7izxoQS7B183hX8xGna2fPSifxF66GI86JdRKGQOaVkJEL6dOstW/MafD7OQIRiQQuBr7fwTZ3A3cDpKend1JkjtLqRlbnV7a1+vNbE394CFMGJXDzlNYWvyV+r2tqcPr393wGBSucET3NDc5zfTNhzA2QMQPSp0Nssn9jNaYb83shAK4AVnbULaSqzwLPAuTk5KgvgymtPtri/zyvkgL3Qiutif8WS/y+c7jWaeXvWekk/+J10HIEEGfs/sTb3Yl/GkQl+jtaY3qMrlAIbsSP3UIl1Q2szqs6SeLvwzenDrTE7ysNB50Tu3tWOIl/33rQFmcGzgHjYMq3YeAMSJ8KEb39G6sxPZhfC4GIxAHnAt/srH2WVDc4SX93FZ/nV7YtrRgbHsJkS/y+VVd5tLW/Z4WzrCLqLLKSMhFyf+DMz5M2xeblMaYT+XL46OvALCBRRIqAR4BQAFV9xr3ZXGCJqtb5Ko5WS7eV8ZNFm7+W+G+1xO87h0qdvv09nzkFoHyb83hIBKRNglkPOS3+1Bwb0WOMH/ly1NBNHmzzEs4wU59LjA5jeP8Y5k3LYOrgBEYmWeL3usZqyP8U8pZC3jKo3OU83iva6d4Zcz0MzHVm47TVtYzpMrrCOYJOMTo1jv+bl+PvMHqWliZnJE/eUti91Dm5qy0QGuWc1J14u9PVkzQWggPmT82Ybsf+O43nVKFi59HEX7ACjhxyVtoaMN7p4x9ynnMBl7X4jek2rBCYjtVVON08u93dPTVFzuPxGTD6OifxD5oJEfF+DNIYczZOWQhE5A/A91W12n1/IPCCqp7v6+CMHzQ1wt5V7lb/x1C6yXk8PA4GnQsz74fB50HCIP/GaYzxGk+OCFYAq0XkPiAF+CFwv0+jMp3H5XKmZW7t7tm7CpobISjUGcY5+2EYPNsZ1x9kC+AY0xOdshCo6u9EZDOwFKgAxqtqqc8jM75TXXw08ectg/oK5/G+mc4kbYPPc07y2lh+YwKCJ11DtwI/BuYBY4D3ReQOVd3g6+CMlxw+5JzY3b3UKQAVO5zHo/rB0POdxD94ls3XY0yA8qRr6FogV1XLgNdF5G3gZWCcLwMzZ0EVyrbAjsWw8yNnXn5Xs3MhV8YMZ5H1IedBvyybodMY41HX0NXgzBKqqvWqukZEJvs8MnN6mhqdVv+OxbDj71C913k8eSxMv9dp9adPhZAw/8ZpjOlyPOkamgY8D0QD6SIyFrgH+K6PYzOncmg/7Py7k/h3L3UWXA+NdJL+zAdg2EXW3WOMOSVPuoZ+DcwB3gVQ1Q0iMtOXQZmTUIXSjbB9sdPy3/eF83hsKoy7CYZf7CzMYvP2GGNOg0cXlKlqoRzbl9zim3DM1xyph/zlsOMD2LEEDu0DxJmobfaPneTff5T19RtjzpgnhaBQRKYDKiKhwAJgq2/DCnDVxU6Xz/bFkP+JM66/VzQMmQ0jLoGhF0J0X39HaYzpITwpBN8GnsS5mKwYWAJ8z5dBBRyXC/Z96T7R+8HRq3l7D3Qmbht+sTNds83fY4zxAU9GDVUAt3RCLIHlcK0zpn/HYqfLp67MmbwtbSpc8KjT8k8cbl0+xhifO2khEJH/BU66PrCq/otPIurJDuxxRvjsWAwFnzrr8YbFwbALnFb/0AsgMsHfURpjAkxHRwRr3bczgCzgT+773wC2+DKoHqN1lM+Wv8L2D5yLvAD6DIPJdzvJP30qBIf6N05jTEA7aSFQ1ZcBROQ7OFcWN7vvPwN82jnhdUOqULIeNr/jFIAD+c5i7AOnw5yfOcm/zxB/R2mMMW08OVkcD8QCVe770e7HTCtVZ0x/a/I/uAeCQpxpm8+5D0ZcBlF9/B2lMcackCeF4H+AL0VkKSDATOAnvgyqW1B1lmbc/DZsedeZ0iEoxLmq99x/gxGXWn+/MaZb8GTU0Isi8gEwxf3QgwE7DbXLBcVrj7b8a4qcefuHzIbz/t0Z6WMrdRljuhlPl6oMBsrd2w8XkeGqutx3YXUhLhcUrnYS/9Z3oaYYgnvBkPPhfPeVvRG9/R2lMcacMU8mnXsMuAHYDLjcDyvQcwuBqwX2fn40+R8qgeAwZ3jn+Y/AiIudpRuNMaYH8OSI4GpghKoe9nEs/uVqgT2fHU3+tfshJNxJ/llXw/A5EB7r7yiNMcbrPCkEeUAo0PMKQUsz7FnpTv6LnKt7QyJg2IWQdZWT/MNi/B2lMcb4lCeFoB5YLyL/oF0x6LZXFrc0O1f1tib/+gon+Q+/yGn5D7vI1uo1xnQJqkpedR6fl3zOmpI1nJd+HlcPvdrr+/GkELzr/ure9m+G1b+Dbe9BfaWzgMvwOe7kfyH0ivJ3hMYYQ0ltCZ+XfM7q0tWsKVlDeUM5ACnRKUwfMN0n+/Rk+OjLPtlzZ6vZB1+9eTT5D70AekX6OypjTIA70HiANaVrWF2ymtUlq9l7yFlmNiE8gSlJU5icPJkpyVNIi0nzWQwdTTr3Z1W9XkQ2cYLJ51R1TEdvLCIvAJcDZaqafZJtZuGsgBYKVKjquR5HfroGnwc/3GWrdxlj/Kq+qZ61+9e2Jf7tB7YDEBUaRU7/HG4ceSNTkqcwrPcwpJNmH+7oiGCB+/byM3zvl4CngN+f6EkR6Q08DVysqntFpN8Z7sczwSHOlzHGdKKmliY2lG9gdamT+DeVb6JZmwkNCmV8v/HcO/5eJidNJjsxm5Ag/+SojiadK3Hf7jmTN1bV5SKS0cEmNwNvqepe9/ZlZ7IfY4zpSlpcLWw7sK2txf9l2Zc0NDcQJEFkJWRx26jbmJI8hfH9xhMeEu7vcAHPryz2heFAqIgsA2KAJ1X1ZEcPdwN3A6Snp3dagMYYcyqqSkFNQVviX1O6hpojNQAMiRvC3KFzmZI8hZykHGJ7dc1rkfxZCEKAicD5QASwSkQ+V9Udx2+oqs8CzwLk5OScdLEcY4zpDKV1pW0neD8v+ZyyeqdDIzkqmdnps5mSPIUpSVPoG9k91hb3ZyEoAipVtQ6oE5HlwFjga4XAGGP8qaG5gbWla1lRvILP9n1GQU0BAPFh8W2jeqYmTSU1JrXTTvB6kydzDc3AmXZ6oHt7AVRVB5/lvv8KPCUiIUAvnNlNf3WW72mMMWdNVcmvyWdl8UpWFK9gbelajriOEB4cTk5SDtcNv46pyVMZFj+MIAnyd7hnzZMjgueBHwDrgBZP31hEXgdmAYkiUgQ8gjNMFFV9RlW3ishiYCPOZHbPqepXpxe+McZ4R31TPatLVrOieAUr962kuLYYgEFxg7hh5A3kDshlYtJEwoLD/Byp93lSCKpV9YPTfWNVvcmDbX4B/OJ039sYY86WqrLz4E5WFq9kZfFK1pWto9nVTGRIJFOSpzA/ez4zUmaQEp3i71B9zpNCsFREfgG8xbFzDX3hs6iMMcYHao7UtLX6VxSvaDvJOyx+GLdm3UrugFzG9xtPaHConyPtXJ4UgtaVyXLaPabAbO+HY4wx3uNSF9uqtrX19W8o30CLthATGsPUAVPJTcllxoAZ9I/q7+9Q/cqTuYbO64xAjDHGGw42HmRVySqnr794JZWNlQBkJmQyP3s+uSm5jO47mtCgwGr1d8STUUNxOCd6Z7of+gT4qapW+zIwY4zxRIurhS2VW5zunn0r+KriK1zqIi4sjunJ08lNzWX6gOkkRiT6O9Quy5OuoReAr4Dr3fdvBV4ErvFVUMYY05HKhko+2/cZnxZ/yqp9qzh4+CCCkJ2YzT1j7mFGygyy+2QTHBTs71C7BU8KwRBVvbbd/UdFZL2P4jHGmBPKq85jWeEylu5dyobyDShKQngC56Scw4yUGUwfMJ348Hh/h9kteVIIGkQkV1VXQNsFZg2+DcsYE+haXC1sKN/gJP/CpW1X82YmZPLtsd/m3NRzyeyT2SMu6PI3TwrBd4CX3ecKBKgCbvdlUMaYwFTfVM+qklUsK1zG8qLlVDVWESIhTEqaxM2ZNzMrdRbJ0cn+DrPH8WTU0HpgrIjEuu/X+DooY0zgqGio4JPCT1hWuIxVJas43HKYmNAYclNzmZ02mxkpM4jpFePvMHu0jlYo+6aqviIi9x33OACq+oSPYzPG9ECqSn51Ph8XfsyywmVsLN+IoiRHJXPtsGs5L/08JvabGHAXdflTR0cErau5n6gU21TQxhiPtbhaWF++nqV7l7K0cGnburyZCZl8Z9x3mJ02m+Hxw7vlzJ09QUcrlP3O/e1Hqrqy/XPuE8bGGHNS9U31rNq3io8LP2Z50XIOHj5ISFAIU5KmcGvWrcxKm0VSVJK/wzR4drL4f4EJHjxmjAlw5fXlfFL0CUsLl/L5vs854jpCTK8YZqbOZFbaLHIH5BLdK9rfYZrjdHSOYBowHeh73HmCWMCu0jDGoKrsPribpYVLnf7+io0ApESncP2I65mVNosJ/SfYdA5dXEdHBL2AaPc27c8T1ADX+TIoY0zXpapsqdrCkoIlfLjnQwoPFQIwqs8ovj/u+8xKm2X9/d1MR+cIPgE+EZGXVHVPJ8ZkjOli2if/JQVLKKotIliCmZI8hdtH3c65qecG/Aye3Zkn5wjq3esRjALCWx9UVZuG2pge7GTJf2ryVL415lvMTptN7/De/g7TeIEnheBV4E/A5cC3gduAcl8GZYzxD0v+gcmTQtBHVZ8XkQXtuov+6evAjDGdQ1XZWrWVvxf8/WvdPpb8A4MnhaDJfVsiIpcB+4AE34VkjPG11uS/pGAJfy/4uyX/AOdJIfhv94Rz9+NcPxAL/MCnURljvK6j5H/X6LuYnT7bpnEOUJ5MOvee+9tqwJatNKYbaZ/8l+xZQuGhQkv+5ms8WaryNyd4uBpYq6p/9X5Ixpizoapsq9rm9Pkfl/zvzL7Tkr/5Gk+6hsKBkcBf3PevBfJxpqY+T1X/1UexGWM81Jr8l+xxun1ak//kpMmW/M0peVIIxgAzVLUFQER+C3wK5AKbfBibMeYU8qvzWbR7EYsLFlvyN2fMk0IQjzPVRLX7fhSQoKotInLYZ5EZY06osqGSxQWLWbR7EZsrNxMkQUxOmsz87Pmcn36+JX9z2jwpBP8fsF5EluEsVTkT+JmIRAEf+TA2Y4xbY3MjywqXsShvESuLV9KiLYxMGMkDOQ9w6aBL6RvZ198hmm7Mk1FDz4vI+8Bk90M/UtV97u9/6LPIjAlwLnWxbv86Fu1exId7PqS2qZZ+kf2YN2oeVwy+gmHxw/wdoukhPDkiAGgESnBOHA8VkaGquryjF4jICzjTUpSpavYJnp8F/BXnxDPAW6r6Uw/jMabHyjuYx6K8Rfwt72+U1JUQGRLJBQMv4IohVzCp/ySCg2wWeONdngwfvQtYAKQC64GpwCrgVJPOvQQ8Bfy+g20+VdXLPQnUmJ6ssqGSD/I/YFHeIrZUbiFIgpg2YBoLJixgdvpsIkIi/B2i6cE8OSJYAEwCPlfV80RkJPCzU71IVZeLSMZZxmdMj9XQ3OD0++9exGf7PqNFW8hMyOTfJv0blwy6hMSIRH+HaAKEJ4WgUVUbRQQRCVPVbSIywkv7nyYiG3DmL3pAVTefaCMRuRu4GyA9Pd1Luzam87nUxT9L/8mi3Yv4aO9H1DXVkRSVxO2jbueKIVcwpPcQf4doApAnhaBIRHoD7wAfisgBwBsL1XwBDFTVWhG51P3+Jzz7parPAs8C5OTkqBf2bUyn2nVgV1u///76/USFRnHhwAu5YvAV5CTlECRB/g7RBDBPRg3NdX/7ExFZCsQBi892x6pa0+7790XkaRFJVNWKs31vY7qCioYK3s97n/fy3mNr1VaCJZgZKTO4P+d+ZqXNsn5/02V4NGpIRCbgXEmswEpVPXK2OxaRJGC/qqqITAaCgMqzfV9j/KmhuYGP937MorxFrNq3Cpe6GNVnFA9NfoiLMy6mT0Qff4dozNd4MmroP4FvAG+5H3pRRP6iqv99ite9DswCEkWkCHgECAVQ1WeA64DviEgz0ADcqKrW7WO6HVXli7IveGvnW3y05yPqm+tJjkrmzuw7uXzw5QzuPdjfIRrTITlV7hWR7cBYVW10348A1quqt04Yn5acnBxdu3atP3ZtzDEONB7g3d3v8ubON8mvzicqNIo5GXO4fPDlTOw/0fr9TZciIutUNedEz3nSNbQP50KyRvf9MKDYS7EZ06241MWa0jW8ueNN/rH3HzS5mhjbdyw/nf5T5mTMITI00t8hGnPaPCkE1cBmEfkQ5xzBhcCa1nUKVPVffBifMV1CRUMF7+x6h7d2vkXhoUJie8Vy/YjruXbYtTbVg+n2PCkEb7u/Wi3zTSjGdC0trhY+2/cZb+58k08KP6FZm8npn8N3x32XCwdeSFhwmL9DNMYrPBk++rKI9AKGux/arqpNHb3GmO6stK6Ut3e+zdu73qakroSE8ARuzbqVa4ZdQ0Zchr/DM8brPBk1NAt4GSjAmYY6TURuO9Wkc8Z0J82uZpYXLefNnW+yongFLnUxLXkaD+Q8wHlp5xEaHOrvEI3xGU+6hh4HLlLV7QAiMhx4HZjoy8CM6QxFh4p4a+dbvLPrHcobyukb0Zc7s+/kmmHXkBqT6u/wjOkUnhSC0NYiAKCqO0TEmkem22pqaeLjwo95Y8cbfF7yOUESRG5KLg8Pe5iZqTMJCfJ0dnZjegZP/uLXichzwCvu+7cANpDfdDv51fm8tfMt3t39LlWNVSRHJfPdcd9l7tC5JEUl+Ts8Y/zGk0LwbeB7QOsw0U+Bp30WkTFedLjlMB/u+ZA3drzBuv3rCJEQZqXN4trh1zIteZot8mIMpygEIhIMbFDVkcATnROSMWdv54GdvLnzTRbtXkTNkRrSYtJYMGEBVw+92ub5N+Y4HRYCVW0Rke0ikq6qezsrKGPOxJGWIywuWMyftv+JjeUbCQ0K5YL0C7h2+LVMSppkUz4YcxKedA3F41xZvAaoa31QVa/0WVTGnIay+jL+vP3P/GXHX6hqrGJQ3CB+mPNDrhhyBfHh8f4Oz5guz5NC8GOfR2HMGdhYvpFXt77KkoIltGgL56aey82ZNzM1eSoi4u/wjOk2TloIRCQc50TxUGAT8LyqNndWYMacSFNLE3/f83de2/oamyo2ER0azY0jb+TmkTeTFpvm7/CMHzU1NVFUVERjY+OpN+7BwsPDSU1NJTTU81H+HR0RvAw04YwSugTIwlnI3phOV9FQwV+2/4U/7/gzFQ0VZMRm8KMpP+LKIVcSFRrl7/BMF1BUVERMTAwZGRkBe0SoqlRWVlJUVMSgQYM8fl1HhSBLVUcDiMjzwJqzjNGY07a5YjOvbn2VDwo+oNnVTG5KLt/M/CbTBkyzk7/mGI2NjQFdBABEhD59+lBeXn5ar+uoELRNLKeqzYH8wzWdq8nVxD/2/INXtr7ChvINRIZEcv3w67lp5E026ZvpkOWpM/sZdFQIxopI6wLzAkS47wugqhp7+iEac3JVjVW8seMN/rTtT5Q1lJEWk8aDkx7k6qFXE90r2t/hGdNjnbQQqKpdcmk6xdbKrU73T/4HHHEdYfqA6Twy/RFyU3Kt+8d0GRkZGaxdu5bERM8uSLzrrru47777yMrK8ngf0dHR1NbWnmmIZ8xm1zJ+0exq5uO9H/Pq1lf5ouwLIkIimDtsLjePvNkWezc9wnPPPefvEDxmzS3TqQ40HuC5Tc9x8ZsXc/8n97O/fj8P5DzAR9/4iIenPmxFwHhNQUEBI0eO5Pbbb2f48OHccsstfPTRR8yYMYNhw4axZo0z/qWqqoqrr76aMWPGMHXqVDZu3AhAZWUlF110EaNGjeKuu+5CVdve+5VXXmHy5MmMGzeOe+65h5aWlq/tf9asWaxd68zPGR0dzX/8x38wduxYpk6dyv79+wHIz89n2rRpjB49mocffviY1//iF79g0qRJjBkzhkceeQSAt99+m/PPPx9VpaSkhOHDh1NaWnrWPysrBKZTbK/aziOfPcKFb1zIk188SUZsBk+e9yR/m/s3bht1G7G97JST8b5du3Zx//33s23bNrZt28Zrr73GihUr+OUvf8nPfvYzAB555BHGjx/Pxo0b+dnPfsa8efMAePTRR8nNzWXz5s3MnTuXvXudWXa2bt3Kn/70J1auXMn69esJDg7m1Vdf7TCOuro6pk6dyoYNG5g5cyb/93//B8CCBQv4zne+w6ZNm0hOTm7bfsmSJezcuZM1a9awfv161q1bx/Lly5k7dy7JycksXLiQb33rWzz66KMkJZ39zLnWNWR8psXVwrLCZby67VX+WfpPwoPDuWLIFdw88mZb8N10ikGDBjF69GgARo0axfnnn4+IMHr0aAoKCgBYsWIFb775JgCzZ8+msrKSmpoali9fzltvvQXAZZddRny8M13JP/7xD9atW8ekSZMAaGhooF+/fh3G0atXLy6//HIAJk6cyIcffgjAypUr2/Z966238uCDDwJOIViyZAnjx48HoLa2lp07dzJz5kz+93//l+zsbKZOncpNN93klZ+TFQLjdbVHanljxxu8vu119tXtIzkqmfsm3sc1w64hLizO3+GZABIWFtb2fVBQUNv9oKAgmpvPbKIEVeW2227j5z//ucevCQ0NbRvWGRwcfMy+TzTcU1X593//d+65556vPVdUVERQUBD79+/H5XIRFHT2HTvWNWS8pqKhgie/eJKL3riIx9c9TnJ0Mr+a9Svev+Z97si+w4qA6ZLOOeectq6dZcuWkZiYSGxsLDNnzuS1114D4IMPPuDAgQMAnH/++bzxxhuUlZUBzjmGPXv2nNG+Z8yYwR//+EeAY7qX5syZwwsvvNA2gqi4uJiysjKam5uZP38+r7/+OpmZmTzxhHdWB7AjAnPWCg8V8vLml3l759s0uZq4YOAFzM+eT3Zitr9DM+aUfvKTnzB//nzGjBlDZGQkL7/8MuCcO7jpppsYNWoU06dPJz09HYCsrCz++7//m4suugiXy0VoaCgLFy5k4MCBp73vJ598kptvvpnHHnuMq666qu3xiy66iK1btzJt2jTAOdn8yiuv8Mwzz3DOOeeQm5vL2LFjmTRpEpdddhmZmZln9TOQ9mfCu4OcnBxtPRNv/Gt71Xae/+p5/l7wd4IkiKuGXMVto25jUJznc5wY4y1bt24964TYU5zoZyEi61Q150Tb2xGBOS2qyhdlX/D8puf5tPhTIkMimZc1j29mfpP+Uf39HZ4x5gz4rBCIyAvA5UCZqp60j0BEJgGrgBtV9Q1fxWPOjktdLC9azvObnmd9+Xriw+K5d/y93DDiBuv7N6ab8+URwUvAU8DvT7aBe03kx4AlPozDnIUmVxOL8xfzwlcvsOvgLgZEDeBHU37E1UOvJiIkwt/hGWO8wGeFQFWXi0jGKTa7F3gTmOSrOMyZqW+q5+1db/Py5pcpqSthaO+h/PycnzMnYw6hQZ4veGGM6fr8do5ARFKAucB5WCHoMqoPV/Pattd4betrHDx8kAn9JvDw1Ic5J+Ucm+LXmB7KnyeLfw08qKquUyUYEbkbuBtoG8JlvKu0rpTfb/k9b+x4g4bmBs5NPZf52fOZ0H+Cv0MzxviYPwtBDvBHdxFIBC4VkWZVfef4DVX1WeBZcIaPdmaQPV1edR4vfvUi7+W9h6pyyaBLuCP7DobHD/d3aMYEhMWLF7NgwQJaWlq46667eOihhzo9Br8VAlVtG2wuIi8B752oCBjf2FS+iee/ep6P935Mr+BefGP4N7ht1G2kRKf4OzRjAkZLSwvf+973+PDDD0lNTWXSpElceeWVp7WGgTf4cvjo68AsIFFEioBHgFAAVX3GV/s1J6eqrCpZxQubXmB16WpiesXwrTHf4uaRN9Mnoo+/wzPGbx5dtJkt+2pOveFpyBoQyyNXjOpwmzVr1jB06FAGD3amX7/xxhv561//2nMKgap6PC2eqt7uqziMMwvoR3s/4vlNz7O1aiv9IvrxQM4DXDf8OqJCo/wdnjEBq7i4mLS0tLb7qamprF69utPjsCuLe7AjLUd4d/e7vPjVi+w9tJeM2Awenf4olw++nF7BvfwdnjFdxqla7j2dFYIeqKmliXd2v8OzG5+ltK6UrD5ZPDHrCWanzSY4yJaiNqarSElJobCwsO1+UVERKSmdf57OCkEP0uxqZtHuRfxu4+8ori1mTN8xPDrtUaYNmGbXABjTBU2aNImdO3eSn59PSkoKf/zjH9umvu5MVgh6gBZXC+/nv88zG55h76G9ZPXJ4j+m/Ae5KblWAIzpwkJCQnjqqaeYM2cOLS0tzJ8/n1GjOr+bygpBN+ZSF0sKlvD0hqfJr85nePxwnjzvSc5LO88KgDHdxKWXXsqll17q1xisEHRDqso/9v6DhesXsuvgLobEDeHxcx/ngoEXECS26Jwx5vRYIehGVJVPij7h6fVPs7VqKxmxGTx2zmPMyZhjJ4GNMWfMCkE3oKp8tu8zFq5fyKaKTaRGp/L/cv8flw66lJAg+xUaY86OZZEubnXJahauX8iXZV8yIGoAj05/lCuGXGFTQRtjvMYKQRe1bv86Fq5fyD9L/0m/yH78eOqPmTt0LqHBVgCMMd5lhaCL2VC+gYVfLmRVySoSIxJ5aPJDXDf8OsKCw/wdmjGmh7JC0EVsrtjMwvUL+bT4UxLCE3gg5wGuH3G9LQdpTA83f/583nvvPfr168dXX33llxisEPjZ9qrtLFy/kKWFS4kLi2PBhAXcPPJmIkMj/R2aMaYT3H777Xz/+99n3rx5fovBCoGf7Dqwi6c3PM2Hez4kJjSG7437Ht/M/CbRvaL9HZoxgeeDh6B0k3ffM2k0XPI/p9xs5syZFBQUeHffp8kKQSfLr87ntxt+y+L8xUSGRnLPmHuYN2oesb1i/R2aMSZAWSHoJIU1hTyz8Rney3uPsOAw5mfP5/ZRt9M7vLe/QzPGeNBy78msEPhYeX05C9cv5J1d7xASFMKtmbdyR/YdtiKYMabLsELgI/VN9by8+WVe3PwiTa4mbhhxA3eNvou+kX39HZoxxhzDCoGXtbhaeGfXOzy1/ikqGiqYkzGHBeMXkBabduoXG2MCzk033cSyZcuoqKggNTWVRx99lDvvvLNTY7BC4CWqyoriFTyx7gl2HdzFuL7j+NWsXzGu3zh/h2aM6cJef/11f4dghcAbtlVt4/G1j/N5yeekxaTxxKwnuCD9AlsTwBjTLVghOAuldaU89eVTvLv7XWLDYnlo8kNcP/x6mw/IGNOtWCE4A3VNdbzw1Qv8fvPvadEWbh91O3eNucuuBTDGdEtWCE5Ds6uZt3a+xcL1C6lqrOKSQZewYMICUqJT/B2aMcacMSsEHlBVlhct54l1T5BXnceEfhN4avZTjO472t+hGWPMWbNCcApbKrfw+NrHWVO6hozYDFsc3hjT41ghOImS2hJ+8+VveC/vPeLD4vnRlB9x3fDrbGUwY4zXFBYWMm/ePPbv34+IcPfdd7NgwYJOj8MKwXEOHTnE85ue5w9b/oCIcGf2ndw5+k5iesX4OzRjTA8TEhLC448/zoQJEzh06BATJ07kwgsvJCsrq3Pj6NS9dWFNrib+sv0vPLPhGQ4cPsAVg6/g3vH3khyd7O/QjDE+9tiax9hWtc2r7zkyYSQPTn6ww22Sk5NJTnZyTExMDJmZmRQXF/ecQiAiLwCXA2Wqmn2C568C/gtwAc3Av6rqCl/FczKqyseFH/Prdb+moKaAyUmTuT/nfrL6dO4vwhgT2AoKCvjyyy+ZMmVKp+/bl0cELwFPAb8/yfP/AN5VVRWRMcCfgZE+jOdrNpVv4pdrf8kXZV8wOG4wC89fyDkp59iJYGMCzKla7r5WW1vLtddey69//WtiYzv/eiSfFQJVXS4iGR08X9vubhSgvorleEWHivjNF7/hg4IPSAhP4MdTf8w1w64hJMh6yowxnaupqYlrr72WW265hWuuucYvMfg184nIXODnQD/gsg62uxu4GyA9Pf2M91d9uJrnNj3Hq1tfJViCuXvM3czPnk9UaNQZv6cxxpwpVeXOO+8kMzOT++67z29xBPltz4Cqvq2qI4Grcc4XnGy7Z1U1R1Vz+vY9s/n8lxct57K3L+PlzS9z6aBLWTR3EfeOv9eKgDHGb1auXMkf/vAHPv74Y8aNG8e4ceN4//33Oz2OLtEX4u5GGiwiiapa4Yt9ZMRmMDpxNP864V8ZkTDCF7swxpjTkpubi2qn9YqflN8KgYgMBXa7TxZPAMKASl/tLz02nd9e8Ftfvb0xxnRbvhw++jowC0gUkSLgESAUQFWfAa4F5olIE9AA3KBdoTQaY0yA8eWooZtO8fxjwGO+2r8xJvCoasAP/z6T9rRfTxYbY4y3hIeHU1lZ2SX63P1FVamsrCQ8PPy0XtclThYbY8zZSk1NpaioiPLycn+H4lfh4eGkpqae1musEBhjeoTQ0FAGDRrk7zC6JesaMsaYAGeFwBhjApwVAmOMCXDS3c6wi0g5sMffcZyBRMAnV013YfaZA4N95u5hoKqecI6eblcIuisRWauqOf6OozPZZw4M9pm7P+saMsaYAGeFwBhjApwVgs7zrL8D8AP7zIHBPnM3Z+cIjDEmwNkRgTHGBDgrBMYYE+CsEHiZiLwgImUi8tVJnr9KRDaKyHoRWSsiuZ0do7ed6jO3226SiDSLyHWdFZsvePA7niUi1e7f8XoR+c/OjtHbPPkduz/3ehHZLCKfdGZ8vuDB7/mH7X7HX4lIi4gkdHac3mDnCLxMRGYCtcDvVTX7BM9HA3XuldnGAH92r9vcbZ3qM7u3CQY+BBqBF1T1jU4M0as8+B3PAh5Q1cs7OTSf8eAz9wY+Ay5W1b0i0k9Vyzo5TK/y5O+63bZXAD9Q1dmdEpyX2RGBl6nqcqCqg+dr263EFgV0+0p8qs/sdi/wJtCtkwN4/Hl7FA8+883AW6q61719oP2ebwJe92E4PmWFwA9EZK6IbAP+Bsz3dzy+JiIpwFwgkBaNniYiG0TkAxEZ5e9gOsFwIF5ElonIOhGZ5++AOouIRAIX4zR0uiVbj8APVPVt4G33oed/ARf4OSRf+zXwoKq6AmQZwS9w5nWpFZFLgXeAYf4NyedCgInA+UAEsEpEPlfVHf4Nq1NcAaxU1W57lGiFwI9UdbmIDBaRRFXtbhNYnY4c4I/uIpAIXCoizar6jl+j8hFVrWn3/fsi8nQA/I6LgEpVrQPqRGQ5MBYIhEJwI924Wwisa6jTichQcWdEEZkAhAGV/o3Kt1R1kKpmqGoG8Abw3Z5aBABEJKnd73gyzv9Zj/4dA38FckUkxN1VMgXY6ueYfE5E4oBzcT5/t2VHBF4mIq8Ds4BEESkCHgFCAVT1GeBaYJ6INAENwA3tTh53Sx585h7Fg897HfAdEWnG+R3f2NN/x6q6VUQWAxsBF/CcqnY4nLir8/Dvei6wxH0k1G3Z8FFjjAlw1jVkjDEBzgqBMcYEOCsExhgT4KwQGGNMgLNCYIwxXYCnkze6t50pIl+cbBJHEYkVkSIRecqTfVshMN2aiPyLiGwVkVf9HYu3uKdp2C4iV7rvv3T8P7uI1Hbw+gj3jJhHRCTR1/Ear3kJZ6oKT+wFbgdeO8nz/wUs93THdh2B6e6+C1ygqkXtHxSREFVt9lNM3nCLqq49kxeqagMwTkQKvBuS8SX3TAMZ7R8TkSHAQqAvUA98S1W3qWqB+3nX8e8jIhOB/sBinKv6T8mOCEy3JSLPAIOBD0TkByLyExH5g4isBP4gIn1F5E0R+af7a4b7dX1EZIl73vznRGSPiCSKSEb7w3IReUBEfuL+foiILHZPqPapiIx0P/6SiPxGRD4Tkbz2LXcReVBENrknn/sf93t80e75Ye3vn+HP4KdydE78YhF58Wzez3Q5zwL3qupE4AHg6Y42FpEg4HH3th6zIwLTbanqt0XkYuA8Va1wJ+0sIFdVG0TkNeBXqrpCRNKBvwOZOFeIrlDVn4rIZcCdHuzuWeDbqrpTRKbg/EO2zj2fDOQCI4F3gTdE5BLgKmCKqtaLSIKqVomzYM04VV0P3AF4mrh/ISIPn+Bn8J/Af4qzHsCngEd9wqbrE2ftkunAX9pN1hh2ipd9F3hfVYtOZ4JHKwSmp3nX3TUCzqyuWe3+IWLd/1wzgWsAVPVvInKgozf04B/yHVV1AVtEpH+7fb+oqvXu/bTOTPkccIeI3AfcAEz28HP9sP1iPu3PEbjnNXoFeEJV13n4fqbrCwIOquq403jNNOAcEfkuEA30EpFaVX2ooxdZITA9Tfs5X4KAqara2H6DDlpKzRzbXRre7n06+oc83P7tTxHfmzhHJB8D61TVG5PR/QQoUlXrFupBVLVGRPJF5Buq+hd3wR+jqhs6eM0trd+LyO1AzqmKANg5AtOzLcFZGQ0AERnn/nY5zopauLtw4t2P7wf6uc8hhAGXQ9u00vki8g33a0RExp5i3x/itPwj3a9JcL9XI04X1W/xvFvopMRZIvEC4F/O9r2Mf7knuVsFjHAP/bwTuAW4U0Q2AJtxuhtb1/8uAr4B/E5ENp/Nvu2IwPRk/wIsFJGNOH/ry4FvA48Cr7v/eT7DGYqHqjaJyE+BNUAxsK3de90C/NbdTx8K/BHoqGW22F141orIEeB94Efup1/FPWulFz7jfUAKsMZ9pPOu+7yB6WZU9aaTPPW1IaWq+k8g9RTv9xLOkNRTstlHTcBzD7PM6ayFY0TkASBOVX98kueXAQ+c6fDRdu9TQCd+LtN9WdeQMZ1IRN4G5gFPdrBZFfCSuC8oO4N9RIjIepwjl6+NMzfmeHZEYIwxAc6OCIwxJsBZITDGmABnhcAYYwKcFQJjjAlwVgiMMSbA/f/Cek/B4gM8MAAAAABJRU5ErkJggg==\n",
+      "image/png": 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",
       "text/plain": [
-       "
"
+       "
"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -754,7 +754,7 @@
     "fig, ax = plt.subplots(1)\n",
     "n_eff = mode_data.n_eff  # real part of the effective mode index\n",
     "n_eff.plot.line(x=\"f\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -788,7 +788,7 @@
     "\n",
     "print(\n",
     "    f\"first mode effective index at freq0: n_eff = {n_eff[f0_ind, 0]:.2f}, k_eff = {k_eff[f0_ind, 0]:.2e}\"\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -809,14 +809,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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77mYl+XwAfwxgGetWVANw3xHKJcTWoCD+GmJ0SAcLMUIKooN7CU3yCgD3N7P9oxZm7Bgg5lHqz8G2PnDZcqkU/BuiH0TZ2ym7/0TdDyL6U7g/RDkplywbd67UJpdrLfFdq7lfWtX9R6oeiyiWY2yi6G9Rv+7l6FdSq8X6nX3qrMFPruZt1BkwL+DYY01dFKIVW0gHp36yqc5d9/8ql7Nx5Oamd2fKR0/fO1M+rnpSOI8dAICdU0HnxpixR6rhj3HeQqy1w6XgSzZfugsAsGyhvOLnV6uLANZ96KLsNde9tZjSNeo81oNuxs6EQ+IrVypFHe/fAYmP3Ew5yB99AOcYjrtwLADgqFosh/ezvRzjz4XHLFSCPAcsyH/n9G31d2SJX18tUVBpOfW5btbB7fwEx4QC6eBeBnP/BuDIqAURYqthBVIkYqRIBwsxAoqkg3sZzL0KwD+R/AaAuunEzF48MqmE2CLUxnziKsYC6WAhRkRRdHAvg7l3Avh/AL6HwqwuCzEGGPQXJXpBOliIUVAgHdzLYG7azF42cklyYJgxjxp9OYBmf47o91CO/hBJXLl4ftrzC04zxgwKxznbCQCYtdDuTC3Un/Ff4bQfy7E9l3WqHoeudZ+ii0OlHkfO/BjKawg26NV4pMcmKgW/kmUuhPMWVoHW4rEWYzGt+HPcZ87/cuL5GJsJAKzaLg9sYOJjGrWgKLNCMVIKq4PbKqa21RvjzGW/vqIPWST6yEXutz3EnTttd3jmabuC7jm4Gtq5ZSno1lsXg269Yyno3n3V4KN2sBRcFhemsrld6zLF5Tp3ljNm81bD2vyxJ7lXS0k8uemp8PzoK7etfDQAYKf7yO2p7QUAHF/eBQC4x87Qzr12hH6evC08f89MEPCG+dDPGw65X3fDAv5iOeuWucxDWVGb8ry27lLDDdnyGPo1F0UH9zKY+wzJ8wB8ElkT/93tbxFCdCP4a/T3ZSa2JNLBQoyAIungXgZzMd3LqxrOaVu8EINiRK2anyIheQKAPwdwTzN7LMnTATzMzN6Vm1CiFdLBQoyCAXQwyQsBPB7APjO7f4vrZwO4BMCP/NTHzOx1HdobSB93HcyZ2X16aagQdAlF0mmbfNxGHpl2k3hkdiqYwOMyaiQuJU57Kpa4zXyn7QnHalhe3e4hSbaXwq9s21SQZa4cjjPlIPuMizjtRz9d35aeErflx7iJa74isBqPfmHZl0CXPAHxkVrFj8FQsFBa8OPBUN/TSq4x2PBT83xchl2pzK/LUku2vTNdVu2yDX4CQ5XknBfwIgDvBvAaL/8AwIcBaDA3RmwpHdxE+7SE6TLrFLOuLUchm97r3juDEnzcSSHUyP0+dw4AoPqaCwEA3/rqPQAAV94ddG4MD1Va9OVbX46rlX35tJRNeVgrhXBMUU+tp0BcS/pSS8ppeKpsSJL4nTFXCnJsZ1hm3VMNy6v38OXle+4I30enhq8anHlM0MkPfsQdob0/ex4A4Cf/+8cBAMvVsEx729L6e0rfYfqOm9NCpuXJW7McQAdfBOBtAN7Toc5XzOzxfbS3YX3cS9DgZ7c6b2adOiCE6IIZUKu2zpm7Sew1s4+QfFWQxyokJ08bFxzpYCFGwyA62MyuIHnKEMUZSB/3ssz64Iaf5wA8GsC30Xk0uiXIOOOWsq9yOtkAEZ1W9+CeAIBFHAAALNidAIBZhtngbndmPRphtrV7KszOdk6HoL+7punlcNw+Zd5+eM5cOZT9MqZLoRwlJUPZLFSIk5K1WiivuTFr2U3PS348Ugn9W/AK82uxHGaDc1WfTXrSaSuFllcQZoc7GWaBOxDew8Hyf4Tn12etwJoH4YzE4JuRCTC09U2tlqu/xiLJY+F7SUg+FMChzreIHJAOHgJV13mHVnwT2iX/CAD43teDbvqmW+RuOBQUzS2LYdVhHw4CAA6W9wEAFmthk0BMUB9XFyrVsCmsFgOnxyDo9UBmqQmolrler+9f3/XA6tHS5xfqAd3L4bjmAdpX5vcEOXxzXA2hPzu8f2d4f2P/4/vY6oxYBz+M5HcA/AeAV5jZtR3qDqSPe1lm/Z3GMsk9AD7U6wOEEO2xfAdzLwNwKYCfJPk1AMcB+JU8BRLNSAcLMTo66OC9JK9sKF9gZhf00fS3AdzbzBZIPg7AJwCc2qH+QPq4F8tcyiKAifThGGYoknC9vf9GOUnnFS1vs7Vgsat4yJG1Uji/y8K2+WiR2zsd7j96Npjcds8EWfbMhFnj7ukwW9s1FY47psKsbc4tYnPlUJ72crnU2qxVjRa5WujLcjU8b9nLi5VQnq+E8qG1UP/gqpdXQ3l6xd/VWpC/6rPNFfelm3X/lRhaZdb73bj1vV//jEkPVWLGXC1zZvZtko8EcBoAArjBrMFUKsaVidXBo8SS0B9rSfmulfD3/s93Bd3zzT8I5284FHzS/n0x+PHu81WTA/wxAOBILfjYraxm03hV66kLVzPl+p9QTOO1bmpLBQ6HuJIW22Ga+rCSaX+lFCyBy+WDAIAFtxjeXQoWuB8vnuD9Odr7twcAcNofBF0T/aPj+2h8T2k6r6LTRQfvN7MzN962538LP19G8m9I7m2Xlm9QfdyLz9wnsf49WQJwOoCP9PoAIUQbDKjmu5s19cV6EEn5Yo0Z0sFCjIgR6mCS9wDwYzMzkmch/O3e1aH+QPq4F8vcGxt+rgC42cxu7aXxopHuyOwUwHKqlLXMzViY/W1zy9QawywrWuzirtXoIxctcnvnwn+0Y2eDLt87E2ZOe6bDbG33TBi475wO7W3z87MeIHLaLXZl34GV5nquuvPnmlvgVjyA5lLdJy7Ic2g1+Mbt8uTUc+VQf7q+TTaU12qh/nIl9Ge+vLNl/494ufE9NVnmOrzvxj5MKgagVtuY822rbfEkXwvgNwHc6dVebWaXdWhGvliTQXF1cNOO9LRC+62G6e726EsWWVoPyQcAOLQSVgfu8q3+J21zf+Op8NAd5aDjtldCvRXuBgBUSh7g3I0kdV+2eHRLWila1Ny6ZXElod7HdlYvr5cEIY76kHGXqx/j7tYpP874Ksd2BHm3W5B/hwc3jv2bdn/pfcvRd9AjFDS8p/QdNkUQaKLL9TFX0gPq4A8COBthOfZWAH8EYBoAzOwdCEukLyBZAbAE4OlmHV/IQPq4F5+5L/fSkBCif2obd0K+CK23xf+lmb2xuXoz8sWaDKSDhRgdG9XBZnZul+tvQ9DRvbY3kD5uO5gj+alu8VF6qVNsGnezZtN5pfF6ZuGxgjwe3ZpbpqqeAybGkTtqJusjFy1yJ8yGesfNBgvcMbNhNrV7W9hBtWNHOD+3M8ysyj7rLHtYu2j0olvSzAPMxY1W1aV4DNeXF0J/FheDvDs81c2c74Qq1+PqhYbjbtjoczdfDfViGrJtlu1/fB+N7yl9h80xjIqFDRCwcgTb4gH5Yo0V0sHNNFqK0nR/1fqu0cByaSlTXqwEf97FtaBXqr7qEbVM2XVjPTVi/Rj0UtnjilYSy1mN4bn1lQPz0ALIytM75Ux7zZa6UkaeunxpasdStn9x9+qiG98WK8FS2PieqmmsT0v9kguSyNQZRAdvAn3p406WuUeQvLTDdSL4bgghNkh1gyb+DrzIfS+uBPByMzvQrqJ8scYe6WAhRswIdPCGGFQfdxrMPbGH+1e7V5kkOmd8iDOm+uVGn7nk3nLyaqctWKS2+exq1X3L1hAsUzGzww73b9g9HX6nx0yH2dPe2TCdOs4tccfsCjuqdu5x37ujfdfqbp/FeTRwbndr10xW9jqr7t9xJLRfWwwzs5lD3u4BLx8Mx/K8W/QQd8F6PDr3uVvw3a6xP3H37mzsv5+P76Pc4CeXvsPURy59/02/nyZ3hPHemWXWUZFsZFv8+QD+BEEh/AmANwF4Xof6xfXFKgaF08HpDvLmiAFpFpdkB3qDpaiWWOKqtayP3HIpG7dyweOx1WNkVuJqQrzfXILOfl7NeqkddZtYn/U70+35Uf7Yn9i/BX9d8x4rNL6PZa6/p/Qdpu84tdQ1p0/obLnLO4JAShcdvNkMpI/bDubkpyHE6Omg+vreFm9mP44/k/xbAJ/qUl9/42OMfj9CjJ5xWTge9O99I3HmikNTLtZ+b2+fAaLJZ84tcTMxl6oFi9m0+zvEHKtuUMPO6RhPznevzoQZ09E7Q67TXUeH8uzxod7U3tA+jw6+aDzKn7/Nd4tOu3xx92lMyrrm/h5LoT0eDpa/0o7gR8GZVe9ruB4zR6z6LtgjbpE7VInyM9Of6Vro0Iz7d8yUwvnZSpB3qtToM9d5N2vfpL/fMdtZZeBQZ4UkTzSz2714DoBr2tSbR+uwfARgZnbU0IQSYqisW7nSXM4x33NkKQmev2Ahu85hz1pzwHfoz3tC6iNVj43pxs413+UZd3jW/Gs/PjfNwdpktep7mJD4p6W7ZpPn1zyGaJQvyhvlP1KN/Qs65oDHBj3sOn/Bwvtaaoj1mb7D9B2P+2pHvwxbB2+EYenjrT2YEyJnqhsMGtxmW/zZJB+AoBhuAvD8Vvea2a4NPVQIIQrGRnXwsBiWPtZgrg86WYqY+HPFHUbr5XB9puw7ptyyNe0Wunh+zo87PS7cLo8bt8t3sW7f6b5se90id7znGTxuR3jQ0WH3KHaFWEPY4dtZp10et4yh5rPAtbi1yS1x24Llj+5jNxX9TtyXYvtqeP6uZT96HLqdqzH+XCnbT5/9TjN7vuwWvcb3lL7DlMLFmTOgahubFbbZFv+ujbRF8ngAdROpmf37hoQSYsg0+VhZ427WLj5ztcOZ8qHS3QCAbZWgM/ctBX1zYDXowENuqVrwDAtLCPevWdCJlVo4xl2zdQtdYjkb3HqVjVMX24/Pq/ru2SjPmvsdL8XdrR4VoOQWp9JqHKyE8wcr4T3F99H4ntJ3mL7j5t2tk62EB9HBo2Kj+rhrL0g+nOTnSf6A5A9J/ojkDweQVQjhmLX+bAYkn0DyXwH8CMCXEax5n9mcp4tekQ4WYnTkqYMbGVQf92KZexeA3wVwFYq2YD4gjblDU6tRKRntT9MtUe7HNRVjBXlMohn3ZZvxHKpzbpnbNuVx6ObcIrfLMzrsDveV3EeubpE7xpfXdwfLrW3z67PuO5da5lbcV26bTwSms/8lShXfJbvkmSXmw/O3L3jGieVKRt7oExf7Ey2S9f4yRiN3+a39O2zOzVosDEQl31nhnwB4KIAvmNkDSf48gGfmKZBoSWF1cNPu1vRLlOnuyXVLUc2yVqRKdTlTXqlmLXPzpZAYpVRy3bMccpge9t2c86UQxSf62kWL1Wot5Jde8/YrHpSznpvVsjlZ69asuhWrxywK7o9sSXy6WtMPrTH3obNS+C8SM1esWcjRurwcVm/2lUIu13lPFNP4ntJ3mL5jWOpDl/5+sr/AcbfcjYEObmQgfdzLYO6QmWm2LsSQMeTur7FmZneRLJEsmdmXSL4lT4FES6SDhRgBY6CDGxlIH/cymPsSyf8L4GPAehI3M/t2/7JuHVKrUsnjKUXLVN1C5/Xq0cf9thmfZc36LGvGc7LWMzrUt726xS36yO0KFjrzI3aE2ZhNe8aGxDLHuXC/TXnU8WSXK+Iu1x3L/vysPHX5XN4of+xPvX9Jv+P7KLr1rSO2HpU9Jw6S3AngCgDvJ7kPIeq4GC+2rg5OLT2Nprsmn7msVWl5LbubNVKZCvVWysHnbMWC5S1a4FJLXPQjq1vi6ha56DMX85lGC1v0oatm+pBaqWI5dslYzZaR+OQx7m6tZJ4f5VsrBXlXy0H+5VKwuC26xfGA53BdqAXL3HLlYDg2vKf0HTb5zDX5MI635a0r+evgRgbSx70M5h7ix8aYVwbgUT2LKIRowjBQbtZh8ESEBNC/C+BXAewG8Lo8BRItkQ4WYgSMgQ5uZCB93HUwZ2Y/v2HRukDyMQDeihDa/+/M7PWjetbG6N1q1GyJKyflwJT/EEOgsW6xQuY4FY/ui1aeCseS5xPknP/qZt1CNzuTHIMPnPmxnc+ceZmepw+e83W9nenM8+LzozxRvqlE/nLSv9jf2P/4PhrfU/9WurT+pLkTMe9Z4fMBfNjMbgNwcZ6CiPZsJR3czceKtv43nuYJrVWz5dW6xSwQrU5La8FStVDe5+eDzqs17VLNWsDaW+Ase74uYGuLXEo7Cx3qfY05X+NuVs8t6zHhKr6LNeZuXfFjqXRX5ny5FL4D1qqL3s/VTDuhS9mEIpbq1AnzietO7jq4kYH0cS+7WXeTfDPJK/3zJpK7NyJp0m4ZwNsBPBYhB9m5JJVnUGwZDMHE3+qzSewC8DmSXyH5IpInbNaDRe9IBwsxGsZABzcykD7uZZn1QoRI8k/18rMAvBvAk/oSs5mzANxoZj8EAJIfQjAzXjdgu5vGIBkKSk05CeP52HaY8ZT8SN/lWr8tZnKIljb3ebOpqUwZsRzrlRKZk/rxfsb76/dFUxsy8tTli/K27FX7/nZi4AwQE0CeJn4z+2MAf0zypwE8DcCXSd5qZr+Qm1CiFdLBTsYS1GQlSixj1cQyV826H2W9w/Kn3rcmP7RoAaxXTPDdtSOSq+iMyzLroPq4l8HcT5rZkxvKf0zy6v5FbeIkALc0lG/Fum9IHZLnAThvCM8TYqyw8XG+3QfgDgB3ATg+Z1lEM9LBQoyAMdLBjWxIH/cymFsi+Qgz+yoQAlgiTgU2ATO7AMAF/uyxWqBvzsXXO7U2vgaxxZgDNc4aLG6frrtn+A8xXpz7vLHi/h3RB87LKCfZFer3VVreH8v1evF58VBL5IvytuxV+/52YpD3OynkqUhI/jaCtec4AH8P4DfNbGytMlsY6WCHjRb+JPdymkGGpZlMuVwKoQBKfn66HHb65+0zty5/3dE4uVJKrpey5xOfOdZ95qYy5+Uz15pxGcwNqo97Gcy9AMDF7qNBAHcDeG7/ojZxG4CTG8r38nNCbAkMQCVfXXgygJea2dW5SiG6IR0sxAgYAx3cyED6uJfdrFcDOIPkUV4+3PmOnvkWgFNJ3gdBgTwdwDOG1PaQ6N0y1LSzKpnRxKuVOKmru0eEH6pejsf4H6ziOVyrlXCsecYF8yNXfLa4spocg0dI9H2zaGFL48ytZes3t7OWeV5t2TLyRPkqifzVpH+xv7H/67HR2+9O686kW+4I24Av4bAws1fl9nDRM1tJB7Pb3wMbdr8ziRjglrfIdHlnpjw3HfaMzE3tAQDsLIUVrEHjzKGdxa4pblzrUUNqkatbGLtY4KKFsVw/znq/QwSDGY8rF4+z7B5nbq26kJGtVssagFNLXePu4laMv+UuXx3cyKD6uO1gjuQzzex9JF+WnI8PfvMgDzazCskXAfgswrb4C83s2kHaFGKSiDuphGiFdLAQo6VIOriTZW6HH3e1uDaU4baZXQbgsmG0NW40W+qiBS57rHi9qvukrbkv2motzMZWamGWtroajp4WEFOLPhtc8Lhw20I085hblb77NPrOsVtu1iVveN5nZvNHMu2bPy8+P8pTl8/lXfNmY3/q/Uv6Hd9H/9a44mC2bqkUogXSwU1+cWy4lP36KpfmMuVoiYvsnL4HAGA3wvEe1Ta5WUuem7XUa27WoPvquVk9c0P7zA6W7UsbixzrPnDRIpe1xE15OqDUEjdXCvm5tyH0f5fnZj2qFv473VEK2YIOTd+BlFqa8SGJ1YfEj9lS/74JywhRJB3cdjBnZu/0H79gZl9rvOYOuEKIASnKrFAMH+lgIUZPUXRwLxsg/hrAg3o4t+VotCqlOy9rzJbXamF2VrfIef2q+yCsuiUrWriW3RdtqRJ+RUeWw2xs+3yYDU4d8tnetjBLLE0lMdk8t2rMqYppzxSRWubWfOa1GC1zbpE7EGajtQM+C/XnrcyXM/JE+aK8q3XLYrZ/9f56/9fc16JWav8Oi261M+Tj9UdyHh0sO2Z21CaKI7pTWB3c5CPXZifn+vX1r6wSZzOXpspZy9xsOfvfeBeOAwAcXwu+cifOBcvW3Gpoc7oWdORh9z1jyfNVd4t3mfgBR6KFbt1a1c6/LKYF2phFLvZzzuNI78SxAICjaqF8tFvijvZsPrXl0P+a59Ve8xy1ALBazsbiqyW7W6vJ91qTJS65nu59Hjcfurx0cCPD0sedfOYeBuBnARyX+GwcBSR7wIUQfZOXv4aZ7QIAkn8C4HYA70XYJfmrAE7cdIFES6SDhRgtg+hgkhcCeDyAfWZ2/xbXiZAq73EAjgB4rpl9u0mGIenjTpa5GQA7vU6jz8ZhAL/S6wOKRKe4Z+kunyqS6OPRAufbPNd8PrDGtcz5ZT8uVMN/sPm18CuaX3HL3ILPzva7xa0UfNqm3LLHVbfYLbX2patncohx49yCB69vh0O75ha5yv4wM1vZH+474s+P8kT5orxR/no/vX9rmMmcj++j8T01xTRKKGLcuWq+E9UnmNkZDeXzSX4HwB/mJZDIsOV1cGq5a7SSNfvMZS110Xcssrt2DABgz1Sod/y22JZbrFaCxasWVxVK7ifsOqxaysahq3kct+hnxug7Vx9nZ/3PesctgvVdrOEY48aVvd9TbnGbZjhuQ+jvzlr4r7KboT9Hz0xn+rtYCf1fqoT3ccR9BcPPd2ckSd9xk5UyGQiNl92tNwbQwRcBeBuA97S5/lgAp/rnIQDOR4ug3A0MpI87+cx9GSGdxEVmdnMvjQkhemcMdlItkvxVAB9ycc4FsNj5FrFZSAcLMVoG0cFmdgXJUzpUeSKA91iIz/V1kntInmhmt7epP5A+7rTM+hYzeymAt7WK+m1mT+j1IWNL03p/v7evW4ribC1SsWzmvxUGC1f0JVv1XULRcrVcDecX18KsbGEtCHPI48RtXw2zqZkFjw/HGL8ttDu7Go7lxSBHaUewrHG7+8rNtFmVcUueHQly1Pz+6qHwnJUDYSa2cDBY1g4shFngQZfnUCJv3GQb+7PmlrfY39XaVOZ9NL6n9B0ObIkb951Vtm4gzYlnICwDvDVIg69h7GI9bl22hA7um3U9Fi1VkekkzlzczRnZ6Zaqo3yV4mhPELFUSfyTK54hArP+RPddQ9ZCxlrWcla3WtV/U9GK1WvW1KzVK22XyfOjPFG+KO+sr4JsL4d6u6ZLmf7e5f2fr4b30fie0ne4WprPlKu1gq3ud9bBe0le2VC+wLOh9EqrdHknISyltmIgfdxpmfW9fnxjr40JIXonzAo3dm8rfw2SxwD4MIBTANwE4KlmdqBdG2Z2E8LsUYwn0sFCjJAuOni/mZ25abIMqI87LbNe5ccvx3MkjwZwspl9d6MPHG/SnTrpTqvsDCtjmUvurSb+EmtuiVpy/4pomVphsEwdcavUos8OD62F2dQ2t8xNl8LsizFeXdw1uhru37EY2ps7GCxg5W2rmWN0fajHn/PpSAwrFOPHVZfc98194xYXw/HQUtg5dfdKmP3tXwny3O2WuUPRMudBe2J/Yv+mffa4ZNOZ99H4ntJ32GyZS/MEptcnzaeOgyyzXoRmf41XAviimb2e5Cu9/PtNTyX/Gp13T714o0KJ4VFEHdw1w0PT7tXW1ipgPRNCpMlnrh6mL7DTdeiu6SDDTr99rhzvp0vQWcbeVwxGk9Gm2/Oj/LE/sX+xv/X+r4b3MWfr7yl9h+k7Tn3mrMmHLlUrScYIpD52ea+eDKSDu9FTurxh6eOuoUlIXg7gCV73KgD7SH7NzF7W8UYhREcGyQvYxl/jiQDO9p8vBnA5WgzmAFzZ4pwYU6SDhRgNI87NeimAF5H8EMLGh0Nt/OWGoo97iTO328wOk/wNBGe+PyI5kbPC4dPoM5fdvdrsMxdMX8vu47ZU8jJDPLcjnv/vcJwtlcNsYTruPvVfVZxFLFfDdGuxEurvXA7tbjscLF2zM+E4PRVmRuVy9LULrcUJVNXjw61VPKODx1xairtU19wy53Id9PP7PQPEXSvu27dqLn943hEL/Vkuhf6VkY3lFN9H1mcuiTY+cZa2/hmyHjmhQVncAeCEls80u7ixTHK7mR1pVVeMBdLBDhssd6mVqJxYkeZqWf+vHbNBZ+2YjvVjNpqgw+pZeHy1oFo/+m7WmOHBLWNxF2ssr1vMevWRa0e1Zbvp86I8dflc3rr8tdi/QOzvDrfM7fBVn7mV9feUvsP0HTO1nBaAjepgkh9EmDzvJXkrgD+Cb402s3cgZFZ5HIAbEUKT/FrL5w9JH/cymJsieSKApwJ4Tb8PEEK0xgyoth+vDuR8a2bWymm+EY9j9i6E8Bc/QfIMAM83s9/u9TliU5AOFmIEdNHBXe61c7tcNwAv7LW9QfVxL4O51yEkYv6amX2L5H0B/GuvAhaJpgwFjT5zSU67ilvaIqtTwRK15D5kqwjlFQuWqwW3YM1VfSfVSrTIhdlTzNkaLXLzvgMrWsp2TIX75jyjwlw5zO6mvVwutf5ej753a/XME6H95VqMSZR9XvSNO7iatcgdWAnPO1RdzfQn9i/utFryaOar0TJXXX9PzXkBu/nQTTZdTPwbcb79cdz67l/++7rUfwuAX0JYDoCZfYfkz/X5TDF6iquDmzI+pLS3BKVWorirM7INWf+v3bOh/rF+es1XOZb8j3CxGixcRxiMIkcQcrTG3KyVWtBZa35ct5RVkrLrqbq+6qa3Yn33Z/ZyrZ7jteTtx/zXHqnArWarniniiPu7zXLO+xPjyZUz/Y39n49+2Svr7yl9h90tcen1xCo55rlbR7zM2i9vwQD6uOtgzsz+HsDfN5R/CODJfYsphGhiyHrkUgDPAfB6P17S9flmtzCrcAddIxJDRjpYiNExPmO5wfRxLxsg7oWQBzAmdv4KgJeY2a39CDkOpDtnuu2saq7fPndoalWqWtYyFy1UKwxRulcQZn8rPuubL4fI22XzX8laqLdWC5asI5VsPLeDHjto+1Qobyu7/0M5yOxuEZh2i1ycP63HpwsVYg+i5W/Nu7zsGR2W/L/SkQr9+aHC/FrNy1mL3AEcDtcZ+rNSdcucxy+a8nyK8X00vqcmy1zTjDZ9//39Gea/cyrLgKlkWvlrvB7AR0j+OoCbEZblOnELyZ8FYAyJIF8C4PoNCSRGRpF08CghszHQppOMZ8fOhr+1hx0bYqf97MuCVvzOXwUd9KV9RwMAbjgULFu3LIYMCft4EABwcCYYuhdr+wEAK9Wg61YqoT1Ww6pLzGdai7rNyxajIUTrlH9pM2Z88NyrMedsqZ6L1XOwToXMDjEX647SXgDAHs81e7ztAQCcvCPcf9ru0P7PHx+iE53x4nD+n94c9Oj8WmhvuuG9pe+w6IxB4PZGBtLHvSyzvhvABwA8xcvP9HO/2KegQoiEyvD9NR7dRzO/hRCg8iSELfOfQx8+HmLTkA4WYkRsVAePgIH0cS+DuePM7N0N5YtIvrQfCYtKpwwQa9XsbtYlhtlRpZQ9H6N4R0vVoVIoV91KtVzZCQCYd/+HQ77bdNtUqDdXDscZ3/06U4q7YEP7ZZ/9ldpMPmL06xg40Q1uWI3Hes7YMKtcSuLIxV2r0UduwWexsT+xf0seu3YJB/w57n9SHWEGiDHHLN/crGa2HyGZsxhvpIOHQNzNuXs26KzaE88BAJz+zQsBAItfjZkegs6dKrlFbPFYAMB0NVwvl8NxETH2p0cC8FWJStwB6oa4KqNPXPxjj3rN6/kO0miRK7slrv78qWCJ21beA2DdIndMNVjk7uGWunv6Nt1TPYvvmccEHXz6Q+/y/j4v9P/tH/f3sbP5JW0x8tbBjQyqj3sZzN1F8pkAPujlcwHctdEHCiHWyWPpl+TvmdlftAtWqaDBY4d0sBAjIm/3m2Hp414Gc89D8Nf4Sy9/DW3ipUw8TblaO2eEMKzHRYs7jSJr1Wx+3FrMTeo7jqLFKu5IqriFK1ruVhh96UIu1DkLs6jZWpitzfgOpJkYv82PZcZ8fUHWqXp+v+buAutdrsTYRTHDhMXcqsFathqPXHH5gpwxjtyqh8VZq8VjsLxVfVdvPVaSz0rj+ar7k4R3tP5zuCeJO9ct48OY7ZTqxiDpvAbkOj8qePBksHV0cBO9+ymnsT0PJ6G6bl4IuvOy24Kl7d8e+gUAwMHV+wIAblkKuvLWxfBHecdS0D/7qsEn7mAp+Mod8VWG5VqwxK3rOveVq/vMRfna7WrNno/1WYvZcVrvXo1+baVS1gJYWwwmuYrH8pz3VZ0bPxP6t+eLob83zB/r78Njgza8pwqy77BfP+ZJI0cd3MhQ9HEvu1lvRog+LoQYMhuNcTQgTwPwKQB7zOytuUggekY6WIjRkZMObmQo+rhrOGeS9yX5SZJ3ktxH8hKPcySEGIAY46jVZ8T8DMl7AngeyaNJHtP4GfnTRV9IBwsxGnLUwY0MRR/3ssz6AQBvB3COl5+O4LvxkL5FHjOGGaokXRKsJhsgonN/xZcnYwLjaDoveRDd1VJYnp3ybelld4pd8MCQU/Vt6+6M6064ZWbLJV/2jUEfS9Z6y3ktpo7xJeUa43JrNoVNLMfl4hgUuX49LhPXlxpi/WwgzeYUOOtm/KZl1YKFImnCclsZfgeALwK4L0Kuz8b/+ObnxfhQWB3c7NrSrXr7ZdYYKiRy53SS09xXE+9YConlr9o/5e34pi4PrzTvy7WHSx5mqRTcE5ftcOY5q+5KU6kmy6x1Hef6rNtGrqgL6zrWbSzRBcW7mab3qpZD/ZWSBzm2sHx690LYEHHLYlhW/k45brAI7S5UQv8OWJD/zvL6e1pZy77DboHcuzLuri/56eBGhqKPe0m0tt3M3mtmFf+8D8Bcv9IKIbJEf41Wn5E+1+yvzOx+AC40s/ua2X0aPhrIjR/SwUKMgLx0cEaGIenjXixznyH5SgAfQuj70wBcFs1/Znb3RjogxFbHYKjmOC00sxfk9nDRD9LBQoyAvHVwI4Pq414GczGK/POT80+HlmSEGIgx2Eklxh/pYCFGRFF0cC+7We+zGYKMJV1ClWQuJ74E1aRu9I1DTN1S95WLv4JsqJK1Jp+6WA73l5Pr9WPSfv35bVbU063mdb+M6EuX+mnU/UHC9W4+cem2+/Xz1aQMoGkbfPpXNtmhSFIM60GbhWjH1tLBvYeDSv2Sl9cOZcoHcHOmfKQcQorc5n7HU9XZzPUa3R8Ywf931UOCVDzcUvQHTn3kot/vejn6yrnOi3os1VdeNg9mTK9fizGG2VqHxrBOMWTJcin0a6F0JwDgLmZTJ5Y8VEmk4uGlYj8b/eTSd5i+477DRY05RdLBvVjmhBCjwDA2Jn4hhNhyFEgHazAnRE4YxiLGkRBCbEmKpIM1mBMiR4oyKxRCiEmkKDq462CO5BcBvMnMLms4d4GZnTdSyXKga9y5Dj50lvikscn/K16PvmOhbExjq2V93pj40qW+dfXnJfVT0vp1udrEDYq+dOn11Bdu3ccurV/teN/6hcZ3OJiP3NjHlUswTJqHiciDIungYcb2rHlsy0ilmtVxRxLdtVw6CAAoeUzOVCeu+wdn/YHrx1rU3dEXLtZfy9zf5CvX9a886lDX4fX7A9Va1eWddjmi757HLC3FOHKH/Jj1r27XzxgzNPYrPCtN55V9x0WL/VkkHdxLnLn7APh9kn/UcO7MEckjxNbBgGrNWn6EaEA6WIhRUCAd3Mtg7iCARwM4wVPK7B6tSEJsDULASmv5EaKBg5AOFmLoFEkH9zKYo0cd/20AHwXwVQDHD/JQkk8heS3JGknNMMWWxGCotfkI0YB0sBAjoEg6uJcNEO+IP5jZRSS/B+CFAz73GgBPAvDOAdsZKX350KUx6Joaa+37UT/N1j519fpNPm+tc622843rl/Y5+Nr5wkXa+8QBPfjFZRtvf61lW5PHJM4AxaazdXRwP7E9E6oedy2S5hVltbW/cbMInf2Am/x/6/Xb6LYYTy65HsusfwdkfedQj1dHP8T+ZPtRrXXzs+6tn+HcWlInzc2a1f9FiP1ZFB3c9ZvfzN6ZlK8ys+cN8lAzu97MbhikDSEmnWDir7X8CBGRDhZiNAyqg0k+huQNJG/0lHvp9eeSvJPk1f75jWH3ITL2oUlIngdg4nZtCdENA1ApgHVRFBvpYFFUBtHBDNuF3w7gFwHcCuBbJC81s+uSqh82sxcNJGgPjGwwR/ILAO7R4tJrzOySXtsxswsAXOBt6ptPFAiDFcTEL8YP6WAhujGQDj4LwI1m9kMAIPkhAE8EkA7mNoWRDebM7BdG1XZedPSh6/Yfok+fuuifEZ/RdLlt3Lh2ArRbUe9zSa9tXLp2D+7Sfh9/SEXwkWskzAo3vqRK8iYA8wj/WSpmJkd2UWcSdPBAsT1TdZD657KSVOjXn3hA/982vnIp7Xzn1q9HH7r4ndDa33r9fQzQzw32sR3jrrO76OC9JK9sKF/gE5vISQBuaSjfCuAhLdp5MsmfA/ADAL9rZre0qDMwY7/MKkRxMVSROhT3zc+b2f5hSCOEEFuLjjp4/xAmyJ8E8EEzWyH5fAAXA3jUgG22ZDhbH/uE5DkkbwXwMACfJvnZPOQQIk/CrLDa8iPEKJEOFmJgHXwbgJMbyvfyc+vtm91lVk+j8XcAfmYYcrciF8ucmX0cwMfzeLYQ44PBuGETf2gA+Jz7Mb2zxXUhWiIdLATQRQd341sATiV5H4RB3NMBPKOxAskTzex2Lz4BwPUbfVg3tMw6AJ38Abr6fjTd0M4Xrd3D+/VFGJa1p18fu63rE9eNOCtsQy8m/keY2W0kjwfweZLfN7MrhiqkEJvIKGN7Dq4D2+i+Njpuo3lLm7eYuNx0/+muzx+kn4P5OE+aDu+igzvfa1Yh+SIAn0UI/HqhmV1L8nUArjSzSwG8mOQTEALI3g3guUMRvAUazAmREwZDtclJu4/7zW7z4z6SH0fYXaXBnBBC9MAQdPBlAC5Lzv1hw8+vAvCqDT+gD3LxmRNCAICh0uZfN0juILkr/gzgvyNE9RdCCNETG9fB44Ysc0LkhCGbSqdPTgDwcYallykAHzCzfxySaEIIUXgG1MFjhQZzI6Kd70CTD0j9hg36wLFNe8NmyMFtJ823YjQYqljrXq3VnSFQ5RnDlUeI8WKYsT03LsTGfN82/LjoO9ez3/UQvws2ua/5s3EdPG5oMCdEThgMFRZDkQghxKRRJB2swZwQOWEwVK0YikQIISaNIulgDeaEyA0rjL+GEEJMHsXRwRrMbTL9+hi09bGrNzgePguT7zuRB8Xx1xBiMxhqbM8RyjLK9ofnd71xGYpDcXSwBnNC5ITBUCuIiV8IISaNIulgDeaEyJFaQUz8QggxiRRFB2swJ0ROFGlWKIQQk0aRdLAGc2NO8X0WtjBWnJ1UQuTNVtGVW6Wfm0KBdLAGc0LkRJgVTl7aGCGEKAJF0sEazAmRGwaL0duFEEJsMsXRwRrMCZEjtVoxZoVCCDGJFEUHazAnRE6E6OPFUCRCCDFpFEkHazAnRF5YcXZSCSHExFEgHazBnBC5YTArRowjIYSYPIqjgzWYEyInDCjMTiohhJg0iqSDNZgTIjesMM63QggxeRRHB2swJ0RemMEKMisUQoiJo0A6WIM5IXLCAFhB8gIKIcSkUSQdrMGcELlhsILspBJCiMmjODpYgzkhcqM4Jn4hhJg8iqODNZgTIjcMZsVIJSOEEJNHcXSwBnNC5Eox/DWEEGIyKYYO1mBOiNwoTsBKIYSYPIqjgydtMLcfqNwMYG/4eWyRfIMxKfLde8B2PgtU9ra5Ns79F1sX6eDhIPkGQzo4gWaWtwx9Q/JKMzszbznaIfkGQ/IJMd6M+9+A5BsMyTd5lPIWQAghhBBCbBwN5oQQQgghJphJHcxdkLcAXZB8gyH5hBhvxv1vQPINhuSbMCbSZ04IIYQQQgQm1TInhBBCCCGgwZwQQgghxEQzsYM5kv+X5PdJfpfkx0nuyVumRkg+heS1JGskx2ILNcnHkLyB5I0kX5m3PCkkLyS5j+Q1ecuSQvJkkl8ieZ3/Xl+St0xC5Il0cP9IB28c6eDOTOxgDsDnAdzfzH4awA8AvCpneVKuAfAkAFfkLQgAkCwDeDuAxwI4HcC5JE/PV6omLgLwmLyFaEMFwMvN7HQADwXwwjF8f0JsJtLBfSAdPDDSwR2Y2MGcmX3OzCpe/DqAe+UpT4qZXW9mN+QtRwNnAbjRzH5oZqsAPgTgiTnLlMHMrgBwd95ytMLMbjezb/vP8wCuB3BSvlIJkR/SwX0jHTwA0sGdmdjBXMLzAHwmbyHGnJMA3NJQvhX6Q9gQJE8B8EAA38hZFCHGBeng7kgHDwnp4GbGOjcryS8AuEeLS68xs0u8zmsQzK/v30zZ/Nld5RPFguROAB8F8FIzO5y3PEKMEulgMW5IB7dmrAdzZvYLna6TfC6AxwN4tOUQMK+bfGPGbQBObijfy8+JHiE5jaBE3m9mH8tbHiFGjXTwUJEOHhDp4PZM7DIryccA+D0ATzCzI3nLMwF8C8CpJO9DcgbA0wFcmrNMEwNJAngXgOvN7M15yyNE3kgH94108ABIB3dmYgdzAN4GYBeAz5O8muQ78haoEZLnkLwVwMMAfJrkZ/OUxx2VXwTgswiOox8xs2vzlCmF5AcB/DOA00jeSvLX85apgYcDeBaAR/n/t6tJPi5voYTIEengPpAOHhjp4A4onZcQQgghxAQzyZY5IYQQQogtjwZzQgghhBATjAZzQgghhBATjAZzQgghhBATjAZzQgghhBATjAZzBYHkb5F8tv/8XJL3bLj2d8NISEzytSRvI/m6Qdvy9p5G8kaSnxpGe0IIkRfSwSJPFJqkgJC8HMArzOzKIbf7WgALZvbGIbZ5NoKsjx9Wm0IIkSfSwWKzkWVuhJB8MMnvkpwjuYPktSTvn9Q5heT3Sb6f5PUk/4Hkdr/2aJL/QvJ7JC8kOevnX0/yOm/7jX7utSRfQfJXAJwJ4P0eVHEbyctJnun1zvX2riH5hgY5Fkj+GcnvkPw6yRN66N9rSb6ioXyN9yf26SKSP/C+/QLJr5H8V5JnDeP9CiFEJ6SDpYO3ChrMjRAz+xZCupY/BfAXAN5nZte0qHoagL8xs/sBOAzgt0nOAbgIwNPM7KcQ8ui+gOSxAM4B8F/N7Ke97cZn/gOAKwH8qpk9wMyW4jU3+78BwKMAPADAg0n+sl/eAeDrZnYGgCsA/OaA3f9PAN4E4L/45xkAHgHgFQBePWDbQgjRFelg6eCtggZzo+d1AH4RYab2F23q3GJmX/Of34fwB3cagB+Z2Q/8/MUAfg7AIQDLAN5F8kkA+smJ+GAAl5vZnZ5a5v3eJgCsAoh+E1cBOKWPdlvxIzP7npnVAFwL4IueiPt7Q2hbCCF6RTpYOrjwaDA3eo4FsBMhh+Fcmzqp42JbR0ZXAGcB+AcAjwfwj0OQEQDWbN2BsoowC+1GBdn/Q439W2n4udZQrvXYthBCDAPpYOngwqPB3Oh5J4D/jTADe0ObOj9B8mH+8zMAfBXADQBOIfmf/PyzAHyZ5E4Au83sMgC/C+CMFu3NIyiulG8CeCTJvSTLAM4F8OUN9ClyE4AHAQDJBwG4zwBtCSHEKJAOFoVHo/MRwrBNfc3MPuB/uP9E8lFm9v+SqjcAeCHJCwFcB+B8M1sm+WsA/p7kFIBvAXgHgGMAXOL+HATwshaPvgjAO0guAYgKCmZ2O8lXAviS3/tpM7tkgC5+FMCzSV4L4BsAftClvhBCbBrSwWKroNAkOUPyFACfMrP7d6ubN9S2eCFEwZAO5tmQDp54tMwq+mEBwHkcYsBKAH8D4MAw2hNCiIIjHSxaIsucEEIIIcQEI8ucEEIIIcQEo8GcEEIIIcQEo8GcEEIIIcQEo8GcEEIIIcQEo8GcEEIIIcQE8/8BcS5NZ++HVVgAAAAASUVORK5CYII=\n",
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ZssUwf+LEia7p7t27o2XLlrj88stx4MABnH322QEeABERUQzzM07HG3bMiCj2+HklLiMjI6DiHFOmTMF7772HzZs3+3wY+uKLLwYA7N+/nx0zIiIiEe+YSbFjRkSxJ8gf+JqmYerUqVi7di02btyIDh06+Fxn165dAICWLVsGtC8iIqKYx46ZFDtmRBR7gvyBP3nyZKxcuRLvvPMO0tPTUVhYCADIzMxEamoqDhw4gJUrV+LKK69Es2bNsHv3btx1110YMGAAevToUZ8jISIiij3smEmxY0ZEMUdRrVBsVq+vB2LJkiUA7GOViZYvX45x48YhKSkJn3zyCRYsWICKigq0adMGI0aMwKxZswJuOxERUawLdpyOFeyYEVHsCUEqozdt2rTBpk2bAtomERFR3OIdMyl2zIgo9qia/cfb60RERBQejNNS7JgRUezhlTgiIqLIxTgtxY4ZEcUemw3wkrvudewUIiIiCi3GaSl2zIgo9vBKHBERUeRinJZix4yIYg9z14mIiCIX47QUO2ZEFHt4JY6IiChyMU5LsWNGRDFHsVmhWL2Mj+Itr52IiIhCinFajh0zIoo9mmb/8fY6ERERhQfjtBQ7ZkQUe5giQUREFLkYp6XYMSOi2GO12X+8vU5EREThwTgtxY4ZEcUeXokjIiKKXIzTUuyYEVHsUTUfH/jxmbtOREQUERinpdgxo3pRoIS7CQ1CQ3x+QEQtPlRMRBEoUmNmNMS4SD13QHScv4jDOC3FjhkRxR6bj9x1W3zmrhMREUUExmkpdsyIKPYwd52IiChyMU5LsWNGRLGHH/hERESRi3FayhTuBhARBZ2q+f4hIiKi8AhynJ43bx4uuugipKenIycnB8OHD8e+fft8rvfWW2+hS5cuSElJQffu3fHBBx/U9YiCgh0zIoo9NpvvHyIiIgqPIMfpTZs2YfLkydi2bRvWrVsHi8WCIUOGoKKiwuM6X3zxBUaNGoXx48dj586dGD58OIYPH449e/bU9+jqjKmMca5BqxwpEVZRKYCKP8E4T6za1ICYIkFEDaTe8aEhYqOPeOfrGBoifgXl+0ioz6WH8xhI2/ldwCHIcfqjjz4y/F5QUICcnBzs2LEDAwYMkK6zcOFCXHHFFfjb3/4GAHjooYewbt06PPvss1i6dGlA+w8W3jEjotjDVEYiIqLIFeI4XVJSAgBo2rSpx2W2bt2KwYMHG+YNHToUW7durde+64N3zIgo9nDgSiIiosjlZ5wuLS01zE5OTkZycrL3Tasqpk2bhv79+6Nbt24elyssLESLFi0M81q0aIHCwkIfjQ8d3jEjotij+shbV/mMGRERUdj4GafbtGmDzMxM18+8efN8bnry5MnYs2cPVq1aFeqjCDreMSOi2OMrDYJ3zIiIiMLHzzh95MgRZGRkuGb7uls2ZcoUvPfee9i8eTNat27tddnc3FwcO3bMMO/YsWPIzc310fjQYccsxkXGQ8kRemPWcGhBKAYRBQ9Xxw12zIgoyAKKpwHFzgaIkYHEO0ksE489mLHKr3Pq81w24HcMJYDvCgEUConL+O9nnM7IyDB0zDzRNA1Tp07F2rVrsXHjRnTo0MHnOn379sX69esxbdo017x169ahb9++PtcNFXbMiCj2sCojERFR5ApynJ48eTJWrlyJd955B+np6a7nxDIzM5GamgoAGDNmDM466yxXOuSdd96JgQMH4sknn8RVV12FVatWYfv27Xj++efrdkxBEKG3MoiI6sGq+v4hIiKi8AhynF6yZAlKSkqQl5eHli1bun7eeOMN1zKHDx/G0aNHXb/369cPK1euxPPPP4+ePXti9erVePvtt70WDAk13jEjotjDO2ZERESRK8hxWvNjbNqNGze6zbv22mtx7bXXBrSvUGLHjIhij6Z5f+YvgMHFiYiIKMgYp6XYMSOi2MPiH0RERJGLcVqKHbMo1LCVFr0/huhfRaX6Psroz/r1TE3TglAxsZ7VmgJ5X+OyglMgbDbA6mWsMltg45jNmzcPa9aswd69e5Gamop+/frh8ccfR+fOnV3LVFVV4e6778aqVatQXV2NoUOHYvHixW6DVxJR5Kt/BUZ53JJuVxojg1ECwENMksQ7Q0zxUcFRERatayxynQeP30fcj9/je+LxO0YIzqGH7wrS8+DpO0EA8T+mY32Q43SsYPEPIoo9zitx3n4CsGnTJkyePBnbtm3DunXrYLFYMGTIEFRUVLiWueuuu/Dvf/8bb731FjZt2oTffvsN11xzTbCPjIiIKPoFOU7HCt4xI6LYo8JHikRgm/voo48MvxcUFCAnJwc7duzAgAEDUFJSgpdeegkrV67EZZddBgBYvnw5zj33XGzbtg2XXHJJgAdAREQUw4Icp2MF75gRUewJ8ZW4kpISAEDTpk0BADt27IDFYsHgwYNdy3Tp0gVt27bF1q1b67UvIiKimMM7ZlJR1THbvHkzrr76arRq1QqKouDtt98Od5OIKAJpNtXnDwCUlpYafqqrq31uW1VVTJs2Df3793eNdVJYWIikpCRkZWUZlm3RooVrkEuieMA4TUT+8DdOx5uo6phVVFSgZ8+eWLRoUbibQkSRzM8rcW3atEFmZqbrZ968eT43PXnyZOzZswerVq0K9VEQRR3GaSLyC++YSUXVM2bDhg3DsGHDwt2MBlX/ylAy9a0WJd+G4nFZs9fWeF4veDTNx5UXRV79x7me8czUsdKVYX9+XgnyMI6H7L2K6epNgfKzDO+RI0eQkZHhmp2cnOx1s1OmTMF7772HzZs3o3Xr1q75ubm5qKmpQXFxseGu2bFjx5Cbm1u3YyCKQtEWp4MTY2XxUB73FMX9a5eiJEraZRJe9z9GirFOE2KVIQY64p04TxHjmmEbkn0L8SuQCo2Gc+06l358H3Edv6dzYpbOd57Dup4/wHgO7a9bPKxolWzLw05k8T8eYz3L5UtFVccsUNXV1YbUpNLS0jC2hogajJ8f+BkZGYaOmSeapmHq1KlYu3YtNm7ciA4dOhhev/DCC5GYmIj169djxIgRAIB9+/bh8OHD6Nu3b92PgyjGMU4TxSl2zKRiumM2b948zJkzJ9zNIKIGptlUaFbPdyUDzV2fPHkyVq5ciXfeeQfp6emu58YyMzORmpqKzMxMjB8/HtOnT0fTpk2RkZGBqVOnom/fvqzISOQF4zRRfAp2nI4VUfWMWaDuu+8+lJSUuH6OHDkS7iYRUUMIcu76kiVLUFJSgry8PLRs2dL188Ybb7iWefrpp/HHP/4RI0aMwIABA5Cbm4s1a9YE+8iIYgrjNFGc4jNmUjF9xyw5OdnnMyNEFIOCnCKheXxYQJeSkoJFixax6AFRABinieIUUxmlYrpjFk1C9gByHQp6+FPEw7mM5weUvT+4LHtA1xN/Htz1VdzD40PQ0nnuy3peXygaori/rtS3UIinIiGSjoKnv6GYeVA4EPzAJ6L6kMZZD4UqJIU+ZEU+AMBsSpXMS3Hfk0lf3yQUB5HFQ0PBD02PSapQkMIwrVody+rzjDFOKHDh3LZhWaENkkIgYsyRF/wAXN83DK8nCIuK3yESHcuK84TzYzhX7tPi++Pp+4TzHKq1ins4z5WTTa2Srm9TK6Xz5fuSzAwg1scMxmmpqOqYlZeXY//+/a7fDx48iF27dqFp06Zo27ZtGFtGRJFEs2nQbJ4/1L29RkR1xzhNRP5gnJbzq2O2e/fugDfctWtXJCQEt9+3fft2DBo0yPX79OnTAQBjx45FQUFBUPdFRFGMV+IozjBOE1FUYZyW8usTuVevXlAUxa/nLADAZDLhf//7Hzp27FivxtWWl5fndxuIKI7xA5/iDOM0EUUVxmkpvy+Vffnll8jOzva5nKZp6NatW70aRURUH5pqGCNV+jpRrGGcJqJowTgt51fHbODAgejUqROysrL82uiAAQOQmur+gCsRUYOwaYDVy9W2OM1dp9jFOE1EUYVxWsqvjtmGDRsC2ugHH3xQp8bEi/pXYPRQGUpagdH9LTZWJXKvVmQyrCOvrqhXO3KfZ5+vb9csWdZYldF7xSRfVRsBY0Up1zyxUpVQPdFVaVF43SZWnJJUtRKXVQ3Luldz9Py60AZJexXN6jZP83TssgpOHtKHZH8XsV6pUVM1aF7SILy9RhSNGKd9Cyj2BrS+++e0SZEPAZBgTnObl5rY1G1esjlDX0fYVgLct6tCryRo1apd0zWaXinQqp4Rpmvs/7fpr9sc8wBA0xLc5oufmIqnCo1ilWIpMe47zqXhe4NefdJsShLmJ7jNSzDrFxUSxPkm/fwmKfZlxPNngr4PkRX28yaePwCotpUafq+0nJauL6v2bPNwy0dWrTmQiBQrMZ1xWi6qqjISEflFBTyNVOB6nYiIiMKDcVoq4I6ZpmlYvXo1NmzYgOPHj0NVjWduzZo1QWscEVGdaPB+CTI+L8RRnGCcJqKIxzgtFXDHbNq0aXjuuecwaNAgtGjRAorHwY6JiMJDs2rQzF5SJLzltRNFOcZpIop0jNNyAXfMXn31VaxZswZXXnllKNpDRFRvrPZE8YxxmogiHeO0XMAds8zMzKCPe0LwUOQDkD1ULBbWMM53fztlDyArhodt/S/oYTLpD806H7Y1C9s3Cw/uGh62dcw3Cw/disuK802a+HCwoxgJ5McrUh0PHYtFNVShQIZNeDjaplnc5qma/OFp17LCPKvwkLSqitvwv1CIJin0odZ66Ni+oPty9vUlM2UFQTwuHOOYu05xjHE6GCSFlyTFqQBj0QonszlFumxKYqbbvCaJ7dzmZdvO0l9HI9d0Y2FAcGdthDM2PU6UaVWu6VKTXriizHTKNV2l2edXC6/X2Cpc02JREOd5UIXYLGbGGgqBOL/HiDFH+G5jKFrh+G5hMonfFfQiHrJCH0lm/TyIxVFSFH06Hc1c0xlqhmOe/l6kmYXvNEJzyq324yjS9PMAACcSfzX8rnkocKJKY7qH+C0LQB7jtK+CKlGMcVrKd7m7WmbPno05c+agsrLS98JERGHgvBLn7YcoVjFOE1GkY5yWC/iO2XXXXYfXX38dOTk5aN++PRITjVeKvvnmm6A1joioLjQV0LxcaIzXD3yKD4zTRBTpGKflAu6YjR07Fjt27MDo0aP5UDERRSTmrlM8Y5wmokjHOC0XcMfs/fffx8cff4xLL700FO0hIqo/5q5THGOcJqKIxzgtFXDHrE2bNsjIyPC9IElHZw/ONur3ALL4UK1ZfPDWsV2x+If4eqIpVZ9W0gAASY7/A0CK1tg1nazp+01S7dtIEv7cEoVps7g/4XgTFGd73A7BjfO5WatwicUmPExrE+ZbYH8gtwb6g7k1il54o9qkPzxdpZTbX9fO6OuL06r+DIdNrXa0RSj+IXyyOF+3T+sFRFzHYHP/FNI8XDJSJJ9YgZT48PS3qcXIwCG8EkfxjHE6AEG4m6hIioKYJMW4AGPRCiex0IfTuWlNXdOdM/U2dk7XY0dxjX0fRyr1ePtLhR6zCyv1+HzcphfOKDadBACUJ+jfGcRjMByPM0wKFT80odCUoZCFt7w0wFA8xfl9RTxP4neTxAS9vc6iH6nmJq55jYUiH1lqc9d0jjndNZ3b2L6P1o3089cmVW9vVpL+HWBfmf0c7iupVTDtjPHXCvNJt8MCgCqlxG2e7O8CCLAel+zvM0YKejFOywVc/OPJJ5/EzJkzcejQoRA0h4io/jSb4vOHKFYxThNRpGOclgv4jtno0aNx5swZnH322UhLS3N7qPj06dNBaxwRUV1E45W4zz77DM899xwOHDiA1atX46yzzsKrr76KDh06MCWNAsI4TUSRLhrjdG2hiNsBd8wWLFhQpx0RETUUTVOgaZ6vtnl7LRz+9a9/4aabbsKNN96InTt3orranvZaUlKCRx99FB988EGYW0jRhHGaiCJdtMXp2kIVt+tUlZHqSZrT7iGrVJKjLHuWDDAOzuiUKAzI6JScoOdgi8+NOYnPNSWahGfIhEEcG2tZ9v/b9OfK0oRBpdNM+p9WaoL9GFLM+rEkmfVzkCQcYqIw7VzE5Me/TedAmzYh9doiXG2pEacdC1UJz3RVWvXpM6pVmLb/Qys3lbvmlZuKXdNVij5Ap0WxJ6N7yisXn0ertpa5H4PqPhil5mHQ6IAGqJRtI0Zy1D2JtitxDz/8MJYuXYoxY8Zg1apVrvn9+/fHww8/HMaWUTRinA4VD893S+Z7esYsQXF/7jsDaW7z2jXWA9+VZ+mDQ5/7n7+4pm1/XwYA+HpLrmve9tN6TE4QntkyVQjPtjkeBVPNwrNiJv35MMNz0iaLfZ4QczTDs9wWodXO86BK5hmnTa4BpuWDSovfTVJM9ranKfozZlk2/bmyXOG5vVaN9O9Hv3N81endVI/fF11aqO/vkVtc02cPWQsAqLLpz64BwK+VxvdG9v6JxyOS/V04lpbMi+GBpD0IdpzevHkz5s+fjx07duDo0aNYu3Ythg8f7nH5jRs3YtCgQW7zjx49itzcXMkaRqGK2wF3zA4fPuz19bZt29a5MUREwaCqClQv+emqGllX4vbt24cBAwa4zc/MzERxcXHDN4iiGuM0EUW6YMfpiooK9OzZE7fccguuueYav9fbt2+foVhSTk6O3+uFIm4H3DFr37691zFRbLb46/UTUYRRFWjePtQjrGOWm5uL/fv3o3379ob5W7ZsQceOHcPTKIpajNNEFPGCHKeHDRuGYcOGBdyMnJwcZGVlBbxeqOJ2wB2znTt3Gn63WCzYuXMnnnrqKTzyyCN1bggRUbBomvdszUjL5JwwYQLuvPNOLFu2DIqi4LfffsPWrVsxY8YMPPDAA+FuHkUZxmkiinT+xunS0lLD/OTkZCQnJ0vWqJtevXqhuroa3bp1w+zZs9G/f3+/1gtV3A64Y9azZ0+3eb1790arVq0wf/78gG4fUt14eobJZHJ/OxMl45iJY4FkoZVrugJFAIBy7YRrXrKi56tnCmOFNIH9tm9mgp4T3jjR7JpOT1SE+fbptAT9X2CqvihSzPp8YTUkmuzzxaNVFH1Z8cFQZyqyRbjCYhH+wVcJt8srHdNnrPr5KhcWLrOI8+356ik2IQ9e01/XTHoSdDXseeyNFT1HvRH0c11s/k1vryEv39FeW4XbPFULwjgocSjaHiq+9957oaoqLr/8cpw5cwYDBgxAcnIyZsyYgalTp4a7eRRlGKejn034jCqpFsYbfecj1/S32+yx5ivhubJ9JXpwOFKhj515HMWu6WLzcQBAhaqPyVVt07/8is9AW232cT1VYexNVdO3C018Nlr2UJA4zpm+rGsbws1b8dk1wzNtjoVs0OOmzaxPW1R9urosS2+7YwxVFfr5abRNj889hXPpPMe2CIsNsczfON2mTRvD/Pz8fMyePbve+2/ZsiWWLl2K3r17o7q6Gi+++CLy8vLw5Zdf4oILLvC5fqjidsAdM086d+6Mr7/+OlibIyKqM9WmQPVSNcZbXns4KIqCv//97/jb3/6G/fv3o7y8HF27dkXjxo19r0zkJ8ZpIooU/sbpI0eOGJ4BC9bdss6dO6Nz586u3/v164cDBw7g6aefxquvvupz/VDF7YA7ZrVvKWqahqNHj2L27Nn43e9+V6/GRDMF9fui53l9SVVGD5V+ZFWBzIr7H7B4FyxZ1e+oWRV79SGLSX89XWvqmnbeJQOA5on27TZJ1m99ZSbpx5CVpF+1y0y0X/lKT9CvgDVK0C+TpQh3nVLM+vxEx3yzyfftIZvjTplF1c9NlU1vW5Uwv8Jqn19m1eeVWPS2F9cI82vs8xOrhffHop8Hm3AFsNpRuTFZqLCVrOnnN1k4r1VKidsx1LeqkyK9WgkEcnNN9neoBbSFyBBtd8yckpKS0LVr13A3g6Ic43Tk0jT35/ssknmnqvXP3a2n9Njx1Sx9mX0l9sqFhyv0ir/HHZkvAFCkHHNNn1H1yo7VNfa/jxohS8Mm3hETpp3zNTHLQ4h7mvGWl9txiPM0RVjWsV2bUDVYFbcrTDvbUG3S7+RVmYtd0+WOO4AAcNqk3xE7VtECAHC4Qs9c2VeS5ZruPEuPA86KzuJ5B9zfGy0OqyeGir9xOiMjw9AxC6U+ffpgy5YtAa0T7LgdcMcsKyvL7aFiTdPQpk0bQ7lIIqJwUVXFa0WnSKvKOGjQIK/FGj799NMGbA1FO8ZpIop0kRind+3ahZYtW/q1bKjidsAdsw0bNhh+N5lMyM7ORqdOnZCQELTMSCKiOou24h+9evUy/G6xWLBr1y7s2bOHY1JRwBiniSjSBTtOl5eXY//+/a7fDx48iF27dqFp06Zo27Yt7rvvPvz666945ZVXAAALFixAhw4dcN5556GqqgovvvgiPv30U/znP//xa3+hitsBf0IPHDiwzjujwMkKfXgs/iFJhUswuacyJmn6wI2pQqqdRbGnC4ipjuIA0mKhD2cKY/MU/WpBs2T9X1HzJP12f1aiPSUhM0lPhWicqKdKpCbqKQvJSfp0oiPd0SwMgilenBD/0dps9nNiserpi9U1+vmoNBT0sB9HSY0+EGW68GUlxaxvI9GV/6zPs6j6eaiy6uenzGyf9nR+zwjzZe+LNJXRw3stmx9pnY1wUlUTVNVTGii8vibja+DKcePG4eWXXzasM3ToUHz00Ufwx9NPPy2dP3v2bJSXl0tfI/KEcToAnj44pRfC/R/xVvOwrFjAwqkS1W7zSqr1lPhTifrn1VmpQhGtBHsjG5n1WJZm1derVjJd01aTvg/noNCGYhvitJBeaHJMq0IKnyam0hvOn680P2E9RwwTY5kYAxVh2jlfHHQ6QZhOEh4TSIN+zGma/Vw0StDPj/OcAUCiUEzseJV9fkm18X2r/d7I3j/A8/stF8CyMRzYgx2nt2/fbhgwevr06QCAsWPHoqCgAEePHjWM8VhTU4O7774bv/76K9LS0tCjRw988skn0kGnZUIVt/066nfffRcWi/yPUeaDDz5AZWWl7wWJiEJA1RSfP4FwDly5aNEij8tcccUVOHr0qOvn9ddfr+9hYPTo0Vi2bFm9t0Oxj3GaiKJJsON0Xl4eNE1z+ykoKAAAFBQUYOPGja7lZ86cif3796OyshKnTp3Chg0b/O6UeVPfuO3XHbO//OUvKCwsRHZ2tl8bHTlyJHbt2sWBUYkoLDQfA1d6HdRSwp+BK5OTk5GbmxvQdn3ZunUrUlLch7wgqo1xmoiiSbDjdKSob9z2q2OmaRrGjRvnd4nKqqqqOjeI/OFpHLNEt3kJivsfRzL0FIAURU/LszhS7WzQ0wnThKqOGUnuFRjF9MUWyfp62cl6qmLTZHsqQGaq/nfRqJH+ekpj/SqvWUjTMDuaKWb4KUJpVU0Vqj05dm0TLgDbKvVlq8r1c1NRYT/mRpX6uUkRxokxC+fE+U9EHB9NrPZYZtPXS9Hs6RSpmvz8iudd9r7I3j8/b2pTLeF4xmzjxo3IyclBkyZNcNlll+Hhhx9Gs2bNfK8IuI0r5ayit337dg4wTX5hnA4fWRqbmBooshnG/bKrMrnfuayw6lXoKix6HLAJjw8455qFuJgofK0zG6b1+GJW7NNWD2mEqqK30ZVqqAmDj8L9GOpO366Y1ihLcRTnOY/BvgVxWj8O57kQz48YUcUxyyocX0MqrMZ0zNrvjU2VH7vs/Q4svTH+RNuz4LWFKm771TEL9CG2G2+8scFKWxIR1WbTTLB5yU+3OQburl1WPDk5uU5jpFxxxRW45ppr0KFDBxw4cAD3338/hg0bhq1bt8JsNvtcPzMz0/C7yWRC586dMXfuXAwZMiTg9lD8YZwmomjib5yOVKGK2351zJYvX17nHRARNTTNR366c3yUNm3aGObn5+dj9uzZAe9v5MiRrunu3bujR48eOPvss7Fx40ZcfvnlPtfnZyzVF/+GiCia+BunI1WoPnNZNzcsJFcBPFTfE2/zuxb1VJVRsl2z5C1O1PT0ulQhfaHGUW3QAj3NLs2kv95IqGaUmWi/x9w0Ub/t3zxZT0nMFtIWm6bbB7FsnKWnLyY30W/xmzOFlIVGekqCkuaYTvJ9xwE19nZoZ/Q2qBV6ykFSibDvIvv8pGL9dXOZkBYplONypjBWCtUey4XBqMXz4xysO1k8v8Lr4nk3Syowyt4/T++17O/C49+QNB8gtgfJ9HfgyiNHjhjuGtTlbplMx44d0bx5c+zfv9+vjhkRhZ4G989CRV5+EdLKeR4+U2RpbKokZREAbKp7BcYqU4XbvHJVj2VlYlVhq5hW79ymflyq5Bg98RxffDEkBAZhG/4JtL3OcyGenyqhueXCW1RmsS8jnncAqFKM743s/QPk77endFZI5wdS8TPCc/z85G+cjjfsmBFRzFHhPcw5X8vIyAhJOtcvv/yCU6dOeR2oskmTJl4HpxSdPn06WE0jIiIKO3/jdCRpiLjNjlko+fnmBb5ZT8U/JOOYyYp/CONwJZn1bSVp9jtUicKDtCnC68LNLDR23DHLFO6YZSXpV5KaND7jmk5vYp+fnKNf5UlorrdBaaIXxVAyhPamOu5eJArHJTzEC+EqGCz2q1VKpd4GpVS/a2dqpD/AqyTZ754pir6seGWmxqYf8xnHnbISq3ge9GXF85Oo2k9QkvBQcpJJfz3Zqh9zgklW/MP/cczqzdPfZqQ/besnm6p4z10PsNqTt4ErmzZtijlz5mDEiBHIzc3FgQMHMHPmTHTq1AlDhw71uM0FCxYE1AYiilTud41UD0UiLKp7oY9KlLjNK9eau6ZLLXpMKRLG3yyz2L+6nrHp+6qGnh1iEcbgEsffUh1fecU2ioUqPI1vpgvGV2bvBVMM05L2qib9dfHYxGN2noszNvGc6XGhqEafLnV8hyjXjO9PpWJ8b2TvX+226WI7M6W+gh2nG0JDxG12zIgo5gQ7RcLbwJVLlizB7t278fLLL6O4uBitWrXCkCFD8NBDD3lNjQy0WAMREVGsiMZUxoaI2+yYEVHMUTXjDVXZ64FwDlzpyccffxzYBr2oqqpCTU2NYR6r5xERUSwJdpwOp2DG7Tp1zNavX4/169fj+PHjUFXj7ej6jHZN7gJJZVMkBSHE8T30efpySWZh/BNHCl+ilih9PUWYbmy2v+/pifrt+3Rh7LK0xkKxjeb2f10JOfrdA1N2I71BTRrr0+lp+nQjR4pjonAMQmogxL89i3MQEiFlMVVPp1SEAiIJzgethYd404R/UOlVwrTFnn7YuEb/pyKmLxrOnyPFJFGRv24WCojI3hfZ++eJ7O8iRrIQgyLarsRVVFTgnnvuwZtvvolTp065vW6zMSWGAsM4HXweiy7IxrAKpPiHWuo2r8SkP5+SatVj5/FKPU4U1djjXomQflduKnNNV0LfrkXT46FVtU+LY6oZ0holaYShS8vTt6sJBUHENjjbZhPGV3MeAwBYhGJaleL4Zo6xQU1CupypRvzs15ctttrfF/G8A+7vjafiH7L321Pxj1gp3lFf0RanawtV3A74AZY5c+ZgyJAhWL9+PU6ePImioiLDDxFRuNk0xedPJJk5cyY+/fRTLFmyBMnJyXjxxRcxZ84ctGrVCq+88kq4m0dRhnGaiCJdtMXp2kIVtwO+Y7Z06VIUFBTgpptuqvNOiYhCSfUxPoq318Lh3//+N1555RXk5eXh5ptvxu9//3t06tQJ7dq1w4oVK3DjjTeGu4kURRiniSjSRVucri1UcTvgjllNTQ369etXp51RcCgebnTK0ttMkpHTExUhpU6ozpfgWN+sCamOQhXEJJN++z3FkcqYmqDfvk9LEdIX0/VbuOZM+/ZMQvVFQ/piUyEPNzPdNamlOpYXCyh4SmWstqcXKKlCtcNE+Z+3yWpfz1yptz25TG9vWrl+HKlV9mWcxwsYKy2K58eZIpogvA/i+RXPu+x9kb1/nt5r8k6DAtXj+ETGseoiwenTp9GxY0cA9rx0Z5ndSy+9FLfffns4m0ZRiHHaf57SyhTZbMXTuFTuaWyqJk95s9qq3OZV29xTGctMJ1zTJpMQO6pauKZLHWNslZn0u6BihUcxDa9GLXdNWxxtsNr0FEibqsc9VdOnNc3i+L9wjIYUvUAqNArLOr50a5CnfKoef/FOE6o1aiZ7XLea9PfCojVxTVdV6Y9UHDcdBwCUQT/vgPt7I3v/AA/vt4d0VvkYefK/w1hOe4y2OF1bqOJ2wN/6br31VqxcubLOOyQiCjVN8/0TSTp27IiDBw8CALp06YI333wTgP2KXFZWVhhbRtGIcZqIIl20xenaQhW3A75jVlVVheeffx6ffPIJevTogcREYxGDp556qs6NISIKBptmgk1yV1J8PZLcfPPN+O9//4uBAwfi3nvvxdVXX41nn30WFouFn6kUMMZpIop00RanawtV3A64Y7Z792706tULALBnzx7Da/6Ohk0NR5YKZxJuD4upds7pBGEds5CqlyhsKsmRLpBs0lMAk5KE9EUha1FxjkzdWEhJFKsvputpjZowjUb2NAMtUR+Y2VMqo5Ji37aWoKd8KJIBqAEAjkGolUZ6SoI5VX4czuNLEtIjxPMgnh/neZOdU8B43pmiGFrRVob3rrvuck0PHjwYe/fuxY4dO9CpUyf06NEjjC2jaMQ4HSKe0s1keY8eqzK6p8JVWdwHmBZZE/R1qs16NcJqzZ6eKKYpylIW7fsVBpt2pC2qhvRFsSqjPmCzK+3OUKlRqDYnnBNZ2p04TzxNmmJznwcPlSEdKaRiOqXYXvHYLCb9mGvM9nNRZdLTESuEtM8ik/59o1y1pzJWWYsN7a/93sjev9ptc83zWMUzwgJQmERbnK4tVHE74I7Zhg0b6rwzIqKGEG0PFR85cgRt2rRx/d6uXTu0a9cujC2iaMY4TUSRLtridG2hitv1GmD6l19+AQC0bt263g3x16JFizB//nwUFhaiZ8+eeOaZZ9CnT58G23/o1P8OivzumPvYWOJSCcIvzgupiuGOD6TTCY7pBKEohjlBnzalCHeHUhx/ZslCOk1ykodpvXiH5pz2o/iH5pivWIUrecnCw7iGfSQa21WrveJxOI8vwcN5EKed5028IC2eX/G8y96X+t9F87R+/I2BpUHx+uBwpD1U3L59e1x66aUYPXo0/vrXv6JJkya+VyLyA+N03QRSdEHR3D9jNQ8VK1Sb+/wawx0qO/HOTKVFv8tTbj4uLGOPcarH8cjkd5j0O2JiMQ7xjph47N4LVQRynmR3z5x3zuy/iOdRiGeacxwzfZ5N1QuXWIWxy0zCmGbVjmmT6ZT0dbNJ/25hsdkLqYh3EWvvBwC0Wq+75svibBwW9AhEtMXp2kIVtwP+JqiqKubOnYvMzExX7zArKwsPPfSQ2yCWwfbGG29g+vTpyM/PxzfffIOePXti6NChOH78uO+ViShuRNv4KNu3b0efPn0wd+5ctGzZEsOHD8fq1atRXS2v7EbkDeM0EUW6aIvTtYUqbgfcMfv73/+OZ599Fo899hh27tyJnTt34tFHH8UzzzyDBx54oF6N8eWpp57ChAkTcPPNN6Nr165YunQp0tLSsGzZspDul4iiizN33dtPJDn//PMxf/58HD58GB9++CGys7MxceJEtGjRArfccku4m0dRhnGaiCJdtMXp2kIVtwNOZXz55Zfx4osv4k9/+pNrXo8ePXDWWWdh0qRJeOSRR+rcGG9qamqwY8cO3Hfffa55JpMJgwcPxtatW0Oyz0glG+8qGEw+bhuLe1UceQgm4cldRRjnzLApZ4EMMQ1RKNKhJSRI58M5X1zP5OHYnct62K4ibte5DZOYhyhMCsfhPD5FOE5fZ9/XeQxEqN7rWBetueuKomDQoEEYNGgQbr/9dowfPx4vv/wyv9RSQBinG5Y0Nc1jGpvkjqVNksroSK2rTV56Ivq4zpnHQhhiamXtidqE8djq2S5qONEap2sLdtwO+Fvf6dOn0aVLF7f5Xbp0cQ2uFgonT56EzWZDixYtDPNbtGiBwsJC6TrV1dUoLS01/BBR7NP8+IlEv/zyC5544gn06tULffr0QePGjbFo0aJwN4uiDOM0EUW6aI3TtQU7bgfcMevZsyeeffZZt/nPPvssevbsWeeGhMK8efOQmZnp+hGrpxBR7LJBgVXz/GOLsIeKn3vuOQwcOBDt27fHK6+8guuvvx4HDhzAZ599httuuy3czaMowzhNRJEu2uJ0baGK2wGnMj7xxBO46qqr8Mknn6Bv374AgK1bt+LIkSP44IMP6twQX5o3bw6z2Yxjx44Z5h87dgy5ubnSde677z5Mnz7d9XtpaWlMfOiL43sEk+rj+oS4V81xi1m81aypwj8iQ1Enxy/iQ+dC9UTFKlSLEqsqOueb3SsYum/P6nW74nzXemICszgpHIfz+DThOH2dfV/nMRCheq9jnaYphvdM9nokefjhhzFq1Cj885//jLgvzhR9GKcbliL7AulhvDhFVpHXlOQ2z2zSBwM1Ca8nmhu5pqOtKqPIdc48jqtnkixrkr/uoSqj4qrKmCB9nVUZwyva4nRtoYrbAXfMBg4ciP/9739YtGgR9u7dCwC45pprMGnSJLRq1SpoDastKSkJF154IdavX4/hw4cDsFeeWr9+PaZMmSJdJzk5GcliqXUiigsqvHegI627e/jwYQ78S0HDOE1EkS7a4nRtoYrbdRrHrFWrViF7eNib6dOnY+zYsejduzf69OmDBQsWoKKiAjfffHODt4WIIpevik6RVu2JnTIKNsZpIopk0RanawtV3ParY7Z7925069YNJpMJu3fv9rpsjx49gtIwmeuvvx4nTpzAgw8+iMLCQvTq1QsfffSR24PG0an+1wZk1Z5Uye11cSmrmMngKpKk/2uwCf8wxGmrY9pqEwZ8tOrTapWQNuGYVqqFylPVNR6m9ZpTzkqKmpiy6GGAacVS47a+531YDO2yt1c4ZuE4nMdn9XAexGnneROzF8TzK5532fviaUBS/0X69aWG42sMlEgfH4UoUIzTwSVNT/S4sCQ9UTIPAExCiqJTormx27yUxEx9OiHLNd3YlOOartbKAQA1arlrnjhtsenx0Jn2aJ+2x0MxbU9MdYRhwGtHXBFTHYVBoRXDEwHev0kbzqnjS60htdNHqqKYhiimd5oN0/rd10RzCgAgyaSfX3E6WdGny1X7OHtV1mJDm6ssJYbfLbZyyKi1Uh4BD+mNkA9I7kkspz0yTsv51THr1asXCgsLkZOTg169ekFRFMOXdydFUWCzhbZY6ZQpUzymRBARAYAGBZqXL1beXiOKRozTRBRNGKfl/OqYHTx4ENnZ2a5pih7yu2jiHTH3aauwjk24l2wRimLUqParWdWqfrWrpkaftgkXjxIqHFffyoXR0FPP6NOJwsO6wthizkIgivj8gYc7ZnCMtK5UCjsuE65slQn7c7RDq9CvCortFY/DeXzO4wUAi7Bb8fw4z5vsnALG817/u2PkTbSnSBAFinG6AXgs6OE+X1HkX6/MphS3eeLdMafGiXqxlEzo07k2/c5jqWIvWFFmKnLNqzTpd3iqTPrQA7I7aVYh8NkMd8/0eOcsFKJBv6NmuEsmuXsm3uWR3SWzz3fEWUW8MyYW7hALetjviIl3xhLM+p1H550xwHhHLMWUAQBIhX5+07UmrukMVS+kUmhKAwCUJMqHdXAy3FkUaJr7WHTwULxLk/0deRzPLXYxTsv51TFr166da/rnn39Gv379kJBgXNVqteKLL74wLEtEFA78wKd4wzhNRNGEcVou4OIfgwYNwtGjR5GTk2OYX1JSgkGDBoU8RYKIyBfVR+66GgG56+eff77fDw9/8803IW4NxRLGaSKKdNEQp2triLgdcMdM0zRpo06dOoVGjRpJ1qBg85QGJxvzSlXc51lUPSiLqXZWx/o24YHVGuGShZjOV+UoilFp1f+EzlTpaQZpZXpaREKJIyUxVU+bMCV4GNvcIqRLNHI8uJyopzR4TGW0ONIIKsRURiF9sUhP41CL7MvYSvTjrC7T0xfF43AeX5VQ5KTGkNIppH06zptVeB/E82sRHvhVTe7vi+z9Y8pj3URDGV5nOXEAqKqqwuLFi9G1a1fXuFPbtm3Dd999h0mTJoWphRStGKf957HIh/TLl4e4JUlbNCnyIQASzO6pjMnmDLd56ch2Teeoege7ZYqewpdSY99voqrHyFKh+IVi0uOaonhou4yHolVOYlqjMQXPV4dfaIOjPb7SFwE9hdFT+qJ4/lIUPW2xMZoBADJUfV4TR8oiADRJ1venVtnPsWoyHoPFfMbwe425wv2w4D7+GQDYJN+/AMjTFj0sq0gWjZWCINEQp2triLjtd8fsmmuuAWB/cHjcuHGGcUdsNht2796Nfv361bkhRETBEg0DV+bn57umb731Vtxxxx146KGH3JY5cuRIQzeNohTjNBFFi2DH6c2bN2P+/PnYsWMHjh49irVr1xo6UjIbN27E9OnT8d1336FNmzaYNWsWxo0b53H5hojbfnfMMjPtVxw0TUN6ejpSU/UrF0lJSbjkkkswYcKEOjeEiChYou1K3FtvvYXt27e7zR89ejR69+6NZcuWhaFVFG0Yp4koWgQ7TldUVKBnz5645ZZbXBepvDl48CCuuuoq3HbbbVixYgXWr1+PW2+9FS1btsTQoUN9rh+quO13x2z58uUAgPbt22PGjBlMh2ggsvQ2j8tK0ghscK8UZEhVFAbisjj+GVgUi/T1KmG63Ga/klFm0f+EyqqFVMZyoXrSSUdKokmvypggpEgqNUJVp0pJ5UahaiOEqo2GJ0OdKZDC+lqpPoaLVqSnOFpP2lMOqk/q2zojtFc8DufxOY8XMJ4Hw/lznDcLkqSvi+dd9r54GvNEJpC/i3hk04xjzMlejySpqan4/PPP8bvf/c4w//PPP0dKinvqE5EM43RoeUp7lKUJeq7K6J7i6KweKMpUm7qmsxL0dXJSxX3ZU/HUar0Drorp9Sah8rAQ120mq2NZYSxPk95esfKg4qjQqInjjUFembBuhHRLw9hl+rTJ0TazcE4ThJTEREWfToV+Lhur6QCATEU/P02S9PRF8VxWWO3nuNKqn3cAOCNUvLT/flp6FLL322P6qOROUISFpAYR7Dg9bNgwDBs2zO/lly5dig4dOuDJJ58EAJx77rnYsmULnn76ab86ZqGK2wE/YybexiMiikT2ak/eHipuwMb4Ydq0abj99tvxzTffoE+fPgCAL7/8EsuWLcMDDzwQ5tZRtGGcJqJIF+44vXXrVgwePNgwb+jQoZg2bZpf64cqbvvVMbvggguwfv16NGnSxGdFElYPI6Jw0+D9CmSE9ctw7733omPHjli4cCFee+01APard8uXL8d1110X5tZRNGCcJqJo4m+cLi0tNcxPTk42PD9bV4WFhWjRooVhXosWLVBaWorKykpDKrhMqOK2Xx2zP//5z66T4OtBOhJ4GjCwnnUHPKWxiWkJTlatym1etaJXDxKrCtY4BkgUUxmrbPrrFRY95aDcYj+IkgR9XlqN/g8lqVxfT3GUFdI0fb/JNfq0uUJvt6mRnnKopDlSDpLEFAoPHOmQ2hm97aqwXVuJ3p7qInt6QXmxnnJYVK6nQhQLx1HiOGbn8QKAMC614fxYHOmJNcJAkzWq/k9MPO+y90X2/oUsZTHGB7O0avYfb69Hmuuuu46dMKozxulwco9RJpP861Wiyf3LnjgAslNjIf0uQ0jnb6KHLVRaJdWRrfoCidBjmRlCxUNHyqDYRkWVpxG6pg2fmWKKXl2HXpClf3pog6S9JmF98djEY052PFaQZtbXS0/U1xPP5SnHOS6zGd+f2u+N7P0DgBpTmds8m+rHd5c45m+cbtOmjWF+fn4+Zs+eHbqGBSAUcduvjpmYFsEUCSKKdJrmve8Z4/1SikOM00QUTfyN00eOHEFGhv7sYDDulgFAbm4ujh07Zph37NgxZGRk+LxbFkoBP2N25MgRKIqC1q1bAwC++uorrFy5El27dsXEiROD3sDYJLkL4inPVnG/GuXxjplkuzbJQ7oW4c5NpfCQr/OOTrWi3805I9zFqRCuypVY7FedUoU7Zokm/aqVIlxesznG/aqp0bfbqEJvQ0qxfofJnFrjNi0+U6sIxT80IQHZeRg2YRgzW6VQsEMo7lFRYZ8uqdQfzjxdrf9DP1mtH8dpxx2zEvGOmVU/z+L5cZ63ROHqXaWmT4vnXfa+yN4/z3fMJFcpPS4bf4VCNChQvdya1gK8be2rDK+macjPz8cLL7yA4uJi9O/fH0uWLHF7KFjUpEkTvweqPH1a/sA5kQzjtJzHMcukJIUbPBRzkBV5MAVS/APuRVoaC/E0PVFvd2Nhsylm5zb1100BHGPdMzKCEVMC30ag7XWeC/H8pAg3scRz6TzHjWuEcVMBpGjG90b2/gHy99tT8Q9NNt9jD8U91sv+jqNxbDN/43RGRoahYxYsffv2xQcffGCYt27dOteYZDINEbcD7pjdcMMNmDhxIm666SYUFhZi8ODB6NatG1asWIHCwkI8+OCDdWoIEVGwBPuOma8yvE888QT++c9/4uWXX0aHDh3wwAMPYOjQofj+++89VmdasGBBYI0g8hPjNBFFumDH6fLycuzfv9/1+8GDB7Fr1y40bdoUbdu2xX333Ydff/0Vr7zyCgDgtttuw7PPPouZM2filltuwaeffoo333wT77//vsd9NETcDrhjtmfPHlf1kTfffBPdu3fH559/jv/85z+47bbb+IFPRGEX7GfMvJXh1TQNCxYswKxZs/DnP/8ZAPDKK6+gRYsWePvttzFy5EjpemPHjg2sEUR+YpwmokgX7Di9fft2DBo0yPX79OnTAdhjbUFBAY4ePYrDhw+7Xu/QoQPef/993HXXXVi4cCFat26NF1980Wup/IaI2wF3zCwWiyu/85NPPsGf/vQnAECXLl1w9OjR4LaOPPBU/MN9bCx58Q89369KKMhRabLPr1LKXfPOqPq4YKXCLf4Us/1WbqI4rpjw52TTxHG/7LkDFVZ9/cZV+n5TS/W0vuQkfToxwX4L32wWC4noexOvpths9tQAi1XPU6iu0dtTKYy3Vm5xpDIKx1MsvH6yRt/GqWpHkZMafWelwrhrZzT9/FSZ7OfNLJyHRFXfh3je5cU/3N+/eExDDIaGrPZ08OBB110Jp8zMTFx88cXYunWrx45ZbQcOHMDy5ctx4MABLFy4EDk5Ofjwww/Rtm1bnHfeeQG1ieIb43TDUnwUshCZJSlvKar78yyNkvU41EjIrjMr+iebMw3MJqT1W4Q0eZthWhjHzFGgSkwNFMcuE+fr03Ut8uGLvl35fvW2ifNsQpEtw7EJx+w8F+L5ESOqeC4bOVIZGyUYC3akVBvfG9n7B3gYy06WDksuwa6enJeXB83LbbaCggLpOjt37gxwT7pQxO2A/2rOO+88LF26FJ999hnWrVuHK664AgDw22+/oVmzZnVqBBFRMNnHR/H+A9irPWVmZrp+5s2bF/C+CgsLAUBadtf5mi+bNm1C9+7d8eWXX2LNmjUoL7d38v/73/+ykAMFjHGaiCKdv3E6UoUqbgfcMXv88cfx3HPPIS8vD6NGjULPnj0BAO+++64rdYKIKJycuevefgB7kYSSkhLXz3333ReW9t577714+OGHsW7dOiQl6YVqLrvsMmzbti0sbaLoxThNRJHO3zgdqUIVtwNOZczLy8PJkydRWlqKJk2auOZPnDgRaWlpXtakupBVIfJYlVGTjGMmpCI61SToKXWVQgXGGtjnV2t6KmO5SZ9OsQnjo1Q7cwr12/4W1T19EQDKHGOtiOmCjRL0baWY9ONJMetpDYmO+WaT73+dzsqPFmEsFrENVcL8Cke6o7NdgLHqYnGNPu1MYSyq1ttVYtPTMMXz4zxv4jgqlYp+nDViKqPN/X2RvX+e3uuQjW8WI/zNXQ9Gtafc3FwA9jK7LVu2dM0/duwYevXq5dc2vv32W6xcudJtfk5ODk6ePFmv9lH8YZwOgJ8V1uz8v5btKY1NHHPLKRXu6dOZyfr6zYSXLcJjApWOD7IKm57Kd0Y5o0+jxDVdo+qxyqraY5FF1WOSMZXR6jbfEHMM8SeQWCRuw34cmjBPFdIQFU0/ftUxLbZXTB2sUfRzekaompis2AsvVdiEmCw87iCeS+c5LrMY37fUauN7I3v/gEDTFmXLekgVlf19RnqPxU/RON6oKFRxu04JsGazGVarFVu2bMGWLVtw4sQJtG/fHjk5OXVuCBFRsGh+/ARLhw4dkJubi/Xr17vmlZaW4ssvv/RadleUlZUlffZn586dOOuss4LWVoofjNNEFMkaMk6HQqjidsAds4qKCtxyyy1o2bIlBgwYgAEDBqBVq1YYP348zpw543sDREQhFuzc9fLycuzatQu7du0CoJfhPXz4MBRFwbRp0/Dwww/j3XffxbfffosxY8agVatWhrHOvBk5ciTuueceFBYWQlEUqKqKzz//HDNmzMCYMWMCayzFPcZpIop00f6MWajidsCpjNOnT8emTZvw73//G/379wcAbNmyBXfccQfuvvtuLFmypM6NiWaywf0CGczS0+CAimzQYU9VGSWpcDbNPWVOTFWsVvQ0rmrYA3a1kPJQZtYHyDNrwp+Lxb6eRdVT9c4IKQLlYmpgor3/n5agz0s168ummPVjF8bRRKIjhVG8eqAIVZQ0IQ3BeUbEdEqLcEqrbEL6h83ZXn1eubBwmUUV5tsXFtMXi6BX8itT9PNTbXOkMpr0Kk4Jip4GIZ532fsiTWX0mCYi+7uo/6dYNA5SKaNB8TqIdKADTPsqwztz5kxUVFRg4sSJKC4uxqWXXoqPPvrI4xhmtT366KOYPHky2rRpA5vNhq5du8Jms+GGG27ArFmzAmorEeN05FIUs9u8RLjPa5asf0b1bVbmmu43XY+I//2nPY5sOK6nq+4r0T9zjlQ0dU0fV4pd08VJxwEAFaqeblVt0+NatVXfn2KzP+6gqnoMVMX4JczXFEdwFVPthFQ8RThOxWT/7mASYqTJpH+fSDDrx5GckG7/v1n/vtLI1Nw1naXqd4FztCzXdJtG9m13ztTbMCinyDXd8w593188ZY+pZZZ0iBJrvV+y94/qJthxuqGFKm4H3DH717/+hdWrVyMvL88178orr0Rqaiquu+46fuATUdjZNMDq5dEHW4D9T19leBVFwdy5czF37tzANuyQlJSEF154AQ888AD27NmD8vJynH/++fjd735Xp+1RfGOcJqJIF+w43dBCFbcD7pidOXPGrSw0YH/YjSkSRBQJgj0+SkNp27Yt2rZtG+5mUJRjnCaiSBetcbq2YMftgDtmffv2RX5+Pl555RVXmk5lZSXmzJnj94PuVD8eqzKq7qlwFpv7QMaVin4r32pyf90kJA+K6XclJn2+zZF2V2Vt7JpXJlQ+KhEGd05NsK+XYtbXTzLrt6iThEGqE4W8RbMjBcLkx91sZy6yeIVFyEhEjTjtWKjKpldBqhQu25wRzqNzAGmx+mK5kBIinh/neavU9PNbCX1arCgle19k7x+rL9aNr/z0SMhdnz59Oh566CE0atTIlRrpyVNPPdVAraJYwDgd/cTBjzOT9dRB9c9/cU13/WoZAKBii14p0AQ9JieYhHTACn38ukSbfXmzWV+vQqg2KKYcVjtS963iAMpCAUGbIlZadLZZjFvCesLgzM4URrOQsmhob4KetphqzgJgTF9satPTF3OFFMdWwmjcv3NkJfZuqsfprpecck2rf77FNZ25aK29PYp+/ii0oiFO19YQcTvgjtnChQsxdOhQtG7d2jU2yn//+1+kpKTg448/rlMjiIiCSXP85+31cNu5cycsFnuZ62+++QaKh7LdnuYTecI4TUSRLhridG0NEbcD7ph169YNP/74I1asWIG9e/cCAEaNGoUbb7wRqampPtYmIgo9m+Y9Pz0SctcXLlzoGkNt48aN4W0MxRTGaSKKdNEQp2triLgdcMcMANLS0jBhwoRgtyV+yIoIKB5S1jT3XrcGi2RBfRBGkcVWIVlOX79GGIzRmYonDtxoFaoviWmP1Yo9NaDMrA9WmqLpKQDJqp6SkOQYoDFJ+HNLFKbNwv7MQhWeBMXZHrdDcOM8pVYh9c8mXG2xCfMtjkEsa4TBLGsU/TirhUG3qxwpjDWa/lyGRRWn9fREm2MwbzH9UBVSOmzCYN82oZKVa1nNfZ6myd9rSFMcPf0NReCnW4hFQ4rE+eefj6NHjyInJwcdO3bE119/jWbNmvlekcgPjNOhIP+MlVXPlVXZBQCr5p7GXqq5P/f3c7keQz/4Vf9cOHDJJ67p4pqOAIAjlXoM/aVC/3ArrNTjx3GbXmmx2GSvxnhGSLuvUvWqjMa4Zo9LxqqM4rH5Gmxa/rpzG4qwXZuQ9igbTFqsiGgyyVMr1Qq9qqJVtac1lgmPXOz/sKNrOmu9fi73ldnP8c/lxuBQ+72xwv39E49HFEhV5XgUDXG6toaI23XqmO3btw/PPPMMfvjhBwDAueeeiylTpqBLly5BbRwRUV1omvf+aCT0VbOysnDw4EHk5OTg0KFDUFUGawoexmkiimTREKdra4i4Xady+SNHjkTv3r1dDxFv27YN3bt3x6pVqzBixIigN5KIKBAqvF+TjIQu0IgRIzBw4EC0bNkSiqKgd+/eMJvlY+T89NNPDdw6imaM00QU6aIhTtfWEHE74I7ZzJkzcd9997mN15Ofn4+ZM2fyA5+Iwk5VDdkt0tfD7fnnn8c111yD/fv344477sCECROQnp7ue0UiHxiniSjSRUOcrq0h4nbAHbOjR49izJgxbvNHjx6N+fPnB6VRscJTRRklgNHMZdtQPOW5S55Hsvkoy24VnqcyOUrZis+YmRQ9z7vGpD+vlmBKAgCYFf0ZtXJFL1ObIMw3mRyleYVyvGZhWXG+SXhOTnHkm5s0+dUIwzE5/nlrwrN6qiI+b6afG5vjPInzxOfurOKzYM5lxWftxFx7VdyG/bwanjET8s7F+ZosH136PJmnZxrqd48/EqsdBVO0XIm74oorAAA7duzAnXfeyY4ZBQXjdAA85UsFUFRNNqyJp2fMqm2lbvNOJP7qvqDwaFNhZSPX9I6T+tc21bHfMzY9JpUJz7CVmvR9lZn0MvFVWqlbW2qE59GtNskzZoZYJsSqQIZ0EWOgI/4ano0Xn70WTp/z/Irn2WbW21Bt0k/WGU1/3ud0ub1Iw5EK/Xm9/5rFkv36vsut9vNWpBmfyz9hNr431Rb3989+HCEa7iYS8/mCJFridG2hjtvu1SJ8yMvLw2effeY2f8uWLfj9738flEYREdWHM3fd208kWb58OTtlFDSM00QU6aItTtcWqrgd8B2zP/3pT7jnnnuwY8cOXHLJJQDsuetvvfUW5syZg3fffdewLBFRQ4vWK3FEwcA4TUSRjnFaLuCO2aRJkwAAixcvxuLFi6WvAfbB1Ww2b9mjREShYVM1w3AJsteJYhXjNBFFOsZpuYA7ZizpTESRTnP8eHudKFYxThNRpGOclqvTOGYUAh4fQHYPsB7zbiUPmtok64vFPQCz23xnERA7k9vrAGCRFgpJEJbVt2uWLGuYlrTB0F4/HoWUDeRoKLYh1P6RPkhseLBZXzaQgh7OG++eX3dvg7HBsgEqPb3ZsvXj9WPMXTQOXElEkUT2GSuvCKLBv8JbAFBlKXGbV4Sf3eadMeuDP/8qFNNKsCW7LasqQvEq6IWqaoRBmq3qGWHaXmRDVuQDMBancs43FvwQYpwYo2QxSJinKfq04tiGeA1BU9zjKaDHVJtQmEscgLrKpJ+rctMJ1/QpJRVArWJkql5sTGRV7NsWzx/gXuxD9v4B8vdbXtALHgqmxN/FFMZpOXbMiCjm8AOfiIgocjFOy7FjRkQxx6YBNi93EG1x+oFPREQUCRin5dgxI6KY46vULrM+iYiIwodxWo4dMyKKORo0qF4eHY71AbaJiIgiGeO0XMAds8suuwwDBw5Efn6+YX5RURFGjBiBTz/9NGiNi1WyPzYF8oeKpZcMJAU97NuVFM6QFpQQlxMLVdjna4qHB1YlRToUD8VBPBUNkc3zVdxDtn5t0mIa4uvCg7WyZWVFPMT5ntd3LzXtaVvGhSRtkH4IeVrf/w+sePxw45U4imeM03IBxV4/17dvw/1zWtWqJUsCVpt7PDsjiQdVpmLXtEnRC1bI4qGh0JWkeJXbtOosaiUW8RC3YRGmHfM9FfwIqGiFWJDL8R3CsF2dTdWPQ3EcvyoUKLEKxTZMJv2rrEkpEaadhce8FxgD9ONUaxXscJ4rvV3yoi7y99vTd7X6BaBYiemM03IBd8w2btyIb7/9Fjt37sSKFSvQqFEjAEBNTQ02bdoU9AYSEQXKpmkwec1dj9NPfIoLjNNEFOkYp+V834qQ+OSTT1BYWIhLLrkEhw4dCnKTiIjqx1ntydsPUSxjnCaiSMY4LVenjlnLli2xadMmdO/eHRdddBE2btwY5GYREdWd6shd9/ZDFMsYp4kokjFOywXcMVMUez52cnIyVq5ciTvvvBNXXHEFFi9eHPTGiR555BH069cPaWlpyMrKCum+iCi6adDz16U/4W4gUQgxThNRpGOclgv4GTOtVs7nrFmzcO6552Ls2LFBa5RMTU0Nrr32WvTt2xcvvfRSSPdFRNHNpmkweflYj9fcdYoPjNNEFOkYp+UC7pgdPHgQ2dnZhnkjRoxAly5dsH379qA1rLY5c+YAAAoKCkK2j3DyXO1JUjHK0x+rpFqjdEkP6zsrSxleVrxXcDSs77F6otnDfF/rBY+vqo2y6oqe16tvpUUv23BvgH/Led1X/PGVBhGvKRIUHxingyCQisgBfJzY1Eq3eaqkerJi817Z2BNDhUYf1YQ9VhA2bMNHtWDh4GXLivMUw3cLZ8Vj4djEioiaIkw6z4/8nNhUedVFZ8Xnup4/e/tq/a7Jq1ZrsgrYmvx7hTT+x2EnhHFaLuCOWbt27aTzzzvvPJx33nn1bhARUX2pmo8P/DgMghQ/GKeJKNIxTsuF/lZFGFVXV6O0tNTwQ0SxT/PjPyIKP8ZpovgUqji9aNEitG/fHikpKbj44ovx1VdfeVy2oKAAiqIYflJSUup6SEER1o7Zvffe63ZCav/s3bu3ztufN28eMjMzXT9t2rQJYuuJKFLZoMHq5cfGjhmRXxiniSgUQhGn33jjDUyfPh35+fn45ptv0LNnTwwdOhTHjx/3uE5GRgaOHj3q+vn555/rc1j1FnAqYzDdfffdGDdunNdlOnbsWOft33fffZg+fbrr99LSUn7oE8UBTfN+ta12cQQikmOcJqJQCEWcfuqppzBhwgTcfPPNAIClS5fi/fffx7Jly3DvvfdK11EUBbm5uQHvK1TC2jHLzs52e0A5mJKTk5GcnByy7RNRZAr2Q8WzZ892FTZw6ty5c73uFBBFA8ZpIgoFf+N07fRmT58ZNTU12LFjB+677z7XPJPJhMGDB2Pr1q0e91NeXo527dpBVVVccMEFePTRR8P6LG5YO2aBOHz4ME6fPo3Dhw/DZrNh165dAIBOnTqhcePG4W1cCMmuJkgrNQL+V/XxVFkqoG3a3NricVEfFZF8N9ufjFs/qxx6bIT39f3LdQ6gDfW8Y8NnpLwLRbWn8847D5988onr94SEqPn4JGoQ0RinQ1YR2dNHjKx6r+Je0S84T5qEqIKwj0qMnkgrNHr8PiJWa3R+3xCrTAoLC98xjOc9BOfQw3cFn9UrDQuz2jLgf5yufQc9Pz8fs2fPdlv+5MmTsNlsaNGihWF+ixYtPF5E7dy5M5YtW4YePXqgpKQE//jHP9CvXz989913aN26dYBHFBxR883iwQcfxMsvv+z6/fzzzwcAbNiwAXl5eWFqFRFFIhtsUDwMgeB8PVAJCQkRle5AFGkYp4nIX/7G6SNHjiAjI8M1P5h32Pv27Yu+ffu6fu/Xrx/OPfdcPPfcc3jooYeCtp9ARE1VxoKCAns+aq0fftgTUW3OK3HefgC4VYOrrq72uM0ff/wRrVq1QseOHXHjjTfi8OHDDXU4RFGBcZqI/OVvnM7IyDD8eOqYNW/eHGazGceOHTPMP3bsmN8XVRMTE3H++edj//799Tu4eoiajhkRkb9UP/4D7CkSYkW4efPmSbd38cUXo6CgAB999BGWLFmCgwcP4ve//z3Kysoa8rCIiIhigr9x2l9JSUm48MILsX79en0fqor169cb7op5Y7PZ8O2336Jly5YB7TuYoiaVkYjIX5qiQfPw7AKg5+37myIxbNgw13SPHj1w8cUXo127dnjzzTcxfvz4ILWaiIgoPvgbpwMxffp0jB07Fr1790afPn2wYMECVFRUuKo0jhkzBmeddZbrIuzcuXNxySWXoFOnTiguLsb8+fPx888/49Zbb63bQQUBO2ZRKJA/1oAeYJZuwFdRDD/UuzR54M8DBa6exUMAFvSIIPbcdN+5687UiEBlZWXhnHPOCWu6AxGFTr0Lb9W7yFYo456PeOcjlgUjVjm3oXjclHD8iuJYx9PGvBcpC67QFPmKx/jvb5wOxPXXX48TJ07gwQcfRGFhIXr16oWPPvrIVRDk8OHDMJn0ZMGioiJMmDABhYWFaNKkCS688EJ88cUX6Nq1a8D7DhZ2zIgo5jgz1L29Xh/l5eU4cOAAbrrppnpth4iIKB6FKk5PmTIFU6ZMkb62ceNGw+9PP/00nn766TrtJ1T4jBkRxRxVUX3+BGLGjBnYtGkTDh06hC+++AJ/+ctfYDabMWrUqBAdARERUewKdpyOFbxjRkQxR4VqGPNG9nogfvnlF4waNQqnTp1CdnY2Lr30Umzbti2kA+8SERHFqmDH6VjBjhkRxRwbrPCWEGB/3X+rVq2qZ4uIiIjIKdhxOlawY0ZEMUf1MXCl2iAFZYiIiEiGcVqOHbMY56vSj8cqU64NBKNSkOMfl+JjXw0tKMcWwO7isOpSuGjQfDxUzPeCiALj6XMj1NWPgyJCqwaL2w2o6qWBewXHkAnC9wbGHzvGaTl2zIgo5qiKCsXLl514zV0nIiKKBIzTcuyYEVHMUWGp1+tEREQUOozTcuyYEVHMUTUbvD1UbH+diIiIwoFxWo4dMyKKOaEeYJqIiIjqjnFajh2zOFffhyt9Fg8x7Cx6H+SM14dQo5UGGzQvV+K0OK32RETBF0h8qHehkBCJhBhX72JlQNjOZSScv2jDOC3HjhkRxRwbbNC8BPF4LcNLREQUCRin5dgxI6KYo2neP/C1OM1dJyIiigSM03LsmBFRzLGX2WUZXiIiokjEOC3HjhkRxRzNR4pEvOauExERRQLGaTl2zIgo5tg0q9dHsVXN2mBtISIiIiPGaTl2zKheWImIIhFz14koEjFm1h3PXWxhnJZjx4yIYo4Gzcf4KAzwRERE4cI4LceOGRHFHE1TfVyJi8+HiomIiCIB47QcO2ZEFHM0zeK1olO8pkgQERFFAsZpOXbMiCjm2K+08UocERFRJGKclmPHjIhijgoVitcyvPH5gU9ERBQJGKfl2DEjopjDK3FERESRi3Fajh0zIoo5qmaF4jV3PT4/8ImIiCIB47QcO2ZEFHN8faDH6wc+ERFRJGCclmPHjIhiDj/wiYiIIhfjtBw7ZkQUc3w9NByvDxUTERFFAsZpOXbMiCjmqKoVimLy+Hq8XokjIiKKBIzTcuyYEVEM8vWBHp8f+ERERJGBcVqGHTMiijnMXSciIopcjNNy7JgRUcxh7joREVHkYpyWY8eMiGKOplnhfeBKreEaQ0RERAaM03LsmBFRzLGnQPADn4iIKBIxTsuxY0ZEMcj7Bz4Qnx/4REREkYFxWoYdMyKKPT6uxCFOr8QRERFFBMZpKXbMiCjmaLDBa4pEnF6JIyIiigSM03LsmBFRDNJ8ZEHE5wc+ERFRZGCclomrjpn+IGF8vtlEkcP+bzB0D/dqcXu1jSiaMU4TRQrG6XCIq45ZWVmZYyo+x0YgijRlZWXIzMwM2vaSkpKQm5uLwsJCn8vm5uYiKSkpaPsmovpjnCaKLIzTDUvR4qgepaqq+O2335Ceno6ysjK0adMGR44cQUZGRribFlSlpaU8tigVy8cnHpvz32CrVq1gMpmCup+qqirU1NT4XC4pKQkpKSlB3TcR1U+8xGkgfj7veWzRg3E6/OLqjpnJZELr1q0BAIpif+AwIyMj5v5hOfHYolcsH5/z2IJ5BU6UkpISdx/kRLEi3uI0ENvHx2OLTozT4RPcLjAREREREREFjB0zIiIiIiKiMIvbjllycjLy8/ORnJwc7qYEHY8tesXy8cXysRFR8MX6Z0YsHx+PLTrF8rFFi7gq/kFERERERBSJ4vaOGRERERERUaRgx4yIiIiIiCjM2DEjIiIiIiIKs7jvmB06dAjjx49Hhw4dkJqairPPPhv5+fl+DXwXDR555BH069cPaWlpyMrKCndz6m3RokVo3749UlJScPHFF+Orr74Kd5OCYvPmzbj66qvRqlUrKIqCt99+O9xNCpp58+bhoosuQnp6OnJycjB8+HDs27cv3M0ioijCWB09GKejD+N05Ij7jtnevXuhqiqee+45fPfdd3j66aexdOlS3H///eFuWlDU1NTg2muvxe233x7uptTbG2+8genTpyM/Px/ffPMNevbsiaFDh+L48ePhblq9VVRUoGfPnli0aFG4mxJ0mzZtwuTJk7Ft2zasW7cOFosFQ4YMQUVFRbibRkRRgrE6OjBORyfG6cjBqowS8+fPx5IlS/DTTz+FuylBU1BQgGnTpqG4uDjcTamziy++GBdddBGeffZZAICqqmjTpg2mTp2Ke++9N8ytCx5FUbB27VoMHz483E0JiRMnTiAnJwebNm3CgAEDwt0cIopSjNWRh3E6NjBOh0/c3zGTKSkpQdOmTcPdDBLU1NRgx44dGDx4sGueyWTC4MGDsXXr1jC2jAJVUlICAPw3RkT1wlgdWRinYwfjdPiwY1bL/v378cwzz+D//u//wt0UEpw8eRI2mw0tWrQwzG/RogUKCwvD1CoKlKqqmDZtGvr3749u3bqFuzlEFKUYqyMP43RsYJwOr5jtmN17771QFMXrz969ew3r/Prrr7jiiitw7bXXYsKECWFquW91OTaiSDB58mTs2bMHq1atCndTiCgCMFYzVlNkYZwOr4RwNyBU7r77bowbN87rMh07dnRN//bbbxg0aBD69euH559/PsStq59Ajy0WNG/eHGazGceOHTPMP3bsGHJzc8PUKgrElClT8N5772Hz5s1o3bp1uJtDRBGAsTp2YjXjdPRjnA6/mO2YZWdnIzs7269lf/31VwwaNAgXXnghli9fDpMpsm8kBnJssSIpKQkXXngh1q9f73rYVlVVrF+/HlOmTAlv48grTdMwdepUrF27Fhs3bkSHDh3C3SQiihCM1bGDcTp6MU5HjpjtmPnr119/RV5eHtq1a4d//OMfOHHihOu1WLjCc/jwYZw+fRqHDx+GzWbDrl27AACdOnVC48aNw9u4AE2fPh1jx45F79690adPHyxYsAAVFRW4+eabw920eisvL8f+/ftdvx88eBC7du1C06ZN0bZt2zC2rP4mT56MlStX4p133kF6errrWYPMzEykpqaGuXVEFA0Yq6MD43R0YpyOIFqcW758uQZA+hMLxo4dKz22DRs2hLtpdfLMM89obdu21ZKSkrQ+ffpo27ZtC3eTgmLDhg3S92ns2LHhblq9efr3tXz58nA3jYiiBGN19GCcjj6M05GD45gRERERERGFWWQnaBMREREREcUBdsyIiIiIiIjCjB0zIiIiIiKiMGPHjIiIiIiIKMzYMSMiIiIiIgozdsyIiIiIiIjCjB0zIiIiIiKiMGPHjIiIiIiIKMzYMaOwGTduHIYPH+51mY0bN0JRFBQXF4e0LXl5eVAUBYqiYNeuXSHdFwC0b9/etb9QHxsREVFdME4zTlPDUjRN08LdCIpPJSUl0DQNWVlZAOwfur169cKCBQtcy9TU1OD06dNo0aIFFEUJWVvy8vJwzjnnYO7cuWjevDkSEhJCti8AOHHiBD777DOMGDECRUVFrnNAREQUKRinGaepYYX2r5rIi8zMTJ/LJCUlITc3twFaA6SlpTXYvrKzs9G0adMG2RcREVFdME4zTlPDYipjHDhx4gRyc3Px6KOPuuZ98cUXSEpKwvr166XrHDp0CIqiYNWqVejXrx9SUlLQrVs3bNq0ybDcpk2b0KdPHyQnJ6Nly5a49957YbVaXa+vXr0a3bt3R2pqKpo1a4bBgwejoqICgDFFYty4cdi0aRMWLlzoSh04dOiQNEXiX//6F8477zwkJyejffv2ePLJJw1tat++PR599FHccsstSE9PR9u2bfH8888HfN4KCgrcrpC9/fbbhiuCs2fPRq9evbBs2TK0bdsWjRs3xqRJk2Cz2fDEE08gNzcXOTk5eOSRRwLePxERxQfGacZpIoAds7iQnZ2NZcuWYfbs2di+fTvKyspw0003YcqUKbj88su9rvu3v/0Nd999N3bu3Im+ffvi6quvxqlTpwAAv/76K6688kpcdNFF+O9//4slS5bgpZdewsMPPwwAOHr0KEaNGoVbbrkFP/zwAzZu3IhrrrkGsuzZhQsXom/fvpgwYQKOHj2Ko0ePok2bNm7L7dixA9dddx1GjhyJb7/9FrNnz8YDDzyAgoICw3JPPvkkevfujZ07d2LSpEm4/fbbsW/fvjqeQe8OHDiADz/8EB999BFef/11vPTSS7jqqqvwyy+/YNOmTXj88ccxa9YsfPnllyHZPxERRTfGacZpIgCARnFj0qRJ2jnnnKPdcMMNWvfu3bWqqiqPyx48eFADoD322GOueRaLRWvdurX2+OOPa5qmaffff7/WuXNnTVVV1zKLFi3SGjdurNlsNm3Hjh0aAO3QoUPSfYwdO1b785//7Pp94MCB2p133mlYZsOGDRoAraioSNM0Tbvhhhu0P/zhD4Zl/va3v2ldu3Z1/d6uXTtt9OjRrt9VVdVycnK0JUuWeDxe2b6XL1+uZWZmGuatXbtWE//Z5Ofna2lpaVppaalr3tChQ7X27dtrNpvNNa9z587avHnzvB4bERHFN8ZpxmmKb7xjFkf+8Y9/wGq14q233sKKFSuQnJzsc52+ffu6phMSEtC7d2/88MMPAIAffvgBffv2NaQM9O/fH+Xl5fjll1/Qs2dPXH755ejevTuuvfZavPDCCygqKqrXMfzwww/o37+/YV7//v3x448/wmazueb16NHDNa0oCnJzc3H8+PF67duT9u3bIz093fV7ixYt0LVrV5hMJsO8UO2fiIhiA+M04zTFN3bM4siBAwfw22+/QVVVHDp0KOT7M5vNWLduHT788EN07doVzzzzDDp37oyDBw+GfN+JiYmG3xVFgaqqAW3DZDK5pXNYLBa/9hWM/RMRUXxhnGacpvjGjlmcqKmpwejRo3H99dfjoYcewq233urXlaFt27a5pq1WK3bs2IFzzz0XAHDuuedi69athg/Fzz//HOnp6WjdujUA+wdd//79MWfOHOzcuRNJSUlYu3atdF9JSUmGq2ky5557Lj7//HPDvM8//xznnHMOzGazz+MJRHZ2NsrKylwPQQNokLFTiIgo/jBOB45xmmINO2Zx4u9//ztKSkrwz3/+E/fccw/OOecc3HLLLT7XW7RoEdauXYu9e/di8uTJKCoqcq03adIkHDlyBFOnTsXevXvxzjvvID8/H9OnT4fJZMKXX36JRx99FNu3b8fhw4exZs0anDhxwhUwamvfvj2+/PJLHDp0CCdPnpReubr77ruxfv16PPTQQ/jf//6Hl19+Gc8++yxmzJhRvxMkcfHFFyMtLQ33338/Dhw4gJUrV7o9vExERBQMjNOBY5ymWMOOWRzYuHEjFixYgFdffRUZGRkwmUx49dVX8dlnn2HJkiVe133sscfw2GOPoWfPntiyZQveffddNG/eHABw1lln4YMPPsBXX32Fnj174rbbbsP48eMxa9YsAEBGRgY2b96MK6+8Eueccw5mzZqFJ598EsOGDZPua8aMGTCbzejatSuys7Nx+PBht2UuuOACvPnmm1i1ahW6deuGBx98EHPnzsW4cePqd5IkmjZtitdeew0ffPABunfvjtdffx2zZ88O+n6IiCi+MU7XDeM0xRpFq52cSwT7+CgdOnTAzp070atXr3A3J+Ty8vLQq1cvLFiwoMH2uXHjRgwaNAhFRUVu47AQERF5wzgdeozT1NB4x4zIYfHixWjcuDG+/fbbkO/rvPPO83hFkoiIiNwxTlOsSwh3A4giwYoVK1BZWQkAaNu2bcj398EHH7gqR2VkZIR8f0RERNGMcZriAVMZiYiIiIiIwoypjERERERERGHGjhkREREREVGYsWNGREREREQUZuyYERERERERhRk7ZkRERERERGHGjhkREREREVGYsWNGREREREQUZuyYERERERERhRk7ZkRERERERGH2/xmrIej0R0OQAAAAAElFTkSuQmCC",
       "text/plain": [
-       "
"
+       "
"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -829,7 +827,7 @@
     "ax1.set_aspect(\"equal\")\n",
     "ax2.set_title(\"|Ez(x, y)|\")\n",
     "ax2.set_aspect(\"equal\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -850,14 +848,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "
"
+       "
"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -865,7 +861,7 @@
     "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 3))\n",
     "mode_solver.plot_field(\"Ex\", \"abs\", mode_index=0, f=freq0, ax=ax1)\n",
     "mode_solver.plot_field(\"Ez\", \"abs\", mode_index=0, f=freq0, ax=ax2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -888,14 +884,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "
"
+       "
"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -904,7 +898,7 @@
     "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 3))\n",
     "mode_solver.plot_field(\"Ex\", \"abs\", mode_index=mode_index, f=freq0, ax=ax1)\n",
     "mode_solver.plot_field(\"Ez\", \"abs\", mode_index=mode_index, f=freq0, ax=ax2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -925,14 +919,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "
"
+       "
"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -941,7 +933,7 @@
     "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 3))\n",
     "mode_solver.plot_field(\"Ex\", \"abs\", mode_index=mode_index, f=freq0, ax=ax1)\n",
     "mode_solver.plot_field(\"Ez\", \"abs\", mode_index=mode_index, f=freq0, ax=ax2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -972,7 +964,7 @@
     "# Makes a mode monitor with geometry of `plane`.\n",
     "mode_mon = mode_solver.to_monitor(name=\"mode\", freqs=freqs)\n",
     "# Offset the monitor along the propagation direction\n",
-    "mode_mon = mode_mon.copy(update=dict(center=(0, -2, 0)))\n"
+    "mode_mon = mode_mon.copy(update=dict(center=(0, -2, 0)))"
    ]
   },
   {
@@ -985,41 +977,18 @@
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "
[15:54:13] WARNING: Default value for the field monitor 'colocate' setting has  \n",
-       "           changed to 'True' in Tidy3D 2.4.0. All field components will be      \n",
-       "           colocated to the grid boundaries. Set to 'False' to get the raw      \n",
-       "           fields on the Yee grid instead.                                      \n",
-       "
\n"
-      ],
+      "image/png": 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",
       "text/plain": [
-       "\u001b[2;36m[15:54:13]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Default value for the field monitor \u001b[0m\u001b[32m'colocate'\u001b[0m\u001b[31m setting has  \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m\u001b[31mchanged to \u001b[0m\u001b[32m'True'\u001b[0m\u001b[31m in Tidy3D \u001b[0m\u001b[1;36m2.4\u001b[0m\u001b[31m.\u001b[0m\u001b[1;36m0\u001b[0m\u001b[31m. All field components will be      \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m\u001b[31mcolocated to the grid boundaries. Set to \u001b[0m\u001b[32m'False'\u001b[0m\u001b[31m to get the raw      \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m\u001b[31mfields on the Yee grid instead.                                      \u001b[0m\n"
+       "
"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "
"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
     "# In-plane field monitor, slightly offset along x\n",
-    "monitor = td.FieldMonitor(\n",
-    "    center=(0, 0, 0.1), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\"\n",
-    ")\n",
+    "monitor = td.FieldMonitor(center=(0, 0, 0.1), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\")\n",
     "\n",
     "sim = td.Simulation(\n",
     "    size=(Lx, Ly, Lz),\n",
@@ -1032,7 +1001,7 @@
     ")\n",
     "\n",
     "sim.plot(z=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1046,13 +1015,15 @@
     {
      "data": {
       "text/html": [
-       "
[15:54:14] Created task 'mode_simulation' with task_id                          \n",
-       "           'fdve-bc0cf21a-7395-4ea6-9794-5c3668537f92v1'.                       \n",
+       "
10:40:28 Eastern Daylight Time Created task 'mode_simulation' with task_id      \n",
+       "                               'fdve-7f8b020f-56a1-4196-8c06-251daebbb2f4' and  \n",
+       "                               task_type 'FDTD'.                                \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[15:54:14]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'mode_simulation'\u001b[0m with task_id                          \n",
-       "\u001b[2;36m           \u001b[0m\u001b[32m'fdve-bc0cf21a-7395-4ea6-9794-5c3668537f92v1'\u001b[0m.                       \n"
+       "\u001b[2;36m10:40:28 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'mode_simulation'\u001b[0m with task_id      \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'fdve-7f8b020f-56a1-4196-8c06-251daebbb2f4'\u001b[0m and  \n",
+       "\u001b[2;36m                               \u001b[0mtask_type \u001b[32m'FDTD'\u001b[0m.                                \n"
       ]
      },
      "metadata": {},
@@ -1061,13 +1032,28 @@
     {
      "data": {
       "text/html": [
-       "
           View task using web UI at 'https://tidy3d.simulation.cloud/workbench?\n",
-       "           taskId=fdve-bc0cf21a-7395-4ea6-9794-5c3668537f92v1'.                 \n",
+       "
                               View task using web UI at                        \n",
+       "                               'https://tidy3d.simulation.cloud/workbench?taskId\n",
+       "                               =fdve-7f8b020f-56a1-4196-8c06-251daebbb2f4'.     \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \u001b]8;id=663925;https://tidy3d.simulation.cloud/workbench?taskId=fdve-bc0cf21a-7395-4ea6-9794-5c3668537f92v1\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[2;36m           \u001b[0m\u001b]8;id=472421;https://tidy3d.simulation.cloud/workbench?taskId=fdve-bc0cf21a-7395-4ea6-9794-5c3668537f92v1\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=663925;https://tidy3d.simulation.cloud/workbench?taskId=fdve-bc0cf21a-7395-4ea6-9794-5c3668537f92v1\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=472421;https://tidy3d.simulation.cloud/workbench?taskId=fdve-bc0cf21a-7395-4ea6-9794-5c3668537f92v1\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=663925;https://tidy3d.simulation.cloud/workbench?taskId=fdve-bc0cf21a-7395-4ea6-9794-5c3668537f92v1\u001b\\\u001b[4;34m-bc0cf21a-7395-4ea6-9794-5c3668537f92v1'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                 \n"
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                        \n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=507167;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7f8b020f-56a1-4196-8c06-251daebbb2f4\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=845094;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7f8b020f-56a1-4196-8c06-251daebbb2f4\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=507167;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7f8b020f-56a1-4196-8c06-251daebbb2f4\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=439071;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7f8b020f-56a1-4196-8c06-251daebbb2f4\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=507167;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7f8b020f-56a1-4196-8c06-251daebbb2f4\u001b\\\u001b[32m-7f8b020f-56a1-4196-8c06-251daebbb2f4'\u001b[0m\u001b]8;;\u001b\\.     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               Task folder: 'default'.                          \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=665547;https://tidy3d.simulation.cloud/folders/639eb096-a602-4b56-a502-cac1f18f9557\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                          \n"
       ]
      },
      "metadata": {},
@@ -1076,7 +1062,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "44b0dacc8e494527a99412ee08d738b6",
+       "model_id": "6f6129b90d814768ab01b3ae525a5365",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1100,11 +1086,17 @@
     {
      "data": {
       "text/html": [
-       "
\n",
+       "
10:40:30 Eastern Daylight Time Maximum FlexCredit cost: 0.025. Minimum cost     \n",
+       "                               depends on task execution details. Use           \n",
+       "                               'web.real_cost(task_id)' to get the billed       \n",
+       "                               FlexCredit cost after a simulation run.          \n",
        "
\n"
       ],
       "text/plain": [
-       "\n"
+       "\u001b[2;36m10:40:30 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost     \n",
+       "\u001b[2;36m                               \u001b[0mdepends on task execution details. Use           \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed       \n",
+       "\u001b[2;36m                               \u001b[0mFlexCredit cost after a simulation run.          \n"
       ]
      },
      "metadata": {},
@@ -1113,25 +1105,11 @@
     {
      "data": {
       "text/html": [
-       "
[15:54:20] status = queued                                                      \n",
+       "
                               status = queued                                  \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[15:54:20]\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                      \n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-    {
-     "data": {
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-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                  \n"
       ]
      },
      "metadata": {},
@@ -1140,11 +1118,19 @@
     {
      "data": {
       "text/html": [
-       "
[15:54:24] status = preprocess                                                  \n",
+       "
                               To cancel the simulation, use                    \n",
+       "                               'web.abort(task_id)' or 'web.delete(task_id)' or \n",
+       "                               abort/delete the task in the web UI. Terminating \n",
+       "                               the Python script will not stop the job running  \n",
+       "                               on the cloud.                                    \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[15:54:24]\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                  \n"
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use                    \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or \u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or \n",
+       "\u001b[2;36m                               \u001b[0mabort/delete the task in the web UI. Terminating \n",
+       "\u001b[2;36m                               \u001b[0mthe Python script will not stop the job running  \n",
+       "\u001b[2;36m                               \u001b[0mon the cloud.                                    \n"
       ]
      },
      "metadata": {},
@@ -1152,10 +1138,14 @@
     },
     {
      "data": {
-      "text/html": [
-       "
\n"
-      ],
-      "text/plain": []
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1163,13 +1153,11 @@
     {
      "data": {
       "text/html": [
-       "
[15:54:30] Maximum FlexCredit cost: 0.025. Use 'web.real_cost(task_id)' to get  \n",
-       "           the billed FlexCredit cost after a simulation run.                   \n",
+       "
10:40:37 Eastern Daylight Time status = preprocess                              \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[15:54:30]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get  \n",
-       "\u001b[2;36m           \u001b[0mthe billed FlexCredit cost after a simulation run.                   \n"
+       "\u001b[2;36m10:40:37 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                              \n"
       ]
      },
      "metadata": {},
@@ -1178,12 +1166,9 @@
     {
      "data": {
       "text/html": [
-       "
           starting up solver                                                   \n",
-       "
\n"
+       "
\n"
       ],
-      "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                   \n"
-      ]
+      "text/plain": []
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1191,11 +1176,11 @@
     {
      "data": {
       "text/html": [
-       "
           running solver                                                       \n",
+       "
10:40:41 Eastern Daylight Time starting up solver                               \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                       \n"
+       "\u001b[2;36m10:40:41 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                               \n"
       ]
      },
      "metadata": {},
@@ -1204,17 +1189,11 @@
     {
      "data": {
       "text/html": [
-       "
           To cancel the simulation, use 'web.abort(task_id)' or                \n",
-       "           'web.delete(task_id)' or abort/delete the task in the web UI.        \n",
-       "           Terminating the Python script will not stop the job running on the   \n",
-       "           cloud.                                                               \n",
+       "
                               running solver                                   \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or                \n",
-       "\u001b[2;36m           \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.        \n",
-       "\u001b[2;36m           \u001b[0mTerminating the Python script will not stop the job running on the   \n",
-       "\u001b[2;36m           \u001b[0mcloud.                                                               \n"
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                   \n"
       ]
      },
      "metadata": {},
@@ -1223,7 +1202,7 @@
     {
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-       "model_id": "52ff7a2ba44145bbaa6844154dea340b",
+       "model_id": "a077603190324a44b83b7d4f85cda66e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1237,11 +1216,11 @@
     {
      "data": {
       "text/html": [
-       "
[15:54:38] early shutoff detected, exiting.                                     \n",
+       "
10:40:50 Eastern Daylight Time early shutoff detected at 8%, exiting.           \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[15:54:38]\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected, exiting.                                     \n"
+       "\u001b[2;36m10:40:50 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m8\u001b[0m%, exiting.           \n"
       ]
      },
      "metadata": {},
@@ -1260,24 +1239,11 @@
     {
      "data": {
       "text/html": [
-       "
\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
           status = postprocess                                                 \n",
+       "
                               status = postprocess                             \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                                 \n"
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                             \n"
       ]
      },
      "metadata": {},
@@ -1300,11 +1266,11 @@
     {
      "data": {
       "text/html": [
-       "
[15:54:42] status = success                                                     \n",
+       "
10:40:52 Eastern Daylight Time status = success                                 \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[15:54:42]\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                     \n"
+       "\u001b[2;36m10:40:52 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                 \n"
       ]
      },
      "metadata": {},
@@ -1323,13 +1289,15 @@
     {
      "data": {
       "text/html": [
-       "
           View simulation result at 'https://tidy3d.simulation.cloud/workbench?\n",
-       "           taskId=fdve-bc0cf21a-7395-4ea6-9794-5c3668537f92v1'.                 \n",
+       "
10:40:54 Eastern Daylight Time View simulation result at                        \n",
+       "                               'https://tidy3d.simulation.cloud/workbench?taskId\n",
+       "                               =fdve-7f8b020f-56a1-4196-8c06-251daebbb2f4'.     \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mView simulation result at \u001b]8;id=166321;https://tidy3d.simulation.cloud/workbench?taskId=fdve-bc0cf21a-7395-4ea6-9794-5c3668537f92v1\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[2;36m           \u001b[0m\u001b]8;id=343380;https://tidy3d.simulation.cloud/workbench?taskId=fdve-bc0cf21a-7395-4ea6-9794-5c3668537f92v1\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=166321;https://tidy3d.simulation.cloud/workbench?taskId=fdve-bc0cf21a-7395-4ea6-9794-5c3668537f92v1\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=343380;https://tidy3d.simulation.cloud/workbench?taskId=fdve-bc0cf21a-7395-4ea6-9794-5c3668537f92v1\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=166321;https://tidy3d.simulation.cloud/workbench?taskId=fdve-bc0cf21a-7395-4ea6-9794-5c3668537f92v1\u001b\\\u001b[4;34m-bc0cf21a-7395-4ea6-9794-5c3668537f92v1'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                 \n"
+       "\u001b[2;36m10:40:54 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                        \n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=177219;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7f8b020f-56a1-4196-8c06-251daebbb2f4\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=393601;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7f8b020f-56a1-4196-8c06-251daebbb2f4\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=177219;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7f8b020f-56a1-4196-8c06-251daebbb2f4\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=34485;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7f8b020f-56a1-4196-8c06-251daebbb2f4\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=177219;https://tidy3d.simulation.cloud/workbench?taskId=fdve-7f8b020f-56a1-4196-8c06-251daebbb2f4\u001b\\\u001b[4;34m-7f8b020f-56a1-4196-8c06-251daebbb2f4'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m     \n"
       ]
      },
      "metadata": {},
@@ -1338,7 +1306,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d100d7eeb2c2477ab2f0a36c337bc8ab",
+       "model_id": "58566d15e81544d799fd50caf9e650b3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1362,24 +1330,11 @@
     {
      "data": {
       "text/html": [
-       "
\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
[15:54:43] loading SimulationData from data/simulation_data.hdf5                \n",
+       "
10:40:55 Eastern Daylight Time loading simulation from data/simulation_data.hdf5\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[15:54:43]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/simulation_data.hdf5                \n"
+       "\u001b[2;36m10:40:55 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/simulation_data.hdf5\n"
       ]
      },
      "metadata": {},
@@ -1388,7 +1343,7 @@
    ],
    "source": [
     "job = web.Job(simulation=sim, task_name=\"mode_simulation\", verbose=True)\n",
-    "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n"
+    "sim_data = job.run(path=\"data/simulation_data.hdf5\")"
    ]
   },
   {
@@ -1409,14 +1364,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "
"
+       "
"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1424,7 +1377,7 @@
     "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
     "sim_data.plot_field(\"field\", \"Ez\", f=freq0, ax=ax[0])\n",
     "sim_data[\"mode\"].amps.sel(direction=\"-\").abs.plot.line(x=\"f\", ax=ax[1])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1439,7 +1392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 19,
    "id": "4cb3fdb9",
    "metadata": {
     "tags": []
@@ -1472,12 +1425,12 @@
     "    structures=[waveguide],\n",
     "    sources=[mode_src],\n",
     "    monitors=[monitor, mode_mon, mode_solver_mon],\n",
-    ")\n"
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 20,
    "id": "6d86abc0",
    "metadata": {
     "tags": []
@@ -1486,13 +1439,15 @@
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      "data": {
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[15:14:53] Created task 'mode_simulation' with task_id                          \n",
-       "           'fdve-58775b76-2c3f-4ede-9c56-ff331ad4eb07v1'.                       \n",
+       "
10:40:56 Eastern Daylight Time Created task 'mode_simulation' with task_id      \n",
+       "                               'fdve-52ecb464-5f00-433f-9cf2-a8b294108646' and  \n",
+       "                               task_type 'FDTD'.                                \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[15:14:53]\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'mode_simulation'\u001b[0m with task_id                          \n",
-       "\u001b[2;36m           \u001b[0m\u001b[32m'fdve-58775b76-2c3f-4ede-9c56-ff331ad4eb07v1'\u001b[0m.                       \n"
+       "\u001b[2;36m10:40:56 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'mode_simulation'\u001b[0m with task_id      \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'fdve-52ecb464-5f00-433f-9cf2-a8b294108646'\u001b[0m and  \n",
+       "\u001b[2;36m                               \u001b[0mtask_type \u001b[32m'FDTD'\u001b[0m.                                \n"
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      },
      "metadata": {},
@@ -1501,13 +1456,28 @@
     {
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-       "
           View task using web UI at 'https://tidy3d.simulation.cloud/workbench?\n",
-       "           taskId=fdve-58775b76-2c3f-4ede-9c56-ff331ad4eb07v1'.                 \n",
+       "
                               View task using web UI at                        \n",
+       "                               'https://tidy3d.simulation.cloud/workbench?taskId\n",
+       "                               =fdve-52ecb464-5f00-433f-9cf2-a8b294108646'.     \n",
        "
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       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \u001b]8;id=541023;https://tidy3d.simulation.cloud/workbench?taskId=fdve-58775b76-2c3f-4ede-9c56-ff331ad4eb07v1\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[2;36m           \u001b[0m\u001b]8;id=680144;https://tidy3d.simulation.cloud/workbench?taskId=fdve-58775b76-2c3f-4ede-9c56-ff331ad4eb07v1\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=541023;https://tidy3d.simulation.cloud/workbench?taskId=fdve-58775b76-2c3f-4ede-9c56-ff331ad4eb07v1\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=680144;https://tidy3d.simulation.cloud/workbench?taskId=fdve-58775b76-2c3f-4ede-9c56-ff331ad4eb07v1\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=541023;https://tidy3d.simulation.cloud/workbench?taskId=fdve-58775b76-2c3f-4ede-9c56-ff331ad4eb07v1\u001b\\\u001b[4;34m-58775b76-2c3f-4ede-9c56-ff331ad4eb07v1'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                 \n"
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                        \n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=537778;https://tidy3d.simulation.cloud/workbench?taskId=fdve-52ecb464-5f00-433f-9cf2-a8b294108646\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=114294;https://tidy3d.simulation.cloud/workbench?taskId=fdve-52ecb464-5f00-433f-9cf2-a8b294108646\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=537778;https://tidy3d.simulation.cloud/workbench?taskId=fdve-52ecb464-5f00-433f-9cf2-a8b294108646\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=325787;https://tidy3d.simulation.cloud/workbench?taskId=fdve-52ecb464-5f00-433f-9cf2-a8b294108646\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=537778;https://tidy3d.simulation.cloud/workbench?taskId=fdve-52ecb464-5f00-433f-9cf2-a8b294108646\u001b\\\u001b[32m-52ecb464-5f00-433f-9cf2-a8b294108646'\u001b[0m\u001b]8;;\u001b\\.     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               Task folder: 'default'.                          \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=53671;https://tidy3d.simulation.cloud/folders/639eb096-a602-4b56-a502-cac1f18f9557\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                          \n"
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@@ -1516,7 +1486,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "58227ec9e8a54bcaa575b0393149b391",
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        "version_major": 2,
        "version_minor": 0
       },
@@ -1540,11 +1510,17 @@
     {
      "data": {
       "text/html": [
-       "
\n",
+       "
10:40:57 Eastern Daylight Time Maximum FlexCredit cost: 0.025. Minimum cost     \n",
+       "                               depends on task execution details. Use           \n",
+       "                               'web.real_cost(task_id)' to get the billed       \n",
+       "                               FlexCredit cost after a simulation run.          \n",
        "
\n"
       ],
       "text/plain": [
-       "\n"
+       "\u001b[2;36m10:40:57 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost     \n",
+       "\u001b[2;36m                               \u001b[0mdepends on task execution details. Use           \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed       \n",
+       "\u001b[2;36m                               \u001b[0mFlexCredit cost after a simulation run.          \n"
       ]
      },
      "metadata": {},
@@ -1553,25 +1529,11 @@
     {
      "data": {
       "text/html": [
-       "
[15:14:54] status = queued                                                      \n",
+       "
10:40:58 Eastern Daylight Time status = queued                                  \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[15:14:54]\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                      \n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
+       "\u001b[2;36m10:40:58 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                  \n"
       ]
      },
      "metadata": {},
@@ -1580,11 +1542,19 @@
     {
      "data": {
       "text/html": [
-       "
[15:15:00] status = preprocess                                                  \n",
+       "
                               To cancel the simulation, use                    \n",
+       "                               'web.abort(task_id)' or 'web.delete(task_id)' or \n",
+       "                               abort/delete the task in the web UI. Terminating \n",
+       "                               the Python script will not stop the job running  \n",
+       "                               on the cloud.                                    \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[15:15:00]\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                  \n"
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use                    \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or \u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or \n",
+       "\u001b[2;36m                               \u001b[0mabort/delete the task in the web UI. Terminating \n",
+       "\u001b[2;36m                               \u001b[0mthe Python script will not stop the job running  \n",
+       "\u001b[2;36m                               \u001b[0mon the cloud.                                    \n"
       ]
      },
      "metadata": {},
@@ -1592,10 +1562,14 @@
     },
     {
      "data": {
-      "text/html": [
-       "
\n"
-      ],
-      "text/plain": []
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1603,13 +1577,11 @@
     {
      "data": {
       "text/html": [
-       "
[15:15:06] Maximum FlexCredit cost: 0.025. Use 'web.real_cost(task_id)' to get  \n",
-       "           the billed FlexCredit cost after a simulation run.                   \n",
+       "
10:41:11 Eastern Daylight Time status = preprocess                              \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[15:15:06]\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get  \n",
-       "\u001b[2;36m           \u001b[0mthe billed FlexCredit cost after a simulation run.                   \n"
+       "\u001b[2;36m10:41:11 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                              \n"
       ]
      },
      "metadata": {},
@@ -1618,12 +1590,9 @@
     {
      "data": {
       "text/html": [
-       "
           starting up solver                                                   \n",
-       "
\n"
+       "
\n"
       ],
-      "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                   \n"
-      ]
+      "text/plain": []
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1631,11 +1600,11 @@
     {
      "data": {
       "text/html": [
-       "
           running solver                                                       \n",
+       "
10:41:15 Eastern Daylight Time starting up solver                               \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                       \n"
+       "\u001b[2;36m10:41:15 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                               \n"
       ]
      },
      "metadata": {},
@@ -1644,17 +1613,11 @@
     {
      "data": {
       "text/html": [
-       "
           To cancel the simulation, use 'web.abort(task_id)' or                \n",
-       "           'web.delete(task_id)' or abort/delete the task in the web UI.        \n",
-       "           Terminating the Python script will not stop the job running on the   \n",
-       "           cloud.                                                               \n",
+       "
                               running solver                                   \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or                \n",
-       "\u001b[2;36m           \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.        \n",
-       "\u001b[2;36m           \u001b[0mTerminating the Python script will not stop the job running on the   \n",
-       "\u001b[2;36m           \u001b[0mcloud.                                                               \n"
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                   \n"
       ]
      },
      "metadata": {},
@@ -1663,7 +1626,7 @@
     {
      "data": {
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+       "model_id": "a1e98ce8c2ab42189eda6339a1225958",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1677,11 +1640,11 @@
     {
      "data": {
       "text/html": [
-       "
[15:15:14] early shutoff detected, exiting.                                     \n",
+       "
10:41:22 Eastern Daylight Time early shutoff detected at 8%, exiting.           \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[15:15:14]\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected, exiting.                                     \n"
+       "\u001b[2;36m10:41:22 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m8\u001b[0m%, exiting.           \n"
       ]
      },
      "metadata": {},
@@ -1700,24 +1663,11 @@
     {
      "data": {
       "text/html": [
-       "
\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
           status = postprocess                                                 \n",
+       "
                               status = postprocess                             \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                                 \n"
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                             \n"
       ]
      },
      "metadata": {},
@@ -1740,11 +1690,11 @@
     {
      "data": {
       "text/html": [
-       "
[15:15:20] status = success                                                     \n",
+       "
10:41:27 Eastern Daylight Time status = success                                 \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[15:15:20]\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                     \n"
+       "\u001b[2;36m10:41:27 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                 \n"
       ]
      },
      "metadata": {},
@@ -1763,13 +1713,15 @@
     {
      "data": {
       "text/html": [
-       "
           View simulation result at 'https://tidy3d.simulation.cloud/workbench?\n",
-       "           taskId=fdve-58775b76-2c3f-4ede-9c56-ff331ad4eb07v1'.                 \n",
+       "
10:41:29 Eastern Daylight Time View simulation result at                        \n",
+       "                               'https://tidy3d.simulation.cloud/workbench?taskId\n",
+       "                               =fdve-52ecb464-5f00-433f-9cf2-a8b294108646'.     \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mView simulation result at \u001b]8;id=376233;https://tidy3d.simulation.cloud/workbench?taskId=fdve-58775b76-2c3f-4ede-9c56-ff331ad4eb07v1\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[2;36m           \u001b[0m\u001b]8;id=352124;https://tidy3d.simulation.cloud/workbench?taskId=fdve-58775b76-2c3f-4ede-9c56-ff331ad4eb07v1\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=376233;https://tidy3d.simulation.cloud/workbench?taskId=fdve-58775b76-2c3f-4ede-9c56-ff331ad4eb07v1\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=352124;https://tidy3d.simulation.cloud/workbench?taskId=fdve-58775b76-2c3f-4ede-9c56-ff331ad4eb07v1\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=376233;https://tidy3d.simulation.cloud/workbench?taskId=fdve-58775b76-2c3f-4ede-9c56-ff331ad4eb07v1\u001b\\\u001b[4;34m-58775b76-2c3f-4ede-9c56-ff331ad4eb07v1'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                 \n"
+       "\u001b[2;36m10:41:29 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                        \n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=272256;https://tidy3d.simulation.cloud/workbench?taskId=fdve-52ecb464-5f00-433f-9cf2-a8b294108646\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=283834;https://tidy3d.simulation.cloud/workbench?taskId=fdve-52ecb464-5f00-433f-9cf2-a8b294108646\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=272256;https://tidy3d.simulation.cloud/workbench?taskId=fdve-52ecb464-5f00-433f-9cf2-a8b294108646\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=795148;https://tidy3d.simulation.cloud/workbench?taskId=fdve-52ecb464-5f00-433f-9cf2-a8b294108646\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=272256;https://tidy3d.simulation.cloud/workbench?taskId=fdve-52ecb464-5f00-433f-9cf2-a8b294108646\u001b\\\u001b[4;34m-52ecb464-5f00-433f-9cf2-a8b294108646'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m     \n"
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@@ -1778,7 +1730,7 @@
     {
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       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e96fc811a7d34f52b540bc4c4ea4c6d3",
+       "model_id": "dc3a5258cfa84f099f743eb0da2d256a",
        "version_major": 2,
        "version_minor": 0
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@@ -1802,24 +1754,11 @@
     {
      "data": {
       "text/html": [
-       "
\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
[15:15:22] loading SimulationData from data/simulation_data.hdf5                \n",
+       "
10:41:30 Eastern Daylight Time loading simulation from data/simulation_data.hdf5\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[15:15:22]\u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from data/simulation_data.hdf5                \n"
+       "\u001b[2;36m10:41:30 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/simulation_data.hdf5\n"
       ]
      },
      "metadata": {},
@@ -1828,7 +1767,7 @@
    ],
    "source": [
     "job = web.Job(simulation=sim, task_name=\"mode_simulation\", verbose=True)\n",
-    "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n"
+    "sim_data = job.run(path=\"data/simulation_data.hdf5\")"
    ]
   },
   {
@@ -1841,7 +1780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 21,
    "id": "09f13155",
    "metadata": {
     "tags": []
@@ -1849,14 +1788,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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1rCQKNyLSSBW1Few+stthsu+eoj0nXXod4BVA94ju9hDTI7IHyaHJOldJAMgtqTSDzAlzZE61CV77MH96tw8xe2MSzOGl6GBtpignp3AjIqdUVl3GzsKdDiuW9hXvw2bUn6AZ4hNiTvQ9tmqpR0QPkkKS8LBo47K2zjAMsooq2PqLoaX80pPvH5McFUivYz0xvY/NkwkP9GnhqsWVKdyICGAOLe0+spvNhzez9fBWNudvZn/J/pPeG+kXaU7yPaFHRkuvBcxDIg8UHq03tFR0tP7Saw8LdIkOchha6hkfQrCfevbk7CjciLRBhmGQWZpZF2QOb2ZnwU6qbdX17o0LjLMHmOOrltoFtHNC1dLa1Fpt7Dtc7jC0tD27hLKq+kOU3p4WUmKC7ZN8e7UPpUdsCP4+WrEkTU/hRqQNKKosYmvBVrbkb7EHmpMtvw71DaV3VG/6RvWlT1Qfekf1JtwvvOULllbHMAwOFBxlU2YRGzKK2JRZxI6cEipr6g9R+nh50CMuxD7Jt3d8KCmxQVp6LS1G4UbEzVRbq9lZuJMth7ewOd8MMhmlGfXu8/bwpkdED/q060OfKPMjMThRQ0sCmKuWNh0sYuOxj02ZRScdWgrw8aRXvLmHjLkEO4TO7YLw1iGR4kQKNyIuzGbYyCjJcAgyO4/sPOnKpY4hHe29MX3b9aVbeDe8PTW3QcydfbfnlLAxoy7InGz5tY+nBz3jQ+iXGEa/xDD6JITSMTIQT+0hI62Mwo2ICymsLGRL/ha2HK77KK0urXdfhF9EXZCJ6kuvqF7a2VcAc8LvvsPl9l6Z48NLNdb6O+J1igqkX2IYqcfCTI+4EHy81CMjrZ/CjUgrVVlbyY7CHQ5h5mQHR/p6+tqHl/pG9aV3VG/aB7XX8JIAkF9a5RBkNh4sorSyfs9eZKCPvUcmNTGM1IQwQgPUsyeuSeFGpBWwGTbSi9PNEHMszOw5sodaw/GH0PHzlvpE9aFvOzPIdA3vqk3xBICKaitbs4vN4aXMIjZmFJFVVFHvPl8vD/q0D3XolUkI91cgFrehcCPiBIcrDrM5f7O9R2bb4W2U1ZTVuy/KP8o+2bdPuz70iuxFsE+wEyqW1sZqM0jLL3MIMrtyS7H+4uBIiwW6tAtyCDLdYoM14VfcmsKNSDOrtlazrWAbG/M22sPMofJD9e7z9/KnZ2RPe5jp264vMQEx+m1aAPOoguNLsDdmFLElq/ik+8lEB/uaw0tJYfRLMCf9alM8aWsUbkSaWHFVMZvyN7E+dz0b8jaw9fDWepvjeVg86BzWua5XJqoPncM64+Whf5IC5VW1bM4stgeZjQeLOFRS/8ylAB9Pc3jpWJDplxRGbIgOjhTRd1KRs2AYBjnlOazPW8+G3A2sz1vP3qK99e6L8Iugf3R/+rYzN8frGdmTQO9AJ1QsrY3VZrDrUKlDkNmTV8ovRpfwsEBKTDD9k8zJvv2SwugaHaxl2CInoXAj0ghWm5U9RXvsvTIb8jaQezS33n0dQzoyIGYA/aP7MyB6gDbHE7uj1bVsPFjE2v1HWHvgCOsPHDnp8FJ8qB/9jgeZY3vKBPjoW7ZIQ+hfishpVNRWsPXwVnuY2ZS/qd7EXy+LFz0je9I/uj/9Y/rTr10/Iv0jnVSxtDb5pVWsO1DImv1HWLu/kG3ZJdT+olsmyNeL1MRQe5DplxhGdIifkyoWcX0KNyInKKwsNHtkcs1eme0F2+stxw70DqRfu35mr0zMAHpH9cbfy99JFUtrYhgGafnlDmFm/0l2+o0L9WNgxwgGdQxnYIcIusVqeEmkKSncSJtlGAYHSw+a82XyNrA+dz37S/bXuy/aP7puiClmAF3DuuLpoQMABaprbWzJKmbt/kLWHjjCugNHKCx3nDxusUC3mGAGdgxnUMcIBnaMoH2YwrBIc1K4kTaj1lbLrsJdDmGmoLKg3n1dwrqYQ0zHwkx8YLzmywgAxUdrWJ9xhDXHwsymg0VU1Tqeiu3r5UG/xDAGdYzgnI7hDEgKJ9RfS7FFWpLCjbitozVH2ZS/yQwyeevZnL+ZilrH3Vq9PbzpHdXbPvG3X3Q/ncEkgNmzl3mkgnUHjoWZ/UfYnVeK8YtVTBGBPgzscLxXJpxe8aE6f0nEyRRuxG3kH823r2Ban7eeXYW7sBpWh3uCfYLremWiB9Arqhe+nr5OqlhaE6vNYEdOiUOYOdneMp2iAjnnhDCTHBWonj2RVkbhRlySYRikF6fbh5g25G3gYOnBeve1D2pvDzP9o/vTOawzHhb9Vi3HlmRnFJkTfw8UsiGjqN6SbC8PC73bhzKwQzgDj4WZqCCFYZHWTuFGXIJhGOwr3seqnFWsPrSadbnrKKoqcrjHgoVuEd0chphiA2OdU7C0Onmllazbf8QeZrZll9Q7hynY14sBHcIZ1DGcczpE0C8xDH8fTR4XcTVODTdLly7lqaeeYt26deTk5PDJJ59wxRVXnPL+adOm8eabb9Zr79mzJ9u2bWvGSqWlGYZBZlkmq3NWs+rQKlbnrK43+dfP048+7frYw0zfdn11qKQAx5dkl7H2hDBz4CRLsuNPWJJ9jpZki7gNp4ab8vJyUlNTmT59OhMnTvzV+59//nn+8Y9/2B/X1taSmprKpEmTmrNMaSG55bmsPrTa/MhZTXZ5tsN1X09f+kf3Z0jcEAbFDqJnRE+8PbUKRcBmM9ieU8LP+wr4eV8h6w4UcuRojcM9Fgt0jw05NsQUriXZIm7MqeFm/PjxjB8/vsH3h4aGEhpat5Ll008/5ciRI1x//fXNUZ40syOVR1hzaA2rD61mVc6qenvMeFm86NuuL4PjBjM4djCp7VLx8fRxTrHSqhiGwZ68MlamFfBT2mFWpRdS9Isw4+dtLske2MGcKzOgQzghOh1bpE1w6Tk3c+fOZezYsXTo0OGU91RVVVFVVWV/XFJS0hKlyUmUVZexLnedfZhp15FdDtctWOgZ2ZPBcYMZEjuE/tH9CfAOcFK10poYhsH+gqP2MPPzvkIOl1U53BPo48ng5AiGdo5kUMcILckWacNcNtxkZ2fzzTff8O677572vscff5w5c+a0UFVyosraSjbmb7TPm9l2eFu9pdldwrowJG4Ig2MHc07MOdpjRuwyj5hhZmVaASv3FZBT7Lgs28/bg4EdzDAztHMkfdqH4u2pMCMiLhxu3nzzTcLCwk47ARlg9uzZzJo1y/64pKSExMTEZq6ubaqx1rC1YKt9RdPGvI3U2ByHCpKCk+w9MwNjBxLlH+WkaqW1yS2pdAgzGYWOE4B9PD3olxTGsM6RDO0USb+kMHy9tJJJROpzyXBjGAZvvPEG1113HT4+p5+D4evri6+v9qVoDlablZ1Hdtp7Ztbnrq+3A3B0QDRDYofYA01cUJyTqpXWpqCsip/3FbJy32F+SitgX365w3VPDwupCaFmz0ynKM7pEK5l2SLSIC4ZbpYsWcLevXu54YYbnF1Km2IYBmlFafY5M2ty11BaXepwT7hvOINiB9mHmjqEdNDurQJAcUUNq/aZvTIr0wrYecjx747FAr3jQ+3DTIM6RhDk65LfokTEyZz6naOsrIy9e/faH6enp7Nx40YiIiJISkpi9uzZZGVl8dZbbzk8b+7cuQwZMoTevXu3dMltimEYZJZm2sPM6kP195oJ8g5iYMxA+4qmruFdtQOwAFBWVcua/YX2oaat2cX1zmXqHhvMuZ0iGdY5kiHJkYQGaDWTiJw9p4abtWvXMmbMGPvj43Njpk6dyvz588nJySEjI8PhOcXFxXz00Uc8//zzLVprW3HiXjOrclaRU57jcN3P04/+0f3tw0w9Invg5aHfrgUqa6ysO3CEn9IOszKtgE2ZxfV2AO7ULvDYnJkozu0UQaSOMhCRZmAxjF/+LuXeSkpKCA0Npbi4mJCQEGeX43RFlUUOYUZ7zUhDVdfa2HiwyB5mNmQUUW21OdyTGOHPsE5R9qGmmBA/J1UrIq6uMT+/9St3G2MzbGw7vI3lWctZnrWcLYe3YFCXb7XXjJxKrdXG5qxi+zDT2gOFVNY4hpnYED+GdY7k3GMrmhIj9HdHRFqewk0bcKTyCCuyV7A8azk/Zf3EkaojDte114yczPEjDY5vnLdm/5F6p2ZHBfkcmzNj9s50jAzQBHIRcTqFGzdktVnZVlDXO7P18FaH3plA70CGxg1lRPsRDG8/XCdni11+aRXL9uSzZHc+y/YcprC82uF6qL8353aKsIeZrtFBCjMi0uoo3LiJwspCVmQd653J/omiqiKH6ynhKYxoP4IR7UfQL7of3h5alSJQY7WxIaOIJbvzWLI7n61ZjseTBPl6MTg5whxq6hRJz7gQPHRqtoi0cgo3Lspqs7K1YKvZO5O5nG0F2xx6Z4K8gxgaf6x3Jn44MYExTqxWWpOsogqW7s5nya58Vuw9TOkvhpp6tw9hVEo7RqVE0z8pTEcaiIjLUbhxIQUVBfyU/RPLspaxMntlvd6ZbuHd7L0zqdGp6p0RwFyivTq9kCW7zeGmvXllDtfDA7w5L6Udo1LaMbJrO9oFa3m2iLg2hZtWzGqzsuXwFpZlLWN51nK2F2x3uB7sHVzXO9N+ONEB0U6qVFoTwzBIP1xuDzM/7ytwWNXkYYH+SeHHemfa0bt9KJ4aahIRN6Jw08ocrjjsMHempNpxDkSPiB723pm+7fpqAz0BzN2AV6YV2OfOHCx0POMrNsTPDDPd2jG8c5R2AhYRt6afjE5Wa6s1e2cyzd6ZHYU7HK4H+wQzLH6Yfe5Mu4B2TqpUWhPDMNh5qNTsndmVz9oDhdRY6+Zc+Xh6MCg53D53JiVGq5pEpO1QuHGCwxWH7cu0V2avPGXvzMiEkfSJ6qPeGQGg6Gg1y/YcZsnufJbuzievtMrheofIAPtQ07mdIgnUoZMi0kbpu18LqLXVsjl/sz3Q/LJ3JsQnpK53pv1wovyjnFSptCZWm8HmzCL73JlNB4s48agmf29PhnWOZFS3dpzXtR0dowKdV6yISCuicNNM8o/m1/XO5KyktLrU4XrPyJ5m70z7kfSO6q3eGQEgr6SSpcd6Z5btyafoaI3D9W4xwYzqZvbODOwYjq+Xp5MqFRFpvfQTtYnU2mrZlL/JHmh2Fu50uB7qG8qw+GGMbD+SofFD1TsjgHn45PqMI/a5M9tzHIcog/28GNk1ilEp7TgvpR1xof5OqlRExHUo3DSRXYW7mPbtNPtjCxZ6RfZiRIK5sql3ZG88PfRbtsDBwqP2oaaf9h6mvNpqv2axQN/2ofYw0y8xDC9toici0igKN02kR2QPOod2pluEuZHesPhhRPpHOrssaQWqa22sSi9g0U5zmfa+/HKH61FBPpzX1VymPaJLFJFB2kRPRORsKNw0EQ+LB5/85hMttxUASiprWLwrn++357J4Z57DEQeeHhbOSQq3z53ReU0iIk1L4aYJKdi0bTnFFfywPZcF23P5eV+Bw74zUUG+jO0Rzehu7RjWJYoQP22iJyLSXBRuRM6QYRjsyi3l+21moNmSVexwvXO7QC7sGcuFPWPonxim3hkRkRaicCPSCLVWG2sPHGHBtly+33HI4ZgDiwUGJIVzYc8YLuwZQ+d2QU6sVESk7VK4EfkVR6trWbo7nwXbc1m0M89h7xlfLw9Gdo3iwp4xnN89Ridqi4i0Ago3IieRX1rFwh25fL89l2V7D1NdW3eqdniAN+d3N3tnzkuJIsBH/4xERFoTfVcWOSYtv8wcbtp+iA0HizBOOOogKSLAPtw0sEO49p4REWnFFG6kzbLZDDYcPMKC7WYPzS/3n+mbEMqFPWK4qFesTtUWEXEhCjfSplTWWFmx9zDfb8/lhx25HC6rtl/z9rRwbqdILuoVy9ge0TrqQETERSnciNs7Ul7Nop15LNh+iKW7D1NRU3fcQbCvF2O6R3NhzxhGdWun/WdERNyAwo24pYyCoyzYfojvt+ey9sARrLa6CTRxoX72+TNDkiPx8dL8GRERd6JwI27BMAy2ZBXz/bH5MzsPlTpc7x4bzEU9Y7iwZyy924do/oyIiBtTuBGXVV1r4+d9BfZAc6ik0n7N08PCoI7hXNgzlot6xpAYEeDESkVEpCUp3IhLqai2snBnLt9uPcSSXfkOB1IG+HhyXtd2XNQrhjHdogkP9HFipSIi4iwKN9LqVdVaWbIrny8257BwRy5Hq+smBEcF+XJhT3NC8LDOUfh5ezqxUhERaQ0UbqRVqrHaWL73MF9syub7bbkOPTQJ4f5c2jeOi3rG6kBKERGpR+FGWg2rzeDnfQV8uTmbb7YecjjDKTbEj0v7xjEhNZ7UhFBNCBYRkVNSuBGnstkM1mUc4YtN2Xy95RCHy6rs16KCfLikTxyX9Y1nYIdw9dCIiEiDNDrc7Ny5k+7du5/02nfffce4cePOuihxb4ZhsCmzmC83ZfPVlhxyiutWOYUFeHNxr1gmpMYzJDlCZziJiEijNTrcDBgwgKeeeorbb7/d3lZVVcVdd93F66+/TmVl5WmeLW2VYRjsyCnli83ZfLk5m4OFFfZrQb5eXNQrhgl94xneJUqb6omIyFlpdLiZP38+t956K1999RXz5s0jJyeHyZMnY7PZWLZsWXPUKC5sb14pX2zK4YvN2Q4HU/p7ezK2ZwyX9Y1jVEo7rXISEZEm0+hwc/XVVzNs2DCuv/56evXqRXl5OdOmTeOZZ54hIEAbpQkcKCjny805fLEp22GnYB8vD8Z0a8eE1HjO7x5NgI+mfImISNM7458u1dXVWK1WrFYrcXFx+Pn5NWVd4mKyiir4anM2X27OYXNmsb3d29PCyK7tuKxvHBf2jCFYB1OKiEgza3S4ee+997j11lsZOXIku3fvZuPGjVx//fV89913vP3223Tq1Kk56pRWKK+0kq835/DF5hzWHThib/ewwPAuUVzWN45xvWIJC9BOwSIi0nIshmEYv35bncDAQJ5++mluvfVWe9uRI0e4+eab+fbbbykpKWnyIptSSUkJoaGhFBcXExIS4uxyXE5heTXfbM3hy005/JxewPG/PRYLDOoYwYTUeMb3jiUqyNe5hYqIiFtpzM/vRvfcrF+/nm7dujm0hYeH8/777/P222839uXEBRRX1LBg2yG+2JzDir2Hsdrq8nD/pDAu6xvPpX3iiA3V0KSIiDhfo8NNt27dqK2tZfHixaSlpTF58mSCg4PJzs7myiuvbI4axQnKq2r5YUcuX2zKYenufKqtNvu1XvEhTEg1A41O2xYRkdam0eHmwIEDXHzxxWRkZFBVVcWFF15IcHAwTzzxBFVVVbz66qvNUae0gMoaKz/uzOOLzdks2plHZU1doEmJCeKyvvFc1jeOTu2CnFiliIjI6TU63PzpT39i4MCBbNq0icjISHv7lVdeyU033dSkxUnzq6q1smz3Yb7cnM3323MpP+HE7eSoQC7rax5/0C022IlVioiINFyjw82yZcv46aef8PFxXAHTsWNHsrKymqwwaT6GYbAtu4QP1h7ks03ZDgdUtg/z57LUOCb0jadXfIgOqBQREZfT6HBjs9mwWq312jMzMwkO1m/3rdnhsio+3ZDFh+syHTbXiw72tZ+43T8xTIFGRERcWqPDzUUXXcRzzz3Ha6+9BoDFYqGsrIyHHnqISy65pMkLlLNTY7Xx4848PliXyY8786g9ttLJx8uDcb1iueqcBEZ0icJTJ26LiIibaPQ+N5mZmYwbNw7DMNizZw8DBw5kz549REVFsXTpUqKjo5ur1ibRVva52XmohA/WZvLphiwKyqvt7amJYVx1TgKX940nNEC7BYuIiGtozM/vRocbgNraWt577z02b95MWVkZAwYM4Pe//z3+/v5nXHRLcedwU3S0ms83ZfPB2ky2ZNUdgRAV5MvEAe256pwEUmI0dCgiIq6nWTfxA/Dy8uIPf/jDGRV3oqVLl/LUU0+xbt06cnJy+OSTT7jiiitO+5yqqioeeeQR/vOf/3Do0CHi4uJ48MEHmT59+lnX44qsNoOle/L5cG0m32/Pte9H4+1p4YLuMUwamMB5Ke3w9vRwcqUiIiIto0Hh5vPPP2/wC15++eUNvre8vJzU1FSmT5/OxIkTG/Scq6++mtzcXObOnUuXLl3IycnBZrP9+hPdTFp+GR+szeSTDZnkllTZ23vGhTBpYAK/6deeiECd6SQiIm1Pg8LNL3tTLBYLvxzNOr7C5mQrqU5l/PjxjB8/vsH3f/vttyxZsoR9+/YREREBmEvQ24qSyhq+2pzDB2sPsj6jyN4eHuDNb/q1Z9LABHrFhzqvQBERkVagQeHmxJ6RH374gXvvvZe///3vDB06FICVK1fywAMP8Pe//715qjzm888/Z+DAgTz55JO8/fbbBAYGcvnll/Poo4+ecr5PVVUVVVV1PRut/WDPX7LZDFbuK+CDtQf5dtsh+67Bnh4WRqe0Y9LABM7vHoOPl4adRERE4Azm3Nx55528+uqrjBgxwt42btw4AgIC+OMf/8iOHTuatMAT7du3j+XLl+Pn58cnn3zC4cOHue222ygoKGDevHknfc7jjz/OnDlzmq2m5pJRcJQP1x3ko/VZZBVV2Nu7RgcxaWACV/RvT3SwDqoUERH5pUaHm7S0NMLCwuq1h4aGsn///iYo6dRsNhsWi4V33nmH0FBz+OWf//wnV111FS+//PJJe29mz57NrFmz7I9LSkpITExs1jrPVHlVLV9vyeGDdZmsTi+0t4f4eXF5v3iuOieR1IRQbbInIiJyGo0ON4MGDWLWrFm8/fbbxMTEAJCbm8s999zD4MGDm7zAE8XFxdG+fXt7sAHo0aMHhmGQmZlJ165d6z3H19cXX1/fZq3rbBiGwer0Qj5cl8lXW3I4euxsJ4sFRnZtx1XnJHBRzxj8vD2dXKmIiIhraHS4eeONN7jyyitJSkqy94AcPHiQrl278umnnzZ1fQ6GDx/OBx98QFlZGUFB5snUu3fvxsPDg4SEhGb93E0tq6iCj9dl8uH6TA4UHLW3J0cFctU5CUwc0J640Na/b5CIiEhr0+hw06VLFzZv3sz333/Pzp07AbP3ZOzYsY0eLikrK2Pv3r32x+np6WzcuJGIiAiSkpKYPXs2WVlZvPXWWwBMnjyZRx99lOuvv545c+Zw+PBh7rnnHqZPn+4SGwhW1lj5btshPlibyYq0wxxfcBbo48llfeOZNDCBczqEa9hJRETkLJzRDsVNZfHixYwZM6Ze+9SpU5k/fz7Tpk1j//79LF682H5t586dzJw5kxUrVhAZGcnVV1/NY4891uBw09I7FBuGwYaDRXywNpMvN2VTWlVrvza0UySTBiZwce9YAnzOaD9FERGRNqHZj19YuHAhCxcuJC8vr94Gem+88UZjX65FtVS4yS2p5OP1WXy47iBp+eX29oRwf646J4HfDkggMSKg2T6/iIiIUxgGHC2EwMgmfdlmPX5hzpw5PPLIIwwcOJC4uDgNoZygqtbKwh15fLD2IEt253PsAG78vD24pHccVw1M4NzkSDx0AreIiLiTmkpIXwq7vobd30FYEtzwndPKaXS4efXVV5k/fz7XXXddc9TjsjZnFjHljdUUHa2xtw3sEM6kgQlc0ieOYD+dwC0iIm6kNBf2fAe7voV9P0JN3eIYqkqhqgx8g5xSWqPDTXV1NcOGDWuOWlxa1+hgrFaD2BA/fntOe646J5HkqEBnlyUiItI0DANyt8Gub2D3N5C1zvF6cDx0uxi6XQIdR4K38zaabfScm3vvvZegoCD++te/NldNzao559zszSslOSoITw07iYiIO6itgv3LzN6Z3d9C8UHH6/H9IWW8GWpi+5qbtDWTZp1zU1lZyWuvvcYPP/xA37598fZ2HG755z//2diXdBtdooOdXYKIiMjZKT9szpvZ/Q2k/QjVZXXXvPyg02joNh66joOQOKeVeTqNDjebN2+mX79+AGzdutXhmiYXi4iIuBjDgPyd5nDTrm8gcw1wwqBOUCykjDMDTfIo8Gn9K30bHW5+/PHH5qhDREREWkptNRxYYQ417foGig44Xo/tY86dSbkY4vqBh4dTyjxT2jlORESkLThaCHsWmGEmbRFUldRd8/SF5PPMuTMpF0Ooax1p9EsNCjcTJ05k/vz5hISEMHHixNPe+/HHHzdJYSIiInIWDAMO7zHnzuz6Fg7+DMYJG+8GtjOHm1LGm/NonLRsuzk0KNyEhoba59OceCK3iIiItCLWGsj4uW65duE+x+vRveqWa8cPcLnhpoZy6tlSztDSZ0uJiIg0q4ojsHehGWj2fg+VxXXXPLwheaTZO5MyDsI7OK/Os9SsS8FFRETEyQrSjvXOfAsHfgLDWnctINJcpt3tYuh8Pvi2vW1KFG5ERERaO2stZK6uCzSHdzteb9fdnAjcbTwkDAIPT+fU2Uoo3IiIiLRGVWXmMNOub8xVThVH6q55eEGH4WaYSRkHEZ2cV2crpHAjIiLSWlQWm7sDb/8M9v4AtZV11/zCoOtF5nBTl7HgpwU+p6JwIyIi4kxHC2HX17D9c/N0bWt13bXwZOh+qbm6KXEIeOrHdkOc0bu0cOFCFi5cSF5eHjabzeHaG2+80SSFiYiIuK2yfNj5hRlo9i8DW23dtagU6Pkb8yOmd7MeRumuGh1u5syZwyOPPMLAgQOJi4vTeVIiIiINUZIDO74wh5wyfnLcUC+mtxlmelwO0d2dV6ObaHS4efXVV5k/fz7XXXddc9QjIiLiPooyzN6ZHZ/DwVWO1+L7m2Gm528gsrNz6nNTjQ431dXVDBs2rDlqERERcX0FaWaY2f4ZZG9wvJYw+FgPzQSX3lCvtWt0uLnxxht59913+etf/9oc9YiIiLie/F1mmNn+OeRuqWu3eEDSMOh5uRloQuKdV2Mb0uhwU1lZyWuvvcYPP/xA37598fb2drj+z3/+s8mKExERaZUMA3K3mmFm+2dweFfdNYunecJ2z8uh+2UQFO28OtuoRoebzZs3069fPwC2bt3qcE2Ti0VExG0ZhjnMtP0zc9jpxEMpPbyh8xhzyKnbJRAQ4bw6pfHh5scff2yOOkRERFofmw0y1xwLNF9AcUbdNS8/czO9HpebuwT7hzmtTHF0VrsBZWZmApCQkNAkxYiIiDidzWoeRrnjczPQlObUXfMOMHcJ7vkb80/fIOfVKafU6HBjs9l47LHHeOaZZygrKwMgODiYu+66i/vvvx8PD48mL1JERKRZWWvMzfS2fwY7v4Ly/LprPsHmGU49L4fOF4BPgPPqlAZpdLi5//77mTt3Lv/4xz8YPnw4AMuXL+fhhx+msrKSv/3tb01epIiISJOrrYJ9i81Jwbu+cjyY0i/MPPag52+g02jw8nVSkXImLIZhGI15Qnx8PK+++iqXX365Q/tnn33GbbfdRlZWVpMW2NRKSkoIDQ2luLiYkJAQZ5cjIiItqaYC9i40e2h2fwtVJXXXAqKgx2XmHJrk88DT+9SvIy2uMT+/G91zU1hYSPfu9beG7t69O4WFhY19ORERkeZVWwV7voetH8LuBVBTXnctKPbYHjSXQ4dh4OHpvDqlyTQ63KSmpvLiiy/ywgsvOLS/+OKLpKamNllhIiIiZ8xmgwMrYMv7Zi9NZXHdtdDEumMPEgaB5oq6nUaHmyeffJJLL72UH374gaFDhwKwcuVKDh48yNdff93kBYqIiDSIYcChzbDlA9jyEZRm110LjoPev4XeEyF+gE7adnONDjejRo1i9+7dvPTSS+zcuROAiRMncttttxEfr22lRUSkhRXuM8PMlvfh8O66dr9Qs3emzyToMFxDTm1IoycUuzpNKBYRcQNl+bDtY7OXJnNNXbunL3S7GPpcDV0v1ConN9LkE4o3b95M79698fDwYPPmzae9t2/fvg2vVEREpKGqSs09aDa/by7hNqxmu8UDkkeZPTQ9LjN7bKRNa1C46devH4cOHSI6Opp+/fphsVg4WYePxWLBarU2eZEiItJG1VbD3h/MHppd30BtRd21+AHQ92rodSUExzqvRml1GhRu0tPTadeunf2/RUREmo3NBhkrzTk02z6FyqK6axGdzUDTZxJEdnZWhdLKNSjcdOjQwf7fBw4cYNiwYXh5OT61traWn376yeFeERGRBjEMyN1qDjlt/QhKTtgQNijWXOnU5yqI76+VTvKrGr1aasyYMeTk5BAdHe3QXlxczJgxYzQsJSIiDXdkP2z50Bx2yt9Z1+4bYu5F03cSdByplU7SKI0ON4ZhYDlJai4oKCAwMLBJihIRETdWfhi2fWIGmoOr6to9fSBlnDnk1HUcePs5r0ZxaQ0ONxMnTgTMScPTpk3D17dueZ3VamXz5s0MGzas6SsUERHXV1UGu742h53SFtWtdMICySPNpds9JoB/mDOrFDfR4HATGmourTMMg+DgYPz9/e3XfHx8OPfcc7npppuavkIREXFN1hrzkMotH5jBpuZo3bW4fmYPTe/fQkic00oU99TgcDNv3jwAOnbsyN13360hKBERqc9mM4eajq90qjjhQOXwZHOlU++roF2K00oU96cdikVE5Ozlbjt2ptOHUHywrj0w+thKp0nQXmc6yZlr8h2Kf+nDDz/k/fffJyMjg+rqaodr69evP5OXFBERV1OUcWyl04eQt62u3SfYnD/TdxJ0PA88z+hHjcgZa/TfuBdeeIH777+fadOm8dlnn3H99deTlpbGmjVruP3225ujRhERaS2qSs2VThvfNTfaO87D+9hKp6sg5WLw9j/1a4g0s0aHm5dffpnXXnuNa6+9lvnz5/PnP/+ZTp068eCDD1JYWPjrLyAiIq7FMCDjZ9jwHzPY1JQfu2CBjiPMQNPzN+Af7tQyRY5rdLjJyMiwL/n29/entLQUgOuuu45zzz2XF198sWkrFBER5yjJgU3/NUNNYVpde2QX6Pd76HsNhLZ3Xn0ip9DocBMbG0thYSEdOnQgKSmJn3/+mdTUVNLT0096mKaIiLiQ2mrY8x2sfxv2fg+GzWz3DoTeV0L/6yBxiCYGS6vW6HBz/vnn8/nnn9O/f3+uv/56/u///o8PP/yQtWvX2jf6ExERF5O3w+yh2fQeHD1c1554LvT/A/S6AnyDnVaeSGM0eim4zWbDZrPZD8587733+Omnn+jatSs333wzPj4+DX6tpUuX8tRTT7Fu3TpycnL45JNPuOKKK055/+LFixkzZky99pycHGJjG3bcvZaCi4gcU1lsHlK54T+Qta6uPSgGUq81Q01UV+fVJ3KCZl0K7uHhgYeHh/3x7373O373u981vkqgvLyc1NRUpk+f3qhen127djl8Yb88xFNERE7BZoMDK8xAs/0zqK0w2z28zFVO/f8AXS7U8m1xaY3+27t58+aTtlssFvz8/EhKSnI4d+p0xo8fz/jx4xtbAtHR0YSFhTX6eSIibVZxJmz8L2z8j3kS93FR3WDAdebk4CD9oijuodHhpl+/fg6ngv/ylHBvb2+uueYa/vWvf+Hn1zwnuvbr14+qqip69+7Nww8/zPDhw095b1VVFVVVVfbHJSUlzVKTiEirU1tlnum04T/mGU8cm4XgEwx9fmtODm5/jiYHi9vx+PVbHH3yySd07dqV1157jY0bN7Jp0yZee+01unXrxrvvvsvcuXNZtGgRDzzwQJMXGxcXx6uvvspHH33ERx99RGJiIqNHjz7trsiPP/44oaGh9o/ExMQmr0tEpFU5tAW+uRee6QYfTIO9PwAGdBgBV7wKd++CCc9DwkAFG3FLjZ5QPHjwYB599FHGjRvn0P7dd9/x17/+ldWrV/Ppp59y1113kZaWdopXOUkhFsuvTig+mVGjRpGUlMTbb7990usn67lJTEzUhGIRcS8VR8xjEDa8DTmb6tqD46HfZPMjsrPz6hM5S806oXjLli106NChXnuHDh3YsmULYA4b5eTkNPalz8jgwYNZvnz5Ka/7+vo2eA6QiIhLsdkgfYk57LTjC7Ae+0XOwxu6X2oOO3UeAx6ezq1TpIU1Otx0796df/zjH7z22mv2Zd81NTX84x//oHv37gBkZWURExPTtJWewsaNG4mLi2uRzyUi0ioUZcCGd8zznYoz6tqje5mTg/tcDYGRzqtPxMkaHW5eeuklLr/8chISEujbty9g9uZYrVa+/PJLAPbt28dtt932q69VVlbG3r177Y/T09PZuHEjERERJCUlMXv2bLKysnjrrbcAeO6550hOTqZXr15UVlby+uuvs2jRIhYsWNDYL0NExLXUVMDOr8xhp31LsE8O9g01T9/u/weI66c5NCKcQbgZNmwY6enpvPPOO+zevRuASZMmMXnyZIKDzd0rr7vuuga91tq1ax025Zs1axYAU6dOZf78+eTk5JCRUfdbSXV1NXfddRdZWVkEBATQt29ffvjhh5Nu7Cci4vIMA3I2msNOWz4wN907LnmUOezU4zKdwC3yC42eUOzqtEOxiLR6Rwth8/tmL03u1rr20ETzwMp+kyG8/txHEXfWrBOKj9u+fTsZGRlUV1c7tF9++eVn+pIiIm2XzQppP5qBZtfXYD32vdXTF3pMMIedkkeBR6N38BBpcxodbvbt28eVV17Jli1bsFgs9pPAj2/kZ7Vam7ZCERF3VpQB698yJweXZNW1x6Waw059rgL/cOfVJ+KCGh1u/vSnP5GcnMzChQtJTk5m9erVFBQUcNddd/H00083R40iIu7FZoO0RbDmddj9LfbJwf7h5jEI/X4PcX2dWqKIK2t0uFm5ciWLFi0iKirKfojmiBEjePzxx7njjjvYsGFDc9QpIuL6jhaaPTRr50Lhvrr25FFwzjRzbxov7cslcrYaHW6sVqt9VVRUVBTZ2dl069aNDh06sGvXriYvUETE5WVvMHtptnwItZVmm2+oOTF40A0Q1dW59Ym4mUaHm969e7Np0yaSk5MZMmQITz75JD4+Prz22mt06tSpOWoUEXE9NZWw7RMz1GStrWuP6QODb4Q+k8An0Hn1ibixRoebBx54gPLycgAeeeQRLrvsMkaOHElkZCT/+9//mrxAERGXcmQ/rJ1nrno6WmC2eXhDrytg0E2QOFgb7Yk0sybZ56awsJDw8HD7iqnWTPvciEiTs9kgbeGxCcLfYZ8gHJIAA6+HAVMhqJ1TSxRxdS2yzw3AwYMHAUhMTDyblxERcU1HC83dg9fONXtsjut8Pgy6EbqOA8+z+jYrImeg0f/qamtrmTNnDi+88AJlZWUABAUFMXPmTB566CG8vb2bvEgRkVYla73ZS7P1I8cJwv1/DwNvgKguzq1PpI1rdLiZOXMmH3/8MU8++SRDhw4FzOXhDz/8MAUFBbzyyitNXqSIiNPVVMK2j49NEF5X1x7bx5xL0+cqTRAWaSUaPecmNDSU9957j/Hjxzu0f/3111x77bUUFxef4pmtg+bciEijFKbD2jfM4aeKQrPN0wd6XWkOPSUM0gRhkRbQrHNufH196dixY7325ORkfHx8GvtyIiKtj80KexfCmn/Dnu+xTxAOTTInCPe/ThOERVqxRoebGTNm8OijjzJv3jx8fc2dNKuqqvjb3/7GjBkzmrxAEZEWc7TQXMK9Zi4UHahr73wBDL4Jul4EHp7Oq09EGqTR4WbDhg0sXLiQhIQEUlNTAdi0aRPV1dVccMEFTJw40X7vxx9/3HSViog0l8x1dROErVVmm1+o2UMzcDpEdnZufSLSKI0ON2FhYfz2t791aNNScBFxOTUVZphZ87p5PMJxcanmBOHevwWfAOfVJyJnrNHhZt68ec1Rh4hIyyjcd8IE4SNmm6cP9JpoDj21P0cThEVc3BnvLpWfn28/KLNbt260a6fJdSLSStms5sTgNa/D3h9wmCA8aLo5/BQY5dQSRaTpNDrclJeXM3PmTN566y1sNhsAnp6eTJkyhf/3//4fAQHqxhWRVqK8ADa8ZfbUFGXUtXcZaw49db1QE4RF3FCjw82sWbNYsmQJX3zxBcOHDwdg+fLl3HHHHdx1113axE9EnMswzE321rwOWz8+YYJwGPT/Awy6ASI6ObVEEWlejd7ELyoqig8//JDRo0c7tP/4449cffXV5OfnN2V9TU6b+Im4qdpqcwfhn1+BnI117XH9zLk0vX8L3v7Oqk5EzlKzbuJ39OhRYmJi6rVHR0dz9OjRxr6ciMjZqSiCdfNh1atQmmO2efqaYWbwjeYEYRFpUxodboYOHcpDDz3EW2+9hZ+fHwAVFRXMmTPHftaUiEizK8owe2nWvwXV5iG+BMXCkD/CgGkQGOnU8kTEeRodbp577jkuvvjiepv4+fn58d133zV5gSIiDrLWw8oXYdunYFjNtuieMGym2Vvj5evU8kTE+Ro95wbMoal33nmHnTt3AtCjRw9+//vf4+/f+sezNedGxAXZbLBnAfz0/+DA8rr2TqPNUNP5Au1NI+Lmmm3OTU1NDd27d+fLL7/kpptuOqsiRUR+VU0lbH4PVr4Eh3ebbR5e0PsqGHo7xPV1bn0i0io1Ktx4e3tTWVnZXLWIiJjKC2DtXFj9GpQfW4HpGwLnTIMht0Boe6eWJyKtW6Pn3Nx+++088cQTvP7663h5nfEGxyIi9RWkmb00G9+F2gqzLTQRzr3V3EXYT0PJIvLrGp1O1qxZw8KFC1mwYAF9+vQhMDDQ4bpOAheRRstYBT+9ADu/wn40QlwqDLsDev4GPL2dWp6IuJYmORVcRKTRbFbY+aU5SThzTV1713HmJOGOIzRJWETOiE4FF5GWVV0OG96Bn1+CI/vNNk8fSP0dDJ0B7bo5tTwRcX0NDjc2m42nnnqKzz//nOrqai644AIeeughl1j+LSKtQGmuOUF4zetQWWS2+YfDoBvNQyyD6+98LiJyJhocbv72t7/x8MMPM3bsWPz9/Xn++efJy8vjjTfeaM76RMTV5e0wN93b/D5Yq8228GRzKXe/yeATePrni4g0UoM38evatSt33303N998MwA//PADl156KRUVFXh4eDRrkU1Jm/iJtADDgPSl5nyavd/XtScOMYeeul8KHp7Oq09EXE6zbOKXkZHBJZdcYn88duxYLBYL2dnZJCQknHm1IuI+rDXmsQg/vQCHNh9rtECPy2DoTEga4szqRKSNaHC4qa2ttR+UeZy3tzc1NTVNXpSIuJjKElj/Jvz8KpRkmm1e/tD/D+YeNZGdnVufiLQpDQ43hmEwbdo0fH3rDqWrrKzklltucdjrRvvciLQhxZmw6lVY9yZUlZhtge1g8M0w6AYIiHBufSLSJjU43EydOrVe2x/+8IcmLUZEXETOJvjpRdj2MdhqzbaobjBsBvS5Grz9Tv98EZFm1OBwo/1tRNo4w4C9P5jzadKX1rV3HGluutflQnChxQUi4r50OJSInF5tFWz5wOypyd9htlk8odeVZk9NfH/n1ici8gsKNyJychVHYM2xk7nLcs02nyAYMBXOvQXCkpxbn4jIKSjciIij8gLzaIRVr0F1qdkWHG8GmgFTwT/MqeWJiPwahRsRMZXlmZvurZkLNeVmW3RPGH6nOQTl5ePU8kREGkrhRqStKz0EK16AtW9AbYXZFtsXRv0Zul2qScIi4nIUbkTaquIsWPGcuUeNtcpsa38OnPdnSBkHFotTyxMROVMKNyJtzZEDsPxZ2PhO3UGWiUPMnprOFyjUiIjLU7gRaSsK98Gyf8Km/9ZtvNdhhBlqks9TqBERt6FwI+LuDu+BZc/A5vfBsJptnUabw08dhzu1NBGR5qBwI+Ku8nbC0qfMIxIMm9nW5UKzpyZxsHNrExFpRk5dBrF06VImTJhAfHw8FouFTz/9tMHPXbFiBV5eXvTr16/Z6hNxSYe2wvtT4eVzYeuHZrBJGQ83LYI/fKhgIyJuz6k9N+Xl5aSmpjJ9+nQmTpzY4OcVFRUxZcoULrjgAnJzc5uxQhEXkr3R7KnZ+WVdW48JcN49EJfqtLJERFqaU8PN+PHjGT9+fKOfd8sttzB58mQ8PT0b1dsj4pYy18KSJ2HPd8caLOame+fdDTG9nFqaiIgzuNycm3nz5rFv3z7+85//8Nhjj/3q/VVVVVRVVdkfl5SUNGd5Ii0n42dY8gSkLTIfWzygzyQYeRe06+bc2kREnMilws2ePXu47777WLZsGV5eDSv98ccfZ86cOc1cmUgL2r/cDDXpS83HFk9I/Z0ZaiI7O7c2EZFWwGXCjdVqZfLkycyZM4eUlJQGP2/27NnMmjXL/rikpITExMTmKFGk+RgG7FtsDj9l/GS2eXhDv8kw4v8gItmp5YmItCYuE25KS0tZu3YtGzZsYMaMGQDYbDYMw8DLy4sFCxZw/vnn13uer68vvr6+LV2uSNMwDNj7g9lTk7nGbPP0gQFTzAMtwxTURUR+yWXCTUhICFu2bHFoe/nll1m0aBEffvghycn6zVXciGHArm9g6ZOQvcFs8/KDc66H4XdASLxz6xMRacWcGm7KysrYu3ev/XF6ejobN24kIiKCpKQkZs+eTVZWFm+99RYeHh707t3b4fnR0dH4+fnVaxdxWTabuZR76ZNw6FiY9w6AQTfA0JkQHOPc+kREXIBTw83atWsZM2aM/fHxuTFTp05l/vz55OTkkJGR4azyRFqOzQrbP4WlT0PedrPNJwgG3wRDZ0BglFPLExFxJRbDMAxnF9GSSkpKCA0Npbi4mJCQEGeXI22dtRa2fgTLnobDu8023xAYcguceysERDi3PhGRVqIxP79dZs6NiFux1pgHWS572jytG8AvDIbeDoP/CP5hzqxORMSlKdyItKTaatj0rnlKd9GxIVf/CBg2EwbdCH7qTRQROVsKNyItobYKNrwNy56FkkyzLbAdDLsDBk4H3yDn1ici4kYUbkSak7UWNr8HPz5eF2qCYmHEnTBgKvgEOLU8EWkYm81GdXW1s8twez4+Pnh4eJz16yjciDQHw4CdX8HCR+DwLrMtpL25m3D/68Dbz7n1iUiDVVdXk56ejs1mc3Ypbs/Dw4Pk5GR8fHzO6nUUbkSaWvoy+OFhyFprPvYPh5F3m3NqFGpEXIphGOTk5ODp6UliYmKT9CrIydlsNrKzs8nJySEpKQmLxXLGr6VwI9JUcjbBD3MgbaH52DvAXP00bCb4hTq3NhE5I7W1tRw9epT4+HgCAjSM3NzatWtHdnY2tbW1eHt7n/HrKNyInK2CNPjxb+Z+NQAeXuYxCefdox2FRVyc1WoFOOthEmmY4++z1WpVuBFxitJD5oGW698CWy1ggT6TYMxfdEq3iJs5myESabimep8VbkQaq6IIVjwPP78CtRVmW9eL4IIHIbaPU0sTERHQzCiRhqqpgOXPwfOpsPyfZrBJGAzTvobff6BgIyLNZvTo0dx5551n9RoWi4VPP/30rF5j/vz5hIWFndVrtAT13Ij8GmstbPwPLH4CSrPNtnY9zJ6abuNB3dUi4gJycnIIDw93dhktQuFG5FQMA7Z/BosehYK9Zltokjmnpu/V4OHp3PpERBohNjbW2SW0GA1LiZxM2o/w7zHwwVQz2AREwsX/gJlrod+1CjYiwujRo5k5cyZ33nkn4eHhxMTE8O9//5vy8nKuv/56goOD6dKlC998843D85YsWcLgwYPx9fUlLi6O++67j9raWvv18vJypkyZQlBQEHFxcTzzzDP1PndVVRV333037du3JzAwkCFDhrB48eLT1nvisNT+/fuxWCx8/PHHjBkzhoCAAFJTU1m5cqXDc+bPn09SUhIBAQFceeWVFBQU1Hvdzz77jAEDBuDn50enTp2YM2eO/et55JFHiI+Pd3jepZdeypgxY5p1U0SFG5ETZa2Ht34Db18B2RvAJwhG3Qd/2gTn3gpevs6uUERakTfffJOoqChWr17NzJkzufXWW5k0aRLDhg1j/fr1XHTRRVx33XUcPXoUgKysLC655BIGDRrEpk2beOWVV5g7dy6PPfaY/TXvuecelixZwmeffcaCBQtYvHgx69evd/i8M2bMYOXKlbz33nts3ryZSZMmcfHFF7Nnz55G1X///fdz9913s3HjRlJSUrj22mvtwWTVqlXccMMNzJgxg40bNzJmzBiHOgGWLVvGlClT+NOf/sT27dv517/+xfz58/nb3/5mf/2OHTty4403AvDSSy/x008/8eabbzbvhohGG1NcXGwARnFxsbNLkdYkf7dh/O86w3goxPyYE2kYX99rGKV5zq5MRJyooqLC2L59u1FRUVHv2qhRo4wRI0bYH9fW1hqBgYHGddddZ2/LyckxAGPlypWGYRjGX/7yF6Nbt26GzWaz3/PSSy8ZQUFBhtVqNUpLSw0fHx/j/ffft18vKCgw/P39jT/96U+GYRjGgQMHDE9PTyMrK8uhngsuuMCYPXv2Kb8WwPjkk08MwzCM9PR0AzBef/11+/Vt27YZgLFjxw7DMAzj2muvNS655BKH17jmmmuM0NBQh8/597//3eGet99+24iLi7M/TktLM4KDg417773X8Pf3N955551T1ni697sxP78150batuIsc6+aDf8BwwpYIPV3MHo2hHdwdnUi0sr17dvX/t+enp5ERkbSp0/dysmYGHMjz7y8PAB27NjB0KFDHfZzGT58OGVlZWRmZnLkyBGqq6sZMmSI/XpERATdunWzP96yZQtWq5WUlBSHWqqqqoiMjDzj+uPi4uy1du/enR07dnDllVc63D906FC+/fZb++NNmzaxYsUKe08NmBvwVVZWcvToUQICAujUqRNPP/00N998M9dccw2TJ09uVI1nQuFG2qajhbD8WVj9GtRWmm3dLoHz/woxPZ1bm4i4jF/uomuxWBzajoeYppxfUlZWhqenJ+vWrcPT03H+X1BQUKNe62xrLSsrY86cOUycOLHeNT+/urP0li5diqenJ/v376e2thYvr+aNHwo30rZUl8OqV2H581BVbLYlDYWxD0PSuU4tTUTcX48ePfjoo48wDMMeJlasWEFwcDAJCQlERETg7e3NqlWrSEpKAuDIkSPs3r2bUaNGAdC/f3+sVit5eXmMHDmyWWtdtWqVQ9vPP//s8HjAgAHs2rWLLl26nPJ1/ve///Hxxx+zePFirr76ah599FHmzJnTLDUfp3AjbYO1Bta/CUuehLJcsy2mN1zwEHS9UHvViEiLuO2223juueeYOXMmM2bMYNeuXTz00EPMmjULDw8PgoKCuOGGG7jnnnuIjIwkOjqa+++/32HybUpKCr///e+ZMmUKzzzzDP379yc/P5+FCxfSt29fLr300iap9Y477mD48OE8/fTT/OY3v+G7775zGJICePDBB7nssstISkriqquuwsPDg02bNrF161Yee+wxMjMzufXWW3niiScYMWIE8+bN47LLLmP8+PGce27z/UKp1VLi3mw22PIhvDgIvrrLDDZhHWDiv+HmZZBykYKNiLSY9u3b8/XXX7N69WpSU1O55ZZbuOGGG3jggQfs9zz11FOMHDmSCRMmMHbsWEaMGME555zj8Drz5s1jypQp3HXXXXTr1o0rrriCNWvW2Ht7msK5557Lv//9b55//nlSU1NZsGCBQ50A48aN48svv2TBggUMGjSIc889l2effZYOHTpgGAbTpk1j8ODBzJgxw37/rbfeyh/+8AfKysqarNZfshybQd1mlJSUEBoaSnFxMSEhIc4uR5qLYUDaQvhhDhzabLYFtoPz/gznTAMvnfArIr+usrKS9PR0kpOTHeaQSPM43fvdmJ/fGpYS93NwDSycA/uXmY99gmH4n8x9anwbN9lORERcj8KNuI+8neZRCTu/NB97+sDgP8KIWRDYuOWRIiLiuhRuxPUVHYTF/4BN74JhA4sHpE6G0fdBWKKzqxMRkRamcCOuq7wAlj0Da/4N1mqzrftl5l410d2dW5uIiDiNwo24nqoy+PllWPECVJeabR1Hmsu6Ewc5tzYREXE6hRtxHTYbbPqvOVn4+F41sX1h7EPQ+QIt6RYREUDhRlxFxir49l7zpG6A8I7m8FOvidCcJ8uKiIjLUbiR1q04E354GLZ8YD72CYZR98CQW8DL16mliYhI66RwI61T9VH46f+Zh1vWVgAW6P8Hs7cmOMbZ1YmISCumcCOti2HAto9hwYNQkmm2JQ2Fi/8B8f2cWpqIiLgGTVaQ1iN7A8wbDx9ON4NNSAJc9QZc/42CjYiIk7z00kt07NgRPz8/hgwZwurVq51d0q9SuBHnK8uDz2bAa2MgYyV4+cPov8CMNdD7t1oFJSLiJP/73/+YNWsWDz30EOvXryc1NZVx48aRl5fn7NJOS8NS4jy1VbDqVVjyVN1+NX0mwdiHITTBqaWJiDQXwzCoqLE65XP7e3tiacQvjP/85z+56aabuP766wF49dVX+eqrr3jjjTe47777mqvMs6ZwIy3PMGDXN/DdX+BIutkW3x8ufgKShji3NhGRZlZRY6Xng9855XNvf2QcAT4N+9FfXV3NunXrmD17tr3Nw8ODsWPHsnLlyuYqsUko3EjLyt0O382GfYvNx0ExZk9N399pvxoRkVbk8OHDWK1WYmIcV6jGxMSwc+dOJ1XVMAo30jKOFsKPf4e1b4BhNU/sHno7jLwLfIOdXZ2ISIvx9/Zk+yPjnPa52wKFG2le1hoz0Pz4d6gsMtu6XwYXPQYRyU4tTUTEGSwWS4OHhpwpKioKT09PcnNzHdpzc3OJjY11UlUNo3EAaT5pi+DVEfDNn81gE90Lpn4Bv3tHwUZEpJXz8fHhnHPOYeHChfY2m83GwoULGTp0qBMr+3WtPzqK6ylIg+/uh93fmI/9I+D8B2DAVPDUXzkREVcxa9Yspk6dysCBAxk8eDDPPfcc5eXl9tVTrZV+0kjTqSyBpU/Bz6+ArQY8vGDwH2HUn8E/3NnViYhII11zzTXk5+fz4IMPcujQIfr168e3335bb5Jxa6NwI2fPZoWN78DCR6A832zrMhbGPQ7tUpxbm4iInJUZM2YwY8YMZ5fRKAo3cnYO/ATf3AuHNpuPI7vCuL9DykXOrUtERNoshRs5M0UZ8P2DsO0T87FvKIy+FwbdBF4+zq1NRETaNIUbaZzqclj+HPz0AtRWgsXDnCh8/gMQGOXs6kRERBRupIEMA7Z8AN8/BKXZZlvHkXDx4xDbx7m1iYiInEDhRn5d1jr45j7IPHbMfViSuQlfj8t1YreIiLQ6CjdyaqWH4Ic5sOld87F3IJx3F5x7O3j7Obc2ERGRU3DqDsVLly5lwoQJxMfHY7FY+PTTT097//Llyxk+fDiRkZH4+/vTvXt3nn322ZYpti2pqYRlz8D/O6cu2KReCzPXmWdBKdiIiEgr5tSem/LyclJTU5k+fToTJ0781fsDAwOZMWMGffv2JTAwkOXLl3PzzTcTGBjIH//4xxao2M0ZBuz80txduOiA2ZYwCC5+AhLOcW5tIiIiDeTUcDN+/HjGjx/f4Pv79+9P//797Y87duzIxx9/zLJlyxRuztahrfDtfbB/mfk4OA4ufAR6XwUeOoJMRERch0vPudmwYQM//fQTjz322Cnvqaqqoqqqyv64pKSkJUpzHeUF8ONjsG4+GDbw8oNhd8CIO8En0NnViYiINJpL/kqekJCAr68vAwcO5Pbbb+fGG2885b2PP/44oaGh9o/ExMQWrLQVs9lg3Zvw/wbA2jfMYNPrSpixBs6/X8FGREQaPTe2tXDJcLNs2TLWrl3Lq6++ynPPPcd///vfU947e/ZsiouL7R8HDx5swUpbqbwdMG88fHEHVBZBTB+Y9jVMmm8u8xYREaFubuxLL73k7FIaxSWHpZKTkwHo06cPubm5PPzww1x77bUnvdfX1xdfX9+WLK/1qj4KS5+En/4f2GrNpd3n3w+DbwZPl/yrICLiegwDao4653N7BzRqf7LGzo1tLVz+J5rNZnOYUyOnsOcH+GpW3Sqo7pfB+CcgNMG5dYmItDU1R+Hv8c753H/JbhPTDpwabsrKyti7d6/9cXp6Ohs3biQiIoKkpCRmz55NVlYWb731FgAvvfQSSUlJdO/eHTDHAp9++mnuuOMOp9TvEkoPmaugjh9wGZIAlzwF3S9xbl0iIiLNxKnhZu3atYwZM8b+eNasWQBMnTqV+fPnk5OTQ0ZGhv26zWZj9uzZpKen4+XlRefOnXniiSe4+eabW7z2Vs9mNScKL3wEqkrA4gnn3gqjZ4NvkLOrExFpu7wDzB4UZ33uNsCp4Wb06NEYhnHK6/Pnz3d4PHPmTGbOnNnMVbmBnM3wxZ8ge735uP05cNlzENfXqWWJiAjmnJc2MDTkTC4/50ZOUFUGix+Hn18Bwwq+IXDBgzBwOnh4Ors6ERGRFqFw4y52fgVf/xlKMs3Hva6Ei/8BwbHOrUtERFzWr82Nba0UblxdcaYZanZ9ZT4O6wCX/hO6jnVuXSIi4vJ+bW5sa6Vw46qstbD6X7Dob1BTDh5e5rEJ590DPm1jwpiIiDSvX5sb21op3LiizHXw5Z/g0BbzceK5MOE5iO7h1LJERERaA4UbV1JZDAsfhTWvAwb4hZknd/e/Tid3i4iIHKNw4woMA7Z/Ct/cB2WHzLa+v4OLHoOgdk4tTUREpLVRuGntjuyHr+6Gvd+bjyM6w2XPQqdRTi1LRESktVK4aa2sNeYBl0uehNoK8PSBEbNgxP+Bt5+zqxMREWm1FG5ao4yf4Ys7IX+H+bjjSLO3JqqrU8sSERFxBQo3rcnRQvjhYVj/pvk4IBLG/R36XtOoI+pFRETaMoWb1sAwYPP78N1f4Ohhs63/deZKqIAI59YmIiLiYhRunO3wXvhqFqQvMR+3624OQXUY5ty6REREXJTCjbPUVsHyZ2HZM2CtBi8/GPVnGDoTvHycXZ2IiIjL0s5vzpC+FF4Zbp7gba2GzhfAbSth5F0KNiIi0io8/vjjDBo0iODgYKKjo7niiivYtWuXs8tqEIWbllR+GD65Bd6cAAV7ICgGrnoD/vARRHRydnUiIiJ2S5Ys4fbbb+fnn3/m+++/p6amhosuuojy8nJnl/arNCzVEmw22Pgf+P5BqDgCWGDQDXD+X8E/zNnViYhICzIMg4raCqd8bn8vfywNXH377bffOjyeP38+0dHRrFu3jvPOO685ymsyCjfNLW8nfPl/kPGT+Timj3nIZcJAp5YlIiLOUVFbwZB3hzjlc6+avIoA74Azem5xcTEAERGtfxWvwk1zqamApU/BihfAVgPeATDmLzDkVvDU2y4iIq7DZrNx5513Mnz4cHr37u3scn6Vfso2h70/wFd3medCAaSMh0uegrBEp5YlIiLO5+/lz6rJq5z2uc/E7bffztatW1m+fHkTV9Q8FG6aUukh+HY2bPvYfBwcD5c8Cd0v0w7DIiICgMViOeOhIWeYMWMGX375JUuXLiUhIcHZ5TSIwk1TyVgF70yCqmKweMCQW8xhKN9gZ1cmIiLSaIZhMHPmTD755BMWL15McnKys0tqMIWbphLTC/xCILITXPYcxPdzdkUiIiJn7Pbbb+fdd9/ls88+Izg4mEOHDgEQGhqKv/+ZDW+1FIWbpuIbBFO/gLAk8PB0djUiIiJn5ZVXXgFg9OjRDu3z5s1j2rRpLV9QIyjcNKUI1+myExEROR3DMJxdwhnTDsUiIiLiVhRuRERExK0o3IiIiIhbUbgRERERt6JwIyIi8itceXKtK2mq91nhRkRE5BQ8Pc2tPaqrq51cSdtw/H0+/r6fKS0FFxEROQUvLy8CAgLIz8/H29sbDw/1CTQXm81Gfn4+AQEBeHmdXTxRuBERETkFi8VCXFwc6enpHDhwwNnluD0PDw+SkpKwnOV5jAo3IiIip+Hj40PXrl01NNUCfHx8mqR3TOFGRETkV3h4eODn5+fsMqSBNHgoIiIibkXhRkRERNyKwo2IiIi4lTY35+b4BkElJSVOrkREREQa6vjP7YZs9Nfmwk1paSkAiYmJTq5EREREGqu0tJTQ0NDT3mMx2tie0jabjezsbIKDg896Hb27KCkpITExkYMHDxISEuLsctyW3ueWofe55ei9bhl6n02GYVBaWkp8fPyvLhdvcz03Hh4eJCQkOLuMVikkJKRN/8NpKXqfW4be55aj97pl6H3mV3tsjtOEYhEREXErCjciIiLiVhRuBF9fXx566CF8fX2dXYpb0/vcMvQ+txy91y1D73PjtbkJxSIiIuLe1HMjIiIibkXhRkRERNyKwo2IiIi4FYUbERERcSsKN25u6dKlTJgwgfj4eCwWC59++ulp71++fDnDhw8nMjISf39/unfvzrPPPtsyxbq4xr7XJ1qxYgVeXl7069ev2epzF419nxcvXozFYqn3cejQoZYp2EWdyd/nqqoq7r//fjp06ICvry8dO3bkjTfeaP5iXVhj3+dp06ad9O9zr169WqZgF6Fw4+bKy8tJTU3lpZdeatD9gYGBzJgxg6VLl7Jjxw4eeOABHnjgAV577bVmrtT1Nfa9Pq6oqIgpU6ZwwQUXNFNl7uVM3+ddu3aRk5Nj/4iOjm6mCt3DmbzPV199NQsXLmTu3Lns2rWL//73v3Tr1q0Zq3R9jX2fn3/+eYe/xwcPHiQiIoJJkyY1c6WuRUvB2xCLxcInn3zCFVdc0ajnTZw4kcDAQN5+++3mKcwNNea9/t3vfkfXrl3x9PTk008/ZePGjc1en7toyPu8ePFixowZw5EjRwgLC2ux2txJQ97nb7/9lt/97nfs27ePiIiIlivOjZzJ9+hPP/2UiRMnkp6eTocOHZqvOBejnhs5rQ0bNvDTTz8xatQoZ5filubNm8e+fft46KGHnF2K2+vXrx9xcXFceOGFrFixwtnluJ3PP/+cgQMH8uSTT9K+fXtSUlK4++67qaiocHZpbm3u3LmMHTtWweYX2tzBmdIwCQkJ5OfnU1tby8MPP8yNN97o7JLczp49e7jvvvtYtmwZXl76p9hc4uLiePXVVxk4cCBVVVW8/vrrjB49mlWrVjFgwABnl+c29u3bx/Lly/Hz8+OTTz7h8OHD3HbbbRQUFDBv3jxnl+eWsrOz+eabb3j33XedXUqro++oclLLli2jrKyMn3/+mfvuu48uXbpw7bXXOrsst2G1Wpk8eTJz5swhJSXF2eW4tW7dujnM+xg2bBhpaWk8++yzGmptQjabDYvFwjvvvGM/ufmf//wnV111FS+//DL+/v5OrtD9vPnmm4SFhTV6qkFboHAjJ5WcnAxAnz59yM3N5eGHH1a4aUKlpaWsXbuWDRs2MGPGDMD84WAYBl5eXixYsIDzzz/fyVW6r8GDB7N8+XJnl+FW4uLiaN++vT3YAPTo0QPDMMjMzKRr165OrM79GIbBG2+8wXXXXYePj4+zy2l1FG7kV9lsNqqqqpxdhlsJCQlhy5YtDm0vv/wyixYt4sMPP7SHS2keGzduJC4uztlluJXhw4fzwQcfUFZWRlBQEAC7d+/Gw8ODhIQEJ1fnfpYsWcLevXu54YYbnF1Kq6Rw4+bKysrYu3ev/XF6ejobN24kIiKCpKQkZs+eTVZWFm+99RYAL730EklJSXTv3h0w92B4+umnueOOO5xSvytpzHvt4eFB7969HZ4fHR2Nn59fvXZx1Ni/08899xzJycn06tWLyspKXn/9dRYtWsSCBQuc9SW4hMa+z5MnT+bRRx/l+uuvZ86cORw+fJh77rmH6dOna0jqNBr7Ph83d+5chgwZou8Xp2KIW/vxxx8NoN7H1KlTDcMwjKlTpxqjRo2y3//CCy8YvXr1MgICAoyQkBCjf//+xssvv2xYrVbnfAEupLHv9S899NBDRmpqaovU6soa+z4/8cQTRufOnQ0/Pz8jIiLCGD16tLFo0SLnFO9CzuTv844dO4yxY8ca/v7+RkJCgjFr1izj6NGjLV+8CzmT97moqMjw9/c3XnvttZYv2EVonxsRERFxK9rnRkRERNyKwo2IiIi4FYUbERERcSsKNyIiIuJWFG5ERETErSjciIiIiFtRuBERERG3onAjIiIiv2rp0qVMmDCB+Ph4LBYLn376aaOeX1lZybRp0+jTpw9eXl6/euDnihUr8PLyol+/fo2uVeFGRADzIL4//vGPREREYLFY2Lhxo7NLapWmTZuGxWI5o2/uv2bx4sX219ZJz9LalJeXk5qayksvvXRGz7darfj7+3PHHXcwduzY095bVFTElClTuOCCC87ocynciAgA3377LfPnz+fLL78kJydHZ9acxsUXX0xOTg7jx4+3t50q7EybNq3BQWXYsGHk5ORw9dVXN1GlIk1n/PjxPPbYY1x55ZUnvV5VVcXdd99N+/btCQwMZMiQISxevNh+PTAwkFdeeYWbbrqJ2NjY036uW265hcmTJzN06NAzqlXhRkQASEtLIy4ujmHDhhEbG4uXV/1zdaurq51QWevj6+tLbGwsvr6+Tfq6Pj4+xMbG6qBJcUkzZsxg5cqVvPfee2zevJlJkyZx8cUXs2fPnka9zrx589i3bx8PPfTQGdeicCMiTJs2jZkzZ5KRkYHFYqFjx44AjB49mhkzZnDnnXcSFRXFuHHjANi6dSvjx48nKCiImJgYrrvuOg4fPmx/vfLycqZMmUJQUBBxcXE888wzjB49mjvvvNN+z8l6OsLCwpg/f7798cGDB7n66qsJCwsjIiKC3/zmN+zfv9+h7iuuuIKnn36auLg4IiMjuf3226mpqbHfU1VVxb333ktiYiK+vr506dKFuXPnYhgGXbp04emnn3aoYePGjVgsFoeTmpvK/v377cNOJ36MHj26yT+XSEvKyMhg3rx5fPDBB4wcOZLOnTtz9913M2LECObNm9fg19mzZw/33Xcf//nPf076C1ZDKdyICM8//zyPPPIICQkJ5OTksGbNGvu1N998Ex8fH1asWMGrr75KUVER559/Pv3792ft2rV8++235ObmOgyl3HPPPSxZsoTPPvuMBQsWsHjxYtavX9+ommpqahg3bhzBwcEsW7aMFStWEBQUxMUXX+zQg/Tjjz+SlpbGjz/+yJtvvsn8+fMdAtKUKVP473//ywsvvMCOHTv417/+RVBQEBaLhenTp9f7xjtv3jzOO+88unTp0sh38dclJiaSk5Nj/9iwYQORkZGcd955Tf65RFrSli1bsFqtpKSkEBQUZP9YsmQJaWlpDXoNq9XK5MmTmTNnDikpKWdVz5nHIhFxG6GhoQQHB+Pp6VlvLLxr1648+eST9sePPfYY/fv35+9//7u97Y033iAxMZHdu3cTHx/P3Llz+c9//mOfDPjmm2+SkJDQqJr+97//YbPZeP3117FYLIAZPMLCwli8eDEXXXQRAOHh4bz44ot4enrSvXt3Lr30UhYuXMhNN93E7t27ef/99/n+++/tExg7depk/xzTpk3jwQcfZPXq1QwePJiamhrefffder05DXXttdfi6enp0FZVVcWll14K4PD+VlZWcsUVVzB06FAefvjhM/p8Iq1FWVkZnp6erFu3rt6/gaCgoAa9RmlpKWvXrmXDhg3MmDEDAJvNhmEYeHl5sWDBAs4///wGvZbCjYic1jnnnOPweNOmTfz4448n/YaVlpZGRUUF1dXVDBkyxN4eERFBt27dGvV5N23axN69ewkODnZor6ysdPhNsFevXg7fTOPi4tiyZQtgDjF5enoyatSok36O+Ph4Lr30Ut544w0GDx7MF198QVVVFZMmTWpUrcc9++yz9VaB3HvvvVit1nr3Tp8+ndLSUr7//ns8PNSJLq6tf//+WK1W8vLyGDly5Bm9RkhIiP3f7nEvv/wyixYt4sMPPyQ5ObnBr6VwIyKnFRgY6PC4rKyMCRMm8MQTT9S7Ny4ursFzVSwWC4ZhOLSdOFemrKyMc845h3feeafec9u1a2f/b29v73qva7PZABo0MffGG2/kuuuu49lnn2XevHlcc801BAQENOhr+KXY2Nh6w1nBwcEUFRU5tD322GN89913rF69ul54E2mtysrKHP59p6ens3HjRiIiIkhJSeH3v/89U6ZM4ZlnnqF///7k5+ezcOFC+vbta++93L59O9XV1RQWFlJaWmrfcqJfv354eHjUW6UZHR2Nn59fo1dvKtyISKMMGDCAjz76iI4dO550wl/nzp3x9vZm1apVJCUlAXDkyBF2797t0IPSrl07cnJy7I/37NnD0aNHHT7P//73P6KjowkJCTmjWvv06YPNZmPJkiWn3FfjkksusS9R/fbbb1m6dOkZfa6G+uijj3jkkUf45ptv6Ny5c7N+LpGmtHbtWsaMGWN/PGvWLACmTp3K/PnzmTdvHo899hh33XUXWVlZREVFce6553LZZZfZn3PJJZdw4MAB++P+/fsD1PtF52ypL1REGuX222+nsLCQa6+9ljVr1pCWlsZ3333H9ddfj9VqJSgoiBtuuIF77rmHRYsWsXXrVqZNm1Zv6OX888/nxRdfZMOGDaxdu5ZbbrnFoRfm97//PVFRUfzmN79h2bJlpKens3jxYu644w4yMzMbVGvHjh2ZOnUq06dP59NPP7W/xvvvv2+/x9PTk2nTpjF79my6du16xvtqNMTWrVuZMmUK9957L7169eLQoUMcOnSIwsLCZvucIk1l9OjRGIZR7+P4BH5vb2/mzJlDeno61dXVZGdn8/HHH9OnTx/7a+zfv/+kr3EqDz/88BltKKpwIyKNEh8fz4oVK7BarVx00UX06dOHO++8k7CwMHuAeeqppxg5ciQTJkxg7NixjBgxot7cnWeeeYbExERGjhzJ5MmTufvuux2GgwICAli6dClJSUlMnDiRHj16cMMNN1BZWdmonpxXXnmFq666ittuu43u3btz0003UV5e7nDPDTfcQHV1Nddff/1ZvDO/bu3atRw9epTHHnuMuLg4+8fEiROb9fOKtDUWo6n7gkRETmL06NH069eP5557ztml1LNs2TIuuOACDh48SExMzGnvnTZtGkVFRU1+9EJLfw4Rd6aeGxFps6qqqsjMzOThhx9m0qRJvxpsjvvyyy8JCgriyy+/bNJ6li1bRlBQ0EknUYtIw2lCsYi0Wf/973+54YYb6NevH2+99VaDnvPkk0/ywAMPAObqsKY0cOBA+/yChu4NIiL1aVhKRERE3IqGpURERMStKNyIiIiIW1G4EREREbeicCMiIiJuReFGRERE3IrCjYiIiLgVhRsRERFxKwo3IiIi4lb+P07f7VH4KPbcAAAAAElFTkSuQmCC",
       "text/plain": [
-       "
"
+       "
"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1864,7 +1801,7 @@
     "fig, ax = plt.subplots(1)\n",
     "n_eff = sim_data[\"mode\"].n_eff  # real part of the effective mode index\n",
     "n_eff.plot.line(x=\"f\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1877,7 +1814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 22,
    "id": "c169a724",
    "metadata": {
     "tags": []
@@ -1885,14 +1822,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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sxznn2p4nYM45AIJ0fbuYMLPyE7OSPkf0UIhzzi1JnoA554D6d8SqqWMlnk70gIhzzi1JbZWADQ4O2vr99597RTenkeHhZoewh57e3maH0DF+ffPNW8xs9Xy2qWUCFvej9XSi9mIbgfOAp0s6iqgK8j6iPvCcc25JaqsEbP3++3P99dc3/LiqwZOi1sSxxiq5+eaZh/Obfr6LiX0++zr66KoewmuYdv6993R33z/3WlOFNezl3szOqDD7CzU7gHPOtbm2SsDmo5Y3z1ZLnuphtutVi2s5fV/tcE1rGeNirmGjrpWPBemcc43TsQlYLVRz02yHRKIVtVMiNpdaJqjN5AmYc841jidgi5S8+bZ6MpGMdb5Jw0LOrZpjzBRTO13LTuEDbTvnXON4ArZIrZwo1CpJWOg5mrTgGGrZDq0eKsXT7knZUhlo2znnWoEnYM45wKsgnXOukTo2ASuVUNSrVKLVSmRKml3qNdt+FhNbO1RP1vIz14xz9ATMOecap2MTsJJaJACtesNParXEa7Z9LzbWVm/Av5jqyWaeU5jq+H8HzjnXMvw/7ixa9QZfC808t1q1n2r1RCxpti8CrRK/gtaIwznnloIllYBVUwLTKjfDaiwkaWnV8zNpUY3220mr/g7UonE551wnaloCJikLXAd0xXF83czOa1Y87aSTq1IXUk3ZDu3D2kGYCpodgnPOLRnNLAEbB55pZkOS0sBPJV1tZr9oYkzOLVleBemcc43TtATMzAwYit+m46nz659cXbVTu7BWE/g1c865hmlqGzBJIXAT8FjgAjP7ZYV1zgXOBdhvv/0aG2CLWQrto2rFqyXnL/AqSOeca5im/sc1s4KZHQWsA46T9MQK61xoZhvMbMPg6tV1j0lm5akVtHI806dW1erxtYogUFWTc865xWuJpyDNbIek/wFOAW6r13Ha6Sbc7FgX0y3ETJpdEtUq1ZML7Yai3vHLC8Ccc65hmvYvV9JqScvj193As4G7mhVPJY0uOWmF0qR6Hr/Z5zY9jlb53bbC7x2ibiiqmZxzzi1eM0vA1gJfituBBcDXzOy7TYzHuSXNu6FwzrnGaeZTkLcCT6rFvupeNdOg/TdTo0uDZrqWjb4WS+F3Wy3vhsI55xqnJdqA1VMr3wCbkWxMTzSadX0qPaXY7KrX6RaalNVi0PFaDWI+H94NhXPONU5HJWD17npgMTfpVkkEk3G0YkytpNrfd6vGP1/eDYVzzjVORyVgSY26Kbbrzbdd4262Tv5cdUoXE5LeXMVqw2b2H3UPxjnnZuBfeZ1zQG2fgpR0kaTNkm5LzFsp6YeSfh//XFGnU3kr0Af0zzK9pU7Hds65qnRsCZhzbn5q3A/YF4FPA19OzHs78GMz+4ikt8fv31bTo0a+Ymbvm20FSb11OK5zzlXNS8Ccc0DUDUU1UzXM7Dpg27TZpwFfil9/CfjTmgU/9dj/uNh1JJ0i6XeS7o6TxenL10v6H0m/lnSrpFMXE7NzbunxEjDnHMB8OlkdlHRj4v2FZnZhFdvtbWYPx683AXvPJ775krQBeBqwDzBKNMrGD81s+xzbhcAFRJ1DbwR+JelKM7sjsdq7ifou/IykQ4GrgANqfxbOuU7lCZhzDphXI/wtZrZhMccyM5NUlycNJP0l8EbgXuAm4HdAFjgReFvcLu2fzOyPM+ziOOBuM7sn3t/lRKV3yQTMgIH49TLgoVqfh3Ous3kC5pwDIFX/bigekbTWzB6WtBbYXKfj9ABPNbPRSgslHQUcDMyUgO0LPJB4vxE4fto65wPXSHoj0As8a4ZjnQucC7B+/frqonfOLQneBsw5B0AYqKppEa4EXhW/fhXw7UUHXdmlMyVfAGZ2i5n9eJHHOAP4opmtA04FviLt+RiDmV1oZhvMbMPq1asXeUjnXCfxEjAH7NnBaK36oZqro1rvj6x1LDK5mkLSZcDTidqLbQTOAz4CfE3Sq4H7gT+v2QGn+p2kLcD1wM+A683s/+ax/YPAfon36+J5Sa8GTgEws59LygKD1K9UzznXYTwBcxWTpHqNjVjpOJ6EtYZMDasgzeyMGRadXLODzHzsvSQ9DnhKPL1F0mrgF0TJ2P+bYxe/Ag6WdCBR4vUy4OXT1vkj0bl8UdIhRG3MHq3haTjnOpwnYEtcoxIt1/pSHdITPkBc4vV/RAnSY4iqCd8EPAeYNQEzs7ykNwA/AELgIjO7XdL7gBvN7Eqijlw/J+nviRrkn23m3yScc9XzBMw5B9S2CrKZJJVKvp5MVJV4D1Hp1yuAm6vZh5ldRdS1RHLeexKv7wCeWqOQnXNLkCdgzjmgcxIw4KdEida/AFeY2UiT43HOuT00LQGTtB/RMCV7ExXhX2hmn2xWPM4tdZlU2OwQamUfJtt/vVZSiigh+znw81L/Xs4510zNLAHLA28xs5sl9QM3SfrhtN6mXZ3JzNuBOaBzSsDMbBPwzXhCUg9wDvBe4ECidl3OOddUTUvA4iFJHo5f75Z0J1EHiJ6ALSH+BGTr6JQETNIyovZfpVKwJwG/B75D1DWFc841XUu0AZN0ANE/yV82OZQlyUvBHEAm7Jh+me8mrm4E3gf8araOWZ1zrhmanoBJ6gO+Afydme2qsLw8lMd+++03fXFLWWgSsxRLgZbiObe6TikBMzPvct451/Ka+pVXUpoo+brEzL5ZaZ3kUB6DLTiUh0nlqRb7mD41iswalhQ1Ovma7frWYuoUqUBVTa1O0uckPXGGZb2SzpF0ZqPjcs65pGY+BSngC8CdZvaJZsXhnIt0SgkYcAHwHkmHA7cR9VCfJRqAewC4CLikeeE551xzqyCfCrwS+K2kW+J574w7QGx5jRyqp6QRJUf1bg9W73NoRolU6ZjtXq1ay6GImsnMbgH+PG7esAFYC4wSfdn7XTNjc865kmY+BflToK2+cje7uqlRN/p6JWH1irvZv5eSdk/EOqgErOTV0/sWlPQm72/QOdcKOuMrb521WlufRsRTyySiXu3LWu33UtKu7cPCQFVNbeRVFead3eggnHOukqY/BekWrt7Vk6V9ttrTne2U2LRTqVinVEFKOgN4OXCQpCsTi/qBbc2JyjnnpvIEzDkHdFQV5M+IOnkeBD6emL8buLUpETnn3DSegM2inUpa6mm+JWHtUNrj9tQpCZiZ3S9pIzBmZv/b7Hicc64ST8Cm8aRrZrMlYp50za7RT7MuRAf1hI+ZFSQVJS0zs53Njsc556bzBKxDNLKtUTMSiE5KjFu1XVjQQdc4NkTUzc0PgeHSTDP72+aF5JxzEU/AYp10g3edqd6f0bDz/gS+GU/OOddyPAFzrklarSQs6JA2YCVm9qVmx+CcczPxBMw5B0A66Jw2YACSfgtUym4FmJkd0eCQnHOubEknYF7t6NykDqyCvDr++ZX4Z2kA7s80IRbnnJuiIxKwVqvKaaZ2eNpuPpZCktwqv7NaVkFKuo+o360CkDezDTXbefWebWZPSrx/u6SbzeztTYjFOeem6IgEbL6Wwk3duflK174N2DPMbEutdzoPkvRUM7s+fvMUfPg151yLWJIJmGt9SzVJrlSa26hr0YHdULwauEjSMqJ2X9uBc5obknOdJZfLsXHjRsbGxpodSlNls1nWrVtHOp2uehtPwDqYV822r2YkoPPoCX9Q0o2J9xea2YXT1jHgGkkG/EeF5XVnZjcBR8YJGN4hq3O1t3HjRvr7+znggANQ532Jq4qZsXXrVjZu3MiBBx5Y9XaegDnngHmVgG2pok3XiWb2oKS9gB9KusvMrltchPMj6U3AfwK7gM9JOhp4u5ld08g4nOtkY2NjSzr5ApDEqlWrePTRR+e13ZJqD2HSkq3aWojS9ZrvVIvjucZLB6pqqoaZPRj/3AxcARxXx9Bnco6Z7QKeA6wCXgl8pAlxONfRlnLyVbKQa9DUBEzSRZI2S7qtXsfwm3rlRGqm+bVOpBqRuLnaCANVNc1FUq+k/tJrogSobn/js4US/zwV+LKZ3Z6Y55xrAwcccABbtlT/LM9f/dVfcccdd8zrGH19ffMNqyaaXQX5ReDTwJdrsTO/iVfPr5WbroYPQe4NXBF/I0wBl5rZ92u29+rdJOka4EDgHXFSWGxCHM65Bvn85z/f7BCq1tQSsLhNyLZmxuCci6SDoKppLmZ2j5kdGU+HmdkHGxB+Ja8G3g4ca2YjQAb4yybF4tyScN999/GEJzyBs88+m8c97nGceeaZ/OhHP+KpT30qBx98MDfccAMA27Zt40//9E854ogjOOGEE7j11lsB2Lp1K895znM47LDD+Ku/+iss8RDZxRdfzHHHHcdRRx3Fa1/7WgqFwh7Hf/rTn86NN0bPCPX19fGud72LI488khNOOIFHHnkEgHvvvZcnP/nJHH744bz73e+esv3HPvYxjj32WI444gjOO+88AK644gpOPvlkzIyHH36Yxz3ucWzatGnR16rl24BJOlfSjZJu3DLPBm7OueqFQXVTq5O0BsDMimZ2s5ntiN9vNbNbk+s452rv7rvv5i1veQt33XUXd911F5deeik//elP+ed//mc+9KEPAXDeeefxpCc9iVtvvZUPfehDnHXWWQC8973v5cQTT+T222/n9NNP549//CMAd955J1/96le5/vrrueWWWwjDkEsuuWTWOIaHhznhhBP4zW9+w0knncTnPvc5AN70pjfx13/91/z2t79l7dq15fWvueYafv/733PDDTdwyy23cNNNN3Hddddx+umns3btWi644AJe85rX8N73vpc1axb/L6Tl/52a2YVmtsHMNgyuXt3scJzrWIFU1dQGrlrsOpJOkfQ7SXdLqthzvqQ/l3SHpNslXbqgSJ3rQAceeCCHH344QRBw2GGHcfLJJyOJww8/nPvuuw+An/70p7zyla8E4JnPfCZbt25l165dXHfddbziFa8A4PnPfz4rVqwA4Mc//jE33XQTxx57LEcddRQ//vGPueeee2aNI5PJ8IIXvACAY445pnzs66+/njPOOAOgHANECdg111zDk570JI4++mjuuusufv/73wPwqU99ig9/+MN0dXWVt12sZrcBc861iA4ajPtISbtmWS6irikqL5RC4ALg2cBG4FeSrjSzOxLrHAy8A3iqmW2Pu9twzgFdXV3l10EQlN8HQUA+n1/QPs2MV73qVXz4wx+uept0Ol1+OjEMwynHrvTUopnxjne8g9e+9rV7LNu4cSNBEPDII49QLBYJavD/smP+4zrnFqdTqiDNLDSzgVmmfjPbd5ZdHAfcHbdlmwAuB06bts5rgAvMbHt8zM31ORvnOtPTnva0chXitddey+DgIAMDA5x00klcemlUoHz11Vezfft2AE4++WS+/vWvs3lz9Ke2bds27r///gUd+6lPfSqXX345wJRqzOc+97lcdNFFDA0NAfDggw+yefNm8vk855xzDpdddhmHHHIIn/jEJxZ20tM0tQRM0mXA04l61t4InGdmX2hmTG5pmW2UgKX2pGibVC82wr7AA4n3G4Hjp63zOABJ1wMhcH6lJz0lnQucC7B+/fq6BOtcOzr//PM555xzOOKII+jp6eFLX/oSELUNO+OMMzjssMN4ylOeUv67OfTQQ/nABz7Ac57zHIrFIul0mgsuuID9999/3sf+5Cc/yctf/nI++tGPctppk9+tnvOc53DnnXfy5Cc/GYga8V988cV89rOf5WlPexonnngiRx55JMceeyzPf/7zOeSQQxZ1DWRtNEzN0cccY9dff32zw1iQWgwHVMuE4Oabb67Zvmrl6KOPrst+6zEUU6snZz3d3TdV0Vt9maSbbn1oR1W/gCP2WX6zmR2z8Oham6Q/A04xs7+K378SON7M3pBY57tADvhzYB1wHXB4qcF/JRs2bLDS01nOdYo777xz0YlIp6h0LSTN+L/Y24A55wAIWzypbKAHgf0S79fF85I2Ar80sxxwr6T/Aw4GftWYEJ1z7a4NWnS0H5ntMdVjv25P9br2sx2nUwSqbmoXkh4jqSt+/XRJfytpeRWb/go4WNKBkjLAy4Arp63zLaLmE0gaJKqSnP2RLOecS/AEbJEadcNvtWO3kqV+/rUiVTe1kW8ABUmPBS4kKtWas7sIM8sDbwB+ANwJfM3Mbpf0Pkkvilf7AbBV0h3A/wBvNbOt9TgJ51xn8irIBWrVm30yrlZvp7RYrfI7KMXR7te73asgJZ1tZl9MzCqaWV7S6cCnzOxTkn5dzb7M7Cqm9RVmZu9JvDbgzfHknHPz5iVgHazWpUKVStyqmWrNS7vqo52rICU9BviUpL0Ts3OSzgBeBXw3npdueHDOOVeBl4DNQ7ve9CvFPVtJWa3Pc/r+FlJS1A7Xvt1Lwtopakl/D/w9sIzoi+Q24Dzg9rjd1jDwJeDJwAfN7F5JBwJfaVLIzjk3hZeAOecACANVNbWI1xINsr0s7lh1fzP7hJkNmtkAcCRwCvA24GYAM7vXzD7axJidcy3i+9//Po9//ON57GMfy0c+8pGmxOAJmGt4Q/75VFd6dWPjBFVOLeK9ZvbITAvjnul/CNwCfB9A0lGSpj/N6JxbYgqFAq9//eu5+uqrueOOO7jsssu444475t6wxlro/2nr8iSgcTrhqc52jV9SVVMrMLPLqljtmUTDCu2It7kFOKh+UTnn2sENN9zAYx/7WA466CAymQwve9nL+Pa3v93wOLwNmHMOaN0G9rOR9AfgY2b22cS875rZC4Ccme2cljQWGx2jc66y937ndu54aFdN93noPgOc98LDZl3nwQcfZL/9JvtaXrduHb/85S9rGkc1vATMOQdAqOqmFpMDniHpP+PG9xCN5QhRg/yXA6GkgyV9CvhZU6J0zrlpvATMOQfQMtWL8zRiZn8h6R+Bn0h6KVCq+30j8C5gHLiMqPPU9zcnTOfcdHOVVNXLvvvuywMPPFB+v3HjRvbdd99ZtqgPT8Ccc0B7VkES955hZv9P0s3ANcDKeN4IUQL2ruaF55xrNcceeyy///3vuffee9l33325/PLLufTSOQfJqDlPwGbRbo2onVuMFqxerEayd/ofSXou8IPZnnY0sxfNtMw51/lSqRSf/vSnee5zn0uhUOCcc87hsMMaXxrnCZhzDmivKkhJrzCzi83sO5KeambXA5jZ/ZK+D1wBvBhYA1wcb3YGMGPXFc65pePUU0/l1FNPbWoMTW2EL+kUSb+TdLektzczFrc0mdS2PdfXWi2HImrA33ZyDMZPTVt2kpn9L/BUM/sLM/tOPL0ceFodYnHOuXlrWgImKQQuAJ4HHAqcIenQZsXTKKUbvt/0W0u9fi/t1B+Yqpzm3E9j/rY1w+vk+15J5X6/4qGIemsch3POLcicVZCS3ghcbGbba3zs44C7zeye+DiXA6cBje+Otknme7Mv3ceLpTEHE8tyhehdvjg5dzQfvd41UQDgkaEJHt49DsD1t2zirk272bx5mF1bhtm96T4ARrZsnO9pzFvP4Dr61xwAwMBgL3vt1csT1vTzh+6trO3vAmDvvqhHgYFMCEB3avJapeJimHTcaCl5FYP4mnp+O381HGaoEX/bNsPr5Pu/B66VdA/Rx2R/oiGMnHOu6appA7Y38Kv4CaOLgB+Y1eQr/b7AA4n3G4Hjp68k6VzgXGBKx2nOudqaR0ndoKQbE+8vNLMLE++r+ttepCdIupUosXpM/Jr4/UEAZvZ9SQcDT4iX3WVm4zWOwznnFmTOBMzM3i3pn4DnAH8JfFrS14AvmNkf6h1g/I/9QoCjjzmmPepymqBS4UWpFCiIy4gCTZYapVMB2VRAmApIZUJSXT0AhJkeChMjdYszzPSQ6uohFZdshXEc6VRAOlT5PEoxVyrJaqfuEtqqqtmq7iR+i5ltqGcoVThkrhUknTVt1pGSMLMv1ykm55yrWlVPQZqZSdoEbALywArg65J+aGb/uMBjPwgki7TWxfPcIiSfZAvimphUlOvQkw7py0S/8v5smhW9XeweyFPIFynk10TbhGlGtj3ExFCta5wh07eCnpX7kF2+ip6BLAADA12s6O2iP5umL5OiJx1OibnUSLGdntBrVyrma7Wruv9tm9n9Vax2bOJ1FjgZuBnwBMw513TVtAF7E3AWsAX4PPBWM8tJCoDfAwtNwH4FHBw3jH0QeBnw8gXua0kptXMqJqqMSglKqMl5qbi0K1OMfvZnUuVtVmbT5Fd1kw7Fw10h23rTAIwszzK2ejVjQ6PlJCw3OkRudBf58VEoFqoIMCTV1U26e4B0dx+ZvhUAZPu6yfZm6OnPsHIgauu1dlk3a5ZnWZlNs7I7RX+cIGbiYq7SOST7qCqda2lWUIPkrF0aytdV7a5B3f+2Jb3fzP5pjtV2JNeRtBy4vJZxOOfa0znnnMN3v/td9tprL2677bamxFBNCdhK4MXTv3GaWVHSCxZ6YDPLS3oD0fAgIXCRmd2+0P21i+SNfr7VU6XVS7uYmniUZ5bnpEtLUqVypCJhEM1d09dFTyZkZXeGdSt72DEyAcDOkRxD43l2j+UZGotKxfK5PLmJIlYoUsgXKcYN/c0MsyguSQTxscNUgMKAdCYglU7Rly2VuqXo60qxrCfN8p64kX1XKpoyKVZ2p+mKM61sHHO6tM/EeZWSsUqJlxeULUL1VZCz76Yxf9t/JumnwKNEA2w/ZGab46RvBbAX0VOYySRtGDiwxnE459rQ2WefzRve8AbOOmt6S4XGqaYN2HmzLLtzMQc3s6uAqxazD+dcbdSwCrIRf9tvJnqicTlRkneQpOuAJwP3EyVbxUSP+AFRlxj/VceYnHNt4qSTTuK+++5ragzeE/4sSiVU9aqeKu13oSVhSUG5Qm4y1mBa9V0mDMnFpVerelIMZAMKxQy5opW7r5goFMkXjImikS9GJSL5eHnRompPK1a+HgpKjf2jxvSpQPEUlWZlApEKRSYMJruSCEQYQDoIWJ4NJ0u84phLJV+17GrCqxtnUKMSsEYws6uBq0vvJfUSJV77mNlEPO9PEpvkgfvNrP79rDjnqnP122HTb2u7zzWHw/M+Utt91oknYC2gFgnBZHuoPZeVnnzEilFZAbA8Hd9sS8cuhxDEN2KhSt1uVnOT1tT+fS25n9IyTS6NMqkiy9OJQDRbH8HTY3Y10UYJ2HRmNizp1FLyFTvVzN6WXE/SR6fPc865ZvAErJ1VuGEqOS9uMK9CfE/KT6BCDoBg92aUn0C5UYrjYzAxFm0yNopNjGK5CchFXSZZPkdhIg/FIsV8gWIhboifTBwlgjDK7oJUCEFAmEmhVBrSUYN7pTMo002Q7YZM9BRk0JXF0t1YKkO4ezMWxi3XUlEbMQujnwTh5GlPT85mTdZc1Yrtm4ABmNkN02Y9G5iebD2vwjznXDO0SUlVvXgC1o7iJGtKslV6nXhKsZR4aWI0+pkbIYhfs+MRCju3UNy1ndzOnYzvHAJgfOcwud0jjO8aZzzuNT8/mic3kqcwUaAwXihXTRYS+VcoylWNYVdImAlJ96RIdafoinu37xroIt3fQ9eyXrqW9QGQXraMYGAFwbJBwuGtFDPd0emko37JyETHKidigErJWJx4TSkI82RswWrZBqxRJD0O+Aywt5k9UdIRwAeIGtsflOigFaAfuL4JYTrn3B48AatCvduCVR/ILCUUcWzJm6jycQKWizpWDcZ2U9y5FYDClofIbdnM8ENbGH10O0MPRwnY8KMj7N4xxtaJAjty0fF25QrkFnDuaYmBdMjydJQUrcqE9C/P0ru6h761UQLWvXoFvfsMk85NUNz+CMGyVUD0WFsU9NRkCxIlYJXagJWuURMTsbbqfDWp2Z/vhfkc8FbgPwDM7FZJjwVOBT4MJAcC321m2xofonOu1Zxxxhlce+21bNmyhXXr1vHe976XV7/61Q2NwRMw51ykPduA9ZjZDdM66s2b2X2SXj99ZUkrPQlzzl122WXNDsETsHZUrnqsVAVZqQ1YPmr3VRzeRSEuASvu2sbIpq3s3vgoux7Yxc64BOzB0TwPj+UXVOI1Xc6MrRN5tsZN0P44ItaO5dl35xgTw9HMgfE8CkR/JhXFloragCkVtRGzdKm9WaVz3fMJyT3ah7mqtWMVJLBF0mOIa6Il/RmwJl52Uzx/eod5BzU0Quecq8ATsE6TSFRKN9TSz+LEGDY2DEB+ZIyJ3SOM7RhndOsom8aiRKdWyVclOTMeHssTSmS2Rm3RMv1dZFeMkB+JYrP4YYBgWuzWnqUz7aU9r/HricaKfYKkB4F7iYcgMjPvdNU517I8AZuHZNueprcHm0aztg8rNdAvJmYZli9ETzXmjUJ8PvVKvkpyFh2rmI+PE8dR7lusWKEkb5rSuRrhjOs0Q9u2/SppwwTMzO4BnhX3AxYAI8DbJV09yzY3Nyo+55ybiSdgHaJU9VapOs6C6NesVLrcvUOQSZPuzZLpzZDpz7BsIioBW5Evsj1XxXiPC7QiHbIsHZLpj+JIdadI92YJMlFsiqsgSzFXqlJstWrGtk+8YrMm8S1G0gBR6de+wLeBH8Xv3wL0Ac+aYVMDntmIGJ1zbjaegC1QM5+MLCdbyRtmKSkJEr/S0hOEpXZV3b0Evf3RrIF+ulYM0zc2jhWs3Gt+99ZRNo3l2TJRYKRQmxtyTxjFNpgJWZNNsWxVN31roqcg+9YO0LWin9RAP0FvP+runRJz+RyS5zW9o9cWS8jaVqF+iXcdfAXYDvwceA3wLqLvH6eb2S1NjMs556riCZhzLtJGJWDAQWZ2OICkzwMPA+vNbKy0gqQs8DfAiUQlXz8BPptcZyaSTgE+STR2xOfNrGKPkZJeAnwdONbMblzcKTnnlhJPwBapoe3CSiU9Ffu62nOelXqTL0RPFKpnebnVVLh8L7oVkuruIjPQR+/eOwEY2DbC6h1jjO0YZ2J39KTirlyB0YIxki8yXjTG4/ZaxeS4k4iueBykrkD0pAK6w6gfsFJ1Y3Z5F9nlWbpX9pBdtQyA7sFlpFcNEi5bRbhiL+hZHseenXIOU861dM0rlXw1qDSsU6odk9qpChLIlV6YWUHSxgqJ1ZeB3cCn4vcvJyo5e+lsO5YUAhcQ9aS/EfiVpCvN7I5p6/UDbwJ+uZgTcc411gMPPMBZZ53FI488giTOPfdc3vSmNzU8Dk/Aaqhh1ZIVeoAvD8VdGsonMa/YFb9XUG5jFQ7uS9C3jHDFajL77GZgeDcAhdERcsOjFMYmyI9GCVh+bJzieI5CLj+lwXwxMSh3EKg8GLdSIWE6RdCVJpXtItUdJVFhNkO6t5uwuwfFVaFBTz9BTz/K9mEDqycTr7hHfMLSkEST51U+/3KHrI2rguzExKusvbqhOFLSrvi1gO74vQAzswHgiWZ2aGKb/5F0x/QdVXAccHfcwB9JlwOnAdO3fT/wUaKOYJ1zbSKVSvHxj3+co48+mt27d3PMMcfw7Gc/m0MPPXTujWsZR0OPtkTMdpOuaXJWqXf4Su3C4uTFUtlyKUdhYC/UuxKtKBBaodxnWLpYIFssgBmy+IZsBsUCFv+ccczAoNQOLURS1H5LwhR/zOJ5FoSJHu6j98UwRbF/78nzmD7c0JQSsPomXB2dZM2m2D5twMysmkdgb5Z0gpn9AkDS8UA11YT7Ag8k3m8Ejk+uIOloYD8z+56kGRMwSecC5wKsX7++ikM75+pt7dq1rF27FoD+/n4OOeQQHnzwwaWRgEl6KXA+cAhw3FJqO1Hvm/uUrhkUvS7GSZ8FUIhLrUbCPooBGEbRJucbUDDDmByZxixR3ZjIHxMFYATJ04pfBwgpUWMIhFL0M5icGSICwUjYU56v8n73vF5LNT+qN2vzwbgrOAb4maQ/xu/XA7+T9FuiUrIjFrJTSQHwCeDsudY1swuJ+iljw4YNrdV3jXNN9tEbPspd2+6q6T6fsPIJvO24t1W9/n333cevf/1rjj/++LlXrrFmlYDdBryYePw2tzA2JRmaTKBKSknVRDxqdq4IE/GTjdvH8owXjKGJAiO5AsPjUWnXWL7IUK7A6Hie8Xy07vB4gVyxyHiuQLFo5f2VjltKkjLxk5RBILrSIekgoLcrpCsVlVh1d6XoS4dkUwG9XdFHrycd0pcJ6QrF9rECmfiJyXj4yPI+w0SGp/jwpeN6QlYj8dihHeSUBW73ILBf4v26eF5JP/BE4Np4CKQ1wJWSXrSUvkw61+6GhoZ4yUtewr/+678yMDDQ8OM3JQEzszsB5HdO51qGtVc3FHMys/slrSBKplKJ+XN1xPor4GBJBxIlXi8jasBf2n4nMFh6L+la4B88+XJufuZTUlVruVyOl7zkJZx55pm8+MUvbkoMLd8GLNmGYr/99ptj7aWhVPJVTBSBlV4VEvWCpZKqsfjnaL7IaC4q1XpkOMf2kQm2jObYvHOMrUPjAGwdmmBsJMf4yAQTY1Gp2MRYnsL4KIWJEQq5cYrx2JLJKisFAUGpgX+6izDTQ9jVTSabIpONPmZdPRmyPWlW9WVY1Rc9GbDXsiyD3WlW9GTYOV6gOx3F2p2a2s4rk3hdKg0rnX+Q6H7Wc/pF6LAqSEnvJ6om/AOTfyJzdsRqZnlJbwB+QNQNxUVmdruk9wE3mtmV9YvaOVdvZsarX/1qDjnkEN785jc3LY66JWCSfsTkoLhJ7zKzb1e7n2QbiqOPOWZJt6GY3n4/+baUdyVqB4lzLcbjYX+GxotsGYmqmR4dHmfjjjEe2DrM5h2jDO2InuAf2TXO6PbNjO/eyviurTWJu2tgVfSzfxXdK/Zi50AXm5dHTztuHepmv1W95M14ZGiCwZ64p/44qQpVqoKc3N9kFeSexypdI0/EFqCNGuFX6c+Bx5jZvOtWzewq4Kpp894zw7pPX1B0zrmmuP766/nKV77C4YcfzlFHHQXAhz70IU499dSGxlG3BMzMZhoKxC1SscKTlBbPSzaML4/vWGp4nyswNBGVau0Yy7N51xibd4yyc8soQ1t3ROtseYCxnY/WNN5SIje+aysTQ9vJDe5HIb+8vLwrHdKXTTE0kacnHT040B03AiuUz2syo7JpWVbyelRqtO+qY/nc3Cu1l9uA5cDmJsfhnGshJ5544uR9pIlavgpyqav2M1JKvJIfKiuXikUvxgvFcsP60fE8u8fyTIzmmRjLMb47SpJqnXxNN7bzUcKuHjK90ZBDE6Npdo/ly43+x+OHBEoxW4XzKiVjs/VDkLxunpNVxxpUAibpfKLhg0oftnfGJU619mHg15JuA8ZLM83sRXU4lnPOzUuzuqE4nah36tXA9yTdYmbPbUYszrlYY6sg/8XM/rnOx/gSUUepv6U8VIRzzrWGZj0FeQVwRTOO3W6SpTezlYZVag812zrBtA2U6ES1rjfiIJw8VnJ2NSfg6spyHVcFOWJm/9bsIJxzrhKvguwwya49FLdWLzVkTwdBuU+unkzIsp40O3vSZMcy5OKG8oWJcUa3PVS3+LqX703XwCqyvVFj+66eNMt60vRkov7CuuLW9uG0Pr6S51VNrubVjgtQfeI9KCnZ5cKF8cMy8/EGSWcR9Uz/FjPbPs/tq/ETSR8GrmRqFeRc3VA45+bBzJZ8t1ILaVPmCVgbCio0Pi99+ANNzksHUztI7e8KGS9EXUWsyKZZuyxLvhD94aTixu9dPb1kl69mbMejTAztAKAwMbLgWMNMD5m+5QBkl6+me2A5PQNd9A5E3VCsXdnD2mVZVmTTLMum6e8Kp8RcOodk0lU619l6y3cLUH0CtsXMNsy2wmxPQQOfIRpH0eKfHwfOqT7Qqj0p/nlCYt6c3VA456qXzWbZunUrq1atWrJJmJmxdetWstnsvLbr2ARstjEX23Wsv1LYlU6tlKCEiVNLTUvAetMhQW/0enVfF2EY0JdNsVd/F1vifsC2DU8wPNLHxOgaxuN+wHLjefITBQq5HIXcGJbPx3EUsGIRBQFSiFLRxylMZwnTaVKZkHRXiq64H7BMd4rengwrezMMxv2ArerPsLI7w8ruNHv1Zsr9f5ViLp1D8rxmKwFr019tS6jlUETVPgUt6XPAd2t24KkxPKMe+3XOTVq3bh0bN27k0Ufr+wBXq8tms6xbt25e23RsAtbJSklGsgPSUmlYOCU7iTO1VGl4H6MrFSdgPWmWZVOs6cswsrzIWD4q/RidKDCWLzJeLDI6Ec0byxXJF4pM5IvkClY+ViGRCYZSuSQqHYpMKiAVBmTTAd2ZuHQtCMimovfZVDSvJx3QnQ7JBGJ5NiyXeJUSr9KQRFOGIop/+lBENdagoYgkrTWzh+O3pxN1F1GvYz0fOAwofzU1s/fV63jOLTXpdJoDDzyw2WG0JU/AnHNAQwfj/n+SjiL6hnAf8Np6HETSZ4Ee4BnA54E/A26ox7Gcc26+OioBm63asdJ67VoVWZIMP6xwLqV5cQEURbNy7/kru0PMoGgpjER/W1j0vL5N7dTVEttWFRvJdmmTMwNAaLJxfbxcghXZcMZ2XW3+q5rVXJ/bhn1OG9QNhZm9siEHgqeY2RGSbjWz90r6OHB1g47tnHOz6qgEbL6SN75mJGPVJoxVsT1LL4LSvPjGmirmoRi13+oa34kKeSjm4p/ROirmoJDHCvlyWy9Ky82izjpnKimJu5dQEEYZUxBCmCq3DVOYgjCFBeloGWBhCoI0FqboGt8JQbSuxT/L62nPriuoNG+empmE1/T3XwOWa0wVZAONxj9HJO0DbAXWNjEe55wrW9IJWFK9S8XqnWxpWrIFoDjZIheN8xjkRlE+eh3u3kxxaCfF4d0Uh3dSHNkNQH54jNzwKLnhMfIj0f0rP5YjP5ajmC9SzBUp5qJjJPOwIIAgfpIySAcEqYBUNh1NPd0ApHuzpHu7SfVmCXr6o3V7lxH09hP2LSMcehRLRU11iuloG9Jx050g8VEtJWWVrs08k7J2eFijYSW2HTYYN/BdScuBjwE3E31kPtfUiJxzLuYJ2DS1KBWrW8nGtMRLyfflEqz85LxS4jUxHC0bH4bhqLulwpaHKGzdxNiWbYxt2cnolp0ADG8ZZWzHGGPbxsrjRu7MFRnKFxktGOPFYnlsyWQ0AZNdRnQFAd2h6EsFLEsH9GWij1l2ZZbs8iy9g910Dy6L5g0uIzu4knDVGti5H+pdEe0vPrfSMZSefLy3dHVLuVbF0rEaqFfi02olX2UdNhi3mb0/fvkNSd8Fsma2s5kxOedciSdgs5jvDbhRN9Zy4pVMwMolYJMJWCkZU5yIMbqLwvboUeHCts2MPPwoQxs3s+vBXQxtGgJg6/YxNo3leXQ8P6+xW4rAeJyYjRcL7MrDI3HXl6X0aPXOMdZkU6xakaVvTVS6NrDvCMVcgR4FFLY/ShiXbqn0M9UVn14isZzMvKK3iThs+jo1UOn32mqfiVoodt5g3GVmNk6iM1bnnGs2T8CccwBYoeOqIJ1zrmV1RAJW71KGdirFoFK1ZCEq2SiODlMc3gVAfvduxrbvZviRIYY2DbFpa1QitXE0z658bauiShE9Mp5ntGDkilbuIj1MBaR6u8n091Ac3jXZNizbN+UcKrV7a6a2+kxUyRMw55xrnI5IwNy09mDT58U/LZ/D4mqmYr5AYXSc3FiO3FCOXblonVonX9PtyhfYlQtYNRTFkRvLURgdp5gvTImvFHPpHJLpzuS8sK6xLjUN7AesIRT1g3ImcJCZvU/SemCNmXlfYM65pvMErEOUGqKrwrxye6ggQKl0PEsoFRKkQsKukK7RUgN6ldty1UNXILpCEcZjPgapEKXCOJ50uSuLUsyVGtjXq9F9q6v305DFifzcK7WXfycqgH0m8D5gN/AN4NhmBuWcc+AJWOdJJielJCbuwkGZLMr2ApDu66VrWR+9gyPkR/PsMz5Z8vXIeJ6hfO1LQ/pSAXt3pdinO03PYA8AvYPddC3rI93Xi7K9KJOdEjPTk0hXN8VCZz0FCRxvZkdL+jWAmW2XlGl2UM45B01KwCR9DHghMAH8AfhLM9vRjFjaUaXSLhSXWlXqLysdPUkY9C0v9/UULFtFz5oJFIqwK01Xf3Rf6t4ywr7bxtg+lmd7PBbkrnyRoXxhXk9FBkBfKmQgFbAi7op/RTZFdmWW3sEeeveO2nj1rFlJ9+oVBMtWES5bFcUIFOOYS+cw5bw0dymZm79Oq4IEcpJC4hpsSathXh9j55yrm2bduX4IPNHMjgD+D3hHk+JwzsWKE/mqpjbyb8AVwF6SPgj8FPhQc0NyzrlIU0rAzOyaxNtfEA2S6+ZS7v8qboSeKPmR4nnJkqJS56WJTk0VLw9X74O6ugmXbadnzSoGdkb9gI3vGCY3NMy+Q2NMDMcN5Ydz5EZyFCYKFHJFinH1pCXaiikQQSqKJ0wHhJmQdE+adG+aTG/U7izdl42qPpf3klkWlYAFfcsI+1cQDKxAy/eimIl6wLd06ee0KsnoZPc4/z2ukZu3TisBM7NLJN0EnExUYPynZnZnk8NyzjmgNdqAnQN8tdlBtJVpiRgk2nkl5xE3d+mKq+pSGZSJ2oDZwF4EPcsJB/fF8uNkJqI+Kvty49jEWPREYjw2oBXyUMhBPg/FYjQeJNEA3eWQpHLnqQQBpFIQplGYQukoDqXSURuvdBdBV5wchhksFU3FvsFobMh4fvK8ylWRVEi8POmqiWKHdEMhaWXi7WbgsuQyM9vW+Kicc26quiVgkn4E5e6ekt5lZt+O13kXkAcumWU/5wLnAuy33351iLSNzfWEYGlga+L2VPSWFxX69qr+MHO8n4tN+wlQqbl3oW/1PPfsaqnNqhdncxPRx03AemB7/Ho58EfgwKZF5pxzsbolYGb2rNmWSzobeAFwstnMvVqa2YXAhQBHH3NM2/Z+WY+uAypdtWJiZiF+WYirCvNFK8/bPl5kIu4UNV80xuL+vyYKxkShyETemIirpPLxOvmiYWYUEscoFo0gHgMyjM9REqlgcsrEXUtkUiITBmRCkU1FJVqpQKQDkQnF9vEiYXyZUqV9lvc9eY5B+Tjzv2bV6MROVqvRKVWQZnYggKTPAVeY2VXx++cBf9rE0JxzrqwpdTeSTgH+EXiRmY00Iwbn3FRWKFY1tZETSskXgJldDTylifE451xZs9qAfRroAn4YdVbNL8zsdU2KpW7q1WFmpQKaUqlUsg/VfPxmLB8Pkl0oll9vHc2zYyzHjrE8u8ZybI8b3G8bmWDnSI6hsTy58ahKKjeep5Avks8Xo0b4xVIj/MljKYAgmGyEn0oFhKmAdFeKdFf0MevLpljWk2ZlT4YVccP8gWya5dkUy7Npto8VyKbiDmHDaF/Z0ic0mLyWFldmhhUqQ2txyUu/t6VWEtZmyVU1HpL0buDi+P2ZwENNjMc558qa9RTkY5tx3HY3PR9IVjcWp1U3QlSdCFHiBTCcK7JjNEqqNg2N8/DucTbtGGPj9hF274oa3A/vHmNseIKxXTuYGNoOQG5kN7mxoclxGasRhKSzfaR7+sn0rQAgO7CcbG+G3v4s/QNRI/t1K3pYszzL2v4iW0dyLO+e+pEMKvV5FidjihOxIJF1lS6JJ2LzV8h1TBuwkjOA84i6ogC4Lp7nnHNN1wpPQXacepV8lZQSr2RaUGpGV0jMLCVjpURs93ie7WNRSdf20RwPbhvlvi3D7N42wu7t0WDcw9seZmz7I+RHhxYZZIHcyE5yIzsZ2bIRgFR3H9kVezO6ci3jo1E3E+O5IoWikU0FbB/LERd8kQ6iErJMEJ9XogQsmDYkTzIRDep87TuZdVhP+PHTjm+S1B+9tUV+qJ1zrnY8AeswyecZShVKpZqlsXyRkVx0k905mmP7yDijwxOM7J5geNvDAIw8upFibrwuseVHhxiJB9sOU+sASHWl2N6TYudohpFcgbF8OCXm0jlMfU7Dk6x66JRG+CWSDge+DKyM328BXmVmtzU1MOeco3k94bsaK9rU9l8QVceZQRGLJoNcwcgVjGIx+lnIFynkCxQnxqOpTslXOc5cdJxCvhBPxSnxlM6jFHPpHOY613oyqTx1sg7sCf8/gDeb2f5mtj/wFuInquci6RRJv5N0t6S3V1j+Zkl3SLpV0o8l7V/j2J1zHc4TMOccEHXEWs3URnrN7H9Kb8zsWpKd4c0gHj/yAuB5wKHAGZIOnbbar4EN8XBqXwf+X62Cds4tDZ6AdYhAUx4UBKKG6BIEiICov610GE+pgJ5MSDoTku5KkelZRqZnGanuvrrGmeruI9OzrPx0ZDoTRnGkgiiuuF+wUsylc5jrXN3iWbFY1bRYkl4q6XZJRUkbpi17R1zq9DtJz13koe6R9E+SDoindwP3VLHdccDdZnaPmU0AlwOnJVcws/9JdKHzC2DdImN1zi0x3gaswyiRrZQ7NY1H8elJh/Rlol/58myawb4udo/l47Edo17olUoxun0TYzu3zO+px9nEwwhllw3SvWINvStW0LcsGopoYKCLwb4ulmfT9GVS9KTDKTEnO3dtlqXyFGQDu6G4DXgxURVhWVzK9DLgMGAf4EeSHmdmC/0gngO8F/hm/P4n8by57As8kHi/ETh+lvVfDVxdaUFyJI/169dXcWjn3FLhCVgdaNpTerUWVHj6r5SghJqcV3qiMBMXF/VmQgYterpwVW+GApAOAzb3ZHikPxquqG9ZltHVg4yPTjAxPAzAxOhO8qNDFMbHKOTGKBbidkDJBC0ICeJxHMN0lrArG5V2dS8j0xvV+nR1Z+juTdPd38XecQK2V/x6VW+GwZ40vZlwSsylc0j2hF8619Isf/KxNgoTjXkKsjQgdoWk+jTgcjMbB+6VdDdRadTPF3ic7cDfLiLUOUl6BbAB+JMZYiiP5LFhw4alkck756riCVgbKd2vSnnX1MSjPLM8J1N6kQ7iRVbu3mFNXxc96ZDBnjTrV3azO+50dWgsz87RPCMTeXaPRfPGcwXGJwoUi0WKeSiUqqGSLeEDEcYdsQapqFPWrkxIVzqkP+5NtSeTYll3ir5siv64c9b+rhR9mRQDXSlWdafJxJlWV9wha2bakEQwmYxVSrxqmYu1aslXMq5aJvlW/ZMNg5JuTLy/ME40Fmtfouq8ko3xvAWJqzffCRxA4n9d3G5rNg8CyYFn18Xzpu//WcC7gD+Jk0bnnKtaxydglW5QjbqxVjpOLW6YlXYRxOVBybZRpUSllLxkgsmxIFd0hwxkAvKWplCc7DW/SNR/WDT2Y2ksyKi0rWjR2I9FKl+/cgyB4nZaIhVAKk7MUoEIAxGQHOsRUhJhKJZnwz1iXmrJVjM7fy0Wqj7mFjPbMNsKkn4ErKmw6F1m9u35xrZAlwBvBX7LZI8m1fgVcLCkA4kSr5cBL0+uIOlJRFWop5jZ5tqE65xbSjo+AXPOVadYwypIM3vWAjarquRpHh41syvnu5GZ5SW9AfgBEAIXmdntkt4H3Bjv82NAH/BfcVXqH83sRYuI1Tm3xHRsAjZbSVOrlYrVZL8V5gXxl/64Ng8FxfIAjgPKQ1iMhvOxyflRx1vFKM7kYI+luKuJv3R9yz+jEjCToteln/EyI3o/EOQS605/QLfCA7utUWg1b63an9g8SsDq5UrgUkmfIGqEfzBwwyL2d56kzwM/BspVhGb2zZk3Ka9zFXDVtHnvSbxeSILpnHNlHZuAzVczk7IFSyRIKidQiaQpbiSvQjTOo/ITUIh7ot/9CMqPYWMj2NgIxbFoKCIbG8bGR7HcOIWxaLtivkBhfIJivkAxV5jSFYEVDZXGZiy1AUuHBKmQsCsT/cxGrdGU7kJd3SjbS5CNhiJStgdle7BUlmBoC4RRGzVLRdtYGLdki5+kjA8ULUskca1qoclWcrtGfQ4b9RSkpNOBTxE9evs9SbeY2XPjUqavAXcAeeD1i3gCEuAvgScAaRKDKjD5VKRzzjVNWydg9b4xtexgzHGSpSklVKWG8ZP3q3LiNR49zRjkRsqvbfsm8ju3UNyxhfEduxnbthuAiZ1DjO0cZWLXBGO7okKD/Fie3FCO8UKR8aKRK7UXm9oGn3SciHUFoisMSPelSWVTZAeiJywzAxmyy7rJLOsju7I/Wnd5P8HyQcJlg4RDj2Jd0ROTxXRPtOOu+PQmHykoF4aVUpQpv50WSMZatYRrLoVcYxIwM7uCyQGypy/7IPDBGh3qWDN7fI325ZxzNdXWCdh09brxtUwiZrPcIOPYVJwcKkb5UsnXWPRzdBeFnVsBKGzdxMTmTQw/tIWhTdsZfiRKzIY3j7B9aIJHx/PsiG/IIwstGdkCPWHA8vgpzNVdKVb0Zejdq4fevaNEq2/NCnr3GSOTz1PYvplw2aoo1lIiFZeIJROryZKvStehuMf6jdKuiVdJm/VyX42fSTrUzO5odiDOOTddWydgjU6MmlEtVEnF6sbZqiDj8R2Lw7splhKw3dsZ2byDoYe2seOPu9i2aQiAh8byPDRWu/H+RgrFcgL30Fiefcbz7DM8wcrRUl9ihlIpwu4uiju3olSpujKqorRSSVjFc53aH1g0q/klYLXWqM+5Nb8NWK2dANwi6V6iNmACrIpuKJxzru7aOgFzztVOByZgpzQ7AOecm0lTEjBJ7yfq9boIbAbONrOHmhHLQrVMteR0yYb5cXVk6WcxP4FNRNWRhdFxckPDjO0YZ2zbGFsnou021bD0q5JNY3m6goCebVEcYwNdZFcOUxgdxybGsLjaNJgWu81W/dpk7V71WFLINaYn/EYxs/ubHYNzzs2kWfU1HzOzI8zsKOC7wHvmWN/NQVac2ii/kmlPLyYVzCiYzau3yoUoxsearhxPsTglzkqqOlc3b8WCVTU555xbvKaUgJnZrsTbXhbZo1MzSyBapV1Yqe2TKjRWtyD6NSuTRZloDMYwmyHV001mIEPXQIbBiaikabRgPDpRv1Kw1ZkUg10hXQNRW6/MQBRHmM1Mia8Uc6WuJlqhnVenlHolNXAwbuecW/Ka1gZM0geBs4CdwDNmWe9c4FyA/fbbb6bVlpRyspUsBarUJ1ap76xU9CShuntR7wAAYf8ysqtGKUzE/YJ1Rev2PjLCirGQrRMFttYoEVuVScU/Q/bOpujdu4e+tX3R8dYsJ7tqgLB/GeodQN29U2Iun0PyvKYlYI1MyFoh2a9Xol+Y8ATMOecapW53Lkk/knRbhek0ADN7l5ntRzRe2xtm2o+ZXWhmG8xsw+Dq1fUK17klz4rFqibnnHOLV7cSsHkM1XEJ0ZAf59Urlkape8P8UklPxb6uonmlqjsA4i4disWoK4eguzg5MPaqNXSHKdK93WRX9NO7JuqIdXzbblbtGGNs5zjju6IG8RMjOUYLRUbyNrUjVqLBvUNFA3EnO2LtSYnuMCDTky5XN2aXdZFdnqVrZT/ZFVFHrNlVy0gtX0W4fJDUqjVYV1QyVuqItdwjfvK8pg1xVPEa1VAnVjdW4u27nHOucZr1FOTBZvb7+O1pwF3NiKNeGpWIJfdeThES+YelslPWLwYhSkfztGof0v3LCQf3IbPvbvrGE0MRjY1SGJsgPxb1H1YYz1EYz0VDEeUnhyJKthlSGEwORZQqDUWUJuxKk8pG3dmH2QzKxkMRdcVDEXX3E3T3YJkein2DWCpa1+I4mzkUUasmXnXrcNgTMOeca5hmtQH7iKTHExXb3A+8rklx1NVcN8pFJ2hzNUwP4gSpNL5iurvcY36hf+9oIO5iHswIysMbRYNwp80SA29PG6x7zriSJVTB1MG5FUwOyk10jQpBCkMU+veuMIB3Y8Z7bNVkq5EKE53VDYVzzrWyZj0F+ZJmHLfV1OOmPyU/UlRqVIiLxYqAxeVmI+qiaEZRXRSJqhOjdQ1T3BuE4p1ZaVtAMz+yqmmvA0UvgnhJEETzQ4kgPvdAUaFdIDGqDErMJ153j+N4rlQXXgLmnHON4z3hd4hKBVNFmzpodqEYtdkCyJtRKBh5M4oWLQMoWLR+vmDk41KvfCHqH6wYr1ssVr5RB0EiqZIIgFQoUnHpVSoUgSCUESbWTUmEIeSK0bLyAohK6aCcsCXP1ROx2iq2WqfCzjnXwTwBc84BMDFDYu2cc672PAHrEJVKg4JypWBcihSKML7HdgWiIJGxqNqRRCmZEZUyFZna2H9Ks7A5Yik35UrEIcVVkIHK9ZUhQoqepMzEJWSl7WCy5MtLu+qv0ggFzjnn6qPjE7D5NnTvpMbYqtCOqnQ50oFIU2r7lVjOnuvWKo7y+2nLSwlaOtCSSrRm+2w243PoTcCcc65xOjYBW+gThpW268SkrJQFBXukQ8mV6x7O1MN1zmXew3w/j8n1G/X58ypI55xrnI5NwGqpdDPspETM1U8zxwRdDK+CdM65xvEEzLkaa5UB2ufLqyCdc65xPAFzzgGegDnnXCO1VQL265tv3tLT3X3/tNmDwJZmxFMjHn/ztfs5VIp///nuxNuAOedc47RVAmZmq6fPk3SjmW1oRjy14PE3X7ufQ63ib1QbMEkvBc4HDgGOM7Mb4/kHAHcCv4tX/YWZdeQwZc4511YJmHOufhpYBXkb8GLgPyos+4OZHdWwSJxzrkk8AXPOAY2rgjSzO4HyuJ/OObcUBc0OoAYubHYAi+TxN1+7n0NN4i+YVTUBg5JuTEzn1uL4sQMl/VrS/0p6Wg3365xzLaXtS8DMrK1vnh5/87X7OdQq/mL1q26Zq82ZpB8BayosepeZfXuGzR4G1pvZVknHAN+SdJiZ7ao+NOecaw9tn4A552qjlo3wzexZC9hmHBiPX98k6Q/A44AbaxaYc861CE/AnHNA87uhkLQa2GZmBUkHAQcD9zQ1KOecq5NOaAMGgKS3SDJJg82OZb4kfUzSXZJulXSFpOXNjqkakk6R9DtJd0t6e7PjmQ9J+0n6H0l3SLpd0puaHdNCSQrjdlPfXcx+ClbdVIN4T5e0EXgy8D1JP4gXnQTcKukW4OvA68xs2+KP6JxzracjEjBJ+wHPAf7Y7FgW6IfAE83sCOD/gHc0OZ45SQqBC4DnAYcCZ0g6tLlRzUseeIuZHQqcALy+zeJPehNR/1mLMo9G+ItiZleY2Toz6zKzvc3sufH8b5jZYWZ2lJkdbWbfWfTBFmiuLxeSuiR9NV7+y7gPM+ecq1pHJGDAvwD/CLRlV95mdo2Z5eO3vwDWNTOeKh0H3G1m95jZBHA5cFqTY6qamT1sZjfHr3cTJTD7Njeq+ZO0Dng+8PnF7muiaFVNna7KLxevBrab2WOJ/v98tLFROufaXdu3AZN0GvCgmf2mQ/oVOgf4arODqMK+wAOJ9xuB45sUy6LEpRdPAn7Z5FAW4l+Jvnz0L2Ynq0gfnTdDzP43VMQYJHP0Yo7VBspfLgAklb5c3JFY5zSi3vwhqi79tCSZ1eZJhld98Rh2FnO12JVzrsY+efIF7L//4nvJaYsEbLZH2oF3ElU/trRqHsuX9C6iqrFLGhnbUiapD/gG8Hft1t2BpBcAm+MnBp++mH3tRRf3MspB9My63u8ZZh+yizlUO6jmy0V5HTPLS9oJrGLamJxxH2nnAqxfv77qAA7sWsWu/Oi8A3fO1V8mPfv/yWq1RQI20yPtkg4HDgRKpV/rgJslHWdmmxoY4pzmeixf0tnAC4CTa/Utus4eBPZLvF8Xz2sbktJEydclZvbNZsezAE8FXiTpVCALDEi62MxeMd8d3cnQ2keZeHh/uglnKAXLU+RWdrGN3MrFhb10xH20XQiwYcOGqv+uzz/jmrrF5JxrDW3dBszMfmtme5nZAWZ2ANE31aNbLfmai6RTiKqRXmRmI82Op0q/Ag6WdKCkDPAy4Momx1Q1RRn7F4A7zewTzY5nIczsHXFj9gOIrv9/LyT5ive1aX+6uZOhGdf5Lbt5PH2Y2faFRdw2qvlyUV5HUgpYBmxtSHTOuY7Q1glYB/k0URueH0q6RdJnmx3QXOKHBt4A/ICoAfvXzOz25kY1L08FXgk8M77mt8QlSUvWTezsv5PdTFToE3+UAn9gmJ+zvePrH6nuy8WVwKvi139GlPy2Q8m1c65FyP9nOOdKTtIqGyLPcayYMv96trE3XfzIHu2IJ13mEifj/wqEwEVm9kFJ7wNuNLMrJWWBrxA9vLENeFmp0f5MNmzYYDfe6J36O7eUSLpppqHb2qINmHOuMX7CtvQgmdyh5OmL/z3sJMejTHAbu8Mmh9cwZnYVcNW0ee9JvB4DXtrouJxzncOrIJ1zZWaW38AybmRHed4N7OARxp9hZvMYr9s559xsvATMOTfF93k02IdscSsT5DAKGGZ2bbPjcs65TuIJmHNuCjMzScf+ku2/ymM8zPhhzY7JOec6jVdBOuf2YGY3dhGwnDRmdsfcWzjnnJsPfwrSOecaQNKjwP3NjiM2yLRe+9tEu8YN7Ru7x704+5vZ6koLPAFzzrklRtKNMz0a38raNW5o39g97vrxKki3IJKOlXSrpKykXkm3S3pis+Nyzjnn2oE3wncLYma/knQl8AGgG7jYzG5rcljOOedcW/AEzC3G+4iGbRkD/rbJsTjnqndhswNYoHaNG9o3do+7TrwNmFswSWuBnwLjwLFmNtzkkJxzzrm24AmYW7C4CvJy4EBgrZm9ockhOeecc23BqyDdgkg6C8iZ2aWSQuBnkp5pZv/d7Nicc865VudPQboFMbMvm9lL4tcFMzveky/nWoukiyRtllTxARlJp8VPM98i6UZJJzY6xkrmijux3rGS8pL+rFGxzaaK6/10STvj632LpPdUWq/Rqrnecey3xE+8/28j45tJFdf7rYlrfZukgqSVjY5zJl4F6ZxzHUrSScAQ8GUz26ObGEl9wHA8/NQRwNfM7AmNjrNCXLPGHa8TAj8kegjoIjP7egNDrKiK6/104B/M7AUNDm1WVcS9HPgZcIqZ/VHSXma2ucFh7qGaz0li3RcCf29mz2xIcFXwEjDnnOtQZnYdsG2W5UM2+S28F2iJb+RzxR17I/ANoOmJQEmVcbecKuJ+OfBNM/tjvH5LXPN5Xu8zgMvqGM68eQLmnHNLmKTTJd0FfA84p9nxVEPSvsDpwGeaHcsCPFnSbyRdLaldBrp/HLBC0rWSborbALcNST3AKUQJe8vwRvjOObeEmdkVwBVxdc77gWc1OaRq/CvwNjMrSmp2LPNxM9HYgEOSTgW+BRzc3JCqkgKOAU4m6nj755J+YWb/19ywqvZC4Hoza6nSSU/AnHPOYWbXSTpI0qCZtcIgxrPZAFweJ1+DwKmS8mb2raZGNQcz25V4fZWkf2+T670R2Br39Tgs6TrgSKBdErCX0WLVj+BVkM45t2RJeqziLEbS0UAXsLW5Uc3NzA40swPM7ADg68DftHryBSBpTeJ6H0d0D2756w18GzhRUiquzjseuLPJMVVF0jLgT4jOoaV4CZhzznUoSZcBTwcGJW0EzgPSAGb2WeAlwFmScsAo8BeJRvlNU0XcLamKuP8M+GtJeaLr/bJ2uN5mdqek7wO3AkXg860w9m+Vn5PTgWtacaQW74bCOeecc67BvArSOeecc67BPAFzzjnnnGswT8Ccc8455xrMEzDnnHPOuQbzBMw555xzS1K1A7/H654k6eaZBoCXNCBpo6RPV3NsT8Ccc851FEl/K+lOSZc0O5ZaiYcB+p2kF8Xvvzg9CZA0NMv23ZJukTQhabDe8baRLxINU1SNPwJnA5fOsPz9wHXVHtj7AXPOOddp/gZ4lpltTM6UlDKzfJNiqoUzzezGhWxoZqPAUZLuq21I7S0eAeKA5DxJjwEuAFYDI8BrzOwuM7svXl6cvh9JxwB7A98nGqlhTl4C5pxzrmNI+ixwEHC1pL+XdL6kr0i6HviKpNWSviHpV/H01Hi7VZKukXS7pM9Lul/SoKQDktVTkv5B0vnx68dI+n48QPVPJD0hnv9FSf8m6WeS7kmWVEl6m6TfxgNyfyTex82J5Qcn3y/wGrwvLu26RdKDkv5zMftbgi4E3mhmxwD/APz7bCtLCoCPx+tWzUvAnHPOdQwze52kU4BnmNmWOFk6FDjRzEYlXQr8i5n9VNJ64AfAIUS9qP/UzN4n6fnAq6s43IXA68zs95KOJ7pRPzNethY4EXgCcCXwdUnPA04DjjezEUkrzWybpJ2SjjKzW4C/BKpNmD4m6d0VrsF7gPdIWg78BKiqTZIDSX3AU4D/Sgz03jXHZn8DXGVmG+czOLwnYM455zrdlXEVHMCzgEMTN8qB+KZ7EvBiADP7nqTts+2wihv1t8ysCNwhae/Esf/TzEbi42yL538e+EtJbwb+AjiuyvN6q5l9PRHTUOK1gIuBT5jZTVXuz0U1gzvM7Kh5bPNk4GmS/gboAzKShszs7bNt5AmYc865TpccBzAATjCzseQKs5Rc5JnaXCeb2M9sN+rx5O7niO8bRCVw/w3cZGa1GKD7fGCjmXn14zyY2S5J90p6qZn9V5zIHmFmv5llmzNLryWdDWyYK/kCbwPmnHNuabkGeGPpjaSj4pfXAS+P5z0PWBHPfwTYK24j1gW8AKIbNXCvpJfG20jSkXMc+4dEJV098TYr432NEVWFfobqqx9nJOmFRKVtf7vYfXW6eEDvnwOPj7uQeDVwJvBqSb8BbieqNkbSsYoG/X4p8B+Sbl/Msb0EzDnn3FLyt8AFkm4lugdeB7wOeC9wWXxT/RlRlwOYWU7S+4AbgAeBuxL7OhP4TNwOKw1cDsxWUvL9OOG7UdIEcBXwznjxJcDpRAniYr0Z2Be4IS7ZuzJuF+amMbMzZli0R9cUZvYrYN0c+/siUdcWc5KZVbOec845t2TE3TVsMLMtDTrePwDLzOyfZlh+LfAPC+2GIrGf+2jgebmZeRWkc84510SSrgDOAj45y2rbgC8q7oh1AcfolnQLUUndHv1YucbzEjDnnHPOuQbzEjDnnHPOuQbzBMw555xzrsE8AXPOOeecazBPwJxzzjnnGswTMOecc865Bvv/7xNTNVWeeGMAAAAASUVORK5CYII=\n",
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",
       "text/plain": [
-       "
"
+       "
"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1900,7 +1835,7 @@
     "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
     "sim_data.plot_field(\"field\", \"Ez\", f=freq0, ax=ax[0])\n",
     "sim_data[\"mode\"].amps.sel(direction=\"-\").abs.plot.line(x=\"f\", ax=ax[1])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1913,7 +1848,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 23,
    "id": "ebe430c7",
    "metadata": {
     "tags": []
@@ -1921,14 +1856,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "
"
+       "
"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1936,7 +1869,7 @@
     "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
     "sim_data.plot_field(\"mode_solver\", \"Ex\", f=freq0, val=\"abs\", mode_index=0, ax=ax[0])\n",
     "sim_data.plot_field(\"mode_solver\", \"Ez\", f=freq0, val=\"abs\", mode_index=0, ax=ax[1])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1955,27 +1888,50 @@
    "cell_type": "code",
    "execution_count": 24,
    "id": "1088ea2b-6411-41b6-92a8-8c66a6c486b9",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T20:59:35.159315Z",
-     "iopub.status.busy": "2023-08-18T20:59:35.159133Z",
-     "iopub.status.idle": "2023-08-18T20:59:58.398177Z",
-     "shell.execute_reply": "2023-08-18T20:59:58.397581Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
[13:59:35] Mode solver created with                                    web.py:80\n",
-       "           task_id='fdve-ea498b47-738e-4a8c-aea3-21d5f1a83878v1',               \n",
-       "           solver_id='mo-edf74f79-26da-45d6-81f1-8ed812458d87'.                 \n",
+       "
10:41:31 Eastern Daylight Time Created task 'mode_solver' with task_id          \n",
+       "                               'mo-f4bde0a7-44a1-4372-81f3-ffe961bc1126' and    \n",
+       "                               task_type 'MODE_SOLVER'.                         \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:41:31 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'mode_solver'\u001b[0m with task_id          \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'mo-f4bde0a7-44a1-4372-81f3-ffe961bc1126'\u001b[0m and    \n",
+       "\u001b[2;36m                               \u001b[0mtask_type \u001b[32m'MODE_SOLVER'\u001b[0m.                         \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               View task using web UI at                        \n",
+       "                               'https://tidy3d.simulation.cloud/workbench?taskId\n",
+       "                               =mo-f4bde0a7-44a1-4372-81f3-ffe961bc1126'.       \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                        \n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=530254;https://tidy3d.simulation.cloud/workbench?taskId=mo-f4bde0a7-44a1-4372-81f3-ffe961bc1126\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=68997;https://tidy3d.simulation.cloud/workbench?taskId=mo-f4bde0a7-44a1-4372-81f3-ffe961bc1126\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=530254;https://tidy3d.simulation.cloud/workbench?taskId=mo-f4bde0a7-44a1-4372-81f3-ffe961bc1126\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=421376;https://tidy3d.simulation.cloud/workbench?taskId=mo-f4bde0a7-44a1-4372-81f3-ffe961bc1126\u001b\\\u001b[32mmo\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=530254;https://tidy3d.simulation.cloud/workbench?taskId=mo-f4bde0a7-44a1-4372-81f3-ffe961bc1126\u001b\\\u001b[32m-f4bde0a7-44a1-4372-81f3-ffe961bc1126'\u001b[0m\u001b]8;;\u001b\\.       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               Task folder: 'default'.                          \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[13:59:35]\u001b[0m\u001b[2;36m \u001b[0mMode solver created with                                    \u001b]8;id=491643;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/plugins/mode/web.py\u001b\\\u001b[2mweb.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=539613;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/plugins/mode/web.py#80\u001b\\\u001b[2m80\u001b[0m\u001b]8;;\u001b\\\n",
-       "\u001b[2;36m           \u001b[0m\u001b[33mtask_id\u001b[0m=\u001b[32m'fdve-ea498b47-738e-4a8c-aea3-21d5f1a83878v1'\u001b[0m,      \u001b[2m         \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m\u001b[33msolver_id\u001b[0m=\u001b[32m'mo-edf74f79-26da-45d6-81f1-8ed812458d87'\u001b[0m.        \u001b[2m         \u001b[0m\n"
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=343331;https://tidy3d.simulation.cloud/folders/639eb096-a602-4b56-a502-cac1f18f9557\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                          \n"
       ]
      },
      "metadata": {},
@@ -1984,7 +1940,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ce8fcf3059c64b25af07daa85ad6a293",
+       "model_id": "2fc60fcbbb0247b6b96684c1a47cf8c8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2008,11 +1964,51 @@
     {
      "data": {
       "text/html": [
-       "
\n",
+       "
10:41:32 Eastern Daylight Time Maximum FlexCredit cost: 0.006. Minimum cost     \n",
+       "                               depends on task execution details. Use           \n",
+       "                               'web.real_cost(task_id)' to get the billed       \n",
+       "                               FlexCredit cost after a simulation run.          \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m10:41:32 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.006\u001b[0m. Minimum cost     \n",
+       "\u001b[2;36m                               \u001b[0mdepends on task execution details. Use           \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed       \n",
+       "\u001b[2;36m                               \u001b[0mFlexCredit cost after a simulation run.          \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
10:41:33 Eastern Daylight Time status = queued                                  \n",
        "
\n"
       ],
       "text/plain": [
-       "\n"
+       "\u001b[2;36m10:41:33 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               To cancel the simulation, use                    \n",
+       "                               'web.abort(task_id)' or 'web.delete(task_id)' or \n",
+       "                               abort/delete the task in the web UI. Terminating \n",
+       "                               the Python script will not stop the job running  \n",
+       "                               on the cloud.                                    \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use                    \n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or \u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or \n",
+       "\u001b[2;36m                               \u001b[0mabort/delete the task in the web UI. Terminating \n",
+       "\u001b[2;36m                               \u001b[0mthe Python script will not stop the job running  \n",
+       "\u001b[2;36m                               \u001b[0mon the cloud.                                    \n"
       ]
      },
      "metadata": {},
@@ -2021,7 +2017,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1ef90212bc2e46cab80dd8bac3e32105",
+       "model_id": "",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2045,11 +2041,11 @@
     {
      "data": {
       "text/html": [
-       "
\n",
+       "
10:41:38 Eastern Daylight Time starting up solver                               \n",
        "
\n"
       ],
       "text/plain": [
-       "\n"
+       "\u001b[2;36m10:41:38 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                               \n"
       ]
      },
      "metadata": {},
@@ -2058,11 +2054,11 @@
     {
      "data": {
       "text/html": [
-       "
[13:59:36] Mode solver status: queued                                  web.py:93\n",
+       "
                               running solver                                   \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[13:59:36]\u001b[0m\u001b[2;36m \u001b[0mMode solver status: queued                                  \u001b]8;id=899166;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/plugins/mode/web.py\u001b\\\u001b[2mweb.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=166603;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/plugins/mode/web.py#93\u001b\\\u001b[2m93\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                   \n"
       ]
      },
      "metadata": {},
@@ -2071,11 +2067,11 @@
     {
      "data": {
       "text/html": [
-       "
[13:59:39] Mode solver status: running                                 web.py:93\n",
+       "
10:41:51 Eastern Daylight Time status = success                                 \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[13:59:39]\u001b[0m\u001b[2;36m \u001b[0mMode solver status: running                                 \u001b]8;id=619091;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/plugins/mode/web.py\u001b\\\u001b[2mweb.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=530299;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/plugins/mode/web.py#93\u001b\\\u001b[2m93\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m10:41:51 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                 \n"
       ]
      },
      "metadata": {},
@@ -2084,11 +2080,25 @@
     {
      "data": {
       "text/html": [
-       "
[13:59:57] Mode solver status: success                                web.py:103\n",
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
                               View simulation result at                        \n",
+       "                               'https://tidy3d.simulation.cloud/workbench?taskId\n",
+       "                               =mo-f4bde0a7-44a1-4372-81f3-ffe961bc1126'.       \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[13:59:57]\u001b[0m\u001b[2;36m \u001b[0mMode solver status: success                                \u001b]8;id=381787;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/plugins/mode/web.py\u001b\\\u001b[2mweb.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=620487;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/plugins/mode/web.py#103\u001b\\\u001b[2m103\u001b[0m\u001b]8;;\u001b\\\n"
+       "\u001b[2;36m                              \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                        \n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=376966;https://tidy3d.simulation.cloud/workbench?taskId=mo-f4bde0a7-44a1-4372-81f3-ffe961bc1126\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=992225;https://tidy3d.simulation.cloud/workbench?taskId=mo-f4bde0a7-44a1-4372-81f3-ffe961bc1126\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m                               \u001b[0m\u001b]8;id=376966;https://tidy3d.simulation.cloud/workbench?taskId=mo-f4bde0a7-44a1-4372-81f3-ffe961bc1126\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=472097;https://tidy3d.simulation.cloud/workbench?taskId=mo-f4bde0a7-44a1-4372-81f3-ffe961bc1126\u001b\\\u001b[4;34mmo\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=376966;https://tidy3d.simulation.cloud/workbench?taskId=mo-f4bde0a7-44a1-4372-81f3-ffe961bc1126\u001b\\\u001b[4;34m-f4bde0a7-44a1-4372-81f3-ffe961bc1126'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m       \n"
       ]
      },
      "metadata": {},
@@ -2097,7 +2107,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f3aec92bbcff47e181f320603cc6f989",
+       "model_id": "5129550df9334f44b3fd7cfe0b80bf21",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2121,11 +2131,11 @@
     {
      "data": {
       "text/html": [
-       "
\n",
+       "
10:41:53 Eastern Daylight Time loading simulation from simulation_data.hdf5     \n",
        "
\n"
       ],
       "text/plain": [
-       "\n"
+       "\u001b[2;36m10:41:53 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5     \n"
       ]
      },
      "metadata": {},
@@ -2140,18 +2150,11 @@
    "cell_type": "code",
    "execution_count": 25,
    "id": "907597c2-ae51-490f-a437-0e9a431117b6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T20:59:58.428085Z",
-     "iopub.status.busy": "2023-08-18T20:59:58.427844Z",
-     "iopub.status.idle": "2023-08-18T20:59:58.567987Z",
-     "shell.execute_reply": "2023-08-18T20:59:58.567364Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -2164,7 +2167,7 @@
     "fig, ax = plt.subplots(1)\n",
     "n_eff = mode_data.n_eff\n",
     "n_eff.plot.line(x=\"f\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2179,14 +2182,7 @@
    "cell_type": "code",
    "execution_count": 26,
    "id": "123d354e-2635-449d-be1d-02a4690c220c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T20:59:58.570730Z",
-     "iopub.status.busy": "2023-08-18T20:59:58.570552Z",
-     "iopub.status.idle": "2023-08-18T21:03:11.998268Z",
-     "shell.execute_reply": "2023-08-18T21:03:11.997418Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2216,9 +2212,9 @@
     ")\n",
     "\n",
     "dummy_source = td.UniformCurrentSource(\n",
-    "    size=(0,0,0),\n",
+    "    size=(0, 0, 0),\n",
     "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
-    "    polarization='Ex',\n",
+    "    polarization=\"Ex\",\n",
     ")\n",
     "\n",
     "mode_spec = td.ModeSpec(\n",
@@ -2226,6 +2222,7 @@
     "    target_neff=2.0,\n",
     ")\n",
     "\n",
+    "\n",
     "def create_mode_solver(resolution):\n",
     "    grid_spec = td.GridSpec.auto(min_steps_per_wvl=resolution, wavelength=wvl_um)\n",
     "\n",
@@ -2237,7 +2234,7 @@
     "        run_time=run_time,\n",
     "        boundary_spec=td.BoundarySpec.all_sides(boundary=td.Periodic()),\n",
     "    )\n",
-    "    \n",
+    "\n",
     "    return ModeSolver(\n",
     "        simulation=sim,\n",
     "        plane=td.Box(center=(0, 0, 0), size=(4, 0, 4)),\n",
@@ -2251,7 +2248,7 @@
     "    mode_solver = create_mode_solver(resolution)\n",
     "    local_data = mode_solver.solve()\n",
     "    n_eff[0].append(local_data.n_eff.item())\n",
-    "    \n",
+    "\n",
     "    mode_solver = create_mode_solver(resolution)\n",
     "    server_data = web.run(mode_solver, task_name=\"mode_solver\", verbose=False)\n",
     "    n_eff[1].append(server_data.n_eff.item())"
@@ -2261,18 +2258,11 @@
    "cell_type": "code",
    "execution_count": 27,
    "id": "6354bde2-aa7e-4541-83ff-90e7f836fcfb",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T21:03:12.000945Z",
-     "iopub.status.busy": "2023-08-18T21:03:12.000738Z",
-     "iopub.status.idle": "2023-08-18T21:03:12.165383Z",
-     "shell.execute_reply": "2023-08-18T21:03:12.164882Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "
"
       ]
@@ -2285,7 +2275,7 @@
     "fig, ax = plt.subplots(1)\n",
     "ax.plot(resolutions, n_eff[0], label=\"Local\")\n",
     "ax.plot(resolutions, n_eff[1], label=\"Server\")\n",
-    "ax.set(xlabel=\"Resolution\", ylabel=\"Progagation index\")\n",
+    "ax.set(xlabel=\"Resolution\", ylabel=\"Propagation index\")\n",
     "ax.legend()\n",
     "ax.grid()"
    ]
@@ -2307,18 +2297,12 @@
    "execution_count": 28,
    "id": "24625b82-fafe-4667-9896-08b12d768681",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T21:03:12.167405Z",
-     "iopub.status.busy": "2023-08-18T21:03:12.167266Z",
-     "iopub.status.idle": "2023-08-18T21:03:12.339853Z",
-     "shell.execute_reply": "2023-08-18T21:03:12.339266Z"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -2330,17 +2314,13 @@
    "source": [
     "# bottom layer\n",
     "bottom = td.Structure(\n",
-    "    geometry=td.Box(\n",
-    "        center=(0, 0, -0.8 * wg_height), size=(wg_width, td.inf, wg_height)\n",
-    "    ),\n",
+    "    geometry=td.Box(center=(0, 0, -0.8 * wg_height), size=(wg_width, td.inf, wg_height)),\n",
     "    medium=td.Medium(permittivity=wg_permittivity * 2),\n",
     ")\n",
     "\n",
     "# top layer\n",
     "top = td.Structure(\n",
-    "    geometry=td.Box(\n",
-    "        center=(0, 0, 0.7 * wg_height), size=(wg_width, td.inf, wg_height * 0.8)\n",
-    "    ),\n",
+    "    geometry=td.Box(center=(0, 0, 0.7 * wg_height), size=(wg_width, td.inf, wg_height * 0.8)),\n",
     "    medium=td.Medium(permittivity=wg_permittivity * 2),\n",
     ")\n",
     "\n",
@@ -2355,7 +2335,7 @@
     "\n",
     "# visualize\n",
     "sim.plot(y=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2371,12 +2351,6 @@
    "execution_count": 29,
    "id": "fe98731f-5a6c-41d6-af92-3fb7a1e09f95",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T21:03:12.341907Z",
-     "iopub.status.busy": "2023-08-18T21:03:12.341689Z",
-     "iopub.status.idle": "2023-08-18T21:03:26.774589Z",
-     "shell.execute_reply": "2023-08-18T21:03:26.773817Z"
-    },
     "tags": []
    },
    "outputs": [],
@@ -2398,7 +2372,7 @@
     "    mode_spec=mode_spec,\n",
     "    freqs=freqs,\n",
     ")\n",
-    "mode_data = mode_solver.solve()\n"
+    "mode_data = mode_solver.solve()"
    ]
   },
   {
@@ -2414,18 +2388,12 @@
    "execution_count": 30,
    "id": "96ad19e0-c987-4dbf-8040-aa98746f10c6",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T21:03:26.777747Z",
-     "iopub.status.busy": "2023-08-18T21:03:26.777525Z",
-     "iopub.status.idle": "2023-08-18T21:03:27.174777Z",
-     "shell.execute_reply": "2023-08-18T21:03:27.174350Z"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -2438,7 +2406,7 @@
     "fig, ax = plt.subplots(1)\n",
     "n_eff = mode_data.n_eff  # real part of the effective mode index\n",
     "n_eff.plot.line(\".-\", x=\"f\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2454,18 +2422,12 @@
    "execution_count": 31,
    "id": "e516a125-2fc9-48b1-b5ff-cc0c0a8c694f",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T21:03:27.178056Z",
-     "iopub.status.busy": "2023-08-18T21:03:27.177895Z",
-     "iopub.status.idle": "2023-08-18T21:03:30.193541Z",
-     "shell.execute_reply": "2023-08-18T21:03:30.192882Z"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -2489,7 +2451,7 @@
     "            origin=\"lower\",\n",
     "        )\n",
     "        ax[j, i].axis(\"off\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2505,17 +2467,11 @@
    "execution_count": 32,
    "id": "bbfba9cb-34f2-4063-90ba-1f888ae89777",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T21:03:30.199827Z",
-     "iopub.status.busy": "2023-08-18T21:03:30.199521Z",
-     "iopub.status.idle": "2023-08-18T21:03:32.319338Z",
-     "shell.execute_reply": "2023-08-18T21:03:32.318605Z"
-    },
     "tags": []
    },
    "outputs": [],
    "source": [
-    "mode_data_sorted = mode_data.overlap_sort(track_freq=\"central\")\n"
+    "mode_data_sorted = mode_data.overlap_sort(track_freq=\"central\")"
    ]
   },
   {
@@ -2531,18 +2487,12 @@
    "execution_count": 33,
    "id": "ef04ae6c-5251-401f-b81d-d6ef1d7510f2",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T21:03:32.322726Z",
-     "iopub.status.busy": "2023-08-18T21:03:32.322448Z",
-     "iopub.status.idle": "2023-08-18T21:03:36.016507Z",
-     "shell.execute_reply": "2023-08-18T21:03:36.015918Z"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
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       ]
@@ -2552,7 +2502,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "
"
       ]
@@ -2580,7 +2530,7 @@
     "            origin=\"lower\",\n",
     "        )\n",
     "        ax[j, i].axis(\"off\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2588,9 +2538,9 @@
    "id": "e5ce0939-69b3-4768-9ec2-fe01527a98f3",
    "metadata": {},
    "source": [
-    "Note that depending on particular situation the mode sorting, whether default or by hands, might not succesfully resolve tracking of all modes throughout the chosen frequency range. Among possible reasons for that: \n",
+    "Note that depending on the particular situation, the mode sorting, whether default or by hands, might not successfully resolve the tracking of all modes throughout the chosen frequency range. Possible reasons for that: \n",
     "- A mode is physically disappearing at a certain frequency point.\n",
-    "- A mode is not included in calculated number of modes, `num_modes`, for certain frequency points. In this case increasing `num_modes` might alleviate the issue.\n",
+    "- A mode is not included in the calculated number of modes, `num_modes`, for certain frequency points. In this case, increasing `num_modes` might alleviate the issue.\n",
     "- Presence of degenerate modes at certain frequencies."
    ]
   },
@@ -2611,18 +2561,12 @@
    "execution_count": 34,
    "id": "b995922a-c9d7-449a-8437-e3b0f19e3821",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-08-18T21:03:36.022436Z",
-     "iopub.status.busy": "2023-08-18T21:03:36.022171Z",
-     "iopub.status.idle": "2023-08-18T21:05:14.279059Z",
-     "shell.execute_reply": "2023-08-18T21:05:14.277521Z"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -2634,9 +2578,7 @@
    "source": [
     "# Use the previous mode solver with the appropriate settings\n",
     "mode_spec = td.ModeSpec(num_modes=12, track_freq=\"central\", group_index_step=True)\n",
-    "mode_solver = mode_solver.copy(\n",
-    "    update={\"mode_spec\": mode_spec}\n",
-    ")\n",
+    "mode_solver = mode_solver.copy(update={\"mode_spec\": mode_spec})\n",
     "\n",
     "mode_data = mode_solver.solve()\n",
     "n_eff = mode_data.n_eff\n",
@@ -2683,7 +2625,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.11.0"
   },
   "title": "Using the Mode Solver in Tidy3D | Flexcompute",
   "widgets": {
diff --git a/ModesBentAngled.ipynb b/ModesBentAngled.ipynb
index 59c60148..aac26b5b 100644
--- a/ModesBentAngled.ipynb
+++ b/ModesBentAngled.ipynb
@@ -26,14 +26,14 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3D import\n",
     "import tidy3d as td\n",
     "from tidy3d import web\n",
-    "from tidy3d.plugins.mode import ModeSolver\n"
+    "from tidy3d.plugins.mode import ModeSolver"
    ]
   },
   {
@@ -111,7 +111,7 @@
     "        axis=2,\n",
     "    ),\n",
     "    medium=mat_wg,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -167,9 +167,7 @@
     ")\n",
     "\n",
     "# Flux monitor along the ring propagation direction\n",
-    "flux_mnt = td.FluxMonitor(\n",
-    "    center=[0, radius, 0], size=[0, 3, 2], freqs=[freq0], name=\"flux\"\n",
-    ")\n"
+    "flux_mnt = td.FluxMonitor(center=[0, radius, 0], size=[0, 3, 2], freqs=[freq0], name=\"flux\")"
    ]
   },
   {
@@ -226,7 +224,7 @@
     "ax2 = fig.add_subplot(gs[0, 1])\n",
     "sim.plot(z=0, ax=ax1)\n",
     "sim.plot(x=0, ax=ax2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -327,7 +325,7 @@
     "for axe, mode_index in zip(axes, range(num_modes)):\n",
     "    for ax, field_name in zip(axe, (\"Ex\", \"Ey\", \"Ez\")):\n",
     "        ms.plot_field(field_name, \"abs\", f=freq0, mode_index=mode_index, ax=ax)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -364,7 +362,7 @@
     ")\n",
     "\n",
     "sim = sim.copy(update=dict(sources=[mode_src]))\n",
-    "sim = sim.copy(update=dict(monitors=[field_mnt, flux_mnt, mode_mnt]))\n"
+    "sim = sim.copy(update=dict(monitors=[field_mnt, flux_mnt, mode_mnt]))"
    ]
   },
   {
@@ -713,7 +711,7 @@
     }
    ],
    "source": [
-    "sim_data = web.run(sim, task_name=\"ring_mode\", path=\"data/sim_data.hdf5\", verbose=True)\n"
+    "sim_data = web.run(sim, task_name=\"ring_mode\", path=\"data/sim_data.hdf5\", verbose=True)"
    ]
   },
   {
@@ -764,7 +762,7 @@
     "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(15, 5))\n",
     "ax1 = sim_data.plot_field(\"field\", \"Ex\", z=0.05, f=freq0, val=\"real\", ax=ax1)\n",
     "ax2 = sim_data.plot_field(\"field\", \"E\", \"abs^2\", z=0.05, f=freq0, ax=ax2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -834,7 +832,7 @@
     "    freqs=[freq0],\n",
     "    mode_spec=mode_spec,\n",
     "    name=\"mnt_fwd\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -887,7 +885,7 @@
     "ax2 = fig.add_subplot(gs[0, 1])\n",
     "sim.plot(z=0, ax=ax1)\n",
     "sim.plot(x=0, ax=ax2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -921,16 +919,14 @@
     }
    ],
    "source": [
-    "ms = ModeSolver(\n",
-    "    simulation=sim, plane=msource.geometry, mode_spec=mode_spec, freqs=[freq0]\n",
-    ")\n",
+    "ms = ModeSolver(simulation=sim, plane=msource.geometry, mode_spec=mode_spec, freqs=[freq0])\n",
     "modes = ms.solve()\n",
     "\n",
     "f, axes = plt.subplots(mode_spec.num_modes, 3, tight_layout=True, figsize=(14, 6))\n",
     "for axe, mode_index in zip(axes, range(mode_spec.num_modes)):\n",
     "    for ax, field_name in zip(axe, (\"Ex\", \"Ey\", \"Ez\")):\n",
     "        ms.plot_field(field_name, \"abs\", f=freq0, mode_index=mode_index, ax=ax)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1285,9 +1281,7 @@
     }
    ],
    "source": [
-    "sim_data = web.run(\n",
-    "    sim, task_name=\"angled_waveguide\", path=\"data/sim_data.hdf5\", verbose=True\n",
-    ")\n"
+    "sim_data = web.run(sim, task_name=\"angled_waveguide\", path=\"data/sim_data.hdf5\", verbose=True)"
    ]
   },
   {
@@ -1327,7 +1321,7 @@
     "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(15, 5))\n",
     "ax1 = sim_data.plot_field(\"field\", \"Ex\", z=0.05, f=freq0, val=\"real\", ax=ax1)\n",
     "ax2 = sim_data.plot_field(\"field\", \"E\", \"abs^2\", z=0.05, f=freq0, ax=ax2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1420,7 +1414,7 @@
     "ax2 = fig.add_subplot(gs[0, 1])\n",
     "sim.plot(z=0, ax=ax1)\n",
     "sim.plot(x=0, ax=ax2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1768,9 +1762,7 @@
     }
    ],
    "source": [
-    "sim_data = web.run(\n",
-    "    sim, task_name=\"angled_ring\", path=\"data/sim_data.hdf5\", verbose=True\n",
-    ")\n"
+    "sim_data = web.run(sim, task_name=\"angled_ring\", path=\"data/sim_data.hdf5\", verbose=True)"
    ]
   },
   {
@@ -1811,7 +1803,7 @@
     "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(15, 5))\n",
     "ax1 = sim_data.plot_field(\"field\", \"Ex\", z=0.05, f=freq0, val=\"real\", ax=ax1)\n",
     "ax2 = sim_data.plot_field(\"field\", \"E\", \"abs^2\", z=0.05, f=freq0, ax=ax2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
diff --git a/MultiplexingMMI.ipynb b/MultiplexingMMI.ipynb
index 121f9f3a..40a29512 100644
--- a/MultiplexingMMI.ipynb
+++ b/MultiplexingMMI.ipynb
@@ -34,10 +34,9 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web"
+    "import tidy3d.web as web\n",
+    "from matplotlib import pyplot as plt"
    ]
   },
   {
diff --git a/MultipoleExpansion.ipynb b/MultipoleExpansion.ipynb
index ba3630ee..7b1819ac 100644
--- a/MultipoleExpansion.ipynb
+++ b/MultipoleExpansion.ipynb
@@ -27,9 +27,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tidy3d as td\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import tidy3d as td\n",
     "from tidy3d import web"
    ]
   },
@@ -128,9 +128,7 @@
    "outputs": [],
    "source": [
     "# adding PMLs\n",
-    "boundary_spec = td.BoundarySpec(\n",
-    "    x=td.Boundary.pml(), y=td.Boundary.pml(), z=td.Boundary.pml()\n",
-    ")\n",
+    "boundary_spec = td.BoundarySpec(x=td.Boundary.pml(), y=td.Boundary.pml(), z=td.Boundary.pml())\n",
     "\n",
     "# TFSF source\n",
     "source = td.TFSF(\n",
@@ -157,9 +155,7 @@
     "\n",
     "mesh_override = [structure_override]\n",
     "\n",
-    "grid_spec = td.GridSpec.auto(\n",
-    "    min_steps_per_wvl=min_steps_per_wvl, override_structures=mesh_override\n",
-    ")\n",
+    "grid_spec = td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl, override_structures=mesh_override)\n",
     "\n",
     "# defining the structure objects\n",
     "structure = td.Structure(\n",
@@ -636,16 +632,14 @@
     "    omega = 2 * np.pi * freqs\n",
     "\n",
     "    # import libraries to carry out integration and Bessel functions\n",
-    "    from scipy.special import spherical_jn as jn\n",
     "    from scipy.integrate import trapezoid as trapz\n",
+    "    from scipy.special import spherical_jn as jn\n",
     "\n",
     "    # function for calculating the current density\n",
     "    J = lambda Ei, epsilon: 1j * (2 * np.pi * Freqs) * td.EPSILON_0 * (epsilon - 1) * Ei\n",
     "\n",
     "    # function for calculating volume integral\n",
-    "    integrate = lambda Data: trapz(\n",
-    "        trapz(trapz(Data, x=x, axis=0), x=y, axis=0), x=z, axis=0\n",
-    "    )\n",
+    "    integrate = lambda Data: trapz(trapz(trapz(Data, x=x, axis=0), x=y, axis=0), x=z, axis=0)\n",
     "\n",
     "    # defining the current densities\n",
     "    Jx = J(Ex, eps_xx)\n",
@@ -706,16 +700,12 @@
     "            delta = 0 if alpha != beta else 1\n",
     "\n",
     "            integrand1 = (3 * (ra * jb + rb * ja) - 2 * rj * delta) * (jn(1, kr) / kr)\n",
-    "            integrand2 = (\n",
-    "                (5 * ra * rb * rj)\n",
-    "                - (ra * jb + rb * ja) * r**2\n",
-    "                - (r**2 * rj * delta)\n",
-    "            ) * (jn(3, kr) / kr**3)\n",
-    "\n",
-    "            Eq_ab = (3j / omega) * (\n",
-    "                integrate(integrand1) + (2 * k**2) * integrate(integrand2)\n",
+    "            integrand2 = ((5 * ra * rb * rj) - (ra * jb + rb * ja) * r**2 - (r**2 * rj * delta)) * (\n",
+    "                jn(3, kr) / kr**3\n",
     "            )\n",
     "\n",
+    "            Eq_ab = (3j / omega) * (integrate(integrand1) + (2 * k**2) * integrate(integrand2))\n",
+    "\n",
     "            Eq += (1 / 120) * np.abs(k * Eq_ab) ** 2\n",
     "\n",
     "    # magnetic quadrupole\n",
diff --git a/NanobeamCavity.ipynb b/NanobeamCavity.ipynb
index 9ee03350..a5d373bd 100644
--- a/NanobeamCavity.ipynb
+++ b/NanobeamCavity.ipynb
@@ -27,11 +27,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tidy3d as td\n",
-    "import numpy as np\n",
+    "from typing import Callable\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import tidy3d as td\n",
     "from tidy3d import web\n",
-    "from typing import Callable\n",
+    "\n",
     "# defining a random seed for reproducibility\n",
     "np.random.seed(12)"
    ]
@@ -124,11 +126,7 @@
     "        x = structure_index / np.ceil(n_tapered_structures / 2)\n",
     "\n",
     "    # factor is 1 if the structure is not in the tapered region\n",
-    "    factor = (\n",
-    "        1\n",
-    "        if x >= 1\n",
-    "        else 1 - max_dip * (cubic * x**3 + quadratic * x**2 + linear * x + offset)\n",
-    "    )\n",
+    "    factor = 1 if x >= 1 else 1 - max_dip * (cubic * x**3 + quadratic * x**2 + linear * x + offset)\n",
     "\n",
     "    return factor"
    ]
@@ -219,12 +217,11 @@
     "\n",
     "    # defining the number of unit cells for each side\n",
     "    positions = range(\n",
-    "        n_constant_structures\n",
-    "        + int(np.floor(n_tapered_structures / 2))\n",
-    "        + n_constant_structures % 2\n",
+    "        n_constant_structures + int(np.floor(n_tapered_structures / 2)) + n_constant_structures % 2\n",
     "    )\n",
     "\n",
     "    # iterating over the unit cells\n",
+    "    previous_lattice_constant = 0\n",
     "    for i in positions:\n",
     "        # defining the current value of the unit cell given the position\n",
     "        current_lattice_constant = lattice_constant * taper_factor(\n",
@@ -245,9 +242,7 @@
     "        for idx in range(len(original_params)):\n",
     "            original = original_params[idx]\n",
     "            max_dip = max_dip_params[idx]\n",
-    "            tapered_param = original * taper_factor(\n",
-    "                max_dip, i, n_tapered_structures\n",
-    "            )\n",
+    "            tapered_param = original * taper_factor(max_dip, i, n_tapered_structures)\n",
     "            parameters.append(tapered_param)\n",
     "\n",
     "        # defining the center\n",
@@ -257,7 +252,7 @@
     "        new_unit_cell = unit_cell_function(center, *parameters)\n",
     "\n",
     "        # adding the unit cell to the geometry (or initializing the variable if is the first unit cell)\n",
-    "        unit_cells = new_unit_cell if unit_cells == None else unit_cells + new_unit_cell\n",
+    "        unit_cells = new_unit_cell if unit_cells is None else unit_cells + new_unit_cell\n",
     "\n",
     "        # the separation to the next unit cell is the mean value of the lattice constants of the current and next unit cells\n",
     "        previous_lattice_constant = current_lattice_constant\n",
@@ -376,26 +371,17 @@
     ")\n",
     "\n",
     "ellipse_geometry, _ = assemble_unit_cells(\n",
-    "    unit_cell_function=ellipse_uc,\n",
-    "    original_params=[0.2, 0.3],\n",
-    "    max_dip_params=[0.4, 0.3],\n",
-    "    **args\n",
+    "    unit_cell_function=ellipse_uc, original_params=[0.2, 0.3], max_dip_params=[0.4, 0.3], **args\n",
     ")\n",
     "ellipse_geometry.plot(z=0)\n",
     "\n",
     "square_geometry, _ = assemble_unit_cells(\n",
-    "    unit_cell_function=square_uc,\n",
-    "    original_params=[0.4, 0.3],\n",
-    "    max_dip_params=[0.4, 0.3],\n",
-    "    **args\n",
+    "    unit_cell_function=square_uc, original_params=[0.4, 0.3], max_dip_params=[0.4, 0.3], **args\n",
     ")\n",
     "square_geometry.plot(z=0)\n",
     "\n",
     "sawfish_geometry, _ = assemble_unit_cells(\n",
-    "    unit_cell_function=sawfish_uc,\n",
-    "    original_params=[1, 0.5],\n",
-    "    max_dip_params=[0.4, 0.5],\n",
-    "    **args\n",
+    "    unit_cell_function=sawfish_uc, original_params=[1, 0.5], max_dip_params=[0.4, 0.5], **args\n",
     ")\n",
     "sawfish_geometry.plot(z=0)\n",
     "\n",
@@ -485,7 +471,7 @@
     "    point_monitors = [\n",
     "        td.FieldTimeMonitor(\n",
     "            center=tuple(positions[i]),\n",
-    "            name=\"pointMon%i\" % i,\n",
+    "            name=f\"pointMon{i}\",\n",
     "            start=0,\n",
     "            size=(0, 0, 0),\n",
     "            interval=1,\n",
@@ -534,9 +520,7 @@
     "\n",
     "            # creating the monitors\n",
     "            flux_monitors.append(\n",
-    "                td.FluxTimeMonitor(\n",
-    "                    center=mon_center, size=mon_size, start=start, name=mon_name\n",
-    "                )\n",
+    "                td.FluxTimeMonitor(center=mon_center, size=mon_size, start=start, name=mon_name)\n",
     "            )\n",
     "\n",
     "    return (\n",
@@ -629,25 +613,27 @@
     "        override_structures=[mesh_override],\n",
     "    )\n",
     "\n",
-    "    kwargs = dict(unit_cell_function=unit_cell_function,\n",
-    "                  lattice_constant=lattice_constant,\n",
-    "                  max_dip_lattice=max_dip_lattice,\n",
-    "                  original_params=original_params,\n",
-    "                  max_dip_params=max_dip_params,\n",
-    "                  central_gap=central_gap)\n",
+    "    kwargs = dict(\n",
+    "        unit_cell_function=unit_cell_function,\n",
+    "        lattice_constant=lattice_constant,\n",
+    "        max_dip_lattice=max_dip_lattice,\n",
+    "        original_params=original_params,\n",
+    "        max_dip_params=max_dip_params,\n",
+    "        central_gap=central_gap,\n",
+    "    )\n",
     "\n",
     "    # assemble unit cells for the right side of the nanobeam\n",
     "    unit_cells_right, pos_x_right = assemble_unit_cells(\n",
     "        n_constant_structures=n_constant_structures_right,\n",
     "        n_tapered_structures=n_tapered_structures,\n",
-    "        **kwargs\n",
+    "        **kwargs,\n",
     "    )\n",
     "\n",
     "    # assemble unit cells for the left side of the nanobeam\n",
     "    unit_cells_left, pos_x_left = assemble_unit_cells(\n",
     "        n_constant_structures=n_constant_structures_left,\n",
-    "        n_tapered_structures = n_tapered_structures,\n",
-    "        **kwargs\n",
+    "        n_tapered_structures=n_tapered_structures,\n",
+    "        **kwargs,\n",
     "    )\n",
     "\n",
     "    # rotate and adjust the position of the left unit cells\n",
@@ -657,9 +643,7 @@
     "    # assemble and position the tapered output if applicable\n",
     "    if n_tapered_output > 0:\n",
     "        waveguide_tapered, pos_tapered = assemble_unit_cells(\n",
-    "            n_constant_structures=0,\n",
-    "            n_tapered_structures=n_tapered_output * 2,\n",
-    "            **kwargs\n",
+    "            n_constant_structures=0, n_tapered_structures=n_tapered_output * 2, **kwargs\n",
     "        )\n",
     "\n",
     "        # rotate and position the tapered output\n",
@@ -719,9 +703,7 @@
     "\n",
     "    # substrate\n",
     "    substrate = td.Structure(\n",
-    "        geometry=td.Box.from_bounds(\n",
-    "            rmin=(-999, -999, -999), rmax=(999, 999, -height / 2)\n",
-    "        ),\n",
+    "        geometry=td.Box.from_bounds(rmin=(-999, -999, -999), rmax=(999, 999, -height / 2)),\n",
     "        medium=td.Medium(permittivity=substrate_index**2),\n",
     "    )\n",
     "\n",
@@ -736,14 +718,10 @@
     "    ]\n",
     "\n",
     "    # boundary conditions\n",
-    "    boundary_spec = td.BoundarySpec(\n",
-    "        x=td.Boundary.pml(), y=td.Boundary.pml(), z=td.Boundary.pml()\n",
-    "    )\n",
+    "    boundary_spec = td.BoundarySpec(x=td.Boundary.pml(), y=td.Boundary.pml(), z=td.Boundary.pml())\n",
     "\n",
     "    # determine symmetry based on structure configuration\n",
-    "    if (n_constant_structures_left == n_constant_structures_right) and (\n",
-    "        n_tapered_output == 0\n",
-    "    ):\n",
+    "    if (n_constant_structures_left == n_constant_structures_right) and (n_tapered_output == 0):\n",
     "        symmetry = get_symmetry(polarization=polarization)\n",
     "    else:\n",
     "        symmetry = [0] + get_symmetry(polarization=polarization)[1:]\n",
@@ -815,9 +793,7 @@
     "sidewall_angle = 0\n",
     "\n",
     "# defining a unit cell function depending only on the taper parameters\n",
-    "unit_cell_function = lambda center, x, y: ellipse_uc(\n",
-    "    center, x_axis=x, y_axis=y, height=height\n",
-    ")\n",
+    "unit_cell_function = lambda center, x, y: ellipse_uc(center, x_axis=x, y_axis=y, height=height)\n",
     "\n",
     "\n",
     "sim = create_simulation(\n",
@@ -1379,11 +1355,11 @@
     "\n",
     "    # iterate through all monitors and combine the signal\n",
     "    i = 0\n",
-    "    name = \"pointMon%i\" % i\n",
+    "    name = f\"pointMon{i}\"\n",
     "    while name in sim_data.monitor_data:\n",
     "        combinedSignal += sim_data[\"pointMon0\"].field_components[polarization].squeeze()\n",
     "        i += 1\n",
-    "        name = \"pointMon%i\" % i\n",
+    "        name = f\"pointMon{i}\"\n",
     "\n",
     "    # create the ResonanceFinder instance\n",
     "    rf = ResonanceFinder(freq_window=freq_window)\n",
@@ -1528,7 +1504,7 @@
     "    energy_density = np.abs(Efield.Ex**2 + Efield.Ey**2 + Efield.Ez**2) * eps\n",
     "\n",
     "    # calculating the temporal mean value\n",
-    "    delta_t = energy_density.t[-1] - energy_density.t[0] # signal duration\n",
+    "    delta_t = energy_density.t[-1] - energy_density.t[0]  # signal duration\n",
     "    energy_density_temporal_mean = energy_density.integrate(coord=(\"t\")) / delta_t\n",
     "\n",
     "    return energy_density_temporal_mean, eps\n",
@@ -1574,7 +1550,7 @@
     "\n",
     "\n",
     "Vmode = mode_volume(energy_density, eps)\n",
-    "print(\"Mode volume: %.2f (lambda/n)^3\" % Vmode)"
+    "print(f\"Mode volume: {Vmode:.2f} (lambda/n)^3\")"
    ]
   },
   {
@@ -2117,7 +2093,7 @@
     "asymmetric_energy_density, asymmetric_eps = get_energy_density(asymmetric_sim_data)\n",
     "asymmetric_Vmode = mode_volume(asymmetric_energy_density, eps)\n",
     "\n",
-    "print(\"Mode volume: %.2f (lambda/n)^3\" % asymmetric_Vmode)\n",
+    "print(f\"Mode volume: {asymmetric_Vmode:.2f} (lambda/n)^3\")\n",
     "\n",
     "asymmetric_df, asymmetric_signal = analyse_resonance_monitors(\n",
     "    asymmetric_sim_data, start_time, freq_window\n",
@@ -2155,10 +2131,7 @@
     "    # iterating through all flux monitors and calculating the Q factor\n",
     "    for coord in [\"x\", \"y\", \"z\"]:\n",
     "        for direction in [\"\", \"-\"]:\n",
-    "            delta_t = (\n",
-    "                sim_data[direction + coord].flux.t[-1]\n",
-    "                - sim_data[direction + coord].flux.t[0]\n",
-    "            )\n",
+    "            delta_t = sim_data[direction + coord].flux.t[-1] - sim_data[direction + coord].flux.t[0]\n",
     "            flux = abs(sim_data[direction + coord].flux.integrate(coord=\"t\")) / delta_t\n",
     "            dict_fluxes[direction + coord] = (omega * Energy / flux).values\n",
     "\n",
@@ -2195,7 +2168,7 @@
     ")\n",
     "\n",
     "for i in directional_Q.keys():\n",
-    "    print(\"Q %s = %.2f MM\" % (i, directional_Q[i] * 10**-6))"
+    "    print(f\"Q {i} = {directional_Q[i] * 10**-6:.2f} MM\")"
    ]
   },
   {
@@ -2233,7 +2206,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.0"
   },
   "title": "How to model a nanobeam cavity using Tidy3D | Flexcompute"
  },
diff --git a/NanostructuredBoronNitride.ipynb b/NanostructuredBoronNitride.ipynb
index 8a24d7e6..07c32deb 100644
--- a/NanostructuredBoronNitride.ipynb
+++ b/NanostructuredBoronNitride.ipynb
@@ -44,9 +44,8 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web"
    ]
diff --git a/Near2FarSphereRCS.ipynb b/Near2FarSphereRCS.ipynb
index e9307560..5a538e32 100644
--- a/Near2FarSphereRCS.ipynb
+++ b/Near2FarSphereRCS.ipynb
@@ -32,12 +32,12 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3d imports\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n"
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -101,7 +101,7 @@
     "domain_size = buffer_PML + 2 * radius + buffer_PML\n",
     "\n",
     "# construct simulation size array\n",
-    "sim_size = (domain_size, domain_size, domain_size)\n"
+    "sim_size = (domain_size, domain_size, domain_size)"
    ]
   },
   {
@@ -145,7 +145,7 @@
     ")\n",
     "\n",
     "# Simulation run time (s)\n",
-    "run_time = 2e-12\n"
+    "run_time = 2e-12"
    ]
   },
   {
@@ -181,7 +181,7 @@
     "mon_size = 2 * radius + 2 * buffer_mon\n",
     "monitors_near = td.FieldMonitor.surfaces(\n",
     "    center=center, size=[mon_size] * 3, freqs=[f0], name=\"near_field\", colocate=False\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -226,7 +226,7 @@
     "    theta=list(thetas),\n",
     "    far_field_approx=True,  # we leave this to its default value of 'True' because we are interested in fields sufficiently\n",
     "    # far away that the far field approximations can be invoked to speed up the calculation\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -268,7 +268,7 @@
     "        2,\n",
     "        3,\n",
     "    ),  # these are the (x, y, z) factors by which fields are downsampled\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -317,7 +317,7 @@
     "    monitors=monitors,\n",
     "    run_time=run_time,\n",
     "    boundary_spec=boundary_spec,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -358,7 +358,7 @@
     "sim.plot(y=0, ax=ax[0])\n",
     "sim.plot(y=0, ax=ax[1])\n",
     "sim.plot_grid(y=0, ax=ax[1])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -715,7 +715,7 @@
     }
    ],
    "source": [
-    "sim_data = web.run(sim, task_name=\"sphereRCS\", path=\"data/sphereRCS.hdf5\", verbose=True)\n"
+    "sim_data = web.run(sim, task_name=\"sphereRCS\", path=\"data/sphereRCS.hdf5\", verbose=True)"
    ]
   },
   {
@@ -1224,7 +1224,7 @@
     "start = time.time()\n",
     "far_fields_downsampled = n2f_downsampled.project_fields(monitor_far)\n",
     "end = time.time()\n",
-    "n2f_downsampled_time = end - start\n"
+    "n2f_downsampled_time = end - start"
    ]
   },
   {
@@ -1268,7 +1268,7 @@
     "\n",
     "print(f\"Local near-to-far: {n2f_time} s\")\n",
     "print(f\"Local near-to-far with downsampling: {n2f_downsampled_time} s\")\n",
-    "print(f\"Server-side near-to-far: {n2f_server_time} s\")\n"
+    "print(f\"Server-side near-to-far: {n2f_server_time} s\")"
    ]
   },
   {
@@ -1293,7 +1293,7 @@
    },
    "outputs": [],
    "source": [
-    "far_fields_server = sim_data[monitor_far.name]\n"
+    "far_fields_server = sim_data[monitor_far.name]"
    ]
   },
   {
@@ -1324,7 +1324,7 @@
     "# get the RCS for the local, local downsampled and server-side cases\n",
     "RCS = np.real(far_fields.radar_cross_section.sel(f=f0).values)\n",
     "RCS_downsampled = np.real(far_fields_downsampled.radar_cross_section.sel(f=f0).values)\n",
-    "RCS_server = np.real(far_fields_server.radar_cross_section.sel(f=f0).values)\n"
+    "RCS_server = np.real(far_fields_server.radar_cross_section.sel(f=f0).values)"
    ]
   },
   {
@@ -1436,9 +1436,7 @@
     "    \"--r\",\n",
     "    label=\"$\\\\phi = \\\\pi/2$, near2far local downsampled\",\n",
     ")\n",
-    "ax.plot(\n",
-    "    thetas, to_db(RCS_server_phi90), \"--g\", label=\"$\\\\phi = \\\\pi/2$, near2far server\"\n",
-    ")\n",
+    "ax.plot(thetas, to_db(RCS_server_phi90), \"--g\", label=\"$\\\\phi = \\\\pi/2$, near2far server\")\n",
     "ax.set(\n",
     "    xlabel=\"$\\\\theta$ (degrees)\",\n",
     "    ylabel=\"Bistatic RCS (dBs$\\\\mu$m)\",\n",
@@ -1447,7 +1445,7 @@
     ")\n",
     "ax.grid(visible=True, which=\"both\", axis=\"both\", linewidth=0.4)\n",
     "plt.legend(loc=\"best\", prop={\"size\": 14})\n",
-    "plt.tight_layout()\n"
+    "plt.tight_layout()"
    ]
   },
   {
diff --git a/NonHermitianMetagratings.ipynb b/NonHermitianMetagratings.ipynb
index 3c24bab6..a17541fc 100644
--- a/NonHermitianMetagratings.ipynb
+++ b/NonHermitianMetagratings.ipynb
@@ -38,9 +38,8 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
     "from tidy3d.plugins.dispersion import DispersionFitter"
@@ -76,19 +75,19 @@
    },
    "outputs": [],
    "source": [
-    "lda0 = 1.15 # simulation wavelength\n",
-    "freq0 = td.C_0/lda0 # simulation frequency\n",
+    "lda0 = 1.15  # simulation wavelength\n",
+    "freq0 = td.C_0 / lda0  # simulation frequency\n",
     "\n",
-    "w1 = 0.083 # width of the first nanostrip in the unit cell\n",
-    "w2 = 0.162 # width of the second nanostrip in the unit cell\n",
+    "w1 = 0.083  # width of the first nanostrip in the unit cell\n",
+    "w2 = 0.162  # width of the second nanostrip in the unit cell\n",
     "t1 = 0.08  # thickness of the first nanostrip\n",
     "t2 = 0.03  # thickness of the bottom layer of the second nanostrip\n",
-    "t3 = 0.022 # thickness of the top layer of the second nanostrip\n",
+    "t3 = 0.022  # thickness of the top layer of the second nanostrip\n",
     "s = 0.35  # center-to-center distance between the nanostrips\n",
     "p = 1.14  # grating period\n",
     "t_au = 0.06  # gold layer thickness\n",
     "N = 9  # number of unit cells\n",
-    "inf_eff = 1e3 # effective infinity"
+    "inf_eff = 1e3  # effective infinity"
    ]
   },
   {
@@ -171,7 +170,7 @@
    ],
    "source": [
     "# define gold medium\n",
-    "Au = td.material_library['Au']['Olmon2012stripped']  \n",
+    "Au = td.material_library[\"Au\"][\"Olmon2012stripped\"]\n",
     "\n",
     "# define Ge medium\n",
     "Ge = td.Medium.from_nk(n=4.6403, k=0.2519, freq=freq0)\n",
@@ -183,7 +182,7 @@
     "fname = \"misc/Cr_Sytchkova.csv\"  # read the refractive index data from a csv file\n",
     "fitter = DispersionFitter.from_file(fname, delimiter=\",\")  # construct a fitter\n",
     "Cr, rms_error = fitter.fit(num_poles=4, num_tries=10)\n",
-    "fitted_nk = Cr.nk_model(freq0)\n"
+    "fitted_nk = Cr.nk_model(freq0)"
    ]
   },
   {
@@ -191,7 +190,7 @@
    "id": "2f0b68de",
    "metadata": {},
    "source": [
-    "After the fitting, we see a warning that the fitting RMS error didn't get lower than the threshold. We can inspect the fitting result of chromium to see if the fitting is sufficiently good. Here we do see the fitting is quite resonable.  "
+    "After the fitting, we see a warning that the fitting RMS error didn't get lower than the threshold. We can inspect the fitting result of chromium to see if the fitting is sufficiently good. Here we do see the fitting is quite reasonable.  "
    ]
   },
   {
@@ -248,21 +247,23 @@
     "# define the nanostrips that make up the unit cells\n",
     "grating = []\n",
     "for i in range(N):\n",
-    "    geo = td.Box(center=(-s/2+i*p-(N-1)*p/2, 0, t1/2), size=(w1,td.inf,t1))\n",
+    "    geo = td.Box(center=(-s / 2 + i * p - (N - 1) * p / 2, 0, t1 / 2), size=(w1, td.inf, t1))\n",
     "    grating.append(td.Structure(geometry=geo, medium=Ge))\n",
-    "    \n",
-    "    geo = td.Box(center=(s/2+i*p-(N-1)*p/2, 0, t2/2), size=(w1,td.inf,t2))\n",
+    "\n",
+    "    geo = td.Box(center=(s / 2 + i * p - (N - 1) * p / 2, 0, t2 / 2), size=(w1, td.inf, t2))\n",
     "    grating.append(td.Structure(geometry=geo, medium=Ge))\n",
-    "    \n",
-    "    geo = td.Box(center=(s/2+i*p-(N-1)*p/2, 0, t2+t3/2), size=(w1,td.inf,t3))\n",
+    "\n",
+    "    geo = td.Box(center=(s / 2 + i * p - (N - 1) * p / 2, 0, t2 + t3 / 2), size=(w1, td.inf, t3))\n",
     "    grating.append(td.Structure(geometry=geo, medium=Cr))\n",
-    "    \n",
+    "\n",
     "# define the substrate\n",
-    "substrate_geo = td.Box.from_bounds(rmin=(-inf_eff,-inf_eff,-inf_eff), rmax=(inf_eff,inf_eff,-t_au))\n",
+    "substrate_geo = td.Box.from_bounds(\n",
+    "    rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, -t_au)\n",
+    ")\n",
     "substrate = td.Structure(geometry=substrate_geo, medium=SiO2)\n",
     "\n",
     "# define the gold film\n",
-    "gold_film_geo = td.Box(center=(0,0,-t_au/2), size=(inf_eff, inf_eff, t_au))\n",
+    "gold_film_geo = td.Box(center=(0, 0, -t_au / 2), size=(inf_eff, inf_eff, t_au))\n",
     "gold_film = td.Structure(geometry=gold_film_geo, medium=Au)"
    ]
   },
@@ -314,29 +315,26 @@
    "source": [
     "gaussian_beam = td.GaussianBeam(\n",
     "    size=(td.inf, td.inf, 0),\n",
-    "    center=(0, 0, 5*lda0),\n",
-    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0/10),\n",
+    "    center=(0, 0, 5 * lda0),\n",
+    "    source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 10),\n",
     "    direction=\"-\",\n",
-    "    waist_radius=5*p,\n",
+    "    waist_radius=5 * p,\n",
     ")\n",
     "\n",
     "field_monitor = td.FieldMonitor(\n",
-    "    size=(td.inf, 0, td.inf), \n",
-    "    freqs=[freq0], \n",
-    "    interval_space=(2,1,2),\n",
-    "    name=\"field\"\n",
+    "    size=(td.inf, 0, td.inf), freqs=[freq0], interval_space=(2, 1, 2), name=\"field\"\n",
     ")\n",
     "\n",
     "flux_monitor_right = td.FluxMonitor(\n",
-    "    center=(23*p,0,0),\n",
-    "    size=(0, td.inf, 2*lda0),\n",
+    "    center=(23 * p, 0, 0),\n",
+    "    size=(0, td.inf, 2 * lda0),\n",
     "    freqs=[freq0],\n",
     "    name=\"flux_right\",\n",
     ")\n",
     "\n",
     "flux_monitor_left = td.FluxMonitor(\n",
-    "    center=(-23*p,0,0),\n",
-    "    size=(0, td.inf, 2*lda0),\n",
+    "    center=(-23 * p, 0, 0),\n",
+    "    size=(0, td.inf, 2 * lda0),\n",
     "    freqs=[freq0],\n",
     "    name=\"flux_left\",\n",
     ")"
@@ -365,26 +363,26 @@
    "outputs": [],
    "source": [
     "# simulation domain size\n",
-    "Lx = 50*p\n",
+    "Lx = 50 * p\n",
     "Ly = 0\n",
-    "Lz = 7*lda0\n",
+    "Lz = 7 * lda0\n",
     "\n",
     "# simulation run time\n",
     "run_time = 5e-13\n",
     "\n",
     "# define simulation\n",
     "sim = td.Simulation(\n",
-    "        center=(0, 0, 2*lda0),\n",
-    "        size=(Lx, Ly, Lz),\n",
-    "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=30, wavelength=lda0),\n",
-    "        structures=grating + [substrate, gold_film],\n",
-    "        sources=[gaussian_beam],\n",
-    "        monitors=[field_monitor, flux_monitor_right, flux_monitor_left],\n",
-    "        run_time=run_time,\n",
-    "        boundary_spec=td.BoundarySpec(\n",
-    "            x=td.Boundary.pml(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
-    "        ),  \n",
-    ")\n"
+    "    center=(0, 0, 2 * lda0),\n",
+    "    size=(Lx, Ly, Lz),\n",
+    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=30, wavelength=lda0),\n",
+    "    structures=grating + [substrate, gold_film],\n",
+    "    sources=[gaussian_beam],\n",
+    "    monitors=[field_monitor, flux_monitor_right, flux_monitor_left],\n",
+    "    run_time=run_time,\n",
+    "    boundary_spec=td.BoundarySpec(\n",
+    "        x=td.Boundary.pml(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
+    "    ),\n",
+    ")"
    ]
   },
   {
@@ -421,7 +419,7 @@
    ],
    "source": [
     "ax = sim.plot(y=0)\n",
-    "ax.set_aspect('auto')\n",
+    "ax.set_aspect(\"auto\")\n",
     "plt.show()"
    ]
   },
@@ -461,7 +459,7 @@
     "ax = sim.plot(y=0)\n",
     "ax.set_xlim(-6, 6)\n",
     "ax.set_ylim(-0.1, 0.1)\n",
-    "ax.set_aspect('auto')\n",
+    "ax.set_aspect(\"auto\")\n",
     "plt.show()"
    ]
   },
@@ -819,9 +817,7 @@
     }
    ],
    "source": [
-    "sim_data = web.run(\n",
-    "    sim, task_name=\"nonhermitian_metagrating\", path=\"data/simulation_data.hdf5\"\n",
-    ")\n"
+    "sim_data = web.run(sim, task_name=\"nonhermitian_metagrating\", path=\"data/simulation_data.hdf5\")"
    ]
   },
   {
@@ -865,8 +861,10 @@
     }
    ],
    "source": [
-    "ax = sim_data.plot_field(field_monitor_name='field', field_name='Hy', val='abs^2', vmin=0, vmax=3e-4)\n",
-    "ax.set_aspect('auto')"
+    "ax = sim_data.plot_field(\n",
+    "    field_monitor_name=\"field\", field_name=\"Hy\", val=\"abs^2\", vmin=0, vmax=3e-4\n",
+    ")\n",
+    "ax.set_aspect(\"auto\")"
    ]
   },
   {
@@ -900,12 +898,13 @@
    ],
    "source": [
     "def cal_C_exc(sim_data):\n",
-    "    I_r = sim_data['flux_right'].flux\n",
-    "    I_l = -sim_data['flux_left'].flux\n",
-    "    C_exc = (I_r-I_l)/(I_r+I_l)\n",
+    "    I_r = sim_data[\"flux_right\"].flux\n",
+    "    I_l = -sim_data[\"flux_left\"].flux\n",
+    "    C_exc = (I_r - I_l) / (I_r + I_l)\n",
     "    return C_exc.values[0]\n",
     "\n",
-    "print(f'C_exc = {cal_C_exc(sim_data):1.4f}')"
+    "\n",
+    "print(f\"C_exc = {cal_C_exc(sim_data):1.4f}\")"
    ]
   },
   {
@@ -939,22 +938,23 @@
    "outputs": [],
    "source": [
     "def make_sim(s):\n",
-    "    \n",
     "    # define the grating structures of each unit cell\n",
     "    grating = []\n",
     "    for i in range(N):\n",
-    "        geo = td.Box(center=(-s/2+i*p-(N-1)*p/2, 0, t1/2), size=(w1,td.inf,t1))\n",
+    "        geo = td.Box(center=(-s / 2 + i * p - (N - 1) * p / 2, 0, t1 / 2), size=(w1, td.inf, t1))\n",
     "        grating.append(td.Structure(geometry=geo, medium=Ge))\n",
     "\n",
-    "        geo = td.Box(center=(s/2+i*p-(N-1)*p/2, 0, t2/2), size=(w1,td.inf,t2))\n",
+    "        geo = td.Box(center=(s / 2 + i * p - (N - 1) * p / 2, 0, t2 / 2), size=(w1, td.inf, t2))\n",
     "        grating.append(td.Structure(geometry=geo, medium=Ge))\n",
     "\n",
-    "        geo = td.Box(center=(s/2+i*p-(N-1)*p/2, 0, t2+t3/2), size=(w1,td.inf,t3))\n",
+    "        geo = td.Box(\n",
+    "            center=(s / 2 + i * p - (N - 1) * p / 2, 0, t2 + t3 / 2), size=(w1, td.inf, t3)\n",
+    "        )\n",
     "        grating.append(td.Structure(geometry=geo, medium=Cr))\n",
     "\n",
     "    # define simulation\n",
     "    sim = td.Simulation(\n",
-    "        center=(0, 0, 2*lda0),\n",
+    "        center=(0, 0, 2 * lda0),\n",
     "        size=(Lx, Ly, Lz),\n",
     "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=30, wavelength=lda0),\n",
     "        structures=grating + [substrate, gold_film],\n",
@@ -963,9 +963,9 @@
     "        run_time=run_time,\n",
     "        boundary_spec=td.BoundarySpec(\n",
     "            x=td.Boundary.pml(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
-    "        ),  \n",
+    "        ),\n",
     "    )\n",
-    "    \n",
+    "\n",
     "    return sim"
    ]
   },
@@ -991,11 +991,9 @@
    },
    "outputs": [],
    "source": [
-    "s_array = np.linspace(0.35,0.77,5)  # collection of s for the parameter sweep\n",
+    "s_array = np.linspace(0.35, 0.77, 5)  # collection of s for the parameter sweep\n",
     "\n",
-    "sims = {\n",
-    "    f\"s={s:.3f}\": make_sim(s) for s in s_array\n",
-    "}  # construct simulations for each s from s_array\n"
+    "sims = {f\"s={s:.3f}\": make_sim(s) for s in s_array}  # construct simulations for each s from s_array"
    ]
   },
   {
@@ -1745,19 +1743,18 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(len(s_array),1, tight_layout=True, figsize=(8,12))\n",
+    "fig, ax = plt.subplots(len(s_array), 1, tight_layout=True, figsize=(8, 12))\n",
     "\n",
     "C_exc = []\n",
     "\n",
     "# computer |H_y|^2 and C_exc for each simulation\n",
     "for i, s in enumerate(s_array):\n",
     "    sim_data = batch_results[f\"s={s:.3f}\"]\n",
-    "    Hy_abs = sim_data['field'].Hy.abs\n",
+    "    Hy_abs = sim_data[\"field\"].Hy.abs\n",
     "    Hy_squared = Hy_abs**2\n",
-    "    Hy_squared.plot(x='x',y='z',ax=ax[i], vmin=0, vmax=3e-4, cmap='hot')\n",
-    "    ax[i].set_title(f's={s:.3f} um')\n",
-    "    C_exc.append(cal_C_exc(sim_data))\n",
-    "    "
+    "    Hy_squared.plot(x=\"x\", y=\"z\", ax=ax[i], vmin=0, vmax=3e-4, cmap=\"hot\")\n",
+    "    ax[i].set_title(f\"s={s:.3f} um\")\n",
+    "    C_exc.append(cal_C_exc(sim_data))"
    ]
   },
   {
@@ -1793,10 +1790,10 @@
     }
    ],
    "source": [
-    "plt.plot(s_array*1e3, np.array(C_exc), c='red', linewidth=3)\n",
-    "plt.xlabel('s (nm)')\n",
-    "plt.ylabel('$C_{exc}$')\n",
-    "plt.ylim(-1,1)\n",
+    "plt.plot(s_array * 1e3, np.array(C_exc), c=\"red\", linewidth=3)\n",
+    "plt.xlabel(\"s (nm)\")\n",
+    "plt.ylabel(\"$C_{exc}$\")\n",
+    "plt.ylim(-1, 1)\n",
     "plt.show()"
    ]
   },
@@ -1835,7 +1832,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.0"
   },
   "title": "SPP on Non-Hermitian Metagratings | Flexcompute",
   "widgets": {
diff --git a/OpticalLuneburgLens.ipynb b/OpticalLuneburgLens.ipynb
index 71a70b56..e91468bd 100644
--- a/OpticalLuneburgLens.ipynb
+++ b/OpticalLuneburgLens.ipynb
@@ -13,9 +13,9 @@
    "id": "549ddc87",
    "metadata": {},
    "source": [
-    "Luneburg lens is a prototypical gradient index (GRIN) optical component. A classical Luneburg lens is a spherical lens with a spatially varying refractive index profile following $n(r)=n_0\\sqrt{2-(r/R)^2}$, where $r$ is the radial distance, $R$ is the radius of the lens, and $n_0$ is the refractive index of the ambient environment. Plane wave incident on a Luneburg lens will be focused to a point on the surface of the lens. Compared to a usual refractive lens, Luneburg lens is abberation-free and coma-free, which enables a wide range of applications in modern optical systems.\n",
+    "Luneburg lens is a prototypical gradient index (GRIN) optical component. A classical Luneburg lens is a spherical lens with a spatially varying refractive index profile following $n(r)=n_0\\sqrt{2-(r/R)^2}$, where $r$ is the radial distance, $R$ is the radius of the lens, and $n_0$ is the refractive index of the ambient environment. Plane wave incident on a Luneburg lens will be focused to a point on the surface of the lens. Compared to a usual refractive lens, Luneburg lens is aberration-free and coma-free, which enables a wide range of applications in modern optical systems.\n",
     "\n",
-    "However, it is practically difficult to construct such a lens due to the required gradient index distribution. In the microwave regime, high-gain antennas based on a Luneburg lens design can be achieved by, for example, using concentric ceremics shells with different densities. In the optical frequencies, such an approach is generally not applicable. \n",
+    "However, it is practically difficult to construct such a lens due to the required gradient index distribution. In the microwave regime, high-gain antennas based on a Luneburg lens design can be achieved by, for example, using concentric ceramics shells with different densities. In the optical frequencies, such an approach is generally not applicable. \n",
     "\n",
     "In this notebook, we demonstrate the numerical simulation of a practical 3D optical Luneburg lens. The structure consists of a large number of subwavelength unit cells. Using an effective medium approach, each unit cell can be approximated by a local effective index, which can be tuned by the filling fraction of the dielectric polymer in the unit cell. By varying the filling fraction of each unit cell such that the local effective index follows $n(r)=n_0\\sqrt{2-(r/R)^2}$, a Luneburg lens is constructed. This design is adapted from [Zhao, Y. Y. et al. Three-dimensional Luneburg lens at optical frequencies. Laser Photonics Rev. 10, 665–672 (2016)](https://onlinelibrary.wiley.com/doi/abs/10.1002/lpor.201600051). In the simulation, a linearly polarized plane wave is launched towards the Luneburg lens. Through the visualization of the field distribution, the focusing capability of the lens can be assessed. We also compare the practical Luneburg lens design with the idealized case, which is simulated using [CustomMedium](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.CustomMedium.html). The comparison result shows the practical Luneburg lens design is very optimal.\n",
     "\n",
@@ -38,14 +38,12 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "from scipy.optimize import fsolve\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
-    "from tidy3d import ScalarFieldDataArray\n",
-    "from tidy3d import PermittivityDataset\n"
+    "from scipy.optimize import fsolve\n",
+    "from tidy3d import PermittivityDataset, ScalarFieldDataArray"
    ]
   },
   {
@@ -82,9 +80,7 @@
     "ldas = np.linspace(5.25, 7.25, 10)  # simulation wavelength range\n",
     "freq0 = td.C_0 / lda0  # central frequency\n",
     "freqs = td.C_0 / ldas  # simulation frequency range\n",
-    "fwidth = 0.5 * (\n",
-    "    np.max(freqs) - np.min(freqs)\n",
-    ")  # width of the frequency gaussian distribution\n"
+    "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))  # width of the frequency gaussian distribution"
    ]
   },
   {
@@ -109,7 +105,7 @@
    },
    "outputs": [],
    "source": [
-    "a = 2  # period of the unit cell\n"
+    "a = 2  # period of the unit cell"
    ]
   },
   {
@@ -138,7 +134,7 @@
     "n_0 = 1  # refractive index of air\n",
     "\n",
     "dielectric = td.Medium(permittivity=n_d**2)\n",
-    "air = td.Medium(permittivity=n_0**2)\n"
+    "air = td.Medium(permittivity=n_0**2)"
    ]
   },
   {
@@ -146,7 +142,7 @@
    "id": "77ca0842",
    "metadata": {},
    "source": [
-    "The unit cell is a simply cubic with voids. We define the width of the polymer frames to be $w$. By tuning $w$ from 0 to 0.5$a$, the fillig fraction $f$ is changed from 0 to 1. Since the Luneburg lens structure consists of a large number of unit cells with varying geometries, it is convenient to define a function called `build_unit_cell` that takes in $w$ and the center coordinates and returns a unit cell structure. This function can then be called systematically later to construct the whole lens."
+    "The unit cell is a simple cubic with voids. We define the width of the polymer frames to be $w$. By tuning $w$ from 0 to 0.5$a$, the filling fraction $f$ is changed from 0 to 1. Since the Luneburg lens structure consists of a large number of unit cells with varying geometries, it is convenient to define a function called `build_unit_cell` that takes in $w$ and the center coordinates and returns a unit cell structure. This function can then be called systematically later to construct the whole lens."
    ]
   },
   {
@@ -167,9 +163,7 @@
     "    unit_cell = []\n",
     "\n",
     "    unit_cell.append(\n",
-    "        td.Structure(\n",
-    "            geometry=td.Box(center=(x, y, z), size=(a, a, a)), medium=dielectric\n",
-    "        )\n",
+    "        td.Structure(geometry=td.Box(center=(x, y, z), size=(a, a, a)), medium=dielectric)\n",
     "    )\n",
     "\n",
     "    unit_cell.append(\n",
@@ -193,7 +187,7 @@
     "        )\n",
     "    )\n",
     "\n",
-    "    return unit_cell\n"
+    "    return unit_cell"
    ]
   },
   {
@@ -222,7 +216,7 @@
     "R = N * a  # radius of the Luneburg lens\n",
     "r = np.linspace(a / 2, R - a / 2, N)  # distance of each unit cell to the origin\n",
     "n_r = np.sqrt(2 - (r / R) ** 2)  # desired effective index at each unit cell\n",
-    "f_r = (n_r - n_0) / (n_d - n_0)  # corresponding filling fraction at each unit cell\n"
+    "f_r = (n_r - n_0) / (n_d - n_0)  # corresponding filling fraction at each unit cell"
    ]
   },
   {
@@ -250,9 +244,9 @@
     "w_r = np.zeros(N)  # width of the polymer frame at each unit cell\n",
     "\n",
     "# solve for w_r from f_r\n",
-    "for i, f in enumerate(f_r):\n",
+    "for i, f_val in enumerate(f_r):\n",
     "\n",
-    "    def func(w):\n",
+    "    def func(w, f=f_val):\n",
     "        return 1 - (a - 2 * w) ** 2 * (a + 4 * w) / a**3 - f\n",
     "\n",
     "    solution = fsolve(func, 0.5)\n",
@@ -264,9 +258,9 @@
    "id": "4790a4d6",
    "metadata": {},
    "source": [
-    "With the obtained $w$ as a function of radial distance, we are ready to construct the Luneburg lens strucutre. This can be done easily through calling the `build_unit_cell` function in a neasted loops over the $x$, $y$, and $z$ coordinates of each unit cell.\n",
+    "With the obtained $w$ as a function of radial distance, we are ready to construct the Luneburg lens structure. This can be done easily by calling the `build_unit_cell` function in a nested loop over the $x$, $y$, and $z$ coordinates of each unit cell.\n",
     "\n",
-    "Thanks to the symmetries, we only need to build a quater of the Luneburg lens structure. This drastically reduces the total number of Structures as well as the number of grid points of the model."
+    "Thanks to the symmetries, we only need to build a quarter of the Luneburg lens structure. This drastically reduces the total number of Structures as well as the number of grid points of the model."
    ]
   },
   {
@@ -288,14 +282,10 @@
     "for x in r:\n",
     "    for y in r:\n",
     "        for z in np.linspace(-R + a / 2, R - a / 2, 2 * N):\n",
-    "            r_local = np.sqrt(\n",
-    "                x**2 + y**2 + z**2\n",
-    "            )  # radial distance of the unit cell\n",
+    "            r_local = np.sqrt(x**2 + y**2 + z**2)  # radial distance of the unit cell\n",
     "            # build an unit cell if the radial distance is smaller or equal to the lens radius\n",
     "            if r_local <= R:\n",
-    "                luneburg_lens.extend(\n",
-    "                    build_unit_cell(np.interp(r_local, r, w_r), x, y, z)\n",
-    "                )\n"
+    "                luneburg_lens.extend(build_unit_cell(np.interp(r_local, r, w_r), x, y, z))"
    ]
   },
   {
@@ -337,7 +327,7 @@
     "# define a field monitor in the xy plane at z=R\n",
     "monitor_field_xy = td.FieldMonitor(\n",
     "    center=[0, 0, R], size=[td.inf, td.inf, 0], freqs=[freq0], name=\"field_xy\"\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -377,15 +367,13 @@
     "    sources=[plane_wave],\n",
     "    monitors=[monitor_field_xz, monitor_field_xy],\n",
     "    run_time=run_time,\n",
-    "    boundary_spec=td.BoundarySpec.all_sides(\n",
-    "        boundary=td.PML()\n",
-    "    ),  # pml is applied in all boundaries\n",
+    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),  # pml is applied in all boundaries\n",
     "    symmetry=(\n",
     "        -1,\n",
     "        1,\n",
     "        0,\n",
     "    ),  # symmetry is used such that only a quarter of the structure needs to be modeled.\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -422,7 +410,7 @@
    ],
    "source": [
     "sim.plot(y=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -430,7 +418,7 @@
    "id": "abf14287",
    "metadata": {},
    "source": [
-    "Submit the simulation job to the server. We name the simulation data `sim_data_practical` to distinguish the simulation data for the idealizd Luneburg lens later on."
+    "Submit the simulation job to the server. We name the simulation data `sim_data_practical` to distinguish the simulation data for the idealized Luneburg lens later on."
    ]
   },
   {
@@ -807,7 +795,7 @@
     "    task_name=\"practical_luneburg_lens\",\n",
     "    path=\"data/simulation_data.hdf5\",\n",
     "    verbose=True,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -862,7 +850,7 @@
     "sim_data_practical.plot_field(\n",
     "    field_monitor_name=\"field_xz\", field_name=\"Ex\", ax=ax2, vmin=-5, vmax=5\n",
     ")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -904,10 +892,10 @@
     "fig, ax = plt.subplots()\n",
     "for z in zs:\n",
     "    I = sim_data_practical.get_intensity(\"field_xz\").sel(z=z, method=\"nearest\")\n",
-    "    I.plot(ax=ax, label=f\"z={z} $\\mu m$\")\n",
+    "    I.plot(ax=ax, label=rf\"z={z} $\\mu m$\")\n",
     "ax.legend()\n",
     "ax.set_title(\"Field intensity\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -946,7 +934,7 @@
     "sim_data_practical.plot_field(\n",
     "    field_monitor_name=\"field_xy\", field_name=\"E\", val=\"abs^2\", vmin=0, vmax=25\n",
     ")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -995,7 +983,7 @@
     "# index of refraction array\n",
     "# assign the refractive index value to the array according to the desired profile\n",
     "n_data = np.ones((Nx, Ny, Nz, Nf))\n",
-    "n_data[r_mesh <= R] = np.sqrt(2 - (r_mesh[r_mesh <= R] / R) ** 2)\n"
+    "n_data[r_mesh <= R] = np.sqrt(2 - (r_mesh[r_mesh <= R] / R) ** 2)"
    ]
   },
   {
@@ -1003,7 +991,7 @@
    "id": "3838716d",
    "metadata": {},
    "source": [
-    "The numpy array is converted to a ScalarFieldDataArray that labels the coordinate. A custome medium is then defined using the classmethod [td.CustomMedium.from_nk](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.CustomMedium.html#tidy3d.CustomMedium.from_nk). Finally, the lens structure is defined."
+    "The numpy array is converted to a ScalarFieldDataArray that labels the coordinate. A custom medium is then defined using the class method [td.CustomMedium.from_nk](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.CustomMedium.html#tidy3d.CustomMedium.from_nk). Finally, the lens structure is defined."
    ]
   },
   {
@@ -1027,7 +1015,7 @@
     "mat_custom = td.CustomMedium.from_nk(n_dataset, interp_method=\"nearest\")\n",
     "\n",
     "# define the ideal luneburg lens structure\n",
-    "lens = td.Structure(geometry=td.Sphere(radius=R), medium=mat_custom)\n"
+    "lens = td.Structure(geometry=td.Sphere(radius=R), medium=mat_custom)"
    ]
   },
   {
@@ -1065,7 +1053,7 @@
    "source": [
     "sim = sim.copy(update={\"structures\": [lens]})\n",
     "sim.plot_eps(y=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1445,7 +1433,7 @@
    "source": [
     "sim_data_ideal = web.run(\n",
     "    sim, task_name=\"ideal_luneburg_lens\", path=\"data/simulation_data.hdf5\", verbose=True\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1489,10 +1477,8 @@
     ")\n",
     "\n",
     "# plot Ex at the xz plane\n",
-    "sim_data_ideal.plot_field(\n",
-    "    field_monitor_name=\"field_xz\", field_name=\"Ex\", ax=ax2, vmin=-5, vmax=5\n",
-    ")\n",
-    "plt.show()\n"
+    "sim_data_ideal.plot_field(field_monitor_name=\"field_xz\", field_name=\"Ex\", ax=ax2, vmin=-5, vmax=5)\n",
+    "plt.show()"
    ]
   },
   {
@@ -1524,10 +1510,10 @@
     "fig, ax = plt.subplots()\n",
     "for z in zs:\n",
     "    I = sim_data_ideal.get_intensity(\"field_xz\").sel(z=z, method=\"nearest\")\n",
-    "    I.plot(ax=ax, label=f\"z={z} $\\mu m$\")\n",
+    "    I.plot(ax=ax, label=rf\"z={z} $\\mu m$\")\n",
     "ax.legend()\n",
     "ax.set_title(\"Field intensity\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1558,7 +1544,7 @@
     "sim_data_ideal.plot_field(\n",
     "    field_monitor_name=\"field_xy\", field_name=\"E\", val=\"abs^2\", vmin=0, vmax=25\n",
     ")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1566,7 +1552,7 @@
    "id": "0835790d",
    "metadata": {},
    "source": [
-    "Lastly, as a direct comparison, we plot the field intensity around the focus for the practical and idealized Luneburg lens together. The resutls are nearly identical, which validates the design of the practical Lunebug lens."
+    "Lastly, as a direct comparison, we plot the field intensity around the focus for the practical and idealized Luneburg lens together. The results are nearly identical, which validates the design of the practical Lunebug lens."
    ]
   },
   {
@@ -1608,7 +1594,7 @@
     "\n",
     "ax.legend()\n",
     "ax.set_title(\"Field intensity\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1645,7 +1631,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.0"
   },
   "title": "3D Optical Luneburg Lens Modeling in Tidy3D | Flexcompute",
   "widgets": {
diff --git a/OpticalSwitchDBS.ipynb b/OpticalSwitchDBS.ipynb
index 211b10f9..8271ccdb 100644
--- a/OpticalSwitchDBS.ipynb
+++ b/OpticalSwitchDBS.ipynb
@@ -59,8 +59,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web"
    ]
@@ -137,7 +137,7 @@
     "l = 1  # length of the waveguide in the simulation\n",
     "Px = Py = 0.1  # pixel sizes in the x and y directions\n",
     "Nx = 9  # number of pixels in the x direction\n",
-    "Ny = 9  # numbre of pixels in the y direction\n",
+    "Ny = 9  # number of pixels in the y direction\n",
     "N_gst = 3  # number of pixels for GST\n",
     "buffer = 0.8  # buffer spacing"
    ]
@@ -194,7 +194,6 @@
     "\n",
     "\n",
     "def create_design(pixels):\n",
-    "\n",
     "    # parse 1d parameter vector to 2d array\n",
     "    pixels_2d = parse_to_2d(pixels, Nx, Ny, N_gst)\n",
     "\n",
@@ -265,7 +264,6 @@
    "outputs": [],
    "source": [
     "def make_sim(pixels, gst_state):\n",
-    "\n",
     "    # create silicon structures\n",
     "    design = create_design(pixels)\n",
     "\n",
@@ -402,7 +400,6 @@
    "outputs": [],
    "source": [
     "def objective_function(pixels):\n",
-    "\n",
     "    # define two simulations\n",
     "    sim_a = make_sim(pixels, \"a\")\n",
     "    sim_c = make_sim(pixels, \"c\")\n",
@@ -468,7 +465,6 @@
    "outputs": [],
    "source": [
     "def direct_binary_search(pixels, iterations):\n",
-    "\n",
     "    num_of_eval = 0  # number of objective function evaluations\n",
     "    best_score = [initial_obj]  # best objective function values\n",
     "    print(f\"The initial objective function is {initial_obj:.3f}.\")\n",
@@ -760,7 +756,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.0"
   },
   "title": "Direct Binary Search Optimization of an Optical Switch | Flexcompute"
  },
diff --git a/OptimizedL3.ipynb b/OptimizedL3.ipynb
index b0ad4288..281d5d8d 100644
--- a/OptimizedL3.ipynb
+++ b/OptimizedL3.ipynb
@@ -25,12 +25,12 @@
    "outputs": [],
    "source": [
     "from os.path import join\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
-    "from tidy3d.plugins.resonance import ResonanceFinder\n",
-    "from tidy3d import web\n"
+    "from tidy3d import web\n",
+    "from tidy3d.plugins.resonance import ResonanceFinder"
    ]
   },
   {
@@ -58,7 +58,7 @@
    "outputs": [],
    "source": [
     "holes_file = np.loadtxt(join(\"misc\", \"optimized_L3_holes.txt\"))\n",
-    "xs, ys, rs = holes_file.T  # in units of um\n"
+    "xs, ys, rs = holes_file.T  # in units of um"
    ]
   },
   {
@@ -92,7 +92,7 @@
     "\n",
     "# Simulation domain size (micron)\n",
     "size_z = 6\n",
-    "sim_size = [(nx + 1) * alattice, (ny + 1) * alattice * np.sqrt(3) / 2, size_z]\n"
+    "sim_size = [(nx + 1) * alattice, (ny + 1) * alattice * np.sqrt(3) / 2, size_z]"
    ]
   },
   {
@@ -110,9 +110,7 @@
    "outputs": [],
    "source": [
     "# Initialize structures\n",
-    "slab = td.Structure(\n",
-    "    geometry=td.Box(center=[0, 0, 0], size=[td.inf, td.inf, d_slab]), medium=si\n",
-    ")\n",
+    "slab = td.Structure(geometry=td.Box(center=[0, 0, 0], size=[td.inf, td.inf, d_slab]), medium=si)\n",
     "\n",
     "holes_group = []\n",
     "# Add all provided optimized holes\n",
@@ -128,11 +126,9 @@
     "        if ix > xmax_opt or iy > ymax_opt:\n",
     "            xp = ix + (iy % 2) * 0.5\n",
     "            yp = iy * np.sqrt(3) / 2\n",
-    "            holes_group.append(\n",
-    "                td.Cylinder(center=[xp, yp, 0], radius=r_hole, length=d_slab)\n",
-    "            )\n",
+    "            holes_group.append(td.Cylinder(center=[xp, yp, 0], radius=r_hole, length=d_slab))\n",
     "\n",
-    "holes = td.Structure(geometry=td.GeometryGroup(geometries=holes_group), medium=air)\n"
+    "holes = td.Structure(geometry=td.GeometryGroup(geometries=holes_group), medium=air)"
    ]
   },
   {
@@ -192,7 +188,7 @@
     "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
     "source.source_time.plot(np.linspace(0, 5e-13, 2000), ax=ax[0])\n",
     "source.source_time.plot_spectrum(times=np.linspace(0, 5e-13, 2000), ax=ax[1])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -297,7 +293,7 @@
     "    ux=ux,\n",
     "    uy=uy,\n",
     "    apodization=apodization,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -350,7 +346,7 @@
     "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
     "    symmetry=(1, -1, 1),\n",
     "    shutoff=0,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -381,7 +377,7 @@
     "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
     "sim.plot(z=0, ax=ax[0])\n",
     "sim.plot(y=0, ax=ax[1])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -717,7 +713,7 @@
     }
    ],
    "source": [
-    "sim_data = web.run(sim, task_name=\"L3_opt\", verbose=True)\n"
+    "sim_data = web.run(sim, task_name=\"L3_opt\", verbose=True)"
    ]
   },
   {
@@ -775,7 +771,7 @@
     "ax2.set_xlabel(\"Frequency [Hz]\")\n",
     "ax2.set_ylabel(\"Electric field [a.u.]\")\n",
     "ax2.set_title(\"Spectrum\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -862,7 +858,7 @@
    "source": [
     "resonance_finder = ResonanceFinder(freq_window=(1.8e14, 2e14), init_num_freqs=100)\n",
     "resonance_data = resonance_finder.run(sim_data[\"time_series\"])\n",
-    "resonance_data.to_dataframe()\n"
+    "resonance_data.to_dataframe()"
    ]
   },
   {
@@ -900,7 +896,7 @@
    "source": [
     "fig, ax = plt.subplots(1)\n",
     "sim_data.plot_field(\"field\", \"Ey\", val=\"abs\", z=0, ax=ax, eps_alpha=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -946,7 +942,7 @@
     "ax.plot(wavelength / Lx * np.cos(phis), wavelength / Ly * np.sin(phis), lw=2, color=\"w\")\n",
     "ax.set_xlabel(\"normalized $k_x$\")\n",
     "ax.set_ylabel(\"normalized $k_y$\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
diff --git a/PECSphereRCS.ipynb b/PECSphereRCS.ipynb
index f8366db5..a0cbdbd8 100644
--- a/PECSphereRCS.ipynb
+++ b/PECSphereRCS.ipynb
@@ -28,10 +28,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tidy3d as td\n",
-    "import tidy3d.web as web\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
+    "import tidy3d as td\n",
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -478,8 +478,8 @@
    "source": [
     "plt.plot(rel_freqs, rcs_tidy3d, \"red\", linewidth=2, label=\"Tidy3D\")\n",
     "plt.scatter(rel_freqs, rcs_analytic, c=\"blue\", label=\"Analytic\")\n",
-    "plt.xlabel(\"Relative frequency (2$\\pi r$/$\\lambda_0$)\")\n",
-    "plt.ylabel(\"Monostatic RCS (2$\\pi r^2$)\")\n",
+    "plt.xlabel(r\"Relative frequency (2$\\pi r$/$\\lambda_0$)\")\n",
+    "plt.ylabel(r\"Monostatic RCS (2$\\pi r^2$)\")\n",
     "plt.xscale(\"log\")\n",
     "plt.yscale(\"log\")\n",
     "plt.legend()\n",
diff --git a/PICComponents.ipynb b/PICComponents.ipynb
index 526c3eb0..ea573043 100644
--- a/PICComponents.ipynb
+++ b/PICComponents.ipynb
@@ -30,11 +30,10 @@
    },
    "outputs": [],
    "source": [
-    "import tidy3d as td\n",
     "import gdstk\n",
-    "\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
+    "import tidy3d as td"
    ]
   },
   {
@@ -653,7 +652,7 @@
     "            offset=lambda u: -bend_height * np.cos(np.pi * (u)) / 2 + bend_height / 2,\n",
     "        )\n",
     "        # check if mirroring is needed\n",
-    "        if mirror == True:\n",
+    "        if mirror:\n",
     "            path.mirror((x0 + 1, y0), (x0, y0))\n",
     "\n",
     "    elif orientation == \"y\":\n",
@@ -663,7 +662,7 @@
     "            offset=lambda u: -bend_height * np.cos(np.pi * (u)) / 2 + bend_height / 2,\n",
     "        )\n",
     "        # check if mirroring is needed\n",
-    "        if mirror == True:\n",
+    "        if mirror:\n",
     "            path.mirror((x0, y0 + 1), (x0, y0))\n",
     "\n",
     "    cell.add(path)  # add path to the cell\n",
@@ -798,14 +797,14 @@
     "        # define cosine bend for x orientation\n",
     "        path.arc(R, -np.pi / 2, -np.pi / 2 + bend_angle)\n",
     "        # check if mirroring is needed\n",
-    "        if mirror == True:\n",
+    "        if mirror:\n",
     "            path.mirror((x0 + 1, y0), (x0, y0))\n",
     "\n",
     "    elif orientation == \"y\":\n",
     "        # define cosine bend for x orientation\n",
     "        path.arc(R, np.pi, np.pi - bend_angle)\n",
     "        # check if mirroring is needed\n",
-    "        if mirror == True:\n",
+    "        if mirror:\n",
     "            path.mirror((x0, y0 + 1), (x0, y0))\n",
     "\n",
     "    cell.add(path)  # add path to the cell\n",
@@ -915,7 +914,6 @@
     "    medium,\n",
     "    sidewall_angle=0,\n",
     "):\n",
-    "\n",
     "    \"\"\"\n",
     "    This function defines a directional coupler and returns the tidy3d structure of it.\n",
     "\n",
diff --git a/ParameterScan.ipynb b/ParameterScan.ipynb
index 139c48dd..14bc2a4c 100644
--- a/ParameterScan.ipynb
+++ b/ParameterScan.ipynb
@@ -34,14 +34,15 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "import os\n",
+    "\n",
     "import gdstk\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3D imports\n",
     "import tidy3d as td\n",
-    "from tidy3d import web\n"
+    "from tidy3d import web"
    ]
   },
   {
@@ -91,7 +92,7 @@
     "# space between waveguide and PML\n",
     "pml_spacing = 1\n",
     "# resolution control: minimum number of grid cells per wavelength in each material\n",
-    "grid_cells_per_wvl = 16\n"
+    "grid_cells_per_wvl = 16"
    ]
   },
   {
@@ -150,7 +151,7 @@
     "        offset=[lambda u: -0.5 * offset(1 - u), lambda u: 0.5 * offset(1 - u)],\n",
     "    )\n",
     "    coup.segment((0.5 * length, 0))\n",
-    "    return coup\n"
+    "    return coup"
    ]
   },
   {
@@ -276,7 +277,7 @@
     "    )\n",
     "    monitors = [mon_in, mon_ref_bot, mon_top, mon_bot]\n",
     "\n",
-    "    if domain_field == True:\n",
+    "    if domain_field:\n",
     "        domain_monitor = td.FieldMonitor(\n",
     "            center=[0, 0, wg_height / 2],\n",
     "            size=[td.inf, td.inf, 0],\n",
@@ -296,7 +297,7 @@
     "        boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
     "    )\n",
     "\n",
-    "    return sim\n"
+    "    return sim"
    ]
   },
   {
@@ -305,7 +306,7 @@
    "source": [
     "## Inspect Simulation\n",
     "\n",
-    "Let's create and inspect a single simulation to make sure it was defined correctly before doing the full scan. The sidewalls of the waveguides deviate from the vertical direction by 30 degree. We also add an in-plane field monitor to have a look at the fields evolution in this one simulation. We will not use such a monitor in the batch to avoid storing unnecesarrily large amounts of data."
+    "Let's create and inspect a single simulation to make sure it was defined correctly before doing the full scan. The sidewalls of the waveguides deviate from the vertical direction by 30 degrees. We also add an in-plane field monitor to have a look at the field evolution in this one simulation. We will not use such a monitor in the batch to avoid storing unnecessarily large amounts of data."
    ]
   },
   {
@@ -322,7 +323,7 @@
     "# Waveguide separation in the coupling region\n",
     "wg_spacing_coup = 0.10\n",
     "\n",
-    "sim = make_sim(coup_length, wg_spacing_coup, domain_field=True)\n"
+    "sim = make_sim(coup_length, wg_spacing_coup, domain_field=True)"
    ]
   },
   {
@@ -349,7 +350,7 @@
     "sim.plot(z=wg_height / 2 + 0.01, ax=ax1)\n",
     "sim.plot(x=0.1, ax=ax2)\n",
     "ax2.set_xlim([-3, 3])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -726,7 +727,7 @@
     "job = web.Job(simulation=sim, task_name=\"CouplerVerify\", verbose=True)\n",
     "\n",
     "# download the results and load them into a simulation\n",
-    "sim_data = job.run(path=\"data/sim_data.hdf5\")\n"
+    "sim_data = job.run(path=\"data/sim_data.hdf5\")"
    ]
   },
   {
@@ -755,14 +756,12 @@
     "\n",
     "    amps = np.zeros(4, dtype=complex)\n",
     "    directions = (\"-\", \"-\", \"+\", \"+\")\n",
-    "    for i, (monitor, direction) in enumerate(\n",
-    "        zip(sim_data.simulation.monitors[:4], directions)\n",
-    "    ):\n",
+    "    for i, (monitor, direction) in enumerate(zip(sim_data.simulation.monitors[:4], directions)):\n",
     "        amp = sim_data[monitor.name].amps.sel(direction=direction)\n",
     "        amp_normalized = amp / input_amp\n",
     "        amps[i] = np.squeeze(amp_normalized.values)\n",
     "\n",
-    "    return amps\n"
+    "    return amps"
    ]
   },
   {
@@ -803,7 +802,7 @@
     "for amp, monitor in zip(amps_arms, sim_data.simulation.monitors[:-1]):\n",
     "    print(f'\\tmonitor     = \"{monitor.name}\"')\n",
     "    print(f\"\\tamplitude^2 = {abs(amp)**2:.2f}\")\n",
-    "    print(f\"\\tphase       = {(np.angle(amp)):.2f} (rad)\\n\")\n"
+    "    print(f\"\\tphase       = {(np.angle(amp)):.2f} (rad)\\n\")"
    ]
   },
   {
@@ -842,7 +841,7 @@
    "source": [
     "fig, ax = plt.subplots(1, 1, figsize=(16, 3))\n",
     "sim_data.plot_field(\"field\", \"Ey\", z=wg_height / 2, freq=freq0, ax=ax)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -873,7 +872,7 @@
     "\n",
     "ls = np.linspace(5, 12, Nl)\n",
     "split_ratios = np.zeros(Nl)\n",
-    "efficiencies = np.zeros(Nl)\n"
+    "efficiencies = np.zeros(Nl)"
    ]
   },
   {
@@ -1657,7 +1656,7 @@
    "source": [
     "# submit all jobs\n",
     "sims = {f\"l={l:.2f}\": make_sim(l, wg_spacing_coup) for l in ls}\n",
-    "batch = web.Batch(simulations=sims, verbose=True)\n"
+    "batch = web.Batch(simulations=sims, verbose=True)"
    ]
   },
   {
@@ -1769,16 +1768,16 @@
     }
    ],
    "source": [
-    "batch_results = batch.run(path_dir=\"data\")\n"
+    "batch_results = batch.run(path_dir=\"data\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Load and visualize Results\n",
+    "### Load and Visualize Results\n",
     "\n",
-    "Finally, we can compute the output quantities and load them into the arrays we created initally.\n",
+    "Finally, we can compute the output quantities and load them into the arrays we created initially.\n",
     "\n",
     "Then we may plot the results."
    ]
@@ -2401,7 +2400,7 @@
     "    amps_batch.append(amps_arms_i)\n",
     "amps_batch = np.stack(amps_batch, axis=1)\n",
     "print(amps_batch.shape)  # (4, Nl)\n",
-    "print(amps_batch)\n"
+    "print(amps_batch)"
    ]
   },
   {
@@ -2415,7 +2414,7 @@
     "powers = abs(amps_batch) ** 2\n",
     "power_top = powers[2]\n",
     "power_bot = powers[3]\n",
-    "power_out = power_top + power_bot\n"
+    "power_out = power_top + power_bot"
    ]
   },
   {
@@ -2444,7 +2443,7 @@
     "plt.ylabel(\"Power ratio (%)\")\n",
     "plt.ylim(0, 100)\n",
     "plt.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2470,7 +2469,7 @@
     "batch.to_file(\"data/batch_data.json\")\n",
     "\n",
     "# load batch metadata into a new batch\n",
-    "loaded_batch = web.Batch.from_file(\"data/batch_data.json\")\n"
+    "loaded_batch = web.Batch.from_file(\"data/batch_data.json\")"
    ]
   },
   {
@@ -2535,7 +2534,12 @@
    "outputs": [],
    "source": [
     "method = tdd.MethodGrid()\n",
-    "design_space = tdd.DesignSpace(parameters=[param_spc, param_len], method=method, task_name=\"ParameterScan_Notebook\", path_dir=\"./data\")"
+    "design_space = tdd.DesignSpace(\n",
+    "    parameters=[param_spc, param_len],\n",
+    "    method=method,\n",
+    "    task_name=\"ParameterScan_Notebook\",\n",
+    "    path_dir=\"./data\",\n",
+    ")"
    ]
   },
   {
@@ -2711,7 +2715,7 @@
     "df = results.to_dataframe()\n",
     "\n",
     "# take absolute value squared of output 2 to get powers to the top port\n",
-    "df['amp_squared_top'] = df['top'].map(lambda x: abs(x)**2)\n",
+    "df[\"amp_squared_top\"] = df[\"top\"].map(lambda x: abs(x) ** 2)\n",
     "\n",
     "df.head()"
    ]
@@ -2748,7 +2752,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you are interested in this approach to parmaeter sweeps, we highly recommend checking our our `Design` plugin [tutorial](https://www.flexcompute.com/tidy3d/examples/notebooks/Design/) for a deep dive and also the [documentation](https://pandas.pydata.org/docs/getting_started/intro_tutorials/01_table_oriented.html#min-tut-01-tableoriented) for `pandas.DataFrame`."
+    "If you are interested in this approach to parameter sweeps, we highly recommend checking out our `Design` plugin [tutorial](https://www.flexcompute.com/tidy3d/examples/notebooks/Design/) for a deep dive and also the [documentation](https://pandas.pydata.org/docs/getting_started/intro_tutorials/01_table_oriented.html#min-tut-01-tableoriented) for `pandas.DataFrame`."
    ]
   },
   {
@@ -2793,7 +2797,7 @@
     "    task_name = f\"SWEEP_l={l:.3f}\"\n",
     "    sim_data = web.run(sim, task_name=task_name, verbose=False)\n",
     "    amps_arms = np.array(measure_transmission(sim_data))\n",
-    "    powers = np.abs(amps_arms)**2\n",
+    "    powers = np.abs(amps_arms) ** 2\n",
     "    efficiency = np.sum(powers)\n",
     "    ratio_0 = powers[2] / efficiency\n",
     "    return efficiency, ratio_0"
@@ -2857,7 +2861,7 @@
    "source": [
     "for l in np.linspace(5, 12, 3):\n",
     "    statepoint = {\"l\": float(l)}\n",
-    "    job = project.open_job(statepoint)    \n",
+    "    job = project.open_job(statepoint)\n",
     "    compute_transmission(job)"
    ]
   },
@@ -2958,6 +2962,7 @@
     "        job.init()\n",
     "        print(\"initialize\", job)\n",
     "\n",
+    "\n",
     "# make 5 points between 5 and 12, note, 3 have already been computed\n",
     "init_statepoints(5)"
    ]
@@ -3033,11 +3038,13 @@
     "\n",
     "from multiprocessing.pool import ThreadPool\n",
     "\n",
+    "\n",
     "# make a convenience function to just call compute_transmission only for uncomputed jobs\n",
     "def compute_transmission_cached(job):\n",
     "    if \"eff\" not in job.document or \"ratio\" not in job.document:\n",
     "        compute_transmission(job)\n",
     "\n",
+    "\n",
     "with ThreadPool() as pool:\n",
     "    pool.map(compute_transmission_cached, list(project))"
    ]
@@ -3081,7 +3088,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ls =  np.array([job.statepoint()[\"l\"] for job in project])\n",
+    "ls = np.array([job.statepoint()[\"l\"] for job in project])\n",
     "effs = np.array([job.document[\"eff\"] for job in project])\n",
     "ratios = np.array([job.document[\"ratio\"] for job in project])"
    ]
@@ -3109,7 +3116,7 @@
     "plt.ylabel(\"value (%)\")\n",
     "plt.ylim(0, 100)\n",
     "plt.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -3139,7 +3146,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.0"
   },
   "title": "Performing Parameter Scan in Tidy3D | Flexcompute",
   "widgets": {
diff --git a/ParticleSwarmOptimizedPBS.ipynb b/ParticleSwarmOptimizedPBS.ipynb
index 7625b837..ff43038a 100644
--- a/ParticleSwarmOptimizedPBS.ipynb
+++ b/ParticleSwarmOptimizedPBS.ipynb
@@ -34,14 +34,12 @@
     "# uncomment the following line to install pyswarms if it's not installed in your environment already\n",
     "# pip install pyswarms\n",
     "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
     "import gdstk\n",
-    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n",
-    "import tidy3d.plugins.design as tdd"
+    "import tidy3d.plugins.design as tdd\n",
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -456,7 +454,7 @@
     "    widths = np.array(list(params.values()))\n",
     "    sim_te = make_sim(widths, \"TE\")\n",
     "    sim_tm = make_sim(widths, \"TM\")\n",
-    "    \n",
+    "\n",
     "    return {\"TE\": sim_te, \"TM\": sim_tm}"
    ]
   },
@@ -477,7 +475,7 @@
     "\n",
     "The input for this function is a dictionary with the same keys as the output of `fn_pre`. The optimizer replaces the `Simulation` objects it receives with `SimulationData` objects, meaning we can easily access the appropriate TE or TM simulation.\n",
     "\n",
-    "Note that this FOM is a maximising function; all the optimizers in the `Design` plugin are maximising by default so the sign does not need to be changed."
+    "Note that this FOM is a maximizing function; all the optimizers in the `Design` plugin are maximizing by default so the sign does not need to be changed."
    ]
   },
   {
@@ -491,22 +489,12 @@
     "    \"\"\"Calculate the power for TE and TM polarizations in different regions of the PBS.\"\"\"\n",
     "    # Extract te power at the bar at the central frequency\n",
     "    P_TE_bar = (\n",
-    "        np.abs(\n",
-    "            sim_data_dict[\"TE\"][\"bar\"].amps.sel(\n",
-    "                mode_index=0, direction=\"+\", f=freq0\n",
-    "            )\n",
-    "        )\n",
-    "        ** 2\n",
+    "        np.abs(sim_data_dict[\"TE\"][\"bar\"].amps.sel(mode_index=0, direction=\"+\", f=freq0)) ** 2\n",
     "    )\n",
     "\n",
     "    # Extract tm transmission at cross port at the central frequency\n",
     "    P_TM_cross = (\n",
-    "        np.abs(\n",
-    "            sim_data_dict[\"TM\"][\"cross\"].amps.sel(\n",
-    "                mode_index=0, direction=\"+\", f=freq0\n",
-    "            )\n",
-    "        )\n",
-    "        ** 2\n",
+    "        np.abs(sim_data_dict[\"TM\"][\"cross\"].amps.sel(mode_index=0, direction=\"+\", f=freq0)) ** 2\n",
     "    )\n",
     "\n",
     "    return float(P_TE_bar + P_TM_cross)"
@@ -529,9 +517,9 @@
     "\n",
     "In this optimization, we put an upper and lower bound for the $W_i$ to be 450 nm and 300 nm. 5 particles are used for a total of 40 iterations. As discussed above, this means the entire optimization will run 400 simulations and cost 10 FlexCredits. Since this notebook is mainly for demonstration purposes, the numbers of particles and iterations are kept small. To really achieve a design with high performance, larger numbers should be used. To ensure the final result is reproducible every time we run the notebook, the initial positions of the particles are fixed with a random seed.\n",
     "\n",
-    "There are three hyperparameters in PSO, namely the inertia weight, the cognitive coefficient, and the social coefficient. Their values can significantly impact the performance of the algorithm. The best values of them depend on the specific problem so can take some experimentation to determine.\n",
+    "There are three hyperparameters in PSO, namely the inertia weight, the cognitive coefficient, and the social coefficient. Their values can significantly impact the performance of the algorithm. The best values of them depend on the specific problem so it can take some experimentation to determine.\n",
     "\n",
-    "We also include a very low `ftol` value that must be maintained for 8 consecutive iterations as an early-stop criteria. This means if the fitness stops impoving the optimization will finish early."
+    "We also include a very low `ftol` value that must be maintained for 8 consecutive iterations as an early-stop criterion. This means that if the fitness stops improving, the optimization will finish early."
    ]
   },
   {
@@ -546,12 +534,12 @@
     "\n",
     "n_particles = 5  # number of particles\n",
     "\n",
-    "# Set initial positions to for reproducability of the notebook\n",
-    "np.random.seed(1)\n",
+    "# Set initial positions\n",
+    "np.random.seed(1)  #  use a fixed random seed for reproducability of the notebook\n",
     "init_pos = np.random.uniform(W_min, W_max, (n_particles, M))\n",
     "\n",
-    "particle_swarm=tdd.MethodParticleSwarm(\n",
-    "    n_particles=n_particles, \n",
+    "particle_swarm = tdd.MethodParticleSwarm(\n",
+    "    n_particles=n_particles,\n",
     "    n_iter=40,\n",
     "    cognitive_coeff=1,\n",
     "    social_coeff=1,\n",
@@ -559,12 +547,14 @@
     "    init_pos=init_pos,\n",
     "    seed=1,\n",
     "    ftol=0.001,\n",
-    "    ftol_iter=8\n",
+    "    ftol_iter=8,\n",
     ")\n",
     "\n",
     "parameters = [tdd.ParameterFloat(name=i, span=(W_min, W_max)) for i in range(M)]\n",
     "\n",
-    "design_space = tdd.DesignSpace(method=particle_swarm, parameters=parameters, task_name=\"PSO_Notebook\", path_dir=\"./data\")"
+    "design_space = tdd.DesignSpace(\n",
+    "    method=particle_swarm, parameters=parameters, task_name=\"PSO_Notebook\", path_dir=\"./data\"\n",
+    ")"
    ]
   },
   {
@@ -1346,8 +1336,7 @@
     }
    ],
    "source": [
-    "\n",
-    "cost_history=results.optimizer.cost_history\n",
+    "cost_history = results.optimizer.cost_history\n",
     "plt.plot(cost_history)\n",
     "plt.xlabel(\"Iteration\")\n",
     "plt.ylabel(\"Cost\")\n",
@@ -1685,7 +1674,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.0"
   },
   "title": "Particle Swarm Optimization of a PBS| Flexcompute"
  },
diff --git a/PhotonicCrystalWaveguidePolarizationFilter.ipynb b/PhotonicCrystalWaveguidePolarizationFilter.ipynb
index 47ce361c..fa4ee372 100644
--- a/PhotonicCrystalWaveguidePolarizationFilter.ipynb
+++ b/PhotonicCrystalWaveguidePolarizationFilter.ipynb
@@ -29,13 +29,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
     "from tidy3d.plugins.mode import ModeSolver\n",
-    "from tidy3d.plugins.mode.web import run as run_ms\n"
+    "from tidy3d.plugins.mode.web import run as run_ms"
    ]
   },
   {
@@ -64,7 +63,7 @@
     "lda0 = 1.55  # central wavelength\n",
     "freq0 = td.C_0 / lda0  # central frequency\n",
     "ldas = np.linspace(1.48, 1.62, 100)  # wavelength range of interest\n",
-    "freqs = td.C_0 / ldas  # frequency range of interest\n"
+    "freqs = td.C_0 / ldas  # frequency range of interest"
    ]
   },
   {
@@ -86,7 +85,7 @@
     "si = td.Medium(permittivity=n_si**2)\n",
     "\n",
     "n_air = 1  # air refractive index\n",
-    "air = td.Medium(permittivity=n_air)\n"
+    "air = td.Medium(permittivity=n_air)"
    ]
   },
   {
@@ -117,7 +116,7 @@
     "\n",
     "D = 0.4  # width of the input and output waveguides\n",
     "\n",
-    "inf_eff = 1e3  # effective infinity of the model\n"
+    "inf_eff = 1e3  # effective infinity of the model"
    ]
   },
   {
@@ -137,15 +136,15 @@
    "source": [
     "# define the silicon slab\n",
     "si_slab = td.Box.from_bounds(\n",
-    "        rmin=(-L / 2, -N_rows * np.sqrt(3) * a / 2 - w / 2, 0),\n",
-    "        rmax=(L / 2, N_rows * np.sqrt(3) * a / 2 + w / 2, t),\n",
-    "    )\n",
+    "    rmin=(-L / 2, -N_rows * np.sqrt(3) * a / 2 - w / 2, 0),\n",
+    "    rmax=(L / 2, N_rows * np.sqrt(3) * a / 2 + w / 2, t),\n",
+    ")\n",
     "\n",
     "# define the input and output straight waveguides\n",
     "si_wg = td.Box.from_bounds(\n",
-    "        rmin=(-inf_eff, -D / 2, 0),\n",
-    "        rmax=(inf_eff, D / 2, t),\n",
-    "    )\n",
+    "    rmin=(-inf_eff, -D / 2, 0),\n",
+    "    rmax=(inf_eff, D / 2, t),\n",
+    ")\n",
     "\n",
     "# get the union with both silicon geometries\n",
     "si_geometry = si_slab + si_wg\n",
@@ -223,7 +222,7 @@
     "    size=(td.inf, td.inf, 0),\n",
     "    freqs=[freq0],\n",
     "    name=\"field\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -247,7 +246,7 @@
     "    grid_x=td.UniformGrid(dl=a / steps_per_unit_cell),\n",
     "    grid_y=td.UniformGrid(dl=a / steps_per_unit_cell * np.sqrt(3) / 2),\n",
     "    grid_z=td.AutoGrid(min_steps_per_wvl=steps_per_unit_cell),\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -274,14 +273,14 @@
     "    center=(0, 0, 0),\n",
     "    size=sim_size,\n",
     "    grid_spec=grid_spec,\n",
-    "    structures=[si_structure, holes_structure], # note: the order of the structures is important\n",
+    "    structures=[si_structure, holes_structure],  # note: the order of the structures is important\n",
     "    sources=[mode_source],\n",
     "    monitors=[flux_monitor, field_monitor],\n",
     "    run_time=run_time,\n",
     "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
     "    symmetry=(0, -1, 0),\n",
     "    medium=air,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -311,7 +310,7 @@
    ],
    "source": [
     "sim_te.plot(z=t / 2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -352,7 +351,7 @@
     "sim_te.plot_grid(z=t / 2, ax=ax)\n",
     "ax.set_xlim(-0.5, 0.5)\n",
     "ax.set_ylim(0, 1)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -576,7 +575,7 @@
     "ax2.set_aspect(\"equal\")\n",
     "ax3.set_title(\"|Ez(x, y)|\")\n",
     "ax3.set_aspect(\"equal\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -605,7 +604,7 @@
     "sim_tm = sim_te.copy(update={\"symmetry\": (0, 1, 0)})\n",
     "\n",
     "# define simulation batch\n",
-    "sims = {\"TE\": sim_te, \"TM\": sim_tm}\n"
+    "sims = {\"TE\": sim_te, \"TM\": sim_tm}"
    ]
   },
   {
@@ -850,7 +849,7 @@
    ],
    "source": [
     "batch = web.Batch(simulations=sims, verbose=True)\n",
-    "batch_results = batch.run(path_dir=\"data\")\n"
+    "batch_results = batch.run(path_dir=\"data\")"
    ]
   },
   {
@@ -1010,7 +1009,7 @@
     "sim_data_tm.plot_field(\n",
     "    field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\", ax=ax2, vmin=0, vmax=4000\n",
     ")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1054,10 +1053,10 @@
     "plt.plot(ldas, 10 * np.log10(T_tm), label=\"TM\")\n",
     "plt.xlim(1.48, 1.62)\n",
     "plt.ylim(-50, 0)\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Transmission (dB)\")\n",
     "plt.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
diff --git a/PhotonicCrystalsComponents.ipynb b/PhotonicCrystalsComponents.ipynb
index a1f62007..550fac28 100644
--- a/PhotonicCrystalsComponents.ipynb
+++ b/PhotonicCrystalsComponents.ipynb
@@ -34,8 +34,8 @@
    },
    "outputs": [],
    "source": [
-    "import tidy3d as td\n",
-    "import numpy as np"
+    "import numpy as np\n",
+    "import tidy3d as td"
    ]
   },
   {
@@ -5724,11 +5724,12 @@
     "    # axis\n",
     "\n",
     "    x_length, z_length = (\n",
-    "        tooth_width + spacing\n",
-    "    ) * tooth_number - spacing, tooth_height + base_height\n",
+    "        (tooth_width + spacing) * tooth_number - spacing,\n",
+    "        tooth_height + base_height,\n",
+    "    )\n",
     "    start_x, start_z = x0 - x_length / 2, z0 + z_length / 2\n",
-    "    \n",
-    "    axisArray = [0,1,2]\n",
+    "\n",
+    "    axisArray = [0, 1, 2]\n",
     "\n",
     "    # create list of points as vertices for polyslab\n",
     "    points = [(start_x, start_z), (start_x + tooth_width, start_z)]\n",
@@ -5745,7 +5746,7 @@
     "\n",
     "    grating = td.PolySlab(\n",
     "        vertices=points,\n",
-    "        axis=axisArray[axis-1],\n",
+    "        axis=axisArray[axis - 1],\n",
     "        slab_bounds=(-thickness / 2, thickness / 2),\n",
     "        sidewall_angle=sidewall_angle,\n",
     "        reference_plane=reference_plane,\n",
diff --git a/PlasmonicNanoparticle.ipynb b/PlasmonicNanoparticle.ipynb
index 0df39155..b0a5151e 100644
--- a/PlasmonicNanoparticle.ipynb
+++ b/PlasmonicNanoparticle.ipynb
@@ -27,12 +27,12 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3d imports\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n"
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -113,7 +113,7 @@
     "sim_size = [(radius + buffer_pml) * 2] * 3\n",
     "\n",
     "# define PML layers on all sides\n",
-    "boundary_spec = td.BoundarySpec.all_sides(boundary=td.PML())\n"
+    "boundary_spec = td.BoundarySpec.all_sides(boundary=td.PML())"
    ]
   },
   {
@@ -153,7 +153,7 @@
     ")\n",
     "\n",
     "# Simulation run time\n",
-    "run_time = 10 / fwidth\n"
+    "run_time = 10 / fwidth"
    ]
   },
   {
@@ -241,7 +241,7 @@
     "    name=\"near\",\n",
     ")\n",
     "\n",
-    "monitors = [monitor_flux_out, monitor_flux_in, monitor_n2f, monitor_near]\n"
+    "monitors = [monitor_flux_out, monitor_flux_in, monitor_n2f, monitor_near]"
    ]
   },
   {
@@ -284,7 +284,7 @@
     "    run_time=run_time,\n",
     "    boundary_spec=boundary_spec,\n",
     "    shutoff=1e-8,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -324,9 +324,9 @@
     "zoom = radius * 2\n",
     "sim.plot(y=0, ax=ax1)\n",
     "sim.plot_grid(y=0, ax=ax1)\n",
-    "sim.plot(y=0, ax=ax2, hlim=[-zoom,zoom], vlim=[-zoom,zoom], monitor_alpha=0.2)\n",
-    "sim.plot_grid(y=0, ax=ax2, hlim=[-zoom,zoom], vlim=[-zoom,zoom])\n",
-    "plt.show()\n"
+    "sim.plot(y=0, ax=ax2, hlim=[-zoom, zoom], vlim=[-zoom, zoom], monitor_alpha=0.2)\n",
+    "sim.plot_grid(y=0, ax=ax2, hlim=[-zoom, zoom], vlim=[-zoom, zoom])\n",
+    "plt.show()"
    ]
   },
   {
@@ -689,7 +689,7 @@
     "    task_name=\"plasmonic_nanoparticle\",\n",
     "    path=\"data/plasmonic_nanoparticle.hdf5\",\n",
     "    verbose=True,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -720,7 +720,7 @@
     "scattered = sim_data[\"flux_out\"].flux\n",
     "\n",
     "# Power scattered in the forward direction\n",
-    "RCS = np.squeeze(np.real(sim_data[\"n2f\"].radar_cross_section.values))\n"
+    "RCS = np.squeeze(np.real(sim_data[\"n2f\"].radar_cross_section.values))"
    ]
   },
   {
@@ -804,15 +804,9 @@
     "\n",
     "\n",
     "fig, ax = plt.subplots(1, 3, figsize=(15, 3))\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"near\", field_name=\"Ex\", val=\"abs\", f=f0, ax=ax[0]\n",
-    ")\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"near\", field_name=\"Ey\", val=\"abs\", f=f0, ax=ax[1]\n",
-    ")\n",
-    "sim_data.plot_field(\n",
-    "    field_monitor_name=\"near\", field_name=\"Ez\", val=\"abs\", f=f0, ax=ax[2]\n",
-    ")\n",
+    "sim_data.plot_field(field_monitor_name=\"near\", field_name=\"Ex\", val=\"abs\", f=f0, ax=ax[0])\n",
+    "sim_data.plot_field(field_monitor_name=\"near\", field_name=\"Ey\", val=\"abs\", f=f0, ax=ax[1])\n",
+    "sim_data.plot_field(field_monitor_name=\"near\", field_name=\"Ez\", val=\"abs\", f=f0, ax=ax[2])\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(5, 3))\n",
     "ax.plot(td.C_0 / freqs * 1e3, to_db(RCS_mie), \"-k\", label=\"Mie\")\n",
@@ -852,7 +846,7 @@
     "ax.legend()\n",
     "ax.grid(visible=True, which=\"both\", axis=\"both\", linewidth=0.4)\n",
     "plt.tight_layout()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
diff --git a/PlasmonicNanorodArray.ipynb b/PlasmonicNanorodArray.ipynb
index 9f2bcc74..6ca7a1a6 100644
--- a/PlasmonicNanorodArray.ipynb
+++ b/PlasmonicNanorodArray.ipynb
@@ -31,9 +31,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web"
    ]
@@ -125,7 +124,7 @@
     "\n",
     "In addition, we notice that the unit cell structure has two reflection symmetries. Therefore, in the case of normal incidence of a plane wave linearly polarized either in the horizontal or vertical directions, we can utilize the symmetry to further reduce the simulation domain size by a factor of 4. For more details on symmetry, please refer to the tutorial [here](https://www.flexcompute.com/tidy3d/examples/notebooks/Symmetry/). Next we are going to take this approach and only model the unit cell.\n",
     "\n",
-    "\"Schematic"
+    "\"Schematic"
    ]
   },
   {
@@ -255,7 +254,7 @@
     "    run_time=run_time,\n",
     "    boundary_spec=td.BoundarySpec(\n",
     "        x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
-    "    ), \n",
+    "    ),\n",
     "    symmetry=(1, -1, 0),\n",
     "    shutoff=1e-7,  # reducing the default shutoff level\n",
     ")"
@@ -765,7 +764,7 @@
     "\n",
     "plt.xlim(min(ldas), max(ldas))\n",
     "plt.ylim(0, 1)\n",
-    "plt.xlabel(\"Wavelength($\\mu m$)\", fontsize=15)\n",
+    "plt.xlabel(r\"Wavelength($\\mu m$)\", fontsize=15)\n",
     "plt.ylabel(\"Reflectance\", fontsize=15)\n",
     "plt.yticks(fontsize=15)\n",
     "plt.xticks(fontsize=15)\n",
@@ -804,7 +803,7 @@
     "\n",
     "    # plotting\n",
     "    E.plot(x=\"y\", y=\"z\", ax=ax[i], vmin=0, vmax=1e3, cmap=\"jet\")\n",
-    "    ax[i].set_title(f\"$\\lambda$={res_ldas[i]} $\\mu m$\")\n",
+    "    ax[i].set_title(rf\"$\\lambda$={res_ldas[i]} $\\mu m$\")\n",
     "    ax[i].set_ylim(-0.2, h + 0.2)\n",
     "plt.show()"
    ]
@@ -1646,7 +1645,7 @@
     "\n",
     "    # plotting field profiles\n",
     "    E_norm.plot(x=\"y\", y=\"z\", ax=ax[i], vmin=0, vmax=5, cmap=\"jet\")\n",
-    "    ax[i].set_title(f\"$\\lambda$={res_ldas[i]} $\\mu m$\")\n",
+    "    ax[i].set_title(rf\"$\\lambda$={res_ldas[i]} $\\mu m$\")\n",
     "    ax[i].set_ylim(-0.2, h + 0.2)\n",
     "plt.show()"
    ]
@@ -1715,7 +1714,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.0"
   },
   "title": "Plasmonic Nanorod Array Resonator | Flexcompute"
  },
diff --git a/PlasmonicWaveguideCO2Sensor.ipynb b/PlasmonicWaveguideCO2Sensor.ipynb
index a6bd26a0..48c4ca75 100644
--- a/PlasmonicWaveguideCO2Sensor.ipynb
+++ b/PlasmonicWaveguideCO2Sensor.ipynb
@@ -23,9 +23,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
     "from tidy3d.plugins.mode import ModeSolver"
@@ -246,7 +245,6 @@
    "outputs": [],
    "source": [
     "def make_sim(n_phmb):\n",
-    "\n",
     "    # define the phmb medium\n",
     "    phmb = td.Medium(permittivity=n_phmb**2)\n",
     "\n",
@@ -1173,7 +1171,7 @@
     "plt.legend()\n",
     "plt.xlim(np.min(ldas), np.max(ldas))\n",
     "plt.ylim(-35, -3)\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Transmission (dB)\")\n",
     "plt.show()"
    ]
@@ -1219,7 +1217,7 @@
     }
    ],
    "source": [
-    "sim_data = batch_results[f\"n_phmb=1.51\"]\n",
+    "sim_data = batch_results[\"n_phmb=1.51\"]\n",
     "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(8, 4))\n",
     "sim_data.plot_field(\n",
     "    field_monitor_name=\"field\",\n",
diff --git a/PlasmonicYagiUdaNanoantenna.ipynb b/PlasmonicYagiUdaNanoantenna.ipynb
index 5fd66b61..18820b7e 100644
--- a/PlasmonicYagiUdaNanoantenna.ipynb
+++ b/PlasmonicYagiUdaNanoantenna.ipynb
@@ -56,11 +56,11 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
-    "from tidy3d.plugins.dispersion import DispersionFitter\n"
+    "from tidy3d.plugins.dispersion import DispersionFitter"
    ]
   },
   {
@@ -88,7 +88,7 @@
    "outputs": [],
    "source": [
     "lda0 = 0.57  # operation wavelength\n",
-    "freq0 = td.C_0 / lda0  # operation frequency\n"
+    "freq0 = td.C_0 / lda0  # operation frequency"
    ]
   },
   {
@@ -155,7 +155,7 @@
    "source": [
     "fname = \"misc/McPeak.csv\"  # read the refractive index data from a csv file\n",
     "fitter = DispersionFitter.from_file(fname, delimiter=\",\")  # construct a fitter\n",
-    "al, rms_error = fitter.fit(num_poles=6, tolerance_rms=2e-2, num_tries=50)\n"
+    "al, rms_error = fitter.fit(num_poles=6, tolerance_rms=2e-2, num_tries=50)"
    ]
   },
   {
@@ -193,9 +193,7 @@
     "\n",
     "    feed = [\n",
     "        td.Structure(\n",
-    "            geometry=td.Cylinder(\n",
-    "                center=(0, 0, 0), radius=r, length=L_f - 2 * r, axis=1\n",
-    "            ),\n",
+    "            geometry=td.Cylinder(center=(0, 0, 0), radius=r, length=L_f - 2 * r, axis=1),\n",
     "            medium=medium,\n",
     "        ),\n",
     "        td.Structure(\n",
@@ -210,9 +208,7 @@
     "\n",
     "    reflector = [\n",
     "        td.Structure(\n",
-    "            geometry=td.Cylinder(\n",
-    "                center=(-a_r, 0, 0), radius=r, length=L_r - 2 * r, axis=1\n",
-    "            ),\n",
+    "            geometry=td.Cylinder(center=(-a_r, 0, 0), radius=r, length=L_r - 2 * r, axis=1),\n",
     "            medium=medium,\n",
     "        ),\n",
     "        td.Structure(\n",
@@ -227,9 +223,7 @@
     "\n",
     "    director_1 = [\n",
     "        td.Structure(\n",
-    "            geometry=td.Cylinder(\n",
-    "                center=(a_d, 0, 0), radius=r, length=L_d - 2 * r, axis=1\n",
-    "            ),\n",
+    "            geometry=td.Cylinder(center=(a_d, 0, 0), radius=r, length=L_d - 2 * r, axis=1),\n",
     "            medium=medium,\n",
     "        ),\n",
     "        td.Structure(\n",
@@ -244,9 +238,7 @@
     "\n",
     "    director_2 = [\n",
     "        td.Structure(\n",
-    "            geometry=td.Cylinder(\n",
-    "                center=(2 * a_d, 0, 0), radius=r, length=L_d - 2 * r, axis=1\n",
-    "            ),\n",
+    "            geometry=td.Cylinder(center=(2 * a_d, 0, 0), radius=r, length=L_d - 2 * r, axis=1),\n",
     "            medium=medium,\n",
     "        ),\n",
     "        td.Structure(\n",
@@ -261,9 +253,7 @@
     "\n",
     "    director_3 = [\n",
     "        td.Structure(\n",
-    "            geometry=td.Cylinder(\n",
-    "                center=(3 * a_d, 0, 0), radius=r, length=L_d - 2 * r, axis=1\n",
-    "            ),\n",
+    "            geometry=td.Cylinder(center=(3 * a_d, 0, 0), radius=r, length=L_d - 2 * r, axis=1),\n",
     "            medium=medium,\n",
     "        ),\n",
     "        td.Structure(\n",
@@ -284,7 +274,7 @@
     "r = 0.02  # radius of the nanorods\n",
     "medium = al  # material of the antenna\n",
     "\n",
-    "antenna = construct_antenna(L_f, r, lda0, medium)\n"
+    "antenna = construct_antenna(L_f, r, lda0, medium)"
    ]
   },
   {
@@ -331,9 +321,7 @@
     "# create an electrical point dipole source polarized in the y direction to excite the feed element\n",
     "d_dp = 0.004  # distance between the dipole and the feed element\n",
     "pulse = td.GaussianPulse(freq0=freq0, fwidth=freq0 / 20)\n",
-    "pt_dipole = td.PointDipole(\n",
-    "    center=(0, L_f / 2 + d_dp, 0), source_time=pulse, polarization=\"Ey\"\n",
-    ")\n",
+    "pt_dipole = td.PointDipole(center=(0, L_f / 2 + d_dp, 0), source_time=pulse, polarization=\"Ey\")\n",
     "\n",
     "# create a FieldProjectionAngleMonitor to perform the near field to far field transformation in spherical coordinates\n",
     "theta_array = np.linspace(0, 2 * np.pi, 200)\n",
@@ -370,7 +358,7 @@
     "\n",
     "# visualize the simulation setup\n",
     "sim.plot(z=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -404,7 +392,7 @@
     "    monitors=[n2f_monitor, flux_monitor],\n",
     "    run_time=1e-13,\n",
     "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1097,7 +1085,7 @@
     ")\n",
     "sim_empty_data = web.run(\n",
     "    sim_empty, task_name=\"empty\", path=\"data/optical_yagi_uda.hdf5\", verbose=True\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1155,12 +1143,9 @@
     "P = np.squeeze(P)\n",
     "D = 4 * np.pi * P / P0  # directivity of the yagi-uda antenna\n",
     "\n",
-    "P0_dp = np.array(\n",
-    "    sim_empty_data[\"power\"].flux\n",
-    ")  # total radiated power of the point dipole\n",
+    "P0_dp = np.array(sim_empty_data[\"power\"].flux)  # total radiated power of the point dipole\n",
     "P_dp = (\n",
-    "    1e12\n",
-    "    * sim_empty_data[\"n2f_monitor\"].power.sel(f=freq0, phi=0, method=\"nearest\").values\n",
+    "    1e12 * sim_empty_data[\"n2f_monitor\"].power.sel(f=freq0, phi=0, method=\"nearest\").values\n",
     ")  # angular radiated power of the point dipole\n",
     "P_dp = np.squeeze(P_dp)\n",
     "D_dp = 4 * np.pi * P_dp / P0_dp  # directivity of the point dipole\n",
@@ -1173,7 +1158,7 @@
     "ax.set_rlim(0, 8)\n",
     "ax.set_title(\"Directivity\")\n",
     "ax.legend((\"Yagi-Uda antenna\", \"Dipole\"))\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1236,7 +1221,7 @@
     "ax.set_zlabel(\"Z\")\n",
     "surf = ax.plot_surface(\n",
     "    X, Y, Z, cstride=1, rstride=1, facecolors=color, antialiased=True, shade=False\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1252,7 +1237,7 @@
    "id": "9e95638d",
    "metadata": {},
    "source": [
-    "Performing the near field to far field transformation using the [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionAngleMonitor.html) is a great way to reduce the computational cost by limiting the simulation domain only to the vicinity of the antenna. However, there are certain limitations. For example, the transformation assumes a homogenous background medium. In practice, we often encounter inhomogeneous background such as when the antenna is placed on a dielectric substrate. \n",
+    "Performing the near field to far field transformation using the [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionAngleMonitor.html) is a great way to reduce the computational cost by limiting the simulation domain only to the vicinity of the antenna. However, there are certain limitations. For example, the transformation assumes a homogeneous background medium. In practice, we often encounter inhomogeneous background such as when the antenna is placed on a dielectric substrate. \n",
     "\n",
     "Alternative to using the near field to far field transformation to obtain the far-field quantities, we can simply extend the simulation domain sufficiently far into the far-field zone. Here we harness the power of the highly optimized Tidy3D solver such that the simulation is still fast even when the domain size is large.\n",
     "\n",
@@ -1313,9 +1298,7 @@
     "\n",
     "# add a point dipole source\n",
     "pulse = td.GaussianPulse(freq0=freq0, fwidth=freq0 / 20)\n",
-    "pt_dipole = td.PointDipole(\n",
-    "    center=(0, L_f / 2 + d_dp, 0), source_time=pulse, polarization=\"Ey\"\n",
-    ")\n",
+    "pt_dipole = td.PointDipole(center=(0, L_f / 2 + d_dp, 0), source_time=pulse, polarization=\"Ey\")\n",
     "\n",
     "# add a flux monitor to compute the total radiated power\n",
     "flux_monitor = td.FluxMonitor(\n",
@@ -1347,7 +1330,7 @@
     "\n",
     "# visualize the simulation setup\n",
     "sim.plot(z=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1706,7 +1689,7 @@
    "source": [
     "sim_data = web.run(\n",
     "    sim, task_name=\"plasmonic_yagi_uda\", path=\"data/optical_yagi_uda.hdf5\", verbose=True\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1755,15 +1738,9 @@
     "# evaluate the radiated power at 7*lda0 away from the antenna\n",
     "P = np.zeros(len(theta_array))\n",
     "for i, theta in enumerate(theta_array):\n",
-    "    Ex = sim_data[\"field\"].Ex.sel(\n",
-    "        x=d * np.sin(theta), z=d * np.cos(theta), method=\"nearest\"\n",
-    "    )\n",
-    "    Ey = sim_data[\"field\"].Ey.sel(\n",
-    "        x=d * np.sin(theta), z=d * np.cos(theta), method=\"nearest\"\n",
-    "    )\n",
-    "    Ez = sim_data[\"field\"].Ez.sel(\n",
-    "        x=d * np.sin(theta), z=d * np.cos(theta), method=\"nearest\"\n",
-    "    )\n",
+    "    Ex = sim_data[\"field\"].Ex.sel(x=d * np.sin(theta), z=d * np.cos(theta), method=\"nearest\")\n",
+    "    Ey = sim_data[\"field\"].Ey.sel(x=d * np.sin(theta), z=d * np.cos(theta), method=\"nearest\")\n",
+    "    Ez = sim_data[\"field\"].Ez.sel(x=d * np.sin(theta), z=d * np.cos(theta), method=\"nearest\")\n",
     "    P[i] = (\n",
     "        d**2 * (abs(Ex) ** 2 + abs(Ey) ** 2 + abs(Ez) ** 2) / (2 * Z0)\n",
     "    )  # we multiple the power by d^2 to normalize it to the power at unit distance\n",
@@ -1775,7 +1752,7 @@
     "ax.plot(theta_array, D)\n",
     "ax.set_rlim(0, 8)\n",
     "ax.set_title(\"Directivity\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1825,9 +1802,7 @@
     "inf_eff = 100  # effective infinity\n",
     "# construct the substrate\n",
     "sub = td.Structure(\n",
-    "    geometry=td.Box.from_bounds(\n",
-    "        rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, -r)\n",
-    "    ),\n",
+    "    geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, -r)),\n",
     "    medium=glass,\n",
     ")\n",
     "\n",
@@ -1839,7 +1814,7 @@
     "refine_medium = td.Medium(permittivity=10**2)\n",
     "antenna_refine = construct_antenna(\n",
     "    L_f, r, lda0, refine_medium\n",
-    ")  # construct the mesh override structure\n"
+    ")  # construct the mesh override structure"
    ]
   },
   {
@@ -1902,9 +1877,7 @@
     "sim_size = (Lx, Ly, Lz)\n",
     "\n",
     "pulse = td.GaussianPulse(freq0=freq0, fwidth=freq0 / 20)\n",
-    "pt_dipole = td.PointDipole(\n",
-    "    center=(0, L_f / 2 + d_dp, 0), source_time=pulse, polarization=\"Ey\"\n",
-    ")\n",
+    "pt_dipole = td.PointDipole(center=(0, L_f / 2 + d_dp, 0), source_time=pulse, polarization=\"Ey\")\n",
     "\n",
     "flux_monitor = td.FluxMonitor(\n",
     "    center=(0, 0, 0),\n",
@@ -1934,7 +1907,7 @@
     ")\n",
     "\n",
     "sim.plot(y=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2296,7 +2269,7 @@
     "    task_name=\"plasmonic_yagi_uda_on_glass\",\n",
     "    path=\"data/optical_yagi_uda.hdf5\",\n",
     "    verbose=True,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -2339,22 +2312,14 @@
     "# evaluate the radiated power at 7*lda0 away from the antenna\n",
     "P = np.zeros(len(theta_array))\n",
     "for i, theta in enumerate(theta_array):\n",
-    "    Ex = sim_data[\"field\"].Ex.sel(\n",
-    "        x=d * np.sin(theta), z=d * np.cos(theta), method=\"nearest\"\n",
-    "    )\n",
-    "    Ey = sim_data[\"field\"].Ey.sel(\n",
-    "        x=d * np.sin(theta), z=d * np.cos(theta), method=\"nearest\"\n",
-    "    )\n",
-    "    Ez = sim_data[\"field\"].Ez.sel(\n",
-    "        x=d * np.sin(theta), z=d * np.cos(theta), method=\"nearest\"\n",
-    "    )\n",
+    "    Ex = sim_data[\"field\"].Ex.sel(x=d * np.sin(theta), z=d * np.cos(theta), method=\"nearest\")\n",
+    "    Ey = sim_data[\"field\"].Ey.sel(x=d * np.sin(theta), z=d * np.cos(theta), method=\"nearest\")\n",
+    "    Ez = sim_data[\"field\"].Ez.sel(x=d * np.sin(theta), z=d * np.cos(theta), method=\"nearest\")\n",
     "    if d * np.cos(theta) > 0:\n",
     "        P[i] = d**2 * (abs(Ex) ** 2 + abs(Ey) ** 2 + abs(Ez) ** 2) / (2 * Z0)\n",
     "    else:\n",
     "        # inside the substrate, the impedance of the glass needs to be taken into account\n",
-    "        P[i] = (\n",
-    "            n_glass * d**2 * (abs(Ex) ** 2 + abs(Ey) ** 2 + abs(Ez) ** 2) / (2 * Z0)\n",
-    "        )\n",
+    "        P[i] = n_glass * d**2 * (abs(Ex) ** 2 + abs(Ey) ** 2 + abs(Ez) ** 2) / (2 * Z0)\n",
     "\n",
     "D = 4 * np.pi * P / P0  # directivity\n",
     "\n",
@@ -2365,7 +2330,7 @@
     "ax.plot(theta_array, D)\n",
     "ax.set_rlim(0, 25)\n",
     "ax.set_title(\"Directivity\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2405,7 +2370,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.11.0"
   },
   "title": "Plasmonic Yagi-Uda Nanoantenna | Flexcompute",
   "widgets": {
diff --git a/PolarizationSplitterRotator.ipynb b/PolarizationSplitterRotator.ipynb
index d3f6a028..e71ecb10 100644
--- a/PolarizationSplitterRotator.ipynb
+++ b/PolarizationSplitterRotator.ipynb
@@ -44,9 +44,9 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "import gdstk\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
     "from tidy3d.plugins.mode import ModeSolver"
@@ -996,7 +996,7 @@
     "plt.sca(ax1)\n",
     "plt.plot(ldas, T1, ldas, T2)\n",
     "plt.xlim(1.5, 1.6)\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Transmission\")\n",
     "plt.legend((\"Wide waveguide\", \"Narrow waveguide\"))\n",
     "\n",
@@ -1005,7 +1005,7 @@
     "mode_power_share = 100 * np.abs(mode_amp) ** 2 / T1\n",
     "plt.plot(ldas, mode_power_share)\n",
     "plt.xlim(1.5, 1.6)\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Power share at Port1 (%)\")\n",
     "plt.legend([\"TE0\", \"TM0\"])\n",
     "plt.show()"
@@ -1476,7 +1476,7 @@
     "plt.sca(ax1)\n",
     "plt.plot(ldas, T1, ldas, T2)\n",
     "plt.xlim(1.45, 1.6)\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Transmission\")\n",
     "plt.legend((\"Wide waveguide\", \"Narrow waveguide\"))\n",
     "\n",
@@ -1485,7 +1485,7 @@
     "mode_power_share = 100 * np.abs(mode_amp) ** 2 / T2\n",
     "plt.plot(ldas, mode_power_share)\n",
     "plt.xlim(1.45, 1.6)\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Power share at Port2 (%)\")\n",
     "plt.legend([\"TE0\", \"TM0\"])\n",
     "plt.show()"
diff --git a/Primer.ipynb b/Primer.ipynb
index 1fdc3030..4ae6046c 100644
--- a/Primer.ipynb
+++ b/Primer.ipynb
@@ -28,9 +28,9 @@
    "outputs": [],
    "source": [
     "# First, let's import the main packages we'll need\n",
-    "import numpy as np\n",
     "import matplotlib.pylab as plt\n",
-    "import tidy3d as td\n"
+    "import numpy as np\n",
+    "import tidy3d as td"
    ]
   },
   {
@@ -100,7 +100,7 @@
     "    td.log.info(e)\n",
     "\n",
     "# correct way\n",
-    "m = td.Medium(permittivity=2.0)\n"
+    "m = td.Medium(permittivity=2.0)"
    ]
   },
   {
@@ -149,7 +149,7 @@
     "\n",
     "print(my_box)\n",
     "print(your_box)\n",
-    "print(my_box == your_box)\n"
+    "print(my_box == your_box)"
    ]
   },
   {
@@ -184,7 +184,7 @@
    ],
    "source": [
     "print(my_box.json())\n",
-    "print(my_box.dict())\n"
+    "print(my_box.dict())"
    ]
   },
   {
@@ -341,7 +341,7 @@
    "source": [
     "monitor = td.FieldMonitor(size=(2, 2, 0), freqs=[200e12], name=\"monitor\")\n",
     "\n",
-    "monitor.help()\n"
+    "monitor.help()"
    ]
   },
   {
@@ -449,7 +449,7 @@
    },
    "outputs": [],
    "source": [
-    "pec_medium = td.PEC\n"
+    "pec_medium = td.PEC"
    ]
   },
   {
@@ -478,7 +478,7 @@
    "source": [
     "lossless_dielectric = td.Medium(permittivity=4.0)\n",
     "lossy_dielectric = td.Medium(permittivity=4.0, conductivity=1.0)\n",
-    "lossy_dielectric_from_nk = td.Medium.from_nk(n=2.0, k=0.1, freq=150e12)\n"
+    "lossy_dielectric_from_nk = td.Medium.from_nk(n=2.0, k=0.1, freq=150e12)"
    ]
   },
   {
@@ -510,7 +510,7 @@
    "source": [
     "anisotropic_medium = td.AnisotropicMedium(\n",
     "    xx=lossless_dielectric, yy=lossy_dielectric, zz=lossy_dielectric_from_nk\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -560,7 +560,7 @@
     "\n",
     "# models\n",
     "lorentz_model = td.Lorentz(eps_inf=2.0, coeffs=[(1, 2, 3), (4, 5, 6)])\n",
-    "sellmeier_model = td.Sellmeier(coeffs=[(1, 2), (3, 4)])\n"
+    "sellmeier_model = td.Sellmeier(coeffs=[(1, 2), (3, 4)])"
    ]
   },
   {
@@ -612,11 +612,9 @@
    ],
    "source": [
     "freqs_hz = 1e12 * np.linspace(50, 200, 1001)\n",
-    "print(\n",
-    "    f\"complex relative permittivity at freqs_hz = \\n\\t {lossy_dielectric.eps_model(freqs_hz)}\\n\"\n",
-    ")\n",
+    "print(f\"complex relative permittivity at freqs_hz = \\n\\t {lossy_dielectric.eps_model(freqs_hz)}\\n\")\n",
     "\n",
-    "ax = lossy_dielectric_from_nk.plot(freqs_hz)\n"
+    "ax = lossy_dielectric_from_nk.plot(freqs_hz)"
    ]
   },
   {
@@ -778,7 +776,7 @@
     "print(s1.inside(x=np.linspace(-1, 1, 5), y=np.zeros(5), z=np.ones(5)))\n",
     "\n",
     "# plot the geometry at a cross sectional plane\n",
-    "ax = s1.plot(y=0)\n"
+    "ax = s1.plot(y=0)"
    ]
   },
   {
@@ -811,7 +809,7 @@
     "dielectric_box = td.Structure(\n",
     "    geometry=td.Box(center=(0, 0, 0), size=(1, 1, 1)),\n",
     "    medium=td.Medium(permittivity=2.0),\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -854,7 +852,7 @@
     "    source_time=gaussian,\n",
     "    pol_angle=np.pi / 2,\n",
     "    direction=\"-\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -938,7 +936,7 @@
     "    stop=3e-13,\n",
     "    interval=5,\n",
     "    name=\"flux_over_time\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -974,7 +972,7 @@
     "first_4_modes = td.ModeSpec(num_modes=4)\n",
     "\n",
     "# have mode solver return 4 modes around the target effective index\n",
-    "complicated = td.ModeSpec(num_modes=4, target_neff=2.0)\n"
+    "complicated = td.ModeSpec(num_modes=4, target_neff=2.0)"
    ]
   },
   {
@@ -1016,7 +1014,7 @@
     "    freqs=freqs_hz,\n",
     "    mode_spec=first_4_modes,\n",
     "    name=\"modes\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1038,9 +1036,9 @@
     "\n",
     "- [td.PML()](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PML.html) defines a standard PML, with an adjustable number of layers.\n",
     "\n",
-    "- [td.StablePML()](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.StablePML.html) defines a PML with 'stable' profile, which can reduce divergence at the expense of more layers.\n",
+    "- [td.StablePML()](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.StablePML.html) defines a PML with a 'stable' profile, which can reduce divergence at the expense of more layers.\n",
     "\n",
-    "- [td.Absorber()](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Absorber.html) defines adiabatically increasing conductivity values at the edges of the simultion, which can dramatically improve stability of simulations involving dispersive materials, again at the expense of more layers.\n",
+    "- [td.Absorber()](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Absorber.html) defines adiabatically increasing conductivity values at the edges of the simulation, which can dramatically improve stability of simulations involving dispersive materials, again at the expense of more layers.\n",
     "\n",
     "As before, these layers *add* to the simulation size defined in [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html).\n",
     "\n",
@@ -1069,7 +1067,7 @@
     "# standard absorber on x, PML with 20 layers on y, no PML on z (periodic BC)\n",
     "boundary_spec = td.BoundarySpec(\n",
     "    x=td.Boundary.absorber(), y=td.Boundary.pml(num_layers=20), z=td.Boundary.periodic()\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1113,7 +1111,7 @@
     "    structures=[dielectric_box],\n",
     "    sources=[dipole],\n",
     "    monitors=[mon1, mon2],\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1178,7 +1176,7 @@
     "ax1 = sim.plot_eps(x=0, ax=ax2)\n",
     "\n",
     "# add the FDTD grid boundaries\n",
-    "ax2 = sim.plot_grid(x=0, ax=ax2)\n"
+    "ax2 = sim.plot_grid(x=0, ax=ax2)"
    ]
   },
   {
@@ -1212,7 +1210,7 @@
    },
    "outputs": [],
    "source": [
-    "import tidy3d.web as web\n"
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -1224,7 +1222,7 @@
     "\n",
     "The web interface provides a number of basic functions, but usually, the most convenient way to run a single simulation in one line is with [sim_data = web.run(sim)](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.webapi.run.html#tidy3d.web.run), which simply performs all of the necessary steps under the hood.\n",
     "\n",
-    "The output of the simultion is a separate data object called a [SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SimulationData.html), which provides an interface for post processing and visualizing the data."
+    "The output of the simulation is a separate data object called a [SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SimulationData.html), which provides an interface for post processing and visualizing the data."
    ]
   },
   {
@@ -1580,7 +1578,7 @@
     }
    ],
    "source": [
-    "sim_data = web.run(sim, task_name=\"web_demo\", path=\"data/data.hdf5\", verbose=True)\n"
+    "sim_data = web.run(sim, task_name=\"web_demo\", path=\"data/data.hdf5\", verbose=True)"
    ]
   },
   {
@@ -2500,7 +2498,7 @@
     "print(sim_data.log)\n",
     "\n",
     "# get a copy of the original Simulation, so it also doesn't need to be stored separately\n",
-    "sim_data.simulation.help()\n"
+    "sim_data.simulation.help()"
    ]
   },
   {
@@ -2546,7 +2544,7 @@
     "flux_data = sim_data[\"flux_over_time\"].flux\n",
     "flux_data.plot()\n",
     "plt.title(\"flux over time\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2586,7 +2584,7 @@
     "\n",
     "Ey.real.plot(x=\"x\", y=\"y\", robust=True)\n",
     "plt.title(\"real{Ey(x, y)}\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2623,7 +2621,7 @@
    ],
    "source": [
     "sim_data.plot_field(\"fields_at_150THz\", \"Ey\", val=\"real\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2742,7 +2740,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.0"
   },
   "title": "Guide to the Frontend Interface for Tidy3D | Flexcompute",
   "widgets": {
diff --git a/RadarAbsorbingMetamaterial.ipynb b/RadarAbsorbingMetamaterial.ipynb
index bc7a557b..95f2d77d 100644
--- a/RadarAbsorbingMetamaterial.ipynb
+++ b/RadarAbsorbingMetamaterial.ipynb
@@ -26,11 +26,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import tidy3d as td\n",
-    "import tidy3d.web as web\n",
     "import pandas as pd\n",
-    "import matplotlib.pyplot as plt"
+    "import tidy3d as td\n",
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -52,15 +52,15 @@
    "source": [
     "# geometry parameters as defined in the paper\n",
     "\n",
-    "p = 12e3    # lattice constant (um)\n",
-    "a = 1e3     # windmill width (um)\n",
-    "b = 4e3     # windmill arms length (um)\n",
-    "c = 1.9e3   # half of hexagon size (um)\n",
-    "d = 3.8e3   # hexagon size (um)\n",
-    "g = 2e3     # windmill gap (um)\n",
-    "h1 = 0.2    # composite material thickness (um)\n",
+    "p = 12e3  # lattice constant (um)\n",
+    "a = 1e3  # windmill width (um)\n",
+    "b = 4e3  # windmill arms length (um)\n",
+    "c = 1.9e3  # half of hexagon size (um)\n",
+    "d = 3.8e3  # hexagon size (um)\n",
+    "g = 2e3  # windmill gap (um)\n",
+    "h1 = 0.2  # composite material thickness (um)\n",
     "h2 = 2.6e3  # PMMA thickness (um)\n",
-    "h3 = 0.05e3 # metal layer thickness (um)\n",
+    "h3 = 0.05e3  # metal layer thickness (um)\n",
     "\n",
     "# operating frequency (Hz)\n",
     "freq1 = 5e9\n",
@@ -98,7 +98,7 @@
    "outputs": [],
    "source": [
     "# material properties\n",
-    "resistivity = 60 * h1 # (Ohms um)\n",
+    "resistivity = 60 * h1  # (Ohms um)\n",
     "pmma_index = 1.51\n",
     "\n",
     "# modeling the Al@SiO2 as a 2D medium\n",
@@ -253,7 +253,10 @@
     "\n",
     "# monitor to calculate Flux\n",
     "reflection_monitor = td.FluxMonitor(\n",
-    "    center=reflection_field_monitor.center, size=reflection_field_monitor.size, name=\"reflection_monitor\", freqs=freqs\n",
+    "    center=reflection_field_monitor.center,\n",
+    "    size=reflection_field_monitor.size,\n",
+    "    name=\"reflection_monitor\",\n",
+    "    freqs=freqs,\n",
     ")\n",
     "\n",
     "\n",
@@ -292,9 +295,7 @@
     "# defining the structures\n",
     "\n",
     "substrate = td.Structure(\n",
-    "    geometry=td.Box.from_bounds(\n",
-    "        rmin=(-1e12, -1e12, -h2 / 2), rmax=(1e12, 1e12, h2 / 2)\n",
-    "    ),\n",
+    "    geometry=td.Box.from_bounds(rmin=(-1e12, -1e12, -h2 / 2), rmax=(1e12, 1e12, h2 / 2)),\n",
     "    medium=pmma,\n",
     ")\n",
     "\n",
@@ -624,10 +625,9 @@
     "# fields at the metasurface plane\n",
     "fig, Ax = plt.subplots(1, 3, figsize=(16, 4))\n",
     "for i, ax in enumerate(Ax):\n",
-    "\n",
     "    sim = list(simulations.keys())[i]\n",
     "    sim_data = results[sim]\n",
-    "    sim_data.plot_field('field_monitor','E','abs',ax=ax,shading='gouraud')\n",
+    "    sim_data.plot_field(\"field_monitor\", \"E\", \"abs\", ax=ax, shading=\"gouraud\")\n",
     "\n",
     "plt.show()"
    ]
@@ -652,10 +652,9 @@
     "# reflected fields\n",
     "fig, Ax = plt.subplots(1, 3, figsize=(16, 4))\n",
     "for i, ax in enumerate(Ax):\n",
-    "\n",
     "    sim = list(simulations.keys())[i]\n",
     "    sim_data = results[sim]\n",
-    "    sim_data.plot_field('reflection_field_monitor','E','abs',ax=ax)\n",
+    "    sim_data.plot_field(\"reflection_field_monitor\", \"E\", \"abs\", ax=ax)\n",
     "\n",
     "plt.show()"
    ]
diff --git a/RadiativeCoolingGlass.ipynb b/RadiativeCoolingGlass.ipynb
index 42ed7a79..833f64d9 100644
--- a/RadiativeCoolingGlass.ipynb
+++ b/RadiativeCoolingGlass.ipynb
@@ -29,13 +29,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pylab as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
-    "\n",
-    "from tidy3d.plugins.dispersion import FastDispersionFitter, AdvancedFastFitterParam"
+    "from tidy3d.plugins.dispersion import AdvancedFastFitterParam, FastDispersionFitter"
    ]
   },
   {
@@ -70,20 +68,20 @@
     }
    ],
    "source": [
-    "# Define Paramters\n",
+    "# Define Parameters\n",
     "# radius and location of the sphere\n",
     "radius_Al2O3 = 0.25\n",
-    "radius_SiO2 = 4 # exp is 6um\n",
+    "radius_SiO2 = 4  # exp is 6um\n",
     "box_size_xy = 20\n",
     "box_size_z = 100\n",
     "\n",
-    "vol_Al2O3 = 4/3 * np.pi * np.power(radius_Al2O3,3)\n",
-    "vol_SiO2 = 4/3 * np.pi * np.power(radius_SiO2,3)\n",
+    "vol_Al2O3 = 4 / 3 * np.pi * np.power(radius_Al2O3, 3)\n",
+    "vol_SiO2 = 4 / 3 * np.pi * np.power(radius_SiO2, 3)\n",
     "vol_box = box_size_xy * box_size_xy * box_size_z\n",
-    "num_Al2O3 = int(np.floor(0.2 * vol_box / vol_Al2O3))   # 20% of volumn is Al2O3\n",
-    "num_SiO2 = int(np.floor(0.3 * vol_box / vol_SiO2))   # 30% of volumn is SiO2\n",
-    "print('num_Al2O3:',num_Al2O3)\n",
-    "print('num_SiO2:',num_SiO2)"
+    "num_Al2O3 = int(np.floor(0.2 * vol_box / vol_Al2O3))  # 20% of volume is Al2O3\n",
+    "num_SiO2 = int(np.floor(0.3 * vol_box / vol_SiO2))  # 30% of volume is SiO2\n",
+    "print(\"num_Al2O3:\", num_Al2O3)\n",
+    "print(\"num_SiO2:\", num_SiO2)"
    ]
   },
   {
@@ -164,12 +162,14 @@
     }
    ],
    "source": [
-    "# permittivity of Al2O3 \n",
+    "# permittivity of Al2O3\n",
     "mat_Al2O3 = \"misc/mat_Al2O3.csv\"\n",
     "\n",
-    "advanced_param = AdvancedFastFitterParam(weights=(1,1))\n",
+    "advanced_param = AdvancedFastFitterParam(weights=(1, 1))\n",
     "fitter = FastDispersionFitter.from_file(mat_Al2O3, skiprows=1, delimiter=\",\")\n",
-    "medium_Al2O3, rms_error = fitter.fit(max_num_poles=6, advanced_param=advanced_param, tolerance_rms=2e-2)\n",
+    "medium_Al2O3, rms_error = fitter.fit(\n",
+    "    max_num_poles=6, advanced_param=advanced_param, tolerance_rms=2e-2\n",
+    ")\n",
     "fitter.plot(medium_Al2O3)\n",
     "plt.show()"
    ]
@@ -247,7 +247,9 @@
     "mat_SiO2 = \"misc/mat_SiO2.csv\"\n",
     "\n",
     "fitter = FastDispersionFitter.from_file(mat_SiO2, skiprows=1, delimiter=\",\")\n",
-    "medium_SiO2, rms_error = fitter.fit(max_num_poles=8, advanced_param=advanced_param, tolerance_rms=2e-2)\n",
+    "medium_SiO2, rms_error = fitter.fit(\n",
+    "    max_num_poles=8, advanced_param=advanced_param, tolerance_rms=2e-2\n",
+    ")\n",
     "fitter.plot(medium_SiO2)\n",
     "plt.show()"
    ]
@@ -274,7 +276,7 @@
     "freq_end = td.C_0 / wl_start\n",
     "\n",
     "freqs = np.linspace(freq_start, freq_end, 100)  # freqeucny range of the simulation\n",
-    "freq0 = (freq_start + freq_end)/2  # central frequency\n",
+    "freq0 = (freq_start + freq_end) / 2  # central frequency\n",
     "freqw = freq_end - freq_start  # width of the frequency range\n",
     "\n",
     "# distance between the surface of the sphere and the start of the PML layers along each cartesian direction\n",
@@ -282,7 +284,7 @@
     "buffer_source = 1 * wl_end\n",
     "\n",
     "# set the domain size in x, y, and z\n",
-    "domain_size_xy = box_size_xy \n",
+    "domain_size_xy = box_size_xy\n",
     "domain_size_z = buffer_PML + box_size_z + buffer_PML\n",
     "\n",
     "# construct simulation size array\n",
@@ -309,23 +311,27 @@
     "Al2O3_geometry = []\n",
     "geometry = []\n",
     "for i in range(num_SiO2):\n",
-    "    position_xy = (box_size_xy - 2*radius_SiO2) * (np.random.rand(2) - 0.5) \n",
-    "    position_z = (box_size_z - 2*radius_SiO2) * (np.random.rand(1) - 0.5) \n",
-    "    position = [position_xy[0],position_xy[1],position_z]\n",
+    "    position_xy = (box_size_xy - 2 * radius_SiO2) * (np.random.rand(2) - 0.5)\n",
+    "    position_z = (box_size_z - 2 * radius_SiO2) * (np.random.rand(1) - 0.5)\n",
+    "    position = [position_xy[0], position_xy[1], position_z]\n",
     "    sphere = td.Sphere(center=position, radius=radius_SiO2)\n",
     "    SiO2_geometry.append(sphere)\n",
-    "    \n",
-    "geometry.append(td.Structure(geometry=td.GeometryGroup(geometries=SiO2_geometry), medium=medium_SiO2))\n",
-    "   \n",
+    "\n",
+    "geometry.append(\n",
+    "    td.Structure(geometry=td.GeometryGroup(geometries=SiO2_geometry), medium=medium_SiO2)\n",
+    ")\n",
+    "\n",
     "for i in range(num_Al2O3):\n",
-    "    position_xy = (box_size_xy - 2*radius_Al2O3) * (np.random.rand(2) - 0.5) \n",
-    "    position_z = (box_size_z - 2*radius_Al2O3) * (np.random.rand(1) - 0.5)\n",
-    "    position = [position_xy[0],position_xy[1],position_z]\n",
+    "    position_xy = (box_size_xy - 2 * radius_Al2O3) * (np.random.rand(2) - 0.5)\n",
+    "    position_z = (box_size_z - 2 * radius_Al2O3) * (np.random.rand(1) - 0.5)\n",
+    "    position = [position_xy[0], position_xy[1], position_z]\n",
     "    sphere = td.Sphere(center=position, radius=radius_Al2O3)\n",
     "    Al2O3_geometry.append(sphere)\n",
     "\n",
-    "geometry.append(td.Structure(geometry=td.GeometryGroup(geometries=Al2O3_geometry), medium=medium_Al2O3))\n",
-    "geometry = tuple(geometry)\n"
+    "geometry.append(\n",
+    "    td.Structure(geometry=td.GeometryGroup(geometries=Al2O3_geometry), medium=medium_Al2O3)\n",
+    ")\n",
+    "geometry = tuple(geometry)"
    ]
   },
   {
@@ -369,19 +375,25 @@
     "plane_wave = td.PlaneWave(\n",
     "    source_time=td.GaussianPulse(freq0=freq0, fwidth=0.5 * freqw),\n",
     "    size=(td.inf, td.inf, 0),\n",
-    "    center=(0, 0, box_size_z/2 + buffer_source),\n",
+    "    center=(0, 0, box_size_z / 2 + buffer_source),\n",
     "    direction=\"-\",\n",
     "    pol_angle=0,\n",
     ")\n",
     "\n",
     "# add a flux monitor to detect transmission\n",
     "monitor_t = td.FluxMonitor(\n",
-    "    center=[0, 0, -box_size_z/2 - (buffer_source+buffer_PML)/2], size=[td.inf, td.inf, 0], freqs=freqs, name=\"T\"\n",
+    "    center=[0, 0, -box_size_z / 2 - (buffer_source + buffer_PML) / 2],\n",
+    "    size=[td.inf, td.inf, 0],\n",
+    "    freqs=freqs,\n",
+    "    name=\"T\",\n",
     ")\n",
     "\n",
     "# add a flux monitor to detect reflection\n",
     "monitor_r = td.FluxMonitor(\n",
-    "    center=[0, 0, box_size_z/2 + (buffer_source+buffer_PML)/2], size=[td.inf, td.inf, 0], freqs=freqs, name=\"R\"\n",
+    "    center=[0, 0, box_size_z / 2 + (buffer_source + buffer_PML) / 2],\n",
+    "    size=[td.inf, td.inf, 0],\n",
+    "    freqs=freqs,\n",
+    "    name=\"R\",\n",
     ")\n",
     "\n",
     "# add a field monitor to see the field profile at the absorption peak frequency\n",
@@ -410,15 +422,15 @@
     "# set up simulation\n",
     "sim = td.Simulation(\n",
     "    size=sim_size,\n",
-    "    grid_spec=td.GridSpec.uniform(dl=wl_start/20),\n",
+    "    grid_spec=td.GridSpec.uniform(dl=wl_start / 20),\n",
     "    structures=geometry,\n",
     "    sources=[plane_wave],\n",
     "    monitors=[monitor_t, monitor_r, monitor_field],\n",
     "    run_time=run_time,\n",
     "    boundary_spec=td.BoundarySpec(\n",
     "        x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
-    "    ), \n",
-    ")  "
+    "    ),\n",
+    ")"
    ]
   },
   {
@@ -458,12 +470,10 @@
    ],
    "source": [
     "# Visualize source\n",
-    "plane_wave.source_time.plot(np.linspace(0, run_time/10, 1001))\n",
+    "plane_wave.source_time.plot(np.linspace(0, run_time / 10, 1001))\n",
     "plt.show()\n",
     "\n",
-    "plane_wave.source_time.plot_spectrum(\n",
-    "    times=np.linspace(0, run_time/10, 2000), val=\"abs\"\n",
-    ")\n",
+    "plane_wave.source_time.plot_spectrum(times=np.linspace(0, run_time / 10, 2000), val=\"abs\")\n",
     "plt.show()"
    ]
   },
@@ -602,7 +612,7 @@
     "task_id = web.upload(sim, task_name=\"Simulation\")\n",
     "\n",
     "estimated_cost = web.estimate_cost(task_id)\n",
-    "print(f'The estimated maximum cost is {estimated_cost:.3f} Flex Credits.')"
+    "print(f\"The estimated maximum cost is {estimated_cost:.3f} Flex Credits.\")"
    ]
   },
   {
@@ -929,6 +939,7 @@
     "web.monitor(task_id, verbose=True)\n",
     "\n",
     "import time\n",
+    "\n",
     "time.sleep(20)\n",
     "print(\"Billed flex unit cost: \", web.real_cost(task_id))\n",
     "\n",
@@ -988,10 +999,10 @@
     "R = sim_data[\"R\"].flux\n",
     "T = -sim_data[\"T\"].flux\n",
     "A = 1 - R - T\n",
-    "plt.plot(td.C_0 /freqs, R, td.C_0 /freqs, T, td.C_0 /freqs, A)\n",
+    "plt.plot(td.C_0 / freqs, R, td.C_0 / freqs, T, td.C_0 / freqs, A)\n",
     "\n",
     "# Save the absorption spectrum as as a .txt file\n",
-    "np.savetxt('data/Abs_4-20um.txt', (np.transpose((td.C_0 /freqs, A))))\n",
+    "np.savetxt(\"data/Abs_4-20um.txt\", (np.transpose((td.C_0 / freqs, A))))\n",
     "\n",
     "plt.xlabel(\"Wavelength (μm)\")\n",
     "plt.ylim(0, 1)\n",
@@ -999,7 +1010,7 @@
     "plt.show()\n",
     "\n",
     "ax = sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\")\n",
-    "ax.set_aspect('auto')\n",
+    "ax.set_aspect(\"auto\")\n",
     "plt.show()"
    ]
   },
@@ -1037,7 +1048,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.11.0"
   },
   "title": "Radiative cooling glass coating | Flexcompute"
  },
diff --git a/RadiativeLossesModeSolver.ipynb b/RadiativeLossesModeSolver.ipynb
index 5aa18ce7..cf2004ea 100644
--- a/RadiativeLossesModeSolver.ipynb
+++ b/RadiativeLossesModeSolver.ipynb
@@ -37,10 +37,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tidy3d as td\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
+    "import tidy3d as td\n",
     "from tidy3d.plugins.mode import ModeSolver\n",
     "from tidy3d.plugins.mode.web import run as run_mode_solver\n",
     "from tidy3d.plugins.mode.web import run_batch"
@@ -92,17 +91,13 @@
     "\n",
     "    # SiO2 layer\n",
     "    box = td.Structure(\n",
-    "        geometry=td.Box.from_bounds(\n",
-    "            rmin=(-td.inf, -td.inf, -100), rmax=(td.inf, td.inf, -0.11)\n",
-    "        ),\n",
+    "        geometry=td.Box.from_bounds(rmin=(-td.inf, -td.inf, -100), rmax=(td.inf, td.inf, -0.11)),\n",
     "        medium=td.Medium(permittivity=1.45**2),\n",
     "    )\n",
     "\n",
     "    # Si substrate\n",
     "    substrate = td.Structure(\n",
-    "        geometry=td.Box.from_bounds(\n",
-    "            rmin=(-td.inf, -td.inf, -100), rmax=(td.inf, td.inf, -1.11)\n",
-    "        ),\n",
+    "        geometry=td.Box.from_bounds(rmin=(-td.inf, -td.inf, -100), rmax=(td.inf, td.inf, -1.11)),\n",
     "        medium=td.Medium(permittivity=3.5**2),\n",
     "    )\n",
     "\n",
@@ -520,9 +515,7 @@
    "source": [
     "# function to decide if a mode is a valid solution\n",
     "def isMode(mode_data, mode_index, radius=1, freq_index=0):\n",
-    "    E = abs((mode_data.Ex**2 + mode_data.Ey**2 + mode_data.Ez**2))[\n",
-    "        0, :, :, freq_index, mode_index\n",
-    "    ]\n",
+    "    E = abs(mode_data.Ex**2 + mode_data.Ey**2 + mode_data.Ez**2)[0, :, :, freq_index, mode_index]\n",
     "    center = (\n",
     "        E.y.min() + (E.y.max() - E.y.min()) / 2,\n",
     "        E.z.min() + (E.z.max() - E.z.min()) / 2,\n",
@@ -539,7 +532,7 @@
     "# function to find the first valid mode\n",
     "def findMode(mode_data, radius=1):\n",
     "    for i in range(mode_data.Ex.shape[-1]):\n",
-    "        if isMode(mode_data, i, radius) == True:\n",
+    "        if isMode(mode_data, i, radius):\n",
     "            return i\n",
     "    return None"
    ]
@@ -694,12 +687,8 @@
     "for i, mode_data in enumerate(batch_data):\n",
     "    idx = findMode(mode_data)\n",
     "\n",
-    "    Real.append(\n",
-    "        float(mode_data.n_complex.sel(f=mode_solver.freqs[0], mode_index=idx).real)\n",
-    "    )\n",
-    "    Imag.append(\n",
-    "        float(mode_data.n_complex.sel(f=mode_solver.freqs[0], mode_index=idx).imag)\n",
-    "    )\n",
+    "    Real.append(float(mode_data.n_complex.sel(f=mode_solver.freqs[0], mode_index=idx).real))\n",
+    "    Imag.append(float(mode_data.n_complex.sel(f=mode_solver.freqs[0], mode_index=idx).imag))\n",
     "\n",
     "fig, ax = plt.subplots(2, 1)\n",
     "ax[0].plot(sizes, Imag, \"o\")\n",
@@ -708,7 +697,7 @@
     "\n",
     "ax[1].plot(sizes, Real, \"o\")\n",
     "ax[1].set_ylabel(\"n\")\n",
-    "ax[1].set_xlabel(\"Size ($\\mu$m)\")\n",
+    "ax[1].set_xlabel(r\"Size ($\\mu$m)\")\n",
     "\n",
     "plt.show()"
    ]
@@ -846,12 +835,8 @@
     "for i, mode_data in enumerate(batch_data):\n",
     "    idx = findMode(mode_data)\n",
     "\n",
-    "    Real.append(\n",
-    "        float(mode_data.n_complex.sel(f=mode_solver.freqs[0], mode_index=idx).real)\n",
-    "    )\n",
-    "    Imag.append(\n",
-    "        float(mode_data.n_complex.sel(f=mode_solver.freqs[0], mode_index=idx).imag)\n",
-    "    )\n",
+    "    Real.append(float(mode_data.n_complex.sel(f=mode_solver.freqs[0], mode_index=idx).real))\n",
+    "    Imag.append(float(mode_data.n_complex.sel(f=mode_solver.freqs[0], mode_index=idx).imag))\n",
     "\n",
     "\n",
     "fig, ax = plt.subplots(2, 1)\n",
@@ -993,12 +978,8 @@
     "for i, mode_data in enumerate(batch_data):\n",
     "    idx = findMode(mode_data)\n",
     "\n",
-    "    real.append(\n",
-    "        float(mode_data.n_complex.sel(f=mode_solver.freqs[0], mode_index=idx).real)\n",
-    "    )\n",
-    "    imag.append(\n",
-    "        float(mode_data.n_complex.sel(f=mode_solver.freqs[0], mode_index=idx).imag)\n",
-    "    )\n",
+    "    real.append(float(mode_data.n_complex.sel(f=mode_solver.freqs[0], mode_index=idx).real))\n",
+    "    imag.append(float(mode_data.n_complex.sel(f=mode_solver.freqs[0], mode_index=idx).imag))\n",
     "\n",
     "fig, ax = plt.subplots(2, 1)\n",
     "ax[0].plot(grid_step, imag, \"-o\", lw=0.4)\n",
@@ -1037,7 +1018,7 @@
     "    resolution,\n",
     "    npml,\n",
     "    delta_override=None,\n",
-    "    size=(20,15),\n",
+    "    size=(20, 15),\n",
     "    nun_modes=10,\n",
     "    target_neff=1.547,\n",
     "    bend_radius=None,\n",
@@ -1112,7 +1093,7 @@
    ],
    "source": [
     "# visualizing the mode solver\n",
-    "ms = mode_solver_SiN_wvg(10,12)\n",
+    "ms = mode_solver_SiN_wvg(10, 12)\n",
     "ms.plot()\n",
     "plt.show()"
    ]
@@ -1152,13 +1133,12 @@
     "            size=(size, size),\n",
     "        )\n",
     "\n",
-    "        mode_data = run_mode_solver(mode_solver,verbose=False)\n",
+    "        mode_data = run_mode_solver(mode_solver, verbose=False)\n",
     "\n",
     "        idx = findMode(mode_data, 0.4)\n",
     "\n",
     "        l = mode_data.to_dataframe().iloc[idx][\"loss (dB/cm)\"]\n",
     "\n",
-    "        \n",
     "    loss.append(l)\n",
     "    sizes.append(size)"
    ]
@@ -1189,10 +1169,10 @@
     }
    ],
    "source": [
-    "fig,ax = plt.subplots()\n",
-    "ax.plot(radius,sizes,'--o')\n",
-    "ax.set_xlabel('bend radius ($\\\\mu$m)')\n",
-    "ax.set_ylabel('minimum plane size ($\\\\mu$m)')\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(radius, sizes, \"--o\")\n",
+    "ax.set_xlabel(\"bend radius ($\\\\mu$m)\")\n",
+    "ax.set_ylabel(\"minimum plane size ($\\\\mu$m)\")\n",
     "\n",
     "plt.show()"
    ]
@@ -1285,7 +1265,7 @@
     "        nun_modes=5,\n",
     "        bend_radius=r,\n",
     "        bend_axis=1,\n",
-    "        size=(20,15),\n",
+    "        size=(20, 15),\n",
     "    )\n",
     "\n",
     "    mode_solvers.append(mode_solver)\n",
@@ -1353,7 +1333,7 @@
     "        nun_modes=5,\n",
     "        bend_radius=r,\n",
     "        bend_axis=1,\n",
-    "        size=(20,15),\n",
+    "        size=(20, 15),\n",
     "    )\n",
     "\n",
     "    mode_data = run_mode_solver(mode_solver, verbose=False)\n",
@@ -1449,7 +1429,7 @@
     "        bend_axis=1,\n",
     "        resolution=r,\n",
     "        nun_modes=10,\n",
-    "        size=(20,15),\n",
+    "        size=(20, 15),\n",
     "    )\n",
     "\n",
     "    mode_data = run_mode_solver(mode_solver, verbose=False)\n",
@@ -1533,13 +1513,9 @@
     "):\n",
     "    fiberDiameter = 2 * fiber_radius\n",
     "\n",
-    "    fiber = td.Cylinder(\n",
-    "        axis=0, radius=fiberDiameter / 2, center=(0, 0, 0), length=td.inf\n",
-    "    )\n",
+    "    fiber = td.Cylinder(axis=0, radius=fiberDiameter / 2, center=(0, 0, 0), length=td.inf)\n",
     "\n",
-    "    structures = [\n",
-    "        td.Structure(geometry=fiber, medium=td.Medium(permittivity=coreIndex**2))\n",
-    "    ]\n",
+    "    structures = [td.Structure(geometry=fiber, medium=td.Medium(permittivity=coreIndex**2))]\n",
     "\n",
     "    mode_spec = td.ModeSpec(\n",
     "        num_modes=num_modes,\n",
@@ -1553,9 +1529,7 @@
     "    if delta_override:\n",
     "        mesh_override = [\n",
     "            td.MeshOverrideStructure(\n",
-    "                geometry=td.Box(\n",
-    "                    center=(0, 0, 0), size=(1, 3 * fiber_radius, 3 * fiber_radius)\n",
-    "                ),\n",
+    "                geometry=td.Box(center=(0, 0, 0), size=(1, 3 * fiber_radius, 3 * fiber_radius)),\n",
     "                dl=(delta_override,) * 3,\n",
     "            )\n",
     "        ]\n",
@@ -1647,9 +1621,9 @@
    ],
    "source": [
     "# print the results\n",
-    "print(\"Size: \\t loss (dB/cm)\")\n",
+    "print(\"Size (μm)\\tLoss (dB/cm)\")\n",
     "for i, s in enumerate(sizes):\n",
-    "    print(\"%s \\t %s\" % (s, loss[i]))"
+    "    print(f\"{s}\\t\\t{loss[i]:.6f}\")"
    ]
   },
   {
@@ -1762,7 +1736,7 @@
     "    label=\"Analytical model\",\n",
     ")\n",
     "ax.set_yscale(\"log\")\n",
-    "ax.set_xlabel(\"Bend radius ($\\mu$m)\")\n",
+    "ax.set_xlabel(r\"Bend radius ($\\mu$m)\")\n",
     "ax.set_ylabel(\"$k_{eff}$\")\n",
     "ax.legend()\n",
     "\n",
diff --git a/ResonanceFinder.ipynb b/ResonanceFinder.ipynb
index c52284b2..6ca19427 100644
--- a/ResonanceFinder.ipynb
+++ b/ResonanceFinder.ipynb
@@ -28,8 +28,8 @@
    "outputs": [],
    "source": [
     "# Standard python imports.\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# Tidy3D imports.\n",
     "import tidy3d as td\n",
@@ -72,7 +72,9 @@
     "\n",
     "# Simulation runtime\n",
     "runtime_fwidth = 20  # In units of 1/frequency bandwidth of the source.\n",
-    "t_start_fwidth = 2  # Time to start monitoring after source has decayed, units of 1/frequency bandwidth.\n",
+    "t_start_fwidth = (\n",
+    "    2  # Time to start monitoring after source has decayed, units of 1/frequency bandwidth.\n",
+    ")\n",
     "run_time = runtime_fwidth / freq_bw\n",
     "t_start = t_start_fwidth / freq_bw\n",
     "print(f\"Total runtime = {(run_time*1e12):.2f} ps\")\n",
@@ -84,7 +86,7 @@
     "\n",
     "# Microdisk material\n",
     "n = 3.50\n",
-    "mat_disk = td.Medium(permittivity=n ** 2)"
+    "mat_disk = td.Medium(permittivity=n**2)"
    ]
   },
   {
@@ -975,9 +977,7 @@
     "sim.plot(z=0.0, ax=ax1)\n",
     "\n",
     "plot_time = 3 / freq_bw\n",
-    "sim.sources[0].source_time.plot(\n",
-    "    times=np.linspace(0, plot_time, 1001), val=\"abs\", ax=ax2\n",
-    ")\n",
+    "sim.sources[0].source_time.plot(times=np.linspace(0, plot_time, 1001), val=\"abs\", ax=ax2)\n",
     "ax2.set_xlim(0, plot_time)\n",
     "ax2.vlines(t_start, 0, 1, linewidth=2, color=\"g\", alpha=0.4)\n",
     "ax2.legend([\"source\", \"start time\"])\n",
@@ -1166,7 +1166,7 @@
     "\n",
     "time_response = sim_data[\"monitor_time_0\"].Ey.squeeze()\n",
     "freq_response = np.abs(np.fft.fft(time_response))\n",
-    "freqs = np.linspace(0, 1/sim_data.simulation.dt, len(time_response))\n",
+    "freqs = np.linspace(0, 1 / sim_data.simulation.dt, len(time_response))\n",
     "plot_inds = np.where((freq_range[-1] < freqs) & (freqs < freq_range[0]))\n",
     "ax1.plot(freqs[plot_inds], freq_response[plot_inds])\n",
     "ax1.set_xlabel(\"Frequency (Hz)\")\n",
diff --git a/RingResonator.ipynb b/RingResonator.ipynb
index 7e61230e..1bc9e6fb 100644
--- a/RingResonator.ipynb
+++ b/RingResonator.ipynb
@@ -33,11 +33,10 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import tidy3d.web as web\n",
-    "import tidy3d as td"
+    "import numpy as np\n",
+    "import tidy3d as td\n",
+    "import tidy3d.web as web"
    ]
   },
   {
diff --git a/SMatrix.ipynb b/SMatrix.ipynb
index 84295863..c94c1256 100644
--- a/SMatrix.ipynb
+++ b/SMatrix.ipynb
@@ -36,14 +36,15 @@
     "# make sure notebook plots inline\n",
     "%matplotlib inline\n",
     "# standard python imports\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "import os\n",
+    "\n",
     "import gdstk\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3D imports\n",
     "import tidy3d as td\n",
-    "from tidy3d import web\n"
+    "from tidy3d import web"
    ]
   },
   {
@@ -104,7 +105,7 @@
     "# Straight waveguide sections on each side\n",
     "straight_wg_length = 4\n",
     "# space between waveguide and PML\n",
-    "pml_spacing = 1.2\n"
+    "pml_spacing = 1.2"
    ]
   },
   {
@@ -170,7 +171,7 @@
     "        offset=[lambda u: -0.5 * offset(1 - u), lambda u: 0.5 * offset(1 - u)],\n",
     "    )\n",
     "    coup.segment((0.5 * length, 0))\n",
-    "    return coup\n"
+    "    return coup"
    ]
   },
   {
@@ -257,7 +258,7 @@
     "    monitors=[domain_monitor],\n",
     "    run_time=50 / fwidth,\n",
     "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -287,15 +288,15 @@
    "source": [
     "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(15, 10))\n",
     "ax1 = sim.plot(z=wg_height / 2, ax=ax1)\n",
-    "ax2 = sim.plot(x=src_pos, ax=ax2)\n"
+    "ax2 = sim.plot(x=src_pos, ax=ax2)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setting up Scattering Matrix Tool\n",
-    "Now, to use the S matrix tool, we need to defing the spatial extent of the \"ports\" of our system using [Port](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.smatrix.Port.html) objects.\n",
+    "## Setting up the Scattering Matrix Tool\n",
+    "Now, to use the S matrix tool, we need to define the spatial extent of the \"ports\" of our system using [Port](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.plugins.smatrix.Port.html) objects.\n",
     "\n",
     "These ports will be converted into modal sources and monitors later, so they require both some mode specification and a definition of the direction that points into the system.\n",
     "\n",
@@ -352,7 +353,7 @@
     "    name=\"left_bot\",\n",
     ")\n",
     "\n",
-    "ports = [port_right_top, port_right_bot, port_left_top, port_left_bot]\n"
+    "ports = [port_right_top, port_right_bot, port_left_top, port_left_bot]"
    ]
   },
   {
@@ -378,7 +379,9 @@
    "source": [
     "from tidy3d.plugins.smatrix.smatrix import ComponentModeler\n",
     "\n",
-    "modeler = ComponentModeler(simulation=sim, ports=ports, freqs=[freq0], verbose=True, path_dir=\"data\")\n"
+    "modeler = ComponentModeler(\n",
+    "    simulation=sim, ports=ports, freqs=[freq0], verbose=True, path_dir=\"data\"\n",
+    ")"
    ]
   },
   {
@@ -415,7 +418,7 @@
    "source": [
     "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(15, 10))\n",
     "ax1 = modeler.plot_sim(z=wg_height / 2, ax=ax1)\n",
-    "ax2 = modeler.plot_sim(x=src_pos, ax=ax2)\n"
+    "ax2 = modeler.plot_sim(x=src_pos, ax=ax2)"
    ]
   },
   {
@@ -1011,7 +1014,7 @@
     }
    ],
    "source": [
-    "smatrix = modeler.run()\n"
+    "smatrix = modeler.run()"
    ]
   },
   {
@@ -1431,9 +1434,7 @@
     }
    ],
    "source": [
-    "smatrix.loc[\n",
-    "    dict(port_in=\"left_top\", mode_index_in=0, port_out=\"right_bot\", mode_index_out=0)\n",
-    "]\n"
+    "smatrix.loc[dict(port_in=\"left_top\", mode_index_in=0, port_out=\"right_bot\", mode_index_out=0)]"
    ]
   },
   {
@@ -1466,7 +1467,7 @@
    ],
    "source": [
     "S = np.squeeze(smatrix.values)\n",
-    "print(S.shape)\n"
+    "print(S.shape)"
    ]
   },
   {
@@ -1508,7 +1509,7 @@
     }
    ],
    "source": [
-    "np.sum(abs(S) ** 2, axis=0)\n"
+    "np.sum(abs(S) ** 2, axis=0)"
    ]
   },
   {
@@ -1517,7 +1518,7 @@
    "source": [
     "There is a little power loss since the coupler was not optimized, most likely scattering from the bends and coupling region.\n",
     "\n",
-    "Finally, we can check whether `S` is close to unitary as expected. S times it's Hermitian conjugate should be the identy matrix."
+    "Finally, we can check whether `S` is close to unitary as expected. S times its Hermitian conjugate should be the identity matrix."
    ]
   },
   {
@@ -1534,7 +1535,7 @@
    },
    "outputs": [],
    "source": [
-    "mat = S @ (np.conj(S.T))\n"
+    "mat = S @ (np.conj(S.T))"
    ]
   },
   {
@@ -1570,15 +1571,15 @@
     "imreal = ax2.matshow(mat.real, cmap=\"RdBu\", vmin=-vmax, vmax=vmax)\n",
     "vmax = np.abs(mat.imag).max()\n",
     "imimag = ax3.matshow(mat.imag, cmap=\"RdBu\", vmin=-vmax, vmax=vmax)\n",
-    "ax1.set_title(\"$|S^\\dagger S|$\")\n",
-    "ax2.set_title(\"$\\Re\\{S^\\dagger S\\}$\")\n",
-    "ax3.set_title(\"$\\Im\\{S^\\dagger S\\}$\")\n",
+    "ax1.set_title(r\"$|S^\\dagger S|$\")\n",
+    "ax2.set_title(r\"$\\Re\\{S^\\dagger S\\}$\")\n",
+    "ax3.set_title(r\"$\\Im\\{S^\\dagger S\\}$\")\n",
     "plt.colorbar(imabs, ax=ax1)\n",
     "plt.colorbar(imreal, ax=ax2)\n",
     "plt.colorbar(imimag, ax=ax3)\n",
     "ax1.grid(False)\n",
     "ax2.grid(False)\n",
-    "ax3.grid(False)\n"
+    "ax3.grid(False)"
    ]
   },
   {
@@ -1692,7 +1693,7 @@
     ")\n",
     "ax2 = modeler.batch.load(path_dir=\"data\")[\"smatrix_right_bot_0\"].plot_field(\n",
     "    \"field\", field_name=\"E\", val=\"abs^2\", z=wg_height / 2, ax=ax2\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1802,7 +1803,7 @@
     ")\n",
     "ax2 = modeler2.batch.load(path_dir=\"data\")[\"smatrix_right_bot_0\"].plot_field(\n",
     "    \"field\", \"int\", z=wg_height / 2, ax=ax2\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1853,7 +1854,7 @@
     "map_horizontal_l2r = (top_coupling_l2r, bot_coupling_l2r, +1)\n",
     "map_horizontal_r2l = (top_coupling_r2l, bot_coupling_r2l, +1)\n",
     "\n",
-    "element_mappings = (map_horizontal_l2r, map_horizontal_r2l)\n"
+    "element_mappings = (map_horizontal_l2r, map_horizontal_r2l)"
    ]
   },
   {
@@ -2442,9 +2443,9 @@
     "    freqs=[freq0],\n",
     "    element_mappings=element_mappings,\n",
     "    verbose=True,\n",
-    "    path_dir=\"data\"\n",
+    "    path_dir=\"data\",\n",
     ")\n",
-    "smatrix = modeler.run()\n"
+    "smatrix = modeler.run()"
    ]
   },
   {
@@ -2483,7 +2484,7 @@
     "print(f\"top to top coupling       = {LT_RT:.5f}\")\n",
     "print(f\"bottom to bottom coupling = {LB_RB:.5f}\")\n",
     "\n",
-    "assert np.isclose(LT_RT, LB_RB)\n"
+    "assert np.isclose(LT_RT, LB_RB)"
    ]
   },
   {
@@ -2839,7 +2840,7 @@
     "modeler = ComponentModeler(\n",
     "    simulation=sim, ports=ports, freqs=[freq0], run_only=run_only, verbose=True, path_dir=\"data\"\n",
     ")\n",
-    "smatrix = modeler.run()\n"
+    "smatrix = modeler.run()"
    ]
   },
   {
@@ -2877,7 +2878,7 @@
     "\n",
     "print(\"output from run_only port     : \\n\", s_matrix_left_top)\n",
     "\n",
-    "assert \"right_top\" not in smatrix.coords[\"port_in\"]\n"
+    "assert \"right_top\" not in smatrix.coords[\"port_in\"]"
    ]
   },
   {
@@ -2907,7 +2908,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.0"
   },
   "title": "Scattering Matrix Computation in Tidy3D | Flexcompute",
   "widgets": {
diff --git a/STLImport.ipynb b/STLImport.ipynb
index 3f8cb3c1..666806f0 100644
--- a/STLImport.ipynb
+++ b/STLImport.ipynb
@@ -12,7 +12,7 @@
     "\n",
     "To use this functionality, remember to install `Tidy3D` as `pip install \"tidy3d[trimesh]\"`, which will install optional dependencies needed for processing surface meshes.\n",
     "\n",
-    "We also provide a conprehensive list of other tutorials such as [how to define boundary conditions](https://www.flexcompute.com/tidy3d/examples/notebooks/BoundaryConditions/), [how to compute the S-matrix of a device](https://www.flexcompute.com/tidy3d/examples/notebooks/SMatrix/), [how to interact with tidy3d's web API](https://www.flexcompute.com/tidy3d/examples/notebooks/WebAPI/), and [how to define self-intersecting polygons](https://www.flexcompute.com/tidy3d/examples/notebooks/SelfIntersectingPolyslab/).\n",
+    "We also provide a comprehensive list of other tutorials such as [how to define boundary conditions](https://www.flexcompute.com/tidy3d/examples/notebooks/BoundaryConditions/), [how to compute the S-matrix of a device](https://www.flexcompute.com/tidy3d/examples/notebooks/SMatrix/), [how to interact with tidy3d's web API](https://www.flexcompute.com/tidy3d/examples/notebooks/WebAPI/), and [how to define self-intersecting polygons](https://www.flexcompute.com/tidy3d/examples/notebooks/SelfIntersectingPolyslab/).\n",
     "\n",
     "If you are new to the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials. "
    ]
@@ -32,12 +32,12 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3d imports\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n"
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -109,7 +109,7 @@
     "source_time = td.GaussianPulse(freq0=f0, fwidth=fwidth, offset=offset)\n",
     "\n",
     "# Simulation run time past the source decay (around t=2*offset/fwidth)\n",
-    "run_time = 40 / fwidth\n"
+    "run_time = 40 / fwidth"
    ]
   },
   {
@@ -215,7 +215,7 @@
     ")\n",
     "monitor_xy = td.FieldMonitor(\n",
     "    center=(0, 0, 0), size=(domain_size, domain_size, 0), freqs=[f0], name=\"xy\"\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -277,7 +277,7 @@
     "_, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 4))\n",
     "sim.plot(y=0, ax=ax1)\n",
     "sim_ref.plot(y=0, ax=ax2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -331,7 +331,7 @@
     "_, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 4))\n",
     "sim.plot(y=0, ax=ax1)\n",
     "sim_ref.plot(y=0, ax=ax2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -362,7 +362,7 @@
     "\n",
     "# update the simulation objects to add in the new monitor\n",
     "sim = sim.copy(update={\"monitors\": list(sim.monitors) + [monitor_eps_xz]})\n",
-    "sim_ref = sim_ref.copy(update={\"monitors\": list(sim_ref.monitors) + [monitor_eps_xz]})\n"
+    "sim_ref = sim_ref.copy(update={\"monitors\": list(sim_ref.monitors) + [monitor_eps_xz]})"
    ]
   },
   {
@@ -1041,7 +1041,7 @@
     "sim_data = web.run(sim, task_name=\"stl_box\", path=\"data/stl_box.hdf5\", verbose=True)\n",
     "\n",
     "# reference simulation\n",
-    "sim_data_ref = web.run(sim_ref, task_name=\"stl_box_ref\", path=\"data/stl_box_ref.hdf5\", verbose=True)\n"
+    "sim_data_ref = web.run(sim_ref, task_name=\"stl_box_ref\", path=\"data/stl_box_ref.hdf5\", verbose=True)"
    ]
   },
   {
@@ -1079,7 +1079,7 @@
     "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(8, 3))\n",
     "sim_data[\"xz_eps\"].eps_xx.real.plot(x=\"x\", y=\"z\", ax=ax1, cmap=\"binary\")\n",
     "sim_data_ref[\"xz_eps\"].eps_xx.real.plot(x=\"x\", y=\"z\", ax=ax2, cmap=\"binary\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1133,16 +1133,10 @@
     "plt.show()\n",
     "\n",
     "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, tight_layout=True, figsize=(12, 3))\n",
-    "sim_data_ref.plot_field(\n",
-    "    field_monitor_name=\"xz\", field_name=\"Ex\", y=0, val=\"real\", ax=ax1\n",
-    ")\n",
-    "sim_data_ref.plot_field(\n",
-    "    field_monitor_name=\"xz\", field_name=\"Ey\", y=0, val=\"real\", ax=ax2\n",
-    ")\n",
-    "sim_data_ref.plot_field(\n",
-    "    field_monitor_name=\"xz\", field_name=\"Ez\", y=0, val=\"real\", ax=ax3\n",
-    ")\n",
-    "plt.show()\n"
+    "sim_data_ref.plot_field(field_monitor_name=\"xz\", field_name=\"Ex\", y=0, val=\"real\", ax=ax1)\n",
+    "sim_data_ref.plot_field(field_monitor_name=\"xz\", field_name=\"Ey\", y=0, val=\"real\", ax=ax2)\n",
+    "sim_data_ref.plot_field(field_monitor_name=\"xz\", field_name=\"Ez\", y=0, val=\"real\", ax=ax3)\n",
+    "plt.show()"
    ]
   },
   {
@@ -1240,7 +1234,7 @@
     "sim.plot_eps(z=-0.7, ax=ax2)  # STL with a box with a hole\n",
     "sim.plot_eps(z=1, ax=ax3)  # STL with a box with an indent\n",
     "\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1310,19 +1304,15 @@
     ")\n",
     "\n",
     "# make two simulation objects, one with the unionized shape and one with the non-union one\n",
-    "sim_union = sim.copy(\n",
-    "    update={\"structures\": [td.Structure(geometry=obj_union, medium=medium)]}\n",
-    ")\n",
-    "sim_nounion = sim.copy(\n",
-    "    update={\"structures\": [td.Structure(geometry=obj_nounion, medium=medium)]}\n",
-    ")\n",
+    "sim_union = sim.copy(update={\"structures\": [td.Structure(geometry=obj_union, medium=medium)]})\n",
+    "sim_nounion = sim.copy(update={\"structures\": [td.Structure(geometry=obj_nounion, medium=medium)]})\n",
     "\n",
     "# plot both simulations\n",
     "_, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 3))\n",
     "sim_union.plot_eps(y=0, ax=ax1)\n",
     "sim_nounion.plot_eps(y=0, ax=ax2)\n",
     "\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2015,11 +2005,17 @@
    ],
    "source": [
     "sim_data_union = web.run(\n",
-    "    sim_union, task_name=\"stl_icecream_union\", path=\"data/stl_icecream_union.hdf5\", verbose=True,\n",
+    "    sim_union,\n",
+    "    task_name=\"stl_icecream_union\",\n",
+    "    path=\"data/stl_icecream_union.hdf5\",\n",
+    "    verbose=True,\n",
     ")\n",
     "sim_data_nounion = web.run(\n",
-    "    sim_nounion, task_name=\"stl_icecream_nounion\", path=\"data/stl_icecream_nounion.hdf5\", verbose=True,\n",
-    ")\n"
+    "    sim_nounion,\n",
+    "    task_name=\"stl_icecream_nounion\",\n",
+    "    path=\"data/stl_icecream_nounion.hdf5\",\n",
+    "    verbose=True,\n",
+    ")"
    ]
   },
   {
@@ -2057,7 +2053,7 @@
     "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(8, 3))\n",
     "sim_data_union[\"xz_eps\"].eps_xx.real.plot(x=\"x\", y=\"z\", ax=ax1, cmap=\"binary\")\n",
     "sim_data_nounion[\"xz_eps\"].eps_xx.real.plot(x=\"x\", y=\"z\", ax=ax2, cmap=\"binary\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2110,16 +2106,10 @@
     "plt.show()\n",
     "\n",
     "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, tight_layout=True, figsize=(12, 3))\n",
-    "sim_data_nounion.plot_field(\n",
-    "    field_monitor_name=\"xz\", field_name=\"Ex\", val=\"real\", ax=ax1\n",
-    ")\n",
-    "sim_data_nounion.plot_field(\n",
-    "    field_monitor_name=\"yz\", field_name=\"Ex\", val=\"real\", ax=ax2\n",
-    ")\n",
-    "sim_data_nounion.plot_field(\n",
-    "    field_monitor_name=\"xy\", field_name=\"Ex\", val=\"real\", ax=ax3\n",
-    ")\n",
-    "plt.show()\n"
+    "sim_data_nounion.plot_field(field_monitor_name=\"xz\", field_name=\"Ex\", val=\"real\", ax=ax1)\n",
+    "sim_data_nounion.plot_field(field_monitor_name=\"yz\", field_name=\"Ex\", val=\"real\", ax=ax2)\n",
+    "sim_data_nounion.plot_field(field_monitor_name=\"xy\", field_name=\"Ex\", val=\"real\", ax=ax3)\n",
+    "plt.show()"
    ]
   },
   {
@@ -2157,7 +2147,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.0"
   },
   "title": "Importing STL File in Tidy3D | Flexcompute",
   "widgets": {
diff --git a/SWGBroadbandPolarizer.ipynb b/SWGBroadbandPolarizer.ipynb
index 3a52870a..96fa19c4 100644
--- a/SWGBroadbandPolarizer.ipynb
+++ b/SWGBroadbandPolarizer.ipynb
@@ -38,10 +38,9 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
     "import gdstk\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web"
    ]
@@ -1309,7 +1308,7 @@
     "\n",
     "plt.plot(td.C_0 / freqs, 10 * np.log10(T_te), label=\"TE input\")\n",
     "plt.plot(td.C_0 / freqs, 10 * np.log10(T_tm), label=\"TM input\")\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Transmission (dB)\")\n",
     "plt.ylim(-35, 0)\n",
     "plt.xlim(lda_min, lda_max)\n",
@@ -1767,7 +1766,7 @@
     "\n",
     "plt.plot(td.C_0 / freqs, 10 * np.log10(T_te_ref), label=\"TE input\")\n",
     "plt.plot(td.C_0 / freqs, 10 * np.log10(T_tm_ref), label=\"TM input\")\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Transmission (dB)\")\n",
     "plt.ylim(-35, 0)\n",
     "plt.xlim(lda_min, lda_max)\n",
diff --git a/SWGWaveguideCrossing.ipynb b/SWGWaveguideCrossing.ipynb
index 242c5df2..bbe89a7e 100644
--- a/SWGWaveguideCrossing.ipynb
+++ b/SWGWaveguideCrossing.ipynb
@@ -78,9 +78,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tidy3d as td\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import tidy3d as td\n",
     "from tidy3d import web"
    ]
   },
@@ -958,9 +958,7 @@
     "        mode_data = mode_data.assign_coords(\n",
     "            wavelength=td.C_0 / mode_data.f\n",
     "        )  # add coordinates for wavelength\n",
-    "        avg_tr[j, i] = np.average(\n",
-    "            np.abs(mode_data) ** 2\n",
-    "        )  # calculate average transmittance\n",
+    "        avg_tr[j, i] = np.average(np.abs(mode_data) ** 2)  # calculate average transmittance\n",
     "        db_data = 20 * np.log10(np.abs(mode_data))  # convert to db scale\n",
     "\n",
     "        # plotting\n",
diff --git a/SbendCMAES.ipynb b/SbendCMAES.ipynb
index 1f258b66..d09e856d 100644
--- a/SbendCMAES.ipynb
+++ b/SbendCMAES.ipynb
@@ -72,14 +72,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "import math\n",
     "\n",
     "# uncomment the following line to install cma if it's not installed in your environment already\n",
     "# pip install cma\n",
     "import cma\n",
-    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web"
    ]
@@ -1055,10 +1054,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# make the misc/ directory to store the GDS file if it doesnt exist already\n",
+    "# make the misc/ directory to store the GDS file if it doesn't exist already\n",
     "import os\n",
-    "if not os.path.exists('./misc/'):\n",
-    "    os.mkdir('./misc/')\n",
+    "\n",
+    "if not os.path.exists(\"./misc/\"):\n",
+    "    os.mkdir(\"./misc/\")\n",
     "\n",
     "sim_opt.to_gds_file(fname=\"misc/optimized_sbend.gds\", z=0)"
    ]
@@ -1097,7 +1097,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.0"
   },
   "title": "Optimizing a Waveguide S-bend Using CMA-ES | Flexcompute"
  },
diff --git a/ScaleInvariantWaveguide.ipynb b/ScaleInvariantWaveguide.ipynb
index e1ed3eb6..bff145ff 100644
--- a/ScaleInvariantWaveguide.ipynb
+++ b/ScaleInvariantWaveguide.ipynb
@@ -33,9 +33,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
     "from tidy3d.plugins import waveguide\n",
@@ -133,7 +132,6 @@
    "outputs": [],
    "source": [
     "def make_sim(d):\n",
-    "\n",
     "    dummy_length = 1  # dummy waveguide length in the z direction\n",
     "\n",
     "    # define structures\n",
@@ -225,7 +223,6 @@
    "outputs": [],
    "source": [
     "def solve_mode(d):\n",
-    "\n",
     "    sim = make_sim(d)  # define a simulation\n",
     "\n",
     "    mode_spec = td.ModeSpec(num_modes=1, target_neff=n_H)\n",
@@ -279,7 +276,6 @@
     "results_1 = {}\n",
     "\n",
     "for i, d in enumerate(ds):\n",
-    "\n",
     "    # solve for the mode\n",
     "    mode_data = solve_mode(d)\n",
     "\n",
@@ -326,7 +322,7 @@
     "plt.scatter(ds, n_eff_1)\n",
     "plt.ylim(n_L, n_H)\n",
     "plt.title(\"Effective index\")\n",
-    "plt.xlabel(\"d ($\\mu m$)\")\n",
+    "plt.xlabel(r\"d ($\\mu m$)\")\n",
     "plt.ylabel(\"$n_{eff}$\")\n",
     "plt.show()"
    ]
@@ -419,7 +415,6 @@
     "results_2 = {}\n",
     "\n",
     "for i, T in enumerate(Ts):\n",
-    "\n",
     "    strip = waveguide.RectangularDielectric(\n",
     "        wavelength=lda0,\n",
     "        core_width=W,\n",
@@ -427,7 +422,7 @@
     "        core_medium=mat_H,\n",
     "        clad_medium=mat_L,\n",
     "        mode_spec=td.ModeSpec(num_modes=1, target_neff=n_H),\n",
-    "        grid_resolution =20,\n",
+    "        grid_resolution=20,\n",
     "    )\n",
     "\n",
     "    mode_data = run_mode_solver(strip.mode_solver, verbose=False)\n",
@@ -476,7 +471,7 @@
     "\n",
     "plt.ylim(n_L, n_H)\n",
     "plt.title(\"Effective index\")\n",
-    "plt.xlabel(\"d ($\\mu m$)\")\n",
+    "plt.xlabel(r\"d ($\\mu m$)\")\n",
     "plt.ylabel(\"$n_{eff}$\")\n",
     "plt.legend()\n",
     "plt.show()"
diff --git a/SelfIntersectingPolyslab.ipynb b/SelfIntersectingPolyslab.ipynb
index d8b30696..51d8a399 100644
--- a/SelfIntersectingPolyslab.ipynb
+++ b/SelfIntersectingPolyslab.ipynb
@@ -23,13 +23,11 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "import gdstk\n",
-    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
-    "\n",
-    "from tidy3d.plugins.polyslab import ComplexPolySlab\n"
+    "from tidy3d.plugins.polyslab import ComplexPolySlab"
    ]
   },
   {
@@ -83,7 +81,7 @@
     "    reference_plane=\"top\",\n",
     ")\n",
     "s.plot(z=0.5)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -139,8 +137,8 @@
     "        sidewall_angle=sidewall_angle,\n",
     "        reference_plane=\"top\",\n",
     "    )\n",
-    "except Exception as e:\n",
-    "    pass\n"
+    "except Exception:\n",
+    "    pass"
    ]
   },
   {
@@ -171,7 +169,7 @@
     "    axis=2,\n",
     "    sidewall_angle=sidewall_angle,\n",
     "    reference_plane=\"top\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -211,9 +209,7 @@
     "polyslabs_group = s.geometry_group\n",
     "\n",
     "print(f\"The number of sub-polyslabs in the list is {len(sub_polyslabs)}.\")\n",
-    "print(\n",
-    "    f\"The number of sub-polyslabs in geometry group is {len(polyslabs_group.geometries)}.\"\n",
-    ")\n"
+    "print(f\"The number of sub-polyslabs in geometry group is {len(polyslabs_group.geometries)}.\")"
    ]
   },
   {
@@ -247,7 +243,7 @@
     "struct_list = [td.Structure(geometry=s.geometry_group, medium=mat)]\n",
     "\n",
     "# 3) directly obtain the structure with a user-specified medium\n",
-    "struct_list = [s.to_structure(mat)]\n"
+    "struct_list = [s.to_structure(mat)]"
    ]
   },
   {
@@ -269,7 +265,7 @@
     "    center=(0.5, 0.5, 0.5),\n",
     "    grid_spec=td.GridSpec.auto(wavelength=1.0),\n",
     "    structures=struct_list,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -316,7 +312,7 @@
     "sim.plot(z=0.95, ax=ax[0])\n",
     "sim.plot(z=0.75, ax=ax[1])\n",
     "sim.plot(z=0.5, ax=ax[2])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -346,7 +342,7 @@
     "lib = gdstk.Library()\n",
     "cell = lib.new_cell(\"SI\")\n",
     "poly = gdstk.Polygon(vertices, layer=0)\n",
-    "_ = cell.add(poly)\n"
+    "_ = cell.add(poly)"
    ]
   },
   {
@@ -377,7 +373,7 @@
     "    slab_bounds=(0, 1),\n",
     "    sidewall_angle=np.pi / 4,\n",
     "    reference_plane=\"top\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -404,7 +400,7 @@
     "    center=(0.5, 0.5, 0.5),\n",
     "    grid_spec=td.GridSpec.auto(wavelength=1.0),\n",
     "    structures=[structure],\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -435,7 +431,7 @@
     "sim.plot(z=0.95, ax=ax[0])\n",
     "sim.plot(z=0.75, ax=ax[1])\n",
     "sim.plot(z=0.5, ax=ax[2])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -500,8 +496,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "After going through this tutorial, you have learned how to define a self-intersecting polyslab. If you are new to `Tidy3D` or the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials and [example library](https://www.flexcompute.com/tidy3d/examples/) before starting your own simulation advanture. "
+    "After going through this tutorial, you have learned how to define a self-intersecting polyslab. If you are new to `Tidy3D` or the finite-difference time-domain (FDTD) method, we highly recommend going through our [FDTD101](https://www.flexcompute.com/fdtd101/) tutorials and [example library](https://www.flexcompute.com/tidy3d/examples/) before starting your own simulation adventure. "
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -509,9 +512,9 @@
   "feature_imag": "",
   "feature_image": "N/A",
   "kernelspec": {
-   "display_name": "tidy3d",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "tidy3d"
+   "name": "python3"
   },
   "keywords": "polygon, Tidy3D, FDTD",
   "language_info": {
@@ -524,7 +527,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.0"
   },
   "title": "Modeling Complex Polygon Geometries in Tidy3D | Flexcompute"
  },
diff --git a/SimpleModeSolverGUI.ipynb b/SimpleModeSolverGUI.ipynb
index c7a0075b..527bcd0f 100644
--- a/SimpleModeSolverGUI.ipynb
+++ b/SimpleModeSolverGUI.ipynb
@@ -93,229 +93,296 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tkinter as tk\n",
     "import math\n",
-    "from tkinter import ttk, messagebox\n",
+    "import tkinter as tk\n",
+    "import traceback\n",
+    "from tkinter import messagebox, ttk\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\n",
     "from matplotlib.figure import Figure\n",
     "from tidy3d import Medium, ModeSpec\n",
-    "from tidy3d.plugins.waveguide import RectangularDielectric\n",
     "from tidy3d.plugins.mode.web import run as run_mode_solver\n",
-    "import traceback\n",
+    "from tidy3d.plugins.waveguide import RectangularDielectric\n",
+    "\n",
     "\n",
     "class WaveguideGUI:\n",
     "    def __init__(self, root):\n",
     "        # Initialize the root window and set the title\n",
     "        self.root = root\n",
     "        self.root.title(\"Tidy3D Waveguide Designer\")\n",
-    "        \n",
+    "\n",
     "        # Create the main frame that holds everything\n",
     "        self.main_frame = ttk.Frame(root)\n",
     "        self.main_frame.pack(fill=tk.BOTH, expand=True, padx=10, pady=10)\n",
-    "        \n",
+    "\n",
     "        # Left frame for parameter inputs\n",
     "        self.left_frame = ttk.Frame(self.main_frame)\n",
     "        self.left_frame.pack(side=tk.LEFT, fill=tk.Y, padx=5)\n",
-    "        \n",
+    "\n",
     "        # Right frame for mode solver visualization\n",
     "        self.right_frame = ttk.Frame(self.main_frame)\n",
     "        self.right_frame.pack(side=tk.RIGHT, fill=tk.BOTH, expand=True, padx=5)\n",
-    "        \n",
+    "\n",
     "        # Create a Matplotlib figure and canvas for displaying the waveguide cross-section\n",
     "        self.fig = Figure(figsize=(6, 4))\n",
     "        self.ax = self.fig.add_subplot(111)\n",
     "        self.canvas = FigureCanvasTkAgg(self.fig, master=self.right_frame)\n",
     "        self.canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True)\n",
-    "        \n",
+    "\n",
     "        # -----------------------------------\n",
     "        # Waveguide Type Selection\n",
     "        # -----------------------------------\n",
     "        self.type_frame = ttk.LabelFrame(self.left_frame, text=\"Waveguide Type\")\n",
     "        self.type_frame.pack(fill=tk.X, padx=5, pady=5)\n",
-    "        \n",
+    "\n",
     "        # Dropdown to select waveguide type (Strip, Rib, Slot)\n",
     "        self.waveguide_type_var = tk.StringVar(value=\"Strip waveguide\")\n",
     "        self.type_combobox = ttk.Combobox(\n",
-    "            self.type_frame, \n",
+    "            self.type_frame,\n",
     "            textvariable=self.waveguide_type_var,\n",
     "            values=[\"Strip waveguide\", \"Rib waveguide\", \"Slot waveguide\"],\n",
-    "            state=\"readonly\"\n",
+    "            state=\"readonly\",\n",
     "        )\n",
     "        self.type_combobox.pack(fill=tk.X, padx=5, pady=5)\n",
-    "        self.type_combobox.bind('<>', lambda e: self._on_type_change())\n",
-    "        \n",
+    "        self.type_combobox.bind(\"<>\", lambda e: self._on_type_change())\n",
+    "\n",
     "        # -----------------------------------\n",
     "        # Parameter Frames for Each Waveguide Type\n",
     "        # -----------------------------------\n",
-    "        \n",
+    "\n",
     "        # Parameters for Strip waveguide\n",
     "        self.strip_frame = ttk.LabelFrame(self.left_frame, text=\"Strip Waveguide Parameters\")\n",
     "        self.strip_frame.pack(fill=tk.X, padx=5, pady=5)\n",
-    "        \n",
+    "\n",
     "        ttk.Label(self.strip_frame, text=\"Core Width (um):\").grid(row=0, column=0, padx=5, pady=5)\n",
     "        self.core_width_var = tk.DoubleVar(value=0.5)\n",
-    "        ttk.Entry(self.strip_frame, textvariable=self.core_width_var).grid(row=0, column=1, padx=5, pady=5)\n",
-    "        \n",
-    "        ttk.Label(self.strip_frame, text=\"Core Thickness (um):\").grid(row=1, column=0, padx=5, pady=5)\n",
+    "        ttk.Entry(self.strip_frame, textvariable=self.core_width_var).grid(\n",
+    "            row=0, column=1, padx=5, pady=5\n",
+    "        )\n",
+    "\n",
+    "        ttk.Label(self.strip_frame, text=\"Core Thickness (um):\").grid(\n",
+    "            row=1, column=0, padx=5, pady=5\n",
+    "        )\n",
     "        self.core_thickness_var = tk.DoubleVar(value=0.22)\n",
-    "        ttk.Entry(self.strip_frame, textvariable=self.core_thickness_var).grid(row=1, column=1, padx=5, pady=5)\n",
-    "        \n",
-    "        ttk.Label(self.strip_frame, text=\"Sidewall Angle (deg):\").grid(row=2, column=0, padx=5, pady=5)\n",
+    "        ttk.Entry(self.strip_frame, textvariable=self.core_thickness_var).grid(\n",
+    "            row=1, column=1, padx=5, pady=5\n",
+    "        )\n",
+    "\n",
+    "        ttk.Label(self.strip_frame, text=\"Sidewall Angle (deg):\").grid(\n",
+    "            row=2, column=0, padx=5, pady=5\n",
+    "        )\n",
     "        self.sidewall_angle_var = tk.DoubleVar(value=10.0)\n",
-    "        ttk.Entry(self.strip_frame, textvariable=self.sidewall_angle_var).grid(row=2, column=1, padx=5, pady=5)\n",
-    "        \n",
+    "        ttk.Entry(self.strip_frame, textvariable=self.sidewall_angle_var).grid(\n",
+    "            row=2, column=1, padx=5, pady=5\n",
+    "        )\n",
+    "\n",
     "        # Parameters for Rib waveguide (initially hidden)\n",
     "        self.rib_frame = ttk.LabelFrame(self.left_frame, text=\"Rib Waveguide Parameters\")\n",
-    "        \n",
+    "\n",
     "        ttk.Label(self.rib_frame, text=\"Core Width (um):\").grid(row=0, column=0, padx=5, pady=5)\n",
     "        self.rib_width_var = tk.DoubleVar(value=0.5)\n",
-    "        ttk.Entry(self.rib_frame, textvariable=self.rib_width_var).grid(row=0, column=1, padx=5, pady=5)\n",
-    "        \n",
+    "        ttk.Entry(self.rib_frame, textvariable=self.rib_width_var).grid(\n",
+    "            row=0, column=1, padx=5, pady=5\n",
+    "        )\n",
+    "\n",
     "        ttk.Label(self.rib_frame, text=\"Core Thickness (um):\").grid(row=1, column=0, padx=5, pady=5)\n",
     "        self.rib_thickness_var = tk.DoubleVar(value=0.22)\n",
-    "        ttk.Entry(self.rib_frame, textvariable=self.rib_thickness_var).grid(row=1, column=1, padx=5, pady=5)\n",
-    "        \n",
-    "        ttk.Label(self.rib_frame, text=\"Sidewall Angle (deg):\").grid(row=2, column=0, padx=5, pady=5)\n",
+    "        ttk.Entry(self.rib_frame, textvariable=self.rib_thickness_var).grid(\n",
+    "            row=1, column=1, padx=5, pady=5\n",
+    "        )\n",
+    "\n",
+    "        ttk.Label(self.rib_frame, text=\"Sidewall Angle (deg):\").grid(\n",
+    "            row=2, column=0, padx=5, pady=5\n",
+    "        )\n",
     "        self.rib_angle_var = tk.DoubleVar(value=10.0)\n",
-    "        ttk.Entry(self.rib_frame, textvariable=self.rib_angle_var).grid(row=2, column=1, padx=5, pady=5)\n",
-    "        \n",
+    "        ttk.Entry(self.rib_frame, textvariable=self.rib_angle_var).grid(\n",
+    "            row=2, column=1, padx=5, pady=5\n",
+    "        )\n",
+    "\n",
     "        ttk.Label(self.rib_frame, text=\"Slab Thickness (um):\").grid(row=3, column=0, padx=5, pady=5)\n",
     "        self.slab_thickness_var = tk.DoubleVar(value=0.1)\n",
-    "        ttk.Entry(self.rib_frame, textvariable=self.slab_thickness_var).grid(row=3, column=1, padx=5, pady=5)\n",
-    "        \n",
+    "        ttk.Entry(self.rib_frame, textvariable=self.slab_thickness_var).grid(\n",
+    "            row=3, column=1, padx=5, pady=5\n",
+    "        )\n",
+    "\n",
     "        # Parameters for Slot waveguide (initially hidden)\n",
     "        self.slot_frame = ttk.LabelFrame(self.left_frame, text=\"Slot Waveguide Parameters\")\n",
-    "        \n",
-    "        ttk.Label(self.slot_frame, text=\"First Core Width (um):\").grid(row=0, column=0, padx=5, pady=5)\n",
+    "\n",
+    "        ttk.Label(self.slot_frame, text=\"First Core Width (um):\").grid(\n",
+    "            row=0, column=0, padx=5, pady=5\n",
+    "        )\n",
     "        self.first_core_width_var = tk.DoubleVar(value=0.5)\n",
-    "        ttk.Entry(self.slot_frame, textvariable=self.first_core_width_var).grid(row=0, column=1, padx=5, pady=5)\n",
-    "        \n",
-    "        ttk.Label(self.slot_frame, text=\"Second Core Width (um):\").grid(row=1, column=0, padx=5, pady=5)\n",
+    "        ttk.Entry(self.slot_frame, textvariable=self.first_core_width_var).grid(\n",
+    "            row=0, column=1, padx=5, pady=5\n",
+    "        )\n",
+    "\n",
+    "        ttk.Label(self.slot_frame, text=\"Second Core Width (um):\").grid(\n",
+    "            row=1, column=0, padx=5, pady=5\n",
+    "        )\n",
     "        self.second_core_width_var = tk.DoubleVar(value=0.5)\n",
-    "        ttk.Entry(self.slot_frame, textvariable=self.second_core_width_var).grid(row=1, column=1, padx=5, pady=5)\n",
-    "        \n",
+    "        ttk.Entry(self.slot_frame, textvariable=self.second_core_width_var).grid(\n",
+    "            row=1, column=1, padx=5, pady=5\n",
+    "        )\n",
+    "\n",
     "        ttk.Label(self.slot_frame, text=\"Gap (um):\").grid(row=2, column=0, padx=5, pady=5)\n",
     "        self.gap_var = tk.DoubleVar(value=0.1)\n",
     "        ttk.Entry(self.slot_frame, textvariable=self.gap_var).grid(row=2, column=1, padx=5, pady=5)\n",
-    "        \n",
-    "        ttk.Label(self.slot_frame, text=\"Core Thickness (um):\").grid(row=3, column=0, padx=5, pady=5)\n",
+    "\n",
+    "        ttk.Label(self.slot_frame, text=\"Core Thickness (um):\").grid(\n",
+    "            row=3, column=0, padx=5, pady=5\n",
+    "        )\n",
     "        self.slot_thickness_var = tk.DoubleVar(value=0.22)\n",
-    "        ttk.Entry(self.slot_frame, textvariable=self.slot_thickness_var).grid(row=3, column=1, padx=5, pady=5)\n",
-    "        \n",
-    "        ttk.Label(self.slot_frame, text=\"Sidewall Angle (deg):\").grid(row=4, column=0, padx=5, pady=5)\n",
+    "        ttk.Entry(self.slot_frame, textvariable=self.slot_thickness_var).grid(\n",
+    "            row=3, column=1, padx=5, pady=5\n",
+    "        )\n",
+    "\n",
+    "        ttk.Label(self.slot_frame, text=\"Sidewall Angle (deg):\").grid(\n",
+    "            row=4, column=0, padx=5, pady=5\n",
+    "        )\n",
     "        self.slot_angle_var = tk.DoubleVar(value=10.0)\n",
-    "        ttk.Entry(self.slot_frame, textvariable=self.slot_angle_var).grid(row=4, column=1, padx=5, pady=5)\n",
-    "        \n",
+    "        ttk.Entry(self.slot_frame, textvariable=self.slot_angle_var).grid(\n",
+    "            row=4, column=1, padx=5, pady=5\n",
+    "        )\n",
+    "\n",
     "        # -----------------------------------\n",
     "        # Common Parameters (Used by all Waveguide Types)\n",
     "        # -----------------------------------\n",
     "        self.common_frame = ttk.LabelFrame(self.left_frame, text=\"Common Parameters\")\n",
     "        self.common_frame.pack(fill=tk.X, padx=5, pady=5)\n",
-    "        \n",
+    "\n",
     "        # Core, Cladding, and Box indices and thicknesses\n",
     "        ttk.Label(self.common_frame, text=\"Core Index:\").grid(row=0, column=0, padx=5, pady=5)\n",
     "        self.core_index_var = tk.DoubleVar(value=3.47)\n",
-    "        ttk.Entry(self.common_frame, textvariable=self.core_index_var).grid(row=0, column=1, padx=5, pady=5)\n",
-    "        \n",
+    "        ttk.Entry(self.common_frame, textvariable=self.core_index_var).grid(\n",
+    "            row=0, column=1, padx=5, pady=5\n",
+    "        )\n",
+    "\n",
     "        ttk.Label(self.common_frame, text=\"Clad Index:\").grid(row=1, column=0, padx=5, pady=5)\n",
     "        self.clad_index_var = tk.DoubleVar(value=1.0)\n",
-    "        ttk.Entry(self.common_frame, textvariable=self.clad_index_var).grid(row=1, column=1, padx=5, pady=5)\n",
-    "        \n",
+    "        ttk.Entry(self.common_frame, textvariable=self.clad_index_var).grid(\n",
+    "            row=1, column=1, padx=5, pady=5\n",
+    "        )\n",
+    "\n",
     "        ttk.Label(self.common_frame, text=\"Box Index:\").grid(row=2, column=0, padx=5, pady=5)\n",
     "        self.box_index_var = tk.DoubleVar(value=1.44)\n",
-    "        ttk.Entry(self.common_frame, textvariable=self.box_index_var).grid(row=2, column=1, padx=5, pady=5)\n",
-    "        \n",
-    "        ttk.Label(self.common_frame, text=\"Clad Thickness (um):\").grid(row=3, column=0, padx=5, pady=5)\n",
+    "        ttk.Entry(self.common_frame, textvariable=self.box_index_var).grid(\n",
+    "            row=2, column=1, padx=5, pady=5\n",
+    "        )\n",
+    "\n",
+    "        ttk.Label(self.common_frame, text=\"Clad Thickness (um):\").grid(\n",
+    "            row=3, column=0, padx=5, pady=5\n",
+    "        )\n",
     "        self.clad_thickness_var = tk.DoubleVar(value=2.0)\n",
-    "        ttk.Entry(self.common_frame, textvariable=self.clad_thickness_var).grid(row=3, column=1, padx=5, pady=5)\n",
-    "        \n",
-    "        ttk.Label(self.common_frame, text=\"Box Thickness (um):\").grid(row=4, column=0, padx=5, pady=5)\n",
+    "        ttk.Entry(self.common_frame, textvariable=self.clad_thickness_var).grid(\n",
+    "            row=3, column=1, padx=5, pady=5\n",
+    "        )\n",
+    "\n",
+    "        ttk.Label(self.common_frame, text=\"Box Thickness (um):\").grid(\n",
+    "            row=4, column=0, padx=5, pady=5\n",
+    "        )\n",
     "        self.box_thickness_var = tk.DoubleVar(value=2.0)\n",
-    "        ttk.Entry(self.common_frame, textvariable=self.box_thickness_var).grid(row=4, column=1, padx=5, pady=5)\n",
-    "        \n",
+    "        ttk.Entry(self.common_frame, textvariable=self.box_thickness_var).grid(\n",
+    "            row=4, column=1, padx=5, pady=5\n",
+    "        )\n",
+    "\n",
     "        # Wavelength, Grid resolution, and number of modes for the simulation\n",
     "        ttk.Label(self.common_frame, text=\"Wavelength (um):\").grid(row=5, column=0, padx=5, pady=5)\n",
     "        self.wavelength_var = tk.DoubleVar(value=1.55)\n",
-    "        ttk.Entry(self.common_frame, textvariable=self.wavelength_var).grid(row=5, column=1, padx=5, pady=5)\n",
-    "        \n",
+    "        ttk.Entry(self.common_frame, textvariable=self.wavelength_var).grid(\n",
+    "            row=5, column=1, padx=5, pady=5\n",
+    "        )\n",
+    "\n",
     "        ttk.Label(self.common_frame, text=\"Grid Resolution:\").grid(row=6, column=0, padx=5, pady=5)\n",
     "        self.grid_resolution_var = tk.DoubleVar(value=25)\n",
-    "        ttk.Entry(self.common_frame, textvariable=self.grid_resolution_var).grid(row=6, column=1, padx=5, pady=5)\n",
-    "        \n",
+    "        ttk.Entry(self.common_frame, textvariable=self.grid_resolution_var).grid(\n",
+    "            row=6, column=1, padx=5, pady=5\n",
+    "        )\n",
+    "\n",
     "        ttk.Label(self.common_frame, text=\"Number of Modes:\").grid(row=7, column=0, padx=5, pady=5)\n",
     "        self.num_modes_var = tk.IntVar(value=1)\n",
-    "        ttk.Entry(self.common_frame, textvariable=self.num_modes_var).grid(row=7, column=1, padx=5, pady=5)\n",
-    "        \n",
+    "        ttk.Entry(self.common_frame, textvariable=self.num_modes_var).grid(\n",
+    "            row=7, column=1, padx=5, pady=5\n",
+    "        )\n",
+    "\n",
     "        # Optional parameters: Target n_eff and Bend Radius\n",
     "        ttk.Label(self.common_frame, text=\"Target n_eff:\").grid(row=8, column=0, padx=5, pady=5)\n",
-    "        self.target_neff_entry = ttk.Entry(self.common_frame, width=10, \n",
-    "                                           validate='key', \n",
-    "                                           validatecommand=(self.root.register(self._validate_float_or_empty), '%P'))\n",
+    "        self.target_neff_entry = ttk.Entry(\n",
+    "            self.common_frame,\n",
+    "            width=10,\n",
+    "            validate=\"key\",\n",
+    "            validatecommand=(self.root.register(self._validate_float_or_empty), \"%P\"),\n",
+    "        )\n",
     "        self.target_neff_entry.grid(row=8, column=1, padx=5, pady=5)\n",
-    "        \n",
-    "        vcmd = (self.root.register(self._validate_float_or_empty), '%P')\n",
+    "\n",
+    "        vcmd = (self.root.register(self._validate_float_or_empty), \"%P\")\n",
     "        ttk.Label(self.common_frame, text=\"Bend Radius (um):\").grid(row=9, column=0, padx=5, pady=5)\n",
-    "        self.bend_radius_entry = ttk.Entry(self.common_frame, width=10, validate='key', validatecommand=vcmd)\n",
+    "        self.bend_radius_entry = ttk.Entry(\n",
+    "            self.common_frame, width=10, validate=\"key\", validatecommand=vcmd\n",
+    "        )\n",
     "        self.bend_radius_entry.grid(row=9, column=1, padx=5, pady=5)\n",
-    "        \n",
+    "\n",
     "        # PML (Perfectly Matched Layer) usage toggle\n",
     "        ttk.Label(self.common_frame, text=\"Use PML:\").grid(row=10, column=0, padx=5, pady=5)\n",
     "        self.use_pml_var = tk.StringVar(value=\"False\")\n",
-    "        pml_combo = ttk.Combobox(self.common_frame, textvariable=self.use_pml_var, values=[\"True\", \"False\"], width=7, state=\"readonly\")\n",
+    "        pml_combo = ttk.Combobox(\n",
+    "            self.common_frame,\n",
+    "            textvariable=self.use_pml_var,\n",
+    "            values=[\"True\", \"False\"],\n",
+    "            width=7,\n",
+    "            state=\"readonly\",\n",
+    "        )\n",
     "        pml_combo.grid(row=10, column=1, padx=5, pady=5)\n",
     "        pml_combo.set(\"False\")\n",
-    "        \n",
+    "\n",
     "        # -----------------------------------\n",
     "        # Simulation Parameters Section (Currently empty, can be expanded in future)\n",
     "        # -----------------------------------\n",
     "        self.sim_frame = ttk.LabelFrame(self.left_frame, text=\"Simulation Parameters\")\n",
     "        self.sim_frame.pack(fill=tk.X, padx=5, pady=5)\n",
-    "        \n",
+    "\n",
     "        # -----------------------------------\n",
     "        # Buttons for Actions (Plot, Solve Locally, Solve on Server)\n",
     "        # -----------------------------------\n",
     "        self.button_frame = ttk.Frame(self.left_frame)\n",
     "        self.button_frame.pack(fill=tk.X, padx=5, pady=5)\n",
-    "        \n",
+    "\n",
     "        # Button to update/plot the cross-section\n",
-    "        ttk.Button(self.button_frame, text=\"Plot\", command=self._update_plot).pack(side=tk.LEFT, padx=5)\n",
-    "        \n",
+    "        ttk.Button(self.button_frame, text=\"Plot\", command=self._update_plot).pack(\n",
+    "            side=tk.LEFT, padx=5\n",
+    "        )\n",
+    "\n",
     "        # Frame to hold solve buttons\n",
     "        solve_frame = ttk.Frame(self.button_frame)\n",
     "        solve_frame.pack(side=tk.LEFT, padx=5)\n",
-    "        \n",
+    "\n",
     "        # Solve locally (on the user's machine)\n",
-    "        ttk.Button(\n",
-    "            solve_frame,\n",
-    "            text=\"Local mode solve\",\n",
-    "            command=self._solve_local_mode\n",
-    "        ).pack(side=tk.LEFT, padx=5)\n",
-    "        \n",
+    "        ttk.Button(solve_frame, text=\"Local mode solve\", command=self._solve_local_mode).pack(\n",
+    "            side=tk.LEFT, padx=5\n",
+    "        )\n",
+    "\n",
     "        # Solve remotely (on server)\n",
-    "        ttk.Button(\n",
-    "            solve_frame,\n",
-    "            text=\"Server mode solve\",\n",
-    "            command=self._solve_server_mode\n",
-    "        ).pack(side=tk.LEFT, padx=5)\n",
-    "        \n",
+    "        ttk.Button(solve_frame, text=\"Server mode solve\", command=self._solve_server_mode).pack(\n",
+    "            side=tk.LEFT, padx=5\n",
+    "        )\n",
+    "\n",
     "        # Variables to store mode data and waveguide object\n",
     "        self.mode_data = None\n",
     "        self.current_waveguide = None\n",
     "        self.current_mode_index = 0\n",
-    "        \n",
+    "\n",
     "        # Initialize the interface with default waveguide type\n",
     "        self._on_type_change()\n",
-    "        \n",
+    "\n",
     "    def _on_type_change(self):\n",
     "        \"\"\"Handle changes in the waveguide type selection.\"\"\"\n",
     "        waveguide_type = self.waveguide_type_var.get()\n",
-    "        \n",
+    "\n",
     "        # Hide all parameter frames\n",
     "        self.strip_frame.pack_forget()\n",
     "        self.rib_frame.pack_forget()\n",
     "        self.slot_frame.pack_forget()\n",
-    "        \n",
+    "\n",
     "        # Show the parameter frame corresponding to the selected type\n",
     "        if waveguide_type == \"Strip waveguide\":\n",
     "            self.strip_frame.pack(after=self.type_frame, fill=tk.X, padx=5, pady=5)\n",
@@ -323,52 +390,52 @@
     "            self.rib_frame.pack(after=self.type_frame, fill=tk.X, padx=5, pady=5)\n",
     "        else:  # Slot waveguide\n",
     "            self.slot_frame.pack(after=self.type_frame, fill=tk.X, padx=5, pady=5)\n",
-    "        \n",
+    "\n",
     "        # Update the plot whenever the waveguide type changes\n",
     "        self._update_plot()\n",
-    "    \n",
+    "\n",
     "    def _create_waveguide(self):\n",
     "        \"\"\"Create the waveguide object based on current parameters.\"\"\"\n",
     "        try:\n",
     "            # Define materials from user-input indices\n",
-    "            core = Medium(permittivity=self.core_index_var.get()**2)\n",
-    "            clad = Medium(permittivity=self.clad_index_var.get()**2)\n",
-    "            box = Medium(permittivity=self.box_index_var.get()**2)\n",
-    "            \n",
+    "            core = Medium(permittivity=self.core_index_var.get() ** 2)\n",
+    "            clad = Medium(permittivity=self.clad_index_var.get() ** 2)\n",
+    "            box = Medium(permittivity=self.box_index_var.get() ** 2)\n",
+    "\n",
     "            # Common parameters\n",
     "            wavelength = self.wavelength_var.get()\n",
     "            grid_resolution = self.grid_resolution_var.get()\n",
     "            num_modes = self.num_modes_var.get()\n",
-    "            \n",
+    "\n",
     "            # Optional parameters: Bend radius and target effective index\n",
     "            bend_radius = self._get_bend_radius()\n",
     "            target_neff = self._get_target_neff()\n",
-    "            \n",
+    "\n",
     "            # Use PML or not\n",
     "            use_pml = self.use_pml_var.get() == \"True\"\n",
     "            num_pml = (12, 12) if use_pml else (0, 0)\n",
-    "            \n",
+    "\n",
     "            # Create ModeSpec object with user-defined parameters\n",
     "            mode_spec_params = {\n",
-    "                'num_modes': num_modes,\n",
-    "                'bend_radius': bend_radius,\n",
-    "                'num_pml': num_pml,\n",
-    "                'group_index_step': True,\n",
-    "                'precision': 'double'\n",
+    "                \"num_modes\": num_modes,\n",
+    "                \"bend_radius\": bend_radius,\n",
+    "                \"num_pml\": num_pml,\n",
+    "                \"group_index_step\": True,\n",
+    "                \"precision\": \"double\",\n",
     "            }\n",
-    "            \n",
+    "\n",
     "            if target_neff is not None:\n",
-    "                mode_spec_params['target_neff'] = target_neff\n",
-    "            \n",
+    "                mode_spec_params[\"target_neff\"] = target_neff\n",
+    "\n",
     "            if bend_radius is not None:\n",
     "                # If bend_radius is given, bend axis is set to 1 (for curved waveguides)\n",
-    "                mode_spec_params['bend_axis'] = 1\n",
-    "            \n",
+    "                mode_spec_params[\"bend_axis\"] = 1\n",
+    "\n",
     "            mode_spec = ModeSpec(**mode_spec_params)\n",
-    "            \n",
+    "\n",
     "            # Retrieve parameters based on selected waveguide type\n",
     "            waveguide_type = self.waveguide_type_var.get()\n",
-    "            \n",
+    "\n",
     "            if waveguide_type == \"Strip waveguide\":\n",
     "                width = self.core_width_var.get()\n",
     "                thickness = self.core_thickness_var.get()\n",
@@ -388,7 +455,7 @@
     "                sidewall_angle_rad = math.radians(self.slot_angle_var.get())\n",
     "                slab_thickness = 0.0\n",
     "                gap = self.gap_var.get()\n",
-    "            \n",
+    "\n",
     "            # Create the RectangularDielectric waveguide object\n",
     "            waveguide = RectangularDielectric(\n",
     "                core_width=width,\n",
@@ -405,10 +472,10 @@
     "                mode_spec=mode_spec,\n",
     "                grid_resolution=grid_resolution,\n",
     "            )\n",
-    "            \n",
+    "\n",
     "            return waveguide\n",
-    "            \n",
-    "        except ValueError as e:\n",
+    "\n",
+    "        except ValueError:\n",
     "            # If user inputs invalid values, show an error message\n",
     "            messagebox.showerror(\"Input Error\", \"Please enter valid numbers for all fields.\")\n",
     "            return None\n",
@@ -416,28 +483,28 @@
     "            # Catch any other exceptions\n",
     "            messagebox.showerror(\"Error\", str(e))\n",
     "            return None\n",
-    "    \n",
+    "\n",
     "    def _update_plot(self):\n",
     "        \"\"\"Redraw the waveguide cross-section plot based on current parameters.\"\"\"\n",
     "        waveguide = self._create_waveguide()\n",
     "        if waveguide is None:\n",
     "            return\n",
-    "        \n",
+    "\n",
     "        try:\n",
     "            # Clear the previous plot\n",
     "            self.ax.clear()\n",
-    "            \n",
+    "\n",
     "            # Plot the waveguide cross-section\n",
     "            waveguide.mode_solver.plot(ax=self.ax)\n",
     "            self.ax.set_title(\"Mode solver cross-section\")\n",
-    "            \n",
+    "\n",
     "            # Update the canvas to show the new plot\n",
     "            self.canvas.draw()\n",
-    "            \n",
+    "\n",
     "        except Exception as e:\n",
     "            # If plotting fails, show an error\n",
     "            messagebox.showerror(\"Plot Error\", str(e))\n",
-    "    \n",
+    "\n",
     "    def _create_mode_window(self, mode_index, mode_data):\n",
     "        \"\"\"\n",
     "        Create a separate window that displays the properties and field profile\n",
@@ -445,12 +512,12 @@
     "        \"\"\"\n",
     "        # Create a new top-level window\n",
     "        mode_window = tk.Toplevel(self.root)\n",
-    "        mode_window.title(\"Mode {}\".format(mode_index))\n",
-    "        \n",
+    "        mode_window.title(f\"Mode {mode_index}\")\n",
+    "\n",
     "        # Frame to hold mode properties (n_eff, group index, polarization fractions, etc.)\n",
     "        props_frame = ttk.Frame(mode_window)\n",
     "        props_frame.pack(pady=5, padx=10, fill=tk.X)\n",
-    "        \n",
+    "\n",
     "        # Extract mode properties from mode_data\n",
     "        n_eff = float(mode_data.n_eff.values[0][mode_index])\n",
     "        k_eff = float(mode_data.k_eff.values[0][mode_index])\n",
@@ -458,71 +525,68 @@
     "        te_frac = float(mode_data.pol_fraction.te.values[0][mode_index])\n",
     "        tm_frac = float(mode_data.pol_fraction.tm.values[0][mode_index])\n",
     "        mode_area = float(mode_data.mode_area.values[0][mode_index])\n",
-    "        \n",
+    "\n",
     "        # Create labels to display these properties\n",
     "        props = [\n",
-    "            (\"n_eff\", \"{:.6f}\".format(n_eff)),\n",
-    "            (\"k_eff\", \"{:.6f}\".format(k_eff)),\n",
-    "            (\"Group Index\", \"{:.6f}\".format(n_group)),\n",
-    "            (\"TE Fraction\", \"{:.1f}%\".format(te_frac * 100)),\n",
-    "            (\"TM Fraction\", \"{:.1f}%\".format(tm_frac * 100)),\n",
-    "            (\"Mode Area\", \"{:.2f} um²\".format(mode_area))\n",
+    "            (\"n_eff\", f\"{n_eff:.6f}\"),\n",
+    "            (\"k_eff\", f\"{k_eff:.6f}\"),\n",
+    "            (\"Group Index\", f\"{n_group:.6f}\"),\n",
+    "            (\"TE Fraction\", f\"{te_frac * 100:.1f}%\"),\n",
+    "            (\"TM Fraction\", f\"{tm_frac * 100:.1f}%\"),\n",
+    "            (\"Mode Area\", f\"{mode_area:.2f} um²\"),\n",
     "        ]\n",
-    "        \n",
+    "\n",
     "        # Display the properties in a grid layout\n",
     "        for i, (label, value) in enumerate(props):\n",
-    "            ttk.Label(props_frame, text=f\"{label}:\").grid(row=i, column=0, sticky='e', padx=5, pady=2)\n",
-    "            ttk.Label(props_frame, text=value).grid(row=i, column=1, sticky='w', padx=5, pady=2)\n",
-    "        \n",
+    "            ttk.Label(props_frame, text=f\"{label}:\").grid(\n",
+    "                row=i, column=0, sticky=\"e\", padx=5, pady=2\n",
+    "            )\n",
+    "            ttk.Label(props_frame, text=value).grid(row=i, column=1, sticky=\"w\", padx=5, pady=2)\n",
+    "\n",
     "        # Create a Matplotlib plot for the mode field\n",
     "        fig, ax = plt.subplots(figsize=(6, 4))\n",
     "        canvas = FigureCanvasTkAgg(fig, master=mode_window)\n",
     "        canvas.get_tk_widget().pack(pady=5)\n",
-    "        \n",
+    "\n",
     "        # Plot the electric field (absolute value) of this mode\n",
-    "        self.current_waveguide.plot_field(\n",
-    "            field_name=\"E\",\n",
-    "            val=\"abs\",\n",
-    "            mode_index=mode_index,\n",
-    "            ax=ax\n",
-    "        )\n",
+    "        self.current_waveguide.plot_field(field_name=\"E\", val=\"abs\", mode_index=mode_index, ax=ax)\n",
     "        ax.set_title(\"Mode profile\")\n",
     "        canvas.draw()\n",
-    "    \n",
+    "\n",
     "    def _solve_local_mode(self):\n",
     "        \"\"\"Solve for modes locally and display results.\"\"\"\n",
     "        try:\n",
     "            self.current_waveguide = self._create_waveguide()\n",
     "            if self.current_waveguide is None:\n",
     "                return\n",
-    "                \n",
+    "\n",
     "            # Solve the mode problem locally\n",
     "            self.mode_data = self.current_waveguide.mode_solver.solve()\n",
-    "            \n",
+    "\n",
     "            # Create a separate window for each mode to display its properties and fields\n",
     "            for mode_index in range(len(self.mode_data.n_eff.values[0])):\n",
     "                self._create_mode_window(mode_index, self.mode_data)\n",
-    "            \n",
+    "\n",
     "        except Exception as e:\n",
     "            # Print error details for debugging\n",
     "            print(\"Error in local mode solve:\", str(e))\n",
     "            print(\"Full error:\", traceback.format_exc())\n",
     "            messagebox.showerror(\"Error\", str(e))\n",
-    "    \n",
+    "\n",
     "    def _solve_server_mode(self):\n",
     "        \"\"\"Solve for modes on a remote server and display results.\"\"\"\n",
     "        try:\n",
     "            self.current_waveguide = self._create_waveguide()\n",
     "            if self.current_waveguide is None:\n",
     "                return\n",
-    "                \n",
+    "\n",
     "            # Create a small progress window while solving on the server\n",
     "            progress_window = tk.Toplevel(self.root)\n",
     "            progress_window.title(\"Server Mode Solve\")\n",
     "            progress_window.geometry(\"300x80\")\n",
     "            progress_window.transient(self.root)\n",
     "            progress_window.grab_set()  # Make it modal\n",
-    "            \n",
+    "\n",
     "            # Center the progress window on the screen\n",
     "            window_width = 300\n",
     "            window_height = 80\n",
@@ -531,75 +595,79 @@
     "            x = (screen_width - window_width) // 2\n",
     "            y = (screen_height - window_height) // 2\n",
     "            progress_window.geometry(f\"{window_width}x{window_height}+{x}+{y}\")\n",
-    "            \n",
+    "\n",
     "            # Label inside the progress window\n",
-    "            message = tk.Label(progress_window, text=\"Solving modes on server...\\nThis may take a few moments.\")\n",
+    "            message = tk.Label(\n",
+    "                progress_window, text=\"Solving modes on server...\\nThis may take a few moments.\"\n",
+    "            )\n",
     "            message.pack(expand=True)\n",
-    "            \n",
+    "\n",
     "            try:\n",
     "                # Update the GUI so the message is shown\n",
     "                progress_window.update()\n",
-    "                \n",
+    "\n",
     "                # Run the mode solver on the server\n",
     "                self.mode_data = run_mode_solver(self.current_waveguide.mode_solver)\n",
-    "                \n",
+    "\n",
     "                # Close the progress window after completion\n",
     "                progress_window.destroy()\n",
-    "                \n",
+    "\n",
     "                # Create a separate window for each mode result\n",
     "                for mode_index in range(len(self.mode_data.n_eff.values[0])):\n",
     "                    self._create_mode_window(mode_index, self.mode_data)\n",
-    "                \n",
+    "\n",
     "            except Exception as server_error:\n",
     "                # If there's an error during server solve, close the progress window and show an error\n",
     "                progress_window.destroy()\n",
     "                print(\"Error during server mode solve:\", str(server_error))\n",
     "                print(\"Full server error:\", traceback.format_exc())\n",
-    "                messagebox.showerror(\"Server Error\", \"Error during server mode solve: {}\".format(str(server_error)))\n",
+    "                messagebox.showerror(\n",
+    "                    \"Server Error\", f\"Error during server mode solve: {str(server_error)}\"\n",
+    "                )\n",
     "                return\n",
-    "            \n",
+    "\n",
     "        except Exception as e:\n",
     "            # Catch any other errors\n",
     "            print(\"Error in server mode solve:\", str(e))\n",
     "            print(\"Full error:\", traceback.format_exc())\n",
     "            messagebox.showerror(\"Error\", str(e))\n",
-    "    \n",
+    "\n",
     "    def _reset_values(self):\n",
     "        \"\"\"Reset all parameters to default values.\"\"\"\n",
     "        # Reset waveguide type and parameters\n",
     "        self.waveguide_type_var.set(\"Strip waveguide\")\n",
     "        self._on_type_change()\n",
-    "        \n",
+    "\n",
     "        # Strip parameters\n",
     "        self.core_width_var.set(0.5)\n",
     "        self.core_thickness_var.set(0.22)\n",
     "        self.sidewall_angle_var.set(10.0)\n",
-    "        \n",
+    "\n",
     "        # Rib parameters\n",
     "        self.rib_width_var.set(0.5)\n",
     "        self.rib_thickness_var.set(0.22)\n",
     "        self.rib_angle_var.set(10.0)\n",
     "        self.slab_thickness_var.set(0.1)\n",
-    "        \n",
+    "\n",
     "        # Slot parameters\n",
     "        self.first_core_width_var.set(0.5)\n",
     "        self.second_core_width_var.set(0.5)\n",
     "        self.gap_var.set(0.1)\n",
     "        self.slot_thickness_var.set(0.22)\n",
     "        self.slot_angle_var.set(10.0)\n",
-    "        \n",
+    "\n",
     "        # Common parameters\n",
     "        self.core_index_var.set(3.47)\n",
     "        self.clad_index_var.set(1.0)\n",
     "        self.box_index_var.set(1.44)\n",
     "        self.clad_thickness_var.set(2.0)\n",
     "        self.box_thickness_var.set(2.0)\n",
-    "        \n",
+    "\n",
     "        # Simulation parameters\n",
     "        self.wavelength_var.set(1.55)\n",
     "        self.grid_resolution_var.set(25)\n",
     "        self.num_modes_var.set(1)\n",
-    "        \n",
+    "\n",
     "        # Update the plot after resetting\n",
     "        self._update_plot()\n",
     "\n",
@@ -612,7 +680,7 @@
     "            return True\n",
     "        except ValueError:\n",
     "            return False\n",
-    "            \n",
+    "\n",
     "    def _get_bend_radius(self):\n",
     "        \"\"\"Retrieve the bend radius value; return None if empty or invalid.\"\"\"\n",
     "        value = self.bend_radius_entry.get().strip()\n",
@@ -622,7 +690,7 @@
     "            return float(value)\n",
     "        except ValueError:\n",
     "            return None\n",
-    "            \n",
+    "\n",
     "    def _get_target_neff(self):\n",
     "        \"\"\"Retrieve the target n_eff value; return None if empty or invalid.\"\"\"\n",
     "        value = self.target_neff_entry.get().strip()\n",
@@ -632,12 +700,13 @@
     "            return float(value)\n",
     "        except ValueError:\n",
     "            return None\n",
-    "            \n",
+    "\n",
+    "\n",
     "if __name__ == \"__main__\":\n",
     "    # Instantiate and run the Tkinter application\n",
     "    root = tk.Tk()\n",
     "    app = WaveguideGUI(root)\n",
-    "    root.mainloop()\n"
+    "    root.mainloop()"
    ]
   },
   {
diff --git a/Simulation.ipynb b/Simulation.ipynb
index 28f77ef3..ce105fa6 100644
--- a/Simulation.ipynb
+++ b/Simulation.ipynb
@@ -24,13 +24,13 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "import h5py\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3d imports\n",
     "import tidy3d as td\n",
-    "from tidy3d import web\n"
+    "from tidy3d import web"
    ]
   },
   {
@@ -62,7 +62,7 @@
     "fwidth = 1e13\n",
     "\n",
     "# apply a PML in all directions\n",
-    "boundary_spec = td.BoundarySpec.all_sides(boundary=td.PML())\n"
+    "boundary_spec = td.BoundarySpec.all_sides(boundary=td.PML())"
    ]
   },
   {
@@ -87,7 +87,7 @@
    "outputs": [],
    "source": [
     "# Total time to run in seconds\n",
-    "run_time = 2 / fwidth\n"
+    "run_time = 2 / fwidth"
    ]
   },
   {
@@ -124,9 +124,7 @@
     "\n",
     "\n",
     "# Rectangular slab, extending infinitely in x and y with medium `material1`\n",
-    "box = td.Structure(\n",
-    "    geometry=td.Box(center=[0, 0, 0], size=[td.inf, td.inf, 1]), medium=material1\n",
-    ")\n",
+    "box = td.Structure(geometry=td.Box(center=[0, 0, 0], size=[td.inf, td.inf, 1]), medium=material1)\n",
     "\n",
     "# Triangle in the xy-plane with a finite extent in z\n",
     "equi_tri_verts = [[-1 / 2, -1 / 4], [1 / 2, -1 / 4], [0, np.sqrt(3) / 2 - 1 / 4]]\n",
@@ -139,7 +137,7 @@
     "        axis=2,\n",
     "    ),\n",
     "    medium=material2,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -171,7 +169,7 @@
     "    size=(td.inf, td.inf, 0),\n",
     "    source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
     "    pol_angle=np.pi / 2,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -204,17 +202,11 @@
    "outputs": [],
    "source": [
     "# measure time domain fields at center location, measure every 5 time steps\n",
-    "time_mnt = td.FieldTimeMonitor(\n",
-    "    center=[0, 0, 0], size=[0, 0, 0], interval=5, name=\"field_time\"\n",
-    ")\n",
+    "time_mnt = td.FieldTimeMonitor(center=[0, 0, 0], size=[0, 0, 0], interval=5, name=\"field_time\")\n",
     "\n",
     "# measure the steady state fields at central frequency in the xy plane and the xz plane.\n",
-    "freq_mnt1 = td.FieldMonitor(\n",
-    "    center=[0, 0, -1], size=[20, 20, 0], freqs=[freq0], name=\"field1\"\n",
-    ")\n",
-    "freq_mnt2 = td.FieldMonitor(\n",
-    "    center=[0, 0, 0], size=[20, 0, 20], freqs=[freq0], name=\"field2\"\n",
-    ")\n"
+    "freq_mnt1 = td.FieldMonitor(center=[0, 0, -1], size=[20, 20, 0], freqs=[freq0], name=\"field1\")\n",
+    "freq_mnt2 = td.FieldMonitor(center=[0, 0, 0], size=[20, 0, 20], freqs=[freq0], name=\"field2\")"
    ]
   },
   {
@@ -249,7 +241,7 @@
     "    monitors=[time_mnt, freq_mnt1, freq_mnt2],\n",
     "    run_time=run_time,\n",
     "    boundary_spec=boundary_spec,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -602,7 +594,7 @@
    ],
    "source": [
     "for m in sim.monitors:\n",
-    "    m.help()\n"
+    "    m.help()"
    ]
   },
   {
@@ -645,7 +637,7 @@
    "source": [
     "# Visualize source\n",
     "psource.source_time.plot(np.linspace(0, run_time, 1001))\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -656,9 +648,9 @@
     "\n",
     "For this, we will plot three cross sections at `z=0.75`, `y=0`, and `x=0`, respectively. \n",
     "\n",
-    "The relative permittivity of objects is plotted in greyscale.\n",
+    "The relative permittivity of objects is plotted in grayscale.\n",
     "\n",
-    "By default, sources are overlayed in green, monitors in yellow, and PML boundaries in grey."
+    "By default, sources are overlaid in green, monitors in yellow, and PML boundaries in gray."
    ]
   },
   {
@@ -692,7 +684,7 @@
     "sim.plot_eps(z=0.75, freq=freq0, ax=ax[0])\n",
     "sim.plot_eps(y=0.01, freq=freq0, ax=ax[1])\n",
     "sim.plot_eps(x=0, freq=freq0, ax=ax[2])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -733,7 +725,7 @@
     "sim.plot(z=0.75, ax=ax[0])\n",
     "sim.plot(y=0.01, ax=ax[1])\n",
     "sim.plot(x=0, ax=ax[2])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -827,7 +819,7 @@
     }
    ],
    "source": [
-    "task_id = web.upload(sim, task_name=\"Simulation\")\n"
+    "task_id = web.upload(sim, task_name=\"Simulation\")"
    ]
   },
   {
@@ -869,14 +861,14 @@
     }
    ],
    "source": [
-    "estimate_cost = web.estimate_cost(task_id)\n"
+    "estimate_cost = web.estimate_cost(task_id)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can now start the task, and if we want to, continously monitor its status, and wait until the run is successful. The [monitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.webapi.monitor.html#tidy3d.web.monitor) function will keep running until either a `'success'` or `'error'` status is returned."
+    "We can now start the task, and if we want to, continuously monitor its status and wait until the run is successful. The [monitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.webapi.monitor.html#tidy3d.web.monitor) function will keep running until either a `'success'` or `'error'` status is returned."
    ]
   },
   {
@@ -1121,7 +1113,7 @@
    "source": [
     "# web.start(task_id, solver_version=\"improve_python_overgap-0.0.0\")\n",
     "web.start(task_id)\n",
-    "web.monitor(task_id, verbose=True)\n"
+    "web.monitor(task_id, verbose=True)"
    ]
   },
   {
@@ -1162,7 +1154,7 @@
     "import time\n",
     "\n",
     "time.sleep(4)\n",
-    "real_cost = web.real_cost(task_id)\n"
+    "real_cost = web.real_cost(task_id)"
    ]
   },
   {
@@ -1294,7 +1286,7 @@
     "sim_data = web.load(task_id, path=\"data/sim_data.hdf5\")\n",
     "\n",
     "# Show the output of the log file\n",
-    "print(sim_data.log)\n"
+    "print(sim_data.log)"
    ]
   },
   {
@@ -1334,7 +1326,7 @@
     "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n",
     "sim_data.plot_field(\"field1\", \"Ey\", z=-1.0, ax=ax[0], val=\"real\")\n",
     "sim_data.plot_field(\"field2\", \"Ey\", ax=ax[1], val=\"real\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1958,7 +1950,7 @@
    ],
    "source": [
     "mon1_data = sim_data[\"field1\"]\n",
-    "mon1_data.Ex\n"
+    "mon1_data.Ex"
    ]
   },
   {
@@ -1986,7 +1978,7 @@
     }
    ],
    "source": [
-    "ax = mon1_data.Ez.real.plot()\n"
+    "ax = mon1_data.Ez.real.plot()"
    ]
   },
   {
@@ -2025,7 +2017,7 @@
     "fig, ax = plt.subplots(1)\n",
     "time_data.Ey.plot()\n",
     "ax.set_ylabel(\"$E_y(t)$ [V/m]\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2059,7 +2051,7 @@
     "eps_centers = sim.epsilon(box=volume, coord_key=\"centers\")\n",
     "\n",
     "# at Ex locations in the yee cell\n",
-    "eps_Ex = sim.epsilon(box=volume, coord_key=\"Ex\")\n"
+    "eps_Ex = sim.epsilon(box=volume, coord_key=\"Ex\")"
    ]
   },
   {
@@ -2103,7 +2095,7 @@
     "ax1.set_title(\"epsilon_r at centers\")\n",
     "ax2.set_title(\"epsilon_r at Ex locations\")\n",
     "\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2140,7 +2132,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.0"
   },
   "title": "First Walkthrough in Tidy3D | Flexcompute",
   "widgets": {
diff --git a/StartHere.ipynb b/StartHere.ipynb
index 63077f1e..1e6ed4c3 100644
--- a/StartHere.ipynb
+++ b/StartHere.ipynb
@@ -7,12 +7,12 @@
    "source": [
     "# Quickstart\n",
     "\n",
-    "This is a minimal Tidy3D script showing the FDTD simulation of a delectric cube in the presence of a point dipole.\n",
+    "This is a minimal Tidy3D script showing the FDTD simulation of a dielectric cube in the presence of a point dipole.\n",
     "\n",
     "Before running this notebook, make sure to have:\n",
     "\n",
     "1. [Installed tidy3d](../quickstart.html#installation-of-tidy3d-python-api)\n",
-    "2. [Generated your free API key](https://tidy3d.simulation.cloud/account)\n",
+    "2. [Generate your free API key](https://tidy3d.simulation.cloud/account)\n",
     "3. [Optional - Configured your API key](../quickstart.html#installation-of-tidy3d-python-api#linking-registration)"
    ]
   },
@@ -27,9 +27,8 @@
    "outputs": [],
    "source": [
     "# import packages and authenticate (if needed)\n",
-    "import numpy as np\n",
     "import matplotlib.pylab as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
     "\n",
diff --git a/StripToSlotConverters.ipynb b/StripToSlotConverters.ipynb
index c198fae9..74785dfe 100644
--- a/StripToSlotConverters.ipynb
+++ b/StripToSlotConverters.ipynb
@@ -27,9 +27,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
     "from tidy3d.plugins import waveguide"
@@ -781,7 +780,7 @@
    "id": "dbdc290a",
    "metadata": {},
    "source": [
-    "We define two postprocessing functions `plot_convertion_efficiency` and `plot_field` to plot the coupling efficiency of the converter as well as the field distributions.\n",
+    "We define two postprocessing functions `plot_conversion_efficiency` and `plot_field` to plot the coupling efficiency of the converter as well as the field distributions.\n",
     "\n",
     "For this design, we see a coupling efficiency above 95%."
    ]
@@ -804,8 +803,8 @@
     }
    ],
    "source": [
-    "def plot_convertion_efficiency(sim_data):\n",
-    "    # extract the convertion efficiency from the mode monitor\n",
+    "def plot_conversion_efficiency(sim_data):\n",
+    "    # extract the conversion efficiency from the mode monitor\n",
     "    amp = sim_data[\"mode\"].amps.sel(mode_index=0, direction=\"+\")\n",
     "    T = np.abs(amp) ** 2\n",
     "\n",
@@ -813,12 +812,12 @@
     "    plt.plot(ldas, T)\n",
     "    plt.xlim(1.5, 1.6)\n",
     "    plt.ylim(0.8, 1)\n",
-    "    plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "    plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "    plt.ylabel(\"Coupling efficiency\")\n",
     "    plt.show()\n",
     "\n",
     "\n",
-    "plot_convertion_efficiency(sim_data_1)"
+    "plot_conversion_efficiency(sim_data_1)"
    ]
   },
   {
@@ -1382,7 +1381,7 @@
     }
    ],
    "source": [
-    "plot_convertion_efficiency(sim_data_2)"
+    "plot_conversion_efficiency(sim_data_2)"
    ]
   },
   {
@@ -1876,7 +1875,7 @@
    "id": "f1fdc872",
    "metadata": {},
    "source": [
-    "The convertion efficiency is also close to 100%."
+    "The conversion efficiency is also close to 100%."
    ]
   },
   {
@@ -1897,7 +1896,7 @@
     }
    ],
    "source": [
-    "plot_convertion_efficiency(sim_data_3)"
+    "plot_conversion_efficiency(sim_data_3)"
    ]
   },
   {
@@ -1936,7 +1935,7 @@
    "source": [
     "Different designs of the converters have their advantages and disadvantages. The choice of the design should be based on fabrication capability, constraints on device footprint, and the required coupling efficiency. In this notebook, we only simulated the ideal designs. To be more practical, simulations that consider the fabrication constraints need to be performed. As introduced in the referenced papers, the pointy tips of the tapers will have a finite width due to the finite fabrication resolution. Simulations with more realistic device geometry will be extremely useful for design and testing.\n",
     "\n",
-    "In addition, the design parameters used in this notebook are optimized for the specific waveguide widths, thickness, and materials. For a different plateform, the parameters need to be re-optmized, which can be done using the adjoint method as provided in Tidy3D's adjoint plugin. Users follow a similar procedure as introduced in the [tutorial](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin5BoundaryGradients/) of optimizing a waveguide taper."
+    "In addition, the design parameters used in this notebook are optimized for the specific waveguide widths, thickness, and materials. For a different platform, the parameters need to be re-optimized, which can be done using the adjoint method as provided in Tidy3D's adjoint plugin. Users follow a similar procedure as introduced in the [tutorial](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin5BoundaryGradients/) of optimizing a waveguide taper."
    ]
   },
   {
@@ -1973,7 +1972,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.0"
   },
   "title": "Strip to Slot Waveguide Converter | Flexcompute",
   "widgets": {
diff --git a/Symmetry.ipynb b/Symmetry.ipynb
index 790ec8a4..ffda43f7 100644
--- a/Symmetry.ipynb
+++ b/Symmetry.ipynb
@@ -20,8 +20,8 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3d imports\n",
     "import tidy3d as td\n",
@@ -78,8 +78,8 @@
     "# Material\n",
     "n = 2.00\n",
     "ns = 1.46\n",
-    "mat_brick = td.Medium(permittivity=n ** 2)\n",
-    "mat_sub = td.Medium(permittivity=ns ** 2)"
+    "mat_brick = td.Medium(permittivity=n**2)\n",
+    "mat_sub = td.Medium(permittivity=ns**2)"
    ]
   },
   {
@@ -101,9 +101,7 @@
     "\n",
     "# Substrate.\n",
     "sub_box = td.Structure(\n",
-    "    geometry=td.Box.from_bounds(\n",
-    "        rmin=(-_inf, -_inf, -_inf), rmax=(_inf, _inf, -size_z / 2 + sub_t)\n",
-    "    ),\n",
+    "    geometry=td.Box.from_bounds(rmin=(-_inf, -_inf, -_inf), rmax=(_inf, _inf, -size_z / 2 + sub_t)),\n",
     "    medium=mat_sub,\n",
     ")\n",
     "\n",
@@ -163,461 +161,98 @@
      "data": {
       "text/html": [
        "\n",
-       "    \n",
+       "    
\n",
        "    \n",
        "    "
@@ -647,9 +282,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "job_no_sym = web.Job(\n",
-    "    simulation=sim_met_no_sym, task_name=\"sim_met_no_symmetry\", verbose=False\n",
-    ")\n",
+    "job_no_sym = web.Job(simulation=sim_met_no_sym, task_name=\"sim_met_no_symmetry\", verbose=False)\n",
     "sim_data_no_sym = job_no_sym.run(path=\"data/simulation_data.hdf5\")"
    ]
   },
@@ -671,461 +304,98 @@
      "data": {
       "text/html": [
        "\n",
-       "    \n",
+       "    
\n",
        "    \n",
        "    "
@@ -1149,9 +419,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "job_sym = web.Job(\n",
-    "    simulation=sim_met_sym, task_name=\"sim_met_no_symmetry\", verbose=False\n",
-    ")\n",
+    "job_sym = web.Job(simulation=sim_met_sym, task_name=\"sim_met_no_symmetry\", verbose=False)\n",
     "sim_data_sym = job_sym.run(path=\"data/simulation_data.hdf5\")"
    ]
   },
@@ -1169,7 +437,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -1181,8 +449,8 @@
    "source": [
     "fig, ax = plt.subplots(1, 1, figsize=(5, 3), tight_layout=True)\n",
     "\n",
-    "ax.plot(wl_range, sim_data_no_sym[\"R\"].flux, \"-k\", label=\"symetry=(0, 0, 0)\")\n",
-    "ax.plot(wl_range, sim_data_sym[\"R\"].flux, \"--r\", label=\"symetry=(-1, 1, 0)\")\n",
+    "ax.plot(wl_range, sim_data_no_sym[\"R\"].flux, \"-k\", label=\"symmetry=(0, 0, 0)\")\n",
+    "ax.plot(wl_range, sim_data_sym[\"R\"].flux, \"--r\", label=\"symmetry=(-1, 1, 0)\")\n",
     "ax.set_xlabel(\"Wavelength (nm)\")\n",
     "ax.set_ylabel(\"Reflectance\")\n",
     "ax.set_ylim(0, 1)\n",
@@ -1243,7 +511,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "wg_medium = td.Medium(permittivity=wg_n ** 2)\n",
+    "wg_medium = td.Medium(permittivity=wg_n**2)\n",
     "wg_structure = td.Structure(\n",
     "    geometry=td.Box(center=(0, 0, 0), size=(2 * wg_length, wg_width, wg_height)),\n",
     "    medium=wg_medium,\n",
@@ -1293,9 +561,7 @@
     "\n",
     "    # Include a mode source in the simulation.\n",
     "    source_time = td.GaussianPulse(freq0=freq, fwidth=freqw)\n",
-    "    mode_src = mode_solver.to_source(\n",
-    "        mode_index=0, source_time=source_time, direction=\"+\"\n",
-    "    )\n",
+    "    mode_src = mode_solver.to_source(mode_index=0, source_time=source_time, direction=\"+\")\n",
     "    sim = sim.copy(update={\"sources\": [mode_src]})\n",
     "\n",
     "    return mode_data, sim"
@@ -1370,13 +636,17 @@
     {
      "data": {
       "text/html": [
-       "
[19:56:33] WARNING: Use the remote mode solver with subpixel averaging for      \n",
-       "           better accuracy through 'tidy3d.plugins.mode.web.run(...)'.          \n",
+       "
10:48:50 Eastern Daylight Time WARNING: Use the remote mode solver with subpixel\n",
+       "                               averaging for better accuracy through            \n",
+       "                               'tidy3d.web.run(...)' or the deprecated          \n",
+       "                               'tidy3d.plugins.mode.web.run(...)'.              \n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[2;36m[19:56:33]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Use the remote mode solver with subpixel averaging for      \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m\u001b[31mbetter accuracy through \u001b[0m\u001b[32m'tidy3d.plugins.mode.web.run\u001b[0m\u001b[32m(\u001b[0m\u001b[32m...\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.          \u001b[0m\n"
+       "\u001b[2;36m10:48:50 Eastern Daylight Time\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Use the remote mode solver with subpixel\u001b[0m\n",
+       "\u001b[2;36m                               \u001b[0m\u001b[31maveraging for better accuracy through            \u001b[0m\n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'tidy3d.web.run\u001b[0m\u001b[32m(\u001b[0m\u001b[32m...\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m or the deprecated          \u001b[0m\n",
+       "\u001b[2;36m                               \u001b[0m\u001b[32m'tidy3d.plugins.mode.web.run\u001b[0m\u001b[32m(\u001b[0m\u001b[32m...\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.              \u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -1386,271 +656,98 @@
      "data": {
       "text/html": [
        "\n",
-       "    \n",
+       "    
\n",
        "    \n",
        "    "
@@ -1679,31 +776,9 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "
[19:56:34] WARNING: Default value for the field monitor 'colocate' setting has  \n",
-       "           changed to 'True' in Tidy3D 2.4.0. All field components will be      \n",
-       "           colocated to the grid boundaries. Set to 'False' to get the raw      \n",
-       "           fields on the Yee grid instead.                                      \n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m[19:56:34]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Default value for the field monitor \u001b[0m\u001b[32m'colocate'\u001b[0m\u001b[31m setting has  \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m\u001b[31mchanged to \u001b[0m\u001b[32m'True'\u001b[0m\u001b[31m in Tidy3D \u001b[0m\u001b[1;36m2.4\u001b[0m\u001b[31m.\u001b[0m\u001b[1;36m0\u001b[0m\u001b[31m. All field components will be      \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m\u001b[31mcolocated to the grid boundaries. Set to \u001b[0m\u001b[32m'False'\u001b[0m\u001b[31m to get the raw      \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m\u001b[31mfields on the Yee grid instead.                                      \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "monitor = td.FieldMonitor(\n",
-    "    center=(0, 0, 0), size=(td.inf, td.inf, 0), freqs=[freq], name=\"field\"\n",
-    ")\n",
+    "monitor = td.FieldMonitor(center=(0, 0, 0), size=(td.inf, td.inf, 0), freqs=[freq], name=\"field\")\n",
     "sim = sim.copy(update={\"monitors\": [monitor]})\n",
     "job = web.Job(simulation=sim, task_name=\"mode_sim_no_symmetry\", verbose=False)\n",
     "sim_data = job.run(path=\"data/simulation_data.hdf5\")"
@@ -1725,18 +800,35 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Simulation domain Nx, Ny, Nz: [424, 146, 86]\n",
-      "Applied symmetries: (0, 0, 0)\n",
-      "Number of computational grid points: 5.5482e+06.\n",
-      "Using subpixel averaging: True\n",
-      "Number of time steps: 2.0982e+04\n",
-      "Automatic shutoff factor: 1.00e-05\n",
-      "Time step (s): 4.7664e-17\n",
+      "[14:48:55] USER: Simulation domain Nx, Ny, Nz: [424, 146, 86]                   \n",
+      "           USER: Applied symmetries: (0, 0, 0)                                  \n",
+      "           USER: Number of computational grid points: 5.5482e+06.               \n",
+      "           USER: Subpixel averaging method: SubpixelSpec(attrs={},              \n",
+      "           dielectric=PolarizedAveraging(attrs={}, type='PolarizedAveraging'),  \n",
+      "           metal=Staircasing(attrs={}, type='Staircasing'),                     \n",
+      "           pec=PECConformal(attrs={}, type='PECConformal',                      \n",
+      "           timestep_reduction=0.3), lossy_metal=SurfaceImpedance(attrs={},      \n",
+      "           type='SurfaceImpedance', timestep_reduction=0.0),                    \n",
+      "           type='SubpixelSpec')                                                 \n",
+      "           USER: Number of time steps: 2.0982e+04                               \n",
+      "           USER: Automatic shutoff factor: 1.00e-05                             \n",
+      "           USER: Time step (s): 4.7664e-17                                      \n",
+      "           USER:                                                                \n",
+      "                                                                                \n",
+      "           USER: Mode solver at f=2.9979245800e+14 with plane size (127, 67),   \n",
+      "           direction: +                                                         \n",
+      "[14:48:56] USER: Compute source modes time (s):     0.7546                      \n",
+      "           USER: Rest of setup time (s):            0.4769                      \n",
+      "[14:48:59] USER: Compute monitor modes time (s):    0.0001                      \n",
+      "[14:49:02] USER: Solver time (s):                   2.5560                      \n",
+      "           USER: Time-stepping speed (cells/s):     1.03e+10                    \n",
+      "           USER: Post-processing time (s):          0.2314                      \n",
       "\n",
+      " ====== SOLVER LOG ====== \n",
       "\n",
-      "Compute source modes time (s):     0.6101\n",
-      "Compute monitor modes time (s):    0.0169\n",
-      "Rest of setup time (s):            2.2775\n",
+      "Processing grid and structures...\n",
+      "Building FDTD update coefficients...\n",
+      "Solver setup time (s):             0.7243\n",
       "\n",
       "Running solver for 20982 time steps...\n",
       "- Time step    839 / time 4.00e-14s (  4 % done), field decay: 1.00e+00\n",
@@ -1745,9 +837,8 @@
       "- Time step   2517 / time 1.20e-13s ( 12 % done), field decay: 5.19e-01\n",
       "- Time step   3357 / time 1.60e-13s ( 16 % done), field decay: 1.32e-07\n",
       "Field decay smaller than shutoff factor, exiting solver.\n",
-      "\n",
-      "Solver time (s):                   3.9805\n",
-      "Data write time (s):               0.0072\n",
+      "Time-stepping time (s):            1.8160\n",
+      "Data write time (s):               0.0153\n",
       "\n"
      ]
     }
@@ -1760,7 +851,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Below we can observe the fundamental mode propagating thoughout the waveguide."
+    "Below we can observe the fundamental mode propagating throughout the waveguide."
    ]
   },
   {
@@ -1770,7 +861,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -1801,7 +892,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -1828,7 +919,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -1855,7 +946,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -1882,7 +973,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -1925,271 +1016,98 @@
      "data": {
       "text/html": [
        "\n",
-       "    \n",
+       "    
\n",
        "    \n",
        "    "
@@ -2221,7 +1139,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -2231,7 +1149,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "
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       ]
@@ -2241,7 +1159,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "
"
       ]
@@ -2251,7 +1169,7 @@
     },
     {
      "data": {
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",
       "text/plain": [
        "
"
       ]
@@ -2280,7 +1198,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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M14Gt66oG7RHitm3I0vHuyTNdcU9xl+85OXZto3ePY9rBdbaVq5ALV2w3W+/hra7Hsf2SEO62A2MMdyYuUqWv3HSpO3Ilcuo229d1OO7VNr0eeKx+XvXxbh3Vv7s8Kim1hNs0Ttd1rOs2MHf8Ir4pQ3Vdu71Ju1JeezViDxxbz/hVnv/LKgzuSWek4p7gsm3wJtpu8LV93iWuy1ZdtG0c+s8P2a5LtJu1PsZaH2GTwDqSfLrpnmV5B9bOS8gqwVaic1m1Rk4t9U8u3WU5tt3cVPva2rbapseH2/D1qo+3iNfVx6tFJbbx+pJStz0Sc9O47Hlc5GV97G8uiMsYp7sm1696/KsmHhVUo3aPsfTsrD2nl1ELbP3mmJHfY7ateHy4KXLhIm14qw1W8vRucKy9OvA/m8v0RYJHOPR/G9ruj6y0G4P5+7p8px5aDywTPEtXegw/5a9ZQUWRZHIb20x4qHj8pTvuF3onZKb3SG+xRkzNUP53l3wX3mj7Ko+/9M6tdunh4ap+2n3w86qPd6u4yvEvmquvxOvs171+pNRt5Um4zf0ILmNIHlDs/FWN1HUz81cxHGsdvIviVhKQVlwcxzhzF33+L0ImXcSxrx3t1wfXqUS4pHOYUBJR1QG8O1zBXl2KJDhyP+EyZOWBdnNRQqoko44loo4hnq5rcGvrWEJY6U2EoNLXIndMrlerqUriKeCCxNQl7cl1ta2lfc1IqqX2Ve3Sw8ddKIGvcx8a1cfbxH3x8a7q3x1N9D9CPH5S6qod5xvY/3W+aCeI+z04IlRibfS6XF8cZ3HdkbhsLodDv13c3w3ldXBxt6UU/hhs5XAocczea8jfPcKWM6e+L9oBv7CMtvOpLD7vxyhS1s7xnndcKi6IqxBMl3EUj2zDE5OlVTC1/d0uLmuvyv/5Ovs1sQ0YoU1oShSs9nG03VvYRr8nw9ry2F/QV1N2ITQJdJjUWj7Vcj+XAW30PWSVE1XURCk17VeQMROVlTGYqam0gkorpzIxttLvWGlP97Jt4Ui7VImp+4vr9t+qj3exY0VUH4+nW2d2TFGJcn+PHY+blLoJcunI7W7MKF3h2KuG7JAB2nIWbiGXw9ZvJr+/o6SiW8ddM2YXMThbOR3W8Doz7fcW8TmaPJ9LDnyJA06+WeqorD2zxzj+tcP9eHDoPXRVMuqY9ru0HdG03a61udoWbxeHSINDZMF19HvKMD1D+bjR7hlNTOk2cmR7WSKkDpFRS0TUIQJqiXS6zl6KU/ufve7DlLRyoQjhQ+4j6PMkY9J1GJPvBykFle5flKqpVRxDSPnlbRbnL4OttgUku3SVtlVxR7hOf++IfVUfbx3Vxzsud9RFfLvXJSrmcZJS18F+b6zfNEbHdNJvw5gtGBGD6ct3CYuP3qFY+yNHuBfzNBy73YpROHQnb8OGbZLoG7kb8u/NUbHFS5vcavnmisNY60gHP3fsgp/9xiw5MWUDMwS4+bNo4CfPdVjqgC+d51YnZW19xf3FNRFRl3UWD7Zhab/SbuX1IjNlJ7g6gzeDIxRSJvhVezX7n9cUMcci/cduNp/aT6C0bmLvhEjQ7Wel3ZSEUklGhZBJmvm2Mr9NUE3IrOLNvdYnuWq58RnhpLJp6XXye4Ocg0rIK2OY6ErbhLzex2WU7pdJ+1slpsp3nH4PAse1resgp3TbMvodWbznol2StjUjzCtJdT+x1SYusa76eJi/g1eOqVF9vGUs5ekDttVUa4d87IKDx0VKXdYwbaxfNU5rhukCI883ikNGZOl01Oh12s2aY3sNL+Q1+TywbKCWTnm9g3eLjPqEEV9hzVeMCGH5WtdKOa9Vxkm/Wzp2JaduBxuEVIL3PB987nhvOXOGkmMg8wjsSszIKkANa0+f5clzfFH1VO18Pxxc5h24ploA5k6j+n4Rx9Ho9hMbdDCxXXlMyIVKTN0xSgJ9QigUNmvJdm2Rm/owW2HH+rvYwGj7gjG5PQi5KQoX+d1Ce/FhmZAqyaglIuogSYVQqKrUdS4osPK6hYUXwNIrgBbW6Xe/MUJY5W3kquVdE4xJ6ygSVSbMySmtmgpYcZjWBmbWiKgFImu2L30Pjm1fqj3JNL1bddsSaP5J/668tmqX7g5XfN8JLqQCrj7e8aeoT6f6eAlL/t1Fq54u/eYx4PGQUhc1FBchoY6Jb180dGsaxhs2XpPOfM4UMHnhLhk0NzdCk12Vjm25jw0jdpFcDsDcOF1FFn9TpUcF2jCsyunViKOGw9II53FGrDy2OsyFyjdX3ABmHW6fOuLGx2fSj3kbvZ3ezYqTBihHPzn50w51wlKolN6fPudD8u/aAb+fuMygyxoZtaSK2iIkFpzH1XYsahYAhhp2BAnJAZwoE6rDd3s4hkDX2yzZLE04lPtdQLJW+j+mqY0Lhc2DIbZ7JYkgChcqCISVNqSJopKMEhFqSVCFkFVP/Ju8L01syfZAJnvWVFS8zeVQvsrnJFSAUWomvQ0hpD4FGb2cyagAZvAMMkG1RE6JamqRHSvIJP0OzMsW2pX67WXbVro/x7at+GHyM+R2dQxhXnG/cJXBl639VB/vXvp4N+3fAVfz8ZbIKlrw79aIpi2f7jHicZBSRxiPo0moI8MULiRdv01mfWP0cbETaKbXkeTMss1CmAWgjMOWvDliyViV0nkAs44dbzcdoVzc5xUIq6tA7mdZhrk0SpP3RHEmse84+Z3sb9LJvCBJNRHXqO+VmLohbDlf0hn3I9sNP0ZyiqeJrFpSzMnujUmKgEmnWjv5hqYqFCArUZBn02+XOi2HyKdKTt0vXBcZVb73ltQLhfM4cRg32jCQnVvdhkN8V/A6D6DJxNTatdR2dz1Y6q9oIsAxYT4h0PX/Xv7nW4RCeTxg8j/OiPcJWWDyd2k3mkQAAOJubCAAzsPYZto/UZD3ZUlGJdIphJTcVpyPEAAXfyjrWHUV0u8Afuc6H1L/Q37vvfqung+nHhV/Qc+K1EvcTsgoE5eZxKXYRD4ZWDLpnWIMh+kZmERMUbxzlkxSVJn4TrGG9yGklQmZsJJupC37FrrNLLWpcpuCuFprW7zugu1rqW2ttStNmEubWiPMq126fRzj4xXzm7k8F9vXFX28myKmqo93az4egEmYs+BCPt6Cyqokqmhyj6YrS5+u9Of0No8BD5+UOubB3zJOgjUyas1ILY0sbvx29bjXZbi0wdC7X2O7Zb4wMCaKv9PYn/697Fong7zA6NGasTrGUC0ZqNIwrbHzk22uyXItyuZl0FYSgqoTS4RTMbCojclRyUZll5qt3yjdvIRKTN0wJiPBC3ZCOtlOyCmXOuhrCpOkDvCUnHtTdqYVDEjlYVkYHQtFMldgTk7VjvbDxrGd9JV335o6ZpOUWCIkwO1YCIQQ7NRBnBz7cBhWxc1h5uhrW1aS6IpcMMW2M1JzBcGYqSOlSIOgVVEmAN4hkJ3bPT/GtiLqFj8jN8vR+iVCyoVMMnkEOI+kghJllBBRsj8hoVwIiXzKpFQmm8rl6bzUibkjOydWve+XyCkyJhNRZGbLiSJplUiqkJRQQlB5H0DgvoclJqcc+PptzFxDhu8hwTBJpQbKEjkVlhVSa/Yktaml9Xp5xNHta6ltUQPAJXuU2hU16ngmO+xho+pjxd1i4113VG7EJXtVfbzq4xW4Lh+PFLVU+ndyz9YSqAd93EeIh01KHWksjmLI1ffNXC/F9Bh58bUnbVxCaSwUcz5LMgsclMkbPZ+2kWuV0aQ4Wx43Ohca0vkDpoZqLV9DaZTmxi7OKzk9im30sdI6XA9K06wNSCmV19uXhsqk35jJ+rVko0sqKoQwOZ9DpZuBSkxdK7bsBDB14vwI43rAjTDjOTt6/R5h7IFxgPfTMRnTdNxBaDoYa6NTFkdtqYkjvZY3ltFcGe0tlVOGlhNMr42ALZFVx6yruHlsvTcOdIy3Eu7PlFHaMdRKh+AzGeFHGO8QnON27D382E8Ob8gCTcvtuNtlxy+qWgw1MYzP13CZm8ZW+/BFWxBV59hP/3c/JuWnPz9D8J5Jo3Hgnw/x//flGLNCtFvGRvvVtDxPltdFu8frGoiCRWyfMYRANtk9E3ibAAAhKvDERoLfg0IyCQHlPCujNCk1+rguklejIp7OR74/g+P5wQd4H/L+QsAYvZIh3kun9peIKbndhapK4AqPyxYva61+4vVI80w45d+08XlqrFpnDIgMWuJlrWWSqk3bGDTEKimeD2hIfhvJqngMIaQohEwClXZD3n/abrhx0ZYgeGBkWxPGPrct76LiOOZUdEKKHtHGWm5HpmlT26KTUz5faridpPfpiNB06R0a4PNzYVfsUbVRt4eL+n2HCsus+HbVxzvg48WVOVS5+nh62ZaPJ/6dRMroKJn0+3QP58ctianH5M89bFJqDUujtUeWMt7Mm3HISB3ax8KxjyoregxDvWKwgML4SB6G5Jz6nHNGOnQrmKkvNNF+5At5Kf8CUBq0A0asMFLHlnTW266en8rDsLmdfgeYfCCCZsql4wi4OBqJEEcdw9R4uRBgME1AqhOIGsRRyfgjraLS11wy7Y/JWD1ILKlJHDtzxo8wboDv9wj9nh256NQB4E5FO3AnuhkQolOPxnNHGkAOfzJ8HGD+DB8IM1gtgS3Ysj+1M377uA5Caut3S4RUUO1YiArXJwfSR1I1jAM7kEM/eeeGpmXHsPMgISFCh+BGdgaDB2Dze8XW9nbb0P0aba/0/59I9eCB/hzeO4R+D3gf/3MmDoKQkluVqqLTxES7BYaebZ3NpJSQlOkV1iISBci2D4CRSmkATKDJ+1kPkmUniacBmZByfkpY+QAMzjP55JiAGpyHj/OalPKBSaiSgHIeidByPuQwQPV9VMuOgZBNDZk0b4vvWQkFkPE8P/L6lghkMCWlfCSlPKElJqd8MGgtATEgxUgfjAALg2DivZR7agC7cs6T9uTHrIoqQ9kjmSnktkwRSe/UvrzLZNQRbQzjABAhDD3bISJ4RAK0Q1ZnUgMEk+eD535vtDtVLXWPcYH33KKPNrF/t+Tjles22lb18a7u483Tp6w7R2s+nj7mRXw8rZLS/h2fc1y3oI4KIWBNNfUY8XBJqTUjoA3KUuWgYv6QceJtpEOmiamwvW25T3WsNMIDzEZ5wtaoTwFDuQvAoRFqPnb8k4FRbLgYsEnYT1JYqO0P5XBQ1ZMMKWe4MF5ijKZS+PXEolpCr43SvFKO7D+obfIxed/ZkJRS0GPl8sBUMg9MjYd0CGVJNkSRHIqS+LxOpPHT5ZK/gaJhM2DmXPI46Gtdko0C60z7UtnmSljdLJZCYYSIMn5EePU2/P4V/Gc/CffOpxH2rzC88wJkCdQ2MNbCPHkOQxb09A2Ybgc0Lej0KU/jSC9sFzvTcaTaNvm5jQ6/hMEE/TpPTCZVYuqh4DoIqTWFlH53KTWDJqOMHwHXJ4UMxgH+7CVP9y8Rzl4ijAPC/iWCc/DDCO882ndx+7XP343w7vfCdDuYp7F9Nh1CPIZ0BKvzd0vYct40geAGGNcziX72khUsL99B8I7/62FIas9ETDqH4Jb3byzNFVKiCiXLihZr2eYRT41VarumBboTtjti/yKBFZpdJudVH0eH6YlCavT5e+/47bofHQbHqqjB+wkhde6YeOIpE1H9yJ/zkdf1o4tTvvZ+9Pk4Cx9g2k9ZI6e0WmpGQhWfhgy6hq+7awiWDLrGwpLBk87GeUJLBEvAieVtTiyhtUxMnTQEa3jaWoNdY2EAdBawJsAGg4Yk1xQTVWXoXlJD+RFm3Ke2VJKbYVTtZ+hzG+r3gGMFXigJT1HlHdPOpE1J+2lbmN1TmKaFOX0K03Sg06cItgVsF8P7Yigf3+n5zut77/ax4fcJFt9zYV0FnH5zhI83I6uqj8fbofp4F/bx4v7Ev1utcopMTOkcUo/Vn3u4pJTGGlu9sW41dnjDUC3l2DALv50ZpmiEfDmyo4zTRQxV+g2mRgtAMlqBJETHpuWlETPKEIlRSsaMoCraFIZLrtnYS72YxXDo5KJLhkqMFHA4wSiwnmRUz5fncFFMDFXK21DMm9xhBDDN2RC9rmB4uWbcg2G1VAjZSMm5luy6nL0oqPT5BcXQ5+tdNmQVN4ySpJZOjx9ZHXV+BvfOp+Hf+QzOP/MC+09+FsZa2F0H2zY4eXMAdS2Cd6DTpxz6ZC133iXMZaIYaFh9YpvYlvRQVxz1TT0M6XD4Skw9BNw0IaWWL6mjJGRLlFFh/4odx/1LhH7PZMWrd+D7AeefeQE3jHB7diB3w4ju+RMmHHZP+BinY26H6ZPE7pe6RRWXwAJ5npVRmpwamTCIqk6/fwkMPfyrdxDGAX6/hxtGBOfh43SbLIgf4qltG55vuxRmZYaB+x/jgEDEJFU7AN0O8A50cgqYMdmfACAl0FZ9ozwaL0qmTE4Nnr/3URX1avDwPuDV4CZk1OAC9iMrpV6cj3A+4FXvEhE1J6emUxf3xacYpz6kPk7wIS1bguSPMjKNIXgAQJGAaiO5JKSTnp40hK4hvGgIXWPRNZSWnbYWZAyGJqC1HieWMPiANu5/iB5SS8T9kCgJ4H4KO0sm9i4mZx/fdVDqqERGDX0momKbSqq7GBLK85GUGnoE51MbC96nNnaonVFsW7ZtQLsdTNOCnENoOxBZPmbTAsawEi+0UQJW+g/VLt1nrBFS6bteFvzFfDyVmH/Tx9PKvUM+XqnyoznZAxR+3lV8PHAfkX+PdR9P8vQ9UB9vzb/T+fxmRRkilnw8ACkXn96m9PH0b4718cS/k5Zg1TLJN6WJqceOh0lKbXTOU1JF2U4z5LJMO15qu4mRAlKYzeJ6tW+RFQctLQawGQMfz3FmqC5CTmnWPBkmmqyTUSJZl/I1ANzxi9uaJjYFUtNkoGiqwIj30PCBEfyIVDkpn1y+JMWAO5+Nk/4uI5gl2y6GRvocozJKg8vGSVfNkRwPfOzC4F0isSiwwKLLSKVRBiqRVGa2fJ5cFEVy0djBU8ttMnwFgy7nYIDyCnQYYGLaN4zZY2HX7z2U7TCSg+fsJfyrdzB+6hN48YufwKc/9n/j4z/3S2h2Dd79FW+ie9bh2Zd+Htqnp9h97hnad70B2j3lzn23gxdVAcAhfYjEVOqAxGWaSAauRkwduMZKTN0QtgipA9tfSCEl7bTIHyUKKYwjk1Cx/YZ+zyq/fo/hs29j/8m30b/zEi9/8RPoX/T49C98BuN+xBe+7wvw7v/Pl+LZF48wp0/Zzj1/N2BGBO9hjJ/Zsop7gKDCrPpz+P1L+Jdv8//+1i/C7c9x/mkmIIeXZ3D7OXGgYcSRsgSKhBR1ijBoW1DX8PeuQbPrQG3D6lCyoN0ToOW8ZHT6FK7fg3ZPYdqobvE8+h8AGM8qUemvhNhHcLF/MDompAYfsB8dXg0eg/OJjDobHPaRZDobmHR6Zz+iHx1e7EeMPuCsdzjred1+cAg+YFRT7wP8yP0TP3q42JFxo0fwIT12PoRESoUVUsooUirlJyGet5GUspZADYGMATVMWjWthVHTXWtx2jEpddpZNGTwbNegayye75pEUp00hF1D2DuPlgwGZ9FawvPOxrC+pEeDjfkug4yIRTuiCW0znvPy8TyTmlFpGfo9E5yR6PZnL+GHEeO+h+9HuGGE70f4YcjEZ8+2wwsxtRDGVxKedtehfXrKAz7vfga7OwEA0O4pPBH3Wk+I389br7L6rru/WPL/Sr9NCKYyT+JFfTxJt6B9vEJ88CB9PK2aWvPxRMCAx+/jAVgsIHFZHy9GRSsfL/t3HB2zTEy9DniYpJTGihJq0SDpddrxKo1RQUalePfYQdMGKiX21Ak+lwyWMmTwDnBCXC2z6ryuSHpcqqKAbHySXEex5mRjnDzlefkekyYnI6Yk9MbafL9iYlGzFB7EB2Z1RvDYygUiBigAKs/C3FC5yJ5ng4M4mukn1W2kss3yPB+TK+Lk/cgynp8bq0PJRRdVUkYbLzMhk1qbWfU23hcZedQJRTnvQzZelkLK0UAGgJ+z6qKaKi9jabv0H1S11K1hM4+AKA72rxDOXuLlxz+Ft//bx/G//vX/gx///38K7z2x+PWfPMPpu3d4b+9w8uZT+GGMapM973/sQURA2/Hz7FvOjQEgUMN2SueZ0oopsFI0EVML576Z/HyrM14769ePQ4TU0rsvYiu3xpJaeFEhFTwTUhJmI0qZV28jnO/h3/kM+ndeYf/Jt/Hy45/E+Wde4q2f/wTOPr3H/++XXuBTvcP/14VIOjRo3/MSoWn5XUnqXRM8dJhMDeG7ZZQJfFX/yXgHPw4I53uE/Uv4l+/g1cc/ieHVHvtPfhbDyz2Gl+cYzpgocL1HcAG+ULCQFVLKwHZMStnOgqxBs2snRJTddWh2J6CuQfd8D2MJ7fNnHHK1ewqMA5PzAMw48MALNcDYAE20ed5PevPSB9GE1KveYe88XpyPGHzAZ/cDBh/wzvmYiKgX+wH96PGZVzx9sR8xOI/hfIQbPcbBYRw8fPzOy5h4GvsewTv4sYcb4/eB+4ZSECBIf1L1+URZT4VjKh8AoJgIntoWhixs04HixzZCRhFsQ2haC2oITcvfbUNoTxq0liIpRXjzSYuuITzbtYmgen7SoCVWQ7XE/+fOBjzpLISYcsRV+WbtKRFSHP4JPyYSSkhtf/aSie5xwPDOC/hhxPByD9+PGPfncPt+QlCN+wHeBbjeTdoagMX2ptta96xF+3SHZtdheLVH+2SHp7unrLprWoSmA7USTkz5vamvaS3fXcXtovhvZu+6JdGB+HKX9fGWcpsBx/l4C2TUrfp4qnhE9fHirVwYkZ+JDDZ8PE1O6eIRwLaP52N4X+njAVk1JYopTZaVaqnH6Ms9PFLqkEpKbZOMjhgcIDPDxf4mZWnV78qKIKnjoGLcdWLXlIBRf3f88kxklZZ46mUaC6M+6XFSEk8xYqFg0dk4xfLJKXeDVLJpuTFLJRJrAe+SgQtjJqeS8gIAqGGVhXT0JkZ/+9EQCacPWcopMk5trHSS0SVDJQlG5TvAHUsAWRoflGE7wmCtKL8BzO3vksEqq9yIERt8NmCDD2xkiEBkUtJynVCUBxmZjQoGsAjwEoMc1g2Vjt02gU8gVWmIVmtL+lnVUreHrRLWxhqcWoNTSzh5o8PJGx26N07RvfEE3XP+0OnTmAOjhTk55Wm34+pUtkvVppI90JJshWDMKnk0IQLKbbZIgkog3AxKZe/Ser1OzXNVsoXfxfxiqT2qHBRpdNQAJjTRQfNMehK/K0AWpt+zeqXfowPge1bLBO9x8kYH1zt0ZNCRAUWCPoXZrOV7rLhzrNoo1Z+Bd/DeIzgH10fligvwg4N3AeNZrJ7mebmArInqlfj+tFEdZE1a5pSaKnRtVMiMIDSRyIxKlng+JvatTAhTEqFoWx65DyIQ1ZT3OpE5Ut/hfPQpNO9Vn0P0eiGeegfnmIAaewfvPMaeFVFjz/1F159NyCghqIJ3cJqUcst9wVRxVykjDFkQWfix536b9N+8gxl72KZD6E7jb5rsf5ZOmfXofUAflVavej5aPzqcjxz2x/fMYHAeZIgdQ+L7Iw6Y5JKa0soFRHWSBm379B+G2IeWULykghr4v3f9mBRTrneTtuZ6h+DCYlsLab6B7QDXe9h2hCPinHfSL09Ew8XTaFTcMTZ9wrlK6sI+3kRwcE0+3kpy/hv18Zo22gjPodDVx0vQft6Wj6ermxIZWOXj8Tz7eHx8M/HxdEXTNR8vKEEBh/+9fnh4pNQhqER1k8oeYw9oaaLt8m9KhZTOnxENVYprl6ogUREVelYu5KpDQ3rJolBR6eSfwp7LyI501rVyqoyTNwVDoqXwAJIcXpYZVcVmxpQTIVgL03TJQAWiWJkkVvuKySFN26V7xE5LHEnik4iOjzgx8zCMlDMBwppP2XNdgtkHNjxldZvBe7i4zqspV8bxhZESln652k1Z9UawxaLrSjflVFe7AbJCqo3ryDB7ziy6n5Zg9gRrgNaFtCwQUt4GS2LAQqp4ow1VHoHg7w2ZxDIZhBTuV+mCu8OsmAJyB0V3VGxHeNYQPqezeP6Fz/HkPad4+gWfg93nvgu7934O6NmboNOnoOdvcqJzCVuxbSppHWKOKSmTPpFqoyCjZEqZkMgnXcmoe4ctcqpcp+ZzLom4rfbbOb4TRsWqpNFRseWifvMjDI0gaxM5gHFg5cbuJU6thbGEZtehf/sM1Fp8zi++g94H2E7l91Ft3wATMkEnO6+4YRxLCAavUg/EXD7jkMOqBlavjPsBw9kIPzi4XsKqclgawKFmxhqQJfjBRaWUz4RU79F6j+Bz/4xiKJ91HrZrQNgjNB1MVNoFqdjmXAodK0lP/WpnR0k5QbF/MTgfQ/k4wfmrGJp31rsYtjdXSA3nLqqkXCKjhv05gncY+zNWR53vF5RSWT3FpzlXSklf0BQOaqmUMmRBbTdRSrmmA/VnoKaDH5mcat0JrCV45+FHCz/yf2Mbwksy6CMJ5XyI/Ro+btcQHAEnDQGjx67xIG9SH0sUUj6OgKV7nfrhmRAIkrRcqnPGpObhfM/k076HG1ghlRRTcflwxuRn/3JAKJRSh9oabxfvmTUwluCGEdSPMYl6bjOTJNWyr2qX7h4bhPPBbcUPFB/P9VMyqj+fEVGJhNKVIIFNHy/boqmPt+TfyXYaN+HjSZL/m/LxUjieIqKu6uPpqqa35eMB2xVNgSw82PLxrPEgE/P7Gf5N8veieqqzOTcfJ0tnHw+eVVU+LChPi3u+JCR4DAKDx0dKRSwmMl+S3soosNdSdSXlDIE7DmKUxoFfWtF4JcJp6GNnTZWwLarQLMW/h5KUUkbKFwaMiiR4xk4NllcGy3YNMIwxZ0OLgCEz5Yiyv6bjEaI2dgBVwjwpwxw8Ac6l3DQSCsR5adT9PcCkawY9LxNjhpTYTkYuM1M+L8E8yvd4rwY/ZdX7WCEHQKqAMy4YrNJAueJ+W3W/l8gogI1YWflGWHG5RjY6hDgojAE8z7v3MWmoj8sCLFkgAMEEOG9AlNl0E8xigr5JCVXDU2Hdy1jpivsDQzblujh54wRffNri808snrznFCdvPsXJm8/RPt3B7J6kROdSkQrdCYKhKSElxLuQUYowmhFSpNddkpCquF84RjUl/7tHIpySakq1j6Saip1QDtSJv+lOYMYBUhmNwMqHdn8O348ccuoCvmjXcAWvNzrYXcf5g1I4wqqmouI2ULaVi4CKJOWdhTmjRD56F2CthetdIpzST2NIlSimmDgQ5VRcXuSe0n2fVLWvOJ90TeU1lpcdT6fsvFMc0LEEDMVtKZ0XQwY6x9P0+zzcTpRM033kdXqZzJuyv7fy+7Vt9HPGn+n56s/WtfIynlKxbtMBusB7w1gLDGM8BkUiKU5dzD1mDZzjNuKB2K4IxgYQCB5+1takPVJrYTsLG6fUNqnt5hw99T33qKF9vCJUb5IrqqwAuSI4mETKHPDxlvw7PqW5/Z3auqv7eIacUmDdnI8H4Cgfb4gFJW7bxyv9O2Dq4/H8nIjSPp6jMPHxfAggb2J4s0nf2U76TDo5oLWA9wagTN6RYRUq59hiH68koziCZv2eS9/sseBhk1JKWpgSnC8w5FKWNogcUXXGtHHSCinjHbxUAClL1Ypiyjl4qRaiFFRSBtvHl6wXebuuFhKl775g0NeqiJRIxkpCxiJrnuY7/mtTRZtYgUSXmw/RgKUyuUTAyWkaAQ/CoDf8nU7YsRXjBXFy4/00Ico+C+LPI6ukApjtDoo9H/20FLPI6LWc/nz0yUiJgdqrkssitRe2vB9dWgdkFr1k0NfKL5fQBkrm559c7UYbMl16uSUCGWBsbGLTWzJovUFLrJbynpn4DpRknQbMohOxnJ+Kkd8QCT1jgQAzY9o9AIqGr+aVul3IM6M76fm5a7H73DfghxFf1DV4/oXPsHv3E7zn134l2qc7tJ/3RUkdZXZMSqHbMWlgO542bVaArimjzLzjHTRBVaIqo+4vyvtfEgsbOcB00nteLASVUkUFPy0pDQCUB2ow8qiqsTy1u6cI/R70/E3Y5+9G+3kvcfLuZ+jffoWTN3b48k+/wvMvYcXfyZvPUml2OQlNloZKoN8/GMr5SZoWpuWQ4ZM3n6HZ8YDW+HIPu9uz0qV3k9xSAFIYldHkUySgdE4pIeibSGC2T0552Sn3T+zuJNlNtF08ny7aU5W4l+Yhy0vgcIwcltGmUW+uTOd8wGlnUz+iHz3saBA8h8MHH2CGTOzkZOXR/noH23RwYw8/DqCmSwqp4B28KKRcVkmtVWKekEwqhM+QnSimqGknuaWaroEhg+6Eian2pOE8Uzbnlnp60sCSwWnHCc4lEfpJ7LvwR+4RTZL8bt5l+S98fDc1SMmXQ9Ny2GXsa9odYGPYnh1GUMvbUTumPmyz87DdNKcU5y4Li+1Mckm1pw2TUrsO7ZMdmqc7nH7uu2B3nDTftDHMSXLxFJdR7dLDw2LOqAUfD7rK4x35eMH5uUpqwccDkJ+LAz4eAJiT3aPw8c5iWPGWj6d9PWDdxxMlaImL+ngAq0fXfLxd9PGkiqn4eERG6grFbbKPR8HAxdv7GFRPl8HDJqVWoMt6LuXTmIWYlQyqyNUlRjiqo5KxUqz5RO7pXIp/D94nQ+XEcA1jMlIli64N1VIVkdk1FLJOr8ori1zZECE4n4xWcB7eEsh5kCXYHRAoV+1Kscfych4BWIvgCcZbBOf4hR0dHUmWXN7PrSTPEmomdiIgxMR4gW+3yu8wnfoYfxyiUfMzMiobJzchoZwPiUU/pJZagjZg2kABKMgn2aedkFO8IccYA547wJ6nRCGF0wijrqcNmZSvAWBF1IRsCtOp3Fubo/gOMu0Vt4cQS06npJTWwsakvu3zJ3j6hR7dG09w8uYzJqNOn8LsnmSFVNNM80bFDkPKG7WmjJqdxyVJp0pI3T9sKV421h2Vb0orpxDVUmR5xDRwdTPTNDDYsYO5ewIC0LzxLhginH7em6wCfPMZ7K6DbRsgOtSgqpR6kIi2y3YNEwhdyw6Yz/0YJgksj9KvVEWTMCrbRTVUx+qVVClNT4lYGUC57SSlUEzsK9iye+kSAETXa54nxBh4CjNHRA8y7QceDefKdwbBG1hLcPCwNlZQaloEbxMxlc5P5Y6S3FieFCm1ED4GZIWYJqOMJqWsTYRUJqqIz9HGc7WxMl8893QNK84Xf+I9o5yrMlX/O3JoKw3MALF/6dL/GFKCZg7PdP0I2zZwGLMSr224n2w9rPcwSXnC/5Wxy21MCE9RRzW7Dva0g+1ilceu4YiBaoseJjb8PIFJ20yjZyY+nhDDipC6KR9v1b8bivNe8fF89N+0j2eimnTm49nhxnw8mQ9h6kPdtI/nvJ/5d1f18YCFlCxH+Hiynr8s+3hQCVTIsL/nYloW5wOM8vH0dA2SH/ixklaPkpRKUCF48GNmfzXLm5LfjbnsdYwvTuz5+Z6NVIx9l1xSQdY5j3HfJyMlJWvd/hwANkvYalmnNlZlFRENKhh0YckBJIZ8mT1vp529fsylmHc7jjcWmbl3OWmeGG+AR7gVk56qMuhPMXYmBspH9lxig12MDZb44r3LeR3OR/5+HmWaPGWmXQzVq94lxjwbrFIxNWfRk7GShHgbFkDL1a2ZxxmXnVVLBieNm8x3jU0E1WlnU/LQJP80hkdlQ8hV+qwBeTZyxhAQZZ8UzGwUzyOkJIImvhBq/oO7xarDDyTlAWKScnryBN1zrqYXvEf75JT3oaXi4wCMPefgoTETUCbwc4vY6fB53aTCXsoLxEM0l66wt7Wu4m6wFYJVrlPzqX2WScflnSnL0ntUqYr16PM45vejDNp4x9XSYlvunj9F85TbOpOrLXd+N9pSrbx3yyACnM/EecjfgzFsN1pWlgCAffYM1HL/x+67RK77fkSj+kM6t4ruu+g+S+qHdFyh0XYtV2p8ukt5yqhje8lFHrgNJQVA08YQ5mZCpCbFVGzOZAykLAgZpHyQkuvDG8kXQjhtmagQtZTkW5J+Rh+JEOc8jHEwZNBEBVUTAqw1/Hg0Ft4H2HGYJTj3kovGzXNKlSiVUgATXwAmRBQ1LYgMmtbCENB0FsYYdCesmGpaQtNZWEs4OeEKe6KMehKn8v20tSknSktckEXuVRNJKiOEXjnypUMqY0i5wYJS6oQJbZCF9ZxnLHiflCB+GOHaAdQ18P0IigSVqFTK/D06744QnaK8a3YdmqenaHYduudPWElysotqqQ6grNxM6ihth2qI3/2GJny0T+KnKqlDPl6KjpEImQUfjwsw8PN7UR9vy78Dqo93mz4ekP28i/h4ANJ7YcvH2zWEwYdVHw/g/FJIqVrAfl7UK2RBwgFV6iPBwyWlQtGRLtdJJ3ppm/RgYdohlxxSksAusedR0jn02Vj1ewTn2FA5DyfTVC1kiKy5T1LPFHOsOmouyhKz/NhP5pcg+Rc434J8j8lDoxHyMdbYds2MPddS0SwZ3YO6No5ceRV7nB1e07QII7hkrh9Zji0Gnw6ruwIygcLhZrxsUPHDSdaZvsu6gN75VAXH+YCzfpwZqn7CqCsmPbLzWV6fS4mWTL+GVKwjMhiBJNO3Ub7Z2Tyi2I+ep8lwceeOz8FOjB2QqzuILJ7huaKNA1oCQCHlpQqRaZJqFnzuxT1WyUZLoyZICirUEL5bhwpTkpAY03GJaiGkpDMOII3iyYgdyM5tn1RKWSOWjiGSKjH1MHFJQuqo35QElg6Rn3T48/uyrGBFXYMWp7CnHJIl+dCYkFpw+iruJwxBhxxjHFhhAsDucnJfAHBq0M+1Y+r32MnuspMlDlQmEE5YxdI2ibCirk3HNjHtgInEZlJKlWGqW5djWEJsEHkGz+/4FpLM1oNGk97jfcNnfyLv/TgdXDxO8u08mmBTP8ONHg0sqwPIwBX5pZxU1vI6fG/5OU0OqsolJQosCd+zjQU1UQ3VGNiGIjkV520mpBpFQEmoHn+3KSxFkvNyyF4OdTzIzSzlphOIUioOzGAcUjhvaFoQABpGBPJodh1cJJlGZOc8OJ/amXb2bTp8VlgBYHVU28DuTlgtteuYjIqEpm5XE3tUbdPDg/bv9DK1bpYP6h77eDK/5eP5fmRC/wH7ePvos531LpFOl/XxxP5eh49nTSajSh/P+XBlH2+AR0MWwXB6lbDgmL1O0S4Pi5Q61KleMkY+E1SJ7cXU+TIhpEoMkyTmQx79xdDD93tm1scBbn8OrwzVuO8nhkorpsRw8TzHv0sJ5bWY+KOUUjpBaBzVk8SOIon3Q5OY9MSe77LhokhahV0HM4xo+QAxD4BLhssACOMA0wBh6FO1hlw1KX9f/GsCJkSJD2KsfK66EI3V+ZjZ8/3ISfBenI+JOZcSzf3oJ0bqrM9he73zXL1B2HPpMMf54EMyVMcopcRwieR9HAyMMRgpJwttLU3YdNdZnI8+dvZ86siWhsvH/3AXu1Tno0drDQZvQIHjkA0MC13CnGTS99SYpRA/LjG6xUA9VinovYHunIvKKXWGO9BuBxrG5IyVyXyDJKL0bq7ACh7wgDFKYi05AKBGfLfUUhWvFVZVUpiGv88GcErSSjr4wCzsyMTQVIBHd2mXnUDEPGiZpD0uB1DFDWAtxFOpMWFYJRXGgd/93keCkdA+HWJOS4JrG7hhhGsbVq0oNcts92rkvxzlF8VUs+uYiBAys8glJYl9jbXwklOPJKR5/YXGCh/+SJ6kljyGuI7VQR6AnSml9BQAeuvRDw7GGFjnQeTgY1hG0wa4kZUTviWuehcC/MhkkhtzKN+FckolUkoUU5mIImLn1TaUlFGinJLlXcuE1LMd55IShdRpl5VTu8YmQqqxcb8x1NEak+7fwX6DKNao4RIv1gI+5jQFUiJpVlFxKF0TE0dLO/GUySjfDhduW3Z3wqRUJMft7oRVyl20SbEimWm7xVD4xWuquF9YInMjoaLzS3E1dR9D9KSIVfbxhJB6aD6e2VOylxfy8SIu6+PJkqv6eOK/XcXHm4gNrsnHIzLoh2Ufr58Q+ss+XlKQji75eIMP8CZg8AYtCGNU2Mo9NCZEXy8rruReP3bL87BIqS2syTaLzrUJbCQmrK9WSUV5dQ6ZyXJOTUgJez68lPC984mhku/Bewxn/PLUJWzZSEkZW38jBotZ9DEbr103iUG2Ow8aBoTdSdwfJekzyeijyDwBYIjJaZsWGHrAtvN7Lay6/mtilQVttAKCqsKAxJyfj7ksMye604bKox9dYtJ79XE+4GxwyUiJgXIFKeXHkM5Jq6bWoCvWADxKyCQUGzNjTOr8+fi/9J2FNcyqdw1NDJfuzOZcVRZi2ttAiVEfKKC1wOA9LFmOzQ5AUCF8opqSe4rItAccR0ZV3DBKh48iOWW4bC/agUds+x2aXY8xhsBAOR6C4B0M2lyeOu3XygYXU0cB28RUVUvdb1xEJaWwlX9jdR8LZBTvK1aSKRxpyREjqj9RSZkTDpMxTZeI2pnsorar20Npn3Sob2DyOgTPZI8feSQ9kgl0+hRhbOG9h21aGHrFg19CSkXFAABI0t90mJQfiZL6JSmm2pjrhwi0Y+IrkVIxSW8KAY2Egs6vt6aYIkQiKuR5MlxsxAUXwyp4EGnwAbuG5yWMr0lkVE66K6PnZ2TQNx5u9DDECdCt9QghYOx96m+4kZflwTHLXaYw74vId10db9oPmY7q674IGYOmmy6TpOadzeF608TmNofvxTC9XcOEVEs5fI+I7xkZdU/jVD+5kih5MhATcyGatuME5wD/l3EA1ET1ivEOxns0AELXwLUjq6echxuaSTJpALO2pcP3iDhRvgzM0pMnPBB0+pTbz8kuDRAFYxCoSeGqkzZV2qVqp+43St8vJjfnqun9zMeTtCxX9fH84CIpte7jHR2+d0Efr8wzpX08IagWfTzBa+jjAUjCAmDZx+PcgTTz8URluuXjcSgfwVsJDSScG1ZLDcTasjZw9T4XAmz08QAsqqYeOx4FKbXayS5C8xKWRoWDTnznJ4op+Lzc9zxKk6otSGK7BWM17qdVQoSMkvnScPGpXYCUIiXpVAYvV7jJclE2ZH16WdvoLISOVRnOEsgRXKx0QuOA0IDzhzhKUtd0L2IYEYd1BGw/8sXfomKO+baGaNRyXLKU/5xKNafM+VkK43Ns+EY/6fQFH6LhygZLpJ1esehHkVJK4ikGTIyXJJ4LDY+OogfcyvBhGep30hB650HGwhNfuwsBFO8PRePkfMxBhdypBjAJ35OXwep9B0DxXCuuEVuJpmV9VBvk+eiM61CUlvNepDLVqsMg1ZYAXLgS0CS3VEXFGoLKJXXsT9LooqpYJe2WXK4C1HY5PKZplx2+Ujm1hNqGbx6GALipvYrTRCZ4z7YLSDmCaOcB7NNuyHn42N+QqlTpEKJkUaSUFGqxUSUlbSYppGKonolJvSXJeVlh9BjVnYm5kAwAg5BC07zJuaUoVuPz1sCFHLZXKqYEdjTo4/MQolJKqvRJPySRSNbP+iD+gAOVnCblQMlIPskIvSUY4qmQUbzMJHWU/jyJCrDTrkkKgK7JOaS0MkrukU33zRzXl1hQCQcT1bqSeLnpEMaeq+C5OGDjHWjHqg1BsD4ppqTaGYBZ2yKdUyrmJBOSM4fsddEW2UxuHqOSqng4WOiXSege+3su+Xis2LufPh4A2Av6eGJjxcczloB+fBQ+no8qqSUfT5RTF/Lx3LaPx8UswqV8PMl1RcZicExCNcFMfDwXAtpiPxfriT0ePApSSmOSoDXOpwStIj80dppvSufGiGF7uiRo6PcI4wC/32Pc93D7Pkk5h5dnU2lnLInsegc3OAQXuDyyC2meDRYbKl0y2UfmVyBGLF2bitsyUaItyyXm2LaWp7HqCLWWv+9HNDt+Qbfew+259LJNo04uyedFMUWt4xFIgDsOfXxRx06o6U6mzPlKzHGInxRrHBU/3rMKaPAe+9Enmed+5E+ZP0rmzyKjLoZq7Jk9HweXjJQfoxEco8RTGbEUjaKYdG4Kxf0uRihldBJA6vDpkUjpEDatBZGBGz3HHjecNFVij0XiKdMnXR6pcIHl8t6HNHI7kIGNOaVkBMKrTOYhZLWUGH1jAkKk2F9X43bX4I54oUKIISYh+Njx5jAYM/Yw48AJWduGl0l1JVW5qVRPVVTcNZiQGmKOmNim4zui2XVcNahTCYUbVkoFavhZqE7g7eIQiS7byJSQQq+APiU6p9hXIoCV5FK4QVQI0ZEDkPKtTA6R8qWQUkzZRNTrMuZJIbV7yg5Wl5NTB1quSKqvgQC4GKIejOFzNgGWeHlrCa0LIBMHhWJf4DwmvqUY4gfkkuLidIhD9Uo7UOJkqQGyWT8kaCJq3v8Q9VSZQFxG8YHpSL6E7eWqgDTJhyIhiE8muaTshJx62jVJIdUSj/yfNIQ2DqBxUnhOzGsJUS3F90e6SxNfrVRIeaSE53RymvuXQOpXwnv+f7zn4h5NC3PiYKUKtuTtcXNCStqV9I1JQuFVQvyUS2r3hInP3ZNojyyC7fKA0UUIqmq/bhYXVPdOK7DH737Mvt2glFJDP/Px/DCq6XkkoB6Wj5eqr98DHy8lOL9FH29Lfarv8eR+X9LH04R/6eOVgxcAoSWeHzzfNfbxPMjQRHjgg/LjYnqWQ3joqVgeHSkFYCrbXFhuFlhfSXCeWHPvmJDyXi3L1RTkIU8jNhJbHOOImT2PYXpRzul6NzFcsoxPLTPqgjIOllxuaSzVjPJuZ4DOcu8KAPnIkKuEo8ESjHUwLsQSuUgvdornbawu6exhKF+7EXm0qM+84/tY3OOle1tCYo1F1ulCXu4io64T2EnlhdlHmHOJJU4xxiFLO2cSz+Vk5zxfXIuMtsURz0AGJrDhEpm+xCOnqTfw5BG8SSOlPl1HDOeLeabKUqYykuAkWbkP8Carp5buq5z+2l33YZpfquKGccDh0yPGnOhcq6IodaJFPZVCm0qZ9VInuHaMK+4Kun3G9mwaVviRWobY3o21x5PltV3fPAzF4O+p7UpVRLVaKjr5ISniorJFhVAFMFlJafcewS6/XxMZpfcliqiWlSw6d1RW4tmskgJyyNUxlyuZfRGJqhB/6kUpFUAGaKyBCzFJrYVKaiutt0Ef80KNarl2QnpjMDgP4wOITBrBX8t/IpB1VHgYQkLJOnGc9Ah/a0lV/51PNSElCc4t5SqEZAznkTJI6igbE5wbTJ2eo4W7MRQ0KaaAyX9pAFZMSZ9TcmwBXK4eiG2Ak1Ob6CAvtauU5LlVbUnlcBSFlITJTyqBbqg1K4F+/7CYY7PELNewm/h1eTOVqDz6eFPF1MP38dwwwqK5VR9PL7+PPh4Q/bxL+niDy6qoLR8P6Z4Erq4efbx0fhLtUthUnVeKt3u8ES+Pi5RaM0aSS0oYX+04ioJKh+5JnLGKOfb9wCqpM1ZJDa+WFVLD2Yhxz6y5SDvHs5Eb7tmYjJIfsgTRBU3Q5AdoqTiDEOnWePXdoN3ziKTtuNKK7SzHGe8psucG3jWJeXe9Q+M8bNtMiDV5oUtyvK5rmQUny1VSAJbvN+B7Zoy6xzHR8lIyPFHyALEqQyRhvKrI4Dn53T4y5OejT+z5O/sxMednvcPgPMbeIYSA4XyMLDobp8Smjz4ZKDFYXkIywTJe+e4XkouSIgGSaiV9KMnmyXiubOM4h4MPlktPh4DRGDQdV+OxBXt+Pk7vk8QbD87Dk8EQXzyu4abpPFfwiYWD8r1FfgnovFPT+x9U9YeKG4fKyQIPBOO5zLqMGIOfKTo5RTg5BYl0OlYiSqO53S6HPsUQv0mIQR4ynx8/QjtuJWqi89cPiWgoYQgBKoRPvycNqzbzd+Q8Q21U8nkHnHAOIIqJZM34FMY70OlT0O4pzOnTrJCgZqJsSe2Ups5hxQ1gjTzX9gpirxqAPAw6VUjBcN6fpkWwlpUHTct9g7FHiKXSwxhDr8S+TY6lSEwgKUKFiDKajDpRic4TsRCVdlElBdtM7eICgZByH0WixRtwfj4K2DU2OUJtdEwGH2Ii9PzdhYDOcnlvVkU1OB89nsXwk7PohPajx+inVaLKsuVXKV0OLJct1+XLmzgFpHx5zn2S1FMmq6BEFdXarJDaxSTq8puGTLp/NiqkJK/UpH0tfWzDREDwgPXcjsA2Q6rLGs/qjeAdTDMg+F2MYMhtykibApbb1UKbEjJK3qOSlwzdSVbb2aiYombdLpWodurusVaxUvkmnDN4SGF60qYkX3Dp40kOqSUfzw8OrvczH48VVP7WfDyKiqmL+HgAJgnQr+LjiV+nv1+3j+edxzi4mY+XU7TcrY/nLME6WvTxcph3zANGnF+qjb6ehO8NPsA6ziHM95GjYcqiVgAefbLzx0VKrUFIJ2mAa8y6Kmk9qYYiiqkYw56+92OuuhDlnF7JN8U4CWuuGfRxcDNDdTGDlUew5DfWABSPk9CxcdIJ8/zgEDwbs2D9RAHmhwGENimmeEQqdhC8A7xNy7gz0BwvrYU2YPl6fUBSE8kgwujDMnMeO40T1jwy5yLj1IRUXsYjAj52koN3WRkX52fnWhgsKoxWWt8QMAKA55HQMTLuo4FtMGXxLaVOKiCdUw/nI6tOfD/gc24pH5cDooiaMuYCyTtVccc4IjwmGAMTc2uYpkUQhUDKMdVN80otqaSWHK/r7CDXzvbDxTEhWhfdVyQsJkntJP8QMG2rSS3VInilflHqlpQbbaud1TZ4t5j8925icwxZhAaQymmAKKSi3fKOQ0gSIcUhIcHH/oNA8ua1MWRE2T6jFC2THHx2ShxkAoGmbUtfSpRHaZVPIlSC4dxSlEt3i2KqJVZLtQC8NTzCbQ3IG6BDIpoAwPmcAxIAOnCCdOnPaKXVhJTy0/f4Eqw68TKRbvnRZFRWSNmUxzLljiKCpViynGIeqTiV/mXKK0Um/c9C7MltllNbGr3P77q56g7UwNjA/crYlgDw/+yjes8T4BfC1lOfrV1sU4Yo5iHL7UgIKQi5uRTuSQvv1Yr7g4sW6gjZl8mrXCKmJL9U6eOJbycf8fEkd9SSj6cJqZvy8dC7RDyhszDx3I718YJzCETw/RiJqdv38bR66LI+nhBSKXwvEmq37ePJsuR32mzjz0ePLoV5T308r+7L6r089p5jJq56sHg4pNRFOtiKGZ8QTlopBSCVuI75pIJ3SjEVjdUoZUA5sZ3vY0lQSXgXp8KeT1n0kVnes8j27lnR0yvmXL7L1Wm5ozZegBodMwBick5CgDVA77kT0cnIXjSMrie0pzkxniTLs53FQGNKkC4yRheZ9HHfw7YN/DDC2sigO4dAYrj4flGUcm5JOn2I9eVSqJkkwAspNM2HWCYzBFVxwU2S3vWjx9ngMPYOzvkk6xwHDz9O2XRtqISMEgMVJBTTTY3VUgUpmRpr4eJ9MmRhmw6GLPxoQTHvRRPZc/6NJDzl+9qACaR9HIU8612SeXJH0qfO7dD4lFfKxxeST59YNhSxsh6y5NNL2B/Yb/QBi0z7Gnx42LHId4olEkA7dIH4T6GGR4vdiGA7HrnvdilUgcaeO89SXUopptBw/p3ZKC4ph0yOG6cT56xQVV1JJVU77HePKxBPyUEk4ved2lcwhlUySlWcwk4JMQQntkN5f3YnuVMkKoVxYFvkHedtOX0aq6g1nLtlSdmypZKqbe5mMPnvOWcGQlTNReIAwUdVWyYSzQk4rxRZVh80LYz3XE5c+k8qDGtNzVCqWhIhVeaXilXbUoU0yUkWiYWZgnRB2ULGZNUwBcDHpObE786AgCed5XAMYzB4E6vgGgwuxEp9AW2sHDU4gms5Ya/0ZWTQ6VwppPLgE3eCtGJKTwEekFtCs0JKATk/JfclaEJQnTQqfC+STBKm11qTVVBpCrQkyc7j1BIaAgwMWmLySVRSkvh8cUBMv/uU6o7laR3gx9ieDPclk0IqEk0xjxSH83Eus4n6bqM9JbWdtKcylK+d5pEKtl0gObNKavK+rLbo7rEVtlemcFF+YBj67O+JstP5VR/P7XuexhxS45nDuB/uzscjw6qaK/p4bmD3/7p8PJlK1Ebp47FSKofuHePjiU83nLukkLqoj6f9upv08QAOuewbnxRSSz6epUgyOkl0noUHzgc0BPhYRR1m6seVeaUeo8/2cEipQ1jLI7W0TJW0lDAGzZxLPikhpnxMrMhVFwYmqWIFBjeMk5KfwpYn5ZRSRw0uJCOlDVXJpANzYxVPHIA2XLlqTGs0AREAEKx3aAA46yOLzteaSoxGZt0rWafdeTiMsM4jkMQj59jr1Nl0PDoanINplKQzeAB2ds+1xHMaWxsfyDDv1OmOXSoLGg2REFLCngtJxfkMPdzI/6Prz/hYY8+GaxiU0Vpn0AUTo5XyD/g0MmdFHk4WXmKSRzZYMmpoRgNjPGzDEs8B+ZqkrHTXsNTfxpEEMjm/lB5x0PBcU23xPuuWIEZtKVa54mahR4eD8TAhLrMN4AYEakCxohXaQZU+l3CV2IGWnBdLYQVLDlnZgT7UiS7XV/XKw0dJWh1DYs1C9jKS+TCKsADmYXwAv0utTZ1bDkHlCld+0oaFIM0hpjWk9I6w9N9HVVy2WWMiqaZkpVUhWJQH9lKekgViSpNGQkKpfaVKjUCquJcIqEhIYSFsb5GIX7pcICXsdt7Edy5Pu1hkhIhAJoC84RCOOGov6oWWosNFPr2vWyL4EHDazRUAWh0AaFJq+lyWailbeB5WqigXiqlmSTlVEFFkOLG7jXmjJDxRFFKtlRA+XtfFxOYGEqpnYAkpZG9VnK3uvail+GSl/fAgjdiVEDzbEOfisjYp7ibhn007DdlbalPSb9O5yoTclLYkJJRW3Nnm8Lu04t5gtfK6hiaktB3y2cdzw8g+nFeqoqgsEh/PRbGBKKSqjzcNJNOXs+TjATjaxxN/ThRSkjtqy8cb4/QufTwAfN4xdG9q8/0kfzA3w7nwAEJIHcBjzSv14EmpTaOUHiAkxlc6UrPtBImYUgnOdXlQJecMLpf/TEZqcGoapZ2949KXPhusQbHoANJ0S96pZZ0ylQ8IcZ+yjNl1DCzrNC5LO100XL6zcIODsQTbciLSLOn08DZfp0mdTHXvdGfgiJeDDjXTPxUp41TWOc3DIN+TpDMSUs55pZqS0D1mzYN3EzIKQFq+FGusDZeWgoukEwCo6YCRp0Yl2DRk4aJk3FCU+MdEnJNqOZbJKUmIp6+xHz0aMpOE5w3MpA2EkEcl9P2TkYnpvV42WKGk2yuuB4ecfqNGjAFIctdg21TRyjiXqkuJYkp3oHPFMsOde2CxE70aGrWkkqqE1MOF/B9r7W6DmFpVS0XSCQArphQCZMQ1htYEyk6lJigAwDuuouUcV03rdtyOo7plMTfaoeusuBmU7QKKSAc77sGPMDZW4QsGnHQkti2yIGsRmo5zAkXHxpTv1hVSKr1vNYGQElBLuGehEp3kI1uygfM2YwxAMLBRJcU5GKNqCpwjBN4gmIAutnFrPCsTYl4p7zk3igvIZFQTlc2Bq00BiOW/87zz2Snj+eXpIZQqqTSVULt42VItsI39vpaIq+VRSU7FdSlszyR1lAGTdhOFFCFW3EOqukfG5JA+da7BUCajFOlsEENngkm2g23PGAdjGlZmFO1JQkUnTuZCm9psT9H2zCo2CiEly9eI8mqL7i9Czok48Q2DVzZIF7LKPl4O04sqqZSe5TgfLyukjvfxyvC9193H62PS8uTn3aCPB2Q7cVM+HoBJkat8rZFvV8IDfa/KCBcWF7we6VkeNim1UBVgad2q7FCRVmsqKSlrnMrQRoWUfLSR8oWxkuX7kTswZy52ZMJU3rkUb7wUayywJrPpwqSLoUKU/sF5dDK6th/RtBbOsporVW5p+QE15OC6mABvJ7my4gM+jHCWQJ0YcIk9jk+ghEUeiQAdsheSSkrY475gzfVn0MZpzMz52LsJe+7HHn7s4caepZ0uG7CJxNMdx6J7IFVEE/Y8TeOLTbPtwI5LiEZluh8NvOHYYxuTZvVNnCalFGVjHXhkVu4PEBl1/X5Y4ZUCsiJqSxn1GGWf9woTBz/2Yz3YyROlgXSCg4fpAEpKAZqM5i46Y7TljClCKr0p9aj1JQmpioeJY4kpABPpZfA5lE8vpoZ/YxseWY37gR+j8mBkgiLmcTQA6PQpt2XbRWJKOX/iFJJqz3KeFTeHJUJT2oYiKnUYX6qeFhPemhCyPfEjJ6+O6RMg6+OI+ywhNZAJTCAppWYKUHCb4+2baeJpbRvLdfpSgVRlzxseG2AxFBNSJhJTXCAkwCOSJQggQ2itqhoVw/b4uy2U3sAgFYzF4UgqAZXQvCCmjuSj8m0Tp1X6cioPlqzT5BNXzmPFheSI0vO66p4lIZqYjGISio9lDCb7ZMJKyL61kyV+95GFCfH/1O1I3oGqLRk/cp9dtSfp36f2BGy2qURoApP2NGtLtpm2o4KQWg0prvbp/sP7KTkVi1mlqntlPqmomJLv1cfDQR9PEyrA1Xw8rxRR983HI6WYIjpNiilg2cdzbb5GqcwnPh4PVMRE516IyqmwwCO+l14zPGxS6rI4lkSJDDoAlnXqRHmqNKgkMdff07LCMLGUMzfAyyilMtOQxqjjdqKq4XxD+ThIJUGttYmFTufp43cbt1mrMqKrnXhJgtde+v7qa9UdtqURQ0l8p0sn6+9i/IQd10ZJs+blct7PulJKE07BOaDtJp0v3q8t9sPlQjlKNMdVG8/loA3Nr3Oa+Dykl5IX6avK4SnJzkPIktlDo6wX7PNWXAeWlFOilkJ07nlIJlUGTYl92y5WRFlQBmhnLO3zgEIqoiqkHimuQzG18ptFYsoQjB8BQlLOSAgOn0ufCCkAuaIVNUw+6ZCZrespv1fcHMo2QsTyHmWzAKRcYqxY8ZPeSCKkpG2FOCQcPFa7m0I8qe8Twh3gdgPkxLMLYVaz5OYb7YZiyISJBBX7cCYqWOPlRpKKCa0Q81FxwnNrfCScEBPX5jCVNjqEehmQE/7KdyCvExwip8qBpLIan5BFsk5IJ9lmTkzFyl5RXqXJKElkPskdpdRQOcn5huNUtCkdxpfakfGcYxFxwAaqv6Lak7SvacGi9Ta12p50Wyptkf7wTZxfT8XjQEFU5OJOLEDIm13Nxyt9verjXdzHC/7ufTw/9qlan/eBVeALPh7AijEXAixyGN/adS/hooMUjwmPkpRa7GALqWT97EVpQsisqgrd0wx6IqGirFMecj/kmOKSQR8Hp2KLA4ZooPSyi0g7gWkCvIm0E7wcoMymF7eBBl5gO37k3ODSvFRvCI7jjb3zHFebZK05+Z1GiHJqqDxdS5D8R74wyECWtJf5F85LBj1N55JOP3r4cUjsuev3CG4p3ngq7dyKOS5jjfO1ANR2s3KiTsUkA10crSB48vAj/0mNpzgK4AGVBO+kIfSjS2VEJekdmdyhdSuKP1m6cfsrbgsr6oOkloohIQgGJjQqObkBqIEhFUIAzImohY5zctRKhcmSOmqpU13JqMeDLXKqXKf+24ByUVbKMDGF2GanZBY7ibFUtLwDgk/tF7bjdbHUOppuMWwm7a+qEW4fuj+kCSKA80h5j0BY/v8tkJJVh5a3VaoWXl60xURcLpyHbKLJdVK2bsv+JQXMQtEHCJFiYAE4sMNgEDjnShzg8dGR4HkDjxCXc2ifB2/XihODkH4rV6xzhmTyCdP5Faf0ItAhHpp4irdgOh8VUPk+ZPKJ12e1kzFGzU+JqDJMT+blOEv0VEqeD0042RzaKfZCRy7EtqQVeXmHC7ZtqU2lNrPSlpRiiufVe1QrN/WyYt8VDwTybtLRMEASHvh+hPccupd8vaiSSvmCo89X+nji092mj8cbXs7H8ynR+QhjLYfztc299vF8uF8+Ho3cn9E+XvCEYPgabMN+Xg8U15wrrVuTi1clYsvjtc/9+zhIqbUHJY6yaIXTZSAM+uSQUSXF69UoolqmE9pNv4diGVa31dASTpdaLk/L5fN5xTy7qfJGX0fwHsYSUuUClxP+5R8sdDIvCGG9S0ZYKs+UjHLwYXE6XbbMiuvplrHaknjmY2VppwdSvgMt98z7mj9eIYTUEdwqBaqxnBDxOJSHYNFoxa1BHD4hprTzJ1WtRHUAgIuITxUBa6qBxU5yQUhdKVSvdrwfLpZUenodMCNOofItbuWamqimDAF+zCE4RqkakN9QgewsZ8uUhKB5G664O+j2E8OvAEz//1LxAnBMXJgqDTALQbBYhP7fi5CpCRkly5dsG9F82wUQYuQeTMzVIe/G6byN6iljomoqdrtSfyoqqWDy4BAZE8MAp0QVUJJR8/f6oYGlJYGDVUqlRE7RlCQSIomXTYkovY+SjOJ1ed+LBNXayRaktyHM7EmJZEf4IjIButmegMU2VbanrbakfyPk1dJ+Kh4Gjsl/tNLfl4H4PJ9VUr7wBZcIJvHvpsuu18db2u5YH296rSq6Y8HHY0KqfW19PMSp9u2m+2nUsgBPrOwqfbs1tZTzAW01LxM8TFLqEg/IhfflszoKQCZn/FzaqWWSopryzivpJndwcozxNHzv2HhjNkaS6C4bKyDvV5YnSScMeh/QEVdgIBDHHMfkfGRNOm/jAl8nRQPVctiiPIqSFFC+J4M1u59FB+EAp6JHFnm+LKWcJZ1BSTy5EkNMihdlnZL0TqSbPpYPXmLPD7HoJdlklIEiIQ98Po6xLPEMMfbYxUR3nLuTYOJ5GsPnbBYrNOTPZSSczgd4Mov5pipuEdrxL5xvo76zI088Wtx4hFQie95xnoXmbSmi9Pql89o654rHgfL/XAol1etKkih4SNZkrT5O7z+V5Jq3CdyOAaDxiXDlbZbDUFfJqEfYFjVRd++wYa+kHZiSIFBhVClvZ5FceIJDJOnSsjX1lJ6WBPwCIUVmmkfRgxVTAbmCkXRB5L0b9PeQQ/J4nZluK79FmMzrbXj58kv9oj3atafDTAgqWaZ/ZybLMlEl8/P1q+vU8XRY4SxXpVbfLbWjMJ1OcsAW6yY4tj0t2JVJzqitdrTy+/uIyw9dviaY5BgSv24aDTP17Xz074pQODUvflvvw8zHK5VSV/Xxei+qKTPZbs3HQ+9ALW36eJ78po+3+K5a8PEOjZtfh4+Xqu1dwsdbJSAP+HgOPWzTTXw8Qy75eEJEidLJKEJN/FIhoUY/JaYc5Xahp7NzfM2UUw+TlLoMDjCk+mFc23bOnvvEPPtYNUCjjB3mZXMWfe17+coliLE6rJSC2lbgfeD0DtGwHkJJyN00NJt8LCSmV59fCr/008oLvG6LRZ9eoyGaMOSaMQ+eq6R5z5LXZRZdnWc0tPJdG7AtpNKhR6ilLqOoes3s3e2jDI3RahRCTAALpNCYoiO86ogBx6mh1pYds67icaEkoQ6tK5QOs029WqBC/ACkhMZ6XzqB9SQ0pjx+bZN3hwV7NSEVsPD/Q4lXJioWOw27OhKT3FBrBMNWaPKK4k6IKfkOKHIqBHilGpLTNkIyiRlW1xdU/yq/n83kWSlf8WEjca0/klagjTd2ORilCSIzWZ7nJqTVAZKKjz8no/L25QnNVVGzdrQUKpw2lplraEvTgx4mo7a+V9x/rBGaB33BbX+HQ/myamrLx8vLru7jia3RSqklHw+IfmlZwm0D1+3jre3qWB9P+zvi42lc1MfTv9PY8vGmv5v7eIZs8uO07+99gFX3vrzmi/q4rxteH1LqWCw8Tb5YVobzLRE8+iHymEo2gWW2XG+z9ExL6NWavSmNnt5GDFwpP02yTr+gcDoGlzBkF/mJfoCFeV4jaLakmVtMOU/nJxW8T0YLAMrEeMEVxuyIMNGwEj+XGfSNl6FKGLi2ruIeouzYykgxAFDuOAWwImWplPmFyaZj1le8vjiGqCzD+yKSpaEFW6XbNrLC6qGqD15LLP03C+TU4v8v28rX6zyXY9dthe0lEomnqY9kzOK5+hUSaa54ytsd07257tf1McrorafMrOxgqULv0pablXxXiMWJGmrt5G6jLV3m3Vpxt7jOaBnZ5YJfp5etCQ/y9yVy6uZ8vPm65SHmu/DxLoIlH28Nh1RP679ZvoYtHy8RXuRgVSJ0IyR2SXiFOTlVCg8OEVJXSdXyWPB6kVJFI7rMyEtJ6pQoVUjHJLG8SENcM1qaPc/qqTyVdUuvWr4meeg8gr15VZQ+74PbLDzIvmCn12KHvZ8qopa/H3+9Syz6/NzYiOkqfGu4jDqsYo4HdQc3HPIHdR0PGKH4Xu/7Aq6B9Kz3NePVS54+zHuy9F8/HMf9+tXA90tffEybur1e3dr5rLWXh9OO7jMMgKdPL//7ly+v7VSuHysv7FQU1OflJhjAGxjHUzonwBPCmYU/swh7C79vEPoRft9iPA8Yzju4Hhh6h3EIGAfDybdHh/Nzgvce544H9Uc/YvQOgxuxj6qoM5nGQfM+flwA+pCrc7oQ4NQpr4EADAHoYODjdweumAnPqqlgCKPnqfdASwbGE1oYeN/CDAad62CdxdA36NoWdmww7luQbzD2HSg0GPoOjWnRNi1s08L4BrZpgMaCGgtjiVNMNIY/g0GwQBgBB+BsBJwH9g7oHdB74OwcGDyw3xvsXcB5b3DuDPq9QX9u0PcGw54wjMB4zgWfhr2BdwTXW7jBwPWA64mVUoMkOm/gR48wBvh+RPAt3DByuJxrEbyHHy2AmErFA4Dd9vGSqtsCwcKHGMZnWiBYBGoR0MJ7gkEDHwieGhhj4a2FCQQPgocFLMFZi0AAGQIcYSTCGAgDCAMMrDc4M0DoDM69gbMGzQj4FjAt0DjAUkBoOHtjZ2NKPEwLUgDLYdYyfx9xjH16vUipUpJnzIW7FmQJW3oYYw2MNcDA88coKCVG+Kjjp9/M9yHL8/dyurJPmzsFxlJigm8D9oghPrvwhBEZGDKJmNJVFIAhhdgRWRVlkmOGp6onOpqYOkRI8bnJuRAMmU3fzZJZvL6Kxwuds2S2LNqBcuT62BHrQ7+pqDgWh7jyZWVJsU3RnstwH73sseM97z2961OoqKh4xDh7dXbp31b7dA+hR8xuk1WuqLgBvDrCPr1epNQxWCBkiGhiD4ydbmMW2B4ik2RSBADGwJos3SyT1ckyxCTlMd3MdJ+Q7ZZPXS9fI62oOHeKG16aiLrE72jp4lZgYzJwADBkQGRAxiz+3JBdjRdP1RRWli8RU3JPynKhiQCz02WcBG/7fqwRVEJM2Y3fU+HYLa2ruH+YJLrFlIDyQCpFrtdRHAI8lNuDl+WFum2IXankVYXGVckmIZq0tcxJosNkH7o980hfrtiVEkzj9SSpKioqKioqKioqGK8PKbWmcDEEIOYIkm2i2ma26URRZFhVZA3gmOwpc0sx3yOJ6mSZUWU9cxjelJzCaqidkE2ynzydLpdt9T5IygRHNVdJUJUgSyCiW1NPXVQ1RGQQKIDIwKsrZYWSTR/hwEpyCVD5obxbvMaSjJoqslgVVe5z6TjGsLJLvovS6+A1JiLqCEXZJYip6v/dLEpCCshOvS4L7AM79qKY1A48/zbWewq5LUhiXlIqS6/+UUq/zVj6v8vkvxWPE1tk1LFEFJDJqCUiSlcjE6IVYNtkDLdjCwNvVLuN+Xyk+ekqaY8Jn3jr7N6H7i3Zq1LFqf//NRKyvNCtnIeL7zZNvi+q68ykChwwVeOVibfZdvpZ8mPDWXQnyxB8DgsqkyWrfeSKlHGqB7VUzP4kTcSxFQmPxUaludWE8amfoxJ8H0gWH8zCOl2soKh8OGs7OK4dybb6t5OFChdqUyrMRc0ebEd6W73sMeITb11eZXXjWKrqGXx+Br1Pz6YJAPwI43rAj6D+FeB6hJdvw5+9ROj38C8+izD28O98Fn4ccf7pl/DjiP6dl3D7AeO+R//OK7jBo3+7hxschpcDXO8wno8Yz0b4IeBVP8IF4OXo4QGcOQ8XgMGHWfW9wRfV91YuVTwH8eUoTrvY+E4twRoO1+vIoDVxSgZPGwJZQvu0AXWE5qRBe9qgOWnjMouT5ztQ26B7fopm18E+PUX75ATtkx3o9AlMdwL77N1A04CevwnTnsCcPkNoTvjTPkGwDcLJMzgQzkYfw/c8esfXezY4nI8eb+8dBh/w9vmAc+fxsnd4sR/x4nzA269G3uZsQD96nJ0NcN5jOB8x9gHj4DD0HJo3nDu40cH1ezjXc2jeMMCPA1zP7Xbsz1L+qGMrrAPKl4sV9fK0hbEW1nagpoMhC3uyA4jQnjyDIUK7OwU1hKYlNI0FNYSTUwtjDE52LaghPD9tcdpZ7FqLN5802HUN3jxt0TaEd500aCzhXV2DrjF43jV40lk0ZHDacLhmS0aF75kifM88Ojv1MEkpXSHmphDJGPJRLROJGfmkzaxBiOQUEz0GtrUILsA61l7ayEt1xKU7M4lk0NGcmJpWX8gow/CmxFQmp/J8NmbWMMkk5yjfDellQrTRTA0GzEmZxVC2xaSR27fakgF5o+YpEVTyMWRgvEnkTvmheE5SvtNYD+PzOVIbl/s5gaSJqXLd2nWzKkoRX00HQ5SMF58LwVqCbSiRUKTOWa7dkkFTXK8lcynDIr+rWRruFmvOXVCJLgcfEhHlvEx5JZOzIT/zNHW0jJl2mpOSClmd6cJcRbVFUJWkxUN+sVVcXBG1pYbSBNS0PU+JCUm5mDreqj1bY2AJaIjbViLRfZh1rh4jUfrkCvlebhpb9grATNEJ5P9aqzytbCdV62SgbfPoEuKZl0iFOZ0zA9Ad87C8DLmjbvTvwpQ4SmRUcmRDJpzSMkVE+YXt4rq0nVkivQJgkIugFFWiJjg2t2UxeDbphxUq7sXqrWaBjKImbx+rY862U595JU19brlYhw+ZkFpSBqdtFpZJWwKmlQkv06YIZpZ/hWJkgnR1y3bEv39YTt9VSO+r5KO6cSwRlAErOaUCv1OiM0WNBxy3oGABf8bEBeck8vC9Q3s+wg0j2mHE6M4xuh7dboAjh/OTczjj0Q89HBznlBodHBzMOMKFALJMxjRx2iOgM0DvA7oYIWMhg5FhMiCpseXjiR/XxXdpl0ipTFQ9aRsYazh/VGvRngbY1qM58ehOPGxn0e0sqA3oTlvYFuh2LewJoT2xsE88TBdApyNM18CcOpjOw5x6hCbw58QgEBBOmFijMYooRs6H1DgAA8/3FGB9wNjw/GgDehNwag16BJgx4DxOhzgNCFxEw3rABrjRw8ODWgAmgDxh7AM8xe3IIHgHSwbBG/hhq8L6ko/nos/G66jheWrFX/OgxoOaBvaEfWbbBRgC7ImHtUDTAU1rQA3QnBAMAc1pgG0CTk4DmsbjdEfYPQG6JuD0KeeKOukCdg2w6wKetITTFnjScR/ptOV20FlM3mdlTqmHZJ+OwcMkpUoQ5R6ShuGRHLL2SsbaWJoJp8gSgo0vPhUrJ9+NNYlkkrC8IZTKpjAhpOIekK3uvGUdq5TS83pqyMzCDUmdcw5ZIyZehIzTJFWpKLpEpRKKaiEqjHKjyJrpIcziVOeWmuaLGmaqpsNKqTlhVf5OT0mRUnpd+X2yT00SHGk5LqOASscoflrJqtvFkoPH8yF1xDUhNfj4nyWvPHasvUkvIB8CCDmENS2LpcyNyQURdE6fUoGybmXyOT/kl9vrjMuqog4poqaKh+kyISNCUNVEg2wfYky6gUHgfI4UEpnKDqtJxGltdncL3X7WQoyFjHS6PUDsmiybt7Zkt4rl+t0YVPjyoq1Ty2xcZmNYP6m2tISjCak1Mmqi0BjjCfukiArOAeIUAZlsWiOk1sgo2WZN5S9VoKD6G5IrM67Xy5mocolQMpqgitUVeVlI8yAqtP7xHoIQkkIMwMIg5hYhJeuB5bal25LeltdNz2atPQG5TQUJH4ZO/RDbWGTUJm1LOX71PfgAcYRPYmjZLywH5bXgIBTtXEfDWIOJj7cUDRP3iKv6eOV2enrIx8vXT4m8XvPxkv24Qx9Pvt+2j6e3OW6ZyZEtxbXpKCB93TWf8ByPg5QqEAwpaXVEfAAnoz6pXLVJL+1AMS+QqGGch7EE2zZwlkBdA+s93J5grAe1/KK3rVSvy9X3ggvA4JL6aV4xb57kvCwhmrfL82VS85I9twZJ0pnYdGtALcF2lsPyWoJtLWxnmUCLy+VaSZFR/LHc4UmdnAWjJWTVggGj2I2REfJBvQ5IVF3xwe0aVkqdNISu4X2dDQa2IXjnYRvuXnhLcPCwlufJt5Nj+rHn/8E7NJiy5cEtyzrXDJdWXGkyipoO1LJKykaVFDUdbGNhLYEaVqVRVE0Z4uuwDfH/Fa+Rr9tmpZTJU/nPrVlOzG/ACoRbzE9fsYIlMqDslEu4niikeiX5Ph89yPDzCgBtkJGRwIpBI6O+IdmEoEKjItcNMgYOaqQ4KR4UKSq/j/NLbesxKlYeMy5CRh2rjJLflsoot0BESRsXFeAYqxGdNIQ28AggYGApwATDo6JRxbBEJtT2d/NYynm3FWIsBILz/N87X5IOgGheNMmwBRMNVx71NWlUGEoxqkeKRT0q9k83Fors/IRQEDLJew7vEaLJj7xOCCYX18lyTUbFvoOQTryv4ruoosYhHtZlgikSUKnPockptxbQU0DlskTpSEknQDtNDfeLch8uklXqO7UcshLIJnIq9ZWp4ZtumzTQC0Mw1DCBRYBxIyumhMxSkLahieqldqTJTOkTX609QeVmlPaU1XSW+F0q7039HrXg96fQFmKXpClVouqBQfwTKXZkLT+fZGGsA3UNgvOwXQs/jNFHYvfYnBFsB4xnBGNDioQB2Mcj59F5c2kfT39fIp6u28ejtpn4eNQ1yz5eunULPt4CLuLjyecYHw9ALGh1dR9Pppfx8ahplY/XgshMfDyJgLFN9vM6W0b95Hky8mESiz/Zdr2ueJSk1IWwxQBHg5U21YmtY2gfWZYMGsshaMKqJ3bdGVjPHW9reOhIh+/JuLCE8UkHbMlQlfPb4XuYfMhS/MQwMhVyKCF8hmg7z5Tq8Eyml4Q1gFedzTVFkJAzXkLfovJMQvr49ybmmLJJxRTIwtgoo1c5oLZE8qtMus37NbMPLcybaMNzcnaZl9G7JfZcGyy5LyXrLtSUkU77gQ4a/6bitrH0t3idPyp2xj2y0zfEkF+AX14usPPOKhMOcwIFUOBOc6kckI6zKKdm5xRCan9Lyqm1dlLJgfuN6wjVm+9zmZCazCtSQlRRiXSNJCsAtBaxLcewm1jW2mO5cMdWW6y4GczaiEwLQkpIBU2uA2zPAvK2sk8hI5ZyAGkbZYlJJAMmDwKyigWUqXMW3IWkHgXY/gUzbTM+LLet6UUr5ZPPxFMiqQoyKoWEjMOciBISKjpKiLlNEvHkXC6mUg6IHRu6l27cdFBQ8qEaokRaSV8Icj5NlxxzKIIKWkWgB2dVfigjeaIMwdiGvwsBtUBECWbVODFvR5qQ0m0JmJJTsqut9gTkNmXJpHY4aU8kJ2FYkVe0Iw/AIKSCOvrKql16RCj7+naaqgVA9IkMnMeqjweX/Tnt43G6FuCQj7dko67i4/F5X4OPJ7ZiCUcqp9Z8vCWF0G34eEsRLFs+Xk7HUuQplvNc8PGASHpv+HmH8Dr3sx82KVXklgrG5BhjFUMfVsiOyYNVsujeJ3UVtcyiG6L0XVh027k45fOwE6WUhSGD3X6Ej0yvC4FDZSN73sfT79NLeMqq87LjlFIAsItTiTHuyKDdNczcdsyi84em07Zhxjwy59S2ccqsOmhKwEyIqQtIO3nEih9YNjLZGFFUDp2PPrHnXUPo1by3hNDE++ulw2LhRg8KTAaN6e+1GAEExe4bsqAi3tgfUErpZOY6GZ6xdsKe2+4UhghNZ/kaY9JBaphNt/GlIQy6VoSJQqxJKqlshG1i0jOLvtqkcXgb4PU2ercBeYJLlZQ4aQE5ZK93AYNjB/6dfky2wRppG9KtYYWJdKYtuGNjaYOYil3olBQdlyemKh4mjiGkjlVIibIBmCv+SjLKhTwFgJOGgCarXFwATGx/Ovl5DeO7HaypOoEpISVEgv6/tRpOlst/70OI5Lqsm6oENHS/JvULKPdx2uhAudhOwoJSClHtkkKc+es051AZjheJJ/iRySfvYRyTSsb1eV0ICEMPPw5MPsl06Jl0GvtMPg1x1L4krOQ7kAis4EQxlfuvfikFxQK0U5mcZ610ABbVUCYqptB2cR3BNB0CEUzbsYPXtGlq2i72qYV06gC4GC6Yk5kbalghVTI4cl2Q9rPcjrTCUrclABjiPXEhwPvLtScArCKJ/aqyLaFoRzYSnDok1GL+rqwDNQ8AMRm/UfPZ13P8DLQdDO1ZaNA2MP2QvnuMsB0/Uxf18Ybotx3r4wGHlVIAK6SsuT8+HhVKsMv4eHp6kz7emlLqoj4eNXTQxxOfTvw68fcaMmiJ0Fp5303JxnROGyHojxkPm5QClkP1BIq0MmQRjMnVQsrtBMpgGbIIxOF71DUwe25wXkLaPCVZpB1ioy/C94zjWFhyAV1w+SXq2WG0NjugOQHe3GhpTCvsZSmnNlwi8WxaC9sq+WZLsB2BWpuSstsuV9jLUk5Vec9mQz6Vjqv7dkwMt5L16J+SQeow5KTfBEthEs4nCc/JErwPoCjxTIbLc66S0FgAnNjcRiPkKCcyL5PgmZWqDHxZRYJzCeuMhts2qjJDwy8oq9RoKdm5fI/STn1N5TWKrHNNOVYaKiEh9PZm5beH1lVcHofUKklxgOzAibrkfPR4NTi8GlwOVZFOiISnxlFdUMy1EUlKE8yiYkqOqdUIOtfUZRVTr+OL8r7iNpOZS7stw/U0ITU4j8EF7J2H9wGvBpfebwBgyfK7y4fYSUVUx4RVeyfnUdvdzUG3E2kXQduqgoAMMkXA6BHz4fF/LyoY70Pqx5T5p4B1AiENxChnp41hD97k8BBRjhIhq1tgkh0MCPORmaXcUT6SVH6M8yMro4aenZlxYKJp6LNCSqZDj6DIqQlxFcmn4D288+l7iN8BpKkvlFKhIKjKwjOkyCiZ6vyf0/QLNhFNIAv0e5hYmCW0nJcljHEaySgh0wxZ5qKiYx8ktA+AMZ7JqOCRMp2npO+SqyrnTpTprB0tEJtDvB9le+I2tE5MSZtKaqno9HGuRg5xkraEGEJMyApkiV6Qd2qMAp28LytZfo9hONeZCYVvaLi/HoCJ+IBzDXnlzxFs1yJ4DzvE8D3b34mPx9PX08dLBawu4OON6jzui4/XKXJq6uvl+yLknb5Xs3PEVFX8mPHgSamELRnxUtlcIh7hii9bJqAKtRRZgBwnNXecayk4D3KemWWAc01hBGfkZ+Y8pLwBAcZ5BGfhyaN1HlFpqJRS0XAZQAzVmtFaSnBHWI47bi0/MM2uiQZJxRvHOGNqberAUNvARmNlW2W0YqdGGPQ08iYJ8qyFX6jsUt5zkzoLbJSGqBbzJnAnNOSOqX6ATwpWvXdsoGwjhorZcwAITWCyKs57L/HImS2XhHfcOTymMgOSkeLv2VBNJZ5T9jzljrIE22RG3ZBBa7OhOkmGi1QFPjFU2WCVLDqAFMOtz7q0WwbZyL0mNu1eoXTwgOzkSWd8cJ4dOu85p1SUA7dB7AIB8OgswYFD97yJKhNExxHLoXx8vDCpPrSESkw9PmwRUmvQzt5ayJ6E2mh1Q1CEFJMTHoPP5NSu8Ri8weAMGiKYRDhkR09mg3iCFbeKKWFZhu0pZUuYKqT2o4P3SKSUkAaamNpqe6T7M2Rgfe4L+CAddg8f+N3JhDw3Fgs+jnTaua3mtrPZ5FVons4dJYRUGAcmmyIhFcYhkVHhfD8loGQbRUL5YUwEVHAeXhFSqY8Y+yB+QTlVYlr1WcioaaLi0tEUxcckR07bAc3ARNUYpyc77ttJuKF8mo77x22X+soheC40SJQJvoVaeKH4npSWsf1oQooJTrYhLirtRHG31J6AbXuW+k4L7cnTdKCHAhdf4JB4blchTIlOpHdpfffdJ2yKEgSpwmRUw9js64EsTIPs2xHBqyrr4g8F62F7yRt8v3w8IaHuxMdTp3/dPp7c32N9vAb3w8eTfMHzyurZx8uq4Kmfl6poH2FjHqu44OGQUkWo3qFt+Zk3HBcPZAliqYpKyRyXDBbLm+Vl7ocG5D2s5w4HAPj4kAcX4HoXGfQWUl4ydTZcVFk5j2bwGAcHFww6Ui/mUCbAmza6MoRPExWaNQeAZtdE5jwnuxP2vD3lde1pw8t3HayScVLbotl1qUMDspwsMxquZMyj+gzAeogk8ks8kSPIHQYKJhmulgwGkxlmAOgavn99nD/1FmcAzCjVGKLaQ1Qfng2gswbO8Wg8ALiRWXWdGM8rJl2WaZTVGJbySdnGJkNFDV9T0zKb3rQWtuGpGKtdy8nMTzs7kXfqj0g7lwwWGQlf0KqouCyuJ8jIxOpfsvk/VVwch0Jh0ghx7FQH1RnfJ5UUTyXuvJW2awEhplpLKc8UEYc6mdixlvw8KewgZBUdkEd9l9RSl7ne2l7uFseQTGtYU0kBWR3Dx8gOYIgqKQlJ1TmEtEJqcNyOBx9wNjg4H1IYFhmDzjKV2hI7gSHkPC4mdsKX2ldtczeDUiUl9kqIwhxmNQ3V1CGamYxk+zZ4dh6Ghfw/zodJbg3piIu9ay2l917r+b3Xeg5VOYm2rQUBFOBDVLYYUVdl5Z0xZk6VSJU8Cdtzo/rew4QAf/aSiaZ+n8mpPpJQ53t2ePo9D2oOPfwwwvVjIqFcnEof0fXDRCFVqqf4tFQaipUwvklFMKWUmheloThIyuF61E4dUXFOqW2YoCKCGXvu05zsmKTqdqysageg2yGMA+j0KYfzAQjUAM7krKjUIFXjU9DvvoDYhqJCaigUlrlN8fyUlMqhfBdtU0QGrTjy3qClgNab1JYQnUhEtSZRrJymwviM4eT5VS11j1D6hHq+HBzXfmDyXzyHq46ICqlm5uOxb8ffbeeKZ/BufDwiTUbZ5OOJT3esjyfhfI/Bx/OeK/NZYNXHK/07/g+nFdOv4uPJMknNIj7eaWcn/p3kSJbwvRzCF5ViMW+iKe4p1LLyvj8mPBxS6iowUcKs5sv1AOYGK0k7cxW+xKTHkD4apnHHXlmc4D1LO11AsHG55JwEWHHlczWGraoMwLbBkmpdJsoKS0KKWXSpvsCyzpQAT40KUNsWUvCFai22SIJ3kZxS6UGLiW6FSFEqocFz2VBHmW2efEyMT7YEQ4FfCg043njiqbH7zYYtJy/k/8bxiKgyVL4wWlSQUmUycyIzMVa2YQVAZtNzVYZUcY/moXu6IoMopPJ/nA0WgJTkPN9PkzzKWl70fuAYrkBCFQK4ky0d8cFzB/zEBvg48u99gKOAVvJDhUg+AaAQnfqYY+o6O8u14/1wcQW+anVfErYnxAUwJcZcbKvihHqfncrBebiGOA8RcvvXJOrasWsbvDskUjK2AlZ8ZlJ9iPnwhJAafEh2DMhEgiitBIPXFfVkubyfWS2a8uARYGPyscEFwALkQ8qx52GyHZRzXGg0SVGhHNlUgS+SVbqyHn/3nMDcO4RhSMoojAOCcxj3fXJafT/Cew+3507euD/nq+nHRES5Pg5mRsKqJKUO5ZXKiYwzKeVVYmYbc8YYS2mfDQAM8fcu94G882iAlIsqkOe+b4tUmUzuhwGruoy1KWTPhBzmy1EKFib4WYoMITfFVqQciyoM2CUCConMlKnYlaU2BeR2Jeu5D6XaVAwVbm1uXykc3kp74fYDw23dxjBQW63P/cOxAoUiekb7gBwZ41I0TOnjhbZJOaUktM92FuhdJHxFyXP7Pp7tYgiZIqQu4+NRshUPz8cLgaNl5Flmv29ame8iPh5f4ryA1UV8vFRxr1CBrfl4uvLe6r089p4fud1DwOMipQwBwc2XSbLzpfCyWOKWkkKKmD0HUgx+GHsQgKaQVvth4M5GVFJR62GsgestqHUwlo0VWQPvAqglNLsAN7CiysfYZO88x8pGCyUdE++nFks33lRZgbKh4iklFp2sji0mNKf8oHXPWl63YxVYs+sikz5VSNm2AZo2jZrxp+NlUe6YygZLqWB9b/W5J4UPT0XC2FpJZuoxEuEkGnYXWWYeAeP/tB99erD70eOMDNyYZbREDt4HWEtwzqNpA1y8t7KdczZtH2LHR1BK56WjJ/c9xTkbEy/Z5FELZbyajg1X09lYMjQrpJ7tGlgyeBLZ89OuwWlned6yQqq1PHLXkrDpsWNOWQmlfThRRoXApJUx81Ct1yUe+b5Aq6QkdE+mOgfPfnQ4H13KJyU5pVprMHjOn+JsfKE7ljKXYXxL9VF0BBSTAIer8VW8HtiqtjdVzSx/Xw/bmyqlfMg5pUSxYA2wHwm7xrIz6Q0CZSIhLlpMLFxxfVhT2Wl7VapbxkgQjB7xP9b/NefFy+SUh4shfUAkHoqDTirOGgMyHpY4fxQZ7hdIu5HR8Z0lDN5gZwE0BBmmCSarW3xUkgoRoh09IaFM8FOVlB/hz89Y/SQKqX7PKqnzffze83fnMe5ZIaVJqXF/zt/7EcH7tC6rp6TP53nqverz5f4fz0/7IpPk5taAJoOQsaqypeSI6vCcZhdDT6Kqv9mdpP6d2/exDxiTO3uH0EellHfcB/YOaLtIXBHMTu45sVrKEIf0hZz8PA26BPmuFcK8THKRnY8+hfmWUyE5fUBqU7IvaU9L7SqrjSm1qdZ6tMQpE8h4uGCjAi8r75htNymRvtggbZeWnqVqp+4YMR1LiZQDzXACTmnPKVVLG4mMbgciO/Hxwu6Ek27fsY8HIJFQwP3y8cSvQ7hfPp4O/7ttH08UUtrH6xrCk85i19jk47Ukvh6Sr2fSPV1Pcn48Pfgw8bhIKYFmyDVJJckXl5wzYxJTHKJSKlir8k35CdvM7LkFdQ18PyZWnSwh2IAQlUpskCxMLGmcEuRZjkV2vYe1lrcjfmDEEFFBoxvVu5Ika7LcdnGkTCe8i8y5fLcqvliuhXRcsc5LkNRSOayRTyqryhLRV9zH2d9RzMtL3kYliDU8kEdiyPyUNe8aOzFWmnFGzC3lTeDRU8OOO0Dw8Z57o++jjykQDDyFibIq+OmZGmWo+LxzyU+JKTY6vtgSOBKUDZgxJhk1rZAqk97pOONctQKpzKgsW7JRZGIVqySkL9fX3tJt4pBCJY8Wx0TRcRTNR4WUVJp0PsggL/+uUEstHrvKSiruCKJmALITKiqpfvTwneSDYd+BHcnjGmtt1jcPCclcWg6ocE9FQmpVXMqLV4Tx9eORpFT8cDieT3mkAIINwGACKJKb8ICjkPJHhdhCfEB8/x/RWrTSQlQ/kqhcFFLec0W9lGMpKqK0MioRUOdJETXuOfm5rHO9g5f0DpGI0s4qwP3C0mES1T1ZAz/kfqyUd+d1BGO9IqNsDDkaWD3hCGP8DauiCC724UJUSgGAswTyBNMOMA34eoeelRIxN4skCzYhsFpq6V4eQFYIS3vSSiifCKhRKSwHP80vJW3pGFIKDVKbkhB4cuwkiwp5cIFzS0VlRwi5PZVgldfrWRXrvmKWV2pNnKAhPl4sahVEMSV+UdfADOON+HjBB9joT13Ex5s+4/fLxytxVz6e9wFWneNN+3jWzvMEL0XDlPmCJQJGjpnOR0i+2f1ciJB5pHh0pJTEvE8IqAlbLsvjw65Y4BS613ZRKdUhkIPpOAkkAenFDiCx5yGOSonhsrHDMu6HyJ7b2DnxqSPiBzdl0cWQScd+Q8adJdwmGivKRit2WBYNVscsusQX28iYN7sTZtTjOmMJdndSMOgdf287/t60+b5J4vgNiacBUrJlA+48SrLJlkLsPMROcJNDOnQHpGumLHrXELPpJla3iMbLOQ83+vQ9eMC5zLYLe56SUK8NG0MZLQk1IFUZQsUYl4aLGkJr5/minnSimGonbPqusdg1rJRqbFRJRWMn5YzFYJUsuolydWOQckmRCvR7vObrfkM7eeKoi8JERn4lufm585xbquccPLuG0AZKHfNdfOa3KrZUVNwFpE2mML2QQ7rOeofz0eNp53mEMKppyAPO57xABjVU5raw8bqbbBNE6RKTiUtoseQOk7xhL/qRl40e+9GhH9mmOa9JKT8hDzIhRem7Lp2dcitOwvz4tzkMi9vcaWMBCrABcEp55xW5YHXonlTcC4FVUsHH3FExqXm/B8YB/uwlq6P6PcL5Hn4YMbxkpdTwcg8/DKyY6ke4YYSLyijp9wkJ5Xqf8o1mBYWPRFTu72nV1BK0OmqtD6gdVttJbinu/7ldH/t2Xcon44cB1LZoY+gSAM415V1US3kemfeOi/80LcLQAt0J3ztqmExLCc/jPTbSjsJEIRxCQIDkUnQYXFZKDY7bjQ9ICkvJW9aPHv3oMfqS6MzfdduyJPlqKIXRSD9raAJa6wE0aCkk59CSxeADjDXwUYkconjKHjBNVS11w7hgyF4wMZ+w/k4NTBs4rCv5eDEqZuT4VtoxUesKhdRFfTx59q/bxwOWhQeUnvm78fEMbs/Hc6NHE8Kd+3iGDLrWXsjHk3zBSz6e5FKUyBcgkmRynkfal4duhx4FKcVVQRYecCJ+E5YJztX3ZLAk75SE8NlYHndEYtFBBIrlQqmL1RqGBoEItGBgGmi5YGTkvYfrTUqIJyNk0hEp5Z1LKNlzAKBWpJ3TWOKU4Dx2RgzRRL6ZqjJIMswYZyzyTQiDrqpVJCY9En3HMOfp3CNpIiaCbzcnw0uEismyT1GN8DQbsEaNsgLcKTkDCqkmJ8njKS/zYwBs7Cz5CxisCZNuoo2eGyoALOk08yTmudJeoZayLDGXa7eTD9J1lgYLyNJZubdbfwXLah+4xbqHOOTkSV4W2UzysngPlXOHnfjc4QbIRPUJGVUG++J5o1gSXP/3im3k6mXHQ7adVO3zomjgkK4+hXVRaustSU4Zk0L3fODWbY1ZDSutzt/NIye3j3ZLqTtFEaVDq8R2aULqrHdwE3sWVkip7IQAgFN/rqMAwMJTwCAJz+M5tCT59kzOUYYc9nnQSoZMpJgQONeIVGuSPCQxdxTGISczjwSUHwaeKkKKt3EYzsYJGSUDkEJMBc/fAcD101xSui8ifcFSPQFgMhAJ5DwzwUVSZXBwPTupwfsUpiIhQOkexz7LuOdE6K7lXFfWDqm/x7lZiNVScZ5ELbVSeU9D1HQAYuLzGB6qQvFyiF5URcVQvd5lcjMTnW5TLcXtSdodJYIqrwcAirmpiNuy8Rgc98WEjNXNZyvvXcU9xgKJIpXW2d9TiqkGnKrlGnw8Ceu7KR+Pp3QvfTzg8j5ePwJdM1fVAtnHM2Tgx8JeWtxbH69rKPl4rTUcUlz4eEu25XW1No+ClAKQCSggt84yh5Q8XCXjq9aZpuPRskax6Cc7GCKmvWKGf0B1JOIokx9GuHYADUrG7TyojVVZuphXYDfPK1AaLL3/yWUu5BYQAgpYyi2lKq6IUdp1cV0L6nh5ZtZPWC3W7SDVWBKL3nb5exFrPGPU9TkbqRrHvRFjABPLOyN2MAEeFQUAjB67aKhcZNR1B0RY9ByPbJRR83BtQB9zEojxEhZdxyeHwpGaNyktq8yGKzHpxkxY9VLCKbHFJ8ponXY5p5Qlg6ddEw2Vwa6xaGxMjmeQFFItURyJyPmi5MwIBiGqpEzIVRtMNPy1H3W3KIVNUr2sVErtoyPXj7Gii+rIC1K5WEzj9wX6vz72f98iKbd2UYmBuweZdUJUcdXzdZH0OQRNeE/IbyAloU52qGhHPpKorHBwOOsd53MRgsob+MDaKF+opGoY6u2hbAWagAKyvXKRrJTE5kkpFadng8Or3iUbdhYVn+cbpJQQUSep887v9L6xqYMv2+rBmsYanI8+duSZTGiFTEBOWM3XEbIxXFL0RJWUVNJLSinnUk4ptz9PiijOJTVEpdSolg+JjBrPHLzzGPfc3xv3HPI3VcrzOYwxLE+qc8n3JaQBKjVYJdWWdX4ZUUzZjs+h2bFjGryHOSO0p477nx2rnLyoQWI/VpxsKwVelJokADDdju8V2Qm5Jwo0SCSBIrh1Droy3JNzkQWMLiuj9pGAEqWl2BEhqC7atvrRomssTjsL5wNOWwsywC4A+4bJqYECWitKZq5q6wP3sWDmdqmaqXsOncbFBK4YGWIy/yYrpUCWQ1OHPkXF3IaPt0ZKreWPWyKl7qOPJ31T7eNZA8Ae9vFOGsKrPg7Wx+fe+ZAiCB6ij5fySBH7cqWPxzkUc6V1nRP4dfTfHhYpdUjCmYgmFU9MQjipB6r4TQAbLkMNjA1JISXJHeEdAiQinV9EFgC5WGpSDJdUbBhGuH6YlBP1zsPGCiySawBAGjHLBstP5peQZdw0k3RPcl4R5dK/E9a8zQkwJ3LuNhllNlA58Z0YLqTkd2YeDrkVvmemjrT0G1tLXFGHkMgpwWACdqmz1qB33Gl91Rt0TcBZP05IKR0yIMbMtSFVd/E+oGkzcy5GastB05JObi5xZFLKmdqykl42WCcTFt1ODNVpZNpLQkoknTaSUZLgPCupVu5tvKdA7ETp9nIEOVVJhpuFdMYBcZyygzc4HxOeK2VBmOaPkpEUu/Bf8v8fOwXIBIHM6+2A/IRV1dzrCyGm0jvNZCJCSE9WJZmkmCGYSX4+MhLaYjBgriTgsJusntmPDk8c2/uBQqp6hRhiVcmou8EasRkgIXw5b5SLKpbz+J8O3uPF+YhXvcOL/ZBIhLM+J65dyyclxEGvRpQbMjjtApwnOG/ztkaKP3AfYTAhJlEnuIZzjfi4Pw/JNbUOXXUveJdD97xjQmrkSnt+v4frR7izHi4mM09kVAznE0XUuB/TVMgo1zu43iciisvEA328HzKVcvF8/ssQu61H1rv4o65noqppbSSoHFfm6mxWT7lmUiW6jX1QkqpjcWqd5E/dg3ZAGAcmubsdjKeoJIt5fMoqfCvtSCp3ppxQSh0shJQM0gwuJFLzVSQ5+9imJHxvGhq63r6kzPz56HHSOPSjhfMtnOfQIh+A05YdRBdYPUWGmFBbKuEYURWb9xBLyc4jgRKCz6lamgYYiQUILVfRFOL1Pvp4Mr/l40kerKv4eIiJzi/q4xHY497y8VqncgKu+Hgu2OTPbfl4+vlf8/FSiOQ1+HiiiLpJH68hk+9d6s9n5Za+148dD4uU0pgkM18wREBOgLdQcS99L0L8Qkz6FmKyRwMATcf7Q3zYpIykdyzfTNJrfqm7SAD5YURwLlZeyYbKt00qEUxtZuJlH7zrY+KNReKZDdRiQrvCUGk2XeZpt+N7IVUX2khGRWOV8m01bVSbNVPW/IiSoWyOROkTEEl0jjt2uUIDQLGj7OGtAXnw1GSZuJZtW3ITwyVGQ0u8yw+QRyaXGPR0r8VACSlAebpFRk3lm1Z1uIU9Z3Z8F3NnJGNlKRkyLoUtZINRpEI2WKI+S/dYExIrJJYsq32q20dSIkBexAHnjjvcZ73DO/sRz3fZLBPlpIiinJP2IOGYhDlRpf/3Y0L3ynZSVVIPAxdRS21tm7dRJJTJqg0hoChwW6MQuA3GBMFCGOgEngCTEu/sR5x2DRoyOO9savdaRVGN0f2GEAocshcSOSV58M76EWe9Vkspx0G9Z3XbsMYsqp2BTDTkd2tWswAeTTApQbX3nOg2KO3KoXYufcac4JzJKUQyKoxDJF/Y4eSQvTEnOJfQPUVIjWcj3OAwno0IPrBySimlBhfQR+dJyKghTAmpQzkDs0oq5mNJ6ip+9rrg0Dof+4hNcnzF0QqFIyx9SN9z2J4feErDiNA1fA9ivh3jHcKIGM63HbInRxFVsAaHfyIRU9KWZJCmdzlU76wf0yBjVkwVTmmhhtB9NumDAbnPKPOnnQUaDhm0sXpoG9Vc5XVUPCAsiRMK3880HRPRTTvz8YxnNRWAgz6e25/zkW7ZxwMwqbB5HT6eiSKMu/LxWDmV97Xm4500hHNVEEjI6SWS+r74eGTMqo/XEsX7lSNdZvf1NeofPVxS6hjosDxqcujewjagJndpuhOYcWTWXYxT0wL9nufl4fVctSF4h65rEZxLuQcSW+64HDCARFL5aKwApJLBQT2NZRWW+WVpeefcaHG+AZsMmDZe2oAlGWfTZkN1sptJO6GT4FmLQA1LYeWeltLO8nwjOcL5u0J0ag0reyigBYHiCLw1gI0dJ10OWjqtrTUYGg4NEaN0PtoJ4VTmHHA+cDlrbayK6Ras7kgrAwVAVVXQbPp05FeY9GSIlKE6keWUv+dlsTKfmVZtmBEQ8SVgoaSzB6+q4iaxNTLjA7e7IY78vOwdPvOqx2dfDfjsqyFtJyErE+kv5TZgKSe/F1WUtEuj9gFsq6QqIfWwcVliak0tpXNLmajKcD5kZRQBIZiofjBgHoGTmQo5RfE37+xHfPbVgIYMnPc47SzOR4/WUtzneoOahF5V3Dik1xGg80rx8gDEHHiclPpscHh7zwqpT704j0qpES/2I3rnMUrOpFiuu4QOjdg3XMnoVJXUloSxANA1lttPw+3FBVWl1mfFVGcBUA7f0+eeEHyq1GVCziEln+BFJdWnsL1xz99dkejc9R7Dyx6u9yl8zw8ew1lWTHkfcOYyESUkFJNTmZA6hpjKatlMTsm0k5F3A3TOwBqP08GByMD1LuaWCnCtqgAYQwftMHJfcRijUsrBtg1G2/P6puVbN8bKfGPMNzWOMNazCiX4aUU+3Z4ClEoqtqOokJJ34H6MCqkhq6L6kduUkFGinjobXArbkWTGa23sPPaPX3QWnSU82zU4Hz2edC6pGp517AadOA9vKSq7ABGFesWaH86gVXGn0GopTUQRpj5e43m64OMZskxYHePj7bob8fH4tLNvJ9PXyccbvJ/5eJmAujsfD0BSRR3j46Vk5kf4eHqQuRQVvA4qKeCRklJcgU+ppYwIDJGWTUAsSU6IEk/jW4QRik2X2HoAbQs4SqNGAYCJFfoMEVx80QuznuL1nQdNDNbUgPGyIwxWwaRrIwWApZxa5lkkvTOxkoohmw0SRUkrUZJxQsqFSmJAMVDxPqfQyMn9X398tLwTiAogsEEi4pF4IqANBgN46o2JcmnpLohjxaPuZfiezC8lWhUDNV7SYDWFwZoy6VMyShNVLcVEd8QVFmRqk9HKBIRMm0g6bBEBomaQqd5eiIrq190f+CChfFJSHanD/ckXPf7Pp17hrD/BF755jjeftHgS8wi05FN5XEuI+cNCdOzi2JNBcvQl8sCHZcdfJ5IuQxG2hCs1bOH+YcuElav0tkKcaueRt5mqmGRe2q5U0XJR7TBG2/tqcHg18PQzZwM+82rAL37qDGdnQ7KFfayuxW2/SqQeEvSosw6TF3XUWe8SISVkVMrvEX9b5u2QEKlABr2ZjkrLaHg/OozeAqPHaSs5+XK4hgus3BNIO92Eyi0VvIvklFcElY9Jin3MDaO+D6yAkpwxrudE5n7wMXeUY6WUIqNcYEJKK6UuTkrlaflJ6yPr3BHQe8AGwPoQiamoiup4npMxewRn4IcRBFZ3ePLwnvup3nnYyX2x6Z4ZuY9HQNsduVYpiODVJ1fYcxNHc5KSYVJ1K1bZ8tMwHWlnoeFiN2Y06AGc9UxGNcR5SRsyKXH+1r2vuKdIFfYokc0lZF2qzB5/lwpbAeznANnHk+1WfDxgqnRa8/GCc/DkgRYTkuqoSzvCxwPWFFO35+MZHfuP6/PxbEwIrn08PZX8q9fh4/HxMgkl8/NP9vEATBRTaz6eEFLi2+XohzywrH02PV2CjoZ5jHjYpJQK4QtxlofGCqacCCHYKcsrDx01MH7kkDzFCBs/Ah1AbQd/TizvbFpO9ugcT72DPTll2ffQsxEaOWmmFdlz7PCIlDN1bgoDVU4FXpFVVKi8SgadLytKO6PBMrFioFRUEAMFIBkpxOR2iVGX5HfamFmLYLlEaLAd3yer5J06iXwBQiZHTGAiJgBoSDoRBgEhlsyMUm7i0fr9yNOWTJR5E8ZUsYzvzdDGii2xVycVW4BssLSBWmPQnbrX84otc2knMCWp0sdIZQn5jiTZbIR0ip3yFFtsc3zxrrFR9aJGRJUqxhpFNIWsjLIUWXbMQ7ZSqFdc/Ejt2b2EdvAFKRTGe/yft/f4H594hV/8X5/FL/5f/wVPPveL8OpswJvPT/Crv+RdePNJi/c+7fDspMGT1uJ5x6Vln3YWxgSMlNuINTlBK8DLXAiJoJQqKhKCJfCqRZCZkhllWynf8Y/15XhfcaiPdQwRBcRXZUFEybZZ/q5VDuxMDp5DXF72DoP3eKd3eDU4vDgf8dbLHp980eM//z9v4zPvnOO//18fx/7tT+DVr/oqnH3xGzjtLL74XTsMPr5/w5REqC7h/UMIuT0kdYvz+OSLc7zYj3jr7XOcDQ7nZ0MipMaYyFuS0GpI0lguq41UXtuNnkt+eykPnjv+XeOUM2A5KTUFzi3lPKwhjD4k9R+37SMMk89kVPAOYRjitJ+G6sUKe27PuaX6lz2CC0opNaJ/McA7j/7VABeAF5K02zMJJZ8yp5QmpoDjE51bY9BF49vFd8CppaSc2sX3Ac7G9FsJCwq7EOcNfMw5ZZ2Hi/1Gt+/ToCYnNSeEgRVSYei5bygDuRvEVAiST4pJ7NGrqo0+5pKKH0mWrxOcv9iPWTk1OLiR83KVpeClDHwJim2oaQnWEs47i7OhxYs9K1ue7Rq851mH1lJSB3Jbn7adapceHpI4wVD083xU/lDsCxHohLf1orBSPh76PeAdqO3YPgxDshMk+efEdlzSx/OFYupKPh4wIaGA6uNt+XjA1M+7jI/HuX6jH0cmfScy2FlFXG34eAYm+nQSAYFczGh2px8/HjYptYaYvNzovFPA4sMERCa9yC2VHkJ4ZpbjC1jY8gBwmdzJNL6kJRGkvLjHAabl5aSMF2+6HG8s0GVIxSjly5zKOyWXlCwz1m6TUdYq1jxOJTRRMehSOjUx5mKcoJLfHUh0ToaNUjA5DwIZdobJcLUTMizD8MGk6k6tpRgP7EHBAGDFCHdEbdwXpybk/QSQsWkkVecTKA3VODNYcwMlaFYMVmmk+JwzGVUaKk1CMXEVqzAQpWVi3CVMKzHjMKsEAMHAIUyq7i2RUxX3Cznk1KM/G3D26V8CAOxfvYnPksGnXnDOAq7SyG1/F+Py+1h1yonixAeA5EXGbSVxECaH5ZAx7CyIo4LLK6cq7heuQxkl+/EhV8wSQoqVDbl61n7MSa9f9A6feTXgs696fPZlj1cvznH26f+Ns0//Es73Xz5ROwBVlXDXuMrddx7TkAmlXvFjSCRBmWiWwxPi8WOS+0AhNcogJI1quKJ6lmpwS+1GiJyjmpSuvqeR+m9zkmVJ3SD5mnxUFOXzP0ww8bowuxa9vRyR1Dpr9LYhLjOTZTIv28v7IfiQYs98cqADjJ1e26KSQ+6JjijgC169vos83vo+OJ+T46ePCtGTqlosdpu2tVIphdGDS8hzP9F6ghs9nCUV7iPncPz5VjxASMJzUCamYgXJ5NPFfMKimhJlIHz0pbwDIjkrvh3QJh8vDD2oxczHC47VUsDUx6PiWas+3rqPJ7+/Lh8PmEfB6OWljwewn8bnLPNzMkpUl0R58CDlg41Cg0TgSYGi6C+WRaqAw5Euj61v/vhIqQm5FFIcMVqlkFqowCescPA+sejQidKDB7UdwtCynLOJU2HKB46/F3VUSpwZq7kA0RhFph1AlosDeZlgK+ZY55SSeOg0pbycbDJMABsqAJlNJ4JUWlhk2KOhCnGkQRLfhfjboBLhTWSexf3lDkJQZAs4vMizqgOIuU2CqDqIS/N6yRcBDI4iU07RMaIkAR9kFFeqWiTnKRuppABIuTJ0J2j9VhfviETyCOnE2+QcF7wNskRTGSUgSjqV4crhejQhoygpoiIbj0w0Tc7PsDQ2GMAiGzsy+VzXXyMVtwH5z/TLpUyqCHAp2+HVZ9GcnOLFZ/YYe4//ddriVczPMrxxgictv6R5VInD+p60Fg1xW20Q/2/LnWwf24L0AIxBrE2VX4CJnFIdenle5PQUtzVDuW3FzeCmwvS0UklIJ7GTo2PVyeiz0uHV4DD4gM/uBww+4NNnA14NDv/n7XP8r0+9wltvn+OdT53h1YtznL/4FMb9ixTGBShCX9lLeTZqE7o98Fv5iO2M/s/y+6xrKFUfct4m0sBZDzNEh8aHybuWjJTXzgop2xCMMWg6i9ZSyil12llVyahJx9o1PICjE8eKcyCDOLPOfVnkJuYRTcoB77gqFRCrUxEayRPjPVrnMO57NM7HcvBc9p2sAbUWtrOwLSc6t62FGxy6/QgXgDPnkzpqCFkpVYbuXUYppRVT1gCt+i6qqXbXwLYWzamN0wbNrkF72qB92oGsQft0B2obNE93uXz8acfVu052kwrMRqkuUjJkubcqIbI80zlHSoipDPjt05KHswat5/wrANL/3cfQTSCH3J1FRxVgm0aWiVByJhFUS20NiEqphtC0Fl1rJ21X+mDp/i68yKpdugfQRa2WClxtbYsYFQNEH6/jHGji453E6ThyHimfo2FMTPQfhn7q1xU+Xij9uYLIfaw+nhBPCNfv44ky6q58PLk+sQkpl+GCjycklCauphEzOSXLmo+XVVLbFmdVnPAIDNXDI6U2jFEoSKQk36Rm3YDpmGQAICQ2HdTE7+BtgmeW2bk5U64NhxifaMy0RByIiipNVCG+9EqjhakhS8ZJoOZT8nObz8NoI6bihpORWmPYxVABSe6ajJXIN0v2/EBlBhNZYGHSfYiqnmAQTICHQRyriB0A7vBZstHoTNVQKScBccUUdtBNcqoApJwTwLbBEujR2aWOiTYUJQEly8RAAZlNF0UUgBkZxRLQaOQic84GasqeC9FUnkc5b9J+8jygRg4X8BgM2UPBoXsdvMN4fobzsxFkCS9iPp7nu5476U+4Som3hPPRwce4e28NOmQb5jxSGB4/Ody2pW1q1dTsHC6pmqrk1M3gusL1pttMy7jrxNYh5LA6J1PPkvnBcf6owUeFlIs5pEQh9WrA2fmI8/2A4dzBDz382CfCInfu+LhVyHn/sPaXyPtK3nE6v4bz7EyQanwhcGU8TRClstsmklEpjI/fkbm8Nk3ydch3ztOhEp2r0WjuzKvrKC8k9pFSXw/ICoFUXbhLfSJDnJvFtg1826IBUpW6ZtfCDSNcb0UAEfcX+wIxf1PjAuzAJJQ1gPVa8WRiGJ+Z5ZVagyajAKTwvdbkhOfyaVoLYw26py2MNWh2DWxnEyHFZFqszBWvk7+3aRm1Teo3GnWfUtl4+a+PqMwlyl1rDAZwlTzrTfo/W5qWWe9Hj5PYtoDcPwvNVDUFgNV2MLMqW0QmtTWyBFLtK+WIkX6WGliseEDY9AlNzCmstt3w8dBS9s/Elyt9O+dmPh2iQCFc0McriaqZjyfHxevh43k/9fFaYvL+Jnw8YO7nXdTHk/ePfh9dxccTQmpJgPC64OGRUiV0iJ42TMKOw6e44hl0aB9i515knQDgRgA27zt4gDz3LGLFERMC55gSdr3bzdlymXfZgPE0J9pMWCCmFlEYr4lxUuuTYYrrcscif0/J7QCIskwz5MlQWTFaaqRRMehr4ZEAP4DegOONCTDBAJ4VPiaYmLeE14cQYCHLAhoyaKKMdtewYcmJThVjLgoAH2aGSa/TOCaEpOyoZAMV50tmXRkqWS6yTTFQMqIgRkrUAoS5kkCYfG2w+LhYtFzldum8TV5fcbOQ3CZLsAS0IDxpCS5YfMnnPEkhCrb5ZjStxZvvfYqTkwZf9p4neLZr8Hlv7PDumPj8jZMGrSU8aS2kgmNDJsa+SxuS88BENZfOD+ujMZetzLe0fcXVQWabmErvq4Xt19qhrrAn8wCr6Hzcp41OpDFyFK6gw53EqNSLCqidUh682J+gay3Oz0c8feM3YxwcvuDL342v+oJn+MI3T/Gsa6LahaYqqbKtVQfxVkHI5WCkfQiRQIY72buG8OykAZHBl7z7FL07wbuedDjrx0mltKXy3MA07D1XMmKCQFQyrJJiZZSU137SWljD08bydGdZ5fKkZYVVZ/md14odjO/TCaQ/YzOVRKdAkApzQw9LxJXmuh1sv0f7dI9ufw7Xj+ieP2UyKlbjc2d9nO9jVS6H4Ywrc7mek577waWE6MExoeKkOmFfpHHQpF7stAjRxacf+xhSFr6TqWWCr2MiaqLg6jjkpz1lUopECdU2sKc8bZ6egohYKdU1sLsTrsTV7WBOn7JCavcUpu1AT57DtB1Cc4JgO76XWjGl2pMlTrbOxBv3i0Jn0Tr+vlNVqUYXuCx8CHi2a1L1Ts4xNabQX8k7pUvBL+WQ0WE3QkQ96SxOO4vTrsHnPuuwayze2HGuRlHgSbuRwT39nqyq83uEwuebJTsv07cg+jWRhLq0j7ekiFry8XTxqurjHe3jBdy+jwdM/TxSHdnSl5P11+3jab9tTYBQ9okeYw/pYZJSh9RS8txI6gCD2fYmhOlLVBmlQDGBnFJRQYwTkI2XfBokAybHEUPG51HINxfi82fSziOxqp7SEs+43ESGfVLiE3zPJnHDiLHWcl+I5oZKb0MNQPN9pFMykRGOvVsXRw1BbJQk/EwMl07AK7JLT3n0viHAS9WekB0yMWSArhSEybxgiUU/BotMOhXzydDkah/aOOV7ktny6bKpYVoko5DXLZ2f7BuYG7Lp9kdddsUVkf5/w+3Em5Aq6733aYeuIXzOsxN86gufAwCe7VpYMvjcZx2Xsu4snrQWJw3hecdOoZBSKfwgvRRzGxJ/Rrcd3dFOy9N5rhNSh1Db0s3hEDG1tb0QU4SomktEQyYe5O1olIojpM4lgKhEMAZ4AovBc0jSLhJLJ5ZwYpmw6J3He9/YoR8dzr7sTQDAe9/Y4c0nLT7ntMWTVgjV2F43cuVV3C2Mic69B7pIhMiINcDhnc9PGgwuYD+6RBBIJdy1ykc6aexSpVqxabqENhkmP1tiAlSIzYb4vSqkfC4QwioZaVqsBuAEs0HCXARkQcSl4ENMbOy7HTAOCGMPOt+jGQd0Mfnx8PIsVeLz/QgnydD7IVbni+tStT4/KR0vZJRU8AOyoiKsyKWEnMrVuEzKNZPIqdVKXFH11bVJFUVdVEhFEiqViW/aFLKHpgWdPuV1uye8rO24ZHwkpELDSZElDAhAdMhi1S2WS6CDgfPSh6Nkf04aSurL05bgA7j9hIA3d21MeMxhPkJGlSXgD7UzrbrbNRatNXjeNWiswbvUIE8XC9JIKOgaqr26x5Ck5UqYAGNhPJKfl3y6JR9P55zTPp4fk6LKRP/h2nw871cJn/vu40VXjuUfV/DxXHymtI8n21ynj1fm5itxUR9P7oH8rExLoPvWSz5e6d9psUPZP38d8DBJqRJraqlynUJAJo9m24ihit+NiWSVjwbKWJhAKaSPdyj78/m38kDHdVQaMiCH9Ml5HUlObYby2fw9seNicOT6gGlssGbLZb40VMXvZ9+PhHaGYJDz3WjDBcAjJ7EzgUfxZdReCCpO4BzSflOJaXU83WEpDdVlWHQ5VlqnDBOAGXlklNOljcykGt4GGZXWL5BRS2qXpc5UVUndDeR/dSHncTImJEcLAN4VCahdQ3i+Y5PcRWfsWcyzIoSUjCq3NuchKwkpSZBfKqSuSkhttZ3aSb95bBFTBvMwvgsTU2GumLJpz+xYwhsEQko6zWXvuY3KZ3AeXUzAL2oGUSM865rUlnMhh9yxXVJ4Vtwspu0k2yhijTm8CbDErYAVngHnBvDBYnDcdgYf8MQRBp+rJJUJyzWkOAigk8bmEtpSqVYKg5QVjFprmEAgTlCbVDkmE/QT6D6eiX0+28T+G5MqBgBZyz1D77kARKzCZZqOCauYS7RtWs4pI1X6FPk07jm/qO9FMTWAy8OrT5kEGZmUEsjyreTHopgysVJe+hDBdi2HIMYqXc2um5NVkh8q5pvRZJTpdrw+5tiik1MmnmzHaguyKKtzpXud2paBjXbDGwAU0AZ2YL1F6q9ZYzB4/m9zzsSQFFRSicsHqBw02Tk91M5SfhfKOchYYZenbWxPlvJzkJUL1S49BCS1VEFMLfmAAFZ8PPE5Ch9PCFe97I58PABTAiqe34P38cKCj4dtH6989h+ij1f6d0uE1OuiHH+4pNRSuF40PEEtYtBqUrlUNVgbIiDvWwim4AGLpIgKehs/3dYkelcdU35TLJ/ITXF50kDH9IuhzTstDZWZLqe8vjRqpaFKDLyso8L4LRgwcTiESffRcZbbZCjE+xaNTRDDYtI941wnvF7upPw+q6Yic67vS8CEFr+I2mALE4JIGxPo5WaybTYyBSlg5iTT3OhN1y8d++DxN66h4vphxPMH3+sQO+htDGN41tlYIpewd20qkw3kJiuJ8XeWS8nmKo2ZfFoio8qXnW4/S6MvlZB6GLguYgqAqr7IHUhJTuqDFE2Yjm5SHGG0xM26IQvngSctK6dYzRBi8YkYvhWPLc7gznIITUNAl9pxbJf6Wl6TDthdY6nNAPK+zmEYLbG94P/K4rSxqegCq1oQSQOV71Hl/liC2A2dG6rMFSUltCXfEEV7pm2fJIltSEiQnHdpYpvEKSPAhCb3WfwIOE7aHYKHsS0QPOj0KcLQsxMZkx6Hc1ZKSUGbVNjGxe9AXrdSMh5Q4Xpqmcxv/l9FWXhdlUuWL1blkoTuAE9VVa7VMvFkOUwvVuXyNiuiYJucGFkIqqL/J892IrRhYEO0N1FB5UJAZ21sKzbmr2P74T2SIjO3p2n7Arb7dEmNqdqXVDwWolPIzZQ4nnIC49JZnPwXm/9UxY1gw+8DkFRPiZgC4igMV96biAri9gDyOpuXJZUUcO98vNn8a+3jTe/offPxlny4Lf9uq3++HSVz6IoeBh4uKbUFLckECgM1Zb0nFRokOfqSekohqHUm+NhL9xDya050hel+dBx0MX+la16ZnxkoYGKk0jXpbRYMVtBPZmmsjj1NY9Jogs8+ezJqABucVBlMOfVQDlO0Z7FzE68z3e98vFB0ua/D15kNws4Y9vmxlogo4DAZtbRNue/ynK4SglVxcWwRBYQcZ2/ilEdkcljDScOO10BmNsqz5JRtVWdcIqTyeS43hppD6mHhOoip+T7NxNaKDdbhfQbRyfQm2lWuomYMK6da0s7ktGHodtzQPGFoekWZuX0tr6XiZiAkutipbEui7TIxUW1UsXTgAUAu383qljkpte4hlHk6dGEQydEhds8ABQGfbZ9OVG3MSmddToMoOal80bF/Y5voNMWy8eJgdicw4whJcmzIIlXmcrlCl050bHQlZmCSh0ZUEraszOUulr7BqGTH6boAzPLJAImMQtthkghZyKeiMlfaR5MTIOepWVdHbeSbkf+PK0wFdk5FeVkkPzaUkx+T5y67o9yWBhfA1WWFlNpuY3NSalrxWBNSZjawk88dqGT5Q8Esv5RAfBmJlMGGnxe3v7KPdx3+nTr3pfnq4+njH32qm1jzqdK5Fse6jI+35NuVeN1szsMmpZZYc2BmZCZN1pbGSpFV8mCm9Vbt32Z2XI4h+y7Y8sVtgGUjeVMGCwuGZE2SufB90cgVrPumkStPD9ogxc4nBxunh05urzXZUIVkuNR1qe+T5cUxed30gb5kKqkZluzElkO/RiitMd+r2y+ey7LBXNrv2jYV1wPxfzQRQJDniauNAJzkkX1Ag44CPGgiVxZkNUHcv8mhKZPYdcxHYGQZ/y6fSzrXqo56sEi2dMGepf7yyvZp1E31Fj2KkU2lkgLYbtoAZOGpySXsPRNdHgYhjkAvtWMhnCRfSxvJhtaa1Fap+I2+norrwRKpmcI347xUT9JhCy7aK4+AEExOLh0JEq1cKXOBLEH/r1O7pexabDPSZrcI+KS40+1sckBKOUdDsEDgylyS/Bg08rRpEbznClrBA61PCvnpNOYN1aooAGFkUkrnlTmqyI1ev4ZLJj8GkMvFLyVAViTTRBEhyigd5rNQJn6Sbwa5PQmRaYwsQFJe2gAEyrljPAKcF3uT7YkO4UnfN/p9k/sjU5PbVvlOFXuk8zDqdr9ml3BgWcUNYU0tJd8jAspFha8XhFwSP6/6eJfx8Sg/2tXHW/jtsQKDq/TNH5P9edikFDA3UEdsox/kZESoMFjAArmlJJkoHuRCcZW+y36wYEBWGPorYWl/RxquGUtefl9izTeIqNlpYOqwl86RDK5LrhMgGy5rlEFSnWrtSAn0b9K16ZO4JqztaivE7lijNvtdeewV9vwY4/SYDNh9xKSdw+Q4eSGpxOEzLF02FouElEZ20LaJpyVHfqmpXIWQqngYUE0u4ZBqKopl0u+XRjZF2yCtRMQnF23Duu3KseW8KiF1N9Btgkz+p4VU1/lAxG6lakrGTBLYlm1Bj2hrumge+pDPRa9bIqMAJJJe5jWJlQ9Ik36YlIlP1ZkBpPA9PwKWECQvjU6ArIipEDwTP4FdsFSlSyr5KQLKABOl1GR5mr9cVS6tUEoqJ7W8TIQ8y0GzREQpsmlp/WQbQ8t9QuR3IIWQjhsQPdiFPDNsS5j0tBCCM/YPYTac1eW2JecASBuS79tJh+U3U/tUrdG9xYYfOImYATDLN6W3rT5e3O76fLyllAGvu49XmpI1Quoi5/RY8PBJKWBukJZklEvGAnMjMp1VRFWxcibJnMS1xtGqNcr2upjzNawYj7DU2LeMGTDp8Kx1PFa/yy4KpjioZVsj90BWg5S30mh3Swyd/KZ4bK8rxngNF8k5cCgnFHCcGuoyx97avuJq2AypEjkzxUokFBBgQDKQHqbkgd7P1ijLlgx4jdC8KhlV28/9wpZiCsj/59GqKSDlgwA0QcWOZAAm6ineT0BhkTfbsCYTZL5UxGxda8XVMSEnMXckrEp0z4qp6f+u/3MZrU7tQbWE6Xt7+Q+8aAhESb7nMEDZn5nYxOnBtPPFzimTTSblmUnKqeCRjLTMi8IpFbYJuS+pctCYoAgnlaNmtkxO64LD+7O+3AEnMpFPxfrF3DOmyDujP3Jsmaf5sSanhUxM6TbFP2EqidvYvF0BN9+2dPtZWl7apSWyvNqlO0BBBi0uK9rkpLksRcwURFX18S7m4y29T4CsmgKqjwccFhhc7FjL2z5kPA5SClhnypeMl14uWCOpdD4qjfJQC8eZ2LCJ8SxGum4Sa+z20vKF6zxaHrp1LMyNFrBOTgFTJ0l+pG/5miET6DwDW+U/rxvreXtWtl/Zz2WVUFurH6MBu69YcvQkAWRKLC39eWUoQpi219kLbYOk0ttvvfguSmRu/abifuAYcmpJNSW/yfL6MGk7Ohl63peoZ3henEqNmfnWzmFBKBzj+FXcLEp7BSDlw5sp5MzcZgHzcAzBar69A6T4sUU/lhQwW8cAlHqCkHJMCalkQNP+ZNDVtjLpBCjH1ftpH28hDGixf1qE/1wYl1VIHJNvxpTbmOlyonn/UGHSlkxWTElfTueUkSIL2pbIu9DH3wDztsW/Xz++xlK7KbctSSp9ddUuPRAc8vkWfL1FP6/6eNXHW8B1+HhL/t1l+uWPEY+HlALWjZFeJ7gkSQVsEFXA3JAt7f+OWPSEjYSUi52MY5ddENpR0g9kabyAubOkd7J0N3V5z+uKMV7DMarurbt1FXXAMQarEgq3g1ItteXolfl7BEUhkQuPxGzJgC/70qvt5+FgU7EXp8eQU8DU5vpJGytJhzDrFG614608Z2uOX22D14+1tiIEAoBEpANzEgFAIqmAZaIKON5hOCYE4lCOvE3iQPcPI+kkOaYATMkpIKqnwoyg4usr8soQJsqLyfqlys8bfcA19cWiCqK8Ng1adi5nBJT+vkZE6XVb+WaW+stQ70GocB4zz2WXCCr5Ieakd3l7jmlfa23rkF1aqnqFyW8OH7viBnGMz7fl6235eWud9urjHY3q413ev9s65GO2O4+LlBJsGapyG421EMBim81KBFKad9MoXf1hvwyOqqCwtc1FGPkFLI3mTwYaiu00/MYjKmMSS1WlZge5ZRybh+AYI3PRy3jMhuu+YpOYUo6erJRO+YWPs7DsOkdfatt5uCg7fyWWbO7S73R7slD2VW+Hq7ffQwqp2hZvDkthF3o5h/LF/37BXpXkk19oIxc/p4uT7ssK0pUDCMkkxItaDCB7QJqgUsmQAUUcaSKqmE/rS9HEAYf12lRTS8uOVVItfV8jo9aODdWOoFQTJpNMum0BJSme93MbbQuYKu70NaTtJ/u6/HlUXDPWImVkXYm1tC5q/aq/VH28VVQfbxnXIS44dl8PHY+TlBJsGaq17YGLkVkHkust7uIGWfSLlO5MOPSbyxixDayN0K6N5MtvBFu5e9awasyuAZdJgHkRw3IZG/TYDddDRJngf2lkaMkyHHrCrlsKXNvO48GWcgo4Tj2VtlXtNm23sM9j2/AxIaW1Ld4ulogpALOEtUAUFqn/RyevvdI5LOyibD/HkAbbB6GpY6qc0ExO+WmfUJM40spNUbGr7HMu9SmP7EMedQ0XWVcsWyWiyvmt5MeHzkPvEtttC8j/c9m2eN31GIOL5Hi5VNuquBsc478du+0Rqqfq462j+njXLzR4XfpCj5uUAi7XATj0EB6jqNr6+UWM57G4Bqnl0fu4hmNtGaC1Z2+LZT+IaFSuKyHedRqI69jV62KwHgKW1FLANL4eWB4Zukgmgut86dX283hxVGevmN+0tQds6bFteC1Eq+J2cVl7NRu9voE/8BjCfemwB1VS8h1YIJwiJooI1ecpEyLLPhJs3m+5rDydKzpSmyF96SBHhuZQSVptEFXHzMtuCzWeXp5/Ow3RW1RGXHP7OjZv54XaV8Xd4yI+35Yvc0hNtYFwBKl1YVQfbxv31Me77G5eVxvz+EmpEtdBCF2XwbkOI3NV3IdzOBJLMtCL4r486Nd1GvfleiqmWBop0iPFso3gmBfpTanraht6fXBIOSXYGtHU+9K4Shuujt/d4iL2arbdQm6Qy+DQaPSx7eHgdktqpo35xZLygoXKzPwjf5TDexSpdAwuq4Q4JvHx0u+PJKTSYY6wO1vqzMn53WL7qnbpEeCiETP6d4KH7Ofd9fEvgMfi4131FO7DNdwVXj9SSnAMQ37V/RyD62DRb9Lo3IJBW4wvPmCRrvLMXgeRfps243U2UA8Zh+LrgauNDNURmIrL4P9t746WWwWhKICamfv/v5z7lJk2FUSFA8Jaj5002oSclO0Bz4RJuaGSWvJXIvdQ47OPVDD1UVyrTl6xbj1udn+cWma3bclAaffPSe4vk/i/KRVi1ZLZ4Pin7BKgs/vKXNxvZu99+e7O2z9eu/G1bWrTlDJ7R936/RLmeOZ4CerJX+uGUjm11vxfOdYoOp9X6qpsDaPXAYVqLiV3RIs6D/h2tda2GE7GaF+ltapkqEQucb+23CPRQZHqps90TqRCnj9hVWFoVEvx/jNX95RJ/d1beoxcuUNo6nlqKXkqtWlCLZbZ5Y4xEnO8LtSRPKFUiVpdVaMZtVj+kPsAtyhmURSmtbT8As4dD0pduZrZ8tj0UVKrSt+uo+HTfZlDbjuH3MXJkv+dju7UHOXsOdy9g1eBozE2yvjaNrVpGTPO80aoPwdmnOOpGdcJpe4q/dBHF7UHFKO7Sj74kUVNIeLI2X2kzjwf1JYaX8bu/GrUqsd001258/KVx4+mRWB1wt0x1qqEqE38MuI87wn15SZzvPUIpaIsUEBGpIgwKmOTpzJ211I7TL9z/KbO3gjnCd0VkXftumGZMca8zPPC+dzORSgFAMChJSYBZ7uiSp/jCQY47yXGGAC/CKUAACAlYlPkXgYIogBYm1AKAABKjLjHzB5hEwAPIZQCAICajkKhu6GV0AmASQilAACYyvBbE00cKr22bXvoHd0B6GDeb0QAAAAAhiWUAgAAACCcUAoAAACAcEIpAAAAAMIJpQAAAAAIJ5QCAAAAIJxQCgAgyKv3CQBTUEuAWQilAAAAAAgnlAIAYBo6SPrzHgBQSigFAAAAQDihFABAkHfvEwCmoJYAs/jX+wQAAGb2WcpkEgnUZqkk8HQ6pQAAAAAIp1MKAKChT4eUjimgFvUEmIVOKQAAAADCCaUAAAAeRpcUMAOhFABAABNIAIDfhFIAAAAAhBNKAQAEcgt34A41BJiJUAoAIJBlfMAdaggwE6EUAECQz2Ryr9NB9wPwU65OCKaAWQilAAAAAAj3r/cJAACsRJdUO9+v43vnZ7Tx/Vq/Nt08Ney9jjVf273nSX1m9s4DWMPe92vJ40rolAIAaMjEDWhJjQGe7PV+v13EAAAAACCUTikAAAAAwgmlAAAAAAgnlAIAAAAgnFAKAAAAgHBCKQAAAADCCaUAAAAACCeUAgAAACCcUAoAAACAcEIpAAAAAML9B0VGoM+OPoh9AAAAAElFTkSuQmCC",
       "text/plain": [
        "
"
       ]
@@ -2290,7 +1208,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "
"
       ]
@@ -2300,7 +1218,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "
"
       ]
@@ -2310,7 +1228,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "
"
       ]
@@ -2340,7 +1258,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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Fm/49mzLq+cZ1FXSH0EiphoaGhoaGhoaGhoaGhqPwpAy2G24fjgnP21eu4fnDk7rvGynV0NDQ0NDQ0NDQ0NDwAqL2kmpkU0OJRkA1lLjqb39se9I8pRoaGhoaGhoaGhoaGhoaGhoanjqaUqqhoaGhoaGhoaGhoeEFRFNGNTQ0PCm07HsNDQ0NDQ0NDQ0NDQ0NV0ILz2poaLgOrktyN1KqoaGhoaGhoaGhoaGhAUBTTzU0NFwP1yW0W/heQ0NDQ0NDQ0NDQ0PDC4h6ENkIqYaGhsfFVduRppRqaGhoaGhoaGhoaGhoaGhoaHjqaEqphoaGhoaGhoaGhoaGFxBNGdXQ0HDTuGoYX1NKNTQ0NDQ0NDQ0NDQ0NDQ0NDQ8dTRSqqGhoaGhoaGhoaGhoaGhoaHhqaORUg0NDQ0NDQ0NDQ0NDQ0NDQ0NTx2NlGpoaGhoaGhoaGhoaGhoaGhoeOpopFRDQ0NDQ0NDQ0NDQ0NDQ0NDw1NHy77X0NDQ0HBncZ2ZFV2tX+Ua4Rqv19DQ0NDQ0NDQ0NAwj0ZK3SDCFXMf6paD9W4jXnF4qpowseH5w4tWq5/1522kWENDQ0NDQ0NDw/OERkodwFVJpid9/UZiPWFclWR6ktdvBFbDLUKrjbcHh36LRlg1NDQ0NDQ0NDTcNbxQpNSTJpmeNK77/l84MutJk0tPA9f5DI3IargEz2sNMVds48q21FzhfH/LnyHX/X2fgxazoaGhoaGhoaHhjuK5J6XuOhF1E6i/g+eOpHoeSKibQPk9NIKqAbebhLoqkXQbcJPv+TYRXHU9aS1qQ0NDQ0NDQ0PD08JzSUrdFBF1i8YMAICbGg8d+n5uLWH1NIin20ZuPQ6x1AiqFwJP65e9mwTS/jcdoFIDb5SaLevjk30CPInv9KaIrsvq1S1rKRsaGhoaGhoaGu4wnitS6ipk1G0jnI7Bse/5ccY65Xf4zAmqmyKJbhvZdCyOed/HEE5ynUZO3Xk8iV/waRBOhwii24pn8Z4flwg75re8CeKqKasaGhoaGm4Vmu1Fwx3BdcQzz3xM/hTwXJBSl/241+2D37bQv2Mr5GVv+9h6/UwIqus8VJ5X8uoqhNMx5zVy6k7hJn+lmyKeniRR8zTVWAEolFJP57WPIYOu+/1ehcw69FmvS1jN1dVb1po2NDw2lH6+n50xtLu24Q7gJvvql12r9ZcbroEnwR/su+bzRFbdeVJq7ke6rC5ct7LEJxzOUUNVA5SbytY3d5nL6vQTJaiu8oB5UmVnoJ4ySRXrh9+xr3/ZeXPH24P21uFxfpHrkCqPQzDdJInzTBRJRYun94Tv3TTkO7vJsEBfhCBe/dzd93HZ73oV0qqpqRqeNZ53EummcdPfVyO5Gm4Uz2Li+NBrtn70C43bIlyR9/E8kFN3mpQ6lpA6puLcFOF0TJN5bDN2zHuqiavJe7mCwXn9Sofq9o3dAMc8YJ4QCXWjhNMxHa8jOnvHvKcd4gq4HgnViKlbg+v+Clchhq5KWlyXdLpJcudJqpdM9TpP09/ppggkutb13sO+93EZYVa+3lVVVVLP2zC14abQSKfbjct+n0ZaNRyNq/bZ95W/yX5viz54IfEkyKh9l7xKbzHEu09M3VlSqq4U8dCxAx3ty5q5JyGO8lcsf3AMU73BQ01juKR6l5X5GJLq2uTUoYfLNY9dSuhcpfPzJGZj/BWvuechp6TGHujs7VRZpecfno2YeqY45ps/liy5jOg45jrHkiVPkgy77utcGTFfvNcK/RNQSj2Od1NNDh36Hg8TT/uvOY/D30N5jeuGATZyqmEf7hrJpLS5vNAtRgxX7Y3eLK76ezcS6wXEXH/8cfroVz33pm00Gu4Urh1ZdUOvf9Ve211XTd1JUuoQITXZP9MJ39ccHdNfD08xfE8Xg5BDL1uPVerPVzaL9fexLzxwrjJH3Fz2v73Y97DYs/8gEXVZ5+XYB9PTlAvXhNGhMvXnKzp38r3sVVU1YuqZ47Jv/CZIpMuucVuJrOtc/8rXLVqzfdn3Hgc+xmt95mPD8Y4hh2piqL7mZeF7c8RSeY1DJFcOVdxbBBqNmHrRcZtIqLtOMF0H1/nMz5LIqutLI6meczxJj9lj+73XVUM1FdWdxnXIqOswBMe+zlVJpruqmrpzpNQ+QqrcL+RLSNvl+fM14Pphf5eXOYR9Y4+6w7+3csX9ZJFWakeVNXk9fo26yfRxl7DSavc7khJHM7P1w+KIGZBZ4mmuI3KF2RR16Ed7WmTV7IPKX1omqn3EXVGWz5PTJ59WVFONmHom2PctHyIw9hEU+86ZK3/V61/n/Vx23mXnXuU6V0X9utuogS2tn1qNBdf/m/N7Uny940pf9rq71zmOHJp7fSk/H76X1y/3wJr/kQ4RZvX7aaqp5xtPmnR61kTSs379x8V1CKab/MyPS3C18MDnGIfGCzP97ytbchyavJ1cWDrS1/SWusxeo+FW4Cok1DFFL03AdsW+pvSd5ix7yrF3rZq6a8TUnSKljlFIxRgvJaPq82a9qQ7UlxtVTO34Pu3r6O8nsOYqncLu+9RKTT6XXK9sMqW5lBtGboCnUrmLxnvnAXMMETX7oJr7ca+myLr02HVQXu9QtrzqmIq75aNS01kZWS93XeYp1YipZ4bHJZeuQlo9iWseKn/onGPOveq1jr5OdU257vWz3+15nZnLzSuUSpJp/3X2nbuf/DourUUmqsp9u+/tsrJl+X1KrMcJa2y43XhSJNSTJn/uOrl0XVz3c9+UWmrf69/c9Zuy6k7imAlsHElEyW++p22qr3Ew4dBNZLZuCqpbhZskow5d61gSSmrbvtpRXmeOoKqjmu4aMXVnSKnLFFI1GRVinClTXm96wUOeVPW5O+/tmtGjenamuaxwM+dU+xSm+pqa1Co3Y/U+VbEp5/nqHF188DDJWJXfaamY2q/o2jPLsY+ICseUryvFgZmUS7ZnH2436VNV4lDHfVYdtauCKrfV5JhPZaJioimwF5XWuQY0xdRTR/3N1oTFMYRRWeYYMqksc+jYMcevW2ZfuUPlS1y1Rh5DXG0LZeGDQik1h6uSKJe1CtdVNhHmFVhz16Qy+xVV02uoyXEf9/tRZdKpPH/32peRWeX5LZTvbuEmyafHJYRuklB6UcmpYyFk0U18T4eIp2Oufz2V13y9bWTVLcK+vvrcWCHsHtuLQ76uxfNf+so7b6uc8J05b+d9XNaXvkrZhhvFTRFR81zB5bZBx/BTh1o36VrJ+HxubH5Xiak7Q0odQlkJSkIqxLwNzBNPE6Kqqn5TEuvy177GO9+bPU8r7NwNStUdeTU5BlDHfyLlK8Ma1W55eS0fY75azCRVmCGolFKTkL36BjiIYx4we8vFnX1XJrv2EV/1sfo1azyOcspj70Oo/I1SGJ5s611yapawUprJKo1YyqVA38WlcuWGG8chQuoQ0XQsybTvvKuQWvuOXZXMuox8mzvnUNnLztlffv8xW5BSp1ZjOXNPXJWMOp5sOhx6V79unaBinlyav2Z5rV0ia3rNObVWHeq3n4iqv+z9JFVJTjVi6m7gpkioq5IZ1yU/boI0aQQVoSR/blJZ9bjE09z511VYNUXVLcElhNReMurQZPXcy1zWl/AzUQrl/O2+kL59vrDHElStb/5EcSwZta/YMTzAZQTUXKTVMW+rrLEy9pfxeT02v8vE1J0gpQ6ppEr/KM8FfUFGleQUlWOCKuZr0PZupdqtTLvvrVYfXRVzdWRHDVVt65n9EyJKKmpFWpWEVU1W7SWpZgiqQzfAbMWvHxxHPmD2klAzJJOaeTjNzaJMHlhz58xsX0tFdRn2KKLU3HFZL8gseagqOaYzIRWZlILSUEqT3VQMACygNRFTct2mlnriOIaQOoZUuow0OnZfuf+ya16HTLsJRRYA6D1PUG2uVj/VzAuY0KX101WHpXZpO16RjQp7ZmDDnh7MZSqnQ8dLAmof+YSco7Mqs0tc5fN2lVL1dUU9VRJVOcRvn6JrVx1F5zVi6i7gumTUkyKf7hKp9SRf72majSttrv16V1VW1a9z1fC+mwoHlHrfyKmniGMIqbJPH8PlE9V7oOo+Lg+IDpJVBzimHZLqugqqpp66cTwuETV3jVqMEibH6nML0cze613+/uainhSPz8ux+V0npu4EKVWi/lFT5ztkAsrxwTFEJqeQwvt2yCi+YklclfXDF7UnTDrtVaW8wrNrrp+3ozYoB3xci2RPPlSQTHw0bTOjlAksVR3LpJVS5XWI+9CJ9Dh8AwA0k18TUzsoHjCzZFT9gDlEZO0hs1SMlxNg+5ZyPoDoiw5M1ZmZ7dw8xo9fdqIm6yavpwelkE3ID9X0MCyIKChF5JTt6RB62g4O6ZY3BSHViKgnhmMJqWOIpWO3jymjr3leua8kjUqiSEggdcnxfGy6vXt8jxfEEU/XOUIKALRfAv+H1k/evMKqKncMMRX39HZqkqq+Vqi2y+PluXL9fcdDMQkjqMmmfdtEQqEir9TMeXMEmKrKYGcpJNWuwio2YuoO4Cpk1E0RSsdc5yaucZVy1y1/pWuba6qP/JMjpo4lh/aV33feTZBpMfiD7+dYVdYx76WRU88IcxPDIUzHCjFM/6TcnmuUfdx9EQZqsl0RVWU/+ZBAqg7zawTVU8eTJqL2qaEOEVB1mfLy00Rt02vPiU8UaCwu4/NybC7E1F3FnSOlSkglKYmmTEKRAjNG6gSHSD9eWUHktxfiKaRONm9zzUvbe2em98+K70OtAjCTilcOAHkQqTHZJ2UyYcXEkQwYFRFVHhIKGKEUdfilwkZudOlwJqG0yp9VF7Pusr+UDqbsSccwsDUhNSGqKkKpJrFmztt3jtpznXQOig5d8KlzEuX6vD+/7ZKkqpqjq3ay6k5a8cOmjpM29F74WLkfyJ1YUrhNSamoNJS29EANjval2RuTHqwtjO/poyacrko2Hdo+dEx+5eucL+2UEEM14TRHNgnJVBJC6fyikThEPl1GXKVylwye95FSwS/S+uLBAguznyiaw75ByrGEU31MCKgJKeVl335yKoaIblKW9mfCqiaQ1CxR1bHqqVOsOp4hmvIyE1v8Tg9uEzlVhvCVxNg8MdXw7HDpPXUFguZQ2esee9xzjy2Tyl6TNLrOa93kdR+HAFLG3DjpdYhMukxRdSiE8BCBdpXwv8NldSOmniT2qZvKMUNZTvr2weXxwbH+UkI+aZ0mZWM5OVtN0koPapJQqLgWAHpt6a9zmUvVU3LsWHP01l8/CjcdoneMKuoyMqomoibHquitfW9U7Hs0FIzmfhWPzw2PzaX/TiIcdXV7nVuAW09KTRjEYl9JSAUmnlwg0mnwET5GDJ6UUoMPGHmflM2d9rwPoP2ZhEIuE3b30fZuTfJVrTYzbE1tSJ7Ip2owaAqFUyao8sBRK0VlZvZR2UxiyfvQTEYJcaUVq6WQCSsZH9I+KSvsLCmrVGRihBVZchPsfNo0u3H5bEf9gFHSUZgrUymsoveZZOLzoxvo/IJsknKpTMgElZTd2ccdtJ2OyUxH5qhZRiGYZABgMvGUSSg9v0+b3FHWhkLytKH9tgP6BT3AwhLQdItTaGCPbKVzIIyvPQBvBPu+wanqSO0opWqSqF6vy5ak02XX0VpNCCZVbZeEkjY6kUiZcNIT8knqr5Sflt0lp6R8Pjbdrl+rRD1YVjMKqrl9c7gYF8Av0PqbPu1VnHTbg+XjTIheva9sG+bIJdovbVWYbofd8jUZFXxI6zGEyfFEUqXjcVI++Di9Tphu03Nvqn7yxXOxXA+YP7aP+JK/HOJXklHTsEFBU0s9fewjo54EQTS3/9h9e8seII8OvUd9Q+qrq5Z9FrgKUXVZ2XDg+KFz58iufeXnyKq5/tV1CLjL+mm7x5tq6qmgjmJIfXdH44GSjHID9f+DgwounzdHTBUTuIkw4v6xUppIJ6XnrTC4zK6KqiCcKh+qo9VTTTn12HiSRNQhEuoyAqq2Diojt4A8ESeimX2fRcbqAGB0hAk01raaw/TEgqdQh5hisySmbnsI360npQ4hAMk3Sn5s6iBH+EDbow/YuIAxELE0cgM3+kxKAfncEOKUjIqxUFKhOifXnkOJHWqU46akeKpIqUwgqVnCqiaraF++XscvYkJJXE0JqhiJjIoAVFSIColg8lzpg4rQkdhYUUoZjlmt2dmjUc92zBFSM6TVHBkVPZFQiYiqSalRSCmfyaaafJojnkQ9VZRN1yn3l8f2fdxyo1JGRSCrp0pllDYTwioKMSUdIyG1LHvjdD1dJ3goAKrrSSEVHBA7VsMFqKgRFSYzOw1PHru+SiWBtEskXUYuzZFR+84XIkpIKCCTS9qoKeGk1Q7BdIh8KomnOcKpJqayYkqnMuVy7lh5Xl0mbe8L8duz3w99Wl+8dIpF302Oz5FQc/vrAcqUjAo7ZdK+6lhNKhGJtEtc7SWZKrKqPiYkFb2GTmWCD9CdRgwRlreFoBLyqCSWPEuMNdczIZmAWHhMlV2gfLxUQoliarqvqaVuG25a6XQM8XQ0YTVDQu173X3E09NUXN30Na6Xfe4q6iBzsIw5QAbt6xHF4Hd+t+h3FVSHyKi597XvvR76DJd9vsfx0mq4Gcx6xcZABFQIiaSibTcZF+xcy5ipF6uaZqOmfZH6zUoDWp5c9YXyJK6KMSuo6sndSj0VayJqn3JKXuMQ2sRxwjGE1LFkFHA9Qqo8Vu+vyaiAzFdI9FZpHVSLWgDiAxQiE08K0JHH70CMCkqXKinZfzfD+G41KTVXYWovKdlXklGDjxh9xBtbh63zeDR4bF3A6EMqS+fFRCaFdP70z4V6H52QTNWr5TEQwml3qSf7ZN3y+uQvkUsF6aTovHJba4VOZzWVUUDHDWVncllZt+n1IxTLBDVnCaR1lRjdkp2NSsEgSwYnEELJ80OjmO0AsPOASQ+ZogycI8LJjZl4ciNdfhwmBJOUgfeJYJolrVg1FX3gwVxI6wANHMtBZAjVgHJm0HkZ9g3GtdZQpvjjbV2sK2MmBNYkrE9rKNtDLZbQJ/ehFivoey8zWWWJxNKWv1+eEQph6i1V/WbtoXd9HFJJZYIpL2V/zzePLGsS6lBZ25mJ+qkknWrCSRkF01H9Mb0suS52JpFJ+VhW9s3V0foYgFRGS93FtP6XdV/2zS5LIjcpq65HUgmWm0xCvfIZ/zdOl+NsuWNJqLJc3NdOpNC6mfYlhNSRDtwGleeV7VLZTtXH/OAny+Ajwij7qjKjT8RUSVzRvkxaiarK8XUGftYNISZVlOeJC1mXZyy/JIaQiS05X9RTpXKqRFNLPT3s3D9HEEWHjl12/qHtmrSoy9YE03VIrauQZnPvafe8qz8rb0pVdT2V0CUTaXtC9y5TNAn0EQrytM2jkFp5tVO+eE91KOAhwmofDoUMHibAWijf00Btw6GCT4oo5UZadxuo4BHW5xQV4cZiIrr4jcoIA9tNIwu0AaxlIoqXsg7kPrfJUQco/lQEE1J+SlAB2GeOfrTvVFNOzeK2KaNisS+pxEPeLkUzMUpUV5xEcNXRXAIZJ3RaozMKndY46Q20ApYgAkopVUig2GKHiamdTGm3HLealCoxV7lKXyjZ9vzjjiHgYiQy6mzrcDEGjCFgcLnKlYRSST4NjjvfLhwkqMpr1OtzKMP45ogpo0tSaIaIYtKqLCvrvWWiSesJUdUZhS2vW8P7dITWCmOg8lor+EjHghG/KgrTQ8gKKlqnpUqqKjoO0M28t5sl4XiF+ikpn7xLEtwJIRXDLhkly5KIciMtxyGTUCV5FUJ6UIXRzQ/qmHSKk2O+8G4JqEkq2Z8+4iVyuYlnTjU4rwfwSmvonm5P09kdIqC+nu4sVNdDuRN64IaAuDwhvYJ1TJ3n3yA2IuqZolRJTVVOu4qnOTKqDMdLxBGTUtrkfbrTE4KKCKcpYVWSU5nAuowgna+zNdlUlgWAfUQWUAz89hGvgjkftroMDg8U7SY/+uyrb4Fdup0yh0J19yolSyVlra4UJeYegikdK9qjyb6iTarbLiHV7QxZJUTTLhllE/EURj8hosKYCSzZr4xC9BGa1VQSepcJJ3oeiOLJR6DXJflEqqnSBH2OjGp4ujh0n1wlpG7u2EHyaQ8RVe4/REA9DvE197q53ExY8A2F+F0WKvi4OBRSV+Iy0ibOfAcxhFmVE7BL3hxDGNVKpB3llTZHfZ6SoDr0WuV2/R73HduvomrE1I1hn2fUvrISvhccMA4IbkDcnCNuN4humExKAyBrDI40iEJILZYpsgDaQAUhpwIUE1PyZFKgCXh4R30S7Cc7FPT1lVPA9fvkL1Df/XEIqcdVRs2Zlsv+WhVVklMRJICpySiJ4Npwn2pk+6AyYiuLSwI6o3HSAdoxScUKqhjzOHzuM9+lEL47Q0odQqoEgX74rSNC6uPrEWeDw2+9vsFrDzfwIWLrAoxWWFgieIRI2jIBNbiQyChZjjxb7F1AjDyjHCRcgpdlxS5qfmloXkrpUhiNJmVD8nKyOh2XELzeTsmocnuxc8ykMpaXvdUTNVQn5xqdlFSd0UlFpbXC0mhoTdtEfrHARrF6UAHQip3/OXyDVVShcpVS1UzHXDw4YoDyQyKiomPSadhMSajioRO3mykp5UYatI0OfnA0IBscog/wIy3DOO4M6oSoCiMNTMNASzqHG5ZCeVCH0aR6uMfU+JAZtGZCQIgEIgmIhNK8NJ2F7vM6XYeMSOVz6c5CG43lqy9h+ZYN9MkDqMUSWJ5CLU7SA3KWjGp4oqhNxPP+aYherxVWTBwLEVWqoYSIEkWUkFCmF8KorENyzBRklKI6xOV1Uc+ATH7q3iaySXddIpU0E6U1USXnq46zPUpIqXQAy31FB3HHM63cniGogGIgsW+/4BLFw3iR74H+0z4T3WJeKbU3++a+cN45ImpfuHC5LYSVqD+lY13uG4dZVWf01M6FSZuW27myDQSImC/bPT/wOayqCkxgRR8TgeUHn5RUVD4f60efSKmsoiIyaiw6gbSPdtSElGy3EL5ni72k0QEy6FrXSMrJeQIqtRtXfD+7Kqs9BPbM9mUKrH37jjl2lTKPg6sohI49Vu+7VNE0Ie8rpWmldrpsXZXtK393oSKhSlP2QyF3l2Xtm/sMcr2rKLAabhCVlYdEXKjgAD8gXDxE3G4Qzl5HOH+IOGzgz84AAJ778XbZU59lsaRoAttBrU6htIHql0Q0LVbUR7EdlTGGvE8Asr1g5ZT0nxWbpNfeVACScirOpeiT+0HrXUN0YJ5caiF9CTcZqvc4ZFRp+zNHRolAJhTcxBgCC2Yito76TBLBtfUhiWk2zDusecwp4/3T3mBhNO4tLF5ZdljYCMviFK9pHB4jjdMDh+7dxdpwJ0mpSWUS+VxECh+4GH0ipT56PuD/9zuP8MGPXgAAvA8wRuNk1U1IqcEHeEd/4qvhRk8cyUgdcs/kVAhETMVQVNyixscQMZeuPBFRSqW2Q2k1IabSOofhKa1gLB0zlgeeMgBlsslohVVvJoTVgsko+qNjJ1WZpdUTSaBWIJJLKYy8XNiYjlvNflga03hWMhmBwgF/qdILqo4H9wMRU0JGuRFx2FBonZBSw4bC8dzIfwORUgXh5DdD2iZiatwdgBVlJEyFBlzFYIu3JyEsM+EtAKYmwzMtpigMdurAjFpFVCp2aaGMQreipV12ibRKBIDRiD5g+3ADP/ikjLn/1gsoo7F4eUS4/zK0NvR9p9A9/mNNW8vC9/QxNTrPy14rdJUaqtdTRZQQT9oo2CXVBbuyiXiqSSg6NiWgavLJ9F1S5gnxVBKiymginDhEVBRMynZTkomJJ5HEp3BTOU+OIRNYyVy0yB5ZNI5pXbwgYmVEmvYJ9q1XcF3+DdxLb4U/nZtCm5nBBXZ9LuqMoHIsMj1/KDkDKzgzGTVMQouT6hOYKELlnAkpz4rQKQnvEmEVmKj3w7hDVtXtpFu7HZJK1t2aOvtuQ2X0QJ5UfvDo2Y/KKJrxM6Ki0jkLX0k8NbXUs8Vl5uZz5NCh8vvW55RQ+gDxdIiUml5rl3S6jOia+zyXkW7191RnT56WPTwFfdnx64L6H/u79HP9E0GdPXpOBSSEjCnW62P1eqj326443k1eJylJZ65VLk1JELGaqiSjjskceMhjqi63j+hqaqknhDmVVBlVETzgB2DYIq7PETcXCG98FO7hGxgeXmD7+qPJJHN3uoLSGv2DE5hlD7vsoYcNYDvo1SmgDXQIiExOwY6kqLKF/xkrp2rfqUROcabrEilr3xHk1NGqqbn9Vy1zB/GkfKOODdU7RhklflGijHJhPxl1MXqEEHE2+MRZrAePRxuHs80IHyIebaj+ivDklXsLvHzSYWSV+knQWFoakAe2TDARUDFbhJTfwW1VRtW4k6TUHMq0iiKLO9s6vHEx4LdeO8dHfvthIny6hU2kjndENLnREynlA8atr/ZFBEdEVeABgPyFmYdmjbqzpLWZ7FPaQPNgTYgnIa7kfdakVElUaaOx3tKxZZeJJ/kTgmo9EEG16g2xq72B0YHJKSKf6P1NlyEqrika4BwAUQEmAkFFRBQMLWZC+EJlWh6K+HDxjXKOiCghpVgFlUip7YYGYcMmDbzchsL1/HrYIaLC4CaDLbcZEuHkRz8hoGo1QBmyIp4qfvCTDFVzWanqVOslarVMHaLVMSGlOw3TG5ieyCltNLrTrtiXCSo/eDz8zYcYz8dMnPmAk09+FXbZw4rCrBw8P9mJ2oZLUD8s5iChTwCFRhkF9ADA5GP0Ed5HKOOhjaZ6y2q86GMiKGOgF1NGw/gAzURm9CHtU4Y61kprmBAQtIYOAXpwUEYjJOLKsTpqQ53+ypeBjPj1xJQ/FmRUrMioMmPkJHskkA1JC3JK8TKqKSEFcEex2gdkImsOaiiI4uEMyu7etKomS/aYrlLZsHu8Nl0tw4lLbztkcgoVOVUqqWoVVakOFdVUrRC9jIxKRFSlmsptYpiQ9tIeSln6mLl9HKo2sm4b59DUUXcDx/gw1cTRPgKq7v/Mn6tnzzl0rZ33UZBJE9U6r5cEUZkRecptT9uROUJqdhLyEi+PQ8TWVVATSjXqlObAPEG1S0yVE61FuRh3ypRlw+Q8aSMOE0103vz+HNq8ezwGDz13LV2V9RVxdYmiquEWYjIxFBF4wiZu1/Bnj7D9+BnWH3sDFx/8GGIIGNcO2igsXj6FXfYI4wh7ukI4XaIH9UUCAGV78lwVfymA+huaExFxX4TGPaSYUhAllE6TT6nWl35R+8zMawLrWNXUVb6r54SYuoJd86XnzrWFtH//+fsIKTlPCClfK6UqQmr0JTkViiRsAa9fjDjbjDjbOLx+MWI9eLxxPiCGCNsbGKVSNFdvNV1LExEV0+dSe4m5u4Q7S0rNVSLxmJIf/aNnW3zw9Q1++zc+ht/+zz8P06/Qre5h9er/C/3i/4LtDIatQ3ARw9Zh3Dq4MWDcbBHcgHFzRp3x7RrBDfBuQBgHIqMcexTxcu6hu28GsOyIaR606a6H1gba9pmo6npSMFg6ZvoVlDYw1kBbDWMyKWWshjEaFz0RWLY3MEZDW52IqlVv0FsySeut5u3pPlFPuY6M1EbeN/qYVFMra9hcrcgCVsoHJ9GrKMgoNikUQorD9eLmIqujxoGWwwZxHBNRFbcbhNHBbYZEOPnNAD+M8JshHRMCSgZTbkOKKLcZ0z4hpWj2PxD5GIG1D8mIN0CMfGPaNw1PyQTUHDElx4ApESGk1DQsi5bytzIaGsA9q9FrheXKwi4t+tMO/b0OujPo73UYzkb8j1/7KH5743DmAtY+4M88HPDgUz6Brv3WDUmRxUOq4dZgjpzyMWKMVEekfvU6D9pNiKREGYiMCkw0mZ6NywcO2RsN9EYxmZRD/IRcyuq83TBRAJMQvVJFBcz7m+XzctgegKSqSutAIq5QtIHJhLQKybtSGF95Xtrcz8DqCw3g02n94WvQbvf+2Bmc7POOQqEs2BO2l8qWRFRJOMk+l8mp9PpMRoWBlkImlSHIaV8ip0aUoX1lyJ8Q9ceoRRMRNQnlC8l3SlTFNRkl9VfUy3NoZNSzxT5z832E0T7VVElAzRFGO9cpSKdJX2iubEUulUpyYFd1LmVkW4ghXZFQ5fn588sxTM4BMsF0iKCqj+0jpW6KjKqxj5yqB2L7CKTy2JwdRebay0iFKSkly1BcZ2dfcb3y/KktRm4jaxIqVNslaVWXnSO+SqJq+tkPh/3NqaaeJ7WU/Kq3RlBR+tAKeByRxgnnDxEuHmHz0Yc4+63X8Oi3PobX/utr8EOA2ziYzuCl/+sB+nsdTj/xPvr7J7CnS/j1ALPq0d8faKIseFJxuxHolzTpxr83hfgZoKMJMCKqdF4HAGMzMVURQjuKqdJrqlBM0Wc9IkvfCxTO96zC9eT80si83Jf9onI2PQnXGz35Rm1cSEvhJLZMQj3a+kRGrQePj51t8Wjj8LGzAednW2zXDm989IJIqU7D9gbbrUshffe5P751gdTmxsCUt0nxOW+LDuHYduXOklLHQAzJKdyOGhjTr2D7FbqFhe0MhenBQ4+5s1N2hg5h34xPfXz/A226PwAphl5pw7M7eud6gKGHuFIIIUJpfuizQZrW9JAPKkLxdyCG6qVBe2nYPvneIhEw0mHzEeh2Sj0GDhEkB8yF0+nJ4Nen7emxWAza2LjXF50fVkFJ58jH6cCpHEjlgVZJPKE6JtvzSqk5ckqMfvdBwlyMos5uyqLWGXQri/6UBvkrQz5Eaz8zwLtEwk6/w91+aN12HFJG1cdKhRTSvReTYTRAZBX98gHKKAQEKE/dHg8PA1LQoTfQCAheAVwNND+1IntLAQ468O8/IimoACTiqVRRAblMaWIu95+U0V1BprASStZjQSqV6ioANFvJx4SoirIt4PNT2dJDpmqz58x6BWFrivWLNLM+LbS/LSrVT3XZWd+osmydFVTOL9VTYZoZFMgeGaEgoMRHSkIVSjKqJqySZ94lXlL0GXh/RUilfayWqgmpkowCdhVTgkZI3W5cpoqqw/Hm1N9AnnRLiRAOkFC6IK73kU+1FyeAie0Bna9S36UkmnRRfo7AkmNz+8v1klSahGJXZNNl28cem8OhpDr1sUPbfpZ82lVClSTT7P4Qc0gMW1tMyhXkVLLAiNPtdJ0JWWVzeUkOUUwE7yOpUibTqqyYqe/4YvlDYXr7VVXPE24NGbUHqgx7T4pjer66zQC3GTCeD9g+HOAHj40LMMph+XALAOhPt+S92tvsr7jsSWknIfJCUAUijCJA2/Si6X0kYirGrMgWxdQxoXhybGb/jVpq3GFi6nEUUjUeRyG1b5+8P1FLlSRWmUVPxuUUwZW9owYXsB48BudxMVD43jD6JJAZLihayPdLuDFgYFJqPTi6TtCTsau8h9uKY9/ac0VKyQ8iDzgx/z653+PeWz4Vqze9BS+95RXce2mJz/p/v4Leavyvj1zgbONwcTFgux4xbj22nUZwPbTtEYPHuDlDcAOZx7ohqaYAIIzTh57gUrWUMZOZxVIhRSqpjlVRfTqmbQet1Y5KSpc+U7xM6qmJt5SemKWLSipl8VOFETp39CZZvjg7GLAbn3rUA60MsymyU9AANeQBrITyaE8zFg6AMVA+TFQbNBgeoXtLZt/V4JjKRChPWaOiCQievh9lPJRR8IPHAkDwAYYzIhiVjXpp8KTQ66kCICulrqaSkm1RSq3YYF78hHqtcM/SvuX9BUyvsXiwQHfaYfFggdWbVrCnS9x765vhN1u4tccnfPgcH/rNR/jQ1mH5pkUiFiZfvTxIG5445roAhwhIYBquR+U5OxmrEb2PMCqi12qimgIAO4bkS+aHAG1Uyp4m2ffC6FnZ5JKfmaintBnYtHyqoLpu5j29J7Ok7NPV9lFZ9+YMzSdta/WtX5LC3RfZ9/xHfht+JvseHawnGnbVUrS/IqfKiYoZFVWZvVMUTLJdEkp0yfmsoPsy8M2rqCKHJIeUrEFUUdkzL0xUUMA0+54QU26kz1GTUT5mQv9QmPM+zJmda6B25Gh4gqj7K/PkkpkQSpM/yRzL5U3VrykJJ+qrMIlU+GpOLQxUIpSETNrrwSl9FD39A+azGQNIfZ+6jGzTUs9mS5bzy+25MvU6cPnz4Kqo1YhzWaHn9rnq2HTSMszsm2aedjPH6jKTv1h6smZyauLVmsowYRWzfQaVXU58Xaderzk0Ogaf++luSG1neTwRWzr335U2sxEQLwo5daswEzKvYqCwPVZLhQvykVp/9AyPfvsMv/3GFpsQceYC9at/8xFWb2K1sY9pckYM0M2yR28M0C/z60pd6HqeTAupfaOJMovIXq0KGtGzp5TGbigfv/9LFVP7JtFewMx8x5Irc8UOnXusQipf67CHVBmyFyKH7PE4sjQuH0PEmr2uRx+Jcxg83rgYcDF4fOxswMV6xMXZFmevb7A5H3H2oQ8guAF2eQ9mscTqXo9+YfHSSYfzwaPTOYyvfM8cyZd8n+8anitSag6GCRq7OsXi3n2s7vVYnvZ45d4CC6vx+gU1VgP7R5GXXkDQAd5beKdh+xWCNpBhi3SwahKqjHnfZ5wpnToAsx07CdkTQmpyLHXmph0xzZ24soOXyiq10zma62Cl9RnSSVc1WzYfq5nb10hqA4BnLarBakotXyTKUsZM1Br03WkoE7g8PQrIbJwH6AjQQSOaCPSkOtPQMMEDkHA6IZ5UIqkAIQXykgZSU+WTkFmC0k+qJKVKI+tOkeKp1+QvZXoDuzIwnYFdWnQr+rOnS/QPTrB4+R78psfJJ6wQfMD9j6xx5kIyv9YHVCKX/gYNzwRJJYVygK52VItSh+p6FnyA4XtGQvvysQiNaUpvyhYZyAfKh6xYKogXpfneEtUU32tCfMprlucETOtfeW+m91ZeG6y8CDnDUmpD6xnt9L6KY9rsvOe9KsF9ZNUBVeFOeMaxZNTc/hlCqnydUvVJp09JJ7pUVq2V15J95bGpkXBJfE17bqKMmn7MMLsOTFVO0/W4Z4mGW4xDyvBSHTVHSGULgi7tq/suxprcZ1EK2qoJOQUA2uqd5C6pP8NlEmE1Q0D1nLlYlpLwpd6X//ROf2iOpErrqW8k+6Z9o7rvVPa76j7UIVJqn2jq0GCrJqVK3ychiOT8PLMuxFE+pwxLkWVNQu0STmGHjBo4HFp8UMp9ktV6jqjyLrdxJUnljZ4QVEAmo3RQCC6yGksjBgPv/CQKAeA+utYI45R4Aqbkd6mamiOiGjn1DFF6NwaP6LKfrNuMcBuHcx+w9pmU2rgAu3ZwGyrTrSxZgHBfhpTH5M8I20GNQ+6HcBh+6m9I/6EkemIA1J728yrG4wUxdbRa6g4STjeFY7sU+1RSh8+ZtqGHrhmLdzJVSWXSynlp7yipmrSLW0eKqc3o4UYPNwa4McAPGxLDjENqbxyXSe1nHZYtbNQdx50lpZTaldfJw7wzCtYo3Ft2uL90uPfyEsNb3op7Ly/xpldP8UkvL/FJD5YwGni0WaasdB+zGkPnYKyCdzH5tgxbg+BWMMOKZ1xoBiYGjzAyAz8zszL7vmuvBSMzkWJ03k86e0rrwuBcOmzZ+Jx8o7JCynZEXtneoDdTlVSZfa9USfXsI0WdLKBj9VUnBElSS+XOlULugCWSqlivPvREJRWVSmlTaXbBANqTQsKw4WDwiA5kQuiGlF7eFEbNAPmrmCLMUWZBSJFB60lxwJnwwujhTYDSijo1Rk2ySHUzpr2UrEulFOelGoC2D/7sOwqpOsvayvBv12n09zqYzmDxYAHTGyzftES3sli8fA/LV19Cf/8E/SsvA27E/U95M+yyQxg97G+f495bTtE/OIVZ9SRFlnCnF/TBdZtQZtrL+9TkuKnqRcf7Su8xy5kaJWOj6fXEJD9n3yv8pjoib6WsKA7JN8rm7ZRxz6R9ZaheyuJXKqe6nJFvx7i89pCa84+SdnCfUuqAj5S0nxNT87m6Xu3rLvJ292mfhe5kd0Y2oTJYBYrwPeCwKqral7ymZnymdjym6nA+MTrnRA9zPlG1uXn0PofvJcPzwgydrzFncE6KqTL7Xll2qpyStlK898qsekK2ZvVoPpb80lo4363BnK9TSUhl5TalTtc2q7plv5BQooYqk7QI+STElKi909LqRDqJurteLirVd0kwlcfkr+M2RPozWvGEpcrb8pqa21wJ0zMKuVy1j9YlrA87xBWQyamyazTXT9pHRtWYI6cmtju8LJVRoSCJpTkqySm59yTMZH4fEoGU1QFskcHbI580VkSVDMBKIksGV4MLiewq96WlvKYkG/LV0mWySogq78xEReWdz313S0vJ0if74YYdVWY5wdyIqGeM4jmcn4v83Boc3NpjOB/ZXzWTUmcuoL8YMZ6PGJaWJnpPaLzmNgNssVS2Q7Q94AbAdVAW9BpuoBvceyTj8xhyFr4Y543PjzEuf4GJpWeFY8P2YrVPVFIAkdhlCJ+v2s6R1ZujD5Dkazlsz6XwvfXg4YYctjduRgwXb2A8fyO1OWaxYsKKw/xcSN5VpOZ6Pggp4A6TUvsgP4tR1DlZ9QaLVYflaYfV/R5vfrDAq/d6nHQandF4+YTIoIvBw4eIM6UQY4Q28oBj8sMoKL2Adx2CGxN76UX11OX0tofS0daZZdIsYz3ryKF6x3TsjCmIK172RhcE1LST1luTZgXzMe54yQxk0bnSReeN9vFneRxtYJUxSwaxUZeKh2k4XwR4YG3hOTuYgUUYHc10GQ0Ni+ADVKB1IrEUE1K7A05RAKikMKBWRvsIzdkSaNCUU276GKFBhAEnfd9r5gsUHVXsZt2bKKOWFqbX6E97Woqp+WkPe7qEPV2iO6GlPnmA6AYsXr6P6ANWH19jXDssHiwobr6z+Tu9ixrOFwy1v5QkECgVdXMZGrWhLI1CPNXG5t1KjmVT830klO5sLrdDWFFmGiUZajiz3oR0qg3OC7JJjiUTdCGtykx7E+J6ug02FU11ObsRT7cxQ8DOdPh8cU/400+APy17ItN2QpXbZSbLYjuZsVZZ+HSdla/MyFeGfNeZ9QrySszP07FxmqEvEVbBI7CnVJ2FL2cinSepavPzcS3HONtjJwkiPLQhckrCny2Q2kqAvdG8aMfz99qIp7uFWu29o5Dqdskobbs0WVb2WWyXSaoyc3B5rJvps9QklEyoyXrZh+kN9YnmiKeO1VWdTApx2HxJNuVjmWySPpECVefU94GaTsgh94dy/0iOqcl2WSZ9v1f8berbaCd8A5hkpC7LSOpyORZinu2XAVgEm/gW6qlMOMUd8mrk/lMaiIUpYUUkFRFgg5+SUZmAyiqA7Qw5JeujzxmzY4jwJiQ1leZjStMx5UqxyhJB2lmZNNB6ErYniinpwzdV1C1FMdEj2WP9QM+ngSeUN1xHNyFi7SNOB08T0iOd41n97UcHm5TFnnx9vebnryi4dXo9FU1OHFQrofaF4RXEU/KhuoSMelG9pY4N3TsGl6mk6tC9g2VLRRSknYzcfk6VVWIhJGS+kEclSS9t2uB4AtBFuMHDD+vCKmhMk0DBBXiX201pe/1M23+XcSdJKTUjk9JQUCpCKaDTGp2OWPUG95YdXjrtsX2wwP17REi9em+BewsLoxQeLGmQNDhmJDksq5QRK60QXICxEuJn4UYij0QmPJfpY+/7r2YhgVIpVfopYEcZNSGpSjKKlVJ9kWlPOm6ikCr3lZ0/+r40qaTScuopNe2gqUnHSxezhun3mf/giJy1AtHTOpDitEUxRdkwQpq9UvydqmCAEFLYoIS7mNAXaihessoNqJRS2sP0Gn4w0J2fZpXquJMz6OSjYmWmjQ2A88Cr9pLa360UUqokpGxHv7sQCt3KJrJBSClRSvUPKGNIf/8U3YMTdPfvQa9OEV2H/sEJog84+YQzhNFj8fIputMl7LIndZkxhSJt96G096F3Rx5gtw3Hfmsl8Vsuczhnua2w6M1eMsouLYcom6SI6lZMKolvAht8EilFGfXEU0EIKNlWXZ/Ip7Ts8raQTuK3MCkL8mGISgHaZoJJsw+czDqb4lj5V5Sp6+0OUVUsy6fBZIC25/sfyvXTT0B3Oj1e3s3lADKt1p3SGDJ5FcKEnJLkFWmflKVY8UkZFVw+Fhx1YMeKlErk1JBIK9mnR1raoqwoq9xmmBBVsu2HkTr2nNU0+gC7pGPj2iUFlVt7hGXIGU2Ng+k17fcBegzA4FNHyccIrxUrqHKIc1ZJqYNkfvr+0XylbhqlB1vZH9ktR8RxOWmWFVLHZwUuJ89Kv0ujcr9ElouClFr1dkJASZlO60Q+dWaXeBLSqdNZ9S1lASKlFCgET/ozScmqc3ss/RsF6uNI10bKyj75NpUq+kaQMtNtAFPi+7qZcctndEnK8zKRUMV2MiPHNERF+jKyr1QI7IT0FUSWD3T9REqJn0oip0IirWrCqvZe8SGSaqAgo9aDSwO4bTGIk7I+0kBOlFOe1VJZBc8EuiaiPGgFXxBNYcYYXQOIZl4dNRfW9zxl4LvrKC0L6r5YSnbE3oq07gHOHlxmfFR1aidWZdXJVBoaSoSQ29Iys+llIdC7WVBz23JM0rXnCbealNIqP1inc67TMpFnZDUk/EyhCwonncGwtPjEBwusB49PenmJT3ywxJtOepx0BkYBJ50G0KVKIsaVgwt4yGSUNpStyA0Gnh98ttcsHaYK45Nfx24F23nPIufW89ll9nktHJK7l7OMcx28MmSv3CedO+nYWZNnDsXgnNRSMuuYw/SOlZvPISpVpFdVREzxDUim5zy/aHtE7aFYESBhMRpUef3gSA3FCitVhu8VpJR44fjewQ8BpgvwveZQFUPeOD2RUbqTUMCcDj0sbSKp5HcWVVXtt7Lze7O3FQAYJqPKsCohGuyyg+k1OlFK3T+B6SlszywXWLx8jwipk/tQyxOosMTilZehtMZ4sUb0ActXH6C/fwK9OiWSwHY7KpS9hFMjop44ai+RnGVRTdZLhZTtsopOFHVCRsm2Ngp2ZRP5ZJcc+rvqmXBaQPd2Qk4RCZVTHhPh1AElKdUvd8pAG8DaTDrxMmgmQI3NRJSonPiYkFGl+imRu2E6QEqDo1DO9sfJrFSMPJgong61MqCEDMouzgFgBQD40NmI0xlSuVY8ALuqB60UD1ht2p/bx0xGA+VgFzmbT+D2ytOUvgo+k1dCUFXHdHD5XOeIfBo29PmGDflWDZtMYI1EWhkhqriM2wwTMioMDm6zJaJqTaSUWQ7puOkcgo8wPWV4VIYSSGjDs9SsRHVjJqYo3Dnyd6Am61clpxqePPaF7dVm5qSIyglYhHyy/ZSMsj1NqNneJIV3x7YCJRm1r5+y6k1SvZdLazL5JISTHEt9GVaAK7ASHHQPqjTJNiWeZF2O0X3N92pwACIU369CJovysVZITkjqWl1Z7FNz9X4fSTXzfN5RjgI76tFYkf6p7RWFKgAYmTTgIYG2iZgqJ9+EpCoJKyqjUwhLAJisItLKVSTUlkPyZDmGCMfE1LicllkPviCjfAp5cUJKhYh1J+EsIYXwGeMRY4QbaFubAOMDnKK+tXc6qWJy5utp2Cp4H/0k+7PzNdwuUF8qP1uAIlS1kJVQ0o/A6rqZexRCUDU0PD7qYWKdFTVnHJ3JAFp5oe7LvjpxsbijFfdWk1JXQXo2g0kUnjlbdQar3uClkw73lxar3mBpc0dmaQ1CRFJVAWTOaLSiB6FWZLhoNZTy0J7IohSrbrPZYk55m99XKembqIlS/2CazliIqFL9JNlnajJKVFSl90KphhJpO+3blcQnQqoIz8udNZplzANmtdM40wxhlrYfugeEhErKh/IGVRoRZRY+UkvFccjklGYCy3bpdZQPML3FnEEwAEQTYEEPH8fbACg0c/CFabNH9Bqe1VWmI+Nkb7IRsWSjSiRVMeuSfuviPZQzNkJISXYzCcESzx/NxJSQC6SaItJA9xZmuUjqJ9UviTzoejLk7Jewyw26kxX6ByfoTlcUasVllBAFZaVruHUo7zMK88yKOiVZ8ZJaahqaJ0u77FN4nuF1u6RsjFKXZsmofgnVsQKKiSnVyXqf65tSgOkT4ZSIJtvzfS0kVUVAVQOcGCOHcoQ9M/L0ndSZTmR2HyhVAHES10/7pph7gK+LZAlnY4DfFr9Fxban9iYRUZh46skxCeFJpBSXkaZgTnlhVEcq2K6n/aWayjvEgoyKTEYpN2SSyhJxpfoFqar6JamjFktAVFJuhHIjKarGkX7z4NGxp4rbDNCjQ+hcbhO1Tv58nr3DAJCfVKAMj2VGUyB79lkYmOAAyksEKEnwcHkGvoZnj30zs2UGvamJOau7bc76SyF5qLYVltwfk35JqX5a9XZCRpX9k6U10AoT4kmO5310x1md+zBCQnUcgme02iGgTHFPEtk70iSYkMEl6SQkspBTIeyWKVLHQ65ZEVQ1GXXI8mH2t6gGKTvkVKk8rfeJr2epQmUySpUTCUpBKw1bTTJEqEnWqQk5FWjSYAwRMSr2WaHsUM7QsaXNpJSopGhJ2aRCBIzySVm56oWc0hi4b+5DhNUKW45o6K3GevDYAKkv7V2AtnFSn7WNCJH6zTKhTPXYp/rciKdbBKUB+GKdUSo92e9S+keUhIgm9MrJPW1U6oMnj8wiO7DSu9EEU+/L1n9uuD6kH5iy3Rd/Iv6g52gP3fUwPXlYk0djTypkW5xTTXo+L7jTpBQLpPJMl6YZ1yVnWrm3oI/3yS/TjPgnPljilVWHewuL+z0du9dTZ8fHOPEuGFxAb+kheNYZ+BCxGX2aiaEwvpgUUsExEx9jIqcOvvdCISXbQj7tS32s+XPVqqhy/aSnRjSTU2bHe2FpDYxG0enTSSFFHTxgYU0ipkhJpfOso+LBF9QOGzvbbNeNueJpS22nIS1dMYuB4oFQxI8rzYNebSjDVwhJPu07B8PhKRKG4jdbmg3pbTIEDj7AFubAXgZfbPYrKdNzGvVp2nR6K7uk1N7fmh+GAKCTUiobUmelVD8ho/r7p7w8QXe6hD59AH16H2p5Sn/BQ5/eB7TGckMhPstXH0DfexlqdUpkQ79EZAJhJ/ypPWSfCXbDOYtkAkDqTHWshlJGoT/tEnFpV9l7TBmF7nRJJNXpMqmh7OkKWmvY0yWF852uMgElJBSTUnp1mo8JAWU6JpaIhAq2o4EL/0VtSBEl2wDGgCJNboT3MkjxREJJuEcqkwc1cyEgEjMv/iUADXaA6T65juwXlM1wrcTZrBWAlwEAv/aRcyxX05lVYKoEzQbHOTynND7uyuOiMlXzIUNTpQa1paTkKJUdFkZZupalMp3Oqg0VHOAdEVPBpX3KjaSm8kxc+UxMiXoqrM+zYmocoYdNOjaek+LSnW8QQoA7XycVlT7fUEjf+QaRCSg/UPvrNi4PBgePpdcYPWUpHUJWR4kaUKjDppJ6+lBHtPulYqQ0N5ewPWN7mH5JSVU6tg5YWGirYTsioYSMMlZj1WUi6qQgpXpbbHeGSCajkwJKCKcTTuCyNLk/Yvle6TTdH73JhJPRhfqU70cFAH7YVR8K2Rsc9SdErShKxZDJqZ1Q2iJJQSKWSt+iIgtnzshZkB4FGXVsCNjk9yvJqVLlI2XKpBISXi1JJYqEFLoOvZbJhHqyQZMy1lQTE/Qs0EDfJ/UrEVIcxhvycgwaMUZ6XiDCBQ7v8xEbNi+/GH0mrkLEtg+JvFpzBqqLwWNw2fj3Ysjra6vhXYDhZRmFADhSuoce3nnOukzfv6iltDZp3vQYsup5COGrW+GnNtZVejqTL9vcv0/7ZKkkokIDtkseqqanibp7PIYBqA04NQpLS3YHussWBzQ53CWCisgpvl8miVem99jEA7OOPNjXtk5I4hl14wxuNDnRHZqQ1urmfKWUUgd9pbRSCDGmSCytMJn8nJSFgue7RAMIClBRQSvqw1JEUYQJdCHNFxNuQgcag/sQd0kpCXG3GqZfwvRL2NU9RO/Rre7B9CtYFjKIspjG8ExmQSXbnKmyXz5n/bmv+YVeA1d9qTtHSkmFFZ+JWfWODAZY3k0dIFJJSWdH2g4K9aN9nc7ycQDJi0li2gFg0ArKqVTZlaN3EDh8jNLYXv45sk+vVCRVPDRxkIyqiShZ1gxsbWjecwdPTM1F5i4dORkkaZUHVZJxT77nucos1zjqQx9oHMmHhn2ldOEzRV8UNRyGfKUoKwY1EbrvUoewNED3cPzQ4ZA7Xiod0no0ObQPcAA0k1MKQWdzdD/QB6xD9o4npehzm55DMjsipYiQMsWD1cKs+hRmZUTZwiFVSbliO8SgiUgYR9hlD7uk81IYljbTB2r5Pde/S8MTw1z677nZjWkIH+0TQjOZlxcm5mJgrjvylJL6YpaLXH9YHZVUT/0SasHkU6GUSktriYgSsslmcmqOjPKR0t1SyAbPkvvIRrlleMd0Rl1m1ccQOA4/zhroynZJRk1IqJjj86UjU6c8B3al09tN/gEebhy2Ivcv+5eJPJyqoqazVDlzF4CkwK2JKfK1CbzOKlQt4dARJpAnogncDgfxu4nojIICKQ40AKstjLWAJjIK3kAFS0oq6dArTceUBpQqCH4P5YvBctE+RDfALskENoYeanCIy0Wun6NlJSm1rWSET20jhT6zgkrauqIuB+yST3PG542kuj2oFVOiktITA/QiI7DmJCyyraZK7vpP/KJKqwFRPC1Yzb5ki4FETvUmqaGEeOoKNZTRrJQC3adWIasLQ/Zpg5C2Qu7GOCWnmLCK3qeEArH0cysTETD5JNkxJxk0eTsW5FQqgypU41hSY8YPrOwnpf2JpNKTQXYKzZaBfZ2QokhkoY3JxBOTU0pbVs3mEG6Enjrf3vGzqYc1Gi7yc4Dvdc8q0hipvfOR+vAKGp0BMABeywQEK9ulYXfUVsoEsit8WQBMvFoGFwAmpDT3q42h7MqZqFKJ4NPaIBQKwPL7bcqpWwrxuRPPTJ7gpQzXmidDWC2VEsCYiTJKGQ2tdY5skPtm5n465O1TZ/+Nl4x1Jmj976cOaoOOKIddokrP9FsE4r1MKs8cEFRnY6VssWGilDJGJ99FwxM/QXuYfgWzWGYfxkopte9931XcelKqZE2FzZwro0AVIijJnqLZN0ph9PTjv7SweGlhcdIZrJh42nYh/bid8Vhyh2lwAaue0jbeX7odo0UxW0x/MTIhVah9DtC9MqucFFPFTHtJLAEkTZal7BcCappZL4foldlq5EYRBVnpy5DJOw55NKKUmkripQOoVJbEp1AUJb/DgVuBzc2lwVYAdXaiAiIpphQAWOSOUNmRk23XQdmRYr0tZTyMw4Y6NsMGZkkdRlFEhSLTVOAwP1JMjaR+muwTxZTj3y8fywqpKRkVDpBSumAehJiSB6E8GIU0SH4/XZeUUt3pko6dPoDql+QTdXIfarEE+gU1mKcPAG1g3ICV0dD3X4bi8qpfAh2RCSi9fPb9PnPrDU8cMnAXlZRR5CMloXqmM+hWFnbZwa5MUkj1909YBUX1pDtd5bpzsoTuO6jlKaA1e4wVpFRSTJEqKmohowyF4/E6LCmloukpBENmv33EOPgiVGN36cLUNySEPBM+Fm3mnPEtMJ9W3BXrgBhFhsl2uazXBeMm1/H/72+9gW6ZB4Rl+F7ygSraY0k/X+6fk2TXaehrQ2ZTrC/N1JB5werWFHpULyEZxBYwdpFClpQoQdzAWYQGKFlfDlDBwZ4+QBwHhM05hfhtSSkV3YCwPocOAWZzjjCM0BcbhGUPtxlQhvQZHwBcwA/sI7XmWWAfoYxH8BZhDDA+otdUX0qyteF2Yd+Aa5Jxr/CX0raH7Q15N7GKyXaGVFK9Qbcgld9Lqw5GK9xbWiysxr2lTeqoe0ubVFCdIasFIaIWNq+niUQF9CbfA5aJ344n8HoNVgdus4pQyCeXlYO0pG0M250EAmG7ISLCjZlwGqskA6KG4iQCMYj/5LRPAbB3TZhuA1Nl1OOE7wmxIv0K+t3yetnfSCFL0g/pKGHP3sQW2tCEhjbU7xDvwWISI01aKJ0VthzS3ctzxNi0b2C17MjPEifPgwAsjOJsfZRlbyNKKefT+tKyCtNo+BhxtjG4GDxWvcfZxk3sN86ATEApIQK57+YiKIlRj+AGruOFWgqYTbDwvBJVt6JprlVTYOuPKN5orFISe4p+CbPcwJ4usXxpgeXLS7xlcYa1j1hy+3DvXo/lSwus3rTC4sECiwdL9A9OdiwysnVBlyZ2y6QvOVuwTuOYHbVU3Xfe1+eeK1+QzcdkED64/6plbhnKcf8+HOIDynNLtVR5TwsxJWqpybUVKaPK/Urle0QpBR0jogICE+yG2StDJyNoerVF1IALWFqNkEKSaSJmPfjUVgEcReADlH4AN7wFMXh0S1JKLU96LFYdXj7psOpMfjYaEZocVkA9q/v7qlOMt56UmoMwlRrUYPkYOQtKTD+M0USohBixtAZbH7C0Jpl6C6HSaQ1vgE4HdFohsNTTKJVkdgBgtN8ZfACYkFMAJuvAbtgIMFVOlIMbWe4jpRYz5NQcGVWWoRn5rGYyMpOvMBkUleoyeY+ikKKbMWeZySRUcZNWv88c5OECNjhWwG6Dzv5S0Ia9pEgxJRn6lA6IFpTq13AZ2xUklU4dCsXqtahDMj4Pg0MweVuyT6FD7sSJz0LV0QQwMU9Pn2tmhnOS3ajoLEpn0HDGDzGiNj3JkE1HSind7T4o04PYMtEUQzqu+yXiYgldhGClDmMpN5bvWc88FBueKHZNztXsIN2oTGKKSb4oo0qFVMqqxxn1SpPz5Csmf/2SSKmCkEK/IDLKLqmedLSNyUBiiQhgYDWUCzkUQwYSjtVLjpVSG+cRAgqvkDAxuaVBB6f3DpIqN++TNlUIKNpmpaqbLqWcrB9alnDbXO8/dj7Aul1Sat/SzrTNeWn2ThyUbXFXEFdaAWPllTOGwJnDgKWlds5zx8NrmiSIEN8c6mhpAL3hQSaA6B0lk2DVFE0E8CNfKRq0T0ITNLTnjH22owjrgoQKPWeIZaUUtWEOftA0mOO6qYMMhtUeNVR6h8W+ppC6DTikBqi9pGgfkppbQqRE4d0XNgPZyDyH6+14QmlRSZkUptcbCVOggUAiooys57BW5TakovYj1Xc/TMgpCtsboGJE2K4hmStL77WUNEDug5KcciMppzjkvySgZNILAAJPaoWRiKtQ9B92iKkZguq43ykTTrPLQv2hO7pvNVtWyLODiKpNeo4kkkrC+oR8sh31tdyQSamuR3RDysSqFyvq3wWaxFDB0wRHDFDaJlUZtEZvl4jit4fcj/T8LIyczEJx8IlJGY8BIMBpnRRTo4+p7QWA3vLzQdpjpzGgIKY0oC0lKRK7oqmR/6EQveeTjLoTqAigUuUHbaj/s+xhVxbd0kIP2ZOMJvQkUYyGlr5TT6F7urOZgN37p5mQUruElKDOEjzTt96JUqjOvfJ38oJjHzF1CHvJ5plrCWElRJeGSkl1lCIFu+bzYmQVvQYQeFwds1JehxyZtOAwvt5qOF5uLGfV7gwnU1shBg+7POVQeApxN4WIRCxA6P1kZdf0M1zxC3rGuJOkFDBlP4U4EaLF8kzySUeqH5l5v9cb3OttMjoHKERPOwCgmb3OhVQRO6PgAw2ySqUUDZT8ziw+MJUQC8rt2kS3HOiU2/PklJkck7A8OpYVUeIXJSSUEE6lMkrCS+ZSJkvmmvI7VaoIVWGCim4ACUER9dTuHTD1Mop53RCxEqOBipKJT5N83tJvovj3jW6kjo4sxwEqLImMkk7jSBmjSq8HLdJ79oIo1VD1jKZ4S8XJjCdn96o6j7WxerktHcNyO8nEjeYZZz3pDIoJteks9HJJHb0lef3o0/tEKKxOoZYn0IsVgunJO2Z5Sp0/N8IApJJiPylIGJYYlQpBVf4uDU8MJeGUPQ521SKlp1RKKqDZFL/TKcTTLi13sHpWRmW/KPKRIjN8vVxSdkbbk1+U7fJyeQr0Cw7NW1Ld6Za8vaC6Ynp4aJrFdrnzL7PaRCblULyN8xj9blYl8QMJMWLjQlqWxFNuU8u2NUwUUTuq1KKNHdN9mVWq4itVegnUqlU/5Lr/wdfOYHoJ7S3UjRK2x/vKbKmdhKhV7XStXi29/Gip0yC9LLu0OqlZhbSqM44tLA3UxfRZBuSJAFPAELit10sYAxgEqHGDGByU6WmwrsnonAaLDmpYIthzKDciaEO+VNogugGdNgibTW6/OkosIR59FPrM37ePMGP2zPODL+p1REA2PC/vCyGuSgJrjsxquHnUoUqy3Jd5LyunNIyh8DxtOdNeZ+ivN8lD6t7SwrJSqrdmkmzmpCMz81VHM74nhVKqN+yDabJCyijFSyaGwabkW1YCus1EJUikVCahxFctZacszf/FYy14Ug4WxJNkqAzsVykTWbXKek4pJRNbAKoEKVNvSuB6nlKixk4KKQ71BpAmvuaUUnpCTPEAXXM/ROf+yISwWiyzQorVVJIcI5RZWm2XSCqYPilwFatuld0iaoMFT655reE0PV86zeF5np4tVis4E9F5ahPHEGGUyyHaNsJoYMPqKACpb578W5yaeA3GAKCXPh2RXFK3Y026mnxPtAx8TxGlr1TMURbkJ2JpcjZ46tcEj8XL9zCer3FyvsH9T74HP3gszkdoo3Dvk++hv9eRUurle7CnSyxevg/TkV8rKcpzf0nqcKrvrJRKCVyqZADpfc2RVYe8pGZJqwOqqsv27fse7yiOUUsB+8ikXbUUgB1/qVotVZI6ErI3IaYA6KgAzV6okbylDGhfiDSmDorsFowS5apK5NGoA0K0qX1a9dSe9FbjYzw2t50B8DJiiOgWFsZqnL60xCv3erxyb4GTzuCkM9wn5LE/ckifViol3rlruLOklEBkdpHnXkTNExXNMAPASZeX0uGRgaHmctIfkP05ExQ99JbWYCwGTltnqkFSnrkvl5dhdzZ+OtiR9bnZ+TpEBECagU9eJioPoISMEkJOQvXEX6oM19tHSJVhe4mIOuqTElLWFyCF8Yk6SgZJUducCYezStEmh/2xGiqOAxS6HNrH6qroBij2dFCFigoAVEezl7obdsgoU3QoAUwIq7BnpvMy1DOYIqtP2T/ED6izeZay54fh6jR1AtViRSF8i1X2eFA6h/GtTqnB5IeqXqwQiofnzt/eN3x3H2J3CdnsPBucl55SpJLibFYdGeGn7I1d9ooShZRZ9ZmQ6kkxh66HWp7QfbE8pXtgscr+UKKMqsgpx+ETPkQMe8goIaHGEBLhdDGWWZUCh2DESbt5IWm8B5cIp21FVJUk1MDhfkI6hRBpJj3kcGnpaJR+fomgqrNcSfs85MH4+cMBdjm9n8sEFLQsyCmlsNG7iSiEtCpVInPef6IaIT8d8tU56XPW1JRcYmTliGc/nUCE1daG5LPjjYIJNJgzWjG5E2nwzs8H25+QX864Ie8pAEjZw0hBpcHEv+d2lJNKhBCoboaAGEiFZZcL+GGE6WXQO7A/noYZDKKPyVsq+0pln4W83tRRdwXl4JwG8EV9VzlJi+FOsvhGlQop2be0Ggujk6G5+EaJOkr8pEpSasGTZUJKkan/QKooT2GpatxyqN6G+g7jgDhsENyAuCUj/7i5yMSTqKRYKRVGBz+4FPLvNkMK9feFDQCRUnlSKxNSkhhlPlkKgFQOyCRUbQGwz6dSyCZBIqSSaip7V5ZehKSaUkxIlRmA7cRGQEiprMDtUuZWZTTMcuDzznNYk3gVygCenzdx2ORw8Y4Vm8HTMgYiFoJHNB1lljI2kdChWlK/M6umQkd9cQkJH30ELBIRBQCrPqRkRQAwOI1oKWGN0oAO0/Y6zhBRl94TzxlBJbXumQxjZ8P2dMpgGTlqQ4ig9HuxEtwse3SnK/T3T7B805omRNhHavFggf5eh+7+CezpEt0J2RwY6S9xpAE44/A0IkFPzf/n/mpCqvxMlxFSlymkXlBCSnCTxNTkGC8D5ompKOcje0mpYp+OCkHFFMZXmp5brThDaUQICt7QKwWdI5kWlqK77i8tLgaF+8vMH5CKU8H7wKSUgTEaL6063F9a3F9aenbqrLSXsbm8z8u+02eBY1/2TpBSZeUqK5+S2oSs2gGoc64iDZ5WBeF00pk0AycET8flpLIISUMzMlSxOh50jV7Dd9n/BAAGnyvTHCHlZu4KW9WKSz1MCgUU+HOmEDz5HNy42YKcAqbE07wBbzbdnSOjhG01OiukkglzRU5p7Knw0nCnYN6Y9idiKgYKN+GwtMgPKRUoLTr6BcXw2h5g9ZMQTuBZE1FDwXVk6AseaAFAmTUHSEoqBA9delcVWXTmvKRkf4ljwvcAJC8HABReBSSJ/MTPoZyRlBkb7uBl/5+Ovh9tgR5Qy1P6rZakpkpG1dqkmZ29Kqnn4OF1F7GrmEK6R1Vlbi4mnaYipHQR7qn7IlxvseK6s0pEp5iYw/SItqOwvYKc8srCB2rTHJNPA89YD4Xf0xiISMp+H0RCbT0TTyO1l5IlqVRDrZmUEnKq9Odbj5ysoMhsGkOk7eTZR8SSZDkVcgrAhKwSTD3+8ncdxjzw2JwN0Ftf9Sfzj1OST5PtNCDPg3LN3lBbTcaVYgItZs9CPOVsqRQWvu7NREWVs5EFjJ7Kjp4G8WOI6HTAGMQQOpNTvaE2OILM0qMBglYwysJ0yxzGJ6ntXQ6lVppDdLg9haimgodZLpISRI/UbfBdDgeKPsKPPtVV+dNawTBRdqjuN9xelAoqWuo0mJcELSZ5FalJHZc/CdlbWsOkq06zvOIfJUrATqukjBLz8t5Q59siUEjeuMlheuMGKkYO33OAqKKEjBKFFG+XmSj9ZpuIKMnaS6SUL0gpl0goz1lux7Uorj2TVEJA5Sy9deZegNuuYhuo1FKXyANLYmres1JNt4WcktBao/MzReuUBdhw1mazFDuBrJTyyx7KGBrEFyoqw148URsod5LaD2V7+s5tl7ZVvySCIYUPD4idZDx0QLeE1RYwGiEqRATO1Fr0KW0O29NBkXpXcxIMBywt4Hv6/uqwaiPtNbfPUaOaTNBFHR+fO8LpGNzGJjkqnQjJNF6gGEwaE6xOEYJH99IDLEcHP4y4d7GBHzzGtYM2CqtPuAe77FkltUJ3uiQ/zq6HPrlPz7kyA7H0tyVMtVZIidH/4yqknpSH1FXK3QE8LjEFFPxBpZjSOI6Y4lHqHsUU29Lweq2YAvSkvyMT0sHSe1gVE5KioF8vMjUjGWxfvdfjzQ+WeLC0SSklIewyFlelWOSWqaSOnX68E6RUjcRYRqTUjJROm7R0VtOAykYiYGJvoDWwsmYi/wboGioCsIDVBi5odJpn/A2b9HZxx5w3RAlf2Z/9ifbN/xS1KXid4QnIhFNJQgF5xr70hsr75gkooFRTFQ9qICmhaN/0/ZRklFaZ/Cu3S/JKfp8aUZHnBHzgWQ9+sMRAjX0MeRZN0jIjN9YqOM7G56CspfTM3idlFAAaQGFKOJVZcvKxImsOkA3VJWPOocw4VUdlX8dl4s0xk8kDHAIhWXFKDwfYLkvhS0NRNqNOxuXp8zioE7q+XqwQxCOIM6fJg3Qys0Nv5Ll6eN1VTEP3kAYOpuNsV0ktZdKAYBLuKRka+yWRkuwbheQl1QH9kv2jFjlcryPCKtglfEE8DT77Rck+Ip7IqHz0TEr5AOdz+u6LUVRQHlsXsB5cIp2yQqogo3yAdyGRTlMyCjuklKwDZJwNYEJMxfIen1mW68HlR9/56x+H6XM7vTsIz4PxkpACACOhMpx9TDKOyUBdaUzIqZSRjBW7WTFlkmKqzE62sDof48QdJ52BNQpblwf2ojLxkQiwEAGjSUKeQ58oa58GwNN3pEodRXHhoCVEemT/GPaRAQAroczsmSMeNbq3MCHADAbaeKqrrE7WRqeU60YB4VouEA1PG2XoHiB1f6qSkgG+sTkDX2eovs6ppMScdcGhe6KQOukkw15WR5VKKcqghUxAiTqKlVJJGSVk1OacPKIGIqAwDgibCzIxX58nFVQYHS0HhzCOiYQqySgipDz84BF8RBhp6QefiNgo20w4ifm/H7KxtpBTQpj7qq9YclH71IMTL1I13WeKvmH2IeRjQjj1BXEo2ci6nJVMGwXdbWezApeKKQof77J/YWdhl+RNpXlwr5cnRG4PyzRJIs8itaDJx8hKKdg+qaYsP5sADa8jlAJcoD6m8tI/Npy1NSumBCHSZxXPwdJj1YeIaCOrySgrX4xZLSUhfGEu45q77I55PlHWxKc2xBXVVBnC50Pux5qAaHoae/BkrFmfY5Ge/6RodBtS9i1evgezXKB/cEIJYGbsDSTpS+ovaQN0nOSlkVHPHDW5tA9SR69CTiXVlLShBQGVzk18g4xxyeRcRUWTeiqSeor5BCHTA6unupgTiY0+pFBkrRRva9xfWjzaOKx6g/XgseJ2W/qDb33lBG++t8BLi25CSnU6234kjqD63E+Tnipf6zo9vTtDSs2xpZo7uTpGeGCi9AH7V0REWE3hd0aLMWz+8QyzWlEB0DxnzC75AFVMwzMyXZWmXIgpIPuYzBFTh1CbH88RT7I/rVcElJQTEorKZyVUvobaUUQp5LC8+vUkNPIQIXUw494cpMGmX25ySGbsobkyixIIu9JikoAb8lUyhsgn6UAUpBQqFRR8JqJUTVQBk31KrgXk8+T1udxlnz4ppkQSXg50JR1zuW4Kg1EmpRIhVaqf0gNODOEthfaZbkJA7XhJ6QPpap/TB9ptxpxipJwNz6oT6ShP/UFSNiWTic5EbHbFbF/q0OUOFjR1uHxEyiDqA9KfCzGZl2eFlE/kVA7TI8KqVEEN7MOXCaopKTX6gOACPBNTMUS4ke4pN/qJUiqEiOB48Fbsi5KmvSCfYvAIB8goQUlKuWGNIMqhGY+dMgOZRx6sKK0ReIAuKixtFYIP0EYjaDLXjZ28B5JlJ0WXjVUyDVpmpa2DD7ljIp5/Gwd0Md+rgcPiIGnVQc8pBGphPT/bfACgFZSERwdPnX5j03rKxtX11B7bnsh6bpu0hB4HPamLVFezqq+GUZmMan5Rdwuz90QV3qqrOpxDV/MssPhPSiY9nfosKidgAU8wVkQ9gqMQPZmEKdYRHPlGiScUm5ZHySzphqSMEsLJbwb40dFyGIl82gxEKiVyakwElJBRbi3he4Gz+4akfEpkFBNVQkL5OCWfPPcjaTvfCJffE/n+EZSklFEKJkTAhTzBoRXCGKCMQvQGgIfpNfxI5LEfPHThC2dXFHobOOzQ+ADD3nEAsnm6D6A0B+BMnHT/d/wcClqnvlSC1qRM73rqv4nqJbisgOHJR6MsABrQGaXgAWhEris82NNkJNxphaAVRpmQ1TkB0Jw3q9YKwVPdVWw+HKp63pDx1Mmoep23owopcYdiskj1S+rLr06hhg36+wMWL9/ne3wLZQz6+ycc3pczD6dsxGJvMKeO0jb3m9Ql/lEV8ZTKFvsAPDlC6gXpu1+FnJorUvMIc5n50lCpIKdK1ZQcq1VTQbHpeRrIcsipofYKoPBjab87ft0xPTOzVc/FkPurMin58kmHk448GBdJVUWfQcbrMravv4v6+3uSeNww4DtDSgHTClVWOqUUTKI0iKKUZ33kdI1Wx4kcvAyhI6IK8EEh8uxyiHmWJfCgLURRShVk1MxMV7jsjpn7bFVtKTseJfFEZTHdVvmBK6elG0ulW6MgmqTMdP/0mkjXK4moeh+QVVJCVO1AaUAx6ZTufFZFRQUlA6wYeF8AdDKHoaXM7McueUzJMTlH4s+VXEPWgexL5TMBFUvCCjiskErb15B0T1RTpUycjwmRwAO/mI5VD0XNGdGYnAKQ/WGURjSOzmGFVErTLB5UYnguaCF8zxTTme88iABQhF6It1T2kpLMMZJtL2VpFB+pYtavzLAXtSHTWbsAbI+4RyHlfMTASqk1k1AXo8cYaOl8xMZ5bD2RUWdb8od6tHEYnE9ElJBSgwtYjx4hRLjBI8a89C7ukFBOBneJsAoIrIYMbkBg8on+KBlBTU7NLQFkwsp3ad/m9Q/DdEWigr1KqdIAOmci09pAWyJztOXwFzZ/ptuuVE4plmOTqmrsDXlUjaRAWQ2sKinUJuveJJNooxXuLWwK63OWVFNkEkzK3s4oxGhgWE4eoNBJpygCihVT9KGkl1fMTCsFNWxof8/JJDhTmVkCmkOYTN+Rb5944w0+eUlJFj5lFLQnFsoohbF5SN061IPxucH5VDWVw1VFMSVKqV0vqRxiKib94iMlSqmF5dA8nUP3rCbyaqKQCh5qvKDwPbclI/PgEC/OiIw+fwiEgLA5J3XUsEmG5uP5mointZBRWwrVY0VFSUq5tSMCauOSIspt3ISEcmsONR5ZRRWozQRAbWdBPgXk9VoZddNKqZLM08CE2Ov52dKzMojCwUUpxQbo55wBammTgsouLbRRsCtSnhhWR/nlQM8fzhRMYbzUHnTBp/A9PQ6IXQ8dAvVNAHpeLcGkYg/YkH3utIPpljDaIkaFMfXtdYpW0Epj5AHeyJELywhWTRmc9GR8vh6mSqnBa7gx19kyiUVZzy8L33sew/vqWqfwdBUW9KIzZBSke2q5JvSIXQC8gVpQGJ4BoPolwukD3F+dQrJlAoA6uT/NPFzZGihtKPkL97VDTURJVEIKPd0low6amDci6sZRDpf3DbfLulsWqc8tw9x0GeVUtKnZV4rHkYrMzQOrooi44klFvp4IV2IE988MvI2I0VCUQIw46QJ8xHSy94Qme89eWgJASj72plWH+5ys7aWlRU78kRPdGK0m4/F939nTwHXJqTtFStUgcqSU5CGZngfQAzkKuxkUM5H5PIC9k+ShJzGhgWTDIsfTimR2Mar0RQtJBZRqKT7IfbrL1FK1SgqYtF+T46Ui6RABRfsrYqkiosr9QFZDTa9REU8F4VS+F7kBjkUK4wt5RiRCVEdFg87EUqrY/GBAzD5P4IGWijqXnSGkJsdYXQXkBiiWKihRSOEScorLHkTduZ8hpMRUs041m8goYEoqlWF4/F0RScWBMYWH1NzDU855kR9otwET0rk6ltQHhWntRD1VKlMmoQa6qFtTOXlUKs3wSf2KoAdHENVnyA/QEJEUUiHGZCpLYczZxHzwpVG5Txn1ar8oUUMl9UARoleH74lflHcBgTNrCinl3TBLRs2RUrOKqeQXV3Q+xgFQu6SU3kdKBSGnApT2mUSenM+JFaICENJvaqDhPbsU6AjlaGAUnELUCt5oDJxJymiFLZv1Ds5j68STis4nWXiEDjQRMvJvOXryVwk8II78e4aIlBIkct1QRUhv5BBq2jYAp0dXmqd8tEHEmBRSUhe10aQgm1FJpc9dPSJK5VTD7cVlhFVSTM30T6ZemdluIKnBWcldK7YVd67JM5QnnETVV/85R2H6bqSQfDeQwbkbaVDqRgRWQpUZ9YSQkhC9MLgUojcJyeNtCc9LYXwSWjx4zky6S0rVZNTjk1K763PL8i8gJxiYXCsC4Nl45elgNNIfikRIQZRUCjGQWjeaCN0FBDgEk59cuuvg4aBHBxU0wjDSc02bpLKMbpj6fDoHdHrnN1WhmGhUmutFnKj7gUh1J4pFhXx+IbGmdbFE3V8lW4nZr/yFRhJ8PCsUBBV5z/LEiYq0zZmCFYiQUsFDh5CycEc30HO68GRVi2Uy5xe7C+obycQvRyNcNUxvboK3MjE/ipB6wcP0roO5KKoaaUx3ybm1cgrY9ZwCdtVTpaqKlJxEWimVPac8p+qLKqKHTu9Fhpadjkk5PBbtlmTXk5C9pajmq2fq3LP4md6/13z9O0dK1fK9RDAp+uEjIlcsJqVAP1zg7ERCrmRih5peH0lVBZQqKO7QCzsap69dppecoSwS5voYl/E4OwPVPWF+9bV0UQ1qogmYVpJcifeX2ykz855KIqu+Ti5czBYAgMmZNQBQhxLIsyS1wmnmWHpglfuQyahYXT+/gbJs9eNUGUB2tmvMHT/0wGAyID3QMDPLUj4IAaCYoSlVVLRPkb+WtkkSP3moyoBzRmJ86XtteCKYI6MFiYiS9illT8qhUnVKb4gHAod+poxIlkI5I4ftJYWdoaVk0nMBSQUqM9Globl4SvkYsfVkcL5l9dOQQvOmoXpJJcWkkxt9WsYwDdFzg594SLlhSESUqKKCGyckVElSARy6t0cxVa8DQPR9Wnebc/JQwnTQPa+QYn+WiTLKwOsNhbfZIamnPB+zfQ+lFYKLcNrD9gaBZ+rllpXffA1SVkniDDHqFQNMoylRh7e53Vpa9vYJpDTpjMbG+ZyZJdU7amvGEBGUQs/+HAg+ZTyNnpaSrTRaThrRdYiD+N3l8D1J3jANLw2TkCBtNBnDYPr8oIFyI6duE44JYcqqEn7+J+Ixh0nVmSYlw6/m+pkyTGoNBZVIq0yoUKcb3kF5l8P0HJub+yFl14vDpvCQIoWUmJuLkXmtkMrbA4f0RYxrhzCSQXKtjpJ9QlhtB5+IKCGbRl6W5FS5PERO0fpx98E+xdQk3BFTZZRRCptAasWBl8YDPYf6LTjEzwwc6hcifJdD+yj8L0J3kqFOIfoAs+xT+J7hZ5LWnNEvBJmfRQye7v0Q6BlVZEumZoD7RXoAtIXyFMbXmR7gUBlpv8Q7lrxRAyd6IJVUFxT56HEbWpruD/xZpc5qRRMBUpcPEbCy/rypoy7DMyGm5IFYPhwBRGOhOAmSAvXvo9JQYQFleujVfeDBK9BvejOdIEqpygYjkU9Kw8tv3NRQdxr7VEA1WTVXbOfcRDzRZmrDin5KOR7PvADdLYlsYk5BCCxAQXJkSVt/Yil02Ae6vthmhBix4T6TeEGvbPaQouzOpC4WlXGtkDo4Hn8KuO7L3jlSSjAXyqdV9piSmE4AUJpmh4nAypni5EJUP+icCJndzrK8uqIJhMQShBvoXOsDP2U9lq0r2xzhNHfuZSTVTvnJdVRRfu4aO2+bG9aw08DGuoi8WshlJ8RS+r7NlEyKU8Jp8ivIMVOVA5FXs2XnMKeUuirq9K9q5kE2Rx6J0qU8nq4lZBSTUxUZla7dCKlngkME1LTc5WWyQqowH04pOXVS5pG/VN7mwqkeJTVeiIX0uCDheZ+EKQth5XxMyinPCR62nFVPsuzNKaSSmXnI2fOEkKL9TIpxqF7pDRUczXqGMYfvlcf3kVGHQvgAIJYiSDdAxSkpJeEZ5WAkK6QMvBvIFJdnY7Xt02so7ZN5LwB455EylgWVzNoN6DcAaF+Z4c8rlRRT8j33LqC31GkxydeQfpNOR/YuYL8uQ5lfQqQJFxWzKo4Uv7Egq1XOcCRgpZQyhgeLJrUnNFAVb6mZurgHLePe7cLjeOio6kGv9dS3p0YOM8thykJiCXGqeLZXvKcUkEPxJTy//GMvSArJ58QlxXr0PmfWC2FXKcXroooS3yghn4SQEsUUmZj75CnqWVU6BNJ67yOf9pFR+1RTl6FURfmYVYc+YkLqDRMSisgpzw68PkbeBgAKodaewzGhk0k7AFama84S6CnTZ2+SGTwA6N6SUmpwQG/z/s6yf6dhbzoP5TggT7w80+8bqf9W/N7Sr091QnxbFE0si7eUCbvP2rouSv0UT6mG41DXymdCUjFBFWUzGiAqCuWTsYI2UKaH+NCm/rzYYNQTtrUKSl6rqaCeG1w1zA/I9f3YMD8ocGSWmhBUEqlVhvkpTQVkn1EKARFRk++U1xF9ZR8k76OXZ6yi8HajcyKz20BI3dRL3VlSCtj9wgM/lIVRMWn/fiKl4D4rwimiLLmPkngaFhnHRsdd1pRdFmZ3qAIfOvOoin9JI52+xhj2f5CCNJr72tUhUgnYIZb2/nSXXecmsO/Bc+hhN/twrEIUy2PHztg03Cqk7EnsJVVCzQz852Z0qawhf4SZDldd9+mhmRWhPsaCkMo+edkMPWcdlfWdPw7Di9XScwhfypwXI0JBSJX+UXWYXq2QErLqUBjf3Ox24FnUdFzlsiUhJcvye41s1uuDT4opIadSeOBcOKFTgNUUnhIigqLvwkDTdx+QvyMm/QYO39v/PQNasbKXVRta0e+kY34+xUgEVVmdIpDD9cq6oaZTIxK+V9evdHyuTjYG6rnBPvKqzEI5B1Nk+QV2J8rSdVTVoS6LhYqIAj3nVYyJlMZkKWQVkVBRlvV6sU9MvYWQonJFxjxZjtm4vFRB5TC93WPlsiaoZB8td78X6YXUd5eE4wkhJds5JLYknSIrpiKGUCQZ4POEWDOKZnkDJ5tQXkF5Isr9EGB60PcBIPgIlb6ngKhpCWDyHaMDeUxJJmTOqkwEPw/62S5BlX6iiXQM8x6pyXJjF5LgR2BYTTUHpRXgZXm9jvzz6C11a1D3dwtiSjJ3p2iJop1Q9e9RK53Ka8+QTC0j3vOJqxI0JR+w050plVLIXENRYIcEizHrDkPaJ6+VC8sEcQ0hoEpRTW3lAzw7ZdRN4WhS6i58zr394GPfvNq70fAYiKgIoEvC2x7ndV44tAfec4NjVVXALhEwGThWJAow09ECPRhFJVWGJmdVaCY7gGlyBx8oM19JkABl5jgkDyohpELhwyfbopyi9UzkZEPyXbIpiDoCU+JpHylF6/uJ5hgCxFNKaX3UIENeP2g/GTSm19YagUNW8vUMfRdKIYSYZs0CfxfSL5bvLXkThpyhT75r+e5FyUvmv3TdoKYKX5msiZFm7kKMl05QKGOAYCZ+WeIplctoFJsJQqSKUb8MdF90nPiZL+sZQBKLTBKD8FIrBRU9iQiiQecGynrWWZhxyxkgPSw0FkOAMRrLzsIEjYV2WCiLFSxW1qELBkvTw3oN6y2M1eicgRkVus7Aektm6YYUgsoyyWWE7ArAuKHQvXEDRAcMayKd3BZYnwN+BNYUroeLCyKjLtYUvrfdAtuBvJPWW2AcaRkisB2gfEDcDFBbBzUGqC2TWoMDBk/7Rg89BkQXEEef5KSGffhsjBhjhIqAChE6RnRMPGluC2UZpaEtG1yGwWELiBIaSI20kaUqZs2FwBFvURTqM6Wg2ENU1umrjqSYHCO0MtAqQilyaNUGwECThaT4jFDRUzY0cE95M0B1hkZErgOUovAqksECfQRggWEEogGsR4SBMh2UB6L1FLrXe0BZoAtEPAyevEYDEWEqRCgXoWKE9hE2RHSDgxs9zBhhnId1AYvBQbuAbjMAzuNiM1Jo5tYhbEc456EGD+8D9DjAjQF2HOHHLZzbIgSH4EaMnkPJPSt2/bh30uPQM+Zp4cJ0lxfaA3V+fnTZ29LXnr4PBbqTzKFCe65QPvMbyXgVPK8j5ZaL83jsjPH34fT00iJ3Wim1gyqMa3a9ZshrnyLMKG72PWyehqKmxDEERB0iVmFucHrU9a947FBDdciU7lDFPnjeJZK1Y36pp6F6K7FvXFh/m4dCJjOBgJ0yZbl6f8PtQxm+ES+ZuZWZ6bRdGPSLcmCidqlDVQEOg6BZ58lMtBwv/DeA3WyfViv4KmynDJnQHCpBGVDF1FjBs++f1pTtVEVZN8W5YpArZFFxDECUEMZwza5DzHeZ0lPD+Ly/8pQqM5CZbIZeHxfvqfoYXavIXFZ8n5NtnbNC1d+pfNdWQlFU9dtoGYDmGbRSgaLk9S77enxWn6R9OyGQ861q4LorSpMGwuu/+sPP+i08V2jTMQWOHhXcTVw+lGkAgP4P/KVrn7t885tv8J00NDQ0TLG+uLi0zN0mpeaIJ/EeAADvin3zfjsTeTiQ4trTNXeMt/eQXXKtm8A+Yqn2ZLpMZjoT7qWq/bOS1np9n7T1CnHTk9DIuX17TOPn5I3lNXavU+7ff059Xn3upNwN9fZqv7A5n6+SEMhEQdzZL99yACa+QDLoBLJyUEIjfGwk1V2ADOQjh5ToYvJzjgggAqor1qWsh7Ih+7KV/izVNWQWHZEIKqMUgpK6FNmHI06SRsz9AZk4MYFIKxVY+RCIlBEvpRjZwyQAsDopssSfibyaqI6Ln5OE7xnbkxKp6yfZMyWcogy/K/fnL21KPtUhejUhNSk3QzppDuMjs/Nul6jSGpoNnyXsSWsFw4bnSvbz0qhs0Lvvu6a/KSE1NT7ObchcimBV1Yns7bJLeM6tp31zdfKa4TANDQ0NDQ0NDQ0vHu4uKXWIkCrji2Mk+beku9YWUQWkjx6IuEoph8t9RWx7+TrJZPsQQbVvX42rxCtXZJKaK6t3SaNDvkQTkkoVSg1Jka40pQYQUqs4fWfYUcR9lxDypyaj5oiomoSaI5/2xeHuK0fXL8rG3bL1+6nfV41jVFW7Sqh4OHthIbtXKk7SHOfjLP8vwnki6CcKHMJjtJBYTGDFSAkAFCbZVEJsxNSTBvl9XP4lHzOGl/CA6D3QWQ4n4AxH4qXCx1WxzSfn9pDDKlJ4hlJcd9hzRMJAdERnNEbPWYsMUnYjMSkmxY5Gb8mYcVt5IBleL/2kAJXIKUIAHGWdi0EhMgEVg6d0u+Jb6n0irIIbYKSMnhqe18QUffy8DgBR5ex72vZQfKgmpiaEVEVGpXMLskoIKWP7RFQZa4iEUgraKhirMxnFyiljadtYTX8V+bRIBJVOKinDiqjOKFhTbuukSpPfVBRwQmoTARmSP8+OYjj58vjJNq2W/jw5dOUyVdSLzlO9/Jlf86zfAgAkVSCtT+s41WcN3VG97hb3oLSGWZ3CdiuYfolu2cEYhcVJT+F7pxbaaCyXHe4tLU4XFi+fWHRW49XTBTqrcX9hsbQaS2tw0mksjMa9hUGnNWWWVMDSaFhN2YZ6o9FpQPkByjsoDuNTbgPlRyi3zdn3theUfW+zRvQDwvoccI6y720p+5473yK4IvveZpxm3wsRbu0o095mN/ue20j2PfKb2o45+16I0+x7KRvfAeNz2h8n90Q9ibYPpTdX9pLK63UWvo7bipTVUJY671t0hrLvdeRnaJcWuqPkBXZpoYxCx/vsysJ0BrqzlH2vszDLjpanK2ilYU8XML2FWSwo+5m10KtTMqI+uUdt6eoelO2Brke0C0TTIdol9dG7JaKxiKbHGIDBB4yeMsVufICPwNYFjCHgbOux9QEXY8D54DCGiEdbh83g8fp6wOgCXr9wON86nG0cNpsRwQdszh28D9heDPA+YtyM8MMGblzDr88RQ8C4PaNnzMjhe5xoA8AkVJy277YqdPPaa7P7n1WzPTduiJF8zZzcd+yrOPiAMdL+jfMpSQvA2WlVnsRR4FBX5Mmaso9dPiuh5ieMpaxgrl+fju3p/h3T9b7t/fNb/vaOG4PvKzOTGCthLpt74geK/TuTfVkwk/pd4oMWA3EPzlG0w7jlFyYRjVqeAKandrJbICoLmB5QGtHYInFNFQ32jCxerttu3D1Sqq5A5Q8eZn7gGKDcQAaL2iDansgpCcaXMsGRsqo8fybji4px0lGfzBzXD6VjDBBrE9FKJVV7xQA5dGRvitKKjFLl/rmsElrPn1NV8KgUyxaKMroyTRZiqhLXi7cJkB8scwRUSSjlVJrVfsSizPSasn9yDpevyaoUTR7keP4kdSac+qc9JlNOTUaUP60MIAFSliiUD0Txf6Bv0WiV1CviGyGfafS0NvLD2eiILtL1jEZ64CpQpjV5CJfEFHD7H37PEw4RVcEH6E6ysiEZ7wYfoJMxb4CSzFE6kJ+KNojjCGV5nRVFKjjKTup5GRzgDaA0OtNDc8YPHxVs4VPSGcBog9FHGKUxss9RCHE6KOKKI5n3hJAShc968PBGYw2kz0R+StRMaB9gTIC3AdpRKnJtl7zsENyYSCiADM5pMLDa8ZvaMRaf85YSEsVn+ZldnkL3q7SdCKdisC7L3TA9nUgoIJNU2naZaGKPHCGjdCKlANsZaK1oaTWWnYHRCqveYNUbLKzGqpd9Nm33hgbzJ51BpxWWRkNrlTrgS2tSquAy3M8opMEoPD/zgkvPP8XPzehGqkNuJH+ecWQvn7Eyic4EFX3XIYXtpd+7IKvKZrTMQPYi4HH8Xm4Sh0gppTS06aENk1Jcn61dwHYLmG6J0VjYTiPYDtrS0nYGobfQnYXuLay1WPUGZ12PhdVYLDtErWF6g43VgNXoeotgFExnEJWCs/QA1EbBaVrv1QoxOKihpwZjvED0DnBbIqzGFbDpqZ6ue8AN0J1FdCMw9DDDBmoYETtL7ed5R15SF2tgcFDLHtgMVJ/tlgy8FyO1u72D5kx8sXNUnwfK0NcPHjFEdIPH6OPEQ2pgM/QOh7PvAfNG5/vuiYmBdzVhNVVITpeaj/fcVvdMUnWcRU9pBd0baN7WHf1OhokquyJSyiw7aKNgTxdQRsMu+/x7n6xo3+mSlidL6L6D6pf0t1hCLU+pjq1OoWwHtTwlQsoQKQVjge6E+pr9CtAWY9TJP8958tJzjr7v7Ujf/VY5bFzAVgcM2mPjAi7CiK0K2HiDNTzO7Ih1sLgIDkOwcKPHphsRVMC203AIGA0QuggXA5x1CG6AMz2i8vDOIyrQgLDoK5cTvmki965ixu/lWbTOdVRFKO4VmYQdPJFSREwCF6PHxejxyHk83FD9GPmmOuloomZhNTpN60tNEzdLa1JfWYFIq7LPrWVit+gzp2iFgqwqI07y5DK/77T/crKq3rWvRrX+eoVjrXSOEI/ste+pRCgTcUoVVUW8ASg6gH0IEWPmFjSRUmG7BkJAGDaII/W34rBBdDy5AkDJRNH9N0Gd3odenkJpi2gMYs+JaWxPdhdCUAHT8btsP0VQjMXVcfdIqRJlRSi3E/tYkEw8OENwiJK5gbdVDNQhL8gs6ZxPiChOQz5RJciARyrubHjDdJ+QSjuYEFD84ENBTMmsf+1/UpBVJVGVVFC1YkppJp2E3CmVUFQ+yvdZnC9OJBGFGioEGmAeWeFL8gjYJZ7myKjLiKg5EmqOgKrJp2TcLOR1nB6nfcV73WNqVXci5wgHrRXgc+ex9ILROpNURFDlEKqo6IMpkjwBRC9BcUZJUUl5mRkKkh6Uigc+QyM/VUUh1Z5rzw6TelUdqwfzkgkqH5f9AdEIGeCZdOLwvfJBKm3YREHKszrIJKcQoaKWipFUC6MnImMEh4hF2g5GAdAYdMDCagzWwIcwUUxJBjnD4XmaDb5jiIg6AhzKJ6op+uyh6OR1E+UUfTQmobRGDGZCTsl3ASBl4ZlMHPATz4+5vbL9isg8hpoho2RZk1GinCoVU6KMEuIpk1N5aSyF6hmriSi2RCoZvRu2JwqpBe/rjUZnVFJAiAdVp7Nyin5X5LYg/alkTjyZcAlhMnOXlFKz2QynJNS+elrW5Vol9SKRUXcZOwrD4DHXbSxVxnNJDyRLZCie32TwL88xyg4Zyz5ARJowi9pQLoI6W6S1gO2o7ZNlN6a2EJxsQI8OUQeYzsLDwXT5M2gfEOCIoPEB0RsKUS7qc+mPpor6DgAYPEzImfCATLhqVmVIlrtMRs0vCYr30c65/sQxpJSQUbJPyCijuB0viCfTkypK1FGmM/R9GAXdCWGlOTOsJjKKCSnTWSijYXifMhq67wBLf8p2OfmGbNuOfjuOXih/06hNXudkDrleUF2JkbvhTPaVxEUJP9NnqxXxT9sa9q7gWbfQ5etPoir49yY1Ii03LmRianAIBSkVItCZAB95AkfuK/7djVLooaEU9Z/LPndUEQaKMlTKkIfv6wCk6AMZM2ilePI39+ukpxGrBCNzkQryKS/rm7cohwLPRA01Q0jJMY60mqihKk4Bw5b6scOG+lnbNZNRI+L6nJYbIqUiq+/R9dCan4WLFU82d1DGcuZSEpqoGPJ4fCZy6WniOsTU3SKl9oTLCamUCCg/UFjCuKV9LPmOpgPiEtAu3/ysooIfqDKV8rlhQx0bNyZSSjrqk3WAtotQhqtATSQ0FdGUiCed9k/2Veop2C6fpw3J/qoQlIm3FkjxFHXFrkoZ6RQWnQYhtKLiCs+LVPlq7ytMpbcl8VQTUSUJlTohBZHlAybKpxgz4VSSTSLflX3ygJpkEJPOcYjF/mnHRoiosm9z1QFVNoiemkaLikm8YCSdcWcUqx1I9UAqhwgTFKzOD0eAZox8oDACFwCrYzKWltekf0RoGeQHI+1B+nztIfd0USqmaCadsqcZECGltKKlJz+nMDr4wcEsA/xA4ScA4DdbmCUQhw0UP7RSO2A7qC4CakNSX2lHpGOmLXpj4YsO2oAIpSK8jlDKIFjaP4YAo+heskahcwGjD0xckUpqcAG9NRicx+AC1oPHlpc+RKw7Wo6LgOACvA/wjrLSOc7S5kZSIYTAqdgDk1gBHP4HeMcKnYo0iTJxgCkZVXshBWfxkNf7l16FthyyPeMrVXpHyX7xiZLwRCGgxCtKm3zMdobIKVOE6LFiqjM6KaN6VkHZYru3BidyrDPojMLC6DTzu2SCihRTGic9KaR6DufrtKJ1zYNSrWCi45CoLT33/EAhUX4Ahg09+9yIuKUsZhImBTfCb7YIg6PQpyGrpsLoqI76CD/4rJbycSd7Y8PtwjzxtFsm32eReUzKhhZ8gNYK3gUMJsBoha2j+1Tu+1VvAGhsnIdWwIafd94AnSP1glGRB58aafJFKRhlYbolxFYBeqB2zFmqv8s8SIjBU8fdjeQ3Z3tEN6DTBgieVDw+wG96+NEhDA5ms6W2dTMg+AC7JOUU1fEAP3iEkRRSbk3vQUL7oiipuO5PtwP3LbBDVtVKKTl2DMos03Ohe7K/JJ8AJAJKG8WherKdQ/QMK6Y0k1Km15l0WvbUrrFCyi4XiZQyy57C9ZYLIp76Jc3wL1gpZTtWSPXQq1NEpShUz/SItkM0Pa13C0Rt4RU9k4YUnhUxcNieC8DoAzaenj/yt3GknNo4j4uBnj+DC9jycnCBnxtZySltU1ImV6rbfTgmQ+tdw1z1u67a4bqoOcSy/+/5XnIhUr+3IKM+cjHg4+sRb2wdfvNjF+n3BoBX7i3QW42XTzosLYUOn3QGndE46XTqa3eaJ3uMnoS9G568N7FUT8WdkHggj01UzBPPobhfdXG/K6V2Pq9O18nY1y3fl9zohcB1iaiZfccQUTuWPXVYXh2pBUyIKBU8hZizAj1uuZ+1uUiqKApF32B4dIEwOGxffwQA0F0H3VucftI59P2XYYYNDMDKU0vXURoKPdcbm8fjE7+dp0tQyfjyqlXz7pBSeypYCtsDEiuZSCoOS1B+RNiuoZYqK6YSq+lz+AITUlE65sOGyCgOIUnL4CmcAUAK4Ssr8yXGsMB08BOBrJKqpPVyLBFXJqfpVkw6QQgoABgHWjeZlErHbIfoSA4IYEJQScURFZnSFoieX3/+4aSgp6qpI3AsISWD5NSZK7ZLIgoARv7u88wIz54x2TRHQJXkUwhZWVUSWZP09ntm4vb5QEx9H1SxH5OsWZ0WUkqldfHsSeda6qRDU+pphNz5lAe1D8DGeSytgVHcpY8qkYBRVFKYekxNP8sL+HB7xpgbiExUUTzQmYZHkUJKwvcotM/Tg49VnMqNlJDJcTtlOmrfPEt8lQZCBwUHYy3ACijoCBN5li/Vs4iOVVELC+giTMFwx0oruq+mWfhcsT5VUBinMGgF5YhAjTFCaQrfk9A+7wNi0DSQiERMKR6XUsp4gxhsGlwkBdmelN3letD50Wf7FUxfdBb3KqVIzSReUABg+EYUNVRSP5VKqYKMkuPGavQmK6IkRE+8o1a9RW/1JHyPvHcoNM8aCtlb2ExQlUQ3eX4h/4mnhgLUTNhemsETQmocaNLFDalORe95IJ7D92RJdTXsNTi/zuC74XZg9v5JJFB+hgLi8VL+BfigSC3ZUVsxsmKSlFI59M1rHqiBnmkqivJIwWjLfREHFXlmWAeoSL4aMTjyLnIj0OW+GUD3beA6bL1PbakyGg7UCQ4806N9rsvKaGjjoI2CNzQxoHmpjEoElOkNEbFMdihuE/xQkHgFSQVWXgA3H74nhLgyRRslpJQonZioKompkoySMD4K5cvqJ7vsi7A9GijZRFDRMdUvAW2g+yXQ9ZmQWqyIpLId9V81/27aUJ9T28m6KL+lnxbAxASHcHmuOyGSd9cYRIVX171pfSzrKiXbiEVdfv6IpptAGhs84depw/ZKSJ+/nKj2gcL3ti5gPZKn2OsXIz56NqQJMel7rPgeuL+08Pzo7/gFO+mfGIAI8UD9cyPvg30YIx9VETpmpZQopwDMq6fE4ZXVU/uUU/tQTh5f9t099/33xwnTm8EsIXWZKqpY3yGkCrW5RGphZMsJUUIVk3xhfU59rM0FwmaD8WKD4eEF/GaL7etnPEnSQ/cW3ekSi65H7JeIpxviBRYnREhF9omNmhMb7SGjngIx9bjtxN0hpUqUlaPMnieVwA1QMaYZ4LA+JylcCFAnSA9AAFkhNWxSLGfcbnJsZ/CZ1XRjJqFYPTUfxpD37cNEHQXqIAGANjmMTvYdpYLiY+j6ogwRUnJMdT35zegNEV5yDdtDGU/m6cGRMoorL5F+5CeljGV5NYc/akvEVAxZLTXjKVXOdpQEVBqkMhklnZBSUZX3UWUffdhRQgkZVZJTNfHkqjI+FB2ePWqqsiMzF5IA0KzNPtjJAD1nJUvrhSqqXJcB5sJTDPwYdDKEhSWCSR4+MpN4PpB0OQRA9QZGA6PK5tUICkqz7Jiety2M75ZAwjxELaV9gB/KdkXBjx5+8FDaIbBCSpZmdHAAOmkn2D8PwVPcue1I7WPFMTzm9lNb0iVoC2UsE6IUuuJ15E4X1U9vIxZeYwzUEVy4gDFELCyZoS85XG/bkxfV4DusBw8XIismsmLqgpdpBpvXfYxpNtsXs9lZMYUc0sEKKoAGGjENLnZnwAXpsTHmSYHTl98Es6hCrPkG0wUBpVjVKMfFvFzMymVAKOdMlFFKJQKqDM876U1BSlVKKVZBLURN1RlqF9gbY2nFJ4PKGUWqqFIp1RtWWipMFVJiGu1Gega6DeAcwuY8PfvgRsQtPxeHDdxmgN8MpDBhlYks/eDhRyKt/Oi5vtIzsQxRamqpu4FsYC+JAwJCIOK7VEgBGt5FAKRCGbUiwplVCnnJqsNqsqbTkUNPIwCDYLh5MqyKjuSTCGhoZWH7EyKgRiI2ZF3FSG0Yk1PRDWnmGW6EWqwQ3UDHgoculH9+cIghwK8HhBDgNwOi92SAzipAKROkLHtLERnFiimu79kHMJNR9J0W7VVB4IY65HUPaytkl0DztqidAG6XeL+QU9oo7k+ygrMX0konMoq8pTR0b3l/TUYZUkhpDbMijxMaLHVZGaUNKaNsz+bmXaGUIkNzaItol6SU6tjU3HRp3UHD+zhRSPlIypgxUF/vYvQYQ14KMbFxAetRVFJ+RyU1eqqjwQd4JxMZsZjU8Bwanic3JIHGi4pn0VrLa8qjOxTb1EeisD3HdeDR4PCx9YjfebjB77y+xv/+6AW8C3Aj/W7rwWPVG6wHj7Olxb1lh5dPOnRap+RAJ8Gg0wGj1Uk1FaKEwzPRpFDYalAyocjqqQja1kw4l55TNTklyikNpD6LkFP7yCX5Tl5YcupxyahjwvQel4wq/amZjEqTfMwr7COjhkcXcJsBw8NzjLx+/iFSSnUrm9rn6AOWWkOdPqB++8m9FMaXo58MvS+jMwn1FFVSj1v17gYpdShsL63HVCGS+okVUnFzjrA+px/RdkCPJLFTnsL3wrBB3JxzFpcLehitzxPDiRAQx4HDFKijkmaMfaCZOBSE1CWZiNL7TsQTk1I6b5fHSpJK/BCU0UQ0yecCqCPAhJPSmhRTrIyKbkxx/RFCUmkafEga865HVHzTCQGlNJS2ZDDKqqpMPmEqw1HzlV9IJmAadicPGpkBkXUhlaSRFTJq9Fn5NPJ3LCbMku1m9GFCOPnJsUxOCcEkHWcfYjLVlO28nsuUy8swVY4ARk8zay2KgWqnNZFJnvb5aFiJErGwcj2DoGKhlKLMIxJPDyAZOFIoH+A5fC8WzUU5S3PMTEzDzcPHaSiGDNpNyAopPwR+GNEASBkFP3DY3kCKAL8mE/AwjGTOrQ2FXgWPaDvAe0rwEDpSSSG3nVFb+u21g+mWMNomOboPVC88k5c+8EAyKiJCNGU/6rRCiBoXI92jEtY3Bsq2NYaAk95MyKjV4BIRtS1JqWIpfyOTU2nwEMuBRCajhLQCCgVHRYIkj6Mhk1LLez3sctpepww7iYTKobeKiaikmlJqoqCSkLypJ1ReLmypkLITcqovSCYxZRWSug41IJWUqCyRDc2FlNJ0DatAz8NxQ889t6HnZEFOYdhyyF6ekEGSm3PYXkFE+WFMg3UKc/KFqi//iRJm11Pq+Puk4dli1lMsKE5YIPciEy0h+8gB+dl6weS0ZMDSHhgpthxb5xG05medKINpoRTVFZXUCxqGMw7B0aQjqT6dvFnKKtr1ZP7K4XuwHSlJLffteL/tlzA8ex2WPfzgEJY9og8wiZSiUOnoS1LKJaWg9AlzCB8TUqHct0tGCfnx2KRU6jMW5FQRpqe05nWd9mn2hZK+pWEySvelP1Q3UUhJ+J7uLH+/4hnFYXraUIaompzqOEOUXdKSQ/RSJinbU8heFAPrmIysR+6TCfk0+piy7m14oiJtV2F75Z+X50cZDh5LYmpeZfsi4Wkpoq6CmMYGpZ0HmKikfu96DHi4GfGxsy0+/HCL8zc28D5g3Pr0/D5fWJowK+rDCaun5Bnqi/tLVFNaR8ryG8GRChSxECOSz2utmqIzp55TCkgEWIzzqingOHLqmL66TDg/F3gcQuoQGQUcT0hNQvkyITVJipbU5mxi7lhtzibmMrkXt8w1uBHjozP4zYDh4QXGizWGhxfYvn6G4WzE+YfPEX1Ed9rB9Abd/TOYvkN3uoR56Zzqj3OJdBK1VP4M4Wiv56eBY9uVu0FKHUJtNFZUkMDmYWF9jnj+kDiTfkk3tV0CIHJKygmTGdbnQPA0axwC/MVFIqLC4JLfQA5h8CkLETAlpObIKSGXynXpWOROhUlEVElYKaPheR8dH1JHASCiKbKCKrJKKo4jXT94UliFnDYdbKCmAMrkJegX9D2CBq4IjogphLw/Vf7jwvhEJSUPmUROzRBS8rUl4snLDElk0qlUSNExlxRSsVBL0bVEIVUOeIWAKuXeUgaYV0q5arteF5jiiSDrthio5gFrHozKYNXb7Bslg2Ppp2pHhFP2WA0prv6MwwU6I6GBFtBI4XshYsf0fPc3eo4eZrcYEpYi6igyw6V1H1F4SbHyZDDQHQ/0C4VUDCHf+1rDgiXmWkOFkPylkmIKgOpJ5QgA4q+nCvWj7ZYwRmNUKoXljZ7ITcMz2KLsC9FgwSmYO01p0reGyKktK6lCJO+HEMlbhgatdSdxqpgCcka/uUFuukeLgQVQqaT2EFIA4IfcVp0+6GH6kDqwguTLVqqlinBbAJNsg7IsCedynxiWy72eFFIzPnKihhJlFJFSJhHOEqY3R0ZpVkgZBSafBiaf2GORySnlNjSYH7bzCilWm4TNhsinNamk3GZLz0AmqGTg7YeQ1FKBFSLZ4FnCb8qQpds0/Gk4hOg9tSPJt01nojhGKCapRC1V38NyfwwuTCZqxkBqzFHFFLoOANHonYGZKZZWW5iOMg0px6pQVkqRKbon5VTXQweP0FOWo9j1pHDvC4sGziyphw0MK+OjD7BCRAkZO1FK0YRAOTnpKwVr4MmDlKWyUEZJv3CHkLrEj7RW2Ge1lN5RTqU+ZU9tvTwnxCNKFf1G03eJlNKyLAzMldGZeOIQPdV1eb1fUp91eQpoDb1YpTC9yGF6YPIphe1ZUk55aO67TZVRNJkoHkLUzxGFlDxf5NkyhpKE8kmhu6OSCtmbsFZJJULqSGXU80ZcPcvWeG6Ot3x+x7QPldF5xBgCLgaPRxuHh+sRm4uR2qGtg9IKtiMLgDeK57skYemtRoganfboIofAB1J+GgXoqGBU4Ik66g/FSNEGOsr6lJhS5XvF5cQUMAm22vleXmhi6gYJqQmuYmJelKsVUrN/zqG0+5Gsejv9qmGEO9/Ajy4RUuOjC2w+vsFwNmLz8U1S45reYPv6GbrTJcbzDbrthiZd3ABtSCkci/cfD/3uT9lbSnBsVbyzpNSsSoq9pFLY3uYCcbtGePQ6wsVDClniGTO9oBTgYjAWzh8Se7ndpA76eL6m2eB1DlmQGWLpcEzJqUottaeDMUnJXCmlhIg62KngDkTqWBQzXEpvph0JVkOlzCdCVBlDkmrbZaWUMUDPZB1AhJXl75o9HZIfTUlCxQAok9na2uicySZAHihETpVhcgFIPgIxUkekHMyWpJSookQRJaonGQALCVWqLoSAkgFwSUaVHWhgl5S6LIzvMg+IOnxPBqgAJmSUGBtLKvhxEXngHzF6zYNtixAAuzQIMafCPRs83thSR7kzRGysLH/PPMMj5FT5uySHxoYbR6mEknUZpE8VUtQhzyF8RKAQAcV1sfNpplwZBeMDRr7PR60RelJKxhBgQ6DBle2IgLYdhd7YHsGNObRiyURz8JMZbOVHRG2wMBa9XSJCYdB0jzompeRe9AFYWgUfgNOePPA2TFKNgWa2ZZkHEle7Z2kfDQDm7s2aKL4s5BYA3Da3UZ/85nuwi9xW76obp8uaXAZKUsqk/VZPlVKihNTsHyf7tEIinkghpadLQ+pUyyqTTpOSTUgro/kYk1QKIMLJk1o4eSsWiT8QHOLmIoepS2eJM7/MPQPHiw0Z7PPzUEL5xrWD23AI3+ARxpA8diTcIhs8T38LuR9kvdzf8ORRmpzHYrJqzpdNwpt08IjBwHsavAW+J1VOYIm1VuQdN1EK5nt3sBo+5AkUyVzXGY0xGCwNPftcoHvGBxoUjuxzJ+oGozr0iz7V+RgCtV/sEZoS2XQrSn5zcm+Sijs6TsPNqkAhquA97I6XKKnldyclK1KqIKP2TVge21fch6yO2rPUkinP7PQjS1JK+pGX9RdhCjLKdlSOVVKJhFKKCCelESriScL0oEkxFcEElAd8lOcD7YuxDN+jYxtWxYye/IN85PA9H3E+OAyOiImzzZjWxVdo8AFu8HAj/XkmqLwL8KJoS6F71d+ezK4NTxbyyA7I6qMQI/vNUd9/y5Oxj7YeHzvb4mNnA9aPtjh7fUOk1MVDbttexmJF/aNQ9OVFnUzJQVTymPIxYsn3UWc0jI87iinDNhgqgliiGcWUkE+JeFLzxFSJWjEl38ULSUw9SYVUXa4kpKp9SehSK6TKY/K8KRVShX/UjkLqfE0KqUcXcJstNh99iPF8g83HN7j4yBrbh1t85MMX8DHiwUPy91u+aYnu9Azd6QrLt5yTd9+wIcK/d6DY9+I9h5Atde4Q7iYpVftJyb7CaCz6Xbf70JOHhnJjOi+6ITGZccwd9JSNZXBwmyFJuHP4QpZvS+fkkE9AjXJWi7anpFQoSClTd2hCSJ0O3XU0qxhCJqcCd0KCJmWEMYg6ZDWUNrzuoRwQLaAChfXAjRT/FTynDTdJHTX5rsVcjT/PhJ3d0wDEYikPHWm0KZwvm5iHODUpl3UhpErPqERYpdC+PLCdhgZNlRcyoN2rwihJqSJEiD7idDkHhzL8JyssDM8kG+641wNweW0K4dM8cM1EnFGAD4Y9tmLKOLNxAQvjMXoDrQJciCnDh6jTDIjkUopSRt/ph9YdQ5ltT7bnuhY+gggpNjEnA/DsVUJ+Uw7GB3g4aB4Mycw21Tu69yMPqLQx09khGYxql9SQEsYXY4AyPVLCBqWxMD0TZ4BXQqwpeE2z2VGDlhHQyjCxrJmQ0olMXtqQQm9LglmIVx8jQj8lqejPTgio6T2a1+eW9ToAjDY/qF857dFx+N6cwnFfCC6Qyag5FWRJQpVqKEloIOF5RoHu8bSPzrUF+VQvk5E5Z9kSpVSdeZb8oqoMs0Uij4lvlBsQOFQdbkQoJmB8GbI3ZrWwHwICZxvL4UryvIoT0qms37vkVGOhbgPqTHzTYznbJQ3Ou6Q6AUjdJGFR3gUMwMyETx7U9zYkv7rOKNigECI930KIWEQeGLIbi5YsWJxZyOrsz6KUQm+XrPbUgDdQwSKyP6Zyhpba8pIn0ZwjrykOU0XwiP0yEVZpH6upZFtXJFXu/2UCKpFTB+wdSjLqWIVO8hjFfnJKVPVAJqPqCc8dEkoIKFZBJQKKiSfxJC3D9mDJ3iFwSGU0HYfqcYilqKQMKaagNIZAg2/qxyGF53lWRfkYc4Y9Du8WH0OaDMlhfNLXuxg81kxOlT5SAxNQEvJN0TWyzZM/EyJq/0C4EVLPDoHHBRJZIRMdgccFA/uJuTHADQ5+WMMNayht4MYHMJYVvEXdsNz/FsWdTJiHQH6a5TpCJtlDBPtHIWUIBYAA6V9PiakS+8ikgP1qqX04lph6rnEscVVin1hkrg9SElL1uRVZpWKkNpyTasz9iXJKFLZ+GIlTGCTTa4Db0ETfmpXmZgxYBcr86jcDqXT5OQR+JpfvJ4lEyvd5h4ip209K7fGT2inDKikEl1JYx4tHCOtz+Nc/hs1HH2LpAz1Ig4c6uU+nrs8RNxfwjz5Oy4sLcr8fHYaH54ghkMROTF3HYkbYRwT28RFj4pKYym+PWdiJQqoio2TGsC9nusgHQPbVM1ziA6BMaVCZZdhSvjasjG5IMuwoHRDueKQQP3pz1Em1PSmmwIPX4AEVKUwQSKbohxqIlOEkFnHh3DnxgRp36ZRIp6NUSm1EYeF3Q4KElBIT5UxE+UlIkBBQNTFVetcA1GkpvWsAwHOnuvSv2a2ePMtRhwEVfjTye+/LxNVbjXtLi4XVWA8WvTUYTgJOezuZMTrpDHyMeGPrcDF6fGw94sMPt/APFuiMxr1oeCCg0ZsIHZEemGbmSVY+4BpR9XRRqqeGENFrwI0e2geY3iD6mNoHGfBT+NlF8pfTRhNhtdnCLBcIg+PsSFvoziIOm+TxQX4fHeKaZ1tWpzkEw0p4Bc9uWx5kmB5aaVjT8QDDAp1N710GGUKURmTlo5DH0pkM3AZ4vtclaYGQVCUZXWfLlEyZvrgXUlalpLjJx3a+a75ft+tcwX/fW1/CyeluWelQCpGYwoi0mhzTColwlrA6zSQRGaXy4FrKaDBhxefyOWKiKtuGVU+dUbTUnN1PSfICzp7nWBEVA5QfeLCdFVLwQ8oqG4NP4ekpG0xBSsmEzHhOIUzj+QZhHLPJ+ZAVUsPZiBgCxrVDGH2hmKKOVfaTKkP3rnJnNDwt7COjQvA0+BLViNZUh9wAN3QwVkM7nnjhvpA8N73TOANgBpVCZbaO/OXWg8bF4NFbjWHJvmouwCiFi1FPzPzFP01CWW1SIUoIa03aLmDsImebFJ8Pn2ezIfcJZ59UMSZPEJ0Ma/kz88z3JNmN99NBQb3uC4KjUNqg2Cff+8yXPv8j6d2BhSr6amWZlAwHyN6i2mCSAKdcN1NSKifA4YQ4hb8otCWjcmPzPtOTUsqSUorKaLrnmXwaRwoxGQM9C2RyUfp9LmSi6WL0RWheNjOXYz5EPNo4VkO5gpiiPt/ZxhFhtSG/Lzd6eBeTWiqEmBRTwQ2zyqiwh4R6Xsmpusv3LJrqudeUfdJvoHFATKTleqTQve16xOZiwPr1DyGMA4aLN6C1Qbe8hxhOKPFIEXbvQ8SqH7HqLda9n0wA60DJXoAAHRSguT8RqH8fVURgk3MJ45P3WvJQkpmvVkTNqaVKYqrOzLevT34MMXUn+/PHkE2XqZ8Ys6bmZbl9Sqt6fy3MKNVTLIRJ5FOhvpUQ8YngZT3AbQa4c0ogM5wP2D7cYv3xNdYf3+Dh+YgPbSlMeRMMllrhwesbbN5YYvnKBn6zherJq0rZjp5dbMGx9759RuTUVduR209K1agYyx12M2aWMgbqUJAPBlUCI7NfbiTShZVUIuEW49Yys5BkYXGbgdlMjzDm7CsAEjlVmlruIlfyTEqJkkanNMPkCRARg4Ji/wGa9XLAmMkpuo6GDtUsGRNXHg4GFtEw2TI6KB+gtUmqKGUBeE2qqeJ7RaDKrQJ7UEWTDJJ3Pt0cOzv36eP8QFGOiUIqBORBaVqWA9pCFcUS71pxJITUvGJqSkrtI6N2DTEz8VSnxJ6g6rOoYhCrbT5PaZVfy1YKqVD6cBj0NmDjFBaGPDuyOoz8EtZpprBLJtNlZsHJzzXz3d+5h9YdQq2Qon3z5SR7i6iSoudMlIXpuR8CTM/7WCkVvYbuXLqWMpSpQ2mN4AM6Y6azwNKx1gbRFAOY0EF1kczLxGeqzN4h7at2gLEw2nK6diJMIn/WECO8ZuIZFH4TEOEDlfERCFphAZXKlP5xACulCmIKwISsku9sh4yqSKry+xVsijjWB0uLZb/7G8k9IYRySU6l9YJ8kjKyr9MSAqB4SSQ+kU1CPpHKw2gK3xQiyhTXlTISmidqKPgyqUdBRgk5xUk/pp0jTkdc+OmIvByhyDgmHjqFQjhlKWMfKVHtSd0Ub6lEnMbyb4Yg3HMPNNwO1IPvyGF7IXiYFPInBFSAMRpe2pxAJujeBcDqpJiSsFZaz9mxymy04GYscLmRFVJyHwZDzogR9KiVAVyMrKLipWGi32g2Rzd9IqPASikEDxhWhwaXZ7ljQAwOygYKW2MyK46UVCK6kZcDlBBWXvpQ1Qw2b0++U1n6+js+PBirPaVgpqSUKsmpcl+5Le09E08AJhmao1LU9iuNwAQTkVJ0zoScYsIKhkgomJ7acu4rpbDdgAkZRZMYNBkpqigJ0xPz8tKXUNRSo8+Tj5LRddbYnNV6YmwuJNSkXxeywblk3StD9Sa/V8Otg6ikUl+e/8I4wA9rhHFANAbBDfBumeqAhPCVSYz8pD/BvpQmP6MkAqNWPuX3kvsLzF0dndm6JKYanhKuEBqoKoJqVlGVis9MQAC5vQe4TeLQb/6rrQ+GwCHMMWLtia4U3iGpcmUCZAYqxluVsOAquHukVImCtVQ8s6ViSGbl4fwh4sUjrD/8OtavfRwAYJc9EALCvZdpFuj8IcLmAu7hGxjPNxjPN9i+/ghhcBgeXSCMDsPZkFJcu7UYXfIDb8xhexKyV2ZaKSEqGWBKSs2l9NWFQkp3pkrlO+5N5SuZUvzoknJKd6SaCiMrpiT8bxkSeadCyDNxMtvW9dOKrRR1QOQvmF3NaQjZpVt21T9bRIoNF/VEjLljUnrRiEIqSbdZKSUha7vp5TMZVSqj1kWZOu28qKBSB8bxA6rsxMRKMTWjlKoJoJzBC+m33UkXz0vbaQyWpPXr0aM31JGXGZ2tC/AnHQ/4gZOOWPSzweP1zYgPvr7Ghx9uYbTCPU7Vt11SR/OkM9CICJo67lD8GyjgMI3Y8CQgJBU1E2WIk0pqKXkg6Y1LbUUXYvIncWudCGwiqTTC6Ohe7y3cZoAyGh0v3fkmmdeazkL32aQ29EsixRcrDsvY9QwxlWfI3IBEsmKlmXVjOYUW+dCFQlUVgIlyKjLZTMR0biOyspLbVGS1JX17UwKqvAXLu7EO31tf5PXP+IRTrE5ovQzfK/uJ0mmUwwqZoFIqN4OKlU60T6XU0BR+lLdFGaX5HCPl0sybZ2VHkeGlIKNEyUGDZTmWvXJC7ZXjRsTtmjpBBSkVhjH5JZYTMOPFmuTlrI4KPLFDKqgR0cdKKRWSUip6Ujj4SHU5q6SmHlNyLzQ8XUj4/+Ey2V8qAFBugLZkEh4cETNqMAjaQOkFq4MdjNeIwSAGsDolwFgyGlZaYT0YnLEiWLwTV+zt8qi3yWNREgCUCikJdy1DXGUpCQAUKJRVCF9TqKgAMkjXCjBmMVEmKrD6UFRV7MmR7rMyNKLyFwGHbqiiP5rKyPbcEtMBznXC92I5ms0djckyln023h+5Lxe1zuRT9Zfad61Tmw9WP0k7PVHA8m8+2R/ypKJjJe0+z0Ehn4SMKpfrwaewvDVnbpV9oo4aXMBmJC87qXtu9IgBGLcu9fXcSP06N5BCyruB1FKVSqr2UzuEq3qC3UY8q5a4njyS7fIbjen5HhFRZuDOyVKCp4x7bnMGt11jvHgD2vYYN+fQtocbOwRnKQFHMTFd2mgIyRV0niQMIcKIUj1SFmsiobLpudyRQRIJVSiJKlE3zYX3Tc+ZqqUeB3dq4vk6IXl7sNdLqnqtvebmJWZC93JbHyG+g0lVO1HNcrtSJMYQv0ESwBDh5AeyYdmEiDdGP/HbHNeiQqfwP3mtyLzH5Pkibb2Mx2uV1FNUTV212t1tUqpE8YOkysCprN1mi+HhBezpEm4zQC83ybwy8CyxHxyTUuvUER/Ptwg+FiEJY1JKefbRkLA9L9L1YqZ4H8qQPQn1InWTmpBUflBsahyhjULwQk7RDI/pDIKPOeRP/GQMZeFSZrfS6d7CDw6mt2nWT9EboIdSyIqpOFKYH91gmmSqZQ2LgVtauTH13gdbBLJfFOJ08Mj7ywFLUkkUg5kQYyKkSlPkHKKXOyfbOTKKVVGiiEokVEFGhRj3K6bCVCGVVFNzaqnyO9f5dzb8m3gbKNuQofA9UWTpQFmIgo3oB50+A0BmyEtr0BmVvp8Nd8ouBo83Lka8fNJhPXoySg8BXVQ8cFeT77zhdqEkqMCmv4CCGz0sDLc1HCpsApSJGNdCSvvkUWIKT5PkN6JJNaVHJqd7Cz06WO9pgBM8hfG6kcL6JN13kVUpsDJA9Us2wbVQaQbdUgIEvQGUhmKSSo7LAEcrDWMk6x+fyx08UlPF4rvYJanqfUCxv2h5ctn57zrGCFOYMt/vDU4Wu+USAVW0edLhLEkqIZxkf70vZQ3Tivdzm8sDYERAuYJoSgNet0tCiaJDznUuE0z8zIveZ+LJjRTOXnrmDBtE71PnJhTqYLfZ0qTLeiDyk30V/ejg1i5n2SuegcnYnFXCjjtTOWxvSkQ13G7sC+XLvjtTM3QhnoIjQ2CZuDFGI+iQnpPGaGyAiUpBJo/E36W3GoOl0POF1fDRJBN0UlIF8lszCp0OE+JKFJaSwZJC/CIUJ2rQyCRVGSY7JYoVjOoBxRFv5b0q9x6A6J18KTnz0WWk1EzoyKFZ93RenTimXJeGqCyjLyelSmKKfhxql2PZZgNpIgH8u8XUF5tOKpQkVIAoZHNo3pzdQvaIoszJdUKMmoyS/p1k1isVU5JhL7gAz8bmMVKfzvuQJxhdVvbVXlLHkE8NTxf1OEFQPkuSBUf6bYlkFPJciHTJFFoqpXY9XTGZqS0VUyFkYWJDw5WR1LJESokHYTrMwhaZlB6Y0B9ChFFlP4vPm1NkPQe43aTUVfykynXugIf1OTnaf/Qhzj98RsqBE8q6Z149p5nAi0cYz9fYvn6G4dE53PkG29fP4AeP7cMt/BAwnA/wg0/kVGSiSsiMPAs8nfktG87Sx8eokPZJGMj/v71zW3Jcx9LzD4BkKitrV+3p3eceOybGj+Ab+xX89L6yw+Ho8NjhHvf09N49u6ryJJKAL4AFLIAgRR1SKWWuL6JKIglSSokigR//WitWoen8Vc+0JopUtvezjKazXqTa+m2m8w6JcathOgttFJqNzcL4KPGmCbNw1lo08D8MKh8faXw+KmdC5RUgClLKmmRP1yG0rxSp9iC5I9KAlML26DOl0DTrkDmkqPxvckiN2cwZF6qiKBVm0KhzMg7UUUG2jpK0kghFSVyjm4ps+8VMWvy72HPNOvYq5HBI/3xi46Y1wWyi0fQGuvHhVjokjTUhhO/jJlTOCSPlj53/bP712zN++vaMP//bE75+ecJfOoPffn+LrtH+s9Ih5DF+7r4S3668UsLLwEP5SHgiQYo/31q6PiCFCI86OjT9b9vFqmfaaIz9CNMO/jp3u/VOySfvnBxa5pQKeefIVZny0YUy4JT8tumqiW+zCky0Dj4ERGmTQkBYbpFs9h2Ig6CYqwTwQhWt4wMmpaP7sjrACtDlthSjasO+e3ai//quwd1dWi5/A8kdRQeczphliTBtuh9l1VzCgDaKSkDMZzNxYVBemxAyRFXA/GEo+TJLxFxJyEy5cCixJoXf2e0Qw9GpvD0Xp8ghRS4on9R8xPDoZ/bIDUX3QFpve4vnrXdwktMv3RcxeU6IW+py4A4pILl3MtfI6N1TozbQ4X7mrIFrjC+mYay/VhmNcfRO4HG08X43GI1nrfDYe8GJnFKUU5Gq0RqtcNs1cVujU1sqIkBuqbKAAK9kSeuAVLWPQmkzt1RwLsaCBkxo9vdLBaN9uJvW3VSMDp9hdEuGZe5QqF5Dasu7KGe5SYAKi/wamIT93G3q2wWRP16OHPzkyBjbJBHKZU5VEqIcfP+Mis0AYEUsktA0LWxBk4uIfbqpw32Ik4rVvp1zGLZjnFykfFHkhqIQPqq0ZweLcRijaGHtCEtCvi3+SeW9q4S+NyB9ZySyc2cSL0qSQorzY2ktPWLhRLBJYiCPnPLL+X2HU/aRaoYTANO+6RUlOCcuW5SaoWrJs6lTT9X0hqdtCMe7x7d/uYduDW6+/wplNG7uv0A1Hfr7R/RfHrD9eo/tlwcM9094+vkZth/x+LcnH6ZwT2ELfgDYj6nTvWWz+zQrvIQ/4VzswJAwZcbwGJJ9tmGg6UWoIEb1YfDZ+kdv9/PlIrklPyZFDo4pAFGRVcbEyiwj/TgAPwgd4B0TgBf29BjLAsc8XQBgQiU/IA6iXGZJr3ewrHOF64Fm01LVPHJJkRhlaZk6MkUOAS48PRQzZ6N1mZ2bi1E0g1YKVVyk8u6pPuuo8LLB/s8P6veC9Z7s9oqq3AAwTQelDYamg27aGOJArilyVJlG42el4sxy1/j9P28a9KPFzw9b/Phti6/fnvHw5Rk/37b49tTjQ2diIlFHAmBFdpqzCF+V3feCsJhGtNYoxSlyAyQxitwBYUC0HdEGMcqGBOhcnPJhfP5aoIzC8NRDaY32tvfXDlb0gAok6K5h5cBNDO0rhSrfNhelsopNXLACYsjfJIEukB5pX2OSAJWFl0xn9oGKM0BXxKmZARunv1cA/h0AoLv/K7JA5eL6pcobPZAJT75Nxf5d5iPgVu8x5T0EEAUnqpaYJVIeWB4bLjzx7cEybrd9EJpyAYpC9Nw4RuHJUo6o7RBt5ZQvqmdh6uTSG/uU98COluVXDKETxX3RAnG2j+6Lh4hRb2se8DLg/QUuRJUOKTo/rfYJz2PonvUTKyQy+/07aNvCNTr0h7045auHGi9ODQ6m8Y6koR+htMK299taozNxyj/vYXRwQ8VtJhOqYlujQ2EBVnRATwUrALHqJVXGLIUrgMQqFR2O5KgCEHPCRcckEO+jpcOydFfyNoTaczqozBhiHcuDVLhHS+coiUu0jfph1CY611m/DMBEcOIFKWI+wEJ48sdAFJ+sczEHKBegaGJxLgco7+/1oX/G+3R+MjH15bJtFFYY+nOU2HwMjzaI/rTeMkFq8rlXBKq3ELr3VuBRC/4axyeGw+8zCFHAcrVdjlGpqyEIs2gf7gybT+6oEImUVUvtkQqXFS4BWvSawhnf/wVwlaLUEjQ4oPjN8ekZz1+e8fDXR7S3DW7/7otPAvz0AKef0H95QP/whOH+Cf3XB2y/bdHf9158uu9hR4vttz7OAvNO9+iA3pWd7vReaB1XPo1K64wCLBR659B6jzi6kOB2dA6dddBj6NCER2fC31c5U7VxGEPFPm0GaPhQPQDQ8LHUGsDYhwToYUCitIYKic39QCgkN6dqfMEtFcvIZ8mPD/Ozxlm7bDbOZTeVONMeOzjIZs24S6pWUW/LQvV4Dqlo6S6cUePIrd15VZYxdMajU2phFq2cneHPVZxZ9p+lCZ0h23RwtsMYvtcx5Jqyo8UjCRRa4UPnO+nftgP60eEvX57xr1+e8fh1i4dvW3z41EfHGH2m7+yadpWMLndTjs4BSgXrblrXWRecjg5uNFAmOafGdoR+0sE9aaJw7UWpIVbypJLgU+GpYZU9UxVPAJmoBSCr/KnaPFluFLKDIxA8kTq1Kao/AcgS8AJJuPLtg4BeVJuaza/iDzD7eZsHDRKlzLe/wNQGFhNxinV4SYQuf/9FxS1H60hoon14pS4gJk7OKnfR9YUJVgCyMvR0feeiEgAmOHmRqhShomBlk8BEjigKS/fup3GSP5G7hQE/4ONWcx62x3lvnatrh4tUlFtKgwlVfQ9lKFQ4tXO2g2tMEKOoq+ALe1ijMfQqJkXXWmEc/ONgvKvpqdGxmmXHxCj+2BTrAESx6iYu6+iEmBQdCOujKKWSK4LEqbKKZrkOSKIWkAatqSDCdJBbDnePTRlT6rp8MVUlzQXh6Ipyjm3DbEGJKErR8Syrhhr6Z7QtK1LB+mRD1kfz509ZGbkUoWo5QOfSLuyaYCR3VCZGVRxSPJcUIO6oS4X//qKzslFoOgPddnB2hOk20E0Hc7OB6W59pdDGp0bJhW8TrxNa0b80RuNuKa14CL7KHJIAqvmkgPx3nhyUyz/+U+WT8q91skO9PDsque+DU3o5r1R4LafUcig14C/wFG43ydtnfZ+Urhe8b2sMEATSJEJp6LaFaRu40afi0a3xGoVR+DYorwU44Nb457rVPpd06/ve6TXYGXiFzqiS6xWlKicanXyUR4MSl3/952/409+e4KyDbg2Gpx4f//Ar6LbB408/Y/vlAU8/fsXzl2dsv/U+bK8f8fzFh+3d9yG+fXRRhKo5pWrCFFCG7qUODT3nj70DNPxAtKOO2eiXm947ppqQY8pZBztqmJDLg9RWejSjhaHKcuHRdd4VQc4p3TVeADFbP8BrOl9Rpm3hBoQTPwwgqTIN+/xjPgVa56ZeEZqNmzyPiYxdKAuPaAGPM24xfC983szGTdbutJycUpRfINm5xzhjxhOcu8zqPWYzZmMUpWycTSs7MP7PHjOnVNmRUWwAXTqlND22Lcbu1idk3G58jg4SG4aUuJMcYR86g4ftiD/9+ID7L0/4t3+9x/1Pf8aHj/+Ar08DPm9HDGPK42JlQPjqkPDEBajcMQUAjl0/HEbtPYlbm64RdD0wavDXhRDq60N8TZaXTrc6y1dHRRN4/jrThnOyM3G/KDhFkcrfKkiU4tv88Uy2j2YzQvSP758KO6RtykxFqLmS59k2vj1uW745908NgP/on//P/4p+M1TbTWbB5wYsc6XeuUhFoVAs7xddl20/pG0s14C1admLS2NMds/b0vFIcOLrqRBHzElGuaDCfWPcUkWX1KYs4OHbTHNG8XshTdIAiPdFfn8kaiHuIlhdDtnsbnEvswAwbLOJFTqvbbuNky4mVHWj+5sJIhVVotVNXvTDt/VOJ6VVdFNRcRC6XphGx3VGqSwEZ+5fM7stHJOtAxBFLe6cKEP6vIMK8Xm+LX1eXJAqB6G1MI1DKMVfXnCFF3jgE4C8HTmY+La8Opl/fA4pDPgkoF+22bppnp4d/1zK21kWnaFcm5QviHJ+ApgIUNalqnq8LweA9eVG2J7CoLeT/txc2J6IU69P7jQM/SGN5JpsDZpWo918TNeftkO7+Yh206K9adDdNOg2DW47E/41sehCq0MRheDapLBgqqRL1xueT1IjPY8mbsVy0YG/591/l3Am5oQvEnVYPr8YXxJmWBwslEN09Kuw3udnNSk/szZQ2gJN5ycaQ8qLBsC46WC1hrn152lz26DrWwxPA7oPLT5bh8+twdY6fGw0Oq3Q3jZobg2aTecnhSmVBhWkeCNcryi1Epr9vR8tvtz3uP3xEaYzeP63b9BtE0P2+schVg+i3FH90xA73VvrBSmehAzAXqJUHpLjn3c6PYLlkgG8S6LTqTKXHi10KA2JzkCNPteUzymjAGjY0YVS8hZuVHDGD0xo4EdZ//mgjQYwlPTYK8JjGuiVN+TMKXXYj2FOu+YdqrITxTtCRNl5ok5OnFWjjo1F7LDwksDkjvLrUmckWbuL8D3WyfGf3dQtNStKWYMyPMLYETrcQAcAOto+DYzR0I0LHXHfIf8W3Ak/fnvG43bE47dnPH7bYvvwDcPjPYaQcH9cYSl302g+4RXhOeb88jTGPBe4i1k8Fmeug9MuLauJUKPYOm4hLgUpxQSk1HZZkEpCU2rL32N+LCY87RCjJutREaCWspHSfgO79TWdrx9fQRmDrGy71lEAimHNwMSNSJ+8q6zL3g61G+vXUY3ptbK2DkB2nc8Y/WSGCjkG/edl441KhXuHhoUL4aFKK3+vCX+7Nhpj+Fu1VoBN4ikJrf55SkrNBddcbBUujbnk5nxbFJ+QTzvZIFLZHlDGQula2FMHnktRjVR5Fhi1Deusz8ekFYY+F6xSyF8Ss6htFLvC9a6jfWbEKL6tKdaV+/nl/Hi8bcOusUshQbPLR45IJ27ESv+ofM7XDcW6Umzi68r9S+GpbFvut41Cu8v7Yq7olwURCkjCU61ttUpycKCW/TQvQNl67qgdglSJCFSvh1Iqcxq1OrmevChl0NzeYXw2aEPf2nQbNK0O/ww6k3LXxdBgCvslEZyN2TQTouP7YHfzJUcTVdxdA7+mrnVJvevu+0pH1U631Nyxgfnj89fmz8nEAQAmTOpoDbQd1NADTQvYETo6pPxjs+kwbi2aTY/2tsHmacDHRmNrHW6NxkYrNJsGzaaNEQ7lROxbEabelCjlVEheGZRKmil++Osj/td9j5+2I/7ljwN+/6evaDYN2tsGj397iknN+/se/eOA7UOPrXX4ubewAB5HW82IXyY3n+t0c1GKlskdRQ4IeiR31KhTZ987KxxG5yvx3SCF77jRwXQmVuRSRsEZDWXSjVPpENJhNNSoYdsGQI+RDRzphg07+txSTRdKjqdwvpj0HJj+WNdcHOixErpHs2WxapNlyTFZHgKeg6C0esfn4Z8NFQ6sze3d45DnlKLSwJT4cgzVqaquqZlOjf8IlhNiliF8ShuM0SnVwTQddNNh3D5BaQ07fA6lbC2aVmP7OOD5scfX1uCnr8/onwf8vz/+BQ8//jOev/6E/uFnfP7Df5h0TIErs+9eObW8UvkAfUp0QWmViVNt4aSk7VpT5b3cHUUFEmidZi6o5JRSWSifbptMgKJQvTxcr60KT2VIH8DdVCkcL4bikbgUhNgY3gfkIX1h2bcpXFJckOICVOacWr5Bm8fU1vzwG5jbmYEGF3e5S4oLVYUrKrmkxnwdrS/D8wA0WRnhPNyPXFRlqJ4b7cRJNW77SZuYQ6pP+/OQPTs6X5J4dNAtvSYt+5xlYz/CdDpzTemQYNhPpKgskXkfJlKSmwrYJUzV7qOSreW8lCF7BAlPAGKi85Qr0cSwWn5vy5ZJuNb5PtW2Qbjy65K7yi8jClAkunNxivYBEF1XtB/gr5tKFe2K/WJuqHI9F/m58FQkT+ZU89O80M24dt9fFKn4BGBY79j2srow9dsm69n2tC4dwxb7cYEpWw6Th0DueqLXKivk1ULuyNk+53aKoad8W+Fyn3NGiRh1fkIK3kk+ttYotNYLS3edwQ8fO3x7GrB9HvD4y9/5McD2CUobfPrFLTZ3LW4/3uDzXYfPH1r88LFD1xh8/6FF12h8aH1V69vWYBNcUjeN8S6pkKOu0alyp9Epj5xm/TXukuIiFndOZeIWua4WPoNZh9XKz/Aq+/5rQ/hq7ZYEJeoX2qmxgjLtKRSCE5DcUj4BM9w4+Ha6gXMsx7JSULZFlu4GKPqmBu04wnRBmNrc+OrYbeP76EajvWvxD//7C0bnsGk0mk2D737/Ebc/fET33R30hw++EnbbAU2b51GtFAG6Jt6UKJVhTDabTwLP1oYwvHtKCDvC8nAGmzrWO1+CrphIDqj5drk4VYbtpUd6XhOx/DYK1VFhIErP6YQmBwS5H7jTgYfK8LAaIssFQ5w4w59SapoMAfTZAH22nGYfqUPFZzJH6yYzk9RpdY5mU/2NwOrUgXXaV6GLf3N41ABsJURIa7NqgLQ040zH4x3xXcfyHaE2mx0cMEJrhe3zgGH7GIUzf9w0m7vrZiS24ZenzBW1tI3/7nVcV14jEMNZVHicE6SSk0nBdBSGxwWslCeqFKN25ZYqRSwAPhk6kOWUmhWh+G+AkqIXywATnVY4psrr1NJvTFvmKrv5AL2Z/rongxAmUKmFMD2FIueUDZ2WKPKnZWVZDikACDP7sBYq/P6N6cPf49/zaFJVVc2EJ2oTiy8YcsD69rSfb2cBDHCjgoqOKSra4MP0onMKFgYGI3yya1rnXW8WHcgxnN8P/UH9Ou6mAiRc79KZu49lohUbzI9hvS7axPudZcvGAOhhg0jF77G1iRu/TDnlcvGpFKuoDW0H0j0/W8eEK6ImYMVtlXaTNpUb7pzj4dRVvWxFkAKSkJStY21tRYQq96sJTXFbRXCifZZErFJ8ovWpTRLzS3EpS5tg07WzdK3XJgznxCjeZvp5vQ8xis7IS700a4XMwUT9olZr3HY+JK+7abC5azEOFn2jYYxGd9ugvWlwe+Pb3IRiCbedQdfoWMGTQvS0TmF7KeecioIT/+ny50pN+9y1dXxbff3prg1XKUgR58gttTaMj47B1vk8VGxbvHEEp1QZxqd9VXsAUG3nK72G8D2z6fwE4d2A9tFPGt59usHYj+juWpjOoL3r0Gz8P9V0ocCQTiYcpaZFfi6ItdeV6xWlZhRSp5QfFHUb3Hz/EW60+Pv/9Hv8l//xIwBEFfLDL29hOoPuroMdHYanPlYToip7VPraVyJysfIed0ilnBjTPBnlYLTmlIqOCMoDFdwPOjgZeM6X5tZ/Xc2midvJ+UD5YKjMe7Pp4mCSysCbTQcd4lh5SXh1s4nl39XNxv+Iuo3vFN5s4uepmg5oGsB0cErDmdZ/5roBdEM9w8kPgy7MRilAez1aOxXzS902Bq12aI3C86DCTcI7nJ4a/7hpNPrR4dPG4olV3RssVd+zseoe5ZjilfmeJpVakotq6P15NFA+FZZAkzpNMdcBJRsurN+0jsNzc6RTNO+A0z+qwKe0QtPqWD5bN952bIyGCRZln3NDo71p8Jt//Hv0z7+NHbtf/v4Tfvf9Br/+tMFN42PkFUSAujRqYpVRCu2MYN0FodV0holP5HTS6XoRhCd+7ShdUaUIlRKeJwGKnFBzVfiy6nsxtt0LTiRAJXEqiE4zCc8zJxW8EBUr8gHxugIgXVtqs0FZBT5VvwmGNrZNH779/BvYGadUlvyyUoVPFY+YVN9zmUDFnVAAsuTl1CYmQY8JzrfAOEKH/czQT441V3XPsoTmlPScV+QjB1XpnuIJzynn1Lg1ofJjnvB87MfMNcVzp22tg1YK28q3IcLU68Ir8PnlvArf2kE6CU4AYAsxmd/v+HLtMbtPmlyMKtuvOV48FhO00rrp8zLnU35pybfVRKWaKAWsH2SuFarmBKiSmiAF5KLS3DFzx1TRthCbyuelsOSf193kXCyqb0+C05rjzB5vnPbRls7vXcdPbd+Ol/PSLscUps5/FSpMzLVG48Y5fOz87/x3328AAN9tGvzf29ZHVoQ++y++u0HXaPzwscPHTYvbzjukWq3xodVoTXBKhVC+1vh+8ybkuOMOKR1ev3RI0dw+OaR43ql9HFLltaJ2SXjTDqmSNcLUnDOq2DcTptY6popjRceUaUJbQDm2zVmg9e/FT8aMcNr4fl7T+j7x0HvBatjipu3g+i3MbYfxcYv+4Qndd18x9gM+/uYOAGJy8w+//QU233+H219/D333HdTmLrqlHI2/499KJ9jliFRrT8frFaVKWGlwZQycNmjvbmH7AZ/+/S+jI2jcjkF1vPEDsw3gRov2tvFlr0Pp6yhGhSpDvPoQTxIbE4iv6CjEzg/L38KTEifHEx9QsuXC7eAFrOBW2HRZKA4JUbzMuwkiVHQ7cDGqaf2AsmOiFC03bRKkdJN+AEr7zz2KUar6I9BAFEaUAzQUrHLh4k43Q+VbNoAO8vOo88dWA731olVvbXRK3TQao3XoGiorPMYSwredyUL8SKSissKTii078xl00Wruv/d6J23nOaBUnN0l5wvNAnvhySew1nHZC1Gd0dEJNRoN93e3GEcbc2/87ocP/sbbmhAnryelqYXXY8k5xVkK1yNXpKmE5pVhes1tM70u7BCjSMjOrhVtNy9EtT4ENc4C0TYevtfkFfqoYl8Un5ig7apilErr2LXeb6sLU9nzAtenL8G1H+BuKr9dZ1NHnXVuMgEK8CISK/wQt9H+zvp9ssdwzQhilOMCFLmkQjg1hvCZDT6/nep9fhRlR7+fHaGbFrAWuvWV+caWCU5awwYBwlmLMTxyB5Uy2ucsNENKvq7JQaViAnQ1Kh8yrn3ouA3rTGeA7YgOyML1UmJ/clFNEXHqOlgaxPOQv7IUNgA4w0WiurDEn9fFpSR4jXHd1NXM99GV/cvnu7aVr+OPO38RnxOn1m4/lF39j6XtU1Fq2Tk6NwFXPreV9XlbNmhcEI7q+9YFqF3Hqr1/4fJRxXPKA3UTwu9++NhlERSUmP+7TQOjFT5/6PAhJDgnEepD6GP7vFKIglRMcq59qJ7CNGQvdklUek9lyF7176gJTZWVx1wm3oQgRRzjmKoIUwCmrqk1jqkylI8nPi9C+eB8Oh0H+PEzHc/6XM3KjlBt6/tPTYsOwMDSaDhr0d55odWH9WncfP8duk8foG/v0rhdVyZwr5yrFKW8jY7FfPJ/uoHa3EED0J9+wE3T4tNo0X36EISlgQ22UvLOOGu8HbIZ5DSjTEKUDWF+SZwCALeiZ82FKFpOCYB9mF0uUk0TDmc5YNjfQXlcmk2oOhEGlqm9ic6FKDy1bRxgqiYMOm9u/XvrNmkwWYhR5JCCafyPnItUM2il4OAA7R+VU3AaoO4fVdJobHJNWYvomNo01ucqGW2qzBfyFTwNFjZU7aMKfbwqHwA8hnwsfFuWh8ouV4WZy4NAz4H5mcnsHGD5LOYStrZmWga7Y0kZAWQ3XhLmjFb41acNfvvpBh+7Bh9vGmyMjqGfKtiRc8vxW7p7XQfTinvJVWKUz8dDeeSyXHNFFc5xGx77MYra5JiMbqr75VxSAKrhe+na4QUR7p7iIX/5daqeQyoLCV4K3StzSa2pxoepmyKyEL5nHjSAP/jnP/0fmKfSDrCQU6SSZ6qc2ec5oTL3E1sGgggFTPNMzeSYyivq2Ul+KRvzTvXxfndIbqlxO0YXVKrQ5yYOqbIaH+VcrFXjo7D4uUp8wvkp3VJp/bxbai6sb/l18rA/ZUxMnM9FqlLMqopHw5yIFMJcmQBmi79tSXzSlb+rLlAlUWxX2xpr2+3LWqFlnzC12jpbrFsWq6ai09J+c2JXTeiqiVBL72fNObrPfm/JJXVJaIVJtWit/MSUUYDTQBNy+3zsGrTaV0dvtcKnTYMfPnaxX8/70ZsQqndDSc6VitEE5IqKYXxahddA7DsbJkZppHA+LkYtOaSyvFjsb1vjjgLWOU3elBjF2ZV4fKnNXCies1PHFKPqmArHczS5qMLko7WAU1BOx4lIAIBpoY2BazpftbZp4WgSceihms7fG7sN9F2P9vMWm+enrL8WoxRCHil99wn6u+/9GL27CZFLyru35sbhVyRWXaUoNYFm17UBXBMFFfP5B+jbO9x2G2x+9Qu4fhs75AR17ssS2zzsYa4MN5DfmGiGeQ5eHYtXn6LHskrVXFWrOaEKwGpHAw0CfXvWLjyiaaLI55jgx09+7phy2qTvYRK+p6CUgwk5R4xSUCRQOQDa34SUS+JU6wycA26szgSnJEh5kYrEqdE5DKENb2+dw2iBPnxPXICaliyui1MAqlVm+GP5vEatKs9S6Wp/M9WzbTgUG/+h1fi8acPskQ43WMRYeNGgzk8KZ8qTnacqZQBC5U0gtFUpx5QGCVZBoKLnT0N0UAFgzsq0jkL7Ys651mTLPDyYBK0lQVxVrkG1axWAyfUKQHYsvgywwWRNiOIDuYo7ItuPURtwA0D/1AD4z/75P/039Jv8vlAdcMwNVAqRqpZnCmCz9+Fe4jfZbLm8BwGIQtPSvYgLVdk2m9y95HYaeQ7F4PYlMSot+9cet2l92s+GUL1ScPKPFul5SnTuihD3/Fq5q1iIcB64KDSX9HyJXWF/paMKmBGpwvNSKFrlcBrqbav7MwGr5r6a22/f7URN+DolpWA0xz4CTVpXcU+tyMu01lVVLi+KXytEqDXLa9+7OKteBw0vRlnnsjQgRnu/CiUo18H1xCetqTuuyWmuFNowGbwJkQktrTchqgDInFHA+cQo2r/kXYXr7eLE4XwA1olTyuSpHMLxquKUUj5tA3cvGQvVNMAweId70/m+IqVi2NyBUjZQ7tGWrnEmjeVV00Hd3kFtPnhBq9nkBhGKWOIRBVfG9YhSszmkrE82xoQTtJ0PF7u9A7SGDieBG7YxLwfgbzQGWJyJrglRVPEIQFWcmv8T2GCKiVGAH8CV68uS7Hu5EloKpekmbgQSomJ7npi4CaeEnhGedL4u/gj8HzH7t2v4Th+F8WkE55QCYL1oBRtcVEphtIBTZJRU0MoLTG0QnFqnmCgVBCpDQpR3VAGIbqrRalgHjG0Sqmyo+lcreTwvQtlieb0oBUzLRnuBSbPndYHK6JRwkW6WdNOjm6pWKopRN43BpjHxJq7YzZK+g5L3cE+7FJIgRXnpvGDrK5kBnU5illGAZcUUyEXVhSnFjpx6owMVPiAXld6W4cFDFh4MIIlaHV17KEn6bvembz8VrGh9TWSnbXHfyjr+CExFfDompzqYNPVr0vDUpuc//guGTZ9td5UJhvIazychaveByT1ix/2DBKe0PBWl7GiLfabiU83RCwDjNglNADKRyYUiH7XwdF/p1TuiAKwWo2ptqNIqIC6pS2HOLeW35c4lvo6obSvFrNryrHgVRCoAEzcV7JgJOzXhrCakza1b6wQD+kzAqm5fMQio9RIPdU4dKpTs6quWQtOu11vrsNolENVC/ebe077i09r3uGab3y4uqVPBnVEU5K1VfYKC+q4+IbnDaBSMNrCh39SPOk5Al7l9KWk5uaEAxMiDMm8UPQeQiVP0fkmQ4mIUvT96LeA87qil/d8kJ67MN8k1Rb9tnmvKWZ/YfKZOYso1pYFxgGPjrZjCgcbMId8UhfIBiLmm3DjGIjrxfVDfN1TaU90G+uY2jMfzsfjs58Afy+cXxmWLUnMnFSmb/OQhtVBrwG4A08EYA9dvoe8+JUWyT9WNSJGsLvOKSDyMIiwDC7PlS8zZyRfCVGJnp1Y6ndvc+XLI2TIrWvFkwjxBeSk2sbxR3DEFwP8oAL+si/35n6z8RdgEwcnbopCq33kpyltznYKFg9OAdb5KngXQOQXrAAcvOjnnf+wjiU7OwVokd1S4IZE4Reso5I7EKwrR823AtuWiE99Gx+PsMMlFSkEJyG92RqtMbKJZHr4cn4d8QwDQ6jTzswnJGm+Mv2m2Og/do+PTt1S7ob2rm9wLYIHq7cufPyqrRkaD9VR9j3eoyCHlwrKK6x5HWhfWDzaeS108ZxBzlgGIolXplgIAntcuLlMBhtbE/cttdAweiuzb6ixUmfYD6iKTKraV22tOU86cAFXb9rDt4vOf/vs/4akLuZ0Wfsi7hKqaSMXbuAURK4pR4TslYYmeU9syZLzcb+zH6jYSnNK2qehE78PadP3csutlWdyjJkLVCoBMRan0+fF9S2TYdz5qSc+BuphD7OOi2iUY7RKLuHBD50VNJJoTeOYcSkuC0LnD8dYe51SunWPD/Za2zTm2quJQRQQ7RNRaej+HClBrHWPC6VFKwZFDiuSqoGC12idw2cBfD1rdYHQujguA6SSx0SoKBaXQRP1oni+Ku6KA8wlR/JhLvOs+Oh9jHhPSF7bxvKTZ8NWylc7CqXAsck4xjYJcU9lrOgtYE567tK4JoYF2yB95YRx66XCfS+PxBpan0tGp2BhFLNF7uORqfHNctii1C60BFxKNBdFEAV4ksYP/grqbWJ4RdvTJeIMzSgEpmSwAVQhTipWlVUys8utsnrxs7/c+zYkCIBOe/LZKrpViXZl7JQt3KYQowItJ3pxRF6OojX8/QYAqBacjEqvpYMelGZJsGYCBwkhJcq2CAYKDyotWCt5FZZ0fAFun0GIqUAFekOEilaVcUWwQxYUqAFGsova0juCDqFKcWvv3E1yQApLNmK8rBSia8Unt81kgnww9zfZEuzFT76/vUnXdlAnOKWSP3FI8jC/NFwJgDqmULLpYV4ha/vgp3A/WxXxUevSClA15qVQmWNlMRCKRCkjumlKMAhAcUoXoVMmfR23z9Vx0ytvy4xGlGFUTopbEKeK5v0nPf76HaZ9n21bFqGLdsosqCU3l/lxwqrXlYhEtJyFrXpziridqy481J0TtEpeAlCdqTniidbXjEPV17PODcAlwJ1JtG1AXU+a2LTmv1jqcaH2t8z4rklTXYuK8WvM3zLUr3/O+vHSI2L7H39V+KVRwUehZKULNrT+HGLVmu/DyKN/pB3V9Ym4pnfLSKgdA+0gLqDRxrXmHC1xE4pO76XW4GOXXJVeU31+cURfJMSF9lf33ck5xYWoS9he+T+3/43mnopBFb4GOCWTaQGyTjc2DABVS6cyNza9RkAKuQZTiJwz78h3gT5z4RQEKXayCBNd6ccpZoNnk1Y+AeMyyZLffVNjn1jij9rmB1TottWS9lTwqpbMqU1F9g/RYnJzlyTtX0cqVJ7ji21S+jRxSZVuGn2HwIUhJYPL5pIzywo6DisKPc4Ch5xrBGeXfq4WfDUHW3rupYhtaD5cd06/zJAdUIUixr7UmPtUsxWtLNAPJ9UTw+yZ3SPm2yJejnTjN+OSx7HnJWnJI0SOJV/FrZ84r/q7khncaltxSKdF5LjJx8co/prbcLZW2I4lPM9sQ3seuNkn4YpZ3XRebeAWpOSEqX8dnoqbbyzblttRmum5N2Ex5zMdxE9f97Y8/4hGPq49Rmym3lQtDWfyiJlT547lJG759TqDi+5bbktCO8FgXjMplOvJSm6Vt+yyXz4XXhc7r8ve0S3Q5ZPBeE7uqwtPMsavCz0IuqaX9dwmgy2F763ip5OZznEJQmQvjO/T1TulWOtW++72OSOXngKbe4qQ1bdC+uE+j4QUpl/qqo1WwYdBA44Dqsalfw4SncOj42n55XoCi7Xx/f8zytUSIOhv7OqfKdmudU9BZ3qmkN9CXY7IKzI5ex1lf1SusK3WIeG1Z897JGQUgpdnJo5Ym+1xJ6B5wDaLUArEKX6FUpjCzoEyqlJAsiVHMcmfadDKEko6xZDe7MSt206rewA4M4QMqnRbupJoTnuhvZevK5YmgxNeVQtTMfpmIxYWn2sm/AI8d1/ATG/55ckoBNJgngckP1uk3r5xi6nK6CRmQeOVCDh4AUFGM4kKVfy9R1pkVrOj1ObVB1BFfe5b4OnNQhQ+jJj759SpfVpiUq6UxfmZBZoKUcF644ERilCc5pSghuidt37Vc5k+oPfbOn2+9c7EDtVP4Cr8Fw0SSUrACCtFpQbgq2/htanZbbd/YbmUvrSZuPY9p3fOXZ2izjctrKqkCuZhE1IpdTMUpt7BtXnAqt5fCk3++n0C0RoTiy/sce26fpe3C67OUY8pvX+8Mqrme1h7nkJDBJSHrUJbcYquPsUPg2Vf42kcwOoRTu6rWtjtW3Drle8nbiiD1kvCxwWQ9VKxsXYuqsM7BaHqO2C2a5KVW+XHj+rhuWaiqtYnrs9dZJ0aVx15CBKk9WOOcWmpXuKqyhOjA1D3FCQ4qAHn+qYnLKtcslLLZMarvh72XLLKpIjxNjCVXxPWJUsWXm0b6TRCeFBQ6/8W3OglR9EVbJkYBmaoZIYWzXI90YqqZ7Xv/LQU1y122buZ5TbDiotPkWLsea8csxKidx2Jksx8kqJA7KTg06KtQIQG6A0LFPkwcTzYM6ul7Sjc0JkSlb5Htp9g6Nthj77U201K7YTocPpJSWL5x7bIBlyJTzXJMsz3cIcWPVbvpCqeldEuVwlQetsdzSeXuKb8uHYfEJGo7v46LSYptx2T9dJ2Hv/+5NkblHXxTdMxKXWjXdmDqKsy2rQjRW+KJDTLu//yAUa93SnGWKq7WHJS1MLZDt8+JOhbLbeb2W9v20P3XHKN8/8LrMOeaqrdd52ZaEgHWikm7BKK1gtma11pK7n5yht1NXotj/vZTOpXO0Xa6r1yNXoPUUwnLSkE7l40VFBycYmJUYI35diIqLQhQ1faT44kQdVEc65yqbJt3T2GSewpg5qkZFxXHrRXRiuc1o8i1hu0R1yFKlYpmLYxP63hi+DjjJEgli52NV5PMKUWUN6CJIFWRIY4RpehvqeDKq2Ct7YxTaY2INXuCl9srIlf1+AuCFL/gc1tuFFiYOAXkAhU5lUwQqRD2pUEttaMwQHqhiaBVmT0pv7nazWwuMs9lTpf9WVMOdnojnN4k52Z8auKUjvvWw/Zq70E4nvXCVOmkAvg3RCJVOobK1vPBfemU8rhCSErr/TLfVopZ02PX9kP8W6fOqvwY9ROt1ra6X7/uujt3vCeXRoEP2wFW5aPCfZ07u8LQ5o5X22+XSLOPkDUnGq3ddogodsx+MgS8LPYRp6b7zjud1rSda7+PoCR5gY7nJKGAB1cKfBnH1u7jyJXo3JRuqdp4YXEiO7AQvZcdO3/t6U77uKH437Dm9eaQ/vcLcEqBqnBP1XZd6sTwROnZ/m7F5M/S+HyXUaT6Ri+T6xCldpAlJosCi/+PJxXzGypiFK0vBKvJ6yyd0PvexHZ18la6qHaedAuxpNWTvHzOK/KsEbh2wG80QF2cAuoClW+fBuYkUsVjZy6otL4UrGhfU9wqqm6o2bFmELz2dEvphdvTXERdTXhK25JDirdXM9skbO912CVMAcjEqUS5Yl6komPkx8z3nrqt5pfTunmByq9zxfIawWn5mCXsKrSzbf31cp7Ztfx+sBjU9Pp9SEjZmrvAnIC1VriqtTu2zTGOrVO9RxkGXi7lIP0QkcofZ71QNdd+zf7HiBMiaNV5ibDI19w/P5ZcfS6BNcIUsDyRvS+17kft6rZmIjlrv8d7EDHqTBTi0s52ZdtKuB6AZRcV5aFaeA9rhNTqC6wxisy/sYvkekSpBbcUEIYqJohTXINSAGDYvsxCFxsVVrrKuqX24bCnYenEmdtWFbBWOK1qxyuTnC6JT3uc9PxmE12NyC/GrJ5hJlBBpa+UD2AnMeMo7mZIQ1guQM27n2bf/kKVvePvJrVZmnj02g2zdFTNHCuF9mES9lfuVzuucFpqwhRQc0fR+t0i1ZwYRcct958XnvJjzwtL9d9B3r78O6q77HytetvVTRfhotSXweKmIkrtwz4C1qGuqqV9j3FivUS7ubZz7WVIeF3MDeJP5aiaHvc44Wqf44sYtZtLqxS4/rhypbl0loQpokwBEhuegKWJ2119ZBGiroS17qldbas5pSr3QKMrZpfjRaKJM2rufV2JIAVckygFzAtTDKd0+vxpU1Gu0ZVdYFURqpaSpc2opQex9mRZaLdKgFpaX+lITn5YZzipM+Eqhuf5FTV3JP+zfSWO6edQE5NmbwZqXrAqB87HFI1aOwavvc/aruXfzcUov1yxJq94LeE8cNdUvn4aHlcL0eNtefuaIFUXCqYvzl1Y0/dab1++p/p7nt+3ZNexjqFnr9lbF4smnJJrEqrO0X5pH+FtcCpH1fS4e1ThO+Hxd3Huqnqn5rUFuJcXt0SEukbmhKkSlU1S739zWRM5sKZvLGLUFbPWPbWm7YKAVROr5qKyltiZM+qKBSng2kQpYKdjCkgXr7SKfSnWVr+kemhe0eGYPYFeoGNyArEKwGKY4OzJfar15VthIXvA9EJezob4RmoiFNGn7RZmSWrOquy1ZsWn4jhzDV/gxrLkmIovOxfiV207dUcBIki9JnRelt/XkqNpfpBf/+LmBKLpa0zdVXPvIWe/0Lv8WPt1HPdxUe3Dlv3Nj6PDeGJRapfINL/f6Y6/61jHCGGneg8yZHzbrBEFjhGuTilqHCIwvbao8xpcyt8sgtPbpiZM8W0TQl9hbmK5PPa+HNoTkf71lbCP2WRpzFukElrCHSKI7bPtygQp4BpFKWCVYwpIX3imRpYdoLnSjtmB1p9kL8I+r3uICLXmdc7wt8/NhpRiVmw/cS+lBuW7Lc+OpbEuH2utEYpOzZqXXPo2lmZ/RJC6DMpQvpJaxb1pm2VxaM5VVdu/dow5QWGdYJYf8xCHzLyz63jyZNsO4xGVNOdf45h91+98KiFrzXHWvq81x5IhpQDk4sKpnFWHvY/9xRZxSp0XEaLeF6UwtXafUyJi1DtlH8Ho2P1eYnx9hYIUcK2iFFAXpoCJawrYMTdvKsLVhAO+3NrN8wU6XHuVfzyV+2qfY1WYE5mA+g2Amu26yNuF20fZdVy0+h54M6mdQaf+xg+1G9fD/o5/P8LhzLmmOEuD+33EoTUn9VSkOuQ9lcLW4crMqfJH1eBOqa11wCuH7+0+1v4HO+T1X0IMK5FhpTDHvqLDa4pYwPWJOpeGiEzCLmrjhbVdg/IW9VJdCulLv2FqY91jnU1z7MpZtYYrFaOI6xWlgLpDam4dZ8FVtfhy+yifJ+gs7SU41XgJwWrftgssiVPZy7HnS03LG8PScdeIO/vGqB/7qRxaGe+Q5ItyE70c1ohTNXY5ofK2axKUzx9/ut9+r7PE3Ht4yfxDeeU54JxDy1O6vw79jF46vLCGDD+FU7NL1Hht0eq9I6KTcCoOcU29dDdX+tHvlENdVGuP+1r7XwDXLUoBh4lQ+3xxWdnGV/zCT/Xahx7nBf/2rKrGHgIVZynsbx+y2ZhXCN0jjrnZ7dpVbqSXyTFOu11iwalEpGPC8eZYSpL+UvBccfaFwvdKXlZkO83BT/UeZSgqXAIvIYq8VaFLBCTh0lk7kf0SrykIE9ak/Xnt93FlXL8oBeyXEZ9Ye8Jc45f92gLWEewjUHGW7hv73L+u7QYkVT/eNqcqInuI66l+nMN6g2sSlp+zMlvplLqUq/xL5dDKX+P0x5ThrPBeEPFGEF6Xub7sIWKV9IuFF2HX+PkQ0eoa9Yg9eRuiFHGqTPbEOZXONZzjhLygk/5Qgark0Pjz1+ZU90q56b4dTiVScdaIFMe6mM4htsyxbwW/13yvx3IuYe/C7oyCIAjCO0f6usLVcEFj7UvibYlSBP+yjxGW3upJc4V/19LN5lRW3rd0P5Ob8/tg19XtVL/0lxA7zhWuVxOZyup7+hUk6XM6w/ZFRCdBEARBEAThXLxNUYpzKoHqmrlCEWof9klw/pYRIUooeQln1ak4tSjz0iLXJYtIx/JO74yCIAiCIAjCBfD2RSnOKUo0viZvXFw6FYeKM5ckZonAJLwEh17NruHKs49oNBbP9QX99o/hgu5WgiAIgiAIgrCK9yVKHcK+QtBbTqD+xjm2Wt+pjy0Il8LSVU2uZOdFhCdBEARBEAThLSGi1KkRseldIWKT8N65FJHkpa+8l/J3CoIgCIIgCMJbQkQpQRAE4erZRzSyxXMRnARBEARBEAThdRBbjyAIgiAIgiAIgiAIgnB2RJQSBEEQBEEQBEEQBEEQzo6IUoIgCIIgCIIgCIIgCMLZEVFKEARBEARBEARBEARBODsiSgmCIAiCIAiCIAiCIAhnR6rvCYIgCIIgCIIgvENUsexe5V0IgvCW2Pc6Ik4pQRAEQRAEQRAEQRAE4eyIU0oQBEEQBEEQBOEdIs4oQRBOTenA3IU4pQRBEARBEARBEAQA+w8oBUEQgMNFbhGlBEEQBEEQBEEQBADinhIE4TAOFbQlfE8QBEEQBEEQBOEdQoNIEaIEQTg1a68r4pQSBEEQBEEQBEEQBEEQzo44pQRBEARBEARBEN4hpZNhbfiNOKveB7XzofbdSx6y98Ha370qHnchopQgCIIgCIIgCIIwi4OE+r1HRIASOArpnODPj0VEKUEQBEEQBEEQhHfMGsFJxKi3SSkyHfs9H+q+E64DNfOc43ZsL5GcUoIgCIIgCIIgCIIgCMLZEaeUIAiCIAiCIAjCO2afXDHimHpbzH2fakUbvn7fPELCdcJDeefY9xwQp5QgCIIgCIIgCIIAYH5AyUUHER5en5f8DspjK/bPsX+clxQrRQh9ffh3vkak3AdxSgmCIAiCIAiCIAiLiDBwWbzU97Gr4t6Sg2qNi+YQRAR9fXjeuVN/HyJKCYIgCIIgCIIgCADqg85TCCAS+nc+DvmsD/nOawLVvkmu+f4iPp2HYz7rJVHy0GOKKCUIgiAIgiAIgvAOmRtgloPWUwhKIkidj0M+a/rOD/2ejnXSiCB1Pk7xWe8SpPY5j1aLUjXFU9RMQRAOobzhyXVEEARBEAThdeGiAn88JoH1miTaS+3ORfm3n+p4xNJx5157V6LxfcLs1lDLE1U7zi4x4tDP8tR/7xrH1qmcPnPHfe0xztq/75jz/hS/5b0Tnb/2BUMQhLfFa1+sBUEQBEEQhPdL6Qhb0/6l+6+nrm72FtilQ7y0TjEn2i29D9FO1qGcc/JZCYIgCIIgCIIgCIIgCGdlb6eUIAiCIAiCIAiCIAiCIByLiFKCIAiCIAiCIAiCIAjC2RFRShAEQRAEQRAEQRAEQTg7IkoJgiAIgiAIgiAIgiAIZ0dEKUEQBEEQBEEQBEEQBOHsiCglCIIgCIIgCIIgCIIgnB0RpQRBEARBEARBEARBEISzI6KUIAiCIAiCIAiCIAiCcHZElBIEQRAEQRAEQRAEQRDOzv8HMwhqok97g3kAAAAASUVORK5CYII=",
       "text/plain": [
        "
"
       ]
@@ -2350,7 +1268,7 @@
     },
     {
      "data": {
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",
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KinuBZ43IqWl9FRUVFRUVFRUVFftRlVIVFRUVFU8dzxohVeJZ/mwVFRUVFRUVFRUVT4JKSu1BjW5XPC1UJ7bizYQ3w3h/M3zGioqKioqKioqKimNRSakrUImpirtGdV4r3kx4M433N9NnraioqKioqKioqDgElZSqqKioqKioqKioqKioqKioqLhz1ELn9xThjkPq+lQkYTHc7fupyttWVNwWqnKooqKioqKioqKi4s2NSkrdE9w1CbXv/e8dQXXXRNS+964kVUVFxRMgoqaFV1RUVFRUVFRUVAgqKXUAFG42ov+0CairMHd+d0ZUPU0C6hDMnd8NElX3fGhU3Afc5RypJOxpoo6RioqKioqKioqKE0ElpQ7ETRBTN01GHfNyT8opybnfGjl1U07UMa9zU86UvOcTvl4lpCpmUZWC18I+e1s+FuJTJuJvAnWMVFRUVFS8SXFT/tVJXfcrTgc3tUZ7xtdXlZS6RVzHSN4WMXFTBNaNpvkdOklvy+G6aQKrOmcV18V9VwgKblAp+CS27i7Upoe8x3Vt4BOl8N33sbLv/KpNrKioqKh4Atzm9f+Y164E1pscT2MtdsvZOk8blZS6JRxq2K5jW5/UIF9lSMuXv4qgurZRvmoyHzPZn9Qw3BbhFMMzZSwqbgH3nWA4BDekFDwE9y31+dYVpCXqWKmoeLZwF2uXiopnBPf1+i+oJNUzjvu6BnuG1lWVlDoCh6TwXWU0n/T5N4F97zE1qlcRVNcipnZN7H0T/jaNwSGvPZ3sh5JUBxqLe3atrbhNPOlYfhoXxmOI2xu+MF7XJu57Wpzcnjv2GLN2q40i7pKgvw6u83uX5/kMLKQqjse++flM+XZPa+3yDM2rN81YqZjFbWad3OT4OfkyABUZt72WOvT1j7HjzwA59WyRUk8yiG7gR9xlOHcZx32GNsa7pymUUnuN6i6C6mC1wNzvc+g+AOopOFxRxsVVksmrnKwbUk09CWlZL473AHedsnpTTssxkuEbIhwOGeu3aSWv6wjdiHrqkN//NsnNJx0bx7xOVZSeLO5DuQHBvbm83Zdo+j0tJ3DTY+ZJXu/ejJmKWez1kW7oPW67Nm9VU50AnkZN45t6zV3+5q7H7jmeHVLqJhfoe37IXWqpYwipuWP3kVC3ucQpP2l5DkplyznnZN1IW/PpbzbzG+4kosItfis6fyvT949zRNSciuoIYuqu1HNPlG5Z8eS4yZTV6xx/nefvu6g9BWXUsVNh12tdVej8mBRnYLeK9JDXOhpPm7ASHJv2fNVYOsEF1JsJ913Ne8icvN0TuCdk1C48JUflPo+bJ1XIVtwebmsd8CTX45uwMbeqqq44Dvcpa+G6wdx9a6cTXFedPil1nQX6VT/SE168p4ZralznCKjpp9jFUYUbUFDpgnDyxX5VGkh+n/IbCIUJ1ip/Ttm7l/QofwO5PfldRgTQlHS67dS+8rf2u43DLNc091o3oBa5bl2yW1dxVByPIxSBBz+O21cP7h2Cu1SEhxC3M7hJpSmwn+gvH4oxbtlbH8fE/BymJL1g+qyj5twx42TPb3/r42Jq42ZP4siU5xNcQD2ruM9EwqG4E5LqvhNRc7jF1NlncdwAlai6Sxy6DrhusPa6z5u7fu96qUPHS033u0M8ia2+6xIKxzbh2mfTT0w1ddqk1HVqE+16/AkkcMcYudJRuoqI2kdAXffir3a8rlZq9P7ijwVkYkrOXdL8yrS+g+3oIWqokpA6QE1Fr3G9byTKB901JqaTvTw3VlSpGMYKqifEk0aIDlVx1IvfHeEGL2hHkw2HKAr17rFbvt/WGD9WKXgkjlGkAvtJqGu9/+T1piTVrojnjahI04vdQnrzE46JufeatX/XcX4rMVVxS7j1eXmKODGHpeLNiUMIqZu4/u8LRB2jcJoG7I9BXZvfAq5rr2+43MZNBAtn1+HTQPAJ2/PTJaUOWawfk4Kwj6iaXLjLFL4tFdTMfjGW5TuI/SwJovKlttVV86d/jHJKzxhc2eVjHBlCVZ4/HyTH6vR5FL9uXvDNGtSpMoq3aYKG+cfpmPJE9pBVu/btQvFbzu0n+NH9qCZOlg/F64TkyI1+ESG2yrFUvmZx6K6xNIdj2tXvukDWi98d4Bp26uiU1Se90Pkdz5+qBMWCTQiLvUrBHTZ0ipG93PfYASrTfOyOB0BKqPK2nzl2y1zOqEfzOcyrSIFrKEmn968aH7cxLnaNCWD2Nyzt39Zp7Irk3TKpWXEc7krlcpNKhWPxxOqpu46CH4onnS/XJKduY8w8zfGxC/uUsBU3h1myaebxfcH9redPXnOvALo4eNdM2FV7F9jfKCo9f8/bC6qC6glwmwTUddTph5aaOTCja24dnobLDj9z5757iNMkpa5y9K6rTHjCH3FKSM0ZzikZtYuUKA3plHi67kLAi9Jp8mJCVlHKCu0WA1gqqzQ/MxTkVKma2huJnCOkDiGj5lL9rkM+7jof4GCWWaE0AjMGJJBjJsYploTUHmJq9vRm9h27WJumDc39PpWYukUcaadmL2pzF7TrKkSPwa70rFItWCgFAYybAhwx1g+p+zQlo+Y+6THEfZjY13EUVM2+XqkeTcdOzq9csM7Nq4Pm244xcpSi9Kr918FV6ZozKlJgx/iYvt6JLqCeFdw0uXAbXYQPdQTvJW5bWXWgQ3OfcNNj5Jiu0k+CG1XcVSQ8KSF1aHx+33ElYTWdsdNrPR1/mHp69P7y3L1nuY1aeuMK3BYZdSwRtY+EuomsiXK9Nc3WuWrtfQLrqtMjpQ6IKB80UOaiutM0rGnEf88POkdI7SOi8nHbj5WvMzpmsnQ8Vq06jRDoZBbjLBlF91V6LCJCYUJOxQgNUgrIc3cuDmLYJqPSbxbHBNQuVVX52x5Td2oO099SxsRIHTXZJ8qodD+SkkzGRqme0nuIqQnSeJjZB2w75emYyf19Ko65OmDyPvVCd8PYR0jtIhqOSFudTVe9UQJiYmumaa4y1uU2xkN7FLmZGfdzo/kYUj/t36E0zY/P7MR+pdRUNSoov5JE5MtjExUpnXOed8D2InTWudll94BtEn96GzPj4hbHBIBs+9Ix/DtPxsbk5lg9tcs2nsAC6tRxLC9wG2TTk+JJyIijHMRD59JtE1DXPYdDGxMccNyhw+C+jJdDz+PQdVAlpm4W+wip6XogYNtvKo/f9fpX/bZq8iLTzBKPGR9qZh0mRNVVzVNuQkX1pl+3H2trn0ANdRCvcEBmxJWlZq6wwaOMnZj90sRFpTfa73PeV5wWKXUMIXVVFHlm0Ty9O/vj8rZM4ds6TSakdpFR5f5tw5vJp3xcfl3BvqkV47bxFJSfdkRE8XN8JNJpRETJ+zNBNUdO6RgTMTXraJWEVOF4JTIqTvcXDtjEGdtSUU1vT99736QsHws6fU55TAFEVil5rCCneDwovr2lnpIhcyiROb1/RP0xwfTCqfl1rqoDVompW8bM+NyZvjq5fWsprLswo5JS5bgHgOjzPBHIU1gtOEvITubjVWN/H7E/fY3yuNFpzSmlRlxPHGWraaW20vlGc0ptq063VKTYnncy57bm2pSs3HUt23p8Zlzc4ZiA0pmn2jcuBHPjY3qeJ7aAOmUc4qffFKlwW9zEVZesQx24K0mGJ4imX+u462Lf/Jkjj3cdt88puuIUjqqxevihR+G6S5ljurBeV/FScTWm42JKSO0K4h+asj/F9PpOx4+v8UAZyOf33Qr0YyuAPEdS3ZSK6k1JUF3Hhl7Dfl+LhNq1bp8ee93riay5xB8tPcOA+bXV1J7f83XW6ZBSxxJS+xbpc69TRngnkf/R0NrDPhLhlIfJHBklRNSUhCoJqKnKKjtuBUkxHe87DO6cf7CthioIKn5OOkZlkkoIqjlySoipXWzYiJCaI6OEiJoQV6PjgG3CqtyOvpDdk3r2/lQhV+4PuqirpbdJKv7bIqc41UlpHkM7xgyAyZiYd8jp+AOWcTFHeYKqxNSd41Bb9aTpq1sRmCckJKZqpumcmGzTxVGICLGLxfA/RCk4R0iN7d/+BeihdflKuOJrmVNKAeO5sLfGHpNUEmnVSh2f4jxHSO1SlM4cf6UKb/LYlZj7nfbZzuI6unPRJNg1PuQcT2gBdao4BWLhuu+96xJ2Kw7cvjl1pPN0aBHcvU1V5l5j7vjpuvcJcWUn1Bt5l8NxyPsdo0qp66K7w74A1dSHmgvwY/L8OUx/z33X9/L4dJ2X/XtIqmm6X5nqd5UtKk/9WILqmRyrd0REATN2+MAMhiuDx3tIqX22f3bdlTgKonDSOqskpsrjT2gddTqk1C6UJEVBegCACn503CzSD5WLWid5XCxStbTeKiY2dehLhVRpOD1bCy/EFJNQ288hokeMrbyfDxEhxuQsyRzxMwTFlLQoJaiGb0uWWrqvFIxWUBBDHGGUSmSVBjlWRgtBRektJqX2xURMqTliY0pIBc/73IiISvtLkiq49BoqRkTvAf5do/y+8oUUv3csf3sASpvxOZX3JS9XG0AbaEOPRaVGhFNJQEFpJucySRXZQCht6X5wyUmfElPlr5QvsLsVdsDVihAgX0h9jDCsgBMSsSxQ/0xeuO4DjiGkRkTrDNmwSzmzS0GDK6Iz+7BFSJE9SPsnqa1TxWA6VkWyn0oDPoxt5y7iAXn8A5kg2rX4BPKcAYrHJgpTADsXpn3BQvU+wo7NxZiQSoS9LFYzaaVA6kRdLGSFvNdKwQMw2DPvJuPjSjWpPOfAMXHj46F8bN+Y4O1IYTdVUYldrMTUnWKfw35oO/Z9eFopW9PGHiWm027OgYs7jj046HWok7MLBxbEPXQqbNVwm3uBXeTUzJyb+16vM16e9vgQ7DuNQ8bL9HXqkup6OGQ8yLpYfB4fwD4RPT4N7u973X3Xdto3DkrNXePl8V2KaWBGRbUj1e/YNL+bUoeeBI4ho65BQh2thppbV10RINxbembf2j09UA7Ywt8s1k1KW1pfaSRiai/u8ZrqNEmpXQMpkRm7F/FbmEZ7lU4FrXcqXqbPnZ7KxJlK5FRhUIWA8GGbiCpJqBDy84YgpJQ4b3H0nrsgxjSTUnKfCSml0hhupIOcRlJMKSgYHYGg4BHRaEpvUZrIqKjo/Y26wgJOfhvFhJNyw3hfQUzB0THR9fTdhwAET6TThIyK3o/uj946fRmZjFJMPkEbmsT8WOT7QlIpYzIBJYSUtnyfIzQaUMGNaqUosGpq5zjZJqRo/x7HeyZ9qYQ4w4BcSPPzRTU1/V5O/bp1EphTwPD+2Xpqcxe0q9JY90RiBFtFp7cOKAh3pSFkfZw4L0rUgjEkMja9xHTchwCY/QpTAFcqTAGMFqH0nLHKlB6Lo9crjxeUyigXYlJOZVI3zxWJcEaVF6AaatQYQmak5t4oWuVzlucbPkczN+GmpBLvm0tvVgVRPzsmbnU8ABJW3jsmSqKpeO25sTFLTFXcC1zlLx5LMNxE23bBviLDwGGE07XUwfsCD+X57VpzHtqNaRd2PX/iiOyc23NE1DXm3lWFqfcdN3rOLY6Jq87jpotRVxyHrYyPyePTQL88R3ysXUH+/Px8+6BrOwdy5ToO5DGy1fCpeJ+tY5RK7z1tkFLOMslgmPtO9o3NZ149dQwZddXxT0pGzRFHc2urfWTUddfuSo+DgSrSuktpKJ2FEMl+p3VkEfArX/cEgn2nQUrtGQCzaWG+H5Ee8Ex+TC5+Ze2gMuob0yJbfnApZs2KGWOLAcAkTiKZIhNLRBqFSE6PkFLC9hMZBXSOCJSNC/ARGHxIEYAQiJgSMsr5TEaFGJMC65CFoRglo1UiLRre2RidlE+Noccbo2EUkVR5n+LogMrFzQHYSK9LKzxgajKVd6SKCp62vidnxBMZpfj3wtAT4eQGREfHxGIfmIiKrqfvfBjoDYSkAg5b8E2UUSoxci3ttw3vZ3KqaRNxpdolvcRiRWNCW/pTmrcKsC2NIW2htCXFFOyI1CwJKRkXcqGV+ja+uAjLxRfAlhO+9Run35p/CZMd6bL+lzynvMjVFL4nwBXOyjRikpScBRE7TWEdXbzKYyb76PV5fPiClJ0haIEdixomZdVUKQgkxUu6CJaqQc121th0TEpl1YYuoOIgFRdCmQPThWdSlJZq04LQB7bvC7EP5Pkh08MXE0UWjeshf+yLwQNDoSjlCSB7xsooUZDSPiHiTVKfZgVVahIRIqumqClEnKTRzv2mpWp06zo2UZTSvoKoSq85GQ87xkL5WRNmyPvR9bJQQU0VpPylbUf15HllVI/e7OTrIJwC9i0T9jmHO9UwB5AJx7gW+15ulmuYqsInDxNpPH7iSCXB26OueTuch70pH9PnHbL/UJRzoiyMNyKCwxZhtdVoQM7lwDk2Um3v2A/Mj5FDPvHRY2HHE6/6NHNjJD1XbZNTVTF1eyjXxsD8ukD8qyHQmlm2pU+V/aLtQZQC9CkzZO7aTmejQWNju5RJ+Rw+14mKSpIJVfHBymDxKDXwGgqq6TOeCfXUMbbwpkmonXZ9B3k0WmdNyKdpgHB67Na+uH+NVqzLo1LkU4rPaRpaX9k2+5sF/XmKaXynQUodiJEKp3TsZBE/Pb6I/JZRX4n4xnLgFBFeFUNyroSLKSFMfo7yk4EVVZQPRFQNIWDwRDB1jsiowWciKjCZNYTA6qlMSAFZwgrMp/IJyjQ9AGjMhJQKOV2vCfSZlpEdKUMkFD1Hw7IxjwpQAYgqQkUFHWkfIAuRCTHFv40KDvCO7kv6XnCAc0Q2uYFIKSGj+i4TUd4jym1WTAEgwgrYVkzNoFRHxSk5Ja9jG8AYRG2gbIPohkRUyfEBgLItYJFUBJFVdjES5RPF+KhJ6mDBTiVVR+GAlxGhqSy5THOajugUAWKVm4oqjc97eyF6M6C0PXN1pPYRUnOPT0gtGe9xFzm7h4wAUBAQA6XaOeR016QU5PeU57BaEDLWyy5spS2Nkexm0b52F0bRzwkhJXZ0Fzk1VZmWj039Qx8jOpfvDz5i47L93JXeHCMtYn3Mj0HLxGTCnileUUodpSTd8btvjQlRmAqZCeR9Mac4R+AJxsOQvow0HkakpSfCEcgEZEk8yfWS4zYcgCaUUT1WIacv/QQWTc8ajiGkriKirnIrriuK2fW8STYogP1KhF01FXdeI3dFsHcRUkcUwp3DVZ2ZtpoITF9vmrJXqlUL+zsigcs5t+t2gUMIqUPJqOuMh2MIq+l7zlmW8lwPGSvTxyruFqVfJT6VC3TtH0LYW95EskJCnL+2RxTrZlrEQ355DSCoCB2zUmqfimo74BuTD0bvh3zs5Dx3KahuSj1178buPSOkttLzdpBSqnxsGkgelZ4Zr+dm12mFTzuCrMVFLGH49Vn4EBEyJ1H6mwesue8jTpqUmqqkRHGgHCul3IaO8X1KA0vPNUWqFiYL66R84dQx2xYR3ogYTRERzj86MfZkHH0k4okMJRnIwZPR3LiQbncuYAgRGyalSnKq9wG9o+N9iOg5x6R3nt4jZLWUn64eC0jk32g1+mutgdEKC6vRWg2jFZbWwGhgYej+wmg0RmNhNZaGVFNnjYFWCgurJo6WgsKMxQsO8D05Ua6n3yU4oO+A4BG6CyKjNh1i31Gq3qYjJ5tJqdB1iCHA9w5hcIg+wPM2+kBkFTvnwe82WpolDcqQQkoZDdPQNNCthTIaurEwjYVuG8A2UNqQQkobqMUSyjZQ7ZIUVHLbtlCNo7ETAxNa+WJBC8BirECc7Kyok3EzTfP0QmwWahDZTn93qxWspp+gNUxOAcywx5Faap/UveJIXOF8TAtXJ3JBiNoYiKxlAj0pX+Qx5zBKWw0eobg9irSEgFFNtatICGBEuNJ9nQippBLUxW3bQnPkBqyMil6iN3QxjDHkx5Sm1ZfU6yvUUgF57E+jn0JElYR+BClKASQSf6oupdfO+wTyWHeZx/6vXvQ4L78KrVJqMyALWTVKd07EvqFlqNGR05yzWopq7pEFEMUUtIKKRQrflHQq6+0Fh5GiVMZCcGlRUxL0cToeihTnTE4dPhbSeChSm3eOB22gmzar67QluyP19bSBYoVx5N8/RfXK9PiqlroVHOr7y3HTVFjgeoTTrrIC1+SmRihVCMBYiQAUBEWMo3SdUu0yR0zNOnhTpwSFs3NMxH3uuEP2p/Pfn2aLyAGKJNko5owfz5/0lKlqamaeTcfFzn0zAbPpR5+OiRsbCzMvpidrnKlKZQvFWAGwU1G+D8cQBBW7IWMoZxDk9XHvaW1A/lPA5RCwcR4+UtYJwOuHEItyJWqUEQJQJojWCo3OZUwarUc1dsvMAwCpxm6uuau2FVIRW+qpUUMUOnjUkKiExjxZuk/xVA79k1BO3SURdZ3UvGnguHzdXcFjYGvtPrdWC5z5EwfO+ikzgcq1mmTsAOyDaqjFCmp5Rj4nUAgi2PYoPVpXpYF7IjhpUqrENM1FBc+Legf0GyI3CkzrBsHSglkVNTFUZCeLC1ZHgEkHNZbFYXIRBhNUBTkVQcZSiKgNk1GXg0eItPWB0vmEiOpdQM/3hZRyvN1FSk1Jil2ElJBRRE5p/jM4a4moerCw0EphsBELG5OBb0I25lYbQJOjSIW0ATNjDlXEyOmW3yURUOsLUkd1F4jDQPv7DnHo4bqeCKiuhx8cQu8QhgHBh0ROCSkV+DYNgwkpIKoonqDa6ERK6dZCaQ3TNlCa7hMpZTNBtSQViQ6kENAhkIKK0/kQAjnpvKAbKaVmkPLfJ4RUjLvJKDlurt6YYGmJ/DKaXsdolcbpTSz+Ko7EIZFymRtT9eCUjHLDmHgoL2jA+KImF7ZCNTidE0CeFwBIHQigTF2Ncts2dMFrWiIpQkBkcgoWecyzDU1qwWnkfvrRY1YBJoVUBALIthFhldVSvQ+J4C9JpyGEESlVqqqElCpNY+eynVq7gNjHtDibW8QKKSWL2CHQgtKzAqqFJgKKGF+KhBpAR5UUU1BgRVfc9oymkbRditIYgH6TCMlpmvPo93/CsQAg//5MTEWAVKMApTuXjwVfXE/D9pjwLqV4bn32Sj49FewjGXYpcq9StxxCOhxScuAqbMW+Ju+bGxPkzyCpMgd1xNyDLULqkGh7iV3Xgatw4NxQowDYhHCS2xxJ30qdPQCHjJWs7t4/Hm5jLAD0m5bvXapU5jCnXJl2LZ4jMStuHjKeRAFdpuZThghd3zeJlPIc7A/Y+JxZItAqly5ptIL2REItLMXQgpBTUSGEMCKnaAwgM0ea7IiP9JoeRRfy4v121aAqC6iLekrG5K76U8fWnpKPfvLD80gyCjhCuTq10zvUUEnpJPvKFD1ZW80EkoWMCrJGKwOIG+IjYt+lNVochnFwUcA8hXI9rbdFARUCVNNScG+i5pKMnZTZdULrqdMnpbZkcUxI+R7oLhD7DuHiDcTuMjGQShuo5RndXqwyOSWqmKZllqWoHaU0YAM7Wy2idxT5xVjCSUQRRftFIbV2HoOPyXB2PmA90L5HvYMPEa9dDvAh4nE3oHcBl73HuidFlGz7wSOGCO8DoqT3MQkjjtd0nlJZD472MCFjrKZuelbDWI3WaKyEjFpaLKzGg6WF0RovnDVorcZ5a3DWGDRaY/CxUAoooGGiBwpBi3M5/o1UcFB+gHId/S5uQHj0KpFRF2/Q/e4C4fISruvhLjqEENC/cYEYAt1ncioMDr738L1H8BFhoN/V9/xd+IDoxysPZSbfQauhjGIyykAbBbtsSDm1bKF5a5ctdNOgOV9CGY324SWU0Yj9c6SYWp1D9Usip4KnMbQqxww75tqQMbE61dGRVvSuGDMx0riZqkKmjngZFRLn2yiFs0bjQWuxtPS5ECKCJgWbirS9Mo2o4laxlZ4Vci085Tq6mA39SCk4Ih3Ki5z3iZRK0Ra5DeT7B6a0zilgoA1Uw9Jhm7eqaYm04AiOXqzIbrJCShbwUmMqaowukNmxyRHQ6TwQgqlzHiEAnQ8IIabtEDJhPghhO0l1FuFk6aBs1nkOvHLZY1FI643OiqitxSxHVBtN0VYhqZYmQmtgaU2R3seLWW4UoSLtM0qNHDORdqeae0JI+YFv91AxImzW9LuzwjQOffrN03a6sJncPmosoCCg5LeXfcbQAomJySjKOiYtVbscXUsjAGVaIsP0ZPFUzdFTwaGE1C5yYQ77CIerFDPHgAjhfH9Uo2WSUjOnSJh2ohVbtZdoKKPlc2nY2EFEzUTlD4ru7zqNOQej3DdROyWCatr9EsCIrzpQJVXen5JR07EyCtw+xbEATFQqc0hOvkqKKlGsVGLq6ULIKQpORQrO+4BHvcPGBby+cXjcO3QuJF9KMkskC0QyQlrO/CBSKm+1IuUzXds1Gh1HdXZDkYYu9adCyLWnosrjK0aVGkVdRU6NiNOCSB8dizzXAGCaZlq+3uh7w+GqqTsbx4cqpI5Qk964Moq3o5Iaad94/Z6PyQr2kRpKytGUtZI3a8RyLdd3CP1AwgsWW4SeSC7J4GkfnkEtltBnz0EHD706p7W3DbS2AlKmzikH9e4/KXUIG1oey0W1ERxCd4G4voB/9SWEx6/l59oG+uwhLaz7Li2qk5Mljle7LIq8srmwReQXIJq9TOEDO1dsOF2IuByIwX/cEyl1OXhcDh7rweO1ywG983j9ciDjekmkVDd4uN4j+IB+40g0waSUGzxCiExQ8aLA5e+jlKwCgNIKSisYJmZsazIpZTRsq2EbA2002qWFUQrPMxn1uGuwag0eLBu8cNawEgewbKiXVo9SWHwQuWth4YKDcgOU76H8AN9dIG46hMtHwNDDP34NcdOhf3SJ4aKD73r0b1wgDA79G5eIIaB/3MMPHm7t4boBvg/wPX0fw9oheiLoYogjQir4kIgoANCNkFJERCmjYJcW2mjYpYUyCu2DBqYxsCsLu2xhli2a8yV0Y+G6HqaxaAcHs1xAuQF6eQa4gbKTmpbVI+SYwzO5WaBM2ysJKRdk3JAiSggnqS8mapDBB073BB71DkPxeZ9fWn4PgwXn8fnC4QaQ0uRjnFFsoC60bhqzLWH5/uiC5ntSwbhhRDwIERGDB5iIGCmn3EARmcEltaAoYURJKLe3zi2ls+p8WxSEnMqqjNlOY22afHvooZoWIQSynW3+zCNiiknW8nsIKFKeQ54TITKx7ynVufMhjXuJkCayKiLV5/OxTHOmraRRA1lJ2nd5gP/yGx1WUqtdK1ge/C3bupYXromEUkQ+aYW0qB3YDoZARH1jNAAFrViBFUhpFVm1kRLkyohbqZDyAzeEcKMxEYd+tJBJJJSMjzscC2lM2AZoWtrHBGVS0nE9PqUt15zaVhnn7+H0onqngGPd/bKDlagXD3mdQ0mHJ1XKSBqMQBUvKgRFmTJTvn9yDAvCQYipEiNnbuTIzBBL4qhM9s1H5fcTU1c2atF6e+1bKKBGxFKpipoQVCm9oySvyrl3hWMzJaR2kVGlwqV8vDwGk2MPhfxmMhbm0qJkabSLqJpDqV4pScxjyh0cQgZU7EceM3kra+feU8YJ+VEBr64HvHLR4/XLHq887lMwH0AKuD9cWrRWY9VaPFxaGK1w3loqV+KkTAl1FzcupGt7E3KjJ6MUGqoUyal9VDNScd1XSeuT2lNii45RTpXH7Ko7JXNvVw200feI/Pr7cCfr/kPVoAc+dutklLzmLjKqVEUNPSnYJ+vytF6TEjWuT8Fmf3kJ3zv4jrKCwuAwXKwRfYDrKBPCLlsoo7F44SHa586weGGA0hoheOIo2iVglznLK30Wk9ZVp4T7T0pdA2lA8UI+vPEKLn/plbTQtudLtLbhSK8ZyeFoS4oXOrihhbQsqr3iqD+l8ZWIyIYzxrKYeUhklGxfXw+47D1ev+x5S2TU4/UA7wKGjYMbAtzg4QYP7wLcQKSL6x1iIFmg5050gbcAEEMYpWIoTsEwlhwHbVsorWFbA2MUbGtgGwNjNNzgYayGjxGt0fAh4uHSJmfurCW1VBMUlvx9nvkArTQpe4yadzh4Asehz7WjOG3PP34M1/Xo37jEcLGGuyCCKgwO3esbhMFj88YGvg9wncOwJqWU6xxCiOg59a1nUqeMmk2nZCsOp1ZoJSVnaaG0QrOyMK2B61wipdpzB9NuEAYHzbWnTEtjw/QObfkePGai65nAZGMFpG1erBUpeTHmv4CRIz4lowJvxWF/5aLHI67a7EPEx71lhQf8G20p1ipuB4dcaMvI+taFVVbOrKjTngrsB05/8p7Gk+vzYjdwKlXIKVXGDNDej9JZ0WBnSiu9ZWEnmHwQImInAWFMqqOWu1Pq3AygKHhdOkS7nBytwBF8WuhJhFoilz56mADAaHIYokrzZgmDIZD90YqUUUbS6rSCDxEthPTKk8H6bLvPGoPWxpTWDBTpzkWktOF9jdawskCVLqW81Tp3M5WaEwf5MqWDOP2T+nSOyD9SzJk8pjx/r1yDAMHPjoWoaTxcNRZkHMi+ciwAGCmklNZESMn11DZcd0pUdjqprmYRA11HpwXP9x1fSasbxZRAKFGSDFOCQakx+TRHOGyREsXqYJc4Znoes6lZJclQOHAUgKETUKAaQqkLJoBYpNokNcwOYmqE5ISM7XhyVIp9WwTUnLIKO1RVVyEVzCoCocWXkWpO6Wx3Y2l/pREBLUrycwv+Kk5eP70Pb1PpAYzJpukYuZKgumIsHDIO6DMXx6SxEEfPUeVXPfq+xuNZniPnPlWVP0nKZ8VuTMcW0v2YtlHGXcwq6CFEdM7j8cbh1x5v8MrjHv/v1XUK3gPAYtWgNRq9C1i1JpU/kaCT0QpoC/KLlVJwITV4SpclA8AHejwCURqfsD0xiKkreZAxGNXOouhKzY+pkhjdldIHYLaRAzA/Vw4Zs7dKTD0JIXWb6qgttav4q5Ogg5BQQK7vWRBSW6r1TREodH0uTeN6hEsSWwwXHULvMFx2cN0Gft2PfF8AaFYklpCgom4s9IOhKOfhx4XOZ9ZIpxTse3ZIqZLdjAFwDmF9gXDxCK//z/+LV3/+F1OdoMULD/C2ZUtFq12fU1SKBXZSSjUtpQNqA8XlgyKAVMg1OCjT5tNggynFy7tCIfW4d7joSR31+iWRCb/2uMe697i87OFdQHc5wPUew8Zh2Hi4vsfQPUYMHm59geB6uH6NwOk9wXGaT9F5K9X2AEZbzaSUWaygtYFdPoBuWph2iWb5ANo2WJ43MFZjedbCWIXLdYtfW1g8f9Zg3XusWnq9laTyhYil1SndZcFESGnXVAwU9R96hMtHiJePEDdrhMevwXcbXP4qbTevPUb/xiX6ix7dqx1cN2DzRo9h7bB5Y4PBRzx2AR0TUY9ZNbEWhUQYFwoHwKk0dNsoIqGMyoRUqxVWFwNarXDOjubzDSnHmvMG7XkDu7JYvWUD3Ri4iw5m2cJ1GzRnK4TBoX1IRscAZCiaFmgp3zcqDRXciKiTtD3P6qghUOFGUYGIsq5USrmyoHOI2HCk6Of+3xs0hlg9t/745/GWVQOjFFyIMBqI7MTHupK6Gxxz8S2PVeT8K7MixSaA2He0dQMwNFB80VNhkrIFJi2KlC0D5AvrvgLXZXFzYJy+Z8m27UrfA5MRpJBaAEojGmoMIXX7pCtbcgaKiyPpiWjhZTSgIiuKEBGYqFpaQ2okUYWGCKNoDg06wAaV0veAogh6yAvaaQrfppgIz60arCibO6XuAUV3UiZoJH1P5P1GAQtL3500gRBCSmyNKtIAlQI7xAVSDUOVCsIrramZBtc0VADX4vOZuHEDbxv6bWVMDAOPlxsaC/z7XzUWJHVvtA9gG6gKNUZMtQ4OIpgqCXUr2BWsKGu5AFNnMZMP5YPp2gJc2Sl2GiiJGJ/IrvOaOkqqKEAtZLaMku027lmtoCAkFZFXKvKL8MVRqxnHrCSkxBFJxWiLDkty2+dOmFOnZ0s1u0tFexUKokkV91MHTNnH6kQAqZ5baicOQGmL1EmTm1HMEVOlWiU3YslEVDk20v09Y2GqgnnScSCPJTJKHt8aCzn4IeRleq1IY6MkENJSUm0TAOW5VmX57SNwdbQIKmXROSqDcjkE/NJra/x/v7bGr/7aJV79lccc2PdQWuHBC0s0C4PHZy3apcWDhcXzZw4Lq7E+a9Bag55VVEtrOPsjYGE0tJfUfArCD4Gu/1JLshEaVOeBMqeaQsQopQ9g4umaqinaTzimU99TI6Zuk5C6ySLmu8ioZPdz3agtddSEjEoKqU1HKqm+Q+g6+MHBXdC2zAhylx36xz3Wr3YYLgY8+qXHiD6iOW9gWo23/sYBDz+OxCfNwwf0+7uB1mwzwe6ttdMx666niPtPSpUy5EOx43jprGaFkJJoPzhKbAwyQWVyfQ1gHO2dUQHMqVKk1knZTa0sUJ7+IqXipb8YeW7ERDYFZkV3/YlSKqVqsFqqJKhK0irwNvqS0GpG5xH4HHzcLqouyh76nJMoaZwavm1nSLrlpZpYnqP5gdjgcj9QnMfWH4ptvl28KxlZ/j1aXonQMWry/PyY5rpUkg4YfIQyfH6h7PjH6TFXdbkqjIaoKEQdkvepUX0orRUQgCAXNlmIm/ztSp68DxHdjLN5HaVUXWDdDeZSkCNfYFTTUm66bZiI8LRv6KF0yPYo8NZ7wLZMQmgo2Z/m/O6LUO64lwmJEVE/qR+U1FGlemqXQmqqljoANOwp9S2C7IlWClpHKkSq8lwN7HUSgUVKQlFUCUKk16Lb8rXlQU5kEt02hRMjtaRk35Rw0sVWM5mlkOd36RDJ59qLOWUZLyCiClzkkoti2sLGhqc4FvjYVAC/UEcp6c7IZBuAREzOpvBV3Diu8x3n62aeM1PSAdgmovL+rKIpSYfp8+Tc5joHJ4ds4myZNIly/ZZMOAlRFQsyKtd6EacwIsIgkw8+sCqBVQ6mfMsyhYPv76wpImSUdNAUZ6cgrrIDFMcdU9Pb7e+OqfT2mjTNMyB1kQaYfIIQT/n7FhKLOqI6Vk4VHZvKzz4hpqQxxZSAktp/JRk1VVRNx8J0HADbY2HXOABkLIzHAR27eyx4xEROxqi2nidEQSjvC1G1dQb7UWOAt4PA40+6lA+BmkKtOZi/WTvKLOkuoLTBsOIMB+OgtcJaqaSSkhT/jaP7rdFJeR00vZnXEQhI26CAwA1NdCCFtRA54mtIh2tRTSWlJr3EmJiaGdtzY6dUTW0df8PE1J3jSRRSVx6/KwJTklYTUmeXnS8eE9+5bCiT9/Ef13ylWlI+1YzKTbsodc93JL7oLwYMFwM2b2zwxgUFm899SBk8VFN5SMosqV+V14KhqA1xmrj/pNQTQmmNxQsPcP4xb0X73DmWb3sOixceovn1n5wdv3IhUBZrLRUA2iLaJaJpEG1L900DaAsnxEYicPIiTjDtkgaMU0Sk7hPVftLwNsB4jRhaGF78yyLGMNEUpPvWDPGUP79Jf9q2UMbAtqt0XzcNbLuCtm0qfK6NhrZ0HlIIXTr0Sbe+RuuiOxU7ZkVEagxRAIjDIilCBqahIm6mbWAaC99YmNZTkW4Ahr3FZmmBzmFlNHwMgAbfjgA0Ry7IWZ2m8AHbaqmcvqfSbVFPGUU1p0yroRtNWy6Knv5m2mzGwA2ExRlkZp26TjkoVk0ZbbluTURrcmoBLGBDVoU0RhVpe4W6hB3lIQQMHx/xykWPl97o8NrlgOfP2qTooGO3TrPirlBeyCb7R3dVkRIshQqDA0wDZS1HZBqa57ZJF0IMpJJRYUmP7em4NpeuJUgpfEWR6+T8lMWtS9s4aQwRlSKbqDRv1bZKqiAnSohDKMXB8xYAFEchFYw2MCqkGmkhRgw6KwgHHuyikJKgAJAdIrHDZfreeWtxLvyKOChsG4BSMZVJqpTSV6TxKQBWlzZxTGDJZ50i1ygMWS1luFA81y6UtBwVC4KJf4NyLMDx7yXF729qLAjRVN6+aixgrMqYJSZjwOwq6p5H804Z82l60/tjQqokHfLt3B3zqg6xZafM1BWzDCBNTkCUCFPkzpj5Oij7ynlKaxOqD6eUkM0xqxe5iHGao2xjFMS2AAaTCHoMUH7gLdX6SwSUNChgFVVqAQ7kGoCiXJQ157T9d/ryd8zPMrW1nIty30waVEjqtRDEHDiQ64xSGtE2tC+GXCc1sv3h+RdI6JE6oMoat+xyOu0YLMe6YiyI0jUpWEMcjY25cbBrLEzHAd3OXVLz2EAaC4Zts5BWRuci1TIOpqSlYWKqfP85Z7+qpW4PMWbiMrINGnxWSz3uHF553OPR4w0u3tjg8cu/BL/p4LrHvI75BLRnZ/AuwHsqieJjxKqn685CCCpWPa9akzr8SUMnXdgWpp2gowTC6PofI9XVDVHNElNz6XwKuxVTQh6VxGxJTE2J0pMlpm4qZe9YhRRvr1XMnDOVRvWjhCiaUUjFoU/1kl3XY7hYU+3kR5fwXY/1q2v0jwd0r3a4fGWNi8sBv7ge4CPwwGos1w5nv3IBu7RoH55huOjQNi0ppZp2FPDIt0+TnXo2SakkTTaIxkA3lopVny2pgv3ZQ+qaZltA2jQCaaE/TUtIXfgUESrJydJ6ZyQyRQF3sbQFTGE1JCqTP4rOxJIxUIEJJgCxuF/WlBp/FWb8/Ml9Semj9yFSTPNfIslGfyU5cuVH2/OheQGFAVrrXCrBUFc8bRSi3A46nZcJpHYKATCsHspbIJvb8fcui4pW5wVMmc5nJgubOVBh9KsdpeiJPJA6UlPIZU2l23QeHnRR00GUG5q7BOlR8XppTT8EhQetgY+UWtlzccb0mesq6X5BLnZ8ewvTukLlMVwrKG1H+8PkZajOFIKneeZ9IhumhERZU2oLZTrf1mN5X5meNfuZdmBudE6j3kqBukaqnAoyPkWVmj1oTWl8ErGc21fOc4GQYCUhlV5/bt/MvFKTcy63c59378zc811GzIyJ0SGGFklSI3H2ZfT+cTBHTpbYpSKeHntVPbGSeCpun1L9g1NHOaXKAufymATXShWMEFJXERBhRESEGVIqr49KMqLkJaZTTRfXbyEShIgY+DpOyki+zeQxjHxWJp4iqWbkvqQN7/6iJo5KyE4KJK1jn8PCRFTqinoN0nhKGMdJ8CAiBw9giuuFbYAghHHIqyNtSd1VzFNas0wdvfFcDMWYKImqGFGkUOffPwUNfJwlKHeNA3mv9NsXv890HKTbPBYa/q7K4IGMCyIqIxCoSLXUAVJcnDoANEYgRaz5XIBRwfN75di/SREid+JzgdRRvYffdPD9GsOaSCnXr6FtCzcYuMHAGDq2V2qrKcrGUeDdNNwgKGCkmNJRQcfIhBSNAB8jGr5HJBARU0YhNTWhtcf2+T/pGLqOgu+ZwVVNIa7CjqDxdI0+CkqUCimI/Q5s38NYIVUopsLgEEOAl856RYc930sDLwfXOXRrh8cucHkaGTeaaigPpLaKRWmGFBh5RvDskFISYZYLrLWpZlT71hdwHgIWLzyEfu5t0A9fgHn4Frqoc6t1GWSzkSWpkWIbuq0tYFtEbUedo8QozVMRBCF3WkuF9yT1yrIiSFsFC5O66ymtEAMpmejxJpFQ2rYIlnJadTNWU6WvhYkoPakpRYXOWSHVLtEuLL03FzyXbnzLxiSVVPrj+imiJpAo1C5p6fRcIN+xbaBbD91amNDCrFr4wcE0FnZpATg0KwtvApreU/Hdix5AoGgYdIpmTQudT8nA7Ixu15Ra8ud4YKlOjF1SwXO7tGhWFrox0I2BaQ2ruiyUlm5UOi0WZx18MWRFNDUCMCyppzU7XZqkExAsOPoyjjrL58iLLIO3rJpU82bVGrz9QYuzxmDJyg2FnMd+1e9TcQM45uLAzs3oIigXSt9DxYiwWbP8l9vKplz1QJF4kQeXkfhpVB6Y7bYmyF3XCnJhqoARxdRilVSkUtQ6deFrl1CWlV7aQlikyFF5KHLqUioXwN33cre9GKmzTgBSZ0oX4qjrXrnNnSlzvbXSMQIwmj+SHrJZ58nwS486nPHXI/MLyFFSKXAuxc6lrtS0pbRsrVYwGsk2gR0exYow2uYxoOK24kK5IasvfA84N+6653MBTWkxDGC7M6MslIDjx0I5Dorbqdi9pPGljoysLLYN9GJFCkBgVGhZxUgLqsJUqlgUXJ5DVU3dKqbiFFEmCPkg5FRWg9NjjmsKdc4jBIwacwgBMTc/hYSQWm+iBNgXwJPrd1LF6KyGKRsRALkjZuqWqakejNbUNVMBiIauh40GoBViyDWmSqJjZKPTfKTumKljagxAv0F0AwUHi6h5IqKGHlFs9oF2WsipaTMK2u6Yn5PmA6Mu0kX9P8Xq//QHAKyOUtrSe6vARHhWwEUgdUeVephks/ePBWnMQnaePteIpJK48JFjoRwHWmGnnd43FkjdGlnpQkWrIyukVFSp5FhdPz1dpGUScnOg3lPqXu+kKVSA6x7DbdZJKTVcvAEAsO3bYKwjYrKlzt3r1sCHmNVSLvD1u1gHRFqfh0hZC0ZRkBgcHAfyukLprLCTMSMKpjSklRD8WRE1LWwuCqc50mrfWDxGLbUPN6r8uw5hcpNpe3vqSM0+vuc5OeVaCKhMRCXb7j3ZT163h36A7zldr3dU0Lzr4XuH/qLnpl2UttdfDHjsAi58HJFSPgLDmpt8dT1C73KgY+LzX4l7vpY6WVIqqpnWuCVS16IWqs0KKX3+EHp1Tml4XKwcMbAiRV5bFWkoOR1FUhGiNiktJcQsuZQIIrCdG2/4Ajrb4alI2zOGothaKxhLMtFgDbwDjG0xLWgOsJhUuvBp3if1BoqtkFJCbGnbprQ9ben9DKfuKX5/IdAsq6TKOis5MpXLTc5KRcsUHj0mpogA1NCcFmcai9Barv9FZBCAnM43GLQpPYcvTlol+XfeKt5mMgcYd98zCmhUTuMzCkTKceqeaUlBRvcNVJHCl7pTSfv0GUIqeg+YJjmepF6hxZ5SnEbAHXCUotx0FVFE55iUhJKmWUmRIcUWH7Y0hTeOtmeNSYThlSRhXWXdHkrbFMLVF+aSkAqOGjUUrWMjk1NwA90uHJy0HfpU48z3jl825O57M2REqfwTgrXswqfbhsh7cXDcQCmEzcAERK4rBK2JXGmRx7tnYqJM45qrpRULx1cUGIEIqrIT5SVL7zcuOzjJAebofO/DqGafC9s2eX2Z3/v19YB1jMku28IuG63QGj1J1VNwmupL+RgTKSzR+2AU2qIAqo7s6FwVFhXFhXfZ4Q2OHN6twpl9dnbLIpu8Df2A1HWvqH+X3mrPWCiJdiHglaE6YrGsK2apNTFCYDVGTkUKAB0nTm56kz0Lohh4pb6tnqq4eex2+8fHZBJpTCL3viSIgY3zWwRE53yap0JA9X6sTtg1R0vsmpvSDdNoUsdoReR2oxWaoNHomMhlw0O+0Tqn9HHRa3E6NTuRo4tnESmHd2PyOHiyu32XAgcleQzvEcoggrQJP9JOAwfMT2MQpQtm30G3y6SYikxOiRetAKgm5nGgDX02SR+fppjHWBCVBUEZ6LZ0mt41FqaklA+yjVfa6n3jAECqDyR2uhwLS2uoHuAVYyGC1kMqggIIcX83xn2kQcXxSOq9K1AOCyFK5S+4SCl6rqfO5H0HbRsEuc9qKu9pqwqlVFJb8RiUYLAQ84B09iRfQ3PKnaSWTmtDUTozpe1hYlMCcrre+LPN15eaw7FpfLOvgXswdg8krA5K23uS99uRBrhVawrYUqcnlRTA/nkZdMjKJqonNSS7HwZSSYXBw/cBvvfwvUcfqGnXmp3ZPkQYFRGGgDD41IUv+pDtuWTlPAOKqZMlpXZCZ8UUXYQ99PlzQAjQ589Bnz8HtTzP6icgt1MUSO0TKRApufuyryhIG0NR+HHGqgp5E1RMZI7VCq01o7ak695jAJiIAmIQMkmx8IAWIsENUNrAW+m+R06h1JeaIqft5TRAqSll2xbaaljuNmeshm3JIZX7q5aUUrJdWFZJsfqGIlIYdb+Ri/XkRJDSH4s/ZRvoxiL6ALtcIAwONrRoVj20UTRhjYbvAxSvEpRWsEOA6clBNeyULjlKMVdTSiCkVFMoplpNjqZdWmijWR2lYZcWdmVZOaVZwdVCtxa6oRpYUhtLsWS+LJCvjBkpIkriQb4jWkRxSoTKaUpGce55HCvvFHLNGqUUzpi0E3WIdEVcSlFmja0LYOWinjLm8r8LQkoFcmSi6xE3HTkyfZeIqdhzd49Nh+jHkuDQO4SQL1rSUABAahowhRCsumE1lNGp1ptmpyepA5fLpI5RtoVqmtSdFECuY2KRVKtRUhEl7cUUtUqYfEqNC0TtyIRU50LqYiodKUPEiJzyMeZoabG47J3fbtAgKqouL+N+5bUOy7O4FSxorRmR8qmuXqGMChHYuIDBajSGIqiLVP+Nri+abpJNCizrj4WcPzARyU4vddpzmZASh3d9Qb97d0HthQvSMnRdWuzEEDiSlscGAATu0njVOEjXCybctdZs8+xobKjFkuwdE5VYrDJBxZ9Lsw1MxFQMxLpDPxMLqGcJAdhO1yu2qZYQqxc7FxCiEMU0DzYuFOqYTBive5qLG5eJiN6RXSody12YC+TJ/FwUwTOjVeoGvOTal5ZrM8p8G1TEGQwiq2xUAKAjdamV76L02EobzYpGUUiFzbqwz2yz1xdkdwtyKvRDKnIraRvXtdO75qc08qEu0w0Cd2ZSg5DHpKBE8EDLdQiXHFAMFlAxtxUvx0XM29I+e1ZLOQ4WlGPhcvBwPAZknHT82697+pybGTtd2up9Y6EM7so4KMfCqqXanBsf0GiNhY3Jbs+NBatV7qIWaE2b5gNyOt/T9+KfbWiFnWt3eXzv84sDSoWhNCii27mhlCCPuwDAjMafDzE1FxqRYjcqJ6q4VdzWWmNHLcB9DSumQYfgIzf6IlWx+LCmmAs7U8ulLEeqGbEniHciAb7TJ6VSakAsiA+b8+uX59BM2Oizh6SWWqzgDRV4VFzkEcA4MlsW5+WIL0xLBJUhybPnC9aoTTIjyz9JFl6muolz44NOstFVa2CcQnCZfKHTYM0MW8NoNbxrYMKKlBTMyIZ2ycf54rmcElgWOtcGxhoidhoqZG4bQ+l6VqFZWCil0DYGq5b+zgpCioqcU2E/aouepdPAjmt2mb4hBXIDdZOKAAyTUrol0gcA7NkSSpO0URmP4KktpjIKyigiqYxC9BEtL4Ry+t5YJQVsp+9J6p7WipRQjYFdUcpic97wfZtS+OyyhW5scs50S3+mtSPV1946PIVjriLJgCW9hxpi8bkbjoQk5d24+4viaGCMwFlroHWWxD9sDc4ak5xmDeRW9AoHR2IqDsQRF7tUk0QwKqyYCakUeR96IiCEkCjaysYQMFwQKeW6nkmpIZEQ8ucHjsTvSQuRCLyoo8ZkVEOpvsuWCIllD9NamOVAUfl2Ce09YtNSnTvb0Jhrl0CLZGPBaauIzajzmnTRyQ4P4ALJ5C8HirZfDh6dD3A+K6UuB6qhdsm11HrnCzJqTE7NOTtl+t5LjzZYOFZUjpzeTEaRo2NhtEr2sLW6iJRmYlg+F801Ba8AhIigqX4NHcYE+8jhdSlNCL5PhFRYXwBuQOgugKEnwrInstJ3G/ieJN1pLIQAL0opJqdENVWOhV3pQeU4kD/TEBGvW5tIqebcMVE5EEnpBiLUClIq2oYUVmBiSgpwlil8MQDqiKKcVUF14ygdNLqfA20BJRExVcVEXA60XfN87VxAx0TDdH6WaoRyfgLYSUzNEVIlCbFrfg7eoDEBjaa6cqJolCYtsJoVywCCgtdFgWt+71HHJSGKmZxKdnpD8zF2F6SM6i5TQCEMDo7TLaTjUgwBrtsAwNb8nM5NQWmrp3YaAOxywcpWIaUs7HJDRLLroSzbZ9PRHPU+k8pNzMXPg0tBhJGd5jERkYMIA/+OJRl5OYRsu/kxSbG6ZGJy3btkpzduWy21j5iayzSY2mkKouaxQESlwdJqrm1mctkJVr4COo0DrSPXj1K5CHWpSjlQiVJxPVCq2/Zvf+hXLsH3OcRiTMUdZPiYkAJdrvakk940dqXnHaOketPiPgS6QpHix5gSUaPO7VegrH+6twbsHpxSjc7TJ6XmwOSUaijdTTWUwqcWKyJCuFZUKlQeFTkDk7bVMHb0enMdpK6yVUJGBVEWmXGkr7U0gFOL0tZAOZWYfOXGpJRn0ioGDc8Fa2Pw0DuY2dJACxlljIbSgG1z/ShRSFH63lgZRQ6YSfWkSDLPXU50zr3ee6GW76ysf9BQyoduG5gQKHWvaaB9ILIHgB0clNGIPsIXqUbaiIMVoYyC9RFm8ABUUVNq+3xSNywmo5SQUq1OSilTpPA1K6ovpRsioGjbZLUAq73GtR2ucLAKw6mUdPyRcUcRW84rBZR0AuLPrUSVJuo7cCTQUMc+rdMYkxTBinuKafHcGHIHj6FPOekounrEoU/Eg5BRvttkh8eHFJEHyOmht2K1zOTimKLvJQmhNcxApFSQ+RcCdGNHr2WW4A5rXKfFNrTliLzmriDxgIWCRKTLaHxSW7BzM/gcdV/3HhtuAy0Orzg9JTk1cnbYWIcQ4TfZllxsHDoETrEek1LlVpStPkTu0BOTcyOOzUZxvTttmCjWqQPYlel7aUzQuAhlfSjXj8aBEFLi8LquZwd4k5QYpXKqVGRcNQ4AJGdXlBehdzDLAD0MiMvFaKw0hV1WQ08ElBtobEhh53ZRKAInxZTB8vNqqu4FyiVNQFaAR0gx66yEku6wlJ6VCal17+FCxONuSOoYUUrtmp/A7lpCKf2e5+K0Lqfl+SmExcYFYAmEqAGruaZV4LpTAUNQ0AGw2jDZQuk2aiapJdnokBWtqWZU8VeqF6XbUgwBft0nmyzz03cUKJ2bn/uUUnN2GiCnRxkNGxaIPsDwa2gfaJEfAkLqKE0KKhhDn0EbKBtGwdm5Ji1ZQRdzF7QQ8p8f1/rbuID14JNdLseC22GnU0rnFeNgl51e8Jgox0JK9XMAoKEVjYMhaAQVMXgqk2G1QVRU13Nqq++OkqiYQikK0Kb7vKVMgDwOtFXQVqcyJ7mJk5Qsyc2c5po4Cca3S4HBWBi1qyFSevya17NdS/ZDCalD1/zP5OV22iDoqv1X4MoyQdqkBhUQNaqsh+U+cuBXbic7rjW0idTxnsuuNCp3hwfybd2IKGO++/vos54QCTXFSZNSUWmI4HrcVt1xMcsGasnGxDbQy3OSLZs2qZ+UYqdtOvCEfCpT9rRFLFRSYienFyzFHV1S2p6mjg1LawDnabsEO1RE9rjigtwbIp600YgxwvUa3gfYxlC3id7TwiDEVBBdTr9sH1p8FEiNKIDqJqUaVlzcXBtSTa0WFq3VeP6swcJqPFharFqLh0uL89aiMQoLVkktrEGjKR1R1FKa60uNvpNUT8pSi3uuP6K4Ho3qlzDawPJCSiacEcd6cNBGwfeeyaMGvvdozj2ij3AdHed7j+AjYqH/lQVelr4r7qKneJGnYFeWDAKTUUROKVZKtdS58XzJ6oAVDHdztMuW0plsQ/UbEjFF9czmupIl5ytSFxyjNMCFNaGploEk7KmoUipfer7wpQqIUGg5lfJhNDCKirwScVjUMSt+l/w6z+Ql6TQgyqjyNteRSuQTEw9xfZHUMq7r4bs+KaSGy/WIkJBofPQxzYUwSErIeF6UKOeE1FDTRiUyVhmN5kzG/xK6aeCXLcyqJ2VjCJSGCyA21E0TC3aCJADg2SYXdlbqlKTacByBl6h7x+l6l4NPqqk3ugEbF/C4G9AzKSVqDHF8u4GKmHoXtu2k1Ifo87x8/PoadkH121LXUUX2UmmFZWNSSkhrNc5YQdpajU3R8bIxFIUHfyajgKC5YHpQCIaUkBFZAZnGQFFLCsFRgXtO2UwKqctHiG6Av7yksbDu+bcfijHRpVROqVEQfGTHNx40DgBQPT8eA8qopL7QRQpz6ImwDINje0iF+FW7pMBPIAUdgFFKZ2RlVKnEqLh5zH23e7Ki+PGYtjROhSjONd6IICYCauDULdk+7txofk7n5rr3GHzYOT+BsWpLrlOiGJc1jATPtFZYTebnuveJsBLVVIgx1Rlqgs4F03ndCE3X2qiQSKrygimKRlFLJdUq2+e46RAu3wDcgOFijTA4DBcdQu+SnRbVVFa3ju104Hm5b34CIMW41qP5KUrWUtHdnK1onp4voZseTfCI0rAieE4r5lID1tJ89A3N0XIcCQnF48GzKlTGQuemCqmANzqXfv+dY2HwlK7CdX4ACroCuHIszNnpxlAwdW4siJ1+sLTw0YwKoTeaggdGhVQLUDo5G5BaKm33rJvmsrkOiUNUzEOCr340BpDqwRlF9R5FtUwZHxp2+QBKm5QOa1fnsO0KtjWpkZPUz03P1SoF3+W25oBTWUO37NgNSMdHKcNR+FyihJYMEqVGn2kOWl1dB7b8HtLzDnvK/cSBZNEWOVR2Fz6GcNp1bMrXDfmY6RbIJVm0iEAG2idklA5J7KK0QdSB/Hivk+gisOgihgDTGt5yuZilxWqgYM8DvtY9sBoro9CsKGvHSAmZluu8GjNPRJ0oOXXSpFTCzOARkgqKC7OGANW0CNOaUNIKd/KSsTgmkSoTXLXA05okwOSTqILZH9ct6Z0fpfEJOgDBBURLDpODhwoKaJHyouViLhdxaak7zq2mi7dcyI2QMZy+p42Gbc3oor6lkLJSTLTsaiKLOn6ffQa1nBxlqltqWexTkXPjA6uiSJ1ESqmQFmXK+HQ7mgDAInhRkMVRpDH6TEbRW0v9HJbBGwXT0MKuWVm6n5RTbVIKjGqpGJ3OS0kNna1uOFcYg0kKikSDRDFFUl1gaj7lZ9VyDIqOfFqnwp1Se6p8fdnuO7PrRncqjsdWd5CihawUSpRuaoEj7SEVSBzg16SYkv2uG7acHN9LV6e4UypMURsmaQ1F65XWCJ5aHSuv4TSl7bmuh+aovDKk1tRNzx+Da66UnUkmaSD7IEqMVOw8SHHcrJoSpYX85fQg+ht8QHBUFN0xORWjOL2F4rTPA931EdCBxbNkK8UJ1lqhAyhVeZI2AuRizaKKCpbOmRoRUKoHObulArLANE2nrL016qgYkoJOOrl4Vl7I2JD9MQTq0tJ7VksJIRUOGgcAYDzV0Qg+19NIp8z22DQWKphETsXGouz8p4Iej2nYvLibUWJUPF1MxSmyviHyWAjk3N02yBxllVSZNit/60K9WM5PKTY8nZ/AOMUm15HMj8l1XocIYzXWQFJwlwqZ3vk0V6kTZ8BA/d0Rot7q0huYhNj+YrLCD+CxO7XV3Gwi9EMOEHQ9ovcjQsp3PYIPcGs3stPHzE9Sh4et+SlrG1nUh4ZryDWSwk21/6LroZqGtoHqrsp8TJ2CZxy3wKmc8vPkphRFMXNWTElq3rp3xe1iLPDv7x0pw8LWenb/WCjtdIwRxmgMANBjpGy1hZ02mgpbN7w2CyEiKJU7AAoRK+rAGri7V9BFuFuzH6JTjV6dmkNJbUzdr1PXcW1bGFaiaMv+UKG6nP4BSIFdQEpfjM9nrs6PmjluLgBc7lLYVkHJa8yNwL0JKTMPnuSafoY82qta2kdMzZFMQD5+7rnTfek5k4ykQg2V7qfbBtA+qaIAjJsINSR+MI1Jtt20JimjVobKQ6wMd4jnAIQ8f9Q0bO79TxSnQUqVA2Q6wIQxlZbjSlPaXQyIoSV1jtJQ1lJxc6kLVXaDAnIR1pI8EUKKo/3RZEJL1jLTXGOlqL2sdAABAhbWQCuSNQtZJCQCXSgNjNbwIeBx5/BwaXHZ+1F00ceYFnGeI41l9D+E+SW+vJ9cwDUrsMg4k+JJCpkbrfBwSbn4D5YWrTV4uLRYtZSPf9YYLAxtKcKkU+0iwxEAxaqc0RRPBc4tYCiVEgDU8pxbn1N3GIAWl47JHz84aKOJWW5sSlkSh4ycrQA/+C0VwF4J/B5liNIapog6mmVL6XrLFlpr2PMl1dQ5O+PWykuo1Xm+3bSp9XLq3jhlqwuVjJYUUhBbFCOSYqpUSU3zyY0mJZU1VKh1WbSit1olskp+D1Gxjb6LPRe+ihtCUTdKtmUdofRXqqSGPteQ6i4QelLCDBcdfLehCLxE4geH/nEPzx083FrStnixP4Tk7ABI0XiBzAMAiZyV+aAbk4ja9nyg7cOzFOUJg0tqRtM66srnmqSAhG2gnKPAQPAkZy6/E6Aodh5TAeVcn8Rjw9H3i97hsvd4/bJH7wJeu6To++POofcBrvdwHHknMgqJlBJ7SW/NyowhX8jXFxtYJ/WVStLeUJpzY9BrhU1jYFuDVW+w5kh82ayC6pfk77YxCkFruIbi5fJZp6Za0oOI0BPFXJ9q1UhNKX95SW2EH12yYm4N35FaipRSEf0FKTCGtUtdXeT390MmKOfGAYC06BHSXlQZdiXNHgzaB0TWS0pn9B6Ga5m1AHRL9cZgG0S29WkB1ejxXACr58rrelEMv+J2IelYgij7iscDSCU1eE7bKxSMoo6RObnuHda9x2uXlKr1+uVAjQg6h8BEhBvG65ipSgbI5JOgVMbI2qVUem+0Qrew0Ew8iDrm4TKg5860on5YWo3LgVOQVe7UFqNKHQKTIkZslXRQDW6rllRYXyB2l0nB2L9xSXb5jYuRnR7WEjjYb6enczN9B4XCe9f8tEtHTWIaSwrzYp4CgF06GCDVqIxsq2O7JG9XCHH5zEbGQU7Zi4gpVW/jPC6HgA2nVl9wkOBR57DuHR51jmy0C3i8cURiblwio7yj318CCaVybtdYKNVyYq+1pW1vNdZGY720MCqTlESGmdFYoA6NkVM6We2iArTSVNw8ZvWTbEXgGiOl+Z2kw38CUCnfPQdqlcpB2IYD40v2XR4sLZbnLTZrh9ULH02d9xoKGK+eeytsY7A8b7FYWTQLi3ZpsWroeaKga61JKuhGayyY6BYfh2roIpXIkK0QY3KuEhCWAHBaZxc+Uj72MOxK3bvqKnnd8Xmj4/qaqXNXPq9US808LyqVA787Xp+IeF53kLueSgsoKeeD/DtFFQBruTmE33ocwQALCSh6QGvoEKBMbkSm2KfVEkzsegQfU4f56CMWnUuk//ONxtJqLF9YYvWWFez5kjJ32iX7mg2mDdhG3MaJqaVOg5Q6Bqx+Unxb9sn+qBSRVuVjwJiQmil2Pnp9vjktxjdSDEWKqIdCKSW1pRqt4TU9ty0UUr1TWLU5jQ8ou0JE9BzVkZpT5UUcwCwxJYs5gC7iAJLsvZS+yl+WP9uUpy8KnEbrZJilQCTlWzPpdZVSSlRpxlBr8VL2KEROoHoIoVRIDbmjlAXgpcinoYWusNBJFRJCUkiNTkEcL5a+K6NHZJQUd85FnYmQyh1tcl0pxU5XUkqJjFK6HIrMc24MleeE7QsTRU6ySirGfFGafscaCpFrSyWFFJBqSsn+inuAuQtsUsZwxJ3by0qtuOj9bL0o37ucAlIQUnTbJyenVMnQ201sFpPUAFJ9tsgXSHGOTGvgG09EMddCUbqn6A7XnQIAw51BRUWgRO0Ts5MzW6uE95BDHIsIPNgJHtcfGf1JxL3YitMrpBSliBRpSSEiDIVSagigFlyAtmrkBJW2U2kF5RTWwJZSSs5t8BGDjmg0klKKUqEUOzLA7FK0JC+DjIGswIhuSG2EQ09KOdrKfWkpTFvX5TROIe19T+8RCpKuHAcAklrOeM0BAZ0WS7JcIDVednT94KCbJgULqAaZpwUadxVTskiT8T5H1B9T7LziRjG39I8xp24BSIoSmaNlg4KymHmpZkzKxYKQEhIikVIyhwoyZouUUgpShkBrBR0ioo2IgdcUKqdy9VxvUs7BaI+No7EV2pg6Ag4+wvMKOLC9me1yNKm1FNgux+CpngjbOZmLomRN6kUmpHzv4dY0d2ROOiHrDrDTHtvzE7BQJtttqQnXgOpVAYDvWTHVO3itYZZ8zuVnwES9WFyryrEh31NgG11+lyHG4vf3W7ZalFFSfsL12W6XaqkYxsHecixopSDNfCSDIIYIHfXo8up6jzBJ36P1bEyqqfw5xuO6JN/m1K1C6FU8OYTwA7Bd9mMGip2rssFSa3QqW2EbyvoAlrCuh9Im1cw1JtfNFd8nq6OK21L2QufUPa1yGYyyju743LYJqVK5VKbtlaTPdG0/DRaPSrGUz5v9bg7DvXIJjiCtDk7jmxJTe1LyRq+pNVe3x5ZoRU3fI50+peklxZJmTV9RW0oZw7ZbQ7PtjkGnzvOmpSBis7LpmrDidfnS5k7wOXOHfU1TKKUOIZ+mopt7iNMkpYqBFfkuArGYCqTIiTEANmQVVXDEJtoWUdtMFEyKnAPYbq+o9Tidb+6UAE7PI4Wf1JNqjMYiRmiu1DeEkKSgQyDj6ptcQHfdm7Soe+D8VmFIAHu7Ss1hu4VuLgxZtjpvLUUJLCuljFKkiuI6UnKblFJEUImxNjpLXLckrJImqQ1gWiA46MUKgduHawDRDdQ+XDp6tReI3qdIX1ksdNpZquwwJkVDdw6dSWFnijrSNDDS+Y8L+U7rqCijoVfnREC1S6iG1VGskBLmGu2CCNCCvZ4qplQMicHWCkktFQveXYmzPpUDgxfpiLCsmAoROdde0wUwXUR5XVVeKKtK6vZR1o3aQpHalhydoqi1FMyVlA/X9Rgu13CsmCKF1IAwePSPB/jBF6RUSKSEY3VML3ZiYibK+ghNEYWXFFaq42YSYQVQrbfcXpm6ZhofELqO5vKmows1FwPWTcspIdujTRwAUmNIml7kouZUn0SK5UrkvVRI9Z2DG0glNWx8IqOIoIrJ6U0kHzs0fpPPZfP4Am5BiwuldapR4noPYzWCM9BWJwfKNgaPAbROp3qAbTfAt1SPrzEKQ1AYPLkvgyf7LwV0Z4N3RYpQKAvdD7njou/IBg6Xa/h1j+GS1XN9wOaNTVJKCSklBJUQlIOXMRBnxwGAVFzTNlKrJiuk/OBhGhoLVHOqQ+CC+AIpkN4sOvpYrs/F74OHZlVYFEXGVBHF1/XSPlbcLUo1XwDddiESjyFEceCaUj6mum7r3rNChmpLDT6gXw+sjvFEGLOikeapkFISTMrzMwZKNRNIweKyWYu2REJ5T/tijLzYj+iLMghJLdOS6hsWcD5iUJJ2xkWumTSOUNskXdGIAiGk4ubSCdN3m0K1SDWkxE53r28QBo9h7TBcDDQXL4aUXn3o/Jy102uqt9mcN0lV3qxsWgOFomFFIpFbC6MNgtYwixWgc8FzWidvk8OU/MiqfE57lJQ9l+qLhWSnH3eO60k5PN44uN6j35Cz1U/sdHCFas750TiYjoW5xj22ycpW76lmUAgRepDAQU7nA3JAYWkNACqCb5igDCGrBZNYTmx2XSzdCZIyDUBQpFgTn0J8rMYoLCL5JCECb32wwGXv0Q0eDzoab8OKxvvq4QK20VietWiXFg8WNtXMff6sSRkhrdWpS+O4dq7meq0mKys5E0YUUkbPE1L5/rxC6rYIqX1Kp6uG8a2o/64innY9Pk21ww0TU/KaKL4XQ4aAtZDjYzSXILAAYJHSoaVkizR2CYH92j75t4bXlxK484NLtlkZzV3eLXRjsHjOwby8BgAsnmthWoMHH32O5duew+KFh1DLc+jVOfmetkmcRkz+JvMbsjY7obXU6ZBSh7CpStNAKhVPOg+4qM2IKCifs/VexTZO7odi0ZZq+7CKSYyPdFQDchpfoGrW6VvXonAqiosarbAK0l0qp4eUnUmkdsNVrZSB7XbK0olk3LXEJIJKlFJnDbXNXbDMeWELyWqSrUqKmKLPjXmDFxXyBAlMGgK5dXgjHZpyqgcAqODRgMgmpTUME1HS9j4Mjmo2FGRUaqk8Q0yVeb2ylbb3AFJR51RDatnmOlLSPrldEim1WG6l7KW0PRlfqUD+DuVdoRqQ70268QE5sjJNEc2dQBQTWpQuipAJKUnbUyqTWFehStHvCNNc+RRtF3UMd3Qq6kWFIStjXNcn4ikTELSltK1AC/5IZFRZBwbIxJQQEZJ+66OC8RENOwjRs7pgoPolNFc0ojcwjYNvuNB10eGPnDbP9UoK9VcMAEwe8xhH4GMZtQ6Ru2UhqaSmkfdu8AguJELK8WeOIWanp3eIwSO4nrYlKVXUlBo2F2TD2eEJ3LUntjapN5QPKC+XWiugRUqxptQ9alwhaikKOuRIfDmN02cv67fEwGNhQk4OREaJYq5sLy9EpKQDidPr1j6p5kTNQGNh/zjoOVDSRk/NFHyE7zWaFRPkTE4aHzG0BoYVU8pohGZI9aXEcU/dvWR8ez9edVxH1l9xJa5SHFz1OJAVjAIhImSOhljWecvzc9179Ew4SErtsHGJlPKei1szATE3P6cwnGYmc9PbBsZrBEfEszZcmNhSnSn0wFrltQ6pY1g5pRQRUZHWbkJEALsJY0kDEXI1FkEEUUlJ0CzXeyM7LYSU61yy1cPaIYaIoXNH2+k+KLTcbbgpVERSc0SgjIbxIQXbwuDgjYbvXVJLTWtjAch1paZfgyjmmKAUez2EkFRSZa2/Nf8JEekGj+CoUU/wYayUktqJjrsSun7r/QEipcROx8B2OlDQgM4xIhgNqTuljUa0Eetep3EgtcaGEGACZRh4HdPWFyl9O+uMld8L9jv6lc/aj1Itte+YMrDacL2dBXfWPGuJWHpu1aA7a+C4Xi4ALM8a2MZgtWq4cVMmoVYtpe9Jyt6SOziKzyOpgjL3SkJqlI2ArKYC5gmpOcJpFxkljwNjMgq4XYXUrfoA1yWmZnAlMQWMCCgRmqg5jWMhcKFjMErlS89RGjE4fpzIKWUMgJbWu7bNXAOv41LAjn1aDUCZPnVLzX5sgGvywkiCygDQnDdEWJ0t0ZxT+p6IIWAbqKbdqoF9Je4xSXU6pBSwc9BGpZNaio7jBYQMHMXLK0nbm9STmn0fIJMJ5b5dTykYfSEIwErAqAHDg7LRQOdJ/jnwxS9EoNG02JPt0lJL8ZKM2kwUUj7k1JFyW2JMStFnEDLKFuRUa3K9Ba0oiqQVmIzSKX9b6khZzumWz5trF9EUHi0ti8lS1vNSbcjRUMudBCxN8Ng0iMMAbRtinZcdEDxCPyRSSogocYpDOI6U0mV75VIxJURU03K+LiujtIZarEg2adtkEBR33tOLVeoyKOSn1CObGos5FUAZIQIPIWAs+5WxVt42oLRQaGlRX1zAC3JKojdzqITUHaFUT01S97aKnA9F/bQhd9kjQmrIhETh6LjOkXrBh+TsBGw7OgJTLKQSIcFOT9N7AC0V1U01TGi+um5ISsMwEKks6SLaDVxfSgpe++TYydsnJYYUTI45PSgTUyF1eJqmgkj6TyKk2PnxnruThgDfrxGDh3c9AkewwsBF2V2+9Pn1BQBN6TG2gUmR+RWU1jl1qJg74vj2DX0fkhLiAnWlaozKzg5/pvJzT5HbzhcOL5OVsmhJ5CTXIXCdKOR8knwPa5cUU4MnIkoc3qFIw5pXSkU0nB7RalqEryLQlI0jQpniqQE0qYizbm1O3xt6pCKgE6d3RMTJ/Xu8SHqzQOYikAkISWOS9CYionJx8zKlVopZS6qWEA8hZlWMGzIJ4fs1QiKlQuqWNSWmlDYIbqC5GDw0pwjH0CJaXuxb6XKsYYxHtBGaO/Cte1+QxiGpZpzPJJAP5H7sSyCVhg2JjBqYTCuLm/P6xKfAwZAJqXWRxtdRfaW1H8/PubkpMIrsc6MiEVNaAZ1LdUODH6c8EkHVpJRr3w8pMh/6Adr2o+sNFTzf35RCZq2cp5Mx4MNs8GBgGy2/vQQSgpMxQb9/cP3WWJgbBwByIWvb0lhoV9ChgTT/kd/esHIMQBoH0pXR6JCUudPrD6Vbc+oe8lwIEamrdsXtgK4+tB71vBbOjX/omhQ1oFVMJFKIwFvO2rROkA6fw4bWI2dnbeooLjWjnj9rYbTCC2fNKCNE/JtGq1Qvasm16BqjUm0pWV+n9fYOdRSAgrgilGTTPjJKjkn7p9/VzDh8EoXUvcCRiikA49Q7YFs1VTx3Lzklr4uCmIKmrADJqJIsrFg08FGKsnxkzcOdh+lcpDzNgKg1Iq+LW0PdISWoq7SG6TZcx/gSoXfQvLZsHzQwjWWV1AM0Dx+QSmp5Tj6nNuRnMr+xUyV1Imus0yKlSkykeCUxFRXVp6B1dsiFVLWlQaXUmHBCMZj1DHFQkiryDAUEkKpFS9pVovwVoCMQFLSm+xERK0t1hhqvMHjNnUoy4x9CxNBIxykkUmrksBURKmA/ISUYEVMq50OLkdWKu0spKpwtCqm5on6N0ZQyBiK1RC0lKXyz12v+7mNR2FYFT6l82vKEbhCZkFJuQGyXSTESg+cFYIByPYx0pEpSSa7xkOox7Enf01O11LRrXlEXyjaAMZmA4k6BypikilKJqVaZiDJtJqQME1TTQnTyvUCUTMhFNfnyoWU1pHbXMTBKsqIUdMyqKDWK2owJqZq6dz8gBc9jQUIhBMSBU0OGHH336z45/0RIZTJK0veGziUnpwvb6hhxdnwcL6wlNYSICHJ4VkbBRwVc9KNGAEREAEo7mNbAdT1M18MCRW2pngqeF7WR9kFq86X0oMLpLdUXEn3v/VghNXB6yLDhQufdBWLwcP2aSBxWYozS94Y8m1x3gRCIINe2hddUJDWnjZwnx4/O1UJrh9gYrAdSSl1yPbt1T51UKahAHcKok+CYiNs1+2LZcY/VUqmW1DDw79/nwslrT+Nhopq7GEIipAb+7Us1hqAcC6aw5UOk64CPAW1QWIUBMUSYnrucFoU5TcudGNc9wnIBb8jxNYbSD5VtaWxbHhP5w2LeqlXcBqZLhF1LBlHmShpfKOyHdMUMcZuIEEKqH3wxPyVtj0jkodsguB7e9fB9R0rnmfkZmHwSaFZKaVZKmcUSxrYItgWwgvK51pQxOqXyrTnwtikCe6SSiWgKxZcoGeXzbimmUgChULSyGjCpFjmFz3diq8lODxdDUrGKkvFioODj2tP83GWnAczOz1Yr9IFstFEK5yCFLJ0qK615jdNwGpOQU3bZUh04Tg8mJyqklPItJe/kexFllKjmhhBHpKRs10OhkurJTkdJ5XQBvu/yWNh0I6VUKJRS5VhQ2kD1hm01tWMPwRdjYcFugUuKKdsY9ByInSMohxCgPTAEjSattZkguULmJGu2Xfcrrg+lKCU3q41i8jca9j0etBZaeTy/tAhxkXydDaf4S9MmIaNWLd1+sLDQSuGsoXGxmmSGZB9IjYqZ71JGyfteVxk1zWS4KTLq0KF4Z2N2hlzaeczccceSU1PVlDwnVffUW1xCek2+rZSs06UEhaHGPXw+SjgGbegcnKPHbUvrOL7OqXZJt4Mncip4LBo6xhZNa9rnzuG6DdrnOkrHPl9CNxbnH/s2LF54CP3gBaizh9Crc0RDqXvJz5z4mKdYAuF0SSlgdjBt7S/3TdKnZiWAjGnK3qHQqqgLpCnaEiTqouli15CUClprqjUSOaddkxND7cQjLEtlHK9ShkIZJQtFwTTFa3pOwLgmgewviahMTolMVaWIgeRPS3RAjK48b8zu77KQXITeWO52aPMiqF3kvFwpHN6QM6NCoFpTISC6hgt0huTgRDeMuyHscYKTMqssSicLnpRO2Ob7UsOgJKN03qqmzd0Zp+oos6P73hUQgkouysD8hSly9CjwhVtk5uUFsXbcu0VML5h7LrRJCQPkSE6JVKNJOqRxWqqXqLHcjqljk++5kDfvL1Ux4txILSlxegSl7WjZ3gDs+BiVnmd8BHqPsLRQPqQ6KHZl4HsP3bBaUVJnm+3PRJ8/zKaEpK8OWSFVQhShkq5MqSPFH3ckjZyGEdwAL+lAw5AcXYnE55o1+US976FCLqCrbYswAJ7nvnZESpOpovcMjU7n4BWdl+Nz9ezozn1G2j0z+yTyFiZjJBSK0DQe5LfPxc1D8dtIup7nGjny25djYfQdF799q4l4MKyYoufRY9ZHBB3SmKMxSARVVq76dK66UL6o9Fk811HjKGPR4avi/iLw/AKQipsDmCi36S/ZJH6OdNij2m6iXOwRRGm0I33PSzqZNgDX7UiklQSkAHjXwlhTFEyndH/pSjx3jmVhaz9ZS40wJWmmilYZ78U8lfmZ52XZWS8kBSOpozLZN0caA+P5KYQUHRNheG62PqJBfg/fe4QlzU/fUxqJzNFSUT6rZCw/+w7IOabvce47FjvB33eu7xfpPJiQktS98m/fWJD0PQBQweRAo85jwAaNoPi6IPWq9vyF5MfOj4NRR8aKG0eZwidqKQBbXfikVq/hQCwppSLOGsMkaYN1v0DviHwEsNVJXBRWUjdKa4Wl2e6oN1VG3be6UeVrzeHeEVIlDk3Vuynl1PT4ffWmJki8tNEpMBE1ZpVT4HpTKnInvaCzb2rGdaeoIzXV82u0gV1SgNdddKl0DACY5QKmsWgfniUySi0ofW+n4KH4Pkbf1/T2PcTpkVI7BldSMRkmmoI4QSanjnBqVVm4fPYSNP3RdvyIWuwlG0iFiKgAFamgrY5CLtDxPuQOTI02LIun13AhE0+kGMiOTVkAExClFL3mrovo1rmKWqogK4yi6KIUxAZK9VTurJdrR5GhtPwijVZsrIui2pgxclqIGgWlpOh5kUKZ8nMdpcLFAC21aMraDcCWOirLJMdO/VVQBSmValiZIpVQjhHlVGpzPzYAoSxmLq8pZNSo0LlKRBW/+Px5IcuXaYeajWiLosog0sUq8NjDRB6M/QqpGtV7Ohi1qpUudUkh0+eULVZLSS2pMv2jLGpOKXukkOrDOH2vdLzm0rb6UNSUYnUMKaUAIND+zqVaUgDg1h6m8dw1pCjcaHSqJZTrlUxStQqUpyNOovMUhe99SA0e0l9Rq0bqSLmeU0Qk8s6RKd+vk+Mj6SDpz7fpfSVKr7QhJaZtkzJDGZPShZxe0T6tqGMfqMsTWozTggKpSAYfKD07xNRtdRbTRZecI9erKbsuhlSrxo/qRsntzgX+/cnhLdOD5tQY5Tgoa0r5GGhMGA0Wx8H0Ho0PXEsqp3IOa0et6htJ3zPJOY+uh2qa8Rjf8R2oqOdq4efvaBpBveeLq1PB3C+SO2JOaqEFqZGGUae9TTE/KS0rTuoIOQTXw/VrmqdDP0qvFcUUML6GTwtc64ZStkxYpTmd6wuRSkYber4xHsZorDUpLAGkekI+5OAekcuiBNpPTklHVOlcJza6rPXmuoFJKZ/qSImyVeanKFkfu8PtNKmkcupeW6zpjCc7rTuXn8PzU7oS2yHXAJQUPhU8lM9rqeRYzdaUku9L1HK5OYXUFUspnNJhkdP2KL1a1HLrLSVrSuUcsmpu11jQtoUypkizHqvjY2i5ED7XF4OBGzyUVuiLTnyylQBvCBFg0VipkAq1KtSdYEpMydq09LEgmSdcU+qsMWhZEXjWaDxYWC5+HpPflNPxdFI/7SteXvo9Cir14jDFOnoXCbWLgJLPhOKY8jjgbtPz7sW6f58iatdx02NnHpuqg8Z39VZ6X1pz8A0Vt88rBVRjAHQRVBP1bBSjASA4rpm8YC7CQcdI16vUTZm3mzVtWUFlNh1ax01uemoWI1k5+uEL0GcPoc+fg3rwPGBaRLsgf5OzjlIaX/nBT4iQAk6RlJrDHJuqNa22EmElKXxH5llecdxUYkq5z/TWKiIx+jEiKadUBLSmGgY+AFDZOEntKYnmUZ0peqwkqq5LSgG5tWkyshojIkohq21KmapEBXIxd6Ri2+DnzBnSpBYSR0K+S2M5vdKwQ6KB4HLnJU2Eoo4xSSFhs4OjUKS7yO9xIClVKqWAgoDifXJfiKpEOGnL92cIJy3kVGEUysevkFQmhRTGzvquC8iuvHsgE1JT3IdrUUWBMtIjdYTSfb4g+jBedPPkJ4VKTI7i+A+YU0xtKwJktOXoOz1XiCr6i4VKRt4/+KyY0YWKZ/TxvL9yzAXsTiUq05LldoxxLF5ghyJF2P0k4j4lpCZR+On9oKkOVplKpIIfRfzpefL88TmW55wcnljU69n1RUhNqXIMsBJD/qRGSijHQKGWSylWwNYYKB3euXHgY2RHV5xitfVcE/K4k/cffYQihTqNWT+vwKBA0a4vo+Jpo5yTpW2R++mxQnEiyHNE5kxJCO+en/Sc+c5rcj8AFLQKHjGY4rUjgs7vK0qp+c+2rTY//gsa22Ugj/8wmSNJWbg1D6dzdPf8zDH78rlyn+x3SDbCFO8dYYpzuyn4wqalfaKmY6VUZHsZ49R2svp3oozaZaNLJLusPa19Zmx8iBHqAPs8/TzlGKegsUqB3BL5G664bZQ+1rRur2GS6qwxaU44VgoPPN6X1sAalYipuySjxgHg8TFp/8znneIma0XdC0JqikOVU3IscLMKqgJl47PZ2lNKZ3UUkBRUUTpqi48oh8vx2lCpGu+RCrMXW8kIilyvMIg4wrZQWkMvz6EWKxJumHYrbW+aBXaqOF1Sak4xxUj5oGYyGOWxY1Oq5IfmuyWjr5nBUZEuYmJQYiKkkFrMSuFEKqSoRrULUmQy5tdOtVbk3OXY4poad7s4449QmK6SRAKyUVQTgokeK/OokYtmT+8Xz9+yeUojGmpNHENIZBNiy3LHMC4eJ+wzMHJgtiJ4bBTUZN9hX8ieVE1R0PF21DFv1ElvMpZKEqq4L8+L02Mn5wGMialDIJE+UxTgLC9qc5Ga6f6KO8QhYzWUqVqZlJBOTqKO8X2AH3xROwjp9lQdI/enyIXOIwBdLMADAJ1sgxs8LMCpICql8KXUrdGfT5336KX8zuj7HMTZmU21SOkfMUXigxROlrQgTgdy/XqrZk1SYxT+ju/XFBgo0kKSckobBMeFdV2LoBW8UQguUOpsUUhXuqTmFKFt53LrG4hjO5cPzN/f9Pv1PS2ARI0hbeVdMRZELVfe35W+B2DkeOVuX9ud+QCgGaSDITuHg4fvTVL3lSl8pYJ1ZHIOtdMVd4JjuJkUGAtl2mqen97lrmrSaa9M1ZI5Kgop31Pra5mnU8g8LFVTDnnx6nnxHsIS4ALrmhWKQc6DFZfpPCcE28HklKhZg5BPTBZzAwKZlzGEZKeDjwhDGDUemNrlUsU4Nz9RNB+QOUlptpTK1HBzn9ZH6IFshO89tFGjc5JzTIEOIdb2qRiRx4ekWYuyKBE5xRjoXU7x9dwdMdtrn8fC0Cf13DSVcxcpVRKUABBNMS6MgefbwbUAdE7pdAHaBPiGznEzGQv7yl/MfRc1k+92IOvScg1c+ljQJJbSCghaHH5qIHXWGLgQsT6jcSNKqV0FygFsEU9yf+r30Hlcj3g6hHQqX28OzwTxdBX2+eT7CKgDEaecgcnPV3P1LUPR9GFmnZYa90weK1VVU382iSYCNwXiLYLj4yMi20LJDkqZO8szIqNMi9gsyNe0S5QlY6Kx877lCZFUp0tKCUqGtRx0xe2duZWHvPYxp6JUKk4d2PARyRSLgtSAKrp6pKKiEMIK6TExRSVhVSIUxxyCqaEqiafR/cLY7uoUMSraJ599jpACmJzhOJ8Gy8iovSaUyXLJsu5M4cSOpJLAFmGVGOkjo55xenGYEk/Fvjniajr5d5FRo+fvQKmOKi/Mh0BS+ab7ytfe9VjFPcAoVTVMHspqqRISfS/VMfOKGEwcsOmbU6SRVFF0e1xoN6tl9KgLW0hReLmfz62UMc1Eu9PFe7zbF/YQ2K5bk16S1RekjpKXFEcmjByaXdH3baUURveVMcWx09ccn4P4SeV5jtVSR6oxynObfudJPRf5fkyKtVJhUf7e+ffMbzE/DmR/qZpTxRhSY9Xc9ovw6U/GauAGF7tUczUV795gbqjKrlLtF2ImowAk555eI29jyGqlLUXLDoXUvhT8OYVjVtpQwCupdAqlzD5bkj8n2bud03WOOMZkjvo8P2Urt8s6b+O5um2z5yBzUuZnabMDxnY+qWjl/Ytaf2PS+EBl+RXYpZQDsmoq35+3x1cppaSm2Nx4KcdTiWk6phBocs4uZEIqjNbj28hpZBW3jV01prTs4HIp1L2cA2o6Ygg526QUDty3WlDl683hTUFCHYN9KXzHHn9Ayt/2YXqsrNqX9lf4sMlvNRgHZiNl/MTYjAQXMYac9lfyGooaaKXmWWUzrbIszNzJn9ja6vRJKWD7S58jqvYdfw1sETzpBhNOk0enBEO+WArxtI19vszRjg5jWvhaMLd7n3E9SoHDpFRSIGn5tG2euMBY8QTkKHu5b5cxum7kfd9YmHtMj/dtGbNj7+/BoReaEDErMX/mL1TPMMpUuHJbpoWU2BXpH3dzmlPqyD+15fTI8WVsOvg4cjiAMSGV9z2ZszPtLiq3k8O7w7Es379sMb+dsjclToqiysCoXk2J2feKces8y89At6fP2XqZ0XmgcLBCQUbRue23ddnplfeOW7fnxoGka8oYmMLPeGXZ8Z1P38wn5cfbipPCvvXGiIyYmZ90fzwXwwwpkR8votFabxFR5evQ7TINTI/OI8zMyxvBzDieS62dPpbOZZKmN5e2J8/QxXOyiy7Bgu30sjAiyWI6pzL9dyf2rKN2PTJn+wCkIvclOVkGD+jt5smo8rctIcTUqOh9GKdb0ziIUCGPxRjjFkFVcb8x62MphQhak4So0Oi8XpmWNtn1WpVUegZwrA9/DEklh0yP3/WWccZPxbw/m46ZyZgYiSrmavrJWrQgoQ7Jvjk1HExKnZw5v4UfZp/dmCMF9j/5Sa3Q6VixCBykVju5MXbDuO4veuXYqzgt7CFP8v5cS2gccZ/vKDVLRCCTTiXhMCUfynpC49OMW0qesCcF5FDMOcBzDqXUKaH33o6Wz0Xb5wm08jOMyaj0moVzHHY43jfq9F6BUKjUABSpNOPjxmOAHdS51wMSMZWPLWtKqdFrXIVEUFUSKuPi4um+//Snk/sx8kWa6vCoSA69CoAKASoAuhtgBg8zRKjNAOsDmvUGbvBouw5h8Fhueqh+gOkd0Dt456EGB+8CrNtQmpbbwLse3tMfpbl1W2RFgidiSjo2aqWgYoDxBgbgxisaCgHt0EFHj8XGwQaDVnu0yqCFhbUei9ag7QKaYKEuPOzCwgQLHS10Y6CihVIKajCcuxqB/pL+ugvAdcC6AzYd0G2Avke87IDNgLjeQA0OanDQziFsPLT31ARiGGA4VU4FStFoYoCOwCYExBjRFjZ7wDyaSB12DQAdqH5koynGp4I0PAFMANQwQA2a+rQ4D60inZ/RUP0AbAZg0wNdT7L+ZUfbdk2qKm+AAcAQAa+gfITqPVSM0GsH1TuYjYfdONiNQ9Nt4HuHxWaD0NNY0L2D7j10P8ANHnpwCG6AcRtEN/B4kLFAjTGCH64cC9q2gB+o+U70aSwYraF7BYuIZuhgosZi42CswbJxaJTFQjssrIcJBk0b0HgD23ioXsO4BqZR0M5CecsCBc1bRd8vyWyQ8sCA7YXb01yPnZ9f+6nqadunK1BreN0vRFSfbR/2fzelkThwZJdLKedndp4ADrBPz4ZSagb7/IMbZ6XnoktX1I7ZpQra+5qHPHYIjlEHFcqgnaqgGcJp+hWXv0ec2QeMpdbTT3gbqjHBLvUYsK0gOySKsi91bvr4jeCYzhUV9x+FOkfp+d9PaQWlFbTRMCoAiEWHJqo1UgrfDd8sR0r5ymW762nra3ldPRm42ijqtlecq95xvsdgbj6amUmjuDkDvTfXnTFhFE3feo6k0/rpPj96nfwYNTso9+f3nHxPdxju1EZBF2y01gqGSaXSrtJvGdNtH2P63adjYVxXShXjKasxDm2LroymsTHzG7xZsXzHO572KVQ8Iaaj+eyuT0A8QZm8uxisG8SStx99+29V8YToLi+v/dxqnyoqKm4T6wPs0zNBSm2RG3sek45lhyyt9/oY08JnRf0jeuO52khx+zm7nj9qLzWzr3y9A3F0DaVJ/SQ1rZ0kXeamRbxpZz59DsSGyOoG3p+6aZXHFTJceQ4wJqX219g6HFfV2Brtm8knp8finsfGReTldcrxd9AY24ddhZLn6qtNH6u4t0hOPd+WLRFBM6RNQT7M7ZcaJNNDxkRWUfCT709HijZqi4yZI87mCKFjkAuPFmSZRKsxJoS0VlvxIiGT5rp3zZ3zFhllzOxnmBJzAM3t6XmWn4FuT5+z9TLpPCIAFO+vtUYozrckAucgBNOYSIqpE6yMhzEpmbe7iKe5/YrJMRmvO88tdS+tJFVFRUVFRUVFRcUYJ09KzSlwtvaPFDjqqFo9QCYO5ittYEwozZFRksIwIaFGxczChLzaQXql95m89yFQJeGUdm7no0al0rHQXGSt6DSXXkeex7xHnL6mnCLGhFSuucKPMxGVj8NWN8JdnQiBgrg6Qkw6140QQCaa0v1xR8JcFD6moogeYyIKIBZOK4Ug5FQkeX2I80TUaNexSjgZS3J3WlNtXyOAiqcPIVC0oRoqo4c0PLaJCCIDNGKII0JJujFNiQhTjs0CUxXM+LXUaL+eqKKEkJD7+dyKc50jIURNOZkHRo1VUonc0WPSR2tSSGmtCrMlaqYxCTWnfKKvYXpM+Rpm8pq6uE3vWZ6DnjnP0fke26ppRI5NvnMtBKXi+4pVUxom+KSGKn//kowqb5eY/tZzZKXclrE3R47S6U8JPznnHYTUm8QWdS+9dGfvNXclnK6V5L6PMV2DfaRU0D5E+EDpZT4AjzYOl4PHegh41Dt0LuClRxtcDh6//FqHbvD4tcc9um7AsHHoLh2CC+jWA4IL6C8v4H2P0Hfwm466rg1r7r7X0fkd0H1P2wZKG9h2BW1baNvArM5hbItm9RBaKyzPGxhr0K4s2oVB01q85eECrdX4mOcXWLYW73i4wFlj8MKywXMLg7PG4C2rBo0CHiwMGq1wZiJUfwnVX0JvHkG5DcJrLyFu1vCvvYzYd+h/7RW4iw361x9h8/oFXNeje+Ux/BBw+coavvfoH/UYLhw2g8eFo46YF9yY4MKFUfe9EHd13wNatjlGgTvxKZxb6ph6bjRaTfcXjUFzbtE+bNGsLJYvLGEajeXbHsAuWyzf+hyaB2don1vBPv8WqHYJ88LboRYr6BfegWgXCIuHiO0ZYnuGS68w+IjHvUcfI15b01h4rXN4g8fFS4826HqHX359g3Xv8cZFj6F32Kw9hg2lcnYXA7zzcJsLSuFcXyC4AW4ou++V6XvzY0HbFgBg2iWUMbANjQWzWEK3SxjToj07h7Yay1VD2zOLZmGxXDZ464MWy8bgY15Y4qwxeMfDBZZW42FrsWo0zhqDhwsLo4GF1jCav3vufihrPbGZ0wY/V67v7inu0j5diTkfJwYoz13K2F9SrgMQoIYNlB8A3yN2lyN/CsaS/Wja3ElbikenYInU6OHHtYaEbWa7bBfbWP64c9kjM0G72cZHx9a33Yc7uqbeVfreMYKDqw6de625enNh9Lg8Nx+3lfFTHDMVVATE5MsC4yYXETH5vZ4bL/gYU/dIylIAltbAaoVGAa3RUIo6S2q2RWKT5rtCXvGl3COcLCk1p47KgyATGOVg0oo6NsR4eKqF4tcdHT5HLokR5LaOiAHS9lFxu0c5RsU4fgzAqCVk6sQlxWHD6P4o137mwh29n3cAkgFmJ0Eb2sdOlzjHAHJVfyaklLaJsIK0ndR5G4OC0pZaUtKb5FOMuetJBOBDXgDHmFvAR1kUFxNVftOBa9dQK2J5zpjcmitCnB20yVdRKhuSGkolA0D7eRGo1d5WstTFI6YuHsnJ13nBEnnsaYyJ0dmxVW6vgApuNPagmDaTC+uu7pP7GgFU3B4O6YqhNbTRCEaTYocVKKbV8L2BaWiOmpae32qyIX2I8DyYDu26VqZpScvxVqutP9sY6EbDtCb9CTFVqmQUnzPEtgC03VJS7obMKSF55K+1GsZqxBiJjOH73kfo0MCws6ItOTa2XcG7HgBGtaJi8Iiqze/XrqCtz45v08LYFtq2yQky7AArrWCshrZCCNG2tXrrfHVhL9JPi4mDsqs4ZfH9Tb9fGgf5t4ghQjcaFtQSHpAFlU63aLfe2eFrnK6XnV75/ZtiXJjGwLT8/o2B5vu6sen3J1WfHl9r5j73mwlPUO/lRjC9PKbFNPf+ZUmz1KuLAVQbLADBOITBww8ewXq4wSMEi9B7hF7D9R7OWwzaYTAOQxzgfUAfe3gf0UdNRJS28MpSLSFtEIJHMC0REoZaxJVkxJQg1k07JqWaFs3yHNq2CIsVzctlA2M1sLTAsgGWFm7VomkN/NkCobWIZwvExsAvLdSygWoM4tIiagALQxdlE4FGUY0l4wGngeEMsBqqv0DUEfpsCRUB5TzU4IEIqLMF4tpBLT2U8YhdQFyAWtlHBc1rhRgBC0VFuVWklvcxwujt2m2G6xgpdj6UVrBsfzQHDDTbxLigz61WLWJjENsGamWBRQt1tgSWC6izBdRqCZytoNoljc0F3YddAsszoD0H2jPAKyBExI3nAKSDGjyiGRCNQxw8grfw1sH1BrEJ2AQLZzwGNWCjHNzgsQkNggsYFI2FARph6BFsC2fWNAaYnNrVfQ9AtstNC60NXJtJKRkXcbWEMQr6rIUxGuasARYWdkFjQS0twtkSoTWIZwsEq+Fbg9DQX1zQWIhG01artJhLKt37WFPqSfC07ROwm4wS/ykYqOCJjIqAQiQyKqwR1o8Q1xcIj15FDAGRr/26XZLyuF1C2Zb8ncWKtnJftSkwSBM6IqpI62lNQZyoOJirURBSHKTfSViVEe/82GiIXNU8qXzuHO4pYXUsjs102Xf4IcTTHOlEzx1n8JSvV/IJhxBPSVRR+LM+RAwhIARgiBSc2DgPH8nP3biAEBU6rhvVGFqLPbek4MnDhrZGA0tDpLnltTs0NQJQE3JKPuspkFMnS0qVmI0KYm4AZYJCyIC5wVv+cKKO2qlwmaTile0dkzH1QkD5vF+IKvlzdExw/ZiECrlYrJBVdGDgt52QUnLsxAkYOQWaa32Ywmm0vCDkrbLZSCtW20RtoUQ1JfIo+ezKpM87NaqRJ6d87/J7lB22SEE1P3kBJNZ4KNoLCzFVOlhTYkreY8s5TCk2fL9QhpQRMSGpGqlFo2ktEgOrrTR9NiqCGUU+Bq3EUMk4o4UoFBvHOfXELkJqjqAqFVB8jIoRMcnWAr/55AJUVVL3C8VvobRBNKWCJ6ftjZVIChhYLWNUUU+oVLaQ5aLxHdFqNds9bV4Ro1Lq3pY6Ruf0QVHo6AlpMvp4u9QxBTR2XyynwQMpOKs0Uo3HrJoy0AWxLvfj3DnsUkoZfo1UoyrfF3WUStt8TuX5jlVSmdy+ckGg9CgwAGBESiaiB8gKNZNri5Wqufzb5TEwVkntGwdq5rbYQpXGnbx/WdsqpZoWqi5Mv/+5BXvFvUM5XssxIPfTYwURK1BacWFqRYW4J3NKBZ5TQJqf8zXgpqrFyfycqBjLem9KjVWMtOX5o+ZJ4+O+oLFdBvL4T2nWQ7ad2mgYFyZK1mx787w8dH4qTO23LmwEnU+hZr0i7fdYmBmbJt+zKEgV20up/xe1/D7j305rg4DxGCg77AGTsTBjp0djROU08332efp5yjGu9mRV1BXU3UGNyKnCv/IOyvdAcAjdBeJmjdBdIFw+onHjqOBacANgGyr+3zRERIVAa+MFb0NAZJJKGUNBZAnEy3loC0SPKGtozeSS+Dwz62pVjJS4VSkX9BwRGch6T7q5la+3L4h8bID5HvoAN6WEuo9kFPm1EzIqjMmojQsYfMAQYtpuHJ1pY6jBhzXZzreGwpteRyDQ/sg+514f8wRwcqTUrvpRZXrYbDpYjHQxChFRKRhEBKhZ2Z4XkgG0MNteIsibF+qngnBCcJmMioGY/IKIUsEjckQILFeGG4h0kvvBI7qBSCbPnUiEcGJjm+5f0eEoXegLpVRSRmlNUQSJGMi2aelx25BBty2UZWVUaDPxoTSibaGiJcWU0oAaG98AUUpxd5lAk3a67bmT2OBjIp9k8g4+IBTbREiFuCWJvKrwuS4W1qLIkP2yT2uFRmdHTG6TXFIl9rqRSKWKMIGimA0TV0Flp1QpcHohRzp3kJw7a4xND5PHfB5TCC5HbZSmyE7kSI6khRW/2+j97tlF6lkHKecYQhLzVtkWurEIPkA3FsZ7mNbCLltEH+E6IrDd2lF6X++hh8CqQAruG6XhI7D2AYDilBA1o5DJjpkopVZGJ2XMA0sReLu0MK2GXVmYxsCuSKWjG5v+DG/JXtBf+lyCyTgrp4Dm+WSNQhM1WqNhWYXUWg0fIhqjgRawA9my4LITE8ISitUXYhO1baFdT2Q/kJVTvsnfwWIJu1D5eI7EmwW9HimpGtjGwLYaxmhoq/i+QWN0OkfDCobWaLINKe1jzwJhKvUX58o2FBQwOn230Yc0DuzKwPf0+f3goYzCsvcwPgIIMIF+27UP3Dqefvy59CDhlVo2SitDaUHLQi23aEkt16wsKbOWNqm17LKFafMYSESabUdjey6VQb6DvSRVrYt3a8h6unKfgkeEgoLicVOOZa2Q5mS5bRtS7rnBAzDwLiDoAO8tfEmsS5CnqPlWqhpLlETUSCVjSR1p2hWMNbANKSdtS7dta6CtxqoxaK3GgucnbZHUjPS5WAizawxK6QIJ5gkJYkghqFv6Mz7ALsm2mJY/V58/z9IHGB/hOUIO0JzrJWUyxtTpcgqZm0mxyPPTKEU225CdtitD26VFs7I8Pxuam3yedM5N+jypht40Van8ChSrvLSkL9N32Bj6Tn0wWFhW73YawUb6PRraF3yAckBcruAd2WhfEEoxeATLaXx7lFImpe+taLtYQtsWtl3BtEseA0TO2cbAsK1ui3FQbrUCGp1TsbUe+3P6ZKVPJ4apSmpS2iStcX0PNWwou6S7ICLq1ZfgH72K8Og1XPzyryH6AN9tAAD2fAXTWNjzJc2B5ZJVUw3UYknXW7nftLR+4UC92JqoVM4Mmfylcic6z5utmrxALonC9jSm6Dbv92OyaDoFR2U55g7a99i+7/opXUsPJaLuioSaI6Dk8V0k1JhzGJNQQ+HPBt52PiCEvL0cPEIEOhfQOY/eBTzqHHrn8ZjX+avWoLUGH/vCEs8vLJ7n68tZY6CVgVGRxxYLIwofExinF+8S19wnnBwpVWI6mJDux7S/JKhiJDZRFFNzg1emZ7yiBtDuk8rqKCGqcvqeIzXL0CO6geSlTEZFrqsQ+y4TUYmg2lZORS/qKVFM7U73GikuOLUmAsnpITJqgNIasRlogeAGciqZIKN2xmy0leNIgSVqLwaKHsj77SBSQhznzpbKqMEHJqPCaPIOvFBzXvJsMykl6inJwU21qvZYu2nNl5yiNyaljFZwmhcsRiPwgoWcO4UQFaunAhqjyZDpCBUVNGJWToHS9o6KyM4RUhLZmX7H0+hRCVavbV24psRUxfUwXSTMLRoYaeES+Xf0k+OKNKcIUshkBZKkylG6nGk1oo8wrSFCilUypIYqi1znfa1Gemya0jpN39PIjo/WisinFO1XKXXPtGak4NkXid8VSUxfHWheDdNaR1rBaCKnJMrtY+FAWA3lA6TOE9hRlfSPMCDVIInBJ4fGx3wuxrRQOkycXnodqWNjbG4Nrq0uHJh8XpbP1RSKjeln3Of0jiKjMtdZjSHfcUgpfAZqrTl9L9AYMDQm0Pvk2HqfxwB97picW7k/Lm4/p5qjsVCq5ZLyoy1SOPU4dU9NFTASDCk/6w10a6y4fRQNKkeBnGnKqtFZuReCh9YKMSoYtg3BmTQHYwh5HcNzMwafg2eMuTpvQkhRai2ppIzlMclksCil5s5Ry3W/UBvNYmq3UsmDXO5AGeo6mm20pNaSnc5qUpqnDQLaoIo02phSZsdp12OV93R+Nslmc7DM6DRHyWbL9aJQW3KH1GSrJ59n67PvQKkwlxIHW9+x2AmdFabGargh0HnYnEIt4yD91q7fPp/i8ZGSldOrlTYwln5/JUotjWSz5s5xn3KqxNF1ASuOw446uVv1c0tfKjjEvkPcdAjrC8TLR+gfXaJ/4wJhcBguOgraDQ6msfCDo+DJ4NCcewr4lI1Q3EDrbO+JqAUQtaiYDHnLRbpeZB9IRcqgSKQSK6hmvZBi7a1i3CamyiDxTSioDlnjPwVf4EkJqScho+j5uwmpucdKMgoY10Uu0/NiBBzXhepY6bThuoGifhp8wMYH+BBxOQSEGPF443DZe6x7h8edw8YFPO4cfIh4sLRYWI1Va5IQonOkBl01REbJeauYa8QKhyGf5VSIqZMipeaKmpd1pISxFGZTahglZY0CACIgdg1Yjxwl0UJusYxXfkyRbKZC5aVCKrhMSPmeCaoeKkaEzZoWYn2HODAh1Xe0EBv69JiopOLQE+M/OK7tEGjLf4HvA5mcmmLUuUuk6y397BLRNhw1S8ooiRw0DdUbkPuLJZQbaB+rpiSdT4GNpTaYipuJhJI6N1kp5UKEk4kaAjoX+PZYwliyzb0P6B1NaJE39rz1IVxJTI0X0+M6MOL4iuKh0TopohZWw7CEstFkGBpNhNTSxFSIjjN6YVhWKcSUDB6qK6XIiHF6lYynUconkC9Kct9vX4AS8el7Uk0ZmwgAxcdGTuVTuoi2VGLqqUPqEaTabk2L2HdQDakTdeNhWB1jVm1aVPmex3tvoYxCDBG+54V459D6CMMKqbWkvU7qrwlKh6wpyKmVYSXQkt6jOW9SxN2uDEXgly0s/+mmIbVUa1lV2WTVJRNtu6A4lUVrwASKWgeOwIsCacWqg3VrYJyCa8gRpXRdmy++AQDOEQORTLH18K5HcOwIe88OcL70NWfPQS9YDWIzGWXaFZTWaBcWSgPNwiYVRrOwMCarMM5agxX/tazEaJLCEkmJAex3cJQ2iEkxR9+hSfa6gQXgl/RZmt6nYITvA3SjEXykmmMXPdoQYVRWYAgZJXWlpuOAxoAopTIZJQqMpLoo1HJ22fDvb2FW7bZirmmS8lZNHd9qe+4UsobZdT/vp/moVFaKj8gQozGEyCkEQGsNfIhYtYavzaSQ0loRB+8DYohQDgAW8K6BdlQPKASP4CjyGwZSgM+mbDExry09TxeElG1MUi5qVuaIYqpUSa1ak9QxpQpaiBX5vKSYmnwpQk5pXSjNNZHgzQDfWtjlAgBgeH6KYir4JqXZAqScOodD4Pkphc4H/i32FToHkFSspJBShZLVoD1vYFe2UEyxXV62bK8XpOgq5iet+Qqlx1TNOvleGq0xKFKtGhXQ8JpJxgCAtA0hkkJKKQpKOgo2GqPh9ArekZLV2JbHQr9FVk7Hgm7oe5WxIEpWUWXZ1iT73C4stNFJJSX2ubVsp5Ws83Sy1UktPzcOJpg6d/fZ2Ts1TEujjAL+vgeGHuHiDcTuEuHxa+heeR2Xv/oqLv7vy3DdgGHNSpO3rKBbi/bhWZoD7rzjfT3ZFg7Cq80aarFCtA3UQMSoYsW0KKekYHoqa6I0nZfSgIopvS+l9pZBmMnaW9L7Rql9ZXDzScmpQxVRd5QxcQgZddvKqPI9hEgqH4/T/XuUUZKqN6eM6lwotkROdY6UUZeDR+9FFRXwuBuw7j0edURKrQePzZquh68uLGUIgPxarRTOGoMQIx4uLIKKaJmQ0vHAUjH3GCdFSu3CNG0vqXEKhtMHADrCRKQcTME03UvkutI5zYAG4I5mQ3wSheEUmamw+s7RxbbviIga6HZ0A+KGSSgmp+KmQxgcfO8QmIwSUioMZGR9PySCCkAiqeZQklIpUsZR7DEpZaG0hl22RGAFj9iblIedFFMNTRQVGpasWiBYcqQSOTdJ3+NUu0ROcRpfz+qoy8FvkVJCRl0OHj5ErHsPFyJ6F9A72ifklGdyS24D+0mpkpiS9CAAiYxqrYHRCmfJwVQYgoZWCkursVEKCxvRaIVlpDTDhgdHo3WqOUWCGJKDl/m+e8cQsE1G7VDfpMcKIjSClXGjCyG/aUlMzb1OdRJvH6WkOzk6ZutPMUklKSGmkfS9wAXOG/g+ECnlI8q6GXoIMIMoo5iQiCpF3oWQSAX51bi4dcPRdSEetKGULaU1pe012dmhv4bOk9NClG0oxaVIExaZezrHUaQ9jmov5ZRZjdbE5EhKihAAaN7aJpMcMVKB5hANYtAAVik9KLLTI06O74tzWSxh2riVImRbC6UVbKt5y2lBjYEx5PwIaSZ/KXVP6yLVqahXt2P+5xSArAKI/B2mVM4imKA5TUhSg3xvEUNAGAKCDwBa+MFD9x6tz47vXAH86TgoVXNS4L5hR5eIySIliAuc22ULI4RUa8ck1FSJMXV8q925F9CKgiVe1rG8VTETN1KLqWGy0kchJDLx40NEz/PTWOoSGZxBWeMnaKoBFxMR4RFEKTWD3MRgrF6keZhTtbSleWo4fXaOiGgNp9maPN6NVlfWCZLrpjioYNui2yal8JlAthoATNcDaBALBjiGmGo8RcrbSKm1LZPFDc/HQ+ZnsyQbVRLGQiDL/DQyN9tmnLqXUmsLm3PFXJRH5VysUTCBbJ63MRGUYq97pxHYRttGIygKJARWoCit4Pk6oYNHmKRyTjGXymnsmIw0hm6LUssUgY2FHadaS/eq8vpDDZFyN+UUpFZVMXXrmK55y+ZQRQaKErvRk0qqf+0NbF57jO6VN3Dxq48xrB2GC64pNXjYpUXoHcyyRXO+JHUxB/x0Y9F4T9esdgkVAvk8HJAHQOuZ4AG2Uan2FDCrnCrJKWjsVk4xEjklzgOwHTi+K+XUU8RNKaPocXnuPBklx1yljBIyCthWRon4pawVNfB6S3zY0qftWICR1VADehfw2uWAx53D443DZj3Au4D1Y7KBzYICL7/a5gDo80sLrYDOeSzZ5ipFvTkiKACgFI0qEdNc5X7eF5wMKXUIw1qm6QkJEkCpXwEAgoLntBb6oVR6TgkfqRuNkFOR07HScbKojhODCSQjWhJUgdP0RoRU3yEOQ1JGhfUFog9wXY8wOITeJTLKdRtyOPqsmAqimJKC5xPF1KiVuBTflMUSk1FCRBleVJnGssJAowFywd3pQpGLoeumpe9EiCgtxbbHiPxHkzymbnuSrtf5kLoObAqiyseIx52DY1Kq55zb3oVEUAk5BZSKqXwGcnsrbW9Gwi1O5aoNMFqhdyFFWX1rRil/ABDEw7QAoKEVfUojC6sIrl0GGMhYUyl9VBZD4O9QTS/IMra2vs8wGX8FEcrfNVWQ5bEQdX7OPpzAReuZgNIoL6HUUKCIwHMdNwRfLJwa6NbB+hZhcNDGceoek1JF4WnfB6hOUdOozgGQtJD9pFRKt2hy+klJQCijMhHB0XazbNNt3diiFl3h8BgzbqvMY6wcaUphTEhFdn6TWiqrMYxWWPcGfVJKCSwC24AQiKiLwcK7hkj8stZU4Vw0i3OYhaSGaE7VU8mxKRUYtjGpllRrSoc3O72NIWdH6pSIszOuU1KMhYKoTB2AUh2/AappEzEJIBGVobWwK7Llvg+IPiL4mIiqVASdiao2kVL7xwFAzi6AVEuMCEpSSFFNKdkSIUXObnZ6RUWSa4vx+DYT1Vy1N7eCq52g/Y8Dsv7JR0nqgGEVlGaVidQTaq1Pzn/vAgatEDgNXxRTSikYH+CTcmh+fpa1poCcgptS9biuG6ljTCKPcx0hnZSLRJbZNE9pbmaVjNZ5sT7LOUjdGIiSMWTC1TaAG1I9tViQx3bZQhs3ChhGT50yASB4sVERTe8RDpyfklKtdE6tbs4b+uyJkLJpfqZaUnzbtBZl12U1IY2jUrPzUr4bSs3L9rrRFLDLtf/IcQK4TINkHDAZJUEDpT20o9/SW0pB946c/lFqXzEWygL3JgUmdErdFELSNtSYQmr+Te30wlLgYBQ84O2oUcUBbtxVR5yCI/hUsS/oWh5T/I0yTfoOvuvRv3GB/tEl1q92cGuHnkkpxUpfAGhD9pkMB+CND2mbRj3bIVnHxOBzCwLbcme/FjkCbenx4NLcoUIeIdWFSUt9YKyGKrBFTk1VU8BsKY+ryOTR93iVYuqWrslX+fAHuPj52CPVUXNk1PTxY+tGAfOEVKqDzBk/4tNO60Z5Jqcue4/XL4ekjtqsB7ghoLuQOqgWtg14Yz3gtUuL58+I0Gq0wuAjjAoI1oyyxPYKaHC/U/hOhpQqMWY0Z9L2hPQIRIIMrMxpJIVCq9T9bTpIgXzxJQqB00tY5qKw4wcvyQGuI5UKmrNCKqwvyJBu1gjFvjA4IqN6l0gp120SCeW6PpNSIcD3HsFHUk/5WBBT29OaHBOOWra5lbyWAp1NVjholqDrxiIMDspoNGcDFQZ0AzTXudIA0AwIWkMvVohSYFtqS03qG2VGGRgCuLCbTwqpy8HDMRElE/cR59MKk3zZZ0Iq/fGCVxxRn+prRUy78AFInXjKzjxS58ConLYnju9ZsZCRRW3fElHlo6E0I/5sjeaFV8wF0D2z1WLMSiMwqi1TKszmiM6JBFciMaO0Pd/TbW0B0yJGkzslskx4lMbHr1WJqNsDpbWG+QVIqWZTKjkG0TZkc6ykgFCtOdu79NQmBPjeofUB0dtERJnGwA8evvdoVqSusUvLZEWeGyWk/guAVB/INIbrkIzTQpTRaM/bnLZ3voRdLkgls2xzpHGx5NQQSuPb5eQArMRQRNJI5LohI42lNQgRWDUGfUEUW63QLy3JngGav1bDGA/vA7VFDxHG+mQLgpP5yQ5fk89n8eActqXvR9tsF5Kz2+ikjBKH98HCpqhVazUeLBtyfhuTnB1JCWlMVmLI593+Ilj6r1QquBolvaZd0vez7KGMRjhbJZuutIbvpHh74LHgqdYUE1R+8HybCTtOp5qOAwBFHRo9JiiFlGppDCijYM+W5IyvWjRnqzQO7LJN7bdlUZ+VGCoTlHP1pJQsyqtNelqQdC0fObahqOX0oDm9lhVSS6thVE7Z6rnpgNFqFCwKTGJ5F4iA8AHBBdg2MBEhtqlwEsJE/amoPpAQEaKAEaWUUopStqzGiufmg6XFw6UdkVNLJiNsQRqLupkiy4oDktMvha+jDau5JGjQLhFCgAkehm10OF/BNwOv1bjzstHQDalOgw9pfrrOIfiQSGVg9/wUO13OT0mvtktKU5M062Zl0/y050uYtknz0ywXZFPaZQqAKNswIW7nCSkQQUNjIxb2jQjGpdUwATjjtZMPcUTu9FzXybvAa6KYxoJ3oSAvzWgcTMdC2fVUggdEUGaFlBCV2mqcF2PhrDV4sGxSqvUyEZTSuAaJoNQqj4FD0vgqbhA7VFKKb6vgKMuEVVKxu8DmtUfYvPYYly+v8ej/PYbrHN7oPdUO5qYgYfBwnUNzTn6VWbaIrGyMPsC3Fg3P5Wgbsn0D+ztyrfJcX9cSka4CN3ORelPajsiVREz5ABjL+7AdmJmm9e2qN3UoMfUkiqlb8AmuS0jdRLrePkJqqo4CyoZZmV9IJFTMpWekeLmUpKF6xzldj4qYcyaQj1gPPqXrvX7ZY+MCkVG9x+V6QL9x6NcO3WWPfuPRvfEGYvBozp6DbQ0WS4tfawweLi0+9vkltKKUQKMUDS+VP4dk5gSVM73uKwk1xUmSUvuQ2c2CsCqkdpm0okj8dBBrJhEAJrj5mExUZRmc1FLKbz4mEaR+CSRHniOBUVRTbkipen7dIwTqGOFZKeU6rinFpJTkSHuuJTJ1NNJpFMoJWshQZ6bAHZqiNykyYFhtZcFOjdaI3qe0Pt84KNMDWiM6WrjI51GytQUZted3SRLIEKmeIMvVpcue/GXiyWPd5/Q92fZcLE7qE+QFTUzvIaTU1L7qwvkS5zOGCK/VuOgvHydpgbLQsry/EaIvBFjO5fUxQnOalOZjy/E4FzJT5biRfUJITUmpHVETFWMiRKVmlApAjIHGaSWe7gfmyKlJCp8yHJWTukLG0DxsLRQX7AxNjsSHwcH0JpMU3ALcG1roayOqyhydL5FalwMwjUmFcbVRSQ0j+0xryKlhImKUDmJ0UsTk6LtOqXv0ZvMd1kSRQc4ANxLglL7GKDSBCGNXpO/JdhUM1iDHJdrsvOSpE0fpImIXVMjzgeqQyPehJ6QUUtTdsCO8aswk8q6T0lJUUpQOAu7OqVJwY2fkXWuKzqaxIKo56Yra5NpSnCYUBgcjUd/BIXoFM/iUHiT2PqV4GmmOoWfHQR4/REZJIXPdSIc9mwrdl8GMUnlrmpy6l+pJmUKJMVOzht642qenCVoljCFEjWjEy/SmfDumsS9qRknfEhLZTxSNSgNB5WtvnrOxuG5PSCmekxJMEhJCOqtJeq3WajQvS2VMK+oYU6Qpj1Kzdnw5hZIxcgAhNYwx2c6Z1qa0IADwvA2DhfT6JKKK5mM0AYCd2OkIg/n5Wa7pyvmptB7Z6aSQkvlZphcWKinqutwkRfzINhfzsRwb5fdk2D4PYZzKCeQ6Y/IbAEDviUSSNGsAUI5+1+BojKQU7GK8jEgpnWtzSUHzpJozE8WUygHGxcROt0a6JqutcS2BAzVRt5bfR8UNoVwPHaOYkmZPISD0A/lMg4NbO7jOoXMBa0812x5yFzN/Tj6UbkwKukdP3UFDCFCe10yeU/5FHcXblD4XPCv9zOy5bZNOknGSiaQRiTSt8XpTxNRV3+M9ueY+TYVU+Ri9XmF3CsELvVbcer50gQ9MUkl6nxBU0tDLx1xeRmofS+Cm9wFu8PmvD3C9x9A9BiAq4SXcQMduRJARDfvQsfhu9jNP5RH3lag6CVKqHDTTQRbSfSGeYmIzRSE1eMrnPGtNIpm8pglfsqJAZkm1YjGKUqCuauCrkailSiNSLOckDSu43F1vs07Mftx0CN0F4qaD63q4iw5+cHAXa952RFJ1Pfzg4PsA1w2IPmJYOyKiOPIdPNUQATIpVaqlykWM0rl7kixk7IqcDCqKSQ6F63qYVhZKOtWyaiQtkLvjqHaJ2JAnRx35NBCaLZWU/GiBa1VIYfPOB1ZKEZO88QFvdNKBwCcm+TEzy+veY/A0Wd1ACgg3cKSVI4vlogbAaGFTqqSAvKCRRU3pfG44yrbuc3HMjQucphBGqURpjBrNXfkiKz0CbCCndmosJHUoEVIxQHkH6c6YOoyUKX3yVU6iIcoPdPzmEqHvuEA9yYmjOIHa8uuxYiUEwGxfBCtuAZq/b7ETKjcGKJ0dWEtRt3ZJu1bngBtIoWh7NIEk5r6xRFx5Iq5iCDDLPqX7urVD8BFh8KNUrlgoKqegrlG5e5PYB3FwlNaZjOKi1s15VsY050siIpbnSdmjFqSUgbXjsSjfiXw97ABQCi3Nk4XV0EHhrMnz14dMRPUuq6YedQ4rZ9Avso0QslrsRORFA00pXkwUc3d1voBd5g5+SmVSShzgMgVk1ZqRQqq1Gi+cNWi0xlljsDC8taSmpAYK+bNOFwJSTypqA6UtYAN9dwsaC3p1jjgMMG6AbljSzSrX4WINMziY1sL3DnbZpACGBC9yal84aBwAGBGT2qh0fSA1rYyBJZQxaM6WrJZaQp+dkUJqdZ4UX9JuW5pjlH9zrbNrR767g1ZAQO5ELCoRVTweI10uGpD6d8mdK8+CwaAVhhBTkIZICJ/mpwRz1o0ZBZNEITOdn4JEWqTrNu1PRIQldZSoJCWV1miVuhU9WFqsWlbILCwaozhtS+Gs0VhIGpfJZATN02KCFl0wEehaqppIzV4AKNdT3Q5QRFoIqdA7KK0RhiEpz/fZ6WPmZ+6yd/X81E1DWy70THb6DHp1zmsFmp9oF9lO6zk7TfY5BFoBNwa0HmITPXidymG0Ro8IqYXV2HAphDQW+Pcfq6RioXafHwcyFiTVHEBSzImdFhtttMLDpU12Wuz2eWuxtGSjG6Ozgs4oDiLQUl8IuHJO5DpT6l46dM8KtrruTdP3JLDfZz9q/eoa3WsdfmXt0IWI1wfPKa8DHvQcmOfxpY2C6X1ObzUacblIpU6MD9QYyrbApkNqBCBppCjIFE2klTImdSEvH1dsL8tUPiKdQs5YuAq71ugTYurg5133/a6Bq1RS133eLgpzSkjtQizeY5qyJ69fpuzRNqckB1Z0ll3jy1pSqTxNkbK37l0qaN67gMdcP2qzdhi4nlR3scbQPcbm9Zep+cMwwCyW6J5fYHFh8fi8xbqnWlJDyB3qzQFd+E4BJ0FKXYUYx1sgDyQip1iF4xWs1qk+lBBZ0+cCWSUlP/DsBeiqSct1Emj0emCknAqjIuaijpIC5+XCxa09gg9wnUspGTHkRYwsagAURrdg0E1IEW4isSyU4ehnawBQJIGi6WyUBweA2qiSM+ygQ+C2qZ629Ibbip4ZlN+vT8yyqKSYTQ65kPlmmqo3eHhHjHLwAd5lMsoNXOxXFrnFaYREMubFbRmhVUohWu4GA58WvSFEdPwaZXH0VSgZbzI8jY4IOiuliDnfbwS2xlOZslcSUlMVVfo+i+OCo+iN61lVYdPrRZVfHzBXX2wqQXVniEpxKqY4AdNi5yQTl9pSmm2GHqixgFm1CL2jWi3FwiT6CG8Ugs9FdUsbMYU4OJLqK4SEKGKUyQ0QpNOeKSLvkqYn6i6UW6UPjt5RcdmYlVJapU5f1EiAHAggE1RnBbkkisYOgBLFRRF5L5WUqiCJbUspermNuEoOr9IKy8YUahA9KpYr90WBoTl1l+qTIEfeMR91J2e3HBPccnryXSrtaSwAMC2lcRsfEJcLBA4eCEkZfUADWnhT3RpR1uqDxgGA5OxKGuf/397bNTmOI1uCBwBJURFZWdW3x9ru7LyM2bzM//8z+7pPs2trNvdWd2VmRIokMA8OBxwgKFHxqYj0YxYmBUmRlIgPx/Hj7pw/zHDYd8whxQopbieGqwbKnDt1knOgqZhTvC9M5eK3YgFuABiEpCpJOREth9sikQ6yIEGuyBfDuU7ABOS5t+qfjNRP07wdydJIRjFZbK3BcbN/EumUw4I5nNbG75DzQwLYVjEmAtVEhbxZj9XdgGAfUwEZALATKxvJvupAlhbbWHKcvqZ/8jgt+2c5PrPzICpZU//MFT3rvH+t79uCBanjqIhGDHmLYx7gIznp0/OnXGM0wC1VW3iIz1WqpMycVXOtdsBtoTVO94KYlFVb+f+klnO57VpTFtigdm4+1CLu06JeT0iylvPPeXa85LXRFKioB1WxzO+XU3TmRyLYxBQogVXlS16XoUdet8nry77SIo9bYXMtm7pWP218Jqmltj5boamW+oA2/Z6wvXLf5XO1wva2PrtSZIXyOH70S72uFdcgdRRWBbikSiqt4+McuCwBfj7BzyfMPx/oM6f4Gp2tnFM5KbTEj+U/TDrzbXxoUko2HCaZlpCru82echd9+7nExNk0gXoHDI7KJtZKKYYtDJaY6wMhqaWszSYMGyv5ZiIhwATUfKJQPc4j9fiI6ccjkVCPJyynKSmk5h+PmB5mzI8T5gfKD8N5B6bvU1I/hDjI1skxAa66Re9TOJohJRDnjDHORJk3Sb+XE1X18kuI1b2QwkWCILrceKCKgQBC7DRmnmHsDISBQsZWHZqUZvRMYvyt50p79MrxtlwakxVS/3qY4GeP00/6DeYTEVNLVE0Fj0ROLXOZKJVfpcy2TpRJSikycKj0u0E3ebguJmmNUkk2tKlaH3mAZx+AkSTsmBccOpueh40DX6gGCfa2mfhMqLztnBKVg/NEIe6rfkvOz2NiDiozP1Jp3B9/wX//F+zxnp7V4Yjg5ig7nhFsV5BUV0l9FS8LY3OrYOWU7YAOpJYCcjliH8kI69D3j+jGEyU/95RQl8J7fxJRy+G+kyiIEHOasPddhvkCrKLk0L9codNxEYTjQKrK6EXkpNZdzE2CrocdoyrmeC9UMgO1QUtKKbhula+EQ4OcoXbtbAA8K6UQkzjSsc4YTPwdQkje94fTgi+iAAKT2XV1zmQUxP40PeQ++eX3I4YxkvS2LHxQL2h4scs5ar4cOlhj8NvBpaTPhQojJlR2lhZ0Lnncy1CZEBOlYiFC2R6O8NELar3POS7mCbAWbpywjD/hYi5CNx4QFo/ufkyFMpKxLpwg3BZa7YBuhV5zEQx6lWGaLraF/n6k0KH7rLiw4x3A6ouUrL3PirktlZTixbEKIwE72rY/Y6N6nFUvOd8bnWuIainAYvIWvXAscdJr7huzp5yQMvx+q38CZVLsGpyAeqt/UoESVjJ2qRLg2Dn0jkpoO2OiOoZyIB2iwqqPBLIxYn6uGLpgLEwcw4yxiSC2fkGwsTgBgDCf0FuHMJ2SQqobhxxe9PiT1Ocb/bPVN9OziWNza5y2AzsPDjmh+TGrWmEdzEgqxqSSOoz0OoyUh5LH6apP8jjNv42L0qHgrLCfAyW2D8ASiPSbloDjsOAuKs0fTln1XheqkeP0nnYAYHOclnlBv4w9uqie661Nz53Ucg7OIKnlqCojq+WQSCp2aF6qvNdyXH/s5eENYBV1EZVSMR2KP03w0ZE/xeTm32Lo3j8nD2eA7wu93j9SaB8552l9wBXNO+/hpwnLRCTuMs2wQwzTi1Wt4RcK+/esVHeZtOJjui7f5yokLzqFPdrqpnN5oFrHboTxfSTsFVHtDdu7BL9xMKuk5LVk2B5jiWQQIAtSBFHROBRjIofw0Xi3rIp0sWJ44RC+04L59ID54Tvmx2+0fo0pPeaJjpE5lfl6Iax/Bx+I4/hog9CHJqW2EDh8b8lZ8R8Xj35eMPUWzrqYBCwU5R5ZWUnvySgxBqliWojyuIJoaOWJkaiY9jBPyQih8LycV4oH1uW0pLA9eo2k1MNckFG5xHfZKRguGlbsERzCQgtAkXPKLVbcqks5p1wME3HTDO8slonCQ4ylQRrTlD0J/B3PKqVER5X37QNOS84hJReWDyfqpD52WD+HFHdLqqmSjEqlpStiih4TGY3WiqS7nhbcIcq/Z7ukkL7gcxiPmQ1Ojr7bz1Tpb0mhCbR4jsZ8NNL3aruTGqpQPbFSSiin6EvkcrPx906lcX8+UHho11Our+kE9MfzbVPxvjCUBNOIhboRIbEBoISaAAwrEwF0o18RDTaG25Ix1eVFzpgrdTKYoJJkFFAuemRlTiKlYqWaFMZ3EMRDT0REN9D/US0TrNtNPPDiNxjO52bROxqRe0uKAsBi7KIHKuRqmENn43gxp/dzY7ED5Gqcs8gMcn/ocNggpTorCyB0K4Lq0OUqXl0KD7KUb8XS5zkcZJfz3Yjk91Elx8nvQz/RwjCWqmaqfbGsvIik09CnRMu2n1eLXUlOyXbAbWG1+I35BVl54aR6ztocojeMsR30RaJ70w8VGbX+IVQ5dTuQT8KCVIu0nZSMrIbprYUNISoD2Vi3wEwKKdnneK4cquq5wLpqrqyeC2xXzD0IMoL7pyxQwoRUb01SSGXFlBUJznM4Vot44PBa+kE6BD/DxnE6dH1MfrxQ6LK1iUzuQGH9APWrJfar4D1m95NO168rKgO5b6ZnIsbqepwGQE4Dm/O6yTA+JoftMFJpe67uKhKc+yqMtpn7z5hUidpE27i3Ft5yoRdKcTAtPqnnfLDAADFW57bA4/PPBil1qR3QubJqlsfpVltw1iRCSoZwEhmZVXSsaG2N1To6vSOqiIHkqPWlHeRjWhNWRp08rX2WAEzRLvec9mQJMM4npVRSRwHFe452Sc+/UmvlA9d4suN3S9m0U/G0+7ofUEF1CVuk0zXEVXm+C/ulSkmqpHyu0AesK8LzX1KEspLfe/h5Smqp4Bf46YTFOvjZkwhDjJEtBGAzh/FHwIcnpTiG0of8MJjwmGJFt79OC/4Vk9311sJ7YOxo8uHEZJINNVGiLKvspfLIjo4gNVJUGYT1wislOQdK8sYvREDx3xTVUjGHFBNR0/cTpocZfvI4fZvgF4/TjwlLQJajhoAp3vZJNvoQVoQUENDH98fo1TwsAcvJUh6DJcANnEcqwA20qEnJdfue7tVZuHmi3zqGiwW/5BjwBgnC/cPzc/GUW2JaPKZonLDqgXNKffs5F+F6088cvjdPpGCaT9Rp59NDJKWm1W+/Ki0djUkbF8+uG+C7AUvXwweXjFIO42M8RkMqVxrycJYM7GPPMcYB3rAxth402OtmEb2NfiY1VFRJmeVE6qdlyiRVHcLHyppAigquPuK//Qn//S86pB+A8Z6qlDBpmv7Mp5uI3hWXSGkBUlSK395aknUKtVQAcp4PXojMExE8Px+IoBhGhDkqphaPLiqjWCHjK887QH0CaCulAFBoh1DJ2BU5FT3y45hIB8skxDASEcV5hMb7pIzhPCVJMVWF89m4GAyI4T0+oLM0Bt/1Do8mkm7WwLuQlB7OGCx9wLGnZI9EaneVBz6G9VaLHQB4/JH79r//PqIffbHgBShZb01OyWTJvTUYO8pTeBfzT931Lm63KZeUi0SbTa/52ilcL6qlYGfq226gitPWJYWUBRCGEdY5YFngux7dMMJF9W3wHl1UXnDlVl+p5fa0AwAFKWVtGaLHpBTnDaPnb2EOx0xU8r5+iHlqupKglJ7da8cjHb9eHMYY2BBSQmsiiYmEspyfIioZKUWKxaEL2Rsc++S0EDExzqSi4f75cHINAmKbNK5RExHUL12TnBo7UurISnusirnrs6KR+zArGTnU1iL7lGT+P1JJTdSWXU/EcbQvgiO1VJhPsM4hTBNCN8D6BW58LJIxh8WjP1FOKqlq3eqbjHqs5r4IQCilhkhO9Ym8ptxRfa6IGRVS6HpgOKSxOY3TUsnI145twiErWnMBU3rWfZRhLzGscl4Cxs5TNaohJKLyNHdFWzjNS3ruNUG51Q5KgrJUzCWllDHJaZCIKJeVUtw+xs5F5Tq1g1SFMZKURYoxVCpXxYujzqOasNPOqiNHgCw2sGJRl+a4+Jq2u8pGow/uuvaLYGt+03nvIljh+973sCDA2azXkJVI+X9nDcpcee1xRYor6qqsexFC2OkVfX98eFKqhheV9h4XUkh9+znjrxOTUvRgvnoXQ0Ki9E2cg71APi6YjAeCoURitIak6n0kZ7aF0qFGIkjiK/0fc0hNU8ojxUnNl5PH/DDHEL45KaXmacFDTKT2sARM0RBkMopfeTCWYXv8/2ANnAlYgknKnmEyBVtLIXwOtqdBeH48wViL+fFnUkuEeYrSVZ//WtXiiucSCUSfiUAfkJLQy/xRrJBiSSMrpJaZyKn5NFPMbSSjlp+PhVKKbiEaePHVslJqZlKKKkT5/oRuOMbPHuG6mJy8K6v/OGcxAXiIiaOJlFowe5crMCTVWkCPslqfNHCMMSVR5GdSSC050Tkny0+KKW5P0WsbABg/wz9+h//+L/jvFL5nuh5hvCdjNCqvgveQXAg9H53g3g2mWpCbkEP6mJgSpBR6qsCGrieC6ucDgh8RhhHwHnYmMjNMp7TAWQoiolTIFLdSGWPJAx/f26GP+Ufya1ZG9WmRk8JDrBPJzUXiXB4fm0nOAXhe+AIu8E/EuV8oh4gXYxyTU2MXE0z25ITwIaRwW/6bGwveBzHz/ZevBwxjLmHe2SpMKOagkVW7eksJcQ8xue4Y80iNcdHT2Zzg3PHCl+cTUwkprSXCzs9E8gcHhI7GgA55AQlQKAEvfvsBmCg03Awj4Be4mPPPn6ZCfSHbwaW2wO0AQCIjjYsqPmtjLhp6ZTIqqS4OR9oXF8ArQqpObK6G9k2ByahAaXljInRurwawUSduDMaQw7c4ZUBOfE6hXL1dYj+1WDxS/wRKAqLVRyW2+iarYJxF6pO9ywQEvRocukxKyb7s4viTCKmW3S5VftaRYyeG21KIclywWgdMcZwcFoS+T+SxG+M4PU9kA145TgPr/rlWNbqkioK1SRlVklO58AARxmKc3rBjbVSbcFtwJraDQDYyHHAHl8KrU1tYAiZvcXDkfDw4KpU+eY/F82u4OFafaweAUExxrjDRFthp0GoLrGq18blzOyBiCsVYXZNTilfCBeJpk7CKkISuByCppKLytsifiOKYrBpP15SElHSmvCVRpXgWjHm6WuocrDVYYpgSj5NAHKfqcrYQ++S9CXLK2Diex+qPDPlegodJ/mqXcmvtDOB5V3xYUupc0jPaH3MdLB4/Jo///e0EfEFKlPtf/ABv8kJGkjk2xa1HAyzGay7GwFgidQInoBMLreT1RvY4p/uJ5I0MufG8WIgVkihsj5PShrRtnpZKHZVD93jbOnwvfx9SSyH+j0RIpXLIpwVLJKGWU5aPU2JAItCcJ6+7XbLqKwj1lwn8W3nUT8YHup2AnOScyZssdcyS/lNM6LbMHn7Ocbd+5m0nIW/0WeY4n+B9Jv+K3z8+D++XRFDxZLPEcD7bDVhmwHYUnmCdh3OWrr94mKiWaieuW7dBHnxkpZbkhY0qKMOJ4sV7w2RUIjGz8osTCBvQAlUSUj///IbxMMLOJxg/7prgNa/UK0EqqDiZ9bnnwQmgQcQUOg/rHMXOs2Tc5tAL4xeE0yOR3TMRFiESEsEvcFGdyf1ga6EDyMVOHLOYAInX43A8KiEuiAkRogXraIFmTM5PspFLqkVEcBiNAxHnFDYNAAbgpLk2k1I8hnXe4OAop43vYvEBT8dPXPxBjO/cb49ihfH7sccdpWJL4z8Qc8WBHBmscOqtScmdeaFTe+QzGYVERvH3az53EZ6bckvxeGoo/MWGQGFBbJwwQcnhuvMptoWJnv8wFe2A2sD5tlASlJzE2eYxh9sFtwVe7DJhKRR+qS3UhNQ5VKSlElavi6j9vniMFWrrEHKuncHZXA05NqneRiW0jf3TmZhzyqa5nktZ+yEkogpYqxsk2MmWlMwWibBOBQbiJJtCapk8jmSxtUjV1XrL1fYyOWUN1vnegJKscR2ikZBVyMOB+iz3l1gdFTHnTCKPPSlcDY/NV47TQDVWV/1TEsUQ47IZqEpm0V+jQiq4ofxuG32VQ/aYwAFIMedBSjp6JmxjtdvCz5jEnNvCtFhhA8Z2dGVbkO1AKlHlOC3bQm4TKBRSVijkKJk7zka/sD33AdZ4HwO71eaiHIGVhKyF7R3c4DBYgyUYjJb69WCjQ35w8S8fa/suq8Bj4Q4mBhDXBYg5aIt+5gQRLV8Zdc5E+bqV/+ma+U4eW51vt01/A/PrnjkIQCqKwaAVGu/bTzhtKaksSN0kr8WqWR/Ku7QpCslgQi7MMyHbe3ycNbkAyNBR0QdZCMS5/MqVZG03wHYD3HBE8Au97wYq7NGVDpl6HPoMxPmHJaVqBA7Di6F4sw94XCgs7D9/nPD//fmQPHPT0uHfjhN6a8lbE3JsaD2RWWPIWx2l7NyNpvjwO9flhWTwZf6B1n16jn8WiS4XIoD8RH+skiKlFCmkTj7gMRJTD4tfhfG1ckoBkpRipVT2ZHlwSV8PfD+hWzpKArhYdCPFZ8+PJxhnc7ihtfCniYgdQU6FZbk4sXCSOCampiXnk3qIYXtcNprD9lJS84kSmy+nR8ynByynRyw/H8rwvemUCJyaWU45pboBCwDbU0dnQovD+lyX80wZu8Bagw4Oy+xhTM6FUOes4d++q8wUIwycrJoAzDID8ymF7Zl5ovC94IHTI6kdmGTgiodAUp6YeO/LP/83/F9/4uH//xOPf/5Fk+zvf0foBiIKRRhg4Ap8itfDFeF8ZKAgFgdAJqai4RWChxkjsRlD9rhwAuRihsNWeTuQCWOAcp5EyFwlpjaOeKEDJj+lYWYT8VCrZdB1iVAJxhIpZQwRUnHRw554aTBFYVQOL7ZsMFha7ADwFug8jcUhAKcul9vlsKFp8ek9E96sWgTy2C6d749d7qf/9bcR4zFk4phDRHihE5VastJUfk9zxeBsTALMSc2BzlEYSJcWTkjhu3KUCIZ+WwQDE0Si4YVIHW5T5p4SpZrTCFlEg1Vy4G3JWeBFCPlStAPZFprtAMjPnn4UetYAEWKuXAgXbQGg5w4QIUkXid/VZOVc+v4XLKkbMJ4/M1h1yDCGHHHW0LPhhOcOBsaBlOOBk6EDtqccnQcmnkT/ZGKYVTRcNhtAs48ytry61kqjf903C5LKMVnB27IiKpFSSS2VyeOCkIiLyOANqVjrqlnG0hxuOxg/U4GTaIcgEsSsjkpzOZDH7MY4XeeTyrcSr9vqn5Iwti6rpqRjoR8yUSxC9mAsQhfJKdut+mgiJi2NwZ2N5H78DTkyhPY5+AAcFpuePxNP8n09VgPZYVmj1RbqdgAgPXsAMe8Zmm2hE8/eRoetSeM2V0vNi0oiKpuPRPGWEHOIieGyduhTaHl/7DDc9/jyn7ZY53ztLH7vLYb7Hv19j/5+QH/s0I29KNxySJUq3dBR+Cs7AK2NtpEkptgmKh16m0SUfI3vg9y+cWxzbjy3vmzte8r8+YJzbj2/PPUzNTFV7lsTU0wl8bn4GCamQvXZ9FOH6JiI8x6kSpTVdjwURxmxDbFS9OITaS/z6i0dcRJMRv2cKV3Ew6HDaVrQDRY+dFhmj+HudwS/oBu/IPgF/fEL3HBE11t0vUsFPZgAc1cOTsxi3Co+DSklwY3Ze5rwfs4eP04LhscJf/6gsL0f05I8ORLk9bExuadF74DFUxx9CFHwQHX44vv2A+bBpFZMtSC9Y569+0s21lhVlP/Q/B8oS1TGs4O7J+3jioP0evLkSeCSzD4qgvziYRYT802JyjDy96oWOdeAwvdYvZDPycnfuESmF/+HuMjy86lQEOWwyPKved35RNLIZYG3C+VpkZ7KWGIWjfC2EM4nmGPwICHbhTVcbjhu9yKRuc+hevDzinxIC06JfkL4+Yjw4y+c/vqB6ccjlscTVRNZhDLCbSdiVLwxTExsHt+vsBECmyp5AkU74IUOHV5JfXcsdJqkhF+y1y+WQKa8bNX9+iUqwBaY4GKwTwNnkmm2elItQ2YnQx1inW5D9McW+VRva42TcpFsDX3GJoMiEvg+wMUFj3xf3HccakP92vi+Z0cRSWpW740wpph4Qv3s6SbFj9Amo1r/G2upzThyOhBX6tK5ueKQ3L5qC8Y02/GqHWyo51S9+XaQPmDOK+XFPhOVIymcLxgEQ6OYi2kO4GkbLRrINnKGyOPeIuUbWgKpZ2R1IiD3xRYhIZHJ4vg/K2QsExHxdQcBURNSF8MajChKAZCqKPi4yumylRU8KQYRHUg8nsa+avqBxu0UjutXY7vZIKXq/DbJWRC3rdRTgowC30/KXdc11VFJ/S++dz1YMVnJBX+WqBbg/w0AOCAEys3KRKU1TFCWtu1LtAP6f01QWstklc3t2dBdsvMgLTSRScmUakFeFyhUdLe8sPtlwJW0hy6pn0ZLazgmpY7OoB87dPHPDZZUUpGEsrEwABXzoD8mnox12VknVVLcxyDmR+47NeHE+1p4rqppS3X1C0CqpZ4CJtvr4Ya38fnpfxrL2A5Nha0ia+WjWsrzfOQpHYSPJBgX4Bk6yqvHEVtDR2rRrnfwnl67oYMfv6A/fomq9yPcYUTXOzhRYMfGa1n7uUKLPyUpJbGEgP/49hP/6z8f8B/fTvh//3zEP74eKLbc2ST3ZaOFQjJ8lHwH2MXgro8VZ7rcCdi7v1gDFz1OlKC2Q1jIa2asA4n6Gt5oIOf68Fx9hRKO+4nD93LY3iSUUVIhJcP4gLKT8hWd4XAXeg/YuI1m5YfFwxlLYXwxfI9Lpi6nBW6YU6VA21O5VOModM6kvFJLDt2rlCIhsJItpHxSTKjJssCpzGUskelnIsmW2Rd5pPw0VTmlONF5VkrRbaxVIZxLivYvCH3+389UMcz5BctMFfmWjkg6Jsa4YmCLnEqeu9imjCnzVbBKitRRE7CcYKZHmCWqpE6PCPMJ4fEHLfxirizMUw7PEdL/8PgD//F//z/4+ec3zN8fsUwzDn/8hjtp6Gr1vfcD54mqn0GlpmJykiov5jxiYVmoPdQEZaWMarWNREo0SKv1bdZSdBGyxV7CqIbBPNFrP5QEVjLQsnKUwpl9VILRsrYVMirzsZWvXKETmD0XSKBFzOPsK+87K6aQFjrcRwtFRnz9ecqz+PfTDB9nQp4HAKznBm+T2qL3tG+M5c9pAR6l2BYADKnfEGCi/NvFKq61Z4uLGaR2EHxZAIHbxUxjYKHAmCoC+5XbQhAhnKkdpvw0lqpEchiRc3TfrMCwHVZKTU3q+uY4Z4jn/6mtJg8xKHwrGMT2TKQDAHgXEILJ+S1CgO9cUq8DSIVkWM1O2wh7HD0MmY+D30migb8LkWlIofNMrnGEqlRFyfybJjqP6DI2qt8tjOuofyE6CXixabvUf8NC+aEM96+e/59jSG52OvD4Tj/K5dwh6TsX+W3imOtcXvQWBBPNP6xWTLmvnCg4wUrGpJDqSBkm+h+3DUk8OSeKBAUTQ6sp5yqVVzepsrUHVm2h1Q6Ap7WFuh3QtnZbKLeZ9Lm0HZmQSsrZarxuKvg+0aLw5tDKQxgLvphhRH9/wuGPL7j7/ojp+4T/6z8fsZwWfJ88nAH+7d/vMdwPGP824vD1gOF+wOGPL7BDh+G3e9ihQ38/ktrq/pjO3czJVuVt43lO5mSr8yfWaV6aoX0NNVXRp+VvwdgTtvcB5lfpGJHYo5aSxJRUQgFk6/G5uX/69DmT9vMcx2HI3kSVPqKTJo4fPpA4xYcYBu5JmuK9iWe2iXjnVBNcDMJ5wB37oiAHR249nBb80xginWIY38+DA/Dfo2LqHq6zuP96wG9fDvjjrsdxcLFQgwg9T2MfPjRJdfOk1LWyP2B7gmDy48dpwWNsEAdYIYqJ7GeUQS02pIlLggkWEydeZ1AOLOlGMtseABQx0PI1/lkXYBwlPTPOwDqLwedKeif27JmAwRqcPHuLQoyjDqvgLBm+J18Hm6V/KQlvSgDI95Pv0br8v41JNRPRFkN7EirvmjE8MJjqvnKyyhQnK9hfKmoYf4+4ODLRI2g8Vc5bovIJIMJJKqUKAw7In+ccUpGQav1Za+iRGiPuId/z+i9/L/mamoL8t66ox9sqFQwrXnKifEFInR4RJlJG+WmuvufGJLYXn7BU7C2CqiiW7YAXNGE60QYO9xCKudQuOLxzqsL3pFKwIiSa92E5Z1XOWxasp/+7IZ+jJqAAhI7u0XQA0JUKmfrP7AsdTYsZXvSAwn+8Jy+7T2HAXM0zhgPHyUKSU3S+dc6SSfwc0xLQ8c9lQzFOLQiAy2HO1pKFYiPRNC0heeMneLgYvkKqiaSLS9+rkVs1Q/5W/H98NSEQIcWEcySkQhXi+x5twXiLMJ1IkcEqPVbY9UOhomOll6qh3gZbBv85WFCOViZbk9GOTFSlk4NIKvIeZ0LCIfdjAKkf8HpCEhSdLftI856qfiNJByATD/TeFEa6VMDUpIM04GXux3yh0oGwUkzxAjPknp6VZ5nUoh0+jfcheFIxV31991pCXl/831rspsWtY3Kqax9zTskIXryZGCeAFOLJpKUPVHGK24Mx222h1Q4ApLbwnHYArNsC75NE1Ppc24TUU0arD7wuvE0IUofXVJyw3/Y93Digv+9x+DpgOXnY7xOMMxjuaftw36M/dnAxZK9WSLmokDLsuBYKKV53yHQGvE32nU1CilGlMNiys181bO8G597nEFPlvjzHXD53zi/F4xh/lueTmpjigg8hICqhDGApr56NDJmNJ2PFVB8r+Czep/C9U0f2EyumTrPP+ZajsTr9vEPwAf2BFFL9ocNxoPA9zo8nc6ACaPIVHw03T0rtRTIyDE1UnaVKK0sI+J//9Su+jH0iEO4Gh98PXSoP60xOjOgMioSIvaN8Ic7ShNlbkyvAmOhdK24keoVdTvpqphPQ9XAAzDBiHPrIyo/oxgOW04Tu/hv8NOPw9YEUUt8nzA8zlmnB1+8T/OJx+jYh+IDpcV4lOZd5pSSYiAKQckoNllhZ25OMleKr6W/40sP2DuPvB9i+w/DbHbr7Ef39EcPXO3TjgO6Pf6PJ4P4reRTGe6AfENxQGkUR1pB6YHEGvw0O0+LRW4PJd7iLCdZ/nBYcB4c/7nr89Tjjn8cep8VjfOixzB6nL3PMLfUlVuYLmGNSeFZILTMt5LfC9zLhlBObG0tySWMNhoODsQb9oYNzFl1PA0E3ONwfOgydxd+/DBg6i9/vBmpHxx5fBoexc/gyOBw6i7s+lo1PJFa+h6QisZRkE91QGLFJYTdPCIexVEOIPBWhGxCGEb//j/+G5fGUQizv/ts/YO6/wh7vU66flWdG8bqoFzJbx8hXIHrbSR1V5yMJMc9YmKeYNNdv5phKobaiqAKd/nyuklY1pxBDQMKc85Mk5ZT3wHQCDnQPFgAn4efwkFItZVdKSg6BDgHJy74EkkmzAupxXqh8eAB+zkt89TF5bq2Yyu/PlZv/8T1//389THhEWBPklnPUeHDyXGcNZmsxRWfBoYt5BZBfx84BMQGwiWHTIdDYG9IiLj9zAKVKylORg6SQ4lx5p0d63j8f6DUqK2WC81W7abSFizlrttpCI4eUsZbUpl1P14z5ptgpEPwSj48LYmmk7xmPLiyUFU9Dy9gHssGfiJogjkdUywjDHZBEE6llgLwI8ElVla8hS6Gw2upayDVbS/ECZJKh3ldvLxRV4vP5YlwZFfQjSCJKjPUh5hVN6qOaXPac1zGetl49XZOLUH5M/hiVIoP2V4qMglAz5TbbthWsyQsyIC4KrYmJ7xFztEi1HB0nc7fUbeG12oHcLttC/i5lm6jbM7eD+vyrBPjIxyveCFIpdRhhjvewAI7/+APGWXTjgOFLj7AETA/krB1/PxBhdTfCHQd04wH93VgopMyBqsVShco+F3KJpFcqEFDlZ8sqxKw4XJFRVtjg/B3kdxHbVn1ZvjJeUyH1SvNrUiqd8ZA8lZjiO5aKKdrOBHdI/XlLMZX2mUiqB1JMOUNjkwlMnPPJIzEPm8LV+8BOSkqH0zuDaQkp19ShsxhnymN9HBxOs8dx6HCaPX4bO/yIeZW/PR7wMC348scIACls799/H/GPrwf84+uI3w4Ox94RT+EM5S5e/W6m+j//zreMT0NKSTA51TuDsbP427EvHhBtj6VibU5cy5VauHQslRomEopl3rJSS/3Q8wQfk513lPaaY/oxT8S+eo8BwDLQz79EpYufZgqPezyhO54SKXUaO4QlYLifKfn340xhfjHEjz67bdCw+soNtBTqj1363w2WEv4dHVzvMHyhhcbw9Y5Iqa/30QMxYvjtDuYwwo53VN1lGGkwT6WFXR6Q5fVBg4gzJHscIzt87B3l9Rq7XI2gkjf+M3pru96lxOfzyaeKfCEEzCf6fZcl55/abBup6gH9JjYSdCa9AsOhg3UW3eAwxsRyX8YOh87i97seQ0fk2bF3uOuJkDrEOF8K/7Qpl8XmQGAsYEIODQiUlJzaSyhVKZyTYjrR+5kUM8ZaDH/7W06eCsB++SNNniuvjOJ1sIeEspZYl/jc5fEhhowktZz36ZkyIZnIhqiOaREQfpoT+cDEg6z22arsVJQZ9xZ+QiIiXN/BiPxCRa4p/k4yZwkrploqqTOQ3TWH+iKH5HmkhOZMOLEaKr36vI9l0fw6N4ip05J75I9pQYip13i8B3Kp8SWQ48I7gz5YLH7BCEcybwPKI2A84CzxdItH72zKSyDW6ts4pzBLSihfqaNOpIxi5eQrtgXrLBnvzqV8UqT+dWVeIiATUgCMd2eVWfWCWPH24OfXMv5lKEStkCpOAKSwPiAv9FkZKCseheoET0mC27h8cb9bpERNRtH7y9XWCmIKSKsaEkwaIpjkPBBKAorIKhTK2NAaF7fySaUveH4B2lz4Nl5X5LCoIL21MJULO1YQwGQVHR0T4nniZwQRxeQVn+gt2kHaJ8mrjfYr/60/X3/21hd2Hw7cd0yV8sBE3WahPqoqwnY9+vtjihgwziIsHvMjOam7cYBxFv39EW4c4PoO3f0I6yzc3R05WIZISh3GVdXKzYIBtcO3VkedC9drvF4kpFopYK5x2nyAtcA5Ygoox4hWKB/QDufj+aee66R6slZNtcL5OHuqiwS8IbYKvYv2PZvIhu7ImgDMHtZwuB9YhJ7SPSwx/9RxIFKKq/U9HMkoPQ4Ozhr8/cuA3+8GfDl0SfTQW5vSS3CuxNbYtUWo3yJunpS65NErtoGJI/Li3PUuPSBW5FDuDyKrZCUlLhMrkyI6CxgY9JauRyqpdRnhWt6NSDIgVp4y4x1M11Nn6XrYfiCP8jjBjQf404T+foQ/zZi+P8JPE+bHE/wp5nKKSpj5cUpEFFXxCzkPVVpsxM4n4kQo3M7AOgoLdPG36I5EvrhxSPJVd+RB+5gGb9d3sHd3sOM9TNfD/vY3+h73X2G6HsH1VG3JiQSa8hnG3y1Er9ldT/JDDsGh5+MxdhbfR2KOf7+bcZoXfHuc8TNW5+MqfafFw3tSSgGAj/8HHyhkRjSYYm6L98IheZZD8pwlpVTv4uBA1Q2OUfk0dBZfxh5DZ/Hb2GFwlr6DMzj2DmNU3N0J5lqGRTYJKa7ayOqy2H6TtN55mK6jcJeolDCRhDAilMsc74kM4JCq8Q72t7/F5xIVCq56JrUHtXV/ihdDCtOrySs2wAp1pU/jA6lM4uthvJg/yMoqja0k2I3CBEUhBpkkt3pv+j4bgfHV9EORX8EejrHqXiSpXU/tXHoP4/dOlwQtVhbEXEw+jxWAgTVUbMLOgHcWvaO+PnaknDoutgjfy2XnZW4pupZcGP8ccq/8968jjnf5Z9hTZrx2ZHDJeXZ2kPqWyXhSS5q48C16V2wTwTqqama7kuQxFhgA2w/w0XgOMUwuDGM7fM8vsOL5P7stANkwF3nGiqp71ub20DDgZXLl5sL30pikeDK2DP3N400kHMDGdelR3oIz4jrVIkIqrBh8vrMhrRvYQzqcJavS59pJzotNUg3FY7ncbmwKUU3+6oqgSv2c2/dGiLwx50mpXXP21uIXWI/BLUKrPl8DTEyFEBdsop0A5bONoqmUlwpYh9hQrr2zlzxzL/sJqD2X4BA+oGwn1+DjLAFvGKJ/pb5jqAomhgPNpfdf4a2D9R6Hrsfw2x2GrzSZ+1MmqVhFZaylynoyT5S1MIdjIqfY0SZtHViXK8vW89hrk1FAQUhdbbfvnVPfYO7dWs8XtxFf96imTBpPSnLqqaopPiap2kMkpwxSODKrpoyhfZ7/j6qp3kbFfizqwUopH7jCtEtK/zEmOv9y6FIl+p9VVfehI5HDl7HD2Fl8GTp8Hbu07uysFMqU3+0DcVEJN09KXQIznQEhVQRxseUee4shKnM4hxRDVuaQVVrIY1ZWasnlg1GG7dWEAy8ug6dfNg6oZrwjT5p1CIeRElgPFJrlhxHWL7B33wHvcXikhNV+mhMp5U8zgvdYThOFYpzmIiwDwGZoBnu6AVThGOT5Jg94n2KrnfAqpEGbFVEDeRHM8Z68Cndf6Pt2IxFTQi1VLDwNLfQ6/rV6iyjawuQpGeG0BBx7hy8DdcqvYw8fAr79nLH4gB+nBad5SQnRFx+KzluH6dQJM2VoDr/yH3d6VmgNncXQuVTlYOgsxs7BWSSik4g1Q5LMjtRRdwOpBjj5HIX75EWtBVKFL0SFFKujArcfSV5wRT7X52TI87y50ARAk+pwoAVgfB715Kl4X5AySjwLa6kduCGHt1lHz3ymhNc1KUkERAxXTXmnlhzmWVXfo9M21DHS+8Y5FACkyk6cQ4GrRfUVEdENKUw0xOTmcNGT6Ihoo32soszXy8YB9YBgDExMmBtHMzhQ8lwuO98vJiqlmNR2kZDyRZlxoKzyBJTGzKOYrf9+N2A8ZiVIXc1LVnViEoorPLHqc3A2V/SyZBh1zsDGUHJr8mfkd6f2YGGMpRx6ISaIRxwTmKQOnp5D8ERMzScip+sKnfL515W9rm0LQH7+Im+gKdrCutpXMuBbbcKWY9FmVSLFq0Ma+GsPciYcACIdtgiFGpK8ahFVdMw67+JT7l+iPltNRAFrkmGtpDpzQbE4TsNY1Y2CcFKuCCo597ZUUo0iEJv3cWnbOSJKvpcKqY1zr9qG/J9zOlUkpiQha/KRSSrGa7SF6hLxmPPXaLUT2t5uK1vXVbwCeM6QDv8DFdawcd6xx3uE+QT3+2O2jxktB5t1OcWKDNETYepFzihbklIrRxvPbcB1ZNQ5Mvg5ZNSlfdcc84LYE8oHbDtTtlRTjGLeSvvjZ0XvLcL2xLFb5JTDOlfe4kPMpWiKOSQEg97R/sl7eEdhe4cu2qu9L9JQtJyq8vs6S4XZOOXQb1VeKRbKGOBDElESH56UYhjxapg8Ctzf7GpCSmVgxSRK25FyDKTk1RZROWVS2N7Kw8bhOUAesDi/ihsQ/JwGQAA02E0nWGvBpdcRlRE2hmH4SEKxDJXJKA73S9X7lqwYqmFdvl6qQBfDBl3fJXLKDR15EcYxeQyMdWWs9WEkhdR4HwfuLhNRrJLaeDYm/sbBAi4AsFQWs48SycmGuNjL4ZNLjMtdPHAclpSonsmo00zfm8N0tvLH1M9cElIAMHQukVEAcBflkoMrc4sxGWUNEhHVOyKmepsrczF5ycqI9o8iVDImgMtKEyERBaKGyAr4OZFSXM7BsiKmVjx0XVwA2hVBqAvAd0BDHbVakLBybplhbCYq5fE2hFxinMnIrgeXGQdiW2DSCigJCZxpi6IgwMUy4y2DjT2HTpQet7Yw3rYIUSL2SQLNDgZjKB+Ti10DNoDLzg+1N2oJWGwuOS9JKanElOrJJQSYKf8aX3qHcYgGSiKi8iKmVWqcxwNnTaGq5WqbNn2X0vNuTGlAFW1AvCbFlPdEVHGbiA4P6xw97zh/sKKO81GltkBfvsyxx9svtIdcic/mtsD7G4q6IqzB1l5lg6L8fOu7b/2veDXsIaaAtve5BpMQ0taSnun6us/FJdLhHBkl72EXIVUhKabYfmsooIoE/3tSZctznNu/Y19TfVG/rwmpK9BqK4Agp9BeCAIlaQm8TlvI517vuWQGKSH1/iDHTFQhGl8QvGzXGtvlNZWnvmj8SA7/es6DsG2k4jvaTrUqim2bUM9l8R5WyihT9aUzpNSr5426tO8DgX+pveQUsJ63gPXcJcP66vG/RU7JfUxO+SiAITVVQOHBMdHOtAbOOlrLBpPsVRvZe1JOlVWkl2p8TNWfozr/ENPFcOheUuHzeGsyD8K/x0fChyalWGbu4wLAWaqI1yPHi1J+ErN60C2PmZxEWRkFZDIqEVPID768oUxEBQCmA5EKydNNg6gZ74B5LhIam7vfyjCMeYKL3u0+lYCfskoGKJQyrXAMAE8PyahlrBymI0gPCtNxCJzINv2VA6KzxBpbYWv5QGGWPpDKQJZ8n3xIckcq/d4u956UEDFERxo79fNO95KedyYdOba3dyb9z4NACsWJi9Ac9mlS7qhWmGetrFuNCzxpuKgk8PO6Wg8rpXyfCCtOjpryUdQGLJd2ZqKDJ1VJDFQlnxUvgAvkU/Ku+xzuYWJi3PRsEwnpEUKfkl9zfhLTHTI5iQYx2aq0FrefhZVjRCSxZXU1WXJcGGa+9hjW1Z3qpJ9SJcOXi2M0r26Io46VnGLc/hKAYOM4bpl0pvtZOu4PNOYu0dsEiOp7jTXeN2Hm/P2uT+F7hVioGCtKL5R0YvD/PIek98jjTD13JEjFgidCzwRh4FqfF4/LTG3CzzFfU3R0ADR+ALnUvAzjrH+Ep7YHJqvinJJKzMdn6isCsn7uZysTtcYkJateDGyT7D0uG+G5sTqUYRLND6MUD9UKmEsqq2vRsrfrVtIiotK+1jmbF7Kl2knM02WztLmviTkgpdtKoX/blUi5/9fY5VQ612fOLXTPqTWwJqHqbUBJThVtpWoXsk28RXtg7Bk96gWcbC97l3Yfawl4wxCEL9tLpKMGQjgAkVAww0ipAxZRlRhYz3PCydK0b8TclOYyAKsiHXV4XnyfXveQUPX7Pfmi6s9cs+85x74SZN/aG9IHrOex1nm2lFPpOGHHFUU4RBixzIuXh7KQ9rF9moo6IKTceQCSQzQEwFuK4jp027YqTxstUgpA4Qjt4ppzcDLfNZKd+pyqoe+ND01K1aCHkV9DYOYoRFY0H1tMNiazouTZRvlgxf8WKMP2Wl6tZHTHMAxD4TpcncUA2dvN6gf+aExUy6E6iKopAFTFD0g5ZKQnwLRWXunLNhaakYSqwzMKDwIrIljS2nMoDodmuGKhyYNvazDlBR3JIU2UQEbiEDyIWDgT4BYaGBbBZk/WFEyyr/LGAFkddW6AS8q4pJQzqdN3iZQySR3B75mwStUaRd4xF5UQshQ1K+pa3jSSgiK3EQsgxIlXhHWl9mKYiLD07INPRq00YJuln9N11FR6N0iCSiY8DyKBJ5Bre1gQQWEA4/PnA4dwivwm1Bbi2BUiIb6Uqhig33efNekQjbDA36E2wnibICcK4kGcY08S6+S5iuNx9lAZLIjGQkpeEj1hHNMfCW7rTKrkxMOAVE3yWDF2uT+Q2lEsvmOn5S2lB6osN79JTsXvY8T7yz+AzeOAGEOD7VJOsjyl+VztC8iEdgz9ZAK7aA9pvtjRHqR6TrYHoPGMS7KJ7lmorBqEFJ1YEHLpYh/RjPrYkEQDUBJYq32Vsd88n3hfWyXnusFZvmtP92l+bvuDcs+m6kWO3RvvizxTQF5M83FA+bkalQ25e66+ZoH6FEJq4/wtYqreDuBiImI6pn37z20L9TXPYU9VvfoIVUm9EWQECpBt5khUQcyBMAYmZOfJJikFFETUyr7ZQUbR9sZcpoTUk1CPHecgx5w952nln44mdtyfx5vNpOjpc/Q866ToQLRFjSTA6Fi2XUMwkQ8obdUAUBJ0S+ftq9EmKfaFKp/XnOfWm7f3lPfhQ5BSZ70zKNVSPlDIlxeMpQ/tRlwaJSUpJfcnAgOZeOD74hPnPAMdheoxcSNzBIU+q5uk+gGAOc5pIZEY/ypv0MWEtS0PeGNxsfIYiJAd08VKgZ1QQQjlQ6GIiPli0nbbIdRJtZHzrMAH2MjsUh+MndayYkqWgqccMnN88FM08qgSV2iG5kiGuZWUVS4MkwpiM1wn5xuTKomtMM8cqlMNGMgKCVNPULLaiOtIIQMIkkGoolrKKFbZSNQTZUUaNEs+7zBEFTsgFy2N/4uE5yD1Dyso0wKHn7UDUD93L45Bbh/FZ9PFzpDU5+4fyEaa3Ja0wIJIqI04SVRJzyOAlUcReVw3JsbnI1D/iolLuMR4CDHkFwAXzWPiib1TgCxHHsnp/MXi9vxVbS5Yifve4a5fL3b4TlchHKbtxKBjyr4vQ/d47DDiXPybJNWFNI5ZGZnIpjhd+zl+0fNtghV4CU9sE0G8l69bbYL2bRBQjbaRjq+ue/Z/xdXgJteyhepQCHnsNmHDBvr2NWs90B6l1V5cCkto3XfrE1cRDDUxFbH6Vq4iquQSoXYgvlTb3iD+dy9wr7iP2iaX28tzmlX72NUmnkj6XBOqsue5X0NGKU/1TAibGECuwifsJRPK+RFsN/PaSdpMjFZ/vXYuk/satszmeer3fG55e1v97qVIqKcc/w54qnIKKMffuo9KBWdxPTnuCHKplTtxq6qoPI7WnCbdS7ZHSUWVj23ZquvztezRQviASoXfsDnrNEMfYYz6EKRUjRZTaoGCmOKKTkC78ks6l2QWKzKqlsDJ0sFnJzQjVC/8v7EwfgZsvPdgYIIYXOOCVXq6udOkcIxGKIapiChWTxlbT/0oyKjimA0J6yrcQiijQnGc2TXo1UoIF8koB1JOwZtY1cCksvDO0msXS4yfC8+pZY+SmFrlFBP/8xwhSSgAiYiqFRKtwYFVYCg+c0EhURm1xqLIT1FU9Kkm7K3qPavFn9x+oZy04hURn18Rxhe3F2OFODaR2jw+cGUmJqH4HGmCdJmUYOwlIqp20DTSxP+bXsLWeFB9tkYmqctrs1LKQ47h2TiQyScB0BiCTFg5VMaN6A7O5B1dlEPX95Q+xk4IQUTxMTUZJbe3JNQXFRmVymLVNoCsgGQVZXxPL2WbWIUCPbc9yH2tNsH/bxjwrbZxLsmy4n1xjshitBYBm+d7RcXuUwiDJyleaufDxrZENK9C7M+083OK90ufrXDVYvcJfa+pSuBrV8dJtJQMb4FrnvVHWLx9Cuxx5FlkxqBWl9suRRJQFIEB054rWyid88xcJrZdJKMKkuuNyKg9+5967A1hK1/UFlqcwKVzXVJy8iPl/FN0jqyQQnV8S0XF+x2i/ZrWiGds1ZqMqmxRKYBgOzMLZUyxRi2+b3nam1V7fkhSSoIWLdH4D+RhZ1KDJs2GO6d5nnxAXoTw/yZdS56mOGX0KKcB1FgEPwPGlbmCYiJrIz3ZMSSLPANhrZroybAxfDyj4RFYfc1zHmeTFwT1gqJVVWIVgmFKr3fYSGRrTUkaApQ7hmN1faCCmixxzOoHyiMTQk4ZmhQSAQhiGKoHnKbz7Vxnhym2ccem96ba3l6A1qqJLYUdAIjxSZBQKD2slQoCcOKZb+ekkOeUZJQu/t4A54ysFTGVD0tVm+qwTKGUSk06hQKW/d+EhnLuySTExrhxjmzg/4Wh12xzjfYnx3EX6Vgmolpx/EDtoQLykbx9++vOIlxvcCZVA63vKd1ysX1NTslj6pLitdKqQNU+ijGA82mEbGxvquM22sSm1/gSGs/oYptobDub1HVv+9Dx6kVxjmjaNODPnG/lIHxnY3fP5feqqGjHBgkFrMd6CRlmvROXDn1SLshzn7liYXyOcNrTXviQ924fjGtuQxVSr4hW/6qRci4CSV0OpPyKhbqcsXHOs8rBrXmoRUCdO57v+dJ1W5+7dv9zj79hvJR6qj4Xn++iggo5YwSwLweVtFEBrI6neyuvuyflDFDamEBOG1GsQbdP1TznreHDkFJ78h7IMsZbicxqbIVs0L6KqBDXa59MElOV4sXEBLYhE1ApPMci54kRIRkpX0gI14VibKlogOYguisMg/fXZBRvv+DBq5PSA7SElOSUQRm2Q7+HcJKkRWj5P7DOXZF+irB+xhJnn3daWJrmArQO+awHDD7/qlKjBE/IctITu9JZtnJUbJ0zffQMIaWLv9fBNcQU0Gy8iYY1kYiQz32jDazGiOfc/9a2c97Byuu46WmsTw2sKjnJEuN1efFUZp4v35BSAzJ8ez0BOFO+l/9fqth0zkg459CQx68vIMaBVvtI25HIy1W7kK8Rb9ImqvcXjXYdk24e13iq99i3Ox3eV+Fau/pVDPFz83Gr7V7oi88uQLL38y/cr/YsHrd+/ltoG1u45cXbp0VlE5OznlXEiNvi2kiohotXXNmuzsxBm/NZ6/+XyA+1Z/9LfeYD4SnqKcY5B0x9TrbZ6gp+AIqqorSvJpfoM8lGFTdCKYX43OW9uJ3jzCVHqLQ7P1rYHuPDkFLA9cRUcSCkzG4bW5VaakLqrOewoYQAGuSUWKwWHm9BUhX7amn3tYuNrQG0WFA0FpLi/YqMiudZ5RCRl0VWQjAxBbTJKQDb6ghmpHmBWl3nmkouLaKqDs8B2p1fHtMio1oqiRbSV6oWpIVyiu939bOKDdwuLuWU0MXf+6HhDSzKigObzGoiIgBkJVVYP7NzZPRT7re4B7O9vyIb6PgNQqo6bxq3sSam5DhejxmMwlMFrAZmaQgU1y1421yJU95XDVPsLw+oySjattOp0er/TEydaR/tdvF2bYLu4Uy7qP9vtY8L51e8HvjJvVRo3p5rvTX2kgoXD7uk5pBtd+9xb40XIqyk3d3CNeoGPt+tQEP8bgRVfyvmQumb33LePQEX57LWtr0k1Nb5nnLMc47/BLh2fAEuz3Wtc9Zrt3NV/NJ5GqOCJKrSPTxj8Kjt0C1HaOt4+szTr/0W+FCkFNAmpoC8oAHKpGESFwKedixEznz4nKeblS6FtzsSGKFtzDSTFrv1cU/CuYVDtXh8kfCLCCamuFxwi5yKb5K8samKEuE6wPM9betOm7c8VSFB23aqJIAVIbVST50ziusVdevcrffntimeh9bzajxjacCUj4GJCL/eyWNIjebGJ+IJBtlTSozX24D2OJ4k1ZWD4VJ5cWdeRikFbBHZ9TFrp8bWU2lu30VMi7ZxqV28dpvY2r7HWNdx6SZwiWSQ2GvQPpW8egqeY2Rf9dFLxJQ8rsZLqBWvwVP7zs7P7SE0gfPP5qO0kRZufF338bDDXiojCCr7CNWa6Tn3sYVrCKhL59qz/6U/9wnxEgT4njA/YE32AOtQP5i1b/mSjXotSqWW3F6uN+PtfDh8OFIKWBNTQGlYXSPze3asuBxMG8QUAMHwr73WQTThOqFt8xr8fzrB03OEnCOnLpFRdMx5FURxaqyVEDJEB8jklA9htUCFeOZbEkj+7B5cCtHJx5Xfof58a4C4tDDdZSBtTdKMpxjHuvB7W5wztPg9kNWScrxgSEOoRVCJz7/4s9w636VcCTsI6uJ0jfGctwNl7P+WpBpY919Z5nfr3Ly/6ZDY6Ketb7SlsD133U205hQ02kdSUJ1pF6/Rv3e2i3Qbl8YhHZfeHdcQU3vwGmTErXt4z2JrzH6t838AbD3PW28fH7kZ3jT22EtSQQzkdRXv38JT5sJrqlnK+70EJaNeBdeG9zGeoqJKny0Ip7VtCpQk1Z6aDpdSzqT7atzHJULqI8yhH5KUAraJKWBNTu3BuUPr81yleBH3U5YJXpNUJdMfZVEbia23KktcwkqiKu976/+tRegZhZWEJGZairZaBWE2mOWV6kF8Fd7qrjQZto6+mFdmtW97UXqOkApyf0VSnPW4XjNBPUdOrHgezpGLrWdZqacKrMqMM848x1up5nShrcnxXHYROV4AbRVsEpA2vFZbOKeU2sJWKO7epMkXlbZAm7BcOTIquAaZSVfcvt4LtgvggqEOXEc86bj0Lqj73WvhPQ3jZ1+67qfPPc+t4In3sxJmPv9ObnLhdIO39Dmx05FXtLPN+U9iX/vendPtpY97qc8pmuPHa6mo5Pm37EO5cl/ZqHtvRO6+4GTdOsUtjqstfFhSCtj2sF+aKPc+m90Pca+sG1iz/IxGKEY6d3EC9po/R7N+xWJgKw/IlWqIdChK0nBLBdFSP8jSnBJSWfVU7A3ZuaSSeBGcM3z3hAa8prdGcT2uGB8uqeFqo+m8IYaryYVL1ysv/vqkQq3g2Ou1YuwyAlrX3dGnt+aHFx0N9pCaEUUo6Hu2i3QTT7iGjkuKV8KL90vg7cPyXhov3N9eWnGnUABYz4ON/vfsIgF77uE1jn3Jzyqa2OIKLuGSikqen3HuOi+9VryqiuwHwYcmpYB9kr1rH9KzGcV6YXnJ043WONQI22kf+DxcI1F9ShgGX6ahhDingmiqH4pjM1qJjJ+Dc7/wHpYaeKGBYa/h+1aTpeLpuBSOCVxPLp5TVb0Unks+XHF/e8uLX6OE3covWEQni2SR9b3swaVDr5pPWn3+CeGab7IwvLbtPUVNpXgX7PEa3zrexDi/4ES4SbxyP3sN9dRb4qMv6j48tuzePU77t8RLXFvnvDfDUxVUwPaYcKmq31vhmkivW8eHJ6UYT2VC5ef3YPOwLTXETk/3WQXEM73ae/Gs8JwnYEsF0SrPCZQE1VsN5eeY7WuVElvHFyF8zRO+kOGrE+D74pJi6trnvOd5vmYlqFdqT5ccDdeEGW31uTKn1PUT957DX80Y2ENWv3fbuPY8OjbdPG6dqHp32/vW1VPv1MdumaR69zaj2MY5e+ka9flL3s8tnUfxbDw1DxXjFubEz0RIAZ+IlAJu4AGcI6YY5zzgYv+rKyC28IrhOk3SSew/p4A4F7P7FrjUtl5ULXH2QjqhfWi8hPrtGmPsPbx5L9RGn6uCfemqnHvxIn19D0G59Ts/p/jFU6ChCr8sXqKpPzW9ws3ikgL2ve7jRvDU5/vp2oniMq5NZSHxUo695+JG+6Ei4zkqqhrXjEt7LvGUce7duZBn4FORUq+NXc/50qLzuSE75869Fy8Z7vWEAXdPLrC9CeaA55cWfkoHfum8ZBfVUorPhed41W/VyHll5RRwXV8/159M9f75OemeeYJzuLat3Gr7kPgI96h4E3z6ee+lHAzXnvuT4dO3E8U2nmIvvUff+IX642fHSxJVW3jpMe0jk1EMJaVeC3vlpU/xqr3mwPdG+YkuhVteI/l+i474q7HVijfCrYd87MEbGmLPlVu/JN68f2tbUSg+F7Q/KBT7cWtzoPbfXwq1zXcLdijwudaaSkq9Jl6bcHoPz/kLnusaBcRLSyKvPecefKaBQfHGeE1v+kviBfv/cyo17Upy/g4Kyr141qnPhH3fBNRQVygUCsVr4TXnQJ2/FDtxjY34UgTWZ19nKim1E29aTe3a870FXvlaL6mAeOs++1KDhIbwKZp4zxwlH9hAO9cvn5vo/OagbUShUCgUvyp0HlLcMD68jflGUFJqB168LamnexNvEcf7HLz2wKLElOIi1PhSXIK2EYVCoVAoFArFB4GSUreC967YcsOLmKcmOn7paysUCsVzoUOKQqFQKBQKhUKRoaTUreKGSaL3hJJECsXnwXPySikUCoVCoVAoFIqPD2U+FAqFQqFQKBQKhUKhUCgUbw4lpS5AhTmKt4YqRxS/En6VMdbg1/muCoVCoVAoFArFXigpdQa6gFC8F5SYUvxK0LFWoVAoFAqFQqH4NaGklEKhUCjeHZ+ZmPrM302hUCgUCoVCoXgONNG5QqFQKG4CTN58FqWgklEKhUKhUCgUCsV5qFJqA7qYULw3PsvCXKG4Fp9h/P0M30GhUCgUCoVCoXhtqFKqAV1MKG4FAdoeFb8mZLv/KASt9lWFQqFQKBQKheI6KCmlUCgUipvGrYf1KRmlUCgUCoVCoVA8DUpKKRQKheJDoEX+vDVRpQSUQqFQKBQKhULxclBSqoIuOBS3Bg3hUyi2sdU3nktWaZ9TKBQKhUKhUCheH0pKKRQKheLT4RypZKr3SkApFAqFQqFQKBTvA62+J6ALE8Wt4lZz6SgUCoVCoVAoFAqFQvFUKCmlUCgUCoVCoVAoFAqFQqF4cygpFaEqKcWtQ9VSCoVCoVAoFAqFQqH4TFBSSqFQKBQKhUKhUCgUCoVC8eZQUgqqklJ8HKhaSqFQKBQKhUKhUCgUnwVKSikUCoVCoVAoFAqFQqFQKN4cvzwppSopxUeDqqUUCoVCoVAoFAqFQvEZ8MuTUgqFQqFQKBQKhUKhUCgUireHklIKhUKhUCgUCoVCoVAoFIo3h5JSCoVCoVAoFAqFQqFQKBSKN4eSUgqFQqFQKBQKhUKhUCgUijfHL01KaZJzxUeFJjtXKBQKhUKhUCgUCsVHxy9NSikUCoVCoVAoFAqFQqHICLjOCW6ggg/F02FCCCq6UCgUCoVCoVAoFAqFQqFQvClUKaVQKBQKhUKhUCgUCoVCoXhzKCmlUCgUCoVCoVAoFAqFQqF4cygppVAoFAqFQqFQKBQKhUKheHMoKaVQKBQKhUKhUCgUCoVCoXhzKCmlUCgUCoVCoVAoFAqFQqF4cygppVAoFAqFQqFQKBQKhUKheHMoKaVQKBQKhUKhUCgUCoVCoXhzKCmlUCgUCoVCoVAoFAqFQqF4cygppVAoFAqFQqFQKBQKhUKheHP8H80F+9Q6+Sf/AAAAAElFTkSuQmCC",
       "text/plain": [
        "
"
       ]
@@ -2360,7 +1278,7 @@
     },
     {
      "data": {
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R9azQ5/WS+YgvGhF1W9DnVe0toZJSUzwEg3XKMb5kRummeCjG7NhxVoP14uGBdK0RXg+Kk5tcl0PfqW31YuH10Icr7g/Pe4zwvH9/CXVMUvEg8RB8uLvGAxYzvKj28K5R7S2hklIvqwF7iVnzY3gZjdr0nF4vBupFxMvQvV5GxcldXZeXlQR56P34Zb0uFXePl3GMcN+os/wVLwxeVj/uNvEC+4TVHh/H68Xevv5Iqdej8Zqe8wtmkJ4Vr0eDVkmq54PXYVd7ELiP6/IyEnkvA+p1qTgVr8exwn1A2rWOQyruFffhz71IPuNt+W4vAEF1F7b4RTLvd2kKX2Z7+/ogpe7SqNynwXqJDNKz4qEbtNu2Ja8XFv154kV64N0GXgalyX1fk9pmLyYqMVVxCJWMuh+8zM5SxQuE2/K7XiTC6RQcO96b+HOyz3vyBZ/FFj8kM37KsT6rmXwZ7e3LTUrdtsF53gbsLuKE79kgPStua3D5vI3b3O/fll15GQ3V88bz7i8VFRWHUYmpiikqGfV8UMcgFXeCZ/XBnrcPd9d4lqiYO/YFb2KLX3bzPT2/m5rLl8nevpyk1E0Nz4tAYl3XIBz6jZsYpJscwx3jpgPL2zRoNzmG6xqIYz9xXXvzMhmq54mX/cH4EPG8r8nLoJh6GVGJqQpBJaSeP2Kq44+KW8Kp/tRt+HEPhbw65qvdRMhwy+TUqXb4Jub6odj4U23gsxbceRns7ctJSl0Xz2KA7ovIuk9ZZoovDDF13+z6bRq5Q/u6ieGojvD94z6feaf0vYf+wKl4OfEi9d1KTFU8FGfl9YCXwVGqeM44xc96kfy4+8JNlFGn+oW34AeeYoevY6ofql2/jRzAp/p/D93evlyk1G0brhsaKnMLBi5NjcEp+1wyIHdpuO4I1zE+p256I4IrPbsVNGbfQjyLE3fd0L+qmLoZ7uL596wP1btw/m/sxD+rnbuhbXmRxiXPRIA8h/Z7nrORt03SH0Ilpl6/eKiOy8uMh+4oVTwnHHtG3pF66jZ8uPvCyFe8jqjhlOiYZyCmDtnhYyb6JDLrFnyz+8LUBzx2fodspf7q0mYP2ed7eUip2zJe19z2rozX3H73iKq9DU4MwbsO4fQcVFO3Kfe8Frl1R0Zubr9zRNUU1zEsp7DodWB4Ol5EQuq6v3On1/o2JfIviCrz3nCbSVrvsO3u07m/C9tUianbwU3b8Hm4DJWQenHxvMYfD6n/Vig8q093H35cvGPyyh5/vutjX/QRj/mGh8YSNxhn3ISQugsi6q6pxVNbZXrcx/y/UwtXHRvjPESf7+UgpQ4ZlGc0XCcZq7s2TABg7cFjOaqsuilTrre7Y+fxNqSex/ZxzKjdx/yIPeE4tNGaO6djKqpDDPpDM1L3jeedi+wufvvYNT/Jgb+r2cNr2Jfbknrrz2K63j02d0wnbXoX7XeNXIC3TeLfNu6igmglpq6H22yrY/u67a5WCakXH3cx/rir+3tuv7WLvQBYeo7ety93H2qqcA1C6ICPuKimOmW9fHbicczZ4esSUYf8o1Nb/T6EVAHACfqCDGnBQ+d3TFU1tZ8vW1qXh09K3ZSQOvDZovE6xWDdpqHSRmDutxWLPj3mgyTVIYLqGDn1nCozHPr4ICv/DMbtNo2atjNLv6tbVh/3ofC/Q+RUJaauj+dBSD3rb56aABF4hut+HwOwWyC/7yon3DPdL/cVCnBHUvu9n7nRLxTcd8LOSkwdx/NoH/nN6uhX3AS1z76kuK7ftrD9QRLqmC93D2lejkKe5cf2L9sd8BEX1VRLY665scQJ44tTCanZ7WYcrkNnfqp/Fu+InbLslx3b/bP4fvT9eZJqjpyas4kPLZTv4ZNSc7gmk35tEupZlFm3BWNvZoSAw87fMcNzB8TUTQmp67DsS1flkEG5TWNmDwyhxOZMj3GJVV9SUb3sDPpDwrPE018X18kxdm1n/y5t3T3JxUe7niynr4/t/9oP9vtuvxsQe/fZV+f2Wcnz54cXoXn1Mdy0v1WV1MPBs9zXL0J/BSo59VwwfV7OPD/v25czdynHSQHpOjIcjTmi6pBvODduuAVfb9o6Uzt9qq8218yn+Gd3eX+GlE6yR8/i+wHLAoW5MenLMAH3cEmpZ2SyZ43X1HBdi5W/g+6fAi3mDNOSwdjr3fuSzoOG6ND6Y799A9xG7PEphu06Ru0uDFng35o1GKmw7hpxsmqOpDpmpJbw0Njz+8BtXPebOvjP6lQtEZIC/fHJTsE17N+NcAty8UVi+rqHsrB+2kzXum8eYPs9a3j0Mcy12yHy/Lbs1MswWLtNvKhtcRNn/1n75LMM3W7qM74oeF7nfl1i6kVt5kpO3TJOfWaqdXu+3HUIqFnfcGlQccfqqZnntZmdcZw+62fIq7nn/xHfMBm77/vNvV8YV2g7fIiMmvprx3y1OT/tVPXVHG5i864VojfZ9pDvN0dYGTNPzE3TvojvN7WlhxRTD8Hfe7ik1BKOsOk3IqNOMVx3qJAyM+nUkjHzTsnUiMi5TUL99iScN1FN3SFuSkhd18A9i3G7CaZGQd7q45rKQqds+hyLPiWnXjb2/K7xPAip5xlqdvQBdR1C5TYGYDeUi88ezoHPpJ2mOaUO7evWKhPeFkl1qq0+0n6n9r/76qeH2vo2BlTVDhKu0wZzkyU3wXVVx3ft7N/WHOJ0Py86SXWbc6d6X3d13jfd7X33W4NKTD0zTn0WLhFSc2TUTXy5Q8dyg2O8VWjiSK+e+Iiz/uER33DkF96R8ED7a4d8tWN+2nKUzLFjudldekjtpGHMcn7SuYni6fFYYxbt6tT/Sym9lMTUwySlTjEY12HSF793gHg6wdjdmsMxXWdsYdCzEZmoqqZGRR/KKQx5/vDuCKvrqBxOZdqXDNth5n762WnM/DHMJsqcDJb0WzEWe8z6REk1p6CaMuhThcFDNlIPAaf25VNj6Z/pWGautL7GhQg94do/i2R+aZdzA6slG7RgZw4pfE7JMZcm9iSm5YZYarvpZ6NjXnp/G+03t3LaptfI/XCs7W67f+qBlEDbKaDaqrvASaEGd8QuHNrvIUfhtpz9+6oePvc7z4uous+K6XdBzh3bxV311UO/cayvApWcuhFu6tPF032ykT93bFxzsqrq7sQIAJar6c34hKO3+r2xY/9wj5za382eT3gNf09aec5nm/PVlvy06/hop4oYboowc9/Pj/2u7/fpfe2FBKYZG6R9RiVKeFl8vodJSs3hyMA/4wRCapGMuiYpdRODdeN7yAjbzU6XGJ855jvG5VKjt6hYuAmuqyg5RkgtkQLXYeWPHcMS5m78PWZcmY3MhM84aDGlkYJqyqAvXR1tgKpSYB7P+ty6KSG15OzfZJhzTD13LZw4S3jTAZl8b1GteU17c4hUOZVQOZa37cb30IFnxp213/T3b2i7r5t4dAnTX38WdeezDqiqDVzGfTj5S797V8TUfRIzh47hvpv2eZ/3VOV9XbwIhNTS795V4uSKCW4aLndTMuomUTZ3TErNExzzaqnypRlCiV+blPaFC3pb9g1nVVPTpUL2qybvgX1C6phwYImMOiQymH5v+t27QFiw63PRMNNImL0oGEUoTSfm5LtL/t8hPMTxzsMjpa5hGGalncdUUYcIqExazXx2Sj6qE5A7kL7p7YT1hnJIpow4L01CZshnc1JlBsTuOzn3QELdpErDnPTzEBE1JaAOGcDRftSRPKth22PE+QrL+pBSNmKiftPXS15aM2HRJwy6sOdaNTVHSswZqRedOb9L3AchdRP58vxvjR9sGkG9zh+llAkBrZ7K/Q1Hrv0hu3dohvIYhEDHmBxP+vMTbdD+fXxa+4bJ670Bhmo72hd9KIOGk26XF7T9rjOApHXLh3KoTwqkb+7Zwuek7nyIA7VnwU0c/Pvw+XW/Gj3LZjrcEjF14zCOW9KznBrWcR+hbjcZq9x1Oxwip5bu46XmWbIvz6uvLpFTVTF1TRx7/h1SSO09Y2cIqAO+3N5+D4UDHiK7Dmx3LSw8s0c+wXS7Q/6h3lbGDfr9nH84EVKlI4RUPkb5+mQsNvXVTvXT5sbQp/pp1ymCdSqWJnn3SKgFn08+0z6fnEJ+u+D7AWP/T/aZ/b6JYurY+OhF9vkeHil1DHMG4QAhtUdG6ddzTsUR2ehBVv4UqHC83EGDNgSFyRYjsSjxlF2ye5W0tZHvK9XUwZji56CgOpWQuq6R2/t8wbgdChk8BcYU2jsbFiQyNrIeJjvEso1mxoUVnzpsS+qpOdXUi2yAHjJui5A6JXx0/7fnt5nOwmTu+brqqSVCZYlMOdXWzc3MAePZuYWZub37d/r+BEJF2m2cU4poiqnjmNuO960f+EJs7N1bh54dwPNpvxNwrO2etU/q/ekcecfyI9wVMVVBuAkhdZ12P6QwzhP2k22W1CinKqYOkqi3TBXo/V2HoLptIuU6w5PbbgO9z0Pk1CnnfB1C6pT93WZfPbWfVtwCDkWeHCOkFp7B420Xnr0zvuLR/MEnKssPYpoTeLJ+lPB8SiYd8g9x3GaO/MMZ33AvB7EcJ687FPI/56vdpp92zEdbugLXuW31JK+GtIhMcOax9ZLPB8BymojpJOjU96P9m9E5jMedaeQflvNKe8TU0hjqRcXDIqWOqaROcQAOGbAlEirOfPeY87F0vMdwgOEu4XfChhvAcNe1ZZtk7KxTskdOCWL5/l0lu7suDjnzpxgx/VlM+4aNthnvf9+AqmO4wUBOG4JMJilFk7y3PNounxkaBPFvmmzkzF6I3yyBJdsssOdzRqo6ebeDY4TUMUJ17rvX+32+5rzTPKurZlS06mfvkk/s4OxA8NCs5DGo0VUyilzhgZWof5Ksn9ggTUjp+1e36yk5CqZKqSGmUf/X91wA35usnoows8RU/vq0/a4zq3sMS+2nXubzPpAr8FD7Acu29boQhWc+fN7pXr/E9YmpiuM41G5TR38pz8VN9g3QdTu0D+lT40H1+Niu4/TPbXqIgHkWPmFefD51WpZP/pCC6Do45RxOIaFuqy0OtcFNzvlYH6Vtjhzfkd+QI17az7SfTlVThxRTlbI6gmM+3RSTccjJftx0v3M+3XQ/k+8s+nbXPYdDOJQraumzQ/4hUJRR1haiytjx93J0TRorp9TwIY8pDoB8rfnxxHjMse+j5fXKRwNwLT+N9qf8xelnN7whj9kdo+720foZfy9vk7RN2VdITf2/ZMxs5IyFGY2j5vCQfL6HRUrdFHOO1IIR21NDTQ3faF/7BnDO4UhBB9fMwzhXtp8ak8lSDAp9HoiIkt93TWbJR7PmE3JqlHMqRcVkLDDjzwmnOPNTQ6cdKdleHCMxmrLPETmFso3+LW3HwgnemVPaTHk1lWIadmyRiEW3KEx7MvQ9MVRCXMWUZtlz3R5CaFUcx7MMGI+Fnx4jpI7NCpX9nH6U1hg1ayPHVB5cAJEAVjn/cv/sPZzmCJWlQaD6zlHMhBjvkeRgG6S3n2BKqJzaplkpNRqfJqS033ajkFkmUqT9hNhzS/eaJvTmng2HZnOXsJcfQtYFIqgm5NRc+8U07qe6/ejz/cFj+e71+iKA2TbV+xJ7FXF9YupZBlWvB6LrVNXJITLqWdro0HenhNWS43/I6T/2KJ4SJMe671LVpDlM93WIpDpVQXXbuA1C7pQ2OdQWEeng+R+6h3U/PUaY3kU/nZJVuo9OiSn6fL+xKjH1jFia7J97fh4io9Q4Zs+fW/qOXo62OeDbxX1fL82sExjr5j+YrD/VP9RpXUz+bNlHpP3ZkY9oEpD0sGEaMTNdh7EfltepMZmMA7SPNiWiNMl0iIC6iZ+29AyZS2AucDNGffrsPOTr0fa8Lk8Ep9E24vcF0KSoRNHw9OxoHzGlMg7Nx1j8wvLs3FdLPSQ8HFLqRJVUxqGZ/akxmjoQmoxSRNSYsPJ5nymEbIxSXmqv5zgplbQREtWSrLMOxrmxMeK/MUGlbkxjYZgZn7vt9oipm+AZiCsiWW723UUVlDJ2U0MnRipMjJv+fuLtiiHdd2APGTGBNmYiYJN11hg4a2D4qjhLP0wkVcoDHmMMkVOGJJ/ZOE9JBszIOieOXmm3qpa6DZyaDw3YJ0Dp+8v9d7q/U2Z6soo77ecnkxW632hiZel6TwdnRmzYhJgf2c58kJP3yk7s5cLTtisr0GXwNB+CJqc2JaSW2nNuwDSnlAJY8ST2INvbMSmsib1ZcmNOXYaZNtSfHXqWTc9/bpYTTE7lTQ9PLugZzbn2O6SUOtQH8zZascc7EtJU2lPbK4AJU4yJqfE+q806FTdtEh0ePl5/e40c07jC0BxBpR1/TUwtOfkj8nQmzEPv+3rHuvzZKDG/2m7aVMeImbvAHCG1NHS57TaZtsWUnEtpnsTTq+YIqWNE1H300SVi6hAqMXVDTJ+N8bi44CARNUNg7QkLJvvXvh1tEuhayr4nvt0sATXNTaW3BzBXdGpEVlm3t93UNwTASpoFokqNs5BSjrKB5btSxgqybcR4wlD0C0d8vmNjiiUyakksMEdAiZ9G68e+GlCae7Ru5mYNR27KuQnHPVJK/LqJnyfbCmkl/l4e+7DgIBk6KW1Psu8HOeexQkr7ePoUUtofQz3E8dHDIaWmWJhZng2XwMzs9NSIBb9HRBkmnhDLZ2Kkkh/4o563ibReGzE+jkNMeT6+bGREalmMDZq2bGMdGRJ+bZqmsNzGApGXtqHjsa6QUzOKKQAY5Zp6gaENGzBv7MTQBWWYtIGT9Z7Jp5ASYqTtxHANoRi6KdMfTxjBWbYCLhsSA2cMrDV5XcvXuXWGjRcNXowRw5bgrDDpKCy6DOw0ySC/a6AMGQoxdQ0n7/WC2xwk6n1NFVJL6qhp34VaB5SBvJ4hOgYdImpVPwHKw9LZRDNhxsCBiKn88NWEkxDz2Zb5sc1cmn2U/WiMJOimrJvYpTLnRJ/v5UhSbScDjhDLvT8lnmWbuTbsg36d0IT99jNG2o6SdTtr6L5jD8XyTTa6NNPBrrTh3OTG3CD7SPuNBp5Tu85tmIyh55mxgIl7+5EBYFD9T9tLqPfTGcxj2A9TLsSelqOLFF0GWbKtDjO9TvLzm+D1Zv8OKaSmZNRBlcoNflv3aLFFS2R8nhmWdemwYmpqM/U62r/edrkTT0eUh85T9828fZ6plm30vu+PmDqmEDuUA2Z+f2McapdpQQ3dFpqcWiKm6HvFZsh+BHPE1WxozYFjXD72AmfMpA+l0bFEdfyaPNXbVtwAS4rhqMYmertDz9TJJJB+Juvn7p6oIEYinyZ+XPbv+PXoM/luPt7jPt8sFBk15xMCIIGC3lb5gyNBg/iQQkDlyb+GU740QAAJIrSIQfzHFGFsc5SYkt4+R0jpccVULLA0XpPthHwSPw0ovlnk8QtQfLLy/WWf7RRRwRzGQgOj1vM6GV+znzf9jNbPiRMIZaKuiBOklbNwwRSCSo9DgUJ65UnTmXN4KOOdh0tKaczNNqvPDlZkGM34MyElZJRyKlIIREAJ+cSkVBqYlPIDG7RirIpRO10pZcSwMDturAOGngxO2wEYYJoWyUbABjK81gFMThk2Jtmxi2w07MKMzalE1KHY5htiTxaN08mCY4SUGMgpOTVHRolKYggxGztt6LQTfD1SipQohU03aPmz4OS4yGAVcopawbIBCkAmD6xuH6MfBmqABGbSJyO1h8ycv0g4dVZ5aZA/JaT0PqczR3o/2rJNdz0dHMtMC30xe/lEeiaTiQBjCrESEyDDITMZ8OnXJnrMKkllmyWINDzP4CWSk08VncbCJJ6hi/Hg7JwMXISQ2iesShtqBxcAvDpUH1OuwKfl1C7/LLVZ4JtHnK1oiGDZm8FfUNsaIfamn+m2O9KGeTAJjNov8TraL0gxZcZqqWnIo7adS+TUISl9Pix1/roPAgAsD5bSvuTcKXs0Z5KWFFNTVJs2xnWbYkkdBWhycbr+er9iJ50mjvZdnHk9BhBliswin5pYeo5EndrjA3fZwW2kHfT+dLipHLccx30TU4cIqX3V7X5b3la7TAnlQyTUaD8nEFJzZNSz9s+UptVWS7+bP87TxwLA9ca2FYcxjXiZElKLES6pfC7k0p6ogEmpJQJqz6ebCg+mvt4JqVsAAG4Sxqf9P2BMTM2tc2NSCg35ikkTVg2NEfYm/yLKeMw1nDpE+cXg8VhWWu2LGOLIzozHFfJ5SpqcKgTWdLw29dEAYOAX4ptNBQP7xBTG7/fC+XASpuMKa2mGspBLtF77eC7Kd8dElYuaoEoYYhEnOCtEPbWdSQbJsI2LPFYHEE3KYyltVx8K4XQKHgYpdWigPoViykff2zNMM4qA6NmhYOeh3yHFgNhvySANfSGfZDn0SDEiacJKGTYA2bilsH8ehj2frIqaKKFgLUzTkePWdrRtt87klWk7oGlhmq5s6xxgPWAbJGNhUgNEm1VT2YGRY4BlGWHELMc6lYROMHd/63UG4xtmuv1o9mlpf/Icwj4hFeKYjAqpOKfisA4xIkZgGyIivw+JiCghpXwgY0fkVFFKhajZdzGg+0fpFBlF50UKqGyIDNA6C2cMGsdklDWZrKLPgFVD23TO0gyjIfbcGRC5aABEYsxHDPmoLcdxxqfi9eDg3XRweCxsb45I0moATaAA9DCdElH6Ab0UXjqHIhOmB5vMujhLg23HMywuESngrIFlYmX0QBObGD3ZxNCTTRQl6dROosxI7g3iNGQgxQMj2AZGz8oZyzbLIrmOPqMvjNQ+WeXDJNQQUm7HIabR/a/bb9p2l0N5vfER6JFnrqT9XJT7mQYFrTUIie9Dx4SUxb7aTNovDNRmgZ4NWTEl7acnQLyocpfbLsv08wRGU9rPsjLKsc1HN24/rTSDDAapTUIqpH2Ihcyfth9w2O6NZ/0S9T/e3MlMoJrt017rXs48nu07tZrMTe3WyzSYA5bPZUn5NKeQ0rO0ZjL4Pvwb5fVeNxF1FL91apusoFMKKq1MOeRAZPs6o+g7RKZeN2Qt/55uh3zPp1F73YSM0TiWcPtUHCLnliY5bq1dVJvkwhry3qSjailgTFZLv9T7ubW+yQc9GrPO9E3dLxPGpClQ2vFZq0e+bjD166b+GtQED68bCQfUJJlWI08jXOTZKj5amooKlDpq73UISNqvA8a+nfh6wOjZrXNNzfl9GqbMfo1yR+0poWSdqKEa9getzaSUkaga/gxNy/4gf6a2sW1Hquo8/mogoX3GxfFEoW2QogfQQM3WjUDjBuWTpTI205P7MrYILJgYuH0G8b9iysTTWDDA+4ljoYB8fyQeSCUqRt+LWil1LD+wU0ZjGpZHn0+IJ1O+p30+ipApAgWJmLGGfD4AaG1RUxFRldBYsxg54yyNo2CVct+oyT5gpDZfGju9iP7ewyClDuGQSgoYz0YvfF+2MdmoEUue/ECGxg9kZPyA5HtaL2RUv6XdiMEbeqQQkWJE5LtC3s8enxgY7pzW0cy3cUw4WUu/ax2M3od1OaxP2HgjscfRAd2KnB/bKNbbABEw5nASYXVwBz++6QN2xNQv4JSZKHHyZUBaFADFyRpCZKKJCCZRQw2BDF5ICTuWTex8HBFRQ4zktPEfUAyZXzi4xpY4YsevnTVorYWz5ZxWiYin4AzaxGwTYiagIjt2ZOASIjtyJvGg7IiRUePllyLO+KFB3+1z+Y4SxoSUkAFTckpILC1hnpoSa4EhyoPRjGZdEHnGhftNApkBGWDPztwv2MRMqEQ/tpOaiAf2iRXrAAw0uGJlp3E8+LFNsUUU4wFYselpNGOXw8+gyGmUQU9WR/IASO41sQG67QZ1iDsf0cTShq2z9H0h91hRFhLgQO0p91BEUZjNqm51SMFoIO3H4eBH2w+jWU+R8At5Z6jBaBsAKUVSnM08+zKhr9pP+qAm9DWZd6z/AeP+x78ERFaZcT931uTZvsR2iu4D5EFVDoPB7BRJxS1hiZASaKffzHxvilO2KUSUdupNVqrE/P3i2Mt9JrZz6tzP50+aJ6RGBM01RjASaLFEOElfnVP3LZEv03XHnslTZbnsY4b/WFi3TEjtqaiu2TZz7XKoTaaqMf2phLIINKGqCal5FdXSMer9zW+j22DaN/NxwYwUfddVTFWcjpMqmeuwvqnwQBFHIyGBCAw0AaUjXaZE1AGxgRBO4t9pn2+KqQ8ovt9onfIDZRvj+Bk/SecCPwDWjdVQmjizlt4zGUU7nhlbNKBJwujLmMIwAzIaf7k8JtwL4UvjSQEZ88oYdzrZFWJRRGkyCsDIR5OxXCauZCyiiKm5tCsAZv22OSJq6ss1MwZC+3TTpYx76D1goyKhrBm9J8WUiBGAmAJ/1+ZxERApfBgSQlwiZySfKb1OcDPjd/28OVVt/iLh4ZNSGnFsxA7m6WBnITtf4ijEgLjbAH5A3FySMdpekWHbbZH6LRFTuy1SiPBbIqfCpkeMEbH3RFaFgDAwQx/iIltelFJERllnYZyDbRsYZ+HaBrZrYKxFs+5ouxUppUy3Jta77WB6Xnd2QUYqBmLLGzYiokwAG2wgh3+Ug+HZ9JlEusDtz/JMbxU96MoPfBi+saYzemoWPztShYn3rIYiogm4GgJiTNiGCB/os60n47f1RD5t+gAfE3of0fuAwK/FuPkZIzeFkFAAK574fddYOGtx1jk01uCsc3DWYN1YtNZi1diskDqPFpQU2KF1BuvGwTLBYC2F8hkxYKoRR8YI1cG7a4wcI3G2VCfNzj8KIbU0e5SSnkUCtj4gCjGayoMa2I+V1zHsMvviDM24SFjoqjEwMGhtQsNx54ZnYGKSSpBxpOAxoQd8T7bEb2n90BMB7wdEJu0lhFlmH+dIlTzTJzN5bMPQtLCrMy66sC52x0quJFMq8UEreuh1H1ImUwYeqEjbiSoyE82ptNnlZTm837kasLGl/SSctrUW1oLvP6BNpJrijAxwhma8kqKG99ovRhi/o7b1W8D70n78TAFAA0wAoxBw1X4Ayixo25UZT1HJth0kD4TkiKCNuf14IBlVHxRSnuwhncfOl4HitO8d6ncAuL1M7nvSdgY0WHPGIEaayUtsz8g8pZHS85RqMqXFKwRL7XEoP5TeRiuktPptREqlWAzfnKMoIaWzB2iJwFXjCcdOfTIlhDjRKCUTAKNjXxiEyBhgP48fv+YvFjs9+f7c4U620G0n20s4NIBRPjQARSF0Q0JKUbt7255CTB1TjImaVG+rP8/v93e92Da6XQCUEBS22xKGstQPZf2UKJWwalpX+uXoWKRvLhEYS32T++O0b+buZkqDR7PfL/Wxy+qqlnoGHBERjELhtXqbfTh4D8RQxidqnJL21k3eyzIERPbfQvbpyl9U7+mwhIzisMDJ+mMQgspOImeElKJ1FlYEC/JnLRz7h1nIAMB0a1o2LY0TnCvRNN2axg9teW/kvVM+YbTEEKiJr1xIRV2jmO1JGqmvUyrqdRlf+FgmCcU3k/EF+WNFFTXEmImmrRdSqvhjvY/ZJyt/ceSjLZFSS6KCKZoZEmq6pD87EiE07PMBIN+PiavWlfGlNeNImfG406C1cbytQ1ZbIZJgIU8GJJ4gPUC4PxQhwstFSh3D0iBK/eUQPT8UZdRug+QHIqT6LWI/wG97xMETKRUiwnaHMHgyWGzEQk9GLSpySmNKSBlns1LKdg1s28A6C7dewTiLOHgYZ9GGSN+JgQyKGL6mBXY2Oy154N7a7JhQfPCJWCCklu7no51+6WEj+WYwnn069TjzYJQHoUJIDaE4V6KC2vmIIUTsQsxk1JOtZ1LKZ3IqRNpWk1JLqimBGKqRMcqElMGqseg9kVE732DVWITOoXPFcW6tzWF+MnPgTETnLJLh3FJp3yWbyyNVcXu4jdnQ6eyR9FtNUCVwSCkr+4Q4FWUfUCTMgmkOszYmtNZgMCkr8pwlYtMmA8OTaCmBVVNp5CWYlJSqhwZ8MtgTUh5+KIO8aT69mTwKaSIhz0UYVmvK32Ed5ZuyDRBZPSX5H2JUciQZ/JT7XQiW8b0O7HxQg56x9Hvry/le+QAMqv2cxRCBVYOcH0Bi/2Gp/ZwBDF/D0UyUmuiQsII8WOZw8NRviYyaGQzP5qDIM6Q04Eye8gpChRtY5/IkgwEAmfUUJZpcB9UHC7lX+p+2kzJ7qduPTnHc90Ta3tqUB1BoAJsMnIlZeQbQzB6RDkzocR8U2XnF7WLpmaAd+v0E6DOElIyR5LXe/lAYjqzSBQukn/JzfzpukGfcIQJgpDqdkC+ybo50EfWf4DohHPqLQqAmJvWBEloxNwG0pJYCxuc/t4leJ4dwSKEzS1Cpz+aIutH8rfreye0jjmk+Ptn3mISSZ40MYWaGMrk9poRUDo+ZJaPGfXMpckL3zaz20DlyVN/U41CtmJJjk+d5VUvdD8xMxx4pukV9zM/THL2y29DS9/tkVL8tSikmoqIQT+zPibhACw4iR79kUkoIq4liSj47eF4qHG6qkJLPp6KF7CNai7AtJJVte/IZZSzWrct4IYQylpgmTZdwP2OK32csEAzgGnWPORJ/TEL4tH2R8UXgcZq8TqmMbcU/02ONnR+TUfoz8cN2e/7Yvo+miSpgnpyavp7DKHxvJvpF3jdqnfh7ANA1Lvt9I3GCMWgjjZPWjYWNBpHJqZAMguNxZ+MQKTYPrSNbmhJyUIFJ+8+Vl8EMPUxS6gQmfXZbPTASeWJQ23mPJAopPyBePQZCQLx6gtRvMVxuELY9/LZH2PYI/QB/uUWKEcOlKKcGhD4ghoQ40DJJxYAZ40RGxsA6A+MMXOtIIdUVlVSzppxSzcUarmsxXG7h2gbNRY9mvYVpOxh/DtN0sDEi+YEGRWyYrBie7PTFRdmMruQkDHkZxCzD4AgbuzCgHa0Th0oNCEQtNTfSEude8shkqSc7pkNIuBoChsjLEHE1RAwxYtMHPNl69D5g0wf0Pipyit4PISJ6UltEVl0kZv7nEp4LOWCMgbEGrrEwhpZdSwbq0bqBswavrBt0jcWjdYuusXhl3eCsdVg1FkCTQwpzrLEZyzBFSaBnFAX6OhzLLTUzJqy4JuZmpJO6Z7Kij1/PhZvK38Bk6dPeYwhjZV+RM8sDln5Dxgea0JQcZatgcy6k1hmcNYXdcZQ5cWwLcj4pD+MHUvdEj3T1lGYfLx9ngh5MSu0RKygzhnu58ni2znbrovDs1jBtR85H25U8eE5yJrncztKOWinlY0IfIt/j5Z6fEixC7gHAblN6/YcvezwyRSUlisVVE9Fag/PWoXUR561DYoLFGiDx7JU4eg6ADnE0YSAyandFbXf1hIg8ntzA0Jd8hSon4VL76ZwQWSHbUftBwiLPgZwjogHlgYjt6BpL2w1R2i5h4/fbTvLt6ZnLuX43VZc1zmCIjnKXmRZAzLkTDGgQJeGQNLhSyeOh1FAJ1wo9fkizgc8D8gzYU5jwZ9r5z46/DoeR5/RMdeNTJpzyeztWZAs55YwdPfM1MSX5zaYKFCH36TXhWD6+uTLiS5CQXqA8Yx2xUbn/gsMrNAETgcWqtxpz10L/Vj5PPlZZeyoZMqccS6rN7qp95Ks6ca/YIKM+sxz6JypVff4H+yQw3y9PIEhpJ3bcJ4HZfml1MmfVJ/P5YXwtTlFLVVwTUzujw+Jz8RCaNIuenq8jMoqJp7RTE2o8VgnbHY3tB4/Y+yIsGDxCP1AaFiUuiIqs0iRU6EUhRdc7hZhfl0OfkPkqfM9y5y7he/LejIQKALJYIQsZugaubUYRNhJVY9sN+5FFVW2GgX7bcyL0bg20A8DjCNN0dG/wOIJIWcspAeR+Lur1lO0FcgQLqf5TGWuoCUN5LcqokWCAx7l9ILJJSCgRDGhyakpG9ewz9TLeY3+N2p5tm7zXExgTI2omgwhjSkU9+cw18swyI+HBmJSyWTFVBAoOnUTFWIOz1mUFVR6zNzRBF9gurhqysysWLdD4SSb36HVOxaHGTg/VsXuYpNQEc7Mis5WNgKL80Q8lUUl5Ck1J/baE7/kB/vFrCL1H/+QK/nIDv+0xXG4RB4/+8QYxJAyXPUIvpBTlk/JbkX8Kmz7D9DMh5TpyPpp1A+MMmnUD1zm4zqK76GCcQXu1hm0btBdrNOsOftujvVjDrTu0MSDl8I6WQjgkwXqWapJaitqjGMQk7LhVZJQuH87bHVJIyT0w6xhMr8MhUlHaxVAIUhAHkB0UMzkGcWS0U7/zZPB2PmYnS5z8pzuPqz7g6XbApg/82qP3EZc7jxQThp1H8BEhRPghIsUEz0logqf3c6cg4xfXWCKlXFk2Hc1yXK0aGGvw9IzIqDeeB6wai03f4E3nHc64H4iBQmMxxAgbgcY6ZspLOfX9KnvIavNR2EX+/OHFGL+IWBpq6m4xcp6SPAyLcxBRCALpu6KIuhqIFHjah/wA1w/kKRr1YOxcCQV91DlYSw+tVSakaBkoQJ3CQRPGA77A4cyBFVHbS6ShJ2JFwpj7LdJAJD0ABF7KgG1upk+k5umcSHTTrWHPLortkiIOKSKFlicS2lE7TkmVjQ+4Guhez/c8E9AxJWyGsCf17reKlHq6w9NY2lAGCuetzYrFVWMREnDeJpw1DgOb0BANjFUjABkgBw7fC9x2fkC8fJyfK6I4i1dXWVWrB8DSjtJ2AODWNJvZrDuaiGBCyqx4MNl2RFi1Ccl5nuUs1zRy/9QhzkJIXQ0ROx/wpKcw56d9yAPFPsSRXH6KlVKESjhyjIkHWPKsEWKqME3T/HhaLXVK6PEDHnfdOm7aDjqPlDj/RvYnfRmACSpcZiSrOUACGDXrl8ddESyNZGWkLSQAiJjK4w2ejJJZYWtK6PIUOqfJXIEDTbSIbxhm7LO0iUYOy7OcRDaUvinhHUSmplllUISZJUunhNQhpfN40ul4TiNR4dIxqHUTsk7u52Pto7fZO56F9hHyTtomSUgflscguf9N+qQzqk9qMkrnFIJS0xyamKazU+8l95DJ/TKxOsSwY24wniA1ZlnFdwxy7SqugamdieN+YCJXR/dDUSHrtCvar+sHhMET8cRpV8K2zylXYs+fqWcyPaNJZBD6wCqplH07UlclpZQ6LEbQKCF7ppBR1hZCSokWaAzlss8oZJWMrcaklINbd3viBnt+Tsr0rLgmIYMJgccS+8rrFLiQSvQ8STjNKaXsrRBSiexLAjLhJH5ZnvhSaVQuOULlikUBIhiQdTIG6TUhlVL2yUKg5VQ8QN2k2LkpUbUEIaCMLTmjjDXsKpssPrDWjP09KVSlxkXyl8mozuV0Liv5nAmpIdBYKajxEE04JwARzrps810Cq6nGz4eHHDXzUpBSAMrsHbA8aBIFUIoAJwA3MiBKbFDyrD8buH5LyqhND3+5wXBJ74cnVwh9wO7xDqGP6C97hD7Abz1CzwZsIHJqmoBNQ/KSNK3LZJSdkFKhj3DdOOZYJKHGWaQQyfAAdNy8NNYiJ/ILga72EiGkBoeYEFJTJYiGnr3WxNQeTpBX731NKaZysuG5XfMXs5Fixl6Hn4hTr9VRT7Yemz7gtc2AGBP6zYDgI4ZdQAgRwUf4ISD6YvCCV4md9w6XnH3btLDWwDYWzpGRagYH21jEEPM1FLZdiChnLXxM6ISMChHWAEOwaEuKGNrWqNlEg9FAU0s66+Dn2XFsNnr+O/IQHDsFZV1xoqaQSmXW0gwKYBFIQIcQU5YOy8z2XEJ9ITVbx+opW/JMOasG/fqHp/nljOXqLJT7yQA5r1GGtWRWJnL1FONoJlDIKNuJ2kcIFJnBY/WPVaS4HdujfGhGKzuQq1jS+dFrwGKdRN1DKkU9q9aEcubnrcNZh0zoieInK85Gf3b027PPfj3xYZtR2yXQpEFWsa6LOgoAgmozacNMSvGA06zWmdCj122uwgrryiTDNDegHJ5qR+orlsLsrEVrqdiCDI6iM7DGYYhx1IbAvqSdvs95zOxY3TBFEgeZk3lGIIfzyeubhPRVtdTNsEf6TZ/TmpBKYyJgbkyxlwxXFFOTXJUpUrEDAEQE8D0j/Ub6w+jQeCkVd3WICCBKqZK7JEZVqWkyYw7sj8tGlZas4UE/z0jzR62TYzF7hOAeyWeKsllIl+zkmNPJqVL5TYhbah+5VxJIkZTACqVEY7NcV246oaf2qceoc6XWT2knBLDtlSpTpXBESgBsyu2CxKXNjYT07sMCY0JKQspDqa426o/AbJ+cS8w896yj1BZx/zuTSVyi2CueG2aIcCoYEkeq7eR7SlreE0kFP1Cald4zCRU5DcuQ07AIGVVSs6S9yJfQh0xACSklSqnEURWFkCo9eypKMOrhqJVR9J4IDk1MOfYRhZSybSGnXOcQthaOFVIyoZVCQGxbNKFDCpFEDc7man0pBljH6bKtAziqJg08pkhFyKDVUdrfHk/EjoUCYodzOpWofbKQlVJ9oOiVnY/Y9D4TT0JGbfSSiajgI0XJjEgpZF8tE1SKiMpKKWXXpt1pbBr4ejDxJGR6VkrJdbIYkVLGGPQsTli3LpNSPZNSIaasqOp9xHnnEBo+psYiJsonFU3CYA2AmBOmh5jQ6Nk8vFx+3stDSgH7D6dpKJ+xXE6b42ljQLJbVUI05PCKuLlEePoEfttj97Gn8Nsddh9/Cn+5RX/ZY/uxLUIfsP04LfvLAcPWYxMSNiFiSEDPJEkfS5JY/WAXQsoZg856OAOsrUFnDc6cxapzsK3F+o0r2NaifzrAdQ6rjUd71sBvz9ENHs16Rftbd+isAxqSZQIYhX0gxf1ZGq2MUiF7uay6kn/Pt7lUzTEjYmrpuoxKvE5g5Jf07Klt8n7Lb4zJqTIDyDJRJqE2QwnpebolhdRrVz0vB7y2GeD7gN1mQAgR/cYjhIjdxiP6CN/3CP0GwfcIO5IAR99nUioqZ9LmMu0OtulgrIPjZM5NdwbXnaHpGrQrB9dYDLsA1xj4PmC1ajLz/2jd5EToazZa6yYipDKD7CxywrzSBmUwCWCUOJgv016k1usRt2G89T7mCFsdEqG/Qw/rUiVOZM4pEcEkyQwBVpzwDiRXzxDsyGnQ0CVoW6WUOm8paf6qIbKg4XBQkh3T92Twz0mISLZtLRAt4Dq6DyU5ZggwXA00WYsU10hNB8QAO6lOUw6OQ/eE3OJcUpBiDUKucBhftE0JQZs4E9aQbNmCjt9FEIkLICaHwRbniiTQlgdFNqusAGCj9vnKusX5OuXEk5Qo3uCslTaUggO2tJ81sGyPrDHj9jOW8jAEyudkV2dcPTUAktic36emRYoBTbdGigHuWPtJm1kLszqj69K0eSkJz5OUjlZtKIpTUjEkOK7M2FhSO1mTEJIrTmlj0UoYczre93T43nnr9pLth5hgrEHgp4SE8YkEPe09OCYEe7qb6qGzz6yXDFOeQxMjgkKYoCgReJlzy2mVilaqAHuVjvPeM0lryv1sbS6+AhvKZKGxSC4yMWXzjIvcEVrRI4SUhOv5SHZUHKBxBadS6EBXNp2D9K08S+3KTLkUkQAokb/lSQIDwIg9ABEvVMKblgZSKry81r+hVc1LfTtmpVE59ikRF/lZkmAQIpFTiJQPKZmiNEsok3jTghoAcll2/RuntJcud56T9PqYbYNjQ2mYAjJkBHKom1STspDKVjJm9FmtpyvB0ntfxpRaybfQF1NmDcvExyiUlPPwSWVTGEowLXZHcws6t1T+CYPFCaeK24NJKduhHOUy9KSQ8n1Jw7K5RAoRw9U2h+j5yw1FsyykXwlDgN8EDtcjMspvKJJirJgiRVSOpEiSSwn8vhC8c3ATWyPvO76pRKxgrJkopEwWLdjW5c9EzNCsW8o/fEHRNUPXoFmvYNsGcfBEToUA4xxijJybOJB/ECjfZ4qBxmpKxJF9ucm4TPwPiQIgVToVnRkC5UaVNCo7FcUSEvlmOx/xdDtkIqr3RFIJGdUPIYsEhIASscA+OZUyKRU8h1WKWENVZAQw8uE0tD8nS/kDANc4pY7CiIxqOlLa24bsYd86yvPFaVy6xmLDha5eWQc4a9CvG3SNQ99FnHUOHSulhORvufiVM0CbDGIyWQCyNPH3UPGwSaljYWBzsycL8clS2pzKhQ6sMCp/Ould6EM2XPQ6Yth69JEIKCGkNoEe9L16uIdRJ0psfBIAmwmqEIiCQQ+sAAwbDxcs2jO6XKEPZJxYfmqsReAk6LkSFqu+jCQh9gM9vG3DpcjpNaLHqMIVq8dkcFoSa+/PDs0RHLP3h+FRmbS9EGYLtcWnBJmE5iUZXPIATGSiUmI0D0SZjZ/GMveewvUkZ5SE6ImB82z4NCHl+w1dfyajfE/ubOR2piYLiFymHQAcG3MAsG2HwMbMg9h0gH4TsAiBnD6Jn+5UmNEQEjuKErZEs59FeTMmQuSdwXIojKAqCp4/ZCgr120/PIKvoQNC4uobsTgnMuAZlZ/NqiGu7GEUCcXKOj07vwgevBt+baxDspESY8ZA92rbsbMakKKFiS6/Hu/KFWKlIQm5zo1k2i5vo1U+U+XmaJ/KgTaZGKLZ+dbRrH3jDGwEZG6bqgxSm3mVOF3i+YXQk4oo2anidtTKxEWVFLdXXiq1mbGOSCNpM8/Ks8hFKU5pP+tIacUEHywlPs37n1GnHLzMpqjNohk7yUBRncWUct/T/U62FaVU46SvjUkPsUFCAKUjVNCpRNHrgVC6KU6V8AuJQt+ZtKd2RISQEhJgQTk1VUGL4sTo53qKVKDFFsffAEiG1IFZxZ6PrdjGHJqnFFIpzVd1EnJFVzqVYgfL7cZLtgGUj4/GZ5EfnJZneebKcpeQs7IcEVO8zPZLtzOQx0XSjtJ+TtsVbjfHyihR74SYYB0RUrCJlFNcIAVJdF1CMhUVmRB2uTLnpDiEtPuhNpNy5/ncreHnFtkPNGBnykKqB0s5nqlzpZPtZxJUlFFZMdWf3A+1Uqr0w1RClJgkRb4Wk2uKQkgdUu1XPGfoYivs+4jfJkudO0rnkIohKr8u5jC9YeOJeNr6ERkVONRd+3hTXw84rqpjryf7f/R9+qxLgZ6l1iDxpE4MdI+TKsvCcaig6xyMI8LMOAMbLfy2hw0RDYoKW+fIskAew4En2020vHSFgJIJCLt8NuS1ptEEbYyFsIoxKRuTcgJzyQ+l/8RH6zmXr5BRMUQEL6RULKSUIqPofcwiAgAIipQqxNTyuQQgK/2FjLKKlEqRxqyJySmjrg9A16uBQ7KAMeV3BkvPE1GX7zwp0DtOyaGTqLeOyKcSYTGeJHhZ8bBJqSVM486DZ1WUoyoDSpZrwgAT+lwRKedK6beUN6r3JcH51Rb90wHD5YD+skccIvpLYtaf+og+JmxjwiakPVJqjjEXI+QMbeOMycsh0euzmHAR4ijnFACVRL1Uh5CcLm7doQVg/DkQQ67SZ0FOEE30MTnVtPSQ50S5opJwjis9WTuaoQP2Byf78vOZa6JCWvLgy9nRZ3op7eRDyoMtiVEOETmfTEgJm4FmRp/0PjPw9BfxeDvgqg/46NMdeh/x2hXnktoM6HcefgjYXg6IrJAKPsJvL+H7DcJui9BvMhmVwlgppUP4NKMeWSmVYoD1XSY8XdPB2DfABQvXeERn4RpqrKe2JMh7tKbZgQtObLjzkUOHaMbRGKAPKbd3Apd3j8il10UVEfHyMekvMqbPi5TkoazDTWRGG6MwE43WUV6eVUPOgjhHcw6VdqJ0aVmp7tFaUk416nMhIvb8VjVzbFyDFDtyBrpV7uMSJiaEt2k6uhc0Ia5grM1VX3JZ4obCzXJuJFH5yEy1ECzKmchkUBLVj4GTNwDWyaLldmxtotAbIagbO2o7W1I34VHX4LxLhYhiUmrtqDTvOSumhLxyBlklZTAhp8Tptg1gfUkY2lK1VEnantuvaWFiRPIcFhnCfPsBRAIqtRmadhTKh6bh31VJ4kUNoPfHbSiJ7lsLJGczYScJzWNKeRCllRNz/W/adlLWGNCzxZRzR8J5iIQoeRFMMjAYJ+ysuF1MSSfdd8tcWSGesiol9LSOx1KZFGDlCoXP8HMRmJ1wyko/x+Szpf5q3EDvG1L5UUxaRHIxj0Ms278EZGXpEJFzysWIPPN+NZSwECkhPsRxoQOZ0BLoSktSaVOX7xbCde0S2wSqjGStRZv3oUKJLdA5ooA6JxOOoHZMEdj1kyqnvjh+S5OtWtnDSlJnGyq40FCBCLQdqzWAPtBEYx/4WZMoF5IU+KT7OuaCGjHOF9aQNiuFDsYGwFlVgVOVOheimlSYYPVkUZ4mZzknFzvZ8hzLZB63V+CiEdIHPbedvJeqa6oPIqpQI+5/1ISqD3LF1+TkuePJbqYIg46MZPCY5j3Ll8OU532exN2zjTXZ+SK0L3BdTIUGOfVKqbKXuEKwVEofLre5Srq/3CL0Hv5qixgi+qcD4hAwbDwik1JEQkX4TREgDCFlf058uwiM1mlfT4/r5tRSc0opHUHjDNDysusDR9Xw+jXlp23PSB1lW4t228C2DnEIsK1D2wc06zbn+ZRK8HYoSdM7nmi0AKQysmk7oou9L2lflCI2JRkjlAlxeU3+2rgKuiildlyQZuvjnkLqCef2lRy//RCyUGDYESnlB51ahfP99n4UxRJ4GYdh5Ktpny3GsWJKI49zgRERpdVStuX8XE0Hq6JjbNOi6SKsMWi6AGMNmtbBOQvbGDQDRcrESM8RHcZ33rlRlUBnDQJPstpAyvUhJBqvJSBxLqlnMS8vojDh5SSlGLpyjMx+mOiL06U/i/qBxq81k85/KcYcM5yXMZWBQEx7BmpqrASimiqGif46S8oI2d9ZTLDMhseB5KTBWTRrMjLGWWK9OZY4Dkwy+QFpGGA6fs1ssYnkLCXb0CAzz1wyaRQBGJ/lzNJxs0M305FP6ttKor+3Xi0TipGL4EGoGLw8M5qydD+XGBV1VJS/Ur1hxMiLOooVUpIQTxj2MCGegu8Rh35k+KbGTKSu8hpgw+d7NmItYuQEiZFnOUxJyhfjuMxpiGY0oyCvk2oHGQSl/MdKKlQy6nlAD69OfU7oEIq8LpXZ/TnMDX6BcY4PeT+qGHWLMM5R/obRD1JxBWNtJlZ0XilR+9DryTLv+OYBpuK4OlP6f8R+yAWw31ZlH/L5eKlVJ9cuFDAdfM+1AQ8GafOZ9nNK2iW7nVm397sLECXe0mdu4kw5YxBntj/UrWJMCJYUJeULCTamnCAaWJagy68lGntV3ANmmzkrVYoiZUQG8LNRlAkAZkmpBCKlUyalkRXaxjZIQY1L5DcnxyTkfgSp7RL/FD3/S94SHxKFjjAZtd0rJb7fl3OuPsntAYdotQ7JYjAJLYpiCiAb3jRG7YdVUZn8Bxx4ctT3ROoJoef7ovSRNBLq3PMl0AS9bQDT0zi26Uj5kxIpTQG4TFhxCXEkOiYuG2eNziEll6u0W2nHUgkLQK5utdR2zlK4NIBc6jxwOHXjSBXQOoMhGoDLnEteuQgipQVG/vL43BdSNAwjUlSqruUqpsCiCj8rTKPjSq+kyEu2gUlkd42JWUGlUU3Qi4+cPxfIPhwlJY/Fj+v9yJcLfYSulC4qKQnTE3JKCCn5myOjpktgPPaYpm+hJSCqeHktETSdNVSvpMwYQKJqbB9gnUVwpa/L6yApDTqXo2hkXVoHSr0QiPiP/QAr4XoxkHI7sD8TA7iML4Birw9eA6XskXaICcrHIRsiESG98tHk9cDEU/Did6cc1ULRLKlURp9EsUiqlUWlVBj7b3PiAnkdQeMsrZQSwQEtI6K1sCw6AAAPcA4qQOehou3Z3/MRaOxe9UDJNyVt40yZCIwxSeahlxovJyk1IptYDRU9kJoiFeck4IYHVlHKhHKZ89gPpexnToDHFRZEJshV9uTGG9RNKIYL0JLOseNaKBjDhiuhFxGRIecKEdiEhC4F2A3JR0ktNcB1papVVklte7gQMbQbmsHj3C9YRX4Yx1yhiZylpoRUyEyQehgnV5LBO0UaAcuKqT3o8L2Z8BK9PyGjdPLSmIh1l6WopHwklcmWQ+AkVln+NjlUL5Q45SHA9xyf3FOMsu8DQqA+EH1PfwO9FkIqTuSfKY4VDeI8FmMVcriOZdZe1sdITLmxibso9afeR0qG5yOctegDDeJ8SHmwnVCIzpQMy2WLUioiwTjMlgmtuF3MEUPTzyXfhCYOdZ/WjpQQURJ7H3lmSRyEEJHJSY25WWoJQztvqfreECmRdeqockebgIFtTHSmVLWMxQEwfoDxWyB6pKevUZ+/fJIr2SR5+HMJZsgDf1JthirIuRyyl/MitW3JKcWhgKNyxDJDzQMFIWBDQlZL9mwHtlxBTioW5hLDHMIjob3SdleX5fh++3KH8wTsl+YtFQxbZ3HeWqwbp647zWRZJokdUByo0MP4HT13dpTjIl09yTkLqXLhkJOw5gStR9oPXMJZ8nKRyqyDuXiFlufcdlqZGlvAFpI/q04DqU2GSNVthhhz9T2pWppDoCYqk2n/sxyiJDmkVlll5rI6z1qDNvFDDZQwPoGUUkKmJ0N93KRxxTL6RsUc5sz7KaF7ObWOkdxoGCsPmHiCJgOkb8dAuVr4/k9hPPG1RApkpSQrJGEdVd+0DkjrEn4DHhe4CKvHI3zv00y83PfUd1/besQEPOFKu1sf8mQU5SbZJ6Y0WV9KertcNWnV2JzfsZV8dY7tBCydpvDGbHsbVjJ0zqAxIBvqexqH+i2TUzuqGuZ3dM8zqZKTNM9dL8kNyjP0aBrAkUIqNStStjZrJNeiaTq4Zg2fwCG3gGOC2Ev4XhQyKmaFlLTl1pfEw8VpDHuEnrShbjsAo6pS6yZi3Vj41qFh75qUrMC6oeDHYMbzE7kvcr8zoYcZdtT/hisas28uqb24CuwppFRW6Do36n+wHsl1MMpHKDdJk8OWrKSUUFX4jnnpc2qpE772+sWzKKgYQj4AKH5cLARV5DA+v5GQvJgLVVGxKlFNRex4PLER5WDazxe8RE4By/mkpAdotZTOKSWRM/K6pH6J/D6iY3FEE5qcVD0G8gljiDDOIAVW+jApFThU0XIkRpJiK37gfFKhhPSBlVFqeQhiGspwsoy7IvspkopEKuxJVV8hpbbsk8UgOaSQFVLiq/k+ZH9NRATTvL9ZXBDCnv8mrw9hTiFlWGXpVDSMkFMpBhjfo+nOKJ2LXeVwvnHO2RLm17tC+E+L8QhCTLnQ1U3xIiqilvBykVI6ySFQpNDiKADIseS8PZJUbhClVORdRcQ4NmTEtKfMsNNPFGMEjGOJ5T0tsRdbHAG+e+U7JiuuaD9kpGSfklQv9AHGGYQ+olnHbGQMV40AgCYwA970FBriOFwEKAoH63KbpOBphj7H2xsi72aYccn1cO2OPpl5mpJbQkhJrgjJH5WXKEx7TJwzIiTleMY8Y9qHkkcqlw+NKq45xBznTN0gjcimOcnnEiFFn4myQRFS2DeC9FcYc/03HjDHfK6AUs9Ezs2SgGhSluVL0lebymfirlC7Xr+S1cuGuxoETqtSLsn1hRQQcmWIkWf5lfPPpOtm0AkiS6w9QGEpGo4doVyClkvLSpJEST67oogymMblPrSXXFrsZWAlRBgKkSLL3QaRiRWpaBNDybsHYFQdFCCZOAAuVWxJMdB0sEGSoyvSvO2Yw1b2W7VtSoWYlWSaErY7sDw8ppSLHPShOFbSdv22nPhHL3sMxmRHlCrwRcTkcihJG1MeMJrGURJjttlRS3n0ZAg79KnnktQcHh43l+N1gaoAAcj2e9p+xlnYtqGEmWsJ+wvUfqtITlc75AmH5Dx5em7cfgBKCBQTo1sfsA0RO3bwY0wjYk+3n8zgaaxYfi4VZlprMTQphz9FqwkpIvoGRDTWAaxmM2leLTXNh1eTnd8cJ7dXTn2QsmpHEp2byKkQ2BYgqpLrTKweCkFN3EdzCfK2Q5Rcc5JYOjgyVFblNJkeIlJ+/udJKX6/48knyR8ZYswJc3dqLDBFCaOIXLa7yTYXQA6lAGzO9whIfqhSac8ZtskGFGI27Hi5zUS18TukoSc7ymFGWWnG4Wfji0fkfA7bbbl6abeGbTukFJGCzomUkIxF4zoi3iI9/5MZ55WS8DxRSMnzRgrDUMUrn8uyAxi1iUBIqVUT2LmK2HDJ89AltqVAm2iyJDhgFajqZ2MtpnefUf1QSFEhREUZpZ9Jmdg/EkKePPU/sKoB1pEtbTvA+JzgHCnmEKV8HCeoeJdUzBV3jAWCIZNQMeYJnxRJLUV+nFTRC3lZ1FPk52kCStRRsm4TivDgGCk1vWt0b5IoGf0MnEbRlHVFEd5HVoQPtHep0h6GAAeH0HOOPk71kmwcjdGSpXO0IeQxl5mqiEKAaZQfmCIAts18FjKmldcSAZAvTxxHfGhfRwgpeZ9iUonLkRVSkjcqckRLFg34Pr8XUirGkKNbtGJqLvXK0rNqkZSKRa7UqO1oW84vbB2Cb7l6q4G1dD7OWaYlEgLiXlvoSJkQU1aavp7wcpFSGir+dS+MD8BIBzc1aHNxpq6U5gZAsklEcviShIoYjvkVY0FDXZFkznnFJXbYqPenn6ZITkWKmQJLM5lMgzC4QSWx4xvUtpyHIPoyIOQZIRNDJu+MJNa04+5i9Azmicert5uy6oGNvlZIidJE5+OhSnvjsD3JF6PloXN/YvBOha64oF8f2naOURcJKP2J0TOZMQfmQ6ymypiRAwzkSm4JpfJF5P546CwfEnP+PHHTAab+HpFRKZOtiU2BqHd2PuSYe3EOXtt66Mok4hjIDJNGmaW2cOwInHdU5WOIKStVSriwgTEWHStScpdSg3ATA0z0iLtNrmADPyBePs7EiuRqCJseMUYusxwyYS+wzsK2VA3Gtw1s18CtezTrDsn3sH6A8T2sECvdGmihKn51OZxCCFa5z4V8ljxyOx/xlJUST3c+qyRkll/abntVboCPPN5hE5Fn+mWWf1gldkKBVZOgZzQb62CZJJdyzuLIi+OO6El5u70kB/TqMdJui7i9RNpt4bc9wrYfJV6NwzBqP5tJKQe37mCthTvrYdsGzXoLnF3A+AEWNNlguIqfaVbUXtqxosn9rEQdYhq1385HPN35rNSTSjji2PcLDr2QoeLIrxqLR+umKPg4H09rDdY8mLbJYN2Q4oVsVWJyqswWix2reDbMzUUI0SdP8BIdMnbEjSYEmGQVm5B2GyJUtpdACIhaMTkhVkzDmZealgioti2z8wDQDLASjioFWKLk9ymIGIftiypayOghJLy2GXKuklJmvNz/c0oprfbZ9CErloWoAiisjyriUjXccWGKMSHVWpMVUvDbTEjZYYM09AiimtxejhT6SbWJOE46l58RtamQUusLmLaDPX8Ftu0QUwQaj9RGChtJEa1b0zmahCA2jM9bxhcl9wvd71d92KuGNW07YE4pZdkOkA3tvUOIbba7MlkSE7BzpPjoHBBskprY5WKr55CJgdRSYSD76QekzWVR7Q6s0svEnnoG6XxmTQs0LZLbsjK3zRVQzdrkiAEEnrCNDZItx6PzSuWgzjkllClOesWJWFJI5RzAYbKtej8zLjfOwYSZ/U2QssIojd4DyHkUNaZkU1Bj9LkULcA+ISXr7Gjb4i9K5IyM2/S0SUgJUOO5kIojn0LKru1SIu8U4siXzetjuPWJmalI4+j2k/ZOcf5aTEklUcXRdvtCgOlr/d0pUmQiT4kL5PsiOkhcuXC6zRJiTJShISUKoz6CbFONmfULNabPdynIM7vt0V9+/ngYpNQhYzW3fjrDwXHnlCFMOV48Ewhg1KkPwTqTSQXrDM1cxzBir8sfzUgVdns+9GHMiJvReqvWHYKot0iyyXHTbQM79OSoSCU+gKre8Gwn2EGVnFJQObdox+IhAAZ+VMUkY5IAEhh3/oTDZJSoo4SQksTQMpsvkv1xHqmYDZ6oS4YYWSK6P4Bamh3dPxUijUZklBsTU8vfm2fWLVccK1UcxmSUuSZDJI6lDIrkvY+UO4KIjzLonFYHGh335H0lq26Guce/kE9CRGWVFDBS920l5FS9/p3LnpM+jh2Dub4sjtMZl5k97xwerVt0jUU4bynRrjUYAuciMBSeFiKFH4j6iA66KHygC0AwkRKffhxhu8NwyYUgBg+/3ZG9YcWmzEZm8tVZWA4zbi7WsM6iuThDWHdoLzihsh8o34wfyNECgMYDqYWGtJ/Y0i0TKk/6QCRUiPjYFTmlr131e22XFT+7Ysc+/GSHy0xKWbyybtA1FjsfswqI2o4deWMoea/kb9NmRc/u8yx+IfSeIPVbhKsrDJdESk1LU0v70a6oDaX9LBN6Utq5vVijHTzcOTmxZrVG7NYU1remnDMm+kxM5eqlTOrFhNx+0nZP+4A+RHz8asj9rygm9pUSU4f0vPPoGourvs39ctVYrBuLKKo57n9DiFyZqzhxCfTI4XH33j12ior9tlVULzukrUSdYvTYSJNT0QP9Liskp6XXJYxP7MDoN/j+t11LCaa7NcwwwLQtEapMFtgVkGzPYWqelNopAlyhTWypj6SQ2jKpuvUBl3yff/yqV8SKz9WcQuKwj5mJKWO4pLc1WLcul+k+68rz/rxzWDcOgGVid9yO4kC0rJIShZTtNzDDFdBvEZ5+nAjqpx8Hhh6R2zDttrndtOI0H59SStq2yQUO7MWW1D5DT+TUozfBdJ7udc411TRrwBrsZExpxiopmeCTkD0hpJ5sPTbchiEmbKUcu4+z7WctlUN3hkipjsnp3sf8bOqDZXIaGNqIbTBYRYsuTRL25jxmTIoKIbXbFHt69TiHQItaKvYDdCRDvjZtQxPLTOahoX6HpkPiiqjGOqADj4fb0vcTK6dQxktzeQor7gkHKswa65B4vC6XxzgLTNJf3hRL5BOtK8tTq+/JM62E5u3vU9YLaXUMkutYvpijf0IE2v3tp7aGdkJEz109Rud8MVknkSP5+NT7dMCH21dAHX59qPqeJqYE0yiY6e8mzqeq9zFHlgLks14nz6s1yIVj8vGoq/MyDXceBik1xS3EHO/t0hUjBiYmrLUkLec/11n4raFym604XAY2GE5MBwwqHiZMBi7T91odJd+XygoSRzx63zoYx78/WRpry6y6tSrsY+amiFzCHSiVFKby5Kw0szyHDQAczjclpk6UNgPzyhMZ4AghldRrvY0cRSGz0uxMBjB2lvR7STxnjJSQ54R0ycA2HSInJjcuwjYdnDI45XT3Y5I1KWWbLqukbNPBtrR0mZyiAbDj6nvWGlilNqA/ei9JhyseBk6ptFOSm3OuHiFVVeiJqHueKKcqEyvTAXdvcjx6x8kTAeScKKFJlFvKlNxkQ4iIjdubzaWEu8ox9QOrIijMJGx3RKZs+71KNuJMaadAbJJlpwAQ1anLM3Zia9NuQzPVvqdwFclto2y9VgMSAV3CT4TUy/njWCUhjmkmpVLCsCn31NOtR3SFlAKISHGW2lIcU6mCOcQ0Ooa9S87HTCFNvVKV9IjbbSb0hNxLIWK42o6q4+j2y8RU28Bx1RwXQlbvGs4zBevoejmXSYWE/VwQQpDqfFtDZKeUFVLTiji6/00H5EJ6St8TtUSIDpuObCTlnaIk52K3Q6L37ZHBWQ09vmfoe04nN0+UdxEcbpZ8X/KicR5OXWJ9tEvupy7GHMoreX6S72lQ7QcirJpVGZPEOBa15/uuhOKLWloT0EJISb/d7XwuLQ4gK6ZLElqT329BDoDYAh0aHVlVNC1GYQxyXi7KBarUZVrhs9sQibIhtU+8fMzEflFMSsn2abED2zVwQkwPPrejqH0QA1K3hnUOyfYwcZWV8M42cJbGn9NbSZRSUcJ1WS0lbbfhkF7fBxVWU4g9Y2hcJdVOc9XNiRJNwv+GmOBYmdXalCch5fo6Ue2q8G2TYg5xlNxloo6SMD7K++ohIVsa8qxxIcKuWRg8sCJt6FkoHGB5PGxSKk5r7oc1q929gVXR04T/GsmYkpJFioUATGhLlUV6TlpLYw6jnqe0qUGK7EvxUv/ZYOCiKJhoGQw4GkbU00VYIOF0Og2LwzKBeaz6nvh/5TXnZ8zb0p9hgYRx5Ce6jsdV7BvS+ajxF7eBjgAyo/az2Xcc+caTa6Q/0HbFWYMhcoEUUzbShXfENjTWICjfx0z/kuFxJIW/BUQ1we8QxT5yFegUA5F9bYfI4bzymZynVj3Nn9q+wED2M418sdnPa0fbu8bxOdDzxLI/ZyxoAsQUJdSc7yf+n/ipzsh+Slsf47UkX+RDwsMkpZYwlXQKtHJKr2aVUIaEVqkb1nYcbhIjXNugWXPFvSEguIjuokVoHXDZo2PL05qEztLfNAmeICRdbaGQUmdcPrg1BmeODNG6sXAdGRpaOti2vLfKObHHHpwTxlhmR6EHgXbcZgZMTMlD2kweFEtV9aY/PaOQAspMQg7VU+SUJDzXiiBpP92WGntVyNQNb5kQisEihAibLJrOwfiIFBsENnDR9/CsbtLs+LSKgyb9xEiW8qBkqJruDK47g21adKuGSoNKmVBn0bSUSFlysnQjgqqcUzZqZt8YJTWom8MpiW8rng1p0r+BErpXlH/jMtxD4PwnA830y+z0bz/eovcRr10NJTm/xNX7onzRDsFV62Abi3XrsOlDJlMkce9F5ziJN83mn7WWbnWAQ6e0HfB5Vjr1JeSsf0wqn/7JJRMrPYbLHVepGXL1Gg3jTB4kdY+2cG2DOHi4dZeJLDd4CuuJEeb8FQCAjVRFNCm7LaouyYUkYXtPeo/HW48nW4+PPt3hqg/Udn3IiTMTKyUAwO/K/fDktQ36QER107kcvqMJlvPOobVUibR1BltvYZjUGxGROvSRVWbShsNrjxG2PXYff4r+8SXi4NE/vkIYPPqnNMMvxTQ0qKCFQbNuYZ1Be7GFW3do2ZGNvcda8nSt1vT75xuyWYEcXJMiomGVWUzcfiWP1NPe47IP+J2nPXof8NuPd7k883YIiJ6SjkpRBu3Qb1lh4hzZ0rPWjUKfzjqHcN5iiBYxuVFOGSCiTYYLMwDGpFEIX8XtwmA/dA8oiaWnNiCHobI9yMpJrQC8usqhqEuklDhBLofvdmhjoHw+1iK1Q1avmG4F4zoiVXPKBc7RmBLnkypKKSkxTuo+6sObPuC1zYCeK2f1Ow9JmAsQKSUTWqJcdo76cbuist0fiwnrln5XHKjN0ABwpbKktB9KGgbKGdXD+C3MsIPZPkW4eox0+QThtd8hteTjj8Nve/RPrhB7j+Fqk9WmYYGUcl2T1ZLt+Rls12D1ph6ubeA4dFeSfdvzQGGQbYTxZ0ADOOOy+l4QUsqFVGgyxOMp29HXrgZsh4Bh50dl2eU5NG0/AGhaB2OpnTeOQiBFLeWsyaqpmBLOuAjHeUhIbULkCd08VGGFnlQrlD4nz6J49RjwA4bLzV4Y9FQFIZMiri2h4w5A8hROKjkNIwCzNpActCl6akchxhIl3hcTbQEElBC+Kp66JqYCgwOCg+yvpUgkVKAJcmN4nG5tDnFF2+aQYdv2SCHCDg0c55WSsYfriKhKISE4m0P4rKPnsLEG663nvm5z0aohyf2ecoQMoFVNJucFFsypq8YV+MaElBYn6NdrJiiyr7huMhFFS/IPSVXpso9o26bYkEzUsYpdCtGoUFcoMiYdUKcBY3/EqnOxlp7tJc3NmIihsWkYTQj2wWZFVAyiWOLr0tB1QNfAo5BHUYQCLB6wTE4FFW43l+R8jpSaVuAbkVIt5aJzyseT6uoiPHDdGfnijYXlMaVz9L7pqL3lWaN9vtXktRSNyYViVN+wygeUOCVjDiumHkpUzMtFSt0UchMCNMtsHS2dhQ10M8tMi3WGbngZrLDj5wYH9AFnKtwNVjPkZkJMjY2RVkZZIBNSnTVERGVjQ4anzKAXlZQoEDKh5saE22g5hzwYxcE4iVm1lHx/gZhaIkuuk99pCdPy5QBm2OcSoxttceSdK2El0YtElAyPzYarJC3Pxx3D6O6ZY9PFUNFfC9dY2KaopOS1Nk6i0HDKEF3HeEy3rWTU7eLU3rpHVmCaZ2o+DxrNUPvRLHVgQkDCJkLup2k0EwMANiZsUUL6dBlvUcSEmWPTyKE7HOYruV/iwDP5w0AOAOeUGjY0O+03gcNPxg96SrTp4LoEvzFIIcF2kteuQexp1j/5ngaSkhNkkuQ8k9mqFLy0YUyleotWSvVcscVz6InnhKC+L3bKDwmuj9mx2rDqTCf2baxhNYGjkJdYjmWaB2VE8Musvu9zxZ/QD1kd4bcDJVcdQq4ANHWoYnAjFWx+5rQN4jAgDEzyuaFcrygKgzQh9caTAEGpzXqlkliqhKMLQ+jKMsYYpIbWbQB03L4bdrCF1JOwwSnmFbRA4oHWXDhfxbPDiNpgCSrpeVL5oiT3UfKsjpKEwqxUCb3kRYplfKISxBpnqRALQCG7QN63qFWWnNOYioMXle2UvGc5/5Hn6sieS4inEno2ylUSVO62ZOCCECzzeSkl3+V8eyITepKLK6slpbhBX/LJhW2P0A8IG1KfJs7NR8c1VkumSMVsmtBl1Rnl8Yuw56QuTb6nNA2+52NwtEwNjNkf++lJvml58hApqXBuRzUpogv9CCmVJ0iiyaXpBy6SIIRUqTCFTGrJtUwTcl+/NimR4xkDJTOXZxLb0qwyG3zOa6hDyAHABFbkRioOZLnfSXJ5wzlYpzZz9jqD7p3bGL9WXBO5MuJUUEB+WxLVlHWADbA8xtf+kXUWKfCzlAtHicpIKqy7zsIEw0nEI1xMWTyAqAmo/UMsSqnxuimuQ0q1o3X0HSKBzUiwIKop15G/6jqbSdniKxY/0eT2ssBMZE3iY0wnPICleaZbimKKyJR9v2zVWGyEqHIWsUmch4kmriS6hO63iJgMXOMQPPtb8lxSVdCj7+FYIRXtOOLlWqSUSt/iuEBC8fE6JqC6HBlDyn8D58Z20Tmbw5xztMySn8qChCJGgPIHVdheVkztXxvzQAdMLx0pNVI/caU9GA4XyZXl7EjdYxwlQJSqHKajxJDtxQ7e9WiHMzZiLAHupFISxZCGIcB1DqEPaDYe51y1YevjwWoMc4ZIDE7TMgHVObRnDWxreenQPWrhWofuUUfJb9sGzcWaZ4E6VnTRevB5ZQWYYsBPSdA2IprU61yV70SVVN4dL0k5osPzMPt6CfkGFWlkSmgtz7xHg5Ak9KaBs+PKO72PuATdtMTEEyHlHKmnfGsRQovWnyH4Ur0hxbCXRI+aoEg9rZZvduscmte0Fq6xaFcNnLN5NnZ11uKsdXi0bvAK57CQnDaSi6V1ijG3pcoPwHml+L3M5hpWU4lJepim6eVAVk/xDH9KnKCfCSJKMh3xmHN4iMrno097yjd12cMPAX6Io/AJDccElPStprWIPtIMjaX8Hmddg7CmML6rIZBaJVKBtpGDpXLwSa6OuLnEcLmBv9xi9/En8JekmNo93sJvPXaPSSk1XA45n0GZcSyDPeMMVm9Y0SAxJLRnff7ZLkY0a37gb68AgAYaM8QUtWHMpcxFZfbxqx5PeYZ/0wc85bYbdiGTejFEcmRVTqnt5Q4+JLiGVIt+COh50mHDuVF6TwnQJWk3Vfg0SMmOc+blHDyhhOr0W0TOI9U/vsTu408xPLnCsPHYfmyL0Af0lz3N1g5htv2ss2gvSCkVh4Bm3YxCJV3boAkR5vySHP1+S8+xbt+5TwlctbAkiH+6I3XEx7ntPnrZw/cBu81AfW8IWWUy1/9EZdbsPJqWlCRPt9TGr6yLMwq0WDfssLbsjDZAMpLXzGRC91RCfUILViicOi7NimkhgyX0jNUqJVyv5JmLG1L7+cttDuf1TJJM80oJseLOaHziRCG57tByFTnTc36kdU8hfMErYppnxMH3P9vOXYi47H1WRsqyH6jv9huPEGJW+wgpLX1YyHxjDZqWJohSbDPhGphIkb+dECuKmBKCQpwIM1C4nvE7GL9FvHqC+OTjCE8+hvDxj2K42mL7O4/htzvsPvaUiP3LHZegJ3I654UBxk5mS+PB9oLyyvntDs16hRQj2nMatyJGWAB2fU7fDwNgLFzbwVlW16rplcCEviSEf7IltdRmM8APAdurAdGL2oyI/WWlFI1zQmi40hQRgKKcDDHhydYjdAmvrBxaHzG0TFJNh5ApAsGX/icKPc7DJQopCYUOXHRjmlPKOovATnkcBrjB0/jYWbj1Cmm3pZ9rWiL2hhbGtaTCSZHUUqnlKII4jq44dE+Z08ayFctIxpJrIZfTpOJvWAC2QQIoZJUntCSUnZZUrTaHwHIqFgCwLU2YuHXM1cyF3Ak9+XQx0LoUEpoN9f/QU0EFqcgHSDQMHeahauxzKPmHi0/YmjE5pX3Ddt0wETVWR7nOolm3MM6gu6DCMt1Fx3koi6/o1qviK3YN7HpN9rfpSDWoq3tKMSz+SxNCMPsiqbyWv9ZZtJYUDnkZE9bSNl2i0L3Y5kI0O2Vve2exsxQSLASw+GhNKxMODiG0SDHB9WezvlqKAXEYF4+YS3quMc0hXMgpy6F5E3KKRQfyHLHWZMKwaR0cR8c0HM1wsWrgrMEj9vXI91P+n7NYOfL91uL/8V+jiEoLo4gplEiaw13uhcbDIaUOyjotzKF0cnPMurw3tjDAwhZn4qYwzLZtKZRvIGegPQsIbMiMM1nqCQDB0Q0jss85KaeGNj4NO29Zjtk5NOsmh2+UzxzJMFvKM2A4Bljk8fI+SzGdK+d3avuI8U8RmJlluwvoEL+l2cg5FEljycMkoTciEZXcEADQe4vU0A84Z2E5t4FcQ+MjIjPcKbosA7UHjNk4wTmFslhjSrgek1PWlvfdYtjeWCkl52QAzM1tWyYXjsx7H2TPX1Q550OHhJ8C0q/LDLGUyZVcHjuZ6VcKqejTSO0TJqOcrJgygdUqVEbXeKNmqQN23pLah/P6yD22N3BORSGVhgGIsaikegovCYOH35JKath4pJDyMvKMOkDOnmV5vOsc/NbDtQ5+O8A6A7/tSUrPCgupnmSkosqElJKcMjLYk2qFoo7I1fZE2RMKoTJSTKjEp76PgCvJe11j4EGV/dyoDWNuP5lkkOPJ7cbLFEitJIoSabs4SIhOyCqpMAT4rc+hj3PtZ1zMA1EKj/RozuhaOB0yFSMQQr5+I0IviYKv9EPpCzqXTO9jDhcNoaj0JJfMUv8DyE55kLoKHbJy7RH3v5xoXX7baAUcSiXIilvF1LafPIkqSfJFfRcjV4pj8inErFCRPk5KKT9S/KUwyV/iHGLv6T3f99D3/wk5Q+dKi5MidKzu8UOA72NRSvF9STPo5XlNKh8KPXPOEoFtDfoQR5NaAHI/1u1rpJ11cnhfCD34gci7TQ+/3SFsSCnltwP6yz4rTf2Ww/cUKQUATWhg+xKa1qwjwoYnSHsPb3u4nG+pB7yHsYXYGx2jasOo2lHOMaSUC+dET+04UpsdaMMUIywrpUSpFpiQyvtXqrM5SE7DnBtPqhKKetcXhdTo2TQTvpcC97lgEVglEvj6mp4qvxprc//eS2txIgml+8J1xq6ve1w3RzCTI9lGsA8nvk6yDsZGqvAJIhstALPtc0qWnJtxaPKkDuCRguRRYl+OJ4mSk3AyetZZFh64IOF7RokPkAkqiZA5hFxABePIGVnO+YbGGbRnhZwSH1FC/bNiSsL1xFfs2pz034mfyGTsWLhAfnAypog4GMcUUxblHsgKKRYOiJ/mbMnhKT5a1xRVv9jZIViYmBADEdy2SRMlkwH6QMfUNXu+WuR0K8GWaJeS+P1wcbOcU2tUpGpaxKotOYGdUkBxWhYhqVzDxWpYZFDOu4TtiWKsyyQUFYWQtC06v7C+BFqkcCpeZF/v4ZBSx2Atj2rVQ8TaXEoc1hUSyjrAUZw4MfENMcUi4/XMrMcA64c84Jdks3HwsO0VUoxozrYIfYR/NOTwi+JgkBM5VQ+MDputUE5UZ002KpoBlxky4wzaizWXBO+Y7W7Rnq9hnM2KKXt+Tsy3VGOS2UhXZhBM21FbaKMjSQPl9cQg3TWskfw29DolNmogRZBUNGydzcZ+7SyCLYlzAdpOyrnrECZd6lkc2cvOIcWEdhWU5D/xQCzmaj16dnCuCoSEBIpkUzPnlNTc7jHlbzxvcwn1R2t6/YZ1i9YZnLcODS9JMUWzsdaUOGJStyYYcCJhUz7XpUEfOnv+omIq3z80/MiheokqRPpJYvOnPEP9dOux25DqqN94Uu7sOC9SjPlBKxDJcNNFNK3NKoDgI17jfv7KuoGzFluvkluHiEbkzhIPxwPxpJMZ99vsQA2XW/irLXaPd9g9ptn93eM+270YU541BArh3rJNowEgDxBCYrK9w+AshkuasXZDX2Y9dT5AI8QKhc+JWmoIFGqXZ/h33GaskhC1T/QRvqe285syCN5ebtCB7ktpu6Z12DUDgo94sm7gY8Ib+4BHqwY7zgUWncjJlZxaco9IkvOBE8JvewxXm5zkfPd4h/7pgO3HRSk1jGZgdft1bFekvYCSZ0rCtocLnu3vt4jWwnKJ+bnBPuXkieME56r/bYaAnttwezmMlXpH+l8MkWycJRXqa46c0fPOwVmLTRdw3lKFriFGWOsQ4umqKOqr6cFK019E5AGqCtUzSUKZOBRN2wNONu05fNdfbnLBA1FK+e2kj0gIWohwPLEnioW43cJ2pCyEtUQ+SG42CSVGIVUT6N733H+vVMjz062H7ykP0m7jMew8fB+x29Dseeg3oxn0UcJcT3kfASA01I8BwPcB/WocGjiFyRNHpEwyOrn59grx8gnC0yfon1zBX26w+9hThG2Pq9+5ROgjdo938BuPwEopIfdz27HD6VqH5qxB6COaNSs9tqyOvzgjtal1VMl0ewnrHEzgSpyYD/PQIXvyJ5Mg/Y5sQfSJl8PRNhSnselsHgsBwFNRn/aeEyFLPqs4qlI7eqRm5S6Tbaw+DUzq+W1RTAUOKZ9TSsWBHHFNVknoo+ME+2SvOWIieqrCFyOMicCBkL6qiLpl6OgLafOcQ8rAQPy4BrCeUtzahionghTWcI7IxoYqKyY/oAMQugah91TEivMEh8HnZXvBaQlChN94zpWp/bmowuxpHYCcRzNIzjp1/y4VYxLk1At8n1MTlMTlU7U55YgyOV+U2AXjLBNVNqugsjqqa0sUDVdAdufnxS9csVqqW5NKqlszOdUUH9Au+4KG/0S5YwA0NqF1BtZaDKHk3bIc1WGNwcADHSmK0PvAVXwddpx/rvcRm9aNqqeWCdpClouvJmr4xKkGogrXHvtwR0J0s7quKEEdT6rYht9LwvKm5IlyTclPKktrDc5al8UHNB4npZSzpeLzWetw3rrs/1mDrJRaM2HVWE50zyF+MtEgll1fIXm2P6TR0sMipbSRmjNcsh6hvJ5KD6cETL7h6EGOdiiVjPotjHVoL/o82AJQKiRxvoxmTRJPMVbtWYMoRiwkfkimvQS2+ZDZ8ADICeoy492WOOH2Yk2D/3WRZIqCy511+TOSXor8ksP3uCJcrqygWPC9drkObjCTdJNZJGOIaU+mhPA4YwAHDJGYrJbjj6M1iI5M5LpBDh3xnHdCwtxElSIqiK01lEdChfnkpS8zpEtlSY2V6gpcYSEbKZsT3Um5aS3bpNLJLc47ChFaN5RQmZLdCWNODLvhSh95bJm4bQwpX7R8VjAdiM4ZqheZOX+IyEokleRcwpKiIqYkdKL3gZ2rgJ6VPX6ITA6wcqXfIvo+J9sXSNJF4CL3TT9IYv6I3pishOk9ESpFpSXKrTGxkpUR7Ix6zn0iOaT8JtByS3+hD7gcYpa061LGzhicJQPHs4yui9mmNWvKrSS5pULrKceMzFhL8mXdtkCuFjUEqkY4cqg4D5JX7ThsBype0G+oXYZyvsPuEsZR/jeghbUk029ayYsUso0Q1YRWSswrzXjW3Q9UmbAvs/qe1WWiNAt9wHbjEVLCJpT9ShvKbCkue7jOob/s0YQGzTpgWHvYlq6NbRuqJNW0RVGgj8ciV7qS6y85Xza9z/ZQFHoStjfsAnzvEfoNOfYz/Y/ajsKHYkw5hK9dNdhYqrx11oVS/TBSaHtW7SWTVVzOFCWXGwUaVUzxLGb7ILGncqPlfD6cQ0r+Rrl8WEEp78VJkzET5UHT1WoplCo4i8ATfinG0X2fj0NBiP2Y+28hVLLKj/tu8BG+Z6Uf913fb0jlExSh4koFpxQDQnMOwMIPAcYaBL5HJG+VKLLEudETP1lhE0QhNWQixW97+MtNJvaHDRU48FuP4XLINiGw46WJaWsNmnUD1xUVpYQYtTFmQt+/cg673nK1T7LdCB5wMY8b9PhAzkHGRjmfFE/MRVaXhhDz8+dYGxrr4BvOr+kCj4PCyEb3PuZrKEqtveoGWnkaRXkaKY9UVugNRb3LqjNpGwAwjqp1OVbDBGthWw8PoO3JXiVPSfaTHygf1zSv2SS/FUDjpcBjr5syUjS9WnEMWRW14P8lDuMzKZLv4wcaC3druqYr9udigLMOxtK9In6c7T1yjrZNn4UHriVb5jqqFCyqKZ37Ufw6ER7oBN3i782JETS0MIHeF3HCiJiydo+UsiJgUJEzxmkfcZX9wpGPqMUJqxK6Z9ouJ4uX0L1km7FiSgsX5ByMpBVJWQkVIvkuCJEFApZS3En4XkycD9hl1WvHOXU3vc/qIZ2LrneWFOw8gdhk5aYUYGDiKQkplUaiAgAHhQUaWf2tSCmdE0ryRAGkrqfk8WqpVFHyd9Y5NJZSatD7Bitev26IjMp+ny0he9n/M4XUk+fNXI6pQ1X3XnRf72GRUlMsyT6ZJCEVlKW44xRpyaSMxMYajkk2bZeTbZoQAOtgL96Qkyq2LedC4NlBSX7uzjoyWMyux5GEPbDREinzmKHVSRiJDbeKFW+y3L1Zc/K0rkgwRSkl67KhWV8A1sJ2a0AYb4kRllhrJqiSbWi2QYcxuqa04Slk0w2IrFPkzVLNhHgng2QSIigXgoMBbEILCxsT1g65Ek6uTmANWpv4t2yW164aW0J8fCkfrZN7buT9iHEvxk2gQ2yAMsgfJbIzZhSad8Z5fgpT3o6M0rqxWSElxmntSOIpxl4bHGcMjMrHkkvGGmQDli+VNl7XvmoVp+JY387lxFmxtFMD9a0iUohY4eX2kp2BgMA5MPLvtUOe4U/dGYw1pcrcUJJ1S78e+C/G5fF0ijoenwb/NOingT/9ETlVCJWIQYXWCTpL6zpr0G45L8nWUx6HIYxCMOzg6Xd9D8zMZHEmOyL1JK+DItw2PYeaqbCdYUuz+8H3CP2WEuH25Q6I/Ra+8XBM4shDXodA6t/QyeL3jlCeR3wO0n6BE5tTG/rRX+gDNiGijwlDQs5TAZA9GxJVdHUGcCFlpRmF/7U5JDDm0JVy7eycUipxFBb3g5GzONv/iJAatk9pID6MSam0CjB8rsAF9bedR5MoP9fWljbceVUKPo4rmOlr7KqFuheMVFKYfz0KRVV/mpAqfXvIlTg1Ykh5bGOcVWSUo77rLKzvkXy7n2xaKgLLvpK6/1LK96UQ7qOwvSHA9/3Idur7Y6+6UgxEsHYNVefl8LN+REzNKN5Jv5HD9kxKlHtr6KkK59BnJZnf9uif9pmM8luP7Ws7eh/IjgL7uUfPQiL7KePIkDCc0WRp+wqFJoVtX8gwXpqUSO2WIh/n+N7KCl6xBRJyNwrfJUIv+v5oG0pVKqCj1AWdgwsmE+BiS4eQJtdSH1QhpExKo4IRicPJxa7q8NE85pbv56IbLVIYOK8UkRFChko4oCTaN3GGEFV5zYQMqWF6t4ilKnwz6xM4r5dtAEcEsFRIFDIKHI5p2o4iL5oByVrEfgvXtLAdjQPs5ZYiXjYdYowIa5rgIfLTZ9Vnfh9LvjcRHgDIxV20+ECUUktihNHpK0JK3md7ae3IP3Sdy/7gtKqpVNZz69Xe+pN9xKYFulUmpKidmyJemEBkIJkwMQYBCVLoq+OoFmuKjzaw+tuHlO8j7aOJQuqsCyOfTI+VR0UZFDk1jWxJym8rhNRpXRIoSjZJWA6UPK4iPMiFs1TaGPH1usbm1DFnrBgXcurRuoEzytdrXH59zopzqcQnbSuTC5LeZY6cGp3H8VN9ofDwSKk5IspQsllJiJdMnEg8I0xqctheEoMGUPwxgOQ6JUEkp8xai+QHWOeAEGC6K6py4ge0F2vEENFtz4vhCpGrIbFzIH+xxNfPQVdWyhURVKUE27aFnHLj0p6m7YjtZrmlsN45wbkw3xLK5yjmemRwJKTR2v1k8EJW7cUVP3swmK5eQuFlhhPcggsA0lxSQiFfEA2vIWKKbkTKVUIhIQYDy0aHkNA4SpTXOmLa141FiNjLQ0OGz4/IKZkRnf4tQRRYpdwptdFqREo1ucS8Y0ln6wxWzmLdkFxzLN9ko+RKaVBJqEqdh0qoJw7jk/A+LenUoXtzLPmLzpy/iNDdIOfCObJ9gsrhkYQcIpWKqKQo3EQtmVAZtk8RdjRTvRc+5UkpVZI3PoJzdG/5nuzcpidZ9FUf8AnnotYqJaxzNb5J5Tiw0kcG/l6rpDaUoPspDwoee1K/9LHkWQCQq4gOCXBDRBcSmrWHsQb90wHdxQDblep08EPJizRThSuxwkaUEkIyb/pAYWm7kAmpfufht0/JoRqGrJiIuvre5hIwDWLbs5P1BgANl0AHtkNRSgmpQuFvlvOFTS6+zOwHqRA1ziVFbUcqqe3Go48Jl4EUZpsQR0lSdYJTgEMhL3sAHYa1R3M5UJ4pzpchVaXmKhiKei/3w1jySUkbispM9z9pP7+5hCQSHfVt38NylRs6/bNR/zPGYNMHzi3FIZfRjO6FhHkHL6ZinxKZ/2zL9GcVx3FSUykiqFThLERUGlgl1Q/ZJsQJKRCGAL+ZVOB0MSsCBKEl2xR7j2AtGs6HloZ+Np8cQKMBTeoLWbRjMsX3dN8XQrUnQpptpw49y8emEtemGBGaDsAZwsrBOVUcQf1N+yqpjwyQgqq6N+RCBwMTUsPllgpFXA7wG59DoDeXQyamt3FcGEcckT4mdMHgLOwQQ0TbU8GDGBJWrJQaLrcUsnNGv5vajnJbBUcEj5kf8o/OTRTiklvOk9Is9FvEoc8E/7QNreRyaTpuQ8A2Fr4PNMnoIzaW8iZ24kgqxdlsX2RSNIViT0WRN87L1dOzaesXoxIoxInsZANSyniAqkBahzQMMJ308x7oVqXqpBzPEVT10zPgWN5g9u9o21TWOxYdoIOEqZqG+zkn/U8hAE0LtzrjMFAiVrvVFogRcbuF5MKTyR3x46YVHYWs0mGiQnSmTETxUk2sTf0/XR1dBAol597YJyzV1ssfCRcKQWWFqGIfUcgpEiK0xQ9sW0gi+DkfEU0DuC77ycm6ka+Yo2v0pVOEFGxCAyqk01p6Tp81Dq09zUc7b10mrDfDfpEqqbK6lwfvgL+2t1QDdm1/7GRAkRPQ2/FSfDudA1jIJ/2+5DQ2Iz9w3Tg4C6wc55LipOatkFCc4NxaajtjwP4fVL7h0u46wflDzxv88EipY7AWSIpN53UpuTHbKwbQsVIqEdOOFjm3lGbaweRU2m2R/ADXtHAxwrVkzNy6GxkzoIT3idQZWDZMo6RqbHSsGCRmxCUxne3aw4aGyanRNtYBTSHkMjGnCKkpGTUipFT7TiswXEctJQ9tYcclLE9aRRRSmpgSxZRNZPBCNHBMTBkAcEBKBkAkw2wSbGSllDW5bDEZPsu5aEgSedamHJJz3jl4RUqJnP2QcRNMCSmgGK7rGiXNkOd8PEYMkhB03J5JPww4ppvbUZbPTh9WALc32Axp7IDrPlZkyBwPz+F6cRgyITUNnwLIKYhDh2AdrO8RQgunZozGfXj8+4fOK5fQlaS3LFWPA8nZMzmUMCKktFMFgJ0q+twZCq8IXNWGwpzJRsacQHcSxjODyDPsI4fKS9tJO0ZErr4i7RdjQAzlrqD1PLhsOhqchrIf3X4l0fGhRisDUqoCI45VzCHdlDsmoI9p9CdtqK9OH5FJPYAd8lylTxS5ahKElS3HQAqJ5f4XQtrrf3PhewJjHULTM0Ha5X25oO1oHP/+Ld1UPE1RgeUZ02NYCpkDsNenaEwTi7PG6gHdL+l7TKx0DhFFSSBO3WjiTiWhRQzTQ9iD3CaaTElMdEqxgNx/pxWZVOgZnc94PZGvCbEts+3TSampDbAGYyJYjSNJ3TPskXehJ1sq9/82UggvoEmp8e84k7AaIoIjG0yKU1GZcrtmYloVO+C8fNM2zOHCygZEaU9+Bum2CZP30oYRyMmFYxT15IqeF4lsgY12NIbK1zAlpKUnUSa7lV1V/Ub6XuQ/eaYARYGSgkVAyKFYUexk24yIA6ix+hxy1emKu8chtZQWHgBETKH4FyZ62NUZ261Q7Il1pASyjirkMpHqmpaWfiCbNlBIKIBZv076TOilqlsho6bElGCaw0hHygCFiJLXIkygw7aZgFryDwtRZYtYoSnLoz5i0xEhpSJopr7hXJJzCWGVCXCTWFBgKYLDGBljGqCxsBEHfbQhJgw2IiYwUcWpLuJYMLDkp2k77Wfs9tJrgZswN3O+XTNDTs35e7JNp3w5ayg/FED+n+VoGFlKuN4qF75SVRgVIZWJKZSwPYGcgplZ96LjYZJS09hihnSvQkYpSacog3Syc7hi9NR+Dccni1Ez6wsaMHHoh0iJKWdIT7N7no2TkgFjMsBaetgJIUXHXHI+QeTQHJpjmpYS+LGhyQaF43/3jJDjvFHZwMyE6k1zSk2NzySGeI+QegZMiSlMiChngGQAkwxskpl+ujlTKmWZSa0ANNbRTGpkNUBMXC0LOYfJEEouGvk+raNtAJTkeznfgVKSYOxsa+QqGpldR66YIEnXRYbZurIseaNMlnI6a9BYot1aDtlrJwZKGjElwLBiSgyXZs9zWKHZd9weiqF6qKBcUmoQHscqHx06tRnlkuK8SDxDLWqVFGjWevQb3VmptAme+e/XVAmNc7vo35FcVjmXx95Bx2zDcnn3rPYJOeRs2HpsQlH5iGJqmui8jwYhmayYCilhvfWwztK+hkDhP4OH7bmCXJwnViTvkORE0uXM+72wHQo785tLWvYbhB3nlNqVfQ7bp0i2QcPtKEoJzxV6/BDQc9ivbj9tfzKyzS/nIGFOdI4hhz36rc8hj6KSkJxS00TnfWS1hDE4CwlQ4X/tWTO6RhLCN62+Jxjl5FGDPcnHQ9UeY+l/u21uv8X+x46qPMv8cMbLCGsDJU9XfbC1tqi1mBhrLamr0kKV2orbwUmc1TSflNgDruwm1fW0WoryozFJOoTFwi4DwCXYKZdLzinlhzwheIiQjhGje18T0lRhU/JJeSakSnLusNsUAkqFnmXyPQaEFeUaCusVk1uUm2+papx2AHLVuOgRByoSEbfbnJQ7bHv0T4ccttdf9tj6iKc+4vKADaDQFoPeGso1t/NYh4juskUKCX7jYR0l/W7P16MQPhs9pGiEtWV2XWN0bpxHSsIghdgLHL4Xh0LyzYXvuaZD6DnHVe/RtDZfE+fGzqVcx9n+x+1ZKu8FVuntV4IdNvxs2kiIFe9zKH3OdQ6hDXCdz6pS4xwlvm57Fb4noc+SV8qMw/dmYI5IpGSyteIIDoTx5WgYgIQHBjDoyMczlny8FFkV2CHZHqbhPFMpUiVM7bfFSIo47leZQBafLsaxLzfj1+lJO6AQT1p8MCWnZk9bEVI6ckY+y/mAgbF/qF4ba3OVdamgN/IVl3xELnaV/UOd5ma6zlgk9hvz8RoAMKDHtsiXyVezMIjgQlUWaKLBCqZUUT7gowXlmxVl97hi6Ph98dOCGtvOCQkORbwINDk1VUrlJYfQ0bri79nsh5X34vNpcqp1Fs6wX2hp2VijwvOIAJPv0fri4+nKjdrPoyvyMPEwSSlgXuop67gS314Y35R84TxTiD4rpugB1JTZLmNgsCaDZR3ge6DtgIGqQ8EzYZRLmYeRQQOQZc65k4hRmzLl2egoAwOQEQGI4QcKEaUTl8+QUSNV1CRkUdjvg+F60qaMWUJq6fUEOv5eP781MTUN5QMwq5qS9w4U0kfKKTAzT0nGQ+KkylLdiSOSnKHZ+daWJJsD38VDJEdpJeVflcED9g2ZvJ0O8MRgaYMk8cYtfzZnlMq6YvCMUUntJqy4NRTKqEMdc8LB3I7zx7i0ruL2MFYjHX4I5rAJpc7JSpuhzEyLM6CRlJ0pfzHnXJH9a6XKsQGyzDDmmWheymA/hqKGGv8hrydQvwzJ5PUWZiSZLjlS9lUSlA9lf1A3d09K25V2DAf+ioOhy5uPlBK8b9nnKQOZcswygJ0MWidVeUobYvK6tF9ghVRZUnvqa6FnZ/WzZwmSx4XaUDn2TFjFlMbtMmkbjVH/C6XtJNloVO13ahtW3B/0Y8Bcw3EW9Z8oT2hd6ZP6fXJUbS+GBNuW9Xv7XCCjAezlHZraNrlfReGj+63se/qnfzcqQiLyZ2JT5HeWMHqW6hBokH2JsbSThP1IWCDlkisqSVFKylKIfCGnZNscZhdYtZrtCqs1Ztpy6ZEvz4WpPaWltonLbThq4xCUXRiHyMylQjhB2Dm+XiOnf9zn9DrjzKjPidLXalWp2n8dEr0AOEZMKfXUXjW+FGnEETllC1CKDzSgaJcQIDmnxAczrJBCDDBxTX1BhAbyHJ/x60wcj1mkjzrgtE6tofxB7QvKZ0v+oSah9l4LEWXHvmJO4wLsR84Ysxg5c8jHE78tISGZcYSLs6ygsjR2Yf3irI/WWlNIKfVaJnM1SQXM+2lagUnbaPtzvcui+MJCQCkCSMgnWa/9PYDOx3Lo3axIgcmoIiYYixEcR8CUSnvFx5N2L8fH6ybn8JB8vYdLSgGHFVNSgjhGuhn1dlr5Ezx9HjlpXopIXPEpBZ9nC8vMoSqTDOTP88NNP6ivIUUHUAwQUIylVkw5t08YaWIJoOTlQNnOjj8btYGsnyOhpsoo/dmcYTpBOTUlpvR6AIgweS5KnHhhf8WmxFQILVIpGEQr7HjZNiJlVQV9ZstnifYvn6VUlFW03zJ7J5dwSR01h2ywbHlf2HQzIpZGscF8vvJeyzOtGRslUUoRmSdGtyS+mzLnD9lIPRToB99Sb8kKm1hme2TmmMJNaKaaZqv7kgtFQs9mckoBoDwenkKnoqf8PtE7mqHmRJN7oajTgxTbJrZShebIMvQh/01Dz3QIWrlfEudEUo6WZQKGQ9giV7TR+fc0obPY3opo61khkUN4/Dh0J0xm+KMvtpZm/x2CdTD9BmG15hC0M1IKRB2+E0fkzTGkUIi90JdqPWEI2RnVuaS0UwoIiU5tuAmRVVMJzqR8HSQcKufAyARByNdU28x9opL+BlaFRG5LaqdStfBo/3MOtt9Q8mo/IFoDCUNNcaJqkUGlK30lFRciV+GreI5QSc618k/nUykho4nVURGR72uAiADjDNADxqWcwDd2lOA8cOLp1Db79/tc9TNGfq7HkvsxpULSECFa+u/0/p+qfADAtmw3h4FCUX0klU8aFzgJh5xNFb6XIuXgiiGWYg492QFRS25CsZubELFRic7FDoSsHLQAIpyxvI3FelNK0oc+5iqfYfBoBpooHZFkbvnZL8olCYFMMXHyYGoT3YZLSqmslvI924EeMbX52oQwztepicY9zk9d+zKmDrn/ZZUe9z0JidTV0KSCWU5EPQSk0IzI1JRJPEXkzVxjk8rUUjVNdwztT+hoFrBPYsQP42cb+3i5QEKKSOyjmbiiLwZfSHf5TI95VJjr1Kej1fN+3YiYnbMNp/h/2vcD5gkqvd0h/5A+KMsTfcSj+YSnOYfl+KDUUkhEpJjiq2U/LZF6it5f30cDruenjaNbxs27mMdOYZpfChiPSfYIKuXvjT6f+HxCOomfN/6OzmNVPhN1q/h/eV8oPiMw71c/NDxsUgooN8cCOWXoOV4+m5AtRm4+AIZvCkRPhk7vWx7qKTKLUQxY4qXIzRdzMyzlR5kSOjNEUDLzuZz2jJA2MLL+lO+dQkbNHevSugVoYip/Hch5poCinAJAYX0AYr4Ji4JKh/fRtsifJeXcpFSE14XcKsqsmD8zxQhCG7fx8S75ostxvYocUsaEvqNJqWJ8gH3jZFBUWKP2MYWcmpJb8r3pMVXcP6YkhiaKAOQZeY05lcri7PTMLLXsd6r00Q/2UzAijIJSD6TywNcJuvUgYKoGkm1kP2k0w03Hb2YGd/reHO1fnZeozHS7zLaR2r28T5NBppApS3mljiK3l57h3z/nabuNB1CJQ3ewt73sL6prUzY6cSIEmO2DWWkW5ttwdITTPqrVUqr9pr8l57wE/QkpZKvxehEw7Ws6XErUKPI6Lyc+1zSkJaqw10NqKY1DM95p0n/n1VJKbSNKVLtvA/LvpcP3/t6zdaKSlNcplDLycs9rMl8vAcnDZ7JCUqsqiXyxozxdM42RT2Tu+T/3DBAyWbdF+Wy+Dcs5Tp9Z5f6fYnoNZXJtvFL1Dc47OP69fdWUtq8xRDjYWUVVOaf588CRsL2Ke8ZSOJ/0VUP/TNKT7uLbqAgYUUpLepcFnw7AYb+OlVn58ObupSPP4lH6Fv296fPuQKRK4vY46utZvX7iI84JFZRfOEdI5cMA+3bgHMFMrpB7kpAMRbZkQmrGR2s51O+wjwbAHfbTppdgyWIfG8dNc0vJeY7ez6qUzPj9gs83/mxMPOnf12SUfGd2XwfP5uHg4ZNSggWyhFj0uLhdIV+6XII4Gx9xKoA9Q5R3t3cHHBwpnX7sBz5fNFZz+1KM+17FvOsSTNcgnw5hiRgRGzGaIRel1P7BzJBFZR5r2tL6Mk0HYdN9H/M5TyWlNKbnPCaKyruRkcvrxkZu9H1jqDrhwr7nfrvixYNW3yQVOiUYEywzA+gZAkFIgenvnESqzDhU9H5MKMT8ekyqyHoLjJypsu3k+FWC0IOHtXDo+6G1QrLEPadU1uffjgGAU04UqwNOUUItpNieDkTnzm9K6GmFWd4ybzNu25D2Hb299jswQxsVSZS/rwgper8flkfv5/uffp3bcSKrL+c+d72qoXpuWBjfjDAiV5XChDEXPpUihWjEEGGZGCik1dS23Dx8apqgew5jcnXBhmY72xYS/gCpMrOT8Xt1z+hCEbJfbSunobuy3kHb0JTt7t7svyKoS/je6eR0OQVtE4pSaY6Q1ttIBVgzVX1McFshvNqm6op7un0FMaRRGM40LL0c3AntNVHuVNwx5vwWRUwBGPt4Shk8yksXy/pD9m42hHnJJh7y924bS31uZv2t+IgnChO0mMCxJGg8oVSO5bZ8NPm9OVwjqOWZseTzzflcSz7fdD92tL58MOv/Hfi9h4qTSal7vM7PjlPIlwOfPahzfUGh29DgNHfj2uEae9ufuoOX6A6ueKkxl/tkbhvtCMxvs2/VdEz+IcjMtB7sT8mpXMFpSjZgTJgGRT7kUJvrBvnLvtNMTimda2BP+aSX40ffXJvJPvVy9NsnPCj2FCGTc55L9DttDconY/Lr8XEX1QUd52nk3hI0mUfLeWJ0fAwhO6TH2vH1DHN5efv7nFtnZkavc3LZnKQQFM4SPTD0QOhh+kt6v9kA/RbYbIHtDrjaALse2PUw/QAzePrzHtYHWFY62kwCJyAAligp2GBgPf2ZwQPbnvZtDb12O3q/2gB2BZgroKWwM+MjzC7A7gJwuUOz6WEud3BXV1jtPNb9BrYPCMMWbudh+ivY/gqh38ING6QY4YftiJQyyeYQnsY5WAus/A7WGayHLVZNxLrnvyag21q0dkCzcXChgW0DTGpgvKMEy7tL4PISZtjkNkubbW4r6z1sCDCDh4sRTYowMcLyX5sSQooI6pJZFpq1CbDRwEbARKAxFtYHmAFAP8CuqE1NP9A12u6AZkvXrAXQXAJDgtkGmD7AbjzcZodmN8BdXaLbBqx3G6DvEfoeGHrYoYfh0F0XesTQ5zaMnHNHt2PjGpgUsPJncNaiG3bodg0626DbJnTJYbUzcBjQbiwa4+FcgLUeJjiY4IDGAuESGPjYr0pbYtsD/QBstrnvYTfkvmdDAAJVNDRQKvpgyPfeGZiVhRlA33eG2qqxtH84arN2A/gEpAZwLeAtKW1aT0vb0CA3AUraUe6xJO2ibsV78JbTxcXNv3sH9ul5Y7nFtUF8IEq4pZO5dreafiEsvD4NWdlz7W9WvO5wgn16eZRSCnsKmsnnZuaz0fNkOlsN/dxZZnDnxt3XfQ6dyrzuM6WH1Tbls/E3j+330La3juuozPZmN6YzlAszIcdmRW5rRuSICu0kae6peb3mEv4fO5aKBwGdp0PeA8PeNofe07r9m9cqefAhWFWWWPZlFIOsq0FKmEn+7mRfTs/8OJv/bgJJMlmO05CcWc7LOnLuVFVCvdRYmt0v+9q3m6eQ6GZybqNzHuK4kqZsg/GzR7eZm86w8bWQNpQy0TeBUQk7jc5ZkT+X1/P9b9pXNebyM7ze8Ht+3yc/70O4Vbz5Hn5DehNnhcH/6x5+87Ygx3x2WzsM/DcAkAKYH+Pl/3P86zLYPwPwe27rmO4J2lF59NyO4sXH//nokxt/9+wtb7nFI6moqKgYY3t1dXSbl4aUmpmk4FhVkjrrfDtAIWfyDH8s0aky0Vwy92MvMdtcrOs0zvXUXER0POP3h+JTT41Npe+PY1GBNIpbHX+23z76t0OaJ62u7W/METyL4ZHjUEod902fp711OodCTmIYj3xvKW781LxgwDIZpGPbl2K+pwkHAUi1RCNVE43J65Jr9ttHZOV2nNjw4DFWvBBwXKGDkhgaGF1tBcXhJ1XKTEw/V+PUBIG1Zo8McNbMxsrvISfPHP/WlIwSAb1UhgIo15sYU1nvFImjX5f9nkamLB369Jw0uSJtA4/ZNjQqUajhyjbGupNyGJkFGn+aJ2Lu/ObaRNpw3H7jNrTg5JeTc95rvwNhNNIv5vqCfDYim9zx/qdf53acFHko5z5/vSoqKioqKioqKl5/ePCk1BIZBYwJqZASTCLH3piSQFuSroU0fg1w3o40JrfmCCsAuZKIfKaFOvP5MwjTwfixym0AciZ/a1J2isQfsVz9YJw8uziOQGkDajNKN5cTiAs5xcdoTMndZA1mM6jEdA1i6hAhdSIZtUgoqYqIQtLoyol72yS1DZBLwtLHhyso6jCVPXXApIri1LkDgJSraDSU6J3LsdIfk1SOQ2iYbDIqmeEeGSUVQ1hmYeyMqldi8eV1xb1i7l7XJNGcmik7+G6/wtHeNpNty+dcklaTAlklddqNa7iaKb0uhIgQJSHppWKloEmVsaJK9qOJLuP4HpkhPjRRr0kt3X7yp9tl2kbyfrH98jbjfTprTif1gHwOmqCaO+cx6ZRyW0o7TdVU+rWopPaIooXkqXOY64PWGsSZfnew/7nxUkhRfU2m7Tclp0b7Va9fBtLq//7/fvPW9zk/SWT2NphLoCrVXRsDen6EHmbogehh+qcwYYDZPEHqt4iPP4q4eYJ4+QThyWsYrra4+s2PImx7bD/6BH43oH/cY9h6roQm1flKBTTjDJp1g3bdoLlosXpljfZijfUnvgHN2Rrnn/yJMOeP0HziJ8O+6ZNgzt+AcPFJSO0al+4COx/x0V3A5S7gN5/2+PDVDr/ztMf/+vAlLncev/k7lxh2Hpev7dDvAnZPL9Fffhxht0W/eQ1x6BF6Cd8bV46zTYfm7ALOdWjP3wC7WmP9ypuxOmuwfrTCxRs6nJ11eOtbLnCxdvj//K5X8Khr8HvfsMKbzhq8sXN448qhGa7gHv8GzLCB/7//X8Snr2H3m/8H2w+/hs1HPo6nv/lx9I97PP6/jxE2AR97bYdtiHjsIy49Vd/bTqrvdXy9LhqLzhpcNBZvaCzWzuIT3riCO3N4w+95A7o3dHjl93wi1p/4Bqzf8kasPvlTYB+9Ec2nfBpSe4bwyu+Gb8/x2i7gtT7g4xuP//14h6e9x//z20/w+Mrj1Y9cYrcdcPm4x/bpDruNx/bJ7yDutthdfgwpBvjN5WL1PdetYdsO3dkb4VZrdBdvwurRBVbrBudv6NCuGnzymy9wsWrw/37LBd78qMNbzlf45EcdLlYOn7Ru0DqDi3AJM2zhLj+CdPUY4aO/hfDxDyNdPcXVb34UfrPF1Yc/jrDtsXuyhb8cMGw9/NaPwpkBUqe6zsK2Dt1Fi/aiRbNqsf7EV+DWHc4/+RPRnq/hXnkj7MUrsGevwL7hE2HPLhBXj5CsQ+oeAbZBajsk1wHGwicJ4y4T2wByxbH8hjGbUP7kO/3usfnwh5/3IexhKepF+2RA8d1y24+uC20TUtr7nvh6UuktpfI7Jb3Bvl9X/D+dxmBy7DcIWbd2PC7I62fU0tbuV3qzS5Xe1Dpd6W1aYCmLGCbvpwWUDNQzZeRLxYlPMv48b4OJjwagVLRP16uIOPURZb1AO+M3yLF3UmVElRA+r1uqiqgTyuvvi0DBiQiB6RnlGyaD0ff3BAjHipI9ADxoUuqYOkrUTyGW6j3GpFx2UQiooCufxEJGDZGWngmnISQMkUp3D1x2eAgpG7tS6l0M4PhhNT1mYJ/McayW0IbDWpMHkM4YtK5sQ58BrbWwlpYA0FgyJPI9eU2/kWYMEUbv5SaSlHXEdZRjEsjLk4ippTA6rVKaKprEiMwZNt4+bxO9Moz0Gp4MXfI9ECOVM+fSv0JCJT8gl7uW5KA5JwZvOzVm00Spc4aK1QJwjl87oGlpm6aldW1HhqzpyrqGjJAMfuBaXjY5n0EmqmIYt0uk3AcpGlJYWVWVQyforMk6bx1Wha4t3QpO3bdWkVJdY4k8agxcsHCNBbCGW60BgJyAMJ9E1nVn5BA0HWzTwTYt/1nYxsI1ZkSAFad0siMzecA5x86kzUvXufzXWYOQTHaaOrZTUkmqnC99Jn+tEDAt7cc6A9fZMfGlSh3PEVS0Ws7HlvaTEL7GwDYdXAx5OUrWm0o72qaDbUv7ydJYA+dsJlbkt7Sq7RiIoJG2s7COCCnXOnTWo+XRnrRhSGYUAumM4XYFzpwdtaVcB+MM7b9tYLumhFk6N1Zegp8napJD/7XOwljkfmO8435E7Reb7mj/c93ZqP8RIUVkV9fY/FtWhVm6/Kwxo+N82fAs+V6u9Ttzofxjhq98aPizFIHQAkNDz86GnnumSUg7B4QtYAKQPEzYwSTAnK1oH+sd7e8MNKvlHBI8/SQbAtPRvW3OGmDdwKwamPM1sOpgLs5g1h1wfgasV8DZml5fXACPHgHtGWDPkHxEcgHRBaTUwCeHmFrEywRvPXarBG8DdjuHPnlsV0AfPIJpMMSA4HoE47BESrn2DK7pEM4ekU04O0daOdjVCs1qBbtq0a/PcHbewZ+dI3QN4vka6axBWjlgxXmlwhNgsDAXFzAmwD09h9n0MFdb2PMOxifY8xWi9eguPXoY2GTQwKIxEY7vV8eDxUwYO4PGkQ1pG4fOGZiLDnbdwD5awZ63MBdrmEdncI/OYV55BHN+Qe3YngOvvAK4NYILSE1ANB5hcPDOY1gHBHgM64RdGtCvHLaDxYCA7W6DaBv0fkDwPY2nXTtPSq2oDeP6EdxqjXT2BqDrkFYNmrM1sGrgz84R1w3ixQXieYd00SGer2DWDmnd8OwGAN+A4hQHmN0FsH0MIMCcr4CUYM/XiNbC9AGIBsZYcr9jBPqY2ywBwLoBnEXqWmDVAusOOFvBrDuYszX1xfMz4Pyc/h5dAKszYHVOz6HVOZJrgaYDXEc2lcmPQzmlNA+V0mTwXzZ7MXBP9ukUnEJGaSJKfLwsLkCCZ9szRCKdQgRvS35cjMCQyJ+LMWWfTd4PfBCD5GvkQgNSlIOECPLcniGprpnxAyDfDFDPaOUHTicSxbeb+ofiE2ofsZVtDviIzhTSyRn5jPdjyzpIhE52/tjvCryMoayLXhqdBQKJnispApj4aKH4ZpF9siwU0L7ZxE9LSjiwJCbYW38dLPh28tmsvwfANB2tb1oez1raxmk/UPl/lskrk9j/YwGDA2CBZEmYwIO0HGFj5qJjHnAKlwdLSs2R0LJODJaw5kX9RGRMRCFbMnGlCKkQC3MeYsLOU+leWQ4xMSmFsoyRjVQhpMQoaaZ9CZrxdhYjYqpl77G1FtYAbbR7BFVwJKyJ3PeiIycjJao6YpIBrDSQQTKJSnSCDF5MpW3kPVBYdIAeBqKcknGtVk4dJKZOIaT0urn3U0IqeDJ0zLAbIaIirU9DT4QTgNRvaTn0IyIqE1IhlG0lgacYOTaUAA6Wd91XQdniXEsYUdOS0WJjBD+QgfID0K3JaGHNDqVXzHpCsla1jdtvl+hJcZUiDCwRVxEko5sjoSoxdacYk7cGS0PQ7KjPKEumChRZp7GkpLITlaWQKvLZIZhMpBayyLBDBICIJDNW8ozC90TBmbcZv9YzgqIayuFnOmn7wnHOhYRJ25V2nFf5GOuQ1G2cQ/wm7VjC2PaVZodAYbb8O5MwyHH+J2qLiHH7aKu6H75X2ltfCx26Z3jwcwgy2KQ2tOM+yP1m1C4n9D/bdGOVlBlfj2upzCruDfoZnsxSQOo+SNVYSGtaR4SCdQaRVVIAMtls870+/yt58D4Dm52w8h4Y209A7MCMgu+IytROXpd97duaKUbj0ew4KDLYKnLflrxyVhH1vUlM9NNYUYhque+FnG6VDS056kyZQJC8cjNtuTQClefC1J7SkuyYtEuctOOsjR2Fkk8UIDO2YOGSQ29grCtV1bS9U3aQfs/COBWNoPqcbif6c8U2H7GZFfeDY4QUMFE0KR8vZf+LK9WmIjLwmoxaEBcMcV9YMOfXTX06XdV4VGX2RLXUnIp9b2lM9g+BQi5ZA7S8spVnLdvK1lm4CAzG5NfiI4IniQBRmBk4S21lHGCTQWJf0CQDw/7z4gNi4o/kPxEOZJJK+WghFN+MBQPioyHGso5JqDQMo23za2AsNgBywRchrbR68tRiMNrOlJyqVo3n2CYpH6+83uYxdHIuE1BJ/EDr6JgtEemJySk0ILIJQJIImcSSEGPHPp5MKIGiZ2YFCHJtHoiv9yBJqUMKKaAw6GI8vKifDN10JgGwhg0YfdYH2t7HhD5EDCyjHkLEzkcMTE4NMWbF1BBo2xATrvqQDVPvy+slY6Whjc/4z2YVRaNmmjtnYZmQanmGW4zRmgc6en3ryEh1zir2m0JFjJBgHObnDEqIoyKrApDDHqfE1I0xVUjNhegpwmVq4MiwKVVUvyPj5QdEP5Bx8wPN6PVbMli7bd5GCKnYD0gxIvQeKUbE3iOFmCtZldLBZOyWqoVphUJ25lnBIK+bdUeD0q4lBp0VUma1ZoPUwnRrmLaDWZ/TAG91RgqpFIl55LA+GDuekQgexinDJAoEbtdR76uKqXuB4dknILHjxGREpPuya2z+O2sdtq1DjAndinIahZ7S5IpKKvp+L19RVvh0Z3CrNdr1I3SrBk1n6a91o99prVUDnJmb2NhMqhhnYUWBMzSwrUOzbnIYDrYeF66opfqY0NmilAJI2XPGNmjNr5t1g+aM9uVah2bdZaWPzCjNeSom2y6T/7rGYtVYdM7COzpfAGi6BsAZmrOeT4udqklepHb9CM264TZcw3VnaLoGTevyX+f4N7j9WjXwG/Fm2WMu52C7Bm5o0Kw7+I3P5x5Dg0ccstM7i84mOJNy2A4wdkjPnEVrgDNnKASK/2zryjUSWyODo5l7W6txW/WM6RpquxgTms4iJofQnY1Uokf7X7dG051Ru3UWTUv7PJv2QacUg1apQYwZt+eJqFTX6ZD8mgchEyHG5L5EkyodTNvBdR7GsjqvbZBCRHtGs9bWGQwAbItcpVMUfbZ1aM8auLaB66jPyn2fB+pC6B54Lul7P5NSjYULog61aLoGoe+QWC0ppENixyWrfJxW+XX5tcskEuCacp84WyYMgTIeTSAnQnI/mraD6daw6zXc2ZZs3LpDFyLaixbGGXQXHezWIyQPZ4BtJBsAYKSWBOi+X7MdOF+xDb5o2Z40cOsOzXoFd0bXyDQtTNsh2YZC0Iwt+VEnQ9HRuLOxpDR1ltqAZ/xFEQkAwbm9NjTWoZE27NZwTUd2tHOwbFvsTDvOhvCqPJzF0SNnznYNXIi0jBGubdCeJQwAmrNxZVghrNqzhhSqHdvKtsl91zrLinU36us51+dSfk6FQ/liqY+8UJqoFxanhuuJQmoIRSkVmEASZZSP5HNdDQExJWzZj4uRfLsYE3bsvw0qGkZ8ut5HeOXDzfl14tP5yXsAR/0+wRIhJa+bPb9w3j/U9xU915WQYcZHHPuK9B3aD81lWyQ0MJzaRRw+GduplAIzKinDqicTBkhoOKIHvCcS6ZCPNgyFjFJEVQoRYWDfjJdLflrk1wDyZ/I6960jqqmSrmJ/WSZZ7EF/z1gL1zVjn48jZsi3YyFCty7rV/TarC9gnUOKXPEzBiK0XCyRMujITkk1WRZwzhJTDwQPkpQ6hIhisPISbCxYMWRNeYhkZRSTUz6S8YopYecDhiBGDbgaQiajNkPIZFTvYzZYvScS6zqkFDBHSJk9Q3PWNXDWYJXfOzI+IWLlaF2MKRucmBKitYjJMKseaXbRibE3cEgIkVRUmhmfqqZkVjJin5jSM61HMckfNQrZ4/VTQmqOcUf0hahS6icxeKKSmpJR9J6MnN/2IwNX3g/ZqGVjx+8Pla+fMunaSNm2hXEWsfe0bt3Buh3ceoXUtDAxwHYByKx/KHJP66iks84r5bqxcix4mBhoIG+lbROSxG4fnYqsOIZlrdP1IKTwaJ04VuwISNhTDp1q+/0QUoY4XLZt4fh1zodkxvmQ6LemKq5lZHXM3ow8haCFPowUUp01HDmRsrMhYX057Mxw6FnrsqMgs9Y2q3PYGTnwULUG0CGQ4pjmHFDOIlgLy20DkENqYqAwV9V+tmlU++lrcIN8UkbZAcvnxDPy4pxT6GLkdint1qnTDSlNFBJKns9tZ3mf09n/U+53a6gv6HOTdqOlyU46lYDvs4M62o+EjXL7UX9E3pd1FnogPfr9yibdOvTz+jpIPDs7d88Z50ipK+/lGRep70VtExBKRU1JmZFVKiaHm46ek/lZt6+WXILcilopJYn1rZNwaLKfoemo/1qXHVut/JtVSjZj9ep+MYXx8cSEMYkxmjW3sG2byRDXOaSQ4DqLGCy6nkKhBSGVHDWijFzbYkNtS+HAEgItBIvh39IEX76eM9dVCH7djmO1Kd3fKQZqmzjJu6fa0FpXnkX5tbomdqw4JXK8nOOiRi8rrmjsM+03hvue9K8IIkEFRoVMS98TZ1L2W35L7Od8/ztElFbcLjQhNVqvomBkKYSUiAxSArY+IEaMIl0kykXIKFrStlP/Tft0EjEDIL/Xvl3Pn2lfby+X8ORE9orQTO5D/Xrub6UIefIRbfYVs4/Y0uRmGxNNpCkfMcaE1pnsI0YLoLHIun4DGHL4kEAijjRzi5pU/I09X42FAyYMWRV1zEcTwUAaesTBIyofLfYeUYQDTEAFRVBpPy0tEVMTMmrq002rQY+VUUxGtU3eNkcRCPkkY1kmp4SksjwRY6yFXa9JGcWkPmKg6BkZ47cd0G9pm7XJKqisoxc/UEfFzCmjHmDKlgdHSi2F7ek4Y22wQhQWnVYaAyQVyiYMuyihtj4y+ZTwdOcRE/Ck96Sc8gGbPuCqD9j0HiEmPNnScpPJqZgNUvARiZn5rOKanEAO0xGDpHKjdG48q3TWOThrcN7JrLPDWeewaix9ZgzOWwdrAN84NM5g7RLaaNDahFWybPhYtmlTlmxKSJ/JVmeGmJoJ5XsWzIXszSqkgFGonskxzMXQgQmnkaEbekQhobaXSCHAb8nQhU2PMHjEwcNvd8TCb/uxAWSDmEJC6EUlJddxbMh0eIKEOblO5ZNZd+ScrTsYZ9Gen9HyYg3bkooirdc0ExgCUtvBxojUtBRWEAPMqjgOSYcs6gcAq6fmwvhGLLrGAzFWLxLonuDXAKIiupe2N1ASbMMzVNbirGtw3gVsOoemc0gpoV01MMYghFRmbKxDimvEYcgElZBRMkvtujO06xbtimaom46UPmedwzn/Uf45w46AHJ+wyyr0pKGZHcdqhrTu0Jw1iCHBbwf4rQfQ4VHY5QHYIAPE7FBxmDETVhctkSjtGc3ud49aNOsWzbrLah9IiKs4VJO+aQzy8bfOZPt31jmEmLBdESk37CyABik+grEOoSGVTwoBUQ0Im7MLUho0HZr1RVaZtStquzW3nyiyWg6Nbp0kAJ3YQZnd59wBRWXWoFm3aM8a+G2DFBPW2wZtHxBSym201H6PGrLf3UWH5syhu+jQXtD+skqi5fYTibhqP2tIgp/7oaW+uOLzOuO+AmDU/4BH2RFNMSDstupUx/2vWV+g6dy4/3Wl/+0rzUo/nOP7RuSpAezCZxXHcdLkkbGg+DsOGRiRKw6mbVmtEqivgQboDWTgn0YhD2W39ExszpqsGHJn1GdtR4N1SnjCKh/nZivIUpGDQm50zo6UkrFzWeXXDAGxY5WP73M/XcopZds2K/6armHFUAmzG5Hfk4ZMotS3lnI/xkCz30MP01FC97Dt0V5QjsDuYgvrDPx2lUnl9rLHEBI6dpK0DQAop1zL6iqym01eNhf0G/JnuvIH29AxGbs3/hSMzo0nR2xj6f4dSHHqfF/GOUxU7eeUWqs27EgpydfEshpYFKeS53DqnI/6omFCv+2yPXVdgxQjmvWKjn3wo6/FkEbjM1IrUO7CZt3l/ifjLtu1rCpTSj3uf6R6Wyb1pqh6qJvjkKBomkdKK/60Qiqo3L9XA6mhxJ8j/6687kPEpg/w7L+FGLMfV9YVUqpncivGhOhj9uvExwMKwSHvU5ychzpJXdim5K1WimFFiBujyPGGNhbbp33Ec/YRxVfsmjJuER9x1dDz1zfkK563LvuIIRFRddY4VvkDKfAYx5J/SD725GmSySjyz0YKqRQRdxsim7SPtr0EQiAfTT5TPlrsPflovc9CgbDdV0xFvv9DH+jeDzHbACp6UNSTcn20mnJqKzRKaHAhvmW9PNNKvlCefGDiSQgod8YTnV1LdsdauLMtjLXF/vRrHiuuqS2aFjaGQsg3XUkqjw4peBjHuYWBwhaKn7dkqx6Ar/fgSCmNJRumDVYEE0ScnE6YX6MS1IVIMtAhkLESRl0rpDZ9wJOtx6b36H3Eky0tnzIptR0Coo8IIWYyKoTI9ypJQ5cGBMaW3C95ds5ZbEU5wUTVxYqUUhse3J93DpueDNOjdYuGHZrWkky7jcSGr5JFyGmOKFwiJqrJQDHEhnJogb5vURRTh6ojSVtf2zmYWuqlbTTrPlVPRU/GbRqa53sybrttJqfS0GO43CLFiOFyi9gTESWGT4wgKaWIgApD4NeRiapCSmmDpiFGi4gokwdCtnVw3Rau4/A9ZxE2PWzXIA4etm0QL9Zwg0ezJhk5+m1m0FNLA2vbdnTexhZCb0LWJWOB6GBMLAYLM7N+D8A4PVRYQ3lal6CdKiFVJKxp3TqkmNC0dG2aQfZ0AQnfC5NZXFFHNesLClsRR4BDpzoVOiUOVct2YOn2HuVaYaWfDRHNukPoIxNJNBhYDw3dMwnoUkIfSzU+OV8J32vWpBKQ0DPXuhx2Vmb7S/z9XtuiqLJyWDIT9PkcG8ttSO0UU5vPSRCURMd2azQdOaTiQDWtg2PbW8LOhFQxOSfT3hHm0SWHOtltPrdm3SEOPp97CgnNuoGxBmeBwh43QVd/RVafOWOwbsi5ymGPnOhcwlF06F4OVZpTvbCfRaToOIS0k9BH1f9SJKcUQHZCp/1PQnak33WrBrYxmdST/QupJ87o3PNlj+SruDNEuU+XqvaYorzLOdL4z3ZMSnWKnFqTw0DS3hIuIcoVucedDp+SvishdtaW0Clgzw5Yo+4/I45XRNdYhJhYbUr3f/QJQIfoSxiqzLCPQ89sDj1ruobuffljwkuHCu61Izi0SHJ+mB6Ww+dst0ZqOyaOO6QQ0T3iyamNVzn1DJo+oN16TrxM+5b8UXLPt2clbK+96IjgV2SLDt0zTYtoTK7uS8c5UWwYjBQXnbPYNXTukdsAXYPozxBzCORyGxJB3eXQZ1FLtm5sS0vBHrmW+qAKESTEEKU6GFh1xv0oEDmaQoRjUtQBeRJRCFJSlY3tpCgYwGEzJXxvhoSaeX1iuqCKE/D/b+9tt9vWmaXBwgcpyk72fuY9a83M/V/hnHP2ThyRAOZHdwMNiJQoW3YsB7VWIkuiSAoCQXShuvpS2l69Xcre8hLXaRuWmJBtVnSmi8Rzmoz6tcQcz8nzU/NvDjETUDGUx7CkKq5bEx8UYupyR9EVb+W5JqEkPhT1vCiZf3lL9iu+jFH/KAGDxIiSZfNrifDW4Nvk4RoLliFaHLy0sSz4WfJFtlK93qwvvKriVJK1IYICEwPFaUI8/XrJaqn4899KMCAxWvjJWSwxUpx2WqqsljgvNN9kEirO9BhOFLORUirlKpz0Wvk7n/ZGHCeovehkYdjk5zreo7lYUQsLUeUGD/tD3fOYoPIvhyr+89MMO02IywwTI8wwkPe1HyCeU1b8q2ShKHBaHwCTLN2B1mK6B4v1HoqU0tf2pUErb5PKv5hY+paEhFHbgPOJIw1mLyFiCSTtnEPCPy9LHsD+eVnw4xQyGfXPzxkpJixzQFgiljkyGcVKKcWkXySlGkbcekkhAeXiW4MUE4ylqg/OGpyWiO+Tz5LR0dMAFHxiMzwLaxJspCTGwRqACThJ4ZN2Eb+oGxLxALxytVpkhbegMTk3KSGy1BMy8C3MwM9zTuWLLy8I84LwciqPpxnLvy9nZNTpX3qcf1JZ4TCHPNCFWczz0uZgJqkJkjqgU3X80cONFuEUy2D3UktBaf8RI08m06+f9N3GiSbq8wC4oXhoqbapSqiuGcV/EWnno8Aak9WFyaS86iRpDC5Svv9sUp6kPym1zy++5uPiYS31PWuPCMsIKymecixOOdOEgCakRE2ZSRVns5eH5XPSSFkdQWopM3AwFSPs4Fnp4zCeiCwNpwg3OODfE2JMufqeJqUOfC2M3wa4wWWFjygnrPjLZG+Zkr7XEisW4pUQSTlQBTvKU2pwMCxTBwbERZvAl/0Nh2cMk/ieWFL5DOcqMwnYtCcTsELsidKM02jc6BFn+n6e1WbDC5FS4/OAMBKJlkKCY9UUUJNS1pJCQnxoxJdKVFJuHCjIGsascJMKL/l8UII/+f0lUDyOHqeFA/uUqjakXdDkK8W42v+cd8pHqjyOVfu5rJAgXzMUQ/R8ffApo/TLTlFt4/Y7tvrsJbWzSkMTX6nkWVHiByCGfN/y0wELADeTgoWUwguvSivvDWu5r4r/2ZDJKSE19HW/ppIUAiWrTV0hU2Ts9Ezsh+Dyyrj2RotNNV1JN5PKkX5gT6rBUeXSipAq/i0SRGbT5aSUyM4XcuhwhBkn+OkF/pnI3fnfCcaeKP15LNdYGG1O7ZNzz+nSnK43fhsw8BgwPhMRNTxP8M9HWvQaJzomj92ikgqpnhMDJRD2tvaoITLOIATylwIsnPie7GpDHgP04oivx+lcHcxc8DbkRyGLsjcLkMko8ZbK/TrGykjfcuoMBYoyDh+KB5/4u/ji+bLpJ9UQUmsV9faic1r70JqbAyXOS6mkyqVExuVisSIeUhLH/Voifs5BiQqCIqdCJqdOLCyQ+G05BaSUEJZ0FtsFTtuLSy08qEmpfd5FuqhCIaJKfCiKTcvXjhYw+MFhsQYnVkNJjBhiwoHJ+hBjjhFlDB2cwS8ZZ4zM2SyCT0CI8NYiRCKOtUrt7L5RFaJKnMGxlGJTOV2P//16yWl64eVXVkHpGC3OCxFVopCKkWOziOVlqQQDmpQSQUFFTMlvocbVNp2yRS70wONv/l2YcALYK7GJ9eRvP3lYt8AfybcuTiPCvNC4FSPczIs5J1rcdTHCTQeae8cAI2nTw0hE7DwA44EtbwKSxZoUD2hNz1t88ljvoUipSxBpJ6AuHq6gJ/+sITUQYoLhxd5I1x5eOHXvhZVSv5aIf3mg+v9+nPhxxj8vC36eAn7+WhCWiNPLTOVDfwkpFRB58CLFVKzk4gK94lzJnz13ckeTAZkcGWswzgHGGCynAOctfp5cDiS+Tx6jp/3LDX50Ja85SrqEmkw5EzE4Iq5SMjkFKXJlPlmRiPhA749qcGM/pEYhJex7zj1WflFpJlloOr0gnmac/vcHUog4/e+PTEaFeWFSakY4BZz+nStSiga8gHCKeYBb5pBXY4qSoVaS6SpZ1hbfHFJI8Srn8wDrbJ6MxnmBYwWFTLBkFQ/WwcSI5P+lpvEjrT4Ym2902WNLTAZtBBLL17VSilP4sJJa0fF6GGOqiSnpeNaRV6QNkSreGRwSpTQQuRzx4xSIcGaSO3KQZd2CZQ5I0SGEodqvczSZoSCK0s4ORwoMvk0ex7E8Tr4UPxiczT4elNVbJuFlpX0Exgh3JAJK0k8OsirNqSfhFGCcIaJFTQL0TVz6vwRWlH42UerJ0xHD80Q3ZTmuNjsWnyYDJu7JvPPAKS1PI7UhAPw8DfjpLbWfKDU8tacfHBXBUNfB9HzE+EyE/3DwZDLvLA7HIf82QqwMzvAxJX3vnNBLRlb1R6RhhpsOiCFieDqSIfTzxGlOrNadQ25DzxOttfaTMeTw1wFutDj8dYB/4pSdp4lW4cYJdpyyN86qUgol/XCwpMCigN7j76cB48nmewxAaZB+iFgGy8H+dv8ThdQ4DfCjw99PQ+5/3yef03e8K8TUWjrUJbw1bbyjRqWWMhbJRCYCuC9bn8lpLDMtmMRAqklOSRcIcSlFQ/TrUuhDSIHheSJigNPWiUhhUpXJMPpHeedCXBrQte+5/z4xqXPkx39/cYlxDhItFwaJKWHxQzUnK3MvIlCsoTHAWqMI1qI6EEKlhcw5QzKwbgBSRHIDzPQMMz0RkRsDxpcTrLUUfP0kcnd5meFGi+UnGfRSWnRJMZFxgoIcC3/kMWDwOPznG9xxxOE/34nw+/Yd9vk7FUmZnpHcgORGekS9aCuoVFJC7I8OfmYCavEInttwdLvacDxS+uN4oFTecfL5HnQcfU4h8vk+pH5fdRNN1lEKKReFwSHSGAfke1ImQgfy69ReMtL3dCqNnw7UVlxcA5lsHQupbz0vztiSxrcRyHUP8/tiTXAQ1Xu1NQspFEMETqHEcHNM+DlTut2POeCFs1rIAzhkocE/L0UpNYeI5RRYCVUEBoWUkgyYkr4X2aQ78kJNVLFe8TO6pJtH5W0mMaFj70bxaGtjQ2MBP7qilOJ4UQQMpwMpsCU+PI4SK4ZMWAF07cfkcoVBAAgOGBa6P48OkIp8EeDk/5XvIJksKl4zMVD7MAmVlhPSz38RT6SWij9+IMWI0/9QjDb/eEE4zQgvpywYIFKqFgzEmWMzzmgJp1iRUTpWk6Ix8hhS6Uut51cLie0s1iogIy+KEOldUoSFqPJHXwkS3HDi1zzcCy3OCEkVuWCDDxEDgCTKzWFA8iNVh7eOWn8EYD1lfbVWLQa1h/CaAOGT42FIqWv5xpeQeJg7k4iySqoMgkJipZynvCbpPIWYlVHCnmulVFwiwkIDVYoBgQeslpjSpJSUI05x5FVJB5uKB4BzFguz444DrZkHHYCqP7hI5nyjpwGIBpzE/0pq3lrbrpFO8tKr1FCvhbqArq0CV8bPMQCBy4ZydR3JOw7zgiAV9kT+KRJQVkPFOWD5uSBFGvjotZgHuJP4hCVUSga5hcogBVApZxcTxihMveXzTZlNX14WRA7qxQA9zjP9PS8kF+UyqFLqFDGQefmayqwxkN9s3hQfOt/4EaG9cEQBYm1Rq8hKsailQkwYB4fZGvjZYUGAT5w2kRLMUv/GjmXcfnTnaScqXeLAfj5ZnQKcV48DijxYborLiVJMT4VApXQ0D2DJsuUikbZZTSjqQTGZzYHVNHAaX6m6Z0SZJaviK74y1kApfcDVY8r3lFTIEBNOg8MCpfYxBtHSZFIbnfvRwrpUJne88tgqsEbVfjJeyvnkduNHnfIEP8ANc1ZKzWx07MOA5SeReeEUWBoeVtuPglHPkx4mqKSCmTLSpI5VUqx022k1UumHxVfqxEqmEBP86IAT3XfSUFIMrvU/mRj70WFwtlJJaYWL9rTK9xlsp5N2vA1tmv1uUXQ27Wejc/F9ihF2JHKipE+FylBWG8hm/6lRpenqPitpCnL977gPiSdRWxhmcBbJSxofp6GORFJjdEjRIkWXDX/p+5lMrmZfTx4L1lL3tFJS2lcC5kzuWQ/jfSH3/ZAVp346wFiL5eVUTHQtBVzW2VXzXTfaXL1QUvX885S9Svw0loq+wwh4n9P2YGx9jqoNdbGIg7f4KQuYzsLGlItHhCXCRCB5ixQNgNKGpbhESSfKRQ5U+qM2Zy7V985/Wxr3TTbel3EUy4keWamXQoSdKY0PAM2NB3AKaWk7nbKXU8WdZT+pkYyFtVJP+v4r5kM9re823NpekvminydI+p5U0YvsL0UqqRBTrpAnZuaraXo5lku8CFjS9pa5qKQ0GdUSUZLWWtKFr5BSjThBf8bGcBYbYvTZH0IWjggW0UakaGBdJHKKCXk9dukYke73QLQl/VFSh21KebzQo9HZz7Wm1pHXOH5J+ZGJK/47nOoYTdLzsqfUie4nQjytEVLLy1KRUTpWEzJqVjFbHb9tQ6qgFhJKCKrEr9EihAsJAy8kZqUbz9tkYSGFUjAIWGBHj3BaYAcap+xpgbWWv/cMC5AhPPcFEy23YSjeUnJvTqUa32584ljvYUipvZDVIFH5JL7QAncYUgKV1Q15j6oy0CAmainxjZLH//4x4+cvUiz8+jkjLgm/XmaEJWL+FbCcFsTlhOX0kwaruQxcLSElyOkkuYIWpeKQtwF7HMzkcTBMkYKECDhPCo1/AZwWutGf+IYfYqpK7s42kUqMB5wYE6Khi3M4O6Pm/MzW32V1+6Y4oiWdZPDSKX070vtyW0q7xsgpeyQVlYEtG5tLqh4rpU7/zJj/JaXUr//5hRQTTv/MCHPA/LLgFMnf5cQrMTLQaca9hRBTpVqWwThHKuX8ssCNLiukKBWKTYWV/0EMapLPRp7pMAHLQN8vsPmdJqH4XwqB0vtSBJLJ7WxSWq2a0fH+kFVfwxNsY7jiSaJqKPAWT4MFMFTXLgD8PAX8NwcDMyszRUZeHcOy58hACh8/Ovx1HDB6i//6NmL0Fv95GrJK5WkgxZSzK5UAZSLufTbKtQCG5yOpeZgwFeUD5cRTf16eR5XD365Uk2JweB5hncH4F6WaDN+fMH5/hn+eYJ+eYFlZYA5H9q/xNbHCbTo4iyEkTM5iGRzmGPGfpxGjd7kqKkCrp6dhyauf4vO3vJR9Hr8fMB6JlLLOYpw8Rmfxf76NeBod/n6ix+fRUds5y6uINis3TPmyHJA6mMOR/PC4DcfvpJKIIXLRgxNViwoJw9Hnyda5wkRIKVIjTH8fYAeP8fsTxr+e4J+PGP96gpsOsMfnYnDsS0CqQeokYPIWc3SkDGZDiZ9PQyZGT4eI//E2rx4vc+A09LqPO28yKSVqvb+PpJCi/ufwn6cBz6PH02Czsboo9qgNi+pM0lz3og9t20hpH9GXADY1t0CypBTh15L1NC9ZZmCYqW8BMOMEZx08K3wDe89RsDHTfmNEW5FIvDWGpwl2HOia9wOnnU0ww4iozLnBSimgEJcDEyhzsHjmqsR/P9GYR8qHkhITFpo3RZWK0xoOi+LAWFMpJUdWm0pqtTbplnGT5prkTRpsopXtFJE83b/N0/faoP+JTG7jvMCNQ2UnkFRlqSp9TxnoekVGHf7zjcaC//Mf+j3+/i/Y579gnr4j+QnJH0gl5UdVabosxArBL2S0pEDKYueJ70UxRJo/N7YUbRsWtRmNA+NxwPPBF6UkK1on73BwNA4QKYZ1fz7nkQL3v8NEZNQ4IVmHYZmzL1QYSHUQp0NVEl5So9w45LZzRzI5d09PrNCbVMrjWPpfVkt5ucnmeesedAXV27FmcA55jrJIHHL8ptL22Fvq5xzYcmXOhapEIfU/bL8i86tl1gKDwO/RfS+cXrLIgGK6iMi+tqKUCkupkizkFLBNTGlCCkBWOAsRJUUYdEGRwL5tC6fNi+2A8xYhOI5ziZSPMWHmazrElGNEES0sSjVlDQkdAIuZU6/nEDE6W9ngSPvzFyvfpbEPyYbmOavllD1/4wvbp5wWzD/Y4FxlsYR5wfJzyWl6opSqFVP0G4ldxM8QERIRUGtCgjVCakstlSshmvrvUbKQOLYeLfmKjpZiPWeA6YXmyMMzWbVIzBdONMdzo81ZBgByGl+KEZHnfj5GOD9QXMueyWZgv+FlgbELjUNxKAOnxH3AQ2fFfDlS6jWISVVPaJRSIarKCzEppryk6MVFXqsJKSGjNJsuSCHkAahFztvnxyA5xwsPHiGyTJ7Px5bypO0//R2By5JFmfBdM5rdSp+4qKjaIJqomtyN/lItVgb8XBI08MpFiHmiUszwOD0vpko1dYop/5MB7idPEDUptZ6+V/6msmz5jDABCExEhROvJs8BcXSslCL5eYwRhs87K8D2YjXHuONeuJSeV23H15G+3qwpN8OsUkmJV7GQZdYAMHpaQTktET8BpOhyUGBM/ZuK54AfaIIixtJidikqFUnXEmPZrRLcyZgSoIrixg+wA5nKxoEqyknaznAMpSplsNy365LcpXy5zabf4oVUvKTGcjx7TqYURQ2rvKx4EoENjy0WJvUWDrDcQp5Sy4nVPic+taiuW0epOhJUHYfzdJ2skjJksC7q6LXKe/mfKD/8SOSUs/xdB0RuO6kwE07ubHUtnx8r0fw0cOEEn6uXiS+PrarujSWAatJO9BgtJsNCDklfof5Hn5kGhxcAke89a96IEsyLOu+oqhVKhdhRSFArnlZ1s63dO4Twk7877o+UEvWRLShfqUrJxGOCQVFBxROtAItSqt6NLUoVVvfZccipU+AJ95mfzwqESAE4Td6KEsBi9CkTLOPgsudLCBEmGiTP4xQvztVKqaKQyn167Z/ZTjdNCTww+VKFz4/kxzVOeSzQqY+S8igkv8z3cop+JlZ8vvZz1UJWmkq6sCiyjB9JJeV8JlVSXE/fA85VZ7Koqat9OZeQTGk/OKwopSxzSbW5eUkPdPl+V2wkhIzWq562+jv7mllHfSVGuicBsKyIz7+BtTCuUZqJMk8V1dAeUtngvDXZ34AQkR0fD+0npV+TuK2ofVJO8dv6l2MoWfBjxUtKKftE0T0vVoRUKziQbJg411kxrWVLizNSKvIY6CRlb0RcToq0igiKtAoLjV1U0CFmcp3i2YjkLeIScbIGY7RVXKjboWQJnbf3WjrlFgz/KEYR3/m7h5L1QXFYic/CvJTXYmS/qFJoSgpQSfGp7OOcyadS+Vmet3/vJaWcKfN2iemcORciVLEeDzejNbntwykgOQs3phxrFr/ABJwC3BRVrFqUxjFE8snL2TIux4NG9yP2kFpTPl3MivnE+FKk1NYNNyt6molETPXAFtL5QFal7c0Bi/oXQixeUi//IiwnLD//pUGLCaoUCou+ppaSG2L0Y97GWAcXj6Sm8fLZMQelzllEYxBchHW0grVGRK2hJlEKQ569WmByMCCmuAYcTLTtudLGF7GV25rzXy3XAFQTUuc5VzmUfYDaKH9T6wClLmhzuAsZFXPVhnCKLAUNOV1PFFL/LBEzD3bCvmtSSg9uui1lkBIW/eiISZ8TMVQhReDfE9xMZngxkBxfgvZwWmgFlaWcKUTylIrsFyX/2EwwrU3iemreb0NWRanXrAEVVmCzczF5BiImR546c+TVZRvhjMHPkVU+S1Ar/6GUJm6CvpFXRI6KRCGPOYv/+nYgpdQ0ZJXUwSullGWCB0wQZ0LFwx6OSMdnJOuQnv5C8iMOrIqQVLQwLxj+fanK9a6t8htH/4anCcZZjH890wr/X0+kmHh6Ii8UP+aV62Q9BSEVqWJgTIK3BpN3eBrY24EXEyQg/bZEHLzFryXi28nX5Z1TQvhV9vn97yOenpGr1ogi4u+nojKbuO1K+zl4a85VPaySgvVZUWKPz0jWYvj7L7jhR05bksor5DP1UiZpG+3nppEUU88TEXvPx+wnZZ//IkXW9AxzmOi3k6CUf1uLMpZ7FKXU00BKYWsMwjelklhi9qKQPghgtf9ps2lps9GTUuqY242UERMTfEP2lZJ2JJJv172k41UQJbk1tBQkV0FMgBM/NDUGZB8LHg9ijMCBCm/Y5YQ0z3AALKtWdKluDcP9OKebDiMpIv0Ic3yG8WNW+cGNNOasGE2L4nRwBhMrAZ4GGj//8zTgx4mu/58nGjv/4fHTD66qoCWBZ1bnSdqeqH2swd+sNv2e/ZDIE41I6SZ9DyXwSdYDHkh+onOPC5zjOZ61SKcXKoCwzLkip6gGdLnztv2cIlb8RGof+/wXjB9gv5NSyn7/v2CPz4jDEWk8kp8UE1QhkPmt3rMz4utE6XhHrqQo88jj4vDfHGj5ofbWAZDbsDVjPhw8V4UuCqm/n8ZKqTs5+jc4WiDJyrMElp9yJUO7wLgxK06lZDpAKS6DdfATK+JPJbjVqNL3xMNseoYZBuqHA+9fxk2596j0R+mPeootR4lNEN6xD224cqkNJfsFQK4gKUSKXHuU8RJL5kusU/V+qXtZEIFBIAV1XMgrKluziLqaFVLL6ScRUOxhG5dTVkYJGdXGejcrpZRCilL2QlZOAURaAUAUoYIfsQCU0sfpe7Qf9iVmctZGm+/rv3huRIILk43i9T8h4ySbpurzacXupVVMyXdW/5IQUqxklAU5IWXCqamsx/FZmAPbncTs8yuxmmS0RJQYjSoYb6ulgGspfKkq0iPpenp7KkJD6XujNQhG79fCxZSzY+afBo4tXBY3I4Ziii5jvhs8AquJ44m98YTgnGdasFGpkIY9l0ktFWmhNUWqwrc1f9oqePXJ8JCk1HsM/ppkiI1KSrPqkr5QjMyJRc8MOv8Ly+lM2nmJlMrn4ZpByVrFyHskYzgIS1xJkJn+FQa8/V4C8fIAJPXkvLR5roiEOuhaU0ndFESsKKaSoWoBcoEkRJoYt5/L28tE0iHZdSWQDvCAQkzR3wm6OoOUDC2EU2HZ5TVtmLdOStUDda2aMvnzx5hgmfWn80yK/S//Ys6/vkEl1fHbIVX3LoG83kQpReqlaA0iG58DRDBJOp9IrYWUaslnUbXo9JJv01BIAmVqXR7tqocPrUjb7OkBPwAhwAzs4zEO8GqVS0w642nJXihy3ckKv2FvEWOJULFMsEgZcwoS6lV+48dsNFsHpcXHySqVj2cDcgC5DPJpcbltdFuGmLCoEe/b5PE0IRMrQuhJe07e5lQTaT99DmdDIp+zqCJEUWJefsBOMbff8nLCwBM0AJmMSuq5EFLSZmQWfeAywrr9BvqNePU/G0QDZ0oYUvFxCqSNteH5QG33bRoqcuoX90F5Lm0qj9J2oi47jtyGTEblfsf3HgnqV6tutc3ZZVIfC7nmEhmmpkT342QMmZxLf5aFtIWe2wkwjohpXUgA0CloQ3WNm2Eo5uZsph5btaHeT77uStGWwVoEq9Oe61Q06WOB0/d06XaBTt+b+BqQMeDInmiSaiZEqiamJLUl8vzBC7GRIowbkeKSU8RgHcwyw3DQYZYT7PBClgMhwm2QUlZ8kAZPxL0fYZ+/0/6YjDbjhOQGJvZYJWU9zVni+UIsVYLlNowSrDocx4hF5iwDteMLwEpds9p+ZA5P1b+OY63WFXPzUdJ2mZSWuagMA3k40GM/E0J2oGpUeTwd2HxipH7q3JxVZ60nlxB6UhERPF5qc3Ot0svKwPZcOj4E1E8vz6NkCpSS2BTVMVyJ485juVKxr8R0QsTIc4ntNMG0ZsmiPaTaf0BRPK5B7vG68FX1HecT7DAigsYJHS9GVs3QsRyiTUUhFS2SKUqwFOv4cInn80iZt2pPKdl+WLlPx6T18KjiOhEGJFFH5U3kdf1amTOSSkgyWVLObMlqKTYzL3FavSBQiLV1bylNTF3KIYmJ5kiUDVPiuVPUqimT9z1aUmohZ0dQnG51jMkxp7H8t01ZIVuUYwExRtg8v9bEXiyP3NbXrFkuVuH7pHgIUmpL/LP1umX5nah/DABvSwBR0tRKQOF2TnzlApe/aQBTJncs5bw0SK3vt1zEwpDLYLM1YGm0EnORTOeUE1s8PErlAFJLWG4nUk4g+7bI5E/aSiZ30lJ6nGpbTz9PKESiAVYvEErjW8pIITJxriwAyyab1vOEJJF/lA00UQZyVSAzjHAjp8Oxn4V1FilcvzAD57DXUk/NsNfP9eekXcUAPSQio5wh83P5nOWBV37R2O7sGnRajlZIaQ+ZlVXmVTwIe/7ZUWVqMoyhbpzJXVDKN630W1ZG2TwZkNcmrnQUUsLT6NRKX1H7aNTVk+rUs++jhzXANw4KZJXaGqkEyGbTxrCxDBv0Wg/w6rSxjlf3KQg140tOI4lcRSpGUkoByCthAAWjAMgPwZGnh3GWKm9xcCXqHr1iTek8/qwvZuWmAaIhIiWmMjYO3DZzSFkpdRx9bjv5NytPqf/37wnTU8pm80JGfTt4DM6otlN+XKyMNGhIeaUywTDS+8dnpPlEyhI2dLaDh3s5kfFxiFiej6wwIS+euhw8ew9MIyxXkBJSStQl9vkvSmc5PlOA70Ze7S9tKD6AzhiyDTIpt19I1HYk5XeZBC1KqVCRoholPcdlM+Nvk8dgLb4fHAYr3jGkzpokuHc2t5/ci+VaWbsd7x2VutLqNsjKdwKIfAIASeHVpt0pAiO4yquDmZ6AhVKoEELxdIwBNns+EmGQ5y/ZiLuks5FSaiCVChMqknZWCh0kpegGKSVZKfWL+9YcE365yIRUxPgy5zEgKyUP631Yz51kQUDG0G/TkInqiQlW2yxCAeJtQ+cBazAMVN47pshqIg/nHNJ8ojLf8wnm5V9a/f71k9ovBmARPy72mtRtZ12uVAhrWe0zwj59J0JvOJKP1DAhDUfAeSysICkqCP7ORlK5wUopm7+7QBeOyOqSC+0nY4GMoVR5c8jjqdyDBvblm5zNilPTjKO58qPzQCRizx6Qqy5bkFIqDQNVXV5OcKNqO1WFSlJOjUoXpf43sP8elVuvxkyVupoJfnXNdPwepOrv+ocQ1bTun9eyR26BcQ5YkGO06j17HqvR81It+2x/un+uvu+qv6/FgbQdKRf1cwC7FoAE2kvpPWDc+d1c2sI6w38z8cKemiFQMZjW3kCfM5TBhk65W/t7bc4ukLPT1fbKMdrjluIt1T4utLdZ29ElMPFZfUqLO75Q3PYQpNRroImTPJlhRVCr9mlXYtdWZkNMFRNfiCnFpGvGfJU9v8SY12TU7u/J0umWlPL57+LjIatSeZWK20SMb7NXS56scOCl0vikTXUb3wI9nLRqKJoMKFliMsUI3YoxOpFUiScY9LkXGBtzHnaydcoQ5WnLcwN7w4AgbDz9XZj31V8yk1GAZszlc+IxJHYHWr0F4Gx1lL63y4O1PK+gSSmjyhdfwxcaxN4T1rxuAqo/Zw2l8NEYlHKa3+AMYjQ4eLrWZl6Rmrky3BwSBmsxxyI/B0h+rSHXeyYHXDGSfuISwWQwzUoqZ9lbytRKH9WXknVA4hQ+6wqxAhCx4gcMhxdgmRGmXyTHViv88mgyKUW3GiGl8ko/m5qbccppPEJIJesohU95fOTxyRpEJIzOVlJsWdGLiQzl55BwmmImVKTtTmO5Rv6vbyO+f1PEHivLJC3oG1eSkxS00ZXUR0l/zO1mDKsTFgqoBsBMRBTZGJE8rdRH/wPD4QQ/UdqeexFPCikHX7efVkq46UAB6jjBikGvIvTEqLdKfczEFDIxNViDxO0XkkzWfL7PhSHhODgyixeF2UpAqitqiffW00CvfRs9qaNsUehRBUMmFy3y4lGbutfJpfsgpXOSL7Ifori/5V9Up9iblBeBEAOpfoA8JphlJnLFOqoUtMxEWIXzuU6+h4lSxbrcVyWNL7mB0s3kuuf+q3ubzfMVur4PyeIQmPAMNgcGP+fA/TEgxEJK/dogVoGiOBWl5ZEN1CX9rE051fNEUSAFFRB7PwHmRGoGSf0yFsYf4JiUSsfn2gxYr4prsL+fVKETxY+0YfIHuuY5ZQ9uRBomwPpcfUyrpOQcpYpgTin3RaEr241+qQpwtIsiQBkLD4qUcrZU3zwOLo+jpJwsylMquGFW/flyCp1diKwEj6ecfYBZ+hKRosiePiv9z2liio3TdRsKIcVE2Fn6eEtM5d/+vC91u6nbsdVkpEJMZ69prGWEiC/wNQh5INez5bkFgHruzWjJJ+McjBIPtBXWt4ipdt+adGr/tpqcchQTWPV++13o9fUb6BY59R6KZOPOybvL27dtYrOnriZydAhnAeRJNYRMkzmN/hsocVn5bDuaaUJKoxiel6qhe0NJOXeppnwNRTkmZNSFkmSbPsIr8d0DxH1fipRqU2csDKLycskeKqa8J4qBnE6gtnPWwtmYb7ja0DGXfuRVQBoY5vy3yBfbnOGzykpNDvH5P72NgZhI6nK7g9NGksVMcuQAgCZTtgQFjlVS+rvKKrpVj6gJqTMyqr0YrnT29v6Q99eSUwCM8yxTTERCpQhjuPRlErUUT1KGkZVSI6Ua+RF2nGG5dD0AMmCNXPWKK4G5E5nOGWdgQhloLIrqifKJy4BGjwloiCkLzarXzHpbyYG8F0xJaWCiTHw36IZDj22Jd+OU1wYTdVRpzxcVWpP2sMe4809AuW3dB0I8tekMotRc2z4l/hxIvTgjYmDPMWeoaIGNCSE5HJwQKxZhII8EMfPUkP5FRLOkYjlYA0VKUVAlXkguE/Xl3EQpxYMMkBIpF4zJxIqsPlvnyKtkOcH5gVR/XB1yLfXEqICgBKRDrhSXCRWvFD66+pZqW8M5/pGD04nNuUOS6jHIHknkL2Ex+7rtfqh5wf95HvF0BKt3CnmiiT15XtqvjJmtp1S+NkXVOZIHD5YZYqZrnaPVfT+SsmSas9/CZvsxCZ8VJrr9hNAbD1VgtaaWNADfE5G9uaKSpeuiH4MNmKNFHKn/UTuf9z/dds5SKqXlIDUToExMWUt9X5rNApsEVDtRlgWlexNWfwL/xQKe68gLHJK2x4RI8ny/iUXRKAGHH2AUqWKukSrO8XXhMsmVWN2nzbm37lsGdI2GlCpvvsEqsoX7H6WeCqESNkmpQqyW9D1R+2QfNGuzBxP1aSJuvAViMpmYQkxYrIFzI9IQebDlcSEsMExOmfEAsyxE6mUT4HVSKhvMW04N9qRmjcZSpT/nqeKeGwDxkWKSTBTeMdXqEmdK+jNgMXleNJuIkJO0XSGlXtN2dN/hsdOZ2lPOyVJoDVHtZdUpm8YnAGaIsM5RQRg/IDmab5tlRuI+eUv/y8Sq8pJKuu/tUZwzuoLq92BN1VOb91v1t8lenc5ZBEQ4bxAWiq1SSrDewCYLExOip7RRu2Jc7lFnuayn710mZbb8pIwyO6+q8Clzfqsq8DlXig3oggOSmqy//6F5rgkXTWxJG64tFuU5I518IW0NzW8Qa99fY5nIc44V4BYmqsqiL/yc4yHy2TVwcAgDtaFUrLMhYlTXWkgJwUqWSu39JORUSfGrt9lC7StVP0qFdV2Fb6jeM3AjxexutDnuzJVobXme4z6e59nqtevKuK+GL0VKCYR0MiaxZwsTDawoAFg9lajzBKTKm8Qak81b9SBmvYWdiRiSC91aQ6uGlkp2xuVEDDbnv6cYYHlQA2pWW57rf3YY4fwI66mykvUj3HiE8y6X/aQqRyaX35bcfSpbzAaTLJU+eIvjQKSUTAZkpX+wRWUxMFknj5mkUoFrJqHWKrppr6jmBi5eC0BNCsigUAJjGtgMgBQXwJTjGSCv1CZ9PGOoVLUK+gxP7gYgV7SbmZyiimEn+MlnH6fh6GGdwfKycN4wsQbBAoETdiUlT0tE2+FCs+fV4GSAQZFVlhVbxppcccs4U1WIocdS6Uhk58mYHLCTKS3lF0ubJKsql90wmeq4P6whQjBKKjF43OC/yT6K1mqcMTlBXwItAHhhY15JvwBq0kCOI6R6VsHwiv6BU82eRjIsHp2oKMEEQrOewpWjEq9KI01AXGCeQMETE1FYZiQ2/KxW+MVLQHlKAahWqQGUgOAgflIDcDjSsf1UBQnSh60pZAo1j0FKoup0WVU2eZtNTkNCNj4FgIUHnRc1y/q/nw/49o3+FkLKGeDgHT9qY25WpbnyO4r6KJN60n4+ArEQ6MY6TnE6If2iMr/p9ELtxWlPutJm1YbSfhJUcdtJOpQ9PtPY4Cduw6EE+ba0X0q84JCA4IrBuBkdEqgNQyLz6DlGzMHlYDaI54bqd9RlyoRWVFETk1JCSOnuNVibiT36TUv6Di1+nJud91FsG6THrSFm5hc/xwoqKrtuOC2WW9pakFu35DzEkt5nLI8JhjwtxqkeB4D9pIqxiJJq6oaaWGFSVQf6hq99R+tCmVCduZrmHBK8M3gKCS+LxRyJjH5R/miXiBVAVZ70jsdJW42n4onUpmeERCtVLgIBgOUF0dFNRE7FBWY5kHIqnGheExcgLFS1ikupizo8Gwa35IihQjei5qHok4kVTyopKu6UkOQR5ClVqQQsXbsDLCaXMLNlwsweU5O3CBF4WaTwBqeVXySlpO3Ef8sUMkopUMsCCYpthGpOSimlxREhQungrJ6LC/WnFGGW512kHjWhGj+HkcZM5b+VrCukaP6n2j3Vfkd7FFGrSqrrH/tjcQ9iT6uB2gwSWbwHgJktPYKzMKYcWBY/yK5lRIwJxjryC2axAN2fXRXjpRhypfXsq9QQWWsxIADoauxCROl/7jBVJJUbJ1V1meJRP7gcH1pnc0Xm0ZU4UVJyJd1+VNeqzB/Fsw9oFi6vIBm6j+RsDVvmLUkW1pY5E1EA4MYBKYQsIPBcmRgAhiP57Q7RI4hpOy/o25cFQ4hwS+Q4jb0uDdiAPLGXlMmPaxX4BGvV1OnxnJTSMZ4FkVPyb+Dz8xPFlX7ycKODnzz80ZMggoURpRKtz4IEyfBp+8eb8AAKKcGXJKUEokiQYDBPwNX7MhmWC7GsgNcrP1opJQabKYlqiqSUiZlrmqCdEBsGvE3Nq8goP8I4xYpzEEPPuTyxKKR8KVssg40w3/K3DDIlZaKk7YlcWiukcrCXL0Q+R+CcjFpTSO3o9Fn2vKGY0sFOXiVLEYgg4i/SBM2IikOUCJLG58mM2QwD0kKDn1S3ciNtI8+NW+BGixgc7EAGzW50wClk/6cy8LRpfPVzaa9a3lkGLWHVHZMHkiPtRqfUUjavHghTrgd05H9c3lml69HkNoIMjNTrHR8KCwpGgP2qLDE9h1VbRxpf4srsTJMC7eusY87XrlRRccbwan49q4gpwWy5JK4Rz/rtsLL6pwgp/ZiJlUilbWEtK67cefBw5biXEJvJhqh+pL10e7ZqHyltHkUpJO3PbRZzSShwgQlz0WDy4neS76wfY01G6b+zhL2ZnKQQytgXAgX5l46rT6Eq7EGPEmy2ajxNSNX7KH9s+SeImkQ/b9MHtia78vIVbqXjjlgjuOR+YqwyPWePqcSqqdw/d5BSxrmclpWEUGlJFklFRz2OErFqYJFANdtS9kMabII1qQouHBsci4q+VFHisYI7cTbtNyUtT9RQQkh5V0hXa5UamscNgMaGEGmOFVKCSUBglZRzYyFYDBN6cSEWKy481ylzrOpq0+pnbv9MgBtDyihuy5CQ02yl/UIsREpkYnpmdWSEkFNEVCMntVhYkyCrJVWq9Eq7SZvJ3FM8TcVPThZIWvsIC5yl7ubf3XA1SDGNB0ofRCHurCsl0zfTH4HzPqjaUhb0KpWUQieSPj+sUvVIDOetQVCk6RJTFdPBW7hgEZZI/C4TAlJESkzQF5SYLVpHRuNMTunqe0YppGJDSGnUaXcbaikWO6wppnQ8mMUKTXxoOP4T8qkVWmTVty0ZQ0JMiYULUO7BZ/fqtTgjL2zwAoQjS5VMTllecH85lUrEQCapIme4iDE4AMTA5u6VL7BHmAPGKJYptZXDrBRSWimFJoWvxHEylpUj1FkvFMsBqMgoUUyN3H5ERtlKIaWJKFJJcSVQtmQwzsKNQxYkGB0DAoAiLL86viQplcmohHxhDUzEeNWnxYMkJbphzpFW1g680ffJ48cp4PtEzfRtUX4bS4LlCnzOWaT4DVaqIziHOJ8yERWWYgB8dq6cgubHI7PhRxhr4ccj3HiEH0cMBxqADtMA6w0OxwF+cPh2HHLJ3b+fBhxHj/88DRisxd9TMZX8zhLq55FW/g6OJgkjT7q8FYVUMb40vGqHWFbutm70SLFezUs0odFIqFeYgJL+JoOCfIJSBkkx5fzE58GTtsUBYaZJ8WyARGbCJlI9rXSYeHJBq4YDAD9R+y8vJ6QYEUSR5AzsQBf6cORSyKcI+++M8RQwhojBkPH5aEl1IWVHTyvMgEzMZKA6OovRGkzW4OiYoHoe4UaL4XmAGxyG5wHjtwH+aYKbRgzPR7gjVdXKqTnjVCrryIqyn2rvKOtpgiu/gTxukVOdtHoXGFOvnopqM4GIDNhUFArgFClnYXlFHwAiSlnjX0vMf0s1mUwi8PZ16i0T0dHmCYSsgEdH5sXGADaZqsRyRlSr9WGB4RV9nH6RyuflX/JCOb1kD49s0KtXqnWbVAoJrpA0zESo5CCWVxC9JxUGwKm6ZcwRTxRptwhdBprUENRmgZ4rldkcYiZXAODnqZzfP6cF0YMNf20OPGOUgBe5ahwAIqxAhN5ZG4raIS5cwnehf8uS2yz9+lm3X4znK/1r7Xd6yQqpxCmQiAEYyNzXxAHGKs89QyQCEYDqFBMq5URICacQMYeEH3NASPQYE6q+16bvyXin+97BW7jI95FkOLVKtiupAJYVL1ZdI5bv1Z2Eej8IzypNrLjWyldK/B2pPxsYSDVMz306IrmBiBW5TtfKgqv7jF5IERIKACn6WoJFVY0jdQqdHS0wChmSuL9YjI4IkZDIvF8rJKW0eUuubikytAJQxlZRT4pCShSAemU9UFOxqrW09RIjKYEAODtQ3z9M2RIhL/xdUqLz75IfmZSJ+VrmAi1zrK7tyARV5EU0/Z2FILImVeOdtN1b2wtolac0FozOZlW+M4B3pdIzHSfRQg0A73QVVprvwY1ETAUqcNMqzS71QyG6Sl80mfCriD4hrJoU6GqoV0/kt2hxrRLvn4y3KKIMr/oZyYixdP3PkfpeTMipqEGZ9/88LZmo0qmp/3D/X05UpCUsEcvskGLCMlDFzmWwSDEhhAEpPtHfC6ujVIX1rJTaUX0PAMSihf4uSikAmYAytpBMOU0vk1J19oyxBsPBw1qD4+ByBUxduEE/1xWGRc2YiXhrqvs0UMZg9QWQTCwEstwnPMgrEMjKeMRAcdrpBcNzyB6a8USPYZhhnEWcF9jxBDeeEEOEHWakGOGngHAKCDM9xjliPAVErmieIj1KBUFdNR3YX30PKFOmVh0FlBjPDy5nvZDIgEgp4wyGI6mfsjpqcPBHIqH801QV/PHTAXak4jVuGqlaqMoiIFsLS0oznQnD7f9qfEIF1UOTUtasqW4KCyqrY3rFVQILkreX12VS7fhmOZuS2qZzcAdnEXkAAGigACL86LEAcDwYiTpAyzU1c94y4zpf2HHKnh9H+MFRmV1hxFmSOQ51yV0pWazTTHQJblFIGRQmnCZJEgiUYEFPkoxevWshr+ul8JVO3t6wtwaDbM/G6QSWA3ejJmGyaoYUaWWV/RlgfQ5yk1RYmU9ZSWAHDxci/HQAAIR5gRsdVcHjPGViuCPCKeAAACc6q1PU+cq2WonVAZow6pK2NzUs+jAp6ebgmDl3cIOHExkn/zPWljLv4ivm3Pkq3hYBtfV3x4fhWmzNQmel3DRZpVJUPykHZXNo3ot0/UqqDgVBCYCFtRRt6v1YIXXSjglhijTR578lPS+X+I1RESpznTbRkCrJRoD9BOgS4r68QCkALUwMMMnlIFeUGGtBmv4eYoQqJZ2lnRZ+rEk9+vysTpFS/STdiQh1l4BgExCAwSZEQ8dIyVTk2Fbb5Udux+wzISSetJmquLWr/WyE+DNI26UgpaEtjddyve9QnUk7liIOiduqBPVVgKr6XlT+PUSucnqkK+nOgrziys/XfGQ09vJSnb/axp4UPoB7PDPlLWlV7i+pBObWl7TxyEwMXD1e6ANkr5FCAGhiAFqpogkDnC9kAVrhTkopZwySSRhA5D48XdPOWLhAPn2Rg5EY6TlwnWQRfxVZ2NTPRQG41r6SDhlSWYiIgeaTKdJ4L1dmIWxt9tZc8VauzjehTL20cknaS/4OkYj7Kt2suSatTUAkf1VYA5tkfmhvaq/8PZr2ysopDnKl+I4U2NnyiEvcH2XOJ5X4RC2V+5i2dIjx/L6ld6r7oTxv+2L7T85Ht39KVwPajt8Dq/qTLHKIKojSdmmuf1oiDt7ixK8fI1WZTb7u5CFEeLh830spwSwxK6iMNUjRIfoBUvTKxnU/qRhXRAkNGdX6CWsfY01GZZ8oFRc6f+4zXOLD+p+k8OmiDaIAtfk5coEwKURy8W4i11NUAxjHLmRxE7NgQOaAFshFcOzMixQ8uHmAslps5DS+UDyA2XcquMi/QYJ1BjGk/NyHBMcTvVL9vI7b1nylWqWUvJbVY9ZkCxbHpBR5YBEh6NkOxuYYz5Z4j1P1HKfriVLKjj4rp2gbsW6xZN0AZNXUV1dMPTQppWEBRKVSELVUW9Ejmz3mQKa8JytHopSaI1V3WiKVw5WL/OfJ479Hh7BEDAeHGCJOvwLiErHME8LyN0JIiAut1MemxK9AV1hwnjv0YPnR5Rxh8ZIajwMGR+W2j6PD98nz36SQGh1VPSqmvDYbm1tjMiGlERNIx5GQfSWsQVlxTxHi7WTWgsOWrV0hQGSfAB3D4XyiCdQTlLVVxKzeas4jsaoK4hcwjDmwMzECywnjXwGRc5WFifbTiOHlhOH4AzFEjN8GhFPE9J8Jy8uCcApYXhbEkLC8LEghYeFBTucmC9pByw5Fsil5xaKUOvx1gBsdhu9P8NOI8a9nDE8T3JHUUlodReWfXZ5MiSeFaSdPLRHVPt+a7f5ByBPYO+1DiHFtdm5WDiKrS5ar8NmUOP2LPO2cZVPuUDxQ5pjwcw5KBVT7obQpFKOkR7iIOdi8Wi1BQ0wSGFCMGaKBsewnI+crqkSe4COcSOXz8oNIlJd/iUj5+S+pfWIgb6kQEVTluDZ1z4l/wHQolbsapVRiZalxQ0lzaa51GbMjuDQ5q3xelogf3FY/5lBUZpHa9BRK24WYcPpZBsL/fZmxOJTiEC5mbyWp5jfEopQyhrxWjANiosA4JhrX8jkHUkiZuCD++kmk089/i9rsF6umTuQtFV5+0W/KK4a6/cRfwLHC0ywnWkGLkYg8NvtFDOzVE2FEYaDakNoNmZwTAmrJiryikKK2jPjFfTKkdOYpk1N3bEnVmXzCwP4+ZBJPd+HB0Qq2kYDeyOorT3Y1EZKvrVo11U3OXw9RZ+bnOCethJCKif18VIoUKUxMVqSklZT+i+PqyuJJksm1FcWUUqqA7rEtASCLjo4GUdgkgy09emvgo8PkiVQNPiHGcq/W5JRA38dr/7MSkMixhYzKwYotgVricYDGfyJ7kkksVKTzznMb/lLGqMVR9R3XkAPj/LzMd2NKisSj8VFI+/Ld1c+BkjruHGCtRYx0zbZp0HvbSqf76HYiM3NAbFK8GjdkPGi/d5QTtgbO2Ho8C6wIZ4Ih30N4rMv3jktYm7dq5RR7dkrfFEVaS8xJu2t0cdTHQIinBFIwRkuWY09DygVPaCyja0+sTk6Lw+gdkVEjPf44hVzhOP9jpfVyCpSWy2RUCJG7Gv0NAIHvjZLyJ9dMajqM7pb19LyQugBgZe5mDSzHo9m83BU7l0xO8dzvmCuH0qOoocRr+OApfnRGqgujLuTC8aLEjpLaJwsBQlidQcUipIIdYYyFPSS2XgkAk1DJOqRhQGT/35FTb+3gEecFw2nC8nJCnGe4aUQKkbJcQkSQbJcTK6VCQpyVUiokBInR+DmA/Jo8j4qNSurvtjKeVGoXyxX5nUTIIOl4xhWllB1YMcWqKBEaOOUf5acDqai4GrWbRrjRw02HUgBkeqK0vemZBQojK6bK4s2uSusPhi9DSmnomNAooopWhOnvQk5xOg2QJaAi/xysAVjm6K1B4EBBJuMySSeWdkEMEcvJ8aCVyuMiDPDKuVrkAQZAll/6wWb2ezh4GGvwfGAT88njaXSZkHoaXS5jrqvsifJL+3hs9WFZLU28WuoMVhVP6w2+j5hKqIP4FnpylgOPPYotnqwYqfjA+ctUWaX4TVkA/nmC4QEOQA74Sin2hNN4gj/6TEolJqUA5IEwrVDsMqBJLrEdbDa8E3JqeB5hncH41xF28Bj/eiKl1DTCP085bU8MjLNsU6VKAoCJS5k0XaiC+BUHrY/GmiLz+mdMVmNGAOCAL0jgx34oaxdVTllh1c8ciVwRk95fKyW59UQf7AUSU8ypaHOMsJbMq310OdCqDq/7UaPygVL5QKl90nzC8kLS9fAiacqRfduoHxonpqD83JJvGjWUg/EzpcEtVL00q6RipOitJaFz4FVUPpIyM6t0vZclIiYi9k5LzD4rISac5nJd/JgD0onaMPgEwCHEyGOQZZWZwRwThpSqY+v0p7YdjSL4sjpqmanqHhuex5cXnmQttNLKbSftKKaX2fhyBgbnKtIqAUjziU6jUZtVqq3mN5fzz4bwsaQ7/Voiq8wKoSdt2EJUxPBkjwNYHFxCNKQuXStmLEqXMtktr7+FdLo3YfWnIAJ14Y6chs+LPjl9KZIL0JYabzvq4n0plYqkTTXqFCGkhERtkckbkMLH8YJAJkksze1GZ0tqm1ae8petfdPK323QVVRA5wTKWncTAjAxAWZYyUVclczDmIxKa2TU5ZvNRXJKkVHXzLhN812dA2wCIpPKVq/w8nEHnBNIW+0jhJ1c0/k9oHi8YrsdI5AXS5xRC6Uy32NvwkxWNWq9jK0+2TzPhJQop/Q2qg27Sur3ou0rBkycWGBIBtFaABFDpHv3IVlS+zoDoHgE/8qLLPQolTo1MRViwom91GpSiu6TKSYizlk5JSRUZHIKOCemqnNXNyxj1cKXNTw8kkpJvIylul5O3zMme0Vp36gnRU6Jobm8losQOPF3syWzRr0mcRhdr4WYAnBeHCc094qsPjREpiwzp9pydVYAdiSlONiGwIeINHosjrymwuxJUR8CzSFDhBs9wmmBmyIiz5lClb4XsxcVPdJzmS+1sVsM21ezzfNXU8gpawtB5aRqOvlKCymVq+yxGkqTUbSw6OCmEdZauONYq6NU7Af2lS6EFGfNSMZM3ZE2v8ej4UuQUm1JdqAQGzFPEOj17NuibuSGLzZh2ydHbzwNLgejc0jMOC84LRH/vMw4LRH/O3nKSX5ZECTVYYkInJcMlI7fGsXm6gZq0LFctch6i2lwlQRzLSdYSu6KQooqm5RKJwdmvLOcFefEVEJe2INpgmezp7NfSRU7Xwnc2I163wAlqBJCSkuz9fFkAOSKKmI0aJyjiY1npdpywmAd/HSCm0aEFwqoh+cjwmnG+P2EOC8YX06IpwVhXqhCX4gIc6gGO+B8QJNBzI28ylHJN33OITbWwj9PcOOA4WmCHT2G5wnu6QnGD7BP32lAmp4yO278WCbuQhgAZcV6hRhcI6S6Afp9oIlvDZoO8d+mTiUOqVThs+DxRySeSKxsokoiT4NF4OqeS0h4GuJq+h5QUnC1n4cQ01ItilbDDI7eVRX4nGGjWSZgch+TYgJuoInF9JxXcNLpBe5wJG+kZYZ9LqloAM7SzwBQLjxAyj++0WJQvmnDCPv0HWYYczWuqjS8alNnRSlBFTKP3mWV4sTffY4Jkydi5dvoqvS9mBJ+jeXa+H++T3h6JtVI8UAxZ233NBD5L9VKyyqius6EKHYxe+XYY6IiDCDyyPqBFFLzidoUgOMqfJA2rDoVy7b9AF2J0xyONIEZJ9jjM7XxeChVC3X7GboHJF45htXG9+yrx5PiOVJfnEPEPJyn7+l+l38TNcn13Fb593Am34Nym6Ge5Mp+Vv8+bxFqj43X/0SscaN7EFPKSjVJOxP1AThlCikiOV/8eZQZd6aycuDfpBaszQ3E2qBR+iZjK0WKLBjK97u0MCCpfABgkiFSnj/orclJ0jYmSlUDqtoSl1qvJaK0OioHaQZZid6q+7bmO6JMk9/uEjHWehNpQmoNsvCR+J4D8K1Gtw3KYkaCKYbwq+0DbLVRS0TpLWWMMCiqx11tooi9qIgr8RqFsVnFrz1Pi0pK98uVdJed/VJ7UbVE32v9orqQ6nbouRSgCWl5n+I3yY+xTKwGUUp5i4H9OWNyZcFqDHmhTxZeTktRTQGo3suLWlLNU8V2KRYiKiuldvaRrAKW68eWmDD7GLFwQZNQQrAdmteOo8+vj1wQq63ILAUIpCo7xYriM6d8lPgcJKZeXec29dwxp9lK1dFxQk7lcw5pPkEqpSdOfTRc2dlNM+JpRpgXxHmplFL0PCDw6/Ja5EcA2Z8qxZhJKVFGyYJfXBEVrKEopWx+LqQUgFwxXRTtoowSA3cApIpyNqfuyXvWWbjpQHM6aR8/UoaMfu1wpLkfk1XJuiJIWMuAefAY7yFJqa2AEEBefYk5GCw3br3CJBJnAYmiDKIzOKRS7ncOEYDHHCMFdYPDKUR8nzx+LRF/n4iM+nkqg9tJDWJAUVQBdTndNv2hrYxw8CUvWA803yefGe+Ds1XweW2A0ekSa9ATU5kgQE0EaKMLiqUN6N+hPbSeoOQ/FSG1ljaYj2dBOcwyeQaIwAFIAgkOBAGkOOWS9sPhBX56wRgi5h8vjUT0Vx7khI2PPACKmgEoAxxQBi0AeTDKecOjrwYh40i2aTmFUBhyM04UbB6feXA65gBUymdniHmxeMhoL5lLaXxrbdjxJmj1X0qpSiXO5DgK4ZsSTaiMMTmNT0aFIfcjTyvT1mQ/qVnS9lJdCa2kmdSklEw8nKGVM5q4mTNSpf4ydC0lNqpEmigN7QlECFuHdJhI8TM9ZfUPQKQvneAKKeVcJlSqR0lRtQ44HOm4Ut7c1qSUTIhMAqcgGoyukOdEqMRs1vtrCaumvTEBL2oA/K+nAU9PdfuRMa/LBr3aq8/zP8cEY1b3JGRSKqUIuKGYjctYFAOlPf6itD2jvKUQw3bbQY1rQkoNY56o2OMzlTf3Ex3fD8pAuv6RHXfQQQ8TxiH6kurwy8WsQGtN4tu+lytv2eIhU63G6kWRvDBiVDBbt6MO+G3+u2y7ha6SugwJ9FsyRN6LBtnPEVAkAN9fygJIfT/evD/rY2+kSwFl7MvpYknmIfI8nc3hBBakQpLvwC9SZWQgv5fnHjShoZQf1POxNehKkbp7Veo+qH6sCKA1NXgL/VvoY2wRHvrVPepducdElOspJhq7Esu0YkIm7+gYfD1jf/uskXT0ulJDon7Pqnaj75M4zZx+ISIeUHmMEsnACgbpPzJXlPkQbuyTubHqvpmJKNR9Uv80LTmo3+sm5/eBFhVIfGdMylYI2VfOAs46OBPhAhFW1F9KUas5WsRIxaxiKo86RV2Ip0JG0T1Zvxc47V1bKWhrBXlNP25BjzFrMaE89/Y8RnTWVq89cVrZkQs45YqXKhbUpJR+b3B1EQKjCCm6d2syvpwz2adwcRVWMhrnmSD2OX403gOnX9A2K1Q1eCBLkmEAAs2FzDKTPzMv1umYTMioFCJiLIqpFMikfk1xXpRSZVzQ8dtqv8tEVBknhHzSWQBSPd24opACit1CJqny3/z9M/HEGT15wXHMZFQmqqS9jKkWGivT8yvikEfBQ5JSr4Wk1GjITVuqOAyulJWkwI1W73wSI3QxDTc48uC2xITjSAx7y6i3A1eLdtDRbPjoXVW686jklyVIopXpooyiQKqqnmBkYrC9QrUGuRm3E9jXdvhrxz4jpHYiGSosLGaY8Nr8ko3mlYGwsWSqKgTVyNJQO3gioDiPuTD19WAHrA9oWwOVDEqZKVe5xLnCgnhIsfIhB+55MNoYfPLBH3cQegRoUvX1++DCCwZU/c6knMbnuMemVIgpbcY4WErjk+drEKJJV4cSksBbChBo5QtZHUpkQH1hJmNg2NcFdjlb8TKi9GFlYuJyyFg4UatVSinvPPihuinDD7CHIxEqTKJIae6s9lOkiuXzowWsxIQ78uDi+DgxlsnobMVAvhAqRgmSnrzD04DsiVJX2gImXwg9aT+ZpFlWmpUTtEjRcPEFy23IE7MxlgBR/LOWmeTp2ndwpf0A5KINZhjLBEbUa9bRcbhyVC5z3vy2oiiR399ZISRIQSGTdCGn5pgQ+Z44b1wAslotlcl0pS2dBuCMqdL26HyE/OiM0ntgy+xc39fbbbTJtGyX/QtXjPR3qW/XUvnUuWgllA7+1zx7gEL0F0VNWRQAagKGtmdyKn9JeihETHO6a1+hIVxou7ovA1hV9l3q3nnxrzmPTBhufGYLOogXiIZEk0MxUVVYacsM9dm97bNGRsnzVjmmt7/cLufElD5+affzvplumD9u9U3pl7S/ugV0v3zrvKBjH9a6iuW+nowUfZH5EyumUgK8Q4xEUknhA8vpdzInm23KlTuFoDpmVRTdf39VpJSk/tVk1Fqst5byruHVgKLjQXksnqGOH6mPHlSsqH0dnUUmoET1rRcrS/EBe3avdmqeKAtHZZ7Ibb72JazlAaZWTNGPowQGQ8rb0xqe8oWzjhY4Hc1t0jKT+jtGOD/AxUgWEhyLif+mJqoArHqbJl7sq0ipHVUR89fLsZ3L72lyyqrnrSjBcByY57x6UXZoFmllsVG/Nk6UsqcLgUil2ibV+CvYtXwpUkqGIqAEgHkFqrlRt4Gh4yok1iRM3mGwZWI+WF41HkolJ6nsJHnJZEKMyoy4HaDWBiffDEIy4LQDDFAGD+844NxYmdZld9fSJWSVr52sij9KK+OX025Jpbd2/7PPr5inmva1/GFbDM6NJa8LXZ3FWBjvS/Dnh2zKnJUJXPEq/SI2XlJopDy7HgBjQ0atDWjZM0cx7HlQGsbMegNgdYilFCZhxzlgz6SUDEZu4GBdBZrCkDdtkpuSB6wMa/cFDx27kYlPnKulgLKqp72lrJBPBkAsK34hJp4ESJBCv9XEhpzaEwVAZa8m906poqK9PGRC4m2puilpe0LCZHWKXFMiDQaoPLtW47kRJi4whyNVmWGPgFw5DtgkVUS+TStlZbUnSr+Wfu5Lifj2ppsrbrFSCgCCEfNjamNvKe06JjruHOJZuz2r0/uvpwHPT6UNtUF8bjuUVUNnS3sKySLQ7ZfciOxozG2YUoRxA5Dim9qvmqQoRVnyQ2k7Iam4nSWQo3GfVHg2UZ90ls3aDZeP9ylXGJT+d63vUZsZrmSr+2QJhsXc2EKqcPEKLLe1fq5/c/k98mvoaFEtHClo0klInNVtmADgF3K71/u15Xdo7id6t1thmKhyzhUmhTTSVgt6P5eEJ5lsMVS0IbFMVb5r9nJSJ7tOd23sv2nZlogCcJOqr/0dNDGVt9n4rJ5GXmqTrEASgkjaOF+bK+3CT17bNlsEXUtEbyGTjPxc+mUAWD2F7NMVVJvxN7xL35RtWpJUzm9TxbZDJdX5q+u4tAAo8yhniIiKkWMnHq9S4krDhoprAOX+PwebF6dI/VsWqmTB5TxVPVXPAWQLgJAuk1FrIgQdA/rmgr+kmAL0/E7aoggOpHp8LsziNCl1vtgGFFJLrByyNxcA70x1j3a2zBfPxA2KEE5WzYtTJIYh1nMguIHmkH6Etn3QcVelHo8xq/DTXCo9exYa5AJi8jyIqfm5QmpNTKBjOU1E5dd0FoyQUyrWM65UkKb3VEVpma9xFb1K7S6f4cXdvL2oqAbaVtL1klJIVYTUJbGC/o0eBA9PSm0Fg1nuicuDnExmhG2XnHtjKZWP8uotD1oUVjqTMJuUq8CElGADZZENyVTeJUBdtreFHnDo+xSfkjqdpCajWjWErOpL2V0dkFpwMMfBQA5Cr2BtoqSRCb8d+9LYtf2elS4jbLuw9HRGWTEVF7qwQ6DtpFqL+LZYl5VUKQZSUMXIrH/MRBZihJMSr2rAOzudhk1vB6YsxRQySg1AoojK6igO3HN57GYw0m1w1iZVRGfRpu880gD1qFgjyPOKdCorT5ZT+ZylYCqAfRJydEilblNqUk6an7D1OaFr/JyQlkmGyROMlZM3FslE5FLcoBsjJCUGKBW5uLKKQbk2NpU+rlSQrAgnudk6JUtu+7rqsxZlXM/km1pRF8JNillow2Npt8WVsZgqlZbgTNqvNebVq4aZQGnarW0/2gGPTzrFWAx7jQHgKY0ziEqqsQZX7Qf+LRK3YV41Y4LqjLxe+3n5PmBMguEgUL6Lk0BeKoclwFjy5LnU93TbAaXtZD5Xk1N1tb2WkMpfe/XsO+6FVgWt/aUAsB+ROZsHXFr41Q0n/wAAHmxJREFUv6YoXVOXaEKqHCOpv+uAv909J49Ur1Wp1ChzwkzUpHOiaQ8qYvQCIVWfy/q+1oip9hgatyhy2kVYOTfdJno73S7A7W2z1i6X2qStiq2Jz5R4uMznVEjVCJmv131zrV+tneOevqn3oxVSmpDac7yOt+FaRWMA2Vsqp6kawKSipKa+LQbiETEaJmepd84h0rzBGlZO2TrV35b4bRY/IvVa6+/ZxnyCrRQ+11zs5/51yM91uruQUPIZy99Bk1LAtnpejm2AipAqMaOe61wnlPNinBBPet5mgZQcTOJ4LS40F/KASY3fm6rGnJVTQIlhJGYLoZBJYhshxV+YqDLqtTZ+y+e9kcKniSh6rmI6dT5Z6aVJKREeMBGVMwWEgGrJKCCrorRwIc9/ZYG4JaC2qqlX4oTHm0U9BCl17WZSbQstU268paBvhhwZxjIjlxU2m+SipQEq+rJarM2GhXEHkJn2GFMOhqrqLhe+gFUDU2a+eSABcGGgKey5V0QUnbsKpmwZZNrVaE1QbQ44kEkrclu27699RmBwhYjaqpSiwQOepOpVH7cexkSquhc5mE6RLua40OA3jKS8WpZamaCZ+pyG1KTSxFjYeOA86NawZZA9G4iAYvismXEt1WwVEIAK4A0F7vydc7vIY0tWvWZAesBB7FZszG+uYm0c0vtqCXKLwi1pxVQTGSEkughTKpasydbBmL4qZHU7n8NKENASAHKdi4olky9ors18s/N83Y85jQ+5mtxAih+ZoLdV3tbABHJN3JhCRgFlJUikydajVQTSOG5y2hkc4BKNz1LivCwCGD4/Wek2JVBTvM/TYDHJpYYSRJ0RKqr91lLRVtsvpVIdKnIp87CU9pMUqFvaT1JM8qTFVm1HbelW20/Ip2g4XdEZbj9WB1hebbb064uXCoDzNpR9bgTn0gfp9UJ2SLqfTMjXFFJtQHtNJXVp8eQSXvmxTwsd3Gu0aimAfpvzoD6Rkom3FfPwNYJjDVupxWfpT9V76thYJwf0+/V5MKkCZdCdj8HXsRx/g1C6BWt3x7YttoirvbgX0bFKxJmmTYB3aRfdJufqKbPrWJoMDSnlSnxt3wznPPlq/7zkbbzVP/f0zbZbdpXUPrTzqa25lKS868U98H0KNlFhA1tUlqQAJEuElKTQQcKQXKX+BZAtEWJEFdfRa01hmVTiuEJKoX6+cvFeu57be1cbD+ptdFxYTN4L8aRT6QFkAkpiRKCe1+Q5YBMn6nt0Nfe5+EV43hMXGLUAB6DEZIHndTxnzMpxMFkF5BgNQK2iAmo1lFZIQcVr7WvAesy25t+p4dz5a7JA2JBT1eu6srSoqHhbLVAAFIElC7bVoqxtnpf53tmCbruIqzNjrogYPhsegpS6hjW1VHlvPY1PBkC5d+XJOgwc2PdDMe6yWkyl1omcGqD8NHjACrYexAAeqFb6d3WeNww0a2SUKCOyIqIip7YJqb3Qk91rK3r7dxovP7+E5mLK52f5b85vTrxfGSQx2IacsucDzECyUqiBz7SD3MVTOx+wziSevmbIZVBKKrBcHZCuDDRnSqr8+srnOm7GXoJ8bSwC5PqTCbeaZIFJcyacpLJMTq9oWd6Nc6Njl4m/noDINjIGaALGqhmhGFdSf5PAhYkQJoCRijSachE3ynGrz9Kmprymb66aUNFky0Z/FWJFzONhKQWSxkr+ThZcIt3AgX83PrxXuy2pjHX7rZEoLaG3NgaSUhNA4rRiuYa5bYzzpf3y76tOaKP9qraTL3hp8oJ6payd6CfV/4qqSi3WADAmnffBC+P+pQBUt5Vt+uMa9hhF62N2ELaIqS1cUkSLmkf3SIvzIP7qMTb2Tccv+9oT9F/DJTJGznttVGnP8dKdclX9Y9pt2v29f0dtVWPSFkDdHvReOZ/3aJfXkHNCnuZ+p35H6dd5oVmNLWfneGunwfk+Lu3iVuKwE1L3g3Qlo+7/MneKJuU0Xln0MzyPMkCl/gXIgzJEqsg5wLBCigkqA36NjifxHYCzGE/elzivVUrthdPXz0pMqLfR75d4seyntXLQxJber06pl+3a+7Ntruuzy1kW3vhvNoCp5iEGAJzP854qRoPap8RoKUGUUfmLiTfwmt1Bq9SP7C28V1BwCa3YIL/ekFINCZW311kzvL3OHDir+KkXH+Uza3Pmdm74BfCwpJSKoerXFbOub8gSDAK1Ysrk7Hm60kQ6vMa4xyTBDFV9AoAEl1n3Ld+NPQNUPRjVr+nBBSgDzFplk5xK0QwyemDRr8n+H6JPywWY1+xrYkYUG1o1lb+YDEayjeNHpfKoU2g0y65+zD2D2sqgpb1gAJyRTGkHMy7bnZFTawPTGmPe4g8mqLbGj7fuqw2EtI+HjDlkJE18hElGjVFlNT/lbc+Pp5UOGq2apFKemPo1IaTOVD55ZxbAyoRBqnClyMvtfC2qVbHLpFRDjrb9HVDkVNPX8/eixQPLkSctMlCaY0zIpcxp4mjyeem2XNQlOrpSxU9/17Xxsm2/qm3U96IFD0VGyfON9ruoFt3ZfoCawLgVyXf+XmXxRXw4jE2s7CoTVlrMKd9uy8tm7d6x1ZayfXv/ya/nbUy1n/zZ80O9Go9wy7snimpcCBp6XeI3eq/eljasf79XTuvPzkPtvnkf1fmtfUYjk1AyZiKpFMSmDxmzSShcWTc8wxaRt3o9fGBvWyOmgHNySqBJuzXsbZe19tC71W1wab6piSmgkEVa0SfbAcgvfkQfveTn1avtvR1b8zJZhJI+lMqPnu//Mp+SOM5IjJZEdU7zgdq3jjJPElKlTJdz0LEdsO7pWWXEXOgDrVLPXbgGdF8uJFTzPAsPyjxEz/fotfMYcUtJr/d9bc5TQeY1shgnUxooMYAUzokLKCuJ50ExUlyUUsn+UMbocGV+Ke9lhX5DQp3FaypWuygmaP2Bt9Li0BJSG3Geeu8s3qON8rwwtfM4/rtVPa3Gf2siBYkX109+83t9JjwMKbU3dSZvD5yl8cn2wqzTdqYEiVBqBYO8Smx4iUYMwFMiZV9MNDh6KxMdczaoAddLggJ1brEmn+S70etlor42yADng4rsby1ABerB71Zc85y6iFtUUS3yIHieygcgp7mIoiO/BpAiQQJoIah0Cg2bENO28qhWcq9JPlEGIuBc3ZCqgWcjsNRklN52axBqj6MHs+rE7Prfl17rOMO1sah63Wyn8gnBAt6fMyupUWt73bjm2sBfztWo9zYJqQbJWOr/mewAXSuy+tWQKUn9fREr/XdVAXRpF/k7Fh+JahyX3aU6+BaiRf4WkPnnetvp410lpFrI90iKTDcuj0H0YpO+d/GLr1y/7ZixRmCtnZopxJ6e7MsiTcR5f6z64pVxv72vXGtT4JyQarH2aldJrUNUJbeiTecz+fX7BtxbRFR+f4OQ2jqLShG0Qk7pfW71mfYcbulbl6ZRH0lI6WOe+2yVv3X7Xvuet7bL2WIJ1seCFrrPrpnzx+ac9WkZfGwfveVQnap6HdoMGJlDNZm4VYwncRyMuv+DYjmJz3Q8VwjbUmSGPlMT+Htju7U+eEmU4DYuhvb+uTc+PFcqy/OaiGo/38aIWp11kZBqsEZMwThSRQGofDXpi2FVNa5jNPWYySygkEJKVJDPo4nTzAYpdYmsqggoQfPapVhPL0iuxX/5sZ3DAedE1KXP6GPYjc88EB6GlHoL9IWoPaaAevK9FtSUAQ1VGkhKUu2pDnyqGxd3ybbKwsVzVcPtpQFG3i/v1WTU6qR/JSAo7aPO4RPJpnJwnOWbQDbTWyOmJGAWI2EhncS3JkYAjl/nC3il6h9QSKlqcHSNCfHaOetzQRMc7g3KW9JKHi+RUfqx4yq2iKQ9uIUkXzPdbccjPZmi18Gv33YtXrrGgfPJhVGfqb+MFBHg5yK1lhUsw+l6soEuFX8JFWmy3XeT7usrKON3IVY0idJOUPPhpUS82rW1paJN3i4fZ739rg7p0n4r7VLIqab99mCr/RpCb01lRt9HFlP4o4rYo+9XyCnaD70upOmtaNV75fXzdpXzufbZtfdvxee5w70ftoipS4opeh/8/uXx8VIb7hlXL6lAyzbbe7qWSr2lGNo61r1IKH383wlNzrW41hbVfm4kodbO4TX73vJBo/fqfVzrb2/pq5cIUznP1+674zJaYgqoSai1OA7A+XtZYcWLUygkFW1Pj8U/se53ehvaTx3blfdKT3tN2nGL+h64cW+8EB/K+2tp9FsxohBU5bP1+axeS+08Rky+GMmiLO5rxTh/xpjyt47RWluILaIKUNeaiAv0thrqtV0j9IUF/DbWAzYIoWtxm92YA8trG2KEs/e3jv9AeChSam8gmC/KPMCgSLzNZXIKUDdATqfRqTSS1qe7+ubA9Yo70p6c/DWmm853Wy0h7wPYDAguHf/DodQYmZgCzgY7AJvpfLoMO20YIdJRQJFRWr5ZpdGk9ddv+Q7y8ZbRFrRqKP3+hcHtbFDTf7ds+dZ2G+fa8XrsIaYAqLLc5+ooIVReg7XrHzhfMZNzPf8CNcmUmpeFnALU9QOgTCKuYKsfbq3wbJAq0s4lFQ3ZzHhtLM+HRCEB85mbc8Jlq+3kHGhf5ZyqT+uxBwCcjF+WU4pX2i/t+MHba3RP+619DnKvoL9zKiS2++WqYm8ntlRTwL57z59AIL0XtogpoA76gcuBv4b2+LmGPd47a/Ok16heiuqhvNaSIpdIqrfiFhLqXsde+84tLrXBvc/nUhu85hhbBKre37WKe8B9+uqt/bQTUtdxi+ocwObCnl6AalXn+t7VqqhhSkwHlNBiK6aj9+pRdWvWc8sQtnVtrM3KV4t/7IwP6fn1+HFLaXUVejGuJaYM/ZcX41RcVpFNtiGb9sZpVwioXa9vfKddr7eLj2sxn12fl12c9+4RIlzJLnikmO+hSKk9WBvk9EBWJNwlhUY+BwipJZ9bIbBk0NNMuik3ehnc6PXX3enbj61VK7nm2bF7m+q424TU2jd5d9LqIjFV1FKFtV4hp1b2l9TnSEWlWX6bB78cJ7Iy5NXfQeMaI772qP5eJaP087XB6ZMOPo+OrZX6a8QUUMYkep0e9dj0FlwiVPbvRF1b4hdQKRaVAkgZXO7edz7B/ddBRUS1xJRaTa34M/W19aKErcbS8/EVWB8b1wips++W4u3td8s1emv77dnlxkIOULfbW7DVvherl23sq6ft3QctMQUgp0ttf+Y+x94K2t5avUzP81pcUg+9Bq9RQ72HEP3Sd25hq4D6Y9ph73feIlG3+um1fb9nX+0eUu+HtTnUmuJc7k2tPQtQ5ldVf2+V6XywrA5VZNVqTAfkDpfV7M25r1W13P29NzrzWTy2QkLp19fuoZfIqNX3L5xT7dOnCCZGNd9pYrWrcZrsQ+0/x2mp/KpVnNbEaJUqq9rpjXNVjY3PnJmMb8V86r1dgoGVedy1zJi9i5KfGQ9HSt3q5wJgVaEAnK8GV4y77Bi46E0i26SVEeg1N8RVs8izbS4PQvoz15QT9Pp2ULB2/LtgZSBbTWVpiCkA5+l8DfQgVaXI6H1f8sKx5zs17TY7sOrpcold3xxoVoioCwPfJS+ZR2LMPwJbY8de7CWmgFrFCeC8QlAzmdp9Dhuv71GeXAzuz0hfBadI4iuTiy2c9dNX3EjPJ6LlC8kUZW2SeK6UUvu8MgnbPR5+0vbTpJ48F0j7bZWMp21uw57+2Z7HymE3t7sFfyKXlWOpjfdbNQpw2yr/PXAtwN9UTmyNv20g2X7ug3vCexBRl47xGoLqPXDpe29dx5eIqfLZx+urHeu4loartzlbPMkEyjrxBNQiZNMoks9iOn0NtQrDlXNc7wr3u6bWrp9rMeJmzGf2bbtGRl1U1+cdnC/EycsAzjNcGjIpVyCuVFAbcRpnvVSHV+8nvd17YmWedXFe1j6/8N5FsqsRIWySXFv4xDHfw5FSW1hj1tegFQpAvcLUqqfOgspqP+2AdX7A10ye1z5yMf3hwkR+DyF1fqyrp/jxWCOwBMLEt9vlgZHZ/y3mfCdeNdF4LSklx9xK+bvExF863iceiH4nPoKYWtuuLVOet3vDuej9vn4HG9db83pFEt+Aq2Ttjf302ti/Vvr8bB8X2uxNqWQrY1I+l49ovzdc8x/ZPz+CkOq4jDU1ykcd9xLeMjbvSW97b/yGJr1JOfVex38LtogpwWftqx33wy3zsjaOA8pvpeM5vUAonwPO1cA6DhSse2/eT423hj0iBdpuX4y4Fh9eU9ZfVYcDZ4TUapaLVk0BK9uZc5/NG9Tkq8KUi594G26qan4DUXVVefVaVf2D4CFJqbeoE7RqSnBJPUX7qJl3oGbfAUrne6/BaV8q3fagBJwHEtdWqS8d69L2N2Fn8JtfAyoGHkCtmgIaxlEPeMifp/e01HND4nlvrLLqa3edK4PQCgl1Ezu/5/WOm3BpTALOxyWgTuu797lsnce17c4/2KgM1/rw7jPb2O+tr2Flcrny3hpECVTxvVf8Em5uu3b8ujSxasazXbjDtVyllTavt4jvNLXb208vbd+xH9cUU8B50H2vwP81wfw9p1Pt13gPbuF3kE+XsHU+j/Ld9xBTW/gd/bbTVa/Ha2M6rTaPzU++FsvR52vPziQH0se94PHY7nPN//PW8W5vf32Ncqrd/x6bgna/N11NjUJ8VTUFKP8KvR3y36+K0do0wq1zvDXGe4v66LVx39ZrW1kxXyTme0hS6hJeo05o1YWXPF5a9l2gWfh7Y5sYOn/nGhlF27yNkPoQ3KDWqJQG2rNFtgdqtUL+cGHoq0O8WQuwjdXBCNg3IO0ho9Y+98kHoc+AW1bltnBJobm2/zWC6i3H3sKrCKktYnjrmnwr7phWeum3lO9de0pd3tfrTmJn+93r2nxF++1Jm5DtBB/dT/d8bi8+GWfw23At2Nf4XcqQ9z7qvUiqz0ZE7cFr0vwu7eM9sYdIXcNH99tOSL0dt9yPtnw6gfNMGMFWPHdLLKc9hy+e44099pbr6R7qKeCyUOGuseyaagq4HKs1cdquGO1Wb85bsHe/F7Z7ExEFnMV/X5GQAh6YlGpXym/ddi2l45KCSrDFvlefu8MN8RpzvvX2HhKqHGNj3xeP/H4D1up7wEXVFHBOzJypp4B1I5SNY1eLJPdQTV0bCHYOQoKbZKOvPac/EPcipoB9qqm1z90Ldwnw95Aor70+XntNNNhq762vuPX77m2Sm36nB2m/6r7YvH+JSL0XPkId9YDcwbvitcH+e+M14+/eYPYSHpFcugd+1/e+9br+Sv21YxtbnsHAttjgWiYMcF70JO/nQo9ai+NaVdTm7/9B962tOHH9u65td1mgcK64unAyrVUBsPr8YqYLcB6rrcyjzjJfAKwVozJbsfid4p9NkcHeY90j9vtChBTwwKTUJWwFf1srvu2FuUlS5e23j32vSkXXjgNc9vbY65Fy9rnLh3wfNdglYurS+xuk1dmgB6xf5PEKs35P5n3Pfm4loa7t90EGoa+Ia6opjbdObG+5JG++freI4fb9e+IV+9y7SGGax/bva/u/GXvGtnvilW23t68CH9dfe7re++MzBPv3COzvQUx1fAzecl3rn/jR+2zHbWgXDfeKDYDbY7lb4rj3UuW9JaUv72N1+/MP3JpKvxuXrAywI1ZrVVQae3ymDC7Pv96CO6inMi74Ar/Ju+oB8fCk1K0pM/pzwFblln0Dm8Yewuq12NPNrvnSXJsIvGtgtgd7g7dr6UMXCCoA66l+LS4NhG/FJVNyxi6PmbcoJb7AwPVeuIdaSrA3UPqIyfWbr91r1+c9cId++VGpZjfhGrF3L7yh/W5RHj9Ef1Xo3NZ1/A5yqgf2HW9B77NfE9fU5ltig0sFToB9sdwtcdxaOuB7YO/uXyNSuCVbZrcH6S22BSuFX4AdYgK9jy2sWba8B27Z/z3iv1uVUA8W7z08KQXsX+ndmzZzSUXVYktV9R64xRB5zwDy6Vas9wRvFwios/dXtrl0wV8cCO+Mm8yN78nIP9gA9Ttwb2JK8NEr+bdct7s2vXbtvRY39MlbfptL37/1lHrLGLf7o+9BTt257R6lv+5FJ6Ruw70qGP2O4L2rpT4/3uMa3/OTP0of7riOPZkwwHWSSuNVcZxZdwW5J26dre+JEV9j23JX24I15VTz+lZ8tJrutwlbCKz3witjxbvGf18spvsSpBSwb0KyN6C5adV4YxDYKj2+B6+txrV34HjXdJ97YK8q4x4klsLFgfCVuLmyVnXgOxNXX2zwek/ck5gSfFTQ9CHX7D0Ilj+1Pz5I291yH7zXse6JTkjdB48UnHdi6vPid6bj9i7xeLh2LV+bo92ywHJJVXUJn2EG8xYiKu/jDZ9d3+GFGO6aHQuw+dlVNdUlfIDAYA/uVmH51u0ecI79ZUgpYN8k+haPjD0X46ZC6wPKvO/Faz762/08bgnc9poH77lAV43PP4dvzqs/+4AD02fAexFTW7i1Etq98KZd/oa+9R6/y2vxJ7TdW+6Dt+7nXvjdt6+O34dOTH0+/Pb5ZMdD4lpMdy0Tpt3PFm5RVX1m3Fsd/6brdk8RK2BfxsvKtu8Sl30U3jv+e+C2+VKk1GuxJQfdg7cGmbfs71a8dlefagLxGi+btygRdgyGb97Xa/HAA82j4iMJkE913XV03IDP1Hc/0al0/CZ0Yurz4DONDR2Pib2ZMMB94rhHGTtec219qG3LnvjtPQQInw33iN3uaePyifElSanXph1cuwbfY4X5rbjXIT71xOG1XjbXKuu99hzeC53E+pT4TMqcDsJbJqD3PH7H50L/XToEnZj6/fjU88qOh8I9Csfcw4/ykfApsmT2kk5bFfZu/cwj463f5wu0x5ckpQT39sS4JfXvPfAe4+TDDb73Mgt+zQB4T7zn4PEFBqbPhk5MdXR8bjzarazj/fGRvmgdBQ83r+x4CLz1em675VcbFt5y2b37Nfua2O1RlVF7ca9Y7QvFfF+alBK8Z0WhR7z3fokJw3tUAnvkC/uRz/1B8NWIqS8xDPDjR/0uvc0+J77C79Lxfujk1MfgS8wtOz497nU97+mun2nI+BLChPewVxF8FtLqo+KxLxj3/RGklMaj5g6/BV9+ovDV2fQ1fMHB6BHw1Yipr4KP+F2++jD6qOi/S8dedHLqffDl55gdnxIfcT1/xa79Ka7XLix4Pb7w9/zjSKkWX5Wk+hSDzu/Cewx2vxtfeBB6NHwFpclXHB7e63f5im0FPD7B+lV/l473x3uq5/8U/NFzzI5PhX49X8envl6/Ysx2b/whMeAfT0q1uFf56/fEpx5cPhv2XMi/exD8Qwabr4ZHJKf+hKHjNUanf0K7rKH34Y4/HbeWi//IY1/DZz63jo7fgd95PX8WPPS1+7v9fn8XehwIoJNSr8LaBf9eA91DDy6Pgo8cBPvA8+XwKIqTPpSUNjArr/3J6H24o2Mdn3kO9pnPraPjM+Irqqq+/DjwVS1aejx4hk5K3QlfflD409AHi44b8FkVJ31Y6tgL3Vd6P+7o6Ojo+Mr4jErFHkvuwK3xWRcZPAw6KdXR0dFxJ3wWcqrPazregt6POzo6Ojo6ttEJpAdBJ48eBp2U6ujo6LgzPlJ10udFHe+Ftb7V+3NHR0dHR0dHR8c90Umpjo6OjndEG2S/JajvAXvH78a9iarepzs6Ojo6Ojo6/mx0Uqqjo6PjA9GD8I6vht6nOzo6Ojo6Ojo6XoueaNnR0dHR0dHR0dHR0dHR0dHR8eHopFRHR0dHR0dHR0dHR0dHR0dHx4ejk1IdHR0dHR0dHR0dHR0dHR0dHR+OTkp1dHR0dHR0dHR0dHR0dHR0dHw4OinV0dHR0dHR0dHR0dHR0dHR0fHh6KRUR0dHR0dHR0dHR0dHR0dHR8eHo5NSHR0dHR0dHR0dHR0dHR0dHR0fjk5KdXR0dHR0dHR0dHR0dHR0dHR8ODop1dHR0dHR0dHR0dHR0dHR0dHx4eikVEdHR0dHR0dHR0dHR0dHR0fHh6OTUh0dHR0dHR0dHR0dHR0dHR0dH45OSnV0dHR0dHR0dHR0dHR0dHR0fDg6KdXR0dHR0dHR0dHR0dHR0dHR8eEwKaX0u0+io6Ojo6Ojo6Ojo6Ojo6Ojo+PPQldKdXR0dHR0dHR0dHR0dHR0dHR8ODop1dHR0dHR0dHR0dHR0dHR0dHx4eikVEdHR0dHR0dHR0dHR0dHR0fHh6OTUh0dHR0dHR0dHR0dHR0dHR0dH45OSnV0dHR0dHR0dHR0dHR0dHR0fDg6KdXR0dHR0dHR0dHR0dHR0dHR8eHopFRHR0dHR0dHR0dHR0dHR0dHx4ejk1IdHR0dHR0dHR0dHR0dHR0dHR+OTkp1dHR0dHR0dHR0dHR0dHR0dHw4/n8fuREqOUv5FwAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "
"
       ]
@@ -2370,7 +1288,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "
"
       ]
@@ -2400,7 +1318,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
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@@ -2410,7 +1328,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "
"
       ]
@@ -2420,7 +1338,7 @@
     },
     {
      "data": {
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",
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zbg0NDQ0NDQ0NDQ0NJZp/97Bw6O/R/Lw7RyOl7grNOD0trP2ezYg1NOxEUwclNLVUQ0NDQ0PDI0fz8Z4emtrqztFIqZvGYzJMt3mtuxKd3xQeonFoZFXDVXBXduOe78NGwCzxEIgpd0cXoFvpwYaGhoaGx4rH4OM9hms8BA/Jf2o+3q2ikVJXwUN54B/KddwXrvv979KINEPWwLjP57blVHthcVfE06HX0IiqhlVcx1Y2e/YgcB2702xDw73hofbTXkQ8Bl+v+Xg3gkZK7Yv7MhLNON0eHoKDXl5DM2BPFw/9WZbXdwv34QPgRF44PAQi6jLwNTYH9AXCXdjCfT+jvXOvhLuwLft+RrMdDTeGu+ynPfQ+4VPBfRJG/NntPbMXGilV4iF1lh76Z+z7+bcVvrcPrmoI7ivpeWPbnx7u+1m8Cm6YLH0E3Mi9gn2q67bTYyCh1tDIqSeAp2DrduGJv4cfq/1o5FXDlXBb9uq27eBjtLO3idv2827C7rf8VHuhkVKMu8qv9JjO/RRwFyTPXaidGtv+OHGd5/MhSZa9a/feA8dNO5TXPd1V/T/nm/P4qPAi9UFuWU16H3isRNRVIL9rszEvMG7aZj1Wcuup4Lb9vNvy8Zpft8CLSUrdxoN+3XPe8DWp+zZm4vPVPSql/NrDftXr2cd43GZYYGPbHwf2ub8eymjavvfPE3TIHjuu4lDelQ962efs8gmbauqB4r77FQ8Jj0zV/CKRT/ug5bd7QXBTNuuB+Hj37ts9UFR9vau01X34eM2vi3ixSKmbfJgfgIG6dePkrnF+eaxzVzuXvv5DuauNVgmrXbguY34bjHtj2x8XHlqn4ipkU1NO3TsOcTIfoj/K19T8wEeAu7JZt/U591HU5IHYx/sgo27rI2/bVjQy/InhJuzJVc9xjc++Vd/uOn7dbeKa/t5amx3s592Uj3cTAoQH8g65S7wYpNR9EEhXOObKhugmjcxtjCpcVSllb0mZEYzfoe19JSb+suu+SYb8gXWGX0jsuh/Wtt0HSbWvgnDXvbTn/Xaog3KIE3UvzsOhv9cBz6PC5e21T/vs24T34bCWv9kucqo5ifeMBzD4diO4TWX0Pp95x+/km3quHxqZfej1XCeEGGh251HiLlMmHLD/vft3D8UWr8HesP9yU37eVVVRN+HbvYDRCU+blLpLhvs2jdNVjNJ9q8KuQ0rdlAxz7VyXtecKY38lJv4qrPtDYeobbg7lb7rn/XzVjsylo0P7vjDv6F66qhNVHndrTsRNdXRvuR13NeOVQv381X4YpdZ/iLW8Lh5PWzX16JRht+igPRrcVIjeA7ejD418uinUvtchz99jym/3SC7z9nDb4oHb8PHu07d7KPb6EBLokPPs07YVX6/22x2UBuam/cHy2Cfu1z1NUuqBGKe9DNMhRumWDGgN6orOSPp8eS4Ah5zP2zS7w7m5cfDDfqAxO4isugp7flWn9gUxYg8Gtd92DzLqNqTa+5xz54jQVbZV7rObUP0cghtNZHsbnbY9nsk1tdRaW12ViLoq4bQPdp1bElal07dG3Dwm5/AylC3zIL/WbebEeyjOkMRdhXjc0jv5UDv6FKt87msfDn3+HqrteYCXdH+4DQJ9j31uzMd7TL7dTSP4ew/O17spH+8Gog0Wxzxhn+7pkVL7PrA3aAQuNUyXGaXLjt/LOB5C+tyis8Vwcz7Pywc+TGqfr7Xzobf54i7Dt6+BUHovY7a3IbuKEdtnX3nMEzZiDwIHEFKL++LQ0bLrKukAQOvd9+euz7gBx+oyp2avx37PzzjYobhJh/yyEbZrtOG+xNUugugqb4LydIf2JXVxAhd+yctUUw/VObwuZGs8iK93lRDkq+5XwU2R9AflErmp53mffeX+13wv32Qo777nOwQ3SXzvUl8Cu699l93Y5/l7KLbnAVzCw8JNkjnX8fN29eHu0r+7ad/uuuc7JHXBDfp6lxJc+/hEu776Dh/vSv7docKDJyw4eFqk1E06E7dloC47945texmmO2Tcd55nLXzvJgkSPs8+30OxEVlvw2jILutgXmZkyt+/kIjKe+dSpn1fFdU++z1BA/ZgsU+4Xs1O3JbKc9c9unJ/+hsmooBLlDsHnuvOFSfX/W1uMTzyEDJq17e4ig+5zzGyj8ifX37jmmrqRXPC7j3Eb+0ev8G+1V1Vjzrkc/YmsG7j3XxLuM28crepsrzqZ14lZHjxGXyulXPcFzH1otnBG8U1SKEr+Xm35d/dpW93077hdW3ggb7erft5e/Sf6bOu0Od7woTTPng6pNR1R/euYpgONUrV0J21mIwrfp+bGOXEDXQchVJKSaXUgThotHPXvntsqzL10RjuUFxdZkTWmnJFrVIlA8rrWRy0R2e5EVO3gz2fufhbs93wbrFP2vf6nf7qPRo/QNwHKwkms9uw7BRINZXcJg4qv0Hp/Kx9w6smOl8jqfZWTO1DJO6zrcRah6p8zsVyGcIn22RtPZA7buUV1m4pt6vztrplf8Qmz5xCWuvCRlZOKaUu/a0eimLhtnEv6ql91J6XrL/xlAV3gR2q0RI7382MGwp93oWbDuUFDieaHtivuFBg1iBtTHbsnsUX7tL+vABm7uq4jvroUNu17+Dh6nlrI0Y3S2jdqW93S7grX29fP2+htlqz1XbFr7rMv7vMt9vXp9u1/RHiaZBS13lYb4qMumsjdQWjtrcxuhGj5fL5K55THUCkXMmJqoUq8Xpg1XDI33CVea+do8a276NUiR+2hxHa1WaNmLp9VEmmywmpKxHUiw+ScfDL81Xv1RrhxNdayJQPLq8bsA8hdZUR+xvNIXUZrmoX157Z8lk88NnMiKpFSJzcVh5XuS8O+KzLsEYUJpLQQysF70lB5UCqKe/9Qu3wIqqlSty7ekriKmTUdcOTr4p9n6W169sjCe7B7+hbfP9eheTfh4i67q9xU6KqyyJyyuustbIXBHh27FVDvW8BD+ASHjduipDax8/bd0DxBgj/h+Pb3Q7u1der+G7l73hQ3xnY6d+tvjea3/YESKkberBv1SjtMwK/13n3NGxXkJXeeE6q6SKde7oAJrO+7wEP265YYbV2nlo1vVpS5trx5brKPovPLVj3S1VVK0x71W4dyrLfccf4hcOu5zbMK+9yMiqu91Vyqrp8Fexzr9KFpI9VKr93ChVVvP3kZ5TqqeIzaiqfXQqf7Nhieddd64ruvHQwFC4Z5a79DsVvcJDjXbM5a50RYFVtBqS2qimkuN3402UzSvJpl7qqPK52/CGQmlJd2OvYr4OnX8vTPk7lxJT8rUpi6kVRS5W4U3LqEnu0V168m1IaXhW7PmOf998eJcqv9I6uvZv3HPG+Drlfs7G7foVDHv+r2oqDsDNn1PKpsKgTWWxnJJikKsmpu3rmXkBzdvM4wOe71H5d4msd5OPt4bc9CN/uLmzyQ/X19ljOFFZrPl7Nt9qheVhczy511BNURdXw+EmpGg5SLd0MGXUwEXVop++KncAbDw/cd39n83kZvrdLplhDpv5Y37Yqvax2Lpcsdo2pv9Sclyx7ZXmnqupApn1VsbKvGqPhbiAJqcU6n5b3IEMYe43Sl9dwyP0g7tVITi1esC7KkmNC9ANHt9YUPvtYndo+/Olro+CXX9hlHdA9bG+J2j5Czp11Qg5sxzVCKt8n/I7V4+r7SuzTjS2d3xpRZL3PHC4dllyxv/fYSUw1JNyKo7xnf+DS/tKVRvlvn9BYDcWo4ZAwikPf0ddQRu6DfXLLXZWIOoR4ugOKqiCq809kkqpWlIG/v2z58t2xy/bctF1qJu5AXFN1lNmwy8iofYmoA0ipS327fa6rdj27zrfPtpvYv8RD8fVq0SiFLV6EZu9rm0sfD8X7ovb+cG7xzrj0fbFr/RPy8x4vKbWvEdqHJQd2G4FdBNQ+xmiXUdrl8Fz1cy/7zLVjLlt/yH7zNn32vAXmyqt3n4fokvjgxSWxomNtn0WnMi0vOq5Kp/OUDLySx61sK1UkEIZWMO0LJZU8n9LVpHqryfTWRmFfUNb9VrHvc+JcJKCiOsrLdS4/1z6jdtgxWgTsNWLk5X1a7BPPrZikEveLmF0jxkoCSi7XyJQ1hc8usOPB3RN+jHQ8f6rqxmqbVYeiSh6GdWXIZe2YXeA2EopIJTpKnvcpCKoyr1T20d5n6qiShCoJqJpqaqlWW36O2+Ferv1MpRkVVhRKeWhFt5VXKuxLxJVGUkxdhkZY5ffGrTVFjVxf66McOlC38lk3gsIu7azstHg3XiF/ZEXtXB0Nv2wwaodzsZZXbrHtEhJqX1XkZVb4kJDem+QdLxtz0IrI8Lh/diH5e0OeSyqobpsUf8FN19VxRd9k58DSig1b9bcu8fFW/bun4ttdBQcQPPuuP8jXs/ny3r7emp93mY+35t9JyCYu8k9Vc7hehidCTD1eUqqGQx/ePVnyvRjyXU5Nue+KcTrICJbnP9iQVToh1i7W0fWurN+F8SKf7w54WHQ91E+Z5frygV8Npassl0ZmadysYLYL4yIZfa3zdeVIaa0zKi8JhbGU+5Ys+w6CaifT/0QM1oNGzd644vn2LqoGlZvDslvamr06BcVzmansKqGjxT7lfZspn4r7he9RLxkp52jX8rP5EgQhVVP3lITJPqEo0klgx4NXRRXODnIqElOVz7qUjFp7H8h9GJVRvYX6zCGpztaOFZBtWBJSNTLqUqJKfOpSPVX5ilfwLJVSsPDxd1MAvE9kFDwArbLf1YF+O6l4Y1KxoY4bVU+t2Z5LHLm9+i+XfcZNYe38ayPPO/bJnIx4zA5ng7HidFTJqStiH9XpZcrIfexu7Tz167kDjVTlI2T4ng3bM8JJ2pdwjZzXjvflNqv9Ijdlf5oNuwUc4vddxYbt6+PVPqfm392ib7emnrpJ386vHKNW/LadeIC+ni99LiAnjYT9XvXxRD8v8++wPEfm1wmf7kX1554WKSWx9oADe6uidhqRfQ1UxQjue16147NWrxWFARIGRBoTX15bZf/acZeCz3m+BZsVd/4M0Pt3QqvGTZtlX6SUZMrjxLw0cNKwqcJQlfNVQ7bLiMn92YiVBqxYtzBgogO8KgONTu3SkGXX/YQN14NGrdNQ/CnvAEuklHI2bcMlHRJg9TetKfb2Jlp5X63z+1++dJWGgl4QUzDrThYTUiUZdRlhgri+cKBWchTx6HiZpwhYEhy7sDP/F6+T03hhux1bkniH9ezQegf40K78yK44rLIVaoQUtXHerhlBFc6QiEBx6Xs7s6jCV7xEFe88QUgFFZTR7JwRGUXXByh4aKjMmWyKqMNwG+TU4plYbF95Ni6xY3dVBerSinkLwqliyyrrSrJq5/tavKszh2NlEGDn9wnT2jO89vzum1/ukNxyN6mkug5KZRSA+B6g7QrW5+8Kur51corfGTdlf5oJuyXsJGhW/L5DbFhl3cIvc+X2y89V7eNd4tstiKWKbxefgj19O3nspbgkbUH87JpKv4JVEusavh6Q/L1Lfb0aabXoM4f+ctFX5muSPt5qOo0KQbWTnNrXn3ui/t3jJKUuG3mrPfh7EFF7seTXNUxrxy+2VwwRGx42IuI7+XKb3F4YnXjO0hgVbbRqrPYxYucpfM+9/jFgPl/fd40tL9eXxi5sj0aoPJ+M2Y3rpDHT1W18PgUsDVEx9SVZFR1+3l5x+ovjF+cNxmYvGWhFPbUYkd1FUj1Rw3bfyJ5vn4ftKTcDdoayU1p2M5T368+mxMpLOLtf48bdxCuTTXE+yJwptEpHkioSVErTeuUppE9V8iQFSEJKklELwmRPgorPWhsBV0ij5UoRUaIEOaW9hwskyOIpkkThWqil2A/YgzgUbVx7xmV4pFeCnMKck4HyMrmdVsiobBk+rF86rLyeWlMQUpmTW3y2mLeFt7lLHcG/idFEUxkNmOAgDgakmtJERnlF59KgH6lWja9hP1ybnFq7z2X/pNZ3WevfAJcr028SkjyqbV9znGrP3ZoNlcv8vuZ18n0t37EFt1VVp1beybUwaKD+bNP2JQlVs6u7VFOHqKV2hfnuOu4msDtcmAgrHWKh5TtyUXQhHBvX3wAx1azXHWKNkLpM8byvHav5ePv4d2ufi6UvtvDtnI1PVs3vy4ipFTHC4pjyfNl+V4iKOQQVX68uQqj7egD1dxe+njimSlgd6uvt4+cVPl7s05UDu/KcpW9X8+v4p6qppl4Af+3xkVJ7ElIZdiXiXDNQcl5Md5FRexFRe5JQ3tmqgeJti+/G22pObdhnYYRq57lsP4k1SSgAbFP4nt+eYRFqVJFnRuicZFIVw1PuV91HrjMFKSWMlMeUn08asJL4krl1IAwRO+5Fx3JhfKLBCYeFRc/nrHVQwzbl/TIEyOfXUybQi/s0Yur+wc+/S4SHckRO+WmEcxaYp7Br/rzsetH6ecc+fLwxAJajPYv7N8xH4onvY9MB3sHr9MqIqqnaPVd+9UuIFN4HWKp6SshXeAoFS86FpoxN0ILkYGIKSlXdp52ElCStgLyDysvVC2U7UiGiRcdHQcMrPs/uzodDvR05ZMX6nIxiMhC8f/j2ZZtzm+winEo1AjeBXL+WcLg3CkYp9NCA9jBQlJNU06/mPK1ruFnsTU7Jd00JEV4M4HKytuwb8T587OIir0lO1d5du/JCASkh7uL4sH6tAIrfv5x3HBWX72yerhWM2PEuPpSQWiOj9lFNLVSSFau5i2jaRyV1lXBgCZUMf4RWgJUDF57eB6yUkorasuhCPMRjZ267fQiqZsnuGDdBSK35eZeRUZf4dzXfLk7XfLsK8XQl324focEuImqXj7cPrurnxX2Wvlzm71X8wdzXm/L9ClLKa71CVIW+8mV+nvDxovrJhX9a5/6dROYj+p3vCODFIqYeHylVokIi7UxQW+scrRBF0ThdRkDVDNOaUSoNUlh20ojUjJA0PoXBydZFw1cxaiskV3VbcWx2PQW8dcuVFyM2YXb68IeAoyFuUqb+QGVxxCsqqUNYcFoXPssIgqk0aNrk6woiCtrUP6MwaFrKRgu2PWPaS5a9oqJahE7tw7AzCp5qL8VUw82jdMoECQU3Q0/ncBfncNsz+Itz+HmCH4PCkJ87ec+W92vlhb36DO1SDYb7WGlDo1Dy/tVduF/D9XR07/iwXumOiCsHIBC2Xmm4QIwwSWK9p2VHtIh1SzXP0rlab9oUEuahQQ6KVuR4mLDNKCJFjCbVFLQK6qniZNzxDGo1uDn+VgBSmGVJVKHoAJenrSik6C+0rxbt6LvQsaEOkA8koArrnEckmLj9uE29B6bQWGV7p/19IK/8gkzKiad0/c7J9UviyXraJ51HnDMcq0Njb4xGbxSOOoOTXmPTBVsZnEaj6XjTvLlbwd7k1OoJLinS4ASZG/bP+kEonpXrElE17HqnrW2rrVdaVE9I2+OIuFy/S/m8cGgK5yMkR1d69zta2sEyr1yNiKqRUKsEVWUA4JDqqKvKqR22uxbuezWk8yhxZ0v7TiHDSXHJ7whSRJGCygjSysPDXEOd2czXLeJQm1EhpNYIpYUtK/df8/GEP1n178J8SUCVvl3yvw7w7fYRKawID7LPCljNO4UVH28H1nw82lb6dusRMbW+6tq2zNcL62u+Xjy29PWK/vBiORyvuY9MJ8r/5Dqnl/5dzbcria0VYmovPAHf7vGTUpehJJou277mcFzS6dplqNaY8csMVTomGarMeFWM1KXqqjVlFXLDI0ktVxikNQOVEWEXY5ydzraAdcloFCgNmDbS8Ohsv9i5riifcgZ9XfWEBVE1ZcQTGzM/TwvD5Tk3lra0j1iGE8bQmExVpYLx8dRQyRhBEEwuXVs1+XHBpEeGnc/J+zU8PBQOmfIOfhqBeSJCansGP4/wF9usIxHv566PL1vVDaTuq7xo4z0rsZA+8/1u03HapXtY3L/sgPkwhZ3zF7B3gFc77ztXECREsATyxMnQvnBK+Gquk6wIFn8fT2FfKpxPKQUU4WDKKxjFn1+cCMBihFP+uSLnVzlQUTraoh0yEhqInRGlNLwG4AClXLIJtNNqW7Iqyokpk1DspFpH35GmSzKKyKqcVKL5dbUTE1XlvvwbTeF94DwwhXeAVFwZreCcRx+I+F4rGOXQa7NaScthXanQcHV4HOg478gjUiWk1vpF8QJumJiSz0rtfCq9T6vH+USkZ+epkEPVfI98WvYx+BRrlytzivBncMGIPd/dksAHlqTTXkTVSo65q+aXWyOaLlNM3URYXyyaAFZChfWhtS0Q8tWBQrgBOOWhvUq57IQhcp6IKQegcJd3X8f1v0rDNbE6SCR9t7BctVOlLRP7Xubjrfp30te6zLerEVCX+XDyc+X6G/Tt8qas77Pm32X7HODrAeF9Vfp6QOaXVX09Xr9CVHlt82Xh6+V9ZF338+J+Lvlj0sfj7+GRBiNCpyZEEpeNF/b3+bshO9eOvFVPCE+HlJLGZY0hL/Yt97nUQMl9eBSdK2kJppyNRCSjQkhOJJ7k9ppRsnZJNJXGqSCz2Jh456Kh8dbF83jr0j7sRLh8uWa4pGFbGLBdHdZxwpvC/PP/6yPwQ7/cR+8wUOyAVwwVT7XWi/VxXtO8FstpnxUWvOvDvsHoFOTVqlqFDRSTA11PRkeqqcI+KFRUHA7FBi1j17UwcisM+M5kq4KnWozENtw+tM7CRNg2cQ4pt30Of/4c9mMfhnvto5ien+Pi48/CoeH+HTrovoPpO+ihp/uo6xf3VvyMsmOwMiq1817uBzp3N8TPUv2AqJziED7TJWWPmwGTXiceRF5YR6FlsyMSpZxGUgWJ3JjskiiJX4dHvMOl91pn+YoUKGeRhkcHFUI0qFPQaRoxV75wNph44txebqZ1MueXd5B5v/w8xmNpWhllFPYAAHQ/iGe7o2fSDWQTuiGoKYdIHJS5pdj5lG3K89TW3K4ek/WYnIP11K4c4neZsil9Vv5VStLKit9vsvSZo3UYZ4eLmabWedjQPm9+6QgvHXV443EfzmFw1GnAKThDxKEXjEmzUreHVdVUTa2jdd6fis+BA+ZxqSyMDlwgPUr1gMQliXOruMTx2ZU8d7GvsI1ZThE60XJekMs784vURsZ5v3Iwij+Gx5tqn49ELJUKKfksM2ldElSSgFqr4Mn702flpLPch69Fogz5pWPrbFPNpmfH3cAtEd8RSsHopG5jFa1U1GoFQCtSSIWwb1PYIBnaVwvda2TUHYHt0z6QIcelDwcgq4AM0GAbkA1AlSTUmo8n/bOqf1f4en4S/uCabyeIJfbjXOGnebf06a7q29HlLNt2l4+3D2pElS6Iqav6ejxVxTYd1kVfj1YuFE9l/1n1fdyW9Ydrfp48dpePF+x/JKw8Cxyor+x1F96zXSK3ZDEhIBMr7NHg++33gPF0SClUWHLBZsvlbL6Ub/K6FUJqzVAtjNMhhkqQUJmzU7DvfqJt0kiVBkoaIWmYcoIqhAzuYdx4O2NfZl1Nc5y/+Phz+D6/1Q5lzGtGKBJOwqhJYood+3hcYcCk8QJAjjcQ44y9cNxJnZIbLghjlc07G5Uou1QoUJpeiiW7HnP1IDLrQNFhlcooMR+Zdt7Gh7wgLPud4pBOEkPaj3mCnye4s9cwvvYcFx9/hvP/+TEAgB46KKPRnxxDDx26zUDkVCCplDFAID/lizXeI5WXmHTYvCRd+3BvM9k1T0TQcudIU7YfWVCA1D46fRdl4j3mpPMDBEUUOUesjGIShckoSW6sKXNkWEUfPAen6atyviIVKB2vAOUAr4jwgEhnu5rLhO27TWF8tEzOt8z7VXYegWRXy5E9P49kBxzZA9UP8N5FYkopDW9nKEOhkMoLItqTEfDS+URyMJmQmh05hxcztSVPJ+cjEcUhfrMg/bLQvapzmS/LYyZL5x3nREadjzPG2eH17QzraBuf2750hKNOY7IdJh3ISHWwdoeatnmC18YqObXzoNQHknnxFqRtRTlAhx9ITB1IQpX5IROK3CIA/FwcvxIGXc0jGWx/Pb+IGBmHyweX8ou/1kCRkzZBEFJrZFRZAZWPBZCpU2N3WXyWtA375JkrsUZG7SqSQMft3EwEkvhMrRSmGILn47uBCSrvPQ1eeAWtqfgFtdXhocPNBD1SVAQMVT/PJaKqtGnRT5un/f27y3xEAC74TU74bNInK/26uP4A324xv8O/q+2/D9bC9y7z8+T8Pr6e3F8X/p8S/h0fr4MfysIELwgl7qdFX47FCxU/T/UhWqHr1308DbqXdJfetQ70LuD7TrkmGCjwpEipCOEoLhjylf13qaMyVVTBmDtOTDyPCxIqKqSiE7PDQBVqqpJ4kqQTGyafbUvGqEZQ1RRUNaOW9gkdGNEriCx8padQMup6Tp3PZ//Xx+G6JRse9w29AWmY0row6lUooHh/ubwgmQQRlYxSn22Thqw0YpJtlyqoBTuuNbxQU3lWXHU9Of/SAHY5AcA5fFRQocgQH5lompPmLUZSS6KVmXbOXUHfetHmDbeAMsSy2CZHTJjkwDzBPnuGs9/+KD72G/8D/9///X+gP+7w6v/6KobTHief/Aq6zYDhlROYzRFM38EcD1BawwzhBVuMFF16mbXRIzGvuh7oSSWlhg2gDbSz8NpAH5+SM4bQKe9otEc5LJwuUsoEAiqoa8agsGElz2TrBIpU8UikUfBARBlyOHqj0WsHoxROBiLRnA/qKZI1QDsQaVUyGm4GXCCI5xHKTqFCIpFR7vw5dUhnSkgf7XUpxV/cDsVoW9dDGUNt2vWkROtTZ8TLjolSmfIMIAfNe8ofxaqoKbTn2WhhvcfZZOG8x3Z2mJzHzIopoWxygRBMYXyXeH7i95TTcXaYncf5aDHOFuejxcfPJpyPFr/7iS2mixmf+OgZ5snh0z/rTficT38FQ6fxSSdD/EzOBcPqhRKyumLD7SAjp9YGPEq4QNZyoYagHvQFYbtIznvVkfcVcsoDl6qg0jmWuUvidy/IKV98phxhlyXHZQLcqIqKSqkwGs7vZc4hJ0bGyWFh4g5J1Vx+R6BaxZR9RX6eylx9clskpwoCis/Pob7yGCD9ZLUcdLQ+b9ZSdVmeL+63w+5cRkatIaqcuOqnUtBaxfyCvVFBXUvf26hQEZSLLRRGSL4qmi16nKilXIlq6GiXlmpPzIEgCr5d5uNNY0Y8Rf9tGpN/F/oIpT2U/lgkoUaa2mKZfTsnfTTeVvhtZWRMNs38P/FsV/y7cn2Jq4TvacH41vw8Wp98vbJPuysCJvp/oj+sg98WSa2wzQRSipd13y2VVaV/x76a1vBBuJDWBb+uH3IfL0wBAJ1b+neg9wfsnN4LqSFDapbV5nzSeFyk1IoSKlsvpOa1sLvVc9YIKR4pl2QUj5aXhqcwUtGI2WSwakbKjbTNTvMq+cRGyE3FvsIQscEpjVwitnw0NHZM8cqSfKJ9csOVjJ40XtKo1dtVC2no6x96Bld0EvNQPZGkksmoaJSEoTIqI6t4HzOYeCztoxfGiBny3ADRXzRUgbDSQ7dKViWj1i9knJGEYpa9ozAoLyWg/ZSpqZipj+oJabx0t1BN7avOiccpjSxZniRN1vJoNOyPld/DK52qNu1w7Pw84uJjz3D2Pz+O3/4/fgf/r//Px/GWI4P/x2sjNm88whsni6NXjmCnGf3pBt1mgJnmeC/uS0QtLlu81I14RhakyTxBaQPnLFQ/UA6Oroc6Cs6TVYCeowMWvz87TYFsSkQKKaPOJhuJqe3s4DxwEVQ1kkRhWOEwaEV5irRS6HUipNjhAOh2P+7IudQAYADtUa3wpjzIxtsZylkoOwJuDnm+KPm8n8jhJnXbSBL8mhokc24TyecBqKNjet6nkZ73rofenKbQSN+lkMjgtLLygZ1RVphxu17MROydTRaTczibklJqsi7uw4omIJFJQF0dtQ9IBWVhAyl1Nlo828742PMR2+cjXvvoGbbPJ/zOf/53mM4+geHoa/Dq6YBXTwb8r284xuTW79uYQ3Ql4VRTSd0OItEsw8EVJeKG1+FBQXDe6DlRzhJpO4+ZakAOyNEhgrSthbpKrITg7VU6PBxf3tWLY+VxYv9FoYgib58krLKQjVCtKaqolE5kFA8wAVAIyuigkIZ3+bsayEnBgDzZeVJEleqoVOAgz9XHRFSNhNpV/EDmnZPb5T68n8T+xNRi1bqq6hJbpYVh4HdFH9bx4IWZFY46HwcvnPIYfKjIGsL5tFKxeEYZttdMzwOFUC4qDjuWvslCBeXydaXak9/3QCw+kw1IMfnO0Su8LEh5P43w1iWiqfDbcp/PVkkq7xzsmI4HcpGB9Ou8dWneldvYz6v7d7St7uPJ4/bFMkRv6eMBdT8vX1aZr0fLOttWihHY1zNDV/UDJWEl+9FRqNAnAksZndJZ8MBimAKA7wdwTqooWOA2HYIayvWZOp5Vs9XcosDSFwvfYVE454nicZFSh6IwOrXKKFWHsRKqFw2VJJyC4SlHzlfJKJ4KttyOc0ZEZYRTMFYle+6mua6gKgwVG6nSQEVSKmxjQgrIjZZ3dUNWsuilAQMAIzqeFx+/gM2k8cVo1KoqSlWNlQ5GireVpBQx5WS89HZaGK5SEeUi0UTta6YuM1rScLmJmG3jXDKEzK6zU9r11Bm3FjAmMOc269jS1EblFNhRDYnzFMIIK33pRftm96+QI+eM+0p+jYa7hdIAbFS80WiJisQPh+cdvXKEtxwZvOWow+knn2Dzxg1OP/llDC+fYHjlBN3pMSmlNkNGkNZQjmbxM1yTT2vO9yZeuKTi6YmA6gZ6MXdhuR9SviP5V/vqADhK3ofcHEQmhbcxNKwhh8NGu6ChVe4c9VFRUzobOhBTdM7eqBiyoRQpcZSi3FIKdafCKwQyWAMufR9SPQJwNguhFUGEgMgPAYi8eTF3lwZ6HjHrIwHNbQxNSslFRRYBrRATvXP1QB+mvaF27B21dK89tAccJUuhNu50pgLgPCuSkOJ5UzA+5bIE5406Gy1emi1OBoOh0/hdozFPFtpobF59C5Q26I86HA8Gx4OJvxmXaOffhh3ChvtDfn+HdWJkN+bGCPNekQMYFcB8jg7UFwomSmUkZL9UTO0K0zuEpFocu/95q+qqMKDDobcSPtgFL48tB32kg+GLwSVeRx8e5tevl0kmIJFQ8Vo84AoqLoXh+WwZyAmpuK5CSJUwSsX9+Fkl21LsF1bIc+gVSqckm9jWL8gpYYtKfqo0Uyn3YFJJsbKW1FOINkeFPy1IqBoh3izTI0NlQLD06TLRwsLPCyF7TDyN26qPl5FRgZiXPhqTUdKPI99vWogQqgTWJX4dkIsK2K/j9QBWBAe5fye3yWMlar5e1r6VGNiaGorWJz9PbmMBgjyW/b01cUL094yCMiO00bBbQTRNBdEk/Dr26aSaShkNb104diaiyggxAadiAOBNmDrhzwGRyPJOJ/+uENJQqgbh85X96CdOQNXwNEkp+cN7RyPfdqYOVRiJliP7kjnnhLdSIYV5TuEbTEZdnNNHjNvcSEnCShBXJTPuxnmxjgkoL4yZJJ/YmEkDZUebkU5sfNxEpJAdpYrKhXUc6icMmcuVUimfi5CGh6fN7hghi20qSKn/+bEt5uLhkrYrOksqLUcZts4NldJLwxRJqV7Hfc3ArLnJ9mcjZgazYNp1YNaZrNLCgJmgIilZdHnMglnveDol9dSwQWj8xKprTaoqunh6KqMjEKaSUZcObMx7E9R8xsGbgVu2KaDuEfTbiVFv+ac7uk+GTSSdTj/lBL/vpQFvfGnAp3z+J+PoDS/h9NPfguHlE/SnGwqdK+TEQEFAlblbKtsACGJUR2dLhQp/6b4N5Ek/hDA+DQxH8LqDN0PKuSZDVwSYgFLKE7nhQj4PRYSJdoBWHlorTNbFZ30KlU5qIXxrI+CSnNp01Elg4oND+HTILbKAVDdoQ+8GpaEGBx0UBymfAJPOHKJbUYIASRkJRBm3Otqk349JKZFA3mtDpHJw+ktoRcSACXxTCA6Cgob1Hr1GbMve6JDg3IRpXhmvDO2RbcufBSRSSrYb78ehgJxb6mwitdTHz0b8nyc9PvL6Bc6f/V5sX/s4XnrDBm846fHypsNRp9EbnYjDChm1ppJquH3wKC6A9O4wHb1jdMh7ZmgARAHQR8fRcYuDM5xvrSwzXqqkdhBLVZLoPiCJqWKTB8L3FblEggoq5ovkZQ7VkyPjrJbymsI1nCODtQMeyPI+7apmWiqkSjJql0JqDWWIW7kssSxvUzvh7s2H5KMq7UgZvsfEVG8ohM+EAQsO49OK902kFdAIqQeLSrgx90OUTDYmySfnUs5IjoIJIciOFZ/Owl9sczHBPFF4niSgwjYXCCYWGbhxjr4cEUzJt5N+nBP7AOSb7fLrvPWw0beT4gKPnLhKhBP7drwsfTuaLv252jO3b0htLTdb6ePV1ung2wHIfD1ezv01lZFUAGB6syCuSn8v+YpdVEVl/l/h6/Ey+3msvuJImdSXIx/P90IxBSyJrG5I7zVWTOmO+vAa6R4t1MoRa/NPCE+TlALyantXBTPnMUxDdLZiqF6Y1ggpwZoDWLDmuTGaVskqGW/sg8EqFVB2tAvl09KI5aSUJKKcqOIE5MapNFQxEXGlyXifTrT/c+vgKyPu/EiZoIpIRkqM6vMIvqXROC1IKWbYmdE3ls5oegNnHamgrIc2Cro3sEAwTtROzLCbwUCZ9G300MGHcykrclQVElYe/VVGQ6ODN/R7mWCIaMR4oHuhG4hcCuFQdB+5VB3NWXhHBJTyJq++cEVQXHLrTt06duVh0RpwgFdpJIRkux2FdXU99MkJjt7wMl76tFfxv/zfXsPxm49x+umfhOHlU5y85Q3QJycU5sWkhgwrAaDKvEbOQsmSwmHdwkWQah5ZYVIqpgLBGkmqmkJqhUSJH8Mde8WKGCKpnFdwCnDKB0eMqsTB6NBx8lUng5qVnIde6zQfRsEVxOh3cQ3AuhYhq6oVOgYeLldM9QP8NF6iBEntK/NKQcyrmE9ut9KMoULiXv5erIMiJZhHrzUmOBiH2JYAtyORVlpREnqpQusLd0vmYuF2Y7JQrmPEPFXOY9NpbDqN48HAOo/jweDZp5zi/LTHG17Z4E0vHeF0MEQeahVUUrnjJ3+z+N2z69vZTA1XhPN520bFVGHbvHLJfnGYuXfAcBTea6KfhHxwai/so37aF5XPruV+2xd7KbPSBy2fae8g1cvKuzyP3B4DSJKIcd5Xw98Wl3JAV1grtVA+mZqXueP4ErtIq0Oxb/678rNlwvNOKxgNmioVyaj03lCZzVlcfRvoe7jYJ72FTOci5wu/rvT3OF9UHqZnc8JJ+HelIqr07Zzw8SQZBQBustGPY4JqqYZaElFrJNQ+/twuscGh3kj5dLCPR/MqWyd9PcO+nPD1gJykUsZVCSpvfRZJQ36dgjbhewLRHwTIt/PWwYs0GKWvp6zoZ5e5igHy2eYwONENUNOY3mHzFAsReQDKhfejMfk96h0uZeaBF8bmPBlSalF5jyGY8Z2Jw4Sck1VSqfx3Yso5OXFGPlkLJxVThYTTCYY8GaRpwZTXliUJtctQ0XIyaqyQksQTl1ofRVUrJqKsR6yKRduubsAAoBe/x0cuLKbMKEHML1VSvCwVUybE+cv1vO8gVROFosoMOjNeyVCphYqKluU+esGo22LZ9b2QgYYQv0CI6d5mlcy81kSGx0TTwchwYnQgVHFII65Ki4pcl/TtiEB1oIzOL4YBe5Aoc3VpTVI3TnxreiiloV56FWbYAO6zYF59M07+t8/A//LHPg59fIruUz4D6Hro01eypPjxtLLUuiySIPIZ8LpdybjpcsX9yJ8jiSltgOGInFIzRFUPTBeVPWXomVIqhEZ4KM/PKimlvFeUy8NQmJ5mfjbc35P1gFl3QHj0GyA1lEx4zg6HQgjTCHZCElWrvxkraYOUOnZYB0DNM2TuAG5zrsxTPSX/XrHwgVSgkUOfKaOMaEuhPONLjm1IbEEoJ++hg2HotInhNZNWmYqpVtWwRCKfknNJiqakMmDnDkBWch1IKozJerz+KS/jYnb40P/9k3A2Wbx61OGkN3h10+FNxz0Go+NvJR3DkkRsHNTtobwNareFZhJdaUA7KBfuWTfDcbJudxr7WbEfxSj7ZcV7KSOzD31niXMvPnMRJpFCJOR2Pm6RkB37EVhZifGV66OnFqSW8j6Ga9zUO9p7ZInNawnNJTLiSIdQvB00TK6gDDaCQ274CCWfWSUGIsJxyNWPkgA9lLOSP3X5/crKquVnEukdFLTBrnU65CgM34sVUvFeqV6EWN/6WneHGulUruPlMCBIdESBImyPQ/ak/xb9vnGbQvXY9wv7zNsQBbOl/hYvu2mKvt5yG/l2duSQPbsQF6R1Dm5aRscAiOuloIDf8aWPV/p2chsq2+S6tLzfT7QI5c1U1vk66cet+XpA8vGk/8ekFUfIlNEvutfRv2MVFe/Dvp4KPqL07boNFRJynLt16qKf14XUGRza121sUkM5F/t6UmwAUF+QIrQ05SEbjmh79p4q7AinlOD5cluJJ2KHngwpBSAPVQEuZ8xLFB2ZzPGLI4ApLEYqpDIFVcgZFWOKFwx5IqQSm55knyztLI1WJJqCIkouy30AZEatJKPklI1VaahKcorneR+5zJBGbBZtf24dJpUbJ4CNkC+MkhdkVJpngzVohcl7aABD7Rm0NpJTXvRYzOCjzFMZBR9Yb2bX47LjfTQwAMAc2XKpniJ0ACZo9LCY43FKa0oIHcKQ4ggySLlC5JMIBYjx6zruSwbM5PfkIeUYynvfOXpXrxmuJ2LQ7g0rI3Qx8SaQcrJwMmszEOFxfEqKkZNX4N/4yVDDBubVN5Mqh8PJgDwRoukRSxbzPeN0Go3hXXnkphJ6EiGS+cbqI7ysTd3h2vFSpPCSZQ/mMueDR+m1VnDOZ/lLGLUOTlyOjpKqKmpWVTYr3yUqpxA6FVn4o05qEGMWxFRW4RArKotLpNjKpywxtWuvtWfM86IQ2zINc/r1k2FJRgGJ9CuVBkCKMmJdAYdU2s7jZKCwwaNO42J2Uc320tAtCClWfvH3aQnObx+H5Lf3QAzno5C9GUCwYd4ntZTO+0/Vc+0bilB7JsvzincjXUvx+YIky3J58MAjQOu9i+9a5UNlPWuz93b6yOBkXEXRVRJRh/ZPa6e8pqJBhuLKZUaNhIpEk0rPfiJ8CGlQIhFe8jg+l/wMxGOX17n4nmKFz9avGwn5uVrx4MUOQmpfNOXUowCnaWEiOhLV3qfiVQyp+OQoGFnEKogOZF5froguCSk3ihQsnDeKI10qvh0LD1Lki6+oqOpk1L4ig338uTUfT2LNZkiUPl5tnVRMsa9nFDD50CdBKnQjtxsPGGehgwgAAHwgobzhwYbk9+ng20lfzwCwI6gfrjmSaE5J04cOTjtEP2+aoUX0jJvmkCIw9cHhQjkdGaUQSaogOqiJZJzLE56/oHhcpNQ+0sx9z7MG2XkpwvaIpCI1lCwFWibFi/LNSqgeSz3XpJwyRG/NYJXKqDyXVG6wxlBivWas1gwaUFdK7aOS4m2D+J2ezw5Wx/FCAOtqKakGICOUG6zRJZXUFAzV6Hy2bXQKQwj3s6MNYXzEhrOs1QwGdsyVU86auA/JQU0W2ldKPL118FyG1Bla74hB53Aegw7ASAoJgEZZWAkFRLJAdoIjOVVLlnoNrBJSDTeDWhgfx42HnCxeAwhqIwdAuQ30Gwfg1Tk7j+fQGHkudqb4b56jDeIRPW9tUnICqQADk+dA7mhdEsIXQwaHDSU5Hxy4nLn3DqpDKHOusvs02hjnMTtgtA4OwGhp9H47c/U9H3IeJVXPxAk4iwp87FRonVRQrOSJSc6VwklvQnigIDK0IgEh/JLdcDPgQPkl5hFqvoDyDmq+oAGG7XPqtAalbFbUQg5USGJKkHtR6cbTfki5CDYnucqM7xehTkEIu+PKWtYDc6hoOIeqhmMYfNgG28/TyXF1LR/PAeROXVY+PYxALhPJ60hQudDOStht6ZRyhcMjY3Dc6+w3NEphMOQQDqGCIpNhrKaoqaQaIXVzqBFSax1h+X5XoPtTd11UkfgwXUWFeOJTRmXPjusqsSQ0xH0ibGNZYWtR+l3ml3E2KSZ4W5efDyACjj9rlYTeA0nRLN4XHNoXPr98V3vvsyTnXHUPANyKIqqGqGwsfjImoYGl+omJJ96Fn1Ojk+qIfwc+Pz/TbBMiSYXiNyyJxH3692W+s5X7Sq6rniZMF6qocnB71zXED2rE1J1hTS0FLPtfa6htK/w8fr97FhxwrqlpXES2SMEB+3flPqSUyiNf1siohXpKRMFwP0n6aCw0mHwuPNjXt2PsGwlTNGZ17a6IGF6X5xEuyam0bgj+Yx/8w0GrmOaFw/76EHJnBgOlVQzVk6F9JqyTvp7uTSAMaZ8OiP5bTEQ/JKrEi0Tp5AsSMQVNoXzQuW/H91Tm39HJ48DjzkiuffJIPSHb87hIqbtCYbBkrpaIIolnDJsRzPnyz+asudh/l6SzjBuuEVK8/zzZhbHal4zaZcB4Hlhn0WWryedrAl1H+dgkgySnuVIql22u/zmowKaTgmp0OZtOfcwwQmIUMNpATNGUlE0eDg7KkoNkwdLLYHhcYsi9YTWdg9OO6lwFht0H4+i1gzcuklUegOK8PjJcQIy6UjLVPtxr3eWdtJsiahtuBpcRU87RsK938N1AU0lAsmrJpU76ajgME+EsJ2fChEkpWR2GO1YQ9ixecniJijAz1fdAN9D9yJLkUHEEXVBO6A7ezoAJ4aVFUzCB4hDsj6cOlfMeF7MLpJTDdg6JuEVCbq4Gt1ZliZ0oLu/da48jr4PtCNWVOgOjAeuCg+UTYZJ9fx/a1c5U6t7NFJ50cU7PbSD7/LjNk6ACkfxbqyYWywcbQ8UMtBajsKGCSz8AXB3WzkFJWX81e0lOOfqbncd2dpicx0VB9vEUQCL7CtlBSUoZrTBrjZCbHi6EArpY3TDYQK+Euik5n70SqggwSeljBUH5OXGKRkjdBS4jpPYhN/i9r0tyoHIO71J2RB/CSPkzZbhZOnb3BfC9KsPFjLh/AQPDCdi7pIaIZJPl0u+BiOJiNvz8s6qqMgjgFRJBddUk7FcgL/YlnGrq1DWUVTUlEQUkMiojm0VuOW5/nk8hvjkBZbjNvIWyYeAlTCNRKIi/vfo75XwIf1ZhPt6XWq8r8yT2IaHKc9R+x0ZM3T+u0B+OJBQgohakIEEuuywKxhf+nFyfRcqI/L81QirNyyiZXGggI15Kf60ko65CSu1LSK21bu3OTyRUGRGTT2U0TOnbVT/E1fczCkAQIgBJOQUA2qV1EWO8UihLPqqbKDpGRrywn6eE38dTB0D3iD5eIqNEgnMX+n2h8qy3FugOpF9eENvyZEgprzT9Zpe8YK6rFikduujwCcgSm7vgBDFV306EE4fryUR2srwnk1YS0gildX4xLQ3UVbHPN3aoG65dsD4ZHLpGonbYyLGskxL5qsV+0qh56wOBpAGj4KyD0SEpOnQgmog9d0A1wefab7X6na2D0g6Kc0yX5BQEEXVFyDCjVewqjd1wsyiJqQAPpOFmIISMeMo/zuRUOFaVTtM8Qrn8t+aEm5GQGrexQkzMcTcG8iQoOGWlPs59RpfJIz8GPlSGU10PdbSB1wbaOUrKDlAlviNN16ZU5sCxs+nFn3WsmgKRUc7hbLJR2XMxk1JqOwfyxKYKUbbwyExUSTkYDUxWw2hFib6dz6ryGaXQg5RUyiU7svDdvIPySJVXbWpPzBPc+XNq44vzPK9EIPo4XDu7BbgtWSEV2o3zUfG8A6DmCepEQemOzscKCuEM+0BEudCONrTPaInUO5ssJkcV8JzzuLCJ9Isjqy61p3U+c0yNVrEdjQZ67aEVsPGANQrWq9Bb0Og1Yi4aADFhudEU2ldzVjmnlSSdpJPL51n0QRshdatYI6SY4EiEktwvf4CkQiWpeXxmC5iUTERUqhInP9uvjLrLMDEASwWOWuYKSuGhBkZ1VOQ2KKC8rFrLttbMRF65OSqoqgQVsCjtnV9snaw7CDc00FSL3pVYhtTlRJRURGXhblHJEPYJJFWsZjbP6T3G6/hdJnOPLdRol5BTrIoq1KVpvUqDO6w2LXMeyn0r587mdfF5knhqJNTDxD0O1PrCFyyXy3Wl33YZcsIoiQ1oeTktfbuaX5gfUyek9vXvgNzHy/039tWWZBN9rqr6ekal8/DU8eYiNND6/X1Mbz04vzj9JrsHGmq/ZTqX8PGyY67n20W8QHbmcZNSlxkfkfDO34WRKgkruyScdpFQ6TT7GapyP2ddlVwqjVK5Lm1bN2C7VFKHtKwkppKhyY3VmoEqY5dLQyXXlfvJuGOA2s4YBe9SzPEhIEIrVG8wLiMb4u/bZwfsJoauURUofq5S64nxSrxARu5esCvnUtw2pLLgQU1lfej4eypXrKaLmHjbz2PstHMISAwZnicipS62cR7Owm4vMgl5zSbJ+Hkue6v7jsLMQmJvBxChYlKOJMUdfFb5AMEe61AZikf0KIRvsg5b6zBZh7OJpheWCBXrKKTPOo9xdiEsbYWU0gpHHZFRQ6fRa41NR58TSSmnIkndaQ3lgz1zihSR8rQ+SI5CiWh3cU4k1Plzasvtc/gpTAMBWErz66SURlZGeDMBWkNvTul574aQ78sRAdh7eDMDVgHGLd5tPnwH7ylsb7JE5F0IUuqcyb5A/o2zi+05BtJvrU1lux73JpBJ1JbO6GB/HSZHZB8PDyiVCKnBkMM6hPN1IJUEdJdIivKxEPONhLo9LJJCV7ZJMsp7LJRNct2iT+BysikPF0VMhE/hpKz4qyfgr+UrKRPxsyKSiNQQepoRoiKRfrhHe60CiTLQtg5EUHHorvfwkTyZo7JKcZ5HzuPnl8/ndXFpMR5UeaX9z185t1REAUJxoEUbCpJPIeSZCzniKExyIhLKzYnYD22XkVI2yBImDi0XxL4Mh5bf16ZEwemiU2GOmPMwzMeqpnGfLiemlI7blAm5GlmVKvdRTI7Rcny9lekUSmKqEVV3gwcQIbCLpKiB80gd9hk1AmndCqy5jjXfrra+dp5DW7kkp9Z8siQsWAoPanmqaudJ23zYnnbwzl/GM2Ug1ZOOh/iQiuUucSXRzBOzN4+blNoH8geTpMAuo1ZsU9qQAdKGpHwi5CqGvgAhV5CFdhrWphs6lgufkiLBB5JEJs/WALzx8EbBQUMb6uARaaKhjCdFj6UpBsCOQeXjaF0PF5IEA6woGjQpiRw45xLidKk8ykkh6y8jj9JzLw2ZfEx0+FskJl7IOdPyrip8vI5D+2qVGWRSPK7SEKvwGRUrLiixjX4brt5Ay/SzcvW+5GimUqU67ifjjPlPc9xx2ZGSOWd0MR/upXgv1lCu15q8gl3HNNwfduRWsR7RSbOOFH9HpoPpQrifmwHdkXTY2dRpFkUXME8pXG8eI3ESK76Mc0zEKRVT8t41wWZ556Ctg7GOQrVCCJ8HYuJ0mYeFvowY/as4sjZIzmVI2UVQ+UyWFD/no8XsPM5D7oRxTg4KK3tYXTN2lDfqZDCwXTI8zqgY2jeFXAODITuwM7ol5pghlQQrokghtY3LbruFnWbYbcodCKQBCNmJMX1wehxVc+kA6KGP6jWAVFIeQfVmbVQQ1AZRJBnAYY6T83E6BzKKVGcWYyCsxtkFss9mRF9UKkVSSmcKqqHTILU6qaMm5yMxACDmhyHbLpKQBnJKeQc1noWBoQ4wHUwI++T7o+HhQSqYoo1yS/VTynEmSCn4GFIK0D3qXAjJ9Qh5ztI87cP3tXiOi3sNSGGfQF4Vkp/3jdGRpKJE+h7GUe6RzlBOOat9SNbvI0llQgVMheCIAKSmAmi9m2Pej5hPD0tVX9afvAm11A2Bw3DlMqOmjIoqKKGM4udaA9S2CkQ+zWNUl0biyTlaZnLKzcA8p4rVQr0b8/iIMOhF1cMixUHsI8Vw8zDth0RQiUqnCiCJHFeK5amzFPZn3EJVpdhG0U0RRlPv/7dsEDiEmFJ6NaKASEyd3WfcJ/cAYqGY4Acqo6Ed+W6yEhsdl/pTch86Dfl1ynoALvoaNd/Os+9ng0IUGsZZAAqDlgRNGOURuZbYfyuVRkvfrvT5ALZql/l3u7Dm58ltpU/H28rK6nWfrqzEnm+XFdhjZfUdvl5a1tHvk36eHGDkbfwb0+8q1sniQEySlz6d1imncA2FSnMnHsD75abxNEipinGKMea8vSLfjfM8uqF8vk2b8GhP6UYKhktpFw2WClMPMnDKOpihi46ftjrEnfYxJhVIihonHBpnHSVqCxYgxbAGR8+kY1ToXUQJaA/YyUJbDx0S3Y5OEEvB+GitKDxNAdarwDLTuXg+KaLyed5HLkukROessQSOtcJQecBqRomXayQU77NunBJZVZJRXBKUE9+lZSoJSooGLhualmOJ0eBg6r4L5UapfKjuO5ihT0ZN66g4SURWPpInDVbsRIV96NYrCKmaBJ1RzstwsdrQ6BM0YveNfd7VMqkvz1tPIS1lwurOKBx3lFj6eDihjj0QnPs5VsKiE4fCCzOF7PlxC7fdYjrbwluH6fk2JNm8iCpNN87ZtZVKqW5zBD106DYDeufo3teGCJTgAPiuh+ZcSHYOnXwX2WiPFLZHuY5IJcWKnmejxXZ22M4Wr29njLPD+TjDBlJqX6XU+WAwdAYng8FLmw6bTpOjqhX6YESOHIWkOa+CY108F0EJodh5GrcUqhcUUu7sdfhpxPR8C7sdBSllY4LTXUqpbnsEZTTsZoAeOvQnE/RmImIvhPf5fgAGBzVsUsVGDm9BIAqQwvY4P9fFbHE2uaiQev2C2vD17YzzkdvV7lRKDaEtO61wPBDZZ10PoxVe3nTYdIgqFK0A6w10sPMKCp0JicuNwqbT6BSgLp5B2SlOfX9EigQzwJue7hdTKBR4/hI0Mutw7MpLVAupYwKKySgriCgb8kTNLt2HHH47OV9NtM8huhxKyvMA2TwAWWipREmeAohhplqp7JnXWmFjKPfZUWeiHTjuDJSie1QDGIyG0R6dUE8NZkN8RU05pVMYNWDy0D4ExVDZD10oY+8ORESF/iF8Fp7H2wEs8kXJ8NtSZdYFgkZN50kVZceo6KWwvS2RdtszCn2WYc9BvesnUZiDSSlWT4kBkzINhi4cfz0EKXooyhGnoUBHtWBH11P4uTZAP1BBCTPE/j4MEVY+/J4Koh/FP3GplirR1FIPAjGli+Q4Q9SMUqmgCHzYQRsAU04ehCnl13SAttHH44JGeujgwj1rhh52nGibJZU0QH6bcQ4qpl0Rip6KbwcAyjpooyjflHbojUJnU97g3H9Lfh7n15XbUKwDsPD5eJ5R+ne7FFoS9Srr+bbSp+Nth/p6ctoFn47JJzMkIYLu9aqvR76gjkIFGTEgCSoz9ElsMHThGN4n+G/Rt2MVpxbrhTgByIjwLMpF4EUsTvU0SKl9sfIDLxh0xUyoyYgnjylsNmJe5/toB2Vs3EbnYRVUYla9KxRSPZFYpu/IOI22qphiGbOyipJ0w8Tyl856GJhQwhIAHAYgM1iSMdcFiw4kFVUel5zma1OJyI8JUqoXnUmJfUkpYKmGusxAMSHFxogJKa7MoMK8Ngq6Zzl3UkjpbP+COY/5d/JlLVh0/v1ZJZWTUXKfYgSQ9zEmjdwJrBoppVEV2r6ARu2ucAgZxfuTyoCeZ1IPpUpq1gGTo/wrvU7kbleO8GYfIJN0cihEIp+44qcTVT4XnX0n7kej4cI97LSG7UO4iqzmF5I3emsBU7nnZBtJVUVwYmfhmHJ42ThbnI+k5LmIyp7cWZVhZkAYsRcO6zA7GKVCYm8d1RgUSqioglVtrFSqvUI4JDlKnDuK2s9Nc6ysY8eJEpuOqSw0t6s2mvq2gbieETqroV1tP0MPIR9YUJ7F5OdFjpXspxb3EodCxYqFIX8Ut5v8W2tPea6h07CifcfZ0brYfstmk31PUrEIBYWdyGGdL8jBVxrQSWHi42COSJAvnfoddos/tpFTV0fZdmWSbLZVTE7lyigOx6P9LmYXiSfOYVaSUVtBSo3WRcLZRkLe701K8XPPZOpkPYwGjgwtO+fRO3r+N0ZHcqYPyfsVFJTy2dvSKMAqBQ+gU1T9lFTxcwxTjk4sQAOYsbFcIpIvUzbf4vtYKVWVg6rC5tXycnFOLs7rpsSUlVIQYY2RkJpHIvTtSNsmGiCJRSEutiHX4UjzgYxiIkpWLpMFggBUiX6eKqOhtmPog11Q32zoKR8iV7kSZFQs2OESAaGNgVcuVjpVSofCHV32TlDQWVhlrTpiwz1hD7WUVyp7XKvbYzSMEB+UCEQD+3ishKJNISk2V/kWRZGkior8QB8VUyZEu5S+He2vYGGJSAn+ntJUHU5bByMEB1SBPFQiBxa+Xa6Koq+zy8cr52vLa7guKSXnpa8HIIuOqfl6ADIxAYsPar4eE1SskGJlu/T3dvl50r9PPl5hF2KocB4pUxUMCFy2PVzo5fs8Qjw+UuoyI6Q14PORDGLMc3ku/+iKzycZdDFCogyNtvgZNOoCUHlHgF58gCg3bJPyxbnYuLMeo+zTWwdrNHTfwzsH25Nzo/tQWjRMdT+j29CLmqvsUdU9B2+7rHwo7RMcjlCZr6zYx1Uc6DJpFJONVZk7SsYZc0vXckntqtLQeQcEQcYnHXWYw29RM1ByfUx6i8uNVamGkvLMqHAKsk0zkFFIhkhlxksqoyQRxY6lYWVUZNGTOqrc1m0GMlq9kJDHcvBhOoiRvHCPxZE8Y2LCTh/IiDJfVHb/rtzj2TOxC0/UuN00DnGCpTKKl5mMYoXBaEkptbUOoyWH7dk446jT8L7L8vMc9RuyU90Az44+X1cYffbjFnZ7gXlLqh43zZien8ONIYzPpUowkpjiFy7fv26cafRvmuACUX6kNfQwwQ8b+syLLfywgeq6GMoXv2v4vlR1j5xOqZTiHEjPtjNe3854tp0wzg6vb5NSyjpyXmWeGc0dEeGQvrTpcNTpSLiMsyP1hCdnGQCOZofe6OhMlwSLCmSUcjaqzeTf+NpzuGnG+NoZ5u1FaNdtJPlKUorblG1Htxkwa43udAMz9NHx6k8tPf+OFFIAoJ0FFP1GMSkwJ+MMRIAN4VDb0KbcnqN1+PjZhHGmtj0bbVRKMSnF5GDZptyeTEYBSUEFUOL4yWnooIQBKyo0DTp0GjgyCno8g7IT9PknADsCz34XbtxCn7wC1Q/w3QZ+OKb3bLcJiqkekLYr+3EuJ6fic7C6Z8Ma5LMgVVIpRA9RzcnFCuZAJJXJ9Xn5YnaRhHoWlHtn4R5kNSSTpQBieKkko2ohpgCyezPmPxsMjNY4Hgy6YBMozxzt02uFk96gN2GqNU4Gg04j5kEjh82JfGgduqELoWcjlNWZjcM8Al5BcV9TbitREFJ7ORwHgkP0NAAXuKm1/GxlqJ5SiLn4ODQvhjaqoByzhRrKzkQ42xHKUjEIP4/LPHxsR4PS1DsHez7COZepTVm960I4tCuIKTnYJ99XAKJaXfdd7IuZ0AfrT49Tf2vYQPU91OaUHMNxS/2tzSlU16UwY+/JM+L+F4DISskwPqmOa32o+0PNJ6ypFuWfDs92DJUyUMFh44ETAGLAT6ipgo9nzER9blZJAbB6hBs66HGG6ycqJhP6CGboKBJGDBDa0cJtkm9nR3rZl74d+3NcdZ32CT7fZHESHLB5Ctt2+HY8X/p2vL5cJ9fvi7UQvpr/t6+vB5AaCkAUGwDk09E0CQ6kKuoyX09GvZRRMDr46nrooLWGOR7oOjZDJMIhCgNJlSYpMoMfyPuEasu+vBfL+1OiZluesL15fKTUbSALk/LZiyaOp0mWHOQI0khvCNvTIRGmDrHHIHmxtzYaIyC9ZG1gbLQJDLuIS2UZpzdp1EjKPp32WVifsyozjkAwasZDBWOmrIoPsZ0sDICOySq3K3wP1SlAMtH4eYUR68TQ0rFRmKNRSuvlY3WZakqul0QUAJhS6XQJGSWlnWygeB9plLIkxUNi0SVrbvouSjnZiHHlrZSIk5n0xKjzemVkKJ/otJYjq1cZaX3ChuuucR2HVxJSnAA4hcIgJq0+mywl/fbASe+hVCAg4HHUd/kLq6aYQgoFpg7PRKqeaY7kCY1K+6xIAiktLUwI1YuKHqOhxxk62BYqnSz/Qv4joDpQIE1CUkqFqnEx31Ge+2icKRTNOeqAyUSfDoDVRD4DiCQKQE7qODtSSniuOueCsxYc3rKnVF6vLcpAB5UUk3lumkgxNab2pJxdeXtymzpLhJ5lgjs4XLrvoMNvooXyDJbVZ/3OgRePIIoLbTpZF9tUOvxMSI2zI3VKaCvZpl4ruEp4VCSxoool5bQqoUBhVJrVFJbzzIxw2zMK3wm5XUiNwOF7UqEWXrvAle2WQiOm9sEulZRURrkQXkyV81LVx1nkMZtC4YLJ+SxH3PMxhZEyGbXMcZYr+DiMD0j5plJy80RK8X06dDp77q3rYHQirZw3sN5TeJ+mCpJaKVhD0XibzgCgMuBek3nwSsEqAI4KoVB4FxEWygGcUwpaAw6gTJ3F/XrPCZgBIprKx5W7XiUhpYOWip9hTiKvENRSnPA9qwRrEyF1cR6JJ789g3c2VSwNAyV2nDE/38I5h/n5ORXl2S6VUkxOlSF8skiNTKXAfTC7HaEHIqV032d5frQJyYudBVwI3et6YArhf/NIPdk4GK1TKHoMzwy+AEdRlGqpRkzdL/ZQTEVw/ijRn0pRMTb6eQhFSGQYX7ZPB6iQiF/3HYkJhg4YZ4D9hcKXY/vkMEMH91tFu2fIj3R1347JKaVTgSZv09RZF1MW6NGih4q+XT0dCxbTPnKveV9p39A9RqmWuo6vByD6aLuEBzUySuYQ3uXrMaENYCE8YLuSqac4CiaGDCcfbxm2Z7J9whfJ+/NCcHApnrideZykVCnzjwQScgk1azaDHHvBRko1le6gMCe1lO5iwksMgPaeqnZwqAVPgRiOAGeh+h5+onwhfqap6icYZ+E45GMj2PLwEraRPSd2vRNSZg65KV/c0sH01tHULRVSzLiz48RMOzPviTBLzhXHOTvrohNTli91lU4kw3gHvEbzn7bpYBXLXAUpZfKHi0kmNjxyHRsgPo6NEJAMVkpStzRcHJIXk9kJuaYWscMZKSVC9igfmM6PkUQUs+ZB7ZRNWR3F7DmQ5TiICql+CIqoLrtnWSWV3cN87++6x3kfIFchSKP2xA3cdXGok1sL1wMQw/U8cqWUdcD55PD6SITUx84nnPQOG0Oj+YNxGLzCaIChGygsikf54odacBU+Tm5uxynOk7LnIqor3cQElofmZ84omNFAmRmDdXAhJx53iMzxQBUsj7dEonBCdbtJip4s2XkK8UkJuUW1uNHifJxxPqacUp84n+CcxzxacoJnlxEoKuSMUVrBzQ46hJcdDykE1jpHyc+9psTcgfBjMozDCYtfLSTkpZA6qrBHoSesirLbMSrQeN6OTqhXc2KKpeLazOimOdoNkxHXGmYTkp4zOTWPpD7zebLznBzwWZjUFNRlZ6ONqrNnW2rb88liHonk4wEIgIipSO51FLKpBCHFSpTzcaYQKedgnc7svEJSrnVakZM6X0CNZ9Djc7jz53Cf+Cg5qM5BHZ9Cn7wMzbbKdClsRjkAHUmvync7z+8Bfrs0ciphLZ9UTSWVtoWk5khhxRwmGvPCXcwhP9wc159P+X3IpNSz8IyPM5FYbnawoW/BZKnlfkhF9MD9Bm10CtfQCrrT2PQmqiY5D9rQaby06XEyGBwPBi8fdei1wuSoQudkDS46h5PewMWE/sJB0jzV6EyokmrnlATdOyjlADvXiakSd/ie1aDfsxRk8RVIQoqTx3P1wi4oJ3tdKKTcHBVSOuSUwsU57PY5qWbPXqe8hs9fB5yFffY62cio2p2iupTzHbINJWKKlCGs/uB+q0TZ35OOJuWGoQFC3XfoTzeklHp+THn8tiO6zQCz2caKsto5yo0IUD4/AOgRCSoiIIqbsUx6vkZGNZLq7qHEbxIQ3y8Qfp2bKbTTkd8Xw+UDEUsig9Cn4FxBTE45Bz8LH68bAGehQ/J+u72A2yS/TZKubnO0yEN5iG/H/bbyGWHfjv09YH/fjppr6d+VKR5K/+4ylEUqpK8n/Ty5bR9fL/PrhK8HJMUU9bt2+3pmyIntFPFiltExPDUaZnNE1zpsFj6eLK4QFVJSMdUNQEd9HumreW2S/wbkUTISL4g9eZyk1J6gZOcFAbVwyG2+LBz9mGuKFVNB4ulnANqSsbKJmGKnBtrkuULCVPPUWvi+g51mYthF6Uk7dcJgTWFdXs7db4gxNyIOn2XPTDitEVVyHzZovD+vk4YMSEarXC9RKgWMs8D/pPnjV49gdSKOSkiDQ82tFuulwaJ9cvKJt9UIKEAQVwXjXeaIyoinvl+uqyXBY7VTSUZpTck0ZRLOLiXnDBcd948KKVWE4dXu25rBEvd4prZquBKu49jKYz2TIUhEDTt7HAZzNlk8u5jxbJzhvMfZRPfbplPQIUkldAcv75PyM21BZI8hIXcIAbaTDU5AesYtig6D9ZjNhA6A1RqGwyl4BHueKHSFc1lxHqTs+7LKwidVT1RVUFgeO6hZiNlMap55ohBkclrTedk5VUoBA51z1AoYkSU/Z+XEZB16zQk+gw2r1c0KIXIxT1ZQSZHaLOWQssHJctOM+XyGsx7zdg7OVJ6DQhkFI0g/GtFkGz8Hef8MN07QXS8UWkJ9VgGFRiZZPrepVJrJeRucfzun8MLYwbTUnt55+M5DG42xSyFVALLcPxImqio4PwSg5jkP59k+h3v+Gtz2OVRPzp/TGno4CmHwM5TpyMFHCJ+XCoRrOHZNNXU1sI2StoqeY59XfLQUOsoVHy9mh2cXc0yw/2w70zouYjDZ+GzzvWhnL55zUv3Fyp2CNAUCkRIIaaUVjNEwHb3/595AF3nQeGpdH8MEh/D8cbU+qfwbjKZ8fioQUi4pplQI52NFKCumvJvpHS8VUzsSnmf38mUh9QdAQ8EWdzsTU3IZWCqktCSmVMrTorxLCqmQL0rNU1JCjhdw2+cUrjduU/je9jncOGF8/QxunDGdbWMI+XS2hRtnjM/HQErNkdhnUkqGKTGcdVnfsFagxgwaujfojzuYwZAKldUrYerGGZ116AFg2FDRDmdjSDq0Dnmmwu/nqYJo/N2L3FINDxRqx3PI+d8Q/MOoVAlqSOMBDGFwqKfcUEBM2RIjZAAg5JOK+zgLswH5d5bStEjFHldA1iGFAhNUvE36dlquDz5aCtsTPl5FhAAsfbsFEbXi28ltjNK32xelr6fE8i5fj5dLX4+318QGgCSrL/f1OPKFiShWRHGhKnl8VEmZ3I9T7LsZEwgnsU3uKwipMlSPQ7l97R0hl18gX+5pkFLC+MSKCw7BAAXDI5IER6dOdnq5k2G48xFYc59KBKPXsWISs+eKQy+MIYKq64mYCrlCvLNQU8gpFRQNKlTM0pyYOBgxzktSY80BiLj7KSiYUniflD3Lc2SJI+M2NkRMRCUVlZSO5vuss+g1kkpbC/y/af6lT30JziQ1gyqMVY1FlwoO+ol1XC9LrPI+bHBoWVeNEYAUhleooOS2NQVVSUJlrDgbKCARUSzpZJLKLMkp1Q0kHQ5seaaQ4nVBUZAluo73shillcqq2LgF4/4CGbfr4Cqv4dpgEoftcZ4WqlyVCKkxhLwwMfXadoZ1wNmGOh6vOINJESngjY5Jd4mhCc+UDRWMpLoyhO3xiBsTUqVSCkgKQwBQReJyG54JtkGJjHJRoUVfdF06zxX4nE+hdBy2F0PMbCKjaIrorDKUpvCb2JEJyikMiGSM0SpOyYkOibqdUK3VpOhefKdpAoKtpATnUyKophnT+Yx5G0ipc87R5TM7KKX1yigqQhE6SzZIxe0UKrSWCc/5eoqE53zZHAop25RCoagNzkdKGj9OFnNQSpHyLIyehhNppeDgobtkL+zsMKJOSpX5uBQ7tzLB+TyFsL2glDp7HfbsLI4s6q4HxgtgAJTbBDXCTLYLJrxTsRwlvAJB1Yip64FUhVyUAZEAnSzdZxdB9XQ22aiKeradcD7akNuMiCo7O0xhOk9Ekkql1My5UYqRewYTUQAopE4rmE7DGA3daXS9hTGk4rvQqiClkpLyZDBUpS+opZxHCFnj8FUiHZicUhTTBuVByW11B6gZXhNpg0BUKcx1YqrWpreQT4qhFYXdMLFXXkFJSBlNRBQrppQgmImMooIFkpxSbgbGi1ChdEvhelypdJ6y/HusjuL8hhevkUJqfD7BjjaQUhZuyvOmAnQP1JRSMnwnheAEYqo3mLcdzKAxnFqYwcRcPgBiP1gbTf1T7qMPpHJRXR/6XCqSj/4axHjDA4GMnOH5EJJL0TU6T0GgFJGTZVRM5uP1UYSgAErwH/w6FfoRep4y/y76cYXIwAlfb02IwOsA3Ltvdwju2tcDkv+2JjQAEAkouY/uJVG1FBvE/MBBbABgKUSoEVciAiYqpDhPsKlEcNVEBy8QHi8pVbLg5TpOeA4gC3Eq8vWUTj0TU2ywAJPO6x2g5nAThhCLieLROVSPSSc2RjGMDyDnA6BcJTbEDgcnxMttYV8fVFgy+WNpkCIpVTFS3loh0VxWNamV35XHy+VyXu5T/XmmFF706me8At8vbzVVjBZmlesik66zZVkWeFHdQpJPwaiUxksXx5XkFYBonGICchB5RFOhcNI6seSA2FcYKDZmWhg4IBmqoIpayDmBaLCq21Ij5uED0qDJ9q0ZuRfY8N0m+DVeklSslqoRUs8uZnziYsZHn42wJx7PjntY7/HGuYdRHrOjmH9tOvpdy9/O0bNuA4nCeaSYiJrPrVBLJaUkkDoDveuKDoGCHUkNEDtN4wTdhQpLNti6FULKgRxZDp2bHOU2OgukCU+3k4WbHebRwlpyXDmsp6wKZqKN6KJyCgDOi2TdHHLWexVVRfXQPSTbLtRfrJTiJPGskiJCasJ0TmTf+JzsNReRYCidSCmyRQ7TYGCsg9kMmLcjdN/H9tWC5PM2V59lIaGeLjERUy4oz2wiowJZYEObcntyEtQyX4/2mprAkyrEO48xhERxZUSgXzSbBmKVH1lxD+MW7vlr8OfPMX78NczPtzCbIwop6nr441N6H3YjFAZy/OL7tVBJNafw1sF3mkxw7kK4KBNSnPcuJjIPxNQ2qKHORotPnI1BKWXxibMJk3UYz6dAStGzzeSUtSFM1zm4eaT73lk47g9xagRWWIfwdx2SxZrOQHcaxih0g4ExGvPkYDoFax263kTS+3gwIa8V3UdDp2GUwlEnnDHvoRUl7DeKuo4s1FIK0F7BhAEixeF7DmKAADkxRV/ioN9hH8LqKpSW4oTCgpDKquux0jEjl8f0PHMOqXkLzDMppELIngtKKfvsddhpjgqp8fXnMXRven6B6XzGFMio8fkINzlM54mU4jL3o/NZoR1WuNbKxA9hUKLbdFEtNZwOMIOGHR3MoHFkXXRI3ZRUJ2YzYOjPgMFRNUAAPoTb6H4A3Ez9LkdhmjGU2gf/wbkU49nwcEGdBHo2lSNimX9bz/mBk/AghvuJcD7Mc+bHSeLJT6kqsXI8OGgjSRUH7pyDEfO7xAhlBcq78O3K5V2+XUlcMcp0LPnPsPTtyuXb9PVkTuCdIgNd8dlE+pVMaADEEL0oUEDy/9AFMlyv+HFlShYmqviefUH7PY+XlAJyEkp0YIn9Tp1aDuOT4VEpJrxw6pmYAogUcC4x7MGYKa624h0wHEF5n9h0GdbC4Rj9QI5G19O2IBtmJRUAKDZubhMNGRs+dUT7mMKY0f7JoPFyzajxfDaN621maKRBWivPWy4vcDHG2c2b6qQUsMNASYJKGDQ2QHK9PEZX1pXlO5dMezJEdN6KYeJrYIY8S24n5i8zcKGyXl4Jct0oLcL46MPT/ct9Jbh0j/M+QMsldQXchEpK5pJKoXvs9HFp9ZQTaAoJf8fZ4mzUmJxD51QkdOKx5ShKSVIi74RknZ4g9S7DI9JxHhouKqvMQGWS7TQnu8IqKTpgtT3k2a1PRIgs+85/MnSHTGsipDjfDEAkioWDUuR4GpA6wrEjE89J+Y+sC8o0oQ5ag/Iejkkh7lyKDqFUpnIbcsiJrHbKMIOBHW02pWNV9rvw3+LdASycWicctaS8y9syqppCiBS1KavFwsgoJzq1nsrBOw+vfEgL5uHUMmTPuhQCqbVKeTmDM4tYKn4mwnKe4EZSSNhpht1eoBs2lK8rfNfo3MdpyKPlkHJLyXu9qaVuHDFcrkKgl2AiVBKizvusYEFUPmbhekSIchjpPIWw0nEL7yysIKVknwRAfNe78A41LiQZdgO06+FDNT7fMaGqoYPacysS958EtRSH8k3OQVvAdpR7zoS8cx5EYqtAzDGR7TzdS4b7l0D2jqVPWekT3WJIBic0l4nNmYTy3sf58hhOaJ6ItzAvCeLwbJKzPdPzySkqOKdg+ONBEC4IwXn35kDku8li3k6wYwrbY6XUZH0ko2iaWjKRUrSsARiVytsb57HZzuF91mE2M5yl/p23BqanwQOzIfLf9jRgo4yOgw/J9trYv848o5JcDHZokey84WFDacDbZT9YI0XHOCTCin3KDqBBMFZO6fSe5v4XT4M6KouU4SImgfxi4UEktpzNfDtgKUQAru7b8Xnk+tox5foadpFV1Jz156H08+S6mq+XwvjWfT0AqyIDXpbkUzxfxT8DhPAgiA0Wvp2McCmUUtn5AhmVIleEr8fRLzqtW/h2LzgeNylVohbGBzIgXpJXIqSJw2F8jCGXyw7Q4iXt6AHhDjSv90As0a7EC11zh4/zlLCzUTDotM5m+/L6mGuEjyvXWR5ZTM6ML/a/bH38XFQMlDBCawx51YgJUurV3/vpwNEQl2sGCiiknSsqKiXCANMLQZBJl6yP5zVyXb7/Yh2TTXzuCnkll3nfjAQN95sLyewSISWUe2tElCoIVSDdvxqJmFLICKsWtnc4ruvAyuPzqlaIVa1YucPhL+chdO/1bVBKOY/XLyjPxdlk0RsdlFI+EJchjK94RjISipVSIWzPhSk7AkCSb8vYf47TV0Zh0jO6Deeqo/LHzoYRvxDixuRCzYvlCoMuhH5N1q/mPaIwMxeUUjYmObcyebhS4RFQMRdSUuPreD5SSrlE3ISRdz7T6m/MNjR0LN2YKu1x+eZ5m8L3eLnM58Btq42K5J93LibitNsRZuhSdUSt0TlLlXz4vbAgpHy8j2ROKQ7dk5XMRpFHigkBJ5RSfE0cEpXuUw0bHHpZxe+4klMKSI5szCcV1FJu3FKume0ZxtfOML7+HP3phnI0DBv4ixAuczynd3YMVVySrNdVS73IxNRCrbnXMYmosg5R5Tg5vyhaQCRUKljwbDvj+cWM6WLGPFlcnJMyajyf6dkeZ9jxHHYeiZSyFnY8p8+dx0wppQThboLDEJVSRxvoboDpBszDMUxn4JyHDtU3u16Dq3eycjJW6+sMhfd1gNE2qnCOZqpi2WkDBCGM9hTOR7tQbim4OQ1esvICoc8S1FK0d2Gfa0qotft6j/u9ppqiaqNhu1BI0TJiHilWSSmkJOcctqfsHIkorrqn3Ax3cU5k1MU2C99z2y3m51vM2xHj689JKfXaGebthPH5iPHZhHk74+K1C9jRYXo+YZ4szgMZNTqPbUFKlWXqZYWuIXyhY0M5sDbWY5gcjrczhe/1hoioQER2x4bCqVklNZBjabcX0H14l2lDqhdtSBnWDyF9Rw9cEpK5iqbyvD8Ixa1M6eI1kp/HpGLw83hwJBZt4XuQi8p0gaSt+XhRVCCUUKW/Z3lZKKrEcjo++Xa+5sPV/Lc1n89KH++GfLsDcB1fj+Yv8emKbaUPGAmmsK706+K55PJlIgUkUqr08bJB4yJx+d7+ndbrYoIXAI+flJJqqWKdD6MZ2fqSyWXWOyAmRxfHeEedZ6W440wVqaJxA0RyRBc/MxJbSgHoFiQVDUwVRqYwLqo0ZggMO3IDJtcpsS+kUVojqcTxERWDt3AXdrHm20RKDa++AmyG5T5rrHrN4BTH1PbJjJspjdMlxJPcl41S+Xkrxq8kocqkdTW1U2aowr6+MGaZwaq0QbbskD0Hq4nz1tY13BqkSseFhMFAUBAJhQ8TCzYoWyx30EMeqgOr8ubXUCFNYm4BIBIo3HlPiTDFKF0cqcttg0oLq5+fORlSLcUJ0YWaKRE5yJadBrQjisE5n7l8vlAK8WfuU8o4KgDk+awllZQYiawVg8iqnAqyxwdPSu7Hf7Sc5wTcaUt3QH6/WXxv71K7Aol4Ikc9fstETDkPp331vGtgZzcqLEJYQexwOws3TeAcZ0SY2uzdskBTITwIuBX6ilWP0m7Jv5HJ5JkqPXJScxdUj24eYeeRCKiJFFIuKAdsCAkGlqSUhJLvbhBRRdyJATqQSlApuD4kUi9srNG6sLFJTemciqooVkhVLkBMd9zLjwCcT6tGcCnZn+XnW6hJ+Vku00nwu8KFanouhIyzwlSG6o3OY/K5UgpAnAIlKZWmowtl45HW6dFCaQUb8yY62JHssRtn+I2LkQbOOugeuUq1BueqXPkCjYR6eJC+oBzALVNeuDn6cOzn+UBOKe6PM2lV8/EGqsqmvV/175Szi2VACBGw9O3Il9vt28XwQ7EtRuCI41Hsw/DOHubbXQUVX29h31d8vWzfG/T1aoTXZX5elpi8FAmU6+RnrhBWq4TUC4zHT0oBGStevhToRiiq+oh9PAAYQV45l5LehXVslFJceZp6sZwZqnCubH+QA1SeQ06VOE/m5FXmlXAcpKNYZdDl9VT2W+y7dkwNtePOt3F2+MzPBY43adtKZzPDTRmxYj+ptCpD3dbma4ojeS95eS55Dq2Xx9TIogWJJa6rOFeV8PIu3sOL8K6mlrpXxMddODhcycqJPC2sOHi2nWG0wvlk0RuqIDc5B+8NkTaCqOQ7LiOOhHPAIWXSIbBjUE4JgkpDUyhX6PG7gUgWN1FYr2JCoRi5ywgpgYyEc4ngsN5jjuF1PobWSQKFK/B5lyc6ZxUEOg3tVAhNU3Czh9PBAc6cYxsdT25rTta9CjGCmdrRwobvz21HSrTUvtRWwckxOguPVEbB9hy+RxVx7DRT5VQOD4wjqy7l6areS1QFjduD23kuSAEOaXSOKpvF6mYzq62YFUWoeO5iGJ+1DkqrRXuuQYU/hDAfP4/w00h5ZrYXFL4TVBR2mmHmifJvzFM4hkJDlRI0SBj4abhbsJKTQepOH/OYxftKqB45sT7nh5uC0pETms+ThZs9pu0EN4+Yts9gL7ZETo3ncM7CXiSllC+dKnY0RnIsHBcGcRZ6HqA7ysVpugGj1jBWx1xpsRACgPPehrxSdN6zkXKmXViFfnYwSmGyHoCD8wYIxSiMosTpVtEr2EMllapD/o5WGrHsHaeNuM5Iwg2gVEmxOopD91QgdaJ6Kiij4Ob4TJNSKpCHHLoX1FL8jNMfzadiEBPm86Qs5QTnz2YH64Hn1kVi6tzWlVIslN2llLJGYfIKNpTGG86maIe5Ouq0maGMwrwdYbYjlKEKfc5o6HkKldZszAuUBph9qrjnHSXFvkpir4a7B/eNua8MJF/P5b5bJDQ4hFxsK5ejogpY+Hir/l2Yr/p3+/p2u/y1u/TtDsFd+3pi3719vYpPl45b8fMqPt7i+Mp0cbw4T1VI8AL6bE+DlGJI1VQx73md3FfAL1YnBVWW5BAAYJbGB0vjBZNvl+VlI0rDwOGAsjNTi2nna+ZtffoMiXK5qmYQhFh+7P4dqowUA4DuPM7ql98EnBzTOc0+Q07hnGuJP/dR/9TISaTfeeeDnxmlijEDFoST/IzV81fJrQqZVRo8VAxmhYjdeY+/gMbtUFzVfdhJdJSfIfZlJc+a6sA6ZJXj/I5rXCp9kkKHpoLgifkFWCnFaiANh5Qzidb5aFvKhJniw6vXJNuFiRCpkuJrYGc3kVO0P2+L53Aeynk4TfMmhOpwPipWRNwGZNLQUjHFOaV4O0BEn9MOxpi4nUgs8UyX7VjrAFbaVhbEKXN00SE+U0TFNi6UaCq0nw4Vu7zLt+9qS67WFZUWMdTBRXIvS94ac5KlvC3K+/wdtha+dwN4kUP4DkWtndhWlerD8v7z4s8F1ZQLSkSX5Y6ipOaslpKKKWnLpGJKzlvOy6JNPM64Y3inIhnbBZJbGx1D+CSBm+VMC99LeyKhOKTZpQxSoYpqIkiuhFt8D8u8UldCaWuy/DOcZ68ISwIWz7gb5wWBz+8UF0inUUxlcnNWSNVD+FSWY2p0wKABG4tZ0PFG0aCFsqSQ8kbmU0yDDTxwQ1/dxagEdV1HvOHhoHzepGoKiMqppKhC9PM4CqYkjbJ3lvDxon9XHlMQVzv9uzvy7VBewwoWft2B2Mff21ng4TZ9vZqfJ9evFIraSR7t8vH29e9q53qB8LRIKaB+o0gF1doPXd7I3tUHa6XRkIevha7IF3vlPKvYsX0vsuiy8191332xOYvuhX3TZwCnJzd37kMe1j32Pdgo7rNtJTRxVZp51fUlEXvZdTU8OFiHhbO0k1wp1XArqJXzTeWByUlQHLanldgnJ7ToXK5WgE18mOx46SpRt4uYApARUPmpWbGq477yuJtESe6tFYCQxF4MgwzXo0Vb1n4DVkyl5avZ37IdgWXYXdlGJfHE1fYW1+h8xg/NgtSq7JxPYw4NlzuBNxES0EJkbgTXeXScIIBLW7UkQJkktaGQgIsElCSkspAwBJKA1cHOQoecUm4eoTiUIpxH94MgulIuKcehwRW7syT+8/vb++Sv1gYDKLl5ZQCUEZ3c68tqtMqJ6H32zy6llspK+mBCdQsg5UsF+AdMG2OoWwqPkyRPbtc4ZNlRPsMQtmc9Zd1Kqqh8HsjD91g3ab2HiW2h4nGD5uTootKqS2HVTE7J95kTtmkvNUizO48XNaKAl/U6kbOXj7fm34nzrJ2/hkfh290lHoKvd6Cfd5CPt++6Fwh7k1KPepTxKsTCgTfGo26fW0EK17OnbwJOT+/xWp44XnAj9hDhd3QunEcWWgbUFS8yobUX+zKUNktnSeYpAjLVUy2ZJRNTtN3DGBXPI8+Z9mcypkiseU1IVQ9PST1VXEdUTfjM+/IVh5OW98uPlEGM+sfPldca2zNXceXzjtrVpGNKx4jWp/ZcKlXXMvvsRkk87ZrP1pm6WGkv9VkkpMqwgbxaUIgDS/s99k7zIXj+/O4/c+2n4/VR8U3zygXHyHoqOuwc4AB9MUNPFmZy6MYZ3ezQn1+gnyz02RbdZNGdjxi2E9TFDL+dSSk1TnCzg744g7Uj7MUZ7Ejhe7Md4eYpKqiSUircE+JW0uE+0d5BdwP0CGhv0QEwF2dQrsdgOmg74EgP6LxGp2YcKYPOG/R6Rmc0TGcxDAZ9Z6GtgXY91GxgegPjQnLbuQuVnKhMHT3Hihwao+AVoOwI2BnezaGNQsgbXDVtQ0Q2Sq6BrCR4mJqO1sdcNhBSWZoqJpUDY6YgfsfyI2PuN5WWtYrTGMPnHTCe0fXPW/qzEzCdA+MF3b/TCGwv4M/PgLNz+LMtcDHBn19ATTPlEp1mqHGCni3UOEM7C+MsjKewbeUoLK7zHrNz0M6jD/HtJnyvmmOiw/czToVcUgq9VtAOUFBQyqNTGnAKerJQcBT2CQ9czFAdgK0GtiMNwlxMJCHZjoBTgO4BbwAYKNXDm54GYpQCuk36TXTxe+2pur8TXKOf7e/DPj0iHP4uXiNAbkcN/CRxSKPfmhO+68Q1QrspLVexh316ekqpCso+tRxJWutv11av7rvi9Kx1t3f5SLtKl+/zzO3lP1xzlHQfqK3FcZj/+NbCm90Pqr7GiOI+h5ajh9Xz7Dx+fevaprXuSK1MM33G/te06x7e57s23C6UUqt2QSuueJRKcnPpciPKmGtFnW8KlVKL37WWJ0CWvwVA1fQC0aSNhi2skhKxKFkVvixmX55Ph3VF4shrIon88nwwjotKLK5JFcfn7ZfaE7G61t7QRRnhRXUYbk/RdiEPE89TKWKVHaOMyo6h9UV7Clw14aVsw2ydXVdHpWOW5zN7GU92tk2xOi/ZTFVudNrvBSLUj9/ylvu+hIaGK6PmSt/LUCNL1/bhs//PW76WB4bt2dmVj232qaGh4TZxvod9enKklOxr+2I9962tT86+L47LyrjzPp73EaPNi+PkZy33W91X7FEmGU3rcwdCvotLv7ckKHxBZdVIq5rvvA/1tGsEXZ/N+LQw/z/PZjjM2fa9HB2sxEJXVi4k68WRuyTtpVskSSN5XH6MWqzP9s0+u9zXpwSklf3i+SrXxMfFe0nsw/c4izHyc6PhgcJooBNkiiRVqihDKlagKslPtFGwqBAW2T467pvOdQl5UISO1i6f729JwMXPt0Tm2EqcSklG1UiXm0JJDq2VLtZGw1qbrsHmYXvxmMpvoIyqEn2HomxHIORdkZ9VIe84XHNXO5brukCiVlHLlaBNUJvwn1mQe1fCC0RiNTQ0NDQ0NDS8KHhSpNQuQsp7KgIKkMO0RkZJIirmRVjsy8uXbA9b0v7i+iqft/wOvnLc8jtKckgSYmXYioxCWWyrMFNreQzWcr9I6PMpzn/sfIJT0/q+K85lLaFo6RhJFUTp82TbxHx0hsW+GeGkgqIA8vh8v5A6v0pgpX2YZPJpeyBELZYkFP/eiu+jkEwYAHS8X3JyCsjvYYekqPHiOxbRTg0VhJ/nYGi1f46WmPMQdH8aRc5+SUjRX6iSpEV1pLXzlgoVo4EpkVBcjQhIRAsnONdRyRIUPkJdRfO62G+/EAWdPVPrhJQOf0orKKcoMsIpeB2LNsfqe4oVZkJNpjUpyYy6hMy7BphQ0UZHso7by4GSKXP1PbktfkejY9vGc5btWKtUU2lbaRdrCjsmnKT6TCP9Bo65o4wsX5JUu9qSQkrFOzAaqqCC0vx9NXTfQfchJCqUXJblle8iF96tKfsPwPmHP3znn7kcpMq3+ZDfhyL2uKhCSD7tgAvnYB1wNlqcTRbnk8OzyeJ8svjdsxFno8XvfGKLs9HiY89HnF3MmMYZ2+czrHW4OBvhZo+L85Gq7108h7vYws0TVd/zsvreJHJK2cymcU4p3fVQxqDrjymMr+thjk9hzIBucwLTaRwddzCdQX/U4ejYoOsMXjodMHQab3n5CEOn8MbTI7y06XA6GLxy1OPIaLxyZNAbhVc2PbQCjo1GrxU6DfSGQsUGQ70CNW8B57JqdZRzJoTv1apoyfs9hO95DtHTHbwI3/MhVMwDmBxCIYcU0s19MOtDXzVUSSx/81r1PYpKpPdKF2xD/F7jGX2XeYRyI9R0ATWdQzkL++zjwDzBvf4JuO1zuLPX4c7PMJ1tMX78OebtBS4+8RxumrH9+BnsdsbFayPG5yPmixnja1R97/nsMDkfq+9NLq++BwBTlicv2Twdqu8ZhTBV2Bhat9EKp52m+ZcG6EFj84YNTG+wecMR+tMew8snGF4+Rn96jKM3vYxuM6B79Y3QmxOok5ehj0+hjk6gTl6i9u+P4bWB7zbhdxO/ky7KvPNvK/GISPT7sE+3DY565Xkg2Tya523Jl4u33o5tl/l4q/6duLbSv6v5dm4P367MoHAd3678zJvEmp8HHO7r0fnq2+RxsRKrOK4UEWR+XrlPXM79rqWPl87JZ9Oys7+yTX7O4jPE/IuEJ0FK1cio0gB5Dyj4kDAx3QBye2l8+Fnmh5zX1ToAvE8IjQ/n8dl5JIkUk/6GjmD6jNTZSB2PfbYV1+rq67PzZAYvtWFtX4l98vJ252f4f4b53/joGeYdqr2aUCAjkVaUSzLMqdyX92OjVK6Py6GDI89z2Tat5fnUguSiqlTJ2GmVjE9+HT7rLJbXppSPhoxJLL6HmZySLz++x72vGzj+jV9EQ3dfUExEqqRi4/wXWqvk7HQax0OHlzYdXt50OO4NjoxGbzR6rVOlM1GqOn6GJoqUQ6X4j0kQMxjM2znOA5QPSUMSKIE46bWYN5FYkGoXZQKxsFLuV/OXBrJnxSgVFGE6kiiRjAqkiOk0nNXBfmk4TUlrjSCklA7kkFbQnYrzktAbOpOFSPL2nfd+IFNUpvIxMEMHN80wg4GbLOxI7aS0gh1d1o70e9A1mSG1PxN9SmsYJmiMhg6hgtSe3LZ1R0YmJNZCudRl31tTFbHQtsZQ4mcLB92RSko7vk7ENuffwXC7Fu25Bh/+lNLw2kB3A1Q/QA0bmM2EbjPAWwfTdzB9B3Q9VNcDXQ+o4JTLcsh8YU8R95FXcZd/4ZF1TlTIV8SJwpUHMIcOzGCBycFPFv5ihrIOTvXQRw7Gd+hGC61HWDNh7i1mjLDWYUIPZx3m7gjzaGH7DebhHG4eYS+OKZfUEeWUsvO4Wu1J90RKmW6A0gZuOIbue+huQL95CVYbqOMBvlMwxz280dDHHeajDhgM9OkA3WngpQFm6NC/NED3hp7No46e100HozXUkSE705HR1kbBGLZRIBs8BVtsJyKlPOeVqhNStKIgpQLJQV+wI/tTyynFL3efpjFvnOdVqU8qP5b6DeFQ7mvocFtoIv6dAhzbxk1PRNt8Acxb+HmAmjrKnaUc/DwCsFBHHVRH9rfrO1gH+KGHcR5qmtFZB9/N0I6II2cMjNfA6DC8fgHjgVk5KOehvYfTRErFXFkrjrGpkFJHOpBSRqHv6HdUpz29v06PoAcD8/IGetNDn25gXjmF2hxBv3QCvRmgXnoJONpAnZwCx6fA5oSmgZSC0kC/CYSUYcOZfsMnQko9tbyvfAvVfEHnfebn1Xw/9vPqx4d1FR+P/bvkiwFQSJWW+bqcr/p2+Ta14tst16fzqMK3S+/vm/Dtrg5/b75e2pZ/ZrlNh8FNOp84Pvh5l/l4JblU+neSlJLrWICgATiVrscV7fAi4NGTUrV3Vy0Uj5ZDPkO5b3FsaahqRgpx3W4jVZJQ0kjVDBRwmeFC2keQTtKwWbfcN7ULk1HFsmTg3eXzu9Yx+vMxzn/8bMTkl7da6ezsWi5DVACpvkC2nO+Tjs8MkTBkq0bM1gzVcn3NeLlAEKnQIVThxeCVj6WblaJRSgtPhsmzMoqWub6PNGYA3bOmWC7fL957KKWqCqmmmrpbaJVKWWsoaOXj6K9USQ2dIGt4vQr3LpJq7krXYBS80XCTi8tObCPChMkKLQiWlFepzFdF641cWP182dnI1D0qEVJaKXoWtAKcj4opqexhIoVJlPTRSxLFiOd2F7xS0AulmYHWGo6/f0UhZa2N7aihM+WZzOOVSL+UU4pyTon2vGJom/x+nfjekugDgq3R/JsHx69ov7XO4Rpkp18HJ41JNaUNkVR9D2VG6CFXSoUPXZ40llF+RM7cE4QGPYslTCB4taurO4dOY7IOptPw3sN0HM4b1rkBRlTYo6qMSRXldF0pZaJSikgpJqSYpDKdIYKayWsxPxh9iY1VkeRlVWp0KGqPQVlt8hHDeQ+jVJW79EoT2RymkbDXaVDCRzJd/FmybdoomEHDjgbG+jgw0vUGmCwGblwHWL2u3pBKKSalgERKDTr9mYEGUkwgHOXggB66SPzzoA2AMBixPhiwt21uNuvBgW+pywipXWSULfymmo83hY1SZFCKCDI/bbEO4ni/+Oxdvl3p12Xr9vDt9lm+Lm7K16PtyNZd5uvxtl2+HvfFs3UufU4pRuiNrvp4FoDR3Ha0wavcv6Purc/8u7isAB18txcVj56UkpCPUc0IRUc//PDA0ihZtzRQ/LCnkIWlYZLEU0k6sfFZLi+N0BS0mNaJ7eFaJ5sMTV5hKs3L0vJpWzpnOS3XzXuQUvsQVEdBkg8A//H/9wlcHBFJVRt1XzVIYj46XNKhrayjqV7s160cZ4Sh6tlhZCOk0jl7Dt8Rxk2rpVGrLy/ZdjJOHiqE2yklOl6a2XUfP4MMH41ea6WS9BfFPe4F6y6MG9//DbuhcL1QH3m8YvYRiVjk39kphaNOw3rguDd42XqMG4c3vzTgpU2Pl48MjnuDk96g00DHL0Q3UZiIF1We+PPYKdAUMkUKFXLs7MgOXhfzCvEaJie64y44EgZm0OiPO+ihQ7cZYIY+deS1gep7cKiWV7paaleJ50Mrer6GTmN2HkedxthpDGEKgMp3z4lE8d7Dzi5KpKWqSncaxmh0vUHXG5hwLvmnxTNqBLG7+hxw6FnXA/MEPXTQzlH4mXUwg0G3oVemrFronQ8lxpNTYgZDRNlgooPUbTpa3gzQfQcz9NBDBzN05OgJBVpJynCHSUe7kmwKf1/rPIbQlhdGw3dUtdE7A+c95pG+nhysUYqUUsZo6C6opoITP3QaR8KJL0EVIakMuwkhR970UMMG6viUwoJeOYEyGv3pBvr0FajjU6ijDdSwiSFKUW1wQ5VwF9d5raMfNwK/G7GPfWOlo1LBAXAKvdbotYfTGr12cFph02kYpXA80DPx8iZ1J73z0em3sw/54hzmXmMeOtjZwY7boJA6hXc2/HGlRiKnlCErpQIhwiRUDN3rNLpew3Qa/VEHYzSG4y4unx51OB4M3nDSY+g0Xtr0OOo0TgdSoh73BkedxsZobDpSpkZFZ3h/93EemSpKeR+XFdtjqWJl0ircv8r7pZ30rn5/70F41X7HLGogqqiD7QDglIfyZBe9J/WUdT72gboQMkjfrwOMA9wAD0AfUSibOqLKyspZKGehtUY3zdADvXPcNME7Bz2MUR3ah3eLHV0gqhz67Qw7Wkw2hIt6nqbBYnaqozMJRNvHiqlNp6N97Y876F5jOB1gBoOjVwZ0xx2Gl08oVO90A7MZ6J22OSLlZk/qTdUPNO2GoIrqrhda3Eiqe4MThNEiRFmQUWX4q3W50MAL/2+u+HgcZjpZd6l/F33Aim9nHeK5a77d7Ja+2ppvV9uPr59xiG+3a/0a1tTVV/X1eHqZr8dTqR6XxyY/jD6r9PXkcs3P69kfDCfgd4P08dL15/6dRlD8q+TfGZX8NQ8PI/gJfle8aCKCR01KZS9gsY5fxlJSyT+sZCRp/5yEkvMpJK9u1DwSQz7ZJfHEBBOTSbkBWxop2sdlBgoAxjkZHmmgcgPkqsasJKt4m9ynXFeblvPAMnZZYnORlFIfez5iOy9DfcrR+JqR2jVdM0Zy+8IoBXJJbi8NGDt20oD1gf3ujaKkxsJ4SQMV9wERWcy48/5xm9FBLUX3nwkj0xrEqFs2UAAg5k24h1P4aX4PcxP6wELJPFOSmHrRDN0huAoxVTp/vBx8uoxMpEp6Pjo8vVaRsBk6g5PBBCdQviTDy02EhwAAKhX4ZO4nGrn2MaeUMj6GknGOKVlNTmkaWTa9iQ6FVEkprp5GB+xsQwarK2CXHYah00n5pBSU9jGkzM4uKi3iuUJVO5oihZoJxVkKD0QMEbysuqdXKqoASAEgwus4FDIqAqj9DAzsaFOeqEw0lsL2UvieFlOdtW02Wr+iJOLnVd4P/B3LP91paOehZkft65K8nbnMmG8hkH1aTEsiSpL3zvlU9Z7ftzHfSgfVDTFET4ewPbM5Cg5fn9o4fEfOy+KVSmExle9/FSfvRSak9gET56oweuUyIAdguIOuYZ0jct0ZDJ0NYcgG4+wwaUVEs3LwzkQyHGAifAPvHN0LgZRyazmlwj2jM2WUhjEK3WAiQW0Csdr1Bpve4HigPyZuj8K014mAYvtL4fGsYKWpYoci/MERAUWhjhymd0nhiYKcomW9TkgdgNjHrZBRu46hiCIiqIzoJ2Rhhc6GZ1NB6Y6eTybtLf0+qhsAR+G5AKADQWk2pGzz1sFbjwlAt+mhtA2597iiqoKeHMxkYb2CUcExj1+BVMYpBDwNMgzBVjHZ3x93YV6jOxbTnoh/GgDoYUKOO/D1R9sr8t3JBluxQ03N+bgQQ/REn1kKD9jPs65ORiU/zmU+Xo2ImqU/uCCllgRUzbcDEvF0iG+3r093Vd9uLefU7pxR1/P1eHqor1cTJyz6S8LPWyOpOqNgnMIU7A/fS074eCRc8HCG+5v0EjU6RMpoUAQAKQagvYIXnARUUq6+yHi0pFTtuZDrHJIRisw4EjFllMpIKKmQYkUUs+ZspJxP+07OwTlkxBMbnTRfN1CTWxonJp54ejE7YXzcwnitkVMlgZUZqcDax3wELoUBRiJPrhMNGkfdioZf64tdjNs4/4mPnmM75MeV7/PoIIkHkh0knpfbZfhOdKTU0kgtiKYVMir/S+qAo3CclP3nZJWGVnZpwJSikAWVOrxGKfSO5ifrocPxRiu4mD9KwQVyCpqd+2DcoFKOCE+d5aX0mDqb3FbczDUSqhFTtwP257RKIQkUQ05KN+KMKKwEAE56g8l5vOo8xpcGnA4dXhpIJXXU0X1KSikANs9bEqE5bKGD6/voJPAItXcukikcQuZi4nMmo4g8iYqenlRSmjv0fQc99IF0GFL+o7VE5yDyVc/hOdAag9GwncfxYGAd2b1NbzBqUlIYpwHMwV9TmQ1KOaUUupDTqesNuiE5n0fC+aRnM6mK2MFc/mDsiCVSSHVDVJt1wcEy44w+Kjl8bD/vHNzgI8kHENHHxF53bJJaIJA03K7kLPVRCaJiEvDUpvIZVcFvlMTAYDyGjhz445AzrI9KKVr23sf25PdiVF91RPKxM8+qMyYNOT9XCUrrHBxIQyopmAEYSBXltMHwhldICXbyMqmkNqdQwwYYjgAz5DlaarlZmuN369DgvhGR5txP8l5RQQ9No9fOKFif3okXnYZWwLTpsvcrq+vG2eETWgVyeYadHbrewFoHNztY6yjf2TyQ+MjnfRMA+TtepfDdpO4LSikTlFKdxvFRF1RRIT/fYPDqyYCjTuOVTY/eKLwUbMVJUKNuOo2jLn9/G51UUp0CqaBsUEqVf1IldcWwPlJS7d7nKiSrVEyxWoodIu9D3ilP/QbrFYzu4E34PgDgHbydgW4DpWdoZ+G1oftEa/i+B5yFnqmYjZtmKKNhtxexyEF/OuNi2JI9Pe5gRxuVUm5yNLU+2FWX7oNi4ITz9akQGsh5EDMl6pCUUv3LJ2RvTzfoNkfoTzcYXjmhwgubE3qPDaTcZNLcK5VUUjKZubRRN1FJtOHW4XyKJvCQiqRcZBB9vuCXseDABn8u9+vS/BT8pVJwIMUFo02+Xem3jbPNtq35djt9POGbrfl2vF76b15sj+2VbV9vV9kvA/JUACXkK1wSV+zLyWOzgbIdvh6AbCASqPt4a77eEP06s9gnbjPUf+yjP5cLEKj/JX08l/l4fFwPDY5tMYryAsMpeEVT+po0bzRimiH1gvpnj5aUkpCPh1Q18TY2NiawkV5sZ2deElLMmluPzGBJxpzJqG0wENI4lURUNGDWZySUNECJlLLCYNUIqvwvHscxzWGZjRIbJGmwrOV9knGpdQhrhJVcn/0GJdM+JVLq+SfOcd7nnUyJXcQTr+dtWsl1iAoLXpeF+IjjjDA0QEEyFX81IoqdvqHT6LTMTeEiSRWNkMsJqsmpaMRs6HVuOg04wGlE0qrTRFyQwULGqktiimUoNWWfAR0bFVFoYXxXwU2opYBADHofyURoUq1A+zDSQ2EjkzNwzuOVTRcdJQrdC46RVmlEPjpCQSUV8/iIJOdhxHrWGmYAzChUB1wprk/LACJ5Qh38nsLMhkSk6KETxI1OoWbATvKA1QiTQgxhHTqDoXNRWYERsL2Jjqt3lFNKdozk894NJk4Hk6sgeKoVotqMc8XQ71R5ApT4Tn0PP4+kLus7yl9jXUh43qEHKQAiKWU97GiBXpwuKKR0IKfMYKgdNwPM8QAdiEOlNSkP+oGmUiVVEDV82VJKrkObcvge2y5+LzDovUCUtbTfsXIhh+11OrantH/UbnknyTMR7smZ7XQH3/WAG6A2FL6nT14GtIY+PoXeUOheRkgZEcJHF1RXIDSV1J2DSFyyX0YDPSiJfh+SvB1ZUhpNjgb57IZanO+XCzHQdh6Uj/NkYWcHZx3sTH0OJqe4vwIgPP+5AxPf7ZpJVCIoIpHam0hGDZ3Gy5sOL236OD8YjZOeFFJHHdld/huMjiF7fTYNgwGcyNw7KGfjshLJzTNCqjRcPOsrIXw3BOoHCAdTbCMFts+IqRAnQoNdnqe0v9EUuucB+t5KQwXllBpmCkV3Nr6D1OYUmEf0zsKNUyxuwPmb3DhnU28d5u0MOxIhZSebCCnrs/5kVtVU88BKyBUV3ldmoMIc/XEeIt2fbsL0WITvHUENG+hhA4SiDCrYX9UPkZCKhHnDowa/o5xPg7hrwoPZJdFBSUZJHy8nn5LQgPdl38w6n9lB61zm++XbKn7d7FJOq7nw8QrfznspKLiabye3xfar+Hv7oPT1MkJqh6/H09LX4/1iDlLh6wHJx5O+niSsaqTUUebjaRitF9visUqhN458vXDMrEM/0+nMxyM1lYr5ZElFRfaWVFMhP3UQHuigvlKei1kJJ+4Fw5MgpdbADntSTKV4el7O9meCKhBbziNWNZldUkcxwQQsyShXMVBs3EabG6Pc+NjMkM2lYaoYqmiMVggoH/dBHCHn/QEsjgEEe+6E3NXtNlycB0JCTSl8b/t8xLbnTkURklIzVIKAkmXNeX9JOvE2qaKQzisbr3mi5Tnsu52Suqo0WJzjhgkoMkwORitYl8gpOSpstILzOhihFKpnPRkw60n+CTacrFJRNFTZG2B2NCLtFeVqIT7Kw/ugfYr3bVJDxd+jco+zmqrhargKMVU7VoXRER75YDmvCWTjSW+iMzA5n0ipwWAwoSS5VoCbKI+H91DeLa4tq7zXpzAKqhxnQ8iYhZssDDRV4AvqKQAi91HKdSRzH+m+Ezk3RKhZ0XFXipVh4QUbnsfeaPSGnhMmPU5CqA8QOoPhmfbeQ80VpZRmpRSFAx0HR1SG6PCz2YdKchymo6BifqoMWgOwUaUklVIAhaQY5+A2A1wg9LtjBzsyuU95UpxQSmmjQvVClUJINkOsQsfqM1JJpbBBImXWH1oNlpgHss9o9IGMkjmlmKRKv4mCC23L7wdJ2LPqRBJSR0IlVSqleISZBm2Q8kqZATAzzPEpEeKnr0B1PfQJ5ZPSm1MipMxA6qoidO+mqvA1QupqCLx5nBoNWKfCMnWuN4E33RiNSXlMTmcqZX7+xpnfmR7PtjOs8zgfLTl0mVIqOVnSqQKQOSMAUtXNQKAqrbApbMDLgZQ6Hro4//JRh14rvBSmrI46CTml8txRIfRXcdgqADtD2TknpNYUUqXEgMP0ZLieczemtlm+CXJCipdrxBQVYJFTAFDQmkL4FAC4AXAzEc5WQbkBONJRYQdt6NzzRCkGhgkDABsUU7rvSDU1dHDjDB0UU/N2jIQ+k1JsU9nOLkPMUxGJkpRiG8s57JTR6E+OoQcipziPlNqc0nvs6DgSUaSUIqLcizx3UiV1W2Riw82CX3sll1IKEZxPKipJUqUoGPbnKEQvElHWRR+PRQkXwSeTRJRUQ5UklSSfSjJqzbdjO+miACH5ZaVvl4QHdd+O2iEnf9dEBzX/bh+s+XprBVbWfD2A0jbwNunrAYDpkm2gZZ35ekorjHNdjDCK5ZKEss7E9xfvY31413Wa1HEdD3KShdWBaXKKpqxqn6IFDrmCvYpqKBMGs03w2WQX1Ynt+gUxP4+SlJLPjBfrZC4pZsalbDNK45Cc+jjS6+jlzobKumSoRiHjLA0VG6M5ElBuQUqVxmicXdVQATkBFQ2VdZTHY3YZkWRntyCerFBMLUiqQiKfVFUOXuRy4D8AWY6HfJobqkUp5/kizp7/7odw1h0BQExcCuRGi/NH8FSL5eiwxWW9YrCWaqnInodcNJKw4k7teSC4dEfnrVXrWWPPSfGhl0ZNyjiFY1zmR8CMyK6Tw64wQEfFFDQZLl5WOmfRd93j0si13FJXAzfNvk6uFr+LEsv0rqKQ4S78RtYoWAV46DT6ooCjzuClIyKkNkYTMaUANY80as3JduOHhmekH6D7Ed1mgN0SKdxtBtjwfHjrYIcZ3nYLAoX2pWTmkTw5HtBtjmiEOYw806hyH8mpGGpWhPEplZxazo/FyZEB4CSE75UjhtZ5nBuq4NX1eacJAJV1B7DpqcNQqiKOB0oQ35uUr4uLDmjF6o/Fr0a5kEK+FNVROIo62sCYiUbbtY42jhQAGt0mOVayPblNKYQvd5R036HjUftNCh1ByMEUk+wGkiZrz1CBkcKKdPYdjwcTiYDzccYcOlLj7HCuFcbOZSraeN5gK/tg89ixPwnnk6GQnJ+L4UPoHg+i9N0AH+y8sxOUNtDjFv7iHPrlN4awvQ1cfxzC/YZckSDvoSuG7zUyagldUXCW64mkQGxAHrnVYXS392EHQ3vTq7vDZB2F8DmP3qiQRLyLz/dLkYyaMc4OF7Nb9HGycBQRigIkR4bfmbl6OVdG0jMgSClOYh7mmYzqg2Jq01ERiYGdkqBYHkzIWWSCOtXOUPMYlEGslqoopNZIqZKQumXwpxdjrjGPqhbElAUNeimwgotDCBWM6mC64CLY0G8zVGNUOQulNEw/wA3n8P0AP43g/GBq2MCMW/SnW9jtBew4Y36+hXMO8/NzOOvgphlunGGnmXJPOQc3znTtLnfAk1IqVSzVwQ6zPY4h5n1PttVodKcbIjBPTuidFYowqK4ncqofYhgferK9lBtPhKaXRLkulmtoCqs7R83G+RCtQgIFkdg8+IsyNYtUSLEaSqqi2NcrSanSjysVUWcj9RvOxbTm2wHIiHr27dzss21VAYLw7XIiymX+3D6+HR8X50vfbk9c1deT83ycVMnWqjBLJS37g7ECqyLySWmFi7gt9/X4XQKkFAiyD8QKqhiabkPaFe+Tj+d8UEiFfn8H6BAZ0/tEXBG5FcQD2sM6UvKHXwEedF4SFaT+VnxXP3F/7VGSUjcNtmWskuKXOTv7APU/XDASnIgxJrfjqZCIWocslnh2shOWCCnpjJUdNe6k2dktZe5xeX8yig0frU/GqjRUuRHjEaua4arP03UkpZQdt+AqETJ5aW2eDVksxc4JTgtj5YTRYieLkvsCPiRL1poqeMUH2xI55TWgQuLytF9QaGiFEYBxeeI8nnIFixTfbbN9WFll4aGVATSpnmIel0CYu2C5dGCQnAKcUzAm3HORTGK1HhFRhzhdbDAbro/rqKaAFAbjAllDpGF4GXkVLDFVSTvqiIgiiTDyqk9rjk/oRGuj4YRSyk2hk28d5QkB4I2DEvWDOYk3l8xmJQ9PY1LuvkwKK0aPK51weevJsuu9IDvGEMYHAGNQ9/DIFHfY4nWykqEgi9kp5Xn6DB2UWTqGDF76G5lgU0LyXtUNoETMOijFKIwPAPTUwWuHjtvWuehAcZtGpym0a1SdhXnddzFkj1VaMcnuDqdGIYjUOIwvEN7SWWflFIDUnoEUrzn8eQ6pQrauUxLQGlKHX0F3A+A95ZfyDmpzAmgdCamokCqr7q2F7R2ARkjth9KWcaVLQIQRRBKURnWJDKWBEa8BQIfiHzp0xum+0krhQjkAXXzn8f3HfZ8xOG6z6PPwfuU9y6iFYbCDwGQU55Q7DoUiNpFQJXK/N0RKGaUiIdXpPIeUVrRO87iP21MhtSvZ+R0SUvEjqw46Tbm6k1RMeSRHKVNMKQjFVGiDbqAcU+H76iPAsfLLWWCeaBreE0Yb6H6k8L1phtYazjnY7Qi/sYmUCkQVfZR0il0iorjYBhDfTRwmyGHmcWBloAIL0Cblj+r7lD9KVtvrmIxKuaRQqqTEb7iwVY2EulfUCKnVfYP1c8gJKfLvkp+XpiGtS+HjTTYfVEt+m4s2blfalZpvZzNyClj4fVIxNVcUUvNSXHBd3662fBmUNsBcLBfz+/p6VHiG1pmOctlJgkp3SfGtnBIq8NzXAwDfJYW49PUYUuHLUwAYOiDw5ZlyfLIevUHm49mgBLDBkXPOR//OqVC0ypPy2EHBgO5Js2e83lMXEjw6Uuoy40Nhd0vFlEMer8kJJWPYk1BJOSYCENhzEYa3ppBKLLqPccbno91JRtVIqHF2mAq2XBouIBkjqZzifSQBtUY+uWzZwdulAZPTBaNeMWLlsrZTnN++9hFsTZ8ZJmBpnCRzXrLp0kCR86gzI6YWfzqpnwSzztJOuS0aNa7sFfYxncZ2IqevdNSkOip1uk0krmRlCBuIB5Z9Gg9s4WI4X5R6hlA+reh89MIMCZqDWsowix4M2K57nKs5yITn1WfmiRu5m0LZRLtMkVRMAUilX33ou/M6AEZrWAccGU/lrTVw2lPo6HGnSSV1sYWyUxixz5VSShugp8612ZBSxVsx6ryxsVy3tw52Er0FpFFoHRVAQxxt7k43FMZ3Ejr1RyEp7NFGlM4uEnOH76/BYayIuY5OeoMLkYcmyqhnqtw1zi5Lgs6QEmp+/jqh7Hlp0+MkqKRYBSFLvWuVqmqV97pX9Nx7baJqSQE0rzWGVyzslpSfMQSl7+LIviSlyhF9Dl8xQx8VaN3pBv3pccxroo6OY7vG8BGdyJr4OyuutofodF/MqV17o2BPeoyzCSN7pE7hUD7p/EtIR59HBzkXD7cpq904bwLf2zaEORilMFoP022gdAftHbwdoZWGdjN8t4Hvevpu3SZU8uoSGbWW6HwHGgl1fdTUUpyWNdqooJzRPj1DVHyB9OYbQ7mmqGBDUhpY73Fx3EdFQU09Xib1ZfB8bWBIJqRl1aDRwBGr/QLxxM8/K7h6TcnMFYIaKoRFUy4pTmwLdAiE00TK1EwhFQbbIkklyCi1Qkp5mRxP6b0Smh8KbjoOR5LrSpBCiqIF0mi8UE+Cw0Z8SD/QQZmO8kx5qpaonIXvhqggU90Rff+TLfw8wo9bYJ7gL7bwI60zF1vqY45bwDn4aYzvI7ahPobt5VMmpXg+DpQIpZQyQenKdlwbsqtxWcdQPXQ99NEx2SET8kixQrVGmqulPb6qmrPhZlG7z8tVLggMfFBKcRSM86kPzb4bT8lmLRVSvF2S66R+SutqitDz0a4SUezbzRP7eEFBZZ0QIRQhfcK3c8EusRjAhaIDV/Xt5Lq15TWs+XnA1Xw9ANAdJew03RCWh4Wvl/INprQtMdw79D+7PkXOmE5jnkhZNU6heIYSqvPKwKd1pJoC6B1oOw/rdfTxuAI7K3tZMYVUTgSAw2B0Ch1FKDwBhGp8YSCa2x1L3+Mp+2yPjpS6Dsp4TUZp1Er2nBnyuL/sPPl8H66wt6ygsM6aS4eBCSnOCZXPL5lzNlpViadzcPN4EBnFxk0aq5KEqsk9y2UnSCk3j0SOrJFSLjdK3piq0eIy0cqxwXJQmirBlOSU7gY4aGB28FrBgBVVLjhVaRvATimxkt55UMVmatMJKTcGd47liG6aZ6ODbB+jvIhzp5kw9gjnPAxXQ2OCNKinKOQov2lLgxXbvnlodw7+GfYlpxRYJaU4CiaG8ynx6jFhBJ9Dz6JKqgzbA6JiieZ1UN2kZOek6NHQQSnFo85ejETLXFQc/hAr7sVcUkOsTqS4419UiVu0jwiVS0opiOqUuWxaPl8cflYSKLX4/6EzsfhAb0IuKSaheDRNIeS4ql1ocNNCBT7VD2RreuoIeW2issmEdjPBfgKIZJ/OEp0nx6nbHKW2DQq0WFq9H+h34yTnNYKGf2qVqjkaVp1xIQVDdizPKZXOcSHy+8g2lUSfLOAgiSpuvxLyfZqqGIWE54aIPd8dAY4SoHuTSEwoDW+61P4HOHjN1F0fu9RSvF0HIp2VU4aLbYTnmhRyRDjzu6v3CpP22IZ+CYesswLLGiKsNp2OeTljkRbxTpWQhBQnnJXVb7lQCOeGoimFQVPovMJxZ6BYBQVEFSrbWSVIazgOz5szJZQqlaolIVUL2ytxi6opv9IJ8BnhlMJLvE+qKVaKy21e0W+hEX6DQEoReaPTPSRUvKp3UBzyJxS1mGnQxM8jfDcAzsLPExWUcI6WrY3kFJCrpeh0ifAHAD0EgxveR5GQCnY1W3e0ieHRKtjaaIc4sXkgpEoiqswldV1FZ8PN4TKRAnD5+8KD1S2cH7F+hIsDwDUfb7d/x1EvTDBFoilGtiD5eqWPVyii7Jz7bdK3WyOjLiOi6iF8t0BKCV9PGQMbfDpgt69H6igNO49BhTUulFQmFKOyCBEpHQC4aNuUVnCzj74eVYUlUYAGR68s30Pl7wi42Lcawnb28dzKaIP1RDz1YX6FiojYp4/zVImpJ0FKXcaUMzuO2JFKEjqf7ZOH7jHZBCRjlaZJ4hnJK8fV+8oEdmtxx8V+PjdUOaO+jDeW6qh9yCgXS/aOVaMGJBKqFoO8i5RahO8JUsqO57CG1BlVGecKUw6AwvQqJJX80/0AYIIPHRCtTTRqvhvAIX5J0slKOgUVpJskW9eQYX0AoBwx6KOlKntS0lmGF9Cywzgr2Cj71LDaQ4dKNzYYPjaA2lPcsQ5GixxP6sTHe5aVe5UQPo6Xj/cwEK3di8aw3xf2ac6MSIy/T7hHvEoScUe//VHIa6LGM3KO5gsoJ8JIGFrHcDM92FimewihEkxAdZsBLoxEx1HoEJoGYJGbI1bcCwopvTlJlYqOjnNVjxEhD+IrEjGk0GtyXLUGJmtiXj4ikCiuf7QO56OJo45ACo3N2lEryAopJ0EptekocTGH6MT8MTpV1SKVVIWYUiTzhu6gOsqbBQBqnqC6KTpQQz/Abbew0xxzds1h6gsHShkNLchBIqcGSmw+JLWZ3pySsxQT7XbR6fNKRQdIhzblRMxO+Vi1EQCsN+g1vUM2gVA6GUzxrrHV8KhETCVFaGpXjSOTlGcUKphGAj08ZkvqmpHerjBaYXP0EpR3UCGMD7ojEkppcm7RyKW7hFRErW3LQoz5KQnV2bQT/SZN7yYb3pXWkKrqpDeUZ8whqcxj0mCXpzzwqVgME5o8X4MceWaCm3PF8T3Za7Ix8ZnX/LwQEa5BRSOMIKKY+FfeQdkLIqTslA0CKDdTXqkiVG9BRJUV95iwwt0TGc7nZFT67X1GbitPUQMukE/cPzAKMGEgxXpAOx8IPYWuPyEFnaNcW947qH4KoY7H1IbDS4CboTkH4jzDzyMppHg6jRTmxzlNwzyw7gDH/iMX2QAiycRFKuI7MUxp8CSQapKE4nMZJqWkIirYKN6nDDHe9Xs20upBgAtWSXBEjFRJMWI+u+DnxT5ZUEnZwsdb+nm+kqpFhPGtKKR2+XZSHVWSUXaWftxSZLCPbxfX3xUpVfHxDvH1mMxyIlLGBNWUd7xs4DXl52XVFIBVXw9Anr5FpHtglfnsPCB8P1agRyV68PG0SgKWMoyP1/G7le9BtrscCZO1OUlZ92r3p4InQUrtQubAi7jNXQx7uU0qWFglFWONXa6CKfMi5AwrIrO+2C5Y9DWFVFVBJRLcEasemHJhkFj9tMtorZFR+xFSS9l6lijPOXi1dIQjcbQyXbDmxkQBZPb7cIJNrem6uiHuE79D19PDH4wHs+RSEeWDKkkqppRRsW1LFp2VB6WqowxDYCadiVA4Dyd6gBxznO41JaZ1ZdTiHhV3enP2HiAqzgv/rB13eEFkpgJgfMhlEhwkdoySQ+Spw+xsUtrwKFIgqQzH7Ad1lLY5KcWI+To4pxSro/ohjSxz/o0+jTSvKVw4HISJKU7O7Tw5kIALUwLlPgvkSnB+ZucXFeSAulKqzB/D6ghWEhmdq7aqkM6I1lER5gEaZQ9trTeAMiPl7hKqs1qbslIq5pTabFKyXQ5/5DYVyrMyfwmDywsbBVh2yLWC0xq9ps9nBcqmS23F7VmqPMs2zfL09Cbm5uJcO+zgM1gp44CYj8Z6spsjKCy1G04SKYWlIwDkpG4jzO8fHMbnffptuGIoraMNikegWS3H6hvNUxUSvALOm1x57qTKnI4vFehAIqMAiBxoCBUoVSSh+B7l+1Tx/SqIKUlGxXUKgB2JYJpHIp4KMormQ3/HhcG1B5hHag3L/mya16HSL4f0mVC8gAbOQvu7lASdfw2rKKwPvaY2URrwPdlPNxPRY+dANM1A52LYuea+ZiCnwH007jPKvmVQo8qkyUxGITimkYgK88qkHDTQHVypfJJkFEAkFbCujirD9hoeBHb5cCWYAKjBOp9uPeHfSeEBfV7ut82Vfv4u/04qpFKy8t2+XR6ul4fq1Qipctsu366c52U5pfkVG7fW1jF/cO7n5fskH09u3+XreWcpZ+Xi82y2zTmd/D8u3hD8t5qvFwn5oJiyIVKl5r/XQsx5e68TByCFBxwNw3mEnUv93Gr7BTVBzdctwYMITwmPmpS6zCaV+aJqzv1NokwEW0uu22lyPOVIdQmlVDbKtQal85LpWlNelJLRVmJdSf5k+5lkGHhfXl4jj2g/vbfh2lWFocaix2WTG7XaceW8XKeLto7s+RVZ6DLHRbmt/G0vSzh+EwnJJdvOo9prqKmoGm4QtbLgTEYV5FLcrjQleWQnSCbW5epP4Rw08hs64n14UXMejWmk5JHOwXQ9hZqF/B2157QaDsHheoE8oYTVJpbTRj/Am6KENp0M8Bx6GsrZKqDTHgoaG+NDiVuPSYdE3Vpjci6EoFHySHZey1AzIKivwr6cR+YoJDVPFbZoyiopWep9EY6mNHndzsJ7R7lGNOVNUaHMueomIr3niezfuKUQzHnKwvjiKTnHCYeThGlWinzYQB+fklM1HEWVVBZCIi8ztKdSpPhwBtj4EProTcztNFlqy8lqTJ1bDZOSYJvF+Xl6TSGQm86gM4noo9xSpEjhJvQ+P6dWlGtKKR8IBAMbKhhxu6vi9+Tjykt7ah2u+0TZvtRdz7fx+1AHRoqXfdZ/SkldgURVSMIxVbyiZSaZkkIhqRjSMXy+5f3J+sZ4/6igHoyhuek+iqSVzgkqrYBOIdjVCco5UkAJWxvJKOcWeaOq4Xs1XJGMKsPEqqe+0pnD+StdyvA2EVWsqZ3YUbOKTKNGoZxSHrNToX0HmH6g34HbUJJ5sl2BRPSVoZFAystVa9vQrgu1UrCVTpL5TDiV4dBifkE8VT6LyahM6bZLLfVAiMinjkMIqdtCpylR9i5IP0CF0OE9BUfxmLICcc13u2xbzbcryaC18yRy6XAfj89Vu841/02qpS47T+18ad1h1lJWdAfq/vtt4yo8xVMjph41KSVRk2rWcF1ySmsFK8p/m0pPupYLZQ3xxq/YGKWJLiWZoUojk05FzZ/SgHYKvjBe8QEVxkYZA2/twiCRdmF/eWYN+xBTpbHa77y5wZKSTrldklblPun4VE6Ul3ma/qiju2bQaoTTLpT7a7U0diVhdij2udUcQjXvhttHLaSDO94yPIRH6JmkUqGykMuPr3XegdBx6AA4m5yVMFqsjQGshZ97etaHTbQFtQ4IgJSbQxuqUsRKnkCksMqHKxWhSAYbQ82Ck8h/RlOFExXC+Fg9MWkK4dvODkde48gQccGFIoB1koIrznF+ql6nnDInvSHxWAzhCY6VcGiLBqBr1wbKUz4kKEUOVVBdektqSxVIKR9G5fw8hspUZa4vHdtWdT1gTAopGTZUyZBVU0zw8Sh+MbKvwM43KRa4ClpKzhza0xEJNGlSpFFiVprSdh3aNG9UHZ15Fe0Tk1JM9EVCypA6RSpYHCvMnY8dJKUESQUFF5MqC4KAO1Oa30EqI6343E+pw/XQUCOmgERGGbljceTa7yKf2ZJ8ikVoxH7ydizvzRJ8r6qgilKQ9y/tY4LaVCtEImlBiJSkf7DDqyF6BVmidlxn1hO9KklxQ+SGJAprV+yL30ApH0gnIgI1L8NDuWAfHLe/D6SUT0SgMgAMTD+EcGOktpS/AZDUZ5X2XcUKOZRVgRWEUpVMWjTCAUqQRjo9eNwnX9Xt4fspYWhlDYR9UAoMGKVfx9PSt7uMhKoJGNI+u8mpmn+3K11LuV+5fi0JOn9W3cfb38vJTcPlnYw1ZROwTmCZPQYa9sGLJCJ4MqTUTUIrHg1KSTrllHMayN6XVoryLwgFTTkvK8qUeYkGo2GVxwRAxfM6kvB56tRrp2LPK8oQObQMGk6z9FTDu2Rk3DwmGacO24pQPsVyT71/bqlyXq6Tz6gZNjCmz/bZJ8543/jishJfrTKDrLTHjHisthcTaCa2nCs3mI726Y0sj17/4zw35Xpal6uhtEIscS8RwxKEemAXHsCAUYPELkKKySdRyYnUONsYFgKAOtkL9VExMs8kCkDhejGMIfx1Pbg8t3IWiMUKRCgKEEMhlNYAK3t0kSBWm1hGW5bOlolha6P8rJTyXsEoSpbcGwXvyY72GthaFZMgT5rWdyGWVYbzSHKCnxsmTTifDBMnHILGpd05ObKp5ZMC4BWglKaqSyHUTCkNfeSJjHL0p4yBn0Yo5+C7HpQLJZF9sJbasGxboZTiROpcDQrDUSCjkkoqK0tegEkdrzyM5udfUR6pMFjSh0TTvaYqh7bTWWlras/83SXVSqRe48qJHAap4zq2T/In98STQXuPSTi6Ml8QQNc+mBAOBlIrKASyDT4SU7IT1oipm0NNjVYSU2tQYh8F1IkEUWUuO0gSCGFVUkYlXDbAUpKW8VqAZENdGgBIhMiK6mmNiJLnk4pWkANYXmZNyV3FFUgN7o9eemql6nKoCtbUkpKkYoLKKEXJg4MNt/BRocZJ7+l38NEuGJfmNRSU6qCDeinWNwCW99AVSSmgIDqLw3i9vL/jPaR33M/7XMeudQ13iqv0h42mgRZr6d2nfRh4CYP/Opv6qv+2zM+4zEu0ZSIq5KkluFidLfpviv04D8p6ooNPmHyozGfbISbY5dvx/K5pOX8IdhFSNVHBmq8HIIbm6VB8RnfDYr9UoY/9unoVPukHRh9PJWHCsIevt8vHi8Vhwnq9WL6aCutFIaYeFSl1U5LNXacJIahZ1SinyFA4RQlmoVU0WL3XIS6fXmS9Jm5aVpSyjvIpDF0KR+m0wkVRojtONSXldnPIN6R5ShVtiMEOVQQCKcXbXLhwSpxnSIbdUTWpZdnQMbLebirjlC8vG7orEV4nEp2b4RjG9Kus9mVlQjPjFEKVlNbRUC3LhOqMeKJtuXGSpBSTUdFwSQMWHLGyKhWXCc0rVelFvpukOmAnT4tErSlha8pxkcxOHPy7IUvkvb9yuGLDHtiDkFLOQtkRsDP0+BywI9xrvwt//hyc8FUNG5hX30wKGm3ASVcjuPPbdQA6ejZC3jRWRHnOLRee60ic1Ea5YvW+ZbLYmD9KkFEI1dWqpbP5lAi2UwNkcXXMe+Y9YLSBdcCR0zEZsswxw4RUmfg4kic6JTpm8qQ3pCpiMkpOWS1ltFrG6fN3AqC68H7wnipMdVRNSntPIZDzlOdAKWxi/InYSeW21Hm7Zu2pNFWp04mcyhLsggk+ejmZIAPhECYbSLXBUHs673ESylaXyaWpTZe3AKtMmAznnF99tFOIZBSPGMp+lfP0M1skImqyDh85GzFZH3+vk97gDZueztGncESwo+sbMXXbWCOm1vaN25m0sXO49+csB1NSEa079VK5YtZCpmrraudcqJfEspP21+f7SuKpUOkw+RSfZ36+40cuVQpy+z7kFIdex+9XhJfVkqKrQN4iPAdcFREg0scrv1f/OClQ852t95jccmRfK4UJabBVgfsjPqqpaD9Ee8TXy+v4GuP8glhUSEeGAcKwsRpyGFey6k5Ufd7RBvJzObxTIb0TjKb3h9Y6PQ/7ylgaIXVnqP3GayTkGtjPk+ACIuzn0f2SBmxokCf4eB0luz7qdMgjZUL1bXE+rWJVdenjTVrBzQ5KqYVv58Jzzb6f9x4qJD03ht7tfIwP/UUdlNveWbgw1X0PzjUF5H5dSTyV/h2w7uMdgoyUMkuCSq+JEeRyRXSwFCfkhBOZUxXJpjUyqvT1WHggfTz283L/j5Y7ncQI7OP1gYBKg3dY+Hd8P0UyX+3u28i+T3zP7NjnseNRkVKXgaWKcTkom/YFjzQpLn2sVTihD0wnqZZINQUAgaSKUnYFpylxJAQbKhP2jrONWfuPBHFVTkc4oNNRNRWrwikfkrPRN3baZ/tonSunlPPwTsE5vZBoam2igorD72pGTKNg0MNdUzNmDGOTETJHGxiTEtSVRkmuqxko4DDjxIx3qYxSSkUWPdsWWHI2YnEfwZqXpNRRYbhk5arsT6XcFuxYyimAyKIndZQSnUA2YOogcqrMUaWBnYTUUzJq94KVzmsWbsfOEecxsSMwXsCfP4f7xEfhtmdwz1+DPj6FH7cp5xDneArhtxH8/DEBxYljUZBRXN1oBfJnj0SA1oF84Q56VyfdyjYQ4WalR5GH6SwPjzkAVwgpXscvdVvcs5w80iPY4MX5l5+567vIHDJSNRouMu1ql23rLSmrOBF9Ldnnsj1zx0Y6p9WO+EobcsUgukyPXQml43FhqkO44xTzY2lySrkzDg8XGCzN1RnCu5IrFE3W42yyuJgd/sfrW2xnh5eHDic9fX+aatgw8uxDBTBO8LlWcabZqJtDjZhaQ0ZIyeTfXITBBlvDqiPev3auivO+UFnu4+CXdrVcXyOsdoTh1ZywXTZzjZza+T32IeEORGlmNaqZIFZR2tjaslEKLhiPGCqpqQiLYq2mIJw47C9bH/dLv3V6lv1efZvS3tVtYv2mjrnSFOcZ41Bj6ut7RQ8Efy/6HnuiEVKPAlohvGtEiGogp4yidxG/CJN6WPh64d5gH0/6dTSvgviA7oeLuRQncL9GAZ0GZnrnS98OcKlKnAsJuoEoPmDllAYCcZWiYlgVBSThQfLjzKo6SteiYAofj3EZSVULwattr6ZgWfH1gCQ8WCOjAGTKqFx0wPNI0TElYSV8ttLXy6NhzP+/vXNpdiRX7vsfKJLn0T1zZ+5DGikUtiQvvdFSG38Df3CvvfDCsheOsWSH5HvncafPdB+SBS+ABBLPQhWLPCRP/iI6+rAK9WCxKgv5R2bCR+H7NszHI59PKzA/j9m8JIA4ulaNlPj3xN2IUkqp4gvJjsEoV22/tb19oRsDGGeEjLEvqe2gEGaP1BhU/uJ+xGDFqqOdkGQ/jqHwpgubtNOFDm6aUDs9984p6elUony6UVLb98m0oXw2h6NzIsaD8bPxxbM82O0A+JpY8cwOtH0wZvR/FikVddzizicZvsfDF7/s8avfwzw8x9e7UfC8lEusNyxCiglPgDVGQAjbJGOTilO2rY4MFxehKDqgZJRIGaeXTGgzZOuenPNF6URa5TVwbP0bmsY6FBImw6YQhCuNPmfMti2nKAkXJE21ozQSNz22Otrps80vP2H89RP2//w/8fL9/8UP//Q9/vm/fI/Hbx7x1//4d3j45it8+O630M/P0I8fQiodvejpGUqn0U7S9fyyFJ66B9gZ9rSdxVJtdjBuZjijtY3w2uxYmsMO5niw8r3ekNH0wpRxQoixg/s+esZOFx8iafYsomd/HKNIqZIolUZK7V30IY1gjoPCDhpKGRxGnl5iU1NI+Kj+ZiQY0lTmZrTC4WFfn8q8cn0N/Vb7V3utx9GJfSPUaK8tlJu5Sm9gjgcbtcWiOVwAEsCvoQnTUu9dhBldxy8Hey2/HI7smto2h2Ms9pXq9/D6UvYz2KxnYZndnp3XaPDlOOJlf8Sn1yP+959e8MdfXvHf/umP+Pzyir/9D7/Df/yb3+Dvfv8MrWzdr92wtSl+rkaW0i5aiu17lnMozIK/U0qCXyZGUXSUs1/6yye77vDZtjscUJtJrVh8l+xP8eROrICYOlP0f8up4n2ZSv3LYHuHaL+KLUs2CMW2gTCz5opChlLUz7W2gfqytmh5EKMJEq6B8JVbNgGIoytbNVlrE7acWlulNbjcZceSSE+lgN1gbc3T1vbFNhp+xqtQl0zn96d7xwmXZ06U1IjywA0AP8MoxR1sBw0cR5/poqnupY8sBHAAtLI+ns2OsYLt0RgMr8doRu7XwxE75r/x2YQzH884n475cfTZGIPjwflpFb/Oz9o+ImoDIIqoAoJIRX/bNrkQn2fBdEYMOmrFzrlopQuiVCxW6ZDytqGI8RAJxX047u9RdsywiQMOgFiU4hkwqRi10WnE1BAFIzzthri8gQ5ZMA+btB4nKzHhsmmiKCkUx+B8SYQB+fu51I29l0G7uxGlUtb4bUhFpxccpfFB26mORxcxpZWb7hEKcAVmybgdxxAp5fNIkwgpYmCRUjxqitejotQ+MjxHJ1TRMoqUsn1J40JBR2CAF3HUgZxYbshcp2kzRCIVhYfS3/x/opTTPBzCrTU8fcCwefCfW+o5/Z8aJl6UnBsmAJHBAcDCOENqHhmnkoqepuilRiqNjALgjRQP46R/FBqeClJa2RBgIMwQFKfvue+GspFKmQoCvAP7dBu0IohGEhjcaBhz8sbDHjjsMb684PXPn/DL//kJ//L9z/j2h8/45m//hOPnV6hBY/fVK7YfPvuZ2nxKHYBsgoH0GS2sA+C239veOnMOjSvITQW8DWDFk3G0yzZBNOH/lNFZ59AfloInjPGCFE2TfHQilC3KbRtSlA6JKdG+3E29BXUmXftBQxsD7UQwsI4p7YucqVr3Kpv5yf1u40h1pYIgRdFpoCi1xAbygqJq466fv5Y2As0WsRjCMVXdEacBFxLWjBvUNa6AeEiBhItYCoKU/38MqQRcUCJoZBgInZtBK+AYO5vUEbfX1QpiR2Ojo375fMCPL6/4X//vBT98esWP//YJn3/+ET/+4QN+ennCp9cH7Edja125dGL6bQzijpj/7gi27F46XtdGUZACMttG0Z6KBPbRirhZais9F2kUIXeA/MHL9313naZzMB7j85o7SUtHzSGTilMdQgeP/tdKeXvqd1GIUC0smoSLUGlkZds9nXmgrnMp77MlRhG+RMIYol9Gl+p9GJ2oMNosAwNVHLTIFokgdbX0RH8qFWfFaBY1p7VxglRI4xvde3Ez2HfhqEmwCnaTR00d2En4OsKFbJg4M8ad2yGesOp4GG12jAl+3fE4uuyVtkBlrwf5dhu/HkAkVA2oBRzkfl2rfhWQ+3ZALESVxCmKerLb576e/bstRNX8PR4RxcWpNAOmlgVTCjwI0VCxILXVlPECP/kOpe1RppW/TspFlKKdwRJd+3fS97lLUYoK5yoXObLRBsrEM/tEIc/KhnWOilL2XDjxSBFWyk17PEJrjdGNwFMxdK6Yj8bOfmSdAGe8jtqOZm/iqc65ih4+h6iqI1tOUVSldf6f4UapbKwAeDWeC1f2c6ywA/Db82XUlpP64k/7z/7vr377e2y2j/baZxHsPLQxGCMfbs2XRUYqzKIXCpU3jJYOUUi1AuUlgSkVoSh0kxuzrbM2FOlE6jkvxuwNFSscHCKlaJ12NQ6swfJTMZOyzsI76f5V7v7VzLnjRYhrI5fC+VGpWMX/jQeY/SvGz5/w+ucXvP78gj//yy/477+84revRzz/13/F47c/45ufX7D7+hkP33zE5sMThu0Gw+MOatDQg85GpMLhwrHHY/xwav+8uI7AzhXQpNn3KF1wa2tUmdfPVpim4udbuvmcEDWwyB72gFOElPEilI2UOowGXw4j9uOIzwcrRo2jLc4NAIdjXAMpOnd3P5O4ux1tZ8DWWtA+bc+maFgx+KhcgJKzz9moPRfXxqN3uPH6xaY3f36xzvaXz97hNhRRut/TRY53aS+0FQ23to3aPVpn/eHRi1wKsPWlxoObxe9ga1kZUxwB5u+Pw2ijwfZHGyFFaXNHQ9fXeLGPxCMAeD0GcYr/T/AinLWCnPy98/J6xOvBClJ//OUVP73s8cMfP+Hzpz1+/P5/4Muf/4Rf/t13+PFljz9/PuDLYXS/GRPYjMHAXD+pf/d2+Agp/j+bpIFS+Og5Gb/86kVrHPZscoBKJGFt1D2xZdFdWROupiKqWmJSOjhWE8HYTJp+m2SZGgYY5ZLZXA1AQ7UAqW4cRUnpOHrKr5s4X0rNo0ErY8L7n/qv7qxgohgu2/A4wqU3K2jq91EKwBieNdufrRdXzwYKGorXVIH2Uipx6Rjx8crLuamKImqZILW1BflwNEc8bQZAO3HcKGgnTgEKaWKFVjLId43wn4kPfqUoBSjjIlNM8OsofRzauMHkAYMaXYkWg61R2Lt7ZD8a7LWdKZgP9NiIZR0N0PCIqC8+Yir37b4kvlzRx2v4dQDCOsNFpz7fjtb5a5j5dfXnMJ3TokQ6qx2fadyLTxVfD0AkNlEbHmDg20/4e2mGSxpsUPL1sprBSWRUqBccfDw+IUzw9UI9Ya1CWh+Jo9r5dhTxmnVPaUBbqUiYKujnd8FdilKEVhSOG0aAidaLlAquGmVDoJWyw+8U4gltsIUdmQe0e5BHbKFsUVel7c1zJHWTQj1ZfQ8XoUSdfjJCpLhTUfTjOEaGCkBmyLjqnrZNDRrARSluxHKjRsvT68UNVc1oPbyGTujD8xZml86+lxsnLqAEtZwZK8WMFTNcXJTiBglAZIyAfHYMMj5pWypazoWoyHC5qdj9jAsdYhQQp+vxAsIkSClS0BWaKjr/PagFF6Teg6J+MyQFeBVFHLjIgvH1gMPnV+w/7fEn99x8+tcXHPdH7D5ucdwfMB5H7PYHJ0o9QA0aw3YDNcwftT0CUEzQGpyTqLd29rjIWuoBStsIIQWECCE9uO81BFGnwugmSR+tGbWdt9GKKLYjZzt4lF5GHT1uWkItKePq4ZBATQkX8H9vNVxnEzDKsKgiVRzHVwb+t+ECFS9mbvavkSBl9knBc6AQXWE/+0dRDzDa1piia6w2Wyhjr6GpzP400vn76wd/PX0KpEvfowgkLkh9cSkC6bvhwN4bc6FO9nE0+PX1iJdXK0r98OkVr7/u8fLzF3z+tMfnn/4N+5efsP9ywK+vR/z6enTnpZ0YZe8Pg1CjZq1plIX5RFc+KSDOZ6nzEVPGRVXTs+EEqalIwmI6MVEUmvZFAcroeloyP062rauZx9v7OnB8H1x8SuCCFFtY/p/+1oXoqFLbCiF6H1Tu1C/XTpjiy6xwZUUqHjEV6qXmAlAYaM1FJd6W90GyFMGCXSkJTSXzU42MatgqrVUc3eVuZB5Ra3du++MYVTxbnxPGjXsCSqL4vTqB90rpNqLb3kqTITJXuVRXw/y8UVFU8Oh8yBGD0m5wR7v71PY3BtdnIV/PBm+rKEPmyyH27ai+MPfhvqT+25i/t1OhCgi+HBeaovUU2Vzx74C6j1f6PEUqRkXBBxU/D4h9PVofRU+p5HMiQAGpHxcve0hEKV4nquQPkvBEfh4XowD4cixUfoUCDgD4Nty/086/U86/04oi98o+Wyly8965KVHKOiPhM3Xs+XIaTaIXayxM5YxuRJq/1G3incLgXuTaKJ9KshtsdMDRqdDD0QCDjYo6GjsdNwlPe3eXkaPwMIRpuUenpo/G1qIad0Go4pFUPFIqXjYWxSjelj6nwlX1bxOLVEQpUgrIRyT4No+v4e+P3zxgs3vMDVXy0ufKt2/DRKhQo4tPw5n/nS7b6NxghXZl8SktVp4KUFohpOqR2q2DgQIQi1Ra+cioaJ0O6Xq0HzJYYdplMsjumqAeAUXGjbfhXV3N9oHCcmEmUwXOW7P3uNpND99+xPNffIPv/uEv8Z9fR2yeBnzz73+DzeMGz3/xNTaPO+y+fvaRUnq3gdIaw86ab6X1LHGqNjMcpQWq7dZ/Vjtbx4pSB/XTBxg9wAw7GwEwbHwkQHQMpYLAoO3LVbvxe62soGuFKY2ttqIJdQZIpAIas+8xsZc6AtQxeNxYIWg32Jc/dRz4zCccowClN8Dg5gc0I5TSUI/WER9ho5zMYW/FOXK6mSBVS9/zs+0BNtJsGOw1ddFo2O5glIYZ7EyLfvY9pXzUmSbnUgEDFAZXg2mrAWy0d7iORkOrI0Zj3ExBdsBkvw2RUumgSOkat4jfOYOrlXHEx8cRzzsb5v7r0xZ60Djsj3j88J9weB3xh7/5Df7qm0f87uPO/1Y1qCMmBc8vi7+k6Ux6XKwFbGSPHoDB1kTTTx+sGLXZQh32NmWVxFyXvqd6IqVa1CKISmJRKeKpJHQV6lpGbUuCFIuKciutzXDiUhQBRRFTPEKKR0/R9rQ+nExmTzUTiHjfNhSJN/YHZJMHUDDI0T1QJAJTyp8elI0SAaI+LZCITwXxqiVI2W16Ramy7ZmKsCrBa13xmlJ8plaq82n7ejxigc7HCuNhYFr55VqJMHWtBHHR+SrJenpW/HPDMmOUQXhmdPDzqFi/KwPpRdqtZjPbDvadunEqKI/y3h9df2YcJ327IDy5QuWFoIND4rN1+XRj/Dy3/Du+3q+b0TcoUfPxgLqfByDy9ej/Xp+v6MN5/29w/+dZMamvl/lqytYEBvisxCr6m2e/eOFKq8y/i309Fflsad+Ghl1JKL/3/s9NiVK9pO9prQAkghZ/1ngbZZQTqcKL3STilHGz8RmjsB3sg791s/JFU5tvkM1+FD7HIlCtngo5ELYNU8oTo3QoGqm8PX3m/6ciFtFaxikte/gSnprfffuMLw9P/nMpJaSUMlJbttG5waL/Y4Ols3WbQltq45Xtivhk28QiGTnH6RT1+WcwgxeEKFLOS2IUGSyuoqeCFLf5qeAkAQcXpiVE8dnczAijlC0ifthj+PgRz98dMDw+4MN33wKAFZ4Gje3zE/Rug83jDsPjzk5hu91Yx2tjow+jtBKi4MRlzhUrcA4giFKUwrfdhWW7R+uMDVvrWA22ELqh1JSC0zdol0Yy2meBSiaNsPfqZhzwuAEeN2HWNmsnbcN0Fj4uKMWRh/Fo1Ebb54f+99Px6vCMxdfKCUFmdKndYxDazAj1rGxECEVLUaoS4CNBAAQHvBBt4a+pHlzx+MFet2Hn/t8GgY/+pddT2QGXrVbRdzgag+fdYEdllXICH0vdG0P0FICo4HnkXBbseLqIb0MiFx37abfBx8cNXg8jfvtxZ0d5//prAHCC1AO+edr6ThwAJ1OGVL7U4xNn8PxE13Vqhk1Ca2sD6DkZRqjNxj4nFC1F4hSAasHzlkDVUcepmr5XXZ6I5yUxCrGwxQUo+1nHf9O6NB3P1xfYxNuxf1Hhc0rrS78j3P3vhJLR9QtspL19Rgb2mfdhgbjfOig74EqPvXfmXZ8WbB1/9LltiAWp2ED4nzO5/HPqQsXbNVdnRddbBc6p30XlEbbaXVOoaNv4/EK/itsiQOzRrRCen/i5UUbh6J4JGzkX+3l0Owx6KPh4IV0PQCZScf/OBx74QITYt+MTD0TRUNUAgzz9vuXblZbXfLpa5PTciOpa2n+tNEDJlwMw6evFy3S2nAtPAAtKmBlokPp5JcEqtTX09UrBBjSG7NcBUdmV92xb7kqUSmfg8wXKDY3Qw30OBodeSLzNiFLUFHBEyNknxWs0VuX1efrKFtsdR9uBgFZhBofR4KgTgcrl9G+doSLRIzZcbnujI6cgVd/tstxQlQyb/Ts2bun2tXWcQ8VY7Tah8/nthx1eH3fR+o3OH7seIxUvi6Oe6O9UeEr3ww0SEBslapMu48YJgA/RTIUogBczb4tR1AYI6rk9n2lByt+/yWVMRSu/XBSq9Wk5bTW4U7LZQm220M9fYzeONqWOivW76CeqH2UjpLah7lMS5WR3XXDi0qgBXmiS2pMQ5Yqeq+3OR/j4ouososeLUPy7AHFkD6xDQfe7d5Y03MgkWVED5SYMHxRPOQYwNCKlSE+j2UxQqsVmnyGf1ko/Qe1ZcJEOatjAjCOUSYQqGj0/HsMJUBTUeIw7EqkgBVgxSikm5IWoKAwb77RGTrDS9lWj7NWiDgxdU3t5FKgmBqB9BO+g7HuFZjXcJu8cescATgDkWU8Fs86Lmx9Hg62md5Y95m6jvUD18fEYdaB/9/EBXz9u8bwdbLTCQDUUltmlex8tvASLLh89I7BijYK7j110oRkPgFJQxqUBU6HzObWlWjSEqmZ9qYn6UYbbBMUmbSgUIq8JUX4/IRfFX6+o/cyUvepXcgK077MiRIL4AVYDGxGiXHSIe8yp5pRSwYbQFeAzVZMdsFFV/kL460NFGXzfUNN2sQHZsrsttunxXZjeEnHRhzL8lgh9qLjfV3IQo8G/BQ+DiFO3CX9ufKQgFZwaSfQFwGqzVX08l66n9VD17+jRsBOy5L4dfw8/bnQWxVzKegGQRU7R3/b/ad8u/bv0mR9nLlM+Xvq519ej/0vCVRZFxewBCVAAqiIUEHy7UwMOQrSX3a9m5x+EcPjzEyx3I0ppEoic4wCEvHJ6WRM00w8Qwjy5OMVf6nAv7RHGK+gAMJhQ58Nu56aUJSNnyiHRPBSajBcQRwWky4MzwLYdw7LUWUjb0joA4DWP01DtXvW8RzVXn8JD9vd/+ID903O1bY+h4n+nHQ+7DtGydPpyHu1UEpuitkxg4saH2peXIzGA8Sx6PCIKiFVzv54JUfx8WmJUasqiIuegY8XfT7gQSQ/bKFvHyeiN/U0fP/gII/30AcO3r3j4SztBgOG1UCiqyTlSPh0sFZWIaJqPijPGRKmSyOVTVLiA4tN2Nl5Q8SJKEtmjAJ+6RwqC0+idzQKMs5dHV/9p66dZj+tJlQj3fxwSDTAxyglfA+tEaBSeA00imy2ObpQG9Agftz+67zYerEi1GW0Knv2h2ifKHNDRR0RsgvNKYhS/vnoDM+Qpkb7Wi1a2PvFoncUjjbi6a25gsBsAg8G/f9IoXgC+42z/ji94LT2n9D4qR/ca91vYi/0waGwHjeetxm8etr74J/1W9Bv64wB5RJuwCtXXAI/09NM3sfubpaXZ58CJtSPVlTMhEpTS/zZx9Gg2+cNJX2TiDmmsTyOSsgil2qx4XHzi68jmFqKpkIpStH2U6qfjSKr0q4AGTEO0FBAipFIhym5jRwWoXhv1ZYHQ780ipli4Iv910oCm1D5vslMOd9mULT8xQ6gSLR73eXh/K+1r1WpxunERL4Ar9nd0/nQep30NYYI52ohG7v/RM5JGFgLhuVED1RcLzwL5edzH49GE3MfbJ1HI6Tu25dulfh3fBkC3b1eKZJzjz82Niuql5eeln1NfD+BCc/K54OvR+nRdGlww5eulyyhCn/t4QBC206wXfj6lQAMelJD6e6kf18M9DNbdjSg1BY+aAmiUnotZru6JCgIWX0eRU4MXqZQfcQodaOVH/9MoKgBeZQfglfYtnOGhG9CJWt5gaZUZMu1OPDNObhisbrTK63kb2w7F5ek2KdyYbY7h1nrcDdjshmpIJxAbH6AsOGXLG8aotD7NV24ZptJy2ndLiAJCJEPqOAOoilFALkjRN+0RpERpv3ISx8YoDU1pc74o8NaLSCopwluNbGJtgCQCoCJUhXpHTIii/dIsUhS1Q+k5JDqRWFKKFqjgpy4HoNxLszRbFN3CxtjOQKtv1HI2/IveHZu3LUVJGaWhXKpe9t1cDUGY0dadIoe75GQX9uv3w65VcRauhkNKUKfGwL4PKJ1vgLJFWQHw2YRKUbw0GYdtGt4vFMlQmgqe3lF04VNRS5PoZpJ3EUInkOp9PWwoUsrNRFP5riJInYfZbwkSZrMdhYgpF04OYyUTUNoraDZO91wper6A8KytyQwRqrpN43MaTQWgLEbR58Lzn4lZbh/V8yudsht49QOqaZ+VlqcDrWDrqJ9J/dGw8zDwClZThu0XCP0hHj3F4bY7TbHL5hVdoevCIy65vQfK/S3uHKZ9p1Itzh6CnCe8FWm2DEHPiBcQFaJgBR9tGPl5rn6PMeyHdT6ecgNnJsxoeRyNr+8Y+XcueqrHt+NRzEAiVOmwzLZJBKcTfbt0uxJTglXLxwNyPw9Y5uvxY7X8vaW+Xk/AAZCLUbS9XRcPtnGbw69C9FpBbo9Cu/dhXW5elLIikIVHSwHIIqaA8KImaJYR43Y2mnADGtfxH828EafqOh3CQcPIlPvftQ1qOFPXmSHJDBIzaHx9qQ1vl7eNLkuhVkDdGJVWDZtw0n/11SOOT4/+c8tu6WRlLlaV1/HtuICUti2JTPm+6mHg3Bjx80mFJwBRpFK+LheapkQofvzUcEWjhey4fllhH8KZ0Tr0ArjIM9hUlxGAGrYYtruoXlGp/kqrEG+2nrcBmvVR0vQ7w5dxAYXqougN+y4shU+HbeyLlWwa2TwadSIb62wlyH6wOifMaUn7SOl7uSTk8lHv6DMqtofOHxufpqeM8WlJoP+TWRQBVnclFacSp5MvK9aWod+LpfFRGpG/ngZupiA7Yns0NmJqNGGmqKOhUV3bOffRvEOoK/O0rb9/gHr9GLt/tm6iAHLc4YOv+0UFhm29B9sR5aOXwrqsclmVhrHeGJTR/rkgwRZ6IsrK2Kda1ZyepVFTnWKOSQ1Hax+lFMFInCpEUTWWFW0uO04mXid/k83kf/uMAMUiJL1+ZNx+3WcWvU/9Hj9VfCWiqSQ2jYmY1PJf275r2OfSQsolBy3q4xT6/KVoBL685RD24kWPk/YilODPQVd72GcjDTjg/h5/NmqRhb5M2oSPV/PvWr4dAB/BzNfZ9e4YiW/XimIOy/t9u3TbbN2yR3SWnwec5uvZ9rEvl/p6aRsy8y1fr+bn9fp4Nf+uFmCQ+X7IaV7XOzA8NydKlQyTQi5MRdsgGI8wGk/traGiffgRJZMbM8XX075LI06K5e5HHQf4HGa6dyh/n86ZZiGgzsIWsTHbQvnZVMaR5fTraaMGAGmdlu5ZVJjP2zNb07APj9PTRuO4rXcei+p5Jk4l6wuCU7pty2DZdvH2qVEC2iJU9LlgpMrrE6MzZZR4Rytpw9v574ScKUHqHgzZTcBTNagGi5+FidUrApDVKOI06qNkzpeq10cpRvL4dSoSm5oRPT0FiRV1/MKoJDlSLSeqdBEih6PDySBByk8zXDg/m1oJb9h91Ic/qA4RUkpDqTQNqe7I1iKm/H5TpzX9m6GBaBYhqiUT4g/KE3XQdSHxz4uCHfVj7PWw8PdRrX5MdL7MpioEAUqpkGYp5ud8dF/bqaLmY2hDtaTiA2kvPPmoqEGz/dqIK8NPiB9TJaL6qUyJVTWbVdiuKEKlnwttipFVU4JUK1ISaXS/FZ5rznfoL9j/+S/Mo6gIXtaCBmg5A1QsRLGvlwpWWXRUzUSsMPpfGqhI19XeE7SO74LeFzV6UmREnLos3O/j0VKtZyN+luJngj8zdN+3fLxRTft3Vd9Ol4UqH6Gs42e1KkTp1mBSfs1SvzC0XahCTVDy84CaQJW0qfh7/cEIyedKsAGQitd1P6/l480JNqiuj87p/ViSmxOlgH5hin5IenETZKT8/qLRIruXVvSUXez/sEV6vUFD1K6Ul18akUqjqni7kK9fPgbYdwfyEMuu2VIq60v76EGbcGt9+7TB+NRTsrI+UpULT/X16T5KIhNQFn5sG5Utr4lOvF1tdC7bF/8eHSIUb5ceh59TWJ+fW7q/0nphPUySrmJgRSjARhv52kXG1Swih45m6fM7akTgUBMU0lNan0spKT5aKKzP6qKk0VOp6JJgHQD4OihQ1GGMhagBqmgTObajmN+srRd82oFInY/oelBUku/FkgilAAwhwoMc88oMi0Wnsuaw8murw71RI4hqJhL0jEKoiZGO9sJ2lMlpzN9BcbpD+q3S32I0YSIH3yZNx0GwoQCykcVBx1Mk029FDqO/Jd9RR2xtTr5yJDKRHaNlDoqaAmAjp4AoktDfETy6MDVJp9aU6qArLa7WZupZTj4XRaj071SM4usbwpTv0yIXpgBkWQE8cgoIGQFA6NcC8AMA/jtwP5Ucc76QNedP/VC547ydOeOjXOrDpI4eMO0M8jb277wfOBcRpy5HS5gCENWYImqZMnTLp5kydhu7rpUtYz+74AF2jql/t9Uhkry0b2BZFDNwPt9uLdbw9dL9zPH1Wn4esF6Awew2iKn5c/fKTYpSPaQGCkBupEDr4dbHNwnPN/ZURKqqmMVDlf3xghEgZZ0KKsKfS+hEjFG4M+8shNPijsFGx04e0hEuoOBG1POFlxgtvQ3S+/N2wLjtHwmtGqvCE5kuyQSb5HOp9kC6XU2h7gkPLx27JC6Voj1qbUvHTM8zPdd0v8IFIbGJfwYAeu4Vq8fC664ANm3MLV903IRS5JSnVQ+F/i+lovDlutCWHw5BmAKQiVNA0jGkjUDr7P+lmgJp89pI0xxxw0dMAV6csr8RnOjkBKr0u6aCVOu3KIlR6bbJ9pFdoFNTKM64BaAu/KnwDhmZy+Q74fTVC94UfyfF1K9vqUPHCwz7ZSoWpPx3FRs2i1UvFxemNKyXQ/dlIrjbY+tou+jvwvMyp47Sakwdc4ZA1bSt6WfmWU2m/02gEAtTQHDCI9M10a8Fkugo2jnivmXa122R9hXL9mJ9WgMWdj1bXtgm7f+tPTmMiFPngZ4FYsrvqz0Xma/H9pNGFdZ8PDqP8G51UclueXj/GtaOvsUS3678XJ7bt6uJW7VIqBbX6OuVgxEqtqLDx5tre9LzKX2H9Jj3xF2JUi0DBdTFKSCPnioZLL5fvh1QfnErdja10alotKnHUfAGNXYQxuQWLUcb5MtSg1duW7/9azZNsYp120HDDImT1flE1ToFpaSPosM6Q8jJHvrIWNQFokXRTZVtJrdDzlR9hdIxSm2EE0nEqMiZA0LaCxXM9lOpJxEGI837wyiJVC1H5px1UZggZQoCij8FFUb3Qzg8Szlxy0qj+kTLqamJttURLnZeGfzapIuSS+/Nd2/6UU/URIdzyq8hFTrmwlTcJr+uaZpkFKGmKlES0Rtq+h1Tsuu1+nlp7a/QPrdpYqranEOMyiCbMhZEJgRxyu6DRRdyIYsfI9vBSpFTc8WuifbFelQ9UVSJDW6mAS4kLjcR+rdR3zbrQ+TfpyQmZY93slmxlmijY7dWQMZU33Gq71MaGATKfavaPpYi4tT69Ph9pYCEljhF+6EDlAIR7Cr3TqUTQVmosucR//olwcoun+fbTfl1wLq+3VxO9fWAPn9vqa+Xb9cWoOz50LL+ts32jXNN95fu8964WVGKv4w5JQOVtuM/eI9AlXbu7f6C2g1+TG6YouPw/THjl2wfpQEmIllsqHJ6hSjbtv56XNI1TI+t2FO00wpmKHcEppjqutWiIHoEKrv/uthU2k9LQDrH9vYcc2Z97852wkxqzlsK1WSBFQQUi8DhaXuhfXufak6UwRppfWm7UnRPelgkaSb8s7sj09mdgCCmzKH2Qi93Iho74lEd7BqHr8mc8nhFx0l2OKipENg6VaWilJ1aejnPzolqdTlK7yJ+7FpaDtFTXyXtlKUjiNG0yO1dsf10NrxjLnIJ0mfBjLngPcZtiax+VEW4zcSrM9AsdJ7SOo/ausIgQNE+zrHH6SGYs03fxrB1QCxO+e2yzlnex+uJhpoTBVXczco3bGt39RmsWJtsXdsRXJO61C+0KJVuqbUD8qgpoC3alibCAvJ6a7Tv0nOTvlOLvh3bT+rbAQo9kwq0fbvyXTXXtztFmFoQONUWhk/w9UpC/BxfrTmx1NLtsvOZFt5a3Euf6GZFqRapMAU0RKwkegqI1fTQDq4d7Y8MjPHHJMj5IqiQXnQ+FeOXnm84gSSkuhS6GYlWsWiWt02Pz/fdpjhjSvpgRqJe0kFNN51hvZpqeofo1GzbIeh0iUgzhajqfvJFswxz6djChUhrsrBiweQgZcWCk21rnLNOSrM4L5ALUiuknQDlwQEiLtDb2P+SXhBQvya+sDNzyIFyFBov4NAo/F6t/TXjOvJ6MrzQMWDtCI388mLH/J1Uckhrnc+p8P6p9JxqNJuKbRMXpNZOnbk3znJZWjan9SwA7QiqQj2qaDlmCkZrsVTUT+kVo0r7WkGES/u6xXqrnX1c2zb+zB//1sx0qZ249C/aI0L5tsV2/YLU2nZJoqfmUbzHURZCa89D+iykpVyAOIIKiO/xzMdLj9vw70ops0smFej17ezxab9tMt9ubSG509ZPDnSt7OvN9fOWClG+TXaO/YJUafE99ZVuXpSqKee8M56255SUdN+2Ir5wA9QaYUqjqQge6ul3W1FtSikVfN/pfqZGuHun92z7ItNPAE/f2xXS93pZI/QTqJ/xnM5M7Thz9t3bSbJt619q6fft2VaYQerMFeqpZMJUVKeIfozh9OnSO5ycrlSUSi0Uu31BRJlISymN8Oeie1oHL9BbjW66o9HYuFb7htE0iT32bUnkBOJoMzoPnsoHJO8qFUec5f3M/JvUBg34e6T33dHTmZqq/9VOb+47j3vjol87TblLPpeEl/Kjo5FV3K03jo95CqcKPh0zigITAwQLBwZaFCP/6VySNsTsPm50Xm4fE+c1Fd26RirQWlH2c/pVl3zmRJw6jR6fr/YslJ6DNO21J2OGSN+n6WQuvh2JRTfk252Lt/D1lgYn1PZ5ruCC1jncGzcvSgHtkM6ags63BSrCVmGECWhHUtn2tO+yWs6XTnck8vNKFfzSMWr0duhP7RZGARcKi5+o3vOdatYa5WsZw3lq9UyBq3o+y8UoQASpN6cQdRALUyFFjDt7qWB0SlrLZPTBzJoopxYlzuo8oD+adWq/F4NfgzmO8wUiJ2pFXe06uHX2/9Lob+0yplEYs86psKw120zJ7om5urJrUKoP5Yhm6uNk6X4Tz84Z0/iKdApQwIlRqlPrTqTW1+3p4xLFKHhMR7BOntsFbuKeK7vEGbw0Ik6dRsvnqwYxnJgxA8S+15SPV/PvbsG3Oxdv6ev1lz3p9/HWEqNq53Kv3IUoBUwLU0TLWHFqNaj8fpK85PSApQd/Tt5+LS2iFGUVHbqVJ5fuq3Eec2dNycK32fY8fa9lMOYyZ1enPvSLRa2J/U6Fs65lqOfsS5jJVOoLgGKNorHcjpjxKPedY42KU1ZNNev8nHYOS8JUdDzWbgn10azeHVSiQ4ipQs29++9YVxLsSvVk8jTxsKBUSwYoR/r6464Q0VCi1WmbM+Xxe7Fhb/I1S3asJkJVCpX3iDaX1pymWCy4r5ECuPDYrZqqxFSWADHV162ew7mMxULWSg0CVniXrEAle0vA9ADWnOegdf9X/byCj7eKf3dlvt2S2fk4a/l7a/l6ZxG0Gvs7RQR/rz7d3YhSQFuYIniHfmpfxNQIU2l0id+op+bt1xT4aB9++0qDbJ/rjl5lMz1FjoayM4+d4QFaY/Q+32e71dT3uJQQBfR3Vu7ReF01rbQ+gtdi4e2mqIkmc5hT9+jUY/HDtkbt+Tl07Ovit3RLpOrdZu76GZRmHQLqkb7A9LuphVn4Dql94/c0w0yNN//ONYG9ESGVtZloNyUCFaOtFnBqdGfG3P29sfq2pK8L9Eeq9opAteiruSyuGYh1Bvfesg8l0VPLmXoOeiMJe9+j5/Lv3sK3q9Vp6mVNn29tv+gUP2+JEBWO21z97p/xuxKlgD5hCqiP0tf2yakVkkxpFe+N9jdxF/Yo8MX9NjboUctndyX4PtOyNEsCC+ZvEjFXpe/Oae7e3/QOzyGqnXoMYQG1KINCdE36XM33XdZxdlarh7KAOSOdpx5jNj0OOG93yjFOoCTild5VrffTSWexNKKto6hwadf3aseu7mv1RH4C/QJVysRztbqYNMUax3sjwaon5bl0f83p79ZYW7w6lbUjbNfY99pI9FTMnJT/qUGvJX5ej4/X499d0rebdagL3mSnHOocvt4p6cDRflaKipq7z1vl7kQpYFltkjmj9KUboidf3++/obiXKM0MMUUrN3kup4Zwzn/g1nvi1hzJrx+j/yBLjMnSq3HPhusq6U1/Sdq1nLClUQOLHLulaSat88fymn5LWPWeX1pDqnefFRaN6Fa2bUX89titpVEOS1Npalvdky27ia/SEqZ4G86KEzJcPedO+ZvJXDs6Z1B26pi3xr04gNng1pucxXXQG4xA9EQR9kQQ9vh4TcHK76fRKNv3Oj7SqX7dWrxFip8/dve+z+PjSZBBzl2KUsRSh2fJC7tXqALaN/hUKmCNU9Iw8nNIjr9kZ0n63ikntGoI6OJzmH8S5xy5O8dxhTNSiZoCcFKqy0nns2a7E1maQnKxe/1GHPBWZ3vOO8rv7wxRDnMmj7gnbu779QhTafuUtcTct2Kt5/fCdvTUPi9xHW7rct5TX+rWf6tTOTUYAZgfQTjHx7s2327Nwf+1uDVf7xKZLkuPdavctShFnDoaPzf8OT1uiSWCVXYOnWr8bOh6rbAPAItn3zt3N24Nh+tUQ7GmnXkvRuuqaaV+zSka/FbMOZfOtlPRUiWu/l4+0282570yJ2Wnd4KPt6B1Ctdwfu+a3lTWqe3ncA4h6y1s7Bva9amaqL0sffzOIZBc0hSI3bldTrn310x1XRKZHB23s/zLLNbw7c7AOS3lqX7eWwUXnHLsW+ZdiFLEWi9q4PTw5yWj1tk5zIy4mstahuItXe41R/rXMBBr25j3aLRunlMdvbVZO9VPuAhzUhbmpKdfCjFdN8Qlbdat25YrO/+1U6R7uNVnW/pT98XctL4SPal+tWNz1vLvrsm3uybW8vWuJdDgvdqidyVKcU41GCmt+6d310tuwjUirs4CO746MX3vHJzzgT/nN32vhurmmDtbVY1TnMC3nn1KODtLBlp6Tcgpr8T3lDbzrrjHFL2l3Jg9rD1TlxSrrgmxMe+DNQIAgNN9vLX8uzf37W6Icz3jElxwPt6tKJVyztGkU6OqWixJEXwvXOpBv8RhxGjdKHPrspS2vwau5TyEKmuMCnPeyuSIrbsxzjEhwLVyh3ZwzQyCa0dsiwCs7++dy8cTIbmPcz/X59q92KMcEaUSLmEE5tyHpxz2TW94dmy9sKbUWyIOmbAa15au18PKztfSEPj3wrk6sbfSeRW7dyf02o1rsoV3KDQtZc5zeE22ReyHsIS1oqhSzh2NLPf7OkiduutDRKlO3qqzv0ZaoFDmGmyEGKp3xLWLU+Kc3R23EAUhNvAdcm7xSmzZ2bmEgCW2Qbg0l6zBVru9r/RVfbO8pRkRGzYPEaUWcg1hlafe67du+G7pWRfDJHjesi6LOGvvlmtI9RY7KMxC7NVdIM+9cGu8pY/33n27lFswH2Lj1kFEqZW5BrGql0s9Q+/pWRXDJCzinThfCvfXYTqVa7geYrcEQRAEoc0t+HjyOj8f0lc6LyJKXYhbMGRCP2KYBGEZIkwF5DoIgiAIwm1zrvpUwtshft7lEVHqjbnVwpL3iBggQRAEQRAEQRBOYa5PIT7eeREf7/oRUeqGuIaaILeOGCVBeHskWkq+vyAIgiAIFom2Wg/x9W4TEaXuhGt5AGsG9FrOTxCE6+A9C1Pv9XsLgiAIgtDHW/hOawph4vsJcxBRSlgVb4BUYZkgCIIgCIIgCIJwdYjPJrwV72PKJ0EQBOHqeI99H4mSEgRBEARBEISAiFKCIAjCm/GehCkRpARBEARBEAQhRkQpQRAE4U15D8KUCFKCIAiCIAiCkCOilCAIgvDm3LMwJYKUIAiCIAiCIJQRUUoQBEG4Cu5RmBJBShAEQRAEQRDqyOx7giAIwtVAwtStizm3fv6CIAiCIAiCcAkkUkoQBEEQBEEQBEEQBEG4OBIpJQiCIFwdtxoxdWvnKwiCIAiCIAhviURKCYIgCFfLLdWZEkFKEARBEARBEOYhkVKCIAjCVXPtUVPXel6CIAiCIAiCcO2IKCUIgiDcBNcmTl3LeQiCIAiCIAjCrSKilCAIgnBT8JS+SwtDIkQJgiAIgiAIwnqIKCUIgiDcLGnNqbVFIxGhBEEQBEEQBOF8iCglCIIg3A1rpfiJGCUIgiAIgiAI50dEKUEQBOHu6J21T8QnQRAEQRAEQXg79FufgCAIgiAIgiAIgiAIgvD+UMYYGSgWBEEQBEEQBEEQBEEQLopESgmCIAiCIAiCIAiCIAgXR0QpQRAEQRAEQRAEQRAE4eKIKCUIgiAIgiAIgiAIgiBcHBGlBEEQBEEQBEEQBEEQhIsjopQgCIIgCIIgCIIgCIJwcUSUEgRBEARBEARBEARBEC6OiFKCIAiCIAiCIAiCIAjCxRFRShAEQRAEQRAEQRAEQbg4IkoJgiAIgiAIgiAIgiAIF+f/A8DKJ0bVe9qKAAAAAElFTkSuQmCC",
       "text/plain": [
        "
"
       ]
@@ -2430,7 +1348,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "
"
       ]
@@ -2456,31 +1374,9 @@
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "
[19:57:07] WARNING: Default value for the field monitor 'colocate' setting has  \n",
-       "           changed to 'True' in Tidy3D 2.4.0. All field components will be      \n",
-       "           colocated to the grid boundaries. Set to 'False' to get the raw      \n",
-       "           fields on the Yee grid instead.                                      \n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\u001b[2;36m[19:57:07]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Default value for the field monitor \u001b[0m\u001b[32m'colocate'\u001b[0m\u001b[31m setting has  \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m\u001b[31mchanged to \u001b[0m\u001b[32m'True'\u001b[0m\u001b[31m in Tidy3D \u001b[0m\u001b[1;36m2.4\u001b[0m\u001b[31m.\u001b[0m\u001b[1;36m0\u001b[0m\u001b[31m. All field components will be      \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m\u001b[31mcolocated to the grid boundaries. Set to \u001b[0m\u001b[32m'False'\u001b[0m\u001b[31m to get the raw      \u001b[0m\n",
-       "\u001b[2;36m           \u001b[0m\u001b[31mfields on the Yee grid instead.                                      \u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "monitor = td.FieldMonitor(\n",
-    "    center=(0, 0, 0), size=(td.inf, td.inf, 0), freqs=[freq], name=\"field\"\n",
-    ")\n",
+    "monitor = td.FieldMonitor(center=(0, 0, 0), size=(td.inf, td.inf, 0), freqs=[freq], name=\"field\")\n",
     "sim = sim.copy(update={\"monitors\": [monitor]})\n",
     "job = web.Job(simulation=sim, task_name=\"mode_sim_tm1_symmetry\", verbose=False)\n",
     "sim_data = job.run(path=\"data/simulation_data.hdf5\")"
@@ -2502,18 +1398,35 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Simulation domain Nx, Ny, Nz: [424, 146, 86]\n",
-      "Applied symmetries: (0, -1, -1)\n",
-      "Number of computational grid points: 1.4378e+06.\n",
-      "Using subpixel averaging: True\n",
-      "Number of time steps: 2.0982e+04\n",
-      "Automatic shutoff factor: 1.00e-05\n",
-      "Time step (s): 4.7664e-17\n",
+      "[14:49:17] USER: Simulation domain Nx, Ny, Nz: [424, 146, 86]                   \n",
+      "           USER: Applied symmetries: (0, -1, -1)                                \n",
+      "           USER: Number of computational grid points: 1.4378e+06.               \n",
+      "           USER: Subpixel averaging method: SubpixelSpec(attrs={},              \n",
+      "           dielectric=PolarizedAveraging(attrs={}, type='PolarizedAveraging'),  \n",
+      "           metal=Staircasing(attrs={}, type='Staircasing'),                     \n",
+      "           pec=PECConformal(attrs={}, type='PECConformal',                      \n",
+      "           timestep_reduction=0.3), lossy_metal=SurfaceImpedance(attrs={},      \n",
+      "           type='SurfaceImpedance', timestep_reduction=0.0),                    \n",
+      "           type='SubpixelSpec')                                                 \n",
+      "           USER: Number of time steps: 2.0982e+04                               \n",
+      "           USER: Automatic shutoff factor: 1.00e-05                             \n",
+      "           USER: Time step (s): 4.7664e-17                                      \n",
+      "           USER:                                                                \n",
+      "                                                                                \n",
+      "           USER: Mode solver at f=2.9979245800e+14 with plane size (64, 34),    \n",
+      "           direction: +                                                         \n",
+      "[14:49:18] USER: Compute source modes time (s):     0.7681                      \n",
+      "           USER: Rest of setup time (s):            0.5214                      \n",
+      "[14:49:21] USER: Compute monitor modes time (s):    0.0001                      \n",
+      "[14:49:24] USER: Solver time (s):                   1.5950                      \n",
+      "           USER: Time-stepping speed (cells/s):     4.54e+09                    \n",
+      "           USER: Post-processing time (s):          0.2719                      \n",
       "\n",
+      " ====== SOLVER LOG ====== \n",
       "\n",
-      "Compute source modes time (s):     0.7195\n",
-      "Compute monitor modes time (s):    0.0027\n",
-      "Rest of setup time (s):            3.0073\n",
+      "Processing grid and structures...\n",
+      "Building FDTD update coefficients...\n",
+      "Solver setup time (s):             0.5203\n",
       "\n",
       "Running solver for 20982 time steps...\n",
       "- Time step    839 / time 4.00e-14s (  4 % done), field decay: 1.00e+00\n",
@@ -2522,9 +1435,8 @@
       "- Time step   2517 / time 1.20e-13s ( 12 % done), field decay: 7.62e-01\n",
       "- Time step   3357 / time 1.60e-13s ( 16 % done), field decay: 7.91e-06\n",
       "Field decay smaller than shutoff factor, exiting solver.\n",
-      "\n",
-      "Solver time (s):                   1.3242\n",
-      "Data write time (s):               0.0037\n",
+      "Time-stepping time (s):            1.0635\n",
+      "Data write time (s):               0.0105\n",
       "\n"
      ]
     }
@@ -2540,7 +1452,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -2582,7 +1494,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.0"
   },
   "title": "How to Set Up Symmetry in Tidy3D | Flexcompute",
   "widgets": {
diff --git a/TFSF.ipynb b/TFSF.ipynb
index aa1b76f9..b8ee3105 100644
--- a/TFSF.ipynb
+++ b/TFSF.ipynb
@@ -31,12 +31,12 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3d imports\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n"
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -77,7 +77,7 @@
     "grid_spec = td.GridSpec.auto(min_steps_per_wvl=cells_per_wvl)\n",
     "\n",
     "# boundary conditions: PML on all sides\n",
-    "boundary_spec = td.BoundarySpec.all_sides(td.PML())\n"
+    "boundary_spec = td.BoundarySpec.all_sides(td.PML())"
    ]
   },
   {
@@ -118,7 +118,7 @@
     "    pol_angle=0,\n",
     "    angle_theta=np.pi / 6,  # with respect to the injection plane's normal vector\n",
     "    angle_phi=np.pi / 5,  # with respect to the injection plane's normal vector\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -214,7 +214,7 @@
     "    center=source.center,\n",
     "    size=[size * 1.2 for size in source.size],\n",
     "    freqs=[f0],\n",
-    "    name=f\"flux_out\",\n",
+    "    name=\"flux_out\",\n",
     ")\n",
     "\n",
     "# make a surface flux monitor across the simulation domain to measure the total injected flux\n",
@@ -222,7 +222,7 @@
     "    center=source.center,\n",
     "    size=[td.inf, td.inf, 0],\n",
     "    freqs=[f0],\n",
-    "    name=f\"flux_inj\",\n",
+    "    name=\"flux_inj\",\n",
     ")\n",
     "\n",
     "# make field monitors along each cardinal plane to look at the fields\n",
@@ -248,7 +248,7 @@
     ")\n",
     "\n",
     "# collect all monitors together\n",
-    "monitors = [monitor_out, monitor_inj, monitor_freq_xy, monitor_freq_xz, monitor_freq_yz]\n"
+    "monitors = [monitor_out, monitor_inj, monitor_freq_xy, monitor_freq_xz, monitor_freq_yz]"
    ]
   },
   {
@@ -299,7 +299,7 @@
     "sim.plot(x=0, ax=ax[0])\n",
     "sim.plot(y=0, ax=ax[1])\n",
     "sim.plot(z=source.center[2] - source.size[2] / 2, ax=ax[2])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -641,7 +641,7 @@
     }
    ],
    "source": [
-    "sim_data = web.run(sim, task_name=\"tfsf1\", path=\"data/tfsf1.hdf5\", verbose=True)\n"
+    "sim_data = web.run(sim, task_name=\"tfsf1\", path=\"data/tfsf1.hdf5\", verbose=True)"
    ]
   },
   {
@@ -679,9 +679,9 @@
     "# print the escaped and injected flux\n",
     "print(\n",
     "    \"flux_inj per unit area: \",\n",
-    "    sim_data[f\"flux_inj\"].flux.values[0] / source.size[0] / source.size[1],\n",
+    "    sim_data[\"flux_inj\"].flux.values[0] / source.size[0] / source.size[1],\n",
     ")\n",
-    "print(\"flux_box: \", sim_data[f\"flux_out\"].flux.values[0])\n"
+    "print(\"flux_box: \", sim_data[\"flux_out\"].flux.values[0])"
    ]
   },
   {
@@ -722,19 +722,13 @@
     "\n",
     "    _, (ax1, ax2, ax3) = plt.subplots(1, 3, tight_layout=True, figsize=(12, 3))\n",
     "\n",
-    "    sim_data.plot_field(\n",
-    "        field_monitor_name=\"freq_xz\", field_name=field_name, val=val, f=f0, ax=ax1\n",
-    "    )\n",
-    "    sim_data.plot_field(\n",
-    "        field_monitor_name=\"freq_yz\", field_name=field_name, val=val, f=f0, ax=ax2\n",
-    "    )\n",
-    "    sim_data.plot_field(\n",
-    "        field_monitor_name=\"freq_xy\", field_name=field_name, val=val, f=f0, ax=ax3\n",
-    "    )\n",
+    "    sim_data.plot_field(field_monitor_name=\"freq_xz\", field_name=field_name, val=val, f=f0, ax=ax1)\n",
+    "    sim_data.plot_field(field_monitor_name=\"freq_yz\", field_name=field_name, val=val, f=f0, ax=ax2)\n",
+    "    sim_data.plot_field(field_monitor_name=\"freq_xy\", field_name=field_name, val=val, f=f0, ax=ax3)\n",
     "    plt.show()\n",
     "\n",
     "\n",
-    "plot_results_freq(sim_data, field_name=\"Ex\", val=\"real\")\n"
+    "plot_results_freq(sim_data, field_name=\"Ex\", val=\"real\")"
    ]
   },
   {
@@ -791,7 +785,7 @@
     "    ],\n",
     "    size=[td.inf, td.inf, 0],\n",
     "    freqs=[f0],\n",
-    "    name=f\"flux_above\",\n",
+    "    name=\"flux_above\",\n",
     ")\n",
     "\n",
     "center = [source.center[0], source.center[1], source.center[2]]\n",
@@ -804,7 +798,7 @@
     "    ],\n",
     "    size=[td.inf, td.inf, 0],\n",
     "    freqs=[f0],\n",
-    "    name=f\"flux_below\",\n",
+    "    name=\"flux_below\",\n",
     ")\n",
     "\n",
     "# collect all the monitors together again\n",
@@ -815,7 +809,7 @@
     "    monitor_freq_xy,\n",
     "    monitor_freq_xz,\n",
     "    monitor_freq_yz,\n",
-    "]\n"
+    "]"
    ]
   },
   {
@@ -863,7 +857,7 @@
     "sim.plot(x=0, ax=ax[0])\n",
     "sim.plot(y=0, ax=ax[1])\n",
     "sim.plot(z=source.center[2] - source.size[2] / 2, ax=ax[2])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1218,7 +1212,7 @@
     }
    ],
    "source": [
-    "sim_data = web.run(sim, task_name=\"tfsf2\", path=\"data/tfsf2.hdf5\", verbose=True)\n"
+    "sim_data = web.run(sim, task_name=\"tfsf2\", path=\"data/tfsf2.hdf5\", verbose=True)"
    ]
   },
   {
@@ -1255,10 +1249,10 @@
     "# print the escaped and injected flux\n",
     "print(\n",
     "    \"flux_inj per unit area: \",\n",
-    "    sim_data[f\"flux_inj\"].flux.values[0] / sim.size[0] / sim.size[1],\n",
+    "    sim_data[\"flux_inj\"].flux.values[0] / sim.size[0] / sim.size[1],\n",
     ")  # using sim.size because the TFSF slab extends to sim edges\n",
-    "print(\"flux_above: \", sim_data[f\"flux_above\"].flux.values[0])\n",
-    "print(\"flux_below: \", sim_data[f\"flux_below\"].flux.values[0])\n"
+    "print(\"flux_above: \", sim_data[\"flux_above\"].flux.values[0])\n",
+    "print(\"flux_below: \", sim_data[\"flux_below\"].flux.values[0])"
    ]
   },
   {
@@ -1293,7 +1287,7 @@
     }
    ],
    "source": [
-    "plot_results_freq(sim_data, field_name=\"Ex\", val=\"real\")\n"
+    "plot_results_freq(sim_data, field_name=\"Ex\", val=\"real\")"
    ]
   },
   {
@@ -1334,9 +1328,7 @@
     "    medium = td.Medium(permittivity=epsr)\n",
     "    size = (td.inf, td.inf, zsizes[idx])\n",
     "    center = (0, 0, zpos + zsizes[idx] / 2)\n",
-    "    layers.append(\n",
-    "        td.Structure(geometry=td.Box(center=center, size=size), medium=medium)\n",
-    "    )\n",
+    "    layers.append(td.Structure(geometry=td.Box(center=center, size=size), medium=medium))\n",
     "    zpos += zsizes[idx]\n",
     "\n",
     "# make a TFSF box\n",
@@ -1372,7 +1364,7 @@
     ")\n",
     "\n",
     "# reuse the monitors from the first simulation\n",
-    "monitors = [monitor_out, monitor_inj, monitor_freq_xy, monitor_freq_xz, monitor_freq_yz]\n"
+    "monitors = [monitor_out, monitor_inj, monitor_freq_xy, monitor_freq_xz, monitor_freq_yz]"
    ]
   },
   {
@@ -1421,7 +1413,7 @@
     "sim.plot(x=0, ax=ax[0])\n",
     "sim.plot(y=0, ax=ax[1])\n",
     "sim.plot(z=source.center[2] - source.size[2] / 2, ax=ax[2])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1776,7 +1768,7 @@
     }
    ],
    "source": [
-    "sim_data = web.run(sim, task_name=\"tfsf3\", path=\"data/tfsf3.hdf5\", verbose=True)\n"
+    "sim_data = web.run(sim, task_name=\"tfsf3\", path=\"data/tfsf3.hdf5\", verbose=True)"
    ]
   },
   {
@@ -1809,7 +1801,7 @@
    ],
    "source": [
     "# print the escaped flux\n",
-    "print(\"flux_box: \", sim_data[f\"flux_out\"].flux.values[0])\n"
+    "print(\"flux_box: \", sim_data[\"flux_out\"].flux.values[0])"
    ]
   },
   {
@@ -1844,7 +1836,7 @@
     }
    ],
    "source": [
-    "plot_results_freq(sim_data, field_name=\"Ex\", val=\"real\")\n"
+    "plot_results_freq(sim_data, field_name=\"Ex\", val=\"real\")"
    ]
   },
   {
@@ -1929,7 +1921,7 @@
     ")\n",
     "\n",
     "# specify PML boundaries on all sides\n",
-    "boundary_spec = td.BoundarySpec.all_sides(td.PML())\n"
+    "boundary_spec = td.BoundarySpec.all_sides(td.PML())"
    ]
   },
   {
@@ -2020,7 +2012,7 @@
     "sim.plot(x=0, ax=ax[0])\n",
     "sim.plot(y=0, ax=ax[1])\n",
     "sim.plot(z=source.center[2] - source.size[2] / 2, ax=ax[2])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2107,7 +2099,7 @@
     "_, ax = plt.subplots(1, 2, figsize=(8, 4))\n",
     "sim_ref_empty.plot(x=0, ax=ax[0])\n",
     "sim_ref_empty.plot(y=0, ax=ax[1])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -3158,7 +3150,7 @@
     "# planewave simulation without objects\n",
     "sim_data_ref_empty = web.run(\n",
     "    sim_ref_empty, task_name=\"planewave_empty\", path=\"data/planewave_empty.hdf5\"\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -3189,7 +3181,7 @@
     "for field, field_empty in zip(\n",
     "    data_ref.field_components.values(), data_ref_empty.field_components.values()\n",
     "):\n",
-    "    field.values -= field_empty.values\n"
+    "    field.values -= field_empty.values"
    ]
   },
   {
@@ -3259,7 +3251,7 @@
     "fig.colorbar(im2, ax=ax2)\n",
     "fig.colorbar(im3, ax=ax3)\n",
     "\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
diff --git a/THzDemultiplexerFilter.ipynb b/THzDemultiplexerFilter.ipynb
index 300e86d6..c9c12619 100644
--- a/THzDemultiplexerFilter.ipynb
+++ b/THzDemultiplexerFilter.ipynb
@@ -52,12 +52,11 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
-    "from tidy3d.plugins.mode import ModeSolver\n"
+    "from tidy3d.plugins.mode import ModeSolver"
    ]
   },
   {
@@ -94,7 +93,7 @@
     "freq0 = 380e9  # central frequency\n",
     "lda0 = td.C_0 / freq0  # central wavelength\n",
     "freqs = np.linspace(375e9, 385e9, 301)  # wavelength range.\n",
-    "# To ensure we resolve the spectral features, 301 frequency points are used.\n"
+    "# To ensure we resolve the spectral features, 301 frequency points are used."
    ]
   },
   {
@@ -124,7 +123,7 @@
     "n_si = 3.405  # real part of the Si refractive index\n",
     "\n",
     "si = td.Medium.from_nk(n=n_si, k=k_si, freq=freq0)\n",
-    "air = td.Medium(permittivity=1)\n"
+    "air = td.Medium(permittivity=1)"
    ]
   },
   {
@@ -167,9 +166,7 @@
     ")\n",
     "\n",
     "ring_in = td.Structure(\n",
-    "    geometry=td.Cylinder(\n",
-    "        center=(0, 2 * W0 + Wg + R1, t_si / 2), radius=R1, length=t_si, axis=2\n",
-    "    ),\n",
+    "    geometry=td.Cylinder(center=(0, 2 * W0 + Wg + R1, t_si / 2), radius=R1, length=t_si, axis=2),\n",
     "    medium=air,\n",
     ")\n",
     "\n",
@@ -223,11 +220,9 @@
     "\n",
     "# si wafer\n",
     "si_substrate = td.Structure(\n",
-    "    geometry=td.Box.from_bounds(\n",
-    "        rmin=(-inf_eff, -inf_eff, 0), rmax=(inf_eff, inf_eff, t_si - t_wg)\n",
-    "    ),\n",
+    "    geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, 0), rmax=(inf_eff, inf_eff, t_si - t_wg)),\n",
     "    medium=si,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -311,7 +306,7 @@
     "    size=(td.inf, td.inf, 0),\n",
     "    freqs=[freq1, freq2],\n",
     "    name=\"field\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -381,7 +376,7 @@
     "\n",
     "# visualize the structure to make sure it is set up correctly\n",
     "sim.plot(z=t_si)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -450,7 +445,7 @@
     "ax1.set_title(\"|Ex(x, y)|\")\n",
     "ax2.set_title(\"|Ey(x, y)|\")\n",
     "ax3.set_title(\"|Ez(x, y)|\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -671,7 +666,7 @@
    ],
    "source": [
     "job = web.Job(simulation=sim, task_name=\"thz_demultiplexer\", verbose=True)\n",
-    "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n"
+    "sim_data = job.run(path=\"data/simulation_data.hdf5\")"
    ]
   },
   {
@@ -726,7 +721,7 @@
     "    vmin=0,\n",
     "    vmax=0.01,\n",
     ")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -773,7 +768,7 @@
     "    vmin=0,\n",
     "    vmax=0.01,\n",
     ")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -816,7 +811,7 @@
     "plt.xlabel(\"Frequency (GHz)\")\n",
     "plt.ylabel(\"Transmission (dB)\")\n",
     "plt.legend((\"Through port\", \"Drop port\"))\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
diff --git a/TaperedWgDispersion.ipynb b/TaperedWgDispersion.ipynb
index 8dcad275..00e3061e 100644
--- a/TaperedWgDispersion.ipynb
+++ b/TaperedWgDispersion.ipynb
@@ -29,9 +29,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
     "from tidy3d.plugins import waveguide\n",
diff --git a/ThermallyTunedRingResonator.ipynb b/ThermallyTunedRingResonator.ipynb
index f52bf010..173f97a7 100644
--- a/ThermallyTunedRingResonator.ipynb
+++ b/ThermallyTunedRingResonator.ipynb
@@ -30,9 +30,8 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
     "import tidy3d as td\n",
+    "from matplotlib import pyplot as plt\n",
     "from tidy3d import web"
    ]
   },
@@ -107,7 +106,7 @@
     "\n",
     "TiN_k = 28e-6  # TiN thermal conductivity W/(um*K)\n",
     "TiN_s = 0.598 * 5.240e-12  # TiN volumetric heat capacityJ / (um^3 * K)\n",
-    "TiN_sigma = 2.3  # Electric conductivity of TiN, S/um\n"
+    "TiN_sigma = 2.3  # Electric conductivity of TiN, S/um"
    ]
   },
   {
@@ -142,7 +141,7 @@
     "\n",
     "# an alternative but equivalent way of defining a medium specification\n",
     "SiO2 = td.PerturbationMedium(\n",
-    "    permittivity=SiO2_n ** 2,\n",
+    "    permittivity=SiO2_n**2,\n",
     "    perturbation_spec=td.IndexPerturbation(\n",
     "        delta_n=td.ParameterPerturbation(\n",
     "            heat=td.LinearHeatPerturbation(coeff=SiO2_n_slope, temperature_ref=300)\n",
@@ -164,7 +163,7 @@
     "    name=\"TiN\",\n",
     ")\n",
     "\n",
-    "air = td.Medium(heat_spec=td.FluidSpec(), name=\"air\")\n"
+    "air = td.Medium(heat_spec=td.FluidSpec(), name=\"air\")"
    ]
   },
   {
@@ -241,7 +240,7 @@
     "# thickness and z position of box and cladding combined\n",
     "# make structures overlap to ensure no gaps due to numerical roundoff errors\n",
     "box_clad_zmax = h_clad\n",
-    "box_clad_zmin = -h_box - h_wafer / 2  \n",
+    "box_clad_zmin = -h_box - h_wafer / 2\n",
     "h_box_clad = box_clad_zmax - box_clad_zmin\n",
     "z_box_clad = (box_clad_zmax + box_clad_zmin) / 2"
    ]
@@ -347,7 +346,7 @@
     "        operation=\"difference\",\n",
     "    ),\n",
     "    medium=Si,\n",
-    "    name=\"wg_ring\", \n",
+    "    name=\"wg_ring\",\n",
     ")\n",
     "\n",
     "# heater outer radius\n",
@@ -398,14 +397,7 @@
    "outputs": [],
    "source": [
     "scene = td.Scene(\n",
-    "    structures=[\n",
-    "        box_clad, \n",
-    "        wg_ring, \n",
-    "        waveguide_top, \n",
-    "        waveguide_bottom,\n",
-    "        heater,\n",
-    "        wafer\n",
-    "    ],\n",
+    "    structures=[box_clad, wg_ring, waveguide_top, waveguide_bottom, heater, wafer],\n",
     "    medium=air,\n",
     ")"
    ]
@@ -487,7 +479,7 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(1, 3, figsize=(20,5))\n",
+    "fig, ax = plt.subplots(1, 3, figsize=(20, 5))\n",
     "scene.plot(x=0, ax=ax[0])\n",
     "scene.plot_eps(x=0, ax=ax[1])\n",
     "scene.plot_heat_conductivity(x=0, ax=ax[2])\n",
@@ -523,7 +515,7 @@
     "z_buffer = 1  # buffer in z direction\n",
     "\n",
     "# difference between thermal and optic simulations\n",
-    "heat_sim_buffer = 3\n"
+    "heat_sim_buffer = 3"
    ]
   },
   {
@@ -575,13 +567,19 @@
    "source": [
     "fig, ax = plt.subplots(1, 3, figsize=(10, 3))\n",
     "scene.plot(\n",
-    "    x=0, ax=ax[0], hlim=[-w_heat_sim / 2, w_heat_sim / 2], vlim=[z_sim - h_sim / 2, z_sim + h_sim / 2]\n",
+    "    x=0,\n",
+    "    ax=ax[0],\n",
+    "    hlim=[-w_heat_sim / 2, w_heat_sim / 2],\n",
+    "    vlim=[z_sim - h_sim / 2, z_sim + h_sim / 2],\n",
     ")\n",
     "scene.plot(\n",
     "    z=z_wg, ax=ax[1], hlim=[-l_heat_sim / 2, l_heat_sim / 2], vlim=[-w_heat_sim / 2, w_heat_sim / 2]\n",
     ")\n",
     "scene.plot(\n",
-    "    z=z_heater, ax=ax[2], hlim=[-l_heat_sim / 2, l_heat_sim / 2], vlim=[-w_heat_sim / 2, w_heat_sim / 2]\n",
+    "    z=z_heater,\n",
+    "    ax=ax[2],\n",
+    "    hlim=[-l_heat_sim / 2, l_heat_sim / 2],\n",
+    "    vlim=[-w_heat_sim / 2, w_heat_sim / 2],\n",
     ")\n",
     "plt.tight_layout()\n",
     "plt.show()"
@@ -605,11 +603,11 @@
    "outputs": [],
    "source": [
     "bc_air = td.HeatBoundarySpec(\n",
-    "    placement=td.MediumMediumInterface(mediums=[air.name, SiO2.name]), \n",
+    "    placement=td.MediumMediumInterface(mediums=[air.name, SiO2.name]),\n",
     "    condition=td.ConvectionBC(ambient_temperature=300, transfer_coeff=10 * 1e-12),\n",
     ")\n",
     "bc_wafer = td.HeatBoundarySpec(\n",
-    "    placement=td.MediumMediumInterface(mediums=[air.name, Si.name]), \n",
+    "    placement=td.MediumMediumInterface(mediums=[air.name, Si.name]),\n",
     "    condition=td.TemperatureBC(temperature=300),\n",
     ")"
    ]
@@ -661,10 +659,10 @@
     "dl_interface = h_heater / 2\n",
     "dl_bulk = (h_box + h_clad) / 15\n",
     "\n",
-    "heat_grid_spec=td.DistanceUnstructuredGrid(\n",
-    "    dl_interface=dl_interface, \n",
-    "    dl_bulk=dl_bulk, \n",
-    "    distance_interface=dl_interface, \n",
+    "heat_grid_spec = td.DistanceUnstructuredGrid(\n",
+    "    dl_interface=dl_interface,\n",
+    "    dl_bulk=dl_bulk,\n",
+    "    distance_interface=dl_interface,\n",
     "    distance_bulk=h_box,\n",
     "    non_refined_structures=[\"wafer\"],\n",
     ")"
@@ -693,7 +691,7 @@
    "id": "15a264d8-c2e0-4e24-8ae7-e14fbe0efc48",
    "metadata": {},
    "source": [
-    "By default for convenience purposes, heat monitors return data on equivalent Cartesian grids in the form of [SpatialDataArray](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SpatialDataArray.html)'s. However, if the unprocessed data and/or the original unstructured grid are of interest, one can request that by setting `unstructured=True`. Additionally, the option `conformal=True` allows to request the monitor to be meshed conformally on the unstructured grid. This is useful for visualization purposes, because plots of slices of tetrahedral grids maybe be confusing for visual inspection. Here, we will create monitors with and without conformal meshing for demonstration purposes. In total, we create 3 planar monitors: horizontal through the waveguie structure, horizontal through the heater, and a vertical one."
+    "By default for convenience purposes, heat monitors return data on equivalent Cartesian grids in the form of [SpatialDataArray](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SpatialDataArray.html)'s. However, if the unprocessed data and/or the original unstructured grid are of interest, one can request that by setting `unstructured=True`. Additionally, the option `conformal=True` allows to request the monitor to be meshed conformally on the unstructured grid. This is useful for visualization purposes, because plots of slices of tetrahedral grids may be confusing for visual inspection. Here, we will create monitors with and without conformal meshing for demonstration purposes. In total, we create 3 planar monitors: horizontal through the waveguide structure, horizontal through the heater, and a vertical one."
    ]
   },
   {
@@ -703,9 +701,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "temp_mnt_ugrid_wg = td.TemperatureMonitor(size=(td.inf, td.inf, 0), center=(0, 0, z_wg), name=\"ugrid_wg\", unstructured=True, conformal=True)\n",
-    "temp_mnt_ugrid_heater = td.TemperatureMonitor(size=(td.inf, td.inf, 0), center=(0, 0, z_heater), name=\"ugrid_heater\", unstructured=True)\n",
-    "temp_mnt_ugrid_vert = td.TemperatureMonitor(size=(0, td.inf, td.inf), center=(0, 0, 0), name=\"ugrid_vert\", unstructured=True)"
+    "temp_mnt_ugrid_wg = td.TemperatureMonitor(\n",
+    "    size=(td.inf, td.inf, 0),\n",
+    "    center=(0, 0, z_wg),\n",
+    "    name=\"ugrid_wg\",\n",
+    "    unstructured=True,\n",
+    "    conformal=True,\n",
+    ")\n",
+    "temp_mnt_ugrid_heater = td.TemperatureMonitor(\n",
+    "    size=(td.inf, td.inf, 0), center=(0, 0, z_heater), name=\"ugrid_heater\", unstructured=True\n",
+    ")\n",
+    "temp_mnt_ugrid_vert = td.TemperatureMonitor(\n",
+    "    size=(0, td.inf, td.inf), center=(0, 0, 0), name=\"ugrid_vert\", unstructured=True\n",
+    ")"
    ]
   },
   {
@@ -733,7 +741,7 @@
     "    boundary_spec=[bc_air, bc_wafer],\n",
     "    symmetry=(1, 0, 0),\n",
     "    monitors=[temp_mnt, temp_mnt_ugrid_wg, temp_mnt_ugrid_heater, temp_mnt_ugrid_vert],\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1247,9 +1255,7 @@
     }
    ],
    "source": [
-    "perturbed_scene = scene.perturbed_mediums_copy(\n",
-    "    temperature=heat_sim_data[\"all\"].temperature\n",
-    ")"
+    "perturbed_scene = scene.perturbed_mediums_copy(temperature=heat_sim_data[\"all\"].temperature)"
    ]
   },
   {
@@ -1500,18 +1506,14 @@
     "    scene=scene,\n",
     "    size=[l_optic_sim, w_optic_sim, h_optic_sim],\n",
     "    center=(0, 0, z_sim),\n",
-    "    grid_spec=td.GridSpec.auto(\n",
-    "        min_steps_per_wvl=min_steps_per_wvl, wavelength=td.C_0 / freq0\n",
-    "    ),\n",
+    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl, wavelength=td.C_0 / freq0),\n",
     "    sources=[mode_source],\n",
     "    monitors=[field_monitor, through_mode_mnt, drop_mode_mnt],\n",
     "    shutoff=1e-7,\n",
     "    run_time=run_time,\n",
     ")\n",
     "\n",
-    "perturbed_optic_sim = optic_sim.perturbed_mediums_copy(\n",
-    "    temperature=heat_sim_data[\"all\"].temperature\n",
-    ")"
+    "perturbed_optic_sim = optic_sim.perturbed_mediums_copy(temperature=heat_sim_data[\"all\"].temperature)"
    ]
   },
   {
@@ -1579,10 +1581,14 @@
    ],
    "source": [
     "optic_sim = optic_sim.subsection(\n",
-    "    region=optic_sim.bounding_box, remove_outside_structures=True, remove_outside_custom_mediums=True\n",
+    "    region=optic_sim.bounding_box,\n",
+    "    remove_outside_structures=True,\n",
+    "    remove_outside_custom_mediums=True,\n",
     ")\n",
     "perturbed_optic_sim = perturbed_optic_sim.subsection(\n",
-    "    region=optic_sim.bounding_box, remove_outside_structures=True, remove_outside_custom_mediums=True\n",
+    "    region=optic_sim.bounding_box,\n",
+    "    remove_outside_structures=True,\n",
+    "    remove_outside_custom_mediums=True,\n",
     ")\n",
     "\n",
     "fig, ax = plt.subplots(2, 2, figsize=(10, 6))\n",
@@ -2314,7 +2320,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.0"
   },
   "title": "Modeling thermo-optic phase-shift in a ring resonator using Tidy3D | Flexcompute"
  },
diff --git a/ThermoOpticDopedModulator.ipynb b/ThermoOpticDopedModulator.ipynb
index 6b485519..0b502f5a 100644
--- a/ThermoOpticDopedModulator.ipynb
+++ b/ThermoOpticDopedModulator.ipynb
@@ -31,10 +31,9 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web"
+    "import tidy3d.web as web\n",
+    "from matplotlib import pyplot as plt"
    ]
   },
   {
@@ -99,13 +98,19 @@
     "acceptors = []\n",
     "\n",
     "acceptors.append(\n",
-    "    td.ConstantDoping(center=[0, 0, 0], size=[2*x_total, 2*h_core, td.inf], concentration=conc_p)\n",
+    "    td.ConstantDoping(\n",
+    "        center=[0, 0, 0], size=[2 * x_total, 2 * h_core, td.inf], concentration=conc_p\n",
+    "    )\n",
     ")\n",
     "acceptors.append(\n",
-    "    td.ConstantDoping.from_bounds(rmin=[-x_total, 0, -td.inf], rmax=[-x_i, h_side, td.inf], concentration=conc_pp-conc_p)\n",
+    "    td.ConstantDoping.from_bounds(\n",
+    "        rmin=[-x_total, 0, -td.inf], rmax=[-x_i, h_side, td.inf], concentration=conc_pp - conc_p\n",
+    "    )\n",
     ")\n",
     "acceptors.append(\n",
-    "    td.ConstantDoping.from_bounds(rmin=[x_i, 0, -td.inf], rmax=[x_total, h_side, td.inf], concentration=conc_pp-conc_p)\n",
+    "    td.ConstantDoping.from_bounds(\n",
+    "        rmin=[x_i, 0, -td.inf], rmax=[x_total, h_side, td.inf], concentration=conc_pp - conc_p\n",
+    "    )\n",
     ")"
    ]
   },
@@ -126,7 +131,7 @@
    "source": [
     "# let's define a material here for our Charge simulations\n",
     "si_doped = td.MultiPhysicsMedium(\n",
-    "    optical=td.material_library['cSi']['Li1993_293K'],\n",
+    "    optical=td.material_library[\"cSi\"][\"Li1993_293K\"],\n",
     "    charge=td.SemiconductorMedium(\n",
     "        permittivity=11.1,\n",
     "        N_c=2.86e19,\n",
@@ -161,31 +166,41 @@
     "core = td.Structure(\n",
     "    geometry=td.Box(center=(0, h_core / 2, 0), size=(w_core, h_core, td.inf)),\n",
     "    medium=si_doped,\n",
-    "    name=\"core\"\n",
+    "    name=\"core\",\n",
     ")\n",
     "\n",
     "left_slab = td.Structure(\n",
-    "    geometry=td.Box(center=(-(x_side + w_core / 2) / 2, h_slab / 2, 0), size=(x_side - w_core / 2, h_slab, td.inf)),\n",
+    "    geometry=td.Box(\n",
+    "        center=(-(x_side + w_core / 2) / 2, h_slab / 2, 0),\n",
+    "        size=(x_side - w_core / 2, h_slab, td.inf),\n",
+    "    ),\n",
     "    medium=si_doped,\n",
-    "    name=\"left_slab\"\n",
+    "    name=\"left_slab\",\n",
     ")\n",
     "\n",
     "left_side = td.Structure(\n",
-    "    geometry=td.Box(center=(-(x_side + x_total) / 2, h_side / 2, 0), size=(x_total - x_side, h_side, td.inf)),\n",
+    "    geometry=td.Box(\n",
+    "        center=(-(x_side + x_total) / 2, h_side / 2, 0), size=(x_total - x_side, h_side, td.inf)\n",
+    "    ),\n",
     "    medium=si_doped,\n",
-    "    name=\"left_side\"\n",
+    "    name=\"left_side\",\n",
     ")\n",
     "\n",
     "right_slab = td.Structure(\n",
-    "    geometry=td.Box(center=((x_side + w_core / 2) / 2, h_slab / 2, 0), size=(x_side - w_core / 2, h_slab, td.inf)),\n",
+    "    geometry=td.Box(\n",
+    "        center=((x_side + w_core / 2) / 2, h_slab / 2, 0),\n",
+    "        size=(x_side - w_core / 2, h_slab, td.inf),\n",
+    "    ),\n",
     "    medium=si_doped,\n",
-    "    name=\"right_slab\"\n",
+    "    name=\"right_slab\",\n",
     ")\n",
     "\n",
     "right_side = td.Structure(\n",
-    "    geometry=td.Box(center=((x_side + x_total) / 2, h_side / 2, 0), size=(x_total - x_side, h_side, td.inf)),\n",
+    "    geometry=td.Box(\n",
+    "        center=((x_side + x_total) / 2, h_side / 2, 0), size=(x_total - x_side, h_side, td.inf)\n",
+    "    ),\n",
     "    medium=si_doped,\n",
-    "    name=\"right_side\"\n",
+    "    name=\"right_side\",\n",
     ")"
    ]
   },
@@ -211,18 +226,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "contact_medium = td.MultiPhysicsMedium(charge=td.ChargeConductorMedium(conductivity=1), name=\"contact\")\n",
+    "contact_medium = td.MultiPhysicsMedium(\n",
+    "    charge=td.ChargeConductorMedium(conductivity=1), name=\"contact\"\n",
+    ")\n",
     "\n",
     "contact_p = td.Structure(\n",
-    "    geometry=td.Box(center=(-x_total + w_contact/2, h_side  + h_contact/ 2, 0), size=(w_contact, h_contact, td.inf)),\n",
+    "    geometry=td.Box(\n",
+    "        center=(-x_total + w_contact / 2, h_side + h_contact / 2, 0),\n",
+    "        size=(w_contact, h_contact, td.inf),\n",
+    "    ),\n",
     "    medium=contact_medium,\n",
-    "    name=\"contact_p\"\n",
+    "    name=\"contact_p\",\n",
     ")\n",
     "\n",
     "contact_n = td.Structure(\n",
-    "    geometry=td.Box(center=(x_total - w_contact/2, h_side  + h_contact/ 2, 0), size=(w_contact, h_contact, td.inf)),\n",
+    "    geometry=td.Box(\n",
+    "        center=(x_total - w_contact / 2, h_side + h_contact / 2, 0),\n",
+    "        size=(w_contact, h_contact, td.inf),\n",
+    "    ),\n",
     "    medium=contact_medium,\n",
-    "    name=\"contact_n\"\n",
+    "    name=\"contact_n\",\n",
     ")"
    ]
   },
@@ -256,7 +279,7 @@
     "# create a scene with the previous structures\n",
     "Si_structures = [core, left_slab, left_side, right_slab, right_side]\n",
     "\n",
-    "all_structures =  Si_structures + [contact_p, contact_n]\n",
+    "all_structures = Si_structures + [contact_p, contact_n]\n",
     "\n",
     "scene_charge = td.Scene(\n",
     "    medium=air,\n",
@@ -350,11 +373,17 @@
    "outputs": [],
    "source": [
     "charge_mnt = td.SteadyFreeCarrierMonitor(\n",
-    "    center=(0, 0, 0), size=(td.inf, td.inf, 0), name=\"charge_mnt\", unstructured=True, \n",
+    "    center=(0, 0, 0),\n",
+    "    size=(td.inf, td.inf, 0),\n",
+    "    name=\"charge_mnt\",\n",
+    "    unstructured=True,\n",
     ")\n",
     "\n",
     "potential_mnt = td.SteadyPotentialMonitor(\n",
-    "    center=(0, 0, 0), size=(td.inf, td.inf, 0), name=\"potential_mnt\", unstructured=True, \n",
+    "    center=(0, 0, 0),\n",
+    "    size=(td.inf, td.inf, 0),\n",
+    "    name=\"potential_mnt\",\n",
+    "    unstructured=True,\n",
     ")\n",
     "charge_monitors = [charge_mnt, potential_mnt]"
    ]
@@ -381,7 +410,9 @@
    "source": [
     "convergence_settings = td.ChargeToleranceSpec(rel_tol=1e-10, abs_tol=1e10, max_iters=400)\n",
     "\n",
-    "analysis_type = td.IsothermalSteadyChargeDCAnalysis(temperature=300, tolerance_settings=convergence_settings, convergence_dv=0.5)\n",
+    "analysis_type = td.IsothermalSteadyChargeDCAnalysis(\n",
+    "    temperature=300, tolerance_settings=convergence_settings, convergence_dv=0.5\n",
+    ")\n",
     "\n",
     "res = 0.005\n",
     "mesh = td.UniformUnstructuredGrid(dl=res, relative_min_dl=0)"
@@ -441,12 +472,12 @@
     "    monitors=charge_monitors,\n",
     "    analysis_spec=analysis_type,\n",
     "    center=scene_charge.center,\n",
-    "    size=(2*x_total, h_core, 0),\n",
+    "    size=(2 * x_total, h_core, 0),\n",
     "    structures=scene_charge.structures,\n",
     "    medium=scene_charge.medium,\n",
     "    boundary_spec=boundary_conditions,\n",
     "    grid_spec=mesh,\n",
-    "    symmetry=(0, 0, 0)\n",
+    "    symmetry=(0, 0, 0),\n",
     ")"
    ]
   },
@@ -799,7 +830,7 @@
     }
    ],
    "source": [
-    "charge_data=web.run(charge_sim, task_name=\"modulator\", path=\"charge_modulator.hdf5\")\n",
+    "charge_data = web.run(charge_sim, task_name=\"modulator\", path=\"charge_modulator.hdf5\")\n",
     "# from run_drift import run_drift\n",
     "# charge_data = run_drift(charge_sim)"
    ]
@@ -843,7 +874,9 @@
     "    ax[ind].set_xlabel(\"x (um)\")\n",
     "    ax[ind].set_ylabel(\"y (um)\")\n",
     "\n",
-    "ax[2] = charge_data[\"potential_mnt\"].potential.sel(z=0, voltage=voltages[1]).plot(grid=False, ax=ax[2])\n",
+    "ax[2] = (\n",
+    "    charge_data[\"potential_mnt\"].potential.sel(z=0, voltage=voltages[1]).plot(grid=False, ax=ax[2])\n",
+    ")\n",
     "ax[2].set_title(f\"Potential field - bias {voltages[1]:1.1f} V\")\n",
     "ax[2].set_xlabel(\"x (um)\")\n",
     "ax[2].set_ylabel(\"y (um)\")\n",
@@ -880,12 +913,12 @@
    ],
    "source": [
     "# let's plot the current as a function of the applied voltages\n",
-    "_, ax = plt.subplots(1,1)\n",
+    "_, ax = plt.subplots(1, 1)\n",
     "# since this is a 2D simulation the current is provided in A/um\n",
     "currents = charge_data.device_characteristics.steady_dc_current_voltage.values\n",
     "ax.plot(voltages, currents, label=\"current\")\n",
     "ax.set_xlabel(\"Bias (V)\")\n",
-    "ax.set_ylabel(\"Current (A/$\\mu$m)\")\n",
+    "ax.set_ylabel(r\"Current (A/$\\mu$m)\")\n",
     "ax.legend()\n",
     "plt.show()"
    ]
@@ -908,11 +941,11 @@
    "outputs": [],
    "source": [
     "# cladding dimensions (um)\n",
-    "heat_sim_width = 20 \n",
+    "heat_sim_width = 20\n",
     "h_cladding = 2.8  # thickness of cladding\n",
     "h_box = 2  # thickness of buried oxide\n",
     "\n",
-    "# define center of the cladding so that the device sits 2um above \n",
+    "# define center of the cladding so that the device sits 2um above\n",
     "center_cladding = (0, h_cladding / 2, 0)\n",
     "center_box = (0, -h_box / 2, 0)\n",
     "center_heat_sim = (0, (h_cladding - h_box) / 2, 0)"
@@ -940,17 +973,17 @@
     "freq0 = td.C_0 / wvl_um\n",
     "\n",
     "# charge perturbation parameters\n",
-    "library_si = td.material_library['cSi']['Li1993_293K']\n",
+    "library_si = td.material_library[\"cSi\"][\"Li1993_293K\"]\n",
     "n_si, k_si = library_si.nk_model(frequency=freq0)\n",
     "\n",
     "# convert to permittivity and conductivity\n",
-    "# permittivity_si, conductivity_si = td.Medium.nk_to_eps_sigma(n=n_si, k=k_si, freq=freq0) \n",
+    "# permittivity_si, conductivity_si = td.Medium.nk_to_eps_sigma(n=n_si, k=k_si, freq=freq0)\n",
     "\n",
-    "# Empiric relationships presented in M. Nedeljkovic, R. Soref \n",
-    "# and G. Z. Mashanovich, \"Free-Carrier Electrorefraction and \n",
-    "# Electroabsorption Modulation Predictions for Silicon Over the \n",
-    "#1–14- μm Infrared Wavelength Range,\" IEEE Photonics Journal, \n",
-    "#vol. 3, no. 6, pp. 1171-1180, Dec. 2011\n",
+    "# Empiric relationships presented in M. Nedeljkovic, R. Soref\n",
+    "# and G. Z. Mashanovich, \"Free-Carrier Electrorefraction and\n",
+    "# Electroabsorption Modulation Predictions for Silicon Over the\n",
+    "# 1–14- μm Infrared Wavelength Range,\" IEEE Photonics Journal,\n",
+    "# vol. 3, no. 6, pp. 1171-1180, Dec. 2011\n",
     "\n",
     "ne_coeff = -1.91e-21\n",
     "ne_pow = 0.992\n",
@@ -969,17 +1002,17 @@
     "Ne_range = np.concatenate(([0], np.logspace(15, 20, 20)))\n",
     "Nh_range = np.concatenate(([0], np.logspace(15, 20, 21)))\n",
     "\n",
-    "Ne_mesh, Nh_mesh = np.meshgrid(Ne_range, Nh_range, indexing='ij')\n",
+    "Ne_mesh, Nh_mesh = np.meshgrid(Ne_range, Nh_range, indexing=\"ij\")\n",
     "\n",
-    "dn_mesh = ne_coeff * Ne_mesh ** ne_pow + nh_coeff * Nh_mesh ** nh_pow\n",
-    "dk_mesh = ke_coeff * Ne_mesh ** ke_pow + kh_coeff * Nh_mesh ** kh_pow\n",
+    "dn_mesh = ne_coeff * Ne_mesh**ne_pow + nh_coeff * Nh_mesh**nh_pow\n",
+    "dk_mesh = ke_coeff * Ne_mesh**ke_pow + kh_coeff * Nh_mesh**kh_pow\n",
     "\n",
     "dn_data = td.ChargeDataArray(\n",
-    "    ne_coeff * Ne_mesh ** ne_pow + nh_coeff * Nh_mesh ** nh_pow, \n",
+    "    ne_coeff * Ne_mesh**ne_pow + nh_coeff * Nh_mesh**nh_pow,\n",
     "    coords=dict(n=Ne_range, p=Nh_range),\n",
     ")\n",
     "dk_data = td.ChargeDataArray(\n",
-    "    ke_coeff * Ne_mesh ** ke_pow + kh_coeff * Nh_mesh ** kh_pow, \n",
+    "    ke_coeff * Ne_mesh**ke_pow + kh_coeff * Nh_mesh**kh_pow,\n",
     "    coords=dict(n=Ne_range, p=Nh_range),\n",
     ")\n",
     "\n",
@@ -1012,7 +1045,7 @@
     "        conductivity=Si_k,\n",
     "        capacity=Si_s,\n",
     "    ),\n",
-    "    name=\"Si\"\n",
+    "    name=\"Si\",\n",
     ")\n",
     "\n",
     "# create thermally varying SiO2\n",
@@ -1070,17 +1103,17 @@
    },
    "outputs": [],
    "source": [
-    "# create objects for heat simulation \n",
+    "# create objects for heat simulation\n",
     "cladding = td.Structure(\n",
     "    geometry=td.Box(center=center_heat_sim, size=(td.inf, h_cladding + h_box, td.inf)),\n",
     "    medium=SiO2,\n",
-    "    name=\"cladding\"\n",
+    "    name=\"cladding\",\n",
     ")\n",
     "\n",
     "substrate = td.Structure(\n",
     "    geometry=td.Box(center=(0, -h_box - h_slab / 2, 0), size=(td.inf, h_slab, td.inf)),\n",
     "    medium=Si,\n",
-    "    name=\"substrate\"\n",
+    "    name=\"substrate\",\n",
     ")"
    ]
   },
@@ -1093,8 +1126,7 @@
    "source": [
     "# update silicon structures\n",
     "for n, structure in enumerate(Si_structures):\n",
-    "    Si_structures[n] = structure.updated_copy(medium=Si)\n",
-    "    "
+    "    Si_structures[n] = structure.updated_copy(medium=Si)"
    ]
   },
   {
@@ -1159,12 +1191,12 @@
     "\n",
     "bc_air = td.HeatBoundarySpec(\n",
     "    condition=td.ConvectionBC(ambient_temperature=300, transfer_coeff=10 * 1e-12),\n",
-    "    placement=td.MediumMediumInterface(mediums=[air.name, SiO2.name])\n",
+    "    placement=td.MediumMediumInterface(mediums=[air.name, SiO2.name]),\n",
     ")\n",
     "\n",
     "bc_substrate = td.HeatBoundarySpec(\n",
-    "    condition= td.TemperatureBC(temperature=300),\n",
-    "    placement= td.StructureStructureInterface(structures=[substrate.name, cladding.name])\n",
+    "    condition=td.TemperatureBC(temperature=300),\n",
+    "    placement=td.StructureStructureInterface(structures=[substrate.name, cladding.name]),\n",
     ")\n",
     "\n",
     "heat_bc = [bc_air, bc_substrate]"
@@ -1219,8 +1251,8 @@
     "\n",
     "device_volume = (\n",
     "    w_core * h_core  # core\n",
-    "    + 2. * (x_side - w_core/2) * h_slab  # slabs\n",
-    "    + 2. * (x_total - x_side) * h_side  # sides\n",
+    "    + 2.0 * (x_side - w_core / 2) * h_slab  # slabs\n",
+    "    + 2.0 * (x_total - x_side) * h_side  # sides\n",
     ")\n",
     "\n",
     "input_power = voltages[1] * currents[1]\n",
@@ -1230,8 +1262,7 @@
     "print(\"Volumetric heat rate: \", volumetric_heat * 1e3, \"mW / um^3\")\n",
     "\n",
     "heat_source = td.UniformHeatSource(\n",
-    "    structures=[struct.name for struct in Si_structures], \n",
-    "    rate=volumetric_heat\n",
+    "    structures=[struct.name for struct in Si_structures], rate=volumetric_heat\n",
     ")"
    ]
   },
@@ -1253,7 +1284,13 @@
    "outputs": [],
    "source": [
     "# set a temperature monitor\n",
-    "temp_mnt = td.TemperatureMonitor(center=(0, 0, 0), size=(td.inf, td.inf, 0), name=\"temperature\", unstructured=True, conformal=True)\n"
+    "temp_mnt = td.TemperatureMonitor(\n",
+    "    center=(0, 0, 0),\n",
+    "    size=(td.inf, td.inf, 0),\n",
+    "    name=\"temperature\",\n",
+    "    unstructured=True,\n",
+    "    conformal=True,\n",
+    ")"
    ]
   },
   {
@@ -1331,7 +1368,7 @@
     "    grid_spec=grid_spec,\n",
     ")\n",
     "\n",
-    "heat_sim.plot(z = 0)\n",
+    "heat_sim.plot(z=0)\n",
     "plt.show()"
    ]
   },
@@ -1669,7 +1706,7 @@
     "heat_sim.plot_property(z=0, ax=ax[0], property=\"heat_conductivity\")\n",
     "heat_sim_data[\"temperature\"].temperature.plot(ax=ax[1], grid=False)\n",
     "heat_sim_data[\"temperature\"].temperature.plot(ax=ax[2])\n",
-    "plt.show()  "
+    "plt.show()"
    ]
   },
   {
@@ -1693,18 +1730,15 @@
     "for n, (v, i) in enumerate(zip(voltages, currents)):\n",
     "    input_power = v * i\n",
     "    volumetric_heat = input_power / device_volume\n",
-    "    \n",
+    "\n",
     "    # update heat sources for each structure\n",
     "    heat_source = td.HeatSource(\n",
-    "        structures=[struct.name for struct in Si_structures], \n",
-    "        rate=volumetric_heat\n",
+    "        structures=[struct.name for struct in Si_structures], rate=volumetric_heat\n",
     "    )\n",
-    "    \n",
+    "\n",
     "    # update the simulation object\n",
     "    sim_name = \"thermal_case_\" + str(n)\n",
-    "    heat_simulations[sim_name] = heat_sim.updated_copy(sources=[heat_source])\n",
-    "    \n",
-    " "
+    "    heat_simulations[sim_name] = heat_sim.updated_copy(sources=[heat_source])"
    ]
   },
   {
@@ -1877,7 +1911,7 @@
    ],
    "source": [
     "# plot first and last case\n",
-    "_, ax = plt.subplots(2,1)\n",
+    "_, ax = plt.subplots(2, 1)\n",
     "thermal_batch_data[\"thermal_case_2\"].plot_field(\"temperature\", ax=ax[0])\n",
     "thermal_batch_data[f\"thermal_case_{len(voltages)-1}\"].plot_field(\"temperature\", ax=ax[1])\n",
     "ax[0].set_title(f\"Voltage {voltages[2]}\")\n",
@@ -1916,7 +1950,7 @@
     "    size=(heat_sim_width, h_cladding + h_box + 1, wvl_um),\n",
     "    run_time=1e-15,\n",
     "    grid_spec=grid_spec,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1945,30 +1979,36 @@
     "    name_thermal_data = \"thermal_case_\" + str(n)\n",
     "    therm_data = thermal_batch_data[name_thermal_data]\n",
     "\n",
-    "    temp_interpolated = therm_data[\"temperature\"].temperature.interp(x=target_grid.x, y=target_grid.y, z=0, fill_value=Tref)\n",
+    "    temp_interpolated = therm_data[\"temperature\"].temperature.interp(\n",
+    "        x=target_grid.x, y=target_grid.y, z=0, fill_value=Tref\n",
+    "    )\n",
     "\n",
     "    # now deal with charge distributions\n",
-    "    e_interpolated = charge_data[\"charge_mnt\"].electrons.sel(voltage=v).interp(x=target_grid.x, y=target_grid.y, z=0, fill_value=0)\n",
-    "    h_interpolated = charge_data[\"charge_mnt\"].holes.sel(voltage=v).interp(x=target_grid.x, y=target_grid.y, z=0, fill_value=0)\n",
+    "    e_interpolated = (\n",
+    "        charge_data[\"charge_mnt\"]\n",
+    "        .electrons.sel(voltage=v)\n",
+    "        .interp(x=target_grid.x, y=target_grid.y, z=0, fill_value=0)\n",
+    "    )\n",
+    "    h_interpolated = (\n",
+    "        charge_data[\"charge_mnt\"]\n",
+    "        .holes.sel(voltage=v)\n",
+    "        .interp(x=target_grid.x, y=target_grid.y, z=0, fill_value=0)\n",
+    "    )\n",
     "    # convert to SpatialDataArray\n",
     "    coords_charge = {\n",
     "        \"x\": e_interpolated.coords.get(\"x\").values,\n",
     "        \"y\": e_interpolated.coords.get(\"y\").values,\n",
-    "        \"z\": e_interpolated.coords.get(\"z\").values\n",
+    "        \"z\": e_interpolated.coords.get(\"z\").values,\n",
     "    }\n",
     "    e_interpolated = td.SpatialDataArray(\n",
-    "        np.squeeze(e_interpolated.values, axis=3),\n",
-    "        coords=coords_charge\n",
+    "        np.squeeze(e_interpolated.values, axis=3), coords=coords_charge\n",
     "    )\n",
     "    h_interpolated = td.SpatialDataArray(\n",
-    "        np.squeeze(h_interpolated.values, axis=3),\n",
-    "        coords=coords_charge\n",
+    "        np.squeeze(h_interpolated.values, axis=3), coords=coords_charge\n",
     "    )\n",
     "\n",
     "    psim = optic_sim.perturbed_mediums_copy(\n",
-    "        electron_density=e_interpolated,\n",
-    "        hole_density=h_interpolated,\n",
-    "        temperature=temp_interpolated\n",
+    "        electron_density=e_interpolated, hole_density=h_interpolated, temperature=temp_interpolated\n",
     "    )\n",
     "\n",
     "    perturbed_sims.append(psim)"
@@ -2002,7 +2042,7 @@
    ],
    "source": [
     "sample_region = td.Box(\n",
-    "    center=center_heat_sim, \n",
+    "    center=center_heat_sim,\n",
     "    size=(heat_sim_width, h_cladding + h_box, 0),\n",
     ")\n",
     "\n",
@@ -2014,14 +2054,13 @@
     "    eps_doped = eps_doped.interp(x=eps_reference.x, y=eps_reference.y)\n",
     "    eps_diff = np.abs(np.real(eps_doped - eps_reference))\n",
     "    eps_diff.plot(x=\"x\", ax=ax[ax_ind], vmax=0.15)\n",
-    "    \n",
+    "\n",
     "    ax[ax_ind].set_xlabel(\"x (um)\")\n",
     "    ax[ax_ind].set_ylabel(\"y (um)\")\n",
     "    ax[ax_ind].set_title(f\"Bias: {voltages[n]:1.1f} V\")\n",
     "\n",
     "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n"
+    "plt.show()"
    ]
   },
   {
@@ -2089,7 +2128,7 @@
     "        plane=mode_plane,\n",
     "        mode_spec=td.ModeSpec(num_modes=1, precision=\"double\"),\n",
     "        freqs=[freq0],\n",
-    "    )\n"
+    "    )"
    ]
   },
   {
@@ -2268,13 +2307,13 @@
     "power_mw = np.array(voltages) * np.array(currents) * 1e3\n",
     "\n",
     "# plot n_eff\n",
-    "_,ax = plt.subplots(1,2, figsize=(12,4))\n",
-    "ax[0].plot(power_mw, np.real(n_eff), '.-')\n",
-    "ax[0].set_ylabel(\"$\\mathbb{Re}(n_{eff}$)\")\n",
+    "_, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "ax[0].plot(power_mw, np.real(n_eff), \".-\")\n",
+    "ax[0].set_ylabel(r\"$\\mathbb{Re}(n_{eff}$)\")\n",
     "ax[0].set_xlabel(\"Power (mW / um)\")\n",
     "\n",
-    "ax[1].plot(power_mw, np.imag(n_eff), '.-')\n",
-    "ax[1].set_ylabel(\"$\\mathbb{Im}(n_{eff}$)\")\n",
+    "ax[1].plot(power_mw, np.imag(n_eff), \".-\")\n",
+    "ax[1].set_ylabel(r\"$\\mathbb{Im}(n_{eff}$)\")\n",
     "ax[1].set_xlabel(\"Power (mW / um)\")\n",
     "\n",
     "plt.show()"
@@ -2316,7 +2355,7 @@
     "ax[0].axhline(y=1, color=\"k\", linestyle=\"--\")\n",
     "\n",
     "ax[0].set_xlabel(\"Power (mW)\")\n",
-    "ax[0].set_ylabel(\"Phase shift ($1/\\pi$)\")\n",
+    "ax[0].set_ylabel(r\"Phase shift ($1/\\pi$)\")\n",
     "\n",
     "ax[1].plot(power_mw_per_100um, 10 * np.log10(intensity), \".-\")\n",
     "\n",
diff --git a/TimeModulationTutorial.ipynb b/TimeModulationTutorial.ipynb
index a2d7534b..2992a806 100644
--- a/TimeModulationTutorial.ipynb
+++ b/TimeModulationTutorial.ipynb
@@ -26,15 +26,15 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
-    "import xarray as xr\n",
     "import matplotlib.pylab as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3D import\n",
     "import tidy3d as td\n",
-    "from tidy3d.constants import C_0\n",
+    "import tidy3d.web as web\n",
+    "import xarray as xr\n",
     "from tidy3d import SpatialDataArray\n",
-    "import tidy3d.web as web\n"
+    "from tidy3d.constants import C_0"
    ]
   },
   {
@@ -65,7 +65,7 @@
     "spacing = wvl_um\n",
     "\n",
     "# simulation size\n",
-    "sim_size = Lx, Ly, Lz = (1.0, 1.0, 4 * spacing + t_slab)\n"
+    "sim_size = Lx, Ly, Lz = (1.0, 1.0, 4 * spacing + t_slab)"
    ]
   },
   {
@@ -123,7 +123,7 @@
    "outputs": [],
    "source": [
     "permittivity = 2\n",
-    "mat_passive = td.Medium(permittivity=permittivity)\n"
+    "mat_passive = td.Medium(permittivity=permittivity)"
    ]
   },
   {
@@ -144,9 +144,7 @@
    "outputs": [],
    "source": [
     "modulation_freq = 0.1 * freq0\n",
-    "time_modulation = td.ContinuousWaveTimeModulation(\n",
-    "    freq0=modulation_freq, amplitude=1, phase=0\n",
-    ")\n"
+    "time_modulation = td.ContinuousWaveTimeModulation(freq0=modulation_freq, amplitude=1, phase=0)"
    ]
   },
   {
@@ -167,7 +165,7 @@
    "outputs": [],
    "source": [
     "modulation_amplitude = 0.4  # this is a relatively strong modulation to permittivity\n",
-    "space_modulation = td.SpaceModulation(amplitude=modulation_amplitude, phase=0)\n"
+    "space_modulation = td.SpaceModulation(amplitude=modulation_amplitude, phase=0)"
    ]
   },
   {
@@ -189,7 +187,7 @@
    "source": [
     "modulation = td.SpaceTimeModulation(\n",
     "    time_modulation=time_modulation, space_modulation=space_modulation\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -209,7 +207,7 @@
    "outputs": [],
    "source": [
     "modulation_spec = td.ModulationSpec(permittivity=modulation, conductivity=None)\n",
-    "mat_modulated = td.Medium(permittivity=permittivity, modulation_spec=modulation_spec)\n"
+    "mat_modulated = td.Medium(permittivity=permittivity, modulation_spec=modulation_spec)"
    ]
   },
   {
@@ -247,7 +245,7 @@
     "    medium=mat_passive,\n",
     ")\n",
     "\n",
-    "modulated_slab = unmodulated_slab.updated_copy(medium=mat_modulated)\n"
+    "modulated_slab = unmodulated_slab.updated_copy(medium=mat_modulated)"
    ]
   },
   {
@@ -271,7 +269,7 @@
     "    size=(td.inf, td.inf, 0),\n",
     "    center=(0, 0, -Lz / 2 + spacing),\n",
     "    direction=\"+\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -291,15 +289,13 @@
    "source": [
     "# We are interested in measuring the transmitted flux, so we set it to be an oversized plane.\n",
     "# The monitor frequencies are set to span several sidebands.\n",
-    "monitor_freqs = np.linspace(\n",
-    "    freq0 - modulation_freq * 3, freq0 + modulation_freq * 3, 500\n",
-    ")\n",
+    "monitor_freqs = np.linspace(freq0 - modulation_freq * 3, freq0 + modulation_freq * 3, 500)\n",
     "monitor = td.FluxMonitor(\n",
     "    center=(0, 0, Lz / 2 - spacing),\n",
     "    size=(td.inf, td.inf, 0),\n",
     "    freqs=monitor_freqs,\n",
     "    name=\"flux\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -327,9 +323,9 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:34:54 PDT\u001B[0m\u001B[2;36m \u001B[0m\u001B[31mWARNING: monitors\u001B[0m\u001B[1;31m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;31m]\u001B[0m\u001B[31m contains frequencies outside of the simulation\u001B[0m\n",
-       "\u001B[2;36m             \u001B[0m\u001B[31mfrequency range \u001B[0m\u001B[1;31m(\u001B[0m\u001B[1;36m2.878008e+14\u001B[0m\u001B[31m, \u001B[0m\u001B[1;36m3.117842e+14\u001B[0m\u001B[1;31m)\u001B[0m\u001B[1;31m(\u001B[0m\u001B[31mHz\u001B[0m\u001B[1;31m)\u001B[0m\u001B[31m as defined by the \u001B[0m\n",
-       "\u001B[2;36m             \u001B[0m\u001B[31msources.                                                           \u001B[0m\n"
+       "\u001b[2;36m15:34:54 PDT\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: monitors\u001b[0m\u001b[1;31m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;31m]\u001b[0m\u001b[31m contains frequencies outside of the simulation\u001b[0m\n",
+       "\u001b[2;36m             \u001b[0m\u001b[31mfrequency range \u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;36m2.878008e+14\u001b[0m\u001b[31m, \u001b[0m\u001b[1;36m3.117842e+14\u001b[0m\u001b[1;31m)\u001b[0m\u001b[1;31m(\u001b[0m\u001b[31mHz\u001b[0m\u001b[1;31m)\u001b[0m\u001b[31m as defined by the \u001b[0m\n",
+       "\u001b[2;36m             \u001b[0m\u001b[31msources.                                                           \u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -344,9 +340,9 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0m\u001B[31mWARNING: monitors\u001B[0m\u001B[1;31m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;31m]\u001B[0m\u001B[31m contains frequencies outside of the simulation\u001B[0m\n",
-       "\u001B[2;36m             \u001B[0m\u001B[31mfrequency range \u001B[0m\u001B[1;31m(\u001B[0m\u001B[1;36m2.878008e+14\u001B[0m\u001B[31m, \u001B[0m\u001B[1;36m3.117842e+14\u001B[0m\u001B[1;31m)\u001B[0m\u001B[1;31m(\u001B[0m\u001B[31mHz\u001B[0m\u001B[1;31m)\u001B[0m\u001B[31m as defined by the \u001B[0m\n",
-       "\u001B[2;36m             \u001B[0m\u001B[31msources.                                                           \u001B[0m\n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: monitors\u001b[0m\u001b[1;31m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;31m]\u001b[0m\u001b[31m contains frequencies outside of the simulation\u001b[0m\n",
+       "\u001b[2;36m             \u001b[0m\u001b[31mfrequency range \u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;36m2.878008e+14\u001b[0m\u001b[31m, \u001b[0m\u001b[1;36m3.117842e+14\u001b[0m\u001b[1;31m)\u001b[0m\u001b[1;31m(\u001b[0m\u001b[31mHz\u001b[0m\u001b[1;31m)\u001b[0m\u001b[31m as defined by the \u001b[0m\n",
+       "\u001b[2;36m             \u001b[0m\u001b[31msources.                                                           \u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -366,7 +362,7 @@
     "    normalize_index=None,\n",
     ")\n",
     "\n",
-    "sim_modulated = sim_unmodulated.updated_copy(structures=[modulated_slab])\n"
+    "sim_modulated = sim_unmodulated.updated_copy(structures=[modulated_slab])"
    ]
   },
   {
@@ -417,7 +413,7 @@
     "\n",
     "sim_modulated.plot(x=0, ax=ax[1])\n",
     "ax[1].set_title(\"Modulated slab\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -444,8 +440,8 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mCreated task \u001B[32m'unmodulated'\u001B[0m with task_id                            \n",
-       "\u001B[2;36m             \u001B[0m\u001B[32m'fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1'\u001B[0m and task_type \u001B[32m'FDTD'\u001B[0m.\n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'unmodulated'\u001b[0m with task_id                            \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.\n"
       ]
      },
      "metadata": {},
@@ -460,9 +456,9 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mView task using web UI at                                          \n",
-       "\u001B[2;36m             \u001B[0m\u001B]8;id=503939;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001B\\\u001B[32m'https://tidy3d.simulation.cloud/workbench?\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=928814;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001B\\\u001B[32mtaskId\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=503939;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001B\\\u001B[32m=\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=855657;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001B\\\u001B[32mfdve\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=503939;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001B\\\u001B[32m-9aec2409-f35\u001B[0m\u001B]8;;\u001B\\\n",
-       "\u001B[2;36m             \u001B[0m\u001B]8;id=503939;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001B\\\u001B[32m7-4f04-bd69-4eed3ff94767v1'\u001B[0m\u001B]8;;\u001B\\.                                       \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=503939;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=928814;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=503939;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=855657;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=503939;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001b\\\u001b[32m-9aec2409-f35\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=503939;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001b\\\u001b[32m7-4f04-bd69-4eed3ff94767v1'\u001b[0m\u001b]8;;\u001b\\.                                       \n"
       ]
      },
      "metadata": {},
@@ -512,7 +508,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:34:56 PDT\u001B[0m\u001B[2;36m \u001B[0mstatus = queued                                                    \n"
+       "\u001b[2;36m15:34:56 PDT\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
       ]
      },
      "metadata": {},
@@ -539,7 +535,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:35:05 PDT\u001B[0m\u001B[2;36m \u001B[0mstatus = preprocess                                                \n"
+       "\u001b[2;36m15:35:05 PDT\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
       ]
      },
      "metadata": {},
@@ -563,8 +559,8 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:35:12 PDT\u001B[0m\u001B[2;36m \u001B[0mMaximum FlexCredit cost: \u001B[1;36m0.025\u001B[0m. Use \u001B[32m'web.real_cost\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m to get\n",
-       "\u001B[2;36m             \u001B[0mthe billed FlexCredit cost after a simulation run.                 \n"
+       "\u001b[2;36m15:35:12 PDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get\n",
+       "\u001b[2;36m             \u001b[0mthe billed FlexCredit cost after a simulation run.                 \n"
       ]
      },
      "metadata": {},
@@ -577,7 +573,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mstarting up solver                                                 \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
       ]
      },
      "metadata": {},
@@ -590,7 +586,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mrunning solver                                                     \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
       ]
      },
      "metadata": {},
@@ -606,10 +602,10 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mTo cancel the simulation, use \u001B[32m'web.abort\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m or              \n",
-       "\u001B[2;36m             \u001B[0m\u001B[32m'web.delete\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m or abort/delete the task in the web UI.      \n",
-       "\u001B[2;36m             \u001B[0mTerminating the Python script will not stop the job running on the \n",
-       "\u001B[2;36m             \u001B[0mcloud.                                                             \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
+       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
+       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
       ]
      },
      "metadata": {},
@@ -636,7 +632,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:35:19 PDT\u001B[0m\u001B[2;36m \u001B[0mearly shutoff detected at \u001B[1;36m16\u001B[0m%, exiting.                            \n"
+       "\u001b[2;36m15:35:19 PDT\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m16\u001b[0m%, exiting.                            \n"
       ]
      },
      "metadata": {},
@@ -672,7 +668,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mstatus = postprocess                                               \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
       ]
      },
      "metadata": {},
@@ -699,7 +695,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:35:23 PDT\u001B[0m\u001B[2;36m \u001B[0mstatus = success                                                   \n"
+       "\u001b[2;36m15:35:23 PDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
       ]
      },
      "metadata": {},
@@ -724,9 +720,9 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mView simulation result at                                          \n",
-       "\u001B[2;36m             \u001B[0m\u001B]8;id=127825;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001B\\\u001B[4;34m'https://tidy3d.simulation.cloud/workbench?\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=54272;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001B\\\u001B[4;34mtaskId\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=127825;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001B\\\u001B[4;34m=\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=760282;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001B\\\u001B[4;34mfdve\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=127825;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001B\\\u001B[4;34m-9aec2409-f35\u001B[0m\u001B]8;;\u001B\\\n",
-       "\u001B[2;36m             \u001B[0m\u001B]8;id=127825;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001B\\\u001B[4;34m7-4f04-bd69-4eed3ff94767v1'\u001B[0m\u001B]8;;\u001B\\\u001B[4;34m.\u001B[0m                                       \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=127825;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=54272;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=127825;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=760282;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=127825;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001b\\\u001b[4;34m-9aec2409-f35\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=127825;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9aec2409-f357-4f04-bd69-4eed3ff94767v1\u001b\\\u001b[4;34m7-4f04-bd69-4eed3ff94767v1'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                       \n"
       ]
      },
      "metadata": {},
@@ -776,7 +772,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:35:24 PDT\u001B[0m\u001B[2;36m \u001B[0mloading simulation from simulation_data.hdf5                       \n"
+       "\u001b[2;36m15:35:24 PDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
       ]
      },
      "metadata": {},
@@ -791,9 +787,9 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0m\u001B[31mWARNING: monitors\u001B[0m\u001B[1;31m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;31m]\u001B[0m\u001B[31m contains frequencies outside of the simulation\u001B[0m\n",
-       "\u001B[2;36m             \u001B[0m\u001B[31mfrequency range \u001B[0m\u001B[1;31m(\u001B[0m\u001B[1;36m2.878008e+14\u001B[0m\u001B[31m, \u001B[0m\u001B[1;36m3.117842e+14\u001B[0m\u001B[1;31m)\u001B[0m\u001B[1;31m(\u001B[0m\u001B[31mHz\u001B[0m\u001B[1;31m)\u001B[0m\u001B[31m as defined by the \u001B[0m\n",
-       "\u001B[2;36m             \u001B[0m\u001B[31msources.                                                           \u001B[0m\n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: monitors\u001b[0m\u001b[1;31m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;31m]\u001b[0m\u001b[31m contains frequencies outside of the simulation\u001b[0m\n",
+       "\u001b[2;36m             \u001b[0m\u001b[31mfrequency range \u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;36m2.878008e+14\u001b[0m\u001b[31m, \u001b[0m\u001b[1;36m3.117842e+14\u001b[0m\u001b[1;31m)\u001b[0m\u001b[1;31m(\u001b[0m\u001b[31mHz\u001b[0m\u001b[1;31m)\u001b[0m\u001b[31m as defined by the \u001b[0m\n",
+       "\u001b[2;36m             \u001b[0m\u001b[31msources.                                                           \u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -807,8 +803,8 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mCreated task \u001B[32m'modulated'\u001B[0m with task_id                              \n",
-       "\u001B[2;36m             \u001B[0m\u001B[32m'fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1'\u001B[0m and task_type \u001B[32m'FDTD'\u001B[0m.\n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'modulated'\u001b[0m with task_id                              \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.\n"
       ]
      },
      "metadata": {},
@@ -823,9 +819,9 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mView task using web UI at                                          \n",
-       "\u001B[2;36m             \u001B[0m\u001B]8;id=625663;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001B\\\u001B[32m'https://tidy3d.simulation.cloud/workbench?\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=384394;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001B\\\u001B[32mtaskId\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=625663;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001B\\\u001B[32m=\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=87714;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001B\\\u001B[32mfdve\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=625663;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001B\\\u001B[32m-a0b394b0-5bb\u001B[0m\u001B]8;;\u001B\\\n",
-       "\u001B[2;36m             \u001B[0m\u001B]8;id=625663;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001B\\\u001B[32m4-4314-adbc-007c20c63855v1'\u001B[0m\u001B]8;;\u001B\\.                                       \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=625663;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=384394;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=625663;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=87714;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=625663;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001b\\\u001b[32m-a0b394b0-5bb\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=625663;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001b\\\u001b[32m4-4314-adbc-007c20c63855v1'\u001b[0m\u001b]8;;\u001b\\.                                       \n"
       ]
      },
      "metadata": {},
@@ -875,7 +871,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:35:25 PDT\u001B[0m\u001B[2;36m \u001B[0mstatus = queued                                                    \n"
+       "\u001b[2;36m15:35:25 PDT\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
       ]
      },
      "metadata": {},
@@ -902,7 +898,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:35:34 PDT\u001B[0m\u001B[2;36m \u001B[0mstatus = preprocess                                                \n"
+       "\u001b[2;36m15:35:34 PDT\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
       ]
      },
      "metadata": {},
@@ -926,8 +922,8 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:35:40 PDT\u001B[0m\u001B[2;36m \u001B[0mMaximum FlexCredit cost: \u001B[1;36m0.025\u001B[0m. Use \u001B[32m'web.real_cost\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m to get\n",
-       "\u001B[2;36m             \u001B[0mthe billed FlexCredit cost after a simulation run.                 \n"
+       "\u001b[2;36m15:35:40 PDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get\n",
+       "\u001b[2;36m             \u001b[0mthe billed FlexCredit cost after a simulation run.                 \n"
       ]
      },
      "metadata": {},
@@ -940,7 +936,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mstarting up solver                                                 \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
       ]
      },
      "metadata": {},
@@ -953,7 +949,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mrunning solver                                                     \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
       ]
      },
      "metadata": {},
@@ -969,10 +965,10 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mTo cancel the simulation, use \u001B[32m'web.abort\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m or              \n",
-       "\u001B[2;36m             \u001B[0m\u001B[32m'web.delete\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m or abort/delete the task in the web UI.      \n",
-       "\u001B[2;36m             \u001B[0mTerminating the Python script will not stop the job running on the \n",
-       "\u001B[2;36m             \u001B[0mcloud.                                                             \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
+       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
+       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
       ]
      },
      "metadata": {},
@@ -999,7 +995,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:35:48 PDT\u001B[0m\u001B[2;36m \u001B[0mearly shutoff detected at \u001B[1;36m16\u001B[0m%, exiting.                            \n"
+       "\u001b[2;36m15:35:48 PDT\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m16\u001b[0m%, exiting.                            \n"
       ]
      },
      "metadata": {},
@@ -1035,7 +1031,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mstatus = postprocess                                               \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
       ]
      },
      "metadata": {},
@@ -1062,7 +1058,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:35:51 PDT\u001B[0m\u001B[2;36m \u001B[0mstatus = success                                                   \n"
+       "\u001b[2;36m15:35:51 PDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
       ]
      },
      "metadata": {},
@@ -1087,9 +1083,9 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mView simulation result at                                          \n",
-       "\u001B[2;36m             \u001B[0m\u001B]8;id=48449;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001B\\\u001B[4;34m'https://tidy3d.simulation.cloud/workbench?\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=477427;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001B\\\u001B[4;34mtaskId\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=48449;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001B\\\u001B[4;34m=\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=713954;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001B\\\u001B[4;34mfdve\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=48449;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001B\\\u001B[4;34m-a0b394b0-5bb\u001B[0m\u001B]8;;\u001B\\\n",
-       "\u001B[2;36m             \u001B[0m\u001B]8;id=48449;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001B\\\u001B[4;34m4-4314-adbc-007c20c63855v1'\u001B[0m\u001B]8;;\u001B\\\u001B[4;34m.\u001B[0m                                       \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=48449;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=477427;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=48449;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=713954;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=48449;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001b\\\u001b[4;34m-a0b394b0-5bb\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=48449;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a0b394b0-5bb4-4314-adbc-007c20c63855v1\u001b\\\u001b[4;34m4-4314-adbc-007c20c63855v1'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                       \n"
       ]
      },
      "metadata": {},
@@ -1139,7 +1135,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:35:52 PDT\u001B[0m\u001B[2;36m \u001B[0mloading simulation from simulation_data.hdf5                       \n"
+       "\u001b[2;36m15:35:52 PDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
       ]
      },
      "metadata": {},
@@ -1154,9 +1150,9 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0m\u001B[31mWARNING: monitors\u001B[0m\u001B[1;31m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;31m]\u001B[0m\u001B[31m contains frequencies outside of the simulation\u001B[0m\n",
-       "\u001B[2;36m             \u001B[0m\u001B[31mfrequency range \u001B[0m\u001B[1;31m(\u001B[0m\u001B[1;36m2.878008e+14\u001B[0m\u001B[31m, \u001B[0m\u001B[1;36m3.117842e+14\u001B[0m\u001B[1;31m)\u001B[0m\u001B[1;31m(\u001B[0m\u001B[31mHz\u001B[0m\u001B[1;31m)\u001B[0m\u001B[31m as defined by the \u001B[0m\n",
-       "\u001B[2;36m             \u001B[0m\u001B[31msources.                                                           \u001B[0m\n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: monitors\u001b[0m\u001b[1;31m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;31m]\u001b[0m\u001b[31m contains frequencies outside of the simulation\u001b[0m\n",
+       "\u001b[2;36m             \u001b[0m\u001b[31mfrequency range \u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;36m2.878008e+14\u001b[0m\u001b[31m, \u001b[0m\u001b[1;36m3.117842e+14\u001b[0m\u001b[1;31m)\u001b[0m\u001b[1;31m(\u001b[0m\u001b[31mHz\u001b[0m\u001b[1;31m)\u001b[0m\u001b[31m as defined by the \u001b[0m\n",
+       "\u001b[2;36m             \u001b[0m\u001b[31msources.                                                           \u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -1165,7 +1161,7 @@
    ],
    "source": [
     "sim_data_unmodulated = web.run(sim_unmodulated, task_name=\"unmodulated\", verbose=True)\n",
-    "sim_data_modulated = web.run(sim_modulated, task_name=\"modulated\", verbose=True)\n"
+    "sim_data_modulated = web.run(sim_modulated, task_name=\"modulated\", verbose=True)"
    ]
   },
   {
@@ -1186,7 +1182,7 @@
    "outputs": [],
    "source": [
     "photon_number_unmodulated = sim_data_unmodulated[\"flux\"].flux / monitor_freqs\n",
-    "photon_number_modulated = sim_data_modulated[\"flux\"].flux / monitor_freqs\n"
+    "photon_number_modulated = sim_data_modulated[\"flux\"].flux / monitor_freqs"
    ]
   },
   {
@@ -1215,7 +1211,7 @@
     "ax[1].set_ylabel(\"Photon number (a.u.)\")\n",
     "ax[1].set_title(\"Modulated\")\n",
     "ax[1].set_xlabel(\"Frequency (freq0)\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1223,7 +1219,7 @@
    "id": "1cd383f1-ffb5-45a8-893e-16b9ae50ec08",
    "metadata": {},
    "source": [
-    "The 1st sideband is clearly visible. They are offsetted from the source frequency by the modulation frequency. The 2nd sideband is weak, but still visible.\n",
+    "The 1st sideband is clearly visible. They are offset from the source frequency by the modulation frequency. The 2nd sideband is weak, but still visible.\n",
     "\n",
     "Next, let's examine if the photon number after the modulation is conserved."
    ]
@@ -1267,7 +1263,7 @@
     "# summed photon number in the modulated system\n",
     "photon_number_sum_modulation = (\n",
     "    photon_number_peak_modulated + photon_number_sideband1 + photon_number_sideband2\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1287,9 +1283,7 @@
    "source": [
     "photon_number_difference = photon_number_sum_modulation - photon_number_peak_unmodulated\n",
     "photon_number_rel = float(photon_number_difference / photon_number_peak_unmodulated)\n",
-    "print(\n",
-    "    f\"The relative difference of photon number with modulation is {photon_number_rel}\"\n",
-    ")\n"
+    "print(f\"The relative difference of photon number with modulation is {photon_number_rel}\")"
    ]
   },
   {
@@ -1318,7 +1312,7 @@
    "outputs": [],
    "source": [
     "integrated_photon_number_unmodulated = float(photon_number_unmodulated.integrate(\"f\"))\n",
-    "integrated_photon_number_modulated = float(photon_number_modulated.integrate(\"f\"))\n"
+    "integrated_photon_number_modulated = float(photon_number_modulated.integrate(\"f\"))"
    ]
   },
   {
@@ -1336,15 +1330,9 @@
     }
    ],
    "source": [
-    "photon_number_difference = (\n",
-    "    integrated_photon_number_modulated - integrated_photon_number_unmodulated\n",
-    ")\n",
-    "photon_number_rel = float(\n",
-    "    photon_number_difference / integrated_photon_number_unmodulated\n",
-    ")\n",
-    "print(\n",
-    "    f\"The relative difference of photon number with modulation is {photon_number_rel}\"\n",
-    ")\n"
+    "photon_number_difference = integrated_photon_number_modulated - integrated_photon_number_unmodulated\n",
+    "photon_number_rel = float(photon_number_difference / integrated_photon_number_unmodulated)\n",
+    "print(f\"The relative difference of photon number with modulation is {photon_number_rel}\")"
    ]
   },
   {
@@ -1367,7 +1355,7 @@
     "$$\\delta(y, t) = A \\cos(-2\\pi ft + q y)$$\n",
     "Where $f$ is the modulation frequency, and $q$ the wavevector.\n",
     "\n",
-    "Below, as an example, we consider the wavevvector $q=\\frac{2\\pi}{\\lambda_0}$, where $\\lambda_0$ is the wavelength corresponding to source central frequency. With phase-matching condition, the wavevector along $y$-direction in the 1st sideband will take values $\\pm q$. Let's do a quick qualitative analysis of what is expected in the transmission spectrum regarding the 1st sideband:\n",
+    "Below, as an example, we consider the wavevector $q=\\frac{2\\pi}{\\lambda_0}$, where $\\lambda_0$ is the wavelength corresponding to source central frequency. With phase-matching condition, the wavevector along $y$-direction in the 1st sideband will take values $\\pm q$. Let's do a quick qualitative analysis of what is expected in the transmission spectrum regarding the 1st sideband:\n",
     "\n",
     "- The frequency of the lower branch is $f_0 - f$, where $f_0$ is the source central frequency. However, $\\frac{c}{2\\pi}|q| > (f_0-f)$, so the wave is evanescent and is confined to the slab. The transmission at this frequency is expected to be the same as the unmodulated medium;\n",
     "- The frequency of the upper branch is $f_0 + f$. Since $\\frac{c}{2\\pi}|q| < (f_0+f)$, the wave is propagating. It is expected to see up-converted photons in the transmission spectrum.\n",
@@ -1383,10 +1371,8 @@
    "outputs": [],
    "source": [
     "# let's consider passive dispersive medium described by Sellmeier model\n",
-    "mat_passive = td.Sellmeier.from_dispersion(\n",
-    "    n=np.sqrt(permittivity), freq=freq0, dn_dwvl=-0.2\n",
-    ")\n",
-    "unmodulated_slab = unmodulated_slab.updated_copy(medium=mat_passive)\n"
+    "mat_passive = td.Sellmeier.from_dispersion(n=np.sqrt(permittivity), freq=freq0, dn_dwvl=-0.2)\n",
+    "unmodulated_slab = unmodulated_slab.updated_copy(medium=mat_passive)"
    ]
   },
   {
@@ -1415,7 +1401,7 @@
     "\n",
     "phase = td.SpatialDataArray(2 * np.pi * ygrid / Ly, coords=coords)\n",
     "\n",
-    "space_modulation = td.SpaceModulation(amplitude=modulation_amplitude, phase=phase)\n"
+    "space_modulation = td.SpaceModulation(amplitude=modulation_amplitude, phase=phase)"
    ]
   },
   {
@@ -1436,7 +1422,7 @@
     "modulation = modulation.updated_copy(space_modulation=space_modulation)\n",
     "modulation_spec = td.ModulationSpec(permittivity=modulation)\n",
     "mat_modulated = mat_passive.updated_copy(modulation_spec=modulation_spec)\n",
-    "modulated_slab = modulated_slab.updated_copy(medium=mat_modulated)\n"
+    "modulated_slab = modulated_slab.updated_copy(medium=mat_modulated)"
    ]
   },
   {
@@ -1462,9 +1448,9 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0m\u001B[31mWARNING: monitors\u001B[0m\u001B[1;31m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;31m]\u001B[0m\u001B[31m contains frequencies outside of the simulation\u001B[0m\n",
-       "\u001B[2;36m             \u001B[0m\u001B[31mfrequency range \u001B[0m\u001B[1;31m(\u001B[0m\u001B[1;36m2.878008e+14\u001B[0m\u001B[31m, \u001B[0m\u001B[1;36m3.117842e+14\u001B[0m\u001B[1;31m)\u001B[0m\u001B[1;31m(\u001B[0m\u001B[31mHz\u001B[0m\u001B[1;31m)\u001B[0m\u001B[31m as defined by the \u001B[0m\n",
-       "\u001B[2;36m             \u001B[0m\u001B[31msources.                                                           \u001B[0m\n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: monitors\u001b[0m\u001b[1;31m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;31m]\u001b[0m\u001b[31m contains frequencies outside of the simulation\u001b[0m\n",
+       "\u001b[2;36m             \u001b[0m\u001b[31mfrequency range \u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;36m2.878008e+14\u001b[0m\u001b[31m, \u001b[0m\u001b[1;36m3.117842e+14\u001b[0m\u001b[1;31m)\u001b[0m\u001b[1;31m(\u001b[0m\u001b[31mHz\u001b[0m\u001b[1;31m)\u001b[0m\u001b[31m as defined by the \u001b[0m\n",
+       "\u001b[2;36m             \u001b[0m\u001b[31msources.                                                           \u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -1479,9 +1465,9 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0m\u001B[31mWARNING: monitors\u001B[0m\u001B[1;31m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;31m]\u001B[0m\u001B[31m contains frequencies outside of the simulation\u001B[0m\n",
-       "\u001B[2;36m             \u001B[0m\u001B[31mfrequency range \u001B[0m\u001B[1;31m(\u001B[0m\u001B[1;36m2.878008e+14\u001B[0m\u001B[31m, \u001B[0m\u001B[1;36m3.117842e+14\u001B[0m\u001B[1;31m)\u001B[0m\u001B[1;31m(\u001B[0m\u001B[31mHz\u001B[0m\u001B[1;31m)\u001B[0m\u001B[31m as defined by the \u001B[0m\n",
-       "\u001B[2;36m             \u001B[0m\u001B[31msources.                                                           \u001B[0m\n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: monitors\u001b[0m\u001b[1;31m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;31m]\u001b[0m\u001b[31m contains frequencies outside of the simulation\u001b[0m\n",
+       "\u001b[2;36m             \u001b[0m\u001b[31mfrequency range \u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;36m2.878008e+14\u001b[0m\u001b[31m, \u001b[0m\u001b[1;36m3.117842e+14\u001b[0m\u001b[1;31m)\u001b[0m\u001b[1;31m(\u001b[0m\u001b[31mHz\u001b[0m\u001b[1;31m)\u001b[0m\u001b[31m as defined by the \u001b[0m\n",
+       "\u001b[2;36m             \u001b[0m\u001b[31msources.                                                           \u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -1490,7 +1476,7 @@
    ],
    "source": [
     "sim_unmodulated = sim_unmodulated.updated_copy(structures=[unmodulated_slab])\n",
-    "sim_modulated = sim_modulated.updated_copy(structures=[modulated_slab])\n"
+    "sim_modulated = sim_modulated.updated_copy(structures=[modulated_slab])"
    ]
   },
   {
@@ -1509,8 +1495,8 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:35:53 PDT\u001B[0m\u001B[2;36m \u001B[0mCreated task \u001B[32m'unmodulated_sellmeier'\u001B[0m with task_id                  \n",
-       "\u001B[2;36m             \u001B[0m\u001B[32m'fdve-24b5af63-da83-4464-9c80-1271190025e1v1'\u001B[0m and task_type \u001B[32m'FDTD'\u001B[0m.\n"
+       "\u001b[2;36m15:35:53 PDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'unmodulated_sellmeier'\u001b[0m with task_id                  \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-24b5af63-da83-4464-9c80-1271190025e1v1'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.\n"
       ]
      },
      "metadata": {},
@@ -1525,9 +1511,9 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mView task using web UI at                                          \n",
-       "\u001B[2;36m             \u001B[0m\u001B]8;id=79961;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001B\\\u001B[32m'https://tidy3d.simulation.cloud/workbench?\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=178911;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001B\\\u001B[32mtaskId\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=79961;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001B\\\u001B[32m=\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=209127;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001B\\\u001B[32mfdve\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=79961;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001B\\\u001B[32m-24b5af63-da8\u001B[0m\u001B]8;;\u001B\\\n",
-       "\u001B[2;36m             \u001B[0m\u001B]8;id=79961;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001B\\\u001B[32m3-4464-9c80-1271190025e1v1'\u001B[0m\u001B]8;;\u001B\\.                                       \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=79961;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=178911;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=79961;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=209127;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=79961;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001b\\\u001b[32m-24b5af63-da8\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=79961;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001b\\\u001b[32m3-4464-9c80-1271190025e1v1'\u001b[0m\u001b]8;;\u001b\\.                                       \n"
       ]
      },
      "metadata": {},
@@ -1577,7 +1563,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mstatus = queued                                                    \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
       ]
      },
      "metadata": {},
@@ -1604,7 +1590,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:36:02 PDT\u001B[0m\u001B[2;36m \u001B[0mstatus = preprocess                                                \n"
+       "\u001b[2;36m15:36:02 PDT\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
       ]
      },
      "metadata": {},
@@ -1628,8 +1614,8 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:36:06 PDT\u001B[0m\u001B[2;36m \u001B[0mMaximum FlexCredit cost: \u001B[1;36m0.025\u001B[0m. Use \u001B[32m'web.real_cost\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m to get\n",
-       "\u001B[2;36m             \u001B[0mthe billed FlexCredit cost after a simulation run.                 \n"
+       "\u001b[2;36m15:36:06 PDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get\n",
+       "\u001b[2;36m             \u001b[0mthe billed FlexCredit cost after a simulation run.                 \n"
       ]
      },
      "metadata": {},
@@ -1642,7 +1628,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mstarting up solver                                                 \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
       ]
      },
      "metadata": {},
@@ -1655,7 +1641,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mrunning solver                                                     \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
       ]
      },
      "metadata": {},
@@ -1671,10 +1657,10 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mTo cancel the simulation, use \u001B[32m'web.abort\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m or              \n",
-       "\u001B[2;36m             \u001B[0m\u001B[32m'web.delete\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m or abort/delete the task in the web UI.      \n",
-       "\u001B[2;36m             \u001B[0mTerminating the Python script will not stop the job running on the \n",
-       "\u001B[2;36m             \u001B[0mcloud.                                                             \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
+       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
+       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
       ]
      },
      "metadata": {},
@@ -1701,7 +1687,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:36:14 PDT\u001B[0m\u001B[2;36m \u001B[0mearly shutoff detected at \u001B[1;36m16\u001B[0m%, exiting.                            \n"
+       "\u001b[2;36m15:36:14 PDT\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m16\u001b[0m%, exiting.                            \n"
       ]
      },
      "metadata": {},
@@ -1737,7 +1723,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mstatus = postprocess                                               \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
       ]
      },
      "metadata": {},
@@ -1764,7 +1750,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:36:17 PDT\u001B[0m\u001B[2;36m \u001B[0mstatus = success                                                   \n"
+       "\u001b[2;36m15:36:17 PDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
       ]
      },
      "metadata": {},
@@ -1789,9 +1775,9 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:36:18 PDT\u001B[0m\u001B[2;36m \u001B[0mView simulation result at                                          \n",
-       "\u001B[2;36m             \u001B[0m\u001B]8;id=476169;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001B\\\u001B[4;34m'https://tidy3d.simulation.cloud/workbench?\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=174910;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001B\\\u001B[4;34mtaskId\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=476169;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001B\\\u001B[4;34m=\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=484794;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001B\\\u001B[4;34mfdve\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=476169;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001B\\\u001B[4;34m-24b5af63-da8\u001B[0m\u001B]8;;\u001B\\\n",
-       "\u001B[2;36m             \u001B[0m\u001B]8;id=476169;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001B\\\u001B[4;34m3-4464-9c80-1271190025e1v1'\u001B[0m\u001B]8;;\u001B\\\u001B[4;34m.\u001B[0m                                       \n"
+       "\u001b[2;36m15:36:18 PDT\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=476169;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=174910;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=476169;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=484794;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=476169;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001b\\\u001b[4;34m-24b5af63-da8\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=476169;https://tidy3d.simulation.cloud/workbench?taskId=fdve-24b5af63-da83-4464-9c80-1271190025e1v1\u001b\\\u001b[4;34m3-4464-9c80-1271190025e1v1'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                       \n"
       ]
      },
      "metadata": {},
@@ -1841,7 +1827,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mloading simulation from simulation_data.hdf5                       \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
       ]
      },
      "metadata": {},
@@ -1856,9 +1842,9 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0m\u001B[31mWARNING: monitors\u001B[0m\u001B[1;31m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;31m]\u001B[0m\u001B[31m contains frequencies outside of the simulation\u001B[0m\n",
-       "\u001B[2;36m             \u001B[0m\u001B[31mfrequency range \u001B[0m\u001B[1;31m(\u001B[0m\u001B[1;36m2.878008e+14\u001B[0m\u001B[31m, \u001B[0m\u001B[1;36m3.117842e+14\u001B[0m\u001B[1;31m)\u001B[0m\u001B[1;31m(\u001B[0m\u001B[31mHz\u001B[0m\u001B[1;31m)\u001B[0m\u001B[31m as defined by the \u001B[0m\n",
-       "\u001B[2;36m             \u001B[0m\u001B[31msources.                                                           \u001B[0m\n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: monitors\u001b[0m\u001b[1;31m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;31m]\u001b[0m\u001b[31m contains frequencies outside of the simulation\u001b[0m\n",
+       "\u001b[2;36m             \u001b[0m\u001b[31mfrequency range \u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;36m2.878008e+14\u001b[0m\u001b[31m, \u001b[0m\u001b[1;36m3.117842e+14\u001b[0m\u001b[1;31m)\u001b[0m\u001b[1;31m(\u001b[0m\u001b[31mHz\u001b[0m\u001b[1;31m)\u001b[0m\u001b[31m as defined by the \u001b[0m\n",
+       "\u001b[2;36m             \u001b[0m\u001b[31msources.                                                           \u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -1872,8 +1858,8 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mCreated task \u001B[32m'modulated_sellmeier'\u001B[0m with task_id                    \n",
-       "\u001B[2;36m             \u001B[0m\u001B[32m'fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1'\u001B[0m and task_type \u001B[32m'FDTD'\u001B[0m.\n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'modulated_sellmeier'\u001b[0m with task_id                    \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.\n"
       ]
      },
      "metadata": {},
@@ -1888,9 +1874,9 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mView task using web UI at                                          \n",
-       "\u001B[2;36m             \u001B[0m\u001B]8;id=938244;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001B\\\u001B[32m'https://tidy3d.simulation.cloud/workbench?\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=162297;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001B\\\u001B[32mtaskId\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=938244;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001B\\\u001B[32m=\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=960938;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001B\\\u001B[32mfdve\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=938244;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001B\\\u001B[32m-82b3a3b4-c3a\u001B[0m\u001B]8;;\u001B\\\n",
-       "\u001B[2;36m             \u001B[0m\u001B]8;id=938244;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001B\\\u001B[32m8-4c39-88e8-92b7dda67fe2v1'\u001B[0m\u001B]8;;\u001B\\.                                       \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=938244;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=162297;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=938244;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=960938;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=938244;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001b\\\u001b[32m-82b3a3b4-c3a\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=938244;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001b\\\u001b[32m8-4c39-88e8-92b7dda67fe2v1'\u001b[0m\u001b]8;;\u001b\\.                                       \n"
       ]
      },
      "metadata": {},
@@ -1940,7 +1926,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:36:19 PDT\u001B[0m\u001B[2;36m \u001B[0mstatus = queued                                                    \n"
+       "\u001b[2;36m15:36:19 PDT\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
       ]
      },
      "metadata": {},
@@ -1967,7 +1953,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:36:29 PDT\u001B[0m\u001B[2;36m \u001B[0mstatus = preprocess                                                \n"
+       "\u001b[2;36m15:36:29 PDT\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
       ]
      },
      "metadata": {},
@@ -1991,8 +1977,8 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:36:35 PDT\u001B[0m\u001B[2;36m \u001B[0mMaximum FlexCredit cost: \u001B[1;36m0.025\u001B[0m. Use \u001B[32m'web.real_cost\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m to get\n",
-       "\u001B[2;36m             \u001B[0mthe billed FlexCredit cost after a simulation run.                 \n"
+       "\u001b[2;36m15:36:35 PDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get\n",
+       "\u001b[2;36m             \u001b[0mthe billed FlexCredit cost after a simulation run.                 \n"
       ]
      },
      "metadata": {},
@@ -2005,7 +1991,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mstarting up solver                                                 \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
       ]
      },
      "metadata": {},
@@ -2018,7 +2004,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mrunning solver                                                     \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
       ]
      },
      "metadata": {},
@@ -2034,10 +2020,10 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mTo cancel the simulation, use \u001B[32m'web.abort\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m or              \n",
-       "\u001B[2;36m             \u001B[0m\u001B[32m'web.delete\u001B[0m\u001B[32m(\u001B[0m\u001B[32mtask_id\u001B[0m\u001B[32m)\u001B[0m\u001B[32m'\u001B[0m or abort/delete the task in the web UI.      \n",
-       "\u001B[2;36m             \u001B[0mTerminating the Python script will not stop the job running on the \n",
-       "\u001B[2;36m             \u001B[0mcloud.                                                             \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
+       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
+       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
       ]
      },
      "metadata": {},
@@ -2064,7 +2050,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:36:47 PDT\u001B[0m\u001B[2;36m \u001B[0mearly shutoff detected at \u001B[1;36m76\u001B[0m%, exiting.                            \n"
+       "\u001b[2;36m15:36:47 PDT\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m76\u001b[0m%, exiting.                            \n"
       ]
      },
      "metadata": {},
@@ -2100,7 +2086,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mstatus = postprocess                                               \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
       ]
      },
      "metadata": {},
@@ -2127,7 +2113,7 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:36:50 PDT\u001B[0m\u001B[2;36m \u001B[0mstatus = success                                                   \n"
+       "\u001b[2;36m15:36:50 PDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
       ]
      },
      "metadata": {},
@@ -2152,9 +2138,9 @@
        "
\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0mView simulation result at                                          \n",
-       "\u001B[2;36m             \u001B[0m\u001B]8;id=959758;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001B\\\u001B[4;34m'https://tidy3d.simulation.cloud/workbench?\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=518564;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001B\\\u001B[4;34mtaskId\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=959758;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001B\\\u001B[4;34m=\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=619446;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001B\\\u001B[4;34mfdve\u001B[0m\u001B]8;;\u001B\\\u001B]8;id=959758;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001B\\\u001B[4;34m-82b3a3b4-c3a\u001B[0m\u001B]8;;\u001B\\\n",
-       "\u001B[2;36m             \u001B[0m\u001B]8;id=959758;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001B\\\u001B[4;34m8-4c39-88e8-92b7dda67fe2v1'\u001B[0m\u001B]8;;\u001B\\\u001B[4;34m.\u001B[0m                                       \n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=959758;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=518564;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=959758;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=619446;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=959758;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001b\\\u001b[4;34m-82b3a3b4-c3a\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=959758;https://tidy3d.simulation.cloud/workbench?taskId=fdve-82b3a3b4-c3a8-4c39-88e8-92b7dda67fe2v1\u001b\\\u001b[4;34m8-4c39-88e8-92b7dda67fe2v1'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                       \n"
       ]
      },
      "metadata": {},
@@ -2204,7 +2190,7 @@
        "\n"
       ],
       "text/plain": [
-       "\u001B[2;36m15:36:51 PDT\u001B[0m\u001B[2;36m \u001B[0mloading simulation from simulation_data.hdf5                       \n"
+       "\u001b[2;36m15:36:51 PDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
       ]
      },
      "metadata": {},
@@ -2219,9 +2205,9 @@
        "\n"
       ],
       "text/plain": [
-       "\u001B[2;36m            \u001B[0m\u001B[2;36m \u001B[0m\u001B[31mWARNING: monitors\u001B[0m\u001B[1;31m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;31m]\u001B[0m\u001B[31m contains frequencies outside of the simulation\u001B[0m\n",
-       "\u001B[2;36m             \u001B[0m\u001B[31mfrequency range \u001B[0m\u001B[1;31m(\u001B[0m\u001B[1;36m2.878008e+14\u001B[0m\u001B[31m, \u001B[0m\u001B[1;36m3.117842e+14\u001B[0m\u001B[1;31m)\u001B[0m\u001B[1;31m(\u001B[0m\u001B[31mHz\u001B[0m\u001B[1;31m)\u001B[0m\u001B[31m as defined by the \u001B[0m\n",
-       "\u001B[2;36m             \u001B[0m\u001B[31msources.                                                           \u001B[0m\n"
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: monitors\u001b[0m\u001b[1;31m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;31m]\u001b[0m\u001b[31m contains frequencies outside of the simulation\u001b[0m\n",
+       "\u001b[2;36m             \u001b[0m\u001b[31mfrequency range \u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;36m2.878008e+14\u001b[0m\u001b[31m, \u001b[0m\u001b[1;36m3.117842e+14\u001b[0m\u001b[1;31m)\u001b[0m\u001b[1;31m(\u001b[0m\u001b[31mHz\u001b[0m\u001b[1;31m)\u001b[0m\u001b[31m as defined by the \u001b[0m\n",
+       "\u001b[2;36m             \u001b[0m\u001b[31msources.                                                           \u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -2229,12 +2215,8 @@
     }
    ],
    "source": [
-    "sim_data_unmodulated = web.run(\n",
-    "    sim_unmodulated, task_name=\"unmodulated_sellmeier\", verbose=True\n",
-    ")\n",
-    "sim_data_modulated = web.run(\n",
-    "    sim_modulated, task_name=\"modulated_sellmeier\", verbose=True\n",
-    ")\n"
+    "sim_data_unmodulated = web.run(sim_unmodulated, task_name=\"unmodulated_sellmeier\", verbose=True)\n",
+    "sim_data_modulated = web.run(sim_modulated, task_name=\"modulated_sellmeier\", verbose=True)"
    ]
   },
   {
@@ -2254,7 +2236,7 @@
    "outputs": [],
    "source": [
     "photon_number_unmodulated = sim_data_unmodulated[\"flux\"].flux / monitor_freqs\n",
-    "photon_number_modulated = sim_data_modulated[\"flux\"].flux / monitor_freqs\n"
+    "photon_number_modulated = sim_data_modulated[\"flux\"].flux / monitor_freqs"
    ]
   },
   {
@@ -2283,7 +2265,7 @@
     "ax[1].set_ylabel(\"Photon number (a.u.)\")\n",
     "ax[1].set_title(\"Modulated\")\n",
     "ax[1].set_xlabel(\"Frequency (freq0)\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2332,7 +2314,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.11.0"
   },
   "title": "How to apply time modulation to materials in Tidy3D FDTD"
  },
diff --git a/TunableChiralMetasurface.ipynb b/TunableChiralMetasurface.ipynb
index bb9d1425..ddaa6586 100644
--- a/TunableChiralMetasurface.ipynb
+++ b/TunableChiralMetasurface.ipynb
@@ -29,12 +29,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
-    "from tidy3d.plugins.dispersion import FastDispersionFitter, AdvancedFastFitterParam"
+    "from tidy3d.plugins.dispersion import AdvancedFastFitterParam, FastDispersionFitter"
    ]
   },
   {
@@ -313,6 +312,7 @@
     "    geometry=td.Box(center=(0, 0, -t_al2o3 / 2), size=(td.inf, td.inf, t_al2o3)), medium=Al2O3\n",
     ")\n",
     "\n",
+    "\n",
     "# define a function to construct the GST layer given the state\n",
     "def make_gst_layer(state):\n",
     "    if state == \"amorphous\":\n",
@@ -457,6 +457,7 @@
     "    rmin=(-Px / 2, -Py / 2, -t_al2o3 - t_gst - lda0 / 2), rmax=(Px / 2, Py / 2, t_al + lda0 / 2)\n",
     ")\n",
     "\n",
+    "\n",
     "# define a function to construct a simulation given the polarization and GST state\n",
     "def make_sim(pol, state):\n",
     "    sim = td.Simulation(\n",
@@ -1188,7 +1189,7 @@
     "plt.plot(ldas, A_LCP_C, \"blue\", label=\"LCP_C\")\n",
     "plt.plot(ldas, A_RCP_C, \"blue\", linestyle=\"--\", label=\"RCP_C\")\n",
     "plt.ylabel(\"Absorption\")\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.legend()\n",
     "plt.show()"
    ]
@@ -1224,7 +1225,7 @@
     "plt.plot(ldas, A_LCP_C - A_RCP_C, \"blue\", label=\"C\")\n",
     "plt.title(\"Circular dichroism (CD)\")\n",
     "plt.ylabel(\"CD\")\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.legend()\n",
     "plt.show()"
    ]
diff --git a/UnstructuredData.ipynb b/UnstructuredData.ipynb
index d5f652a0..c8347a13 100644
--- a/UnstructuredData.ipynb
+++ b/UnstructuredData.ipynb
@@ -32,9 +32,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tidy3d as td\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
+    "import tidy3d as td"
    ]
   },
   {
@@ -83,8 +83,16 @@
    "outputs": [],
    "source": [
     "tet_grid_points = td.PointDataArray(\n",
-    "    [[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [1.0, 1.0, 0.0], \n",
-    "     [0.0, 0.0, 1.0], [1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n",
+    "    [\n",
+    "        [0.0, 0.0, 0.0],\n",
+    "        [1.0, 0.0, 0.0],\n",
+    "        [0.0, 1.0, 0.0],\n",
+    "        [1.0, 1.0, 0.0],\n",
+    "        [0.0, 0.0, 1.0],\n",
+    "        [1.0, 0.0, 1.0],\n",
+    "        [0.0, 1.0, 1.0],\n",
+    "        [1.0, 1.0, 1.0],\n",
+    "    ],\n",
     "    coords=dict(index=np.arange(8), axis=np.arange(3)),\n",
     ")\n",
     "\n",
@@ -286,8 +294,8 @@
    ],
    "source": [
     "_, ax = plt.subplots(1, 3, figsize=(15, 4))\n",
-    "heat_data_slice.plot(ax=ax[0], cmap = \"coolwarm\")\n",
-    "heat_data_slice.plot(ax=ax[1], grid=False, cmap = \"coolwarm\", cbar_kwargs={\"label\": \"Temperature\"})\n",
+    "heat_data_slice.plot(ax=ax[0], cmap=\"coolwarm\")\n",
+    "heat_data_slice.plot(ax=ax[1], grid=False, cmap=\"coolwarm\", cbar_kwargs={\"label\": \"Temperature\"})\n",
     "heat_data_slice.plot(ax=ax[2], field=False)\n",
     "plt.tight_layout()\n",
     "plt.show()"
@@ -324,7 +332,7 @@
     "for x_pos in [0.25, 0.5, 0.75]:\n",
     "    tri_grid_slice = tri_grid.plane_slice(axis=0, pos=x_pos)\n",
     "    assert tri_grid_slice.name == tri_grid.name\n",
-    "    tri_grid_slice.plot(ax=ax, marker='o', label=f\"Slice at x={x_pos}\")\n",
+    "    tri_grid_slice.plot(ax=ax, marker=\"o\", label=f\"Slice at x={x_pos}\")\n",
     "\n",
     "plt.legend()\n",
     "plt.show()"
@@ -360,8 +368,8 @@
     "tet_grid_line_slice2 = tet_grid.plane_slice(axis=0, pos=0.4).plane_slice(axis=1, pos=0.2)\n",
     "\n",
     "_, ax = plt.subplots(1, 1)\n",
-    "tet_grid_line_slice.plot(ax=ax, marker='o')\n",
-    "tet_grid_line_slice2.plot(ax=ax, marker='x')\n",
+    "tet_grid_line_slice.plot(ax=ax, marker=\"o\")\n",
+    "tet_grid_line_slice2.plot(ax=ax, marker=\"x\")\n",
     "plt.show()"
    ]
   },
@@ -427,7 +435,7 @@
     "    _ = tet_grid.plane_slice(axis=2, pos=2)\n",
     "except:\n",
     "    print(\"Error\")\n",
-    "    \n",
+    "\n",
     "try:\n",
     "    _ = tet_grid.line_slice(axis=1, pos=(3, 3, 3))\n",
     "except:\n",
@@ -546,14 +554,14 @@
    ],
    "source": [
     "heat_data_interpolated = heat_data.interp(\n",
-    "    x=np.linspace(0, 3, 20), \n",
+    "    x=np.linspace(0, 3, 20),\n",
     "    y=np.linspace(-2.5, 2.5, 11),\n",
     "    z=np.linspace(1, 5, 30),\n",
     "    fill_value=300,\n",
     ")\n",
     "\n",
     "heat_data_slice_interpolated = heat_data_slice.interp(\n",
-    "    x=np.linspace(0, 3, 20), \n",
+    "    x=np.linspace(0, 3, 20),\n",
     "    y=[0.01, 0.02],\n",
     "    z=np.linspace(1, 5, 30),\n",
     "    fill_value=\"extrapolate\",\n",
@@ -692,7 +700,7 @@
     }
    ],
    "source": [
-    "new_data = (tri_grid * 2 + tri_grid.abs + np.log(tri_grid)) / tri_grid ** 2\n",
+    "new_data = (tri_grid * 2 + tri_grid.abs + np.log(tri_grid)) / tri_grid**2\n",
     "new_data = new_data.rename(\"processed data\")\n",
     "new_data.plot()\n",
     "plt.show()"
@@ -722,7 +730,7 @@
     "tet_grid_loaded = td.TetrahedralGridDataset.from_vtu(\"tet_grid_test.vtu\")\n",
     "\n",
     "assert tri_grid == tri_grid_loaded\n",
-    "assert tet_grid == tet_grid_loaded\n"
+    "assert tet_grid == tet_grid_loaded"
    ]
   }
  ],
diff --git a/VizData.ipynb b/VizData.ipynb
index f1fe5116..51575d87 100644
--- a/VizData.ipynb
+++ b/VizData.ipynb
@@ -35,11 +35,10 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pylab as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n"
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -53,7 +52,7 @@
     "\n",
     "First, let's make a [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html) so we have data to plot.\n",
     "\n",
-    "We will add each possible type of monitor into the simultion to explore their output data separately."
+    "We will add each possible type of monitor into the simulation to explore their output data separately."
    ]
   },
   {
@@ -166,9 +165,7 @@
     "            name=\"field_time\",\n",
     "        ),\n",
     "        td.FluxMonitor(size=(0, 3, 3), center=(2, 0, 0), freqs=freqs, name=\"flux\"),\n",
-    "        td.FluxTimeMonitor(\n",
-    "            size=(0, 3, 3), center=(2, 0, 0), interval=10, name=\"flux_time\"\n",
-    "        ),\n",
+    "        td.FluxTimeMonitor(size=(0, 3, 3), center=(2, 0, 0), interval=10, name=\"flux_time\"),\n",
     "        td.ModeMonitor(\n",
     "            size=(0, 3, 3),\n",
     "            center=(2, 0, 0),\n",
@@ -177,7 +174,7 @@
     "            name=\"mode\",\n",
     "        ),\n",
     "    ],\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -214,7 +211,7 @@
     "    monitor_grid = simulation.discretize(monitor)\n",
     "    num_cells = np.prod(monitor_grid.num_cells)\n",
     "    monitor_size = monitor.storage_size(num_cells=num_cells, tmesh=tmesh)\n",
-    "    print(f\"monitor {monitor.name} requires {monitor_size:.2e} bytes of storage.\")\n"
+    "    print(f\"monitor {monitor.name} requires {monitor_size:.2e} bytes of storage.\")"
    ]
   },
   {
@@ -263,7 +260,7 @@
     "simulation.plot(x=0.0, ax=ax1)\n",
     "simulation.plot(y=0.01, ax=ax2)\n",
     "simulation.plot(z=0.01, ax=ax3)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -294,7 +291,7 @@
    "outputs": [],
    "source": [
     "# get rid of scatterer for normalization\n",
-    "simulation0 = simulation.copy(update=dict(structures=[simulation.structures[0]]))\n"
+    "simulation0 = simulation.copy(update=dict(structures=[simulation.structures[0]]))"
    ]
   },
   {
@@ -999,7 +996,7 @@
     "    task_name=\"scattered waveguide\",\n",
     "    path=\"data/simulation.hdf5\",\n",
     "    verbose=True,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1058,7 +1055,7 @@
     }
    ],
    "source": [
-    "print(sim_data.log)\n"
+    "print(sim_data.log)"
    ]
   },
   {
@@ -1100,7 +1097,7 @@
     }
    ],
    "source": [
-    "print(sim_data.simulation.size)\n"
+    "print(sim_data.simulation.size)"
    ]
   },
   {
@@ -1133,7 +1130,7 @@
    "source": [
     "# get the flux data from the monitor name\n",
     "flux_data = sim_data[\"flux\"].flux\n",
-    "flux_time_data = sim_data[\"flux_time\"].flux\n"
+    "flux_time_data = sim_data[\"flux_time\"].flux"
    ]
   },
   {
@@ -1561,7 +1558,7 @@
     }
    ],
    "source": [
-    "flux_data\n"
+    "flux_data"
    ]
   },
   {
@@ -1608,7 +1605,7 @@
     "# flux values (W) as numpy array\n",
     "print(f\"shape of flux dataset = {flux_data.shape}\\n.\")\n",
     "print(f\"frequencies in dataset = {flux_data.coords.values} \\n.\")\n",
-    "print(f\"flux values in dataset = {flux_data.values}\\n.\")\n"
+    "print(f\"flux values in dataset = {flux_data.values}\\n.\")"
    ]
   },
   {
@@ -1616,7 +1613,7 @@
    "id": "46e4f0da",
    "metadata": {},
    "source": [
-    "So we can see the datset stores a 1D array of flux values (Watts) and a 1D array of frequency values (Hz).\n",
+    "So we can see the dataset stores a 1D array of flux values (Watts) and a 1D array of frequency values (Hz).\n",
     "\n",
     "### Manipulating Data\n",
     "\n",
@@ -1648,7 +1645,7 @@
    "source": [
     "# Selecting data at a specific coordinate\n",
     "flux_central_freq = flux_data.sel(f=freq0)\n",
-    "print(f\"at central frequency, flux is {float(flux_central_freq):.2e} W\")\n"
+    "print(f\"at central frequency, flux is {float(flux_central_freq):.2e} W\")"
    ]
   },
   {
@@ -1676,7 +1673,7 @@
    "source": [
     "# Selecting data at a specific index\n",
     "flux_4th_freq = flux_data.isel(f=4)\n",
-    "print(f\"at 4th frequency, flux is {float(flux_4th_freq):.2e} W\")\n"
+    "print(f\"at 4th frequency, flux is {float(flux_4th_freq):.2e} W\")"
    ]
   },
   {
@@ -1704,9 +1701,7 @@
    "source": [
     "# Interpolating data at a specific coordinate\n",
     "flux_interp_freq = flux_data.interp(f=213.243523142e12)\n",
-    "print(\n",
-    "    f\"at an intermediate (not stored) frequency, flux is {float(flux_interp_freq):.2e} W\"\n",
-    ")\n"
+    "print(f\"at an intermediate (not stored) frequency, flux is {float(flux_interp_freq):.2e} W\")"
    ]
   },
   {
@@ -1724,8 +1719,8 @@
    },
    "outputs": [],
    "source": [
-    "# arithmetic opertions with numbers\n",
-    "flux_times_2_plus_1 = 2 * flux_data + 1\n"
+    "# arithmetic operations with numbers\n",
+    "flux_times_2_plus_1 = 2 * flux_data + 1"
    ]
   },
   {
@@ -1743,8 +1738,8 @@
    },
    "outputs": [],
    "source": [
-    "# operations with dataset (in this case, dividing by the normlization flux)\n",
-    "flux_transmission = flux_data / sim0_data[\"flux\"].flux\n"
+    "# operations with dataset (in this case, dividing by the normalization flux)\n",
+    "flux_transmission = flux_data / sim0_data[\"flux\"].flux"
    ]
   },
   {
@@ -1752,11 +1747,11 @@
    "id": "d86475df",
    "metadata": {},
    "source": [
-    "There are mny more things you can do with the xarray package that are not covered here, so it is best to take a look at [their documention](http://xarray.pydata.org/en/stable/) for more details.\n",
+    "There are many more things you can do with the xarray package that are not covered here, so it is best to take a look at [their documentation](http://xarray.pydata.org/en/stable/) for more details.\n",
     "\n",
     "### Plotting Data\n",
     "\n",
-    "These datasets have built in plotting methods, which can be handy for quickly producing customizable plots.\n",
+    "These datasets have built-in plotting methods, which can be handy for quickly producing customizable plots.\n",
     "\n",
     "Let's plot the flux data to get a feeling."
    ]
@@ -1805,7 +1800,7 @@
     "flux_time_data.plot(color=\"crimson\", ax=ax3)\n",
     "ax3.set_title(\"flux vs. time\")\n",
     "\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1823,9 +1818,9 @@
    "source": [
     "### Complex, Multi-dimensional Data\n",
     "\n",
-    "While the flux data is 1D and quite simple, the same approaches apply to more complex data, such as mode amplitude and field data, which can be complex-valued and muli-dimensional.\n",
+    "While the flux data is 1D and quite simple, the same approaches apply to more complex data, such as mode amplitude and field data, which can be complex-valued and multi-dimensional.\n",
     "\n",
-    "Let's use the [ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) data as an example.  We'll access the data using it's name `'mode'` in the original simulation and print out some of the metadata to examine."
+    "Let's use the [ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) data as an example.  We'll access the data using its name `'mode'` in the original simulation and print out some of the metadata to examine."
    ]
   },
   {
@@ -1855,7 +1850,7 @@
    ],
    "source": [
     "mode_data = sim_data[\"mode\"]\n",
-    "mode_data.amps.shape\n"
+    "mode_data.amps.shape"
    ]
   },
   {
@@ -1904,7 +1899,7 @@
     "ax1.set_xlim(150e12, 250e12)\n",
     "ax2.set_xlim(150e12, 250e12)\n",
     "\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1930,14 +1925,14 @@
    },
    "outputs": [],
    "source": [
-    "# sum abolute value squared of the mode amplitudes to get powers\n",
+    "# sum absolute value squared of the mode amplitudes to get powers\n",
     "mode_powers = abs(mode_data.amps) ** 2\n",
     "\n",
     "# select the powers at the central frequency only\n",
     "powers_central_freq = mode_powers.sel(f=freq0)\n",
     "\n",
     "# sum the powers over all of the mode indices\n",
-    "powers_sum_modes = powers_central_freq.sum(\"mode_index\")\n"
+    "powers_sum_modes = powers_central_freq.sum(\"mode_index\")"
    ]
   },
   {
@@ -1949,7 +1944,7 @@
     "\n",
     "Data from a [FieldMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldMonitor.html) or [FieldTimeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldTimeMonitor.html) is more complicated as it contains data from several field components.\n",
     "\n",
-    "As such, when we just grab the data by name, we dont get an xarray object, but rather a [FieldData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldData.html) container holding all of the field components.\n"
+    "As such, when we just grab the data by name, we don't get an xarray object, but rather a [FieldData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldData.html) container holding all of the field components.\n"
    ]
   },
   {
@@ -1977,7 +1972,7 @@
    "source": [
     "field_data = sim_data[\"field\"]\n",
     "field0_data = sim0_data[\"field\"]\n",
-    "print(type(field_data))\n"
+    "print(type(field_data))"
    ]
   },
   {
@@ -1987,9 +1982,9 @@
     "tags": []
    },
    "source": [
-    "The field_data object contains data for each of the field components in it's `data_dict` dictionary.\n",
+    "The field_data object contains data for each of the field components in its `data_dict` dictionary.\n",
     "\n",
-    "Let's look at what field components are contined"
+    "Let's look at what field components are contained."
    ]
   },
   {
@@ -2015,7 +2010,7 @@
     }
    ],
    "source": [
-    "print(field_data.dict().keys())\n"
+    "print(field_data.dict().keys())"
    ]
   },
   {
@@ -2646,7 +2641,7 @@
     }
    ],
    "source": [
-    "field_data.Ex\n"
+    "field_data.Ex"
    ]
   },
   {
@@ -2771,7 +2766,7 @@
     "\n",
     "# Colocate to custom coordinates\n",
     "# Note: some or all of the x/y/z coordinates can be provided\n",
-    "field_data_colocate_z = sim_data[\"field\"].colocate(z=np.linspace(-1, 1, 21))\n"
+    "field_data_colocate_z = sim_data[\"field\"].colocate(z=np.linspace(-1, 1, 21))"
    ]
   },
   {
@@ -2841,7 +2836,7 @@
    ],
    "source": [
     "field0_data_centered = sim0_data.at_centers(\"field\").interp(f=200e12)\n",
-    "field_data_centered = sim_data.at_centers(\"field\").interp(f=200e12)\n"
+    "field_data_centered = sim_data.at_centers(\"field\").interp(f=200e12)"
    ]
   },
   {
@@ -2849,7 +2844,7 @@
    "id": "8cd0d9e9",
    "metadata": {},
    "source": [
-    "All these methods return an xarray [Dataset](https://xarray.pydata.org/en/stable/generated/xarray.Dataset.html), which provies similar functionality as the [DataArray](https://xarray.pydata.org/en/stable/generated/xarray.DataArray.html) objects, but is aware of all of the field data and provides more convenience methods, as we will explore in the next section."
+    "All these methods return an xarray [Dataset](https://xarray.pydata.org/en/stable/generated/xarray.Dataset.html), which provides similar functionality as the [DataArray](https://xarray.pydata.org/en/stable/generated/xarray.DataArray.html) objects, but is aware of all of the field data and provides more convenience methods, as we will explore in the next section."
    ]
   },
   {
@@ -2880,7 +2875,7 @@
    "source": [
     "# get the field data on the y=0 plane at frequency 200THz\n",
     "Ez_data = field_data.Ez.isel(y=0).interp(f=2e14)\n",
-    "Ez0_data = field0_data.Ez.isel(y=0).interp(f=2e14)\n"
+    "Ez0_data = field0_data.Ez.isel(y=0).interp(f=2e14)"
    ]
   },
   {
@@ -2922,7 +2917,7 @@
     "ax2.set_title(\"with scatterer (real Ez)\")\n",
     "ax3.set_title(\"without scatterer (abs Ez)\")\n",
     "ax4.set_title(\"with scatterer (abs Ez)\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2973,7 +2968,7 @@
     }
    ],
    "source": [
-    "ax = sim_data.plot_field(\"field\", \"Ex\", y=0.0, f=200e12, eps_alpha=0.2)\n"
+    "ax = sim_data.plot_field(\"field\", \"Ex\", y=0.0, f=200e12, eps_alpha=0.2)"
    ]
   },
   {
@@ -3014,15 +3009,13 @@
    ],
    "source": [
     "# downsample the field data for more clear quiver plotting\n",
-    "field0_data_resampled = field0_data_centered.sel(\n",
-    "    x=slice(None, None, 7), z=slice(None, None, 7)\n",
-    ")\n",
+    "field0_data_resampled = field0_data_centered.sel(x=slice(None, None, 7), z=slice(None, None, 7))\n",
     "\n",
     "# quiver plot of \\vec{E}_{x,y}(x,y) on plane with Ez(x,y) underlying.\n",
     "f, ax = plt.subplots(figsize=(8, 5))\n",
     "field0_data_centered.isel(y=0).Ez.real.plot.imshow(x=\"x\", y=\"z\", ax=ax, robust=True)\n",
     "field0_data_resampled.isel(y=0).real.plot.quiver(\"x\", \"z\", \"Ex\", \"Ez\", ax=ax)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -3076,7 +3069,7 @@
     "time_data = field_time_data.Ez.isel(y=0).sel(t=slice(1e-14, 5e-14, 1))\n",
     "\n",
     "time_data.plot(x=\"x\", y=\"z\", col=\"t\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -3115,15 +3108,15 @@
     "Ez = Ez0_data.real.squeeze().to_numpy()\n",
     "\n",
     "# save x coordinate to a numpy array\n",
-    "x_array = Ez0_data.real.coords['x'].to_numpy()\n",
+    "x_array = Ez0_data.real.coords[\"x\"].to_numpy()\n",
     "\n",
     "# save z coordinate to a numpy array\n",
-    "z_array =  Ez0_data.real.coords['z'].to_numpy()\n",
+    "z_array = Ez0_data.real.coords[\"z\"].to_numpy()\n",
     "\n",
-    "# save the numpy arries to .csv files\n",
-    "np.savetxt('./data/Ez.csv', Ez, delimiter=',')\n",
-    "np.savetxt('./data/x.csv', x_array)\n",
-    "np.savetxt('./data/z.csv', z_array)"
+    "# save the numpy arrays to .csv files\n",
+    "np.savetxt(\"./data/Ez.csv\", Ez, delimiter=\",\")\n",
+    "np.savetxt(\"./data/x.csv\", x_array)\n",
+    "np.savetxt(\"./data/z.csv\", z_array)"
    ]
   },
   {
@@ -3149,7 +3142,7 @@
    "outputs": [],
    "source": [
     "df = time_data.to_dataframe(\"value\")\n",
-    "df.to_csv(\"./data/time_data.csv\")\n"
+    "df.to_csv(\"./data/time_data.csv\")"
    ]
   },
   {
@@ -3320,7 +3313,7 @@
     "import pandas as pd\n",
     "\n",
     "df = pd.read_csv(\"./data/time_data.csv\")\n",
-    "display(df)\n"
+    "display(df)"
    ]
   },
   {
@@ -3390,9 +3383,9 @@
     }
    ],
    "source": [
-    "fig, (ax1, ax2) = plt.subplots(1,2,tight_layout=True, figsize=(8, 4))\n",
-    "sim_data0.plot_field(field_monitor_name=\"field\", field_name='E', val='abs', f=2e14, ax=ax1)\n",
-    "sim_data.plot_field(field_monitor_name=\"field\", field_name='E', val='abs', f=2e14, ax=ax2)\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(8, 4))\n",
+    "sim_data0.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs\", f=2e14, ax=ax1)\n",
+    "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"E\", val=\"abs\", f=2e14, ax=ax2)\n",
     "plt.show()"
    ]
   },
@@ -3405,7 +3398,7 @@
    "source": [
     "## Conclusion\n",
     "\n",
-    "This hopefully gives some sense of what kind of data post processing and visualization can be done in `Tidy3D`.\n",
+    "This hopefully gives some sense of what kind of data post-processing and visualization can be done in `Tidy3D`.\n",
     "\n",
     "For more detailed information and reference, the best places to check out are\n",
     "\n",
@@ -3444,7 +3437,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.0"
   },
   "nbdime-conflicts": {
    "local_diff": [
diff --git a/VizSimulation.ipynb b/VizSimulation.ipynb
index bae6de1c..1977a233 100644
--- a/VizSimulation.ipynb
+++ b/VizSimulation.ipynb
@@ -7,9 +7,9 @@
    "source": [
     "# Visualizing geometries in Tidy3D\n",
     "\n",
-    "This notebook provides a tutorial for plotting `Tidy3D` components **before** running them, to get a sense for the geometry.\n",
+    "This notebook provides a tutorial for plotting `Tidy3D` components **before** running them to get a sense of the geometry.\n",
     "\n",
-    "We also provide a conprehensive list of other tutorials such as [how to define boundary conditions](https://www.flexcompute.com/tidy3d/examples/notebooks/BoundaryConditions/), [how to defining spatially-varying sources](https://www.flexcompute.com/tidy3d/examples/notebooks/CustomFieldSource/) and [structures](https://www.flexcompute.com/tidy3d/examples/notebooks/CustomMediumTutorial/), [how to model dispersive materials](https://www.flexcompute.com/tidy3d/examples/notebooks/Dispersion/), and [how to create FDTD animations](https://www.flexcompute.com/tidy3d/examples/notebooks/AnimationTutorial/)."
+    "We also provide a comprehensive list of other tutorials such as [how to define boundary conditions](https://www.flexcompute.com/tidy3d/examples/notebooks/BoundaryConditions/), [how to defining spatially-varying sources](https://www.flexcompute.com/tidy3d/examples/notebooks/CustomFieldSource/) and [structures](https://www.flexcompute.com/tidy3d/examples/notebooks/CustomMediumTutorial/), [how to model dispersive materials](https://www.flexcompute.com/tidy3d/examples/notebooks/Dispersion/), and [how to create FDTD animations](https://www.flexcompute.com/tidy3d/examples/notebooks/AnimationTutorial/)."
    ]
   },
   {
@@ -29,11 +29,10 @@
    "source": [
     "import matplotlib.pylab as plt\n",
     "import numpy as np\n",
-    "\n",
     "import tidy3d as td\n",
     "\n",
     "# set logging level to ERROR because we'll only create simulations for demonstration, we're not running them.\n",
-    "td.config.logging_level = \"ERROR\"\n"
+    "td.config.logging_level = \"ERROR\""
    ]
   },
   {
@@ -76,7 +75,7 @@
    "source": [
     "cylinder = td.Cylinder(center=(0, 0, 0), radius=1, length=2, axis=0)\n",
     "ax = cylinder.plot(x=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -120,7 +119,7 @@
     "    medium=td.Medium(permittivity=2.0),\n",
     ")\n",
     "\n",
-    "ax = box.plot(x=0)\n"
+    "ax = box.plot(x=0)"
    ]
   },
   {
@@ -163,7 +162,7 @@
     "# plot each axis of the plot on each subplot\n",
     "ax1 = box.plot(x=0, ax=ax1)\n",
     "ax2 = box.plot(y=0, ax=ax2)\n",
-    "ax3 = box.plot(z=0, ax=ax3)\n"
+    "ax3 = box.plot(z=0, ax=ax3)"
    ]
   },
   {
@@ -171,7 +170,7 @@
    "id": "15ac6190",
    "metadata": {},
    "source": [
-    "The `.plot()` method returns either a new axis (if ax not supplied) or the orginal axis, so you can add more objects to the plot, or edit it through the `ax` handle."
+    "The `.plot()` method returns either a new axis (if ax not supplied) or the original axis, so you can add more objects to the plot, or edit it through the `ax` handle."
    ]
   },
   {
@@ -209,7 +208,7 @@
     "ax.set_xlim(-3, 3)\n",
     "ax.set_ylim(-3, 3)\n",
     "ax.set_title('my custom title: \"just a sphere\"')\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -257,9 +256,7 @@
     "ax = box2.plot(y=0, ax=ax, facecolor=\"blueviolet\", edgecolor=\"black\", alpha=1)\n",
     "ax = box3.plot(y=0, ax=ax, facecolor=\"black\", edgecolor=\"black\", alpha=1)\n",
     "ax = box4.plot(y=0, ax=ax, facecolor=\"green\", edgecolor=\"black\", alpha=1)\n",
-    "ax = sphere.plot(\n",
-    "    y=0, ax=ax, facecolor=\"sandybrown\", edgecolor=\"black\", alpha=0.5, hatch=\"/\"\n",
-    ")\n"
+    "ax = sphere.plot(y=0, ax=ax, facecolor=\"sandybrown\", edgecolor=\"black\", alpha=0.5, hatch=\"/\")"
    ]
   },
   {
@@ -317,9 +314,7 @@
     "    ),\n",
     ")\n",
     "\n",
-    "monitor = td.FieldMonitor(\n",
-    "    center=(-L / 4, 0, 0), size=(L / 2, L, 0), freqs=[100e14], name=\"fields\"\n",
-    ")\n",
+    "monitor = td.FieldMonitor(center=(-L / 4, 0, 0), size=(L / 2, L, 0), freqs=[100e14], name=\"fields\")\n",
     "\n",
     "# make simulation from structures\n",
     "sim = td.Simulation(\n",
@@ -334,7 +329,7 @@
     "    sources=[source],\n",
     "    monitors=[monitor],\n",
     "    run_time=1e-12,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -367,7 +362,7 @@
     "\n",
     "# put the grid lines on the 2nd one\n",
     "ax2 = sim.plot(x=0, ax=ax2)\n",
-    "ax2 = sim.plot_grid(x=0, ax=ax2)\n"
+    "ax2 = sim.plot_grid(x=0, ax=ax2)"
    ]
   },
   {
@@ -404,11 +399,11 @@
    ],
    "source": [
     "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 10))\n",
-    "ax1 = sim.plot(x=0, ax=ax1, vlim=[-2,2])\n",
+    "ax1 = sim.plot(x=0, ax=ax1, vlim=[-2, 2])\n",
     "\n",
     "# put the grid lines on the 2nd one\n",
-    "ax2 = sim.plot(x=0, ax=ax2, hlim=[-1,1], vlim=[-2,2])\n",
-    "ax2 = sim.plot_grid(x=0, ax=ax2, hlim=[-1,1], vlim=[-2,2])"
+    "ax2 = sim.plot(x=0, ax=ax2, hlim=[-1, 1], vlim=[-2, 2])\n",
+    "ax2 = sim.plot_grid(x=0, ax=ax2, hlim=[-1, 1], vlim=[-2, 2])"
    ]
   },
   {
@@ -451,10 +446,9 @@
    "source": [
     "f, axes = plt.subplots(1, 3, tight_layout=True, figsize=(10, 3))\n",
     "for ax, axis in zip(axes, \"xyz\"):\n",
-    "\n",
     "    ax = sim.plot(**{axis: 0}, ax=ax)\n",
     "    ax.set_title(f\"axis={axis}, position=0.0\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -498,7 +492,7 @@
     "    for ax, pos in zip(axes_range, positions):\n",
     "        ax = sim.plot(**{axis: pos}, ax=ax)\n",
     "        ax.set_title(f\"{axis}={pos:.2f}\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -541,7 +535,7 @@
     "for ax, axis in zip(axes, \"xyz\"):\n",
     "    ax = sim.plot_eps(**{axis: pos}, ax=ax, alpha=0.98)\n",
     "    ax.set_title(f\"{axis}={pos:.2f}\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -577,7 +571,7 @@
     "    for ax, pos in zip(axes_range, positions):\n",
     "        ax = sim.plot_eps(**{axis: pos}, ax=ax, alpha=0.98)\n",
     "        ax.set_title(f\"{axis}={pos:.2f}\")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -651,14 +645,14 @@
     "# make a star structure with silver as medium\n",
     "silver_star = td.Structure(geometry=poly_star, medium=Ag)\n",
     "\n",
-    "# plot the structrue alongside the medium properties\n",
+    "# plot the structure alongside the medium properties\n",
     "freqs = np.linspace(1e14, 2e14, 101)\n",
     "position = 0.0\n",
     "axis = 2\n",
     "\n",
     "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 4))\n",
     "ax1 = silver_star.geometry.plot(z=0, edgecolor=\"black\", ax=ax1)\n",
-    "ax2 = silver_star.medium.plot(freqs=freqs, ax=ax2)\n"
+    "ax2 = silver_star.medium.plot(freqs=freqs, ax=ax2)"
    ]
   },
   {
@@ -713,7 +707,7 @@
     "\n",
     "f, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(10, 4))\n",
     "ax1 = cube_source.geometry.plot(z=0, facecolor=\"sandybrown\", edgecolor=\"black\", ax=ax1)\n",
-    "ax2 = cube_source.source_time.plot(times=times, ax=ax2)\n"
+    "ax2 = cube_source.source_time.plot(times=times, ax=ax2)"
    ]
   },
   {
@@ -760,7 +754,7 @@
     "position = 0.0\n",
     "axis = 2\n",
     "\n",
-    "ax = freq_mon.geometry.plot(z=0, facecolor=\"blueviolet\", edgecolor=\"black\")\n"
+    "ax = freq_mon.geometry.plot(z=0, facecolor=\"blueviolet\", edgecolor=\"black\")"
    ]
   },
   {
@@ -799,7 +793,7 @@
     "position = 0.0\n",
     "axis = 2\n",
     "\n",
-    "ax = time_mon.geometry.plot(z=0, facecolor=\"blueviolet\", edgecolor=\"black\")\n"
+    "ax = time_mon.geometry.plot(z=0, facecolor=\"blueviolet\", edgecolor=\"black\")"
    ]
   },
   {
@@ -888,7 +882,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.0"
   },
   "title": "Visualization of Geometries in Tidy3D | Flexcompute"
  },
diff --git a/VortexMetasurface.ipynb b/VortexMetasurface.ipynb
index 25ef3243..4b835437 100644
--- a/VortexMetasurface.ipynb
+++ b/VortexMetasurface.ipynb
@@ -45,11 +45,12 @@
    "source": [
     "# importing libraries\n",
     "\n",
-    "import tidy3d as td\n",
-    "import numpy as np\n",
-    "from tidy3d import web\n",
+    "from warnings import warn\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
-    "from warnings import warn"
+    "import numpy as np\n",
+    "import tidy3d as td\n",
+    "from tidy3d import web"
    ]
   },
   {
@@ -177,24 +178,22 @@
     "    # Create arms and cylinder\n",
     "    arm1 = td.Box(size=(length, width, thickness), center=(0, 0, 0))\n",
     "    arm2 = arm1.copy()\n",
-    "    cylinder1 = td.Cylinder(\n",
-    "        radius=width / 2, center=(0, 0, 0), axis=2, length=thickness\n",
-    "    )\n",
+    "    cylinder1 = td.Cylinder(radius=width / 2, center=(0, 0, 0), axis=2, length=thickness)\n",
     "\n",
-    "    # rotate each arm around its left corner in oposite directions\n",
+    "    # rotate each arm around its left corner in opposite directions\n",
     "    arm1 = arm1.translated(length / 2, 0, 0).rotated((delta / 2) * np.pi / 180, 2)\n",
     "    arm2 = arm2.translated(length / 2, 0, 0).rotated((-delta / 2) * np.pi / 180, 2)\n",
     "\n",
     "    antenna = cylinder1 + arm1 + arm2\n",
     "\n",
-    "    # rotate an translate the whole antenna\n",
+    "    # rotate and translate the whole antenna\n",
     "    antenna = antenna.rotated(theta * np.pi / 180, 2).translated(*center)\n",
     "\n",
     "    return antenna\n",
     "\n",
     "\n",
     "# create a dictionary to define the parameters of the anthenas in each octante\n",
-    "# to create the appropriete phase change\n",
+    "# to create the appropriate phase change\n",
     "dicNumerator = {\n",
     "    0: (90, -45, 1.1),\n",
     "    1: (90 + 45, -45, 0.9),\n",
@@ -229,7 +228,10 @@
     "fig, ax = plt.subplots(1, 8)\n",
     "for i in dicNumerator.keys():\n",
     "    vAntennaBlock((0, 0, 0), *dicNumerator[i]).plot(\n",
-    "        z=0, ax=ax[i], linewidth=0, facecolor=\"navy\"  # patches kwargs\n",
+    "        z=0,\n",
+    "        ax=ax[i],\n",
+    "        linewidth=0,\n",
+    "        facecolor=\"navy\",  # patches kwargs\n",
     "    )\n",
     "    ax[i].set_axis_off()\n",
     "    ax[i].set_title(\"\")\n",
@@ -291,20 +293,15 @@
     "\n",
     "        # condition to remove the 45 degree row in the lower half of the metasurface, for it is more similar\n",
     "        # to what was done in the experiment\n",
-    "        if (abs(angle - 45) >= 0.1) and (((posX == 0) and posY > 0) == False):\n",
+    "        if (abs(angle - 45) >= 0.1) and not (posX == 0 and posY > 0):\n",
     "            # antenna parameters for a given octant\n",
     "            armArgs = dicNumerator[octant]\n",
     "\n",
     "            # adding to the structure\n",
-    "            if (metasurface is None) == True:\n",
-    "                metasurface = vAntennaBlock(\n",
-    "                    (posX, posY, structure_z_position), *armArgs\n",
-    "                )\n",
+    "            if metasurface is None:\n",
+    "                metasurface = vAntennaBlock((posX, posY, structure_z_position), *armArgs)\n",
     "            else:\n",
-    "                metasurface += vAntennaBlock(\n",
-    "                    (posX, posY, structure_z_position), *armArgs\n",
-    "                )\n",
-    "\n",
+    "                metasurface += vAntennaBlock((posX, posY, structure_z_position), *armArgs)\n",
     "\n",
     "# define the medium e create the structure object\n",
     "medium = td.material_library[\"Au\"][\"Olmon2012crystal\"]  # built-in gold model for the\n",
@@ -423,7 +420,7 @@
     "    size=far_field_monitor.size,\n",
     "    freqs=far_field_monitor.freqs,\n",
     "    name=\"fieldMon\",\n",
-    "    colocate = False,\n",
+    "    colocate=False,\n",
     ")\n",
     "\n",
     "# creating the simulation object\n",
@@ -438,7 +435,7 @@
     "    sources=[source],\n",
     "    monitors=[far_field_monitor, field_monitor],\n",
     "    run_time=run_time,\n",
-    "    boundary_spec=td.BoundarySpec(  # all bounderies are pmls in order to simulate a finite structure\n",
+    "    boundary_spec=td.BoundarySpec(  # all boundaries are pmls in order to simulate a finite structure\n",
     "        x=td.Boundary.pml(), y=td.Boundary.pml(), z=td.Boundary.pml()\n",
     "    ),\n",
     ")"
@@ -998,11 +995,11 @@
    "source": [
     "# running the simulation\n",
     "results = web.run(\n",
-    "    simulation = sim,\n",
-    "    task_name = \"vortexMetasurface\",\n",
-    "    folder_name = \"data\",\n",
-    "    path = \"data/%s.hdf5\" % \"1\",\n",
-    "    verbose = \"True\",\n",
+    "    simulation=sim,\n",
+    "    task_name=\"vortexMetasurface\",\n",
+    "    folder_name=\"data\",\n",
+    "    path=\"data/1.hdf5\",\n",
+    "    verbose=\"True\",\n",
     ")"
    ]
   },
@@ -1228,7 +1225,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.0"
   },
   "title": "How to model a vortex metasurface using Tidy3D | Flexcompute",
   "widgets": {
diff --git a/WaveguideBendSimulator.ipynb b/WaveguideBendSimulator.ipynb
index 90dd19fd..331af1a6 100644
--- a/WaveguideBendSimulator.ipynb
+++ b/WaveguideBendSimulator.ipynb
@@ -50,14 +50,15 @@
    "source": [
     "import tkinter as tk\n",
     "from tkinter import ttk\n",
+    "\n",
+    "import gdstk\n",
     "import matplotlib.pyplot as plt\n",
-    "from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\n",
     "import numpy as np\n",
+    "import scipy.integrate as integrate\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
-    "import scipy.integrate as integrate\n",
-    "from scipy.optimize import fsolve\n",
-    "import gdstk"
+    "from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\n",
+    "from scipy.optimize import fsolve"
    ]
   },
   {
@@ -75,13 +76,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "num_freq = 30 # number of frequency points \n",
-    "num_theta = 100 # number of circular bend points\n",
-    "nm_to_um = 1e-3 # conversion factor from nm to um\n",
-    "min_steps_per_wvl = 15 # grid resolution\n",
+    "num_freq = 30  # number of frequency points\n",
+    "num_theta = 100  # number of circular bend points\n",
+    "nm_to_um = 1e-3  # conversion factor from nm to um\n",
+    "min_steps_per_wvl = 15  # grid resolution\n",
     "\n",
     "# euler bend parameters\n",
-    "R_eff = 4 \n",
+    "R_eff = 4\n",
     "A = 2.4"
    ]
   },
@@ -113,8 +114,8 @@
     "    \"GaAs\": td.material_library[\"GaAs\"][\"Palik_Lossy\"],\n",
     "}\n",
     "\n",
-    "materials = list(media.keys()) # list of materials for the combo box\n",
-    "shapes = [\"Circular\", \"Euler\"] # list of shapes for the combo box"
+    "materials = list(media.keys())  # list of materials for the combo box\n",
+    "shapes = [\"Circular\", \"Euler\"]  # list of shapes for the combo box"
    ]
   },
   {
@@ -139,6 +140,7 @@
     "    y_circle = r - r * np.cos(theta)\n",
     "    return x_circle, y_circle\n",
     "\n",
+    "\n",
     "# function to calculate the coordinates for an Euler bend\n",
     "def euler_xy(r):\n",
     "    L_max = 0  # starting point of L_max\n",
@@ -154,12 +156,8 @@
     "\n",
     "        # compute x1 and y1 using the above integral equations\n",
     "        for i, L in enumerate(Ls):\n",
-    "            y1[i], err = integrate.quad(\n",
-    "                lambda theta: A * np.sin(theta**2 / 2), 0, L / A\n",
-    "            )\n",
-    "            x1[i], err = integrate.quad(\n",
-    "                lambda theta: A * np.cos(theta**2 / 2), 0, L / A\n",
-    "            )\n",
+    "            y1[i], err = integrate.quad(lambda theta: A * np.sin(theta**2 / 2), 0, L / A)\n",
+    "            x1[i], err = integrate.quad(lambda theta: A * np.cos(theta**2 / 2), 0, L / A)\n",
     "\n",
     "        # compute the derivative at L_max\n",
     "        k = -(x1[-1] - x1[-2]) / (y1[-1] - y1[-2])\n",
@@ -183,7 +181,7 @@
     "    def circle(var):\n",
     "        a = var[0]\n",
     "        b = var[1]\n",
-    "        Func = np.empty((2))\n",
+    "        Func = np.empty(2)\n",
     "        Func[0] = (xp - a) ** 2 + (yp - b) ** 2 - R_min**2\n",
     "        Func[1] = (R_eff - yp - a) ** 2 + (R_eff - xp - b) ** 2 - R_min**2\n",
     "        return Func\n",
@@ -259,9 +257,10 @@
     "def get_ldas():\n",
     "    lda0 = float(lda0_box.get()) * nm_to_um\n",
     "    bw = float(bw_box.get()) * nm_to_um\n",
-    "    ldas = np.linspace(lda0 - 0.5 * bw, lda0 + 0.5 * bw, num_freq)  \n",
+    "    ldas = np.linspace(lda0 - 0.5 * bw, lda0 + 0.5 * bw, num_freq)\n",
     "    return lda0, ldas\n",
     "\n",
+    "\n",
     "# function to define the bend simulation\n",
     "def make_sim():\n",
     "    # get user input values from the gui\n",
@@ -272,7 +271,7 @@
     "    t = float(thickness_box.get()) * nm_to_um\n",
     "    w = float(width_box.get()) * nm_to_um\n",
     "    shape = shape_box.get()\n",
-    "    \n",
+    "\n",
     "    # define simulation wavelength range\n",
     "    lda0, ldas = get_ldas()\n",
     "    freq0 = td.C_0 / lda0\n",
@@ -288,9 +287,7 @@
     "    bend = line_to_structure(x, y, r, t, w, wg_mat)\n",
     "\n",
     "    sub = td.Structure(\n",
-    "        geometry=td.Box.from_bounds(\n",
-    "            rmin=(-2 * r, -2 * r, -2 * r), rmax=(2 * r, 2 * r, 0)\n",
-    "        ),\n",
+    "        geometry=td.Box.from_bounds(rmin=(-2 * r, -2 * r, -2 * r), rmax=(2 * r, 2 * r, 0)),\n",
     "        medium=media[sub_mat],\n",
     "    )\n",
     "\n",
@@ -326,9 +323,7 @@
     "        sources=[mode_source],\n",
     "        monitors=[mode_monitor],\n",
     "        run_time=run_time,\n",
-    "        boundary_spec=td.BoundarySpec.all_sides(\n",
-    "            boundary=td.PML()\n",
-    "        ),\n",
+    "        boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
     "        medium=media[clad_mat],\n",
     "    )\n",
     "    return sim"
@@ -353,7 +348,7 @@
     "def plot_sim():\n",
     "    t = float(thickness_box.get()) * nm_to_um\n",
     "    w = float(width_box.get()) * nm_to_um\n",
-    "    \n",
+    "\n",
     "    sim = make_sim()\n",
     "    ax1.clear()\n",
     "    sim.plot(z=t / 2, ax=ax1)\n",
@@ -366,6 +361,7 @@
     "    ax3.set_ylim(-2 * t, 2 * t)\n",
     "    yz_canvas.draw()\n",
     "\n",
+    "\n",
     "# function to run the simulation\n",
     "def run_sim():\n",
     "    ax2.clear()\n",
@@ -377,7 +373,7 @@
     "        \"View your job in the web GUI at \"\n",
     "        + f\"tidy3d.simulation.cloud/workbench?taskId={job.task_id}.\\n\",\n",
     "    )\n",
-    "    text_widget.insert(\"end\", \"Simulation started. Please wait.\\n\") \n",
+    "    text_widget.insert(\"end\", \"Simulation started. Please wait.\\n\")\n",
     "    text_widget.insert(\"end\", \"Once completed, bending loss will be automatically plotted.\\n\")\n",
     "    text_widget.see(tk.END)\n",
     "    root.update_idletasks()\n",
@@ -387,6 +383,7 @@
     "\n",
     "    return sim_data\n",
     "\n",
+    "\n",
     "# function to plot the simulation result\n",
     "def plot_result():\n",
     "    _, ldas = get_ldas()\n",
@@ -497,21 +494,21 @@
     "xy_canvas = FigureCanvasTkAgg(fig1, master=root)\n",
     "xy_canvas_widget = xy_canvas.get_tk_widget()\n",
     "xy_canvas_widget.grid(column=0, row=6, columnspan=2)\n",
-    "plt.close(fig1) \n",
+    "plt.close(fig1)\n",
     "\n",
     "# add a plot for bending loss spectrum\n",
     "fig2, ax2 = plt.subplots(figsize=(5, 3), tight_layout=True)\n",
     "result_canvas = FigureCanvasTkAgg(fig2, master=root)\n",
     "result_canvas_widget = result_canvas.get_tk_widget()\n",
     "result_canvas_widget.grid(column=2, row=6, columnspan=2)\n",
-    "plt.close(fig2) \n",
+    "plt.close(fig2)\n",
     "\n",
     "# add a plot for yz cross section\n",
     "fig3, ax3 = plt.subplots(figsize=(3, 3), tight_layout=True)\n",
     "yz_canvas = FigureCanvasTkAgg(fig3, master=root)\n",
     "yz_canvas_widget = yz_canvas.get_tk_widget()\n",
     "yz_canvas_widget.grid(column=0, row=7, columnspan=2)\n",
-    "plt.close(fig3) \n",
+    "plt.close(fig3)\n",
     "\n",
     "# add a text box to display information\n",
     "text_widget = tk.Text(root, height=12, width=1)\n",
diff --git a/WaveguideCrossing.ipynb b/WaveguideCrossing.ipynb
index fc1dcff5..cccf70c8 100644
--- a/WaveguideCrossing.ipynb
+++ b/WaveguideCrossing.ipynb
@@ -36,12 +36,11 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
-    "from scipy.optimize import fsolve\n"
+    "from scipy.optimize import fsolve"
    ]
   },
   {
@@ -79,9 +78,7 @@
     "w_out = 1.1  # output taper width\n",
     "w_m = 0.75  # amplitude of the cos function\n",
     "l_t = 5.3  # taper length\n",
-    "inf_eff = (\n",
-    "    1000  # effective infinity used to make sure the waveguides extend into the pml\n",
-    ")\n"
+    "inf_eff = 1000  # effective infinity used to make sure the waveguides extend into the pml"
    ]
   },
   {
@@ -99,7 +96,7 @@
    "outputs": [],
    "source": [
     "si = td.Medium(permittivity=3.67**2)\n",
-    "sio2 = td.Medium(permittivity=1.45**2)\n"
+    "sio2 = td.Medium(permittivity=1.45**2)"
    ]
   },
   {
@@ -192,7 +189,7 @@
     "        rmin=(-w_in / 2, -inf_eff, -h / 2), rmax=(w_in / 2, inf_eff, h / 2)\n",
     "    ),\n",
     "    medium=si,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -306,7 +303,7 @@
     ")\n",
     "\n",
     "sim.plot(z=0)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2139,7 +2136,7 @@
    "id": "efab5310",
    "metadata": {},
    "source": [
-    "The cost is reasonaly so we can run the simulation."
+    "The cost is reasonably so we can run the simulation."
    ]
   },
   {
@@ -2419,7 +2416,7 @@
     }
    ],
    "source": [
-    "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n"
+    "sim_data = job.run(path=\"data/simulation_data.hdf5\")"
    ]
   },
   {
@@ -2468,7 +2465,7 @@
     "sim_data.plot_field(\n",
     "    field_monitor_name=\"field\", field_name=\"E\", val=\"abs^2\", f=freq0, vmin=0, vmax=3000\n",
     ")\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2510,15 +2507,15 @@
     "T_cross = sim_data[\"flux_cross\"].flux\n",
     "\n",
     "ax1.plot(ldas, 10 * np.log10(T_through))\n",
-    "ax1.set_xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "ax1.set_xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "ax1.set_ylabel(\"Insertion loss (dB)\")\n",
     "ax1.set_ylim((-0.3, 0))\n",
     "\n",
-    "ax2.plot(ldas, 10 * np.log10(T_cross/T_through))\n",
-    "ax2.set_xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "ax2.plot(ldas, 10 * np.log10(T_cross / T_through))\n",
+    "ax2.set_xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "ax2.set_ylabel(\"Crosstalk (dB)\")\n",
     "ax2.set_ylim((-30, -25))\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2553,7 +2550,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.93.9.16"
+   "version": "3.11.0"
   },
   "nbdime-conflicts": {
    "local_diff": [
diff --git a/WaveguideGratingAntenna.ipynb b/WaveguideGratingAntenna.ipynb
index 13fe5d55..ab6b440c 100644
--- a/WaveguideGratingAntenna.ipynb
+++ b/WaveguideGratingAntenna.ipynb
@@ -29,9 +29,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
     "from tidy3d.plugins.mode import ModeSolver\n",
@@ -142,7 +141,6 @@
    "outputs": [],
    "source": [
     "def cal_n_eff(p1, p2):\n",
-    "\n",
     "    # define the top waveguide\n",
     "    top_layer = td.Structure(\n",
     "        geometry=td.Box(center=(0, 0, gap / 2 + t / 2), size=(td.inf, w - 2 * p1, t)), medium=SiN\n",
@@ -228,7 +226,6 @@
    "outputs": [],
    "source": [
     "def make_sim(L_0):\n",
-    "\n",
     "    # calculate L_p\n",
     "    L_p = (lda0 + L_0 * (n_full + n_s - 2 * n_a) - L_f * n_full) / n_s\n",
     "\n",
@@ -237,7 +234,6 @@
     "    # define the grating structures\n",
     "    structures = []\n",
     "    for i in range(N):\n",
-    "\n",
     "        structures.append(\n",
     "            td.Structure(\n",
     "                geometry=td.Box(\n",
diff --git a/WaveguidePluginDemonstration.ipynb b/WaveguidePluginDemonstration.ipynb
index 0ae7629e..bac20edb 100644
--- a/WaveguidePluginDemonstration.ipynb
+++ b/WaveguidePluginDemonstration.ipynb
@@ -13,7 +13,7 @@
    "id": "b0eccebd-d6a9-446c-a37a-8dfff5656d28",
    "metadata": {},
    "source": [
-    "This notebook demosntrates the use of the waveguide plugin to quickly set-up waveguide simulations from usual geometries."
+    "This notebook demonstrates the use of the waveguide plugin to quickly set-up waveguide simulations from usual geometries."
    ]
   },
   {
@@ -31,13 +31,12 @@
    },
    "outputs": [],
    "source": [
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
+    "from matplotlib import pyplot\n",
     "from tidy3d.plugins import waveguide\n",
-    "from tidy3d.plugins.mode.web import run as run_mode_solver\n",
-    "\n",
-    "import numpy as np\n",
-    "from matplotlib import pyplot"
+    "from tidy3d.plugins.mode.web import run as run_mode_solver"
    ]
   },
   {
@@ -278,9 +277,9 @@
    "source": [
     "## FDTD Simulation\n",
     "\n",
-    "The waveguide structures can be used in Tidy3D simulations as conventinal structures.  The origin, lenght, and orientation of the waveguides can be selected at creation to fit the most common simulation configurations.\n",
+    "The waveguide structures can be used in Tidy3D simulations as conventional structures. The origin, length, and orientation of the waveguides can be selected at creation to fit the most common simulation configurations.\n",
     "\n",
-    "In the following example, we simulate the effect of directly coupling strip and rib geometries without any tapering and compare the result with the modal overlap calclulation between the two modes."
+    "In the following example, we simulate the effect of directly coupling strip and rib geometries without any tapering and compare the result with the modal overlap calculation between the two modes."
    ]
   },
   {
@@ -310,7 +309,7 @@
    "source": [
     "length = 6.0\n",
     "\n",
-    "strip, rib = [\n",
+    "strip, rib = (\n",
     "    waveguide.RectangularDielectric(\n",
     "        wavelength=np.linspace(1.5, 1.6, 11),\n",
     "        core_width=0.45,\n",
@@ -321,8 +320,9 @@
     "        length=length,\n",
     "        origin=(0.5 * length if slab else -0.5 * length, 0, 0),\n",
     "        sidewall_angle=np.pi / 12,\n",
-    "    ) for slab in (False, True)\n",
-    "]\n",
+    "    )\n",
+    "    for slab in (False, True)\n",
+    ")\n",
     "\n",
     "fig, ax = pyplot.subplots(1, 2, figsize=(10, 4), tight_layout=True)\n",
     "strip.plot_structures(x=-0.5 * length, ax=ax[0])\n",
@@ -375,7 +375,7 @@
     "\n",
     "ax.set_ylabel(\"Rib mode index\")\n",
     "ax.set_xlabel(\"Strip mode index\")\n",
-    "ax.xaxis.set_label_position('top') "
+    "ax.xaxis.set_label_position(\"top\")"
    ]
   },
   {
@@ -851,7 +851,7 @@
    "id": "220e4461-2d68-415d-adb1-5f76ec01e8c3",
    "metadata": {},
    "source": [
-    "Looking at the power transmisison, we see almost the same result as obtained from the overlap calculation:"
+    "Looking at the power transmission, we see almost the same result as obtained from the overlap calculation:"
    ]
   },
   {
@@ -882,7 +882,7 @@
    "source": [
     "rib_transmission = data[\"rib\"].amps.sel(direction=\"+\", mode_index=0)\n",
     "fig, ax = pyplot.subplots(1, 1)\n",
-    "ax.plot(rib.wavelength, 20 * np.log10(np.abs(rib_transmission.values)), '.-')\n",
+    "ax.plot(rib.wavelength, 20 * np.log10(np.abs(rib_transmission.values)), \".-\")\n",
     "ax.set(\n",
     "    xlabel=\"Wavelength (μm)\",\n",
     "    ylabel=\"Transmission (dB)\",\n",
@@ -1317,8 +1317,8 @@
     "strip_reflection = data_tm[\"strip\"].amps.sel(direction=\"-\", mode_index=1)\n",
     "\n",
     "fig, ax = pyplot.subplots(1, 1)\n",
-    "ax.plot(rib.wavelength, 20 * np.log10(np.abs(rib_transmission.values)), '.-', label=\"Transmission\")\n",
-    "ax.plot(rib.wavelength, 20 * np.log10(np.abs(strip_reflection.values)), '.-', label=\"Reflection\")\n",
+    "ax.plot(rib.wavelength, 20 * np.log10(np.abs(rib_transmission.values)), \".-\", label=\"Transmission\")\n",
+    "ax.plot(rib.wavelength, 20 * np.log10(np.abs(strip_reflection.values)), \".-\", label=\"Reflection\")\n",
     "ax.set(\n",
     "    xlabel=\"Wavelength (μm)\",\n",
     "    ylabel=\"Power (dB)\",\n",
@@ -1335,7 +1335,7 @@
    "source": [
     "## A Note about Accuracy\n",
     "\n",
-    "By default, the waveguide class uses `grid_resolution = 15`. This value is enough for quick mode solving with effective indices relatively accurate (usually within 10% of the best estimate). If higher accuracy is desired, `grid_resolution` can be increased to higher values, for example, to improve the calculation of derived values (group index, coupling length, mode losses, etc.), but for the absolute best results, the server-side mode solver should be prefered.\n",
+    "By default, the waveguide class uses `grid_resolution = 15`. This value is enough for quick mode solving with effective indices relatively accurate (usually within 10% of the best estimate). If higher accuracy is desired, `grid_resolution` can be increased to higher values, for example, to improve the calculation of derived values (group index, coupling length, mode losses, etc.), but for the absolute best results, the server-side mode solver should be preferred.\n",
     "\n",
     "The function `run_mode_solver` starts the given mode solver on the server. To access the mode solver from the waveguide plugin, we simply access its `mode_solver` property, as demonstrated below.\n",
     "\n",
@@ -1441,7 +1441,7 @@
     "    core_thickness=0.22,\n",
     "    core_medium=si,\n",
     "    clad_medium=sio2,\n",
-    "    sidewall_angle=np.pi/18,\n",
+    "    sidewall_angle=np.pi / 18,\n",
     ")\n",
     "\n",
     "grid_resolution = np.arange(15, 46, 3)\n",
@@ -1594,6 +1594,7 @@
     "    alpha = 2 * np.pi * n_complex.imag * n_complex.f / td.C_0  # µm⁻¹\n",
     "    return 1e4 * 20 * np.log10(np.e) * alpha  # dB/cm\n",
     "\n",
+    "\n",
     "print(f\"Curvature loss: {loss_dB_per_cm(bend.n_complex).item():.1f} dB/cm\")"
    ]
   },
@@ -1744,7 +1745,9 @@
     "ax[0].set_ylabel(\"Effective index\")\n",
     "ax[0].grid()\n",
     "\n",
-    "ax[1].plot(radii, [loss_dB_per_cm(data.n_complex).isel(mode_index=0).item() for data in bend_data], \".-\")\n",
+    "ax[1].plot(\n",
+    "    radii, [loss_dB_per_cm(data.n_complex).isel(mode_index=0).item() for data in bend_data], \".-\"\n",
+    ")\n",
     "ax[1].set_xlabel(\"Bend radius (μm)\")\n",
     "ax[1].set_ylabel(\"Curvature loss (dB/cm)\")\n",
     "ax[1].grid()"
@@ -1757,7 +1760,7 @@
    "source": [
     "## Coupled Waveguides\n",
     "\n",
-    "The `RectangularDielectric` class supports modeling coupled waveiguides by passing an array of core widths (and a corresponding array of gaps between adjacent cores)."
+    "The `RectangularDielectric` class supports modeling coupled waveguides by passing an array of core widths (and a corresponding array of gaps between adjacent cores)."
    ]
   },
   {
@@ -1791,7 +1794,7 @@
     "    core_thickness=0.22,\n",
     "    core_medium=si,\n",
     "    clad_medium=sio2,\n",
-    "    gap = 0.15,\n",
+    "    gap=0.15,\n",
     "    sidewall_angle=np.pi / 18,\n",
     "    mode_spec=td.ModeSpec(num_modes=4),\n",
     ")\n",
@@ -1894,7 +1897,7 @@
     "    core_thickness=0.22,\n",
     "    core_medium=si,\n",
     "    clad_medium=sio2,\n",
-    "    gap = 0.06,\n",
+    "    gap=0.06,\n",
     "    mode_spec=td.ModeSpec(num_modes=1),\n",
     ")\n",
     "\n",
@@ -1907,7 +1910,7 @@
     "\n",
     "# Plot a cross-section of the field component normal to the gap\n",
     "ey = slot.mode_solver.data.Ey\n",
-    "_ = ey.squeeze(drop=True).sel(z=0.55 * slot.core_thickness, method='nearest').real.plot(ax=ax[2])"
+    "_ = ey.squeeze(drop=True).sel(z=0.55 * slot.core_thickness, method=\"nearest\").real.plot(ax=ax[2])"
    ]
   },
   {
@@ -1925,8 +1928,8 @@
    "source": [
     "Besides using lossy materials for all waveguide regions (core, and upper and lower claddings), it is also possible to create separate medium layers along the sidewalls and top surfaces of the waveguide to model localized losses independently.\n",
     "\n",
-    "In the following models, we exagerate those regions and decrease the domain size only to show a close-up of the resulting geometry.\n",
-    "Decreasing the domain size is only advisable if we know the modes will have properly decaied to insignificant values at the domain boundaries.\n",
+    "In the following models, we exaggerate those regions and decrease the domain size only to show a close-up of the resulting geometry.\n",
+    "Decreasing the domain size is only advisable if we know the modes will have properly decayed to insignificant values at the domain boundaries.\n",
     "Also note that the colors in each plot correspond to different materials."
    ]
   },
@@ -1965,7 +1968,7 @@
     "    core_medium=si,\n",
     "    box_medium=sio2,\n",
     "    clad_medium=td.Medium(permittivity=1.0),\n",
-    "    gap = 0.15,\n",
+    "    gap=0.15,\n",
     "    sidewall_angle=np.pi / 12,\n",
     "    surface_thickness=0.03,\n",
     "    surface_medium=td.Medium.from_nk(n=3.4, k=0.008, freq=td.C_0 / 1.55),\n",
@@ -1985,7 +1988,7 @@
     "    core_medium=si,\n",
     "    box_medium=sio2,\n",
     "    clad_medium=td.Medium(permittivity=1.0),\n",
-    "    gap = 0.15,\n",
+    "    gap=0.15,\n",
     "    sidewall_angle=np.pi / 12,\n",
     "    surface_thickness=0.03,\n",
     "    surface_medium=td.Medium.from_nk(n=3.4, k=0.008, freq=td.C_0 / 1.55),\n",
@@ -2004,7 +2007,7 @@
     "    core_medium=si,\n",
     "    box_medium=sio2,\n",
     "    clad_medium=td.Medium(permittivity=1.0),\n",
-    "    gap = 0.15,\n",
+    "    gap=0.15,\n",
     "    sidewall_angle=np.pi / 12,\n",
     "    sidewall_thickness=0.05,\n",
     "    sidewall_medium=td.Medium.from_nk(n=3.2, k=0.01, freq=td.C_0 / 1.55),\n",
@@ -2024,7 +2027,7 @@
     "    core_medium=si,\n",
     "    box_medium=sio2,\n",
     "    clad_medium=td.Medium(permittivity=1.0),\n",
-    "    gap = 0.15,\n",
+    "    gap=0.15,\n",
     "    sidewall_angle=np.pi / 12,\n",
     "    sidewall_thickness=0.05,\n",
     "    sidewall_medium=td.Medium.from_nk(n=3.2, k=0.01, freq=td.C_0 / 1.55),\n",
@@ -2059,7 +2062,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.0"
   },
   "title": "Using the Waveguide Plugin in Tidy3D | Flexcompute",
   "widgets": {
diff --git a/WaveguideSizeConverter.ipynb b/WaveguideSizeConverter.ipynb
index 8ef36209..c0b70cc1 100644
--- a/WaveguideSizeConverter.ipynb
+++ b/WaveguideSizeConverter.ipynb
@@ -15,7 +15,7 @@
    "source": [
     "Note: the cost of running the entire notebook is larger than 1 FlexCredit.\n",
     "\n",
-    "It is common to have waveguide components of different widths and potentially thicknesses on a photonic integrated circuit (PIC). Therefore, having a low-loss waveguide size converter becomes a necessity. The most common and simple size converter is adiabatic waveguide tapers. However, to achieve low loss and meet the adiabatic condition, the taper inevitable needs to be very long, which is not ideal in many modern high-density PIC designs. To aleviate this shortcoming of the conventional adiabatic taper, novel designs of compact size converter have emerged. \n",
+    "It is common to have waveguide components of different widths and potentially thicknesses on a photonic integrated circuit (PIC). Therefore, having a low-loss waveguide size converter becomes a necessity. The most common and simple size converter is adiabatic waveguide tapers. However, to achieve low loss and meet the adiabatic condition, the taper inevitably needs to be very long, which is not ideal in many modern high-density PIC designs. To alleviate this shortcoming of the conventional adiabatic taper, novel designs of compact size converters have emerged. \n",
     "\n",
     "In this notebook, we aim to simulate different types of size converters and compare their performance. We first simulate linear adiabatic tapers of different lengths. Subsequently, we will demonstrate two compact designs: one based on Luneburg lens and the other based on semi-lens emerged from segment optimization. These novel designs achieve ~-0.5 dB loss while being only about 6$\\lambda_0$ in footprint. The linear adiabatic taper can only achieve similar performance while being 30$\\lambda_0$ long.\n",
     "\n",
@@ -38,12 +38,11 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
-    "\n",
     "import tidy3d.web as web\n",
-    "from tidy3d.plugins.mode import ModeSolver\n"
+    "from tidy3d.plugins.mode import ModeSolver"
    ]
   },
   {
@@ -68,7 +67,7 @@
    },
    "outputs": [],
    "source": [
-    "td.config.logging_level = \"ERROR\"\n"
+    "td.config.logging_level = \"ERROR\""
    ]
   },
   {
@@ -97,7 +96,7 @@
     "freq0 = td.C_0 / lda0  # central frequency\n",
     "ldas = np.linspace(1.5, 1.6, 101)  # wavelength range\n",
     "freqs = td.C_0 / ldas  # frequency range\n",
-    "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))  # width of the frequency distribution\n"
+    "fwidth = 0.5 * (np.max(freqs) - np.min(freqs))  # width of the frequency distribution"
    ]
   },
   {
@@ -126,7 +125,7 @@
     "si = td.Medium(permittivity=n_si**2)\n",
     "\n",
     "n_sio2 = 1.44  # silicon oxide refractive index\n",
-    "sio2 = td.Medium(permittivity=n_sio2**2)\n"
+    "sio2 = td.Medium(permittivity=n_sio2**2)"
    ]
   },
   {
@@ -170,15 +169,13 @@
     "\n",
     "# define the substrate structure\n",
     "sub = td.Structure(\n",
-    "    geometry=td.Box.from_bounds(\n",
-    "        rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, 0)\n",
-    "    ),\n",
+    "    geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, -inf_eff), rmax=(inf_eff, inf_eff, 0)),\n",
     "    medium=sio2,\n",
     ")\n",
     "\n",
+    "\n",
     "# define a function to construct the simulation given the taper length\n",
     "def linear_taper_sim(L_t):\n",
-    "\n",
     "    # vertices of the taper\n",
     "    vertices = [\n",
     "        [-inf_eff, w_in / 2],\n",
@@ -237,12 +234,10 @@
     "        sources=[mode_source],\n",
     "        monitors=[field_monitor, flux_monitor],\n",
     "        run_time=run_time,\n",
-    "        boundary_spec=td.BoundarySpec.all_sides(\n",
-    "            boundary=td.PML()\n",
-    "        ),  # pml is used in all boundaries\n",
+    "        boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),  # pml is used in all boundaries\n",
     "        symmetry=(0, -1, 0),\n",
     "    )  # a pec symmetry plane at y=0 can be used to reduce the grid points of the simulation\n",
-    "    return sim\n"
+    "    return sim"
    ]
   },
   {
@@ -283,8 +278,8 @@
     "L_ts = np.array([10, 20, 50, 100])  # taper lengths to be simulated\n",
     "sims = {f\"L_t={L_t}\": linear_taper_sim(L_t) for L_t in L_ts}  # make a simulation batch\n",
     "sim = sims[\"L_t=20\"]  # take one simulation (L_t=20) from the batch\n",
-    "sim.plot(z=t_wg / 2)  # visualize the simualtion setup\n",
-    "plt.show()\n"
+    "sim.plot(z=t_wg / 2)  # visualize the simulation setup\n",
+    "plt.show()"
    ]
   },
   {
@@ -688,7 +683,7 @@
    ],
    "source": [
     "batch = web.Batch(simulations=sims, verbose=True)\n",
-    "batch_results = batch.run(path_dir=\"data\")\n"
+    "batch_results = batch.run(path_dir=\"data\")"
    ]
   },
   {
@@ -944,14 +939,14 @@
     "for i, L_t in enumerate(L_ts):\n",
     "    sim_data = batch_results[f\"L_t={L_t}\"]\n",
     "    T = sim_data[\"flux\"].flux\n",
-    "    plt.plot(ldas, 10 * np.log10(T), label=f\"{L_t} $\\mu m$ taper\")\n",
+    "    plt.plot(ldas, 10 * np.log10(T), label=rf\"{L_t} $\\mu m$ taper\")\n",
     "\n",
     "plt.title(\"Insertion loss for different linear taper lengths\")\n",
     "plt.ylim(-10, 0)\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Insertion loss (dB)\")\n",
     "plt.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1067,7 +1062,7 @@
     "        vmax=50,\n",
     "    )\n",
     "    ax[i // 2, i % 2].set_xlim(L_t - 5 * lda0, L_t + lda0)\n",
-    "    ax[i // 2, i % 2].set_title(f\"{L_t} $\\mu m$ taper\")\n"
+    "    ax[i // 2, i % 2].set_title(rf\"{L_t} $\\mu m$ taper\")"
    ]
   },
   {
@@ -1085,11 +1080,11 @@
    "source": [
     "Besides adiabatic tapers, there are many novel size converter designs that can achieve both compactness and low loss. The first example is a taper design based on the principle of a Luneburg lens, which we will demonstrate here. \n",
     "\n",
-    "A Luneburg lens is a spherical graded index lens where its refractive index varies with positions. The most common Luneburg lens employees a refractive index profile of $n(r)=\\sqrt{2-(\\frac{r}{R})^2}$, where $r$ is the distance to the center of the lens and $R$ is the radius of the lens. The result of the graded index is that light propagating parellel to the lens will be focused on the edge of the lens.\n",
+    "A Luneburg lens is a spherical graded index lens where its refractive index varies with positions. The most common Luneburg lens employees a refractive index profile of $n(r)=\\sqrt{2-(\\frac{r}{R})^2}$, where $r$ is the distance to the center of the lens and $R$ is the radius of the lens. The result of the graded index is that light propagating parallel to the lens will be focused on the edge of the lens.\n",
     "\n",
-    "On the other hand, waveguides with different thicknesses have different effective indices. By applying the same principle of a classical Luneburg lens, we can design a spherical waveguide region where the thickness of the waveguide varies such that the effective index follows that of a Luneburg lens. Consequently, the incidet light from the input waveguide can be focused into the output waveguide and we achieve a low loss while having a small device footprint. The device design is adapted from [S Hadi Badri and M M Gilarlue 2019 J. Opt. 21 125802](https://iopscience.iop.org/article/10.1088/2040-8986/ab4fa3).\n",
+    "On the other hand, waveguides with different thicknesses have different effective indices. By applying the same principle of a classical Luneburg lens, we can design a spherical waveguide region where the thickness of the waveguide varies such that the effective index follows that of a Luneburg lens. Consequently, the incident light from the input waveguide can be focused into the output waveguide and we achieve a low loss while having a small device footprint. The device design is adapted from [S Hadi Badri and M M Gilarlue 2019 J. Opt. 21 125802](https://iopscience.iop.org/article/10.1088/2040-8986/ab4fa3).\n",
     "\n",
-    "To use this design, we first need to obtain the relationship between the effective index and the waveguide thickness. Here, we ignore the effect of finite waveguide width and only consider the effective index of a slab waveguide. This will leave room for further optimization, which is another topic that we will not cover in this example notebook. We will first use the [ModeSolver](../notebooks/ModeSolver.html) to solve for the effective index. Note that the effective index here is that of the waveguide, not to confuse with the effective index used in a 2.5D simulation. The simulation here will be performed in full 3D.\n",
+    "To use this design, we first need to obtain the relationship between the effective index and the waveguide thickness. Here, we ignore the effect of finite waveguide width and only consider the effective index of a slab waveguide. This will leave room for further optimization, which is another topic that we will not cover in this example notebook. We will first use the [ModeSolver](../notebooks/ModeSolver.html) to solve for the effective index. Note that the effective index here is that of the waveguide, not to be confused with the effective index used in a 2.5D simulation. The simulation here will be performed in full 3D.\n",
     "\n",
     "\"Schematic"
    ]
@@ -1114,9 +1109,7 @@
     "for i, t_slab in enumerate(t_slabs):\n",
     "    # build the slab\n",
     "    slab = td.Structure(\n",
-    "        geometry=td.Box.from_bounds(\n",
-    "            rmin=(-inf_eff, -inf_eff, 0), rmax=(inf_eff, inf_eff, t_slab)\n",
-    "        ),\n",
+    "        geometry=td.Box.from_bounds(rmin=(-inf_eff, -inf_eff, 0), rmax=(inf_eff, inf_eff, t_slab)),\n",
     "        medium=si,\n",
     "    )\n",
     "\n",
@@ -1150,7 +1143,7 @@
     "    mode_data = mode_solver.solve()\n",
     "\n",
     "    # add the effective index to the n_eff array\n",
-    "    n_eff[i] = np.array(mode_data.n_eff.item())\n"
+    "    n_eff[i] = np.array(mode_data.n_eff.item())"
    ]
   },
   {
@@ -1191,9 +1184,9 @@
     "plt.scatter(n_eff, t_slabs, label=\"Simulated results\")\n",
     "plt.plot(n_eff, t_fit(n_eff), label=\"Polynomial fit\")\n",
     "plt.xlabel(\"Effective index\")\n",
-    "plt.ylabel(\"Waveguide thickness ($\\mu m$)\")\n",
+    "plt.ylabel(r\"Waveguide thickness ($\\mu m$)\")\n",
     "plt.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1201,7 +1194,7 @@
    "id": "8cb3d5f6",
    "metadata": {},
    "source": [
-    "We will construct a circular Luneburg lens region where the hight of the Si layer changes gradually such that the local effective index follows $n(r)=n_{edge}\\sqrt{2-(\\frac{r}{R})^2}$, where $n_{edge}$ is the effective index at the edge of the circle. To minimize impedance mismatch at the interface between the input/output waveguides and the Luneburg lens, the edge of the Luneburg lens will have the same thickness as the waveguides."
+    "We will construct a circular Luneburg lens region where the height of the Si layer changes gradually such that the local effective index follows $n(r)=n_{edge}\\sqrt{2-(\\frac{r}{R})^2}$, where $n_{edge}$ is the effective index at the edge of the circle. To minimize impedance mismatch at the interface between the input/output waveguides and the Luneburg lens, the edge of the Luneburg lens will have the same thickness as the waveguides."
    ]
   },
   {
@@ -1239,9 +1232,9 @@
     "\n",
     "# plot the thickness profile of the luneburg lens\n",
     "plt.plot(r, t_lens)\n",
-    "plt.xlabel(\"Distance to the center of the lens ($\\mu m$)\")\n",
-    "plt.ylabel(\"Si thickness ($\\mu m$)\")\n",
-    "plt.show()\n"
+    "plt.xlabel(r\"Distance to the center of the lens ($\\mu m$)\")\n",
+    "plt.ylabel(r\"Si thickness ($\\mu m$)\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -1249,7 +1242,7 @@
    "id": "c4dc99d6",
    "metadata": {},
    "source": [
-    "From the above thickness profile, we can construct the Luneburg lens strucutre using a series of [Cylinders](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Cylinder.html?highlight=cylinder). The radius and length of each cylinder follows the above plot. "
+    "From the above thickness profile, we can construct the Luneburg lens structure using a series of [Cylinders](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Cylinder.html?highlight=cylinder). The radius and length of each cylinder follows the above plot. "
    ]
   },
   {
@@ -1270,12 +1263,10 @@
     "for i in range(len(r)):\n",
     "    lens.append(\n",
     "        td.Structure(\n",
-    "            geometry=td.Cylinder(\n",
-    "                center=(0, 0, t_lens[i] / 2), radius=r[i], length=t_lens[i]\n",
-    "            ),\n",
+    "            geometry=td.Cylinder(center=(0, 0, t_lens[i] / 2), radius=r[i], length=t_lens[i]),\n",
     "            medium=si,\n",
     "        )\n",
-    "    )\n"
+    "    )"
    ]
   },
   {
@@ -1301,17 +1292,13 @@
    "outputs": [],
    "source": [
     "wg_in = td.Structure(\n",
-    "    geometry=td.Box.from_bounds(\n",
-    "        rmin=(-inf_eff, -w_in / 2, 0), rmax=(0, w_in / 2, t_wg)\n",
-    "    ),\n",
+    "    geometry=td.Box.from_bounds(rmin=(-inf_eff, -w_in / 2, 0), rmax=(0, w_in / 2, t_wg)),\n",
     "    medium=si,\n",
     ")\n",
     "wg_out = td.Structure(\n",
-    "    geometry=td.Box.from_bounds(\n",
-    "        rmin=(0, -w_out / 2, 0), rmax=(inf_eff, w_out / 2, t_wg)\n",
-    "    ),\n",
+    "    geometry=td.Box.from_bounds(rmin=(0, -w_out / 2, 0), rmax=(inf_eff, w_out / 2, t_wg)),\n",
     "    medium=si,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1385,7 +1372,7 @@
     "\n",
     "# visualize the simulation setup\n",
     "sim.plot(z=t_wg / 4)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1764,7 +1751,7 @@
    ],
    "source": [
     "job = web.Job(simulation=sim, task_name=\"luneburg_lens_taper\", verbose=True)\n",
-    "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n"
+    "sim_data = job.run(path=\"data/simulation_data.hdf5\")"
    ]
   },
   {
@@ -1801,7 +1788,7 @@
    ],
    "source": [
     "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"Ey\", vmin=-50, vmax=50)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1855,18 +1842,18 @@
    ],
    "source": [
     "T_luneburg = sim_data[\"flux\"].flux  # transmission of the luneburg lens\n",
-    "sim_data_linear = batch_results[f\"L_t=50\"]  # simulation of the 50 um lienar taper\n",
+    "sim_data_linear = batch_results[\"L_t=50\"]  # simulation of the 50 um lienar taper\n",
     "T_linear = sim_data_linear[\"flux\"].flux  # transmission of the linear taper\n",
     "\n",
     "# plotting the insertion loss of the luneburg lens and the linear taper\n",
     "plt.plot(ldas, 10 * np.log10(T_luneburg), label=\"Luneburg lens taper\")\n",
-    "plt.plot(ldas, 10 * np.log10(T_linear), label=\"50 $\\mu m$ linear taper\")\n",
+    "plt.plot(ldas, 10 * np.log10(T_linear), label=r\"50 $\\mu m$ linear taper\")\n",
     "plt.title(\"Comparison between Luneburg lens and linear tapers\")\n",
     "plt.ylim(-3, 0)\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Insertion loss (dB)\")\n",
     "plt.legend()\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1882,7 +1869,7 @@
    "id": "54e7ae80",
    "metadata": {},
    "source": [
-    "The Luneburg lens design introduced in the previous section requires a more complicated fabrication since the thickness of the Si layer needs to gradually vary. This is not desirable in many cases. There are a number of other designs that circumvent this issue. One such example is introduced in [Siamak Abbaslou, Robert Gatdula, Ming Lu, Aaron Stein, and Wei Jiang, \"Ultra-short beam expander with segmented curvature control: the emergence of a semi-lens,\" Opt. Lett. 42, 4383-4386 (2017)](https://opg.optica.org/ol/abstract.cfm?uri=ol-42-21-4383), where a compact waveguide taper is segmented into 6 segments and oarticle swarm optimization algorithm is used to optimize the shape of each segment. The result of the optimization is a semi-lens structure that also outperforms the conventional adiabatic taper of the same length by a great margin. \n",
+    "The Luneburg lens design introduced in the previous section requires a more complicated fabrication since the thickness of the Si layer needs to vary gradually. This is not desirable in many cases. There are a number of other designs that circumvent this issue. One such example is introduced in [Siamak Abbaslou, Robert Gatdula, Ming Lu, Aaron Stein, and Wei Jiang, \"Ultra-short beam expander with segmented curvature control: the emergence of a semi-lens,\" Opt. Lett. 42, 4383-4386 (2017)](https://opg.optica.org/ol/abstract.cfm?uri=ol-42-21-4383), where a compact waveguide taper is segmented into 6 segments and the particle swarm optimization algorithm is used to optimize the shape of each segment. The result of the optimization is a semi-lens structure that also outperforms the conventional adiabatic taper of the same length by a great margin. \n",
     "\n",
     "In the previous models, the input waveguide is wider than the output waveguide. In this case, we model a beam expander so the output waveguide is 9 $\\mu m$ wide and the input waveguide is 450 nm wide. \n",
     "\n",
@@ -1912,7 +1899,7 @@
     "w_i = [w_0, 1.7, 3.3, 9.06, 10, 3.19, w_6]\n",
     "L_i = [1, 3.61, 0.05, 0.7, 3.11, 1.03]\n",
     "\n",
-    "L_tot = np.sum(L_i)  # total length of the device\n"
+    "L_tot = np.sum(L_i)  # total length of the device"
    ]
   },
   {
@@ -1920,9 +1907,9 @@
    "id": "5d0141c0",
    "metadata": {},
    "source": [
-    "The width of each segment of the device is described by $w(x_i)=(w_i-w_{i+1})|\\frac{x_i-L_i}{L_i}|^{m_i}$, where $i=1,2,...,6$, $w_i$ is the width at the begining of each segment, $L_i$ is the length of each segment, and $m_i$ is the curvature of each segment. The optimized values of $w_i$, $L_i$, and $m_i$ are listed above.\n",
+    "The width of each segment of the device is described by $w(x_i)=(w_i-w_{i+1})|\\frac{x_i-L_i}{L_i}|^{m_i}$, where $i=1,2,...,6$, $w_i$ is the width at the beginning of each segment, $L_i$ is the length of each segment, and $m_i$ is the curvature of each segment. The optimized values of $w_i$, $L_i$, and $m_i$ are listed above.\n",
     "\n",
-    "The structure of the deive will be constructed using a series of [PolySlabs](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PolySlab.html?highlight=polyslab). The vertices are calculated from the above equation and the optimized parameters."
+    "The structure of the device will be constructed using a series of [PolySlabs](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.PolySlab.html?highlight=polyslab). The vertices are calculated from the above equation and the optimized parameters."
    ]
   },
   {
@@ -1979,7 +1966,7 @@
     "# construct the semi-lens structure using the above vertices\n",
     "semi_lens = td.Structure(\n",
     "    geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(0, t_wg)), medium=si\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -1987,7 +1974,7 @@
    "id": "c08344ce",
    "metadata": {},
    "source": [
-    "The simulation will be defined in a similar way as previou simulations. Since in this model, the output waveguide supports higher order modes, we add an additional [ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) to investigate the mode composition at the output waveguide. Ideally, the fundamental TE mode should be dominant."
+    "The simulation will be defined in a similar way as previous simulations. Since in this model, the output waveguide supports higher order modes, we add an additional [ModeMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.ModeMonitor.html) to investigate the mode composition at the output waveguide. Ideally, the fundamental TE mode should be dominant."
    ]
   },
   {
@@ -2042,7 +2029,7 @@
     "    freqs=freqs,\n",
     "    mode_spec=mode_spec,\n",
     "    name=\"mode\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -2085,7 +2072,7 @@
     "\n",
     "# visualize the simulation setup\n",
     "sim.plot(z=t_wg / 2)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2466,7 +2453,7 @@
    ],
    "source": [
     "job = web.Job(simulation=sim, task_name=\"semi_lens_beam_expander\", verbose=True)\n",
-    "sim_data = job.run(path=\"data/simulation_data.hdf5\")\n"
+    "sim_data = job.run(path=\"data/simulation_data.hdf5\")"
    ]
   },
   {
@@ -2503,7 +2490,7 @@
    ],
    "source": [
     "sim_data.plot_field(field_monitor_name=\"field\", field_name=\"Ey\", vmin=-30, vmax=30)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2548,7 +2535,7 @@
     "plt.plot(ldas, 10 * np.log10(T))\n",
     "plt.xlim(1.5, 1.6)\n",
     "plt.ylim(-4, 0)\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Insertion loss (dB)\")\n",
     "\n",
     "plt.sca(ax2)\n",
@@ -2559,7 +2546,7 @@
     "plt.xlabel(\"Wavelength (nm)\")\n",
     "plt.ylabel(\"Power share at Port1 (%)\")\n",
     "plt.legend([\"TE0\", \"TE2\", \"TE4\", \"TE6\", \"TE8\"])\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -2597,7 +2584,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.0"
   },
   "title": "Waveguide Size Converter Modeling in Tidy3D | Flexcompute",
   "widgets": {
diff --git a/WaveguideToRingCoupling.ipynb b/WaveguideToRingCoupling.ipynb
index 5754beb1..6ec45e4a 100644
--- a/WaveguideToRingCoupling.ipynb
+++ b/WaveguideToRingCoupling.ipynb
@@ -33,10 +33,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "import gdstk\n",
-    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web"
    ]
@@ -1614,7 +1613,7 @@
     "plt.plot(ldas, np.abs(t) ** 2, label=\"Through port\")\n",
     "plt.plot(ldas, np.abs(k) ** 2, label=\"Drop port\")\n",
     "plt.legend()\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Transmission\")\n",
     "plt.xlim(1.5, 1.6)\n",
     "plt.ylim(0, 1)\n",
diff --git a/WebAPI.ipynb b/WebAPI.ipynb
index 142bf26a..31d7a74c 100644
--- a/WebAPI.ipynb
+++ b/WebAPI.ipynb
@@ -28,7 +28,7 @@
     "import tidy3d.web as web\n",
     "\n",
     "# set logging level to ERROR because we'll only running simulations to demonstrate the web API, we dont care about the results.\n",
-    "td.config.logging_level = \"ERROR\"\n"
+    "td.config.logging_level = \"ERROR\""
    ]
   },
   {
@@ -68,9 +68,7 @@
     "\n",
     "# create structure\n",
     "dielectric = td.Medium.from_nk(n=2, k=0, freq=freq0)\n",
-    "square = td.Structure(\n",
-    "    geometry=td.Box(center=[0, 0, 0], size=[1.5, 1.5, 1.5]), medium=dielectric\n",
-    ")\n",
+    "square = td.Structure(geometry=td.Box(center=[0, 0, 0], size=[1.5, 1.5, 1.5]), medium=dielectric)\n",
     "\n",
     "# create source\n",
     "source = td.UniformCurrentSource(\n",
@@ -98,7 +96,7 @@
     "    monitors=[monitor],\n",
     "    run_time=run_time,\n",
     "    boundary_spec=td.BoundarySpec.all_sides(boundary=pml),\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -477,7 +475,7 @@
     "web.monitor(task_id, verbose=verbose)\n",
     "\n",
     "# download the results and load into a simulation data object for plotting, post processing etc.\n",
-    "sim_data = web.load(task_id, path=\"data/sim.hdf5\", verbose=verbose)\n"
+    "sim_data = web.load(task_id, path=\"data/sim.hdf5\", verbose=verbose)"
    ]
   },
   {
@@ -503,7 +501,7 @@
    "source": [
     "## Job Container\n",
     "\n",
-    "The [web.Job](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Job.html) interface provides a more convenient way to manage single simuations, mainly because it eliminates the need for keeping track of the `task_id` and original [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html).\n",
+    "The [web.Job](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.web.api.container.Job.html) interface provides a more convenient way to manage single simulations, mainly because it eliminates the need for keeping track of the `task_id` and original [Simulation](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html).\n",
     "\n",
     "We can get the cost estimate of running the task before actually running it. This prevents us from accidentally running large jobs that we set up by mistake. The estimated cost is the maximum cost corresponding to running all the time steps."
    ]
@@ -613,7 +611,7 @@
     }
    ],
    "source": [
-    "# initializes job, puts task on server (but doesnt run it)\n",
+    "# initializes job, puts task on server (but doesn't run it)\n",
     "job = web.Job(simulation=sim, task_name=\"job\", verbose=verbose)\n",
     "\n",
     "# estimate the maximum cost\n",
@@ -999,7 +997,7 @@
     "job_loaded = web.Job.from_file(\"data/job.json\")\n",
     "\n",
     "# download the data from the server and load it into a SimulationData object.\n",
-    "sim_data = job_loaded.load(path=\"data/sim.hdf5\")\n"
+    "sim_data = job_loaded.load(path=\"data/sim.hdf5\")"
    ]
   },
   {
@@ -1344,7 +1342,7 @@
     "batch = web.Batch(simulations=sims, verbose=verbose)\n",
     "\n",
     "# run the batch and store all of the data in the `data/` dir.\n",
-    "batch_results = batch.run(path_dir=\"data\")\n"
+    "batch_results = batch.run(path_dir=\"data\")"
    ]
   },
   {
@@ -1551,7 +1549,7 @@
     "    sum_intensity = float(intensity.sum((\"x\", \"y\")).values[0])\n",
     "    intensities[task_name] = sum_intensity\n",
     "\n",
-    "print(intensities)\n"
+    "print(intensities)"
    ]
   },
   {
@@ -1889,7 +1887,7 @@
     }
    ],
    "source": [
-    "batch_results = web.run_async(simulations=sims, verbose=verbose)\n"
+    "batch_results = web.run_async(simulations=sims, verbose=verbose)"
    ]
   },
   {
@@ -2078,7 +2076,7 @@
     "    sum_intensity = float(intensity.sum((\"x\", \"y\")).values[0])\n",
     "    intensities[task_name] = sum_intensity\n",
     "\n",
-    "print(intensities)\n"
+    "print(intensities)"
    ]
   },
   {
@@ -2117,7 +2115,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.0"
   },
   "title": "Running Simulations Through the Cloud | Flexcompute",
   "widgets": {
diff --git a/WidebandBeamSteerableReflectarrayWithPRUC.ipynb b/WidebandBeamSteerableReflectarrayWithPRUC.ipynb
index ecbc38e4..043aea0b 100644
--- a/WidebandBeamSteerableReflectarrayWithPRUC.ipynb
+++ b/WidebandBeamSteerableReflectarrayWithPRUC.ipynb
@@ -42,19 +42,23 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "from typing import Union\n",
     "from copy import copy\n",
+    "from typing import Union\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
     "# additional imports to aid in geometry processing and post-processing of results\n",
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
     "import shapely as shapely\n",
-    "import xarray as xr\n",
     "\n",
     "# tidy3d imports\n",
     "import tidy3d as td\n",
+    "\n",
     "# design plugin for performing the Bayesian optimization of the PRUC\n",
     "import tidy3d.plugins.design as tdd\n",
+    "import xarray as xr\n",
+    "\n",
     "# dispersion plugin for fitting loss tangent to complex-conjugate pole-residue pair model\n",
     "from tidy3d.plugins.dispersion import FastDispersionFitter"
    ]
@@ -151,9 +155,9 @@
     "l3_t = 0.813 * mm\n",
     "structure_t = 2 * prepreg_t + l1_t + l2_t + l3_t\n",
     "# Used to define grid discretization\n",
-    "dl_xy = 0.1 * mm # resolution in the xy plane\n",
-    "dl_hole = 0.05 * mm # grid resoluion around ground plane holes\n",
-    "dl_z = 0.05 * mm # resolution along the z axis\n",
+    "dl_xy = 0.1 * mm  # resolution in the xy plane\n",
+    "dl_hole = 0.05 * mm  # grid resoluion around ground plane holes\n",
+    "dl_z = 0.05 * mm  # resolution along the z axis\n",
     "# Metals modeled as 35 microns thick\n",
     "metal_t = 0.035 * mm\n",
     "\n",
@@ -217,20 +221,20 @@
    "source": [
     "class PRUC:\n",
     "    \"\"\"Class representing a Polarization-Rotating Unit Cell (PRUC) for reflectarray antennas.\n",
-    "    \n",
+    "\n",
     "    This class defines the geometry of a unit cell containing four arrow-shaped patches\n",
-    "    connected by vias through a ground plane. The cell can be rotated 0° or 180° to \n",
+    "    connected by vias through a ground plane. The cell can be rotated 0° or 180° to\n",
     "    encode binary phase information.\n",
     "\n",
     "    Attributes\n",
     "    ----------\n",
     "    S : float\n",
     "        Size of the square unit cell aperture in microns.\n",
-    "    s : float \n",
+    "    s : float\n",
     "        Spacing between arrow patches and unit cell boundary in microns.\n",
     "    l_head : float\n",
     "        Length of the arrow head in microns.\n",
-    "    l_stem : float \n",
+    "    l_stem : float\n",
     "        Length from via center to arrow tip in microns.\n",
     "    w_stem : float\n",
     "        Width of the arrow stem in microns.\n",
@@ -382,7 +386,7 @@
     "        rotate_45 = td.Transformed.rotation(axis=2, angle=angle)\n",
     "        strip = td.Transformed(geometry=strip, transform=rotate_45)\n",
     "\n",
-    "        geo_tuple = tuple([strip])\n",
+    "        geo_tuple = (strip,)\n",
     "\n",
     "        metal_regions = td.GeometryGroup(geometries=geo_tuple)\n",
     "        return metal_regions\n",
@@ -448,7 +452,7 @@
     "        list[td.MeshOverrideStructure],\n",
     "        list[tuple[float, float, float]],\n",
     "    ]:\n",
-    "        \"\"\"Returns the geometry of the metallic portion of the unit cell, \n",
+    "        \"\"\"Returns the geometry of the metallic portion of the unit cell,\n",
     "        mesh overrides around the holes, and the vertices of the arrows.\"\"\"\n",
     "        arrows = self.make_all_arrows()\n",
     "        bottom_geometry = self.make_bottom_layer()\n",
@@ -467,9 +471,7 @@
     "\n",
     "        # Get arrow vertices for snapping points\n",
     "        arrow_verts = [\n",
-    "            (vert[0], vert[1], self.z_top)\n",
-    "            for arrow in arrows\n",
-    "            for vert in arrow.vertices\n",
+    "            (vert[0], vert[1], self.z_top) for arrow in arrows for vert in arrow.vertices\n",
     "        ]\n",
     "        return geometry_group, mesh_overrides, arrow_verts"
    ]
@@ -508,7 +510,7 @@
    "source": [
     "class PRUCArray:\n",
     "    \"\"\"Class representing an array of Polarization-Rotating Unit Cells (PRUCs).\n",
-    "    \n",
+    "\n",
     "    This class handles the construction of a reflectarray consisting of multiple PRUCs,\n",
     "    including substrate layers and mesh refinement specifications.\n",
     "\n",
@@ -637,9 +639,7 @@
     "\n",
     "        substrates = [sub1, prepreg1, sub2, prepreg2, sub3]\n",
     "\n",
-    "        patch_box = td.Box(\n",
-    "            center=(0, 0, top_l1 + metal_t / 2), size=(array_sx, array_sy, metal_t)\n",
-    "        )\n",
+    "        patch_box = td.Box(center=(0, 0, top_l1 + metal_t / 2), size=(array_sx, array_sy, metal_t))\n",
     "        ground_box = td.Box(\n",
     "            center=(0, 0, top_l3 + metal_t / 2),\n",
     "            size=(array_sx, array_sy, metal_t),\n",
@@ -702,13 +702,9 @@
     "                # Add translated geometries\n",
     "                translate = td.Transformed.translation(dx, dy, 0)\n",
     "                if get_cell_variant(i, j):\n",
-    "                    transformed_cell = td.Transformed(\n",
-    "                        geometry=geo_unit_cell_1, transform=translate\n",
-    "                    )\n",
+    "                    transformed_cell = td.Transformed(geometry=geo_unit_cell_1, transform=translate)\n",
     "                else:\n",
-    "                    transformed_cell = td.Transformed(\n",
-    "                        geometry=geo_unit_cell_0, transform=translate\n",
-    "                    )\n",
+    "                    transformed_cell = td.Transformed(geometry=geo_unit_cell_0, transform=translate)\n",
     "                unit_cell_geos.append(transformed_cell)\n",
     "\n",
     "                # Add mesh overrides\n",
@@ -782,65 +778,67 @@
     "top_l2 = top_l1 - l1_t - prepreg_t\n",
     "top_l3 = top_l2 - l2_t - prepreg_t\n",
     "bottom = top_l3 - l3_t\n",
-    "substrate_width = 2*S\n",
+    "substrate_width = 2 * S\n",
     "\n",
     "unit_cell_1 = PRUC(\n",
-    "        S = S,\n",
-    "        s = s,\n",
-    "        l_head = 2.5*mm,#l1,\n",
-    "        l_stem = 3*mm,#l2,\n",
-    "        w_stem = w_stem,\n",
-    "        w_head = w_head,\n",
-    "        w_via_pad = w_via_pad,\n",
-    "        w_cnx = w_cnx,\n",
-    "        d_via = d_via,\n",
-    "        d_hole = d_hole,\n",
-    "        metal_t = metal_t,\n",
-    "        z_top = 0.0,\n",
-    "        z_ground = top_l3,\n",
-    "        z_bottom = bottom,\n",
-    "        bit_version = True,\n",
+    "    S=S,\n",
+    "    s=s,\n",
+    "    l_head=2.5 * mm,  # l1,\n",
+    "    l_stem=3 * mm,  # l2,\n",
+    "    w_stem=w_stem,\n",
+    "    w_head=w_head,\n",
+    "    w_via_pad=w_via_pad,\n",
+    "    w_cnx=w_cnx,\n",
+    "    d_via=d_via,\n",
+    "    d_hole=d_hole,\n",
+    "    metal_t=metal_t,\n",
+    "    z_top=0.0,\n",
+    "    z_ground=top_l3,\n",
+    "    z_bottom=bottom,\n",
+    "    bit_version=True,\n",
     ")\n",
     "\n",
     "unit_cell_0 = copy(unit_cell_1)\n",
     "unit_cell_0.bit_version = False\n",
     "\n",
     "array = PRUCArray(\n",
-    "        unit_cell_0 = unit_cell_0,\n",
-    "        unit_cell_1 = unit_cell_1,\n",
-    "        Nx = 1,\n",
-    "        Ny = 1,\n",
-    "        array_sx = substrate_width, #Make array large enough that the substrates extend passed the simulation boundaries\n",
-    "        array_sy = substrate_width,\n",
-    "        prepreg_t = prepreg_t,\n",
-    "        l1_t = l1_t,\n",
-    "        l2_t = l2_t,\n",
-    "        l3_t = l3_t,\n",
-    "        substrate_medium = RO4003C,\n",
-    "        prepreg_medium = RO4450B,\n",
-    "        metal_medium = patch_metal,\n",
-    "        dl_hole = dl_hole,\n",
-    "        dl_xy = dl_xy,\n",
-    "        dl_z = dl_z,\n",
+    "    unit_cell_0=unit_cell_0,\n",
+    "    unit_cell_1=unit_cell_1,\n",
+    "    Nx=1,\n",
+    "    Ny=1,\n",
+    "    array_sx=substrate_width,  # Make array large enough that the substrates extend passed the simulation boundaries\n",
+    "    array_sy=substrate_width,\n",
+    "    prepreg_t=prepreg_t,\n",
+    "    l1_t=l1_t,\n",
+    "    l2_t=l2_t,\n",
+    "    l3_t=l3_t,\n",
+    "    substrate_medium=RO4003C,\n",
+    "    prepreg_medium=RO4450B,\n",
+    "    metal_medium=patch_metal,\n",
+    "    dl_hole=dl_hole,\n",
+    "    dl_xy=dl_xy,\n",
+    "    dl_z=dl_z,\n",
     ")\n",
     "\n",
-    "mask = np.ones((array.Nx,array.Ny), dtype=bool)\n",
-    "mask[0,0] = False\n",
+    "mask = np.ones((array.Nx, array.Ny), dtype=bool)\n",
+    "mask[0, 0] = False\n",
     "bool_array = xr.DataArray(\n",
     "    mask,  # The value (can be False too)\n",
     "    dims=[\"x\", \"y\"],  # Names of the dimensions\n",
     ")\n",
     "\n",
-    "all_structures, all_mesh_overrides, arrow_snapping_points = array.make_array(bit_mask=bool_array, ground_width=substrate_width)\n",
+    "all_structures, all_mesh_overrides, arrow_snapping_points = array.make_array(\n",
+    "    bit_mask=bool_array, ground_width=substrate_width\n",
+    ")\n",
     "\n",
     "via_center = unit_cell_1.calc_via_offset()\n",
     "scene = td.Scene(structures=all_structures, plot_length_units=\"mm\")\n",
     "fig, axs = plt.subplots(1, 2, figsize=(10, 5), tight_layout=True)\n",
-    "scene.plot(ax = axs[0], z=top_l1, hlim=[-3*mm,3*mm], vlim=[-3*mm,3*mm])\n",
-    "scene.plot(ax = axs[1], z=bottom, hlim=[-3*mm,3*mm], vlim=[-3*mm,3*mm])\n",
+    "scene.plot(ax=axs[0], z=top_l1, hlim=[-3 * mm, 3 * mm], vlim=[-3 * mm, 3 * mm])\n",
+    "scene.plot(ax=axs[1], z=bottom, hlim=[-3 * mm, 3 * mm], vlim=[-3 * mm, 3 * mm])\n",
     "plt.show()\n",
     "fig, axs = plt.subplots(1, 1, figsize=(10, 5), tight_layout=True)\n",
-    "scene.plot(ax = axs, x=via_center)\n",
+    "scene.plot(ax=axs, x=via_center)\n",
     "plt.show()"
    ]
   },
@@ -891,7 +889,7 @@
     "    source_time=time,\n",
     "    direction=\"-\",\n",
     "    pol_angle=np.pi / 2,\n",
-    "    num_freqs=1\n",
+    "    num_freqs=1,\n",
     ")\n",
     "\n",
     "R_mon = td.FieldMonitor(\n",
@@ -906,7 +904,7 @@
     "        wavelength=td.C_0 / freq_stop,\n",
     "        override_structures=all_mesh_overrides,\n",
     "        snapping_points=arrow_snapping_points,\n",
-    "        dl_min= 0.018 * mm,\n",
+    "        dl_min=0.018 * mm,\n",
     "    ),\n",
     "    structures=all_structures,\n",
     "    sources=[planewave],\n",
@@ -944,8 +942,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def compute_flux_polarization_components(mon_data) -> tuple[float,float]:\n",
-    "    \"\"\" Compute flux in the +z axis direction, but decomposed\n",
+    "def compute_flux_polarization_components(mon_data) -> tuple[float, float]:\n",
+    "    \"\"\"Compute flux in the +z axis direction, but decomposed\n",
     "    into x and y electric field polarizations\"\"\"\n",
     "    Ex = mon_data.Ex\n",
     "    Ey = mon_data.Ey\n",
@@ -959,20 +957,22 @@
     "    flux_y = Py.integrate([\"x\", \"y\"])\n",
     "    return flux_x, flux_y\n",
     "\n",
-    "def find_frequency_bounds(value_dB: xr.DataArray, threshold = -1) -> tuple[float,float]:\n",
+    "\n",
+    "def find_frequency_bounds(value_dB: xr.DataArray, threshold=-1) -> tuple[float, float]:\n",
     "    \"\"\"Find the frequency bounds where value_dB is above the threshold.\"\"\"\n",
     "    value_dB = value_dB.squeeze()\n",
     "    peak_amp = value_dB.max().item()\n",
     "    peak_frequency = value_dB.idxmax(dim=\"f\").item()\n",
     "    if peak_amp < threshold:\n",
     "        return np.nan, np.nan\n",
-    "    \n",
+    "\n",
     "    below_threshold = (value_dB < threshold).values\n",
     "    low_freq = value_dB.f[below_threshold].where(value_dB.f < peak_frequency).max().item()\n",
     "    high_freq = value_dB.f[below_threshold].where(value_dB.f > peak_frequency).min().item()\n",
     "\n",
     "    return low_freq, high_freq\n",
     "\n",
+    "\n",
     "def calculate_fractional_bandwith_metric(mon_data: td.FieldData, threshold=-1) -> float:\n",
     "    \"\"\"Calculate the fractional bandwidth metric given a simulation result\"\"\"\n",
     "    flux_x, _ = compute_flux_polarization_components(mon_data)\n",
@@ -991,7 +991,8 @@
     "    fractional_bandwidth = 100 * bandwidth / freq0\n",
     "    return fractional_bandwidth\n",
     "\n",
-    "def make_structures_and_gridspec(l_head : float, l_stem : float) -> tuple [td.Structure, td.GridSpec]:\n",
+    "\n",
+    "def make_structures_and_gridspec(l_head: float, l_stem: float) -> tuple[td.Structure, td.GridSpec]:\n",
     "    \"\"\"Recreates the structures that are affected by a new arrow head length and stem length.\"\"\"\n",
     "    # Update unit cell parameters\n",
     "    array.unit_cell_0.l_head = l_head\n",
@@ -1372,10 +1373,12 @@
    "source": [
     "f, ax1 = plt.subplots(1, 1, figsize=(5, 4))\n",
     "result_df_process = copy(result_df)\n",
-    "result_df_process[\"l_head\"] = result_df_process[\"l_head\"]*1e-3\n",
-    "result_df_process[\"l_stem\"] = result_df_process[\"l_stem\"]*1e-3\n",
-    "result_df_process = result_df_process.rename(columns={'output': 'Fractional Bandwidth (%)'})\n",
-    "im = result_df_process.plot.scatter(x=\"l_head\", y=\"l_stem\", s=75, c='Fractional Bandwidth (%)', cmap=\"magma\", ax=ax1)\n",
+    "result_df_process[\"l_head\"] = result_df_process[\"l_head\"] * 1e-3\n",
+    "result_df_process[\"l_stem\"] = result_df_process[\"l_stem\"] * 1e-3\n",
+    "result_df_process = result_df_process.rename(columns={\"output\": \"Fractional Bandwidth (%)\"})\n",
+    "im = result_df_process.plot.scatter(\n",
+    "    x=\"l_head\", y=\"l_stem\", s=75, c=\"Fractional Bandwidth (%)\", cmap=\"magma\", ax=ax1\n",
+    ")\n",
     "ax1.set_xlabel(r\"$L_{\\rm H}$ (mm)\")\n",
     "ax1.set_ylabel(r\"$L_{\\rm S}$ (mm)\")\n",
     "plt.tight_layout(pad=0.2)\n",
diff --git a/XarrayTutorial.ipynb b/XarrayTutorial.ipynb
index 6a3a0dcf..1691aaf4 100644
--- a/XarrayTutorial.ipynb
+++ b/XarrayTutorial.ipynb
@@ -27,10 +27,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "import os\n",
     "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "from tidy3d import web"
    ]
@@ -637,7 +637,7 @@
    "id": "54a11880",
    "metadata": {},
    "source": [
-    "The data array has `.values` and `.coords`. The `.values` are just the raw, unlabelled data. The `.coords` are a dictionary mapping each dimension of the data to a set of data. For example, mapping the flux values to a specific frequency. We can inspect them by printing them out."
+    "The data array has `.values` and `.coords`. The `.values` are just the raw, unlabeled data. The `.coords` are a dictionary mapping each dimension of the data to a set of data. For example, mapping the flux values to a specific frequency. We can inspect them by printing them out."
    ]
   },
   {
@@ -749,7 +749,7 @@
     }
    ],
    "source": [
-    "# extract frequecies from the monitor data\n",
+    "# extract frequencies from the monitor data\n",
     "freqs = sim_data[\"Flux monitor\"].flux.f\n",
     "\n",
     "# extract the transmission flux from the monitor data\n",
@@ -757,7 +757,7 @@
     "\n",
     "# plot the transmission in dB\n",
     "plt.plot(td.C_0 / freqs, 10 * np.log10(T), \"red\", linewidth=3)\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Transmission (dB)\")\n",
     "plt.ylim(-10, 0)\n",
     "plt.show()"
@@ -795,7 +795,7 @@
     "# ensure the directory exists\n",
     "if not os.path.exists(\"data\"):\n",
     "    os.makedirs(\"data\")\n",
-    "    \n",
+    "\n",
     "np.savetxt(\"data/flux_data.csv\", flux_data, delimiter=\",\")"
    ]
   },
@@ -1478,7 +1478,7 @@
     "\n",
     "In addition to the support of standard mathematical operations (`+`, `-`, `*`, `**`), `xarray` is also compatible with [numpy](https://numpy.org/) so we can directly apply `numpy` operations such as `square` to an `xarray` DataArray. As a demonstration, we extract the transmission of the fundamental mode in the positive direction (`sel(mode_index=0, direction='+')`), take the absolute value (`.abs`), and square it using `numpy`. Then we can plot it in dB scale. The plot is similar to the previous plot from `Flux monitor`, which is expected as the waveguide is single mode and most of the transmitted power is at the fundamental mode.\n",
     "\n",
-    "Note that in Tidy3D, we implemented additional convenient functionalities that are not avaiable in the origianl `xarray`. Taking the absolute value (`.abs`) is one example. One can achieve the same result by using `abs()` on an DataArray. "
+    "Note that in Tidy3D, we implemented additional convenient functionalities that are not available in the original `xarray`. Taking the absolute value (`.abs`) is one example. One can achieve the same result by using `abs()` on a DataArray. "
    ]
   },
   {
@@ -1504,7 +1504,7 @@
     "\n",
     "# plot the transmission spectrum\n",
     "plt.plot(td.C_0 / freqs, 10 * np.log10(T), \"red\", linewidth=3)\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Transmission (dB)\")\n",
     "plt.ylim(-10, 0)\n",
     "plt.show()"
@@ -2873,7 +2873,7 @@
    "source": [
     "overlap = waveguide_mode_data.outer_dot(gaussian_beam_data[\"monitor_0\"])\n",
     "plt.plot(td.C_0 / overlap.f, 20 * np.log10(np.abs(overlap)))\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Overlap integral (dB)\")\n",
     "plt.ylim(-2, 0)\n",
     "plt.show()\n",
@@ -2934,7 +2934,7 @@
     "overlap = integrand.integrate(coord=[\"y\", \"z\"])\n",
     "\n",
     "plt.plot(td.C_0 / overlap.f, 20 * np.log10(np.abs(overlap)))\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Overlap integral (dB)\")\n",
     "plt.ylim(-2, 0)\n",
     "plt.show()\n",
@@ -2993,7 +2993,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.0"
   },
   "title": "Advanced monitor data manipulation and visualization | Flexcompute"
  },
diff --git a/YJunction.ipynb b/YJunction.ipynb
index aa7018b1..3c14810c 100644
--- a/YJunction.ipynb
+++ b/YJunction.ipynb
@@ -33,10 +33,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "import gdstk\n",
-    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web\n",
     "from tidy3d.plugins.mode import ModeSolver"
@@ -969,13 +968,13 @@
    "source": [
     "# extract the transmission data from the mode monitor\n",
     "amp = sim_data[\"mode\"].amps.sel(mode_index=0, direction=\"+\")\n",
-    "T = np.abs(amp) ** 2 # transmission to the top waveguide\n",
-    "T_total = 2 * T # total transmission at the two output waveguides\n",
+    "T = np.abs(amp) ** 2  # transmission to the top waveguide\n",
+    "T_total = 2 * T  # total transmission at the two output waveguides\n",
     "\n",
     "plt.plot(ldas, 10 * np.log10(T_total))\n",
     "plt.xlim(1.5, 1.6)\n",
     "plt.ylim(-0.5, 0)\n",
-    "plt.xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "plt.xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "plt.ylabel(\"Insertion loss (dB)\")\n",
     "plt.show()"
    ]
diff --git a/ZeroCrossTalkTE.ipynb b/ZeroCrossTalkTE.ipynb
index 239b7cc4..3f2fc30a 100644
--- a/ZeroCrossTalkTE.ipynb
+++ b/ZeroCrossTalkTE.ipynb
@@ -38,10 +38,9 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "import gdstk\n",
-    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import tidy3d as td\n",
     "import tidy3d.web as web"
    ]
@@ -490,8 +489,8 @@
    ],
    "source": [
     "# inspect the grid\n",
-    "ax = sim_eskid.plot(z=0, hlim=[-1,1], vlim=[-1,1])\n",
-    "sim_eskid.plot_grid(z=0, ax=ax, hlim=[-1,1], vlim=[-1,1])\n",
+    "ax = sim_eskid.plot(z=0, hlim=[-1, 1], vlim=[-1, 1])\n",
+    "sim_eskid.plot_grid(z=0, ax=ax, hlim=[-1, 1], vlim=[-1, 1])\n",
     "plt.show()"
    ]
   },
@@ -787,7 +786,7 @@
     "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), tight_layout=True)\n",
     "\n",
     "    ax1.plot(ldas, 10 * np.log10(crosstalk))\n",
-    "    ax1.set_xlabel(\"Wavelength ($\\mu m$)\")\n",
+    "    ax1.set_xlabel(r\"Wavelength ($\\mu m$)\")\n",
     "    ax1.set_xlim(np.min(ldas), np.max(ldas))\n",
     "    ax1.set_ylim(-80, 0)\n",
     "    ax1.set_ylabel(\"Crosstalk (dB)\")\n",
diff --git a/ZonePlateFieldProjection.ipynb b/ZonePlateFieldProjection.ipynb
index ac1ed5e8..661dbf24 100644
--- a/ZonePlateFieldProjection.ipynb
+++ b/ZonePlateFieldProjection.ipynb
@@ -32,12 +32,12 @@
    "outputs": [],
    "source": [
     "# standard python imports\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "\n",
     "# tidy3d imports\n",
     "import tidy3d as td\n",
-    "import tidy3d.web as web\n"
+    "import tidy3d.web as web"
    ]
   },
   {
@@ -104,7 +104,7 @@
     "# define material properties\n",
     "air = td.Medium(permittivity=1.0)\n",
     "SiO2 = td.Medium(permittivity=n_SiO2**2)\n",
-    "TiO2 = td.Medium(permittivity=n_TiO2**2)\n"
+    "TiO2 = td.Medium(permittivity=n_TiO2**2)"
    ]
   },
   {
@@ -130,7 +130,7 @@
    },
    "outputs": [],
    "source": [
-    "# because the wavelength is in microns, use builtin td.C_0 (um/s) to get frequency in Hz\n",
+    "# because the wavelength is in microns, use built-in td.C_0 (um/s) to get frequency in Hz\n",
     "f0 = td.C_0 / wavelength\n",
     "\n",
     "# Define PML layers, for this application we surround the whole structure in PML to isolate the fields\n",
@@ -140,7 +140,7 @@
     "length_z = space_below_sub + height_sub + height_lens + space_below_sub\n",
     "\n",
     "# construct simulation size array\n",
-    "sim_size = (length_xy, length_xy, length_z)\n"
+    "sim_size = (length_xy, length_xy, length_z)"
    ]
   },
   {
@@ -149,7 +149,7 @@
    "source": [
     "## Create Geometry\n",
     "\n",
-    "Now we create the ring metalens programatically"
+    "Now we create the ring metalens programmatically."
    ]
   },
   {
@@ -178,12 +178,13 @@
     "# focal length\n",
     "focal_length = length_xy / 2 / NA * np.sqrt(1 - NA**2)\n",
     "\n",
+    "\n",
     "# location from center for edge of the n-th inner ring, see https://en.wikipedia.org/wiki/Zone_plate\n",
     "def edge(n):\n",
     "    return np.sqrt(n * wavelength * focal_length + n**2 * wavelength**2 / 4)\n",
     "\n",
     "\n",
-    "# loop through the ring indeces until it's too big and add each to geometry list\n",
+    "# loop through the ring indices until it's too big and add each to geometry list\n",
     "n = 1\n",
     "r = edge(n)\n",
     "rings = []\n",
@@ -210,7 +211,7 @@
     "\n",
     "# reverse geometry list so that inner, smaller rings are added last and therefore override larger rings.\n",
     "rings.reverse()\n",
-    "geometry = [substrate] + rings\n"
+    "geometry = [substrate] + rings"
    ]
   },
   {
@@ -254,7 +255,7 @@
     ")\n",
     "\n",
     "# Simulation run time\n",
-    "run_time = 40 / fwidth\n"
+    "run_time = 40 / fwidth"
    ]
   },
   {
@@ -303,9 +304,7 @@
    ],
    "source": [
     "# place the monitors halfway between top of lens and PML\n",
-    "pos_monitor_z = (\n",
-    "    -length_z / 2 + space_below_sub + height_sub + height_lens + space_below_sub / 2\n",
-    ")\n",
+    "pos_monitor_z = -length_z / 2 + space_below_sub + height_sub + height_lens + space_below_sub / 2\n",
     "\n",
     "# set the points on the observation grid at which fields should be projected\n",
     "num_far = 40\n",
@@ -340,7 +339,7 @@
     "    size=[td.inf, td.inf, 0],\n",
     "    freqs=[f0],\n",
     "    name=\"nearfield\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -411,7 +410,7 @@
     "    monitors=[monitor_far, monitor_near],\n",
     "    run_time=run_time,\n",
     "    boundary_spec=boundary_spec,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -457,10 +456,8 @@
    "source": [
     "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 8))\n",
     "simulation.plot_eps(x=0, ax=ax1)\n",
-    "simulation.plot_eps(\n",
-    "    z=-length_z / 2 + space_below_sub + height_sub + height_lens / 2, ax=ax2\n",
-    ")\n",
-    "plt.show()\n"
+    "simulation.plot_eps(z=-length_z / 2 + space_below_sub + height_sub + height_lens / 2, ax=ax2)\n",
+    "plt.show()"
    ]
   },
   {
@@ -854,11 +851,7 @@
     }
    ],
    "source": [
-    "import tidy3d.web as web\n",
-    "\n",
-    "sim_data = web.run(\n",
-    "    simulation, task_name=\"zone_plate\", path=\"data/simulation.hdf5\", verbose=True\n",
-    ")\n"
+    "sim_data = web.run(simulation, task_name=\"zone_plate\", path=\"data/simulation.hdf5\", verbose=True)"
    ]
   },
   {
@@ -867,7 +860,7 @@
    "source": [
     "## Visualization \n",
     "\n",
-    "Let's inspect the near field using the Tidy3D builtin field visualization methods.  \n",
+    "Let's inspect the near field using the Tidy3D built-in field visualization methods.  \n",
     "For more details see the documentation of [tidy3d.SimulationData](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SimulationData.html?highlight=SimulationData)."
    ]
   },
@@ -902,7 +895,7 @@
     "near_field_data.Ex.real.plot(ax=ax1)\n",
     "near_field_data.Ey.real.plot(ax=ax2)\n",
     "near_field_data.Ez.real.plot(ax=ax3)\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -932,7 +925,7 @@
    },
    "outputs": [],
    "source": [
-    "projected_fields = sim_data[monitor_far.name].fields_cartesian\n"
+    "projected_fields = sim_data[monitor_far.name].fields_cartesian"
    ]
   },
   {
@@ -987,7 +980,7 @@
     "im = projected_fields[\"Ey\"].real.plot(ax=ax2)\n",
     "im = projected_fields[\"Ez\"].real.plot(ax=ax3)\n",
     "\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1033,7 +1026,7 @@
     "ax1.set_title(\"$|E(x,y)|^2$\")\n",
     "ax2.set_title(\"$|E(x,y)|$\")\n",
     "\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -1070,7 +1063,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.11.0"
   },
   "title": "Zone Plate Modeling in Tidy3D | Flexcompute",
   "widgets": {
diff --git a/convert.py b/convert.py
index 7ac3bf52..942051cd 100644
--- a/convert.py
+++ b/convert.py
@@ -1,18 +1,19 @@
-from subprocess import run, CalledProcessError
-from bs4 import BeautifulSoup
-import nbformat
-import yaml
-import re
 import glob
 import os
+import re
 import shutil
+from subprocess import CalledProcessError, run
+
+import nbformat
+import yaml
+from bs4 import BeautifulSoup
 from nbconvert import HTMLExporter
 
 output_dir = "./html"
 css_file_name = "example-notebook.css"
 
 
-def creat_yaml(meta, anchor={}):
+def creat_yaml(meta, anchor={}):  # noqa: B006
     data = {
         "layout": "example",
         "custom_css": "cobalt",
@@ -37,7 +38,7 @@ def creat_yaml(meta, anchor={}):
 
 
 def read_template():
-    with open(f"./_template/template.html", "r") as f:
+    with open("./_template/template.html") as f:
         html = f.read()
         return BeautifulSoup(html, "html.parser")
 
@@ -85,7 +86,7 @@ def write_css_file(style_tags, output_dir):
 def write_template(meta, file_name, create_css=False):
     html_output_file = f"{output_dir}/{file_name}.html"
     css_output_directory = f"{output_dir}/css/"
-    with open(html_output_file, "r") as html_file:
+    with open(html_output_file) as html_file:
         content = html_file.read()
 
         # Parse the HTML content using BeautifulSoup
@@ -175,7 +176,7 @@ def generatorOG(metadata, description):
             result = run(cmd, shell=True, capture_output=True, text=True, check=True)
 
             if result.returncode == 0:
-                with open(input_file, "r", encoding="utf-8") as f:
+                with open(input_file, encoding="utf-8") as f:
                     nb = nbformat.read(f, as_version=4)
                     default_title = ""
                     for cell in nb.cells:
diff --git a/ruff.toml b/ruff.toml
new file mode 100644
index 00000000..f2639ac6
--- /dev/null
+++ b/ruff.toml
@@ -0,0 +1,45 @@
+target-version = "py39"
+line-length = 100
+extend-include = ["*.ipynb"]
+
+[lint]
+typing-modules = ["tidy3d.components.types"]
+select = [
+    "B",      # flake8-bugbear
+    "C",      # flake8-comprehensions
+    "E",      # pycodestyle errors
+    "F",      # pyflakes
+    "I",      # isort
+    "NPY201", # numpy 2.* compatibility check
+    "UP",
+    "W",      # pycodestyle warnings
+]
+ignore = [
+    "B006",   # allow mutable defaults for notebooks
+    "B007",   # Loop control variable not used within loop body
+    "B008",   # do not perform function calls in argument defaults
+    "B028",   # stacklevel
+    "B904",
+    "B905",   # `zip()` without an explicit `strict=` parameter
+    "C408",   # Unnecessary `dict` call
+    "C417",   # allow map
+    "C901",   # too complex
+    "E402",   # module level import not at top of file
+    "E501",   # line too long (handled by formatter)
+    "E722",   # we'll allow bare excepts in notebooks
+    "E731",   # allow lambda assignment
+    "E741",   # "l" is ok as a variable name
+    "F401",   # imported but unused (common in notebooks for exploration)
+    "NPY201", # allow numpy<2 for now
+    "UP006",  # type annotation with Tuple[float] messes up pydantic
+    "UP007",  # use x | y instead of union[x,y] (does not work)
+    "UP035",
+    "UP038",
+]
+
+[format]
+quote-style = "double"
+indent-style = "space"
+skip-magic-trailing-comma = false
+line-ending = "auto"
+docstring-code-format = true