From dc4e338ec0ca03b04c212dfdb10dc634b95cccf0 Mon Sep 17 00:00:00 2001 From: Lolistone Date: Fri, 29 May 2026 18:29:43 +0000 Subject: [PATCH 1/5] patch config --- docs/examples/vivaldi_antenna.ipynb | 84 +++++----- src/palacetoolkit/plot_farfield.py | 165 ++++++++------------ src/palacetoolkit/{s_plot.py => postpro.py} | 4 +- src/palacetoolkit/simulation.py | 14 +- 4 files changed, 123 insertions(+), 144 deletions(-) rename src/palacetoolkit/{s_plot.py => postpro.py} (80%) diff --git a/docs/examples/vivaldi_antenna.ipynb b/docs/examples/vivaldi_antenna.ipynb index 380ea2d..8527e16 100644 --- a/docs/examples/vivaldi_antenna.ipynb +++ b/docs/examples/vivaldi_antenna.ipynb @@ -601,16 +601,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Info : [ 0%] Difference \r", - "Info : [ 10%] Difference \r", - "Info : [ 20%] Difference \r", - "Info : [ 30%] Difference \r", - "Info : [ 40%] Difference \r", - "Info : [ 50%] Difference \r", - "Info : [ 70%] Difference - Filling splits of edges \r", - "Info : [ 80%] Difference - Making faces \r", - "Info : [ 90%] Difference - Adding holes \r", - " \r", + "Info : [ 0%] Difference \r\n", + "Info : [ 10%] Difference \r\n", + "Info : [ 20%] Difference \r\n", + "Info : [ 30%] Difference \r\n", + "Info : [ 40%] Difference \r\n", + "Info : [ 50%] Difference \r\n", + "Info : [ 70%] Difference - Filling splits of edges \r\n", + "Info : [ 80%] Difference - Making faces \r\n", + "Info : [ 90%] Difference - Adding holes \r\n", + " \r\n", "Copper patch surfaces after boolean cut:\n", " dim=2, tag=13\n" ] @@ -677,36 +677,36 @@ "name": "stdout", "output_type": "stream", "text": [ - "Info : [ 0%] Difference \r", - "Info : [ 10%] Difference \r", - "Info : [ 20%] Difference \r", - "Info : [ 30%] Difference \r", - "Info : [ 40%] Difference \r", - "Info : [ 50%] Difference \r", - "Info : [ 60%] Difference \r", - "Info : [ 70%] Difference - Filling splits of edges \r", - "Info : [ 80%] Difference - Adding holes \r", - "Info : [ 90%] Difference \r", - "Info : [ 0%] Difference \r", - "Info : [ 10%] Difference \r", - "Info : [ 20%] Difference \r", - "Info : [ 30%] Difference \r", - "Info : [ 40%] Difference \r", - "Info : [ 50%] Difference \r", - "Info : [ 60%] Difference \r", - "Info : [ 70%] Difference - Filling splits of vertices \r", - "Info : [ 80%] Difference \r", - "Info : [ 90%] Difference - Adding holes \r", - "Info : [ 0%] Fragments \r", - "Info : [ 10%] Fragments \r", - "Info : [ 20%] Fragments \r", - "Info : [ 30%] Fragments \r", - "Info : [ 40%] Fragments \r", - "Info : [ 50%] Fragments \r", - "Info : [ 60%] Fragments \r", - "Info : [ 70%] Fragments \r", - "Info : [ 80%] Fragments - Adding holes \r", - "Info : [ 90%] Fragments \r", + "Info : [ 0%] Difference \r\n", + "Info : [ 10%] Difference \r\n", + "Info : [ 20%] Difference \r\n", + "Info : [ 30%] Difference \r\n", + "Info : [ 40%] Difference \r\n", + "Info : [ 50%] Difference \r\n", + "Info : [ 60%] Difference \r\n", + "Info : [ 70%] Difference - Filling splits of edges \r\n", + "Info : [ 80%] Difference - Adding holes \r\n", + "Info : [ 90%] Difference \r\n", + "Info : [ 0%] Difference \r\n", + "Info : [ 10%] Difference \r\n", + "Info : [ 20%] Difference \r\n", + "Info : [ 30%] Difference \r\n", + "Info : [ 40%] Difference \r\n", + "Info : [ 50%] Difference \r\n", + "Info : [ 60%] Difference \r\n", + "Info : [ 70%] Difference - Filling splits of vertices \r\n", + "Info : [ 80%] Difference \r\n", + "Info : [ 90%] Difference - Adding holes \r\n", + "Info : [ 0%] Fragments \r\n", + "Info : [ 10%] Fragments \r\n", + "Info : [ 20%] Fragments \r\n", + "Info : [ 30%] Fragments \r\n", + "Info : [ 40%] Fragments \r\n", + "Info : [ 50%] Fragments \r\n", + "Info : [ 60%] Fragments \r\n", + "Info : [ 70%] Fragments \r\n", + "Info : [ 80%] Fragments - Adding holes \r\n", + "Info : [ 90%] Fragments \r\n", " Physical group 'substrate' (dim=3): pg=1, tags=[1]\n", " Physical group 'air_box' (dim=3): pg=2, tags=[2]\n", " Physical group 'copper_patch' (dim=2): pg=3, tags=[13]\n", @@ -1461,7 +1461,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": ".venv (3.12.3)", "language": "python", "name": "python3" }, @@ -1594,4 +1594,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/src/palacetoolkit/plot_farfield.py b/src/palacetoolkit/plot_farfield.py index bb71748..9d70861 100644 --- a/src/palacetoolkit/plot_farfield.py +++ b/src/palacetoolkit/plot_farfield.py @@ -7,12 +7,10 @@ from scipy.spatial import Delaunay from scipy.interpolate import griddata - def clean_column(name: str) -> str: name = re.sub(r"\s*\([^)]*\)", "", name) return name.strip() - def compute_field_magnitude(df: pd.DataFrame) -> np.ndarray: Ex_re = df["r*Re{E_x}"].to_numpy(float) Ex_im = df["r*Im{E_x}"].to_numpy(float) @@ -35,44 +33,34 @@ def compute_db(magnitude: np.ndarray, floor_db: float = -25.0) -> np.ndarray: return np.clip(db, floor_db, 0.0) -def extract_eplane(df: pd.DataFrame, tolerance_deg: float = 2.0) -> dict: - """ - E-plane (xz-plane): phi ~ 0° and phi ~ 180°. - Returns a dict with each half stored separately to avoid - the diagonal slash caused by joining them in one array. - """ - phi = df["phi"].to_numpy(float) +def extract_eplane(df: pd.DataFrame, tolerance_deg: float = 5.0) -> dict: + phi = df["phi"].to_numpy(float) result = {} + # half1: phi~0°, theta 0->180 maps to polar angle 0->180 mask1 = np.abs(phi - 0.0) < tolerance_deg if np.any(mask1): - d1 = df.loc[mask1] - a1 = d1["theta"].to_numpy(float) - m1 = compute_field_magnitude(d1) + d1 = df.loc[mask1] + a1 = d1["theta"].to_numpy(float) # 0° → 180° + m1 = compute_field_magnitude(d1) idx = np.argsort(a1) result["half1"] = (a1[idx], m1[idx]) print(f" E-plane phi~0°: {mask1.sum()} points") - else: - print(" E-plane phi~0°: 0 points — try increasing tolerance_deg") + # half2: phi~180°, theta 0->180 maps to polar angle 360->180 mask2 = np.abs(phi - 180.0) < tolerance_deg if np.any(mask2): - d2 = df.loc[mask2] - a2 = 360.0 - d2["theta"].to_numpy(float) - m2 = compute_field_magnitude(d2) + d2 = df.loc[mask2] + a2 = 360.0 - d2["theta"].to_numpy(float) # 360° → 180° + m2 = compute_field_magnitude(d2) idx = np.argsort(a2) result["half2"] = (a2[idx], m2[idx]) print(f" E-plane phi~180°: {mask2.sum()} points") - else: - print(" E-plane phi~180°: 0 points — try increasing tolerance_deg") - - if not result: - print(" WARNING: No E-plane points found at all") return result -def extract_hplane(df: pd.DataFrame, tolerance_deg: float = 2.0): +def extract_hplane(df: pd.DataFrame, tolerance_deg: float = 5.0): """H-plane (xy-plane): theta ~ 90°.""" theta = df["theta"].to_numpy(float) mask = np.abs(theta - 90.0) < tolerance_deg @@ -93,11 +81,9 @@ def style_polar_ax(ax, title: str, db_min: float = -25.0): ax.set_rlabel_position(45) ax.grid(True, color="lightgray", linewidth=0.8) - def polar_plots( df: pd.DataFrame, label: str, - filename: str = "farfield_polar.png", db_min: float = -25.0, ): print("Extracting E-plane...") @@ -107,20 +93,46 @@ def polar_plots( fig = plt.figure(figsize=(11, 5)) + # ── E-plane ────────────────────────────────────────────────────── # ── E-plane ────────────────────────────────────────────────────── ax1 = fig.add_subplot(1, 2, 1, projection="polar") style_polar_ax(ax1, f"E-plane ({label})", db_min) if e_data: - for key, (angles, mags) in e_data.items(): - if mags.size > 1: - db = compute_db(mags, db_min) - ax1.plot(np.deg2rad(angles), db, linewidth=2, color="tab:blue") - ax1.scatter(np.deg2rad(angles), db, s=18, color="tab:blue", zorder=5) - else: - ax1.text(0.5, 0.5, "No E-plane points found", - transform=ax1.transAxes, ha="center", va="center") - + parts_angles = [] + parts_mags = [] + + if "half1" in e_data: + a, m = e_data["half1"] + parts_angles.append(a) + parts_mags.append(m) + + if "half2" in e_data: + a, m = e_data["half2"] + parts_angles.append(a[::-1]) + parts_mags.append(m[::-1]) + + if parts_angles: + # Combine + all_angles = np.concatenate(parts_angles) + all_mags = np.concatenate(parts_mags) + + # Sort to ensure sequence (0 -> 360) + sort_idx = np.argsort(all_angles) + all_angles = all_angles[sort_idx] + all_mags = all_mags[sort_idx] + + db = compute_db(all_mags, db_min) + + # Close the loop (connect last point to first) + e_angles_closed = np.append(all_angles, all_angles[0]) + e_db_closed = np.append(db, db[0]) + + ax1.plot(np.deg2rad(e_angles_closed), e_db_closed, linewidth=2, color="tab:blue") + ax1.scatter(np.deg2rad(all_angles), db, s=18, color="tab:blue", zorder=5) + else: + ax1.text(0.5, 0.5, "No E-plane points found", + transform=ax1.transAxes, ha="center", va="center") # ── H-plane ────────────────────────────────────────────────────── ax2 = fig.add_subplot(1, 2, 2, projection="polar") style_polar_ax(ax2, f"H-plane ({label})", db_min) @@ -140,19 +152,14 @@ def polar_plots( fig.suptitle(label, y=1.01) fig.tight_layout() - fig.savefig(filename, dpi=200, bbox_inches="tight") - plt.close(fig) - print(f"Saved {filename}") + plt.show() def three_d_plot( df: pd.DataFrame, label: str, - screenshot: str = "farfield_3d.png", - show: bool = True, - off_screen: bool = False, - n_theta: int = 360, - n_phi: int = 720, + n_theta: int = 360, + n_phi: int = 720, n_smooth: int = 100, taubin_pass_band: float = 0.1, ): @@ -164,8 +171,6 @@ def three_d_plot( phi_raw = df["phi"].to_numpy(float) # ── 1. Interpolate onto a regular (theta, phi) grid ────────────── - # ✅ Start from 1° and end at 179° — avoids the pole singularity - # entirely. The tiny holes at top/bottom are capped later. theta_lin = np.linspace(1, 179, n_theta) phi_lin = np.linspace(0, 360, n_phi) TH, PH = np.meshgrid(theta_lin, phi_lin, indexing="ij") @@ -173,16 +178,14 @@ def three_d_plot( pts_src = np.column_stack([theta_raw, phi_raw]) E_grid = griddata(pts_src, E_raw, (TH, PH), method="linear") - # Fill NaN holes with nearest-neighbour nan_mask = np.isnan(E_grid) if nan_mask.any(): E_fill = griddata(pts_src, E_raw, (TH, PH), method="nearest") E_grid[nan_mask] = E_fill[nan_mask] - # Normalize E_grid /= np.max(E_grid) - # ── 2. Wrap the φ seam (duplicate φ=0 column at φ=360) ─────────── + # ── 2. Wrap the φ seam ──────────────────────────────────────────── E_wrap = np.hstack([E_grid, E_grid[:, :1]]) TH_w = np.hstack([TH, TH[:, :1]]) PH_w = np.hstack([PH, PH[:, :1]]) @@ -203,9 +206,7 @@ def three_d_plot( # ── 5. Extract surface ──────────────────────────────────────────── mesh = grid.extract_surface() - # ── 6. Cap the small pole holes ─────────────────────────────────── - # The 1°–179° trim leaves a tiny open ring at top and bottom. - # fill_holes() closes them cleanly with no spikes. + # ── 6. Cap pole holes ───────────────────────────────────────────── mesh = mesh.fill_holes(hole_size=10000) # ── 7. Taubin smoothing ─────────────────────────────────────────── @@ -217,38 +218,22 @@ def three_d_plot( normalize_coordinates=True, ) - # ── 8. Plotting ─────────────────────────────────────────────────── - def _build_plotter(off: bool) -> pv.Plotter: - pl = pv.Plotter(off_screen=off) - pl.set_background("white") - pl.add_title(f"Relative E-field magnitude ({label})", font_size=12) - pl.add_mesh( - mesh, - scalars="E_norm", - cmap="viridis", - smooth_shading=True, - scalar_bar_args={"title": "Normalized |E|"}, - ) - pl.add_axes() - pl.camera_position = "iso" - return pl - - pl_save = _build_plotter(off=True) - pl_save.show(auto_close=False) - pl_save.screenshot(screenshot) - pl_save.close() - print(f"Saved {screenshot}") - - if show and not off_screen: - pl_show = _build_plotter(off=False) - pl_show.show() - -def main(): - filename = ( - sys.argv[1] if len(sys.argv) > 1 - else "postpro/horn_antenna/farfield-rE.csv" + # ── 8. Plotting (notebook) ──────────────────────────────────────── + pl = pv.Plotter(notebook=True) + pl.set_background("white") + pl.add_title(f"Relative E-field magnitude ({label})", font_size=12) + pl.add_mesh( + mesh, + scalars="E_norm", + cmap="viridis", + smooth_shading=True, + scalar_bar_args={"title": "Normalized |E|"}, ) + pl.add_axes() + pl.camera_position = "iso" + pl.show() +def load_data(filename, freq): try: df = pd.read_csv(filename) except FileNotFoundError: @@ -265,7 +250,7 @@ def main(): label = f"m = {m0}" print(f"Processing mode: {m0} ({len(data)} rows)") elif "f" in df.columns: - f0 = df["f"].iloc[0] + f0 = freq data = df[df["f"] == f0].copy() label = f"f = {f0:.4f} GHz" print(f"Processing frequency: {f0} GHz ({len(data)} rows)") @@ -273,20 +258,4 @@ def main(): data = df.copy() label = "all data" print(f"No 'm' or 'f' column found; plotting all {len(data)} rows.") - - polar_plots(data, label, filename="farfield_polar.png") - three_d_plot( - data, - label, - screenshot = "farfield_3d.png", - show = True, - off_screen = False, - n_theta = 360, - n_phi = 720, - n_smooth = 100, - taubin_pass_band = 0.1, - ) - - -if __name__ == "__main__": - main() + return data, label diff --git a/src/palacetoolkit/s_plot.py b/src/palacetoolkit/postpro.py similarity index 80% rename from src/palacetoolkit/s_plot.py rename to src/palacetoolkit/postpro.py index 5c098e2..05f2551 100644 --- a/src/palacetoolkit/s_plot.py +++ b/src/palacetoolkit/postpro.py @@ -1,7 +1,7 @@ import pandas as pd import matplotlib.pyplot as plt -def plot_s_params(csv_file = r"C:\Users\loloc\Desktop\Epsilon\palace-course\lecture_3_waveports\results\patch_antenna\port-S.csv"): +def plot_s_params(csv_file): df = pd.read_csv(csv_file) @@ -24,6 +24,4 @@ def plot_s_params(csv_file = r"C:\Users\loloc\Desktop\Epsilon\palace-course\lect plt.show() -if __name__ == "__main__": - plot_s_params() diff --git a/src/palacetoolkit/simulation.py b/src/palacetoolkit/simulation.py index 72dcd19..864d140 100644 --- a/src/palacetoolkit/simulation.py +++ b/src/palacetoolkit/simulation.py @@ -496,6 +496,7 @@ def generate_palace_config_from_entities( L0: float = 1e-3, solver_order: int = 2, absorbing_order: int = 2, + farfield = True ) -> dict: """Build and write a Palace JSON config from entity definitions. @@ -583,6 +584,11 @@ def generate_palace_config_from_entities( boundaries["LumpedPort"] = lumped_ports if wave_ports: boundaries["WavePort"] = wave_ports + if farfield: + boundaries["Postprocessing"] = {"FarField": { + "Attributes": sorted(absorbing_attrs), + "NSample": 32000 + }} output_stem = Path(output_file).stem output_folder = f"postpro/{output_stem}" @@ -739,8 +745,14 @@ def generate_palace_config( "Materials": materials, }, "Boundaries": { - "PEC": {"Attributes": sorted(pec_attrs)}, + "PEC": {"Attributes": pec_attrs}, "Absorbing": {"Attributes": sorted(absorbing_attrs), "Order": 1}, + "Postprocessing": { + "FarField": { + "Attributes": sorted(absorbing_attrs), + "NSample": 64000 + } + }, "WavePort": waveport_entries, }, "Solver": { From 264a8c31ca16cc2fdc72454c2bce34837aa21fc4 Mon Sep 17 00:00:00 2001 From: Lolistone Date: Sat, 30 May 2026 19:47:02 +0000 Subject: [PATCH 2/5] patch config --- docs/examples/coax_to_waveguide.ipynb | 2 +- docs/examples/open_ended_stub.ipynb | 2 +- docs/examples/step_in_width.ipynb | 2 +- src/palacetoolkit/plot_farfield.py | 36 ++++++++++++++------------- 4 files changed, 22 insertions(+), 20 deletions(-) diff --git a/docs/examples/coax_to_waveguide.ipynb b/docs/examples/coax_to_waveguide.ipynb index 092d6b1..c3a1e69 100644 --- a/docs/examples/coax_to_waveguide.ipynb +++ b/docs/examples/coax_to_waveguide.ipynb @@ -721,7 +721,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": ".venv (3.12.3)", "language": "python", "name": "python3" }, diff --git a/docs/examples/open_ended_stub.ipynb b/docs/examples/open_ended_stub.ipynb index 4a0dc7a..c58f38f 100644 --- a/docs/examples/open_ended_stub.ipynb +++ b/docs/examples/open_ended_stub.ipynb @@ -881,7 +881,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": ".venv (3.12.3)", "language": "python", "name": "python3" }, diff --git a/docs/examples/step_in_width.ipynb b/docs/examples/step_in_width.ipynb index 9ec52df..0c7d5f8 100644 --- a/docs/examples/step_in_width.ipynb +++ b/docs/examples/step_in_width.ipynb @@ -631,7 +631,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": ".venv (3.12.3)", "language": "python", "name": "python3" }, diff --git a/src/palacetoolkit/plot_farfield.py b/src/palacetoolkit/plot_farfield.py index 9d70861..eddc637 100644 --- a/src/palacetoolkit/plot_farfield.py +++ b/src/palacetoolkit/plot_farfield.py @@ -47,11 +47,11 @@ def extract_eplane(df: pd.DataFrame, tolerance_deg: float = 5.0) -> dict: result["half1"] = (a1[idx], m1[idx]) print(f" E-plane phi~0°: {mask1.sum()} points") - # half2: phi~180°, theta 0->180 maps to polar angle 360->180 + # half2: phi~180°, theta 0->180 maps to polar angle 180->360 mask2 = np.abs(phi - 180.0) < tolerance_deg if np.any(mask2): d2 = df.loc[mask2] - a2 = 360.0 - d2["theta"].to_numpy(float) # 360° → 180° + a2 = 180.0 + d2["theta"].to_numpy(float) # 180° → 360° m2 = compute_field_magnitude(d2) idx = np.argsort(a2) result["half2"] = (a2[idx], m2[idx]) @@ -93,7 +93,6 @@ def polar_plots( fig = plt.figure(figsize=(11, 5)) - # ── E-plane ────────────────────────────────────────────────────── # ── E-plane ────────────────────────────────────────────────────── ax1 = fig.add_subplot(1, 2, 1, projection="polar") style_polar_ax(ax1, f"E-plane ({label})", db_min) @@ -109,26 +108,29 @@ def polar_plots( if "half2" in e_data: a, m = e_data["half2"] - parts_angles.append(a[::-1]) - parts_mags.append(m[::-1]) + parts_angles.append(a) # already sorted 180->360, no reversal needed + parts_mags.append(m) if parts_angles: - # Combine + # Combine and sort (0 -> 360) all_angles = np.concatenate(parts_angles) all_mags = np.concatenate(parts_mags) - - # Sort to ensure sequence (0 -> 360) - sort_idx = np.argsort(all_angles) + sort_idx = np.argsort(all_angles) all_angles = all_angles[sort_idx] - all_mags = all_mags[sort_idx] - + all_mags = all_mags[sort_idx] + + # Normalize globally so both halves share the same 0 dB reference db = compute_db(all_mags, db_min) - - # Close the loop (connect last point to first) - e_angles_closed = np.append(all_angles, all_angles[0]) - e_db_closed = np.append(db, db[0]) - - ax1.plot(np.deg2rad(e_angles_closed), e_db_closed, linewidth=2, color="tab:blue") + + # Close the loop only if data actually spans ~360 degrees + if abs(all_angles[-1] - all_angles[0] - 360) < 10: + plot_angles = np.append(all_angles, all_angles[0] + 360) + plot_db = np.append(db, db[0]) + else: + plot_angles = all_angles + plot_db = db + + ax1.plot(np.deg2rad(plot_angles), plot_db, linewidth=2, color="tab:blue") ax1.scatter(np.deg2rad(all_angles), db, s=18, color="tab:blue", zorder=5) else: ax1.text(0.5, 0.5, "No E-plane points found", From 7b0f69c9e83f35a38d9c3502b75715c8f9705ec5 Mon Sep 17 00:00:00 2001 From: Lolistone Date: Sat, 30 May 2026 21:02:59 +0000 Subject: [PATCH 3/5] new generate_palace_config, adjusted patch antenna dimensions --- docs/examples/open_ended_stub.ipynb | 16 +- docs/examples/patch.config | 17 +- docs/examples/patch_antenna.ipynb | 2604 ++++++--------------------- src/palacetoolkit/plot_farfield.py | 36 +- src/palacetoolkit/simulation.py | 5 +- 5 files changed, 597 insertions(+), 2081 deletions(-) diff --git a/docs/examples/open_ended_stub.ipynb b/docs/examples/open_ended_stub.ipynb index c58f38f..009f177 100644 --- a/docs/examples/open_ended_stub.ipynb +++ b/docs/examples/open_ended_stub.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 1, "id": "6cf089f3", "metadata": { "execution": { @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "d854dab2", "metadata": { "execution": { @@ -135,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "12cc3554", "metadata": { "execution": { @@ -187,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "f151f6b0", "metadata": { "execution": { @@ -244,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "e4112dbf", "metadata": { "execution": { @@ -701,7 +701,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "89d4552b", "metadata": { "execution": { @@ -767,7 +767,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "45a75202", "metadata": { "execution": { @@ -799,7 +799,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "8f221339", "metadata": { "execution": { diff --git a/docs/examples/patch.config b/docs/examples/patch.config index 60b7837..8ad4829 100644 --- a/docs/examples/patch.config +++ b/docs/examples/patch.config @@ -38,8 +38,7 @@ }, "Absorbing": { "Attributes": [ - 6, - 7 + 6 ], "Order": 2 }, @@ -53,14 +52,22 @@ "Excitation": true, "Direction": "+Z" } - ] + ], + "Postprocessing": { + "FarField": { + "Attributes": [ + 6 + ], + "NSample": 32000 + } + } }, "Solver": { "Order": 2, "Device": "CPU", "Driven": { - "MinFreq": 1.0, - "MaxFreq": 7.0, + "MinFreq": 3.0, + "MaxFreq": 4.0, "FreqStep": 0.05, "SaveStep": 5, "AdaptiveTol": 0.001 diff --git a/docs/examples/patch_antenna.ipynb b/docs/examples/patch_antenna.ipynb index a1f82d4..cd73a60 100644 --- a/docs/examples/patch_antenna.ipynb +++ b/docs/examples/patch_antenna.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, "id": "9437354e", "metadata": { "execution": { @@ -42,9 +42,9 @@ "source": [ "import gmsh\n", "import math\n", - "import os\n", - "import json\n", "\n", + "from palacetoolkit.plot_farfield import load_data, polar_plots, three_d_plot\n", + "from palacetoolkit.postpro import plot_s_params\n", "from palacetoolkit.simulation import run_palace, generate_palace_config_from_entities\n", "from palacetoolkit.viz import view_mesh\n", "from palacetoolkit.mesh import (\n", @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, "id": "4049c4db", "metadata": { "execution": { @@ -110,26 +110,40 @@ }, "outputs": [], "source": [ + "# Patch dim — unchanged, resonance estimated at 3.296 GHz ✓\n", "l: float = 0.030\n", "w: float = 0.029\n", + "\n", + "# Ground plane — unchanged\n", "l1: float = 0.06\n", "w1: float = 0.06\n", - "l2: float = 0.008\n", - "w2: float = 0.003\n", - "w3: float = 0.001\n", - "h: float = 0.0013\n", - "air_height: float = 0.025 \n", - "air_margin: float = 0.025 \n", + "\n", + "# Notch — widened to match feed line\n", + "l2: float = 0.01136 # inset depth for 50Ω match (was 0.008)\n", + "w2: float = 0.00971 # notch width = 2 x w3 (was 0.003)\n", + "\n", + "# Feed line — widened to 50Ω\n", + "w3: float = 0.00485 # 50Ω microstrip on RT/duroid 5880 h=1.575mm (was 0.001)\n", + "\n", + "# Height — unchanged\n", + "h: float = 0.001575\n", + "\n", + "# Air box — unchanged\n", + "air_height: float = 0.03\n", + "air_margin: float = 0.03\n", + "\n", + "# Design frequency — unchanged\n", "freq: float = 3.3\n", - "filename: str = \"patch_antenna.msh\"\n", "\n", - "# dielectric properties of the substrate\n", - "mu_r: float = 1.0\n", + "# Substrate — unchanged\n", "eps_r: float = 2.2\n", "loss_tan: float = 0.0009\n", + "mu_r = 1.0\n", + "\n", + "port_impedance: float = 50.0\n", "\n", - "# lumped port impedance\n", - "port_impedance = 50.0\n", + "# Filename where the mesh is loadeds\n", + "filename = \"patch_antenna.msh\"\n", "\n", "wavelength = wavelength = 3e8 / (freq * 1e9)" ] @@ -153,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "id": "e6a08089", "metadata": { "execution": { @@ -203,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "id": "474cc986", "metadata": { "execution": { @@ -268,7 +282,7 @@ "kernel.synchronize()\n", "\n", "# Gap bewteen the gropund plane and the bottom of the lumped port.\n", - "gap = 0\n", + "gap = 0.0001\n", "lumped_port = kernel.addRectangle(-l1/2 + gap, -w3/2, 0, h - gap, w3)\n", "kernel.rotate([(2, lumped_port)], -l1/2, 0, 0, 0, 1, 0, -math.pi/2)\n", "kernel.synchronize()\n", @@ -301,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "fdbadb7b", "metadata": { "execution": { @@ -324,59 +338,58 @@ "name": "stdout", "output_type": "stream", "text": [ - " Physical group 'air_box' (dim=3): pg=1, tags=[2] \n", + " Physical group 'air_box' (dim=3): pg=1, tags=[2] \n", " Physical group 'substrate' (dim=3): pg=2, tags=[1]\n", " Physical group 'top_conductor' (dim=2): pg=3, tags=[8]\n", " Physical group 'ground_plane' (dim=2): pg=4, tags=[7]\n", " Physical group 'lumped_port' (dim=2): pg=5, tags=[9]\n", - " Physical group 'air_box__None' (dim=2): pg=6, tags=[16, 17, 18, 19, 20, 21]\n", - " Physical group 'air_box__substrate' (dim=2): pg=7, tags=[10, 11, 13, 12, 14, 15]\n", - " ppw_near=200 ppw_far=7\n", + " Physical group 'air_box__None' (dim=2): pg=6, tags=[15, 16, 17, 18, 19, 20]\n", + " Physical group 'air_box__substrate' (dim=2): pg=7, tags=[10, 12, 11, 13, 14]\n", + " ppw_near=400 ppw_far=7\n", " SizeMax=0.0130 transition=0.0227\n", - " global: 12 curves, SizeMin=0.0005\n", + " global: 12 curves, SizeMin=0.0002\n", "Info : Meshing 1D...\n", - "Info : [ 0%] Meshing curve 45 (Line)\n", - "Info : [ 10%] Meshing curve 46 (Line)\n", - "Info : [ 10%] Meshing curve 47 (Line)\n", - "Info : [ 10%] Meshing curve 48 (Line)\n", - "Info : [ 10%] Meshing curve 49 (Line)\n", - "Info : [ 20%] Meshing curve 50 (Line)\n", - "Info : [ 20%] Meshing curve 51 (Line)\n", - "Info : [ 20%] Meshing curve 52 (Line)\n", - "Info : [ 20%] Meshing curve 53 (Line)\n", - "Info : [ 30%] Meshing curve 54 (Line)\n", - "Info : [ 30%] Meshing curve 55 (Line)\n", - "Info : [ 30%] Meshing curve 56 (Line)\n", - "Info : [ 30%] Meshing curve 57 (Line)\n", - "Info : [ 40%] Meshing curve 58 (Line)\n", - "Info : [ 40%] Meshing curve 59 (Line)\n", - "Info : [ 40%] Meshing curve 60 (Line)\n", - "Info : [ 40%] Meshing curve 61 (Line)\n", - "Info : [ 50%] Meshing curve 62 (Line)\n", - "Info : [ 50%] Meshing curve 63 (Line)\n", - "Info : [ 50%] Meshing curve 64 (Line)\n", - "Info : [ 50%] Meshing curve 65 (Line)\n", - "Info : [ 60%] Meshing curve 66 (Line)\n", - "Info : [ 60%] Meshing curve 67 (Line)\n", - "Info : [ 60%] Meshing curve 68 (Line)\n", - "Info : [ 60%] Meshing curve 69 (Line)\n", - "Info : [ 70%] Meshing curve 70 (Line)\n", - "Info : [ 70%] Meshing curve 71 (Line)\n", - "Info : [ 70%] Meshing curve 72 (Line)\n", - "Info : [ 70%] Meshing curve 73 (Line)\n", - "Info : [ 80%] Meshing curve 74 (Line)\n", - "Info : [ 80%] Meshing curve 75 (Line)\n", - "Info : [ 80%] Meshing curve 76 (Line)\n", - "Info : [ 80%] Meshing curve 77 (Line)\n", - "Info : [ 90%] Meshing curve 78 (Line)\n", - "Info : [ 90%] Meshing curve 79 (Line)\n", - "Info : [ 90%] Meshing curve 80 (Line)\n", - "Info : [ 90%] Meshing curve 81 (Line)\n", - "Info : [100%] Meshing curve 82 (Line)\n", - "Info : [100%] Meshing curve 83 (Line)\n", - "Info : [100%] Meshing curve 84 (Line)\n", - "Info : [100%] Meshing curve 85 (Line)\n", - "Info : Done meshing 1D (Wall 0.120498s, CPU 0.107369s)\n", + "Info : [ 0%] Meshing curve 1 (Line)\n", + "Info : [ 10%] Meshing curve 2 (Line)\n", + "Info : [ 10%] Meshing curve 3 (Line)\n", + "Info : [ 10%] Meshing curve 4 (Line)\n", + "Info : [ 20%] Meshing curve 5 (Line)\n", + "Info : [ 20%] Meshing curve 6 (Line)\n", + "Info : [ 20%] Meshing curve 7 (Line)\n", + "Info : [ 20%] Meshing curve 8 (Line)\n", + "Info : [ 30%] Meshing curve 9 (Line)\n", + "Info : [ 30%] Meshing curve 10 (Line)\n", + "Info : [ 30%] Meshing curve 11 (Line)\n", + "Info : [ 30%] Meshing curve 12 (Line)\n", + "Info : [ 40%] Meshing curve 13 (Line)\n", + "Info : [ 40%] Meshing curve 14 (Line)\n", + "Info : [ 40%] Meshing curve 15 (Line)\n", + "Info : [ 40%] Meshing curve 16 (Line)\n", + "Info : [ 50%] Meshing curve 17 (Line)\n", + "Info : [ 50%] Meshing curve 18 (Line)\n", + "Info : [ 50%] Meshing curve 19 (Line)\n", + "Info : [ 50%] Meshing curve 20 (Line)\n", + "Info : [ 60%] Meshing curve 21 (Line)\n", + "Info : [ 60%] Meshing curve 22 (Line)\n", + "Info : [ 60%] Meshing curve 23 (Line)\n", + "Info : [ 60%] Meshing curve 24 (Line)\n", + "Info : [ 70%] Meshing curve 25 (Line)\n", + "Info : [ 70%] Meshing curve 26 (Line)\n", + "Info : [ 70%] Meshing curve 27 (Line)\n", + "Info : [ 70%] Meshing curve 28 (Line)\n", + "Info : [ 80%] Meshing curve 29 (Line)\n", + "Info : [ 80%] Meshing curve 30 (Line)\n", + "Info : [ 80%] Meshing curve 31 (Line)\n", + "Info : [ 80%] Meshing curve 32 (Line)\n", + "Info : [ 90%] Meshing curve 33 (Line)\n", + "Info : [ 90%] Meshing curve 34 (Line)\n", + "Info : [ 90%] Meshing curve 35 (Line)\n", + "Info : [ 90%] Meshing curve 36 (Line)\n", + "Info : [100%] Meshing curve 37 (Line)\n", + "Info : [100%] Meshing curve 38 (Line)\n", + "Info : [100%] Meshing curve 39 (Line)\n", + "Info : [100%] Meshing curve 40 (Line)\n", + "Info : Done meshing 1D (Wall 0.368538s, CPU 0.373135s)\n", "Info : Meshing 2D...\n", "Info : [ 0%] Meshing surface 7 (Plane, MeshAdapt)\n", "Info : [ 10%] Meshing surface 8 (Plane, MeshAdapt)\n", @@ -385,119 +398,139 @@ "Info : [ 30%] Meshing surface 11 (Plane, MeshAdapt)\n", "Info : [ 40%] Meshing surface 12 (Plane, MeshAdapt)\n", "Info : [ 50%] Meshing surface 13 (Plane, MeshAdapt)\n", - "Info : [ 50%] Meshing surface 14 (Plane, MeshAdapt)\n", + "Info : [ 60%] Meshing surface 14 (Plane, MeshAdapt)\n", "Info : [ 60%] Meshing surface 15 (Plane, MeshAdapt)\n", "Info : [ 70%] Meshing surface 16 (Plane, MeshAdapt)\n", - "Info : [ 70%] Meshing surface 17 (Plane, MeshAdapt)\n", + "Info : [ 80%] Meshing surface 17 (Plane, MeshAdapt)\n", "Info : [ 80%] Meshing surface 18 (Plane, MeshAdapt)\n", "Info : [ 90%] Meshing surface 19 (Plane, MeshAdapt)\n", - "Info : [ 90%] Meshing surface 20 (Plane, MeshAdapt)\n", - "Info : [100%] Meshing surface 21 (Plane, MeshAdapt)\n", - "Info : Done meshing 2D (Wall 0.142372s, CPU 0.14019s)\n", + "Info : [100%] Meshing surface 20 (Plane, MeshAdapt)\n", + "Info : Done meshing 2D (Wall 0.923452s, CPU 0.912526s)\n", "Info : Meshing 3D...\n", "Info : 3D Meshing 2 volumes with 1 connected component\n", - "Info : Tetrahedrizing 2561 nodes...\n", - "Info : Done tetrahedrizing 2569 nodes (Wall 0.0235287s, CPU 0.020498s)\n", + "Info : Tetrahedrizing 4960 nodes...\n", + "Info : Done tetrahedrizing 4968 nodes (Wall 0.114148s, CPU 0.109978s)\n", "Info : Reconstructing mesh...\n", "Info : - Creating surface mesh\n", "Info : - Identifying boundary edges\n", "Info : - Recovering boundary\n", - "Info : Done reconstructing mesh (Wall 0.0425806s, CPU 0.038769s)\n", + "Info : Done reconstructing mesh (Wall 0.217504s, CPU 0.211456s)\n", "Info : Found volume 2\n", "Info : Found volume 1\n", - "Info : It. 0 - 0 nodes created - worst tet radius 4.22528 (nodes removed 0 0)\n", - "Info : It. 500 - 500 nodes created - worst tet radius 1.21844 (nodes removed 0 0)\n", - "Info : 3D refinement terminated (3524 nodes total):\n", - "Info : - 0 Delaunay cavities modified for star shapeness\n", - "Info : - 0 nodes could not be inserted\n", - "Info : - 18179 tetrahedra created in 0.0395464 sec. (459687 tets/s)\n", + "Info : It. 0 - 0 nodes created - worst tet radius 5.52105 (nodes removed 0 0)\n", + "Info : It. 500 - 499 nodes created - worst tet radius 1.67588 (nodes removed 0 1)\n", + "Info : It. 1000 - 999 nodes created - worst tet radius 1.44902 (nodes removed 0 1)\n", + "Info : It. 1500 - 1499 nodes created - worst tet radius 1.22861 (nodes removed 0 1)\n", + "Info : It. 2000 - 1999 nodes created - worst tet radius 1.13379 (nodes removed 0 1)\n", + "Info : It. 2500 - 2499 nodes created - worst tet radius 1.17638 (nodes removed 0 1)\n", + "Info : It. 3000 - 2999 nodes created - worst tet radius 1.00538 (nodes removed 0 1)\n", + "Info : 3D refinement terminated (8004 nodes total):\n", + "Info : - 2 Delaunay cavities modified for star shapeness\n", + "Info : - 1 nodes could not be inserted\n", + "Info : - 44119 tetrahedra created in 0.460338 sec. (95840 tets/s)\n", "Info : 0 node relocations\n", - "Info : Done meshing 3D (Wall 0.114736s, CPU 0.110719s)\n", + "Info : Done meshing 3D (Wall 0.872377s, CPU 0.868986s)\n", "Info : Optimizing mesh...\n", "Info : Optimizing volume 1\n", - "Info : Optimization starts (volume = 4.68e-06) with worst = 0.0319047 / average = 0.67364:\n", - "Info : 0.00 < quality < 0.10 : 17 elements\n", - "Info : 0.10 < quality < 0.20 : 63 elements\n", - "Info : 0.20 < quality < 0.30 : 140 elements\n", - "Info : 0.30 < quality < 0.40 : 297 elements\n", - "Info : 0.40 < quality < 0.50 : 753 elements\n", - "Info : 0.50 < quality < 0.60 : 1236 elements\n", - "Info : 0.60 < quality < 0.70 : 1207 elements\n", - "Info : 0.70 < quality < 0.80 : 1373 elements\n", - "Info : 0.80 < quality < 0.90 : 1387 elements\n", - "Info : 0.90 < quality < 1.00 : 741 elements\n", - "Info : 193 edge swaps, 0 node relocations (volume = 4.68e-06): worst = 0.207267 / average = 0.687205 (Wall 0.00172274s, CPU 0.001706s)\n", - "Info : 195 edge swaps, 0 node relocations (volume = 4.68e-06): worst = 0.207267 / average = 0.687158 (Wall 0.00214301s, CPU 0.002136s)\n", + "Info : Optimization starts (volume = 5.67e-06) with worst = 0.00414796 / average = 0.67099:\n", + "Info : 0.00 < quality < 0.10 : 59 elements\n", + "Info : 0.10 < quality < 0.20 : 153 elements\n", + "Info : 0.20 < quality < 0.30 : 307 elements\n", + "Info : 0.30 < quality < 0.40 : 605 elements\n", + "Info : 0.40 < quality < 0.50 : 1833 elements\n", + "Info : 0.50 < quality < 0.60 : 3482 elements\n", + "Info : 0.60 < quality < 0.70 : 3780 elements\n", + "Info : 0.70 < quality < 0.80 : 3834 elements\n", + "Info : 0.80 < quality < 0.90 : 3488 elements\n", + "Info : 0.90 < quality < 1.00 : 1496 elements\n", + "Info : 485 edge swaps, 12 node relocations (volume = 5.67e-06): worst = 0.115668 / average = 0.683955 (Wall 0.0168896s, CPU 0.017073s)\n", + "Info : 496 edge swaps, 15 node relocations (volume = 5.67e-06): worst = 0.115668 / average = 0.684073 (Wall 0.0201504s, CPU 0.020496s)\n", "Info : No ill-shaped tets in the mesh :-)\n", "Info : 0.00 < quality < 0.10 : 0 elements\n", - "Info : 0.10 < quality < 0.20 : 0 elements\n", - "Info : 0.20 < quality < 0.30 : 25 elements\n", - "Info : 0.30 < quality < 0.40 : 298 elements\n", - "Info : 0.40 < quality < 0.50 : 735 elements\n", - "Info : 0.50 < quality < 0.60 : 1243 elements\n", - "Info : 0.60 < quality < 0.70 : 1208 elements\n", - "Info : 0.70 < quality < 0.80 : 1403 elements\n", - "Info : 0.80 < quality < 0.90 : 1386 elements\n", - "Info : 0.90 < quality < 1.00 : 747 elements\n", + "Info : 0.10 < quality < 0.20 : 7 elements\n", + "Info : 0.20 < quality < 0.30 : 12 elements\n", + "Info : 0.30 < quality < 0.40 : 602 elements\n", + "Info : 0.40 < quality < 0.50 : 1835 elements\n", + "Info : 0.50 < quality < 0.60 : 3431 elements\n", + "Info : 0.60 < quality < 0.70 : 3832 elements\n", + "Info : 0.70 < quality < 0.80 : 3860 elements\n", + "Info : 0.80 < quality < 0.90 : 3524 elements\n", + "Info : 0.90 < quality < 1.00 : 1497 elements\n", "Info : Optimizing volume 2\n", - "Info : Optimization starts (volume = 0.00031355) with worst = 0.00663094 / average = 0.672232:\n", - "Info : 0.00 < quality < 0.10 : 38 elements\n", - "Info : 0.10 < quality < 0.20 : 85 elements\n", - "Info : 0.20 < quality < 0.30 : 240 elements\n", - "Info : 0.30 < quality < 0.40 : 367 elements\n", - "Info : 0.40 < quality < 0.50 : 794 elements\n", - "Info : 0.50 < quality < 0.60 : 1898 elements\n", - "Info : 0.60 < quality < 0.70 : 2323 elements\n", - "Info : 0.70 < quality < 0.80 : 2579 elements\n", - "Info : 0.80 < quality < 0.90 : 1848 elements\n", - "Info : 0.90 < quality < 1.00 : 793 elements\n", - "Info : 291 edge swaps, 5 node relocations (volume = 0.00031355): worst = 0.2062 / average = 0.685631 (Wall 0.00241841s, CPU 0.002436s)\n", - "Info : 293 edge swaps, 5 node relocations (volume = 0.00031355): worst = 0.2062 / average = 0.685747 (Wall 0.00304061s, CPU 0.003056s)\n", + "Info : Optimization starts (volume = 0.00044901) with worst = 0.00712449 / average = 0.666614:\n", + "Info : 0.00 < quality < 0.10 : 71 elements\n", + "Info : 0.10 < quality < 0.20 : 244 elements\n", + "Info : 0.20 < quality < 0.30 : 482 elements\n", + "Info : 0.30 < quality < 0.40 : 845 elements\n", + "Info : 0.40 < quality < 0.50 : 2071 elements\n", + "Info : 0.50 < quality < 0.60 : 4459 elements\n", + "Info : 0.60 < quality < 0.70 : 5547 elements\n", + "Info : 0.70 < quality < 0.80 : 5509 elements\n", + "Info : 0.80 < quality < 0.90 : 4198 elements\n", + "Info : 0.90 < quality < 1.00 : 1656 elements\n", + "Info : 741 edge swaps, 12 node relocations (volume = 0.00044901): worst = 0.10223 / average = 0.681125 (Wall 0.0208262s, CPU 0.021018s)\n", + "Info : 751 edge swaps, 13 node relocations (volume = 0.00044901): worst = 0.108948 / average = 0.681296 (Wall 0.0259946s, CPU 0.02634s)\n", "Info : No ill-shaped tets in the mesh :-)\n", "Info : 0.00 < quality < 0.10 : 0 elements\n", - "Info : 0.10 < quality < 0.20 : 0 elements\n", - "Info : 0.20 < quality < 0.30 : 63 elements\n", - "Info : 0.30 < quality < 0.40 : 367 elements\n", - "Info : 0.40 < quality < 0.50 : 798 elements\n", - "Info : 0.50 < quality < 0.60 : 1896 elements\n", - "Info : 0.60 < quality < 0.70 : 2323 elements\n", - "Info : 0.70 < quality < 0.80 : 2614 elements\n", - "Info : 0.80 < quality < 0.90 : 1860 elements\n", - "Info : 0.90 < quality < 1.00 : 791 elements\n", - "Info : Done optimizing mesh (Wall 0.0129572s, CPU 0.012025s)\n", - "Info : 3524 nodes 23507 elements\n", + "Info : 0.10 < quality < 0.20 : 9 elements\n", + "Info : 0.20 < quality < 0.30 : 27 elements\n", + "Info : 0.30 < quality < 0.40 : 830 elements\n", + "Info : 0.40 < quality < 0.50 : 2102 elements\n", + "Info : 0.50 < quality < 0.60 : 4436 elements\n", + "Info : 0.60 < quality < 0.70 : 5560 elements\n", + "Info : 0.70 < quality < 0.80 : 5600 elements\n", + "Info : 0.80 < quality < 0.90 : 4233 elements\n", + "Info : 0.90 < quality < 1.00 : 1643 elements\n", + "Info : Done optimizing mesh (Wall 0.122415s, CPU 0.123794s)\n", + "Info : 8004 nodes 54051 elements\n", "Info : Optimizing mesh (Netgen)...\n", "Info : Optimizing volume 1\n", - "Info : CalcLocalH: 2358 Points 7047 Elements 4606 Surface Elements \n", + "Info : CalcLocalH: 5518 Points 18615 Elements 9234 Surface Elements \n", "Info : Remove Illegal Elements \n", - "Info : 153 illegal tets \n", + "Info : 232 illegal tets \n", "Info : SplitImprove \n", - "Info : badmax = 53.301 \n", - "Info : 24 splits performed \n", + "Info : badmax = 60.0977 \n", + "Info : 38 splits performed \n", "Info : SwapImprove \n", - "Info : 14 swaps performed \n", + "Info : 27 swaps performed \n", "Info : SwapImprove2 \n", "Info : 0 swaps performed \n", - "Info : 123 illegal tets \n", + "Info : 175 illegal tets \n", "Info : SplitImprove \n", - "Info : badmax = 54.6473 \n", - "Info : 27 splits performed \n", + "Info : badmax = 66.684 \n", + "Info : 38 splits performed \n", "Info : SwapImprove \n", - "Info : 6 swaps performed \n", + "Info : 11 swaps performed \n", "Info : SwapImprove2 \n", - "Info : 3 swaps performed \n", - "Info : 66 illegal tets \n", + "Info : 8 swaps performed \n", + "Info : 77 illegal tets \n", "Info : SplitImprove \n", - "Info : badmax = 3309.82 \n", - "Info : 24 splits performed \n", + "Info : badmax = 3449.76 \n", + "Info : 22 splits performed \n", "Info : SwapImprove \n", - "Info : 3 swaps performed \n", + "Info : 9 swaps performed \n", "Info : SwapImprove2 \n", + "Info : 4 swaps performed \n", + "Info : 17 illegal tets \n", + "Info : SplitImprove \n", + "Info : badmax = 5945.37 \n", + "Info : 6 splits performed \n", + "Info : SwapImprove \n", "Info : 1 swaps performed \n", - "Info : 12 illegal tets \n", + "Info : SwapImprove2 \n", + "Info : 0 swaps performed \n", + "Info : 5 illegal tets \n", + "Info : SplitImprove \n", + "Info : badmax = 317.821 \n", + "Info : 1 splits performed \n", + "Info : SwapImprove \n", + "Info : 0 swaps performed \n", + "Info : SwapImprove2 \n", + "Info : 0 swaps performed \n", + "Info : 3 illegal tets \n", "Info : SplitImprove \n", - "Info : badmax = 357.875 \n", - "Info : 5 splits performed \n", + "Info : badmax = 317.821 \n", + "Info : 1 splits performed \n", "Info : SwapImprove \n", "Info : 0 swaps performed \n", "Info : SwapImprove2 \n", @@ -505,164 +538,164 @@ "Info : 0 illegal tets \n", "Info : Volume Optimization \n", "Info : CombineImprove \n", - "Info : 23 elements combined \n", + "Info : 32 elements combined \n", "Info : ImproveMesh \n", - "Info : Total badness = 14074.5 \n", - "Info : Total badness = 13912.6 \n", + "Info : Total badness = 33653.5 \n", + "Info : Total badness = 31439.3 \n", "Info : SplitImprove \n", - "Info : badmax = 69.4619 \n", - "Info : 1 splits performed \n", + "Info : badmax = 65.2629 \n", + "Info : 0 splits performed \n", "Info : ImproveMesh \n", - "Info : Total badness = 13918.6 \n", - "Info : Total badness = 13912.1 \n", + "Info : Total badness = 31439.3 \n", + "Info : Total badness = 31153.7 \n", "Info : SwapImprove \n", - "Info : 358 swaps performed \n", + "Info : 1015 swaps performed \n", "Info : SwapImprove2 \n", "Info : 0 swaps performed \n", "Info : ImproveMesh \n", - "Info : Total badness = 13192 \n", - "Info : Total badness = 13130.2 \n", + "Info : Total badness = 29240.2 \n", + "Info : Total badness = 28834.8 \n", "Info : CombineImprove \n", - "Info : 0 elements combined \n", + "Info : 1 elements combined \n", "Info : ImproveMesh \n", - "Info : Total badness = 13130.2 \n", - "Info : Total badness = 13129.5 \n", + "Info : Total badness = 28822.6 \n", + "Info : Total badness = 28805.6 \n", "Info : SplitImprove \n", - "Info : badmax = 48.8037 \n", + "Info : badmax = 60.0639 \n", "Info : 0 splits performed \n", "Info : ImproveMesh \n", - "Info : Total badness = 13129.5 \n", - "Info : Total badness = 13129.5 \n", + "Info : Total badness = 28805.6 \n", + "Info : Total badness = 28805.1 \n", "Info : SwapImprove \n", - "Info : 24 swaps performed \n", + "Info : 199 swaps performed \n", "Info : SwapImprove2 \n", "Info : 0 swaps performed \n", "Info : ImproveMesh \n", - "Info : Total badness = 13093.6 \n", - "Info : Total badness = 13085.2 \n", + "Info : Total badness = 28591.3 \n", + "Info : Total badness = 28483.3 \n", "Info : CombineImprove \n", "Info : 0 elements combined \n", "Info : ImproveMesh \n", - "Info : Total badness = 13085.2 \n", - "Info : Total badness = 13085.1 \n", + "Info : Total badness = 28483.3 \n", + "Info : Total badness = 28479.7 \n", "Info : SplitImprove \n", - "Info : badmax = 48.8085 \n", + "Info : badmax = 51.4219 \n", "Info : 0 splits performed \n", "Info : ImproveMesh \n", - "Info : Total badness = 13085.1 \n", - "Info : Total badness = 13085.1 \n", + "Info : Total badness = 28479.7 \n", + "Info : Total badness = 28479.5 \n", "Info : SwapImprove \n", - "Info : 8 swaps performed \n", + "Info : 81 swaps performed \n", "Info : SwapImprove2 \n", "Info : 0 swaps performed \n", "Info : ImproveMesh \n", - "Info : Total badness = 13083.1 \n", - "Info : Total badness = 13076.5 \n", + "Info : Total badness = 28416 \n", + "Info : Total badness = 28366.7 \n", "Info : Optimizing volume 2\n", - "Info : CalcLocalH: 2760 Points 10713 Elements 3696 Surface Elements \n", + "Info : CalcLocalH: 6212 Points 24440 Elements 8130 Surface Elements \n", "Info : Remove Illegal Elements \n", - "Info : 348 illegal tets \n", + "Info : 383 illegal tets \n", "Info : SplitImprove \n", - "Info : badmax = 72.2751 \n", - "Info : 40 splits performed \n", + "Info : badmax = 97.3676 \n", + "Info : 49 splits performed \n", "Info : SwapImprove \n", - "Info : 54 swaps performed \n", + "Info : 57 swaps performed \n", "Info : SwapImprove2 \n", - "Info : 1 swaps performed \n", - "Info : 241 illegal tets \n", + "Info : 2 swaps performed \n", + "Info : 257 illegal tets \n", "Info : SplitImprove \n", - "Info : badmax = 97.9241 \n", - "Info : 42 splits performed \n", + "Info : badmax = 120.61 \n", + "Info : 49 splits performed \n", "Info : SwapImprove \n", - "Info : 24 swaps performed \n", + "Info : 26 swaps performed \n", "Info : SwapImprove2 \n", - "Info : 0 swaps performed \n", - "Info : 150 illegal tets \n", + "Info : 1 swaps performed \n", + "Info : 133 illegal tets \n", "Info : SplitImprove \n", - "Info : badmax = 103.264 \n", - "Info : 29 splits performed \n", + "Info : badmax = 120.61 \n", + "Info : 31 splits performed \n", "Info : SwapImprove \n", - "Info : 17 swaps performed \n", + "Info : 11 swaps performed \n", "Info : SwapImprove2 \n", "Info : 2 swaps performed \n", - "Info : 74 illegal tets \n", + "Info : 54 illegal tets \n", "Info : SplitImprove \n", - "Info : badmax = 96.2662 \n", - "Info : 21 splits performed \n", + "Info : badmax = 93.243 \n", + "Info : 15 splits performed \n", "Info : SwapImprove \n", - "Info : 4 swaps performed \n", + "Info : 2 swaps performed \n", "Info : SwapImprove2 \n", - "Info : 1 swaps performed \n", - "Info : 15 illegal tets \n", + "Info : 0 swaps performed \n", + "Info : 12 illegal tets \n", "Info : SplitImprove \n", - "Info : badmax = 165.787 \n", + "Info : badmax = 93.243 \n", "Info : 4 splits performed \n", "Info : SwapImprove \n", - "Info : 6 swaps performed \n", + "Info : 0 swaps performed \n", "Info : SwapImprove2 \n", "Info : 0 swaps performed \n", "Info : 0 illegal tets \n", "Info : Volume Optimization \n", "Info : CombineImprove \n", - "Info : 55 elements combined \n", + "Info : 92 elements combined \n", "Info : ImproveMesh \n", - "Info : Total badness = 19339.2 \n", - "Info : Total badness = 17837.4 \n", + "Info : Total badness = 42401.4 \n", + "Info : Total badness = 39092.5 \n", "Info : SplitImprove \n", - "Info : badmax = 37.5597 \n", + "Info : badmax = 80.0955 \n", "Info : 1 splits performed \n", "Info : ImproveMesh \n", - "Info : Total badness = 17843.1 \n", - "Info : Total badness = 17638.3 \n", + "Info : Total badness = 39101.3 \n", + "Info : Total badness = 38607.1 \n", "Info : SwapImprove \n", - "Info : 650 swaps performed \n", + "Info : 1412 swaps performed \n", "Info : SwapImprove2 \n", "Info : 0 swaps performed \n", "Info : ImproveMesh \n", - "Info : Total badness = 16331.3 \n", - "Info : Total badness = 15960.9 \n", + "Info : Total badness = 35935.5 \n", + "Info : Total badness = 35120.3 \n", "Info : CombineImprove \n", - "Info : 7 elements combined \n", + "Info : 15 elements combined \n", "Info : ImproveMesh \n", - "Info : Total badness = 15895.9 \n", - "Info : Total badness = 15869 \n", + "Info : Total badness = 34958 \n", + "Info : Total badness = 34891.4 \n", "Info : SplitImprove \n", - "Info : badmax = 28.9639 \n", + "Info : badmax = 57.6624 \n", "Info : 0 splits performed \n", "Info : ImproveMesh \n", - "Info : Total badness = 15869 \n", - "Info : Total badness = 15866.1 \n", + "Info : Total badness = 34891.4 \n", + "Info : Total badness = 34882.1 \n", "Info : SwapImprove \n", - "Info : 163 swaps performed \n", + "Info : 315 swaps performed \n", "Info : SwapImprove2 \n", "Info : 0 swaps performed \n", "Info : ImproveMesh \n", - "Info : Total badness = 15751.7 \n", - "Info : Total badness = 15648.7 \n", + "Info : Total badness = 34596.7 \n", + "Info : Total badness = 34391.3 \n", "Info : CombineImprove \n", - "Info : 3 elements combined \n", + "Info : 4 elements combined \n", "Info : ImproveMesh \n", - "Info : Total badness = 15619.8 \n", - "Info : Total badness = 15613.5 \n", + "Info : Total badness = 34348.6 \n", + "Info : Total badness = 34333.7 \n", "Info : SplitImprove \n", - "Info : badmax = 28.83 \n", + "Info : badmax = 49.4872 \n", "Info : 0 splits performed \n", "Info : ImproveMesh \n", - "Info : Total badness = 15613.5 \n", - "Info : Total badness = 15612.8 \n", + "Info : Total badness = 34333.7 \n", + "Info : Total badness = 34332 \n", "Info : SwapImprove \n", - "Info : 75 swaps performed \n", + "Info : 117 swaps performed \n", "Info : SwapImprove2 \n", "Info : 0 swaps performed \n", "Info : ImproveMesh \n", - "Info : Total badness = 15558.9 \n", - "Info : Total badness = 15511.5 \n", - "Info : Done optimizing mesh (Wall 0.57654s, CPU 0.577822s)\n", + "Info : Total badness = 34263.2 \n", + "Info : Total badness = 34189.9 \n", + "Info : Done optimizing mesh (Wall 3.09309s, CPU 3.12883s)\n", "Info : Writing 'patch_antenna.msh'...\n", "Mesh saved to patch_antenna.msh\n", "Info : Done writing 'patch_antenna.msh'\n", - " Nodes: 3654\n", - " Elements: 23666\n", + " Nodes: 8115\n", + " Elements: 53184\n", "Info : Writing 'patch_antenna.msh'...\n", "Info : Done writing 'patch_antenna.msh'\n" ] @@ -693,7 +726,7 @@ "mesh_sizes = {\n", " \"substrate\": wavelength / 12,\n", " \"air_box\": wavelength / 4,\n", - " \"lumped_port\": wavelength / 400,\n", + " \"lumped_port\": wavelength / 150,\n", " \"ground_plane\" : wavelength / 10,\n", " \"top_conductor\": wavelength / 50\n", "}\n", @@ -723,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "id": "3c212d13", "metadata": { "execution": { @@ -750,31 +783,38 @@ "Groups to render transparent: air_box__None\n", "\n", "Mesh loaded successfully with 2 cell blocks\n", - "Found 5152 triangles total\n", + "Found 9954 triangles total\n", "Physical group tags in mesh: {3: 'top_conductor', 4: 'ground_plane', 5: 'lumped_port', 6: 'air_box__None', 7: 'air_box__substrate'}\n" ] }, { "data": { "text/html": [ - "
" ], @@ -816,7 +856,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "id": "8a378602", "metadata": { "execution": { @@ -837,9 +877,11 @@ "outputs": [], "source": [ "output_file: str = \"patch_antenna.json\"\n", - "freq_min: float = 1.0\n", - "freq_max: float = 7.0\n", + "# Sweep de simulación \n", + "freq_min: float = 3.0\n", + "freq_max: float = 4.0\n", "freq_step: float = 0.05\n", + "\n", "solver_order: int = 2" ] }, @@ -863,7 +905,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "id": "9d7b7f83", "metadata": {}, "outputs": [ @@ -888,16 +930,17 @@ " 'Permittivity': 2.2,\n", " 'LossTan': 0.0009}]},\n", " 'Boundaries': {'PEC': {'Attributes': [3, 4]},\n", - " 'Absorbing': {'Attributes': [6, 7], 'Order': 2},\n", + " 'Absorbing': {'Attributes': [6], 'Order': 2},\n", " 'LumpedPort': [{'Index': 1,\n", " 'Attributes': [5],\n", " 'R': 50.0,\n", " 'Excitation': True,\n", - " 'Direction': '+Z'}]},\n", + " 'Direction': '+Z'}],\n", + " 'Postprocessing': {'FarField': {'Attributes': [6], 'NSample': 32000}}},\n", " 'Solver': {'Order': 2,\n", " 'Device': 'CPU',\n", - " 'Driven': {'MinFreq': 1.0,\n", - " 'MaxFreq': 7.0,\n", + " 'Driven': {'MinFreq': 3.0,\n", + " 'MaxFreq': 4.0,\n", " 'FreqStep': 0.05,\n", " 'SaveStep': 5,\n", " 'AdaptiveTol': 0.001},\n", @@ -907,7 +950,7 @@ " 'MaxIts': 500}}}" ] }, - "execution_count": 8, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -922,7 +965,8 @@ " freq_step = freq_step ,\n", " L0 = 1.0,\n", " solver_order = solver_order,\n", - " absorbing_order = 2)" + " absorbing_order = 2,\n", + " farfield=True)" ] }, { @@ -935,7 +979,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "b60b1c59", "metadata": {}, "outputs": [ @@ -943,8 +987,7 @@ "name": "stdout", "output_type": "stream", "text": [ - " Running: /home/martin/.cache/palacetoolkit/runtime/palace-cpu-v0.1.2/bin/palace -np 16 /home/martin/Desktop/PalaceToolkit/docs/examples/patch.config\n", - ">> /usr/bin/mpirun -n 16 /home/martin/.cache/palacetoolkit/runtime/palace-cpu-v0.1.2/bin/palace-x86_64.bin /home/martin/Desktop/PalaceToolkit/docs/examples/patch.config\n", + " Running: apptainer exec --pwd /work --bind /home/loloc/PalaceToolkit/docs/examples:/work /mnt/c/Users/loloc/Desktop/Palace/palace/Palace.sif mpirun -np 16 /opt/palace/bin/palace-x86_64.bin /work/patch.config\n", "\n", "_____________ _______\n", "_____ __ \\____ __ /____ ____________\n", @@ -955,45 +998,42 @@ "\n", "\u001b[38;2;255;255;000m--> Warning!\u001b[0m\n", "Output folder is not empty; program will overwrite content! (postpro/patch)\n", - "Git changeset ID: v0.16.1-51-g4f2e2d97\n", + "Git changeset ID: v0.15.0-13-g2bbc5096\n", "Running with 16 MPI processes, 1 OpenMP thread\n", "Device configuration: omp,cpu\n", "Memory configuration: host-std\n", "libCEED backend: /cpu/self/xsmm/blocked\n", "\n", - "Added 1585 duplicate vertices for interior boundaries in the mesh\n", - "Added 3142 duplicate boundary elements for interior boundaries in the mesh\n", + "Added 1370 duplicate vertices for interior boundaries in the mesh\n", + "Added 3558 duplicate boundary elements for interior boundaries in the mesh\n", + "Finished partitioning mesh into 16 subdomains\n", "\n", "Characteristic length and time scales:\n", - " Lc = 1.100e-01 m, tc = 3.669e-01 ns\n", - "Finished partitioning mesh into 16 subdomains\n", + " Lc = 1.200e-01 m, tc = 4.003e-01 ns\n", "\n", "Mesh curvature order: 1\n", "Mesh bounding box:\n", - " (Xmin, Ymin, Zmin) = (-5.500e-02, -5.500e-02, +0.000e+00) m\n", - " (Xmax, Ymax, Zmax) = (+5.500e-02, +5.500e-02, +2.630e-02) m\n", + " (Xmin, Ymin, Zmin) = (-6.000e-02, -6.000e-02, +0.000e+00) m\n", + " (Xmax, Ymax, Zmax) = (+6.000e-02, +6.000e-02, +3.157e-02) m\n", "\n", "Parallel Mesh Stats:\n", "\n", " minimum average maximum total\n", - " vertices 226 327 423 5239\n", - " edges 1396 1706 1959 27300\n", - " faces 2257 2498 2657 39981\n", - " elements 1087 1119 1153 17919\n", - " neighbors 2 4 9\n", + " vertices 523 592 691 9485\n", + " edges 3317 3531 3783 56501\n", + " faces 5361 5575 5733 89206\n", + " elements 2573 2636 2704 42188\n", + " neighbors 4 7 11\n", "\n", " minimum maximum\n", - " h 0.00355613 0.155272\n", - " kappa 1.13016 13.8543\n", - "\n", - "Estimated current per-rank memory usage is: Min. 43.8M, Max. 57.7M, Avg. 45.1M, Total 722.1M\n", - "Estimated current per-node memory usage is: Min. 724.2M, Max. 724.2M, Avg. 724.2M, Total 724.2M\n", + " h 0.00136422 0.160382\n", + " kappa 1.10926 12.9511\n", "\n", "Configuring Robin absorbing BC (order 2) at attributes:\n", - " 6-7\n", + " 6\n", "\n", "Configuring Robin impedance BC for lumped ports at attributes:\n", - " 5: Rs = 3.846e+01 Ω/sq, n = (-1.0,+0.0,+0.0)\n", + " 5: Rs = 1.644e+02 Ω/sq, n = (-1.0,+0.0,+0.0)\n", "\n", "Configuring lumped port circuit properties:\n", " Index = 1: R = 5.000e+01 Ω\n", @@ -1009,1806 +1049,196 @@ " Lumped port 1\n", "\n", "Beginning PROM construction offline phase:\n", - " 121 points for frequency sweep over [1.000e+00, 7.000e+00] GHz\n", + " 21 points for frequency sweep over [3.000e+00, 4.000e+00] GHz\n", "\n", "Assembling system matrices, number of global unknowns:\n", - " H1 (p = 2): 32539, ND (p = 2): 134562, RT (p = 2): 173700\n", + " H1 (p = 2): 65986, ND (p = 2): 291414, RT (p = 2): 394182\n", " Operator assembly level: Partial\n", " Mesh geometries:\n", - " Tetrahedron: P = 20, Q = 14 (quadrature order = 4)\n", + " Tetrahedron: P = 20, Q = 11 (quadrature order = 4)\n", "\n", "Assembling multigrid hierarchy:\n", - " Level 0 (p = 1): 27300 unknowns\n", - " Level 1 (p = 2): 134562 unknowns\n", - " Level 0 (auxiliary) (p = 1): 5239 unknowns\n", - " Level 1 (auxiliary) (p = 2): 32539 unknowns\n", - "\n", - " Residual norms for GMRES solve\n", - " 0 (restart 0) KSP residual norm 9.390411e+00\n", - " 1 (restart 0) KSP residual norm 8.610250e+00\n", - " 2 (restart 0) KSP residual norm 4.276071e+00\n", - " 3 (restart 0) KSP residual norm 2.151573e+00\n", - " 4 (restart 0) KSP residual norm 8.301657e-01\n", - " 5 (restart 0) KSP residual norm 5.927243e-01\n", - " 6 (restart 0) KSP residual norm 4.472137e-01\n", - " 7 (restart 0) KSP residual norm 3.176477e-01\n", - " 8 (restart 0) KSP residual norm 1.695899e-01\n", - " 9 (restart 0) KSP residual norm 6.737670e-02\n", - " 10 (restart 0) KSP residual norm 4.273955e-02\n", - " 11 (restart 0) KSP residual norm 2.455760e-02\n", - " 12 (restart 0) KSP residual norm 1.662199e-02\n", - " 13 (restart 0) KSP residual norm 8.928176e-03\n", - " 14 (restart 0) KSP residual norm 4.573848e-03\n", - " 15 (restart 0) KSP residual norm 2.681506e-03\n", - " 16 (restart 0) KSP residual norm 1.856789e-03\n", - " 17 (restart 0) KSP residual norm 1.001770e-03\n", - " 18 (restart 0) KSP residual norm 5.843657e-04\n", - " 19 (restart 0) KSP residual norm 3.549690e-04\n", - " 20 (restart 0) KSP residual norm 1.944111e-04\n", - " 21 (restart 0) KSP residual norm 1.077087e-04\n", - " 22 (restart 0) KSP residual norm 6.432707e-05\n", - " 23 (restart 0) KSP residual norm 3.361469e-05\n", - " 24 (restart 0) KSP residual norm 1.774120e-05\n", - " 25 (restart 0) KSP residual norm 1.014999e-05\n", - " 26 (restart 0) KSP residual norm 5.430069e-06\n", - " 27 (restart 0) KSP residual norm 3.176418e-06\n", - " 28 (restart 0) KSP residual norm 1.822400e-06\n", - " 29 (restart 0) KSP residual norm 1.138756e-06\n", - " 30 (restart 0) KSP residual norm 7.458396e-07\n", - " 31 (restart 0) KSP residual norm 4.766722e-07\n", - " 32 (restart 0) KSP residual norm 3.102637e-07\n", - " 33 (restart 0) KSP residual norm 1.859564e-07\n", - " 34 (restart 0) KSP residual norm 1.121650e-07\n", - " 35 (restart 0) KSP residual norm 6.614425e-08\n", - "GMRES solver converged in 35 iterations (avg. reduction factor: 5.849e-01)\n", - " Field energy E (2.054e-11 J) + H (4.474e-11 J) = 6.528e-11 J\n", - "\n", - " Residual norms for GMRES solve\n", - " 0 (restart 0) KSP residual norm 6.340616e+02\n", - " 1 (restart 0) KSP residual norm 5.865490e+01\n", - " 2 (restart 0) KSP residual norm 1.158306e+01\n", - " 3 (restart 0) KSP residual norm 1.067576e+01\n", - " 4 (restart 0) KSP residual norm 1.032869e+01\n", - " 5 (restart 0) KSP residual norm 7.482092e+00\n", - " 6 (restart 0) KSP residual norm 7.160284e+00\n", - " 7 (restart 0) KSP residual norm 6.678148e+00\n", - " 8 (restart 0) KSP residual norm 6.446218e+00\n", - " 9 (restart 0) KSP residual norm 6.273929e+00\n", - " 10 (restart 0) KSP residual norm 5.494908e+00\n", - " 11 (restart 0) KSP residual norm 5.237036e+00\n", - " 12 (restart 0) KSP residual norm 4.738916e+00\n", - " 13 (restart 0) KSP residual norm 4.606677e+00\n", - " 14 (restart 0) KSP residual norm 3.902439e+00\n", - " 15 (restart 0) KSP residual norm 3.802467e+00\n", - " 16 (restart 0) KSP residual norm 3.628411e+00\n", - " 17 (restart 0) KSP residual norm 3.050602e+00\n", - " 18 (restart 0) KSP residual norm 2.483130e+00\n", - " 19 (restart 0) KSP residual norm 2.332310e+00\n", - " 20 (restart 0) KSP residual norm 2.068934e+00\n", - " 21 (restart 0) KSP residual norm 1.894428e+00\n", - " 22 (restart 0) KSP residual norm 1.694681e+00\n", - " 23 (restart 0) KSP residual norm 1.584121e+00\n", - " 24 (restart 0) KSP residual norm 1.372501e+00\n", - " 25 (restart 0) KSP residual norm 1.259096e+00\n", - " 26 (restart 0) KSP residual norm 1.129365e+00\n", - " 27 (restart 0) KSP residual norm 1.047181e+00\n", - " 28 (restart 0) KSP residual norm 9.361363e-01\n", - " 29 (restart 0) KSP residual norm 8.723721e-01\n", - " 30 (restart 0) KSP residual norm 7.023014e-01\n", - " 31 (restart 0) KSP residual norm 5.803797e-01\n", - " 32 (restart 0) KSP residual norm 4.891579e-01\n", - " 33 (restart 0) KSP residual norm 4.299519e-01\n", - " 34 (restart 0) KSP residual norm 3.840150e-01\n", - " 35 (restart 0) KSP residual norm 3.204976e-01\n", - " 36 (restart 0) KSP residual norm 2.860438e-01\n", - " 37 (restart 0) KSP residual norm 2.419242e-01\n", - " 38 (restart 0) KSP residual norm 2.222653e-01\n", - " 39 (restart 0) KSP residual norm 1.869088e-01\n", - " 40 (restart 0) KSP residual norm 1.676486e-01\n", - " 41 (restart 0) KSP residual norm 1.431822e-01\n", - " 42 (restart 0) KSP residual norm 1.249755e-01\n", - " 43 (restart 0) KSP residual norm 1.145922e-01\n", - " 44 (restart 0) KSP residual norm 9.631295e-02\n", - " 45 (restart 0) KSP residual norm 8.305945e-02\n", - " 46 (restart 0) KSP residual norm 7.279482e-02\n", - " 47 (restart 0) KSP residual norm 6.415174e-02\n", - " 48 (restart 0) KSP residual norm 5.584070e-02\n", - " 49 (restart 0) KSP residual norm 4.782108e-02\n", - " 50 (restart 0) KSP residual norm 4.004127e-02\n", - " 51 (restart 0) KSP residual norm 3.350077e-02\n", - " 52 (restart 0) KSP residual norm 2.769418e-02\n", - " 53 (restart 0) KSP residual norm 2.342054e-02\n", - " 54 (restart 0) KSP residual norm 1.897038e-02\n", - " 55 (restart 0) KSP residual norm 1.560070e-02\n", - " 56 (restart 0) KSP residual norm 1.302738e-02\n", - " 57 (restart 0) KSP residual norm 1.085561e-02\n", - " 58 (restart 0) KSP residual norm 8.825434e-03\n", - " 59 (restart 0) KSP residual norm 7.298219e-03\n", - " 60 (restart 0) KSP residual norm 5.953772e-03\n", - " 61 (restart 0) KSP residual norm 5.052888e-03\n", - " 62 (restart 0) KSP residual norm 3.969988e-03\n", - " 63 (restart 0) KSP residual norm 3.283202e-03\n", - " 64 (restart 0) KSP residual norm 2.644195e-03\n", - " 65 (restart 0) KSP residual norm 2.028884e-03\n", - " 66 (restart 0) KSP residual norm 1.680855e-03\n", - " 67 (restart 0) KSP residual norm 1.342876e-03\n", - " 68 (restart 0) KSP residual norm 1.035780e-03\n", - " 69 (restart 0) KSP residual norm 8.346466e-04\n", - " 70 (restart 0) KSP residual norm 6.397107e-04\n", - " 71 (restart 0) KSP residual norm 5.076534e-04\n", - " 72 (restart 0) KSP residual norm 4.016783e-04\n", - " 73 (restart 0) KSP residual norm 3.265330e-04\n", - " 74 (restart 0) KSP residual norm 2.612588e-04\n", - " 75 (restart 0) KSP residual norm 2.175969e-04\n", - " 76 (restart 0) KSP residual norm 1.723256e-04\n", - " 77 (restart 0) KSP residual norm 1.332829e-04\n", - " 78 (restart 0) KSP residual norm 1.068604e-04\n", - " 79 (restart 0) KSP residual norm 8.479557e-05\n", - " 80 (restart 0) KSP residual norm 6.607118e-05\n", - " 81 (restart 0) KSP residual norm 5.287474e-05\n", - " 82 (restart 0) KSP residual norm 4.263942e-05\n", - " 83 (restart 0) KSP residual norm 3.412808e-05\n", - " 84 (restart 0) KSP residual norm 2.665932e-05\n", - " 85 (restart 0) KSP residual norm 2.145235e-05\n", - " 86 (restart 0) KSP residual norm 1.732264e-05\n", - " 87 (restart 0) KSP residual norm 1.377133e-05\n", - " 88 (restart 0) KSP residual norm 1.120795e-05\n", - " 89 (restart 0) KSP residual norm 8.847671e-06\n", - " 90 (restart 0) KSP residual norm 7.300607e-06\n", - " 91 (restart 0) KSP residual norm 6.010169e-06\n", - "GMRES solver converged in 91 iterations (avg. reduction factor: 8.163e-01)\n", - " Field energy E (6.043e-12 J) + H (9.433e-12 J) = 1.548e-11 J\n", + " Level 0 (p = 1): 56501 unknowns\n", + " Level 1 (p = 2): 291414 unknowns\n", + " Level 0 (auxiliary) (p = 1): 9485 unknowns\n", + " Level 1 (auxiliary) (p = 2): 65986 unknowns\n", "\n", " Residual norms for GMRES solve\n", - " 0 (restart 0) KSP residual norm 1.529025e+01\n", - " 1 (restart 0) KSP residual norm 1.436801e+01\n", - " 2 (restart 0) KSP residual norm 1.372859e+01\n", - " 3 (restart 0) KSP residual norm 1.356227e+01\n", - " 4 (restart 0) KSP residual norm 1.346510e+01\n", - " 5 (restart 0) KSP residual norm 1.312508e+01\n", - " 6 (restart 0) KSP residual norm 9.657604e+00\n", - " 7 (restart 0) KSP residual norm 9.402038e+00\n", - " 8 (restart 0) KSP residual norm 8.236024e+00\n", - " 9 (restart 0) KSP residual norm 8.129019e+00\n", - " 10 (restart 0) KSP residual norm 6.276398e+00\n", - " 11 (restart 0) KSP residual norm 6.061222e+00\n", - " 12 (restart 0) KSP residual norm 5.338306e+00\n", - " 13 (restart 0) KSP residual norm 4.034075e+00\n", - " 14 (restart 0) KSP residual norm 3.860492e+00\n", - " 15 (restart 0) KSP residual norm 3.400737e+00\n", - " 16 (restart 0) KSP residual norm 3.283075e+00\n", - " 17 (restart 0) KSP residual norm 3.020883e+00\n", - " 18 (restart 0) KSP residual norm 2.920133e+00\n", - " 19 (restart 0) KSP residual norm 2.625302e+00\n", - " 20 (restart 0) KSP residual norm 2.477593e+00\n", - " 21 (restart 0) KSP residual norm 2.331252e+00\n", - " 22 (restart 0) KSP residual norm 2.125823e+00\n", - " 23 (restart 0) KSP residual norm 1.998180e+00\n", - " 24 (restart 0) KSP residual norm 1.817477e+00\n", - " 25 (restart 0) KSP residual norm 1.682133e+00\n", - " 26 (restart 0) KSP residual norm 1.584896e+00\n", - " 27 (restart 0) KSP residual norm 1.478136e+00\n", - " 28 (restart 0) KSP residual norm 1.377074e+00\n", - " 29 (restart 0) KSP residual norm 1.298881e+00\n", - " 30 (restart 0) KSP residual norm 1.202597e+00\n", - " 31 (restart 0) KSP residual norm 1.058682e+00\n", - " 32 (restart 0) KSP residual norm 9.699973e-01\n", - " 33 (restart 0) KSP residual norm 8.461862e-01\n", - " 34 (restart 0) KSP residual norm 7.656338e-01\n", - " 35 (restart 0) KSP residual norm 6.573611e-01\n", - " 36 (restart 0) KSP residual norm 5.778923e-01\n", - " 37 (restart 0) KSP residual norm 5.087766e-01\n", - " 38 (restart 0) KSP residual norm 4.446451e-01\n", - " 39 (restart 0) KSP residual norm 4.012098e-01\n", - " 40 (restart 0) KSP residual norm 3.524737e-01\n", - " 41 (restart 0) KSP residual norm 3.182990e-01\n", - " 42 (restart 0) KSP residual norm 2.883785e-01\n", - " 43 (restart 0) KSP residual norm 2.665300e-01\n", - " 44 (restart 0) KSP residual norm 2.432266e-01\n", - " 45 (restart 0) KSP residual norm 2.158636e-01\n", - " 46 (restart 0) KSP residual norm 1.905269e-01\n", - " 47 (restart 0) KSP residual norm 1.640913e-01\n", - " 48 (restart 0) KSP residual norm 1.446377e-01\n", - " 49 (restart 0) KSP residual norm 1.122960e-01\n", - " 50 (restart 0) KSP residual norm 9.284925e-02\n", - " 51 (restart 0) KSP residual norm 8.337004e-02\n", - " 52 (restart 0) KSP residual norm 7.112561e-02\n", - " 53 (restart 0) KSP residual norm 6.293922e-02\n", - " 54 (restart 0) KSP residual norm 5.286523e-02\n", - " 55 (restart 0) KSP residual norm 4.714544e-02\n", - " 56 (restart 0) KSP residual norm 3.944258e-02\n", - " 57 (restart 0) KSP residual norm 3.452418e-02\n", - " 58 (restart 0) KSP residual norm 3.037641e-02\n", - " 59 (restart 0) KSP residual norm 2.667717e-02\n", - " 60 (restart 0) KSP residual norm 2.335050e-02\n", - " 61 (restart 0) KSP residual norm 2.063559e-02\n", - " 62 (restart 0) KSP residual norm 1.814354e-02\n", - " 63 (restart 0) KSP residual norm 1.547959e-02\n", - " 64 (restart 0) KSP residual norm 1.340608e-02\n", - " 65 (restart 0) KSP residual norm 1.157106e-02\n", - " 66 (restart 0) KSP residual norm 1.034960e-02\n", - " 67 (restart 0) KSP residual norm 9.000109e-03\n", - " 68 (restart 0) KSP residual norm 7.770770e-03\n", - " 69 (restart 0) KSP residual norm 6.712387e-03\n", - " 70 (restart 0) KSP residual norm 5.811766e-03\n", - " 71 (restart 0) KSP residual norm 4.909973e-03\n", - " 72 (restart 0) KSP residual norm 4.188819e-03\n", - " 73 (restart 0) KSP residual norm 3.667165e-03\n", - " 74 (restart 0) KSP residual norm 3.137172e-03\n", - " 75 (restart 0) KSP residual norm 2.614388e-03\n", - " 76 (restart 0) KSP residual norm 2.219591e-03\n", - " 77 (restart 0) KSP residual norm 1.848222e-03\n", - " 78 (restart 0) KSP residual norm 1.531228e-03\n", - " 79 (restart 0) KSP residual norm 1.247098e-03\n", - " 80 (restart 0) KSP residual norm 1.056007e-03\n", - " 81 (restart 0) KSP residual norm 8.791043e-04\n", - " 82 (restart 0) KSP residual norm 7.491248e-04\n", - " 83 (restart 0) KSP residual norm 6.282302e-04\n", - " 84 (restart 0) KSP residual norm 5.334538e-04\n", - " 85 (restart 0) KSP residual norm 4.559806e-04\n", - " 86 (restart 0) KSP residual norm 3.964526e-04\n", - " 87 (restart 0) KSP residual norm 3.308924e-04\n", - " 88 (restart 0) KSP residual norm 2.735100e-04\n", - " 89 (restart 0) KSP residual norm 2.292264e-04\n", - " 90 (restart 0) KSP residual norm 1.844660e-04\n", - " 91 (restart 0) KSP residual norm 1.507017e-04\n", - " 92 (restart 0) KSP residual norm 1.252616e-04\n", - " 93 (restart 0) KSP residual norm 1.056320e-04\n", - " 94 (restart 0) KSP residual norm 9.036180e-05\n", - " 95 (restart 0) KSP residual norm 7.684615e-05\n", - " 96 (restart 0) KSP residual norm 6.427665e-05\n", - " 97 (restart 0) KSP residual norm 5.058002e-05\n", - " 98 (restart 0) KSP residual norm 4.150111e-05\n", - " 99 (restart 0) KSP residual norm 3.258785e-05\n", - "100 (restart 0) KSP residual norm 2.643361e-05\n", - "101 (restart 0) KSP residual norm 2.150750e-05\n", - "102 (restart 0) KSP residual norm 1.700091e-05\n", - "103 (restart 0) KSP residual norm 1.343484e-05\n", - "104 (restart 0) KSP residual norm 1.043250e-05\n", - "105 (restart 0) KSP residual norm 8.056564e-06\n", - "106 (restart 0) KSP residual norm 6.266025e-06\n", - "107 (restart 0) KSP residual norm 4.781414e-06\n", - "108 (restart 0) KSP residual norm 3.602035e-06\n", - "109 (restart 0) KSP residual norm 2.753150e-06\n", - "110 (restart 0) KSP residual norm 2.064698e-06\n", - "111 (restart 0) KSP residual norm 1.579146e-06\n", - "112 (restart 0) KSP residual norm 1.079283e-06\n", - "113 (restart 0) KSP residual norm 7.758606e-07\n", - "114 (restart 0) KSP residual norm 6.057378e-07\n", - "115 (restart 0) KSP residual norm 4.722287e-07\n", - "116 (restart 0) KSP residual norm 3.625128e-07\n", - "117 (restart 0) KSP residual norm 2.921274e-07\n", - "118 (restart 0) KSP residual norm 2.173717e-07\n", - "119 (restart 0) KSP residual norm 1.708051e-07\n", - "120 (restart 0) KSP residual norm 1.328953e-07\n", - "GMRES solver converged in 120 iterations (avg. reduction factor: 8.567e-01)\n", - "\n", - "Greedy iteration 1 (n = 4): ω* = 5.896e+00 GHz (1.359e+01), error = 3.114e-01, memory = 0/2\n", - " Field energy E (6.900e-12 J) + H (1.079e-11 J) = 1.769e-11 J\n", + " 0 (restart 0) KSP residual norm 2.796824e+01\n", + " 1 (restart 0) KSP residual norm 1.883784e+01\n", + " 2 (restart 0) KSP residual norm 9.339612e+00\n", + " 3 (restart 0) KSP residual norm 9.128056e+00\n", + " 4 (restart 0) KSP residual norm 6.701088e+00\n", + " 5 (restart 0) KSP residual norm 6.194023e+00\n", + " 6 (restart 0) KSP residual norm 5.861187e+00\n", + " 7 (restart 0) KSP residual norm 5.209749e+00\n", + " 8 (restart 0) KSP residual norm 3.756144e+00\n", + " 9 (restart 0) KSP residual norm 3.492471e+00\n", + " 10 (restart 0) KSP residual norm 2.177772e+00\n", + " 11 (restart 0) KSP residual norm 2.043599e+00\n", + " 12 (restart 0) KSP residual norm 1.485109e+00\n", + " 13 (restart 0) KSP residual norm 1.363483e+00\n", + " 14 (restart 0) KSP residual norm 1.285172e+00\n", + " 15 (restart 0) KSP residual norm 1.001252e+00\n", + " 16 (restart 0) KSP residual norm 9.222591e-01\n", + " 17 (restart 0) KSP residual norm 7.987311e-01\n", + " 18 (restart 0) KSP residual norm 6.658529e-01\n", + " 19 (restart 0) KSP residual norm 5.933996e-01\n", + " 20 (restart 0) KSP residual norm 5.317672e-01\n", + " 21 (restart 0) KSP residual norm 4.224128e-01\n", + " 22 (restart 0) KSP residual norm 3.884265e-01\n", + " 23 (restart 0) KSP residual norm 3.117440e-01\n", + " 24 (restart 0) KSP residual norm 2.368094e-01\n", + " 25 (restart 0) KSP residual norm 2.069505e-01\n", + " 26 (restart 0) KSP residual norm 1.696437e-01\n", + " 27 (restart 0) KSP residual norm 1.405684e-01\n", + " 28 (restart 0) KSP residual norm 1.195188e-01\n", + " 29 (restart 0) KSP residual norm 7.751752e-02\n", + " 30 (restart 0) KSP residual norm 6.369878e-02\n", + " 31 (restart 0) KSP residual norm 5.090288e-02\n", + " 32 (restart 0) KSP residual norm 4.003856e-02\n", + " 33 (restart 0) KSP residual norm 2.544123e-02\n", + " 34 (restart 0) KSP residual norm 2.224304e-02\n", + " 35 (restart 0) KSP residual norm 1.198237e-02\n", + " 36 (restart 0) KSP residual norm 8.119442e-03\n", + " 37 (restart 0) KSP residual norm 6.700994e-03\n", + " 38 (restart 0) KSP residual norm 5.134920e-03\n", + " 39 (restart 0) KSP residual norm 2.832810e-03\n", + " 40 (restart 0) KSP residual norm 2.018054e-03\n", + " 41 (restart 0) KSP residual norm 1.593587e-03\n", + " 42 (restart 0) KSP residual norm 9.512836e-04\n", + " 43 (restart 0) KSP residual norm 5.857677e-04\n", + " 44 (restart 0) KSP residual norm 4.617620e-04\n", + " 45 (restart 0) KSP residual norm 3.342570e-04\n", + " 46 (restart 0) KSP residual norm 2.058432e-04\n", + " 47 (restart 0) KSP residual norm 1.487793e-04\n", + " 48 (restart 0) KSP residual norm 1.111368e-04\n", + " 49 (restart 0) KSP residual norm 6.601669e-05\n", + " 50 (restart 0) KSP residual norm 4.707811e-05\n", + " 51 (restart 0) KSP residual norm 2.829957e-05\n", + " 52 (restart 0) KSP residual norm 1.631329e-05\n", + " 53 (restart 0) KSP residual norm 8.148923e-06\n", + " 54 (restart 0) KSP residual norm 3.443602e-06\n", + " 55 (restart 0) KSP residual norm 1.726996e-06\n", + " 56 (restart 0) KSP residual norm 9.255429e-07\n", + " 57 (restart 0) KSP residual norm 5.749016e-07\n", + " 58 (restart 0) KSP residual norm 3.462545e-07\n", + " 59 (restart 0) KSP residual norm 1.923409e-07\n", + "GMRES solver converged in 59 iterations (avg. reduction factor: 7.272e-01)\n", + " Field energy E (3.698e-11 J) + H (1.245e-11 J) = 4.943e-11 J\n", "\n", " Residual norms for GMRES solve\n", - " 0 (restart 0) KSP residual norm 1.741628e+01\n", - " 1 (restart 0) KSP residual norm 1.317921e+01\n", - " 2 (restart 0) KSP residual norm 1.236859e+01\n", - " 3 (restart 0) KSP residual norm 9.904050e+00\n", - " 4 (restart 0) KSP residual norm 8.842146e+00\n", - " 5 (restart 0) KSP residual norm 8.698656e+00\n", - " 6 (restart 0) KSP residual norm 7.633451e+00\n", - " 7 (restart 0) KSP residual norm 7.596180e+00\n", - " 8 (restart 0) KSP residual norm 6.400593e+00\n", - " 9 (restart 0) KSP residual norm 6.340166e+00\n", - " 10 (restart 0) KSP residual norm 5.373216e+00\n", - " 11 (restart 0) KSP residual norm 5.159388e+00\n", - " 12 (restart 0) KSP residual norm 5.060056e+00\n", - " 13 (restart 0) KSP residual norm 4.891913e+00\n", - " 14 (restart 0) KSP residual norm 4.876692e+00\n", - " 15 (restart 0) KSP residual norm 4.420494e+00\n", - " 16 (restart 0) KSP residual norm 4.347649e+00\n", - " 17 (restart 0) KSP residual norm 4.023502e+00\n", - " 18 (restart 0) KSP residual norm 3.798997e+00\n", - 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"\n", - "Greedy iteration 2 (n = 6): ω* = 4.901e+00 GHz (1.130e+01), error = 1.739e-01, memory = 0/2\n", - " Field energy E (7.899e-12 J) + H (1.174e-11 J) = 1.963e-11 J\n", + " 0 (restart 0) KSP residual norm 2.835760e+02\n", + " 1 (restart 0) KSP residual norm 1.006704e+02\n", + " 2 (restart 0) KSP residual norm 9.775731e+01\n", + " 3 (restart 0) KSP residual norm 3.137324e+01\n", + " 4 (restart 0) KSP residual norm 2.762710e+01\n", + " 5 (restart 0) KSP residual norm 1.572509e+01\n", + " 6 (restart 0) KSP residual norm 1.352079e+01\n", + " 7 (restart 0) KSP residual norm 9.829290e+00\n", + " 8 (restart 0) KSP residual norm 9.116460e+00\n", + " 9 (restart 0) KSP residual norm 7.368962e+00\n", + " 10 (restart 0) KSP residual norm 6.848618e+00\n", + " 11 (restart 0) KSP residual norm 5.894354e+00\n", + " 12 (restart 0) KSP residual norm 5.430586e+00\n", + " 13 (restart 0) KSP residual norm 4.242235e+00\n", + " 14 (restart 0) KSP residual norm 4.009218e+00\n", + " 15 (restart 0) KSP residual norm 2.951924e+00\n", + " 16 (restart 0) KSP residual norm 2.905064e+00\n", + " 17 (restart 0) KSP residual norm 2.229543e+00\n", + " 18 (restart 0) KSP residual norm 2.068515e+00\n", + " 19 (restart 0) KSP residual norm 1.629175e+00\n", + " 20 (restart 0) KSP residual norm 1.519449e+00\n", + " 21 (restart 0) KSP residual norm 1.247236e+00\n", + " 22 (restart 0) KSP residual norm 1.182084e+00\n", + " 23 (restart 0) KSP residual norm 9.931370e-01\n", + " 24 (restart 0) KSP residual norm 8.684876e-01\n", + " 25 (restart 0) KSP residual norm 7.578365e-01\n", + " 26 (restart 0) KSP residual norm 6.512453e-01\n", + " 27 (restart 0) KSP residual norm 5.815502e-01\n", + " 28 (restart 0) KSP residual norm 4.667646e-01\n", + " 29 (restart 0) KSP residual norm 4.224742e-01\n", + " 30 (restart 0) KSP residual norm 3.129434e-01\n", + " 31 (restart 0) KSP residual norm 2.886765e-01\n", + " 32 (restart 0) KSP residual norm 2.144262e-01\n", + " 33 (restart 0) KSP residual norm 1.995803e-01\n", + " 34 (restart 0) KSP residual norm 1.623844e-01\n", + " 35 (restart 0) KSP residual norm 1.426765e-01\n", + " 36 (restart 0) KSP residual norm 1.179786e-01\n", + " 37 (restart 0) KSP residual norm 1.008049e-01\n", + " 38 (restart 0) KSP residual norm 8.442849e-02\n", + " 39 (restart 0) KSP residual norm 6.753044e-02\n", + " 40 (restart 0) KSP residual norm 5.529979e-02\n", + " 41 (restart 0) KSP residual norm 3.829408e-02\n", + " 42 (restart 0) KSP residual norm 2.953870e-02\n", + " 43 (restart 0) KSP residual norm 2.315844e-02\n", + " 44 (restart 0) KSP residual norm 1.896347e-02\n", + " 45 (restart 0) KSP residual norm 1.434537e-02\n", + " 46 (restart 0) KSP residual norm 1.148611e-02\n", + " 47 (restart 0) KSP residual norm 8.714205e-03\n", + " 48 (restart 0) KSP residual norm 6.642581e-03\n", + " 49 (restart 0) KSP residual norm 5.424971e-03\n", + " 50 (restart 0) KSP residual norm 4.011649e-03\n", + " 51 (restart 0) KSP residual norm 3.383851e-03\n", + " 52 (restart 0) KSP residual norm 2.392996e-03\n", + " 53 (restart 0) KSP residual norm 1.897091e-03\n", + " 54 (restart 0) KSP residual norm 1.525942e-03\n", + " 55 (restart 0) KSP residual norm 1.155310e-03\n", + " 56 (restart 0) KSP residual norm 9.472898e-04\n", + " 57 (restart 0) KSP residual norm 6.950777e-04\n", + " 58 (restart 0) KSP residual norm 5.450853e-04\n", + " 59 (restart 0) KSP residual norm 4.093101e-04\n", + " 60 (restart 0) KSP residual norm 2.769404e-04\n", + " 61 (restart 0) KSP residual norm 2.011495e-04\n", + " 62 (restart 0) KSP residual norm 1.597891e-04\n", + " 63 (restart 0) KSP residual norm 1.166760e-04\n", + " 64 (restart 0) KSP residual norm 8.108030e-05\n", + " 65 (restart 0) KSP residual norm 5.311617e-05\n", + " 66 (restart 0) KSP residual norm 3.761329e-05\n", + " 67 (restart 0) KSP residual norm 2.785225e-05\n", + " 68 (restart 0) KSP residual norm 1.845645e-05\n", + " 69 (restart 0) KSP residual norm 1.368523e-05\n", + " 70 (restart 0) KSP residual norm 9.207315e-06\n", + " 71 (restart 0) KSP residual norm 6.459982e-06\n", + " 72 (restart 0) KSP residual norm 4.410621e-06\n", + " 73 (restart 0) KSP residual norm 2.684780e-06\n", + "GMRES solver converged in 73 iterations (avg. reduction factor: 7.764e-01)\n", + " Field energy E (5.607e-11 J) + H (2.750e-11 J) = 8.357e-11 J\n", "\n", " Residual norms for GMRES solve\n", - " 0 (restart 0) KSP residual norm 1.027754e+02\n", - " 1 (restart 0) KSP residual norm 1.506808e+01\n", - " 2 (restart 0) KSP residual norm 1.023035e+01\n", - " 3 (restart 0) KSP residual norm 8.138741e+00\n", - " 4 (restart 0) KSP residual norm 8.015960e+00\n", - " 5 (restart 0) KSP residual norm 7.809729e+00\n", - " 6 (restart 0) KSP residual norm 6.987544e+00\n", - " 7 (restart 0) KSP residual norm 6.027841e+00\n", - " 8 (restart 0) KSP residual norm 5.988252e+00\n", - " 9 (restart 0) KSP residual norm 5.227285e+00\n", - " 10 (restart 0) KSP residual norm 4.859580e+00\n", - " 11 (restart 0) KSP residual norm 4.654446e+00\n", - " 12 (restart 0) KSP residual norm 3.803440e+00\n", - " 13 (restart 0) KSP residual norm 2.503526e+00\n", - " 14 (restart 0) KSP residual norm 2.278893e+00\n", - " 15 (restart 0) KSP residual norm 1.811265e+00\n", - " 16 (restart 0) KSP residual norm 1.378134e+00\n", - " 17 (restart 0) KSP residual norm 1.207601e+00\n", - " 18 (restart 0) KSP residual norm 8.830801e-01\n", - 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"\n", - "Greedy iteration 3 (n = 8): ω* = 2.610e+00 GHz (6.018e+00), error = 3.965e-01, memory = 0/2\n", - " Field energy E (1.407e-11 J) + H (1.956e-11 J) = 3.363e-11 J\n", - "\n", - " Residual norms for GMRES solve\n", - " 0 (restart 0) KSP residual norm 4.014905e+01\n", - " 1 (restart 0) KSP residual norm 1.105733e+01\n", - " 2 (restart 0) KSP residual norm 9.940733e+00\n", - " 3 (restart 0) KSP residual norm 4.522717e+00\n", - " 4 (restart 0) KSP residual norm 4.409122e+00\n", - " 5 (restart 0) KSP residual norm 4.136822e+00\n", - " 6 (restart 0) KSP residual norm 3.573754e+00\n", - " 7 (restart 0) KSP residual norm 3.276243e+00\n", - " 8 (restart 0) KSP residual norm 3.141783e+00\n", - " 9 (restart 0) KSP residual norm 2.174584e+00\n", - " 10 (restart 0) KSP residual norm 1.564657e+00\n", - " 11 (restart 0) KSP residual norm 1.205133e+00\n", - " 12 (restart 0) KSP residual norm 5.961049e-01\n", - " 13 (restart 0) KSP residual norm 4.596230e-01\n", - " 14 (restart 0) KSP residual norm 3.251782e-01\n", - 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" 58 (restart 0) KSP residual norm 2.011346e-03\n", - " 59 (restart 0) KSP residual norm 1.590280e-03\n", - " 60 (restart 0) KSP residual norm 1.273882e-03\n", - " 61 (restart 0) KSP residual norm 1.072263e-03\n", - " 62 (restart 0) KSP residual norm 8.611091e-04\n", - " 63 (restart 0) KSP residual norm 6.827127e-04\n", - " 64 (restart 0) KSP residual norm 5.432404e-04\n", - " 65 (restart 0) KSP residual norm 4.502156e-04\n", - " 66 (restart 0) KSP residual norm 3.620082e-04\n", - " 67 (restart 0) KSP residual norm 2.884270e-04\n", - " 68 (restart 0) KSP residual norm 2.363778e-04\n", - " 69 (restart 0) KSP residual norm 1.828014e-04\n", - " 70 (restart 0) KSP residual norm 1.457496e-04\n", - " 71 (restart 0) KSP residual norm 1.103432e-04\n", - " 72 (restart 0) KSP residual norm 7.933350e-05\n", - " 73 (restart 0) KSP residual norm 6.253164e-05\n", - " 74 (restart 0) KSP residual norm 4.925423e-05\n", - " 75 (restart 0) KSP residual norm 3.710760e-05\n", - " 76 (restart 0) KSP residual norm 2.820976e-05\n", - " 77 (restart 0) KSP residual norm 2.091695e-05\n", - " 78 (restart 0) KSP residual norm 1.494082e-05\n", - " 79 (restart 0) KSP residual norm 1.069259e-05\n", - " 80 (restart 0) KSP residual norm 8.199708e-06\n", - " 81 (restart 0) KSP residual norm 6.309852e-06\n", - " 82 (restart 0) KSP residual norm 4.520298e-06\n", - " 83 (restart 0) KSP residual norm 3.167319e-06\n", - " 84 (restart 0) KSP residual norm 2.319675e-06\n", - " 85 (restart 0) KSP residual norm 1.621529e-06\n", - " 86 (restart 0) KSP residual norm 1.181528e-06\n", - " 87 (restart 0) KSP residual norm 8.671319e-07\n", - " 88 (restart 0) KSP residual norm 5.936582e-07\n", - " 89 (restart 0) KSP residual norm 3.931206e-07\n", - "GMRES solver converged in 89 iterations (avg. reduction factor: 8.125e-01)\n", - "\n", - "Greedy iteration 5 (n = 12): ω* = 3.733e+00 GHz (8.606e+00), error = 1.117e-02, memory = 0/2\n", - " Field energy E (9.655e-12 J) + H (1.399e-11 J) = 2.365e-11 J\n", - "\n", - " Residual norms for GMRES solve\n", - " 0 (restart 0) KSP residual norm 4.795421e+01\n", - " 1 (restart 0) KSP residual norm 1.531349e+01\n", - " 2 (restart 0) KSP residual norm 1.164523e+01\n", - " 3 (restart 0) KSP residual norm 1.129954e+01\n", - " 4 (restart 0) KSP residual norm 8.789385e+00\n", - " 5 (restart 0) KSP residual norm 7.504302e+00\n", - " 6 (restart 0) KSP residual norm 7.173030e+00\n", - " 7 (restart 0) KSP residual norm 7.134353e+00\n", - " 8 (restart 0) KSP residual norm 5.870336e+00\n", - " 9 (restart 0) KSP residual norm 5.766143e+00\n", - " 10 (restart 0) KSP residual norm 5.378855e+00\n", - " 11 (restart 0) KSP residual norm 5.326799e+00\n", - " 12 (restart 0) KSP residual norm 4.948554e+00\n", - " 13 (restart 0) KSP residual norm 4.863312e+00\n", - " 14 (restart 0) KSP residual norm 4.654441e+00\n", - " 15 (restart 0) KSP residual norm 4.429460e+00\n", - " 16 (restart 0) KSP residual norm 4.208661e+00\n", - " 17 (restart 0) KSP residual norm 3.911745e+00\n", - " 18 (restart 0) KSP residual norm 3.333842e+00\n", - 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" 95 (restart 0) KSP residual norm 1.547062e-05\n", - " 96 (restart 0) KSP residual norm 1.205655e-05\n", - " 97 (restart 0) KSP residual norm 9.156029e-06\n", - " 98 (restart 0) KSP residual norm 7.208099e-06\n", - " 99 (restart 0) KSP residual norm 5.437121e-06\n", - "100 (restart 0) KSP residual norm 4.088308e-06\n", - "101 (restart 0) KSP residual norm 3.076221e-06\n", - "102 (restart 0) KSP residual norm 2.366142e-06\n", - "103 (restart 0) KSP residual norm 1.712979e-06\n", - "104 (restart 0) KSP residual norm 1.270285e-06\n", - "105 (restart 0) KSP residual norm 9.596586e-07\n", - "106 (restart 0) KSP residual norm 7.287776e-07\n", - "107 (restart 0) KSP residual norm 5.438711e-07\n", - "108 (restart 0) KSP residual norm 3.932093e-07\n", - "GMRES solver converged in 108 iterations (avg. reduction factor: 8.416e-01)\n", - "\n", - "Greedy iteration 6 (n = 14): ω* = 6.593e+00 GHz (1.520e+01), error = 4.054e-03, memory = 0/2\n", - " Field energy E (6.328e-12 J) + H (9.903e-12 J) = 1.623e-11 J\n", - "\n", - " Residual norms for GMRES solve\n", - " 0 (restart 0) KSP residual norm 5.201825e+02\n", - " 1 (restart 0) KSP residual norm 8.260846e+00\n", - " 2 (restart 0) KSP residual norm 7.132908e+00\n", - " 3 (restart 0) KSP residual norm 6.928072e+00\n", - " 4 (restart 0) KSP residual norm 6.826729e+00\n", - " 5 (restart 0) KSP residual norm 4.937336e+00\n", - " 6 (restart 0) KSP residual norm 4.221434e+00\n", - " 7 (restart 0) KSP residual norm 2.516598e+00\n", - " 8 (restart 0) KSP residual norm 1.958586e+00\n", - " 9 (restart 0) KSP residual norm 8.069759e-01\n", - " 10 (restart 0) KSP residual norm 5.067569e-01\n", - " 11 (restart 0) KSP residual norm 2.199639e-01\n", - " 12 (restart 0) KSP residual norm 1.325679e-01\n", - " 13 (restart 0) KSP residual norm 8.638454e-02\n", - " 14 (restart 0) KSP residual norm 5.098286e-02\n", - " 15 (restart 0) KSP residual norm 2.776223e-02\n", - " 16 (restart 0) KSP residual norm 1.498579e-02\n", - " 17 (restart 0) KSP residual norm 7.473449e-03\n", - 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"\n", - " Residual norms for GMRES solve\n", - " 0 (restart 0) KSP residual norm 4.361362e+01\n", - " 1 (restart 0) KSP residual norm 1.679999e+01\n", - " 2 (restart 0) KSP residual norm 1.184658e+01\n", - " 3 (restart 0) KSP residual norm 1.090146e+01\n", - " 4 (restart 0) KSP residual norm 7.162960e+00\n", - " 5 (restart 0) KSP residual norm 7.104618e+00\n", - " 6 (restart 0) KSP residual norm 6.743015e+00\n", - " 7 (restart 0) KSP residual norm 5.453304e+00\n", - " 8 (restart 0) KSP residual norm 5.219683e+00\n", - " 9 (restart 0) KSP residual norm 4.987744e+00\n", - " 10 (restart 0) KSP residual norm 4.440557e+00\n", - " 11 (restart 0) KSP residual norm 3.957380e+00\n", - " 12 (restart 0) KSP residual norm 3.504559e+00\n", - " 13 (restart 0) KSP residual norm 3.349881e+00\n", - " 14 (restart 0) KSP residual norm 2.964426e+00\n", - " 15 (restart 0) KSP residual norm 2.588649e+00\n", - " 16 (restart 0) KSP residual norm 2.174116e+00\n", - " 17 (restart 0) KSP residual norm 2.042338e+00\n", - 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" Field energy E (1.179e-11 J) + H (1.657e-11 J) = 2.836e-11 J\n", - "\n", - " Residual norms for GMRES solve\n", - " 0 (restart 0) KSP residual norm 2.095885e+01\n", - " 1 (restart 0) KSP residual norm 1.700128e+01\n", - " 2 (restart 0) KSP residual norm 1.279826e+01\n", - " 3 (restart 0) KSP residual norm 1.260388e+01\n", - " 4 (restart 0) KSP residual norm 1.222982e+01\n", - " 5 (restart 0) KSP residual norm 1.135461e+01\n", - " 6 (restart 0) KSP residual norm 7.273124e+00\n", - " 7 (restart 0) KSP residual norm 7.219049e+00\n", - " 8 (restart 0) KSP residual norm 6.771282e+00\n", - " 9 (restart 0) KSP residual norm 6.670696e+00\n", - " 10 (restart 0) KSP residual norm 5.672779e+00\n", - " 11 (restart 0) KSP residual norm 5.464718e+00\n", - " 12 (restart 0) KSP residual norm 5.291584e+00\n", - " 13 (restart 0) KSP residual norm 4.972985e+00\n", - " 14 (restart 0) KSP residual norm 4.691226e+00\n", - " 15 (restart 0) KSP residual norm 4.360223e+00\n", - " 16 (restart 0) KSP residual norm 4.126172e+00\n", - " 17 (restart 0) KSP residual norm 3.835810e+00\n", - " 18 (restart 0) KSP residual norm 2.748238e+00\n", - " 19 (restart 0) KSP residual norm 2.487395e+00\n", - " 20 (restart 0) KSP residual norm 2.226518e+00\n", - " 21 (restart 0) KSP residual norm 2.090650e+00\n", - " 22 (restart 0) KSP residual norm 1.754012e+00\n", - " 23 (restart 0) KSP residual norm 1.485724e+00\n", - " 24 (restart 0) KSP residual norm 1.227835e+00\n", - " 25 (restart 0) KSP residual norm 1.158753e+00\n", - " 26 (restart 0) KSP residual norm 1.003005e+00\n", - " 27 (restart 0) KSP residual norm 9.227272e-01\n", - " 28 (restart 0) KSP residual norm 8.091555e-01\n", - " 29 (restart 0) KSP residual norm 7.163858e-01\n", - " 30 (restart 0) KSP residual norm 5.878096e-01\n", - " 31 (restart 0) KSP residual norm 5.188543e-01\n", - " 32 (restart 0) KSP residual norm 4.479567e-01\n", - " 33 (restart 0) KSP residual norm 3.707839e-01\n", - " 34 (restart 0) KSP residual norm 3.264839e-01\n", - " 35 (restart 0) KSP residual norm 2.940634e-01\n", - " 36 (restart 0) KSP residual norm 2.509334e-01\n", - " 37 (restart 0) KSP residual norm 2.250445e-01\n", - " 38 (restart 0) KSP residual norm 1.975293e-01\n", - " 39 (restart 0) KSP residual norm 1.745782e-01\n", - " 40 (restart 0) KSP residual norm 1.482566e-01\n", - " 41 (restart 0) KSP residual norm 1.288868e-01\n", - " 42 (restart 0) KSP residual norm 1.156935e-01\n", - " 43 (restart 0) KSP residual norm 9.682898e-02\n", - " 44 (restart 0) KSP residual norm 8.116963e-02\n", - " 45 (restart 0) KSP residual norm 7.006395e-02\n", - " 46 (restart 0) KSP residual norm 5.542348e-02\n", - " 47 (restart 0) KSP residual norm 4.584017e-02\n", - " 48 (restart 0) KSP residual norm 3.940388e-02\n", - " 49 (restart 0) KSP residual norm 3.259716e-02\n", - " 50 (restart 0) KSP residual norm 2.661210e-02\n", - " 51 (restart 0) KSP residual norm 2.219063e-02\n", - " 52 (restart 0) KSP residual norm 1.770195e-02\n", - " 53 (restart 0) KSP residual norm 1.464524e-02\n", - " 54 (restart 0) KSP residual norm 1.262056e-02\n", - " 55 (restart 0) KSP residual norm 1.073677e-02\n", - " 56 (restart 0) KSP residual norm 8.618787e-03\n", - " 57 (restart 0) KSP residual norm 7.357520e-03\n", - " 58 (restart 0) KSP residual norm 6.152111e-03\n", - " 59 (restart 0) KSP residual norm 5.078881e-03\n", - " 60 (restart 0) KSP residual norm 4.145691e-03\n", - " 61 (restart 0) KSP residual norm 3.397618e-03\n", - " 62 (restart 0) KSP residual norm 2.877800e-03\n", - " 63 (restart 0) KSP residual norm 2.420352e-03\n", - " 64 (restart 0) KSP residual norm 1.885633e-03\n", - " 65 (restart 0) KSP residual norm 1.526516e-03\n", - " 66 (restart 0) KSP residual norm 1.259971e-03\n", - " 67 (restart 0) KSP residual norm 1.058930e-03\n", - " 68 (restart 0) KSP residual norm 8.891881e-04\n", - " 69 (restart 0) KSP residual norm 7.290589e-04\n", - " 70 (restart 0) KSP residual norm 6.006386e-04\n", - " 71 (restart 0) KSP residual norm 5.179014e-04\n", - " 72 (restart 0) KSP residual norm 4.223922e-04\n", - " 73 (restart 0) KSP residual norm 3.490727e-04\n", - " 74 (restart 0) KSP residual norm 2.978151e-04\n", - " 75 (restart 0) KSP residual norm 2.530121e-04\n", - " 76 (restart 0) KSP residual norm 2.104874e-04\n", - " 77 (restart 0) KSP residual norm 1.762279e-04\n", - " 78 (restart 0) KSP residual norm 1.463287e-04\n", - " 79 (restart 0) KSP residual norm 1.246066e-04\n", - " 80 (restart 0) KSP residual norm 1.050930e-04\n", - " 81 (restart 0) KSP residual norm 8.587230e-05\n", - " 82 (restart 0) KSP residual norm 7.221370e-05\n", - " 83 (restart 0) KSP residual norm 6.005627e-05\n", - " 84 (restart 0) KSP residual norm 4.954621e-05\n", - " 85 (restart 0) KSP residual norm 4.128680e-05\n", - " 86 (restart 0) KSP residual norm 3.436564e-05\n", - " 87 (restart 0) KSP residual norm 2.807409e-05\n", - " 88 (restart 0) KSP residual norm 2.300202e-05\n", - " 89 (restart 0) KSP residual norm 1.893240e-05\n", - " 90 (restart 0) KSP residual norm 1.504557e-05\n", - " 91 (restart 0) KSP residual norm 1.196808e-05\n", - " 92 (restart 0) KSP residual norm 9.621694e-06\n", - " 93 (restart 0) KSP residual norm 7.540996e-06\n", - " 94 (restart 0) KSP residual norm 5.955643e-06\n", - " 95 (restart 0) KSP residual norm 4.409449e-06\n", - " 96 (restart 0) KSP residual norm 3.278988e-06\n", - " 97 (restart 0) KSP residual norm 2.493392e-06\n", - " 98 (restart 0) KSP residual norm 1.891437e-06\n", - " 99 (restart 0) KSP residual norm 1.477670e-06\n", - "100 (restart 0) KSP residual norm 1.142729e-06\n", - "101 (restart 0) KSP residual norm 8.822025e-07\n", - "102 (restart 0) KSP residual norm 6.542567e-07\n", - "103 (restart 0) KSP residual norm 4.883829e-07\n", - "104 (restart 0) KSP residual norm 3.803136e-07\n", - "105 (restart 0) KSP residual norm 2.833656e-07\n", - "106 (restart 0) KSP residual norm 2.136411e-07\n", - "107 (restart 0) KSP residual norm 1.581536e-07\n", - "GMRES solver converged in 107 iterations (avg. reduction factor: 8.396e-01)\n", - "\n", - "Greedy iteration 9 (n = 20): ω* = 6.842e+00 GHz (1.577e+01), error = 1.096e-04, memory = 2/2\n", - " Field energy E (6.149e-12 J) + H (9.606e-12 J) = 1.575e-11 J\n", - "\n", - "Adaptive sampling converged with 11 frequency samples:\n", - " n = 22, error = 1.096e-04, tol = 1.000e-03, memory = 2/2\n", - " Sampled frequencies (GHz): 1.000e+00, 7.000e+00, 5.896e+00, 4.901e+00,\n", - " 2.610e+00, 1.906e+00, 3.733e+00, 6.593e+00,\n", - " 1.306e+00, 3.181e+00, 6.842e+00\n", - " Sample errors: inf, inf, 3.114e-01, 1.739e-01, 3.965e-01,\n", - " 8.530e-02, 1.117e-02, 4.054e-03, 3.699e-03, 2.560e-04,\n", - " 1.096e-04\n", - " Total offline phase elapsed time: 7.84e+01 s\n", - "\n", - "Beginning fast frequency sweep online phase\n", - "\n", - "It 1/121: ω/2π = 1.000e+00 GHz (total elapsed time = 7.84e+01 s)\n", - "\n", - " Sol. ||E|| = 9.489561e+00\n", - " Field energy E (2.054e-11 J) + H (4.474e-11 J) = 6.528e-11 J\n", - " S[1][1] = -5.642e-01+1.521e-01i, |S[1][1]| = -4.668e+00, arg(S[1][1]) = +1.649e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 1\n", - "\n", - "It 2/121: ω/2π = 1.050e+00 GHz (total elapsed time = 7.87e+01 s)\n", - "\n", - " Sol. ||E|| = 9.181682e+00\n", - " Field energy E (1.889e-11 J) + H (4.379e-11 J) = 6.268e-11 J\n", - " S[1][1] = -5.419e-01+1.643e-01i, |S[1][1]| = -4.940e+00, arg(S[1][1]) = +1.631e+02\n", - "\n", - "It 3/121: ω/2π = 1.100e+00 GHz (total elapsed time = 7.87e+01 s)\n", - "\n", - " Sol. ||E|| = 8.931942e+00\n", - " Field energy E (1.754e-11 J) + H (4.276e-11 J) = 6.029e-11 J\n", - " S[1][1] = -5.195e-01+1.743e-01i, |S[1][1]| = -5.225e+00, arg(S[1][1]) = +1.615e+02\n", - "\n", - "It 4/121: ω/2π = 1.150e+00 GHz (total elapsed time = 7.87e+01 s)\n", - "\n", - " Sol. ||E|| = 8.734390e+00\n", - " Field energy E (1.642e-11 J) + H (4.166e-11 J) = 5.808e-11 J\n", - " S[1][1] = -4.972e-01+1.824e-01i, |S[1][1]| = -5.521e+00, arg(S[1][1]) = +1.599e+02\n", - "\n", - "It 5/121: ω/2π = 1.200e+00 GHz (total elapsed time = 7.87e+01 s)\n", - "\n", - " Sol. ||E|| = 8.583141e+00\n", - " Field energy E (1.552e-11 J) + H (4.053e-11 J) = 5.604e-11 J\n", - " S[1][1] = -4.753e-01+1.886e-01i, |S[1][1]| = -5.825e+00, arg(S[1][1]) = +1.584e+02\n", - "\n", - "It 6/121: ω/2π = 1.250e+00 GHz (total elapsed time = 7.87e+01 s)\n", - "\n", - " Sol. ||E|| = 8.472500e+00\n", - " Field energy E (1.479e-11 J) + H (3.937e-11 J) = 5.415e-11 J\n", - " S[1][1] = -4.539e-01+1.931e-01i, |S[1][1]| = -6.138e+00, arg(S[1][1]) = +1.570e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 6\n", - "\n", - "It 7/121: ω/2π = 1.300e+00 GHz (total elapsed time = 7.90e+01 s)\n", - "\n", - " Sol. ||E|| = 8.397067e+00\n", - " Field energy E (1.420e-11 J) + H (3.820e-11 J) = 5.240e-11 J\n", - " S[1][1] = -4.331e-01+1.960e-01i, |S[1][1]| = -6.458e+00, arg(S[1][1]) = +1.557e+02\n", - "\n", - "It 8/121: ω/2π = 1.350e+00 GHz (total elapsed time = 7.90e+01 s)\n", - "\n", - " Sol. ||E|| = 8.351827e+00\n", - " Field energy E (1.374e-11 J) + H (3.703e-11 J) = 5.078e-11 J\n", - " S[1][1] = -4.131e-01+1.976e-01i, |S[1][1]| = -6.784e+00, arg(S[1][1]) = +1.544e+02\n", - "\n", - "It 9/121: ω/2π = 1.400e+00 GHz (total elapsed time = 7.90e+01 s)\n", - "\n", - " Sol. ||E|| = 8.332200e+00\n", - " Field energy E (1.339e-11 J) + H (3.588e-11 J) = 4.927e-11 J\n", - " S[1][1] = -3.939e-01+1.979e-01i, |S[1][1]| = -7.116e+00, arg(S[1][1]) = +1.533e+02\n", - "\n", - "It 10/121: ω/2π = 1.450e+00 GHz (total elapsed time = 7.90e+01 s)\n", - "\n", - " Sol. ||E|| = 8.334073e+00\n", - " Field energy E (1.312e-11 J) + H (3.476e-11 J) = 4.788e-11 J\n", - " S[1][1] = -3.755e-01+1.971e-01i, |S[1][1]| = -7.451e+00, arg(S[1][1]) = +1.523e+02\n", - "\n", - "It 11/121: ω/2π = 1.500e+00 GHz (total elapsed time = 7.90e+01 s)\n", - "\n", - " Sol. ||E|| = 8.353801e+00\n", - " Field energy E (1.293e-11 J) + H (3.365e-11 J) = 4.659e-11 J\n", - " S[1][1] = -3.580e-01+1.953e-01i, |S[1][1]| = -7.790e+00, arg(S[1][1]) = +1.514e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 11\n", - "\n", - "It 12/121: ω/2π = 1.550e+00 GHz (total elapsed time = 7.93e+01 s)\n", - "\n", - " Sol. ||E|| = 8.388192e+00\n", - " Field energy E (1.280e-11 J) + H (3.259e-11 J) = 4.539e-11 J\n", - " S[1][1] = -3.415e-01+1.927e-01i, |S[1][1]| = -8.131e+00, arg(S[1][1]) = +1.506e+02\n", - "\n", - "It 13/121: ω/2π = 1.600e+00 GHz (total elapsed time = 7.93e+01 s)\n", - "\n", - " Sol. ||E|| = 8.434475e+00\n", - " Field energy E (1.273e-11 J) + H (3.156e-11 J) = 4.429e-11 J\n", - " S[1][1] = -3.260e-01+1.893e-01i, |S[1][1]| = -8.475e+00, arg(S[1][1]) = +1.499e+02\n", - "\n", - "It 14/121: ω/2π = 1.650e+00 GHz (total elapsed time = 7.94e+01 s)\n", - "\n", - " Sol. ||E|| = 8.490257e+00\n", - " Field energy E (1.270e-11 J) + H (3.057e-11 J) = 4.326e-11 J\n", - " S[1][1] = -3.114e-01+1.852e-01i, |S[1][1]| = -8.818e+00, arg(S[1][1]) = +1.493e+02\n", - "\n", - "It 15/121: ω/2π = 1.700e+00 GHz (total elapsed time = 7.94e+01 s)\n", - "\n", - " Sol. ||E|| = 8.553482e+00\n", - " Field energy E (1.271e-11 J) + H (2.962e-11 J) = 4.232e-11 J\n", - " S[1][1] = -2.978e-01+1.806e-01i, |S[1][1]| = -9.161e+00, arg(S[1][1]) = +1.488e+02\n", - "\n", - "It 16/121: ω/2π = 1.750e+00 GHz (total elapsed time = 7.94e+01 s)\n", - "\n", - " Sol. ||E|| = 8.622383e+00\n", - " Field energy E (1.274e-11 J) + H (2.871e-11 J) = 4.146e-11 J\n", - " S[1][1] = -2.851e-01+1.756e-01i, |S[1][1]| = -9.503e+00, arg(S[1][1]) = +1.484e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 16\n", - "\n", - "It 17/121: ω/2π = 1.800e+00 GHz (total elapsed time = 7.96e+01 s)\n", - "\n", - " Sol. ||E|| = 8.695434e+00\n", - " Field energy E (1.281e-11 J) + H (2.785e-11 J) = 4.066e-11 J\n", - " S[1][1] = -2.734e-01+1.701e-01i, |S[1][1]| = -9.842e+00, arg(S[1][1]) = +1.481e+02\n", - "\n", - "It 18/121: ω/2π = 1.850e+00 GHz (total elapsed time = 7.97e+01 s)\n", - "\n", - " Sol. ||E|| = 8.771306e+00\n", - " Field energy E (1.289e-11 J) + H (2.704e-11 J) = 3.993e-11 J\n", - " S[1][1] = -2.627e-01+1.644e-01i, |S[1][1]| = -1.018e+01, arg(S[1][1]) = +1.480e+02\n", - "\n", - "It 19/121: ω/2π = 1.900e+00 GHz (total elapsed time = 7.97e+01 s)\n", - "\n", - " Sol. ||E|| = 8.848828e+00\n", - " Field energy E (1.299e-11 J) + H (2.627e-11 J) = 3.926e-11 J\n", - " S[1][1] = -2.529e-01+1.585e-01i, |S[1][1]| = -1.050e+01, arg(S[1][1]) = +1.479e+02\n", - "\n", - "It 20/121: ω/2π = 1.950e+00 GHz (total elapsed time = 7.97e+01 s)\n", - "\n", - " Sol. ||E|| = 8.926949e+00\n", - " Field energy E (1.310e-11 J) + H (2.554e-11 J) = 3.864e-11 J\n", - " S[1][1] = -2.440e-01+1.524e-01i, |S[1][1]| = -1.082e+01, arg(S[1][1]) = +1.480e+02\n", - "\n", - "It 21/121: ω/2π = 2.000e+00 GHz (total elapsed time = 7.97e+01 s)\n", - "\n", - " Sol. ||E|| = 9.004700e+00\n", - " Field energy E (1.322e-11 J) + H (2.486e-11 J) = 3.808e-11 J\n", - " S[1][1] = -2.360e-01+1.462e-01i, |S[1][1]| = -1.113e+01, arg(S[1][1]) = +1.482e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 21\n", - "\n", - "It 22/121: ω/2π = 2.050e+00 GHz (total elapsed time = 8.00e+01 s)\n", - "\n", - " Sol. ||E|| = 9.081172e+00\n", - " Field energy E (1.335e-11 J) + H (2.422e-11 J) = 3.757e-11 J\n", - " S[1][1] = -2.289e-01+1.401e-01i, |S[1][1]| = -1.143e+01, arg(S[1][1]) = +1.485e+02\n", - "\n", - "It 23/121: ω/2π = 2.100e+00 GHz (total elapsed time = 8.00e+01 s)\n", - "\n", - " Sol. ||E|| = 9.155482e+00\n", - " Field energy E (1.347e-11 J) + H (2.363e-11 J) = 3.710e-11 J\n", - " S[1][1] = -2.226e-01+1.340e-01i, |S[1][1]| = -1.171e+01, arg(S[1][1]) = +1.490e+02\n", - "\n", - "It 24/121: ω/2π = 2.150e+00 GHz (total elapsed time = 8.00e+01 s)\n", - "\n", - " Sol. ||E|| = 9.226759e+00\n", - " Field energy E (1.360e-11 J) + H (2.308e-11 J) = 3.668e-11 J\n", - " S[1][1] = -2.172e-01+1.280e-01i, |S[1][1]| = -1.197e+01, arg(S[1][1]) = +1.495e+02\n", - "\n", - "It 25/121: ω/2π = 2.200e+00 GHz (total elapsed time = 8.00e+01 s)\n", - "\n", - " Sol. ||E|| = 9.294122e+00\n", - " Field energy E (1.372e-11 J) + H (2.256e-11 J) = 3.628e-11 J\n", - " S[1][1] = -2.125e-01+1.223e-01i, |S[1][1]| = -1.221e+01, arg(S[1][1]) = +1.501e+02\n", - "\n", - "It 26/121: ω/2π = 2.250e+00 GHz (total elapsed time = 8.00e+01 s)\n", - "\n", - " Sol. ||E|| = 9.356677e+00\n", - " Field energy E (1.383e-11 J) + H (2.209e-11 J) = 3.592e-11 J\n", - " S[1][1] = -2.086e-01+1.168e-01i, |S[1][1]| = -1.243e+01, arg(S[1][1]) = +1.508e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 26\n", - "\n", - "It 27/121: ω/2π = 2.300e+00 GHz (total elapsed time = 8.03e+01 s)\n", - "\n", - " Sol. ||E|| = 9.413511e+00\n", - " Field energy E (1.393e-11 J) + H (2.165e-11 J) = 3.558e-11 J\n", - " S[1][1] = -2.054e-01+1.116e-01i, |S[1][1]| = -1.263e+01, arg(S[1][1]) = +1.515e+02\n", - "\n", - "It 28/121: ω/2π = 2.350e+00 GHz (total elapsed time = 8.03e+01 s)\n", - "\n", - " Sol. ||E|| = 9.463700e+00\n", - " Field energy E (1.401e-11 J) + H (2.125e-11 J) = 3.526e-11 J\n", - " S[1][1] = -2.028e-01+1.068e-01i, |S[1][1]| = -1.280e+01, arg(S[1][1]) = +1.522e+02\n", - "\n", - "It 29/121: ω/2π = 2.400e+00 GHz (total elapsed time = 8.03e+01 s)\n", - "\n", - " Sol. ||E|| = 9.506327e+00\n", - " Field energy E (1.408e-11 J) + H (2.087e-11 J) = 3.495e-11 J\n", - " S[1][1] = -2.008e-01+1.025e-01i, |S[1][1]| = -1.294e+01, arg(S[1][1]) = +1.530e+02\n", - "\n", - "It 30/121: ω/2π = 2.450e+00 GHz (total elapsed time = 8.03e+01 s)\n", - "\n", - " Sol. ||E|| = 9.540504e+00\n", - " Field energy E (1.412e-11 J) + H (2.053e-11 J) = 3.465e-11 J\n", - " S[1][1] = -1.993e-01+9.867e-02i, |S[1][1]| = -1.306e+01, arg(S[1][1]) = +1.537e+02\n", - "\n", - "It 31/121: ω/2π = 2.500e+00 GHz (total elapsed time = 8.03e+01 s)\n", - "\n", - " Sol. ||E|| = 9.565412e+00\n", - " Field energy E (1.413e-11 J) + H (2.021e-11 J) = 3.434e-11 J\n", - " S[1][1] = -1.982e-01+9.537e-02i, |S[1][1]| = -1.315e+01, arg(S[1][1]) = +1.543e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 31\n", - "\n", - "It 32/121: ω/2π = 2.550e+00 GHz (total elapsed time = 8.06e+01 s)\n", - "\n", - " Sol. ||E|| = 9.580347e+00\n", - " Field energy E (1.412e-11 J) + H (1.990e-11 J) = 3.403e-11 J\n", - " S[1][1] = -1.975e-01+9.264e-02i, |S[1][1]| = -1.322e+01, arg(S[1][1]) = +1.549e+02\n", - "\n", - "It 33/121: ω/2π = 2.600e+00 GHz (total elapsed time = 8.06e+01 s)\n", - "\n", - " Sol. ||E|| = 9.584763e+00\n", - " Field energy E (1.408e-11 J) + H (1.962e-11 J) = 3.370e-11 J\n", - " S[1][1] = -1.970e-01+9.049e-02i, |S[1][1]| = -1.328e+01, arg(S[1][1]) = +1.553e+02\n", - "\n", - "It 34/121: ω/2π = 2.650e+00 GHz (total elapsed time = 8.06e+01 s)\n", - "\n", - " Sol. ||E|| = 9.578331e+00\n", - " Field energy E (1.401e-11 J) + H (1.935e-11 J) = 3.336e-11 J\n", - " S[1][1] = -1.967e-01+8.893e-02i, |S[1][1]| = -1.332e+01, arg(S[1][1]) = +1.557e+02\n", - "\n", - "It 35/121: ω/2π = 2.700e+00 GHz (total elapsed time = 8.06e+01 s)\n", - "\n", - " Sol. ||E|| = 9.560977e+00\n", - " Field energy E (1.390e-11 J) + H (1.909e-11 J) = 3.299e-11 J\n", - " S[1][1] = -1.964e-01+8.794e-02i, |S[1][1]| = -1.334e+01, arg(S[1][1]) = +1.559e+02\n", - "\n", - "It 36/121: ω/2π = 2.750e+00 GHz (total elapsed time = 8.07e+01 s)\n", - "\n", - " Sol. ||E|| = 9.532918e+00\n", - " Field energy E (1.377e-11 J) + H (1.883e-11 J) = 3.259e-11 J\n", - " S[1][1] = -1.961e-01+8.747e-02i, |S[1][1]| = -1.336e+01, arg(S[1][1]) = +1.560e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 36\n", - "\n", - "It 37/121: ω/2π = 2.800e+00 GHz (total elapsed time = 8.09e+01 s)\n", - "\n", - " Sol. ||E|| = 9.494676e+00\n", - " Field energy E (1.360e-11 J) + H (1.857e-11 J) = 3.218e-11 J\n", - " S[1][1] = -1.957e-01+8.749e-02i, |S[1][1]| = -1.338e+01, arg(S[1][1]) = +1.559e+02\n", - "\n", - "It 38/121: ω/2π = 2.850e+00 GHz (total elapsed time = 8.09e+01 s)\n", - "\n", - " Sol. ||E|| = 9.447071e+00\n", - " Field energy E (1.341e-11 J) + H (1.832e-11 J) = 3.173e-11 J\n", - " S[1][1] = -1.951e-01+8.791e-02i, |S[1][1]| = -1.339e+01, arg(S[1][1]) = +1.557e+02\n", - "\n", - "It 39/121: ω/2π = 2.900e+00 GHz (total elapsed time = 8.10e+01 s)\n", - "\n", - " Sol. ||E|| = 9.391188e+00\n", - " Field energy E (1.320e-11 J) + H (1.806e-11 J) = 3.126e-11 J\n", - " S[1][1] = -1.942e-01+8.868e-02i, |S[1][1]| = -1.341e+01, arg(S[1][1]) = +1.555e+02\n", - "\n", - "It 40/121: ω/2π = 2.950e+00 GHz (total elapsed time = 8.10e+01 s)\n", - "\n", - " Sol. ||E|| = 9.328326e+00\n", - " Field energy E (1.296e-11 J) + H (1.780e-11 J) = 3.077e-11 J\n", - " S[1][1] = -1.930e-01+8.971e-02i, |S[1][1]| = -1.344e+01, arg(S[1][1]) = +1.551e+02\n", - "\n", - "It 41/121: ω/2π = 3.000e+00 GHz (total elapsed time = 8.10e+01 s)\n", - "\n", - " Sol. ||E|| = 9.259923e+00\n", - " Field energy E (1.272e-11 J) + H (1.754e-11 J) = 3.026e-11 J\n", - " S[1][1] = -1.916e-01+9.091e-02i, |S[1][1]| = -1.347e+01, arg(S[1][1]) = +1.546e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 41\n", - "\n", - "It 42/121: ω/2π = 3.050e+00 GHz (total elapsed time = 8.13e+01 s)\n", - "\n", - " Sol. ||E|| = 9.187487e+00\n", - " Field energy E (1.246e-11 J) + H (1.728e-11 J) = 2.974e-11 J\n", - " S[1][1] = -1.899e-01+9.221e-02i, |S[1][1]| = -1.351e+01, arg(S[1][1]) = +1.541e+02\n", - "\n", - "It 43/121: ω/2π = 3.100e+00 GHz (total elapsed time = 8.13e+01 s)\n", - "\n", - " Sol. ||E|| = 9.112508e+00\n", - " Field energy E (1.221e-11 J) + H (1.701e-11 J) = 2.921e-11 J\n", - " S[1][1] = -1.879e-01+9.354e-02i, |S[1][1]| = -1.356e+01, arg(S[1][1]) = +1.535e+02\n", - "\n", - "It 44/121: ω/2π = 3.150e+00 GHz (total elapsed time = 8.13e+01 s)\n", - "\n", - " Sol. ||E|| = 9.036395e+00\n", - " Field energy E (1.195e-11 J) + H (1.674e-11 J) = 2.869e-11 J\n", - " S[1][1] = -1.857e-01+9.484e-02i, |S[1][1]| = -1.362e+01, arg(S[1][1]) = +1.529e+02\n", - "\n", - "It 45/121: ω/2π = 3.200e+00 GHz (total elapsed time = 8.13e+01 s)\n", - "\n", - " Sol. ||E|| = 8.960418e+00\n", - " Field energy E (1.169e-11 J) + H (1.647e-11 J) = 2.816e-11 J\n", - " S[1][1] = -1.833e-01+9.606e-02i, |S[1][1]| = -1.368e+01, arg(S[1][1]) = +1.523e+02\n", - "\n", - "It 46/121: ω/2π = 3.250e+00 GHz (total elapsed time = 8.13e+01 s)\n", - "\n", - " Sol. ||E|| = 8.885667e+00\n", - " Field energy E (1.145e-11 J) + H (1.620e-11 J) = 2.765e-11 J\n", - " S[1][1] = -1.808e-01+9.717e-02i, |S[1][1]| = -1.376e+01, arg(S[1][1]) = +1.517e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 46\n", - "\n", - "It 47/121: ω/2π = 3.300e+00 GHz (total elapsed time = 8.16e+01 s)\n", - "\n", - " Sol. ||E|| = 8.813034e+00\n", - " Field energy E (1.121e-11 J) + H (1.594e-11 J) = 2.715e-11 J\n", - " S[1][1] = -1.781e-01+9.816e-02i, |S[1][1]| = -1.383e+01, arg(S[1][1]) = +1.511e+02\n", - "\n", - "It 48/121: ω/2π = 3.350e+00 GHz (total elapsed time = 8.16e+01 s)\n", - "\n", - " Sol. ||E|| = 8.743205e+00\n", - " Field energy E (1.098e-11 J) + H (1.568e-11 J) = 2.666e-11 J\n", - " S[1][1] = -1.754e-01+9.900e-02i, |S[1][1]| = -1.392e+01, arg(S[1][1]) = +1.506e+02\n", - "\n", - "It 49/121: ω/2π = 3.400e+00 GHz (total elapsed time = 8.16e+01 s)\n", - "\n", - " Sol. ||E|| = 8.676672e+00\n", - " Field energy E (1.077e-11 J) + H (1.543e-11 J) = 2.620e-11 J\n", - " S[1][1] = -1.726e-01+9.970e-02i, |S[1][1]| = -1.401e+01, arg(S[1][1]) = +1.500e+02\n", - "\n", - "It 50/121: ω/2π = 3.450e+00 GHz (total elapsed time = 8.16e+01 s)\n", - "\n", - " Sol. ||E|| = 8.613749e+00\n", - " Field energy E (1.056e-11 J) + H (1.519e-11 J) = 2.575e-11 J\n", - " S[1][1] = -1.699e-01+1.003e-01i, |S[1][1]| = -1.410e+01, arg(S[1][1]) = +1.495e+02\n", - "\n", - "It 51/121: ω/2π = 3.500e+00 GHz (total elapsed time = 8.16e+01 s)\n", - "\n", - " Sol. ||E|| = 8.554594e+00\n", - " Field energy E (1.037e-11 J) + H (1.495e-11 J) = 2.533e-11 J\n", - " S[1][1] = -1.672e-01+1.007e-01i, |S[1][1]| = -1.419e+01, arg(S[1][1]) = +1.489e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 51\n", - "\n", - "It 52/121: ω/2π = 3.550e+00 GHz (total elapsed time = 8.19e+01 s)\n", - "\n", - " Sol. ||E|| = 8.499240e+00\n", - " Field energy E (1.020e-11 J) + H (1.473e-11 J) = 2.493e-11 J\n", - " S[1][1] = -1.646e-01+1.011e-01i, |S[1][1]| = -1.428e+01, arg(S[1][1]) = +1.485e+02\n", - "\n", - "It 53/121: ω/2π = 3.600e+00 GHz (total elapsed time = 8.19e+01 s)\n", - "\n", - " Sol. ||E|| = 8.447615e+00\n", - " Field energy E (1.004e-11 J) + H (1.451e-11 J) = 2.455e-11 J\n", - " S[1][1] = -1.621e-01+1.013e-01i, |S[1][1]| = -1.437e+01, arg(S[1][1]) = +1.480e+02\n", - "\n", - "It 54/121: ω/2π = 3.650e+00 GHz (total elapsed time = 8.19e+01 s)\n", - "\n", - " Sol. ||E|| = 8.399574e+00\n", - " Field energy E (9.883e-12 J) + H (1.431e-11 J) = 2.419e-11 J\n", - " S[1][1] = -1.597e-01+1.015e-01i, |S[1][1]| = -1.446e+01, arg(S[1][1]) = +1.476e+02\n", - "\n", - "It 55/121: ω/2π = 3.700e+00 GHz (total elapsed time = 8.19e+01 s)\n", - "\n", - " Sol. ||E|| = 8.354914e+00\n", - " Field energy E (9.742e-12 J) + H (1.411e-11 J) = 2.386e-11 J\n", - " S[1][1] = -1.573e-01+1.016e-01i, |S[1][1]| = -1.455e+01, arg(S[1][1]) = +1.471e+02\n", - "\n", - "It 56/121: ω/2π = 3.750e+00 GHz (total elapsed time = 8.19e+01 s)\n", - "\n", - " Sol. ||E|| = 8.313399e+00\n", - " Field energy E (9.611e-12 J) + H (1.393e-11 J) = 2.354e-11 J\n", - " S[1][1] = -1.551e-01+1.017e-01i, |S[1][1]| = -1.463e+01, arg(S[1][1]) = +1.467e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 56\n", - "\n", - "It 57/121: ω/2π = 3.800e+00 GHz (total elapsed time = 8.22e+01 s)\n", - "\n", - " Sol. ||E|| = 8.274769e+00\n", - " Field energy E (9.490e-12 J) + H (1.375e-11 J) = 2.324e-11 J\n", - " S[1][1] = -1.530e-01+1.018e-01i, |S[1][1]| = -1.471e+01, arg(S[1][1]) = +1.464e+02\n", - "\n", - "It 58/121: ω/2π = 3.850e+00 GHz (total elapsed time = 8.22e+01 s)\n", - "\n", - " Sol. ||E|| = 8.238758e+00\n", - " Field energy E (9.377e-12 J) + H (1.359e-11 J) = 2.297e-11 J\n", - " S[1][1] = -1.510e-01+1.019e-01i, |S[1][1]| = -1.479e+01, arg(S[1][1]) = +1.460e+02\n", - "\n", - "It 59/121: ω/2π = 3.900e+00 GHz (total elapsed time = 8.22e+01 s)\n", - "\n", - " Sol. ||E|| = 8.205099e+00\n", - " Field energy E (9.271e-12 J) + H (1.343e-11 J) = 2.271e-11 J\n", - " S[1][1] = -1.491e-01+1.020e-01i, |S[1][1]| = -1.486e+01, arg(S[1][1]) = +1.456e+02\n", - "\n", - "It 60/121: ω/2π = 3.950e+00 GHz (total elapsed time = 8.22e+01 s)\n", - "\n", - " Sol. ||E|| = 8.173532e+00\n", - " Field energy E (9.172e-12 J) + H (1.329e-11 J) = 2.246e-11 J\n", - " S[1][1] = -1.474e-01+1.022e-01i, |S[1][1]| = -1.493e+01, arg(S[1][1]) = +1.453e+02\n", - "\n", - "It 61/121: ω/2π = 4.000e+00 GHz (total elapsed time = 8.22e+01 s)\n", - "\n", - " Sol. ||E|| = 8.143812e+00\n", - " Field energy E (9.080e-12 J) + H (1.315e-11 J) = 2.223e-11 J\n", - " S[1][1] = -1.457e-01+1.024e-01i, |S[1][1]| = -1.499e+01, arg(S[1][1]) = +1.449e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 61\n", - "\n", - "It 62/121: ω/2π = 4.050e+00 GHz (total elapsed time = 8.25e+01 s)\n", - "\n", - " Sol. ||E|| = 8.115706e+00\n", - " Field energy E (8.992e-12 J) + H (1.302e-11 J) = 2.202e-11 J\n", - " S[1][1] = -1.441e-01+1.026e-01i, |S[1][1]| = -1.505e+01, arg(S[1][1]) = +1.445e+02\n", - "\n", - "It 63/121: ω/2π = 4.100e+00 GHz (total elapsed time = 8.25e+01 s)\n", - "\n", - " Sol. ||E|| = 8.089000e+00\n", - " Field energy E (8.909e-12 J) + H (1.291e-11 J) = 2.181e-11 J\n", - " S[1][1] = -1.426e-01+1.029e-01i, |S[1][1]| = -1.510e+01, arg(S[1][1]) = +1.442e+02\n", - "\n", - "It 64/121: ω/2π = 4.150e+00 GHz (total elapsed time = 8.26e+01 s)\n", - "\n", - " Sol. ||E|| = 8.063497e+00\n", - " Field energy E (8.831e-12 J) + H (1.279e-11 J) = 2.162e-11 J\n", - " S[1][1] = -1.411e-01+1.033e-01i, |S[1][1]| = -1.514e+01, arg(S[1][1]) = +1.438e+02\n", - "\n", - "It 65/121: ω/2π = 4.200e+00 GHz (total elapsed time = 8.26e+01 s)\n", - "\n", - " Sol. ||E|| = 8.039018e+00\n", - " Field energy E (8.756e-12 J) + H (1.269e-11 J) = 2.144e-11 J\n", - " S[1][1] = -1.397e-01+1.038e-01i, |S[1][1]| = -1.519e+01, arg(S[1][1]) = +1.434e+02\n", - "\n", - "It 66/121: ω/2π = 4.250e+00 GHz (total elapsed time = 8.26e+01 s)\n", - "\n", - " Sol. ||E|| = 8.015405e+00\n", - " Field energy E (8.684e-12 J) + H (1.259e-11 J) = 2.127e-11 J\n", - " S[1][1] = -1.384e-01+1.043e-01i, |S[1][1]| = -1.523e+01, arg(S[1][1]) = +1.430e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 66\n", - "\n", - "It 67/121: ω/2π = 4.300e+00 GHz (total elapsed time = 8.29e+01 s)\n", - "\n", - " Sol. ||E|| = 7.992512e+00\n", - " Field energy E (8.615e-12 J) + H (1.250e-11 J) = 2.111e-11 J\n", - " S[1][1] = -1.371e-01+1.049e-01i, |S[1][1]| = -1.526e+01, arg(S[1][1]) = +1.426e+02\n", - "\n", - "It 68/121: ω/2π = 4.350e+00 GHz (total elapsed time = 8.29e+01 s)\n", - "\n", - " Sol. ||E|| = 7.970215e+00\n", - " Field energy E (8.548e-12 J) + H (1.241e-11 J) = 2.096e-11 J\n", - " S[1][1] = -1.358e-01+1.056e-01i, |S[1][1]| = -1.529e+01, arg(S[1][1]) = +1.421e+02\n", - "\n", - "It 69/121: ω/2π = 4.400e+00 GHz (total elapsed time = 8.29e+01 s)\n", - "\n", - " Sol. ||E|| = 7.948400e+00\n", - " Field energy E (8.483e-12 J) + H (1.233e-11 J) = 2.081e-11 J\n", - " S[1][1] = -1.345e-01+1.064e-01i, |S[1][1]| = -1.531e+01, arg(S[1][1]) = +1.417e+02\n", - "\n", - "It 70/121: ω/2π = 4.450e+00 GHz (total elapsed time = 8.29e+01 s)\n", - "\n", - " Sol. ||E|| = 7.926973e+00\n", - " Field energy E (8.420e-12 J) + H (1.226e-11 J) = 2.068e-11 J\n", - " S[1][1] = -1.333e-01+1.072e-01i, |S[1][1]| = -1.534e+01, arg(S[1][1]) = +1.412e+02\n", - "\n", - "It 71/121: ω/2π = 4.500e+00 GHz (total elapsed time = 8.29e+01 s)\n", - "\n", - " Sol. ||E|| = 7.905847e+00\n", - " Field energy E (8.358e-12 J) + H (1.218e-11 J) = 2.054e-11 J\n", - " S[1][1] = -1.320e-01+1.082e-01i, |S[1][1]| = -1.536e+01, arg(S[1][1]) = +1.407e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 71\n", - "\n", - "It 72/121: ω/2π = 4.550e+00 GHz (total elapsed time = 8.32e+01 s)\n", - "\n", - " Sol. ||E|| = 7.884953e+00\n", - " Field energy E (8.298e-12 J) + H (1.212e-11 J) = 2.042e-11 J\n", - " S[1][1] = -1.307e-01+1.092e-01i, |S[1][1]| = -1.537e+01, arg(S[1][1]) = +1.401e+02\n", - "\n", - "It 73/121: ω/2π = 4.600e+00 GHz (total elapsed time = 8.32e+01 s)\n", - "\n", - " Sol. ||E|| = 7.864228e+00\n", - " Field energy E (8.239e-12 J) + H (1.205e-11 J) = 2.029e-11 J\n", - " S[1][1] = -1.295e-01+1.103e-01i, |S[1][1]| = -1.539e+01, arg(S[1][1]) = +1.396e+02\n", - "\n", - "It 74/121: ω/2π = 4.650e+00 GHz (total elapsed time = 8.32e+01 s)\n", - "\n", - " Sol. ||E|| = 7.843622e+00\n", - " Field energy E (8.181e-12 J) + H (1.199e-11 J) = 2.018e-11 J\n", - " S[1][1] = -1.282e-01+1.114e-01i, |S[1][1]| = -1.540e+01, arg(S[1][1]) = +1.390e+02\n", - "\n", - "It 75/121: ω/2π = 4.700e+00 GHz (total elapsed time = 8.32e+01 s)\n", - "\n", - " Sol. ||E|| = 7.823094e+00\n", - " Field energy E (8.123e-12 J) + H (1.194e-11 J) = 2.006e-11 J\n", - " S[1][1] = -1.268e-01+1.127e-01i, |S[1][1]| = -1.541e+01, arg(S[1][1]) = +1.384e+02\n", - "\n", - "It 76/121: ω/2π = 4.750e+00 GHz (total elapsed time = 8.32e+01 s)\n", - "\n", - " Sol. ||E|| = 7.802610e+00\n", - " Field energy E (8.067e-12 J) + H (1.188e-11 J) = 1.995e-11 J\n", - " S[1][1] = -1.254e-01+1.139e-01i, |S[1][1]| = -1.542e+01, arg(S[1][1]) = +1.377e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 76\n", - "\n", - "It 77/121: ω/2π = 4.800e+00 GHz (total elapsed time = 8.35e+01 s)\n", - "\n", - " Sol. ||E|| = 7.782145e+00\n", - " Field energy E (8.011e-12 J) + H (1.183e-11 J) = 1.984e-11 J\n", - " S[1][1] = -1.240e-01+1.153e-01i, |S[1][1]| = -1.543e+01, arg(S[1][1]) = +1.371e+02\n", - "\n", - "It 78/121: ω/2π = 4.850e+00 GHz (total elapsed time = 8.35e+01 s)\n", - "\n", - " Sol. ||E|| = 7.761680e+00\n", - " Field energy E (7.955e-12 J) + H (1.178e-11 J) = 1.974e-11 J\n", - " S[1][1] = -1.225e-01+1.167e-01i, |S[1][1]| = -1.543e+01, arg(S[1][1]) = +1.364e+02\n", - "\n", - "It 79/121: ω/2π = 4.900e+00 GHz (total elapsed time = 8.35e+01 s)\n", - "\n", - " Sol. ||E|| = 7.741200e+00\n", - " Field energy E (7.900e-12 J) + H (1.174e-11 J) = 1.964e-11 J\n", - " S[1][1] = -1.210e-01+1.181e-01i, |S[1][1]| = -1.544e+01, arg(S[1][1]) = +1.357e+02\n", - "\n", - "It 80/121: ω/2π = 4.950e+00 GHz (total elapsed time = 8.35e+01 s)\n", - "\n", - " Sol. ||E|| = 7.720699e+00\n", - " Field energy E (7.846e-12 J) + H (1.169e-11 J) = 1.954e-11 J\n", - " S[1][1] = -1.194e-01+1.196e-01i, |S[1][1]| = -1.544e+01, arg(S[1][1]) = +1.350e+02\n", - "\n", - "It 81/121: ω/2π = 5.000e+00 GHz (total elapsed time = 8.35e+01 s)\n", - "\n", - " Sol. ||E|| = 7.700174e+00\n", - " Field energy E (7.792e-12 J) + H (1.164e-11 J) = 1.944e-11 J\n", - " S[1][1] = -1.178e-01+1.211e-01i, |S[1][1]| = -1.545e+01, arg(S[1][1]) = +1.342e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 81\n", - "\n", - "It 82/121: ω/2π = 5.050e+00 GHz (total elapsed time = 8.38e+01 s)\n", - "\n", - " Sol. ||E|| = 7.679625e+00\n", - " Field energy E (7.738e-12 J) + H (1.160e-11 J) = 1.934e-11 J\n", - " S[1][1] = -1.161e-01+1.226e-01i, |S[1][1]| = -1.545e+01, arg(S[1][1]) = +1.334e+02\n", - "\n", - "It 83/121: ω/2π = 5.100e+00 GHz (total elapsed time = 8.39e+01 s)\n", - "\n", - " Sol. ||E|| = 7.659058e+00\n", - " Field energy E (7.685e-12 J) + H (1.156e-11 J) = 1.924e-11 J\n", - " S[1][1] = -1.143e-01+1.241e-01i, |S[1][1]| = -1.546e+01, arg(S[1][1]) = +1.326e+02\n", - "\n", - "It 84/121: ω/2π = 5.150e+00 GHz (total elapsed time = 8.39e+01 s)\n", - "\n", - " Sol. ||E|| = 7.638480e+00\n", - " Field energy E (7.632e-12 J) + H (1.151e-11 J) = 1.915e-11 J\n", - " S[1][1] = -1.124e-01+1.256e-01i, |S[1][1]| = -1.546e+01, arg(S[1][1]) = +1.318e+02\n", - "\n", - "It 85/121: ω/2π = 5.200e+00 GHz (total elapsed time = 8.39e+01 s)\n", - "\n", - " Sol. ||E|| = 7.617902e+00\n", - " Field energy E (7.579e-12 J) + H (1.147e-11 J) = 1.905e-11 J\n", - " S[1][1] = -1.105e-01+1.272e-01i, |S[1][1]| = -1.547e+01, arg(S[1][1]) = +1.310e+02\n", - "\n", - "It 86/121: ω/2π = 5.250e+00 GHz (total elapsed time = 8.39e+01 s)\n", - "\n", - " Sol. ||E|| = 7.597337e+00\n", - " Field energy E (7.527e-12 J) + H (1.143e-11 J) = 1.895e-11 J\n", - " S[1][1] = -1.085e-01+1.287e-01i, |S[1][1]| = -1.548e+01, arg(S[1][1]) = +1.301e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 86\n", - "\n", - "It 87/121: ω/2π = 5.300e+00 GHz (total elapsed time = 8.42e+01 s)\n", - "\n", - " Sol. ||E|| = 7.576799e+00\n", - " Field energy E (7.476e-12 J) + H (1.138e-11 J) = 1.886e-11 J\n", - " S[1][1] = -1.064e-01+1.302e-01i, |S[1][1]| = -1.549e+01, arg(S[1][1]) = +1.293e+02\n", - "\n", - "It 88/121: ω/2π = 5.350e+00 GHz (total elapsed time = 8.42e+01 s)\n", - "\n", - " Sol. ||E|| = 7.556304e+00\n", - " Field energy E (7.424e-12 J) + H (1.134e-11 J) = 1.876e-11 J\n", - " S[1][1] = -1.043e-01+1.316e-01i, |S[1][1]| = -1.550e+01, arg(S[1][1]) = +1.284e+02\n", - "\n", - "It 89/121: ω/2π = 5.400e+00 GHz (total elapsed time = 8.42e+01 s)\n", - "\n", - " Sol. ||E|| = 7.535868e+00\n", - " Field energy E (7.374e-12 J) + H (1.130e-11 J) = 1.867e-11 J\n", - " S[1][1] = -1.021e-01+1.331e-01i, |S[1][1]| = -1.551e+01, arg(S[1][1]) = +1.275e+02\n", - "\n", - "It 90/121: ω/2π = 5.450e+00 GHz (total elapsed time = 8.43e+01 s)\n", - "\n", - " Sol. ||E|| = 7.515509e+00\n", - " Field energy E (7.323e-12 J) + H (1.125e-11 J) = 1.857e-11 J\n", - " S[1][1] = -9.984e-02+1.345e-01i, |S[1][1]| = -1.552e+01, arg(S[1][1]) = +1.266e+02\n", - "\n", - "It 91/121: ω/2π = 5.500e+00 GHz (total elapsed time = 8.43e+01 s)\n", - "\n", - " Sol. ||E|| = 7.495243e+00\n", - " Field energy E (7.274e-12 J) + H (1.120e-11 J) = 1.848e-11 J\n", - " S[1][1] = -9.752e-02+1.358e-01i, |S[1][1]| = -1.554e+01, arg(S[1][1]) = +1.257e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 91\n", - "\n", - "It 92/121: ω/2π = 5.550e+00 GHz (total elapsed time = 8.46e+01 s)\n", - "\n", - " Sol. ||E|| = 7.475086e+00\n", - " Field energy E (7.225e-12 J) + H (1.116e-11 J) = 1.838e-11 J\n", - " S[1][1] = -9.515e-02+1.371e-01i, |S[1][1]| = -1.555e+01, arg(S[1][1]) = +1.248e+02\n", - "\n", - "It 93/121: ω/2π = 5.600e+00 GHz (total elapsed time = 8.46e+01 s)\n", - "\n", - " Sol. ||E|| = 7.455053e+00\n", - " Field energy E (7.176e-12 J) + H (1.111e-11 J) = 1.828e-11 J\n", - " S[1][1] = -9.274e-02+1.383e-01i, |S[1][1]| = -1.557e+01, arg(S[1][1]) = +1.238e+02\n", - "\n", - "It 94/121: ω/2π = 5.650e+00 GHz (total elapsed time = 8.46e+01 s)\n", - "\n", - " Sol. ||E|| = 7.435160e+00\n", - " Field energy E (7.128e-12 J) + H (1.106e-11 J) = 1.818e-11 J\n", - " S[1][1] = -9.028e-02+1.394e-01i, |S[1][1]| = -1.559e+01, arg(S[1][1]) = +1.229e+02\n", - "\n", - "It 95/121: ω/2π = 5.700e+00 GHz (total elapsed time = 8.46e+01 s)\n", - "\n", - " Sol. ||E|| = 7.415419e+00\n", - " Field energy E (7.080e-12 J) + H (1.100e-11 J) = 1.808e-11 J\n", - " S[1][1] = -8.780e-02+1.405e-01i, |S[1][1]| = -1.561e+01, arg(S[1][1]) = +1.220e+02\n", - "\n", - "It 96/121: ω/2π = 5.750e+00 GHz (total elapsed time = 8.46e+01 s)\n", - "\n", - " Sol. ||E|| = 7.395841e+00\n", - " Field energy E (7.034e-12 J) + H (1.095e-11 J) = 1.798e-11 J\n", - " S[1][1] = -8.529e-02+1.415e-01i, |S[1][1]| = -1.564e+01, arg(S[1][1]) = +1.211e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 96\n", - "\n", - "It 97/121: ω/2π = 5.800e+00 GHz (total elapsed time = 8.49e+01 s)\n", - "\n", - " Sol. ||E|| = 7.376435e+00\n", - " Field energy E (6.987e-12 J) + H (1.090e-11 J) = 1.788e-11 J\n", - " S[1][1] = -8.276e-02+1.424e-01i, |S[1][1]| = -1.566e+01, arg(S[1][1]) = +1.202e+02\n", - "\n", - "It 98/121: ω/2π = 5.850e+00 GHz (total elapsed time = 8.49e+01 s)\n", - "\n", - " Sol. ||E|| = 7.357209e+00\n", - " Field energy E (6.942e-12 J) + H (1.084e-11 J) = 1.778e-11 J\n", - " S[1][1] = -8.024e-02+1.433e-01i, |S[1][1]| = -1.569e+01, arg(S[1][1]) = +1.193e+02\n", - "\n", - "It 99/121: ω/2π = 5.900e+00 GHz (total elapsed time = 8.49e+01 s)\n", - "\n", - " Sol. ||E|| = 7.338166e+00\n", - " Field energy E (6.896e-12 J) + H (1.078e-11 J) = 1.768e-11 J\n", - " S[1][1] = -7.771e-02+1.440e-01i, |S[1][1]| = -1.572e+01, arg(S[1][1]) = +1.184e+02\n", - "\n", - "It 100/121: ω/2π = 5.950e+00 GHz (total elapsed time = 8.49e+01 s)\n", - "\n", - " Sol. ||E|| = 7.319311e+00\n", - " Field energy E (6.852e-12 J) + H (1.072e-11 J) = 1.757e-11 J\n", - " S[1][1] = -7.520e-02+1.447e-01i, |S[1][1]| = -1.575e+01, arg(S[1][1]) = +1.175e+02\n", - "\n", - "It 101/121: ω/2π = 6.000e+00 GHz (total elapsed time = 8.49e+01 s)\n", - "\n", - " Sol. ||E|| = 7.300643e+00\n", - " Field energy E (6.808e-12 J) + H (1.066e-11 J) = 1.747e-11 J\n", - " S[1][1] = -7.270e-02+1.453e-01i, |S[1][1]| = -1.579e+01, arg(S[1][1]) = +1.166e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 101\n", - "\n", - "It 102/121: ω/2π = 6.050e+00 GHz (total elapsed time = 8.52e+01 s)\n", - "\n", - " Sol. ||E|| = 7.282164e+00\n", - " Field energy E (6.765e-12 J) + H (1.060e-11 J) = 1.736e-11 J\n", - " S[1][1] = -7.024e-02+1.458e-01i, |S[1][1]| = -1.582e+01, arg(S[1][1]) = +1.157e+02\n", - "\n", - "It 103/121: ω/2π = 6.100e+00 GHz (total elapsed time = 8.52e+01 s)\n", - "\n", - " Sol. ||E|| = 7.263871e+00\n", - " Field energy E (6.722e-12 J) + H (1.054e-11 J) = 1.726e-11 J\n", - " S[1][1] = -6.781e-02+1.462e-01i, |S[1][1]| = -1.585e+01, arg(S[1][1]) = +1.149e+02\n", - "\n", - "It 104/121: ω/2π = 6.150e+00 GHz (total elapsed time = 8.52e+01 s)\n", - "\n", - " Sol. ||E|| = 7.245761e+00\n", - " Field energy E (6.679e-12 J) + H (1.047e-11 J) = 1.715e-11 J\n", - " S[1][1] = -6.543e-02+1.466e-01i, |S[1][1]| = -1.589e+01, arg(S[1][1]) = +1.141e+02\n", - "\n", - "It 105/121: ω/2π = 6.200e+00 GHz (total elapsed time = 8.53e+01 s)\n", - "\n", - " Sol. ||E|| = 7.227832e+00\n", - " Field energy E (6.638e-12 J) + H (1.041e-11 J) = 1.705e-11 J\n", - " S[1][1] = -6.309e-02+1.469e-01i, |S[1][1]| = -1.592e+01, arg(S[1][1]) = +1.132e+02\n", - "\n", - "It 106/121: ω/2π = 6.250e+00 GHz (total elapsed time = 8.53e+01 s)\n", - "\n", - " Sol. ||E|| = 7.210080e+00\n", - " Field energy E (6.596e-12 J) + H (1.034e-11 J) = 1.694e-11 J\n", - " S[1][1] = -6.081e-02+1.472e-01i, |S[1][1]| = -1.596e+01, arg(S[1][1]) = +1.124e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 106\n", - "\n", - "It 107/121: ω/2π = 6.300e+00 GHz (total elapsed time = 8.55e+01 s)\n", - "\n", - " Sol. ||E|| = 7.192502e+00\n", - " Field energy E (6.556e-12 J) + H (1.028e-11 J) = 1.684e-11 J\n", - " S[1][1] = -5.859e-02+1.474e-01i, |S[1][1]| = -1.599e+01, arg(S[1][1]) = +1.117e+02\n", - "\n", - "It 108/121: ω/2π = 6.350e+00 GHz (total elapsed time = 8.56e+01 s)\n", - "\n", - " Sol. ||E|| = 7.175095e+00\n", - " Field energy E (6.516e-12 J) + H (1.021e-11 J) = 1.673e-11 J\n", - " S[1][1] = -5.642e-02+1.476e-01i, |S[1][1]| = -1.603e+01, arg(S[1][1]) = +1.109e+02\n", - "\n", - "It 109/121: ω/2π = 6.400e+00 GHz (total elapsed time = 8.56e+01 s)\n", - "\n", - " Sol. ||E|| = 7.157857e+00\n", - " Field energy E (6.476e-12 J) + H (1.015e-11 J) = 1.663e-11 J\n", - " S[1][1] = -5.432e-02+1.477e-01i, |S[1][1]| = -1.606e+01, arg(S[1][1]) = +1.102e+02\n", - "\n", - "It 110/121: ω/2π = 6.450e+00 GHz (total elapsed time = 8.56e+01 s)\n", - "\n", - " Sol. ||E|| = 7.140785e+00\n", - " Field energy E (6.437e-12 J) + H (1.008e-11 J) = 1.652e-11 J\n", - " S[1][1] = -5.228e-02+1.478e-01i, |S[1][1]| = -1.609e+01, arg(S[1][1]) = +1.095e+02\n", - "\n", - "It 111/121: ω/2π = 6.500e+00 GHz (total elapsed time = 8.56e+01 s)\n", - "\n", - " Sol. ||E|| = 7.123877e+00\n", - " Field energy E (6.398e-12 J) + H (1.002e-11 J) = 1.642e-11 J\n", - " S[1][1] = -5.030e-02+1.479e-01i, |S[1][1]| = -1.612e+01, arg(S[1][1]) = +1.088e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 111\n", - "\n", - "It 112/121: ω/2π = 6.550e+00 GHz (total elapsed time = 8.59e+01 s)\n", - "\n", - " Sol. ||E|| = 7.107132e+00\n", - " Field energy E (6.360e-12 J) + H (9.957e-12 J) = 1.632e-11 J\n", - " S[1][1] = -4.838e-02+1.480e-01i, |S[1][1]| = -1.615e+01, arg(S[1][1]) = +1.081e+02\n", - "\n", - "It 113/121: ω/2π = 6.600e+00 GHz (total elapsed time = 8.59e+01 s)\n", - "\n", - " Sol. ||E|| = 7.090548e+00\n", - " Field energy E (6.323e-12 J) + H (9.894e-12 J) = 1.622e-11 J\n", - " S[1][1] = -4.652e-02+1.481e-01i, |S[1][1]| = -1.618e+01, arg(S[1][1]) = +1.074e+02\n", - "\n", - "It 114/121: ω/2π = 6.650e+00 GHz (total elapsed time = 8.59e+01 s)\n", - "\n", - " Sol. ||E|| = 7.074121e+00\n", - " Field energy E (6.286e-12 J) + H (9.833e-12 J) = 1.612e-11 J\n", - " S[1][1] = -4.472e-02+1.482e-01i, |S[1][1]| = -1.621e+01, arg(S[1][1]) = +1.068e+02\n", - "\n", - "It 115/121: ω/2π = 6.700e+00 GHz (total elapsed time = 8.59e+01 s)\n", - "\n", - " Sol. ||E|| = 7.057849e+00\n", - " Field energy E (6.250e-12 J) + H (9.772e-12 J) = 1.602e-11 J\n", - " S[1][1] = -4.297e-02+1.483e-01i, |S[1][1]| = -1.623e+01, arg(S[1][1]) = +1.062e+02\n", - "\n", - "It 116/121: ω/2π = 6.750e+00 GHz (total elapsed time = 8.59e+01 s)\n", - "\n", - " Sol. ||E|| = 7.041728e+00\n", - " Field energy E (6.214e-12 J) + H (9.712e-12 J) = 1.593e-11 J\n", - " S[1][1] = -4.127e-02+1.484e-01i, |S[1][1]| = -1.625e+01, arg(S[1][1]) = +1.055e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 116\n", - "\n", - "It 117/121: ω/2π = 6.800e+00 GHz (total elapsed time = 8.62e+01 s)\n", - "\n", - " Sol. ||E|| = 7.025753e+00\n", - " Field energy E (6.179e-12 J) + H (9.654e-12 J) = 1.583e-11 J\n", - " S[1][1] = -3.961e-02+1.485e-01i, |S[1][1]| = -1.627e+01, arg(S[1][1]) = +1.049e+02\n", - "\n", - "It 118/121: ω/2π = 6.850e+00 GHz (total elapsed time = 8.62e+01 s)\n", - "\n", - " Sol. ||E|| = 7.009916e+00\n", - " Field energy E (6.144e-12 J) + H (9.596e-12 J) = 1.574e-11 J\n", - " S[1][1] = -3.800e-02+1.486e-01i, |S[1][1]| = -1.628e+01, arg(S[1][1]) = +1.043e+02\n", - "\n", - "It 119/121: ω/2π = 6.900e+00 GHz (total elapsed time = 8.62e+01 s)\n", - "\n", - " Sol. ||E|| = 6.994212e+00\n", - " Field energy E (6.110e-12 J) + H (9.541e-12 J) = 1.565e-11 J\n", - " S[1][1] = -3.643e-02+1.487e-01i, |S[1][1]| = -1.630e+01, arg(S[1][1]) = +1.038e+02\n", - "\n", - "It 120/121: ω/2π = 6.950e+00 GHz (total elapsed time = 8.62e+01 s)\n", - "\n", - " Sol. ||E|| = 6.978631e+00\n", - " Field energy E (6.076e-12 J) + H (9.486e-12 J) = 1.556e-11 J\n", - " S[1][1] = -3.489e-02+1.489e-01i, |S[1][1]| = -1.631e+01, arg(S[1][1]) = +1.032e+02\n", - "\n", - "It 121/121: ω/2π = 7.000e+00 GHz (total elapsed time = 8.62e+01 s)\n", - "\n", - " Sol. ||E|| = 6.963165e+00\n", - " Field energy E (6.043e-12 J) + H (9.433e-12 J) = 1.548e-11 J\n", - " S[1][1] = -3.338e-02+1.491e-01i, |S[1][1]| = -1.632e+01, arg(S[1][1]) = +1.026e+02\n", - "\n", - " Wrote fields to disk (Paraview) at step 121\n", - "\n", - "Completed 0 iterations of adaptive mesh refinement (AMR):\n", - " Indicator norm = 1.935e-01, global unknowns = 134562\n", - " Max. iterations = 0, tol. = 1.000e-02\n", - "\n", - "Estimated peak per-rank memory usage is: Min. 145.6M, Max. 160.5M, Avg. 150.0M, Total 2.3G\n", - "Estimated peak per-node memory usage is: Min. 2.3G, Max. 2.3G, Avg. 2.3G, Total 2.3G\n", - "\n", - "Elapsed Time Report (s) Min. Max. Avg.\n", - "==============================================================\n", - "Initialization 0.046 0.114 0.108\n", - " Mesh Preprocessing 0.061 0.130 0.065\n", - "Operator Construction 0.942 0.989 0.982\n", - "Linear Solve 12.635 13.001 12.736\n", - " Setup 4.438 4.438 4.438\n", - " Preconditioner 50.650 52.015 51.666\n", - " Coarse Solve 6.967 8.016 7.219\n", - "PROM Construction 0.167 0.173 0.169\n", - "PROM Solve 0.019 0.021 0.021\n", - "Estimation 0.031 0.038 0.035\n", - " Construction 0.198 0.199 0.198\n", - " Solve 2.011 2.015 2.013\n", - "Postprocessing 0.452 0.508 0.456\n", - " Paraview 6.851 6.863 6.862\n", - "Disk IO 0.055 0.058 0.056\n", - "--------------------------------------------------------------\n", - "Total 87.323 87.340 87.333\n", - "\n", - "Peak Memory Per-Node Total Total HWM\n", - "==============================================================\n", - "Initialization 74.8M 74.8M 74.8M\n", - " Mesh Preprocessing 60.0M 60.0M 134.8M\n", - "Operator Construction 129.5M 129.5M 264.3M\n", - "Linear Solve 1.0M 1.0M 265.3M\n", - " Setup 293.6M 293.6M 558.9M\n", - " Preconditioner 27.9M 27.9M 586.8M\n", - " Coarse Solve 1.0G 1.0G 1.6G\n", - "PROM Construction 0.0K 0.0K 1.6G\n", - "PROM Solve 1.3M 1.3M 1.6G\n", - "Estimation 0.0K 0.0K 1.6G\n", - " Construction 180.7M 180.7M 1.8G\n", - " Solve 0.0K 0.0K 1.8G\n", - "Postprocessing 0.0K 0.0K 1.8G\n", - " Paraview 864.0K 864.0K 1.8G\n", - "Disk IO 8.8M 8.8M 1.8G\n", - "--------------------------------------------------------------\n", - "Total 2.0G 2.0G 2.0G\n", - "\n" + " 0 (restart 0) KSP residual norm 4.492711e+01\n", + " 1 (restart 0) KSP residual norm 4.482172e+01\n", + " 2 (restart 0) KSP residual norm 4.317697e+01\n", + " 3 (restart 0) KSP residual norm 2.734558e+01\n" ] } ], "source": [ + "from palacetoolkit.simulation import set_palace_path, run_palace\n", + "set_palace_path(\"/mnt/c/Users/loloc/Desktop/Palace/palace/Palace.sif\") # where palace is installed\n", + "\n", "run_palace(config_file=\"patch.config\", num_procs=16)" ] }, + { + "cell_type": "markdown", + "id": "125db9c5", + "metadata": {}, + "source": [ + "### Post processing.\n", + "\n", + "We want to see the S parameters, H plane, E plane and the radiation pattern." + ] + }, { "cell_type": "code", - "execution_count": 10, - "id": "9c98b299", + "execution_count": null, + "id": "17031e58", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -2818,15 +1248,95 @@ } ], "source": [ - "from palacetoolkit.s_plot import plot_s_params\n", + "# S parameters.\n", + "plot_s_params(\"/home/loloc/PalaceToolkit/docs/examples/postpro/patch/port-S.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66fc97b6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Columns found: ['f', 'theta', 'phi', 'r*Re{E_x}', 'r*Im{E_x}', 'r*Re{E_y}', 'r*Im{E_y}', 'r*Re{E_z}', 'r*Im{E_z}']\n", + "Processing frequency: 3.3 GHz (32000 rows)\n", + "Extracting E-plane...\n", + " E-plane phi~0°: 544 points\n", + " E-plane phi~180°: 894 points\n", + "Extracting H-plane...\n", + " H-plane theta~90°: 3640 points\n" + ] + }, + { + "data": { + "image/png": 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jx49jwIAB2LZtGyZMmKA6NzExEZ07d4a9vT2WLVuGwsJCrF69Gk+fPsW9e/dgYWGhOnfhwoVYsWIFxo8fj9atW+PkyZMYMWIEWCyWWqeELvfUJCMjA++99x7++ecf9O7dG1999RWcnJyQmpqKv/76CyNGjEBkZCS+/vprPX/C2hkzZgzmzZuHQ4cOYezYsSZ9FiGEkLpn1apVGD58OOzt7VXHoqKiEBcXhx07duCzzz6rsrIIhUI8e/YMffr0QUBAANhsNm7fvo1Zs2bh7t27OHToUIXX69J2KSwsRLdu3ZCXl4cFCxaAx+Nh3bp16NKlCx4/fgxnZ2fVudu2bcOkSZMwaNAgzJ49Gzdu3MD06dNRXFyMuXPn6nXP8r7/Dz74AOfPn0f79u0xZ84cuLu7Izs7G9euXcOUKVNw9+5d7Nq1S8+fsHYGDhwIOzs7bN68GYsXLzbps0gVYkittmfPHgYAc//+fZM/69NPP2X8/f1N/hxD/PbbbwwAJjIyUu343r17q+znpI1+/foxNjY2jEwm0/lamUzGNGvWjAkNDVU7PnnyZMbKyoqJi4tTHbt48SIDgNm2bZvqWGJiIsPj8Zjw8HDVMYVCwXTq1Inx8fFRK5O29yxP7969GTabzRw/flzj6/fv32cOHDig+jomJoYBwKxatUrj+d9++y0DgMnIyKj02aX169eP6dSpk87XEUIIeaOyNkeXLl2Yxo0bG/WZyvf96uzhw4cMAOavv/5SO37t2jUGAPPrr7+aqWTqpk6dygBgUlJS9LpeU9tl5cqVDADm3r17qmMvXrxgOBwOM3/+fNWx4uJixtnZmenbt6/aPUeOHMnY2Ngw2dnZOt+zPBMnTmQAMOvXr9f4ekREBLNp0ya1YwDU2kUlGdLWnjp1KuPv788oFAqdryXVE00LJxW6evUqWCwWjh49igULFsDDwwM2NjYYMGAAEhISKr1+9erVaN++PZydnWFlZYWWLVvi2LFjZc5TTrc5ceIEmjRpAktLSzRu3Bjnzp0rc25SUhLGjh0Ld3d31Xm7d+/W6vs5ceIEAgICEBwcrDrWtWtXfPrppwCA1q1bg8ViYfTo0Vrdz1QCAgJQXFwMiUSi87UcDge+vr5l1owfP34c/fr1g5+fn+pYWFgY6tevj19++UV17OTJk5BKpZgyZYrqGIvFwuTJk5GYmIg7d+7ofE9N7ty5g/Pnz2PChAn48MMPNZ7TqlUrjBw5UqvvW5OKlkV07dpV7dyePXvi5s2byM7O1vt5hBBCDFdyCdC6devg7+8PKysrdOnSBf/991+l1+/Zswfdu3eHm5sbLC0t0ahRI2zZsqXMeQEBAejXrx9u3ryJNm3agM/nIygoCPv27Stzbm5uLmbOnAlfX19YWloiJCQEK1euhEKhqLQ8J06cgIWFBTp37qw6Nnr0aHTp0gUAMGTIEI31UlVT5s3RN+eMprbLsWPH0Lp1a7Ru3Vp1rEGDBujRo4daO+HKlSvIyspSa3sAQHh4OIqKivDHH3/ofE9NEhISsHPnTrz77ruYMWOGxnPq1atXphy6ULadNX2Uzk3Us2dPxMXF0Vr7WoSmhdcReXl5ZdahslisSqfOKC1duhQsFgtz585Feno61q9fj7CwMDx+/BhWVlblXrdhwwYMGDAAI0eOhEQiwZEjRzBkyBCcOXMGffv2VTv35s2b+O233zBlyhQIBAL8+OOPGDRoEOLj41XlTEtLwzvvvKMKxl1dXXH27FmMGzcO+fn5mDlzZoXfx+3bt9GiRQu1YwsXLkRoaCi2b9+umj5fMvguTSqVIi8vr5Kf2BtOTk5gsyvvwxIKhSgqKkJhYSGuXbuGPXv2oF27dhX+bEsqKiqCUChEXl4eTp06hbNnz2LYsGGq15OSkpCeno5WrVqVubZNmzb4888/VV8/evQINjY2aNiwYZnzlK937NhRp3tqolwXPmrUKK2+x5KKi4s1rqsuLi5W+7pz587Yv3+/2rG4uDh89dVXcHNzUzvesmVLMAyD27dvo1+/fjqXiRBCyBua2hwAtFrPW9K+fftQUFCA8PBwiEQibNiwAd27d8fTp0/h7u5e7nVbtmxB48aNMWDAAHC5XJw+fRpTpkyBQqFAeHi42rmRkZEYPHgwxo0bh08//RS7d+/G6NGj0bJlSzRu3BjAm7qlS5cuSEpKwsSJE+Hn54fbt29j/vz5SElJwfr16yv8Pm7fvo0mTZqAx+Opjk2cOBHe3t5YtmwZpk+fjtatW1f4PSkUCq07f+3t7dWeVR6JRIL8/HwIhUI8ePAAq1evhr+/v9ZLBitruygUCjx58kTjcqs2bdrgwoULKCgogEAgwKNHjwCgTJuiZcuWYLPZePToEUaNGqXTPTU5e/Ys5HK5Xm0PkUik8fe6sLBQ7euGDRuWaXvk5uZi9uzZGtseAHDr1i28/fbbOpeJVEPmHjonpqWcqqLpw9LSstLrr1y5wgBgvL29mfz8fNXxX375hQHAbNiwQXVM07Tw4uJita8lEgnTpEkTpnv37mrHATAWFhZq07X//fdfBgDz008/qY6NGzeO8fT0ZDIzM9WuHz58OGNvb1/meSVJpVKGxWIxn3/+eZnXdJnSo/yZaPMRExNT6f0YhmGWL1+udl2PHj2Y+Ph4ra5lmP9NcQLAsNlsZvDgwWpTqO7fv88AYPbt21fm2i+++IIBwIhEIoZhGKZv375MUFBQmfOKiooYAMy8efN0vqcmH3zwAQOAyc3NVTsuFAqZjIwM1UdOTo7qNeW08Mo+ypsWLhQKmZYtWzJeXl5lpr0lJyczAJiVK1eWW2ZCCCHlq6jNofzQZlq48r3eysqKSUxMVB2/e/cuA4CZNWuW6pimaeGa2gK9e/cuU7f5+/szAJjr16+rjqWnpzOWlpZqbYUlS5YwNjY2TEREhNr18+bNYzgcTqX1tY+PDzNo0KAyx5XtCW2mhWtb/wFgrly5Uun9GIZhDh8+rHZdq1atmCdPnmh1LcNU3nbJyMhgADCLFy8uc+2mTZsYAMzLly8ZhmGY8PBwhsPhaHyOq6srM3z4cJ3vqcmsWbMYAMzjx4/VjovFYrW2R+l2pjY/9/LakAqFgunXrx9ja2vLPHv2rMzrFhYWzOTJk8stM6lZaOS6jti0aRPq16+vdqxkQq/KfPLJJ2q9gIMHD4anpyf+/PNPTJ8+vdzrSo685uTkQC6Xo1OnThozT4eFhamNGDdt2hR2dnaq7I8Mw+D48eMYOnQoGIZR6z3s3bs3jhw5gocPH6JDhw4ay5KdnQ2GYeDo6Kj1961Js2bNcPHiRa3O9fDw0Oq8jz76CK1atUJGRgbOnDmDtLQ0CIVCrcs0c+ZMDB48GMnJyfjll18gl8vVpmUp72VpaVnmWj6frzrH0tJS9W9F5+l6T03y8/MBALa2tmrHt27dilmzZqm+bty4cZlpgBMmTMCQIUPK3HPfvn1leotLmjJlCp4+fYpr166V+b9R/l7okmmcEEJIWZraHADw+eefq+0AUZn3338f3t7eqq/btGmDtm3b4s8//8TatWvLva5k2yMvLw9SqRRdunTB+fPnkZeXp5ZUrFGjRujUqZPqa1dXV4SGhqplnv7111/RqVMnODo6qtURYWFhWLFiBa5fv17hEqasrCyD2x4eHh5atz2aNWum1XndunXDxYsXkZubi0uXLuHff/9FUVGR1mWqrO2ibTtB+W95SVD5fL7ObY/ylNf2+PPPP/HBBx+ovraxsSkzIj1w4ECNGcMvXLiAVatWlfvMJUuW4MyZMzh27BgaNWpU5vXSv1ekZqPguo5o06aNxum7ShkZGWoVnq2trdobT7169dTOZ7FYCAkJQWxsbIXPPXPmDL7//ns8fvxYbVsDTXtSlly3q+To6IicnBxVGXNzc7F9+3Zs375d4/PS09MrLA8Arba4qoijo6PRM4r7+/urtt/46KOPMGHCBISFheHVq1daTQ1v0KABGjRoAOBNR0ivXr3Qv39/3L17FywWS3WP0ltLAFBtP6U8x8rKSuvztL2nJsrOmsLCQrWGzqBBg1RbtZTXEKtXr57G/4ObN2+W+7xt27Zhz5492LZtG955550yryt/L2rCfqmEEFKdldfmKB1EZGdnq3UEW1lZqdUHpdseALTK6XHr1i18++23uHPnTpnlQqWD68raHgDw+vVrPHnyBK6urhqfVxVtDz6fb/S2h7u7u2oq+uDBg7Fs2TL07NkTr1+/1mpwoLK2i65tj/LyzIhEIpO0PUrq0KGDqvNi1apVuHXrVplrfXx8NP4fJCYmlvu8c+fOYdGiRZg/fz4GDRqk8RyGYajtUYtQcE0AvEnkFRcXp/r622+/xXfffWfQPW/cuIEBAwagc+fO2Lx5Mzw9PcHj8bBnzx6N2zyUN5KurJCUSUNGjRqlSkBWWtOmTcstj5OTE1gsllqFqQ+JRKL1uidXV1edZggoDR48GDt27MD169fRu3dvva6fOHEiIiIiEBoaCk9PTwBASkpKmXNTUlLg5OSk6gX29PTElStXyrzZK6/18vJSnaftPTVRdgb8999/arMNfH194evrC8B4vbn37t3DjBkz8Nlnn6ltT1aS8vfCxcXF4OcRQgip3Icffohr166pvv7000/x888/G3TPqKgo9OjRAw0aNMDatWvh6+sLCwsL/Pnnn1i3bl2ZBGSVtT2AN+2Pnj174ssvv9R4rqZR+pKcnZ0NbnvI5XJkZGRoda6Tk1OlW2FqMnjwYCxcuBAnT57ExIkT9bq+ZNtF2Q4or50AqLcp5HI50tPT1dYlSyQSZGVlqc7T5Z6alGx7lBzhd3V1VQXOBw4c0On7Lk9MTAxGjhyJnj174vvvvy/3vNzcXGp71CIUXBMAwMGDB9Wm0QQFBam9/vr1a7WvGYZBZGRkhcHs8ePHwefzcf78ebUga8+ePXqV0dXVFQKBAHK5XK/eWy6Xi+DgYMTExOj1fKXbt2+jW7duWp0bExNTJjOkNpT/F9omTqvsem9vb7i6uuLBgwdlzr137x6aN2+u+rp58+bYuXMnXrx4oTZ96e7du6rXdb2nJv369cOKFStw8ODBcqfyG0NGRgYGDx6M5s2bY9OmTeWep/y9KJ3IjRBCiGmsWbNGLegsHRSVbnsAQERERIX16unTpyEWi3Hq1Cm1UekrV67oXc7g4GAUFhbqPXLcoEEDg9seCQkJCAwM1OrcK1eu6JV53NhtDzabjbfeektjO+Hu3bsICgpSjSQr2wwPHjxAnz59VOc9ePAACoVC9bou99TkvffeA4fDwcGDBw3ajaQyQqEQH374IRwcHHD48OFyk9smJSVBIpFQ26MWoeCaAEClwc2+ffswf/581RvWsWPHkJKSgrlz55Z7DYfDAYvFUpvWGxsbixMnTuhVRg6Hg0GDBuHQoUP477//VFOHlTIyMsqdsqXUrl07XL16Va/nKxlzzXV5Zd61axdYLJZaZvPMzExkZmbCz88P1tbWAFCmhxd4k4113759sLKyUguOBw0ahL179yIhIUE1Mnzp0iVERESorXEeOHAgZs2ahc2bN2Pjxo0A3nSmbN26Fd7e3mjfvr3O99SkQ4cO6NmzJ7Zv347evXtj4MCBZc4xdBqdXC7H8OHDIZFIcPz48Qp78v/55x+wWCy0a9fOoGcSQgjRjjJTcnlOnDiBpKQk1brre/fu4e7duxXuDKIciS5Zf+Tl5endsQ8AQ4cOxXfffYfz58+XmU2Wm5sLW1tbcLnlN6nbtWuHFStWQCwWVzijqyLGXHOdmZkJZ2fnMlORd+7cCUA9Y3deXh5SUlLg6empmk6vS9tl8ODBmDdvHh48eKC676tXr3D58mXMmTNHdV737t3h5OSELVu2qAXXW7ZsgbW1tdoOM9reUxM/Pz+MHTsWO3bswMaNGzWuoTa07QEAkyZNQkREBO7cuVPhevt//vkHANTaVqRmo+C6jjh79ixevnxZ5nj79u3LjFJr4uTkhI4dO2LMmDFIS0vD+vXrERISgvHjx5d7Td++fbF27Vq8++67GDFiBNLT07Fp0yaEhITgyZMnen0fK1aswJUrV9C2bVuMHz8ejRo1QnZ2Nh4+fIi//vqr0unaAwcOxP79+xEREVHpNK7yGHPN9dKlS3Hr1i28++678PPzQ3Z2No4fP4779+9j2rRpatthbNy4EYsWLVLrkZ44cSLy8/PRuXNneHt7IzU1FQcPHsTLly+xZs0atXXzCxYswK+//opu3bphxowZKCwsxKpVq/DWW29hzJgxqvN8fHwwc+ZMrFq1ClKpFK1bt8aJEydw48YNHDx4UG0Knbb3LM+BAwfw7rvv4v3338d7772HsLAwODo6IjU1FX/99ReuX7+O9957T++f79atW3H58mVMmjSpzKiFu7s7evbsqfr64sWL6NChg9bb0xFCCDGtkJAQdOzYEZMnT4ZYLMb69evh7Oxc7vRsAOjVqxcsLCzQv39/TJw4EYWFhdixYwfc3Nw0TiXWxhdffIFTp06hX79+qm26ioqK8PTpUxw7dgyxsbEVTusdOHAglixZgmvXrqFXr156lcGYa64PHDiArVu34v3330dQUBAKCgpw/vx5XLx4Ef3790f37t1V5/7+++8YM2YM9uzZg9GjRwPQre0yZcoU7NixA3379sWcOXPA4/Gwdu1auLu74/PPP1edZ2VlhSVLliA8PBxDhgxB7969cePGDRw4cABLly6Fk5OTzvcsz/r16xETE4Np06bhyJEj6N+/P9zc3JCZmYlbt27h9OnTCA0N1fvn+8cff2Dfvn0YNGgQnjx5otbmtbW1xfvvv6/6+uLFi/Dz86NtuGoTM2QoJ1Wosm0x9uzZU+H1ym0iDh8+zMyfP59xc3NjrKysmL59+zJxcXFq52raimvXrl1MvXr1GEtLS6ZBgwbMnj17NG6bAYAJDw8v83x/f3/m008/VTuWlpbGhIeHM76+vgyPx2M8PDyYHj16MNu3b6/05yEWixkXFxdmyZIlasd12YrLmC5cuMD069eP8fLyYng8HiMQCJgOHTowe/bsYRQKhdq5yp9byS02Dh8+zISFhTHu7u4Ml8tlHB0dmbCwMObkyZMan/fff/8xvXr1YqytrRkHBwdm5MiRTGpqapnz5HI5s2zZMsbf35+xsLBgGjduzBw4cMCge5ZHKBQy69evZ9q1a8fY2dkxXC6X8fDwYPr168ccPHiQkclkqnOVW5GsWrVK472UPyPlVlzKrzV9dOnSRXVdbm4uY2FhwezcuVPrchNCCFFXWV3apUsXnbbiWrVqFbNmzRrG19eXsbS0ZDp16sT8+++/audqalOcOnWKadq0KcPn85mAgABm5cqVzO7du8tskenv78/07dtXYzlL1hEMwzAFBQXM/PnzmZCQEMbCwoJxcXFh2rdvz6xevZqRSCSVfk9NmzZlxo0bp3ZMl624jOn+/fvMkCFDGD8/P8bS0pKxsbFhWrRowaxdu5aRSqVq5yr/T0u2F3VpuzAMwyQkJDCDBw9m7OzsGFtbW6Zfv37M69evNZZt+/btTGhoKGNhYcEEBwcz69atM/iemshkMmbPnj1M9+7dGScnJ4bL5TIuLi5Mjx49mK1btzJCoVDt/PLaqSV/Rsrf+4ra3iXbyXK5nPH09GS++uorrctNqj8Wwxhh7gOpta5evYpu3brh119/xeDBg81dHKNYsmQJ9uzZg9evX+uVbIzUPuvXr8cPP/yAqKgorbKzE0IIMZ3Y2FgEBgZi1apVlU7zrSn279+P8PBwxMfHw8HBwdzFIdXAiRMnMGLECERFRamSxJKaT/PqekJqsVmzZqGwsBBHjhwxd1FINSCVSrF27Vp89dVXFFgTQggxiZEjR8LPz6/CxJqkblm5ciWmTp1KgXUtQ2uuSZ1ja2ur1Z6UpG7g8XiIj483dzEIIYTUYmw2G//995+5i0GqkTt37pi7CMQEaOSaEEIIIYQQQggxEK25JoQQQgghhBBCDEQj14QQQgghhBBCiIEouCaEEEIIIYQQQgxEwTUhhBBCCCGEEGIgCq4JIYQQQgghhBADUXBNCCGEEEIIIYQYiIJrQgghhBBCCCHEQBRcE0IIIYQQQgghBqLgmhBCCCGEEEIIMRAF14QQQgghhBBCiIEouCaEEEIIIYQQQgxEwTUhhBBCCCGEEGIgCq4JIYQQQgghhBADUXBNCCGEEEIIIYQYiIJrQqrApk2bEBAQAD6fj7Zt2+LevXuq1169eoUOHTrAx8cH33//vRlLSQghhJDagNodhJgHBdeEmNjRo0cxe/ZsfPvtt3j48CGaNWuG3r17Iz09HQAwdepUjBo1CidPnsTJkydx+/ZtM5eYEEIIITUVtTsIMR8KrgkxsbVr12L8+PEYM2YMGjVqhK1bt8La2hq7d+8GAOTk5KBly5Zo2rQpvLy8kJuba94CE0IIIaTGonYHIeZDwTUhJiSRSPDPP/8gLCxMdYzNZiMsLAx37twBACxevBhhYWGwtrYGm81G7969zVVcQgghhNRg1O4gxLy45i4AIbVZZmYm5HI53N3d1Y67u7vj5cuXAIA+ffogIyMD+fn5cHV1NUcxCSGEEFILULuDEPOikWtCqgFLS0uq4AghhBBSJajdQYhpUHBNiAm5uLiAw+EgLS1N7XhaWho8PDzMVCpCCCGE1EbU7iDEvCi4JsSELCws0LJlS1y6dEl1TKFQ4NKlS2jXrp0ZS0YIIYSQ2obaHYSYF625JsTEZs+ejU8//RStWrVCmzZtsH79ehQVFWHMmDHmLhohhBBCahlqdxBiPhRcE2Jiw4YNQ0ZGBr755hukpqaiefPmOHfuXJlkI4QQQgghhqJ2ByHmw2IYhjF3IQghhBBCCCGEkJqM1lwTQgghhBBCCCEGouCaEEIIIYQQQggxEAXXhBBCCCGEEEKIgSi4JoQQQgghhBBCDETBNSGEEEIIIYQQYiAKrgkhhBBCCCGEEANRcE0IIYQQQgghhBiIgmtCCCGEEEIIIcRAFFwTQgghhBBCCCEGouCaEEIIIYQQQggxEAXXhBBCCCGEEEKIgSi4JoQQQgghhBBCDMQ1dwEIqQ0kEgkKCwtRUFCAgoIC1efKf4uKiiCXy6FQKKBQKHDo0CG8/fbbaNSoEdhsNthsNiwsLGBrawuBQKD6t+TnNjY2YLOpP4wQQgip6xiGQVFRkVpbo3T7QywWq9odMTExuHTpEsaNG6dqd3A4HFhbW2tscyg/t7S0NPe3SkiNQsE1IRUoLCxEbGwsUlJSkJKSguTkZI2fC4VCAACLxYKtrW2ZysnGxgZcLldVod2/fx+xsbFIT08HwzBQKBQQi8VlgvKCggKIxWJVeZydneHl5QVPT094enqW+dzb2xs+Pj7gcDjm+pERQgghRE8KhQJJSUlISkoqt82RkpKCjIwMMAwDALCwsCgTHNva2oLP56vaHc+ePUNERASuX7+uanfIZDIUFxdrDM6V9+bz+fDw8NDY5lB+HhAQADs7O3P+2AipNliM8q+HkDpKIpEgJiYGERERZT6Sk5Nha2urVomUrlQ8PT3h6OgIgUAAKysrrUaX5XI5Xrx4gYYNG1YaCEulUlVll5GRobGSVX6dmpoKDoeDkJAQ1K9fv8yHm5sbWCyWsX50hBBCCNERwzDIzMzE69evy7Q7Xr9+DYlEAnd3d43tDeUxNzc3VRBtYWFR6TN1aXcwDAOhUIiCggLk5uZqbG+U/Do/Px/u7u4a2x3BwcE0+k3qFAquSZ2Sk5ODf/75B//88w8ePnyIR48eITo6GjweD/Xq1dNYMTg7Oxs9INWlktOFTCZDbGysWkX96tUrREREIDExEQKBAI0bN0bLli1VH40aNQKXS5NYCCGEEGOTy+V49eqVqu3xzz//4L///kNubi48PT0RGhpapt0RGBioVcCsazlM0e4AgOzs7DIdBa9evcLr168hFAoREBCA5s2bq7U9XF1djVoGQqoLCq5JrZWXl4d79+6pVWgxMTHw9/dXvbm3aNECDRs2hK+vb5WuZzZlJVeeoqIivH79Gk+fPlXrXJDJZGjWrJlapdekSROaWk4IIYTogGEYvHjxAg8ePFC1Ox4/fgyFQoHmzZujRYsWaNmyJZo2bYr69etDIBBUWdnM0e5QKBRITk7Gixcv8OjRI9XPJCoqCr6+vmrtjjZt2sDZ2blKykWIKVFwTWqNvLw83Lx5E1evXsXVq1fx8OFD+Pn5qb15t2jRAi4uLuYuqlkqufLK8fr1a7UOiIcPH4LH46Fz587o2rUrunbtiqZNm1IyNUIIIaQEhmHw/PlzVbvj6tWrKCoqwttvv63W9mjQoIHZZ4hVl3YHAOTm5uLhw4eqNseDBw8QFRWFpk2bqtodnTt3hpOTk1nLSYg+KLgmNVZ+fr4qmL5y5QoePnyI4OBg1Rtzly5d4O3tbe5ialSdKrnSZDIZHj16pGoo3LhxA1wul4JtQgghdZpyZFrZ7rh27RoKCwvRsWNHVf3YqlUro0/pNobq3O4AgLS0NFy/fl3V9njx4gUF26RGouCa1CjR0dE4ffo0Tp8+jWvXriEgIEAtmPbx8TF3EbVS3Su5kjQF29bW1ujXrx8GDBiAsLAwWFtbm7uYhBBCiNGJxWJcuXIFp06dwunTp5GVlYUOHTqo2h6tW7eulsF0aTWp3QGUDbZfvXqFdu3aYcCAARgwYABCQ0PNXURCNKLgmlRrCoUC9+7dw6lTp3Dq1ClERESgS5cuGDBgAPr374+AgABzF1EvNa2SK0kqleLWrVs4ffo0Tp48iaSkJISFhaF///7o168fvLy8zF1EQgghRG8ZGRn4888/cerUKVy4cAFOTk7o378/BgwYgC5dutTI7Nc1ud0BAElJSThz5gxOnz6Nv/76C35+fqq2YIcOHcw+7Z4QJQquSbUjk8lw+fJlHD16FGfOnIFUKkXfvn3Rv39/9O7dG/b29uYuosFqeiWnxDAMXr58idOnT+PUqVP4+++/0aJFC7z//vv46KOPEBgYaO4iEkIIIZVKSkrCkSNH8Pvvv6vqMmVA3bRp0xq/jWVtaXcAbxK0/vXXXzh16pSqndinTx8MGzYMvXv3rhEzCUjtRcE1qRYYhsHDhw9x4MABHDlyBCwWC8OGDcMHH3yA9u3b17oeySKRBHceP0O75o1hw689lYCyt//XX3/FhQsX0KZNG4waNQpDhgyhLKCEEEKqlby8PPz22284ePAgrl27hq5du2LIkCG1YhaWSCpHRoEYrgJL8Hkcje2O0ufURMoZjidOnMCRI0dQUFCAYcOGYeTIkWjfvn2N7xQhNQ8F18SsoqOjcejQIRw4cADJyckYNGgQRo4ciW7dutX4nlVN5AoG6y5GYNfNaAilCljx2BjXMQizetYHh127KoDMzEz88ssvOHDgAB48eID33nsPI0eORP/+/WFlZWXu4hFCCKmDJBIJzp07hwMHDuD06dNo1KgRRo4cieHDh1fLgFpTAFz6WG6xBK9SCxDqIYCAz8O6ixHYeTMaIqkCfB4bTbzs8TQpF2IZAz6PjbEd3swq230rRnXOZx2DMKZDACLTCxHqIYCD9ZsAvOS9HawtqnVArlAocOvWLRw8eBC//PILHBwcMHLkSIwcORINGjQwd/FIHUHBNalyRUVFOHLkCHbv3o379+/j3XffxahRo+pE0LX6/CtsvBJZ5vjUbiGY07v2JueIiorCoUOHcPDgQSQnJ2Pw4MGYMGEC2rZtS73KhBBCTO7x48fYvn07jh49Cjs7O1XQ1bBhQ3MXTY0yeHWyscCWq1HYeSMaIpkCfC4bYzu+CYp33YyGWMaAxwZcbPlIyReprvews0RqvtjgcrTwtQdYLDyMz1Ud87TjI6dYDJGMAZ/Lxmed3gwOFIikagF4dSAWi3Hu3DkcPHgQp06dQpMmTTB69Gh8/PHHtWJ5Iam+KLgmVebp06fYtm0b9u/fj8DAQEyYMAFDhw6tFvtOl6ZvzyzDMMgrFiM+qwiJ2cVIzBUiKUeIpFwhEnNFeJlWpPE6FoAOQY7wcuDD3Y4PD3s+PO2t4OFgBS8Ha9hb8XQKQqtrzzLDMPjnn3/w888/q34PJk6ciJEjR8LOzs7cxSOEEFKLFBcX4+jRo9i2bRuePn2Kjz76CGPGjDHbdGFdRpzZLEBRA1ropYP5Vv6O2DyyBWIyi9SCbXO2S/Ly8nD8+HHs2LEDT548wUcffYSJEyeiVatW1MFPjI6Ca2JSUqkUv/32GzZu3Ih//vkHw4YNw6RJk9CmTZtq+Yb2v2nbMRBK5bDicTC2YwCmdQ2CQi6DVCpFVoEIiTnFSMguRnKuECn5EqTki5FWKENakQxFEoXRy2XJZcHFmgtXGx5cbS3gZmcBDzs+POyt4OlgBW9HG3g52oDL5ZYp/7iOgdVy2rlyBsO2bdvw/PlzfPLJJ5g6dSoaNWpk7qIRQgipwaKiorB582bs3r0bPj4+mDRpEkaNGlWlI5Ylg0keh60WOFtwAGcb9RFnd4EF0gokVVa+qtLSzwFtAp2x51aMagT+s05BmNw1GNlFkioPtv/991/VQE9oaCimTZuGYcOGgc/nV1kZSO1GwTUxifT0dGzduhVbt24Fn89HeHg4xo4dC0dHR3MXrVwymQyrzr3EtptxZV7zFHBhyWEjvUiGYql+wbMytDXVHxwLb4JwkazsE8K7BuOLd8uuN6ouI9wPHjzAxo0bceTIEXTo0AHTp09H//79wWazzVYmQgghNQfDMLh48SJ+/PFHXLx4Ee+//z6mTp2Kjh07Vklnfunp3MpObj6XjYaeAjxKyDN5GWoSDguQM2/aLeM7BWNWz/qQyhVV1iYpKCjA/v37sXHjRmRkZGD8+PGYMmUKfHx8TPpcUvtRcE2MKi4uDqtXr8auXbvQsWNHTJ8+He+99161SU4mksqRni+CA58NyKUQCoUQCoVIySnE7dgCbLqXrfc0LC6bBU8HPrwdrODjaP3//7753MfRCh72fGz467XGNddTugZjdPsApOaLkJonQlq+6P8/F5f4XIRCsUyvsrFZwKctnNG5njOaeDvAkm+FrbcSsOdWXLUa4c7IyMDOnTuxadMmCAQCzJ07FyNHjgSPxzNbmQghhFRfcrkcx44dw4oVK5CYmIjJkydj4sSJ8Pb2rprn//+MN+XaaGXQSHTT3NceL1MKIJIpVG2SqhjdZhgGly9fxk8//YRz585h5MiRmDt3LurXr2+S55Haj4JrYhTPnz/HypUrcfToUQwcOBDz5s3D22+/be5iAXhT8eYXFGLD5UgcfpgGsYyBJYeFbsECeNrzcS+xCM9TiiodUeaxWfB2LBU4O1nB2+FN8Oxux680MNU07VyXoLZQLPtf8J33JuhWfh6fXYyXqQWV3sPRigs7SxbicqVlXgvvFowveps/o6ZYLMaBAwewcuVKiEQizJkzB5999hmsra3NXTRCCCHVgFgsxv79+/HDDz9ALBZjzpw5GDduXJXVE8r10uefpWL3rdgqeWZdw2WzIFMwqmzmph4AePHiBX744QccPnwYAwYMwPz586tNW5bUHBRcE4Pcu3cPy5cvx9mzZ/Hxxx/jyy+/RL169cxaJrlcjqKiItWHSCTCwaf5OPxvjt735PPYePhVT1hbGme/bVPscy2SyvH24osQSuV634PPZeHUmIZwsheAY8FHgZQFNzu+2aaMy+Vy/Pbbb1i+fDkSEhIwY8YMhIeHV+vlBYQQQkynsLAQ27dvx5o1a2Bvb4+5c+dixIgRJp3hVDLxmLUFFyN2/I0Hcfq3KYh+xrb3R+8mnibPSh4fH4/Vq1dj586d6Ny5M+bPn4/OnTtXy1xBpPqh4Jro5datW/j222/x999/Y+LEiZg9e3aVTcEqTaFQoKioCIWFhSgqKkJeYTEK5Rz4utjByV6AyBwphu+4D1kF870beAjQNdQN6fki/PYoqczrxt4qSy6X48WLF2jYsKFRp8yXt9XXyLZ+qO8uwI3XGbgZmQlRBevG53T3Q1J2MX57mgWxnAGfy8KIVp6Y07shrK3Mk/CDYRhcuHABy5Ytw6NHjzBlyhR8+eWXcHJyMkt5CCGEVK2CggKsX78e69evR3BwMObPn4+BAweaJDeHcv20vRUPY3++rxZIW/NYKJZS09ncWvk74tD4d6BgGJOt087IyMCGDRuwceNGNGrUCIsWLULPnj2N+gxS+1BwTXTy33//YcGCBbhy5QpmzpyJmTNnwtnZucrLIZPJUFBQgIKCAhQWFoLNZsPaxhb7HmXh4P1kCKUK8DgsOFhbIKOg4v0ef53UDq0D3gRpymnbu2/FoFgih7UFB2M7GH8tsqmCa23Kny+UotXSvyCR6ZaYbVgTe4xr4wY7OzsIBAJYW1ubpRf39u3b+O6773D//n3MnTsX06dPp+nihBBSS4nFYmzfvh1LlixBvXr1sGjRIvTo0cMk9Y+yDt1xPQpiOQMWTJeElBjOzdYCuUIpJHLGpLlj8vPzsXnzZqxcuRItW7bE8uXL0bp1a6M+g9QeFFwTrcTFxeHbb7/F0aNHMX78eCxcuBDu7u5VWgaxWIz8/HwUFBSguLgYfD5fFejx+XysuRChcdS2ItYWHDz8umeZ3k5TZ9E2VXCtVFn5yxvhrogVj4OrM9qgsLAQ8em5cLTiwMXRHgKBALa2tlWetO7SpUuYN28ekpOT8e2332LMmDGU+IwQQmoJhUKBQ4cO4euvv4aNjQ2WLVuG/v37mySoVk77PvtfCn6+XXbHEFJzfNYhAD0be5hk6nhOTg5WrlyJH3/8EX379sXSpUsp8Rkpg4JrUqHMzEwsXboUW7duxaBBg7B48WIEBQVV2fOlUiny8vKQm5sLsVgMGxsbVUBdJGXwKrUA9d1tcS8mB5MP/qMx0zeLBXQIdsbNyKwyrxl7ure2TB1cV/p8DSPco9sHINjFFp8f+7fc61r4OeB5Sj5EUgX4PDaGv+2G4Y0FkMuksLOzg739m2C7qka0GYbBsWPHsHDhQrBYLHz//fcYPHgwrYsihJAaimEYnD17FvPnz0dOTg4WL16Mjz/+2Gh1ZcnOZzaLReuna7FW/o7YPbo18oRSow6WJCUlYfHixdi7dy8+/fRTfPvtt/Dy8jLKvUnNR8E10UgkEmHNmjVYuXIlOnbsiOXLl6NZs2ZV8my5XI78/Hzk5uaiqKgINjY2cHBwgJ2dHTgcDiQyhc6V4dU5XXHsn0STT/fWlrmDa6XSI9z6JEWb2i0Eo1p74lF0Glx4EthasGFvbw8HBwdYWVlVSaArlUqxe/duLFq0CN7e3lizZg06d+5s8ucSQggxngcPHuDzzz/Hs2fPsHDhQkyePBl8vnFyfZTerYPPZcPeiou0AolR7k+qJ+XUfj6Xjc86GTfj+KtXr/DVV1/hzz//xPTp07FgwQIIBAKj3JvUXBRckzLOnDmDGTNmwMHBAWvXrkWXLl1M/kyGYVBYWIjc3Fzk5+fD0tISDg4OsLe3B4/HU8vUOe7n+/gnPlfre5ec+m3q6d7aqi7BtSa6ThkvvSbtbV87rO0XAGFRATgcDhwcHODo6AgLC9Nl9lQqLi7Ghg0bsGzZMgwcOBCrVq2Cp6enyZ9LCCFEf1lZWVi4cCH27duHWbNm4csvv4S9vb1R7q2s9w/ejcPWa9FGuSepuSZ0CsTH7QKM2g68f/8+Pv/8c0RFRWHNmjUYNmwYzaCrwyi4JirR0dGYOXMmbt68ieXLl+Ozzz4zeeAnlUqRk5ODnJwcMAwDBwcHODg4qHqq9RmlLs1cU78rUp2Da01Txge38MG+v7Vfh9bK3xG/THxH1WFSUFAAGxsbODk5Vcm08cTERMyZMwd//vknFi1ahKlTp9J6bEIIqWYUCgV27dqFefPm4Z133sGPP/6I4OBgo9y79Eg1ISVZclgY3znYaCPZDMPg0KFDmDNnDho2bKjKME7qHgquCYRCIX744QesXLkSo0aNwrJly+Di4mKy5zEMg+LiYmRlZVUadA3eclunwPqDt71x/llqtZj6XZHqHFwrlRzlB6DzdPGrc7ogLV+MUA8BbHgstU4UJycnODk5gcs1zr7h5bl06RKmTZsGNpuNTZs2VcksDEIIIZW7f/8+wsPDVdsdGStZWV0cqfaws0Rq/v92RrG14KBQ8r/62tOOj6xCESSKN9OjR3cIwP2YbLVZgKWvqSs+6xiAT9sHGm0kOz8/H9999x22bNmCqVOn4ptvvqGp4nUMBdd13JkzZzB9+nQ4Oztj06ZNaNOmjcmepVAokJubi+zsbEilUjg6OsLR0RGWlpZq56XmCXE/Jhv13W3Re8NNnZ7x+Js307+rw9TvitSE4Lo0fTKMKyn3o+RxWCgoKEB2djaKiopgZ2cHZ2dnk26lJZFIsGHDBixevBgDBgzA6tWraao4IYSYSVZWFhYsWID9+/fjiy++wLx582BlZWXwfZUj1TtvRkMk1W2ryeqODUABwJIDvOXjiGfJ+RBK5WrbTxWIpKrlcw7WFmrL6RysLVAkkuDO42do17wxbPhvlmmVPkf5dYibLfbcilVbn97E2171XEsuG408BXiUkGfWn4sx8XlsfNbReGuynz59iqlTpyIyMpKmitcxFFzXUenp6QgPD8elS5dMPgVcLpcjOzsbWVlZ4HA4cHZ2hoODA9hsttp5Qokc3VdfRUq+SK/ntPJ3xLHJ7Y1RZJOricF16enifC4LIpn2bx+l/3/EYjGysrKQm5sLKysruLq6wsbGxmSVj3Kq+NmzZ7F27VqMHTuWKjpCCKlCv/76K6ZMmYI2bdoYbQq4cqR6/99x2H695o9Ue9rxkVMsgUimUCXhmtw1GNlFErXko7oOIujT7tCU9FT5NY/DVpt2b8lhwcnGUu82XHUxvmMgPmlvnDXZJaeKN2vWDDt27ICvr6+RSkqqKwqu6yBl5dalSxds3rwZbm5uJnmOTCZDVlYWsrKywOfz4eLiUuF623bLLhkUWB8a/w4suOzKT64GamJwrVSych21865O0/Z3fdIKLQMc1faelMlkqs4XCwsLuLq6mnRd9p9//onx48ejadOm2LFjB3x8fEzyHEIIIW9kZGQgPDwcly9fxqZNmzB06FCD3+Nr6kh1ySSgXDYLEzoHYVzHQESmF6pGkE2RfNVU7Y7SZS09+r3zRhREMgYcFiCvQRGHJZeF8Z2MsyY7NzcXs2bNwm+//Uad+3UABdd1SEZGBqZOnYpLly5h48aNJpuiIpFIkJWVhezsbNjY2MDV1RXW1tblPis1T4iLz1Lx9annWj+jlb8jdn7aSm06U01Sk4PrkvRNOKepM0QulyMnJweZmZngcDhwcXGBg4ODSX5Hc3JyMGvWLJw4cQJr167FmDFjqKIjhBATOHbsGKZMmYJOnTph8+bNcHd3N+h+NW2kuvRWUJO7BiMlTwgwgI+TdZUtXzNXu0P5/+VkY4EtV6NUI90cNiCvAX0ixlyT/ccff2DChAnUuV/LUXBdRxw/fhyTJ09Gx44dsWXLFoMrN02kUinS09ORm5sLgUAAV1fXCtdR6TsNvKaNUmtSW4JrJWVPtbudJbquvqbVNaFutjg6qV2ZjhHl2vzMzEwwDAM3NzeTBdl//PEHxo8fj+bNm2P79u1U0RFCiJFkZmYiPDwcf/31FzZt2mRwh75qpPpGNESyGhCV4U17Zffo1sgTSs2eB6a6tDtKB9vKpWYWXBYcrHhIr6b7jhtrJLtk5/66deswevRo6tyvZSi4ruUyMzMxdepUXLx4ERs3bsTw4cON/kcsl8uRkZGBrKwsCAQCuLu7l0lSVlpqnhDvrr+BXKFUq2ecn9ERuUJZjRyl1qS6VHKmoGuG9/I6SxiGQV5eHtLS0sBms+Hu7m6S6eI5OTmYOXMmTp48SRUdIYQYwW+//YZJkyYZpUP/fyPVsdh+PcaIpTS+Vv6O2DyyBWIyi6pde6W6tjtKTitns1gGb79qasYayT5z5gwmTJiAt99+G9u3b4e3t7cRS0nMiYLrWuzatWsYMWIEWrduja1bt8LDw8Oo91coFMjKykJmZib4fD7c3d0rzfqsz2i1px0fdxb0MLS41Up1reSMQZ+p4hUlo1MoFMjJyUF6ejosLS3h7u4OGxsbYxVX5cyZMxg/fjw6deqEHTt2wN7e3ujPIISQ2qy4uBgzZ87EsWPHsGnTJoM69GvCPtXVOZgurSa1O5Sz4QJdbDDl4MNqGWwrp/kbMpKdk5ODGTNm4I8//sDevXvRr18/I5eSmEPNnVdrIlu2bEHTpk1hZ2cHOzs7tGvXDmfPnlW9vn37dnTt2hV2dnZgsVjIzc0tc4+AgACwWCy1jxUrVqids2PHDvj7++Ptt9/G3bt3jfo9yOVyLF68GH369MFXX32F33//3aiBNcMwyM7ORkREBPLy8uDj44OAgACttlPSJ7C+PKerAaUlVc2Cy8axye3x+Jue2PVJK62ueRCXg0vP05BbXHY6GJvNhrOzM+rXrw9bW1vExcUhLi4OIpFxM5L269cP//77L/Lz89GiRQvcv3/fqPevyIoVK8BisTBz5kzVsa5du5Z5H5k0aZLadadOnUL9+vURGhqKM2fOVFl5CSHGVRvaHs+fP0ebNm3w7NkzPH78GB999JFBs4DWXYzAxiuR1S6wHtPOH0cnvIPH3/TEscnt4WbHR9sg52odWNc0DtYWaBvkDDc7vqo9cXTCOxjd3t/cRVMRyRTYeCUSay680vsejo6O2LdvH3788UeMGDECn3/+OSSSqpkWT+0O06GR61JOnz4NDoeDevXqgWEY7N27F6tWrcKjR4/QuHFjrF+/XtWonz9/PnJycuDg4KB2j4CAAIwbNw7jx49XHRMIBKrRtvj4ePTo0QP79u1DUlISvvnmGzx/rn0yr4qkpKRg5MiRSEpKwtGjR9G8eXOj3FepqKgIKSkpUCgUcHNzg729vVaVp65Jy5YMbISejTzgYW/43pfVUU3qQTaUsaaJK8lkMmRkZCA7OxuOjo5wd3c36s9QoVBg1apVWLx4MZYuXYoZM2aYdJr4/fv3MXToUNjZ2aFbt25Yv349gDeVXP369bF48WLVudbW1rCzswPwZiuzkJAQ7NmzBwzDYOzYsYiKioKFBTXwCKlpanLbg2EY/Pzzz5g2bRqmT5+ORYsWgcfj6X0/kVSOqPQCfLjlDsTVYG01mwUoGKjtKW2MfZCrWm1od/xv3X2UTluBmhIbwN5xrfGWt4NBHSwREREYNmwYeDwejh49isDAQOMVshRqd5gW19wFqG769++v9vXSpUuxZcsW/P3332jcuLGqh+fq1asV3kcgEJQ7Wpyfnw8HBwc0bdoUHh4eEAqFxig6zp8/j48//hi9evXCyZMnIRAIjHJf4E2ysrS0NOTn58PV1RXOzs5l9qnWRN9p4B+3M92bCqlah8a/o9M08QdxOej/4w2Nyc4AgMvlwtPTE05OTkhJSUFERAQ8PDyMlvSMzWZj7ty56NSpE4YPH44rV65g9+7dcHZ2NvjepRUWFmLkyJHYsWMHvv/++zKvW1tbl/s+IhaLweFwVB1oXC4XYrGYKjlCaqCa2vYoKCjA5MmTceHCBRw/fhy9e/fW+15yBYM1F15h+/VoyBTVI3CqTgnJCMBhszCndyimdg9BRoEY9lY8jP35vlmnjSsAfLzrzUw3QxLu1q9fH3fu3MGcOXPw9ttvY+fOnRg8eLCRS0vtjqpA08IrIJfLceTIERQVFaFdu3Y6XbtixQo4Ozvj7bffxqpVqyCTyVSvNWnSBE2bNoW9vT0aN26s8ZdbF1KpFPPnz8egQYOwcuVK7N+/32iBNcMwyMrKwuvXr6FQKBASEgJXV1etAuvcYgk6rLxM08DruJLTxEPdtfu9fJVeiOaLL2LwltuQlDNyYWlpCX9/f3h5eSE9PR3R0dFG66gCgPbt2+Px48cAgObNm+PWrVtGu7dSeHg4+vbti7CwMI2vHzx4EC4uLmjSpAnmz5+P4uJi1Wt2dnYYM2YMPD094eXlhcmTJxu1Q40QYh41pe3x+PFjtGzZEikpKfj333/1DqxFUjkSsoux6vxLbL4aZfbAuvS0bzsrHnyrcMssUjk+jwNfJ2vYWfGq1bTxB3E5GLb1NhKyiyHSYzkDn8/Hxo0bsWvXLowfPx5Tpkwx+hI4aneYHo1ca/D06VO0a9cOIpEItra2+P3339GoUSOtr58+fTpatGgBJycn3L59G/Pnz0dKSgrWrl2rOmfXrl344YcfYG1tXeF2VZXJzMzEkCFDkJGRgXv37ulUzsqUnALu5+cHW1tbra7TJ6GVPZ+L87M619pp4OTNGqrT0zrqPIo9Ysff5SY7Y7FYsLe3h0AgQEZGBqKjo406VdzJyQknTpzATz/9hF69emHlypUIDw83ygj5kSNH8PDhw3LXdo8YMULVefDkyRPMnTsXr169wm+//aY659tvv8XMmTPBZrOpgiOkhqtJbY8DBw5g4sSJmDdvHhYsWKDX+211S1hW06d912XKNdqtApxga8nDjuuREJvpV+pRYh46/XAFfB4bn3XUL+HZoEGD0KJFCwwfPhwdO3bEiRMnjLJVKLU7qgatudZAIpEgPj4eeXl5OHbsGHbu3Ilr166pVXJXr15Ft27dNK57Km337t2YOHEiCgsLK92iShdPnz7FgAED0LJlS/z8889aB7+VkcvlSE1NRV5enk5TwJV0XWOrHK22sqg7vcK1Ye2TIXKLJfgnNgfj9j3Q6vyrc7ogwKXy32+xWIyUlBQIhUJ4eXkZNeP3rVu38OGHH+L999/HTz/9ZNA0qISEBLRq1QoXL15E06ZNAbxZ69S8eXPV2qfSLl++jB49eiAyMhLBwcF6P5sQUj3VhLaHXC7HggULsG3bNhw5cgTvvvuu3vdaff4VNl6JNEq5DDG+YyA+aR9Q66d916V2h0gqx7I/X2DfnThzFwVTugbjy3cb6HWtWCxGeHg4/vjjD/z+++9455139C4HtTuqDk0L18DCwgIhISFo2bIlli9fjmbNmmHDhg16369t27aQyWSIjY01WhlPnDiBDh06YPTo0fjll1+MFlgXFhYiMjISEolEpyngSql5Qq0D6yUDG+Hv+d1xZ0GPOhVYkze9zD0auaOVv6NW53ddfa3CKeJKyqninp6eSE5ORkJCgtq0SEN06NABDx48wIMHDxAWFoaMjAy97/XPP/8gPT0dLVq0AJfLBZfLxbVr1/Djjz+Cy+VCLi/b5d62bVsAQGSk+RujhBDjq+5tj7y8PAwYMAAnTpzA3bt3DQqsU/OE2HEj2ijl0pcVj4Op3UIwr09DmvZdy/B5HHzbvzGmdguBtZnbl1uvRuFZUq5e08QtLS2xY8cOLFiwAD169MDevXv1Lge1O6oOTQvXgkKhgFgs1vv6x48fg81mw83NzeCyMAyDpUuXYuXKlfj5558xaNAgg+8JqI9We3h4wNHRUaepr3IFgx/OvcS269pVlpS0jAC6JTt7EJeDoVtu48S0jhWex2Kx4ODgABsbGyQnJ+P169fw9vZWZbs0hK+vL27cuIExY8agdevWOHnyJJo1a6bzfXr06IGnT5+qHRszZgwaNGiAuXPnahxVUK7/9vT01KvshJCapTq1PV6/fo0BAwbA398fd+/erXTUvDz6LBszts86BuDT9oG1fqS6riud/Ozg3ThsvVb1HToKAH1/ugUum4UJnYPwea9QnaaJs1gsTJs2DQ0bNsTQoUPx9OlTrFy5UufZB9TuqDoUXJcyf/58vPfee/Dz80NBQQEOHTqEq1ev4vz58wCA1NRUpKamqnpxnj59CoFAAD8/Pzg5OeHOnTu4e/cuunXrBoFAgDt37mDWrFkYNWoUHB21G6UrT3FxMcaMGYO7d+/i5s2bejXqNSksLERSUpKq11yf6a7rLkboFFhT0jIC/C/ZWW6xBMO2/Y1XaQUVnv84KQ/vb7qJXya2rzQbJ4/Hg5+fH/Ly8pCUlIS8vDx4enqCyzXsbc/a2hpHjhzBsmXL0LFjR706uQQCAZo0aaJ2zMbGBs7OzmjSpAmioqJw6NAh9OnTB87Oznjy5AlmzZqFzp07q6ZzEUJqj+rc9rh48SKGDRuGsWPH6tWoB95M080oEGPGkUd4GJ9rUHn0xeey8Vkn/dbAkppLmfzsi94NwGWzsftWDIolVb8gW6ZgsPlqFApFMizo21Dnjp2wsDDcvXsXAwcOxLNnz3D48GGdOrmo3VF1KLguJT09HZ988glSUlJgb2+Ppk2b4vz58+jZsycAYOvWrVi0aJHq/M6dOwMA9uzZg9GjR8PS0hJHjhzBd999B7FYjMDAQMyaNQuzZ882qFzJycno378/bG1tcf/+fbi6uhp0P+BNr3hKSoreo9VKIqlcq+ldgc7WODzhHUpaRsrQJdnZ44S8CpOclWSqUWwWi4WFCxeiSZMm+Pjjj/H8+XN89dVXRtsP28LCAn/99RfWr1+PoqIi+Pr6YtCgQfjqq6+Mcn9CSPVSXdsemzZtwpdffoktW7bgk08+0fn66pC0jEaqCVB2JHv/33HYruWgkDHt+zsOv/yToFeys3r16uHOnTsYOXIk2rZtiz/++AMhISFGKRe1O4yHEprVAK9evULv3r3RrVs3bNu2zSj7yYlEIiQkJIDD4cDHx0fvewolcnT+4TIyCiUVnhfkYoNzMzvrtfdfbVSXEovoKjazEF1XX6v0PG2TnCkxDIPc3FykpKSoMorrkk+gPE+fPkWfPn3Qp08fbN68mf4/CSE1HsMwWLhwIbZv347Tp0/rvCWYkjmTltFItTpqd6irDh0/+iY7k8vl+PLLL3HgwAGcPXsWLVq0MEHpiL4ouK7m7t27hz59+mDChAlYunSpwSNjDMMgJycHqampcHZ2hpubm0H3bLP0ItILKg6sAeDv+d1pxLoEquQqpm3G+Vb+jjg0/h2dOm3EYjESEhLAYrHg4+NjlCy6CQkJ6N27Nxo0aIBDhw6Bz+cbfE9CCDEHmUyGiRMn4tKlSzh//jxCQ0P1uk9usQTvLLsEUSWJKE3hk3f89Zp6W5tRu0Mz5ZKFfXdiseNGTJU+m8MCzs/qDB9H/RLqrV69GosXL8Zvv/1W7r7VpOpRcF2NnTt3DkOGDMHSpUsxffp0g+8nl8uRnJyMoqIi+Pj4GJRhXCiRo8vKS0gvklZ6rqcdH3cW9ND7WTWFXC6HTCaDTCaDVCqFTCaDXC4HwzCqD+B/HRwA4ODgADabrergYLFYYLPZqkyOPB5P9bmxphzXBBKZAkO33MbjpLxKz23ubV9pkrPSFAoFUlNTkZubC29vb6Ns2ZWdnY1+/fqBx+Ph5MmTeif8IYQQcykuLsawYcMQFxeHc+fOwcvLS+d7KEcEt9+IgkRWtU1MawsOxnaoO3tVMwyjaneUbHsoFAq1NgfDMBAKhRCJRLC3t1e1O5QfAFRtjZJtj5Ltk9pO+Xu780Z0lXcIGTLLYv/+/Zg0aRJ2796NYcOGmaiERBcUXFdTBw4cwMSJE7Fr1y4MHz7c4PsJhUIkJCSAx+PBx8cHPB7PoPu1W3YJKfmiSs/zEFjiyhfdavxWW3K5HCKRCFKpVFV5lazIlJUZi8VSq5w4HI5aBaaspHJzcyGVSuHi4qJ6hrICVCgUavdXbo/A4XDUgm3l5zweDxYWFrC0tKx1leD7m27icYIWAbavvVZJzkrLz89HYmIiHBwc4OHhYfA08eLiYgwdOhTx8fF6N0wJIcQcsrOz0b9/f3A4HJw6dUrvDsKVZ19iy7Uo4xauEhM6BeLjdrVrr2qGYSAWiyGRSMq0N0p+DrxpH5RsG5QMnoE3HfcSiQR5eXlwcXEBi8Uq0+lf+t4Mw4DFYmlsdyg/5/P5BicJrW7MuUe2vtPEz549i6FDh2LZsmWYNm2aCUpGdEHBdTW0du1afPfdd0ab5pGdnY2UlBS4urrC1dXV4ADsSUI2Bmy6U+l5p8Lboamvk0HPMgeZTAaRSAShUKj6kEql4PF4agGtpspGGUxXRpfpWSWDbU0Vq1QqhUQiAcMw4PP5sLKygpWVFfh8Pvh8fo0OuHXZtqWVv6NWSc7KPEMiQUJCAhiGgZ+fn8E5DaRSKSZMmIArV67gwoULqF+/vkH3I4QQU0tMTETv3r1Rv359HDp0CFZWui/jEknlSMwuRq/116GoopalFY+DcR1r/ki1MpBWji4r/2UYBpaWlho71UuONGvTMaxLu0PZ0V9eQK8caFC2jZRtDmX7o6YH3MpR7KrOLM5hAf983RMO1rq3Q+7evYu+ffti4sSJ+P7772t026+mo+C6GmEYBgsWLMCuXbtw9uxZtGzZ0uD7KbOB+/r6GjQNHHgzFbz76qtajVjXlKngMplMrTIrGUgrKwllpWHMysLYa58YhoFEIlHrEFBWzMoKr+S/Ne1NV9skZ4+/0a9SYhhGNU3cGH8ryr/lnTt34vz585RshBBSbUVERCAsLAzvvfceNm3apHNdp5pOezMaImnVTaetqeuqSwbSJdsfAMoEqcackWaKNddyubxMG0oikYDL5aq1oWpqwG2OLeQsOCxM6BysV4fRy5cv0bt3b/Tq1Qvbtm0zStJWojsKrqsJhmEwZ84cHD16FJcvXzZ4tEsmkyEhIQFyudwoo3GA9snL3AQWuPZF92o5FVwul6OwsBD5+fkoLi6ukkC6vHKYOrFI6YBbWfkpA25bW1sIBAJYWVnViGBbmyRnoe4CnJ7WUe+s9MpZHh4eHnB2dtbrHps2bcKqVauQmpoKNzc35OXl4dKlS2jVqpVe9yOEEFN5+fIlunfvjo8//hgrVqzQqy744dxLbL5addPAuWwWJnQOwue9QmvEaDXDMBCJRCgoKEBhYSGEQiFYLFaZjm9TL+2qqoRmFQXc1tbWEAgEEAgENSrY1mUWnbFM6hKEee811Pp8ZdsjJSUFLBYLYWFhOHnyJCWvMwMKrqsBhmEwc+ZM/P7777hy5QqCg4MNup9IJEJcXBysrKzg7e1t8B+WLsnL3Gx4uPd1L4OeZ2wSiQQFBQWqgNrCwgICgQC2trawsrIyyxuPubJ2lgy4CwoKUFBQADabrarsbG1tq21Pp7ZJzvRJcFZSUVER4uPjYW9vr/M67KNHj+KTTz7B1q1b0bZtW6xfvx779++HhYUFLly4gLZt2+pdLkIIMabnz5+je/fu+Oyzz7BkyRK9ArvcYglafv8X5FUwD5wN4PS0jgh2s632o9UKhQJFRUWqelYul6t1aJsjR4o5s4Ur89YUFRUhPz8fIpEI1tbWsLOzg0AgMMquHVUht1iCp4l5+GT3PVRF8PTXbO0yiZdueyxbtgyHDx/GBx98gCNHjtSojozagIJrM2MYBlOnTsWZM2dw9epVBAYGGnQ/ZYImY2yzpaTtiLWnHR+X53Q1+4i1MiumMqAWi8WwsbFRvYkbYxTfUNVlSwyGYVQNgPz8fMhkMlUDQCAQGJz4zhS0SXKmb4IzJYlEgvj4eLDZbPj5+WldMbVt2xatW7fGxo0bAbxpYPn6+qJFixa4fv06zp8/j3feeUevMhFCiLE8e/YM3bp1w+TJk/Hdd9/p3FZQZQS/FglJFc0E1zfZU1WRyWSqYLqwsBBsNlvV7rCxsTF7x3V1aXcAb3KTKNsdRUVF4PF4qp+VtbV1tZ9Nt+LsC2y9Fl0lz9Imr4CmtoeXlxcUCgV69OiB/fv3U4BdhSi4NiOGYTBjxgycPHkSu3btQtu2bSEQCPS+V2ZmJjIyMoy2tZAuI9bmTl6mUChQWFioqtgUCgUEAgHs7Oxga2tr9oqktOpUySkp14EpKzyhUAgrKyvVz7G6ZCOXyBTo/+MNvEovrPA8fROcKSkUCiQmJkIoFMLf37/SvaslEgmsra1x7NgxvP/++6rjn376KVJSUtCuXTts2LABFy5cQJs2bfQuFyGEGOL58+fo1q0bPvroI8ycORN+fn46B35VORW8OictE4vFyM/PR0FBAYqLi8Hn81VBYnXLb1Id2x3Am3IpR7QLCgoAQG02XXUqq5I5OpemdgvBnN5l95wvr+0xcuRIxMXFISsrCy1btsTevXur5c+yNqJuDDNhGAazZ8/GiRMncO3aNTg6OiIhIQG+vr46B9jKZEx5eXkIDAzUK8unJt1XX9V6H2tzBNbKEeqcnBzk5eWBy+VCIBDAx8cHNjY21apSqwmUa8D4fD5cXV1VvfD5+fnIyMiAhYUFHB0d4eDgYNYeUAsuG0cntUPzxRcrPO9BXA5yiyV6JTgDADabDV9fX6SnpyMmJgb+/v6wtrYu9/zMzEzI5XK4u7urjjEMAysrK6SlpeHrr7+Gk5MTevfujb/++svghIWEEKIr5RrriRMn4ttvv0VsbCzi4+O1DrCVGcG3Xa+awLo6Ji2Ty+XIzc1FTk6Oamacg4MDfH19q+Vsr+qOw+HAzs4OdnZ2qnZdfn4+0tPTkZiYCDs7Ozg5OVWrEW0Om4U5vUMxtXsIhmy9jadJ+SZ/5s6b0fisU2CZNo2mtkdxcTEsLS0hFApx9epVdO3aFWPHjsXu3bspwK4CFFybybx58/Drr7/i2rVralPBdQ2wFQoFkpKSIBQKERQUZLQpz6l5Qq2ygrsJLHB5TlejPFNbJSs2iUQCe3t7BAYGVrte4pqOy+XC0dERjo6OUCgUyMvLQ05ODtLS0mBnZwdHR0ezdWI4WFuglb9jpclFhm3726AEZywWC+7u7uByuYiNjYWPjw/s7Oy0upZhGCQmJkIul6u2UpkxYwbkcjl69uyJK1euoFmzZnqVixBCdBUVFYXu3btj7NixWLRoEVgsFgICArQKsJUjdbtuxkAoNf3WRNUtaZky6MvOzkZeXh74fD6cnJxgb29PwYoRsVgsWFtbw9raGh4eHhCJRMjJyUF8fDw4HA6cnJzM3sFfEp/HwfHJHaok2ZlIqkDbZZcwvlNQhbM4iouLERsbCxsbG3C5XLi7u+Py5cvo2rUrJk6ciB07dlBb2cSqx29nHbN69Wrs2bMHN2/eVEte5uDgAED7AFsulyM+Ph4KhQJBQUFGfbO5FZlZ6TluNjzcW9jTaM+sjFAoRFZWlqpic3Z2hp2dHVVsVYDNZqsCbbFYjOzsbCQkJKgqO0dHxyr/fzg0/p1KE5y9SivAiB1/GzQ9HACcnZ3B5XKRmJgIT09PODo6ljnHxcUFHA4HaWlpqsBaJBJBJBLB09NTdd7s2bMhEonw7rvv4vbt2wbnWSCEkMqkpqaiV69eGDZsGJYuXapqXHM4HK0C7HUXI7DxSqTJy1ndkpYpO/Ozs7MhlUrh4OCAoKAgo80QJBXj8/nw9PSEu7s78vPzVR38AoEAzs7O1WI024LLxrHJ7ZGaJ0SXVVchlplunrhYpsDGK5FQMIwq/0DJtocysFb+vDw8PAAAnp6euHz5Mjp06ID58+djxYoVJisjefM+RqrQ/v37sXjxYpw9e1bjdlsODg7w8vJCQkKCau2JJjKZDDExMQCAgIAAo/fiLf3jWYWvuwkscG2u6fexZhgGeXl5iI6ORkxMDNhsNoKDgxEcHGyWgI4AlpaW8PT0RGhoqOoN/NWrV0hOToZYLK6yclhw2TgxrSOa+1acX+BBXA7StZiFURl7e3v4+fkhJSUFGRkZKJ2uwsLCAi1btsRff/2lCqz9/f1x5coVtGvXTu3c+fPnY+jQoejVqxfS09MNLhshhJQnPz8fffr0Qbt27bBmzZoywYgywC7ZYV9SbrEEO29UTfKmSV2D0djb3uyBtUQiQUpKCl69eoWcnBy4uLigQYMG8PLyosDaDNhsNhwcHBAYGIiQkBDweDzExcUhKioKOTk5ZX5nzcHD3grjOwVVybO2XYtCbvGbRMPKtsf58+dVgbWjoyMuXbqk1vbw9vbGhQsXsGvXLqxfv75KyllXUUKzKnT27FkMHjwYJ0+eRFhYWIXn5ubmIjk5WeMItkQiQWxsrGqrLWNmoCwUydB6yXkIK5j15WzNwT/fvGu0Z2oik8mQk5OD7OzsN890dq5VwXR1TSyir5KzCmxsbODs7AxbW9sq6VHWJsFZPTdbXJzdxSjPEwqFiIuLU23VVfJ7PHLkCEaPHo1FixahT58+2LhxI3755Re8fPlSbT0U8GZJx4gRIxAVFYXLly/rncyQEELKIxaL0adPH/B4PJw6darCpWNyuRyxsbHgcDjw8/MDAxbWXYzAjhvRJh2NAwA+l43PKpnuamrK3TOysrJQWFhYrUZHjaG2tTuUswqysrKgUCjg6OgIJycns657Vy6f2HkzGiKpaf9mLDgsTOgcjFk96+Pggf0YP3481q5di+7du2P9+vXltj3u3buHHj16YPv27fjoo49MWsa6ioLrKnL37l2EhYVh586dGDZsmFbXaAqwxWIxYmJiNDbsjaHJN+dQKKl4PdWaQW9hUGs/oz5XSS6XIysrC5mZmWpTv2tDxVZSbavklKRSKbKzs5GdnQ0ejwcPDw/Y2tqa/Lm5xZJKE5x91jEQ8/s0NErDTdnBZW1tDW9vb7BYLNVU8B07dmDv3r1ITU1F8+bN8eOPP5a7v7VYLEa/fv3AYrFw5syZarFNHCGkdpDL5fjoo48QGxuLy5cva/VeXDLA/vWlCJuqICP4W152+HVye7OOVhcXFyM1NRVisVgVpNW29+Pa2u5gGAaFhYXIyspCUVERnJ2d4erqatbvMbdYgneWXYLIxJ1SADC+gx8+CObg9OnT2Lx5s1ZtjwsXLuCDDz7A77//jl69epm8jHUNBddV4OXLl+jYsSO+/fZbTJs2TadrSwbYFhYWiImJgYODA9zd3Y0ecMZmFqLr6muVnvf3/O7wsDfutCiFQoGcnBykp6fDwsICHh4esLGxMeozqpPaWskpKRQKZGVlISMjA1ZWVvDw8DD5VLrea69Vuj2XMfdJlUqliImJgbW1Nby8vJCUlASRSITAwECdlmkUFBSgW7duqF+/Pg4cOGD2vVAJITUfwzCYNm0aLl68iJs3b8LV1VXra+VyOV5FRuOD/a8hlpm2idjK3xGHxr+jd9JJQ4lEIqSlpamCMuX61dqotrc7gDczy1JTUyEUCuHq6gpnZ2ez1amrz7+qkjwFHBZwfUYbeHto/zcOAIcOHcKkSZNw6dIltG7d2kSlq5uoFWdiSUlJ6N27NyZOnKhzYA38bw12fHw8oqKi4OjoaJLAWiiRo/+PNys9z9OOb9TAmmEY5Obm4vXr18jOzoa3tzeCgoJqdWBdF7DZbLi6uiI0NBRWVlaIjo5GfHy8Sddk7/9Mcw9tSduvR0NkpEy3PB4PgYGBKC4uxuvXryEUCnUOrIE3+3n++eefuH//PmbPnl1mLTchhOhq2bJl+O2333D+/HmdAmvgzRpsjp2bSQNrCw4Lf8/vjmOT25slsJZIJEhMTERUVBR4PB7q168Pd3f3Wht01hVWVlYIDAyEn58f8vLyEBERgezsbLPUq7N61sfUbiGwtjDt75ScAdLEuv8NjRgxAosXL0afPn0QERFhgpLVXZQt3ISKiorQr18/hIWF4fvvv9f7PlZWVmCxWFAoFKrPja376qsoqGQ6uKsRt91STuNJS0uDXC6Hm5sbHBwcat3077qOw+HAw8MDzs7OSE9PR2RkJBwcHODm5mb0dVFudvxKt+eSKRhEpxeikXfFSdC0xeVyYWlpqVqfp2/DzM3NDRcuXEC7du0QFBSE6dOnG6V8hJC659ChQ1i1ahWuX7+OgIAAna6VKxisufAK266Zdjr4hM7BRp8Bpw2ZTIaMjAxkZ2fDzs4O9erVq3XTvwlga2uL4OBg5OfnIy0tDZmZmXB3d6/SZYYl98LOKBBjxpFHeBifa5Jnfbjlb71mgcycORMpKSno06cP7t27BycnJ5OUr66haeEmwjAMhg4dioyMDFy4cEHvN2/lGmtHR0dYWFggJSVFp32wtRGVVoAe665XeI4VB3ixtK9RnldybZOrqyucnJzq3FTYujA9SxOxWIy0tDQUFBSYZF2URKbAB5tu4llK+Zn267nb4OKsrgY/q+R2W76+voiPj1dbg62Pv//+Gz169NAq6SEhhJR2//59dOvWDb/++ivee+89na839VRWc+1frVAokJmZiczMTFhbW8Pd3b3OZf2uq+0OhmFUyw55PB7c3d2rJBdMaRKZwuT7Ybfyd9R561GFQoFBgwahoKAA586dqzZ7iNdkFFybyJIlS7B7927cu3dP5ylZShKJBNHR0WprrCvKIq6v+gv/QEWD1pZcNv75qids+Yb9wSkDq8LCwlq/tqkydbWSUyouLkZaWhpEIpHRO1hEUjkafX0OFaURcbezxNU53WCl53StkoG1ciq4cg22jY0NvLy89A6w9+7di1mzZuHevXsICQnR6x6EkLonJSUFrVq1wuzZs/H555/rfH1usQRtl10yWWZwCw5wb2FPOFhX3UgxwzDIzs5GRkaGKslmXV12VtfbHSVzwZizg0Xb/Eb60icvUmFhIdq3b49u3bphw4YNJipZ3VG3hguryO+//44ffvgBJ0+e1DuwlslkiI2Nhb29vdoaa233wdbW86TcCgNrALj2RVeDAmuFQoG0tDRERkaCy+WiXr16tLapjrO2tkZAQAB8fHxUa+6LioqMcm8+j4MBzbwqPCctX4zuq6/qdX9NgTXwvzXYyuUO+vr0008xduxYDBgwAPn5+XrfhxBSd4hEInzwwQcICwvD7NmzdbpWrmDww7mXaLnkokm33JrQOaRKA2uhUIjIyEhkZ2fDy8uL8rnUccpcMPXr1wefz0d0dDSSk5OrfI/sABdbtPJ3NNn9O6+6itXnX0Gu0H7s1NbWFidPnsTBgwexc+dOk5WtrqDg2siePHmCTz75BPv27UPTpk31uodyKwxlluXSI2DGDLAHbrpV4et2fK5B66KEQiGioqJQUFCA4OBgeHl5mXUPQlJ9sFgsCAQCBAcHw8XFBXFxcUar6L7u36jSc1LyRYjJqDi7eGnlBdZKPB4PAQEByMnJQWZmpk73LmnlypXw9/fHiBEjIJcbJwEbIaR2YhgGEyZMAABs27ZN51kz6y5GYPPVKMhNNI+Rw36zU8OsnvVN84BSlB360dHRsLe3R0hISK3c0pPoh8vlwsPDAyEhIRCJREbt3NfWofHvmCzAlsgU2HglEmsuvNLpusDAQBw7dgwzZszAzZuVJzgm5aPg2ogyMjIwYMAAfPnll/jggw/0uodCoUB8fDw4HE6FazcNDbAlMgX6bbiOyva4PzrhHZ3vDZSt3IKDg8Hn8/W6F6ndWCwWnJ2dERwcDJFIhMjISIMrOmdbS60qrg8239b6npUF1kqWlpbw9/dHeno6cnNztb5/SRwOB4cPH8br16+xcOFCve5BCKkb1q5di0uXLuH333/XuZ7NLZZgx41oE5XsTVbwf77qiS/fbVAla6xLd+i7ublRUE00srS0RGBgIJydnREXF4eUlJQqG8W24LJxbHJ7XJ3TxWTP0Gd3lK5du2L16tX48MMPERcXZ6KS1X4UXBuJVCrFkCFD0KZNG3z11Vd63UPZeFcoFPDz86t0DaohAfawbbfxXwVJn4A3lWJDL92zKlPlRvShrOicnJyMUtEdGv8OmnrZVXhOrlCK1DxhpffSNrBWsra2hq+vL5KSklBYqNvouJKDgwNOnTqFrVu34uDBg3rdgxBSu507dw7ffvstTpw4AU9PT62vkysYrD7/yqRrrIE3WcGrYio4degTfbBYLLi4uCA4OFi1jKAqR7FNOUVcpmDwPDlX5+smT56MQYMGYeDAgVU+ol9bUHBtJF9//TWys7OxZ88evQJJhmGQkpICkUgEf39/rdcj6xNg5xZL8Cghr9LzftMj4yBVbsQQxqzoLLhsnJreCXaV5Au4FZFR4eu6BtZKAoEA3t7eiI+Ph1BYeQCvSWhoKA4dOoRJkybhxYsXet2DEFI7JSYmYuTIkdiyZQtat26t07XrLkZg45VIkwXWXDaryqaCU4c+MZSxO/d1Ycop4h9u+RuDt9yGRMe/8x9//BECgYC2BdUTBddGcO7cOWzatAm//PKL3skyMjMzkZ+fj4CAAJ3T4OsaYF98llrpObYWHDTxcdC6DCUrt6CgIKrciEGMWdGdDO9Q4esrK1iXpG9graTc0zs2NhZisVina5X69OmD8PBwDB06VO8gnRBSu8hkMnz00Uf44IMP8PHHH+t0rUgqx66bMSYq2Zus4A++CjP5VHDq0CfGZK5RbFNPEX8Ql4MRO/7W6Roej4fDhw/j5MmTOHDggEnKVZtRcG2g5ORkfPzxx9i0aRMaNGig1z3y8/ORkZEBf39/vffD1iXA/uL40wpft+Cw8PcC7fbY1VS51bW9I4lpGKuiC3S1hZtN+Un00gskGhObGRpYK7m4uMDBwQHx8fF6JydbsmQJBAIBZs6cqdf1hJDa5bvvvkN2djZ+/PFHna9NzCmGUMe1mLqoiqzg1KFPTMVco9imnCL+IC5HqyVwJfn4+GDv3r2YMmUKIiIiTFKu2oqCawPI5XKMGDECffv2xSeffKLXPUQiERITE+Ht7W1wUKpNgP0oLqvS+1z/sptWW28p9+Gmyo2YUumKLi0tDQyjW1rbuX0aVvj6gJ9uqH1trMBaycPDAzweD4mJiTqXHfhfL/Kvv/6KI0eOGFQWQkjNdvHiRaxfvx6//PILrK2ttb5OueVW73XXTVIuDguY3CXIpFPBGYZBZmamqkM/KCiIOvSJ0Wnq3Nd39pkudo5sigYupumY6qLHFl19+/bFhAkTMHToUIhEIpOUqzai4NoAS5YsQVpaGjZu3KjX9TKZDHFxcXBxcYG9ve6JwzSpLMAesrXiqSE2Fmyttt4qKipCVFQUrKysqHIjJqes6IKCgpCbm4uEhASdRoE7hLhU+HqBRIE2Sy9CKJGrBdY9e/YEj8cDi8VSfaxYsUKv8vv6+kIsFuu9B7a/vz/27NmDiRMnIjIyUq97EEJqttTUVIwaNQo//vgjGjdurNO1ptxyi8cGjo0KwbCGVmDBNHt6KRQKJCUlITMzE4GBgXBzc6s08SshhlB27tvZ2almSphKcXExkhPj8XzXlya5v/j/t+had1G3Uehly5bB0tISn3/+uUnKVRvRu5KeLl++jNWrV+Po0aOwtbXV+XqGYRAfHw8rKyu4uroatWzlBdhPE3Igq6TOOziubaX3z8nJQWxsLNzc3ODt7U2VG6kyfD4fwcHBkMvliImJgUQi0eo6D3sr2FcyGyO9QIJuq6+ojVgDwOLFi5GSkqL6mDZtml5l53A48Pf3R3Z2tt5bdA0cOBBjxozB8OHDq6QXnRBSfcjlcowcORI9e/bEmDFjdLpWJJVj503Tbbk1sUsImjYIgVwuR3x8vNGn0cpkMlXuiuDgYJ1G7AkxBIvFgoeHh6pdnZmZqdcMtIoUFxcjNjYW7u7uYEsKwYXplm3svhWj0xZdFhYWOHLkCA4ePIjjx4+brFy1CUVFekhPT8fIkSOxZs0aNG3aVK97KNdw+Pj4mGQqtaYAe8Cmivf0ZbOA5v5O5b6uzGiempoKf39/ODs7G7XMhGiDy+UiICAA1tbWiIqK0nod9olKEpsBQGq+GHHpeWpTwQUCATw8PFQf+iYtBN70gvv6+iI5OVnv5GQrV64EwzCYO3eu3uUghNQ8y5cvR0JCArZs2aJzuyGjQAyR1PjrRjlsqLKCczgcBAQEGD3AVk7L5fF4CAwMBI9Xfg4NQkzFwcEBAQEByMzMRFJSktF+v0sG1s7OzoBcihY2le/oo/fzJHIkZhfrdE1gYCB27dqFcePGITY21jQFq0VYjLG7X2o5hmEwZMgQMAyDY8eO6RUYZ2dnIy0tDcHBwXonMNNWbm4ukpOTkckI8Mn+JxWee3JKezTz05xMQSaTISEhATKZzKDEa+QNuVyOFy9eoGHDhlpvu0bKys7ORkpKCjw9PeHkVH7HkFK7ZZeQkl/xuqGeDd2w49M329oEBARAJBJBKpXCz88PI0aMwKxZswxeg52ZmYnMzEwEBwfr1VCMjIzE22+/jZMnT6J79+4GlYUQUv09evQIHTp0wM2bN9GiRQudrpXIFBi69RYeJ+YbvVx/TOuAxt4OasfkcjliY2PB4XDg5+dn0Oy2vLw8JCUlwdXVFS4uLpTXxQDU7jAOqVSK+Ph4AICfn59BnT1lAmv8f7tDLAaraX9YvvUewDV+e5vDBiZ2DsbnvUJ1yug/adIkvHr1CpcuXaJZqxWgn4yOfvnlF1y7dk2vnmPgTQ9sSkoK/Pz8qiRAVY5gVxZY89iscgNrkUiE6OhosNlsBAUFUWBNqg0nJycEBAQgLS0NycnJlU7VujynK1ysKm5U3HidqZoyNX36dBw5cgRXrlzBxIkTsWzZMnz5peHroZydnWFjY6N3grOQkBAsX74c48aNQ2Fh2UznhJDaQyKRYPTo0fjyyy91DqwB4KMdd0wSWHPZLAS7CcocN8YINsMwSEtLQ1JSEnx8fODq6kqBNakWlDMoLC0tERUVheJi3UaBlTQF1sD/tzsOH8b5VVMxPSAdjEy75W+6kCuAzVejdF5/vWrVKsTExGDLli1GL1NtQsG1DtLS0hAeHo7NmzfDzc1N5+vlcjkSEhLg5uZm0NRSXaUJK6+QjozXvNa6oKBAlZXTz8+PejtJtWNjY4Pg4GBVRSWTyco918qCg6bCfyq8n0imwN+PnwMAZs+eja5du6Jp06aYNGkS1qxZg59++sng9c4sFgteXl6QSqXIyMjQ6x5TpkxBQECAUYJ9Qkj19f3334PFYmHBggU6X5uaJ8Q/cbnGLxSACZ2DwOdpbhMYEmArr8nLy0NQUBDs7OyMVWRCjILNZsPb2xsuLi6IjY2tNI/KvHnz1BKjslgs2NjYoHHjxqoZGS9fvgSg3u6YPXUiOrlJjL7GW2nnjSid1l8LBALs3r0b8+bNQ3S06XI41HQ0LVxLDMNg8ODB4HA4+OWXX/S6PjExEXK5HP7+/lXaA7vyzxfYcr38PwI2gOgVfdWOKbe7yMjIgLe3t9GymddGDMNAJpOpPqRSaZnPFQoFGIZRvUFKpVIAUGWiBt4EXFwuF1wuFzweT/V5ya+pc6N8crkcSUlJEAqF8Pf3B5/P13heZHwKwjY/rPBeT77uDjubshnwnz17hiZNmuDly5cIDQ01uMxCoRDR0dHw9/fXKzFidHQ0mjVrRtPDCamlHj58iI4dO+L27dto3ry51tfJFQzWXYzAtuuRMMWW1p+088e3/RtXOqVU1yniEokEcXFx4HK58PX1NXgJTm2mUCg0tjeUn8vlclW7g2EYVbuDy+WqBXlsNltj26Pk5zRroHwFBQVISEiAk5MT3N3dNf6sMjIykJX1ZitcoVCIpKQkuLi4wMHBQXVOeTNDnzz9D13CV8Kl43CYIG2C1n/LJU2ZMgXPnz/H5cuXaXq4BvSupaWjR4/ixo0bePbsmV7X5+TkoKioCCEhIVX+JrX7RsW9S6VHrRmGQVJSEgoLCxEYGEjbbP0/ZeUkFAohEolU/ypHSjkcTplKycbGBjweD2w2W1WRAW+m2qekpMDLy0t1TKFQQC6XqyrI4uJitcqSYRiw2WzweDzw+XxYWVnBysoKfD6fgm68+fn7+voiPT0d0dHR8PPz0xiwhvh54i0vAZ4ml7+lRlaxHHYaJpc8fvwYbDZbr5krmlhZWcHDwwOJiYkICQnRuSEZFBSEFStWYOzYsXj69CkEgrJTNAkhNZNyOvjcuXN1CqyBN9tubbximi37rHhsLOjTUKvGuHIEOzY2FvHx8RUG2MXFxYiLi4ODgwM8PDwooPt/CoVC1eZQtjskEgkUCoVap7yy7WFhYQFra2twuVzVz5rFYqly5yh3eVEG3QqFQhWQSyQStbaHcstLLpcLS0tLtXaHhYUF/R/hzWhucHAw4uLiIBaL4evrW+Z33NXVFa6urqoZdu3atdM6KfDTJ/8i/9Yh3Nm/Cu9uqXjmnT723YmDrSUXX77bQOtrfvjhB7z11lvYvHkzpk6davQy1XQ0cq2FtLQ0NG7cGFu3bsXgwYN1vl65Zrm8xr4pPU/KRZ+fbpX7OpcNRC7736g1wzBISEiAWCxGQEBAnc3KWTKQLhlMy+Vy8Pl8VXDL5/NVgbQuvXe6JhZhGAZyuRwymQwSiUStTDKZDJaWlhRwl5Cbm4ukpCT4+vqWmVLIMAx2/vUESy8llnu9gxUPW3oJcPfuXXTr1g0CgQB37tzBrFmz8N5772Hv3r1GK6vyb06hUOg1q0WhUKBHjx5o0KABrYMipBb5+uuvcebMGdy7d0+nulgklePtxRchNMWQNYCp3UIwp7duM3cqG8EuKipCXFxcmfWndU3pQFooFEIsFoPD4ajV75aWlqrZbNrWGfokNFMG3jKZTK1cYrEYLBZLVSYKuP/3O85ms+Hv71/md7y8NdYl3blzp8J2x+Att/EgLsfoZeeyWfhvUe9yl3locuXKFfTv3x///vsvgoODjV6mmoyC60oop4NzuVwcPXpU5+sVCgWioqJgZ2cHd3d3E5SwYvUW/FHhNJLpXX0x+90324kpFAokJCRAKpUiICCgzk3HUigUKCoqQn5+PgoKCiCTydQCaWXlYYwpMMbM2ll6NF0ZcFtbW0MgEEAgEMDS0rLOVXh5eXlITEyEj4+PalmDcnlGQmY+RhyNrfD6Te85Y8ncmXj58iXEYjECAwPx8ccfY/bs2bC0tDRqWeVyOSIjI+Hk5KTXvvcxMTFo2rQpfv/9d4SFhRm1bISQqqecDn7nzh00a9ZMp2sTsovR6YcrRi+TJZeN8Z2C3my7pcMUUqXyAuzCwkLEx8fDw8NDq10fahuxWIyCggIUFBSgqKhILZAu2YlvaB1uzHaHQqGAWCwu0wnAZrMhEAhgZ2cHGxubOtfJL5fLERcXBwDw9/dXff/aBNbAm7/7KVOmlNvukMgUGLT5Fp4mV03m/8qEh4fjv//+w5UrV2h6eAkUXFfi5MmTGDduHF68eKFXozcpKUn1B1LVwU1sZiG6rr5W4Tk7B3qhbZMQ2NjYID4+HnK5HAEBAXXmDVEmk6GgoAD5+fkoLCwEl8tVVQzW1tYme7Mw9ZYYUqlUVVkXFhaCx+OpAm0bG5s6E2jn5+erpsHZ29sjMTERIpEIgYGBaPn9JeSJyk9+5mDFw+Nve1VZWYuLixETE4OgoCC9lmJs2rQJa9euxbNnz8pdb04Iqf7kcjnatGmD/v3747vvvtPpWolMgaFbbuFxkvEb33/P7w4Pe8OWiZUOsIuKipCQkABPT084OmresaS2YRgGQqFQ1ZEvkUhgY2OjqqONEUhrYup2h0KhUPu+pFIpbGxsYGdnp/q+6gKFQoG4uDgoFAoEBARALBZrFVhrSySVo/niC0bft16fzrPCwkI0adIEX331FT777DOjlqcmo+C6AsXFxWjUqBG+/vprjBs3TufrlUkOQkJCzLJ91Z6b0Vh05kW5r1twWLj3RXskJSXB0tJSNZWltgfWYrEY+fn5yM/Ph1AohJWVlSqgrqoR3qrcb1KhUKCwsFAVbCsUCtX3a2trW+v/v5V/h5aWllAoFAgMDASXy0VMRiG6ram48+nK510Q6Fp1SznS09ORl5eH4OBgnTt2lA3ygQMH4ptvvjFRCQkhprZlyxasWbMG//33n84dZYO23DJJdvBW/o44Nrm9Ue6lDLAZhoFYLFabXVRbKethZeAJQBVMV1U9XNX7XCvbWgUFBSguLgafz4ednR3s7OxqfQewQqFAfHy8ah27h4eHUZc7rD7/ymQ5FXRd9qEchIyIiKiTM080oTH8CqxYsQIeHh4YM2aMztcqMxd7eHiYbV9oF5uKn7t+8Fuws7ODhYUFRCIRnJ2da22gpVAokJOTg+joaERGRqK4uBiOjo4IDQ1FcHAw3NzcwOfza+WILpvNhp2dHby9vREaGoqAgABYWFggPT0dr169QlJSEoqLi0221YO52drawsrKCkKhEE5OTqrlDoGutrDnV7z04YPNt6uiiCrKvVzT09N1vpbD4WDz5s1YuXIlYmJiTFA6QoipZWRkYOHChfjpp590DkByiyUmCaxb+Dng0Ph3jHY/DocDZ2dniEQiWFpa1upEjCKRCMnJyXj58iVSU1PB5XLh5+eHBg0aqDoVamu7y9LSEq6urggKCkKDBg1U/+dRUVGIjIxEdna2KmFabcNms+Hq6gqxWAwul6uWFdwYZvWsjwmdAo16T6Xdt2J02p5rwIABaNu2LRYuXGiS8tRENHJdjsjISDRt2hQ3btxAy5Ytdb4+KSkJEokEAQEBZgvYAub9UeHr07sF4cNQKygUCjg4OCA1NRW+vr61qqITiUTIzs5Gbm4ueDwenJycYG9vb/b15FXdg1weoVCI7Oxs5OXlwcLCAo6OjnBwcKg1lb1yjbVIJIKbmxuSkpLUtpbTZvTaGFMhdaFsfAQGBsLa2lrn6z/77DNkZGTg5MmTJigdIcSUPvvsM2RmZuLEiRM6X3vpWRLG7X9s9DLd+LIbfJ10fy8qT0FBAeLj4+Hl5YXs7Gytt+mqKRQKBfLy8pCdnQ2RSAR7e3s4OjrC2trarB341aXdIZfLkZeXh5ycHNXPx9nZuVbtTKNcY+3m5obCwkKTLLk0VW4FQPe/eWXMdP36dbRq1cokZapJasc7mZExDIPp06dj9OjRegXWBQUFyMvLg7e3t9neSB/EZFZ6ztsuUO277eTkBC8vLyQkJKimLNVUDMMgPz8fMTExiIqKUq17CQkJgbOzs9kD6+rEyspKNaLt7OyM3NxcvHr1CikpKZBIJOYunkFKBtaBgYGwt7eHr68vEhMTkZ//Zj2iNqPX92Oyq6K4Knw+H66urkhKSoJCofuaquXLl+PGjRv444+KO9cIIdXL33//jcOHD2P9+vU6XSdXMFh57gU+M0FgbW3BgavAeAkcCwsLVXkwHB0dERAQALlcjvj4eL3e76oTqVSKtLQ0vHr1CpmZmXBwcFCNUNelXCeV4XA4cHJyQnBwMIKCgsBms1Xttby8vBo/i65k8jIXFxdVx5FyHbaxuAosYaVDdm9d7P87DnKF9v8PISEhmDNnDsLDw2v837ExUHCtwalTp3Dv3j18//33Ol9bHaaDA8DgbXcrfJ0FwNeep9aT5uDgUKMDbIVCgaysLLx+/RrJycmwsbFBaGgofHx8zN5jXN1xOBw4OjoiKCgIAQEBkEqleP36NeLi4lBcXGzu4umsdGCt7FARCATw9fVV+x3f8UmLCu/lJqj6v2NDpoe7urpi6dKlmD59OkQikQlKRwgxNrlcjvDwcMybNw8BAQE6XbvuYgS2XI2GKUKSsR0CddqepyLFxcWIj4+Hp6enapqsch/smhxgi0QiJCQkICIiAiKRCL6+vqrO/NoyC8xUrKys4OXlhdDQUNjb2yM1NRWvXr1CRkZGjfxd0JQVXJnPCIBRA2w+j4NxHU0zNXz79Wisuxih0zXz5s1DWloadu/ebZIy1SQUXJdSXFyMGTNmYOXKlXotzE9JSQGfzzdr1stXKXmVnrOur7vGKSo1McBmGAa5ubl4/fo1srOz4ebmhtDQULi5udEotY5YLBasra3h5+eHevXqwdLSEjExMYiPj4dYLDZ38bRSXmCtZGdnBx8fHyQkJKCoqAhp+RWP0E888MiUxdWIxWLBx8cHWVlZenVuTJgwAQ4ODli1apUJSkcIMbYdO3YgLy8PX3zxhU7XiaRy7LgRZZIyfdLOH7N61jfKvYRCoSroKN0+qqkBtkQiQWJiIqKiosDhcBASEgJ/f3/Y2tpSZ76OOBwOXFxcUL9+fXh5eSE/Px8RERHIzs6uMSPZFW23pQywlVveGut7mtWzPqZ2C4EF1/i/b7tu6rb22traGhs2bMC8efOQnV21M/6qGwquS1m1apXeScyUmSC9vLzM+sb6x9OUCl9nAej7TrNyA8+aEmAzDIOCggJERUUhLS0Nbm5uCAkJgYODA1VsRmBhYQEPDw/Ur18fHA4HkZGRSEpKglQqNXfRylVZYK1kb28PDw8PxMfHo5l3xdnAc4VSxGQUmqK4FVJOD09OTta5IlYmN1uxYgXi4+NNVEJCiDFkZ2djwYIF+PHHH3VOYpaeL4JYZvzgg89lY0GfhnrtZ12aTCZDfHw8XFxcys2YXJMCbJlMhpSUFLx+/RoMw6BevXrw8vKCpaXxps/XVSwWC3Z2dggKCoKnpycyMzPx+vXraj9dXJt9rJW/4xKJBKmpqUZ5LofNwpzeobj+RTej3K8koVSOjALdBlUGDBiANm3a4OuvvzZ6eWoSCq5LSE9Px+rVq7F27VqdE2swDIOUlBS4ubmZdTo4AAQ521T4+vKB9Srdb7C6B9jKPYETExPh4OCAevXqwdHRkYJqE+DxePD29kZISAjkcjkiIiKQmppa7bJ8ahtYKymT24lz02FXzbKGK7m4uEChUOjVC9y2bVu8//77Ou+TSwipWitXrkTr1q3Rp08fna6TKxjsvKbb1E1tfdYpyCjTwZVbEllbW8PV1bXCc6t7gC2Xy5Geno6IiAiIxWIEBwfD19fX7G2+2ojFYsHe3h716tWDi4sLUlJSEBUVhcLCqu/orow2gbWSMnlfbm4ucnJyjFYGD3srtPI3/ozZ6YcfQiLT/u+QxWJhzZo12LVrFyIjTbNVWE1AwXUJ33//Pbp374727XXfyzErKwsAjLqPnb7mHHtS4evJBdr9oVTHAFskEiEuLg6xsbGwsbFB/fr14eLiUmuyjFZnlpaW8PPzQ1BQEIRCYbVaF6VrYK3k6ekJLpeLzQP9KzwvVyhFap7QGEXVCZvNhqenJ9LS0iCTyXS+fvHixTh8+DCeP39ugtIRQgyVmJiIn376CcuXL9f52jUXXmLfvWSjl2lC5yCjTAdnGAbJyclQKBRaJ3itjgG2Mp9LREQECgoK4O/vj4CAgFq/V3N1wGKx4OTkhPr168Pe3h7x8fGIiYmBUFj19bEmugTWSpaWlvD19UVycrJRc9ocGv8OmnnbGe1+APAoIQ8jdvyt0zUNGzbEyJEj6/ToNUUk/y86Oho7duzAsmXLdL5WKpUiPT0dnp6eZh85jc0sRGUzxPq+5an1/apLgK1QKFQ9lzweD/Xr14e7uzslCzEDKysrBAYGws/PD3l5eXj9+rVZfzf0DayBNxW3r68v3GzYEFhU/HZY1VnDlQQCAWxtbfWaRhYcHIxx48ZhwYIFJigZIcRQixYtwoABA9CiRcWJFUsTSeXYdcM0+9l//I6/UaaDZ2dno7CwEP7+/jp1gFenALuoqEi1J7O3tzeCgoJgY1Px7EBifMp9o0NDQ2FlZYXo6GgkJSWZdQadPoG1kq2trWppmrGW2llw2VjQt5FR7lXSg7gc5BbrtnvMd999h5MnT+Lhw4dGL09NQMH1//vmm28wfPhwNG7cWOdrU1NTVQ1gc7vysuLswmwWEOppr9M9zR1gFxcXIzIyEsXFxQgJCYGXlxclKqsGbG1tERwcDFdXVyQkJJilojMksFbicrnw8/PDwi4VT1k0R9ZwJQ8PD+Tn56OoqEjna7/++mv89ddfuH3bPFPbCSGavXz5EgcOHNBrZ5LEzHyI5cZfg2qsrbcKCwuRmpoKPz+/SpehaWLuAFvZoR8bGwtHR0eEhITAzs7O7AModR2Hw4GHhwfq1asHiUSCyMhIs7VL9Q2slZycnCAQCIyaQTzUQ2CU+5T2MiVfp/N9fX0RHh6O+fPnm6Q81R0F1wD+/fdf/Pbbb1i0aJHO1xYWFqKgoAAeHh4mKJnuPCupFLcOb6bXfc0RYCsrt5iYGNU2UZQwpHpRTtkKCQlRVXRVtSbKGIG1Ep/PB8um4vVK5sgarmRhYQFXV1ekpKTonNTF3d0ds2fPxrx586p1QhhC6pqFCxdizJgxCAkJ0ek6mVyB7VdemqRMxth6SywWIyEhAV5eXrC2ttb7PuYKsEt36Cu3RiTVh4WFBQICAszSuW+MwBp4037y9PQEm81GUlKSUepnB2sLk6y9/v7MM53WXgPA/PnzcffuXVy+fNno5anuKLjGm1+AyZMnw8/PT6frSiYx06dn1hQmHX5c4etJBbpN7SipKgPskpWbcnSUKrfqq2RFFx8fb/KKrmRgfeTIEXTu3BnW1taqvVNLi4+PR9++fWFtbQ03Nzd88cUXZdYwd2noVeEzzbXuWsnZ2Vnv5GZz5szB8+fP8eeff5qgZIQQXd29exfnzp3Ta13iij+e4pcnxl+mMqmL4WutlYGwg4ODUbYkrcoAmzr0axZzdO4rA+uDBw+if//+Brc72Gw2/Pz8UFxcjMzMTKOU8dD4d9DY07gj2P+lFOKj7Xd0usbJyQlz586tkx37dT64vnbtGm7duqXX1IWcnBwwDFMtkpgBwJOEyivbbg3cDHqGqQNshUKB1NRUtcqNkobUDFVV0ZUesZbJZBgyZAgmT56s8Xy5XI6+fftCIpHg9u3b2Lt3L37++Wd88803aud52FtVmjXcXOuugf8lN0tPT9e548LOzg4LFy7E/Pnzq12Wd0LqGoZhMG/ePMycOROentrnQAGAYrEU++8mmaRcI9sattZa+d7M4/GMOpuvKgJs6tCvuZSd+y4uLibt3C85Ys3lco3S7gD+tzQtIyMD+fm6Tb/WxILLxobhbxt8n9L+ic/Vee319OnTkZCQgOPHjxu9PNVZnQ+uv/nmG8yZMwcuLi46XadQKJCeng53d/dq8wZ8/GHFFS6XDQS4GL4u3FQBtlAoRGRkJIqKiqhyq8FKV3TKbLHGoGkq+KJFizBr1iy89dZbGq+5cOECnj9/jgMHDqB58+Z47733sGTJEmzatAkSiXpFsfOTipMK+TtbGeX70JetrS34fL5ePdxTpkxBbm5unavkCKlurl69in///Rdffvmlzte+Tkirtmut09LSIBaL4evra/S621QBNsMw1KFfC7BYLDg7O6t17uuTo6Q8paeCG7PdAbxJFOvt7a1q3xjKx8kaXCMkJSzteVKuTufb2Njgm2++wbffflstMv9XlTodXN+6dQuPHj3CtGnTdL42KysLPB4PdnbGTXtviBCXijNYfte/gdGeZewAOy8vDzExMbC3t6fKrRYoWdEpKyV9tpIqSd811nfu3MFbb70Fd3d31bHevXsjPz8fz549Uzs3Lb/iXtm4LPNu/8FiseDu7o7MzEydM4xaWlriiy++wPLly+vcFC1CqpPly5dj2rRpsLfXLbmoRCrD3tumyRBu6FrrvLw8ZGdnw9/f32S7eBg7wJbL5YiLi0NBQQF16NcSys59Z2dnxMbGGmUvaX3WWOvS7lCyt7eHs7Mz4uLiDB555/M4mNA5yKB7aKLP2uuxY8ciJycHp0+fNnp5qqs6HVwvX74cU6ZMKXe9RHlkMhkyMjKq1ag1AHx9+kWFr79ON95+eoBxAmyGYZCeno6kpCT4+PhUu58pMYyFhQWCgoLA4/EQGRmp996UhiQvS01NVavgAKi+Lr29VWUj0+bMGK5kbW0NgUCAjIwMna8dN24ckpKScP78eROUjBBSmQcPHuD27duYPn26ztcuP/0Evz03fNpoaZ+08zdorbVUKkVycjK8vb1NvkbZWAG2WCxGVFQUAFCHfi3DYrHg4uICf39/pKam6pUIVEnf5GW6tDtKcnNzg4WFhV5bb5b2ea9QfNC84lwyunqeVozh23TbecTS0hKzZ8+uUx37dTa4fvLkCS5duoSZM2fqfG1mZiasra2rxdZbSq9S8io9Z1ALb6M/15AAW6FQICEhATk5OQgKCqpWswCI8bDZbPj4+MDZ2RkxMTHIy6v8d3XevHlgsViqD2XSj/r164PH44HFYuHlS+Nny61sZNqcGcNLcnd3R05ODsRisU7XWVtbY8aMGVi+fLmJSkYIqcjy5csxfvx4nXO1FArFOPggxejl4XNZWNCnod5rrRmGQXJyMmxtbf+PvTOPk6Mu8/+nuvq+u6d7uue+kslBwJCIEAgYVggIwroKrhyiCOIi4MWu+nMREF1ExXXXC0QODxDxQrlDOEK45AgkEEgymfvu6fs+q+v3x2w1c/RMd9VUdX1nUu/XKy/ITFf3k5nu+j6f5+SdiRfKUgV2PB5HX18frFarpJl2BXkxm83o7OxEIpHA4OBgxWzwXL+DoiiYTCYcddRRcLlckvkdM6EoCk1NTYhGo0uuCqVVFG44l/964Uq8MRLl3Xv9+c9/Hj09Pdi1a5fo9pDIEbss+JZbbsGll17Ke+hGLpdDMBhEZ6f45RZL4dG3xis+5pgWpySvzWX+R0ZG0NLSAoul8pTCXC6H4eFhqFQqdHV1KXurVzgURcHtdkOn05Uy0PX19QtWKVx77bX4zGc+U+qHy2azaG5unvU+qfYz6PV68eqrr876ms/nK31vJsd1LP4Z4SaGe23y9l7rdDrY7XZMTU2hpaWF17VXXXUVvv/97+PFF1/ESSedJJGFCgoKczl48CAeffRR9Pb28r92cBw5CVoWLz+5a8nl4KlUCqtXrxbRqspwAntwcBDDw8NobW2FSrV4vohlWQSDQfh8PjQ1NfGuWlRYfuh0OnR2dmJ0dBR9fX1obW1dsEqB8zuA6RlAY2NjcLlcs94nUvgdc9FqtfB6vRgfH8eqVauWFPzhVnO9PrT08viZvD0cxMlrqx/GaLFYcPXVV+Pmm2/GqaeeKqotJHJEZq77+vrw17/+Ff/xH//B+9qpqSlYrVYYDPI613NpMC4ekbv1Y+slfX0+GexkMom+vj4YjcYl7ydWWF5YrVZ0dnYiEolgeHh4wUiy2+3GmjVrYDab0dzcjNNOOw0bNmzA2rVrS3+02upKtLds2YK3334bU1NTpa/t3LkTVqsV69fP/lx4bQbYCJ4YPpP6+nrEYjHepfZ2ux1f+MIXlOy1gkKN+f73v4+LL74Yzc3NvK5LpTP4/asjottzxSkdSy4Hn5iYQFNTkyznOJ8MdrFYxNjYGAKBADo6OhRhfQRB0zRaW1ths9nQ39+/oI/qdruxdu1atLa2QqfTYcuWLTjhhBMk9zvK4XA4RCsPv/szx0Hs2WZ/3zMIpsivxPuLX/wiXnrpJbz++uviGkMgR6S4/uEPf4jzzz8fHR0dvK7L5XKIRqOor1/aOiuxSaVS2De8eFRqOMKvfFQI1QjscDiMwcFB1NfXo7GxUemvPgLR6/Xo6upCsVhEf39/2cmZfHqsh4eHsXfv3pJY37t3L/bu3VtaA7Z9+3asX78en/rUp7Bv3z7s2LED1113Ha666qqy/YG/InxiOIdGo0FdXd2sw7tavvzlL+Ppp5/GW2+9JYFlCgoKcxkeHsb9998vaEL4LY++jb8eEH/15adOaBelHFzOlq5qBHahUMDAwACy2Sy6urpgNBplsFRBTrhhoJyPutDGjWp7rMX2O8rZK1Z5eDSdB08dXJE/vx3CDx4vP5htIVwuF6644oojIrB/xInriYkJ/OY3v8E3vvEN3tcGAgFYrVbJB3bwgYvGdnsXP9zWi7xQfiEWE9iBQAATExNoa2sjZje4gjyo1Wq0t7fDaDSWnB4OvsPLrr/+ehx77LG44YYbkEgkcOyxx+LYY48tRUdpmsYjjzwCmqaxZcsWXHzxxbjkkktw0003lX0+0ieGz6Surg6JRIL36g6v14tLL70Ut9xyi0SWKSgozOTWW2/Fueeei+5ufpniWDKNP7zBP4BWCYNGtaTVW5FIBOl0mveebilYTGDn83kMDAxAo9Ggo6MDGo1GRksV5MZut6OjowN+vx8+n2/WgC0+w8vE9jvKwZWHL3Vvt9uig14jvtz79UtDyOT52XXttdfi0Ucflbx3XW4o9kgZ3fZ/XHfdddi3bx/vkfD5fB49PT3o6uoiaqrk5OQkEokETrvz0KKP++I/deGr28VbxVWJSCSC8fHxUg+23+9HIBBAW1ubEjXG9AqQAwcOYN26dUf0MBWupzoajaKjowNarVbwVHCxeGskhHN//vKC3//z596P93d5Fvx+rRkfHwfDMLx7r/v7+7Fu3Tr09vbyvlZBQaF6IpEImpqasHv3bmzevJnXtXsODuLjv+aXIaqGq09dhX8/Y42ga/P5PA4fPozm5maiBpEyDIPBwcFSGXChUMDg4CCMRiOampqO+Eo5xe94j0wmg8HBQdjtdng8HqTTaUFTwaWGZVkMDg5Cq9WiqUn4UOJbdxzCz57lP+uhEk99+SSs8tp5XXPppZdCp9Ph9ttvF90eUjiiMtfZbBZ33HGHoBUYwWAQZrOZKGGdSqUQDAaR0lQ+3M4+urbRZS6DPTw8XCrB4TKVCgocFEXB6/XCbrdjYGAAQ0NDsgproHJmem/fOFHrJFwuF2KxWNny+sXo7OzEmWeeiV/+8pcSWaagoAAAv/71r7Fx40bewrpQKCCfjEJsSfjJDzQL7rXmysEtFgtRwhqYncEeHBzEwMAATCaTIqwV5qHX69HR0YFIJIKRkREMDAwQJ6wB8crDv3J6N644mV8rbDXc9VwP797ra665Br/73e8QiUREt4cUjihx/cc//hFOpxMf+tCHeF3HMAxCoRDcbrdElvGHKwd3u9146tDiA5ZUANY01GZFxkzsdjssFkupT520IXAKZEBRFOrr60HTNBKJBBobG2Udclepp9qhYxAMBmtkTWW0Wi2sVuuCPWSLcfXVV+OOO+7gXVauoKBQHcViET//+c9x9dVX8742GAziu88HIXYo7/KTOgX3WpNUDl4OmqbR1NRUGvTo9XoVYa1QFp1Oh4aGBsRiMRgMBjid0mzUWSpilIfTKgqf2tIurmEA7n/Tj1ufOMDrmk2bNmHjxo245557RLeHFI4ocf2zn/0MV199dcV1DXMJBoPQ6/VEZV2npqagUqngdruxym1a9LE3/8u6Glk1G7/fj2QyCY/HA5/Pt+ShDAorE5ZlMTY2BmC6h3hkZIT3/mYxqZS5vunZ6VUucto4F7fbjXA4jHw+z+u60047DU6nE3/84x8lskxB4chmx44dSCQS+PjHP87rOoZhMDDmw/5JcWc8qFUUmp3CfBluOrjcAdDFyOfzGB4ehs1mA03TGBkZ4b0HW+HIIJVKlZJU+Xx+Xg82STgcDuj1ekxMCN9177boYFjC2r2FuEdA7/U111yDn//85yv2s3nEiOvXX38d7777Li655BJe1xWLRQSDQaKy1plMBsFgsFTqtGc4sujjD0wma2PYDAKBAPx+P9rb2+F2u6te06VwZDF3eNnMEnG+Zc5iUWnXdTRTQEFrxvg4OeXher0eZrOZd0adoihcddVVuO222ySyTEHhyOYXv/gFrrjiiqpX+HCEQiGMS3B0X3FKp+C91hMTE0SWg3NwU8G5HuuOjo6q1nQpHHnMHF7m8XjQ3t6OSCQiaPtGLaAoCo2NjYjFYkgmhd0Y9Boal20VvzQ8UyhiIszPpo997GNIJpPYuXOn6PaQwBEjrn/5y1/iwgsv5H0ohMNhaDQamM1miSzjj8/ng9PpLPV/H924+L+p0vfFJhQKYWpqCu3t7aVScD57sFcixWIR+Xwe2WwW2Wy29DPIZrPI5XIoFArECLVaUW4qOLcuw2q1YmBggHcmVgyq2XU9mFAhk8mU1m6QgNvtRigU4l02dskll+Ctt97Cvn37JLJMQeHIZHh4GDt27MDnPvc5XtcVi0VM+QN4Y0r4hOBy0CrgqlNXCbo2nU4jHo/D6/WKapNYMAyDgYEBGAyGUuKBzx7slQjLsigUCsjlcshms6VSec4PyeVyR9zPBCg/FVyn06G9vR2hUAh+v19mC8uj1WrhcrkwOTkp2F/8yunduPAD4g4wpSkAWX7iWqvV4rLLLluxQ83IrOsRmWg0ivvvvx/PP/88r+tYlkUoFILL5SKmZyeZTCKZTM6aGvhsz+KRtmd7pnDecW1SmwZg2j5u3dbcMnq73Q4AGBkZKU0RXymwLFs6vLLZ7PQgmnwehUIBhUKhJHi49xF3YxwYGCj9P0VRUKvVpT8ajQYajQZ6vR4Gg4HYMjwhLLZui6IoNDQ0lByijo4O3q0cS+VXl2zCJ+54dcHvd7hMcBsM8Pl8MJvNRNwfjEYjdDodIpEIr6EsNpsNF1xwAX75y1/iF7/4hYQWKigcWdx555348Ic/jObmZl7XxeNx3Ls3jPvfCotqD1MEQskcTDr+Z8nk5CTq6uqIXGXFsixGRkag0WjQ3Nw8637MCezBwUEMDw+jtbW15ueJlBQKBaTTaWQymZLPMdP3mOlfzPQ7gPf8EJqm5/keWq0WBoMBer2eiPNNLBZbt6XX69He3o6BgQHodDoiKzRcLhdCoRDi8bgg+2gVhc9u7cDvXx0RzSaGBW7f3Y/vXeDh9dn63Oc+h+7uboyNjS1pEjqJrBxvfRHuu+8+HHXUUTj22GN5XZdMJlEoFGCz1X4YWDlYloXP54PL5ZolRlh28Rtfpe+LRS6Xw/DwMBoaGhbM9K8UgZ3NZpFKpZBOp0sHGwAYDAbodDpotVoYjUZoNJpZhxZ3SHErMdauXQuapkuZbe5A5P4/k8kgHA4jl8tBrVbDYDCU/hiNxmW5TqOaPdbchMyBgQGMj4/XfNprNbuuNxzTgEAggGg0Wnpfy43T6UQgEIDT6eT18/r85z+PD33oQ/jBD35AVJWOgsJyJZ/P484778Rdd93F+9pxnx8PvhsV3Sajlha02zqRSCCdTqO1tVV0m8TA5/Mhl8uhq6ur7H1vpQjsYrGIZDJZ8jnS6TTy+Tw0Gg0MBkPJ7+AEMud3cH7CXL+DZVkwDFPW94hGo6UMqV6vLwX5uSDuchTc1eyx5iofRkdH0dnZSdSGIAClWUs+nw8Wi0XQ76HZYYRaRaHAc8r3Yvz5nQi+EgzD664+sN/W1obTTjsNd911F66//nrRbCGBI0Jc33333fi3f/s33tcFg0E4nU5ibsLxeBzZbBZtbbOz0Gce5cFj+30LXnfmUdLv5OWyjDabreLExeUosFmWRSqVQjweRywWQz6fL4lcp9NZEtVCDxyVSgWdTgedrrzjwzBM6TBNp9OIRCLI5/MwmUywWCywWCy8e/rkoBphzaFSqdDa2oq+vj4Eg0G4XK6a2VlpYnhbnQEqlao0rM9qtRJxn7DZbJicnEQymeQlkt///vejq6sLf/rTn3DppZdKaKGCwpHBjh07oFarsX37dl7XpdNpjIcSyBTEL9f97EkdvPutWZbF5OQk3G43kcHccDiMUCiErq6uRe1brgI7n88jHo8jHo8jkUjMCrJzvofQ38vMarlysCyLXC5XEvPRaBQTExNQq9WwWq2wWCwwGo3L4udYjbDmsNlsyGQyGBoaQldXF3FVgw6HA4FAAJFIBA6Hg/f1eg2NK07pxC929YlmE1MEDgz7eIlrALjiiivwpS99Cdddd92yeB9VC1nvGAk4ePAg9u/fj/POO4/XdblcDolEgph1E1zWmltZNJNe/+K9DpW+v1S4ac80TVf981oOAptl2ZKY5nqkLRYLvF4vTCZTTR0NmqZhMplgMr03GZ7r3Y7FYpiYmIBer4fFYoHNZiMu2grwE9YcGo0Gra2tpTKtWr1P9o0snjXaNxLFMS1O2O12BAIBhMNhIvZjqlQqOBwOBINBXuKaoih86lOfwn333aeIawUFEbj33ntx0UUX8T4nQqEQWj1OqFXjomaWaBVw5bYu3tfFYjEUCgUi7m9zSaVSGB8fR2tr64KB6ZksF4Gdy+UQiUQQj8eRTqdhNBphsVjg8XhqmjWmKGpe0J/LnsdiMYyOjqJYLJYC/KQEmefCR1hz1NfXI5vNYmRkBO3t7URl6rnA/uTkJGw2m6Cf+VWnrsIvd/fz3lG9GA/s9eO4tSmYTdVvI/jwhz+Mz372s3jppZewdetW0WyRG/I+BSJz33334ZxzzuFdthkOh2E2m4nJBkYiERSLxbJRqvUNiwuOSt9fKlNTU0in02hpaeF1AyJ1yFk2m8Xk5CQOHjxYitK2tbVh7dq1aG5uhtVqJSKCr9Pp4HK50NHRgXXr1sHlciGbzaKvrw/9/f0Ih8PEDCsRIqw5jEZj6X1Ss/VXld7G//d9bgCb3+8XvH9SbJxOJxKJBO9hcJ/85Cfx3HPPYXx8XCLLFBSODGKxGB566CFcfPHFvK5jGAbRaBR/eicmqrAG3uu35sPMoD5poolbueXxeHgFXUkdclYsFhGNRjEwMIDDhw8jnU7D6XRi7dq16OzshNvtJqL/WaVSwWKxoKmpCWvWrEFHRwe0Wi38fj8OHTqEiYmJUpscCQgR1sB7rWkMwyxp/ZVU2Gw2qNVqhEIhQdeHkjlRhTUAPHE4gR8+8S6va7RaLT7xiU/gvvvuE9UWuSHrbikyLMvivvvuw0UXXcT7unA4TMxC+WKxiKmpKXg85YcFvDuxuDCt9P2lEI1GEQwG0dbWJqh0hhSBzWWpBwcH0dvbi1wuh5aWFnR3d8Pr9cJoNMp+qC0GTdOw2+1obW3FmjVrYLPZEAgEcOjQIUxOTsoydZtjKcKaw+FwwOl0YmhoqCYi9n3Ni89ZmPl9i8UCjUbDew2WVGi1WphMJoTD/IYhNTY2Ytu2bbj//vslskxB4cjgwQcfRHd3N4466ihe10UiEYDW4O6XhkW3SUi/NXcPEVJ6KiXFYhHDw8Mwm82CMuokCexCoYCpqSn09PSUBmSuWbMGbW1tcDgcxJUkz4SiKBgMBng8HqxatQqtra1gGAZ9fX0YGBhALBaTdQuKUGHNQdM0WltbEY1GBYtYqVhqYF+qndcPvDmFZIZfEO/iiy/GH//4R9nWr0rBihbXL7/8MiKRCD784Q/zui4ej4OiKGIG+4RCIdA0veBgtVVuU9mvV/t9oaTTaYyNjaG5uXlJZchyCmyWZRGLxdDb24vR0VEYDAZ0d3ejtbWVmCnQfFGr1airq8OqVavQ0tKCbDaLnp4ejI2N1VxkiyGsOTweD7RaLUZGRiQ/sIeC6aq/T1EUvF4vAoEACoWCpHZVi9PpRDgc5v1zuuiii1ZcBFlBodbcd999vLPWXFA/rTKInrUG+Pdbzwzqk3QOcm1owHRAUKhtcgvsQqGAiYkJHDp0CKlUCk1NTVi9ejXcbjfRgnohKIqCyWRCc3Mz1qxZA7PZjPHxcRw+fBiRSKTmInupwppDq9WitbUVExMTgvdLS4XZbIZer0cgEOB9rV5D49NbxN8ilCmw6B/nZ8+JJ54Iq9WKJ554QnR75GJFi+t7770X559/flW9ODMJh8Ow2+1EHCgMw8Dv9y96wO0Zjiz6HJW+L4RisYiRkRG4XC5R1hXIIbCTySQGBgYwNjYGp9OJNWvWwOPxELlqRAhcgKitrQ2rVq1CsVhET08PJicnayICxRTWwPS/p6WlBblcTtBhwodqBprNxGQywWg0ErMf02KxgGVZ3s7Axz72MRw4cADvvsuvtEtBQWGaiYkJPPvss7jgggt4XZfJZJDL5WA2iR/Uv2RLG75yejeva4LBIDQaDXHriMLhMJLJpCj90nIIbIZh4PP50NPTU5pw3t7eLnjyM4mo1Wq43W6sWbOmtJe5r68P8Xi8JiJbLGHNYTKZ0NDQgJGREWIC6MB72etAICAocfLJD4g//V+nVkFdSPG6hqIoXHTRRbj33ntFt0cuVqy4zuVy+OMf/8g7elwoFJBIJIhZrRMIBKDT6RbNom9utS/6HJW+LwSfzweapuF2u0V7zloJbG4K5NDQEMxmM7q7u1FXV0dcT5mY6HQ6tLS0oLOzE5lMBj09PfD7/ZI5E3OF9fe//32ceOKJMBqNC362KIqa9+cPf/jDrMfQNI3m5mZMTU1J2tfFJ3PN4fF4EAqFiChtoigKNpttusyUB1arFeeee66SvVZQEMgf/vAHnHLKKbz3tkajURhNZvzlTXFnHujVKnzzrHWgVdULt0KhUDGoLwe5XA6Tk5NoamoSLQheK4FdLBYRDAbR09ODZDKJtrY2tLW1ETl8VCwoioLT6UR3dzfsdjtGR0cxODiIVIqf+OLDTGF9++23i+J3ANOtEQaDgbj+a27YnZDAvtemh14trt+71mtGPpvh7QdddNFFePjhhxGNir+CUA5WrJrYsWMHTCYTTjrpJF7XxWIx6PV63tluKSgUCggGg/B6vYsecK/0L94LUun7fEkmkwiFQmhubhb94JVSYDMMg/HxcfT19UGr1aK7u7vs9PWVjMFgQHt7O1pbWxGLxdDT04NYLCbqa5TLWOdyOZx//vm48sorF732nnvuwcTEROnPRz/60XmPMRqNqKurw9jYmGRRcL6Za2D6Z2u1WjE1NSWJTXyx2+2IxWK8ncWLL74Y9913HzGDfhQUlhPclHA+sCyLSCSC+96K4I7nB0S15/KTO3mv3woEAjAajcS0xgHTP6Px8fHSCigxkVpgJxIJHD58GKFQCE1NTejo6Ji1+WOlo1Kp4HK50N3dDYPBgIGBAYyOjoqeBZ6bsRbT76AoCo2NjaUNLSTh8XgQDod5D3zVa2hcfnKnqLbsG43hTwdSvAP769atw/r16/HXv/5VVHvkYsWK6z/84Q+44IILeGcjI5EIMVnrUCgEg8EAo3HxsfYsu/hBUOn7fCgWixgbGyuthJACKQR2IpFAb28vMpkMVq1ahYaGhmXZ1yQWZrMZnZ2d8Hg8GB0dxejoqCiDwhYqBf/2t7+Nr3zlKzj66KMXvd5ut8Pr9Zb+LBTVr6+vR7FYlKw8vJpVXOWor69HNBqVdYAch16vh0aj4f0ZOuOMMxCLxfDKK69IZJmCwsrk8OHD2L9/Pz7+8Y/zui6ZTCKTZ/C7V8ZEtYem+K/fYhgGoVAI9fX1otqyVMLhMDKZjGTrUaUQ2FxAf3h4GC6XC6tWrYLVaiWqGqCW0DQNr9eL7u5uMAyD3t5e0YRquVJwsf0OjUaDhoYGjI+PE1UertPpYLVaBQ1du3JbF9Q8qlqq4U9vBTEV5D/z5cILLyxbNbAcWZHiOp/P47HHHsO//Mu/8Loul8shnU4vODislhSLRYRCIbhcroqPPaFr8cdU+j4ffD5faWCWlIglsOcebh0dHURUJZAARVFwOBxYvXo1GIbB4cOHl3TQidFjfdVVV8HlcuEDH/gA7r777gVvziqVCk1NTdKVh1e5imsuOp0OJpOJiMmiQkvDtVotzj77bDz00EPSGKagsEJ56KGH8E//9E+8fYhIJAJGY0KmIG7GlGH5r9+KRCLQ6XQwGBav3qklM8vBpaw0E1Ngzwzod3V1oa6u7ogV1XPRaDRobW0VLbi/1B7rav0OYNo3JbE8vK6uDuFwmPfPMZTMiT5AMVMoYiqW4e2bffSjH8Wzzz5LXGWAEFakuH7xxReh0+lw3HHH8bouGo3CZDIRkdGMxWJQqVRVlWXVaqBZMplEOBxGU1NTTQ6JpQrsZDKpHG5VIMZBJ4awvummm/DHP/4RO3fuxMc//nF84QtfwE9/+tMFHy9leTifVVxzqaurQygUIqKs2m63I5FI8I6yn3vuuXj44YclskpBYWXy8MMP49xzz+V1TbFYRCwWQ6vHCVrk48mgUfFav8WyLILBIFFnJTcd3GaziV4OXo6lCuxisagE9KtgZnC/UCjg8OHDgvy8pQprvn4HqeXhRqMROp2O9wpOt0UHvUZcKUgBaKjjH9jv6urC6tWr8eSTT4pqjxysSHH90EMP4SMf+QivknCu54mEknC+B1wtBppx5eD19fU1PSiECGzu5zc4OIi6ujrlcKuCuQddX19fxf6db3zjG6UBICqVCq2treju7oZGoyl9/eDBg1Xb8K1vfQsnnXQSjj32WHz961/H1772Nfzwhz9c9BquPFzsKd1CBppxmM1m0DRNxGAOrVYLg8HA25YzzjgDPT096O/vl8gyBYWVRSgUwgsvvICPfOQjvK6Lx+NQq9X49avjYEQeIXHZVn791olEAsVikagJ4VwvqdfrrdlrChXYuVwO/f39SKfTSkC/SjQaDdra2uDxeDAyMoKpqalFg+Uz/Q5u/ddRRx0Fl8tVM7+D1PJwLrDPJ9mg19C4fKu4fdcsgG8+MYJoNMo78XHOOeesiKq5FSeuWZbFQw89xDt6nM1mkcvliDhU0uk0stls1UK/FgPNalUOXg4+ApuLGvv9fnR0dJRuuArVwR10Vqu1tDpjIa699lq8++67eOaZZ/D444/j7bffxoEDB2b96ewUftM+/vjjMTo6uqjI58rD/X6/qOXhQgaacVAUhbq6OgSDwZrv9iyHzWbjLa6tViu2bdumZK8VFKrk8ccfxzHHHIOWlhZe10WjUehMFtwl8iAzIf3WwWAQTqeTmM0ZtSoHLwdfgZ1KpdDf3w+DwaAE9HnCBfc7OzsRDocxMjKy4M/72muvxYEDB/DGG2/g4Ycfxssvv1xzvwMgszzcZrOhWCzyrgCQou/6zdEYYpkC73Wg5557Lh577DGighZCIOMOKiIHDx7E2NgYTjvtNF7XxWIxWCwWIg6VYDAIh8NR9WEi9UCzTCZTmnIpl1CtRmAXCgUMDg6WosaVBsEplIeiKHi93tLPOxAIlBWJLpcLZrMZzc3NOO2007BhwwasXbt21h+tVivYjr1798LhcFR0Urjy8PHxcdHErNCBZhx2ux25XE7SlSPVYrVakUqlBJWGr4QIsoJCLRAS1C8Wi0gkEsir9LL3W2ezWSSTSTidTlHtWAqTk5OSTAevlmoFdjgcxsDAANxuNxobG4nwI5cjer0eXV1dKBQK6O/vL7vOye12o7W1FTqdDlu2bMEJJ5wgi98xszw8kUgIfj0x4VafBYNBXtdJ0XcNAFNZNW+hf/zxx0OlUuHll18W3Z5asuLuAA899BBOO+003sIqHo/LdgOfSaFQQCwW43XAST3QbHJyEk6nU/ZI7GICO51Oo6+vDxqNBp2dnaLtwDySsdvtaG9vRyAQwNjY2CzHgm+P9fDwMPbu3Yvh4WEwDIO9e/di7969pUPp4Ycfxp133on9+/ejt7cXt912G26++WZcc801VdnqdruRzWbFO+QEDjTjoGkaDoeDd/+TFGg0GhgMBt6H3DnnnIPdu3fz7ptSUDjSyOVyePzxx3HOOefwui6ZTE5PUHaYJei3pnn1W4dCIVitViJmzgDTmeB4PA6PxyOrHYsJbJZlMTExgcnJSbS1tSll4CKgVqvR0dEBo9GIvr6+eZlPPj3WUvsdGo0GLpcLPp+PiCo1AHA6nUilUrzWcknRdw0A65rrEIvFeP1saJpeEQNVV5y4FjJQJJ/PI51OEyGuI5EIDAYDLyEr5UCzZDKJVCoFt9st+DnEpJzATiaTGBgYgMPhQHNzsxI1FhGj0Yiuri5ks9mSYyFkeNn111+PY489FjfccAMSiQSOPfZYHHvssXj99dcBTB9SP//5z7FlyxZs3LgRv/zlL/Hf//3fuOGGG6qyk6ZpuN1u0Q657vrFBwlWGngGAA6HA9FoVJQVZ0vFYrHwFtdtbW1Yv349nnjiCYmsUlBYGezevRsWiwWbNm3idR0X1L/9uX4J+q07qu63LhaLiEQicDgc4hohEJZl4fP54HK5iAiUlxPY3DmYSCTQ2dlJ1E7w5Q6XFa6vr8fQ0FDp7OI7vExqvwOYruDL5/PEDDdTq9WwWCy8AvtS9F0DwNf+3oNCocB7//ZKGKhKsaSEW0QgEAjA6/ViZGSE1y7EUCiESCSypD4NMWBZFocPH0Z9fT2vwWoPvTmCLz7w1oLf/8m/HoNzj+XXB8bZ09/fD4vFQtzOy0gkgvHxcbjdbvj9fni9XqLK2SrBMAwOHDiAdevW1byXTAgMw2BoaAgURYGmaWSzWcFTwaWiWCzi8OHD8Hg8Sx5MePT1jyOeW7hM86ef3IhzNjZVfJ6+vj7Y7XZZZhXMJJ1OY2BgAGvXruUVfLruuuswMDCA++67T0LrFBSWN1/60peQzWZx++23V30Ny7I4dOgQXJ4GbP3RS6KWhdMU8NaNZ8Ckq+7+HIlEMDU1hdWrVxOReY3H4xgdHUV3dzdR5yPDMBgcHIRKpYJKpUI+n0d7eztR5+BiLDe/A5ieSTA6Oor6+nr4/X7BU8GlJBQKIRAIEPX5GRsbw5o1a6q2J5kt4Jgbd4ge5Hv40rXwOq28EnSJRAIulwtvv/02Vq9eLa5BNWJFpfiee+45rF+/npewBsgpCU+lUmAYhvdQtRf7Akv6/kLE43Hk83nibmTAdAbb6XTC5/PB4XAsK2G9HKFpGm1tbchkMojH42hrayPOoVCpVKivr4fP51vSGqzJaHpRYQ0Ax3VU935zOp0Ih8Oyl4zp9XrQNM17uMj27dvxzDPPyG6/ggLJPPPMM9i+fTuvazKZDIrFIpIMLXu/dTgchsPhIEIYcFlrt9tNnADkzsF0Oo1kMknkObjSsNlspXPdZrMR6Y9yFR8ktIEB0xtLKIriVa0WSuZEF9YAMJlW8c7qm81mnHjiiXjmmWfEN6hGrChxvWvXLmzbto3XNdxAERLEdTgcht1u513WnKogBCIp/mWpJB9wwHQpeCgUKvW1CtmPqFA9LMtifHwcNE1Dr9fP68EmBe7zs5RDbuc7k4t+36yh4LUtPk2cw2azIZfLIZ1efLWX1FAUJag0/AMf+AAikQh6enokskxBYXnj9/vxzjvv4JRTTuF1XSwWg9lshsuil7XfOpvNIpVKEVMSHo1GUSgUiAyYc+egVquFVqsl9hxcSaRSKfj9fjgcDkQiESJ9PYqi4PF4MDU1RcT7gZu+HgpVvynIbdFBrxZfEh7TXo90Os17oOq2bduwa9cu0e2pFUe8uE4kEtBoNLIP62IYBtFoVNABd/q6xUu2d7zr4/2ckUgExWKRmAN3Jul0GkNDQ/B6vWhqauK9B1uBHzN7rDs7O9He3g6WZTEyMkJcRnPmISe417mCo/uV7WuqfiqVSgWbzUZERJsT17x2YOr12LJly7I+5BQUpGT37t3YsGEDXC5+g0Pj8TisVitu29Una791JBKBxWIhIgNbLBYxNTUFj8dD3OwUlmUxNjaGbDaL9vZ2dHR08N6DrcCPmT3WTU1NaG5uxsjICDHTuWfCDQPkO6lbKhwOB5LJJPL5fFWP12toXH6y+K2xv/nHiKCBqpy4Js3HrBay7l5LQGj0OJFIlEoo5CSRSECr1UKv1/O+ttdfudTzrZHqI1gkH3CFQgHDw8Nwu92lyDafPdgK/Cg3vIwrjeN2kJKGxWKBTqcTfMhVGlZ2XDu/gJPNZuMtaqXAZDIJGi6y3CPICgpSIiSoXygUkMlkoNYZcNcL4u63BoBLT2qv+rGxWAw2W+UBjbUgHA6Doqglz8yQgkAggGQyWeqx5rsHW4Ef5YaX2Wy20lylcmu65IRbYer3+4kYYsptCeFTki3Fvus7dvdDozcdcVVzZCmnJSA0epxMJomY8sjt2RbC2UdX7jH/yxtjVT9fKBQCTdPEHLgcxWIRw8PDMBqN837PisAWn8WmgtM0jdbWVkQiESKysjPhsteBQIB3KRIADAUXL+Gu9P25mEwmsCwre2m4SqWC0Wjk3Xe93CPICgpSIkRcJ5NJ6PV6hNMM0nnxHfHeqeoye9lsFrlcjggfqFgslgZWyZ3smEssFoPf70dra+us6eWKwJaGxaaCO51O2O12DA0NESFiZ2I2m2EwGBAICJtzJDZWq5V337XY+64LRRaRvArJZPKIqppbMeJaaPQ4m83y3oktNizLIpFI8B5kxrGmwVaxZ2tzq71qW4LBINxuN1EHHNfrxLIsmpqaytqmCGzxqGbdlk6nQ0tLC8bHx5FKpWSwcmFMJhMMBoMg4d9Wt3g/daXvz4WiKJjNZiLekyaTibe4Xu4RZAUFqRBaMZdMJmEymSTrc1zjrS5QH4/HYTKZiJirEolEQNM0EfNvZpLJZDA6OoqmpiYYDPPv/YrAFpdq1m15vV6o1WqMjo4SF/Str69HKBQi4n1gsViQTCarDkJIte9ar9OhWCweUVVzK0ZcP/vss4Kjx3L3GnHCpNyNu1rOPWbxMffVTgznSkiECn2pCAaDSCQSaG1tXbRUXRHYS4fPHmuz2Qyv14vh4eGqe3tqRV1dHYLBIO/D97XBxVso9o1EedtitVqJ2IPJiWshEeRnn31WQssUFJYfzz33nOCKOZPJBA2twroGccVkg1UPu1Fb1WNJ2ZTCBfXr6uqICuoXCgUMDQ2hrq5u0Uo+RWCLQ7V7rCmKQktLC7LZLKampmpoYWWMRiM0Gg0ikYjcpkCn00Gr1Vbdoy7Vvusmp0lw1dyzzz5LXAClGlaEuPb7/Xj33XcFR4/lhisJX8qhUmAXjzxXOzGcxAMukUjA5/PNK8laCEVgC4ePsOZwOp2wWCwYGhoiyqngPlN8Re1/P1khQyvgo2E2m5HL5WTvEzMYDIIjyIq4VlCYzVIr5n68swdvCgjWLUYomUWmilJzhmGQTCaJENfJZBKFQoGoXmtuaKfBYEB9/eJDYwFFYC+VaoU1h1qtRmtrK4LBIKJRcT9DS4GiKMGBfSnguyXkym1dom8vuG1Xn+CquWg0ioMHD4prUA1YEeL65Zdfxtq1awVHj+WGmxq6FMSYGJ5Op5FOp4maEM4wDMbGxuD1enmV7ysCmz9ChDUwfZhwu+X9fr+UJvKCO+T49D9NRtNI5hc/EE9f7+VtC03TMBqNsr8XhfZdb926FS+//LJEVikoLE9eeuklbN26ldc1XMVcgaVw5/P9otuUZVj445WDZ/F4HHq9HlptdVluKQkGg3A6nUQNUA2FQsjlcgu2oZVDEdjC4CusOfR6PZqamjA+Pi5ovopU2Gw2FAoFIqaa890SIsW+6zuf7wOt1QuqmjvuuOOWpe9Bzp1sCbzxxhvYvHkzr2tI6bfOZrPI5/NLHigixsTwUCgEu91ORP8Vh8/ng1arFbTzUhHY1SNUWHOoVCo0NTUhEAjIPrhrJg6HA5lMpmqbKu24NvHYcT0X0krD+bBp0yaMjIwQFTxRUJCTbDaL/fv38/Y9uKC+P55FpiC++Kp2xzUpJeG5XA6JRIKovdbZbBY+nw9NTU28/SFFYPNDqLDmsNlsMJlMpZk8JKBSqeB0OnntmZYKTuNUOxdHijkQmQKLREElqGpu8+bN2LNnj6j21IIVIa737Nkj6IAjod86FovBZDItOWK71Inh3J5tkg64RCKBSCTCK3I8F0VgV2apwprDYDDA5XJhbGyMGIeCm3pf7WCzDLN49PurPHZcz8VisSCVSsk+4VRI37XdbkdXV9eyPOQUFKRg//79MJlM6Ozk16Mo9TCzanZcF4tFYsR1OByG2WyuquWrFnD7rO12u+CkhyKwq2OpwpqjsbERyWSSiOA1h8PhQCKRkH0WDUVRvErDpdh3rQLgsugFVc0p4lpGhIjrVCole9YaEKckHFj6xPBoNAqtVrukoWpiwpWDezyeJZetKQJ7YcQS1hxu9/RgPZIynE6nE5FIpCoH50c7Di/6fb47rmei1Wp5DReRCq7vmu+hv3nzZrzxxhsSWaWgsLzYs2cPNm3axCvwyzBMqWJOr6GxoUncdZc0Nd0zWYlUKgWVSiX7ec+yLMLhMFGtaKFQCPl8Hh6PZ0nPowjsxRFLWAPT/deNjY1ElYdrtVqYTCYiVpXy7bu+9KR2UV+/iOm+a6PRyLuycfPmzdi3bx8xv9dqWfbiemJiApOTkzj22GN5XZdOp2U/WAqFAlKplGjR40oTw585uLDgCYfDRGWtJycnBZeDl0MR2PPhhHVvby9uvvlmrF69GgaDAV1dXbjhhhvmDd966623cPLJJ0Ov16OlpQU/+MEP5j0nieXhBoMBGo2m4tCTwUACmcLi2Vy+O67nQkJpuEqlgk6nE3TILccIsoKCFAgJ6qfTaWg0GqjVamTyDN4ejYhqE8NO90xWgstayz24lAs0kpBBB5ZWDl4ORWCXhxPW2WwWX/va19DR0bFk32NmeTgpOBwOhMNh2cvVuYGq1ZZk906JnwC464V+UGotb7+ju7sbNE3jwIEDotskJcteXO/Zswdr1qzhVb7DsiwymQz0er2EllWGGygiVjlUpYnhf9s3UfbrmUwGmUxm0VUTtSSdTi+5HLwccglslmWRzWYRjUYRDocRDAYxMDAAYDqoEQ6HkUgkalouPDNjzfXi/PKXv8Q777yDH//4x7j99tvxzW9+s/T4WCyG7du3o62tDXv27MEPf/hD3HjjjbjjjjvmPTdXHj4xMSH7oQJMl0U5nc6KEeQnK/RbA8BxHUsL9lgsFiQSCdl/LgaDQRHXCgpLQIi4nul3+ONZZEWeHFRNvzXLsqUNJXLDZa3lFvkck5OTsNlsS56BMxM5BTY3ET4SiSAUCsHnmx5sy/kd0WgUmUympufRzIy1z+dDsVgUzfdobGxEIpGQvTqMw2q1olgs8i6FFhuapmEymar2e9d4xb83pPNFJBgVstksr88ATdM49thjl53vIW/DsQgIGWaWy+XAsiwR4lrMfdKXbGnFw28tLhAOTUSxpmG2iI5Go7BYLMQMMvP5fHA6nZJMMeVWfYyMjKClpUV0B4NlWeRyudLkdW6YFsuy0Ol0oGkaKpUKmUwGAEpTHPP5PPL5PDQaDQwGw6w/Yv9e5paCr169GmeffXbp+52dnTh06BBuu+023HrrrQCA++67D7lcDnfffTe0Wi2OOuoo7N27F//93/+NK664Yt5ruFwuhEIhJBIJIpw4q9WKiYkJFAqFBUvfncbFb4c2LQQPM+MwGAygKArJZFJUB06IHXzXl2zatAlDQ0OldX0KCkcquVwOb7/9tqDMNVcxx/VciznU7KhGa8V+62w2i0KhIOv9B3iv77uaNVe1IJVKIZlMYvXq1aI/NyewBwcHMTw8jNbWVtEnozMMM8vnSKfTyOVyUKvV0Gq1UKlUpcwlF+AtFovIZDKgKAp6vR4Gg6H0X51OJ3rQY24p+Jlnnokzzzyz9P2l+h5qtRputxs+nw8mk0n2oA1FUbDZbIjFYrJ/3iwWC6LRaFVblexGLRqsekzEMqK9vloF1FuNGA2pkU6neW1q4gL7n/nMZ0SzR2qWvbjes2cP7z2T6XQaer1e1g8ey7JIJpOiOqleW+U36x3P9+FHn9g062vxeJz3GjOpSCQSSKVSaG5uluw1pBDY+XwekUgEkUgEuVyudEDZbDZ4vV7odLpZhynDMDhw4ABaW1tL4rlQKMw6HEOhEAqFAiwWC+x2uyhlfNX2WM8dbvfyyy/jlFNOmRXwOOOMM/D973+/bM8cTdNwu92YnJyE2WyW/ZDjghbxeHzB/r5v/u3dRZ/jc9tWLdkOiqJgMpmQSqVkPWz1ej18Ph9Ylq36d+NwONDZ2Yk9e/Zg+/btEluooEAu77zzTqmMlQ/pdLp0/mhoFY5qtGLPcEQ0u/aPRZDJM4sK7GQyCaPRKPvaq0QiAY1GA52u8mRzqWFZFpOTk6irq5NssJoUAptl2dLg11gsBpqmS0F5u90Og8Ew64zn/I6WlpaS38FV1nGCPBwOY3x8HGq1Gg6HA3a7XZRER7U91kv1Pbgd07FYjIhqTIvFUppkLqcfZDabMTk5WZUdmTyDcLpyewkfCkXg9uf6cd4aPTKZDG9xfdttt4lqj9Qse3H95ptv4qtf/Sqva0jpt2YYRlQ7qlm/4YvO/sDk83lkMhnZo2rA9E3e5/PB5XJJPsVdLIGdSqUQCAQQj8dhNBrhdrthtVoFHZpqtRoWi2WWLdlsFpFIBBMTExgfH4fT6YTT6RT086lWWPf29uKnP/1pKXIMTJfLdXR0zHocN/BlcnKyrGB1Op0IBoOIRCJEDKyxWCyIxWJlbRkMJJCrUKIpVjWfwWCQvUxMr9eDYRjk83lejtOmTZvwxhtvKOJa4YjmzTffxLHHHst7mFkulyud+T/e2SOqsAamV97441m0OBce1prJZGT3fwBy+r6BaaGfzWbR1tYm6euIJbAZhim1mLEsW9rmIKQak8ta6/X60tnIVRVEIhH4/X6YzWbU1dUJzgZXK6zF8D1UKhXq6+vh8/lgtVplf3+ZTCYwDCP7506r1YKiqKrs8MezyOTFb1+4+8UBnL/hfbxb0jZt2oS9e/eiWCzKHhSsluVh5QIkEgmMjIxg/fr1vK4jod86nU7Py2YuFb2Gxqndi2egX+gLzvo7JwrlXkkGTPfW5PP5mmXRl9KDnU6n0d/fj8HBQWg0GqxevRodHR2w2+2i/k51Oh08Hg+6u7tL6yZ6enowMTFRsUf7G9/4BiiKKv1RqVRobW1Fd3c3NBoNKIrCwYMHZ10zNjaGM888E+effz4+97nPLcl27pCbmpoiYpCL1WpFIpEoa8uzB6cqXn/uxiZR7NDr9bIPe1OpVILsWL9+PQ4dOiSRVQoKy4ODBw8K8jvUanVpmNmdz/eLbpdeo6oYZCchucCyLDGrwLigvtvtrklr3FJ6sIvFInw+Hw4dOoRoNAqv14s1a9bA6/WK6tOqVCrYbDa0tbWhu7sber0eIyMj6OvrqyowPNf3MJlMOOqoo+ByuUpfk9L34AQ3CZO6VSoVzGaz7IN0uSBKNWe+26KDTi1+UCKVY5BkaN5+x+rVq5HL5TAyMiK6TVIhv6JaAr29vbDb7bzEGMuySKfT8Hq9ElpWGakOuGZn5ed8bcCP4zqmJ4vHYjFR+76FwrIspqam4Ha7axqZ4pvBLhaL8Pv9CAQCcLlcaGtrq8mBTFEUrFYrrFYr0uk0JiYm0Nvbi+bm5gXLa6699lp85jOfKZW8ZbNZNDc3zwqkzNzROj4+jlNPPRUnnnjivGEhXq+3NAyFg/v7Yp8lu92OQCCAcDgse5+uTqeDWq1GMpmc93tusS3ukNIA2l3iVHcYDAYUCoVF+79rAXfQ8imd6+7uxlNPPSWhVQoK5NPT04NTTz2V1zUzz3x/PCtqrzXHhkbboiXhXI+t3OI6nU6jWCzyKg2Vimg0CoZharotRUgGO51OY3R0FBRFoa2tDUajsSZZWY1GA4/HA5fLhXA4jKGhIdjtdni93gVt5nyPdDqNsbExuFyukq/FIaXvQVEUPB4PJiYmRE94CMFisSAUCsk+X8BgMJTm/SyGXkPj6CY7Xh8SNzih16jQXGfB8MAUryy0VqtFR0cHenp6JK8uEYtlnbnu6elBd3c3rxtMoVBAsViUvc+H6/sWm/M3V+5V/ukzvQDemyRJQvQ4Ho+DYRhZyoerzWCn02n09fUhHo+jq6sLHo9HliFwBoMBHR0dqKurw9DQEMbHx8tGv91ud2mSfnNzM0477TRs2LABa9euLf3hSoLHxsawbds2bN68Gffcc8+8m96WLVuwe/fuWbuRd+7ciTVr1iz6O6MoCvX19QgEArJPyKYoqlQaPpcr79+36LWXb20XzQ6apqHV8l9JITZ6vb7q1Rwc3d3d6OnpkcgiBYXlAed78CGbzZbOfG6YmdjsH4sik1+4oimTyYCmacn6iquFlJJwlmVLgfJaC7BqM9hctrq/vx82mw1dXV2yDOuiaRoulwtdXV3IZDI4fPjwgllst9uN1tZW6HQ6bNmyBSeccMIsv6MWvofVagVN07wHd0qBxWJBOp2e9W+Qg2q3hGTyDPaPif9zu2RLGyxG/azhetWy3HyPFSGu+ZDNZqHRaGSPZEkVPT6mpXL0NZObFjnJZJKYgSLBYBBOp1O238tiArvc4SZ3WwFFUbMOut7e3nkHXbU91tzh1trailtvvRV+vx+Tk5OYnHxv8vyFF14IrVaLyy67DO+88w4eeOAB/O///m9V8w6sVmupDFBurFZraUI7x2AggXxxceGv1Yj7vhSyCktsdDrdvH2ilVi9ejUCgQBCoZBEVikokA3DMOjt7RXke3CCQq+hsaFJ/GFLmUIR/vjCTivXEie3qCWlJDyVSiGXy83LqtaKSgJ7bkC/vr5e9t+dTqebFdyfmJiYZ3e1PdZS+h4URZWGm8kd2Fer1TAajbL7QFzmutLPQ6rKGrVKBYqiBPkeiriuIULEdS6Xk11M5vN5FAoFyQTaf561+FTjV/+v1IOUknBu13Ity7LKUU5gMwyDwcFBxONxdHZ2EnG4zYQ76JxOJwYHBxEMTvfUVyusgekocG9vL55++mk0NzejoaGh9IfDZrPhySefxMDAADZv3oxrr70W119/fdk1XHOZecjJjdFoLJVGcjy+f7zidR/f3CKqHdWWZ0mJVqstrSWsFrvdjvr6ehw+fFhCyxQUyGVoaAgAeJcnzvQ9MnkGb49GxDat4p5rEvqtc7kcMpkMEeI6GAzC4XDIuoZ0IYEdiUSICujPZGZwn5s/UygUAFQvrAHpfQ+73Y58Po9UKrW0f7AIWCwW2cU1N9SsUtZYqsqae14cRCbPQKvVKplrkhGauZZifzIfuGFmUt3QR8OVI0Kv9vuJiR6Hw2FYrVYihqrNFNiRSAQDAwOgaRqdnZ2yOyULwR10HR0d8Pl8mJqaqlpYAyj1ZZf7M5NjjjkGzz//PDKZDEZHR/H1r3+9ahsdDgdSqRTvG6rYcMNFZpaG/+iJxYWimP3WHCQMNdNqtaUd63zo7u5WxLXCEUtPTw+6urp4nVcMw6BQKJR8D388i2yF7QRCuGxrx6I91ySI63g8DpPJJKugBaZbBOPxuOxBfWC+wA4GgxgfH0draytxAf2ZcMF9nU6H/v5+RKPRqoU1IL3voVKpYLfbiai0slgsCw5UrRXVDjXTa2hcfnLnoo8RQjrPwB/PCs5cLye/Y9mKa5ZlcejQoWWZuZaq35rj45sqTzX+0RMHUSwWYTQuvLKjFhSLRUQiESIOOA673Q6Px4PR0dHShG252wiqwWg0oqOjA36/H4lEoiphXSu4NWMkTO80m82lSPZgIIFChcd/7cy1ottgMBhKFSxyQVHUERFBVlAQE6EVczRNl+7HTpMWtMh6iaaAK7ctvHe7WCzO6vuWi2QyScQgs3A4DKPRKLs/yMEJ7Gw2i4mJCbS2thKR/KgERVFobm4uTRSvq6uTfXjpTBwOB2KxmKxnLYBSQk3uoHq1LWlXbusS/R6lV09vMxDqdwwMDPAW5XJBvmJYAG5/7urVq3ldR0LmWuppndX0Xb8yHCOi9yoWi4GmadlF/ky4HZLcTSiRSMhtUlVww1k4By4Sichr0BycTifC4bDs/U/c75VlWfz+lYGKjz9jg0d0G0gZanYk9D4pKIiJGBVzt+3qg9iJa4YFQsmFP8vZbLYUUJMTEqaVsyyLcDgsywDVxYjFYmAYBjqdDoFAgIgVltWQTqdLa12j0ajsg7tmotfrYTAYZB9sRlEUEbNWqrUhlMyJfo/a0DS9zUCI39HU1FSqkFgOLFtxPTAwgPr6epjN1ZdrsixLTOZa6sPl7os2VnzMm+Py96HEYjHYbDbZRT5HsVjE0NAQNBoNOjs70dTUJGgPdq2Z2WPd1dWF9vZ2TE1NEZEp5uCyFXL3P+l0utK94LcvDi/6WClKwjlIOGiFRJA7OzuXzQGnoCA2AwMDs9YIVcPcfus7X6j9jmvO75DzrGUYBrlcTnZxnc1mkc/niZg5wxGLxUoZ687OTkF7sOWA67H2er3o6OiAwWDA4OAgGGbhqfW1xmazld0SUmtIaAerdqiZ26KDTuTU9TvjsVLPNdcqUy0qlQodHR3LxvdYtuJ6fHwcjY2NvK7J5XKgKErWNRRSDzPj+KejK5eG//ujiwsLqSkWi0gkEkQdcFNT0/v3WlpaQFFU1Wu65KTc8DKDwYDW1lZMTEzI3ufMwa3CkvvnqFKpoNPpMDQVQaaC3/LnK0+QzA69Xi/7UDMhEeTGxkZMTExIZJGCAtkI8T1mZq798Swy+drvuCah3zqdTkOj0cjerhSPx2E2m4lp98rn8xgbG0NTUxPMZnPVa7rkZu7wMq5EXK1Wz5r4LTcWiwXJZFJ2wU/KINNqhprpNTSObraL+tpczzXXIrOSfQ8y7iwCmJiYmDVRsBry+TzUarWskdtMJgOtVluTYR4fWm2v+JjXBvyS27EQqVQKKpVK9h4wjlQqhWAwiObm5lmHLskCe7Gp4GazGQ6HA2NjY7KXYnNYrVYiIsgGgwGPvlV5SrjLIp0zSkLmWqPR8C7ha2howOTkJLEOn4KClAj1Pbigvtuig17k1X4AsH8sUnHHtdxnLQkCHyBnFRgwfYaPj4/DbDbDZntvPRvpAnuhqeAURaGpqQnRaJQYf0mr1UKv18tuj8FgQDablVXkVzvUTIpd1zO3GQj1PRRxLTHj4+O8D7hCoSBr1hqYPmRr1fP0pe3rKj7mp8/01sCS8nCrwEgoCS8WixgbG4Pb7S7rgJAosKtZt+XxeFAoFIhYgwVMC/58Pi97Nt1gMKB/qvLBsViZ5VLRaDQoFAqyBj7UarWgA65QKCAQCEhklYICmRQKBUxNTS3J99BraHz2pA7RbcsU2EV3XNfS91gIEgR+oVBAKpUiRlxHIhGk0+my7ylSBXaldVtarRZerxdjY2OyZ4s5SKia46o25M5eVyNspdh1vb7BAg09LTuF+h7j45WTIiSwbMW1kOhxoVCQvRypljZUM9gsk5PHsWdZlqjo8dTUFFQqFdxu94KPIUlgV7vHWqVSoampCT6fT3ZBy9ljMplk//np9Xo8fCi56GPe32JdtMxyqWg0GrAsK6vzodFoUCwWeTltBoMBNptt2USQFRTEwufzgWVZeDz8hhzW4txfrOeaZVki/B8SMtfxeBwGg0H2RAswHfCYmJhAY2Pjgr8b0gR2tXusHQ4HdDodMecEJ67lruIjoe+aC+wvhhQ913uGI/jxzp6qbZiLkrmuAdwNiQ9cWbic1NqG/zxr1aLff3VInoFXXGkMCSs5uHLwpqamill0EgR2tcKaw2Qywel0YnR0VPaDBSCjNLwvUDly/IPzN0pqg0qlgkqlknVFCNeewteG5dT7pKAgFhMTE3C73bwywAzDoFgslu7TmTyDu1+ovKWAL4v1XHOfbzn9H1KGmZES1OfKwS0WS8W5M6QI7GqFNfBeeXgsFpM9mA5MB4VVKhWSycWD6rWwQ+7MtVqtrnjmS9FzDQB3vdCPTJ6pyoa5LCe/Y1mL6+VYFl5rG0bDlQcGPHfQVwNLZpNMJks3OzlhWbbkMFVbrianwOYrrDm48nAS1nOZTKbSKiy5OPcXLy/6fQpAZ730DpiQA0ZMKIoSXJ61XA45BQWxEOp3UBRVCmRJUW4JLN5zXSgUQNO0rOdtOp2GWq2WVeCzLItkMslry4xUJBIJpFKpqt9PcgtsPsKagysPn5iYkD2wT1EUTCaT7NtKSJi1Uo3fIUXPNQCk80X449kV73csW3EtpOeahMx1rUuzPr6p8tTwT//69RpYMhsSdl0C0wdGNput+rDgkENgCxXWAEol78FgUPZDjptWKVf0dv9oBJV+AldsFb8nshxCDhixEVqetVx6nxQUxGIpfgdXFeW26KBXi+96LdZzTUJJeDablf3MLxQKYBhG9r5vAAgEAqirq+P1e5FLYAsR1hwOhwPFYpGY7LXcwlav1yObzcpa3l+N3yFVEFCvpuC26JZUFi63D1sNy1JcMwwjeKiI3AdMrQV+NX3XQO2nhpPQewVMH3BOp1PQ9PZaCuylCGsOu92OfD4ve1lUtdMqpeJffvFSxcdccEJrDSwRJmzFRkj2fDlFkBUUxEKMWS96DY0NTbZFrhDGYj3XJCQWZk5Ml4t0Og2dTid7xVw6nUYqlYLTWZ1/NpNaC+ylCGtg+rx3uVxEDMAkpd8Z4N+KJbYNlYapStFzDQAbmuzQa2jBfkc+nydmQO9iLEtxHY/HUSwW4XA4eF0nd1k4N1Sk1jb87tObKz7mR08eroEl0xSLRSIy17lcDolEQtCBwVELgS2GsAams9dOp5OIG5NcfUeDgQTyxcWjnjSAdldtygblLgvnbOCbPXc4HES0GCgo1JJIJLJkvyOTZ/D2WERkyyr3XMstbElIbpAS1A+FQnA4HIJ/HrUS2EsV1hwOhwOZTEZ2YWswGFAoFGRvxaJpWlYb1Go1WJZd9H0jVc/1/vFoqeeam0dRLSaTCRqNZln4HstSXCcSCQDg1TfDvZFqsV96IbipwLU+YE5e5634mH8MhBFK8FvoLpRMJgOapmU/7CORCMxm85LtkFJgc8K6t7cXN998M1avXg2DwYCuri7ccMMNyOXe+50NDg6Coqh5f/7xj3+UHuNwOJBIJGQXdHKVZz2+v3Ip88PXnFQDS6YRsutRbGia5u2ckbDWREGh1iQSCd7DsBiGmeV3+ONZZAvilzVW6rmWW9iSkD0nQVwXi0VEo1HeQZq5SC2wOWGdzWbxta99DR0dHYJ9D5qmYbVaZRdFNE1Dq9XKLvLlrljj5i8s5ntI1XOd+b+ea+6euFJ9j2UpruPxOEwmE6/SHu4XKGc5UD6fL00IrjV3X7Sx4mM2fXen9IbgvV2Xcu63ZlkW4XAYdrtdlOeTQmDPzFhzQzh++ctf4p133sGPf/xj3H777fjmN78577qnnnoKExMTpT+bN79XuaDVamE0GmU/5LjMdS17ZxKZAr7/xOIVGjSA9U32mtgDkJG5VqlUvNeBWSyWUpBTQeFIQcik6WKxOOvMd1t00Gtq23NNgrCVO3vOsiwRe7aj0Sg0Go0odkglsGdmrH0+H4rF4pJ9D67aSe5VYiT0XZNw7leyQeqea87/X6m+h7x3W4HE43He0x5JENdyHi7/dHQTgL0VH/fCIR+2ruG3w5Mv2WxW9gOOWwUm5koOTqiPjIygpaVlSc89txR89erVOPvss0vf7+zsxKFDh3Dbbbfh1ltvnXVtXV0dvN6FqxVsNhsikQhcLpdg+5YKt8oml8tBpyvfJyg2m27aUfExXztzbQ0seQ8SBpoJyVybzeZlET1WUBATob7HvJ7rRhteF3kN5mI913ILW84GOQV+sVhEoVCQ3feIx+Ow2+2iJRc4gT04OIjh4WG0trYuyc+dWwp+5pln4swzzyx9X6jvYTQaQVEUUqmUrNPadTodstnyQahaQcK5X8kGruc6y4ibAOF6roGV7Xssy8y1kNKsYrFYKlWRC7kPlzsveF/Fx1x8j/STw0kZbKLX60UPtoiRwa62xzoajZYdiHLuueeivr4eW7duxUMPPTTv+0ajseZZ47lwK6BqFb3dPxpBrop7+BkbpA0szaWawSJSo1KpVmxploKCmAj1PWaeM3L1XMstbBmGkdUGOSsHZyJFabpYGexqe6yF+B4URcFoNMqeNZa7JHu52CBVz/U747FS+8pK9j2WpbgWGj2W+6Yqd2nWae9rrupxLxySdu+13Ac9IG3v1VIEdrXCure3Fz/96U/x+c9/vvQ1s9mMH/3oR/jTn/6ERx99FFu3bsVHP/rReYecTqcDy7JHVPT2nJ+9WPExelXtBplxVDNYRGqEHHBms3lZlGYpKIiJGL5HrXuuuUGqcp65nBMvtw0zV6LJZUM+n5fE91iqwK5WWC/F9yBhWjcpWWO5xXUlGzJ5BvvHpdhzzZTaV1ay77EsxbXQoSJyi2u5I7cA8IOzKg83kzp7LXeQAZB+sEm1Avv//b//N2sAiEqlQmtrK7q7u6HRaEBRFA4ePDjrmrGxMZx55pk4//zz8bnPfa70dZfLha9+9as4/vjjcdxxx+GWW27BxRdfjB/+8IezrudWYcm1Z5qjVtHbavZaA8Dr158huS1zUalUoChK1oN2JUePFRTERAzfo9Y918ViESzLHvHClhS/Q6vVSjZYl4/A/sY3vjHL9zCZTDjqqKPgcrlKXxPb95BrS8hMSMgaLweB749nkcmLH/Q3aOhS+8pK9j2Wbc+1kNIsOSeFA9MRZLkF/rENVgCTFR/33EEfPrhW/BJZudaRzbWhFoNNqunB/upXv4rPfvazYFkWk5OTyGazaG5unuUEdHZ2lv5/fHwcp556Kk488UTccccdFW04/vjjsXPn/EF13FAPsQa6CaFW0dtqstaPXXMSzPra3w45J2a5loWzLCurw6ygUEvE8D30GhqfPakDv9jVJ6ptNAU4Tdp5X+fuLUe6sJXb7wDea0eTkmp7sK+99lp85jOfQTqdxtjYGFwu1zx/QGzfw2AwIJfLzZugX0vUajWKxaKs1awkCPxKfofTpAWtAhiR9fWlJ7WX2ldWsrjm9c763ve+h+OOOw4WiwX19fX46Ec/ikOHDpW+v9A4foqi8Kc//an0uOHhYZx99tkwGo2or6/Hf/zHf8x7o337299Gc3Mztm7dip6enlnfSyaTMJlMvP6hJJSFk+CIFotF3PWJoyo+7tO/liZ7TUIUPZvNgqKomgzSqpTBdrvdWLNmDcxmM5qbm3Haaadhw4YNWLt2bekPN/xrbGwM27Ztw+bNm3HPPfdU9X7eu3cvGhoa5n2dlImZUkdvq8laq1DbCeGkIWRauMlkQrFYrKq1IB6P48tf/jLa2tpgMBhw4okn4rXXXit9n2VZXH/99WhoaIDBYMBpp52Gw4dnT3V/+eWXsXHjRrS3t+Ouu+7iZavC8oYUvwMg2/dgWOC2MoKdBHFNQnJD7tJ4YHpTSi1WgVWTwXa73WhtbYVOp8OWLVtwwgknzPI7pPA91Go1NBqNrL4HTdOgKErWzLEQUVlrbtvVJ7qwnotQ3yOZTFZ8nNx+B687/nPPPYerrroK//jHP7Bz507k83ls37699A9taWmZNYZ/YmIC3/72t2E2m/HhD38YwHSJ1Nlnn41cLoeXXnoJv/nNb/DrX/8a119/fel1XnzxRTz66KP4+9//jgsvvBBXX331LDuElFeTIGxJsWHL2vqqHvvagF/01y8UCqXyZ7nIZDLQ6XQ1+10sJrCr7bHmDrfW1lbceuut8Pv9mJycxOTke1UIv/nNb3D//ffj4MGDOHjwIG6++WbcfffduOaaa+Y9Hwll4bXIXP/LL16q+JjfXrReUhsqIXfmWsjngHufVnMwXn755di5cyd+97vf4e2338b27dtx2mmnYWxsDADwgx/8AD/5yU9w++2345VXXoHJZMIZZ5wx6/152WWX4Vvf+hZ+//vf43vf+x5GRkZ426ywPCHF7+CeZ6m+RybP4O4XBoT8KCpy1wv98/qu5by3zLRBbt+HFHFdq2nllQR2tT3WK833oCgKNE3LmjmW+8yvZEMmz+Auie5R97w4WLpHCfU9loPfwetO88QTT8z6+69//WvU19djz549OOWUU0DT9Lwx/A8++CA+8YlPlIaAPPnkk3j33Xfx1FNPwePxYOPGjfjOd76Dr3/967jxxhuh1WoRDofR2NiIY445BoVCAb/+9a9nPScJWejlDEVRuPuijfjsfXsXfdz5v3wVr33zNLit4mV4udJ4OQ9aOXrf55aIG41GANOlVtlsdlFhDQA7d+5Eb28vent70dw8ezDdzBvkd77zHQwNDUGtVmPt2rV44IEHcN555817Pm4FgpxOj0qlkvSA+UdvAPni4s9PAej28MtEiY3cB62Q1+fuv5Wi7+l0Gn/5y1/w97//HaeccgoA4MYbb8TDDz+M2267Dd/5znfwP//zP7juuuvwz//8zwCA3/72t/B4PPjb3/6GT37ykwCmM4abNm1CfX09HA7HsigLUxAHUvwOQBzfQ6r9sQCQzhfhj2fR4jTO+rrcm1I4G+SEhLa8Wvsec0vEm5qaAEzfl4eHhysKa0A630NOpPY9KiH3mV/JBn88i3SZ4YhiwA00a3EaBfsey8HvWNKdJhqdniRXbiQ/AOzZswd79+7FZZddVvrayy+/jKOPPhoez3v9vGeccQZisRjeeeed0t8zmQyMRiPOPPNMfO9735v1vEIOOLnfyJwNJBwwALf3ujLH3fyUJK8vJ3L9HsplsKsR1gDwmc98BizLlv3D8elPfxrvvvsukskkotEoXnnllbKHG/CeOFqpB0wiU8An73yl4uPuOn+V7O9JEg5avlQrrguFAhiGmZetMRgMeOGFFzAwMIDJyUmcdtpppe/ZbDYcf/zxePnll0tfu/7667Fu3TrYbDaccMIJWL9e3moDBfmQy+8AhPseM88bt0UHvVoakVdu1zUJ9xbFhvdsqLXvMTODPTo6CgBVC2tAGt9DbnEt95nLvQfktmGh15fyHjVzoJkQqnn/kOB3CA6hFYtFfPnLX8ZJJ52EDRs2lH3MXXfdhXXr1uHEE08sfW1ycnLWAQeg9HeuzESj0eCJJ57A1NQU7HZ7qe9j5msD1ZUkLuUaseEyhXLawK39YRgGv/rXo/G5B96ueM0T+8Zw+obKU8argfu3y/kzYBgGFEXJYoPFYoHX6y0dcg0NDbLYwn0eCoWCbL1wUn4eNt64o+JjdKrprDX3eZAThmFks0HI74E7lCtdY7FYsGXLFnznO9/BunXr4PF4cP/99+Pll1/GqlWrSvf8cmfCzLLDyy67DJ/85CeRy+XgcDiqtlNhZSGn38G9vpD7xcxrNCrgs1vb8Ytd/byeoxqOarBCo5r9uZTzvONQ/C/5bWhpaUFf33RPvtVqhd1ul+1nIed5R4INMz8PciXcFnsvalTAUY1W7BmOiP66l57UVrpHzdQjfKhU0k+C3yFYXF911VXYv38/XnjhhbLfT6fT+P3vf49vfetbQl8C9fXle4MpikIsFsOBAwd4P6eQa8QkHo9jampKVhsGBqZ7KRqqHJz5b/fvxSMXt4lqg9y/B+C9DIicDA4Oyvr65Yb21Bqx3wv9oRSq6ab6zceaEIlEAADBYFBUG/gyPDws6+sD/H4PXL9rNVm83/3ud/jsZz+LpqYm0DSNTZs24YILLsCePXt42WcymXgPk1JYWcjpdwDTvkdvby/vntG59/lt9QxuA6paEciHt0cj2Pv2O9CVyTqRcObKbUMymZzlPMsBJ3DlJBKJlM4+uQiHw7K+PpfgkJO5q87koNxnMlso4u3RiCSvd6IzO+81/f7q5zsFg8GqttzI7XcIEtdXX301HnnkEezevXteDwbHn//8Z6RSKVxyySWzvu71evHqq6/O+prP5yt9rxpUKhXMZjPWrVtXtc2RSASxWAytra1VXyM2Y2Nj0Ol0cLlcstnQ09ODlpaW0sTKX/2rtars9QTrwD+tX3r2OpPJYHh4GN3d3Ut+LqGEQiGkUqkF37tSwbIsxsfHkclk0NTUVApytLS0lHoDa0WhUMDhw4exdu1a2SKnsVgMoVAI7e3toj7vOf/5RMXH/PLCjTjuKC/Gx8eh0WjgdrtFtYEPhw8fRlNTU6kPv9Zks1kMDAxg7dq1VV/DBaaqEdddXV147rnnkEwmEYvF0NDQgH/9139FZ2dn6Z7v8/lmTZb1+XzYuHEjv3+IwopGbr8DmH6/d3Z2oqurq+pryn2+R0IpsBDfuc8Vgbqmjlk917lcDv39/bw+32ITCoWQTCbR0tIimw0jIyMwmUwLthPUgkOHDqGtra1mQ8040uk0hoaGUFdXh0AgAK1WC41Gg+bm5pr3oU9MTICm6UWDWFLT19cHj8dTc7+LI5/Po7e3V1b/KxwOIx6Pl9VEI6EUckVphobSzkas65huRxCiiex2e1XVlnL7HbzENcuyuOaaa/Dggw9i165d6OjoWPCxd911F84999x5TuuWLVvwX//1X5iamip9uHbu3Amr1Vp1PTs3jIBPOSt3A5FzHQQ3yEtOG7hJ3ZwNpx/bClQhrj933168cd3pcJrnl8rxgaZp3r87sZHDBm4qeDabRWdnZ+mG2tjYiLGxsQX3YEsFV4Yj5/RUbsiOmL+HalZvAcAZxzSVbJj5eZALmqZls4F7XT6vz71/+ThmXAQ4HA5jx44d+MEPfoCOjg54vV48/fTTpUMtFovhlVdewZVXXln9P0JhxUKK3wEIO8PL3WO8diN0agrZgri5a4OGhtdunPVapJy5M/8rByT4X3LYkEqlMDw8DK/XC7vdjkAggPb2doyMjGBsbGzBPdhSIud5R4INJPhfi70XvXYjdDSFLCN+T/i6xvfEsVD/azn4Hbw+UVdddRXuvfde/P73v4fFYimN45+7s663txe7d+/G5ZdfPu85tm/fjvXr1+NTn/oU9u3bhx07duC6667DVVddVfXeYaEDEeQeaCH3EAXOhrm89s3TyjxyPpu+u3PJr8/97uT8OdRiv/JMFlu3ZbPZFt2DLRX5fF72tSQMw4h6qCcyBXzkZy9WfNyfrji+9P+kDBmU0wYhr8/df6v5/e3YsQNPPPEEBgYGsHPnTpx66qlYu3YtLr30UlAUhS9/+cv47ne/i4ceeghvv/02LrnkEjQ2NuKjH/2okH+OwgqDFL8DEM/30GtoHN1k5/08lTiq0Qq9ZrajSvrwpFpBwiCtWvseC63bqmYPtlSsRN+DL3Kf+ZVs0GtoHN1sF/01G6x62I3vJeiE+h7Lwe/g9e667bbbEI1GsW3bNjQ0NJT+PPDAA7Med/fdd6O5uRnbt2+f9xw0TeORRx4BTdPYsmULLr74YlxyySW46aabqrZDo9Egm83yMV320fskMfdG6rbqcOcF76vq2hcO+Zb02nz240qFXq9HNputyYFSzR7rxfZgS0U6nS61BshFoVCARlNl438VbLqp8hAzGsBxne+VIJF+yJH6+rlcDgCq+v1Fo1FcddVVWLt2LS655BJs3boVO3bsKF37ta99Dddccw2uuOIKHHfccUgkEnjiiSdqXjqpQCak+B2AeL5HJs/g7bEIr+ephv1jkXl7rklBbv9LrVbLutsYmPY95gaFpKLSHms5BDbLsrL7HtwQLzF9DyE2kOx3ZPIM9o+JP5MonMrNuj8J9T2Wg9/Buyy8Gm6++WbcfPPNC36/ra0Njz32GJ+XnoXFYkEikeB1DQlRSxKitwstYD/tfc3A/fsqXn/xPa9j8JazBb8+VwIiZ/RSo9GApunS2hWpqEZYc8zdgy11ibjcBxwwLa7Feg/sH40gV8XH++FrTppng9zlaYC8O2CFRPHj8Th0Ol1Vh9wnPvEJfOITn1jw+xRF4aabbuItdBSODEjxOwDxfA9/PCt6STgAZArsvD3XMzPXct1nFvI7aolGo+H9uxMbg8FQkwB6JWHNMXcPttQl4rlcDizL8qoWERsuwCJn9px0v8MfzyJTEF8vZQrFWfcnob7HYq1BHHL7HfLVRSwBs9m8LMU1CTYsFr2ttjz8uYNLz17LGUGmKAoGg0HSCDIfYc1Ryww2CeJazADLOVWUg+tUwPo5pZhiZ8/5wq2ikFNcF4tF3gd9IpGQbRiMgoJciOV7uC066DXiu180BThNs+eiVLuTXkrkPvNJsYHzO6RMslQrrDlqmcFOp9PQ6/WylmTn83nQNC3rmSu33wFgUb/DadJKIg4NGtWsHdcr2fdYluLaYrHwFh+kC9taodFoFuz5cVt1+N2nN1d8jk//+nX4Y/xK42ZS676jckgproUIa45aCGyGYZDL5WQvuxXrgKl2iNme688oa4PcEWxA3ih6tT1MM4nH4zUdwKegQAJi+R56DY3PnlQ5+8IXhgVu2zV71ZNKpYJKpZLV99BoNGAYRnaBL7ffodfrwTCMZHbwFdYctRLYmUxmxfgdS7VB7r7zxZIbt+3qgxTvgEtP6pg1E2Il+x7LUlwLjR5zS8vlggRxXcmGk9dVt5bkuJufEmyDRqOR/ecglbheirDmkFpgZzIZqNXqFXHAVDvE7LFrToJZP/u1OGdPbnFN07SskXwhB1wikVgWB5yCgpgsxfeYS0EiAXPXCwPz+q7l9j247JTcAr9QKMjqA6pUKsn6roUKa45aCGwSKuZIF7a1YqEgQybP4M7n+6V5TWb2e2ol+x7LUlwLiR5zN3e5xbXckdNqbKgmew0A7d94FKFEThIbpMZgMCCbzYpqhxjCmkNKgZ1IJGTbqcxRLBZFiSAfc2N1Q8zmloMD04eL3KtZSDjohUaPl0NploKCmAj1PeYKlUyewW9fGhLTtBLpPAN/fHZlmdxnLkVRsgt87j5LQmA/mUyK+pxLFdYcUgpshmGQTqdl9z2qHYglJSRnz6XqtwaA3/5jaFbgbyX7HstSXJvNZkGlWYC8fUckRE6ryRpXm70GgM0C1nNJ3e9cDRqNBiaTCdGoOBMRxRTWHFIIbJZlEYlESgPU5CKTyYCmacE/p0SmgHXferyq0qW5Q8w4uMNFzt4rEiLYK7k0S0FBTIT6HuUGmknlwKpV1Ky+RoCMarHFWtJqgUqlgk6nk933sNvtiEajovmiYglrDqkEdiwWg1arlXWYGUBOabrc5/5CvofbogMtkUuUyRdLgT9u3sxK9T2Wpbi2WCzI5XKldTDVwDnQcvf8cG8oOW2o5pB947rTq3o+FvwHnNVyFdZi2O12hMPhJQc7pBDWHGIL7GQyiWKxKHvkjysPEyps33/TDqTzld8/5YaYcZAgbEmIYAuZ2LlchoooKIiJWNPC3RYdDJraVczInTUmxQa9Xo9MJiOrDUajETRNi3Keiy2sOaQQ2OFwGHa7Xfa1kySUpsvte7Asu6jvIdXvyKilS4E/7j21Un2PZSmuuR8sn0OOK/+U8+auUqlAUZSs0VtuJUalm6XTrK169zXfAWdarRYURfHeFyo2NpsN+XweqVRK8HNIKaw5xBTY3AEnZ48vsLTeq/2jEWSqPOvLDTHjIEHYkhDBZhiGtw3LJXqsoCAmQjLX5fwOvYbGZVvFH2gGAIUiS1xZOCk2kFA1R1EU7HY7QqHQkp5HKmHNIabAzmQySKfTslfM5fN5MAxDROZaTt+DmwGxUFl4oShNde1nZww042zg05bHsqzScy0lJpMJOp0Ofr+f13Vyl0ZRFCW7DXz6jk57X3PVz3vczU+BqfIDWYtVWNWgUqngdDoRCAQEXV8LYc0hhsDO5XKIxWKSHMR8EVqaxRTZqgaYAeWHmM2EBGErdwQbEPZz8Pv9cLlcElmkoEAmLpdLNL/jym1dkpRfqlXUvHVccvsdpNhAgt8BAE6nE6lUSrAtUgtrDrEEdjAYhM1mk/2sy2Qy0Ol0sg8QlXuQaj6fL20RmItUa7jUKgpXbusq/V1IW14oFEKxWFwWvseyFNcURaGxsRETExO8riOhLEnu6C0n8Kstqa929zUAdH3zsaoz2KQccnV1dUgkEryz6LUU1hxLFdjBYBAWiwVarbbygyWkWCwik8kIylx//a97q3rcYuXgHMpgk2mECPyJiQk0NjZKZJGCApmI6XeEkjkwEiSICkV23jouuf0OALz8DqnQ6/UoFAqy/yzUajUcDgeCwSDva2slrDmWKrALhQIikQgRgoiEkvBcLkfEINWF/A6p1nAViixCyfc+/0L9DovFopSFS0lDQwPGx8d5XUPCAUOCwOfTd+S26qruvwaqX9FFirjWaDSw2WyYnJysuvdaDmHNIVRgZ7NZhEIhuN1uCa2rDm6YGV9ROehP4s+vV/eZX6wcnIOEg5aE7LkQgT8+Po6GhgaJLFJQIBOhfke5QaZuiw56tTQu2F0v9M+ayruQDbWE8zvktIGmaSKGmgHTgf1oNMqrLa3WwppjKQJ7cnISZrNZ9lJsgIwzf6nzZsRgIb9DyjVcerVq1qDFle53LGtxzTeCTEJZEgk28BW2TrOWl8B+5p3KvxeTyYRMJiN7sAMAvF4v0ul0VZPD5RTWHHwFNmez0+mU/WAB3lulwOdwSWQK2PajXVU9dv+NZyxaDg6glL2Q88DnhorIKa6LxaKgnuuJiYllc8gpKIiFEL9joVYsvYbGhiabaLbNJD1jKi8w7XfIPUxVr9eDZVnZs9cmk4n3UDop0Ol0cLvdGBsbq+r3Ipew5hAisOPxOGKxGBFnRbFYRDKZlD3rSYLAXyhrLOUWgw1NtlK/NSAssbCc/I5lK66VsnDhCMkaO81a/OZTm6p67Gd/9wbeHY0t+hi1Wg2DwSD6DmchqNVqNDY2Ynx8fNHfDQnCmoOPwA4Gg2AYBh6Pp0bWLQ7fYViJTAEbqthnDQBPf+WUisIamD7gNBqNrL9DhmHAsqysZeHc/ZDPz4FlWaUsXOGIpLGxEYlEgpc4U6lUZYeaZfIM3h6LiGzhNDSFWX3XJAxTpSgKer1e9qyxxWJBLBaTNYPO4Xa7QVEUpqamFn2c3MKag4/AZhgGY2Nj8Hq9sreiAdMDkDUajey2pNNp2bP4C2WNnSatZGu43hmPzaqmWentaMtWXAuNIMstbEkQ+AaDAblcrjStr1o+eFT1EaOzfvY8QonFI9RWq5UIcQ1M22KxWDA+Pl720CVJWHNUI7Cz2Sx8Ph+amppknxAOTPcbZTKZqsU1U2SrFtZ1RhpdnuqeV2jPt5gUCoUFh4rU0ga+Q0UikQiy2eyyiSArKIiFw+GATqcTxffwx7PIFqQReAyLWX3XFEUR43vILa5NJlNp7ofcUBSF5uZmBIPBBcvDSRHWHNUK7ImJCej1ejgcjhpbWB4uqC/3KjBSfI9yPuxtu/okmQMBAOk8M6uaRkhZuJK5rgHLtSxcp9Mhm83KGjVVq9VQq9WCDhc+5eGbvrtz0QFn3M5QufddczQ0NCCdTiMSicz6OonCmmMxgT2zHNxkMslk4Wzi8ThMJlNVwzwSmQLWXPdY1c/9wjeqf2+SUJqVzWZlj6ILLc0ymUzLYh2GgoKYUBQFr9criu/htuig10jngt31wsCsTJFWq5V9/SUJe6ZVKpWglWpSodfrUV9fX7Y8nDRhzVFJYHPl4E1NTbKKWQ6WZRGPx2G1WmW1g/v86XS6Co+U3o65vkcmz+CuFwYke02Dhp7Xc62UhROIkMEi3AEnd98RwzCyi3yhEWSnWYt912+v+vHH3fzUghlsnU4HtVpNRP8T8F55+MTERMkBIFlYcywksH0+H1Hl4AC/kvBN396Batt/9t94Bgza6qdvkiCuSbAhl8vxFvjLaaiIgoLYCPU95vYa6zU0Lt/aKaZps5ibKTIYDLILW87vkLskmysNJwWXywWVSoWJiYnSz4ZUYc2xkMDO5XIYGxtDQ0OD7JswOLj3nNFolN0OuYeZFYtFZLPZeb6HP55FOs+vmpUPl219b8c1N3thJfsey1ZcNzU1YWxsjNdNWq1WQ6VSyTpQQ6VSETGtcinlWTajhteKrk3f3Yloan45PkVRRJWGA9Pl4S6XC4ODg8hms8QLa465AjsQCCAcDqO1tZWIcnBgugcrmUxWFNdMkcXX/7wPuSo/2v/4xoeq6rPmIGGYGSB817eYCMmej42NLZu+JwUFsWlqasLo6Civa3Q6XVm/Q6pd18D8TBEJJdk6nY6IoWYWiwWZTEZ2OzgoikJLSwsSiQR8Ph/xwppjrsDOZrMYHByE1WqF3W6X27wSsViM9xBVKSDhzOe2tcz1Z6XcXlBux3WxWFzRvgcZXrcAOjs7SzeiaqEoiojSKBIOuaXawHdF1/tuerKswLbZbIhGo8SUhgPTQ0asVit6e3uRTqeJF9YcnMAeHh6Gz+dDe3u77DfymUSjUeh0uoolUf/12Dt44PXqnNfHrjkJXju/f2Mmk5F9mBnLssRkrvmWqB0+fBirV6+WyCIFBbJZtWoVent7eV2zkN8h1a5rADiq0TprOq/BYEA2m5X1rFWpVEQMNVOr1TCbzfNawOREq9Wivb0doVAIAwMDxAtrDk5gFwoF9PX1wWAwoKGhQXYhy8GyLCKRCGw2aSbz84GEM3+h7LmU2wvm7rjO5XLQaDS8Ej/ZbBZDQ0PLxvdYtuLaaDSipaUFPT09vK5bKIJcS0joO9Lr9YKGms3EadbisatPrvrx77vpyXkl4gaDARqNpqo1WLWkUCiAoqjSqqLlwsx2A7lbD+YSDofhdDoXfczB8SjufmGoqufT08D6JjtvO1KplOwHXD6fB8Mwsgc/hGSue3p60N3dLZFFCgpk093dLdjvKLvrWqK+6/1j0Vk915wzqyQXpnE4HAiHw7KXqM+E2yDBTXYnybbFKBaLKBaLoChK9n3qc+HaDuWeEVIsFokS13PJ5BnsH5PGD59bRSPE7+ACN0rmugasWbOG9yGnZK6n0Wg00Ol0S+53Xt9sxe8+vbnqx2/67s5Zf6coCk6nE6FQaEl2iAXXY53NZrF69WrY7Xb09/fL/vuqBMuy8Pl88Pv96OzsRFNTU9V7sGtBOp1GNptdMHqczjE4/uancOZPXqj6OV//1hmCbEkkEkTsutTr9bKW7HOzH/hmrhVxrXAkI9TvYFl23sRwKfuuM4XZu65JWYVFyp5pi8WCYrFIhC3Aez3WXq8XXV1diEajs3qwSSWbzaK/vx8mkwmrV69GsViseg92LQiFQnA4HLJn0lOpFGialn2Y2ULiWsod1zP7rQFhFXM9PT1YvXo1MW2OlVgeVi7AUiLIcqLX60t9n3JitVpFGepx8jovr8e3f+PRWSXidrsd2WxW9kN/7vAyjUYDr9cLt9uN/v5+TE1NEXnQZbNZDAwMIBqNoqOjAwaDgdce7FoQDAZhs9kWnBK+9eYn4VtksvxcDtx0Jq8+a45CoYBUKiV7FJuE3qtcLlfawVstxWIRhw8fVsS1whFLd3c3xsbGeIkyriWtln3XahU1a9c1QEZg32w2I5fLyZ7kUKlUxAT25/ZY63Q6dHR0IJVKoa+vT/bfWTlYlkUgEEBvb2/J31Cr1VXvwa4FuVwOiUSCiHVgJKwC44aZlfM9nCatJILw2BYbvnL6bH/hSKiYOyLFtdw3dS56JfcN02KxIB6PiyIY37judPC5ZcwsEadpGna7HcFgcMl2CGWxqeAulwudnZ2IRqPo6+uTvaSfY+bhZjAYsGrVqlk3TVIEdqFQQDQaLds/ls4xOO67OxHMVH8I850MPpN4PA69Xi/7FFMSysO46DGfw358fBzpdBpdXV2VH6ygsAJxuVyw2+28+64X8j2k6rsuFNlZu64BMsQ1TdMwmUxEBH2dTicSiYSsPuFCw8u0Wi06OzthsViIC+5zAf1QKIT29nZ4PJ7SOVLtHuxaEAwGYbFYZF95ybIsYrEYEUF9mqbL+j+37eqDFL+pA5Nx5JnZzyw0c62I6xohRFxrtVoiVmGRUJ5lMBigUqmQTCaX/FxOsxYDt5zN65pN392J8fD0z6Curg7RaFSWbH4167YMBgO6urpgsVjQ19cn+0HHHW7BYBDt7e1oaGgoWy5DgsAOhUIwGo1lo6Vbb34S/gVWtZVj/41nCMpYc5Cw65KUYWZCo8ft7e2yl7YpKMgFRVGCfY9yIk7Kvuu7Xuif1Xet1+tlH2oGkLMKS6PRwGq1yhbYrzQVXKVSwePxEBPc5wL6fX190Ov1WLVqFUwm07zHkSCwGYZBOBwmYihcNptFoVAgoh2t3DCzTJ7Bnc/3S/Kamfzs9hSha7gUcV1Duru70dvby2vgFBe1kTv7SMLOSYqiStlrseCbwT7x+8/gm3/bC7VGC4vFAr/fL5ot1cBnj/Xcg663txeRSKSmIjufz2NycrJ0uK1evbrs4TYTOQV2oVBAIBCA2+2e9fV0jsGmbz/BK2O97/rtSxLWXH+d3NFjUoaZCSlNX24HnIKCFAgR1wsNMpWy7zqdL2Iy+t5rarVaUBQle/WexWJBKpWSPckBTFcihMPhmrcL8lm3NTe4Pz4+XlN7ucxrf38/gsEg2tra0NjYuGj/q9wCOxAIQK/Xy77bGnhvFZjc/cILBfUnoxnJ+q2N2vnDzLg2GT4sN99jWYvrtrY2AMDg4CCv60gQtiSUZwHiloYDwjLYv//HGP79gb3weDwIh8M1O/j5COuZcAed0+mEz+dDT08PAoGAZI4Cy7JIpVIYHR1FT08Pstks2tvbKx5uM5FLYAcCARiNxlkR23SOwbrrn0AoXX1Q7MBNZ8JmXFopdzKZLK2CkZNMJgOdTkfsQbsYy+2AU1CQgu7ubhw6dIjXNZzfUe6s/crp3Ti2RZo1OH94dbj0/xRFEeF7aLVa6PV6IoaJGQwGWK1WTE1N1ew1heyxnhncZxgGhw8fxvDwMBKJhGQBfoZhEAwGcfjwYYyPj8NqtS6YrS6HXAK7UCggGAzC6/XKPsgMeK/fWm4WCqj/4bXhMo8Wh8+eNHuYGTfMlc/vJRqNwufzLSvfY1mLa7VajTVr1mD//v28riOhJJuUoWZmsxn5fF50Qbvv+u28Hv/gvnGsueEpUFpTTQ45ocKaQ6VSoa6uDt3d3fB6vYjFYjh06BCGhoYQjUbLrl3hQ7FYRCqVgt/vx+HDhzE4OAiVSoVVq1ahra1NUDS21gI7n88jGAzC4/GUvsYJaz4cuOlMwT3WM+FKwuU+bEkoCefuPXztePvtt7F+/XqJrFJQWB6sX7+et9+h0+lKJZFzyTNFvDshzT35t/8YmlUaToK4BsgpDQcAj8eDaDRak6SLEGE9E4PBgJaWFqxevRoajQYjIyPo6emBz+dDKpVakoDlJtrHYjGMjIzg4MGDiEQicLvd6O7uhtvt5jUAE5BHYE9NTcFkMhGRtS4UCkin07K3oxWLRWQymXlnfibP4DcvVbf+lC80NT2wcSZC/J/9+/fD4/FUXOVKEsJrLAlh8+bN2LNnD/75n/+56msMBgMikYh0RlXBzKFmcg5XUqlUMJvNpUFPYmEzavDGdafPW71VibPuegcPXtAKl8slmQBZqrCeCUVRsNlssNlsyOVyCIfD8Pv9pcERer0eBoMBBoMBer0eNE1DpVKVhDe31zKXyyGdTiOdTiOTyZSuNxqNqK+vh9VqFSXTabfbAT+yS7UAAJJlSURBVAAjIyNoaWmRNJo6NTUFq9Va+j3KKaxZlkU8HidiR2IqlZL9oOXuO3wcJZZlsWfPHtx8880SWqagQD6bN2/G/v37ebVWzFyFNXdmgT+eRVaissxUjoE/nkWLc1poGAyGmrdflcNqtWJgYADFYlH2Kh6tVguHwwGfz1eqiJSCpQrrmWi1WjQ0NMDj8SCRSCASiSAUCoFhGOh0ull+h1arhUqlKgnbYrEIlmVLgovzPdLpdOl6i8WCrq4uUfxCTmAPDg5ieHgYra2tkv3OOT+MlKGb8XgcBoNhSX6mGCw0zMwfzyKdr76KkA8MOz2w0aR779+eTqd5i+Q9e/Zg8+bqV/6SwLIX15s2bcKOHTt4XWMwGJDL5cAwDO8onJhwEzPldrQtFkspOikmTrMWg7ecjfZvPMrrun+5fxi//jiFbcdtENUeQFxhPRetVguPxwOPxzPr0MpkMiXBPReub4+m6dJB6Ha7YTAYoNFoJMmy1kJgZzIZRCIRrFq1CgBwcCyKM39a/Q5rQDxhzdnDMEzV5WxSwTAMUqmU7CK/XAS7EoODg4jH4zj66KMlskpBYXnQ1tYGi8WCt99+G8cdd1zV13FZY+4ezOG26GDQ0JI4uXNXcplMJoyMjCCfz8sa2Nfr9VCpVEilUrIPegKA+vp69PT0IJlMSnJOiCmsZ6JSqWC1WmG1WsGybClTmk6nEY/H4ff757WszWxp4IS4xWJBfX196fciNrUS2D6fDzabTfb2Lw4SpoQD0yLfbDbP8ymdJi1UFFCUoLPAoFHN6rdmWVaQ76GIaxnYvHkzbr75ZrAsW7UQUavV0Gg0SKfTst7UrVYrRkdHedkulR0TExPIZrOSTAHed/12bP7uThR4fHo/85chWB4awqvfEk9gSSms56JSqWA0GmeVJbEsW4oWFwoF9Pb2oru7G2q1GhRF1fQ9IKXAZlkWk5OTcDgcKFJqvP87OxBIVt+PTgF4V0RhDQCRSAQWi0X2DEkymYRGo5F92raQ0qw9e/Zgw4YNxDgtCgpyQVFUqWqOr7gOh8Pzvq7X0Lhsawd+9iy/9V7VwK3k+vcz1gCY9n+MRiPi8bisZZZc1Vc4HCZCXKvVatTV1WFychKdnZ2insdSCeu5UBQFjUZTmoLOwWWqC4UCDh8+XCopr7XfIbXATqVSiMViWL16tWjPuRQKhQISiQS8Xq/cpiAej8Plcs37+m27+iQR1gBw2dbOWf3W3DAzvv7Pnj178LGPfUxs8yRlWfdcA8DGjRvh9/sxPj7O67qFJnfWEqPRWMpwyolarYbFYil76IuBzahB781n4XeXvp/XdfE8sOGGJ5AToVyulsJ6ISiKgkqlKgV3AJTKxOUIrkjVgx0Oh5HJZKA1O7Du+id4CWu7TnxhXSwWEYlEiOjXicVisleqAMLF9XKLHisoSAUnrvnA+R3l5nFcua0LtEQe2dyVXGJvCRGKw+FALBYjYmo4ALjdbjAMg0AgINpz1kpYL4ZKpQJN0yW/R61Wy+Z3SNWDXSwWMTo6Co/HI/tea45wOAyj0Sh7MD2XyyGTycxLomTyDO58QZoVXGoVVbbfmu8ws2QyiQMHDiw732PZi2uTyYS1a9fyPuRIGOrB9TuTMNTD6XQiHA5LOmzi5DUevHHd6byuYVig+7rH4YsKD0CQIKxJRWyBncvlMDY+gTv2JvH+m5/hda3brMXeb58tqrAGpgUt178uJ1zft9wlYtwwM74Z6D179mDTpk0SWaWgsLwQKq4XGmoWSubASHT8pufsmrVYLEgkErLvu+ZmkkSjUVnt4FCpVGhubsbU1JQoSQ8ShDWJSCGwfT4faJom5ufMsizC4TAcDofcpiAej8NkMs1rg/XHs8jkpbkHFIosQsnZ9zkhQf19+/ahrq4OTU1NYponOcteXAPCDjmj0YhkMlnTHcXlsFqtRESQTSYTVCqV5LY4zVrek8QB4PjvPY2rfvsKGJ71K4qwroxYAptlWbyyvw8fvXcQf907yevaepMGu7/2T4JfezHC4TCcTicRU8IByC7yU6kUdDodr88CN8xsuUWPFRSkghtqxmfTBrcKK5lMzvue26KDXiONS0ZTmNV3rdPpoNFoiFiF5XA4EAqFZPfFOIxGI+rq6jA2NrYkmxRhvThiCuxkMolQKITm5mbZz3mOVCoFhmGIqFRbqO+b67eWAoNm9n5rYPpnwtf/4fwOUn6v1XJEi2uGYcpGkGuJ2WxGJpOR3Q6KouB0OhEKhSR/LZtRg/03nsH7ukffDaDrm4/hjcHqbFSEdfUsVWAP+BPYcP1j+NQf+8F3JM+z134Qr35ru+gZa2C6xyeVSs0bICQH3AEn9yEhZGAPt2LumGOOkcgqBYXlRUdHB0wmE95++21e15lMprLiWq+hcfnWTrHMmwXDTvdWclAURcwqLJvNhnw+j1QqJbcpJerr61EsFgWXhyvCujrEENjFYhFjY2PweDyyl1/PJBQKwW63yz7nhRuiWk7kS9tvPXu/daFQQCaT4e17LNeg/ooR16+99hqvKKNKpVowglxLZg4XkRuHw4FUKlWTHnCzXo1912+HEJnxsdtfxqr/9ygSmYX7tBRhzR8hAjuczOF9NzyGU3/0HJICVra/cd3p6HBLN8wmGAzCZrMR8fsnoSQcECauX3vtNWzYsEH2/dwKCqTADTV77bXXeF3HiesF+64lir3d9cJA2b5ruTPGKpUKDocDwWBQVjtmolKp0NTUhKmpKd7tg4qw5sdSBfbk5GRpGB0pcLvCSZjzkkgkoNVq5/WhZ/IM7nxemn7rcvuthVTMAdO+x/vfz29eEwmsCHH9/ve/H7FYrLTWqFoWiiDXGlJKw9VqNWw2W80OOZtRg4FbzuY96AwACiyw4cYd8Mfml+Qpwlo41QpspsjiG3/ei2O/sxPRLH/nzK6nceCmM+E0Szd4hGEYhMNhIg7dbDaLXC4n+1RcodHj3bt345RTTpHIKgWF5ckpp5yC3bt387pmsaq5UDIHRiKtm84zs/quuXuA3LNnAKCurg7xeFz2Cr6ZGI1GuN1uDA8PVz1wTRHWwhAqsMPhMKLRKFHl4MB01tpsNhORSV+oJNwfzyIjwrDgcnD7rWciJKjv9/tx4MABbN26VUzzasKKENd6vR5btmzBrl27eF23WAS5llgsFiSTSTCMNIvc+eByuRCJRGo6vVPIoDOO425+CvuG35tyrgjrpVNJYA/4E1jzzcfwh9fHBD3/s9d+EHtvFHcieDnC4TAMBgMR2daFBorUGqHR4127dmHbtm3SGKWgsEzZtm0bdu3aJVrVnJR913P3XVMUBbPZTERgX6vVwmKx1KQtjQ9utxtGo7EqwacI66XBV2Ank0mMj4+jpaWFmOngwHSZeigUIuI9wLIsEolE2ZJwp0krWZXM3P3WgDBxvXv3bmzYsKHsCjHSWRHiGnjvkOMDKX3XOp0OWq2WiOEier0eRqOx5oec06zFgZvOhEPP/9P+z794Ce3feBSv9wcUYS0ScwV2Js/gpV4/jvrWozj1R89BaOhl3/XbJS0D52BZFsFgkIgDDljeJeF+vx/vvPOOkrlWUJjDBz7wAUQiEdGq5qTsu+b2Xc/EarUS0XcNTGevw+EwEUkGDoqi0NTUhGKxiImJiQWDKIqwFodqBXYul8Pw8DC8Xq/s1WBziUQiUKvVvM9ZKeDmGJRLMNy2q0+yKpm5+62FVswt56D+ihLXzz777LLsuwbI2TsJTEdrg8FgzQ85g5bGmzeehb9duUXQ9efd8QpO/vlbUFnqFWEtAna7HZTJiYt/9Q+s/dYTuPDOVwX1VQOAwzBdBm4zasQ1cgFCoRBUKhURkzoZhkEymVy24vq5555bttFjBQUp4armnn32WV7XkdJ3bTabSy0rcsPtAyap9xqY9hNbW1sRi8XKJh0UYS0ulQR2sVjE8PAwrFYrET3NMykWi/D7/XC73USUqS80RFXK/dZi9ls/++yziriWmw984AOIRqM4dOgQr+vMZjMRGWNShosA0z8TvV4Pv98vy+tvbHMKLhMHgA/9zws49fs7kZOon2Slk8kz2DMYwtHXP4YP37YH+6aEO14mDYVnr/0g3rxB+jJwDoZh4Pf74fF4iDjg4vE49Hq97KVrR2L0WEFBapZSNVdujZfUfdeT0fcGltI0DZPJRET2mqIoeDweBAKBmralVYNWq0VbWxsmJydnJUEUYS0NCwlsru1PpVKhoaGBiPN9JqFQCDRNw2azyW0KWJZFPB4vm2CYjGYk229drt86kUjw9jumpqbw7rvvLtuKuRUjroX2XVssFiQSCVGW2C8Fo9EIiqKIyV57PB4Eg0Hk8wJTlUvEadZi8Jaz8YfPfUDQ9QPhHLqve7zswDOF+URSOTz5ziQ+95vXsPZbT+Djt7+MeE64h8eJ6ne+c1ZNysBnEgwGodFoiMgUA9NlYiRk0OPxOAwGg9JvraAgIkL7rhfqd5ay7xoA/vDq8Ky/22w2RCIRyV6PDyaTCSaTSbbA/mIYjUY0NzdjZGQEiURCEdYSM1dgMwyDsbExZLNZtLa2yr7iai6kBfXT6TQKhULZsvk/vDZc5gpxmNtvzYl8vv7Ycu63BlaQuAaAU089lbe41uv1UKlUsu9YpCgKDocD4XC48oNrgNFohMViwdTUlKx2nNDlxuAtZ8MmsJr4uJufQq+PjIAFaWTyDN4cDmPbD5/Bxpt24orf7cHOA0v/fe/40smyiGpgOjsbCATg9XqJOOByuRySySQcDofcpiw4NXQxuOjxBz/4QYmsUlBY3hx//PGCquYWagWTsu8aAH77j6FZpeE2mw25XI6IqeHAdGA/FAoRUao+F5vNhoaGBgwNDWFgYEAR1hLDCexCoYDDhw8jlUqhvb2dyLa/QCAAvV5PTA/4Qnu2M3kGv3lpSLLXndtvnclkwDCMoIq5U089VWzzasaKEtdCIsgURRHT7+xwOJBIJGTLFs/F4/EgEomULV2rNf/41pnwWIWtNTjtx7ux/rpHMeCXv/xfbiKpHF7uDeD//Xkv1n7rCfzLL17CYFAcp4rrq17TIF+W1u/3w2g0EjFMBJieWG42m6HR1KbXfCGKxeKCU0MX47nnnsPRRx+tOJAKCgug0+lw4okn8u67tlgsSKVSZUugpey7TuVmr+TiylhJmdSt1+ths9lkD+wvxMz1SiSsWlrpqFQq6HQ6MAwDtVot+8aNcuTzeQQCAWKy1gzDIBqNlg3q++NZpPPSzFMq128dj8dhNpt5Vxos535rYIWJ6+OPPx6pVApvvvkmr+u4iZly9ztrtVqYTCZistc6nQ52ux0+n09uU2DQ0njlm6fhyS+dLOj6VAE49UfPYdO3H0c6R8400loxFcvgQ7fuwsabduKCO1/B/QLXaJVDjr7qcuRyOYRCIXg8HtlsmAnLsgiHw0QMXUkmk1Cr1bydwccffxynny58/oGCwpHA6aefjscff5zXNRqNBgaDoWxgX8q+a5rCrJVcwHRgPxqNEjOpu76+HtFoFJlMpvKDawhXCu71etHY2Ijh4WEiEjMrlZmrVbu6usCyLK892LXC7/fDYrHAaDTKbQqA6VY0vV5fdkq406SVTPiV67cWUhI+NDSEnp4eRVyTglarxZlnnomHH36Y13UmkwmFQoGIDK3T6UQ4HJZd6HPU19cjHo8TUzK22mvB81cdg/85qxFC4oOhdBHrrn/iiMhiR1I5vHDYj7P+9zl84Oan0RcQdyq+XgU8/PnNspWAz2VqagpWq5WIvdbA9KHC7ZKVm4Wmhi4GwzB45JFHcO6550pomYLC8ufcc8/FU089xbu9bKGqObdFB71aGveMYTFvJZfBYIBWqyVisBkw7cs5nU4iAvscc3usHQ5HSWCTkvVfSRQKBQwODiKbzaKjowN6vZ7XHuxakc1mEQ6HUV9fL7cpJcLh8IKtaLft6oNUP7m5/db5fB7pdJq3uH7kkUdw8sknE9FOJ5QVJa4B4JxzzsFDDz3E6xpuuAgJB4vFYiktficBjUaDuro6TE5Oym3KrCjmR048GgO3nI2//tsJgp7r1B89h6Ovf3zFiexMnkHfVALn/uR5bLxpJy6+61W8OyHuv7HJqsXu/9iGf3ztJNDpEBGR+0wmg2g0SkzWGpg+4Ox2u+xlYotNDV2MV199FYVCASeeeKJElikorAzWrVuHhoYGPP3007yuW2igql5D4/KTpeu7nruSi5v5QpJIdLvdSCaTRKxKXWh4md1uR3t7O3w+H8bHx4lJiix3MpkM+vv7oVKp0NHRUeqxrnYPdi2ZmpqC3W6HXq+X2xQA04PMcrlc2YnlmTyDO5+XZgUXML/fOh6Pw2g08u6Rf+ihh3DOOeeIbV5NWXHi+qyzzsK+ffswOjrK6zqr1UqESCD1kOPEi1zMFNYzb7ab2uum9ycL2HIUzxVx6o+ew4YbnsCBcfn+bWLAFFl89+F3cPQNT+BD//0c3hoXN1Bk0lD43/Peh73Xn44Xv3k6WutMsNvtaGxsxMjIiKyfHZZlMTExAafTKfu6K45sNotEIkFESXgmk0GxWORdsvbwww/j7LPPJnJ4jIICSVAUhXPPPZd31ZxerwdN02UF5JXbuqCSKC6Xzs/uuwamhWI2myWmSk2tVsPlcmFiYkJWEVVpKrjJZEJXV1fpcaStEVtuxONx9Pf3w2azobW1dV6PNUkCO5FIIB6PE5W1DgaDsNvtZXvT/fEsMhKtqFWrqLL91nyz1rFYDLt27Vr2FXMrTlzX1dXhpJNOwiOPPMLrOovFgkwmQ8SESqfTiUQiQUSZOjB9M2toaMD4+LgsB8dCwprDoKWx76az8fRXhO3DS2QZfPgnL2Dtfz6KRGb5HYxTsQze/92duPPFQYi9uvCDq+vw4tdPxTvfOQv//P5m2I2zxSsJApubLEvSARcKhWC1WmUfZAYA0WgUFouF90CRhx56aNkfcAoKtYIT13ycfYqiYLVaywauQ8kcihIlQtUqal7fNU3TsNvtCAQC0ryoAFwuF1iWlW01V7XrtrRaLTo6OkDTNPr6+ojrFV8OcL/nkZERNDU1LTocjASBza0G83g8RJzzwHQpfTQaXTCo7zRpJRuUWCiys/qtGYYRNET1ySefxKpVq9DV1VX5wQSz4sQ1IKw0XK1Ww2QyyZqd5dBoNLBarQgGg3KbUsJms8FkMmF8fLymr1tJWM+ky2PB/hvPgE7guzrDAEffuAPDQfnL0Kohls7jnJ88jw/c/DTCKXEmzLfYtbhluwe//9Q6HLjpDPzmshPQ5Fg84ymnwM5ms/D5fGhqaiJmiijDMAiHw0RM2GZZFtFoFHa7ndd1/f396OnpwRlnnCGNYQoKK4ytW7cinU7j9ddf53WdzWZDLBabJxCk3HddKLLz+q6B6eRELBYjZmOJSqVCc3MzAoFAzTPqfPdY0zSNlpYW2O129Pf3IxgMKmXiVZLP5zE0NIRgMIiOjo6yJc1zkVtg+3w+aDQaIqrTOEKhEEwm04Il6rft6pNsUKJBQ8/qt45Go9Dr9byHqK6EknBghYrrc889F8888wzvvmW73Y5IJELEDbGurg6RSISY6Z0URaGxsRHJZLJmAQg+wprDrFfj0M1n47FrToIQqcUCOOWHu7D5Ozvxl9eGMRklo0RuJslsAdf/bT/e9+0n8bZI5d9nrHPjwUu68auPtuDs49bgxKM6YdBWXw4sh8BmWRZjY2Ow2+1EDA3jiEQi0Ol0RAxWS6VSKBaLvH8+Dz/8MLZt28Y76qygcKSi0Whw1lln8S4NNxgMUKvV8+6bUu+7ntt3DUxvCDGZTES1pRkMBrhcLoyNjdVMQPEV1hwURcHj8aC1tRWBQAADAwPEVCCSCLdR4/Dhw1Cr1Vi9ejWvc1MugZ1IJBCJRNDc3Cz7TBWOYrGIUCi04Ps1k2dw5wtS9lt3zOq3jkajVQVJZlIoFPDYY4+tiIq5FSmuu7u70dbWhp07d/K6zmq1Ip/PE1HSYzQaodPpiFnLBUxn9xsbG2tSHi5EWM9kfZMdfbecjSe+uFXQ6weTOVz7l7dxwveewQf+a6fs67syeQaDgSRueewA3vftJ/HbfwxhKSGg9Q1m3PPpzXjk6hPx8lePx5eOM8NtNWD16tW8e2Q4ai2wQ6EQ8vk8UUPMWJZFMBhEXV0dEYduJBKBzWbjbctKiR4rKNQSIVVzFEWVAvtz+crp3bjspHZxjJtDOs9gMjrf16mrq0MoFCJiWBSH2+0GgJqUhwsV1jMxm81YtWoV9Ho9+vr6lCx2Gbhstc/nQ3NzM5qbmwVVn9VaYM8sBydlxgsw3avMDWcux2Q0g4zYfYP/xxe2deErp3eX/p7L5ZBMJnlXzL388sugKArHH3+8yBbWnhUprgHgox/9KP785z/zukalUsFisRBRGg5MH3Kk3ZStVmupPFwqu5YqrGeyttGGAzedCZdJ+HNMxXNYf/0TmIjUPovNFFncuuMQjr1pJ7bdugu37+5HYQmNeKvcZrz6zQ/hsS99ECd2OmAuRBENBZZ0uM2kVgKbxHJwYHqAR7FYJCLjWywWBZWE+/1+PP/88ysieqygUEs+/OEP4+DBgzh8+DCv62w2GxKJxLygNa2isP0or5gmzuIPrw7P+5rZbIZarSbGDwKmfbOmpibJy8PFENYcNE2jsbFxVhabhMSN3LAsi1AoNCtbvdTzspYCe3JysrQqjhRYlkUgEFg0qP+H1+Z/1sVAr1Hhix9aDXrG9MVoNFq6j/DhL3/5C84991yifDqhrFhxfdFFF+Fvf/vbsi4N5zJOJGWvZ5aHS7G6TExhzWHQ0nj9W2fgH//vn/C10zqE2QVgyy3P4Iwf78ZUrDYHZCbP4NsPvYOfPduLdF545nxDkxX3ffYD2Hv96Xjq2g/CYaAxNjaG3t5eaDQarFq1SlQxKLXAJrUcnGVZTE1NweVy8R4eJgXxeBxqtZp3efoDDzyAE044AW1tbRJZpqCwMrHb7Tj77LNx33338bpOp9NBr9eXPVPXeIVVElXDb/8xNK80nKIouFwuTE1NEZW9lro8XExhPRMui20wGNDX14eRkREiBufWGm7+x+HDhxEIiBfQ56iFwE4kEohGo2hqaiKiMo0jHo+jUCgsGEjP5Bn85qUhSV47ky/O2zwQiUR4B/ULhQLuv/9+XHTRRSJaJx/ye4ASccwxx6CzsxN/+9vfeF1nNpvBsiwRuxW5/h3SDjmpysOlENYz8doM+MJp6/Hut8+A0yDsrX/IF8cHbn4aH/vFi+ibSsxzTMSAy1ZvvGm6/FsIFIB/fl8D9lx3Gh655mSc1O2GRUdjcnISPT09YBgGq1atQlNTkySrlqQU2CSWgwPT0VqGYYiJaHM9T3ydgPvuu2/FHHAKCrXmoosuwr333ss7QL9QabjdqMX72xwiWTebVG7+Si7OFpVKRVRgH5CuPFwqYc3BbVxZvXo1KIrC4cOHMTExccSs7UokEujv78fExARcLpco2epySCmwSS0HZ1kWPp8Pbrd7wUCFP55dUoJmMYza2YPMuK1LfNsLn376aajVanzwgx8U20RZWLHiGgAuvvhi3hHkxfqf5IBb50PS5HBgOqtuNpsxMjIiSpZfamE9E6NOjTdu+DB2XXsKvFZhKxTeGI7gQ//9HI69aSduefwABgNJZPIMMnkGI6FURdGdyTOYTOTLPu5HTx7Cz57tFdQfs7rejAev3IID3zkT/3vBJtSZdSgUCvD7/ejp6UE6nUZHRwdaW1t5T3HkixQCO5VKEVkOXiwW4fP5UF9fT0TWulAoIB6P844e9/b24o033sD5558vjWEKCiucs88+G4FAAK+++iqv62w2G9LpdNms5t2fOU4s82ZRbiUXMDuwT8pQVWB2eTjfqsSFkFpYz0Sr1aK5uRldXV3I5XLo6emBz+dbkSKbS1INDg5ieHgYVqsV3d3dcDqdkmZ9pRDYXLUcaeXgABAOh8Gy7IJ2MUUW970iTdYaAD570uxBZpFIBFarlbd/du+99+LCCy8kyq9bChRLQv2zRAwPD6Orqwujo6O8slzpdBr9/f1Yu3YtEb/oRCKB4eFhrFmzhgh7OBiGQX9/P0wmExobGwU/Ty2FdTlGgwls/eFzS34e9f/1nBSKLAwaGpdt7cBXTu+e1YvCFFn8eGcP7nqhH+l8EQaNCpdt7cR5m5vxYl8Auw76sfOAj/frnvO+Rlx39jrUmWdHEIPBICKRCAwGA9xuN8xmc83LmSKRCMbHx9HS0iJ4WBowPQClr68PLpcLLpdLRAuXTjAYRCgUwqpVq4goFwsEAojFYujs5Ddt+KabbsIbb7zBu+JHQUHhPS6//HIYDAb89Kc/5XXd8PAwdDrdPH9lJJTCyT94VkwTS1x96ir8+xlr5n2dZVkMDAzAZDIRVyUUDocxOTmJzs7OJQWJaymsy5FMJuH3+5FMJmGz2VBXVyfplgmGYXDgwAGsW7dOMl+Sm/URDAaRy+XgdDrhcrlq7tcxDIPBwUHQNI3W1tYlBb2npqYQDofR1dVV83/HYhSLRfT09MDr9S4YSL91x3SyRgpoCnjrxjNg0qlL9hw6dAgtLS28WvaSySQ8Hg9eeOEFbNy4URJba438KRYJaW1txUknnYQHHniA13UGgwF6vZ6Y7LXZbIbBYKjJpEw+0DSNtrY2RKNRwas75BbWANBcZ8aBm86EewlDz4BpUc0NG0vnGfzs2V78cMfBUkY7lSvg+08c/L8e6uL/Pa6Inz3bi2237sJ/Prifl7A2aGhcsqUN+27Yjh//60bUmXUoFosIh8Po7+9HX18fWJZFZ2cnOjs7YbFYZBF+YmSwi8UihoeHYTabidgfPROGYTA1NQWPx0OEsOYGxvCNsLMsi3vvvRcXX3yxRJYpKBwZXHzxxXjggQd474t2OBylTNRMpNx5ffeL81dyAdPZa6/Xi2AwSFxm1eFwwG63Y3h4WHBmXW5hDQAmkwnt7e3o6uqCSqXCwMAAent7EQqFiKoYqIZMJoPx8XEcPHgQgUAATqcTa9euhdfrlcWvEyuDHY1GEQgE0NbWRpSwBqaD+mq1esGVV5k8g7teGJDs9RkWCCXfq7SJxWKgaRomk4nX8/z9739He3s73ve+94ltomyQ9U6RgIsuugi/+tWv8MUvfpHXdU6ns3SDIMFh9nq96O/vR11dHTQaYaXMUqDVatHS0oKhoaHSjsxqIUFYcxi0NF771hmYjKbxYm8A3334LYRFmFt2+3P9uP058XYLGjQqfPrEdnzyuFZ4bXroNTQKhQJCoRDi8TgSiUSpdKm1tZWYw4CLqo6MjPDOYLMsi/HxcQBAY2MjEZ/HmQSDQeh0uiVl5cUkmUyCYRjePW2vv/46fD4fPvKRj0hkmYLCkcEpp5wCnU6HnTt34qyzzqr6Oq6yKBaLzXKYuZ3XUmSguL7rFqdx3veMRiNMJhOmpqaWVJ0mBV6vF0NDQxgdHUVrayuvc4EEYT0TvV6PxsZGeDweRKNRhMNhTExMwGQywWKxlNoDSYJlWaRSKcTjccTjceRyOdhsNrS1tcFoNBJxTnMCmytN55vBTqfTGBsbQ3NzM/R6vYSW8odhmNJguIV+1lL2WgPz+625oL7QOS8kvGfEYkVnrgHgvPPOw759+9DT08PrOpvNhkKhgFQqJZFl/DAYDLBYLJiampLblHmYzWY0NDRgeHi46imYJAnrmXhtBnx8cwvevPFs7P6Pbeiok65Eay6Xb+3AQ1efhC9s6yr7/Uu2tOHN67fj62euhcekQiwcRF9fHw4ePIhwOAyj0Yiuri6sWrUKdXV1xPxMOYRmsIPBIBKJxJJLu6SgUCggEAgQk7UGpg84biARH+69916cd955xDkRCgrLDZVKhQsvvBD33nsvr+soioLT6SxbCSblzuvfvTwIZoEVjx6PB+FwmLgJ1xRFoaWlpbSWsVpIE9YzoWkaTqezdI6bzWZEo1EcOnQIvb298Pl8SKVSsg24LRQKiEajGB0dxcGDBzE8PIxCoYD6+nqsXbsWzc3NMJlMxJyFgPAMdqFQwPDwMFwuFxGrNefi9/uh1+sXLb92mrRQSfirmNlvnclkkE6nec95mZqaws6dO3HhhRdKYKF8kOV9S4DD4cBHPvIR/OY3v8F//dd/VX2dSqWC3W5HMBjkXeIgFR6PB729vXC5XJIPo+KL0+lEJpPB8PAwOjs7F3XsSRXWc2mtM+HZ//gnTMUy+NSdr+DQFP8BKioK2NzqAE1TKBaB14dCKOfDGLU0/v2MNdBraBzVaIOKonD3iwNI5RgYNCpc+P4GfGazA2PDg8hkMqAoCiaTCU6nExaLhdif4Vz4ZrDj8TimpqbQ0dFBXOQemD7gTCYTMfeIfD6PeDyOVatW8boul8vh/vvv591Co6CgUJ6LL74Yxx9/PO+1NA6HA1NTU8hkMrMCXdzO67teHBTd1jueH4BWTZftvdbr9bDZbPD5fGhpaRH9tZcC15rW19cHvV5f8edMsrCei06ng06ng8vlQqFQQCKRQCwWQzAYBMuy0Ol0MBgMpT86nU7U4HOhUEA6nS79yWQyyOfzpSqt1tZWYjLUleCbweba0LhZNaSRz+cRDAbR0dGx6M//tl19Zf1NMfi3UzrxldO7S38PhUKw2Wy8fdHf//732LJly4pb/bmiB5pxPPHEE/jMZz6DkZERXg56LpfD4cOH0d3dTYxjz62/am1tlduUebAsWxog0dLSUvZDv1yEdTkiqRwOjMfw9AEffv/aCFK5yuU2l7y/Hl/Y2gK1Wg2NRoOfPTeEX700Mu9xl29pxudPbEQ+n0ehUEA+n0cyk4M/noXTqIbNbJx1kGq12mVxqC1ENUPOstks+vr60NjYyDsaWgu4+0NXVxcx2V6fz4dMJsP7oHrggQdw3XXX4dChQ8RVBygoLFdOOOEEXHzxxbj66qt5XTc6OgqVSjWvFDuSymHjTTvFNLGEQUPjzetPnzX5l4O713V2dko6cEso8XgcIyMjaG9vh9E4v7wdWF7CejFYlkUulytlCrk/xWIRGo2m5Guo1erS/3PDy1iWLQW2KYoCy7Ilf6NQKMz6f4ZhoNVqSz6HXq+HwWAgaqguX6oZcsa1oaXT6YqJIrkYGxsDwzCL6oBMnsHGm54UtHWmGp7/2qmlVhKGYXDo0CF0dHTwuj+wLIv169fjP//zP1fcrJflo2yWwPbt22EwGPD3v/8d5513XtXXabVamM1mhEIhYqZlut1u9PT0IJVKLXiIyAVXptXX1wefzwev1zvr+8tZWAPT+0a3rHJhyyoX/v3MtZiMZvCHV4fx238MIZVj5kwLn842X358A4pFBslkEoVCAeetNSCZcuKv+8PIFFjo1RQ+tsGBT26wIJPJQK1Ww2g0Qq1Ww6NWY41Gs+yFdDkqZbALhQKGhobgdDqJFNbAdDmTzWYjRlhzw+yampp4X3v77bfj85//PJGOhILCcuXf/u3fcOutt+Kqq67idQ+vq6vDwMAAPB7PLDHD7bx+fUj8/dPp/MK919wcD5/Ph/b2dtFfe6lYLBbU19eXKufm7iFeKcIamPazuKw215fPsizy+fys4HyhUEA2my3N4OAeB0yfXRRFgaKokgjXarUl30Pzf37HchbS5agmgx0MBhGLxUpD5kgjm80iEolUrE7zx7OSCeu5vdaRSKRUScGH3bt3Y2pqipcuWy4cEZlrALjlllvw9NNPY+dOflHfRCKBkZERrFmzhpgPGtd3097eTqToymQyGBgYQF1dHerr6wEsf2G9GJn/c0q4mw33/+UyABzJTA4v730HWzYeBZN+/p7RI4VyGexCoYCBgQHo9fpFh3XISSaTQV9fH1avXj3PkZOLcDiMQCDAex3YwYMHsXHjRoyOjhK34kxBYTmTSqXQ1NSERx55BCeddBKva/v7+2GxWOaVpcbSebzv209CbMdNraKw74btpbU6cykUCujp6UFbWxsxbTAzYVkWExMTiMfj6OzsLFUbriRhvVRqsYprObBQBjsUCmFycnLRCgi5GR4eBk3TFYPoUt0ngNnr+1iWRU9PDzweD+9EyAUXXICmpibceuutElgpL2SoxRpw6aWXYvfu3bwHm5lMJmg0GsGrpqTA5XKVolckotfr0dHRgWAwiEAgsKKFNTA9ybXFaYReQ8/6/0rXeM2aio9b6cwdcsYdejqdjlhhzbIsxsbG4HQ6iRHWLMvC7/fD5XLx/pndfvvt+PjHP64IawUFkTEajfj0pz+NX/ziF7yvdblcCAaD8wYwRdN5SRzmQpHFbbv6Fvy+Wq2G2+3G+Pi4bAO1FoOiKDQ0NMBsNmNgYAD5fF4R1gplKTfkjNudzk07JxFuIwyXtFqMz/76NUnuEzQFXDlj6G40GgWABdeBLYTP58ODDz6IK664QlT7SOGIEdcejwfnnXce70OOoii43W4EAgFiDhSaptHY2IiJiQneezRrhV6vR3t7O6amptDX17dihbXC0uEE9vDwMPr6+qDRaIgV1sB02RjDMMS0igDT+yVZluV9wCUSCdxzzz28e0IVFBSq48orr8Rf/vIXXhOtgelSZ5qmEQ7PLgGXcuf1XS+U33nNwQXvSNxaAkz7a42NjTAajejv7y+V1ivCWmEuMwV2X18fxsfH0draSmRVBjCdbR8bG4PX6604AyqSyknSOgLM3m3NBfXdbjdvf+1Xv/oVPvjBD6K7u7vyg5chR4y4BoCrr74a99xzDxIJflOfrVYrVCpVKUJDAlarFRaLBePj4yC1sp8bgJHJZARNEVQ4crBYLNBoNMjlcnA4HMS0YMyFW/vS1NREjI0zs9Z8bfrd736HVatW4YQTTpDIOgWFI5s1a9bggx/8IO644w5e180M7M8847md11KQzjO4+dEDC67loigKzc3NCAaDxKwpnQtFUXA4HMjn86Bpmsg1SgpkQNM0HA4HstksdDodsRlrAJiYmIBer4fD4Vj0cUyRxbcfflcyO2b2W3PVhnzLwfP5PG677bYVHdQnwzusESeccAJWr16N3/72t7yu4w45v99PlJBtaGhAOp0msjycKwUvFApoa2tDIBCA3++X2ywFAuF6rHU6HZqamjA6OsprD3at4N7TTqeTqOh2IpFAPp+veOjOhWVZ/OxnP8M111xDbJWAgsJK4JprrsHtt9/Ou9KMq0SZG9j/yunduOIUaQT2b/8xhB/vXLh9Tq/Xw+12Y2xsjJhqvpmkUikMDQ3B6/XOKhFXUJgL12PNTU/nswe7lsTjccRiMTQ2NlY8q3+8swcPvjkmmS3cbmsuqF9XV8c7qP/ggw9Cp9PhrLPOkshK+TmixDVFUbjmmmvwk5/8pDQ9sVpsNhuKxSJisZhE1vFHrVYTWR4+t8faYrGgo6MDgUAAU1NTRAUoFOSFE9ZarRYtLS1wOByzerBJgsRycAAIBAKCDridO3fC5/PhX//1XyWyTEFBAQA+/OEPQ6/X409/+hOv6xYK7NMqCp86Qbq9sHe/uHh5uNvthkqlIq48fGaPtcvlQlNTE0wmE/r7+5HL5eQ2T4EggsFgqcfaZrPN68EmhZnl4JVmvGTyDO56YUAyW2butk6lUshms3A6nbyeg2VZ/M///A++8IUvrOihekeUuAamp9Mlk0n87W9/43WdSqWCy+UiLntNWnn4QsPLDAZDacjZ5OQkEbYqyEs2m0V/fz/0ej1aWlpK4nDukDMSILEcHJg+4NLptKCewu9973v44he/SOTeWgWFlQRN0/j3f/933HLLLbzPPrvdDoZh5t0Lpey9TuWmN2AsBPX/27vv8KbKvg/g38wmaZOm6d4TUPEBFUEUAVEBF+BAQIaKCIoUkAdEhgtUQHCLgCDgAB4HypSpbAUUEAUESnfpbtM0zV7n/aPviW1ZzThJ2v4+19WL0ZyTOx257+89eTzEx8cH1PTwy21exq7BlsvlyM3NhdFo9HMpib8xDIPy8nLnsXLsLLTLbXIWCJo7HRyoP6nGeJVOMU+N6J4Mwf8fOcuOWrsakA8cOIB//vmn1W5kxgqcVqKPiMViTJ06FfPnz3e5klOpVLBarS6v2eZaoEwPv9au4BKJBOnp6dDpdMjPz4fNZvNTSYm/1dXVIScnBwqF4rKblwVSwA7U6eBA/XmlKpXK5QruyJEjOHbsWKte80RIIBk9ejQqKirw008/uXQdn89HeHj4JR37XK69FvJ5UAVffZRMIpEgKioqIKaHX21XcHYXcfbs8EDaO4f4lt1uR1FRETQaDdLS0i5ZYx1oAZudDh4fH3/N6eB2B4Ovj+RzVpaGa60NBgMMBoPbnfqZmZmtfi+ENheuAWDs2LHIz8/Hzz//7NJ1fD4fkZGRATfyGgjTw5t73JZYLEZaWhr4fD5yc3NhMpl8XFLiTwzDoKqqCoWFhYiLi0NMTMwVK41ACdhVVVUBOR1cp9PBYDC4dYTW/Pnz8fzzz7s8pYsQ4h6JRIIpU6a41bEfHh4Oq9V6ybK0KX3bY/QdKV4sZb1rHcvFYjdR9Of08OYct8VOr09ISEBxcTHKy8sDqg1HuGexWJCXlwe73Y709HRIJJLLPi5QAjY7HTw2Nvaau4MD9Wutlx/gbkp4w7XWZWVlCA8Pd3mT4hMnTuDAgQOYPHkyR6UMHG0yXAcHB2PSpEmYP3++y9eqVCo4HA6/jxI3pVAooFAoUFxc7PNKw9VzrAUCAZKSkhAaGorc3Fy/j0wS33A4HCguLkZVVRVSU1ObtcOkvwO2yWRCRUUFEhISAmo6ODu1LSIiwuUK7vTp09i1axemTJnCUekIIZczfvx4nDlzBgcPHnTpOj6fj6ioqEtCoYDPw303xni7mACufSwX4P/p4a6eY61QKJCWloba2loUFha6vPcOaZn0ej1ycnIgk8mQkpLSrDaqvwM2Ox28Oe0kk9WOzw/mclaWJ7snO9da63Q6mM1mtzr1FyxYgGeffRaRkZHeLmLACZzWoo9lZmbijz/+wNGjR126jq3kKioq/D5lpKnY2FiYTCafBn9XgzWLx+MhOjraGZwCbS078S6r1Yq8vDyYzWakp6e7dOSFvwI2wzAoLi5GeHh4wB3RodVqYbVa3arg3nnnHTz55JOIi4vjoGSEkCtRKBSYMGGCWx377JrLpuded4iRe6VsTRmtV193zWKnh1+8eNGnbSJXgzVLIpEgLS0NDoeDNjprA9RqtfPnpDm7bbP8GbBdmQ4O1K+1Ntm4KZ9UJMCsB6+HgM9zdupHRka6vBQtKysLmzdvxrRp0zgpZ6Bps+FapVLh+eefd6uSUyqV4PP5UKvVHJTMfQKBwDk93BcVhrvBuiGlUunc6MzXlTPxDaPRiJycHAQFBSE1NbVZU5ya8kfArqyshN1uR1RUlE+er7kaVnCujqbn5eXhu+++w/Tp0zkqHSHkaiZPnoz9+/fjzz//dOk6tkO6ace+UibGrcmuHcPXXF8fzr/imdcNRUREQCAQoLy8nJNyNOVusGYJhUKkpKQgJCQEOTk5AbePDvEcwzAoKSlxblzmzhIofwRsm83m0nRwAFAFizkLc2PurJ8ODtQfCWi32936Wi5cuBDDhg1DUlKSt4sYkNpsuAaAKVOmYOfOnThz5oxL17GVHNv4DiQKhQJKpRIFBQWcls0bwZollUqRnp4Oi8VC67BbEYZhUF1djby8POexKJ5MrfZlwK6rq0NVVVWjXcwDBTty5U4F9+677+LRRx9Fenq6t4tFCGmGqKgoPPvss1iwYIHL1yoUCohEIlRXVzf6/3Vju6NzQqi3iui0/GDeVc+8ZvF4PCQkJKCmpuaSkXVv8zRYs9iNzqKjo1FQUECz51oRdn21Xq9Henq6RxuR+jJgOxwOFBYWIjg4uFnTwVlL9+WAi1KN65nqnA7ucDhQXl6OqKgol9tEFy9exJo1a/Dyyy9zUMrAFFitRh+Li4vD008/jblz57p8rVwuR1BQEKqqqjgomWdiY2MhFAo5W3/tzWDNEolESE1NdfYkU0XXslksFuTn56OqqgpJSUmIiIho9nSsq/FFwDaZTCgqKkJ8fHzAHVPlcDhQUVGBqKgol7+ehYWFWL16NWbOnMlR6QghzTFt2jRs3rwZp0+fdum6K3Xsi4V8zHrgem8XE0Dz1l4DQFBQEBITE1FSUsLZ+mtvBeuGVCoVUlNTUVNTQ537LRzboZ+dnY2goCCkpaVd82zo5vBFwGYYBqWlpXA4HM2eDg7Ur7deceDamw+6Y9TtKc6jt2pqasDn810K/az58+fjwQcfxPXXc/MeFYjadLgGgFdeeQVbt27F8ePHXbqOx+MhJiYG1dXVftuh+0p4PB4SExNhNBq9vosnF8GaxefzERMTQxVdC9awchOLxcjIyEBISIhXn4PLgG2z2VBYWIjw8HCEhnp/JMhTVVVVEAqFbpXtjTfewMMPP4xOnTpxUDJCSHMlJSVh3LhxmDVrlsvXhoSEQCaTXVK3c7n2et5PZ5s1PVwulyM6OhqFhYVebxdxEaxZMpkMGRkZCA4Ops79Fqpph358fLzL64KvhuuArVarUVdXh6SkpGaPDNsdDOZtOwuz3fs/q1LRv0dv2e12VFRUIDo62uVO/ezsbKxcuRJvvfWW18sYyNp8uI6Pj8fEiRMxY8YMl6+VyWSQy+UoKyvjoGSeEQqFSE5ORnV1tdfOdeQyWDdEFV3LxHXl1hAXAZthGBQVFSEoKCjg1lkD9V/fqqoqxMbGulzB/fPPP/jf//6HN998k6PSEUJcMXv2bOzduxeHDh1y+dqYmBio1epGnc9crr3+6khBs6aHA/XHhoWEhHg1gHAZrFmX69w3m6+9oRvxL1906LO4Ctg6nQ5lZWVISkpyaaT9g91Z+OpwgVfK0FTDtdbl5eWQSCSQy13vwHv11VcxYsSINjVqDVC4BgC8/PLLOHbsmMvnXgP1lVxdXV1AboghkUic5zoajUaP7uWrYM2iiq7lYBgGarXaJ5VbQ94O2GVlZbDZbEhISPDKFHZvKysrg0KhcGv92KxZszBmzBhaa01IgIiKisK0adMwY8YMlzuPJRIJVCoVSkpKGl276umu4Oqda9WvzZsezuPxnCcReGNpmi+CdUNs575MJkN2djZ17gcwX3bos7wdsM1mM4qKihAXF+fSqSQmqx0rD3FzrnXDtdZGoxE1NTUu7bTOOnHiBDZt2oQ33niDg1IGNgrXqD/iYsaMGW5VciKRCFFRUSgtLQ3IN2CFQoGIiAgUFhbCZrO5dQ9fB+uGLlfR0Y7igcNsNiM/Px+VlZU+q9wa8lbAVqvV0Gg0SE5O9mn5m4vtwIuJcf1M28OHD+Pnn3/GK6+8wkHJCCHu+u9//4usrCz89NNPLl8bFRUFi8XSaGZardEKrlohBkvzjuYC6jvHk5KSoNfrPdqXxtfBmsXn8xEbG9uoc9/TAQriPb4crb4cbwVs9nqlUuk8aq+5KuvMMDajs8sd7Fprdh14eHg4goKCXL7PrFmz8MILLyAxMZGDUgY2Ctf/b+LEiSgtLcX69etdvpZ902+6g2egiIyMhEwmc+tNwJ/BmtWwoqutrcWFCxdQU1MTkJ0ZbYXVakVxcbFz4xBfV24NeRqw9Xo9SktLkZiY6JXNT7zN4XCgtLQU0dHRLv/+MQyDGTNmYMqUKW4Fc0IId+RyOV555RXMnDnT5dM9BAIBYmJiUFZW5rw2Uh4EiZCbZh2PB4RKm3+MokgkQnJyMiorK6HVal1+Pn8F64YaLlHLzc1FUVERnYvtRwzDONuA1dXVSExM9HmHPsvTgM22rUUikct1s93B4Osj+S5d01wN11prNBpYrVa3lsnt3bsXR44cabMbqFK4/n8ymQxvvPEGZs+e7fJGHOyRDhUVFQG3uRlQX774+HhnI725oTQQgnVDMpkM6enpzrM+s7OzodVqKWT7kN1uR1lZGbKysmC325GRkYG4uDi/j/a6G7AtFgsKCwsRExPjt86Ba6murgafz3fr6K3t27fjzJkzeOmllzgoGSHEU8899xx0Oh3Wrl3r8rWhoaEICgpybm4mEQnwbM80bxcRAMAwwDNf/OHSNVKpFPHx8c52RHMFQrBmsUvU2rVrBx6PhwsXLqCkpMTtmYDEPTqdDjk5OSgtLUVERATatWvn1hpgb/IkYJeXl8NsNiMxMdHl6dYf7M7C8gPcTAln11qzbb2YmBiXj95iO/WnT5/u999ff6Fw3cDo0aPB5/OxcuVKl68NCQkJ2M3NgPoKIjk5GXV1dVCr1dd8fKAFaxaPx4NSqUS7du2gUqlQXFzsPM+QcMfhcKCyshLnz5+H0WhEWloakpKS3JoqxBVXAzZ7pmRoaGjAVgAWiwWVlZVubWJmt9sxc+ZMzJ49GwqFgqMSEkI8ERQUhDfffBOvvfaay6djsB37DTc3m9K3PV64Kx1CvvdXXx8rqIHG4NrILfv+2tylaYEUrBsSi8VISEhAeno6rFYrsrKyUF5e7vKMA+Iao9GIvLw8Z13dvn17qFSqgNkXxZ2ArdFooFar3VqGxuXRWw3XWrObmLnTdvjxxx9RWFiIyZMne7uILQaF6waEQiEWLlyIV155pVkBtCl2czOuzt/1lEgkQlJSEsrKyq5axkAN1g3x+XyEh4ejffv2CAkJQUFBAQoKCujoLi9jNyvLyspCbW0tkpKSkJqaGnDnP7OaG7DZn3F2VCIQseud3N3EbPny5dDpdBg/fjwHpSOEeMvw4cMRERGBRYsWuXxt083NBHwept93HVY/3ZWDkgJzNp9p1rFcDUVFRSEoKAhFRUVXDR+BGqwbkkgkSE5ORkpKCvR6PbKyslBVVUV7wXiZ2WxGYWEhcnNzIZVK0aFDB0RGRro8iuoLrgRsvV6PkpISJCYmujw4weXRW8C/a60NBoPbm5gZDAZMnToVb775plvtltYi8H5K/WzAgAG47bbb3Nr8h107UVxcHLC9mTKZDAkJCSgqKrrsDuctIVg3JBAIEBUVhfbt20MsFiMnJwcFBQXQ6/U0XdwDdrsd1dXVuHDhgvP4p/T09ICdOt3QtQI2+zNuNptdOlPS1zQaDYxGo1vhv7KyErNnz8bHH38MiUTCQekIId7C5/Px6aefYsGCBcjPz3f5+ujoaNhstkb7vvwnIdSLJfzXhpMleG/XeZeu4fF4SEhIcM4Wulz4aAnBuiGZTIbU1FQkJCSgpqYGWVlZqKyspOniHjIYDCgqKkJ2djYEAgHat2+PmJgYvy89u5bmBGyDwYCCggLExMS4NaWdy6O3pCI+IuVBcDgcKC4udnaIuWrBggWIjo7GM888w0EpWw4eQwnkEtnZ2ejUqRMOHjyILl26uHQtwzAoKCiAUChEQkICRyX0nEajQUlJCZKTk529Sy0tWF+OxWKBWq1GTU0NRCIRwsPDERoaGnABym634+zZs7j++usDqtIwm83Or19QUJDz6xcoU7Bcwf6MJyYmOisyhmFQXFwMg8GAtLS0gP0Zt1qtuHDhAhISEtyalvXss8+isrISmzZt4qB0hBAuPPvss6iqqsLGjRtdvlav1yM/Px8ZGRnORvHgpb/hWEGNl0sJCPk8nJ7T33kObnPZ7Xbk5+dDJBI1Wmva0oJ1UwzDoK6uDtXV1TAYDFAqlQgPDw+4js1AbXewG5VVV1fDbDYjLCwMKpUqoJadNRf7My4QCBp13huNRuTn5yMyMhIREREu39dktePmubs52yE8s08GpvXvgLKyMuj1eqSlpbnc7mOz04EDB3DrrbdyUs6WgsL1Fbz22mvYtWsXfvvtN5eDmcViQXZ2dqNGfSBSq9UoKytDSkoKpFJpiw/WDTkcDmg0GlRXV8NmszmPOgiUyi6QKjmHw4G6ujrU1NRAr9dDoVAgPDzcpTMXA1XDgB0SEoLS0lLU1dUhLS0NIlHzd771JYZhUFhYCD6f79YRFkeOHME999yD06dPIzU1lYMSEkK4UFlZiQ4dOmDNmjV44IEHXL6+tLTU2XHI4/FgsTnwyJJfcabE9d26r+WniXeiY7zro+M2mw35+fkICgpCQkKCM3S01GDdlNFoRHV1NWprayGVSqFSqaBQKAKigz+Q2h3Av4MhGo3GuWlnWFhYQJTNE00DtsViQV5eHiIiIhAZGenWPYvUBvRcuNfLJa03rmcqXr7/ephN9evb09PTXW4rMwyDBx98EMnJyVi6dCkn5WxJKFxfgcFgwA033IBXX30VY8aMcfl6tVqNiooKtGvXLqDfKKqrq1FeXg6ZTAar1doqgnVDDMPAYDBArVZDq9VCKpUiLCwMCoXCr98Xf1dyDMPAbDZDo9GgpqYGfD4fYWFhCAsLC9jQ6S6NRoPi4mKEhIQ4O48C8cgtVk1NDcrLy5GRkeHy76Ldbke3bt0waNAgvPbaaxyVkBDClaVLl+K9997D6dOnXW7gOhwOZGdnQ6VSOUfHsivqcO/7B7xeTj6A5+9Kx9R+HSBwcfM0m82GvLw8iEQi6PV6xMTEtIpg3ZDNZnPWrzabDaGhoc4Ofn/NBPN3uwP4tzNfrVbDYDBALpcjLCwMISEhLXKG3JWwAZvH48FsNiM8PNytI62A+rXW7+w4h+UHcr1cynoHp/dBvFKCnJwcKJVKtzoANm3ahDFjxiArK8utk01aG/93pQUomUyGjz76CDNmzHBrczP2TbS0tJSD0nmPSqWCWCyGTqdDTExMqwrWQP1ar+DgYCQmJqJDhw5QKBSoqqrCuXPnkJeXh+rq6jZzbiXDMM4znS9cuICcnBxYLBYkJiaiffv2iIqKanXBGqjfrTY4OBh1dXWIjIwM6GBttVpRWlqKuLg4t34XV6xYAY1Gg+nTp3NQOkII18aNG4fQ0FC8++67Ll/L5/MRHx/vPOYHABLCZJzsHO4AsGRfDj7YneXytUKhENHR0dDpdM4N2VoboVCIiIgIZGRkICkpCQ6HA3l5ecjKykJJSQnq6urazCZoVqsVarUaBQUFOHv2LMrLyxESEoIOHTogKSkJcrm8VQVroH4NdmxsLAwGg3MDXnfVH73FTbCWievPta6oqACfz3dryrrBYMDkyZOxYMGCVvm77A4aub4KdppDSkoKlixZ4vL17LrJQJ0e3nCNdWhoKKqqqhqtwW7NzGazc2d3vV4PiUQCuVwOuVwOqVTK+Ru9r3qQ7XY7dDodtFqtcwM7hUIBuVyOkJCQgJiqxiV2x+26ujqEh4ejoqIioH8fPdmvgZ1S+vXXX+PBBx/koISEEF9gl3acOXMGKSkpLl/fdHr4wh3nsGQfN8f3SEUC/PlaX5fWX7NrrCMiIlBbWwuJRIKEhIRWF7Cacjgc0Ov1qKurg1arhcPhcB7jKpfLOR/c8FW7g2EYmEwmZxvLaDRCJpNBLpdDoVBALBa3+u+1yWRCXl4elEol9Ho9hEKhWxuomqx2dJ6zE2YbN1Ets08GXuiZ6PZ0cKB+Ge3OnTtx+PDhVt+mbC4K19eQnZ2Nzp07Y/fu3bjjjjtcvr6mpgZlZWXIyMgIqFHBy21exq7BTkpKahG7QnuL3W53VgJ1dXXg8/mQy+UIDg6GVCrlpCLgqpJzOBwwGo0wGo2oq6uDwWCAWCx2BmpfdBwECnbzMr1e75wKfrlNzgJFdXU1Kisr3ZoODgCjRo2CVqulTcwIaQXGjh2LixcvYtu2bS6/ZzscDuTk5EChUCA6Ohp2B4NFO89h2X5uRr+e7J6M1wd2bNb08Kabl7FrsNlzpNtK47xhANVqtTCZTM4AKpVKIZVKvR6AuWp3MAwDq9UKo9Ho7Dyw2+0+7TgIJOw+AuxU8CttcnYtdgeDOVvOcLJDuFjIw7ie6Zh0dzrycnMQHh7u1qj1P//8g1tvvZU2MWuCwnUzLFq0CJ9//jlOnjzp8vm+bAPfarUiJSUlIILN1XYFr6mpQWlpaUCGD19gp06zwdRkMoHH40EikTgrPG8Ebm9Ucna7HSaTCUaj0fmn2WyGUChsNBIfyNOgucL+jBuNRqSmpjbq2ArEgG00GpGbm4uUlBS3Zo5s3rwZTz31FE6fPo34+HgOSkgI8SWNRoOOHTvizTffdOtYG5PJhNzcXGdnOZcbIgH/7jZ8NVfaFZwN2Owu4m0lYDdktVobjfTabDaIxWJnm4Ntg3gSir3R7mAYBhaLxdnmYNsfdrsdEonE2UEQHBzcJr+P7M94VFRUo7DqTsDmcsbJkZl3I1ohcZ49n5yc7HKb1maz4fbbb0efPn2wcOFCTsrZUlG4bga73Y4777wTd9xxB9577z23rmc3CnB3QwNvac5xW+wGUAkJCQgN5easzJaC7V1uGGAbBm6JRAKRSAShUNjoTz6ff9U3quZUcgzDwGazwWq1wmazOf/OVmxskG5Y8UqlUgiFwoDoxPEXh8PhPMf6aj/jgRKw7Xa7cxMidzYSUavV6NixI9555x08+eSTHJSQEOIP27ZtwxNPPIHTp0+7dXIAu7FqRkYGbAyP06N8ZGIBTrx65enh1zpuiw0ffD4fSUlJAb0RrC/YbDZneGXbHVar1Rm4xWJxo3YH+3G10NbcdofD4WjU7mDbHmwbiGEYBAUFNRpwCAoKapNhuiGdTofCwsJr/ow3J2BrDBZ0eXM37BwktFuTw7B+/B0ez5abP38+vvrqK/z5558BcxJPoKBw3Uznzp1Dly5dsGvXLvTo0cPl6z0dmfIGV86x1mq1uHjxovPogLYc1ppid9pmK7ymAdjhcIDH4zkru4Zhm/1gGAZqtRphYWHOf7Nhmr2f3V7fCBIIBI0qULFY7AzTgbTUIBBYLBbnMVZJSUlX/RkPhIDN/k7abDa3Z7aMHDkStbW12Lx5M/2eEtLKjB49GqWlpdi+fbvLv99N31/e25WFxXuzOSpp/a7DiapLj3Bs7jnWdrsdRUVFsFgsSE5ObpHnHHOpYeBm2xwN2x5AfXuhYehm2xysy7U7HA5Ho3sxDAMej9eo3SESiZyBmoJ0Y2x7rqysDHFxcQgLC7viY68VsO0OBh/szsKKg7kw27y/4d0tiUp889ztsFvNHmWS06dPo1u3bti3bx+6devm9XK2dBSuXfDuu+9i+fLlOHnypFtnADfsRfb1+hNXgjXLZDKhoKAAUqm0Ta2F8pTdbm8UktnAzVZk7GNqamqgUqmcPcgNA3nTypFcm8FgQEFBARQKBWJjY5v18+rvgO3pe8KmTZvw9NNP48yZM4iLi+OghIQQf9JoNLjxxhsxZ84ct44FZWfOhYWFQRUewdkaTuDf83Ibrr1ubrBmMQyD8vJyqNXqgJhZ1FI07Zxn/862OdgQrVaroVKpGnX48/n8S9od15p9R+o5HA7npqlJSUnNygZXC9jv7jzPeQdYXGiQR7Np2eng99xzDxYsWMBBKVs+CtcusNvt6NmzJ7p3747333/f5esZhvFofYO73AnWLJvNhsLCQjgcDiQlJbXJ9btcCITzJluTmpoalJSUICYmBiqVyqXfLX8F7KZrIl1VXV2Njh07YtGiRRg1ahQHJSSEBILt27dj2LBhbk8PbzhzTiCWoPMbO2HmYr4pGq+9djVYN8QuT2OvpaDnOWp3eFfD9nFycrJLMwkvF7BNVjunSzekIgFOvHovqspLPZotN2/ePKxduxbHjx+n6eBXQEORLhAIBFi9ejU+++wzHDp0yOXreTwe4uPjYTabUVlZyUEJL+VJsAbqz2pMTU2FTCZDTk4O9Ho9RyUlxHXsUVtlZWVITk52qxGmVCoRFxeHoqIi1NXVcVTSxux2OwoLCxEeHu72zvyTJk1Ct27dMHLkSC+XjhASSO6//3489thjGDt2LNwZD5FKpYiJiUFRUREEcGBsr3QOSlnv80O5MFntHgVroP59OTU1FVVVVSguLm4zZ0KTlsFoNCInJwcikQhpaWkuL9ETCARISUlxtgUcDgcq68ycBWsAGHNnKoy6+mNZ3T367vTp03jrrbewevVqCtZXQeHaRR06dMDcuXPx9NNPQ6vVuny9QCBAcnIyqqqq3LreFZ4GaxaPx0NcXByioqKQn58PtVrt5ZIS4jq73Y6CggLodDqkpaV5dHycLwM2O4MlKCjI7Q0Ov//+e2zfvh2fffYZjegQ0ga8//77OHPmDJYtW+bW9SqVCiEhISgsLMTkezLwwl3pEDbj6CxXmawOPLrkELKyc90O1iyZTIb09HSYzWbk5eXBarV6saSEuKe2thZ5eXlQqVQeLZlsGLDz8gvw1eE8L5f0X+N6pmLs7XEoLS1FUlKSW/v1mM1mPPnkk5g8eTKts74GCtdumDJlCtLS0vDcc8+51YsskUgQHx/vDL5c8Fawbig8PBzJyckoLy9HSUmJW6+dEG8wm83IyckBj8dDWlqaVza+8VXALisrg9VqdbvnODc3F2PHjsXKlSsRGxvLQQkJIYFGqVRizZo1mDZtGv766y+Xr2c7yQGgvKwUL/XvgB2Te3q7mACAf0p1eH2/2qNgzRKJREhNTYVYLEZOTg6MRqMXSkiI69j9ANjTdLyx2S8bsFf9Xo4VB/O9U9DLGHprPEqKLyI2NtbtTZVfeuklCAQCzJkzx8ula30oXLuBz+fj66+/xt69e7Fy5Uq37hEaGoqIiAgUFBQ4d3n0Fi6CNSskJATp6enQ6/XIz8/3etkJuZa6ujrk5ORAoVB4/cgWrgN2TU0NNBqN2+W2WCwYOnQoRo0ahUceecTr5SOEBK7evXvj5ZdfxpAhQ6DT6Vy+nj1FQafTobq6GgkqGaRXODrLU38V16Gs1jtBmM/nIyEhAeHh4cjLy4NGo/HKfQlpLnYne41Gg7S0NCgUCq/du85sx/enqr12v6akIgGM6jKEhoZCpVK5dY8NGzbgyy+/xLfffkt7LzUDhWs3RUdHY+3atXjxxRdx+vRpt+4RGRkJqVSKwsJCr40CcxmsWWKxGGlpaeDz+dSTTHyGYRhUVlaiqKgIcXFxiImJ4WRKNFcB22AwODdOc3ekfebMmbDb7Vi0aJHXykUIaTlmz56N+Ph4TJgwwa3rRSIRkpKSUFFRAavJgDF3pnq5hP/q+c4evLvzPOwOz9s3PB4PkZGRSExMRElJCcrKymgdNvEJs7n+2Cq73Y709HSvrTW2Oxi8u/M8us/7BRbullpj8H9UCJaI3Z7pVlBQgGeeeQYrVqxAWlqal0vXOlG49sA999yDKVOmYOjQoW5t9MXj8ZCQkODcyt9TvgjWLHaHw7CwMOTm5qK8vJwqOsIZds1dTU0NUlNToVQqOX0+bwdsq9WKwsJCxMTEuL02fMuWLVixYgW+/fZb2kiEkDZKIBBgzZo12L59O7788ku37iGTyZzvby/0SkZmnwwECb3fHLQ6gMV7s/HervNeu6dcLkdaWhp0Oh1yc3Opc59whmEYVFVVITs7GyEhIUhJSfFqu/qD3fXnzps4OM8aACQiPkZ3i8XwTqFITEx0azDCarVi2LBhGDZsGIYMGcJBKVsnOorLQzabDXfffTfatWvn9hRxi8WCnJwcREVFub1GyZfBuimj0Yji4mIAQHx8PKRSqc+eu6WiIzGah2EYVFdXo7y8HCqVCtHR0T49b90bx3TZ7Xbk5eVBKpUiLi7OrQquqKgIN910Ez755BMMHz7crXIQQlqPnTt3YvDgwfj9999x/fXXu3WPsrIyaLVapKWloUpvRff5e7xcynoCPnD8lb5Qyrw3ndThcKCyshJVVVWIiIhAZGSkT+uGlojaHc1nNptRXFwMm82G+Ph4t9cpX4nJasdNc3ZxGqx3jL8VBk0l0tLS3O6Qf/nll7Fjxw4cOXKE2vYuoHciDwmFQqxbtw6bNm3CmjVr3LqHWCx2bhRWW1vr8vX+DNZA/TEfaWlpkMvlyM3NRUVFBW12RjzGjlar1WqkpKQgNjbW540nT0ewHQ4HCgsLIRAIEBsb61awttlseOKJJ/DII49QsCaEAAD69++PzMxMDBkyxO3R2+joaEgkEhQUFCAyRIxbk8O8XMp6dgfQ7e2fvTZFHKhfhx0dHY20tDRotVoaxSZe0XC0WiqVIiMjw+vB2u5gMG/bWc6CNQCM6hYPfU0FEhMT3Q7W27dvx5IlS/Dtt99SsHYRhWsvSEhIwFdffYUXXngBp06dcuseMpkMiYmJuHjxoksblfg7WLMaVnS1tbW0Fpu4zReVmyvcDdgMw6C4uBh2ux1JSUludwzMnDkTNTU1+Pjjj926nhDSOs2dOxcKhQLjx493q0ObXZrG5/NRVFSEtc/ehluSlN4vKACLncHivdn4YHeWV+8rlUqRnp5OnfvEY77q0H9v13l8dbjA6/cF6kPd2B5JGJgqQHx8vNsz7vLz8zFq1CgsXrwY1113nXcL2QZQuPaSBx54AFOnTsWgQYNQVVXl1j3kcjni4uJQWFjYrGAaKMG6IaroiCcCYbT6clwN2AzDoKysDEajESkpKW5Pwfv666/x+eefY8OGDZDJZG7dgxDSOolEInz//ffYtWsXPvzwQ7fuwe4gbrPZUFFWgg+H3uTVMjb1+aFcaAwWr96TOveJJ3zZoa8xWPDZ/hxO7g0APzzXFY9mCBEbE+323jQ6nQ6DBg3CsGHD8NRTT3m3gG0Erbn2IofDgSFDhqC6uhq7du1y65B2AKiqqkJlZeVVz+8NxGDdlNFoxMWLF52947QJ079o7VNj7NrqiooKhIWF+XxtdXM1dw12ZWUlqqurkZaW5vaxFUePHsXdd9+NDRs2oF+/fu4WmRDSyv3++++4++678cMPP6B///5u3cNqtSI3NxdiWQgeWH4KRit32xcHCfkY2zMNU/q2h4Dv3RMfGq7FjoyM9MpZxK0FtTsuxa6ttlqtSEhI4CxU2x0MPtidheUHcmCxcxO7hHwefhyRiqjwMMTExLh1D4fDgcGDB0Oj0WDnzp1u55i2LvBary0Yn8/Hl19+iZqaGrz44otu3yciIgJKpRL5+fmwWq2XfL4lBGug8Sh2Tk4OKioqaEdxcgmTyYS8vDxUV1cjOTk5YEarL6c5I9g1NTWorKxEcnKy28G6uLgYjzzyCN5++20K1oSQq+rWrRuWLVuGYcOGISvLvWnXIpEIKSkpMOm0eKJLtJdL2JjZ5uBkijhAo9ikedjR6pycHEgkErRr147T5Wfv7TqPxXuzOQvWAPD4f8KgCpUjOtr93985c+bg5MmT+P777ylYe4BGrjlQUFCArl27Yu7cuXj++efduge7XtNkMjXa/r+lBOum2B3FbTYboqKiEBYW1qZ7k6kHuX6X/IqKCtTW1vplJ3BPXGkEW6vV4uLFi0hKSnL7yC2j0YhevXrhP//5D1auXNmmf08IIc338ssvY9OmTThy5IjbU0KNRiOyc3KxMceKtcdKYbJy1yEuFQnw52t9IRFxUwc2HMVWKBSIioq64mzAtoDaHfVt6NraWlRUVIDH4yEuLo7zPV00Bgu6vLkbXOVqsYCHxzupMKZbDFJTkt1uM3z//fcYM2YMDh8+jI4dO3q5lG0LhWuOHDx4EPfddx+2bduG3r17u3UPhmFQVFQEi8WC1NRU8Pn8FhmsWU3f1KKjoyGXy9tkeGjLlZzNZkNVVRWqq6tbdIOnacDWarUoKipCYmIiFAqFW/dkGAYjR45Efn4+9uzZ0yK/LoQQ/7Db7Rg4cCAcDge2bt3qdt2i1+tRUFAAaWgE+i85xulo25Pdk/H6wI5enx7ekNVqRUVFBTQaDcLCwhAZGdkmR+XacruDYRjodDqUl5fDZrMhOrp+TTKX7U92KviKg7kwc7gz+HcjMhAZUn/qkLsDFH/++Sd69eqFdevWYcCAAV4uYdvTMoaJWqCePXviww8/xGOPPYa8vDy37sGuVRaJRMjPz0dRUVGLDdZA/etRKpVo164dVCoVSkpKkJubC71e7++iER9gRxGysrJgMpmQlpaGxMTEFhsgG04RLy8v9zhYA8DChQtx4MAB/Pjjjy3260II8Q+BQIB169YhPz8fL7/8stv3CQ4ORlJSEoy1VRjRxb21m8311ZECvLvzPKfPIRKJEB8fj/T0dNhsNly4cAHl5eWw27lbV04Ch8FgQH5+Pi5evAilUon27dv7ZPbkB7uzsHhvNqfB+sZoKSKCRR4F64qKCgwaNAizZs2iYO0lNHLNsUmTJmHXrl04dOgQIiIi3LqH3W7HhQsXYLfbkZGR0Woa3Xa7HdXV1aiqqoJMJkN0dHSbOUuvLfUgMwyDmpoaVFRUQCQSITo62u0p04GotLQU1dXViIiIcHsTEQD45ptvMHbsWOzfvx+33HKLF0tICGlLLly4gO7du2POnDnIzMx0+z5VVVUoLinF9+eM+OZkFWxeOqO6KR4P+PPVvlDK3NujwlUGgwFlZWUwm82IjIyESqVqMUuSPNGW2h1A/WZl5eXlqKurQ3h4OCIjI332ujUGC26b9wunwfqGyCDM6xuDjtd3cHsmhk6nQ58+fdC+fXusWbOmTc4k5QKFa47Z7XYMHToURUVF2LNnj8trO9g11kajESKRCA6Hw6OjfQKRzWZDZWUl1Go1FAoFoqOj3d4IqqVoC5UcwzDQarUoLy8HAERHR0OhULSqN292KnhYWBg0Gs01dxG/kt27d+Phhx/GDz/8gPvuu4+DkhJC2pLDhw+jb9++WLVqFYYMGeLy9exon1KphEajgVEUisdWnvR+Qf+fEMDzfTI42UH8chpOE7bb7YiKiuJ8mrC/tYV2B9B4GYBSqURUVJTPlgGwU8E/P5gLE4fBes3QNMQqxGAYBkKhEElJSS53EFksFgwYMAB2ux0//fRTqxm4CwStv6vOzwQCAdasWQOZTIbBgwdfdvfvK2m4eVlaWhqSk5MhFAqRl5cHm83GYal9SygUIjY2Fu3atQOPx8OFCxdQUlLi0teKBA6GYVBXV4ecnByUlpYiIiIC7dq1Q2hoaKtquDRcYx0XF+fSOdgNHTt2DI899hg+++wzCtaEEK+4/fbb8e2332L06NH45ZdfXLqWDdbR0dGIi4urb7gbayARctdktAFYvDcbi3ae4+w5GuLxeJDL5UhPT0d0dDQqKiqQnZ0NrVYLGnNqmWw2G8rKypCVleWc6RkfH+/T9fXsVHAug3XHaCniQoOQkpKC1NRU2O12FBYWunQaj8PhwOjRo1FVVUXL0DhA4doHJBIJNm7ciNLSUowZM6ZZvwCX2xWcz+cjMTERYrEYeXl5rS58isViJCQkONdFZWVloaioCAaDwd9FI83gcDigVquRnZ2NixcvIjQ0FO3bt4dKpWpVoRqoP26r6Rrr5hzT1dSFCxfwwAMP4I033sDIkSO5LDIhpI158MEHsWTJEjz66KP4888/m3VNw2AdHh4OAAgJCUH79FQMut71WTmu+mx/LrLL62Di8Jzthi63F0xWVhaqqqpoTXYLYTQacfHiRZw/f945GJWUlOTzwKgxWLDiYC6nz9ExWor3H0p2zmAVCARISUlxKWAzDIOpU6fi6NGj2LZtm0f7xJDLo2nhPlRaWooePXpg8ODBWLhw4RUfd63jthiGQUlJCXQ6HZKTkyGRSLguul+YzWao1WrU1NQgKCgI4eHhUCgUrWJtVGuanmWxWJzfJ5FIhPDwcISGhraK71NT7NmYlZWVVzxu60rHdDXV3PcDQgjxxMKFC/H+++/j119/RXp6+hUfd7lg3ZBOb8BbG//EhrO1MNu4bTpKRQKMuTPVZ9PEWeypJtXV1TCbzVAqlQgPD28VI3utqd3BzpCrqqqC0Wh0fp/80R527gp+IAdmDnfW/+DBeHRJViEhIeGS9pXdbkd+fj4EAsE1p4g39/2AuI/CtY9duHABPXr0wMsvv4ypU6de8vnmnmPNMAwqKiqgVquRnJwMmUzGddH9xm63Q6PRoLq6Gg6HA2FhYQgLC2vR67JbeiXHrlerqalBXV0d5HI5wsPDIZPJWt0oNYthGJSVlaG2thbJyclX3XzvWgG7trYWvXv3RufOnbF69epW2RFBCAkM7EjV5s2b8euvvyI6OvqSx1wrWLPMZjP+PpeNEd/lw+KDgd3MPhmY1r8D9090GQaDAdXV1dBqtQgODoZKpWrRx4e29HYHUL+euqamBjU1NQAAlUqFsLAwv56gs3DHOSzZl8Ppcwh4wM/jOyMlMf6KP3/NCdhffPEFJk+ejH379uHmm2/mtMxtGYVrPzh+/Dj69OmDjz76CKNHj3b+f3ODdUPV1dUoLy93eyOlloQNdGq1GjqdDsHBwQgLC2uRm2S11EquYcXGMAyUSiVUKlWL7uhoDofDgeLiYhiNRqSkpDTr9V4pYOv1ejzwwAMIDg7Gpk2b2uR5q4QQ33I4HBg1ahT++ecf7NmzB2FhYc7PNTdYs6xWK17/4RjWnVRzWWQAgETEx5GZ9/hsJ/HLaVrvtdQO/pba7gjktl9ZrRF3zN8D7lZY13uqWwzeeOSWa77eqwXsjRs3YsSIEdiyZQvuvvtujkvctlG49pN9+/ZhwIABWLJkCUaNGuVWsGbV1tbi4sWLiIuLa1RhtmYNKzt2NDs0NBQSicTvb7bN0ZIqObvdDp1OB41G46zYWnoPvivYtUx2ux0pKSku/W42DdgGgwEDBgyAw+HA1q1bXT49gBBC3GWxWDBkyBAUFxdj9+7dUCqVLgdr572sNsz58RjW/63mdCosUD9q91zvdEzt18GnU8SbajpjSyaTISwsDHK5PODrcaBltTsYhoHZbEZtbS00Gk3AdWpYbA4MX3EExwpqOH0eEQ94sns8Zg3o3Oyf/csF7K1bt2Lo0KFYt24dBg0axGmZCYVrv/rll18waNAgLF++HD179nQrWLN0Oh0KCwsRERGByMjINhF6gPo3YL1e76zs+Hw+FAoF5HI5goODA3a6baBXchaLBXV1dairq4Ner4dIJEJoaGjAVGy+YrVaUVBQ4Kyk3PlesQE7MjISI0aMgNFoxPbt21vVWd+EkJbBbDbjscceQ1VVFTZu3Ai1Wu1ysGY5HA78k52PR78865Mp4k/enoxZD1wPicj/dabVaoVGo4FGo4HZbEZwcDDkcjnkcnnArs8O9HYH256rq6uDVquFzWZDSEiIswMjUNq1Jqsdjy/9DadKtJw+j4AH7J18G5JiIly+tmHAPnPmDIYMGYKvvvoKjz32GAclJU1RuPazXbt24dFHH8Vbb72FzMxMj9aNGI1GFBYWQiaTIT4+PmCDJVccDofzjbmurg52ux0hISHOCs+fa3KaCrRKjmEYmEwmaLVa1NXVwWQyQSaTOTsqArWxwCWDwYDCwkKEhIQgLi7Oo9+nsrIyDBkyBDabDTt37mz1SzgIIYHLZDLhkUceQXl5OX788UekpKS4fS+GYTDnxxP44o8y7xXwKoKEPIztme7zjc6uhu2M1mq1MBgMEIvFkMvlUCgUkEqlARMKA63dAdSXiW2zsQMkbJstJCQkoNqx7MZlyw9k+6QzaWyPJMwe8B+3r7fb7fjqq68wceJEt8+7J+6hcB0AduzYgcGDB2P58uUYPny4R/ey2WzO7fiTkpLa1ChjQ2xYZN+wjUYjZDJZo55lf1Z4gVDJsZ0RbKB2OBwICQmBQqFASEhIQHVG+FpNTQ1KS0sRFRWF8PBwj35WjEYjBg0aBJ1Ohx07dtCxF4QQvzOZTHj44Yeh0Wiwc+dOhIaGun0vu4PB/C1/Y80fxTBxvIs464W70jH9vut88lyuYJdRabVa6HQ6AHAG7eDgYL+G2kBodwD1sycazoyTSCTOtlkgdUY0tWD7WSzbz+1RWwAgFgBjeqRi2n3Xe9SBtG3bNgwZMgSff/45hg0b5sUSkmuhcB0gdu/ejUceeQRLlizBk08+6dG9HA4HSktLUVdXh6SkpFa9k3hzWa1W55u5TqeDUChESEgIpFIppFIpgoKCfNpD6o9KzmazwWg0Oj/YrwNb8ctksoDqJfYHhmFQXl4OtVrtlU0C9Xo9Bg4cCIvFgm3bttGINSEkYJhMJgwePBjl5eXYtWuXx3u2qGvrcOxsLsZtvOilEl6ZgM/Dry/3QUzolU9t8DeGYWAwGJwd2FarFcHBwc52h1Qq9emGlv5od7ADHSaTCUajEXq9HhaLpdE0+kAfBNIYLDhVrMGolX9w/lxBQh4Oz7gbqhDPjhTbvHkznnjiCXz55ZcYPHiwl0pHmovCdQDZs2cPBg0ahPfffx9jx4716F4Mw0CtVqOsrKxNbXTWHA6HAzqdDgaDwRk0GYZBUFCQs8KTSCSQSCSchU2uK7mmQdpoNMJms0EsFjtfY0hIiN9H8AOJ3W5HUVERrFYrkpKSPJ4KX1tbi4EDB4LH42Hr1q20xpoQEnDMZjOGDBmCoqIi7NixA1FRUR7dz2q14pXv/8C3f3O70RPr1uQwrBvbHWJh4HcMsyO2RqMRJpMJZrMZQqHQ2eZg62ahUMhJvcx1u8PhcMBsNjtfH/snj8dzvj6ZTIaQkJCAmZZ+Nb7atKyhCX3S8VJ/z2ZkrF+/Hk899RTWrFmDRx55xEslI66gcB1gDh48iIEDB2Lq1KmYPXu2x2+wOp0ORUVFUCqViI6ObvMjk5fDMAwsFouzMmArBLvd7qwQ2D9FIpFXKj5vVXIOhwNWqxVms7lR+ZsGabb8LaFC8weTyYTCwkKIxWIkJiZ6/HUqKyvD/fffj5iYGKxfv552BSeEBCyLxYKnnnoKx44dw65du5CamurR/aw2O97ccAL/+7MCVq7PKAJwc2IoPn7iFkTKgwJis7PmstvtjUKo0WiE2WyGQCBoNLodFBQEoVDocb3krXYHwzCw2+2wWq2N2kxNgzT7IRaLW1wnvq82LWN5ay+BJUuWYPr06fjmm2/w0EMPebGExBUUrgPQqVOn0L9/fzz66KP46KOPPH5DNZvNKCoqAo/HQ0JCQpvcnMpVDMM0qjgaBm4AEAqFEAqFzrB9pb9fqUK5WiXHMAwcDgdsNhusVitsNtsV/+5wOMDj8SASiRpVZhKJhIJ0MzAMg5qaGpSVlSE8PBxRUVEeNwKys7PRr18/9OjRA6tWraJzrAkhAc/hcGDKlCn47rvvsH37dtx0000e3Y9hGOReLEffT49zfgYwSyLk49meaQG12ZmrHA5Ho7BtNBphsVjAMAz4fH6z2h58Pv+y9di1wjXDMJe0MRq2Oxr+GwAEAsElAxAtMUg3xG5atuJgDsw+2j9AIuTjyCzPznJnGAavv/46Fi9ejK1bt+KOO+7wYgmJqyhcB6j8/Hz0798fnTt3xtdff+1xIHY4HCgvL0dNTQ3i4+M92rykLWND77WCLxvCBQKBs6Lh8XjOD6C+00MsFoP9FWQYxhmsGYYBj8drVoBv+Byk+ex2O0pKSqDX65GQkOCVadvHjx/H/fffj6eeegrvvPMOzRQhhLQYDMPgnXfewYIFC7Bx40bcddddHt/z7S2nsOLXQs8L54KR3ZPwyoM3tKhR7KthR4qb0/Zg2w5swG7Y/gDqZymwa5zZNkfD5wDq2y1Xam80/L/WVL+ZrHZU1pnx5W95+PxQvk+fO7NPBqb17+D29TabDRMmTMC2bduwc+dO3HDDDV4sHXEHhesAVllZiYceegghISHYsGGDV3YZ1mq1uHjxIpRKJWJiYlrVm2MgadgD3DQ8A/+u7214dnLD8H213mfiOaPRiKKiIohEIiQkJHhldPnnn3/Go48+itdffx1Tp071QikJIcT3Vq1ahYkTJ3rlXFy7g8F7u85h5cE8mO2+a24G4pFdXGs4643tpG8anouKipxLn5qGbzZUt6V2ITtS/fmhXJh8sYahAZlYgGd6pHr0M2oymfDEE08gKysLO3bsQGJiopdLSdxB4TrA6XQ6526e27dvR0xMjMf3tFgsKCoqAsMwSExMpGnifhAoR2K0NQ03+ouMjERkZKRXOjC++eYbjBkzBp999hlGjhzphZISQoj/bNmyBU888QQWLVqE8ePHe3y/Gr0Zt837BRYfBmwgcI/s8gdqd1xq4Y5zWLIvx6fPKeTzsGNyTySoZB7NrtBoNBg4cCDsdju2bNkClUrlxVIST7Sd7qkWKiQkBJs3b8aNN96IO+64A2fOnPH4nmKxGGlpaQgJCUFOTg6qq6tBfSyktbNarSgsLERlZSVSUlK8sr6aYRgsWrQIY8eOxQ8//EDBmhDSKgwYMAA7d+7E7NmzMXPmTDgcno3qhQUHYVyvdC+Vrvk+25+Dg1mV0BgsPn9uErhMVjvOFNdimY+DNQCM65WGjGi5R8E6Ly8PPXv2RGhoKHbv3k3BOsBQuG5i/vz56Nq1K+RyOaKiovDwww/j/Pnzzs+r1WpMnDgRHTp0gFQqRVJSEiZNmoTa2tpG92k4xZf9+Oabbxo9Zs6cOUhISMCdd96JrKysK5ZJLBbjyy+/xMiRI3H77bdjy5YtHr9OHo+HmJgYJCUloaqqCvn5+bBYqPIhrQ/DMNBoNMjOzgafz0dGRoZXdu82mUx48skn8dFHH2Hfvn247777rvjYAwcOYMCAAYiLiwOPx8PGjRsbff6NN97Addddh+DgYISFheHee+/F0aNHGz0mJSXlkveUBQsWNHrMihUrkJycjJtvvvmS6wkhgSsQ2x49evTAb7/9hh9++AEPP/wwtFrPdk6e0rc9MvtkQOrDtdB2Bhi16nfcNHc3Bi/9DRabb6f+ksBidzBYuOMcbnx9Jx785JDPNtsD6jcuy+yTgan93F9fDQD79u1D165d0adPH2zYsAEymeyKj6W2h39QuG5i//79mDBhAo4cOYLdu3fDarWiX79+0Ov1AICSkhKUlJTg3XffxenTp/HFF19gx44dGDNmzCX3Wr16NUpLS50fDz/8sPNzv/76K3766Sds2rQJw4cPR2Zm5lXLxefzMXfuXKxcuRLDhw/H/PnzvTLaHBISgoyMDIjFYmRnZ9MoNmlV2NHqsrIyxMfHIzExEUKh0OP7lpSUoHfv3sjOzsaxY8fQpUuXqz5er9ejc+fO+PTTTy/7+fbt22Px4sU4deoUDh06hJSUFPTr1w+VlZWNHjd37txG7ykTJ050fq6wsBALFy7EN998g9mzZ2P06NEev05CiG8Eatvjuuuuw9GjR2EymXD77bcjJ8f9kT4Bn4dp/Tvgz9f64uD0PugcL3f7Xu44VlCDx5f9hqO51TSS3QZpDBZM++4kluzLgc3h23buk7cn4+Tr/TCtfweP9gBYtmwZHnzwQcyfPx8ff/zxNdsz1PbwD89bma3Mjh07Gv37iy++QFRUFI4fP45evXrhxhtvxA8//OD8fHp6Ot5++22MHDkSNput0Q86u2nY5dTU1CAuLg6dOnWCzWbDF1980azyPf7448jIyMCgQYPw999/Y+XKlVfttWoOgUDg3EG8uLgYWq0W8fHxzh0lCWlpGIZBbW0tSktLnR1I3gjVAPD777/j4YcfRv/+/bFs2bJm7Vlw//334/7777/i54cPH97o3++//z5WrlyJv//+G/fcc4/z/+Vy+RXfU7RaLZRKJTp16oSYmBgYjcZmviJCiL8FctsjLCwM27Ztw/Tp09GtWzd89913jd6XXCURCZCokuH78XfiieWHcbxQ4/a9XPXXxVoMXX4EAHBrchhWPd0VtUZrizsjm1wbuwN4qFSEZ774A8cKanxeBiGfh3G90jC1n2eh2mq1YvLkyVi/fj127NiBnj17Nus6anv4B41cXwM75epq6xlqa2uhUCguabxPmDABERER6NatG1atWtVoRLh///4wmUyQyWS47777MH/+/GaX6eabb8Yff/yBoqIi9OrVCxcvXnTxVV0ejWKT1oAdrS4tLfXqaDUArFmzBnfffTdeeuklrFq1ipPNAC0WC5YvX47Q0FB07ty50ecWLFiA8PBw3HzzzVi0aJHzvFEAuPHGG9GpUyeEhoaiY8eOeOutt7xeNkKIbwRa20MoFOL999/Hu+++i4EDB2Lx4sUetw/EQj5+eKEHTr7WF8uf+I9H93LHsYIadJ6zCz0X7sXNc3fj3Z3nYffxiCbxPruDwbs7z+Om///edp6zy+fBWsDn4aeJPXB6Tn9Mv+86j4J1VVUV+vXrh99++w1//PFHs4O1q6jt4T00cn0VDocDL774Inr06IEbb7zxso+pqqrCm2++iXHjxjX6/7lz5+Luu++GTCbDrl278MILL0Cn02HSpEkAAJFIhB07dqCiogJKpdLlUeLo6Gj88ssvmDBhArp27Yoff/wRt99+u3svtIGmo9hsL7eno+OEcM3hcKC6uhqVlZWQy+Vo166d10K1zWbD7NmzsXz5cvzwww/o37+/V+7b0NatWzFs2DAYDAbExsZi9+7diIiIcH5+0qRJuOWWW6BSqfDbb79h5syZKC0txfvvv+98zMqVK7Fw4ULIZDJIpVKvl5EQwr1AbnuMHj0aHTp0wKOPPopTp07h448/9riTUSkTo1/nJDxXrMNnB/I8uper2ChttNqxeG82tCYrZj1wPY1it0DsSPXXh/Ox/OC/P0f+6C55rlcaOsYrPb7P33//jUGDBqFLly7YunWrV/aLaYraHt5HR3Fdxfjx47F9+3YcOnQICQkJl3xeq9Wib9++UKlU2Lx581XPyn3ttdewevVqFBUVebWMDMNg8eLFmDFjBubMmYP//ve/Xjuj0OFwoKqqCpWVlVAqlYiOjvZaWGnr6EgM79LpdCgtLQUAxMXFebUCKi4uxvDhw1FZWYmNGzeiffv2Ht2Px+Nhw4YNjdZBAvVro0pLS1FVVYUVK1Zgz549OHr0KKKioi57n1WrVuG5556DTqej4/QIaUVaQtujqKgIjz76KBwOB7799ltkZGR4fE/2zOHlB3J8fmRXQ2IBMK5XRqs7I7u1tjv8eVZ1U1KRAGPu9OzsaqC+bf/5559jypQpmD59Ol555RWP2/bU9vAdmhZ+BZmZmdi6dSv27t172cqtrq4O9913H+RyOTZs2HDVyg0AbrvtNly8eBFms9mr5eTxeJg4cSL27t2LTz/9FAMGDEBVVZVX7s3n8xEVFYV27drBZrPhwoULUKvVNFWcBAyr1YqioiIUFhYiLCzMazuBs7Zt24bOnTsjPT0df/zxh8fB+mqCg4ORkZGB7t27Y+XKlRAKhVi5cuUVH3/bbbfBZrMhPz+fszIRQnyrpbQ9EhMTcejQIfTq1QtdunS5ZEdyd7Abnv0++14ECf3XPLXYgcV7s/HW1jO0+VkA0xgsOJpbjbe3nsHivdl+DdY3J4bi4PQ++PO1vh5vWqbVajF8+HC89tpr2Lx5M1577TWvDZpdDrU9vI+GIZtgGAYTJ07Ehg0bsG/fPqSmpl7yGK1Wi/79+yMoKAibN2+GRCK55n1PnjyJsLAwznp5unXrhj///BPPPvssOnfujHXr1qF3795eubdYLEZycjK0Wi1KS0uhVqtpqjjxq8tNAb9WI9MVFosFs2fPxmeffYYlS5b45fxqh8Nx1QbxyZMnnR1ghJCWrSW2PYKCgvDBBx+gT58+ePrpp7Fnzx58+OGHHrcNlDIxxvZMw+K92V4qqXtW/1aA1b8VAKDNzwJBIGxQdjm3Jodh3djuEHuhQ+j48eMYOnQo0tLScPLkSURHR3uhhK6htofnKFw3MWHCBKxbtw6bNm2CXC5HWVkZACA0NBRSqRRarRb9+vWDwWDAmjVroNVqnWc/RkZGQiAQYMuWLSgvL0f37t0hkUiwe/duzJs3D9OmTeO07EqlEt9//z2WLl2KBx54ADNmzMCsWbO8Nv1HoVAgJCTEeS52SEgIoqOjaVoI8Rn2zOqKigoIBAIkJyd7fQ1Sfn4+hg0bBpPJhD/++AMdOnh2JiVQP209O/vfhmJeXh5OnjwJlUqF8PBwvP322xg4cCBiY2NRVVWFTz/9FMXFxXj88ccBAIcPH8bRo0fRp08fyOVyHD58GFOmTMHIkSMRFhbmcfkIIf7VktseAwcOxMmTJ/HEE0/gtttuw7fffosbbrjBo3tO6Vs/S2jVr3kwWOzeKKZHjhXUoNOcXQCAIAEPY3ult7pp44Gq6bRvHvyzjrqpICEf+1+6CzGhnq8xZhgGn3zyCWbOnIlXX30V06dP98poNbU9/IPWXDfB413+jXL16tV4+umnsW/fPvTp0+eyj8nLy0NKSgp27NiBmTNnIjs7GwzDICMjA+PHj8fYsWM5ndrR0MmTJzF06FAkJCRgzZo1iI2N9er9rVYrKisrUVNTA6VSiaioKK+OHLZ2rXXtE1cYhkFdXR3Ky8vhcDgQHR2N0NDQK/6+uuvHH3/EmDFjMHz4cLz33nvNGhlqjiu9bzz11FNYtmwZhg8fjqNHj6Kqqgrh4eHo2rUrXnnlFXTt2hUAcOLECbzwwgs4d+4czGYzUlNTMWrUKPz3v/+lzi1CWoHW0PawWq14/fXX8cknn+Djjz/G008/7fF79JU2qQoET9+RjPtvjEWHGDmUssA/urSltTs0BgvOl9Vh5+lSrPr/GQSBJLNPBqb197zzXa1W45lnnsHx48fxzTffoEePHl4oXT1qe/gHhetWrK6uDi+88AK2bduGxYsXY9iwYV4PI2azGRUVFdBqtQgPD0dERARtetYMLa2S8ye9Xo/y8nKYzWZERUUhLCzM6w3FmpoavPjii9i0aRNWrFjh7LUlhBDiml27dmHUqFG44447sHTp0iuej+sKdvRy5aFcGP28adXltIRp44He7gjUad9NycQCPNPD803LgPqduseNG4dbb70Vq1evRnh4uJdKSfyJwnUb8OOPP+L5559Hjx49sGzZMk7WcBiNRpSXl8NgMCAiIgLh4eEB+eYdKAK9kgsERqMRFRUV0Ov1zo4bLr5WbOV20003YcWKFYiPj/f6cxBCSFtSVVWFzMxM7N6926ud+2wAm/y/P3GiSON5QTkQJORhbM/6aeNWuwOVdeaACNyB1u5gv5eqYDGW7svBioM5MNuYgJn23dTzvdMw4rZkr3wvG3bof/jhh3jqqae8PvhF/IfCdRvBVUXXlE6nQ0VFBUwmE1QqFY1kX0GgVXKBgmEY6PV6VFZWwmAwQKVSITIykpOfoYaV2wcffOCVKYyEEEL+9cMPP2D8+PG48847sXTpUq917ltsDgxfcSRgRzcBoFO8AufLdTDbHM4jmsbflQ613uKXsO3vdseVwrSAB/jx5LVr8uZINQD89NNPGDduHDp37ozly5df9lQA0rJRuG5j1q9fjxdeeAE9e/bEkiVLONuJUK/Xo6qqCjqdDmFhYYiIiIBYHPhrknzF35VcoGHXVFdWVsJsNiM8PBzh4eGcdcxQ5UYIIb5RWVmJzMxM/PLLL1i8eDGGDh3qtY5Mdl3uvJ/+wV/FWq/ck0t8HuBgAImQj2d7pvk0bPu63XFJmD6QA7OdAR9A4E3sv5Q3R6oBQKPR4MUXX8TGjRupQ7+Vo3DdBjWs6D755BPORrEBwGQyobKyElqtFgqFApGRkV7bJKolo3Bdj939u6qqCna7HREREQgLC+Psa6JWqzF16lRs2LCBKjdCCPGhhp37ixcv9upGqy1hJPty2KDJTiUf3SMF2RU65yZpbED1RsDzZrvjcuViOzoyokKw+tf8FjMy3ZSQz8O4XmmY2s+z86ob2rp1K5577jl06tQJK1asoA79Vo7CdRu2fv16TJw4ETfccAMWL16M66+/nrPnMpvNqKqqgkajgUwmQ3h4OORyeZsNNm09XNtsNqjVaqjVavD5fERERECpVHK2o63D4cCqVaswY8YMdO/eHcuWLaPKjRBCfKyyshKTJk3Ctm3bMGfOHGRmZnp1hlJLG8m+mliFBFV1JliZf4//ahq+gUuD7pUC+dXaHde6R9Pg/PnBXJhsDgQJeHjyjhScKKjB8UKNL788Xje8WyKeuTMVCWEyr80iyMvLw4svvoiDBw/i3XffxejRo9tsu7ctoXDdxmm1WrzxxhtYtmwZMjMz8eqrr0Iul3P2fA1DFY/Hg0qlQlhYWJtbl90WwzXDMDAajaiuroZWq/VZJ8uxY8cwYcIEVFRU4KOPPsKAAQOociOEED/65ZdfMHHiRAgEAnz66afo1auXV+/fUkeyXdElSYmuqSqsPpQHs51BkICH/yQocapYA7ONgUTEx7N3pjkDeWSwEEdPZ6FftxsRLq8/m7npGdISER83xoU2ukeYVIxSrcnPr5Y7XIxUm0wmLFy4EO+88w5GjBiBefPmISIiwiv3JoGPwjUBAJw6dQqZmZnIzs7G+++/jyFDhnAaQBiGgVarhVqthsFggEKhgEqlgkwmaxPBpy2Fa7vdDo1GA7VaDavVCqVSifDwcM7PSKyursbs2bPx1Vdf4aWXXsKMGTMglUo5fU5CCCHNY7FY8NFHH2Hu3LkYNGgQFi1a5NWp4kDrGsn2pluTw7BubHd8+HMWluzL8Xdx/IKLkWqgfk+XSZMmQaVS4dNPP0W3bt28dm/SMlC4Jk4Mw2DdunWYNm0abrjhBnzyySe44YYbOH9es9kMtVoNjUYDgUCA0NBQKJXKVn1AfWsP1wzDQKfTQaPRQKvVQiKRQKVSITQ0lLOp3yyHw4GVK1di5syZ6N69Oz766COkp6dz+pyEEELcc/HiRUybNg3bt2/HG2+8gczMTIhEIq8+R1sYyXbVTYmh+KuoNiCPveKSt3f/ZuXl5WHy5Mn49ddfMW/ePDz77LOtsn1Hro3CNblEw6nizzzzDF555RXExMRw/rwOhwN1dXWora1FXV0dJBIJlEolQkNDW9208dYYrtlp3xqNBrW1teDz+c6OEl9tYrd7927MmDEDarUaH3/8MQYMGOCT5yWEEOKZPXv2IDMzEwAwb948DBo0yOsz2diR7J2nS7HqtwKv3psENm/v/s1Sq9V45513sHjxYpoCTgBQuCZXcfbsWcyaNQu7d+/GlClT8NJLL0GhUPjkue12O2pra6HRaGAwGBASEgKlUgm5XN4qwmhrCdcMw8BsNju/V3a73RmofTnF/48//sDMmTNx4sQJzJw5E5mZmTQFnBBCWhir1YoVK1Zg7ty5SEtLw4IFC7y+Hhv4d63xykO5MFpbwsFQxFUiAWC1czdSbTAY8PHHH+Odd95Bt27dMH/+fNxyyy1euz9puShck2s6cuQIZsyYgdOnT2P27NkYP368T4/TslgsqK2tRW1tLUwmE4KDgyGXyyGXy1vs1PGWHK4ZhoFer0ddXR20Wi1sNhvkcjmUSiVCQkI4n/bdUFZWFmbPno1t27Zh0qRJePnll6FUKn32/IQQQrxPp9Phww8/xKJFi9CjRw/Mnz8fnTt39vrzsLtif304H8sP5nn9/sQ/XrgrHZPuaee1Y8waslqtWL16NebMmYP4+HgsWLAAd999t9fuT1o+CtekWRiGwY4dOzBz5kzU1NRgzpw5GDVqlM+DocViQV1dHerq6qDX6yEWiyGXy6FQKCCVSlvMZmgtLVzb7Xbn172urg58Pt/5dQ8ODvZpoAaAkpISzJkzB1999RWeeuopvPbaa4iLi/NpGQghhHCrqqoK8+bNw9KlS/HYY4/hzTffRGpqqtefp+lIdks7m7mtY88Ll4r4GHNnmtdHqYH6dvD69evxyiuvAADefvttPPbYYy2m3Ul8h8I1cYnD4cD//vc/vPrqq5BIJJg5cyaGDRvm9c1HmsNut0On0zkDHwAEBwc7P4KCggL2TS/Qw7XdbofBYIBer4der4fRaIREInEGaolE4pevbVFREd577z2sWLECDz74IN566y20b9/e5+UghBDiO4WFhXj99dfxzTff4Mknn8T06dM52aiSHclWBYuxdF8OVv2aB4PFDrGQB4uNmsuBggeAASAR8vBsz3SMvysdar3F66PUQH27d8OGDZg3bx7KysrwxhtvYPTo0a1uLyDiPRSuiVssFgu++OILLFy4EHa7HS+99BJGjx7tt3Wu7GZabBg0GAzg8XgBG7YDLVxfLkyLxeJGXz9/dKCwzp8/j3feeQfr1q3DQw89hFmzZtHaJkIIaWP++ecfLFiwAN999x0eeeQRzJgxg5Pp4iw2bEfKg/DxLxfa7LFVgeTW5DCserorao1WTsI0y2KxYO3atXjnnXeg1+sxbdo0jB07FjKZjJPnI60HhWviEZvNhvXr12P+/PkoKyvDlClTMH78eISGhvq1XJcL2wAgkUgglUqdH2Kx2C+B25/h2m63w2QywWg0wmg0wmQywWw2NwrTMpkMYrHYp+W6nBMnTmD+/PnYsmULhg8fjunTp+O6667zd7EIIYT4UX5+PhYtWoTVq1ejT58+mDlzJu68805On5OmjvvPs3emou8N0egQI4dSxm3bRK/X4/PPP8d7770HqVSKGTNmYMSIEQHRJiItA4Vr4hUMw2D79u2YN28eTp06hQkTJmDy5MmIjo72d9EA1JfPZDJdEip5PJ4zcAcFBSEoKAhisRhCoZDT0O2LcO1wOGC1WmE2m2E2m52v3WKxQCgUOjsY2Nfvz5HphhiGwf79+zF//nwcOnQIY8eOxdSpU5GYmOjvohFCCAkg5eXl+PDDD7FkyRJ06tQJM2fOxP33389p/d106vjKQ3kwWu0Q8nmwOahJ7ambEkNxrqwOJquDs52+L6empgaffvopPvroIyQlJWHmzJl45JFHAmJ2IWlZKFwTrzt48CDmz5+PPXv2YMiQIZg4cSK6du3q72Jdgj1Gig3bZrMZFosFVqsVfD4fYrHYGbaDgoIgEokgFAohEok83sDLG+GaYRjY7XZYrVbYbDZYLBZYLBbn67BYLODxeM7ysyFaIpEETJBuyGg0Yt26dVi8eDHy8/ORmZmJSZMmITIy0t9FI4QQEsA0Gg2WLl2KDz74AOHh4cjMzMSTTz4JuVzO+XNfbZ32ddFy/F2s5bwMLRk7A0AqEmDMnfVB2mp3cLLT9+WcOnUKixcvxpo1a9C1a1fMmjULffv2DZhlhKTloXBNOHP27Fl8+umn+PLLL9GxY0dMmDABjz/+uE+P8XKHw+FwhlQ2qJrNZthsNthsNjAMAz6f7wzaQqEQQqEQAoEAfD4ffD6/0d/ZD/aNmsfjweFw4MKFC8jIyHAGdYfDccmH3W5v9Hc2SLN/AgCfz4dIJIJIJGrUGSAWiyESiQK+gsjNzcWyZcuwcuVKxMXFYeLEiRgxYgSCg4P9XTRCCCEtiMlkwnfffYdPPvkE58+fx9NPP43nn38eN9xwg+/K0GCdtkjAxwe7s/D5wRyYbIxzV+u2qkuSEv+U1sFotTvDNJebkV2J1WrFpk2bsHjxYhw9ehTDhw9HZmYmbr75Zp88P2ndKFwTzmm1WnzxxRdYunQpKioq8PTTT2PcuHHo0KGDv4vmMna0uGHAZf9+uUDc8ONav2pNw3jToC4QCBqFeW+NovuD1WrFli1b8Nlnn2Hfvn0YMGAAMjMz0bt374DvDCCEEBL4jh49isWLF+P777/Hbbfdhueeew6PPfYYgoKCfF6WpqPbnx/MhcnmgETIR8c4BY4XanxeJm8LFgugt9id/45VSKDWm2G2M34blW4qPz8fK1aswMqVKyGTyfD8889jzJgxCA8P92k5SOtG4Zr4DMMwOHjwIJYtW4Yff/wRt912G0aNGoXBgwdDqVT6u3g+wf662e12nDt3Dtddd12bOc6BYRj8/fffWLt2LdasWQOxWIxx48bhmWeeQUxMjL+LRwghpBWqrq7Gl19+ic8++wzV1dUYPnw4Ro4cia5du/qtM/dyo9vs2m2pSAClVIRSrcn5+BhFEMq0Zq+W4ZbEUDAM8OfFWo/vdWtyGNaN7Q6DxYbzZXXOjccavk5fB2mWTqfDhg0bsGbNGuzduxcPPfQQnnvuOfTt27dFDk6QwEfhmvhFZWUl1q1bh7Vr1+Kvv/7CQw89hJEjR+KBBx7wS6+yrwXaUVxcKiwsdH6vc3Nz8cgjj2DkyJHo27dvq3/thBBCAgO7WebXX3+N9evXIyoqCiNHjsSIESOQkZHh7+JdEkQ1BoszqMolokY7lUtEfNwYF4pTF2tgttef9zy6Rxp+z6tuNAoeIhZA12A0mcWGYQAYvuIIjhXUOD/XJUmJ/yQosO73IlhsDKRiATrGKnC6uBYmm8M5Cj26RwqyK3Q+2cHbVVarFbt378aaNWuwadMmpKenO7/X8fHx/i4eaeUoXBO/O3/+PNauXYu1a9dCrVbj8ccfx4gRI9CzZ89W26vY2sN1TU0N1q9fjzVr1uC3337Dvffei5EjR2LQoEEICQnxd/EIIYS0YSaTCVu3bsXatWuxbds23HzzzRg5ciSGDh0a0JtoNg3glxsZbhjKlTKx89+RwUIcPZ2Fft1uRLhc2ui+Ta9p7nMFEoZh8Pvvv2PNmjX49ttvERQUhOHDh2PEiBHo1KmTv4tH2hAK1yRgMAyDI0eOYO3atfjmm28glUoxcOBADBw4EHfddVerGtFujeG6uLgYW7ZswZYtW/Dzzz+jc+fOzsZKoBzJRgghhDSkVqvx/fffY+3atThy5Ah69+6NAQMGYMCAAUhNTfV38bymNbY7rFYrDh06hC1btmDTpk2orq7G4MGDMXLkSPTq1avVDtCQwEbhmgQkq9WKPXv2YPPmzdiyZQtqamrQv39/DBw4EA888AAiIiL8XUSPtIZKjmEYnDx5Eps3b8bmzZvx119/4Y477sDAgQMxaNAgtGvXzt9FJIQQQpqtoKAAmzZtwpYtW7B//3506NDB2cnftWvXFh3WWkO7A6g/dm3Hjh3YvHkztm/fjqCgIGdnSL9+/QL+RBrS+lG4JgGPYRj89ddfzqD9559/4vbbb8eAAQPQt29fdOrUqcVVFC21kqutrcXBgwexbds2bNmyBRqNBvfdd5+z04N23CSEENIa1NbWYseOHdiyZQt++uknBAUF4aGHHsIDDzyA3r17t7j6rqW2OxiGwT///IPdu3djy5YtOHDgAK6//npnp8ett97aojs9SOtD4Zq0OCUlJdi6dauzZ1kgEKBXr16466670KdPH3Tq1Cng32hbSiVXW1uLQ4cOYd++fdi3bx9OnDiB9PR09OvXDwMHDkTv3r1b1XR9QgghpCmr1Ypff/0VW7ZswY4dO3D27Fn85z//cbY7evXqBZVK5e9iXlVLaXewYZptd+zbtw8GgwF33nknHnroIQwYMAApKSn+LiYhV0ThmrRoNpsNJ0+exN69e7Fv3z4cPHgQQqHQGbZ79OiBTp06BVwADNRKrry8HH/88Qf279/fKEzfdddduOuuu9C7d2/aaZMQQkibVlFR4awn9+3bh7Nnz6JTp07OerJbt26Ii4vz21FflxOo7Q6r1YozZ87g119/xb59+7B//37o9Xr06NHD2fa49dZbIRYH1o7khFwJhWvSqthsNvz555/Yt28f9u7di8OHD0On0+HGG29Ely5dnB+dOnXy67qcQKjkysrKcPz48UYfxcXFaN++vbNz4q677qIwTQghhFxFeXk5Dhw44AyHZ8+eRWRkZKN2R5cuXRAfH++3wB0I7Q6LxYLTp083anf8/fffCAoKwm233eacCUBhmrRkFK5Jq8YwDPLy8nDixIlGb+ZarRYdO3bELbfcguuvvx7t27dH+/btkZ6e7pM3dF9Wcmq1GufPn0dWVhaysrLw999/4/jx4ygrK0OHDh1wyy23OCv+m2++GQqFgtPyEEIIIa2ZXq/HyZMnnW2OEydO4J9//kFERARuueUWdO7c2dnuaN++PSIjIzkP3b5sd1itVuTl5TnbHefOncOJEydw6tQpSKXSRu2OW265BRkZGQG/nI+Q5qJwTdochmFQUFCAEydO4MSJE87geeHCBZjNZqSmpjaq9NLS0hAbG4vY2FhERER4pQLwZiVnMplQWlqK0tJSFBcX48KFC84KLSsrC9XV1YiJiXG+no4dO6JLly646aabIJfLPX4thBBCCLk6g8GAv/76C8ePH8fp06eddXRxcTGUSmWjdke7du2QkJDgbHvIZDKPn9+b7Q6GYVBdXe1sezQM0llZWcjNzYVAIEBGRobzNd18883o0qUL0tLSKEiTVo3CNSH/z+FwoLi4uFEFkZWVhby8PJSWlkKj0UAoFCI6OhpxcXHOSi82NhZhYWGQy+UICQlp9Cf79+DgYAiFQvD5fPD5fOeGHR06dACPx4PD4YDZbIZOp0NdXZ3zz6Z/r6ysRGlpKUpKSpyVWk1NDQQCAWJiYhAXF4d27dpdUknTaDQhhBASeHQ6HbKzsxu1Oy5cuIDi4mKUlpbCZrMhNDTU2d5g2x9RUVGN2hlN/x4SEgKJRNKo3XHu3LlG7Q673Q69Xn/ZNgf7p0ajcbY32PZHWVkZrFYrFAoFYmNjkZycjA4dOjRqeyQmJgbU2m5CfIXCNSHNZDQaL6lg2L/X1tZesWKy2WwuPY9MJrskpLN/RkRENAr27N8jIiKoEiOEEEJaEYfD4RwhbtjmKC0tRUVFxWXbHDqdDnq93qXnEQgEVxwgYAN0wzYH+xEcHMzRKyek5aJwTQiHGIaBxWKBTqeDw+FwftjtdmdvMvshEokQEhJCIZkQQgghbmNHpC0WS6O2h8PhgEAgaNT2CA4ORlBQUEDtbE5IS0bhmhBCCCGEEEII8RDtKEAIIYQQQgghhHiIwjUhhBBCCCGEEOIhCteEEEIIIYQQQoiHKFwTQgghhBBCCCEeonBNCCGEEEIIIYR4iMI1IYQQQgghhBDiIQrXhBBCCCGEEEKIhyhcE0IIIYQQQgghHqJwTQghhBBCCCGEeIjCNSGEEEIIIYQQ4iEK14QQQgghhBBCiIcoXBNCCCGEEEIIIR6icE2IBw4cOIABAwYgLi4OPB4PGzdubPR5nU6HzMxMJCQkQCqV4oYbbsCyZcsaPcZkMmHChAkIDw9HSEgIHnvsMZSXlzd6zObNm9G+fXt06NABW7du5fplEUIIISQAUbuDkMBG4ZoQD+j1enTu3BmffvrpZT//3//+Fzt27MCaNWtw9uxZvPjii8jMzMTmzZudj5kyZQq2bNmC77//Hvv370dJSQkeffRR5+fNZjMmTJiAJUuWYPHixRg/fjwsFgvnr40QQgghgYXaHYQENh7DMIy/C0FIa8Dj8bBhwwY8/PDDzv+78cYbMXToULz66qvO/+vSpQvuv/9+vPXWW6itrUVkZCTWrVuHwYMHAwDOnTuH66+/HocPH0b37t2h1WrRqVMnHDt2DADQtWtX/P3335DL5T59fYQQQggJHNTuICTw0Mg1IRy64447sHnzZhQXF4NhGOzduxdZWVno168fAOD48eOwWq249957nddcd911SEpKwuHDhwEACoUCo0ePRmxsLOLi4jB+/Hiq4AghhBByCWp3EOJfQn8XgJDW7JNPPsG4ceOQkJAAoVAIPp+PFStWoFevXgCAsrIyiMViKJXKRtdFR0ejrKzM+e/XX38dL774Ivh8PlVwhBBCCLksancQ4l8Urgnh0CeffIIjR45g8+bNSE5OxoEDBzBhwgTExcU16jVujtDQUI5KSQghhJDWgNodhPgXhWtCOGI0GjFr1ixs2LABDz74IACgU6dOOHnyJN59913ce++9iImJgcVigUajadSLXF5ejpiYGD+VnBBCCCEtDbU7CPE/WnNNCEesViusViv4/Ma/ZgKBAA6HA0D9JiMikQi//PKL8/Pnz59HYWEhbr/9dp+WlxBCCCEtF7U7CPE/GrkmxAM6nQ7Z2dnOf+fl5eHkyZNQqVRISkpC79698dJLL0EqlSI5ORn79+/HV199hffffx9A/ZSrMWPG4L///S9UKhUUCgUmTpyI22+/Hd27d/fXyyKEEEJIAKJ2ByGBjY7iIsQD+/btQ58+fS75/6eeegpffPEFysrKMHPmTOzatQtqtRrJyckYN24cpkyZAh6PBwAwmUyYOnUq/ve//8FsNqN///5YsmQJTc8ihBBCSCPU7iAksFG4JoQQQgghhBBCPERrrgkhhBBCCCGEEA9RuCaEEEIIIYQQQjxE4ZoQQgghhBBCCPEQhWtCCCGEEEIIIcRDFK4JIYQQQgghhBAPUbgmhBBCCCGEEEI8ROGaEEIIIYQQQgjxEIVrQgghhBBCCCHEQxSuCSGEEEIIIYQQD1G4JoQQQgghhBBCPEThmhBCCCGEEEII8dD/ARNkDSbpdo6tAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data, label = load_data(\"/home/loloc/PalaceToolkit/docs/examples/postpro/patch/farfield-rE.csv\", 3.3)\n", "\n", - "plot_s_params(\"postpro/patch/port-S.csv\")" + "# H plane, E plane.\n", + "polar_plots(data, label)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d6ae149", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/loloc/PalaceToolkit/src/palacetoolkit/plot_farfield.py:207: PyVistaFutureWarning: The default value of `algorithm` for the filter\n", + "`StructuredGrid.extract_surface` will change in the future. It currently defaults to\n", + "`'dataset_surface'`, but will change to `None`. Explicitly set the `algorithm` keyword to\n", + "silence this warning.\n", + " mesh = grid.extract_surface()\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "dfb9a98cc2944b43a001950f5c59e783", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Widget(value='