From cc6c18c9221063d9467f6a72f6ee2537edafb01d Mon Sep 17 00:00:00 2001 From: Joe Schoonover Date: Fri, 12 Jun 2026 11:15:10 -0400 Subject: [PATCH 1/5] Add tutorial for SCHISM model for lake ontario. This is a minimum working example that illustrates primarily how to do 2D (lateral) particle advection with SCHISM model output. It walks through handling issues with uxarray, which assumes that the input grid is in units of degrees for latitude and longitude. The SCHISM output provided here uses x,y in cartesian coordinates in units of meters. Since uxarray automatically wraps longitude to be between [0,360] we have to overwrite the `node_lon` values stored in the uxgrid object. We show how to rename the SCHISM velocity field components, lateral node coordinate, and vertical grid names to match what Parcels expects to see. Note that we intentionally create a ficticious vertical grid in this example; Parcels currently does not support the LSC2 vertical grid that is used in SCHISM and this is documented clearly in the tutorial. --- .../user_guide/examples/tutorial_schism.ipynb | 453 ++++++++++++++++++ docs/user_guide/index.md | 1 + src/parcels/_datasets/remote.py | 8 + 3 files changed, 462 insertions(+) create mode 100644 docs/user_guide/examples/tutorial_schism.ipynb diff --git a/docs/user_guide/examples/tutorial_schism.ipynb b/docs/user_guide/examples/tutorial_schism.ipynb new file mode 100644 index 000000000..0416a4783 --- /dev/null +++ b/docs/user_guide/examples/tutorial_schism.ipynb @@ -0,0 +1,453 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "99ff9ae6", + "metadata": {}, + "source": [ + "# 🖥️ SCHISM tutorial" + ] + }, + { + "cell_type": "markdown", + "id": "0b99ba4b", + "metadata": {}, + "source": [ + "Parcels v4 supports unstructured-grid model output via [uxarray](https://uxarray.readthedocs.io/).\n", + "This tutorial walks through advecting particles in real [SCHISM](http://ccrm.vims.edu/schismweb/)\n", + "output from a hydrodynamic hindcast of **Lake Ontario**. SCHISM writes its output in the\n", + "[scribe-IO](https://schism-dev.github.io/schism/master/getting-started/output.html) format, which\n", + "already follows the [UGRID](https://ugrid-conventions.github.io/ugrid-conventions/) conventions for\n", + "its horizontal mesh, so `uxarray` can read it directly.\n", + "\n", + "SCHISM output differs from the [FESOM tutorial](./tutorial_fesom.ipynb) in a few ways that this\n", + "tutorial highlights:\n", + "\n", + "1. The mesh coordinates are in a **projected coordinate system (metres)**, not longitude/latitude, so\n", + " we build the grid as a *flat* (Cartesian) mesh.\n", + "2. Velocities are stored at **mesh nodes** over a vertical column of layers, ordered **bottom → surface**.\n", + "3. SCHISM uses a **localized vertical grid (LSC2)**: the number of valid vertical levels varies from\n", + " node to node, and levels below the seabed are stored as `NaN`. We use this to flag particles that\n", + " drift below the local bathymetry and stop advecting them.\n", + "\n", + "If you have not done so already, work through the\n", + "[quickstart tutorial](../../getting_started/tutorial_quickstart.md) first to get familiar with\n", + "`ParticleSet`, `Kernel`, and `ParticleFile`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69cd9463", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:08:42.638984Z", + "iopub.status.busy": "2026-06-12T15:08:42.638746Z", + "iopub.status.idle": "2026-06-12T15:09:11.820289Z", + "shell.execute_reply": "2026-06-12T15:09:11.819467Z" + } + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.tri as mtri\n", + "import numpy as np\n", + "import xarray as xr\n", + "import uxarray as ux\n", + "\n", + "import parcels\n", + "import parcels.tutorial\n", + "from parcels._core.statuscodes import StatusCode" + ] + }, + { + "cell_type": "markdown", + "id": "91dc5d03", + "metadata": {}, + "source": [ + "## Get the SCHISM tutorial dataset\n", + "\n", + "We use three files from a SCHISM Lake Ontario hindcast (6 hourly snapshots), bundled in Parcels' tutorial\n", + "data registry:\n", + "\n", + "* `out2d`: the 2D output, slimmed to the **horizontal mesh** (node/face topology) and bathymetry.\n", + "* `horizontalVelX`, `horizontalVelY`: the 3D horizontal velocity components, defined at the mesh nodes\n", + " over 32 vertical layers.\n", + "\n", + "As in the [quickstart](../../getting_started/tutorial_quickstart.md), `parcels.tutorial.open_dataset`\n", + "downloads the files into a local cache on first use (subsequent calls return the cached copy) and opens\n", + "them as `xarray` datasets:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f071e80a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:09:11.823179Z", + "iopub.status.busy": "2026-06-12T15:09:11.822565Z", + "iopub.status.idle": "2026-06-12T15:09:12.645338Z", + "shell.execute_reply": "2026-06-12T15:09:12.644559Z" + } + }, + "outputs": [], + "source": [ + "grid_ds = parcels.tutorial.open_dataset(\"SCHISM_LakeOntario/out2d\")\n", + "u = parcels.tutorial.open_dataset(\"SCHISM_LakeOntario/horizontalVelX\")[\"horizontalVelX\"]\n", + "v = parcels.tutorial.open_dataset(\"SCHISM_LakeOntario/horizontalVelY\")[\"horizontalVelY\"]\n", + "grid_ds" + ] + }, + { + "cell_type": "markdown", + "id": "407360fa", + "metadata": {}, + "source": [ + "## Build the horizontal mesh\n", + "\n", + "SCHISM stores its mesh topology in `out2d_1.nc` following the UGRID conventions: node coordinates\n", + "(`SCHISM_hgrid_node_x/y`) and a face–node connectivity table (`SCHISM_hgrid_face_nodes`). Two\n", + "SCHISM-specific details need handling before we hand the mesh to Parcels:\n", + "\n", + "* **Triangular cells.** The connectivity table is stored with a width of 4 (so the same format can\n", + " describe quads), but this Lake Ontario mesh is entirely triangular; the 4th column is all fill. We\n", + " keep the first three columns and convert the 1-based indices to 0-based. Parcels' `UxGrid` requires\n", + " purely triangular cells.\n", + "* **Projected coordinates.** The coordinates are in metres (`standard_name = projection_x_coordinate`),\n", + " not degrees. `uxarray` currently assumes node coordinates are spherical and wraps longitudes into\n", + " [-180, 180] (see [uxarray #1524](https://github.com/UXARRAY/uxarray/issues/1524)), which would corrupt\n", + " the mesh. We undo that wrap by writing the raw metre coordinates back, and later build the `FieldSet`\n", + " with `mesh=\"flat\"` so Parcels treats the plane as Cartesian." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "353da75e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:09:12.647865Z", + "iopub.status.busy": "2026-06-12T15:09:12.647612Z", + "iopub.status.idle": "2026-06-12T15:09:12.855683Z", + "shell.execute_reply": "2026-06-12T15:09:12.854879Z" + } + }, + "outputs": [], + "source": [ + "node_x = grid_ds[\"SCHISM_hgrid_node_x\"].values.astype(\"float64\")\n", + "node_y = grid_ds[\"SCHISM_hgrid_node_y\"].values.astype(\"float64\")\n", + "face_nodes = grid_ds[\"SCHISM_hgrid_face_nodes\"].values[:, :3].astype(\"int64\") - 1 # all-triangular, 0-based\n", + "\n", + "uxgrid = ux.Grid.from_topology(\n", + " node_lon=node_x, node_lat=node_y, face_node_connectivity=face_nodes, fill_value=-1\n", + ")\n", + "# undo uxarray's [-180, 180] longitude wrap of the projected metres (uxarray #1524)\n", + "uxgrid.node_lon.values[:] = node_x\n", + "uxgrid.node_lat.values[:] = node_y\n", + "\n", + "print(f\"n_node={uxgrid.n_node}, n_face={uxgrid.n_face}, n_max_face_nodes={uxgrid.n_max_face_nodes}\")\n", + "print(f\"x range: {node_x.min():.0f} .. {node_x.max():.0f} m\")" + ] + }, + { + "cell_type": "markdown", + "id": "0837cd44", + "metadata": {}, + "source": [ + "## Assemble the velocity fields\n", + "\n", + "The two velocity files hold `horizontalVelX` and `horizontalVelY` with dimensions\n", + "`(time, node, layer)`. We rename them to `U`/`V` (so Parcels recognises the velocity components) and\n", + "to the Parcels UGRID dimension names (`n_node` for the lateral dimension).\n", + "\n", + "Two vertical details:\n", + "\n", + "* **Layer ordering.** SCHISM stores levels **bottom → surface**, while Parcels expects depth increasing\n", + " downward from the surface, so we reverse the layer axis.\n", + "* **Vertical coordinate.** SCHISM's LSC2 vertical grid (*Localized Sigma Coordinates with Shaved\n", + " cells*) varies with horizontal position: each node has its own layer depths, and even its own\n", + " *number* of levels (the true depths are written to a separate `zCoordinates` output, not used here).\n", + " **Parcels does not currently support a vertical grid that varies with lateral position.** `UxGrid`\n", + " takes a single 1D column of layer-interface depths that applies everywhere on the mesh. We therefore\n", + " supply a fictitious 1D vertical grid. This is adequate for the near-surface, horizontal transport\n", + " shown here (the lateral interpolation does not depend on the vertical grid); accurate full-depth 3D\n", + " transport on an LSC2 grid would require Parcels to support a laterally varying vertical coordinate.\n", + "\n", + "We also call `.load()` so the velocities sit in memory; otherwise every interpolation step re-reads\n", + "from disk and the simulation is extremely slow." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75a36eb6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:09:12.858273Z", + "iopub.status.busy": "2026-06-12T15:09:12.858018Z", + "iopub.status.idle": "2026-06-12T15:09:15.109306Z", + "shell.execute_reply": "2026-06-12T15:09:15.108471Z" + } + }, + "outputs": [], + "source": [ + "nlev = u.sizes[\"nSCHISM_vgrid_layers\"]\n", + "\n", + "# placeholder interface depths (metres, positive down): surface (0 m) -> bed, refined near the surface\n", + "zf = 250.0 * (np.arange(nlev) / (nlev - 1)) ** 1.5\n", + "zc = 0.5 * (zf[1:] + zf[:-1])\n", + "\n", + "rename = {\"nSCHISM_vgrid_layers\": \"zf\", \"nSCHISM_hgrid_node\": \"n_node\"}\n", + "U = u.isel(nSCHISM_vgrid_layers=slice(None, None, -1)).rename(rename).load() # reverse to surface-first\n", + "V = v.isel(nSCHISM_vgrid_layers=slice(None, None, -1)).rename(rename).load()\n", + "\n", + "uxds = ux.UxDataset(\n", + " xr.Dataset(\n", + " {\"U\": U.transpose(\"time\", \"zf\", \"n_node\"), \"V\": V.transpose(\"time\", \"zf\", \"n_node\")},\n", + " coords={\"zf\": (\"zf\", zf), \"zc\": (\"zc\", zc)},\n", + " ),\n", + " uxgrid=uxgrid,\n", + ")\n", + "uxds" + ] + }, + { + "cell_type": "markdown", + "id": "7b9ee5f3", + "metadata": {}, + "source": [ + "## Build the `FieldSet`\n", + "\n", + "With the mesh and UGRID-compliant dimensions in place, `parcels.FieldSet.from_ugrid_conventions` builds\n", + "the `FieldSet`. It detects `U` and `V`, attaches the `UxGrid`, and selects the `UxLinearNodeLinearZF`\n", + "interpolator (barycentric in the horizontal, linear in the vertical) because the velocities are\n", + "node-registered along the layer interfaces `zf`. We pass `mesh=\"flat\"` because the coordinates\n", + "are projected metres: velocities in m/s then advect positions in metres directly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3a8bee0", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:09:15.111573Z", + "iopub.status.busy": "2026-06-12T15:09:15.111336Z", + "iopub.status.idle": "2026-06-12T15:09:15.119169Z", + "shell.execute_reply": "2026-06-12T15:09:15.118339Z" + } + }, + "outputs": [], + "source": [ + "fieldset = parcels.FieldSet.from_ugrid_conventions(uxds, mesh=\"flat\")\n", + "\n", + "for name, field in fieldset.fields.items():\n", + " interp = getattr(field, \"interp_method\", None)\n", + " print(f\"{name:>4s} -> {type(field).__name__:<11s} interp={interp.__name__ if interp else '-'}\")\n", + "print(\"time interval:\", fieldset.time_interval)" + ] + }, + { + "cell_type": "markdown", + "id": "897796d5", + "metadata": {}, + "source": [ + "## Stop particles that drift below the bathymetry\n", + "\n", + "Because SCHISM's LSC2 grid keeps below-seabed levels as `NaN`, a particle whose (fixed) depth ends up\n", + "deeper than the local water column will sample `NaN`. Rather than feed it a fabricated velocity, we let\n", + "Parcels flag it: sampling `NaN` sets the particle state to `ErrorInterpolation`, and drifting outside\n", + "the mesh sets `ErrorOutOfBounds`. We add a small kernel that runs after advection, records that the\n", + "particle went out of bounds, and sets its state to `Delete` so it stops being advected (its trajectory\n", + "up to that point is kept).\n", + "\n", + "```{note}\n", + "Most of the Lake Ontario mesh is shallow nearshore water with only a couple of valid vertical levels;\n", + "the deep central basin has the full set. We therefore release particles in the deep basin (nodes with\n", + "many valid levels) so they start within the resolved water column.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9eaf228a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:09:15.121349Z", + "iopub.status.busy": "2026-06-12T15:09:15.121159Z", + "iopub.status.idle": "2026-06-12T15:09:15.124946Z", + "shell.execute_reply": "2026-06-12T15:09:15.124155Z" + } + }, + "outputs": [], + "source": [ + "OUT_OF_BOUNDS_STATES = [\n", + " StatusCode.ErrorInterpolation, # sampled NaN (below the local seabed)\n", + " StatusCode.ErrorOutOfBounds, # left the horizontal mesh / below the grid\n", + " StatusCode.ErrorThroughSurface, # above the surface\n", + "]\n", + "\n", + "SchismParticle = parcels.Particle.add_variable(\n", + " parcels.Variable(\"out_of_bounds\", dtype=np.int32, initial=0)\n", + ")\n", + "\n", + "\n", + "def StopBelowBed(particles, fieldset):\n", + " \"\"\"Flag out-of-bounds particles and stop advecting them.\"\"\"\n", + " oob = np.isin(particles.state, OUT_OF_BOUNDS_STATES)\n", + " particles.out_of_bounds = np.where(oob, 1, particles.out_of_bounds)\n", + " particles.state = np.where(oob, StatusCode.Delete, particles.state)" + ] + }, + { + "cell_type": "markdown", + "id": "b2275fb8", + "metadata": {}, + "source": [ + "## Release particles and advect" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75820fb3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:09:15.126973Z", + "iopub.status.busy": "2026-06-12T15:09:15.126794Z", + "iopub.status.idle": "2026-06-12T15:09:16.362213Z", + "shell.execute_reply": "2026-06-12T15:09:16.361455Z" + } + }, + "outputs": [], + "source": [ + "# number of valid (non-NaN) vertical levels at each node\n", + "valid_levels = np.isfinite(U.isel(time=0).values).sum(axis=1)\n", + "\n", + "# release on face centroids in the deep basin (all 3 nodes well-resolved vertically)\n", + "cx = node_x[face_nodes].mean(axis=1)\n", + "cy = node_y[face_nodes].mean(axis=1)\n", + "deep = (valid_levels[face_nodes] >= 15).all(axis=1)\n", + "idx = np.where(deep)[0]\n", + "idx = idx[:: max(1, idx.size // 150)][:150]\n", + "\n", + "lon, lat = cx[idx], cy[idx]\n", + "z = np.full(lon.size, 2.0) # release at 2 m depth\n", + "print(f\"releasing {lon.size} particles at z = 2 m in the deep basin\")\n", + "\n", + "pset = parcels.ParticleSet(fieldset=fieldset, pclass=SchismParticle, lon=lon, lat=lat, z=z)\n", + "output_file = parcels.ParticleFile(\"output-schism.parquet\", outputdt=np.timedelta64(30, \"m\"))\n", + "\n", + "pset.execute(\n", + " [parcels.kernels.AdvectionRK4, StopBelowBed],\n", + " runtime=np.timedelta64(5, \"h\"), # the dataset spans 5 hours\n", + " dt=np.timedelta64(5, \"m\"),\n", + " output_file=output_file,\n", + " verbose_progress=False,\n", + ")\n", + "print(f\"{len(pset.lon)} of {lon.size} particles still active at the end of the run\")" + ] + }, + { + "cell_type": "markdown", + "id": "f4201357", + "metadata": {}, + "source": [ + "## Plot the velocity field and trajectories\n", + "\n", + "We plot the surface speed across the triangular mesh (in projected kilometres), the particle release\n", + "points, and their trajectories coloured by time since release. The lake currents are slow (~0.1 m/s), so\n", + "over the 5-hour window most particles move only a kilometre or two, while those caught in the faster jet\n", + "near the north-eastern outflow (towards the St. Lawrence) travel noticeably further." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55825c33", + "metadata": { + "execution": { + "iopub.execute_input": "2026-06-12T15:09:16.364368Z", + "iopub.status.busy": "2026-06-12T15:09:16.364163Z", + "iopub.status.idle": "2026-06-12T15:09:22.680573Z", + "shell.execute_reply": "2026-06-12T15:09:22.679862Z" + } + }, + "outputs": [], + "source": [ + "df = parcels.read_particlefile(\"output-schism.parquet\")\n", + "\n", + "triang = mtri.Triangulation(node_x / 1e3, node_y / 1e3, triangles=face_nodes)\n", + "surf_speed = np.hypot(\n", + " np.asarray(uxds[\"U\"].isel(time=0, zf=0)), np.asarray(uxds[\"V\"].isel(time=0, zf=0))\n", + ")\n", + "speed_face = np.nanmean(surf_speed[face_nodes], axis=1)\n", + "\n", + "fig, ax = plt.subplots(figsize=(11, 7))\n", + "tpc = ax.tripcolor(\n", + " triang, facecolors=np.nan_to_num(speed_face), shading=\"flat\",\n", + " cmap=\"Blues\", vmax=np.nanpercentile(speed_face, 98),\n", + ")\n", + "fig.colorbar(tpc, ax=ax, label=\"surface speed [m/s]\", shrink=0.8)\n", + "\n", + "for traj in df.sort(\"time\").partition_by(\"particle_id\"):\n", + " ax.plot(np.array(traj[\"lon\"]) / 1e3, np.array(traj[\"lat\"]) / 1e3, color=\"0.3\", lw=0.6, alpha=0.7, zorder=2)\n", + "ax.scatter(lon / 1e3, lat / 1e3, facecolors=\"none\", edgecolors=\"k\", s=20, zorder=3, label=\"release\")\n", + "\n", + "elapsed_h = (df[\"time\"] - df[\"time\"].min()).dt.total_seconds() / 3600\n", + "sc = ax.scatter(\n", + " np.array(df[\"lon\"]) / 1e3, np.array(df[\"lat\"]) / 1e3, c=elapsed_h, s=4, cmap=\"viridis\", zorder=3\n", + ")\n", + "fig.colorbar(sc, ax=ax, label=\"time since release [h]\", shrink=0.8)\n", + "\n", + "ax.set_xlabel(\"projected x [km]\")\n", + "ax.set_ylabel(\"projected y [km]\")\n", + "ax.set_title(\"SCHISM Lake Ontario surface currents with particle trajectories\")\n", + "ax.set_aspect(\"equal\")\n", + "ax.legend(loc=\"upper left\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4d3c1d62", + "metadata": {}, + "source": [ + "The particles drift with the SCHISM surface currents, and any that wander over shallow water where\n", + "their release depth falls below the seabed are flagged (`out_of_bounds == 1`) and stop.\n", + "\n", + "From here, the rest of Parcels works exactly as on structured grids. To go further with SCHISM data:\n", + "\n", + "* Keep in mind that the vertical is approximate: because Parcels uses a single 1D vertical column,\n", + " this tutorial is most meaningful for near-surface and horizontal transport. Faithful full-depth 3D\n", + " transport on an LSC2 grid would require Parcels to support a laterally varying vertical coordinate.\n", + "* Add the vertical velocity as a `W` field and use `AdvectionRK4_3D` for three-dimensional transport\n", + " (still subject to the single-column vertical approximation above).\n", + "* See the [interpolation tutorial](./tutorial_interpolation.ipynb) for the available `Ux*` interpolators." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/user_guide/index.md b/docs/user_guide/index.md index 9e22f1236..70cbbea8f 100644 --- a/docs/user_guide/index.md +++ b/docs/user_guide/index.md @@ -29,6 +29,7 @@ examples/tutorial_nemo.ipynb examples/tutorial_croco_3D.ipynb examples/tutorial_mitgcm.ipynb examples/tutorial_fesom.ipynb +examples/tutorial_schism.ipynb examples/tutorial_velocityconversion.ipynb examples/tutorial_nestedgrids.ipynb examples/tutorial_manipulating_field_data.ipynb diff --git a/src/parcels/_datasets/remote.py b/src/parcels/_datasets/remote.py index eb2206591..e176a1fb2 100644 --- a/src/parcels/_datasets/remote.py +++ b/src/parcels/_datasets/remote.py @@ -63,6 +63,11 @@ def _get_data_home() -> Path: "data/FESOM_periodic_channel/v.fesom_channel.nc", "data/FESOM_periodic_channel/w.fesom_channel.nc", ] + + [ + "data/SCHISM_LakeOntario/out2d.schism_lake_ontario.nc", + "data/SCHISM_LakeOntario/horizontalVelX.schism_lake_ontario.nc", + "data/SCHISM_LakeOntario/horizontalVelY.schism_lake_ontario.nc", + ] + [ "data/NemoCurvilinear_data/U_purely_zonal-ORCA025_grid_U.nc4", "data/NemoCurvilinear_data/V_purely_zonal-ORCA025_grid_V.nc4", @@ -222,6 +227,9 @@ class _Purpose(enum.Enum): ("FESOM_periodic_channel/u.fesom_channel", (_V3Dataset(_ODIE,"data/FESOM_periodic_channel/u.fesom_channel.nc"), _Purpose.TUTORIAL)), ("FESOM_periodic_channel/v.fesom_channel", (_V3Dataset(_ODIE,"data/FESOM_periodic_channel/v.fesom_channel.nc"), _Purpose.TUTORIAL)), ("FESOM_periodic_channel/w.fesom_channel", (_V3Dataset(_ODIE,"data/FESOM_periodic_channel/w.fesom_channel.nc"), _Purpose.TUTORIAL)), + ("SCHISM_LakeOntario/out2d", (_V3Dataset(_ODIE,"data/SCHISM_LakeOntario/out2d.schism_lake_ontario.nc"), _Purpose.TUTORIAL)), + ("SCHISM_LakeOntario/horizontalVelX", (_V3Dataset(_ODIE,"data/SCHISM_LakeOntario/horizontalVelX.schism_lake_ontario.nc"), _Purpose.TUTORIAL)), + ("SCHISM_LakeOntario/horizontalVelY", (_V3Dataset(_ODIE,"data/SCHISM_LakeOntario/horizontalVelY.schism_lake_ontario.nc"), _Purpose.TUTORIAL)), ("NemoCurvilinear_data_zonal/U", (_V3Dataset(_ODIE,"data/NemoCurvilinear_data/U_purely_zonal-ORCA025_grid_U.nc4"), _Purpose.TUTORIAL)), ("NemoCurvilinear_data_zonal/V", (_V3Dataset(_ODIE,"data/NemoCurvilinear_data/V_purely_zonal-ORCA025_grid_V.nc4"), _Purpose.TUTORIAL)), ("NemoCurvilinear_data_zonal/mesh_mask", (_V3Dataset(_ODIE,"data/NemoCurvilinear_data/mesh_mask.nc4", _preprocess_drop_time_from_mesh2), _Purpose.TUTORIAL)), From b027ed1e551c03ed72d399d5a99045bb574dffa5 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 12 Jun 2026 15:25:12 +0000 Subject: [PATCH 2/5] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../user_guide/examples/tutorial_schism.ipynb | 176 ++++++++---------- 1 file changed, 79 insertions(+), 97 deletions(-) diff --git a/docs/user_guide/examples/tutorial_schism.ipynb b/docs/user_guide/examples/tutorial_schism.ipynb index 0416a4783..432c98e7d 100644 --- a/docs/user_guide/examples/tutorial_schism.ipynb +++ b/docs/user_guide/examples/tutorial_schism.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "99ff9ae6", + "id": "0", "metadata": {}, "source": [ "# 🖥️ SCHISM tutorial" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "0b99ba4b", + "id": "1", "metadata": {}, "source": [ "Parcels v4 supports unstructured-grid model output via [uxarray](https://uxarray.readthedocs.io/).\n", @@ -38,22 +38,15 @@ { "cell_type": "code", "execution_count": null, - "id": "69cd9463", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-12T15:08:42.638984Z", - "iopub.status.busy": "2026-06-12T15:08:42.638746Z", - "iopub.status.idle": "2026-06-12T15:09:11.820289Z", - "shell.execute_reply": "2026-06-12T15:09:11.819467Z" - } - }, + "id": "2", + "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.tri as mtri\n", "import numpy as np\n", - "import xarray as xr\n", "import uxarray as ux\n", + "import xarray as xr\n", "\n", "import parcels\n", "import parcels.tutorial\n", @@ -62,7 +55,7 @@ }, { "cell_type": "markdown", - "id": "91dc5d03", + "id": "3", "metadata": {}, "source": [ "## Get the SCHISM tutorial dataset\n", @@ -82,15 +75,8 @@ { "cell_type": "code", "execution_count": null, - "id": "f071e80a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-12T15:09:11.823179Z", - "iopub.status.busy": "2026-06-12T15:09:11.822565Z", - "iopub.status.idle": "2026-06-12T15:09:12.645338Z", - "shell.execute_reply": "2026-06-12T15:09:12.644559Z" - } - }, + "id": "4", + "metadata": {}, "outputs": [], "source": [ "grid_ds = parcels.tutorial.open_dataset(\"SCHISM_LakeOntario/out2d\")\n", @@ -101,7 +87,7 @@ }, { "cell_type": "markdown", - "id": "407360fa", + "id": "5", "metadata": {}, "source": [ "## Build the horizontal mesh\n", @@ -124,20 +110,15 @@ { "cell_type": "code", "execution_count": null, - "id": "353da75e", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-12T15:09:12.647865Z", - "iopub.status.busy": "2026-06-12T15:09:12.647612Z", - "iopub.status.idle": "2026-06-12T15:09:12.855683Z", - "shell.execute_reply": "2026-06-12T15:09:12.854879Z" - } - }, + "id": "6", + "metadata": {}, "outputs": [], "source": [ "node_x = grid_ds[\"SCHISM_hgrid_node_x\"].values.astype(\"float64\")\n", "node_y = grid_ds[\"SCHISM_hgrid_node_y\"].values.astype(\"float64\")\n", - "face_nodes = grid_ds[\"SCHISM_hgrid_face_nodes\"].values[:, :3].astype(\"int64\") - 1 # all-triangular, 0-based\n", + "face_nodes = (\n", + " grid_ds[\"SCHISM_hgrid_face_nodes\"].values[:, :3].astype(\"int64\") - 1\n", + ") # all-triangular, 0-based\n", "\n", "uxgrid = ux.Grid.from_topology(\n", " node_lon=node_x, node_lat=node_y, face_node_connectivity=face_nodes, fill_value=-1\n", @@ -146,13 +127,15 @@ "uxgrid.node_lon.values[:] = node_x\n", "uxgrid.node_lat.values[:] = node_y\n", "\n", - "print(f\"n_node={uxgrid.n_node}, n_face={uxgrid.n_face}, n_max_face_nodes={uxgrid.n_max_face_nodes}\")\n", + "print(\n", + " f\"n_node={uxgrid.n_node}, n_face={uxgrid.n_face}, n_max_face_nodes={uxgrid.n_max_face_nodes}\"\n", + ")\n", "print(f\"x range: {node_x.min():.0f} .. {node_x.max():.0f} m\")" ] }, { "cell_type": "markdown", - "id": "0837cd44", + "id": "7", "metadata": {}, "source": [ "## Assemble the velocity fields\n", @@ -181,15 +164,8 @@ { "cell_type": "code", "execution_count": null, - "id": "75a36eb6", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-12T15:09:12.858273Z", - "iopub.status.busy": "2026-06-12T15:09:12.858018Z", - "iopub.status.idle": "2026-06-12T15:09:15.109306Z", - "shell.execute_reply": "2026-06-12T15:09:15.108471Z" - } - }, + "id": "8", + "metadata": {}, "outputs": [], "source": [ "nlev = u.sizes[\"nSCHISM_vgrid_layers\"]\n", @@ -199,12 +175,17 @@ "zc = 0.5 * (zf[1:] + zf[:-1])\n", "\n", "rename = {\"nSCHISM_vgrid_layers\": \"zf\", \"nSCHISM_hgrid_node\": \"n_node\"}\n", - "U = u.isel(nSCHISM_vgrid_layers=slice(None, None, -1)).rename(rename).load() # reverse to surface-first\n", + "U = (\n", + " u.isel(nSCHISM_vgrid_layers=slice(None, None, -1)).rename(rename).load()\n", + ") # reverse to surface-first\n", "V = v.isel(nSCHISM_vgrid_layers=slice(None, None, -1)).rename(rename).load()\n", "\n", "uxds = ux.UxDataset(\n", " xr.Dataset(\n", - " {\"U\": U.transpose(\"time\", \"zf\", \"n_node\"), \"V\": V.transpose(\"time\", \"zf\", \"n_node\")},\n", + " {\n", + " \"U\": U.transpose(\"time\", \"zf\", \"n_node\"),\n", + " \"V\": V.transpose(\"time\", \"zf\", \"n_node\"),\n", + " },\n", " coords={\"zf\": (\"zf\", zf), \"zc\": (\"zc\", zc)},\n", " ),\n", " uxgrid=uxgrid,\n", @@ -214,7 +195,7 @@ }, { "cell_type": "markdown", - "id": "7b9ee5f3", + "id": "9", "metadata": {}, "source": [ "## Build the `FieldSet`\n", @@ -229,28 +210,23 @@ { "cell_type": "code", "execution_count": null, - "id": "d3a8bee0", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-12T15:09:15.111573Z", - "iopub.status.busy": "2026-06-12T15:09:15.111336Z", - "iopub.status.idle": "2026-06-12T15:09:15.119169Z", - "shell.execute_reply": "2026-06-12T15:09:15.118339Z" - } - }, + "id": "10", + "metadata": {}, "outputs": [], "source": [ "fieldset = parcels.FieldSet.from_ugrid_conventions(uxds, mesh=\"flat\")\n", "\n", "for name, field in fieldset.fields.items():\n", " interp = getattr(field, \"interp_method\", None)\n", - " print(f\"{name:>4s} -> {type(field).__name__:<11s} interp={interp.__name__ if interp else '-'}\")\n", + " print(\n", + " f\"{name:>4s} -> {type(field).__name__:<11s} interp={interp.__name__ if interp else '-'}\"\n", + " )\n", "print(\"time interval:\", fieldset.time_interval)" ] }, { "cell_type": "markdown", - "id": "897796d5", + "id": "11", "metadata": {}, "source": [ "## Stop particles that drift below the bathymetry\n", @@ -272,20 +248,13 @@ { "cell_type": "code", "execution_count": null, - "id": "9eaf228a", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-12T15:09:15.121349Z", - "iopub.status.busy": "2026-06-12T15:09:15.121159Z", - "iopub.status.idle": "2026-06-12T15:09:15.124946Z", - "shell.execute_reply": "2026-06-12T15:09:15.124155Z" - } - }, + "id": "12", + "metadata": {}, "outputs": [], "source": [ "OUT_OF_BOUNDS_STATES = [\n", - " StatusCode.ErrorInterpolation, # sampled NaN (below the local seabed)\n", - " StatusCode.ErrorOutOfBounds, # left the horizontal mesh / below the grid\n", + " StatusCode.ErrorInterpolation, # sampled NaN (below the local seabed)\n", + " StatusCode.ErrorOutOfBounds, # left the horizontal mesh / below the grid\n", " StatusCode.ErrorThroughSurface, # above the surface\n", "]\n", "\n", @@ -303,7 +272,7 @@ }, { "cell_type": "markdown", - "id": "b2275fb8", + "id": "13", "metadata": {}, "source": [ "## Release particles and advect" @@ -312,15 +281,8 @@ { "cell_type": "code", "execution_count": null, - "id": "75820fb3", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-12T15:09:15.126973Z", - "iopub.status.busy": "2026-06-12T15:09:15.126794Z", - "iopub.status.idle": "2026-06-12T15:09:16.362213Z", - "shell.execute_reply": "2026-06-12T15:09:16.361455Z" - } - }, + "id": "14", + "metadata": {}, "outputs": [], "source": [ "# number of valid (non-NaN) vertical levels at each node\n", @@ -337,8 +299,12 @@ "z = np.full(lon.size, 2.0) # release at 2 m depth\n", "print(f\"releasing {lon.size} particles at z = 2 m in the deep basin\")\n", "\n", - "pset = parcels.ParticleSet(fieldset=fieldset, pclass=SchismParticle, lon=lon, lat=lat, z=z)\n", - "output_file = parcels.ParticleFile(\"output-schism.parquet\", outputdt=np.timedelta64(30, \"m\"))\n", + "pset = parcels.ParticleSet(\n", + " fieldset=fieldset, pclass=SchismParticle, lon=lon, lat=lat, z=z\n", + ")\n", + "output_file = parcels.ParticleFile(\n", + " \"output-schism.parquet\", outputdt=np.timedelta64(30, \"m\")\n", + ")\n", "\n", "pset.execute(\n", " [parcels.kernels.AdvectionRK4, StopBelowBed],\n", @@ -352,7 +318,7 @@ }, { "cell_type": "markdown", - "id": "f4201357", + "id": "15", "metadata": {}, "source": [ "## Plot the velocity field and trajectories\n", @@ -366,15 +332,8 @@ { "cell_type": "code", "execution_count": null, - "id": "55825c33", - "metadata": { - "execution": { - "iopub.execute_input": "2026-06-12T15:09:16.364368Z", - "iopub.status.busy": "2026-06-12T15:09:16.364163Z", - "iopub.status.idle": "2026-06-12T15:09:22.680573Z", - "shell.execute_reply": "2026-06-12T15:09:22.679862Z" - } - }, + "id": "16", + "metadata": {}, "outputs": [], "source": [ "df = parcels.read_particlefile(\"output-schism.parquet\")\n", @@ -387,18 +346,41 @@ "\n", "fig, ax = plt.subplots(figsize=(11, 7))\n", "tpc = ax.tripcolor(\n", - " triang, facecolors=np.nan_to_num(speed_face), shading=\"flat\",\n", - " cmap=\"Blues\", vmax=np.nanpercentile(speed_face, 98),\n", + " triang,\n", + " facecolors=np.nan_to_num(speed_face),\n", + " shading=\"flat\",\n", + " cmap=\"Blues\",\n", + " vmax=np.nanpercentile(speed_face, 98),\n", ")\n", "fig.colorbar(tpc, ax=ax, label=\"surface speed [m/s]\", shrink=0.8)\n", "\n", "for traj in df.sort(\"time\").partition_by(\"particle_id\"):\n", - " ax.plot(np.array(traj[\"lon\"]) / 1e3, np.array(traj[\"lat\"]) / 1e3, color=\"0.3\", lw=0.6, alpha=0.7, zorder=2)\n", - "ax.scatter(lon / 1e3, lat / 1e3, facecolors=\"none\", edgecolors=\"k\", s=20, zorder=3, label=\"release\")\n", + " ax.plot(\n", + " np.array(traj[\"lon\"]) / 1e3,\n", + " np.array(traj[\"lat\"]) / 1e3,\n", + " color=\"0.3\",\n", + " lw=0.6,\n", + " alpha=0.7,\n", + " zorder=2,\n", + " )\n", + "ax.scatter(\n", + " lon / 1e3,\n", + " lat / 1e3,\n", + " facecolors=\"none\",\n", + " edgecolors=\"k\",\n", + " s=20,\n", + " zorder=3,\n", + " label=\"release\",\n", + ")\n", "\n", "elapsed_h = (df[\"time\"] - df[\"time\"].min()).dt.total_seconds() / 3600\n", "sc = ax.scatter(\n", - " np.array(df[\"lon\"]) / 1e3, np.array(df[\"lat\"]) / 1e3, c=elapsed_h, s=4, cmap=\"viridis\", zorder=3\n", + " np.array(df[\"lon\"]) / 1e3,\n", + " np.array(df[\"lat\"]) / 1e3,\n", + " c=elapsed_h,\n", + " s=4,\n", + " cmap=\"viridis\",\n", + " zorder=3,\n", ")\n", "fig.colorbar(sc, ax=ax, label=\"time since release [h]\", shrink=0.8)\n", "\n", @@ -412,7 +394,7 @@ }, { "cell_type": "markdown", - "id": "4d3c1d62", + "id": "17", "metadata": {}, "source": [ "The particles drift with the SCHISM surface currents, and any that wander over shallow water where\n", From f1eb2a0423b9c4c012af339b49fa62920d17d8e7 Mon Sep 17 00:00:00 2001 From: Erik van Sebille Date: Sun, 14 Jun 2026 10:27:23 +0200 Subject: [PATCH 3/5] Update tutorial_schism.ipynb A few minor changes to the tutorial - Change to Australian spelling (metres -> meters) (#2376) - increase the number of particles to 1500 (and set as a variable) - Add grid mesh as black lines in plot - Clarify that Parcels built-in Kernels require U and V (not Parcels itself) - Change 'drift' to the more general 'end up' - changed the Delete Kernel to use more intuitive boolean indexing --- .../user_guide/examples/tutorial_schism.ipynb | 32 ++++++++++--------- 1 file changed, 17 insertions(+), 15 deletions(-) diff --git a/docs/user_guide/examples/tutorial_schism.ipynb b/docs/user_guide/examples/tutorial_schism.ipynb index 432c98e7d..49a185595 100644 --- a/docs/user_guide/examples/tutorial_schism.ipynb +++ b/docs/user_guide/examples/tutorial_schism.ipynb @@ -23,12 +23,12 @@ "SCHISM output differs from the [FESOM tutorial](./tutorial_fesom.ipynb) in a few ways that this\n", "tutorial highlights:\n", "\n", - "1. The mesh coordinates are in a **projected coordinate system (metres)**, not longitude/latitude, so\n", + "1. The mesh coordinates are in a **projected coordinate system (meters)**, not longitude/latitude, so\n", " we build the grid as a *flat* (Cartesian) mesh.\n", "2. Velocities are stored at **mesh nodes** over a vertical column of layers, ordered **bottom → surface**.\n", "3. SCHISM uses a **localized vertical grid (LSC2)**: the number of valid vertical levels varies from\n", " node to node, and levels below the seabed are stored as `NaN`. We use this to flag particles that\n", - " drift below the local bathymetry and stop advecting them.\n", + " end up below the local bathymetry and stop advecting them.\n", "\n", "If you have not done so already, work through the\n", "[quickstart tutorial](../../getting_started/tutorial_quickstart.md) first to get familiar with\n", @@ -100,7 +100,7 @@ " describe quads), but this Lake Ontario mesh is entirely triangular; the 4th column is all fill. We\n", " keep the first three columns and convert the 1-based indices to 0-based. Parcels' `UxGrid` requires\n", " purely triangular cells.\n", - "* **Projected coordinates.** The coordinates are in metres (`standard_name = projection_x_coordinate`),\n", + "* **Projected coordinates.** The coordinates are in meters (`standard_name = projection_x_coordinate`),\n", " not degrees. `uxarray` currently assumes node coordinates are spherical and wraps longitudes into\n", " [-180, 180] (see [uxarray #1524](https://github.com/UXARRAY/uxarray/issues/1524)), which would corrupt\n", " the mesh. We undo that wrap by writing the raw metre coordinates back, and later build the `FieldSet`\n", @@ -123,7 +123,7 @@ "uxgrid = ux.Grid.from_topology(\n", " node_lon=node_x, node_lat=node_y, face_node_connectivity=face_nodes, fill_value=-1\n", ")\n", - "# undo uxarray's [-180, 180] longitude wrap of the projected metres (uxarray #1524)\n", + "# undo uxarray's [-180, 180] longitude wrap of the projected meters (uxarray #1524)\n", "uxgrid.node_lon.values[:] = node_x\n", "uxgrid.node_lat.values[:] = node_y\n", "\n", @@ -141,7 +141,7 @@ "## Assemble the velocity fields\n", "\n", "The two velocity files hold `horizontalVelX` and `horizontalVelY` with dimensions\n", - "`(time, node, layer)`. We rename them to `U`/`V` (so Parcels recognises the velocity components) and\n", + "`(time, node, layer)`. We rename them to `U`/`V` (so Parcels built-in Kernels recognise the velocity components) and\n", "to the Parcels UGRID dimension names (`n_node` for the lateral dimension).\n", "\n", "Two vertical details:\n", @@ -170,7 +170,7 @@ "source": [ "nlev = u.sizes[\"nSCHISM_vgrid_layers\"]\n", "\n", - "# placeholder interface depths (metres, positive down): surface (0 m) -> bed, refined near the surface\n", + "# placeholder interface depths (meters, positive down): surface (0 m) -> bed, refined near the surface\n", "zf = 250.0 * (np.arange(nlev) / (nlev - 1)) ** 1.5\n", "zc = 0.5 * (zf[1:] + zf[:-1])\n", "\n", @@ -204,7 +204,7 @@ "the `FieldSet`. It detects `U` and `V`, attaches the `UxGrid`, and selects the `UxLinearNodeLinearZF`\n", "interpolator (barycentric in the horizontal, linear in the vertical) because the velocities are\n", "node-registered along the layer interfaces `zf`. We pass `mesh=\"flat\"` because the coordinates\n", - "are projected metres: velocities in m/s then advect positions in metres directly." + "are projected meters: velocities in m/s then advect positions in meters directly." ] }, { @@ -229,11 +229,11 @@ "id": "11", "metadata": {}, "source": [ - "## Stop particles that drift below the bathymetry\n", + "## Stop particles that end up below the bathymetry\n", "\n", "Because SCHISM's LSC2 grid keeps below-seabed levels as `NaN`, a particle whose (fixed) depth ends up\n", "deeper than the local water column will sample `NaN`. Rather than feed it a fabricated velocity, we let\n", - "Parcels flag it: sampling `NaN` sets the particle state to `ErrorInterpolation`, and drifting outside\n", + "Parcels flag it: sampling `NaN` sets the particle state to `ErrorInterpolation`, and ending up outside\n", "the mesh sets `ErrorOutOfBounds`. We add a small kernel that runs after advection, records that the\n", "particle went out of bounds, and sets its state to `Delete` so it stops being advected (its trajectory\n", "up to that point is kept).\n", @@ -266,8 +266,8 @@ "def StopBelowBed(particles, fieldset):\n", " \"\"\"Flag out-of-bounds particles and stop advecting them.\"\"\"\n", " oob = np.isin(particles.state, OUT_OF_BOUNDS_STATES)\n", - " particles.out_of_bounds = np.where(oob, 1, particles.out_of_bounds)\n", - " particles.state = np.where(oob, StatusCode.Delete, particles.state)" + " particles[oob].out_of_bounds = 1\n", + " particles[oob].state = StatusCode.Delete" ] }, { @@ -288,12 +288,13 @@ "# number of valid (non-NaN) vertical levels at each node\n", "valid_levels = np.isfinite(U.isel(time=0).values).sum(axis=1)\n", "\n", - "# release on face centroids in the deep basin (all 3 nodes well-resolved vertically)\n", + "# release max 1500 particle on face centroids in the deep basin (all 3 nodes well-resolved vertically)\n", + "n_max = 1500\n", "cx = node_x[face_nodes].mean(axis=1)\n", "cy = node_y[face_nodes].mean(axis=1)\n", "deep = (valid_levels[face_nodes] >= 15).all(axis=1)\n", "idx = np.where(deep)[0]\n", - "idx = idx[:: max(1, idx.size // 150)][:150]\n", + "idx = idx[:: max(1, idx.size // n_max)][:n_max]\n", "\n", "lon, lat = cx[idx], cy[idx]\n", "z = np.full(lon.size, 2.0) # release at 2 m depth\n", @@ -323,7 +324,7 @@ "source": [ "## Plot the velocity field and trajectories\n", "\n", - "We plot the surface speed across the triangular mesh (in projected kilometres), the particle release\n", + "We plot the surface speed across the triangular mesh (in projected kilometers), the particle release\n", "points, and their trajectories coloured by time since release. The lake currents are slow (~0.1 m/s), so\n", "over the 5-hour window most particles move only a kilometre or two, while those caught in the faster jet\n", "near the north-eastern outflow (towards the St. Lawrence) travel noticeably further." @@ -352,6 +353,7 @@ " cmap=\"Blues\",\n", " vmax=np.nanpercentile(speed_face, 98),\n", ")\n", + "ax.triplot(triang, color=\"k\", lw=0.2, alpha=0.35)\n", "fig.colorbar(tpc, ax=ax, label=\"surface speed [m/s]\", shrink=0.8)\n", "\n", "for traj in df.sort(\"time\").partition_by(\"particle_id\"):\n", @@ -397,7 +399,7 @@ "id": "17", "metadata": {}, "source": [ - "The particles drift with the SCHISM surface currents, and any that wander over shallow water where\n", + "The particles move with the SCHISM surface currents, and any that wander over shallow water where\n", "their release depth falls below the seabed are flagged (`out_of_bounds == 1`) and stop.\n", "\n", "From here, the rest of Parcels works exactly as on structured grids. To go further with SCHISM data:\n", From 61e480648c21ddbc0422d4453a44536c813d5e44 Mon Sep 17 00:00:00 2001 From: Joe Schoonover Date: Sun, 14 Jun 2026 11:20:54 -0400 Subject: [PATCH 4/5] Switch to using parcels.StatusCodes directly (now public API) --- .../user_guide/examples/tutorial_schism.ipynb | 4078 ++++++++++++++++- 1 file changed, 4053 insertions(+), 25 deletions(-) diff --git a/docs/user_guide/examples/tutorial_schism.ipynb b/docs/user_guide/examples/tutorial_schism.ipynb index 49a185595..42ca3f9e5 100644 --- a/docs/user_guide/examples/tutorial_schism.ipynb +++ b/docs/user_guide/examples/tutorial_schism.ipynb @@ -37,10 +37,1194 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new 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\"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.8.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.8.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.8.2.min.js\", \"https://cdn.holoviz.org/panel/1.8.7/dist/panel.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false;\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0;\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof 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root.Bokeh = Bokeh;\n }\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } 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((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n let retries = 0;\n const open = () => {\n if (comm.active) {\n comm.open();\n } else if (retries > 3) {\n console.warn('Comm target never activated')\n } else {\n retries += 1\n setTimeout(open, 500)\n }\n }\n if (comm.active) {\n comm.open();\n } else {\n setTimeout(open, 500)\n }\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = 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node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n 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undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true;\n", + " root._bokeh_onload_callbacks = [];\n", + " const bokeh_loaded = Bokeh != null && ((Bokeh.version === version && Bokeh.Panel) || (Bokeh.versions?.has(version) && Bokeh.versions.get(version)?.Panel));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " if (Bokeh != undefined && !reloading) {\n", + " const NewBokeh = root.Bokeh;\n", + " if (Bokeh.versions === undefined) {\n", + " Bokeh.versions = new Map();\n", + " }\n", + " if (NewBokeh.version !== Bokeh.version) {\n", + " Bokeh[NewBokeh.version] = NewBokeh;\n", + " 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OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2975658/2313679893.py:7: UserWarning: This is an alpha version of Parcels v4. The API is not stable and may change without deprecation warnings.\n", + " import parcels\n" + ] + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.tri as mtri\n", @@ -49,8 +1233,7 @@ "import xarray as xr\n", "\n", "import parcels\n", - "import parcels.tutorial\n", - "from parcels._core.statuscodes import StatusCode" + "import parcels.tutorial" ] }, { @@ -74,10 +1257,990 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading file 'data/SCHISM_LakeOntario/out2d.schism_lake_ontario.nc' from 'https://github.com/Parcels-code/parcels-data/raw/main/data/SCHISM_LakeOntario/out2d.schism_lake_ontario.nc' to '/home/joe/.cache/parcels'.\n", + "Downloading file 'data/SCHISM_LakeOntario/horizontalVelX.schism_lake_ontario.nc' from 'https://github.com/Parcels-code/parcels-data/raw/main/data/SCHISM_LakeOntario/horizontalVelX.schism_lake_ontario.nc' to '/home/joe/.cache/parcels'.\n", + "Downloading file 'data/SCHISM_LakeOntario/horizontalVelY.schism_lake_ontario.nc' from 'https://github.com/Parcels-code/parcels-data/raw/main/data/SCHISM_LakeOntario/horizontalVelY.schism_lake_ontario.nc' to '/home/joe/.cache/parcels'.\n" + ] + }, + { + "data": { + "text/html": [ + "
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+       "    SCHISM_hgrid_face_nodes  (nSCHISM_hgrid_face, nMaxSCHISM_hgrid_face_nodes) float64 18MB dask.array<chunksize=(274738, 2), meta=np.ndarray>\n",
+       "    depth                    (nSCHISM_hgrid_node) float32 1MB dask.array<chunksize=(295484,), meta=np.ndarray>
" + ], + "text/plain": [ + " Size: 32MB\n", + "Dimensions: (one: 1, nSCHISM_hgrid_node: 295484,\n", + " nSCHISM_hgrid_face: 549476,\n", + " nMaxSCHISM_hgrid_face_nodes: 4)\n", + "Coordinates:\n", + " SCHISM_hgrid_node_x (nSCHISM_hgrid_node) float64 2MB dask.array\n", + " SCHISM_hgrid_node_y (nSCHISM_hgrid_node) float64 2MB dask.array\n", + " SCHISM_hgrid_face_x (nSCHISM_hgrid_face) float64 4MB dask.array\n", + " SCHISM_hgrid_face_y (nSCHISM_hgrid_face) float64 4MB dask.array\n", + "Dimensions without coordinates: one, nSCHISM_hgrid_node, nSCHISM_hgrid_face,\n", + " nMaxSCHISM_hgrid_face_nodes\n", + "Data variables:\n", + " SCHISM_hgrid (one) |S1 1B dask.array\n", + " SCHISM_hgrid_face_nodes (nSCHISM_hgrid_face, nMaxSCHISM_hgrid_face_nodes) float64 18MB dask.array\n", + " depth (nSCHISM_hgrid_node) float32 1MB dask.array" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "grid_ds = parcels.tutorial.open_dataset(\"SCHISM_LakeOntario/out2d\")\n", "u = parcels.tutorial.open_dataset(\"SCHISM_LakeOntario/horizontalVelX\")[\"horizontalVelX\"]\n", @@ -103,16 +2266,25 @@ "* **Projected coordinates.** The coordinates are in meters (`standard_name = projection_x_coordinate`),\n", " not degrees. `uxarray` currently assumes node coordinates are spherical and wraps longitudes into\n", " [-180, 180] (see [uxarray #1524](https://github.com/UXARRAY/uxarray/issues/1524)), which would corrupt\n", - " the mesh. We undo that wrap by writing the raw metre coordinates back, and later build the `FieldSet`\n", + " the mesh. We undo that wrap by writing the raw meter coordinates back, and later build the `FieldSet`\n", " with `mesh=\"flat\"` so Parcels treats the plane as Cartesian." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "n_node=295484, n_face=549476, n_max_face_nodes=3\n", + "x range: 1364569 .. 1722587 m\n" + ] + } + ], "source": [ "node_x = grid_ds[\"SCHISM_hgrid_node_x\"].values.astype(\"float64\")\n", "node_y = grid_ds[\"SCHISM_hgrid_node_y\"].values.astype(\"float64\")\n", @@ -163,10 +2335,1818 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "8", - "metadata": {}, - "outputs": [], + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.UxDataset> Size: 454MB\n",
+       "Dimensions:  (time: 6, zf: 32, n_node: 295484, zc: 31)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 48B 2025-08-18T01:00:00 ... 2025-08-18T06:...\n",
+       "  * zf       (zf) float64 256B 0.0 1.448 4.097 7.526 ... 214.6 226.2 238.0 250.0\n",
+       "  * zc       (zc) float64 248B 0.7242 2.773 5.812 9.557 ... 220.4 232.1 244.0\n",
+       "Dimensions without coordinates: n_node\n",
+       "Data variables:\n",
+       "    U        (time, zf, n_node) float32 227MB -0.3555 -0.3725 ... nan nan\n",
+       "    V        (time, zf, n_node) float32 227MB 0.2883 0.2322 0.3839 ... nan nan
" + ], + "text/plain": [ + " Size: 454MB\n", + "Dimensions: (time: 6, zf: 32, n_node: 295484, zc: 31)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 48B 2025-08-18T01:00:00 ... 2025-08-18T06:...\n", + " * zf (zf) float64 256B 0.0 1.448 4.097 7.526 ... 214.6 226.2 238.0 250.0\n", + " * zc (zc) float64 248B 0.7242 2.773 5.812 9.557 ... 220.4 232.1 244.0\n", + "Dimensions without coordinates: n_node\n", + "Data variables:\n", + " U (time, zf, n_node) float32 227MB -0.3555 -0.3725 ... nan nan\n", + " V (time, zf, n_node) float32 227MB 0.2883 0.2322 0.3839 ... nan nan" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "nlev = u.sizes[\"nSCHISM_vgrid_layers\"]\n", "\n", @@ -209,10 +4189,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "10", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " U -> Field interp=UxLinearNodeLinearZF\n", + " V -> Field interp=UxLinearNodeLinearZF\n", + " UV -> VectorField interp=-\n", + "time interval: TimeInterval(left=np.datetime64('2025-08-18T01:00:00.000000000'), right=np.datetime64('2025-08-18T06:00:00.000000000'))\n" + ] + } + ], "source": [ "fieldset = parcels.FieldSet.from_ugrid_conventions(uxds, mesh=\"flat\")\n", "\n", @@ -247,15 +4238,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "12", "metadata": {}, "outputs": [], "source": [ "OUT_OF_BOUNDS_STATES = [\n", - " StatusCode.ErrorInterpolation, # sampled NaN (below the local seabed)\n", - " StatusCode.ErrorOutOfBounds, # left the horizontal mesh / below the grid\n", - " StatusCode.ErrorThroughSurface, # above the surface\n", + " parcels.StatusCode.ErrorInterpolation, # sampled NaN (below the local seabed)\n", + " parcels.StatusCode.ErrorOutOfBounds, # left the horizontal mesh / below the grid\n", + " parcels.StatusCode.ErrorThroughSurface, # above the surface\n", "]\n", "\n", "SchismParticle = parcels.Particle.add_variable(\n", @@ -267,7 +4258,7 @@ " \"\"\"Flag out-of-bounds particles and stop advecting them.\"\"\"\n", " oob = np.isin(particles.state, OUT_OF_BOUNDS_STATES)\n", " particles[oob].out_of_bounds = 1\n", - " particles[oob].state = StatusCode.Delete" + " particles[oob].state = parcels.StatusCode.Delete" ] }, { @@ -280,10 +4271,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "14", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "releasing 1500 particles at z = 2 m in the deep basin\n", + "INFO: Output files are stored in output-schism.parquet\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/joe/Projects/Geomar-Utrecht/Parcels/src/parcels/_core/spatialhash.py:592: RuntimeWarning: invalid value encountered in cast\n", + " xq = np.clip((xn * bitwidth).astype(np.uint32), 0, bitwidth)\n", + "/home/joe/Projects/Geomar-Utrecht/Parcels/src/parcels/_core/spatialhash.py:593: RuntimeWarning: invalid value encountered in cast\n", + " yq = np.clip((yn * bitwidth).astype(np.uint32), 0, bitwidth)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1434 of 1500 particles still active at the end of the run\n" + ] + } + ], "source": [ "# number of valid (non-NaN) vertical levels at each node\n", "valid_levels = np.isfinite(U.isel(time=0).values).sum(axis=1)\n", @@ -332,10 +4349,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "16", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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/Z8+enameNacbZvndzyh4WrdurbYT9xkKRH4ej9Wc1+TJk3NcD1y/FJb8flzvhMcJe3gs43rgjQN+BvdHirT8HLfzsrz8TfTu3Vv9Fnic4PGT43gciIyMVL+H6x1Xec7iNFxeHjMp8HgcoDDmvvnCCy/c8HtyWbPC88Rtt92mtg+n5brjb4bHUwozTXgza4bCj9+LIo77An8P2o3T0ogYhwgFxplpiroyKvQtQp64fPmypWPHjry9rwZXV1dL+/btLR988IElLi7ONt2HH36oXv/3339zNd8TJ07Y5nm9gdNp3HfffRZvb+9M8/nnn38sgYGBtukrVKhgGTRokOWvv/5y+HmffPKJWu5y5cpZvvnmG/Xa2LFjLTVq1LCYzWbLqFGj1HQ3Yty4cWq6S5cuWXKL0Wi0pKWlWbp3724ZMGCAw2Xj4/r166vh5MmTtmk2bNigpvnss88yzfP06dMWT09PywsvvJDj50ZFRan3Dhky5LrLN3jwYDXdhQsXMn3Hjz/+ONN0TzzxhMXDw0OtLw1uF26fgq6DmjVrWlJTUzO9R3tt8uTJtnFt27a1VKpUKdM+yHk3bNjQUrVq1UzLlpUnn3zS4ufnd93l1L57VrgMWffLsLAwi8FgsBw6dCjb9J07d7Y0aNDgup9lMpnUOnn77bfV/qst+/Hjx9V8hw8fnuN7ExISLAEBAZZ+/fplm2eTJk0srVu3vu5n8zP5fZYuXZrjNCtWrFDT8O+Ntgv3AY77+eefs82H48uXL2+5evVqpvG33nqr2mYxMTHZthP3M216bTn69OmTabrff/9djedvROP2229X2yU/294Ry5YtU5+xevVq9Xz69OkWHx8f9Vvo2rWrbbratWtbhg0bdt39Jadl09Zno0aN1L6ssXnzZjV+5syZ111G7bPsf1dk3bp1avy7777r8H3c37j/nTp1Sk03f/78bL+DN954I9v7rnes5PezPx7kZj8jnIafqfHYY4+pYzWXzZ5PP/1UTbtv377rzo+/Pf4Gs6LtS506dbLk95hVkOWdOnWqev7TTz9d97NzOq7yWO7u7m6JjIzMNL53794WLy8vy7Vr1274PR39riMiIizNmjVT39eevn37WoKDg9VxhfA4e+edd1puBnjc4XpYtTfEsu1UqFMGzovzzHpME25OtH3IvcubFo8eHzplcO/yZpnch8pm/LAA8A4d7/YxpYepcLxbf/jwYXVnjneLC+puyDoozjvrwPG5gXeYeeeRdzqZftGgQQN1t5538pla5QimsNDEhJEg3r2cOnWqLT3N2fBuJKOPTAlhWiHvUPNuLO/6O4rGsEaAd+JZQ0aXSg2mPHL57r33XrXM2sA7vUzdcoZDl5Zql3U9cF3aw+gVa4kuXrzo9HXAz3IUqbWHd23pOMq75PZun4yi0EiGxij26W9Z4Z1uRiuZwjl//nynOHRynTACmFsYsWR0hnequdz8zrzzzjvR2nplhIZRTUZ9c4IRYEYrmRprv18wEs074vwtcX3lxKJFi9Ryc1mcCaOvjmBkgNFrDe5H3BdYE8k0TfvvwN82X8+a6udofyT20UVnb3tG7rj/MlKmbRtGVLiOuQ0YZWDUimmbBV2XTKu0j2Tn5fuRrPWPjLjwWMIoiQb3MUbFGEHXfpPa8cbR7zKn7Zlb8ruf8bjHSBJTW+33DUahtIh6Qcjpe+XlmJWf5eX64LyZOp4fePxghJzbzx5GurgvZnXrzc32Y0SZqafa/pP1t8iIp3Zc5e+I34GRcp57nFlrXlyYoHfqIJRBJN2xwMgvJ58wVY5pKnRAPHfuHJ577jmVvqGZh2hpW0zJyQtMv+O8sw45peU5gilHTLP85JNP1EmQJxumlTFtiDUJOaUEURS99957Kr0lP3bRN4LpQ//73/9U6gjTKHmxyYtmXtg5Oqnxwo+pKkxB0tKSNDieIooCjhcM9gPne72LTdYU8gL4RtuG25PTsb4gq1C3R0sZys2JOa/rIKd0KXuYpsp14WhaXhyR66XdUMhRoPOilxcvTP3h8nH955fcLLfG5s2bVWou+emnn5Qg5zphnRXR1ouWdnW93wL3C0LBmnW/YEos15OWcuoIfkZefmu5gftQTi6pWdcTtxMvAuk8mnX5eWFIsu7bBdkf87vteUFNoaaJNM14iEKNQpo3srR5FFSkFeT7Ed64cTRO+01QwHP/Y/0c0+L4XbhPamI4v7/L65Hf/Yz7Nx17s+4bvBlHCnqDxdH3yusxKz/Ly/XBY5WjuuncwG2Zl+Nfbrafdizhzc6sy88UYfvlZ4olrwd4Q5SilOcMnoN5k0IQBCG/SE2aE+BBm/n6tBVnHjrhgZrjedDmHdrihIKRdR/M9adI006Q9vCCiyYirOnixVbWO5LOgDUCvIijUUHW+gxH0P2StSKsL+KFq70BC4UWI1y8GMxaV0EcjdPgXXlun3///VdFmRxdLHH8tm3b1B1f+7v4Rb0OchPNZCSGFze8s5sV3kDQ1tf1YOSUA6NMrIvh/kzzD0aJGVHgRblW3G+/bnO6KMxLFJY1Hvyt8K679jkkq9W25grIbZPT/ql9T4qcnJzacqpV1D6D878e9uvCnvysi6yvcVtqEdCcIoas8XMmN9r2OcHIBaOdFDRcZzxu0HiE9WgUaNz3GC0qjGNJXnBUa8RxNAchPGazhpX1rYzAavDmVk4UNMsgN/tZTvs3I4m8meYITZTkF0ffK6/HrPwsL9cHjY8omPMj1Cjk83L8y832097DLBnWIDqC50xCQxrWgnOguNOiaqwNvZERTEnF4kTDD85LKINITVqBkUhaHnF0IiBa2od20uGdWkaAWPDP9EFHUICweN1Z8KTJou7cLJ8jWFDPk4qjptzOgCfGrOKJ3z+nxtE8WdPwgamejOzZXyTwIpJRERpLOIo85mRUoMETL9/PO6K8828Pn/POMV/XTEHyCr+no7vMeV0HuYEXCLzLzUiA/WfygocXWBShuU095LwoTBnFouGFFnnVDGiy7q/O6POn2Xzbi2F+Dxbi28NoB6fJerGY9WYDo640vnC0X3Cgw1xO8LtTnDB9KidyWhd//fUXnBF14w2EHTt2qItbR8ufNbJUkP0xN9s+Jxgh480TmjhwH9PMLjieETYthdUZy1YQNJMaDaZjMnKoGV5oF+xZf5c89uSFvET4crOfOYLHPa2NhaN940YiLT/ruiDHrNwuL9cHU3lv1MMtp+XnDQOuS02UafDcy99Ufqz1KcBo1EQBn9OxhDclHN0E4vmKKcRMh2S6ZWlE+qQJBUbSHQuMRNLyCB3ieEFCMcOLEl4I79y5U7mwsR7IvnaMaSK0euYBm2KNdSY8gPOOO+8009mKUQRn2fDzhMDlo+tY586dVUoHU+Focf3jjz+qixLWY+QE67s4FARetDs6cTH9jCdsOvPxTj2Xj8vLyB0jA9ezj+a65TwpqChCGWHjxTijg4wA0CK5U6dO6iKTIpp3ZCnSKLRygu+nWxyji3RZY70eI46s52NaKGu8+Pr11tf14OezNoHrg9uBy8+Tfn7XQW5aCjCSwQt8pudQiNDBjRdIdHe83p1j9tBjiizXCZeVUQbOj/VhmksfU+2YwsO0WC4vRRUvqFh3VFBYc8TfCi3nuU2ZmkSnwawXhhRHdDLk+uOFmtYGgYKMvynexeZvkFE0RkSY1sj9TnOo48UW/15P5HF/YI9A1pryTjhrTfhZTBvmtuP65Q0Yig+uI0a+GG1iihxFsjP48ssv1T55yy23qH2Y35s3YBjZ4f6U1wt7bX/k8vG707WUN0B4kZmbbZ8TnA+//5IlS9TvUIPrhttIe5zfZXMWPD7whhnrbrm/UoTSel1LWeNxnCKC25s3Zrifcz3nNd1XuzHEtFotAs9ju6ObArnZzxzB3x6Xi8clun3ymEJxw9RsNtNm7dj10ii5jDzn8LNp/c+o8I1uaBXkmJXb5eVvmedDZp1w/vz+PLfyOEzXYp7Trndc5bJp9W+M7nIbUpzz3McShPy2kqFQ57bkeZXnce43PK7wpifLA1juQHiTjOuJ25u/Cb7Om0x0kLxRDz1BEIQcKW7nktLGrFmzlFsZXcvoWkV3x2rVqllGjBhh2b9/v0MnrClTpli6deumXOdcXFwsFStWVK5TM2bMsLlD2TsaOoLjb+TuGB0drRzL+FkhISEWNzc39XrTpk3V+MTERNu0N/o8jby6O+Y0kJSUFMuYMWPUstGlrnnz5pZ58+ap72Hv7pbTsmnrwN5Zja55bdq0Ud+Tro50Qxw5cqRl69atltxAB7y7777bUrlyZbVt6JA4cOBAy/r163PtYOnIsW7nzp2WDh06KGcxvqY5qhV0HeTkIkjWrFmjtr22Luj4uGDBghuuA+6fdOTjOuA+U6VKFcs999xj2b17d6bp6KpHJ1POn8vP9TFx4kSH7o507HNETu6O3I5169ZVDm3h4eHKLXXSpEnZ5q05wbVq1UqtP/4G6b6WdV2sWrVKLQN/c/yNcnn5/I8//rjh+uDv6JlnnlG/a76X+wTfe/DgQds058+fV/sN50+HxnvvvVftc47cHbM6sGpwWv6+HMHv/OCDD6rl5jLwmMF1b+9IqLnRZf1OjvYPOkJyeenkqNPpbL/J3G77nKC7H+f166+/2sbRjZTfWa/Xq3V5o99KTst2vd9AVhdBR2iftWTJEnV85vz5u6Ab5pEjRzJNy2N3z549lUOlv7+/csSlU2DWz7meiy1/2w8//LDaVtr30L5nVnfH3O5njr4nP/vpp59WDrx8H/fBFi1aWF599VVLfHz8ddcJHXJ79eqlvifnrR1zctqX8nLM0pb3zTffzNfyJiUlqWM7z63cF+nsyuOZ/bE4p+Mq2bNnj3J15e+R76eba9bjwvW+Z06urbt27VK/CW4fLn9QUJBaru+//942zUsvvWRp2bKl2ne0Y9hzzz2n3KBLqzPfot01LKtP1HTKwHmVRWc+S1l3d+zxvsXjtvFOGdx7vF8m9yEd/5ezhBMEQRCE0gcjvYzw0eTCmZE5wTHsf8hUY0ayc3ISFko+7LXIyOOi3TXg7eOcipiEODN6Nz6h9pGcTJSEm28fcu/xPnQuGXXmBcFiTEbKslfK3D4k6Y6CIAiCIOQbOj4yhZIwxU8o/Zihg9lJtgVmlVAjlD301ro0Z82rDCIiTRAEQRCEfMN6UhousX6YdYVC6UczDnHWvIQyiLg7FhgRaYIgCMJNB40eCqPfo5AdGmQJgiAIzkVEmiAIgiAIgmDDZNGrwRmYxPqgDEfSnJSmqCub0diymeQpCIIgCIIgCIJQQpFImiAIgiAIgpDFOMQ50QtnzUcopc2snTWvMoiINB5AzGacO3dONca8XtNfQRAEQRAEZ8NuSGxaX6VKFdXQvbihs6NJ3B2FgiDGIQVGRBqgBFpoaGjB16YgCIIgCEI+OX36NKpWrSrrTxAEEWmEETTt4FiWmuQJgiAIglAyGgDzZrF2PVLciHGIUGAk3bHASCRNRWStKY4UaCLSBEEQBEEoDqTkQhAEDRFpgiAIgiAIQqaaNA7OwAyLrNmyiNSkFZjir04VBEEQBEEQBEEQbEgkTRAEQRAEQbBhsujU4AycNR+hlCE1aQVGRFoeMJlMSEtLK/haF/KNm5tbibAnFgRBEISbFZMTLfhNku5YNpF0xwIjIi2X/UuioqJw7dq1gq9xoUBQoNWoUUOJNUEQBEEQBEG4GRGRlgs0gVapUiV4eXmJ+1IxNx0/f/48qlWrJttBEARBEArjfGvRq8E58xLjkLLqVOo0t1Jd2UyZFZGWixRHTaBVqFChaLaKkCMVK1ZUQs1oNMLV1VXWlCAIgiAIgnDTISLtBmg1aIygCcWPluZI8SwiTRAEQRCcj9SkCQVFImkFR0RaLpEGkyUD2Q6CIAiCULiYnejKyHkJZRDuPs7KUtShTCI2eYIgCIIgCIIgCCUIEWlCJrp06YJnn31W1oogCIIglFHM0Dt1EMpuuqOzhrKI/HIEQRAEQRAEQRBKECLSyhCpqanFvQiCIAiCUGLZve8QpkyfibKOyaJ36iCUPSSSVnDkl1NEbNu2DU899RSGDBmCzz//vEgaYzN18cknn8To0aMRGBiInj17Yv/+/ejTpw/KlSuHypUrY8SIEbh8+fJ1hd0LL7yAkJAQeHt7o02bNli5cqXt9StXrmDo0KGoWrWqcsBs1KgRZs7MfIKbPXu2Gu/p6anaGPTo0QMJCQm21ydPnox69erBw8MDERER+O677wppjQiCIAhCzuzYsd32mC7CljLa48sMnVMHoewhIq3giEgrAr766iu0bNkS8+fPV42xX3zxRTRu3BgnTpwo9M+eMmUKXFxcsG7dOnz44Yfo3LkzmjZtiq1bt+Lff//FhQsXcM899+T4/gceeEC997fffsPu3bsxaNAg3HbbbThy5Ih6PTk5GS1atMDff/+NvXv34tFHH1XCb9OmTep1Np6miHvwwQdx4MABJfAGDhxoO/H99NNPePXVV/Hee++p199//328/vrrarkFQRAEoThgNC2o78twr3Erxs9ZIRtBEIQiRyz4C5nIyEg899xzyozj008/hcFgUOM6deqkxs+bN69QP79WrVr4+OOP1eM33ngDzZs3V0JI4+eff0ZoaCgOHz6MOnXqZHrvsWPHVFTszJkzqFKliho3ZswYJe4Y/eJ8GGHjOA1GC/n6H3/8oaJuFGlsPE1hFhYWpqZhVE3jnXfewWeffaZeJzVq1FDRvh9++AH33Xdfoa4bQRAEQXDEvvMxiDmwE7fc2RNeMWfK3EpyZpqipDuWTaRPWsERkVbIMNXP3d0d7777rhJopFq1anj++eeVcGPaH9MICwtG8OxTLlesWKFSHbNCQZZVpG3fvl1FvLKOT0lJUWmLWjoII3SzZs3C2bNn1WsctO/UpEkTdO/eXQmzW2+9Fb169cLdd98Nf39/XLp0CadPn8ZDDz2ERx55xDZ/irry5cs7fV0IgiAIgj0nz0Rh1coVGDJoIFwN8QjwOYcDZ3xwISFFvb5m1V4s+vgZWWmCIBQ5ItIKmaSkJCXSWG9lD0WK2WxWNV+FKdLs583P69evHz766KNs0wUHB2cbx+kpLCnuNIGpoQk9RsFYY/fFF18oIcbPo/jUTEr4vqVLl2L9+vVYsmQJvv76a5XeyHRI1rBpKY+MutmT9fMEQRAEwRnw5iNvJvK8rNdbo0XLFn+FHi1+we1t0lA7yAWfPhwM6N3w5VM94ObmVuZWvAl6NThrXkIZRJpZFxgRaYUMzTpee+01lTZ47733qnFpaWkqnY9RLoq1ooKpjnPmzEH16tVVndqNaNasmYqUXbx4EbfccovDadasWYM77rjD9t0o7FivRiMQ+5B3hw4d1MCUS6Y9zp07VxmaMF3y+PHjGD58uBO/qSAIgiA4Zubvc9R5+L57hyK4oj/a3lILnskTYNCnqddrVzNi8gf1ceh0C3i5lc3LJLNFpwZnzUsoe0i6Y8Epm0efIqRVq1YYPHgw7r//fixatAi1a9dWQunQoUPqeVEyatQoFbWikcfYsWOV4+PRo0eVKQjHZ41eMc2R4mnkyJEqYkbRRifI5cuXq6gZXSJZ88bvw0gZBef48eOVOYom0hgx+++//1SaY6VKldRzpjlqr7/55pt4+umn4evri969e6u7mzQ1iY6OViJOEARBEJwBhdmR45Hqr2YO4ul9Hm1b/ogKPhkehOyb6+3mCz8vVzRr1lxWviAIxYKItCK4kzB9+nS0bt1amXQsW7ZMpfZRFLVt2xZFCc0/6NRId0nWh1EQMapFt0Yt5SMrNAhhPR1r6Fhzxlq0du3aKYFG6MRIl0rOj+mLdHe88847ERMTo16n+Fq9erVKh4yNjVWfR8FHQUYefvhh9b5PPvlEWf0zXZICkCmTgiAIguAs9uw/jD17dsPV1dUm1Do0mIsgF7NtmjSzHsfOtsSpi/XRuFkLNG5Qt0xuALMT0x05L6HswZsdvAZ2zsxQJtFZymoTEDsoHmhUQWFBUWEPLeYpQug6mLWuTCh6ZHsIgiAI+YHp+NNmzFKPdS6JMNdahLuq7IW3PkOkrT3dGO2b/ZarkoCiug4pjuX4cEtneJRzzjpIjjfipVariv27CUW7D5W/50foXK3eAwXFkpaImN8fLXP7kETSBEEQBEG46dEyRgLLn0Sn5lPgojcjzgzQXou3q5OM7qjsP67IBVpJxGzRq8FZ8xLKHjr+c1YkDWUzlFasv5y4uDiV1sYUOE9PT7Rv3x5btmyxvc46rqwdy7OmCDJlj725WF/FVLn+/furvl6CIAiCIAha/RmH416X0brJTHjpzaBnYwU9sC9Fh38u1sK+81+ibq2MPp5lGRN0Th2EskfW6/eCDmWRYhVprEeiPfu0adOwZ88eZS7Ro0cPVfukwXopNkTWhoULF2aaB0UenQJpfrF27VrEx8ejb9++ypVQEARBEISyzenzl9Tf3ZaDMNdcCm+X1PR6GetwNs0T4cFvok3rbsW9qIIgCDZcirN/GF0B58+fj06dOtmc/ubNm4cJEyYoswrCHmNBQUEO58Hc1EmTJimRR3FHaNIRGhqqDDpoZiEIgiAIQtmEN26X/7cMevdr6N58DjwMaThh1KFeeuuz0/H+uLD9fnTp37i4F7VEIemOQoGRPmmlN5JmNBpVtCurGQfTHhkR01i5cqWybqcd/COPPKJ6dmmwyTIdmhiBs3cwbNiwobKEzwmmSLKw0X4QBEEQBKH0k5qaqhpWk6XLV8ECC86FLoVbeh+0KJMFs2MDcO/8zli67imMuPsxMQYTBKHEUWwizcfHR1m5v/POOzh37pwSbIyCsY8W0xoJbdp//fVX1ZeLtu2sV+vWrZsSWYT9uNzc3LI1hK5cubJ6LSc++OADq/NM+sDIW25coYTiR8xIBUEQhBs1q167YQuuXbumbsLucT+L8uUvZ6qMio73R1ePzvDz9c2xBU1ZhgUjzqtJE8okzqxH05XNmrRitTBimuKDDz6IkJAQ1Ui5efPmGDZsGLZv365eZxNoDUbHWrZsqUxG/vnnHwwcOPC6F/LXKzJ8+eWXMzVK5kE8J6FGEcgDOIVkxYoV1fOyWsBY3HC7shE21z/73AiCIAiCI44fP4ajx45iaWw0dE0PwivVHwEuCfB3SUBUSnmMqD8DXs0z3+AVMpB0R6GgONPwQ1dGr7uLVaTVrFkTq1atQkJCghJKwcHBSpixJ5kj+DpF2pEjR9Rz1qoxrSE6OjpTNI0pkXSKzAnWuXHIDRRoXB5G9yjUhOKFP9SqVasqUS8IgiAIGlevXsXixYvV41SjEZ9euAhLtUS00jOaY8CupDBltX8tLgwjIkSgCYJQsikRzUBonc+BYosH2I8//tjhdFeuXMHp06eVWCMtWrRQERU6RN5zzz1qHMXU3r17c5xHfmD0rFq1arY6OqH44PYWgSYIgiDYw/r0BQutAo18fe4S0nws4P28i8nl4GVIgZ9rMmLSPPB6k1dk5d0Ak0WvBmfgrPkIpQuJpJVykUZBxhS2unXr4ujRoxg7dqx6/MADDyhHJro93nXXXUqUnTx5Eq+88orqhzZgwAD1ftaTPfTQQ3j++edRoUIFBAQEYMyYMWjUqJHN7dFZaCl2kmYnCIIgCCUL3sS9fMFalrB633kkBroAFsBk1ONCjA+MZgNYWt7BuwNCfSoW9+IKeYA+Arz+e+aZZ/DFF1/IuhPKDMUq0mihz/owNp+mwKIge++995QQYtSKvdOmTp2qin8p1Lp27YpZs2Yp0xGNzz//HC4uLiqSRlv/7t2745dffpFoiyAIgiCUAS5cuKBq1tlj1dezCmqMHA1DOcDsrgOuuiE+xYC0q+XQBf547wHrTV7h+ligg9lJTag5r/xCw7gff/wRjRtLi4RSh1jwl26RRmGlpSlmhVb8Wm759aCF/9dff60GQRAEQRDKDiyTYJ0yb+w2rng7yrfsiDidDoYEC3QmHcwuenieMWDTm09g7ty5ZdaAoDTCjKrhw4fjp59+svXOFYSyhCQKC4IgCIJQKmFtOgVau/IDULFGS0Cvg0sS4JKigyEZcI/W4cP+PdUN3aFDhxb34pa6mjRnDflh1KhRuP32251eviIUDc6y39c50SWytCEiTRAEQRCEUsfu3btx4sQJNKwYir53uyI8LBqGZDNcks3wiAa8zlsQevYyerdvVNyLWuowW3ROHQhdvO0HreetI3777TfVjon1aELppCSItO+++045tPMmDW/orFmzJlfvW7dunSqlatq0KYoTEWmCIAiCIJQ6xr31Dpo0csOOnR546d0dmPDlIvRscxgeV4zwOpWIY/N/wPB+DYp7MYV02I+Whm/akJMAo4s3TUKmT5+uLq4FIT/Qw+LZZ5/Fq6++ih07duCWW25B7969ERkZeUO/jJEjRyqPi+KmRFjwC4IgCIIg5AV/fz3+WBzGDEcbDw7ehv9+dsHyqImqH2pOde/C9TFBrwZnoM2H4svX19c2Pqd+tdu2bVP9bhn5sM3DZMLq1avxzTffqAictOIp+RS3Bf/48eOVA/zDDz+sntMZlF4XEyZMuG6E9rHHHsOwYcPUPjZv3jwUJyLSBEEQBEEodXz39o5MAo3XcXp9MlZETVLPV61aJW1zShAUaPYiLScYwaC7tz1szRQREYEXX3xRBFoZJjY2NtNzCn1HYj81NVWJ/ZdeeinT+F69emH9+vU5zn/y5Mk4duyYiuKWBLMaEWmCIAiCIJQqjp+oi7AsmXAWCzDq9XNKrY154SW0adOmuBav1GNfS+aMeeUFtllq2LBhpnHe3t6qH27W8ULZiqSFhoZmGj9u3DjVUzkrly9fVtHXypUrZxrP51FRUQ4/48iRI0rUsW6N9WglgZKxFIIgCIIgCLngq39GYFRzi4qc2ZNiBipVuQ/fft8MXl7e2LBhA9q1ayfrNB+YoVeDM3DWfIRSRiH0STudy5RZ29uyHCQsFh43si8UBR1THN966y3UqVMHJQURaYIgCIIglGh4cfXrr7/ih4lvY8VMa2pj5tcBt4rL8O231dTzKdNnwsXdu3gWVnA6K1eulLUqILcps4GBgSotNmvUjLWOWaNrJC4uDlu3blUGI08++aQaZzab1XGHUbUlS5agW7duRb4FRKQJgiAIglCiadasGXbt2oWDJ+tDh7Rsrx9LC0QdN6tAI/fdKz3RCoLJolODM3DWfITSRXEah7i5uSnjmaVLl2LAgAG28Xx+xx13ZJuewi9rHSTt+5cvX47Zs2crG//iQESaIAiCIAgllkaNGmHv3r1Yd6A1arlF26JojJ6RWBNQO3RdsS6jIAgli9GjR2PEiBFo2bKlSnv+8ccflf3+448/rl5/+eWXcfbsWUydOhV6vT5bvWOlSpVUC4jirIMUkSYIgiAIQonklVdeUQLt6KEIVPfJEGjETAfHBD0CjdPh76Q79kLxG4cINwfFbcE/ePBgXLlyBW+//TbOnz+vxNbChQsRFhamXue4G/VMK25EpAmCIAiCUOLYvXu36me0ZE1tVPcxZatD25roi4e/qYZVz1UsrkW8abFY9DBb9E6bl1D2KG6RRp544gk1OOKXX37B9aBrpCPnyKJEfjmCIAiCIJQoWPDfpEkTdL4jEN1qZjcKMVuA4XNvR+RX81CtWkYtmiAIws2CRNIEQRAEQShxdWiB1dyx7Dt/h06O/Wb2womxb6uUJmfdrRcyMEGnBmfgrPkIpYxCsOAva0gkTRAEQRCEEsHRo0eV6LqcEINTG6pBr88u0H6PrIB/n/9OPf/tt9+KZ0EFQRAKGYmkCYIgCIJQ7NBiv2nTptDp9Yg+XB0eDgTaNSPwUFtaZVuwbp04OhYWTCd1nnGIU2YjlDJKQk1aaUdEmiAIgiAIxcqJEyeUQCPJp2vCxYFAu2AEalW7gCQkqMay7du3L56FLQOYnWgc4qz5CKULEWkFR0SaIAiCIAjFRkJCAsLDw9XjuDO1swk0EpWmR2jdU7AgWVlo//fff0W/oIIgCEWI3N4QBEEQBKHY8PHxUX9/2NsOng6uShhFu21QMizJyXj22Wdx8uTJol/IMoYZOqcOQtlDx386Jw0om/uQRNIEQRAEQSgWaJ9vsVjQc9ajeCBghUMnx/cmAXs3n8KhQ4dQp04d2VKCIJQJRKQJgiAIglDkDBo0CKdPn0at7o9h4S3LoddlFmfks791GPf6Ydxyyy0i0IoQk0WnBmfNSyh7SE1awRGRJgiCIAhCkRIZGYnZs2dDBz02/JJZoJE0AI8sr47pjy5VNWirV6+WLVSEiHGIUGCkT1qBkZo0QRAEQRCKjNjYWCW8yPpDjeFvyPw6o2gfHK+J6fcuRfXq1aUGTRCEMolE0gRBEARBKBJYfxYQEKAeL97dGq18orPVoSWZgXc6L0ONGjVw/Phx2TLFgDL8cFaftDJq+lDWkXTHgiORNEEQBEEQioS6devCZDLh+5Xd0D0wu0BLNgG+oSdhMRkzCbRr164pgScIglBWEJEmCIIgCILTiYuLw8yZM23PX331VRw5cgT3vtgVD9c57dDJsWq3FFgsaVi5cmWm1xYtWoSYmBjZSkWExYn2+5yXUPZwmv2+zjqURUSkCYIgCILgdP7++2/1l0Lt2KmzeP/995WbwMSnz2QTaGYL0G5MNUQfjsSAAQPQuXNn22tTps9Eqgnw8/OTrVREMNXRmYNQ9uBv3JlDWUREmiAIgiAITmfo0KFKXJH6dcLV373Ha8FVnz2CFtr5KrbM+A8NGzbEn3/+aXvtv5VrZcsIglAmEeMQQRAEQRCcDiNg5Oeff0Zqaioe+qEfIjwOZpvuXJwLoo5cwciRIzFlyhQ1jvVnU3/9zTZN+fLlZQsVIWLBLxQUawTMOSEwnUTSBEEQBEEQnIObATh18gSWLVsG36BAfNv3oMM0x34Dj6FevXo2gWa9KMs8Yb8+t8pmEQShTCGRNEEQBEEQnF6PxlTHca+/qp6vX18Jrnq2qM6c5lih7QXERqbCbN5nG5+cnIxZs+fanrdu3QYGQ5ZmakKh4sxaMqlJK6M4s5ZMhzKJ1KQJgiAIguBUrlyLw2svv6gePzR/KCI8Mgs0EmcCYk/HqkibfeTMXqCReun1bIIgCGUJiaQJgiAIguDUWrRlSxfjzJnTCG51Kya02pptGqMZqN0nBnf0vwPdu3fP9F577rt3qGyZYkCzz3fWvISyhzSzLjgSSRMEQRAEwWnQJGT61Clwc/XFvzOPw5Al7Yl1aMN+D8HlPRcxb948m03/pUuXMs1HBFrxIRb8QkERC/6CIyJNEARBEIQCQZGVmJioHq9auUL9bdbyfjTwQrY6tCe2N8Kc51Zi27ZttvGsX1u4eFkm0xFBEISyjKQ7CoIgCIKQb5b8t1KJrD/+nK+eb9+6Rf29490DDt0cf+r7JypUqIDmzZvb7PY1JHpWMhDjEKGg6PU6NTgDi5PmU9oQkSYIgiAIQp6JiYnBvAULs41PSkpSf3uEns40nlps0lLr48jISNt4+35oJpNJnBwFQRAk3VEQBEEQhLzC6NfChQttaYkd27W2vZZmNKq/wW6mbO97c7I1aublZc2D3LpjT6bXsz4XigepSRMKitSkFRyJpAmCIAiCkGfntqFDrc6LixYtwtoNm22vnT1jjaAFuWV/X/SRGLi4uDh0ciRtWjaVLVECkHRHoaCIu2PBEeMQQRAEQRDyzW233WZ7HB2frP5+M6U2spaRxJuB1HOX0K9fP4cCTerRBEEQMpBImiAIgiAI+ca+puzTD9+Fv58ej/bMPt2P58MAHMFtt/eXtV3CsTixv1mGLYxQFtMdnTWvsoiINEEQBEEQCkya0Yyzp44j6Wxt1Rstq6vjO31Oqsfu7u7Z3tupcxfZAoIgCHZIuqMgCIIgCAU2Elm7eTsmTagMtyxXFnR1/OpcMOIuHYWfn3+m1zre0kn9rV41SLZACUKMQwRn1aQ5ayiLSCRNEARBEIR8N7EmCSkmTJkwHj+crZ1tGhNFWi+rLf/Yl17OVoNmSmlRZi/CSipiHCIUFDEOKTgSSRMEQRAEIc/s3bvX9viXST8iJNgAFwda64oROHXV2uC6SpWQbCYhderUkbUvCIKQBRFpgiAIgiDkmYYNGyLVxKiLBVs3rsWUPyMcTjd+o3V8h1s6qbvrI4YNlrVdwpF0R6GgSJ+0giMiTRAEQRCEPHPi9Hn1d+rkyYDBgAqVs0/DerRvhqxQjx966FHrhYdeLj0EQRBuhNSkCYIgCIKQK5KTkzFr9lz4+fnh2rVrMJnNWLliGSr3vwtXTQeyWWXT1THZEo/q4eHQG/TSC62UIDVpQkHR8Z+Tak11TmoHUdoQkSYIgiAIQq6gQCMUaGT6tGnqr09wC3Tw3p1t+i3HUtTfN8a9jfYdOspaLiVYLDo1OGteQtlD+qQVHMk5EARBEAQh106O9n3RVixbrB4/MnBDtru+jKJ17ByJUU8+o1Ica9cIlbUsCIKQSySSJgiCIAjCDcUZTUJuv60nlvy3Emmpadi6dbMaf0evFhjd6GC2VEe9Dqgd7opWbdqgfv0GsoZLEWbo1OCseQllD7HgLzgi0gRBEARByJGIiAjs3ndQPQ4ICEBqaio++/Rj7N29C+NnN8DT7WOzCTTNNOTOe0apx62aN5Y1LAiCkAdEpAmCIAiCkCOaQGvYsBFefOVVfPrRh+r584t74+lGRx0KNK2Jdf36rdVjs9ksro6lCDEOEQqK1KQVHBFpgiAIgiA4ZPny5daLBRcD2rRsCpPJBE9/VxzZGY7KrkdzXGuMog14MR733Aq4GYCrV68iMDBQ1nIpQYxDhIIi6Y4FR0SaIAiCIAgOGTZsGC5cuGB7HlilLo5tNsNbb84xgkaBNnGDC4yXmqjnQ4cOlbUrCIKQR0SkCYIgCIKQifvuuw9Tp05Vjz39qmDSkuoYGHIJLrqcxZkm0FyqHFGPf57yqqzVUoqkOwoFRdIdC46INEEQBEEQFOHh4Thx4oRtbbyztgteqnEWev2lG64hCrRZK2LU4+9+mCg1aIIgCAVARJogCIIglHEee+wx/Pjjj7bnNZ59C988Pg+3+p69buTMnrg0YPjwi/jgo0/h5eWlxoVUrVpYiywUIqWpJm306NF5fs9rr72mnEqFwkNq0kp5M+u4uDg8++yzCAsLg6enJ9q3b48tW7bYXrdYLHjzzTdRpUoV9XqXLl2wb9++TPNISUnBU089pQqSvb290b9/f5w5c6YYvo0gCIIglD4eeOABJdB00KFTgxcQ/sbHaNT/DHr4xudKoDGCtj4G8A87grvuGYzgKlVsr509cyZbE2yh5ENhZXbSUNgi7YsvvsCmTZuwY8eOXA1ff/01rl27VqjLJFClZaQ8FnRAGW21V6yRtIcffhh79+7FtGnTlBCbPn06evTogf379yMkJAQff/wxxo8fj19++QV16tTBu+++i549e+LQoUPw8fFR86DIW7BgAX777TdUqFABzz//PPr27Ytt27bBYDAU59cTBEEQhBINBRTPsb7unniwS18cDXeHpaoJoeVicnUXlwItMh64JeIIGjZqjBnTpmLm73MyTSPGIUJhM3fuXFSqVClX02rXj4JQ0im2SFpSUhLmzJmjhFinTp1Qq1YtFTWrUaMGJkyYoKJovDvy6quvYuDAgWjYsCGmTJmCxMREzJgxQ80jJiYGkyZNwmeffabEXbNmzZTQ27NnD5YtW1ZcX00QBEEQSixz5i1Qfct4np3w/ZPYuaEGLhypis9+2Yk5r85Cj4qRiEz0xxljZjHGISscF17HahQy5oWX4ObmBj8/P9vrI4YNLpLvJDgXi902L/BQyBtn8uTJKF++fK6n/+GHH1C5cuVCXSYhI93RWUNZpNhEmtFoVP1WPDw8Mo1nWuPatWtV4XJUVBR69eple83d3R2dO3fG+vXr1XNGy9LS0jJNw4gcBZ02jSOYIhkbG5tpEARBEISbkSnTZypRtvfAEVy5cgUrlv+nslOqtAzD3zMroGGYC9xcrdMa9BY8XG0P9p+ughdOdsTCOF+sTfDCf5HZ58sL8ENXrI+//+ln9Xfh4mW4o29v2zS8mSoIhe1EyuvDvLSVYHmMIJR0ii3dkeHmdu3a4Z133kG9evXUXQ2mXTCvuHbt2kqgkax3O/j81KlT6jGn4V07f3//bNNo73fEBx98gLfeeqtQvpcgCIIglCSBRvYfOoatW7fg/XffxpHDh+Di7oPRMzrBW3c4W91Z2lUDfP51x55oP/RbuE2NM56rnW06sxlo2PgI6tStq264MmrGm6/2NWhZz89C6cCsKhR1TptXUZOamoqLFy+qmxP2VKtWrciXpawiFvylvCaNtWgPPvigqj9j/Vjz5s3VHY7t27fbpska4mR6xo3Cnjea5uWXX87kBsRIWmhoaIG+iyAIgiCUNHGmsWrVSrzy4hh1vgt0DYP/549gVPC0TMKLkbFUC/DjL01w8I8PkJxqjYLtOl5XXWrbw2kDGllvmL7y2jjcd6+1YbVer1c3XnftPYAB/W8v/C8qCHYcOXJEXVdmzabSrgt5E0EQnEF+svB8fX1Lj0irWbMmVq1ahYSEBPVlg4ODMXjwYFWXFhQUpKZhRIzjNXhnRIuucRreLYmOjs50t47T0CkyJxgWz0toXBAEQRBKq0DjBaom0BoE90HaI90QUD0KfoYM4aXVm417OxQzfv0eRqSq50/N7ocGHgezibmYNCA+OhVPPv1sts9v2rSpGoTSS2my4Lfn/vvvh4uLC/7++2917VhWa5lKAje7Bb+fn1+elovTHj58WPWiLFV90pgbzIFia/HixcpMRBNqS5cuVYYghIKMou6jjz5Sz1u0aAFXV1c1zT333KPGnT9/XjlGch6CIAiCcDPD1ELNPZG24vP/XpRJnJF1a9cogVbfoyN82nXGZQ/AaDbgbJoOddwYYbBOv/ZAKj75Ybnt/W8uuw2v1c8s0IjRDFQIs5qFfP3l50XwLYWihtb5OieJK86rqNi5c6fyK4iIiCiyzxTKbrrj7Nmzc9Vvj8fiPn365Hn+xSrSKMi44HXr1sXRo0cxduxY9Zg9W6g4aa///vvvqxo1DnzMBplMiSR083nooYeU7T7t97mixowZg0aNGim3R0EQBEEoK0JNE2g8r5rM1sFssWDzlq1qfJBPXSRCB0M8cOWCD76s0AIvhmxFJQNwKkmPJ57KyDCZtqEThoYdyxZBI+XvsLa3Wb48Q9AJQkmgfv36uHz5cnEvhlAGCAsLU+701B+5gRE0BpZKjUij6xPrw9h8mgLrrrvuwnvvvWf7Ei+88IKy6n/iiSdUlK1NmzZYsmRJph4Xn3/+uQptM5LGabt37656vkiPNEEQBOFmZNvOvWjRtGGmcVevXoXJZEaa2ep7npyUhJUrlmPPrh24dOmimiYl4So8Liej3FkvJJg8sPRaE6zxqY2Lv85G+TOpOHbsqJpu48FGaOl73uHd6wlnayBl2xJVMtC1a9ei+cJCkZNTy4X8zquoaoOYacVrR97U5w37rBfFea0JEvLPzZ7ueOLEiTxNzyy/vKKzaPkQZRj+wBmVo2iUH7AgCIJQkqCtPWu37+zXBzNmzVbjunbqoJzqeAqf+utvMJstOHT4MP6e/ycOH9mHef9FoGmwEWs3JGPU03G4fDUa7vBA2/J3wRAWCpObDgf2/YmLSYfg7x+A6Oirar6HjjVATc/UbAKNVwqfn6mKsa1XqOe/TJthMwsRbp7rEG05GswaC4OXc2r3TYkp2Df4k0L7bjSrsb+Id2QeJ8YhRb8PtX33X7h4OKfVgTE5ARtfu63Yfx95genn9j0j80OJqEkTBEEQBMFae82oWGBgoMoSSUxMxKVLl9SqmfHbbGhu5itWr2O1mRJnl+NTEHnyJCaMfw8BwSHYeaAeIjytxh93dXNH/Iym+GJOW+yZ9BpWxfwK7M5Y0zVqhOPEiePq8d5j9XMUaK/tbYgPe81Vz9lrbcfu/bK5bmJKk3HIihXWGwdCyeJmj6TZwwhu9erVlfkhYXbfnDlzlLfGwoUL0aRJE+QHEWmCIAiCUEycO3dOGWKlpaVhz549+PHHH602+YGBqi6bJQE2dOxNZlF/9ekXLSlGE8xGI3786iN4V/HEkY3u8NFbBZpG89qnkVahI2p0G4YrmxfA188PFSsHoWXLVvhpwjdqmul7WyPCMzqbQDOZge7fdsKa9yep58nJySrVsXuXjoW+boTiozSJtM6dOxfq/IX8URaMQzR++OEHTJ8+XT2mmSGHRYsW4ffff1d+GyzVyg/6fL1LEARBEIQCs3T5KqSarCf2r776Cv1eHIlNR5vjv00h2HL+Z/xv1FNAelECa85MNAUxWZCaZsaVuBQl1mb+8gNSU1Lwyp9d4JPlrM637kr1Q0DdaOjcPBBcpQqeem4sdmzZZBNo6w82x5CA7AKNejCwkdEm0JjiaI3gCULJYffu3dmaVl+Pffv2wWg0FuoyCWWL8+fP2/ots/0DI2m9evVS9ZFbtmzJ93xFpAmCIAhCMXLxwgV1F7bf0DfwychlaOEVh/qeifjtVQPmL5is6tFYU2Mw6OHKwUUPN1c9Asq5wWJKU+YgIQHNcNnkkc3wYVFcIA6n+gFGHU4tn4VDBw/gxeeeVK91fKktUs/URmvfOIcpjq1GeiH26gmbQCO39hCzkLIAbfOdORQmbNPEFNzc0q5dO0RGRhbqMgkZ6Y7OGkoy7NV8+vRp9fjff/+1Ocwrp90CNFCXdEdBEARBKEa0mrDj3joEGkzWNCEAbgDa3uqPc+fOoHadurbpGVFjlIv2+lGXrqgoQqB7MBbua4jegcfQwSseeguwO9kLG5KCcPJaANY/9hlgTlHvb/PEY5jz4nIEuV5xmEZ0xQh0HOaGw2t2ZRJoHW/pVOIvloSyBy+EX3/9ddWiKbd1n4LgTAYOHKjag7FdGG8Y9O7d29a3r1atWvmer4g0QRAEQSgmGtWviwP796nHfpHJOJPmimquaep5ogVYOvs82r1udTNjq5p+fW7Fz1N+RYrRrPqgefn4wsXFFcejNqD+X+3xeNzd8A5KQEqkOw689CbcK5xByqUrqDysM954OQ33B1yCu87a38yR3mINWnjry4g/H51JoJGaYSFFsUqEEkBpsuBnr6pDhw7lKZLm6elZqMsklK2atM8//1wZhzCa9vHHH6NcuXK2NEi2EStUkfbXX3/lecY9e/aUH4EgCIJQ5ppK54U9+w+hbkQ9VKpUGcd2T8OjnwzFM4+vhpfBiLdev4aQoNoICgpW09L1MSUlRUWzUo3WGhxXV3fc0qUbVixbjKP/fIN6WzsjMeUaDlxerF6nQAv/9C082mM5Hgm4BIP++hfTvk0uIfnyNfUZk6f+qsaL1X5ZFWnOMg5BobJy5crC/QAhX5QFd8dXXnkFd955J1q3bo0xY8Zke53mTwUhVyKNC5DXlXnkyBHVXVsQBEEQbiY2bNig7sY7Ysr0mXkSNfXrN8D+/fvwxFNP44vPPsGybz7Apik+iIuLU+Ls+RdewpBBA/HbH38qZ8dpM2cjOc0Ek9mszrXuBj0G3TMUxtQUrF2zCmvPzso0/1b9XsZPA2ehoUdajnejeRGdaAIqhUciOS1FpY2xDo6iUxAEQXAMI2V9+/aFwWBAv379lF7q3r27csB1BrlOd4yKikKlSpVyNa2Pj09BlkkQBEEQSiQULnRjtBdpFGa5hdMOG3w3XF1dlRDat2+vEl/Vq9fAxi07sHXTOvw57y9UCa6C8Z99rKYjFGYcaGJnNJmVa6OrXge9XgdXgwH3P/gw7hp0Dwx6PX6dPgUbN2xAjX4DMfmr+ajnkeYwmsFxjMc1vceE/WutdXENGzZUrQDIkCFDSuwdbKFwKU0W/ELJhFvdaemOKJlMnjxZ1USuXbsWCxYswOjRo3H27FmVTdi/f38l4NhOpVDdHe+77748pS7ee++9paYjuCAIgiDkVmBRoNnDE3RuiY+PV3/ZpJrz+uPP+Uoo0bVx8N0DEHX2FBb+uwRJSYkwGNLUhQnfM3HKr0rIGY0WleZI0xC9DnB31dvqPoxmC9w8vTH554lKoFUOr4/fPj+GCHerQHPk3hhvAmq0PGETaJcvX7YJNCICTRAE4frwOHnLLbeoWrSDBw9i8+bNaNu2LX766SeEhISomslPP/1UibdCiaRRKeaFCRMm5HlBBEEQBKGkYp/6N3L4ENtjii0aeCiR5KLHAyOGOXw/xdyR45G291Da6cGaDSA2+jL8/PxUvVmfsS0w7YNY+LscgulyA+w6VR1JqfdaxRksSpzxPYyY8TH7pPFu66Kly/DX7JlIS0tFy3eHYun9W+FrSMthWYAfompg7KBd8Ez1h9l8QQSZkHkfSR+cQSGXpAklFHVsclIoTV+KIvr16tVTA3ukXbx4UUXYNG8PR3Vr10PcHQVBEAQhB9iYlPVhWgTNzc0NGzduRMuWLVVEjHdRXQw6pJqMDgVaTEwMLl2Nxbp1ax1etG7evBHfffMVyrlWxNz94ejld9UW9TLogHbVT+LS5Z04caZJujW/9UUKQl64xF27ig/eewdXrlyGzsUdPX57EjM6LIavIeMzskbSzqYZMLrzFvS/rQd+//132fZCNiTdUSgoZcndMSdYJvbQQw+pIT/kWaQlJyfj66+/xooVK5RCzNrlffv27flaEEEQBEEoabRo0QJL/luZqcfS4WMn4R8YhIjaNdQ4mnh4uWU+nbJGgXdSaTJCIde8ZSvcOnggqgSXg2uKD3QwwMUFmD/3TxhgQJ3Xn0APvwy7e3uB5eG2BwZ9U+j1GRUKFIZ7dm7Ht19/gRGv18LHD/vD2wB46hZnu6Dh88tpeuxO9sTRhAD8r9lyZRf92WefOX+FCUIxM23aNHz//fc4ceKE+v2FhYXhiy++QI0aNXDHHXcU9+IJNyHJhaSN8izSHnzwQSxduhR33323spyUnHVBEAThZiU42Gp/r2Hv3Lhi9Xr1l9EtRtW2bt2qImxsYMrC8cYNGuO2u9/B8YbucG9xEX3qz0Rrz0QVSVuxryt2HWyLc+fPoYIhGPC3ntSzCiwG8P76ywXNWxqUMDt3PgqzZ83Ezh3boHc34ODxOqjpTqfHnL8Da9iGfHwXNvw5E8nndqoaiZEjRyohmdeWAUIZoZTmO7Lc5o033lDW5++99x5MJmsInOnEFGoi0oqOsmDBX9jaKM8i7Z9//sHChQvRoUMHpyyAIAiCIJRUGDnLicjIU+ovHRaNaUbVeoYi7aOPPkLVkKrwd7sNkTU8YQxNQ+/Q/WjjmajqyBgd69pgBbYfaAUPTy/EJF6GZbMH5nerhAH+F9U8EyzAmugKeOupw2ge0QvJSfF4ccxoJCYmqte3HGqMZj5J0Oky37HNSpIZaHPHeezb+r56Puzekeh1623S+0wosVBocTh58qR63qBBAyW8evfufcP3MppBwwZaoX/44Ye28fxd5rUeSBCKWxvlWaTRqUQs9gVBEISy0IR65u9zbI8H9L8950iBDrZ5bNq0CXWqNUCSiyss6WdZPcw2G2neZNWzTgxA+07d8d+i+bg4ayo+NN6LT29JgiHFjJTfUrBz03fQu5pRv3oynnricfXeRXuboGcAhRoF2vW/F8Vg9/6R2LctRQmzZ0ePUak4bnb1aoLgeOdxngU/55UXqlatqgRWrVq11PMpU6aoCNiOHTuUYLseTHFs1qxZtvHsW8WWF0LRYTU2ct68SjKFpY3yLNKYw/7iiy+qfF/m+QqCIAhCaWXZsmXKFISOi61atUatGqGYM2eOElxXrlzJNG3W1jID7+iLP+f/bWviM2XaTPXYbNEh6moUAvWJcL/iiRR/Fyw42Rh9yp9BY48UJZ62nWiDYH9vDB8yCOdOHcGB/ftxdubrQJaWa6Y0YMrkSWjVvyoWfesJP0PidcVZshm4atQhyazHyHtPY+tOIz79/EuMfuYplYJDS/9z5845bwUKNyXcR/PQXeKG88oLbApsD9MWGVmjYc+NRBrrzphunPX6dNGiRahfv37eFkQoGMo45CZvlFbI2ijPIo0hYxbIhYeHw8vLy9ZoU+Pq1atOWzhBEARBKEzORl2yPd6yZbMactug2nbnND2SZoEFRpMFrdp3xJyZ09C/a324bLXA+7w30nbp0OrxC3hr7FDUCGmI1LRy6q00A3nx5dfw7lvjcPToETUupEo7+D7fFT/d8Q+aeSaCgS+XXDilpZiB20YNxNq/xsNiTkOr1m0x4cfH0bBhI/z2229qGp6zWTchCKUB1pT98ccfKgpm30A+J8aOHYtRo0ap61S2vWDPKkbKP/jgA0ycOLFIllkoOXz33Xf45JNPcP78eSXwWZfInmaOYI0uhRZ7nTGtnGLrsccew3PPPVds2ijPIo13F9mQ7f3330flypVLfDGfIAiCkD8OHz6Mbdu2YciQIU491l8vvbCooUjSnLju7NcH8xYsvO70vPDjulAijtEG/rPwYtKC5DQTTBYL2nXoglPHj2H+8qnqhG3aZ1K1bXfdfQ9CKrVFapb2Zb/PmqkEWoB7NbSZeDs+6rAaddz/gGuGmWMOywLEmICtyT5INbrjofa7cDH6I/XaO+99gNBq1ju6hw8fUimOJWWdC2XTgj82NjZbCiIHR7CpOkUZL3zLlSuHuXPn5ioS9sADD8BoNCpnVV5oDxs2TKWiffnll+o4JpQdC/5Zs2YpAxkKNdaK/fDDD6qucf/+/ahWrVq26b29vfHkk0+icePG6jFFG0UaHz/66KPFoo10Fp5x8gBPOLQ0bdKkCW4WeOAoX7686meTNZ1FEAShrEIhotUvOesCX2sKXdSC4dKlS1izZo3qc9a9e3d4enpmSlHM6tzoKJLW//bb8Nc//ypxlmK0usbxDJpGkacEm/U56ydcXfSIPHkCB/bthquLK1q2ao1KlStnmyf7mz3/7NPwDaiBcf/VwtOVT+aq/sJkAWacDMZH8/ohed1WnFg3S41v1KgR3v/oMzVfe+y/m1DyKCnXIdpyVP/5Nei9PJwyT3NiMk4++G628ePGjcObb77p8D28qREZGYlr166p9GNGwVatWpWnlMXLly+rGzDsVSUU/T7U8/P/4OppzRgoKGlJ8Vj6XPc8/T7atGmD5s2bq1RZDTaZpqkMI6u5YeDAgUqksa1DcWijPEfSIiIikJSU5NSFEARBEEqmQHOmmOJFU6qRjZgLP8KmiUHy3YTvsW7tGhUFIzzp/vLLL0hISoMZFuihQ59be6hz2+9z5mWfmQUwms3486+FKp0xzcz3sPbMOj/+ZUTORU+BpoPJbIFBp0Od2rXU4AiTyaym+/QjqwNd26l98HilJdctvdBuqc4+H4QXOsciLeUcotJeUNE89nPbsmVLRpQvS+1cbjl69KjNsEEowzD65WTjkNOnT2e6wM4pikZ4M0XbD5lKxn2b0TBGQ64Hf8P8nfOiOTAwEKdOnVIpbhR3vXr1cs73EXKFLv2fM9Ahb9FYinxmgbz00kuZxnMfWL/e2jrlRtCohtO++272GwxFpY3yLNLouPP888+rQk7escuadymRKEEQhNINBY6zBRqFQ5rRDFeDHoMHD872ec6Gph9sLLp6zRpcvnQJoyb2xfM9jyPRZMDoD71wzz334J33P0RwcAhMsGDBwiXKSt8eii8GyRJSjeoSgQKMeTe8CEylUKN7mV4Hg44CTQeDXgf2m/ZwNWT0mdLl3Lts9epVOH/+HOoNGYMJjf6Gew7pQRRnqRYg3gJM/CcCX4zajYtGq/2/v7+/sh0fPny4w89p3CAi165j2nYQkSYUhnEIrw/ze43I31xKSsoNp6MLJKMfjz/+uIrCsWcVBR9vEI0fPx7/+9//8vX5QslwdwwNDc1VNJbbm/WMTD20h8+joqJu6C7KzAumzXLeDz/8cLFpozyLtNtuu039ZbqIozx9rXGgIAiCUDqh26EzbdrtIzv3jcgs/Lbu2GP7PIrCTVt3ok3LpgWKqlGcsQDcYDAgLi4Owz7uhy/7HFQCyGJJw+9vJMP/J+C/ZUsxfMT96j269ItJTSSZzRYlpBjtolCjYDPoAVeDTjWV5l9tehe9Xgk0TqPu+GoXtzrrnV5HF5dMh5w/53f1uP3IiwhzNWUSaJx3ZCwwfnxzrLlaF+d3LcXFIxsAHLGl8qxevVpdgOYkePOS4lgYQlkQ8sMrr7yiaod4Qc7fL01vVq5ciX///feG792+fTs+//xz9Xj27NkICgpSERGmTLLXmoi00s3pPERjSdbaME2rXA+mxdMFl26ijMTxptWNzkWFpY1c8nPyEwRBEG4+eKGebGSESFcoNWOMomX9PAo0ws/TxNzxIwfyPO/jx4+r/mSDBg3Cvffei073dsSk1yNRwWBCktkq0Aj/+hgAF1cLTkdG2mrItNcyoQMMFGQuOiSnmpGaZlVfTG10ZcjMvjiefzWRpwNq1qyFju1a2WYVHR2NM+cvobyfH1Ys/0+Ni4uLRVC5CAyreSBzBIPpj3ecwbYtSTZRZv0sHaqEVMXXX32p6iocXWxwPTLVhxelubkgIXQ+0xBzBcG6I6YPziCP87lw4QJGjBih9kvWNtHIgQKtZ8+eN3wvzUK0yPGSJUtUVI2/17Zt26rUR6Ho4LHHaQYaOl2eorFMdeVNuqxRM/aJzBpdc9TGgTAixn2R0bQbnQ8LSxvlWaTRbSfrnTv78KIgCIJQeqFAGzJooNPmR8GgsADV009+ttdMjqNt+Ynk8e4na094p5zF5QterITKBmt0SmUfpgso/k0w65CWClRMNxRgxIxijNEzk9k6HaNi2rWlNZVRj1SjCQZOnI6W7qjVphkMeoRUrYoeXbJbPDMtkUO28V4m1PKIzyQQk9lHbUsSXhz3IS5fvoQK/n4Iqx4GLzcDRgwbrC4+rgfP0XkR2YxSaIhjs1DcTJo0Kd/vZdRj3rx5GDBgABYvXmyzT+fFuZTjlB3c3NxUne7SpUvVvqDB50yJdXaabefOnVEiRBrz+P/88091Z8Ieqk2G+fbu3evM5RMEQRCKAPuo1o1SSAiP9bTJ5smJrmv2ooDCTLuZN/P3OdaROqBzx7Y37D9294D+ytgjL7z11lvqbqervxfC24QjZkkMvPQZCpAaKt4MeOv5V4dbH7d+vzvvHKjSDgnr5WifrwSXpsPSa8qonzzc9GoaCjlG0RgVpIzjc4ozjapBFa+7rJHnLtoeh1X3wqbVgLerMdM0l43pp2adAY0bN1IikgLt/hHDUFgGMVzn/fv3d/r8hdJJYVjwFwVMaaTtPsUZr0m13mqMqjVr1qzIlkMofgv+0aNHq4gsjWe4H/z444/KMZT1iuTll19WtvlTp05Vz7/99ltlzU8TEEIL/k8//RRPPfWUw/nv3r0bDRs2zKaHcmLfvn2oW7cuXFxcCk+kMfz80EMPYfLkyZnGdevW7Yad4AVBEISSK9DszUIcWeUfOnRInWS279qnBBqhQNNYtmK1Ouk5xJK7BtGHj51Cs8a5s9lmcTdNSJhq8uwvt+ODXofhpktBmqU21sa4I9w9WZ3kkixAvxcqYc2Mdbb3Dh46HJWDgjLNj73OlEjT6eDu7onk5EQVWbTWneng5+2qnlPYpaZZe6u5ZWlmtmnrDlyOjrOlOiqTkdRUm/DdsD5jGd7+uhq8XTOs/DWm7mN/swOoWqk8fLzccO/Qe24YPcsrtCafNsNq209EoAk3A2zU3rFjR3Vdam+HTsFmH1ERbn4GDx6sDKTefvtttT9QUC1cuFA1qSYcR9Fmf0ykcDtx4oQSUjVr1lSGIOyV5giKfqZTVqx4/RtzGhSKO3fuVA2vC02k8Qt26tRJ3aVgcSZPyBRo/DGwuFMQBEEoPezatStTXRjRxJS98YQm5DZu2W4bR1HHGjDtzqC9QKM4oV09BY2yqM/lrdDdu3chLS0NrVs47jdDx61ff/tDCZ9XXhqrnBs/X3Eb7q99GJqflqsO6FI+Bk/OrQNXvQWrp1zAro3rUKFCBdSqUxeTJ01U/XKOnjyjrPnTTGbV64yRNAo1Fpfp09KsRiDKUUSXKe2R9vnWNErHd1CbNqpnWwdTf/1N1UDc1rObGnfPXXdi8bIVOHjwIHzqB1BqZjIsIfPHrYe3dzn4+JRD5y5dnS7QtEav3H7OblQu3EQ4qyatiKFZCAd76PIoFC085uf2uJ+beeWHJ554Qg2OYBsWexgxyylq5gge319//XXV7iFPqf95IM8ijSc55vnyTgX5559/VLO4X3/9NdchP0EQBKH4Yc+ZnXv22wQZnRb37bOmrGet37CvH9Owj7IdOHzc9liJMlgNN/Jzgj1wYL8amjZthiYNraknGhSEkyf9hFUrrYXayw+2ROfyx7LNgx/pV8GAD4f8hRrh4Xj8iSfx1RfjMWPWbCXQSEjlCkhONam+Z7yLSlGpIlo6ICklxZquw1qzdHt9nV4Hk8Ws/uqv408+Z94C9OtzKxYsXKyeawKNULStXLEcU3/5GbcufAwtA1agkUfm1My9u2IRUb9ZoTSgpgCmyHV3KRxzGOHmoLSmOxL2VPvjjz9UlCTrhTHLdYSyke5Y2DBgxeyS3MJImqenZ+GKNK2HAIvvKNTotsNO3HInThAEofRAa+u5f/2jHg+9565sqYgD+t9ue+woTZERoUyv22kWdT7NclK1FxzJycmYNXvuDZdx584dNpHG4m2m2Y8ZMwYJCQkY8dmteOaOKDT2inH43mtG4KvHVyKseg2Me8vajJS9a5o0aaoer1q3CceOHkWqySrOrkVfxenTp1QEq0bERQzsvhblXUzYdlqPpx9NRPduA9GiRSs7x7LrXzVoAo3s3ncIjRvUVY+3bd2CX36eiMrB9RBmTsb8q3UQWOkAgtPPxkdSXcHrypdffCHbPB2loOYVrgM5Xws3K8zoGjlypGpazOtU/j1y5IhKS5N0R8GZ2BsuFRa5Eml0pHJ0UKfV6YIFC1R0TePq1avOXUJBEATBqRw8cgKbNm1Uj9u2bZdh7pEHfp8zz5byoZltWAVMZofAwXcPgIeHh+15burSNMMOzWWSoqxr167qDjmZt6sd+lY8nuPdVRptDH6zKhKvHcHdjzyh0gVZ16WlIv45/2/ExMQiOj4JJ67NxkODzqKRTwoYz5qwrTweah4LL5011bFbDRNWLfJA5WpfoHuPWzH03hEq3MUG1jfQaTYa1qut/v446Rf8MvlnfDC/Fca0uga9bq0avyreF7thQZLZFWOfiVH1a6ytcWTwUVC09X/XXXcVfGbCzUsxWvAXhPfff1+V4owaNUpZ8X/55ZfKUp11RcHBwUW3IEKhWPCXNXIl0mhrLAiCIJRuKKhOnomyCTSycSMbJGdm+JBBmZ7XqVMXhw9nT+ugs2FymtXmnjrN3UVvO5n27tUdldIt7jOmN2dZoPS/DvqTEYoVLjMbN9MZa/7uDugdeBF6XL5u+sviyxWxbNIKdRe9UePGqpEoxUmLFi2xbdtWNc+4xGQk+E7EFwOvZvRJswD/axGjToz286/gYsGAcXfizzfnoVW7DqgZHg4zcl9nx7TSRx/7H/74/TeMWjgQY5vuyTT/jt6xaLFhCJL2p+L4vx+p8gGtzi+rwUdBUxT53WtUC8mxlY4glGaOHTuG22+/3Xb84A0eHpPoo0D/BDrBCsJNJdLuu+++wl8SQRAEwWkwNc7+gl4zsbgRd93ZzyYQKG7mLViI+Pj4TNMkphhVtIo6w9vDRYktmm9QczVv3hKNG9RxOO9/ly5PXxj+Z1VoOs4l/bl6nEX3PPjgg0qgfbWqB24PPGUThFnhOPYXW3QhGIOar4aXtzfuuiezoNEEmslswZbNG/HGuPhMH8d56y3A0VRX1HZPs82Xjy6kRqhUQaYrhoZVh0GngwtrbZSxiFVYWp0gM3+Bs2fPoH2HjugyMB5LdrRFi4qZBZq2PvCfF45+944qRKeFuAZrvW/t0VWZjRS00TSFaprRUmg9fYSbiRun9OZtXkVDQECASuUmISEhqlUImxJfu3ZNZX8JRcfNXpNWYkQa7wLmpQkgfyBax3dBEASh8Nm8bZdKq6PTlFa7dOL0eaxelTlvniIlp9SR8uXLI+ryNaybt8Dh6zGJqUhMMcHL3UX17dJ6jPEajM2g2UcsJ4F25MRpZZmvlsEu/8mS/oxGI1nZd/Coqnl+fnofPFH7iO1E7WjxEy3AHa8FYcXk1fAPCMArr41z2O+Noot1aPt274aH3urSaP/aomuV8NZPXfHWI/+hpc8VJFn0+P5EY2CZCTrl7GhSDa8ZdtNcK9U/Zj9m0Zh79+zB5+M/wfhlt2BUHRoYXLGJTPvPXbytIvZ897I6z1KkZYUCTau36dOnj9pOeUXbJ8p5iMGXcPOmO95yyy2qFo3CjH19n3nmGSxfvlyNow2/ULbcHUs7ua5JYz+BrKkrOcG7F3ntBSAIgiDkP1J27Vo0/vhzfqZpsgq0FKYmKtdF6/OskauYmBhlSZ8JC5CYZkRSqgne7i4o7+VmFXp5vDu+ft1a+1naxKIuhxNwdHQ0hgwaoETRgx0is4kpRs12xPni+BVfbN0MTHhhBYxpR9C1ew+MGHm/Y7dhi9V5ktGk+Pg4pJj1NsHEv9sSvVFNl4j1Y2cpG/8rRgMenjAQqRtccXjbz0g1paBew6YqBZE6jXb8DL0xXdJ+ffC7cZj00/cICGuBEbXOWNd3lq9Jt/+f9/vh8f7rVfSSF5KM1mkkJSXZav80h82cBBpvppYrV06ZsmS1hGYkNGubBUG4Gfnmm2/Ub4Cw5xV/T2xKPHDgQIc3QASh1Is0nmwmTpyoTgC5tfgVBEEQnA8tpW9o9JGefmePMvdIj1wxVVEZX+QwPfuAqfekv+aq18PL28U2zf0jMtLxyC/TZqgoWnh1a5PQrPzz7zLbPAnFGT8/ol49tGlpFT279h5E9dBg/PXPv+ocwh5oSYmJeHdRX/gajmQsKkWjGfCtmjGOVAkJwbDhI9CwUWOHy0ABumrFchw7cRy+vn4wmozYGV0J4UEXbKmbwW4pqOJltL0nwGDCa8OWoMcH20FZ1rFrL4RWr6lEHiOH2hrU05I/PRrIJtcUY6dPnVRCs/7T/WDQTckk0I6lumJ5bDAm/dUKW1/7ELfddhv63TEQBw4fQ9OmTW31YvPmZQg0kpNxyPpN23DkyGHbNFmFGFsCkMKw9BduUkppJI3pjhq8UfPCCy+oQSh6SmfCbO7566+/cj1t//79UWgirVq1avjpp59yPVM2EbS/GygIgiA4h207rX3MNNas34zjx49lNuHQZb/RZg8jV7YaMAsjOmb1WE1n/c86i/S3qaBU+jxHDs9cF8U6D76XjaCbNa6f7XNZB0WLexeDtV6rS9fuCAvJnJXBiyn79/63ZJESaKdO1UVVN6thiX2KIEvBLp2pg7/Oh+G57pvQoGEjPPa/UTmuszOnT+OjD95Fv4/b4fbqIZg4Zg8uHj2BEd1iYFxZD/V8orH1WkUMr2ytebPHzZ2fq0eVqqG47Y5BCK5aDYnxsYiPi4VeZ4FFheEAo8WCxNQ067q1mLFx43prhC7VoCJy5dwyeqH9fbQ2pk9pgyMzPlXzHnDXPXB3tzpgagLt8PFIh73p7NEajNuW1YFA07a9M5whBaG0mIewXQf/0t2RWWD//vsvQkND0aBBg+JePOEm4c47M9rQEJ7f7M+19mUFzAgpNJF28uTJfM1cEARBcC7tWje3OS1SANFhMStZ0wcZReNkarxVU2TUg6WHkdRfu5veTOXTa+YeOl02K30N3pCjaQg/0b4dC/klffko0Bh944mKdvg3Yv78+XhqUmeEuJ6zjeOiW78D4E3DEp0FI0NOYu07AzF19DSMvP8BeHpmTvPTYOPoX9Y1wR2BJ9R8Hl/kjr5Du2P3uv/wybDW8ArqjMkTZ6sUR3vizcD4HW1Qu6IOB09vQe2I+khLTYF3OV/ExcYoAxJiNDONlOYpFpw6fhjXPBdg9qRkeOlqY8e131HF1ZQprbKj32mMm7YK8biGOwbcBff09aq1CWCzaaPRaBNXvPHJFgSOmqkuW7Ha9lwTaIxG9uzWOVOqpKQ5CnmCd0Kc1YS6CJtZr1q1Cr1790aHDh2wevVqvPfee0qk7d69W2WEzZ49u8iWpaxzs1vwm+3cipctW4YXX3xRtYBg02ou7/r16/Haa6+pcfklX82sBUEQhKLjzJkzqtaXB35bOjkjYDxJ6LLUXHGUIT1byZxhEqJS89IFmn1NmU6f+e6fKvbW65QNv+byeD02b9+lRGCm9EkA23ftSxdoeluaXW4E2p39+uARoxGDOsRnq0M7nOyBcPdkuGX4lSAsLEGJP1ptOxJpdHWjqL21QsYJNcTVhH7PB2L3Oh2271sA71OrUME1KNvn/X2+Gs5PCUZCSrRaF3qDKyJPHkxfR9YWBDaRrIMSbj98PR7HDofZ+qw1949XtWf2pideXol4fJw3Jn6lQ7t27W2fuXLNBpw5c9r2vHnzFqgdXk01/maaqxZls+9zlxXWsTHN0l6gaVE3ukNyf+DFhcOaPUGw2/8duajmB2fNJze89NJLePfddzF69OhMBna8ycGomlB08Iaa1t7EGfMqyTz77LP4/vvv0bFjR9u4W2+9VdUHP/roozhw4EC+5itHaUEQhBLOmjVrlBDZvG03ZsyarQxAlEAj9hdA6ZbHmUw9dBnCi4LMwPqp9KbTmm+IqhEz6NXA6YYNvjtXAo0cPnRYXYTVrFkj0/jdu3epzzDa1aJdD7pKsrZt7l//oEJgIJbt9Mz0eoIFaBC+B2uiM6J10SY9pozZh3I+PvD3y6hFsRedKckp1r+WzKe7i8d81Pft1PN2uLm7YWN8xvvVatMBg0MiEb9mPs4lnFTTXTh/DkEhofD1C1BRNKZ4svEA//L5VqY4lq8IFzZcs+Os0d5UBKjnZ8JHj3vh5/nV8O7b45SoIhRoWu0go2qN6tdRAo3Y9zVzJNAohDds3p4hzrJcGDMtktuZ+xH7rlHMCcLNxp49ezBgwIBs4ytWrIgrV64UyzIJNz/Hjh1zaOrEcQXJRhSRJgiCUIho1ucFeT8vsJkCt3fvHpVaqKUvajCKxfREDoxcacJMM8SwRc3Sp083jbf19erXu1emz8xLTTEFCv9dTrfXp4hkGqaK4NGgJBe3QGka8u+ihenfy4L+dwzE20PWYvkVP6SY6bKox6C3qqhp95xxUePSLMDSi5Vw4uh+mIxGpBpNat3QwCTFaP1LIevrXwGVKgdh3B/VEWvWqffOPRuCGrrTmP5XLfS6w0+JrlfGd8YPUWHZ7vpfLp+g5j/4/v+p+VWoFISQatURGhauhJmHp7daZr7v8qWLCPT2xowL4dAqELgNQl0sOJLihiMprpl6B3ULT1LOiwv/zmh5wG1CwcWoI9dj1pqGKdNmOjR14LSHDx3K/JrdNFo0c/rM39VfT8/MIlgQMmFx8lBE+Pn5KTfyrOzYsUNlIwhFn+7orKEk06pVKxVNs9/3oqKi8Pzzz6N169b5nq+INEEQhBIq0HjhrRlD0B1RM/TwdHNRPcoY9VI28A7OX1o0KUOg2Z3sdJmNQP75dymSU01qaNq0WZ6WUevlnJCYYG2WnJ6OSeHCyN2NTq7LV6/Dtm3blLkIg4MUNW3atkPffnegV6Ot8Ao5gkqhh7D0p9Xqw55sfAFuOmuu/t1BUej+6CMqKsRG1XRWTKVAM1mQbDQhIcWEZKMZfQcOxnfPL0WD1sm4fWBDnD4cixeHH8egZma8OHAzWvVKwt7Jn2PyC62wIt5PLQOHf2IqIbxyL5UauOSv2QivHYFdWzdDrzfg1MnjyukxLi5e1aj5B1ZChYqVcP70Ucz8tD36ruyLWJN17XMV1HZPRSVDmk0E8i83rUHngh07ttm2mWa2Yi/QyKGjJ63jcrJM4zLbus5lx9E8BeFmg43gWRvEC2QttXfdunUYM2YMRo4cWdyLJ9yk/Pzzz7h48SLCwsJQq1YtNdB0kaJt0qRJ+Z6v1KQJgiAUcU8zRzBSwosKCgL2O6NrIi/Y7Y1BeG3OFD3bczsBpEVKtAtxihUXawMvh1b7nJ4XMFN//U0JQI90+78mDSPy9N0YTbJvRK2ERi4tk0+fv4Rjx04qcaFF/LTA292DBqN7j54qbZJitFKlIMxd+iW4lLb6LgrWUGsd2sED+9GwWUurCEp3qNQicxENm+CRp17AyqULsfXgCnzfvmqGW6QF+N/TwZg94TS2LP0Ao394D2FdzyHFZMD5NVURmnRabRMXNzclzpq36QCLyYRy5crj3JlI1G/SAvt2bkM5M9Cj713qM86u/RnmPfVh6JRZMPkaMsxD+HdFdCW4uJ6yRsnSa4CUQUp6CFRLRSVbtmzOvPK0Fay+q/V78nGmWrMcNoJY8Qs3q3EIjULuv/9+FTXjsah+/frq90XxRhMHoWgp4QEwp0FRRnMa9ro8ePCgbd/r0aNHgaKAeRZp1atXx4MPPqh+BFSJgiAIgmPue3kybr3nVvw8dmSOok0zcWAaWpUqVeDj46sEGlPr1EV7DoJME2O+vr4Y0P929dg+UuJqYKNmOz99O+U0ZNBA9Ze1SYTRODoz9s2S9pgb3Fz0SE4zWYVahoeGbZk73tIpx/cu/28Z6DeiN6fXyKWf1DUXSv+AAHTuYnU1PHXyJAJDvLKZe0Rt9bGJRUbj7IViaLVwFfFycXFFrXoNUS28NjavX4XTyQdR0y3FJooOxgegUUQb7Dm4CUe++ADltzwLeLgj+FQU/Ju4wrjfiE49+wIGF2xZuxzNW7WHZzkfBFergd07tiCiYVPERV9F+YAKGPHoM1jy90QsWXwE3jRwsdNp/KzNCd6o6paMA0m++OiDLkhJXYtGtZraUlS16bK2TcgJLXKmaXnNmsXWYiGPUV9xgRTUPsjfhpPSFJ01n9zAVO1ff/0Vb7/9tkpx5PG1WbNmqF27dtEthFAm3B0dLWOvXr2U6667u7tTljnPIo35lb/88ov6AdAt56GHHlJFmlwgQRAEIQOP6vVwd+Mqqmid0Fb9jz/+UCmMjlLPzp2j5fw5W42Thoqw6XTo1KULqlWplOm93t7eWLZyDc6eOZNtfplSIe3OF7/98WfmCXXIl0CzvtW6bBSVWiqlEmw6oGHDRqgZFqJs4nt0zSzW+B0oRFTkLL0Pmzqp53BCDqgQiLVzz+Pil3VRydVqRpJqAXy9TqnHvFtePqAirl66qJpws6br5MljqBwUAl8/f1y9chkeXj7496/ZeOaDmvj+9cOo5paIA4m+uLvySTy23ITjsXXRtPkRnD03B43qtcVZv5NY+vsK9Og/COfOnUZojZpo1LwN0swmpKSlqfNekxa0WwYuJCehsqcX2nXuiaZ9V6K84aJt2TUxuCPRG08/0BNpId5wOX4JOzd/pAT60KHDrY6b6d87a6pqJtIjZ/b7B59zPTJd9EbiTBPoOVlJ28PUVVqWi3ATShs1a9ZUgyAUBTyGMopLh8cLFy7g8OHDCA8Px+uvv66CW9RKRSLSnnrqKTXs2rVL5WA+/fTTeOKJJ1QomRG25s2b52tBBEEQbja+frhtpueaQMtEupW+FgnRLuiVfTFrzgwGZcVeLaQyjp86i+mrVmZ6u6MieaYvKjgTuwganaZiYmJydeGeG3bsPYQ0RgJZ+8G72Fq9Wzo0OuGQFXuRad+7TRN39iTEJ2D+/HnYvGmDavxssEsXdNcDw56PQmj5ahh4bzTWb3kT//3uiX53D0e9Rs1xeP8exMbFwS+wMi5EnUe9RlXQuEVbfPfJmxib8i7iQt0w5dEZqODCVFMg3NeMH+bUw7P3XcGaDf/Aw9MLT7z8Hu4Y9hCuXDiPeDbu1ung6e4OY1oajBbg7KkTSE1LQY2adbB7x1b1noTyvvSPtH2HKKMBbx5rhfXL68Gwezn2rFmmxnt4euJ/o55TbpbcB5RWtQk1awjCXnBlbpSaMa3q12afY8rXVLpa9pTHrDdUeXGhRVS1NEjtZoJQhnGm4UchR9Jot59bxo8fX6jLIpRNC/53330XU6ZMwccff4xHHnnENr5Ro0b4/PPP8y3SdJbc5lTkAO+0fffdd6pQk48bNmyIZ555Bg888ECpCE+S2NhY28ULU4cEQRAKCoXII69Nxk/vPpBpPCMn9pELHoKN6RfZKvDFqBl06NHrVoRUzrCFz5Ppg4WNlc0q5TG3GW/5qVOiZX5KGiNogLsLC65ynpbplBH16mPd2jWZltOGg/empKRg7vrP8enrFgS5puBCmht89EbVHFo7vcy9EIQ7K0XZ3vPZ9qp4se8KPPrsy+h313AcObQPqSYzatauh707t6BOgyYYNbQ3yvn4o2FAJ3z65xbUdU+26lkL8N+1QPRutBl33/8E7n/2FVyOOgd//wCboFy/YjHqNm4Ov4BAJCfE8ySqDFI8vcrBYjRi3szJWBJ3ElNfOYjmnnGIN+vxwrZOOPZDNWDPMRxOXISExFjcPfx+tG7VEm6uVmt9fh+mjmpnZNV7zs54RUVX2fcuvYVCVlT9onpPek2aA4HmaBtr+xVf27ZzrxLVHy89jFGdwuHv5SJRtCKipFyHaMsR+sXb0Htmb16fH8xJyTj97BuF9t0cNXp3BH9Ly5cvd/rnC473oaET18HNq5xTVk9qYjxmPtyh2H8f16tJ++GHH9C9e3fVn4+BLEbSWJ/G5tZam5UiMw6hIJs7dy4mT56sCuXatm2rlCLTdV599VXVfXvGjBn5nb0gCEKpRavvySrQiCbQtPtjNIngtbSLi15dWN83IvOF9I7d+5V5Rk5oIk8JsnQcNbm2N5qwf4390Ni4Oq/we9CchILBzfXGRsHsUWQv0GwNtdPr5Qbe0Rd/zv9bvcYT8flz53DgwD5M/SoFAQbrOvNzTwG9Iy8a9fAxmLEhrgKqeiSq15ThhgW4rf4VLLrlbkz86mM0a9sJIaHVoXN1h6ubG9p26YXNa1fihQ+/xfcfvoF/d/yM+icG452I7eDlaLwF+HZLa7Ro5o3lf8/G8Ceeh7ePL3Zu2YBadevDy7scGrfuCL+ACoiLicbOLevRtFUHJazWr1yCKmHhKB9YCZFffYj/hb0H95qJSL7qAb8VOpxZPhlnUo7AYHDB0IeeQN16jRGfAujT0pQ4I9yONHvhYC/QoEUZ7YxgOnS8RbU9aN6kAWb+Pie9qUL29Wrv4pmTQKtZs5Z6fPZqEkICPPFc11rKPdR+X2atz913353n/UQopZQi45AVK1YU6vyF/FGWatLOnj2rhJqj86TmeFwkIm379u1KmPHAzTScESNGqFBeRESGI5hWOCcIglAWYQ1P1sgX+3a5MFfPPpsoPcdR9TYD0K5Dh0zv4QX39QSahsHuBKbVNvGj6Jqo5wWS/fkt/fFdd/ZDuXL5u8vJz5j26ywlBlgDxfRKe9dJ2xfM4byqCVRb/ZQO6u5jcHAVvP/uW1izepVNzM7+tnYmsxDGnSYcbYTF79WC67U0PPjpZrQqH6uEEjXMsWRf+Joi1Dw3r12Bbn3vhosF2LpxLRq1aoeQsHB4l/fDh5Nm49NXnsS8d8zY92xvNK50HmuOhgN/+aG823lcuXRB9UfjsqYkJyE5NQ1mfZIydrl29QqMqSlo1q4zPL28YUxJQfN2nZGSmorm7SuoxtOefy1HBa/2MCXGw3LtBC6YI+FfoSKGPfosAgIrwsjIl8kCnZn97Cxq37CkWeDmYrA2HtdMYxzUFBIK3hYtWioRn2m9ptf12cPm5PYXOazf0/ql8T3Hjh1FfLJRCTQt2sY7wU2aNMnbjiHcPJSidEdHHD16VDUY5rUoewLa13wKRUNO3ULyO6+STIMGDbBmzRplwW8P08ZpXFNkIo0N23r27IkJEybgzjvvdNj0lLaTQ4Zkv2snCIJQVtAudq9evYoF/yyGURlr6G2pasqVz66u6N6h96gbXxrrN23DkSOHHc6bURFa52fcrcx4TXtuf1HCKBWFg7MMnvjZyqxC6TJ7u8XrnFXT+3jZv8zlGzFssO1O45fjP8baresxcXNXVPJKxE9/1sDxlN2o5WF1YiSccsPWmohcMQtR5hNY112PCrvbo7HvVRxO8sUbs3qjeswllVZ68cI5ZZ3v6+uH1rd0R3xcLNw9PNRSMNpYJawmdvw+BdXd38TKClXhFmOEz75zuFjxIkLD6yijEaY7tu56G2KuXUV5/0BcPHcah/ZsR6OW7WE0mXHs4D5UrBQEV+9yuHLlHBLi4/DCxxPw0ZjHoHdZB/+AQFw4d1oJtPueeQV+/hWQnJQIvc4CN6a+WixgCSH3C+9yPkhNikcq69Vc9TBxHaf3t3MEv+PBgwdsZiJaqqy9sHOU4qjtZ8q2X2XGWFNWSUJCgjKjEYEmlEYYsb/nnntUdI2/hyNHjqi0s4cfflg1uv7ss8+KexGFm5Bx48apoBUjarzB+Oeff+LQoUOYOnUq/v7bmiFSJCLt+PHj2ZRiVniAZ7RNEAShrLNg4WKHDYa1eAcvzu0FWm5qzzSBZk9YWHWEVqmEypUr2yJknFdh9sRixEcTCMr4Iqf0FgcCjaKRER5C8Thq1CgsXLgQW482QTPvSDW+6wOX0e/tpnj0gYtoE3wJ0Wlu+Hpza6T+lIREz2vw8wiExWTGayM84d5gBPRpQOW9V1EuwgLjvjT0HTQS5XzLY9fW9ahVrzFcXF2xb+dm1GnSChUrVcBrrybj5adrYef2ybh0sguORQfjWNVj2L5mJZ5+azxWL/kLrTp2h8HFBRamd6Ykwd3DEw1atkOFSkFISoxXEbeAoBAluCgAlfirVgP/e/0jHNq9HYnxcWjWvgvqNG6G4LBwZQ7C5tuE0TRGQamVmOpIEclUR6POguQ0a+SVAUpraaFdc7h0bL3TuBmY4Zp+65ptC6pXDVIvzf97Efrfflum7cJaP21WWh8+1/Rattlz/1J/a9eug/ZtWjhxbxFKFaU0kvbcc8+p4EFkZCTq1atnGz948GD1moi0ooPnA0f1s/mdV0mmX79+mDVrFt5//311rH3jjTeUkeKCBQtUYKvIRNqNBJogCIJgZeeeA7ZrFHdXNs3KWDPaOWfw3QPyJNDsYT1Z1lQ2e5wt0M5EXcZ/y5banquyJ3Wlb31uSk/bg11tlGYXb4v06HQqEmi/zLzrTQOqNQebo6lXnG28hx7447Wd6DS4AfatX4XKXuEwpK1ArOtFxCfG4JU3vkNyYiK+fPclBF05hnq1muOq1wU8/+RFTPu6NlIso/D+79Vw6VwnhITXhX/FSkpgeXr7ItD9XjT3jQIqAG1r8FS4FpOXmzDn0Ut4+IW30LJTD1w6fwYJiQmIT4jDvm0bUTuiEarUqInoyxdVKmTstWgVZbt4/gz0STpUqBwMf3MiyuuWo3uPGmjSthM8PDyREBcLs9mE1OQknDt1HGG16uL0sYMq2TO4ek2cO3kMBp2Lco7ZtnUDNqxaploGVA4ORq9evVW6jOonp9lgZtnc1pYNjGzqbdvcPqVx6fJV6NW9S6YWDFrKKYU2BbRqfG6HCDShNLJkyRIsXrwYVatWzTSefdJOnbK26xCEwuDWW29VgzPJt3GIIAiCcH127dqp6rXoyJepvghQ9UTbt2/DrNlzc3z/0Hvuwr6DRxEfH4/jx48ValQsN1y5fNn2WIms9Lops14TZbA1tra2aEtP6Uzvpcb3OPoObOfy0dwOaF8+w7Zew88FeP6bWIxuWREXEo+rcVWrhGPIXU+r5tQVq1RFtdp1MePHL7Flxwrc83BV3BphVY00Axk96AwCq47DgV1b0a3/PfD08EB4RGOEBV3MlCZKhnQx4NHkRHh4lVMpK1XD66oIWlpqMmo3aArPcuVw6cJ5JbjWLl+Iru0T4YujiNF1x86N59CoVRPcXuch+BlYywb8tLw8fvuzLgY9/BSO7t+NiEbNUadRcxzatRXh9Roh6vRJnDp+BDVrRyA5Pg6rlvyIL945jEoutO0Pwp33ueKbLz/DiPsfRNdu3W1C1x7VuJvmMzodWjRtpJ7TXW3egoWZ2jTwBoCWJpvV1FlF0ZxQqyjcRJTSSBrTdb28vLKNv3z5svTzLWKsbUKcN6+SzrVr11RfSWYcjhkzBgEBAcrHg9ktISEh+ZqniDRBEIRCgA6FViOH9AiIHR6eHkqgOaJGjXB06tDG9rxZ4/rqAuP8mZPFvp1q1QjFzp071ONKlSqhd6/u2abZtmsf9qSbnWgpeiOGD1ZRnpw4ceIEHqltTQF0RHO/S6ju1QI74//FqFc/QLuut8HFYIBvQAB2bFgNFxdXvDvhVxXhOhszDkBGbzYXnRld+r2IlQs+QqNWHdCl7134b94sVHvUC4Eu8bZaN/5JMOtRr1ZttKn9FepUXIMjV5/G9o0H0aZzT3j7+CEhLgZe5XwQH3sNXTosQI9qVtOOpgGLsHxRO3h5r1cCTeOeLnF4YsQMJdAee/Uj6FxcsG/7ZjRu2RZnI4+rtEgahXh7eeHYoX3433OnUCm9Z1uQixEfTvDF6/d2xZ9//K7cHN3d3DJF0ngDQHtKAbdtxx5s25m9L50GBZqW3mhtT6dD3YgIHDp40Lqdhl1/OwlliFLk7mgPjUJYB/TOO++o57x5wRsun3zySa6t+gXnUJbcHXfv3o0ePXqo1gMnT55UNZAUaXTBZwSX+2R+kKOxIAiCk6ERBiMZmkjJVJOms6Y4OoJNq+0FmkZgYKAqhi9OeKGj1SsRRwKNtGjSAPePGKaGkfcOUS0FHF34U8Cu3bBF9Vrz9C6Ho8esdVLZpwOi09zhExCqnu/avA56gwG7tm5AfMw1VA2vA9+AQJw8elgt45TPjiPGpK13YP7VqkhN80LdRs2xcfkiJMTH4uTRA3hybjcsjfNHlEmPRAtwxaTD+8eb478lwNCubmgRuA+3hv8Ph3ZtwOVLF3DlchR2bFyt0hzLV6iEplWswoa46YE2Pfdj0+HMRi+JZh369HkTZ44fxt+//gD2MY9o3gp6Fxe17BRorFnbvnm9mq+Xp4o92vA0GBFsaIqEhHicPH4sw1pfQwfVAy7NZAHNMGlOQxGWNVKm9VDTBJr9+8PDQlCpciUV4RSBJpR2KMbYr6p3795ITU3FCy+8oPr3rl69Gh999FFxL55wkzJ69Gjcf//9yqjGwyOjvyD3Q+57+UUiaYIgCE4iOTk5I30xPcShzyLUNOHGVEb2t9KgAyNt6Esq9hfwrIUryN3PhYuX4eLFi1bDDJMFdSLq42K01SwkK1xrP2xuhZRYa8PqmvUawc3DCx06xaNjyEC46iz4JyoAQ9tsQ70mLfH7jFT46ilKAMa0Ji1sB4/T8fDw9kZc9HnUCVmH9q3i8OUna/Hc5SeRWtkI38rxSE1xQ4Wjaaj45FbbZwe4mvHX+I2ITtuED6f1RPV6g5RgunL5Aq4GuCDQQB9GK/0bJODOx5Oxtp2/amLtogMqu5jw7Ze/48WXg7F4xQaEBUxDp+B/1H7xbaQBTw25iB533IPu/e7Bga3zcOSkEY0aAu46KOH49e5W0NMNRfUpc7MJNIou9qjjuqNIY08113QXSJYEsrzMvqe11otPSz21dxX977//pGG1kP23yyQAJ6UpOms+uYHu4oxq0IGctb5Mfxw4cKAyJgoODi66BRHKVLrjli1b1M2BrDDNMSrKeu7KDy65VYi5Zfz48fleGEEQhNIE79SSjVt24MQJa72UDc2Mz8HZRXNdLO4as/zSt3evPE3PGqm5f/1je67q9JSroQWnI09BH74Cw7uZYbQwPTHjfRRx54wGHP0nFOdS/lXjWnfuhS1rluGVYT/AW2+9+hsYfBUPzrkXvw6fgxo+QempfNaeat2bH8Svr0fimGk7Th2NQLDH12h3H9CuvSceGzATAwc3wIO99iIx1QWf/dkcsSbAJ13gaJvOz8WC4XevRpd2szD6w++wc8MqLE7pgC/brkaYK+NjQKCLEX9854Ee3/XGpIcXo225GPX+UO9UzPjKFbujA9HA7x9b+srTPUz4a1BvzJ88EVGRW7F4eiy80r/P2rjyeOmfW2GZdA3nYhYiMLAiwmrUUIqL7pG07Kf7Y5qJjy1KlKWZdNam2Ho9LFy3dvFb7TNt7pvF0LdKEIoig4F9enmx/NZbb8kKF4oMRs94nssKbfgrVqxYuCJtxw5rDYLGtm3blHNU3bp11fPDhw+rOxYtWohdryAINz8UF45s8IuamTNnomnTpti8bSfcDNYm2kUB8+7zQkxCRiojozq09dDDgqTUNBy4MgUzH02P9KQLM0ItkWwB3tveDuX2XsXFpDPw8PLGnu2bUKdhM7jraZaR8RmVAuPQtclwnEujcLJGn/j/zVGhuKbbgp4DKyDYwzqedKllQqJhFz54LQ5ad7qJ3y/HxtgABHnGI9CQhoqGjM/wdLfAzc0DE955AXfe/wSmjPoaS/+ph4dDT9jm6e9qROSX38Pn0arZTBgb+6ep76bNj3rMt3sAeh4egdZ9V8BLn9FztJFXLMwLTdi87zv1/KnRL6qURqPZpOaZmGJSkbS0dIFm1llP5ly3aTArW382MednKetqaje7ldWyVSts3bqlSPcZoZRRCo1DaL2/d+/eEl+/VFYoSxb8d9xxB95++238/rvVUZf7INtAvPTSS7jrrrvyPd9c1aTRHlkb2AugS5cuOHPmjHIt4XD69GlVkHn77bfn6cONRiNee+011KhRQ3WEZ8NBfknWFWgwx1MrPtSGtm3bZpoPe7489dRTqm6DPdr69++vlk8QBMHZJCYmFligsSaNAiu/bNu5V0XjUk1QAo306dMHRcGQQQPzNP3xyHNY/t8y9ZgiguLC1aBXx/K9u3dj0otWoaGlxtinyMy+UgW3ex/H74sWYMeJumjxXHvM+PplDGr7DFztztmRaQb8vb0RTFdccM/XTbAn2RNn0lzwwcmGOLuqCiJC2+BSmgdMbAWQPlCYndwRriJ32md66oCu5a8iQG/CS7s6ICb9VMR46cRTDdC1/kjlyBgVeQJp1y4gzPWqTVSSJLMOTdv9DxM2VFACkWifx8/enJjhmrgzxRNHT1SBR0wg9hyyCjhtiDEbkBJaHnVCGsPNzR3VatZV6y0pxaTcM2MTk7H4n3n4ZNxYvDH6Mfz01cc4sG+vSn/kYBV01kiati6Z5qhdvFKgdenaTQSacNMxcuRITJo0qbgXQyhjfPrpp7h06ZIy1GIfzM6dO6NWrVqqhOG9994rupo0NgJkHwp/f3/bOD5+9913VZj5+eefz/W8WMT5/fffY8qUKWjQoAG2bt2KBx54QN2lfeaZZ2zT3XbbbZmaY7MJqj3PPvusahj322+/oUKFCmoZ+vbtqyJ+Wv8hQRCEgvDfyrU4c+Z0gVdi/foNlONTfli5ZgNOncru8mjfDLuw0Hq4sfF0Xmr0Vq5YAYNeD5OFER49PBi+4c21NBPOnz+Lcg4W29pXDRgSeA6u6ZkiAYYUfPvweRzt1gnB7uds051Ic8GAeYPhu8QVScnnMaz2djT2tJ7axobtxf6oIBzRX8Cu+Wfw6zudcXfAWSXGqFdY+6XNx14cBrma8G2ztRi2uTvqBF3GvivBOLsoFNWvWLc/m1L3HvoALht3ZYrmnU7zgKvZgD+f2IS391eFu4tV5TEi+FdMEN76ow+6ddoHD9dULN7bEOW3My0xAbf28kWyxagE4wWjAWN3dUK54yk4cu44LC4WJCQmqdYBFLexSUZYPCdi9kSm1gTip8Md8f1DhzB5wucY8fAoNG7WAkFVquDihfOqLs3CiJpqZWeNVmpCrWpQYF53AUEoFSnoEydOxNKlS9GyZUt1494eKckpOspSTZqvry/Wrl2L5cuXq+AVg01sZk3Hx4KQZ5HGnMsLFy4oUWUPi8Dj4jKakOaGDRs2qBChFoGrXr26urtMsWYPLwqCgoJytLnmXZNp06bZVsb06dMRGhqKZcuWOb2xnCAIZYvT5y/ZIkEaqidYHs4adNZTBiI6YP/+fWqc1jdFi6gx7cz+sQbHMWKWFRbB2zcoLmzate+AOuHV8vQemqiwWTK/NwWa0WSGi8GawMG6Kh+/iohKNSDIzWQTSgeSXeGrB0Lc0lSqh32KoL9LGtwy7g8qUiwGuOx1h277Jmy8shi/9w6DxWK1sffSA/3HHcLIVmvg5u6BLz7vgKCx/6Fn+SvZltU+zZJ46oFbQ05g8pfdoE+1oNK+S0BYEnR79DCmpuLWex5A3MVRQNWM9847Uxv6RCNe/7wSKqQLNHLJ5IJx829Hxa1mrB9/Be7NmyAwNRW+1y5B32gZHmmfEY4LNJjwQ7MVOPCjD+7pWxdRR3fgl++/RLPWHbBr20aYjck4uMZgS9F8JuIA5o0cBJdfF+DfBX+i1+13ILBSIC5eOAcL8yDt8mXUp6Tvu+y9l9e0VaHsoAS9s4xDUHQw3ZEXx1opTqblKOlX+jcZZcmCX6Nbt25qcBZ5FmkDBgxQ0S5G1LS0w40bN2Ls2LHKQScvdOzYUUXS+EOqU6cOdu3apZToF198kWm6lStXqhCin5+fCiEydMjnhNEyrVhUo0qVKspydf369SLSBEHIF0xZ4Ikhq0BT/ZnzeMKwz6e3Nn3W4ezZs7boFJkzb4ESY+mBJqxZv1k1sHZEYRiOaAKRN8UcHcvzKtCynqhTjdY6qhSjWaU3xiWnoXb9xth3bgUqh1lFFcVOFdc0eKQ/zrqa18ZWxqS5HXHLY3/A12BREaoJJxvD67QRW88vRqo+BdEmF1RwyRB9kak+qBnUDMeidmLftE8xscV9aHvnCviylstidULM+jma6DoUF4Azv/0AV50LDE1qYMWSZZj8R2v0bDkDUbFz4VcuyfY5/Ns+4DzWXQmByYP1ZRlXuEaLDl6nANPqrdgXswzGFYvgF1gJ8THReHRgCHS6jHo0xik99Ba084nFF38GY8Kwe6BvsBofv79Jfc74uYFw0UXbpuc7y4Un41KUARfjzyEm+iq8PNzUp1eqXBlXL1+0CmW778b5iEATbkZYliMIxQGdcj///HMcOHBAnfMiIiJUpl9Boml5FmkUVeykfe+99ypxpGbi4oKHHnpI9afICy+++KKKhPGLMFWHZiQUYPZ3kdljYNCgQQgLC1MNT19//XWlUinOeDFBa0umP9qnXxJ2+M7J9pI1bBw0HDmyCIJQNlm2cg3OOqppzemuskrNswov5baXnqvn7mqtu9JIM5rVLWUXvdV5z2i0wJVufOkwskH8KlTKJN4Ia3bvuetOp4kx9lzT0iM1caYJRGY22NfLOcNYgsLUZLIgJY1uhGakGa3mITq9AcuXu6D7gxmmGoyiwb6WKl0ArUssh7f+6I3gf+NR69Nk9Pi2L47HBMKywRMVjh+HSzlXVAmujlHTI/DjyH2qqfSWRH/MWNQWres2xLGoHfDw8sSfz/yMK5WeRufqx3A0rjzGVt+Jhh4U5NmXu57/ZZgsKYgyHcel/Wfw2lftMaJ9lFqeyhVScdnEbZwhfPYn+iPm6gm8/MQx9NlTFzVc0xBvBr453RguiXpcS4qEEWmIaNwCvuX91LBh6SlceTQuU+TNus6AKh4J8GvoiVmfesGgS1XjPr0nGmdSDajqZg2vJpmBa5FeSDEnqOfuHl6qYJ0Nsi9duqBSJO3Xp7Ybcx8bOXxIqblDLRQxpbSZtVBy0DuxGbMeJZtvvvkGzz33HO6++25buRYDWKwVZ4rtk08+WTQizcvLC999950SZMeOHVMnXxbHZc37zQ2zZs1SqYkzZsxQ6ZM7d+5UqpORsPvuu09NM3jwYNv0jI4xx5iC7Z9//rlu5O566UgffPCB2LMKgmBj2oxZmQyLsh9Q7B9aRRiPL7SS13J5KELSzOxdZZ0PRZo9WhTJz8tVRTZSTSb1Xu0i2j513J4RwwY7tckwxRgdqJh98PeiJdle++2PP50SsdOEpnIcZMNlM6ui2GiZ4gzQma0RxhNHOyA67T/4u6b3kXNw2OYrIW7JcHG34NCW6biCSMx59Ce0b90XejOwNXoDzGYT+g57CF+/PgYDLn+A1GqAPsqAihsTofezbpPuA4bj399/wclX9+Jqj57QmXT4vr0rvumZ0Ww0U2TMPwqe/lWAK2cxYPhDaNDGmoqvvU6ZRPEY4Z4AL50FAyqewt4OjfDzBzWVw+T5NBc8sqUHIjeHovKBWCRV1ANngf53DVU3FhnJ279nB+r3movVC/1Rxz1zQ+8FZ8Lx9nubbRco/FxWZFe0ixQyLfPBPlvw1ccs3juC3Tu3qLrH5OQkuBsM6ZHCjJWqPWzYsJEINEEQBCdAXcEomr0Ye/rpp9GhQwcVfCoykaZx/vx5NXTq1End5c1rjQZhiiTtKYcMGaKeN2rUCKdOnVJfVhNpjuowKNLY1ZuwVo2FotHR0ZmiabzQad++vcN5vPzyy5l6vzGSxho2QRDKFlkjVvaCTEXHMqzxbBEz9ZoFSEo1qr5UrNtQjZPNZqu9fLp19bXENJT3tAoy4uGqt07D+jSmv7kZrutMTQdI9l5xFva1bfybVaBlpSACbfGyzClH/M78sowiqn9cDzprI+v6DZsh0bgCfi7WlEdH9WFchWdSPWE4r0cS4tULDZq3xpadS1QGRuOW7TDk0Wfg6u6FwKAquLbgF7QMHYy0a6nQucdgyda/EVa3Adr3GQA373L449uP0MxdB0ukDksmncUL85vj/WbblWOkfWTscFJ5pCRdwvuzW+KJdovVC2kW2KbbGFsJc9c1xpR+1nUZ6p6Cz37cBp/09gBVXI2Y3e5ffKWPwLef7cc50zGE16mHmqGV1S51+OA+nDt+ADHHLsHH4JtJoPJxWoIb6rhlFm5cNrcsp9o4oyvKeVQG4oC01FQlWLmuqf+1VFuudy3iS1o0bZjv7SuUAUqhBb9QsihLNWmxsbHK5DArvBnKrMH8kufbs1euXEH37t1VDRnDeBRq5OGHH86Ts6NmZZ31DjFTcK53R5ufT8t/rXM8e7OxNwadfDS4TCwezUmkMU2STiz2gyAIZQemamsCjTeYtGhPapoZyUaTivqwSTDH20iPSHCg8PJyd4FBr0OKyaxs0VPTUx01Z0L+TTGabJE1g0EPdxeDMs7QLpb52SQgICCbQHKmQFPfmaooHQrM63HXnf1sjymC7NPDc4Mt1Zzi1ZQubdXHW3t3uej1qpeXi0EHdzcXvPfRleu6d21L9MKYeX3hPmcnjN5pqN+kBfoMGomXPvkB46ctwJOvfoCrly4gLLwmnnv3K8THxeDPVW9jxcnv8dfmCWp9jxz7Ftw8PBEYFIIeg0Zi76GV2BnzL44b92D87b+jxxPd8O3JWlgV44+jqe6YH10ZH37UBm41r2JM+xiU01lQjm6JAP6N9cdXUTXx+qw+aJl2NNOyemRxW6CT5Ni2B9FkXG/1PC0lCa1btsC0Xybi048/xJYtm5Ww2n7Jeh7SbPhTLcDVyxYYstTM2bcp4HTbEr3x67Q2uJRyUO2bFSoEqvmpHkXpNwg0Yab+6oDhQwblaXsKZVikOWsQyhzWPo3OGXQlW6Op1l+OXJvnz5+vWpfllzxH0phzSVHEnPd69erZxjMtka/RUCS3cMEZBqxWrZpKd2TTbOZuPvjgg7YajTfffFM1gqMoO3nyJF555RXVD40GJoTFz6yHo0Ck/T4vdlgzx6hcQa0vBUG4uaAlPMXPjFmzlSijgKKGUmJJXb9aL2f5nBe5TMuzRlWynyESUoxKgFDMae+3D7txTqlG9q0ywdMNSpzRfp5RJU7RvmNH1AwLUTeqaO9/9epV9e5WrVpn+pz8ZClcL2LI+bllSbG0p3r1GihXztrPa9/Bo6qnFmFfrbCQytf9nExOlEwHTL/hxqWnoFXfIr2ZtcGgg6tOpyKPq9cEIsFkVG6MJJ41fXSEtABr4v0w5sn22LvkQ6QhDd7lyqFb7zthtJiRmBCPgIqVcHj/LtRv3hZxsbHq+300ZT5W/j0bsQnxCK5eCy263IrUpEQc2rkZdVu2R/WGzRBSuz6uXjiH1JQUrPlrFtbO+wFr52V8Fz3/uWzCq1ObwKCLsY1nVuZr390JJLqi4oEE/BVnxMg7dCjPhWUPsqRyaOYZb7P5J6z+e3DgDmx+MxinT51E8yYN1HJO2tQDvaqcQZzJgK3R7lh8qQKa+V/DtTRXfPxvG4y5bcN1L06umXV49I3BiJz1NaKN59C6XQcEVa6ktq+LS/renOX9zZu3UHXkgiAIgnOgHqKeodFhu3btbDVp69atU/rkq6++ypQGmVvyfKRmj7TFixejatWqmcbXrl1bpSrmha+//loZgTzxxBMqPZG1aI899hjeeOMNW1Rtz549mDp1Kq5du6aEGptms5aNDeI0mAfKkw6L4enIxkjfL7/8Ij3SBKGMQ9GgGV9oPcZ4cawa/qYbWKh/ysWCd+wsKvrAcjHqGF52u+t1KqKmImxGpjRaUxvdXa2CSxu0AIq3dzklHiw66ziKkKQ0M7zSbei1XlW1a4SqZWFjbC2i1qJFS9SvW9MW7aOYLEjqoaN0Tl64UyApEWUxq1o5TzfrqYDGIZ07Wl17iSbQ+P1Wrlh+w+Vw1CpARc50eiXSKGhNFuv6ZHNnDxe9ukva94670arnR/h5TiNYDDp8PKctVr0+G7HmKLi5eiAldYsSs81bdUDbHn3gXT5ANWvWG/RIjE9AeL1G2LF5PRq2aAsTdPDw9UPXAcPg5ReAyKMHsWvdCrTu3gc1m7aGMS0Vx/fuQHijlnDxOABvv0BUDo/AqYN7sH/DCiTGRqt15OPri6o1IzDqFu4zGYLreKobfJdcwvmT62EM8EBk0gEM+m8A7mt5ABdTvTF5ZxtUupaIP+6eC//0Myzf38r3Cjo90hQdhjXFyvUVEWw4hvtCrefMYIMJdYPOq33lo9318dpt8wHswcsn6tvWoyO3y4VXg+Gx4ZgSaCFVq+LBBx5S64QRSq3lg5p/cLBqtFqtWhga1a+Tu51HKNPw2OU0C/4ijqSxJRNN7mg2x1ZPLJGha3iNGjVU2yehaNCiYM6aV0mGrcBYcrV//341aNCV3r65Os/9hSrSEhISlHlIVi5fvpynJqeEQos/nKyW+xqsdaMgvBG8M07Bx0EQBOHQoUOqoaRWe8U0aS3Njxf3Wu0Y/yoDEDNt4XVWx0F6gaSnNmoHSEbdlDOhySrIOI80k9EWpOA1iIq8qQyAONSJqI8jh3igZqSIopApkVYRpDk6Tpk2M1uUY9u2ragdXg2//Z5u3qGDcuDTvgPT1G4UBWGN7szf5+Q8gd1nnr+WghD/G6RVWoDkVJNK78yNGCYUc7/PmadS2rmi2cxafbSBJx2meVqn09Lx6teLwIXzw9Gp8TSYjEzF3KpEWXCVEPj4lkfV6rXQsPUt8PbxtZqzqGicdRvoXV1x8UIU6jZurmqxwmpHID7mGg7v2YbaTVupFMfgmnURHX0Fh3dsQVB4bVRr0BxxVy/B27c8Yq9chJu3D8LqNUW1iMYqDdOL9YMU3ylJMOgyTrBkdmRNpJw5hhPGTZj+WShaNPbDgsV/4eMJT8Ho4wqvC4Bueyp+Ca6I5zpfsr3PaDHgt3FHVcPqJ2ofxdIrlbKJLj69q+5xjEt3O551vCpernfCYVPYc2l6fDu6A7Ye/1jtqy+/8gZc3WgrknnagXf0zXRTUxBuZiZMmKBu9NOEjpEN/o60i2Vea4pIEwoD3hAoDPIs0mgUwsjWO++8o57z5MAaMro9MsolCIJQFFAA/PEnIw5QKdA9u3VW7ThI3bp1EZeYin379toEGqNgTD9UtWbpKsEaAbMKNkbUlFAzmxEQWBEpiQlINaaoejM1LYWZMgfJMLbQghW+vuURFxsDVzdX1KpbHxfOnVHzSIyLQ0qqtZ6LESvOg7Vrnq4Ga+SOUaX0z9VQ7op2F9mMtGmcvXDluimHv0ybYSv/sI+kOEQHBJXPuLFGAWkvtCj2NLMU1pBpkbnGjZugWeOMCM+cOXMcRu3atmuvesxRiGkmLPffO0xNp31d9rtkFgVf69a9J5q3bI09u3chzWhCzbr14evrj8RUa71fQppRDekeLmqdVawSitOnTyGsejiOHTsES1oaKlUJhV+FigiqVR+u3uWh011D29pzkGb2gtl0JxJTDYiLvoJrly/CxdMb3n7+iI2+Chc3d5jSUtT2SEgzqfnHXovGpEPV8Wy9Eypl8VSqK2bPbQ03/VbMmBOOAS0N0OkseHpIOYxInow77wiHj0tdXEg8iDFD1qPFvtZo4ReNS0Y3pJr00Ke3Q+NeGuSZiJT0tE57Lqd4qAvLCZu64OFQq0BzxOjFXbBp2fvq8agnn4F3OW/rTQe7W86sLdRSVwWhLBiH8Gb9Tz/9hDvvvBMffvihbTydwVkKIxQdZck4JCs8hjMTkFHcrC3CClWkUYx16dIFW7duVSfxF154Afv27VP1FMy9FARBcDYUToys2Pf0yhrJZ/SId0uZGs2LAgowGnkwCqZLF0mm9FRFrYRME2cBARURffUyVROqhIRCrzcoYXLpUhJcDTplna+JM15vaHVlep0elYNDEHX+jHoeUq2GigRdvXpFpfd5eZeDh6cLUpKsPayY4sdwXVKaCa56PXR6i3JvMtu58F0PphxqaKmHXDdsIUAxyYt0fi7nZVLizxqtsj/BKXfJ9OdX4lNRubyHbV4UUJ06d1HH9o0b1tvq9PR6i60dgJaaqcGaYU2g2UdtViz/T/21r4vKKuTs2w3QtMXg7oV6TduodFRur5ikNCWMU9O3mXX5rX/LVwpCSloaQmtHwK2cD6r5V7CuX4sFScY0VA4Lx6UzR/FQy6fga7DuMMFeK/DlX8/BlJaKCqE1cf7EYVQMrY6k2ItISbEgKKwWos8egpuLEUmpHvD29cfLt67AwreeQVDteJiOeuPxapuwpOkFNGnICKS1Vyjxcwee++Y87upkzf7wrxSErg02w9XV2lh6yZYGqJMetOTqP5tcDk8Nv4jnPquOplViEeSWgrOpHhh662kcONEQtd3POmyyzXFb432w8hmrWRbruP0DrcJdbev0lT1iuHNbNwhljFIq0hjRaNasWbbxzPRiJphQdJSldMdnn31WeWHQI4MCjQEtptoy8/Dvv/9WuqlIRFr9+vWxe/duFVJmzRh3evYrGzVqlM1xURAEoaBQDPz62x8q7Vlr9Jz1ddu5P/3i9Vr0tYw6MTOU8yJFjJbiqDnnaUKLjwMrBcGYloIaderh+KH9cPPwRlxcDK5evozg0Oo4F3lCzYsROPVR2l/VeLoijGYTDAYX1GaK44E9qNeoKRo3b43ExAQcP7wf3uV81HtVyhrTH2FRhhhmiwkuFjoc6pV60ysbv9ytGwopEnnuIpYsXaoEDQd+hsHCC3OKNWstGL+nq9287cUgBRrTKTWhR1avWpnt8x66b3iOy0LhnDUKp4mx3Nz9VGLaZEF8itHa6FopLYpprd4vo/1BpYCzqN9wH45F1ca21efh5eOL8EbNcWLnVpT3LY+mdXeicfVNOH62PI5cfg0ebhtQ3iVD0dfwSsCEFx9G4469cNdzb6FDiygMa/wFXHUWbDgTghOxzTG4zwKVkrjzcmVMWTUKjTr2wOo3vsDYsb3w7tNH1MXCA4OBFdG+CPfIEGnEP8gdrRuF4qsJrvAp74lJ8zriz5lHkJwQhwFddmLT9oao6p6MI8lemPBKC2zbuh5DbzmndkquK+7Tf25sjjoecQ7XFVfn6LnN8M0T82BCqqrnZn3D9Bm/o1OXLli9Mn3bKXEuAk0oe7DujD13GcGwZ9GiRer6VRAKg9mzZ+Pee+9VjxcsWKCMDg8ePKgyD1999dV8B7HyLNLo6sieYm+99ZbD1+jUKAiCkFeOHj2KvQcOIyYmw0mPZBVoly5dxLVrMQgKDoanp5cSUBn9taxGIIygqWiaai6dcbHP0VoUjFogvFZdHDt8EE2YZrd9CyIaNoGrmzuOHTmghNbu7Zvh7x+IuLhYuBoMKO8fgORkpi9ao1OXL0WhTv3GqFS5Cvbt3IomLdti97ZNaNa6HY4c3IeIBk2RmpyAs8nJql7K3dNLmYrQ1oFRD6sgSbfoT3c/1CJP13N1XLliBRYbTYiOvoZLly+hvH8gvLytESxGvaxRPh3S9BRuOrU+PJhimQUaSZw+f8kW9bKuxMx1axERGS6+jrAXZ3klOT2VMSnFlLGNVKqItZ8c/6maPosFNUKP4/Gu85UlvbnGfrznGYp371iLNrf2x52PPIsq/mtxV6156v21a5+H7syzmLehJ1KbWU90/EoxZh2aDX8Ru2Z8gviYq9jy11Ul0Lia24eeRVPTWZXWSJoGXsA3Jz7CE/+rhnun14BBb+3NqVHeYML82ED09rkMdz0QawZ+jmyAxf/EwseF2zQV7z60CxO/jkRqghuCqjVERPgeeHqXg1c5H1y58AWCq4bh0dGv4+ypYzgfeQJXo6+gRdCZbCYhWhSRf7b8kaAE2nPPj0XjJk3VPn7fCOs2CBs+WKXH6iwl/LazUOIprcYh7L/LoAGdfHkM3bx5s7qRxP67EydOLLoFERzW0uYXXQk/pDGbh32bycKFCzFo0CDVqoyRNXtnx0IXabxLwT5krCWwh3UffE0r0hQEQdDSE3O6kGcDyLl//aMeK1GSrg6sl+d2DaQtQHT0VUya+CMO7NurpmH9WZfut6L/wLugU1756XIn3dhDE2T2JiEKHaNiQLUa4UhMSkRo9Zq4euUqKlcJxcG9u1CnQRM0btkeaWlGJQCr1qiJ5KQkHNy7G1WrB+Dcmd2qXkpncIGruwdOHDmEKlWrwT+wkvpMv4BAFTmr17gFDuzahogGjVEzogEO7d0JL29flPfzx/mzpwEj0x0ZMbI2eVaROvX1rSmL1nRMq+DifPm65kiZkBgLfbmpSPZPwjfjDsGUZEbTlm1xx+DhcHNzV/VoFGuseWNARbkpms02Aw8NRvuuJ9CYS9+mZVOn78iqNs9oVvVmrBWkiKSY9vQ+B7cKKzB7VhR2/HtORTI73nYnQus2QOvG0zM19hxU/yL+7vYINi3+CZfOn8P3073VeEa6uL7qB8WgzzNvo1r7fhhW+QjSLDp8HtkMFnM5dO32EP5b9iMMuhqZFyzLhUBI11A80PyAbbR9c+2LZi+88NUt+GVADBoFRGFXdBUc3xEK3wYZd0xZ8Xf3xKHY8NIxHNi/Bs273gYPD0/EXb2M2+66F4FVQlVT74imrRASFo7Tp47DgjOZmnprj9lE+8dNNbFh5ULUjaiHJk2tKV1siaPBaChFuRZpFYSyxgMPPACj0ahKcVi3PGzYMISEhODLL7/EkCFDinvxhJuUypUrK1dHZhT++++/+O6779R47oPMOswvec6HyOnuLu92O7v5qiAINwe8eUNBxr/rN21Td52YEkeBxmMKBYQmJFgzRpFl0gYT68lM+Gfhx1jwjxnnTtfFjzu7o+VtI7H037+xcMFfyj2RdWN8vzLoUELNOk+znejjY2ohTy8vuLp5qHqhi1HnlG15QGAl1KrXGL7+gdi2cR1MRhMaNmuD2PhEHNizC7UaNrOKuIZNcfjgPrWMqalpqFG3ASx6F/hXrAyDuweq1aoLk8mIPTu2olnbznDz8sG+XTtQp34T6F1coDO4IiS0uq02ThsU6WKMKZV0k0xMMam6rGuJaepvbCJrpYy46/YJGNv3Gsb1ScHufXXQvv8j2LNzC/6cMVUJS6ZUqnWYntLJgJ1K2UxvrK3B7XA9+t9+W762N2vcHLpQclloApJqVH3mtCbgrBusHLwPT/edhifbn8GSL4x4cObtuHrxIqZ++R4uno3EmateNsHCP5eM7jCGhqJ79/txdOcmbDwfbGsizukOJZfDF1/1xXPBh1BJb8Hkw82wYkkzeF0wwdfoD7PRiEUxldPjmLTWN2DO1WAY0+dxIMUVu66F2Rqpas2jr5iAnckeeGtrF4RsBiLfd8OfszvgxLJw+Ow0IDI143tfNemwL6Eygjw7olZESxzfsx0tuvXB8NFvoELV6qjTqBlcvXyQmJoGz/L+6kP2XPXK2B/SuWIyoN+THfDMwH9V+u+jj/3P4Xpn2imHjWuzp6wKQp5gNNaZQxHyyCOPqJZQrHmNiorC6dOnVURDKFqYzeHMoaTfHGAbsIYNGyqN1LNnTzV+06ZNiIiIKPxI2ujRo9Vffjh7m9nb8PPCiwvStKnz77gKglCyI2Xh4eFo06aNer5xyw4cOnQw23TTZ/5ue0xRdvjwofQL6vQUN3OGkEqIj8PpyEjVtJgmHkx//P7rz7FltTcquFprgO6vFIlto4NhiOqDlf8tRpeefaDT60EjRhWVSm8y7enljcSEBGW1Xr68v3JajL5yCf4VKuLo4QMqHbFyFZ0SawY3d+zesQX1GjVDszYd4ebqhvVr/lNRs3pNW6n5NWvXBdHRl9GifRdsXrsCEY2aIc1oROy1qwipXgNb1q1Q768YVBUNW7ZH1LnTOH3yGJq17gA3Fx0O7N2JgIBABFYOQjkfPyTEWVM7aftuFQM6mE0WJKaZVGSJ4tPal8161c5lSIk/hlo+GbVQtd1S4TfMgG6XBmHJ+hnoM2AwgkJCkRgfY62FgxkGM5BmhjIr0We90ZYerXTU+Di/rFi9Xt3N1ooGOX8t7ZSijEKaqZ7aONK6ySrbXUPGhu6N2I/tzR/Dsf1TsXDmz0hIvAcVH45Bfe8YnE71wMubusPjigW33xKNl8dWw6GzB/CNfzi6+J1DZKo3PtpyC1bdPc8Wm32rwRas+7wBPA5dRFyEUQmiV6Y1w6p+F1HBPQlLI+vCctQDtw2ejYquRlTWW5D4f/bOAjqu627iv2UQM1rMksWyzAyx47DDzGmYmZmx4aRhBgecxDEzW5ZkEtliZl4tfufelWQ7Sfu1aZpCds7R0Wq1evuWnu68mf9MuSf7JmtJ0ZnlVgpMBq4sOoquLg/Uu/V4KrqoXvsh1i1KcmNPg54Bzng8hTuvqUWntPF2fRqd5X4Y9P3Ee+dQUbKDvq4OlHFJxKVnULF3B3FpE8BqpqpkD8k5E7lo0QvsXBNGsNpGp13BZUvnse+aXezvf1ueCL37/gfx8/Mffa7FyQ5Biov3lspAnZ/OB7rgwh8tOEQce0R3r0j9HUF5eblUnaOion6/nfmDQxzPf6vJWCX/2bj33nslQRMnBITVcaSSTKhot95667+epO3atUt+F4sqESs5EnUtIC5nZGS44k1dcOEPZF8UZyjForCk/KD8+hlGrIpCCWhro6a2Bk8PTyKiYujr7WXD+rXU1dXK24SFR9DX28OWTRswmXqxDnMQcf3l1+ko26xGjTMYQ0CYB2I9OmhSxzDQ/x1GDy8G+nudqhEqgsPDpKVQBBulpGexp7CAwOAwaZn0DwqjtqaS3InT6GxrxdMvgMJtGxmbnU967gS8vLxpbW6kT6kka/wULBYzgyYT5XuKyBw3ifJ9u0nNGkda7niZ7jhkGiAsOlaqeBq9AZVaS31tJQ01VWTmT8IrIEjOoe3aW0Rmbj7FO7fh4+8vFZyQMZE019fI8mFBxETAh7NXzeGM/h8uw3baHe3yb6prO7A6nM+BgJiQa+33INyqcxZ1C0JmNqNS6/jxm0+oLC9joL8Ti7mXjs4hfH19SUsdy/yjFxIUHDxM5JDWS2GP/ClR+3HFavla/39F1oIsiPTHjVt2UFNTPWo1Hak8EI9B9stZnUrpIcXT+R5p6Vai8HJuS7zOnTYtQ946Ljg9mVPOLMei/JoTjyrF69h7sBqUuNc6ODp6O1dcUIjDoWZKShef7ddz8qqzZOhiGs1HJIKJsJZnbvqA119V8/a3lSSOm86Bp99kjfU+LF6g6VTw9Alf468WWhr4qK3cc9QaLtq0kEUpRVgcSj4tzcSyxRe3HgduDRaUdW14RgZSW7OPNfuelzH+1heH+IvPIh45t4K7Q4p4YsiTynoFPZEdaHQ6otKy8La+yzn5a1Bnw9qqv3DBeVbGzTiKqLgxJMXnEZ24nbFnXIGyTcPQlq2UDW6RATQ333k/XiLFUlhXRTLMYc/9CFwEzYU/Ms477zwuuOACSdIOhxASxEzampFwHRdc+I2xaNGin1137rnn/lPb/LtJ2urVq0clPeHt9fT0/Kfu2AUXXPjvhli8O+fAnDNjI3YwGXohLHsyVt3C+2//hc2bNoxGtwcEBZM7RUHeqdGUfOug/LNt6HS7eeG1KBLcg9EpgmmwKbls5Wx23PQeN58e9DOBp9Om4LvCVPTqarQ6HZ0d7ej0BiKjozl4oFyEz0uCkJadJ/fNMNwV1dPdRUpGLgFBIbS2tFBZvp+J0+eSkTdJzpcJJSw9Zzx9gyYqy/fJ6/uFTdNuZ9Kco2lvbSYlexxDJhPVB0pJSs+mZPcuxuaMl6XJWROm0tvdhdboRu6k6ewvLpBlzCJwJCBsjCxNHpszjpLiXSSnpkobZVBIKGqcsf0y/VEoZ8KWOEzQ5OVRq6YDvVsA176t5d6zrfI5frU+GetqL/CoH50z02q6WDTzOa4/zi7DMjyUXijxosWqYnWVGxcdt4m1a1dzwqJTmTv/aFQKMcXmnIkb6Vc7/ZST5PYEQTtcsfkljPTVjSREStvqsFI2mtI43BEn7kaoaMKKKa2dShVWq4U3Xk0h7M5CIvWDVFm0rCqN49jEQm65rGz4LGofG9dEEJtwL1NTLsANNyZfWztqbxTfc2Pb8bu+Bp1/MD0WLftP05CsO6Q6ZiZa+PPTFlJyvZk6W8u778fwxVefEhO6EOFzdLt44IjHZfAbpPG0rbx22QLsGtA2KfArMWMsa6anrYZzn67mtAWiJiCee9+IYEtpPF4+Xrx72ZcyTETgucuXMv6jIPZu3Er+Ucej0Rk5ddw6Z5iJAqZHD3LUVQkkKT/jkgnfcukE+GRbCGcef6jjSdhjz774ctw9vTFZ5CdOdq2N2oAO+4D09va6yqtd+MMGhwhBYdKkST+7fvz48Vx55ZW/34648IcKDvlX4R8ODhGN7dLG8hOInjQxg+Aiby648N8LMaMkusZ+cZ5o+EzRh598zlvvfeiMtD88bMJ+6GA6op5sXP8uX33Ugrc6jkqLhku/OJoTjZ9yxXFiidnK3ZPA+miM/HOV1IScfx+mtnP9pK3cN/lcFCw9Ikjhm64AXlo8kcpHV9E4tJu5R5+A3uAmkxlFold6dh779+6WBEqoVmlZ4/APDMHo7kn+1NmUlu6lu6Od8dPm4Ontw+DgADu3rpeEbPzU2fL45ubhSea4yVK1MNusNB8olUSruuoAeqGGJKYSbzSi1hvJnjwL69Agu3duISkzl57uTgb7nYmUOjc3HCo1Sr2RoJBw6qoraamrJiF5LEqNnqDwKFrqq4hJSCFQZ6ShrkYqTEIBGgk7keQMBTqDAbPJREBoOB+8YuGT5y0kTDsfXaeDOEUnP+z4kOiEFCbNWUiw4VwCtU7l0Wu440xcDtLYOC2+h7EFCSycoGTx558Ql5gsLasquxKHyvma+vr4HuGW+Fv4afeZIJXi9TdLy6nzsgw9sTqJ2kjapAxKUamlhc/dN5SzjnmdeMMgHVYVbhYFD+duh9wj78tfYyNgzkxWLHlR/pxSOYHpYYfPoXlQ2bSBwcJqYuLzuHTnfBbFF3OBXxXuw6RJvJWuPFOPw3GQJ2+Apro6Vm/9mJT4aXxUH0uK+y68VdDvgLfqU2iq+4rQl5WEp8xGNWRDe6CV4pqv8c9o5oyjx8htapVw90W1XPPJBbjpOtEfNicuyqoV44awHbTQXFctX0+l4sjZwIBMHVfG6keVv1PHOXj7rEXsXf01r/0lBpVKwfXXPIJpKICAwGAZ3JWbl0dISChataDVh1YwX369hPz88YwJDcTNzRmm4oILfxSI44s4UfFTCNu8K9jOhf82/MM2T5GO8/HHH//s+k8//dSVnOOCC//lWL58OZ999pm8LKK9v/3+R5lO1NnZKaO9RW+ZUMRGLGtiQS6DPobDHwaGhuSZzB3bt9Lc1MI1V9fjo7bL2PQ4rYXrjl7P+QudXm0BsbAWdFD0Uv30rJtaYaffPZDFBYeuKzAZueeL+ey8631qencSl5jKSWdcwJjoOHk4K923G4dCLSPx1Rot6XmTKNy5VSYu7tq+iZ6uTkLCI6TV8UDpXml/FPufnjcRrU5PT28PW9evkEEieqORutoqNMLOPWEapiEzETEJJGfly8Lq1qYGtq9bgWlwAIVaQ0xqJjo3D3r7+jBZbWj0Rty9/QgT6ZGtzWzfsoGouESikzMoK9tPv8lMV2c7EfEp0rcuelU0Bg/5JJitDvxDwmSMYGBIGF7+AXgHBGG2WlCqNVx40/24GTVs+eRu1i67h09+fJzA0HDOufo2induQa1xqmx/DTFaE77nHi+TJrdKldN5vbPkWzEaGPJTAvbTn79b6ixUPryQWrwPhGImO8+sdkxmO2aLM8jFMpxQOWSzERgZg0dAMF09PcRHvEeKd498HwSobUS6Own7yD6N7F+lWYtX+hTik6egVKlZ+kUcb7SNYa9Zx3c9vtxVMJuEtBMZUPRTVL6cmhdq+POyORwwO0OtRkJUDlffLroxDoVKyaplr/LZywNcVzWRR5vj+NPBSSwvGMuE1MuoqlnFvq2vYFB00BnSQJu2mZzjxXvuEFRKB2/ecCYfP/EcddZD/1rrrUrsE47h2IU3c7BoO1uXfcPnzWGjgSVlZjU/HEgYDT0Z/WxMH0PR5hiOGmtnToqNbcsCWLtcwYfvttLTt4J777iFb79ePNoBePgLvnXrFr755pu/8Q5wwYW/cybtt/r6nTBlyhQZt384IROXxXWTJ0/+/XbEBWfK8G8VHMIfU0r7h5U04et9+umnf3a9aNMWhW0uuODCfy/EjNkIRBmuqNb4+PPF0rbG8CJSLMKFStbV3cWKH5awb3eRPHsZGj6GurpSOjv7cVhFObOKk4+Lc9rohhGv66bPpsKgtP5iD5SALKW2wctluSg2lXDye+XMOC6T8AX5lG4KxXfXXvar2skZN5n7n34dd3d31q38kdAxkWRk50sylp0/me1b1hMTn0zexOlSlcqbPAuTaYDGhjrsIYIIWfH0cZNkVNgzrQ5BOG0kZY2Tc2rm/n7K9hYyNneCDBuxO+zs2b6JtJzx8rGJeHgRw2+12TDqDU4y29pMbGomPZ3t8rEUi9tnjZPJkfFpWbS1OreTnJ4tEyA1BjfMQyZamrrwCw6XIR8e3n7UVFZIYoxShc2hoL+nm8CQcGJSMujqaKervZUr73+GpuoD9PV0y7m+KbMWyu8iZv/GG25j7Uf+aH/h/5p4rlttKhw9GknS+vv7Rl8h0d0WGxd3iIwdvrg6bFvrNm6lsvLQHOJIaqQgYuKtItTAEfVMQJBWv4AgOrrasZgtBIZYyU99hs4eMztsF6LUtR4RNz/aRiD+1gHbBjzps2t4ZPcUdE0qErwmU25bT9uefTz7zvE8nazAZlahO6DGs7+XnJzj2Lbjc2rXfMZkZRJ3pk7huvgteKvMmO0KxhtFh5Jz+7tMvjRXrmT6udfSULaHLatS2eCXgqJfgcdBJWqbitj4CRw8sJWlq55BpdaQNGkOqtSTOWB+jFitTT5N6/q9yD3lHg6u+wuXbZvP5WML5Xv/7fpMzG16BvZ3EJuez84V37J3tz9L751GiHs3G2pi0e43Ypt76Kxps1VJT7M7vppDiptR4cCgc2aUfv12ICedOoslX39BQkIiqampMsr/j7qQceFfgN/Q7vh7krTHH3+cqVOnkpiYKAmbwPr162W68KpVq36/HXHBZXf8d5C0oaGhX7Q7WiwWBgcHf4t9csEFF/4NGCmRFkrZ2+99OErIRLT9SOeYgFh8i3Jn09Dz3PWCL+vbJ/PjS0bevWcH8T4h9NnhoeoMNt5p5KbbFvP1q8GjXV8JWisfdQQz27MVH7UNjVAzgM397ihsakzA5xVJ7FoRjembEg62/IiHpze7Nzex+utXR/c1Z/wUbrn/KXZu3Uh2/iTiU9Mx6I24efkwNnucvE1W3kRpV6ysKKHqQDmZeRNlrL4I8RBWvqDQcPwCg2lvb6Nw21riUzMIDougv69HKmUd3Z1MnHMM3V0d6HRGBofMJGTmSeuh2Gmd6DzzD8LT25eGqnKqyveTPWmmVMXEc9jb10t4XKLTtijsinoDBg93tq9ZTmRSKgE+vgyZBgkIHYO/WkdLcwPBY6LkHJvozdpTsJXYlAyqKvYTGhHL3l07CIqIlIXY8amZ0jIo7HMTZy+kvaURh1JB2Z4iElPS6OqIpGbATKyb5Wde/iqLmivWHwXf7KS2oZK88ZNGF1FikX/gQMXobUcKpQUJlzMqCsURBE2mcwoyJhIpzTYZfCJUNKuwNopScWnXVMg0y8iI7USpe1n+vYHbz/gCg9igN0QFPcCxJzex7Ws/PBQOxHmC9X1eZBu75eW3W+N49dvpKO1K9C0KvMv7UQhpVkChoPqVZ8g46mbsOiW6VjOayhYscQ6MXr64+Qawq+AVwt49k6uPOQa1sEsa+rglaR3hmkH2DPrwVvEk0vM8WfPOM8TmTcP0zmLC8o6X71lD4wDKlk6UY/T4hcdy6r0voTN60HCwBHdTBNeVzmR2SCl9Nh2LKzLAzY3JyWfx/YWvUvHwDSgMDuzNGrwPgsJsxejmxWB/N+kZk/jmlLdJOf46lENwwvgtRxBqL5Ud604Nnecq8D7MrjryWrorHfSFphMUsplNG9eTmJwi/5lLojbsmTz++ON/60OECy78xyMlJYXi4mL+/Oc/U1RUJCsrzjnnHDmPJkKTXHDhX4UDBw7w1ltvye8iu0PY0kVn2pgxY+SJtN+FpOXl5fHaa6/xwgsvHHH9K6+8Qk5Ozq/aCRdccOHfhxHVRC7I5fzQocj0kQQ+YV1rb21BoVTh4+dPVNAHnDNPkJFupvt2M/0BH+LcnWf93RRwafgelp54OhX3HgoYGllgBur6mPvAOZS99QhZWXH4zM6ncZOG6g2f0WkTIRXbR/8mOHQM19x2nyQkliEzZfv3kJaVJ+NthbLk6e1HY4OI6/eQ+7x53UpSM3NlUuS+4TRGQaTSfPxEFq5ceGeNn8pAXw8FWzeSmJaJX1AIY3MnyTLqns5WzENDFG3fQmJ6Nt2d7ZI0CZtjaGSMtC2KPjSreYiKfUU01VXLqP2gyDh0Hs5ZvsbaKsp275KzYWLmysPbF1NvN8XbN5I7eTrJORPQG/T0Dw7gGxQqn/OS0n2kZI2jfF8RuROny98JC6YIXgkIDpOBEZ5+/ijVWsmnxPxbev5k4sdmY7GJGTovSZY0Wh1qnZGLbryX/Y13ERtnOcLad8Ci4fQfT0B9zwGKGhfLUJPs/Aky2VGjFrqPA4VD6HmHCLlMf1Q45OJfo1QcoaD1DVlHqwKETbLPapPBJ4InCLImZvaMBiPTch8i3btD/s24S1UYh4mHQJTOjGHc2cx8dYiFC5rZ3+3Prm2xaIJNqPQOhqqN+BfaMVR2oDRbsTc0U5M+IJMS77hvMtWbqlmx5Vu8OzLQKg0M5bhRvOwvZC84jcz5p/Llw9dQ9vHzZBkeweyjolHhxY0DR+NuNNHR6Y66wkhwTzRuPgEM9nTTcKAQ/4BQAjqD0Sg19OeqKV+6hrGzT0Chc8OG0qlGNjVS9M0ApXNn4bArUNWr8W+3ouywY+/tpvHO10ib8SdUZjv6yi68Y7xZv2MTMel5RCSNxc/fl+IP7yEtdxp7k0SQyiElrcOmwuqv4fI1E7hpolORi9AM4jf8H7vUrAOzEj+/AKkQiOde2B6VhyV0jkRAu+DCHymCXyA0NJSHH374971TF34G8X/g8ITdfwbK/3CTwNq1a5k/f74MrVm3bh0PPfSQJGnihIFIFf38889/H5Im7nj27NnyDMWsWbPkdStXrmT79u0sW7bsV+2ECy648O8laCNhDyNJfGKVJ8jK9m1bKNm7m4qyEkl8BLx9/Ni6xnlGUiz+RUZClM4ZlDECpfivrIGU2EwUlB3xu9ohNzT9MGTtlb1qiC9xMNJoCQ+PlopTTFwiSWmZzDvuFEkMhfIlwjiS03Pk/YuZsbXLv5PqmE5vlMEhYr/FvJkIAxHq1cQZ86QqJkhDQFCoJFiF2zYwNmeCnD9LHzdJkioxpyYsjyJq32KxoVCqpZ1RozPIABFhXxTVAe0tTezZsVkqbgZ3D8YkpGLw9MZsMaMzO2eo+gcHJTGJSc+hra0ZlVIlu9CGBgcYk5jGQP8AarVGzrkJC6VQ31Iy82VipPgf5OXtx4DFTOGOzUQmpErLaU3VAXxDxsjybU9ff2xWC9lTZlFfWYFPQKC839K9hVL1E8Xa4kXRGtw459gqvticQoxbD0X93vx5fx7N+4LwXtzBtsYvpeJ32bW3otXq5dyYIOfyH+FwVL5QxQSEkiquVyuVKDSgVillPUDPoMXZRze8ALPZbZIsCEI9ODBAfV01Hl4+GLw8GevlJGgCwRobluEKAXEP7TYlCWkOcpqWseu16VQPxhLSZkHZ3MtQrB/eA1Z05a1UNCyj3lSGW0Ag3WvrWbsxn8kx3+GY4M2NQy2EpbyCxarGttdMaFImebMymRi/hdg/38dDF91M09IPSAk+GrsVOnp96Azwku9Dz4NW7J19eAWFkZ7mwdEnX8Kbj/xFPhHivWfbbyZ52kKmnncTKq0ey0Av4Wl5dDXV0bzkU7LNN2P3dkPTbcdY1glhSuw2G1ZzF3tXP83YjHkMBtj5sXiFrGiYftbluPkHcfFDL7J+8YfsWv0D1S/aeeeEROZ6NzBgV/HIwXEorSr2XXSQM4+ZgTYihMDwVi6Zu5UBu4bnf5hGbGs/35btZ878hdgcIiRGJIMKJdIZzy+UcR8fn3/VocMFF/5j0dXVxbZt22SZtTi2Hw6hqrngwm8N0YX24IMPyk5pDw+P0etnzJghVbVfi3+YpAmWuHnzZun7FWEhQkpOT0/nzTff/FkvhQsuuPCfCWFP3rytYDgO3UnQxNzQiOpSebCC5UtfxDtWz9vveBHuHcyugQS27gtljH8T1WYrsY5DCVo7+zzx8ezES22XGY0ftMajXaukvrOGjWV2Jic6NYI2G3xWlYH7/jb5swj+uP6uhynasY1JM+ZKe6JGq5E7IZQAq9nClg2r5YyZaPMq21dM8thsfPz8SEhJx+GwExgowie65SyWuK6pvlYWTAtZwWqxUrRrq+w/E2pbcna+nD0TARUCwqrY0d7Gnq3rZTJiWGwCVouF+PRsrBYzfT1d8nZmEXZhMRObniPj/IVN0SjIkE6Pt38gTTWVHNhfTO7UOTJ+39rajG9AEOW7d9Lb1SmvlypcYDC1ZfvYs3MLk+YdI8mUIDIbln8rVTHPgGCa6uvInjiNg+VlePj6k5E/Vc6gBYRHcGBfMTqjG0HhkbS1NOIXGi4tiuNnH01HaxPm/l65vab6aqbMO4ljcr4g9ZSbsduUGAsa6Wp5k72tB+WC/pSzLsbHL1ASMmdk/qEeOkFyh6zOkm3xe/HdorDJy4N9nbS0NKN390Lv7iP/VvyheA/FxBZw8rgV0rq3rsubOSnb8fYL4MFzvRnRdYSF8bGqZE4OPYDJoWJ5cwQfHf89CoUSu2Md1z5WzvYdvhSXL0O1W8PY2Dn0upmos5ah1Gvwi4xl3pX3Mi76cbk9sc/eerj60ePZuH0MQbEpjJ/kw7Vpd8jAGvGebrs6h6fu/g6L2oR2UM3QhybS0s9EqVSjbujG6mfj3itaufD4flE6wIXHzeOON3MZ6OkgNDmLyLRcqWK21x7E3duHELc6/AMbSJ22gB0/PMzkSRfjY/FjMMjMysKP8QoMZdFtT7H5y7cp2O6sJ4jLmcSUk85jTGI6ve0teAVHMPGEs5my6Dw6asq46YLXeP7K07DaVTjKDQRWDOCI8mf9Z8/KMva9vT1s1vsTFzEDP+M+1tatQW8wMmHydNmNp1YOJ2fapWjMtp2FzJs943c7prjwP4b/UiXt22+/5cwzz5QdmWKxLD4TIxCXXSTt94N46mVFyG+0rf9kiP7oDz/88GfXBwQEyNn+XwuFY6S86A8MYRfx8vKSZx5dFQIu/LvLon+pjPan1/+12/1/WPLDMlpb2+jq7GTZsqXs27tXqlTZefnkTpiKSq2ir7eP/OwXmJPqLCD+pYNkjx0Ken0Yox+gYsCdWd7tkviInIhXmiN5/4Hx7Fn8LIOOPtmLdskdwcRM9OfrA4lcFVaIX2APH5UG8eSitRg1eu596lXiklIxDZpw9/CkYNsG0jLzpJIlCqcFwZLBE+2tqFXOc0sWqwWjhydtjfXs31tEhiiXVqpoa2+jsa6acZNnShLV3FQvz6YKa19vf5/cjkhmLNq+meSscfj4BdDd1YmHlxddHW2ykFgsfkVvWltDHfuKdsjZsMDQMQwM9sttms1m2uqqqK08QEruBNy9vOnt6pDKmXiihHKmUatx9/Khv68Xhd0mE8Y83J1n2MR2dGqNJIFubh6y8LqnswOjuwcFG1eTkJlLf3cXPoEhtDTU0lhTSebE6fR0dchUQIObu1QeLeYhysT+JY2VASQ1FSVMnHmUJHVYbdxxySnUVVU4rZxdHXLWLjImjhPPvJCQsDGYTYdSFJ0zTw6UtlI2rC+msb6P0LAw0rMTKC9tIiu7gTlz66jpU3Pp6WVcc2MgJq07D/1pD0a9u7SWLnnnIB7KQ2TvxuJcvrisiOPuD+eu6dUoFQ4+b4/g5U9m8ujCH0kI7ESrthKstUi1TrzftvW6MTGxEKVajUKhwmYZkopW4qS55C+6mI66SiLT8rhj5kUEiIbq4faHo1/wo/jzfmZc9TBXzXuKfI/6UZtnab8fN7xxBoVfv0VbZYmsTPDyH0N+7hmYrP3sK1tM4xbjEZaaG1c+QXuPgeaK3YSl5snr6vdu57pTCpkTuVX+vKMmnLNv0FO+aSmnHe9OWKiK7zf6MeVPL+Ll48PBgo3EZubL2x4s2kpUUjoKvRsWQfLVDvxDI+jtbMc/MJD7zpiL0u5GcvJJqDuH8NCb2VT9hVR4RVhNZeleBnp7aG0Sj0tBRk4+Rx13Mt7ePujUShnTL0rR5SydRi1lSq3KVW7934L/lHXIyH7E3v4wKr0zFfWfhc1k4sDDt/8ujy0hIYEFCxZIu6PRaPyX3pcLf/s9dPtXBejdDqlK/wxM/b08fHz2v/3z8dcQHh4uhauJEyfKkwPCbSiqbRYvXsyNN94o59R+FyXt8OG4gwcPyt6032I4zgUX/ugQxOtvETBhTZw7azrLVq75VdsX3WbinExbazsvvXAf5z0cR4w6m89uL+HTD99lxY/fS3KiUzZw59bov3kWy1MJVTYDN9xzLHfPfwXlVIO8XljY5vjW89osLfavrQQGhHLX4y9y+5XnMTi4l8JNlSSFSTMkyTnV9K1YxMoLS7j3hku59aHnpLIm7Iy546ditgxJUiEsjAIb1ywnOj5JWgUFIaoo2y+JWGhULBqDUSYMenh7S7VH9I61NTdIQiXsk2qdjubaaspL9jI2dyJu7l6MzXMWnoogEQFhJxTK1s4Nq4iIS8LL1x8PvwAiE9OketXf20N7cz1dXZ0kpOcQMCYaD79AmdIoCJNQgty8vOlqbaFizy4yJ89CrdOjGOiXatKW5d8Sl5xOSFSMjNL39g+S+zjQ14tdpZLWTEG68mcvoK2pQZJEYZUUs3CC7IkUR0+/QDrbW9i1YTUpOeNlCbTof/MSRLO9lXHT59HUUIdWEBwc3P/SB5QUbWfb+hXotHpmLzyJ3u5Ogn0O0tOzjqVfm1CqjWTlTsA/KIAzFrxAlPsQ158K39T5cXTYLrTKXTRaVASrnSmGmT4wb30IeimODnJMSTpXXJzMlrWfoVEc+b65KH4v054LYNGJy1h56T3Yog2omlU8f+zXTI9qHn1vjZwMEJdb7Ub8wmLp7WjihDtflM+t6FLTevnJy1oPX6nAPdeYyiXB+9ApHKzuC6RReRSd9Y+x8a3HOH2O44jttpjNdDfVMuPyBxjs6aC7uZ5dX77GsqWPyd9PuGAaVhrQjFQRAPs3rcY7bhrBKXkYvPwY7G4nbvx0ZkUcCrDJjajjlJteZHqQmhmRJfL+7rFreHCzjr7+AcZkTcWiUKDX6whNzsWiVOHt6UNbdyfVtQ0yBKajqR5sVs645RHevOsKVi27Gw8fP0nexHtk/pkXMevYU1HjwKjXsbdgG0F+fmxY9SNffPgWlqEhwsZEMGvuUQQEBg137DnkGeyRtNaRY8uvObHjggv/Taivr+fqq692ETQXflecccYZ3HLLLbLCyGk9t7Nx40ZJ0P4Z9Vb9zw7HCQ/mbzEc54ILf1SMLKAExKJKnP3+a1iz5m8TtLq6OlauWS/JmIAod64oL5NKRFNjI4WFBZj66yje6I+7qhO7o5O5S3y5/cxjeenFUlLC4eBQmpiuGt3mL8WiC+XiwKC3nOmpqbWgwEnSBHpsamwWJQuPPpsV676QJOT+R84iLmUVCWHO6H2xHVGVnOtbT3H2SZSX3yVn3jx9fCTJ8Pbzl9H2OzatJT0nX85xBQSHyu0LEifIl7D8iXkynV4vCVJQaCjtrc3UVh90umsUSnZsXS+DN0IjogmLiUetM0hSpdJo5LaEJbGzrUWGeghVTYR8iCAQtUaDQtglbXapQgkLpFqvl/bD4Oh4GbGvUqklOXLY7ZQX78RsHmJMbCK+waEkqcdLK6awIPa0t6Gw20kbPxW9zkBrc6MkVDazmaiUDHnZNzBY9q1VlO4RbFFG75cUbyUxaxzd3V1STROKnCjRFojPyMHo4UV/TyeJmXky3VEtVDwxD6hSyWCRDDGvp9MRm5LKmcd/S7j7fiq6d7NvVx/nzHYWVZ91ooqcae1cdMFeJuXqiXJ3ruoF/zomvF2SboEQjfP6kddffxhpTzf0ozgthrnW83ivuIiLM7pG3y8J+kEScwfZWDyW/JT7mDH+T2h69cRc3jn63hJv1WabEo3CTpXZwIOFU8mK0rKp78+UrP+B8SdfTENJEf5GX/l4FEqLTBj+fMVYNmdFo1FbqW30w9isY+K4C1mz5lnerr6UCMM6wtRW2m0qniifwJb3n6a2cBNTLr2P4KQcQlNyqS5YR3BSLha/XtYMPM0kQ6987OsHvFj10Ycore+w8K7XsJj6cfP0AWMzFvHePYzMff/CPdzyTtvo4zGqLKhqnsLhc70k2u6WYiYHraAvOJFVpePk+8Lg4SU79XReAah7+hgwWwlMSOf6lz+ndt8udm9eLQnayZdci9JSw7jwe+hqrebPr3jR3+cgc1wDz9ylYMih4p7ngvnsje3s3LaJa266g4iICGlhtpoduOnVo3Onf+u44oIL/yuYN28eO3bskCqGC/9e/JGCQx566CHOO+88wsLC5PpLpIwK94wgb3feeefvR9L+VcNxLrjwR8bh/WSHXxYf8vc/+nSUdJmtwwxHKGvvORdfIpFvJB5ffBsym9m6eRNrV62gpqZGLgqNRgXXPZtG8Alj8Kwy4a6yjx74Fnp1cNSSDnkwEItMP3U/5UNaYrVmOT+0rteb6k4fdjUGc0VKIV56Mz92h/DN93nk2R3ccmcLedN8GRtmpd6i4eGSSfissFFSXUB0XCIJ0WYWZiyTu324uiH6r3Z0hUKvTZKi9cu/kxa8rs4Oxk2aQUBQiCQaYv+F7VEEUvgHBkur4qBpkJDwSLQ6A1UHyqiuPEBG7gS5XUGoxPMl4vVTs8ZJC6GwEhqMblJVE6pWQ00le4t3kpozXloHU3Im4LAfeuIFWRNWxMaag4RFx0nrXVPNQQZ6ewmKcEbw79mxiaTsfEkQ3QVx1Gql8mc2mdDq9dgsVuw2u1S+AkLD5YJdJDEaDG7UdOzH08tHEjShhgmiKFIjtXqjJIWCdOVMmS0VN0lIx0RJ25sghR0tjdJ6uXfHJtzcPaXFs72pQapy6bkT5ZxdYkau7PNy9/TC23YHqV5iDg1yfM1kznIStBHytWpFCIkGUZ9i+5tjJCMkXWzncMLeb4fBIS2qDm8uX7CTJY+dx7lzD3BSUMPo3yZ6mfAIjWP1lpfl/m7umkBEkFNJE/f6fG0aP9YnMdCnR71Xj0dPG4Fh8TSUFGL0DyM4WYPeJ5Ch/h46mxup2rGG1i3rUJ17HjY96DoVuNWZUTaKEgcoWu3GzarZhBq7qe7zoX5PKNMn3cDqdY9TV7QRdc5UOVMXkTMDN99AerqbeeLAZNYHVGF1KFlVk0TmTHfKt73D8mduImvRnwhNHUendYiV/Z5MNfZIMrd5wIM2exhd9nZnpcDwCYzi5lpW3Hsii665lDf+9OXwDOR6/NTbefbLGaROnoPNYpazCkJN8/QPorutiZ7WVmIyckmfNpeBnm66e3s4J/1afNVW8IAJj/Ue9hlydqY9fUMztVsvZc/BD/nqs4+48vpb5GdFqxEplM45NYHTTjvtHzwqueDCfx+OPvpobrrpJvbt28fYsWPRDJ+QG8Gxxx77b9s3F/53odFo+OCDD3jggQcoKCiQSlpWVtY/ndWh/k8ZjnPBhf9m/NoZMQHxd6PlwcMQP8fExlJaWj7cVeWMQxcQP8sgB4uFDWtXU7Bjq5yRSk5NY/zESXz03ovMv9iD16/0ZkKwRi6CxUl0o1IU+JazJ1PMGTgXs790IBALwC67mqznT8e6tRh9txtlhV8yYO/mpeHb5GfGEhZQzAdrvyYwJIZ5s+oJDY8nMvFY3Adt6M072bKngPuefg1L9wdHELReB+wf9OCL5lg6CwJ5/fFPUBrieWn9AE/feSrtXUbyJk7j1PMvJ3RMFP4BITTV1bBvdwFjc8bLQJCS3YVYzGbCI2MJi4qTqYeiJFqkOIrnx8PLm+aGOhwKhSRFIuZ+56Y1GNy9sIjZMB8/YlPS5WMRJE4UUfsFh9He3MDeXdtIynR2rYlAEEHsBDly9/aVnV9u3j7SdpeQOU72rvkGBkhlTZAsm3mIbat+IDQqjjGxCWj1BhIz8tBotHJ2rrmuirScCaRkC6uiiM73pKxou1QbRZqjOLCXFu2QipzOzZ3epnpMA/1y3wT5FPNnI0QtPCZBRu4L+6fYllDWRJm0sGUGhYTL+gBRm5A0TIbkaztMIkaIsvgWrDMdYWcVbzNBoJd0BHOcbxM6hShXVhCkPjSjuM+kJ1hjxuRQ8PjBHLTb9RiMA5K5fXfzXzCUTef4+4dJnVB4LVpiF5xBx5rPaWup5OZH/Oi9S0eioYt13aF8uSkX971qPBxgrDejbu3C5DGIxs1TFoRbTAM4VJ3y+fWLjGfIYmH7Zy+TsaETu7sHKpMd7YFWzJFGKIWmtz5H6X0lBzzCUfQr8awEdZ0Z7zHxlK37hjHp4+lTaOXnwtTXTfv+YmrqAqmJDpRx+ooGLYFDFnITTmb5yocxenvzp/y/kOyzh4IBD55sDsHsULGmPgHfWH/eahvgTL8K9AoHWwd8qPQ9ltDoVSQEfiJj/EeQ4l9M8ZoBlHojfmNi6WltJDTQn65W8RoppBXVLyRUEvNBRQ9qjQM/zc97SQ9/vUSdQfMkXzJsE/lx62dSPTca9JJEOifURv5GIY9TJ554oiue34X/2eCQiy++WH6///77f/Y7GaxjO/JklAv/Oojjz+HHoH92W/8NEArub6ni/sMkzdvbm8bGRqKjj5w92LVrl5T5XHDhjzxLJvCPkDWxKP/pjJk4+y26p3bvLaVvoI/SfXtltHZsYjLuw0O4ZoeVj956ihsfU3PhXX68uDyWJdd9SNHOpZQVxOKuso1aFkcSG0cQrTVROmAgwTB4REHuyAJcLAl/bI/Eba+Vras/ldeJWPtJmXOITxory6ELtm/Eo82T8y6/nvnHn0p3RzsP3Holy7+6R95eqGCnX3AFEdHxbFpr56Rph7ZfZdbypzXHEPjnGr5a8gO64XKop+e08MzcaPYNaTn2mg6uPHMhl1x/N/OOO1kGc0TFJcp/siL4Q4RUiDCRjvYWGfTh4xsgtyGSHXtE7L74QamkePsmkjNyZM9YtAhtUCgksbErFFKxEl1btQfL5DCySCkU6lN4dLwkPkJNi8/IlbcRapdQzCISUqRq5enrJ3vDvAND6WxpkrNjQvETt0nIGifDQLraWlGp1RgMRvp7u9AZDLKsWnS06Y3u9Pd0M9jfS2xqlgz1EPvTIbfVI9XQwdZmSnZtIyp5rLxeqHStjXX4BYdK+6roRhPXi7CXgJAwSWZE8uC2ld+TOTxvt7dgK9v6NORc7jzYS3teVwBTPdvkLNf6fl+Mdpjq1SFfH/Guuat6LD8WpsFuI8+q2gmeNcCFUcUsCGwaJdoeaitzL1mI0mhE360kpLGZUv1OGes/84xL+OqdN7nvnGM5NaKMTquGuwpnoO1Tkx2/gK8qHsKxdD3PxdyCOcSBYkCFZwl4r6nDau3FYRrAPCmEpm+KmXv9k5IQ9zbXSaXJMyQatUKNZ2AYY7KnsnXHi2TEHYOvIoShOC3bt7+Nf9xYeXvflW3YxgSgNINb3SCKoSFuuNjChaf10257lhcLrqKrZr/4NNHbPUjrBx8TuegSHGrQdTgwNA2AVpgb4dzpy5gQtFs+/pnafsqrfHimLA9Hkw7/HgefbMqlaGwoWoWNktYQ7O06YgfT6fAxYXO0jpLVJpuGmuJtlG74gXlX3CdnIEVQjqebL14eboQplRg9vamvq6OzuYGEpCTabAr8DuuV++l86G6TEXUn7N9bfNgxxFmjIFIeR1BQtFd+FwpDSUnJP3yscsGF/wb8NHLfBRd+DyxatIjc3FzpNjwcTzzxhKyDELNqvwtJ+1cNx7ngwn8rxEJHELT/b57sl/Deh58cQc6ECiT+x4iZkg3rVvPZR+9JlURAdGbNO/p45iw4lqKCHXz5kY1Qo1hEDjL15HbaT0yk1azDTTlwxH0cvqgTS70Bu4Jzlh7Hq1MWM9Z/SMaUC1Ra1GzpDGZdXSQ734vA2LxFXn/PEy/JePyB/l72FBZwybW30tPdiYenD+1tzdTXVBERHcerH39P2b492O02GZkvkhhFouIPXyVz86u1XHyekRarlof3T0S3RElp6wfoVZE/e05SdGYuvceTj2qm8OUHrzN74YmSkAkbn7DvCYVMzIvJ4maNhh2b1pCWLebWnIl2dZUVhE6dLRUw0YUmVqyCPAgIIiQCOKrK9hIQMobB/n6sVpsMTBFEUJxl9Q8Jl4Sru8M5ZyQInkhLrC7bR4ToClMqKdywCt+gEOf5PaVSWiHj03NlHH9XW4tU58TzULRpDZFxSYTHJsrER7Etvd4gbZSVJXvk/JuYkRP7KAiYp48fkUlpcl+62prJn7VAKmLiemGJzJg8S74PxGNvqKqQaY55M44afe6EfTJ7ymysQyb5nKVPmMprD33PubFRHJtXQ2FvAF9vzMZYasGSo8DarMVv0wDXPbWaCGMvP7aP4ZtleYR+0Expwwc09lWhe9mN1NsnseCyQ4S/wuSOIyaQnO4Xefgpnbzyzud68Y06nemLzpEE8tmTP+Pzyy/FrgZjnQq/8k76fZ21DcGxyXR99CFxiYtwqOzo6nppqd/ObusmVBottm/MRGRNxic0GpVWh19CJgOdrWg9fOjraKFp/05yT72aze8+zrbC90cfv0dQBNmnXEn1tpUUbn6F6YPXYW/ogp4+ki+o5/ZLhaplI9BRwV0T7yJ4trP7b3nleOa/fRDVpt34qCJQmCxQ38TBqFbZexcfefCIz1CWsRlDgR5dtwNjzQC2pw5QeHkGDp0DVacS73oHDFpZviGEb/OtZBu66LWpeLI6h7FzEumr28ryl+/nwteWodBoiXLfwezwtzCHGvmx8mqUSj+6WhpoDgrjspVZ3Dd1L34qC54qO8bDP88OuGjFfDxWVbOrr5TwiChJakcwEiAisHu3k8QJgvZrjlUu/LEg3LvDDt7fZFsu/PHwR5pJW7t2Lffc4zxJfTiOOuoonnzyyV+9XfV/ynCcCy78J0FYd0U5+99zpnmEoP10nuzvga+vLx0dQsVwUFJaxvfffk1FRRlajZbbHvDircei5aLwu24/rj7Hgx++/VJ+3no6qghzO3TGUFjSQhQ2QtQDRyhjPz3r3mZT8lB5DkPPVZBzxR7ZE3XWNTMImObPqnVxdL+1goOdb0tSI07ACNvhx2+/yvqVS5k57ximH3WMDOcQypL4vSAMgqRsWreM7794l9rqeqJiE2hqrJOR/qLUeOZRx3HVuW/xw2dZREbNh+4+NPYt1DaYaRhQEWq0HbG/UnFTOggKnkLRrjW0tzZJu6MgPuJ5Eve5a/smWWQtetWEaiSKlIXFsb+/j0kzj5LdZyNnVP2G59hKdu8icWy2vG6gr0+SKP/gMPn8CquieFyiMNs3MESqXc31NTJxUUAobCI4RDwvIvUxNi1TzpsJMiUWxYKgCeVNqGwi2TEpIwc3D2856yYi/gXhEsqal7cvZm2/tGgKIiOCS0QoiLA4JmTmyfAUYXUTpM8nIFiqe8Lu2FB9kDEJKZI8VpXulzNuAeGRGL18GBoclGmXQnXLmzYXNzd3Ogf66WhrJTgsnOnHnMxD559D87G3MKjyJLS2h5LCT2l5vYacpNk09NdxWk4lqaffhtqhIXh3F1XNK6nvOyBJp1DwHr7/B9xmLeTEiEoCNRaSdf0cE7+Xu0/RDYeLOHj+RneCx38lw1wiU7OYmNHO22e8hVal4LUXk1lnDmbH/qWEp2Qz4ZRL+fjO81Hp1MQF5GMNUFPVVI26W0fq3FOIyplOQEwyA4MDcntCOcRhw9QtSrEV+Cdk4VCqmXTZg/TUH2Cgqw2dmxeeIZE4NEYip3rK2bMVWx8hMCELk7WT8+YfIi8CQQbnayswJ3oLE044nk2fv0N09jx8fcfQoqikdtdaMk6+iqKhIlKM/aKfXaq0K5qN+CyvRWvXQHsn1dataF+uIWzcMaiH7BiqelFpFFR+voGHZl1EeEAHPWY9dQcD8dEoCW0LRWhZB7evJmNKLhemPT4a0uKjv5l550eRMeNo7DYrMRV1pMwZGp0FHLQfCm4RKpup2Qtth1AEITHVaeEdKaT/ax1Frmh+F/4u/JeQq+eff55LLrlEpsCKy38LIvnRhd8HfySS1tfXJ+fSf2lWTVQS/G4kbWQ4Tvh9hcXxtxqOc8GFfzcOtysKgvb3YHBwkJNPPpkPPv7siHmytLSx5GSKlEQoraiSRGzCOCdBECRLJDCOy8mUnWXCllRaWsI3Xz3Ng+9n06aYzAfv+nPDou2jB6bjvNpZ9UwEb81VsGrlN9jMDnqsiXgMB4Acbls8Yv8OW9C12+GU9cdy4LyvaTCXSUticGg47z67Ap459N84PCKasVk5LDqugKNnN9No0XHD+yruvO5ijttyDhdecQN7i3cx66jj6O3ppq/5M24/eyP3nQNlZj8ufC2cB2+5kvP+dD0XX3MbmXkTuOeJV3jxifvY953zTJOXjy8LTjidpLEf8/raGcT6dJJh7EPUXhWa9CwuycSn06m6COIiAkMEGRLJjoIU5oyfKoM0xByaUPTFDFZrcwODgwMyIESpUrJ7x2b0Rje5Da1OJ8M2hP1QqlUOh1TmBEES2xMWQ4XDIcuiRfKjUNNE6XWo3iiVTBHhH5WQKhMcBWkSELZDMWcmFDsR/iDm0hRKBeGxCZJMCkIlIGbn5OPo6cLHz1/+zma1YBVda031Ul1LzZ2Il3+gjOAXnWg15fvR6A1y/4Wd02B0lwmXwk4p1EOppun00NsjH5PDhAxEEaTSPNAvCWhp0XbUaiVpeRNZcPqFfP/RY5JIiuei3dooO+a2ly2TASZatYI9799H9qRZNNraOdi9m6zJs7j03qdoqjpA0faN3DHtWa6qi5MzUF5KC3efsnm0FFxA7YDpD5zF95e9TW9XO7u+CUKvcBLwm27YzVPvLGHQ7MaUs6+ht7uL4299jjXvPsOajc5Jx+DETMYedRox+bNQefihFK/j0BA6D1/6O1poLi3ANz4Ld78gbF1t6L38sJpNDPZ24x+TiltwNINdreg8/dC6eZK88AIGWupor9qPztufEqM//fb9uA3ba+Ws5mGzeWGTTiJDGUr5yk+p6lqJZ0gUmadfT2TeLD6sVqFUbSVC00+pyYfPqqezv/QR7A4b7kFh9LRVQVcFWoOecGsUdi8v9roVM1jdSs0jG+k5dQHYFRiaFBibTaiVOpSiJ6+jlXC/nUc8j/4GGy2N9Xxw31UkjZ/Ox+8Oyv0bsUweGDJgRoHNoeDRkgl47FVg0zlrJKJiE+XsqjhuCMu0TqYB/fy4MGfOHNnpI45FLsujC//teOaZZ2SBtSBp4vJfg/hf4SJpLvwrkJaWxieffMLdd999xPUff/yxFLN+LX5VT5pAbGys/HLBhf+V0I/DVbARC+PXX38tS2OjwoN/9ndisf3VV1/JBc+hK52LImEt2rN7t0xelMIQCkpKS2SAxciCa+OmTRQWFsoF98GyzRStCkevbJGbybum6YgzR+LyLRHFvHAgDrMD/tISydST9/P2u4GEGwbwVzsJweHl08Ik+afSicznAG5BDl4tyKX7vjoaLRWERUTx+J/fpmRfMY+/+DZFBduGLYze+AeG0N/wJOfNF+f2bfirB3jqnHIubbmZr198nGlzjmbO0SfQ0tQoVa5T5m2USp4giYk6M5efVYNq+5l8/cm7LDjxdEkGx02ewbS5C9m6fpVU4TLzJzHY18f2zeu4bFYBwcYc/FMnojrdSEebN74/mmixbCQwOJSMnAn09nRJoiYUOqVChaePryQt1QdLqawoJyUzVz6r+3fvQqFS4x8cSnRiGgq1SqYJKjUagsLGSEVG2AYFienr7UahUrFv5xayJ8+ShdLJOeMlGRzscxJEQWQEuWqpqyYwPAqVykpV6R5JsMT9CYK0e+s6IuKTCYuOl/H3YjZMzM0JAiii+VNzJuAXFCr3QzwGUTpdsbeIrvYWktJzpdoh9qe9uVESsJCIaGnLNJkG0Wp1DKkHiUweS1tjnVTfxP0Ita22fL+0TWbkT5WVBbJk2zSIb0iwtE6K+H5xAsBstXHiBVcy87hTWbH4Q1rqazjzqluZtOAkaY8URFB0yK346kMO7ivGah5i4flXMvfks9mzbYN8TmZGxLB7w3IZPjNyMkD886gc0hCtc85tHbCoKesMZu6Uy/lu+ZPoFPGjtxXvpLFHhVNZ5E9wXCoWGzhUGo69500a9m7HJywKr+BIbA7n50oQjd7WRppKdhGcnI3W25+ApFw07t70tTfT01iNh9UmCbVfQjZqo6cM4RCJiXY6cNhsBKROwJGqIM7NE5upl1X16wn26CFV30GT1UCopo88vVkGbGwddOOrOy4lbs5pnHPL6dx54kZUCjtPfmtiT/8AVRvdeNE2BXe9mZYuD8x1RibHXMi6qtcxm/rJu0CcfHBQ+PEzVA4sl49Z6+5N7MxTaNqzGdWnOwjwT0c9YENzsInuVA3WskHCsmewdmcB5491dg8KVFnU+J94Bvl7e/h2ySM0DI4jzf2Q0lxldufehxZiVZox1Ayha9jJ7qFCgkLDiRw+QSBtuAqwOezy2DOS8HjOmafJy65Yfhf+l4JDKisrf/GyC/9eiGPNyLHnt9jWfzLuuusuTjrpJNkjPXPmTHndypUr5Try186j/d0kTcTti1hJNzc3eflvwd3dXRZaiyG6kTkQF1z4T4YgZ4enK4rL557lJG3m3gHWrlnN2uHfnX3GqZJUHX7Q+G6pc1E2ZDJRV18vPyd+AYGyTFim6dmHZ82GY8uFhe2TD95le8FafBPH0LyrlkvOd0evDBzdh3jdIP02BW6qQ4l64cNdVcJydZp/NR9eeRY5CY/J/bj9+Xyypmlp7zNwemSlvOcvOsPZ/XEsH7z2PnZsaLTbparj4+tHRKQ/Tz1wLLt29BAYFCqj8kXfmFC4Zi04Hp2qFIfDMLrI9lVbsGVpJclYv+J7xk2cikql5MdvPmeOUzAchVZpg8BkOjs+oLGuhpI9RaSkZ0tyIuxYO7dukEmFXS1lvPa6B37hPjz1bh1fPb0SxSoFcXFplPd30dbSxCMvvUdXZxumgQFJgEQ32a6tG0kamyUVM5HSGBWvkkRDzGBljJskbydsiwI+Pv50d3fKOTW/wBCp/IvuMtHFljt1jnye0vKnSkVQEDlBujy8fWioOkB1xX4SMvLkduoOlkmlS1guA0IjZDeasEjabTbixmZLAiZIlyB4psF+mRLp4eUjFTJhgRA/ixk3odaJUJIxMfHEpaQ7Z8uqD3Bw/26SssahUaulFdPg7oHB3ZOm2kpqK0qJTc+RBK5w4yoSM8dhGujDOyBIKngipt/aZ5EETjgdNi37hgmzF8r7E/vY190pA1TScvI59/q7Ze+a3s1dRvYLi2Ry5jhC45M48YKLUNGPyj2ars5OjN6+RKZk0tnTh95oZNz8Uygzf0Kibkg+JyLx8cwNCzg9dTdalY3vqpJRl+qx1rXJ92yJWUfK8G3rrUq6o0+j6YunWf/hS0w66xp03oH0d7bKEBCl0Zu+vl507l5YTX3Oz4pSiU90ChaLlUz3tWjchyjsmuIkK0qVTHjU6YRSapFzbCqdgbbyQgITc1B7+mHpasPg5S+vU2vUNFUP8Rf1BLyNg3T2GUjwb2KcTy1mu4rva1JIzMtn7zevsv3FBPTDQR0Pn7yCkz6aSe8XW0B7FG0eDlR9CjxqQaXUE5Uxn4YDm9AFRKCymZj/+De0lRZIm6JvdDJKnTsBSTlseelWGoN2EBYylgH/fmpWrpLx/1pPP6FrcX/tAU4KLMNkV/NiTQ4ivcS0p5+IlBxuul9F0JN6wjQmaix6Hi6ejHZQieWbFWwzb5D7KYj9yedcKo8FFpsDtUoQtBE1bZj9Ae9+8PHPjn1iqH3cOGeaqQsu/K9BqMUilTwyMhIfH59/9+648D+KY489Vp60f/jhh2VftMFgID09nRUrVjBt2rR/LUkTtkYR9z1y+W9haGhI9qV99913vPvuu796x1xw4Z+FkJ7Fovyv2XlGFLTExCRpNzwcP43EPzzoQxC4EWTljmfL5s18t+QbvlvyrbTbCcQnJHHeBZfgFxgwGgYycjJx04Z1XH1DLSfmxki1q8aSxBiN+QgVrNOu4vqP5/HnU5bhobaxd9CD8e6HfM3S+qRxEJ+YQm1NJQ9fvdU5H6ZQcqnVQkraqUQFjIG2H5i/wMBDT0ZiUql570c1nVvKeevJbtSKIBqtIfy43595CY202nRc+byJay84hYx0AxcsChs9QBQM+GA+YJBWuYb6Wmn7EypXUloG1935Ie89EyhvW2dR8n5lJsY6Z6Gxp6c38UmpkpiK0A9BbibPmEtN5UEmJ9xKtL+4lY0PboCz0s9j8XlvM2DqJSQ8gj/ddA+JqRlyO8X7dzNh+hzZCyaSHcUToDPo6e/vZ0x0LF0d7XS2m6S65u7hRUNdFSV7CkkfTjlskQXZgXI+TXRIhcYk0NXRKkujxYyZ0cODLcu/Iz49my6bTapSMSkZMu6+r6uTnOnz6Olol51qgiwJi2N/TxemwUECQsfQ3S56zoKlila5r1iqW4LQibAQQYiGTIMyidHPT0S822VlgiB4ItUxKDwKvZuHVM+Eyrdn+0bC4pLkfvkFhWEeGpIJk8IWKVQt8Z42untJMijuWxDvnWt/JCU7X5I2MR8nCK4gCoKsCoIbPzYLk82Ovc9JXkWXm1jQx2WMQ+3uiZv5PRalvSVtd+U93rxVfKOcywuIjKNHqG1uHoRExXDcM2O5+5IGDCorf6nKoLcwiLWFDt6+ZgmXBlTTlqrhmuppsA8u2TKPS9K3o1PYeacqE0erOznjz2Tr2rcITc0jMD4dr6BwlHp3rEMibt+L7sZqWg/sZUzmJJQGX6z2Vm6b8Tjp3s7gjoqub7ln2/MYHKDz8meor4uW0gL84jKkxdEnPhurUoNZzK5ZLQx0NKE1eqDQGmn+y2JUvpfR5e5AOaBgR4AH+wLCsFlVmGqMhGmGmH7WdHTK+kNqocKBt69RfraGPlxGcM5sFBYwNJtIi6rilUdLUODN63s62FIVy+CQFX1IPN01pZgVetx1BnzjMsm75EGq1n9NRckqDN7+JC04j8DkPIw+gQx2tfHpQ3aWnXYCDoWSoTo9PiJ4xGzBGORD154WFt4aS9iJKfT1GFDv1hNY0YExPwI2KDj2nMvIyJ2IeVDMWTrQqJTyu5AvnaXuzscSEhIik5l/OpvmImgu/C8Fh1x77bWyH+3CCy+UBG3q1Kls3rwZo9HIkiVLmD59+u+3M39w/JFm0kY6+sTXb4m/i6StXr36Fy//NYi291mzZv1ze+aCC/8Empqa/u4o3hGCdnjx60+RlJRMScn+QyXSMuDCqZCtXL6cgJhNFO2Pocem5t7VE1h/w0c89cTDHH/Syej0RhKSkmlubuLrzz/BMlDC+49Fjc7xiNJo5/2LaH04aNZz15qpbL7/G8bcXIlB6YGHdoilWz3ICBySszTfdIUy+KMHXV0dZOaO54Y7HuSNPz+FxWqRytX+vZ+yTwT7JHmy+I0QFHKKxUzeyWBbFCQtXgIhajvnjXVaLIMdA7x6nZUTaxZRuPgzpp1p4uqn4zho9uDTdTlklJh5v7aai6++lf7eXklUssdN5qn7FczuTiD8vHBKW4LxXKampe0rwsZEEZOQRH9fL0HBobS2NMkgkfDIaDatXsrZ+Yfm6MTc3KSMRsrT83Eobdz39Ovy+RXpkOI5Hj91tiREI10p/oIQdbRSX1MpXzNx28LtmwmPjpOXffyDiE/NlEqVCBRJTM+Wr4NMbHQ40On0qNVairauk+EegnglZjlVMxki0tyAr3+ItHPKyHkfX0m6dm9ZT/zYbEm+9u/cQswwiRQY6BW9VlpJpAzu7nIbJQXbGJs/mcDgMDQqtSSJwj65e9sGEsbmSFVNWBQHerslqRJdZ8k5E9AZ3WQ8v5hx8wsKkWEVYuZNvPGEdVI8DhFSImbbBOLTc7BYrXgb3SUBE4EfQiVrqCxn7KSZMnFSXOdU1TaSmJ6DV2AoPZ3OgJUpY94dDa6I9+yir+V2diyN5sH7AmGMig++S0XnG0/5cx/xuP1RhoKUqDsV+BUNccfjP2Icno30M1g44arVLF7rQeXT+3jw9AUy0l7TrCKg2oyXNgi71SLnykSqoVQXVSqspgH5nBp9AqTy5KZu4fT4J1FjYqzXoWTFOO9mvC27GHSEYx7olb1pnqExmIasaL10QK9MY+wrX8tzZ39Lgn8r+9vGcN/663GYTXiuakAREYLS7MCmUdEb6y2ZjGcjqPusaGNjqLI2EKNxriprLUpW/vkJNEYvanZ8hdHdn7C+EGz93Ty8ZTe6YcXtvqA3OOmL5xgyI+22gRlTsFvNNFRW0Nd4gND0KUzKnYO5z2mxDdI1EhIAeyor0evd8bW70XL/SiLyjsFrwIGxvANboJ2KXeuZfsppvHDTVuIC9lLbreX8p2Mxafdx5ZUWjj8rghuueJlv3nmZkDFRHLvoLCKiolGLyNZh2+MIfkrQpkydRkiAS1lw4b/f7ng4hIpx1llnycvffvstVVVVMtFUiAZ33HGHTCN34ffBX5uV/7Xb+iPiV8+kCTi97z/3igqJz6WiufDvhLB9jcyYdXV1yX6/nxZOj9gcc3Jy2blzx8/exzL8Yfhyyf792HFgEx4iof3IYAihkDkoL1nM5seDhI4sv56Zs5pF515G0fOP8pfXX5b/oMT+LFjoxrdLwlAqRciOc3H9U/SLSO0NC9lx8RMysCIzZxwVZfvpGKwnO8PKCRecQXdUBIpCN7w7drCrtYX7n3iJygNl3P7Qk9RVV8vL61YspauznZSY0tGI/RGMrt9GH+ehA2CM1syK5/bw0rkzefT4VRROaGPmtJPQd67mo4INjJ86kxnzjpbWwjXLvpP2x5vufZz7broCj21eRMUlsGP/HnkG8/m3vpBlymX7i0nPyZf3sb+4QN7hgbISqge0RBqdBHXQAbu7g0gIj2fFxg/kayFm2QSxGxjoJzgsQj7fB8v3093ZObrvItlQqF5CpUvLGY9Ko5ZqmojWD4+IoaenU4YwCAVMkJHOtlaqDpSSkjNeznvFpWVJlUqt0cleNfG3TbVVuHl6SVJUf7BcWimFdVtYNAX5El9KlZro5LFyPwUBEiSrdNd2xs0+mp6ONpkGKRCdnCbTEdvbWrBZLDIoRISACBukSHYUQSHle3ZhtVgJioyRJFtAEGBBwHas+VGqasLKKAJLKvcXk5Q9Xt5GFFoL4uUb6FTwxOW2JmGrbJUzbhGJqdJi2tbYIO9HpeqQ18dnj8fN24/Ojjb279gk5+kccUee0BgzM4V7zy7BR1ctf446fQcP//AgMel5NH35KrlRZ2DtNONmVKL2OrJm1CvSi6H+Epq2/cBUz3EMeRvQ9pjR72ugKqgajcEN37ixWAf7pd2vr62Rloo9+CVkoVU507EenHEnnipxYmF4fXfY+/XPCx7l7fUpfNtyNW7eXkyMWY9Ka+C7gi4sdq38TN582hqSA5rley4toJrT0pewKSyWwk0vM2nwOrRNfVg62zHmJmJ316HpMqM62ExVvYp32hOZ5FEDDgUfNY/F1L6bpsJ1BGTMoKTgY8rUOpLHe6JTeo1+bjQKO7aBNnp7NRj1Wsxd4u9BbfDAMyIF05AFa2uT8D4z1fdj7l64Xf5tXZoP5zy7kMRZi6jc+ieKv3qY5Lxj6XHvp2L3Sqm63X5jJ/EBNnn7CC8z933aQK6vAr3C+VzNmJ3BvZcdzaZ9H/Luq89w7e0PovP1lic3hPVRp/7lk0/r1zlN3K6kRxf+l9DW1kZwsHOG/Pvvv5fBXgkJCVJZ+/+SH11w4ddCrHlEaI0IZKqpqZFumcMhwuN+DQ6Z1f8BCAIm5GThuRzxXb733nujvxczGMcdd9yv2iEXXPgtcHg649dLfvir9kUBQdAOh7Dm2Wx2Oc9hsdrll3n4uwg0sIrfizkzuzOq/45bPUfPGImvALWNP1+2GktDPL118by1exqTLhrHpy8GEKUzE6GxYD5sTdw/fFmsQ1f2BmH51tk/mDd+MtExlTz5SSxzbs+Rt/nqrY9YdfejrPzyLjZs+ZHsvAl8/uHbbFq3irJ9e/nL89cwZ9qzvPjnMo65V8v3S9ukOnc4KRMQhw/hiKo0q6TlcgRi2ReqtnLXuFoWfniJDK/YWrCMrr52Lr3mVs699Fo5M9XR2kJKehYRUbFMmDqblz/4mvknnCqTAnPGT+Hp1z+R4SSiqFeUUItuMEHociZMxs3Dg5CwMUydVs/qbj92mYw8VDeWnd8m0dZ/UN5OWBWFZU8sLUUYSHNDrZyZCwmPlPNoRqObDPVITs+RQSRCrRMkztvHXxITESgiOtyEFXDPzq0yxl2oam5e3tIWKJIdhQVRbE8oT92dbdKGONDXKyP0BbESKZBidkwoV0JdEzNSCem59HV3ych70ZMmZtjE70Q5tVCzRBCHuP/yvYUySdHo5iFJlCCJJUXbxTCVDHMQECqS6GyLTk4nOilN3k5se9/OzcMhIloSMnIlYRPWx+CIaGnBFBD3KS67eXjKhEnx7ulqb8OBgs7WFpRavdw3WRng509VyW45M4fOMPpaWy1mEjLH0dPXz7uNMfI9Id4nZWY1m2oj8dUfek8H6qx8cN9ZeAeHYTL1s3TTwxT2fsGSomd4/UAivcPvr3a7glcrs8ibdrb8efvW53HvbsF9sJfu+D727fqexOnHMdTXS1dTLf0dzWi9AghIzJHzYwNdrZg7luKtNkt7i7hv8b1j+DMysj/nTdkHgw08POU6rhj3GZdlvstTx/wZs0KNysOXUL/aI97vgR4VRM+/CL1fKOu2PcY2VrK292M2rbof07at2GrqaE1QUfHppywuz+TZ6qk8enAG/Y2+fPqiL88/nYyjvYSca14lYsZpeE8/murDPjhCcVt+zyVUfv8aC0L/wtJz72DpeXdwWuZq9H5h2IYGUOsM8j169cKC0b8Ld+9kQnwBzXU15F/+GMHpkykrXEpV2SYSpi5k7o3P4GZsPeKxhHoMYlAeei7iDAOUTnEjWTVZzsVuXrv8yM+61e48ofpfEqfuwn+W3fG3+vq9EBQUJAvbxaJ56dKlzJ49W14/MDDgykn4nSFCsX7Lr/9k3HfffTz99NOccsopdHd3y/yOE088UVrl77333t9PSRM7IVJMrrzySiZNmiQP/kI+vuyyy+QZjOuuu+5X74wLLvwrIZQzQar8/MSw/i9DkLOurh5WrviRfXt3SwUsdWyGPCtSUS7CNER/V6hUJOpraxk0DbJxlfsRapQgRRN9nGdNxJL4HP8GTr9LzLccuh/tYWpWo1XFy4VZNFcaqfnKjc66L2W58qxpZTx0rbegcZyT2c9ts07lxdlfMn32fOISk+lqeZcHHujEouzhoaWhXHz6MdSUJRDq7tzwzWnVVL9/OhOP/54X3otmrHuPtBVWW9RctWsGXXu8MPUYianazXl31bPQ32l7FNALFSiikzFRMSSlZvDA069KZa7yQLkkkGIera2tGW9fX2l9bGpsYO7CE6VK5OnpKcmCUNuERTEyOl6mM3Z3dconSYSqzJx/HJ+88yq3n+uOV9BCbP0mYjQH+Wbt91xzx0NSRRMETzwP2XIGTSFnuASR8g8SNsQWent7CAwNx9JrpmjbRrz8AqU90GJ2drgJYiOQkT9JkjVB0gSE5VCQPvFPXKhgYrvlRTvJm3mUvE3mxOmYBgek3VAoWKhUNNdW4e7lI19/oWCJ2T8RDGJw85DJj55+AQwN9OPp6y9VNRGh3xcUIn8vVC7xfpFzah4e8oUv3LSGuJQMZ/CHaVBG0g8M9svofxmtL96LVutwImSITHYURE/MvYl9ENsUELPCQjEp2b5Zzs0JQifqB4SS19rYwNBAn/yblAnTUKg0UlET/W9V+3eTOG6arAmoWbuctz/XsOaGmQQbe9jZEIV9mxt9U8Ft+D3bY4f4k67k2vEfMuMlX/bUJnDbk8GMScnm4yd7qb1pFrFerezpDKVm6xhiezvkc2Tw8mHl8qflNtRaPSlzTmH82Tdg6umUhdvixIfC7AwXsZgG5OurjPWn1QYBYqbKAd12eLIpiQdDS0R93ujnTNe/lCj3Q2cnEwM78PZQ0NPRws5Bb2K1zfL9PuSA9zZZ6WuuJv3SJ2ktWktn6Va8YtLpr69gZ9Ni5waqwD08AfMmDeVx4YRoe/lw4SfSBjojG2JClFz+bgEBaROxeBh5s72NaUJxEzHLLWmkLjyBqtXPc+38sNH5iT/lfc/ivdMw93Zg72vFIyodizSvHoLVt4X1116Fd0QicbNPJW3W8fhFOittGvZs4/uaaaQmf4Fh+LF81RHBNcEV8gyruBuTXcGtc9fw0cdR8n22Zvn3so9v+qzZo2dhh6x2tGqlTHr8pePiT10GLrjw34rzzz9fLpTFDKZQkEfSl7du3UpSUtK/e/dc+B/FBx98wOuvvy5n0gRhE8dTkYAvRKwtW7b86uqHf5ikvfDCC7z88succ845o9cJ1UwkOgq26CJpLvwn4KcLDrGwFclmS35YdkTwx+EQNqmOzk6efvw+Ln4gjPGXJ/LGfa3s2vkld708lgbCeeqqvWjbKnni7WT6taE89kYoRfu2Ej1RO0rUxFyan8r6sw+aRdgeD1sjjRQ3+6hs7LSEs/7aJ+T1YWMimTP/WC49b8fwnJyzrPqY0Ap2zj6bXduXYBmsZ/mH7iilbdLCM8ccpLzxAoLd1x8RkZ4e3Mwb2zqYmtRFYtAkDCfNwqoUyXA72V/9kjyzWGSz8dWnCkrKxhLnMSj/ts6ioKg+El+/apmEKKxxIaHhkjQIktXUUM/gYL9cVG/dsJr68idx94DHHmxF40jmrkdfkEmMHW0tMm1REI6SvUVyxkw8aKGyXXrd7bz/xgt0b1vh3F+1mnnzU9lTsEKqddLyGByGwWiUgSHVB8upOlhB0vBCs2TPLkl6hHoXmzxWEmy/gGCaG+vJmTBNfhcWRhHIIayjuzavlR1mHa3N1B4ok8pVUFiEVOyyJ8/EZjYzMNAnlTOxrZryEoLGRMo5OKGuJWblS9VOb3CTqZC93Z0yil/8TsyRiWJrESAiXjMxDydnwLo62btjMzmTZ0jCJ0ihwLjp8xjo75NEsqp0r9wnYU8UFksRHiL2rb2lUZK9pOGZueLNa0etjqIiQFQBZE2dK/c3JXeiVOFGiJ0gfmLBLsqgjTYbbmqNVM6EyieqBExDZjo72kny+47nr/iU/gEz930czUZTDpoe8N7Vxf2nZHJB6F6puL7WOJarji3i5LFGFAob0xObue/OUL7ctoiCa06hMvEx9ofEouwD3xIbZlFvoFSSOu80QpKyJIHVe/nSXlXCUH8vWq9APB1K2V8mCFvjvu0Ep+Sh8w6irFbNq63xLPSpkmfg32lOZVNdNAW+B8nWm+V12waN9GorGLAjyYs4LSEITFenha6aPXzemIGJYkI0AxT2BbF2XywH3r6bhJNvROflR9wpt6LWu2PpaaOnqlgSdZ1PCJa+Tqpff4GURTdz6inFR5xYmTTeiNvSSChw2ToAAQAASURBVGwaL5QWP74qyWJHQDRmm4q6en+8PDQcdcapgDNxcQQ1679EF5aBV0wmXY11fNUVxim+dfJESIVZw1rlNKZOnsy23W9xcN3XxFz7BCo3XyyD/fgl5nKgSsP5g01MiayjuCeItcUp9Ma5cVbiPsLUFjyVDk6PqGLG0lpOOO0qmiuX8fVn7xMbH090dLR8/+o0ytHxhMNxwrFHy5MqLrjwnzST9sgjj/Dll1/KWTLh1po4cSKPPfYYiYmJ/+/finWo6Kyqra2VVkedznlyTvyvu/XWW3/tI3DhV+CPFBzS1NQkHYYjKfdCTRNYuHChFLZ+Lf5hkiaGj8UH5qcQ1/10MNkFF/4d+Km1cc7ceYQG+h5xnclkcl6QLiBnAIjNBj8s+ZqtGwMIM4iUxgqO+0hHvC4anVh90scd692wO9xQ0I9C0U/SDZ2c99xxTMn5ET+dsBVBlMZKq1UlbY+HK/SNVgWb2sLRKa0sDGyUhE38vnTIjd5iD0lGJkyZwcWXX8fzTz5EfauSMV62UfJXbzGisbnL+abB7m5UiojRbXsoYUy+ml3NBnKDnUSr1w7rmqJIz8qjonQf+5vWw4vrJXmy2az4Bwah02nQqdupqjaRmlLMc2vm4+E5yLv7s/D4yJP1Bdu59rb70ag19HQ7ExsFuRDx+6V7d+Ph4YmX6kHuutdZGn3xUcGc+XEgF5+6gFc+/JaDFSWS1AUEOWcEhAVPkDFhKT3tgj8xZ+FJ0s64ed33PH3vLqJ9zNgcdTy+bR1XnvMyZ150NfOOO1kqckq1WvZACTtkV1cnk2YcRY9IWBwyScVNqzfS3FAnCc3gQL9Uww6U7CEtd4Ikk0I5EzZHoZZFJaTIRasgXYJEihm0vTu3SPugUIBEcqKYQfMNCJaF5ekTpmMeGpQWSBHwIW4zQrpEUIhIgRSzaPuHO9eEoiXsjOJ+RSCJJKwtTewr2EJ6/hS0eoO0OooQkaDwSNx9fKWFUah04m/cvHxkJYAoshb7JvY5Lj0Hm9WGf0iofOPmzpgv70MQDDk3p9VSVrhdPvaMidPRGIx4+gWi1OmorSijsbKcvFnzcSg1DLW14OHRzzHRH0rK66NT8vQlSzlmzgTixnjhP3Y9hYvHc3LGydIUb6k0cu7JH412/Inv/t4l/Pjqo/iERNDz42Ji/IStSIm2vp0zH9jHA2GhlJvbWdMYgFtAqCyeDohNlUqnrcepgJl6OrBbrfgnZmO1g7mjmaZvVvLRmHGs949nyKqisTIATbOCZ4PzGeddicOuIEs9xLtndNBs1aBTOuf4PmuLZv0DpxMx80z69ybQMuCOVmOjrd0DjyE9gXGTqPrhTTKvfR2th58kZD01+/GIFR17YO5pR6/VM2TuoWPnBjrOtEviJyzA4vNXbdVKQuke64epo4merVVUzHT2T2hbleh6bPR0KtljVpOmdZ6k2WnS01y+i74f3ico/xjCZ5/LS+vyKBnnj6faxI72CDobvDGqTGRkn83W1U9jHuhDqWrHZrPgHRhKX3sThY82UhCTij4wHO86OxufDmNxdAtF7x76nxuiseFzlQrfO+fQ5dXCtk3riY6Kkse2v4bF33zHGacuko4BF1z4TyFpa9eu5YorriAvL0+eYBOBH3PnzpU2RlFx8/9BVED9FOeee+4/thMu/PP4DYND+JXbeemll3jiiSckPxFi0rPPPsuUKVN+8bbixIAQoUR/rUiqHxGf5s2b9//eT3h4uLyPiIgI4uLi5MhNdnY227dvHz1R8LuQNHHnYjDu9ttv/1nceXy806Lhggv/SSgvK2P5ssqfkbh9+/fzzVdf0tLchKeXN/PmL8QyVEiY4VDiWYp+aPQMzghZOvyMTqTGjPsEDWazHYXTpSZRZDLy5sc53HvKNhI8BqRd7KW6sfx4vR/FW14jIV7Dw++Op0nlxtvf5RO4fi+FAwOU7lnFJef+gHlQyzHHmlm1NZMwYz+Fg578eel0FJ3riY1PomhfKd1WFV5qZzpKjUVNdXkgE48t5pW1RxHq181H1Sl0vRFByb53mTZ7Adfedh8b1yyncMdWxk+exrKvHuX7jw24qQx02RSc8lIgV035kYlZ8+kf+Iz4nD48vJRsXr+aOQtPkKTn2y8/xtc/gLiEZDkzd9cNl7HkA3Em3rkfYlbm7AUdLL29nw2rfmThojPk9WJmTMDH15+21mZZnC0sjVq9TtogQ71XEj3cYSMsWhfntvLuxHxWL/1akjRBzBob6wjwCaC9tVkGbAiVzWqzSqtjfEq6VIn2FGwlKT0Hjc4g7YFJGTnD0fat5EycTltrk0zfEyqXUJo2LV9CcvZ4SaBE0qJQw4SKJchScna+tBWKGTLfwBAGelU01VTiFxwq7ZO71q+QZEiQP5FMKCA604SFUIR3NNZUSjJodPeUCpmYhxNzZCKFUah3NRUlGA1GxiSmSnWus6VR2h9Do+Mw9fXIecDA8EiZ4ijCTbz9ArBabXR3tEuCKWbWxGMs3LCS+Mw8SeTC45NlP5Z4jgUxdChUWHp68A0MxdPHn472Trl/Fbt3kT6h8Yj3sujk69F9zMtvR6BVipnFFdzy2nTWVSagbzWzbk4Q6W4HR9dcq3vH0F6/GQ/fQGoP7KCnt5HIuHFc9VgZZ0wTz4eOcYpVtLUPUmy5GL13EAOdrehldH43dXs2EpCUjc4rAKu43tNXkrbW3dvw2jSNyggjWBW41SnwKehke2gce/zCuTd+HVO9W+Rn0ccBX3eHsrgjmp1lMSRkBVC64j2ilOfQMWssDrUDTacCY4uVGM88tpRvYKCpUgZ6CDLsFT9OpkyKIBhLfzdWs52wWedRufRVVtafR0pkE+mGbvpsGp6qGsfBLx6nadOXxJx8C/Xff0yO6i4c3nq0PXYMlb30Dup4riGPCd4HsThUfNeYStT8KAaWbeTg1q9xixiLo9TBElsWCg879h4NUxVNHHfRPnZu17J1NTRWlaFXgl9Mqny+2itLSD3mfFY8db38eUxyJpXmDjpXHWDQHi8tnfIz5oDWATd81IPyhE9fb88hgiY7Gn95ldPZO0igr4ukufCfAzFLdjjeeustAgMD2blzp4zUd8GFvweCl4hKBkHUxHjWq6++yvz58yXZF2Tqp1i3bp20x4quMxE2J953xxxzjLTKZmVl/c37OuGEE2R5dX5+Ptdcc410c7355psyROSfcRj+wyRNeC1PPfVU+WDEgxaWpA0bNsidE+TNBRf+nfglNbe6usp5wYEzodHu4Psl31Ba+QMLb0rix9Ux7H53O6+++BwhQRocDidZOJyQ/fRk9MjPrTYV3Q0evF/ew80XOG1D4uz7srYY9n7QQ8pdRWQkh/D5N37cHLGH/Df9OW9RJBWltSyasF6GaHh7LqdsANZvTGBijAOrAz5pD+Wc9HVMyipj/owrwa7FY3AXq7av4d7HnucvLz/LgpPdue5lD6xqOy9vz8f78zqpBt56XAGTs45GpVVQsO15/PwC+NN1t+Lt48MpZ1/InKOP591XX+CBB/UYhiPEvZQO7r+wj6mPKwmJXc+HzwWhVBjoezyA015TcM7xs4iNC+SWO034heq55/Eu6nd50dLUQMvQGHw0TpIiUDvkLvvTinZu5YwLL5eLwz1FO2W3mrR42u2U7dstSZqff6AkOP2DR9pDBdHwyp/A9h/uZaC/n8qD5fj6B9Lb08mOjWskYRG2PqGiibkvb/8gLBYz2ROmMGQyS2VN2DEDgkIlARMzcoKUic6xin3bSM2ZIBW08JgEqUIJsiOOZWqdTsbUi0W7GPgV5ErMrwnlTECkK4qZMaG4iXTH/t5uSZhEeEd4bKIswhZWTXcfP8wVpTJlUpBHocqJtEYR/iGi+f2Dw6SaKSCUpIH+Xkp2bScxx2ln3L9zswwUkSTRaqG0cJu0PQpUFBc41TSFUpI7Ef4h0kaFciZshQZPb3RGI8XrV6L18CEyOR2tKMkWFkS1GlNfLwFjovnk8zWcmqggSPRAAHuG9Dzzlww0ikPpmWeftJ6d51glAXzp23gcJzkYa2ynsD+Av2yayKScJJaveoqjrnmIiq1rqCzbzIR8L0nixGdHfE4yEvawYX0ruqEhqRKJJEuN3iCj9pVaN/paG6XKJqyaouvM6BtE38rVhCRNx6FWomsaxLGnAo81KQzGeZKS2zqq6AkYrHY27klEf1BN2FAYBwzuVC//gPHGBzB7adD22jEc7KbHy/nvrreuHJ1PECrPIKlUDnU1Oy2BAdEoezsImXIKNrOJg9tU/CVyPAGGfrpMBkoqwsjPu54de16jcf0X+I6dzq4fHiM7+kyMA1qsahXl2nIaN4yjMGEMWoWNico2kmKr2DsUTkt4Kq07vyfy6Csov/tO0o69nuy4el6+6Ae5X8fOBqven22eCbKcW0T1qz188YpMwicwmKNufZHCxW9g6e3ALzKBKadeyvqaj5gb5ey+axjSYdnpwWB3Cc1N9URGx8rnyWJ3oLbbUSt/OSds5Y/fuebRXPhdetJ6eg51fQoIheHvURlGrGO+vkc6Ylz4z4aYgf2lOdhfu61fk6EhUj0vuugi+bNQ0X788UeplglL7U8hfn84BFn7+uuvZZXD/0fSHn300SOU3DFjxsi8DiFsiaLr342knXTSSWzbtk0+eNGuLRYoKSkp8rr/70G44MJvBTHo/kuzZyPRu4djZBbDYnPaEZtbWzj25B2cM1XctouH58Hz1x7FiycMUFm97ojZMXH7VT0+ZLl14aVyyBCBLqsgZzq6bBoe2jmZ+BVmblvSjE+QGyfN09BmU9NQ7cnekr9IH/yXn7sT6eYMRzjOp4Xb30/l7vENeHp5snpVMOnBNknMRHGugLiP43wbmPnGNay5+Hm++M754Q8IDObUsy/k+FPPIjA4hKsuOI07T01kXOoMlK1FrNy4jBlzF8gy51XLv5QkZsrMOZxz8VUkJKXS2dlGTdUB/AOCKNi+iQsvckeBc4EnH6uYhwsfy0MPmFAMZz66K+GaM3s46v4WCjb6ECqdJia+fULP7IcDqX3zADffEsbjT9fipxlie78P7y6dSF3NyyQku8uSaRGRHxoeIYu+G+pqpI1g1lHH0S7mt0RQhErJ++93c+29UaQb+mUc/3ttseh3OSNsBWlqaKhDb9CRGfso15xmY2+Tkhuu8eTUC28ka8JUqa7Z7DYCgkPp6+2jfF8R/iFhdHe2s3vHFuLTMjEN6jC4uZGUmSdti8JqGBWXJFMgTQNiPixAkq5i0YWWkSN/7+nji5unN17ePtJqmDVpBn3DHXGm4aCQrrZm2ZE2NNgviYaYBxNfGeOnSgukUO2KNq2RQSNC3RIQ4R1+gSHyPoRK5ujvJX3CVKmOCbKVkJEnQ1CEBVJYGsXPosxakKXYtEx5G5FKKR6ztEMajPK+xG3NYi5NoSAhd7JUzUz9fbT3dNFcfYCojHx8RM9aWwvFHy3jTyecxhmRxQzY1LxWks8tSRtH7SniW7/OyNpdb8mftWUBvOx3PSpvC9YuLZ5FKgxmp/Wos76KiWddjdHTl/3mG0jQt4++pzZ1+7Hk7nOZee0TMhzEKypVWlMZMqHS6VEMquisLsE/bRI2FERNPpaC9x7BoNATZomWH4itQ6uwfLGMcROuYc+Z7sRqO6TiKt6ly8sjcd+jwaPehqKzVyY49jccoHLfclKVOSh6BjBpLJR0bZRJi75jp6F088ZhNTPY005f7T7corNRDz9wS18XAVlzaN2zhrI9YyjzsOEYUqCvVjFjQg+ZeeN5++1VxJz/ogwe2b7/VTReQdgGe7FbBul7oRbdORfy+bkfEO3RD7mwIcSXRQ+Oobt2lyTOhqBodn18Dw8sG3vEfOp5FwfyaPyJ+MSkkXf6NSjV4qSRXdYUuAeEMuHcmwgOCaazoZqm8mJmRR76/Cboh8hbUEblEn80So0M8xEJtSqlOEXx6+1CLrjwW0EsXA/HPffc8/8m34n/3yIpb/LkyXLWzAUX/h6IsDehvP50DlHYZjdt2vR3bUMEkPX29v7VkwPCzigEKh8fH+6//35uvPFGWZouIBQ18fXP4h8iaSJJ7JJLLpFDcO+///4/fecuuPDPELRzb3uLIauzA0iEgfQNh00458ucc2YjEIuVtvY21qz4kd3FO2gs9D2iN+y0gIO8c8nZcMc6rn6kh+dv90TlgK0D7tz8ztEUP3I/XgY3pt90LFX1IbR+toL6nkKgUM60JSW4ceECldBr8FGZeWziBsofvYodt75AgKcCxTABE+ukCEMPmVkTCfXfIQma3IefPEZ5a4NSHiQuvuoG5s4/jui4BGmt6xDWvfyJPPfGB3z6/lv8sO5T3D08uOGO+zn6hNPYvmU9V998FxqNlr6+XvYV75IET2D/7iLSs/Pw9vHlgXta+OhVhSSfbXYFL1bk0Fz3EX22hOHlrxN9NjVKhYMw9+HiOcCogNlnerH1xUG+/+oHPLV3UeapQ9NpI6BrL4VtLZx10ZXytoLgvPnn10mI/ojpMw2s2mnn3OM1XH3b/XK+LSXdWS9w8SkJGC+PxqpRMLTGHWX9CkLHREn1SQSAmNovYU6aVi5op4yx8+DrcMqki3j2g+9ob2mWM2sKpUoqU421VUQnpcres7G54+VcgyBJpuYGgkLD5eyaCLYQCpZKoWTPzs2kjZssiZDoUJO39/aVM2cBIWG01NdKMikIjt7dGXs/ZBqQ6pxITxSEWBAqQbrKirbLmH+hgNkddqnGJWWNk/H+grC1NtZRVrRTzrKJ92vF3iIi4pIwuHtIlY3+3tFtCSIoZuD8gsOcHWtu7tI+qdLqZHhI4cbNMtBE0e+0k9YfKCU8MR13bx9J6KR6plTKioCAiFi5PfGl6NrEg7cZaC8f5PbSY6TSaKxQ8aI5l7gJKwjXWGizqXi0ZAKJY72pOrCNoc5WAr5oxRIRjGrIgfu+NjoD2yXDiB0/R9oZW2sreKE0H0XyJiI1A+wa8OOTXVMxeh+QKtC0qx5D4+nHQGcLbQf34huVIn/2T8yR6tVQzwDBWdOIPLiH/Ru/olT01zns8jkQLGZ/9bd83JSNWVFMqHqQXQOBrFkeg39hH6qWbgZC3OnbWYF/xgyailbT7l4kExt7KvfgsFuJP+0OSdSGultRG9wZG97IbeesRKlczaPLj2HVjh4C06cyZLNjNw2iXFyGLisZpRWeO2EJ05JE3QGcf1wQp7zTQdzFL9FR+CNDbTWykkPt4Q8DrYQVv060x6Hjz8QpHXRU7MMzIZ+hQTMxp9xJ5eInqBjqZe5hVuomDGROvILq2pWseOxyjr3uRjzdbagijkFrcGNgaJD+zjYMnj7EZORjdbx2ZI+OwY49OpTxAXPYsnvFcGz1X2dnZ51+issB48LvNpMmwjwOD6r5e1Q0kSReXFwsHVsu/HfhX1Fm3fN3qrEibV6sl0Qlw+EQP4uQj78HTz31FP39/TIt9Jewf/9++XtB0oTLUKTcj5C03wr/EEkTw8WLFy/+p5JKXHDhn8WIetY9KAIcFKNzZjNmzmL1ypUypVFYGp2dZs7/NHW1NZSVP8+Zd8Sxsmw6dopHFzeOYRXLoXYSk7+83M47r3URN/kkNB1KOlrfwY6NAYeZxfe/T3Kilu3F0Xhr49htMnLefaEkd6w/Yr7HW+UgME0QSCVfb9BwxnSnKtRjg2/rE2lrWUOg95H/AU12Z4qjiEFY3BmG4kcPef34ydMJCQ+XVj1R2izI14a1K2QYxwNPvohK2KxK9pGcliFj70V3mjj7GBAYhIenJ3q9nr7eLtYs/4Gigu0UF2wjNT2Ll55+hMtuv4/OqT20dbnh/V07Q9YBbrrRwDuvDuGrtlJh1vLclsnY7ZtpNGsI1jhDGgYcUNwkAiwgMTWdjz65X+6XsPAJtej6Ox4md8JUBgf6eO+NF4gOf5drTjbicFjJmAvG2ABuOfoCHn7hLX5c/AqFhfFE+RWys2k/p0/vRuOjoqG+mlsfel6SnS/ef4O7HvQTmYqjwRXhHgPYbXaWfvEBJ5xzGQP9PfTVfUx9fRdR8SfLlEShTHn6+sli4rJ9hXj5+Mkkxa62VqmypedPlvs9Nn+KTD8UqoVWzH/5B9JYWyltcMJyKJ7j/WLebdhyONDXQ/3BMpKyxaLbafUUHWmCEImkRy//QPq7OmWQSGxKhlTuBEmzDJlkiXZyTr6cfROR+BV7CiVZFDN/3e1tch4uargPrWDrcpno2NnWLN+oRRtXk5CdL0mhiP0XBM1is+Lp5S+TJLNmHEV3R6d834mkyYqi7aROmo3ew1taSA3efih6t3PVgtdQISyem0hb0chL705B29BOeVETFxqPJtyrm/oeL3o2BhDv70Pp7lWMSctj5/ZXGa88G28C6ArrYcu6D4nOmYa7fzAavZGWsmLqa9TcPXQUBr2Z7nYP3Mq1pMbMZ+OGF+UsmkPtTEJ18wvGrlBht5hlh5hgOS37t8uusPSzbmPMpONoLdnOUE8HIePm0V1TQunilyguOZHmQS+0KiuNbd4Y+rWUFn2ATW2js6VNWhmj5l+Ce2g8/c3VmLtb8c+YiSEwAkNoCoOdbfTV7MUjJJKXzn8VvdIZif/iiW+Rf+A6+bqp1Sq0/tFUfPEgEwZuw9MDpt7qJGgCeel6YhP9qO/oxjMuD3NkjvNA4rBjM/WCuhirQzF68kXMi9lMffQe2EVf3B5MRi/CZ1/Ah3sbSI79lHhdHy1WLU9X5+DIiCK3dQHpNyzmsWs+l4uTAz3LOfayMfR1tJE+6zhi8mfjsDt4dV8Ul6RUyWPPhn4vtuxJILi/j25rtwz1ESeQfubVBrKystm1q0Aq/a7ofRd+L5ImCNo/kiZ61VVX8c0338jxGhHM8PfiwIEDcp5IfH/uuefkPJuYcxNKngiDcOG/N91xzD+oxo6sEUfgTMxW/F1igNiusDuK988vITMzU1Y+CJVXbPfJJ5+UyY6/hLvvvpvfxe4ohuOEzVHIzy648O/EZRecPfphEl0/q1etxCJT/Bxy9kx8CYg+LQ/dy3zxjA8KRTtnJLazuC2Ek/wPBSf4K+H2Y1dy29tzqN6/nJz8Cexa/5lUj2PjE7nkyhs45qRT+fqzD7n+ku8I0Nrl4inHMMDNN/dz3aQhBu0K9ApnZH6lRUtTub9UX84+fS8HXjmb0Jxmvj2QgOGrYKqq3qS6GnZ2eJPt0yWLhN9pi+SDN3Ow+aswbHNQsvNNqZjlT5gizwgt//4bsvIm4ObuTmZOHmq1drScMzM7n+7uTnQ6vUxP9PULoKG+VhbLi9COP521gLc/DeDaS2BTUxOnTdxBVGw8n757Dx5feZGWZuaGR6I48Yo0rjp5M+decBUdEwOhHfz37ZP3cfXdU7j6jgLc1Gber0/C/Ilzdu+Sq28mKjaBFd9/hX9AMMnp2XIfuzs7JOn57L03+GFFAA6HSMR0rhnzQjtkCuHbLz3NE0+1khrgfN6mhZl4ZHkkl0zZwl2PvwRKlZxh2le0k68K88ifXXAouKIrhNTMAA6U7GVPwWZOm/0MabnOBfdX+1bzxEORTJi1gMwJzkHztqYG/IPC8Pbzl+QuLDpOEixBfgVhEnbM3Vs3EJmQgtXSJtUyoWgJwiZshKn5U6Q6JayO4nEJgiYDRfp65QxZbGqmDBURUfjCximCP+LSc/H28ZNET3yJuP7sKbOlLVIUYg8NDpI9dbYkeEK5E8qch5+/vN/B/j5pcxRhIgZ3Tzpbm4hKTpf1ADqhqvT1YvTxxTI0xL5tG2TIiIdfkPxnoTW6yYqAlPRgdJa9tPWESUum2WohP/HjI6Llp02t5sbbG0Cron5tEd4xj7A7LBDNgAK/4kGGHE5L3cyLbmPzpy/T0/E2V97gQ2OTjS7rRNJPuoHq4m34hMcwNNBL4xefEqe+hV4P0HeDZ0kfGJxnF5tLCoicdLSMwRevLSoNXbXlaL380Bjc8Y3PZqCnR6aAekYkovcJoru2DGNoPFqfEPpaGlCtaqMxL0T+99K0KXBrNWMe6KCNRrwTxxE59wJpKQwcf5y8T9kP19sh1UIx4yVsjm6hCcTGlEqCNtq7prbh5aWnFx+69q1DG5SIMTyVrbueZcqFC6QNd6QKXGjKdfvLMBniMLccwCt1Grb+LlRufij17lS0TmV5fyF5hl5Jjr/sGkPaxedSt/Rrqr94mMgTb8ceFIO5L4dbtvUQGtJNt0lPQ2UAXsIZbbFx15UizMN5DIv17GP+6Z28dlsl3z99C5OPP4rLL9JQV24m7f0opp4UgbJLjc+SAfYXfUKT5QDxianyhJVCvNiKI9UzcdxIT/3/48xdcOHfAXEMEwRNiAJr1qyRVRL/SDKkCIcQmQmC3D300ENykS3UuDfeeIPPP//8X7rvLvxrUft3qrH+/v7yOPdT1aylpeVn6tovBY6IWbbPPvtstAz9l/D2229LkrhkyRJJ/H744Qe5/vopxO9+N5ImhuAeeOAB6enMycn5WRzqry1sc8GFXwtRHiiipMXiVSxKWlpbaGlplqEUPn4iSe4N7rlEqAbO2wu1KtDYz7Y6X/LDOw4RB2MrxhPPhIdWoFFrWbJmh1Q4/AKCnAt5P3+qqw7i7SmI0SHrn49miKCQWaRNWMvTX42jV6XklY35xK7voMBqlcrWvZe9R3xiCijqpOo1YfJ09u0p4txFQ4RPP4XuQAWD39Swe9djUiURi/Xo2Hiuv+1eurs6nPNQeflyX41ubpjNJnz9/Ohob5eR/BFRsfIMzp6iguGADgVKhZJtm9bx7usv8pcPQ5kZ44zmXxTRyStbZnL15A2cetYFFO94j7WfR6HEiiLBSnppDtMSXsBts7u0/YlAjrSMHL775B10PVfTPqBHY2lmw7bnmDxjLlNnz5cWzONOPkvaC3cX7WT/7kJOPfdS+ThE8fTmmjTGji0fPam/q8+PmPgkinduJSwkcfT5FL+P9DNJRU6Qs5xJk+RrKgqslz3TQE/gRI6KrKCgJ4Aln+XSXv+WLKde+dnNPHjmoQPvwmQLZ+9eLxMgBVETxColI08IapL8CAjVsbWpnpLCHSRnjZPXifh+qVbq9Iyx26UyJhQ2YbcTtsbW5gZJdnyDQiTJEoEg4ue0/Klo9XpJnBqqKqitKGHsuMmSQAklUMydCSLo5ecv596EglayaxuJ2fn09/dK0iZInG9ImNyG+BJ2RmFzFOmO4nUQlkXx9Hn5B8ltCGIWnZYlZ+bCE9Lkd5EQKR6feA/MSn2XfO+18nHt747h8Y/mE56Sy+5uE/OdPd8SLTYd9bXFMnBEBGn0bv+euN3TsPWZ0LjZ2Nr4NYGxKQyZ+plx6ok8u6hKkjzxcZp3vIUX90U5Z8w0WkLSxrHtkxdxL67C0O+NwmTB0dNJkWK9TCsMzZ7GYHeHVM6UWiND/d0MdLSgD4jEjgq11oDKoKFx12r8E3NRGj3xi0vBV1lKC75SIaxc8g5Z9puxuqnQddvRV3Tgmzeb9p0fEpQzT1oORUCJ+LAMttai9QyQBM9hs2LpbZPvJ7VXEHa/XdTbIHz4v2CdRUlVwS7cozMxhiXh0LoTcvR1mLa9xrT4fVSYdYRohqQCv67Pl4PfvoTDpiBg7jUMdbVJ9c3c34vDMoRDN4HHyzzJDGxg0K5mW100dncd2arxbAsdpHnz57hFpWPt7aDpxVL6ZufjUCkxtikwNphx9PaLYMsj4J3hS27ihaj9Wvny+VW4qUGRA6ct0BCmc4YjVU/TcNJZM1HutVFeupeCrZuYKOKmD1NE3v/oUxmKc7bsdHPBhd8vOOTvhYjf//DDD6WK4eHhMbrQ9vLykr1pfwtiBunBBx+UQoL42xHMmDFDqmou/H4Qdmun5fq32dY/osaKE9SCoyxfvlyKSyMQP4tu578GcdL/ggsukN/F2vJvQfT2ffzxx/KyOKaK+bS/prr9WvzDJE2ciRDRlGIgT3wdDrEwdJE0F37Ps23iAyJUNLHw6unr4603XqO40Km2CAhb38qvhjvRDkOd2Q1DrRbFGGdfk/j8+6vseCQK65pDxr17enuzt3gX3j7+MqK/Xagdnl4886qZh68R82fQaYOPqtLw9qhja8kQ580oYtKU2XQe+I7N+3cz9+jjOf/SK6XNcOPaVTLp78GnXpRl1WtWLuWJB+5k/8sPSSuxUO0iomKYu+BYcsZNJCk1nYLtm4mKiSMoJJSW5kYCg0JkIEDlgbLRx1K6b7c8myT6yCKjYzANmqR6Iw5SLc3R7C7cSVzEoYACgXjvToaGTGzfuolrb0pFpegf3V6ysRf/9BQC7BpmHXUMx518BgXbtrDyx2/5fMmzkrQJS+NRx57EpdfcisNmZ29RgVSPPvzLY9x0Wz9jT9Nx721vEhJ0vAwNef9+Nban4xjn18iufl9eeX8a1Qcfldv6vDSBmzOK5fMpkjG/rI/Dx69adqzNXHiinMubMvtoXnj4dhJfzudpexzqAQuhmgqKqg7IQuquLjGDeEgwMDsg6+TzWf/Jxyy68Cp6utplUIewX+5Yt4LI+GSpcgpyJGbX/IJC6GpvZWzuRBkkIoiySJAUZHHryh9k19mIGuZMf1RJNW3byu/InT5PkjoxvyaOgWExCbIYu2s4GKW6bL+cSROKnPh7nd4oSb+I+PcODJEzYiVF20fTHAWEMhefkSttjkOmQeoOlhKX6SSSgjQKAhebkSdn9QYGBiS7Fd1zQi2ymoeoLdtN7sS1h15Tr4O0lnyOxWqnoCyKsUEdZBp66LSpuWvfZLJz49mz5weOu/EJvnriRmpUO/ENi6K1sExaJRdeeQ9eQeGkx38g4/lHEOnewN4VXxCVPQWdmyeBcWOJzJ3B5rV/Jjg+Cy//CGrrNjM00M3Uq55A6+aNxuigdsdK/BKy0Xr64hM3Fqup32nNU2mxD/bhFhKDTaHEm1pePfEJfHQDdA9quJDLWLzyfdoqC4nuDYWeAfpCDVSWfIdv6mQ843Kc9mU3H0msHXZRJeCQHWjic91TWYwxYqxU1g52ePNxVxTj3RqwO1S81ziWuu+eRxcwhoiT70WtV6M0eLHlUxMhbsIKbaXKouf1tjjWNsSRcdLxlCz/gJalTxF61ov01+zGPW6cJInWvg7KP7JSNz1DfugULRq8OoRwqCPIfwIHdrxJz8Ei3Hz98Y7Lo/z+28maeBVeZncU7b00Joggl2CuSG6UPW37zVqWNiZgjfXiaN0u3A9LzA/VWg69HloL8x7qYtvli/AOXsXaVcsYP2mK/GwcPvcqPlPCIq5V/Tx8yQUX/t0Q6XsC06dPP+J6YWE877zz/ubf7t69WxK8nyIgIID2dmegkQt/DFx//fWcffbZ5ObmMmHCBF577TUZiS9mxwRuu+026uvreffdd+XPgpidc845ksyPHz9+9OSAODEgThD8NYi1m/g7kYvwbydplZWH+qZccOHfGRwiyNkIhqw2vv7yRV77UImHYSyfNcXy8RVK9hV+xZA17oi/32tS89R3M8jd+h4nTnQuXcTCTnR83TluDaeN8aeyokwStLwJkyUxOlBWwrOP3y8Jz5cf17G/Zj4Z53mysSoCvxUeFBS9z2nnXEB7Wxu1dQflDNm4yVNZtmQx1108j+YWG7n5k7j5rof49MO3efLBu+jt6SY9K5fjTj4dd3dP0jKyiI5NoGjnNjJzxmHHjt5goKO9Te6jUNMEUZNRAAoFbm7uuLt7kD9xilSwRKmxQERUNPW1tXj7+qAc9nMuK43i0swSeVmkVy5vjUShKKCjrYWDpVNxLNw7+vw0WbXEhccz1NrIotPPdSa4pmeQnT+Rux5+Rqp1KemZ7NiySdoJ+vp6iE1M5qJT5rN5vS+RbmIVb+GHN71IPfZLQsMz2Lj2G3zvvIwv3KagH7Khal0ht+vjF8DX91pweyqB86OcxDO+pV3ul1CPeocLtL19/Zl/4hl8/uWT3PdAMBMnuvH2ex3MPvZk3Nw92VZTyeLOYOZ7N8nIk/fbotBX6iTxcjhslBTtJH3cJBmdn5I1TgoLvgFBtDY14BcUKufXxPMn9klYS/eLub3cCajUGqKS0tDqDDLpURAzYUUUX0LNEoRMKFf9vT2UFGyVvWlGD2FNbJYzY0IFE0XVog5AfB3YW0RCWhb+waHyNiIFUtgjc6bPk5eNHl50tjQREZ8sCZeXX6BU0LwDgmQ3XOX+3TJRMjIlQ6ZKio4yQfKKN6wgKjUb76AwuSBvaV8pn4fhhgX5eNvNbWx4+RHs7t7cEnUmAf7ddPcZMW/1JlylwWwawOjty6yLbqGzoYa+zjbCkrNJnr6QoNgUSfCKe02jBc8CdVYNq168k/ipCxl3xnUyuCX/7BsJSR1H9Y7VNFUVEZI+nripx6E1usseNHEb76gUbA4F4cp93HXcn/HS9vPplng+a7oRs1KFwd9dJkFePus1vLXOmT8PvYVLpy5n5+yzObDiPWq9AtB6+NK3S0TqBxM240xJzkRJtcM6hKW3E2NEGubuNlRuvjJq3xCRgVLvJi2Q7Vs7+WJMFtu8ouk3azlYFcL46XewbcuzNK95h4CJp5Cb3kKou1OBFojSmPAY0KJu1GO1q8h0m8mmpv30l67FPXkm1sE+7DYHCruNgf0NGBsGcY9PRjPowK3WRLhnHdmzD7JC4c9gfL6cYfPWdzMw/RQKVj+HUquX8fuWg9189cwEpjzlg0pt55ED42gp9yfAZKdvUD0aNCK+2w97nQX6HBrMwZ5EmuMor9jzV2dCXATNhf/UMuuRROZfAyEiiCqen1okd+3aRVhY2K/ergv/GcEh/whEXZgg5iJ5UbwnRDro999/T2RkpPy9uE6QthGIHjWx7hBKrvg6vAhdWBv/GsRJdqH6/lpL429K0n7pg/T3DOG54MJvBXHmV8SrfvTpF/JnsXisr2/gvdfNhBick2i3Ru6l/plc7CeN47qb9/DpSyGjCxsbKpQl/bz79UHueCSFSHfL6IfhaK92Xv4hlSvH7+aZR+7jgsuulmrctZeeybMvOZiQYKTOPJYT/lTOd/MPEhAQKMuwBZm78IrrMA0MSBvdrh1beOiOC9i/KwE/jRv9drj6WzfOOGEuF/wpgNo9/qjw45tmDWfmvcDl199K9rgJGI1uUn2597Zr2bp+CZNnubNlQx/xiZNZcNwiFp1xLkODJlLHZsoI9i6LRcrsworZ2FBP6f7d8vOo0apZvex7MrLHERwSxgcPWKm/O5vxkbWsbolg3ytRTvI1NosvPivC/6gszk/ahxIHP9ZEsm3pN/IxiVJcdw9PFC2ik8soS5ITktMwD5kJC4/A6O4uVUZBRluaGhkj+8acr5Mo2T3nicm8eOpmTjz9fL7+7A2pXAmIzjMZGvLN5yxf8gWrohNGS3mvmFLJq9OCJWnyFCmFGi1Z4ybR1dHGq+/HcNEsoWLBjIf9OfeRzTRVjaeprpqn7zuPJ441o1A5MK9zx1r/HRGxiWi0evKmzmbIZMI0OIinlw9DFjPN9bUy8bG/p5vd2zYQm5IuSZl4nIKgiVkw+dz6B2K2mGWgh7unjwzqEBZHU38/Y2IT5byaiMFPFfZGi1nOjglCLQiamFFTa9TSKtnb1SnJnUOhkJUBYj5N/BwUHimVO/F7sY/eAcGyI0uUT7c11qHVG9AZ9IzRfE/sZDslLfF0NDdKRa2nvVU+Z3GZ46VK093WLFWSDZ99yjeXR3G0Z4tML93Y74nS/Th6O+6GjlZ8vtTQHhWGahACCnsweQzKdEyjTwBJoZEodUY0Bjdaq0oRxROCSIpUwS1tWt6JCGWaezP9djWPlk4ga0ouxZs/IePYC/CNTJBENmXeGUTkznSmrSqU8joxjxacOQPrkMlZaD1k4rbpTxKgc/bnnT1pPwU/FLCnNR610QuH1Yq7l5Oky/8zwOzoEopfV3P1+9ewfGktQ10tROfMwyMiFZVWj6m7TS4IO8vW4xmdLhU0cd+9VcW4hSXJoBKhcjlEZ97BRjqK02kN8JWl2bpaJVqHlbCkY6nb8xkeydPRBbZIUiqEK+fJEbg1toBrIos4a8UiOrv7ULn5kBrWRnTCAba3ZNJdXY5toAe3pFkc+OgaQqtnE2vIICGynhc/2Clfj4eu8uK6b7dzzrj1ZAc3MniZkrOeOJmduzXSlunm482KF9ZiUIlSAng9eTVHv3c+buXdjL+17ojjYbtNgV7pkMR5fb83KwrSCGnrodpcIes2ZNqt6NH7yXFUnOQSJ7xcSpoL/2l2x38GZ5xxBrfccoucJ5LHH7td9lWJeHShdrjwO/ek/VZ2R37ddi6//HL59Uv4KfES84+/Fscff/y/JK/jV5E00aL9zDPPUF5eLn+Oj4+Xrd4jhXEuuPCvhrDyCZjMNrkAaW5qJNjgDAEQH2Wh5dwRXUDhXScTWFh6xFmYSM0QHkeB6j0jsQn7qCxPIdxoGb1NjKGblOnnsXXJ04SERXDDFRdw9+19zE0yyNsk6028/7IbU5M00mJ4/e33kzt+EkHCMtfVIZW3qy48nVfeycRX3TsaWX/TUfW8bTbz7C1GNMOnFk8KbuabN09iw59XStVKzDi98NTDtDaspn5fNHqlsAAG8KePzXy/+AsmTp2JRq2RyY7dXZ3s2LpJdqaJf0Q6rRZPT2+prgmyN278ZBkUcdPdD3HDn86j9+Y8KqLG0znQwPKVj3HKWRcwadpsrrn4DBR7tASnOXvkrosrY/DLiXx4R4fsBBOF0SJGX9yfUBenz10gZ/acaY5Oda+upgr/wGAaLRrChhMgxeK2qCFMEpJzLr2Gcy65iqXfCmKtYMEJp6LV6tmyTsSfK9ENB64IiG8zr5zG51d/z+kXXSXDOkT0/HuvPM3OAhGMYR697bl/CuOY7O/k7NmBda8zredMNAo36ga2sb54E1fe86ScCxOpjkLxEsmOwsYoUFq0Q86Nidh9YXMUSpW7lzed7a1yfkwoXUKlcvPyluRSqGAiHERgr4jsz58yenpPpDJKda2nS4aSiBh+EbHf29kui7EFwROkTyhvoiJA2CXj0rLkIkJc395UL4uyRXpkd0crDVUH5HZFgIaYPbvq5I9I8HQSsgSv93iv4Bl6hMKqUBKZmoW1pxujpxdDLQNUFG6jq6me+9ddypKUA7I+YUdZLAF1doye3vI1K93+JuNaTsbWNQhjNKxe9yEJ+TPRGd0xePvT09aExWzGOzhCqoRuPgE0lRbT8MFanks+m4/9u+k16ekv9CFysAubZYjO+oOyGkAoZX2drZJEipj98Ly5cvYsLHumvF6pVGNz6LDbenDTOvsDRzAnfgstlZU02+ei9vBjTU8wUdpKfJWH0r3cdVYeP3MNC7pvQKnVgULDYFstDqUGg68vtqEBSdAcSi0aNw8s/V3YLUOYervw1A/gPriDJs04zL2dmD/ZgPekqfJNZ2i1o23pR61WyHARtV80pX1GlvV5kGPoxU/pnGeVc6EaGzPjinhtTyS3HO3ggeuFClxGTdcS5r56KwO9g6i0BnynXULDutdpsP3IzU/GoDwsMP/62V8SYXSetBABJs9du5vEvD503oEkZ4zHTX3IKuCvtuKe00TDst1UdLSjUByiXK02LYs+Pg13jQlToxGvVS1YtGXsKyzglLPOl7ex2Bzof8rSXHDhfxAiKERYIoVqNtLjK455grzdeeed/+7dc+F/FHH/oryOf5ikifh9QdBE8o7weAps3ryZ6667jqqqKjmw6YIL/2qIeQoRFCLmWKw2u1Q7DrZD3GGBCCFqO4npddR+kILd0SKvE+u8NruK7nZ30sfmUrxvO6++3c6DVzgHUUWpdHGfHxPTCtmzTsEV55/CmWcrOW229xFEz1dlJueoM2jev4lJ02dKYvZ/7J0HeFTV2v1/00t67z0hCSEhoYTeexGlqNh7r9feFa+iomLFggqiICAgICBNeie0EEJIgPTee5n6f/YeEuB+9yv/77v9ZvmMZNqZc86cc2avvd53rcqKMi5kZ0mik332DGqnwcJbsus9gpZpdRp50l1JSHxCFPx07LA0rfht/Ro2rf2Z34+nolM4yhwFHf3D9CaSnzpIVWU5586cxj8wmF69+5CY0heNSi1NK0TvkiifrK6sQK0SmWEKgoJCpI38BwsWseqn79myd6nMVBM5atNvvJUj+/fKz7jmGsNV7ovjE5t4LTODysoyigrySB08AhdXd9lT99Bt02ltrCI8pjfX3XArdXW1BIeGyxLFB76+g5fv/h0nlZllJbG0LrdidHKW5hnCgGXmzfdgtppleaZYR6EaBoWGk9bqygAnR/5JkVnJ6ePRVJStlPvn+KG96PR6qaTltMYQrqvuMiA51eQjt3vaLfdwfP8uVm+ZJx8XROyB5/6Iwegks8qk7X5tNa3NzUTG9aKpqYHkwSMlKRaESWy4T0AwpQUXaW9v76oOEBlmgmwJ0qZQpMi+MpFfFhbbS9r7CxImyhQvZp6STozidcK0Q6hqYiWFY2NlSSG5Z9OlG6Ra48guE8TM0aNWLkmd3ugkrfZFeaMgei7uHrh4OsikzqAmxsVB0ATCXM2s/mAm/abMIWHgCNqaG6mtKJGZc55+QYQn9kFrMOL5Sz3H8nphV4FLnh2Xlmr52iGz7iHrwHZ+O/CeY4GZENlnKFH9R2K2WLDWVtJSVyWVM5+oRGkmUnbuJDpnV9oKL+K0VUdFWBDKDvDKFs6KjuPUYjJRnXcOz6hEnDx9aamtIKD3MMwy5NmETe8iGsSoPH8cv6RhWIwdnGnXM8ggwtPlU0yOPs6kKHh9ly97LyawoSSRNoWGvsYyJjtfPpcMmjbMLQ3onSOkmYhTYIwk0jZTG9b2FtQuPtJ6X6HRg82K1ieCEXF5zJ/0LRqlnXNV6VxXPZbzS9/Al3BcVS6omjtora2iTHEQXUCc7HMtSzfwTdRAdrtU8bjvaUIVDvdQudvMeWTmbOfA1suW0KHuTcydsIjHl01DqdFhjEhFH5RAR9lZWtyPS3fZzmO3Q0hqV0CjthE6ZDpFB9az64sFND8fg9Oll1TboDVAh3evYax+R8/oCYXE6VuptSr5sKAvqkodHuvr2J/zAR20y2Nz/JTppA4eJq8yYhLnTyGyJYXa0I1u/KOVO/5fIErPli1bJkvcRImjOPZTUlKkmNCNf69yx78l/lp+Her/TUPnN998c1WJxLRp00hKSpLErZukdeOvCdGYueqXX+UMmclqkzPEIg+tob6ecZPLuHgkoEsUF9lEJXWeVOfquPONJl5+0YcGm5p3zgzGb4OSwxdOM2LMRN59ex2EDmDw0BraLUoe8ClE8QS88mgME17z5JGHhRtgx1W/N7/XB1KXc1ZmFaUfT6N33/7yOa1Gh1ajxc8/kCWf+DDwswp81Va5LmuLozB1ZLKl0p/Jfo6G1ByThrT1fpJM6HV6vKRZhZVm69XT3u02R+/c3NeeJyvjOLGxGgoKYeyk63nz/c+or62htaVFEiKtTsu239aT3G+ALE8U5V7iIiGImlCt8i5eIDo2XgZai/I8gYwabxKcWuRAWSgWZ1sc9vplxUUkJveTjpKfznuTg7u/5OjOSFxVCsrNmfQfcRuzb36RA3u2cfOtrgSrl/DKXZPp8AwlwN7Ezq2fMPvOB6VaJciSxWKWwdpNjfUcO7SXgOBQSooKePLt25j64EnctB2sOJ5C++ZDhIRHyf4uYelfVV4m1+eNxeOx3ruVcF0TR5t9WPPDAGAjWp2eZ99bQGVpCRnHDjJs/LXSpVHEEghCKPrPhAmIf3C4VIaEQuYwC3Hkkjm7eVBbWS5LSIXzonBjFOvcq/9gdEYnaccvDixB0MR3HhQWKUmfKHMU2ySy0YTq5TALscsyR7FcAaEECoImCJhQlzIO75H5ZoI4VhQVyO8kulcfuX61FaWSFPmHRUtbfvHdrV/0BW/Ovmz/LrQXn5Ez+On9V3np+424evlKtU6UD4r9KgboyaOmcHTXV4y1PoqyXoNGb+dg2VpJ3lKnzqbfpOspy8uhraUFF29f/KMSZKmniKsQJY/iGNS5eKFzdsPc0UZzbSW+scm4+ARSsn8FCZmTsZttaDtMZOnOoXVyJazfSGlaIrIJLaZ2qaip9UZKM49J50ODOHE0Wrzj+sm6/6LTJ1mY0Jd873P0NzQQo7kcoD4rcSNLFp5Cp+rN6vY+VARl01t/kiC14/zbUedObfZRfF285LFtVxuwW6005aThEp5IR0M1NouZxsy9OEcPRK3X89LYJTIPUSDOp4JpI9r5ck8vjmd+hXeP8ei0TlTZj9BRW0zAzLdlSWRr9kWyjgVxPsifQG09M93zMSohq8PIQfexYDlDh0KBc1fpP8xIvshX857jUFFv3IfehZePgfWvHCTMrZlWG7Tbhfql4oOiZOZFpOFxydGj2qKF1CAGnZtFRY88SluyiXQ20WxT8EFpIrWVrvi5KHBv9WXMiHR6vHEjFq2B9vPO+KS3Y7aUYNXYiO/Zj2tvuBVPN1fp8Cr6Up11ajmZpVZdVvI6jUO60Y1/RURFRclbN7rxt8Bfy6/j/5ukiR9v4ZTypxDynvjh7UY3/looraxl65YtWAQxE6GxNqgoK+XHxV+jdKuhWWFk5sPlfPJxCFYFLMhPpHmRgSaXYn5eXMvapU2MHXELaqWZE0cWyMFd34FDCAgK4Z37P8WgdyIvO/SqQOpnn9ZQ0mQkxuUySdvZ5MbCV3uRk/MZdz7wGLE9e8k+NJlRplTLmbypM27gm8/nk35iBJ+vNzHRv4KXorIYfz6Z8b0ucN3X1+AZ1MyJbTFkfP+RdHsUzpGBIY7P/2F+f3rO3SxLB6usSubtGyp0I1oaz1GZF4OryibDr2//5CSvPP0If3jhdc6cPoGXjw8ubu6k9BsoFR2RRSZIgyBvX348T56jo8dPlr1ZTaoGqcaJ/bDklR5o55tJcKnhdKsH33+Zilq9j7DwKMLCI0k/eYwfv1vA0czeOCsdioaf2sp3GxOYmvQaW/b3YnS4Q/mpue8AQTGLsXQo5GDx5rsewtvHl8bGejJOpZHQu698XUx8L/oPHsFPi76gbNcyMstvERoAbpYcDqXv5Lk/fkRLc6NU2oRBiVDi7PsP8Ub5VFpC1DjnWVAW7pfb1m/oKPm8UKUGjpokVUmTuQMvXz/aWlspLynAw9vhupR+9IBU2oS9vkDWyaOyjNE/JFyWO4rySFQqqsqKZe+ZuyAbajV5587Q3t5KbO/+Ug0TRE+oN4KQidJIQfSE6Ud+dqZDnQMOb99Av1ETpRtmY62wf7fSf/Qk2QcnSJvVbJb9lY0NdVJtzDmVRo++A6UbZUF2pvzc3MwMFlRM4XafHEmg19eF0FYaIzPe9q37iQm3P0xQTC+aaqsxuHtSnpdNtHCGrCzlt9/nSfIo1tPg4sZNb3yJq7cfmfu3ERjfT+4Dc2MRiuZzKPwGoBZ9e411qHV6NGodptYmSbzCU8fRXFvBqIfeYNO7j3GgugDfmCSqijJpb6xn8N0vYm5rQevmg7W1mfyjO/GMTqbDVIWTd4DMCtO4eNLeUIPW1Vv2jhWt/xlLv3vJ8g1gTtx2emgbus6xuo4m2mvKUG9QYG5P5ag1mu8NzcQaqqg1G1h8ehAluz6gKT+D0KmPo/f2xm414xqZIidtooI7uK/3KtrNahYcikGtaEKnckxIdKGjAO8JT9NwaiO1WTulCidUL78ht6HxDEGh1qBy9qXm02UE3XgXKzX9uRjqjUFlIb02EJPVj/5hd/JLfR53eZZe9WN63f1x7LjpIKbaIhas6UGoqyP2waCA463uzC0YyMV8f1yi0rp6ZRP07cR4ltPi486b9+US4+wo63VT2hnlXM7WCxo0da20moR6W0/WM4tpb2ogMqYfzR4unC3ZKycmxl13IwaDk9wPahFFd4mXXUnQBLqNQ7rxr6ikiQlckYW2a9cumYn1pyryL7/88rdbmX9ziCuO8i+4rH9H/H+TtFtvvVWqafPnz7/qcWFtecstt/wl160b3eiCuNBu3bpFDjykpbbZwrIlizC1pJG5Pwyjyp8Ki4InM67lww/34tVbxbIPdlB5bqUkIXPeX8DJtEPs27VeEpRR4yZz010PEBIWLlUYkUn2xvOP02xV4aWxdKlx9VYtT0zMZsOhYEL1LZztcObV5eM59etcAgKDZc+HsM0XWWA1lZWyR02UnQ0dOY5fViyluGwn4/0ul1n0dWph9LxEfrpvsSSHhfk/kZCUzPOvv8PRg/tkr9nQkWPZuvE7GqvvpTrZDbdqO+nrP5fv/3h5Ci5Kh0KjVcCch5SkxK7m3keeZPzka6Vy1NHewY/ffcHKHxcRHGyiutpKW5ud5ZuTCPSFeZ9tY9n3PaUxyNiJ10ib/d07v8Pt5QcxqX1Rmso4tO0jabHv6e1DVWUFO7dukEYeKvXVv7ZajQgPtzIszOHAJ+CltvHowhGcWubC/U++QPqxw12h2xVlJez57TOSkmq5kBvAmKnP88aHX7Ng3hus3DxHvkYQ3nsef4Gk/oOlq6Mgm9lZp7ntkWf5/O0XcTl/Cv/AEPIry2S/26OvvCuVyKaGBkmUBfEwOrlwZM82EvoOlD/awt0xsf9gtEKlCgpD7+Qk1TVBYiPjk+TgtqayTKpuQu0SsplaqaK8KB8PX3/cPf0Il8ehtSt0uvDYQYIiekiCVpCTiU9ACP5hkUQolbh5+UhzD1EWKTLPhB181vHDRCf2kUqXeL8wChHErr2tVbpONtbV0GvQCGkiond2kSY0whBFYMPGvmxI6gkqO1XnvIgqcjgOCjt/EbmgULXI7WysqcTDLwi9qydxg8ZSl7uLozv2EdBzGPGDx0qFq6mulh6DxtPe3EBq1EGmBy+UZhbp1fF8d+oZCk+nEZDQHzpM1BScx+DuhUIrTFRUuDrbyTjZnyCnSrbsK+OVhf2JGXU9bv6hlJ87iVdsX2k44hOTJN0p9e4+MgdN5+pJa20FNosJa51QG62yJ8xpHzREefKbbzRhupP4q2zU25TMS4uj7vxvVJsOMFwTT7XFldWaFFwN7TS0GDCdd6NXv3s5k/Y1dosZm7ldkiyVszeK5lJ+mPEWRpVVjg1HROfgpmuTBFeQRUGKCs1KFn22C5tLB+59rkPvF40mMEHu046STKytdeDkg8bVn+rqYto3H8NQ1ovfx/RCqbVhrdPiVKXE1dmPr/Z503P8doYYHOeAUMuOeiQR5qGnoHY/Pp5XWzcblBYuHA/BUKWAyZcfF+sVHV/KqQ5nAuMdBkid1yF/QxOaRjPq3FKOtGynw9bC1Idfor2lmYw9m+moqid5yCgmXjNLKrLy3FQLFc3xflHyfCVEpuLM667p/oXpxn8KaZTzF9o/f8tKtSeeeEKOR0Uumggt7ja2+/tB7Pu/1P5X/IPXO4pstf8KixYt+tsah2zbtk3mCAgcPnxYpoAL55wrnU3+lMh1oxv/W4L2w7IVkqAJkxDx3/6dy1nwTR3xHqHoL5Uw+antfJq4F58UMXNm467xXtzx020ce+tb9u/azlMvv8mjz7wiywGdnFykEYaHpzeb16+i74DBhIRFMufjHrz/7FlclFbOdBj4bssgyqt3cNNYf/x7zcRUXUnG6Y+l4+Fn3zmyWESfmOhfEfb8yX0HSCMPpTKXgKBgIiOjsFHR5W4l/tF5BUki0qf/QEaMmcDdDz0ubeJF35Uw45j/5fc8fNeNbN36AWx1vC841GEZa+ocdV364RPWAsLgoTAvjx5xCXKg/s5rz7Ltt7VknksgytgmB6blZjWBmlb5+cteNTD6D1kc3bOPNUvfJSX1OiZPm8n6X7+S+V3C9v+6G26RIdyCIAWFhOFxqT/qmwOJfDjukOyTq7fBF0dTgDSabUrclY4ZS/H/GlUYGae2yXK6mLhecr22bVpL2sF32LHUX4YhW+1l3PzKPST0nMNXy39jqzAVUUDvfoMk6Tpx5IAkW4J4hUTE0KvPABL7DmTV4i8ouJhNUr9BTJh+E0ERMfICfmz/TgaPmSTXWTgu9uo3SJKfhro6Bo2ZJMschdGGIGetbS2SXApFKzAsUvbTefsFSlKWk3GCnv0GS6v7HkkO1a+xrlZug6dvAFWlxbL3LC45FYvVIklbWI8E6cIoCJswARElkCKXTayHq4eXLF0UBE3Y6ddXV9HW0kr+uSPE9R0oyyErivJx9fSRgdTSudFikeRQrzdTkBVDsPtymswq7vn4WjSHmrG6VUoy2nPwaDx8A2isreL8ycPE9B2MxuhCe1szt/V6gtD+FZivV7Ds3ARyqlpoyt3Oo9MPYdBa+C17BhODvumaoeztnYVH2++QNA6VwVVawXe0NGNXaaTbpChffGzS9/TwqJYKz6yx0OSTxKH6VFrrq/FPGixLDIUiKEiiCCNvqSxEqTXQWldNc2UxKo0G1/Ak2htrcAmOoWjXKnoWzWBPRCytdi0+2mZymnwoLIxArd8jSd3R018RfC4es34KzS52lC0K3C/aMdQ5fr6ais5K4xBDcC8sDdWkxp/sMt0Q54iHvq2rD0yQ0a3NbiytSMRvYBDnlr6LpbES73FPyJNTOG/qAuOx2ZUoLGbMLbUYoweTm7ECXb4/PTruxBrujeBjLkUWlI2tlH50nrdDh3Nv9BG81R3sbwriaEkEoX0jaN9ZxOIDepKubZf9ZdZLpcuuRXZu9Dsl8/yEuibXVQHzepygdFkmBWVqh7W+OOdFAGt9EG3+ajwNTmjtWkyqDsJShmIpv8CYSQuoKi3CQ6+T6q5Bo0arUjgUuk5XtCvGN7ffMvsffsDTjW78b7F06VKplk2efMUMSDe68VdGXV3df8hOO3PmDPX19YwePfp/vdz/b5ImPrRPnz7y74sXL3aFBIqbeK4T3T8C3fhL4cdlK2WfiyRpNjsdpg7eejWfKM//+Fof9eXShhCNjcGj8/Ddfyfrty9k5LiJDBo2WhqOHD96UIZEC5ONPqmDZe+GUMU+eOsVmipepCDUCV2pBfv5ndJJsra5lAub5srlJib34bnX3sHXP5C0w/sYM2GqtKoXTfob166SZY4N9aLMQrTg6PkyZxwP9Dgnx0lbG705+O4pmYP23qcLKSsplqHKfv4BksIJMlRVUc5HXy2hvKSEzDOnqKutoVdSCvfdMp0fFgym/xtrCVRbqLcp+PiYCDjOwNPbWxqXlBUXs33zBl5bMJMo40m5vmKgFyDUwSucLzd8aMBNFSx70N7deY79K8JY//sRdm3/jdETp8oAaqEMWm2O7LCykkJ0OgVvjznSZeVd2G5k/e0/SmL5QtoQXu93EJ3Cxqa6IC58t4+Fi50oybueNatiefS5t1kwbw470pNQ4rBVF+vy8st+jE59hYHDR9O730CMzi6ybLG2pork1CGydFOENItA8faONrIz0rh5Vjo9e1jYsbeInFObCNAfo93ihU5/u1TGTqcdJKHPQDk4vXA2QxqCiAF4R1ubzEULi47DTetBQc5ZAsIiHT1NNpvsNxOW/D37DJT5bWVF+ZQV5hEem0BLcxP5585IExGBY3u2kjJ8nOzjEv1qorzScd1DGokIRU4QNJFlVnQ+i/h+g3BV+0iyJt4jetnEY+7eflJRK8w5K8scTdUVtLY0UXTqKD0HjSTG622CLgkxgng8d/N6pq+2UXooh6HX3SwJmjAvEYhMHoBCq6ehqpxA3XxCDY5gbNGHNSH8U/amPcd7t67CW+0o+7s3+WPZH3Uljm74BHuYloi+Q7DbFbj5B9NcX4dKraXdXI/PFZb4ggQ4mbdSltUTV79gdEZPmYPWWHwBrWegfE3VxUw8ontj8PCTO0dlcKO1pkwSvrARszjx9fOcaqwhOPJO9ppiUeusWGq1eBYriYqewtkTS2Re4IWq3fRdH48qPgJlhw2n3EaajA51Vtj1u8cOkOWWamdPrD7NNNjA9VLT+pUW+gLby2M4kh2Dm0pNSMgEWqu3cH3PFCqbXdmZHYnC6A3NNUKmlAYkzskz0Pj2oOHwUjJWv0vvqNkkxfugaWrhhLmDmuqjHH2+mrLXJmEwdNDQ6IS9SI9dayZKn8Kv7+1myJA+3OFbJAOlhzg18PH9axnq/R+DdcXkRahLh7x1Ghktrw3k+/RB6BoUKFSif3EYJzJ+RWMz4eQbRGluDt6e3tDWjEF9qWlPEFKVQvajqf4kKE1MeIlSxzFjxvzFg1e78S+Ef9JyRxE6HBkZ+bf7wG78y6mx/xusXbv2zwoMwv7//3I8/n+TNFHn241u/K2QcTZHDtIFORO9aOK/hoZGgj3+o9tPtQVclI4yQAFR9FPZ5oJR7SkVIpEJJlQuUY5YV1tNXM9eeHn7SiVMlBlOvGYmRw/tY8Oa9y/neXn5MP+rH0jpP5DcC9lSlRHWqv0HDqG9vY3ho8dLp0BBZrZuXMe7b7zAhkODmRTmJmfNvzjhyeuTD7Pytsew+dtwTiuk8MwinnxkMXU11XJ9xGeVFBWiUqtoqK+RKsyJtENy2T3iEyTxEMQ0ukccJ3es5P62O2nq34qyQMuZHz9n8LBRMrNMOEwe2rdbKkk1yqsdAcxXBBCLLXNTXTI6AB4Y2cxrt63jsWdeYerMG2XGlyi1Ey6QIjPs9y0b2bBmJfe9PhFX1YWuZfY0tmKlDbsZLr7fwrTet2H2VOC0dxe7V6ocA+MUDQOGlDJz8o1yvWqtuq6LreJSOamwvC+4eIHgiCipCsr1NZvw9Q+isrxUqqc+foEsfOVJ7rzvGDMSHd9Nyk11tNn2y3w1haKEtJL3eOvDwdx475MyX02U5zVnnsLLx5+j2z/moZlb8DRa2XLclXOVz0vjD9EXJhwfCy5kyVBsUfooSGlbcxP2xt08OmkZRq2VjYcjsMe+KMmVUL6qK8uoq6qU5EyWMp4+RkxSP7wDgqTboHBurK+uxMXdCxd3b0nEhHInes4E0RNZaILANTfUSXOQ+P5DpD2+MC0RPYQxfQaQe+Yk0WNbrvoejZ5aSgtPM/62hxg9+16y0/YTFp+Me0CwzEwTal6TMC8JvOwGKaDUwLrPn2Lh7T5d5424+J9oNTLA2IrGDudMGhqYzDe3fkfP0CWUNHvxwZHXUGk9qco961ArW90Icq2R7xXkZ21uENvefYhRT8zDXYq9dhlCbdQ7odEb8Y3ri12lxdQsyJ2de5O/Y1zIfiob9cw5+CzmtlfJXPE+uT99Trz6ZczOYKwHlwtNNLY2SCaY+MgXnF30HOnZK+jTMB2D2UCbu4GzNb+jcfHCJaI3Nybv4/HUtVhsSt7OGsLaRh8GGqsx25XkmQwMMzbL4+SiWcOmo73QVWoxVJqJdgnh523heLpskfvki739mfvbWKxtDahsVhRGLxTKdrT+8fjeMJ/anQu4/5FdPHiTww32p03t3P6IiaaSSsrfPYjXpEkoLeBUakdX2cbkB8t5/BkflIqiqwKo4zzq/ku3ss7XigmVVOdalOkG3M610Vp0kRKvc3gFhdNUXiBVMzEZYWttkj1ngpCplZfJmfhb8ye9aJ3oJmjd+FfEG2+8wZw5c2R5mcHQabfUjW787SFMwYTz/ciRI3nuuef+9mHW3ejGXxNisHz8+DGp9nT2ou3ctoVN69eQHOPPtCG6qwY+rir4vjyM63yKUStsrK8LIm1LHIqLGwiLjMbd01P2/wiFSAQ8Czm6vLQEi9XxrwgdfuGN9yRROJN+Ag9PL/oPHMqenVtkaWTf/oOoqa6UxK6kuFAahAiSJXB4324WffUJN9w0gcnhufIxwYMe6FPLC9omDn3tSKIXZXxvvPsxA4eO4OC+3QwYIiyy4fSp45I0hkVEYbWWM2TEGGk+IXrMxLrW1lRz2z0P8f3Cz9my/FVY7thHwsHxrflfoNXpyDpzmvlzXycsWsuTY9PlLLygakItWVAUQ6J7HT7aNi40u3GDT2nXfjbZRWmUnfQTacyYfZs0+Dh98jhRPeKkm6YgavV1NVS29sRqv9BVHieUPCejK3G9kth1cCXG9E2SAM9ZMa6LKAvEu7VTdCn3a96KkUTcs45QbYfMVHv7t5GyXFIoVYIQd3R0kH8xB//gUGmPX1VRJklaaVE+v63+kc8/7CmX0/mdG64YfyYH2ti4fDGjp86S9wVxDo6IoejUbN68UyH3hXjfzAGN3PriY0T3ny/LTpVqDSGRPSSpEyWRFnMHSz99h1VfnpH9dQI3Dc1l7B23MnL2Z/iHhhOTkCxLJEU2mnBxjOmdirOrq7TUP3/6hCxV9AsOx+jsKhU1EYcgFMfYPgNkFpkIqT6ffoywuF4Y3TwkuaotL0Hn5Iyrj58kcHvX/kRZdC/6ue3DSwVtdlhcFI+nbzUVhbmSIAr1zODqKcssS85n4hMaxbljBziuiWN8cCVhapskU8sqI9GEKym0WInUOLap0QqvnR3J0OB8NAor2/N7MueZg/QMVcv9FORcw4yeS1hy4kEaK4rIPb6Xql4DqQs/ToimFYVFx+hgPft9teSl7aJfj36YTe149OiHqblBlj4K10mrUk1bXRX9fTdwTcRh+dkhHi08N/gLnjJ/iErvzKmFz2M/lImfKoAQ73rGzbzAkfxcKtriURtciJn9KtlLXuLoxUWSmJmL6lDpnfAbPAtXTQ1PDVwje840ShuvJexlWtZE9jg30mzWYbZCtlc+aqWV/VVRaAs1uOVaMFysY+R9BXiKmZ1LuHXQSd7dfT2mqoug0kFLLXZzO+aqC2j8E/AefhsPzP606/U3T9Hz8k8TqK5spypjJ4nO2dx7aw/O1vqyK8vMQ+trLk9KXDpmxb+lZg2eqg5Zfum41l197bsyoqPJqkKhtmM5cZJDLeuwt9qZcsfDkoBplQpctRpUChEaK2z8HW9UqxTyJoNkFVcbhYgA625047/DP2uY9fXXXy+PcTEJER4eLn+nr8SJEyf+divzbw5x/fmLhVkr/tG1tD8PUXH4fzFV7CZp3fiHhSjLEWWOwmpfqGjrVv3EbXdn8/n8CNxUjoFmZ3O9OH+FUhTq0sCkp2fSmJ1JuG8kLi27OZBxmFfnfiTLBt959VnOpu/Cw62VzCwrw0aPZ9T4yVx/811SXRN5XyJ3S9jC90xMlorOgMHDZPlaY0N91+yI0WBky6a1BIdG0LNXEnEJiVINcw25DnCQNC6tW1ifHvjjR1RMHD6+fowa53D7i++VJMOnNVotfVMHSqVF3AQEQczJypSff/TgXtatWi4JojAVufWeh2R5pYeHl/xcJ2dnme311Sfz5Dam7YsnWNvaNdBbnBfBC0O3EeIWh82uxiPCn8g1jSQbmmmxwcfnRF9ZNq2tzVQKq3sxrhMDQK0Wd08v6T4oevBajzXzQX5PbgnOpsOu5J30gTQ3L2L0pGnMuvUejuzfzdoVSzhTE4PFni/JnFiFCovIbFPKEG7D4Rxur78Je08LXFSh2r0dH78AGbotyjUFzmdlYHByuhRi7YJKo+bwrm2S5JxpdSfMXVjTO7570W/XqRmKzCgvX3+2/vITKYNGYHB2YeXXj5C+yes/lEoMm92DT1/4iPAe38gQa9/AEGoqynD18OTcyaPs27wWJ1XUVWpHyiQfVi+cz6hrbyRx0Aic3T1kqaPIqHPzcncoZx5eRCakSNKr0xtkr5AwBBGGIsIMRJh2tLU0odHp6dFngFTURB9RSd55aipKiUzsQ0N1Jc0N9ZQX5mL7tS/3aMeQ5FFKTqMPZ7f0wCegkNryUhRqccS3ShMOsUPcvP1RaY1kH92LuSmBP3iNordHMSVt7hzcl4DlXDZzCwdxa8AZtNhYWppI/WZ/VvZ0TDQYL6jwevzqXD+l9TRnf19N7NjrObZ2Eap9A/ikcQy/DlhDuMi1G1TFNdv8Sbn2PKa2ZsxtzaiMjjqp8jOHZH5ZUmQj94xcipezI6uwEx5OtbSUF+AekUBY6jD01uW0uvXgq0VtaJQwGxg8MYIPDjWhdXIh4aEFNFw8RVNhJgbfcJxCe6H1DCap5/quoGvxfYkctPwzAeS6+2M3K1CaoCDI0VPZXOGMR6MCzaEc8muP0RGil5NAnV9zox1qtryHU+JkFCJrUO8qTV50of2xWzrQGuxSIb8kRMu+Mb3rRVp3nmLk+CC2fm9AQRGKa4qwBLdh6wpOuLx+Agn6jqvcykT8+/Ymd5INjeiUjlB5YfVfblEx7+IAtNVgtrRjx0byoFFE9ohHr1KhU6kkQdOohYrmGMjo1Ep5E1Bd0cPaiSvjc7rRjX81iCBrkVMlTO66jUO68bfClX4cArJNpKyMTZs2cccdd/yvl9tN0rrxD4nVazfIXB+T2UaH2cKZ08dZ9Fkuoe4aWR7WCcUVs9DinwttbmjbVeSmr5NUSfSdvf7OJ9xw2928N+cFAgP3sWGhj5zBLjerGDD+OA0/1zF42GhJAkRJXnNTk+zxEp/j4eVFdWU5nl7esixSODBGx/bko3deZ8+Orbg4K7DhxIwbb5PEqiKjlaPNrqQ6N8r12dzgQ+7eo1S7lbJ353bZ17V44ec8+MSz3H7PQ109aaJUUSh7Wzaspd+AwVSUl9LQUM+2TetZ8s3nvPjxQG6coiSv6gh3Tf2R+V8uJzA4xFEWOWo8pUWFFBcWMGDgCNw1RVftywAXUTJno6L9giz7rM7K5pWnnqZ2sAJ1jZrin76TpiODR4zB4GSUhEysvFBqRH+ccEScdv3NrFq6CFPbnayN6o/eAlXHVkn1a/zk6TKPrUd8Ir+uXoZpqwvv9U3g+oDzNFvVvLFjFHZbFoNGjGPnjtV4n9mPj28AJUX5khi+/O4ComMTZB+YKKnr3X+ILPn09vCio8OE0cVF3hfK2OtrJtA6cxtR+ib21gWiUNiZ7lVAu13J3MxBGJ33Sie7+JRUvnv/Dfrfm4JSUXjV/hAK0sZTSZw7tYDjB3bKUGvhwCi+76qyErLSj0mCeKzNhWFODtJSY1WQWTKWzLQPufvFuVL5Evb8OelpMgNN9JoJExKxnMCIGFqbGyX50zu54B8eLe33j+/eil9IOG4+/hiEoUl1pTQJEfvZOzCEoJh4qTw1VJVQlnce76AwSjetANt95ARHoamDwP11pJXm06P/EGkWkpdxnPBe/XD1CcAuQp09vGXZ5IUNv6DxfJxM/whUTUp8DrSRW13C/n2JpEVFYLcpMGc74b0jF9csXzDq0OdXs+OuAOL0FzFe6uVaU9WboysXYPQKQO/iTsv63+mhHUjEiPau/envbmPN5x0UKDdy4JiNlyYeQXCEr/1ncrzcwFujXkZ3aSq9M4dP3GuzK/h07Iv8XjCK+1bX4qIJwWxtQ9NVqatgXO8C3v6tAlNjFfrgRJzCe2MMiqe56CxonbG0t1KuUZNrhkiN41qQ1mZAseocxtRERPa0ts5GY5QbdrUCQy0Yyk3UtxZxwXaa3YVTmdC3hRitSSrOX1f2QNFyjppfX8O572w03hGovaOwdbRirjiH1c2fXS0uDDU2ScJ1uNUJ5ZhZJJ8bxCOP/N41YSS28+473Via78kdEeWynPRKXBK8HNctO+Sb1Dy9ZxqqfC0WPys2Ezi5mbBYVGgydXgdqaQ6oB4uQPKg4VI9kyRNIyY/OgmZAq1KiVaj/A+zziK4uhvd+HfoSROD4q1btzJ0qIit6cbfG/+c+tf/P0Rw+pUQk/nCq+PDDz/8b50f/yt0k7Ru/E0gyg+Cg4MZNsxR3vdf4fsff5LKmdlmI+P0KVYs/Z7B/doJdQ+6qmyoE002qLNqONjszaLfhhOrquC4SsXTL7/F5GtnyRmNgryLrF35IzlnglDKrizw11iZsyyRe/rukMYYleXlDB4+moCgIFRqpSQgpcVFsndM9AkJ18OYuJ48cNtMlIo6ygpi8dFYabIqmHjfamnFv+X3JXQ88QjGG6xY7QqKvjonpe6hE2NZ8p4nrio7pSY1KQNexFc4BCYkknHqhCx7VClVssRQkCRRFikI3A/fLuDpj27hjzcclb9zvZxg48EePHrLH/n022XSHn/84CSaGmsw6BXk2pz4uWgUT0Q4jEqEUvbjb4H06p3CgsUrZcj1yh++Y/v6t1BuUMrGVhE/8OYHn8t/6+tqpSKo0+mkoigCkk8dP8q9jzxFfW0tv61bCHsc+90/MJgnXnhT/h3VI16WhA4YMpKdu79CrXiCdd79cW6yUXZqBd6+/tx45wPc/sCT/PrzD+TnnmfI6Anccs9jMrBaKGAVpcVSYXL38MJis0h7fUHajEZHDto38/+I34EC5uVOod1HhfFYLXZfIwsHDUfZoqTnMSjK/Y5rb71flrhkpafRlDSLEksxQZfKFi+aVDx9ejTF35fIZXu5ZDEjdiHOahtbjhk4WfYyTfW1Ujl9YvdU7up/GC91G0vPJ6M+jTQYEcqKIHGiLDZx0EipiuqNTrKPTZQdiu0RyDp2SJIyQcJFSaModXTz9pN9Z8UXcyjLv0B0cipaJ2faGusl6Rsc/B59Qk9iTlHyQ+z1PHT9Dpx3/0xPr1Fo9FqybYdorK2kz+gp0n4/ovcAUKplyaXIM3PXpvHHZ+ysX9pIzqkCFI0e0GbCpCrHRDuGFSdRC+MnJfRrK+F06xZO7S6kV+o0MsrTqDlzJ60JaiJ0dZxsCmTHwd74RGRwZvMy4sbdwP6Fc0jUhtBgBddOe3cFDOplZxC/MSv+MgF5afh3vH7qZvTKyyereGpLsws9ta2E6swQrSIxem/X84JvCILT+doyE3TUV6IPjEelc0Kl1WNuqsUpOA6lwRW7zcLZPE+WRMSR5FRGm03NioK+FJ1cSa8yK67eESjrW9BnabAHeaBus6K7WEORm1qIkJzPTubjYn+inKtR2VTsz4ui9+BUzmXspf7kajwmvohC5ySJv8Y7UrqDvnF8HGNiz+Kj6sDNpmCMVxFnfAPx8HBHoaiX1yZBRCstGj7+bQpnJ6TzUcyxrm2UKnDnNl7aVyZUKKu0BP5cxvELSzEpTZJgmujASelKrrad5vYmBg0fQ0xoqIOcXSpnFORMdan37Mryxk6kJDmiBbrRjX9kcvWXQkhICK6ujmtwN/6+kJejvxBLU/yDs72/ll9HN0nrxl8dnT0QxcXF/+1rl/y4vIugFRUWgXo524+G0mwTh+rlsNtOtNrhk7J41s9PoWDDd9jUn5BeX8P9jz/LlOnXy9yybZvWyUF3U2MjFnvwVe83iwGOSsXJY0e47oZbLwVAO/KOmhsbZLmQUInCI6MoKSni7huvQa2sYdfBnnirHWqCk9LOlwsCGJFyXpK8bb99jGqrSpYuChXKycmZN+e04nxpsCqcFr/b0of5z/zAWx98Tv9BQ2QUgCi3jI1PoKKsjObmJrlOYr37DC3vcmYUiHZq4dTxbH5dvYIP3n6VOYun8PyEHDQi66pRzw1jGsn4dATJwaXsOhrLxpc+4emX/yhJ5qyb7mDC1Os4dewoFy9k0ysxhYjoWE6kHZS5bQK557NxcXGlV0o/OspKZN+WKPV8fd6n3Hj7fZQWF0pTEaEoinUUpiZNTY2yh27s5OmyL23N+jflTJIggX4BQbz2/pekpA6hoqyYx19+W+4bQUbLSxyqn+g/E2WfaQf3SFdHgZyzp2W5pYC7uxdjr7meLWsX4eMfJLflbN4FadIxsmgWjS1VbMg6LHvFhMoletsEeVIeKOa21CncGnuSRquW73cPxri4nJz8bXj5BfDg9ftxV9vk/r0mtY2lTz1OUNzzUgkLXGfhm8yx2DUKvNIaKS35lbDYBOnOmH5wF71Sh8mg6NrKcqmi+QSFypJG4djY0tRARM/eaA0GqQg319dKlU04iwrlTLhKevoFYnB1o7K4gPyz6YSH59Ovt2M2Tqu0Mbv/Sr4dPZmzR/aRe97RR+Hq5cvke57CJywGjZMrHaZqDC7uWGqraC/bzcOjV8iL+vi5viz6ZTV/eNWK3tmV6sKLOHv6kHFmFUmKRpYuLKdniFAPNTz/nh9f/XgIU1sL7RvaWN4+CJXBgqlKj+8ZK1a/aMqLzxDcewgxI67lZFY5y+uDmOBagrsSeeucOBEErfPHVIWdGnM+lVbwvaSOFZtV/DFnNJt7r7/qR/fKSZdiiyAfdhnr8N7Ffpxf/iZB4+7DK3kcdotJxgGIkGlLaz0oVdQfqGRdWDIHXSNpNumoyfOkX8r97D/0Dk7lgaiUWhrP5xOc0RN/r76UuGkpKj2Mxiuc1jMlnDgWxWMTshjqKQxXTvNxWSqrzvTkiG2rtOdXuYcIdo7SyUv2p539vAzj/T35bewvcl/f7pdLdqwzsYHNXdtRZ4WPi4RpioozL0fRvOI4TpfUxGqh5FaHcItfkVTYRJTFN6UJ6IoUlBUdoYFqRx2l+IFWq2lXNcoJnemjbmDQsOE0t1slQTNqHT/fovfsP3NTFn1oSUlJ/+11txvd+FeBUC6EScNXX30le9K60Y2/BdraRNSLHaPRKO8XFBRIx8eePXsyfvz4//Vyu0laN/4mmD37v8/mEb1PFptN3mRmUcevLHjOGbu9RQ7oGq0KqUR1Nt+vq/fgjW/HYt6ZR96ZdzFZTPTuk0piymxUKqUMdB4+ZgIDh4yQroyCjL2zrS/vTD4sO0VyTFp+2NQfq/V3onvEy2X6BQZxITuLnKwz0lq/sqKcXdt+k8HQp0+moVaYyD4ehYvqcrmXtLVX2CWReeyZl/jDC6/z65oV6PUGjh09KImWSnnZQEBAq4WLOefkOnn7+EnFTuwfoVZJw5TDB2ltbZG9YTuyIrk+qNDR46WA7DYXuYzVK34gNXU0j40733Ui93ZtZ/LTNfx4byZ7PTzIu/gbI8dNYtqMGzm4b5fMZhMQId4d7e3ExCVIwpOQmCLLLf38A4ntmSjVvztnTaKi5Aj3PerP7xs6MLrOZOyka+k7cAgGvQEXdw/Uaq0kZ2qVCpPZzKTrrmfA0JGUFhWQfuIo4VE9iOgRi1br6M0RRhr7d26hz4ChUqkTAdTFRfkyG004J8b0TJLER4RYR4mBsSjhc3WTTpqPvjyXoeOmcHDHZun62CMxRRp9HDuxTeaPTZl9FzPuekTuZ7EtIybPYO0PX5E6/z4+TxiPutmKdd92jhTvkcpXdXkpBqXzVcdg70lh/PTxzwwaP42tv71H6tDJuBu8SL+4j8rSQp7/dDG+ToUo/DSy10wcqCKkukfvfvL4Fd+dMAoRg2vPgGBJ1jy8vairLKco5yyRvftLI5Q2kT+mUMjHhROkd3AE9ZqVl48pBTirwVy+jWsefA3PgDDaW5voOWCEDDDWu3lSV1FC4dlThCWmYnTzImHoQUnUO3HdVFde+0SHb1gMA6bfRWjSQA6t/oae4fvpGeL4IRHH3BvPuFIS+i6nf1tK6e/fMdrlYfLtnrjX2XA6XcKZxjQ8w3vIUkzfmCTyN37Dt9PvZodPDdN9zjDbrVKer2KdKy3ge+lgLDYr2X6gnbCISEY6F2Oxq/i6oC/1Zz3IS9DQWyU6sURuGNTZkOYogrB8XJZEOzrK21zIOhtGRK9Z5O9YhEfCcFnmqAuIF96StBZnovMOoSUrA9fDk8kPcQGTAmOxAq1FhbtHT+rrzqLUOqFy8qC4JZPi0kwoBW1APG6D78LS1kjUnu8ZetPlHff49Uc5tqYXRy6A3dyGufI8KicvqaJhNaN0i2ZKww9orrieCYLW+b1JJU2YAV0Mx1CpQNECtzzuyavvi54yO9+XJ7DjQG++ONaG54xa6tud6DjqivPmDA53HJGOdDt27SPnfI5c5sBBg+V14sC+ffJiY9RdbYTwp2WNS5Yu7yJoAitWrJDX325049/BOET0orW2thIVFSUHzH9qHFJbW/u3W5l/c/w7hVlfe+21zJgxgwcffFBmo6Wmpsq+/urqapkZ/dBDD/2vlttN0rrxV4UoXfyfnlzLVq6+5OIo8iXs9Ost7Mkv93ioFJcJmvg30tCCzd3IuZPLpWI2573P2LF1I3WVPzDluiA2bm3h9unzufvBJ7j9/kcZNX4KS5/ZyOmMJyCxDfMZZ+o2r5D2+4K8iEG2cHn08vaRodSrl//A3Nee5a4nInj8eRfSz3pzvj4WF1XmVevdItwTj/ZGrT6Li6srbm4e3HHfI/I54c4o+so+OnwNn47YI4NrhSvi5ytC0enrpaIket0y0k8QGdVDmmjY6mqkQUh4YT6fvPcmpt88eTmgL7Misylsc2bO0xpiYuPJu3iBwUk3AKuuWh+PMG/c3NuJS0hi5LjJ3Hzn/VK9iYjqgdlkkdsnVKbQiGhpNKJUq6Ra5u3rJ41DqqurePGJe3H3aibnaCR6MeK808hnO/ewbHEZT73ytqSm5ppqTB3tMsxZ9O2dPZNOv4FDJcmqKC9j7JTpMohaqGQnjh6g36Dh0ikyQRAVixXfgCCpMqWGR0r1rb2tTQaAt7d3SBVTQARPizJIMTj18Q8kLqmPLBcUfWRi4FpdUY6bh7cMrRYZaOI5obTV11QxYdZtpO3dzoFDnxFYGiWNPVqaGwmJiiVl8Eh2/rqSXc3eTHOtksSuyqJg59FUsk58zsfr90nHzJz042S1NBERn8T1Dz3GA+PnEuxklsfjt78n0GR4gpjkVDz8AuhobaEgOxODkzNeQaGyR6y9pQlzRztu3r5EpaRKlU0QtNP7d0hnR1fvAFnCd3Tbeur79KDRWildSgXEuOaDz8O45bZFPLXwFzQGZ84d3oVPSARmqxWdwYh/VAJmi5WCs+lY+6uvMsEos+opyzlN73HT0eqNUtkc8+BruLo8h92e03UeWcSEx5x7uO+Vm3l3sS96zRoKyo089uxo0g0naCmoYNDdL8rt8Azvibm8HPNuN9Ij3GnrpcJNfYQobTOFJmd2NIQwyq1AEpofipIx7a1gRWp/9nr0oNmkpeKcD16HzSwZmcBUz2xclRZ2NoRSb9Pjo20iu9mfnWfiEWzT1qzCJU9NoC2MPJuVhpzDuPYchVLvIkOuDUHxMotQodZR/uOPxI6+A7sadJUmlIVV2N3c0BBCwI3zpAJmaa7HVF8qSZvaM0QuR9Fcj3+Y6HN15AgKiMP9mx/PsCoSlEZPND5Rjsd1ztzSZzcPjz2B1aLq6o3908ubnEgxGWS5olOJGfLL2H7yNJu3awh/4FF0dle8z5qxHDrJrpWbsF4qvxYQBE2UzQwY0JcB/VPkd9aJstIocnMvMvv6GTKsV6DTrdFkhfDwCPnYjGunsnHjRjloKC8vR6/X/4+uv93oxr8CPv7447/3KnTj3xAnTpzgo48+kn+vXr0af39/2ae2Zs0aXnvttX9OkiZm60WmxbJly+SPSUBAgHTmeeWVV7p+nMTMtMi8WLhwoUz0HjBgAAsWLCAh4XKdvShteuaZZ+SPlZAcRUjnF198IXuguvH3g/g+OgnanxI18b0KR8KsnFz8fTzYtmM3Fov9MkkTBgAnXZg+srHLbKDIpCVOJ9QKxzLOtHmgKUaWzYnes+ysM7g6b+K7j/yF7x+z+ujpPW0az477BE9vH5599W1efuoh9n/6hhzgC4IkCNrCpb9gsVpIO7yfiVOny2ULS/j577zOg6+N4+P7hH18MwOHw57aEuko2Dl0EqVLUz4cT8aX3zJ+yrW4uLrJEj0RxiycG8dOnMqKH76j8LtaJp+5gdCUSkr3OrHzs495+qU58vWiHLJ3n/5ynWxWqzwvhLom+sGmTr+BpSveIymrH0dc/MjKPilLIT/88nveef15qpuy+aYgnicizspcsuwOLR89eppbZ9/PA489K2cURQmngLuHpzQoEUTs9KljJKX0k4qPyCgTPXt9BwyRLozbNv4i1cQf08aiVxZc+r7gxuHw6kN7ZRSCiB4Q5ib5eRfp3dcbVw8vmUPnKuzkdToZTC16t0QwtSB9w8dOlkqjKP/UG40YjU6y50xAEDdhaJJ+/DBxvZLlYxmnjuMT4AhEFqzjlx+/YlDKWVyd1bS3TCBt6x8Z3OckLXkaalxeJiA0ivzzWdKQQyh7oncs88QRXvhoEUd2bubg7xsJiohi9LQbSeg/hJ+/ni9f89rWSRwbfBx/bSu/ZCdhyDHLfWAwujDxlvt49KlwhoZtoqWtgg3HviLYyeG+KY7m60eeZdDUZ3ngjx9LctjUUCfzzoS65+rpTVVJAbmZ6USnDKCl2VEKJ6z1BUGOThkoM9paGurJyzxJYfYZ2ku8yZzuxECjQzmWZ4y3B5VFR6kouCAJnVdgGDalVvah6V08MZksGD182fHDp5xw7kWSTw3J+kYabGpeyRhGcIyKs/u2cu2zH6Bz95akLc3Tj2mRucRpLTI/b1l1GBptM4/P2IHuUkNZmH8rcQnfs+MoDLn3NbwjE6g8fxq9lz9GTz/a9uwmIGsQeW2hzLMacNO1U1rvRkuzjgPe0XRY1DSc9yCs0UD9IVcuBLqi6FDgegE0B7PZei6B7EBxnkJemRcKlQh+t9Jaa0B/WivvC7HaNbcDU0uj3OMWs4WhUbm4KyvYmdebVid/bE01uCdPouDHpyg+cYgYWySKtg4ueLfTkHkEt9QbsLQ0YKo4j9ovHo1vLB2lmajcArDZlY7st8YRnDXtJkFn7SJcRi0ERxgp2PUxTinXY0y8hp6aLbw/81DXNexPyzU7SW+eWcm7+QOx1mhlmaRQ3zrsrThp/Kj87muaaqvlNUJEXai1KhIT+sr3xsbF883XX+Ls7FB3ryRoAsMGp8rbn3NoFKpZaVGe0N1wcXHper471Lcb/27GIf8XJ71u/GUhrmDKv+Cy/pEhxlri2iuwbds2qaqJa/jAgQNl6eP/Fn9Xkvbee+/JuuElS5ZI0nXs2DHuuusumRj/xBNPyNfMmzdPSoXff/89PXr04K233mLcuHFkZ2d37ZAnn3ySDRs2yLIOLy8vnn76aaZOnSptWMXAtxt/H3TO8nZY7FJRu/O2m0k7cZqzZ69WotLtdkwWYTvdSdIcRE3pdAMPvfEe9z0VxkWTC++vHs4E3VqmzNJzqsWThZuH4Jt9llNNjUyZfgPz336NzxaGYafZ4SBnh7HR+SQm9WXrxrVMmzWbD79czI4tG8m7kCPLIOMSk/Dy8pWmF6kDh1JbXSVDpffu3CaJ2phr27rsvcXyYlya+DCvJzeHZNNmU/HG9hSOf7yAlH4DeOPdT2SwsTDe8PD0pL6+lrraWl568z0+encOzTscs98ij8s/IJBjRw7I3q7xk6/D09Ob9o52Hr33Zk6mHZb9c8JmX8yCiyBtYQhQXl0kSxfHTZ4m123qjBv5+pN5uBifZNzweLz9myn6oZiO2otMmDKdPTu3yTJGkf9WX19HZvpJ+os4AY2agMBgbFbRLxYoVcvgsAhp928wOKPV6aVxSX6ro/n6cp+NmqbGehk+7enpJQlXQu8+cqAr8udCI6Jkj5mru6ckXoJ8VpSVSFXFYHCSy8g4mSYVvOamRknSRKljr5RU8i9ks2vjfIb3bsSgVbC7IIKg8Fcl4Zr30mN8/WUlKT6O8riqtm/x0Dus9xV9YF3aM+zZ9yiDx06RdvZCfRNGMHF9+ktVbdLsu0geOlpa4ot1Fk6Lbh5ekkyFbathy6kBWHUKgi50cCzzWxIHDJMMKef4DzzzhyOSLPno4c7RV080mBUKLmQcZ/PShQyeOJ2QHgmohaGI2SRz0Ny8/GQotfi3paGOnBOHiUrqi0bvDOZmrBYzHe2t+ASHExARy55ffmBBwZ0E9ziAj8pOpUXJh6cGolCksXvlIm586UNUOgNKjU5+KXUVpdSVF+NmtdFQUYpmszevu07E172R2mZnOna7ERBUR3bmLml8Y25rITdtD4UqM6+FjSDJvZRqkxO7j/aiV6Qgavuu2j6fWHcSp07Hv9cA1M5ueET2RO3kQdyk2zix7AMqPE/jcS4C273XUuZkR12nwKnSTnWIEYUV3PPsKGoaqFu8jIjBN8rjRJ/fwLmKfbj+HkROsqMH0qXSSopfBaVWZ0oLtXicbUddUivLK+019Vz0qRAnDfOfreaGxJ3yPSWNu5iw4HHM7TbUzl54Dr+b0r2LKdPoUKg02IqbMIT3xbXPTGztjWgCeqLQu0sbfWEAYlfpsbXUYSo7S1V7DG8XmPgoch9+l34Rqyzgfc+TFL80n5aTq+Qs0ag/aLqI2Z+af1xJ2Oqtek5nhuGar0JX1UJDqyNaIiwuidTRE2jKzaK6vAR/X2+GDB0qTQ5mXndNFzn731xnu9GNf+dyx8bGxi6zEPH3f4VuU5G/Hf6dyh2jo6NZt24d06dPl+6iIsRaoLKy8v90zP1dSdqhQ4dkScaUKVPkfdHkKQb1gqwJiEG7kK5ffvllyUoFBKET2Rc//fQTDzzwAA0NDXz33Xf8+OOPjB07Vr5m6dKl0uHn999/Z8KECX/HLeyGGECcyTovQ6k7eyWuhDBSMFvtsk9HELROoibs6rdv3iDJyMKvO2vI93IcmPsSRET3oL7uJOk11UydfiMGg5GmxgYu1HnSz8uhWohzurDDCT8/b6prK9BpdRidnBkzfgr2cZPlLMfZzNOoEzSSNImcLqE0ZZ4+xfIl38pPPFAQxDTfUkdmlAIutjvz5vDd7Jr6CE3VFzl6eLkke/M++0aqYGaLRYZCjxo/SdrM9x80FKvFwszZt/Pjd1+y8PP5ksh98FM0Id41vPfWu2zZsI53Pvmapx+6k6zM03y0ZiDX9iqnzaKg1aamqqmD2ZOyePqlzxg0bBRKpYL2jg6S+qRSUVrCLz9/hP1Xx6+g0cmJN+d9joeXt1xhGzZZxlhTrWDMpGuoqa6S5ZiirFC4JlaUObZNKHoih01ksgni1tbawuavkuj7RimpTrXUWtW8vG4oOt0FlAoV+3ZupU+q6Ctrl+Yhyf0HUltdLUsdjx/eT1xisgzAFgpZRvpx2YMmIO53mE34+gdhUyjwDQ7jXMYJXnnkdvKzQ2Rfkph2fe+pXPpMvIvkgZPJykunt7d31zHjc3X0FCP7qLn1plcZOmGavC/61UTItM1iobG+VhK3oPBomYOm1htQm80MnTJTks1Nyz4mJDIW/+AIdh/fL4lB0sARHN+zjYj+OV0EXcCgsnO6Q0vcJbv2r4p7Ep3kSkVRPl6BIZjNHTLMOjfzJEOH+uBsLKe5Ocihnqk1XepZa1MD2ccP4R8WRUB0T1kmOXjabPasWUL62lDunuJKiEst1lYtC1N34ZIbzfLt9ZQ3NmDuaEOlNchjSBy/ouROnC8+EbGc2voTiZoXKA12R9Nkx/dQFWcaT+ARGEbhmTSCEvvL0sqqNcvIiXmZTJ8wFG0qPE/a6WiuZ3uzHze4lSG61cqsCjaWTiTtp/m4BkdRmXMKzzCRs6cgtP9YWmvKKTq+i6Li/ST9GIRrVCLqdiu2rIu4RASh0OtRF9dxQZtHWUU66vPeBBpjKbQWUarIp3Llp/RreBG1u40VL6zA361dnrPvfz6ArSeVHKz7BavKjtrTn/aMi/gOnc20BHH2OxDk2kTfmDp27W5AYfTGOW40uoCeNJ78FYXBDX1QoixptNSXoPQI5c7+e3lh9BY6LCoeWX0zezJ0suxRG9hLlk7uW+nBxgfCuNOnQMZzmGwa2oxGhvd+Er3r16z84QRGrcKRy/ffOJbZFTZUKHAqMKHIKeKk2UEsUwYOxcfZiejUwQS46WTfrEC3PX43uvF/g4eHh8ykEgHW7u7CZfU/nqBibCEe78wi7UY3/pIQJY0333yzJGeimm/QoEFdqlpKisii/SckaSLHQihpOTk5UiVLT09n//79XTXFeXl5sgzySmcUYQ0+YsQIDh48KEmaUMuEScCVrwkMDKRXr17yNX+OpInySHHrxH838/Lvjk53xv/NjG1BSYUkaH8OgqDJoGrR2mHvJGhwaP9uJk85yGcfBFNp0QrJAo3Gxld7Qvn5uTRaWppwd/dgwODhkngIxUa6Efbuw6PX/orHgR4kONVxocOZd9aP4uzRL5h+wy3StVGUnQWFhEriUFdTQ3Njoyx76wyqFrb3n8x7k+LCXI6eTSHFPY1Wmxi0arnQ5sLLLwdg4iTb1s+VuWh3PfAYE6+ZwelTxxk5dpIklULtEkpYR1u7dG0UpErY2J9IOyzXdeWuIIa6lclB6bqP3Bh2ewarli6WZOeJ7+7kvuQDV5ydZuKNcPBQFNOmLWDQ0JFkpJ+iMD9XbvPMm26nZ+8UNCo1zq6u9O6bKktLRHyAcFYUBE24MQr1zmK1SmVZ2PD3HTAUk6mexoY6ss+dlb1koslVKHuRMbHSkbHs6E/Mf+EWqqM1+FRZ2bdiLlNn3oSPnx/nz2Xy2J0zGDmyFB9fHcsXjePuR+dgajdLt0ehznn5+NPW1ipdHcU+Fj1lgoyL8tTKcmGBr5R//7rie3wHJ+Ohquwa/IqYZj//SratXUHkNTNptu3B5VK9g/iJ7QzKFig26WSf165Na/ALCMbg7CpLFgXOpB0kvs9A6qorqaooxVUQxMAQmhvrmX7PY4TGxJO2eyv1NZWMnn6zJG8KezM39XuRYJdLiu6lD8poN3LfoakkhJRQ2exG5a++WM15aD085b4W2yP6xG6f+isD/R3ZbOk2Peuy38U3JEpep0zt7dL4o0fKQFR6I/VVFXS0tcqeQa+AEAq++Rxt63OUeQez45FF+GocvVIPXFPFMz8sw+w8QjpJBvVMkRlsHijQu3nTc9gE0n5dSumJn0ksFi6IZs61H6Ay/xw3vLUYr5BIGQkR2LMfph/m43+wBovKBaXJjKGsgV0XNvFt9uPkhWXhou7gUFUELcVBOPsEUnB0B0kzHsBstqB18qCjqY646Q8TPvpGjn33Bpl56wjz7EDdAeer1mOvshMSOxqT3kRF3kGc/CMoL0qjqHUHCqUKt+gU6nOOUZD9Oy8/ppEErRO33X6Md75X0WFvRm30RuPqgf/oP+IWEUSr/Ti6KzMRd/+Cyul6GTitcvaUj3qPfkgSSYXWiLmhDEtTPf6eNv44YaPcj07CWfXmH4h+4Q/Y2ptQGD1R2BqoP5jL5GdLu0pqgrRmZodnsMOjF0u+8cGoFQM8h3pruRSg/qfjQHE+CyfLn6tjsIueNYOIFFCjQo3GyUigny/OajUuelUXQetGN/5h8U9U7rhz5048PT3/qlbo3fj/h8yN/AvtOMU/+Bcwa9YsyWnEZEHv3r27HheETahr/5Qk7fnnn5dKWFxcXFeP0Ntvv91FBgRBExDK2ZUQ9ztrPMVrxOBSzKT86Ws63/+neOedd2SfWzf+Z/D29pYONVeStv8JYauoaWD3rp3UNHXg7iQcDi8PTERJo1DQBEG7eD6HPTu3S9MOMdi99UYT90wQ36dF2tULK0ZxjZ83IZtCxfUceHq5zOV6+OkXMXWYCAwKoaqijNm338Nv61fz6DUNRAy+FXVLB2XHf5KfN/2GWzl+9CA9E5N56sE72LrpF5KTNBQUmomKHSZzwjJOHpN9WyKk+qPN19LX46x8r1EJWquda+OPY7GbmTv/S1QaNT179SYkLEISPJGRJnq/RB+ZIG/CLOPIgb2k9B8glyFMMQ7s2cGsaU+T4Owoe+wc9N3zYiyf/GE7PiI3rZfDbv9PEaA1cTYjkxf/8KBcTly8jtgEPZ+8p+P9L36U+0CUBopeMVFKuPnX1fRK7ktNVSUlRQV8/en7tLe1y/UdN/k6rFYLPn7BkoGMnjBV9tCJ/rygkHDOpB+XzpRadT7OfMC+T5s51WFn0PDR3PnQk1JpnPPcIxzJ6kM/d4dFfpXpCGOnXcf8RevoldxfKpbC8EOl0eDp7SvVxPLSIuksLvLFRKnl6ROHJIFLTztI9LV3UmLZRKjGKge7zTaoqQunpeUCnpVq5hUn8mBQJmrsrKiIwt3QxgiXCmotWp7fJCZoTkunyeDoeFpq0vG2/kxNYxjRva/Byd1DKlklBbmSJIksM9Evl3XiKEmDRpAybIx0ZJQukm1tGHhWEjT5HQk3MKuCzY1+fLJvBPrFjWTFxaGps+JXXUT62VPcMnkOF9IPMXyQnrSTx3jkzsvh2b1D2rnn4fuY8tg3lBdcwD8sGr/IODpamyVxFqSurb6IgRE7if9iCPffupKG/T/jaQrC+YnLhhICTaWrOF9pYfCMO+QJ0VBVRmtjvTRfcfcNZNx9z3Nw9SI258xzHLfu3oy69wX8okX/rh2t1on2pgZCew9iz7b3CO45AC06KspzMJvaadnixM+pA1Co7FCixT+3GSefYFrrKiW5Mnp601pbLm3vtRodOmd3EmY8RNGxXZQc2YK5tRGngHBUeicqytKlahc95T78+o7F0t6MTalDY3TFZumgKn0P+Zu/xR4nnBovw6yF9pYqwq9/FUNwL5oL0jEGx2FR69jU5MM4lyrZe3myzYWLDc5UrXkCr+F3Y4joi9rVH7tCg625BmxWVHpXUGiIi8vvOqfEvzqVldot76Lxi0PfQxiRuKLUuYDy6hn2EbF57K6PQqu7mpCJtr0rIwPE3yL4e1eLE0vK+5KeF4a2TIm6xYTV3IHKqMPTxxd3nRa1UoGz7vLP7m033/jfXke70Y1u/NcQE/ediIiIkJVUf64PvqjIEfnSjW78NSDMQsTtSgiXx/8L/q4kbeXKlbI0UZQuip60U6dOyf4yoYRd2fz55062/64+9b96zYsvvshTTz11lZImTupu/OcXQOFQs2jJMu6+4xbpJPbn1LWamhop7YqewU1btnc97ums/Q/fTUtLOydPHicjI51zmYdInRlPTYuKYSOtjJ7k2lWu2DW4EuVmCugZV4Ji0ERZlnd0/15ZZifcEYWyJEjSwmVr+Wju6+z44RX5vgFDRvDe59/i6eODl48PH7z1Kgf3bqIoN1aqFKJ86bWVtTz1wO28+f7nbFrncEp08XJkpXVCp7RJgvbgE88xdtI1cjBvtVll35ggaW4eHuRkZToUOREwq1ISHBqGWqXm4oUc1q36SRqCFAmTDpMed31r1+TinkPIMkHhcLjhcBy3BV/EcInPdu6HPJNO3hfmJl/smMJ98Y5SvBqLisTUG/hu+S6yMk/RJ3WwJJr9Bg6TROzs6VM8cd8tjJ4RyuK39agVp3j2la3U170l942w+Nfp9TJs+vSJNIoLC3jpyfuYt3IIjw8SeVVQb1HSZ2gN46cKt0YDn8+bQ8+RU0h2c1iEC/hobQSGFXDy6AESeveTSpa4HTskiOpgqSJt27CK1podhEUEE5r0IPHJqXSYLTi5uGEsKOO+tAk8m3QArcLOglP9qSzdhIe3DxmHfqH9i8dZPzhZymhOqxpo6+3N+1E2VKUqemRXyc/yDgxm87KX+PHtC+jk/jvEgnWrOV/2tnR0jIhPlOYe7j5+1FSU0qNPKi6e3nJi4MzRA0Qk9GbpR2/y1CfuIvXk8vXBquGdn6bhsbGQg6c+wysvCE//QI6ePkbi4JH0HTOJGTH3EOLUzvVxSDOOzh4MQUqb2+GXL+Zx95yPpRrVWFNNU121tNQPiu7JQ6PelsYlAoPSYkkckk9e0ylOtfdisMHxeI1VQY5tBvtXLaTXyMk4eXhjRyF70vzd/TC6etBjyAQSx99A7skD8hwL6pWKuaOVvON78YnqhdZgRuPkzrD736Ao/QDZezfQWF+GT0wirs2hFK9fQlzlPVhctejKOzC2Wqi5mEFA4iBMJjMWa40s6a09n45Hjz5yvToaagkfMZPYKffS2iDcPjsw+oagVGsxNdaidvagvboYU0MN+oAYGT7dkJuBV9Io3GMHsq9jKyNNhwlT22izw/dVPTA1/Erd2X04RQ3AKSgec7vIvNPzfcEAzvgWoLTbOVwVjm/PAOwFdqr3f4+Xzhmd0lEHa6rKRWVwQeEcgErvQk6pliKzghCNwx02o0NL4NixFK5ZRHvuflxHPIEucgjLS8t4MDRfxnMIDHWr4/EPDrMx35PZUTVdxiCiM7LS4ogDyTVpWFMTQXGHF6fLg2m84IFzvgLX3HbUGQUcb9xMG40MGDQDHxedzDQTyroocRSTkn9qDtKNbvyj4J+pJ+1KCJLWWfr4p9b74rnucse/Hf6detL+Wvi7krRnn32WF154oSvDJTExUSpkQukSJK2TkXY6P3ZCNOJ1qmviNcI6XTg/XqmmidcMHjz4z36uKJkUt278zyCUytGjR7Nl+84uciaImsC583nExUR0vU483kXQhEvjlXUOl/rNci/k8OnHH0mXwjvudOfAmihUChMWuxa1QivLicS7xCl55Yy1yFA6VRqKpfWCNKYYO2kazc2Nsg8rPCpavljYn7/27kecTT9JnwGDZK/amdMniYmLlyWIG9et4rPNE/DVOAiGGCI9McvMu3/IYNakYbg4g16vYOmaRKY9WoiX2ibL61YURoutZfzkadJWXgyCnVxcJDGTqlFZqcxDE71gogxRkDfxmq8//5DFX33K8IlRfLchkbT9h3hswS289cBO/LRt/FoQxbJX1vPq3Pm89fLTmNaVcavbBG7pl067VUGEsZk6i46Xn1ZKtXlg/xlcH3uiS/r3Ult57M0ATh47xLDRE9i3axs/L13EuTPpkngJVa1H0kBWfFiGs8JRVrb8A09CUp5l8c/7yMw4KYmddJYU27nkG6KjE7g1tbzrM0TY8yNzvCnIrJKEOPf8OQaPuoFm23ncLgV0i/fW1Ltx8uhB6cKX2CdVGngk9htITW0Nc59/mE8WKZgYIdahhhNFD/HF0pnccPejDBo1kfU/fcsUt1d5LnAmyjYbQRXlVJQUMeWmu9i1YQ15276iX+kE8i+eIS3vCNoDevomjaPV3sDaE3sZfd1s1nz7Cc/Mt1wiaA5cP8VOYupj3PnMG/QaOBwXNw/Ki/JlxpvW6ExDjSixVNJv9ERqq6rIOLyXxeceYrDnZkLUjqM3QG3irbhF3PZJmVTkjK6u6J2cuPnZP5IwaCQlWc8Skny5ZE+g6ZID6NZGX3yce3Do6BKUGr00sTC6uGG1iOMdsk4sJiDp8oRAqIcVd1cbzXVWXssazS2Rx3FVmlhV0gt7foDsszu59RdG3fWUNCnxj1Gg09i4NeJBwpNKKGwK4evmZzmffgaLqR2dsycB8f2lTb9S74ytuQFX/yDiDakE9R6Mk3cQlo42Tm/8gZOrvyQvYxVxfsNpaiwno+oISo2WuKn34KypZ0hsMUXVRs4YhmBubcLg6YdrRC/sSg1mq1mWb7p4h9DRUC375uw2K+115eg8A7AqtaidPemoK8c5og9KvVGWKR476c0nHv2INFZS2eHKlsze9OobxJnjX+KeOAankASUGiO1mftoN6so69tbjvjMVQbcGxXE2uKotm/D2tqA2uiO1WpG4x0h7WHtSjWWljoKLlTz4fRkRrrn0mFXsbYiEUOPSAZFPszRsuW0pv+C++TX+WjZBGIfXcMUt8tVAwN7FLNnVzB14bVolSJgG3Y1+/LpucH4OLdQ0+ZMVYGnnCzQNirwKbfgdK6W8uy9nLUclstI6TeQcWNGodU4DsykJEcpTLepVTf+ofFPVO74P5mgb25u7o6j+Bvj38nd8V+SpAnLyj+dSZSDRRF+c2lGRJCw7du3dzXeCUK2Z88e6Qwp0LdvXxlWKF5zww0iMwo5i3LmzBnpDNmNvwwEKW4zWUCr7roQChw+fEje/myjrpzrd0A8f/LkKZb9uJi62ire3zQMN38N13mVoJRDfEcZkYA4IsotCtIa/chpdqWHsRFnnYlFF3ujXe7G73u2SEVLWFiLMkdR4idKBcXfCrsdH29fBg8fLQ0wmpoacHP3kL1nF85lyrK7Fs3Vh71QPgT2ZvRhkGeDVNfm/rKJaz6fzrCJeeQWerH32TWMHDtREp/6ulqpVolBvSCcib37yhBoQWC8vH2pq62WitqFnHOSoN3+0gN89chOdMpWbu9j4Oezm7lvnAUPHw+OHVsvbf9FcHbWmdOsWb4E3SED69odA3cno7NU7MQxLmYAXd2jaLedxK68XKPdaHOi6kK2/B187ZlHePCdMezeEIlBYWNVmpFPdg3AWbG2S5UU5WJR8UqyMtNJ6jOA9auWkXn6pDzvsjPTGTP6PlrtW/HE1vUZHUZfViz+ios5n7BxewxZGet5M7cPT4WflMrXyrIoMo7sok+KO4NHjqOurkaWfGq0Otb99B1NhjbGh18+1/uEwMGt83H38sXbNYNXX/Fn0bdzaE8Lkv1lJ+tqmHXPY0y79X4GjZnCllU/sHPvcunOmDxwuFQdM3MPyPc/8vqHxCT14dmbJlKhHoPdfnmQXWPVSCe9xqZGeQy2Njdx8cwpYvsOlHEdhefPyfgAZzdPGR0gdmLbr67crp/K9ymbCdeYJem7boyF2bNVmL3nEN9/mCThYvu2LP2a6BmGqyYTWm0K7soeLY/qnOOhRDpVyp682vISmuuq8QmOwMU3iJbWNg5fPEyLDYxCpRH72Q5NLQ7luWG1F+8NnCAzlLU5WsIvhbAGxifT2lCHWqtFrdEyNvptol1FGDpEuhYwIvBTGi0v0t7ahtrgIstgnd19aCgvxNpSxcvDXiLCpZLGDg1/3Ps8NbZYwodOQ+/pR8fpxRQWLSCv0IJnZAIjH5qLv5+GL6a+ikHlmJlZfHY267In0FCUg87VE7tSibmxloaibNwiHREKbTWlNJfn4RSWRFtjA5b2Fqwd7ajd/KU0bGpw7JPyX36nJGYM7ww4iYcmnwmmeuZZ+qD1DKL+9HZUXkJ9a0VtcKNowRziH5qD1QAudeBS3IGptUmWX9pFdEVzNXaFCpXRE2tzDQq1Flu7DW1ALzZsceFwSoTsV6uvcMXQpMBoNODnmkJpyWYs9cV05Bby675AJk2pdvRSKMS1QcGscY4SKfEdH23z4L3To2i76IYtSzxmxrPdhlNRM22Z6Zxs3oYJR7+z2O8zbrmHKWMcirVAt0lIN7rx10FndZS4zr/66qsyyLoT4rfzyJEjJCc7rk/d6MY/C/6uJO2aa66RPWihoaGy3FEEvwm7/bvvvrvrZBPlj3PnziUmJkbexN/i5BMuKgLCrv+ee+6RtvvCfl80j4rMNKHKdbo9duMvAzeD2qGg2QXBbqO0pBhnZxf8AvzlCKa0uIR169ZIi31hHT9w4GCmTrtWBrRu27qV3buWkTQ9ii+f9SHa2dEveOXgthNiONNsV/PGT+M5PfdV+Zgg8z5+RVKxEmV6DzzxHKaONml3L4iTCE8W6lGPuAQCg0OkU6EgaKL8KiQsHBcXNzy8feVyti/pxdjX8uipa5OmIB+eTuH2t+MY5OmIBhBU4pnpNt4Iepf0Tw2ytLFHfAJPvvC6VP+yMk7JsGlBAkUItruHl1wPh9ue3UF2ss6wduUySd5GzMi9St0ZF9vBrcUXcPcbwI2338Pjz74qA6Yfe+YVbrv3YZZ/v1C+39xhQqvTEhkTxw23383jd91ER2sOb2UO4K3EQzgp7RxvcWXeA8e55e5UvvhwLoOHT+Ot27JxUwojC7h1gI2dacXkmzVEaB0W9jVWJUf3NTJseDlP3X8LjU3n2LEjGn+9iZ1ngnj8jk28fW4Ib8cfxFlp51SbC6/fdpjJd8fwy6smuX8m9Wrl14w8Jn9zByqTHffDJ2V5ZUBIKM2trag0WnZt20By/8H8vukXkp56EJN9HZ36tfjaWzs0hIR9yeP3iXNdx3P3RDDkukaCY2eSMmg4YT16ynJHkavm6RfAY3/8WMYDVJUUYbHZ8AsKpaO9jaryEkm+RVbZsv2p9L6mgt6GRuqtGl7eMwq15py03RcB0OJ9vYePk46Pbp7eBJhM0pFTlEEKS/yw2ASyti2kR8VdOH1/dY9S6rQw5j6zjMQhY9A5u2Kqraa6rITyrJ7s7VtBP0OzJPxflcVSsCJSlviEZjSQWbuD8IQUtILYVVtoN5uxCcOWtH0UHm/mu5pwrnN39LEtqYilT0IUu/Z8ROnOH0nInY1FpcBY10Bmxx6Mbp5E9x8hj7ELR3fjERSJWx+Rj3V5v3q45fH7gleZ+NT7nD+4Db/YPpjMVVjNJq4d8IskaAIuWjO3Jn3J/FOfShIx7+HTDPIRXZIR/JIzmlUX7qEq5yTjEzZ2ETSBSRG/8POxgXQ0VOFkMNGuCkfl5InRLxK71gmViAgwWzAEJUh7fLvVgqVK9COqsKu08hxpzMvAGJpIR2khHyf+jrvacWyOiyzkaKorBysMmBursZla0Xo48vKUzl6Ur11HQuAoNFYriqJqLni2Q5kZbUAc7WXn0Pr3xNJcg91qwlJ0Ek1gkswqq95wHl19L6zuOnSNCpxLbChbOtA6X+p3tlnRhfXn52VWHhh9kT7GFvmwp8p2OQNNKJ26JjpqDHiesuGSXU9t1hFOmHbJiADH/rdKlbb/wGHMvmEmWo2YEulGN/4J8U+mpInxo/wou52MjAxZ2dMJ8bcwcxBjw2787dBd7vhPTtI+++wzOePx8MMPy/JE0YsmHBuFlWUnnnvuOTnjLV7TGWYt+p46M9IERMq3COUVSlpnmLXIVesuJ/nLQ1wAf1m9it9+2ygHxgKCHE+5ZhpLf1hCWIyewTcPJW15Nr9t2sCWzZvkBfLDeW78MC8ChULoCw7lrBN/KsKJpX5SkIxpxRF5f8r0G6XRQ11NNU88/wZDRoymob4GjVpDvwGDcXFzlwN0Xz9/SZSElb4gVsIlMiwymnlzXuLoQUcGlCBDe1fM57Hmh7GN1mItUnFk7ls8+POfD8AUdvWiZ0vkfAl3RhFYLcqXWltaHMrLpR8Ao7MzB3b/TmJyX4JCw6W6tn3Tr+gNBs7WeWMPzO9aZqVZg0arZem6bVRWOMoKBckU+Vri82695yFaWpqly6JGo5V5XiK3bNYtd/L5B28zRXMXU6Nux6Zro3D594RHxTB+6nUsW/QlsRMeRKfIumobnJRHuWXVDfxh0n40ChuLlyagVudzaN9O8i7mcOhMHNEGR4Dytb3hyOMdNK3JYu6wwewrD4O1mdg6TnDnw16oFI7MJ4EBcQ1kTXxFqmUWi5knX5pLWHScdGwUCIuOl+YRIkNNd07DDzVh3ORVIM1S1tcGsXi5mlHhli6iLqrBHnvRnbVrFdJC/8LZ01KNEDifmS6Jn1COKisdBF9jEGqWibxzZ4hJSJE5aIa1+TyumIFTYAttZQYC1tRJxcs3IJiiC+fwDwmX30lzQz1N9bW4eHjJklCjqzv1NVXc9IfXmP/knRzZ8Q57mocyw61Crm+TDTaeG0xh9udUFhXQ1tQkA6uFGmTams0TUdcQG1xKXbsTZTsDqV/zDe3tLRR7aKkpL+aO1z/Cyd0Hb7sSFy8fabtffD6T1tPn+HLvbNZG1mC2qKhN9ybykhJYV1XM3vJ38Y+Mp7LgvDwHBlx3B21NjWiNTtKp8fyxveR3uJBkaHM4DwIb63qRf2w16b/9ROTgiZgsFly8fbDY7bj6XP7+BHTGRra98wCjbx3DIJ9TXY9fF7OTZSenofP0p8XZYY/fCYPGjH3Xw6xd4IqPsZ3CRm8eWf8Edt9w2oWxiLMHSrUGpd4Nc7NwTbVjCIimo6lBkrmO+goMIUko9S64Rqfi4nSZZMrlJ5XR/s0F/MY9jMrohrlJ9IMpcEu9ieot79N/VhFv/0ErnTcfer6Cn62j0LgHofGO5t5Bu7l3yBEKa525/+tRVLc7nHsVSi05H71O32HP4aQwoKlsorWxmTL7YVQufii0TpJnib8jVZfLT/VKHLb7l+6fbndFWkwqFSgr6skxHZMVA94R8QQEBTFsyDCC/QNwd9agvaTYjx8zkm07dsu/RQxJt5rWjW785dHp6iiydj/55JPuPLRu/Evg70rSBNESdvudlvt/DuLH+Y033pC3/wxCtRGET9y68deB6EUTKtpvmzay4dd13P/JRDxjXNj8qZH0LUv44fvFrP+9J1N6mlEo8kh73oOvlvTFPbyOLcuV3D+z8L/MFhLoHAiWWpQcOhDLudzX5f1dWzcy4ZoZTJt5E2czTnJ4305JvmbedAcurq6UFhVIAnVgz+80NjQycuwEho4YS3BoOHfOmsxND4ez/ece6JR21pw2cMvE0xxY/SF2h0eIRMZyd070M5Kib5U9aD+VhwtqQOJQT+a/o0ajaOPZ596krKRIql0iT62uuoojB/fRf+BQWVIpgqJFOZ7IBxMYOmosq376noyvb+an18oZ41Eq3QgffsOdPv0H0VBfLxW+1tZmjh85SExcT6J6xKEoVxARJXLgaigrLsLNw1O+dsS4KRTl57Fz+1qatjoiA0Q/2dvzv5JB2AKute3saPRhqluVvF9mUfHNB9m4Gr/lu83DyS/K4nzO1zz89CtsWPMTI4bPxENz7Krv4MnbDPhqRGndAU5VHKbvM+dk393JBl+u9SrrKoHMa3eSis51s+/khtsfkDbyRlc3GUPQYTIREhFNbXUlSf0GUXhiLV9/dRuLhjaDCRIqsvju9pL/cAzU6CI5tOM3pt3+IG4+/mh0ehQqFcmDR8oySGFc0lK+gidnn8BJbWPdAR+ien2ARm9g7Kxb2bJiMeMN96O1CcLehM1jBU/9IZqU0cPIOpVFc2ODLH0RqlnGySPE9xskSVtlcQHO7p5EJfbhmrsfZ8XHb/JbZjwTB1aiV9rZVRtAyw5HVltbcyP1VWVSHYvolcyGhR8x1KkP56Nj0bTYCEgv4lx9riSV0cH9GX3rQ4Qm9KWprkb2aTXX18gJjuQx13Fsyy8EbWygPlio0TBVdZYHPj3C3JYoNhc/w++bc8g7eYiUCbNIHH2NNP5oM5sxikBsMVFQt5Vr3Su7+jfPtxvYcCCZkMQSco/uImbEtRhcPGiqLsPS0crm6kD6uNTgrXKUVq6q6kFt/lbUbn6S9HR+t+Ic2PL67aTc9gIbCsJJdi8hWWeWZMVFZWfDd6J/1HHMhbhUc3PSL3xx9FZ5vzpjDy4RyWBqoDH/DHrvEFQuatmTJ0xEtG4+WFFjbqrGq+8kjrQtZaJzgyzDrbbC+jofxzqY2lBqjdhM7bQWnsYY3oeY297gnad/7Aqr//p9P7Y+Ow3UOgZEFfD6pN/lewNdmvjxD5uZuuB5abWvD0/F2lDGsb3voPNLYM6TWiYOb2Tv4WZeXTMBzaWwdYXNSitqXO3mrn2xsdETN5WZPJMzSwr6YavQoTI5JpoMIset2cSQmXcR4aEnxtsFg04lw+dvumEmy39e02UN3oluotaNfwb8sxqHLF68+G/3Yd34L/HvZMH/L0nSuvHPp6Jt3LCeo2d709fjonzssYVKJs97mSeGrGBqgqNkSaCfoYXvHsqQfz8+5j8/vTqJW5lFiQIFjVYVfzw3kD7nfuP3wliprizLCOPxqcspzV/FEy+EczTNzMdzV8hMs3sefpKX/vAgJflp/Lg2QZp+PHTPy6xZ8SNRPWIlaZr3WIcsDRSfNbt3G+OLYvFQ2Sjp0JCUkoV/YAJn9y7jxT8+hmFCFe3VenY8/hFePuGs+NiMu9LRY7L6c2/8Ez5h/JTrpGJntVhJ7jdAujZ2Kj6iV02oeMLGPyExhd59+rN7wzyon8EX7cEcP7IKi6WD4aMnyP4zQT5FP13f1MHSIbK0uAilQimJzq5tm1m66Eva29tkdMA1M25izgdfcF9BHqUlhUREx8r37NyyiT6pg+g/aBhHjiym+p0H2Xr7efz1zfz+lTMKay5Jfftz/kI6AcEhPPDUC/RK7sf6n5eiVqjYXhfInT6F8iIoBu6+mstKZ7KfFS8vJbW1LWz5PpHQx+sY6VZGmcnIG+/1wck5hxvufIAzJ9JkiaJwcawqL8WuVEmFTezzpP6DWb7wY6ybFxNxNJmTp3aT+uuYq44DQRB2Nbuz6kNPqRwKiHLE2spy2YfqExiMuaOD52+bzKm9Kvn9iWXfNLyKMbfOYsJd3zB+1u0ykmHb2m8kCfvhlxhuGeQYfNe2z+Jw9V34hUTg5u1HTXkp0cmpOHv6YGlvI+9cBj2SU2UgtW9ImDQT+XDAXlnuKT5nmncZiywr5ffs6R/E2JStDPT8HNtABc+ow/jm2/n0sU5BpVBzuGArOqMTj32+Ar+IWNrr8xkRtRabuY2ffgvCIzxV9qT5hUeTMu46dm/7kPjeI2TQ8dsrz126KCuJCPmIvPI/Ep7UHxf/CIxuHrTUVWFwdqc457RUd6fd2nZV4HaQpgPXE3Zc/b3wM5ZisBTS3GBDoVJzeuOPnFZEozYoiNZXc6HNi9+O9KVnihOnimtJa9eSrDfJyqQtjd44uRtJ+/Z1fE2z+NSrH0uiRO+p48eykyR1IsLjDMqKw1j8BuEUFIfS6CHLDA3+0diUGum+aW6pp6UkG7vKoUA355/GEBjLwvxUSoMycFWbONIYSpW2D76RLVTu+R6tfywG/xi0AQnYTG0ERhmv6HN1lCY3H12E3SmSgWOvvs6EedVTufwhuS5OCZMxJs9A5RHKHQP38OydJrmPe0W44pN4gaeeSKO61o4xahAvruvH/FmH0Csgy6RlYXE/zh8JRaWxo2hS4ZZnx5jbQKu7hYaCMkLiknDWKQn3dMJJr2b6tClds/idqpn4V5wbP61c/T+51HajG39//JOVO3bjHw9XOnT/JZb174huktaN/xHCIkXY+Emwt9HH/XI5kL/GxquPbGea23/uvROmdYQCd55jnT0eAu02eKugJ5vmR3N2zRcoDQas7d/TUBSF/tL02/2987j49WTenZyFEis3DlTSd/JYbhv6vezZOpl2mLLCeHzUDtv0w7+FEJxwjvKyEvqkDEOrOHPV+nQO8IN0ZpZt789zd5uIionj98WvwxWTcD1uvAM35U9XGW6kDHZl1bLF/OGFN/Dy95OllUJVE/821NdK5VdkgIVFRsmN/G7lRj6b90dW/bRYlkg+NG8oj81spKGllJunv0dUbDxu7sLyHekOKSz70w7vIzsrk28//5A7XhvJC3fWYrVkcf+9d2K2fEN8r97U1dYQJQa+JpMMsFap1JLA3TVrAkeWvkH10ThJ+ERu20tvfUT/QUPx8w+Sqt3BvTtlaWVK/0Hs3/UrVR89QdbdZ0lwq2L7xUgWD9wjt5VL5XONjTb0BidaDq5jZc1MFvqp8K1o5sCm95l12/24eXgTEh2PWquXJKbNbEIrShP1OvQaDcMmXkdccn/W/vA1h49uoMPSzoFj0ZyKzyRJ1y6LX5dWhfD5e8O4cPxTwnvEc/rQdgYm7sNFoWDvyX707D9OBlNXWRrRK92u+j6jk3SsW/wFwyZP57p7H2fEtTcw77E7uM4RUSfhqbfTWvABmRY9scn9ZZmjUquVvWp2u42+oybSUF+Hm5cvQVGxsn9Mr7y6LPfplzu46/5QLrbtZIjnpkt9SnY+eFHFuq3hnD93RJYkxqUOJ3XqDehd3GmoruD+Pk/ir3OoTrE3qfkobRiNdXUUZaUz9vbH8QwI4fjWX4icrkOj6CysA2e1nSXP3crEx+fiEhBJfUWJDLBura+lo6UZu9aFHLUfJrtDhRLrc77DiF/BHpb+motRo8Zi+yPvnphDbkOsNBEp+24/v+qnonaxoGu2Myf6IG7P1PDK97W8lz+avp6FtFi1bMnpRc8AC/uLzlC+5GfyhzxBdQR4XzrNWy4NupwvnRsDQ81sfe5npr2eTUvsI5KQ2c0mVK5+2ERvmdiVKh2G0GSHy6TFhDEkURK4jM0aSkYMQ6Oy0VBnRFOppicJVOuO0HL+CPqgJFC0oXbxpKi8kWyziliNo0fuVIcWv6njuPjFV2zd4s9DQ1U4XVrHzA4jMbOvp+DXddTv/AhD3Bj0of2ZckMwdrsIU3e87vpeVdywE34+quama79n2RENp2pnkDjWSHGzO6V5vjiXqvA82Uz78WMct+5GbTTQ0daIRq9n5MSppAR74GZ09MBs2rRJhpeKCo8rIcx/uksdu9GNbnSjG/9T/Lu6Wnbjvyhr7LTZ77St/WHZCs6fz5HKiNX6Hw+Zca510jChc2b9ShLWiT8laPtanPm+KJw794xm5V0WTq/+ENQWfH3csdmt6BQOBUPOlitgxnDhAnmpgd8OQ0NLiI9PYsfmjcQlJOCjtnS9Xqhmt7w1XTosVteUsLXhchi6sNC+clLPyQUu5pzjm2Vr+ey75fzhxTm8/dGX8jltSTsF5svzGPVWBft/r6NHfC+UKrU0MRELE6qX3WaXyxEBxS6u7hTm51FTU0VtdRV9BwyWBO2md57g41sr6GFoo793G7t3BPPjNwvkc0cP7ZXB08LcJCIqjsVffcKEex5jwQMlxOjbiHPuYOtyf+bNeV72s4kcNNGDZbFYZVi0WqOW/Vo/bdzHM6+/h7dvACPGTWbux98yaNgozpw6LnuvDEYXkvsNkkrRg0+9JLcj59dPOfWKmUdT0jj9romFFZE02KDBpuCz3DjMZph910PU1FSwc+0rHP/qRTavfZvBIycw++5HpDHIlp+fpTF/HOf2TaOsMBdPX3+sZjNNDfUEh0cR2aMnT839jIWbDuPk4orbrjLu3DaTO84N4dpj4/nmyT6c+ulN6droFRDM47cs5oaB55iRmsUzN//I/q3rOLZ3O8HR/TnSdrkftcKiJD97KOczThCWkIyLhzfZp0/QZlDQbFPIY6Xzhk9vNiz+jItnTkqy2lxfS27mKVl6KKzm5fFRXyd710S55sFW165jRSiMQ2NVXDNUzcNj110VjqxSQNnFHHoNG8fjC1Zy08vz8QmOxNndm+qygwTo27uOTW+DhZ9fnkheRhrufkFYFGr6XXcn98z/iRLnGKquyK/OM6sJjhnEzu/myXUsyTpFa2sLBg9f3IIiaa4qZdPxMJbV+5NpUrOn1YkXM0bw8KelGC5ZpSoVdib4zaO+JBfP6CRqMg7iu64D4+8Gfh66lpn9chg7uIY9X9goPh7Ij+mDWH0iFdMpN7StdnSuXtjMZho2NLG4JpIzJpX8rA/K4/i8JpLzJhEv7oBWo+ChyWm0VxXRcP64LO00N9ViaW2g8UIadpUetewxE4YgbSidvJjVax8/jl3Ho5pzNGd4oD5rxLXIhrLditrVF2tbPZamKsz1JdK9cWrvUqIvEbTT7XrmFQ1C4ePH4PB7OLhZwadV0exrNfBrsxufFffDFurOQN+p+MRfT1v2LtSunuwqjrvqGtBF1vpbaCyOpuxCOPE1B1kz8zcqVmpxPwTup5uhoISLnkWYLM2gVhLRdyizHn6W+JBgXA2OaQ1BwkRZ+MrVa6+6jnajG/9sEE7Jf8lbN/79IOqj/pK3f0d0K2nd+LNoamril/Ub5d+inO/48WNknzuHi6sHmdUqevlcdnvTKuDdrCReiDvdpcBciStJW71Nwb4mL+ZuGUPO89/SYNouSwdF4PFDT79MW2sLT913E4eb3Bns6ui7KjIrOVAWxKAejV1EsNyswz8wkLLyYtQaHVVWCz4qh/LRaofcc+6yj+rUyaN8M/dFttxego+xmYZWHe8mHJPB2EINWLQ9Hi+fcknoRoyZQEr/gTg5ubDk688pzdzA3T9fx5NT9qJTWvlmVRwqRREDh47E1c1dhmcf2b+HISPH4hsQIPvD1q5cytefvk9zUwPenkr0zqH4+AXi5eNLj8HFXTEDAr5aMwf27MBitdIruT8m4fpntcrAajEoN460dYVaCxgVdhrrCriQnUXq4OFSKTS1d8iyS9EbduzwfrkOSSn92LJ+FZnpOwnw285vawwMHf+8jLwQdvNC7fLy8aOqsoyPv1vJxjXLObBrGxa1icKz61j39rN8028o6hro2LROumcmpgzg2hvv4NjBPZxJP874a2YRHBbJF+/PobBiC8fX+FxSc5r49cR77N3WRL+hwoZeIcsXrTYbaXt/x96ewxdfBJKZ/itHfxhPkdmf2qIsMst+k86NE2+8k5yGXYQbHd+lOG5CXGH36g/xChlIS2kJf9h1O7P7HsNd3cGytL4oWi7IMHChYFaWFVNdWoyrzpWPSxN5PDADg8LOnmYfyk70o6xgJ6G9kqV5hxc7CUnOpcTcg/rqCkztbeScOExDbQ2YTTx3aDI3JB3DQ9XOvV7FXaV+ogwu36wiXGOVx+PBVhcGj7yZwxtXMnj67TTWVFKcfQa/iB7s3PcrjdPB5ZI7YLvwnHBO5LcFb3Hfp6tw8RE9TY2k/baKc0fP8+bMVGb4ZGOyq/js/ABCjHpya/dSlZ+Nf0J/mf3V0lAn9011QTYFe3cxP+gGvDybaGwyYjrhhvIuPQqFo0RXrK/JaGPznLsY8tBc3EOiOX7wM5KTryPMw2EWI6DXKIg6tpVc3bVypxvzWrE5a2ivr8QjqjdnVn/J0pmvcsA/CrNVRW6hH2qDhdGpFSg0DmYpFqUNcefC8/MJnfgggR6NxAc1cOyiB+WKcFn+KHLLWorOog/uxdjgHbwxYRN2u5ahZODa2MqSlamYslqp8XPBdDIf557jsFk65KSCSufGO1N/kX1xYr17G9qpKvFEU6dC5+5OoHokXy5qh5udSHGuwqdJgbpBgd3dSFiRL1V2G+aqXJZuGUxYjxz6u1aQrDNxKerPMcGjAqPSztcv6shLvA3tZ/WUZW/ihPUsdqH52u1EDRxH6tjJOGtVRHs64+MqynoV3H6LI++zUy3rJmnd6EY3utGN/wu6SVo3rsJNN90kG9vXrNsg77e0tPLBvHfIz8uV2VCi36pvn2rKc2PxuNS7dLrdyPK5TszZcR7RmvXBwh48Ptkx8hGBvZ3ERKhtX1b0YPW9fqSffEs+dv3t9xEbn8iQUeMoyr0gTTiGj5nE1GFHeHL1MNydW1l9tDfpf/iW8KMJDHSrpEKEO28ez+GDn5M6ZDh7d2zl4d8e59mxe6V74ZdnUji2YhUjx0zA2dWNZYvewfCLkySATjofMj+7jeSYUs4cDWXny/MZP/laWpqbu3y2d2zZwJsfLODem6ZROudz3l2bTO6FC9RWH+XZ1+ZKe3xBAD08veg3cCjNLU04u7rK9fjgrVe4/dWb+PSB49JgodaiJGXwCTratew7G0lr9GlJEMVHXWg3SHLh5e1HbU0VPn7+lBQWkJ97Xu6bokxfKkcq8VU79nOZWUVjo12S5vKyUhobGii4eJ6R46fI9wgXxZNHDzH/7ZeJGuRH0YYY2VMliMSLX73D2Qx3MtJPEJfYG1tNlRxwFuRe5I4H/8BdjzzDiaMH+eydV9m1+lW89/pL0w8nZxc+/O5nAgJDZTB46tDR9BkyWhK38uJCdm1Zz4L9Q9AqCrqOoZG9Vdw6ew4+/oE0NDTQf9gYSUC3rv6QtE06SXIUY93ZkXmY8WMdvY0h0XE88sdP+H7eqzjNHE2L7XfHfrqkYqENknEKFcV59F7Txtq0QXjq2/HLVrHl2FYGTriWrJNHiOszQLpuNhQX8dvP97IjNRaVykbrYQ98TKcxOLugUmmIVNzC8GCHFX1l62o+2Pg8AdG9iE4eSFO9w/jFeWk7Pw4Yjc1gYdpt3+N/aRJArM8TmaMYGXiRFouW1cf7EapskY6NwtzE3S8Y/6h4KovyOL/jDJ9WDOYmrwtyY36s6EGIb18unHqdCycOEt1vqOz19AwMoe18Lr/veIDdUXH4KVt42PsMivutnDqnxNelgmviD1JjjWNregLl58/i6h9Cy8UsnNdrqQkLRFT7+p5qYuWkHkQZ0vBVQZ1NwYK8VLRONWRuWsLtr9zH62O/w0W/l1a7ousHQGzTriPpmMrziQ8dS5u3M2fO/YLO3YeYG1/g9MJn6Pi5jqwhgmyBoUSJqs3O6ohIAoIy8FIK0w8FiysG0Vr6AzFV81j9qlqq4O2jlNy3+mFOl7nKGABDRH9J2Canpl22t7fD9BEXmTXyIkfOGhk1vQCVkwfO8SOlIqfxC8DaUoOyk1FdQoSyltZib1RNHShtdq5r28SLYT5yeRO9Snk9z8yhbYGY2hwZc+KzTMWn+ePn0wgYW8XzvfYx3cNhhnO50BQZmREeW0H2mDii7E7oKtw5V7OP5AmziEgejJdRQ7SXM73je1BQ4HBura+vx8PD46praTe68U+L7p60bvwf0d2T9n9HN0nrxlXoJGfCxMNmt7PsxyXoPatZ9PMYsls9MJbamTUghw5MVFqUnGpx5YVvJ1G663P5PptdyfMPFXDwHj/G39WDAE07k/wr5HPCyvp0bhAZJ7+V9/sMGCLLAYvyc2lpapKEpamxgZ6JvTm453dW3rtRDmCzz2+Rr39oxFlixt6BKyoydi2Rrnz3PfKUXMbmJxbSMfYmbDYFhw+tlWWHs++4j8DgUKbNnM3239ZTWJDH7m2b2fPgx+xXq2U/17jJ1/LKWx/S3t7e1Vs2dvI0qqsq+fan9Wxc9zPFhfkyF+3aWTcTFhFNQ0Od7IUTJY7Cnt/HzZ99u39n2eKv6NNnKE/cmSXJUWf/25fLY7hmaAbatQ3cFzCK+3qepNZk4JlbyqSBSHVlBfX1tVINc3Zx4fpb7+aXFUvwOn6eW7dfw+ODDmC1qZg3xwn/wGbCI2PkuopP6DNwKA/fNp2jB3fz6rwwVAobpvYKHn8zFmelI+tJ4Ml73Bk39SPe+PBrqVy6uXvKssne/ZUy30zcT+o7gGVbDvHbmp+kq6R/YDATr7uR5uYGMtOP07vvQDxkaHg5DXW1nM0Q5LONsy3e2OwFXcUIpWYdWJsoubifhKGzaW1vY8Gbz3L7+7EYlJejCIbEK2W55RPvfCENO9y9fWV5pPMZPfNL47nDL0cu88v8RHyDa+hob6WPyDgzfcHu1wIwqKCkEYZMcGbCzfejMxqkKUif0ZPZ9MNXqPftwuPUECxmG/6GGrYc/IWBE69j6Qev8chyB0ET8DXaOL/nKVrb5xHRMxnvgBDi+g/h2L4vSKmeRF1BDa8OS+WRYEdw99LyOEqWBrGwZwSKDgVeh1opaTqLs7sXrl5+UulSaTSyD9NaXcuyHUPY3KOnI0g505Oo5hYG9NUxIvJ3GnWxmA0JBMSKHDVnjBvTcQ6LZ90Hv+IkSGE89O4XQajv4ksK0l58NAP5UXuvDKt2D4oga9eXpMbdirLZQl7lYYr3DKRNNZRgQx3ZDX5kHY+kV8w1HD74JS9ObMNNGGAoFIii0Qsmlew73FAXQfCAmzm5cg6Hc5ZADrhHJdNjxqNoXTwJSJ3ChfVfMLTuFWwuenQ1rbSfO8f2Xgm0KHT46ZrIbvTlTG4EfSPvYM6rO7qOB53Sxo83fs6ebD8eXfcACq0eS1Mt5zp0iCTLTmWsk38N6NnKg48EsabkUUnmHINFQZAVHGp1YqixRb42x6ziHJ44ZdXSkpdNvvl3blkUeBXxmzoxnZ3P1nKufZvjqqbRo/WLQavUU/HtGQJebcfuIQimyDiEgEsTT5kdWkpb3LHpwOZqIMgcL0maWiXUMwPx/q4olQpJ0Lr7zLrxr4h/VnfHbvzjQHHpv7/Usv4d0U3SutGFpct/lvbknbDa7DQ0HOPk9hC0ikJs9kIUMY7nOgdB41zriX7sZ+aOncbJp2ycOvuDLANc9WWhvNUU9Lgqc2jBoF14fXYDx5es5ZY7Czl1uo4fFm5g8/qfefvjhfLz169axobfoxkT7SjZ2l3lyaT+p9AZ9Jxc/7k0yRg9YQrXzLgR/8Ag5n32LT8t+YY9O7bIkjfhhChCpBd8OJeJU6czdPR47rj/MRk4/dSLb8rMNGFvHxvfi6Q+/aUjo+j1EeWDAs3NjbLJX5CzOx54TJpgCHOSuIQkSXBc3NykQcTF8+ekotTWVkZAYLBcft+48aDIueqo0rnoZWTAlm0LiciN5aK3L6dPHcXVzYP73n4Ws8Uk3+vu4Y2zqzseHSaefe1d5r7yFP5nQ3jDL5iCgmy5jm99tFCWWgpiJzD3lT/I8sOTF3qTaHQYutx1bQwrqy4HeQo02VRcyDkrM8zEOldXlcvyQpHD5untQ3VFGXkXzxMRE8ewcVPJOXMSNw8vScJ0Wr0jONpipr2ynFNHDxAd3wurzXGs7FibwPf3VDDGrZxaq4b9ZwOpu9COSrGN0yXb+GjpLI4f2EWy7Ras9vyuRtgqi0Zedhtqq0gdPUnu26CIaPL2ruEXn4dYmdQPe5sS/01WLpz5mL4jxqE3OvPx3DL0lxw3A13g+df0HDmyl5SRE6ipKMU/JIL73/qMb159HKt1BzqDkbbmJuL6DaHPyIl88dHztNhc0V6hyiz/1If8yvf5fONN+EQM5rZXPuTXr+ZxdNtaWYKqWfU2t/WNkj8VqlN67Fu2ot6sIDyyDy3u1aQf3cyI2fdJpchUtBm/iAg6QmLRGZ1x3ZCNKaKnZCOBZxsZPjONZ28JRaEoxGJ/nj/ueBoU0QycdR97f/iImwb1xvmSeioQ4au8qsczyfcIR35qI/XWp4kbeyPndvzMjn3vXj7PPsnliP5JDjlbUTao8MqwYW5scDynEq6Gl7G2qgc76sIpKPTBw6QiKHE0teU5JN39FmqdEaWzJ7aOdrSuHpKsHTn2GRGxk2ivLKKodj+698Jpee1eVEYLlgYtznlqXEx6bM7CNMNhltK57iNiK0j12c/BylG0Fmbw85m+BHtUE6dvJEBpxeMKKStumIbqh77FOW6EJPLawESprM05MZ4ZPdPQKy0crI+gqt4d32CoOl+FxdrChRZXBigul0UX23TUPzqIiK9ruGg/T93urwiY+TYav1Duml7FkOg6uX5+Kpt0clxXFohNbeO3ijjKL/rgXmVH2dCGxcOxLQlREUT7OMvyUNF71o1udKMb3ejGXwvdJK0bXRlonRDqlcVqp6y8jG+/CuzqM7vS+vrKfyM0Vr5MOsDc7+Pou+AW5r+VhlFl53SbkcoOO24ah+uigKfSzrzpxzDMDJGlULZrzSRcP4X3bzrGB2++yOPPv0FLYyFjosWA2IER3rWMefgOdnyxhL2nLkoFQRAbX/9AmXsVGBwmre5vv/dRPpk3h01rV/LRwnjCw5W88ezTHNw3lWdfn0tVZTlBoaGMmjCJpoYGMk+fpK2tVZJKQQ6PHzlA3wFDcHFxl+HMA4eNlP1erq7u0h2xrbVVqk6C6Iiyz9KSItnb5RcQKO21hRK2/+ivZDQMIUHfKvebKDd76rFyBg+fhLuHF6dPppF3IRsPT29uvutB4nomSsMPjUYvyZIItz5+eD/Dx0yUqt2qZYtkiPewMRO48Y778QsIluWOLS1NHNq3ix2/rWfIqy8Rb1h5eR9rbKSdCCfFvYpEfbMsOVVY7cx5x4cF77/BI886MgfzL+bIZfdM6iOzyMTIVvTCiRD4NpMJJ5sdKwrcPH3oEIHI7p7UVJbTI7m/NCtx9vLD1d0T1f7dfNk0lo/ClGiz2tj5/oouIpYUBB2VX8rQ5+NberA4sJhZHg4TmHXno1Br86kqK0ZvdKLDbGbQuKkc270Vj11ric8cTXNjDecbDsiw8oQho/nm1SdQvhctxRWZwaIAn/hQVj8+T2alxSbHMj7wfu6+rZ2nronk6Q8G0mExyBLIPqMmkX3iCJbaFj4vT+V+v3NS8XRWCvICsf5w46gVLNrmTu/h4xkz+14ie/Vl3Zfv0bFzEwHpY7CrVLg0VLO98SBmcztna3dI1Sx16o2Mvv1xxgS+xsBhp+W2p1f6c/yG+9jx/UfE1A8j2DeBcvMFZky/nA8neEkvtw957d0Ixj72Np5hsZSzkjZbqywL5dIx5C6iAC69p8ym48L+jTRXlzHo/jcIHjSJmgunMTXXo9I7s/fjP+DxawlqLz/ULR3oCmpJt57F2S+UtHZnRhqb5eeWWxSsOppEQ60bxlIFTrnVMktNpdGhd/Om9vwJXCJTZCi1S9QAYm+LoHTfz5w/9TN2qxmn4Dhay3Ix/FyOIj4UYzu45LXL8+Cnikg8Q84SqHK4U3VeK5ztv1O66RS+o+6mMl3Jh34j8Xdt5DrvTG70KEeY81dYYYeyH/+PvfMAb+Jat/arLkvuvRdcsDHGBozpvUOoSQiQBNIb6b2d9F5I7wkhgRAghBZKqKF3m15swNi4927Lqv+zt7CBnHPvf89Nzj0niRaPHhlpNDOa2ZL2mvV9aznqNlG5bjZe3SahC00Gu43cLZ341GxAY7DSXOeGskgDShsR2njO6c7z8JM2whcaidQ2k2dxY0FJCmZfNaE9RuF/tiu7y76h8dgatF7+RMdddF8U48ldaeWL0z1R/uyOw2zGp7oOVXYxJt8WMo+vJjQ6lq7JHdFpVJd9X7pyz1z4U8JV7ujCb4Sr3PG3w0XS/uK4jKCJ8iyHnb179rB54wbKykq451gwigu1Cm3q2a/dG8XfagfcFJyD+nk1RpXTmTHN0Mz8qhAUrXbi9U7lQ+BSMwyBsR3OsqPHDaxc/SrbN6/DYlXK0si2xcTuWSI8ZU/SqeNHZLld7wGDpdolCIsgS8IwY8+OLaxauohtxzPo61vj7EtZE0GnASvZuKY3U2feIhUoo7s7Cm8FYyZeTWlxkSRHpUWFjJng/L9Yr3BaFCqdcHCsrqrEw9ub1lYzhzL30negMAoJpYsiA2+RbVZUwNoVS6Qz4RMrJnFdkDMfTmx/51k3Tuw8S3n2T5II7jzZjXSPOprsSibf9A47t3UiqXOaJH9im4Igd+mWIcvRklLSePS5N2SvmigD3fHLepmhFhkTS2hkDIHB2XJC3GTX0nBhIi8gNJjNzyzk4Pb7eOKh7UwJLMLTYObRaW40Nm1j945fGDNpqiRgkR0SJPkM8A8iJLKFOe+/hpv3Jl5+1AOz2cFzLxkYNP41DO7u2B12fIJCaGpqoramWh77B158l5ceuAn33MNSlWsN8JPbvxTBiTHo9KeoXfEjYTPc8bqwn/ckn2L7OC21TY1UVZbj7R9EdEo6tzzztnRhXHHMqQ6FxyZy7+yvqa0olWTt6+JEnog8Iu3fS61KvlrTi5bGbdKhMTXyGSIMTjfFaG8r10/ezg+brqVT70HS9Ebkp9kbGli8rA9ruyfxUudfGOLuVFMEQsO1zHv5YQweX9Cxe1/0nr54Bobw1VN3kq88SHBMAsVnTsqg6gGTbiYsIUU+1lhTiam+goxUJ0ETSA0spfL4DyT1HU5lYR5nT+zBwzeQeqWfDDdo+xw1BkeRs2s9gXEpdBo6ie1ffcWXU6IY5VmI2aHgi5IUhvnnkqqvp96m5sXTveiZMYSd29+jrjiPwKQeBHfK4PyBzdL1MbrvWPbvfJ+wzgPxMAZR3JxJQ8k5ogddxVtnAzgdfhR3pYW1FQmYTxgJO9SIurqZFh8FFSeyCB80DYWbF54duslzjMUkGbFHZCdixt1NxPCb0HiL8G0H55a+xfFfPiG99h486hWYm+o46VlA/towasb2ZLLfcQYbG9E6IM+qJDNwLI25H2IqO0uAIGqLW6hIj6E42JvSpCz81U0caQrjaHEUPbrNJKfkEOWHVuLRdQLNZ3ehDU6k+JV1hF5xJTo7uJU50Jc1Y9PqcLS00ljnzr1rryCiUyFNFi3FxX5o6hSgUWGobcIQloyp9AxGo475e/ozOTkLg8L5HbOryY9WswY3lZqAA5Uczl5IOYWQC7GdujBp6o3otX//k6lVOb9HXT1oLrjgggsu/J5wkbS/IAoKCvh40RoSg52Bq+0EzW5n5YrlrFi2hPDwaMK8O1BW14SH30VStqHeB0OrFXc3G52NzdJ+vO05lcKB9oK5Qhs0ShszbhzBim/XEnjBBU4YDASqnYYWQk0rt+qwKDWSoAhC0thoZWFZBFcFFchlfqoLQrHTLo0rfP0CiIyOlQ6MglBl7d3FiCsmUV9fw+pli+mW3psu3k7jB7FfQs269+1ezHtlqTQIEc6IarWaDnEdqSwrk6WNWSt3kdFnoCwn9PD0pCD/HG5Gd8rLyrDa7Rzcv0v2jgnFLL3XAGmCIUiTgNniLB9bvXwxPfsMon/H4vbjIfY9NcqCl5cPVZUVvPTLWHp6npDPeSjtfPt5CNffOpdHnnudI5l7yeg7iN1bNklb/rKSQiKiO9ClW0+uu2WWLHEUhGzVsoWy327spGuIiImTfXwhB2v52+kePBybhRYHc08mUVBymoJvX6LzC53byYDokRo9IZDH7lsu+wEVSjUGd0+0Wj25p0+ycO6nZGavJ3etBxoxbTXAl280kdjnDm685zG6ZPSluqKMsooKSvNzSe7Wk4TOXbn+3ifJyzlB5o5NeDvc+b4qmJv88xAFl8db3Ti5rysa3TnO5R6is19S+7kR+zV9VjQP3n4QtVor90/0yfUeOYG4Lt2pr67C089flhvW11aj1bnJ1x5cF8uJ6fnoVBZe3DcQ1Rbn8d61egm3XXt580N4vCc/jH+JFweMkJlxKf2GsvTj13H/ZT/q/V1Y+ngCvTvulRN1MXJXlkfh7l1G1i9rSOo9CIvZRHBMPI/N28COpd/SWFNFfPe+9Jp4PXa7A3cffxqqyqivr+No5ias/UHTFkUhygvjYjm1YjczZi+WZZd7l83lnbPwTMIefFU2cs1ufHKoFwndFBxdvxi/qHiqtu3nk90vsiyyBpNFTeUpX7ZHxxPqW0dNs4Gqg37EtDTIbZzfv1kqZEqVBt/4rihV8MMnVlL94sgvL2L41cdQqvxJv+1lyo/vIXeNFx/2HIxC5cBUbCCg2MGZo8sorjuKcP0xBscQnDGWlqoS1B4BqDR2TJVFNJfn4R7ZWW6zqSgHESmt0hsJ6j+N5ooC9h+YjcbDD6upEUexGcN3KWwJnkZOdABHIo5jVJnZWxNDVV0wPYKvJqt6FXWH11NaeIxu1U9h9/HhG3NftO4WWhq1UKAHnYUO5gjK7TaaT+/ELaY7arUKhWcMZ997ke5d78GtrhVLZRU5AbWoz1bik3YfptxEju7Iw5AUhaHFznsjV9HrnmJKGjQMn1ZJfrmNnC/uJtfoxeDCbtw0q5oSq4F1hUlYzxvQ1dqgoRmPxEhq86q46u7HCQsIJMAocv+cNZmiD00oaG1wETQX/mxw9aS58JvH0O9ona9w9aS58GdHmyW0UM5i/d3Jr2giKsDY/nxtXR3rf17GD1n9CPRoYXFJAk99FciH9/2Ml85OZpMn9z3WgZwVi6RpR1S4lf2/REvlTJoP1IZTdsCXp0YeluVUJVYFi/K6UHsmi47dz/LRhj7YVfDlwW48m7GTZGMNhRY9z+0eTFPxVqJj4xl/1XQ2rlnBgyNO8NnjN4CnA7dfYMuKt51EKTiU+XM+4ejBTLnPfQYOoay0WBIvoXhhES6IGty15vYspJMFQZjN56QNfozVKpv/vbx9MLWa8HT40C2jr1TTTM1NlBQXkn3iGGnpvaRxhnBM7DtgqCRlWq0OjVaD0d2TU8cOs2v7ZvoPGUFUdKwkUWJavq0ynGGeVe2leIerfbDZiunUpSu+nhdDwAX0KjuHMvdIcxOZcTbnY7766G3eWTeC2zq3olQ0MHvhKpYv8uTA7m0cPbSbH7Z2J8bHxBefL6S8agJDR09g08ZPGO6YxfSQa/FucbB3zbsYjEZiopLJajSQ5FvkJGrAjuwgSksKZTnn8kVzCIuIJiE5leqaajb+tIRrF0xBq9jZvo/i3FaUnOPYkUzsLV8TH9vC4d0JoHLH3/AjRRWJJPcYRUhEFDfOOErn2CrWZYczMX8MBk0r538Ko+rkd7JksrGulmyTOxEetU5FFjhQ3pH8U4toqq/l+MG9dOzei9bqCpll1rFbL5QKBRXlZTIoO6F7LwweXiy4eTleKhEjDd/0/5moWdV4BwRz6sAuFpVOplP0QekMKRwLvz6Rgkpzgp0rFxIWm0hUUhemPfwC8199HIVyOdXxj/HANAf9fQs4Uh/I+oXdiEhopqasiIqCc5zPPkZMak8Ri0V4QmcCIzvgFRSBzmDAt3U1eoWdAt/ROBRK1n/5JstuDGSsZ7lUgbc0+eJQDcVm28vRjUvpMX4GDRUl7FmcxC0Tx+Lt1kRhpR/2TA+8FAHklG7AJyoRg08AugVFVHaNc/ZLnTFj2avjTFIUqlYIOGnBpnVe8PAMjaLVYsXo7ofCUsXVPZbRLSBXPhcTBAsWJvL0zw9hVyjRlxVy+pN3SKt7DounEr9KO+5Hy6h31EiCFpw+goihM1BpddSeOYghJB6Nhw8qd18Mah2o9ah0Btyju2ATUrfKDZXeQsLN71FzdBPNxTl4+Rp49q4WfGyn+WLRJk7nj+SbVG/UWhvNNXoMxWo8vcPx9R5AbdEBvFKGk/Xzy4SHDcUzOxjziBQRl4ixxIGusoUWU718L2qjJ2q9EbXRB4/EAbTkZbJ7z0uoPYNQ2ptY/bI3Q3rFUW/5jpvmjmd7lomyZc/z0rP9GZDkLC+N9DKzfHkA4x7vgS2zlAZzCQc+/4GcjWnoY9wIiNATWGHCUFhDfVgRuZl76DF4NFFBwfJikrv+8muagqi5bPZd+NPCVe7owm+Eq9zxt8OlpP1FcPTo0cv6KG654Vp5L64GCwXLbLVz4uQJdu7rSKp/mZxEd4s9wG3j+jOmX2cOFv0ol1cqD9JrwBCeevkdWs0W4tLHkTLKDUtCBnnLKinMWsZ3PX155f1E7Chp/KKQnMItsnzv+rQteHh60dS4nWF2G0njryPIL5ayvT9x6uRBXpz9qZRXrr35Ll5/9lF2PuQsd1MolQwfPZ6pM25hypiBRHeoYPZ70TS1OJgy7nFOHDnEtBtvo6a6isryQhrMXbFozIKv8XVRLN8+uppJ11wvbesDg0Oor6th6+Z1khT6+vnLfRNKl+grC42IxsPLB61WS11tNVaLFf/AIKk4rV+1jM5p3Zk/51Pmf/kRChrYtvZ1bMokevYdwjdffID2/cd54+FWhvoVklPvw619dhObkEh5SRHz9w5m/Jjz0lJf/P4tOBGNh4dF9sWJ8sFvP/+AwY8/wJ0pq9o/mI9O1RPa5T3qGxVsOp5OX3engvfeI55MvnsRY6+cI9W01StmS6VNIK1HH2kOkrVnO2/PvwfT9K10MVaztSSC127bRWKXrsyafgXzVvjTJdjC/pNWFnyZLAnp0dwwytOdSqdAbqsWm03FxOszmZHiVG+mDz4sCZ+IVrA5zjD5rnnc8lgEV0Rd2H7oIRreriZzeyoa1S7Onz3FkMnXUnTuNI9vGc2zAzcQqW1iY1UE+z6NlK8pLy8lsmOyLHmsKi8jpe9QLBazLDENiYmnub4Wuymf978IxPvCvikukEhvn3oMQf2o3bWZDcvSKBlrpJt3CTtKo6n4PEQqWNK5LzZRqnLJvQZx0wvvs3j2cxR88xl7eZRtwanoyiB8Ty3bTh8ibdBo3Ny9JEHz9AuU2WnhSWnSwVFgRPAs0vycsQPn6r7lmV23U34uhxe3PMrK5DwZIn3wdDS+RTZ8Q6OpqyhF7e5FYHxndsx5H0/FS1T6KdHVKvDbU0kh5bj7BcurhcOmT6a3/xKs9iR2rutDUV4mxyu20CXkCvz9OmK21ZLZ8AtG/1BCUgei9QqgqaaM5spiAr2PXlaOnBxczjTfB1lY8gT+nXpxZvWX5G9bRIr3UOwNTRR4VtNwtojo4dfj23kwKoM3ajd3PGLSZMadw6HEYTGh8QqSwdQ2UxMqNxFjoMNUWYiloQqtL3jEdEWhVLHwb1tJC3Ca2gxPz6f3iO+hYBItUUbcm6GbrZRnv12H1mjm6bfd2K6ZgpD/Cg79DOet+Ocm0Sl2IppGG7aqevK9ilGoNLgFRmO3mFCY68FUjf+wu6G5kobTu5gxwcyQXsVymx5qKz/cshTLzfD0l95o1Jk4HF7tx8NDZcHvqhAU6iQ6/pJHQUZnsvfNxzcmibzVX9FU4SR0wsQmJaM/fYaORqVQYNSqMOgu/lzuPXCInulpLgXNBRdccMGFfxlcJO0vgpSUFHm7FAsWLZFXy0TZlpj2ms1mOvsJauOc5IkSsL7+eeT5puBW6kXHiFRsnib2bNvELdeMka6HUbHxeFliOTR3N1qlim49+/PS03kMDyuVk8XRHxZzTd8HOf7BCtnTJMr0NGo1FRVlssfqfM1yEhI7895XC/EPCGL96uV8+OYLrN0ST0Z4C5UWDWMml5Nz6jgrf1yIgyp+nheImmY5Sc/cl0BY5HfExCVQWlzI4n396eZe6ix1dECstQxzq1k+fz7vrCRkomSyS9cMWVopyFlFWTG5p0/Rq/9gWVIpVDMvH19pLrJr62aZhSYmbWk9erFm2WI+fedVnlx4Jc8MOIJGAVXmFnoO/k6WYe5d8SaaqtFs1SSwfdsq2dc2bMwE3n/tOXqurGd849UM7pbD+exgltz9CXc8+KQ071i5aJ4kJcpERbsluTwPomRUYyVt8Exi3ZwKV9tzV88MZf3qrQwaMZ7RE6dJ+3qjIMH1dUR2iGf7pjWYVy5j5eErWK3VcPrMSpqa6jm0bwcL9nRjWKDThn5MFwWlU4+wbZ0Gw8/V3BAzmjuS9mOyq/no3Qzs9uOMTGpqn/yL99zmnid25ZkXg/HzuhCefOG53pOrePWt2bh7+XDN3Y+T2ncQvyxbgGFeIU/lTMTmBj77TTTV/YJfcBihMfG4+/hSVngeN6OHdBY9tmebVL7EOTu8dSkf3b9MmnwItG2/3q7A0ziA7EN7CItLomn1SnJKJ3DUpxPuea1E6M3srK0mJDqOwLAISbaMPv5EdUrjqdk3MDplBRbbXN5/IZ365lCOtGzC1NRI5/4jULsZsTQ1UFVWIl0ljV6+MqjaZjOT6nsxFy7Gq4Xs9U+g1ulRzDvJkb7dsGvAPc+BZ1UltWWFhHfuQXNtFXG9h7P3h88pXjeXLkFj0SrdqPQr5/Qvm+g59W7sjbkseGILeqUPCkUpK7t8x6SrC2U546Hi5air3LC2tqD39qffXa/JkGdTfZW8t9usrKsIJsOzXIZnCxiVDm6cqCb60Cs8v/tVoodOo3zfMrblZSI5vUJBUPpIAjMmofXwxtLSgKW5XhIurcGLVkH+SnPRB8c5j/e5IxgiklGo1E5bfLVOui46rGaMEckk+i9uH5+CxPeZWM6HL/+NlOjJ+HvG8fayVXi5Oa8Wff6ChkEfNGFJ7I9X6mgaz+6ncucCdh79CENYJ1othZhzCgkYfBsOqwU3Tz+UBm9QREtzGxTBuEWkEJ66WQQ/XPbZEGXOL9zixvWrhnCrY7f8LhPa4/5mb1ptahS+Shyh/gRX2Dmt1qAwN9NDHP+aEvSOFqkw+/v6olepJEn7tYp26tRJSdJccOHPCle5owu/eQxd0g7ze6zrrwgXSfsLQzoSCidH4eJncxAYHC4fv/RK/ACPcq5e8zOl1giePZlE+XtBGE4dpLaygsEDJ3HqbBb7d24lJDySksLzkswMSnU6M4p1CHOHMcOO4bX5KhateV2W5/n6+snSw9YnX2TrhjUkpaRKgibs3oXD4nMfJdI/slGuI1Rr4fsfwkiNPypNP257fzRaRVb7ewhQW4mM7iDJnXBdDPe5WFIo5vHhQeK9WenVf4g0+RAW/UJxm/flh2z/ZQNnsk/g7evN8NGT6TNgKMWF5zlx9CApXXugVKnkvon3JNS0stISli2eR9/+E7m1z4n2D4+f1s6Dz7hz+uRUWltb2bR2JVaLmclTZzJx6vVUlJVxyz2P8uUHb2Dc6kGum4GqijK69+rHsLGTMLh7kNA5Va6rYZ8v+/ob6WVwZpxlNblTXGKlY5oXJ03uhLjXtZcKrluvoKayEm+/ADJ3bZEkOzg8kqGjJkjDh6fe+IRvPnqL1WtflOuK6pBA70Ej2PXLeqI8Gi7mSQn1K90bja6CnVvnMFZ7H+8rx+Bts3Jw22yUOh1VNg2Bqtb211xKIusURsqaFUR7VbXv265CEb1wiHtf+xhN8zK6h27j4SdTePPlOaS1DiEyMJ6T5/aQn3OEUTNnyXLBxro6zh7NomO33qC0Eh6fjIdvAJZWE17hv8hw8EvP7UGTG2+e6E2ot57DzRsZdOVMFrzxBJGWZpLi+lFVV8DSzPXEd+0p1eLG2hr83M4RZXuBlqBkxvReiVq+Aytvf7yXgKgc/EIjmPLIy4TFd8JmNlNbWkRddTVhCcm0tjSTe2gvraYmWvqDs0POaToRPOYaMnd9wcnd3/P1TWfo1dXCgcxQnj5Sjd1qIWXElRSdPEhATBKTnv2CVW/cz8Z9bzmPoVJFyqhpJA6aSGrQV7hd0tc5qo8bRr8Q4gdPlr1nzTVl0ho/us9Y6otzqcg5SGDXwVhF6W5kJ5auzUR7dRJXeJ+jl5vTNl4gPUXLwVue5ofvYxmZEIDJGsRdn3XjpGMSajcDzRVCsVJK4lWTcwBDaAIqqwWHw47K6C0VNhEqLdweRcyAKDu02GzoPQKwNddjt7ZKta3QqiZe6zRFEd2alV2HAN9yNG8pGq9A3N0uqlriLs74BXtWnMcndST+va7CKz6DmqzVUJ9P3yHBVGhHUec/DJ0gkPWVWJtqZBSA2GZTXhbGyBQWH+jNDb02EXBJbEHb949XsoOrfpjANcP2U2bTs7akE82lRrzE14TNjt1hls6jOrUCd4MBo2cMPm5qvHUa1AolaqUCo079dyTNBRdccMEFF/7VcP3y/EXR0OAsXROT1zaEhIZgEn4Rl+QVJbs5FRI/VStPJ+2g/i0vBvhFy1LCbyoq+OL1axkz/COuvsWLQ1Vj+O4xHRXmUwTrrHISJiaw55q9qK939pYoVUoO7NkuCYper5cGFmLyp3czyHI74Z446xVhLuEkaQIeGrPs6Tp17AinTwbTNMKp8gmISaGbQS8JpzCGWHI+gW5JB+SVdPHOlubEolQekeHPTY0NUj1btmg+X3/6Hi/MH84jQyzyQ/DTkV3cc/NUXv/wa3kV3d3dA5VaIwmaTquTDo5Wm5Xz584SFj0Kq+PyLDSVhyeFBXnc/cizTJ42k6x9OxkycgJqjUZa1yd36Ua/QSPYtG6ltPwXwdDiLbh7eOHh6U3XHr2lrb1t73pmfTqJvqNOQ4uCDY+cRKd3oyTvFx5ZMp7HJ2wmStfIyuOJzP/oeyZN68V9MyfwwYpkrkhspKQW7rj9UwaOvoW0jL68+eViTh0/zPxPZ9NtYBl335TL2TPhbKkJIyXkjFO1AyKMLXw2J4CnnlCxYs2b7ePC3dMLXx8/Hjs4iNe7bsFXZWFPgy+JhjoiNBaKrRpeeLc7DZ0DaBq5iRhdA5sqojg6x0nUa4rf5IM7nWNtWAIYPCN4/60T5JzMJDy2I7e/+D46d09p1CLcO9MGjKSpvkYSgeDoWKpKCsncvJqWjpFYHTXtKqPY53htK+Uf+dNiP47Ry4fYtHTuefdbVn7+Nhs2foG7ty/9xl/DoKtukCHc9vod3Jjy5oV1nLns/HlrHDKPS1ju9x57FVWlJVIt8g+PwegbJMmi6FMLT+7Oinf/xpzro5nu78x8W9cQRFFhFL6hMTz/jIbrxtTKdcaPO82pihbWZV6HT0gUBi9fNAYvVDo917z2HdWFeTRUlWIztxLTcxg6Lz+KtJ7SQKXtoqFWCRPH+/L9vM8Z/uQXeEfGSfIoFC+tTzC+fuG0VJeL+jyUbl7kv7uAHyKforGLG91isuTnQByvXKueQT1KGJlwTq5Xp7Lz5i1ZDH2tJ8agaFprK3ALikGp0WGMSpHERWX0xVZfgVtwPOb6Svk6jWcACrOJlpLTaL2CsLc203j+KDrfMFCoePVcD+6KysKosLKlMYTD1fH06/oY56q/YOdPHlJda0OFDQqTBhEYcZqS3Ysx15bglTSQqMFX8dMd7xPqIS5IbOW1HSoW7BmCtaYAs8mEUpmA3tMHVYdUlAo7rVYN09bcyxW9ljPKq4hEnUV+9g+btOQ1+VJUHcRXj4ygtoMZq5cBrzIlnmebcZSWU+B1Ro71yMQueGnteOnUeOu1aJVKNColKknSVFLNFZhx7VR5/+13C/+3X70uuPDHgKsnzYXfCFeY9W+Hi6T9RVFe7SRNAmI+LtQ0hULNl0uCueeaUjlJrLYp8VHa2698h6gtdPWvkH+LUsIpfudpvieT+6K9cDhM9A87jee8CG69L5m338vGXWVhY30wv6xOpbrgazL6DiQyqgPeXsJMwyrJSaupjKDQCPLPnaa2uhI3g5Hv18UwfGYJRqXTce+nykiqKk4QEhbO7q8XcnffK7i9cxbNNjWz1wwi+8SbPPTMK7zz0tMcfcfIHbP6MTA8j135kSy9YS29+g1GLcqzQJY3zvloNumDJnD/4BPtk9gJqTZeUx/i2KEDDB55BTWVFdJYxMfPn/LyMhbN/VzOnMVkrSR3GcWmUELVddLBMNes5cEZRxkxJhmj0R2TWk3fwSOprCiTboWiD0/Y3Dc2NdKz7yB69B0ky7XKRRmdXi/LNMtLirnvyZd4+Yl70WQfxL4piZyTR2UZ2413PcTn771KpwUaFm8djFXRxJbd30kyuXb5Qh5aOpIbOx+X7y/AH778UsEd92yg15DRMsds9Q/z6TBWzZxZehRYSA9QkVVdwTP5qcwIPkVHfSuBGhvXDlBT/biFteuuJaFLN5oa6vH09uHdp+/D/rUX1xy8Tp54j22N1A8zovM34TioJXfuO7gZ/Hjt2I3YjEoCDjeRX7pS9vFNma68rAxy6tXevPD4cabe/zQ9R01Ea1rH6ORP5WT7tc89SEgZQFrYNo5k+5FTPIO6ynLOZx+n8rSSzwbEcGfguXZTFk+VnU/fWkJlrZ0X30oi79hBOvUcwLSHX5Sh1h06pXHu+CEqCvMI997O9RnzLyMJIj+u7f/5VjW9e0xjz+bvGXb9nbQ0NdFYV01YfLLshyvJPUWHtF40NzUxML2Y2wOdZZ/7Gz15dvMoAk/b8AuJpP/gKhwI0xfn+x17dQAvPfWVJPxdr5iOWquTJijnD+0itHMGYV1601xTIRVtS0sTP68z8UiKAv8LMRZiHdPu8GbBN3YOLfmIrtfch0OpRql3x9rcgMYolCkFJYe34RXTBb27L7rv89lWm8y3PiX0MlRQa9Pyxtk+9L+mUBTqtb9/rdrB0U/vxyuuG4kzXsTSWAsaPWqDJwqFClNlAUqtHktTDbaWBkxVRRgilGCzyX40rW8Ear0WY1iiVOHU7n78sjiAs1eMxKC1UFTtja1QrM/KnNfjCPevaf++ybWo+aQ8mfOVgcSF+GE0BHEm62sC0scxNu04oR4XL9Lc3ucXnr9jOfbmZoyRnXBzN2DTqtEZPbE018pg7BpdOnPyr+Zjv1Km9MvCXdfKnuoozp8PQt+oQG12EDI/n00N83E4bHiHx9PaVElLcTXJQyYQFOBLgEGLTqWSJY5CQdOLPjStSt4LjBo+RB5rcUFIYOXKlYwfP/5f8wXtggsuuODCXx4ukvYXRYfIUHZc+LuxqVna7u/btV2qUc88rcDdXUlYiJrdayKl8iAuqh1v8SbYwznREhCTyP7+F5wDL0woe3iWMTtxOMkxS3CPjsddUUN9+WuSlDz+0tvU1lTJEGpLayuHDuwhKCSM8tIS2Tem07kx/sppfPnc20z3n8Ww9BOcrAkgb04IJUXreOa19/nwzRdZMXMp51OH41BY2bXzPVK6pjN89ARJxIThSMD+YLLCIjhxdAE+vn48/eq70mpfvL/wqGjyck8TOmY6Spx2+G2IiPflcOY+eg8cSkVlOSsWz5P29D9+P5ewaDP3PxaGu2c4zz5oJFDlNPCosynplV6D0qHj9vsfo76uFpvdRkBQiMxF27VlI4mdU6WDY2JKGlqNhoqyEknc2hVNhUIe9249+/Htyq0s+34u1ZXlxHdKJa5jEj36DSU0qgOL5nzE0p9fR6vT03fIKGlusuGnJSRHVF9Wrx3i6WDfto2yN60wP5ddm9fy1dtDUCmc0QQCCd71rLrCwTW/KC+z6O/b35enH/6JGfc/SdbOLUSEfk5OTiLZ5xZx162t2KwaThTn4rHVl4H9rqHcdI4GaxUN9VWo139JYGgkB3KO0trcJLPETpu8GeBd176NvFYjbu4eVBSfJztrD2/OmNMe2vzOfTWoWSH/Hp5Yw9urnmb7zmTC4hI59NW7fP7Vc0x9LE8Gorch3sdOnA98934V8d3exjcohIj4Tnj7+cvEuLjOadRWljM15dXLCJrYnzNmLaVWPSa7itezBuCv1cow79amBnz9g/H2D8QnIAhsVpmdZ25ppqGimNeeUKC+4B3aw72eK5Un2Z2tpqj+CPvqu9DZw9kfJZbIbAogY+AM9vzwGW6e3sT1Hombl6/sURMlsW3NdW7e/lTlZVOydgdHHvZmqFSRnOs4ZPfDXedH+clMNG7uGAIjMdVV0ViSj85iRa1zwze+u7zQEtl/EseXfkDoqXTeCx3HgqBaGk16arO9waLhmn7ZRGpESDlsbPSj24gnOLz1Pc4ue4eo0bdRn7MPzw5d6R9zlIkD1pHX0IHPDs2gxW7D6JaI2t0XS10FvimDsDTVYjE1SOMSo62IWb0/Q20q5ONtPcj1ikDZDJ75oKprxT+stT2AXKDM4saqE6koCrT4BtQS0tTKWZWG1oqztDjcL/tcmh0w4YMpjGk6yJ6sfL6c8zdCBl6HT5ch6D280CVmoLCaUFiNlMxZz6dvVhE8eiL2CG8MNSo88qxozlVSEy0clOx4R8RLN1j3yHgSu/cmKiZGqmbCSVSvVqLXKHHTqdGqFDK4ug0/b9gsHR3FWJg0fuw//6Xrggt/MFyISHXBhf8VxMVKcfs9oHT1pLnwV0Jb+Y6w7/74/bcpKsgn2CMWG3Y0Bl/cVQGcP5FJ6uA87nw6hhwiWfOlje/frCU9wjnJW18fQMm5ILp1PSYn+OJDJMwmNt/9LeZZnXhpcUfmPvUzNruV1z6ai6e3t+w5S+/VX25bXJEWkyXhuKhUKmQpoigz3L39F1bd9g6ZYRG0NDuDk2fefi/de/Xl/TmL+OnHBezcsgl3Dw+ZISYyz0R22PAxkyQBXPLd15LA3PPIMwwcNlra69fX1siSSbVKI0sQvQsbWVsfyESvcjlxPGXSseL7PEaNr+DLD96STovjr/GkVx93bL4d+ezuanRiwcH6C0WcTnir7Xgby5l854uEhEVRX1fNkrlzOZt9nOrKCsKjOhAeHSPjATRaHUq9G54+vhSfz+P0qeMMGDYavZuIQRB9b8FotVVSTTyTfRwv7wL8fQx8/NpSZs56lcdf/YiTR7Lo3megXPbzd16UZaI/5XdkvE9Je0j4lpJgrNZTnM0+wfn8s7Kca1dFKNMDz8urMuLU5zS7cyzvR7bVjqRzUK5TnRKleyeiaazPYtFn7xDVfR/3DG+WZKFDioKFS4yMGXaGmI7JMi9t1eZPCAqPYsTVMwmJ7kD2of1UlhSSPnAEfcdM4tW7ruXjpf0IuraRLoZa8luNPDNvOD4BVRTlnuHkgQ18eIOh/VjKEtVLCP9dY5TcNeYk+wpP89PnNrI/fIFHx0/l8YT9GBU2QjXOHiSx3z4qO+HRNr5+/kEe/mQRlcXnpdmH1Wzi+OZXcRt1+fjPt6i5b/dw8vNDUFjAb68Fk7ZQOonWF2bi7TcSByoO/bKGjj36SadSUYIZENHh734snh6/j5dKGtn2Uh3PvmNE+3QISfpajrb48Elmb0LqW1BrdLTUVaNS2KWqJt6kZ0AIpaePotC6yTw+g7cf3uGxvLg7Fkff3YRqTBxs9mPh8W4kB3ix5/w8Cg5sJm7EtdIkxG63yb5J4bYoLnrovfzxS8wgOG0wxYe3oHw/CMfIQbw4YiMjpuVTVKLnlfMZdPEuotrqxvLTqWi8PYmKHcG5I6uIHHkL7lEpJIcV89aY+fK99aOCYN9iHll0AzrfUJpLTqNy85QErSH3EG4hcbJP7Nub3yXCo1ESsRFd1zBqxnjUKm80lS3YSsuYd9SPl/o3y2iERgesro5CddKNW2IOcv9d++Q5vyc7gqmv2jh8LpbsVi1RGjMmoXaiYOHgn+X+zBynZ+R1iVw9dAGlOxZLJTGkz0T0fqG4+YWReNX9nFv9OSfnviYHkVCijW4B6AODqD52QjpiZkyciUOlxUsvlDJnSaNBrSLC2w2d2qmcic/Mpbk8Hh4e8oJKWzaaIGsuuPCnhrxK9DuxtN9rPS78oeAqd/ztcClpf2Hs2rGDxYsXYrbX8fSmMUR71pPiUUONtYX5Re7s3nU7AUvqeeT6D2glh649+/Hsa+nkl6zDf8Rgmk/5cnzlfLQ/JDGwcyt5rQZGeNS0T21em3aMw2WPcPKrD9m1ZQM33PkAqd0y0Oq0eHn7Sq3B29tXlvsJpa2uthaN3o0p19/CtBtuk1loQpWaOOU6PL28OJy5n+49+3H7/U9yzczbZRaYweguFStTczM6N2ePW5duGdLoo7qinP27t+Pr709gSBgVZaWolCqGjZ7Azq1fY3rrYX6clIunupUTH4uyzhziE5N55+Wn+XjrKG6LF26QwmZeTK4vHjcRESX4kPjZEVlcBUVWuZ/lpcVsWruCz999hSe/G8g9/aqwOaqY9dDPDBr9MQ0NddJlUpR3xsQn4e7pQ3NTs3zfVotFKmynTx5j3ufv8tKaoTzc2SYJQYu9jF6jJ/Lgi0vp1ru/JNgiRiC1Rx++++xdzr6t57ZHBzApIpvsKn9WPNJKUFgE8V264SEVJcj6NJh1L/rTy6OaMrOOAG0LFec7siBLw+u2RKmAbimKZtPLFkkiMnds5pYngkC4aF4gTb0ibFScimHWa5XUmK5n6KTpaHQ6WltapPlJx5hsenaxcOL0cRYsbCA8NoGGRT/xVP1VBCXV8Fj3XTzbfy0zPjtLzz4BPPOIO82CnFwgl1U20ft4kagJOizuB0ZaeX9lN56/IYubvY5htWp4N6cLT6dk4qG8aGKyeakv63ea8PGfxXuLPPCPHoNPQCDP/+2S7IkL5+/qzRPQLzbifWQfKoWB8K5hbDn6PedzEgg1LqHZ/iN3vh6EWTMcq9VGSe5xRqbvx1t/lvU1QUzwK+VCEoBUAl+8w53EDmpqqrJ4d/WdtAYpsDRqMB5WQ0O1jEkQb8wzMIz6imKaaqpl35eATqeVBK2lroZOI6aw4cnnePD1mzB6mairMaA+bkBnd0Pv5Ud5dhZRfcfh5hsk+9AUWiMtFQVYzGa5n1qjJ0nTHsE/pR/H5r3IC5PNTOqYJ7cTH9HEzZUnmXFgIjarCnW+hsDaVvyNMeTarJhqyvCO7crQTgsvU2bTwoo4+s4M3COT8e48kOAeY0CpwUv0hKnVKNRaSdDaegWNaoga/gtLnjqBl188LepWdswqxbF2BAM9C0l1a+Bq7wJydBXcOWZ/+7b6dFRx//Td3DfsF4SHSb1NQYNNQ7jGfNn+9Ituosvb91Pz/C9UWAo4t/IDQjLG4D/uNmrOHiZu7C0kjJhOceYGmkQkR/E5WpuriBswnsjkbqDSEuiulrb6osxUELQgox4vgyhe/hUuyH+TJ1whCZqofBRRJiIfzRVg7YILLrjgwr8SLpL2F8WNN9/K3DlfEh8Vza4dAfiqT120VddD34R9rAw5y7N+Y+j2xvUcOP8N3XoPJCQ8iiE+k9mwYhElp3cxYOQ4Pnz6LHcdOkaP7m6MWuV0iBQQyo5Hz3o670/n+JEsvv74HWmakdQ5ld4DBmM0elBXWyOdCA/u3y1VLxFWndFnIO6eHjJQWjzv7euHf0AwgcFFvPfas+zZvhmFUsHEa65n3JXTiYjuQElTE4cP7CWlW4bcdlVFBTa7nU6pXTGbLdI0RJA6o7sH/QaPkGHUm796kpCfI6iprpT78Mgzr7F2xQ+kdh3AyJjz7e9D+asLgflmDS0OYSYAH63thlJ1nsioWJqbm2R8wLBnb+Nvgza3f7gWvOdNQp9H+GTBGtwMBtnXJAxDBETPmHB6FL1wHTun8dLjswjvGs5dnZzqVhsJeOhJX9at/IE7H32ehtpa1q36gZ8WfsPTc9K4fuBmSisUPHl1GM36ag5mbWPslJk0NzYQGhktyyPvuG83V/hUyvfh59bS/l5mZWRz3d1prK0exsQZm1i/sgabowN3Pl7BxpKejPSpkGWCbZNkcSxefNiH4Mh3sDfsxuDuRa0pBm/33Xz9N2cswqBYM0FRedx2dTl11ZVE2xpY9ggYRJliGJw8FIdOUYn7hbJFUc62qi4AhUnNpOCS9tLLtvcv9jk1wcHuXR2JcXf2Ur6amsl7ebE8GHPWOWYV4KuyM3WAmGhbGPx+NTWW7yhqUNPZ1xn83IYGO2gX6sjf9C1F1tNotQpuvTKCt94JIsTgJHRuCgdPPVDNM4+8SfHWtdw4zY1eoQVyX0RH0hPnuvBGzJF2VU3cXTdC0Eo9E00LmX7vNBTYMZwuhQRoPdNIcFyytOEXKzHVVhAYmyyVO4O3Pw2VJTjMrXiFROGoqcX8US7KjFTEqfLMbqKVOkz11QQmdAWbGZvVIlUim7mF5uoyaU3v5hMg1yvcD33ju+Kf3IfYdNGHdhHBXs14LLeh8NKjq7KiP1VKYUC5JFrCNMRSX86AiMtNVfIselInPs2prXMo274In4QMlFo39N4BNOQfY2LG6fZlxfFpdkBjagaBPgqKKjNR6Y2EDpnJ6vnw6mNH0CocUiWbc+syqUlfqp5OHeS8MCE/GyoHOoXwiLwIsUy1TYU2zI7f+MnE/XyW7MBsig6so8PAifL40VJHS205XUZMoankrMzG83IT6hi4aZSYbQ7ctc6+M2+dDk+dhgAv3YUNOE+mVNF+pZi2hVcLouYiaC782eGy4HfhN48hlwX/b4aLpP0FcfpcAcuXLuGat6/ihjFF+Kqdzm0Cl07GBxurmNuhhOqoYNyqjJSVFjN84jX4B4UQGZvA2eOHSe7aA29ff7av/4lP336OMouKII1zoptt1lJ23oei44cYMbyVO+5u5dRZLXfesEha7r/+0dwLRKxaEjRR1igIhXBUFFb5YpZUeD4fLx8/SfLuvWkKn/3QgaUfemJxwP2vf8djd2/g1Q++xmAw0rPfEGprq8jcI5wVx8ncMWHkIWzEhbW+j68/jY0NRETF8PnCVRzct4t9u7YREhZBl649CImI5Iv336B72jgqLPVEqYUFufOYHGg2EqZtocaq5a6Hgmk4G4TR0MKuPd9y/a330Cm1G5m7t8vyzJB0szSVaIMok6wsy+f8uTOk9x6A0d2TgryzkiiKPjS/gCDpxLhswRyqa6rYeSAAo/Li5FRsv87sKcOpRaab2Wzl5x8XMOTxWJ4dlS3nkrGR8PHi83RPO8OsJ1+h97AxUnHLO3MKc6uJ/pE17SWpl0YsCCT33Mv+1Tu5b5RRnncxU/36DT+6Xa/F7aU0rg89QZLu4v7IUtddnRgaI3rcqthcUEw9bigVzvgDsf4+cRYaG2p59d1o7rvKIV0K2+B3wRSjDYJWlVZ4M/UC4ZLj75IsNoEAjYkwrbX9daJTSGlV0GBX4K5wrk/xq2MerHMQpLVI0qC95D17qyB11BH2rDstc9x2bA2hc6CgXpdb/EdozSz8IFgYyWO+4O4u1iH2N8VQy/5md3oaLxpctK0/Qm/lmzcX4O9mY/kuNTdde4rkgWMlWXAT6o3eG0NKD0zVpdIR0S76qRRKys6eICy5B+lT7mLX3NdJqq3Cxx6OiQZyyELv4UNkxjAMfiEUZv6C1jcU9+AoPIJjUBm8MNWUYzU1tYeaJ097hK3NKxhv3ynVSvHoziZ/rAWF1Kzbj4fWl5K4IM4dXEJw+mj0nn50iSwg0nCRxIvP2cvnemIP8yTVfSj7i76nZNv3hA6chrm+iiS/bJ4fvf6yY7CiNpCcsjBi0sMJbOzHwV3vYQwM5dUhS9Fd0k8oSPo3ZSFcE1gif4hOmjWo7RpCNJaL5/GScSPI/FmzkrmVnaipM2LQKVAEBeB/9hyFdhslB7cQmNQDnV6DysOD6vxs/IxqdG5q2WsmxpNWrcRDjAG9Fo1SiZebBm+jRvajBQQEUFHhNEYSHypByn5c/hONjRfPsSBngqi54IILLrjgwr8aLpL2F8TCBd9x52OevDDt8GX9Nb8OCxSTumaTDkvFednTldA5jeamRsqKC6VdfHznrtIevrK8hMTOXZl+83106v033lrVC4tSwbxD6QSsOk9E7xbmvR2MAzv9OpgIOdCXGcNOsHjel7L8T+R7efv4SYK2fs1yQsOjiBVlgSHC1lshbe9ff/YRuo8NZmqqMz9MzN3eecyI+/uH2Lv9F0IjImWpoujREploQp0SvWfunt401NbLPLRBw6+gsaGegKBQWfYoyiJ7DxhCS0uLVPJEMHdcYifOFx7g8a3DeGfwOoLVrWQ1+PHw5FAMSXHUHtvK2dMb5D6IUOyZdzzA9BvvlMYf9fVO6/XjhyIp6KIi4gJZPdHshsnkkGpZVId457G1O6gsL+W1p++XvXldM/rw4/wvibt6BkHqHZedi3OtOp66K4euvQZg8PAiN2sfp08c4Yl0/8vOX5S3DavVQlh0LMcy9+Dh5c0HLz7AHQ+FSHKp1JnbiVobxMT1zul6amJjUV1iSy+IyNkt81h3+0zWT76KeTcuIUHr7BH6+HQyL3Q60r7s4HATBxrUl2WoiZsoIXv46ovGC5eSw3o7eF4yAb8z7jRlVqfa8Y9gQckpk4FUg7NHTuzH1vx4dpTFcH/XvZgdClL19fhekqV2cVsKdCoHbU+Ju4YIUW4LCSnd6BR4/u/Gvlh/WxmmQFuAd5sr9almH977Jo3rZh6X5bLX+RY6exZl6poIuHYqQjMHWzn4Xme+eTGL+PT+kkBZWmpx8wlBa6tkVvfHCHVvpqjVgxdb7kNtcKdjnxGUnjxATtZGTgpzEaEqhUQx9IG3pUmIUM68wmJBq5dlgQI2swlLUx3NQqFLTKelsR6dpx97tnTk+bAG+noXkN/qwdxTPdHFemI5uInjLYdRHNYQ0nM8Ha64HYVaTas177JzIEjamXNhuNeCsUmF3j8M7Fb0Rnc5lj64ZVu722YbzjT64TinB4UZ3bECFGoNnY27GN/TGcXQhiKbgq9ye7CtsZwgQwPHa0PR1ir4tOcGPJQO+Rm/9GLH+gZv3snrQ3GZH+Tp0dU4ULRaMJmdJj5qtQqVzYTKakWjUGBwU+NrcP7EuWtEZ5sCrUqJUSNsXxx4uKnxdHMSNIF2gnaJA+6VE8dJUnZpeaNLRXPhLwGXBb8LvxHOXvffx/FD8Rc9Gy6S9hfDwsVL+fzTj8na7obiUuXgwgRaKAZC9RBXreeXdaDggzrOnVqAb4BwuwvizMmjdOnRV+YVGT08aW5poqaigorSIq64+nqpEN3S9SO0Wh12+xZZRrh4U4qY9rarI138KxnU6zrW/PyZ7FM7dyabbhl95X7EdewkVYWGxgaqqip5/pG7SOqczbzvAjCpPIWfYvs+iz6x2I6dOHpoPz37DUKt1mJqaZHmIcKlb9vGn+k3dIQkgDabnWlj+xMaVICPt4Zmaz969R/KqPFXyuXF9kUp4oQp1/PEPTcRMq8DD6ybQou9heOrPqaxZbd0LxeW/Hc+9LQ0KQkIDpHlmw21NRw7clAqecJyP3h7IdN8JnND3320mLR896iNwOBKBgwdI/dRZI+98uR95BTtY9PPkcToTRwr/ZEdh3V8++LBy8r8hKYwfaJZlmteNfMOdm5aS0lhvny+uslNTqLbzEA0CgdrtnXiuivv5taHX+DVx28h+1A4wZIsNlJoVmNBQZ7JwGBP53EUr/NWOXitj8iLujjZzmx2R6swcuPD2xndT8vpWh8ePNuNylJvXuqy7rKJvFi7xXGRjInJe5VVjbu7syS0DW0lbeJ+eWUIg7wqZdZaW6B2k0NBtlkns9gONHuTYajFX22X5Ykfnu3KviV+/O3effhpTXxXkETFt95k751P7jW3YteqiOxSzPPDf8FdaSVaa5ZGJAJ7m/w5XBnMA9FH5ZjZ0BDA8c0xso8u+/B+QPTeXcSKOh/m5XZlcdrmywhIhU1Bs0PJ1oYgftrbFeO8A3xj6o85VMvxflnMij0kL2wEqex4Ki8GK/cZ6s3aL3xY8c6TTH/pS1rqawnuqOD6jE8JMTrVx1BtAxN7vM1VwwvoPHIq/a67n97T76emspRA90qG9W5l596TnKvV4xeTREttBWoPP+oKz2CtOsl1Y2toMWtYlz+O1sZaroz4mqgoJV/mBrF0cRzb0xO4LuIk12rOsqYhiaikkZQc/5TwAVcTNegqlA1nqCiuxpHYkxZRFnjhPWtFILTVhC5PXLCppKWlAJ+EdOrzjsrwbIPm8nJEgf5epfxUCLriRqoaz+OmsxIS4Ly40jauxTbeLE6lvMKf1mX+2EcWcXviCU7V+jNl+QQSuhXxfPhBAtUXS1VLLUbOnwzFeEqFscyK4WwddbnHyCcHtd5AVHwnVBqNDKbWqBQYtUrc1Cp5E0RMZJ+JzDO1SikVNWGv32ag9GtcagziImUu/BWhsDtvv9e6XPjrweXu+NvhIml/IYgA1iceexiFogx3bcxlz7XNVRoccG3mWGoerOJAzjz5mHCQa25q4Jk7pjL66hlkDBjOvm0bSErtTmBwGAqbnbikFMpLiph6wx2MGHclu35ZL4vai/LPsSP7PJOTTO0T9NxWA60WlSzDE2pPcmo3/AICqampJiQ8kvO5Z9j88wpWLP6O1L6tzH3JF4WkAnXU2pzlagLr6/xpbiyUTmyiV07Y1h87nElddRXzv/qQ5I4l7N7wFNWNnaVStjYrg+HBTnZxui6PYSM/JKPfIHR6gyyxFOYO8Ymdueram9i0diU11UvlsqLn7b47nic8Moau6b0xmVrYvXUj1dWV0l7fK9oXnZtRujJed8s9fP3x23QsSGX1ulQOHt5JcWEej74wm+i4jtRWV7Fk/hfs2rKezTk9SHarlcckI8TClrmeeCub2onOviZPPlzYiX2H5sljHRYVg0av570XHmXdiQyG+TgDtQWxFoREr4SR8RaefcuXJ2Y9yqwFQwjWnGw/x2EaZ7lgQYOCRrsoN7t47kUV2qXz1dmfZvDg+ybuHmHF4TDTwa0M7Pt54ZFGRu40yWXaiNriqhDW742h+5hdiM4ecaYWFSVSVlhGsUVF6AVF0X5hcr6+LoB3Fg6nqf9u7kw7LdcjvsyLzAYeemECLXFa3M7ZsXuZiBpaSVGlD5rv1WRveItXTt2OIyYQw7kWNBWHqK7PxbFkNh4qXzZ/VkLRtY9iDtLSuVsuU1OPktvixdx1fdB8ncOP98xA69OK6aQnPj+dlIYz4i1vqPZnlJ+z5LfaCm9n9cfxnQ9nP9ATp7/wXoFau5oJa6ZDlQb/XVaqW4s59enT9Em4gT0L3Fl//7Uo3a080Xkb0wILnJ8nO2yoiibUtwPlPjnsW/4tkx58SeYEanXll9nSBwQoiOs8iv0/fCL72bpfdSdR/qU8O3iO7AucGgev/ZzN+j098Y7pgtlSht7Dgy+u+wFfbas8f30i1lNVq2dgbLVc57AHIKl/HqvuisdHb4EYGJdwgqHD9bIfbsyVycye8AR6lZ16q5K7Pz4seyDbxoL4gRiYdJr9dbGcaT4g9XW/jt3xjEiU/WiryqKYGuIMyG5Db/catCm1aFYVMW9LFV3j42iyFVNhVRCgFno67Gz2YOPpTqhz1WR4n+eDoevka8eGFxBfWsRDs8JovWkQs0dtQq9wcNKs5aeyRFRNSjzPW9DtOc3OmoW0OppBoaT/lTcQ6C2URQUGjQrdBUt9oaAZtWo5vgQ5EyRN4FJrfRdccMEFF1z4T4SLpP1FcODgUdasXkVZdSm5JxPQKi5eoW43DFHAebOeqt0+NCnms/9AAqGBKjZUh/DR+4MIKDjPmh++JD45lV4Dh8vGe0G0BETmk3B6O5y5m7T03txy7+NUVZRwNuck986cTIeMUQzxL6TQbOC5DcOpPb9KlviJK9nBYZHS4VGQQUG07r1hAivWBPHq/RoabDqUiovufBaHkidyU6hucaNkXgwlRS+Qmt5Tvk70m1WWlfL6sw9z4mwKHQ0h8jUbcmq5/p4UBgc5J64C8V4WQkNLZVbbSd0hqeCJ/CMRqK3XGxg9cQpxHZMJCY+QkQGVFaVk7d1FaUkhfv6BsqRSoVRRV1ONSScMI5BufVddd4skfGK9h47uJCounlvve4LELl1lCabcn5+WkJCUir/G1D5JF/dG5cVeLXG3+qsWFr/5vXSmvP/Zt7CYLfz4zacYuyfR1+ti5plQPtvOobi/erCCh0wNlFu8LzPgaFv3AL9amu1/35vWtg5RHnhsfg63bfLA4ahpV8DifRsoIQeHI6r9deLMvL+gLyfe+ISqb2cyNPE0+8tDyfsglAbHcq54egzrXt4tJ+eihPDlvFQ2fNod5art3P3KGiKO9aObZyUFFj1Prh9B6eJ5VLUWyrLVupYqar/uSpBXFLWa87TQRNa2d0msSKHW3CoVWEGchXpZUlRAq62F7MWvYHB4USoy2TJmoNIb8M3JYce5+fg+Ek5s0EAUjVUUGc9gNZsJi4zmmtHVzPoujQjPOn480xnLIh+Or3mTWRofVn+skQRYEIuNtWF4rQfd4TM0N+RTrM1FqVKyK+dreSwCn+5MbM9r+NQ2gHMvZtHRo5JfqqPYfjCJELuZ5LTBnDy2TY55T79A5mXF8nLfinZS1FHbTHRaOI1VfTm4aj6JQ69iUvpS2X/XhilDzvD0TSvQe/oy5MF3SUxR4KdrbX8+2a+ZFh+nI6ccGwrYuD4eX7eLfV4dvE3o+3vhWGjnoYHftPeJeartfH3PUUosKsK0tovlji1eWPz12OVREKXGdox6NYa4zny3KZlx0x+XwfNt2xQqvCrKzIhZdXSNd/7EiNLRaquCDysiaLBrWVvUCUueO4Gn7Qwdf6x9fIpx1t2/Ev3E0Rx5dBe9vw8j6dGOVLS6U57nj0eBAm21ibqGAknQDF5+DJp+O5FhIVIpc9eq5b0gaDqNEpVCgZdBg0qlaC9rvBTXT79G9pwtW7n67780XXDhrwxXuaMLvxEuC/7fDhdJ+4vg+PFjHD+6h+KcePzVQh25OKk6bNLSUOXJ+Zom7rmziLqzz3IsM47EEPEtbeXawAIKZu3jp5d6kZSazvafVzD0iiulbf7Jg8LCXEHH5DRpgJHea4BUlEqKzpN3+hTxSZ3pmtGXh3ttJGHgVQR5R1BxaAmF58/x6gdz0Lu5UVlRQe6ZbGJiE3jx8Xv5dFksPcOcCpNRaWsnGjLfy+TO2wOXS6OQhvo6aRDSo88gAoJDcZQWs/T7uUx7/So6Gg63v/dh8RYCU/rQ6tjaPuEV70znEUHm7h3c/egzcqL/zMN3cvjgelZu7kygm4X3P/6RHZsziI1PwtcvkPTe/eV7bmluluWcMuMtKITiovMczTogFcGwyBim3zyLmXc+iE6nl8TswJ4dcvYpYgdET5LeYESlbGVZWQgdI4/L3hsx/T3TqqOT3jnhNtnhq6+z8fDy4ZWP59NqsUgTkP3bN+M3bCQtjmr0/6CBSxyjQLWNJb+k0Sk88+8MOOR7/1W/FRe2f8zkRo1Nx+z1/Thf/jVrTw9hYEaNPFhivXvqAinJzWFzvR+DPavkMVxVG4hieSlNtlq23fIt1dGTocVEeesSbFg5/O03jOZxYq6spaTOC/tiN3JWvU4jzv69GX3OEjvmdlQWJeHFpWQ68iWzUBkUBHgEkVd+kLz6g/J8jxgzHjc3N04cO4pWq2GbqIooAABf70lEQVTKdTfSs3dfeU6Ez4yIWNj480qOHz0iDVz2bX2D4JBQKsrLUKgUVJkLqcx3qsMGkzvTbrxDjtUv3n+dhVN8CUsbhaa6heqjP9LkqGPT8jqm9LmR8aPOc7Q+gHW/pOKflcuO/C/kOrx8/bj2nidorKsh50gWxzN307qnHA1a8u5JwzhuIpgVeJ+2o8uv5LTyIO7evhSfPERoUhq/zGnmeDd3uhoa5XkSROfh+7Yw47MmLLYm6otzqcRwGZGuc6jodeWLnNr9Nds+egL3x57E3NuppLb1+lVYtcRdYjwTbXAa4HDJMgET+hJW0Aet28rLcv9EOaib0t7+/SB6wiaHn+EjYyzh2gRqzOXU5x6R70OUJVsbqzHblRgvKe/c2eRJeYU3utDL1Sqx7i8O9cVUYUTZoMQ7Fwxna9mz3crEkU6lVRyHUosWm68d9259aNq+lJWbPiUoZQgRIT3xqHZgqywi3z8XSpUMu+4OosNC8dCqMYi+M7H/WpUkaOL//79GhnkLFv3Dx9vMQVylji644IILLvy74CJpf1L82oFM9GRdN92Cv9o5O7904nfaZOTZ9ePxm3MWS8kiYpNSCA8Q5VPOmZ1YNMFQTXOUlsCGcLJPHZT27iKcWRhhuOndpKLQVFstnRjraqpkbpPo2SotLsDTrZRXXvEjc/8aFv1QT2hEFK98MFeWDTbUN0gjj9RuvVk87wuOH9pHRHDX9n0Te3DOoqHBrqao1cBrnw3EZs0kLjGZm+9+VJp96HV6WUYoArFPnzpGktfA9iyztiv71Rv28klJArcG58jSsZ8qwzi4O4sp14sgbRUbVi1j+6afOXImmUS9s+Tw/Uc8GTh1M5l7d8jQ6TazELHf4jG1WkNMXEdCQiMkIRMul4KUiTK6YwcP0CEhSapuYl/379rC+pVLOH3qED0H6Dh6oJE1T99D7gN+9AwqZNOqSM7Zg7n7qp0Eqk18+20nysvPMnPWjZLczfngDTb+vIj9RzvRyS2T1ktq/NsUiEvRNbKVcO3l1vP8yu687V5M0e/Y25fjz3qAxURL8x4a7bV8dO0mNKtGMiI8V5KU75/vhlXxC7eOUxL39DjsKgVN3+s5cfZDeSwUGjt7Tn4ltyNMWwSp2r9nFwe/fY3cBUH4qIMobT2DiWbiEhIZMHg4C+fN4eCi5zG6u7O/oYGIiEgefORxvL295FW4hoZ6GSLs7+8vibTAuHHjnONXnGOlQpqwiAm+Z3Q48XfOkqYQzY1N7Nm9i9LSYgIGDaJHr35YbDZO52TLstaOSZ1QKDVyPYOGj2bLhrXk/7y7/TgFhYQSGRPLyke/5uRHo/APi8Iz/xhnWrbK50dOuIYuvQegVqkhLILETl1Iyegr8+XKiwuo3LOKjlX1RLlnoFcoKQ8vomTXKSbd+yyxXbphtUNS9z6Y6nNQXMzzRqW2EjJoHPmbTrDtq9e4fnj6ZYYsxSYjJn8d8Zpu7C9eQnmxmUVV4Yz2LpT9cvMq49l9JprXM34h+RJHzjYIZUwYtnzfexUPrxzGx+s8eGWikzC3QfQoXvr9YFS3YgpQSudFAXtzLXpbIw6FGt/waN5YFcxzE4ulg2aORc0H+T1RntOycm9HJo46IXsixaWHdfXBtNbpCV3Rgr2pAXV1C2WFh5l9ZDdR1/ViZGwN5TYN7+RnYC8XCrqF6A49OH0yC0v1OQ4c3SxVSFHeLL57Rk+7kaSoCALddWg1ou8MaQzyX6lmbejTM50DBw4wduzYv1PQ2mz2XXDhrw6XBb8Lv3kMuSz4fzNcJO1PiqlTp5KVlUVOTo4MX2212ene2e2CZnI5RnvVsH3IIbYXd6PPkbFsP7CSbSdTGNvFWR7YZIdNVdG4F5o5eGAbvQePwGG34eXhSX2dTV5Vr6+u4ETWPjp16SadH4Vi5OsfwKO3prNrpTdqPFFc78n423tw3fDNnDyaxfgp19PU2MgHr9+Et/YXJl7jy+xC4X7YKPdSXIcX93PPdmLFM774OKycOPGlJD3PvPGxVEE8PL1oNbWwa8tGunTvKcOtm9epWdwvhCt8SuTk/ZNznSgpX8GP9/Tg+4kZCJHB55dT0qly0IixHD24TypwPQZfSYj28GUT1KtuT2DTj+tJSEoh+/hhuoswaRR4eXrx809LWTT3M/wCgxg2dhJdvLyl3b/NZnESuT3bWbZwLkey9vHWsu68M9FCml8kXioHVkcgNz//PbvuCSJLa+DoqS94dfVApvoXyw9lzYA9/PA67NywlgM7t6ILK6I0JwZvVUt7Bp1Ae3nkr+akR+q9Cfe/GK1gu+TD3jbpP9aip9mu4ZujXVhz9VbKrM7eIjER7to9g6OHs1hxezZHgkegs5lpqlkjn88/vRvz4/nERieQn3uKJks9N91yB93SMzhzJhuTqZXYuHhJvMZNmMgPC+ezZ+dO6kxl+Pj6MnnM1QwdPlwex+5dUzmwf68M9I6MiqJLaposF22Dh6envF0K9aXJ4mKcqBTtCqnRaKSpqYm0rl25+67bKC0tZd3GX9qXDfbvjR1Hu8BitTu49tpr6du7N7t376K1tZXETsl0Se0me7Y0opxx+8+cLVgvx7RQT6def6NUjAVMVjtWm1gjxMbGE9MhHrPNTtbe7Wz76Qeyrducx1SppO/4afgFhWBqaqTo9HGiO3fniTc/ZcWb4KV0hnl/U9oJU4wfXYKGcaRoI5HeXhdLYh0QoWtCU+3AXuG0hR8cu4Pr/Arl+Wy2O9h0PJG6rUFcW3Ml28YulC6Jl46NNpMZES/wyEtbuWGMDy9PrL1McPo1vWmyK7G6qfAxBkvfHrXDjKebTprzqBtLyW8YQ99PM0ke4sbZ2iAqT/jhn2PDcqySToPquOHb/tTa9ezL64A+W4O2soY9J76kUZoAKdDo9Cz7sgtveDRj7JaAvVyL90nQnKumwafG+R015TqUKCg6l4Naq5MXkkK8vQn00qFTK/9hrtl/hfj4eHlrI2UipLrtbwGXzb4LLlz4wvmv7Hb/Wfxe63HhD+ju+Put668IF0n7k0JMtLt3705cXBwLflyJze6gqjEUh8M5obu03FEYSPT0KmKzZ3eabc2yH2jn8dvYdvgNMkYFsra0A8eWxFJ+cL7MHrvmtgckQdq6biVdevSmqrxUWtCLx8RkVIRDC5fH44cPcOczQWgUF3tmBncsol+f8axe+j3X3XovD9w8hZ/XqIjzcDrste2XIFcLq4PZVhLFplmVnMleIhWQfkNG8cRL76LRqCUx69azrwyoTu3RS+ZQjZk0ldVLv0RrfJLZ3cxQpcSwNVMej3275+B5wpuWlmbZP/bwM6/RUFdLVUU5lRVleHjEcbLVSG9DY7uz4i8b3KgoK8HLy4vuvfpKEiB6oe696RomPxrDZ39rxWQ5z/Srvmfo2CeYedt9FBXks2LRtxSbDvHtvAjijAkEaWovO+aCVLz0hD8xnx+TJZ9eqR25J61YWt+LZaZ3baXr6WS8tFaW7GxmUh9PvJR/r4z9+jewwqbk7cPp/Ph4GeErm0jRt9AqesGOp7LrdBSTMo7T07eMvZWhfH6lGoe5hYLKlTTYqwkIDGTCxCtJSEzEz9ePY8eO8t23X3My5025bj9/f+6+9348PDzYuGE9hSV5RMdEc8tttxN3YdKb2qVLeyCwhFbNTTfewg0zb0KtUdO/T0+27rioWCV0TCA9PV0e1+TEuH9qjB8/dYaE2CjZS1haWStJVGig01q/DcHBwaSn9+DAAeHi6NwvMaVvg3ABFIiPj5O3y4+pg1tuuY2xY67g+PGjUh3s2rWbvDAglRoFtLTaMFvttAqyZlfisDtJUbee/YlPTuPsqWMyFiGpe2/nYVHY0WnVdOreC7PNhlEznLE/a0ntVE5hszfZxyMJqLbToXMS7tE72FYQSpqfMyxaXLDYJ3LOPEAdaER5Hu4Z5+znEhBh4W/33sQzXw7j/Px93KkewBNDdtNJZ/6HP3DuKjOKW4ez7NxPTI5xEvq28XlpL2O52f2CO5uTCjua6/HUOlDp1PL46VQ6sr4/w4Z5CiJTYwmta0F9qoBDjauoqyzjvYHLSb/yCTytWoxnq3AzNmLWteLvE0pSRn8SunSXUR71G34i84vvyEi7Em+1D7XeBRw4uEE+HxAYLMsXw0PD8NQ5yxiDPEWm4m//6Rbh1OJClstm3wUXXHDBhf8kuEjanxhi0rp0xWpZevjlZx9zPvcQ/fd3INjzgvn+hYm9mH8dbQjE73gDmYc203v4WFJ69OXsyRd4/oZ3OJ71jVwuKq4jD770npxQma1WOqX14MThTFpbmmVQdFx8EmUlRWg0Wjy8vCT5yTf743AUte9TpVWDl1ssZSUr2LJuFcHxVcR5+LQ/f6lxxtyX49my8Hss9hbGTJ7GtTffQ2nxeTnpFYQtKaUrKpVa7o9QrnyC/Rkz8RoO7d/FmvlP4bs2gIaGOjnZfvbNj/Fw9+R09vELJNXBj9/NYej4BsaO9cRiVjN87Fma6t350WTAT2Nm4b4u/LxgPrfd9xh2h53gkDBKS4pk31zS1Rl8NPOMsxdIB1tXRxKR8BoDh41h1ZIF7N2zjdzTMYSpndlQf2fSIcswRcCuHXNrK+Nu7oxGcTF7TCDR4DRluXeQVZZv/lotO9jiRrdLgodFaPP0ewaza/m3tNLMdVMm4XNlR0ynFeR88wX1tkq2oSJYnwD6YxTXOq38RZ/XuOETmTBpslSK2pCamkrKm7MJCgriwP79RERGtqtcCR0T/+GYEwROlCdeBkGMVErUCjvR0dHytnv3bjbsOY5WVUhFiXOxIwf3/1M9QJeSumB/7/92uayszPaQ5/8pBLEXxzwsPEze/hGkgYbDIRW5VoudFrMVlU2BXuVAqfQgpXtPbA6h3CmwiTEUEyekP04f3EtIQjJ+weFsuO0ZTDc9j81Dh2+Jjf4eh3n5w/2oFBHU287waVk0yR5VHG/x4fOjfdCZFdgrmvjy8zBp9nEpuhobmf/dCnrdomfhzV9y4NGHueOqk9wQmoOP6vLxt7khBKtVxae39GXxq5W80W0PIWobVTYFGhyyb/GQyciC093Q1tlxaJ3jMTA0HC+dWEKElKukgdDoCVOY9/FbZK54TkZZCHVblP1279VfKmV7Fj8vcw8FwT13LBsvH1+ZMejt5YP1whWGUVdMxtNoYM/WNVLl1up0ZPQZyJCxkzHoddK5UeTCuevV0p3xvypp7NEjg/379/1/rfUvrTpYuHChJGpin0WPowsu/NXhKnd04bdCXBD970rP/9l1/RXhIml/Yoim+MbmJl554TnKSopJGHc7E5/2J7b6JEcLNvLJ5/6EROpZURTOD4/XkrfzRWI6diapa4ZUxvyCwhg1ZSb3CmKm08tcNYXdLvvOsg8f4Ni2h0mMr+f7r2vIy7Nw8w0+NDRamb+gnuS0HiSnpfPqjN10y+pOT2MlNTYNj2UOwmQtICg0nKx9O1BGJGBzVLT3j7VhX4uRvL1FkqDd9sBTjJ40leaWFiKi4zB6eGFqbpaTRZvFTM7Jo7K0UoRD+wYG8uI7X5Cfe1o6SwrSMXnaDTIw+0jmHiZdc71cdsKgbtz7fgyP9LbhcFgZm+gus9zErdyipneKlha3tXj7+tGpS1fqa6vkJLG0IJ+ck8e49+00tJeEP3ur7FKh27BqKWtX/kC3O24nUL32H7onSjsWB8ToTFQXJvDMYgNvXXVE6hRty/+a1F1a4SeeO97sxlVDfPlsfQt9PKplSdqjS/rzy/JP5TJxcfEcP7AM9SG1JCfiFhYeTqupldLqbNy17owcNYZBgwbjHxgo1ag2BAQEMHrE0MsypOLiE4iPiWh/rK1ETIT9uruLY4csM1yyTBhRXMSMa50T4F9DqLw5Z/P+7vFL1YzfE8LFr239YjLeBqGQ1tVdzN7730AcE6EoidJIg05FTZMZlXRM1NCssEoCZ3c4MBg8Zd+mcEQVqmRdSQHJGf1IGzSKw18/Q58+03A367n56yPtnwdPlYM4t3puX3U1docCfZ4K47kmmjyUDBroLpW5X0OU0z70egBfFNxM5huzeW/JIDY+OJ6fJq9s/5krtcGS/C645atQNrWybOJ8jgy7Ad8b/TGhpiXPgHtYM43NerRHdQQcLmV/xWb52t69+uGuc4ZAqy+MB5+gQB588jky9+ykKP+sLDvu0bsvKUlJ2Ox2+vXszs6dO6TpzuSrryGjd3+0etGM58BiFbb8gsiqGDlmPENHjKGuvh6DwR03vZYWs00eXxE+LY61/v9jn19cUvx3KplAp07J/+X5aytxXLx4scssxAUXXHDBhf8IuEjanxBtje/Nzc08/dgjDBqt4ZO3ElHxCyvrwnh7zXBCvw5k4JDPGXv1DOnEGOHpyfDHXyKtz2DpPiiImN1upcfA4disFsymZvyDw6RhyI71K/FTv82XL4kJmif3zfCkyqQi2M05G7rv6XgmjKhk/Yof0Cm0PDIVPKZch71eSczJKtZu+YF7n3yZqspyNn+3ku/v7c4472LZN7W6NoR91eHsXhxLo+lbEjunMWrCFLltQcL8A4OpLiuRJFJYglstFmnW4eXjg59/qAyX9vHxk6YPnVO7U1SQh91uk31NGX36S0e61csWUVx4nlFpPu2OcpciSGulsOkk0cGJvPT2x5ItuXt44unl3c6cDmVGUttlnzRZEMht1cnJnlClBItyL1dSaNEQo3Fan4updI1NwaK8ODR6C7eFOwmKp9LBs1c3Sxe9NlxqeiJwqcmHMH44YTJw9x2J5J9fxNQe0UT3m4ztXBnHT30t9+GBhx+jS5culJWVcuTQIVmC+uLzzxIVFXUZYbryyivRXjDj+P8hoUPkf6lIZGZmcuR49mWlZ2Jy3Ea2xL0YiytWrJA9X0uXLr1smf8rJz1B1H+970I5mf/94t9tG4K4+Bi1mCw2VAqbPCYWmx2T1UZzo+gja0Sl1WK1WaXpSEtjPWkiBF2j5Im7dzKyu/rvukZDtC08m/ALiz6IpiHPgLk6j2zTAQ6VBhMV13IZ+W+Lc6iw6bH16khGwxXszV1Jwd2beayxJ3dPr6fFruSz0mSKTwQTkt1Cq61QOnHmbPwSNkJEUA/8leFUVJyiRdeKweFJqaacBqrpM2AIHSKCnNbKwv3xgrIqFC6Fl47YqyZLkl9cXHxZj2DnlC489shDrP55gyT2Py7/Se6oIGc2m1AiRW/fhRpLnRp3Ny1eBufYbGq1ygskGqXi/1veKMbUsEH9WbBoCRaLRY6ptgsKJ04cp0e3Lv/la11Oji64cAlcFvwu/Ea4etJ+O1wk7U+ItonJ22++Rkwy/PCOD6oLNtvX+xayLSOb3LWRqM6oOXJgN1Nue4A+w8ayY8NPaPV6mXm2a8NPdE7vTXNzI9lZewmLiUOt0aLXu0liM2WiGofD1m7T3UbQBFK96okbP4Ptn7zI1BtnsX3TGg49+Yx87qRWy7irr2fQ6IlUlBTz/Zcf8MUTCbw/tj82qwrFtwUc378QtHZ0ej2Pvvo+Hj5+lJcWoVQoqa2pluVRJ49k0a1nPwKDQlGrVJK8VVWUYja1yPJHUc6o1mrR6XTyi0I4UFrNJgKCgmQPnVj32RYPUtya24la22S3yqbE09uf9F79pK1+TVUlAUHBFBUWSkIryrZsaw8yI3I0d2Tso9miZdHrXXA4jvH9159RXV3B7qWfcWPKvbw/9We66JvlPvirHfiaK+gV4XSPbIPD4XSsa4NQ2US+1T8y/Zh3vgOvjqjkXMMiSbBMllIOrZ0tn/P29mHJkh8YOnSo/H9LS4ucIAtnxEvHxu+Jtglw2+RZTJL/0XYEQRNoI2iXLvN/NTkWx+vX29p74NDvvh3FBbKmEU6qotRT9sE5CU2L1YZdXPSw2SQZb26oR6tRc9vdfRmV7gweFyKcOOdiSIhsuRS3FjqnnePqr85RYwF/DdTZfbj+fR3c6EGsWx3ZLR70MtbIyIp9LV4syUlD0+QgyDsczkN4h3jefXI/H7zlT+wd96Js1RF4vBUOH2NH1XKplN9+30OsWf4j53IPUmB19vCpUVNhteLl7cPkq6YwZuw41GrnSU5MFNEUfuzaueO/LCX8NdqWuXTZ1T9vpLKy4jLiLseV8C0QZZdalVTb/hFBGzNyGGvWbWx/rYAg/dMvOc+XqmnZ2dl07Njxf3lmXXDhrwNXuaMLv30QuZxDfitcJO1PgG3btlFZWcnkyZPbHzufn49KU8i+HyOk5XwbxLVpf20zJyqKJBkryjvLW4/dwYCxV5Haqz+Hd28jMDySmMRkvP0DsbW20rlHX1nqKBS2lvo62YNWZtYSpndexRdE7VL1R/RG1YYqCQmPwmqz8OH8n2TvmtVuIzwqFrOpSZLD1PTe3PPkK3zw6lOo12ukMYNQ6oRxSf/h45l518Po3AyYrFasFivHDu2n96DhCD/vvLPZ5Jw4RFSHjtJxUeRiiZgBEWosSFpoWCS7tm6kqryEwSOukPbt+3dvp7PMMoum1WRi9pdD0d/xMx31DeS3GvBWm7E4VLy9fAA1Ve9Jh8Py0hIOZ+6ha3ov3I0G0rpncOvdD/HSUw/Sub6M+R16c+b8ITIPfUDaFbF88547RpUX73zv4KMnXqfuqh7iiLSTwKHxDQRqLhJaQc0+ykwmpkMtg31KKLfoWZ0fxQMdjyMisnc0+DL/YGeu6JzDweJgPrpiNzXmQqKiorn3gYdkf5eXuxsGg4GMjIzL+mmEqieVvd8ZtbW1rF279rKywf/JBP0/Ua3o07O7vJlMJhYtWSYfGzBwkOxxq200ERLgcxkZ/Z+grSRPuA7Wt1hQKpx9W0qr0I0c0mlVKlB2e3udvVp9OXEXn6GZJ4fwQydniaH4jIkv64ALwqeX0s7su5q5akZ3LMMjwaFCX2KHriZMJi36E1o8D5fR5Fsu9+fq626RY2T+5+9z6qWn8fLypQItNY2VqNQq7rn/QZI6JZGa/LR0bSwuKpJjR5jJCLVRlA336dvv7xRVgfiY33ZOx44adtkxFqqr8007PyBCtdOqxTfQ5ZLhtClXXlamK8ajGIdVVReD3n8N4XjrImkuuOCCCy78EeAiaX8CFBUVyQmKmOS0WUrn5eWye10k6gtZZ22oscE1/rmM/1HF7EP3UPGFJw1nNrFt9RJ5a0OvYWOZcf+TaNVaDO6eso/mwNYNRHaIxycgkGturmTFfF/81WaOmjzJafDl6sA8SdbmlHXEcBDKSgoxuntK44RmUwt9Bo3A4O4hs6t8gsLwDgjmiquvk+RM5Ju5e3iR2KUbqRnCsdGTg7u2YBdB2Z3T8AsNJ0WtYeOqpfgGfMqnTzlJ4Rfr13LvzHdk7lpS51SpShzYvZ01K67njedEOaOCW2d9gFo5iMeffVU6UKZn9CEiKoa8H+YyO2si1RoNjjMnweiPUW3l8Jk5cmL7+H23Ed+xEzNvvRO9Xi8NPpoa6hg5drw0SNmzYwvL138lSy4FVn2sIVjtnGC+MxP2bfYmQO00W2gjsz6qSwiayG+zKXh10loMOh969ZjKqSNbKGxYy/Ir7kLhraf4x6WUNX/FSl9/dPpcasxF3HjjjcyZM4d/F0Qf16VoUzD+yBDn91KieWlW1qVk9J+BGEOiZE+U64lzL9U1URfYapGkTedQ0Wx1Onbu3uNJ70QtcXqzVNHWNwSQnRVNdYKKALVzB37df60RAdI9oql69n0cWjW++kDyPzmG0S2QKL+ONAbZ2L97C736DSIyNEhu/7EnniZz3y527tiO3Wblismjefjhh9mf5Qx/v3ryBKk+C7IvyLi3939tyPJ7Q4wjcaGppKSk3YSmvr6+nZzJfshLnEMFcRSPiddd2lvo5+f3D41B/hMvErjgwn8sXBb8LvxGyLL438nwQ/EXNQ5ROC6ts/qLQkwE2n7kPX+VyfRHgZhUNphs0vVMTGROnMjk1fsuN3FoQ9sZL7EpuW73ON6P20yHoEbOmNy49/MxBJ2wsmLd+3TJ6MsND/yN4HBhc66lvrYabFb8g0O5f/poKm2tRN00BWuJiupvluN7/zCUvnoMe7Q0HF7F2dNH+H5TltyeUMeaGmrx8PajsaFOmgIINauxqRGNTivVr9bmJpoaGmRJpVIoQiqV7EUR7pQiey1fWN/PGE19TkS7yYZYd2y/cjp3Hcvt9z9BWUkB9988jsrsDk7nRVH25wDvyFzufuhJZtxylwxezty/l2cfnUVB/t+bV3Tq7c/6RQH4qa0crXenZ9JBHn/mFSZeNZVjx46QktpNvod7bp1BbWsuXy1JQqey0cu79rIyxWKzihCNsyT0UtTbFNIQQpRZfno2nnv6r5GTYqFYiEmnIK3iPbdB5Ie5u3vIMTpk8CCSk5P/cJPNf5UhyL8Kl5K0tr65S8ma+Nq81Fjlf4K6ZgsWq132qIn+K6GotSlrrVYrn7/7PNd/P5oWpYYD2R3w2KEliu189FoOARdcHNtKc0VA+zvFHVnyfl8UW9eRmb9FPi8+Z2K3hEGHp5cXg4cMZ9q06Vxz9ST+SKioqGgvY5S49FfqwmEPD49g6KB+/2c9jS648FeYh7TtR68xL6DWiHqO3w6rxcSeNc/829+bC/+3Y2jTwfMYPX6f893UUM/QrpF/uTHkUtL+JGiboAgV7XzBeQ4ePEGlSYGfru0q9MVl2/4OUNl5NnU7yR4N8rEuhmb+dstGHn3rSkYOu56NW77H08dXWt431NUQEBwmPyg71v/Eo699xE9zbuH9K77D3U3BO16NvPT06+3b8PEP4MWP52M2meQGvX395ZX7/LPZ+AYESZJzaP9uegwYJu3thbmFQutG0fnDhEbHSVImFCqdwYC7t5Zz2cdYMvdTvJMjnGXOl4Q4Dxh1FYvnLCC9z0CWfvMcQ67tcBlZMihg5KQpLP5uLpHRHeg3aBj9Bgxg6dqtHDt6mLyzOZw9k8O+XTvJOXWcRd+FEqxukevu7tXIp+uG8PxNnzDqiglo1Gpmv/Y8Py6cJ4lUwfkkQtTO8qo2wwdBHEW5mo/6coImHheEcdxLAxkxpYpTOQEceGmPfO7xp55Fp9PKLx/hlph96iRNjU1cPXUqg/r1lhNRoQj8s8TgPwV/tAn0pfvbRgIu7W3635wHL4NGGoqIzEJRBqlQqKQtvyh9FMpaYmwSCyb+RPrA+wlusuBeXs6m/N0kHU3g0c/86eJbzsbKCFpbteTV+XJ+ezgBO/Mp1JQTEhbO/Y88jd5NL50QVdil+YbYzz8aQRMQ5iNtyqYsg7xQ+ihIrbyi6oDCwoI/5NhywYU/Alw9aS789kH09xUgv2Vdf0W4SNqfCGIys3HDOuZ/+43Ms0rrreDY3g54af5eRROotCnwUFovusI5wE/dSnOkAt0Zd0zNTdRUV1Gan0tQWIRU00SPWGJqD2oqSvhithVfuW4Hz91ipKR5Bl+9Po/UHn156KX3pEJWWVpEfV0dSqWKUwfXkBb7Fap6K+9/4kls2lQs5lYqyooR3Tn+QaFEJqZIEmdqqOfU0Syp4okSyaCIGDy8vGndW8+2pggGGZ2lTQdajJQe0cpcpqSUDzi07oIdvB2MF5rk8swaDK0h1NZU0atvP8ytzVRXmmXpo16n5e1XX+CGx8OY/6ie4+fSMKouqljimAQEWCkuLKCsuIDvv/mCTevX8MPeDOK86glWmy4LqK61ws76ID79KII3HjlGos7UTuCOtxh4fWEaOz79gv1fO4N4RS/UtGuvJy4u9rKJ/xuvvXLZuXVNRP89+D3KHi9Fm328IGWNrVasNuc519iUjBl/FfNKiti2+jm8vH2laY/oD1OfyuSJ3q2kZdyAziMM5elCzpauwWZrpdzXSkVlGXfd9yA+nu7SZfGaqyZKV8U/CwRZKy8vZ+36Tdxw3fSL/YF/+RoQF1z4F8Ll7ujCb4TLN+S3w0XS/kQoLy+TBG3Ga6OwxkZy9oAvNebVeGmcREGg0Q6lVi0VFj3vHEvHe1senz5SL58T1GRNTSS6MgWFjTlS/TJ4eBAWE8uR7YvYtWUPNruKfsOuoDA/l1sHipKvi9sPGdVM/91XkHf+lCQcvv5B2O0OgiJj2bxqCU/dOpdArTMRqe8njfjFPCqJT89hY9Hr9JKwCbS0tlLX2IBnQAhqnR6lWoPFaiU5vQ/L53/Bk+/eRfLks6hwkLUoHmvdajRauLKz8/UCgqD9VOtPk03Lh8v60ZS/nqTkLnh5eWOxmNm2eSNd0rrz/luvcu3Tqbxzfam0R++eBkebDdg1Fkm6hCK2dEVnVKqdHDmUxbo1P7H6aE9G+Jb/Q/t+DxVM6rwHm2MHpw/3Y/bnHvhqW/lqXTLz7luIxXYYL29vunfvgZe3Fz179SY4OES+9tqpV8s8sx9//PEPVx74Z8U111wjL3iI89HWeycUZdEH2hacfDhr3z9N4kRZsriJ8keRodbcakOj9pAludknjkp1V2SNpXbvKT9DC77+hEP75l62DtGTFeffkRk33SRLYIW1/f6sI38qgtaGwMBAaZYj0NZ3KyAucoh+QhdccMEFF1z4s8FF0v4k+OKruXzx6ceczEkiweMsDsdZFP0uPi8MPU6btdy5biyZ935FU2sVCkWWVMZOnfHl2r91JLM+lAMbEwk4egizYjNGvZV7J2Rw7EAcd/UG08MKbn/ZnTnvvoS7pxf7m2Lo517frsr9kheLh6KQmqoK/IKCKS8uRKlWo9Hr+eG72bw3y1uSOsFrjCoH026/kgWffcbAcVMICo+S6sL29T8REduRyLhEqb55BwRhbmkic/smElK6EtepC0fnvknQmel46QOhcANHju5Fa9RItarNJ0XcffZZOvkLcnCP3M6RI/u5/Z4HyT2TQ3zHJPoPHCRzs3bv2MqjH/e/jGz5acxct2EYnWNK2bcuge0ffU7/gSLLSiNJVEf3OqmwtTk2XuJlQIVVhVKlxMPdh9M7dzA1wygnkVVVmdJhcsyoKxh7xXiM7u6MHTVc2uO3qTUyFPmCW52LoP1nQBA0ARFrIIhCGy4l0YnxMe1ETuDSbK7/H5ISO3LmzGm0aiVmq13e0rp2o0d6dyw2UdznkGPsoSeeob6+jhNHj8hxl9olDV8/H6nG3nj99Pb1DRnYlz8rBvXv/XePCUfO/6mzqAsuuPA/h6vc0YXfDJeU9pvhIml/Anzy+Ve8+NwzdEqoJ8HDqcr8ug5YEJgXzvXE8pMnFpqISezMUx8vwG6zUVFSyGf3vsmRPd/iblSw/nAcAcYIqRTtLPEmxq9WrkOncPD8YyZytk1h37Hv2bg6mOCJOsqtWl7P6Unranfyz+0lokO8jAQQytiJzL24Gd2JDDUhfOzUF0hUkwPyWpOxWn7kxMF9eAeFUFFTTVKPvmh1emqrKjhz/BCxSV0keQmJSZBmIi99uYQvXn2KDRvnY2ppJjImjl4DhpKWfpwii4YwjUWSprV1AeT+cIbs8nUEqUJ5+e2P6DtgEG46LQ211ezfu4uICKed+M78CK7wF0qak3hlN3uyeOYnrPfxobZmGZFR0YweO659wp5V50dUQGk7Qfs8L4Y+wWW02NQ89VI0Fusprrz6JmI6dGD3rp001NcTGxtHr969ZT6b6DkTqkcbXITsPx+XErRfn7O2cXHpY4I4NDY2ynPdRt7aLOIvJXB9e6VLkibGuE7ttOq32Z0hz6Kv0m53fpDFOPP18aH/wIHoxEUAD+cYatu2Cy644IILLrjw54KLpP3B8fW33/H5px8z/R4Dt08XmU4X+6ku7UFrtkNloweW/HxpBBIRlyiNOex2G+VF57n7xfeoKC3Eq/U1Aozl8jViepgW5CRo8v8KCFRbMPdK4cVrt/DkNRa5/jhdK+nZ+bz0w1dYsfDorE/x9Palymalx8DhrP9xPksXhMlA7Tby+HVRHOY9ZfLvgnNnKDl/TipowjWvsb5WKlaxKd3k81qtDj+9G25aDdWlxdxw/1PMvO8JSgryKc3NQaH9nDdv8cDhEGYMYt1RfP5Ud8yapUTFdGDuop9wc9PLQN6dWzaR3CWV9IzeaLUagkNC2PpSKW/M7sSYkDzOtXjwxYvdRcwx/foPJDg4VJI7m9WMzW4nKDiYB8YXU7M4lk4+Vaw5EcPLE5bISbaYkDc0ZJLRsxe9+vSRIdtXT7mm/fgJ1UyoZy78NSDGQ1vJ5KJFi9rLJX+t/LQ5RYZHRNAzPY0flq3EYQebXYFS5VTSBKSNv1LBlCsntK/7rwqXeuaCC/9iiKu04vZ7rcuFvxxcFvy/HS6S9gfGnj17eOC+u+nd08LrN4TiwHzZhE7AcaEU8aPCZM6/U09e9neyp+aa2x902tzjoGvfIbKsyi8whNZSD9Hd1r6Neruzz6oNBiV4d6xjwgQ/7I7W9pK/4WPLmP24FyZFAwlde5Bz8ggBIWHojUa2/7wC4/32y8x57DVqMo9+Lcst+11xFe6+ftLiXwRsnzq4n6RuGbi5eVKRfwxj61eUlOpoVo1EIYhf/6Gy3FJY1m9d9iTzPlZdmOg6fwt6+pVz+4a3cWDn1Xc/Q6VSs3fXdjL69MPLL0Aek317d5HSpSsPPfY0jz10L9UzY9kV3o3q5hL27H+fgYOG8PCjT8q8OavVSXzVSiV3zbqPd95+g1szzkonxvr6TGl1npTUCYPBSLfu3UnunHKZwiEm5/Hx8aSnp/+PzqtLWftzQYyF/+6ctmV9CTt5gYlXjGbl6p9RKgVBc4Y4yx876ZTlvBjgggsuuPAvhcs4xIXfCOdv1u9zGBUud0cX/kjIz89n8JAhBMX6M+dzYRRgaydMlTbYVB5O7nkTsx87RU1ODZAjyUqPQSNI6dkPNw9PqV55ePmg1ujIPrSPgNBw9u1MoE+/Ejq7NUuTka/KE3ki9FQ7mxd0pTVAwclmL5I9yttJ4bFmHwZnjGTF9lcoOJMtA6x17h6YrTbOnjjC1sYMxnlWSqIm+uNmdT6F95xAnnhGTXBkDI111Ri8fDBVVRKX1gOjTwA7V33Fy7ctxudCRtR327N4e3a4DLcWpY5rF37Ndx/b8FA6CVpbn9iJJm86hCdS2VRCeGQ0Gq2OfoOHs3XTBrpn9GHfnt2kdctAqbBjMHrw4uvvsHLpInYf3SAnwDNuuJmhw4dz9myO/KFqt/tHQXhkBK+++TZZB/ZTUV5OcEgo3dLTpTW/tAa/5IukzbJdWOe74MI/grjQ8GusWrveOd4UCiZPGCtDnb/9zhnG7FKQXHDBBRdccOGvAZeS9gfFQ488yufLOzItpQkFzome4wKhOG7y4pVH4jm++XNiEjox+aF70BrcSenZH5vVgtXuQKt3kxNEoUAJi/uYxBSRbM6IK2fSO/VLBn52Gy0qHbWZPnhdaeG68LNyGz/WRFCX7cMjXynQf+lLnK6BYy3efLC9P510Zjw9FcwY/BIRnmZyShXc91xHmXX24oKhHBp3hPtDTuKpdO7nzNFufLewhHcfuY3Bk6YT3iGegPBoaivLOH/uNMFpm9oJmsDkvipuvm4vi+Z8RHJaOmuWfIvHyyGXZab90uDFG58MwaFZRGhoBBGRUezesYWUtO6kdY0gxX8SfceZZY/eyeqpePs5zQjuvf9R7DYL7u5GaqqFomeVpMvR5kQiLyw6/9ZpdfTp61Q9fk3M2shZv3792LFjh2tS7cJ/CxFi/mtcP91ZItvQ0CAJmgsuuODCv8Xz4XeqUvyLiiB/ebh8Q347XCTtDwYxcftx+U+YTeeZLgjar779jrToeezpgRzf/La0wH/xkwU0trTg7uFJbW2NLC/08/GnoriAyNiO8jV11RWyT62ptoayojzuevw1Prv5MRnWrNZqefAtE9999BgqXwUNp7wIWJvPycM7uf+h6eiHd8JRqSJ8WwsHy9eycE0SUZ5muV+JwQ5uvzOXLatbOPbamyh2z+LBuScv29/BNwzn6enLSBswgphOqZKgVZUWs2b+52TcH4LdUdGWY0uTXcnRY7F4ag/w0XfbaGxopMCiIVJrkeuqtil47JOx5H3xOVXmYqbNvBWUKob1OUqC/6soFHZpqy/2Tdwn+y5kT30wSqUHjY31KBUKLJbWi78ov/qBaiNkzjBt50IGg4GrJ0+Qf7cZRAwbNkyG8brKFl34LWgjaMKFtA3CdESQOJdhiAsuuOCCCy78ueEiaX8AWCwWtmzZIm3AlwiCZrUTGCTs7KsuW06UEb5/LhXdgSb5f+GyaLFZOX3sEJ3Te1NamIfRy1cSF1GCKBwetRoTe5Y8xtrVx6mstKA3GBl+5XW8+NUy1v/4LX6BwdRXV7H2rtcICAnHxz+QnSeO4OnjR8G6H9Bt1RMVn8SunBPSzTEoJAkHlnaeE9bBjaDwaKrKi8naMJutjT0Z41Etny+1KlmzvRPuXr9QWVKIxWaTdv0egcHMuLmQG7o2Y3YgXSHr7Uo5WOPcnYTs+Rt1/LzKyNS3+/HYHcfRKW18sL8XrfP2S4LWObUbV06bSfah5dw0cuXfBXkLCJ7l63eK8tIeKFRC1ZA0zLls2zIXHrmo1l1kxdOmXIlWq/278yUImgsu/E/QRuz/u5LYX4+x5uZmV1+aCy648K+FtC/+naS032s9Lvyx4JLSfjNcJO0PgCVLlsj7eQsW4bA7ZPhtROwIduV/Tp8oUbJ4QR1SQM+AUk52HkykPR5TSxM2q40e/Yewb/sm4pLT5GtPHNhFas9+qO15TE58mDvSwfJqNE+u6s3hxVZWffcFDpuN62Y9ht7NQEtToyR0xw/sxmaz0mfEODIGjaC6olzml+WfOcmQidMwNTezpLiJpNhj6ETvmgPm5SfRMyWAZatmS4Lzt6Uj2TPsKH7qFn44lophv1UqdjabhdwTh4mIT2L3rlf54X5RxumEKLmw2dQEa50KXRsm3juUv037nA8yb0atCsJSeY4i2168vH155OkXyTt7Gj8fJ6n7RxDHLT7qCDUVPVFfQtDa0d6Ldjk5Cw+PaDd5uBQu5cyF/w1Eeeyl4+v/B6Gku3rTXHDBBRdccOHPDVfIzn8wrFYrhw4dorHFjFnISW0Xoxzg4+vL/AVXUG2+fHLXaleharHQ3NyIzs1Ic1MDxQV50iRk14ZVWM0tJKX2kPbw8X6f4qa6yNZvHpFFvV8nkjr0YOOyBdKcQ7gtCghHyKtuuJHZr1h4+PofObXpBrlM135DGDPtZnoOHSOdHL96so67z/bkk4oobsnpz+6lnWi1NUnTks69B1Iw+0u2vNmDBe8OwvCJBrtXAa0tzfS54mo8fQMoK8gjMN1pANLmDCTyosIEQbvkfTbYYMu2JNQaDVt2zWHDtlfYcuJ7DO7uPPbMy1RVVsgespJKf861CsroRInt8ot6udXlvPPWa9LyX0yU5Q2FLH1su106gRaT439E0Fxw4X/bkybMZVpbW//b5dr61FxwwQUX/i/DrH+v2z+Dbdu2MW7cOEJDQ+Xv7/Lly/9Vb9OF/wML/t/r318RLpL2H1j+tGDBAt566y2Zq9W1a1fuvvN2vv1mDq1mZ79UG2eIT0zm65/SMF2IMzls0jP/5wyaK7ZQXVHGNbfex6oFX/HgtFEs+nQ23334GvdfNYxZE/px8/Cu5J7Pv2zbXkoLDQka7EUKSdB2rvtJbiwgOJQOHZPp3+kFuoWcITGwldmPgptmNy/eOY3a6kp8g0MZOO5qyrdvJmd2GJ9/Mowzn8QTkVXBrn3LSekziB5Dx2K3W9i/7DkaDv3IkXNz2bppAaNn3Ine4E5UYgqnDuxm225/8iwXDRWsF1wb29737iYPpmwcj2pbEw67neHjrmLm7ffx5Itv8OATz+FmcHNmSikUCPO8j5bfxLulsbxXGc07pSnkWRU02CHXouLd9ZGcPHGM11996eKBuLCdKVdOlH1Bgph16ZLKoMFD/g9GgAt/JUyZMkUqaSL8/b9DWw+aMPoRargLLrjgwv+JBf/vdfsn0NTURGpqKh9++OG/6t258H+Atgvtv9ftf4OPP/6YmJgY9Ho93bt3Z/v27f/lsiUlJUyfPp2OHTvK39z777+ffzdc5Y7/gVixYgWLFy9m+ivTSevTyK6fA1gzez41NTXce/+DToVHqURlt2NXjOK6B3Rs3LCUxhrRi3YUD08vbrr/SUqPPsLC97TY30/g85Od6EkxnoGNvD/XyLkdocx+eTdTV3jKbYoPQJDajk9wLXXSVgO8fH1QK1VkH8lErdUR3rH+sg/KlMcTyTsdxuJP3uKlb3/C6OXNuBl38NO3rxMYHoVPYDAHjx3E3cuHSXc+jMHDm/viO5G1ZR25x7IIjYlj+sPP4+0XiL5uDmFu53CzVnLmk1M80fc6bk/J4liDL6neFfQ3Nsht1toUPPHqaMxr8zhv24FSqSI1vTd+Pt5oVBfVL7VgdSpBXh0YWoxkrRzLPrcW1KGNFLX6EKBt5EyDPwURYQQkVHE65wBVVZUkJ3emuqJUqhtubm5MnnCF3G7XLp3+HUPBhb9IT1pYWNj/d9krJ45jybKVKP+iVxRdcMGFvwZGjx4tby648FuwaNEiSbQEUevbty+fffaZHFcnTpwgMjLy75YXFS3CU+Cpp57inXfe4T8BLpL2HzRZE8TAZDLx06pVPLtsIk/13C/7zGbFn2RW2Azm3vMFBefPExIegdLmQF5ct0NGv0EMGDaGgwf2SDI1cNQESo7fyiPXa9ovYz3S+Vi7S+IXj5q5qltvnvSoaA+ubrs47xNZjzaqO5zaSfaRLHoOGolKrebUof2c7Gqkj7FBLiuqL7fXhNNB78bJok84fSRTqm9Dr55JdHIa6xbOkQRqxc/pDEusoMV6H+8s7Y8qYAojr72F5sYGCk4dI63PYBR1tzEzNV+ud+Sb8EScNy/32YxaAQM9anlyRzqZcbX4a018vbsH1rXn2Fu8QBLVKTNux0sEW9sdsq9MvA1B1gRUSidhc1OrifdQsPG1L4mYcBUH1NEyKNhqVYBZjd/QYVTkHMBhNTNy2GDpoDdquEs1c+E/C6IXra30VsRn/CP7fhdccMGF3wMikkfcfq91CdTX11/2uE6nkzcX/pz4d/uGzJ49m5tvvplbbrlF/v/dd99l3bp1fPLJJ7z66qt/t3x0dDTvvfee/HvOnDn8J8BV7vgfAEEKZM8ZyIDkluZmpnU9035ydAqYPuyo/PvcuVxZ+iecCAVUCoUkJSZTM/2HjiaqQzzuilU8OsNyuVR8QS0Tr9UoYEjnbHThFzPIBMptCnKLglDYnI/r3dzk+v0DgnAzGLnlg0hW1fuRaXLj9ZJEVOfduPPhbIYPMdDS0EBCt15otA08OuVLDq+qZencWkYlVUiy5a62c/ek7bx04zjKT/xEoLeDjt16sWvNUgYkFbebn4j9fO42wwUtz4nr046z6P4UPn1sCMo3TZy37ZWT1VmPPE1K13RJzqRydgEWmx29RoVWrcTTTUOQl45QbwPNLec4PfdDfBc3YK3RQpUeHAqsYgeBoKAgeS/Kz9r+dsGFfyVE+cX/BvO/X/y774sLLrjgwr8SEREReHl5td/+0UTZBRf+O9TX1192+6/6uUV0TWZmJiNGjLjscfH/Xbt2/WEOsktJ+zepZikpKXTu3Jnjx487H7wgc7VlI9WZdaB1Dj5BXsqbDfJvT09PqQ5pRCC0RkWrwo7V5pDGF+WlRQQEBuNpnNdOetrQ4ABPhVMxEz1smXUhHNzQgYxZGzAonE6MX5XHozqux2TOla/p2W8oxefPYW41kdilK+9PfpBnLU/QHOPG7bEH+GTsRrncqP5hzHzyBeasDWfNQgsBGidBjPe93FnRXWNnz55o0kLm4nDM5f1lbqz5tombbvAkWmNp32f9JZb3AuU2N/ZtewM3ozsajZb62mrGXXUtHaI7YLXbUV3o12nrQ9OohdmH0zFP/N2G8RMn8d28bzi97EtCOzxIS6gapRU0Hv4YQyPb3RldLo0u/F8hISFB3v4nEHl8PyxdIf9OSnKV37rgggv/QohrtfbfcV1AQUGBnMO0waWi/cnxL5DSIiIiLnv42Wef5bnnnvu7xUWft6g4+fUFd/H/0tJS/ihwkbR/A0RZo/hyEgpaO0Q1gAK8vX1ITe3KzKnFrF7qS5DGzKkWI89dW4i3jw8pXVKdDoRKpwwqFCRB0trGsDAMybYmMjihRK5TkJWDLToe3j+ER7vsw0dnYnlFB7I2J5C4t4rB5nCunGbgVKMfu3cl4b8pl135awgKDScuqTON9fWyt6zV1MK0Ox5k3gcv8PW8JGb0viD9XSiVvPfJKAIVTQRoLv9Wv5Qs1loVpIVcfP72iS08OOssT+1+iDf7bCBaa8JX5WhfXrytk616nvlmKCHBZdTUlZOQ3IX0nv1I6hiPXqPE5lBis4nXOL1/tBolGkHa/sEXw5hRIzh5/ChZWVn4VtWgVzmdowQefvpZV/mYC//REMHpbTh58gQ9unX5p6z7XXDBBRf+neWOgqBdStJc+HPj93RlVFxYzz9L9H/9GynMt/5Iv5sukvZ/CKF2iawzgV17D1z+ZHsoGNx4yy28/cbrxEYfx2g0SqcjDw9PHnjo4XYiIRQjwdLsou9KEDYlaJQKWTZZU5PB0yuPM35YM+fN7ry1bSANL2cypmCPDLL28qtAr97LspIi/I4E8sRb5fgHd8Daup6zDZUYjO5MmH6TLHU8d/oEBncPjmbtZfRV19HU0MC0oRsu33UF5FuMdPVpuEwBE+qcgF60fgHLq8K5Lbig/XkbEBOaxoH7fuS6B+4gIyObzzvvQHTSifUcanHnpo+n4vHdERqaa0jp2oOp186U5Y1eBg02ux2l3akqimpHpfJyu/w2CHfGoqIiaev78MMPk5uby7KsU7QEhsrnVVaYPMLVpOzCfz56pndl74GD8m9xkeeaqyZJgxsXXHDBBRdc+LPD839I9IU7upgv/1o1Ky8v/0O1s7hI2v+xi9vfnQC1WuahCWRk9GTfvr1STXv+pVc4cvgQhQUFeHn7kNGzp/OKwSUXtmRpn0op1SpR8me22tCphaOhAkv9dO6c8g1HD+7A4XBajsZ1TKbfkJHknc2hpbmJ4WMnE5+UQmFBHvt3bqGhvo6U7t0YP2UGEVEdOHPyKOERUZw8fICuPftitliJjk/C4thw2cDZb3Lj1b2D6T18ESEqeztBezA3HZNDQ7p3MXuqIji8I5q0a5fSVd8sbfW/LoulY2AS5w69Q+XbH3MwbhDPv9+FiUFnKDMbeOn7Efgs3MuO0uWoNWoGDx2JRq3ETeMkquI9q/5BV+XgIUPZuXXTZY8J97xLyxjjNx7kjYW/YFc5ULXCj5sO0SU25DecYRdc+NcjMTFR9nJs2bIFs03B4h+Xyx5KV4muCy648Lvif2Gd/9+u659AY2MjZ86caf//uXPnZGasr6/vP3Tlc+E/E7/FOv8freufgVarlT3fGzZsYNKkSe2Pi/9PmDCBPwpcJO3fCKHwCPImJlmiBDIpoYO8teGb+UrSuna7/EW/GqiCqOk0SlQ2h3SCFC6HovzRw+jGtTfeQe3EKVRVluPt64ePr798vchXuxRR0bFERneQbowqlVIqUtnHD+Pu6Ym70Z3oDvGYTSaOH87k3KljfF3WgZlBudIQZGeTJ/f8Mh7v9VruDxrIo0l70CrszCtNYvuiNNwLzRyI7oy+Bvy2F3Crz2SSkgqoaTZStimUToYauQ8NLRVU7f8W8z3XsrrPdDTVdrTrD7OjfBkanZZb736QsPBQ6dj4331YhU25u7s7kdOcx/a/gkGvlf1o4ibg7qb9J86cCy78+xASEiJJWZsjrLi54IILLvxZcODAAQYPHtz+/wcffFDez5w5k7lz5/4b98yFPxIefPBBrr/+etLT0+nduzeff/4558+f54477pDPP/HEE7LK6ttvv21/jbgY0HahoKKiQv5fEL5OnTr99UiasLvMz788UFngrrvu4qP/1959gEdVpX0AfyeZFMDQSwgl9CaKtKUpIETaAhGQolFBUVddBBRYF5AFUdf28KCirCjgoqKoH8LSBEFBxQRQepMgUgIGSAIkIQFS5n7P/yR3mAxpZG4y5f5/7t1k7szcmXvmDJl33nPe8957MmbMGFmyZEme6zp16iTbtm2zX0ZlFwxhwweWK1euSO/evdWaCHXr1hVPMmDAAFm3bp36Xf/m+9ixY3kCNmfYF719pxw9GnvDdV07dcgZMqkqIuZUeMQwwCwVpNnEmp0TyQTVqi41a+QEZ9iPoC5LTVbLWUMM99UEhUdQVcQm2Vk2qVi5nKSnporV3ypJCeelwi0hErs/Vho1ayk7t34vMx9PlpUzB0r5oKtyNLaOVFurybVtW2Tj9tNyYOKDogVrEhQbIKHrz0jM7x9IgF95ucVSSRKy4yRwcoiE9J4gFptF6h6Pl0MBv0hQcDmZPu9j+fTtVyT216XS5NIeKVelphy8skusgQHy9MTnpWGjRhJk9VdBGjZHWGh63769N7RhYdmFfl1aysfLo+VEUor4ZWpSKYvfV5D3SE3NWTdQh3//mE0jIsNgiI5Bc9Ju9jg9e/ZUc4fIu7m7BP/IkSMlKSlJZs+erRaqRrE+fA4PDw9X12MfgjZHbdu2tf+O6pCfffaZuv2JEyfEHSyaG98JiFJRfUV34MABueeee2Tz5s3qTYog7dy5c/LRRx/Zb4OIFilv3VNPPSWrV69W365Uq1ZNJk2aJBcuXFCNW9x1hFDGE0OIkpOTS3VSa0k/SKGUKM7bWZ7CIw7wkiKjhk6NwYeaDT8RiGmSreX8BJstZxQCKiTm/I5/lBG85ezH7cpXqCDpaWli8feXW0Iqqm8XXnz2MWndpq+Uq9JWrInpcurAejmZlVOlsnGjDhIW1F6uJiTIocTvJE2Sxc/fX2zZ2VIhpJJcTU+ToOBgqd+4hcSfPiEpF5Nk9PipUqdeuGg2TQ7t3iZ7dkRLevpladCwsfTq01/qhNXJqdTob1Fl9fV5Z3GJ6fLCxLH2tsgv0C3ItWuZMmDQHPvfDhxy7apJEhyMGXFERERlp6w+hxT3efToOkOsVtRadl1W1lX5Ifolt58blW0fijl8Rn1uNMLl1BTp0rKO6fqQW9MHWNnb0WuvvSaNGzeWHj162PdhHlZoaGi+98eLtWjRIvnkk08kIiJC7fv0009Vic5NmzZJ3759xZOU9Jvu/AI0fSgfApOYHbskNvaI/ToEMciqgb/+fyrwUmGYqoaoQ4CWkYUMnE2wPJoKWnKDOIufRa6mp6sARsvOlksXL6j9Q6PGyvKlC0VkvQQGBcuVrMtStUaodOwRIZvX/J8cu/qr/XlgMeyu9wwSyc6SgMAgSb6QIPt+iZazcSdVIZBmrdtKm45dJTUpQWy2bOnY+S7p2q27GnKpZ80wDw1FUVSp/dyvUyLu7q4KgehuJkADLIQdgCUMrmXZL+MxiYiIiIjczWPGeCFbhAALY0gdK/RhgnzNmjWlcuXKKnh75ZVX1GVAtiwzMzPPYnVhYWEqpYnF6goK0jBE0nEBPET93kSfi6IHJnqAhstoj1927btxiGTusEi0rB8CuNwmDhA/sfrb5FpmbqCWrUmWnqbODezUUEj8L7d6/m13dJAGjZvKvl075EpamtQNbySNWtwqVj8/ade+kxw7ckiyMjOlQdPmElKxsqoSiWGYyIJVCK0tdQbdp4LIjCybus+lhHgJ9PdTC3QjKLPid1SrtPqp4ZlYmBpGDLtXLl/NkhpVctaSc2V4FwK0AT1bycpv9qrLd7atL4GBHvN2ICIiMuVwR/INpVGC32w85lPpypUr5dKlS2qIo65///4yfPhwNR4U1X1mzJghvXr1UsEZMmworYksU5UqVW5qsTqscv/iiy+KN8O8tvyySAEBAdK1U3u1Oa8LocrQ/7xNBcSOUCESwdG1TJFMiyZ+ueMdMTRSrXPmb1HDJ1Vlktx/b6tWqSLd73ZYyT13IWmrf3lp176j2qUnvnDXCn4WNQcu2+Zvf6sh+MJz87f4iZ/kLCWAbFkQgjM/nCOeV075xvuGDFalxo2sNv79J9ESmJ6h2mZ73G55v0G6PPHUIyqrRkREREQkZg/SMGwRQRkyYY6T/nTIjqFCCwK2tWvXytChQws8VlGL1aGii14tSM+kOa9i7sluNoOkt4UqQz9imH3/xYsXZdXa9ep3BFjlAqwqq6YCstx21L8AQ8CG/ZjfhoxXzvU512GYYE6wlVN5UW96XI2FpdU8OBzLljv/Tc1701Rg5vjlCBbm1odqOs49A6wXZyQUn7mclipWS85CiMgYTpgwXvYd3KkKzxAREZkVRs7oo2eMOBaZjztL8PsKjwjSUOERc8i+/vrrIktPI0g7evSouoy5asgKIdhwzKZhsbquXbsWeBxk4YpapdwM0GbOc7kwXBKFV5CN1IdVIgBDUKWvdYIgSyWb9KIbuXO5cir55L6THN5QKMWB+6MQiZ86Xs5SAWoZFgRrKrjLKQyC/7p17Sz+kiXbftl1Q9bQKFOmTJHsCuUlJK01wkyxpqXKCxOny7/mTFfFaG677TbjH5SIiMgbcLgjeXl1R1/gEeO6UL0R88z++te/Fno7lNKMi4tTwRpgoToM78PidDqU1ESVyMKCNCoY2tNxNfaoUcNlzEMPSNTI+6Rzx7ZqHbV+ET2kWdNm6ne1rprFcj3QCgy44d00NHJg7lBILERtVeu6BQf6q0WpMeQRWTO11pufRUbdN1haNW+sKgPpbBZjv0tAwZmtW7dK/7vvFtvxU5L9xwnJSLggKXsyVdWgNWvWsIsQERERkXkzaTabTQVpWKTQar3+dFDqfdasWTJs2DAVlGGNgmnTpkn16tXtq4fjg/zYsWNV2X2U30dpfqyZhiyIXu2RjBlWicwjFvPTF/TDOnR3dsmZe1YcesYOpfIxBw3Jsc6du0h43VD54v9W2G8XEpJTFATLMOiLfGdl5a44bRB9zpkN4y8d+qE1wF8tCeHYD4mIiExHDXUx8FhkPkyleX8mDcMcsZjco48+mmc/1jjbv3+/REZGSrNmzVQQh58xMTH2D/Iwd+5cuffee2XEiBHSrVs3KV++vFo3rbhrpFHZQgEQXfMmDW54nRwX+IYH7x9h+HBH9B9UBP18w8fS84Gu6h+Sa/42sTVOk7S0NNWfiIiIiIjcxe0pA3xYzm89bVTy27BhQ5H3Dw4Olnnz5qmNPB9eL0c7duaUwAcEY1ujd6i18pyLoyAD93DUKFm2bJlLpfcdg/vu3bvLG6tnSe/ICNm0ZqNsfW+FzJw5U5o2bery8YmIiLyVBQXYDCqdb9RxyLuwBL8PZNLIXD79/Ev1EwEXdOvcQf3Us2X5LVWGoAzXI0AzCoZt7t27V8aNGycpKcliCaigFkXHEFsiIiJT0wuHGLWRaas7GrWZEYM08iiYg5YfI7JnzrAkARZH//F3P7HWuF2ioqIMfwwiIiIiopvFII3KlF6ZMzEx0b7Pcc5ZaZTbLwyWGYCjP3xY6Np6REREpoHkl82gjYk0U9cNMWozIwZpVKb69O6pArEaNWqoy/p8RGTQ9GyZHjg5QkEPGDUqZ5ikUaX4R0/9SP2O9feIiIiIiDyB2wuHkPk4Dl3EwtngvKi2s1WrVqmfRma7sIRD7JYPpEGDBoYdk4iIyNuxcAi53om4mrWrGKSRW2F9u+LONyuNeWkM0IiIiPJbJ82gcYoc7mhKrO7oOg53JI+TXzBWGgEaEREREZEnYiaNPAqDMSIiIjczsnQ+S/Cbk5Gl8y1iSsykEREREREReRBm0oiIiIjoOpuB2Qsci0yHdUNcx0waERERERGRB2EmjYiIiIjsWIKfXMZUmssYpBERERHRdSwcQi5iCX7XcbgjERERERGRB2EmjYiIiIiuYyaNXGQxsAS/hSX4iYiIiIiIyN2YSSMiIiKi65hJIxexbojrGKQRERER0XVcJ41cxSjNZSwcQkRERERE5EGYSSMiIiIiO66TRq5iCX7XMZNGRERERETkQZhJU/NjNdUYKSkp7n49iIiIyGT0zx/65xG3Y+EQMmJKmlEl+MWcGKSJSGpqqmqMevXqufv1ICIiIhN/HqlUqZK7n4aITcOYR+OORabDuiGuY5AmImFhYRIXFychISFiyQ378a0Wgjbsr1ixogFN7f3YJmwT9hO+f/hvCv+d5d8e4yGDhgANn0eIiIBBGibm+flJ3bp18+0RCNAYpLFNisJ+wjYpLvYVtgn7Scn4+nvHIzJoOg53JBch52HYcEeLmBILhxAREREREXkQZtKIiIiIyIGWk00zBOekmRNnpbmKQVoBgoKCZObMmeonsU3YT4qP7x22C/tKyfH9wzbxCBzuSC7icEfXWTSPqfdKRERERO4sEIa5cRENnxGrnzFfUmfZrsmm4/MkOTnZp+cUUt4+dPhkgoQY9HqnpqRIy/AaputDzKQRERERkVPZfJbgp5LjYEfXsXAIERERERGRB2EmjYiIiIiu02w5mxGMOg55Fc5Jc53PZtJ+/PFHGTRokFoYEgtUr1y5Ms/1s2bNkhYtWkiFChWkSpUqEhERIdu3b89zm549e6r7Om6jRo3Kc5uLFy/KQw89pMbfYsPvly5dEm9tF0d/+9vf1G3eeuutPPuvXbsmzzzzjFSvXl213+DBg+X06dNe2y5GtImv9ZWi2mTMmDE3nG/nzp1N3U+K0ya+1k+K+/45fPiwev1xPiEhIapdTp06Zdq+Upw28bW+UlSbOJ+rvr355ps+20+8onCIURuZjsXg/8zIZ4O0tLQ0adOmjbz77rv5Xt+sWTN13f79+2Xr1q3SoEED6dOnjyQkJOS53eOPPy7x8fH2bcGCBXmuf+CBB2TPnj2yfv16teF3/FHw1nbR4Q8oglb8QXU2ceJEWbFihSxbtky13eXLl2XgwIGSnZ3tle1iRJv4Wl8pTpv069cvz/muW7dOzN5PimoTX+snxWmXY8eOyZ133qm+FNuyZYvs3btXZsyYIcHBwabtK8VpE1/rK0W1ieN5Ylu8eLEK0oYNG+az/YSIqFCaCeA0V6xYUehtkpOT1e02bdpk39ejRw9twoQJBd7n0KFD6j7btm2z74uJiVH7fvvtN81b2+X06dNanTp1tAMHDmjh4eHa3Llz7dddunRJCwgI0JYtW2bfd+bMGc3Pz09bv36917dLSdrE1/tKfm0yevRoLTIyssD7mLGfFNUmvt5PCmqXkSNHag8++GCB9zFjXymqTXy9rxTnbzLeS7169TJNP/EU+mehiDpPav3qTTBkw7FwTBybzNOHYuMStfjkDEO22LhEU/Yhn82k3YyMjAz54IMP1NAIfNPnaOnSpWpoxa233iqTJ0+W1NRU+3UxMTHqPp06dbLvw5AV7IuOjhZvZLPZ1LeOU6ZMUefsbOfOnZKZmamyjjpkllq3bm0/Z19rl6LaxKx9BRmAmjVrqqw0vvE/f/68qftJUW1ixn6C987atWtVe/Tt21e1Dc7Ncaib2fpKcdrEjH3F0blz51QbjR071rT9hIjI1IVD1qxZo8b4p6enS+3atWXjxo3qD6IuKipKGjZsKKGhoXLgwAGZOnWqGpaC28HZs2fVH1hn2IfrvNHrr78uVqtVxo8fn+/1OK/AwEA1j89RrVq17Ofsa+1SVJuYsa/0799fhg8fLuHh4XL8+HE1VKtXr17qgxQW4zVjPymqTczYTxCkYkjaa6+9Ji+//LJ6L2EI2tChQ2Xz5s3So0cP0/WV4rSJGfuKoyVLlqh5emgTndn6idtxMWtyEUvwu87UQdrdd9+txqsnJibKhx9+KCNGjFBzjvR/5PFNuA7f1jVt2lQ6dOggu3btknbt2qn9GDPvDKM58tvv6fBh8u2331bnd7PP3/mcfaVditsmZusrI0eOzHO+OFcEJ/j22/GDlVn6SXHbxGz9BFkjiIyMlGeffVb9fscdd6isxvvvv28PSMzUV4rbJmbrK44wHw1BqvMcPTP1E7dTg8sMKvjBuiGmxOqOrjP1cEdUh2rSpIkaDrFo0SKVLcHPguAPY0BAgBw9elRdxjecGJbhDMVH8O2et/npp5/Ut7z169dXbYHt5MmTMmnSJFVYRT9nDA9FBS1HuJ9+zr7ULsVpEzP2FWfIRCMgcTxfM/WT4rSJGfsJRibgPdOqVas8+1u2bGmvZGi2vlKcNjFjX3H8N/fIkSPy2GOP5dlvtn5CRGTqIC2/b9tQ4rcgBw8eVGPi8eELunTpIsnJybJjxw77bZCJw76uXbuKt8G8q3379qnsor5hzD/mYm3YsEHdpn379uqDgj7kBlCJC8Nx9HP2pXYpTpuYsa84S0pKkri4OPv5mq2fFKdNzNhPMDytY8eO6kO3o9jYWBXAmrGvFKdNzNhXdPiiFH3CeX642fqJ27EEP7mIJfhd57PDHTHm//fff7dfxhwRfMCuWrWqVKtWTV555RW1xgr+4OHD1Pz589V6K5hTopdIxqTtAQMGqG8+Dx06pLInbdu2lW7dutm/+UTJbQxL0UsjP/HEE6okcPPmzcXb2gXZIrSNI/xRxLeT+vlgAjYmc6MtcFvcDxPab7vtNrXWnDe2i6tt4ot9pbA2wYZ1BlEaG++fEydOyLRp09S5DxkyxJT9pDht4ov9pDjvH3yhgaGg3bt3V0PMMf9q9erVqsiKGftKcdrEF/tKUW0CKSkp8tVXX8mcOXNuuL8v9hMiokJpPmrz5s1qRLXzhjLZV65c0YYMGaKFhYVpgYGBWu3atbXBgwdrO3bssN//1KlTWvfu3bWqVauq2zRu3FgbP368lpSUlOdxcDkqKkoLCQlRG36/ePGi5o3tkp/8ys2j/caNG6faply5ctrAgQNVe3lru7jaJr7YVwprk/T0dK1Pnz5ajRo1VEns+vXrq/3OfcBM/aQ4beKL/aS4759FixZpTZo00YKDg7U2bdpoK1euNG1fKU6b+GJfKU6bLFiwQL3+KLefH1/rJx5dgr/mY1q/0KcN2XAsM5ZPN3sfOnYmSTufmmnIduxMkin7kAX/V3gYR0RERES+DtlMZC0jaj4mVr9AQ46ZZcuQTecXqmGnFStWNOSY5Pl96NiZJAkx6PVOTUmRxnWqma4P+exwRyIiIiIqAZbgJxexBL/rGKQRERER0XUM0shFLMHvOlZ3JCIiIiIi8iDMpBERERHRdTa9totRxyKzFuE36lhmxEwaERERERGRB2EmjYiIiIjsNM2mNiMYdRzyLpyT5joGaURERESUt3CIUcMUudITUYlwuCMREREREZEHYZBGRF6tZ8+eMnHiRDHLc2vQoIFYLBa1Xbp0Se3773//K5UrVxajjRkzxv5YK1euNPz4ROThJfiN2si0wx2N2syIQRoRebWvv/5aXnrpJVMEfbrZs2dLfHy8VKpUqVQf5+2331aPQ0RERGWLc9KIyCNlZGRIYGBgkberWrWqmE1ISIiEhoaW+uMgCCztQJCIPJDNJmIxqOAHC4eYuAC/MSkwC0vwExGVDmSnxo0bpzYMy6tWrZq88MILojkMg8EwvpdfflkNsUNg8Pjjj6v9y5cvl1tvvVWCgoLUbebMmVNo5gvB3T/+8Q+pU6eOVKhQQTp16iRbtmzJc5+ff/5ZevToIeXLl5cqVapI37595eLFi+qxf/jhB5VB0of5nThxQt3n0KFDMmDAALnlllukVq1a8tBDD0liYqL9mGlpafLwww+r62vXrn3D83SGc4+IiJB+/frZ2wHDF+vXry/Tp093qb2TkpLkL3/5iwwePFiuXr2qzh/nsmHDBmnbtq2UK1dOevXqJefPn5dvvvlGWrZsKRUrVpT7779f0tPTXXpsIvIBHO5ILuJwR9dxuCMRlYklS5aI1WqV7du3yzvvvCNz586VhQsX5rnNm2++Ka1bt5adO3fKjBkz1M8RI0bIqFGjZP/+/TJr1iy1H3OwCvLII4+oIGzZsmWyb98+GT58uAqEjh49qq7fs2eP9O7dWwV+MTExsnXrVhk0aJBkZ2er4KxLly4qQMQwP2z16tVTPxHU3XHHHfLrr7/K+vXr5dy5c+q56aZMmSKbN2+WFStWyLfffqsCIzz/giBoQpvs2LFDtQc8+eSTKgDEeZbU6dOn5a677pIWLVqooaDBwcH263Dcd999V6KjoyUuLk49/7feeks+++wzWbt2rWzcuFHmzZtX4scmIiIiY3C4IxGVCQQ7CMwQnDRv3lwFXbisZ8wA2Z3JkyfbL0dFRamACoEZNGvWTGW0EMwh6+Xs2LFj8vnnn6tAJSwsTO3D8RBUffTRR/Lvf/9b3njjDenQoYPMnz/ffj8EbDoMsUSGzXE44X/+8x9p166dur9u8eLF6pxiY2PVYy1atEg+/vhjueeee9T1CMDq1q1baJsg27dgwQKVlUPQt3r1atm9e7cEBARISeC54PEjIyPt2UBHyFR269ZN/T527FiZOnWqarNGjRqpfffdd58KNJ9//vkSPT4R+QbNZhPNoOGOXCfNnPDXx6h6HxYxJ2bSiKhMdO7cOU/QgIwVslvIYOkQPDk6fPiwPajQ4bLz/XS7du1SQwcRzGHYob5hCCOCEcdM2s1ARgzBi+MxkakCHBcbhlninBznyiEYLQoyfUOHDpVXX31VDZHEcy+JK1euyJ133in33nuvysw5B2hw++23239Hxg7BqB6g6fswBJKIiIjci5k0IvIYmEPmCAGXc7DhOI/Nmc1mE39/fxVU4acjBFaA+Vg3C8fFkMjXX3/9husw/0wfSlkSmAOmP19XjoM5e5jjhmGLGHqZXxbPMUOHdnXO2GEfzpWITE79O8vFrMkFTKW5jJk0IioT27Ztu+Fy06ZNbwimHLVq1UrNGXOE+VTINuV3PxTFQIYN2aAmTZrk2fThi8gmfffddwU+JoY7OmfpMNTx4MGDqnCJ83ERWOInAh7Hc0QhEgw/LMqkSZPEz89PFfBABuz777+XksAxPvnkE2nfvr0aNvrnn3+W6DhERGLTjN3ItNUdjfrPjBikEVGZQKGK5557To4cOaLmjaFAxYQJE4oMYBBQYR00BDyY54XCF47z1hwheMM8NlRZRNGM48ePyy+//KIyYOvWrVO3wTws7Hv66adVYZHffvtNzTnTKzUiEENxE1R1xD5klv7+97/LhQsXVPVDFPr4448/VHGQRx99VAV0yNJhjhcyWHi+Bw4cUHPmEDgVBlkvzG1bunSpmkv2z3/+U0aPHq0CvJJA4IpjtWnTRgVqZ8+eLdFxiIiIyL0YpBFRmUDghHlTKA2PoOeZZ56RJ554otD7IIP15ZdfqkqNqPr4r3/9Sy3knF/REB0KhOCxEOBhThjK0CPoQpEPPZBDgLV37171XDCP7H//+5+qPAkIABHsIItXo0YNOXXqlCoMgoqRCMhQrh/PBQEmlgrQAzEUM+nevbt6PAw7xPwwZLUKkpCQoAI7VFzEecLMmTPVY6HKY0nhPBAEoxiKXmafiOjmS/DbDNqYSTMjluB3nUUrbIIHEZEBsJYZytej3LvREGShEAgqF5oBMn1YF85xbbjShrlqWFoARUmIyHelpKSoL596BQ4Xq6VkVWadZWmZ8n3GV5KcnKzWYyRz9KE/Ey4Z9nqnpKRIWI3KputDzKQRkVe6du2aWrMMc8UcS+ibAUrkY4gl/mCVJmT09IIrRGQemk0zdCPz1g0xajMjVnckIq+EQhsY1oiqi1jfyyywnEBmZqb6PSQkpFQfC0NL9fl/qGJJRCaBYYpiM/BYZDqs7ugyZtKIqNRt2bLF8KGOGHqHIRAolFHSxZ+9UXh4uL2yZFGFSVxVs2bNPFUsiYjKyvz586Vhw4YSHBys5vf+9NNPbHwq1T6EL0FxO9wea4i+//774k4M0oiIiIjIY4Y7fvHFF2re7fTp02X37t1y1113Sf/+/VUhJ/IO7i7B/8VN9iFUgx4wYIC6HW4/bdo0GT9+vCxfvlzchYVDiIiIiMhe9KGnZYihhUO2aCtuquhDp06dVNVbLI+ia9mypRpB8eqrr/KV8oI+dDbRuCIfKSkpElq9Uqn2Icz1XrVqlRw+fDjPvGxUgo6JiRF3YCaNiIiIiK4zrPx+7nYTMjIyZOfOndKnT588+3E5Ojqar5KXcGcJ/owS9CEEYs63x5I7KFCmzwMvaywcQkRERER2WZIpohl4rNxsiKOgoCC1OUtMTFRrUtaqVSvPflw+e/YsXyUv4fx6G3GslFLsQ9if3+2zsrLU8dxRPItBGhERERFJYGCghIaGytaz6wxtDSzlUa9evTz7Zs6cKbNmzSp0fUZHWNbXeR95bh9q2jDv6+0NfciSz+3z219WGKQRERERkapqhwIKGC5mpPw+HOeXAYHq1auLv7//DRmP8+fP35DpIM/jrX0oNDQ039tbrVapVq2auAODNCIiIiKyf8jG5s5MDMqgb9y4UYYMGWLfj8uRkZF8lbyAN/ahLl26yOrVq/Ps+/bbb6VDhw5uW+aHhUOIiIiIyGM899xzsnDhQlm8eLGqtvfss8+q0umotkdkRB+aOnWqPPzww/bbY//JkyfV/XB73G/RokUyefJkcRdm0oiIiIjIY4wcOVKSkpJk9uzZEh8fL61bt5Z169ZJeHi4u58a+Ugfio+Pz7NmGha9xvUI5t577z0JCwuTd955R4YNG+a2c+A6aURERERERB6Ewx2JiIiIiIg8CIM0IiIiIiIiD8IgjYiIiIiIyIMwSCMiIiIiIvIgDNKIiIiIiIg8CIM0IiIiIiIiD8IgjYiIiIiIyIMwSCMiIiIiIvIgDNKIiIiIiIg8CIM0IiIiIiIiD8IgjYiIiIiIyIMwSCMiIiIiIhLP8f/OEZvT4Y2lxwAAAABJRU5ErkJggg==", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df = parcels.read_particlefile(\"output-schism.parquet\")\n", "\n", @@ -415,7 +4443,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -429,7 +4457,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.12" + "version": "3.14.3" } }, "nbformat": 4, From 0b6a6c8a1381e9a8ee373a337625d59b02924e0b Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sun, 14 Jun 2026 15:21:24 +0000 Subject: [PATCH 5/5] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../user_guide/examples/tutorial_schism.ipynb | 4061 +---------------- 1 file changed, 16 insertions(+), 4045 deletions(-) diff --git a/docs/user_guide/examples/tutorial_schism.ipynb b/docs/user_guide/examples/tutorial_schism.ipynb index 42ca3f9e5..bf6b1e725 100644 --- a/docs/user_guide/examples/tutorial_schism.ipynb +++ b/docs/user_guide/examples/tutorial_schism.ipynb @@ -37,1194 +37,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "2", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = true;\n", - " const version = '3.8.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", - " const reloading = false;\n", - " const Bokeh = root.Bokeh;\n", - " const BK_RE = /^https:\\/\\/cdn\\.bokeh\\.org\\/bokeh\\/(release|dev)\\/bokeh-/;\n", - " const PN_RE = /^https:\\/\\/cdn\\.holoviz\\.org\\/panel\\/[^/]+\\/dist\\/panel/i;\n", - "\n", - " // Set a timeout for this load but only if we are not already initializing\n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks;\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - " if (js_modules == null) js_modules = [];\n", - " if (js_exports == null) js_exports = {};\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - "\n", - " if (root._bokeh_is_loading > 0) {\n", - " // Don't load bokeh if it is still initializing\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", - " // There is nothing to load\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - " window._bokeh_on_load = on_load\n", - "\n", - " function on_error(e) {\n", - " const src_el = e.srcElement\n", - " console.error(\"failed to load \" + (src_el.href || src_el.src));\n", - " }\n", - "\n", - " const skip = [];\n", - " if (window.requirejs) {\n", - " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n", - " root._bokeh_is_loading = css_urls.length + 0;\n", - " } else {\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", - " }\n", - "\n", - " const existing_stylesheets = []\n", - " const links = document.getElementsByTagName('link')\n", - " for (let i = 0; i < links.length; i++) {\n", - " const link = links[i]\n", - " if (link.href != null) {\n", - " existing_stylesheets.push(link.href)\n", - " }\n", - " }\n", - " for (let i = 0; i < css_urls.length; i++) {\n", - " const url = css_urls[i];\n", - " const escaped = encodeURI(url)\n", - " if (existing_stylesheets.indexOf(escaped) !== -1) {\n", - " on_load()\n", - " continue;\n", - " }\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", 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was not successfully loaded we reset\n", - " // everything and try loading again\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_is_initializing = false;\n", - " root._bokeh_onload_callbacks = undefined;\n", - " root._bokeh_is_loading = 0;\n", - " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", - " load_or_wait();\n", - " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", - " setTimeout(load_or_wait, 100);\n", - " } else {\n", - " root._bokeh_is_initializing = true;\n", - " root._bokeh_onload_callbacks = [];\n", - " const bokeh_loaded = Bokeh != null && ((Bokeh.version === version && Bokeh.Panel) || (Bokeh.versions?.has(version) && Bokeh.versions.get(version)?.Panel));\n", - " if (!reloading && !bokeh_loaded) {\n", - " if (root.Bokeh) {\n", - " root.Bokeh = undefined;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS 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scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n const shouldSkip = skip.includes(escaped) || existing_scripts.includes(escaped)\n const isBokehOrPanel = BK_RE.test(escaped) || PN_RE.test(escaped)\n const missingOrBroken = Bokeh == null || Bokeh.Panel == null || (Bokeh.version != version && !Bokeh.versions?.has(version)) || Bokeh.versions?.get(version)?.Panel == null;\n if (shouldSkip && !(isBokehOrPanel && missingOrBroken)) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = 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window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.8.7/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.8.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.8.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.8.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.8.2.min.js\", \"https://cdn.holoviz.org/panel/1.8.7/dist/panel.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false;\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully 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recent versions we use the root._bokeh_is_initializing flag\n", - " // to determine whether there is an ongoing attempt to initialize\n", - " // bokeh, however for backward compatibility we also try to ensure\n", - " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", - " // before older versions are fully initialized.\n", - " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", - " // If the timeout and bokeh was not successfully loaded we reset\n", - " // everything and try loading again\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_is_initializing = false;\n", - " root._bokeh_onload_callbacks = undefined;\n", - " root._bokeh_is_loading = 0;\n", - " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", - " load_or_wait();\n", - " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== 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Bokeh.versions.set(NewBokeh.version, NewBokeh);\n", - " }\n", - " root.Bokeh = Bokeh;\n", - " }\n", - " });\n", - " }\n", - " }\n", - " // Give older versions of the autoload script a head-start to ensure\n", - " // they initialize before we start loading newer version.\n", - " setTimeout(load_or_wait, 100)\n", - "}(window));" - ], - "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = false;\n const version = '3.8.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = true;\n const Bokeh = root.Bokeh;\n const BK_RE = /^https:\\/\\/cdn\\.bokeh\\.org\\/bokeh\\/(release|dev)\\/bokeh-/;\n const PN_RE = /^https:\\/\\/cdn\\.holoviz\\.org\\/panel\\/[^/]+\\/dist\\/panel/i;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n 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!== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n const shouldSkip = skip.includes(escaped) || existing_scripts.includes(escaped)\n const isBokehOrPanel = BK_RE.test(escaped) || PN_RE.test(escaped)\n const missingOrBroken = Bokeh == null || Bokeh.Panel == null || (Bokeh.version != version && !Bokeh.versions?.has(version)) || Bokeh.versions?.get(version)?.Panel 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css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false;\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0;\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true;\n root._bokeh_onload_callbacks = [];\n const bokeh_loaded = Bokeh != null && ((Bokeh.version === version && Bokeh.Panel) || 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toinsert.length;\n", - " var html_node = toinsert[nchildren-1].children[0];\n", - " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var scripts = [];\n", - " var nodelist = html_node.querySelectorAll(\"script\");\n", - " for (var i in nodelist) {\n", - " if (nodelist.hasOwnProperty(i)) {\n", - " scripts.push(nodelist[i])\n", - " }\n", - " }\n", - "\n", - " scripts.forEach( function (oldScript) {\n", - " var newScript = document.createElement(\"script\");\n", - " var attrs = [];\n", - " var nodemap = oldScript.attributes;\n", - " for (var j in nodemap) {\n", - " if (nodemap.hasOwnProperty(j)) {\n", - " attrs.push(nodemap[j])\n", - " }\n", - " }\n", - " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", - " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", - " oldScript.parentNode.replaceChild(newScript, oldScript);\n", - " });\n", - " if (JS_MIME_TYPE in output.data) {\n", - " 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The API is not stable and may change without deprecation warnings.\n", - " import parcels\n" - ] - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.tri as mtri\n", @@ -1257,990 +73,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "4", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Downloading file 'data/SCHISM_LakeOntario/out2d.schism_lake_ontario.nc' from 'https://github.com/Parcels-code/parcels-data/raw/main/data/SCHISM_LakeOntario/out2d.schism_lake_ontario.nc' to '/home/joe/.cache/parcels'.\n", - "Downloading file 'data/SCHISM_LakeOntario/horizontalVelX.schism_lake_ontario.nc' from 'https://github.com/Parcels-code/parcels-data/raw/main/data/SCHISM_LakeOntario/horizontalVelX.schism_lake_ontario.nc' to '/home/joe/.cache/parcels'.\n", - "Downloading file 'data/SCHISM_LakeOntario/horizontalVelY.schism_lake_ontario.nc' from 'https://github.com/Parcels-code/parcels-data/raw/main/data/SCHISM_LakeOntario/horizontalVelY.schism_lake_ontario.nc' to '/home/joe/.cache/parcels'.\n" - ] - }, - { - "data": { - "text/html": [ - "
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interp=UxLinearNodeLinearZF\n", - " V -> Field interp=UxLinearNodeLinearZF\n", - " UV -> VectorField interp=-\n", - "time interval: TimeInterval(left=np.datetime64('2025-08-18T01:00:00.000000000'), right=np.datetime64('2025-08-18T06:00:00.000000000'))\n" - ] - } - ], + "outputs": [], "source": [ "fieldset = parcels.FieldSet.from_ugrid_conventions(uxds, mesh=\"flat\")\n", "\n", @@ -4238,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "12", "metadata": {}, "outputs": [], @@ -4271,36 +279,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "14", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "releasing 1500 particles at z = 2 m in the deep basin\n", - "INFO: Output files are stored in output-schism.parquet\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/joe/Projects/Geomar-Utrecht/Parcels/src/parcels/_core/spatialhash.py:592: RuntimeWarning: invalid value encountered in cast\n", - " xq = np.clip((xn * bitwidth).astype(np.uint32), 0, bitwidth)\n", - "/home/joe/Projects/Geomar-Utrecht/Parcels/src/parcels/_core/spatialhash.py:593: RuntimeWarning: invalid value encountered in cast\n", - " yq = np.clip((yn * bitwidth).astype(np.uint32), 0, bitwidth)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1434 of 1500 particles still active at the end of the run\n" - ] - } - ], + "outputs": [], "source": [ "# number of valid (non-NaN) vertical levels at each node\n", "valid_levels = np.isfinite(U.isel(time=0).values).sum(axis=1)\n", @@ -4349,21 +331,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "16", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df = parcels.read_particlefile(\"output-schism.parquet\")\n", "\n",